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Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China
Exposure to air pollution is one of the greatest environmental risks for human health. Air pollution level is significantly driven by anthropogenic emissions and meteorological conditions. To protect people from air pollutants, China has implemented clean air actions to reduce anthropogenic emission...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10305355/ https://www.ncbi.nlm.nih.gov/pubmed/37368580 http://dx.doi.org/10.3390/toxics11060481 |
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author | Qian, Zihe Meng, Qingxiao Chen, Kehong Zhang, Zihang Liang, Hongwei Yang, Han Huang, Xiaolei Zhong, Weibin Zhang, Yichen Wei, Ziqian Zhang, Binqian Zhang, Kexin Chen, Meijuan Zhang, Yunjiang Ge, Xinlei |
author_facet | Qian, Zihe Meng, Qingxiao Chen, Kehong Zhang, Zihang Liang, Hongwei Yang, Han Huang, Xiaolei Zhong, Weibin Zhang, Yichen Wei, Ziqian Zhang, Binqian Zhang, Kexin Chen, Meijuan Zhang, Yunjiang Ge, Xinlei |
author_sort | Qian, Zihe |
collection | PubMed |
description | Exposure to air pollution is one of the greatest environmental risks for human health. Air pollution level is significantly driven by anthropogenic emissions and meteorological conditions. To protect people from air pollutants, China has implemented clean air actions to reduce anthropogenic emissions, which has led to rapid improvement in air quality over China. Here, we evaluated the impact of anthropogenic emissions and meteorological conditions on trends in air pollutants in a coastal city (Lianyungang) in eastern China from 2015 to 2022 based on a random forest model. The annual mean concentration of observed air pollutants, including fine particles, inhalable particles, sulfur dioxide, nitrogen dioxide, and carbon monoxide, presented significant decreasing trends during 2015–2022, with dominant contributions (55–75%) by anthropogenic emission reduction. An increasing trend in ozone was observed with an important contribution (28%) by anthropogenic emissions. The impact of meteorological conditions on air pollution showed significant seasonality. For instance, the negative impact on aerosol pollution occurred during cold months, while the positive impact was in warm months. Health-risk-based air quality decreased by approximately 40% in 8 years, for which anthropogenic emission made a major contribution (93%). |
format | Online Article Text |
id | pubmed-10305355 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103053552023-06-29 Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China Qian, Zihe Meng, Qingxiao Chen, Kehong Zhang, Zihang Liang, Hongwei Yang, Han Huang, Xiaolei Zhong, Weibin Zhang, Yichen Wei, Ziqian Zhang, Binqian Zhang, Kexin Chen, Meijuan Zhang, Yunjiang Ge, Xinlei Toxics Article Exposure to air pollution is one of the greatest environmental risks for human health. Air pollution level is significantly driven by anthropogenic emissions and meteorological conditions. To protect people from air pollutants, China has implemented clean air actions to reduce anthropogenic emissions, which has led to rapid improvement in air quality over China. Here, we evaluated the impact of anthropogenic emissions and meteorological conditions on trends in air pollutants in a coastal city (Lianyungang) in eastern China from 2015 to 2022 based on a random forest model. The annual mean concentration of observed air pollutants, including fine particles, inhalable particles, sulfur dioxide, nitrogen dioxide, and carbon monoxide, presented significant decreasing trends during 2015–2022, with dominant contributions (55–75%) by anthropogenic emission reduction. An increasing trend in ozone was observed with an important contribution (28%) by anthropogenic emissions. The impact of meteorological conditions on air pollution showed significant seasonality. For instance, the negative impact on aerosol pollution occurred during cold months, while the positive impact was in warm months. Health-risk-based air quality decreased by approximately 40% in 8 years, for which anthropogenic emission made a major contribution (93%). MDPI 2023-05-25 /pmc/articles/PMC10305355/ /pubmed/37368580 http://dx.doi.org/10.3390/toxics11060481 Text en © 2023 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 Qian, Zihe Meng, Qingxiao Chen, Kehong Zhang, Zihang Liang, Hongwei Yang, Han Huang, Xiaolei Zhong, Weibin Zhang, Yichen Wei, Ziqian Zhang, Binqian Zhang, Kexin Chen, Meijuan Zhang, Yunjiang Ge, Xinlei Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China |
title | Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China |
title_full | Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China |
title_fullStr | Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China |
title_full_unstemmed | Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China |
title_short | Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China |
title_sort | machine learning explains long-term trend and health risk of air pollution during 2015–2022 in a coastal city in eastern china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10305355/ https://www.ncbi.nlm.nih.gov/pubmed/37368580 http://dx.doi.org/10.3390/toxics11060481 |
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