Kadoya Syun-Suke, Nishimura Osamu, Kato Hiroyuki, Sano Daisuke
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, Aoba 6-6-06, Aramaki, Aoba-ku, Sendai, Miyagi, 980-8579, Japan.
New Industry Creation Hatchery Center, Tohoku University, Sendai, Miyagi, Japan.
Water Res X. 2021 Feb 12;11:100093. doi: 10.1016/j.wroa.2021.100093. eCollection 2021 May 1.
Wastewater reclamation and reuse have been practically applied to water-stressed regions, but waterborne pathogens remaining in insufficiently treated wastewater are of concern. Sanitation Safety Planning adopts the hazard analysis and critical control point (HACCP) approach to manage human health risks upon exposure to reclaimed wastewater. HACCP requires a predetermined reference value (critical limit: CL) at critical control points (CCPs), in which specific parameters are monitored and recorded in real time. A disinfection reactor of a wastewater treatment plant (WWTP) is regarded as a CCP, and one of the CCP parameters is the disinfection intensity (, initial disinfectant concentration and contact time), which is proportional to the log reduction value (LRV) of waterborne pathogens. However, the achievable LRVs are not always stable because the disinfection intensity is affected by water quality parameters, which vary among WWTPs. In this study, we established models for projecting virus LRVs using ozone, in which water quality and operational parameters were used as explanatory variables. For the model construction, we used five machine learning algorithms and found that automatic relevance determination with interaction terms resulted in better prediction performances for norovirus and rotavirus LRVs. Poliovirus and coxsackievirus LRVs were predicted well by a Bayesian ridge with interaction terms and lasso with quadratic terms, respectively. The established models were relatively robust to predict LRV using new datasets that were out of the range of the training data used here, but it is important to collect LRV datasets further to make the models more predictable and flexible for newly obtained datasets. The modeling framework proposed here can help WWTP operators and risk assessors determine the appropriate CL to protect human health in wastewater reclamation and reuse.
废水回收与再利用已在水资源紧张地区得到实际应用，但未经充分处理的废水中残留的水传播病原体令人担忧。卫生安全规划采用危害分析与关键控制点（HACCP）方法来管理接触再生废水时的人类健康风险。HACCP要求在关键控制点（CCP）设定预先确定的参考值（关键限值：CL），在这些点要对特定参数进行实时监测和记录。污水处理厂（WWTP）的消毒反应器被视为一个CCP，其中一个CCP参数是消毒强度（，初始消毒剂浓度和接触时间），它与水传播病原体的对数减少值（LRV）成正比。然而，由于消毒强度受水质参数影响，而不同污水处理厂的水质参数各不相同，所以可实现的LRV并不总是稳定的。在本研究中，我们建立了使用臭氧预测病毒LRV的模型，其中水质和运行参数被用作解释变量。对于模型构建，我们使用了五种机器学习算法，发现带有交互项的自动相关性确定对诺如病毒和轮状病毒LRV具有更好的预测性能。脊髓灰质炎病毒和柯萨奇病毒LRV分别通过带有交互项的贝叶斯岭回归和带有二次项的套索回归得到了很好的预测。所建立的模型对于使用超出此处训练数据范围的新数据集预测LRV相对稳健，但进一步收集LRV数据集对于使模型对新获得的数据集更具可预测性和灵活性很重要。这里提出的建模框架可以帮助污水处理厂运营者和风险评估者确定合适的CL，以保护废水回收与再利用中的人类健康。
