European Community's Seventh Framework Programme.

No abstract

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.

h i g h l i g h t s < LUR models were developed in 36 study areas in Europe using a standardized approach. < NO 2 models explained a large fraction of concentration variability (median R 2 82%). < Local traffic intensity data were important predictors for LUR model development.

Objectives To study the effect of long term exposure to airborne pollutants on the incidence of acute coronary events in 11 cohorts participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE).Design Prospective cohort studies and meta-analysis of the results.Setting Cohorts in Finland, Sweden, Denmark, Germany, and Italy.Participants 100 166 people were enrolled from 1997 to 2007 and followed for an average of 11.5 years. Participants were free from previous coronary events at baseline.Main outcome measures Modelled concentrations of particulate matter <2.5 μm (PM2.5), 2.5-10 μm (PMcoarse), and <10 μm (PM10) in aerodynamic diameter, soot (PM2.5 absorbance), nitrogen oxides, and traffic exposure at the home address based on measurements of air pollution conducted in 2008-12. Cohort specific hazard ratios for incidence of acute coronary events (myocardial infarction and unstable angina) per fixed increments of the pollutants with adjustment for sociodemographic and lifestyle risk factors, and pooled random effects meta-analytic hazard ratios.Results 5157 participants experienced incident events. A 5 μg/m3 increase in estimated annual mean PM2.5 was associated with a 13% increased risk of coronary events (hazard ratio 1.13, 95% confidence interval 0.98 to 1.30), and a 10 μg/m3 increase in estimated annual mean PM10 was associated with a 12% increased risk of coronary events (1.12, 1.01 to 1.25) with no evidence of heterogeneity between cohorts. Positive associations were detected below the current annual European limit value of 25 μg/m3 for PM2.5 (1.18, 1.01 to 1.39, for 5 μg/m3 increase in PM2.5) and below 40 μg/m3 for PM10 (1.12, 1.00 to 1.27, for 10 μg/m3 increase in PM10). Positive but non-significant associations were found with other pollutants.Conclusions Long term exposure to particulate matter is associated with incidence of coronary events, and this association persists at levels of exposure below the current European limit values.

PM 2.5 , mass concentration of particles less than 2.5 mm in size; PM 2.5 absorbance, measurement of the blackness of PM 2.5 filters, this is a proxy for elemental carbon, which is the dominant light absorbing substance; PM 10 , mass concentration of particles less than 10 mm in size; PM coarse , mass concentration of the coarse fraction of particles between 2.5 mm and 10 mm in size; RB, regional background; RH, relative humidity; ST, Street; TRAPCA, Traffic-Related Air Pollution and Childhood Asthma; UB, urban background; US EPA, United States Environmental Protection Agency.

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Consistent and predominant cramped synchronized GMs specifically predict cerebral palsy. The earlier this characteristic appears, the worse is the later impairment.