作者:Zou, B (Zou, Bin) ; Pu, Q (Pu, Qiang); Bilal, M (Bilal, Muhammad) ; Weng, QH (Weng, Qihao) ; Zhai, L (Zhai, Liang ; Nichol, JE (Nichol, Janet E.)
期刊： ResearcherID 和 ORCID IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 卷: 13 期: 4 页: 495499 DOI: 10.1109/LGRS.2016.2520480 出版年: APR 2016 查看期刊信息
摘要： Satelliteretrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM2.5) concentrations. An accurate estimation and mapping of PM2.5 concentrations depends on the highresolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM2.5 and AOD. Take the core portion of the BeijingHebeiTianjin (JingJinJi) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithmretrieved AOD at 1km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM2.5 concentrations. The GWR model shows significant spatial variations in PM2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with groundlevel PM2.5 concentrations demonstrates that PM2.5 concentrations predicted by the GWR model (R2 = 0.75, RMSE = 10 mu g/m(3)) correlate better than those by the OLS model (R2 = 0.53, RMSE = 16 mu g/m(3)). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM2.5 concentrations over urban areas.
关键词:Aerosol optical depth (AOD); geographically weighted regression (GWR); moderate resolution imaging spectroradiometer (MODIS); PM2.5; simplified aerosol retrieval algorithm (SARA); urban area
