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

Setton, Eleanor; Hystad, Perry; Keller, Christina

doi:10.1186/1476-072x-4-26

Cited by 15 publications

(2 citation statements)

References 14 publications

(12 reference statements)

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“…Values for M are calculated based on building area within 27 land-use categories. The data used in Equation 1 were derived from three sources: a ) locations for postal code centroids (Postal Code Conversion File, version 4D) and demographic information such as QAIPPE are from the 2001 Canadian Census ( Statistics Canada 2004 ), b ) street network data are from the 2001 CanMap (DMTI Spatial, Markham, Ontario, Canada), and c ) land-use data are from the 2001 British Columbia Property Assessment (BC Assessment, Victoria, British Columbia, Canada) ( Setton et al 2005 ).…”

Section: Methodsmentioning

confidence: 99%

Healthy Neighborhoods: Walkability and Air Pollution

Marshall

Bräuer²,

Frank

2009

Environ Health Perspect

218

130

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BackgroundThe built environment may influence health in part through the promotion of physical activity and exposure to pollution. To date, no studies have explored interactions between neighborhood walkability and air pollution exposure.MethodsWe estimated concentrations of nitric oxide (NO), a marker for direct vehicle emissions), and ozone (O3) and a neighborhood walkability score, for 49,702 (89% of total) postal codes in Vancouver, British Columbia, Canada. NO concentrations were estimated from a land-use regression model, O3 was estimated from ambient monitoring data; walkability was calculated based on geographic attributes such as land-use mix, street connectivity, and residential density.ResultsAll three attributes exhibit an urban–rural gradient, with high walkability and NO concentrations, and low O3 concentrations, near the city center. Lower-income areas tend to have higher NO concentrations and walkability and lower O3 concentrations. Higher-income areas tend to have lower pollution (NO and O3). “Sweet-spot” neighborhoods (low pollution, high walkability) are generally located near but not at the city center and are almost exclusively higher income.Policy implicationsIncreased concentration of activities in urban settings yields both health costs and benefits. Our research identifies neighborhoods that do especially well (and especially poorly) for walkability and air pollution exposure. Work is needed to ensure that the poor do not bear an undue burden of urban air pollution and that neighborhoods designed for walking, bicycling, or mass transit do not adversely affect resident’s exposure to air pollution. Analyses presented here could be replicated in other cities and tracked over time to better understand interactions among neighborhood walkability, air pollution exposure, and income level.

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Section: Methodsmentioning

confidence: 99%

Healthy Neighborhoods: Walkability and Air Pollution

Marshall

Bräuer²,

Frank

2009

Environ Health Perspect

218

130

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“…Spatial data including population, building age, and social and economic status were obtained at the dissemination area level from Statistics Canada. The number of fireplaces and the number of bedrooms came from BC property assessment data (38). In addition, a woodstove usage variable was included as the product of an emission surface and population density.…”

Section: Methodsmentioning

confidence: 99%

A Spatial Model of Urban Winter Woodsmoke Concentrations

Larson

Baribeau

et al. 2007

Environ. Sci. Technol.

104

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In many urban areas, residential wood burning is a significant wintertime source of PM2.5. In this study, we used a combination of fixed and mobile monitoring along with a novel spatial buffering procedure to estimate the spatial patterns of woodsmoke. Two-week average PM2.5 and levoglucosan (a marker for wood smoke) concentrations were concurrently measured at upto seven sites in the study region. In addition, pre-selected routes spanning the major population areas in and around Vancouver, B.C. were traversed during 19 cold, clear winter evenings from November, 2004 to March, 2005 by a vehicle equipped with GPS receiver and a nephelometer. Fifteen-second-average values of light scattering coefficient (bsp) were adjusted for variations between evenings and then combined into a single, highly resolved map of nighttime winter bsp levels. A relatively simple but robust (R(2) = 0.64) land use regression model was developed using selected spatial covariates to predict these temporally adjusted bsp values. The bsp values predicted by this model were also correlated with the measured average levoglucosan concentrations at our fixed site locations (R(2) = 0.66). This model, the first application of land use regression for woodsmoke, enabled the identification and prediction of previously unrecognized high woodsmoke regions within an urban airshed.

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Models of Exposure for Use in Epidemiological Studies of Air Pollution Health Impacts

Bräuer

Ainslie²,

Buzzelli³

et al.

Air Pollution Modeling and Its Application XIX

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Observational epidemiological studies have had an important role in understanding the public health impacts of air pollution. In such studies, accurate assessment of exposure remains a major challenges, especially in studies involving large populations. Here we review state-of-the-art approaches to assessment of population exposure in epidemiological studies with a focus on approaches applied in the Border Air Quality Study (www.cher.ubc.ca\baqs.htm). The strengths and limitations of these methods are discussed and future research needs identified.

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

Cited by 15 publications

References 14 publications

Healthy Neighborhoods: Walkability and Air Pollution

Healthy Neighborhoods: Walkability and Air Pollution

A Spatial Model of Urban Winter Woodsmoke Concentrations

Models of Exposure for Use in Epidemiological Studies of Air Pollution Health Impacts

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