Arsenic (As) contamination in groundwater is a global crisis that is known to cause cancers of the skin, bladder, and lungs, among other health issues, and affects millions of people around the world. Due to the time and financial constraints associated with establishing in-depth monitoring programs, it is difficult to monitor and map arsenic concentrations over time and across large areas. The goal of this study was to determine the most accurate Geographic Information Systems (GIS) interpolation method for mapping the effects of bioremediation on groundwater arsenic sequestration across a local-scale study area in northwest Florida (~900 m2) over the duration of a nine-month period (pre-injection, one-month post-injection, and nine-months post-injection). We used groundwater data collected from 2018 to 2019 to visualize arsenic contamination over time. Measured arsenic concentrations from 23 wells were grouped into three categories: (1) decreasing, (2) fluctuating, or (3) largely unaffected by the bioremediation procedure. The accuracy of three interpolation methods was also investigated: Inverse Distance Weighted (IDW), Ordinary Kriging (OK), and Empirical Bayesian Kriging (EBK). Statistical results using the leave-one-out cross validation (LOOCV) process showed that OK consistently provided the most accurate predictions of arsenic concentrations across space and time ([Root Mean Square Error (RMSE) = 0.265] and accurately predicted regulatory arsenic concentrations below 0.05 mg/L in nine of 11 wells, while IDW and EBK only accurately predicted four and five wells, respectively. While it was shown that OK tends to underpredict arsenic maxima, this did not affect the overall accuracy of the interpolation compared to results from EBK (RMSE = 0.297) and IDW (RMSE = 0.272). Overall, these interpolations aided in the interpretation of the extent of bioremediation, revealing the need for repeated injections to continuously remove arsenic from the groundwater. The study will provide guidance and evaluation methods for international and governmental organizations, industrial companies, and local communities on how to understand spatial and temporal distributions of arsenic contamination and inform bioremediation efforts at various scales in the future.
Keywords: Arsenic; Bioremediation; Contamination; GIS; Groundwater; Interpolation.
Published by Elsevier Ltd.