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Sainte-Foy-lès-Lyon, France

Morelli X.,French Institute of Health and Medical Research | Morelli X.,University Grenoble alpes | Foraster M.,Center for Research in Environmental Epidemiology | Foraster M.,University Pompeu Fabra | And 21 more authors.
Atmospheric Environment | Year: 2015

Outdoor noise and particulate matter concentration share common sources, including road traffic in urban areas, raising the potential for mutual confounding in epidemiological studies of their health effects. While some studies evaluated their long-term correlation, little is known about their short-term correlation. Our aim was to study the correlation of short-term noise, ultrafine (<0.1μm) particulate matter number concentration (UFP), and traffic flow in urban areas. A secondary aim was to document the temporal variability of these short-term measurements. We simultaneously measured traffic noise levels, UFP concentrations as well as motor vehicles' flows for 20min in 141 locations, on one to three occasions, in three middle size European cities (Basel, Girona, Grenoble). The reproducibility of the short-term noise measurements and traffic counts over time was high, as reported by the intraclass correlation coefficient (ICC), which quantified the agreement between repeated measurements (ICC=0.86-0.97, according to city, for noise and ICC=0.93-0.94 for traffic counts); this was not the case for UFP number concentrations (ICC=-0.11 to 0.14). The Pearson correlations of simultaneous 20-minmeasurements of UFP number concentrations and noise levels were in the 0.43-0.55 range, depending on the city; correlations between noise levels and vehicle counts varied from 0.54 to 0.72; and correlations between UFP concentrations and vehicle counts were lower (r=0.15-0.37 depending on the city). Measurements during as little time as 20min of outdoor noise and traffic, but not of UFP, were strongly reproducible over durations of a couple of days or months in middle-size European cities. In these areas, on the short-term, noise levels and UFP concentrations exhibited relatively moderate correlations, which may allow adjustment for mutual confounding in epidemiological studies, thus allowing to disentangle their possible short-term health effects. © 2014 Elsevier Ltd. Source


Morelli X.,French Institute of Health and Medical Research | Rieux C.,Air Rhone Alpes | Cyrys J.,Helmholtz Center for Environmental Research | Forsberg B.,Umea University | Slama R.,French Institute of Health and Medical Research
Environmental Research | Year: 2016

Risk assessment studies often ignore within-city variations of air pollutants. Our objective was to quantify the risk associated with fine particulate matter (PM2.5) exposure in 2 urban areas using fine-scale air pollution modeling and to characterize how this risk varied according to social deprivation. In Grenoble and Lyon areas (0.4 and 1.2 million inhabitants, respectively) in 2012, PM2.5 exposure was estimated on a 10×10 m grid by coupling a dispersion model to population density. Outcomes were mortality, lung cancer and term low birth weight incidences. Cases attributable to air pollution were estimated overall and stratifying areas according to the European Deprivation Index (EDI), taking 10 μg/m3 yearly average as reference (counterfactual) level. Estimations were repeated assuming spatial homogeneity of air pollutants within urban area. Median PM2.5 levels were 18.1 and 19.6 μg/m3 in Grenoble and Lyon urban areas, respectively, corresponding to 114 (5.1% of total, 95% confidence interval, CI, 3.2-7.0%) and 491 non-accidental deaths (6.0% of total, 95% CI 3.7-8.3%) attributable to long-term exposure to PM2.5, respectively. Attributable term low birth weight cases represented 23.6% of total cases (9.0-37.1%) in Grenoble and 27.6% of cases (10.7-42.6%) in Lyon. In Grenoble, 6.8% of incident lung cancer cases were attributable to air pollution (95% CI 3.1-10.1%). Risk was lower by 8 to 20% when estimating exposure through background stations. Risk was highest in neighborhoods with intermediate to higher social deprivation. Risk assessment studies relying on background stations to estimate air pollution levels may underestimate the attributable risk. © 2016 Elsevier Inc. Source


Aguilera I.,Swiss Tropical and Public Health Institute | Aguilera I.,University of Basel | Foraster M.,Center for Research in Environmental Epidemiology | Foraster M.,CIBER ISCIII | And 18 more authors.
Journal of Exposure Science and Environmental Epidemiology | Year: 2015

Noise prediction models and noise maps are used to estimate the exposure to road traffic noise, but their availability and the quality of the noise estimates is sometimes limited. This paper explores the application of land use regression (LUR) modelling to assess the long-term intraurban spatial variability of road traffic noise in three European cities. Short-term measurements of road traffic noise taken in Basel, Switzerland (n=60), Girona, Spain (n=40), and Grenoble, France (n=41), were used to develop two LUR models: (a) a "GIS-only" model, which considered only predictor variables derived with Geographic Information Systems; and (b) a "Best" model, which in addition considered the variables collected while visiting the measurement sites. Both noise measurements and noise estimates from LUR models were compared with noise estimates from standard noise models developed for each city by the local authorities. Model performance (adjusted R 2) was 0.66-0.87 for "GIS-only" models, and 0.70-0.89 for "Best" models. Short-term noise measurements showed a high correlation (r=0.62-0.78) with noise estimates from the standard noise models. LUR noise estimates did not show any systematic differences in the spatial patterns when compared with those from standard noise models. LUR modelling with accurate GIS source data can be a promising tool for noise exposure assessment with applications in epidemiological studies. © 2015 Nature America, Inc. All rights reserved. Source


Jacquemin B.,French Institute of Health and Medical Research | Jacquemin B.,University Paris - Sud | Lepeule J.,French Institute of Health and Medical Research | Lepeule J.,Joseph Fourier University | And 28 more authors.
Environmental Health Perspectives | Year: 2013

Background: Errors in address geocodes may affect estimates of the effects of air pollution on health. Objective: We investigated the impact of four geocoding techniques on the association between urban air pollution estimated with a fine-scale (10 m × 10 m) dispersion model and lung function in adults. Methods: We measured forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in 354 adult residents of Grenoble, France, who were participants in two well-characterized studies, the Epidemiological Study on the Genetics and Environment on Asthma (EGEA) and the European Community Respiratory Health Survey (ECRHS). Home addresses were geocoded using individual building matching as the reference approach and three spatial interpolation approaches. We used a dispersion model to estimate mean PM10 and nitrogen dioxide concentrations at each participant's address during the 12 months preceding their lung function measurements. Associations between exposures and lung function parameters were adjusted for individual confounders and same-day exposure to air pollutants. The geocoding techniques were compared with regard to geographical distances between coordinates, exposure estimates, and associations between the estimated exposures and health effects. Results: Median distances between coordinates estimated using the building matching and the three interpolation techniques were 26.4, 27.9, and 35.6 m. Compared with exposure estimates based on building matching, PM10 concentrations based on the three interpolation techniques tended to be overestimated. When building matching was used to estimate exposures, a one-interquartile range increase in PM10 (3.0 μg/m3) was associated with a 3.72-point decrease in FVC% predicted (95% CI: -0.56, -6.88) and a 3.86-point decrease in FEV1% predicted (95% CI: -0.14, -3.24). The magnitude of associations decreased when other geocoding approaches were used [e.g., for FVC% predicted -2.81 (95% CI: -0.26, -5.35) using NavTEQ, or 2.08 (95% CI -4.63, 0.47, p = 0.11) using Google Maps]. Conclusions: Our findings suggest that the choice of geocoding technique may influence estimated health effects when air pollution exposures are estimated using a fine-scale exposure model. Source


Bentayeb M.,French Institute for Public Health Surveillance InVS | Stempfelet M.,French Institute for Public Health Surveillance InVS | Wagner V.,French Institute for Public Health Surveillance InVS | Zins M.,University of Versailles | And 14 more authors.
Atmospheric Environment | Year: 2014

Introduction: Exposure to air pollution has been associated to mortality and morbidity in numerous studies. However, few studies assessed retrospectively long-term exposure at a fine spatial scale. Aims: To contribute to the assessment of long-term exposure to air pollution of participants from the French GAZEL cohort, we estimated atmospheric PM10, PM2.5, NO2, SO2, C6H6 and O3 levels at 2km resolution over France, from 1989 to 2008. Methods: The spatiotemporal concentrations of selected air pollutants were estimated at a fine scale by combining (1) the CHIMERE chemistry-transport model (2) mesh refinement and (3) data assimilation with geostatistical analyzes. Assimilated concentrations were assigned to participants according to their residential zip codes, taking into account residential history. Results: Despite a decreasing trend in concentrations for all pollutant concentrations, levels remained high in some French regions, especially for PM, NO2 and O3.Annual median concentrations at the cohort participants' zip code of PM10, PM2.5, NO2 and O3 were decreased from 1989 to 2008 by 27%, 29%, 40% and 16%, respectively. The largest decreases occurred for SO2 (86%) and C6H6 (85%).Validation showed high correlations between observations and final modeled data (R above 0.75 in 2007) for PM10, NO2 and O3. Conclusion: The modeling process enabled us to assess air pollution over 20 years (1989-2008) at a fine-geographical scale, with acceptable agreement being found between observations and models for all pollutants. © 2014 Elsevier Ltd. Source

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