Assessment of temporal and geographic variability in lead in drinking water for a system under different compliance conditions

Lead concentrations in drinking water vary geographically and temporally within the water distribution system. Understanding this variability is key to assessing and addressing elevated lead concentrations and protecting children who spend extended amounts of time in multiple locations such as their schools and their homes. 

The objective of this work was to evaluate how temporal and geographic variability in lead concentrations in drinking water systems affects (1) assessment of corrosion control efficacy and (2) potential childhood lead exposure changes during pandemic-associated school closures. Three main conclusions were reached through evaluation of a unique data set of extensive residential and school sampling, along with compliance sampling during a Lead and Copper Rule (LCR) exceedance. First, drinking water lead concentrations at homes can be divided into two types, and data from sampling at sites confirmed to be Tier I could be used to assess corrosion compliance between regular LCR sampling times, while sampling at sites without lead plumbing should not be used for such assessment. Second, the targeted Tier 1 sampling prescribed in the LCR is sufficient for determination of ineffective corrosion control when compared to other geographically targeted sampling protocols using non-targeted and targeted sampling methods. Geographically biased sampling informed by the location of clusters of elevated lead concentrations, the prevalence of lead sources and customer self-selection bias all result in similar compliance sampling outcomes as the targeted Tier 1 sampling protocol. Third, children’s potential exposure from lead in drinking water changed during school closures and was affected by the interventions taken to reduce lead in drinking water. Differences in the occurrence of elevated lead concentrations at schools and in homes within their corresponding attendance areas lead to differences in the probability of potential exposure under different school closure and reopening scenarios.