title: Real-Time Estimation of Satellite-Derived PM2.5Based on a Semi-Physical Geographically Weighted Regression Model.
abstract: The 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 PM2.5mass 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 PM2.5mass 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 PM2.5pollution during winter. Seasonal average mass concentrations of PM2.5predicted 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 PM2.5was 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.
authors.affiliation: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. tianhaozhang@whu.edu.cn.
authors.forename: Tianhao
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Shanghai Institute of Satellite Engineering, Shanghai 201100, China. liugang8243@126.com.
authors.forename: Gang
authors.initials: G
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. zhongmin.zhu@whu.edu.cn.|College of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China. zhongmin.zhu@whu.edu.cn.
authors.forename: Zhongmin
authors.initials: Z
authors.lastname: Zhu
authors.affiliation: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. weigong@whu.edu.cn.|Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China. weigong@whu.edu.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Gong
authors.affiliation: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. jiyuxi_ss@163.com.
authors.forename: Yuxi
authors.initials: Y
authors.lastname: Ji
authors.affiliation: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. mavis_huang@whu.edu.cn.
authors.forename: Yusi
authors.initials: Y
authors.lastname: Huang
mesh_terms: D000336:Aerosols / Q000032:analysis*; D000393:Air Pollutants / Q000032:analysis*; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D005843:Geography; D016014:Linear Models; D052638:Particulate Matter / Q000032:analysis*; D063809:Satellite Imagery / Q000379:methods*; D012621:Seasons; D000068916:Spatial Regression
chemical_list: D000336:Aerosols; D000393:Air Pollutants; D052638:Particulate Matter
keywords: aerosol optical depth; fusion by DT and DB; national-scale PM2.5; real-time estimation; semi-physical geographically weighted regression