Spatial patterns and influencing factors of intraurban particulate matter in the heating season based on taxi monitoring

作     者：Chong Liu Yuanman Hu Yu Chang Miao Liu Zaiping Xiong Tan Chen Chunlin Li 

作者机构：CAS Key Laboratory of Forest Ecology and ManagementInstitute of Applied EcologyChinese Academy of SciencesShenyangChina College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijingChina Key Laboratory of Watershed Geographic SciencesNanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjingChina Shanghai Key Lab for Urban Ecological Processes and Eco-RestorationEast China Normal UniversityShanghaiChina 

出 版 物：《Ecosystem Health and Sustainability》 (生态系统健康与可持续性（英文）)

年 卷 期：2022年第8卷第1期

页      面：214-227页

核心收录：

学科分类：07[理学] 070602[理学-大气物理学与大气环境] 0706[理学-大气科学] 

基　　金：Funding for this project was provided by the National Natural Science Foundation of China(Nos.41730647 and 32071580) 

主　　题：Particulate matter taxi monitoring heating season spatial pattern urbanization Shenyang 

摘      要：Urbanization has introduced a series of environmental problems worldwide,and particulate matter(PM)is one of the main threats to human *** to the lack of high-resolution,large-scale monitoring data,few studies have analyzed the intraurban spatial distribution pattern of PM at a fine *** this study,portable air monitors carried by five taxis were used to collect the concentrations of PM1,PM2.5 and PM10 for five months in Shenyang during the heating *** results showed that high concentrations of PM were distributed in the suburbs,while relatively low concentration areas were found in the central ***,industrial and development zones had higher concentration values among the eight observed *** PM concentration exhibited strong spatial autocorrelation based on Moran’s I index *** factors were the most important influencing factors of the three pollutants,and their total contribution rate accounted for more than 80%among the 13 factors according to boosted regression trees *** taxi monitoring method we proposed was a more efficient and feasible method for monitoring urban air pollution and could obtain higher spatial-temporal resolution data at a lower cost to elucidate the region’s dynamic air pollution distribution patterns.