Northeastern Section - 53rd Annual Meeting - 2018

Paper No. 36-2
Presentation Time: 1:55 PM


LOMBARD, Melissa A., U.S. Geological Survey, New England Water Science Center, 331 Commerce Way, Pembroke, NH 03275-3718

Arsenic is primarily a naturally occurring contaminant in many groundwater wells. The U.S. Environmental Protection Agency has established a maximum contaminant level of 10 mg/L for public supply wells, however domestic wells are not regulated and do not require testing. Therefore, domestic well users are more likely to unknowingly consume water containing arsenic concentrations greater than 10 mg/L. In cooperation with the Centers for Disease Control and Prevention (CDC), the U.S. Geological Survey recently developed a statistical model to predict the probability of elevated arsenic levels (>10 mg/L) in domestic wells located throughout the United States. The model used geologic, geochemical, hydrologic, and physical landscape features, available at the national scale as GIS coverages, to predict the probability of high arsenic for the continental United States including locations where domestic wells have not been sampled.

Collaborating with epidemiologists under a grant from the U.S. Geological Survey Powell Center, results from this modeling study will be compared to human health outcomes associated with exposure to arsenic, including certain cancers and pre-term births and low birthweights. Further, a machine learning modeling technique, boosted regression trees (BRT), is being used to develop a new predictive model. Preliminary results indicate that the BRT model more accurately predicts the occurrence of elevated arsenic levels than the original model, which used logistic regression (LR) methods.

A second related study with the CDC includes varying parameters in the models to assess the potential impact of drought on arsenic concentrations. In both the LR and BRT models, average annual precipitation and groundwater recharge are important variables. This suggests that drought conditions may affect the likelihood of elevated arsenic concentrations. To evaluate this possibility, precipitation and recharge values representative of drought will be incorporated into the model. Changes in the probability of elevated arsenic levels will be assessed and compared with domestic well water use to estimate changes in the population exposed to elevated arsenic under drought conditions.