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Differences between magnitudes and health impacts of BC emissions across the United States using 12 km scale seasonal source apportionment

MD. Turner, DK. Henze, A. Hakami, S. Zhao, J. Resler, GR. Carmichael, CO. Stanier, J. Baek, A. Sandu, AG. Russell, A. Nenes, GR. Jeong, SL. Capps, PB. Percell, RW. Pinder, SL. Napelenok, JO. Bash, T. Chai,

. 2015 ; 49 (7) : 4362-71. [pub] 20150318

Language English Country United States

Document type Comparative Study, Journal Article, Research Support, U.S. Gov't, Non-P.H.S.

Persistent link   https://www.medvik.cz/link/bmc16010506

Recent assessments have analyzed the health impacts of PM2.5 from emissions from different locations and sectors using simplified or reduced-form air quality models. Here we present an alternative approach using the adjoint of the Community Multiscale Air Quality (CMAQ) model, which provides source-receptor relationships at highly resolved sectoral, spatial, and temporal scales. While damage resulting from anthropogenic emissions of BC is strongly correlated with population and premature death, we found little correlation between damage and emission magnitude, suggesting that controls on the largest emissions may not be the most efficient means of reducing damage resulting from anthropogenic BC emissions. Rather, the best proxy for locations with damaging BC emissions is locations where premature deaths occur. Onroad diesel and nonroad vehicle emissions are the largest contributors to premature deaths attributed to exposure to BC, while onroad gasoline emissions cause the highest deaths per amount emitted. Emissions in fall and winter contribute to more premature deaths (and more per amount emitted) than emissions in spring and summer. Overall, these results show the value of the high-resolution source attribution for determining the locations, seasons, and sectors for which BC emission controls have the most effective health benefits.

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$a Turner, Matthew D $u †Mechanical Engineering Department, University of Colorado, Boulder, Colorado 80309, United States.
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$a Differences between magnitudes and health impacts of BC emissions across the United States using 12 km scale seasonal source apportionment / $c MD. Turner, DK. Henze, A. Hakami, S. Zhao, J. Resler, GR. Carmichael, CO. Stanier, J. Baek, A. Sandu, AG. Russell, A. Nenes, GR. Jeong, SL. Capps, PB. Percell, RW. Pinder, SL. Napelenok, JO. Bash, T. Chai,
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$a Recent assessments have analyzed the health impacts of PM2.5 from emissions from different locations and sectors using simplified or reduced-form air quality models. Here we present an alternative approach using the adjoint of the Community Multiscale Air Quality (CMAQ) model, which provides source-receptor relationships at highly resolved sectoral, spatial, and temporal scales. While damage resulting from anthropogenic emissions of BC is strongly correlated with population and premature death, we found little correlation between damage and emission magnitude, suggesting that controls on the largest emissions may not be the most efficient means of reducing damage resulting from anthropogenic BC emissions. Rather, the best proxy for locations with damaging BC emissions is locations where premature deaths occur. Onroad diesel and nonroad vehicle emissions are the largest contributors to premature deaths attributed to exposure to BC, while onroad gasoline emissions cause the highest deaths per amount emitted. Emissions in fall and winter contribute to more premature deaths (and more per amount emitted) than emissions in spring and summer. Overall, these results show the value of the high-resolution source attribution for determining the locations, seasons, and sectors for which BC emission controls have the most effective health benefits.
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$a Henze, Daven K $u †Mechanical Engineering Department, University of Colorado, Boulder, Colorado 80309, United States.
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$a Hakami, Amir $u ‡Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario K1S 5B6, Canada.
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$a Zhao, Shunliu $u ‡Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario K1S 5B6, Canada.
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$a Resler, Jaroslav $u §Nonlinear Modeling, Institute of Computer Science, Prague 182 07, Czech Republic.
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$a Carmichael, Gregory R $u ∥Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, Iowa 52242, United States.
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$a Stanier, Charles O $u ∥Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, Iowa 52242, United States.
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$a Baek, Jaemeen $u ∥Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, Iowa 52242, United States.
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$a Sandu, Adrian $u ⊥Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States.
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$a Russell, Armistead G
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$a Nenes, Athanasios $u ▲School of Chemical and Biomolecular Engineering, Georgia Tech, Atlanta, Georgia 30332, United States.
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$a Jeong, Gill-Ran $u ◇Korea Institute of Atmospheric Prediction Systems, Seoul 156-849, Republic of Korea.
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$a Capps, Shannon L $u □Atmospheric Modeling and Analysis Division, U.S. EPA, Research Triangle Park, North Carolina 27711, United States.
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$a Percell, Peter B $u ◆Department of Geosciences, University of Houston, Houston, Texas 77004, United States.
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$a Pinder, Rob W $u □Atmospheric Modeling and Analysis Division, U.S. EPA, Research Triangle Park, North Carolina 27711, United States.
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$a Napelenok, Sergey L $u □Atmospheric Modeling and Analysis Division, U.S. EPA, Research Triangle Park, North Carolina 27711, United States.
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$a Bash, Jesse O $u □Atmospheric Modeling and Analysis Division, U.S. EPA, Research Triangle Park, North Carolina 27711, United States.
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$a Chai, Tianfeng $u ■College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, Maryland 20742, United States. △Air Resources Laboratory, National Oceanic and Atmospheric Administration, College Park, Maryland 20740, United States.
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