Exposure characterization is a central step in Ecological Risk Assessment (ERA). Exposure level is a function of the spatial factors linking contaminants and receptors, yet exposure estimation models are traditionally non-spatial. Non-spatial models are prone to the adverse effects of spatial dependence: inflated variance and biased inferential procedures, which can result in unreliable and potentially misleading models. Such negative effects can be amended by spatial regression modelling: we propose an integration of geostatistics and multivariate spatial regression to compute efficient spatial regression parameters and to characterize exposure at under-sampled locations. The method is applied to estimate bioaccumulation models of organic and inorganic micropollutants in the tissues of the clam Tapes philipinarum. The models link bioaccumulation of micropollutants in clam tissue to a set of environmental variables sampled in the lagoon sediment. The Venetian lagoon case study exemplifies the problem of multiple variables sampled at different locations or spatial units: we propose and test an effective solution to this common and serious problem in environmental as well as socio-economic multivariate analysis.
|Data di pubblicazione:||2006|
|Titolo:||Spatial analysis of the bioaccumulation of distributed pollutants in clams Tapes philipinarum: the Venetian Lagoon (Italy)|
|Rivista:||COMPUTERS, ENVIRONMENT AND URBAN SYSTEMS|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.compenvurbsys.2005.09.003|
|Appare nelle tipologie:||2.1 Articolo su rivista |
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