Characterizing Population Exposures to Particulate Matter and Nitrogen Oxides in an Urban Area: Comparison of ISC3, AERMOD and Land Use Regression Models
Date Issued
2010
Date
2010
Author(s)
Yang, Tzu-Hui
Abstract
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 produce different estimations to influence the epidemiological results. Although the model performance of LUR was better than air dispersion models, the air dispersion models considered air pollutants transport and reaction in the environment also provided higher temporal variability. Therefore, air dispersion models and LUR could be integrated to improve the exposure estimates and provided reliable predictions to assess the health impacts on cohorts.
Subjects
Air pollution
Exposure assessment
Air dispersion model
Land use regression
File(s)![Thumbnail Image]()
Loading...
Name
ntu-99-R96844009-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):b31e3ef434280bd28cbf2d4334ead6dc