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Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015

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Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015. / Malley, Chris; Seltzer, Karl; Shindell, Drew; Kasibhatla, Prasad.

In: Atmospheric Chemistry and Physics, Vol. 20, 14.02.2020, p. 1757–1775.

Research output: Contribution to journalArticle

Harvard

Malley, C, Seltzer, K, Shindell, D & Kasibhatla, P 2020, 'Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015', Atmospheric Chemistry and Physics, vol. 20, pp. 1757–1775. https://doi.org/10.5194/acp-20-1757-2020

APA

Malley, C., Seltzer, K., Shindell, D., & Kasibhatla, P. (2020). Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015. Atmospheric Chemistry and Physics, 20, 1757–1775. https://doi.org/10.5194/acp-20-1757-2020

Vancouver

Malley C, Seltzer K, Shindell D, Kasibhatla P. Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015. Atmospheric Chemistry and Physics. 2020 Feb 14;20:1757–1775. https://doi.org/10.5194/acp-20-1757-2020

Author

Malley, Chris ; Seltzer, Karl ; Shindell, Drew ; Kasibhatla, Prasad. / Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015. In: Atmospheric Chemistry and Physics. 2020 ; Vol. 20. pp. 1757–1775.

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@article{b1be6ab50a52458f95fff7d7e79242f1,
title = "Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015",
abstract = "Long-term exposure to ambient ozone (O3) is associated with a variety of impacts, including adverse humanhealth effects and reduced yields in commercial crops. Ground-level O3 concentrations for assessments are typically predicted using chemical transport models; however such methods often feature biases that can influence impact estimates. Here, we develop and apply artificial neural networks to empirically model long-term O3 exposure over the continental United States from 2000 to 2015, and we generate a measurement-based assessment of impacts on human-health and crop yields. Notably, we found that two commonly used human-health averaging metrics, based on separate epidemiological studies, differ in their trends over the study period. The population-weighted, April–Septemberaverage of the daily 1 h maximum concentration peaked in 2002 at 55.9 ppb and decreased by 0.43 [95 % CI: 0.28, 0.57] ppb yr−1 between 2000 and 2015, yielding an ∼ 18 % decrease in normalized human-health impacts. In contrast, there was little change in the population-weighted, annual average of the maximum daily 8 h average concentration between 2000 and 2015, which resulted in a ∼ 5 % increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. Trends of all agriculture-weighted crop-loss metrics indicated yield improvements, with reductions in the estimated national relative yield loss ranging from 1.7 % to 1.9 % for maize, 5.1 % to 7.1 % for soybeans, and 2.7 % for wheat. Overall, these results provide a measurement-based estimate of long-term O3 exposure over the United States, quantify the historical trends of such exposure, and illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics.",
author = "Chris Malley and Karl Seltzer and Drew Shindell and Prasad Kasibhatla",
note = "{\textcopyright} Author(s) 2020.",
year = "2020",
month = feb,
day = "14",
doi = "10.5194/acp-20-1757-2020",
language = "English",
volume = "20",
pages = "1757–1775",
journal = "Atmospheric Chemistry and Physics",
issn = "1680-7316",
publisher = "Copernicus Publications",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015

AU - Malley, Chris

AU - Seltzer, Karl

AU - Shindell, Drew

AU - Kasibhatla, Prasad

N1 - © Author(s) 2020.

PY - 2020/2/14

Y1 - 2020/2/14

N2 - Long-term exposure to ambient ozone (O3) is associated with a variety of impacts, including adverse humanhealth effects and reduced yields in commercial crops. Ground-level O3 concentrations for assessments are typically predicted using chemical transport models; however such methods often feature biases that can influence impact estimates. Here, we develop and apply artificial neural networks to empirically model long-term O3 exposure over the continental United States from 2000 to 2015, and we generate a measurement-based assessment of impacts on human-health and crop yields. Notably, we found that two commonly used human-health averaging metrics, based on separate epidemiological studies, differ in their trends over the study period. The population-weighted, April–Septemberaverage of the daily 1 h maximum concentration peaked in 2002 at 55.9 ppb and decreased by 0.43 [95 % CI: 0.28, 0.57] ppb yr−1 between 2000 and 2015, yielding an ∼ 18 % decrease in normalized human-health impacts. In contrast, there was little change in the population-weighted, annual average of the maximum daily 8 h average concentration between 2000 and 2015, which resulted in a ∼ 5 % increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. Trends of all agriculture-weighted crop-loss metrics indicated yield improvements, with reductions in the estimated national relative yield loss ranging from 1.7 % to 1.9 % for maize, 5.1 % to 7.1 % for soybeans, and 2.7 % for wheat. Overall, these results provide a measurement-based estimate of long-term O3 exposure over the United States, quantify the historical trends of such exposure, and illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics.

AB - Long-term exposure to ambient ozone (O3) is associated with a variety of impacts, including adverse humanhealth effects and reduced yields in commercial crops. Ground-level O3 concentrations for assessments are typically predicted using chemical transport models; however such methods often feature biases that can influence impact estimates. Here, we develop and apply artificial neural networks to empirically model long-term O3 exposure over the continental United States from 2000 to 2015, and we generate a measurement-based assessment of impacts on human-health and crop yields. Notably, we found that two commonly used human-health averaging metrics, based on separate epidemiological studies, differ in their trends over the study period. The population-weighted, April–Septemberaverage of the daily 1 h maximum concentration peaked in 2002 at 55.9 ppb and decreased by 0.43 [95 % CI: 0.28, 0.57] ppb yr−1 between 2000 and 2015, yielding an ∼ 18 % decrease in normalized human-health impacts. In contrast, there was little change in the population-weighted, annual average of the maximum daily 8 h average concentration between 2000 and 2015, which resulted in a ∼ 5 % increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. Trends of all agriculture-weighted crop-loss metrics indicated yield improvements, with reductions in the estimated national relative yield loss ranging from 1.7 % to 1.9 % for maize, 5.1 % to 7.1 % for soybeans, and 2.7 % for wheat. Overall, these results provide a measurement-based estimate of long-term O3 exposure over the United States, quantify the historical trends of such exposure, and illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics.

U2 - 10.5194/acp-20-1757-2020

DO - 10.5194/acp-20-1757-2020

M3 - Article

VL - 20

SP - 1757

EP - 1775

JO - Atmospheric Chemistry and Physics

JF - Atmospheric Chemistry and Physics

SN - 1680-7316

ER -