Cross-sectional associations between tap water lead levels and service line materials are not fully understood. As a result, stakeholders are unable to make use of routinely collected tap water samples for myriad decisions. This analysis leverages a novel data set associating field tap water observations with service line materials under varying seasonal and sampling conditions. The analysis demonstrates a staged approach that leverages interpretable model results to strategically filter the sample to boost material diagnoses. Predicted precisions guided by interpretable model results easily exceed those from an incumbent black box, Random Forest model. For example, precisions of 94% were achieved for samples collected between July and October, when lead more naturally leaches. Precisions for the 7% of households with the highest predicted probabilities exceed 96%, which could help municipalities locate lead service lines. Models were also used to inform customers about their risks, predicting lead with a 95% probability when either a single sample exceeds 20 ppb or multiple samples exceed detection thresholds. These results should help stakeholders make better use of tap water samples for risk mitigation and regulatory compliance. Similarly, the staged approach, using interpretable model results to guide classification, can support other water research domains.

中文翻译：

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通过现场自来水采样识别铅服务线

自来水铅含量与服务管线材料之间的横截面关联尚未完全了解。因此，利益相关者无法利用常规收集的自来水样本进行无数决策。该分析利用了一个新的数据集，将现场自来水观察与不同季节和采样条件下的服务管线材料相关联。该分析展示了一种分阶段方法，该方法利用可解释的模型结果来战略性地过滤样本以促进材料诊断。由可解释模型结果引导的预测精度很容易超过现有黑盒随机森林模型的预测精度。例如，在 7 月至 10 月期间收集的样本的精度达到了 94%，此时铅更自然地浸出。预测概率最高的 7% 家庭的精度超过 96%，这可以帮助市政当局定位主要服务线。模型还用于告知客户他们的风险，当单个样品超过 20 ppb 或多个样品超过检测阈值时，以 95% 的概率预测铅。这些结果应该有助于利益相关者更好地利用自来水样本来降低风险和遵守法规。同样，使用可解释的模型结果来指导分类的分阶段方法可以支持其他水研究领域。当单个样品超过 20 ppb 或多个样品超过检测阈值时，预测铅的概率为 95%。这些结果应该有助于利益相关者更好地利用自来水样本来降低风险和遵守法规。同样，使用可解释的模型结果来指导分类的分阶段方法可以支持其他水研究领域。当单个样品超过 20 ppb 或多个样品超过检测阈值时，预测铅的概率为 95%。这些结果应该有助于利益相关者更好地利用自来水样本来降低风险和遵守法规。同样，使用可解释的模型结果来指导分类的分阶段方法可以支持其他水研究领域。