Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China.
Sci Total Environ. 2017 Apr 15;584-585:318-328. doi: 10.1016/j.scitotenv.2016.12.145. Epub 2016 Dec 28.
China has received increased international criticism in recent years in relation to its air pollution levels, both in terms of the transmission of pollutants across international borders and the attendant adverse health effects being witnessed. Whilst existing research has examined the factors influencing ambient air pollutant concentrations, previous studies have failed to adequately explore the determinants of such concentrations from either a source or diffusion perspective. This study addressed both source (specifically, anthropogenic emissions) and diffusion (namely, meteorological conditions) indicators, in order to detect their respective impacts on the spatial variations seen in the distribution of air pollution. Spatial panel data for 113 major cities in China was processed using a range of global regression models-the ordinary least square model, the spatial lag model, and the spatial error model-as well as a local, geographic weighted regression (GWR) model. Results from the study suggest that in 2014, average SO concentrations exceeded China's first-level target. The most polluted cities were found to be predominantly located in northern China, while less polluted cities were located in southern China. Global regression results indicated that precipitation exerts a significant effect on SO reduction (p<0.001) and that a regional increase of 1mm in precipitation can reduce SO concentrations by 0.026μg/m. Both emission and temperature factors were found to aggravate SO concentrations, although no such significant correlation was found in relation to wind speed. GWR results suggest that the association between SO and its factors varied over space. Increased emissions were found to be able to produce more pollution in the northwest than in other parts of the country. Higher wind speeds and temperatures in northwestern areas were shown to reinforce SO pollution, while in southern regions, they had the opposite effect. Further, increased precipitation was found to exert a greater inhibitory effect on SO pollution in the country's northeast than that in other areas. Our findings could provide a detailed reference for formulating regionally specific emission reduction policies in China.
近年来，中国的空气污染水平在国际上受到越来越多的批评，无论是在污染物跨国界传输方面，还是在由此导致的不良健康影响方面。尽管现有研究已经考察了影响环境空气污染物浓度的因素，但以前的研究未能从源或扩散的角度充分探讨这些浓度的决定因素。本研究从源（具体来说是人为排放）和扩散（即气象条件）两个方面，检测它们对空气污染分布的空间变化的各自影响。使用一系列全局回归模型（普通最小二乘法模型、空间滞后模型和空间误差模型）以及局部地理加权回归（GWR）模型，对中国 113 个主要城市的空间面板数据进行了处理。研究结果表明，2014 年，中国 SO2 平均浓度超过一级标准。污染最严重的城市主要位于中国北方，而污染较轻的城市则位于中国南方。全局回归结果表明，降水对 SO2 减排有显著影响（p<0.001），降水每增加 1mm，SO2 浓度可降低 0.026μg/m3。排放和温度因素都被发现会加剧 SO2 浓度，但与风速没有发现显著相关性。GWR 结果表明，SO2 与其因素之间的关系在空间上存在差异。排放增加会使中国西北地区的污染程度比其他地区更为严重。西北地区较高的风速和温度会加剧 SO2 污染，而在南方地区则会产生相反的效果。此外，降水的增加对中国东北地区的 SO2 污染的抑制作用大于其他地区。我们的研究结果可以为中国制定区域性特定减排政策提供详细的参考。
