Eleanor Setton

Built Environment Influences on Healthy Transportation Choices: Bicycling versus Driving

A growing body of evidence links the built environment to physical activity levels, health outcomes, and transportation behaviors. However, little of this research has focused on cycling, a sustainable transportation option with great potential for growth in North America. This study examines associations between decisions to bicycle (versus drive) and the built environment, with explicit consideration of three different spatial zones that may be relevant in travel behavior: trip origins, trip destinations, and along the route between. We analyzed 3,280 utilitarian bicycle and car trips in Metro Vancouver, Canada made by 1,902 adults, including both current and potential cyclists. Objective measures were developed for built environment characteristics related to the physical environment, land use patterns, the road network, and bicycle-specific facilities. Multilevel logistic regression was used to model the likelihood that a trip was made by bicycle, adjusting for trip distance and personal demographics. Separate models were constructed for each spatial zone, and a global model examined the relative influence of the three zones. In total, 31% (1,023 out of 3,280) of trips were made by bicycle. Increased odds of bicycling were associated with less hilliness; higher intersection density; less highways and arterials; presence of bicycle signage, traffic calming, and cyclist-activated traffic lights; more neighborhood commercial, educational, and industrial land uses; greater land use mix; and higher population density. Different factors were important within each spatial zone. Overall, the characteristics of routes were more influential than origin or destination characteristics. These findings indicate that the built environment has a significant influence on healthy travel decisions, and spatial context is important. Future research should explicitly consider relevant spatial zones when investigating the relationship between physical activity and urban form.

The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates

Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79–0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ∼16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63–0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0–2 h spent at least 10 km away, 0.66 for ≥10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates.

Creating National Air Pollution Models for Population Exposure Assessment in Canada

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.

How Far Out of the Way Will We Travel? Built Environment Influences on Route Selection for Bicycle and Car Travel

Current travel demand models are calibrated for motorized transportation and do not perform as well for nonmotorized modes. Little evidence exists on how much, and for what reasons, the routes people travel deviate from the shortest-path or least-cost routes generated by transportation models. This paper investigates differences in total distance, road type used, and built environment features for shortest-path routes versus actual routes for utilitarian bicycle trips (n = 50) and car trips (n = 67) in Metro Vancouver, Canada. Bike trips were, on average, 360 m longer than the shortest possible route; car trips were 540 m longer. Regardless of mode, people do not detour far off the shortest route: detour ratios (actual distance/shortest distance) were similar, with three-fourths of trips within 10% of the shortest distance and at least 90% within 25%. Differences in the built environment measures en route suggest why bike commuters chose to detour: the actual routes had significantly more bicycle facilities (traffic-calming features, bike stencils, and signage) than did the shortest-path routes. Compared with shortest-path routes, cyclists spent significantly less of their travel distance along arterial roads and significantly more along local roads, off-street paths, and routes with bike facilities. As expected, car trips were more likely to be along highways and less likely to be along local roads than predicted by the shortest route. The results illustrate factors that might be included in travel models to more accurately model nonmotorized transportation and provide guidance for how dense bike facilities need to be when infrastructure to support cycling is designed.

Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide

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 (NO2) concentrations in Vancouver (BC, Canada) was applied to Victoria (BC, Canada) and Seattle (WA, USA). Model estimates were compared with measurements made with Ogawa® 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 (R2=0.51) than in Seattle (R2=0.33). Scenario 2 produced improved R2-values in both cities (Victoria=0.58, Seattle=0.65), with further improvement achieved under Scenario 3 (Victoria=0.61, Seattle=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.

Modeling residential fine particulate matter infiltration for exposure assessment.

Individuals spend the majority of their time indoors; therefore, estimating infiltration of outdoor-generated fine particulate matter (PM2.5) can help reduce exposure misclassification in epidemiological studies. As indoor measurements in individual homes are not feasible in large epidemiological studies, we evaluated the potential of using readily available data to predict infiltration of ambient PM2.5 into residences. Indoor and outdoor light scattering measurements were collected for 84 homes in Seattle, Washington, USA, and Victoria, British Columbia, Canada, to estimate residential infiltration efficiencies. Meteorological variables and spatial property assessment data (SPAD), containing detailed housing characteristics for individual residences, were compiled for both study areas using a geographic information system. Multiple linear regression was used to construct models of infiltration based on these data. Heating (October to February) and non-heating (March to September) season accounted for 36% of the yearly variation in detached residential infiltration. Two SPAD housing characteristic variables, low building value, and heating with forced air, predicted 37% of the variation found between detached residential infiltration during the heating season. The final model, incorporating temperature and the two SPAD housing characteristic variables, with a seasonal interaction term, explained 54% of detached residential infiltration. Residences with low building values had higher infiltration efficiencies than other residences, which could lead to greater exposure gradients between low and high socioeconomic status individuals than previously identified using only ambient PM2.5 concentrations. This modeling approach holds promise for incorporating infiltration efficiencies into large epidemiology studies, thereby reducing exposure misclassification.

Mapping bikeability: a spatial tool to support sustainable travel

The built environment has been shown to influence active transportation. Although spatial data for the built environment is increasingly available, there has been little effort to use existing data and knowledge to define and map ‘bikeability’ as an approach to promoting travel by bicycle. Our goal was to build a tool to identify areas that are more conducive and less conducive to cycling. We used empirical research to develop a bikeability index and geographic information systems to map the index across the Metro Vancouver region. Results of an opinion survey, travel behaviour studies, and focus groups were used to identify the components of the index and their relative importance. Pertinent geospatial data layers were scored and combined using a flexible weighting scheme to create a composite map highlighting both high and low bikeability areas. The bikeability index was comprised of five factors shown to consistently influence cycling: Bicycle facility availability; bicycle facility quality; street connectivity; topography; and land use. For mapping purposes, we created corresponding metrics: density of bicycle facilities; separation from motor vehicle traffic; connectivity of bicycle-friendly roads (local streets, bicycle routes, and off-street paths); slope; and density of destination locations. Using empirical evidence to combine data layers for these metrics we generated a high-resolution (10 m) bikeability surface for the region, depicting bicycle-friendly areas and areas where cycling conditions need to be improved. Built environment interventions for specific locations are informed by evaluating scores for the five individual component layers. Mapping bikeability provides a powerful visual aid to identify zones where changes are needed to support sustainable travel. This evidence-based tool presents data in a user-friendly way for planners and policy makers. The overall bikeability score and its five component scores can guide local action to stimulate changes in cycling rates. It uses widely available data types, thus facilitating easy application in other cities. Furthermore, the flexible parameters and weighting scheme enable users elsewhere to tailor it to evidence about local preferences and conditions.

Opportunities for using spatial property assessment data in air pollution exposure assessments

BackgroundMany epidemiological studies examining the relationships between adverse health outcomes and exposure to air pollutants use ambient air pollution measurements as a proxy for personal exposure levels. When pollution levels vary at neighbourhood levels, using ambient pollution data from sparsely located fixed monitors may inadequately capture the spatial variation in ambient pollution. A major constraint to moving toward exposure assessments and epidemiological studies of air pollution at a neighbourhood level is the lack of readily available data at appropriate spatial resolutions. Spatial property assessment data are widely available in North America and may provide an opportunity for developing neighbourhood level air pollution exposure assessments.ResultsThis paper provides a detailed description of spatial property assessment data available in the Pacific Northwest of Canada and the United States, and provides examples of potential applications of spatial property assessment data for improving air pollution exposure assessment at the neighbourhood scale, including: (1) creating variables for use in land use regression modelling of neighbourhood levels of ambient air pollution; (2) enhancing wood smoke exposure estimates by mapping fireplace locations; and (3) using data available on individual building characteristics to produce a regional air pollution infiltration model.ConclusionSpatial property assessment data are an extremely detailed data source at a fine spatial resolution, and therefore a source of information that could improve the quality and spatial resolution of current air pollution exposure assessments.

Spatial variations in estimated chronic exposure to traffic-related air pollution in working populations: a simulation.

BackgroundChronic exposure to traffic-related air pollution is associated with a variety of health impacts in adults and recent studies show that exposure varies spatially, with some residents in a community more exposed than others. A spatial exposure simulation model (SESM) which incorporates six microenvironments (home indoor, work indoor, other indoor, outdoor, in-vehicle to work and in-vehicle other) is described and used to explore spatial variability in estimates of exposure to traffic-related nitrogen dioxide (not including indoor sources) for working people. The study models spatial variability in estimated exposure aggregated at the census tracts level for 382 census tracts in the Greater Vancouver Regional District of British Columbia, Canada. Summary statistics relating to the distributions of the estimated exposures are compared visually through mapping. Observed variations are explored through analyses of model inputs.ResultsTwo sources of spatial variability in exposure to traffic-related nitrogen dioxide were identified. Median estimates of total exposure ranged from 8 μg/m3 to 35 μg/m3 of annual average hourly NO2 for workers in different census tracts in the study area. Exposure estimates are highest where ambient pollution levels are highest. This reflects the regional gradient of pollution in the study area and the relatively high percentage of time spent at home locations. However, for workers within the same census tract, variations were observed in the partial exposure estimates associated with time spent outside the residential census tract. Simulation modeling shows that some workers may have exposures 1.3 times higher than other workers residing in the same census tract because of time spent away from the residential census tract, and that time spent in work census tracts contributes most to the differences in exposure. Exposure estimates associated with the activity of commuting by vehicle to work were negligible, based on the relatively short amount of time spent in this microenvironment compared to other locations. We recognize that this may not be the case for pollutants other than NO2. These results represent the first time spatially disaggregated variations in exposure to traffic-related air pollution within a community have been estimated and reported.ConclusionThe results suggest that while time spent in the home indoor microenvironment contributes most to between-census tract variation in estimates of annual average exposures to traffic-related NO2, time spent in the work indoor microenvironment contributes most to within-census tract variation, and time spent in transit by vehicle makes a negligible contribution. The SESM has potential as a policy evaluation tool, given input data that reflect changes in pollution levels or work flow patterns due to traffic demand management and land use development policy.

Geographic variation in radon and associated lung cancer risk in Canada

OBJECTIVE: Radon is an important risk factor for lung cancer. Here we use maps of the geographic variation in radon to estimate the lung cancer risk associated with living in high radon areas of Canada.METHODS: Geographic variation in radon was estimated using two mapping methods. The first used a Health Canada survey of 14,000 residential radon measurements aggregated to health regions, and the second, radon risk areas previously estimated from geology, sediment geochemistry and aerial gamma-ray spectrometry. Lung cancer risk associated with living in these radon areas was examined using a population-based case-control study of 2,390 lung cancer cases and 3,507 controls collected from 1994–1997 in eight Canadian provinces. Residential histories over a 20-year period were used in combination with the two mapping methods to estimate ecological radon exposures. Hierarchical logistic regression analyses were used to estimate odds ratios for lung cancer incidence, after adjusting for a comprehensive set of individual and geographic covariates.RESULTS: Across health regions in Canada, significant variation in average residential radon concentrations (range: 16–386 Bq/m3) and in high geological-based radon areas (range: 0–100%) is present. In multivariate models, a 50 Bq/m3 increase in average health region radon was associated with a 7% (95% CI: −6–21%) increase in the odds of lung cancer. For every 10 years that individuals lived in high radon geological areas, the odds of lung cancer increased by 11 % (95% CI: 1–23%).CONCLUSIONS: These findings provide further evidence that radon is an important risk factor for lung cancer and that risks are unevenly distributed across Canada.RésuméOBJECTIF: Le radon est un important facteur de risque du cancer du poumon. Nous utilisons ici des cartes de variation spatiale du radon pour estimer le risque de cancer du poumon associé au fait de vivre dans les régions du Canada fortement exposées au radon.MÉTHODE: Nous avons estimé la variation spatiale du radon à l’aide de deux méthodes de cartographie. La première a fait appel à une enquête de Santé Canada regroupant 14 000 mesures du radon dans les habitations par région sanitaire, et la deuxième, à des estimations antérieures des régions exposées au radon par la géologie, la géochimie sédimentaire et la spectrométrie gamma aéroportée. Nous avons examiné le risque de cancer du poumon associé au fait de vivre dans ces zones exposées au radon à l’aide d’une étude populationnelle cas-témoins menée entre 1994 et 1997 auprès de 2 390 cas de cancer du poumon et de 3 507 témoins dans huit provinces canadiennes. Nous avons combiné les lieux de résidence des sujets au cours des 20 années précédentes avec les deux méthodes de cartographie pour estimer les expositions écologiques au radon. Des analyses de régression logistique hiérarchiques ont permis d’estimer les rapports de cotes de l’incidence du cancer du poumon, après avoir tenu compte d’un ensemble exhaustif de covariables individuelles et géographiques.RÉSULTATS: Les régions sanitaires du Canada diffèrent considérablement en ce qui a trait à leurs concentrations moyennes en radon dans les habitations (intervalle: 16–386 Bq/m3) et aux zones géologiques fortement exposées au radon (intervalle: 0–100 %). Dans les modèles multivariés, une hausse de 50 Bq/m3 du radon dans une région sanitaire moyenne était associée à une hausse de 7 % (IC de 95 %: −6–21 %) de la probabilité de cancer du poumon. Pour chaque tranche de 10 ans pendant laquelle les sujets avaient vécu dans des zones géologiques fortement exposées au radon, la probabilité du cancer du poumon augmentait de 11 % (IC de 95 %: 1–23 %).CONCLUSIONS: Ces constatations sont des preuves supplémentaires que le radon est un important facteur de risque du cancer du poumon, et que les risques sont inégalement répartis au Canada.