摘要 

       抗生素耐药性是一种全球现象，暴露于人为化学物质的陆地和水生生态系统成为抗生素耐药基因的储存库。广泛用于治疗人类和兽医疾病、畜牧业和其他农业实践的抗生素会将有毒化学物质引入土壤中。这种长期的环境暴露会导致不利的生态毒理学影响。许多工业来源的抗生素也可能影响原始生态系统，从而破坏整个生物圈。数学模型是整合有关预测土壤中抗生素的归宿和运输的信息的工具；抗生素的去除过程；土壤中抗菌素耐药性传播的量化；从土壤溶液向根部吸收和转运的研究；以及计步学和数字土壤测绘。为了进一步提高我们控制抗菌素耐药性 (AMR) 的能力，必须解决存在的一系列不确定性，包括暴露评估、危害和风险表征，以及优先缓解策略。在本章中，我们反思了土壤中抗生素的归宿和降解，并提出了一些研究，其中数学模型被用作研究土壤中 AMR 动力学的有力工具。

       Antibiotic resistance is a global phenomenon where terrestrial and aquatic ecosystems exposed to anthropogenic chemicals serve as reservoirs of antibiotic resistance genes. Antibiotics extensively used for treating human and veterinary diseases, animal farming, and other agricultural practices introduce toxic chemicals into the soil. Such chronic environmental exposure results in adverse eco-toxicological effects. Many antibiotics from industrial origin also potentially affect pristine ecosystems, thereby devastating the entire biosphere. Mathematical models are tools for integrating information on prediction of the fate and transport of antibiotics in soils; removal processes of antibiotics; quantification of the spread of antimicrobial resistance in soil; studies on uptake and translocation from soil solution into roots; and pedometrics and digital soil mapping. To further improve our ability to control antimicrobial resistance (AMR), it is imperative to address a range of uncertainties that exist, including exposure assessment, hazard and risk characterization, as well as prioritizing mitigation strategies. In this chapter, we reflect on the fate and degradation of antibiotics in soil, and present studies where mathematical modeling is employed as a powerful tool to investigate the dynamics of AMR in soil.

https://www.sciencedirect.com/science/article/pii/B9780128188828000127