Seasonal Variations of PM2.5 Pollution in the Chengdu–Chongqing Urban Agglomeration, China

During the development of the Chengdu–Chongqing Urban Agglomeration (CCUA) in China, PM2.5 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 PM2.5 concentrations and their anthropogenic driving factors from 2014 to 2023. The transport pathway and potential sources of seasonal PM2.5 concentrations were also examined. The results showed the following: (1) HASM was identified as the most suitable interpolation method for monitoring PM2.5 concentrations in the CCUA; (2) The PM2.5 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 PM2.5 pollution; (4) The border area between the eastern part of the Tibet Autonomous Region and western Sichuan province in China significantly contribute to PM2.5 pollution in the CCUA, especially during winter.

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Publication Details

Journal
Sustainability
Published
2024-10-24
DOI
https://doi.org/10.3390/su16219242
Citations
1
Primary Topic
Air Quality and Health Impacts
Type
article
Field-Weighted Citation Impact
0.31
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article

Seasonal Variations of PM2.5 Pollution in the Chengdu–Chongqing Urban Agglomeration, China

Yuan Yao, Kun Mao, Kun Wang
1 citations
Sustainability
Air Quality and Health Impacts
0.31
article

Seasonal Variations of PM2.5 Pollution in the Chengdu–Chongqing Urban Agglomeration, China

Yuan Yao, Kun Mao, Kun Wang
article en
1 citations

Abstract

During the development of the Chengdu–Chongqing Urban Agglomeration (CCUA) in China, PM2.5 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 PM2.5 concentrations and their anthropogenic driving factors from 2014 to 2023. The transport pathway and potential sources of seasonal PM2.5 concentrations were also examined. The results showed the following: (1) HASM was identified as the most suitable interpolation method for monitoring PM2.5 concentrations in the CCUA; (2) The PM2.5 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 PM2.5 pollution; (4) The border area between the eastern part of the Tibet Autonomous Region and western Sichuan province in China significantly contribute to PM2.5 pollution in the CCUA, especially during winter.

SustainabilityVol. 16(21)
Chinese Academy of Sciences (CN), Chengdu University (CN), Institute of Geographic Sciences and Natural Resources Research (CN)
Sustainable cities and communities
Openalex Percentile: Top 36%
Air Quality and Health Impacts
0.31
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