KMS Of Academy of mathematics and systems sciences, CAS
| Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network | |
| Tang, Ling1,2; Xue, Xiaoda1,3; Qu, Jiabao3; Mi, Zhifu4; Bo, Xin5,6; Chang, Xiangyu7; Wang, Shouyang8; Li, Shibei3,6; Cui, Weigeng9; Dong, Guangxia10 | |
| 2020-10-05 | |
| Source Publication | SCIENTIFIC DATA
 |
| Volume | 7Issue:1Pages:10 |
| Abstract | 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), SO2 and NOX 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. |
| DOI | 10.1038/s41597-020-00665-1 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Science Foundation[71622011] ; National Natural Science Foundation of China[71971007] ; National Natural Science Foundation of China[71988101] ; National Natural Science Foundation of China[11771012] ; National Programme for Support of Top Notch Young Professionals ; National Research Programme for Key Issues in Air Pollution Control[DQGG0209-07] ; National Key Research and Development Program of China[2019YFE0194500] ; Appraisal Center for Environment and Engineering Ministry of Ecology and Environment[2019-10] |
| WOS Research Area | Science & Technology - Other Topics |
| WOS Subject | Multidisciplinary Sciences |
| WOS ID | WOS:000578645900002 |
| Publisher | NATURE RESEARCH |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/52371 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Mi, Zhifu; Bo, Xin |
| Affiliation | 1.Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China 2.Beijing Univ Chem Technol, Sch Econ & Management, Beijing 100029, Peoples R China 3.Minist Ecol & Environm, Appraisal Ctr Environm & Engn, Beijing 100012, Peoples R China 4.UCL, Bartlett Sch Construct & Project Management, London WC1E 7HB, England 5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 6.Minist Ecol & Environm, State Environm Protect Key Lab Numer Modeling Env, Beijing 100012, Peoples R China 7.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Shaanxi, Peoples R China 8.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 9.Changan Univ, Sch Earth Sci & Resources, Xian 710054, Shaanxi, Peoples R China 10.China Natl Environm Monitoring Ctr, Beijing 100012, Peoples R China |
| Recommended Citation GB/T 7714 | Tang, Ling,Xue, Xiaoda,Qu, Jiabao,et al. Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network[J]. SCIENTIFIC DATA,2020,7(1):10. |
| APA | Tang, Ling.,Xue, Xiaoda.,Qu, Jiabao.,Mi, Zhifu.,Bo, Xin.,...&Dong, Guangxia.(2020).Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network.SCIENTIFIC DATA,7(1),10. |
| MLA | Tang, Ling,et al."Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network".SCIENTIFIC DATA 7.1(2020):10. |
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