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.

PM 2.5 : mass concentration of particles less than 2.5 µm in size PM 10 : mass concentration of particles less than 10 µm in size RB: Regional Background site SOP: Standard Operating Procedure ST: Street site TRAPCA: Traffic-Related Air Pollution and Childhood Asthma UB: Urban Background site ABSTRACT The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates long-term effects on human health of exposure to air pollution in Europe. Various health endpoints are analysed by using prospective cohort studies in the study areas. This paper documents the spatial variation of measured NO 2 and NO x concentrations between and within 36 study areas across Europe. In 36 study areas NO 2 and NO x were measured using standardized methods between October 2008 and April 2011. In each study area 14 to 80 sites were selected, which represented a wide range of regional, urban and nearby traffic related pollution contrast. The measurements were conducted for two weeks per site in three different seasons, using Ogawa badges. Results for each site were adjusted for temporal variation using data obtained from a routine monitor background site, which operated continuously, and averaged. Substantial spatial variability was found in NO 2 and NO x concentrations between and within study areas. Analysis of variance showed that 40% of the overall NO 2 variance is attributable to the variability between the study areas and 60% is caused by the variability within the study areas. The corresponding values for NO x are 30% (between the study areas) and 70% (within the study areas). The within-area spatial variability was mostly determined by the differences between traffic and urban background concentrations. The traffic/urban background concentration ratio varied between 1.09 and 3.16 across Europe. The NO 2 / NO x ratio varied between 0.47 (Verona) and 0.72 (Heraklion) across study areas. In study areas in southern Europe the highest median concentrations were observed (Barcelona: NO 2 55 µg/m³), followed by densely populated areas in Western Europe (Ruhr area, The Netherlands). The lowest concentrations were observed in all areas in Northern Europe (e.g. Umeå: NO 2 7 µg/m³). In conclusion, we found significant contrast in annual average NO 2 and NO x concentration between and especially within 36 study areas across Europe. Epidemiological studies should therefore characterize intra-urban contrasts. The use of traffic indicators such as "living close to major road" as an exposure variable in epidemiological studies results in different actual NO 2 contrasts. We would like to thank Kees Meliefste, Geert de Vrieze, Marjan Tewis (IRAS, Utrecht University, The Netherlands) for the sampler preparation, analysis and data management. Furthermore, we thank all those who were responsible for air pollution measurements, data management and project supervision in all study areas and especially:

The role of air pollution in chronic obstructive pulmonary disease (COPD) remains uncertain.The aim was to assess the impact of chronic exposure to air pollution on COPD in four cohorts using the standardised ESCAPE exposure estimates. Annual average particulate matter (PM), nitrogen oxides (NO x ) and road traffic exposure were assigned to home addresses using land-use regression models. COPD was defined by NHANES reference equation (forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) less than the lower limit of normal) and the Global Initiative for Chronic Obstructive Lung Disease criterion (FEV1/FVC ,0.70) and categorised by severity in non-asthmatics.We included 6550 subjects with assigned NO x and 3692 with PM measures. COPD was not associated with NO 2 or PM10 in any individual cohort. In meta-analyses only NO 2 , NO x , PM10 and the traffic indicators were positively, although not significantly, associated with COPD. The only statistically significant associations were seen in females (COPD prevalence using GOLD: OR 1.57, 95% CI 1.11-2.23; and incidence: OR 1.79, 95% CI 1.21-2.68).None of the principal results were statistically significant, the weak positive associations of exposure with COPD and the significant subgroup findings need to be evaluated in further well standardised cohorts followed up for longer time, and with time-matched exposure assignments. @ERSpublications Results from the ESCAPE study: what is the association of COPD prevalence and incidence with ambient air pollution?

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.