Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model
AbstractThe real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China. View Full-Text
Share & Cite This Article
Zhang, T.; Liu, G.; Zhu, Z.; Gong, W.; Ji, Y.; Huang, Y. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model. Int. J. Environ. Res. Public Health 2016, 13, 974.
Zhang T, Liu G, Zhu Z, Gong W, Ji Y, Huang Y. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model. International Journal of Environmental Research and Public Health. 2016; 13(10):974.Chicago/Turabian Style
Zhang, Tianhao; Liu, Gang; Zhu, Zhongmin; Gong, Wei; Ji, Yuxi; Huang, Yusi. 2016. "Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model." Int. J. Environ. Res. Public Health 13, no. 10: 974.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.