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Development of an operational model for risk assessment in case of environmental and food chain contamination from dioxins.

2016; National Institute of Environmental Health Sciences; Volume: 2016; Issue: 1 Linguagem: Inglês

10.1289/isee.2016.4352

2169-2181

Pietro Salizzoni, Massimo Marro, Ubaldo Natangelo, Rosanna Desiato, Elisa Baioni, Ivana Bottazzi, Simona Possamai, Giuseppe Ru,

Environmental and Social Impact Assessments

Introduction. Predictive models of the transfer of pollutants over different environmental compartments and to food chain are extremely useful to inform exposure assessment and risk characterization in case of environmental contamination of dioxins. Aim of this work was to develop and validate a new operational model to estimate the dioxins and furans (PCDD/Fs) and dioxin-like polychlorinated byphenils (DL-PCBs) concentrations in bovine bulk milk in contaminated areas. Methods. The model, named MILC, was implemented in a Gnumeric spreadsheet and is based on parameterisations of the mass balance in the different environmental media and of the bio-transfer towards the food chain. The model integrates two modules: (a) one simulating the deposition of pollutants from air to soil and plants; (b) one quantifying the bio-transfer in bovine milk. The model evaluates the uncertainty of the results with a Monte Carlo approach, i.e. considering input data as random variables characterised by given probability density function. Results. The model was validated against data collected from 2004 to 2012 in four different sites surrounding a sinter plant. These involved pollutant soil deposition, feed (6 samples), bulk milk (24 samples collected between 2004 and 2012 from two farms) and soil (51 samples). Comparison with experimental data showed that the model is able to predict the temporal evolution of the measured concentrations of dioxin in milk, and in particular the reduction of concentrations observed over the last eight years, as a result of the decreasing intensity of the emitting sources. Conclusions. We developed a new model for the assessment of health-risk related to dioxin contamination of air, vegetation and soil. The model, validated against experimental data collected in-situ, was shown to be a reliable operational tool that can be used by public authorities and risk managers to devise and inform appropriate mitigation measures.