Identification of heavily polluted areas based on a novel grey integrated incidence model: A case study of the Yangtze River Delta, China. (May 2023) - An, Yimeng; Dang, Yaoguo; Wang, Junjie; Sun, Jing; Feng, Yu
Abstract: Identifying the severity of regional pollution is an important test for the government to develop precise air pollution management policies. By investigating the temporal patterns of six air pollutants, it is found that air pollution in Yangtze River Delta (YRD) is caused by high pollutant concentrations in individual cities. Therefore, a novel grey projection tetrahedron integrated incidence model (GTIIM) is proposed to identify the administrative cities in need of priority control. First, GTIIM model is constructed and its mechanism and properties are discussed. The GTIIM addresses the problem of separating the shape similarity and distance similarity between two time series in existing grey comprehensive incidence models. Then, the GTIIM model allows cities to be ranked according to the severity of pollution, a process that focuses on the numerical and temporal trend characteristics of air pollution time series. Results suggest that (1) the spatial distribution of heavily polluted cities differs for each pollutant. (2) Cities located in the northern part of the YRD contribute more to air pollution, with pollution levels gradually increasing from south to north. (3) The air pollution of ChangZhou, ChuZhou and YangZhou are relatively serious and need to be prioritized for urban air pollution control. Our work provides new insights into recognizing spatial pattern of heavily polluted administrative cities based on time series correlation analysis. Highlights: A novel grey projection tetrahedron integrated incidence model is proposed. The heavily polluted cities for major pollutants are identified by the GTIIM. The spatial distribution of NO 2 pollution differs from those of O 3 and PM pollution. Cities with severe air pollution are concentrated in the northwest of the YRD.