Sarah Henderson

The British Columbia (BC) wildfire seasons of 2017 and 2018 were unprecedented in terms of the area they burned and the smoke they emitted. Wildfire smoke is a complex and dynamic mixture of air pollutants, of which fine particulate matter (PM2.5) is generally recognized as the greatest threat to human health. Very few studies have examined how exposure to PM2.5 influences in utero respiratory tract development processes, so the most concerning prenatal exposure windows remain unknown. In this thesis, all infants in utero during the wildfire seasons (July to September) of 2016-2019 were identified using the BC Perinatal Data Registry (BCPDR). Residential addresses of the mothers and their infants were used to estimate daily PM2.5 exposures throughout pregnancy and the first year of life using the Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM) at a resolution of 5x5 km². Outcomes of interest and potential covariates for each infant in the first year of life were identified through data linkage and compared during critical windows of prenatal respiratory tract development using the Cox proportional hazard model.We found that the sensitive windows for respiratory infections and associated amoxicillin dispensations move to the later stages of development as the respiratory infections of interest move from the upper respiratory tract to the lower respiratory tract. Each 1 mg/m3 increase in PM2.5 exposure was associated with earlier diagnosis of otitis media in the first window (week 0-9) of the Eustachian tube development (HR=1.012, 95%CI: 1.004-1.021). Each 1 mg/m3 increase in PM2.5 exposure was associated with earlier diagnosis of lower respiratory infections in the Saccular stage (week 28-36) (HR =1.015, 95%CI: 1.010-1.020) and Alveolar stage (week 36 to birth) (HR = 1.008, 95%CI: 1.004-1.012). Similar results were observed for the effect of wildfire-related PM2.5 on amoxicillin dispensations related to respiratory tract infections in the first year of life. The statistically significant associations between wildfire-related PM2.5 and overall amoxicillin dispensation were detected for the later stages of respiratory tract development. Our results suggest that it is necessary to formulate clear public health guidelines for pregnant mothers to avoid being exposed to wildfire during wildfire seasons.

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Governments and organizations around the world are increasingly considering public health as part of planning, assessment, and decision making processes for large development projects, such as new transportation corridors or industrial facilities. To date, there are no established or consistent methods for the consideration of environmental noise in assessment processes for these projects. The overarching objective of this thesis is to identify best practices for considering the public health effects of environmental noise when assessing the potential impacts of development projects. The term “noise impact assessment” (NIA) is proposed, including a framework adapted from human health risk assessment and health impact assessment processes.Best practices for the NIA process were identified following a literature review in four key subject areas: (1) health effects of noise; (2) noise prediction/noise modeling; (3) practices in health impact assessment; and (4) practices in environmental impact assessment. Themes and lessons from the literature in each of the four key subject areas were identified and applied to the NIA framework. A total of thirteen best practices were identified.In particular, this work emphasizes the importance of assessing health impacts themselves in addition to noise exposure. It identifies the “percent [of people] highly annoyed [by noise]” (%HA) and “percent [of people] highly sleep disturbed [by noise]” (%HSD) metrics as recommended quantitative and objective measures of the adverse health effects of noise appropriate for use in NIA. At the same time, this work recommends a flexible assessment approach that considers both objective and subjective, acoustical and non-acoustical factors that impact human health, including noise level, community context, and noise sensitivity. Finally, this thesis argues against noise management as an appropriate focus of any noise reduction strategy because it has limited potential to meaningfully change noise exposures.While there is a broad literature relating to the health impacts of environmental noise, and numerous best practices for health impact assessment and human health risk assessment, this work is the first to bring these areas of research together and identify best practices for considering environmental noise in the assessment process for development projects.

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Background: Mobile air temperature monitoring is a promising method to better understand temperature distributions at fine spatial resolutions across urban areas and to minimize extreme hot weather health impacts. The first study objective was to collect pedestrian microscale air temperature data to evaluate different methods for assessing spatial variation in urban heat exposure in greater Vancouver, Canada. The second objective was to develop microscale land use regression (LUR) air temperature models using the data collected.Methods: Mobile air temperature monitoring was conducted on foot at least twice for 20 routes chosen to represent potential heat exposures. The mobile data were compared with 1-minute measurements from the nearest fixed site, with satellite-derived land surface temperature (LST) for runs corresponding with Landsat overpass days, and with estimates from a previously-developed heat map for the region based on satellite generated geographic data. Six independent variables were considered for use in constructing a 30 x 30m LUR model for each run and within all routes in greater Vancouver. All models were evaluated using a spatial leave-ten-out cross-validation (LTOCV) approach.Results: Mobile measurements were typically higher and more variable than simultaneous fixed site measurements. The relationship between mobile measurements and LST were weak and highly variable. The mobile measurement and heat map z-score differentials suggested that spatial temperature variability was well-captured by the previously-developed heat map. The Distance to Large Water Body, Distance to Major Road, Normalized Difference Water Index, and Sky-View Factor were selected as the most predictive independent variables. On average, the best individual route models explained 38.6% of the variation in microscale air temperatures at 20 routes. The overall model explained only 10.0% of the variation in the route areas of the greater Vancouver region. Conclusion: The microscale measurements confirmed that fixed sites did not characterize the thermal variability within nearby streetscapes. They could also be used to generate LUR models for some locations. The strength of daytime mesoscale atmospheric processes may weaken the predictive power of land use variables. Future studies intending to use microscale modelling should collect data within a restricted time range and across fewer routes. 

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