Seasonal Characteristics and Source Contributions Analysis of PM2.5 in Mountainous Cities

Abstract PM 2.5 is an important component of haze, and an in-depth understanding of PM 2.5 pollution characteristics and potential source areas is of great significance for the accurate prevention and control of PM 2.5 pollution. Previous studies have mostly focused on heavy pollution periods or economically developed areas and neglecting the seasonal and regional characteristics of urban PM 2.5 pollution in mountainous areas. Based on meteorological data and PM 2.5 concentration data, this study analyses the characteristics of PM 2.5 pollution in Linyi in different seasons and explores the regional sources of PM 2.5 . Cluster analysis using Euclidean distance was employed to detect the principal pathways for PM 2.5 transport. To identify the most critical sources of PM 2.5 , both the WPSCF and WCWT methods were employed. The findings indicated a discernible improvement in PM 2.5 contamination levels in Linyi in recent years. Linyi’s main sources of PM 2.5 originates primarily from neighboring areas, including eastern Henan, northern Anhui and northern Shandong. Seasonal variations exist in the possible source regions contributing to the PM 2.5 concentrations in Linyi. During spring and winter, there exists a multitude of potential source areas that substantially influence the concentrations of PM 2.5 . On the contrary, the number of potential source areas is restricted and their contribution to PM 2.5 concentrations is relatively low in summer and autumn. The study will provide a scientific basis for preventing and controlling PM 2.5 pollution in mountainous city. Graphical Abstract

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Publication Details

Journal
Aerosol and Air Quality Research
Published
2026-02-03
DOI
https://doi.org/10.1007/s44408-026-00093-7
Primary Topic
Air Quality and Health Impacts
Type
article
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Seasonal Characteristics and Source Contributions Analysis of PM2.5 in Mountainous Cities

Di-chen Zhao, Qian Wang, Qi-peng Zhang, Xiao-yue Mei
Aerosol and Air Quality Research
Air Quality and Health Impacts
article

Seasonal Characteristics and Source Contributions Analysis of PM2.5 in Mountainous Cities

Di-chen Zhao, Qian Wang, Qi-peng Zhang, Xiao-yue Mei
article en

Abstract

Abstract PM 2.5 is an important component of haze, and an in-depth understanding of PM 2.5 pollution characteristics and potential source areas is of great significance for the accurate prevention and control of PM 2.5 pollution. Previous studies have mostly focused on heavy pollution periods or economically developed areas and neglecting the seasonal and regional characteristics of urban PM 2.5 pollution in mountainous areas. Based on meteorological data and PM 2.5 concentration data, this study analyses the characteristics of PM 2.5 pollution in Linyi in different seasons and explores the regional sources of PM 2.5 . Cluster analysis using Euclidean distance was employed to detect the principal pathways for PM 2.5 transport. To identify the most critical sources of PM 2.5 , both the WPSCF and WCWT methods were employed. The findings indicated a discernible improvement in PM 2.5 contamination levels in Linyi in recent years. Linyi’s main sources of PM 2.5 originates primarily from neighboring areas, including eastern Henan, northern Anhui and northern Shandong. Seasonal variations exist in the possible source regions contributing to the PM 2.5 concentrations in Linyi. During spring and winter, there exists a multitude of potential source areas that substantially influence the concentrations of PM 2.5 . On the contrary, the number of potential source areas is restricted and their contribution to PM 2.5 concentrations is relatively low in summer and autumn. The study will provide a scientific basis for preventing and controlling PM 2.5 pollution in mountainous city. Graphical Abstract

Aerosol and Air Quality ResearchVol. 26(3)
Liaocheng University (CN), Gansu Normal University for Nationalities (CN)
Sustainable cities and communities
Openalex Percentile: Top 70%
Air Quality and Health Impacts
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