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.

In a joint analysis of data from 22 European cohorts, most hazard ratios for the association of air pollutants with mortality from overall CVD and with specific CVDs were approximately 1.0, with the exception of particulate mass and cerebrovascular disease mortality for which there was suggestive evidence for an association.

Background:Epidemiological evidence on the association between ambient air pollution and breast cancer risk is inconsistent.Objective:We examined the association between long-term exposure to ambient air pollution and incidence of postmenopausal breast cancer in European women.Methods:In 15 cohorts from nine European countries, individual estimates of air pollution levels at the residence were estimated by standardized land-use regression models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) and Transport related Air Pollution and Health impacts - Integrated Methodologies for Assessing Particulate Matter (TRANSPHORM) projects: particulate matter (PM) ≤2.5μm, ≤10μm, and 2.5–10μm in diameter (PM2.5, PM10, and PMcoarse, respectively); PM2.5 absorbance; nitrogen oxides (NO2 and NOnormalx); traffic intensity; and elemental composition of PM. We estimated cohort-specific associations between breast cancer and air pollutants using Cox regression models, adjusting for major lifestyle risk factors, and pooled cohort-specific estimates using random-effects meta-analyses.Results:Of 74,750 postmenopausal women included in the study, 3,612 developed breast cancer during 991,353 person-years of follow-up. We found positive and statistically insignificant associations between breast cancer and PM2.5 {hazard ratio (HR)=1.08 [95% confidence interval (CI): 0.77, 1.51] per 5 μg/m3}, PM10 [1.07 (95% CI: 0.89, 1.30) per 10 μg/m3], PMcoarse [1.20 (95% CI: 0.96, 1.49 per 5 μg/m3], and NO2 [1.02 (95% CI: 0.98, 1.07 per 10 μg/m3], and a statistically significant association with NOnormalx [1.04 (95% CI: 1.00, 1.08) per 20 μg/m3, p=0.04].Conclusions:We found suggestive evidence of an association between ambient air pollution and incidence of postmenopausal breast cancer in European women. https://doi.org/10.1289/EHP1742

ObjectivesOur aim was to study the possible associations between exposure to elevated levels of air pollution, ozone (O3) and vehicle exhaust (NOx), during early gestation, and adverse pregnancy outcomes such as pre-eclampsia, preterm birth and small for gestational age.DesignProspective register-based cohort study.SettingThe Swedish Medical Birth Register includes data on all deliveries during 1998 to 2006 in Greater Stockholm, Sweden. The national Patient Register and the Prescribed Drug Register were used to collect information on maternal asthma.ParticipantsAll singleton pregnancies, conceived at the earliest in August 1997 and at the latest in February 2006, were included, n=120 755.Outcome measuresWe studied preterm birth, small for gestational age and pre-eclampsia.Results4.4% of pregnancies resulted in a preterm birth. The prevalence of pre-eclampsia was 2.7%. We observed an association between first trimester O3 and preterm birth (OR 1.04, 95% CI 1.01 to 1.08) as well as an association with pre-eclampsia (OR 1.04, 95% CI 1.01 to 1.08), per 10 µg/m3 increase in O3. We observed no association between first trimester NOx and adverse pregnancy outcomes. No associations were observed between any of the air pollutants and small for gestational age.ConclusionsIncreased levels of O3 during the first trimester increased the risk of pre-eclampsia and preterm birth. Air pollutants did not exhibit any effects on fetal growth restriction. We estimated 1 in every 20 cases of pre-eclampsia to be associated with O3 exposure.

Background: Land use regression (LUR) models have been developed mostly to explain intraurban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area.Objectives: We aimed to develop European and regional LUR models and to examine their transferability to areas not used for model development.Methods: We evaluated LUR models for nitrogen dioxide (NO2) and particulate matter (PM; PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE (European Study of Cohorts for Air Pollution Effects) study areas across 14 European countries for PM and NO2. Models were evaluated with cross-validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building.Results: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5, and 70% for PM2.5 absorbance. The HV R2s were only slightly lower than the model R2 (NO2, 54%; PM2.5, 80%; PM2.5 absorbance, 70%). The European NO2, PM2.5, and PM2.5 absorbance models explained a median of 59%, 48%, and 70% of within-area variability in individual areas. The transferred models predicted a modest-to-large fraction of variability in areas that were excluded from model building (median R2: NO2, 59%; PM2.5, 42%; PM2.5 absorbance, 67%).Conclusions: Using a large data set from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted.Citation: Wang M, Beelen R, Bellander T, Birk M, Cesaroni G, Cirach M, Cyrys J, de Hoogh K, Declercq C, Dimakopoulou K, Eeftens M, Eriksen KT, Forastiere F, Galassi C, Grivas G, Heinrich J, Hoffmann B, Ineichen A, Korek M, Lanki T, Lindley S, Modig L, Mölter A, Nafstad P, Nieuwenhuijsen MJ, Nystad W, Olsson D, Raaschou-Nielsen O, Ragettli M, Ranzi A, Stempfelet M, Sugiri D, Tsai MY, Udvardy O, Varró MJ, Vienneau D, Weinmayr G, Wolf K, Yli-Tuomi T, Hoek G, Brunekreef B. 2014. Performance of multi-city land use regression models for nitrogen dioxide and fine particles. Environ Health Perspect 122:843–849; http://dx.doi.org/10.1289/ehp.1307271