Prioritizing municipal lead mitigation projects as a relaxed knapsack optimization: a method and case study

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