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Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution.

ABSTRACT: Land use regression (LUR) models rely on air pollutant measurements for their development, and are therefore limited to recent periods where such measurements are available. Here we propose an approach to overcome this gap and calculate LUR models several decades before measurements were available. We first developed a LUR model for NOx using annual averages of NOx at all available air quality monitoring sites in Israel between 1991 and 2011 with time as one of the independent variables. We then reconstructed historical spatial data (e.g., road network) from historical topographic maps to apply the model's prediction to each year from 1961 to 2011. The model's predictions were then validated against independent estimates about the national annual NOx emissions from on-road vehicles in a top-down approach. The model's cross validated R2 was 0.74, and the correlation between the model's annual averages and the national annual NOx emissions between 1965 and 2011 was 0.75. Information about the road network and population are persistent predictors in many LUR models. The use of available historical data about these predictors to resolve the spatial variability of air pollutants together with complementary national estimates on the change in pollution levels over time enable historical reconstruction of exposures.

SUBMITTER: Levy I

PROVIDER: S-EPMC4763339 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution.

Levy Ilan I Levin Noam N Yuval Schwartz Joel D JD Kark Jeremy D JD

Environmental science & technology 20150225 6

Land use regression (LUR) models rely on air pollutant measurements for their development, and are therefore limited to recent periods where such measurements are available. Here we propose an approach to overcome this gap and calculate LUR models several decades before measurements were available. We first developed a LUR model for NOx using annual averages of NOx at all available air quality monitoring sites in Israel between 1991 and 2011 with time as one of the independent variables. We then ...[more]

PMID: 25692663

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Project description:BackgroundSurrounding green, air pollution, and noise have been associated with cardiometabolic diseases, but most studies have assessed only one of these correlated exposures.ObjectivesWe aimed to evaluate associations of combined exposures to green, air pollution, and road traffic noise with cardiometabolic diseases.MethodsIn this cross-sectional study, we studied associations between self-reported physician-diagnosed diabetes, hypertension, heart attack, and stroke from a Dutch national health survey of 387,195 adults and residential surrounding green, annual average air pollutant concentrations [including particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text]), PM with aerodynamic diameter [Formula: see text] ([Formula: see text]), nitrogen dioxide ([Formula: see text]), and oxidative potential (OP) with the dithiothreitol (DTT) assay ([Formula: see text])] and road traffic noise. Logistic regression models were used to analyze confounding and interaction of surrounding green, air pollution, and noise exposure.ResultsIn single-exposure models, surrounding green was inversely associated with diabetes, while air pollutants ([Formula: see text], [Formula: see text]) and road traffic noise were positively associated with diabetes. In two-exposure analyses, associations with green and air pollution were attenuated but remained. The association between road traffic noise and diabetes was reduced to unity when adjusted for surrounding green or air pollution. Air pollution and surrounding green, but not road traffic noise, were associated with hypertension in single-exposure models. The weak inverse association of surrounding green with hypertension attenuated and lost significance when adjusted for air pollution. Only [Formula: see text] was associated with stroke and heart attack.ConclusionsStudies including only one of the correlated exposures surrounding green, air pollution, and road traffic noise may overestimate the association of diabetes and hypertension attributed to the studied exposure. https://doi.org/10.1289/EHP3857.

Project description:BackgroundEvidence remains equivocal regarding the association of inflammation, a precursor to cardiovascular disease, and acute exposures to ambient air pollution from traffic-related particulate matter. Though youth with type 1 diabetes are at higher risk for cardiovascular disease, the relationship of inflammation and ambient air pollution exposures in this population has received little attention.ObjectivesUsing five geographically diverse US sites from the racially- and ethnically-diverse SEARCH for Diabetes in Youth Cohort, we examined the relationship of acute exposures to PM2.5 mass, Atmospheric Dispersion Modeling System (ADMS)-Roads traffic-related PM concentrations near roadways, and elemental carbon (EC) with biomarkers of inflammation including interleukin-6 (IL-6), c-reactive protein (hs-CRP) and fibrinogen.MethodsBaseline questionnaires and blood were obtained at a study visit. Using a spatio-temporal modeling approach, pollutant exposures for 7 days prior to blood draw were assigned to residential addresses. Linear mixed models for each outcome and exposure were adjusted for demographic and lifestyle factors identified a priori.ResultsAmong the 2566 participants with complete data, fully-adjusted models showed positive associations of EC average week exposures with IL-6 and hs-CRP, and PM2.5 mass exposures on lag day 3 with IL-6 levels. Comparing the 25th and 75th percentiles of average week EC exposures resulted in 8.3% higher IL-6 (95%CI: 2.7%,14.3%) and 9.8% higher hs-CRP (95%CI: 2.4%,17.7%). We observed some evidence of effect modification for the relationships of PM2.5 mass exposures with hs-CRP by gender and with IL-6 by race/ethnicity.ConclusionsIndicators of inflammation were associated with estimated traffic-related air pollutant exposures in this study population of youth with type 1 diabetes. Thus youth with type 1 diabetes may be at increased risk of air pollution-related inflammation. These findings and the racial/ethnic and gender differences observed deserve further exploration.

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Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution.

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Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution.

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