Éditeur(s) : HAL CCSD National Institute of Environmental Health Sciences
Résumé : International audience
BACKGROUND: Women of reproductive age can be exposed to endocrine-disrupting chemicals (EDCs) at work, and exposure to EDCs in pregnancy may affect fetal growth. OBJECTIVES: We assessed whether maternal occupational exposure to EDCs during pregnancy as classified by application of a job exposure matrix was associated with birth weight, term low birth weight (LBW), length of gestation, and preterm delivery. METHODS: Using individual participant data from 133,957 mother-child pairs in 13 European cohorts spanning births from 1994 through 2011, we linked maternal job titles with exposure to 10 EDC groups as assessed through a job exposure matrix. For each group, we combined the two levels of exposure categories (possible and probable) and compared birth outcomes with the unexposed group (exposure unlikely). We performed meta-analyses of cohort-specific estimates. RESULTS: Eleven percent of pregnant women were classified as exposed to EDCs at work during pregnancy, based on job title. Classification of exposure to one or more EDC group was associated with an increased risk of term LBW [odds ratio (OR) = 1.25; 95% CI: 1.04, 1.49], as were most specific EDC groups; this association was consistent across cohorts. Further, the risk increased with increasing number of EDC groups (OR = 2.11; 95% CI: 1.10, 4.06 for exposure to four or more EDC groups). There were few associations (p < 0.05) with the other outcomes; women holding job titles classified as exposed to bisphenol A or brominated flame retardants were at higher risk for longer length of gestation. CONCLUSION: Results from our large population-based birth cohort design indicate that employment during pregnancy in occupations classified as possibly or probably exposed to EDCs was associated with an increased risk of term LBW.
ISSN: 0091-6765

hal-01405823
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01405823
DOI : 10.1289/EHP208
PUBMED : 27152464

Éditeur(s) : HAL CCSD
Résumé : International audience
ABSTRACT :A satellite retrieval of surface solar irradiance (SSI) based on GOES satellite data is presented and validated for the French Guiana with three years of in situ measurements from six ground stations. We propose a particle filter approach combining GOES satellite observations and in situ data. We propose an original observation function based on a joint probability distribution and taking advantages of the characteristics of both the involved type of data. The statistical results are compared with those of existing estimation methods and they are found to be in accordance with them. The daily bias ranges from -12 W/m² (-6% of the mean of measurements) to 12 W/m² (6%), depending on the stations. The daily root mean square difference ranges between 23 W/m² (10%) and 27 W/m² (15%). The correlation coefficient is close to 0.92. There is no seasonal climatic effect on the method, the bias and RMSD remain similar for both the rainy and the dry season, but better correlation is observed in the rainy season than in the dry season. Uncertainties are mainly due to the presence of specific tropical atmospheric conditions during the dry season. The method has been tested through various tests: cross-validation scheme has highlighted the potential generalization of the model in a temporal dimension; the ability of the method to effectively duplicate anomalies in surface solar irradiance time series has also been tested. The robustness of the results shows the interest of the method compared to other existing estimation methods. Because of its simplicity of implementation this method opens new prospects in estimating surface solar irradiance. It is concluded that using a particle filter and original observation function method allow a high-quality surface solar irradiance estimation in French Guiana.RESUMEUne méthode d'estimation de l'irradiance solaire de surface (SSI) à partir de données satellites GOES est présentée et validée par comparaison avec trois années de mesures in situ provenant de six stations météorologiques de Guyane Française. Dans cet article, nous proposons une approche par filtre particulaire combinant observations du satellite GOES et données in situ. Nous proposons une fonction d'observation originale basée sur une distribution de probabilité conjointe des deux types de données. Les résultats statistiques sont conformes à ceux des méthodes existantes. Le biais journalier varie de -16 W / m² (-7% de la moyenne des mesures) à 12 W / m² (6%). L'erreur quadratique moyenne (RMSD) est comprise entre 23 W / m² (10%) et 27 W / m² (15%). Le coefficient de corrélation est de 0,92. La méthode est peu sensible aux variations climatiques saisonnières, le biais et RMSD restent similaires en saison des pluies comme en saison sèche, mais une meilleure corrélation est observée pendant la saison des pluies. Les incertitudes sont principalement dues à la présence de conditions atmosphériques tropicales spécifiques en saison sèche. La méthode a été soumise à différents tests : un processus de validation croisée a mis en évidence la possible généralisation du modèle dans une dimension temporelle, la capacité du modèle à reproduire efficacement des anomalies dans les séries temporelles d'irradiance solaire de surface a aussi été testée. La robustesse des résultats obtenus montrent l'intérêt du modèle par rapport aux autres méthodes d'estimation existantes. A cause de sa simplicité de mise en oeuvre cette méthode ouvre de nouvelles perspectives en matière d'estimation de l'irradiance solaire au sol. Nous concluons que l'utilisation d'un filtre particulaire combinée à une fonction d'observation originale permet une estimation de haute qualité de l'irradiance solaire au sol en Guyane Française.
Canadian Journal of Remote Sensing

meteo-01304574
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304574
DOI : 10.1080/07038992.2015.1040150

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.
ISSN: 2072-4292

meteo-01304579
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304579
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304579/document
https://hal-meteofrance.archives-ouvertes.fr/meteo-01304579/file/remotesensing-07-15831.pdf
DOI : 10.3390/rs71215831