Iraqi Kurdistan Region Ministry of Higher Education and Scientific Research University of Sulaimani
VALUATION OF AMBIENT AIR POLLUTION: A STUDY OF SOME URBAN AREAS IN SULAIMANI CITY AND ITS SURROUNDING/KURDISTAN REGION OF IRAQ
A Dissertation Submitted to the Council of the Faculty of Agriculture Sciences/ University of Sulaimani In Partial Fulfillment of the Requirement for the Degree of Doctor of Philosophy In Environmental Science (Air Pollution) By
Salih Najeb Majid B.Sc. Agriculture Science/ Soil Science (1978) M.Sc. Agriculture Science/ Soil Chemistry (1982)
Supervised by
Dr. Dilshad Ganjo Ahmad Assistance Professor October 2011
I certify that this dissertation was prepared under my supervision at the Department of Soil and Water Sciences, Faculty of Agriculture Sciences, University of Sulaimani as a partial fulfillment of the requirement for the degree of PhD in Environmental Science/Air Pollution.
Supervisor Dr. Dilshad Ganjo Ahmad Assist. Professor College of Science University of Salahaddin Date: 15 / 8 / 2011
In view of the supervisor’s recommendation, I forward this dissertation for linguistic and scientific evaluation and then after for a debate by the examining committee.
Dr. Kamil Sabir Said Lecturer Head of Soil and Water Sciences Department Faculty of Agriculture Sciences University of Sulaimani Sulaimani/ Iraqi Kurdistan Region Date: 16 / 8 / 2011
Examining Committee Certification We, as the Examining Committee, certify that we read this dissertation and examined the student in relate to the content of his dissertation and what it concerns. In our opinion, it is adequate with (pass) standing as a dissertation for awarding the degree of PhD in Environmental Science/ Air Pollution.
Dr Salman Khalaf Essa Professor University of Bagdad Chairman
Dr Shafeeq Chalab Salim Professor University of Bagdad Member
Dr Nabil Adil Fakhri Professor University of Salahaddin Member
Dr Khasraw Abdulla Rashid Assist. Professor University of Sulaimani Member
Dr Abdul-Aziz Younis Talee’a Al-Saffawi Assist. Professor University of Mosul Member
Dr Dilshad Ganjo Ahmad Assist. Professor University of Salahaddin Supervisor
Date of Exam: 27.10.2011 Approved by the Faculty Committee of Post Graduate Studies
Name: Dr. Aram Abass Mohamad Dean of the Faculty of Agriculture Sciences Date: 2 / 11 / 2011
Dedicated To The souls of my Father and Brothers My cherished Mother and the entire family members My wife Negar My son Ara With great love and gratitude SALIH
Acknowledgement First, I would like to acknowledge and thank the presidency of Sulaimani University, the Deanery of Faculty of Agriculture Sciences and the Department of Soil and Water Sciences for giving me the chance and providing all the available facilities to achieve this proposed project. I wish to thank my adviser, Dr. Dilshad G. Ahmad, for his guidance, encouragement and providing me assistance in numerous ways to complete this dissertation. Special thank goes to many member of the teaching staff in my Faculty of Agriculture Sciences, especially for Dr. Nawroz Rashid Abdulrazaq, Dr. Gafur Ahmad Mam. Dr. Mohammad Abdulrazzaq Fattah, Dr. Shadan Hama Khurshid,
Dr. Ahmad Ibrahim
Khawakaram, Dr Ahmad H. A. Rashid, Miss Qadria Salih Karim and the others those who helped me to conduct the locations measurements, Atomic Absorption and ICP analysis, statistical analysis and writing this dissertation. I owe the deepest gratitude to the staff member of Periodic Vehicle Inspection (PVI) center in Erbil city for their technical and moral support and allowing me to measure the exhaust emission of the vehicles. Finally, I offer my regards and blessings to all of those who supported me in any respect during the process of the project and writing this dissertation.
Salih Najeb Majid
Some Quotations on Pollution Shall we surrender to our surroundings or shall we make our peace with nature and begin to make reparations for the damage we have done to our air, to our land and to our water? -Richard Nixon (1913-1994), 37th U.S. President, State of the Union Message, 22 Jan 1970 Who would have predicted a century ago that the richest civilizations in history would be made up of polluted tracts of suburban development dominated by the private automobile, shopping malls, and a throwaway economy? Surely, this is not the ultimate fulfillment of our destiny. -Alan Durning, How Much Is Enough?, 1992 The people have a right to clean air, pure water, and to the preservation of the natural, scenic, historic and aesthetic values of the environment. Pennsylvania's public natural resources are the common property of all the people, including generations yet to come. As trustee of these resources the Commonwealth shall conserve and maintain them for the benefit of all the people. -the Pennsylvania State Constitution, Article 1, section 27 They have poisoned the Thames and killed the fish in the river. A little further development of the same wisdom and science will complete the poisoning of the air, and kill the dwellers on the banks...I almost think it is the destiny of science to exterminate the human race. -Thomas Love Peacock (1785-1866), Gryll Grange, 1860 You know that the air and water are being polluted, as is everything we touch and live with, and we go on corrupting the nature that we need. We don’t realize we have a commitment to God to take care of nature. To cut down a tree, to waste water when there is so much lack of it, to let buses poison our atmosphere with those noxious fumes from their exhausts, to burn rubbish haphazardly – all that concerns our alliance with God. -Oscar Romero (1917-1980), the Violence of Love, March 11, 1979 The tiny particulate pollution from cars, power plants and factories does more than clog your lungs. It leads to development of heart disease, according to a BYU researcher… "It's very different from what we thought previously," said professor and epidemiologist Arden Pope of Brigham Young University, who led the study. While exposure clearly impacts the lungs, "longterm, chronic exposure to air pollution seems to manifest more in cardiovascular disease than it does in respiratory disease." The link between air pollution and increased deaths has been shown in research by Pope and others. His most recent study, however, shows the biological mechanism by which long-term exposure to tiny-particle pollution can actually lead to ischemic heart disease, which causes heart attacks, as well as irregular heart rhythms, heart failure and cardiac arrest. -Lois M. Collins, “Pollution in the air can cause heart ills” Deseret Morning News, 16 Dec 03 For the first time in the history of the world, every human being is now subjected to contact with dangerous chemicals, from the moment of conception until death. -Rachel Carson (1907-1964), Silent Spring, 1962
SUMMARY This study was carried out in Sulaimani city to assess the status of urban air pollution in the city because Sulaimani city was undergoing a rapid economic growth and urbanization in the last decade resulting in increasing air pollution and deterioration of air quality. Sulaimani city, which is the capital of Sulaimani Governorate, is located in the far north east of Iraq and southeast of the Iraqi Kurdistan Region. Its center coordination are (35o 33′ 14.99′′ N) and (45o 26′ 58.68′′ E) and has an elevation of 864 m above sea level. Sulaimani city covers an area of 113.73 km2 and its population was 571507 inhabitants in year 2009. The city has a semi-arid climate with very hot and dry summers and very cold winters. . Currently, the green areas in Sulaimani city account for only 5.57 percent of the total area. Air pollution in Sulaimani city is a synergetic process of many concurrence sources including; combustion of large amounts of gasoline and other hydrocarbon fuels due to the massive increasing of vehicle number and diesel engines, etc. increment of many industrial activities. Other sources of hazardous air pollutants are the classical incineration of solid waste in Tanjaro waste disposal site (landfill), buildings and streets construction and recently the frequent transboundary sand/dust storm events that contributes significantly as source of particulate matters. In Sulaimani city there have been no published research on evaluation of air pollution and air quality issues, therefore, the objectives of this study were to investigate some important aspects of air pollution in the city. In general, the following results and findings have been found throughout the investigated analysis and measurements: 1- Air monitoring data for the following criteria air pollutants CO, NO2, O3 and SO2 in ambient air showed that average concentration for 7 measurements during the measurement period from 31.9.2009 to 13.7.2010 and for 17 locations was highly varied and this is due to the influence of traffic volume and densifying of urban area. However, all the average concentrations for the pollutant gases, except for SO2, were below the legislative limits or similar target values. 2- On the other hand, the average concentration of CO2 and HC at the studied locations showed also similar trend and status to those of the investigated criteria air pollutants and significant correlation were found among them. This suggests that they mostly share the same common emission source and transportation pattern. CO2 as a I
greenhouse gas in most of the heavy traffic flow locations had a higher concentration level than the globally averaged concentration of CO2 which was about 393.69 ppmv in the Earth’s atmosphere, in June, 2011, according to Mauna Loa observatory/ Hawaii. 3- To evaluate the particle matter pollution, the ambient concentrations of PM1.0, PM2.5 and PM10.0 were measured simultaneously at the same time and locations of gases measurement. There was a large variability in the average of ambient PM levels as a whole for all the different investigated aerodynamic sizes among the studied locations and that might be influenced by local source of the location, because, the concentration of ambient PM are a synergic process of both anthropogenic activities sources and natural source. As it was recorded by this study, the frequent numbers of almost transboundary sand/dust storm event during the measuring period of 19.9.2009 to 26.6.2011 were 34 times. Generally, the concentrations of PM1.0, PM2.5 and PM10.0 in most of the studied locations have exceeded the legislative limits or similar target values of air quality standards by the Environmental Protection Agency EPA and European Commission EC. 4- To evaluate the extent of the heavy metals Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb contamination, ecological samples of (settleable dust, soil, plant and rainwater samples) from different locations in Sulaimani city were taken and analyzed as a biological indicator for ambient air pollution impacts. 5- The results of the investigated eight heavy metal concentrations of Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb in settleable dust and soil samples revealed that the concentration ranges were relatively high. When the concentration levels of )Ni, Cu, Zn, Cd and Pb were compared with the legislative New Dutchlist of heavy metals, it has been found that their concentrations mostly were within the optimum to the action level range except for Ni which exceeded the action level in certain locations. Regarding Cr metal the concentrations in all samples were lower than the optimum level. No legislative limits for Mn and Fe were set by the new Dutchlist of heavy metals. 6- Since the predominate plant species in the studied locations were not the same, therefore, comparative assessment test upon the effect of air pollution on the accumulation of heavy metal concentration level in plant could not be
possible
scientifically. However, as for the concentration of the same investigated eight heavy II
metals in the studied plant species, it was found that the highest concentration values for the metal Cr, Mn, Fe, Ni and Cu were occurred in Eucalyptus (Eucalyptus camaldulensis). But for Zn, was by Mulberry (Morus alba), Cd was by Grape (Vitis sp.) and for Pb was by Walnut (Juglans regia). 7- Regarding the concentration of the same investigated eight heavy metals in the collected rain water samples from 15 locations and in two different raining times, there were also large variations between the concentration levels among the locations and also between the two different raining times. As a whole, all the concentrations levels were below the WHO limits except for Pb and for both studied rainfall’s time. The concentration level of all the studied heavy metals Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb except of Zn was relatively lower at the samples of the second rainfall time as compared to the mean of the samples of the first rainfall time. 8- In this study, some parameters of the exhaust emission test for 812 gasoline fueled vehicles of different model years and 175 diesels were measured. This test was performed in Erbil governorate by Periodic Vehicle Inspection (PVI) center, because exhaust gas analyzer was not available in Sulaimani city, but the vehicle models are almost the same in Iraqi Kurdistan Region. The investigated parameters for the exhaust emission of gasoline fueled vehicles were CO2, CO, HC, O2 and Lambda, λ. While for the exhaust emission of diesel fueled vehicles were smoke opacity and K-values (Extinction coefficient). The tests were conducted directly inside the tail pipe of the vehicle’s exhaust. 9- In general, the results of the measured parameters for both the gasoline and diesel fueled vehicles showed a large variability in the test values and that was due to many factors including; model year, fuel characteristic, vehicle technology, engine maintenances and condition of traffic use. 10- Finally, the current study aimed also to survey of the traffic volume or traffic saturation flow rate at the main streets and intersection or crossing of Sulaimani city in order to evaluate the amount of traffic pollution which is remarkably a significant contributor to local, regional and global air pollution. In general, the traffic was heavy at most of the streets and crossings or they were above their capacities.
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List of Contents Subject No. A B C D E F I 2 2.1 2.1.1 2.1.2 2.1.3 2.1.4 2.1.5 2.1.5.1 2.1.5.2 2.1.5.2.a 2.1.5.2.b 2.1.5.2.c 2.1.5.3 2.1.5.4 2.1.5.5 2.1.5.6 2.2 2.3 2.4 2.5 2.5.1 2.5.2 2.5.3 2.5.4 2.6 2.7 2.7.1 2.7.2 2.8 3 3.1 3.2 3.3 3.4
Subject Titles Summary. List of Contents. List of Tables. List of Figures. List of Appendices. Glossary of Terms and Abbreviations. CHAPTER ONE: Introduction. CHAPTER TWO: Review of Literature. Air pollution. General Introduction to Air Pollution. Units for Expressing Air Pollutant Concentrations. Effect of Meteorological Factors on Air Pollution. Air Quality Standards. Criteria air Pollutants. Carbon Monoxide (CO). Nitrogen Oxides: (NOx). Nitric oxide (NO). Nitrous oxide (N2O). Nitrogen dioxide (NO2). Ground-level ozone (O3). Sulfur dioxide (SO2). Particulate matters (PM). Lead (Pb). Environmental effects of atmospheric carbon dioxide (CO2). Ambient air hydrocarbons (HC). Oxygen (O2) Heavy metals in the environment. Heavy metals in soil. Heavy metal content of ambient dust. Heavy metal in plants. Heavy metal in rainwater. Traffic-related (vehicular) air pollution. Other vehicles exhaust criteria. Diesel opacity test and “K” value. Lambda (λ). Vehicles exhaust standards and legislations. CHAPTER THREE: Materials and Methods. General description of Sulaimani city. Motor vehicle growth in Sulaimani city. Air pollution sources other than vehicles in Sulaimani city. Environmental sampling.
Page No. I IV VII IX XII XIII 1 5 5 5 8 8 9 12 12 14 15 17 18 21 26 30 37 41 47 50 51 52 55 59 60 62 71 71 72 73 77 77 82 84 86 IV
List of Contents Subject No. 3.4.1 3.4.2 3.4.3 3.5 3.6 3.7 3.8 3.8.1 3.8.1.1 3.8.1.2 3.8.1.3 3.8.1.4 3.8.2 3.8.2.1 3.8.2.2 3.8.2.3 3.8.2.4 3.8.3 3.8.3.1 3.8.3.2 3.8.3.3 3.8.3.4 3.8.4 3.9 3.10 4 4.1 4.1.1 4.1.2 4.1.2.1 4.1.2.2 4.1.2.3 4.1.3 4.1.4 4.1.5 4.1.5.1 4.1.5.2 4.1.5.3
Subject Titles Soil and plant sampling. Settleable dust (dustfall) sampling. Rainwater sampling. Measurement of ambient gases concentration. Measurement of particulate matter (PM) concentration. Real-Time measurement of vehicle exhausts gas flow. Analytical procedures for sampling and trace element determination in environmental samples (dust, soil, plant and rainwater Samples). Dust Samples Analysis. Percent of organic carbon. Percent of calcium carbonate. pH of the 1:10 Dust: water suspension ratio. Heavy metal analysis. Soil samples analysis. Particle size distribution. Cation exchange capacity. The Percent of organic carbon, calcium carbonate and also the same heavy metals content of (Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb). The other chemical analysis of soil samples. Rainwater Sample Analysis. Heavy metal analysis Total suspended solids. Nitrate ion (NO3). The other chemical analysis of rainwater samples. Plant Sample Analysis. Traffic Saturation Flow Rate (Traffic volume). Statistical analysis. CHAPTER FOUR: Results and Discussion. Levels of criteria air pollutants in Sulaimani city. Carbon monoxide (CO). Nitrogen oxide (NOx). Nitrogen dioxide (NO2). Nitric oxide (nitrogen monoxide) NO. Nitrous oxide (dinitrogen monoxide) N2O. Ground-level ozone (O3). Sulfur dioxide (SO2). Particulate matters (PM). PM1.0: Particulate matter less than 1 micrometer in aerodynamic diameter. PM2.5: Particulate matter less than 2.5 micrometer in aerodynamic diameter. PM10.0: Particulate matter less than 10.0 micrometer in aerodynamic diameter.
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List of Contents Subject No. 4.1.5.4 4.1.6 4.2
Subject Titles
Notable Dust storms in Sulaimani city Lead (Pb). Other examined heavy metals in settleable dust (deposited or dustfall) Samples. 4.3 Heavy metals in soil. 4.4 Heavy metals in plant: 4.5 Heavy metals in rainwater. 4.6 Levels of other gases rather than criteria gases in Sulaimani city. 4.6.1 Carbon dioxide (CO2). 4.6.2 Hydrocarbons (HC). 4.6.3 Oxygen (O2). 4.7 Traffic-related (vehicular) air pollution. 4.7.1 Number of registered motor vehicles in Sulaimani governorate. 4.7.2 Surveying of traffic volume (Traffic saturation flow rate). 4.7.3 Vehicular exhaust emission test. 4.7.3.1 The investigated exhaust parameters for gasoline fueled vehicles. 4.7.3.1.A Percent of emitted carbon dioxide (CO2%). 4-7.3.1.B Percent of emitted carbon monoxide (CO %). 4-7.3.1.C Concentration of emitted hydrocarbons (HC) ppm. 4.7.3.1. D Percent of emitted oxygen (O2 %). 4.7.3.1.E Lambda (λ). 4.7.3.2 Diesel Opacity and “K” value Test for diesel fueled vehicles. 5 CHAPTER FIVE: Conclusions and Recommendations 5.1 Conclusions 5.2 Recommendations 6 References 7 Appendices 9 Summary in Kurdish 10 Summary in Arabic
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List of Tables Table 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 4.1a 4.1b 4.2 4.3 4.4 4.5 4.6
Title
Page No. The horrible total-man breathes, coughs, and dies due to air pollution. 6 National Ambient Air Quality Standards (NAAQS). 11 Terminology along the corresponding particle sizes. 31 List of the top 10 countries of the world by carbon dioxide emissions in 2007. 43 UK Emissions of 16 PHAs Compounds (in tones) and their Contribution 49 Sources. Mean metal concentration (mg kg-1 soil) extracted by aqua regia in urban soils 54 of different cities in the world. Some physiochemical characteristics of rainwater samples from different 62 cities in the world. European emission standards for light commercial vehicles 1305-1760 kg 75 (Category N1-II). Presentation of some demographic data of Sulaimani governorate, Sulaimani 80 city center and for the studied area. Green area coverage in Sulaimani city and green space per inhabitant. 82 Total number of registered vehicles in Sulaimani city (Sulaimani Numbering) 83 during the years 1999 to 2010. Number of diesel generator (Alternator) in Sulaimani city. 85 Amount of some crude oil products distributed by the official sector in 85 Sulaimani and Erbil governorates during the year 2009. Number and type of factories and industrial activity in Sulaimani city and 85 surroundings GPS coordination and vegetation covers for the soil and plant sample 87 locations. GPS coordinates of the settleable dust sample locations. 90 GPS coordinates of the rainwater sample locations. 93 GPS data and meteorological parameters for the studied locations of ambient 95 gases and particulate matter (PM) concentrations. Average volumetric concentrations of some ambient air gases in the studied 106 locations. Average gravimetric concentrations of some ambient air gases in the studied 108 locations. Total annual emission in tones by source category for Hamilton city /Ontario 111 Canada Response in human subjects exposed to various concentration of inhaled NO. 117 Airborne particulate matter (PM) concentrations of the Fractions PM10.0 , 131 PM2.5, and PM1.0 for the studied locations. Movement of soil particles under a wind force of meters/second 140 Notable dust/sandstorms occurred in Sulaimani city during 19.9.2009 to 142 11.6.2011. VII
List of Tables Table 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17 4.18 4.19
Title Airborne heavy metal concentrations of the settleable dust samples (mg. kg-1 dust). Concentration of heavy metals in atmospheric settleable dust samples (ng .m-3 air). Ranges of heavy metal concentration (mg. kg-1 dust) in the settleable dust sample of different sources. Total content of some heavy metals in topsoil's samples (0-15 cm) of the studied locations (mg kg-1 soil). Ranges of heavy metal concentration (mg. kg-1) in topsoil’s and plant samples of the studied locations. Heavy metal concentrations in plant samples of the studied locations (mg. kg-1 dry matter). Range and mean of heavy metal concentration in some plant species of the studied area in Sulaimani city. Heavy metal content in pine tree needle collected from different sites in Tehran, Iran. (mg. kg-1 dry matter). Heavy metals concentrations in the rainwater (wet deposition) samples for the studied locations (μg. L-1 rainwater). Mean, normal and maximum concentration level for rainwater samples collected in two different times. Traffic volume in working days at some crosses (squares) and streets of Sulaimani city. Level ranges of some emitted gases and lambda (λ) values for gasoline fueled vehicles according to models year. Level ranges of the opacity and K values of some diesel fueled vehicles (trucks).
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List of Figures Figure 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8a 3.8b 3.8c
Title Sources of nitrogen oxides-national emissions in 1999. Sources of on-road mobile nitrogen oxides- national emissions in 1999. A simplified relationship of nitrogen oxides emissions with formation of NO2 and other harmful reaction products including O3 and PM. Photolysis of NO2 and generation of ozone O3. The influence of HCs and free radicals on atmospheric ozone generation. Spring ozone concentrations in Beijing, China between 1982 and 1997. Effect of O3 on yield of sorghum, field com, winter wheat, soybean, peanut and cotton crops. Reduction in sulfur dioxide (SO2) emission due to US EPA’s acid rain program. Displays a typical size distribution of atmospheric particulate matter. Cross-section of black carbon makes up the core of a diesel particulate. Carbon dioxide concentration at Mauna Loa Observatory, Hawaii. Vehicles are significant issue that impacting environment and urban areas. Soot particles from combustion of fossil fuels. Composition of exhaust emissions of petrol engines. Composition of exhaust emissions of diesel engines. Carbon monoxide emission rates by vehicle category vehicle category and different speed. Oxides of nitrogen emission rates by vehicle category at different speed. Volatile organic compounds emission rates by vehicle category and different speed. Comparison of Japanese and European particulate (PM10) emission levels for heavy diesel vehicles Map of Iraq showing Sulaimani Governorate. Map of Sulaimani city showing Quarters Location. Average temperature, relative humidity and wind speed for the studied area during the years 1973 to 2006. Soil, plant and rainwater sampling path. (Source: Google Earth) Dust sampling path. (Source: Google Earth). Instruments used and some location of the current study. Measurement path of gases and particulate matter (PM) concentrations. Average values of wind speed for the studied locations of ambient gases and particulate matter (PM) measurements. Average values of temperature for the studied locations of ambient gases and particulate matter (PM) measurements. Average values of relative humidity for the studied locations of ambient gases and particulate matter (PM) measurements.
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List of Figures Figure 3.8d 3.9 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10a 4.10b 4.10c 4.11a 4.11b 4.12a 4.12b 4.13a 4.13b 4.14a 4.14b 4.15a 4.15b 4.16a 4.16b 4.17a 4.17b 4.18a 4-18b 4.19 4.20 4.21 4.22
Title
Page No. Average values of the pressure for the studied locations of ambient gases and 99 particulate matter (PM) measurements. Instruments used for the current study 101 Average volumetric concentrations of (CO) gas in the study locations. 110 Average volumetric concentrations of ambient (NO2) gas in the studied 112 locations. Average volumetric concentrations of ambient (NO) gas in the studied 116 locations. Average volumetric concentrations of ambient (N2O) gas in the studied 119 locations. Average volumetric concentrations of ambient (O3) gas in the studied 121 locations. Ozone and dust injuries in Plant leaves. 124 Average volumetric concentrations of ambient (SO2) gas in the studied 126 locations. Average airborne particulate matter (PM) concentrations for the fractions 133 PM10.0 , PM2.5, and PM1.0 of the studied locations. PM10.0 level concentraion for the studied locations. 138 Dust over Syria and Iraq on February 22, 2010. 144 Dust storm over Iraq and Iran on March 4, 2011. 145 Dust over Southwestern Asia and the Arabian Sea on June 1, 2011 146 Airborne Pb concentrations of the settle able dust samples. 152 Concentration of Pb in settleable particles samples of atmospheric source. 152 Airborne Cr concentrations of the settleable dust samples. 158 Concentration of Cr in settleable particles samples of atmospheric source. 158 Airborne Mn concentrations of the settleable dust samples. 159 Concentration of Mn in settleable particles samples of atmospheric source. 159 Airborne Fe concentrations of the settle able dust samples. 159 Concentration of Fe in settleable particles samples of atmospheric source. 160 Airborne Ni concentrations of the settle able dust samples. 160 Concentration of Ni in settleable particles samples of atmospheric source. 160 Airborne Cu Concentrations of the settle able dust samples. 161 Concentration of Cu in settleable particles samples of atmospheric source. 161 Airborne Zn concentration of the settle able dust samples. 161 Concentration of Zn in settleable particles samples of atmospheric source. 162 Airborne Cd concentrations of the settle able dust samples. 162 Concentration of Cd in settleable particles samples of atmospheric source 162 Cr concentration in topsoil's samples (0-15 cm) of the studied locations. 167 Mn concentration in topsoil's samples (0-15 cm) of the studied locations. 168 Fe concentration in topsoil's samples (0-15 cm) of the studied locations. 168 Ni concentration in topsoil's samples (0-15 cm) of the studied locations. 168 X
Figure 4.23 4.24 4.25 4.26 4.27 4.28 4.29 4.30 4.31 4.32 4.33 4.34 4.35 4.36 4.37
List of Figures Title Cu concentration in topsoil's samples (0-15 cm) of the studied locations. Zn concentration in topsoil's samples (0-15 cm) of the studied locations. Cd concentration in topsoil's samples (0-15 cm) of the studied locations. Pb concentration in topsoil's samples (0-15 cm) of the studied locations. Average volumetric concentrations of (CO2) gas in the studied locations. Average volumetric concentrations of (HC) gas in the studied locations. Average volumetric concentrations of (O2) gas in the studied locations. Total number of registered vehicles in Sulaimani city (Sulaimani numbering) during the years 1999 to 2010. Ranges percent of exhaust emitted CO2 with corresponded vehicle number. Ranges percent of exhaust emitted CO with corresponded vehicle number. Ranges concentration of emitted hydrocarbons (HC) in ppm and the numbers and percents of corresponded vehicle. Ranges percent of exhaust emitted left O2 with corresponded vehicle number. Ranges intervals of lambda (λ) values and the number and percent of corresponded vehicles. Level Ranges of the average opacity of some diesel fueled vehicles (Trucks). Level Ranges of the average K-values (extinction coefficient) of some diesel fueled vehicles (Trucks).
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List of Appendices Appendix 1 2 3 4 5 6 7 8 9 10 11 12 13
Title Some chemical properties of the collected airborne dust samples for the studied locations. Basic physicochemical properties of topsoil's (0-20 cm) for the studied locations. Basic soluble ions, pH, and ECe of the topsoil (0-15 cm) samples for the studied locations. Some chemical properties of the rainwater samples for the studied locations. Pearson correlation coefficient values among and between the investigated gases and heavy metals in settable dust samples. Multiple comparisons tests (Duncan’s test) between the average concentrations of the studied ambient gases for the study locations. Multiple comparisons tests (Duncan’s test) between the average values of PM10.0 level for the studied locations (mg m-3) Pearson correlations among heavy metals in soil samples and heavy metals with some soil chemical properties. Pearson correlation among the investigated heavy metals in plant samples
Pearson correlation among heavy metal in rainwater samples List of some countries by vehicles per inhabitants. Pearson Correlation among the exhaust emission parameters. European Union (EU) vehicle emission standards in gram per crossed kilometer (g/km), according to Euro 5.
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Glossary of Terms and Abbreviations Abbreviation or Terms A.A.F AAS Act ADD AEA AFR AHU AIT annual arithmetic mean ANPR APHA AQC AQI AQM AQS AT atm. ATSDR Attainment area B-pb BS BTEX BUWAL BW C.F CAAQS CDC CDCP CDIAC CEC CEMS CEPA CFC CFPP CL/P cmole
Definitions Air Accumulation Factor Atomic Absorption Spectroscopy In an air pollution control context, usually refers to the federal Clean Air Act. Average daily dose AEA is a business name of AEA Technology Air-fuel ratio Al-Hussien Bin Talal University An Inconvenient True The mean (average) of a set of values of a variable over a calendar year. The arithmetic mean is equal to the sum of all the readings divided by the number of readings. Automatic Number Plate Recognition American Public Health Association Air quality guidelines Air Quality Index; The air quality index is a number indicating the air quality at a particular time in a particular area. Air Quality Management Air quality strategy Averaging time (days) Atmosphere (unit of pressure). Agency for Toxic Substances and Disease Registry A geographic area that the US EPA has designated as meeting the NAAQS for a specified pollutant blood pb Black smoke (Benzene, toluene, ethylbenzene and xylene Bundesamt für Umwelt, Wald und Landschaft Body weight (average) Concentration factor California Ambient Air Quality Standards Centers for Disease Control Center for Disease Control and Prevention Carbon Dioxide Information Analysis Center Cation Exchange Capacity Continuous emissions monitoring system (at stationary sources). California Environmental Protection Agency Chlorofluorocarbon Coal fired power plants Cleft lip palate Centimole XIII
Co CNS COHb COMS Criteria pollutant
CTRE CY DEFRA DEP dL dpa DPFs dS.m-1 DSEWPCAG E EC ECE ECe ECE ED EEA EEMS EF EIS EP EPA EPR ESC ETBE EU FCCC Fe Fo FY g g. L-1 GGWS GHG GIS GPS
Celsius degree Central nervous system carboxyhemoglobin Continuous opacity monitoring system A pollutant for which a NAAQS has been established. Criteria pollutants include ozone, carbon monoxide, sulfur dioxide, nitrogen dioxide, lead, PM10 and PM2.5. The US EPA has more information. Center for Transportation Research and Education Calendar year Department for Environment, Food and Rural Affairs Department of Environmental Protection Deciliter Diameter of particle Diesel Particulate Filters Decisiemens per meter Department of Sustainability, Environment, Water, Population and Communities/Australian Government Emission European Commission European Commission Environment Electrical Conductivity for the extract European Commission Environment Exposure duration (years) European Environmental Agency Emergency episode monitoring station Exposure frequency (days / year) Emissions Inventory System Environmental Protection Environmental Protection Agency, usually the US EPA. Environmental Performance Report Environmental Systems Corporation Ethyl tert-butyl ether European Union Framework Convention on Climate Change Iron Fahrenheit degrees; Fiscal year. For example, FY03 means the fiscal year ending in 2003. gram Grams per liter. Great Global Warming Swindle Greenhouse gas Geographic information system. Global Positioning System XIV
Gt GVWR GWP GWP h H2S HAP
Hb HC HCDES HCFC HCHO HCO3HD HDT HEI HFCs HI HQ IARC ICP-OES IEC IngR INO IPCC ISO ISTEA IUPAC j JAMA K K K k value KAMA KIE kPa KRG KRI kVA kW LRTAP
Gigatons Gross Vehicle Weight Rating Global warming potential. Global Warming Potential plank’s constant Hydrogen sulfide Hazardous air pollutants: One of 188 substances (originally 189) identified as air toxics by section 112(b) of the federal Clean Air Act. APCD regulation 5.14 also lists these substances Hemoglobin Hydrocarbons Hamilton County Department of Environmental Services (Ohio) Hydrochlorofluorocarbon. Formaldehyde. Bicarbonate ion Heavy-duty Heavy Duty Trucks Health Effects Institute Hydrofluorocarbons Hazard index Hazard quotient International Agency for Research on Cancer Inductively Coupled Plasma-Optical Emission Spectroscopy Industrial Environmental Carbon Ingestion rates Inhaled nitric oxide Intergovernmental Panel on Climate Change International Organization for Standardization Intermodal Surface Transportation Efficiency Act International Union of Pure and Applied Chemistry Joule; The primary metric unit of energy. 1 J = 1 kg.m2/s2. Japan Automobile Manufacturers Association Kilo Kelvin Potassium Extinction coefficient or mass attenuation coefficient or mass absorption coefficient Korea Automobile Manufacturers Association Karak Industrial Estate Kilopascal Kurdistan Region Government Kurdistan Region of Iraq Kilovolt ampere Kilowatt Long-range Transboundary Air Pollution XV
lb mbar MDH MDOHFS MECA metHb µS.cm-1 MJ .m-2 .day-1 MODIS MSW MTBE MY NAAQS NAEI NAICS: NAMS NASA NC NCLAP ND NESHAP NETI NIEHS NIST NMHC NOAA Nonattainment area O2Hb OAQPS ODS OEC OHSMS opacity
OSHA OSHIC Pa PAHs
pound Millibar Minnesota Department of Health Minnesota Department of Health Fact Sheet Manufacturers of Emission Controls Association Methemoglobin Microsiemens per centimeter Megajoules per square meter per day Moderate Resolution Imaging Spectroradiometer Municipal Solid Waste Methyl tertiary-butyl ether; an oxygenate added to gasoline to reduce emissions of pollutants when the fuel is burned Model year for production automobiles National Ambient Air Quality Standards National Atmospheric Emission Inventory North American Industry Classification System National air monitoring station National Aeronautics and Space Administration Not collected National Crop Loss Assessment Programme Not detected National emission standards for hazardous air pollutants National Emissions Trends Inventory National Institute of Environmental Health Sciences National Institute of Standards and Technology Non-methane hydrocarbons National Oceanic and Atmospheric Administration A geographic area that the US EPA has designated as not meeting the NAAQS for a specified pollutant. Oxyhemoglobin Office of Air Quality Planning and Standards Ozone-depleting substance OHIO Environmental Council Occupational Health & Safety Management System The opacity of a material (solid, liquid or gas) is the fraction of transmitted light obscured by the substance. For air pollution purposes, as in testing the smoke from a stack or the exhaust of a vehicle, opacity is usually measured as a percent, where 0% opacity means completely transparent and 100% opacity means completely opaque. Occupational Safety & Health Administration International Occupational Safety and Health Information Centre Pascal; The standard metric unit of pressure, equal to one Newton per square meter = one joule per cubic meter Polycyclic aromatic hydrocarbons XVI
PaO2 PC PCU PEI PFCs PM PM1.0 PM10 PM2.5 PMSA POPs PTE PVI PVR RCEP RF RO2 ROS s SAE SF6 short tones st. STAPPA STP TAP TCE TEL TLV TML Tragedy of the Commons
TSP TSS UCAR UFP or UP ultra fine particles UNCCD UNECE UNFCCC
Partial pressure of oxygen Personal car Passenger-Car Unit Periodic emission inventory Perfluorocarbons Particulate matter Particulate matter with an aerodynamic diameter of 1.0 Particulate matter with an aerodynamic diameter of 10 Particulate matter with an aerodynamic diameter of 2.5 Primary Metropolitan Statistical Area Persistence organic pollutants Potentially toxic elements Periodic Vehicle Inspection Pulmonary vascular resistance Royal Commission on Environmental Pollution. Radiative forcing Alkyl peroxy. Reactive oxygen species. seconds Society of Automotive Engineers Sulfur hexafluoride 1 metric ton = 1.1023 short tons Stoichiometric State and Territorial Air Pollution Program Administrators Standard Temperature and pressure Toxic air pollutant Trichloroethene Tetraethyl lead Threshold limit value Tetra methyl lead The concept was clearly expressed for the first time by Garrett Hardin in his now famous article in Science in 1968. It is a concept that states that any resource open to everyone will eventually be destroyed or if a resource is held in common for use by all, then ultimately that resource will be destroyed. Total suspended particulates Total Suspended Solid University Corporation for Atmospheric research Ultrafine particles or ultra particles Particles with aerodynamic diameters less than 0.08 µm. They are emitted directly from combustion sources or condense from cooled gases soon after emission. United Nations Convention to Compact Desertification United Nations Economic Commission for Europe United Nations Framework Convention on Climate Change XVII
USDA-ARS USGS UV ν VET VOCs W. m-2 WB WHO WMO λ
United States Department of Agriculture-Agricultural Research Service United State Geology Survey ultraviolet light, ultraviolet radiation frequency Vehicle emissions testing Volatile organic compounds watts per square meter World Bank World Health Organization World Meteorological Organization Lambda
XVIII
CHAPTER ONE: Introduction Introduction Pollution likely affects over a billion people around the world, with millions poisoned and killed each year. According to some estimates, the health of over one billion people around the globe is compromised by exposure to the pollution of air, water and soil. About 40 percent of deaths worldwide are caused by water, air and soil pollution, concluded by Cornell researcher (Pimentel et al., 2007). Air pollution has been with us since the fire was lit and it does not respect national borders. Air pollution is a general term that covers a broad range of contaminants in the atmosphere. Pollution can occur from natural causes or from human activities. The atmosphere is one of the few resources shared among all Earth’s inhabitants. As a consequence, the air pollutants that spew everywhere can have a detrimental impact on people and the environment locally or an ocean away. For that reason, air pollution can be considered as an example of the “Tragedy of the Commons”. There is growing recognition that air-borne emissions from major urban and industrial areas influence both air quality and climate changes on scales ranging from regional up to continental and global. Deteriorating urban air quality affects the viability of important natural and agricultural ecosystems in regions surrounding highly urbanized areas, and significantly influences regional atmospheric chemistry and global climate change (Gurjar et al., 2010). Past research indicated a link between urban air pollution and increased rates of mortality and morbidity (Vigotti et al, 1996; Ostro et al, 2000; Stieb et al, 2002; Metzger et al, 2004; Curtis et al, 2006; Bell et al, 2008). It has also shown to be detrimental to the environment (United State Environmental Protection Agency (EPA), 2008a; Health Canada, 2003; Health Canada, 2006b). Such findings have only continued to strengthen the concern that outdoor air pollution continues to pose a threat to public health (Samet et al, 2000). World Health Organization stated that 2.4 million people die each year from causes directly attributable to air pollution, with 1.5 million of these deaths attributable to indoor air pollution (WHO, 2002). Indoor air pollution and urban air quality are listed as two of the world’s worst pollution problems in the 2008 (Blacksmith Institute, 2009). The United Kingdom suffered its worst air pollution event when the Great Smog in December 1952 formed over London. In six days more than 4,000 died, and 8,000 more died within the following months (Stegeman et al., 2002). 1
CHAPTER ONE: Introduction A new economic study of the health impacts and associated costs of air pollution in the Los Angeles Basin and San Joaquin Valley of Southern California showed that more than 3800 people die prematurely (approximately 14 years earlier than normal) and this cause loses of about $28 billion annually due to premature deaths and illnesses linked to ozone and particulates spewed from hundreds of locations in the South Coast and San Joaquin air basins each year because air pollution levels violate federal standards (Sahagun, 2008; Kay, 2008). The researchers (Block and Calderon-Garciduenas, 2009) at Virginia Commonwealth University Medical Campus say air pollution has been implicated as a chronic source of neuroinflammation and reactive oxygen species (ROS) that produce neuropathology and central nervous system (CNS) disease. Stroke incidence and Alzheimer’s and Parkinson’s disease pathology are linked to air pollution. They have further reported that recent findings revealed that air pollution reaches the brain and activates the residents innate immune response to become a chronic source of pro-inflammatory factors and (ROS), culminating in (CNS). Changes in the blood-brain barrier are a key component in the way air pollution affects the brain and central nervous system. When the blood-brain barrier becomes more permeable, pollution has greater access to the brain. In addition to that, air pollution causes acid rain, global warming and climate change, contributes to visibility deterioration, damages property, pollutes water resources, can harm forests, wildlife, and entire ecosystems. Air pollution is also a hidden threat to food production and may damage agricultural production in three major ways (Marshall et al., 1997): 1- Direct visible injury, usually to leaf tissue. 2- Direct effect on growth and yield 3- Indirect effects, where air pollution may cause a range of subtle physiological, chemical or anatomical changes which will not lead to detectable yield under optimal growth condition. Today nearly 3000 different anthropogenic air pollutants have been identified and most of them are organic (including organometals). Combustion sources, especially motor vehicles, emit about 500 different compounds. However, only for about 200 of the pollutants have the impacts been investigated (Gurjar et al., 2010).
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CHAPTER ONE: Introduction In recent decade ambient air pollution in Sulaimani urban/ Kurdistan Region of Iraq (KRI) is a growing problem and its scope is starting now to come into focus, while, the city has been dramatically expanded and the urbanization section covered the majority of the surrounded cultivated area mainly due the natural population and economic growth, migration of farmers from the villages to the city and displacement of peoples by the former Iraqi Baath regime. As a consequence of these changes, the relative growth of vehicles of different models, sources and uncontrolled quality has increased unexpectedly; high electricity demand caused using of a huge number of diesel power stations; high consumption of substandard quality of fuel (gasoline, diesel, kerosene, natural gas, fuel oil) has occurred; large number of buildings and industrial establishments have been constructed; large amount of Municipal Solid Waste (MSW) including many toxic and hazardous compounds has been incinerated daily and directly in (Tanjaro landfill) in Sulaimani city; in addition to that, frequent regional and transboundary sand storm and air movement have occurred due to the global warming and climate change. Moreover the green zones in the city were not in compliance to resist and detoxify the emitted primary and secondary air pollutants such as (CO2, CO, O3, NOx, SO2, VOCs, particulate matters (PM), and toxic metals. Smog hanging over the city became a most familiar and obvious form of air pollution indicator in Sulaimani urban, this means, that the natural capacity of the local system and process is so overloaded that cannot assimilate and detoxify(detoxicate) the air pollutant. Therefore, air pollution and air quality issue in Sulaimani city is something that we cannot really ignore it nowadays. Although, Environmental Protection Act (8) in 2008 came in to force by the Ministry of Environment in KRG, but no particular attention is given about the regulation and legislation of the significant impacts and risk of air pollution and to restrict the concentration of pollutant in the ambient air to such level as will not adversely affect the health, well-being or welfare of the community. In Sulaimani city there have been no published researches on air pollution and quality, therefore, our initiative to conduct a study on air pollution was faced and impacted by many limitations and factor constraints, such as shortage of monitoring and measurement stations, lack of sophisticated gas analyzer and sampling instrument, and moreover, no baseline or
3
CHAPTER ONE: Introduction background data on air quality is established. However, the proposal of the current study aimed the following targets: 1- One of the major aims of the research in the current study on air pollution is to introduce detailed reviews on air pollution topics in order to inform other scientific endeavors, authorities and the decision makers about the realistic appraisal or assessment of the risks of air pollution on human health, environment and climate change. Because air pollution in Sulaimani city has not been given a required attention and no action and strategies were carried out to protect ambient air quality, moreover, it is also significantly correlated with most of the other sciences. 2- Evaluating of ambient air pollution and quality in Sulaimani urban by means of the following environmental sample analysis or measurements; a. Concentration measurement of the following pollutant gases, CO, NOx, SO2, O3, and HC in addition to the greenhouse gas CO2 and the percentage O2% in different location of traffic flow and under the different climatic condition of wind, temperature, pressure and percent of relative moisture. b. Measuring the temporal concentration level of particulate matter (PM) of the following aerodynamic diameter 1.0, 2.5 and 10 microns (PM1.0, PM2.5, and PM10.0) or less in the same locations of gases investigation and schedule time. c. Determination of the most heavy metals of the air borne and vehicle source such as Pb, Cd, Ni, Cr, Fe, and Mn in the collected environmental samples of dust, soil, plant and rain in 15 locations. d. Testing of the exhaust emission of a number of different fueled vehicles (gasoline and diesel), model year and type of vehicles and surveying of traffic volume (traffic saturation flow rate) in some locations of the city. e. Evaluation of the “hot spot” and “cold spot” sites among the studied locations, e.g. to identify where the highest or lowest concentration will be detected. 3- Introducing of the results and findings for establishing the baseline air pollution data for further future air pollution studies, in order to address a national standard of ambient air quality in KRI and also to set regulation and legislation about exhaust emission standards of vehicles in KRI.
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CHAPTER TWO: Review of Literature 2.1: Air pollution 2.1.1: General introduction to air pollution: Air Pollution and concern about air quality are not new. Complaints were recorded in the 13th century when coal was first used in London. In historical times, pollution from homes and factories was much worse than it is today, because there were few laws controlling its release into the air. After the great London smog in 1952, the Government decided to act to clean up the country's air (Encyclopedia of the Atmospheric Environment, 2000). In the 12th century, Moses Maimonides of Egypt documented the effects of polluted air on child mortality and morbidity in Cairo as it existed in his day. In England in 1228, coal smoke was determined to be “detrimental to human health,” as Queen Eleanor of Aquitaine had complained that it hurt her lungs (Griffin, 2007). With the rise of industrialization in the 18th and 19th centuries, the effects of air pollutant emissions were noted on greater portions of the population. It was not until the 20th century that air quality management approaches (i.e., primarily smoke and odor abatement measures) began to make their appearance. In 1906, Frederick G. Cottrell invented the first practical air pollution control device (Griffin, 2007). Table (2.1) showed some historic air pollution disasters and events as examples that were recorded worldwide. Air pollution is currently one of the major problems in developing countries. It has bad effects on human life causing diseases in respiratory system and chronic illnesses (McCubbin and Delucchi, 1999), on soils and plants (El Desouky and Moussa, 1998) and on the forest (Zhang and Pouyat, 2000). Air pollutants are any substance in air that could, in high enough concentration, harm humans, animals, vegetation, or material. Air pollutants can include almost any natural or artificial composition of matter capable of being airborne-solid particles, liquid droplets, gases, or a combination thereof. Air pollutants are often grouped in categories for ease in classification; some of the categories are sulfur compounds, volatile organic compounds, particulate matter, nitrogen compounds, and radioactive compounds (EPA, 2008). Air pollutants can present a real danger to living organisms as well as the wider environment and their concentrations vary greatly from place to place at any one time and with time of day and from year to year at any one place (Colls, 2002). 5
CHAPTER TWO: Review of Literature
Table (2.1): The Horrible Total-Man Breathes, Coughs, and Dies due to Air Pollution. (Schnell and Brown, 2002). Locations
Date
Deaths1
Meuse Valley, Belgium Donora, Pennsylvania, USA London, UK Poza Rica, Mexico London, UK New York, NY, USA London London London London London New York, NY, USA New York, NY, USA
1.12.1930 26.10.1948
63 18
26.11.1948 21.11.1950 5.12.1952 22.11.1953 5.11.1956 2.12.1957 26.1.1959 5.12.1962 7.1.1963 9.1.1963 23.11.1966
700-800 22 3500-4000 175-260 1000 700-800 200.250 700 700 200-400 170
Reported Illness 6000 5900 (43%) <320 Unknown Unknown Unknown Unknown -
Common Conditions Low Atmospheric Dilution Fog and Gaseous Materials -
1. Number of deaths above expected average death rate As another example of air pollution disasters or events is the poisons gas attack (chemical gases weapon) in Halabja, Sulaimani/Kurdistan region of Iraq on 16.3.1998, which caused in few hours to die 3500 to 5000 peoples, and 7000 to 1000 illness cases (BBC on this day, 1988; Beeston R., 2010). Generally, the evidence on air pollutants and health comes from different sources of information, including observational epidemiology, controlled human exposures to pollutants and animal toxicology. Pollutants can be classified as primary or secondary. Primary pollutant is as any pollutant that is emitted into the atmosphere directly from its source and that retains the same chemical form. An example of a primary pollutant is dust that blows into the air from a landfill, or as ash from a volcanic eruption, the carbon monoxide gas from a motor vehicle exhaust or sulfur dioxide released from factories. Secondary pollutants are those that are produced by reactions either in the gas phase or in aerosols suspended in the atmosphere from the precursor or primary emissions. Secondary pollutants undergo a chemical change once they reach the
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CHAPTER TWO: Review of Literature atmosphere (EPA, 2008). The best known of the secondary pollutants are certain gases that are synthesized by photochemical reactions in the lower atmosphere. Sources of air pollution are twofold: human activities and natural environmental processes. Human activities causing air pollution are industry, use of motorized vehicles, incineration of municipal solid waste and low quality fuel for food preparation and heating purposes (Boyazzi, 1998). Hazardous household wastes comprise a significant proportion of municipal solid waste (MSW), and therefore serve as the source of many toxic or carcinogenic organic chemicals that are released in the environment through landfill gases or leachates (Leahy J. G. et al., 2004). Land- fill gases are the source of more than 500000 Mg (megagrams) of VOC emissions annually, including toluene, methylene chloride, trichloroethene (TCE), benzene, and vinyl chloride (EPA, 1991). Vegetation, in some regions, emits environmentally significant amounts of VOCs on warmer days. These VOCs react with primary anthropogenic pollutants, specifically; NOx, SO2, and anthropogenic organic carbon compound-to produce a seasonal haze of secondary pollutants (Goldstein, 2009). Petroleum refineries are also a source of hazardous and toxic air pollutants, such as BTEX compounds (benzene, toluene, ethylbenzene, and xylene). They are also a major source of criteria air pollutants like; particulate matter PM, nitrogen oxides NOx, carbon monoxide CO, hydrogen sulfide H2S, and sulfur oxides SOx. Refineries also release less toxic hydrocarbons, such as natural gas (methane) and other light volatile fuels and oils (Speight, 2005). Natural sources are Volcanoes, dust storms, forest-and grassland fires. According to Gassmann and Mazzeo (2000), air pollution may be defined as „„the presence in the atmosphere of pollutants or combinations of them in such quantities and of such duration as may be or may tend to be injurious to human, plant, or animal life, or property, or which unreasonably interferes with the comfortable enjoyment of life or the conduct of business.‟‟ But, air pollution potential is a measure of the atmospheric conditions that are unable to transport and dilute pollutants into the air, independently of the existence of sources. This potential can be determined from two atmospheric parameters: mixing height and transport wind. The impact of air pollution can be viewed from a global, regional or local perspective. Recently, international concern has turned to a number of air pollutants which, though found in 7
CHAPTER TWO: Review of Literature relatively small concentrations, have the potential to adversely affect human health and the environment through long-term exposure. These substances have been given a variety of names including “hazardous air pollutants” and “air toxics” and about 189 of these toxic chemicals have been detected in ambient air (EPA, 2002). There are many factors that cause dispersion of air pollution, including weather condition such as temperature, wind speed and direction, humidity, topography of the area, relief of the area such as flat or hilly, or the local situation of the area such as whether the area is covered by buildings or whether there is ventilation in traffic corridors. 2.1.2: Units for expressing air pollutant concentrations: The authors Colls (2002) and Cornell (2008) have indicated that two sets of concentration units are in common use, volumetric and gravimetric. If all the molecules of any one pollutant gas could be extracted from a given volume of the air and held at their original temperature and pressure, a certain volume of the pure pollutant would result. Volumetric units specify the mixing ratio between this pollutant volume and the original volume and this is equivalent to the ratio of the number of pollutant gas molecules to the total number of air molecules. Examples of volumetric units are parts per million by volume (ppmv), parts per billion by volume (ppbv) and parts per trillion by volume (pptv). The volumetric concentration is invariant with temperature and pressure, and therefore remains the same. Gravimetric units specify the mass of material per unit volume of air. Examples of gravimetric units are milligram per cubic meter (mg m-3), microgram per cubic meter (µg m-3) and nanogram per cubic meter (ng m-3) .Unlike volumetric units, gravimetric units are appropriate for particles as well as for gases. Conversion between gravimetric and volumetric units and also correction for non-standard temperature and pressure could be done using ideal gas equations. 2.1.3: Effect of meteorological factors on air pollution: There are several meteorological factors that when combined together make air pollution worse over a particular location. Complicating factors include complexity in the thermal structure of the lower atmosphere, as well as related local wind circulations can inhibit dispersion of pollutants. According to Leighton and Spark (1997) the condition of air pollution hazardous to people on the ground arise when there are a coincidence of three factors: 8
CHAPTER TWO: Review of Literature 1- A high rate of emissions (E) per unit surface area (or from a point or line source); 2- An inversion layer at a low height, which limits vertical mixing; in particular, the increase of potential temperature () in the lowest km above the ground; 3- A low wind speed (w), which determines pollutant transport. The latter two factors are purely meteorological and therefore highly variable, although there are some seasonal trends. Thus, forecasters need to monitor the product (E, and w) and determine safety thresholds. Meteorological factors that increase air pollution include: light, wind, high pressure (stable lower troposphere). When the atmosphere is stable the air near the surface does not mix with the air higher aloft. This traps pollutants close to the earth's surface. Light and wind keeps the pollutants over the same area. Over time the concentration of pollutants will increase (Haby, 2007). Meteorology has an important, practical application in the area of control and management of air quality. Its significance was first realized when the increasingly heavy use of coal for home heating and industrial power led to episodes of extreme sulfur dioxide pollution during certain weather conditions. The most famous case occurred in London during foggy December in 1952, when approximately 4000 people died as the direct result of air pollution. Four years later, in January 1956, under similar conditions, 1000 deaths were blamed on an extended fog in London. Since that time, the problem has grown as a result of industrialization. High air pollution concentration are not longer local and restricted to urban areas, but can be transported for long distances by large-scale weather patterns (Sorbjan, 2003). Meteorological researches also allow us to evaluate modeling as a tool for the management and amelioration of air quality problems. 2.1.4: Air quality standards: It was only well into the latter half of the 20th century that instruments become available to monitor air pollution levels on a real-time basis. Gas analyzer instruments also made possible the setting of numerical standards for health and welfare as management tools. Air quality standards are expressed today as a given concentration of the contaminant or pollutant averaged over a specified period of time. Air quality standards are adopted by many international organizations or agencies e.g. World Health Organization (WHO) and European Environmental Agency (EEA). Many 9
CHAPTER TWO: Review of Literature countries, local government or individual states have also adopted their own air quality standards (national standards). There are pollutants for which numerical standards have been set. Although, air quality standards have been regulated by many individual countries or states such as; US Environmental Protection Agency-EPA, The National Institute of Public health and Environment of the Netherland-RIVM,
California Ambient Air Quality Standards
(CAAQS), Environmental Quality Standards in Japan - Air Quality/ Ministry of the Environment Government of Japan, India air quality standards, but still the standards of WHO, EEA and EPA are the most important references for worldwide countries. The two basic categories of pollutants in the ambient air that are of concern are termed “criteria pollutants” and “noncriteria or hazardous air pollutants (HAPs)”. Criteria air pollutants are those for which numerical concentration limits have been set and regulated and are used as indicators of air quality standards. There are differences (particularly, in threshold dose, occurring level, bioaccumulation, primary target organ of effects, and human health effects; acute or chronic) between how these two grouping of air pollutants act (Griffin, 2007). Whilst, noncriteria pollutants are pollutants for which standards or criteria have not been established. Although some air pollutants are known to be toxic or hazardous, they are released in relatively small quantities or in locations where individual regulation is not required. EPA (2010) has indicated that the Clean Air Act, which was last amended in 1990, required EPA to set National Ambient Air Quality Standards (NAAQS) for wide-spread pollutants from numerous and diverse sources considered harmful to public health and environment. The Clean Air Act established two types of national air quality standards. Primary standards set limits to protect public health, including the health of "sensitive" populations such as asthmatics, children, and the elderly. Secondary standards set limits to protect public welfare, including protection against visibility impairment, damage to animals, crops, vegetation, and buildings. The EPA Office of Air Quality Planning and Standards (OAQPS) has set NAAQS for six principle pollutants, which are called "criteria" pollutants (Table 2.2). When the concentration of these gases and particulate matter (PM) exceed the defined standards, this is considered air pollution. Mathematical models are the most used methods to calculate magnitude and dispersion of urban air pollution (Chakraborty and Forkenbrock, 1999). Generally, the amounts of NOx, CO, SO2, PM, temperature, humidity, wind direction and speed are measured at ground stations. 10
CHAPTER TWO: Review of Literature After that, dispersion models or interpolation methods are used to visualize the spatial distribution of air pollution. As an example, CAL3QHCR is an air pollution dispersion model developed by the California Department of Transportation. Table (2.2): National Ambient Air Quality Standards (NAAQS). (EPA, 2010). Pollutant Carbon Monoxide (CO) Lead (Pb) Nitrogen Dioxide (NO2)
Primary Standards Secondary Standards Level Averaging Time Level Averaging Time (1) 9 ppm 8-hour None (10 mg m-3) 35 ppm 1-hour (1) -3 (40 mg m ) 0.15 µg m-3 (2) Rolling 3-Month Same as Primary Average 1.5 µg m-3 Quarterly Average Same as Primary (3) 53 ppb Annual Same as Primary (Arithmetic Average) 100 ppb 1-hour (4) None -3 (5) 150 µg m 24-hour Same as Primary
Particulate Matter (PM10) Particulate 15.0 µg m-3 Annual(6) Same as Primary Matter (PM2.5) (Arithmetic Average) -3 35 µg m 24-hour (7) Same as Primary (8) Ozone 0.075 ppm 8-hour Same as Primary (O3) (2008 std) 0.08 ppm 8-hour (9) Same as Primary (1997 std) 0.12 ppm 1-hour (10) Same as Primary Sulfur 0.03 ppm Annual 0.5 ppm 3-hour (1) Dioxide (Arithmetic Average) (11) (SO2) 0.14 ppm 24-hour (1) 75 ppb (12) 1-hour None (1) Not to be exceeded more than once per year. (2) Final rule signed October 15, 2008. (3) The official level of the annual NO2 standard is 0.053 ppm, equal to 53 ppb, which is shown here for the purpose of clearer comparison to the 1-hour standard. (4) To attain this standard, the 3-year average of the 98th percentile of the daily maximum 1hour average at each monitor within an area must not exceed 100 ppb (effective January 22, 2010). (5) Not to be exceeded more than once per year on average over 3 years. 11
CHAPTER TWO: Review of Literature (6) To attain this standard, the 3-year average of the weighted annual means PM2.5 concentrations from single or multiple community-oriented monitors must not exceed 15.0 µg m-3. (7) To attain this standard, the 3-year average of the 98th percentile of 24-hour concentrations at each population-oriented monitor within an area must not exceed 35 µg m-3 (Effective December 17, 2006). (8) To attain this standard, the 3-year average of the fourth-highest daily maximum 8-hour average ozone concentrations measured at each monitor within an area over each year must not exceed 0.075 ppm. (Effective May 27, 2008) (9) (a) To attain this standard, the 3-year average of the fourth-highest daily maximum 8hour average ozone concentrations measured at each monitor within an area over each year must not exceed 0.08 ppm. (b) The 1997 standard-and the implementation rules for that standard-will remain in place for implementation purposes as EPA undertakes rulemaking to address the transition from the 1997 ozone standard to the 2008 ozone standard. (c) EPA is in the process of reconsidering these standards (set in March 2008). (10) (a) EPA revoked the 1-hour ozone standard in all areas, although some areas have continuing obligations under that standard ("anti-backsliding"). (b) The standard is attained when the expected number of days per calendar year with maximum hourly average concentrations above 0.12 ppm is < 1. (11) Final rule signed June 2, 2010. To attain this standard, the 3-year average of the 99th percentile of the daily maximum 1-hour average at each monitor within an area must not exceed 75 ppb. (12) Final rule signed June 2, 2010. To attain this standard, the 3-year average of the 99th percentile of the daily maximum 1-hour average at each monitor within an area must not exceed 75 ppb. The next topics present some information about description, sources, effects, and standard levels of the criteria pollutants. 2.1.5: Criteria air pollutants: 2.1.5.1: Carbon monoxide (CO): Carbon monoxide (CO), also called carbonous oxide, is a colorless, odorless and tasteless gas which is sparingly soluble in water and slightly lighter than air (its density is 1.25 g L-1 at 0 Co and 1 atm pressure). Although CO is also produced in normal animal metabolism in low quantities, and is thought to have some normal biological functions, but it is highly toxic to humans and animals in higher quantities, (Tikuisis et al., 1992; Omaye ST, 2002 and Oxford Dictionary of Chemistry, 2008). 12
CHAPTER TWO: Review of Literature CO has also received a great deal of clinical attention as a biological regulator, because it is known to act as anti-inflammatory, vasodilators and encourages of neovascular growth (Li, 2009). But at the same time the acute toxicity of carbon monoxide has long been recognized and it is a highly toxic gas to humans and animals when exposure to high level, for example exposure to 700 ppm of CO for one hour causes death, CO poisoning occurs when it dissolves in blood and replace oxygen as an attachment to hemoglobin [Hb(aq)], (Jacobson, 2002). The conversion of O2Hb(aq) to COHb(aq) carboxyhemoglobin causes suffocation. Carbon monoxide can also interfere with oxygen diffusion in cellular mitochondria and with intracellular oxidation (Gold, 1992). Low exposure to carbon monoxide causes headaches, dizziness and nausea. Carbon monoxide also plays a small role in ozone formation in urban area and its oxidation to CO2 which affect global climate. Moreover, it is a major atmospheric pollutant in some urban areas (Wikipedia, 2011). Carbon monoxide is produced from the partial oxidation of carbon-containing compounds; it forms when there is not enough oxygen to produce carbon dioxide CO2. CO is also released from coal plants: According to the Union of Concerned Scientists, in an average year, a typical coal plant (500 megawatts) generates 720 tons of carbon monoxide, which causes headaches and places additional stress on people with heart disease (Union of Concerned Scientists, 2008). Carbon monoxide is present in small amounts in the atmosphere, chiefly as a product of volcanic activity but also from natural and man-made fires. Worldwide, the largest source of carbon monoxide is natural in origin, due to photochemical reactions in the troposphere which generate about 5 x 1012 kilograms per year (Weinstock and Niki, 1972). The natural carbon monoxide levels in air is 0.1 ppm (Griffin, 2007), but the average level in homes or indoor air is 0.5 to 5 ppm (Green, 2008). Carbon monoxide is part of the series of cycles of chemical reactions that form Photochemical smog. Along with aldehydes, it reacts photochemically to produce peroxy radicals. Peroxy radicals subsequently oxidize nitrogen oxide (NO) to nitrogen dioxide (NO2) (National Research Council, 1977). Although this creation of NO2 is the critical step leading to low level ozone formation, it also increases this ozone in another, somewhat mutually exclusive way, by reducing the quantity of NO that is available to react with ozone (National Research Council,1977). Simplified, the net effect of the ozone cycle is: 13
CHAPTER TWO: Review of Literature CO + 2O2 → CO2 + O3. The reason for carbon monoxide toxicity is that it combines with the oxygen-carrying site of hemoglobin, the red protein within red blood cells that is responsible for delivering oxygen from the lung to body tissues. Carbon monoxide, like oxygen, has an affinity for ironcontaining molecules, but it is about 210 times more effective in binding to iron-containing hemoglobin than oxygen is. Muscle myoglobin also binds carbon monoxide 60 times more effectively than it binds oxygen and this leading to profound tissue hypoxia, which can be fatal. Since air contains 21% oxygen this means that only 0.1 ppm carbon monoxide in the air will eventually lead to 50% of the hemoglobin being combined to form carboxyhemoglobin (COHb), (WHO, 1979; Dawson, and Snyder, 1994; Ernst and Zibrak, 1998; Tomaszewski, 1999 and Raub et al., 2000). Concentration of as low as 667 ppm CO may cause up to 50% of the body‟s hemoglobin to convert to carboxyhemoglobin (Tikuisis et al., 1992). A level of 50% carboxyhemoglobin may result in seizure, coma, and fatality. In the United States, the OSHA limits long-term workplace exposure levels above 50 ppm (OSHA, 2009). The current National Ambient Air Quality Standards (NAAQS) guideline values for CO by (EPA, 2011), are a 1-hour level of 35 ppm (40 mg m-3) and 8-hour level of 9 ppm (10 mg m-3), (Table: 2.2). In the EU, the current target value for carbon monoxide concentrations is a daily 8-hour mean level of 10 mg m-3 (ECE, 2010). 2.1.5.2: Nitrogen oxides: (NOx): Nitrogen gas, normally relatively inert (unreactive), comprises about 80% of the air. At high temperatures and under certain other conditions of physical state it can combine with oxygen in the air, forming several different gaseous compounds collectively called oxides of nitrogen NOx. The most atmospheric constituents of nitrogen oxides are; nitric oxide (nitrogen monoxide, NO); nitrous oxide (dinitrogen monoxide, N2O); and (nitrogen dioxide NO2 - the criteria air pollutant), (Wisconsin Department of Natural Resources, 2010). The major sources of nitrogen oxides include fossil fuel combustion in power plants, vehicles and processes used in chemical plants. Mobile sources are responsible for more than half of all nitrogen oxide emissions in the United States. Both on-road and nonroad mobile sources are major nitrogen oxide polluters (EPA, 2010) as it can be seen in (Figures; 2.1 and 2.2). Nitrogen oxides can travel long distances, causing a variety of health and environmental problems in locations far from their emissions source. These problems include ozone and smog, 14
CHAPTER TWO: Review of Literature which are created in the atmosphere from nitrogen oxides, hydrocarbons, and sunlight. Nitrogen oxide emissions also contribute to the formation of particulate matter through chemical reactions in the atmosphere (EPA, 1999). Although there are natural sources of NOx (e.g., forest fires), the combustion of fossil fuels has been, and remains, the major contributor in European urban areas. According to European Union vehicular traffic contributes more than half of the emissions of NOx (EEA, 2002). But the contribution to total NOx emissions is even higher in some European cities. Based on data from the 1990s, in London, for example, road transport contributes 75% of NOx emissions (Holman, 1999).
Figure (2.1): Sources of nitrogen oxidesnational emissions in 1999 (EPA, 2010)
Figure (2.2): Sources of on-road mobile nitrogen oxides- national emissions in 1999 (EPA, 2010)
NOx, in addition to acting as a main precursor for tropospheric ozone, smog and particulate matter it is also harmful to human health in its own right; furthermore, it is also a precursor to acidic precipitation, which may affect both terrestrial and aquatic ecosystems. The next topics are some reviews about the three compounds of nitrogen oxide gases. 2.1.5.2.a:Nitric oxide (NO); nitric oxide (common name) or nitrogen monoxide (systematic name) is a colorless, poisonous gas and has a density of 1.337 g L-1 at 0 Co and 1 atm pressure. NO is an air pollutant and would be produced by combustion of substances in air, like in 15
CHAPTER TWO: Review of Literature automobile engines and fossil fuel power plants. In the environment, nitric oxide is a precursor of smog and acid rain (Columbia Encyclopedia, 2011 and Hou et al., 1999). The gas is synthesized via enzyme-catalyzed reactions in humans and other animals, where it serves as a signaling molecule, therefore, NO is an important messenger molecule involved in many physiological and pathological processes within the mammalian body both beneficial and detrimental depending on the amount and where in the body it is released (Hou et al., 1999). Despite nitric oxide being a simple molecule, NO is a fundamental component in the fields of neuroscience, physiology, and immunology, and was proclaimed “Molecule of the Year” in 1992 (Culotta and Koshland Jr, 1992). Nitric oxide is a small highly diffusible and a ubiquitous bioactive molecule. In the troposphere, during daylight, NO reacts with partly oxidized organic species (or the peroxy radical) to form NO2, which is then photolyzed by sunlight to reform NO as it is seen in the following reactions (Seinfeld and Pandis, 2006): NO + CH3O2 → NO2 + CH3O NO2 + sunlight → NO + O The oxidation of NO via reaction with peroxy radicals is an important source of tropospheric ozone, a major constituent of photochemical oxidants that are detrimental to human health and contribute to global warming. Knowledge of the atmospheric radical content and radical distribution provides essential information about the oxidative state of an air mass (Andres-Hernandez et al., 2010). In spite of their importance in the chemical processing of the troposphere, only a very limited number of airborne peroxy radical measurements are available (Brune et al., 1998; Jaegl´e et al., 2001; Hanke et al., 2002; Cantrell et al., 2003a, b and Martinez et al., 2008). Several methods have been developed for measuring atmospheric peroxy radicals and have recently been reviewed (Sadanaga et al., 2004; Fuchs et al., 2008 and Miyazaki et al., 2010). Particulate matter is produced from primary pollutant gases. For example, NO (from combustion or natural causes) is initially oxidized to NO2 and, ultimately, to nitric acid and ionic nitrate, Likewise, sulfur dioxide SO2 is oxidized in the atmosphere to sulfur trioxide, which, in the presence of moisture, forms sulfuric acid and, ultimately, particulates nitrates and sulfates (Griffin, 2007)
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CHAPTER TWO: Review of Literature 2.1.5.2.b: Nitrous oxide (N2O); also known as dinitrogen oxide or dinitrogen monoxide. N2O has a density of 1.977 g L-1 at 0 Co and 1 atm and its solubility is 1.5 g L-1 at 15 Co. Under room conditions it is a colorless non-flammable gas, with a pleasant slightly sweet odor. It is commonly known as “happy or laughing gas” due to the euphoric or exhilarating effects of inhaling it and it is used in surgery and dentistry for its anesthetic and analgesic effects. (Encyclopedia of Chemistry, 2007 and Cameron and May, 2007). Nitrous oxide N2O, as a natural biogenic and anthropogenic atmospheric trace gas, is active as a modulator of the earth's protective stratospheric ozone layer and as a greenhouse gas. It is relatively unreactive atmospheric gas, while NO and NO2 are quite reactive but nevertheless quite stable when isolated. N2O represents an intermediate oxidation state of nitrogen in the biological nitrogen cycle. Its presence in the atmosphere was discovered in 1939 from its effect on the solar spectrum (Weiss, 1981). Nitrous oxide N2O was present at a concentration of about 350 ppb in the atmosphere. The concentration of this compound was maintained below 300 ppb in the global nitrogen cycle before the 20th century. However, recent reports suggest that the atmospheric concentration of N2O is now increasing at a rate as high as 0.3% per year (Banin et al., 1984). Human activity is thought to account for 30% of N2O but tropical soils and oceanic sources release account for 70% (EPA, 2008). However, a 2008 study by Nobel Laureatte Paul Crutzen suggested that the amount of nitrous oxide release attributable to agricultural nitrate fertilizers has been seriously underestimated, most of which would presumably come under soil and oceanic release in the Environmental Protection Agency data (Crutzen et al., 2008). According to IPCC (2007) the atmospheric levels of N2O have risen by more than 15% since 1750. N2O has a 200- to 300-fold-stronger greenhouse effect than carbon dioxide CO2 and has the potential to destroy the ozone layer (Robertson et al., 2000). Nitrous oxide is a greenhouse gas, accounting for around 6% of the heating effect of greenhouse gases in the atmosphere (Roach, 2009). Therefore, the N2O balance is critical to the natural environment. The proposed sources of N2O are chemical industries, burning fossil fuels, and biomass, as well as soil denitrificaion of nitrogenous compounds resulting from excess agricultural fertilizer (Bremner and Blackmer, 1978; Firestone et al., 1980 and Van Bochove et al., 2001). Another critical source of N2O is wastewater treatment plants, in which considerable amounts of nitrogen pollutants removed from treated water are released into the atmosphere as N2O, as well as 17
CHAPTER TWO: Review of Literature dinitrogen N2 gas (Takaya et. al., 2003). Nitrifying bacteria aerobically oxidize ammonium contaminants to nitrite NO2- and nitrate NO3-, which are then reduced by denitrifying bacteria to nitrogen gaseous forms such as N2O and N2. Most denitrifies bacteria in soil and waste water produce nitrous oxide N2O instead of dinitrogen N2 under aerobic conditions and the final product of N2O is released into the atmosphere (Takaya et. al., 2003). Most natural nitrous oxide is as a byproduct of bacterial denitrificaion and nitrification processes, mainly in soils but also in the oceans and other natural waters (Wayne, 1991; Graedel and Crutzen, 1993; and Houghton et al., 1995). Nitrous oxide is a major greenhouse gas and air pollutant. When considered over a 100 year period, it has 298 times more impact on global warming per mass unit than that of carbon dioxide carbon dioxide (IPCC, 2007). Nitrous oxide reacts with ozone and is the main naturally occurring regulator of stratospheric ozone and also causes ozone depletion. A new study suggests that N2O emission currently is the single most important ozone-depleting substance (ODS) emission and is expected to remain the largest throughout the 21th century (Ravishankara et al., 2009 and Grossman, 2009). The regulation of N2O levels in the atmosphere is not only important for the protection of Earth's climate (Kyoto Protocol) but also for the future evolution of the stratospheric ozone layer (Montreal Protocol). A reduction of N2O emissions would decrease the anthropogenic greenhouse effect and it would have a positive impact on the recovery of the ozone layer (Dameris, 2009). 2.1.5.2.c: Nitrogen dioxide (NO2); nitrogen dioxide is a non-flammable toxic gas, reddish brown in color. It has a detectable smell and biting odor. It is a prominent air pollutant and has a density of 2.053 g L-1 at 0 Co and 1 atm (Sloss, 1992; Chang, 2006). The most important sources of NO2 are internal combustion engines, thermal power stations and, to a lesser extent, pulp mills. Butane gas heaters and stoves are also sources. The excess air required for complete combustion of fuels in these processes introduces nitrogen into the combustion reactions at high temperatures and produces nitrogen oxides NOx (Busoon et al., 2004). Nitrogen dioxide (and other nitrogen oxides) is also a precursor for a number of harmful secondary air pollutants, including nitric acid, the nitrate part of secondary inorganic aerosols and photooxidants (including ozone). The situation is also complicated by the fact that photochemical reactions take some time (depending on the composition of the atmosphere and 18
CHAPTER TWO: Review of Literature meteorological parameters) and air can travel some distance before secondary pollutants are generated. These relationships are shown schematically in Figure (2.3), (WHO, 2003).
Figure (2.3): A simplified relationship of nitrogen oxides emissions with formation of NO2 and other harmful reaction products including O3 and PM. (WHO, 2003). In addition to participating in the formation of photochemical ozone at ground level, nitrogen dioxide has its own particular health effects. The acute effects of nitrogen dioxide are both direct and indirect. The direct effects are damages to the cell membranes in the lung tissues as well as constriction of the airway passages. Asthmatics are, in particular, affected by these acute effects. The indirect effects are that nitrogen dioxide causes edema, or a filling of the intercellular spaces with fluid, which may develop into local areas of infection. Among the chronic effects of long-term exposures to nitrogen dioxide is necrosis, a term for direct cell death. In addition, there is evidence that NO2 causes a thickening of the alveolar walls of the lungs, which interferes with efficient oxygen and carbon dioxide exchange across those cell walls (Griffin, 2007). Health risks from nitrogen oxides may potentially result from NO2 itself or its reaction products including O3 and secondary particles. An individual‟s exposure to NO2 from outdoor sources will depend largely on their proximity to vehicular traffic in space and time, given that mobile sources are the chief contributors to ambient NO2 in contemporary European cities. Ambient NO2 concentrations measured at fixed urban sites may not accurately reflect personal exposure to NO2 from outdoor sources, because ambient NO2 concentrations vary widely in most locales due to traffic patterns, the characteristics of the built environment, and meteorological conditions (Rijnders, 2001). 19
CHAPTER TWO: Review of Literature The evidence on NO2 and health comes from different sources of information, including observational epidemiology, controlled human exposures to pollutants and animal toxicology. The current WHO guideline values for NO2 are a 1-hour level of 200 µg m-3 and an annual average of (40 µg m-3) (WHO, 2003). Whilst, the current National Ambient Air Quality Standards (NAAQS) guideline values for NO2 are a 1-hour level of 100 ppb and an annual average (Arithmetic average) of 53 ppb (Table 2.2). In the European Union (EU), the current target value for nitrogen dioxide NO2 concentration is a 1-hour level of 200 µg m-3 and a year‟s level of 40 µg m-3, (ECE, 2010). Children may also be especially sensitive to the effects of nitrogen oxides. A recent 2005 study by researcher at the University of California, San Diego, suggested a link between NO2 levels and Sudden Infant Death Syndrome (Thiemann et al., 2005). The Wisconsin Department of Natural Resources (2010), has emphasized that human health effects of exposure to nitrogen oxides, such as nitrogen dioxide, are similar to those of ozone, and these effects may include: Short-term exposure at concentrations greater than 3 parts per million (ppm) can measurably decrease lung function. Concentrations less than 3 ppm can irritate lungs. Concentrations as low as 0.1 ppm cause lung irritation and measurable decreases in lung function in asthmatics. Long-term lower level exposures can destroy lung tissue, leading to emphysema. The department has also indicated that other effects of nitrogen oxides can Seriously injure vegetation at certain concentrations. Effects include:
Bleaching or killing plant tissue.
Causing leaves to fall.
Reducing growth rate.
Deteriorate fabrics and fade dyes. Corrode metals (due to nitrate salts formed from nitrogen oxides). Reduce visibility. In a study by Yasmin et al. (2007) they have reported that both sulfur dioxide and nitrogen dioxide were found to have significant positive correlations with total particulate polycyclic aromatic hydrocarbons (PAHs) as well as with most of the studied individual PAH species. 20
CHAPTER TWO: Review of Literature Edwards et al. (2009) have indicated that both nitrogen dioxide NO2 and ozone O3 from transportation emissions in urban environments have the potential to react with and remove polymeric binders in paint, making pigment granules more available for subsequent transfer to hands on contact, or deposition in house dust. Nitrogen dioxide NO2 and nitric oxide NO were found in at least 9 and 6 of the 1585 National Priorities List sites identified by the Environmental Protection Agency (EPA), respectively (ATSDR, 2002). In Ohio, over 280000 tons of NOx are emitted annually by mobile and diesel powered engines (EPA, 2006). 2.1.5.3: Ground-level ozone (O3): Ozone O3 is a trioxygen molecule and an allotrope of oxygen that is much less stable than the diatomic allotrope O2. Ozone is a highly reactive form of oxygen and a powerful oxidizing agent. It is a poisonous and corrosive gas with a distinctive or pungent odor in confined areas. Usually, ozone is colorless but becomes pale blue at high concentrations. It is slightly soluble in water but much more soluble in inert non-polar solvents such as carbon tetrachloride or fluorocarbons. Its density is 1.658 times greater than that of air and is 2.141g L-1 at 0 Co and 1 atm (Encyclopedia Britannica, 2011 and WebElements, 2010). Ozone O3 is present in two layers of the atmosphere (Troposphere and Stratosphere), but it has the same chemical structure whether it occurs miles above the earth or at ground-level and can be "good" or "bad," depending on its location in the atmosphere (tropospheric ozone is bad but stratospheric ozone is good), since, stratospheric ozone keeps harmful excessive ultraviolet radiation from reaching the surface of the Earth. Tropospheric ozone (Ground-level or "bad" ozone) is the main component of the photochemical smog. It is not usually emitted directly into the air, but is created by a chemical reaction between oxides of nitrogen NOx and volatile organic compounds VOCs in the presence of sunlight. Emissions from industrial facilities and electric utilities, motor vehicle exhaust, gasoline vapors, and chemical solvents are some of the major sources of NOx and VOCs (EPA, 2010). As it has been indicated by Griffin (2007) ozone which are a secondary pollutant gas and a photochemical oxidant product is formed by two mechanisms: one is a direct photolysis, whereby NO2 is split by blue/violet light to form NO and an oxygen atom O free radical. The resulting oxygen atom quickly combines with molecular oxygen producing ozone. As it can be seen also in the following reactions (Figure 2.4); 21
CHAPTER TWO: Review of Literature
Where; h = Planck‟s constant; ν = frequency in Hertz; and X* = any free radical atom and M represents any other molecule available to carry off excess energy (or M is any non-reactive species that can take up the energy released in the reaction to stabilize O3, typically N2 or O2. The two reaction components, NO and O3, will react with each other to form NO2 + O2 once again. Second is further studies indicated that the key to understanding elevated ground-level ozone levels are found in the chemical reactions that convert NO to NO 2 without consuming O3. It was discovered that a critical component of the overall higher production of ozone was the effect of reactive hydrocarbons. These hydrocarbons are generated by the act of volatile organic compounds combining with OH* free radicals in the vapor phase. (Volatile organic compounds are hydrocarbon-based molecules.). A simplified reaction sequence then becomes;
R = hydrocarbon fragment, but RO2 is alkyl peroxy. And then the original reactions occur:
The overall equation then becomes
A schematic of this overall reaction is given in (Figure 2.5). The concentration of tropospheric ozone has significantly increased in recent years (Akimoto, 2003). To understand the mechanism of tropospheric ozone increase, precise and accurate measurements of ambient peroxy radical concentrations are essential.
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CHAPTER TWO: Review of Literature It is evident that nitrogen dioxide and ozone can react with PAHs (polycyclic aromatic hydrocarbons) via nitration and ozonolysis respectively, giving products that are more potent than the parent compounds (Pitts et al. 1986; Finlayson-Pitts and Pitts 2000). Ozone O3 is an abundant tropospheric pollutant and, especially in the upper troposphere, it is a greenhouse gas (Seinfeld and Pandis, 1998). Ozone formation is a nonlinear process depending on a large number of reactions taking place in the atmosphere, strongly forced by primary emissions of ozone precursors, i.e. nitrogen oxides NOx, volatile organic compounds VOCs and carbon monoxide CO, and favored by high pressure, stagnant atmospheric conditions (Tao et al., 2003).
Figure (2.4): photolysis of NO2 and generation of ozone O3 (Griffin, 2007)
Figure (2.5): The influence of HCs and free radicals on atmospheric ozone generation (Griffin, 2007)
The atmospheric lifetime of tropospheric ozone is about 22 days; its main removal mechanisms are being deposited to the ground (Stevenson et al., 2006). Instantaneous concentration of tropospheric ozone is influenced by five main factors: 1- photochemical production (which depends in turn on the supply of precursor materials and the availability of sunlight); 2-chemical destruction (largely by reaction with NO); 3-atmospheric transport (which may generate a net increase or reduction); 4-surface dry deposition to vegetation, water or other materials; and 5- folding down of stratospheric ozone (Colls, 2007). In a review study on diurnal average ozone concentrations during April or June in Beijing, China, by Zhang et al. (1998), they have found that the peak concentration in the early 23
CHAPTER TWO: Review of Literature afternoon has increased by a factor of three, from 40 to 120 ppb, between 1982 and 1997 (Figure 2.6).
Figure (2.6): Spring ozone concentrations in Beijing, China between 1982 and 1997. (Poupkou et al., 2009). Ozone reacts directly with some hydrocarbons such as aldehydes and thus begins their removal from the air, but the products are themselves key components of smog. Ozone photolysis by UV light leads to production of the hydroxyl radical OH and this plays a part in the removal of hydrocarbons from the air, but is also the first step in the creation of components of smog such as peroxyacyl nitrates CH3COOONO2 which can be a powerful eye irritants. Certain examples of cities with elevated ozone readings are Houston, Texas, and Mexico City, Mexico. Houston has a reading of around 41 ppb, while Mexico City is far more hazardous, with a reading of about 125 ppb (Barlow, 2003). There is evidence of significant reduction in agricultural yields because of increased ground-level ozone and air pollution which interferes with photosynthesis and stunts overall growth of some plant species (Mutters, 1998 and Barlow, 2003). As it is also emphasized by EPA (2010), ozone damages vegetation and ecosystems in the United States alone, ozone is responsible for an estimated $500 million in reduced crop production each year, furthermore, breathing ground-level ozone, a primary component of smog, can trigger a variety of health problems including chest pain, coughing, throat irritation, and congestion. It can worsen bronchitis, emphysema, and asthma. Ground-level ozone also can reduce lung function and inflame the linings of the lungs. Repeated exposure may permanently scar lung tissue.
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CHAPTER TWO: Review of Literature Ground-level ozone causes more damage to plants than all other air pollutants combined. Field research to measure effects of seasonal exposure to ozone on crop yield has been in progress for more than 40 years. Most of this research utilized open-top field chambers in which growth conditions are similar to outside conditions (USDA-ARS, 2002). Previous studies conducted by NC State University and National Crop Loss Assessment Network (NCLAN) showed that ambient O3 pollution can suppress soybean, cotton, wheat and peanut yields by 5 to 15% (Booker et al., 2008), as it can be seen in (Figure 2.7).
Figure (2.7): Effect of O3 on yield of sorghum, field com, winter wheat, soybean, peanut and cotton crops (Booker et al., 2008). The type and severity of injury is dependent on several factors including duration and concentration of ozone exposure, weather conditions and plant genetics. One or all of these symptoms can occur on some species under some conditions, and specific symptoms on one species can differ from symptoms on another. With continuing daily ozone exposure, classical symptoms (stippling, flecking, bronzing, and reddening) are gradually obscured by chlorosis and necrosis (Krupa et al., 2001; Bell and Treshow, 2002 and Ainsworth et al., 2008). Ozone present in the upper troposphere acts as a greenhouse gas, quantifying the greenhouse gas potency of ozone is difficult because it is not present in uniform concentrations across the globe. However, the most widely accepted scientific assessments relating to climate change (e.g. the IPCC Third Assessment Report, 2001) suggest that the radiative forcing of tropospheric ozone is about 25% that of carbon dioxide. 25
CHAPTER TWO: Review of Literature Air quality guidelines such as those from the World Health Organization, the United States Environmental Protection Agency EPA and the EU are based on detailed studies designed to identify the levels that can cause measurable ill health effects. Epidemiological studies have also addressed the effects of short and long-term exposures to O3 and provided important results. The current WHO Air quality guidelines (AQG) for O3 provide a guideline value of 120 μg m-3 (60 ppb), based on controlled human exposure studies, for a maximum 8-hour concentration (WHO, 2003). But The current National Ambient Air Quality Standards (NAAQS) guideline values for O3 are a 1-hour level of 120 ppb and 8-hour level of 75 ppb in 2008 (Table 2.2). In the EU, the current target value for ozone concentrations is an 8-hour level 120 µg/m³, which is equivalent to 60 ppb. This target applies to all member states in accordance with Directive 2008/50/EC (ECE, 2010). Most people can detect about 0.01 ppm of ozone in air where it has a very specific sharp odor somewhat resembling chlorine bleach. Exposure of 0.1 to 1 ppm produces headaches, burning eyes, and irritation to the respiratory passages (Folchetti ed, 2003). Long-term exposure to ozone has been shown to increase risk of death from respiratory illness. A study of 450000 people living in United States cities showed a significant correlation between ozone levels and respiratory illness over the 18-year follow-up period. The study revealed that people living in cities with high ozone levels such as Houston or Los Angeles had an over 30% increased risk of dying from lung disease (Jerrett, 2009 and Wilson, 2009). Air pollution influences the development of oral clefts in animals. Hwang and Jaakkola (2008) have concluded new evidence in a study aimed to assess the relations between exposure to ambient air pollution and the risk of cleft lip with or without cleft lip and palate (CL/P). They have found that that exposure to outdoor air O3 during the first and second month of pregnancy may increase the risk of CL/P. Similar levels of O3 are encountered globally by large numbers of pregnant women. 2.1.5.4: Sulfur dioxide (SO2): Sulfur dioxide is a heavy, colorless, highly reactive, and poisons gas. It has a pungent, irritatingodor. SO2 is nonflammable and readily soluble in cold water, for example (22.97 g 100 mL-1 at 0 Co), but sparingly soluble in hot water (11.58 g 100 mL-1 at 20 Co), its density is 2.858 g L-1 at 0 Co and 1 atm (Whitten, 2004) 26
CHAPTER TWO: Review of Literature SO2 is also a precursor of secondary sulfates such as sulfuric acid, which is a stronger irritant than SO2, and plays a major role in the adverse respiratory effects of air pollution. Sulfate is a major component of PM2.5, which has been implicated in causing adverse environmental and health effects, especially among the elderly and persons with cardiovascular and respiratory illnesses (Koenig, 1997). Sulfur dioxide is produced by volcanoes and in various industrial processes. Since coal and petroleum often contain sulfur compounds, their combustion generates sulfur dioxide unless the sulfur compounds are removed before burning the fuel. Further oxidation of SO2, usually in the presence of a catalyst such as NO2, forms H2SO4, and thus acid rain (Holleman and Wiberg, 2001). As it has been indicated by Speight (2000), sulfur compounds are among the most important heteroatom constituents of petroleum, In general, the total sulfur in the crude oil can vary from 0.04 % on weight/weight basis for light crude oil to about 5.0% for heavy crude oil and tar sand bitumen. The largest sources of SO2 emissions are from fossil fuel combustion at power plants 73% and other industrial facilities 20%. Smaller sources of SO2 emissions include industrial processes such as extracting metal from ore, and the burning of high sulfur containing fuels by locomotives, large ships, and non-road equipment (EPA, 2010). Sulfur dioxide has potent adverse impacts upon both vegetative metabolism as well as animal health. Commonly occurring localized levels of sulfur dioxide from man-made sources can readily reduce plant productivity by about 30 to 50% Agrawal, and Agrawl (2000), and it can severely adversely affect respiration function, metabolism and mortality of many faunal species, including humans. Effects of air pollutants like SO2, NOx and O3 on plant growth and productivity have been described in terms of inhibition in physiological processes Agrawal et al. (2003) and Verma et al. (2000), alteration in metabolic functions and enzyme activities Okpodu et al. (1996), nutrient uptake Agrawal andVerma (1997) and suppression of growth and yield (Agrawal et al., 2003; Agrawal et al., 2006 and Biswas et al., 2008). Many molecular gases can actually enter the stomatal openings of plants and directly interfere with photosynthesis; particulate matter, on the other hand can clog stomatal openings and reduce the gross intake of carbon dioxide by plants.
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CHAPTER TWO: Review of Literature In dry tropical area of India, the seasonal variations and effects of ambient air pollutants SO2, NO2, and O3 on palak plants (Beta vulgaris L. var. Allgreen) growing in open top chambers (OTCs) were investigated by Tiwari et al. (2010 ). Mean monthly concentrations of 8- Hours exposure to pollutants in NFCs (non filtered chambers) was 34.79 ppb for SO2, 30.37 ppb for NO2 and 38.15 ppb for O3 during winter season. During summer, however, the same were 27.79, 26.46 and 51 ppb, respectively. The results have clearly indicated that the ambient concentrations of the gaseous pollutants SO2, NO2 and O3 had significant detrimental effects on growth, biomass accumulation and yield of palak, as compared to the yield of palak plant growing in the FCs (filtered chambers). The reduction in yield of palak was 28.6% and 23.9% during summer and winter season, respectively, since these pollutants, SO2, NO2 and O3 in combination produced synergistic negative influence. (Agrawal et al., 1983 a & b). In a gradient study by (Agrawal, 2005), yields losses of 30 to 50% were recorded in wheat, depending upon the distance from coal fired power plants (CFPP) where the main pollutant was sulfur dioxide (SO2). Sulfur dioxide is a major air pollutant and has significant impacts upon human health. In addition the concentration of sulfur dioxide in the atmosphere can influence the habitat suitability for plant communities as well as animal life (Hogan and Monosson, 2010). Sulfur dioxide emissions are a precursor to acid rain and atmospheric particulates. Due to the largely US EPA‟s Acid Rain Program, the U.S. has witnessed a 76% decrease in emissions between 1980 and 2009 as it can be seen in Figure (2.8), (EPA, 2010). This improvement resulted in part from flue gas desulfurization, a technology that enables SO2 to be chemically bound in power plants burning sulfur-containing coal or oil. In particular, calcium oxide (lime) reacts with sulfur dioxide to form calcium sulfite: CaO + SO2 → CaSO3 Aerobic oxidation of the CaSO3 gives CaSO4, anhydrite. Most gypsum sold in Europe comes from flue gas desulfurization. In contrarily, Asia is undergoing rapid urbanization resulting in increasing air pollution threats in its cities. The contribution of megacities to sulfur emissions and pollution in Asia is studied over a 25-year period (1975-2000) using a multi-layer Lagrangian puff transport model. Although, Asian megacities cover less than 2% of the land area but emit more than 16% of the total anthropogenic sulfur emissions of Asia. It is shown that urban sulfur emissions contribute 28
CHAPTER TWO: Review of Literature over 30% to the regional pollution levels in large parts of Asia. The average contribution of megacities over the western Pacific increased from less than 5% in 1975 to more than 10% in 2000 (Guttikunda, et. al., 2003).
Figure (2.8): Reduction in sulfur dioxide (SO2) emission due to US EPA‟s acid rain program. (EPA, 2010). However, increased pollution awareness campaigns, stringent pollution control regulations, use of low sulfur fuel, implementation of desulfurization techniques, and periodic monitoring have resulted in a significant reduction in sulfur and other trace gas emissions in many cities. Nevertheless, sulfur pollution levels remain significantly above compliance levels throughout large parts of Asia (Calori et al., 2001; Klimont et al., 2001 and Reddy and Venkataraman, 2002). In 2004, Europe celebrated the 25th anniversary of the Convention on long-range transboundary air pollution (LRTAP). This Convention was evaluated as paving the way for extensive and fruitful cooperation among up to now 49 Parties in the region of the United Nations Economic Commission for Europe (UNECE) to meet specific environmental targets (Dovland et al., 2004). In fact, it was not only the first international legally binding instrument to tackle regional problems, but has also brought about tangible results in reducing emissions and improving the atmospheric environment. All parties to the sulfur Protocol had achieved a
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CHAPTER TWO: Review of Literature 30% reduction, and all parties together reduced their emissions by more than 50% (Sprinz and Vaahtoranta, 1994). But, Northeast Asian countries still do not have any environmental cooperation comparable to Europe‟s successful regulatory regime even though both regions have borne similar conditions of the atmospheric problem has been explored (Kim, 2007). Controlled exposures to sulfur dioxide SO2 have shown statistically significant reductions in lung function at concentrations as low as 0.1 to 0.25 ppm. Epidemiologic studies have seen mortality associated with very small increases in ambient SO2 in the range of 10 – 22 ppb. Low birth weight is associated with SO2 concentrations in the range of 22- 40 ppb. The studies assessed in this review indicate that infants and people with asthma are particularly susceptible to the effects of SO2, even at concentrations and durations below the current California one-our standard of 0.250 ppm (250 ppb), (Koenig and Mar, 2000). The current National Ambient Air Quality Standards (NAAQS) guideline values for SO2 as a primary pollutant are a 1-hour level of 75 ppb; 24-hour level of 140 ppb and annual (Arithmetic Average) level of 30 ppb. But as a secondary pollutant the guideline value is a 3hour level of 500 ppb (Table 2.2). In the EU, the current target value for sulfur dioxide concentrations is a 1-hour level 350 µg m-3, which is about 122.5 ppb and 24-hour level of 125 µg m-3, which is about 43.7 ppb .This target applies to all member states in accordance with Directive 2008/50/EC (ECE, 2010). 2.1.5.5: Particulate matters (PM): Particulate matters (PM1.0, PM2.5, and PM10.0; the subscripts indicate what aerodynamic diameters is in consideration) are an air pollutant received in recent decades a large amount of attention for their impact on the environment and human health. The notation PM10 is used to describe particles of 10 micrometers or less, PM2.5 represents particles less than 2.5 micrometers in aerodynamic diameter and PM1.0 represents particles less than 1 micrometer in aerodynamic diameter EPA (2007). But, everything below 100 nm (nanometer), down to the size of individual molecules is classified as ultrafine particles (UFP or UP) (Brunshidle et al., 2003). PM has been described by EPA (2008a) and WHO (2008) as a complex mixture made of tiny airborne particles of solid or liquid suspended in a gas. While Kumar and Joseph (2006) explained PM as a complex mixture of small and large particles of varying origin and chemical 30
CHAPTER TWO: Review of Literature compositions. Particles ranging from 2.5 to 100 μm (micrometer) diameters usually comprise more of dust from agriculture, construction, road traffic, plant pollens and other natural sources. Smaller particles with less than 2.5 μm in diameter generally come from combustion of fossil fuel. These particles include soot from vehicle exhaust and are often coated with various chemical contaminants or metals. Fine sulfates and nitrate aerosols are formed when SO2 and NO2 condense in the atmosphere. The EPA has defined four terms for categorizing particles of different sizes. Table (2.3) and Figure (2.9) displays the EPA terminology along with the corresponding particle sizes. Table (2.3): Terminology along the corresponding particle sizes. (EPA, 2010) Environmental Terminology for Particle Sizes EPA Description Particle Size µm (micrometer) Supercoarse dpa 10 µm (more than) Coarse 2.5 µm
Figure (2.9): Displays a typical size distribution of atmospheric particulate matter. (EPA, 2010) These particles are as mentioned complex and as a result can have a range of toxic effects. One characteristic that changes PM toxicity is particle size. Those that are considered fine 31
CHAPTER TWO: Review of Literature particulate matter, less than 2.5 microns in diameter (PM2.5), are small enough that when they are breathed they have the ability to penetrate deep into the lung and cause damage to the alveoli (EPA, 2008a). Coarse PM (PM2.5 to PM10) does not have as damaging effects to the lungs but are considered irritants and exasperators to the upper respiratory tract. Research on PM2.5 suggests that these small airborne particles are a toxic component of urban air pollution (Samet et al., 2000). Other studies have provided evidence that PM2.5 in the ambient air is associated with increases in eye nose and throat irritation, daily mortality, and respiratory and cardiovascular diseases (Health Canada, 2006a). The effects of particle pollution don‟t stop with the negative health effects; PM can also have adverse effects on vegetation and structures, and contributes to visibility deterioration, acid deposition and regional haze. Particulate matter (PM) has been widely studied in recent years and the United Nations estimated that over 600 million people in urban areas worldwide were exposed to dangerous levels of traffic generated air pollutants (Cacciola et al., 2002). PM pollution is estimated to cause 22000 to 52000 deaths per year in the United States (from 2000), (Mokdad et al., 2004) and 200000 deaths per year in Europe. About 85% of atmospheric particles originates from anthropogenic sources including burning of coal, oil, natural gas, wood and other biomasses; and, especially, internal combustion engines and heat or power plants (Council of Europe, 1998; Wilson et al., 2002; Artinano et al., 2003). Agricultural activities and vehicular traffic may generate local dust concentrations close to the source that exceed environmental guideline values (Leys et al., 1998 and Manins et al., 2001). The chemical composition of PM is extremely heterogeneous and many researchers have demonstrated the usefulness of separating the particles in at least three or four categories on the basis of their mean size: coarse (PM10), fine (PM2.5), submicron (PM1.0) and ultrafine (PM0.1) (Wilson et al., 2002 and Englert, 2004). Several epidemiological and toxicological studies have shown the association between PM and adverse health effects such as respiratory diseases (chronic bronchitis, aggravation of asthma), cardiovascular pathologies and pulmonary tumors (Saldiva et al., 2002; Pope et al., 2004 and Dockery and Stone, 2007). These effects are thought to arise from the possibility of inhaling
heavy
metals,
polyaromatic
hydrocarbons,
radical
species,
endotoxins, 32
CHAPTER TWO: Review of Literature viruses/bacteria, etc. in combination with the particle matrix (Pakkanen et al., 2001 and Okeson et al., 2003). The nose, as a defender of the airway, is strategically situated at the entrance of the airway and acts as an air conditioner to condition the inspired air, before it reaches the more delicate gas exchange areas of the lungs. The average adult inhales around 10000 to 20000 liters of air (about 15 kilos of air) each day, which in mass is much more than the daily intake of food and water (Holgate et al., 2006) The impact of airborne particulates on human health depends on many factors, However, according to various authors, toxicity is bound to the dimensions of the particles because of deposition issues (Okeson et al., 2003), and increasing toxicity with decreasing aerodynamic diameter has been reported (Smith et al., 2000). During inhalation, particle size will influence in what region of the airways the material will be deposited (Granum and Lovik, 2002). The coarse PM is easily retained in the first aerial ways; while fine-ultrafine particles can penetrate deeper into the lungs, deposit in the alveolar region and may also be transported via the blood stream to other tissues and organs, such as the heart (McClellan, 2002). Furthermore, the fine particulate fraction formed through the process of fossil-fuel combustion contains the most toxic components of the particles (organics, ammonium, sulfates and nitrates), providing a biologically plausible mechanism for causality (NJ Clean Air Council, 1997). For all these reasons, small PM can induce stronger adverse effects and is thus considered more dangerous to human health than larger particles composed of the same material (Dellinger et al., 2001). In 2004, the first American Heart Association scientific statement on “Air Pollution and Cardiovascular Disease” concluded that exposure to particulate matter PM of polluted air contributes to cardiovascular morbidity and mortality. On the basis of the findings of this writing group, several new conclusions were reached, including the following: Exposure to PM less than 2.5 m in diameter (PM2.5) over a few hours to weeks can trigger cardiovascular disease-related mortality and nonfatal events; longer-term exposure (e.g., a few years) increases the risk for cardiovascular mortality to an even greater extent than exposures over a few days and reduces life expectancy within more highly exposed segments of the population by several months to a few years (Brook et al., 2010). The spatial and temporal distribution of airborne PM concentrations is also influenced by meteorological factors such as air pressure, temperature and humidity, precipitations and winds 33
CHAPTER TWO: Review of Literature (Olcese and Toselli, 1998). Rain tends to remove pollutants from the atmosphere; while wind currents can mobilize, re-suspend and transport the particles very far from the source (Armaroli and Po, 2003). In another epidemiological study also found an association between fine particles concentration and increased human health effects (Pope et al., 1995). Each 10 µg m-3 elevations in fine particulate air pollution has been associated with approximately with 4, 6 and 8% increased risk of all cause, cardio pulmonary and lung cancer mortality respectively (Pope et al., 2002). Due to a heterogeneous composition of different naturally occurring PM it is often very difficult to examine the effects of PM on the vegetation in specific experimental settings. Particulate matter can clog stomatal openings of plants and interfere with photosynthesis functions (Hogan, 2010). In this manner high particulate matter concentrations in the atmosphere can lead to growth stunting or mortality in some plant species The direct physical effects of mineral dusts on vegetation became apparent only at relatively high surface loads (e.g., more than 7 g m-2) (Farmer, 1993) as compared with the chemical effects of reactive materials such as cement dust which may become evident at 2g m-2 (Grantz et al., 2003). Air pollutants damage plants leaves, impair plant growth, and limit primary productivity according to the sensitiveness of the plants to pollutants (Ulrich, 1984). Limestone and cement dusts, with pH values of 9 or higher, may cause direct injury to leaf tissues (Vardaka et al., 1995) or indirect injury through alteration of soil pH (Auerbach et al., 1997). Air particulates affect the overall growth and development of plants according to their physical and chemical nature (Gupta and Ghouse, 1987and Pandey et al., 1999), and morphology and anatomy of the leaves are altered (Singh and Sthapak, 1999; Farooq et al., 2000 and Shrivastava and Joshi, 2002). Surface dust deposits may alter the optical properties of leaves, particularly the surface reflectance in the visible and short wave infrared radiation range (Keller and Lamprecht, 1995). In response to these adverse effects various biochemical changes also occur such as decreased chlorophyll content and increased ascorbic acid content (ascorbic acid is an antioxidant and thus scavenges the oxidants of leaves), (Krishnamurthy et al., 1994; Mashitha and Pise, 2001 and Gavali et al., 2002). These responses ultimately accelerate the process of senescence (Lee et al., 1981 and Kohert et al., 1986).
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CHAPTER TWO: Review of Literature To assess the dust interception efficiency of some selected tree species and impact of dust deposition on chlorophyll and ascorbic acid content of leaves, a study was conducted by (Prajapati and Tripathi, 2008). It was found that all species have maximum dust deposition in the winter season followed by summer and rainy seasons. Chlorophyll content decreased and ascorbic acid content increased with the increase of dust deposition. There was significant negative and positive correlation between dust deposition and chlorophyll and ascorbic acid content, respectively. Thus plants can be used to intercept dust particles which are of potential health hazards to humans. In a study by Kumar and Joseph (2006) for monitoring the air pollution concentrations of PM2.5, PM10 and NO2 at ambient and Kerbsite in metro city-Mumbai, they have found an 24hour average values of 43, 61 and 22 µg m-3 at ambient site and 69, 90 and 25 22 µg m-3 at Kerbsite for PM2.5, PM10 and NO2 respectively. Particulate matters can affect the climate in two different ways. The "direct effect" is caused by the fact that the particles scatter and absorb solar and infrared radiation in the atmosphere (Peener et al., 2001). The "indirect effect" of particles is more complex and more difficult to assess. Changing in concentration of aerosols in the atmosphere causes variations in the population and size of cloud droplets. The addition of PM into the atmosphere causes the water to condense on to the particles. In United States of America, PM2.5 and PM
10.are
“criteria” pollutant for which the
Environmental Protection Agency (EPA) has established National Ambient Air Quality Standards (NAAQS) (EPA, 2008b; Environment Canada, 2007). The current National Ambient Air Quality Standards (NAAQS) guideline values for PM10 is a 24-hour level of 150 µg m-3, but for PM2.5 are an annual (Arithmetic Average) level of 15.0 µg m-3 and a 24-hour level of 35 µg m-3 (Table 2.2). Among the pollutants regulated by the European community, PM is one of those which more frequently exceed the limit values. According to the European Union Directive on particulate matter (1999/30/EC), PM10 values must not exceed the yearly average limit of 40 g/m3 or daily average of 50 g m-3 on more than 35 times per year. Both values are already frequently exceeded in European cities, especially in streets and other urban hotspots. The projections for 2030 suggest that the PM10 limit value is not expected to be met even in the most optimistic scenario (EEA, 2006). 35
CHAPTER TWO: Review of Literature But, the current WHO Air quality guidelines (AQC) provide exposure-response relationships describing the relation between ambient PM and various health endpoints. No specific guideline value was proposed as it was felt that a threshold could not be identified below which no adverse effects on health occurred. In recent years, a large body of new scientific evidence has emerged that has strengthened the link between ambient PM exposure and health effects (especially cardiovascular effects), justifying reconsideration of the current WHO PM Air quality guide lines and the underlying exposure-response relationships.(WHO, 2003). Diesel exhaust emission in Sulaimani city contributed a large source of particulate matter (PM) produced by diesel power stations and diesel engine vehicles, since, a large number of diesel electric generators are in work (Table 3.4). These particles pose a serious threat to the health of our families and adversely impact our environment. Diesel particulate matter (PM) is commonly known as soot and is the most harmful constituent of diesel exhaust. These particles are so small and can penetrate deeply into the lungs, collecting in tiny air sacs where oxygen enters the bloodstream affecting both heart and lungs (OEC, 2007). Since diesel particulates (soot) are ultrafine particles or nanoparticles and consist of a complex of toxic and inorganic substances mixed with toxic organic gases (Figure 2.10). Diesel fine particles are composed of a carbon core coated in organic hydrocarbons sulfates and metals (Clean Air Task Force, 2008). These particulates of soot are so small and they can enter bloodstream from the lungs. As it was emphasized by OEC (2007), approximately 45,000 American lives are lost prematurely each year from exposure to particulate matter pollution from two sources of particles-21,000 from diesel engines and 24,000 from power plants. In Ohio, mobile diesel-powered source contributed at least 10% of the PM2.5 emissions in the state (EPA, 2006). Fine particulate matter PM2.5 is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM2.5 varies across space and time, the association between PM2.5 and mortality could also change with space and season (Choi et al., 2009). Although air pollution became an important issue worlds wide and of major concern from a health, agriculture, environmental, infrastructure and national heritage perspective, but, in Kurdistan region/Iraq and particularly Sulaimani city still there have been no published
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CHAPTER TWO: Review of Literature research on the air pollutants and ambient air pollution. Therefore, outdoor air quality research being valuable and more research is needed to establish a baseline of air pollutant data.
Figure 2.10: Cross-section of black carbon makes up the core of a diesel particulate. (Clean Air Task Force, 2008). 2.1.5.6: Lead (Pb): Although the high concentration of most heavy metals (e.g., lead, cadmium, arsenic, mercury, chromium and etc.) have a serious detrimental effects on human health, welfare and the environment, but in current time the National Ambient Air Quality Standards (NAAQS) for outdoor air , which has been established by the Environmental Protection Agency of the United States (US EPA) under authority of the Clean Air Act (42 U.S.C. 7401 et seq.) has considered only lead as a metal among the 6 criteria air pollutants and these were (CO, NO2, SO2, O3, PM, and Pb). Lead was first regulated as a criteria air pollutant in the United States in 1976. Many countries now regulate the emission and outdoor concentration of Lead (Jacobson, 2002). Lead is a malleable, ductile, bluish-white, dense metallic element. It is a heavy metal (specific gravity 11.34 at 16°C) and has atomic number 82 and atomic weight of 207.19. Lead is a moderately active metal. It dissolves slowly in water and in most cold acids. It reacts more rapidly with hot acids. It does not readily react with oxygen in the air and does not burn (McGraw-Hill Science & Technology Encyclopedia, 2010). Lead is present in the proportion of 16 parts per million (ppm) in igneous rocks, the most common ancient rock on the surface, and at an average of about 10 ppm in common soils that are far from sites of contamination; natural soils usually have less than 50 ppm of lead but are never lead free (Dharmananda, 2001). 37
CHAPTER TWO: Review of Literature In a study on eleven soil samples from different locations representing the three main soil types found in Egypt: alluvial, desertic, and calcareous soils. The results showed that the total lead concentrations in agricultural soils with no previous history of metal contamination ranged from 23 to 42 mg kg-1 (ppm). Agricultural soils next to major roadways contained approximately two to three times the concentration of total lead compared with soils with no history of contamination (Badawy et al., 2002). A reconnaissance sample of air-borne dust in 14 sampling stations and street dust of 12 locations in Baghdad/ Iraq have been analyzed for their lead content. In addition to that a number of unpolluted dust samples originated from the western and southern desert of Iraq have been also analyzed. The analysis showed that lead (Pb) concentrations ranged between 1495 ppm with a mean of 49 ppm and 24 -280 ppm with a mean of 92 ppm for both air-borne and street dust samples respectively , compared to about 7 ppm average Pb concentration in the unpolluted dust (Khaldoun et al., 2009). According to Al-Saffawi (2006) who investigated some aspect of the environmental pollution in Mosul city-Iraq, it has been found that the concentration of lead ranged between 21.1-100.2 ppm and 27-255 ppm in soil surface of (0 to 5 cm) and (5 to 27cm) respectively. The lowest levels of both the ranges level were found in Baeshiqa (control location), because it is a rural place and has less traffic volume. Lead is a highly toxic metal found in small amounts in the earth‟s crust. Because of its abundance, low cost, and physical properties, lead and its compounds have been used in a wide variety of products including paint, ceramics, pipes, solders, gasoline, batteries, and cosmetics. Extreme lead exposure can cause a variety of neurological disorders such as lack of muscular coordination, convulsions and coma, but much lower lead levels have been associated with measurable changes in children‟s mental development and behavior. Chronic lead exposure in adults can result in increased blood pressure, decreased fertility, cataracts, nerve disorders, muscle and joint pain, and memory or concentration problems (NIEHS, 2010). The analyses of lead in household paint collected from various old buildings in Bahrain was determined by (Mandany et al., 1987) and were found in the range of 200 to 5700 mg kg-1 , which were low as compared to the limit of 0.5% in U.K. and 0.06% in U.S.A. The most serious worldwide lead contamination has been due to the introduction of lead in the form of tetraethyl lead (TEL) and tetra methyl lead (TML) into gasoline, starting in 1923, 38
CHAPTER TWO: Review of Literature with billions of tons of lead released into the atmosphere in the vehicle exhaust. Although lead was phased out of gasoline in many countries, but leaded gasoline is still being used in many countries, most notably in Africa (Dharmananda, 2001). In Iraq and Iraqi Kurdistan region also still leaded gasoline is in use, as it has been reported and supported also by guardian newspaper that tetraethyl lead (TEL) is a killer chemical and nowadays banned in the west for use in car fuel, but Innospec company has arranged an illegal contract to sell an amount of TEL for $40 million through a bribery trial behind the export to Iraqi oil Ministry. The report emphasized that at least $ 8 million was corruptly paid during the "endgame" in Iraq and Indonesia (Leigh et al., 2010), a video clip about the concern subject is available.The status of lead use in Gasoline in Iraq is about 0.5 g L-1 (MECA, 1998). In the U.S., lead emissions into the air declined from 221000 tons in 1970 (after which all new cars had to use unleaded gasoline), to 4000 tons in 2005 (EPA, 2009). Moreover, the Administrator of the Environmental Protection Agency (EPA), under a court order to review the National Ambient Air Quality Standard (NAAQS) for lead, announced his decision on 16 October, 2008 to reduce the standard level of Pb from 1.5 µg m-3 to 0.15 µg m-3 (McCarthy, 2008). Smelting and gasoline combustion primarily cause air pollution with the lead particles reaching the soil through dry and wet deposition (Bolt and Bruggenwert, 1978 and Milberg et al., 1980). Lead pollution, irrespective of source, is of major concern because of its long residence time in the soil and its possible association with cognitive development in children. It has no known biological function and is one of a small group of heavy metals whose increase both in soils and in the food chain is deemed undesirable. Because of this, many countries in the industrialized world have set limits for lead in both soils and food crops to reduce its buildup in the environment and the food chain (EPA, 1993). Lead is a systemic toxicant and therefore damages a number of different target organs in the body, because it is distributed throughout the body and can be responsible for central nervous system damage. Brain functions affected include behavioral changes, losses of muscle control and learning difficulties. Moreover, lead impacts certain key enzymes in the production of red blood cells, which brings on anemia. In addition, there is evidence of kidney damage, liver and heart damage, and damage to the reproductive organs (Griffin, 2007).
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CHAPTER TWO: Review of Literature Lead poisoning (also known as plumbism, colica pictonium or saturnism) can cause a variety of symptoms and signs which vary depending on the individual and the duration of lead exposure (Karri et al., 2008). Symptoms usually develop over weeks to months as lead builds up in the body during a chronic exposure, but acute symptoms from brief, intense exposures also occur. In adults, symptoms can occur at blood lead levels (BLL) above 40 μg dL-1, but are more likely to occur only above 50-60 μg dL-1 .Symptoms begin to appear in children generally at around 60 μg dL-1 (Needleman, 2004). However, the blood lead levels at which symptoms appear vary widely depending on unknown characteristics of each individual (Bellinger, 2004). Lead affects every one of the body‟s organ systems, especially the nervous system, but also the bones and teeth, the kidneys, and the cardiovascular, immune, and reproductive systems (Whitea et al., 2007). Lead also affects both the male and female reproductive systems. In men, when blood lead levels exceed 40 μg dL-1, sperm count is reduced and changes occur in volume of sperm, their motility, and their morphology (Grant, 2009). The current reference range for acceptable blood lead concentrations in healthy persons without excessive exposure to environmental sources of lead is less than 10 µg dL-1 for children, but less than 25 µg dL-1 for adults (Wu, 2006). The current biological index (a level that should not be exceeded) for lead-exposed workers in the U.S. is 30 µg dL-1 in a random blood specimen. Lead exposure can occur by ingestion and inhalation from contact with lead in air, household dust, soil, water, and commercial products. The national ambient air quality Standards for lead are indicated below (EPA, 2008). Pollutant Lead
Primary Stds. Averaging Times -3 (1) 0.15 µg m Rolling 3-Month Average 1.5 µg m-3 Quarterly Average (1) Final rule signed October 15, 2008.
Secondary Stds. Same as Primary Same as Primary
While, the European Commission Environment regulated lead level in the Air Quality Standards guideline as follows, (ECE, 2010). Pollutant
Concentration
Lead (Pb)
0.5 µg m-3
Averaging Permitted exceedences Legal nature period each year 1 year Limit value entered into force n/a 1.1.2005 (or 1.1.2010 in the immediate vicinity of specific, notified industrial sources; and a 1.0 µg m-3 limit value applied from 1.1.2005 to 31.12.2009) 40
CHAPTER TWO: Review of Literature 2.2: Environmental effects of atmospheric carbon dioxide(CO2): Carbon is at the heart not only of life, but also of energy production and climate change (Colls, 2002), moreover, one of the most important cycles of the earth is the biogeochemical cycle of carbon which allows for carbon to be recycled and reused throughout the biosphere, pedosphere, geosphere, hydrosphere, atmosphere and all of its organisms. Carbon dioxide CO2 is one of two oxides of carbon that has the basic link to this cycle, and it is the principal product oxide of carbon formed from the combustion of hydrocarbon fuels. Carbon dioxide (CO2) is a colorless odorless gas, which is soluble in water, in ethanol and in acetone. Its density is 1.963 g L-1 at 0 Co temperatures and a pressure of 1 atm. CO2 is an acidic oxide and reacts with water to give carbonic acid (Donal O'Leary, 2000). Carbon dioxide has no liquid state at pressure below 5.1 atm. The gas of carbon dioxide deposits directly at the pressure of 1 atm. and temperature below (-78 Co) to a solid, and the solid phase sublimes to a gas at temperature above (-78 Co). In its solid state, carbon dioxide is commonly called dry ice. CO2 is a trace gas of the atmosphere, in 1958, atmospheric carbon dioxide at Mauna Loa/ Hawaii-U.S.A was about 320 parts per million by volume (ppmv), but in January, 2011 the globally averaged concentration of CO2 at the same monitoring station was about 391.19 ppmv (0.039119%) in the Earth's atmosphere (NOAA, 2011), (Figure 2.11), although this varies both by both location and time. Atmospheric concentrations of carbon dioxide fluctuate slightly with the change of the seasons, driven primarily by seasonal plant growth in the Northern Hemisphere. Concentrations of carbon dioxide fall during the northern spring and summer as plants consume the gas, and rise during the northern autumn and winter as plants go dormant, die and decay. Taking all this into account, the mixing ratios of atmospheric CO2 have been increasing globally at the average rate of 1.5 ppm year-1 for the past several decades (Conway et al., 1994). In 2009 the concentration of CO2 grew by about 2 ppm (NOAA, 2011). Carbon dioxide is a greenhouse gas; it transmits visible light but absorbs strongly the infrared and near-infrared spectrum. As the chemically most stable non-condensing greenhouse gas, it acts as a critical “climate control knob” (Lacis et al., 2010). Greenhouse gas (GHG) as defined by IPCC (2008), are those gaseous constituents of the atmosphere, both natural (water vapour H2O, carbon dioxide CO2, nitrous oxide N2O, methane CH4 and ozone O3) and anthropogenic such as (sulfur hexafluoride SF6, hydrofluorocarbons HFCs and perfluorocarbons PFCs), that absorb and emit radiation at specific wavelengths 41
CHAPTER TWO: Review of Literature within the spectrum of thermal infrared radiation emitted by the Earth‟s surface, the atmosphere itself, and by clouds. This property of greenhouse gas causes the greenhouse effect.
Figure (2.11): Carbon dioxide concentration at Mauna Loa Observatory, Hawaii. (NOAA, 2011). It has been reported by EPA (2010) that carbon dioxide is emitted naturally through the carbon cycle and through human activities like burning of fossil fuels. Natural Sources of CO2 occur within the carbon cycle where billions of tons of atmospheric CO2 are removed from the atmosphere by oceans and growing plants, known as „sinks” and are emitted back into the atmosphere annually through natural processes known as „sources‟. When in balance, the total carbon dioxide emissions and removals from the entire carbon cycle are roughly equal. It was further reported by EPA (2010) that since the Industrial revolution in the 1700‟s, human activities, such as the burning of oil, coal, gas and deforestation increased CO2 concentrations in the atmosphere. In 2005, global atmospheric concentrations of CO2 were 35% higher than they were before the Industrial Revolution. According to Schimel (1995) and Houghton (2007) the concentration of CO2 in Earth‟s atmosphere was increased during the past century and the magnitude of this atmospheric increase is currently about 4 Gt C (gigatons carbon) per year (this equivalent to 14.667 Gt CO2). Total human industrial CO2 production, primarily from use of coal, oil, and natural gas
42
CHAPTER TWO: Review of Literature and the production of cement, is currently about 8Gt C per year (which equivalent to 29.334 Gt CO2) (Schimel, 1995; Houghton, 2007). Humans also exhale about 0.6 Gt carbons per year, which has been sequestered by plants from atmospheric CO2. It is estimated that the atmosphere contains 780 Gt C; the surface ocean contains 1,000 Gt C; vegetation, soils, and detritus contain 2,000 Gt C; and the intermediate and deep oceans contain 38,000 Gt C, as CO2 or CO2 hydration products. Each year, the surface ocean and atmosphere exchange an estimated 90 Gt C; vegetation and the atmosphere, 100 Gt C; marine biota and the surface ocean, 50 Gt C; and the surface ocean and the intermediate and deep oceans, 40 Gt C (Schimel, 1995; Houghton, 2007). So great are the magnitudes of these reservoirs, the rates of exchange between them, and the uncertainties of these estimated numbers that the sources of the recent rise in atmospheric CO2 were not determined with certainty (Jaworowski et al., 1992b and Segalstad, 1998). CO2 increased by more than 30% since the 19th century and is reaching now a level that has never reached over the last four hundred thousand years (IPCC, 2001). Table (2.4) showed a list of the top 10 sovereign states and territories by carbon dioxide emission due to human activity. The data presented below corresponds to emissions in 2007 and only considered carbon dioxide emissions from the burning of fossil fuels and cement manufacture, but not emissions from land use such as deforestation. The top 10 countries in the world emit 79.60% of the world total carbon dioxide (CDIAC, 2007). Table (2.4): List of the top 10 countries and the world by carbon dioxide emissions in 2007. (CDIAC, 2007). Country Rank Annual CO2 emissions in (%) Percentage of thousands of metric tones global total World 29,321,302 100 China 1 6,538,367. 22.30 United States 2 5,830,381. 19.88 European Union 3 4,177,818 14.25 India 4 1,612,362 5.50 Russia 5 1,537,357 5.24 Japan 6 1,254,543 4.28 Germany 7 787,936 2.69 Canada 8 557,340 1.90 United Kingdom 9 539,617 1.84 South Korea 10 503,321 1.72 Total CO2 emissions 79.60 23,339,756 of the 10 countries 43
CHAPTER TWO: Review of Literature Political leaders gathered in Kyoto, Japan (Kyoto Protocol), in December 1997 to consider a world treaty restricting human production of “greenhouse gases,” chiefly carbon dioxide (CO2). They feared that CO2 would result in “human-caused global warming”-hypothetical severe increases in Earth‟s temperatures, with disastrous environmental consequences. In June 1, 2006, 189 nations, including the United States, adopted the United Nations Framework Convention on Climate Change (UNFCCC), aiming at “stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system.” The global carbon cycle is critical to this objective because its processes define how emissions of carbon dioxide (CO2) from anthropogenic activity translate into concentrations of CO2 in the atmosphere (Houghton, 2007). During the past 10 years, many political efforts have been made to force worldwide agreement to the Kyoto treaty (Robinson, et al., 2007). The Kyoto Protocol is a protocol to the United Nations Framework Convention on Climate Change (UNFCCC or FCCC), aimed at fighting global warming. The UNFCCC is an international environmental treaty with the goal of achieving “Stabilization of Greenhouse Gas (GHG) concentration in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system” (UNFCCC, 2005). The Protocol was initially adopted on 11 December 1997 in Kyoto, Japan and entered into force on 16 February 2005. As of July 2010, 191 states have signed and ratified the protocol (UNFCCC, 2009). Emissions of CO2 by human activities are currently more than 130 times greater than the quantity emitted by volcanoes, amounting to about 27 billion tons per year (USGS, 2010). Global warming and climate change has become a big political issue and a great deal of debate continues about the solutions, because there are other hidden goals buried in this issue. Many people asking and like to know if the warming is attributed to human activity (anthropogenic) ; are greenhouse gases building up in the atmosphere?; and is the warming something they should be concerned about? The issue is that CO2 is the second main contributor to the greenhouse effect (the first being water vapor) and is therefore liable to cause climate change larger than ever in the past. The major contributing factors to this long term increase are fossil fuel combustion and land use change (Houghton and Hackler, 1999).
However, the rate of CO2 increase in the
atmosphere is estimated to be only about half the amount that is being emitted each year by 44
CHAPTER TWO: Review of Literature fossil fuel burning (Andres et al., 1996). A global sink is acting, but its mechanism and its variations in space and time remain misunderstood. In 2007, the Intergovernmental Panel on Climate Change (IPCC) projected that average temperatures could raise a couple of degrees Fahrenheit every quarter century. The panel was comprised of some of the world‟s most respected climate scientists, whose work also faced closed scrutiny by 113 governments (Arblaster et al., 2007). For their efforts, the scientists shared a Nobel Peace Prize that year with former U.S. Vice President Al Gore, whose movie An Inconvenient Truth helped propel the notion of climate change into mainstream consciousness. The two movies film “An Inconvenient True (AIT) and “The Great Global Warming Swindle (GGWS)” shares dramatically opposed views on global warming, and present opposing views on the effect of anthropogenic carbon dioxide emissions on global warming. The Al Gore movie, “An Inconvenient Truth” (AIT), shares the same level of alarmism on climate change and defends the IPCC (Intergovernmental panel on Climate Change) views on global warming. On the other hand, the movie “The Great Global Warming Swindle (GGWS)” was narrated and directed by film maker Martin Durkin and broadcasted on 8 March2007 by Channel 4 defends a very different thesis, claiming that CO2 has nothing to do with global warming and uses every common climate change skeptic‟s arguments to prove his point (Ofcom Broadcast Bulletin, 2008) According to Solomona et al. (2009) the severity of damaging human-induced climate change depends not only on the magnitude of the change but also on the potential for irreversibility. They emphasized also that the climate change that takes place due to increases in carbon dioxide concentration is largely irreversible for 1000 years after emissions stop. Following cessation of emissions, removal of atmospheric carbon dioxide decreases radiative forcing, but is largely compensated by slower loss of heat to the ocean, so that atmospheric temperatures do not drop significantly for at least 1000 years. Many studies have focused on projections of possible 21st century dangers (Hansen et al., 2007 and Schellnhuber, 2008). However, the principles (Article 3) of the UNFCCC (United Nations Framework Convention on Climate Change) specifically emphasize „„threats of serious or irreversible damage,‟‟ underscoring the importance of the longer term. While some 45
CHAPTER TWO: Review of Literature irreversible climate changes such as ice sheet collapse are possible but highly uncertain (Hansen et al., 2007 and Oppenheimer and Alley, 2004), others can now be identified with greater confidence. It is not generally appreciate that the atmospheric temperature increases caused by raising carbon dioxide concentrations are not expected to decrease significantly even if carbon emissions were to completely cease (Meehl et al., 2007 and Plattner et al., 2008). Removal of CO2 from the atmosphere involves multiple processes including rapid exchange with the land biosphere and the surface layer of the ocean through air-sea exchange and much slower penetration to the ocean interior that is dependent upon the buffering effect of ocean chemistry along with vertical transport (Archer and Brovkin, 2008). A review of the research literature concerning the environmental consequences of increased levels of atmospheric carbon dioxide by (Robinson et al., 2007) leads to the conclusion that increases during the 20th and early 21st centuries have produced no deleterious effects upon Earth‟s weather and climate. Increased carbon dioxide has, however, markedly increased plant growth. Carbon dioxide like all the other greenhouse gases has what is called a Global Warming Potential (GWP) which is a measure of the relative, globally-averaged warming effect due to the emission of unit mass of CO2 (Colls, 2002). This value is used to compare the abilities of different greenhouse gases to trap heat in the atmosphere. GWPs are based on the heatabsorbing ability of each gas relative to that of carbon dioxide (CO2), since the GWP of carbon dioxide, measured across all time horizons, is (1) as well as the decay rate of each gas (the amount removed from the atmosphere over a given number of years) GWPs can also be used to define the impact of greenhouse gases on global warming over different time periods or time horizons. These are usually 20 years, 100 years and 500 years. Agricultural soils contribute about 15% of global greenhouse gases (GHG) emissions, a share expected to rise in the future due to the increasing land use and management intensity of agriculture worldwide (Duxbury et al., 1993 and Gitz and Ciais, 2003). Nitrous oxide, carbon dioxide and methane are the main biogenic greenhouse gases (GHG) contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate thus requires a capacity to predict the net exchanges of these gases in an integrated manner, as related to environmental conditions and crop management (Lehuger et al., 2007). 46
CHAPTER TWO: Review of Literature At higher concentrations CO2 has a sharp, acidic odor. Carbon dioxide is not generally found at hazardous levels in indoor environments. At high levels, the carbon dioxide itself can cause headache, dizziness, nausea, and other symptoms. This could occur when exposed to levels above 5000 ppm for many hours. At even higher levels CO2 can cause asphyxiation as it replaces oxygen in the blood-exposure to concentrations around 40000 ppm is immediately dangerous to life and health. CO2 poisoning, however, is very rare (MDH, 2010) The MNDOLI has set workplace safety standards of 10000 ppm CO2 for an 8-hour period and 30000 ppm for a 15 minute period. This means the average concentration over an eight hour period should not exceed 10000 ppm and the average concentration over a 15 minute period should not exceed 30000 ppm (MDOHFS, 2010).
2.3: Ambient air hydrocarbons (HC): In organic chemistry, a hydrocarbon is an organic compound consisting entirely of hydrogen and carbon. The classifications for hydrocarbons as defined by (IUPAC, 2006) are; saturated hydrocarbons (alkanes), unsaturated hydrocarbons (alkenes), cycloalkanes and aromatic hydrocarbons (arenes). The majority of hydrocarbons found naturally occur in crude oil. There are many hydrocarbon compounds which have the potential to be pollutants when released into the atmosphere. Some occur naturally (e.g., tress), others are man-made and as result of fossil and vegetative fuel combustion, fuel volatilization, and solvent use. Hydrocarbons are a major contributor to smog and ozone (Griffin, 2007). The author Subramanian (2009) has reported that most of the organic compounds in the atmosphere 85% originate from the natural source from vegetation. Among them methane is of concern since it is the most important greenhouse gas after carbon dioxide. Methane is produced by the bacterial action, when dead organic matter is subjected to an oxygen-depleted highly reducing aqueous or terrestrial environment as the following equation:
2 {CH2O} (bacterial action) ---------------- CO2(g) + CH4(g) According to the EPA (2011), 47% of hydrocarbons emissions in the atmosphere can be attributed to on-road and off-road vehicles. The strong odor associated with diesel emissions is due to the presence of hydrocarbons. When hydrocarbons combine with nitrogen oxides (NOx) and sunlight, ozone is formed. This is a serious form of air pollution and a key component of smog. 47
CHAPTER TWO: Review of Literature Historically, measurements of atmospheric HC concentration have been expressed in terms of non-methane hydrocarbons (NMHC), because the methane concentration was regarded as a stable natural background. However, it is now recognized that methane is also a man-made pollutant from intensive animal and rice production, that the concentration is increasing globally, and it plays an important role in ozone photochemistry (Colls, 2002). As it was reported by Subramanian (2009) a variety of organic compounds are emitted into the atmosphere by natural and human activities. They are so diverse that it is difficult to classify them neatly. However they can be divided into two categories namely primary pollutants and secondary pollutants. Typical example of an organic pollutant in this category is vinyl chloride which can cause cancer. An example of photochemical smog formation is due to the interaction between terpene hydrocarbon from conifer trees and nitrogen oxides from vehicles. A range of hydrocarbons are found in vehicle fuel, and occur in vehicle emissions. In most urban areas, vehicle emissions constitute a major of hydrocarbons, including benzene and 1,3 butadiene. Also, there is the potential that they may be released to air from facilities where fuels are stored or handled. Benzene is most concerned, as it is a known human carcinogen; long-term exposure can cause leukemia. The concentration of benzene and 1, 3 butadiene are expressed as parts per billion (ppb) or microgram per cubic meter (µg m-3) and the relationships are as follows (DEFRA, 2001); 1 ppb benzene = 3.25 µg m-3 at 20 Co and 1 atm. (1013 millibar). 1 ppb 1,3 butadiene = 2.25 µg m-3 at 20 Co and 1 atm. (1013 millibar). Another class of organic pollutants is the poly aromatic hydrocarbons (PAHs). These include
the
following
compounds:
Acenapthene,
Acenapthylene,
Anthracene,
Benz(a)anthracene, Benzo(a)pyrene, Benzo(b)fluoranthene, Benzo(ghi)perylene, Chrysene, Benzo(k)fluoranthene, Chrysene, Dibenz(ah)anthracene, Fluoranthene, Fluorene, Indeno(1,2,3cd)pyrene, Napthalene, Phenanthrene, Pyrene. They are all, to varying degree, toxic or carcinogenic, and therefore classified as Hazardous Air Pollutants (HAPs). Concentrations of these hazardous compounds in ambient air are usually very small, and are reported as parts per trillion (ppt) or nanogram per cubic meter (ng m-3), (DEFRA, 2001). According to
AEA (2011), the National Atmospheric Emission Inventory (NAEI)
indicated the annual estimate of the total emission in tones of the US-EPA priority (16) PAHs 48
CHAPTER TWO: Review of Literature to the atmosphere in UK
and these include; Acenapthene, Acenapthylene, Anthracene,
Benz(a)anthracene, Benzo(a)pyrene, Benzo(b)fluoranthene, Benzo(ghi)perylene, Chrysene, Benzo(k)fluoranthene, Chrysene, Dibenz(ah)anthracene, Fluoranthene, Fluorene, Indeno(1,2,3cd)pyrene, Napthalene, Phenanthrene, Pyrene. The road transport combustion in 2008 was the largest source of PHAs emission contributing 57% of the emissions in United Kingdom (Table 2.5). Table 2.5: UK Emissions of 16 PHAs Compounds (in tones) and their Contribution Sources. (AEA, 2011). Sources
1990
1995
2000
2004
2005
2006
2007
2008
2008%
Residential/ Commercial/ 705 Institutional/Agriculture Combustion Light Duty Vehicles 317
393
337
246
254
286
329
360
29.61%
465
437
381
366
337
322
297
24.42%
Heavy Duty Vehicles-Buses 1884 and coaches Passenger Cars 144
1299
639
354
314
291
259
212
17.43%
153
189
193
195
188
180
172
14.14%
Other (Paint Application/ 1118 Waste) Public Electricity and Heat 68 Production Other Industrial Combustion 21
164
144
117
114
109
108
105
8.63%
45
35
38
39
43
39
37
3.04%
20
16
14
15
15
15
13
1.07%
Other Transport
10
8
9
10
10
10
10
10
0.83%
Metal Production
3499
2315
40
17
8
10
6
10
0.83%
Total
7766
4863
1846
1371
1314
1289
1268
1216
100%
Polycyclic aromatic hydrocarbons (PAHs) are produced as result of incomplete combustion, with significant amounts produced as a consequence of anthropogenic activity (Zhang and Tao, 2009). Consequently, elevated levels are typically found in urban environments, with strong correlation between levels of PAHs in the environment and population density being frequently observed (Sun et al., 2006; Brandli et al., 2008; Zhang and Tao, 2009). Whereas automotive and industrial sources tend to strongly influence PAHs levels in outdoor environments. Observation of PAHs concentration for indoor environments, particularly at higher latitudes, suggest different contributing sources, largely related to human
49
CHAPTER TWO: Review of Literature activities, such as domestic heating, smoking and age of the building (Ohura et al., 2004 and Li et al., 2005). The limit values of benzene and 1,3 butadiene were set by European community (EC Directive 2000/69/EC the 2nd Daughter Directive) to cover the air quality strategy. But PAHs were to be covered by the fourth Daughter Directive and just for one PAHs compound, which was Benzo(a)pyrene in the PM10 particulate fraction, because it would be used as a marker of carcinogenic risk for PAHs in ambient air. The proposed target value for Benzo(a)pyrene as a representative PAH is 1 nanogram per cubic meter (1ng m-3) for the annual mean total Benzo(a)pyrene in the PM10, particulate fraction and to be achieved by 2012 (DEFRA, 2010). The EC limit or air quality strategy (AQS) for benzene is 5 microgram per cubic meter (5µg m3) since January 2010, and it was 16.25 µg m-3 during 2003 to 2010. But the EC limit for 1,3 Butadiene is 2.25 µg m-3 since 2003 (DEFRA, 2001). As the air pollution is an issue of great public health concern, therefore, a new methods and tools such as passive air sampler for long-term air pollution and quality monitoring for seasonal and spatial variation in Sulaimani city should be used, particularly for determining PAHs, persistence organic pollutants POPs. Persistent organic pollutants POPs due to their wide distribution, ability for bioaccumulation in the fatty tissues, and carcinogen, mutagen and endocrine disruption potential, remain the center of our attention. They are emitted from various primary and secondary source, and the atmosphere often play a key role in their transport within the immediate vicinity of POPs source as well as over great distance (Wania, 2003).
2.4: Oxygen(O2): Although oxygen constituent in the atmosphere is not an air pollutant or greenhouse gas, but in the current study its concentration level has been estimated in different urbanized sites of Sulaimani city in order to assess the anthropogenic effects on its normal level in the atmosphere. Oxygen or O2 is one of the basic chemical elements. In its most common form, oxygen is a colorless, odorless and tasteless gas in air; it has poor solubility in water. A density of 1.428 g/L at standard temperature and pressure (0 Co and 1 atm) makes it slightly heavier than air, it is chemically reactive (Jacobson, 2002).
50
CHAPTER TWO: Review of Literature Oxygen is one of the life-sustaining elements on Earth and is needed by organisms, makes combustion possible and is also used in many industrial, commercial, medical and scientific applications. Oxygen comprises 20.946 percent by volume of the earth‟s atmosphere (Griffin, 2007). It is the most abundant of all elements on earth; it comprises 88.8 percent of its ocean by mass (Cook and Lauer, 1968) and, as a component of most rocks and minerals, 46.6 percent of its solid crust by mass (Jacobson, 2002). In addition, it constitutes 60 percent of the human body (Chang, 2007).
2.5: Heavy metals in the environment: Environmental contamination and exposure to heavy metals such as mercury, cadmium and lead are a serious growing problem throughout the world. Human exposure to heavy metals has risen dramatically in the last 50 years as a result of an exponential increase in the use of heavy metals in industrial processes and products (Dhundasi, 2009). According to Duffus (2002) the term “heavy metal” has never been defined by any authoritative body such as IUPAC. Over the 60 years or so in which it has been used in chemistry, it has been given such a wide range of meanings by different authors that it is effectively meaningless. No relationship can be found between density (specific gravity) and any of the various physicochemical concepts that have been used to define “heavy metals” and the toxicity or ecotoxicity attributed to “heavy metals”. Thus, the term "heavy metals" is both meaningless and misleading. Even the term "metal" is commonly misused in both toxicological literature and in legislation to mean the pure metal and all the chemical species in which it may exist. However, over the past two decades, the term "heavy metals" has been widely used. It is often used as a group name for metals and semimetals (metalloids) that have been associated with contamination and potential toxicity or ecotoxicity. Heavy metals mainly include the transition metals, some metalloids, lanthanides, and actinides There are 35 metals that concern us because of occupational or residential exposure; 23 of these are the heavy elements or "heavy metals": antimony, arsenic, bismuth, cadmium, cerium, chromium, cobalt, copper, gallium, gold, iron, lead, manganese, mercury, nickel, platinum, silver, tellurium, thallium, tin, uranium, vanadium, and zinc (Glanze 1996). Motivations for controlling heavy metal concentrations in the environment are diverse. Some of them are dangerous to health or to the environment (e.g. mercury, cadmium, lead,
51
CHAPTER TWO: Review of Literature chromium), (Hogan, 2002), some may cause corrosion (e.g. zinc, lead), some were harmful in other ways (e.g. arsenic may pollute catalysts). Heavy metals pollutants can localize and lay dormant. Unlike organic pollutants, heavy metals do not decay and thus pose a different kind of challenge for remediation (Zevenhoven and Kilpien, 2001). It has been published by OSHIC (1999) that heavy metal toxicity can result in damaged or reduced mental and central nervous function, lower energy levels, and damage to blood composition, lungs, kidneys, liver, and other vital organs. Long-term exposure may result in slowly progressing physical, muscular, and neurological degenerative processes that mimic Alzheimer's disease, Parkinson's disease, muscular dystrophy, and multiple sclerosis. Allergies are not uncommon and repeated long-term contact with some metals or their compounds may even cause cancer. Environmental bioindicators, such as pine needles, mosses, and lichens, represent a complementary tool for environmental monitoring systems, and could also overcome some of the shortcomings associated with the direct measurements of heavy metal pollutants. Pine needles (Pinus Sylvester‟s) have proved to be suitable air quality indicators for pollutants, especially for sulfur and heavy metals, in many studies such as the study of (Reimann et al., 2001). Environmental contamination by heavy metals became an important issue partly because of the potential accumulation in biosystems, through polluted water, soil and air. Therefore, the current study was also aiming to assess the impact of air pollution on the accumulation of some heavy metals in the environmental samples of soil, dust, plant and rainwater samples in Sulaimani city. 2.5.1: Heavy metals in soil: Heavy metals naturally occur in the environment, but may also be introduced as a result of land use activities. Naturally occurring as well as anthropogenically introduced concentrations of metals in near-surface soil can vary significantly due to different physical and chemical processes operating within soils across geographic regions. Urban soils differ from the rural ones by the fact that they are more strongly influenced by anthropogenic activities. This influence is often reflected by a high degree of contamination. The total analysis of heavy metals such as Cd, Cr, Cu, Ni, Pb, and Zn in soils is commonly done to evaluate the degree of 52
CHAPTER TWO: Review of Literature contamination of terrestrial environment and to determine background (baseline) concentration, since toxic metals belong to the most serious air pollutants affecting our environment. The accumulation of heavy metals in agricultural soils is of increasing concern due to the food safety issues and potential health risk as well as its detrimental effects on soil ecosystem (McLaughlin et al., 1998). These metals have peculiar characteristics including that (1) they do not decay with time, (2) they can be necessary or beneficial to plants at certain levels but can be toxic when exceeding specific thresholds, (3) they are always present at a background level of non-anthropogenic origin their input in soils being related to weathering of parent materials and pedogenic process, (4) they often occur as cationic form which strongly interact with the soil matrix, consequently, heavy metals in soil can become mobile as a result of changing environmental condition. This situation was referred to as “chemical timing bomb” (Facchinelli et al., 2001). The proximity of urban soils to humans increased the probability that soil components, including heavy metal pollutants, may be carried into the human body through inhalation, ingestion or dermal contact (Abrahams, 2002). The sources of heavy metals in soils mainly include; natural occurrence derived from parent materials and human activities; anthropogenic inputs due to different human activities and actions such as, incineration of municipal and industrial solid waste, application of chemical fertilizer and herbicides, long-term using of waste water in irrigation; and atmospheric depositions, particularly from mobile and industrial sources (Schwedt, 2001 and Koch and Rotard, 2001). Table (2.6) shows the means of some heavy metal content (aqua regia extractable) in urban soils from different cities in the world (Biasioli et al., 2006). It was further revealed by Biasioli et al. (2006) that urban soils differed from the rural ones by the fact that they were more strongly influenced by anthropogenic activities. This influence was often reflected by a high degree of contamination. To investigate the influence of metal pollutant in large city on its soils as compared with the surrounding ones having the same parent material, samples within the city of Torino, Italy and a set of surrounding soils developed from the same alluvial parent material were analyzed for determining Pb, Zn, Cu, Ni, and Cr. Results showed that the city plays a key role in concentrating some pollutants, such as Pb, Zn, and Cu within its borders, while Ni and Cr appeared to have a strong natural 53
CHAPTER TWO: Review of Literature contribution. The mean concentrations of the metals Pb, Zn, Cu, Ni, and Cr. for both Torino city and surrounding soils were (149, 183, 90, 209), and 191 and (20, 62, 28, 74, and 96), respectively. Table (2.6): Mean metal concentration (mg kg-1 soil) extracted by aqua regia in urban soils of different cities in the world. (Biasioli et al., 2006) City Metal concentration in mg kg-1 (ppm) Pb Zn Cu Ni Cr Hong Kong/ China 95 125 23.3 12 23 Madrid/ Spain 161 210 72 14.1 75 Nanjing/ China 104 96 104 97 Napoli/ Italy 262 251 74 11 Palermo/ Italy 253 151 77 19 39 Seville/ Spain 137 145 68 22 39 Warsaw/ Poland 53 140 25 13 Torino/ Italy 149 183 90 209 191 In another study by Biasioli et al. (2007) the soils of Ljubljana (Slovenia), Sevilla (Spain), and Torino (Italy) were extensively sampled and analyzed for the heavy metals Pb, Zn, Cu, Cr, and Ni. Results highlighted that the three city ecosystems demonstrated that strongly influenced the quality of their soils, despite their differences in geography, size, climate, etc. Potentially toxic elements (PTE) showed a wide range in concentration reflecting a diffuse contamination. Among the „„urban‟‟ elements Pb exceeded the legislation threshold in 45% of Ljubljana, 43% of Torino, and 11% of Sevilla samples while Zn was above the limits in 20, 43, and 2% of the soils of Ljubljana, Torino, and Sevilla, respectively. In Mosul city / Iraq, three metal concentrations were measured at two depths; surface (0-5 cm), and shallow subsurface (5–25 cm) at five locations, including (Bashiqa) as a control side by (Al-saffawi, 2006). The concentration values at depth (0-5 cm) ranged from 27.0 to 255.8, 4.40 to 14.17, and 85.01 to 197.5 mg/kg, while at the depth (5-25 cm) were 21.1 to 100.2; 3.77 to 8.77, and 76.5 to 172.5 mg/kg for the heavy metal Pb, Cd, and Zn, respectively. The results revealed that the lower levels for all three metals were in control side (Bashiqa), moreover, the concentrations in the surface soil layer were higher than that in the beneath layer and the maximum levels were above the guideline limits set by many countries. In a study by Murray et al. (2004), 14 metals concentrations including; antimony, arsenic, barium, beryllium, cadmium, chromium, copper, lead, mercury, nickel, selenium, silver, thallium, and zinc were measured in soil at three depths: near-surface (< 0.5 m), shallow 54
CHAPTER TWO: Review of Literature subsurface (0.5–10 m), and depths greater than 10 m across six soil units in glacial terrain and throughout three general land use categories: residential, commercial, and industrial
in
Southeastern Michigan. The results indicated that metal concentrations were the highest in the near-surface with Pb present at concentrations averaging 15.5 times that of background in industrial areas and approximately 16 times background in residential areas. Cd, Hg, and Zn were also present in surface soils at levels of several times that of background. The highest concentrations of each of these metals were present in the clay-rich soils located in the eastern; more urbanized and industrialized part of the watershed. Metals detected at elevated concentrations decreased in concentration with increasing depth and distance from the urbanized and industrialized center of the watershed. 2.5.2: Heavy metal content of ambient dust: Dust is a significant environmental media that can provide information about the level, distribution, and fate of contaminants present in the surface environment. As the composition of settleable dust is similar to atmospheric suspended particulates, it can be used as an indicator of pollutants such as heavy metal contamination in the atmosphere (Akhter and Madany, 1993). Furthermore, the elemental composition and concentrations in dust reflected the characteristics of short-and long-term activities in the area (Banerjee, 2003). Humans can become exposed to heavy metals in dust through several routes which include ingestion, inhalation, and dermal absorption. In dusty environments, it was estimated that adults could ingest up to 100 mg dust/day (Hawley, 1985; Calabrese, 1987). Children are usually exposed to greater amounts of dust than adults as a result of pica and play behavior (Murgueytio, 1998 and CDCP, 2005). Currently, there are no guidelines or regulations for heavy metals in dust. The composition and quantity of street dust are important environmental pollution indicators that should be considered in investigations of environmental pollution. Street dust is generally originated from vehicles exhaust particles, particularly for diesel engines and windtransported particles. Heavy metals, which are found in street dust, such as Pb, Cu, Mn, Zn, Cd and Ni are significant for environmental pollution. According to the kind of fuel source and vehicle in traffic, quantity and type of heavy metals vary in street dust (Ball et al., 1991). The researchers Ball et al. (1991) and Peterson and Batley (1992) have believed that atmospheric deposition, abrasion of tires, brake lining and road surfaces, and corrosion of vehicle body work were the predominant sources of heavy metals Cd, Cu, Mn, Ni, and Zn to 55
CHAPTER TWO: Review of Literature urban road runoff in the urban area. Emissions resulting from the combustion of fuel are also an important source of particulates and heavy metals in urban street dust, particularly Pb, and the metals Cd, Cu, Mo, V, Zn that are widely used as additives in lubricants. The use of leaded gasoline gave a boost to the importance of lead level especially in street dust even at the start of 21st century. These metals possessed bioaccumulation and biomagnifications property, and the possibility of the amount of these metals reaching a critical value and threatening human health increased the importance of this issue. Heavy metals accumulated in the street dust, soil, and surface water samples and affected the ecosystem in the world (Al-Radady et al., 1994 and Tuzen, 2003). The determination of metal in environmental samples including dusts, plants, soils and surface water is very necessary for monitoring environmental pollution (Tuzen, 2003 and Zhou et al., 1997). Metals are released into the biosphere by natural processes and by anthropogenic activities. They are predominantly transferred as molecules or particulate matter via the atmosphere, mostly on large scales. The amount of anthropogenically derived metals has increased continuously since the beginning of the industrial revolution, and the awareness and concern about associated environmental and health risks were raised sharply over the last few decades (Gunter and Komarnicki, 2005). In urban areas, metals may come from many different sources, including vehicle emission, industrial discharges and weathered materials (Al-Khashman, 2004; AlKhashman and Shawabakh, 2006 and Li et al., 2001). The monitoring of the metals contents of dust samples was an efficient way of obtaining information on the current environmental state of large areas (Divrikli et al., 2003). In a study carried out by Yetimoglu et al. (2007) fifty-six samples of street dusts were collected on E-5 highway in Istanbul, Turkey and analyzed for determining some heavy metals content. The mean concentration levels of Pb, Mn, Zn, Ni, Cd and Cu were found to be 368.3, 747.8, 431.2, 27.1, 0.3 and 191.1 µ g-1, respectively. Pb, Cu, Zn and Mn mean concentrations in studied areas were higher than levels of these heavy metals according to EPA (1992). Highly significant correlations except for Mn were found between the number of vehicles and heavy metal concentrations. Al-Khashman (2004) studied some heavy metal distribution in control dust, street dust and soils from the work place in Karak Industrial Estate (KIE), Jordan. The samples were analyzed for their content of Fe, Cu, Zn, Ni, and Pb. The ranges of heavy metal concentration in the 56
CHAPTER TWO: Review of Literature investigated area were (58.8-94.8), (1.8-84.9), (15.4-136.9), (1.7-6.5), and (2.1-314.1) mg kg-1 dry soil for Fe, Cu, Zn, Ni, and Pb, respectively. The concentrations were greater on the surface samples but decreased in the lower part as a result of the basic nature of this soil. The possible sources of the heavy metals (Cu, Zn, Ni, and Pb) were anthropogenic and industrial activities from the work place in KIE. In another study, Al-Khashman (2007) investigated the concentrations of the heavy metals (Fe, Zn, Cu, Cr, Pb, Cd, Ni, Mn and Co) in 140 street dust samples, collected from Aqaba city, Jordan. The highest levels of metal concentrations were found in the samples from heavy traffic and the values were several times higher than the control levels. Factor analysis showed that the area was mainly influenced by three sources, namely lithogenic, traffic, and industrial. In a study by Sezgin et al. (2004), twenty-two street dust samples were taken from 14 main areas of E-5 Highway in Istanbul, Turkey and investigated for the heavy metals concentrations of Pb, Cu, Mn, Zn, Cd and Ni. According to the results of this study, Pb, Cu and Zn concentrations in E-5 Highway between Topkapi and Avcilar region in Istanbul were higher than maximum concentration levels of these heavy metals in normal soil. This situation indicated that there is heavy metal pollution in the inspected area in E-5 Highway in Istanbul. Atmospheric samples were collected by Momani et al. (2000) from seventeen sampling sites using a low-volume air sampler and dustfall containers in the city of Amman, Jordan. The heavy metal contents in settleable particles (dustfall) as well as in suspended particulates were analyzed for the heavy metals: Zn, Cu, Pb, and Cd. The atmospheric concentrations of Zn, Cu, Pb, and Cd were 344, 170, 291, and 3.8 ng m-3, respectively. But, the levels of these elements in the dustfall deposition were 505, 94, 74 and 3.1 µg g-1, respectively. The enrichment coefficients of the heavy metals in the dustfall were found to be significant and were 12.1, 6.1, 11.7, and 1.1 for Zn, Cu, Pb, and Cd, respectively. A total of 106 dust sample, including street dust, control site and household dusts were investigated by Akhter and Madany (1993) throughout Bahrain for the heavy metals Pb, Zn, Cd, Ni and Cr content. Results showed that dust samples contained significant levels of the metals studied compared with the control values. The mean values for Pb, Zn, Cd, Ni and Cr in street dust were 697.2, 151.8, 72.0, 125.6 and 144.4 µg g-1 respectively, whereas for household dust they were 360.0, 64.4, 37.0, 110.2 and 144.7 µg/ g-1. These values suggested that motor vehicles were forming a major source of these metals in street dust samples. 57
CHAPTER TWO: Review of Literature The effect of electric waste recycling (recycling of printed circuit boards), which contain many different heavy metals was investigated by (Leung et al., 2008) in Guiyu, China, to evaluate the extent of heavy metals (Cd, Co, Cr, Cu, Ni, Pb, Zn) contamination. Surface dust samples were collected from recycling workshops, adjacent roads, a schoolyard, and an outdoor food market. The analyses revealed elevated mean concentrations in recycling workshop dust (Pb 110000, Cu 8360, Zn 4420, and Ni 1500 mg kg-1 dust) and in dust of adjacent roads (Pb 22600, Cu 6170, Zn 2370, and Ni 304 mg kg-1 dust). Lead and Cu in road dust were 330 and 106, and 371 and 155 times higher, respectively, than non e-waste sites located 8 and 30 km away. Levels at the schoolyard and food market showed that public places were adversely impacted. Risk assessment predicted that Pb and Cu originating from circuit board recycling have the potential to pose serious health risks to workers and local residents of Guiyu, especially children. Road surfaces contributed significant loads to runoff in an urban environment (Xanthopoulos and Hahn, 1990), and the primary pollutants of concern in such runoff were heavy metals, (Pb, Zn, Cu, Cd), (Ward, 1990; Yousef et al., 1990 and Barley et al., 1994). A study carried out by Ford and Dale (1996) to investigate Pb in soil and road dust in two Sydney suburbs. This study found that a large number of residential sites had Pb concentration above guideline limits set by the New South Wales Environmental Protection Authority (NSW EPA) for residential areas. Risk assessment regarding exposure to heavy metal-contaminated dust by ingestion could be carried out to estimate the noncancer toxic (chronic) risk. Estimation of risk was calculated based on equations detailed in US EPA‟s Exposure Factors Handbook (EPA, 1997). Average daily dose (ADD) was determined by the following equation:
Where, (C) is the mean heavy metal concentration (mg kg-1) in dust; (IngR) is conservative estimates of dust ingestion rates, IngR, were chosen for adult (100 mg day-1) and child (200 mg day-1) scenarios (EPA, 1997); EF is exposure frequency (days / year); ED is exposure duration (years); (BW) is an average body weight, BW of 60 kg for adults (Lee et al., 1994) and 15 kg for children was assumed; and AT is the averaging time (days). Then noncancer toxic risk was determined by calculating the hazard quotient (HQ) by the following equation: 58
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Where RfD is Reference Dose (mg/Kg-day) or an estimate of the daily exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Therefore, HQ ≤ 1 suggested unlikely adverse health effects whereas HQ > 1 suggested the probability of adverse health effects (EPA, 1993). An HQ>10 was considered to be high chronic risk (NSDH, 1995). It was also assumed that the toxic risks due to the heavy metals were additives; therefore the HQ for each metal at a location scenario was summed to generate the hazard index (HI). 2.5.3: Heavy metal in plants: On the other hand, the high concentration of heavy metals in soils was reflected by higher concentrations of metals in plants, and consequently in animal and human bodies. The ability of some plants to absorb and accumulate heavy metals made them useful as indicators of environmental pollution. The study of excessive concentrations of pollutants in biological matrices has been reported in numerous publications (Mccrady and Maggard, 1993). Pine and spruce needles, mosses and grasses are widely used as a specific indicator in the study of a geographical and temporal pattern of pollutants (Jensen et al., 1992 and Richardsond, 1995) The main sources of heavy metals to plants are the air or soil from which metals are taken up by the root or foliage. Some heavy metals are essential in plant nutrition, but plants growing in a pollute environment can accumulate heavy elements at high concentrations, causing a serious risk to human health (Alloway, 1990; Vousta et al., 1996 and Sharma et al., 2004). Atmospheric metals are deposited on plant surfaces by rain and dust. Several authors showed a relationship between atmospheric element deposition and elevated element concentrations in plants and topsoil‟s, especially in cities and in the vicinity of emitting factories (Sunchez et al., 1994 and Srinivas et al., 2002). Widespread interest in heavy metal accumulation in plant systems has emerged only over the last three decades, and several research articles reported concentrations of a number of heavy elements in the local crops and other plants as a consequence of anthropogenic emissions (Zarcinas et al., 2004a; Zarcinas et al., 2004b and Wong et al. 2001). The Air Accumulation Factor (A.A.F) and concentration factor (C.F) can be calculated to find out the origin of trace metals in vegetables by using the following equations (Chandra 59
CHAPTER TWO: Review of Literature sekhar et al., 2001). For example, (A.A.F.) and (C.F.) could be calculated for the content of a particular heavy metal in a plant species grown in industrial area as compared to the same plant species grown in a control area and for the same heavy metals content in order to find out the origin of the metal in the plant as follows:
The environmental samples of air, soil media, and four plant samples from Industrial, Semi-urban and rural areas (as a control site) were investigated by Srinivasi et al. (2009) in Visakhapatnam city/ India for analyzing the heavy metals Pb, Zn, Ni and Cu. Results showed that the air environments in Industrial and Semi-Urban areas were enriched with the four trace metals. Soils did not seem to have been contaminated by atmospheric deposition. Remarkable differences were observed between the trace metal content in vegetables of rural areas with semi-urban and industrial areas. Based on the air accumulation factor and concentration factor calculations, the trace metals of Pb and Zn in industrial and semi-urban areas were found to be receiving the contributions from both atmospheric and soil inputs in all the four crops. The concentrations of Pb, Cd, and Zn metals in a number of plants species grown in five locations of different sites (control site is included) in Mousl city/Iraq were studied by AlSaffawi (2006) in both leaves and steam samples. In general, the concentration of the metals varied between 2.83-54.03, 0.22-2.52, and 6.34-82.59 µg/g dry matter in leave samples for all species and ranged between 2.15-29.53, 0.22-1.31, and 7.39-57.53 µg g-1 in steam samples for Pb, Cd, and Zn metals, respectively. The higher levels were at heavy traffic locations, while the lowest levels detected in control site (Bashiqa). 2.5.4: Heavy metal in rainwater: Nowadays, rainwater harvesting is a very common process in many countries in order to assure an independent water supply during water restrictions for drinking , livestock, irrigation or to refill aquifers in a process called groundwater recharge. But the quality of rainwater may deteriorate due to the atmospheric aerosol pollutants, particularly in urban areas. Heavy metals are one of the pollutants that picked up by the rain from the atmosphere.
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CHAPTER TWO: Review of Literature The rainwater dissolve air borne particulates, water soluble gases and also incorporated airborne microbes as it passes through the atmosphere. Its quality may further be degraded as it infiltrated the soil (Okonkwo et al., 2008 and Olobaniyi and Efe, 2007). The study of chemical composition of atmospheric aerosols was especially significant, due to the direct influence on ecosystems (Kanellopoulou, 2001 and Thomidis, et al., 2003). The heavy metals that are emitted in the atmosphere in the form of aerosols, mainly from human activities, were taken away by wet or dry deposition and caused damages to the surface waters and the organisms living there. Also, the metals were absorbed by the plants through the rain, (Steinnea, 1990; Quiterio et al., 2004a and Quiterio et al., 2004b;). Frequently, anthropogenic emissions caused the levels of metal in suspended particles to be above natural background levels (Finlayson-Pitts and Pitts, 2000; Quiterio et al., 2004a and Quiterio et al., 2004b). A great percentage of metals fell through the rain at the place of their production (Nurnberg et al., 1984). However, the aerosols, with a very small falling velocity, were easily transferred by the wind and it was possible to be deposited through the rain at long distances from the point of their emission (Smirnioudi et al., 1998). Therefore, it was expected that chemical components in the rainwater (acid components, anions, cations, and heavy metals) damaged significantly the environment (surface waters, plants, animals, human beings, building). Thus, a good knowledge of the chemical qualities of rainwater through regular physico-chemical analysis is necessary so as to manage its suitability. The concentrations of ten heavy metals including Pb, Cd, Ni, Al, Cr, Zn, Cu, Mn, Fe, and As were determined in 20 samples of rainwater collected at the station of Athens University Campus (AUC) during the cold period 1/10/97 - 31/3/98 by Kanellopoulou (2001). In general, low concentrations were observed for all the metals except Zn and the mean concentrations were (0.88, 0.20, 4.41, 5.87, 1.29, 33.46, 15.41, 3.61, 4.38, and 0.84) ppb for the metals Pb, Cd, Ni, Al, Cr, Zn, Cu, Mn, Fe, and As, respectively. It was found through Spearman correlation coefficient that the metals Cd, Ni, Cr, Zn, Cu, Mn and Fe have common sources (the industrial area of Thriasio), but the sources of Pb were due to a traffic, while, Al and As seem to have their own common sources. In Mosul city/ Iraq, rainwater samples over two season (winter and spring) were collected in fourteen 14 different urbanized locations (including a control site) and analyzed for the 61
CHAPTER TWO: Review of Literature heavy metals Pb, Cd, and Zn by Al-Saffawi (2006). The results for winter season ranged between ND-350, ND-72, and 13-400 ppb and for the spring season between ND-100, ND-16, and 12-75 ppb for the metal Pb, Cd, and Zn, respectively. But the overall average for all the locations in winter were 78.67, 10.12, and 93.37 ppb and in spring were 24.19, 3.7, and 27.58 ppb for the metal Pb, Cd, and Zn, respectively. The lowest levels for all metal were observed in control site (Bashiqa). Table (2.7) showed some physiochemical characteristics of rainwater in different cities of the world (Al-Saffawi, 2006). Table (2.7): Some physiochemical characteristics of rainwater samples from different cities in the world. (Al-Saffawi, 2006). City Concentration in mg/L nnspH Ca+2 Mg+2 K+ Na+ NO3- ClSO4-2 *SO4-2 % Ontario /Canada 5.3 1.20 0.24 1.28 1.36 0.68 0.18 3.22 87 Colaba/India 7.1 2.56 6.64 1.13 3.12 0.40 4.19 2.58 86 Sau Paulo/ Brazil 5.0 1.48 0.36 0.31 0.48 2.77 0.94 2.77 89 Taipei/ Taiwan 4.4 0.54 0.30 0.30 2.00 2.89 3.77 5.62 91 New Delhi/ India 6.1 2.96 -1.29 1.75 -2.52 2.74 84 Guiyang/ China 4.1 4.62 0.69 1.00 0.23 1.30 0.37 19.76 99 Dhahran/ Saudi Arabia 5.5 9.26 1.10 0.45 3.00 11.26 5.12 11.80 93 Amman/Jordan 6.15 5.88 0.96 0.78 3.15 2.91 4.58 11.95 92 Mousl/Iraq 5.2-7.4 9.90 2.97 1.00 3.37 2.12 6.75 10.37 93 *nns is non sea salt sulfate. In another study by Wedyan et al. (2009), the concentrations of six heavy metals (Pb, Cd, Zn, Fe, Cu, and Mn) were investigated in 10 samples of rainwater collected at the station of Al Hussein Bin Talal University Campus (AHU), Jordan during the winter time (1/12/06-3/3/07). The results indicated that there were strong correlations among the heavy investigated metals and the mean concentrations were 0.42, 0.19, 24.87, 14.56, 2.34, and 3.99 ppb (µg L-1) for the metals Pb, Cd, Zn, Fe, Cu, and Mn, respectively.
2.6: Traffic-related (vehicular) air pollution: Urban environmental issues owe much of their persistence to the complex and interactive nature of towns and cities. Nodaway, motor vehicles have a significant impact on human health and environment in terms of air quality, greenhouse gases, ozone depletion, water quality, natural resources, agriculture product, habitat destruction/disturbance, noise and many economic, social and political issues of every country. Figure (2.12) shows a diagram of the web of connections between increased car ownership, use, environmental and social outcomes in urban areas. 62
CHAPTER TWO: Review of Literature One detrimental environmental effect of road transport is its contribution to atmospheric pollution, because automobile traffic is one of the important sources of air pollution (Gramer and Chevreuil, 1991). As it was published by Colls (2002), since the 1960s, the number of motor vehicles in the world has been growing faster than its population. In 1950 there were 50 million cars for 3.5 billion people. There are now 600 million cars for about 6 billion people, with a global production of 45 million cars per year. By 2020 there will be a billion cars. The net growth rate for all motor vehicles is now around 5%, compared to a population growth rate of 1-2%. Vehicle exhaust emissions are a main contributing factor to poor air quality, particularly in urban areas. These emissions contributed to air pollution and were a major ingredient in the creation of smog in some large cities (EPA, 1999). Exhaust gas or flue gas is emitted as a result of the combustion of fuels such as natural gas, gasoline (petrol), diesel fuel, fuel oil or coal. It is discharged into the atmosphere through an exhaust tail pipes, flue gas stack or propelling nozzle. It often disperses downwind in a pattern called an exhaust plume. As a matter of fact, today one of the major issues in road transport is poor air quality and the associated negative health impacts. In particular, diesel vehicles are found to contribute substantially to atmospheric levels of particulate matter PM and ozone O3. Pollutant mass emission rates from motor vehicles vary greatly according to their operating mode. Consequently, changes in road layout or traffic behaviour may have a significant impact on local air quality (North, 2007). The source of pollution from vehicles comes from by-products of the fuel combustion process (exhaust) and from evaporation of the fuel itself and that is either from the direct evaporative emissions or refueling losses (Colvile et al, 2001). The negative health impact of these air pollutants was well documented (e.g. Bernstein, 2004) and tend to be more significant in urban areas due to increased human exposure (WHO, 2005). Gasoline and diesel fuel are complicated mixtures of hydrocarbons, including aromatic, naphthenic, olefinic and paraffinic components. Gasoline typically contains hydrocarbons with 5-12 carbon atoms, diesel fuel 12-18 carbon atoms. As a result of incomplete combustion, complex partially oxidized compounds and soot can be formed (Hart, 1980).
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Figure (2.12): Vehicles are significant issue that impacting environment and urban areas. (RCEP, 2007). In a “perfect” engine, oxygen in the air would convert all the hydrogen in the fuel to water and all the carbon in the fuel to carbon dioxide. Nitrogen in the air would remain unaffected. In reality, the combustion process cannot be “perfect,” and automotive engines emit several types of pollutants. Soot is formed as a residue during the combustion of fuel in power plants and vehicles due to the incomplete combustion of organic products. Soot particles are an impure form of elemental carbon (graphite) and are roughly spherical, whereas graphite has a layered structure. Soot forms accretion of graphite-like precursors. It is known that most PAH compounds are sorbed on soot particles. Soot consists of many condensed aromatic rings containing 1-3% hydrogen, 5-10% oxygen and trace metals such as Be, Cd, Cr, Mn, Pb, Ni and vanadium and also toxic organic such as benzo(a)pyrene, as it is illustrated in Figure (2.13) (Subramanian, 2009). The toxic compounds emitted by self-ignition engines are mostly nitrogen oxides, NOx, unburned hydrocarbons, HC, carbon oxide, CO, but foremost particulate matters PM. The term particulate matter usually denotes soot particles with all the other particles they contain. They are by far more carcinogenic than other harmful components of exhaust gases e.g. polycyclic 64
CHAPTER TWO: Review of Literature aromatic hydrocarbons at a comparable emission level. Due to their microscopic sizes, the above compounds easily migrate into the atmosphere or the human being (in the process of breathing). They are extremely dangerous on account of their small dimensions, as when they become deeply rooted in the lung tissue they are virtually irremovable (Zajac, 2008).
Figure (2.13): Soot particles from combustion of fossil fuels. (Subramanian, 2009). In general, there are four main polluting emissions from motor vehicles and they are: carbon monoxide, Nitrogen oxides, Hydrocarbons or volatile organic carbons VOCs and particulate matter PM, (Colls, 2002). In this connection, mention is rarely made of the fact that these substances constitute only a fraction of total exhaust gas emissions. Considerably, the approximate composition of the exhaust emissions of diesel and petrol engines includes other components like N2, O2, H2O, CO2, SO2, and Pb, Figures (2.14 and 2.15), (Audi, 2000). As it was reported by (HC, 2004), hydrocarbons (HCs), derived from unburned fuel during incomplete combustion, additionally Volatile Organic Compounds VOCs, are a large family of carbon-containing compounds that evaporate easily and arising from fuel evaporative emissions, these may include benzene, toluene, xylene, 1,3-butadiene, acetaldehyde, and formaldehyde. Subsequent reaction in sunlight creates smog and other forms of air pollution. VOCs are cancer-causing agents, although the risk at current levels in the environment is small. Evaporative losses can account, on hot days, for a majority of the total VOCs pollution from current model cars.
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Figure (2.14): Composition of exhaust emissions of petrol engines. Note: Petrol engines may also emit small quantities of sulfur dioxide SO2. (Audi, 2000).
Figure (2.15): Composition of exhaust emissions of diesel engines. (Audi, 2000). Evaporative emissions occur in several ways (EPA, 1994): Running Losses-The hot engine and exhaust system can vaporize gasoline while the vehicle is running. Hot soak (Cooling Down) - The engine remains hot for a period of time after the vehicle is turned off, and gasoline evaporation continues when the car is parked while cooling down. Diurnal Emissions (Emissions while Parked and Engine is Cool) - Even when the vehicle is parked for long periods of time, gasoline evaporation occurs as the temperature rises during the day.
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CHAPTER TWO: Review of Literature Refueling - Gasoline vapors escape from the vehicle‟s fuel tank while the tank is being filled. The author Dora and Phillips (2000) demonstrated that motor vehicle traffic is the main source of ground-level concentrations of air pollutants in urban areas: it contributes practically all CO, 75% of NO2, about 40% of PM10 concentrations and accounts for a substantial fraction of CO2 and O3 concentrations in the air. The awareness of the major role of motor vehicle traffic in increasing air pollution, and hence of its impact on human health, led international and national environmental and health agencies to indicate reduction in traffic-related emissions of air pollutants as a priority in environmental policy. As an example, the European office of WHO in recent years has strongly suggested European trans-national and national governments to take actions in two directions: technological improvements (such as the introduction of unleaded petrol and catalytic converters) and reductions in traffic volume (Dora and Phillips, 2000). According to Tahir and Ul-Haqkhan (2003), engine emissions are responsible for about 60% of air pollution and the exhaust of diesel engine contains water and carbon dioxide as the major components and that is from 80 to 90%, the remaining 10% is as un-burnt carbon particles, nitrogen oxides, oxides of sulfur, carbon mono oxide and hydrocarbons, etc. It has been published by (Langat et al. (2008, that the transport sector is expected to be responsible for 75% of carbon emission by the year 2020, and they have mentioned that nearly 50% of global carbon monoxide, hydrocarbons (HCs) and nitrogen oxides emissions from fossil fuel combustion come from internal combustion engines. Emission rates vary based on the speed a vehicle is traveling and vehicle category. The figures (2.16; 2.17; and 2.18) showed how speed and vehicle category (Personal car PC, and Heavy Duty Trucks (HDT) affected emission rates of CO, NOx, and VOCs (CTRE, 2004). Although, vehicle became more and more important and it is now a key factor in the "quality of life", but at the same time mobile source emissions are important contributors to ambient air pollution and is associated with cancer-related and noncancer- related health effects. Recent work has shown that health effects and ambient air pollution increase with proximity to roadways, suggesting that motor vehicle traffic contributes a large share to ambient health effects (Pearson et al., 2000; Venn et al.2001 and Nicolai et al., 2003).
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CHAPTER TWO: Review of Literature A number of epidemiologic studies reported the presence of associations between residential proximity to busy roads and a variety of adverse respiratory health outcomes in children, including respiratory symptoms, asthma exacerbations, and decrements in lung function (Wjst et al., 1993 and van Vliet et al., 1997). In some reports, truck traffic was more strongly associated with these adverse outcomes than total vehicular traffic (van Vliet et al., 1997; Brunekreef et al, 1997 and Janssen et al., 2003).
Figure (2.16): Carbon monoxide emission rates by vehicle category vehicle category and different speed. (CTRE, 2004).
Figure (2.17): Oxides of nitrogen emission rates by vehicle category and different speed. (CTRE, 2004).
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Figure (2.18): Volatile organic compounds emission rates by vehicle category and different speed. (CTRE, 2004). Most studies used metrics of proximity to traffic as surrogates of exposure to traffic pollution (e.g., residential proximity to major roads, traffic volume at the nearest road, or modeled levels of traffic pollution). Few have measured pollutant concentrations as part of the exposure assessment or for providing information on local air quality (van Vliet et al., 1997; Brunekreef et al, 1997and Kramer et al., 2000). Traffic-related air pollution may be linked to a higher death rate among people who initially survived strokes, hint study findings from the United Kingdom by Maheswaran, et al. (2010). Of 3320 men and women who lived in a specific south London region and had a first stroke between 1995 and 2005. They found that survival after stroke was lower among patients living in areas with higher levels of the outdoor air pollutants of nitrogen dioxide and particulate matter less than 10 µm in diameter for more than a decade (Mean exposure levels were 41 µg m-3 for nitrogen dioxide and 25 µg m-3 for particulate matter <10 µm in diameter). Their report, in the journal Stroke showed that a 10 μg m-3 increase in nitrogen dioxide was associated with a 28% increase in risk of death. A 10 μg m-3 increase in particulate matter <10 µm in diameter was associated with a 52% increase in risk of death. Traffic-related pollutants such as particulate matter, nitrogen dioxide, and ozone were strong oxidants (D‟Amato et al., 2005), leading to the production of reactive oxygen species (ROS). 69
CHAPTER TWO: Review of Literature Air pollution contributed to the development of asthma (Brauer et al., 2007; Jacquemin et al. 2009). Recent studies from the United States found associations between long-term exposure to air pollution and cardiopulmonary and lung cancer mortality (Jerrett et al. 2005; Miller et al. 2007). Cohort studies from Europe have tended to confirm the U.S. findings (Gehring et al. 2006). An analysis of air pollution in central Copenhagen, Denmark by Nielsen et al. (1996), concluded that traffic sources contributed 90 percent of the organic hydrocarbon (such as benzene-related compounds) levels on working days and 60 percent during weekends. The study used several different approaches to assessing the health impacts of these exposures, and concluded that the direct effect of exposure to these organic compounds and other mutagens in the urban air was a maximum of five lung cancer cases each year per one million persons. An early study of the link between traffic-related air pollution and cancer was carried out in Hamburg, Germany by ((Ippen et al., 1989). Cancer frequency for almost 62000 people living in street with high levels of traffic (>30000 cars day-1) were related to about 12000 cancer cases for the period 1970-1972. The study found an excess risk of 6 percent for all cancers, with a 12 percent overall excess cancer risk for men. More importantly, the study, which controlled for smoking, found an excess risk for lung cancer of 34 percent. Somewhat surprisingly, the study also found an excess risk for colon cancer of 68 percent. Several studies reported significant associations between proximity to highly trafficked streets and the occurrence of childhood cancer and childhood leukemia. A study in Denver, Colorado, USA by Pearson et al. (2000), which expanded on the analysis of an earlier childhood cancer study by Savitz and Feingold (1989), was conducted to include calculations of traffic-related density and emissions. For children residing in homes within 750 feet of roads with the highest traffic density (≥20000 vehicles day-1), the increased risk for all cancers was almost six times higher and the risk for leukemia more than eight times higher than for children with the lowest traffic density exposures. In a study by Garshick et al. (2004 ), the assessment of lung cancer mortality due to diesel exhaust from diesel-powered locomotives in 54973 U.S railroad workers for the period between 1959 and 1996 (38 years) was assessed. The results showed that there were 4351 lung cancer deaths. Lung cancer mortality was elevated in jobs associated with work on trains
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CHAPTER TWO: Review of Literature powered by diesel locomotives. These results suggested that exposure to diesel exhaust contributed to lung cancer mortality in this cohort. Therefore, in cities across the globe, the personal automobile is the single greatest polluter, as emissions from a million vehicles on the road add up a plane-wide problem. Driving a private car is a typical citizen‟s most air polluting activity. The negative effects of automotive emissions are high when you sit in traffic surrounded by cars, their engines idling. Everyone sitting in a traffic jam is getting poisoned. Environmental impacts of road transport include emissions of PM, CO, CO2, HC, NOx, SO2, Pb, etc.
2.7: Other vehicles exhaust criteria: 2.7.1: Diesel opacity test and “K” value: Diesel engines are widely used in almost all walks of life and cannot be dispensed with in the near future. The health effects of diesel emissions received attention worldwide. Several Organizations were concerned about the health effects onroad diesel engines present for the general population. For example, (WHO, 1996; HEI, 1995; and IARC, 1989) published information on the potential adverse health effects of diesel emissions. In comparison to spark ignited gasoline engines, diesel engines yielded relatively low levels of hydrocarbons HC, volatile organic compounds VOCs, and carbon monoxide CO (Grumet et al., 1997). As global warming concerns increased, diesel engines have an added advantage with carbon dioxide production 10-25% less than gasoline engines (Grumet et al., 1997). However, diesel engines were relatively high emitters of nitrogen oxides NOx and particulate matter PM when compared to spark ignited engines. Highway and non-road heavyduty (HD) engines (Primarily diesel engines) were reported to account for approximately 40% of inhalable ambient particulate PM10.0 and 60 to 80% of fine particulate PM2.5 inventory (Grumet et al., 1997 and Diez-Sanchez, 1997). In 1998, California identified diesel exhaust particulate matter PM as a toxic air contaminant based on its potential to cause cancer, premature death, and other health problems, and reported that diesel engines emitted a complex mixture of air pollutants, composed of gaseous and solid material. The visible emissions in diesel exhaust were known as particulate matter or PM. Diesel engines also contributed to California's fine particulate matter PM2.5 air quality problems. Based on year 2006-2008 emissions in California, diesel PM contributed each year to approximately 2000 premature deaths (CEPA, 2011). 71
CHAPTER TWO: Review of Literature Diesel engines are one of the largest sources of fine particulate matter, other than natural causes such as forest fires. Fine particles from diesel engines contributed to haze which restricts visibility and contributed also to ozone formation (a component of smog), acid rain, and global climate change (EPA, 2002). Diesel exhaust includes 40 hazardous air pollutants defined under the Clean Air Act, with 15 being known or probable carcinogens such as benzene, formaldehyde, acetaldehyde, dioxin, and poly aromatic hydrocarbons PAHs. PAHs are some of the most potent known carcinogens (OEC, 2007). Diesel emissions are the emissions that emitted into the environment from any dieselpowered motor vehicle. But diesel smoke (black smoke) means particles, including aerosols, suspended in the exhaust stream of a diesel engine which absorb, reflect, or refract light. This smoke is partially burned fuel which not converted into energy (DEP, 2000). Black smoke particulate matter, especially the tiny particles smaller than 10 microns, are a health hazard and have been linked to cancers and respiratory diseases such as asthma (Zero Emission Limited). An opacity meter is used to measure the amount of light blocked in smoke emitted by diesel engines. Opacity means the degree of light-obscuring capability of emissions of visible air contaminants expressed as a percentage. For example, complete obscuration shall be expressed as 100% opacity. But emission opacity standards mean the acceptable level of peak smoke opacity for diesel-powered motor vehicles as established by the Board of Environmental Protection (DEP, 2000). The opacity of smoke is indicated in units called the k-value (k-value is extinction coefficient and also called mass attenuation coefficient or mass absorption coefficient and refers to several different measures of the absorption of light in a medium), (Wikipedia, 2010). The Diesel Opacity Test indicates how much pollution is coming from the engine and whether the engine is running efficiently, by measuring the emission of black smoke coming from the vehicle‟s exhaust. This test is used to improve fuel economy, reduce air pollution and protect our health. 2-7-2: Lambda (λ): Lambda is the ratio of actual air-fuel ratio (AFR) to stoichiometry for a given mixture. Lambda of 1.0 is at stoichiometry, rich mixtures are less than 1.0, and lean mixtures are greater than 1.0. The following equation is often used as the definition of lambda (Wikipedia, 2011): 72
CHAPTER TWO: Review of Literature
Where, AFRstoich is the stoichiometric mixture fraction. Lambda (λ) is an alternative way to represent Air-fuel ratio (AFR) or it is a measurements of the ratio of air-to-fuel present during combustion. But AFR is the mass ratio of air to fuel present during combustion (Wikipedia, 2011).
If exactly enough air is provided to completely burn all of the fuel, the ratio is known as the stoichiometric mixture. For gasoline fuel, the stoichiometric air/fuel ratio is approximately 14.7 times mass of air to 1 time mass of fuel (14.7:1). In practice this is never quite achieved. Lambda is an important measure because it indicates whether the car is polluting the air and needs tuning. Typical readings on a well-tuned engine are between 0.95 (rich mixture) and 1.05 (lean mixture).
2.8: Vehicles exhaust standards and legislations: Air quality laws and regulations are at the heart of air quality management strategies, even as health and environmental effects are the justifications for those laws and regulations. Unfortunately, many people don‟t make the correct choice unless regulations are legislated and enforced. The increasingly greater concerns regarding environmental pollution created from automotive emissions, is leading to more stringent emission control requirements worldwide. This has forced the auto-manufacturers to develop and use various pre-engine, in-engine and post-engine technologies to reduce pollution from automotive engines. The catalytic converter is one of such post-engine devices used to convert the harmful gases like CO, NOx and unburned hydrocarbon emitted from a gasoline engine in to less harmful forms like CO2, N2 and H2O (Ehsan et al., 2005). The first legislated exhaust emission standards were promulgated by the State of California for 1966 model year for cars sold in that state, followed by the United States as a whole in model year 1968. Following that annual reductions in exhaust emission standards, but not fuel economy, were legislated by Environmental Protection Agency EPA (Tom, 2009). 73
CHAPTER TWO: Review of Literature It was reported by (Christian et al., 2008) that motor vehicle exhaust emissions could be sharply reduced within the past 20 years by the introduction of exhaust after treatment systems, modern engine control concepts and cleaner fuels. However, it was not possible to significantly improve air quality in cities with regard to particulates and ozone in the past 10 years. It appeared that the reduction in vehicle exhaust emissions achieved is offset by the growth in traffic as well as by changes in the composition of exhaust emissions and the corresponding reactivity in the environment. According to BUWAL (2000) the emission from gasoline fueled construction engines, measured at idle, must not exceed the following limits: CO: 35000 cm3 m-3 and HC: 500 cm3 m-3. The same directive establishes the following smoke opacity limits: 10% opacity for vehicles equipped with DPFs (Diesel Particulate Filters) and 35% opacity for vehicles without DPFs (Diesel Particulate Filters). It also classified diesel particulate emissions as carcinogenic. Emission standards for engines and vehicles, including emission standards for greenhouse gas GHG emissions, were established by different organizations and governments either as national or international limits. Standards will usually be set to protect the most sensitive receptor. The Environmental Protection Agency EPA and the European Union EU vehicle emissions regulations were the most influential groups. Various terms such as standards, guidelines and limit values were used by the different organizations and governments (Colls, 2002). EPA's emission standards for trucks and buses were based on the amount of pollution emitted per unit of energy (expressed in grams per brake horsepower hour) (EPA, 2002). Some countries and states like (UK, Japan, China, India, Hong Kong, California State, etc) have independently set emissions standards to control the emissions from different vehicles and engines categories. Table (2.8) and figure (2.19) are example of vehicles standard emissions of European Union (EU) and Japan.
74
CHAPTER TWO: Review of Literature Table (2.8): European emission standards for light commercial vehicles 1305-1760 kg (Category N1-II). (Wikipedia, 2011) Diesel Fuel No. Tier Date 1Euro 1† October 1994 2Euro 2 January 1998 3Euro 3 January 2001 4Euro 4 January 2006 5Euro 5 September 2010 6- Euro 6 September 2015 (future) Petrol (Gasoline) Fuel
Gram Pollutants per Kilometer (g/km) CO THC NMHC NOx 5.17 ---1.25 ---0.80 --0.65 0.63 --0.33 0.630 --0.235 0.630 --0.105
P**
HC+NOx 1.4 1.0 0.72 0.39 0.295 0.195
PM 0.19 0.12 0.07 0.04 0.005 0.005
-------
Gram Pollutants per Kilometer (g/km) 12345-
Euro 1† Euro 2 Euro 3 Euro 4 Euro 5
6- Euro
6 (future)
October 1994 January 1998 January 2001 January 2006 September 2010 September 2015
5.17 4.0 4.17 1.81 1.810
--0.25 0.13 0.130
----0.090
--0.18 0.10 0.075
1.4 0.6 ----
----0.005*
------
1.810
0.130
0.090
0.075
--
0.005*
--
* Applies only to vehicles with direct injection engines ** A number standard is to be defined as soon as possible and at the latest upon entry into force of Euro 6
75
CHAPTER TWO: Review of Literature
Figure (2.19): Comparison of Japanese and European particulate (PM10) emission levels for heavy diesel vehicles. (EDC, 2007).
76
CHAPTER THREE: Materials and Methods 3.1: General description of Sulaimani city: Sulaimani is a governorate in Iraq, Iraqi Kurdistan region and located far north east of Iraq and southeast of the Iraqi Kurdistan Region (Figure 3.1).The international border with Iran represent the eastern boundary of the governorate where, it is bound north and north-west by Erbil governorate, west by Kirkuk governorate and Salahaddin governorate, and southwest and south by Diyala governorate. Sulaimani city is the capital of Sulaimani Governorate, its center coordination are (35o 33′ 14.99′′ N) and (45o 26′ 58.68′′ E) and has an elevation of 864 m above sea level (Google Earth, 2011). Sulaimani city surrounded by the mountains: Azmar, Goizja and the Qaiwan Ranges in the north east (with the highest peak of 1709 m above sea level), Baranan and Glazarda Mountains in the south (with the highest peak of 1373 m above sea level) and the Tasluje Hills in the west (with the highest peak of 1118 m above sea level ), (Husain, 2010). Sulaimani city was founded on 14 November 1784 by the Kurdish prince Ibrahim Pasha Baban who named it after his father Sulaiman Pasha. According to a master plan set by the old Iraqi Baath regime in 1981, Sulaimani city was any metropolitan area, residential quarter, commerce and culture center which was located within the boundary of Maleek Mhmood Circle (Circular 60 m Street formerly) and covered an area of 31.35 Km2 (Muhammad,2009). In 1994 the urbanization area of Sulaimani city was expanded by the municipality of Sulaimani to cover an area of 113.73 Km2 (Muhammad, 2009). The increase in the proportion of the inhabitants residing in Sulaimani city caused an urbanization expansion of 60 percent throughout the years 2003 to 2006. Furthermore, in 2007 it has been decided by the municipality of Sulaimani to expand the urbanization of Sulaimani city to cover an area of 350.0 Km2 (Muhammad, 2009), (Figure 3.2). The populations of Sulaimani governorate and Sulaimani city center were 1797508 and 571507 inhabitants in 2009 respectively (Table 3.1), (Directorate of Statistic in Sulaimani, 2010). While the population of Sulaimani city center in 1987 was 339310 inhabitants. When the populations of Sulaimani city are equally spread on its 113.73 km2 area it accounts for a density of about 5025 inhabitants/ km2, or 199 m2 per inhabitants. Considerably, Sulaimani city suffered rapid urbanization in the last 23 years and the population increased by 40.62% between the years 1987 and 2010.
77
CHAPTER THREE: Materials and Methods
78
CHAPTER THREE: Materials and Methods
79
CHAPTER THREE: Materials and Methods Table (3.1): Presentation of some demographic data of Sulaimani governorate, Sulaimani city center and for the studied area. (Directorate of Statistic in Sulaimani, 2010). District Type of the Total No. of Total Area No. of Total No. of No. of Hotels & No. of Population Population Families (km2) Quarters Buildings common Village & houses residential Build. 314572 9795.76 639 303410 646 -Sulaimani Metropolitan 1,526,540 Governorate Rural 270,968 51033 -63399 98 2235* Total
Sulaimani City Center
Metropolitan Rural Total
Bakrajo
Metropolitan Rural Total
Tanjaro
Metropolitan Rural Total
Raparin
Metropolitan Rural Total
1,797,508 571507 -571507 25675 27673 53348 17479 24662 42159 27540 -27540
365605 123985 -123985 5610 5833 11443 3529 4860 8389 5959 -5959
113.73
639 133 -133 10 -10 6 -6 19 -19
366804 112322 --112322 5396 6198 11594 3411 4830 8241 6386 -6386
744 394 -394 25 1 26 2 19 21 2 2
2235 ----65 65 -63 63 ----
The original total number villages of Sulaimani governorate are 2572 but only 2235 are rehabilitated. In Sulaimani governorate there are 16 town and 61districts.
80
CHAPTER THREE: Materials and Methods Increasing levels of urbanization in Sulaimani city were caused by natural growth of the urban population and migration of the rural population towards cities mainly due to a political plane carried out by the Iraqi Baath regime. Sulaimani city has a semi-arid climate with very hot and dry summers and very cold winters. The mean average monthly temperature during the winter months varies between 410oC and 22-32oC during summer. The daily temperature range may vary between 8-16 °C (Figure 3-3). There are an average of 9 days /month with frost in the plains and up to 22 days/ month in the mountain valleys and slopes (Amin, 2003; FAO, 2002). The solar radiation intensity or total solar energy for a day in Sulaimani may reach 6-11 MJ m-2 .day (megajoules per square meter per day), during winter and increases up to 21-29 MJ m-2.day in summer (Amin, 2006). The overall average annual precipitation of Sulaimani center was found to be 687.09 mm during the period extending from 1942 to 2005 seasons (64 years) (Mohamed-Ali, 2008), Regarding, the relative humidity, the highest average value of 69.67% was recorded in January and the lowest average value of 20.74% was recorded in July (Figure 3-3), (Muhammad, 2009).
Figure: (3.3). Average temperature, relative humidity and wind speed for the studied area during the years 1973 to 2006. (Muhammad, 2009).
81
CHAPTER THREE: Materials and Methods In general, the dominant winds in the region are often affected by global patterns of movement in the Earth’s atmosphere and also depend upon the seasons. Southerly wind blows over the Arabian Peninsula developing dust storms, raising daily temperature to more than 45° C. The average wind speed recorded in Sulaimani city in summer season throughout the years 1973 to 2006 was 2.4 m sec-1. While the average wind speed recorded in winter season was 1.5 m sec-1, but the annual average wind speed recorded during the years 1973 to 2006 was 1.9 m sec-1 (Mohamed-Ali, 2008; Muhammad, 2009). In winter, the region is dominated by Mediterranean cyclones moving east to north east over the region, Arabian sea cyclones moving northward passing over the Gulf carrying a great amount of moisture which cause a large amount of precipitation in the region. The total number of garden city parks and recreation places in Sulaimani city are 226 and covers an area of 536.6 Donum. In addition to that the median islands street garden or median strips road garden cover about 105 Donum. These parks, gardens and recreation places enrich lives through education, community beautification and also offer diverse environmental experiences for the city (Husain, 2010). In 1975, the green areas made up 12 percent of the Sulaimani city area and have been shrinking since then. Currently, the green areas in Sulaimani city account for only 5.57 percent of the total area and it is far below the target of 24.7 percent which was set by the directorate of gardens in Sulaimani Table (3.2), (Husain, 2010). Table (3.2): Green area coverage in Sulaimani city and green space per inhabitant. (Husain, 2010). Year Total Area of Sulaimani Green Area Percent of Green Area (m2) City (Donum) Coverage Green Area per Inhabitant 1969 2952 228.0 7.72 6.9 1975 6000 720.0 12.00 12.0 1998 12317 682.4 5.54 3.59 2009 29244 1683.2 5.57 6.10 Planned for 2012 29244 7555.1 25.83 24.7
3.2: Motor vehicle growth in Sulaimani city: Nowadays, motor vehicle air pollution has become a seemingly inescapable reality of life in urban of Sulaimani city. The number of motor vehicles in Sulaimani governorate (Sulaimani Identification Numbering) has increased sharply from 32468 in 1999 to 180912 in 2010, in addition to 68489 vehicles registered with information cards and 6716 motorcycles (Table 3.3). 82
CHAPTER THREE: Materials and Methods Table (3.3): Total number of registered vehicles in Sulaimani city (Sulaimani Numbering) during the years 1999 to 2010. (Directorate of Traffic in Sulaimani City, 2010). Types
Years of Registration 2004 2005 2006
1999
2000
2001
2002
2003
2007
2008
2009
2010
Private 10533 Vehicles For-Hire 9502 Vehicles Trucks 7487 Vehicles Government549 Owned Vehicles Agricultural 4218 Vehicles Construction 179 Vehicles Total 32468 Vehicles %Neta Growth Rate Other Vehicles are:
10606
12224
14370
21876
33511
37185
37094
37174
40307
57611
85301
9548
10284
10094
11030
13769
15262
15735
15961
16120
18708
22587
7490
7604
7729
16818
18324
20356
21647
22597
28745
41072
58537
561
813
946
953
2759
3491
4398
5324
6369
7448
7448
4219
4236
4244
4254
4287
4348
4486
4674
4761
5179
5453
139
179
179
179
179
180
204
277
325
946
1586
32563
35340
37562
55110
72829
80822
83564
86007
96627
130964
180912
0.29
7.86
5.92
31.84
24.33
9.89
3.28
2.84
11.00
26.22
27.61
Vehicles Registered by Information Carts
64849
Motorcycle (Sulaimani Numbering)
6716
a: Calculated by the Researcher
83
CHAPTER THREE: Materials and Methods Therefore, vehicles in Sulaimani governorate increased by 82.05% and 86.78% without and with vehicles registered with information cards, respectively. As the vehicle in Sulaimani was so dramatically increased, the fuel consumption and air pollutant emissions are also raised enormously.
3.3: Air Pollution sources other than vehicles in Sulaimani city: The rapid growth of urban population and industrialization activities in Sulaimani city over the last decade resulted in more demand for fuel and electricity power and this led to operate many diesel and gasoline generators. As it can be seen in Table (3.4), there are more than 455 diesel generators in use to generate 161327 kVA electricity powers for the Sulaimani quarters. In addition to that, there were and still many others generators in use by all the sectors, but there were no available data about them. Moreover, the amount of fuel (gasoline, kerosene and diesel) consumption in Sulaimani city is an important issue, the amount of fuel supply by the official sector for 2009 in Sulaimani governorate were 498570782 liters (Table 3.5). The private sectors as another source of fuel supplying also provided a huge amount of fuel demand, and also there were no available data about the supplied fuel amount from the private sector was present. In Sulaimani city there are many factories or firms and industries that are responsible for the release of pollutants into the environment. Some of these industrial processes produce dust or gases from their activities; these may be released directly into the atmosphere. To ensure that the concentrations of releases are not excessive or likely to be at harmful levels the industries should be regulated. But most of these industrial activities in Sulaimani city and its surrounding were not prescribed for regulation to control pollution under Iraqi or Kurdistan regional environmental protection acts and permitting regulations of pollution prevention and control acts. The number and type of these factories are presented in Table (3.6), (Directorate of Environment in Sulaimani city, 2009). In addition to those factories and industrial activities in Sulaimani, the classical incineration of solid waste in Tanjaro waste disposal site (landfill) is another serious source of air pollutants and cause detrimental impact on environmental and human health in the city. This landfill site is located in 10 Km of south Sulaimani city and has an area of 180 donums (450000 m2) and receives about 1000 tons solid waste daily from the city (Rashid, 2010).
84
CHAPTER THREE: Materials and Methods Table (3.4): Number of diesel generator (Alternator) in Sulaimani city. (Directorate of General Electricity in Sulaimani, 2010). No.
Generator’s Position
12345678-
Sulaimani Quarters German Village Shari Goizha Pac City Shari Shniar Gallary Lawand Kurd City Many other Generators for Hotels, Market, Private Houses Power Supply
Number in Produced Power Use (kVA or kW) 441 153430 5 4000 3 out of 15 1500 3 out of 5 1530 1 out of 3 500 1 37 1 330 No Available Date
Hour’s of operating per day About 8 hours about 8 hours 24 hour 24 hour 24 hour About8 hours
kVA : Kilovolt Ampere; kW: Kilowatt; 1kVA=1kW Table (3.5): Amount of some crude oil products distributed by the official sector in Sulaimani and Erbil governorates during the year 2009. (Managing Establishment of Special Projects/ Directorate of Planning and Monitoring) Governorate Oil Products (Liters) Gasoline Kerosene Diesel Sulaimani 167703244 133908555 196958983 Erbil 165317400 130900129 190457239
Table (3.6): Number and type of factories and industrial activity in Sulaimani city and surroundings*. (Directorate of Environment in Sulaimani city, 2009). Type of Factory (Firms) Number State of the Factory Operating under an or Industrial Activity Environmental Permit or Regulation In Not No Yes Operation Operated Cement Factory 3 3 1 2 Refining Industry 6 6 6 Asphalt Factory 15 15 15 Other Industrial Activity 122 89 33 84 38 (Industrial Place 1,) Other Industrial Activity 100 60 40 86 14 (Industrial Place 2, Tanjaro) *: The farthest factories are Mass and Bazian cement factories which are located 30 km in west
85
CHAPTER THREE: Materials and Methods 3.4: Environmental sampling: 3.4.1: Soil and plant sampling: Soil and plant samples were collected simultaneously on 25.8.2009 from urban locations of different traffic volumes and surrounding rural fields (i.e. rural samples) in Sulaimani city. The exact location was described with GPS reading as it is shown in Table (3.7) and Figure (3.4). A total number of 15 tope soil (0–20 cm depth) and a number of different plants samples of the planted trees were collected separately in each location. At each sampling site composite soil samples of about 5 Kg were obtained by mixing subsamples from 4 sites using a hand auger. The soil samples, free of plant roots, were air-dried, gently crushed and sieved at 2 mm with stainless-steel sieves to avoid any contamination and then stored for subsequent analyses. Hygroscopic moisture was measured gravimetrically after 24 h of oven-drying at 105 Co of about 10-g air-dried soil samples. At the end of sampling, for plant samples, only the most frequent 15 samples of plant trees were selected among all the collected samples in order to give a more scientific chance for a logic comparison between the heavy metals content in tree plants grown in urban and rural locations (Table 3.7). The plant samples were pretreated according to Gupta (2004) by washing or rinsing with running tap water and then acidified distilled water, 0.1% HCl followed by distilled water, dried by a hot air oven at 70 Co for 24 hours after placing them in new paper bags, then gently grinded with stainless-steel grinder, sieved through a 40 mesh stainless-steel sieve and finally stored in plastic container for heavy metal analysis. The samples locations (soil and plant) 1, 2, 4, 5, 7, 8, 9, 14, and 15 (9 locations) were from urban and agricultural soils close to highway and roads, while the samples locations (soil and plant), 3, 6, 10, 11, 12, and 13 (6 locations) were from rural and sites far from the urban and direct traffic impact. 3.4.2: Settleable dust (dustfall) sampling; In this study, dust sampling was impacted by lack of the instrument of air particulate sampler and also by the absent of the ambient air quality monitoring stations in Sulaimani city, therefore, only settleable dust (dustfall) samples were taken but suspended particulates were not possible to obtain or to collect.
86
CHAPTER THREE: Materials and Methods
Table (3.7): GPS coordination and vegetation covers for the soil and plant sample locations. No. 1-
Locations Raparin/ Sulaimani- Karkuk Street
2-
Raparin/ Entrance Kelaspi Village
3-
Bakrajo/ Agriculture College Fields
4-
Sarchinar Crossing Garden
5-
9-
Maleek Mahmood Circle/ Near to Chami Rezan Petrol Station Maleek Mahmood –Entrance Kani Goma Village Wluba Garden/ Beside Wluba Overpass Tanjaro/ Agricultural Field Adjusting Sulaimani- Qaradakh Street Tanjaro/ Near to Landfill Site
10-
Shek-Waisawa Village
11-
Bnari Goizha/ Behind Goizha Appartments Dabashan/ West of Sulaimani- Azmar Street. Sarwari Quarter/ Near to Kanispika Village Orchard/ Beside Abu-Sana Hotel Olf Palma Garden /Near to Khalahaji Crossing
678-
12131415-
GPS Coordinates N 35o 34′ 51.96′′ E 45o 19′ 11.28′′ N 35o 34′ 41.16′′ E 45o 16′ 27.84′′ N 35o 32′ 17.88′′ E 45o 21′ 58.68′′ N 35o 33′ 52.56′′ E 45o 23′ 22.92′′ N 35 o 24′ 20.52′′ E 45o 24′ 20.16′′ N 35o 31′ 20.64′′ E 45o 23′ 25.80′′ N35o 22′ 10.68′′ E 45 o 25′ 48.72′′ N 35o 28′ 17.04′′ E 45o 25′ 50.88′′ N 35o 29′ 16.44′′ E 45o 25′ 50.88′′ N 35o 30′ 59.04′′ E 45o 27′ 57.60′′ N 35o 33′ 30.54′′ E 45o 29′ 06.42′′ N 35o 35′ 31.56′′ E 45o 27′ 01.08′′ N 35o 35′ 41.28′′ E 45o 24′ 46.44′′ N 35o 34′ 27.84′′ E 45o 23′ 29.76′′ N 35o 33′ 56.94′′ E 45o 25′ 13.86′′
Elevation (m) 768
Type of Vegetation Cover Eucalyptus (Eucalyptus camaldulensis)
751
Eucalyptus (Eucalyptus camaldulensis)
731
Eucalyptus (Eucalyptus camaldulensis)
760
816
Mulberry (Morus alba) Red-Bud or Judas Tree (Cercis siliquastrum) Grape (Vitis Sp.); Mulberry (Morus alba) Oleander (Nurem olendar) Mulberry (Morus alba) Mulberry (Morus nigra) Robinia (Robinia psuedoacasia) Mulberry (Morus alba) Poplar (Populus alba); Mulberry (Morus alba) Eucalyptus (Eucalyptus camaldulensis.) Grape (Vitis Sp.); Fig (Ficus carica) Eucalyptus (Eucalyptus camald.) Walnut (Juglans regia)
1093
Grape (Vitis Sp.)
1017
Mulberry (Morus alba) Fig (Ficus carica) Grape (Vitis Sp.)
1067 733 874 676 693
845 786 816
Grape (Vitis Sp.) Fig (Ficus carica) Ash (Fraxinus rotundifolia); Hackberry (Celtis Sp.) Maple (Acer cinerascens); Mulberry (Morus alba) Eucalyptus (Eucalyptus camald.) 87
CHAPTER THREE: Materials and Methods
Figure (3.4): Soil, plant and rainwater sampling path. (Google Earth) Blue numbers represent soil and plant samples location; Red numbers represent rainwater sample locations. 88
CHAPTER THREE: Materials and Methods For sampling the settleable dust (deposited dust samples or dustfall), a sheet of black plastic in dimension of 1.5 x 2.5 m was placed and fixed on 22.8.2009 at a height of 4 to 8 m on the roof of a private building houses in 15 fifteen location to collect a deposited dust particles. The roofs were firstly cleaned and made free from all materials which cause contamination. Sampling locations (2, 3, 9, 11, 13, 14, and 15) included 7 buildings located directly on streets of heavy traffic volume and densifying urban area. For comparison, settleable dust samples were also collected from 8 buildings or locations (1, 4, 5, 6, 7, 8, 10 and 12) located in different quarter of Sulaimani city and far away from direct effect of traffic volume. In addition to that, samples of deposited dust from the frequent and massive dust storm days were collected separately, knowing that the first dust storm coincided with this study happened on 19.9. 2009. Extra samples for the massive dust storm event of 25.2.2010 were collected separately from location (16, 17, 18, and 19). A map of the dust sampling locations and description were shown in (Table 3.8) and (Figure 3.5). The accumulated dust particles were collected in a weekly basis by using plastic brushes and a plastic collector by gentle sweeping motion of the plastic to collect the particulates with the aid of the house or building owner and kept in a plastic container of 100 ml. On the dust storm day falls, the deposited dust were collected and kept separately and considered as mixed sample of the local and dust storm source. The amount of collected dust particles per a week varied from a range of 0.1 to 2 grams according to the predominant condition of the locations. The collection process of settleable dust carried out in dry days only (not rainy) till March 2010, in order to obtain a sufficient amount of deposited dust for the target analysis, It should be noted that the measurements represent dry deposition only, as there was no rainfall during the sampling period. The dry dust deposits (settleable particulates) were sieved through a 140 mesh stainlesssteel (106 micrometer), since tiny airborne particles or aerosols that are less than 100 micrometers are collectively referred to as total suspended particulate matter (TSP), (HCDES, 2010), and then sealed in plastic container for subsequent analyses. Hygroscopic moisture was measured gravimetrically at 105 C o for about 1.0-g air-dried dust samples by using a moisture meter (Model Mettler Toledo), (Figure 3.6 Image A).
89
CHAPTER THREE: Materials and Methods Table (3.8): GPS coordinates of the settleable dust sample locations. No. 1-
Locations
2-
Raparin/ Near to Sulaimani International Airport Maleek Mhmood Circle/ Lovan Hotel
3-
Tanjaro / Tanjaro Mosque
4-
Nawgrdan Village/ Osman Gas Fact.
5-
Charakhan Quarter
6-
Kaziewa Quarter/ Near to Goizha Apartments
7-
Kurdsat/ Quarter 1
8-
Kurdsat Quarter 2
910
Maleek Mhmood Circle/ Beside Zargata Underpass Farmanbaran Quarter
11-
Salim Street/ Beside Khsrawkhal Bridge.
1213-
Chawrbakh Quarter / Near to Sulaimani Stadium Sarkarez/ Dastaraka Crossing
14-
Kanat Street
15-
Main Internal Buses Transportations Center
16-
German Village
17-
Mamostain Quarter
18-
Kurdsat Quarter
GPS Coordinates
Elevation (m)
N 35o 34′ 22.74′′ E045o 20′ 21.48′′ N 35o 33′ 22.56′′ E045o 24′ 22.02′′ N 35o 29′ 00.36′′ E045o 25′ 34.50′′ N 35o 28′ 02.46′′ E045o 25′ 59.40′′ N 35o 31′ 21.18′′ E045o 26′ 32.70′′ N 35o 33′ 32.52′′ E045o 28′ 24.90′′ N 35o 35′ 18.30′′ E045o 25′ 55.68′′ N 35o 35′ 43.80′′ E045o 26′ 18.36′′ N 35o 34′ 50.22′′ E045o 24′ 35.46′′ N 35o 35′ 03.18′′ E045o 24′ 04.92′′ N 35o 33′ 45.00′′ E045o 24′ 25.32′′ N 35o 33′ 01.32′′ E045o 25′ 50.64′′ N 35o 32′ 57.30′′ E045o 26′ 25.68′′ N 35o 33′ 43.38′′ E045o 26′ 46.56′′ N 35o 33′ 41.04′′ E045o 26′ 20.40′′ N 35o 34′ 57.12′′ E045o 27′ 14.41′′ N 35o 33′ 54.42′′ E045o 26′ 22.08′′ N 35o 35′ 43.80′′ E045o 26′ 18.36′′
765 775 668 688 789 999 916 948 830 809 787 815 826 868 854 975 864 948
90
CHAPTER THREE: Materials and Methods
Figure (3.5): Dust sampling path. (Google Earth).
91
CHAPTER THREE: Materials and Methods
3.4.3: Rainwater sampling; Rainwater samples were collected twice from 15 fifteen locations in Sulaimani city, once from the first precipitation or rainfall of the season which occurred on 28 and 29/ 10/ 2009 (first year’s rainfall) and the second occurred in the mid of winter season on 24 and 25/1/209. Sampling locations (2, 3, 5, 8, 11, 14, and 15) included 7 buildings located directly on streets of heavy traffic volume and densifying urban area. For comparison, rainwater samples 92
CHAPTER THREE: Materials and Methods were also collected from 8 buildings or locations (1, 4, 6, 7, 8, 9, 10, 12 and 13) located in different quarter of Sulaimani city and far away from direct effect of traffic volume (Table 3.9 and Figure 3.4). Table (3.9): GPS coordinates of the rainwater sample locations. No. 1-
Locations
2-
Raparin/ Near to Sulaimani International Airport Bakrajo/ Awal Road
3-
Maleek Mhmood Circle/ Lovan Hotel
4-
Qaratogan Quarter
5-
Tanjaro / Tanjaro Mosque
6-
Charakhan Quarter
7-
Ibrahim Ahmad Quarter
89-
Maleek Mhmood Circle/ Beside Zargata Underpass Farmanbaran Quarter
10-
Sarwari Quarter/Near to Grape Orchard
11-
Salim Street/ Beside Khsrawkhal Bridge.
12-
Shek-Mohedin Quarter
1314-
Chawrbakh Quarter / Near to Sulaimani Stadium Sarkarez/ Dastaraka Crossing
15-
Main Internal Buses Transportations Center
GPS Coordinates
Elevation (m)
N 35o 34′ 22.74′′ E045o 20′ 21.48′′ N 35o 32′ 54.84′′ E045o 21′ 59.16′′ N 35o 33′ 22.56′′ E045o 24′ 22.02′′ N 35o 30′ 58.14′′ E045o 26′ 09.66′′ N 35o 29′ 00.36′′ E045o 25′ 34.50′′ N 35o 31′ 21.18′′ E045o 26′ 32.70′′ N 35o 33′ 29.10′′ E045o 28′ 05.52′′ N 35o 34′ 50.22′′ E045o 24′ 35.46′′ N 35o 35′ 03.18′′ E045o 24′ 04.92′′ N 35o 35′ 30.78′′ E045o 24′ 03.78′′ N 35o 33′ 45.00′′ E045o 24′ 25.32′′ N 35o 33′ 12.84′′ E045o 25′ 09.12′′ N 35o 33′ 01.32′′ E045o 25′ 50.64′′ N 35o 32′ 57.30′′ E045o 26′ 25.68′′ N 35o 33′ 41.04′′ E045o 26′ 20.40′′
765 729 775 764 668 789 961 830 809 961 787 801 815 826 854
For collecting the rainwater a plastic bucket of 30 cm diameter and 25 cm depth was placed and fixed at height of 4 to 8 m on the roof of a private building house in each location. The buckets were placed directly before the rainfall date according to the awareness of (AccWeather.com/As Sulaymaniyah, Iraq/forecast). The samples were collected directly a day 93
CHAPTER THREE: Materials and Methods after the rainfall to avoid contamination and brought to the lab, filtrated through Whatman No. 42 filter paper to remove and to determine simultaneously the amount of total suspended solid (TSS) in a limited volume of rainwater sample (250 ml). The Filtrate samples were divided into two portions for each location and stored at 4 Co in a refrigerator until analysis. The first portion used for the measurement of the physicochemical properties that shown in (Appendix 4), and the second portion was acidified with concentrate nitric acid (HNO3) to a pH value of 2 for determining some heavy metals in the rainwater samples (wet deposition).
3.5: Measurement of ambient gases concentration: The natural atmospheric background level of atmospheric gases should remain relatively constant in the absence of other sources. As a part of an on-going measurement program of this study the atmospheric level of 9 gases including; CO2, CO, HC, O3, O2, NO, NO2, N2O and SO2 were measured in 17 locations of Sulaimani city to monitor the changes that occurred in the local ambient air composition and the levels of gas pollutant of the city (Table 3.10 and Figure 3.7) The measurements were conducted 7 times throughout the period from 31.9.2009 to 13.7.2010 by using two separate units of gas analyzer instrument (Model Air Plus from KINSCO Technology/ Korea) (Figure 3.6 Image B). One of the gas instrument units was equipped with infrared sensors and specified for measuring CO2, HC, N2O, but the other unit was equipped with electrochemical sensor, specified for measuring the gases CO, O3, O2, NO, NO2 and SO2. Ambient air pollutant concentrations were influenced by the spatial or time variance of emissions of hazardous substances and the dynamics of their dispersion in the air, consequently, marked daily and annual variations of concentrations occur. Therefore, in the current study the ambient gases concentrations of CO2, CO, HC, O3, O2, NO, NO2, N2O and SO2 were measured in the following dates; 31.9.2009; 16.10.2009; 6.11.2009; 26.12.2009; 11.3.2010; 9.6.2010 and 13.7.2010 and in 17 different geographical site and traffic volume locations in order to cover the entire area of Sulaimani city (Figure 3.6 showed images of F, G, H, and I for the studied locations). The units of measured concentrations were in parts per million for the gases CO2, CO, HC, and N2O and parts per billion (ppb) for the gases (O3, NO, NO2 and SO2) and percentage (%) for oxygen (O2).
94
CHAPTER THREE: Materials and Methods Table (3.10): GPS data and meteorological parameters for the studied locations of ambient gases and particulate matter (PM) concentrations.
No.
Locations
GPS Parameters Latitude& E Elevation Longitude (m) o N 35 34′ 37.20′′ 764 E045o 20′ 15.78′′
1-
Raparin /At SulaimaniKarkuk Street
2-
Sarchnar Crossing
N 35o 33′ 54.06′′ E045o 23′ 23.58′′
760
3-
Wluba Overpass
N 35o 32′ 07.32′′ E045o 25′ 56.34′′
786
4-
Tanjaro/ Near to Tanjaro Mosque
N 35o 29′ 00.24′′ E045o 25′ 34.02′′
668
5-
Tanjaro/ Landfill site
N 35o 29′ 16.68′′ E045o 25′ 50.94′′
694
6-
Foothill of Goizha Mountain
N 35o 33′ 30.84′′ E045o 29′ 10.92′′
1099
7-
Inside Peshraw Tunnel /About 75m far from the North Side Outside Peshraw Tunnel / About 250m far from the North Side Maleek Mahmood Circle /Opposite Binai Petrol Station
N 35o 37′ 39.30′′ E045o 29′ 17.76′′
1237
N 35o 37′ 41.52′′ E045o 29′ 24.24′′
1235
N 35o 34′ 48.30′′ E045o 24′ 01.02′′
804
8-
9-
Summarized Values of 7 Reading Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum
Wind Speed (m. sec-1 ) 1.0 0.5 2.0 1.1 0.5 2.9 1.0 0.3 2.9 1.1 0.3 2.6 0.9 0.2 2.8 1.4 0.9 2.5 0.7 0.1 1.6 1.4 0.4 2.1 0.7 0.3 2.4
Meteorological Parameters Temperature % Relative (Co ) Humidity 29.0 25.8 15.1 7.2 41.2 66.5 27.5 26.7 15.4 8.6 38.9 55.3 29.2 25.7 18.6 13.5 39.9 51.5 28.0 26.9 19.9 10.8 42.4 51.4 28.7 25.0 18.2 10.3 43.6 47.8 27.3 26.2 13.3 8.8 43.2 66.5 24.8 27.9 16.0 13.9 33.2 51.1 23.1 26.5 10.7 10.8 39.0 52.4 28.4 24.6 14.5 6.6 44.0 55.3
Pressure (mbar) 926.1 914.0 931.9 926.2 913.7 931.6 923.9 911.3 929.3 937.0 923.3 943.1 933.7 920.0 939.6 890.0 879.6 896.3 876.9 866.3 881.1 877.0 866.4 881.6 920.8 908.5 925.8
95
CHAPTER THREE: Materials and Methods Table (3.10): GPS data and meteorological parameters for the studied locations of ambient gases and particulate matter (PM) concentrations.
No.
Locations
10-
Salim Street /Near to Khasrawkhal Overpass
GPS Parameters Latitude& Elevation Longitude (m) N 35o 33′ 45.48′′ 787 E045o 24′ 25.14′′
11-
Khalahaji Crossing / Qadamkher Street
N 35o 33′ 56.46′′ E045o 25′ 19.20′′
820
12-
Parki Azadi /At Day Time
N 35o 33′ 53.22′′ E045o 25′ 56.88′′
846
13-
Parki Azadi /At Night Time
N 35o 33′ 53.22′′ E045o 25′ 56.88′′
846
14-
Dastaka Crossing/ Mamostayan Street
N 35o 33′ 50.40′′ E045o 26′ 16.98′′
856
15-
Sarkarez/ Sbunkaran Street
N 35o 33′ 04.98′′ E045o 26′ 40.02′′
843
16-
Bardargai Sara (Sulaimani City Center)
N 35o 33′ 25.80′′ E045o 26′ 36.30′′
849
17-
Internal Buses Transportation Center
N 35o 33′ 41.04′′ E045o 26′ 20.40′′
854
Summarized Values of (7) Reading Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum
Wind Speed (m. sec-1 ) 0.4 0.0 0.9 0.7 0.3 1.2 0.8 0.0 1.8 0.3 0.0 0.5 0.9 0.5 1.8 1.1 0.4 2.4 0.8 0.4 1.4 0.7 0.2 1.6
Meteorological Parameters Temperature % Relative (Co ) Humidity 28.9 26.1 13.0 6.7 43.3 68.6 28.6 25.3 15.6 6.8 43.2 64.8 29.2 25.1 13.9 8.4 40.6 66.0 22.3 35.1 11.5 14.7 29.6 72.4 28.4 24.2 15.9 8.1 40.1 58.0 29.2 25.1 14.8 11.0 41.6 63.1 29.8 22.8 19.5 11.1 39.5 49.4 29.4 23.1 16.2 10.3 39.7 51.8
Pressure (mbar) 922.5 909.3 929.1 919.7 906.3 925.1 916.5 903.4 921.6 916.8 903.9 921.8 914.8 902.3 920.3 917.5 905.0 923.4 915.2 903.6 920.0 914.7 902.0 920.4
96
CHAPTER THREE: Materials and Methods
Figure (3.7): Measurement path of gases and particulate matter (PM) concentrations.
97
CHAPTER THREE: Materials and Methods Each measurement took at least two days, then after the mean concentration of the 7 dates for each location was summarized in one table for valuation of the air quality and determining the critical area of hot spots sites of air pollution in Sulaimani city. Meanwhile, the meteorological parameters of temperature, relative humidity, wind speed and atmospheric pressure was also measured in each location by using a portable weather meter (Model Kestrel 4000 Pocket Weather Tracker) as it is displayed in Figure 3.6 Image E, because the properties and dispersion of gas pollutants are significantly correlated to those weather parameters and also to the amount of precipitation. Figures (3.8a, b, c and d) showed only the average levels of the measured meteorological parameters of; wind speed, temperature, relative humidity, and atmospheric pressure respectively in each location.
Figure (3.8a): Average values of wind speed for the studied locations of ambient gases and particulate matter (PM) measurements.
Figure (3.8b): Average values of temperature for the studied locations of ambient gases and particulate matter (PM) measurements.
98
CHAPTER THREE: Materials and Methods
Figure (3.8c): Average values of relative humidity for the studied locations of ambient gases and particulate matter (PM) measurements.
Figure (3.8d): Average values of the pressure for the studied locations of ambient gases and particulate matter (PM) measurements.
3.6: Measurement of particulate matter (PM) concentration: The real-time concentration measurements of particulate matter PM in aerodynamic diameter of PMtotal, PM10.0, PM2.5 and PM1.0 were measured simultaneously with the gases measurement in 17 locations of Sulaimani city (Table 3.10 and Figures 3.7). The measurements were also performed and repeated 7 times during the measurement period (31.9.2009 to 13.7.2010) by using a Haz-Dust Environmental Particulate Air Monitor instrument (Model EPAM-5000/ USA), (Figure 3.6 Image C&D). Dust particles were drawn into the sensor head of the device and detected once every second and also all data points were stored in the memory for later analysis. Dust concentrations were instantaneously calculated and displayed in milligram per cubic meter (mg m-3) on the Haz-Dust’s LCD in accordance
99
CHAPTER THREE: Materials and Methods with Environmental Protection Agency (EPA) or Occupational Safety and Health Administration (OSHA) reference method. The Haz-Dust EPAM-5000 uses the principle of near-forward light scattering of an infrared radiation to immediately and continuously measure the concentration of airborne dust particles. The measurements continued in each time and each location for 30 minutes for the entire aerodynamic sizes to quantify the temporal and local variation of atmospheric particulate matters in Sulaimani city and the average values of the measured concentrations were recorded. Dust/sandstorms became a notable meteorological phenomenon in Kurdistan Region of Iraq; therefore, the PM levels have been also measured through the dust/sandstorm days during the period from 19.9.2009 to 2.6.2011 as an extra factor affecting the air quality of Sulaimani city.
3.7: Real-Time measurement of vehicle exhausts gas flow: Vehicle exhaust emissions are a major contributor to the world's air pollution problems. Therefore, the current study also aimed to test the exhaust-gas emissions analysis for a number of gasoline’s and diesel’s fueled vehicles and trucks of different type (made) and model year. The test was conducted by Periodic Vehicle Inspection (PVI) center in Erbil governorate (Figure 3.9 Image A, B, C, and D) because the exhaust gas analyzer was not available in Sulaimani city and the vehicles were almost the same in Iraqi Kurdistan Region. The test was performed for 812 gasoline-fueled vehicles by using an exhaust gas analyzer (model Auto-Com 40/ Sweden product) (Figure 3.6 Image K & L) and for the following emitted gases and parameters; (carbon monoxide CO, hydrocarbons HC, carbon dioxide CO2, oxygen O2 and Lambda (λ). In addition to that exhaust test was also performed for 175 diesel-fueled trucks by using an exhaust gas analyzer (model Auto-Com 50/ Sweden product) (Figure 3.9 Image D) to measure the parameters of smoke opacity and K values. The real time exhaust-gas emissions analysis was conducted directly at the tailpipe exhaust flow and in a low engine speeds number or at slow engine running.
3.8: Analytical procedures for sampling and trace element determination in environmental samples (dust, soil, plant and rainwater samples): For the chemical analysis and dissolution of the environmental samples for heavy metal determination the following methods were applied:
100
CHAPTER THREE: Materials and Methods 3.8.1: Dust samples analysis; 3.8.1.1: Percent of organic carbon (o.m%): The percent of organic carbon content in dust samples were determined by Walkley-Black method (wet oxidation by potassium dichromate K2Cr2O7 and concentrated H2SO4) as it is described by Black et al. (1965). 3.8.1.2: Percent of carbonate minerals or (CaCO3 equivalent): CaCO3 in dust samples were determined by the acid-neutralization method according to the method 23c of U.S. Salinity Laboratory Staff, 1954 (Black et al., 1965).
3.8.1.3: pH of the 1:10 Dust: water suspension ratio: After preparing the suspension solution by mixing 1g dust sample with10 ml distilled water and allowing the mixture for equilibrium for 30 minutes with occasional shaking the pH-value (hydrogen ion potential) were measured in the supernatant using a portable pH-meter (model WTW 330i/ Germany) according to soil survey standard test method (Rayment and Higginson, 1992). 101
CHAPTER THREE: Materials and Methods 3.8.1.4: Heavy metal analysis: The total analysis of 8 heavy metal content of Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb in settleable dust sample was estimated from the aqua regia extractions, following the procedure recommended by the International Organization for Standardization (1995). A 1.0-g dust of each sample was digested with 20 ml aqua regia (HCl of 37% and HNO3 of 70% in a ratio of 3:1) at room temperature for 16 hour. Then the mixtures were digested at 130 Co for 2 hour using the automated kjeldahl digester (model Tecator Digestion System Unit 2540 Auto/ Denmark-Sweden, (Figure 3.9 Image E and F). The obtained suspension was then filtered through an ashless Whatman 42 filter, diluted to 100 ml with 0.5 M HNO3, and stored in polyethylene bottles at 4 Co for element analysis. All glass- and plasticwares used were previously soaked in 10% HNO3 and rinsed thoroughly with deionized water. The metals were analyzed or determined using both inductively coupled plasma-optical emission spectroscopy ICP-OES (model PerkinElmer, precisely Optima 2100/USA), (Figure 3.9 Image H & I) and Atomic Absorption Spectroscopy AAS (model PerkiElmer, precisely AAnalyst 800 Atomic Absorption Spectrophotometer/ USA), (Figure 3.9 Image G). All standard reference solutions were from the National Institute of Standards and Technology (NIST), USA. Appendix 1 showed the results of the investigated properties for the settleable dust samples. 3.8.2: Soil samples analysis; 3.8.2.1: Particle size distribution: The particle size fractionation and analysis was conducted by international pipette method as recommended by Black et al. (1965). 3.8.2.2: Cation exchange capacity (CEC): The CEC values of soil samples were determined by sodium acetate solution of I.0 N concentration and adjusted pH value to 8.2 as described by Gupta (2004). 3.8.2.3: The percent of organic carbon, carbonate minerals and also the same heavy metals content of (Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb), were determined by the same applied methods for dust samples analyzing. But, for the heavy metal analysis a sub-samples of 4-5 g soil were ground to pass an 80 mesh (less than 190µm) sieve then a further sub-sample of 1.0g was transferred to a Kjeldahl digestion tube for the subsequent digestion, filtration and dilution process and then metals measurement. 3.8.2.4:The other chemical analysis of soil samples including (soil extract pH, electrical conductivity and the soluble ions of Ca+2, Mg+2, K+, Na+, Cl-, HCO3-,CO3-2 ), were performed 102
CHAPTER THREE: Materials and Methods using the standard methods reported by Gupta (2004), using these models of instruments; pHmeter (model WTW 330i/ Germany); EC-meter (model WTW 330i/ Germany) and flamephotometer (model Corning-400/U.K.).
Appendixes 2 and 3 showed the basic physicochemical properties and soluble ions of topsoil's (0-20 cm) for the studied locations. 3.8.3: Rainwater sample analysis; 3.8.3.1: Heavy metal analysis: The same heavy metals of Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb, were analyzed in rainwater samples of adjusted pH to 2 using both inductively coupled plasma-
optical emission spectroscopy ICP-OES, (Figure 3.9 image H & I). All standard reference solutions were from the National Institute of Standards and Technology (NIST), USA. 3.8.3.2: Total suspended solids: The amount of total suspended solid (TSS) was evaluated in a volume of 250 ml of each rainwater sample as described in APHA (1999). 3.8.3.3: Nitrate ion (NO3-): This ion was measured as recommended by APHA (1999) using a photometer (model WTW PhotoLab Spektral/ Germany). 3.8.3.4: The other chemical analysis of rainwater samples including pH, electrical conductivity and the soluble ions of Ca+2, Mg+2, K+, Na+, Cl-, HCO3-,CO3-2 , were evaluated using the standard methods reported by (APHA, 1999)., using these models of instruments; pHmeter (model WTW 330i/ Germany); EC-meter (model WTW 330i/ Germany) and flame photometer (model Corning-400/U.K.). Appendix 4 showed some chemical properties of the rainwater samples for the studied locations. 3.8.4: Plant Sample Analysis; In current study the plants samples were analyzed only for the same heavy metals of (Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb) as an indicator of the severity of air pollution in Sulaimani city. The plant samples were wet digested by a mixture of concentrated sulfuric acid and concentrated hydrogen peroxide as described by Jones, Jr. (2001). A 1.0-g plant of each sample was digested with 20 ml mixture of concentrated H2SO4 and H2O2 of 37% in a ratio of 1:1 at room temperature for 30 minutes. Thereafter, was digested at 350 Co for 30 minutes using the Kjeldahl digester (model Buchi Speed Digester K-425/Switzerland). Once the digest became clear, was diluted with distilled water, filtered through an ashless Whatman 41 filter and diluted to 100 ml with excess distilled water, then stored in polyethylene bottles at 4 C o for element 103
CHAPTER THREE: Materials and Methods analysis. The elemental assay was conducted using both inductively coupled plasma-optical emission spectroscopy ICP-OES (model PerkinElmer, precisely Optima 2100/USA), (Figure 3.9 Image G)) and Atomic Absorption Spectroscopy AAS (model PerkiElmer, precisely AAnalyst 800 Atomic Absorption Spectrophotometer/ USA), (Figure 3.9 Image H&I). All standard reference solutions were used from the National Institute of Standards and Technology (NIST), USA.
3.9: Traffic saturation flow rate (Traffic volume): The present work also aimed to provide a reliable traffic flow and volume through counting the average daily traffic volume or flow across most of the main streets and roadways and the saturation flow rate at junction or intersections in Sulaimani city, but due to the lack of automatic traffic counter such as pneumatic tubes, inductive loops, piezometric sensors, manual classified counts, video survey or ANPR piezometric sensors, the measuring of the traffic volume capacity or counting of vehicles number flow at the roadways and intersection was performed through two (2) student in each site location for 15 minutes. The counting process was performed in two periods in each location, once during non school day (days when schools were not opened) on 26.7 to 29.7.2010, and the other period was during school days on 24.10 to 27.10.2010. Counting the number of vehicles was repeated (2) times in each location site, once in the morning and rush hours were not included and the other time was in the afternoon between 8:30 am and 7:00 pm of the entire time. Therefore, the mean numbers for each location site consist of (8) count number of the past vehicles and was expressed as a number of vehicles/hour.
3.10: Statistical analysis; Statistical analysis for all measured variable was performed using excel software. Correlations between the gases pollutants, particulate matter and the weather parameters were calculated. Also Pearson correlations analysis were applied among location sites for gases concentrations,
heavy metals in dust, soil, plant and rainwater samples to find out the
correlation coefficient and to test the significance levels at 0.05 and 0.01 levels. Multiple comparisons tests (Duncan’s test) were also performed to find out the significance differences between the average concentrations of the studied ambient gases for the studied locations.
104
CHAPTER FOUR: Results and Discussion 4.1: Levels of criteria air pollutants in Sulaimani city: 4.1.1: Carbon monoxide (CO): The results of average level of the 7 measurements of ambient carbon monoxide, which is one of the primary and criteria air pollutants, during the measurement period of 31.9.2009 to 13.7.2010 and for 17 studied locations, were ranged between 0.2 to 6.2 ppm by volume (Table 4.1a and Figure 4.1). These values corresponded to 0.2 to 7.1 mg CO m-3 (Table 4.1b), with most of the values being less than 7.1 mg CO m-3. The lowest average level was detected at locations 6, 12 and13 but the higher average level was found at location 15 that had a high traffic volume or flow, moreover, this location is also relatively a confined area and the dilution of emitted pollutant gases was slow. However, the range of normal and maximum levels was between ND to 32.8 ppmv (ppm by volume) or ND to 37.6 mg CO m-3, the normal level was frequent in many locations but the maximum level was detected at location 15 Sarkarez/ Sabunkaran Street. In general, most of the locations with high traffic flow monitored a high average level of CO because of the partial oxidation of carbon during the incomplete combustion of carbonaceous fossil fuels, chiefly from the exhaust of internal combustion engine of vehicles and stationary power generators (Table 4.17). The mixing ratios of CO in urban air were typically 2 to 10 ppmv. On the freeways and in traffic tunnels, it could be raised to more than 100 ppmv (Jacobson, 2002). The natural carbon monoxide level in atmospheric air composition is 0.1 ppmv (ppm by volume), (Griffin, 2007). Therefore, the concentration levels of CO in some urban areas of Sulaimani city increased more than 50 times the natural level. According to the National Ambient Air Quality Standards (NAAQS) guideline values by (EPA, 2011), the permissible or standard CO level is 9 ppm (10 mg m-3) for the averaging exposure time of 8-hour and 35 ppm (40 mg m-3) for the averaging exposure time of 1-houre. The same level concentration of 10 mg m-3 for a daily 8-hour mean was set by Air Quality Standard of European Commission Environment (ECE, 2010). Therefore, all the studied locations in Sulaimani urban areas could be evaluated as an attainment area for CO air pollutant (Griffin, 2007), because the CO pollutant level meet the health-based primary standard of both the NAAQS and ECE air quality standard, and that might be due the lighter density of CO (1.250 g L-1 at standard temperature of 0 Co and standard pressure of 1 atm.) as compared to the other air pollutant and could be easily replaced at the lower troposphere. 105
CHAPTER FOUR: Results and Discussion Table (4.1a): Average volumetric concentrations of some ambient air gases in the studied locations. No.
Locations
1-
Raparin /At SulaimaniKarkuk Street
2-
Sarchnar Crossing
3-
Wluba Overpass
4-
Tanjaro/ Near to Tanjaro Mosque
5-
Tanjaro/ Landfill site
6-
Foothill of Goizha Mountain
7-
Inside Peshraw Tunnel /About 75 m far from the North Side Outside Peshraw Tunnel / About 250m far from the North Side Maleek Mahmood Circle /Opposite Binai Petrol Station
8-
9-
Summarized Values of 7 Readings Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum
O2%
21.0 18.6 23.1 20.8 18.7 23.2 20.6 18.6 22.7 20.5 18.9 22.8 20.7 19.2 23.1 20.9 19.1 22.5 20.0 17.7 22.1 20.3 18.1 21.9 20.5 17.7 22.1
Gases Concentrations (ppm) CO2 402.3 365.0 494.0 477.0 385.0 676.0 394.9 365.0 487.0 430.2 365.0 700.0 393.2 359.0 469.0 371.3 347.0 444.0 1159.1 426.0 2311.0 385.3 359.0 487.0 503.9 371.0 1152.0
HC 70.0 58.0 84.0 69.7 57.0 81.0 70.3 56.0 86.0 68.5 57.0 92.0 46.6 31.0 74.0 28.4 17.0 44.0 79.1 42.0 111.0 30.7 22.0 54.0 73.00 59.0 104.0
N2O 28.0 3.0 58.0 26.4 5.0 62.0 24.1 4.0 67.0 24.8 5.0 62.0 27.4 51.0 63.0 12.4 4.0 39.0 22.1 3.0 43.0 11.5 1.0 38.0 22.6 6.0 46.0
CO 2.8 ND 11.0 5.6 ND 12.5 2.2 ND 8.3 2.1 ND 8.7 0.5 ND 4.0 0.2 ND 0.8 5.3 ND 20.8 0.8 ND 5.7 4.0 ND 7.6
Gases Concentrations (ppb) NO 1.4 ND 20.0 27.9 ND 140 23.6 ND 190.0 56.4 ND 400.0 0.7 ND 10.0 ND ND ND 54.3 ND 200.0 7.1 ND 70.0 59.3 ND 260.0
NO2 2.1 ND 10.0 2.9 ND 10.0 6.4 ND 20.0 2.1 ND 10.0 2.1 ND 10.0 0.7 ND 10.0 17.1 ND 60.0 1.4 ND 10.0 7.1 ND 20.0
O3 35.9 5.0 69.0 40.6 14.0 76.0 48.7 10.0 121.0 34.6 3.0 63.0 41.0 16.0 68.0 28.9 5.0 51.0 124.7 8.0 432.0 25.6 5.0 69.0 54.4 14.0 12.0
SO2 80.0 ND 470.0 99.3 ND 260.0 71.4 ND 190.0 106.4 ND 700.0 74.4 ND 580.0 37.9 ND 120.0 270.7 90.0 470.0 40.0 ND 110.0 77.4 ND 250.0 106
CHAPTER FOUR: Results and Discussion Table (4.1a): Average volumetric concentrations of some ambient air gases in the studied locations. No.
Locations
10-
Salim Street /Near to Khasrawkhal Overpass
11-
Khalahaji Crossing / Qadamkher street
12
Parki Azadi /At Day Time
13-
Parki Azadi /At Night Time
14-
Dastaka Crossing/ Mamostayan Street
15-
Sarkarez/ Sabunkaran Street
16-
Bardargai Sara (Sulaimani City Center)
17-
Internal Buses Transportation Center
Summarized Values of (7) Readings Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum
O2%
20.7 16.8 22.4 20.9 17.3 22.3 21.2 17.8 22.6 19.6 17.2 21.1 20.8 18.1 22.3 20.7 18.3 22.7 21.0 18.5 22.9 21.1 18.5 22.7
Gases Concentrations (ppm) CO2 488.8 365.0 670.0 470.1 383.0 664.0 388.9 353.0 480.0 497.4 359.0 921.0 486.1 383.0 865.0 456.8 365.0 713.0 488.1 389.0 706.0 452.0 371.0 664.0
HC 72.4 57.0 92.0 74.4 57.0 100.0 34.5 27.0 58.0 31.7 22.0 55.0 72.1 61.0 100.0 71.9 61.0 104.0 73.3 59.0 113.0 71.5 61.0 92.0
N2O 28.6 7.0 50.0 26.4 7.0 56.0 15.1 3.0 44.0 10.1 ND 31.0 21.2 2.0 55.0 28.1 2.0 56.0 26.4 4.0 54.0 25.9 3.0 51.0
CO 4.6 ND 14.6 5.9 ND 11.8 0.2 ND 0.7 0.2 ND 1.2 3.2 0.1 10.3 6.2 ND 32.8 5.6 ND 15.5 2.8 ND 7.0
Gases Concentrations (ppb) NO 40.0 ND 210.0 82.1 ND 380.0 ND ND ND 2.9 ND 40.0 23.6 ND 140.0 20.0 ND 60.0 37.9 ND 130.0 2.9 ND 40.0
NO2 5.0 ND 10.0 5.7 ND 20.0 3.6 ND 20.0 5.0 ND 20.0 3.6 ND 10.0 4.3 ND 10.0 5.0 ND 10.0 2.9 ND 10.0
O3 49.5 15.0 126.0 50.6 17.0 103.0 42.2 3.0 155.0 40.7 5.0 138.0 28.6 ND 50.0 37.9 5.0 63.0 40.3 11.0 86.0 32.3 4.0 55.0
SO2 53.6 ND 140.0 94.3 ND 320.0 36.4 ND 120.0 27.1 ND 110.0 86.4 ND 460.0 88.6 ND 390.0 94.3 ND 340.0 77.1 ND 220.0
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CHAPTER FOUR: Results and Discussion Table (4.1b): Average gravimetric concentrations of some ambient air gases in the studied locations. No.
Locations
1-
Raparin /At SulaimaniKarkuk Street
2-
Sarchnar Crossing
3-
Wluba Overpass
4-
Tanjaro/ Near to Tanjaro Mosque
5-
Tanjaro/ Landfill site
6-
Foothill of Goizha Mountain
7-
Inside Peshraw Tunnel /About 75 m far from the North Side Outside Peshraw Tunnel / About 250m far from the North Side Maleek Mahmood Circle /Opposite Binai Petrol Station
8-
9-
Summarized Values of 7 Readings Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum
Concentrations (g m-3) O2 274.9 243.4 302.3 272.2 244.74 303.6 269.4 243.4 297.1 268.4 247.4 298.4 271.3 251.3 302.3 273.5 250.0 294.5 262.3 231.7 289.2 265.9 236.9 286.6 267.8 231.7 289.2
CO2 724.0 656.9 844.0 858.4 692.8 1216.5 710.1 656.9 867.4 774.2 656.9 1259.7 707.6 646.1 844.0 668.2 624.5 799.0 2085.9 766.6 4158.9 693.4 646.1 876.4 906.9 667.7 2073.1
(mg m-3) N2O 50.4 5.4 104.4 47.6 9.0 111.6 42.3 7.2 120.6 44.6 9.0 111.6 49.4 9.0 113.4 22.5 7.2 70.2 39.8 5.4 77.4 20.7 1.8 68.4 40.6 10.8 82.8
CO 2.4 ND 11.6 6.4 ND 14.3 2.6 ND 9.5 2.4 ND 1ND 0.6 ND 4.6 0.2 ND 0.9 6.0 ND 23.8 0.9 ND 6.5 4.6 ND 8.7
NO 1.8 ND 24.5 34.2 ND 171.8 28.9 ND 233.1 69.2 ND 490.8 0.9 ND 12.3 ND ND ND 66.5 ND 245.4 8.8 ND 85.9 72.7 ND 319.0
(µg m-3) NO2 O3 4.0 70.4 ND 9.8 18.8 135.5 5.4 79.6 ND 27.5 18.8 149.2 12.1 95.6 ND 19.6 37.6 237.6 4.0 68.2 ND 5.9 18.8 123.7 3.5 80.5 ND 31.4 18.8 133.5 1.3 56.8 ND 9.8 18.8 100.1 32.3 244.7 ND 15.7 112.9 848.1 2.7 50.2 ND 9.8 18.8 135.5 13.4 106.9 ND 27.5 37.6 235.6
SO2 209.4 ND 1230.3 259.8 ND 680.6 187.0 ND 497.3 278.6 ND 1832.3 194.6 ND 1518.2 99.1 ND 314.1 708.6 235.6 1230.3 104.7 ND 287.9 202.7 ND 654.3 108
CHAPTER FOUR: Results and Discussion Table (4.1b): Average gravimetric concentrations of some ambient air gases in the studied locations. No.
Locations
10-
Salim Street /Near to Khasrawkhal Overpass
11-
Khalahaji Crossing / Qadamkher street
12-
Parki Azadi /At Day Time
13-
Parki Azadi /At Night Time
14-
Dastaka Crossing/ Mamostayan Street
15-
Sarkarez/ Sbunkaran Street
16-
Bardargai Sara (Sulaimani City Center)
17-
Internal Buses Transportation Center
Summarized Values of (7) Readings Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum Average Normal Maximum
Concentrations (g m-3) O2 270.3 219.9 293.2 274.0 226.4 291.9 276.9 233.0 295.8 257.0 225.1 276.2 272.6 236.9 291.9 271.1 239.5 297.1 274.5 242.1 299.7 275.9 242.1 297.1
CO2 839.6 656.9 1205.7 846.1 689.2 1194.9 699.8 635.3 863.8 830.9 646.1 1578.2 874.7 689.2 1556.7 822.0 656.9 1283.1 883.1 700.0 1270.5 813.4 667.7 1194.9
(mg m-3) N2O 51.6 12.6 90.0 47.4 12.6 100.8 27.3 5.4 79.2 18.1 ND 55.8 35.6 3.6 81.0 50.6 3.6 100.8 47.4 7.2 97.2 46.7 5.4 92.0
CO 5.3 ND 16.7 6.8 ND 13.5 0.2 ND 0.8 0.2 ND 1.4 3.9 0.1 11.8 7.1 ND 37.6 6.6 ND 17.8 3.2 ND 8.0
NO 49.1 ND 257.7 100.7 ND 466.3 ND ND ND 3.5 ND 49.1 28.9 ND 171.8 24.5 ND 73.6 46.5 ND 159.1 3.5 ND 49.1
(µg m-3) NO2 O3 9.4 97.2 ND 29.5 18.8 247.4 10.8 99.3 ND 33.4 37.6 202.2 6.7 82.2 ND 5.9 37.6 304.3 9.4 87.9 ND 9.8 37.6 270.9 6.7 56.1 ND ND 18.8 98.2 8.1 74.3 ND 9.8 18.8 123.7 9.4 79.1 ND 21.6 18.8 168.8 5.4 63.4 ND 7.9 18.8 108.0
SO2 140.2 ND 366.5 246.8 ND 837.6 95.4 ND 314.1 71.0 ND 287.9 226.2 ND 1204.1 231.8 ND 1020.9 246.8 ND 890.0 201.9 ND 575.9
(g m-3): grams per cubic meter: (mg m-3); milligrams per cubic meter; (µg m-3): micrograms per cubic meter.
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CHAPTER FOUR: Results and Discussion
Figure (4.1): Average volumetric concentrations of (CO) gas in the studied locations. The overall mean value, normal and maximum levels of ambient carbon monoxide in Sulaimani city were 3.1, ND and 32.8 ppmv which corresponded to 3.4, ND and 37.6 mg m-3, respectively. These results were somewhat in agreement with the values reported by (Rashid, 2010) for CO which varied from 10.5 to 17.0 µg m-3 in some locations of Tanjaro/ Sulaimani, but no other background concentrations of carbon monoxide in ambient air of Sulaimani urban were reported or available. The results also agreed with (Al-Saffawi, 2006), who reported a range of ND to 31.74 µg m-3 of CO concentration in 21 locations of Mosul city. Carbon monoxide concentrations in urban areas were closely related to motor traffic density and to weather and varied greatly with time and distance from the sources. The total emission of CO in the United States in 1997 was 90x106 short tones (1 metric ton = 1.1023 short tons). The major sink of ambient CO gas is chemical conversion to CO2 gas. It is also lost by deposition to soils and ice caps and dissolution in ocean water. Because it is relatively insoluble, its dissolution rate was slow (Jacobson, 2002). Clean Air Hamilton, 2007 presented in the 2005-2006 progress reported that transportation was the largest source of annual CO in Hamilton city /Ontario (58490 tones out of 84934 total tones for 6 CO sources), with three time greater emission than point sources of industrial emissions (Table 4.2). The correlation test in (Appendix 5) showed that there were no significant Pearson correlations between the CO concentration and each of the measured meteorological parameter of temperature, relative humidity and pressure. A highly significant Pearson correlation was 110
CHAPTER FOUR: Results and Discussion observed between the concentration of CO with each of the ambient gas N 2O, NO and HC (r = 0.677**; 0.682** and 0.829** respectively, (Appendix 5), significant Pearson correlation was also observed between the concentration of CO and SO2 (r = 0.533*), but no significant correlations of CO were observed with the other measured gases, particulate materials and meteorological parameter. Table 4.2: Total annual emission in tones by source category for Hamilton city /Ontario Canada (Clean Air Hamilton, 2007) Source Category Industrial Fuel Combustion Transportation Incineration Miscellaneous Open sources Total Tones
CO 16,443 9,428 58,490 377 197 ND 84,934
SOx 11.088 421 871 40 ND ND 12,421
NOx 8,414 1,659 12,766 173 ND ND 23,012
PM10 5,430 1,707 1,037 2.0 118.0 21,669 29,963
The results of Duncan test showed that there was significant differences at level 5% between the gravimetric average concentrations of ambient CO between locations 7, 2, 16, 15 and 11 as compared to location 6, 13 and 12 (Appendix 6A), because locations 6, 13 and 12 were far away from direct effect of dense traffic, moreover location 12 had more cover plant. The subsequent increasing order of the average of CO were as follows; 0.20, 0.23, 0.26, 0.62, 0.88, 2.42, 2.43, 2.56, 3.16, 3.69, 4.58, 5.26, 6.03, 6.40, 6.62 7.13 and 7.17 µg m-3 for the locations 6, 13, 12, 5, 8, 1, 4, 3, 17, 14, 9, 10, 7, 2, 16, 15 and 11 respectively. In general, the higher values were at the location of dense traffic and in Peshraw tunnel. 4.1.2: Nitrogen oxide (NOx): For review and assessment purposes, the measured forms of nitrogen oxide were presented and discussed in this topic. 4.1.2.1: Nitrogen dioxide (NO2): Nitrogen dioxide NO2 is individually considered as the only criteria air pollutant among the other oxides of nitrogen NOx for air ambient quality by NAAAQ of EPA, European Commission Environment (ECE) and the environment agencies of the most government and countries. But NO2 is mostly a secondary pollutant and it is not emitted directly as NO2 to the atmosphere but it will form through a series of chemical reactions from emission of nitric oxide (NO) (Han and Naehar, 2006), this is largely due to incomplete combustion of fossil fuels. For 111
CHAPTER FOUR: Results and Discussion example in motor vehicle exhaust, roughly 95% of the emissions are nitric oxide NO with only 5% direct NO2 (Environmental Health, 2000). As a secondary pollutant, NO2 is highly reactive in ambient air. It could undergo complicated photochemical reactions with other species in the air such as ozone (O3) and volatile organic compounds (VOCs), (Finlay-Pitts and Pitts 2000). The results of this study showed that the average values of NO2 for the 7 measurements period and for all the 17 locations varied from 0.7 to 17.1 ppb by volume (Table 4.1a and Figure 4.2) which corresponded to 1.3 to 32.3 µg NO2 m-3 (Table 4.1b). The lowest average level was detected at location 6 (Foothill of Goizha Mountain), but the higher average level was found at location7 (inside Peshraw tunnel). This tunnel is located at foothills of Azmar Mountain and has a 2.5 km length without any ventilation system, therefore, the emitted air pollutants could not be diluted easily. The range of normal and maximum levels was between ND to 60 µg NO2 m-3. The overall mean value, normal and maximum levels of ambient nitrogen dioxide in Sulaimani city were 4.5, ND and 60.0 ppbv which correspond to 8.5, ND and 112.9 µg m-3, respectively. This result was not consistent with the findings of (Rashid, 2010) in some locations of Tanjaro site in Sulaimani city, because he reported the results of nitrogen oxide NOx rather than NO2 and his results varied between 141 to 175 µg NOx m-3. The results were also not in compliant with the results of Al-Saffawi, 2006 in some locations of Mosul city due to the same reason of the previous researcher in Sulaimani city, and his results ranged from 31 to 491 µg NOx m-3.
Figure (4.2): Average volumetric concentrations of ambient (NO2) gas in the studied locations.
112
CHAPTER FOUR: Results and Discussion In Iraqi Kurdistan Region, since it has not been yet mandatory to monitor air pollutants, therefore no further background and baseline data are available about the ambient air pollutants. According to Tham et al. (2008), the concentration of NO2 in Higashi Hiroshima throughout 2006 was above 35 ppb for almost all winter samples. The natural nitrogen dioxide level in atmospheric air composition is 1 ppbv (ppb by volume), (Griffin, 2007). Therefore, the concentration levels of NO2 in some urban areas of Sulaimani city increased many times as the natural level, but still all the average values for the studied locations were less than the standard limit addressed by the National Ambient Air Quality Standards (NAAQS) guideline values by (EPA, 2011), which are 53 ppb for the annual arithmetic average or 100 ppb for the averaging exposure time of 1-hour. In the EC and WHO standard, the current target value for nitrogen dioxide concentration is a 1-hour level of 200 µg m-3 and a year’s level of 40 µg m-3 (ECE, 2010; WHO, 2003),
as the result, all the studied locations in Sulaimani urban could be
evaluated as an attainment area for NO2 air pollutant (Griffin, 2007), because the NO2 pollutant level met the health-based primary standard of both the NAAQS and ECE air quality standards. Mixing ratios of NO2 gas near sea level in free troposphere ranged was from 20 to 50 pptv. In the upper troposphere, mixing ratios were 30 to 70 pptv. In urban areas, they ranged from 0.1 to 0.25 ppmv. Outdoors, NO2 was more prevalent during mid morning than during midday or afternoon because sunlight breaks down most NO2 past midmorning (Jacobson, 2002) In a study on air pollution in Olympic summer games in Beijing/Chine by Vanos (2008) using photochemical model ozCalc, it was found that the annual average level of NO2 was 71 ppb. It was found by Environmental Health (2000), that all recorded annual mean background concentration within the Arun District, west Sussex, England between 1993 and 1999 were consistently less than the annual air quality objective of 40 µg m-3. But in a study by DEFRA (2009) on ambient air quality and NO2 spatial distribution in UK in 2009, it has been reported that the annual mean of NO2 at many roadside locations in excess of the limit value of 40 µg m-3. Nitrogen dioxide is one of the main traffic-related air pollutant and precursors for the formation of photochemical smog and ground level ozone (Finlayson-Pitts and Pitts, 2000).
113
CHAPTER FOUR: Results and Discussion Clean Air Hamilton (2007 presented in the 2005-2006 progress report that transportation was the main source of nitrogen oxides NOx (12766 tones out of 23012 of the total tones of NOx among 6 sources), with 1.5 time greater emission than point sources of industrial emissions (Table 4.2). Sinks of ambient NO2 gas included photolysis, chemical reaction, dissolution into ocean water and transfer to soils and ice caps. NO2 is relatively insoluble in water. The total emission of NO2 in the United States in 1997 was 24 million short tons (Jacobson, 2002). The researchers Zemp et al. (1999) have emphasized that there were a positive association between annual mean concentration of NO2 which were 35.6, 37.3 and 21.2 µg m-3, and total suspended particulates and particulate of less than 10 micrometer in aerodynamic diameter (
114
CHAPTER FOUR: Results and Discussion (Zemp et al., 1999). But no significant correlations were found with the other measured air pollutants and meteorological parameters. Duncan test showed that there was a significant difference at the level (5%) between gravimetric concentration of NO2 in location 7 (Peshraw tunnel) and the other 16 locations, therefore, location 7 can be considered as a hot spot site for NO2 (Appendix 6B). The subsequent
increasing order of the average NO2 were as follows; 1.34, 2.69, 3.46, 4.03, 4.03, 5.38, 5.38, 6.72, 6.72, 8.06, 9.41, 9.41, 10, 65, 12.09, 13.44 and 32.26 µg m-3 for the locations 6, 8, 5, 4, 1, 2, 17, 12, 14, 15, 13, 16, 10, 11, 3, 9 and 7 respectively. In general, the higher values were found at the location of high traffic volume and in Peshraw tunnel. 4.1.2.2: Nitric oxide (nitrogen monoxide) NO: Nitric oxide is the common name but nitrogen monoxide is a systematic name for NO. Nitric acid is a highly reactive radical commonly found in the environment and its natural levels are between 10 and 100 ppb in the atmosphere (Pilbeam and Cairo, 2006). Average volumetric and gravimetric concentration and the concentration range of NO for the whole 7 measurements period from 31.9.2009 to 13.7.2010 and for all the 17 studied locations were presented in Table (4.1a), Figure (4.3) and Table (4.1b). The values of NO ranged from ND to 82.1 ppbv by volume which corresponded to ND to 100.7 µg NO2 m-3. The lowest average level was found at location 12d (Parki Azadi at day time), but the higher average level was found at location11 (Khalahaji crossing), and this may be due to the closeness of the location from many operated diesel power station in addition to a dense traffic. The range of normal and maximum levels was between ND to 400 ppb by volume or ND to 490.8 µg NO2 m-3. The maximum concentration was detected in Tanjaro/ near to the Tanjaro mosque, and that could be resulted from the incineration process of solid waste and wind direction in the landfill site in Tanjaro. Smokers may inhale as much as 400 to 1000 ppm when they inhale tobacco smoke (Dupuy, et al., 1995). Although NO is normally present in our environment, it is still considered an air pollutant (OSHA, 1988). NO as an air pollutant is produced by combustion of substances in air, like in automobile engines and fossil fuel power plants, NO can be produced also by lightning and microbes in soil and plants during nitrification.
115
CHAPTER FOUR: Results and Discussion
Figure (4.3): Average volumetric concentrations of ambient (NO) gas in the studied locations. The primary sink of NO is chemical reaction (Jacobson, 2002). In the environment, nitric oxide is a precursor of smog and acid rain (Columbia Encyclopedia, 2011and Hou et al., 1999). In this study the overall mean value, normal and maximum levels of NO for the whole measurements in Sulaimani city were 25.9 , ND and 400 ppbv which corresponded to 31.8, ND and 490.8 µ m-3, respectively. In a study by Apascaritei et al. (2009) in Bucharest, they have reported that in urban and rural areas the NO concentration was very different because in the rural area there were not traffic emissions. The values were much less, even 15 times smaller. Therefore, the environmental impact of transport was evident in urban air quality. If nitric oxide gas is not emitted at night, it catalytically destroys ozone as it can be seen in the following reaction (Jacobson, 2002).
NO(g) +O3 (g)→ NO2 (g) +O2 (g) So, in the presence of sunlight, the NO2 that forms from the NO incrementally stimulates the photochemical smog-forming reactions because nitrogen dioxide is very efficient at absorbing sunlight in the ultraviolet portion of its spectrum. Nitric oxide NO should not be confused with nitrous oxide N2O, an anesthetic and greenhouse gas, or with nitrogen dioxide NO2, a brown toxic gas and a major air pollutant. However, nitric oxide is rapidly oxidized in air to nitrogen dioxide (Han and Naehar, 2006). In the troposphere, during daylight, NO reacts with partly oxidized organic species (or the peroxy
116
CHAPTER FOUR: Results and Discussion radical) to form NO2, which is then photolyzed by sunlight to reform NO as it is seen in the following reactions (Seinfeld and Pandis, 2006): NO + CH3O2 → NO2 + CH3O NO2 + sunlight → NO + O Direct inhalation of extremely high concentration of the NO gas can result in shortness of breath, hypoxemia, pulmonary edema, and even death (Clutton-Brock, 1967; Zwemer et al., 1992). Although actual amounts of NO inhaled were not addressed in this study, one can only imagine the quantity of NO sufficient to cause death if cigarette smoke contains as much as 1000 ppm. Table (4.3) illustrates some of the responses detected at various concentrations of NO administrated to human beings (Hess, 2001). After exposure to high levels of NO gas (80 ppm), the level of methemoglobin (metHb) in the blood increased (Hess, 2001). Table (4.3): Response in human subjects exposed to various concentration of inhaled NO. (Hess, 2001). Exposure amount of Response nitric oxide (NO) ppm 1 ppm Small decrease in specific airway conductance in healthy volunteers 15-20 ppm A decrease in PaO2 (partial pressure of oxygen in arterial blood) and an increase in airway resistance. 80 ppm Decreased airway conductance in patients with chronic obstructive pulmonary diseases Nitric oxide is manufactured in small amounts in the human body. Inhaled nitric oxide (INO), as a potent and selective pulmonary vasodilator, is a new and encouraging therapy for newborn and pediatric pulmonary hypertension and respiratory failure. Primary respiratory failure in children is the most common pathway leading to cardiopulmonary arrest, which is a significant factor in morbidity and mortality in the pediatric intensive care unit (Thompson et al., 1999). A typical sea-level mixing ratio of NO in the background troposphere is 5 pptv. In urban regions, NO mixing ratios reach 0.1 ppmv or (100 ppbv) in the early morning, but may decrease to zero by midmorning due to reaction with ozone (Jacobson, 2002).
117
CHAPTER FOUR: Results and Discussion In this study the overall mean value, normal and maximum level of NO for the whole measurements in Sulaimani city were 25.9 , ND and 400 ppbv which correspond to 31.8 , ND and 490.8 µ/m3, respectively. Appendix (5) showed that there was a highly significant Pearson correlation between the concentration of NO with each of the ambient gas CO and HC (r = 0.682**; 0.639**) respectively, significant Pearson correlation was also observed between the concentration of NO with NO2 and SO2 (r = 0.507*; r =500*) respectively, but no significant correlations of NO were observed with the other measured gases, particulate materials and meteorological parameter. Duncan test in Appendix (6C) showed that there was a significant difference between the gravimetric average concentrations of ambient NO at location 11 and the locations 6, 12, 5 and 1). The subsequent increasing order of the average of NO were as follows; ND, ND, 0.88, 1.75, 3.51, 3.51, 8.76, 24.54, 28.92, 28.92, 34.18, 46.45, 49.08, 66.467, 69.24, 72.74 and100.77 µg m-3 for the locations 6, 12, 5, 1, 13, 17, 8, 15, 3, 14, 2, 16, 10, 7, 4, 9, and 11 respectively. In general, the higher values were at locations of dens traffic and in Peshraw tunnel. 4.1.2.3: Nitrous oxide (dinitrogen monoxide) N2O: As presented in Table (4.1a), Figure (4.4) and (Table 4.1b), the abundance of nitrous oxide N2O was much higher than NO and NO2, because it seems that during the engine combustion when some of the nitrogen content of the input air oxidises to nitrogen oxides due to the high temperature a most probable form for forming being N2O. The average levels of N2O ranged from 10.1 to 28.6 ppmv and 18.13 to 51.55 µg m-3 for both volumetric and gravimetric averages respectively, The lowest average was detected at location 13 (Parki Azadi at night), while the highest average was detected at location 10 (Salim Street/ near to Khasrawkhal overpass). Close values to the highest average were found in many of the locations which had a high traffic volume. But, the range of normal and maximum levels of N2O was between ND to 67.0 ppmv which equivalent to ND to 120.6 mg N2O m-3, the normal level was at location 13 (Parki Azadi at night), but the maximum level was detected at location 4 (Wluba overpass), this may be due to the closeness of the location from many industrial activities and landfill site, in addition to its traffic dense.
118
CHAPTER FOUR: Results and Discussion
Figure (4.4): Average volumetric concentrations of ambient (N2O) gas in the studied locations. These results of N2O may be superseded by new findings in Sulaimani urban, particularly, when new and more sophisticated gas analyzer instruments were used for monitoring the gas pollutant in the field of air pollution. Therefore readers are advised to determine whether new information and data are available. The mixing ratio of N2O is relatively constant up to about 15 to 20 km, but decreases above that as a result of photolysis. Throughout the atmosphere, N2O produces nitric oxide NO. Nitrous oxide N2O gives rise to nitric oxide (NO) on reaction with oxygen atoms, and this NO in turns reacts with ozone. As a result, it is the main naturally occurring regulator of stratospheric ozone (Jacobson, 2002).
IPCC (2007) has referred that nitrous oxide continues to rise approximately linearly (0.26% yr-1) and reached a concentration of 319 ppb in 2005, contributing an radiative forcing (RF) of (+ 0.16 ± 0.02) watts per square meter (W m-2). Recent studies reinforce the large role of emissions from tropical regions in influencing the observed spatial concentration gradients of N2O. N2O is very stable and inert chemically at room temperatures (Tarendash, 2001). N2O is a chemical compound used as an oxidizing agent to increase an internal combustion engines power output by allowing more fuel to be burned than would normally be the case.
119
CHAPTER FOUR: Results and Discussion Nitrous oxide is not classifiable as a human carcinogen; however it is a narcotic at high concentration (Kuhlman and Coyne, 2003). It is used in surgery and dentistry for its anesthetic and analgesic effects, and also used as inert gas to displace bacteria-inducing oxygen when filling packages of chips and other similar snack foods (Kuhlman and Coyne, 2003). Epidemiological studies showed increased risks of spontaneous absorption, premature delivery, and involuntary infertility among occupationally exposed populations. American Conference of Governmental Industrial Hygienists (AGGIH) used a guideline of 50 ppm N2O based on an 8-hour time weight average (TWA), but National Institute of Occupational Safety and Health (NIOSH) recommends 25 ppm as a time weighted average, and Occupational Safety and Health Administration (OSHA) did not regulated nitrous oxide (OSHA, 1996) The overall mean value, normal and maximum levels of ambient nitrous oxide N2O in Sulaimani city were 22.3 , ND and 63.0 ppmv which corresponded to 40.2 , ND and 111.4 mg m-3, respectively. Pearson correlation was used to calculate the correlation coefficients of N2O with the other ambient air pollutants (Appendix 5). A significant positive correlations were found between N2O with each HC (r = 0.831**), and CO (r = 0.617**), these strong positive correlations may be attributed to the similarity in terms of emission source from the internal engine combustion. But no significant correlations were found with the other measured air pollutants, particulates matters (PM) and meteorological parameters. No significant Duncan’s test was appeared among the average means of N2O for the studied locations (Appendix 6D). 4.1.3: Ground-level ozone (O3); Ground-level ozone concentrations were estimated 7 times between 31.9.2009 and 13.7.2010 for 17 locations in Sulaimani city. The results of this study showed that the average values of O3 varied from 25.6 to 124.7 ppbv (Table 4.1a and Figure 4.5) which corresponded to 50.2 to 244.7 µg O3 m-3 (Table 4.1b). The lowest average level was detected at location 8 (outside Peshraw tunnel), but the higher average level was found at location7 (inside Peshraw tunnel). This tunnel is located at foothills of Azmar Mountain with a 2.5 km length without any ventilation system, therefore, the emitted or formed air pollutants could not be diluted easily. The range of normal and maximum levels was between 3.0 to 432.0 ppbv which were equivalent to 5.9 to 848.1 µg O3 m-3. The normal value was detected in both locations (4 and 12), but the maximum value was at location 7. The results of the study were close to the 120
CHAPTER FOUR: Results and Discussion findings of Al-Saffawi (2006) in some location of Mosul city, who reported the concentration in the range of 66 to 607 µg O3 /m3. But AL-Abed-Raba (2004, Arabic Reference) has found an average concentration of 180 µg O3 m-3 for O3 level in the industrial region of Kirkuk city. Without ozone (O3) in stratosphere, every living thing on the earth’s surface would be incinerated. On the other hand, as we have already noted, ozone in troposphere can be lethal (Davis and Cornell, 2008). Ozone is considered as an air pollutant because of the harm that it does to humans, animals, plants, and materials. It is an abundant tropospheric pollutant and, especially in the upper troposphere, a greenhouse gas (Seinfeld and Pandis 1998). Ozone patterns vary across the countries. In 1997, about 48 million people lived in (77) counties where O3 levels exceeded the NAAQS. The highest O3 concentrations that year were found in southern California, the Gulf Coast, and the northeastern and north Central States (Bernard et al., 2001), but for the first time the highest O3 levels were recorded not in Los Angeles and California, but in Houston, Texas (EPA, 1998). The researchers Apascaritei et al. (2009) have found that the highest O3 concentration value of 0.958 ppm (958 ppb) during three days of observation in 6.5.2008 at 13:40 in urban region of Bucharest, and found the lowest value of 90 ppb in 07.5.2008 at 3:40 in a rural area of Bucharest. All the ozone peaks coincided with clear sky.
Figure (4.5): Average volumetric concentrations of ambient (O3) gas in the studied locations. Ozone is not emitted directly into the atmosphere in any significant quantity, but is formed from a complex series of chemical reactions involving other “precursor” pollutants-volatile organic compounds VOCs and presence of nitrogen oxides (NOx) - in the presence of sunlight; therefore, it is a secondary air pollutant. The reaction producing ozone occurs in air containing 121
CHAPTER FOUR: Results and Discussion these NOx and VOCs precursors as it move downwind; ozone formation can occur over a timescale of a few hours to several days. As a result, a highest concentration of ozone usually occurs in rural areas, and often a long distance from the source of the precursor emission (DEFRA, 2009). Ozone is also like water vapour, CO2, CO, SO2, NO, NO2 and many VOCs gases considered as a variable gas, because its volume mixing ratios change in time and space (Jacobson, 2002). One can observe that the urban ozone concentration increases from its normal in early morning (low solar radiation) to its maximum in late afternoon and then, at night, decreases till next morning (O3 is consumed by the reducing chemical such as NOx and without any accumulated process) after being generated through a complex series of chemical reactions involving the interaction of sunlight with hydrocarbons and nitrogen oxides from vehicle exhaust (Apascaritei et al., 2009). The overall mean value, normal and maximum level of ambient ozone in Sulaimani city were 44.5, ND and 432.0
ppbv which corresponded to 87.8 , ND and 848.1 µg m-3,
respectively. As it can be noted from Figure (4-5) all the volumetric and gravimetric average values (except for location 7) of ozone concentration fell in the range of 25 to 55 ppbv and 49.1 to 108.0 µg m-3, respectively. These values were less than the limits set by European Union (EU) legislation (120 µg/m3 for averaging period of 8-hours) and by EPA (75 ppb for averaging time of 8-hours). Therefore, all the locations still could be considered as attainment area for ozone air pollutant (Griffin, 2007), because the O3 pollutant level meet the health-based primary standard of both the NAAQS (EPA, 2011), and ECE air quality standard (ECE, 2010). According to Poupkou et al. (2009), over the greater part of Greece, the simulated mean daily maximum ozone concentrations ranged from 50 to 65 ppb. More enhanced maximum ozone concentrations up to 95 ppb mainly dominated over the greater area of the two largest Greek Urban centers (Athens and Thessaloniki) and over the continental and maritime areas south of Athens which are under the influence of the urban plume. Sinks of ozone include reaction, transfer to soil and ice caps, and dissolution in ocean waters. Because ozone is relatively insoluble, its dissolution rate is relatively slow. But mixing ratios of ozone in the free troposphere were 20 to 40 ppbv near sea level and 30 to 70 ppb at higher altitude. In urban air, ozone mixing ratios ranged from less 100 ppbv at night to 500 122
CHAPTER FOUR: Results and Discussion ppbv (during afternoons in the most polluted cities worldwide), with typical values of 150 ppbv during moderately polluted afternoons. In stratosphere the mixing ratios ranged from 1000 to 12000 ppmv (Jacobson, 2002). The potential impact of ozone on humans has led to the adoption of European Union (EU) legislation, which has set upper exposure limit for human health protection (55 ppbv during 8hours). Ozone is also considered phytotoxic, the respective EU limit being 32 ppbv for 24hours (Poupkou et al., 2009). Ground level ozone could also cause damage to many plant species leading to loss of yield and quality of crops, damage to forests and impacts on biodiversity (DEFRA, 2009). It has been reported by (USDA-ARS, 2002) that ozone enters leaves through stomata during normal gas exchange. As a strong oxidant, ozone (or secondary products resulting from oxidation by ozone such as reactive oxygen species) causes several types of symptoms including chlorosis and necrosis. It is almost impossible to tell whether foliar chlorosis or necrosis in the field is caused by ozone or normal senescence. Several additional symptom types are commonly associated with ozone exposure, however. These include flecks (tiny light-tan irregular spots less than 1 mm diameter), stipples (small darkly pigmented areas approximately 2.4 mm diameter), bronzing, and reddening. Ozone symptoms usually occur between the veins on the upper leaf surface of older and middle-aged leaves, but may also involve both leaf surfaces (bifacial) for some species. Figure (4.6) presents some images of those symptoms; the images were from both sources of mentioned reference and also from the current study in Sulaimani city. The symptoms might be due air pollution and ozone effect in Sulaimani city, because they were almost occurred at the streets side rather than the other side. Ozone, an oxidant of photochemical smog, is known to cause a variety of respiratory effects, including diminished lung function, exacerbation of respiratory symptoms, and inflammation of airways (Lippmann, 2000). To date, lung inflation has been measured only invasively, by analysis of bronchoalveolar lavage fluid obtained from adults exposed to relatively high concentration of ozone while exercising (Devlin et al., 1991; Kinney et al., 1996).
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CHAPTER FOUR: Results and Discussion
Figure (4.6): Ozone and dust injuries in Plant leaves.
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CHAPTER FOUR: Results and Discussion Ozone causes headaches at mixing ratios greater than 150 ppbv, chest pains at mixing ratios greater than 250 ppbv and sore throat and cough at mixing ratios greater than 300 ppbv (Jacobson, 2002). Duncan test in Appendix (6E) showed that there was a significant difference between the gravimetric average concentrations of ambient O3 at location 7 (Inside Peshraw tunnel)) and all the other locations. The subsequent increasing order of the average of O3 were as follows; 50.2, 56.1, 56.8, 63.4, 68.2, 70.4, 74.3, 79.1, 79.6, 80.5, 82.2, 87.9, 95.6, 97.2, 103.9, 106.9, and 244.7 µg O3 m-3 for the locations 8, 14, 6, 17, 4, 1, 15, 16, 2, 5, 12, 13, 3, 10, 11, 9, and 7 respectively. In general, the higher values were at the location of dens traffic and in Peshraw tunnel. Pearson correlation was used to calculate the correlation coefficients of ozone O3 with the other ambient air pollutants (Appendix 5). A high significant positive correlations were found between ozone (O3) with each SO2 (r = 0.855**), CO2 (r = 0.937**), NO2 (r = 0.961**), PM1.0 (r = 0.821**), PM2.5 (r = 0.649**), and PM10.0 (r = 0. 864**). These strong positive correlations may be attributed to the probability of increasing ozone formation as a pollutant with increasing the level of the correlated atmospheric compounds, because ozone is a secondary pollutant formed through complicated atmospheric reaction involving NOx and hydrocarbons as the substrate and driven by sunlight. These pollutants; SO2, NO2, PM, CO, and O3 along with lead Pb are regulated under Clean Air Act as “criteria pollutants,” referring to the process for developing the pollutants standards (EPA, 2011). But no significant correlations were found with the other measured air pollutants, and the meteorological parameters. 4.1.4: Sulfur dioxide (SO2): As it is presented in Table (4.1a), Figure (4.7) and Table (4.1b), the results of volumetric and gravimetric average levels of the 7 measurements of ambient air sulfur dioxide SO2, which is one of the primary and criteria air pollutant, during the measurement period of 31.9.2009 to 13.7.2010 for the 17 study locations ranged between 27.1 to 270.6 ppbv and 71.0 to 708.6 µg m-3 SO2, respectively. The lowest average level was detected at location 13 (Parki Azadi at night) but the higher average level was found at location 7 (inside Peshraw tunnel), and that was due to the shortage of ventilation system that is unable to transport and dilute pollutants into the air, in addition to a relatively long length of the tunnel (2.5 km) and dense traffic volume. It was shown in Figure (4.6) that most of the urbanized locations in Sulaimani city had 125
CHAPTER FOUR: Results and Discussion volumetric and gravimetric averages of SO2 emission more than 71.0 ppbv or 187 µg m-3 respectively, and that was surely due to a high number of diesel power station and vehicle flow in Sulaimani city in current time.
Figure (4.7): Average volumetric concentrations of ambient (SO2) gas in the studied locations. These results were somewhat in agreement with the values reported by Rashid (2010) for SO2 which varied from 125 to152 µg m-3 in some locations of Tanjaro/ Sulaimani, but no further background concentration of sulfur dioxide in ambient air of Sulaimani city was available. The results were consistent somewhat with the values obtained by Al-Saffawi (2006), who reported a range of 80 to 2407µg m-3 of SO2 concentration in some locations of Mosul city. On the other hand Al-Qaisi (1990, Arabic reference) estimated that the concentration of sulfur dioxide in some locations of Bagdad city were in the range of 114 to 543 µg m-3. These results were not consistent with the findings of Tham et al. (2008), who estimated the level of SO2 in Higashi Hiroshima (Japan) throughout 2006 much lesser as compared to this study, and found the highest concentrations above 8 ppb during January and February and moderately high concentrations between 4 to 7 ppb in December. But the lowest concentrations (below 4 ppb) were in summer that may be due to higher fuel consumption during the colder season. Sulfur dioxide is also a precursor to secondary particulate matter (PM), and therefore, contributes to the ill-health effects caused by PM10.0 and PM2.5. The health effects of SO2 and PM are closely linked, the individual effects of each pollutant only being quantifiable in the last 10 years or so (DEFRA, 2009). Sulfur dioxide is a precursor to sulfuric acid (H2SO4), an aerosol particle component that affects acid deposition, global climate, and the global ozone 126
CHAPTER FOUR: Results and Discussion layer. SO2 exhibits a taste at level greater than 300 ppbv and strong odor at level greater than 500 ppbv (Jacobson, 2002). Sulfur dioxide is a conventional air pollutant emitted into the atmosphere mainly from anthropogenic sources such as the combustion of sulfur-containing fossil fuels (Gupta et al., 2003). Asian megacities cover less than 2% of the land area but emitted 16% of the total anthropogenic sulfur emissions of Asia (Guttikunda et al., 2003). In UK, approximately 85% of SO2 originated from power stations and industrial source (DEFRA, 2009). Clean Air Hamilton, 2007 has presented in the 2005-2006 progress report that industrial source in Hamilton city /Ontario is the largest source of annual SO2 (11.088 tones out of 12.421 total tones of SO2 for 6 sources), with 8.3 time greater SO2 emission than the other five sources together (Table 4.2). Since SO2 was a water soluble and reactive gas, it does not remain along the atmosphere as a gas. Much of the SO2 emitted is transformed through oxidation into acid aerosols, either sulfuric acid H2SO4 or partially neutralized H2SO4 (ammonium bisulfate or ammonium sulfate). The ecological effects of acid aerosols (in the form of acid rain or dry deposition) have received much attention (Koenig and Mar, 2000). The range of normal and maximum levels throughout all the measurements was between ND to 432.0 ppbv which corresponded to ND to 700.0 µg m-3 SO2, the normal level was frequent in all the locations (except location 7 which stand for the location; inside Peshraw tunnel) but the maximum level was detected at location 3 (Tanjaro/ near to Tanjoro Mosque), and that is due to the closeness of the locations from many industrial activities in the region and also to the landfill site, particularly during incineration process. In the background troposphere, sulfur dioxide mixing ratio ranged from 0.01 to 1.0 ppbv. In polluted air, they ranged from 1.0 to 30 ppbv. Sinks of SO2 include chemical reaction, dissolution in water, and transfer to soils and ice caps. SO2 is relatively soluble (Jacobson, 2002). Sulfur dioxide has potent adverse impacts upon both vegetative metabolism as well as animal health. Schwartz et al (1995) studied the acute effects of summer air pollution on respiratory symptoms in children in six U.S. cities. They found that sulfur dioxide was associated with incidences of cough and lower respiratory symptoms, using a single pollutant model. These findings, however, could be confounded by PM10.0. Segala et al (1998) found a
127
CHAPTER FOUR: Results and Discussion strong association between short-term exposures to SO2 and the risk of asthma attack in children in Paris. The overall mean value, normal and maximum levels of ambient sulfur dioxide in Sulaimani city were 83.3, ND and 700.0 ppbv which were equivalent to 217.9, ND and 1832.3 µg m-3 SO2, respectively. The average values were more than the limits set by European Union (EU) legislation for averaging period of 24-hours (43.7 ppbv or 125 µg m-3 SO2), but less than the limits set by European Union (EU) legislation for averaging period of 1-hours (122.5 ppbv or 350.0 µg m-3 SO2). Moreover, the obtained average values of SO2 level exceeded the limits set by the current National Ambient Air Quality Standards (NAAQS) guideline which is 75 ppb for averaging time of 1-hours, and also exceeded the limits set by WHO standard for annual average SO2 which is 80 µg/m3 SO2 in an industrial area and 60 µg m-3 SO2 in a residential area. As a result Sulaimani city could not be considered as attainment area for sulfur dioxide air pollutant (Griffin, 2007), because the SO2 pollutant level did not met the health-based primary standard of the NAAQS (EPA, 2011), ECE air quality standard (ECE, 2010) and WHO (Guttikunda et al., 2003). This is due to that Sulaimani is undergoing rapid unplanned urbanization resulting in more demand for vehicles and diesel power stations for power supply. According to Koenig and Mar (2000) controlled exposure to SO2 showed statistically significant reductions in lung function at concentration as low as 100 ppb to 250 ppb. Epidemiologic studies have seen mortality associated with very small increases in ambient SO 2 in the range of 10-22 ppb. Low birth weight was associated with SO2 concentration in the range of 22-40 ppb. The studies assessed in this review indicated that infant and people with asthma were particularly susceptible to the effects of SO2, even at concentration and duration below the current California one-hour standard of 250 ppb. Pearson correlation was used to provide the correlation coefficients of SO2 with the other ambient air pollutants (Appendix 5). A highly significant positive correlations were found between SO2 with each ozone (r = 0.855**), nitrogen dioxide (r = 0.813**) carbon dioxide (r = 0.903**), PM1.0 (r = 0.864**), PM2.5 (r = 0.703**), and PM10.0 (r = 0.934**), these strong positive correlations may be attributed to the similarity in terms of emission source. Significant positive correlations were also found between SO2 with each carbon monoxide (r = 0.533* ), nitric oxide (r = 0.521* ), hydrocarbons (r = 0.588*). But no significant correlations were found with the other measured air pollutants and meteorological parameters. 128
CHAPTER FOUR: Results and Discussion Duncan test showed that there was a significant difference at the level (5%) between gravimetric concentration of SO2 at location 7 (Peshraw tunnel) and other locations, therefore, location 7 could be considered as hot spot site for SO2 (Appendix 6F). The subsequent increasing order of the average SO2 were as follows; 71.0, 95.4, 99.1, 104.7, 140.2, 187.0, 194.6, 201,9 202.7, 209.4, 226.2, 231.8, 246.8, 258.1, 259.8, 278.6 and 708.6 µg m-3 SO2 for the locations 13, 12, 6, 8, 10, 3, 5, 17, 9, 1, 14, 15, 16, 11, 2, 4, and 7 respectively. In general, the lower values were found at the location of less of traffic volume or far from direct traffic. 4.1.5: Particulate matters (PM): Particulate Matter PM is one of the six criteria pollutants, and the most important in terms of adverse effects on environment, climate change and human health. The marked increase in mortality that occurred during air pollution episodes in the small town of Donora, Pennsylvania, in 1948 (20 deaths) and the London fog of 1952 (4000 deaths), provided evidence of the impact that this pollutant has on human health (Fierro, 2000). Nearly 100 million people in the United States are breathing unhealthy levels of particles, says EPA (Baltimore, 2004). Air quality guidelines and standards were developed in an attempt to reduce adverse impacts on human health and the environment. The US National Ambient Air Quality Standards (AAAQS) established in 1971 included at the first standard for Total Suspended Particulate Matter (TSP) in 1987, the TSP was replaced with particulate matter less than 10 micrometer (PM10) standard with a limit of 150 µg m-3 24-hours average and 50 µg m-3 averaged annually, and in 1997, a standard for particles less than 2.5 micrometers in diameter (PM2.5) was established with a limit of 65 µg m-3 24-hours average and 16 µg m-3 averaged annually), and in 2006, the limit of (PM2.5) was declined to 35 µg m-3 24-hours average (Fierro, 2000). In Kurdistan region still no attentions are given to PM issues and air quality index, although, dust and sand storm became nowadays a notable frequent phenomena, moreover, the rapid unplanned urbanization growth of Kurdistan Region’s Cities led to increase a variety anthropogenic sources of PM such as increasing of huge number of vehicles, industrial activities and buildings construction.
129
CHAPTER FOUR: Results and Discussion In this study, we performed, for the first time in Sulaimani city, a numerical determination of airborne PM concentration for the particulate matters (PM1.0, PM2.5, and PM10.0; the subscripts indicated what aerodynamic diameters in micrometer is in consideration), (Table 4.4 and Figure 4.8). 4.1.5.1: PM1.0; particulate matter less than 1 micrometer in aerodynamic diameter: Particulate matter levels were determined 7 times during the measuring period from 31.9.2009 to 13.7.2010 at 17 different outdoor sites in Sulaimani city. As shown in Table (4.4) and figure (4.8), the average PM1.0 concentration for the whole 7 times ranged between 13.1 to 170.1 µg m-3. The lowest average was found at location 6 (Foothill of Goizha Mountain), and that was due to the low anthropogenic activity and vehicle flow rate in this location, moreover, it might be also due to the wind speed and prevalent wind direction. But the highest average value was estimated at location 7 (Inside Peshraw Tunnel), and that was due the confinement of a large amount of vehicle emission (vehicle’s episode and smoke haze) inside the tunnel and lack of dilution or aeration system. Therefore, the emission of the traffic vehicles caused considerable air pollution throughout the tunnel. There was a large variability in the average of PM1.0 level among the studied locations and the subsequent order of increasing level from cold spot locations to the hot spot locations were as follows; 13.1, 21.0, 21.9, 30.1, 31.0, 34.0, 34.1, 34.7, 35.4, 36.0, 42.4, 45.8, 51.3, 57.0, 61.6, 100.2, and 170.1 µg m-3 for the locations 6, 12, 13, 14, 9, 16, 8, 1, 10, 15, 11, 17, 4, 3, 2, 5, and 7, respectively, knowing that hot spots were areas that experience level of ambient air pollutants or exhaust emissions that pose health risks beyond those from normal background and standard levels (OEC, 2007). Although, no specific standard limit value was proposed for PM1.0 level by the international organizations and countries, but increasing toxicity with decreasing aerodynamic diameter to submicron concentration was reported (Casale et al., 2008). For all these reasons, fine PM could induce stronger adverse effects and was thus considered more dangerous to human health than larger particles composed of the same material (Dellinger et al., 2001). Regulations controlling the emission of ambient particulate matter were based on limits for PM10.0 (particles less than 10 µm in diameter) or PM2.5 in term of total mass concentration.
130
CHAPTER FOUR: Results and Discussion Table (4.4): Airborne particulate matter (PM) concentrations of the fractions PM10.0 , PM2.5, and PM1.0 for the studied locations. No.
1-
2-
3-
4-
5-
6-
7-
8-
9-
Locations
Raparin /At Sulaimani- Karkuk Street
Summarized values for 7 measurements
Average Normal Maximum Sarchnar Crossing Average Normal Maximum Wluba Overpass Average Normal Maximum Tanjaro/ Near to Tanjaro Mosque Average Normal Maximum Tanjaro/ Landfill site Average Normal Maximum Bnari Shakhi Goizha Average Normal Maximum Inside Peshraw Tunnel /About 75m far Average from the North Side Normal Maximum Outside Peshraw Tunnel / About Average 250m far from the North Side Normal Maximum Maleek Mahmood Circle /Opposite Average Binai Petrol Station Normal Maximum
Mass concentrations of the aerodynamic diameters (µg m-3) PM1.0 34.7 16.0 82.0 61.6 22.0 200.0 57.0 24.0 170.0 51.3 16.0 100.0 100.2 17.0 284.0 13.1 2.0 25.0 170.1 71.0 311.0 34.1 8.0 19.0 31.0 13.0 84.0
PM2.5 57.1 17.0 90.0 72.7 24.0 207.0 69.6 30.0 172.0 57.9 31.0 93.0 178.6 22.0 660.0 27.7 4.0 74.0 181.7 102.0 363.0 18.7 11.0 32.0 36.7 12.0 83.0
PM10.0 96.6 19.0 155.0 133.7 40.0 204.0 109.7 48.0 191.0 149.4 48.0 423.0 168.7 47.0 342.0 50.4 6.0 104.0 374.0 243.0 778.0 40.4 22.0 84.0 92.1 37.0 180.0 131
CHAPTER FOUR: Results and Discussion Table (4.4): Airborne particulate matter (PM) concentrations of the fractions PM10.0 , PM2.5, and PM1.0 for the studied locations. Mass concentrations of the aerodynamic diameters (µg m-3) PM1.0 PM2.5 PM10.0 Salim Street /Near to Khasrawkhal Average 35.4 32.3 66.7 10Overpass Normal 12.0 21.0 36.0 Maximum 76.0 53.0 98.0 Khalahaji Cross / Qadamkher Street Average 42.4 57.4 104.9 11Normal 19.0 30.0 55.0 Maximum 95.0 111.0 183.0 Parki Azadi /At Day Time Average 21.0 24.3 48.0 12Normal 4.0 9.0 20.0 Maximum 45.0 48.0 89.0 Parki Azadi /At night Time Average 21.9 33.6 72.1 13Normal 6.0 11.0 25.0 Maximum 49.0 58.0 135.0 14Dastaka Crossing/ Mamostayan Street Average 30.1 46.2 77.4 Normal 6.0 23.0 40.0 Maximum 65.0 79.0 122.0 15Sarkarez/ Sbunkaran street Average 36.0 39.4 79.4 Normal 24.0 26.0 37.0 Maximum 55.0 63.0 135.0 16Bardargai Sara (Sulaimani City Average 34.0 43.1 86.1 Center) Normal 14.0 22.0 60.0 Maximum 53.0 52.0 116.0 17Internal Buses Transportation Center Average 45.8 54.6 109.9 Normal 21.0 50.0 50.0 Maximum 82.0 67.0 250.0 PM1.0: Particulate Matter of 1.0 Micron Aerodynamic Size; PM2.5: Particulate Matter of 2.5 Micron Aerodynamic Size PM10.0: Particulate Matter of 10.0 Micron Aerodynamic Size; (µg m-3): micrograms per cubic meter. No.
Locations
Summarized values for 7 measurements
132
CHAPTER FOUR: Results and Discussion
Figure (4.8): Average airborne particulate matter (PM) concentrations for the fractions PM10.0 , PM2.5, and PM1.0 of the studied locations. T-test t (observed value) t (critical value)
PM1.0 5.4* 2.1
PM2.5 5.3* 2.1
PM10.0 5.9* 2.1
133
CHAPTER FOUR: Results and Discussion Recent toxicological studies have suggested that the ultrafine fraction such as PM1.0, which contributed very little to the total mass concentration of particles (Kittlesson, 1998) but were the main component, by number of concentration of particulate pollution. Additionally epidemiological studies revealed correlation between exposure to ambient ultrafine particles at high number concentration, and adverse health effects (Pope, 2000; Davidson et al., 2005). The width of a human hair is (30) times larger than the smallest fine particle or these particles are so small that several thousand could fit on the head of a pin (OEC, 2007). Hence, it is significant to study the variation of number concentration of particulate matter at urban. When the average levels of PM1.0 level for the studied locations were compared with the current National Ambient Air Quality Standards (NAAQS) guideline values for PM2.5 which is 15.0 µg/m3 for an annual (Arithmetic Average) and 35 µg m-3 for 24-hour averaging time, it appeared that PM1.0 level for most of the locations exceeded that limit. This meant that Sulaimani city could not be considered as an attainment area for this issue (Griffin, 2002), because when any ambient air pollutant exceeded the defined standards, this is considered air pollution. At present, long-range transboundary transport of PM is responsible for a significant fraction of the particulate pollution in Sulaimani city and Kurdistan region, but still anthropogenic sources of PM, especially motor vehicles and diesel emission are the most significant source of PM. The references (Council of Europe, 1998: Wilson et al., 2002 and Artinano et al. 2003) reported that about 85% of atmospheric particles originated from anthropogenic sources. The overall mean value, normal and maximum levels of PM1.0 in Sulaimani city were 46.2, 2.0 and 311 µg m-3 respectively. Over a life time more particulate mattes get into your body through your breathing passages than you would ever care to imagine. Particles can come from; ash and combustion products; Dander (comprised of dead skin cell from animals and people; dust, including powdered stone, other mineral, confectioner’s sugar, flour, coal and powdered talc; dust mites and other microscopic parasites and their feces; Feathers and feather pieces particularly in proximity to birds; Fibers of different type and diversity of origin as mineral, asbestos and natural textile fibers as in cotton, linen, silk wool, etc; fingernail fillings these expect in abundance in nail salons; food crumbs; glass particles; glue; graphite, carbon and other finely 134
CHAPTER FOUR: Results and Discussion ground powders; hair, animal and human-includes fur; insect fragments, actual pieces of body parts, feces; metal shaving and dust; mold and other spores, enzymes and colony particles especially in the warm, dark and moist environment; oil soot, from kerosene, and scented oil lamps; paint chips and dust; plant parts; pollen ( all source); Polymer foam particles; vehicles parts, like tire and brake facing; resin powders ;salt and sugar crystals; sand and soil; wood shavings, wood dust; and etc. In addition to that, particulate matters can absorb toxic gasses, such as the mercury fumes and sulfur dioxide that can lodge in lungs tissue. Particles also carry toxin, bacteria, fungi, or heavy metals (Pro-active Environmental Technologies. 2005). PM is very heterogeneous and consisted of particles varying in size, shape, and specific weight (Gaanum and Lovik, 2002). High significant Pearson correlation coefficients were found between PM 1.0 and each of PM2.5, PM10.0, CO2, NO2, O3 and SO2 (Appendix 5). The coefficient values were (0.928**, 0.963**, 0.799**, 0.717**, 0.821** and 0.864**) respectively, thus indicating that most of these air pollutant were from similar source. In most of the countries, since it is not mandatory to monitor PM1.0 levels therefore limited information is available on PM1.0. 4.1.5.2: PM2.5: Particulate matter less than 2.5 micrometer in aerodynamic diameter: The results of PM2.5 presented in table (4.4) and figure (4.8) showed also a high variation in values of the average concentration of PM2.5 among the studied locations for the same measurements period of PM1.0. Average values ranged between 18.7-181.7 µg m-3. The lowest average value was at location 8 (Outside Peshraw tunnel), and that was because that location was far from residential urban area and vehicular effects. But the highest average value was estimated at location 7 (Inside Peshraw Tunnel), and that was due the confinement of a large amount of vehicle emission (vehicle’s episode and smoke haze) inside the tunnel and lack of dilution or aeration system. Thus, the emission of the traffic vehicles caused considerable air pollution throughout the tunnel. The subsequent order of increasing average level from cold spot locations to the hot spot locations were as follows; 18.7, 24.3, 27.7, 32.3, 33.6, 36.7 39.4, 43.1, 46.2, 54.6, 57.1, 57.4, 57.9, 69.6, 72.7, 178.6, and 181.7 µg m-3 for the locations 8, 12, 6, 10, 13, 9, 15, 16, 14, 17, 1, 11, 4, 3, 2, 5 and 7, respectively. PM2.5 showed relatively a different trends for the locations sequence as compared to the locations of PM1.0 and that was because PM synergy were from different sources and of different characteristic in the locations.
135
CHAPTER FOUR: Results and Discussion The average levels of PM2.5 at locations 5 and 7 were very high as compared to the average levels of the other locations, and that was because location 7 was inside Peshraw tunnel and the reasons were mentioned previously, while location 5 was in Tanjaro/Landfill and in that location a large amount of PM was emitted to the ambient air through the incineration process of solid waste and industrial sources, knowing that many industrial processes were located in Tanjaro. The average PM2.5 levels in most of the locations in Sulaimani city exceeded the current standard level set by EPA (15.0 µg m-3 for an annual ,arithmetic average, and 35 µg m-3 for 24-hour averaging time) .This meant that most of the locations in Sulaimani city could be complied as a nonattainment area for this issue (Griffin, 2002), Available air quality data suggested that pollutant of most concern from the point of view of environmental health risk in South Asia was airborne particulate matter (Kumar and Joseph, 2006). As per a study, levels of PM2.5 at several locations at Delhi, India reported to be in the range of 78-109 µg m-3 (Agarwal et al., 2002). The mean PM2.5 concentration for the background mixed/traffic industrial site in Chennai, India was 35, 46, and 54µg m-3 respectively during 2002-2003 (Rama Krishna et al., 2003). Lazaridis et al. (2008) reported that the mean PM2.5 and PM10.0 concentrations were 25.4±16.5 and 35±17.7 µg m-3, respectively in Akrotiri monitoring station which is situated at an urban background/semi-rural area on Island of Crete, Greece. While, (Kumar and Joseph, 2006) estimated the average concentration of PM2.5 at ambient and kerbsite of 43 and 69 µg m-3 respectively, in Metro city, Mumbai, India. According to (UTAH, 2007) the average 3 years (2004, 2005 and 2006) of PM2.5 24-hour design values in 20 location/UTAH State were ranged between 33 to 63 µg m-3, and the value of 36 or greater indicated a violation. High PM2.5 concentrations were recorded in Dhaka-Bangladesh (up to 300 µg m-3) and moderate levels in Sri Lanka (60 µg m-3) (World Bank, 2004a). In Iraq and also Iraqi Kurdistan Region it is not yet mandatory to monitor or estimate PM2.5 levels, therefore no background data is available and also no other studies were carried out on this issue. Particulate matters undergo a seasonal change and also a prevalent climatic condition. Nowadays, Long-range transboundary transport is also responsible for a significant fraction of the particulate pollution in Iraqi Kurdistan Region cities. The overall mean value, normal and maximum level of PM2.5 in Sulaimani city were 60.7, 4.0 and 660 µg m-3 respectively, these results provided that the PM2.5 level in the ambient air of 136
CHAPTER FOUR: Results and Discussion Sulaimani city as a whole was also exceeded the standard limit of EPA and that could be considered as hot spot of PM2.5 . Therefore, depending on the reviews literatures reported by (Davidson et al., 2005; Curtis et al., 2006 and Tonne et al., 2007), PM2.5 is considered as one of the prevalent urban air pollutants in Sulaimani city that associated with negative health effects. Diesel exhaust emission in Sulaimani city contributed a large source of particulate matter (PM) produced by diesel power stations and diesel engine vehicles, since, a large number of diesel electric generators are in work (Table 3.4). These particles pose a serious threat to the health of our families and adversely impact our environment. The statistical analysis of PM2.5 data showed a high significant Pearson correlation coefficients between PM2.5 and each of PM1.0, PM10.0, O3 and SO2 (Appendix 5). The correlation coefficient values (r) were (0.928**, 0.867**, 0.649** and 0.703**) respectively, but only significant correlation was found between PM2.5 and each of CO2 and NO2 and the correlation coefficient values (r) were (0.598* and 0.521*) respectively, similar correlation were found between PM2.5 and each of PM10.0 and NO2) by (Kumar and Joseph, 2006). 4.1.5.3: PM10.0: Particulate matter less than 10.0 micrometer in aerodynamic diameter: Results of ambient level of PM10.0 during the study period 31.9.2009 to 13.7.2010 at 17 different outdoor sites in Sulaimani city are presented in Table (4.4) and Figures (4.8 & 4.9). Average values of 7 reading of PM10.0 were ranged from 40.4 to374 µg m-3. The lowest and highest averages values were also recorded in the same location of PM2.5 and that was due to the same mentioned reasons by PM2.5. Also the measured PM10.0 level showed a high average variability among the locations, and the order of increasing the average gradient were as follows; 40.4, 48.0, 50.4, 66.7, 72.1, 77.4, 79.4, 86.1, 92.1, 96.6, 104.9, 109.7,109.9, 133.7, 149.4, 168.7 and 374.0 µg m-3 for the locations 8, 12, 6, 10, 13, 14, 15, 16, 9, 1, 11, 3, 17, 2, 4, 5, and 7 respectively, and the order sequence of the locations was complied somewhat with the order sequence of PM2.5 level. This trend of sequence can be explained by the fact that the concurrence of particulate matters was a paradigm of synergic process. These processes are a combination of biogenic and anthropogenic activities (including particularly; fugitive dust, vehicular particulate emissions, diesel power station PM emission and industrial process sources), where the later (anthropogenic activities) presented a more complex issue that drew international and local government attention in recent years, especially with regard to climate change. Nowadays this anthropogenic impact in Sulaimani city became a real fact, because the 137
CHAPTER FOUR: Results and Discussion concentration of human activities in a relatively small area like Sulaimani put enormous pressure on the urban system and led to numerous environmental problems (e.g. noise, waste, air pollution, etc). According to National Ambient Air Quality Standards (NAAQS) guideline by EPA (2010), the current value for PM10 is a 24-hour level of 150 µg m-3, (EPA, 2010). But, according to the European Union Directive on particulate matter (1999/30/EC), PM10 values must not exceed the yearly average limit of 40 g m-3 or daily average of 50 g /m3 on more than 35 times per year (ECE, 2010). When the obtained average means of this study were compared with the standard limit of European Commission (EC), it showed that the PM10.0 level of the most location have exceeded that limit of daily average of 50 g m-3 of EC. An average range from 150 to173 µg/m3 was obtained by (Rashid, 2010) for PM10.0 level in some Tanjaro location/Sulaimani. While (Lazaridis et al., 2007) have reported that the mean concentration of PM10.0 was 35.0 ± 17.7 µg m-3 during the measuring period of (2.5.2003 to 9.3.2004) at Akrotiri Research Station, Crete, Greece, and they reported that the major desert dust on 27.2.04 led to PM10.0 level reached more than 400 µg m-3 with the highest value of 495 µg/m3.
Figure (4.9): PM10.0 level concentraion for the studied locations According to data published by DEFRA (2009), the annual PM10.0 concentration for the most urbane background and roadside sites in UK as a Government Air Quality indicator were 19 µg m-3 and 22 µg m-3 respectively. On the contrary, average high values were recorded for PM10.0 level in various Cities in South Asia such as; 900 µg m-3 for Lahore; 225,135 and 126 µg m-3 for core, subcore and remote part of Nepal (Kumar and Joseph, 2006). 138
CHAPTER FOUR: Results and Discussion Clean Air Hamilton, 2007 presented in the 2005-2006 progress reported that the total annual emission of PM10.0 in tones by source category out of the total amount of (29963 tones) for Hamilton, Ontario were as follows; (5430; 1707; 1037; 2.0; 118.0; and 21619 tones) for the sources (Industrial; Fuel Combustion; Transportation; Incineration; Miscellaneous and Open sources) respectively (Table 4.2). The overall mean value, normal and maximum levels of PM2.5 in Sulaimani city were 109.4, 6.0 and 778.0 µg m-3, respectively. The lowest value was found at location 6 (Bnari Shakhi Goizha), while the highest value was in location 7 (Inside Peshraw tunnel). The range of health effects associated with PM is broad, but is predominantly related to the respiratory and cardiovascular system. All population is affected, but susceptibility to the effects of PM may vary with health or age. The risk for various outcomes showed to increase with exposure, with both short-term and long-term exposure being important. The available evidence suggested that pathogenic mechanism of PM was linked to the number (or total surface area) of particles inhaled and retained in the alveolar ducts rather than to the total mass of the PM (Casale et al., 2009). However, even though the potential toxicity of coarse particles should not be neglected (Brunekreef and Forsberg, 2005; Schwarze et al., 2007). Generally, the particles at the largest end of the size range tend to have short suspension times (hours) and rapidly fall out of the atmosphere. In contrast, submicron and ultrafine PM could remain suspended in the ambient air for days to weeks and could be carried over large distances (Granum and Lovik 2002). Moreover, the obtained average values of PM10.0 in this study showed high significant Pearson correlation with each of PM1.0, PM2.5, SO2, NO2, O3, and SO2 and the correlation coefficients were as follows; (0.963**, 0.867**, 0.868**, 0.774**, 0.864* and 0,934**) respectively (Appendix 5). Similar high significant correlations between PM10.0 and each of PM2.5 and NO2 were found by (Kumar and Joseph, 2005). The results of Duncan test (Appendix 7) showed that there were significant differences at level 5% between the PM10.0 levels among the locations and they were sub-grouped into four groups, the groups were (7); (5, 4, 2, 17, 13, 11, 1, 9, 16, 15, 14); (13, 10) and (6, 12, 8). 4.1.5.4: Notable dust storms in Sulaimani city: A dust/sand storm is a meteorological phenomenon common in arid and semi-arid regions. Dust storms arise when a gust front or other strong wind blows loose sand and dust from a dry 139
CHAPTER FOUR: Results and Discussion surface. Particles are transport by saltation and suspension, causing many negative effects such as; soil erosion from one place and deposition in another, changing both the local and global climate change, impacting global economics (Squires, 2007), much human health effects by breathing dust, increase the spread of many microbes, virus spores and disease across the globe (Vidal, 2009), reducing agricultural productivity. Other effects that may have impact are: reduced visibility affecting aircraft and road transportation, reduced sunlight reaching the surface, increased cloud formation increasing the heat blanket effect and etc. Dust can also have beneficial effects where it deposits. The ejected particles move in one of three modes of transport depending on particle size, shape and density of the particle. These three processes are designated suspension, saltation and creep. Its size and density determine movement pattern of sand-dust particles. Table (4.5) showed an example of soil particle movement under a wind force of (15 meters per second). Fine dust particles may be transported at altitudes of up to 6 km and move over a distance of up to 6000 km (Squires, 2007). Table (4.5): Movement of soil particles under a wind force of 15 meters second-1* (Squires, 2007). Particle size (mm) Period of suspension (time) Comment/description 0.1 0.3-3.0 seconds Fine sand 0.01 0.83-8.3 seconds Dust. Can go up to 700 m high 0.001 0.95-9.5 years Fine clay can go up to 77 km high *The threshold wind velocity (15 cm above ground surface) that can lift up and transport dust grains of 0.05-0.1 mm in diameter is 3.5-4.0 m/s. In the last two years, dust/ sandstorms became a notable phenomenon in most areas of Iraqi Kurdistan Region, Sulaimani city has also received much deposited dust from the dust/ sandstorms, therefore, in this study as an extra factor of affecting the air quality of the city’s ambient air, the number of the event dust/ sandstorms and the maximum PM level of the different aerodynamic size that reached were recorded during the period from 19.9.2009 till 2.6.2011 (Table 4.6). According to the information and satellite or spectroradiometer images (such as Aqua-MODIS, Terra-MODIS, EO-1 Ali, Terra-ASTER), obtained from NASA (National Aeronautic and Space Administration), the originating or emerging sources of the most occurred dust/ sandstorms were from Sahara (desert) and drylands of the adjoining or neighboring countries of Iraq, including (Arabian peninsula, Iran, Pakistan, Afghanistan, Turkmenistan, Tajikistan, Uzbekistan and even China), (Figures 4.10a, 4.10b, and 4.10c). For 140
CHAPTER FOUR: Results and Discussion comparison the concentration levels of PM at dust/sandstorm days with the normal and clear days were also measured and compared. Table (4.6) showed that the frequent number of occurred dust/sandstorms in Sulaimani city were 32 time throughout the recording period of 19.9.2009 to 11.6.2011 and that was really a large number of dust/sandstorms events as compared to the previous years, and also considered as a good indicator for the climate change which happened due to global warming in adjoining countries of Iraq and led to increase more drylands area. The particle concentration level for the PM1.0, PM2.5 and PM10.0 aerodynamic size on the dust/sandstorm days varied between; (16452), (44-676) and (157- 4358) µg m-3 respectively. The large-scale dust/sandstorm events observed in Sulaimani city during the studied period were on 22.2.2010; 23.6.2010; and 12.4.2011. Sometimes the dust/sandstorm coincided with other weather conditions such as rainfall and affected the city. These dust/sandstorms phenomena caused deposition of thousands of tons of dirt and soil particles in Sulaimani city. Comparative assessment in Table (4-6) showed that there was a large variability in concentration between the clear and dust/sand storms day. This meant that long-range transboundary of particles was responsible for a significant fraction of the particulate pollution in Sulaimani city, because Sulaimani city is now one of the regions downwind to the sources of dust/sandstorm that sweep across the neighboring countries of Iraqi Kurdistan Region. After the devastating dust storms that swept across the drylands and particularly from Northern China in 2000, there was much interest in examining and analyzing experience with dust storm mitigation, prevention, forecasting and control. There was a need to document the nature, extent, causal factors associated with the severe dust/sandstorms experienced in China itself and which threatened the lives and livelihoods of millions of people. Because China, is one of the most countries worst affected by desertification, has real and mounting problems to overcome (Longjun, 2001). Generally, desertification-land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities, is the result of processes which are complex and variable.
141
CHAPTER FOUR: Results and Discussion Table (4.6): Notable dust/sandstorms occurred in Sulaimani city during 19.9.2009 to 11.6.2011. No. 12345678910111213141516171819202122232425262728-
Date 19.9.2009 29.9.2009 11.10.2009 19.2.2010 22.2.2010 16.3.2010 19.3.2010 5.4.2010 12.4.2010 21.4.2010 25.4.2010 28.4.2010 12.5.2010 18.5.2010 30.5.2010 7.6.2010 19.6.2010 22.6.2010 23.6.2010 24.6.2010 15.7.2010 19.7.2010 22.7.2010 28.9.2010 4.10.2010 20.2.2011 10.3.2011 2.4.2011
O’clock
Originating or emerging in
7:00 am 7:50 am 6:30 am 10.40 pm 6:50 pm 2:10 pm 6:15 am 6:40 am ----7:30 am --15:30 pm 6:30 am 5:10 am 8:25 pm 6:00 am 5:00 am 6:00 pm 7:00 am 6:15 am 7:40 am 3:00 pm 11:30 am 9:30 am 3:40 pm 6:00 pm 3:40 pm
South west Iraq (Regionally) South west Iraq (Regionally) South west Iraq (Regionally) Adjoining Countries Adjoining Countries South west Iraq (Regionally) Clear day (Sunshine) Adjoining Countries Not measured due to rainfall Not measured due to rainfall Clear day (Sunshine) Not measured due to rainfall Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries
Concentration (µg/m3) PM1.0 PM2.5 PM10 124 249 321 158 346 396 103 154 418 208 334 1.388 452 676 3.303 40 68 208 10 18 22 45 84 305 ------------13 17 35 ------22 44 162 114 231 1.721 57 137 581 52 132 499 31 44 126 30 73 255 258 509 2.148 83 165 682 52 88 344 24 129 209 21 53 157 20 62 306 25 64 427 28 69 185 37 155 868 38 192 824
Duration Several hours Several hours About one days Several hours About 4 days Several hours ---------About two days About 2 days About one days ---------About two days About two days Several hours About 3 days About 1 days Continuous Starting day Continued from 22.6.2010 Continued from 22.6.2010 About 2 day Continued About 3 days About 1 days About 2 days About 1 day Continued from 08.3.2011 About 3 days 142
CHAPTER FOUR: Results and Discussion Table (4.6): Continued Notable dust/sandstorms occurred in Sulaimani city during 19.9.2009 to 11.6.2011. No. 29303132333435-
Date 12.4.2011 7.5.2011 13.5.2011 22.5.2011 2.6.2011 11.6.2011 26.6.2011
O’clock 9:15 pm 5:00 am 3:300 pm 5:15 am 4:45 pm 5:20 am 4:30 am
Originating or emerging in Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Adjoining Countries Clear day
Concentration (µg/m3) PM1.0 PM2.5 PM10 145 641 4.358 18 102 487 16 60 347 29 161 781 23 132 633 24 101 418 13 19 21
Duration Continued from 08.3.2011 About 3 days About 2 days About 5 days About 7 days About 2 days After 3 days of north eastern wind and the rainfall of 25.6.2011
Note; the particles concentration of notable dust/sand storm was measured in German village location only during the period of (19.9.2009 to 11.6.2011), and the location has a GPS coordination of (N 35o 34′ 57.12′′) and (E045o 27′ 14.41′′).
143
CHAPTER FOUR: Results and Discussion
Figure (4.10a): Dust over Syria and Iraq on February 22, 2010. Instrument: Aqua-MODIS (NASA, 2010), A dense plume of dust swept from Syria into Iraq on February 22, 2010. This photo-like image of the dust storm was captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua satellite in the early afternoon (12:30 p.m. in Syria, 1:30 pm in Iraq). Distinct plumes arose from many point sources in the Syrian desert. Within a few kilometres, the plumes blend into a dense cloud that completely obscures eastern Syria and western Iraq. The veil of dust was thick enough that the ground beneath was not visible, which means that people on the ground were probably getting significantly reduced daylight. The large image provided above is the highest resolution version of the image (250 meters per pixel). The image is available in additional resolutions from the MODIS Rapid Response System. NASA images courtesy Jeff Schmaltz, MODIS Rapid Response Team at NASA GSFC. Caption by Holli Riebeek.
144
CHAPTER FOUR: Results and Discussion
Figur (4.10b): Dust storm over Iraq and Iran on March 4, 2011. Instrument: Aqua-MODIS (NASA, 2011). Dust continued blowing over Iraq on March 4, 2011. As it blew toward the southeast, the dust also spread over parts of Iran and the Persian Gulf. The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua satellite captured this natural-color image at the same day. The dust plume was thickest over Iran, having been carried eastward from Iraq. Sediments from Iraq’s sand seas, impermanent lakes, and riverbeds frequently gave rise to dust storms, many of which can spread southward over the Arabian Peninsula and eastward over Iran. NASA image courtesy Jeff Schmaltz, MODIS Rapid Response Team at NASA GSFC. Caption by Michon Scott.
145
CHAPTER FOUR: Results and Discussion
Figure (4-10c): Dust over Southwestern Asia and the Arabian Sea on June 1, 2011. Instrument: Aqua-MODIS. (NASA, 2011). Dust storms spread over southwestern Asia on June 1, 2011. As the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua satellite passed overhead, dust-storm activity extended from the Iran-Afghanistan border across Pakistan and into India. Dust also blew over the Gulf of Oman and the Arabian Sea. Dust plumes often arose from the dry lakebed sediments along the border between Iran and Afghanistan. Thick plumes blew from that region toward the southeast in this natural-color image. Dust also arose from the sand seas occurring over much of western Pakistan, and the sandy desert that stretches for hundreds of kilometers along the Pakistan-India border. Although the storm was thinner than the plumes to the north and east, dust also occurred over Oman. Sandy deserts in Oman could be the source of the dust in that region, but the dust might be arisen in and around Pakistan, and been carried across the Arabian Sea and Gulf of Oman. Dust plumes in this region often originate from dry lakebed sediments along the IranAfghanistan border, and from sandy deserts in Afghanistan and Pakistan. NASA image courtesy Jeff Schmaltz, MODIS Rapid Response Team at NASA GSFC. Caption by Michon Scott.
146
CHAPTER FOUR: Results and Discussion 4.1.6: Lead (Pb): In the present study, the availability of the required instrument or equipment was one of the biggest limitation, therefore, due to the shortage of air sampler, we were not be able to determine the suspended airborne lead which is certainly now has a high level in the ambient air of Iraq as whole and also in Kurdistan Cities of Iraq, because as it was confirmed by (Leigh et al., 2010) in 2010 and through the available video clip leaded gasoline fuel is still in use in Iraq. In fact, the potential exploding number of vehicles in Sulaimani city is the main factor for adding large amount of emitted lead into the atmosphere during combustion of the leaded fuel. For this reason, Settleable dust (deposited or dustfall) samples were taken as a integrated significant media for the atmospheric or airborne lead (Pb), additionally; soil, plant and rainwater samples were also analyzed as a environmental indicators that can provide information about the level, distribution, and fate of lead and some other heavy metals contaminant present in environment. The concentration of airborne lead metal in Settleable (deposited) dust samples collected from local, mixed (local mixed with duststorm) and separated sandstorm samples of 22.2.2010 are shown in Table (4.7) and Figure (4.11a) and they were varied between 12.03 - 315.92; 9.86 -102.33 and 0.62 - 1.65 mg Pb kg-1 dust respectively. The highest values of local and mixed (local mixed with duststorm) were found at location 13 (Sarkarez/Dastaraka crossing) and 11 (Salim Street/Beside Khsrawkhal bridge) respectively. For describing and discussing the lead status in Sulaimani city, the following conclusions can be derived from the comparison of the obtained concentration ranges: -The concentration of lead revealed large variation among the locations indicating the highly heterogeneous nature of the dust samples. In addition to that, the delimitation for mixed (local mixed with duststorm) sampling period was uncontrollable and this may affect also the concentration levels of Pb in mixed samples. Normally, the collected samples from locations of dense traffic contained high concentration of lead. Location of 13, 14 and 11 could be considered as hot spot location for Pb, therefore, human and environment lead exposure at these locations will occur more seriously, as health risks of lead were previously extensively reviewed.
147
CHAPTER FOUR: Results and Discussion -In general, the obtained results for airborne lead from mixed (local mixed with duststorm) and separated sandstorm samples of 22.2.2010 were lower than the results obtained from the local samples, and that was due to the fact that the source of sandstorm dust was from regions far away of anthropogenic impacts which caused increasing the concentration of lead in the Settleable dust samples. The particle of dust/sandstorm will be contaminated with anthropogenic pollutant such as heavy metals and toxic gases as it passes over the polluted urbanized locations. -Currently, there are no guidelines or regulations for heavy metals in Settleable dust (deposited or dustfall). But, to evaluate the extent of lead metal contamination in the Settleable dust in Sulaimani, the concentration of lead and the other studied heavy metals have been converted from mg Pb kg-1 dust to ng Pb m-3 air through a simple calculation either at those locations where both processes of the average PMtotal measurement and Settleable dust collection were proceeded or at those locations where carrying out both the processes of average PMtotal measurement and dust collection were close to each other (Table 4.8 and Figure 4.11b). The Pb concentrations ranged from 0.70 to 44.51 ng Pb m-3, and the lowest value was found at location 4 (Kurdsat Quarter), because this location is far from the direct effect of traffic emission and industrial effect due to its geographical site, but the highest value was at location 7 (Sarkarez/ Dastaraka crossing) and that was due to a dense and high traffic volume at this location. This is most convincingly demonstrated that Pb is largely introduced into the atmosphere of Sulaimani city from the combustion of leaded gasoline from vehicle source. In Europe in 1990, the background concentrations of lead in air were mainly within the 10-30 ng m-3 range. In 2003, the concentration mainly ranged between 5-15 ng m-3 (Aas and Breivik, 2005). On the contrary, at current time in Sulaimani city trended towards increasing level of lead became a real fact because leaded gasoline is still in use. For the period from 1990 to 2003, lead deposition on Europe as whole decreased from about 40000 tons to 17500 tons (2.3 times), and the maximum contribution (about 85%) to the total lead emission was from road transportation sector (WHO, 2007).
148
CHAPTER FOUR: Results and Discussion Table (4.7): Airborne heavy metal concentrations of the settleable dust samples (mg kg-1 dust). No
123456-
Locations
Raparin/ Near to Sulaimani International Airport Maleek Mhmood Circle/ Lovan Hotel Tanjaro / Tanjaro Mosque Nawgrdan Village/ Osman Gas Fact. Charakhan Quarter
7-
Kaziewa Quarter/ Near to Goizha Apartments Kurdsat/ Quarter 1
8-
Kurdsat Quarter 2
9-
Maleek Mhmood Circle/ Beside Zargata Underpass. Farmanbaran Quarter
101112131415-
Salim Street/ Beside Khsrawkhal Bridge. Chawrbakh Quarter / Near to Sulaimani Stadium Sarkarez/ Dastaraka Crossing Kanat Street Main Internal Buses Transportations Center
Source of the sample L M L M L M L M L M L M L M L M L M L M L M L M L M L M L M
Concentrations (mg kg-1 dust) Cr
Mn
Fe
Ni
Cu
Zn
Cd
Pb
17.94 20.34 20.51 19.12 16.83 14.64 17.00 21.80 16.14 43.57 38.36 24.03 19.62 19.16 19.03 21.95 19.17 NC 17.32 NC 66.86 20.35 14.08 16.43 25.03 18.82 18.15 18.17 20.38 16.95
50.89 53.07 42.90 53.61 54.12 53.83 56.80 53.99 46.61 42.69 86.68 58.40 54.94 59.42 57.14 57.78 57.35 NC 52.45 NC 65.82 55.68 52.08 51.88 65.54 60.10 58.25 53.60 65.68 60.57
22964.8 23537.3 26035.4 23269.7 22118.3 20918.1 21525.2 23788.4 24772.8 24351.7 25092.9 25089.7 23540.7 23528.3 22824.4 24377.4 24974.1 NC 21658.8 NC 24624.9 23027.1 21189.0 22289.8 25687.3 23505.7 27007.5 22970.5 22791.0 22999.8
97.91 104.16 92.05 83.62 91.77 85.73 100.16 113.76 85.02 241.16 110.71 99.84 83.1 98.56 93.56 109.08 87.37 NC 81.77 NC 423.86 108.18 71.19 83.76 127.70 86.95 110.61 86.73 93.95 76.48
25.89 35.63 32.10 43.32 41.31 29.93 28.81 29.89 33.13 45.21 29.09 30.98 33.90 37.72 35.07 38.57 40.62 NC 43.72 NC 59.07 54.42 37.24 33.47 108.49 41.31 146.24 47.49 55.24 54.08
140.56 110.34 344.58 146.26 122.19 170.83 82.32 129.82 312.14 323.36 229.73 133.83 108.46 100.08 107.75 99.80 179.63 NC 159.46 NC 139.52 177.12 151.81 124.28 240.00 165.32 283.51 152.66 335.94 244.69
2.14 1.43 2.25 2.13 2.14 2.24 2.44 1.63 4.29 5.34 3.38 3.37 3.25 3.46 2.96 3.98 1.74 NC 2.24 NC 2.14 4.37 2.64 1.94 2.75 2.14 2.44 2.14 2.35 2.14
12.03 11.33 29.03 70.39 44.05 26.72 64.03 9.86 24.02 83.74 42.09 35.78 32.32 29.32 14.37 26.53 77.57 NC 43.77 NC 135.96 102.33 47.59 26.57 315.92 62.49 255.31 59.41 68.31 59.49 149
CHAPTER FOUR: Results and Discussion Table (4.7): Airborne heavy metal concentrations of the settleable dust samples (mg kg-1 dust). No
Locations
16-
German Village
17-
Mamostain Quarter
18-
Kurdsat Quarter
Source of the sample Sandstorm 22.2.2010 Sandstorm 22.2.2010 Sandstorm 22.2.2010
Cr 22.05
Mn 51.43
Concentrations (mg kg-1 dust) Fe Ni Cu Zn 26354.5 117.37 28.30 81.38
24.45
53.19
26180.5
123.91
29.11
73.52
1.95
0.62
21.48
52.56
26251.4
116.40
28.65
78.87
3.30
1.24
Cd 2.27
Pb 1.65
Note: L: Local Airborne Dust Sample M: Mixed samples of Sandstorm and local Airborne Dust NC: Not Collected
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CHAPTER FOUR: Results and Discussion
Table (4.8): Concentration of heavy metals in atmospheric settleable dust samples (ng m-3 air). No
123456789-
Locations
PMTotal (µg m-3)
Cr
Mn
Fe
Concentrations (ng m-3 air). Ni Cu Zn
Cd
Pb
Raparin/ Near to Sulaimani International Airport Tanjaro / Tanjaro Mosque
174.6
3.13
8.89
4009.7
17.10
4.52
24.54
0.37
2.10
249.3
4.20
13.49
5514.1
22.88
10.30
30.46
0.53
10.98
Kaziewa Quarter/ Near to Goizha Apartments Kurdsat Quarter 2
66.1
2.54
5.72
1658.6
7.32
1.92
15.19
0.22
2.78
48.9
0.93
2.79
1116.1
4.57
1.71
5.27
0.14
0.70
Maleek Mhmood Circle/ Beside Zargata Underpass. Salim Street/ Beside Khsrawkhal Bridge. Sarkarez/ Dastaraka Crossing Kanat Street
112.4
2.15
6.45
2807.1
9.82
4.57
20.19
0.20
8.72
111.4
7.52
7.40
2767.8
47.64
6.64
15.68
0.24
15.28
140.9
3.52
9.23
3619.3
17.99
15.29
33.82
0.39
44.51
136.3
2.47
7.94
3681.1
15.08
19.93
38.64
0.33
34.80
10.93
3792.4
15.63
9.19
55.90
0.39
11.37
188.75
96721.0
430.75
103.86
298.66
8.33
6.06
Main Internal Buses 166.4 3.39 Transportations Center 10- German Village (Sandstorm 3670.0 80.9 22.2.2010) Note: L: Local Airborne Dust Sample. M: Mixed samples of Sandstorm and local Airborne Dust. NC: Not Collected. (ng m-3 air): nanogram per cubic meter air.
151
CHAPTER FOUR: Results and Discussion
Figure (4.11a): Airborne Pb concentration of the settleable dust samples.
Figure (4.11b): Concentration of Pb in settleable particles samples of atmospheric source.
In Minsk, Belarus, the average annual concentration of lead in 2004 was 83 ng m-3. In cities and in the vicinities of industrial sources (i.e. at a distance of 1-10 km), higher concentrations were prevailed (Herpin et al., 2004; Wenzel et al., 2006). On the other hand, other researchers have reported that in cities where leaded petrol is still in use or has only recently been phased out, mean air lead levels of the order 200-400 ng m-3 were common in residential areas (Lu et al., 2003; He et al., 2004). The researchers kaul et al. (2003) have estimated 2000-3900 ng Pb m-3 in traffic areas in the city of Lucknow in India, and Hashiho and El-Fadel (2004) reported an average of 2860 ng Pb m-3 in urban Beirut/Lebanon.
152
CHAPTER FOUR: Results and Discussion In general, the values of Pb concentration at all locations were below the standard limit set by National Ambient Air Quality Standards (NAAQS) which was150 ng Pb m-3 of rolling 3month average (EPA, 2010). Similar pattern of Pb concentrations in streets dust (24 to 280 Pb mg kg-1 dust) in a reconnaissance sampling of air born dust in Baghdad city/Iraq were estimated by Al-Bassam et al. (2009). The concentration of Pb in the examined dust samples was fairly in agreement with the range 2.1-314.1 mg Pb kg-1 dust detected by Al-Khasman (2004) in street’s dust of Karak Industrial Estate/ Jordan. On the other hand, according to Al-Saffawi (2006), the amount of seasonal deposited lead in some locations of Mosul city/Iraq ranged between 948 to 116522 mg Pb m-2 season-1. While a very high mean concentration of 110000 mg Pb kg-1 dust) was reported by Leung et al. (2008) in samples of streets dust collected from recycling workshops of printed circuit boards in Guiyu/China. The main chemical species upon emission are chlorides, oxides and sulfates. In particular, oil combustion releases lead as lead oxide (PbO). Historically, lead emissions peaked during the 1970s, with annual emissions estimated at 400 000 tons (Nriagu, 1996). Lead is by far the well-studied toxic metal, and a wide range of biological effects dependent upon the level and duration of the exposure are known. Among children lead is associated with decreased intelligence, growth and hearing impairment, anemia, and attention and behavioral problems. High levels of exposure could cause severe brain damage and death (ATSDR, 1999). This Agency for Toxic Substance and Disease Registry has emphasized also those young children, especially those who are aged younger than 2 years, were particularly more susceptible to lead because their central nervous system is still developing and because they absorb more lead from their environment than the adults do. Statistically high significant Pearson correlations were found between Pb and each of Cr and Ni and the correlation coefficient were (0.828** and 0.845**) but only significant Pearson correlations (r = 0.518* and r = 0.614*) were noted between Pb and each of Cu and Zn (Appendix 5).
4.2: Other examined heavy metals in settleable dust (deposited or dustfall) samples: In order to enforce legislation, measure human and environmental exposure, and show compliance with limit and target values, the concentration levels of ambient metals must be 153
CHAPTER FOUR: Results and Discussion measured at multiple sites. Indeed, in Sulaimani city there are no available or limited studies on atmospheric airborne heavy metals; as a result there is little information available on the background level of heavy metals in the ambient air composition, therefore, the current studies aimed also to estimate some other heavy metal pollutants rather than Pb in ambient air of Sulaimani city in order to provide air quality information for the general public and to inform other scientific endeavors. The concentration of the heavy metals (Cr, Mn, Fe, Ni, Cu, Zn, and Cd) in the examined Settleable dust (deposited or dustfall) samples collected from local, mixed (local mixed with sand storm) and separated sandstorm of 22.2.2010 samples were presented in (Table 4.7, and Figures 4.12a, 4.13a, 4.14a, 4.15a, 4.16a, 4.17a and 4.18a). But the overall ranges for the heavy metals (Cr, Mn, Fe, Ni, Cu, Zn, and Cd) for the local, mixed and dust/sandstorm samples are shown in Table (4.9). Generally, the following trends and patterns can be outlined from the presented ranges of the selected heavy metals. Table (4.9): Ranges of heavy metal concentration (mg kg-1 dust) in the Settleable dust sample of different sources. Heavy Source of Settleable dust samples and concerned locations metals Concentration in mg kg-1 dust Local sample Mixed sample* Dust/sandstorm sample of 22.2.2010 Range L** Rang L Range L Cr 14.08 - 66.86 12-11 14.64 – 43.57 3- 5 21.48 - 24.45 18-17 Mn 42.90 - 86.68 2- 6 42.69 – 60.57 5-15 51.43 – 53.19 16-17 Fe 21189.0 - 27007.5 12-14 20918.1 – 25089.7 3- 6 26180.5 - 26354.5 17-16 Ni 71.19 - 423.86 12-11 76.48 – 241.16 15-5 116.40 – 123.91 18-17 Cu 25.89 – 146.24 1-14 29.89 – 54.42 4-11 28.30 – 29.11 16-17 Zn 82.32 – 344.89 4- 2 99.80 – 323.36 8- 5 73.52 – 81.38 17-16 Cd 1.74 -4.29 9-5 1.43 – 5.34 1- 5 1.95 – 3.30 17-18 Pb 12.03 - 315.92 1-13 9.86 - 102.33 4-11 0.62 -1.65 17-16 *Mixed Sample: are those samples consist of local dust particle mixed with dust/sandstorm particles. **L: are the concerned locations Dust/sandstorm samples had the narrowest range of concentrations for all the investigated heavy metals, while the local samples characterized by the widest ranges when compared to the ranges of the other sources of samples. This could be interpreted by the heterogeneity of the sources, amount and the factors that affected the 154
CHAPTER FOUR: Results and Discussion concentrations of the heavy metals in the different locations, and that was why the concentration of the different heavy metals showed large variations among the locations, particularly for the local samples. No systematic patterns for the distribution of the lowest and highest values among the heavy metals and also between the local and mixed sources of the samples were found. However, locations 11, 5, and 14 were more frequent for the highest values of the metal concentration. While for the lowest concentration, location 12 was the most frequent for local samples. Currently, there are no regulation or standard limits for heavy metals in dust, therefore, in order to evaluate the extent of heavy metals contamination in the dust, the concentration were compared to soil guideline. Regarding chromium metal the concentration at all locations was below the New Dutch list (2001) which was 100 mg Cr kg-1 soil for optimum level and 380 mg Cr kg-1 soil for action level. For Ni, Cu, Zn and Cd metals the concentration limits by New Dutch list for both optimum and action levels were; 35 - 210; 36 - 190; 140 -720 and 0.8 -12 mg kg-1, but no standard limits for Mn and Fe were regulated by the New Dutch list. When the concentration of Ni metal in the investigated dust Settleable (deposited or dustfall) samples were compared with the limits of the New Dutch list, it appeared that for all the locations and for all sources (local, mixed and dust/sandstorm of 22.2.2010) were extremely exceeded the optimum level of the New Dutch list. At location (5 local, 11 local) the concentration of Ni also exceeded the action limit of the New Dutch list, that may be due to the vehicular and diesel power generator emissions effects, because the trace metals Ni and V (vanadium) were the most higher trace metal content of crude oil constituents (Speight, 2007) and it may be that these metals were not removed totally during the refinery process of the oil products .As for Cu metal, the concentration in some locations for local and mixed sources only exceeded the optimum level of the New Dutch list and the maximum exceeding was at location 14 local, otherwise, for all locations the concentration of Cu was below the action level of the New Dutch list. Regarding Zn metal, it followed almost similar trend with Cu, but the maximum exceeding was at location 2 local. The concentration of Cd, as compared with the New Dutch list level, it appeared that at all locations and for all the sources of samples (local, mixed and dust/sandstorm) exceeded 155
CHAPTER FOUR: Results and Discussion the optimum level only for the New Dutch list. Higher exceeding level was found at locations (5 local, 5 mixed, and location 11). About Pb metal the concentration has exceeded the optimum level of the New Dutch list at locations (11 local, 11mixed, 13 and 14). At the locations where both process of PMTotal measurement and dust sampling or collecting were conducted, the concentrations of the examined heavy metals were converted from mg kg-1 dust to ng m-3 air through a simple calculation (Table 4.8). In general, the concentrations of heavy metals in local source of dust for the metal order of Cr, Mn, Fe, Ni, Cu, Zn, and Cd were varied between 0.93 – 7.52, 2.79 – 13.49, 1116.1 – 5514.1, 4.57 – 47.64, 1.71 – 19.93, 5.27 – 55.90 and 0.14 – 0.53 ng m-3 air respectively (Table 4.8, and Figures 4.12b, 4.13b, 4.14b, 4.15b, 4.16b, 4.17b and 4.18b). The concentration ranges showed a large variability among the locations and that was because each location had its specific condition from the point of view of urbanization, climatic and geographical site. The European Commission for Environment regulated a standard concentration limit only for Ni (20 ng m-3 air for averaging period of 1 year) and Cd 5 ng m-3 air for averaging period of 1 year (ECE, 2010), when the concentrations of these 2 target metals in the investigated dust samples were compared with the standard limits of EC, it was found that the concentrations for Cd at all locations assessed compliant with legislative limits of EC. But for Ni concentration the obtained values were exceeded the legislative limits of EC at location (2 and 6) from (Table 4.9). Furthermore, the concentration of the investigated heavy metals were extremely elevated in ambient air for the samples source of dust/sandstorm of 22.2.2010 and they reached 80.9; 188.75; 96721.0; 430.75; 103.86; 298.66 and 8.33 ng m-3 air for the heavy metal order of Cr, Mn, Fe, Ni, Cu, Zn, and Cd respectively, because the concentration of PMTotal in that day of dust/sandstorm event was many folds higher than that of the normal days. Consequently, the concentration of both Ni and Cd has also extremely or seriously exceeded the legislative limits of EC in the whole ambient air of Sulaimani city. For this reason inhaling and breathing of airborne particulate matter in dust/sandstorm days might cause many health hazards and human should protect himself against such events of storm. Past research and reviews demonstrated a link between PM inhaling and increased adverse health effects or even increased rates of 156
CHAPTER FOUR: Results and Discussion mortality and morbidity (Vigotti et al, 1996; Stieb etal., 2002; Davidson et al., 2005; Bell et al., 2008). Because human could become exposed to heavy metals in dust through several routes which included ingestion, inhalation and dermal absorption. In dusty environments it was estimated that adults could ingest up to 100 milligram dust day-1 (Hawley, 1985; Calabrese et al., 1987). Comparison of the current results for the selected heavy metals with other studies; Most heavy metals are of geological origin or naturally occur in the environment, but their concentration variations may be due to industrial, mining, agricultural, waste handling and other anthropogenic activities. The current results almost agreed with those obtained by Momani et al. (2000) when some heavy metal contents in settleable particles (dustfall) as well as in air particulates (suspended) were determined at different sites in the city of Amman, Jordan. Their results showed that the concentrations for (Zn, Cu, Pb and Cd) order were; 344, 170, 291, and 4 ng m-3 respectively in suspended particles (atmospheric concentration), and were 505, 94, 74 and 3 µg g-1 respectively in settleable dust. The concentration values obtained for Fe, Cu, Zn, Ni and Pb in this study were almost higher than that reported by (Al-khashman, 2004) for street dust samples at Karak Industrial Estate, Jordan because the indicated ranges were; 58.8 -94.8, 1.8 - 84.9, 15.4 – 136.9, 1.7 – 6.5 and 2.1 – 314.1 mg kg-1 for the metal order of Fe, Cu, Zn, Ni and Pb respectively. On the contrary, the overall results estimated by Akhter and Madany (1993) for the heavy metals (Pb, Zn, Cd, Ni and Cr) in Bahrain for street dusts showed a remarkable superiority over the results of the present study in particular for Pb, Cd and Cr. Since the reported results for the heavy metal order of Pb, Zn, Cd, Ni, and Cr were; (697.2, 151.8, 72.0, 125.6 and 144.4 mg kg-1 dust) respectively for street dust. In another study by Arslan (2001) in Bursa, Turkey, the mean concentration levels of Pb, Ni, Cd, Zn and Mn in street dust samples were found to be as follows; 210, 67, 3.1, 57 and 500 mg kg-1 dust respectively. Appendix (5) showed that there were significant Pearson correlation between Cr with each of Ni, Cu, Zn and Pb, the correlation coefficients were (0.955**, 0.666**, 0.525*, and 0.828**) respectively. Other abundant significant correlations were between Pb and each of Cr, Ni, Cu and Zn as mentioned previously. The only significant correlation for Fe and Cd was the highly negative significant correlation between Fe with Cd which was (- 0.803**), and that may be due 157
CHAPTER FOUR: Results and Discussion to the high abundant level of Fe which led to an antagonism interaction with Cd. Toxic metals were studied in aerosols, house dust, gasoline, and blood to determine their concentrations and to link them to their origins, and thus protecting public health in different urban areas (Fitzgerald et al., 1998; Hughes et al., 1998), but such a study did not draw any attention in Sulaimani city. Heavy metals in the air might pose a threat to humans. They can cause acute and chronic health effects (Amdur, 1980). These pollutants are emitted into the atmosphere continuously through various human activities, especially in the urbanized areas where inhabitants and industrial activities are concentrated.
Figure (4.12a): Airborne Cr concentration of the settleable dust samples.
Figure (4.12b): Concentration of Cr in settleable particles samples of atmospheric source.
158
CHAPTER FOUR: Results and Discussion
Figure (4.13a): Airborne Mn concentration of the settleable dust samples. .
Figure (4.13b): Concentration of Mn in settleable particles samples of atmospheric source.
Figure (4.14a): Airborne Fe concentration of the settleable dust samples.
159
CHAPTER FOUR: Results and Discussion
Figure (4.14b): Concentration of Fe in settleable particles samples of atmospheric source.
Figure (4.15a): Airborne Ni Concentrations of the Settleable Dust Samples.
Figure (4.15b): Concentration of Ni in settleable particles samples of atmospheric source.
160
CHAPTER FOUR: Results and Discussion
Figure (4.16a): Airborne Cu concentration of the settleable dust samples.
Figure (4.16b): Concentration of Cu in settleable particles samples of atmospheric source.
Figure (4.17a): Airborne Zn concentration of the settleable dust samples
161
CHAPTER FOUR: Results and Discussion
Figure (4.17b): Concentration of Zn in settleable particles samples of atmospheric source.
Figure (4.18a): Airborne Cd concentration of the settleable dust samples.
Figure (4.18b): Concentration of Cd in settleable particles samples of atmospheric source.
162
CHAPTER FOUR: Results and Discussion 4.3: Heavy metals in soil: The total analysis of heavy metals such as Pb, Cd, Cu, Ni, Mn, Cr and Zn in soils is commonly done to evaluate the degree of contamination of aquatic and terrestrial environments. Heavy metals naturally occur in the soil, but may be introduced as a result of land use activities. Naturally occurring as well as anthropogenically introduced concentrations of metals in near-surface soil can vary significantly due to different physical and chemical processes operating within soils across geographic regions. Historically, soil scientists and geologist studied near-surface soil for agricultural or farming purposes to better understand natural ecosystems (Thoronton, 1991), or focused on the health effects in urban areas associated with one or two metals (Mielke et al., 1983). Little information is available on the natural background level of heavy metals in the nearsurface soil in urban area of Sulaimani city. Natural background concentration is defined as the ambient concentration of chemical in soils without human influence ((Kabata-Pendias, 2001; Gough, 1993) In recent years, the urbanized or metropolitan area of the city has rapidly grown without a lot of planning and this resulted in more urban environment pollution in the city. Therefore, the current study aimed also to estimate some of these heavy metals in topsoil’s of the city. The concentration of the investigated metals of Cr, Mn, Fe, Ni, Cu, Zn, Cd, and Pb is presented in Table (4.10) and Figures 4.19, 4.20, 4.21, 4.22, 4.23, 4.24, 4.25 and 4.26, but the concentration’s ranges are shown in Table (4.11). In general, the following conclusions and trends can be drawn for the studied heavy metals in topsoil’s samples; The range limits for all metals were almost wide, that meant that the locations were highly varied in their content of the examined metals, and that may be mainly due to the regional parent materials and pedogenic processes which were the primary factor influencing the concentration of trace metals, and also due to the various anthropogenic activities which were the second most important factors (Zhang et al., 2008). There was not a systematic distribution for the normal and maximum levels among the locations since the source and amounts of the metals were different. However, the normal level of the ranges and for the metals (Cr, Fe, Ni and Zn) was at locations 3 but for the metal (Mn and Cu) was at location 7, it is evident that most of the normal level of the ranges were appeared in rural locations soils were natural and 163
CHAPTER FOUR: Results and Discussion Table (4.10): Total content of some heavy metals in topsoil's samples (0-15 cm) of the studied locations (mg kg-1 soil ).
1-
Raparin/ Sulaimani- Karkuk Street
Cr 33.5
Mn 72.2
Concentrations (mg kg-1 Soil) Fe Ni Cu Zn 19236.7 159.9 34.0 85.93
2-
Raparin/ Entrance Kelaspi Village
42.4
94.7
21034.4
197.2
46.0
117.8
1.5
25.7
3-
Bakrajo/ Agriculture College Fields
21.0
75.4
18206.2
100.6
39.6
76.6
3.9
8.6
4-
Sarchinar Crossing Garden
37.8
61.8
19092.7
177.2
41.0
137.2
3.4
47.6
5-
Maleek Mahmood Circle/ Near to Chami Rezan Petrol Station Maleek Mahmood –Entrance Kani Goma Village Wluba Garden/ Beside Wluba Overpass
66.3
73.1
19030.4
335.4
50.0
117.4
2.6
40.1
43.5
60.7
19570.1
237.8
36.5
95.5
2.9
27.3
22.1
46.6
18562.0
122.5
30.3
83.2
3.2
16.1
77.1
60.4
19760.8
502.9
43.1
118.1
3.2
110.8
9-
Tanjaro/ Agricultural Field Adjusting Sulaimani- Qaradakh Street Tanjaro/ Near to Landfill Site
49.3
65.6
20447.9
198.8
56.0
94.4
2.6
54.9
10-
Shek-Waisawa Village
40.3
67.3
19592.8
241.6
43.5
139.0
3.1
58.0
11-
Bnari Goizha/ Behind Goizha Appartments Dabashan/ West of Sulaimani- Azmar Street Sarwari Quarter/ Near to Kanispika Village Orchard/ Beside Abu-Sana Hotel
26.6
58.0
20179.4
134.3
38.2
100.8
2.5
6.7
26.3
74.4
19729.7
143.7
37.4
89.6
2.9
10.6
25.2
64.2
19658.3
133.2
35.9
90.8
2.2
2.6
22.2
61.3
18998.8
115.4
30.8
90.6
3.3
3.8
Olf- Palma Garden /Near to Khalahaji Crossing
25.3
68.9
19311.5
104.9
35.3
104.9
2.8
37.8
No.
678-
12131415-
Locations
Cd 2.79
Pb 20.0
164
CHAPTER FOUR: Results and Discussion non-contaminated. But the maximum level for the metal Cr, Ni and Pb appeared at location 8, this location is close to the landfill site in Tanjaro and was affected by the emission of the industrial and incineration process. According to Pooley and Mille (1999) the fly ash of municipal waste incineration and different industrial activities contain many human health and environment hazards including Zn, Fe, Hg, Pb, Sn As, Cd, Co, Cu, Mn, Ni, and Sb. Other sites of the maximum levels were mostly near to the streets of high traffic volumes. Table (4.11): Ranges of heavy metal concentration (mg/kg) in topsoil’s and plant samples of the studied locations.
Cr Mn Fe Ni Cu Zn Cd Pb
Concentration in mg kg-1 soil Soil samples mg kg soil Plant sampled mg kg-1 dry matter Range Concerned Range Concerned locations locations 21.0 -77.1 3 -8 1.0 - 9.5 13 -15 46.6 – 94.7 7-2 26.73 – 302.0 12 - 1 18206.2 - 21034.4 3-2 251.7 – 633.3 12 - 3 100.6 – 502.9 3 -8 3.2 – 12.9 13 - 3 30.3 – 56.0 7-9 2.9 – 9.3 13 - 8 76.6 – 137.2 3 -4 30.7 – 82.2 11 - 15 1.5 -3.9 2 -3 2.2 – 16.1 8-4 2.6 – 110.8 13 -8 0.6 – 9.3 4 - 11
To
evaluate the extent of heavy metal contamination in the soil samples, the
Heavy metals
-1
concentrations were compared with the New Dutch List (2001) standard and guidelines for soil. Regarding the metals Cr and Zn, the concentration in all soil samples did not exceeded the optimum level of the New Dutch List (Cr is 100 for optimum and 380 mg kg-1 for action level). But Zn is 140 for optimum and 720 mg kg-1 for action level. For Pb only in soil of location 8 exceeded the optimum level of New Dutch List, otherwise for all the other locations were below the optimum level (Pb is 85 for optimum and 530 mg kg-1 for action level). Concerning Cd, its concentration in soil samples of all the locations were between optimum and action levels (Cd is 0.8 for optimum and 12 mg kg-1 for action level). For Ni, the concentration in soil samples of all the locations exceeded the optimum levels by at least 3 folds and even at location 8 and 10 the concentration exceeded the action level of the New Dutch List (Ni is 35 for optimum and 210 mg kg-1 for action level). Finally, for Cu, its concentration in soil samples of 165
CHAPTER FOUR: Results and Discussion most of the locations (9 locations) exceeded only the optimum levels; otherwise, for the soil sample of the other locations were below the New Dutch List. For Fe and Mn no standards limits were regulated by New Dutch List. From the previous comparison it can be concluded that the contamination of the environment components in Sulaimani city is in its starting points. Comparison of the results of soil samples with other studies; When the results of current study were compared with others, for example with Al-Saffawi (2006), the metal concentration of Pb, Cd and Zn in surface soil of some locations in Mosul city/Iraq were generally found to be close and in the range of Pb 27.1 - 255.8 , Cd 4.40 – 14.17 and Zn 85.0 -194.0 mg kg-1 soil. According to Biasioli et al. (2006), the concentrations of some heavy metals in urban soils of some cities in the world are shown in Table (2.6), it is obvious that the results of the current study almost agreed with the result of some other cities for some metals. The current results of heavy metals in soil samples were consistent somewhat with Qishlaqi and Moore (2007), who indicated that the average mean concentration of Pb, Ni, Zn, Cd, Cr, and Cu were 254.6, 171.4, 117.0, 5.2, 124.5, and 96.9 mg kg-1 respectively in 40 topsoil samples along two sites of the Khoshk River banks in Shiraz, Iran. The study also concluded that statistical analysis can provide a scientific basis for monitoring the heavy metal accumulation in soil and for controlling the future soil contamination posed by human activities. The mean total content of the metals Cd, Cr, Cu, Ni, Pb and Zn in calcareous soil sample in Taiwan digested by aqua regia method was reported by Hseu et al. (2002) to be 0.3, 37.5, 27.4, 29.0, 37.6 and 58.9 mg kg-1 soil respectively, These results were almost in compliance with the result of current study, except for Ni and Cd. When results of the current study were compared with Zhang et al (2008), who revealed that the upper baseline concentration of the metals Cu, Pb, Zn, Cd, Ni, Cr and Hg in Guangdong province, China were 28.7, 57.6, 77.8, 0.13, 23.5, 87.0 and 0.15 mg kg-1, respectively, it was found that concentrations of the heavy metals in this study were higher than those of Guangdong province, China. This may be due the facts that soil are derived from various parent materials and with varying weathered degrees in different regions, moreover, the soils are under different anthropogenic pressure in different urban areas. 166
CHAPTER FOUR: Results and Discussion Heavy metals can be toxic if ingested by humans. They can cause immediate health effects and long term damage. Gardeners are always encouraged to have soil quality tested for the presence of heavy metals before planting vegetables or fruit for human consumption, because they are subjected to bioaccumulation in food-chain. Significant Pearson correlation coefficients were found between Cr and each of Ni, Cu, Zn, and Pb which were; 0.955**, 0.666**, 0.525* and 0.828** respectively, (Appendix 8). Moreover, Pb also had significant correlation coefficients with each of Ni, Cu and Zn which were; 0.845**, 0.518* and 0.614* respectively. As it was observed in dust sample, negative correlation (r = - 0.845**) was found between Cd and Fe. Similar correlations had been reported by Ma et al. (1997), Chen et al. (1999a), Xu and Tao (2004) and Zhang, et al. (2008). This suggested that soil Cr, Ni, Cu, Zn and Pb may be associated mainly with the mineral phase in soil. No significant correlation was found between the investigated heavy metal with the related soil chemical properties such as clay, CEC, and organic matter content, except for Zn with organic matter content (r = 0.581*). But regarding the relation of CaCO3 with the heavy metal content in soil, it was found that the relation was significantly negative with the metals Mn and Fe only and the correlations coefficients were (-0.743** and -0.852**) respectively. But positive correlation coefficient was found between CaCO3 and Cd (r = 0.794**). This significant positive correlation between Cd and CaCO3 may be attributed to the presence of Cd in a form of carbonate and bicarbonate minerals in calcareous soils (Lindsay, 1979).
Figure (4.19): Cr concentration in topsoil's samples (0-15 cm) of the studied locations
167
CHAPTER FOUR: Results and Discussion
Figure (4.20): Mn concentration in topsoil's samples (0-15 cm) of the studied locations.
Figure (4.21): Fe concentration in topsoil's samples (0-15 cm) of the studied locations.
Figure (4.22): Ni concentration in topsoil's samples (0-15 cm) of the studied locations.
168
CHAPTER FOUR: Results and Discussion
Figure (4.23): Cu concentration in topsoil's samples (0-15 cm) of the studied locations.
Figure (4.24): Zn concentration in topsoil's samples (0-15 cm) of the studied locations.
Figure (4.25): Cd concentration in topsoil's samples (0-15 cm) of the studied locations.
169
CHAPTER FOUR: Results and Discussion
Figure (4.26): Pb concentration in topsoil's samples (0-15 cm) of the studied locations In urban areas, pollutants in soil are a major threat because they can easily enter the food chain by dust ingestion, dermal contact or breathing Abrahams (2002). Urban soils differ from the rural ones by the fact that they are more strongly influenced by anthropogenic activities. This influence is often reflected by a high degree of contamination.
4.4: Heavy metals in plant: The main sources of heavy metals to plants are the soil and air (in case of polluted condition) from which metals are taken up by the root or foliage. Plants absorb a number of elements from soil or air, some of which have no known biological function and some are known to be toxic at low concentration. In general, the high concentration of heavy metals in soils is reflected by higher concentrations of metals in plants, and consequently in animal and human bodies. The ability of some plants to absorb and accumulate heavy metals makes them useful as biomonitors of environmental pollution. Therefore, samples of the prevailed available tree plants at the same locations of soil sampling were analyzed to estimate their contents of the same investigated heavy metals in soil samples, in order to assess the accumulation of the heavy metals (Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb) in some plant species, grown in the urban and rural areas of this study. The obtained concentrations for the heavy metals in the studied tree plant samples are shown in Table (4.12), but detailed information can be found in Table (4.13). In general, the following trends and conclusions were apparent from this analysis; Obviously, the range of concentration was wide for all the investigate metals. This trend is commensurate with the large variation concentration of the examined metals in plant samples collected from different location and different plant species, because metal 170
CHAPTER FOUR: Results and Discussion absorption and bioaccumulation by plants depends upon numerous biotic and abiotic factors, such as temperature, pH and dissolved ions in water (Lewander et al., 1996; Demirezen and Aksoy, 2006). The normal level of the ranges for the metals (Cr, Mn, Fe, Ni, Cu and Zn) were occurred mostly at location 11, 12 and 13 and these locations were almost far from direct traffic effects. Moreover, the predominant plant species for all normal concentration was Mulberry (Morus alba) except for Cr and Cu was Grape (Vitis Sp). But for the maximum concentration levels, there was not a distinct pattern of distribution among the different plant species and also between the locations, therefore, no possible dominating factors or sources can be identified for governing the heavy metal content in the plant samples. In general, the plant sample of the locations 1, 2, 3, 4, 5, 9, and15 were characterized by high level concentration for all the studied metals, and that can be connected to high traffic congestion and densifying urban area for these locations. Although, the same plant species were not available at all locations in order to investigate the role of abiotic or environmental impact on the concentration of different heavy metal in one species, but the mean, normal and maximum concentration were determined for the same frequent plant species in the overall locations (Table 4.14). According to this table no systematic pattern was observed for the distribution of the metal in the plant species, however, for the metal Cr, Mn, Fe, Ni and Cu the highest mean values were occurred by Eucalyptus (Eucalyptus camaldulensis). But for Zn was by Mulberry, Cd was by Grape and for Pb was by Walnut (Juglans regia). On the other hand, the lowest mean concentration of Cr, Ni and Cu was found in Grape, but for Fe, Zn and Cd was by Walnut, for Mn was by Mulberry and for Pb was by. In general, the high inter-specific variation of the metals in each plant species was due to the impact of the environmental factors in each location rather than plant species. Comparison of the results of plant samples with other studies: According to Al-Saffawi (2006), in Mosul city/Iraq, who analyzed the metals Pb, Cd and Zn in 6 plant species, the annual mean concentrations were in the range of 2.8-39.4, 0.2-2.6 and 11.1-82.6 mg kg-1 dry matter respectively. Thus, the mean concentration range of Pb was
171
CHAPTER FOUR: Results and Discussion Table (4.12): Heavy metal concentrations in plant samples of the studied locations (mg kg-1 plant dry matter). No.
Locations
Cr 5.7
Concentrations (mg kg-1 dry matter) Mn Fe Ni Cu Zn 302.0 558.3 1.0 7.1 38.1
Cd 5.1
Pb 3.2
9.1
145.0
567.8
6.61
6.6
59.6
7.3
4.9
5.6
68.4
633.3
12.90
5.3
47.9
10.4
3.2
4.1
51.3
455.7
8.53
5.9
62.0
16.1
0.6
Mulberry (Morus alba)
7.4
34.7
629.2
5.72
5.7
49.7
3.9
5.4
Mulberry (Morus alba)
3.5
67.7
364.1
6.05
3.3
43.4
6.2
2.5
Mulberry (Morus alba)
5.1
46.1
514.5
5.74
5.9
52.7
5.6
6.1
Mulberry (Morus alba)
4.3
123.8
460.0
11.21
9.3
50.3
2.2
7.9
Eucalyptus (Eucalyptus camald.) Walnut (Juglans regia)
8.5
78.1
632.1
8.62
8.8
45.6
8.6
2.4
2.1
76.9
342.2
5.91
5.5
31.7
6.3
7.8
Type of sampled plant
1-
Raparin/ Sulaimani- Karkuk Street
2-
Raparin/ Entrance Kelaspi Village
3-
Bakrajo/ Agriculture College Fields
4-
Sarchinar Crossing Garden
5-
9-
Maleek Mahmood Circle/ Near to Chami Rezan Petrol Station Maleek Mahmood –Entrance Kani Goma Village Wluba Garden/ Beside Wluba Overpass Tanjaro/ Agricultural Field Adjusting Sulaimani-Qaradakh Str. Tanjaro/ Near to Landfill Site
10-
Shek-Waisawa Village
11-
Bnari Goizha/ Behind Goizha Apartments Dabashan/ West of SulaimaniAzmar Street Sarwari Quarter/ Near to Kanispika Village Orchard/ Beside Abu-Sana Hotel
Grape (Vitis Sp.)
1.6
97.8
300.7
3.12
2.7
30.7
5.5
9.3
Mulberry (Morus alba)
2.5
26.7
251.7
5.42
7.6
42.2
7.5
3.3
Grape (Vitis Sp.)
1.0
185.1
395.2
3.20
3.3
33.3
9.5
7.3
Grape (Vitis Sp.)
2.7
78.3
505.1
3.73
4.3
37.3
11.1
1.7
Olf- Palma Garden /Near to Khalahaji Crossing
Mulberry (Morus alba)
9.5
43.7
514.7
6.39
7.7
82.2
9.5
8.1
678-
12131415-
Eucalyptus (Eucalyptus camaldulensis) Eucalyptus (Eucalyptus camaldulensis) Eucalyptus (Eucalyptus camaldulensis) Mulberry (Morus alba)
172
CHAPTER FOUR: Results and Discussion Table (4.13): Range and mean of heavy metal concentration in some plant species of the studied area in Sulaimani city.
Mean 7.3
Range 5.6-9.1
Concentrations mg kg-1 dry matter) Mn Fe Mean Range Mean Range 185.2 68.4-302.0 595.8 558.3-633.3
6.0
2.5-9.5
75.3
26.7-123.8
471.95
Grape (Vitis Sp.)
1.9
1.0-2.7
131.7
78.3-185.1
Walnut (Juglans regia)
2.1
--
76.9
--
Plant species Cr Eucalyptus (Eucalyptus camaldulensis) Mulberry (Morus alba)
Mean 9.8
Range 6.6-12.9
251.7-629.2
8.3
5.4-11.2
402.9
300.7-505.1
3.4
3.1-3.7
342.2
--
5.9
--
Mean 7.1
Range 5.3-8.8
Concentrations mg kg-1 dry matter) Zn Cd Mean Range Mean Range 48.9 38.1-59.6 7.8 5.1-10.4
6.3
3.3-9.3
62.2
42.2-82.2
9.1
Grape (Vitis Sp.)
3.9
2.9-4.3
3.0
30.7-37.3
Walnut (Juglans regia)
5.5
--
31.7
--
Plant species Cu Eucalyptus (Eucalyptus camaldulensis) Mulberry (Morus alba)
Ni
Pb Mean 3.6
Range 2.4-4.9
2.2-16.1
4.4
0.6-8.1
8.6
5.5-11.7
5.5
1.7-9.
6.3
--
7.8
--
173
CHAPTER FOUR: Results and Discussion higher as compared with the current study, while for Cd was lower but for Zn was almost agreed. The results showed by the current study was almost close with those indicated by Kozanecka et al. (2002) for the concentrations of Fe, Mn, Zn, Cu, Pb, Ni, Cr and Cd in forest floor plants ( pollution-free regions) of Puszcza, Biala Forest/Poland, which were; 460, 65, 27, 2.5, 3.0, 4.4, 3.3 and 0.3 mg kg-1 dry weight respectively. Since Cd and Pb, being a highly toxic metals pollutant, therefore, more considerable attention was attracted for its widespread distribution and potential risk to the human health and environment. Cd inhibits root and shoot growth and yield production, affects nutrients uptake, and is frequently accumulated by agriculturally important crops and then enters the food chain with a significant potential to impair animal and human health (di Toppi and Gabrielli, 1999). While Pb, is a potentially toxic heavy metal with no known biological function. Pb contamination in soils not only aroused the changes of soil microorganism and its activities and resulted in soil fertility deterioration but also directly affected the change of physiological indices and, furthermore, resulted in yield decline (Majer et al., 2002). Ultimately, lead enriched in the body of humans through the food chain and endangered their health (Liu et al., 2003). In a study by John et al. (2009), plant growth, pigment concentration, biochemical parameters and uptake of heavy metals were determined for Brassica juncea L. in response to leaf stress and increasing Cd and Pb concentrations. The plant exhibited a decline in growth, chlorophyll content, decline in protein content, and carotenoids with increasing Cd and Pb concentrations but Cd was found to be more detrimental than Pb in B. juncea. A Pine tree was investigated for its content of the heavy metals Pb, Zn, Cu, Ni and Cr by (Kord et al., 2010) in different sites of Tehran, Iran (Table 4.14). According to their results which were indicated in the following table, Cu, Ni, and Cr were somewhat in compliance with the obtained concentration level in the current study. On the contrary, for Pb, the reported were higher than those indicated by this study. Similarly, the concentration of Zn was also not in compliance with Iran, because, lower concentration levels were estimated.
174
CHAPTER FOUR: Results and Discussion Table 4.14: Heavy metal content in pine tree needle collected from different sites in Tehran, Iran, (mg kg-1 dry matter). Parameters Urban Highway Industrial Control Pb 39.80 62.30 42.60 14.10 Zn 14.46 18.49 24.16 1.53 Cu 7.93 15.43 10.60 2.50 Ni 10.16 16.70 13.20 1.86 Cr 2.04 3.15 3.97 0.39 Heavy metals enter the biological cycle through the roots and leaves of plants and are enriched in various plant organs. They can directly affect plant growth and an excess dietary intake of contaminated plants could also be dangerous for the health of humans and animals. The chemical composition of plants reflects the elemental composition of the soil and the contaminations of the plant surface indicate the presence of noxious environmental contaminants in ambient air. Therefore, analyzing of various types of environmental samples is needed. According to (Appendix 9), significant Pearson correlation was found among some of the heavy metals in plant samples, especially Cr with each of Fe, Cu and Zn (r = 0.765**, 0.580* and 0.737**) respectively. Ni showed similar correlation with Cu (r = 0.576*), but Cd was negatively correlated with Pb (r = - 0.546*).
4.5: Heavy metals in rainwater: Rainwater is relatively free from impurities except those picked up by rain from the atmosphere. The heavy metals that are emitted in the atmosphere in the form of aerosols, mainly from human activities, are taken away by wet deposition and cause damages to the surface waters and the organisms living there. Also, the metals are absorbed by the plants through the rain, (Steinnea, 1990). Therefore, in this study, the concentration of the same investigated heavy metals in settleable dust, soil and plant samples were also determined in rainwater samples twice, once, at the first rain’s time of the year and second, at the mid winters rain’s time. The results in Table (4.15) showed the concentration of the examined heavy metal in rainwater samples for the study locations. But in Table (4.16) the mean, normal and maximum concentration levels of the heavy metals are given according to their concerned locations. In general, the following tends and conclusions can be presented.
175
CHAPTER FOUR: Results and Discussion Table (4.15): Heavy metals concentrations in the rainwater (wet deposition) samples for the studied Locations (μg L-1 rainwater) Rain’s Concentrations (μg L-1 rainwater ) Time Cr Mn Fe Ni Cu Zn 1Raparin / Near to Sulaimani R1 9 ND 73 48 42 5 International Airport R2 5 ND 48 42 8 ND 2Bakrajo/ Awal Road R1 10 2 22 42 33 ND R2 7 ND 8 43 10 ND 3Maleek Mhmood Circle/ Lovan R1 17 60 137 55 74 11 Hotel R2 9 4 110 41 11 4 4Qaratogan Quarter R1 5 ND 66 49 27 ND R2 11 2 13 40 9 3 5Tanjaro/ Tanjaro Mosque R1 ND ND 72 50 22 ND R2 8 ND 55 47 10 7 6Charakhan Quarter R1 10 7 33 50 24 ND R2 9 ND 12 35 11 ND 7Ibrahim Ahmad Quarter R1 15 4 95 43 32 ND R2 9 2 24 35 10 ND 8Maleek Mhmood Circle/ Beside R1 5 3 67 45 37 ND Zargata Underpass R2 8 12 279 36 16 8 9Farmanbaran Quarter R1 7 ND 68 47 38 23 R2 ND ND ND 36 9 ND 10Sarwari Quarter/Near to Grape R1 5 ND 40 46 25 ND Orchard R2 4 ND ND 34 10 ND 11Salim Street/ Beside Khsrawkhal R1 2 ND 49 48 31 ND Bridge. R2 8 ND ND 35 9 ND 12Shek-Mohedin Quarter R1 4 3 34 50 35 ND R2 ND ND 21 33 11 ND 13Chawrbakh Quarter / Near to R1 6 ND 33 56 32 ND Sulaimani Stadium R2 3 ND ND 38 10 ND 14Sarkarez/ Dastaraka Crossing R1 11 10 97 43 60 ND R2 ND ND ND 36 14 ND 15Main Internal Buses R1 7 6 153 45 35 ND Transportations Center R2 4 15 66 24 11 19 R1: Sampling dates are 28 and 29/ 10/2009 (First Year’s Rain); R2: Sampling dates are 24 and 25/ 1/2010. No.
Locations
Cd 5 4 6 2 5 6 8 3 5 2 2 3 7 6 7 2 2 2 4 5 8 5 5 2 2 2 5 2 8 4
Pb 134 85 153 59 164 53 107 73 104 63 109 72 100 74 131 99 140 37 130 91 123 81 113 98 109 81 158 70 120 51
176
CHAPTER FOUR: Results and Discussion Table (4.16): Mean, normal and maximum concentration level for rainwater samples collected in two different times Metals Rain’s Concentration (µg L-1 rainwater) Time Range Concerned locations Mean Min. Max Cr R1 ND - 17 5 3 7.5 R2 ND - 11 9, 12, 14, 4 5.6 Mn R1 ND - 60 1, 4, 5, 9, 10,11, 13, 3 6.3 R2 ND - 15 1, 2, 5, 6, 9, 10, 11, 12, 13, 14 15 2.3 Fe R1 22 - 153 2 15 69.3 R2 ND - 279 9, 10, 11, 13, 14 8 42.4 Ni R1 42 - 56 2 13 47.8 R2 24 - 47 15 5 37.0 Cu R1 22 - 74 5 3 36.5 R2 8 - 16 1 8 10.6 Zn R1 ND - 23 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15 9 2.6 R2 ND - 19 1, 2, 6, 7, 9, 10, 11, 12, 13, 14 15 2.7 Cd R1 2-8 6, 9, 13, 4, 11,15 5.3 R2 2-6 2, 5, 8, 9, 12, 13,14 3, 7 3.3 Pb R1 100 - 164 7 3 126.3 R2 51 - 99 15 8 72.5 R1: Sampling dates are 28 and 29/10/2009 (First Year’s Rain) R2: Sampling dates are 24 and 25/1/2010. The mean concentration of all the studied heavy metals (Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb) except of Zn which was relatively lower at the samples of second’s rainfall time as compared to the mean of the samples of first rainfall time. This is due to the fact that in summer season a large amount of aerosols and heavy metal has been emitted in the atmosphere and only the coarse and heavier one was dry deposited, but, the lighter or finer one remained suspended, this resulted in increasing the concentration of the atmospheric pollutants, then when rainfall occurred a large amount of these metals would wet deposited with the first’s rainfall time. The range of concentration for all investigated metals was wide and that commensurate with the large concentration variation of these metals among the studied locations. This might be due the fact that a great percentage of metals fall through the rain at the place of their production or close to the emission point sources (Nurnberg et al., 1984). There were also a large variation between the mean concentration levels of the different heavy metals and the trend of increasing order for the first rainfall’s time were as 177
CHAPTER FOUR: Results and Discussion follow; Zn 2.6; Cd 5.3; Mn 6.3; Cr 7.5; Cu 36.5; Ni 47.8; Fe 69.3 and Pb 126.3 µg L-1 rainwater, while for the second rainfall’s time were as follow; Mn 2.3, Zn 2.7; Cd 3.3; Cr 5.6; Cu 10.6; Ni 37.0; Fe 42.4 and Pb 72.5 µg L-1 rainwater). It was obvious, that the increasing trends order of the metals for both rainfalls’ time were alike except for the lowest concentration level of Zn which became Mn in the second- rainfall time, furthermore, the metals Ni, Fe and Pb had high concentration levels as compared to the other examined metals. This can be connected to the combustion of large amount of fossil fuel products by vehicles and diesel power stations. In urban areas road traffics. consider and contribute as a main anthropogenic sources for such pollutants of heavy metals (Harrison et al., 2003; Birmili et al., 2006; Preciado and Li, 2006). It is believed that burning of fossil fuels is responsible for Be, Co, Hg, Mo, Ni, Sb, Sn, V, Pb, Cr, Cu, Ni, Mn and Zn content (Allen et al., 2001; Swaine, 2000). Generally, no distinct pattern was occurred for the normal and maximum distribution levels of the heavy metal among the location. However, for the normal levels the location,
2, 5, 9, 10, 11, 12, 13, and 14 were frequent 5 times or more. But for the
maximum level the location 3, 8, 15 were frequent 3 times and more, where location 3, 8 were at Maleek Mhmood circle and location 15 was for the Main internal buses transportation center. As comparison assessment, the mean concentration of the metals in the studied rain samples was compared with the maximum permissible limits of drinking standard regulated by World Organization Health (WHO).
As a whole, all the mean
concentrations were below the WHO limits except for Pb and for both studied rainfall’s time. The maximum permissible limits standards of the studied metals by WHO are (Cr+6 10; Mn 500; Fe 1000; Cu 1000; Zn 15000; Cd 10 and Pb 10 µg L-1 water) (Gupta, 2004), and that was due to the fact that lead metal loading to the atmosphere in Sulaimani city was high because still leaded fuel for vehicles is in use. It is therefore, recommended that the treatment of rain water and all other forms of water should be a matter of great concern to the Kurdistan Regional Government, communities, families and individuals, furthermore, using leaded fuel also should be stopped because of the health hazard potential of this metal. (Note: no standard limit for Ni was regulated by WHO). 178
CHAPTER FOUR: Results and Discussion In addition to that, heavy metals in the atmosphere are in ever increasing level as a result of anthropogenic and natural emissions (Suzuki, 2006), therefore, it is expected that the chemical components in the rainwater (acid components, anions, cations, and heavy metal) damage significantly the environment (surface water, plants, animal, human beings). Comparison of the results of rainwater samples with other studies; The current results for Pb and Zn were relatively lower as compared with Al-Saffawi (2006) in Mosul city, Iraq since the occurred mean ranges concentration by the author were between ND-225 and 12.7-246 µg L-1 rain respectively, but for Cd was in agreement because the mean ranges was between ND-5.7. On the other hand, Wedyan et al. (2009) in south of Jordan have estimated lower mean concentration for the metals Pb 0.42, Cd 0.19, Cu 14.56, Mn 2.34 and Fe 3.99 µg L-1 as compared to the results of this study, but for Zn 24.87 µg L-1 their results was higher than the results of this study. The fluctuation of this concentration of the heavy metals throughout the different regions or sites might be due to the fact that there were different amount of emitted heavy metals and at the same time the amounts of rainfall also differ, therefore, the solubility of the aerosol compounds was not also in correspondence. In another study by Kanellopoulou (2001) in Athens the reported results for the mean concentration of the metals Pb 0.88, Cd 0.20, Ni 4.41, Cr 1.29, Mn 3.61 and Fe 4.38 µg L-1 were lower as compared with the results of the two rainfall’s time for the current study, while, for Zn 33.46 was more, but for Cu 15.41 was somewhat in agreement. According to Sekabira et al. (2010) in Uganda the determined means concentration of Pb 13, 33; Cd 2, 3 and Cu 14, 25 µg L-1 for two different locations were lower than the results of current study for both of the rainfall times. On the contrary, the means concentrations of Zn 18, 21; Mn 50, 10 and Fe 200, 100 µg L-1 for the two location sites have exceeded the results of this study and for both of the rainfall times. The atmosphere is an important pathway for transport heavy metals to remote’s ocean regions. On a global basis, the atmospheric input of dissolved Cd, Pb and Zn is greater than those rivers, while both transport paths are approximately equal for Fe and Cu (Duce et al., 1991). Pearson correlation was determined among the investigated heavy metals in rainwater samples to understand association between the metal species (Appendix 10). The only 179
CHAPTER FOUR: Results and Discussion significant correlation between the metals was of Cu with each of Cr, Fe and Pb, which were correlating at the level of 0.5, and the correlation coefficient were (0.536*, 0.601* and 0.537*) respectively. This could be explained by the fact that these correlated metals have a common source of emission in Sulaimani city.
4.6: Levels of other gases rather than criteria gases in Sulaimani city: 4.6.1: Carbon dioxide (CO2); CO2 is a trace gas of the atmosphere, in 1958, atmospheric carbon dioxide at Mauna Loa was about 320 parts per million (ppm), but it was in June, 2011, at a globally averaged concentration of approximately of 393.69 ppm (0.039369%) by volume in the Earth's atmosphere, according to Mauna Loa observatory/Hawaii (NOAA, 2011), and this varies both by location and time. As it is given in Table (4.1a, 4.1b) and Figure (4.27), the results of volumetric and gravimetric average levels of the 7 measurements of ambient air carbon dioxide (CO2), during the measurement period of 31.9.2009 to 13.7.2010 for the 17 study locations ranged between 371.3 – 1159.1 ppmv and 668.2 – 2085.9 mg m-3 CO2, respectively. The lowest average level was detected at location 6 (Foothill of Goizha Mountain), because this location was not an urban area and had not traffic congestion problem, additionally, it had relatively a good plant coverage. But the higher average level was found at location 7 (inside Peshraw tunnel), and that is attributed to the shortage of ventilation system in the tunnel of 2.5 km length. This led to the confinement of the vehicular emissions in a limited space inside the tunnel and was not possible to transport and dilute the emitted gases into the air. As it is indicated in (Table 4.1a and Figure 27), the mean concentration level of CO2 in 13 locations of Sulaimani city has exceeded the globally averaged
concentration of June, 2011 which was 393.69 ppmv according to Mauna Loa observatory/Hawaii (NOAA, 2011). Thus, the average concentration level of CO2 was only at location 5, 6, 8 and 12 below the globally averaged level, and that was due to the fact that the amount of emitted CO2 was not proportional to the amount of consumed CO2 by plants to produce equivalent amount of oxygen. Furthermore, no other sinking factors of CO2 might be available in Sulaimani city. This can be associated directly to the rapidly increasing number of vehicles and ultimately consumption of large amount of fuel. In addition to that, the green area in Sulaimani city was also not proportional with the amount of emitted CO2.
180
CHAPTER FOUR: Results and Discussion Carbon dioxide has unique long-term effects on climate change that are largely “irreversible” for one thousand years after emissions stop (zero further emission). The greenhouse gases, methane and nitrous oxide do not persist over time in the same way as carbon dioxide. Even if Carbon emissions were to completely cease, atmospheric temperature are not expected to decrease significantly (Solomona et al., 2008; Cohen, 2010; WMO, 2010). The mixing ratios of atmospheric CO2 have been increasing globally at the average rate of1.5 ppmv/year for the past several decades (Conway et al., 1994).
Figure (4.27): Average volumetric concentrations of (CO2) gas in the studied locations. The increasing concentration of CO2 in the Earth’s atmosphere is known to have a number of environmental consequences (Takle, 1995). For example, changes in rainwater pH are also associated with increasing atmospheric carbon dioxide after industrial revolution. The historical change in the pH of natural rainwater due to increased atmospheric CO2. During 1800-2007 the rainwater pH at 25Co and 1 atm is calculated to decrease by 0.06 units, from 5.68 to 5.62. In 2100, the predicated rainwater pH is calculated at 5.49 using the projected pCO2 (700 ppmv; IS92a) at 25Co and 1 atm (Bogan et al, 2009). In the current study, the range of normal and maximum of volumetric and gravimetric levels of CO2 for the whole measurements was between 347-2311 ppmv and 624.5 – 4158.9 mg m-3 CO2 respectively, the normal level was detected at location 6 (Foothill of Goizha Mountain), But the maximum level was found at location 7 (inside Peshraw tunnel), for the previously mentioned reasons. The overall mean value, normal and maximum level of ambient carbon dioxide in Sulaimani city were 485 , 347 and 2311 ppmv which is equivalent to 869.3, 624.5 and 4158.0 mg
m-3 CO2
respectively. It was remarked that the whole average value of CO2 in Sulaimani city has exceeded the 181
CHAPTER FOUR: Results and Discussion globally averaged concentration of CO2 which is about 393.69 ppm (0.039369% by volume for June, 2011 in the Earth's atmosphere, according to Mauna Loa observatory/ Hawaii (NOAA, 2011). Therefore, the green area in Sulaimani city should be increased; furthermore, stringent regulation for air quality control must be promulgated or set in order to achieve the proportionality between sinking and production of CO2 level.
As an worlds action for controlling the carbon dioxide level globally, political leaders gathered in Kyoto, Japan, in December 1997 to consider a world treaty restricting human production of “greenhouse gases,” chiefly carbon dioxide. They feared that CO2 would result in “human-caused global warming”-hypothetical severe increase in Earth’s temperature, with disastrous environmental consequences. During the past 10 years, many political efforts have been made to force worldwide agreement to the Kyoto treaty (Robinson et al., 2007). Relationships between measured CO2 and other atmospheric constituents were examined through Pearson correlation. CO2 showed significant correlation with each of PM1.0, PM2.5, PM10.0, NO2, O3 and SO2 and the correlation coefficients were; 0.799**, 0.598*, 0.868**, 0.927**, 0.937** and 0.588* respectively (Appendix 5). This positive correlation can be attributed to the similarity in terms of emission source. Duncan test showed that there was a significant difference at the level (5%) between gravimetric concentration of CO2 at location 7 (Peshraw tunnel) and the other location, therefore, location 7 can be considered as a very hot spot site for CO2 emission (Appendix 6E). The
subsequent increasing order of the average CO2 were as follows; 668.2, 693.4, 699.8, 707.6, 710.1, 724.0, 774.2, 813.4, 822.0, 830.9, 857.1, 858.4, 874.7, 879.6, 883.1, 906.9 and 2083.9 mg m-3 CO2 for the locations 6, 8, 12, 5, 3, 1, 4, 17, 15, 13, 11, 2, 14, 10, 16, 9, and 7 respectively. In general, the lower values were found at the location of less of traffic congestion, non-urbanized and high percent of green coverage, but, vice versa, for the other locations. 4.6.2: Hydrocarbons (HC); Hydrocarbons (HC) are of particular interest as air pollutants, HC are a class of reactive organic gases formed solely from hydrogen and carbon through the incomplete burning of any organic matter such as crude oil products, wood or rubber. Hydrocarbons are a precursor to ground-level ozone, a serious air pollutant in cities across the urban areas and it is also a key component of smog (Auckland, 2010). 182
CHAPTER FOUR: Results and Discussion Hydrocarbon (HC) concentrations were estimated 7 times during 31.9.2009 to 13.7.2010 for 17 locations in Sulaimani city and the revealed concentration was in ppm. Results for the current study showed that the average values of HC ranged between 28.4 - 79.1 ppmv (Table 4.1a and Figure 4.28). The lowest average level was detected at location 6 (Foothill of Goizha Mountain), but the higher average level was found at location7 (inside Peshraw tunnel). This tunnel is located at foothills of Azmar Mountain and has a 2.5 km length without any ventilation system, therefore, the emitted or formed air pollutants could not be diluted easily. The highest concentration of hydrocarbons is
observed in densifying urban, where traffic is heavy and its flow is slow, and the limited space reduces considerably the process of air self-cleaning. By contrast outside built-up areas masses of air can move freely and there is no accumulation of contaminants observed (,Ariya et al., 1998). Hydrocarbons enter the atmosphere either directly through tank filling or from the storage tanks by evaporation or as by-products of partial combustion of other hydrocarbons. Aromatic hydrocarbons such as benzene, toluene, ethylbenzene, xylene and cumene are among the top 50 chemicals produced each year and these are also released into the atmosphere during their production (Subramanian, 2009).Hydrocarbons in the atmosphere are both of natural and anthropogenic origin (Nriagu, 1992; Singh, 1995). Anthropogenic ones are commonly concentrated in small areas. This is why their impact is perceived as more harmful (Isidorov et al., 1999).
Figure (4.28): Average volumetric concentrations of (HC) gas in the studied locations. The most significant anthropogenic sources of hydrocarbons are exhaust gases emitted from vehicles. To that add hydrocarbons evaporating from liquid fuels. The emitted compounds are dispersed in the air and spread over long distances. The measured level of emission depends 183
CHAPTER FOUR: Results and Discussion on the level of emission, and their magnitude is influenced by factors such as the location of the emitter, topography of the area, meteorological conditions and reactivity of the emitted compounds (Seinfeld and Pandit, 1998). The range of normal and maximum levels of hydrocarbons was between 17.0 – 113.0 ppmv. The normal value was detected at locations 6, but the maximum value was at location 16 (Bardargai Sara/ Sulaimani center) and that is due to the fact that traffic volume was dense in this location, its flow
was slow and the limited space there reduced considerably the process of air self-cleaning. These results of hydrocarbons by the current study may be superseded by new findings in Sulaimani urban in the field of air pollution, particularly, when new and more sophisticated gas analyzer instruments are used. Therefore readers are advised to be aware about new information and data concerning this issue. A range of hydrocarbons are found in vehicle fuel, and occur in vehicle emissions. In most urban areas, vehicle emissions constitute a major of hydrocarbons and that include benzene and 1,3 butadiene (DEFRA, 2001). According to (Auckland, 2010) the Ambient Air Quality Guidelines for most abundant hydrocarbons are as follows; Benzene (3.6 µg m-3) for annual average, 1,3- Butadiene (2.4 µg m-3) for annual average and Benzo(a) pyrene (0.0003 µg m-3) for annual average. No recommended exposure limit or threshold limit value (TLV) was regulated for total hydrocarbons as a whole. Also the concentration of HC in ppm as a general could not be converted to milligram or microgram per cubic meter (mg m-3or µg m-3) ambient air, because no universal molecular weight for HC was defined or is available. According to Juszkiewicz and Kijak (2003) the levels of volatile organic carbons (VOCs) at the motorway and Krakow center in Poland were 40.430 and 50-2000 µg m-3 respectively. Although the number of the indicated chromatographic compounds were 150, but only 60 compounds has been identified, therefore, 20-30% of that compounds corresponded to unidentified compounds. The road transport combustion in UK in 2008 was the largest source of PHAs emission and contributed 57% of the total emissions and that was corresponded to 681 tons of 16 compounds of PAHs (AEA, 2011)
Exposure to hydrocarbons may cause many negative impacts due to their harmful effects on both human health and the quality of crops as well as their particular role played in photochemical generation of oxidants taking place in the atmosphere with the participation of 184
CHAPTER FOUR: Results and Discussion nitrogen oxides under the influence of sunlight, hydrocarbons are air pollutants attracting special attention pollution (Seinfeld and Pandit, 1998). Carcinogenic forms of hydrocarbons are considered hazardous air pollutants or air toxins and can lead to the development of cancer (Auckland, 2010). As a result, hydrocarbons require a continuous monitoring of their concentrations and structures through further researches in Sulaimani city. The overall mean, normal and maximum level values of ambient HC in Sulaimani city were 60.1, 17.0 and 111.0 ppmv respectively. Pearson correlation was used to calculate the correlation coefficients of hydrocarbons with the other ambient air pollutants (Appendix 5). Significant positive correlations were found between HC with each N2O (r = 0.831**), CO (r = 0.829**), NO (r = 0.639**) and SO2 (r = 0.588*). This strong positive correlation may be due to the fact that these ambient constituents were of the same source. Also, significant correlation was found between HC and temperature (r = 0.508*) and that can be attributed to the fact that the emission of crude oil products (e. g. gasoline and diesel) into the atmosphere in summer season through the evaporation is high and remain also for a long time. 4.6.3: Oxygen(O2); The results from this study showed that the average values of O2 for the (7) measurements period and for all the 17 locations ranged between 19.6-21.2% by volume (Table 4.1a and Figure 4-29) which corresponded to (257.0-276.9 g O2 m-3) (Table 4.1b). The lowest average level was detected at location 13 (Parki Azadi at night time), also the higher average level was found at the same location of Parki Azadi, but at day time (it has a location number of 12). That is due to the fact that plant population effects on atmospheric composition, particularly for O2 and CO2 concentration through both processes of photosynthesis and respiration. Moreover, air temperature, relative humidity and radiant energy would be also affected by plants coverage. Since, this park is planted with many different plants, in sunny day time, more O 2 has produced through the process of photosynthesis and that resulted in increasing the O2 concentration level, therefore the level has exceeded its natural level of 20.946% (Griffin, 2007). On the contrary, in night time respiration process has dominated and more O2 has been converted to CO2. In general, the average concentration level of O2 in all location, except location 1, 12, 16, and 17 was below the normal average level. 185
CHAPTER FOUR: Results and Discussion
Figure (4.29): Average volumetric concentrations of (O2) gas in the studied locations. In nature, free oxygen is produced by the light-driven splitting of water during oxygenic photosynthesis. Green algae and cyanobacteria in marine environments provide about 70% of the free oxygen produced on earth and the rest is produced by terrestrial plants (Fenical, 1983). A simplified overall formula for photosynthesis is (Brown and LeMay, 2003). 6CO2 + 6H2O + photons
C6H12O6 + 6O2
Or simply carbon dioxide + water + sunlight
glucose + oxygen
The overall mean value, normal and maximum level of ambient oxygen in Sulaimani city were 20.7, 16.8 and 23.2% which correspond to 270.5, 219.9 and 303.6 g m-3, respectively. The normal percent level of 16.8% was detected at location 10 (Salem street) on 27.12.2009 and at 9:40 am o’clock, and that can be attributed to the factors of traffic dense and congestion, season and the time of measurement. But the maximum level of 23.2% was determined at location 2 (Sarchnar crossing) on 13.7.2010 at 10:40 am o’clock. Pearson correlation was used to provide the correlation coefficients between O2 and the other investigated atmospheric constituents (Appendix 5). No significant correlation was found between O2 and with the other measured atmospheric components. But, regarding the correlation of O2 with the meteorological parameters, highly positive significant correlation was found between O2 and temperature and highly negative significant correlation was determined between O2 relative humidity percent. That is due the fact that in summer season where photosynthesis processes is high the temperature is also high but on contrast the relative humidity is low in Sulaimani city.
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CHAPTER FOUR: Results and Discussion Duncan test revealed that there was no significant difference between the average gravimetric concentrations of O2 for the studied locations (Appendix 6H). 4.7: Traffic-related (vehicular) air pollution; The object of this study was to disseminate information on the required standard guideline regulation, development of strategies, and action plan to protect the air quality in Sulaimani city. The systematic approach establishes that the origin of vehicular emissions is the result of the interaction of fuel used, vehicle technology and traffic conditions. For these reasons the urban air quality management in the city requires a systematic consideration of the three parameters: fuel type, vehicle technology and condition of use. 4.7.1: Number of registered motor vehicles in Sulaimani governorate; The number of vehicles on Sulaimani roads and streets was multiplying every day during this study and still is the same case, but the efforts to strengthen the infrastructure to support this growth were not enough to keep up. With growing incomes, it is not surprising that peoples want to lavish lifestyle, but what the government and people did not realized was that this mode of convenience will soon turn into an endless of environmental and human health problems. Furthermore, road accidents in the city have increased and killed many peoples in recent years. In addition to that, the streets of the city also headed towards a traffic jam situation. Table (3.3) and Figure (4.30) showed that the number of registered motor vehicles in Sulaimani governorate (Sulaimani Identification Numbering) has increased sharply in the last decade from 32468 in 1999 to 180912 in 2010, in addition to 68489 vehicles registered with information carts and 6716 motorcycles. Therefore, vehicles in Sulaimani governorate increased 82.05% and 86.78% without and with vehicles registered with information carts, respectively. The net growth rate of Sulaimani registered vehicles was more than 25% at the last two years. According to a study done by Crisil Research in India, the number of households who will have a car of their own will reach up to 117 million by year 2015 and this has translated into a density of 17 car owners per 1000 people (CarDekho Team, 2010). While the density in Sulaimani city was 139 car owners per 1000 people in 2010 (The populations of Sulaimani governorate in 2009 were 1797508 inhabitants and the total number of fleet (vehicles) in 2010 was 245761).Thus, the number of vehicle owner in Sulaimani governorate was 8.2 folds greater than India. 187
CHAPTER FOUR: Results and Discussion Therefore, rapid programme and plans in the city should be implemented to assess, reduce and control the impacts of vehicular emission. Moreover, systematic consideration for the three parameters: fuel type, vehicle technology and traffic condition should be taken into account. As it can be seen from (Appendix 11) the numbers of motor vehicle per 1000 people or inhabitants in Sulaimani city were 139 vehicles and that is a good rank as compared with most of the Asian, African and Latin America.
Figure 4.30: Total number of registered vehicles in Sulaimani city (Sulaimani numbering) during the years 1999 to 2010. (Directorate General of traffics in Sulaimani, 2010). Vehicle fleet in Sulaimani city was modified predominantly by addition of new vehicle to a fleet of aging old vehicles rather than substitution of old vehicles by new ones and this caused a significant growth rate in motorization throughout the city, therefore vehicular emission had an important role in urban air quality of the city. 4.7.2: Surveying of traffic volume (traffic saturation flow rate); One of the fundamental measures of traffic on a road is the volume of traffic using the road in a given interval of time. It is also termed as flow and it is expressed in vehicles per hour or vehicles per day. When the traffic is composed of a number of vehicles, it is the normal practice to convert the flow into equivalent Passenger-Car Unit (PCU), by using certain equivalency factors. The flow is then expressed as PCUs per hour or PCUs per day.
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CHAPTER FOUR: Results and Discussion Traffic volume is the number of vehicles crossing a section of road per unit time at any selected period. Traffic volume is used as a quantity measure of flow; the commonly used are vehicles per day and vehicles per hour (Bonneson, et al., 2005). One way of estimating the amount vehicular emission is to estimate vehicle kilometers based on vehicle counts. Traffic counts are translated into Annual Average Daily Flows (AADF) using conversion factors and because the lengths of roads are known, traffic volume in vehicle kilometer can be calculated by multiplication and expressed as million or billion vehicle kilometers traveled. Vehicle kilometers traveled is the total kilometer traveled by motor on the highway system during a given period of time. Vehicle kilometers by passenger automobile are an important variable in the analysis of fuel efficiency, fuel consumption, environmental quality and highway safety (Rudman, 1979). The saturation flow rate or the counted number of vehicles which crossed the signalized intersection points (crossing) and streets in Sulaimani city are given in Table (4.17). The counting has been carried out twice for each location (once in the morning and the other in the afternoon between 8:30 am to 7:00 pm in non-rush hours) during 26 to 29/4/2010 and in neutral
months (unaffected by seasonal variations in traffic such as summer holidays). The number of traffic counts at the crossing points and on the streets ranged between (3176 to 9730 vehicle/hour) and (1974 to 5126 vehicle/hour) respectively. The highest traffic volume was recorded at Sarchnar crossing, this is attributed to the fact that this crossing is the main crossing for entrance and out way of vehicles from or to other governorates of Iraq and Kurdistan Region; furthermore, it covers considerable residential areas in Sulaimani city. While the lowest flow rate was counted in Folkay Khalahaji crossing. The heaviest traffic volume was found also at Sulaimani-Kirkuk Road due to the previous reason. But the lowest traffic volume was recorded at Ibrahim Pasha Street. Nowadays, most of the crossings and streets in Sulaimani city are below their capacity as compared with the traffic flow rate, because the available lanes are not proportional with the increasing number of vehicles and traffic conditions. If the flow characteristics are known then one can easily determine whether a particular section of the road is handling traffic much above or below its capacity. If traffic is heavy, the road suffers from congestion with consequent loss in journey speeds. Lower the speeds causes more air pollution and ecomonicloss to the community due to time lost by the occupants of the
189
CHAPTER FOUR: Results and Discussion Table (4.17): Traffic volume in working days at some crosses (squares) and streets of Sulaimani city.
No. Crosses or streets
1-
2-
3-
4-
5-
6-
GPS parameter
Sarchinar Crossing N 35 o 33′ 54.97′′ E 45 o 23′ 25.73′′
Qazi Mohmad Square (crossing)
Bardargi Sara (Crossing)
Dastaraka Kotri Ashti (Crossing)
Folkay Kawai Asangar (Crossing) Zila Swtawaka (Crossing)
Direction of traffic to the crossing
Kirkuke City to the Crossing Arbat to the Crossing City Center to the Crossing Sarchinar and Maleek Mahmood Circle to the Crossing N 35 o 33′ 20.83′′ Kirkuk and Salim Street to the Square E 45 o 26′ 06.26′′ Azmar and Mamostayan Quarter to the Squre Mawlawy Street to the Squre Charbax Quarter to the Square N 35 o 33′ 25.04′′ Twooy Malik to the Crossing E 45 o 26′ 34.48′′ Mawlawy Street to the Crossing Kawa Street to the Crossing Saboonkaran Quarter to the Crossing N 35 o 32′ 55.58′′ Maleek Mahmood Circle to the Crossing E 45 o 26′ 22.69′′ Grdi Sywan to the Crossing Assoi Dalak Crossing to the Crossing Sarshqam to the Crossing N 35 o 33′ 53.49′′ Twooy Malik to the Crossing Zanko and Majid Bag to the Crossing E 45 o 26′ 59.79′′ City Center to the Crossing Ibrahim Pasha to the crossing N 35 o 34′ 15.61′′ Zanko to the Crossing E 45 o 26′ 24.14′′ Dastaka to the Crossing Mzgawtaka to the Crossing Azmar to the Crossing
Mean* number of passed vehicles per 1 hour 2519 1978 2616 2626 1928 724 Not Allowed 2018 1964 544 496 684 1398 560 1876 1964 1140 860 1252 908 1554 1398 842 1146
Total passed vehicles per 1 hour
9730
4670
3688
5798
4168
4740
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CHAPTER FOUR: Results and Discussion Table (4.17): Traffic volume in working days at some crosses (squares) and streets of Sulaimani city No. Crosses or streets
GPS parameter
7-
N 35 o 33′ 48.64′′ E 45 o 26′ 15.78′′
8-
9-
101112131415-
Folkay Dastaka (Crossing)
Parki Azadi (Crossing)
Folkay KhalaHaji (Crossing)
Sulaimani – Kirkuk Main Road Sulaimani-Arbat Road Peshraw Tunnel Maleek Mahmood Circle (North) Maleek Mahmood Circle (North) Salim Street
Direction of traffic to the crossing
Azmar and Mamostayan Quarter to the Crossing Parki Azadi and Kareza Wshk Quarter to the Crossing Baxy Gshti and Mawlawy Street to the Croosing Kanat Street to the Crossing N 35 o 34′ 15.27′′ Folkay Dastaka to the Crossing E 45 o 25′ 53.04′′ Azmar to the Crossing Kareza Wishk Quar. To the Crossing Salim Street to the Crossing N 35 o 33′ 56.75′′ Chwarta to the Crossing E 45 o 25′ 19.40′′ Ashti Quarter to the Crossing Salim Street to the Crossing Parki Azadi to the Crossing N 35 o 33′ 52.53′′ Sulaimani -Kirkuk Direction E 45 o 23′ 16.62′′ Kirkuk- Sulaimani Direction N 35 o 32′ 09.54′′ Sulaimani – Arbat Direction E 45 o 25′ 54.02′′ Arbat –Sulaimani Direction N 35o 37′ 39.30′′ Sulaimani- Citak E045o 29′ 17.76′′ Citak- Sulaimani N 35 o 34′ 59.57′′ Sarchnar Croosing – Azmar Direction E 45 o 26′ 01.83′′ Azmar- Sarchnar Crossing Direction N 35 o 33′ 35.29′′ Sarchnar Croosing – Arbat Direction E 45 o 23′ 39.59′′ Arbat- Sarchnar Crossing Direction N 35 o 33′ 43.12′′ City Center - Sarchinar Direction E 45 o 24′ 33.38′′ Sarchinar- City Center Direction
Mean* number of passed vehicles per 1 hour 1128 1080 920 834 1212 1284 1170 612 820 854 768 734 2358 2768 1680 1580 290 266 2526 2199 2125 1987 2496 2250
Total passed vehicles per 1 hour 3962
4278
3176
5126 3260 556 4725 4112 4746
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CHAPTER FOUR: Results and Discussion Table (4.17): Traffic volume in working days at some crosses (squares) and streets of Sulaimani city. No. Crosses or streets GPS parameter Direction of traffic to the crossing Mean* number of passed vehicles per 1 hour 16- Chwarbax Street N 35 o 33′ 17.22′′ Qazi Mohmad Square-Charbax Quar. 858 E 45 o 26′ 05.19′′ Charbax Quar.- Qazi Mohmad Square 1188 17- Piramerd Street N 35 o 33′ 33.53′′ Bardargi Sara-Tooy Malik direction Dir. 942 E 45 o 26′ 42.42′′ Tooy Malik direction-Bardargi Sara Dir. 1664 18- Ibrahim Pasha N 35 o 33′ 25.80′′ Folkay Kawai Asangar-Grdi Syawan Dir 1134 Street E 45 o 27′ 02.83′′ Grdi Syawan-Folkay Kawai Asangar Dir 840 19- Zanko Street N 35 o 34′ 13.91′′ Zanko – Zilaswtawaka Direction 1500 E 45 o 26′ 26.38′′ Zilaswtawaka Zanko Direction 1092 20- Parki Azadi – N 35 o 34′ 06.24′′ Folkay Dastaka- Karez Wshk Direction 1212 Karez wshk Street E 45 o 25′ 57.56′′ Kareza Wshk- folkay Dastaka Direction 978
Total passed vehicles per 1 hour 2046 2606 1974 2592 2190
Note: *The mean number consist of 2 times of counting , once in the morning and the other in the afternoon Date and O’clock of calculation are; 26 to 29/4/2010 and between 8:30 am to 7:00 pm o’clock (Rush hours were not included)
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CHAPTER FOUR: Results and Discussion vehicles and the higher operational cost of vehicles. Congestion also leads to traffic hazards (Akcelik, 2008). To be able to estimate the amount of fuel consumption then after the amount of emitted gases into the atmosphere within 1 km of Sulaimani-Kirkuk Street in 10 hours, we assume the average fuel consumption is 10 liter per 100 km. As it is shown in Table (4-17), the traffic volume of this street was 9730 vehicles per one hour, then for 10 hour (consider it as one day driving) equals to 97300 vehicle kilometers. Now the amount of consumed fuel is equal to 9730 liters per one kilometer per a day (10 hours). According to (EPA, 2000) and by assuming that the flow was equivalent to PassengerCar Unit (PCU), then the following amounts 169.3, 1263.6, 84.0 and 25120 kg gas per one kilometer and per one day (10 hours) will be emitted from the gases of (Hydrocarbons, CO, NOx and CO2) respectively. That is why automobile traffic considered as one of the important source of these pollutants (Gramer and Cheveruil, 1991). As the vehiclesin Sulaimani has increased so dramatically, the fuel consumption and air pollutant emissions are also raised enormously. 4.7.3: Vehicular exhaust emission test; Vehicular emission remains a threat to environmental health problems which is expected to increase reasonably as vehicle owner’s increase in the world and the same case also in Sulaimani city is remarkable now. Over 600 million people globally are exposed to hazardous level of traffic-generated pollutants (UN, 1998). Human exposure to these air pollutants due to traffic is believed to have constituted severe health problems especially in urban area where pollution levels are on the increase. Most countries consider this problem a threat to the national health, and make strict regulation to reduce vehicular emission as much as possible. Several studies have showed that there were a strong correlation between the levels of vehicular pollutants and the rate of death due to respiratory and cardiovascular problems. For that reason, the current study has taken this important issue also into account. Although, exhaust gas analyzer was not available in Sulaimani city, but this test has been performed in Erbil governorate by Periodic Vehicle Inspection (PVI) center for 812 gasoline fueled vehicles and 175 diesel fueled vehicles, since the vehicles are almost the same in Iraqi Kurdistan Region. The investigated parameters for the exhaust emission of gasoline fueled vehicles were (CO2, CO, HC, O2 and Lambda, λ). While for the exhaust emission of diesel fueled vehicles were 193
CHAPTER FOUR: Results and Discussion smoke opacity and K-Values (Extinction coefficient). The measurement of these parameters have been carried out directly inside the tail pipe and then sub-ranged properly to different levels in order to assess the efficiency of engine combustion and to compare the influence of model years upon the level range of the measured parameters. Regarding the model years of the vehicles, it has been also subgrouped into 7 groups of model year for gasoline fueled vehicles in order to evaluate the correlation between model year and the ultimate level of the measured parameter. The model year groups were; Earlier of 1985; 1985-1989; 1990-1994; 1995-199; 2000-2004; 2005-2009 and 2010 and the number and percent of frequency distribution with the total number of 812 vehicles were; 9, 1.23%; 28, 3.33%; 239, 28.69%; 26, 3.94%; 57, 7.02%; 382, 47.04% and 71, 8.74% respectively (Table 4.18). But for diesel fueled vehicles, it has been subgrouped to (2) model groups for the total number of 175 vehicles and those were (pre-2000 then 2000 and post 2000) and the number and percent of frequency distribution were; 45, 25.71% and 130,74% respectively (Table 4.19), the prevailed group was 2000 and post 2000 group. The predominance order of model year groups for gasoline fueled vehicles were in the following trend or sequence 2005-2009, 47.04%; 1990-1994, 28.69%; 2010, 8.74%; 2000-2004, 7.02%; 1995-1999, 3.94%; 1985-1989, 3.33% and earlier of 1985, 1.23%. Considerably, the model group of 2005-2009 was the most abundant vehicles, while the model group of earlier of 1985 was the rarest vehicles. For selection the range level of the measured parameter and also the groups of model years some considerations has been taken into account, especially, those relate with normal distribution of the frequency and the significance of the level range. 4.7.3.1: The investigated exhaust parameters for gasoline fueled vehicles: 4.7.3.1.A: Percent of emitted carbon dioxide (CO2%): The identified level ranges of CO2 and the consistent percentage of concerned vehicles are given in (Table 4.18 and Figure 4.31). Remarkably, the percent of the following range level intervals have been established (less than 10.0; 10.0-11.9; 12.0-13.9 and 14 or more) and these were corresponded with the following number and percent of concerned vehicles (108, 13.30%; 105, 12.93%; 351, 43.23% and 248, 30.54%) respectively. But the number of vehicles per each model year could be seen in (Figure 4- 31).
194
CHAPTER FOUR: Results and Discussion Table (4.18): Level Ranges of some Emitted Gases and Lambda (λ) Values for Gasoline Fueled Vehicles according to Models year. No. per each Model Year’s Range % of each Model Year No. Emitted Level Ranges Gas 1-
2-
3-
4-
5-
CO2%
Less than 10.0 10.0 - 11.9 12.0 - 13.9 ≥ 14.0 CO% Less than 0.5 0.50 - 2.49 2.50 – 4.99 ≥ 5.0 a HC Less than 100 (ppm) 100- 199 200 - 299 300 - 399 ≥400 Left Less than 1.00 O2% 1.00 - 1.99 2.00 – 2.99 ≥ 3.00 Lambda Less than 0.95b (λ ) 0.95 -1.05c More than 1.05d
a: Hydrocarbons;
9 28 239 1.23% 3.33% 28.69% Model year Earlier of 198519901985 1989 1994 6 5 72 3 12 62 -10 104 -1 1 -10 34 2 7 94 1 3 49 6 8 62 --9 -9 64 2 5 43 2 4 40 5 10 83 2 9 83 2 5 70 1 3 30 4 11 56 6 11 94 1 5 76 2 12 69
b: Rich Mixture;
26 3.94%
57 7.02%
382 47.04%
19951999 6 6 11 3 10 7 3 6 2 8 8 4 4 13 4 4 5 10 7 9
20002004 7 8 30 12 20 23 6 8 5 21 12 8 11 38 10 1 8 13 35 9
20052009 11 14 163 194 279 90 13 -105 164 70 27 16 289 48 25 20 16 306 60
c: Normal Mixture;
71 8.74% 2010 1 -33 37 60 10 1 -24 42 5 --48 20 2 1 1 63 7
Total per each Gas Range 108 105 351 248 413 233 76 90 145 308 145 85 129 482 159 66 105 151 493 168
812 100% Grand % of Total each Gas Range 812 13.30 12.93 43.23 30.54 812 50.86 28.69 9.36 11.08 812 17.86 37.93 17.86 10.47 15.89 812 59.36 19.58 8.13 12.93 812 18.60 60.71 20.69
d: Lean Mixture
195
CHAPTER FOUR: Results and Discussion
200
Number of concerned vehicles
180 160 140 120 100 80 60 40 20 0
<10 (13.30% )
10-11.9 (12.93% )
12-13.9 (43.23% )
≥14 (30.54% )
1985
6
3
0
0
1985-1989
5
12
10
1
1990-1994
72
62
104
1
1995-1999
6
6
11
3
2000-2004
7
8
30
12
2005-2009
11
14
163
194
1
0
33
37
2010
Ranges percent of emitted CO2
Figure (4.31): Ranges percent of exhaust emitted CO2 with corresponded vehicle number. Figure(4..) Ranges percent of exhaust emitted CO 2 with corresponded vehicles number.
196
CHAPTER FOUR: Results and Discussion The highest number and percentage of vehicles were found by the range level of 12-13.9%, and this might be attributed to the higher number of frequent vehicles in this range or due to the differences in vehicle model or fuel sources. Certainly, whenever the percentage of emitted CO2 is high that means the air-fuel ratio (AFR) is in a stoichiometric mixture and the combustion process is perfect, but in contrast more greenhouse gas would be added to the CO2 level. For example for each one percent (1%) of CO2 ten thousand ppmv of CO2 would be added to the atmospheric composition. Considerably, there was a high variability in the amount of emitted CO2 in vehicles exhaust and the overall means, minimum and maximum levels were 12.62, 0.7 and 15.2%, and that can be attributed due to many factors, such as fuel characteristic, vehicle technology and condition of use. Fuel characteristic are an important component of the vehicular emission control strategy in any urban air quality management plan. The other main components are vehicle technology and condition of use. Each one of these components must be considered in the context of local condition. Industrial Environmental Carbon (IEC, 2011) has developed technology to mitigate or capture oxides of carbon (CO and CO2) combustion gases emitted from transport, electric generators, flue gas, and traffic tunnel exhaust. Concerning, the interrelationships between the investigated vehicular emissions, Pearson correlation was applied to evaluate the correlation among the studied parameters. Regarding the emitted CO2 significant correlation was found with each of CO, HC, O2 and Lambda (λ) and the correlation coefficient were -0.746**, 0.446**, -0.693** and 0.069* respectively (Appendix 12). It is clear that the relation between CO2 and each of CO and O2 should be negative, but the opposite correlation with HC is expected when the air-fuel mixture (AFR) is rich mixture or if the ratio of air to fuel would be decreased, because, meanwhile with emission of CO2 more HC would be emitted. The European Automobile Manufacturers Association, abbreviated as ACEA (French: Association des Constructeurs Europeens) signed in 1998 a voluntary agreement with European Commission (EC) to limit the amount of carbon dioxide emitted by passenger cars sold in Europe. With 18 million cars sold each year. This target represents a 25% reduction from the
197
CHAPTER FOUR: Results and Discussion 1995 level of 186 g/km to an average of 140 g km-1 of CO2 in 2008 and this is equivalent to a fuel economy of 5.8 L/100 km or 5.25 L/100 km for gasoline (petrol) and diesel engines respectively. However, the average for the whole car market for 2008 was 153.7 g km-1, so the target has not been achieved. In addition to that, the European Commission also closed agreements with the Japan Automobile Manufacturers Association (JAMA) and Korea Automobile Manufacturers Association (KAMA) for the same target but the target date was 2009 instead of 2008 and as ACEA accounts for 86.4% of their car sales in Europe (Wikipedia, 2011) 4.7.3.1.B: Percentage of emitted carbon monoxide (CO %); Carbon monoxide as reviewed previously is a poisonous criteria air pollutant, the standard limit for this gas has been set or regulated by both air quality standard and by exhaust emissions standard. It is produced by the incomplete combustion of hydrocarbons. CO penetrates in blood through the lungs and combines with hemoglobin 200 times more easily than oxygen and thus deteriorates the transport of oxygen towards the bodies (Tomaszewski, 1999; Raub et al., 2000; Alkama et al., 2006). Pollution due to traffic constitutes up to 90-95 of the ambient carbon monoxide levels, 8090% of NOx, hydrocarbons and particulate matter in the world, these pollutants posing a serious threat to human health (Savile, 1993). As it is shown in Table (4.18) and Figure (4.32), the following interval ranges less than 0.5; 0.50-2.49; 2.50-4.99 and 5 or more have been defined and those were corresponded with the following number and percent of concerned vehicles 413, 50.86%; 233, 28.69%; 76, 9.36% and 90, 11.08% respectively. But the number of vehicles per each model year could be seen in (Figure 4-32). The highest number and percentage of vehicle was found by the range level of less than 0.50%, and this might be attributed to the higher number of frequent vehicles in this range, in other words, Normally, the percentage of emitted CO will increase whenever the oxidation of carbons in hydrocarbon is incomplete, and that is because of the shortage of oxygen or air in the air-fuel ratio (AFR) mixture. The data clearly showed that there was a distinct variation in the amount of emitted CO in vehicles exhaust and the overall mean, minimum and maximum levels were 1.57, 0.0 and 12.00% respectively, this can be due to the factors of fuel characteristic, vehicle technology
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CHAPTER FOUR: Results and Discussion and condition of use. Generally, all engine exhaust contains a certain amount of CO, but the amount will increase if the vehicle engine is poorly maintained. As it has been reported by Clean Air Hamilton (2007) in the 2005-2006 progress report, transportation is the largest source of annual CO in Hamilton city /Ontario (58,490 tones out of 84,934 total tones from 6 carbon monoxide sources), with three time greater emission than point sources of industrial emissions (Table 4.2). Nowadays, European Union (EU) vehicle emission regulations have been the most influential groups. Acceptable limits for exhaust emission have been defined in a series of European Union directives since 1992 up to 2014 or 2015 for some vehicle types (Appendix 13). The regulated tiers called “Euro” and include the following; [Euro 1, (July 1992); Euro 2 (January 1996); Euro 3, (January 2000); Euro 4, (January 2005); Euro 5, (January 2010) and Euro 6, (September 2014 or 2015 for some vehicle types)]. Currently, emissions of Nitrogen oxides, NOx; Total hydrocarbon, THC; None-methane hydrocarbons, NMHC; Carbon monoxide, CO; and Particulate matter, PM are regulated for most vehicle type, including cars, trucks, trains, tractors and similar machinery. For each vehicle type, different standard applied. Non-compliant vehicles cannot be sold in EU (Wikipedia, 2011). According to European Emission Standards the current standard of exhaust emitted CO for gasoline and diesel fueled vehicles ranged between (1.0-2.27) and (0.5-0.74 g km-1) respectively for most vehicle types (Appendix 13). In a study on exhaust emission for 150 different model year gasoline fueled vehicles by Alkama et al. (2006), it has been found that the maximum concentration level of CO was 1200 ppm and that result is much lower than the results obtained by the current study. At present time, modifying in vehicle technology by the automobile manufactures companies, introduction of gas filters or catalytic converter and application of more stringent standard regulation resulted in reduction of the pollutant emission by 50 to 400% (Alkama et al., 2006). To be able to compare the results of this study with promulgated standards, the obtained concentration in percentage (%) or ppm should be converted to gram of the pollutant per one crossed kilometer (g km-1).
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CHAPTER FOUR: Results and Discussion
300
Number of concerned vehicles
250
200
150
100
50
0
<0.5 (50.86% )
0.5-2.49 (28.69% )
2.50-4.99 (9.36% )
≥5.0 (11.08% )
1985
0
2
1
6
1985-1989
10
7
3
8
1990-1994
34
94
49
62
1995-1999
10
7
3
6
2000-2004
20
23
6
8
2005-2009
279
90
13
0
60
10
1
0
2010
Ranges percent of emitted CO
Figure (4.32): Ranges percent of exhaust emitted CO with corresponded vehicle number. Figure(4..) Ranges percent of exhaust emitted CO with corresponded vehicles number.
200
CHAPTER FOUR: Results and Discussion Pearson correlation showed significant correlation between the exhaust emitted CO and each of the exhaust parameter HC, CO2 and Lambda (λ) (Appendix 12). The correlation was negative with CO2 (r = -0.746**) and Lambda (λ) (r = -0.604**), but positive with HC (r = 0.393**). 4.7.3.1.C: Concentration of emitted hydrocarbons (HC) ppm; The hydrocarbons in fuel normally react only with oxygen during the combustion process to form water vapor and carbon dioxide, creating the desirable effect of heat and pressure within the cylinder. Unfortunately, under certain engine operating conditions, the nitrogen also reacts with oxygen to form nitrogen oxides, a criteria air pollutant. Generally, excessive HC results from ignition misfire or misfire due to excessively lean or reach air-fuel mixture and the ideal air-fuel ratio is around 14.7 times the mass of air to 1 times mass of fuel (Toyota Motor Sales, Emission #1 and Emission #2) Table (4.18) and Figure (4.33) presents the determined interval ranges of HC in ppm and the consistent percent of the concerned vehicles. As it can be seen 5 interval ranges have been established because the ranges of exhaust emitted HC was super large or wide. The range intervals were; less than 100; 100-199; 200-299; 300-399 and 400 ppm or more and the corresponded vehicle numbers and percent for these interval ranges were 145, 17.86%; 308, 37.93%; 145, 17.86%; 85, 10.47% and 129, 15.89% respectively. But the number of vehicles per each model year could be seen in (Figure 4.33). The highest percent of HC was found by the interval range of 100-199 ppm, while the lowest vehicle percent was found by the range interval of 300-399 and this might be due to the lower number of frequent vehicles in this range or to the differences of vehicle model or fuel source. Hydrocarbon emissions increase rapidly as the fuel and air mixture becomes fuel rich (Sloane, 1984; Grimm, 1988). For example, the hydrocarbon mass emitted under rich condition (the equivalent ratio, Ф= 1.15) was twice that of lean condition (the equivalent ratio, Ф= 0.90), when pure toluene was used as fuel in the single-cylinder engine (Kaiser et al., 1991). Knowing that equivalent ratio, Ф is calculated as below; Ф= fuel-to oxidizer ratio/ (fuel-to oxidizer ratio)st. Where, (st.) is the stoichiometric mixture.
201
CHAPTER FOUR: Results and Discussion
180
Number of concerned vehicles
160 140 120 100 80 60 40 20 0
<100 (17.86% )
100-199 (37.93% )
200-299 (17.86% )
300-399 (10.47% )
≥400 (15.89% )
1985
0
0
2
2
5
1985-1989
0
9
5
4
10
1990-1994
9
64
43
40
83
1995-1999
2
8
8
4
4
2000-2004
5
21
12
8
11
2005-2009
105
164
70
27
16
2010
24
42
5
0
0
Ranges concentration of emitted hydrocarbons (HC) (ppm)
Figure (4.33): Ranges concentration of emitted hydrocarbons (HC) in ppm and numbers and percentages of corresponded vehicle.
Figure (4-23): Ranges concentration of emitted hydrocarbons (HC) in ppm and the numbers and percents of corresponded vehicles .
202
CHAPTER FOUR: Results and Discussion The data clearly showed that there was a distinct variation in the amount of emitted HC in vehicles exhaust and the overall mean, minimum and maximum levels were (275.19; 0.14 and 18340 ppm ) respectively, this can be due to the factors of vehicle technology, condition of use and mainly to fuel characteristic or sources. Because several full formulated gasoline blends were used in Kurdistan Region of Iraq for the fleet engine combustion. These gasoline were comprised of 100 to 200 different hydrocarbon species present in concentration above 0.01 wt. %. These species included C4-C12 paraffin, olefins, aromatics, and selected oxygenated hydrocarbons (e. g., methyl tert-butyl ether (MTBE), ethyl tert-butyl ether (ETBE), methanol, etc) that were added as octane boosters or to vary the fuel-oxygen concentration (Schuetzle et al., 1994). Furthermore, the maintenances of the vehicle engine have its important role up on the amount of emitted hydrocarbons. According to European Emission Standards the current standard for exhaust emitted total hydrocarbons THC of gasoline fueled vehicles varied between 0.10-0.16 g/km, but for diesel fueled vehicles the considered or addressed standard is as a mixture of NOx+HC together and ranged between 0.230-0.350 g km-1 and that is according to the different vehicle categories (Appendix 13). Pearson correlation showed significant correlation between the exhaust emitted HC and each of the exhaust parameter CO2, CO, O2 and Lambda (λ) (Appendix 12). The correlation was negative with CO2 (r = - 0.446**) and Lambda (λ) (r = - 0.149**), but positive with CO (r = 0.393**) and O2 (r = 0.257**). That is due to the fact that when sufficient oxygen or the ideal air-fuel ratio is available then perfect combustion of hydrocarbons is occurred and stoichiometric amount of carbon dioxide will be produced. 4.7.3.1.D: Percentage of emitted oxygen (O2 %); The identified level ranges of O2 and the consistent percentage of concerned vehicles are given in (Table 4.18) and Figure (4.34). As it is indicated in the table the defined ranges were less than 1.0; 1.0-1.99; 2.00-2.99 and 3.00% or more and these were corresponded with the following number and percent of concerned vehicles 482, 50.36%; 159, 19.58 %; 66, 8.13% and 105, 12.93% respectively. But the number of vehicles per each model year could be seen in (Figure 4.34).
203
CHAPTER FOUR: Results and Discussion
Number of concerned vehicles
300
250
200
150
100
50
0
<1.00 (59.36% )
1.00-1.99 (19.58% )
2.00-2.99 (8.13% )
≥3.00 (12.93% )
1985
2
2
1
4
1985-1989
9
5
3
11
1990-1994
83
70
30
56
1995-1999
13
4
4
5
2000-2004
38
10
1
8
2005-2009
289
48
25
20
2010
48
20
2
1
Ranges percent of emitted left O2 .
Figure (4.34): Ranges percent of exhaust emitted left O2 with corresponded vehicle number.
Figure(4..) Ranges percent of exhaust emitted left O2 with corresponded vehicles number.
204
CHAPTER FOUR: Results and Discussion The highest number and percentage of vehicles were found by the range level of (less than 1.0%), and this might be attributed to the higher number of frequent vehicles in this range. Moreover, less emitted or output of percent oxygen is attributed to the perfect engine combustion and an ideal air-fuel ratio and this result in more CO2 production. Considerably, a wide range of emitted oxygen percent was occurred in the whole investigated vehicles. The overall average, minimum and maximum level of exhaust emitted oxygen percent were 1.48, 0.0 and 17.05% and this might be due to many factors such as, vehicle model year, vehicle technology and makes, fuel characteristics and engine maintenance. Certainly, exhaust emitted oxygen measurement at tail-pipe of the vehicles provide a good indication of a lean running engine, since O2 increase with leaner air-fuel mixture ratio. Generally speaking, oxygen is the opposite of CO, that is, O2 indicates leaner air-fuel mixtures while CO indicates richer air air-fuel mixture ratio. Lean air-fuel mixture and misfire typically result in high oxygen output from the engine. (Toyota Motor Sales, Emission #2). In lean mixture the ratio of air-fuel exceeds the ideal ratio of 14.7 to 1. The oxygen in the air is never completely used up when gasoline is burned. The main reason for this is the fact that air is only 20.946% oxygen, and the remaining 79.036% is comprised of other components that will interfere with a perfect reaction. The Pearson correlation test in Appendix (11) showed that there were positive significant correlations between the output of O2 percent and each of HC (r = 0.257**) and Lambda λ (r = 0.545
**
), but the correlation was negative between the output of O2 percent and the emitted
CO2 percent. The positive correlation between the outputs of O2 percent and the emitted HC concentration might be due to the present of oxygenated compounds in the fuel or equipping the engine with an oxygen sensor which regulate the amount of O2 supply to the mixture. 4.7.3.1.E: Lambda (λ); Due to the fact that fuels differ in composition from country to country (even from source to source) and because the composition of common fuels varies seasonally, and because many modern vehicles can handle different fuels, when tuning, it makes more sense to talk about lambda values rather than AFR. Table (4.18) and figure (4.35) present the determined interval ranges of Lambda (λ) and the consistent percent of the concerned vehicles. As it can be seen 3 interval ranges have been established based on rich, lean and stoichiometric mixture. 205
CHAPTER FOUR: Results and Discussion
350
Number of concerned vehicles
300
250
200
150
100
50
0
≥1.05
<0.95a (18.60% )
0.95-1.05 (60.71% )
(20.69% )
1985
6
1
2
1985-1989
11
5
12
1990-1994
94
76
69
1995-1999
10
7
9
2000-2004
13
35
9
2005-2009
16
306
60
2010
1
63
7
Ranges intervals of lambda (λ) values.
Figure (4.35): Ranges intervals of lambda (λ) values and the number and percent of corresponded vehicles.
Figure (4-25): Ranges intervals of lambda (λ) values and the number and percent of corresponded vehicles .
206
CHAPTER FOUR: Results and Discussion The range intervals were; (less than 0.95; 0.95-1.05 and more than1.05) and the corresponded vehicle numbers and percent for these interval ranges were (151, 18.60%; 493, 60.71% and 168, 20.69%) respectively. But the number of vehicles per each model year could be seen in Figure (4.35). The highest percent was found by the interval range of 100-199 ppm, while the lowest vehicle percent was found by the range interval of 0.95-1.05 and this might be due to the higher number of frequent vehicles in this range, in other words, more vehicles have characterized by relatively ideal air-fuel ratio and perfect combustion engine. Lambda values lower than 0.95 means rich mixture was available for the combustion process, on the contrary, lambda values more than 1.05 means lean mixture or more air (oxygen) was available for the combustion process. Considerably, a wide range of lambda values was obtained in the whole investigated vehicles. The overall average, minimum and maximum level of lambda values were 1.014, 0.676 and 1.690 and this might be due to many factors such as, vehicle model year, vehicle technology and makes, fuel characteristics and engine maintenance. Regarding, the interrelationships between Lambda values and the other investigated vehicular emissions; Pearson correlation was applied to calculate the correlation coefficients. Significant positive correlation was obtained between Lambda value and each of CO2 (r = 0.069*) and O2 (r = 0.545**), but the correlation of Lambda value was negative with each of CO (r = - 0.604**) and HC (r = - 0.149**), (Appendix 12). The explanation and interpretation for these significant correlations are given previously by the discussion of each CO2, CO, HC and O2. 4.7.3.2: Diesel Opacity and “K” value test for diesel fueled vehicles; Opacity is defined as the percentage of light transmitted from a source that is prevented from reaching a light detector by the diesel smoke. In smoke opacity measurements from a diesel vehicle, a beam of light is transmitted across the exhaust plume to a light detector (Mccormic et al., 2003). The diesel smoke opacity is measured in smoke density% (also known as Hatridge HSU units) as well as in absorption coefficient K-value (m-1) (Rao et al., 2008). Therefore, the opacity of smoke is also indicated in units called K-value (K-value is extinction coefficient and also called mass attenuation coefficient or mass absorption coefficient and refers to several different measures of the absorption of light in a medium), (Wikipedia, 2010). Normally, the reading of the K and % opacity values is displayed simultaneously. 207
CHAPTER FOUR: Results and Discussion The average of three acceleration opacity percent and K-values (extinction coefficient) test results are presented in Table (4.19) and Figure (4-36). The opacity percent has been addressed in the following percent intervals; less than ≤ 2; 2.0-2.9; 3.0-3.9 and ≥ 4% or more while, the number and percent of corresponded vehicles were 96, 54.86%; 32, 18.29%; 8, 4.57% and 39, 22.29% respectively. But the number of vehicles per each model year could be seen in (Figure 4-36). The highest percent of opacity percent was found by the interval range of (less than 2% opacity), and this might be due to the higher number of frequent vehicles in this range, in other words, more vehicles have characterized by relatively low opacity percent or perfect combustion. It is noted also that the second highest number or percent of vehicles in the rank was by the highest opacity interval percent (4% or more) and that may indicate engine malfunction and increased emissions of air pollutants, primarily unburned fuel hydrocarbons (emitted as an aerosol) or soot particles (Mccormic et al., 2003). High opacity percent can arguably provide a good indication of increased particulate matter (PM) production and as a result much air pollution. Since opacity is a function of the number concentration of the particles, the projected area of the particle, and the light extinction (absorption and scattering) properties of the particles. Thus, the ratio between PM and opacity is a function of particle size, as well as the light extinction effects of the particles (Jarrett, 2000). According to (NHDES, 2008) an engine that is not emitting any smoke or emits very little smoke is likely operating efficiently and will pass the opacity test (opacity level less than 20%). The results of this research indicated that only limited number out of the 175 investigated vehicles had opacity percent around 20 percent. Opacity percent values from these tests varied considerably, suggesting that each engine had its specific condition. Moreover, the measured value of smoke opacity was highly dependent upon the test procedures, including ambient conditions, engine operating mode, measurement configuration, and instrumentation. In recent years, the SAEJ1667 test procedure suggested as a standard test (SAE, 1996). This test was developed specifically to identify gross emitting heavy-duty trucks and buses. In general, the overall means, minimum and maximum opacity percent levels were 2.657, 0.0 and 23.2%. Since the extinction coefficient K-value is another measure of the smoke opacity, therefore, Pearson correlation indicated a highly significant correlation between the two investigated parameters of opacity percent and K-value (r = 0.955**), (Table 4.19). 208
CHAPTER FOUR: Results and Discussion
Table (4.19): Level ranges of the opacity and K-values of some diesel fueled vehicles (Trucks).
No. Studied Parameter
Level Ranges
No. per each Model Year’s Range % of each Model Year 1Smoke Opacity, % (Average for 3 Accelerations)
2-
K -Values (Extinction coefficient). Average for 3 Accelerations
Less than 2% 2.0-2.9% 3.0-3.9% ≥ 4% ≤ 0.03 0.04-0.06 0.07-0.09 ≥0.1
Model year Pre- 2000 2000 and Post-2000
Total per Grand each Range Total
% of each Range ----
45
130
----
175
25.71% 21
74.29% 75
---96
100% 175
---54.86
6 4 14 18 8 4 15
26 4 25 66 33 6 25
32 8 39 84 41 10 40
175
18.29 4.57 22.29 48.00 23.43 5.71 22.86
Pearson correlation between the two investigated parameters of opacity percent and K-value Parameters K-value **. Correlation is significant at the 0.01 level (2-tailed).
Opacity% 0.955**
209
CHAPTER FOUR: Results and Discussion Generally, to limit or reduce pollutant emissions from diesels, the U.S Environmental Protection Agency regulates both the quality of on-road diesel fuel and the pollutant emission levels from engines under the pollutant emissions levels from engines under the authority of the Clean Air Act. Additionally, smoke opacity tests are used by many States as a part of inspection programs for control of PM emissions (Mccormic et al., 2003). Meanwhile, the results of K-values have been also established in four range intervals Table (4.19) and Figure (4.37). The range levels of K-value were ≤ 0.03; 0.04-0.06; 0.07-0.09 and ≥ 0.1 and the corresponded number and percent of vehicles for each range were 84, 48.00%; 41, 23.43%; 10, 5.71% and 40, 22.86%. But the numbers of vehicles per each model year are shown in (Figure 4.37). Considerably, the distribution pattern of the number and percent of the vehicles per each range showed mostly similar tend with the opacity percent, therefore, no further explanations and discussions were required.
210
CHAPTER FOUR: Results and Discussion
70
Vehicles model year
60 50 40 30 20 10 0
≤ 0.03 (48.00% )
0.04 - 0.06 (23.43% )
0.07 - 0.09 (5.71% )
≥0.1 (22.86% )
Pre - 2000
18
8
4
15
2000 and Post - 2000
66
33
6
25
Range intervals of average K - values (Extinction coefficient). Figure (4.36): Level Ranges of the average opacity of some diesel fueled vehicles (Trucks).
211
CHAPTER FOUR: Results and Discussion
70
Vehicles model year
60 50 40 30 20 10 0
≤ 0.03 (48.00% )
0.04 - 0.06 (23.43% )
0.07 - 0.09 (5.71% )
≥0.1 (22.86% )
Pre - 2000
18
8
4
15
2000 and Post - 2000
66
33
6
25
Range intervals of average K - values (Extinction coefficient). Figure (4.37): Level Ranges of the average K-values (extinction coefficient) of some diesel fueled vehicles (Trucks).
212
CHAPTER FIVE: Conclusions and Recommendations 5-1: Conclusions; 1- The current study indicated that transport-related and diesel electric generator were significant sources with possibly severe health consequences. The study further revealed that the pollution at traffic intersection is threatening and that motor vehicle remains the dominant reason of urban air pollution, because all the investigated gases exceeded their natural concentrations limits by many times. In addition to that, many other toxic air pollutants have been added to the atmosphere by the anthropogenic activities and natural phenomena. 2- The present study clearly indicated that the ambient concentrations of gases pollutants such as; CO, SO2, NO2 and O3 varied highly among the studied locations, and were considerably higher at higher traffic volumes or densifying urban, including inside Peshraw tunnel, Sarchnar crossing, Salim Street, Sarkarez/ Sbunkaran street, Bardargai Sara, Tanjaro, Internal Buses Transportation Center, and Dastaka crossing and these locations could be considered as hot spot locations in Sulaimani city. 3- The results of this study showed that the recorded averaged concentration of all investigated gases pollutant except for sulfur dioxide (SO2) in Sulaimani city were under the permissible recommended levels of National Ambient Air Quality Standard (NAAQS) by EPA, European Commissions (EC) and WHO standards. However, the pollution levels were distinctly higher with the natural levels. Therefore, all the studied locations could be considered as attainment area for the gases pollutants of CO, NO2 and O3 gases pollutants, 4- The concentration level of the greenhouse gas CO2 in most of the studied urbanized locations exceeded the globally averaged concentration of CO2 which was about 393.69 ppmv for June, 2011 in the Earth’s atmosphere, according to Mauna Loa observatory/ Hawaii. 5- Many locations in the city were affected by high levels of air born particles of PM1.0, PM2.5 and PM10.0 due to a synergetic affect. In addition, comparative assessment indicated that the concentrations of PM1.0, PM2.5 and PM10.0 exceeded the promulgated air quality standards by Environmental Protection Agency (EPA) and European Commission (EC). Particulate matters in air are responsible for respiratory problems and has several environmental impacts. 213
CHAPTER FIVE: Conclusions and Recommendations
6- The concentration ranges of Ni, Cu, Zn and Pb in settable dust and soil samples were relatively high and their concentrations mostly were within the optimum to the action levels as compared to the new Dutchlist. But, for Ni and Pb and in certain locations exceeded the action limits of new Dutchlist and that were due to the influence of local source. 7- Regarding the concentration of the same investigated eight heavy metals in the studied plant species, we concluded that the highest concentration levels for the metal Cr, Mn, Fe, Ni and Cu occurred in Eucalyptus (Eucalyptus camaldulensis). But for Zn occurred in Mulberry (Morus alba), Cd occurred in Grape (Vitis Sp.) and for Pb occurred in Walnut (Juglans regia). 8- In general, all the mean concentrations of the studied heavy metals in the rainwater samples were below the WHO limits except for Pb and for both studied rainfall’s time. The concentration levels of all the studied heavy metals (Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb) except of Zn were relatively lower at the samples of second’s rainfall time as compared to the mean of the samples of first rainfall time. 9- The frequency of sand/dust storms became a common phenomenon in the city and this contributed significantly to the particulate matter levels in the city. Moreover, forming of a thick haze or photochemical smog, which is a hazy, unhealthy polluted over the city particularly in the dry seasons also became a common phenomenon, therefore, the local air quality was getting worse in the city. 10- The results of the present study also concluded that since last decade, vehicles were increasing at faster rate in Sulaimani city. The increased socio-economic status of the residents, the availability of automobiles, lack of integrated mass transport, the increased need for use of transport for daily journey and the increased need for more diesel electric generator have resulted in large amounts of exhaust emissions and air quality deteriorations. Additionally, still no air quality and vehicular emission standards have been regulated or came into force. 5-2: Recommendations; 1- We should be worried about air pollution and attention must be paid to this topic through teaching this subject in the related colleges and other academic 214
CHAPTER FIVE: Conclusions and Recommendations establishment and even in the schools. The reason is that air pollution affects all of us; it can cause many health problems and spreading of diseases , reduce crop yields, affects animal life, corrode different material and cause many environmental impacts such global warming, climate change and acid rain. 2- Establishment of some atmospheric monitoring stations in Sulaimani city and other Iraqi region cities is an urgent need for monitoring the trend and status of air quality and their correlations with the meteorological factors. 3- Almost most people in the densifying urban area in Sulaimani city breathe unhealthy levels of tiny particles, because particulate matter (PM) is now at its critical level in the city. Therefore, action on particles is an urgent need to monitor its levels and composition continuously and giving recommendation about life protection. Furthermore, particulate matter should be put at the top of air quality agenda through the intended legislations. 4- Unfortunately, many people don’t make the correct choice unless regulations are been legislated and enforced, therefore, air quality standard, air quality index (AQI) and vehicular emission standards are the necessity in preserving our air quality. 5- Studies on risk assessment, air accumulation factor and concentration factor with regards to exposure to heavy metals and toxic gases are recommend to carry out in order to estimate the noncancer toxic (chronic), threshold limit values (TLV) and occupational exposure limits (OEL) for these issues. 6- Acute or chronic poisoning levels of lead (Pb) were reported and confirmed by many previous studies, but still leaded gasoline fuel is in use in Sulaimani and Iraq in general, therefore, the present study, as an editorial pollution challenge and humanitarian priorities recommend to bring the attention of the authorities to this serious issue to stop using leaded fuel as soon as possible. Improvement of fuel quality is another recommendation. 7- This study could be considered as key precursor study for air pollution problems in Sulaimani city and provided basic data and information about this issue, therefore, further investigation and academic research are needed to confirm the reliability of the current results, moreover, more sophisticated gas analyzers are required, particularly for measuring volatile organic compounds (VOCs) and poly aromatic 215
CHAPTER FIVE: Conclusions and Recommendations hydrocarbons. Emission factor of the point sources is another important issue that should be taken in account by the future’s studies. Eestablishing an emergency episode monitoring station (EEMS) also should be taken into account. 8- There are many factors that cause dispersion and spatial variability of air pollution, and these
including weather conditions such as temperature, wind speed and
direction, humidity, topography of the area , relief of the area such as flat or hilly, or the local situation of the area. To overcome this problem, it is recommended for the future studies to use either dispersion models or interpolation methods. As an example CAL3QHCR is an air pollution dispersion model developed by the California Department of Transportation (Wijeratne, 2003) or to use remote-sensing technique. 9- For keeping the balancing of the emitted carbon dioxide with the produced oxygen by green plants and as sustainable solution in environmental protection. The current study recommends for increasing the green area in the city. Also vehicle emissions testing (VET) should be enforced directly. 10- Finally it is our recommendation also to avoid doing exercise in polluted atmosphere, and also to avoid buying or eating any edible substances exposed to air pollutants for a long time, especially in areas with heavy traffic, because many of those toxic pollutants either would deposit on them or react with their constituents, particularly, the free radical components of the air pollutants.
216
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المصبدر العربيت اطروحت ممد مت الً كليت. الخلىد البيئً لمدينت المىصل و طرق المعبلجت.)2006( . عبدالعزيز يىنس طليع، الصفبوي- 1 . لنيل شهبدة الدكخىراه فً اخخصبص حلىد البيئت. جبمعت المىصل/الخربيت اطروحت ممد مت الً كليت، مرالبت و حمثيل الخلىد الهىائً فً شركت نفط الشمبل.)2004(. وليد محمد شيج، العبد ربت- 2 . لنيل شهبدة الدكخىراه. جبمعت المىصل/الهندست . ، دراست الملىثبث فً اجىاء بعض المنبطك الصنبعيت فً جبنب الرصبفت من مدينت بغداد.)1990( . جبسم داود،ً الميس- 3 . لنيل شهبدة المبجسخير. جبمعت بغداد/)رسبلت ممد مت الً كليت الخربيت الثبنيت (ابن الهيثم
264
Appendix 1: Some chemical properties of the collected airborne dust samples for the studied locations. No.
123456-
Sample locations
Raparin/ Near to Sulaimani International Airport Maleek Mhmood Circle/ Lovan Hotel Tanjaro / Tanjaro Mosque Nawgrdan Village/ Osman Gas Fact. Charakhan Quarter
7-
Kaziewa Quarter/ Near to Goizha Apartments Kurdsat/ Quarter 1
8-
Kurdsat Quarter 2
9-
Maleek Mhmood Circle/ Beside Zargata Underpass. Farmanbaran Quarter
1011-
13-
Salim Street/ Beside Khsrawkhal Bridge. Chawrbakh Quarter / Near to Sulaimani Stadium Sarkarez/ Dastaraka Crossing
14-
Kanat Street
1516-
Main Internal Buses Transportations Center German Village
17-
Mamostain Quarter
18-
Kurdsat Quarter
12-
4.70 4.01 2.97 4.69 4.65 3.74 3.71 4.27 3.59 5.51 1.69 4.86 5.68 6.61 4.46 6.54 3.80 NC 2.82 NC 5.75 6.10 5.45 4.67 5.27 5.24 3.83 4.37 5.98 5.13 0.95
Carbonate minerals g kg-1 29.94 26.06 28.70 31.14 33.79 27.35 32.44 31.17 28.71 26.20 28.76 26.10 28.53 25.99 31.25 25.05 31.29 NC 37.80 NC 27.31 27.25 25.91 30.02 24.74 28.68 28.62 31.21 27.39 28.65 23.73
pH at 25 Co 1:10 suspensiona 10.30 9.58 10.39 8.28 10.53 8.32 10.20 9.70 9.92 9.04 9.89 8.87 9.90 9.64 9.85 8.85 9.78 NC 9.74 NC 9.73 8.97 9.33 9.31 9.76 8.69 10.08 9.00 9.63 9.57 8.13
0.98
21.01
8.16
0.91
21.09
8.14
Source of the sample
% Organic carbon
L M L M L M L M L M L M L M L M L M L M L M L M L M L M L M Sandstorm 22.2.2010 Sandstorm 22.2.2010 Sandstorm 22.2.2010
L: Local Airborne Dust Sample M: Mixed samples of Sand or Dust storm and local Airborne Dust NC: Not Collected. a: pH of the 1:10 Dust: Water Suspension.
265
Appendix 2: Basic physicochemical properties of topsoil's (0-20 cm) for the studied locations. No.
Carb.1 minerals g kg-1 228.4
CEC Cmol kg-1 31.03
Particles Size Distributions g kg-1 Sand Silt Clay Texture 92.6 415.8 491.6 SiC
1-
Raparin/ Sulaimani- Karkuk Street
Organic Carbon g C kg-1 15.9
2-
Raparin/ Entrance Kelaspi Village
8.4
046.7
41.98
84.5
414.8
500.7
SiC
3-
Bakrajo/ Agriculture College Fields
7.4
267.0
38.75
77.4
395.2
527.4
SiC
4-
Sarchinar Crossing Garden
18.9
246.4
36.52
143.0
419.2
437.8
SiC
5-
Maleek Mahmood Circle/ Near to Chami Rezan Petrol Station Maleek Mahmood –Entrance Kani Goma Village Wluba Garden/ Beside Wluba Overpass
9.9
250.0
28.56
77.8
621.0
301.2
SiCL
9.1
229.1
35.67
78.5
492.9
428.6
SiC
8.8
296.2
34.82
112.8
483.5
403.7
SiC
8.7
241.2
39.10
76.5
333.1
590.4
C
9-
Tanjaro/ Agricultural Field Adjusting Sulaimani- Qaradakh Street Tanjaro/ Near to Landfill Site
9.6
193.7
28.37
269.4
536.0
194.6
SiL
10-
Shek-Waisawa Village
19.0
196.3
41.46
110.4
405.9
483.7
SiC
11-
Bnari Goizha/ Behind Goizha Appartments
12.2
214.0
30.33
136.1
479.2
384.7
SiCL
12-
Dabashan/ West of Sulaimani-Azmar Street
7.0
209.4
32.83
130.0
474.3
397.7
SiCL
13-
Sarwari Quarter/ Near to Kanispika Village
12.6
207.8
37.34
98.6
419.2
482.2
SiC
14
Orchard/ Beside Abu-Sana Hotel
10.1
250.6
29.55
162.8
417.4
419.8
SiC
15-
Olf Palma Garden /Near to Khalahaji Crossing
11.7
232.1
30.59
121.7
435.7
442.6
SiC
678-
Locations
1: Carbonate minerals 266
Appendix 3: Basic soluble ions, pH, and ECe of the topsoil (0-15 cm) samples for the studied locations. No.
Locations
pH
ECe at 25Co
Cations (mmoles L-1)
Anions (mmoles L-1 )
( dS m-1 )
Ca+2
Mg+2
Na+
K+
CO3-2
HCO3-1
Cl-1
1-
Raparin/ Sulaimani- Karkuk Street
8.10
1.25
10.96
0.92
0.89
0.50
ND
7.2
4.7
2-
Raparin/ Entrance Kelaspi Village
8.08
0.74
4.49
3.89
0.86
0.17
ND
3.5
0.6
3-
Bakrajo/ Agriculture College Fields
7.88
0.79
5.17
1.91
0.59
0.17
ND
2.8
0.4
4-
Sarchinar Crossing Garden
8.16
1.94
10.14
3.39
3.89
0.06
ND
9.0
7.3
5-
Maleek Mahmood Circle/ Near to Chami Rezan Petrol Station Maleek Mahmood –Entrance Kani Goma Village Wluba Garden/ Beside Wluba Overpass
8.07
1.85
14.92
1.03
3.58
0.04
ND
6.2
5.6
7.98
0.66
4.11
2.64
1.23
0.13
ND
3.9
1.4
8.06
1.86
12.06
4.22
4.05
0.07
ND
4.8
7.6
7.98
1.62
10.27
3.48
0.59
0.22
ND
2.0
0.4
9-
Tanjaro/ Agricultural Field Adjusting Sulaimani- Qaradakh Street Tanjaro/ Near to Landfill Site
7.82
1.69
10.87
4.73
0.90
0.43
ND
6.4
6.8
10-
Shek-Waisawa Village
7.75
1.13
6.53
4.51
0.62
0.27
ND
8.5
3.6
11-
Bnari Goizha/ Behind Goizha Appartments Dabashan/ West of Sulaimani-Azmar Street Sarwari Quarter/ Near to Kanispika Village Orchard/ Beside Abu-Sana Hotel
7.78
0.60
5.50
1.41
0.48
0.15
ND
3.2
0.4
7.82
0.63
3.98
0.62
0.53
0.16
ND
3.7
0.8
7.75
0.84
5.08
1.91
0.58
0.18
ND
3.6
0.8
7.92
0.78
4.14
1.91
0.70
0.20
ND
4.6
1.2
Olf Palma Garden /Near to Khalahaji Crossing
7.74
2.31
16.13
3.65
4.68
0.04
ND
3.2
1.6
678-
12131415-
267
Appendix 4: Some chemical properties of the rainwater samples for the studied locations. No.
123-
Sample Location
Raparin / Near to Sulaimani International Airport Bakrajo/ Awal Road
4-
Maleek Mhmood Circle/ Lovan Hotel Qaratogan Quarter
5-
Tanjaro/ Tanjaro Mosque
6-
Charakhan Quarter
7-
Ibrahim Ahmad Quarter
8-
Maleek Mhmood Circle/ Beside Zargata Underpass Farmanbaran Quarter
910 1112 13 14-
Sarwari Quarter/Near to Grape Orchard Salim Street/ Beside Khsrawkhal Bridge. Shek-Mohedin Quarter Chawrbakh Quarter / Near to Sulaimani Stadium Sarkarez/ Dastaraka Crossing
15- Main Internal Buses Transportations Center
Rain’s Time
pH
R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2
7.15 6.43 8.08 6.68 7.75 7.09 7.64 6.71 7.72 6.85 7.36 6.42 8.26 7.11 8.26 7.23 7.66 6.74 8.07 6.78 8.16 6.53 7.53 7.41 7.68 7.36 7.63 7.30
R1 R2
7.66 7.15
EC25 (µS cm-1)
Cations (mg L-1)
TSS
Anions (mg L-1 )
mg L-1
Na+1
K+1
Ca+2
Mg+2
CO3--2
HCO3-1
Cl-1
NO3-1
71 35 87 37 147 58 154 34 93 39 53 35 56 49 82 71 92 29 64 40 80 36 84 63 137 43 106 84
279 194 98 147 668 46 384 106 235 177 66 141 816 147 246 135 72 143 78 169 53 152 103 116 646 113 309 163
1.6 1.5 0.6 0.7 5.6 1.5 1.4 1.1 7.9 1.5 1.4 1.1 0.6 1.5 0.6 1.5 2.9 1.1 0.6 1.5 1.5 1.1 0.6 1.5 2.3 1.1 1.5 1.8
0.9 0.8 0.6 0.7 0.6 0.9 1.1 0.8 1.3 1.1 0.6 0.8 1.3 1.1 0.7 0.8 0.8 1.0 0.7 1.2 0.8 1.0 0.7 1.1 1.1 1.1 1.3 1.3
10.8 4.0 13.6 6.0 86.2 8.0 24.4 6.0 19.8 6.0 8.2 6.0 12.4 6.0 14.8 12.0 13.4 6.0 10.8 6.0 12.4 4.0 15.8 10.0 23.4 8.0 23.2 12.0
9.2 2.4 14.6 2.4 19.6 1.2 8.6 2.4 11.1 3.7 3.8 6.1 7.2 3.7 6.6 1.2 13.5 3.7 9.0 1.2 8.3 8.5 10.6 1.2 9.8 1.2 32.8 2.4
ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND
34.8 8.7 23.2 11.6 59.4 34.8 23.2 8.7 31.9 11.6 17.4 8.7 23.2 11.6 40.6 14.5 46.4 8.7 17.4 8.7 23.2 5.8 23.2 14.5 17.4 14.5 17.4 20.3
32.0 26.6 10.7 17.7 75.7 35.5 17.7 26.3 17.7 26.3 17.7 17.5 10.7 26.3 17.7 44.4 17.7 17.7 31.9 8.9 17.7 26.6 17.7 8.9 17.7 26.6 17.7 26.6
0.5 0.2 1.5 0.4 1.7 0.6 1.2 1.4 1.2 0.4 1.1 0.2 0.6 0.2 1.1 0.2 0.8 0.2 0.8 0.3 1.9 1.0 1.8 0.3 1.6 0.8 1.9 0.4
98 44
219 122
0.9 1.1
0.8 1.3
28.6 10.0
13.5 2.4
ND ND
40.6 14.5
21.3 26.6
1.7 1.5
R1: Dates of Rain Sampling are 28 and 29/ 10/2009 (First Year’s Rain):
R2: Dates of Rain Sampling are 24 and 25/ 1/2010 268
Appendix 5: Pearson correlation coefficient values among and between the investigated gases and heavy metals in settable dust samples. Properties PM1.0 PM2.5 PM10.0 Cr Mn Fe Ni Cu Zn Cd PM1.0 1.00
Pb
PM2.5
0.928**
1.00
PM10.0
0.963**
0.867**
1.00
Cr
-0.03
0.04
-0.19
1.00
Mn
-0.25
-0.13
-0.33
0.11
1.00
Fe
-0.39
-0.40
-0.36
0.31
0.40
1.00
Ni
-0.04
-0.02
-0.21
0.955**
-0.01
0.22
1.00
Cu
-0.08
0.01
-0.17
0.666**
0.40
0.49
0.48
1.00
Zn
-0.07
-0.08
-0.13
0.525*
0.14
0.30
0.50
0.44
1.00
Cd
0.10
0.00
0.08
-0.14
-0.47
-0.803**
-0.02
-0.30
-0.14
1.00
Pb
-0.08
-0.18
-0.20
0.828**
-0.08
0.21
0.845**
0.518*
0.614*
0.13
1.00
PM10.0
CO2
HC
N2O
NO
NO2
O3
SO2
Properties PM1.0
PM1.0 1.00
PM2.5
PM2.5
0.928**
1.00
PM10.0
0.963**
0.867**
1.00
CO2
0.799**
0.598*
0.868**
1.00
HC
0.34
0.25
0.42
0.38
1.00
N 2O
0.28
0.34
0.27
0.06
0.831**
1.00
CO
0.24
0.10
0.30
0.40
0.829**
0.677**
1.00
NO
0.26
0.12
0.36
0.40
0.639**
0.39
0.682**
1.00
NO2
0.717**
0.521*
0.774**
0.922**
0.45
0.11
0.44
0.507*
1.00
O3
0.821**
0.649**
0.864**
0.937**
0.38
0.13
0.38
0.46
0.961**
1.00
SO2
0.864**
0.703**
0.934**
0.903**
0.588*
0.34
0.533*
0.500*
0.813**
0.855**
1.00
0.19
0.42
0.14
-0.11
-0.45
-0.41
-0.24
O2
-0.33 -0.22 -0.35 -0.47 ** Correlation is significant at the 0.01 level (2-tailed) *C Correlation is significant at the 0.01 level (2-tailed)
CO
O2
1.00
269
Appendix 5: Pearson correlation coefficient values among and between the investigated gases and heavy metals in settable dust samples.
Properties Temperature o (C ) Relative Humidity% Pressure (mbar) CO2
Temperature o (C )
Relative Humidity%
Pressure (mbar)
CO2
HC
N2O
CO
NO
NO2
O3
SO2
1.00 -0.795**
1.00
0.587*
-0.12
1.00
-0.33
0.21
-0.48
1.00
HC
0.508*
-0.42
0.32
0.38
1.00
N2O
0.716**
-0.537*
0.574*
0.06
0.831**
1.00
CO
0.33
-0.32
0.08
0.40
0.829**
0.677**
1.00
NO
0.13
-0.12
0.10
0.40
0.639**
0.39
0.682**
1.00
NO2
-0.21
0.17
-0.36
0.922**
0.45
0.11
0.44
0.507*
1.00
O3
-0.21
0.18
-0.36
0.937**
0.38
0.13
0.38
0.46
0.961**
1.00
SO2
-0.07
-0.05
-0.29
0.903**
0.588*
0.34
0.533*
0.500*
0.813**
0.855**
1.00
-0.47
0.19
0.42
0.14
-0.11
-0.45
-0.41
-0.24
O2
O2
0.843** -0.819** 0.31 ** Correlation is significant at the 0.01 level (2-tailed) *C Correlation is significant at the 0.01 level (2-tailed)
1.00
270
Appendix (6): Multiple comparisons tests (Duncan’s test) between the average concentrations of the studied ambient gases for the study locations. Categories L11 L15 L16 L2 L7 L10 L9 L14 L17 L3 L4 L1 L8 L5 L12 L13 L6
Mean 7.17 7.13 6.62 6.40 6.03 5.26 4.58 3.69 3.16 2.56 2.44 2.42 0.88 0.62 0.26 0.23 0.20
Groupings A A A A A A A A A A A A
B B B B B B B B B
C C C C C C C C C C C
-3
A: Average CO grouping (mg m ) Categorie s Mean Groupings L11 100.8 A L9 72.74 A B L4 69.24 A B C L7 66.47 A B C L10 49.08 A B C L16 46.45 A B C L2 34.18 B C L14 28.92 B C L3 28.92 B C L15 24.54 B C L8 8.76 B C L17 3.51 C L13 3.51 C L1 1.75 L5 0.88 L12 0.00 L6 0.00 C: Average NO grouping(µg m-3)
D D D D D D D D D D D D D D
Categories Mean Groupings L7 32.26 A L9 13.44 B L3 12.10 B L11 10.65 B L10 9.41 B L16 9.41 B L13 9.41 B L15 8.07 B L14 6.72 B L12 6.72 B L17 5.38 B L2 5.38 B L1 4.03 B L4 4.03 B L5 3.46 B L8 2.69 B L6 1.34 B -3 B: Average NO2 grouping (µg m ) Categorie s Mean Groupings L10 51.55 A L15 50.65 A L1 50.39 A L5 49.36 A L2 47.56 A L16 47.43 A L17 46.68 A L11 46.30 A L4 44.61 A L3 42.32 A L9 40.62 A L7 39.85 A L14 35.61 A L12 27.25 A L6 22.50 A L8 20.70 A L13 18.13 A -3 D: Average N2O grouping (mg m ) 271
Appendix (6): Multiple comparisons tests (Duncan’s test) between the average concentrations of the studied ambient gases for the studied locations. Categories L7 L9 L11 L10 L3 L13 L12 L5 L2 L16 L15 L1 L4 L17 L6 L14 L8
Mean 244.70 106.85 103.91 97.18 95.64 87.92 82.17 80.49 79.65 79.09 74.32 70.39 68.23 63.38 56.79 56.09 50.20
Groupings A B B B B B B B B B B B B B B B B
E: Average O3 grouping (µg m-3) Categorie s Mean Groupings L7 2085.85 A L9 906.86 B L16 883.09 B L10 879.61 B L14 874.73 B L2 858.40 B L11 857.11 B L13 830.90 B L15 822.03 B L17 813.42 B L4 774.21 B L1 723.95 B L3 710.07 B L5 707.63 B L12 699.78 B L8 693.36 B L6 668.16 B G:Average CO2 grouping (mg m-3)
Categorie s Mean Groupings L7 708.62 A L4 278.59 B L2 259.84 B L11 258.14 B L16 246.80 B L15 231.84 B L14 226.24 B L1 209.41 B L9 202.67 B L17 201.93 B L5 194.64 B L3 186.97 B L10 140.23 B L8 104.70 B L6 99.10 B L12 95.36 B L13 71.05 B F: Average SO2 grouping (µg m-3) Categories L12 L17 L1 L16 L6 L14 L2 L11 L5 L15 L10 L3 L4 L9 L8 L7 L13
Mean 276.90 275.88 274.94 274.47 273.54 272.60 272.23 272.07 271.30 271.11 270.27 269.43 268.40 267.83 265.87 262.32 256.99
Groupings A A A A A A A A A A A A A A A A A
H: Average O2 grouping (g m-3) 272
Appendix (7): Multiple comparisons tests (Duncan’s test) between the average values of PM10.0 level for the studied locations (mg m-3)
Categories L7 L5 L4 L2 L17 L3 L11 L1 L9 L16 L15 L14 L13 L10 L6 L12 L8
Mean 0.37 0.17 0.15 0.13 0.11 0.11 0.11 0.10 0.09 0.09 0.08 0.08 0.07 0.07 0.05 0.05 0.04
Groupings A B B B B B B B B B B B
C C C C C C C C C C C C
D D D D D D D D D D D D D D
273
Appendix 8: Pearson correlations among heavy metals in soil samples and heavy metals with some soil chemical properties. Properties Cr Mn Fe Ni Cu Zn Cd Pb OM Ca mi1 CEC Clay Cr 1.00 Mn 0.11 1.00 Fe 0.30 0.40 1.00 Ni 0.955** -0.01 0.21 1.00 Cu 0.666** 0.40 0.49 0.48 1.00 Zn 0.525* 0.14 0.30 0.50 0.44 1.00 Cd -0.14 -0.47 -0.803** -0.02 -0.30 -0.14 1.00 Pb 0.828** -0.08 0.21 0.845** 0.518* 0.614* 0.13 1.00 OM -0.05 -0.15 -0.05 -0.06 -0.03 0.581* 0.08 0.15 1.00 1 Ca. mi -0.13 -0.743** -0.852** -0.04 -0.41 -0.31 0.794** -0.01 0.04 1.00 CEC 0.09 0.26 0.11 0.22 0.01 0.36 0.01 0.24 0.12 -0.36 1.00 Clay -0.04 0.15 -0.16 0.17 -0.44 0.11 0.19 0.14 0.11 -0.06 0.722** 1.00 **. Correlation is significant at the 0.01 level; *. Correlation is significant at the 0.05 level; 1: Carbonate minerals Appendix 9: Pearson correlation among the investigated heavy metals in plant samples Parameters Cr Mn Fe Ni Cr 1.00 Mn -0.06 1.00 Fe 0.765** 0.12 1.00 Ni 0.39 0.17 0.51 1.00 Cu 0.580* 0.00 0.35 0.576* Zn 0.737** -0.33 0.40 0.28 Cd -0.05 -0.22 0.06 -0.02 Pb -0.11 0.06 -0.28 -0.28
Cu
Zn
Cd
Pb
1.00 0.47 -0.18 -0.06
1.00 0.25 -0.03
1.00 -0.546*
1.00
**. Correlation is significant at the 0.01 level (2-tailed); *Correlation is significant at the 0.05 level (2-tailed). 274
Appendix 10: Pearson correlation among heavy metal in rainwater samples Parameters Cr Fe Ni Cu Zn Cd Pb
Cr 1.00 0.24 0.17 0.536* --0.36 0.19 --0.03
Fe
Ni
Cu
Zn
Cd
1.00 --0.19 0.601* 0.10 0.27 0.29
1.00 0.07 --0.64 --0.31 --0.01
1.00 -0.02 0.10 0.537*
1.00 --0.41 --0.42
1.00 0.03
Pb
1.00
*. Correlation is significant at the 0.05 level (2-tailed). Appendix 11: List of some countries by vehicles per inhabitants. (Wikipedia, 2011). Country
United States Luxembourg Australia Italy Canada New Zealand Austria Germany Japan Ireland Switzerland Norway France Spain Sweden UK Greece
Motor Vehicle per 1000 people 779 697 619 571 503 582 558 558 543 542 539 494 494 479 475 458 434
Country
Lebanon Denmark Poland Qatar Portugal South Korea Saudi Arabia Bahrain Israel Brazil Russia Turkey Iran Singapore Iraq/Sulaimani China South Africa
Motor Vehicle per 1000 people 434 408 382 378 374 338 336 322 239 224 212 182 175 158 139 128 123
Country
Tunisia Chile Colombia Paraguay Morocco Jordan Egypt India Pakistan Angolia Sudan Somali Rwanda Ethiobia Nigeria Afghanistan Malawi
Motor Vehicle per 1000 people 110 97 67 63 53 47 30 12 8 4 3 3 2 1 1 <1 <1
275
Appendix 12: Pearson correlation among the exhaust emission parameters Parameters CO% HC (ppm) CO2% O2% Lambda(λ)
CO% 1 0.393** --0.746** 0.068 --0.604**
HC (ppm)
CO2%
O2%
Lambda(λ)
1 0.446** 0.257** 0.149**
1 --0.693** 0.069*
1 0.545**
1
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2 tailed).
276
Appendix 13: European Union (EU) vehicle emission standards in gram per crossed kilometer (g/km), according to Euro 5. (Wikipedia, 2011). Vehicle Category
CO G
D
THC G
D
NMHC G
D
NOx G
D
HC + NOx G D
PM G
D
Passenger car (category M*)
1.0
0.5
0.1
--
0.068
--
0.060
0.180
--
0.230
0.005**
0.005
Light commercial vehicles ≤ 1305 Kg
1.0
0.5
0.1
--
0.068
--
0.060
0.180
--
0.230
0.005**
0.005
Light commercial vehicles 1305-1760 Kg
0.181
0.630
0.130
--
0.090
--
0.075
0.235
--
0.298
0.005**
0.005
Light commercial vehicles >1760 max 3500 Kg
2.27
0.74
0.160
--
0.108
--
0.082
0.280
--
0.350
0.005**
0.005
G is Gasoline; D is Diesel *Before Euro 5, passenger vehicles > 2500 kg were type approved as light commercial vehicles **Applies only to vehicles with direct injection engines
277
اٌخالطخ اعشيذ ٘زٖ اٌذساعخ فِ ٝذيٕخ اٌغٍيّبٔيخ ثٙذف رميُ ؽبٌخ رٍٛس اٌٛٙاء ف ٝاٌّذيٕخ ٚاؿشافٙب ثبٌٕظش الْ اٌّذيٕخ ِشد ف ٝاٌؼمذ االخيش ِ ٚب رضاي ثّٕ ٛالزظبد ٜعشيغ ٚ ،وزاٌه رٛعغ وجيش ف ٝاٌّغبؽبد راد اٌىضبفاد اٌغىبٔيخ اٌؼبٌيخِّ ،ب ٔزظ ػٕٗ صيبدح ف ٝرٍٛس اٌٛٙاء ٚرذ٘ٛس ٔٛػيزٗ ٚثبٌزبٌ ٝاالػشاس ثظؾخ االٔغبْ ٚاٌىبئٕبد االخش ٜػالٚح ػٍٝ
اصشٖ اٌؼبس
ف ٝاٌجيئخ اٌـجيؼيخ. اْ ِذيٕخ اٌغٍيّبٔيخ ٚاٌزِ ٝ٘ ٝشوض ِؾبفظخ اٌغٍيّبٔيخ ،رمغ ف ٝالظ ٝاٌشّبي اٌششل ٝثبٌٕغجخ ٌٍؼشاق ٚاٌغٕٛة اٌششل ٝاللٍيُ وشدعزبْ اٌؼشاق .اْ اٌّٛلغ اٌفٍىٌّ ٝشوض اٌّذيٕخ ٞلغ ثيٓ خؾ ػشع ) ٚ (35o 33′ 14.99′′ Nخؾ اٌـٛي )ٚ (45o 26′ 58.68′′ Eاسرفبػٙب ػٓ ِغز ٜٛعـؼ اٌجؾش ِ٘ 864 ٛزش .رغـ ٝاٌّذيٕخ ِغبؽخ 113.73وُٚ 2ثٍغ ػذد عىبٔٙب ف ٝعٕخ 2009ؽٛأٌ 571507 ٝغّخ ِٞ .زبص ِٕبؿ اٌّذيٕخ ثّٕبؿ شجٗ عبف ،ؽيش اْ اٌظيف ؽبس ٚعبف اِب اٌشزبء فأ ٔٗ ثبسد عذأ .ؽبٌيب رظً ٔغجخ اٌّغبؽخ اٌخؼشاء ف ٝاٌّذيٕخ اٌ %5.57 ٝثبٌٕغخ ٌٍّغبؽخ اٌىٍيخ. يؼض ٜعجت رٍٛس اٌٛٙاء ف ٝاٌّذيٕخ اٌ ٝػٍّيبد ػذح ِظبدس ػبٍِخ ِؼأ ٚيزؼّٓ ر ٌه اؽزشاق وّيبد وجيشح ِٓ اٌىبصٌٚيٓ ٚغيش٘ب ِٓ اٌّشوجبد اٌٙبيذسٚوبسثٔٛيخ ِٓ عشاء صيبدح اػذاد اٌغيبساد ٚاٌٌّٛذاد اٌىٙشثبئيخٕ٘ ٚ ،بن ِظبدس اخشِ ٜضً صيبدح إٌشبؽ اٌظٕبػ ٝف ٝاٌّذيٕخ ٚوأٔشبء اٌؼذ يذ ِٓ ِؼبًِ االعّٕذ ،االعفٍذ ِ ٚؼبًِ رىشيش اٌّشزبلبد إٌفـيخ ٚاْ ِؼظّٙب ال رٍزضَ ثبٌششٚؽ ٚاٌزؼٍيّبد اٌجيئيخ اٌمبٔٔٛيخ اٌّؾذدح ٚ ،رؼذ رٍه اٌّظبدس ٘بِخ ف ٝرٍٛس اٌٛٙاء ف ٝاٌّذيٕخ .اػبفخ اٌ ٝرٌه ٕ٘بن ِظبدس اخشٌٍٍّٛ ٜصبد اٌٛٙائيخ ف ٝاٌّذيٕخ ِٕٙٚب اٌؼٍّيبد اٌىالعيىيخ ٌؾشق classical incinerationاٌمّبِخ ٚإٌفبيبد اٌظٍجخ فِٛ ٝلغ سِ ٝإٌفبيبد ) (landfillفِٕ ٝـمخ ربٔغش ٚاٌٛالؼخ ػٍ ٝثؼذ 10وُ ِٓ اٌغٕٛة اٌغشثٌٍّ ٝذيٕخ ،فؼال ػٓ رٌه فبْ ثٕبء اٌؼذيذ ِٓ إٌّشأد ٚاٌشٛاسع ٚوزٌه ؽذٚس ظب٘شح اٌؼٛاطف اٌشٍِيخ اٌّزىشسح اد ٜاٌ ٝف ٝصيبدح ِغز ٜٛاٌّٛاد اٌذلبئميخ
ِ ،particulate matters.غ اٌؼٍُ اْ اوضش اٌؼٛاطف اٌشٍِيخ
ِظذس٘ب خبسط ؽذٚد االلٍيُ ٚوزٌه اٌؼشاق. اطجؼ ِٛػٛع رٍٛس اٌٛٙاء ِٛػغ ا٘زّبَ وجيش ٌٍّغبِيغ اٌؼٍّيخ ٚاٌـجيخ ٌٚىٓ فِ ٝذيٕخ اٌغٍيّبٔيخ ٌُ رغٍت اٌؾبٌخ ا ٜا٘زّبَ ٚوزٌه ٌُ رغش ٜايخ دساعبد ا ٚثؾٛس ٌزميُ ؽبٌخ اٌزٍٛس اٌٛٙائ ،ٝػٍيٗ رُ اعشاء اٌذساعخ اٌؾبٌيخ فٝ ِذيٕخ اٌغٍيّبٔيخ ٌٍزؾش ٜػٓ ثؼغ اٌغٛأت اٌّّٙخ ٌٍزٍٛس اٌٛٙائ ٚ ٝرٌه ٌزميُ ؽبٌخ اٌزٍٛس. ثظٛسح ػبِخ يّىٓ اعّبي ٔزبئظ اٌذساعخ اٌؾبٌيخ ف ٝإٌمبؽ اٌزبٌيخ: - 1اظٙشد ثيبٔبد ِشالجخ اٌٍّٛصبد اٌٛٙائيخ اٌميبعيخ اٌزبٌيخ
) (CO, NO2, O3 and SO2ثبْ ِؼذي اٌزشاويض
ٌٍميبعبد اٌغجؼخ خالي اٌفزشح ِٓ ٚ 31.9.2009اٌٌٍّٛ ٚ 13.7.2010 ٝالغ اٌغجؼخ ػشش ( )17رجبيٕذ وجيشا ف ٝاٌميُ ٚيؼٛد اٌغجت ف ٝرٌه اٌ ٝاخزالف اٌىضبفخ اٌغىبٔيخ ٚاٌّشٚسيخ ٌٍّٛالغٚ ،أْ ِؼذي اٌزشاويض ٌزٍه ٌٍٍّٛصبد اٌّزوٛسح ػذا اٌٍّٛس SO2وبٔذ الً ِٓ اٌؾذٚد اٌمبٔٔٛيخ اٌّؾذدح .legislative limits ٚ - 2ف ٝعبٔت اخش ،فأْ ِؼذي رشاويض غبص صبٔ ٝاٚوغيذ اٌىبسثٚ ْٛاٌغبصاد اٌٙب يذسٚوبسثٔٛيخ فِٛ ٝالغ اٌذساعخ ثيٕذ أرغب٘ب ٚؽبٌخ ِشبثٗ ٌٍغبصاد اٌميبعيخ اٌّذسٚعخ االخشٚ ٜوزٌه ٚعذد ػاللبد ِؼٕٛيخ ثيٕٙب٘ٚ ،زٖ إٌزبئظ رإوذ ثبٔٙب راد ِظذس أجؼبس ِشزشن .وبْ ِؼذي رشويض غبص صبٔ ٝاٚوغيذ اٌىبسث ْٛوبؽذ غبصاد اٌجيٛد اٌجالعزيىيخ ػبٌيب فِ ٝؼؼُ اٌّٛالغ راد اٌىضبفخ اٌّشٚسيخ اٌؼبٌيخ ٚأٗ اوجش ِٓ اٌّؼذي اٌؼبٌّ ٝاٌّذ ْٚفِ ٝؾـخ F
ِبٔٚب ٌٌٍّ Mauna Loa observatory/Hawaii ٛشالجخ ف٘ ٝبٚا /ٜاِشيىب ف ٝشٙش ؽضيشاْ ٌغٕخ 2011 ؽيش وبْ اٌزشويض ػجبسح ػٓ ppmv 393.69عضء ِٓ اٌٍّي. ْٛ - 3ثيٕذ اٌؼذيذ ِٓ اٌذساعبد اٌٚ ٝعٛد ػاللخ ثيٓ اصش اٌّٛاد اٌذلبئميخ ف ٝاٌؼذيذ ِٓ اٌؾبالد اٌظؾيخٌ ،زا ٌزميُ ؽبٌخ اٌزٍٛس ثبٌّٛاد اٌذلبئميخ ) particulate matter (PMرُ ليبط رشاويض اٌّٛاد اٌذلبئميخ راد االؽغبَ ، 1.0 10.0 ٚ 2.5
ِبيىش0 ْٚ
ِٚب دْٚ
ٚ PM1.0, PM2.5 and PM10.ف ٝاٌٛلذ ٔفغٗ ٚلذ ليبط اٌغبصاد ٚفٝ
ٔفظ ِٛالغ اٌذساعخ ،أظٙشد إٌزبئظ ثٛعٛد رجبيٓ وجيش ف ٝرشاويض اٌّٛاد اٌذلبئميخ ٌٚالؽغبَ اٌّخزٍفخ فِٛ ٝالغ اٌذساعخ ٚ .لذ يؼٛد رٌه اٌ ٝاصش اٌّظذس اٌّٛلؼٌٍ ٝذلبئك ؽيش اْ رشويض اٌّٛاد اٌذلبئميخ ٔبعّخ ػٓ االصش اٌّشزشن ٌٕشبؽ االٔغبْ ٚاٌّظذس اٌـجيؼ.ٝ وّب ِ٘ ٛذ ْٚثٙزٖ اٌذساعخ فأْ ػذد رىشاس ؽذٚس اٌؼبطفخ اٌشٍِيخ ٌٍفزشح ِبثيٓ 19.9.2009اٌ26.6.2011 ٝ ػجبسح ػٓ ِ 34شحِّ .ب يغذس االشبسح اٌيٗ ايؼب ٘ ٛاْ ٌٍؼٛاًِ إٌّبخيخ وبٌؾشاسح ٚعشػخ ٚارغبٖ اٌشيبػ اصش وجيش ػٍِ ٝىٔٛبد اٌغالف اٌغ.ٜٛ ثظٛسح ػبِخ فأْ رشاويض اٌّٛاد اٌذلبئميخ ٌٚالؽغبَ ِ 10.0 ٚ 2.5 ، 1.0بيىشِٚ ْٚب د ْٚفِٛ ٝالغ اٌذساعخ رغبٚصد اٌؾذٚد اٌمبٔٔٛيخ اٌّؾذدح ِٓ لجً ٚوبٌخ ؽّبيخ اٌجيئخ االِشيىيخ
U S Environmental Protection
)ٚ Agency (EPAوزاٌه اٌؾذٚد اٌّؾذدِٓ لجً اٌّفٛػيخ االٚسٚثيخ ). European Commission (EC - 4اْ ٌزمذيش اٌؼٕبطش اٌضميٍخ فِ ٝؾيؾ اٌٛٙاء اّ٘يخ وجيشحٌ .زميُ ِذ ٜرٍٛس اٌٛٙاء ثبٌؼٕبطش؛ اٌىش ،َٚإٌّغٕيض، اٌؾذيذ ،إٌيىً ،إٌؾبط ،اٌضٔه ،اٌىبدِي ٚ َٛاٌشطبص )(Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb اخزد ػيٕبد ؿجيؼيخ ِٓ اٌغجبس اٌمبثٍخ ٌٍزغبلؾ
، settable dustاٌزشثخ ،إٌجبد ِ ٚيبٖ االِـبس ِٓ ِٛالغ
ِخزٍفخ فِ ٝذيٕخ اٌغٍيّبٔيخ ٚؽٍٍذ وبدالئً ثبيٌٛٛعيخ ٌّؼشفخ اصش اٌزٍٛس اٌٛٙاء ػٍي.ُٙ - 5أظٙشد ٔزبئظ اٌؼٕبطش اٌضّبٔيخ اٌّذسٚعخ ) (Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pbف ٝػيٕبد اٌغجبس اٌمبثً ٌٍزغبلؾ ٚاٌزشثخ ثبْ ِذ ٜاٌزجبيٓ ف ٝرشاويض اٌؼٕبطش وبٔذ ػبٌيخ ٚ .ػٕذ ِمبسٔخ رشاويض اٌؼٕبطش Zn, Ni, Cu, Cd and Pbف ٝػيٕبد اٌغجبس اٌمبثً ٌٍزغبلؾ ٚاٌزشثخ ثبٌمبئّخ إٌٌٛٙذيخ اٌغذيذح ٌٍؾذٚد اٌّضجزخ Dutchlistرجيٓ ثبْ ؽذٚد رشاويض اٌؼٕبطش اٌّزوٛسح رمغ ف ٝاٌّذ ٜاٌّضبٌ ٝاٌ ٝاٌفبػً
New
optimum to the
action level rangeػذا ٌؼٕظش إٌيىً ؽيش رغبٚصد فِٛ ٝالغ ِؼيٕخ اٌّذ ٜاٌفبػً action levelثغجت اٌؼٛاًِ اٌّٛلؼيخ اٌّإصشح .اِب ثبٌٕغجخ ٌٍىش َٚفىبٔذ اٌزشاويض ثظٛسح ػبِخ الً ِٓ اٌؾذ اٌّضبٌٝ
optimum
. levelفيّب يزؼٍك ثؼٕظش ٜإٌّغٕيض ٚاٌؾذيذ Mn, Feفٍُ رؾذد اٌؾذٚد ثٛعبؿخ اٌمبئّخ إٌٌٛٙذيخ اٌغذيذح. - 6ثّب اْ عٕظ إٌجبربد ا ٚاالشغبس اٌغب ئذح فِٛ ٝالغ اٌذساعخ ٌُ رىِ ْٛزشبثٗ ػٍيٗ فبْ ػٍّيخ اٌّمبسٔخ اٌزخّيٕيخ ؽٛي اصش اٌزٍٛس اٌٛٙائ ٝػٍ ٝرشاوُ اٌؼٕظبس اٌضميٍخ ف ٝػيٕبد إٌجبربد اٌّذسٚعخ وبٔذ غيش ِّىٕخ ػٍّيبٌٚ .ىٓ ثبٌٕغجخ ٌٍؼٕبطش اٌضّبٔيخ اٌّذسٚعخ ف ٝػيٕبد إٌجبربد ٚعذد ثبْ اٌزشاويض اٌؼبٌيخ ٌؼٕبطش اٌىش ،َٚإٌّغٕيض، اٌؾذيذ ،إٌيىً ٚإٌؾبط Cr, Mn, Fe, Ni and Cuفٔ ٝجبد اٌيٛوبٌيجزٛط Eucalyptus (Eucalyptus ) ، camaldulensisاِب اٌضٔه Zn
فٔ ٝجبد اٌزٛد ) ٚ Mulberry (Morus albaاٌىبدِي Cd َٛفٔ ٝجبد
اٌؼٕت )ٚ Grape (Vitis Sp.اخيشا اٌشطبص Pbفٔ ٝجبد اٌغٛص). Walnut (Juglans regia G
- 7فيّب يزؼٍك ثزشاويض اٌؼٕبطش اٌضّبٔيخ اٌّذسٚعخ ف ٝػيٕبد ِيبٖ االِـبس اٌّأخٛرح ِٓ ِٛالغ ِخزٍفخ ٚػٍٝ ِٛػذيٓ ِٓ اٌزغبلؾ (اٌّٛػذ االٚي وبْ ػٕذ اٌزغبلؾ االٚي فِٛ ٝعُ اٌشزبء اٌّٛػذ اٌضبٔ ٝوبٔذ ػٕذ ٚعؾ ِٛعُ اٌشزبء
28 and 29/ 10/ 2009اِب
) 24 and 25/1/209فٛعذد رجبيٓ وجيش ف ٝرشاويض اٌؼٕبطش
اٌّذسٚعخ .وؾبٌخ ػبِخ فىبٔذ اٌزشاويض ٌغّيغ اٌؼٕبطش ػذا ػٕظشاٌشطبص ٌٍّٛ ٚػذيٓ الً ِٓ ؽذٚد اٌّغّٛؽخ ٌّيبٖ اٌششة ِٓ لجً ِٕظّخ اٌظؾخ اٌؼبٌّيخ ٚ WHO limitsفؼال ػٍ ٝراٌه فبْ رشاويض اٌؼٕبطش ػذا ٌؼٕظش اٌضٔه ػٕذ ِٛػذ اٌزغبلؾ اٌضبٔ ٝوبٔذ الً ِٓ ِٛػذ اٌزغبلؾ االؤي. - 8ؽبٌيب فِ ٝذيٕخ اٌغٍيّبٔيخ ،فأْ اٌغيبسد ٚخالي أجؼبس فزؾبد ػبدٌِٙ ُٙب اصش ِؼٕ ٜٛػٍ ٝطؾخ االٔغبْ ٚوزٌه اٌجيئخ ِٓ خالي (ٔٛػيخ اٌٛٙاء ،غبصاد اٌجيٛد اٌجالعزيىيخ ،اعزٕفبر االٚصٛٔ ،ْٚػيخ اٌّيبٖ ،اٌّٛاسد اٌـجيؼيخ، إٌّزٛعبد اٌضساػيخ ،رذ٘ٛس ٚاخزالي اٌجيئخ اٌـجيؼيخ ٌٍىبئٕبد اٌؾيخ ٚاٌؼٛػبء)ٌ .زا فبْ اٌذساعخ اٌؾبٌيخ أغضد ثؼغ اٌفؾٛطبد اٌّزؼٍمخ ثجؼغ اٌخظبئض االٔجؼبس اٌؼبدِ ٝي
812عيبسح ػبٍِخ ثبٌجٕضيٓ 175 ٚ
عيبسح ػبٍِخ ثبٌذيضي .رُ اعشاء االخزجبس ٘زا فِ ٝشوض اٌظيبٔيخ اٌذٚسيخ
Periodic Vehicle Inspection
) (PVIف ٝاسثيً ٔظشا ٌؼذَ رٛفش عٙبص فؾض غبصاد اٌؼبدَ exhaust gas analyzerف ٝاٌغٍيّبٔيخ ٚوّب ِ٘ ٛؼشٚف فبْ أٛاع ِٛ ٚديالد اٌغيبساد اٌّٛعٛدح ف ٝالٍيُ وشدعزبْ اٌؼشاق ِزشبثٗ. - 9اْ اٌظفبد اٌّذسٚعخ ثبٌٕغجخ ٌالٔجؼبس اٌؼبدٌٍِ ٝغيبساد اٌؼبٍِخ ثبٌجٕضيٓ وبٔذ ػجبسح ػٓ غبص صبٔ ٝاٚوغيذ اٌىشث ،ْٛغبص اٚي اٚوغيذ اٌىشث ،ْٛاٌغبصاد اٌٙب يذسٚوبسثٔٛيخ ٔ ،غجخ االٚوغغيٓ اٌّزجم ٚ ٝاٌالِجذا
(CO2,
) CO, HC, O2 and Lambda, λف ٝؽيٓ اٌظفبد اٌّذسٚعخ ٌٍغيبساد اٌؼبٍِخ ثبٌذيضي وٕذ ػجبسح ػٓ ليّخ اٌىِ( ٝؼبًِ االٔـفبء) ٔ ٚ K-Values (Extinction coefficient).غجخ اٌالشفبفيخ ٌٍذخبْ
smoke
ٚ ، opacityاعشيذ اٌفؾٛطبد ِجبششح ػٕذ ِذخً أجٛة اٌؼبدَ. - 10ثظٛسح ػبِخ فبْ ٔزبئظ طفبد االٔجؼبس اٌؼبدِ ٝاٌّذسٚط ٌٍغيب ساد اٌؼب ٍِخ ثبٌجٕضيٓ ٚاٌذيضي ثيٕذ رجبيٕب وجيشا ف ٝليُ االخزجبس ٚرؼٛد اٌغجت ف ٝراٌه اٌ ٝػذح ػٛاًِ ِٕٙ ٚب عٕخ االٔزبطِ ،بسوخ اٌغيبسح ،طفبد اٌٛلٛد، اٌخظبئض اٌفٕيخ ٌٍغيشح ،طيبٔخ اٌّؾشن ٚاٌؾبٌخ اٌّشٚسيخ). - 11اخيشا ،فبْ اٌذساعخ اٌؾبٌيخ رؼّٕذ ايؼب ِغؼ اٌىضبفخ اٌّشٚسيخ ِ ٚؼذي رذفك اٌغيبساد traffic volume or traffic saturation flow rateف ٝاٌشٛاسع ٚاٌزمـؼبد االعبعيخ ٌّذيٕخ اٌغٍيّبٔيخ ِٓ اعً رميُ ؽبٌخ اٌزٍٛس اٌّشٚس ،ٜار اْ اٌغيبساد ػجبسح ػٓ ِظذس اعبعِ ٚ ٝؼٌٕ ٜٛزٍٛس اٌٛٙاء ف ٝثيئخ اٌّٛلغ ،إٌّـمخ ٚوزاٌه اٌؼبٌُ . local, regional and global air pollutionاظٙشد ٔزبئظ اٌّغؼ اٌّيذأٌٍ ٝغيبساد ثبْ اٌىضبفخ اٌّشٚسيخ ِ ٚؼذي اٌزذفك اٌغيبساد فِ ٝؼؼُ اٌشٛاسع ٚاٌزمبؿؼبد وجيشح عذا ا ٜثّؼٕ ٝاْ اٌىضبفخ اٌّشٚسيخ وبٔذ اوجش ِٓ عؼخ اٌشٛاسع .
H
حهىمة اقلًه نىردشتاٌ وزارة التعلًه العاىل والبحث العلنى جامعة الصلًناىًة-فانلتى العلىو السراعة
تكًه تلىث حمًط اهلىاء :دراشة لبعض املياطل الصهيًة فى مديية الصلًناىًة و اطرافَا /اقلًه نىردشتاٌ العرام اطروحة مكدمة اىل جملض فانلتى العلىو السراعة /جامعة الصلًناىًة وٍى جسء مً متطلبات ىًل درجة الدنتىراه فلصفة فى ختصص عله البًئة /تلىث اهلىاء مً قبل صاحل جنًب جمًد بهالىريض فى العلىو السراعًة-علىو الرتبة 1978 ماجصتري فى العلىو السراعًة-نًنًاء الرتبة 1982
باشراف االشتاذاملصاعد د .دلشاد طنجو امحد
ذوالكعدة (ٍ)1433حرى تشريً االول(
)2011مًالدى
ثوختةى هيَلوَهيِةوة بة ًة بةضتى ٓةهَطةُطاُدُى زِةوغى ثيظ بووُى ٓةوا هة غازى ضويٌَاُى ئةَ هيَلوَهَيِةوةية بوَ ٓةُدىَ ُاوضةى ُيػتةُى غازضتاُى هة ُاو غازةكةو دةوزوثػتى ئةجناَ دزا ،ضوُلة غازى ضويٌَاُى هة ًاوةى ئةَ دةضاهَةى دوايةدا طةغةضةُدُيَلى ئابووزى و بةزفساواُيةكى طةوزةى ُاوضة ُيػتةُية غازضتاُيةكاُى بة خوَوةدى وة دةزئةجناًى ئةَ ذاهَةؽ شيادبووُى ثيظ بووُى ٓةوا و بةزةو خساث ضووُى جوَزى ٓةواى هوَكةهَى غازةكةى ىلَ كةوتةوة ،وة ئةَ دةزئةجناًةؽ ٓوَكازة بوَ طةياُدُى شياُبةخػى بة ذياُى ًسوَظ و ٓةًوو بووُةوةزةكاُى تس ضةزبازى تيَلداُى ذيِطةى ضسوغتى ُاوضةكة. غازى ضويٌَاُى ،كة ثايتةختة بوَ ثازيَصطاى ضويٌَاُى ئةكةويَتة ئةوثةزِى باكووزى زوَذٓةالَتى عيَساق و وة باغووزى زوَذٓةالَتى ٓةزيٌَى كوزدضتآْ .يَوَى ٓاوتايى ئةضرتوَُوًَى ضةقى غازى ضويٌَاُى بسيتى ية هة ٓيَوَى ثاُى ) (35o 33′ 14.99′′ Nوة ٓيَوَى دزيَرى ) ،(45o 26′ 58.68′′ Eوة بةزشيةكةى هة ضةزو زِووى ئاضتى دةزياوة بسيتى ية هة ً 864ةتس ،ذًازةى داُيػتواُى غازةكة هة ضاهَى 2009بسيتى بووة هة 507 571كةع .غازى ضويٌَاُى كةؽ و ٓةوايةكى ُيٌضة ووغلى ٓةية كة هةٓاويِدا ووغم و طةزًة بةالََ هة شضتاُدا ضازدة .هة ئيَطتادا زِووبةزى ُاوضةى ضةوشايى هة غازى ضويٌَاُيدا ُصيلةى هة 5.57%تةواوى ثاُتايى غازةكة ثيَم دةٓيِيَت. ثيظ بووُى ٓةوا هةغازى ضويٌَاُى ٓوَكازةكةى دةطةزَيَتةوة بوَ ضةُدةٓا ضةزضاوةى بةغداز و ثيَلةوة كازكةز ،هةواُةؽ ضووتاُدُى بسِيَلى شوَز هة ثيَلٔاتية ُةوتيةكاْ وةن طاشوَهني و ديصيَ هة دةزئةجناًى شيادبووُى ذًازةى ئوَتوًَبيى و ًوةهيدةى بةزٓةَ ٓيَِاُى ووشةى كازةبا ،طةغةضةُدُى ضاالكى ثيػةضاشى هةواُةؽ كازطةى ضيٌةُتوَ و بةزٓةَ ٓيَِاُى قريو و ثاكتاوى بةزٓةًة ُةوتيةكاْ ،هة كاتيَلا كة شوَزيِةى ئةَ كازطاُة ًةزد و زيٌَِاية ذيِطةيةكاُى تيا ثةيسِةو ُةكساوة .ئةَ دوو ضةزضاوةوةية بسيتني هة ٓوَكازة ضةزةكيةكاُى ثيظ بووُى ٓةواى غازى ضويٌَاُى .ضةزضاوةى ٓوَكازى تس بوَ ثيظ بووُى ٓةواى ضويٌَاُى بسيتى ية هة ثةيسِةوكسدُى زيَطاى كالضيلى بوَ ضازةضةزى خاغاكى زِةق هة ُاوضةى تاجنةزوَ ى ُصيم ضويٌَاُى كة ئةويؼ زيَطاى ضووتاُى ٓةزِةًةكية بةبىَ طويَ ثيَداُى ٓيض زِيباشيَلى شاُطتى ،بيَحطة هة وةؽ دزوضت كسدُى ذًازة يلى شوَز هة بيِاو تةالزو غةقاًى ضةزضاوةو ٓوَكازى تسْ ،بةَ دوايةؽ دووبازةبووُةوةى زووداُى ديازدةى خوَيَ و مل بازيّ كة ضةزضاوةكةى دةزةوةى ضِووزى ٓسيٍَ وعيَساقة ٓوَكازى كازيطةزة بوَ شيادبووُى ئاضتى ًادةى دةُلوَهَةكاُة . particulate matters ثيظ بووُى ٓةوا بوَتة بابةتيَلى شوَز طسُط و جيَطاى بايةخيَلى بةزدةواَ هةاليّ كوًَةهَطاى شاُطتى و ثصيػلى .بةالََ هة غازى ضويٌَاُى ٓيض تويَريِوةيةكى ثةيوةضت بة ٓةهَطةُطاُدُى زِةوغى ثيظ بووُى ٓةوا و جوَزى ٓةواكة ئةجناَ ُةدزاوة ،هة بةزئةوة ئاًاجنى ئةَ تويَريِةوةية ٓةوهَداُة بوَ هيَلوهيِةوةو ثػلِيِى ٓةُدىَ اليةُى طسُطى ثيظ بووُى ٓةواى غازى ضويٌَاُى .بة غيَوةيةكى طػتى ،ئةَ دةزئةجناَ و دةزكةوتاُةى خوازةوة هة ئةجناًى كازى ثيَواُةو غيلازيةكاْ دةضت كةوتّ: A
- 1دةزئةجناًى ئةَ طاشة ثيَوةزية ثيظ كةزاُةى ٓةوا
)ُ (CO, NO2, O3 and SO2يػاُى دا كة ًةوداى تيَلسِاى
ثةيتيةكاُني شوَز بةزفساواُة بوَ ٓة ز ( )7جازةى ثيَواُةكسدُياْ هة ًاوةى ُيَواْ 31.9.2009بوَ 13.7.2010وة هة ٓةز 17جيَطاى ثيَواُةياْ ،وة ٓوَكازى ئةًةؽ دةطةزِيَتةوة بوَ ئاضتى ضسِى ئوَتوًَبيوى ٓاتووضوو هة ُاوضةكةدا ٓةزوةٓا ضسِى ذًازةى ُيػتةُى .بةالََ بة ٓةز ذايَ بةٓاى تيَلسِاى ثةيتيةكاُني بيَحطة هة طاشى SO2كةًرتبووة هة ئاضتى زِيَطةثيَدزاو ى ياضايى. - 2هة اليةكى تسةوة تيَلسِاى ثةيتى طاشة كاُى
CO2و ٓا يدزوَكازبوَُيةكاْ HCهة جيَطاى د يساضةكسدْ و هيَلوَهَيِةوةياْ
ٓةًاْ زِيَباشى طاشة ثيَوةزية ثيظ كةزةكاُياْ ُيػاْ دآ ،ةزوةٓا ثةيوةُدى ًةعِةوى significant correlationتيَبيِى كسا هة ُيَواُياُدا ،ئةًةؽ ئةوة دةضةمليَِيَت كة ضةزضاوةى ٓاوبةغى دزووضت بووْ و دةزضووُياْ ٓةية .ثةيتى طاشى دووةَ ئوَكطيدى كازبوَْ CO2كةوةن طاشيَلى خاُووة ثالضتيليلاْ greenhouse gasئةذًازدةكسيَت هة شوَزبةى ئةوجيَطاُةى كة ضسِى ئوَتوًَبيوى ٓاتووضوو تيا شوَزبووة شياتسبووة هة ئاضتى تيَلساى جئاُى globally averaged concentration كة بسِكةى بسيتى بووة هة 393.69 ppmvبوَ ًاُطى ذوشةيساُى ضاهَى 2011هة بةزطى ٓةواى ٓةضازةى شةوى بةثىَ ى ثيَواُةى ويَطتطةى ضاوديَسى ًاوُا هوَ هة ٓاواى .Mauna Loa observatory/ Hawaii - 3ضةُدةٓا هيَلوَهيِةوة ئةوةى دةزخطتووة كة ثةيوةُدى ٓةية هة ُيَواْ ئاضتى ًادة دةُلوَهَةيةكاُى ُاو ٓةوا
ambient
) particulate matter (PMوة زِووداُى شَوَزةٓا طسفتى تةُدزووضتى جياواش ،بوَية بوَ ٓةهَطةُطاُدُى زِادةى ثيظ بووُى ٓةوا هة غازى ضويٌَاُى بة ًادة دةُلوَهَيةكاْ ثةيتى ضىَ قةبازةى جياواش كة ئةواُيؼ بسيتى بووْ هة دةُلوَهَةى قةبازة 2.5 ،1.0وة ً 10.0ايلسوَْ و كةًرت هيَياْ ( )) PM1.0, PM2.5 and PM10.0ثيَواُة كسا هة ٓةًاْ كات و غويَِى ثيَواُة كسدُى طاشةكاْ .جياواشيةكى طةوزة بةدى كسا هة ُيَواْ تيَلسِاى ئاضتى دةُلوَهَةى ٓةًوو قةبازة جياواشةكاْ ،وة ئةًةؽ هةوةية ٓوَكاةكةى بطةزِيَتةوة بوَ كازتيَلسدُى ضةزضاوةى هوَكةهَى جيَطةى ثيَواُةكة
، local sourceضوُلة ثةيتى بووى
ًادة دةُلوَهَيةكاْ هة ٓةز جيَطايلدا دزووضت بووى وةيا دةزئةجناًى ضاالكى ثسوَضة ًسوَيةكاْ
anthropogenic
activitiesو ضسووغتيةكاُةٓ .ةزوةن هةاليةْ ئةَ ديساضةو هيَلوَهيِةوةية توًَازكساوة ذًازةى زِووداوى دووبازةبووةى خوَيَ و مل بازيّ هةًاوةى ثيَواُةى ُيَواْ 19.9.2009بوَ 26.6.2011بسيتى بووة هة( )34جاز .شياتس هةوةؽ ٓوَكازى كةغى meteorological factorsوةن (ثوةى طةزًا ،خيَسايى باو ئازِاضتةكى) وة توَثوَطسافياى ُاوضةكة كازدةكاتة ضةز ثةيتى ٓةًوو ثيَلٔاتةكاُى بةزطى ٓةوا.
B
بةغيَوةيلى طػتى ،ثةيتى ًادة دةُلوَهَةيةكاُى قةبازة جياواشةكاُى 2.5 ،1.0وة ً 10.0ايلسوَُى PM1.0, PM2.5 and PM10.0هة شوَزبةى جيَطاكاُى ئةَ هيَلوَهَيِةوةية شياتس بووة هة ئاضتى زيَطةثيَدزاوى ياضايى وةيا ديازى كساو هةاليةْ وةكاهةتى ثازاضتِى ذيِطةى ئةًسيلى
) U S Environmental Protection Agency (EPAوة ٓةزوةٓا ٓى
كوًَيطوَُى ئةوزوثى. European Commission (EC). - 4ديازى كسدُى بسِى ثةيتى تومخة قوزضةكاْ heavy metal concentrationهة بةزطى ٓةوادا جيَطاى بايةخيَلى شوَزى ئةَ هيَلوهيِةوةية بوو .بوَ ٓةهَطةُطاُدُى زِادةى ثيطبووُى ٓةوا بةَ تومخة قوزضاُة (كسوًََ ،ةُطةُيص ،ئاضُّ ،يلىًَ ،ظ، توتيا ،كادًيوَ وة قوزِقوغٍ ) Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pbمنووُةى ضسوغتى هة خان ،دةُلوَهَةى توَشى ُيػتوو ،زووةن وة ئاوى بازاْ وةزطريا هة جيَطاى جياواش هة غازى ضويٌَاُى وة كازى غيلازى بوَكسا وةن منووُةى بايَوهوَجى كازتيَلساو بة ثيظ بووُى ٓةوا. - 5دةزئةجناًى هيَلوَهيِةوةى ثةيتى ٓ 8ةغت تومخةكة هة منووُةى تةثوتوَشى ُيػتوو و خاكدا settable dust and soil samplesئةوةى دةزخطت كة ًةوداى ثةيتى تومخةكاْ تازِادة ية بةزفساواُة ،وة ثةيتى تومخةكاُى Cdو Pbبةطػتى هةُيَواْ ئاضتى ديازى كساوى منووُةيى (باؽ) levelsبة ثىَ ى ثيَوةزى هيطتى ٓوَهَةُدى تاشة كساوداثةيتى بووُياْ ئاضتى كازتيَلسدُى
Ni, Cu, Zn,
بوَ كازتيَلسدُدا بوو optimum to the action
. New Dutchlistبةالََ بوَتومخى ُيلىَ هة ٓةُديَم جيَطاى ديازى
action limitsتيَجةزِاُدبوو ،وة ٓوَكازى ئةوةؽ دةطةزِيتةوة بوَ كازتيَلسدُى
فاكتةزى هوَكةهَى .دةزبازةى تومخى كسوَ Crبةطػتى ثةيتيةكةى هة ذيَس ئاضتى منووُةيى (باؽ) optimumبوو .بةالََ بوَ تومخى ًةُطةُيص و ئاضّ Mnو Feضِوز ديازى ُةكساوة بة ثىَ ى ثيَوةزى هيطتى ٓوَهَةُدى تاشة . New Dutchlist - 6هةبةزئةوة ى زِةطةش وةيا تومخى شاهَبووى زِووةن وة يا دازى plane speciesزِواوى جيَطاكاُى ئةَ تويَريِةوةية ضووْ يةن ُةبوو ،بوَية ئةجناًداُى ٓةهَطةُطاُدُى بةزاوزدى بوَ شاُيِى كازتيَلسدُى ثيظ بووُى ٓةوا هة ضةز كوَبووُةوةى ئاضتى ثةيتى تومخة قووزضةكاْ هة ُاو زِووةكة زِوواوةكاْ زِةخطاو ُةبوو هة زِووى شاُطتيةوة.بةالََ بةٓةز ذايَ ،دةزبازةى ثةيتى ئةو 8تومخةى هيَلوَهَةزةوةى هةضةز كسا هة زِووةكةكاُدا ،دةزكةوت بسِى ثةيتية بةزشةكاُى تومخى كسوًََ ،ةُطةُيص ،ئاضُّ ،يلىَ و ًظ ،Cr, Mn, Fe, Ni and Cuهة زِووةكى دازى يوَكاهيجتوَضدا بو و) ، (Eucalyptus camaldulensisبوَ تومخى توَتيا هة زِووةكى دازى توودا بوو) Mulberry (Morus albaوة كادًيوَ هة زِووةكى تسىَ دابوو )Grape (Vitis Sp. ،بةالََ بوَ تومخى قوزِقووغٍ هة زِووةكى دازى طو يَصدابوو ).. Walnut (Juglans regia
C
- 7دةزبازةى ثةيتى ٓةًاْ 8تومخى هيَلوَهَةوة هة ضةز كساو هة ئاوى بازاُى كوَكساوة هة 15جيَطاى جياواشدا وة بوَ 2دوو كاتى بازاْ جياواش ،ئةجناًةكاْ واياْ ُيػاُدا كة جياواشيةكى شوَز ٓةبوو هة ُيَواْ جيَطاكاْ وة ٓةزوةٓا ٓةزدوو كاتى بازاْ بازيِةكاْ (كاتى بازاُى يةكةَ بسيتى بوو هة يةكةَ بازاُى دةضتجيَلى شضتاُى ضايَ كة بةزوازى 28 and 29/ 10/ 2009 بوو بةالََ كاتى بازاُى دووةَ بسيتى بوو هة ُاوةزِاضتى وةزشى شضتاْ كة بةزوازى
. 24 and 25/1/209وةن
دةزكةوتةوةيةكى طػتى ،ئاضتى ٓةًوو ثةييت تومخةكاْ بيَحطة هة تومخى قوزِقووغٍ
ُ Pbةبيَت كةًرت بوو هة ئاضتى
زِيَطةثيَدزاو بة ثىَ ى ثيَوةزى زيَلخساوى تةُدزوضتى جئاُى WHOبوَ ٓةزدوو كاتى بازاُةكة .ئاضتى ثةييت ٓةًوو تومخةكاْ بيَحطة هة تومخى توتيا ُ Znةبيَت تا زادة يةن كةًرت بوو هة منووُةكاُى وادةى بازاُى دووةَ بة بةزاووزد هةطةيَ كاتى بازاُى يةكةَ. - 8هة ئيَطتادا ئوَتَوًَبيى هة غازى ضويٌَاُى هة زِيَطاى طاشة دةزضووةكاُى ئةكحوَشةكاُياْ exhaust emissionكازيطةزيةكى ًةعِةوياْ ٓةية هة ضةز تةُدزووضتى ًسوَظ ،ذيِطة بوَ ( جوَزى ٓةوا ،طاشى خاُووةثالضتيليةكاُْ ،ةٓيَػتِى ئوَشوَْ ،جوَزى ئاو ،ضاًاُة ضسوغتيةكاْ ،بةزٓةًة كػتووكاهَيةكاْ ،تيَلداْ و غيَواُدُى غويَِى ضسوغتى طياُدازةكاْ
habitat
،destruction/disturbanceوة ذاوةذاوى دةُط ) ٓةزوة ٓا هةضةز بابةتى ئابووزى ،كوًَةالَيةتى و زِاًيازى . economic, social and political issuesهةبةزئةوة ،هةَ تويَريِةوةيةدا ذًازةية هة ثيَوةزة
دةزضووةكاُى
ئةكحوَشةكاْ بوَ ٓ 812ػت ضةد وو دواُصة ئوَتوًَبيوى كازكةز بةبةُصيّ gasolineوة 175ضةدوو ذةفتاو ثيَِخ ئوَتوًَبيوى كازكةز بة د يصيَ dieselثػلِيِى بوَ كسا .بةالََ ئةَ ثػلِيِة هة ضةُتةزى ثػلِني و ضاكطاشى دةوزى
Periodic
) Vehicle Inspection (PVIهة غازى ٓةوهيَس ئةجناًدزا ،ضوُلة ئاًيَسى غيلازى طاشة دةزضووةكاُى ئةكحوَشةكاْ exhaust gas analyzerهة غازى ضويٌَاُى ُةبوو ،بةالََ ئيٌَة ئةبيَت ئةو زِاضتية بصاُني كة كوًَةهَةو جوَزى ئوَتَوًَبيوة بووةكاْ هة ٓةزيٌَى كوزدضتاْ ضووْ يةن و يةن ضةزضاوةْ تا زِادةيةكى شوَز. - 9ثيَوةزة parameterدةزضووةكاُى ئةكحوَشةكاْ كة ثػلِيِى بوَكسا بوَ ئوَتوًَبيوى كازكةز بةبةُصيّ
بسيتى بوو هة طاشى
دووةَ ئوَ كطيدى كازبوَْ ،يةكةَ ئوَ كطيدى كازبوَْ ،طاشة ٓايدزوَكازبوَُةكاْ ،زِيَرةى ئوَكطحيِى ًاوة و ثيَوةزى الًبد ا (CO2, ) ، CO, HC, O2 and Lambda, λبةالََ بوَ ئوَتوًَبيوى كازكةز بة د يصيَ ثيَوةزى زِيَرةى تازيلى دووكةيَ وة بةٓا ى كةى smoke opacity and K-Values (Extinction coefficient).ثػلِيِى بوَ كسا وة ثػلِيِةكاُيؼ زِاضتةو خوَ هة بوَزى ئةكحوَشةكاُى طاشة دةزضووةكاْ ئةجناًدزا.
D
- 10بة غيَوةيلى طػتى ،دةزئةجناًى ثيَوةزة وةيا ضتاُدةزة ثيَوزاوةكاْ بوَ ئوَتوًَبيوى ٓةزدوو جوَزى كازكةز بة بةُصيّ و ديصيَ جياواشيةكى شوَزى ُيػاُدا هة بةٓاى ثػلِيِةكاْ وة ٓوَكازى ئةوةؽ دةطةزِيتةوة بوَ ضةُدةٓا فاكتةز هةواُؼ ضاهَى بةزٓةًٔيَِاُى ئوَتوًَبيى ،جوَزى ًازكة ،ضيفات و خةضوَةتى ضووتةًةُى ئوَتوًَبيى ،اليةُى ٓوُةزى دزووضت كسدُى ئوَتوًَبيى، ثازاضنت و بة باغى زِاطستِى ًةكيِةى ئوَتوًَبيى وة بازوودوَخى ٓاتووضوَو و هيَخوزِيِى ئوَتوًَبيى. - 11هة كوَتايدا ،ئةَ تويَريِةوةية ضةزبازى ئةو كازةغيلازى و ثيَواُاُةى كة ثيَػرت ئاًاذةى ثيَلسا ًةبةضتى بوو ٓةزوةٓا كة ضسِىو قةبازةى ٓاتووضوَى ئوَتوًَبيى traffic volume or traffic saturation flow rateهةذًازةيةن هة غةقاًة ضةزةكى و هيَلداْ و ثيَطةيػتِى غةقاًة طسُطةكاُى غازى ضويٌَاُى ئاًازو زووثيَوو بلات تاوة كو بتواُسيت ٓةهَطةُطاُدْ بوَ ثيظ بووُى ٓوَكازى ٓاتوو ضوَبلسيَـت ،ضووُلة ئاغلساية كة ٓاتوو ضوَى ئوَتوًَبيى وضووتاُى ضووتةًةُيلةى بةغدازى ًةعِةوى ٓةية هة ثيظ بووُى ٓةواى هوَكةهَى و ُاوضةطةزى و جئاُى .بة غيَوةيلى طػتى ،ضسِى ٓاتووضوَى ئوَتوًَبيى توُد شوَزو بوو heavyهةشوَزى غةقاًة ضةزةكى و هيَلداْ و ثيَطةيػتِى غةقاًة طسُطةكاُى غازى ضويٌَاُى ،بة واتايةكى تس ذًازةى ئوَتوًَبيوى تيَجةزِيو شياتسة هة تواُاى ٓةهَطستِى غةقاَ و هيَلداُى غةقاًةكاْ وة دةزئةجناًى ئةًةؽ ُةخوَغى ثة يوةضت بة ٓاتووضوَى ئوَتوًَبيى بةتايبةتى ُةخوَغى تةُطةُةفةضى و ٓةضتيازى
(asthma and allergic
) symptomsهة ُاو ًِداالَْ شياد دةكات ضةزبازى شيادبووُى بسِى بةكازبسدُى ضووتةًةُى شياتس هة ئاضتى ُوَزًاي و ثيَويطت.
E
حوكومةتى هةزيَمى كوزدضتان وةشازةتى خويَندنى باالَو تويَرينةوةى شانطتى شانلوَى ضويَمانى -فاكةهَتى شانطتة كشتوكاهَيةكان
هةهَطةنطاندنى ثيظ بوونى هةوا:هيَلوَهَينةوة ية هةضةزهةنديَم ناوضةى نيشتةنى شازضتانى هة شازى ضويَمانى و دةوزوثشتى /هةزيَمى كوزدضتانى عيَساق تيَصيَلة ثيَشلةش بة ئةجنوومةنى زِاطسايةتى فاكةهَتى شانطتة كشتوكاهَيةكان هة شانلوَى ضويَمانى كساوة وةن بةشيَلى تةواوكةز بوَ بةدةضت هيَنانى ثوةى دكتوَزاى فةهطةفة هةثطثوَزى شانطتى ذينطةدا /بوازى ثيظ بوونى هةوا هةاليةن
صاحل جنيب جميد
بةكاهوزيَظ هة شانطتة كشتوكاهَيةكان-شانطتى خان 1978 ماضتةز هة شانطتة كشتوكاهَيةكان-كيمياى خان 1982
بة ضةز ثةزشتى ثسوَفيطوَزى يازيدة دةز د .دهَشاد طةجنوَ امحد
طةالَزيَصانى ()2711كوزدى
شوهقةعدةى ()1432هجسى
ئوَكتوَبةزى ()2011شاينى
