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Chinese industrial air pollution emissions based on the continuous emission monitoring systems network
As the world’s largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO(2) and NO(x) emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significa...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030638/ https://www.ncbi.nlm.nih.gov/pubmed/36944667 http://dx.doi.org/10.1038/s41597-023-02054-w |
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author | Tang, Ling Jia, Min Yang, Junai Li, Ling Bo, Xin Mi, Zhifu |
author_facet | Tang, Ling Jia, Min Yang, Junai Li, Ling Bo, Xin Mi, Zhifu |
author_sort | Tang, Ling |
collection | PubMed |
description | As the world’s largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO(2) and NO(x) emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China’s continuous emission monitoring systems (CEMS) network during 2015–2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China’s industrial emission sources, which can offer insights into associated scientific studies and future policymaking. |
format | Online Article Text |
id | pubmed-10030638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100306382023-03-23 Chinese industrial air pollution emissions based on the continuous emission monitoring systems network Tang, Ling Jia, Min Yang, Junai Li, Ling Bo, Xin Mi, Zhifu Sci Data Data Descriptor As the world’s largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO(2) and NO(x) emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China’s continuous emission monitoring systems (CEMS) network during 2015–2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China’s industrial emission sources, which can offer insights into associated scientific studies and future policymaking. Nature Publishing Group UK 2023-03-22 /pmc/articles/PMC10030638/ /pubmed/36944667 http://dx.doi.org/10.1038/s41597-023-02054-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Tang, Ling Jia, Min Yang, Junai Li, Ling Bo, Xin Mi, Zhifu Chinese industrial air pollution emissions based on the continuous emission monitoring systems network |
title | Chinese industrial air pollution emissions based on the continuous emission monitoring systems network |
title_full | Chinese industrial air pollution emissions based on the continuous emission monitoring systems network |
title_fullStr | Chinese industrial air pollution emissions based on the continuous emission monitoring systems network |
title_full_unstemmed | Chinese industrial air pollution emissions based on the continuous emission monitoring systems network |
title_short | Chinese industrial air pollution emissions based on the continuous emission monitoring systems network |
title_sort | chinese industrial air pollution emissions based on the continuous emission monitoring systems network |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030638/ https://www.ncbi.nlm.nih.gov/pubmed/36944667 http://dx.doi.org/10.1038/s41597-023-02054-w |
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