School of Economic and Management, Xinjiang University, Urumqi, 83000, People's Republic of China.
Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi,, 83000, People's Republic of China.
Environ Monit Assess. 2020 Dec 28;193(1):15. doi: 10.1007/s10661-020-08749-6.
While numerous studies have explored the spatial patterns and underlying causes of PM at the urban scale, little attention has been paid to the spatial heterogeneity affecting PM factors. In order to enrich this research field, we collected PM monitoring data from 367 cities across China in 2016 and combined inverse distance weighted interpolation (IDW) and geographically weighted regression (GWR) model. As a result, we could dynamically describe the spatial distribution pattern of urban PM at monthly, seasonal, and annual scales and investigate the spatial heterogeneity of the influential factors on urban PM. Furthermore, in order to make the result more scientific and reasonable, the paper used selection.gwr function and bw.gwr function, respectively, to optimize model, thereby avoiding local collinearity caused by independent variables. The main results are as follows: (1) PM in Chinese cities is characterized as time-space non-equilibrium pattern. The Beijing-Tianjin-Hebei region, the Yangtze River corner region, the Pearl River Delta region, and the northeast region have formed a pollution-concentrating core area with Beijing-Tianjin-Hebei region as the axis, which brings greater difficulties and challenges to PM governance. (2) The effects of various factors of socio-economic activities on the concentration of PM have significant spatial heterogeneity among Chinese cities. (3) There is an inverted "U" curve between economic growth and PM. When the per capita income reaches 47,000 yuan, the PM emission reaches the peak, which proves the existence of environmental Kuznets curve (EKC). These findings could provide a significant reference for policy makers in China to facilitate targeted and differentiated regional PM governance measures.
虽然有许多研究探讨了城市尺度上 PM 的空间格局和潜在原因，但很少关注影响 PM 因素的空间异质性。为了丰富这一研究领域，我们收集了 2016 年中国 367 个城市的 PM 监测数据，并结合了反距离加权插值（IDW）和地理加权回归（GWR）模型。结果，我们能够动态描述城市 PM 在月度、季节性和年度尺度上的空间分布模式，并研究影响城市 PM 的因素的空间异质性。此外，为了使结果更加科学合理，本文分别使用 selection.gwr 函数和 bw.gwr 函数对模型进行优化，从而避免了因自变量独立而导致的局部共线性问题。主要结果如下：（1）中国城市的 PM 具有时空非均衡模式。京津冀地区、长江三角洲地区、珠江三角洲地区和东北地区已经形成了以京津冀地区为轴心的污染集中核心区，这给 PM 治理带来了更大的困难和挑战。（2）社会经济活动对 PM 浓度的影响在不同城市之间存在显著的空间异质性。（3）经济增长与 PM 之间存在着倒“U”型关系。当人均收入达到 47000 元时，PM 排放量达到峰值，证明了环境库兹涅茨曲线（EKC）的存在。这些发现为中国决策者提供了重要参考，有助于制定有针对性和差异化的区域 PM 治理措施。
