Within the context of global climate change and soaring energy prices, people are
searching for inexpensive and renewable sources of energy; therefore, burning wood for
home heating is increasing. Woodsmoke contains substances known to harm human
health and is a major contributor to air pollution in many parts of the world; yet there is
limited research into the health effects of woodsmoke and existing research suffers from
methodological constraints. As a result, there is interest in producing robust woodsmoke
exposure estimates for health research and air quality management purposes. Studying
health and the environment is inherently spatial; however, research related to air pollution
and health tends to be aspatial. As investigators begin to understand the influence of
spatial processes on research findings, the importance of adopting a spatial approach to
modelling exposure and health risk is becoming apparent. This thesis describes a spatially
explicit model for predicting fine particulate matter (PM2.5) attributable to woodsmoke
from residential heating in Victoria, British Columbia, Canada. Spatially resolved
measurements of PM2.5 were collected for 32 evenings during the winter heating seasons
of 2004/05, 2005/06, 2006/07 using a nephelometer installed in a passenger vehicle.
Positional data were collected concurrently using a Global Positioning System (GPS).
Levoglucosan, a chemical unique to woodsmoke, was measured to confirm the presence
of woodsmoke in the measured PM2.5. The spatial scale for the analysis of woodsmoke
data was determined using semivariograms to identify the maximum distance of spatial
dependence in the data which typically occurred near 2700m. Different spatial
approaches for modelling woodsmoke concentrations were evaluated both qualitatively in
terms of transferability, meeting statistical assumptions, and potential for exposure
misclassification; and quantitatively to assess the association between the model’s
predicted PM2.5 concentrations and observed PM2.5. The baseline model characterized
exposure based on the PM2.5 value from the closest fixed monitor (R=0.51, α=0.05). The
Krigged model produced a seasonal average surface based on nephelometer
measurements and showed the weakest performance (R=0.25, α=0.05). The regression
models predicted concentrations of woodsmoke based on predictor variables available
from census data, typically used in health research, and spatial property assessment data
(SPAD), an underused data source at a finer spatial resolution. Different approaches to
regression modelling were investigated. A regression model already developed for
Victoria performed the best quantitatively (R=0.84, α=0.05); however, qualitative
considerations precluded it from being selected as an appropriate model. A quantitatively
(R=0.62, α=0.05) and qualitatively robust regression model was developed using SPAD
(M6). SPAD improved the spatial resolution and model performance over census data.
Removing spatial and temporal autocorrelation in the data prior to modelling produced
the most robust model as opposed to modelling spatial effects post regression. A
Bayesian approach to M6 was applied; however, model performance remained
unchanged (R=0.62, α=0.05). The spatial distribution of susceptibility to health problems
associated with woodsmoke was derived from census data relating to population, age and
income. Intersecting the exposure model with population susceptibility in a Geographic
Information System (GIS) identified areas at high risk for health effects attributable to
woodsmoke.
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/1278 |
| Date | 08 December 2008 |
| Creators | Lightowlers, Christy |
| Contributors | Keller, C. Pleter |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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