Combining particle-tracking and geochemical data to assess public supply well vulnerability to arsenic and uranium

Abstract

Flow-model particle-tracking results and geochemical data from seven study areas across the United States were analyzed using three statistical methods to test the hypothesis that these variables can successfully be used to assess public supply well vulnerability to arsenic and uranium. Principal components analysis indicated that arsenic and uranium concentrations were associated with particle-tracking variables that simulate time of travel and water fluxes through aquifer systems and also through specific redox and pH zones within aquifers. Time-of-travel variables are important because many geochemical reactions are kinetically limited, and geochemical zonation can account for different modes of mobilization and fate. Spearman correlation analysis established statistical significance for correlations of arsenic and uranium concentrations with variables derived using the particle-tracking routines. Correlations between uranium concentrations and particle-tracking variables were generally strongest for variables computed for distinct redox zones. Classification tree analysis on arsenic concentrations yielded a quantitative categorical model using time-of-travel variables and solid-phase-arsenic concentrations. The classification tree model accuracy on the learning data subset was 70%, and on the testing data subset, 79%, demonstrating one application in which particle-tracking variables can be used predictively in a quantitative screening-level assessment of public supply well vulnerability. Ground-water management actions that are based on avoidance of young ground water, reflecting the premise that young ground water is more vulnerable to anthropogenic contaminants than is old ground water, may inadvertently lead to increased vulnerability to natural contaminants due to the tendency for concentrations of many natural contaminants to increase with increasing ground-water residence time.