PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015

• Published in: Atmosphere Vol. 10; no. 2; p. 55
• Main Authors: Yun, Guoliang, He, Yuanrong, Jiang, Yuantong, Dou, Panfeng, Dai, Shaoqing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2019
• Subjects: Air pollution; Carbon dioxide; Carbon dioxide concentration; Carbon dioxide emissions; Distribution; Geographical detector model; Geomorphology; Haze; Heterogeneity; Lower atmosphere; natural pollution factors; Outdoor air quality; Particulate matter; Patchiness; PM2.5 pollution; Population density; Remote sensing; Rivers; Social factors; Socioeconomic data; Socioeconomic factors; socioeconomic pollution factors; Socioeconomics; Spatial analysis; Spatial distribution; Spatial heterogeneity; Statistical analysis; Statistical methods; Studies; Yangtze River Delta; United States > US; China; Yangtze River
• DOI: 10.3390/atmos10020055
• ISSN: 2073-4433

High concentrations of PM2.5 are a primary cause of haze in the lower atmosphere. A better understanding of the spatial heterogeneity and driving factors of PM2.5 concentrations is important for effective regional prevention and control. In this study, we carried out remote sensing inversion of PM2.5 concentration data over a long time series and used spatial statistical analyses and a geographical detector model to reveal the spatial distribution and variation characteristics of PM2.5 and the main influencing factors in the Yangtze River Delta from 2005 to 2015. Our results show that (1) The average annual PM2.5 concentration in the Yangtze River Delta prior to 2007 displayed an increasing trend, followed by a decreasing trend after 2007 which eventually stabilized; and (2) climate regionalization and geomorphology were the dominant natural factors driving PM2.5 concentration diffusion, while total carbon dioxide emissions and population density were the dominant socioeconomic factors affecting the formation of PM2.5. Natural factors and socioeconomic factors together lead to PM2.5 pollution. These findings provide an interpretation of PM2.5 spatial distribution and the mechanisms influencing PM2.5 pollution, which can help the Chinese government develop effective abatement strategies.