Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study

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 (NO 2 ) concentrations in Vancouver (BC, Canada) was applied to Victoria (BC, Canada) and Seattle (WA, USA). Model estimates were compared with measurements made with Ogawa s 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 (R 2 ¼ 0.51) than in Seattle (R 2 ¼ 0.33). Scenario 2 produced improved R 2 -values in both cities (Victoria ¼ 0.58, Seattle ¼ 0.65), with further improvement achieved under Scenario 3 (Victoria ¼ 0.61, Seattle ¼ 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.

Section: Introductionmentioning

confidence: 99%

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

Poplawski

Gould

Setton

et al. 2008

“…Recent studies suggest that particles originating from traffic are especially harmful (Hoek et al, 2002;Peters et al, 2004). Reflectance of PM 2.5 filters, transformed into absorbance, can be used as an indicator for diesel and other combustion particles (Cyrys et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Determinants of personal and indoor PM2.5 and absorbance among elderly subjects with coronary heart disease

Lanki¹,

Ahokas²,

Alm³

et al. 2006

Self Cite

Epidemiological studies have established an association between outdoor levels of fine particles (PM 2.5 ) and cardiovascular health. However, there is little information on the determinants of PM 2.5 exposures among persons with cardiovascular disease, a potentially susceptible population group. Daily outdoor, indoor and personal PM 2.5 and absorbance (proxy for elemental carbon) concentrations were measured among elderly subjects with cardiovascular disease in Amsterdam, the Netherlands, and Helsinki, Finland, during the winter and spring of 1998-1999 within the framework of the ULTRA study. There were 37 non-smoking subjects in Amsterdam and 47 in Helsinki. In Amsterdam, where there were enough exposure events for analyses, exposure to environmental tobacco smoke (ETS) indoors was a major source of between-subject variation in PM 2.5 exposures, and a strong determinant of PM 2.5 and absorbance exposures. When the days with ETS were excluded, within-subject variation accounted for 89% of the total variation in personal PM 2.5 and 97% in absorbance in Amsterdam. The respective figures were 66% and 61% in Helsinki. In both cities, outdoor levels of PM 2.5 and absorbance were major determinants of personal and indoor levels. Traffic was also an important determinant of absorbance: living near a major street increased exposure by 22%, and every hour spent in a motor vehicle by 13% in Amsterdam. The respective increases were 37% and 9% in Helsinki. Cooking was associated with increased levels of both absorbance and PM 2.5 . Our results demonstrate that by using questionnaires in connection with outdoor measurements, exposure estimation of PM 2.5 and its combustion originating fraction can be improved among elderly persons with compromised health.

“…For categorical exposure classification studies, the relative risks (RRs) of ''living near a major roadway'' are usually reported. For example, Hoek et al (2002) reported a RR of 1.41 for all-cause mortality and 1.95 for cardiopulmonary mortality for those residential locations, but only 5% of the cohort met this high-exposure definition (Hoek et al, 2001). These results would thus imply much more modest risks (1.02 to 1.03) when averaged over the whole cohort, as is usually the case with continuous measures of exposure.…”

Section: Measures Of Effect (Risk)mentioning

confidence: 96%

“…Premature mortality (Roemer and van Wijnen, 2001;Hoek et al, 2002;Finkelstein et al, 2004;Nafstad et al, 2004;Lipfert et al, 2006a, b;Maynard et al, 2007). Increased hospitalization (Hagen et al, 2000;Janssen et al, 2002;von Klot et al, 2005;Wellenius et al, 2005;Lanki et al, 2006a).…”

Section: Introductionmentioning

confidence: 99%

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

Lipfert

Wyzga

2008

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.