The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China

The rapid urbanization in China has brought about serious air pollution problems, which are likely to persist for a considerable period as the urbanization process continues. In urban areas, the spatial distribution of air pollutants represented by PM2.5 has been proved mainly affected by emission,...

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Main Authors: Jiachen Meng, Wenchao Han, Cheng Yuan, Lulu Yuan, Wenze Li
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Environment International
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Online Access:http://www.sciencedirect.com/science/article/pii/S0160412025000029
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author Jiachen Meng
Wenchao Han
Cheng Yuan
Lulu Yuan
Wenze Li
author_facet Jiachen Meng
Wenchao Han
Cheng Yuan
Lulu Yuan
Wenze Li
author_sort Jiachen Meng
collection DOAJ
description The rapid urbanization in China has brought about serious air pollution problems, which are likely to persist for a considerable period as the urbanization process continues. In urban areas, the spatial distribution of air pollutants represented by PM2.5 has been proved mainly affected by emission, urban landscape pattern (short as ULP), as well as meteorological conditions. However, the contributions of these factors can seriously vary with different periods of urban development. Based on multi-source data, 304 prefecture-level cities in China were chosen as study areas, and we used the Geographically and Temporally Weighted Regression (GTWR) model to quantify the relative contributions of three factors—emission, ULP, and meteorological condition—to PM2.5 concentration variation in different periods, namely, the Slow Ascending Period (SAP, 2000–2007), the Stable High-level Period (SHP, 2007–2013), and the Rapid Decline Period (RDP, 2013–2020). During SAP, the relative contribution of emission remained low and the relative contribution of ULP decreased, while the contribution of meteorological factors to PM2.5 concentration variation becoming the dominant factor. During SHP and RDP, the relative contribution of emission notably increased (The largest increase is 28 %), while the relative contribution of meteorological factors significantly decreased (The largest decrease is 16 %). Spatially, the key regions for air pollution control in China, such as the Beijing-Tianjin-Hebei, the Fenwei Plain, the Yangtze River Delta, and the Pearl River Delta, experienced a significantly greater decrease (The largest decrease is 39 %) in the meteorological contribution and increase in the emission contribution (The largest increase is 66 %) compared to other regions. In general, we found that 27 cities in southwest China become increasingly sensitive to meteorological conditions, while the majority of cities (277 in total), particularly in key regions, have shown a growing sensitivity to emission during the whole period. These results prove that the ability of anthropogenic influence on air quality is gradually more effective, indicating the air pollution prevention and control policies in China in recent years have achieved satisfactory results. It is worthy to notice that the PM2.5 level in most cities is still sensitive to emissions. Therefore, strict emission reduction measures still needs to implemented in the future to further improve air quality.
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spelling doaj-art-df22382c33d04db09492f5f74d3bfe7b2025-01-24T04:44:14ZengElsevierEnvironment International0160-41202025-01-01195109251The capacity of human interventions to regulate PM2.5 concentration has substantially improved in ChinaJiachen Meng0Wenchao Han1Cheng Yuan2Lulu Yuan3Wenze Li4State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; School of Emergency Management, Nanjing University of Information Science & Technology, Nanjing 210044, China; Northwest Engineering Corporation Limited, Xi’an 710065, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Corresponding authors.School of Emergency Management, Nanjing University of Information Science & Technology, Nanjing 210044, China; Corresponding authors.College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaThe rapid urbanization in China has brought about serious air pollution problems, which are likely to persist for a considerable period as the urbanization process continues. In urban areas, the spatial distribution of air pollutants represented by PM2.5 has been proved mainly affected by emission, urban landscape pattern (short as ULP), as well as meteorological conditions. However, the contributions of these factors can seriously vary with different periods of urban development. Based on multi-source data, 304 prefecture-level cities in China were chosen as study areas, and we used the Geographically and Temporally Weighted Regression (GTWR) model to quantify the relative contributions of three factors—emission, ULP, and meteorological condition—to PM2.5 concentration variation in different periods, namely, the Slow Ascending Period (SAP, 2000–2007), the Stable High-level Period (SHP, 2007–2013), and the Rapid Decline Period (RDP, 2013–2020). During SAP, the relative contribution of emission remained low and the relative contribution of ULP decreased, while the contribution of meteorological factors to PM2.5 concentration variation becoming the dominant factor. During SHP and RDP, the relative contribution of emission notably increased (The largest increase is 28 %), while the relative contribution of meteorological factors significantly decreased (The largest decrease is 16 %). Spatially, the key regions for air pollution control in China, such as the Beijing-Tianjin-Hebei, the Fenwei Plain, the Yangtze River Delta, and the Pearl River Delta, experienced a significantly greater decrease (The largest decrease is 39 %) in the meteorological contribution and increase in the emission contribution (The largest increase is 66 %) compared to other regions. In general, we found that 27 cities in southwest China become increasingly sensitive to meteorological conditions, while the majority of cities (277 in total), particularly in key regions, have shown a growing sensitivity to emission during the whole period. These results prove that the ability of anthropogenic influence on air quality is gradually more effective, indicating the air pollution prevention and control policies in China in recent years have achieved satisfactory results. It is worthy to notice that the PM2.5 level in most cities is still sensitive to emissions. Therefore, strict emission reduction measures still needs to implemented in the future to further improve air quality.http://www.sciencedirect.com/science/article/pii/S0160412025000029PM2.5EmissionMeteorologyUrban landscape patternGeographically and temporally weighted regression model
spellingShingle Jiachen Meng
Wenchao Han
Cheng Yuan
Lulu Yuan
Wenze Li
The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China
Environment International
PM2.5
Emission
Meteorology
Urban landscape pattern
Geographically and temporally weighted regression model
title The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China
title_full The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China
title_fullStr The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China
title_full_unstemmed The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China
title_short The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China
title_sort capacity of human interventions to regulate pm2 5 concentration has substantially improved in china
topic PM2.5
Emission
Meteorology
Urban landscape pattern
Geographically and temporally weighted regression model
url http://www.sciencedirect.com/science/article/pii/S0160412025000029
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