Int J Cancer

Nuclear power plants (NPPs) release toxic emissions into the environment that may affect neighboring populations. This ecologic study was designed to investigate the possibility of an excess incidence of cancer in the vicinity of French NPPs by examining the incidence by municipality of 12 types of cancer in the population aged 15 years and older during the 1995-2011 period. Population exposure to pollution was estimated on the basis of distance from towns of residence to the NPP. Using regression models, we assessed the risk of cancer in a 20-km zone around NPPs and observed an excess incidence of bladder cancer (Relative Risk (RR), 95% Credibility Interval (95% CI)) in men and women (RRmen = 1.08; 95% CI: 1.00, 1.17 and RRwomen = 1.19; 95% CI: 1.02, 1.39). Women living within the 20-km proximity areas had a significantly reduced risk of thyroid cancer (RRwomen = 0.86; 95% CI: 0.77, 0.96). No excess risk of hematologic malignancies in either sex was seen. The higher than expected incidence of bladder cancer may be due to an excess incidence localized around the Flamanville NPP and the nearby La Hague nuclear waste treatment center, which is a source of chemical contaminants, many (including arsenic) of them known risk factors for bladder cancer. Differences in medical practices could explain the reduced risk of thyroid cancer. In this first study of adults living near NPPs in France, cancer incidence is significantly higher than in the references populations for one of the cancer types studied: bladder cancer

Long-term air pollution exposure is associated with increased severity of rhinitis in 2 European cohorts

Background: Very few studies have examined the association between long-term outdoor air pollution and rhinitis severity in adults. Objective: We sought to assess the cross-sectional association between individual long-term exposure to air pollution and severity of rhinitis. Methods: Participants with rhinitis from 2 multicenter European cohorts (Epidemiological Study on the Genetics and Environment on Asthma and the European Community Respiratory Health Survey) were included. Annual exposure to NO2, PM10, PM2.5, and PMcoarse (calculated by subtracting PM2.5 from PM10) was estimated using land-use regression models derived from the European Study of Cohorts for Air Pollution Effects project, at the participants' residential address. The score of rhinitis severity (range, 0-12), based on intensity of disturbance due to symptoms reported by questionnaire, was categorized into low (reference), mild, moderate, and high severity. Polytomous logistic regression models with a random intercept for city were used. Results: A total of 1408 adults with rhinitis (mean age, 52 years; 46% men, 81% from the European Community Respiratory Health Survey) were included. The median (1st quartile-3rd quartile) score of rhinitis severity was 4 (2-6). Higher exposure to PM10 was associated with higher rhinitis severity (adjusted odds ratio [95% CI] for a 10 μg/m3 increase in PM10: for mild: 1.20 [0.88-1.64], moderate: 1.53 [1.07-2.19], and high severity: 1.72 [1.23-2.41]). Similar results were found for PM2.5. Higher exposure to NO2 was associated with an increased severity of rhinitis, with similar adjusted odds ratios whatever the level of severity. Adjusted odds ratios were higher among participants without allergic sensitization than among those with, but interaction was found only for NO2. CONCLUSIONS: People with rhinitis who live in areas with higher levels of pollution are more likely to report more severe nasal symptoms. Further work is required to elucidate the mechanisms of this association.The following bodies funded the local studies in ECRHS III in this article: Belgium: Antwerp South, Antwerp City: Research Foundation Flanders (FWO), grant code G.0.410.08.N.10 (both sites); France: Ministère de la Santé , Programme Hospitalier de Recherche Clinique (PHRC) national 2010; Germany: Erfurt: German Research Foundation ( HE 3294/10-1 ); Spain: Fondo de Investigación Sanitaria ( PS09/02457 , PS09/00716 09/01511 , PS09/02185 , and PS09/03190 ), Servicio Andaluz de Salud, Sociedad Española de Neumología y Cirurgía Torácica ( SEPAR 1001/2010 ); Barcelona: Fondo de Investigación Sanitaria ( FIS PS09/00716 ); Galdakao: Fondo de Investigación Sanitaria ( FIS 09/01511 ); Huelva: Fondo de Investigación Sanitaria ( FIS PS09/02185 ) and Servicio Andaluz de Salud ; Oviedo: Fondo de Investigación Sanitaria ( FIS PS09/03190 ); United Kingdom: Medical Research Council (grant no. 92091 ). The Epidemiological Study on the Genetics and Environment on Asthma is funded in part by PHRC-Paris, PHRC-Grenoble, ANR 05-SEST-020-02/05-9-97, ANR-06-CEBS, ANR-CES-2009, Région Nord Pas-de-Calais, and Merck Sharp & Dohme. European Study of Cohorts for Air Pollution Effects Funding: The research leading to these results has received funding from the European Community’s Seventh Framework Program ( FP7/2007-2011 ; under grant agreement no. 211250

Spatial variations and development of land use regression models of oxidative potential in ten European study areas

Oxidative potential (OP) has been suggested as a health-relevant measure of air pollution. Little information is available about OP spatial variation and the possibility to model its spatial variability. Our aim was to measure the spatial variation of OP within and between 10 European study areas. The second aim was to develop land use regression (LUR) models to explain the measured spatial variation. OP was determined with the dithiothreitol (DTT) assay in ten European study areas. DTT of PM2.5 was measured at 16–40 sites per study area, divided over street, urban and regional background sites. Three two-week samples were taken per site in a one-year period in three different seasons. We developed study-area specific LUR models and a LUR model for all study areas combined to explain the spatial variation of OP. Significant contrasts between study areas in OP were found. OP DTT levels were highest in southern Europe. DTT levels at street sites were on average 1.10 times higher than at urban background locations. In 5 of the 10 study areas LUR models could be developed with a median R2 of 33%. A combined study area model explained 30% of the measured spatial variability. Overall, LUR models did not explain spatial variation well, possibly due to low levels of OP DTT and a lack of specific predictor variables. © 2016 Elsevier Lt

Association between air pollution and rhinitis incidence in two European cohorts

Background: The association between air pollution and rhinitis is not well established. Aim: The aim of this longitudinal analysis was to study the association between modeled air pollution at the subjects' home addresses and self-reported incidence of rhinitis. Methods: We used data from 1533 adults from two multicentre cohorts' studies (EGEA and ECRHS). Rhinitis incidence was defined as reporting rhinitis at the second follow-up (2011 to 2013) but not at the first follow-up (2000 to 2007). Annual exposure to NO2, PM10 and PM2.5 at the participants' home addresses was estimated using land-use regression models developed by the ESCAPE project for the 2009–2010 period. Incidence rate ratios (IRR) were computed using Poisson regression. Pooled analysis, analyses by city and meta-regression testing for heterogeneity were carried out. Results: No association between long-term air pollution exposure and incidence of rhinitis was found (adjusted IRR (aIRR) for an increase of 10 μg·m−3 of NO2: 1.00 [0.91–1.09], for an increase of 5 μg·m−3 of PM2.5: 0.88 [0.73–1.04]). Similar results were found in the two-pollutant model (aIRR for an increase of 10 μg·m−3 of NO2: 1.01 [0.87–1.17], for an increase of 5 μg·m−3 of PM2.5: 0.87 [0.68–1.08]). Results differed depending on the city, but no regional pattern emerged for any of the pollutants. Conclusions: This study did not find any consistent evidence of an association between long-term air pollution and incident rhinitis

Spatial variations and development of land use regression models of oxidative potential in ten European study areas

Oxidative potential (OP) has been suggested as a health-relevant measure of air pollution. Little information is available about OP spatial variation and the possibility to model its spatial variability. Our aim was to measure the spatial variation of OP within and between 10 European study areas. The second aim was to develop land use regression (LUR) models to explain the measured spatial variation. OP was determined with the dithiothreitol (DTT) assay in ten European study areas. DTT of PM2.5 was measured at 16–40 sites per study area, divided over street, urban and regional background sites. Three two-week samples were taken per site in a one-year period in three different seasons. We developed study-area specific LUR models and a LUR model for all study areas combined to explain the spatial variation of OP. Significant contrasts between study areas in OP were found. OP DTT levels were highest in southern Europe. DTT levels at street sites were on average 1.10 times higher than at urban background locations. In 5 of the 10 study areas LUR models could be developed with a median R2 of 33%. A combined study area model explained 30% of the measured spatial variability. Overall, LUR models did not explain spatial variation well, possibly due to low levels of OP DTT and a lack of specific predictor variables. © 2016 Elsevier Lt

The heat and health in cities (H2C) project to support the prevention of extreme heat in cities

International audienceThe main goal of the Heat and Health in Cities (H2C) French project is to improve the urban climate services to support preventive actions and policies to deal with the risk of heat and extreme heat in cities, with the Paris region (France) as a case study. In response, the three scientific objectives are identified: • Improve our understanding of the impacts of urban covers and surface heterogeneities in the city on UHI, local meteorology and air quality. • Strengthen the synergy between numerical modeling and multi-source observations to study the environmental phenomena in detail, and assess the spatial and temporal variations in population exposure to heat and air pollution, and the associated health risks. • Develop and provide information that is useful for decision-making, in terms of UHI assessment, consequences on exposure in both outdoor and indoor environment, associated health impacts, and opportunities for prevention

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas: The ESCAPE project

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 R2s 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 R2 and HEV R2 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 R2s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites. © 2013 American Chemical Society

Performance of multi-city land use regression models for nitrogen dioxide and fine particles

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

Performance of multi-city land use regression models for nitrogen dioxide and fine particles.

BACKGROUND: Land use regression (LUR) models have mostly been developed to explain intra-urban 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: 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 (PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE 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 R(2)s were only slightly lower than the model R(2) (NO2: 54%, PM2.5: 80%, 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 which were excluded from model building (median R(2): 59% NO2; 42% PM2.5; 67% PM2.5 absorbance). CONCLUSIONS: Using a large dataset 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

Development of West-European PM_2.5 and NO₂ land use regression models incorporating satellite-derived and chemical transport modelling data.

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR(2): 0.33-0.38). For NO2 CTM improved prediction modestly (adjR(2): 0.58) compared to models without SAT and CTM (adjR(2): 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies