Water Quality Data and GIS Mapping Mr. V. LENIN KALYANA SUNDARAM

Assistant Professor Centre for Water Resources Anna University, Chennai - 600025

Water is Precious and scarce Resource • India is wettest country in the world (relatively), but rainfall is highly uneven with time and space (with extremely low in Rajasthan and high in North-East)‫‏‬ • Out of 4000 BCM rainfall received, about 600 BCM is put to use so far • Water resources are over-exploited resulting in major WQ problems • The analysis of water quality data – space& time – past & future (impact).

2

Water (Prevention and Control of Pollution) Act, 1974 • Preamble: Maintaining and restoring of wholesomeness of water – level of WQ • Every polluter (industry or municipality) has to obtain consent from SPCBs/PCCs • Standards prescribed for effluents • Standards like BIS, WHO, European Union (EU), United States (USEPA) and Australia, etc. 3

Major Water Quality Issues Common issues of Surface and Ground water • • •

Pathogenic (Bacteriological) Pollution Salinity Toxicity (micro-pollutants and other industrial pollutants)‫‏‬

Surface Water • Eutrophication • Oxygen depletion • Ecological health

Ground Water • • • • •

Fluoride Nitrate Arsenic Iron Sea water intrusion 4

Major Factors Responsible for WQ Degradation Domestic: 423 class I cities and 499 class II towns harboring population of 20 Crore generate about 26,254 MLD of wastewater of which only 6955 mld is treated. Industrial: About 57,000 polluting industries in India generate about 13,468 mld of wastewater out of which nearly 60% (generated from large & medium industries) is treated. Non-point sources also contribute significant pollution loads mainly in rainy season. Pesticides consumption is about 1,00,000 tonnes/year of which AP, Haryana, Punjab, TN, WB, Gujarat, UP and Maharashtra are principal consumers. 5

• Domestic sewage is the major source of pollution in India (surface water) which contribute pathogens, the main source of water borne diseases along with depletion of oxygen in water bodies. • Sewage along with agricultural run-off and industrial effluents also contributes large amount of nutrients in surface water causing eutrophication • A large part of the domestic sewage is not even collected. This results in stagnation of sewage within city, a good breeding ground for mosquitoes and contaminate the groundwater, which is the only source of drinking water in many cities.

Increase in Urban Population POPULATION, Crores

30

28.5

25 21.8 20 15.6

15 10.7

10 5

7.8

6.2

4.4 3.3 2.6 2.6 2.8

0 1901

1921

1941

1961

1981

2001

YEAR 7

Water supply and sewage disposal status in class I cities 35000

29782

30000

25000

23826 20607

20000

1978 1988

16662

1995

15191 15000

2003 12145

10000

8638 7007

4037 27562485

5000 142 212 299 423

1850 1281 6031023

Number

Popn (lakh)

6955

0 Water supply

Wastewater

Treatment

8

Water supply and wastewater generation and treatment in class II towns of India

3500 3035 3000

2428

2500

1978 1988

1936

2000 1533

1650

1622

1500

1995 2003

1226 1280

1000 498 500 190 241

370

345 128

207 236 67

27

62

89

0 Number

Popn (lakh)

Water supply

Wastewater

Treatment

9

Comparision of pollution load generation from domestic and industrial sources 25000

22900

Industrial Domestic

20000 15000

13468 9478

10000

4580

5000

3510 1776

0 Wastewater gen (mld)

BOD Generation (t/d)

BOD Discharge (t/d)

10

NATIONAL WATER QUALITY MONITORING PROGRAMME •

Network Comprising of 784 stations.

•

Extended to 26 states & 5 Union Territories

•

Monitoring done on Quarterly/Monthly/Half Yearly.

•

Covers 168 Rivers, 53 Lakes, 5 Tanks, 2 Ponds, 3 Creeks, 3 Canals, 12 Drains and 181 wells. 11

Parameters for National Water Quality Monitoring Core Parameters (9)‫‏‬

Field Observations (7)‫‏‬ Weather Approximate depth of main stream/depth of water table Colour and instensity Odor Visible efluent discharge Human activities around station Station detail

pH Temperature Conductivity Dissolved Oxygen Biochemical Oxygen Demand Nitrate-N Nitrite-N Faecal Coliform Total Coliform

Bio-Monitoring Parameters (3)‫‏‬

General Parameters (19)‫‏‬ COD TKN Ammonia Total Dissolved Solids Total Fixed Solids Total Suspended Solids Turbidity Hardness Fluoride Boron

Chloride Sulphate Total Alkalinity P-Alkalinity Phosphate Sodium Potassium Calcium Magnesium

Saprobity Index Diversity Index P/R Ratio

Trace Metals (9)‫‏‬ Arsenic Nickel Copper Mercury Chromium Total Cadmium Zinc Lead Iron Total

Pesticide (7)‫‏‬ BHC(Total) Dieldrin Carbamate 2.4 D DDT(Total) Aldrin Endosulphan 12 Dichlorophenoxyacetic acid

Abundance of Dissolved Constituents in Surface and Ground Water • Major Constituents (> 5 mg/L) –Ca –Mg –Na –Cl –Si –SO42- - sulfate –H2CO3 - carbonic acid –HCO3- - bicarbonate

Abundance of Dissolved Constituents in Surface and Ground Water • Minor Constituents (0.01-10 mg/L) –B –K –F –Sr –Fe –CO32- - carbonate –NO3- - nitrate

Abundance of Dissolved Constituents in Surface and Ground Water • Trace Constituents (< 0.1 mg/l) –Al –As –Ba –Br –Cd –Co –Cu

– – – – – – –

Pb Mn Ni Se Ag Zn others

Use of Diagrams • There are numerous types of diagrams on which anions and cations (in Meq/L) can be plotted. These include: – – – – – – – – – –

Box plots scatter plots Q-Q plots Line Graphs Bar Charts Tables Piper Stiff Pie Schoeller, etc

Boxplots • A boxplot is a very useful and convenient tool to provide summaries of a dataset and is often used in data analysis. • A boxplot usually presents a dataset through five numbers: extreme values (minimum and maximum values), median (50th percentile), 25th percentile, and 75th percentile

Boxplots for total phosphorus at four stations

Scatter plots • A scatter plot is a very useful summary of a set of bivariate data (two variables), usually drawn before obtaining a linear correlation coefficient or fitting a regression line. • It can be used to detect whether the relationships between two variables are linear or curved, and aids the interpretation of the correlation coefficient or a regression model.

Scatter plot of total phosphorus and instantaneous flow at Station 1, three possible outliers are in red

locally weighted scatter plot smoothing method

Q-Q plots • A Q-Q plot presents the quantities of a dataset against the quantities of another dataset (Chambers et al., 1983; Gnanadesikan & Wilk, 1968). • It can be used to determine whether two datasets come from populations with the same distribution. • The greater the departure from the reference line, the greater the evidence to conclude that these two datasets come from populations with different distributions. • If their distributions are identical, the Q-Q plot follows a straight line.

17

15

16

16

19

19

25

23

28

27

25

21

17

18

16

27

15

16

70

27

90 80

15

100

60 50

BOD >6 BOD 3-6

67

60

59

57

57

60

59

57

58

30 20

64

40

BOD<3

10 0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

29

River basin-wise riverine length under different level of pollution 14000

12000

BOD <3 mg/L BOD 3-6 mg/L BOD >6 mg/L

8000

6000

4000

2000

Su T be api rn re kh a Br ah m in M i ah an ad G od i av ar i Kr is hn a Pe nn ar C au ve ry G ha gg ar M ed iu m M in or

0

In du s G Br ang a am ap ut ra Sa ba rm at i M ah N i ar m ad a

Riverine length, Km

10000

River basin

30

Stiff Diagrams • Concentrations of cations are plotted to the left of the vertical axis and anions are plotted to the right (meq/L) • The points are connected to form a polygon. • Waters of similar quality have distinctive shapes.

Stiff Diagrams in Cyprus

!(

yk-27

!(

yk-16

!( !(

yk-101

yk-31

Pie Diagrams Igneous Volcanic

Sandstone Aquifer

Na

Na

Ca

Ca

Mg

Mg

Cl

Cl

SO4

SO4

HCO3

HCO3

NO3

NO3

Calcium bicarbonate water

Magnesium bicarbonate water Alluvium

Shale with Salts

Na

Na

Ca

Ca

Mg

Mg

Cl

Cl

SO4

SO4

HCO3

HCO3

NO3

NO3

Sodium chloride water

Sodium-calcium bicarbonate water with nitrates

Schoeller Diagram Line Plot That Characterizes Water • Semi-logarithmic diagram that represents major ion analyses in meq/l. • Demonstrates different hydrogeochemical water types on the same diagram. • Number of analyses plotted at one time is limited. • Actual parameter concentrations are displayed.

Schoeller Diagram Concentration (meq/l) logarithmic scale

1000.0000

Groundwater

100.0000

10.0000

Surface Runoff 1.0000

Precipitation

0.1000

0.0100

0.0010

Ca

Mg

K Si Na Cl F Major anions and cations

SO4 HCO3

Average concentration of major anions and cations of groundwater, surface-runoff, and rainfall

Piper Diagram Shows Groupings of Water Types Major ions are plotted as cation and anion percentages in meq/l in two base triangles. Total ions are set to equal 100%. Data points in the two triangles are projected to central diamond. Allows comparison of a large number of samples. Shows clustering of samples and water type.

Sea Water Contamination • Na/Cl, SO4/Cl, Br/Cl, B/Cl, K/Cl, Cl/(HCO3+CO3), Ca/(HCO3+SO4) and Mg/Ca

GIS Mapping Used to store, retrieve, analyze, etc. • Point • Line • Polygon

Weighted water quality indices • Water quality indices are usually obtained by assigning a suitable weight to each water quality parameter and averaging them using some type of average functions.

•

The water quality index proposed by Pesce & Wunderlin (2000) is:

where n is the number of the water quality parameters, Si is the score of the ith parameter, ωi is the relative weight given to Si satisfying Σωi = 1. k is a subjective constant representing the visual impression of river contamination. The value of k ranges from 0.25 (for highly contaminated water) to 1 (for water without contamination). WQISA tends to overestimate the pollution due to the use of a subjective constant, which is not correlated with the measured parameters (Kannel et al., 2007).

• k = 1, the above eqn. is called the weighted arithmetic water quality index

• weighted geometric mean function (Brown et al., 1970; McClelland, 1974),

* weights based on the importance of the water quality situation.

Unweighted water quality indices • Landwehr & Deininger (1976), arithmetic/geometric water quality indices

WQIG is always lower than WQIA

harmonic square water quality index

Compared to WQIWA and WQIWG, WQIH is the most sensitive to changes in single water quality parameter (Cude, 2001).

Harkins’ water quality index • An objective water quality index was proposed by Harkins (1974), which is based on Kendall’s nonparametric multivariate ranking procedure

The comparison of results (five methods) indicated that the five indices are correlated well and WQIHR was the lowest of the five. Therefore, it is suggested adopting any of the four indices except WQIHR.

Landwehr (1974) by developing Water Quality Index (WQI) Categories of Water Quality Indices

Water Quality Index

Description

0-25

Excellent

26-50

Good

51-75

Poor

76-100

Very Poor

>100

Unfit for drinking

Water Quality Parameters, BIS Standards and Weighting factor (ai) Parameters

BIS Standards

Weighting factor (ai)

pH

8.5

0.24

TDS (mg/l)

500

0.0041

Turbidity (NTU)

50

0.41

HCO3 (mg/l)

500

0.0041

Ca (mg/l)

75

0.027

Mg (mg/l)

30

0.0681

Cl (mg/l)

250

0.0082

Na (mg/l)

200

0.0102

K (mg/l)

20

0.102

SO4 (mg/l)

250

0.0082

NO3 (mg/l)

45

0.045

Water Quality Classification based on WQI of September 2010, January, February and March 2011 ID

Name

Latitude and Longitude

WQI (Sep)

WQ Rating

WQI (Jan)

WQ Rating

WQI (Feb)

WQ (Rating)

WQI (Mar)

WQ Rating

Well 1

Renganathan street

12° 55' 14.74"N, 80° 13' 51.09"E

95.8

Very poor

61.84

Poor

113

Unfit for drinking purposes

125

Unfit for drinking purposes

Well 2

Near Okkium Maduvu

12° 55' 7.07"N, 80° 14' 2.33"E

141

Unfit for drinking purposes

77.31

Very poor

112

Unfit for drinking purposes

121

Unfit for drinking purposes

Well 3

Indragandhi street

12° 55' 1.01"N, 80° 13' 55.37"E

63.6

Poor

42.73

Poor

93.3

Very Poor

99.8

Very Poor

Well 4

Kupusamy street

12° 54' 54.04"N, 80° 14' 2.15"E

62.9

Poor

56.3

Poor

66.8

Poor

69.6

Poor

Well 5

Kalaimagal nagar

12° 54' 48.69"N, 80° 14' 4.60"E

54.7

Poor

42.4

Good

49.4

Good

56.8

Poor

Well 6

Government well

12° 54' 50.47"N, 80° 13' 55.19"E

52.3

Poor

49.3

Good

58.8

Poor

62.3

Poor

Well 7

Muthamil nagar

12° 54' 54.05"N, 80° 13' 42.17"E

100

Very poor

82.7

Very poor

93.78

Very Poor

101

Unfit for drinking purposes

Well 8

Mahatma Gandhi street

12° 54' 43.34"N, 80° 13' 48.06"E

45.3

Good

32.4

Good

42.9

Good

56.2

Poor

Well 9

Vendraai amman kovil steet

12° 54' 42.43"N, 80° 13' 56.11"E

87.6

Very Poor

64.3

Poor

91.46

Very Poor

99.3

Very Poor

Well 10

Rangasamy street

12° 54' 41.55"N, 80° 14' 2.15"E

73.5

Very Poor

62.6

Poor

71.8

Poor

76.8

Very Poor

Well 11

Sadagopan street

12° 54' 37.63"N, 80° 13' 58.22"E

49.8

Good

39.34

Good

43.1

Good

49.6

Good

Near Kovilampakkam, Chennai

• [email protected] • [email protected]

Reference • Voudouris, (2012), “Water Quality Monitoring and Assessment”, Eds.