The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India

The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India

Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM ≤ 2.5 μm in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are l...

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Main Authors: Meng Gao, Gufran Beig, Shaojie Song, Hongliang Zhang, Jianlin Hu, Qi Ying, Fengchao Liang, Yang Liu, Haikun Wang, Xiao Lu, Tong Zhu, Gregory R. Carmichael, Chris P. Nielsen, Michael B. McElroy
Format: Article
Language:English
Published: Elsevier 2018-12-01
Series:Environment International
Online Access:http://www.sciencedirect.com/science/article/pii/S0160412018313369
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spelling doaj-8554f2fc8bd24c40a42870301cfa45192020-11-25T01:27:01ZengElsevierEnvironment International0160-41202018-12-01121250259The impact of power generation emissions on ambient PM2.5 pollution and human health in China and IndiaMeng Gao0Gufran Beig1Shaojie Song2Hongliang Zhang3Jianlin Hu4Qi Ying5Fengchao Liang6Yang Liu7Haikun Wang8Xiao Lu9Tong Zhu10Gregory R. Carmichael11Chris P. Nielsen12Michael B. McElroy13Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA; Corresponding authors.Indian Institute of Tropical Meteorology, Pune, Maharashtra 411008, IndiaHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USADepartment of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USASchool of Environmental Science and Engineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, ChinaZachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Occupational and Environmental Health, School of Public Heath, Peking University, Beijing 100191, China; Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United StatesDepartment of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United StatesState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR ChinaHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA; Laboratory for Climate and Ocean-Atmosphere Sciences, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, ChinaState Key Lab for Environmental Simulation and Pollution Control, College of Environmental Science and Engineering, Peking University, Beijing 100871, ChinaCenter for the Global and Regional Environmental Research, The University of Iowa, Iowa City, IA 52242, USAHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA; Corresponding authors.Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USAEmissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM ≤ 2.5 μm in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks. Keywords: Air quality modeling, Power generation, China, India, WRF-Chemhttp://www.sciencedirect.com/science/article/pii/S0160412018313369
collection DOAJ
language English
format Article
sources DOAJ
author Meng Gao
Gufran Beig
Shaojie Song
Hongliang Zhang
Jianlin Hu
Qi Ying
Fengchao Liang
Yang Liu
Haikun Wang
Xiao Lu
Tong Zhu
Gregory R. Carmichael
Chris P. Nielsen
Michael B. McElroy
spellingShingle Meng Gao
Gufran Beig
Shaojie Song
Hongliang Zhang
Jianlin Hu
Qi Ying
Fengchao Liang
Yang Liu
Haikun Wang
Xiao Lu
Tong Zhu
Gregory R. Carmichael
Chris P. Nielsen
Michael B. McElroy
The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India
Environment International
author_facet Meng Gao
Gufran Beig
Shaojie Song
Hongliang Zhang
Jianlin Hu
Qi Ying
Fengchao Liang
Yang Liu
Haikun Wang
Xiao Lu
Tong Zhu
Gregory R. Carmichael
Chris P. Nielsen
Michael B. McElroy
author_sort Meng Gao
title The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India
title_short The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India
title_full The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India
title_fullStr The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India
title_full_unstemmed The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India
title_sort impact of power generation emissions on ambient pm2.5 pollution and human health in china and india
publisher Elsevier
series Environment International
issn 0160-4120
publishDate 2018-12-01
description Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM ≤ 2.5 μm in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks. Keywords: Air quality modeling, Power generation, China, India, WRF-Chem
url http://www.sciencedirect.com/science/article/pii/S0160412018313369
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