摘要

    抗生素抗药性是一种全球性现象，暴露于人为化学物质的陆地和水生生态系统充当了抗生素抗药性基因的储藏库。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将有毒化学物质引入土壤。这种长期的环境暴露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。数学模型是用于整合有关土壤中抗生素命运和运移预测的信息的工具；去除抗生素的过程；量化土壤中抗菌素耐药性的扩散；从土壤溶液吸收到根的吸收和转运的研究；计测学和数字土壤制图。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确定缓解策略的优先级。在本章中，我们将反思土壤中抗生素的命运和降解，并介绍利用数学模型作为研究土壤中AMR动力学的有力工具的研究。

    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