UC Riverside

Food contamination is a major worldwide risk for human health. Dynamic plant uptake of pollutants from contaminated environments is the preferred pathway into the human and animal food chain. Mechanistic models represent a fundamental tool for risk assessment and the development of mitigation strategies. However, difficulty in obtaining comprehensive observations in the soil-plant continuum hinders their calibration, undermining their generalizability and raising doubts about their widespread applicability. To address these issues, a Bayesian probabilistic framework is used, for the first time, to calibrate and assess the predictive uncertainty of a mechanistic soil-plant model against comprehensive observations from an experiment on the translocation of carbamazepine in green pea plants. Results demonstrate that the model can reproduce the dynamics of water flow and solute reactive transport in the soil-plant domain accurately and with limited uncertainty. The role of different physicochemical processes in bioaccumulation of carbamazepine in fruits is investigated through Global Sensitivity Analysis, which shows how soil hydraulic properties and soil solute sorption regulate transpiration streams and bioavailability of carbamazepine. Overall, the analysis demonstrates the usefulness of mechanistic models and proposes a comprehensive numerical framework for their assessment and use.