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Article: Quantitative association analysis between PM2.5 concentration and factors on industry, energy, agriculture, and transportation

TitleQuantitative association analysis between PM2.5 concentration and factors on industry, energy, agriculture, and transportation
Authors
Issue Date2018
PublisherNature Research (part of Springer Nature): Fully open access journals. The Journal's web site is located at http://www.nature.com/srep/index.html
Citation
Scientific Reports, 2018, v. 8, article no. 9461 How to Cite?
AbstractRapid urbanization is causing serious PM2.5 (particulate matter ≤2.5 μm) pollution in China. However, the impacts of human activities (including industrial production, energy production, agriculture, and transportation) on PM2.5 concentrations have not been thoroughly studied. In this study, we obtained a regression formula for PM2.5 concentration based on more than 1 million PM2.5 recorded values and data from meteorology, industrial production, energy production, agriculture, and transportation for 31 provinces of mainland China between January 2013 and May 2017. We used stepwise regression to process 49 factors that influence PM2.5 concentration, and obtained the 10 primary influencing factors. Data of PM2.5 concentration and 10 factors from June to December, 2017 was used to verify the robustness of the model. Excluding meteorological factors, production of natural gas, industrial boilers, and ore production have the highest association with PM2.5 concentration, while nuclear power generation is the most positive factor in decreasing PM2.5 concentration. Tianjin, Beijing, and Hebei provinces are the most vulnerable to high PM2.5 concentrations caused by industrial production, energy production, agriculture, and transportation (IEAT).
Persistent Identifierhttp://hdl.handle.net/10722/272226
ISSN
2017 Impact Factor: 4.122
2015 SCImago Journal Rankings: 2.073
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, N-
dc.contributor.authorHuang, H-
dc.contributor.authorDuan, X-
dc.contributor.authorZhao, J-
dc.contributor.authorSu, B-
dc.date.accessioned2019-07-20T10:38:09Z-
dc.date.available2019-07-20T10:38:09Z-
dc.date.issued2018-
dc.identifier.citationScientific Reports, 2018, v. 8, article no. 9461-
dc.identifier.issn2045-2322-
dc.identifier.urihttp://hdl.handle.net/10722/272226-
dc.description.abstractRapid urbanization is causing serious PM2.5 (particulate matter ≤2.5 μm) pollution in China. However, the impacts of human activities (including industrial production, energy production, agriculture, and transportation) on PM2.5 concentrations have not been thoroughly studied. In this study, we obtained a regression formula for PM2.5 concentration based on more than 1 million PM2.5 recorded values and data from meteorology, industrial production, energy production, agriculture, and transportation for 31 provinces of mainland China between January 2013 and May 2017. We used stepwise regression to process 49 factors that influence PM2.5 concentration, and obtained the 10 primary influencing factors. Data of PM2.5 concentration and 10 factors from June to December, 2017 was used to verify the robustness of the model. Excluding meteorological factors, production of natural gas, industrial boilers, and ore production have the highest association with PM2.5 concentration, while nuclear power generation is the most positive factor in decreasing PM2.5 concentration. Tianjin, Beijing, and Hebei provinces are the most vulnerable to high PM2.5 concentrations caused by industrial production, energy production, agriculture, and transportation (IEAT).-
dc.languageeng-
dc.publisherNature Research (part of Springer Nature): Fully open access journals. The Journal's web site is located at http://www.nature.com/srep/index.html-
dc.relation.ispartofScientific Reports-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleQuantitative association analysis between PM2.5 concentration and factors on industry, energy, agriculture, and transportation-
dc.typeArticle-
dc.identifier.emailZhang, N: zhangnan@hku.hk-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1038/s41598-018-27771-w-
dc.identifier.pmid29930284-
dc.identifier.pmcidPMC6013430-
dc.identifier.scopuseid_2-s2.0-85048929579-
dc.identifier.hkuros298821-
dc.identifier.volume8-
dc.identifier.spagearticle no. 9461-
dc.identifier.epagearticle no. 9461-
dc.identifier.isiWOS:000435790500040-
dc.publisher.placeUnited Kingdom-

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