title: Spatiotemporal Pattern of PM2.5Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression.
abstract: Based on annual average PM2.5gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5across Mainland China during 1998-2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry) and natural geographical factors (e.g., topography, climate, vegetation) to PM2.5were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM2.5concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM2.5concentrations greater than 35 μg/m3significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM2.5. Additionally, the Moran's I for the residuals of GWR was 0.025 (not significant at a 0.01 level), indicating that the GWR model was properly specified. The local coefficient estimates of GDP in some cities were negative, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for PM2.5in Mainland China. The effects of each latent factor on PM2.5in various regions were different. Therefore, regional measures and strategies for controlling PM2.5should be formulated in terms of the local impacts of specific factors.
authors.affiliation: Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China.|Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China.|Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
authors.forename: Jieqiong
authors.initials: J
authors.lastname: Luo
authors.affiliation: Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China.|Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China.|Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
authors.forename: Peijun
authors.initials: P
authors.lastname: Du
authors.affiliation: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
authors.forename: Alim
authors.initials: A
authors.lastname: Samat
authors.affiliation: Research Center for Advanced Science and Technology, The University of Tokyo 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan.
authors.forename: Junshi
authors.initials: J
authors.lastname: Xia
authors.affiliation: Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China.|Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China.|Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
authors.forename: Meiqin
authors.initials: M
authors.lastname: Che
authors.affiliation: School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China.
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Xue