Shin, Hwashin H

Climate change is a global challenge with profound environmental, health, andsocio-economic implications. Canada's diverse geography offers a unique lens to study localized climate trends. This thesis models and clusters climate variables, focusing on temperature trends, using Bayesian hierarchical models and clustering techniques to uncover regional patterns and health impacts. Three decades of hourly temperature data from the Meteorological Service of Canada were split into 18 annual parts to capture seasonal variations. Metrics like mean, minimum, and extreme temperatures were analyzed. Bayesian models revealed regional variability, with examples of British Columbia and the Northern regions exhibiting notable trends. Clustering identified regional dependencies and linked temperature trends with morbidity and mortality risks from air pollutants (PM2.5, O3). Summer risks stemmed from O3, while winter risks were PM2.5 driven. Findings highlight the need for region-specific strategies, offering actionable insights for policy makers addressing climate-health linkages.

Author Keywords: Bayesian models, Climate change, Clustering, Temperature Trends, Time Series

This thesis explores the shot-term relationship between exposure to ambient air pollution and human health through metrics such as mortality and hospitalization in Canada. We begin by detailing the organization and interpolation of air pollution data from its partially quality-controlled source form. Analyses of seasonal, regional and temporal trends of all major components of PM2.5, was performed, showing a seasonal variation across most regions and validating the dataset.

A one-pollutant statistical Generalized Additive Model was applied to the data, estimating the health risk associated with exposure to thirteen different components of PM2.5. The selected components were based on those that compromised the majority of the mass and included: sulphate, nitrate, zinc, silicon, iron, nickel, vanadium, potassium, organic carbon, organic matter, elemental carbon, total carbon. Trends based on annual estimates of the association for PM2.5, and its constituents,were compared, showing that carbonaceous compounds, sulphate and nitrate had similar estimates of association. Many estimates, as is common in population ecologic epidemiology, had association estimates statistically indistinguishable from zero, but with clear features of interest, including evident differences between cold and warm season associations in Canada's temperate climate.

A method to model two correlated pollutants (in this case, PM2.5 and O3) was developed using thin plate splines. In this approach, the location of the response surface (after accounting for the temperature, a smooth function of time and day of week) that corresponds to the average pollutant concentration and the average plus one unit was used as the estimate of the joint contribution of pollutants due to a unit increase. The estimates from the thin plate spline (TPS) approach were compared to the single pollutant models, with large increases and decreases in PM2.5 and O3 being captured in the TPS estimates. However, this approach indicated significantly larger error in the estimates than would be expected, indicating a possible future area for refinement.

Author Keywords: Air pollution, Environmental Epidemiology, Generalized Additive Models, Human Health, Multivariate Models, Thin Plate Splines