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Meteorologically adjusted long-term trend of ground-level ozone concentrations in Kaohsiung County, southern Taiwan
Atmospheric Environment 44 (2010) 3605e3608
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Short communication
Meteorologically adjusted long-term trend of ground-level ozone concentrations in Kaohsiung County, southern Taiwan H.C. Li, K.S. Chen*, C.H. Huang, H.K. Wang Institute of Environmental Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan, ROC
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 October 2009 Received in revised form 6 April 2010 Accepted 7 April 2010
Since meteorological changes strongly affect ambient ozone concentrations, trends in concentrations of ozone upon the adjustment of meteorological variations are important of evaluating emission reduction efforts. The goal of this work is to study meteorological effects on the long-term trends of ozone concentration using a multi-variable additive model. Data on the hourly concentrations of ozone were collected from four air-quality stations from 1997 to 2006 in Kaohsiung County to determine the monthly, seasonal and annual average concentrations of ozone. The model incorporates seven meteorological parameters e pressure, temperature, relative humidity, wind speed, wind direction, duration of sunshine and cloud cover. The simulated results show that the long-term ozone concentration increases at 13.84% (or 13.06%) monthly (or annually) after meteorological adjustments, less than at 26.10% (or 23.80%) without meteorological adjustments. Wind speed, duration of sunshine and pressure are the three dominant factors that influence the ground-level ozone levels. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Ambient ozone Ozone trend Meteorological adjustment Multi-variable additive model
1. Introduction Ground-level ozone (O3) is a secondary pollutant, generated mainly in photochemical reactions of its precursors, including volatile organic compounds and nitrogen oxides (Seinfeld and Pandis, 1998). Since high ozone levels may cause severe health effects, many governments have implemented pollution control programs to reduce the emissions of the precursors. Although most primary pollutants are emitted during the course of human activities, such as the use of motor vehicles and the operation of power plants and other stationary sources, high ozone events are also related to the meteorological conditions (NRC, 1991; EPA, 2005). Therefore, the effect of meteorological conditions on ambient ozone level is important in evaluating the ozone trends. Various linear and nonlinear regression models have been employed with statistical estimates to evaluate the effect of meteorological variations on ozone concentrations (Thompson et al., 2001). For example, the probabilistic approach (Cox and Chu, 1993), the multi-variable additive model (Flaum et al., 1996; Xu et al., 1996; Holland et al., 1999), the multilayer neural network (Gardner and Dorling, 2000; Lu and Chang, 2005) and the generalized additive model (Zheng et al., 2007) have been adopted to investigate the effect of temperature, wind speed, sunshine hour
* Corresponding author. Tel./fax: þ886 7 5254406. E-mail address: [email protected] (K.S. Chen). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.04.011
and/or relative humidity on the concentration of ozone. Most related works have focused on daily maximum ozone concentrations, except that Holland et al. (1999) have considered monthly and weekly average concentrations of air pollutants. These works have presented useful results concerning the separation of meteorological factors from human-related emissions. This work investigates meteorological effects on the long-term trend of ozone concentration in Kaohsiung County, a heavily industrialized region, and one of the worst regions for air-quality in Taiwan (Chen et al., 2004). The effects of seven meteorological parameters on the ozone trends are examined. A multi-variable additive model was employed to investigate the long-term ozone trend after meteorological adjustments, since it characterizes the complex relationships among the predictor and response variables better than a linear model does, particularly in characterizing the seasonal (or cyclic) variations of meteorological data. 2. Methods Fig. 1 presents four air-quality monitoring stations in Kaohsiung County, established by Taiwan’s Environmental Protection Administration. Data on hourly concentrations of ozone between 1997 and 2006 were collected from these four stations to determine the monthly, seasonal and annual average concentrations of ozone in Kaohsiung County. In these stations, O3 was measured using an ultra-violent photometer analyzer (Model 9810, Ecotech), with a detection limit of 0.5 ppb and a precision of 1 ppb. Meanwhile,
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H.C. Li et al. / Atmospheric Environment 44 (2010) 3605e3608
Fig. 1. Four air-quality monitoring sites in Kaohsiung County, southern Taiwan.
seven meteorological parameters e pressure (kPa), temperature ( C), relative humidity (%), wind speed (m s1), wind direction (deg), duration of sunshine (h) and cloud cover (%) e were collected from the Kaohsiung Weather Station located at Hsiung-Kong (marked with a dark circle in Fig. 1), operated by Taiwan’s Central Weather Bureau. Table 1 lists the seasonal averages and overall means of the seven meteorological data between 1997 and 2006. The monthly averaged concentrations of ozone (ppb), O(t), can be expressed as (Holland et al., 1999; Lu and Chang, 2005)
log OðtÞ ¼ A0 þ
2 X
PN
½Bk sinð2pkt=12Þ þ Ck cosð2pkt=12Þ
IOA ¼ 1 PN
7 X
Fi Meti ðtÞ
i ¼ 1 ðjPi
i ¼ 1 ðjPi
k¼1
þ D0 ðt=12Þ þ EðtÞ
original) trend when Meti(t) is not included in Eq. (1). Notably, the concentration of ozone and meteorological parameters are all normalized according to their respective maxima. The coefficient Fi of Meti(t) is a weighting factor of the effect of the meteorological parameter Meti(t) on the concentration of O3. The robust MM iterative algorithm (Yohai, 1987) in S-PLUS software (S-PLUS, 2001) was used to solve Eq. (1). The performance of the regression was evaluated using the correlation coefficient (R) and the index of agreement (IOA), defined by (Willmott et al., 1985):
ð1Þ
i¼1
In the above, t is the number of the month (with t ¼ 1 for the starting month); A0 is a constant such that 10A0 is the annual average concentration of O3 at the base year; Bk and Ck together represent a cyclic component; D0 represents a long-term component; and E(t) is the residual in the regression model. The last term Meti(t) represents meteorological variables, such as temperature, wind speed and others. Accordingly, meteorologically adjusted trend can be compared with meteorologically unadjusted (or
Oi jÞ2
(2)
Oj þ jOi OjÞ2
where Pi and Oi are the predicted and measured values, respectively, with a sample size N, and O is the average over all measured data. 3. Results and discussion 3.1. Monthly variations of O3 without and with meteorological adjustments Fig. 2 presents measured monthly average concentrations of O3 in Kaohsiung County from 1997 to 2006, and corresponding
Table 1 Seasonal average and overall means of seven meteorological data between 1997 and 2006.
Spring Summer Fall Winter Mean
Pressure (mb)
Temperature ( C)
Relative humidity (%)
Wind speed (m s1)
Wind direction (deg)
Duration of Sunshine (h)
Cloud cover
1011.9 1006.4 1011.9 1017.3 1011.9
25.6 28.8 26.5 20.6 25.3
75.5 80.8 75.2 73.6 76.3
2.2 2.4 2.1 2.2 2.2
215.4 180.2 204.7 224.0 206.3
6.4 6.5 6.1 5.6 6.1
5.0 5.6 4.7 4.6 5.0
H.C. Li et al. / Atmospheric Environment 44 (2010) 3605e3608
3607
Fig. 2. Meteorologically unadjusted monthly average O3 concentration from 1997 to 2006 in Kaohsiung County.
Fig. 3. Meteorologically adjusted monthly average O3 concentration from 1997 to 2006 in Kaohsiung County.
regression results without adjustments. The plots show that the concentration of O3 is typically low in summer (June, July, and August) but high in fall (September, October, and November) or winter (December, January, and February). The unadjusted monthly average O3 concentration increases at a rate of 25.96% (dashed line in Fig. 2). Regression results agree very well with measurements, with IOA ¼ 0.906 and R ¼ 0.849. Fig. 3 plots the corresponding meteorologically adjusted average O3 concentrations, which show a rate of increase of concentration of O3 that was 13.84% (dashed line) e lower than the unadjusted rate of 26.1%. Xu et al. (1996), Gardner and Dorling (2000) and Zheng et al. (2007) also observed the declines in ozone levels after meteorological adjustments in examining the daily maximum ozone concentrations, indicating that declining ozone levels are associated with the implementation of emission control programs. Meteorological factors influence air-quality or the levels of air pollutants to varying extents. While most ambient ozone is formed in sunlight via a photochemical reaction of hydrocarbons and nitrogen oxides. Table 2 summarizes the weighting factors (%), Fi, or the impacts of the seven meteorological parameters. Wind speed (26.79%), duration of sunshine (19.43%) and pressure (16.33%) are the three factors that dominant the concentrations of O3; they are followed by temperature (14.57%), cloud cover (9.38%), relative humidity (9.03%), and wind direction (4.47%). Viney et al. (2000) and Chen et al. (2003) also showed that low wind and high-pressure systems tend to trigger high pollution events.
Fig. 4. Meteorologically unadjusted and adjusted average O3 concentration in spring, summer, fall, and winter from 1997 to 2006 in Kaohsiung County.
3.2. Seasonal and annual trends in O3 concentration without and with meteorological adjustments Fig. 4 plots the seasonal average O3 concentration from 1997 to 2006. Meteorologically adjusted average O3 concentration increases in each season, and ranges form 1.26e8.64%, high in spring and summer but low in fall and winter. This may be related to higher pressures, lower temperatures, and shorter durations of sunshine in fall and winter, as compared to the spring and summer (Table 1). Notably, the unadjusted average O3 concentration increases seasonally, and ranges from 21.21% to 27.55%. Therefore, meteorological adjustments reduce the average O3 concentration by about 12.57% in spring, 21.17% in summer, 20.94% in the fall, and 26.29% in winter. The meteorologically adjusted annual average O3 concentration increases at a rate of 13.06%, less than the 23.80% increase for the unadjusted annual average O3 concentration (Fig. 5).
Table 2 Weighting factor (%), Fi, for meteorological factor in Eq. (1). Pressure Temperature Relative Wind Wind Duration Cloud cover humidity speed direction of sunshine 16.33
14.57
9.03
26.79 4.47
19.43
9.38
Fig. 5. Meteorologically unadjusted and adjusted annual average O3 concentration from 1997 to 2006 in Kaohsiung County.
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H.C. Li et al. / Atmospheric Environment 44 (2010) 3605e3608
4. Conclusions This work adopted the multi-variable additive model to study the effects of seven meteorological parameters on the long-term trend in ozone concentration from 1996 to 2007 in Kaohsiung County, southern Taiwan. The simulated results show that the longterm ozone concentration rises at 13.84% (or 13.06%) monthly (or annually) after meteorological adjustments e less than the respective rate of increase at 26.10% (23.80%) without adjustments. It implies that the increasing trend after meteorological adjustment owes most likely to increase in human activities and/or long range transport. Wind speed (26.79%), duration of sunshine (19.43%), and pressure (16.33%) are the three factors that dominate ozone concentrations. They are followed by temperature (14.57%), cloud cover (9.38%), relative humidity (9.03%), and wind direction (4.47%). The meteorologically adjusted ozone trend is a good index of the overall impact of emission reduction programs on groundlevel ozone concentrations. Acknowledgements The authors would like to thank the Bureau of Environmental Protection, Kaohsiung County for supporting this research. References Chen, K.S., Ho, Y.T., Lai, C.H., Chou, Y.M., 2003. Photochemical modeling and analysis of meteorological parameters during ozone episodes in Kao-hsiung, Taiwan. Atmospheric Environment 37, 1811e1823. Chen, K.S., Ho, Y.T., Lai, C.H., Tsai, Y.A., Chen, S.J., 2004. Trends in concentration of ground-level ozone and meteorological conditions during high ozone episodes
in Kao-Ping airshed, Taiwan. Journal of the Air & Waste Management Association 54, 36e48. Cox, W.M., Chu, S.H., 1993. Meteorologically adjusted ozone trends in urban areas: a probabilistic approach. Atmospheric Environment 27B, 425e434. EPA (US Environmental Protection Agency), 2005. Evaluating Ozone Control Programs in the Eastern United States. EPA454-K-05-001, Washington, DC. Flaum, J.B., Rao, S.T., Zurbenko, I.G., 1996. Moderating the influence of meteorological conditions on ambient ozone concentrations. Journal of the Air & Waste Management Association 46, 35e46. Gardner, M.W., Dorling, S.R., 2000. Meteorologically adjusted trends in UK daily maximum surface ozone concentrations. Atmospheric Environment 34, 171e176. Holland, D.M., Principe, P.P., Sickles, J.E., 1999. Trends in atmospheric sulfur and nitrogen species in the eastern United States for 1989e1995. Atmospheric Environment 33, 37e49. Lu, H.C., Chang, T.S., 2005. Meteorologically adjusted trends of daily maximum ozone concentrations in Taipei, Taiwan. Atmospheric Environment 39, 6491e6501. NRC (National Research Council), 1991. Rethinking the Ozone Problem in Urban and Regional Air Pollution. National Academy Press, Washington, DC. Seinfeld, J.H., Pandis, S.N., 1998. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. John Wiley & Sons, Inc., New York. S-PLUS, 2001. TIBCO Software Inc., Palo Alto, California, USA. Thompson, M.L., Reynolds, J., Cox, L.H., Guttorp, P., Sampson, P.D., 2001. A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmospheric Environment 35, 617e630. Viney, P.A., Arya, S.P., Li, Y., Murray, G.C., Manuszak, T.L., 2000. Climatology of diurnal trends and vertical distribution of ozone in the atmosphere boundary layer in urban North Carolina. Journal of the Air & Waste Management Association 50, 54e64. Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O’Donnell, J., Rowe, C.M., 1985. Statistics for the evaluation and comparison of models. Journal of Geophysical Research 90, 8995e9005. Xu, D., Yap, D., Taylor, P.A., 1996. Meteorologically adjusted ground level ozone trends in Ontario. Atmospheric Environment 30, 1117e1124. Yohai, V.J., 1987. High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics 15, 642e656. Zheng, J., Swall, J.L., Cox, W.M., Davis, J.M., 2007. Interannual variation in meteorologically adjusted ozone levels in the eastern United States: a comparison of two approaches. Atmospheric Environment 41, 705e716.
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Short communication
Meteorologically adjusted long-term trend of ground-level ozone concentrations in Kaohsiung County, southern Taiwan H.C. Li, K.S. Chen*, C.H. Huang, H.K. Wang Institute of Environmental Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan, ROC
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 October 2009 Received in revised form 6 April 2010 Accepted 7 April 2010
Since meteorological changes strongly affect ambient ozone concentrations, trends in concentrations of ozone upon the adjustment of meteorological variations are important of evaluating emission reduction efforts. The goal of this work is to study meteorological effects on the long-term trends of ozone concentration using a multi-variable additive model. Data on the hourly concentrations of ozone were collected from four air-quality stations from 1997 to 2006 in Kaohsiung County to determine the monthly, seasonal and annual average concentrations of ozone. The model incorporates seven meteorological parameters e pressure, temperature, relative humidity, wind speed, wind direction, duration of sunshine and cloud cover. The simulated results show that the long-term ozone concentration increases at 13.84% (or 13.06%) monthly (or annually) after meteorological adjustments, less than at 26.10% (or 23.80%) without meteorological adjustments. Wind speed, duration of sunshine and pressure are the three dominant factors that influence the ground-level ozone levels. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Ambient ozone Ozone trend Meteorological adjustment Multi-variable additive model
1. Introduction Ground-level ozone (O3) is a secondary pollutant, generated mainly in photochemical reactions of its precursors, including volatile organic compounds and nitrogen oxides (Seinfeld and Pandis, 1998). Since high ozone levels may cause severe health effects, many governments have implemented pollution control programs to reduce the emissions of the precursors. Although most primary pollutants are emitted during the course of human activities, such as the use of motor vehicles and the operation of power plants and other stationary sources, high ozone events are also related to the meteorological conditions (NRC, 1991; EPA, 2005). Therefore, the effect of meteorological conditions on ambient ozone level is important in evaluating the ozone trends. Various linear and nonlinear regression models have been employed with statistical estimates to evaluate the effect of meteorological variations on ozone concentrations (Thompson et al., 2001). For example, the probabilistic approach (Cox and Chu, 1993), the multi-variable additive model (Flaum et al., 1996; Xu et al., 1996; Holland et al., 1999), the multilayer neural network (Gardner and Dorling, 2000; Lu and Chang, 2005) and the generalized additive model (Zheng et al., 2007) have been adopted to investigate the effect of temperature, wind speed, sunshine hour
* Corresponding author. Tel./fax: þ886 7 5254406. E-mail address: [email protected] (K.S. Chen). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.04.011
and/or relative humidity on the concentration of ozone. Most related works have focused on daily maximum ozone concentrations, except that Holland et al. (1999) have considered monthly and weekly average concentrations of air pollutants. These works have presented useful results concerning the separation of meteorological factors from human-related emissions. This work investigates meteorological effects on the long-term trend of ozone concentration in Kaohsiung County, a heavily industrialized region, and one of the worst regions for air-quality in Taiwan (Chen et al., 2004). The effects of seven meteorological parameters on the ozone trends are examined. A multi-variable additive model was employed to investigate the long-term ozone trend after meteorological adjustments, since it characterizes the complex relationships among the predictor and response variables better than a linear model does, particularly in characterizing the seasonal (or cyclic) variations of meteorological data. 2. Methods Fig. 1 presents four air-quality monitoring stations in Kaohsiung County, established by Taiwan’s Environmental Protection Administration. Data on hourly concentrations of ozone between 1997 and 2006 were collected from these four stations to determine the monthly, seasonal and annual average concentrations of ozone in Kaohsiung County. In these stations, O3 was measured using an ultra-violent photometer analyzer (Model 9810, Ecotech), with a detection limit of 0.5 ppb and a precision of 1 ppb. Meanwhile,
3606
H.C. Li et al. / Atmospheric Environment 44 (2010) 3605e3608
Fig. 1. Four air-quality monitoring sites in Kaohsiung County, southern Taiwan.
seven meteorological parameters e pressure (kPa), temperature ( C), relative humidity (%), wind speed (m s1), wind direction (deg), duration of sunshine (h) and cloud cover (%) e were collected from the Kaohsiung Weather Station located at Hsiung-Kong (marked with a dark circle in Fig. 1), operated by Taiwan’s Central Weather Bureau. Table 1 lists the seasonal averages and overall means of the seven meteorological data between 1997 and 2006. The monthly averaged concentrations of ozone (ppb), O(t), can be expressed as (Holland et al., 1999; Lu and Chang, 2005)
log OðtÞ ¼ A0 þ
2 X
PN
½Bk sinð2pkt=12Þ þ Ck cosð2pkt=12Þ
IOA ¼ 1 PN
7 X
Fi Meti ðtÞ
i ¼ 1 ðjPi
i ¼ 1 ðjPi
k¼1
þ D0 ðt=12Þ þ EðtÞ
original) trend when Meti(t) is not included in Eq. (1). Notably, the concentration of ozone and meteorological parameters are all normalized according to their respective maxima. The coefficient Fi of Meti(t) is a weighting factor of the effect of the meteorological parameter Meti(t) on the concentration of O3. The robust MM iterative algorithm (Yohai, 1987) in S-PLUS software (S-PLUS, 2001) was used to solve Eq. (1). The performance of the regression was evaluated using the correlation coefficient (R) and the index of agreement (IOA), defined by (Willmott et al., 1985):
ð1Þ
i¼1
In the above, t is the number of the month (with t ¼ 1 for the starting month); A0 is a constant such that 10A0 is the annual average concentration of O3 at the base year; Bk and Ck together represent a cyclic component; D0 represents a long-term component; and E(t) is the residual in the regression model. The last term Meti(t) represents meteorological variables, such as temperature, wind speed and others. Accordingly, meteorologically adjusted trend can be compared with meteorologically unadjusted (or
Oi jÞ2
(2)
Oj þ jOi OjÞ2
where Pi and Oi are the predicted and measured values, respectively, with a sample size N, and O is the average over all measured data. 3. Results and discussion 3.1. Monthly variations of O3 without and with meteorological adjustments Fig. 2 presents measured monthly average concentrations of O3 in Kaohsiung County from 1997 to 2006, and corresponding
Table 1 Seasonal average and overall means of seven meteorological data between 1997 and 2006.
Spring Summer Fall Winter Mean
Pressure (mb)
Temperature ( C)
Relative humidity (%)
Wind speed (m s1)
Wind direction (deg)
Duration of Sunshine (h)
Cloud cover
1011.9 1006.4 1011.9 1017.3 1011.9
25.6 28.8 26.5 20.6 25.3
75.5 80.8 75.2 73.6 76.3
2.2 2.4 2.1 2.2 2.2
215.4 180.2 204.7 224.0 206.3
6.4 6.5 6.1 5.6 6.1
5.0 5.6 4.7 4.6 5.0
H.C. Li et al. / Atmospheric Environment 44 (2010) 3605e3608
3607
Fig. 2. Meteorologically unadjusted monthly average O3 concentration from 1997 to 2006 in Kaohsiung County.
Fig. 3. Meteorologically adjusted monthly average O3 concentration from 1997 to 2006 in Kaohsiung County.
regression results without adjustments. The plots show that the concentration of O3 is typically low in summer (June, July, and August) but high in fall (September, October, and November) or winter (December, January, and February). The unadjusted monthly average O3 concentration increases at a rate of 25.96% (dashed line in Fig. 2). Regression results agree very well with measurements, with IOA ¼ 0.906 and R ¼ 0.849. Fig. 3 plots the corresponding meteorologically adjusted average O3 concentrations, which show a rate of increase of concentration of O3 that was 13.84% (dashed line) e lower than the unadjusted rate of 26.1%. Xu et al. (1996), Gardner and Dorling (2000) and Zheng et al. (2007) also observed the declines in ozone levels after meteorological adjustments in examining the daily maximum ozone concentrations, indicating that declining ozone levels are associated with the implementation of emission control programs. Meteorological factors influence air-quality or the levels of air pollutants to varying extents. While most ambient ozone is formed in sunlight via a photochemical reaction of hydrocarbons and nitrogen oxides. Table 2 summarizes the weighting factors (%), Fi, or the impacts of the seven meteorological parameters. Wind speed (26.79%), duration of sunshine (19.43%) and pressure (16.33%) are the three factors that dominant the concentrations of O3; they are followed by temperature (14.57%), cloud cover (9.38%), relative humidity (9.03%), and wind direction (4.47%). Viney et al. (2000) and Chen et al. (2003) also showed that low wind and high-pressure systems tend to trigger high pollution events.
Fig. 4. Meteorologically unadjusted and adjusted average O3 concentration in spring, summer, fall, and winter from 1997 to 2006 in Kaohsiung County.
3.2. Seasonal and annual trends in O3 concentration without and with meteorological adjustments Fig. 4 plots the seasonal average O3 concentration from 1997 to 2006. Meteorologically adjusted average O3 concentration increases in each season, and ranges form 1.26e8.64%, high in spring and summer but low in fall and winter. This may be related to higher pressures, lower temperatures, and shorter durations of sunshine in fall and winter, as compared to the spring and summer (Table 1). Notably, the unadjusted average O3 concentration increases seasonally, and ranges from 21.21% to 27.55%. Therefore, meteorological adjustments reduce the average O3 concentration by about 12.57% in spring, 21.17% in summer, 20.94% in the fall, and 26.29% in winter. The meteorologically adjusted annual average O3 concentration increases at a rate of 13.06%, less than the 23.80% increase for the unadjusted annual average O3 concentration (Fig. 5).
Table 2 Weighting factor (%), Fi, for meteorological factor in Eq. (1). Pressure Temperature Relative Wind Wind Duration Cloud cover humidity speed direction of sunshine 16.33
14.57
9.03
26.79 4.47
19.43
9.38
Fig. 5. Meteorologically unadjusted and adjusted annual average O3 concentration from 1997 to 2006 in Kaohsiung County.
3608
H.C. Li et al. / Atmospheric Environment 44 (2010) 3605e3608
4. Conclusions This work adopted the multi-variable additive model to study the effects of seven meteorological parameters on the long-term trend in ozone concentration from 1996 to 2007 in Kaohsiung County, southern Taiwan. The simulated results show that the longterm ozone concentration rises at 13.84% (or 13.06%) monthly (or annually) after meteorological adjustments e less than the respective rate of increase at 26.10% (23.80%) without adjustments. It implies that the increasing trend after meteorological adjustment owes most likely to increase in human activities and/or long range transport. Wind speed (26.79%), duration of sunshine (19.43%), and pressure (16.33%) are the three factors that dominate ozone concentrations. They are followed by temperature (14.57%), cloud cover (9.38%), relative humidity (9.03%), and wind direction (4.47%). The meteorologically adjusted ozone trend is a good index of the overall impact of emission reduction programs on groundlevel ozone concentrations. Acknowledgements The authors would like to thank the Bureau of Environmental Protection, Kaohsiung County for supporting this research. References Chen, K.S., Ho, Y.T., Lai, C.H., Chou, Y.M., 2003. Photochemical modeling and analysis of meteorological parameters during ozone episodes in Kao-hsiung, Taiwan. Atmospheric Environment 37, 1811e1823. Chen, K.S., Ho, Y.T., Lai, C.H., Tsai, Y.A., Chen, S.J., 2004. Trends in concentration of ground-level ozone and meteorological conditions during high ozone episodes
in Kao-Ping airshed, Taiwan. Journal of the Air & Waste Management Association 54, 36e48. Cox, W.M., Chu, S.H., 1993. Meteorologically adjusted ozone trends in urban areas: a probabilistic approach. Atmospheric Environment 27B, 425e434. EPA (US Environmental Protection Agency), 2005. Evaluating Ozone Control Programs in the Eastern United States. EPA454-K-05-001, Washington, DC. Flaum, J.B., Rao, S.T., Zurbenko, I.G., 1996. Moderating the influence of meteorological conditions on ambient ozone concentrations. Journal of the Air & Waste Management Association 46, 35e46. Gardner, M.W., Dorling, S.R., 2000. Meteorologically adjusted trends in UK daily maximum surface ozone concentrations. Atmospheric Environment 34, 171e176. Holland, D.M., Principe, P.P., Sickles, J.E., 1999. Trends in atmospheric sulfur and nitrogen species in the eastern United States for 1989e1995. Atmospheric Environment 33, 37e49. Lu, H.C., Chang, T.S., 2005. Meteorologically adjusted trends of daily maximum ozone concentrations in Taipei, Taiwan. Atmospheric Environment 39, 6491e6501. NRC (National Research Council), 1991. Rethinking the Ozone Problem in Urban and Regional Air Pollution. National Academy Press, Washington, DC. Seinfeld, J.H., Pandis, S.N., 1998. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. John Wiley & Sons, Inc., New York. S-PLUS, 2001. TIBCO Software Inc., Palo Alto, California, USA. Thompson, M.L., Reynolds, J., Cox, L.H., Guttorp, P., Sampson, P.D., 2001. A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmospheric Environment 35, 617e630. Viney, P.A., Arya, S.P., Li, Y., Murray, G.C., Manuszak, T.L., 2000. Climatology of diurnal trends and vertical distribution of ozone in the atmosphere boundary layer in urban North Carolina. Journal of the Air & Waste Management Association 50, 54e64. Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O’Donnell, J., Rowe, C.M., 1985. Statistics for the evaluation and comparison of models. Journal of Geophysical Research 90, 8995e9005. Xu, D., Yap, D., Taylor, P.A., 1996. Meteorologically adjusted ground level ozone trends in Ontario. Atmospheric Environment 30, 1117e1124. Yohai, V.J., 1987. High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics 15, 642e656. Zheng, J., Swall, J.L., Cox, W.M., Davis, J.M., 2007. Interannual variation in meteorologically adjusted ozone levels in the eastern United States: a comparison of two approaches. Atmospheric Environment 41, 705e716.