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.
Sci Rep. 2017 Jan 12;7:40607. doi: 10.1038/srep40607.
Based on annual average PM gridded dataset, this study first analyzed the spatiotemporal pattern of PM 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 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM concentrations greater than 35 μg/m significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM. 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 in Mainland China. The effects of each latent factor on PM in various regions were different. Therefore, regional measures and strategies for controlling PM should be formulated in terms of the local impacts of specific factors.
本研究基于年均 PM 网格化数据集，首次分析了 1998-2012 年中国大陆地区 PM 的时空分布格局。然后，利用气象站点数据、土地覆盖数据、人口和国内生产总值（GDP）等数据，通过地理加权回归（GWR）模型，探讨了潜在地理因素（包括社会经济因素，如道路、农业、人口、工业和自然地理因素，如地形、气候、植被）对 PM 的贡献。结果表明，PM 浓度呈上升趋势，空间格局保持稳定，PM 浓度大于 35μg/m3 的地区比例从 23.08%显著增加到 29.89%。此外，道路、农业、人口和植被对 PM 的影响最大。此外，GWR 残差的 Moran's I 为 0.025（在 0.01 水平上不显著），表明 GWR 模型的指定是恰当的。一些城市 GDP 的局部系数估计值为负，表明中国大陆 PM 存在倒 U 型环境库兹涅茨曲线（EKC）。各潜在因素对不同地区 PM 的影响不同。因此，应根据特定因素对当地的影响，制定针对 PM 的区域性措施和策略。
