In the aftermath of Flint drinking water crisis, most US cities have been scrambling to locate all lead service lines (LSLs) in their water supply systems. This information, which is most often inaccurate or lacking, is critical to assess compliance with the Lead and Copper Rule and to plan the replacement of lead and galvanized service lines (GSLs) as currently under way in Flint. This paper presents the first geospatial approach to predict the likelihood that a home has a LSL or GSL based on neighboring field data (i.e., house inspection) and secondary information (i.e., construction year and city records). The methodology is applied to the City of Flint where 3254 homes have been inspected by the Michigan Department of Environmental Quality to identify service line material. GSLs and LSLs were mostly observed in houses built prior to 1934 and during World War II, respectively. City records led to the over-identification of LSLs, likely because old records were not updated as these lines were being replaced. Indicator semivariograms indicated that both types of service line are spatially clustered with a range of 1.4km for LSLs and 2.8km for GSLs. This spatial autocorrelation was integrated with secondary data using residual indicator kriging to predict the probability of finding each type of material at the tax parcel level. Cross-validation analysis using Receiver Operating Characteristic (ROC) Curves demonstrated the greater accuracy of the kriging model relative to the current approach targeting houses built in the forties; in particular as more field data become available. Anticipated rates of false positives and percentages of detection were computed for different sampling strategies. This approach is flexible enough to accommodate additional sources of information, such as local code and regulatory changes, historical permit records, maintenance and operation records, or customer self-reporting.
Keywords: Indicator semivariograms; Kriging; Lead service lines; Lead-and-Copper Rule; ROC curve.
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