Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China.
Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China.
Environ Pollut. 2019 May;248:792-803. doi: 10.1016/j.envpol.2019.02.081. Epub 2019 Feb 28.
Whilst numerous studies have explored the spatial patterns and underlying causes of PM, little attention has been paid to the spatial heterogeneity of the factors affecting PM. In this study, a geographically weighted regression (GWR) model was used to explore the strength and direction of nexus between various factors and PM in Chinese cities. A comprehensive interpretive framework was established, composed of 18 determinants spanning the three categories of natural conditions, socioeconomic factors, and city features. Our results indicate that PM concentration levels were spatially heterogeneous and markedly higher in cities in eastern China than in cities in the west of the country. Based on the results of GWR, significant spatial heterogeneity was identified in both the direction and strength of the determinants at the local scale. Among all of the natural variables, elevation was found to be statistically significant with its effects on PM in 95.60% of the cities and it correlated negatively with PM in 99.63% cities, with its effect gradually weakening from the eastern to the western parts of China. The variable of built-up areas emerged as the strongest variable amongst the socioeconomic variables studied; it maintained a positive significant relationship in cities located in the Pearl River Delta and surrounding areas, while in other cities it exhibited a negative relationship to PM. The highest coefficients were located in cities in northeast China. As the strongest variable amongst the six landscape factors, patch density maintained a positive relationship in part of cities. While in cities in the northeast regions, patch density exhibited a negative relationship with PM, revealing that increasing urban fragmentation was conducive to PM reductions in those regions. These empirical results provide a basis for the formulation of targeted and differentiated air quality improvement measures in the task of regional PM governances.
虽然有许多研究探讨了 PM 的空间格局和潜在原因，但很少关注影响 PM 的因素的空间异质性。在这项研究中，使用地理加权回归（GWR）模型来探索中国城市中各种因素与 PM 之间的关联的强度和方向。建立了一个综合的解释框架，由跨越自然条件、社会经济因素和城市特征三个类别的 18 个决定因素组成。我们的结果表明，PM 浓度水平存在空间异质性，东部城市明显高于西部城市。基于 GWR 的结果，在局部尺度上，决定因素的方向和强度都存在显著的空间异质性。在所有自然变量中，海拔高度被发现具有统计学意义，其对 95.60%的城市中 PM 的影响为正，而对 99.63%的城市中 PM 的影响为负，其影响从中国东部到西部逐渐减弱。建成区变量是所研究的社会经济变量中最强的变量；它在珠江三角洲及周边地区的城市中保持着正相关关系，而在其他城市中则与 PM 呈负相关关系。系数最高的城市位于中国东北地区。作为六个景观因素中最强的变量，斑块密度在部分城市中保持着正相关关系。而在东北地区的城市中，斑块密度与 PM 呈负相关关系，表明增加城市破碎化有利于这些地区的 PM 减少。这些实证结果为制定有针对性和差异化的区域 PM 治理空气质量改善措施提供了依据。
