Traffic density in California: Socioeconomic and ethnic differences among potentially exposed children

Gunier, Robert B.; Hertz, Andrew; Behren, Julie Von; Reynolds, Peggy

doi:10.1038/sj.jea.7500276

Cited by 187 publications

(144 citation statements)

References 35 publications

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“…The majority of existing research examining socioeconomic status (SES) and air pollution exposure focuses on how lower SES is associated with higher smoking rates (Watson et al, 2003), greater occupational exposures (Rotko et al, 2000), and residences located in areas that have higher ambient air pollution (Gunier et al, 2003;O'neill et al, 2003;Jerrett et al, 2004). This is the first study that has found links between low improved building value, which is likely related to low SES, and increased F inf of ambient PM 2.5 air pollution.…”

Section: Discussionmentioning

confidence: 96%

Modeling residential fine particulate matter infiltration for exposure assessment

Hystad

Setton

Allen

et al. 2008

J Expo Sci Environ Epidemiol

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Individuals spend the majority of their time indoors; therefore, estimating infiltration of outdoor-generated fine particulate matter (PM 2.5 ) 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 PM 2.5 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 PM 2.5 concentrations. This modeling approach holds promise for incorporating infiltration efficiencies into large epidemiology studies, thereby reducing exposure misclassification.

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Section: Discussionmentioning

confidence: 96%

Modeling residential fine particulate matter infiltration for exposure assessment

Hystad

Setton

Allen

et al. 2008

J Expo Sci Environ Epidemiol

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“…Traffic density has been estimated at the county level, 5 the census block level, 44 and within selected radii (50-300 m) of a residence 10,11,30 using publically or commercially available sources or some combination thereof. The traffic density estimate may be based on only those roads for which actual traffic volume data are collected, 11,44 or traffic volumes may be assigned to neighboring roads using models or comparison to roads with similar capacities. 10,36 In one study, publically available data on roadway networks and traffic volumes were used to develop geographic information system (GIS)-derived cumulative traffic density scores identifying roads with greater than 8,500 cars/day.…”

Section: Rationale For Using Surrogate Methods For Characterizing Tramentioning

confidence: 99%

“…36 Data obtained from 93 state monitoring stations for CO, benzene, and 1,3-butadiene showed stronger correlations with census block level traffic density (correlation coefficients, 0.70, 0.69, and 0.57, respectively). 44 Possible explanations for these inconsistent results include spatial or temporal misalignment of the monitoring data and traffic density estimates or difference in the relative contribution of traffic versus non-traffic sources to parameter concentrations in a particular locale. Land-use regression studies combine monitoring of air pollution at a small number of locations and development of stochastic models using data on land use, traffic characteristics, and meteorology to predict measured pollution concentrations at un-sampled locations.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Characterizing Urban Traffic Exposures Using Transportation Planning Tools: An Illustrated Methodology for Health Researchers

et al. 2010

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Exposure to elevated levels of vehicular traffic has been associated with adverse cardiovascular and respiratory health effects in a range of populations, including children, the elderly, and individuals with pre-existing heart conditions, diabetes, obesity, and genetic susceptibilities. As these relationships become clearer, public health officials will need to have access to methods to identify areas of concern in terms of elevated traffic levels and susceptible populations. This paper briefly reviews current approaches for characterizing traffic exposure and then presents a detailed method that can be employed by public health officials and other researchers in performing screening assessments to define areas of potential concern within a particular locale and, with appropriate caveats, in epidemiologic studies examining traffic-related health impacts at the intra-urban scale. The method is based on two exposure parameters extensively used in numerous epidemiologic studies of traffic and health-proximity to high traffic roadways and overall traffic density. The method is demonstrated with publically available information on susceptible populations, traffic volumes, and Traffic Analysis Zones, a transportation planning tool long used by Metropolitan Planning Agencies and planners across the USA but presented here as a new application which can be used to spatially assess possible traffic-related impacts on susceptible populations. Recommendations are provided for the appropriate use of this methodology, along with its limitations.

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“…For example, Weiland et al (1994) use this term in conjunction with self-described truck-traffic frequencies and ''main'' vs. ''side'' streets, whereas in California (Gunier et al, 2003;Gauderman et al, 2005) and in the Veterans Cohort Study (Lipfert et al, 2006a, b), more explicit continuous measures based on annual traffic counts within a specified land area were used.…”

Section: Traffic Exposure Metricsmentioning

confidence: 99%

“…According to Figure 2 in Lipfert et al (2006a), this corresponds to a median traffic flow rate of about 4000 vpd, as a county average. By way of comparison, Gunier et al (2003) reported a mean annual traffic density for census block groups in California of about 19 million VKTA, with a 95th percentile value of about 113 million. One would have expected higher extreme values for census blocks than for the counties used in the Veterans Cohort Study, but this is not always the case.…”

Section: Exposure Measurements and Distributionsmentioning

confidence: 99%

On exposure and response relationships for health effects associated with exposure to vehicular traffic

Lipfert

Wyzga

2008

J Expo Sci Environ Epidemiol

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This work examines various metrics and models that have been used to estimate long-term health effects of exposure to vehicular traffic. Such health impacts may include effects of air pollution due to emissions of combustion products and from vehicle or roadway wear, of noise, stress, or from socioeconomic effects associated with preferred residential locations. Both categorical and continuous exposure metrics are considered, typically for distances between residences and roadways, or for traffic density or intensity. It appears that continuous measures of exposure tend to yield lower risk estimates that are also more precise than categorical measures based on arbitrary criteria. The selection of appropriate exposure increments to characterize relative risks is also important in comparing pollutants and other agents. Confounding and surrogate variables are also important issues, since studies of traffic proximity or density cannot identify the specific agents related to traffic exposures that might be responsible for the various health endpoints that have been implicated. Studies based on ambient air quality measurements are necessarily restricted to species for which data are available, some of which may be serving as markers for the actual agents of harm. Studies based on modeled air quality are limited by the accuracy of mobile source emission inventories, which may not include poorly maintained (high emitting) vehicles. Additional exposure modeling errors may result from precision limitations of geocoding methods. Studies of the health effects of traffic are progressing from establishing the existence of relationships to describing them in more detail, but effective remedies or control strategies have generally not yet been proposed in the context of these epidemiological studies. Resolution of these dose-response uncertainties is important for the development of effective public health strategies for the future.

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Traffic density in California: Socioeconomic and ethnic differences among potentially exposed children

Cited by 187 publications

References 35 publications

Modeling residential fine particulate matter infiltration for exposure assessment

Modeling residential fine particulate matter infiltration for exposure assessment

Characterizing Urban Traffic Exposures Using Transportation Planning Tools: An Illustrated Methodology for Health Researchers

On exposure and response relationships for health effects associated with exposure to vehicular traffic

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