Abstract
The estimation of ambient particulate matter with diameter less than 10 µm (PM10) at high spatial resolution is currently quite limited in China. In order to make the distribution of PM10 more accessible to relevant departments and scientific research institutions, a semi-physical geographically weighted regression (GWR) model was established in this study to estimate nationwide mass concentrations of PM10 using easily available MODIS AOD and NCEP Reanalysis meteorological parameters. The results demonstrated that applying physics-based corrections could remarkably improve the quality of the dataset for better model performance with the adjusted R2 between PM10 and AOD increasing from 0.08 to 0.43, and the fitted results explained approximately 81% of the variability in the corresponding PM10 mass concentrations. Annual average PM10 concentrations estimated by the semi-physical GWR model indicated that many residential regions suffer from severe particle pollution. Moreover, the deviation in estimation, which primarily results from the frequent changes in elevation, the spatially heterogeneous distribution of monitoring sites, and the limitations of AOD retrieval algorithm, was acceptable. Therefore, the semi-physical GWR model provides us with an effective and efficient method to estimate PM10 at large scale. The results could offer reasonable estimations of health impacts and provide guidance on emission control strategies in China.
Highlights
Numerous studies have shown that airborne particulate matter from both natural sources and anthropogenic emissions are associated with environmental degradation, climate change, and adverse human health effects [1,2,3]
The results from the physics-based revision indicated that the surface relative humidity (RH) correction on PM10 and vertical correction on aerosol optical depth (AOD) could remarkably improve the quality of dataset for better model performance, where the adjusted R2 increased from 0.08 to 0.43
The semi-physical geographically weighted regression (GWR) model could explain approximately 81% of the variability in the corresponding PM10 mass concentrations, and the comparison between the semi-physical GWR model and conventional GWR model showed that estimation accuracy can be improved under similar parameter conditions
Summary
Numerous studies have shown that airborne particulate matter from both natural sources and anthropogenic emissions are associated with environmental degradation, climate change, and adverse human health effects [1,2,3]. The estimation of air quality from stationary ground monitoring sites are supposed to be accurate, the consistency of their quality often declines with increasing spatial distance [6]. The accurate and spatially resolved assessment of PM10 exposure is significant in effectively estimating air quality and conducting environmental epidemiologic studies [7]. As satellite remote sensing has been generally employed to make up for the limitation in spatial coverage of ground measurements, a potentially effective method has been put forward to predict. PM10 concentrations using satellite-derived aerosol optical depth (AOD) [8,9,10,11,12].
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