Regional delimitation of PM2.5 pollution using spatial cluster for monitoring sites: A case study of Xianyang, China

Abstract

Fine particulate matter (PM2.5) pollution has been a major concerning issue in China, and many cities have developed emergency plans for heavy air pollution. The aim of this study is to delimitate PM2.5 pollution regions of Xianyang, which is very important to the regional environmental prevention and control. The result showed that PM2.5 air pollution had significant cross-administrative characteristics in Xianyang. Using spatial clustering algorithm under adjacent matrix constrain, this study classified the air quality monitoring sites into two clusters. For each monitoring site, we generated Voronoi polygons and ultimately Xianyang was delimitated into two regions, south and north. The air pollution of the southern region was more serious with 64 days of heavy and severe pollution since 2018, while the northern region had only 10 days. The southern region consisted of four complete administrative districts and parts of three administrative districts. While the northern region consisted of six complete administrative districts and parts of three administrative districts. Visualization of the spatiotemporal characteristics of the PM2.5 air pollution in the two regions further illustrated the significant difference. We suggest when heavy pollution happens, the two regions should be considered separately. If the southern region is heavily polluted while the northern region not, only the southern region needs to implement the emergency plan to minimize the damage to society and economy. Seventy-five percent of the city area, 2.3 million people, 59% of schools, and 43% of GDP would not be impacted if air pollution was controlled by region separately. The sensitive analysis shows that clustering result is robust against different pollution degree and missing values.