The Ohio State University, College of Public Health, Environmental Health Sciences Division, 1841 Neil Ave. Columbus, OH, 43210, USA; University of Arizona, Mel and Enid Zukerman College of Public Health, Community, Environment & Policy Department, 1295 N. Martin Avenue, Tucson AZ 85724, USA.
The Ohio State University, College of Public Health, Environmental Health Sciences Division, 1841 Neil Ave. Columbus, OH, 43210, USA.
Water Res. 2021 Feb 15;190:116763. doi: 10.1016/j.watres.2020.116763. Epub 2020 Dec 24.
Drinking water treatment processes are capable of removing microcystins but consistent operation of processes optimized for cyanobacterial harmful algal bloom (cHAB) conditions is not fiscally feasible. Therefore, utilities must ready themselves and start the cHAB processes as a reactionary response. Predictive analytics and modelling are impactful tools to prepare water systems for cHABs, but are still in early stages of development. Until those prospective models are completed, a method to determine best actions in advance of a bloom event thus improving system resiliency is needed. In this study, an adaptation of the quantitative microbial risk analysis (QMRA) methodology was applied to develop this method. This method and resulting models were developed around the Toledo (Ohio, USA) water crisis of 2014, but being mechanistic, they are easily adaptable to other systems' process operations data. Results from this internally validated model demonstrate how rapid action using both powdered activated carbon and measured increases in chlorine dose can mitigate health risks. Our research also demonstrates the importance of modelling the cellular status of the toxins (toxins either in an intact cell or in the water from a lysed cell). Risks were characterized using hazard quotients (HQ) and at the peak of the crisis ranged from a minimum of 0.00244 to a maximum of 2.84 for adults. In simulations where cHAB-specific treatment was used this decreased to 0.00057 and 0.236 respectively. We further outline how this methodology can be used to simulate water system resiliency to likely and aberrant microbial hazard events to plan for the best interventions to protect public health. This method can be used for other hazards expected to be variable in the future, where system prepatory planning is critical to continued public health protection. Considering the water quantity and quality fluctuations occurring and likely to intensify under climate change, this type of computationally supported preparedness is vital to maintaining robust water system resiliency.
饮用水处理工艺能够去除微囊藻毒素，但为蓝藻有害藻华 (cHAB) 条件优化的工艺持续运行在财务上是不可行的。因此，公用事业公司必须做好准备，并开始对 cHAB 做出反应。预测分析和建模是为 cHAB 做好水系统准备的有效工具，但仍处于早期开发阶段。在这些前瞻性模型完成之前，需要一种方法来预先确定最佳行动，从而提高系统的弹性。在本研究中，应用定量微生物风险分析 (QMRA) 方法的改编版来开发这种方法。该方法和由此产生的模型是围绕 2014 年美国俄亥俄州托莱多的水危机开发的，但由于是机械性的，它们很容易适应其他系统的工艺操作数据。该内部验证模型的结果表明，使用粉末活性炭和测量增加的氯剂量的快速行动如何减轻健康风险。我们的研究还表明了对毒素的细胞状态进行建模的重要性（毒素要么在完整的细胞中，要么在来自裂解细胞的水中）。使用危害系数 (HQ) 对风险进行了特征描述，在危机高峰期，成年人的风险从 0.00244 到 2.84 不等。在使用特定于 cHAB 的处理的模拟中，这一数值分别降至 0.00057 和 0.236。我们进一步概述了如何使用这种方法模拟水系统对可能和异常微生物危害事件的弹性，以便为保护公众健康计划最佳干预措施。这种方法可以用于未来预计会变化的其他危害，在这种情况下，系统准备规划对于持续的公共卫生保护至关重要。考虑到在气候变化下发生和可能加剧的水量和水质波动，这种类型的计算支持的准备对于保持强大的水系统弹性至关重要。
