Integration of ecological and human PBPK exposure models and their application in exposure assessment to POPs in Venice lagoon

Abstract

The proposed PhD project focuses on an integration of ecological and human exposure models, and it is aimed at demonstrating the feasibility of linkage between environmental fate models, bioaccumulation models and human PBPK model on a common platform, integrating uncertainty, variability and sensitivity analysis to allow complete exposure analysis. The project main objective is the integration of multimedia models (MM) simulating the fate of chemicals in environmental media, and of physiologically based pharmacokinetic (PBPK) models simulating the fate of chemicals in human body using MERLIN-Expo in order to determine internal effective chemical concentrations. The project was developed within ‘4FUN’ EU-funded initiative aimed at the development of a unique tool called MERLIN-Expo designed to aid exposure assessment by serving as a platform enabling integration of human and ecological exposure models. The PhD project was developed for the transitional ecosystem of the Lagoon of Venice, affected by catchment densely populated area, industrial settlings, oil refining plants, wastewaters and waste incineration plants. The pollution sources have been affecting different environmental compartments, through the release of range of environmental contaminants to the lagoon including organic (e.g. PCBs, dioxin-like PCBs, PCDD/Fs, PAHs) and inorganic (e.g. Cd, Pb, As, Cr, Zn, Ni) chemicals. Specific objectives of the project involve: 1. Reviewing and selecting the bioaccumulation modelling approaches for aquatic organisms; 2. Implementation of the selected bioaccumulation modelling approach in MERLIN-Expo; 3. Parameterisation of environmental and human PBPK models in order to adapt the models to a site-specific exposure scenario (Venice lagoon case-study), considering peculiar pollutants and environmental media. This step involves data evaluation for ensuring that they support the model of choice, and can be parameterized probabilistically; 4. Verification of model outputs by comparing the model outcomes with actual monitoring data; 5. Application of probabilistic approach in addressing uncertainty and variability of exposure assessment to chemicals; 6. Application of sensitivity analysis in order to identify parameters having the most influence on observed results.