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.