Fine particulate matter (PM2.5) is a leading risk factor for morbidity and mortality worldwide. Recent studies have shown substantial heterogeneity in exposure to PM2.5 within populations, with socially disadvantaged subgroups exhibiting disproportionally higher exposure. However, the extent to which reductions in PM2.5 and its components may differentially affect these subgroups remains unclear. Leveraging state-of-the-art exposure surfaces that simulate annual reductions in total and compositional PM2.5 from major emission sectors under two policy scenarios (20% and 100%), based on satellite observations and a global chemical transport model, we characterized the expected reductions in total and compositional population-weighted PM2.5 exposure by selected subgroups for 2007 and 2016. Under both scenarios, we found that certain subgroups, particularly low-income people, immigrants, and racialized groups, would experience larger reductions in exposure to total and compositional PM2.5. The largest decreases were associated with nitrates, sulfates, and ammonia from agriculture, nitrates and black carbon from transportation, and organic matter from residential wood combustion. These findings have important implications for PM2.5 mitigation strategies aimed at reducing population exposure and associated disparities in Canada.

Exposure to ambient air pollution is associated with a wide range of adverse health effects such as respiratory symptoms, cardiovascular events, and premature mortality. Canada and the United States (US) have worked collaboratively for decades to address transboundary air pollution and its impacts across their shared border. To inform transboundary air quality considerations, we conducted modelling to attribute health impacts from ambient PM2.5 and O-3 exposure in Canada to Canadian and US emission sources. We employed emissions, chemical transport, and health impacts modelling for 2015, 2025, and 2035 using a brute-force modelling approach whereby anthropogenic domestic and US emissions were reduced separately by 20 % or 100 %, and the resulting changes in health impacts were estimated across Canada. We find that transboundary PM2.5 and O-3 related health impacts vary widely by region, with >80 % of impacts occurring in Central Canada, and most health impacts occurring within 200-300 km of the Canada-US border. The relative contribution of US sources to O-3 in Canada is larger than for PM2.5, yet we find that the health impacts from transboundary PM2.5 exceeded those from transboundary O-3. Nationally, we estimate that roughly one in five PM2.5 deaths in Canada is attributable to US sources (2000 deaths in 2015) and more than one in two O-3 deaths are attributable to US sources (roughly 800 to 1200 deaths in 2015). We project health impacts from domestic and US sources to increase from 2025 to 2035 in Canada. Our results suggest that there are substantial benefits to be gained by domestic and international strategies to reduce PM2.5 in the Canada-US transboundary region.

We aim to understand how changes in ambient fine particulate matter (PM2.5) over the last two decades have influenced PM2.5-attributable mortality in a Canadian population experiencing both growth and changing baseline health status. We conducted a health impact analysis using dynamic estimates of population, baseline mortality rates, and satellite-based PM2.5 concentrations to estimate mortality attributable to long-term PM2.5 exposure every five years between 2001 and 2021, applying risk estimates from the 2006 Canadian Census Health and Environment Cohort (CanCHEC) to the population aged 25 and older. We conducted a decomposition analysis to examine the influences of population exposure, size, and health status on trends in PM2.5-attributable mortality. Between 2001 and 2021, population-weighted exposure to PM2.5 declined by 18% in Canada, with improvements occurring in most urban areas. In recent years, these changes have led to 4,400 (95% CI: 3,700–5,000) to 4,700 (95% CI: 4,100–5,400) fewer PM2.5-attributable deaths annually based on log–linear and log–log shapes of concentration–response. However, a growing population alongside higher baseline mortality risks in several regions, likely due to aging, has led to a small net increase in total PM2.5-attributable deaths between 2001 and 2021. These findings suggest that the Canadian population has benefitted broadly from air quality management strategies implemented in North America over recent decades.

Air quality management benefits from an in-depth understanding of the emissions associated with, and composition of, local PM2.5 concentrations. Here, we investigate the changing role of biomass burning emissions to North American PM2.5 exposure by combining multiple satellite-, ground-, and simulation-based data sets biweekly at a 0.01° × 0.01° resolution from 2000 to 2022. We also developed a Buffered Leave Cluster Out (BLeCO) method to address autocorrelation and computational cost in cross-validation. Biomass burning emissions contribute an increasingly large fraction to PM2.5 exposure in the United States and Canada, with national annual population-weighted mean contributions increasing from 0.4 μg/m3 (3-5%) in 2000-2004 to 0.8-0.9 μg/m3 (9-14%) by 2019-2022, led by western North American 2019-2022 annual contributions of 1.4-1.9 μg/m3 (15-27%) and maximum seasonal contributions of 3.3-5.5 μg/m3 (29-49%). Other components such as nonbiomass burning Organic Matter (OM) and nitrate can be regionally as (or more) important, albeit with distinct seasonal variability. The contribution of total OM to PM2.5 exposure in the United States in 2016-2022 is 42.2%, comparable to all other anthropogenically sourced components combined. Comparison of BLeCO and random 10-fold cross-validation suggests that random 10-fold cross-validation may significantly underrepresent true uncertainty for total PM2.5 concentrations due to the clustered nature of PM2.5 ground-based monitoring.

The World Health Organization (WHO) recently released new guidelines for outdoor fine particulate air pollution (PM2.5) recommending an annual average concentration of 5 μg/m3. Yet, our understanding of the concentration-response relationship between outdoor PM2.5 and mortality in this range of near-background concentrations remains incomplete. To address this uncertainty, we conducted a population-based cohort study of 7.1 million adults in one of the world's lowest exposure environments. Our findings reveal a supralinear concentration-response relationship between outdoor PM2.5 and mortality at very low (<5 μg/m3) concentrations. Our updated global concentration-response function incorporating this new information suggests an additional 1.5 million deaths globally attributable to outdoor PM2.5 annually compared to previous estimates. The global health benefits of meeting the new WHO guideline for outdoor PM2.5 are greater than previously assumed and indicate a need for continued reductions in outdoor air pollution around the world.

BACKGROUND:The temporal and spatial scales of exposure assessment may influence observed associations between fine particulate air pollution (PM2.5) and mortality, but few studies have systematically examined this question.METHODS:We followed 2.4 million adults in the 2001 Canadian Census Health and Environment Cohort for nonaccidental and cause-specific mortality between 2001 and 2011. We assigned PM2.5 exposures to residential locations using satellite-based estimates and compared three different temporal moving averages (1, 3, and 8 years) and three spatial scales (1, 5, and 10 km) of exposure assignment. In addition, we examined different spatial scales based on age, employment status, and urban/rural location, and adjustment for O3, NO2, or their combined oxidant capacity (Ox).RESULTS:In general, longer moving averages resulted in stronger associations between PM2.5 and mortality. For nonaccidental mortality, we observed a hazard ratio of 1.11 (95% CI = 1.08, 1.13) for the 1-year moving average compared with 1.23 (95% CI = 1.20, 1.27) for the 8-year moving average. Respiratory and lung cancer mortality were most sensitive to the spatial scale of exposure assessment with stronger associations observed at smaller spatial scales. Adjustment for oxidant gases attenuated associations between PM2.5 and cardiovascular mortality and strengthened associations with lung cancer. Despite these variations, PM2.5 was associated with increased mortality in nearly all of the models examined.CONCLUSIONS:These findings support a relationship between outdoor PM2.5 and mortality at low concentrations and highlight the importance of longer-exposure windows, more spatially resolved exposure metrics, and adjustment for oxidant gases in characterizing this relationship.

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US.

BACKGROUNDImmigrants make up 20% of the Canadian population; however, little is known about the mortality impacts of fine particulate matter (PM2.5) air pollution on immigrants compared with non-immigrants, or about how impacts may change with duration in Canada.DATA AND METHODSThis study used the 2001 Canadian Census Health and Environment Cohort, a longitudinal cohort of 3.5 million individuals, of which 764,000 were classified as immigrants (foreign-born). Postal codes from annual income tax files were used to account for mobility among respondents and to assign annual PM2.5 concentrations from 1998 to 2016. Exposures were estimated as a three-year moving average prior to the follow-up year. Cox survival models were used to determine hazard ratios (HRs) for cause-specific mortality, comparing the Canadian and foreign-born populations, with further stratification by year of immigration grouped into 10-year cohorts.RESULTSDifferences in urban-rural settlement patterns resulted in greater exposure to PM2.5 for immigrants compared with non-immigrants (mean = 9.3 vs. 7.5 μg/m3), with higher exposures among more recent immigrants. In fully adjusted models, immigrants had higher HRs per 10 μg/m3 increase in PM2.5 concentration compared with Canadian-born individuals for cardiovascular mortality (HR [95% confidence interval] = 1.22 [1.12 to 1.34] vs. 1.12 [1.07 to 1.18]) and cerebrovascular mortality (HR = 1.25 [1.03 to 1.52] vs. 1.03 [0.93 to 1.15]), respectively. However, tests for differences between the two groups were not significant when Cochran's Q test was used. No significant associations were found for respiratory outcomes, except for lung cancer in non-immigrants (HR = 1.10 [1.02 to 1.18]). When stratified by year of immigration, differences in HRs across varied by cause of death.DISCUSSIONIn Canada, PM2.5 is an equal-opportunity risk factor, with immigrants experiencing similar if not higher mortality risks compared with non-immigrants for cardiovascular-related causes of death. Some notable differences also existed with cerebrovascular and lung cancer deaths. Continued reductions in air pollution, particularly in urban areas, will improve the health of the Canadian population as a whole.

TPS 691: Methods of measurement, design and data analysis, Exhibition Hall, Ground floor, August 28, 2019, 3:00 PM - 4:30 PM Background: Missing data is persistently a thorn in the side of data science researchers. While there are various ways to deal with missing data, from basic to sophisticated, it is critical to understand the pattern and characteristics of your missing data (i.e. is it missing (completely) at random?). Most importantly, how does the missing impact the dependent variable? Aim: The purpose of this work is to assess the pattern of missing person-year data, particularly for the years leading up to the censoring event (e.g. death), and to examine the impact of excluding or imputing on survival model risk estimates. Methods: We use the 2001 Canadian Census Health and Environment Cohort (CanCHEC, N=3.1 million, 16-years follow-up). Residential postal codes reported on annual income tax filings were used to account for residential mobility among respondents and for exposure and area-based covariate assignment. Missing postal codes were assessed by proximity in years to censoring event (non-accidental mortality) and relationship to other covariates (education, income, etc.). Imputation took three forms, national annual mean, person-year mean, truncated postal code mean. We use Cox survival models to estimate hazard ratios of fine particulate matter (PM2.5) to assess the impact of exclusion versus imputation. Results: Four percent of person-years were missing postal codes, with slightly higher proportions in the years leading up to a mortality event. Imputation method had little impact on the overall PM2.5 mean (7.03 vs. 7.04 μg/m3); however, levels of PM2.5 were notably larger among subjects who died and were missing data compared to survivors (7.57 vs. 7.04 μg/m3) and tended to have lower socioeconomic characteristics. Preliminary results suggest that excluding person-years with the demonstrated pattern of missing can have large nullifying impact on the PM2.5 hazard ratio. Conclusion: Missing data can impact results. Discussion will focus on sensitivity tests to examine missing data.

OPS 51: Air pollution and mortality: what's new? Beatrix Theater, August 26, 2019, 4:30 PM - 5:30 PM Background: Ambient fine particulate matter (PM2.5) is an important contributor to the global burden of disease. An element critical to these estimates, and to air quality standards, is information on the shape of concentration-response relationships at low concentrations. In the largest population-based air pollution cohort ever constructed, we examined the concentration-response relationship between PM2.5 and non-accidental mortality in a new Canadian Census Health and Environment Cohort that combines the 1991, 1996, and 2001 Canadian census cycles linked to mobility and mortality data. Methods: We linked individual census responses with death records through 2016, resulting in a cohort of approximately 8.5 million adults who contributed 151 million person-years, and 1.5 million deaths. Using annual place of residence, we assigned time-varying contextual variables and annual average exposures to ambient PM2.5 at a 1 km x 1 km spatial resolution from 1981 to 2016. We ran log-linear Cox proportional hazards models for PM2.5 with eight subject-level indicators of socioeconomic status, seven contextual covariates, and O3 and NO2. We ran a nonlinear model-fitting routine (the shape constrained health impact function, SCHIF) to examine the shape of the concentration-response relationship at PM2.5 levels below 20 μg/m3. Results: The mean three-year annual average PM2.5 concentration was 7.4 µg/m3 over all person-years of follow-up. We estimated an ensemble hazard ratio for non-accidental mortality of 1.045 (95% CI: 1.037-1.054) for a 10 µg/m3 change in PM2.5. We observed a sublinear concentration-mortality curve predicted by the SCHIF model that results in a 1.063 (95% CI: 1.052-1.073) hazard ratio for a change in concentration from 5 to 15 µg/m3. We found little increase in risk below 5 μg/m3. Conclusions: In this very large population-based cohort with up to 25 years of follow-up, PM2.5 was associated with non-accidental mortality at concentrations as low as 5 µg/m3.

Background: Indirect adjustment via partitioned regression is a promising technique to control for unmeasured confounding in large epidemiological studies. The method uses a representative ancillary dataset to estimate the association between variables missing in a primary dataset with the complete set of variables of the ancillary dataset to produce an adjusted risk estimate for the variable in question. The objective of this paper is threefold: 1) evaluate the method for non-linear survival models, 2) formalize an empirical process to evaluate the suitability of the required ancillary matching dataset, and 3) test modifications to the method to incorporate time varying exposure data, and proportional weighting of datasets. Methods: We used the association between fine particle air pollution (PM2.5) with mortality in the 2001 Canadian Census Health and Environment Cohort (CanCHEC, N = 2.4 million, 10-years follow-up) as our primary dataset, and the 2001 cycle of the Canadian Community Health Survey (CCHS, N = 80,630) as the ancillary matching dataset that contained confounding risk factor information not available in CanCHEC (e.g., smoking). The main evaluation process used a gold-standard approach wherein two variables (education and income) available in both datasets were excluded, indirectly adjusted for, and compared to true models with education and income included to assess the amount of bias correction. An internal validation for objective 1 used only CanCHEC data, whereas an external validation for objective 2 replaced CanCHEC with the CCHS. The two proposed modifications were applied as part of the validation tests, as well as in a final indirect adjustment of four missing risk factor variables (smoking, alcohol use, diet, and exercise) in which adjustment direction and magnitude was compared to models using an equivalent longitudinal cohort with direct adjustment for the same variables. Results: At baseline (2001) both cohorts had very similar PM2.5 distributions across population characteristics, although levels for CCHS participants were consistently 1.8-2.0 mu g/m(3) lower. Applying sample-weighting largely corrected for this discrepancy. The internal validation tests showed minimal downward bias in PM2.5 mortality hazard ratios of 0.4-0.6% using a static exposure, and 1.7-3% when a time-varying exposure was used. The external validation of the CCHS as the ancillary dataset showed slight upward bias of -0.7 to -1.1% and downward bias of 1.3-2.3% using the static and time-varying approaches respectively. Conclusions: The CCHS was found to be fairly well representative of CanCHEC and its use in Canada for indirect adjustment is warranted. Indirect adjustment methods can be used with survival models to correct hazard ratio point estimates and standard errors in models missing key covariates when a representative matching dataset is available. The results of this formal evaluation should encourage other cohorts to assess the suitability of ancillary datasets for the application of the indirect adjustment methodology to address potential residual confounding.

S21: When the Answer is "Big(ger) Data" in Environmental Epidemiology: What are the Questions?, Room 315, Floor 3, August 26, 2019, 1:30 PM - 3:00 PM Use of large administrative cohorts linked to national registries and environmental exposures has facilitated consistent population-based risk coefficients. However, despite their size advantage, the lack of person-level behavioural risk factors (e.g. smoking, diet) is an important limitation with the potential to bias risk estimates. In environmental epidemiology, indirect adjustment for unmeasured confounding has taken several forms. A recently proposed method by Shin et al. (2014) uses partitioned regression. This method does not attempt to estimate the missing risk factors directly from supplementary data, but rather estimates the association between the missing factors and the available factors contained in the survival model using a representative ancillary dataset. The advantage of this method is that adjustment is at the individual-level and can accommodate multiple missing risk factors simultaneously. This symposium presentation will, 1) describe the partitioned regression indirect adjustment methodology including modifications that incorporate time-varying exposure data and proportional weighting of datasets, 2) formally evaluate the method and representativeness of the ancillary dataset using Cox proportional hazard models, and 3) compare it to other indirect adjustment methods. As an example we apply the method to the relationship between fine particulate matter (PM2.5) and non-accidental mortality, but keep the discussion general to any exposure-disease outcome relationship. We use the 2001 Canadian Census Health and Environment Cohort (CanCHEC, N=2.4 million, 16-years follow-up) as our primary dataset, and the 2001 cycle of the Canadian Community Health Survey (CCHS, N=130,000) as the ancillary matching dataset. Our validation tests showed minimal adjustment bias (-1.2% to +2.3%), depending on the modifications applied, cause of death, and covariates in the model. Adjustment direction and magnitude were very similar (<0.5%) compared to equivalent models using a CCHS-mortality linked cohort. Discussion will focus on the generalizability of the validation tests and how to assess adjustment results using sensitivity tests.

Background: Ambient fine particulate air pollution with aerodynamic diameter ≤2.5 μm (PM2.5) is an important contributor to the global burden of disease. Information on the shape of the concentration–response relationship at low concentrations is critical for estimating this burden, setting air quality standards, and in benefits assessments. Objectives: We examined the concentration–response relationship between PM2.5 and nonaccidental mortality in three Canadian Census Health and Environment Cohorts (CanCHECs) based on the 1991, 1996, and 2001 census cycles linked to mobility and mortality data. Methods: Census respondents were linked with death records through 2016, resulting in 8.5 million adults, 150 million years of follow-up, and 1.5 million deaths. Using annual mailing address, we assigned time-varying contextual variables and 3-y moving-average ambient PM2.5 at a 1×1 km spatial resolution from 1988 to 2015. We ran Cox proportional hazards models for PM2.5 adjusted for eight subject-level indicators of socioeconomic status, seven contextual covariates, ozone, nitrogen dioxide, and combined oxidative potential. We used three statistical methods to examine the shape of the concentration–response relationship between PM2.5 and nonaccidental mortality. Results: The mean 3-y annual average estimate of PM2.5 exposure ranged from 6.7 to 8.0 μg/m3 over the three cohorts. We estimated a hazard ratio (HR) of 1.053 [95% confidence interval (CI): 1.041, 1.065] per 10-μg/m3 change in PM2.5 after pooling the three cohort-specific hazard ratios, with some variation between cohorts (1.041 for the 1991 and 1996 cohorts and 1.084 for the 2001 cohort). We observed a supralinear association in all three cohorts. The lower bound of the 95% CIs exceeded unity for all concentrations in the 1991 cohort, for concentrations above 2 μg/m3 in the 1996 cohort, and above 5 μg/m3 in the 2001 cohort. Discussion: In a very large population-based cohort with up to 25 y of follow-up, PM2.5 was associated with nonaccidental mortality at concentrations as low as 5 μg/m3. https://doi.org/10.1289/EHP5204

HEI’s Program to Assess Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution

Abstract Background Approximately 2.9 million deaths are attributed to ambient fine particle air pollution around the world each year (PM2.5). In general, cohort studies of mortality and outdoor PM2.5 concentrations have limited information on individuals exposed to low levels of PM2.5 as well as covariates such as smoking behaviours, alcohol consumption, and diet which may confound relationships with mortality. This study provides an updated and extended analysis of the Canadian Community Health Survey-Mortality cohort: a population-based cohort with detailed PM2.5 exposure data and information on a number of important individual-level behavioural risk factors. We also used this rich dataset to provide insight into the shape of the concentration-response curve for mortality at low levels of PM2.5. Methods Respondents to the Canadian Community Health Survey from 2000 to 2012 were linked by postal code history from 1981 to 2016 to high resolution PM2.5 exposure estimates, and mortality incidence to 2016. Cox proportional hazard models were used to estimate the relationship between non-accidental mortality and ambient PM2.5 concentrations (measured as a three-year average with a one-year lag) adjusted for socio-economic, behavioural, and time-varying contextual covariates. Results In total, 50,700 deaths from non-accidental causes occurred in the cohort over the follow-up period. Annual average ambient PM2.5 concentrations were low (i.e. 5.9 μg/m3, s.d. 2.0) and each 10 μg/m3 increase in exposure was associated with an increase in non-accidental mortality (HR = 1.11; 95% CI 1.04–1.18). Adjustment for behavioural covariates did not materially change this relationship. We estimated a supra-linear concentration-response curve extending to concentrations below 2 μg/m3 using a shape constrained health impact function. Mortality risks associated with exposure to PM2.5 were increased for males, those under age 65, and non-immigrants. Hazard ratios for PM2.5 and mortality were attenuated when gaseous pollutants were included in models. Conclusions Outdoor PM2.5 concentrations were associated with non-accidental mortality and adjusting for individual-level behavioural covariates did not materially change this relationship. The concentration-response curve was supra-linear with increased mortality risks extending to low outdoor PM2.5 concentrations.

This study examines inequity in exposure to fine particulate matter (PM2.5) in New York City and surrounding areas across income groups. By contrasting the sensitivities of public health and equity measures to emissions reductions on a location-by-location basis, this study offers novel yet practical suggestions to coordinate air quality management strategies that prioritize different policy endpoints.Air quality simulations were run using the USEPA's Community Multiscale Air Quality (CMAQ) model and its adjoint version over New York City at 1 km resolution. CMAQ is used to estimate concentration surfaces for quantifying exposure to PM2.5. Second, the adjoint of CMAQ was run to estimate the sensitivity of domain-wide inequity parameters to pollution emissions, on a location-by-location basis for different sectors.Preliminary results show that lower income populations in New York City tend to be exposed to higher concentrations of PM2.5. We find that emission reductions in Brooklyn, Harlem, and the Bronx would be most beneficial for reducing domain-wide inequity, while reductions in Manhattan would result in increased inequity. We also calculate monetized health benefits per ton of primary PM emissions across the domain, with benefits estimated to be significant (as high as $10M/ton) and spatially variable across the domain. We propose a novel approach for monetizing air pollution inequity, and find that monetized benefits from inequity reductions are comparable in magnitude (up to $5M/ton) to health benefits from avoided mortality. We examine hypothetical scenarios where quantified benefits with regards to health an inequity are considered in tandem to coordinate policies that target both endpoints simultaneously. Our results demonstrate that focusing emission reductions on sources that are influential on both environmental justice and public health can yield improvements across multiple policy goals simultaneously.

We estimate monetized health benefits of phasing out the coal-fired power plants in Ontario and Alberta as well as in the US. We quantify these health impacts by accounting for reduced mortality due to chronic exposure to NO2 (In Canada) and PM2.5 (In Canada and the US).We apply the US EPA's Community Multi-Scale Air Quality (CMAQ-5.0) model and its adjoint to quantify the marginal benefits (MB) of NOx and PM2.5 emissions. The adjoint model traces mortality counts back to emissions for each single source location and time. The backward simulations of the model rely on non-linear concentration-response (C-R) functions of single (PM2.5) and three-pollutant (PM2.5, NO2 and O3) epidemiologic models. The simulations are done over a nested 12 km and 36 km domain, covering North America and for July 2010.Our preliminary results show health benefits for specific plants in Ontario and Alberta are between C$ 30k-310k/ton of PM2.5 and $30-270k/ton of NOx. These values range between $30k-580k/ton of PM2.5 for plants in the US. Retrospective analysis of coal phase-out in Ontario suggests benefits of $3.1 billion/yr, while societal benefits of the proposed phase-out in Alberta is approximated at $2.4 billion/yr.We find significant benefits from coal phase-out in both Ontario and Alberta, and even larger benefits in the US. For Ontario, our results suggest that most of the health benefits from Ontario coal phase-out materializes in the province, whereas Alberta phase-out entails larger out-of-province benefits.

Managing air quality through emissions control entails significant societal benefits in Canada and around the world. As the public health impacts of emissions depend on the atmospheric conditions conducive to pollutant transport and transformation, sophisticated atmospheric models are necessary to link public health impacts with sources of emissions directly. This thesis develops a novel method to integrate health benefit assessment and formal sensitivity analysis tools. It employs a reverse sensitivity analysis technique, infused with epidemiological and economic data, to attribute air pollution health effects to emissions sources. This linkage creates a streamlined approach for assessing the damages incurred by anthropogenic emissions, and the benefits of their control, on a source-by-source basis. The findings presented in this thesis indicate that the public health benefits of emission controls vary considerably from source-to-source and by emitted species. A main feature of emission control benefits is their dependency on the composition of the atmosphere and hence on emission quantities. As the atmosphere becomes cleaner with progressive emission reduction policies, the benefits-per-ton of emissions control are likely to change, particularly for pollutants that undergo nonlinear transformations in the atmosphere. Further, the shape of the concentration-response function (CRF) between pollutant exposure and mortality plays a determining role in estimating these benefits. This thesis investigates how both atmospheric chemistry and assumptions about the CRF influence the health benefits of emission control. For secondary pollutants such as ozone, the benefits-per-ton of NOx control are found to increase substantially as the atmosphere becomes less polluted. For primary pollutants such as NO2, compounding benefits of NOx control exist due entirely to a supralinear CRF. The findings of this thesis indicate unforeseen and long-term benefits of emissions control, suggesting that current emission controls make future abatement efforts more worthwhile. Assessment of recent emission trends in North America indicates that we are currently at an important point on the abatement trajectory, where the benefits of emission controls have increased in the past and will do so considerably in the near future. Regard for the compounding nature of emission control benefits can cast abatement policies in a self-propagating, self-rewarding light in the long-term.