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Developing Modeling Capabilities to ...

Gentry, Brian M.,

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Developing Modeling Capabilities to Assess Health Outcomes From Air Pollution /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Developing Modeling Capabilities to Assess Health Outcomes From Air Pollution // Brian M Gentry.
Author:	Gentry, Brian M.,
Description:	1 electronic resource (248 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Public health. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31558717
ISBN:	9798384089704

Developing Modeling Capabilities to Assess Health Outcomes From Air Pollution /
Gentry, Brian M.,

Developing Modeling Capabilities to Assess Health Outcomes From Air Pollution /Brian M Gentry. - 1 electronic resource (248 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Exposure to air pollution is responsible for more than 100,000 deaths annually in the U.S., and this mortality burden is disproportionately shared by disadvantaged communities, particularly communities of color. Regulating air pollutant emissions is therefore important from a public health and environmental justice perspective, but traditional models to predict health impacts from air pollutant emissions can take months of simulation time to consider just one emissions scenario. Reduced-complexity models sacrifice accuracy to achieve orders-of-magnitude speedup in simulation time. This dissertation expands on the capabilities of existing reduced-complexity models and explores applications of the models to decarbonization policy analysis. By coupling air quality modeling with demographic and epidemiological data, this research develops two models, EASIUR-HR and MAINE, which are designed to perform environmental justice analysis for the premature mortality burden from primary PM2.5 emissions and pediatric asthma burden from NOx emissions, respectively. This research assesses the air quality impacts of both light-duty vehicle electrification and hydrogen substitution for process heat in heavy industry. This dissertation also briefly explores the application of machine learning techniques to air quality models. This research produced publicly available models that academics and policy analysts alike can use for exploring the public health and environmental justice impacts of emissions scenarios.

English

ISBN: 9798384089704Subjects--Topical Terms:

534748
Public health.
Subjects--Index Terms:

Air quality

Developing Modeling Capabilities to Assess Health Outcomes From Air Pollution /
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245 1 0 $a Developing Modeling Capabilities to Assess Health Outcomes From Air Pollution / $c Brian M Gentry.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (248 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
500 $a Advisors: Robinson, Allen; Adams, Peter Committee members: Nock, Destenie; Whitefoot, Kate; Presto, Albert.
502 $b Ph.D. $c Carnegie Mellon University $d 2024.
520 $a Exposure to air pollution is responsible for more than 100,000 deaths annually in the U.S., and this mortality burden is disproportionately shared by disadvantaged communities, particularly communities of color. Regulating air pollutant emissions is therefore important from a public health and environmental justice perspective, but traditional models to predict health impacts from air pollutant emissions can take months of simulation time to consider just one emissions scenario. Reduced-complexity models sacrifice accuracy to achieve orders-of-magnitude speedup in simulation time. This dissertation expands on the capabilities of existing reduced-complexity models and explores applications of the models to decarbonization policy analysis. By coupling air quality modeling with demographic and epidemiological data, this research develops two models, EASIUR-HR and MAINE, which are designed to perform environmental justice analysis for the premature mortality burden from primary PM2.5 emissions and pediatric asthma burden from NOx emissions, respectively. This research assesses the air quality impacts of both light-duty vehicle electrification and hydrogen substitution for process heat in heavy industry. This dissertation also briefly explores the application of machine learning techniques to air quality models. This research produced publicly available models that academics and policy analysts alike can use for exploring the public health and environmental justice impacts of emissions scenarios.
546 $a English
590 $a School code: 0041
650 4 $a Public health. $3 534748
650 4 $a Engineering. $3 586835
650 4 $a Environmental justice. $3 528369
653 $a Air quality
653 $a Particulate matter
653 $a Air pollution
690 $a 0573
690 $a 0537
690 $a 0619
710 2 $a Carnegie Mellon University. $b Mechanical Engineering. $e degree granting institution. $3 3765793
720 1 $a Robinson, Allen $e degree supervisor.
720 1 $a Adams, Peter $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 0041
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31558717