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Title: Air pollution control strategies directly limiting national health damages in the US

Abstract

Exposure to fine particulate matter (PM2.5) from fuel combustion significantly contributes to global and US mortality. Traditional air pollution control strategies typically focus on emission reductions for specific air pollutants or sectors to maintain air pollutant concentrations within acceptable levels. Here we directly set national PM2.5 mortality cost reduction targets within a global human-earth system model with US state-level energy systems, identifying endogenously the control actions, sectors, and locations that most cost-effectively reduce PM2.5 mortality. Our results show that substantial health benefits can be cost-effectively achieved by using electricity to replace sources with high primary PM2.5 emission intensities, including industrial coal, building biomass, and industrial liquids. Increasing the stringency of PM2.5 reduction targets expedites the phaseout of high emission intensity sources, leading to larger declines in major air pollutant emissions, but very limited co-benefits in reducing CO2 emissions. Control strategies achieve the greatest mortality cost reductions in the East North Central and Middle Atlantic states.

Authors:
 [1];  [2]; ORCiD logo [3];  [4];  [1]
  1. Environmental Protection Agency
  2. University of North Carolina
  3. BATTELLE (PACIFIC NW LAB)
  4. U.S. Environmental Protection Agency (EPA)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1603660
Report Number(s):
[PNNL-SA-148416]
Grant/Contract Number:  
[AC05-76RL01830]
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
[ Journal Volume: 11; Journal Issue: 1]
Country of Publication:
United States
Language:
English

Citation Formats

Ou, Yang, West, J Jason., Smith, Steven J., Nolte, Christopher G., and Loughlin, Daniel H. Air pollution control strategies directly limiting national health damages in the US. United States: N. p., 2020. Web. doi:10.1038/s41467-020-14783-2.
Ou, Yang, West, J Jason., Smith, Steven J., Nolte, Christopher G., & Loughlin, Daniel H. Air pollution control strategies directly limiting national health damages in the US. United States. doi:10.1038/s41467-020-14783-2.
Ou, Yang, West, J Jason., Smith, Steven J., Nolte, Christopher G., and Loughlin, Daniel H. Wed . "Air pollution control strategies directly limiting national health damages in the US". United States. doi:10.1038/s41467-020-14783-2.
@article{osti_1603660,
title = {Air pollution control strategies directly limiting national health damages in the US},
author = {Ou, Yang and West, J Jason. and Smith, Steven J. and Nolte, Christopher G. and Loughlin, Daniel H.},
abstractNote = {Exposure to fine particulate matter (PM2.5) from fuel combustion significantly contributes to global and US mortality. Traditional air pollution control strategies typically focus on emission reductions for specific air pollutants or sectors to maintain air pollutant concentrations within acceptable levels. Here we directly set national PM2.5 mortality cost reduction targets within a global human-earth system model with US state-level energy systems, identifying endogenously the control actions, sectors, and locations that most cost-effectively reduce PM2.5 mortality. Our results show that substantial health benefits can be cost-effectively achieved by using electricity to replace sources with high primary PM2.5 emission intensities, including industrial coal, building biomass, and industrial liquids. Increasing the stringency of PM2.5 reduction targets expedites the phaseout of high emission intensity sources, leading to larger declines in major air pollutant emissions, but very limited co-benefits in reducing CO2 emissions. Control strategies achieve the greatest mortality cost reductions in the East North Central and Middle Atlantic states.},
doi = {10.1038/s41467-020-14783-2},
journal = {Nature Communications},
number = [1],
volume = [11],
place = {United States},
year = {2020},
month = {2}
}

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