Methods for defining the scopes and priorities for joint prevention and control of air pollution regions based on data-mining technologies

Abstract How to implement the strategy of joint prevention and control of air pollution (JPCAP) to effectively control the severe regional air pollution has become a focus of global concern. Chinese government has adopted this strategy widely, but it is improving the regional air quality too slowly and costly because of lacking accurate scopes and priorities of JPCAP regions. In this context, making use of long-term, wide-area monitoring data provided by the constantly expanding air pollution monitoring network, we proposed new methods to solve these problems, including (i) the method of subdividing large regions into sub-regions by using data-mining technologies and (ii) the method of determining the priorities for JPCAP sub-regions based on the technique for order preference by similarity to an ideal solution method (TOPSIS) by establishing four key indicators. To test the methods, we applied them to a case study of JPCAP for particulates smaller than 2.5 μm in diameter (PM 2.5 ) and ozone (O 3 ) in 15 cities of the Yangtze River Delta, China. We found that the region under study could be subdivided into four JPCAP sub-regions for PM 2.5 and nine for O 3 . The priorities assigned to these sub-regions match their actual conditions in terms of population density, industrial structure, geographic features, and climate, suggesting that the new methods are scientific and effective. Thus, implementation of the new methods should help to mitigate regional pollution problems both in China and around the world.