Multiple Data Imputation Methods Advance Risk Analysis and Treatability of Co-occurring Inorganic Chemicals in Groundwater.

ABSTRACT: Accurately assessing and managing risks associated with inorganic pollutants in groundwater is imperative. Historic water quality databases are often sparse due to rationale or financial budgets for sample collection and analysis, posing challenges in evaluating exposure or water treatment effectiveness. We utilized and compared two advanced multiple data imputation techniques, AMELIA and MICE algorithms, to fill gaps in sparse groundwater quality data sets. AMELIA outperformed MICE in handling missing values, as MICE tended to overestimate certain values, resulting in more outliers. Field data sets revealed that 75% to 80% of samples exhibited no co-occurring regulated pollutants surpassing MCL values, whereas imputed values showed only 15% to 55% of the samples posed no health risks. Imp

SUBMITTER: Mahmood AU 

PROVIDER: S-EPMC11580165 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature