title: Ground Level PM2.5Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO₂ and Enhanced Vegetation Index (EVI).
abstract: Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 μm: PM2.5) is currently quite limited in China. By introducing NO₂ and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM2.5mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO₂ and EVI, where cross-validation R² increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM2.5pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM2.5pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM2.5still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO₂ and EVI in GWR models could more effectively estimate PM2.5at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation 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: 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. wangweicn@whu.edu.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
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. 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. mavis_huang@whu.edu.cn.
authors.forename: Yusi
authors.initials: Y
authors.lastname: Huang
mesh_terms: D000336:Aerosols / Q000737:chemistry*; D000393:Air Pollutants / Q000737:chemistry*; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D052638:Particulate Matter / Q000737:chemistry*; D015203:Reproducibility of Results; D018531:Spacecraft; D000068916:Spatial Regression
chemical_list: D000336:Aerosols; D000393:Air Pollutants; D052638:Particulate Matter
keywords: MODIS (Moderate Resolution Imaging Spectroradiometer) AOD; enhanced vegetation index; geographically weighted regression; nationwide ambient PM2.5; satellite-derived NO2 column density