Traffic at residential address, respiratory health, and atopy in adults: the National German Health Survey 1998

Heinrich, Joachim; Topp, Rebekka; Gehring, Ulrike; Thefeld, Wolfgang

doi:10.1016/j.envres.2004.08.004

Cited by 72 publications

(57 citation statements)

References 37 publications

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“…Numerous studies have identified associations between traffic-related air pollution and adverse heath effects either by characterizing exposures to specific pollutants using measurements from a few central ambient sites (Dockery et al 1993;Pope et al 1995;Studnicka et al 1997;Laden et al 2000), or by some measure of traffic (Oosterlee et al 1996;Garshick et al 2003;Heinrich et al 2005;Ryan et al 2005). Yet, by ignoring the contribution of indoor sources and the effect of residential ventilation, it is difficult to accurately estimate personal exposures, especially in an intraurban epidemiological study.…”

Section: Introductionmentioning

confidence: 99%

Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data

Baxter

Clougherty

Paciorek³

et al. 2007

Atmospheric Environment

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Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and/or factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system (GIS), and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3-4 day samples of nitrogen dioxide (NO 2 ) and fine particulate matter (PM 2.5 ) in 43 low SES residences across multiple seasons from 2003 -2005. Elemental carbon concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally-located ambient monitor.The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM 2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO 2 with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50m buffer of a home and distance from a truck route as important contributors to indoor levels of NO 2 and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and

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Section: Introductionmentioning

confidence: 99%

Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data

Baxter

Clougherty

Paciorek³

et al. 2007

Atmospheric Environment

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“…Several studies have shown that there is a correlation between asthma, bronchitis, pneumonia, and respiratory infections and settlements located near major roads [1][2][3][4][5]. Brunekreef et al [6] revealed that long-term exposure to traffic-related gaseous and particulate matter pollutants has a strong link to respiratory mortality.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

Suleiman

Tight

Quinn

2016

Environ Model Assess

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This paper examines the application of artificial neural network (ANN) and boosted regression tree (BRT) methods in air quality modelling. The methods were applied to developing air quality models for predicting roadside particle mass concentration (PM 10 , PM 2.5 ) and particle number counts (PNC) based on air pollution, traffic and meteorological data from Marylebone Road in London. Elastic net, Lasso and principal components analysis were used as feature selection methods for the ANN models to reduce the number of predictor variables and improve their generalisation. The performance of the ANN with feature selection (ANN hybrid) and the BRT models was evaluated and compared using statistical performance metrics. The performance parameters include root mean square error (RMSE), fraction of prediction within a factor of two of the observation (FAC2), mean bias (MB), mean gross error (MGE), the coefficient of correlation (R) and coefficient of efficiency (CoE) values. The input variables selected by the elastic net produced the best performing ANN models. The ANN hybrid produced models performed only slightly better than the BRT models. The R values of the ANN elastic net and BRT models were 0.96 and 0.95 for PM 10 , 0.96 and 0.96 for PM 2.5 and 0.89 and 0.87 for PNC, respectively. Their corresponding CoE values were 0.72 and 0.70 for PM 10 , 0.74 and 0.76 for PM 2.5 and 0.81 and 0.71 for PNC respectively. About 80-99% of all the model predictions are within a factor of two of the observed particle concentrations. The BRT models offer more advantages regarding model interpretation and permit feature selection. Therefore, the study recommends the use of BRT over ANN where the model interpretation is a priority.

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“…Heinrich et al (2005a) compared subjective assessments of traffic intensity by degree of urbanization with estimated concentrations of PM 2.5 , NO 2 , and PM 2.5 filter absorbance, using GIS-based dispersion modeling, and concluded that the two types of traffic indicators were only ''weakly associated''. Heinrich et al (2005b) also found significant respiratory relationships by considering only the street of residence and not the neighborhood.…”

Section: Traffic Exposure Metricsmentioning

confidence: 85%

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 at residential address, respiratory health, and atopy in adults: the National German Health Survey 1998

Cited by 72 publications

References 37 publications

Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data

Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

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

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