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Hybrid land use regression modeling of fine particulate matter and metal components for application in two Pittsburgh cohorts

Tripathy, Sheila (2017) Hybrid land use regression modeling of fine particulate matter and metal components for application in two Pittsburgh cohorts. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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While numerous studies have linked exposure to ambient fine particulate matter (PM2.5) to adverse health outcomes (e.g., asthma, cardiovascular disease), less is known about which specific components of PM2.5 drive these associations. Because PM2.5 composition varies spatially with sources, characterizing fine-scale variation in constituents is critical to improving epidemiological studies on health effects of source-specific PM2.5. One approach for improving this characterization may be hybrid models wherein source-specific dispersion covariates are integrated into land use regression models (LURs).
The objective of this dissertation was to develop hybrid dispersion-LUR models for PM2.5, black carbon (BC), and steel-related PM2.5 constituents [lead (Pb), manganese (Mn), iron (Fe), and zinc (Zn)], by combining concentrations data from spatial saturation monitoring with daily Environmental Protection Agency (EPA) regulatory data. These models were used to assign residence-based exposure estimates for time windows of interest for two Pittsburgh-area epidemiological cohorts.
The first epidemiologic study examined associations between one-year pollutant exposures and levels of both circulating and lipopolysaccharide (LPS)-stimulated inflammatory mediators in the Adult Health and Behavior II (AHAB II) cohort. We found that exposures to PM2.5 and BC were associated with higher LPS-stimulated IL-1β, IL-6, and TNF-α. Pb was associated with increased stimulated TNF-α (p = 0.02) and IL-1β (p = 0.02), but were insignificant after adjusting for multiple comparisons (Bonferroni correction). No pollutant exposures were associated with circulating IL-6 or CRP. The second epidemiological study explored associations between pollutant exposures and brain morphology indicators (i.e., total and cortical gray matter volumes, cortical white matter volume, total white matter surface area, mean cortical thickness) from magnetic resonance images of participants in the AHAB II and Pittsburgh Imaging Project Cohorts, finding no significant associations.
These results suggest that, although pollutants were not associated with circulating inflammatory mediators or brain morphology in these samples of healthy midlife adults, some chronic air pollution exposures may influence immune responsiveness, influencing risk for future inflammatory conditions. Taken together, these results indicate the public health importance of better understanding relationships between long-term source-specific PM2.5 and component exposures with functional indicators of immune responsiveness and other processes shaping risk for future health effects.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Tripathy, Sheilasht52@pitt.edusht52
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorClougherty, Janejcloughe@pitt.edujcloughe
Committee ChairBarchowsky, Aaronaab20@pit.eduaab20
Committee MemberGianaros, Petergianaros@pitt.edugianaros
Committee MemberHolguin,
Date: 29 June 2017
Date Type: Publication
Defense Date: 14 April 2017
Approval Date: 29 June 2017
Submission Date: 21 April 2017
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 124
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Environmental and Occupational Health
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: land use regression, PM2.5, metal constituents, stimulated cytokines, IL-6, CRP, brain morphology
Date Deposited: 29 Jun 2017 23:49
Last Modified: 01 May 2018 05:15


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