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Estimating exposure to traffic-relat...

McMaster University (Canada).

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Estimating exposure to traffic-related air pollution.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimating exposure to traffic-related air pollution./
Author:	Sahsuvaroglu, Talar.
Description:	206 p.
Notes:	Source: Dissertation Abstracts International, Volume: 68-07, Section: A, page: 3086.
Contained By:	Dissertation Abstracts International68-07A.
Subject:	Environmental Sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR29264
ISBN:	9780494292648

Estimating exposure to traffic-related air pollution.
Sahsuvaroglu, Talar.

Estimating exposure to traffic-related air pollution. - 206 p.

Source: Dissertation Abstracts International, Volume: 68-07, Section: A, page: 3086.

Thesis (Ph.D.)--McMaster University (Canada), 2006.

Epidemiological studies that investigate the association between air pollution exposure and adverse health outcomes sometimes use citywide averages of pollutant levels as their exposure estimate. These citywide averages do not take into account the spatial variability of pollutants within cities, and do not often accurately represent individual exposures. There is a growing field of exposure assessment that is examining methods to measure, model and predict within-city, or intraurban, variations in air pollutants. As yet, little research has been conducted with regard to the differences in health effects when these models are used, and in how these models actually compare to personal exposure concentrations within study populations. It is these gaps in research that this dissertation aims to fill.

ISBN: 9780494292648Subjects--Topical Terms:

676987
Environmental Sciences.

Estimating exposure to traffic-related air pollution.
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245 1 0 $a Estimating exposure to traffic-related air pollution.
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500 $a Source: Dissertation Abstracts International, Volume: 68-07, Section: A, page: 3086.
502 $a Thesis (Ph.D.)--McMaster University (Canada), 2006.
520 $a Epidemiological studies that investigate the association between air pollution exposure and adverse health outcomes sometimes use citywide averages of pollutant levels as their exposure estimate. These citywide averages do not take into account the spatial variability of pollutants within cities, and do not often accurately represent individual exposures. There is a growing field of exposure assessment that is examining methods to measure, model and predict within-city, or intraurban, variations in air pollutants. As yet, little research has been conducted with regard to the differences in health effects when these models are used, and in how these models actually compare to personal exposure concentrations within study populations. It is these gaps in research that this dissertation aims to fill.
520 $a Using data from Hamilton, Ontario, Canada, this study addresses the following objectives: (1) to derive intraurban exposure models for nitrogen dioxide (NO2) levels (a proxy for traffic-related air pollutants) in Hamilton; (2) to use these modeled exposures to assess air pollution associations with childhood asthma to determine whether refined exposure models generate larger health effects; and (3) to measure personal exposure in a group of older adults from an on-going study, and compare personal levels to the modeled exposure metrics.
520 $a Results show that a land use regression (LUR) model is a practical and effective method to predict intraurban variation in NO2 within the city; that this model was able to identify large and robust effects in childhood asthma (specifically in asthma without hayfever in young girls); and when compared to personal exposures in an elderly population study, that the concentrations from the LUR model were not correlated to the personal exposures. This apparent discrepancy may be due to either the fact that NO 2 may be acting as a surrogate for other forms of traffic pollution responsible for causing the health effects, or that the socio-spatial differences between the children and elderly were too distinctly different, or that there were some other variables that were not able to be incorporated into the analysis. Further multipollutant research on similar populations and larger sample sizes may provide more clarity to these findings.
520 $a The dissertation presents the first assessment of how different intraurban exposure models for air pollution predict health effects (i.e., childhood asthma) and how well these modeled estimates predict personal exposure in elderly adults in Hamilton. The absence of an association between the LUR model and personal exposures is another significant contribution of this dissertation, highlighting future research directions. With the implicit assumptions that the quality of the air is something seldom in our individual control, that breathing is an involuntary action, and that a potentially large burden of illness exists, the importance of developing more detailed scientific knowledge on the health effects of air pollution is emphasized.
590 $a School code: 0197.
650 4 $a Environmental Sciences. $3 676987
650 4 $a Geography. $3 524010
650 4 $a Health Sciences, Epidemiology. $3 1019544
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710 2 $a McMaster University (Canada). $3 1024893
773 0 $t Dissertation Abstracts International $g 68-07A.
790 $a 0197
791 $a Ph.D.
792 $a 2006
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