T193 Approaches to Applied Modeling for Industry and the Public

Tuesday, 24 October 2017
8:00 AM - 12:00 PM
Room 613/614

PREDICTING ARSENIC IN DRINKING WATER WELLS IN GLACIAL AQUIFERS IN WESTERN AND CENTRAL MINNESOTA, USA

Paper number: T193-10
Program: Technical Sessions
Time: 10:25 AM – 10:40 AM

Authors

Abstract

Arsenic is a naturally-occurring contaminant adversely affecting drinking water quality sourced from groundwater in geologically diverse aquifers in Asia, Europe, Africa, as well as North and South America. Because arsenic is associated with occurrences of cancer, the World Health Organization and the U.S. Environmental Protection Agency (EPA) have established a drinking water standard of 10 μg/L. In Minnesota, an estimated 125,000 domestic water well users with drinking water arsenic concentrations above 10 µ/L (elevated arsenic). Groundwater arsenic concentrations vary considerably over short distances and regionally across the state. Elevated groundwater arsenic is more prevalent from northwestern to south central Minnesota. Approximately 40% of available arsenic data for groundwater in western and central regions of Minnesota exceed the drinking water standard.

A Boosted Regression Tree (BRT) model was developed to predict the probability of elevated arsenic in groundwater at typical drinking water supply depths in glacial aquifers in western and central Minnesota. Predictive factors used to build the BRT model were obtained from a variety of existing data sets and consisted of about 75 different factors such as well construction, and glacial aquifer/surficial (e.g. soil texture, soil chemistry, land use) characteristics. The final BRT model predicted the probabilities of elevated arsenic in groundwater with about 65% accuracy. Predictive factors determined to be important for predicted probabilities included clay gap (distance from top of screen to overlying confining unit), nearest major river (a proxy for hydrological position in the landscape), horizontal hydraulic conductivity, and distance to the top of the bedrock from the bottom of the well. For example, smaller clay gaps were typically related to higher probability of elevated arsenic concentrations. This is the first successful application of BRT to model probabilities of elevated arsenic in a complex glacial aquifer system. The BRT model was used to generate maps illustrating probabilities of elevated arsenic across the modeled regions at the depth and with the construction characteristics typical of drinking water wells. The BRT model could be used to create a 3-D tool to reduce the risk of drilling wells with elevated arsenic.

Geological Society of America Abstracts with Programs. Vol. 49, No. 6

doi: 10.1130/abs/2017AM-303845

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