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Influencing factors and trend prediction of PM(2.5) concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China

The government’s development of eco-environmental policies can have a scientific foundation thanks to the fine particulate matter (PM(2.5)) medium- and long-term change forecast. This study develops a STRIPAT-Scenario analysis framework employing panel data from 11 cities in Zhejiang Province betwee...

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Autores principales: Zhang, Qiong, Ye, Shuangshuang, Ma, Tiancheng, Fang, Xuejuan, Shen, Yang, Ding, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9476454/
https://www.ncbi.nlm.nih.gov/pubmed/36124159
http://dx.doi.org/10.1007/s10668-022-02672-1
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author Zhang, Qiong
Ye, Shuangshuang
Ma, Tiancheng
Fang, Xuejuan
Shen, Yang
Ding, Lei
author_facet Zhang, Qiong
Ye, Shuangshuang
Ma, Tiancheng
Fang, Xuejuan
Shen, Yang
Ding, Lei
author_sort Zhang, Qiong
collection PubMed
description The government’s development of eco-environmental policies can have a scientific foundation thanks to the fine particulate matter (PM(2.5)) medium- and long-term change forecast. This study develops a STRIPAT-Scenario analysis framework employing panel data from 11 cities in Zhejiang Province between 2006 and 2020 to predict the changing trend of PM(2.5) concentrations under five alternative scenarios. The results reveal that: (1) urbanization development (P), economic development (A), technological innovation investment (T) and environmental regulation intensity have a significant inhibitory effect on PM(2.5) concentration in Zhejiang Province, while industrial structure, industrial energy consumption and the number of motor vehicles (TR) have a significant increase on PM(2.5) concentration. (2) Under any scenario, the PM(2.5) concentration of 11 cities in Zhejiang Province can reach the constraint target set in the 14th Five-Year plan. The improvement in urban PM(2.5) quality is most obviously impacted by the high-quality development scenario (S4). (3) Toward 2035, PM(2.5) concentrations of 11 cities in Zhejiang Province can reach the National Class I level standard in most scenario models, among which Hangzhou, Jiaxing and Shaoxing are under high pressure to reduce emissions and are the key areas for PM(2.5) management in Zhejiang Province. However, most cities cannot reach the 10 μg/m(3) limit of WHO’s AQG2005 version. Finally, this study makes recommendations for reducing PM(2.5) in terms of enhancing industrial structure and funding science and technology innovation.
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spelling pubmed-94764542022-09-15 Influencing factors and trend prediction of PM(2.5) concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China Zhang, Qiong Ye, Shuangshuang Ma, Tiancheng Fang, Xuejuan Shen, Yang Ding, Lei Environ Dev Sustain Article The government’s development of eco-environmental policies can have a scientific foundation thanks to the fine particulate matter (PM(2.5)) medium- and long-term change forecast. This study develops a STRIPAT-Scenario analysis framework employing panel data from 11 cities in Zhejiang Province between 2006 and 2020 to predict the changing trend of PM(2.5) concentrations under five alternative scenarios. The results reveal that: (1) urbanization development (P), economic development (A), technological innovation investment (T) and environmental regulation intensity have a significant inhibitory effect on PM(2.5) concentration in Zhejiang Province, while industrial structure, industrial energy consumption and the number of motor vehicles (TR) have a significant increase on PM(2.5) concentration. (2) Under any scenario, the PM(2.5) concentration of 11 cities in Zhejiang Province can reach the constraint target set in the 14th Five-Year plan. The improvement in urban PM(2.5) quality is most obviously impacted by the high-quality development scenario (S4). (3) Toward 2035, PM(2.5) concentrations of 11 cities in Zhejiang Province can reach the National Class I level standard in most scenario models, among which Hangzhou, Jiaxing and Shaoxing are under high pressure to reduce emissions and are the key areas for PM(2.5) management in Zhejiang Province. However, most cities cannot reach the 10 μg/m(3) limit of WHO’s AQG2005 version. Finally, this study makes recommendations for reducing PM(2.5) in terms of enhancing industrial structure and funding science and technology innovation. Springer Netherlands 2022-09-15 /pmc/articles/PMC9476454/ /pubmed/36124159 http://dx.doi.org/10.1007/s10668-022-02672-1 Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Zhang, Qiong
Ye, Shuangshuang
Ma, Tiancheng
Fang, Xuejuan
Shen, Yang
Ding, Lei
Influencing factors and trend prediction of PM(2.5) concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China
title Influencing factors and trend prediction of PM(2.5) concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China
title_full Influencing factors and trend prediction of PM(2.5) concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China
title_fullStr Influencing factors and trend prediction of PM(2.5) concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China
title_full_unstemmed Influencing factors and trend prediction of PM(2.5) concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China
title_short Influencing factors and trend prediction of PM(2.5) concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China
title_sort influencing factors and trend prediction of pm(2.5) concentration based on stripat-scenario analysis in zhejiang province, china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9476454/
https://www.ncbi.nlm.nih.gov/pubmed/36124159
http://dx.doi.org/10.1007/s10668-022-02672-1
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