Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model. (May 2018)

• Record Type: Journal Article
• Authors: He, Qingqing; Huang, Bo
• Is Part Of: Environmental pollution. Volume 236(2018)
• Page Start: 1027
• Page End: 1037
• Publication Date: 2018-05
• Subjects: PM2.5 -- AOD -- MODIS -- Spatiotemporal modeling -- Beijing-Tianjin-Hebei region; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2018.01.053
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Ground fine particulate matter (PM2.5) concentrations at high spatial resolution are substantially required for determining the population exposure to PM2.5 over densely populated urban areas. However, most studies for China have generated PM2.5 estimations at a coarse resolution (≥10 km) due to the limitation of satellite aerosol optical depth (AOD) product in spatial resolution. In this study, the 3 km AOD data fused using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOD products were employed to estimate the ground PM2.5 concentrations over the Beijing-Tianjin-Hebei (BTH) region of China from January 2013 to December 2015. An improved geographically and temporally weighted regression (iGTWR) model incorporating seasonal characteristics within the data was developed, which achieved comparable performance to the standard GTWR model for the days with paired PM2.5 - AOD samples (Cross-validation (CV) R 2 = 0.82) and showed better predictive power for the days without PM2.5 - AOD pairs (the R 2 increased from 0.24 to 0.46 in CV). Both iGTWR and GTWR (CV R 2 = 0.84) significantly outperformed the daily geographically weighted regression model (CV R 2 = 0.66). Also, the fused 3 km AODs improved data availability and presented more spatial gradients, thereby enhancing model performance compared with the MODIS original 3/10 km AOD product. As a result, ground PM2.5 concentrations at higher resolution were well represented, allowing, e.g., short-term pollution events and long-term PM2.5 trend to be identified, which, in turn, indicated that concerns about air pollution in the BTH region are justified despite its decreasing trend from 2013 to 2015. Graphical abstract: Image 1 Highlights: We created 3-km MODIS AOD data through fusion to estimate high-resolution PM2.5 . We incorporated seasonality inherent the data into the GTWR model. Improved GTWR outperforms standard GTWR especially for the days without samples. We quantitatively evaluated fused 3-km AOD against MODIS original AOD products. Fused 3 km AODs improved data availability and presented more spatial gradients. Abstract : Taking advantage of the fused 3-km aerosol optical depth dataset, we developed an improved geographically and temporally weighted regression (GTWR) model with the incorporation of seasonal characteristics, which demonstrated a better performance over the standard GTWR in generating daily high-resolution surface PM2.5 concentrations for the Beijing-Tianjin-Hebei region of China especially for the days without samples.