Numerical Modeling of Microbial Fate and Transport in Natural Waters: Review and Implications for Normal and Extreme Storm Events
Numerical Modeling of Microbial Fate and Transport in Natural Waters: Review and Implications for Normal and Extreme Storm Events
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DOI：
10.3390/w12071876
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发表时间：
2020-06
期刊：
影响因子：
3.4
通讯作者：
Chelsea J. Weiskerger;M. Phanikumar
中科院分区：
文献类型：
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作者：
Chelsea J. Weiskerger;M. Phanikumar
娱乐区的水质退化可能是一个重大的公共卫生问题。模型可以帮助海滩管理人员作出同期的决定，以保护公共卫生在娱乐区，通过使用微生物的命运和运输模拟。模拟微生物命运和运输的方法因当地水文气象背景而异，但许多参数化包括基本死亡率，太阳能灭活和微生物污染物沉降的术语。使用这些参数化的模型可以预测近岸水体中观察到的微生物浓度的变化高达87%，均方根误差范围为0.41至5.37 log10菌落形成单位（CFU）100 mL−1。这表明，一些模型预测微生物的命运和运输比其他模型更可靠，而且模型仍有全面改进的空间。模型的完善将是不可或缺的微生物命运和运输模拟在面对不容易观察到的过程影响近岸地区的水质。在缺乏直接观测数据的情况下，优化归宿和迁移模型可大大有助于管理污染现象，如与风暴有关的河流羽流的释放以及污染物在海滩的水和沙之间的交换。
Degradation of water quality in recreational areas can be a substantial public health concern. Models can help beach managers make contemporaneous decisions to protect public health at recreational areas, via the use of microbial fate and transport simulation. Approaches to modeling microbial fate and transport vary widely in response to local hydrometeorological contexts, but many parameterizations include terms for base mortality, solar inactivation, and sedimentation of microbial contaminants. Models using these parameterizations can predict up to 87% of variation in observed microbial concentrations in nearshore water, with root mean squared errors ranging from 0.41 to 5.37 log10 Colony Forming Units (CFU) 100 mL−1. This indicates that some models predict microbial fate and transport more reliably than others and that there remains room for model improvement across the board. Model refinement will be integral to microbial fate and transport simulation in the face of less readily observable processes affecting water quality in nearshore areas. Management of contamination phenomena such as the release of storm-associated river plumes and the exchange of contaminants between water and sand at the beach can benefit greatly from optimized fate and transport modeling in the absence of directly observable data.