Asymmetric effects of natural and socioeconomic factors on PM2.5 pollution in Chinese counties

Abstract With rapid urbanization intensifying air pollution, especially PM2.5, which poses a serious threat to public health, clarifying its spatial differentiation patterns and driving mechanisms is of great practical significance. As factors may exert different magnitudes and even opposite directi...

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Main Authors: Biao Sun, Jihong Li
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-03138-w
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author Biao Sun
Jihong Li
author_facet Biao Sun
Jihong Li
author_sort Biao Sun
collection DOAJ
description Abstract With rapid urbanization intensifying air pollution, especially PM2.5, which poses a serious threat to public health, clarifying its spatial differentiation patterns and driving mechanisms is of great practical significance. As factors may exert different magnitudes and even opposite directions of influence across PM2.5 levels, accounting for asymmetric effects is essential for designing targeted and effective governance strategies. We developed a spatial zoning plan for 2851 counties in China using annual average PM2.5 remote sensing data from 2000 to 2015. Geographic detectors and quantile regression models were used to reveal the dominant factors and asymmetric effects affecting PM2.5 pollution in 2015, respectively. The results show significant spatial agglomeration of PM2.5 (Moran’s I = 0.9387), with a pronounced divergence across the Hu Huanyong Line. Combining the spatial classification and temporal trends of PM2.5 pollution, 84.5% of counties need to be treated (523 counties belong to key governance zone), and 15.5% of counties are classified as coexistence areas. The influences of socio-economic factors on PM2.5 (additive q-value 0.7881) are greater than those of natural condition factors (additive q-value 0.7036), and land urbanization is the leading factor. The interesting findings are that the coefficient of population size declines with increasing pollution quantiles—its effect at the 10th quantile (0.1104) is nearly four times that at the 90th (0.0296), and that land urbanization showed an inverted U-shaped curve, while per capita GDP presented a U-shaped trend. Moreover, favorable natural conditions have mitigated local PM2.5 pollution. These findings uncover how socio-economic and natural drivers perform differently at varying pollution levels, offering novel insights into targeted zoning regulation.
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spelling doaj-art-ddf77659c1eb4e4e8306d9f1dc071d712025-08-20T01:59:57ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-03138-wAsymmetric effects of natural and socioeconomic factors on PM2.5 pollution in Chinese countiesBiao Sun0Jihong Li1College of Landscape Architecture, Nanjing Forestry UniversityCollege of Landscape Architecture, Nanjing Forestry UniversityAbstract With rapid urbanization intensifying air pollution, especially PM2.5, which poses a serious threat to public health, clarifying its spatial differentiation patterns and driving mechanisms is of great practical significance. As factors may exert different magnitudes and even opposite directions of influence across PM2.5 levels, accounting for asymmetric effects is essential for designing targeted and effective governance strategies. We developed a spatial zoning plan for 2851 counties in China using annual average PM2.5 remote sensing data from 2000 to 2015. Geographic detectors and quantile regression models were used to reveal the dominant factors and asymmetric effects affecting PM2.5 pollution in 2015, respectively. The results show significant spatial agglomeration of PM2.5 (Moran’s I = 0.9387), with a pronounced divergence across the Hu Huanyong Line. Combining the spatial classification and temporal trends of PM2.5 pollution, 84.5% of counties need to be treated (523 counties belong to key governance zone), and 15.5% of counties are classified as coexistence areas. The influences of socio-economic factors on PM2.5 (additive q-value 0.7881) are greater than those of natural condition factors (additive q-value 0.7036), and land urbanization is the leading factor. The interesting findings are that the coefficient of population size declines with increasing pollution quantiles—its effect at the 10th quantile (0.1104) is nearly four times that at the 90th (0.0296), and that land urbanization showed an inverted U-shaped curve, while per capita GDP presented a U-shaped trend. Moreover, favorable natural conditions have mitigated local PM2.5 pollution. These findings uncover how socio-economic and natural drivers perform differently at varying pollution levels, offering novel insights into targeted zoning regulation.https://doi.org/10.1038/s41598-025-03138-wPM2.5 pollutionSpatial differentiationZoning controlAsymmetric effectsChinese counties
spellingShingle Biao Sun
Jihong Li
Asymmetric effects of natural and socioeconomic factors on PM2.5 pollution in Chinese counties
Scientific Reports
PM2.5 pollution
Spatial differentiation
Zoning control
Asymmetric effects
Chinese counties
title Asymmetric effects of natural and socioeconomic factors on PM2.5 pollution in Chinese counties
title_full Asymmetric effects of natural and socioeconomic factors on PM2.5 pollution in Chinese counties
title_fullStr Asymmetric effects of natural and socioeconomic factors on PM2.5 pollution in Chinese counties
title_full_unstemmed Asymmetric effects of natural and socioeconomic factors on PM2.5 pollution in Chinese counties
title_short Asymmetric effects of natural and socioeconomic factors on PM2.5 pollution in Chinese counties
title_sort asymmetric effects of natural and socioeconomic factors on pm2 5 pollution in chinese counties
topic PM2.5 pollution
Spatial differentiation
Zoning control
Asymmetric effects
Chinese counties
url https://doi.org/10.1038/s41598-025-03138-w
work_keys_str_mv AT biaosun asymmetriceffectsofnaturalandsocioeconomicfactorsonpm25pollutioninchinesecounties
AT jihongli asymmetriceffectsofnaturalandsocioeconomicfactorsonpm25pollutioninchinesecounties