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首页> 外文期刊>Scientific reports. >Spatiotemporal Pattern of PM2.5 Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression
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Spatiotemporal Pattern of PM2.5 Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression

机译：中国大陆的PM2.5浓度的时空模式及其影响因素的地理加权回归分析

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Based on annual average PM2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5 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 PM2.5 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM2.5 concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM2.5 concentrations greater than 35?μg/m3 significantly 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 PM2.5 in Mainland China. The effects of each latent factor on PM2.5 in various regions were different. Therefore, regional measures and strategies for controlling PM2.5 should be formulated in terms of the local impacts of specific factors.
机译：该研究基于年平均PM2.5网格数据集，在1998 - 2012年，首先分析了中国大陆PM2.5的时空模式。然后促进了气象网站数据，土地覆盖数据，人口和国内生产总值（GDP）数据等，潜在地理因素的贡献，包括社会经济因素（例如，道路，农业，人口，工业）和自然地理因素（通过地理加权回归（GWR）模型探讨了PM2.5的地形，气候，植被）。结果表明，PM2.5浓度在空间模式保持稳定的同时增加，PM2.5浓度大于35μg/ m3的区域的比例显着增加到23.08％至29.89％。此外，道路，农业，人口和植被对PM2.5产生了最大的影响。此外，GWR的残留的莫兰的I为0.025（0.01级不显着），表明GWR模型已正确指定。一些城市GDP的局部系数估计是消极的，这表明在中国大陆PM2.5的倒装U形环境库兹涅特曲线（EKC）存在。每个潜在因子对各个区域中PM2.5的影响是不同的。因此，应在特定因素的当地影响方面制定控制PM2.5的区域措施和战略。

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