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ABSTRACT
This study comprehensively analyzed air pollution in Chengdu (CD), a megacity in southwest China, evaluated the Variation Characteristics of air quality during 2015–2018, and conducted Random Forest classification of air pollution data of 2017. The classification results showed three pollution periods: severe (December, January and February), ozone (May‒August), and slight (March and November). These features were combined with potential source contribution function (PSCF), concentration weighted trajectory (CWT) and backward trajectory model (HYSPLIT) for simulating spatio-temporal trajectory of air polluted during each pollution periods. The results show that PM2.5 mainly comes from CD and surrounding cities, and some may be from India, Myanmar and Chongqing; PM10 mainly comes from CD and surrounding cities and some may be from India and Myanmar; NO2 mainly comes from CD and surrounding cities and cities and Some of the pollution may come from the input of India, Myanmar, Chongqing and Inner Mongolia; O3 mainly comes from the urban agglomeration of Sichuan Basin and some areas from Chongqing, Sichuan Liangshan and Yunnan Guizhou. Combined with the meteorological data of temperature, relative humidity and wind speed, aerosol optical depth, planetary boundary layer height and thermal anomaly data, the Monthly, daily and hourly spatio-temporal characteristics and the possible occurred cause of the main air pollution during each pollution period in CD were revealed detail. The research in this paper is critical for pollution control and prevention and provides a scientific basis for studying the spatio-temporal characteristics and sources of pollution in megacities in terrain such as basins and mountains.
Implications: Air pollution has a significant impact on human and ecological health. In 2013, Chengdu was one of the five cities with the most serious PM2.5 pollution in the world. In the previous study of air pollution in Chengdu, it was only for a short period of pollution. It is impossible to fully understand the spatio-temporal trajectory and cause of air pollution. Chengdu is surrounded by mountains, and the meteorological conditions have been stagnant for a long time. The research on the spatio-temporal evolution of the main air pollution trajectories in each pollution period in Chengdu is particularly important. Quantifying the pollution trajectory and air pollution concentration is helpful to fully understand the air quality in Chengdu. The comprehensive analysis of multi-source data such as air pollution and meteorology has focused on strengthening the in-depth research on the transmission law of air pollution, the spatio-temporal change trend of air pollution, the sources of air pollution and the causes of air pollution, so as to help people fully understand the sources and causes of pollution in Chengdu. Aiming at the trajectory law, causes and occurrence time of air pollution, it is conducive for the government to formulate corresponding policies, carry out regional emission reduction and joint prevention and control, improve air quality and minimize the harm of air pollution to the public.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request. http://sweb.cdutetc.cn/tmgc/wxj/20220407.rar
Supplementary material
Supplemental data for this paper can be accessed on the publisher’s website.
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Notes on contributors
Xingjie Wang
Xingjie Wang received a B.S. degree in Automation from Kunming University of Science and Technology, Kunming, China, in 2003, and an M.S. degree in Computer application technology from Kunming University of Science and Technology, Kunming, China, in 2006. He is currently working toward a Ph.D. degree with Geodetection and Information Technology, Chengdu University of Technology, Chengdu, China.
He is currently a Professor with College of Engineering and Technology, Chengdu University of Technology. His research interests include remote sensing image processing, earth exploration, and spatio-temporal big data.
Ling Chen
Ling Chen received a B.S. degree in mathematics education from Southwest Normal University, Chongqing, China, in 1992, and an M.S. degree in applied mathematics, in 2003, and a Ph.D. degree in geodetection and information technology, in 2011, both from the Chengdu University of Technology, Chengdu, China.
She is currently a Professor with the Geomathe-matics Key Laboratory of Sichuan Province, Chengdu University of Technology. Her research interests include remote sensing image processing, earth exploration, and information technology.
Ke Guo
Ke Guo received a B.S. degree in mathematics from Chengdu Geological College, Chengdu, China, in 1985, an M.S. degree in geomathematics from Chengdu Geological College, Chengdu, China, in 1990, and a Ph.D. degree in mineral resource prospecting and exploration from the Chengdu University of Technology, Chengdu, China, in 2005.
He is a Professor and Doctoral Supervisor with the Chengdu University of Technology, Chengdu, China. He is an Academic and Technical Leader of Sichuan province and enjoy special subsidies from the State Council Government. He is currently the Head of the teaching team of mathematics geology in Sichuan province, and the Head of the scientific research and innovation team of high-level resources and environment in Sichuan province. He has long been involved in the teaching and scientific research of quantitative evaluation and prediction of resources and environment.
Bingli Liu
Bingli Liu received a B.S. degree in Mathematics and Applied Mathematics, in 2003, and an M.S. degree in computational mathematics, in 2006, and a Ph.D. degree with Geodetection and Information Technology, in 2012, all from Chengdu University of Technology, Chengdu, China.
He is currently a associate professor with the Geomathe-matics Key Laboratory of Sichuan Province, Chengdu University of Technology. Her research interests include mathematical geology, earth exploration, and spatio-temporal big data.
