Temporal Variability of Arsenic in the Caplina Aquifer, La Yarada Los Palos, Peru: Implications for Risk-Based Drinking Water Management

Arsenic (As) in groundwater often fluctuates around the 10 µg/L health-based guideline, complicating compliance assessment and risk-based management. This study investigates the short-term temporal behavior of As and its implications for compliance at three supply wells in the Caplina aquifer (La Yarada Los Palos, Tacna, Peru), based on a one-year fortnightly time series. At each visit, in situ electrical conductivity (EC), total dissolved solids (TDS), pH, and temperature were measured, and total As was determined by inductively coupled plasma–mass spectrometry (ICP–MS). The dataset was evaluated using robust descriptive statistics, exceedance proportions with Wilson 95% confidence intervals, Spearman rank correlations, simple time-series diagnostics, and comparisons of deterministic monthly schemes against the fortnightly reference. Exceedances were widespread—100% at Point 1 and 91.7% at Points 2 and 3—yielding 94.4% at the network scale, with no consistent seasonal signal. Relative variability was low yet operationally decisive (coefficient of variation (CV) ≈ 7–10%; interquartile range ≈ 1.3–1.6 µg/L), and typical fortnightly oscillations of ~0.5–1.5 µg/L were sufficient to flip compliance labels under monthly sampling. Point-wise associations were generally weak, except for a moderate As–TDS correlation at Point 1, supporting an interpretation dominated by geogenic As under arid, alkaline, and saline conditions, modulated by redox processes, anion competition, and mixing/pumping dynamics. The findings support risk-based monitoring with a fortnightly baseline and adaptive escalation when predefined activation criteria and action thresholds are met, using EC/TDS, pH, and simple redox indicators as operational early warnings. To reduce exposure in such settings, priority should be given to source management, pre-oxidation of As(III) to As(V), and adsorption onto iron media (or membranes where appropriate), while future work should integrate high-frequency sensing, in situ or inline speciation, reactive-transport modeling, and locally trained risk mapping to strengthen contributions to Sustainable Development Goals 3 (Good Health and Well-Being) and 6 (Clean Water and Sanitation).