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Modeling impacts of climate change on air quality and associated human exposures

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TitleInfo
Title
Modeling impacts of climate change on air quality and associated human exposures
Name (type = personal)
NamePart (type = family)
Cai
NamePart (type = given)
Ting
NamePart (type = date)
1987-
DisplayForm
Ting Cai
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Georgopoulos
NamePart (type = given)
Panos G
DisplayForm
Panos G Georgopoulos
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Broccoli
NamePart (type = given)
Tony
DisplayForm
Tony Broccoli
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Weisel
NamePart (type = given)
Clifford
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Clifford Weisel
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
He
NamePart (type = given)
Shan
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Shan He
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
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DateCreated (encoding = w3cdtf); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Climate change critically affects both the atmospheric processes involved in the dynamics of air pollution systems and biogenic emissions including tree and grass pollens and fungal spores. Synergistic action of allergenic pollen with air pollutants like ozone and particulate matter has been reported as potentially exacerbating the symptoms of allergies. This dissertation investigated the spatiotemporal distributions predicted for allergenic pollen and ground-level ozone across the contiguous United States (CONUS) in 2004 and 2047 reflecting the Representative Concentration Pathways (RCP) 8.5 scenario, and estimated human exposures to those pollutants. In addition, Machine Learning (ML) methods were evaluated and applied to local-scale prediction of airborne allergenic pollen concentration.
It was estimated that ragweed pollen season will start earlier and last longer in 2047 under the RCP 8.5 scenario across the CONUS, with increasing average pollen concentrations in most regions. The response of the oak pollen season varies across the nine climate regions of the CONUS, with the largest increase in pollen concentration occurring in the Northeast region. The oak pollen season length was estimated to shorten by 1-2 days for most regions, except for the Southeast and Southwest regions.
Analyses of observed ragweed pollen counts and ozone concentrations from 1990 to 2010 indicate that the ragweed pollen season started earlier at 76% of the monitoring stations, and the annual average number of co-occurrence of ragweed and ozone exceedances (daily maximum 8-hour average ozone > 70 ppb) ranged between 0 to 17 days. Co-occurrences of ragweed pollen and ozone exceedances under climate change were investigated based on simulated ragweed pollen and ozone concentrations. Although the co-occurrence of ragweed pollen and ozone exceedances is scattered across the CONUS, it influences a remarkable fraction of the population. Inhalation exposures to ragweed pollen are higher outdoors than indoors, with significant correlation with pollen concentration. Males tend to have higher inhalation exposures to ragweed pollen and ozone than females. The inhalation exposure to ragweed pollen and ozone per unit body weight decreases with age.
Prediction of ragweed pollen concentration at the local scale, based on meteorological factors and previous ragweed pollen observations, was conducted using ML models including Support Vector Machine (SVM), Random Forest, XGBoost, Neural Network, Decision Trees, and a Bayesian Generalized Linear Model. The model parameters were optimized and the final models were evaluated using a repeated 10-fold cross-validation. Random Forest and XGBoost models outperformed other models, and pollen concentration of the previous day is the most important predictor variable for both models.
Subject (authority = RUETD)
Topic
Environmental Sciences
Subject (authority = LCSH)
Topic
Air quality
Subject (authority = LCSH)
Topic
Climatic change
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10329
PhysicalDescription
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application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xxi, 191 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-c6hd-ns81
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Cai
GivenName
Ting
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-25 14:34:44
AssociatedEntity
Name
Ting Cai
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2021-10-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 30th, 2021.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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2019-09-24T16:24:38
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2019-09-24T16:24:38
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