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<title lang=en>Geographical modeling of exposure risk to cyanobacteria for epidemiological purposes</title>
<creator>Serrano, Tania</creator>
<creator>Dupas, Rémi</creator>
<creator>Upegui, Erika</creator>
<creator>Buscail, Camille</creator>
<creator>Grimaldi, Catherine</creator>
<creator>Viel, Jean François</creator>
<contributor>Institut de recherche, santé, environnement et travail [Rennes] (Irset) ; Université d'Angers (UA) - Université des Antilles et de la Guyane (UAG) - Université de Rennes 1 (UR1) - École des Hautes Études en Santé Publique [EHESP] (EHESP) - Institut National de la Santé et de la Recherche Médicale (INSERM) - Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )</contributor>
<contributor>Sol Agro et hydrosystème Spatialisation (SAS) ; Institut National de la Recherche Agronomique (INRA) - AGROCAMPUS OUEST</contributor>
<contributor>Laboratoire Chrono-environnement (LCE) ; Université Bourgogne Franche-Comté (UBFC) - Centre National de la Recherche Scientifique (CNRS) - Université de Franche-Comté (UFC)</contributor>
<contributor>Département d'Information Médicale ; Université de Rennes 1 (UR1) - Centre Hospitalier Universitaire de Rennes</contributor>
<description>International audience</description>
<source>ISSN: 0160-4120</source>
<source>Environment International</source>
<publisher>Elsevier</publisher>
<identifier>hal-01175538</identifier>
<identifier>https://hal-univ-rennes1.archives-ouvertes.fr/hal-01175538</identifier>
<source>https://hal-univ-rennes1.archives-ouvertes.fr/hal-01175538</source>
<source>Environment International, Elsevier, 2015, 81, pp.18--25. 〈10.1016/j.envint.2015.04.007〉</source>
<identifier>DOI : 10.1016/j.envint.2015.04.007</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.envint.2015.04.007</relation>
<identifier>PUBMED : 25913322</identifier>
<relation>info:eu-repo/semantics/altIdentifier/pmid/25913322</relation>
<language>en</language>
<subject lang=en>Phosphorus concentration</subject>
<subject lang=en>GIS</subject>
<subject lang=en>Environmental descriptors</subject>
<subject lang=en>Cyanobacteria</subject>
<subject lang=en>Amyotrophic lateral sclerosis</subject>
<subject lang=en>Watershed</subject>
<subject>[SDV] Life Sciences [q-bio]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>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 (R2 = 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</description>
<date>2015</date>
</dc>
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