  Lead pipe remediation budgets are limited and ought to maximize public health
impact. This goal implies a non-trivial optimization problem; lead service
lines connect water mains to individual houses, but any realistic replacement
strategy must batch replacements at a larger scale. Additionally, planners
typically lack a principled method for comparing the relative public health
value of potential interventions and often plan projects based on non-health
factors. This paper describes a simple process for estimating child health
impact at a parcel level by cleaning and synthesizing municipal datasets that
are commonly available but seldom joined due to data quality issues. Using
geocoding as the core record linkage mechanism, parcel-level toxicity data can
be combined with school enrollment records to indicate where young children and
lead lines coexist. A harm metric of estimated exposure-years is described at
the parcel level, which can then be aggregated to the project level and
minimized globally by posing project selection as a 0/1 knapsack problem.
Simplifying further for use by non-experts, the implied linear programming
relaxation is solved intuitively with the greedy algorithm; ordering projects
by benefit cost ratio produces a priority list which planners can then consider
holistically alongside harder to quantify factors. A case study demonstrates
the successful application of this framework to a small U.S. city's existing
data to prioritize federal infrastructure funding. While this paper focuses on
lead in drinking water, the approach readily generalizes to other sources of
residential toxicity with disproportionate impact on children.
