Spatiotemporal Evolution Pattern And Underlying Factors In Atmospheric PM_2.5 Concentrations In Zhejiang Province,China

The booming economy,industrialization and urbanization,along with rapid energy consumption,population agglomeration and traffic congestion have greatly affected the air quality in China.Fine particulate matter in the lower atmosphere(PM_2.5)has gained great attention for the past few decades because PM_2.5 pollution is closely related to local potential economic investment,tourism and sustainable development.Previous studies seldom focus on the spatiotemporal change of PM_2.5 influencing factors,more comprehensive and systematic research should be taken to reveal the dynamics and mechanism for targeted monitoring and control.In this study,spatial autocorrelation,Mann-Kendall trend test and rescale range analysis were used to explore the spatiotemporal change of PM_2.5 in Zhejiang Province from 2000 to2019.Besides,based on multi-source data including meteorology,topography,land use and socioeconomic data,XGBoost model and Shapley additive interpretation method（SHAP）were employed to analyze the spatiotemporal variations and mechanism explained by key factors both in the whole province and within the urban core areas.Accordingly,practical recommendations for prevention and control were proposed.The main findings were as follows:From 2000 to 2019,the annual average value of PM_2.5 concentrations in Zhejiang Province showed a fluctuant but downward trend.After 2014,the quality of PM_2.5 had been improved significantly.High polluted spots were clustered around the urban area of Hangzhou in the north,Quzhou City and Jinhua City in the mid-west,and Taizhou City along the southeast coast.The low concentrated spots were mainly located in the southwestern mountainous.The year 2010 saw the change of decreased high pollution and enlarged area with substantial improvement.Generally,the PM_2.5 concentrations showed strong local spatial positive autocorrelation with slight change.The Hangjiahu Plain,Jinqu Basin and eastern coast kept being the high-high spots,while the low-low agglomeration areas were contiguously distributed in the southwestern.The influencing factors’importance was generally ranked as follows:meteorological factors>topography and land use factors>socioeconomic factors.The relative contribution of meteorological factors showed a U-shaped trend,among which sunshine,precipitation,temperature and wind speed were at the top.Differently,the importance of topography and land use factors continued to increase,especially the proportion of forest land and construction land.Meanwhile,the contribution of socioeconomic factors showed an inverted U-shaped trend with factors initially increased but then declined.The SHAP method facilitates the spatiotemporal visualization of PM_2.5 influencing factors and enhances the spatial interpretability of different factors’importance.The map of SHAP values suggested that,although the relative importance of industrial emissions declined,the coverage positively impacted by emissions actually increased.High PM_2.5 concentrations in Zhejiang Province were mainly concentrated in urban areas.Controllable factors were selected in four urban core areas,in 2019 top factors were the proportion of forest land,the proportion of construction land and the edge density of construction land,implying the importance of landscape protection and land use optimization.The importance of GDP and highway mileage in the core area of Hangzhou and Wenzhou was more prominent,indicating the demand of optimization and upgrading of the industrial structure and transportation infrastructure.In addition,industrial waste gas emission stood out in Ningbo and Jinyi hinted that priority could be given to clean energy consumption,rationalization of industrial operations and scattered industrial regions to reduce the diffusion of fine particles.