Spatiotemporal Pattern of PM_2.5 Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression.

ABSTRACT: Based on annual average PM_2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM_2.5 across 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 PM_2.5 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM_2.5 concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM_2.5 concentrations greater than 35??g/m³ significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM_2.5. 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 PM_2.5 in Mainland China. The effects of each latent factor on PM_2.5 in various regions were different. Therefore, regional measures and strategies for controlling PM_2.5 should be formulated in terms of the local impacts of specific factors.

SUBMITTER: Luo J 

PROVIDER: S-EPMC5228184 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature