title: Applicability of a neuroprobabilistic integral risk index for the environmental management of polluted areas: a case study.
abstract: Recently, we developed a GIS-Integrated Integral Risk Index (IRI) to assess human health risks in areas with presence of environmental pollutants. Contaminants were previously ranked by applying a self-organizing map (SOM) to their characteristics of persistence, bioaccumulation, and toxicity in order to obtain the Hazard Index (HI). In the present study, the original IRI was substantially improved by allowing the entrance of probabilistic data. A neuroprobabilistic HI was developed by combining SOM and Monte Carlo analysis. In general terms, the deterministic and probabilistic HIs followed a similar pattern: polychlorinated biphenyls (PCBs) and light polycyclic aromatic hydrocarbons (PAHs) were the pollutants showing the highest and lowest values of HI, respectively. However, the bioaccumulation value of heavy metals notably increased after considering a probability density function to explain the bioaccumulation factor. To check its applicability, a case study was investigated. The probabilistic integral risk was calculated in the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain), where an environmental program has been carried out since 2002. The risk change between 2002 and 2005 was evaluated on the basis of probabilistic data of the levels of various pollutants in soils. The results indicated that the risk of the chemicals under study did not follow a homogeneous tendency. However, the current levels of pollution do not mean a relevant source of health risks for the local population. Moreover, the neuroprobabilistic HI seems to be an adequate tool to be taken into account in risk assessment processes.
authors.affiliation: Laboratory of Toxicology and Environmental Health, Rovira i Virgili University, Reus, Catalonia, Spain.
authors.forename: Martí
authors.initials: M
authors.lastname: Nadal
authors.forename: Vikas
authors.initials: V
authors.lastname: Kumar
authors.forename: Marta
authors.initials: M
authors.lastname: Schuhmacher
authors.forename: José L
authors.initials: JL
authors.lastname: Domingo
mesh_terms: D002617:Chemical Industry; D004147:Dioxins / Q000032:analysis / Q000737:chemistry / Q000145:classification; D004784:Environmental Monitoring / Q000379:methods*; D004785:Environmental Pollutants / Q000032:analysis*; D005663:Furans / Q000032:analysis / Q000737:chemistry; D006207:Half-Life; D006801:Humans; D019216:Metals, Heavy / Q000032:analysis; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D011336:Probability; D012306:Risk; D012989:Soil Pollutants / Q000032:analysis*; D013030:Spain
chemical_list: D004147:Dioxins; D004785:Environmental Pollutants; D005663:Furans; D019216:Metals, Heavy; D012989:Soil Pollutants