A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China

• Published in: Remote sensing of environment Vol. 154; pp. 1 - 7
• Main Authors: Song, Weize, Jia, Haifeng, Huang, Jingfeng, Zhang, Yiyue
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.11.2014; Elsevier
• Subjects: Aerosol optical depth; Animal, plant and microbial ecology; Applied geophysics; Argon oxygen decarburizing; Biological and medical sciences; Brackish; China; Correlation; data collection; Deltas; Earth sciences; Earth, ocean, space; Exact sciences and technology; Freshwater; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Geographically weighted regression; Internal geophysics; particulates; Pearl River Delta region; PM2.5; Regional; Regression; regression analysis; Relative humidity; remote sensing; river deltas; Rivers; Satellite remote sensing; satellites; statistical models; Teledetection and vegetation maps; Pearl River; Far East; Asia; China; China, People's Rep., Zhu R; China, People's Rep; Satellite remote sensing; Aerosol optical depth; Pearl River Delta region; Geographically weighted regression; PM2.5; Relative humidity; accuracy; variability; Modeling; evaluation; spatial variations; Semiempirical method; deltas; performances; Linear regression; Estimation; Space remote sensing; Regional scope; satellite measurements; Satellite observation; concentration; Meteorological variable; corrections; Regression model; Statistical model; PM
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2014.08.008

To estimate the daily concentration of ground-level PM2.5 coincident to satellite overpass at regional scale, a satellite-based geographically weighted regression (GWR) model was developed. The model enhances PM2.5 estimation accuracy by considering spatial variation and nonstationarity that might introduce significant biases into PM2.5 estimation. The model was evaluated and validated against the PM2.5 data collected over the Pearl River Delta (PRD) region, China for the period of May 2012 to September 2013. The evaluation evidenced that, with meteorological parameters assimilated, the GWR model is able to explain 73.8% of the variability in ground-level PM2.5 concentration, a better performance than the two conventional statistical models (a general linear regression model Model-I, 56.4% and a semi-empirical model Model-II, 52.6%, respectively). The vertical correction on satellite-derived AOD and relative humidity significantly improve the AOD–PM2.5 correlative relationship. The findings from the study demonstrated the great potential and value of the GWR model for regional PM2.5 estimation. •A novel satellite-based GWR model for PM2.5 estimation was proposed and evaluated•Comparison of GWR model with two other conventional models was conducted•One case study in China demonstrated the competitive performance of the GWR model•The role of model predictors in the AOD-PM2.5 relationship was analyzed.