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Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

For decades, extrinsic skin aging has been known to result from chronic exposure to solar radiation and, more recently, to tobacco smoke. In this study, we have assessed the influence of air pollution on skin aging in 400 Caucasian women aged 70-80 years. Skin aging was clinically assessed by means of SCINEXA (score of intrinsic and extrinsic skin aging), a validated skin aging score. Traffic-related exposure at the place of residence was determined by traffic particle emissions and by estimation of soot in fine dust. Exposure to background particle concentration was determined by measurements of ambient particles at fixed monitoring sites. The impact of air pollution on skin aging was analyzed by linear and logistic regression and adjusted for potential confounding variables. Air pollution exposure was significantly correlated to extrinsic skin aging signs, in particular to pigment spots and less pronounced to wrinkles. An increase in soot (per 0.5 × 10(-5) per m) and particles from traffic (per 475  kg per year and square km) was associated with 20% more pigment spots on forehead and cheeks. Background particle pollution, which was measured in low residential areas of the cities without busy traffic and therefore is not directly attributable to traffic but rather to other sources of particles, was also positively correlated to pigment spots on face. These results indicate that particle pollution might influence skin aging as well.

BackgroundCross-sectional and ecological studies indicate that air pollution may be a risk factor for type 2 diabetes, but prospective data are lacking.ObjectiveWe examined the association between traffic-related air pollution and incident type 2 diabetes.DesignBetween 1985 and 1994, cross-sectional surveys were performed in the highly industrialized Ruhr district (West Germany); a follow-up investigation was conducted in 2006 using data from the Study on the Influence of Air Pollution on Lung, Inflammation and Aging (SALIA) cohort.Participants1,775 nondiabetic women who were 54–55 years old at baseline participated in both baseline and follow-up investigations and had complete information available.Materials and MethodsUsing questionnaires, we assessed 16-year incidence (1990–2006) of type 2 diabetes and information about covariates. Complement factor C3c as marker for subclinical inflammation was measured at baseline. Individual exposure to traffic-related particulate matter (PM) and nitrogen dioxide was determined at different spatial scales.ResultsBetween 1990 and 2006, 87 (10.5%) new cases of diabetes were reported among the SALIA cohort members. The hazards for diabetes were increased by 15–42% per interquartile range of PM or traffic-related exposure. The associations persisted when different spatial scales were used to assess exposure and remained robust after adjusting for age, body mass index, socioeconomic status, and exposure to several non–traffic-related sources of air pollution. C3c was associated with PM pollution at baseline and was a strong independent predictor of incident diabetes. Exploratory analyses indicated that women with high C3c blood levels were more susceptible for PM-related excess risk of diabetes than were women with low C3c levels.ConclusionsTraffic-related air pollution is associated with incident type 2 diabetes among elderly women. Subclinical inflammation may be a mechanism linking air pollution with type 2 diabetes.Relevance to clinical practiceOur study identifies traffic-related air pollution as a novel and potentially modifiable risk factor of type 2 diabetes.