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Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network

To meet the growing electricity demand, China’s power generation sector has become an increasingly large source of air pollutants. Specific control policymaking needs an inventory reflecting the overall, heterogeneous, time-varying features of power plant emissions. Due to the lack of comprehensive...

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Autores principales: Tang, Ling, Xue, Xiaoda, Qu, Jiabao, Mi, Zhifu, Bo, Xin, Chang, Xiangyu, Wang, Shouyang, Li, Shibei, Cui, Weigeng, Dong, Guangxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536431/
https://www.ncbi.nlm.nih.gov/pubmed/33020482
http://dx.doi.org/10.1038/s41597-020-00665-1
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author Tang, Ling
Xue, Xiaoda
Qu, Jiabao
Mi, Zhifu
Bo, Xin
Chang, Xiangyu
Wang, Shouyang
Li, Shibei
Cui, Weigeng
Dong, Guangxia
author_facet Tang, Ling
Xue, Xiaoda
Qu, Jiabao
Mi, Zhifu
Bo, Xin
Chang, Xiangyu
Wang, Shouyang
Li, Shibei
Cui, Weigeng
Dong, Guangxia
author_sort Tang, Ling
collection PubMed
description To meet the growing electricity demand, China’s power generation sector has become an increasingly large source of air pollutants. Specific control policymaking needs an inventory reflecting the overall, heterogeneous, time-varying features of power plant emissions. Due to the lack of comprehensive real measurements, existing inventories rely on average emission factors that suffer from many assumptions and high uncertainty. This study is the first to develop an inventory of particulate matter (PM), SO(2) and NO(X) emissions from power plants using systematic actual measurements monitored by China’s continuous emission monitoring systems (CEMS) network over 96–98% of the total thermal power capacity. With nationwide, source-level, real-time CEMS-monitored data, this study directly estimates emission factors and absolute emissions, avoiding the use of indirect average emission factors, thereby reducing the level of uncertainty. This dataset provides plant-level information on absolute emissions, fuel uses, generating capacities, geographic locations, etc. The dataset facilitates power emission characterization and clean air policy-making, and the CEMS-based estimation method can be employed by other countries seeking to regulate their power emissions.
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spelling pubmed-75364312020-10-19 Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network Tang, Ling Xue, Xiaoda Qu, Jiabao Mi, Zhifu Bo, Xin Chang, Xiangyu Wang, Shouyang Li, Shibei Cui, Weigeng Dong, Guangxia Sci Data Data Descriptor To meet the growing electricity demand, China’s power generation sector has become an increasingly large source of air pollutants. Specific control policymaking needs an inventory reflecting the overall, heterogeneous, time-varying features of power plant emissions. Due to the lack of comprehensive real measurements, existing inventories rely on average emission factors that suffer from many assumptions and high uncertainty. This study is the first to develop an inventory of particulate matter (PM), SO(2) and NO(X) emissions from power plants using systematic actual measurements monitored by China’s continuous emission monitoring systems (CEMS) network over 96–98% of the total thermal power capacity. With nationwide, source-level, real-time CEMS-monitored data, this study directly estimates emission factors and absolute emissions, avoiding the use of indirect average emission factors, thereby reducing the level of uncertainty. This dataset provides plant-level information on absolute emissions, fuel uses, generating capacities, geographic locations, etc. The dataset facilitates power emission characterization and clean air policy-making, and the CEMS-based estimation method can be employed by other countries seeking to regulate their power emissions. Nature Publishing Group UK 2020-10-05 /pmc/articles/PMC7536431/ /pubmed/33020482 http://dx.doi.org/10.1038/s41597-020-00665-1 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Tang, Ling
Xue, Xiaoda
Qu, Jiabao
Mi, Zhifu
Bo, Xin
Chang, Xiangyu
Wang, Shouyang
Li, Shibei
Cui, Weigeng
Dong, Guangxia
Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network
title Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network
title_full Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network
title_fullStr Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network
title_full_unstemmed Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network
title_short Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network
title_sort air pollution emissions from chinese power plants based on the continuous emission monitoring systems network
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536431/
https://www.ncbi.nlm.nih.gov/pubmed/33020482
http://dx.doi.org/10.1038/s41597-020-00665-1
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