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Understanding Anthropogenic PM(2.5) Concentrations and Their Drivers in China during 1998–2016
Air pollution poses serious challenges for human health and wellbeing. It also affects atmospheric visibility and contributes to climate change. As social and economic processes have increased, anthropogenic PM(2.5) pollution caused by intensive human activities has led to extremely severe air pollu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819118/ https://www.ncbi.nlm.nih.gov/pubmed/36613014 http://dx.doi.org/10.3390/ijerph20010695 |
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author | Yun, Guoliang Yang, Chen Ge, Shidong |
author_facet | Yun, Guoliang Yang, Chen Ge, Shidong |
author_sort | Yun, Guoliang |
collection | PubMed |
description | Air pollution poses serious challenges for human health and wellbeing. It also affects atmospheric visibility and contributes to climate change. As social and economic processes have increased, anthropogenic PM(2.5) pollution caused by intensive human activities has led to extremely severe air pollution. Spatiotemporal patterns and drivers of anthropogenic PM(2.5) concentrations have received increasing attention from the scientific community. Nonetheless, spatiotemporal patterns and drivers of anthropogenic PM(2.5) concentrations are still inadequately understood. Based on a time series of remotely sensed anthropogenic PM(2.5) concentrations, this study analyzed the spatiotemporal patterns of this crucial pollutant in China from 1998 to 2016 using Sen’s slope estimator and the Mann–Kendall trend model. This, in combination with grey correlation analysis (GCA), was used to reveal the socioeconomic factors influencing anthropogenic PM(2.5) concentrations in eastern, central, and western China from 1998 to 2016. The results were as follows: (1) the average annual anthropogenic concentration of PM(2.5) in China increased quickly and reached its peak value in 2007, then remained stable in the following years; (2) only 63.30 to 55.09% of the land area reached the threshold value of 15 μg/m(3) from 1998 to 2016; (3) regarding the polarization phenomenon of anthropogenic PM(2.5) concentrations existing in eastern and central China, the proportion of gradient 1 (≤15 μg/m(3)) gradually decreased and gradient 3 (≥35 μg/m(3)) gradually increased; and (4) the urbanization level (UR), population density (PD), and proportion of secondary industry to gross domestic product (SI) were the dominant socioeconomic factors affecting the formation of anthropogenic PM(2.5) concentrations in eastern, central, and western China, independently. The improvements in energy consumption per gross domestic product (EI) have a greater potential for mitigating anthropogenic PM(2.5) emissions in central and western China. These findings allow an interpretation of the spatial distribution of anthropogenic PM(2.5) concentrations and the mechanisms influencing anthropogenic PM(2.5) concentrations, which can help the Chinese government develop effective abatement strategies. |
format | Online Article Text |
id | pubmed-9819118 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98191182023-01-07 Understanding Anthropogenic PM(2.5) Concentrations and Their Drivers in China during 1998–2016 Yun, Guoliang Yang, Chen Ge, Shidong Int J Environ Res Public Health Article Air pollution poses serious challenges for human health and wellbeing. It also affects atmospheric visibility and contributes to climate change. As social and economic processes have increased, anthropogenic PM(2.5) pollution caused by intensive human activities has led to extremely severe air pollution. Spatiotemporal patterns and drivers of anthropogenic PM(2.5) concentrations have received increasing attention from the scientific community. Nonetheless, spatiotemporal patterns and drivers of anthropogenic PM(2.5) concentrations are still inadequately understood. Based on a time series of remotely sensed anthropogenic PM(2.5) concentrations, this study analyzed the spatiotemporal patterns of this crucial pollutant in China from 1998 to 2016 using Sen’s slope estimator and the Mann–Kendall trend model. This, in combination with grey correlation analysis (GCA), was used to reveal the socioeconomic factors influencing anthropogenic PM(2.5) concentrations in eastern, central, and western China from 1998 to 2016. The results were as follows: (1) the average annual anthropogenic concentration of PM(2.5) in China increased quickly and reached its peak value in 2007, then remained stable in the following years; (2) only 63.30 to 55.09% of the land area reached the threshold value of 15 μg/m(3) from 1998 to 2016; (3) regarding the polarization phenomenon of anthropogenic PM(2.5) concentrations existing in eastern and central China, the proportion of gradient 1 (≤15 μg/m(3)) gradually decreased and gradient 3 (≥35 μg/m(3)) gradually increased; and (4) the urbanization level (UR), population density (PD), and proportion of secondary industry to gross domestic product (SI) were the dominant socioeconomic factors affecting the formation of anthropogenic PM(2.5) concentrations in eastern, central, and western China, independently. The improvements in energy consumption per gross domestic product (EI) have a greater potential for mitigating anthropogenic PM(2.5) emissions in central and western China. These findings allow an interpretation of the spatial distribution of anthropogenic PM(2.5) concentrations and the mechanisms influencing anthropogenic PM(2.5) concentrations, which can help the Chinese government develop effective abatement strategies. MDPI 2022-12-30 /pmc/articles/PMC9819118/ /pubmed/36613014 http://dx.doi.org/10.3390/ijerph20010695 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yun, Guoliang Yang, Chen Ge, Shidong Understanding Anthropogenic PM(2.5) Concentrations and Their Drivers in China during 1998–2016 |
title | Understanding Anthropogenic PM(2.5) Concentrations and Their Drivers in China during 1998–2016 |
title_full | Understanding Anthropogenic PM(2.5) Concentrations and Their Drivers in China during 1998–2016 |
title_fullStr | Understanding Anthropogenic PM(2.5) Concentrations and Their Drivers in China during 1998–2016 |
title_full_unstemmed | Understanding Anthropogenic PM(2.5) Concentrations and Their Drivers in China during 1998–2016 |
title_short | Understanding Anthropogenic PM(2.5) Concentrations and Their Drivers in China during 1998–2016 |
title_sort | understanding anthropogenic pm(2.5) concentrations and their drivers in china during 1998–2016 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819118/ https://www.ncbi.nlm.nih.gov/pubmed/36613014 http://dx.doi.org/10.3390/ijerph20010695 |
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