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Rapidly changing coal-related city-level atmospheric mercury emissions and their driving forces.
Guo, Yaqin; Xiao, Lin; Chen, Bin; Wu, Zhujuan; Chen, Huanxin; Li, Jiashuo.
Afiliación
  • Guo Y; School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China.
  • Xiao L; Institute of Blue and Green Development, Shandong University, Weihai 264209, PR China; School of Business, Shandong University, Weihai 264209, PR China.
  • Chen B; Laboratory of Systems Ecology and Sustainability Science, College of Engineering, Peking University, Beijing 100871, PR China.
  • Wu Z; Department of New Energy Science and Technology, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China; China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, PR China.
  • Chen H; School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China.
  • Li J; Institute of Blue and Green Development, Shandong University, Weihai 264209, PR China; School of Business, Shandong University, Weihai 264209, PR China. Electronic address: lijiashuo@sdu.edu.cn.
J Hazard Mater ; 411: 125060, 2021 06 05.
Article en En | MEDLINE | ID: mdl-33465541
The up-to-date city-level mercury emission inventory is essential for effective mitigation policy designs due to rapid changes in energy consumption and industrial structures in Chinese cities. This study updated the atmospheric mercury emission inventory in 2015 based on the most recent information on plant-specific air pollution control devices (APCDs) and coal consumption for 45 sectors in 215 Chinese cities. Total emissions were estimated at 218 t with an uncertainty range of - 54.0% to 147%, to which coal-fired industrial boilers (CFIBs) contributed 58.1%, followed by coal-fired power plants (CFPPs, 32.7%). Mercury emissions varied significantly among cities, ranging from 0.0218 to 6.89 t. The Logistics Mean Division Index (LMDI) model was then applied to identify key factors driving mercury emission changes in 50 representative cities from 2010 to 2015. Although coal consumption increased by nearly one fifth across the 50 cities, their total emissions declined by 2.36%, largely due to energy structure adjustments and widespread installations of more efficient APCDs. However, key drivers of mercury mitigation differed widely between the cities, being driven by energy intensity improvements in Chongqing and Guangzhou (Guangdong province) and by energy structure adjustments in Wuhan (Hubei province) and Yinchuan (Ningxia province). Mitigation strategies should be tailored to reflect these differences.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: J Hazard Mater Asunto de la revista: SAUDE AMBIENTAL Año: 2021 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: J Hazard Mater Asunto de la revista: SAUDE AMBIENTAL Año: 2021 Tipo del documento: Article Pais de publicación: Países Bajos