Predicting the regional contamination evolution of DDT for 100-years with a new gridded spatial and dynamic multimedia fate model.

In 1996 high dichlorodiphenyltrichloroethane (DDT) concentrations were found in Lake Maggiore (Italy) fish and sediments. DDT was produced by a chemical company located in a subalpine valley (Ossola valley, Piedmont Region, Italy), and ended up in the Toce River, a tributary of Lake Maggiore. In the area surrounding the chemical plant, high DDT concentrations in soil and vegetation were found after subsequent investigations. The quantification of the release from contaminated soil and the following migration toward downwind areas, deposition to the soil, and further evaporation plays an important role in understanding future DDT trends in soil and the atmosphere. To study this phenomenon, soil, and vegetation from Ossola Valley were monitored in 2001 and 2011. The concentration values obtained (soils: 0.05 to 1 μg/g; vegetation 2-100 ng/g), allowed to reconstruct the contamination gradient in the valley and were used to develop and calibrate a spatially resolved multimedia fugacity model. The model accounts for spatial and temporal dynamicity of environmental characteristics such as wind speed and direction, variable air compartment height etc., and simulates the fate and transport of chemicals on a local scale. The dynamic emission of DDT (about 13,000 kg for the 50 y production time) to the air was estimated and utilized for a 100-year simulation (from 1948 to 2048). The results obtained allowed to understand the temporal and spatial pattern of DDT contamination for a long period at a local scale as well as the potential contribution as a source potentially affecting sites at larger distances. Copyright © 2022 Elsevier B.V. All rights reserved.

Citation

Parisa Falakdin, Elisa Terzaghi, Giuseppe Raspa, Antonio Di Guardo. Predicting the regional contamination evolution of DDT for 100-years with a new gridded spatial and dynamic multimedia fate model. The Science of the total environment. 2022 Nov 01;845:157190

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PMID: 35839893

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