The convergence of PM2.5 concentration in Chinese cities: a distribution dynamic approach

• Main Authors: Weiran Lin, Qiuqin He, Haijing Yu
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-08-01
• Series: Ekonomska Istraživanja
• Subjects: pollution convergence; kernel density; distribution dynamic; pm2.5; china
• Online Access: http://dx.doi.org/10.1080/1331677X.2021.1967772

To fill the gap in the research on the convergence trend of air pollutants since 2013 in China and overcome the Galton fallacy caused by the parametric regression method, this study examines the convergence trend of the annual average concentration of fine particulate matter 2.5 (PM2.5) in China’s prefecture-level cities after 2013 using a distribution dynamic approach. The winter PM2.5 pollution in Chinese cities is severe. Hence, the convergence of the average winter PM2.5 concentration of prefecture-level cities is also explored in this study. The results show that during 2015–2019, the annual average PM2.5 concentration level improved significantly. However, the average PM2.5 winter concentration level in 2015–2018 did not significantly decrease, with some cities showing severe pollution levels. The annual average PM2.5 of China's prefecture-level cities exhibit club convergence, while the PM2.5 concentration in winter exhibits ‘unikurtosis’. In the long run, the annual average PM2.5 clusters around two levels, at approximately 35 μg/m3 and 60 μg/m3, while the average PM2.5 in winter is concentrated at 100 μg/m3. In the long run, in the central region, PM2.5 pollution is more severe than in northern and southern areas, regardless of the annual or winter average PM2.5 concentration.