State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; PLA 96941 Army, Beijing, 100085, China.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China.
Environ Pollut. 2020 Oct;265(Pt B):115086. doi: 10.1016/j.envpol.2020.115086. Epub 2020 Jun 26.
In recent years, the Chinese government has made great efforts to jointly control and prevent air pollution, especially fine particulate matter (PM). However, these efforts are challenged by technical constraints due to the significant temporal and spatial heterogeneity of PM across China. In this study, the Affinity Zone Identification Approach (AZIA), which combines rotated principal component analysis (RPCA) with revised clustering analysis, was developed and employed to regionalize PM pollution in China based on data from 1496 air quality monitoring sites recorded from 2013 to 2017. Two clustering methods, cluster analysis with statistical test (CAST) and K-center-point (K-medoids) clustering, were compared and revised to eliminate unspecified sites. Site zonation was finally extended to the municipality scale for the convenience of the controlling measures. The results revealed that 17 affinity zones with 5 different labels from clean to heavily polluted areas could be identified in China. The heavily polluted areas were mainly located in central and eastern China as well as Xinjiang Province, with regional average annual PM concentrations higher than 66 μg/m. The new approach provided more comprehensive and detailed affinity zones than obtained in a previous study (Wang et al., 2015b). The North China Plain and Northeastern China were both further divided into northern and southern parts based on different pollution levels. In addition, five affinity zones were first recognized in western China. The findings provide not only a theoretical basis to further display the temporal and spatial variations in PM but also an effective solution for the cooperative control of air pollution in China.
近年来，中国政府在联合控制和防治空气污染方面做出了巨大努力，尤其是针对细颗粒物（PM）。然而，由于中国各地 PM 的时空异质性很大，这些努力受到了技术限制的挑战。在这项研究中，开发并采用了基于 2013 年至 2017 年期间 1496 个空气质量监测站记录的数据的亲和区域识别方法（AZIA），该方法将旋转主成分分析（RPCA）与修正聚类分析相结合，对中国的 PM 污染进行区域化。比较并修正了两种聚类方法，即带有统计检验的聚类分析（CAST）和 K-中心点（K-medoids）聚类，以消除未指定的站点。最后，将站点分区扩展到直辖市规模，以便于采取控制措施。结果表明，在中国可以识别出 17 个具有从清洁到重度污染的 5 种不同标签的亲和区域。重度污染地区主要集中在中国中部和东部以及新疆地区，区域年均 PM 浓度高于 66μg/m。与之前的研究（Wang 等人，2015b）相比，新方法提供了更全面和详细的亲和区。华北平原和东北地区根据不同的污染水平进一步分为北部和南部。此外，首次在中国西部识别出五个亲和区。这些发现不仅为进一步展示 PM 的时空变化提供了理论基础，也为中国的空气污染协同控制提供了有效的解决方案。
