| 英文摘要 |
The data of air pollutants were collected from seven air-quality monitoring stations (Dajia, Houli, Shalu, Fengyuan, Taiping, Dali, and Wurih) distributed throughout Taichung County from 2000 to 2007. The year and seasonal variations of PM10 concentrations were analyzed. The lowest concentration was observed in summer for every air quality station. Among the three districts, the highest average concentration of PM10 occurred in the Tun district; whereas, among the seven stations, the highest PM10 concentration was found at the Dajia station. Similar phenomena were observed in PM2.5 concentrations, except that the highest PM2.5 concentration was found in the Dali station. The occurrence frequencies of PM10 episode days (PM10≥125µg/m3) were very high in the Tun district. It was noticed that the number of PM10 episode days in the Houli station was increased in 2007 due to the development of the Houli Science Park.
Analysis results of the daily chemical compositions of suspended particles among the four seasons indicated that the concentrations of the secondary salts showed the highest value in autumn. The secondary-salt concentrations were slightly lower in winter due to few showers, and the concentrations of secondary organic carbons were higher in summer. Among the seven stations, most of the samples from the Dajia and Dali stations showed higher concentrations of secondary salts and organic carbons. Both the concentrations of coarse and fine secondary salts among the seven stations showed that the differences were not statistically significant. However, the concentrations of both fine and coarse secondary organic carbons showed statistically significant differences among the seven stations. In addition, the size distribution of secondary salts and organic carbons were mainly distributed in a fine mode. The concentrations of secondary organic carbons were significantly higher in the daytime than in the nighttime. However, these phenomena were not observed for the daytime and nighttime secondary salts. According to the above results, this study proposed that the photochemical influences are more notable for the secondary organic carbons than the secondary salts. Overall, the mass fractions of the fine secondary aerosols (including secondary salts and organic carbons) of the fine mode aerosols were 32~42%, which is higher than the mass fractions of coarse secondary aerosol in the coarse mode aerosols (18~24%).
Air pollutants displayed rapid changes of spatial and temporal variations. In order to clearly discriminate local and external contributions of atmospheric particulate matter, this work applied an in-situ particulate composition technique to characterize ambient pollution in the Shalu and Wurih stations. Under the influence of sea-land breezes, the concentration peaks of PM10 were mainly observed at night at the Shalu station. However, the curve of PM¬10 of hourly concentrations at the Wurih station showed a bi-modal distribution, with the first peak in the morning and the secondary peak in the evening. Analysis on chemical compositions of PM10 found that hourly time series of NO3- and NH4+ concentrations were consisted with that of the PM10 concentrations. Good linear correlations were found at the same time between daily maximum O3 concentrations and NO3- concentrations, which clearly shows that secondary aerosol formation of NO3- by photochemical reaction played an important role in air quality. According to the influences of sea-land breeze transport and the characteristics of local pollutant emissions, the pollutants from Shalu could be transported to the inner land of Wurih, and then reacted with urban pollutants during the day. During the night, the formatted secondary pollutants of photochemical reaction in Wurih could be transported to Shalu. Interactions between the two mechanisms increased the complexity in order to control secondary pollutants.
For the air pollutants data analysis and modeling study, the use of the Rawins scheme in the meteorological model showed significant improvement near the surface wind speed estimation, and hence, could improve our air quality modeling study. Data showed that the 3rd monitoring period (fall season) has the highest average air pollutants concentrations, while the 2nd monitoring period (summer season) has the lowest. Simulation results confirmed that the proposed model could reasonably simulate the variation trends in central Taiwan, especially for O3 and PM2.5.
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