Background: Population exposure assessment methods that capture local-scale pollutant variability are needed for large-scale epidemiological studies and surveillance, policy, and regulatory purposes. Currently, such exposure methods are limited.Methods: We created 2006 national pollutant models for fine particulate matter [PM with aerodynamic diameter ≤ 2.5 μm (PM2.5)], nitrogen dioxide (NO2), benzene, ethylbenzene, and 1,3-butadiene from routinely collected fixed-site monitoring data in Canada. In multiple regression models, we incorporated satellite estimates and geographic predictor variables to capture background and regional pollutant variation and used deterministic gradients to capture local-scale variation. The national NO2 and benzene models are evaluated with independent measurements from previous land use regression models that were conducted in seven Canadian cities. National models are applied to census block-face points, each of which represents the location of approximately 89 individuals, to produce estimates of population exposure.Results: The national NO2 model explained 73% of the variability in fixed-site monitor concentrations, PM2.5 46%, benzene 62%, ethylbenzene 67%, and 1,3-butadiene 68%. The NO2 model predicted, on average, 43% of the within-city variability in the independent NO2 data compared with 18% when using inverse distance weighting of fixed-site monitoring data. Benzene models performed poorly in predicting within-city benzene variability. Based on our national models, we estimated Canadian ambient annual average population-weighted exposures (in micrograms per cubic meter) of 8.39 for PM2.5, 23.37 for NO2, 1.04 for benzene, 0.63 for ethylbenzene, and 0.09 for 1,3-butadiene.Conclusions: The national pollutant models created here improve exposure assessment compared with traditional monitor-based approaches by capturing both regional and local-scale pollution variation. Applying national models to routinely collected population location data can extend land use modeling techniques to population exposure assessment and to informing surveillance, policy, and regulation.

Land use regression (LUR) is a method for predicting the spatial distribution of traffic-related air pollution. To facilitate risk and exposure assessment, and the design of future monitoring networks and sampling campaigns, we sought to determine the extent to which LUR can be used to predict spatial patterns in air pollution in the absence of dedicated measurements. We evaluate the transferability of one LUR model to two other geographically comparable areas with similar climates and pollution types. The source model, developed in 2003 to estimate ambient nitrogen dioxide (NO 2 ) concentrations in Vancouver (BC, Canada) was applied to Victoria (BC, Canada) and Seattle (WA, USA). Model estimates were compared with measurements made with Ogawa s passive samplers in both cities. As part of this study, 42 locations were sampled in Victoria for a 2-week period in June 2006. Data obtained for Seattle were collected for a different project at 26 locations in March 2005. We used simple linear regression to evaluate the fit of the source model under three scenarios: (1) using the same variables and coefficients as the source model; (2) using the same variables as the source model, but calculating new coefficients for local calibration; and (3) developing site-specific equations with new variables and coefficients. In Scenario 1, we found that the source model had a better fit in Victoria (R 2 ¼ 0.51) than in Seattle (R 2 ¼ 0.33). Scenario 2 produced improved R 2 -values in both cities (Victoria ¼ 0.58, Seattle ¼ 0.65), with further improvement achieved under Scenario 3 (Victoria ¼ 0.61, Seattle ¼ 0.72). Although it is possible to transfer LUR models between geographically similar cities, success may depend on the between-city consistency of the input data. Modest field sampling campaigns for location-specific model calibration can help to produce transfer models that are equally as predictive as their sources.

No abstract

BackgroundTools for estimating population exposures to environmental carcinogens are required to support evidence-based policies to reduce chronic exposures and associated cancers. Our objective was to develop indicators of population exposure to selected environmental carcinogens that can be easily updated over time, and allow comparisons and prioritization between different carcinogens and exposure pathways.MethodsWe employed a risk assessment-based approach to produce screening-level estimates of lifetime excess cancer risk for selected substances listed as known carcinogens by the International Agency for Research on Cancer. Estimates of lifetime average daily intake were calculated using population characteristics combined with concentrations (circa 2006) in outdoor air, indoor air, dust, drinking water, and food and beverages from existing monitoring databases or comprehensive literature reviews. Intake estimates were then multiplied by cancer potency factors from Health Canada, the United States Environmental Protection Agency, and the California Office of Environmental Health Hazard Assessment to estimate lifetime excess cancer risks associated with each substance and exposure pathway. Lifetime excess cancer risks in excess of 1 per million people are identified as potential priorities for further attention.ResultsBased on data representing average conditions circa 2006, a total of 18 carcinogen-exposure pathways had potential lifetime excess cancer risks greater than 1 per million, based on varying data quality. Carcinogens with moderate to high data quality and lifetime excess cancer risk greater than 1 per million included benzene, 1,3-butadiene and radon in outdoor air; benzene and radon in indoor air; and arsenic and hexavalent chromium in drinking water. Important data gaps were identified for asbestos, hexavalent chromium and diesel exhaust in outdoor and indoor air, while little data were available to assess risk for substances in dust, food and beverages.ConclusionsThe ability to track changes in potential population exposures to environmental carcinogens over time, as well as to compare between different substances and exposure pathways, is necessary to support comprehensive, evidence-based prevention policy. We used estimates of lifetime excess cancer risk as indicators that, although based on a number of simplifying assumptions, help to identify important data gaps and prioritize more detailed data collection and exposure assessment needs.

R adon is a colourless, odourless, naturally occurring gas released from the breakdown of uranium in soils. Exposure to radon occurs primarily indoors, where levels can accumulate to high concentrations. The majority of lung cancers are due to tobacco smoke; however, radon increases the risk of lung cancer for smokers as well as for individuals who have never smoked. 1-4 In Canada, approximately 16% of lung cancers (3,261 cases annually) are estimated to be attributable to residential radon exposure. 5 While radon is recognized as being causally associated with lung cancer, national-level studies are important to estimate attributable disease burden, increase awareness and develop population health policy. To date, only one residential radon epidemiological study has been conducted in Canada. This study was in Winnipeg and reported no associations between residential radon concentrations and lung cancer. 6 Similar to most epidemiological studies of residential radon, exposure was assessed using indoor residential measurements. While this method is the gold standard for characterizing radon exposure, studies using this method typically have limited statistical power due to the difficulty in measuring residential radon for large populations. Alternatively to using individual residential measurements, two epidemiological studies conducted in the United States and Denmark used maps and spatial prediction models to estimate long-term ecological residential radon concentrations in larger population samples, the approach we follow in this analysis. For the Cancer Prevention Study II cohort in the United States, average county-level radon measurements were linked to study participants' zip codes to estimate ecological radon exposure, and a 100 Bq/m 3 increase in radon was associated with a 15% (95% CI: 1-31%) increase in lung cancer mortality. 7 In the Danish Diet, Cancer and Health cohort, information on geology and housing characteristics were used to predict radon concentrations at residential locations for 57,053 subjects (589 lung cancer cases) and a 4% (95% CI:-31-56%) increase in lung cancer risk was observed per 100 Bq/m 3 increase in predicted radon concentrations. 8 Here we present two national radon risk maps for Canada and estimate the associated lung cancer risks by applying these maps to 20 years of residential histories using a population-based casecontrol study of 2,390 histological confirmed lung cancer incidence cases and 3,507 population controls. Based on the existing evidence for radon as a risk factor for lung cancer, we expect that lung cancer incidence in this population-based case-control study will be increased in individuals living in high radon risk areas. METHODS Radon mapping Two distinct approaches were used to create radon risk maps for Canada. The first (Figure 1a) and a priori best estimate of radon exposure used a recently completed residential radon survey of three-month radon measurements collected from approximately 14,000 households across Canada. 9 The sampling frame for this sur