The presence of waterborne microbial (including viral) pathogens, in wastewater poses a potential risk to human health when wastewaters are reused either directly or indirectly. Therefore, reuse activities need to be regulated in such a way as to protect human health and to this end, quantitative microbial risk assessment (QMRA) has been successfully used to formulate evidence-based reuse regulations. The QMRA approach depends, however, on reliable information about the various elements of the system, including the wastewater treatment component. One point of major concern is the determination of pathogen concentrations, especially viral pathogens, in treated wastewater, as a consequence of their low levels and problems associated with the detection limit of enumeration methods. Therefore, the research described here aimed to develop stochastic simulations from empirical data to estimate likely concentrations of specified enteric microorganisms in final effluents of municipal wastewater treatment plants based on either activated sludge (AS) or trickling filter (TF) as the secondary biological treatment stage and thereby support the construction of functional QMRA models. Wastewater samples were collected every fortnight, during a twelve-month period, at each stage of four full-scale wastewater treatment plants (WWTP) in southern England (two AS and two TF plants) (n = 360 samples) in order to build a robust dataset. Probability density functions (PDF) were then fitted to empirical data and used as input variables in the proposed model, which considered the concentration of the assessed micro-organisms in the raw wastewater and the removal rates in primary, secondary and tertiary treatment stages. Final concentrations of pathogenic and indicator organisms were then estimated using stochastic simulations. The proposed stochastic model was able to predict both accurately and reliably the likely concentration of microorganisms in the final effluent of both systems. Moreover, sensitivity analysis revealed that the concentrations of the microorganisms in raw wastewater and their removal rates in the secondary treatment stages had the greatest influence on the predictive output. It was therefore concluded that, provided due attention is paid to the quality of the specific input variables of the model, stochastic modelling may represent a valuable tool to support integrated water and sanitation safety planning approaches to human health risk management of wastewater reuse systems, based on the use of QMRA models. The approach may also support better design and operation of wastewater treatment processes so as to maximise pathogen removal in support of Sustainable Development Goal 6 Target 3 of the United Nations.

当废水直接或间接回用时，废水中存在水生微生物（包括病毒）病原体会对人类健康构成潜在风险。因此，需要以保护人类健康的方式来规范重复使用活动，为此，定量微生物风险评估（QMRA）已成功用于制定基于证据的重复使用法规。但是，QMRA方法取决于有关系统各个要素（包括废水处理组件）的可靠信息。一个主要的关注点是测定处理后废水中的病原体浓度，尤其是病毒病原体，这是由于其浓度低以及与计数方法的检出限有关的问题。因此，本文所述的研究旨在根据经验数据进行随机模拟，以基于活性污泥（AS）或滴滤池（TF）作为二级生物处理阶段，从而估计市政废水处理厂最终废水中特定肠道微生物的可能浓度，从而支持功能性QMRA模型的构建。在英格兰南部的四个大型污水处理厂（WWTP）（两个AS和两个TF厂）的每个阶段，在十二个月的时间内，每两周收集一次废水样品（ñ = 360个样本）以构建可靠的数据集。然后将概率密度函数（PDF）拟合到经验数据，并在建议的模型中用作输入变量，该模型考虑了原废水中评估的微生物浓度以及一级，二级和三级处理阶段的去除率。然后使用随机模拟估算病原体和指示生物的最终浓度。所提出的随机模型能够准确，可靠地预测两个系统最终流出物中微生物的可能浓度。此外，敏感性分析表明，原废水中微生物的浓度及其在二级处理阶段的去除率对预测产量影响最大。因此得出结论，如果对模型的特定输入变量的质量给予了应有的关注，那么基于QMRA模型，随机模型可能代表支持废水和回用系统的人类健康风险管理的综合水和卫生安全规划方法的宝贵工具。该方法还可以支持更好地设计和运行废水处理工艺，从而最大程度地去除病原体，以支持联合国的可持续发展目标6目标3。