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.

中文翻译：

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机械性土壤-植物吸收模型的应用：青豆植物中卡马西平易位的综合实验和数值分析

食品污染是世界范围内人类健康的主要风险。从污染环境中动态吸收植物污染物是进入人类和动物食物链的首选途径。机制模型是风险评估和缓解策略制定的基本工具。但是，在土壤-植物连续体中难以获得全面的观测结果会阻碍其校准，破坏其推广性，并对其广泛的适用性产生疑问。为了解决这些问题，首次使用贝叶斯概率框架来校准和评估机械化土壤-植物模型的预测不确定性，并根据对卡马西平在绿豌豆植物中的易位性实验进行的综合观察得出结论。结果表明，该模型可以在有限的不确定性条件下准确地再现土壤-植物域中水流和溶质的反应动力学。通过全球敏感性分析研究了不同理化过程在水果中卡马西平生物积累中的作用，该分析显示了土壤水力特性和土壤溶质吸附如何调节卡马西平的蒸腾流和生物利用度。总体而言，该分析证明了机械模型的有用性，并提出了用于评估和使用的综合数值框架。通过全球敏感性分析研究了不同理化过程在水果中卡马西平生物积累中的作用，该分析显示了土壤水力特性和土壤溶质吸附如何调节卡马西平的蒸腾流和生物利用度。总体而言，该分析证明了机械模型的有用性，并提出了用于评估和使用的综合数值框架。通过全球敏感性分析研究了不同理化过程在水果中卡马西平生物积累中的作用，该分析显示了土壤水力特性和土壤溶质吸附如何调节卡马西平的蒸腾流和生物利用度。总体而言，该分析证明了机械模型的有用性，并提出了用于评估和使用的综合数值框架。