Seasonal Variations of PM 2.5 Pollution in the Chengdu–Chongqing Urban Agglomeration, China

Kun Wang, Yuan Yao () and Kun Mao
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Kun Wang: School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
Yuan Yao: School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
Kun Mao: School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China

Sustainability, 2024, vol. 16, issue 21, 1-20

Abstract: During the development of the Chengdu–Chongqing Urban Agglomeration (CCUA) in China, PM 2.5 pollution severely threatened public health, presenting a significant environmental challenge. This study employs a novel spatial interpolation method known as High Accuracy Surface Modeling (HASM), along with the geographical detector method, local and regional contributions calculation model, and the Hybrid Single–Particle Lagrangian Integrated Trajectory model to analyze the seasonal spatial distribution of PM 2.5 concentrations and their anthropogenic driving factors from 2014 to 2023. The transport pathway and potential sources of seasonal PM 2.5 concentrations were also examined. The results showed the following: (1) HASM was identified as the most suitable interpolation method for monitoring PM 2.5 concentrations in the CCUA; (2) The PM 2.5 concentrations exhibited a decreasing trend across all seasons, with the highest values in winter and the lowest in summer. Spatially, the concentrations showed a pattern of being higher in the southwest and lower in the southeast; (3) Industrial soot (dust) emissions (ISEs) and industry structure (IS) were the most important anthropogenic driving factors influencing PM 2.5 pollution; (4) The border area between the eastern part of the Tibet Autonomous Region and western Sichuan province in China significantly contribute to PM 2.5 pollution in the CCUA, especially during winter.

Keywords: PM 2.5; HASM; seasonal variation; driving factors; potential source contribution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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