Spatial modelling of woodsmoke concentrations and health risk associated with residential wood burning.

Supervisory Committee Dr. C. Peter Keller, Department of Geography Supervisor Dr. Trisalyn Nelson, Department of Geography Departmental Member Dr. Andrew Kmetic, Department of Aboriginal Health Outside Member 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 iv 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.