Wei S, Richard R, Hogue D, Mondal I, Xu T, Boyer T H, Hamilton K A
School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, United States.
Wilson & Company Engineers, United States.
Water Res X. 2024 Jul 26;24:100244. doi: 10.1016/j.wroa.2024.100244. eCollection 2024 Sep 1.
People spend most of their time indoors and are exposed to numerous contaminants in the built environment. Water management plans implemented in buildings are designed to manage the risks of preventable diseases caused by drinking water contaminants such as opportunistic pathogens (e.g., spp), metals, and disinfection by-products (DBPs). However, specialized training required to implement water management plans and heterogeneity in building characteristics limit their widespread adoption. Implementation of machine learning and artificial intelligence (ML/AI) models in building water settings presents an opportunity for faster, more widespread use of data-driven water quality management approaches. We demonstrate the utility of Random Forest and Long Short-Term Memory (LSTM) ML models for predicting a key public health parameter, free chlorine residual, as a function of data collected from building water quality sensors (ORP, pH, conductivity, and temperature) as well as WiFi signals as a proxy for building occupancy and water usage in a "green" Leadership in Energy and Environmental Design (LEED) commercial and institutional building. The models successfully predicted free chlorine residual declines below 0.2 ppm, a common minimum reference level for public health protection in drinking water distribution systems. The predictions were valid up to 5 min in advance, and in some cases reasonably accurate up to 24 h in advance, presenting opportunities for proactive water quality management as part of a sense-analyze-decide framework. An online data dashboard for visualizing water quality in the building is presented, with the potential to link these approaches for real-time water quality management.
人们大部分时间都待在室内，会接触到建筑环境中的多种污染物。建筑中实施的水管理计划旨在管理由饮用水污染物（如机会性病原体（如 spp）、金属和消毒副产物（DBPs））引起的可预防疾病的风险。然而，实施水管理计划所需的专业培训以及建筑特征的异质性限制了它们的广泛采用。在建筑水系统中实施机器学习和人工智能（ML/AI）模型为更快、更广泛地使用数据驱动的水质管理方法提供了机会。我们展示了随机森林和长短期记忆（LSTM）ML 模型在预测一个关键公共卫生参数——余氯方面的效用，该参数是从建筑水质传感器（氧化还原电位、pH 值、电导率和温度）收集的数据以及作为建筑 occupancy 和用水量代理的 WiFi 信号的函数，该建筑是一座获得能源与环境设计先锋（LEED）认证的“绿色”商业和机构建筑。这些模型成功预测了余氯降至低于 0.2 ppm 的情况，这是饮用水分配系统中公共卫生保护的常见最低参考水平。预测提前 5 分钟有效，在某些情况下提前 24 小时也相当准确，这为作为感知 - 分析 - 决策框架一部分的主动水质管理提供了机会。本文还展示了一个用于可视化建筑内水质的在线数据仪表板，有潜力将这些方法联系起来用于实时水质管理。
