評估都會地區居民之懸浮微粒及氮氧化物的暴露狀況：比較ISC3、AERMOD及土地利用迴歸模式的預測結果
摘要
Abstract BACKGROUND: Epidemiological studies of assessing the health effects from exposures to air pollution have been hampered by difficulties in characterizing individualized exposure levels for subjects. One possible solution is applying intraurban air pollution exposure models to link subjects’ home addresses to reconstruct individual exposures. METHODS: The study was conducted in Taipei, Taiwan. This study utilized two air dispersion models and a land use regression model to predict nitrogen oxides, particulate matter < 10 (PM10) and < 2.5(PM2.5) μm in aerodynamic diameter concentrations for 17 air quality monitoring (AQM) stations and 66 study subjects in 2000, 2003 and 2007. Two air dispersion models including ISC3 and AERMOD used emission inventory, meteorological data and topography data to simulate air pollution concentrations. Land use regression models were used to predict concentrations rely on the geographic variables as predictors, such as traffic, land use type, meteorological parameters and census data. To evaluate flexibility, the predicted air pollutant concentrations were compared with fixed-sites measurements. Subsequently, the estimations of three approaches for all study subjects were then compared. RESULTS: At the air quality monitoring sites, the R2 between modeled and measured NOX concentrations ranged from 0.56 to 0.75 for ISC3, 0.56 to 0.72 for AERMOD and 0.50 to 0.65 for LUR. For PM10, the ISC3 explained at least 23% variability of the measurement whereas AERMOD had at least 28% explained variance. The R2 value of LUR was higher than ISC3 and AERMOD. For PM2.5, the R2 of three methods were all greater than 0.69 that are applicable to simulate PM2.5 concentrations. For subjects, the correlations between ISC3 and AERMOD predictions are good; however, the R2 between air dispersion models and LUR was not well for NOX, PM10 and PM2.5. CONCLUSIONS: Each approach calculated the variability of concentrations in different spatial-scales, it could