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一个混合建模框架,用于预测沿海水域抗菌素耐药性的时空动态

Xuneng Tong^1,2,3, Zhixin Xiang², Shin Giek Goh³

¹School of Energy and Environment and State Key Laboratory of Marine Pollution, City University of Hong Kong, Hong Kong SAR 999077, China.

Environmental science & technology

|June 20, 2025

概括

沿海水域的抗菌素耐药性 (AMR) 是一个越来越令人担忧的问题. 这项研究开发了一种混合模型来预测AMR的传播,确定了新加坡的关键环境驱动因素和脆弱区域.

科学领域:

环境科学环境科学
微生物学微生物学
计算建模计算建模

背景情况:

水生环境中的抗菌素耐药性 (AMR) 对生态系统和公共健康构成重大风险.
了解AMR的环境动态对于制定有效的缓解策略至关重要.

研究的目的:

开发和验证一种新的混合模型,用于预测沿海水域中抗微生物耐药性基因 (ARG) 的时空动态.
确定影响新加坡沿海环境中类相关ARG (ARG_Macro) 的流行的主要环境因素.

主要方法:

集成基于过程的水力动力学环境模型与数据驱动方法.
利用与类相关的ARG (ARG_Macro) 作为标记基因.
根据新加坡沿海地区每月采集的水样数据,验证了模型预测.

主要成果:

混合动力车型实现了良好的性能,R2 = 0.693和NSE = 0.589.
林氏素,pH值,溶解氧,和温度被确定为ARGs_Macro.的重要驱动因素.
沿海地区,特别是新加坡北部,被确定为ARG积累的热点,由季风驱动的水力动力学加剧.

结论:

开发的混合模型为评估沿海水域AMR动态提供了一个强大的框架.
环境因素,包括特定的污染物和水力动力学条件,在AMR扩散中发挥着关键作用.
这些发现为有针对性的监管干预和未来研究提供了基础,以打击水生AMR.

关键词:

抗生素耐药性基因是抗生素耐药性的基因.抗微生物耐药性抗微生物耐药性数据驱动模型是基于数据的模型.混合型建模混合型建模水力动力学-环境模型

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