Lead is a chemical contaminant that threatens public health, and high levels of lead have been identified in drinking water at locations across the globe. Under-served populations that use private systems for drinking water supplies may be at an elevated level of risk because utilities and governing agencies are not responsible for ensuring that lead levels meet the Lead and Copper Rule at these systems. Predictive models that can be used by residents to assess water quality threats in their households can create awareness of water lead levels (WLLs). This research explores and compares the use of statistical models (i.e., Bayesian Belief classifiers) and machine learning models (i.e., ensemble of decision trees) for predicting WLLs. Models are developed using a dataset collected by the Virginia Household Water Quality Program (VAHWQP) at approximately 8000 households in Virginia during 2012–2017. The dataset reports laboratory-tested water quality parameters at households, location information, and household and plumbing characteristics, including observations of water odor, taste, discoloration. Some water quality parameters, such as pH, iron, and copper, can be measured at low resolution by residents using at-home water test kits and can be used to predict risk of WLLs. The use of at-home water quality test kits was simulated through the discretization of water quality parameter measurements to match the resolution of at-home water quality test kits and the introduction of error in water quality readings. Using this approach, this research demonstrates that low-resolution data collected by residents can be used as input for models to estimate WLLs. Model predictability was explored for a set of at-home water quality test kits that observe a variety of water quality parameters and report parameters at a range of resolutions. The effects of the timing of water sampling (e.g., first-draw vs. flushed samples) and error in kits on model error were tested through simulations. The prediction models developed through this research provide a set of tools for private well users to assess the risk of lead contamination. Models can be implemented as early warning systems in citizen science and online platforms to improve awareness of drinking water threats.

铅是一种威胁公共健康的化学污染物，全球各地的饮用水中都发现了高水平的铅。使用私人系统供应饮用水的服务不足的人群可能面临较高的风险，因为公用事业和管理机构不负责确保这些系统的铅含量符合铅和铜规则。居民可以使用预测模型来评估其家庭中的水质威胁，从而提高人们对水铅水平 (WLL) 的认识。本研究探索和比较了使用统计模型（即贝叶斯信念分类器）和机器学习模型（即决策树集合）来预测 WLL。模型是使用弗吉尼亚家庭水质计划 (VAHWQP) 在 2012-2017 年间在弗吉尼亚州大约 8000 户家庭中收集的数据集开发的。该数据集报告了家庭实验室测试的水质参数、位置信息以及家庭和管道特征，包括对水气味、味道、变色的观察。一些水质参数，例如 pH 值、铁和铜，可以由居民使用家庭水质检测套件以低分辨率测量，并可用于预测 WLL 的风险。通过水质参数测量的离散化来模拟家庭水质测试套件的使用，以匹配家庭水质测试套件的分辨率，并在水质读数中引入误差。使用这种方法，这项研究表明，居民收集的低分辨率数据可用作模型估计 WLL 的输入。对一组家用水质测试套件探索了模型可预测性，这些套件观察各种水质参数并以各种分辨率报告参数。通过模拟测试了水采样时间（例如，第一次抽取与冲洗样品）和套件中的错误对模型错误的影响。通过这项研究开发的预测模型为私人油井用户提供了一套评估铅污染风险的工具。模型可以作为公民科学和在线平台的预警系统来实施，以提高对饮用水威胁的认识。对一组家用水质测试套件探索了模型可预测性，这些套件观察各种水质参数并以各种分辨率报告参数。通过模拟测试了水采样时间（例如，第一次抽取与冲洗样品）和套件中的错误对模型错误的影响。通过这项研究开发的预测模型为私人油井用户提供了一套评估铅污染风险的工具。模型可以作为公民科学和在线平台的预警系统来实施，以提高对饮用水威胁的认识。对一组家用水质测试套件探索了模型可预测性，这些套件观察各种水质参数并以各种分辨率报告参数。通过模拟测试了水采样时间（例如，第一次抽取与冲洗样品）和套件中的错误对模型错误的影响。通过这项研究开发的预测模型为私人油井用户提供了一套评估铅污染风险的工具。模型可以作为公民科学和在线平台的预警系统来实施，以提高对饮用水威胁的认识。冲洗样品）和套件中的错误模型错误通过模拟进行测试。通过这项研究开发的预测模型为私人油井用户提供了一套评估铅污染风险的工具。模型可以作为公民科学和在线平台的预警系统来实施，以提高对饮用水威胁的认识。冲洗样品）和套件中的错误模型错误通过模拟进行测试。通过这项研究开发的预测模型为私人油井用户提供了一套评估铅污染风险的工具。模型可以作为公民科学和在线平台的预警系统来实施，以提高对饮用水威胁的认识。