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Desert dust transported from the Saharan-Sahel region to the Caribbean Sea is responsible for peak exposures of particulate matter (PM). This study explored the potential added value of satellite aerosol optical thickness (AOT) measurements, compared to the PM concentration at ground level, to retrospectively assess exposure during pregnancy. MAIAC MODIS AOT retrievals in blue band (AOT 470 ) were extracted for the French Guadeloupe archipelago. AOT 470 values and PM 10 concentrations were averaged over pregnancy for 906 women (2005-2008). Regression modeling was used to examine the AOT 470 -PM 10 relationship during pregnancy and test the association between dust exposure estimates and preterm birth. Moderate agreement was shown between mean AOT 470 retrievals and PM 10 ground-based measurements during pregnancy (R 2  = 0.289). The magnitude of the association between desert dust exposure and preterm birth tended to be lower using the satellite method compared to the monitor method. The latter remains an acceptable trade-off between epidemiological relevance and exposure misclassification, in areas with few monitoring stations and complex topographical/meteorological conditions, such as tropical islands.

The cyanobacteria-derived neurotoxin β-methylamino-L-alanine (BMAA) represents a plausible environmental trigger for amyotrophic lateral sclerosis (ALS), a debilitating and fatal neuromuscular disease. With the eutrophication of water bodies, cyanobacterial blooms and their toxins are becoming increasingly prevalent in France, especially in the Brittany region. Cyanobacteria are monitored at only a few recreational sites, preventing an estimation of exposure of the human population. By contrast, phosphorus, a limiting nutrient for cyanobacterial growth and thus considered a good proxy for cyanobacteria exposure, is monitored in many but not all surface water bodies. Our goal was to develop a geographic exposure indicator that could be used in epidemiological research. We considered the total phosphorus (TP) concentration (mg/L) of samples collected between October 2007 and September 2012 at 179 monitoring stations distributed throughout the Brittany region. Using readily available spatial data, we computed environmental descriptors at the watershed level with a Geographic Information System. Then, these descriptors were introduced into a backward stepwise linear regression model to predict the median TP concentration in unmonitored surface water bodies. TP concentrations in surface water follow an increasing gradient from West to East and inland to coast. The empirical concentration model included five predictor variables with a fair coefficient of determination (R(2) = 0.51). The specific total runoff and the watershed slope correlated negatively with the TP concentrations (p = 0.01 and p< 10(-9), respectively), whereas positive associations were found for the proportion of built-up area, the upstream presence of sewage treatment plants, and the algae volume as indicated by the Landsat red/green reflectance ratio (p < 0.01, p < 10(-6) and p < 0.01, respectively). Complementing the monitoring networks, this geographical modeling can help estimate TP concentrations at the watershed level, delivering a proxy for cyanobacteria exposure that can be used along with other risk factors in further ALS epidemiologic case-control studies., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Background: Climate change poses unprecedented challenges, ranging from global and local policy challenges to personal and social action. Heat-related deaths are largely preventable, but interventions for the most vulnerable populations need improvement. Therefore, the prior identification of high risk areas at the community level is required to better inform planning and prevention. We aimed to demonstrate a simple and flexible conceptual framework relying upon satellite thermal data and other digital data with the goal of easily reproducing this framework in a variety of urban configurations., Results: The study area encompasses Rennes, a medium-sized French city. A Landsat ETM + image (60 m resolution) acquired during a localized heatwave (June 2001) was used to estimate land surface temperature (LST) and derive a hazard index. A land-use regression model was performed to predict the LST. Vulnerability was assessed through census data describing four dimensions (socio-economic status, extreme age, population density and building obsolescence). Then, hazard and vulnerability indices were combined to deliver a heatwave health risk index. The LST patterns were quite heterogeneous, reflecting the land cover mosaic inside the city boundary, with hotspots of elevated temperature mainly observed in the city center. A spatial error regression model was highly predictive of the spatial variation in the LST (R² = 0.87) and was parsimonious. Three land cover descriptors (NDVI, vegetation and water fractions) were negatively linked with the LST. A sensitivity analysis (based on an image acquired on July 2000) yielded similar results. Southern areas exhibited the most vulnerability, although some pockets of higher vulnerability were observed northeast and west of the city. The heatwave health risk map showed evidence of infra-city spatial clustering, with the highest risks observed in a north-south central band. Another sensitivity analysis gave a very high correlation between 2000 and 2001 risk indices (r = 0.98, p < 10⁻¹²)., Conclusions: Building on previous work, we developed a reproducible method that can provide guidance for local planners in developing more efficient climate impact adaptations. We recommend, however, using the health risk index together with hazard and vulnerability indices to implement tailored programs because exposure to heat and vulnerability do not require the same prevention strategies.

Recent studies have suggested that minority or deprived groups are subject to the additional burden of a polluted living environment. Our goal is to determine whether such environmental inequalities occur in France's leading industrial region, using detailed socio-economic data and advanced Bayesian methods. Associations between proximity to hazardous facilities (i.e., within a 2 km radius) and the socio-economic characteristics of populations are analyzed at fine geographical scales. Noxious facilities are disproportionately located in higher foreign-born communities after controlling for deprivation (Townsend score), population density and rural/urban status. High deprivation also appears as a predictive factor, although less strongly and less consistently., (Copyright © 2010 Elsevier Ltd. All rights reserved.)