Environ Monit Assess (2007) 127:87–96 DOI 10.1007/s10661-006-9262-1

ORIGINAL ARTICLE

Evaluation of the trend of air quality in Taipei, Taiwan from 1994 to 2003 Shuenn-Chin Chang · Chung-Te Lee

Received: 27 November 2005 / Accepted: 18 April 2006 / Published online: 18 August 2006 C Springer Science + Business Media B.V. 2006 

Abstract The data collected from the five air quality monitoring stations established by the Taiwan Environmental Protection Administration (TEPA) in Taipei City were analyzed to assess the changes in air quality. The analyses reveal that the air quality in Taipei City improved over the last decade from 1994 to 2003, as evidenced by the significant downward trends of the various primary air pollutant concentrations, such as CO, NOX , SO2 , and PM10 . An air pollution fee was collected by TEPA in 1995, and several air pollution control measures were likewise taken to improve the air quality in Taiwan. However, although the extreme daily maximum O3 concentrations occurred more frequently in earlier years and showed a downward trend, its moderately high concentrations increased annually in recent years. It implied that after the reduction of various primary pollutant concentrations, the effective reduction of O3 pollution still remains an important issue. Keywords Pollutant trend . Pollutant temporal variation . Urban air quality . Primary air pollutants . Secondary air pollutants

S.-C. Chang . C. Lee () Graduate Institute of Environmental Engineering, National Central University, 300 Jhongda Rd, Jhongli, 32001, Taiwan e-mail: [email protected]

1 Introduction Due to dense population and the increasing number of motorized vehicles, several cities in Taiwan are facing the problem of worsening air quality like in many other large international metropolises. In response to this, the Taiwan Government set new ambient air quality standards in 1992. Consequently, in 1995, the Taiwan Environmental Protection Administration (TEPA) collected an air pollution fee, and various governmental agencies have dedicated themselves to improving air quality by lowering sulfur content in gasoline and diesel fuel, adjusting the traffic signal to reduce waiting time, establishing special lanes for buses, and so on. Generally, changes in meteorological conditions cause more variations in air pollutant concentrations than changes in pollutant emissions over a monthly or seasonal period. For example, Marcazzan et al. (2001) showed that persistent winter thermal inversions caused the accumulation of pollutants in Milan. They also indicated that ambient weather conditions, e.g., atmospheric temperature, relative humidity, and short-wave radiation might affect chemical reactions resulting in the formation of secondary aerosol. Furthermore, in addition to the local accumulation of pollutants due to poor atmospheric conditions unfavorable to dispersion, the pollution contributed by transboundary transportation and secondary photochemical reactions cannot be ignored. The composition of particulates during the winter months in Hong Kong apparently Springer

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revealed their transportation from southeastern China (Ho et al., 2003). In Taiwan, the air quality is normally affected by meteorological conditions causing obvious seasonable variations from the accumulation of local pollution or long-range transport. The results of air quality monitoring show that under stable atmospheric conditions, high concentrations of pollutants appear as thermal inversion layer occurs (Tsuang and Chao, 1999). The PM10 concentration, which has high seasonal and spatial variations, is at its highest level in northern Taiwan during March to May and is apparently caused by yellow sand brought over by northeasterly winds after the occurrence of sandstorms in Mainland China (Lin, 2001). Ozone is one of the major pollutants that cause poor air quality in Taipei City. Ambient O3 is dependent on photochemical products from O3 precursors, regional horizontal and stratospheric transport, surface sediment, and NO titration effect (Tao et al., 2005). The variation of O3 concentration is not only affected by the emissions of O3 precursors, e.g., nitrogen oxide (NOX ) and volatile organic compounds (VOCs) from different sources but also by various meteorological conditions. Vukovich (1994) pointed out that year-to-year variations greater than 64% are contributed by meteorological changes. In Taipei, the occurrence of high O3 concentration is always associated with the strong insolation in the morning, a rapid temperature rise, less clouds, and/or the absence of precipitation (Liu et al., 1994).

In this paper, we analyze the air quality in Taipei City from 1994 to 2003 to evaluate the variation trend of air quality and the improvement of air quality after the opening of Taipei’s mass rapid transit (MRT) system, and the lowering of sulfur content in fuel and diesel oils. 2 Methods 2.1 Area of study Taipei City, a basin located in northern Taiwan (Fig. 1) which has an area of 272 km2 , is the area of study. This city has a population of 2.62 million, and has 6,536 motorized vehicles per square km, among which are 700,000 cars and more than 1 million motorcycles. According to the source inventory from TEPA, except for emissions from three municipal incineration plants, there is no significant stationary pollution source in Taipei City; most CO and NOX come from vehicle emissions, which constitute 98% and 77% of the total CO and NOX emissions, respectively (TEPA, 2005). 2.2 Sources of data The Taiwan Air Quality Monitoring Network (TAQMN) was established by TEPA in September 1993. It consists of 74 monitoring stations around the island. Five ambient automated air quality monitoring stations are set up in Taipei City on top of school buildings with inlets measuring 15 meters above the ground

Fig. 1 Geographic locations of the five air quality monitoring stations (shown in dots) in Taipei City, Taiwan Springer

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level. The monitoring data used in this work include hourly data of SO2 , CO, NOX , PM10 (particles with an aerodynamic diameter less than or equal to 10 μm), O3 , and wind speed from 1994 to 2003. Data were collected using commercial monitoring instruments designated by the US EPA as an equivalent or reference method, and manufactured by Thermo Environmental Instruments, Inc. (Franklin, MA, USA). These instruments include the following: model 43 pulsed fluorescent SO2 analyzer (EQSA-0276-009) for SO2 , model 48 gas filter correlation ambient CO analyzer (RFCA-0981-054) for CO, model 42 chemiluminescence NO-NO2 -NOX Analyzer (RFNA-1289-074) for NOX , model 49 U.V. Photometric Ambient O3 Analyzer (EQOA-0880-047) for O3 , and model 650 (formerly: Wedding and Associates) PM10 Beta gauge automated particle monitor (EQPM-0391-081) for PM10 . TAQMN instruments were well operated and maintained to ensure data quality. Scheduled quality control procedures included daily zero and span checks, biweekly precision check, quarterly multiple-point calibration, and data validation. Additionally, independent quality assurance program and performance audit were also undertaken. 2.3 Data processing In order to avoid the influence of pollutant variation from one single station, the city’s hourly or daily average obtained at all five stations was used to evaluate the air quality in Taipei. Only the average of data collected more than 16 effective hours per day will be considered as a valid daily average such that a better representative daily average is obtained. The O3 characteristics were evaluated by calculating the city’s daily average of O3 concentration (O3,avg ) and the city’s daily average of maximum O3 concentration (O3,max ). Eder et al. (1993) used the daily maximum hourly O3 concentration to analyze the spatial and temporal variability of O3 over the eastern US nonurban regions. This is based on the consideration that the extreme concentration of O3 has a more adverse impact on human beings or the environment. Higher O3 concentrations mostly occur during one o’clock to three o’clock in the afternoon when the boundary layer of O3 distribution was rather homogeneous. Thus, the data collected at near-surface were representative of the entire boundary layer. At night, when photochemical reactions stopped, the NO titration effect would

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dissipate O3 . Hence, using O3,max will better index the seriousness of O3 pollution. Considering mutual exchanges of NO, NO2 , and O3 occurring in the atmospheric environment, the sum of O3 and NO2 is often defined as the total oxidants (OX ) (OX = O3,avg + NO2 ) (Kley et al., 1994; Clapp and Jenkin, 2001). Thus, concentration variations of the daily average of OX were also analyzed in this work.

3 Annual distribution of pollutant concentrations Box plots of various air pollutants, e.g., CO, NOX , SO2 , PM10 , O3,max , O3,avg , and OX from 1994 to 2003 are shown in Fig. 2 (a) to (g). The maximum annual mean concentrations of CO (1.3 ppm), NOX (56 ppb), and SO2 (7 ppb), and their maximum daily averages (4.4 ppm, 175 ppb, and 25 ppb, respectively) for the last decade were noted in 1994 and 1995, respectively. The data indicate that the air pollution due to emissions was most serious during the said years as shown in Fig. 2 (a) to (c). The maximum annual mean of PM10 concentration (59 μg m−3 ) occurred in 1994, while the maximum daily concentration (230 μg m−3 ) for the last decade took place in 2000. However, the annual mean of PM10 concentration in 2000 was far below the level in 1994 as shown in Fig. 2 (d). This observation implies that PM10 may be influenced by occasional incidents of pollution such as the yellow sand transported from Mainland China which lead to a higher daily average concentration (Lin, 2001). The annual mean of O3,max (53 ppb) for the last decade was highest in 2003, while the maximum O3,max (164 ppb) occurred in 1995 as shown in Fig. 2 (e). However, both the maximum annual mean of O3,avg (27 ppb) and the maximum of O3,avg (66 ppb) occurred in 2003 as shown in Fig. 2 (f). The data show that extreme O3 concentrations (the maximum O3,max ) occurred in earlier years, but medium to high concentrations (e.g., the annual mean of O3,max and O3,avg , and the annual maximum of O3,avg ) increased annually in recent years. The annual mean of O3,avg escalated from an average of 19 ppb in 1994 to 27 ppb in 2003; the annual maximum of O3,avg also increased from 51 ppb in 1994 to 66 ppb in 2003. In contrast, the annual mean of OX varied only slightly between 50 ppb and 53 ppb with the maximum daily concentration of 106 ppb that occurred in 1995. The next highest concentrations occurred in Springer

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1999 (104 ppb) and 2000 (103 ppb) as shown in Fig. 2 (g). The data show no obvious upward trend for OX indicating that when an obvious decrease of NOX concentration occurs, the reduction of NO titration effect on the contrary results in the gradual increase of O3,avg , which is equivalent to the gradual reduction of the excess NO concentration. 4 Annual trend of pollutants Fig. 2 Box plots of yearly concentration distributions for various pollutants from 1994 to 2003, (a) CO; (b) NOX ; (c) SO2 ; (d) PM10 ; (e) O3,max ; (f) O3,avg ; (g) OX . The horizontal dash line in each graph denotes the level of 10-year average for a pollutant

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In order to evaluate the annual trend for pollutant variations, simple linear regression analyses were carried out on various statistical parameters (e.g., mean, median, as well as the 75th percentile, 90th percentile, 98th percentile, and 99th percentile concentrations) to estimate the trend line slope, standard errors (SEs), and

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Table 1 The trend line slope, standard errors (SEs), and coefficient of determination (R2 ) estimated using simple linear regression from different pollutant frequency distributions from 1994

Mean

Median

75th

90th

95th

98th

99th

Slope SE R2 Slope SE R2 Slope SE R2 Slope SE R2 Slope SE R2 Slope SE R2 Slope SE R2

to 2003. SE is the estimated standard deviation of slope divided by the root of case number

CO (ppm)

NOX (ppb)

SO2 (ppb)

PM10 (μg m−3 )

O3,max (ppb)

O3,avg (ppb)

OX (ppb)

−0.060 0.004 0.96 −0.055 0.006 0.91 −0.070 0.007 0.93 −0.091 0.006 0.97 −0.107 0.010 0.93 −0.126 0.015 0.90 −0.149 0.018 0.90

−2.05 0.20 0.93 −1.94 0.24 0.89 −2.42 0.27 0.91 −2.88 0.29 0.93 −3.71 0.38 0.92 −3.56 0.73 0.75 −5.02 0.96 0.78

−0.51 0.07 0.86 −0.44 0.06 0.85 −0.68 0.10 0.85 −0.95 0.12 0.88 −1.13 0.14 0.89 −1.28 0.21 0.82 −1.49 0.25 0.82

−1.38 0.35 0.66 −1.32 0.32 0.68 −1.89 0.63 0.53 −2.61 0.72 0.62 −2.82 0.96 0.52 −3.48 1.38 0.44 −3.98 1.76 0.39

1.01 0.13 0.88 1.23 0.15 0.89 1.18 0.36 0.57 0.98 0.29 0.59 0.61 0.48 0.17 −0.82 0.44 0.31 −1.12 0.47 0.42

0.87 0.09 0.93 0.88 0.08 0.94 1.11 0.09 0.95 1.19 0.14 0.90 1.36 0.20 0.85 1.39 0.26 0.78 1.49 0.25 0.82

0.10 0.12 0.08 0.18 0.14 0.17 0.13 0.09 0.23 0.09 0.15 0.05 −0.05 0.23 0.01 −0.20 0.29 0.06 −0.60 0.31 0.32

coefficient of determination (R2 ) over the last decade. As shown in Table 1, the standard error of the dependent variable can be used to calculate the interval of the confidence level of slope (slope with 1-α interval of confidence = (Slope – tn−2,1−0.5α ∗ SE, Slope + tn−2,1−0.5α ∗ SE), where α is the significance level, and tn−2,1−0.5α denotes 100(1−α)% point of the t distribution with n−2 degrees of freedom). The R2 value should be between 0 and 1. When it approaches 1, the calculated slope will have the highest reliability. All the aforementioned statistical parameters have decreasing trends of CO, NOX , SO2 , and PM10 . The decreasing trends of the annual average concentrations are −0.06 ppm year−1 (standard error = 0.004 ppm year−1 ) for CO, −2.05 ppb year−1 (standard error = 0.20 ppb year−1 ) for NOX , −0.51 ppb year−1 (standard error = 0.07 ppb year−1 ) for SO2 , and −1.38 μg m−3 year−1 (standard error = 0.35 μg m−3 year−1 ) for PM10 . The results indicate that these primary pollutants had obvious reduction over a long period of time. Although the primary pollutants had obvious downward trends, the statistical quantities (e.g., mean, median, 75th percentile, 90th percentile, and 95th percentile) for O3,max displayed upward trends. Of

particular interest is the lack of obvious increasing trend for the 98th and the 99th percentile concentrations. The 99th percentile concentration showed a decreasing trend of about −1.12 ppb year−1 (standard error = 0.47 ppb year−1 ). All quantities (mean, median, 75th percentile, 90th percentile, 98th percentile, and 99th percentile) of O3,avg showed obvious increasing trends. However, when the NO titration effect was considered and included in the statistical analyses of the daily OX average (OX = O3,avg + NO2 ), all the calculated R2 values were too small with the maximum being only 0.32. This indicates that OX had no obvious increasing or decreasing trend as shown in Table 1. The increasing O3,avg concentration (shown in Table 1) may originate from a reduction of NOX , leading to a corresponding decrease of the titration effect.

5 Weekly cycles of traffic volumes and pollutant concentrations According to TEPA source inventory, 98% and 77% of the total CO and NOX were emitted from motor Springer

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Fig. 3 Hourly variations of weekday and weekend traffic patterns (hour 1 denotes 0:01-1:00)

vehicles in Taipei City (TEPA, 2005). Thus, air pollution was affected greatly by the diurnal traffic variation. The daily traffic through bridges that connect Taipei City with neighboring towns and villages is shown in Fig. 3. On weekdays (Monday through Friday), the peak traffic occurs during eight to nine in the morning and at seven in the evening. The afternoon peak was relatively mild and lasted for over a longer period. From three to five o’clock in the morning, the traffic volume was lowest for the whole day. Every Saturday, from eight to nine in the morning, the traffic volume was lower than the weekday average, but it was even lower on Sundays. However, during afternoon hours, particularly from twelve noon until two o’clock in the afternoon, and early morning hours from

one to five o’clock during both Saturdays and Sundays, there was a higher traffic volume as compared to that during weekdays. These observations show a typical pattern of city life. Thus, the pollution emission is certainly expected to change according to traffic volume variation. Comparisons of the differences between the pollutant concentrations observed on weekends (Saturday and Sunday) and on weekdays (Monday through Friday) are shown in Table 2. The CO, NOX , and SO2 concentrations during Saturdays were about the same as those observed during weekdays. During Sundays, however, these pollutant concentrations may drop at 81 to 85% of the weekday concentrations. The Saturday PM10 concentration was only 1% higher than that

Table 2 Weekend-Weekday statistics on various air pollutants from 1994 to 2003

weekday

Sat

Sun

Sat/Weekday Sun/weekday

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mean SD Max Min n mean SD Max Min n mean SD Max Min n Ratio Ratio

CO (ppm)

NOX (ppb)

SO2 (ppb)

PM10 (μg m−3 )

O3,max (ppb)

O3,avg (ppb)

OX (ppb)

1.02 0.40 3.52 0.26 2607 1.02 0.43 4.42 0.36 522 0.87 0.36 3.51 0.33 522 1.00 0.85

46.3 18.8 165.2 5.1 2606 46.0 20.3 175.2 14.0 522 38.2 17.0 173.0 8.5 522 0.99 0.83

4.4 3.1 25.4 0.1 2607 4.3 3.1 22.0 0.3 522 3.6 2.7 20.0 0.1 522 0.97 0.81

49.3 25.0 229.8 13.0 2606 49.7 24.8 191.1 13.7 521 44.9 22.7 144.5 14.4 522 1.01 0.91

48.3 26.3 163.7 7.1 2608 48.6 26.2 133.9 7.8 522 47.5 23.4 125.8 7.4 522 1.01 0.98

22.9 8.6 65.6 4.1 2605 23.5 8.9 56.3 4.5 522 24.7 8.7 56.2 4.2 522 1.03 1.08

52.0 13.2 103.7 12.2 2605 52.4 13.1 106.4 13.9 522 50.1 12.4 89.3 14.8 522 1.01 0.96

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of the weekday PM10 concentration, and the Sunday PM10 concentration dropped to less than 91%. Since wind speeds during weekends were not much different during weekdays, the observed pollutant concentration differences were probably caused by the lower traffic volume on Sundays, resulting in lower pollution emission from motor vehicles. Saturday traffic volume was only lower than weekdays from seven to ten in the morning; for the remaining hours, it exceeded the weekday level as reflected in Fig. 3. However, gaseous pollutants, e.g., CO, NOX , and SO2 , had greater decreasing magnitudes than PM10, indicating that the former were more influenced by the traffic volume than the particulate pollutants. PM10 is the sum of all fine particles from different emission sources. It has different chemical compositions and hence different effects on human health (ven der Wal and Janssen, 2000). The important sources of PM10 include dust resuspension, sea spray, tire abrasion, and secondary photochemical reactions in addition to motor vehicle emissions and long-range transport (Smith, et al., 2001; Lenschow, et al., 2001). Hence, PM10 concentration cannot be accounted only from a single dominant source. The production of O3 is from nonlinear chemical reactions (Br¨onnimann and Neu, 1997). Even the Sunday NOX concentration was reduced by 17%, while O3,max did not differ much during Sunday and weekdays. The Sunday concentration was only 2% lower than the weekday level. Instead of being lower, the Sunday O3,avg was, on the contrary, 8% higher than the weekday concentration. However, the Sunday OX was 4% lower than the weekday average; one may speculate

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that a lower NO titration effect would lead to the observed higher O3,avg on Sundays than weekdays. This implied that even though significant downward trends of the various primary air pollutant concentrations were observed over the last decade, the moderately high concentrations of O3 increased annually in recent years.

6 Discussion 6.1 Effectiveness of the implemented control measures From 1994 to 2003, the concentrations of the various primary air pollutants, e.g., CO, NOX , SO2 , and PM10 in Taipei City decreased annually indicating that the various air pollution control measures implemented by the government were effective. Using 1994 data as the reference, the normalized annual averages for all other years (dividing by the 1994 annual average) are shown in Fig. 4. Since the air pollution control fee was collected in July 1995, several air pollution control measures were implemented, concurrently resulting in 17% reduction of the annual average of various primary pollutants in 1996. In July1996, the Taipei metropolitan area started to impose lowering the sulfur content of fuel oil from 3% to below 0.5%. The SO2 concentration in 1996 decreased obviously to 72% of the 1994 level and further to 55% in 1997. In July 1998, the sulfur content in diesel oil was lowered to 0.05%; the SO2 concentration was further reduced to 49% in 1994. Thus, the effect of implementing air pollution control measures has been verified.

Fig. 4 Normalized annual averages for various pollutants, and the number of passengers using Taipei’s MRT (mass rapid transit) system

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Fig. 5 Comparison between vehicle kilometers traveled by motorcycles and small passenger cars, and the number of passengers using the Taipei MRT system

Furthermore, Taipei’s MRT system lines were gradually put into operation with daily passengers amounting to 30,000 in 1996 and 170,000 in 1998. During 1999, the first phase of the MRT network was formed to serve 340,000 passengers daily. Since 2000, the number of daily passengers has exceeded 740,000. With the rapid growth of the MRT system, NOX and CO, which comes mainly from motor vehicle emission, decreased from 79% and 87% in 1998 (based on 1994 concentration) to 69% and 80%, respectively, in 1999. The growth of MRT usage is, however, not well reflected in the reduction of NOX or CO. Fig. 5 shows the vehicle kilometers traveled (VKT) for two-stroke motorcycles, four-stroke motorcycles, small passenger cars, and MRT passengers for the years compiled (TEPA, 2005). The VKT from two-stroke motorcycles increased from 3.32 × 109 km year−1 in 1994 to a maximum of 3.74 × 109 km year−1 in 1996 but persistently declined to 2.23 × 109 km year−1 in 2003. This is probably due to stringent emission standards imposed on new motorcycles effective in 1998. Aging two-stroke motorcycles were gradually replaced with four-stroke motorcycles through owners’ expectation of this regulation change even before 1998. It resulted in the steady increase of VKT from four-stroke motorcycles but a persistent decrease of VKT from all motorcycles. The fastest reduction of VKT from all motorcycles occurred in the year 2000, while the fastest growth in the number of MRT passengers occurred. Although the VKT from all motorcycles was reduced by 18% from 1999 to 2003, the VKT from cars increased by 7% during the same time period. Apparently, this increase offsets the reduction of VKT from motorcycles for the ambient level of NOx Springer

or CO. The causes of the increase in small passenger cars after the growth in the number of MRT passengers were not readily known. However, more parking spaces may have been available in the city because more upper-ranking employees had already moved to the suburban area due to better road access. 6.2 Annual rise in medium to high ozone concentrations The efforts put forth for many years have obviously improved the air quality in Taipei City for the past decade as evidenced by the drastic reduction of primary pollutant concentrations. However, the secondary photochemical pollutant, e.g., O3 ,max , showed obvious rising mean, median, 75th percentile, and 90th percentile concentrations regardless of the decreasing 99th percentile value as shown in Table 1. To evaluate the variations of frequency distribution for O3,max concentration, we divided the data into five ranges: less than 40 ppb, 40 to 60 ppb, 60 to 80 ppb, 80 to 120 ppb, and greater than 120 ppb. The annual frequency distributions from 1994 to 2003 are shown in Fig. 6 in which the percentage of O3,max concentration less than 40 ppb range was reduced annually. Although the percentage of greater than 120 ppb range also decreased, the percentage of 40 to 120 ppb ranges increased every year. As the extreme and medium high O3,max concentrations are concerned, one can make the following observations. The number of days with O3,max concentration greater than 150 ppb summed over the five stations in Taipei City was reduced from seven days in 1994

Environ Monit Assess (2007) 127:87–96

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Fig. 6 Annual O3,max (the daily maximum ozone concentration) distribution in different concentration ranges. For O3,max > 120 ppb, the right axis shows the detailed variations over the years

to one day in 2003. The number of days with O3,max concentration greater than 120 ppb observed was 21 in 1994 that is not much different from the 22 days in 2003. However, the number of days with O3,max concentration greater than 90 ppb increased from 51 days in 1994 to 79 days in 2003. These observations show that the occurrence of extremely high O3,max concentrations (greater than 150 ppb) in Taipei City is decreasing, but one should be alerted that the incidence of moderately high O3,max concentrations (greater than 90 ppb) could increase drastically. This trend of O3,max concentrations is worthwhile for TEPA in setting up its control strategies for Taipei City’s air quality.

7 Conclusions This work carried out the analyses of monitoring data on CO, NOX , and SO2 collected from 1994 to 2003 at the five automated air quality monitoring stations in Taipei City. With a decreasing traffic volume on Sundays, the concentrations of various pollutants from local motor vehicles were reduced. However, the reduction magnitude for gaseous pollutants, e.g., CO, NOX , and SO2 , was greater than that for particulate pollutants such as PM10 . For O3 , the reduction of precursor pollutants was varied; O3,max was slightly lower on Sundays than on weekdays. On the contrary, O3,avg on Sundays was higher than the weekday level. The statistical results show that the air quality in Taipei City had obviously been improved over the past decade due to the reduction of CO, NOX , SO2 , and PM10 concentrations. The accomplishment parallels

the efforts put forward by the government in levying air pollution control fees, lowering the sulfur content in fuel and diesel oils, and completing the MRT network. Interestingly, the increase in MRT usage was not well reflected in the reduction of NOX or CO. Thus, it is viewed that the increase of VKT of small passenger cars offsets the VKT from motorcycles. Although the statistical data for O3 show that the extreme value of the daily maximum concentration (99th percentile concentration) was reduced after lowering the concentration of O3 precursors, the remaining precursor concentrations are still sufficient to produce O3, thereby leading to the increase of the medium to high O3,max (median, 75th percentile, and 90th percentile concentrations). Future research must be carried out to study the cause of medium to high O3,max and variations of O3 precursors. Acknowledgement We are grateful to the Taiwan Environmental Protection Administration (TEPA) for providing the monitoring data. Although the data were taken from TEPA’s official monitoring network, the results of this study were not peer-reviewed by them, and the mention of instrument trade names does not connote their endorsement.

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