Predicting geogenic Arsenic in Drinking Water Wells in Glacial Aquifers, North‐Central USA: Accounting for Depth‐Dependent Features

Abstract

Chronic exposure to arsenic (As) via drinking groundwater is a human health concern worldwide. Probabilities of elevated geogenic As concentrations in groundwater were predicted in complex, glacial aquifers in Minnesota, north-central USA, a region that commonly has elevated As concentrations in well water. Maps of elevated As hazard were created for depths typical of drinking water supply and with well construction attributes common for domestic wells. Conventional variables describing aquifer properties and materials, position on the hydrologic landscape, and soil geochemistry were among the most influential for predicting the probability of elevated As. We also found that certain well construction attributes were influential in predicting As hazard. Smaller distances between the top of the well screen and overlying aquitard (proximity) and shorter well screen lengths were each associated with higher probabilities of elevated As. Influential predictor variables, which are either mapped across the region or are well construction attributes, are proxies in the model for measurable physical or geochemical causes of elevated As (e.g., redox condition, till or aquifer sediment chemistry, and water chemistry), which are not mapped across the region. Our setting shares some important characteristics with deltaic and other high-As aquifers in Southeast Asia: late Quaternary age, complex layering of coarse-and fine-grained materials, low-As sediment concentrations, and geochemical controls on As mobilization. Translating three-dimensional geologic and geochemical understanding of As mobility to quantifiable variables for modeling with powerful, flexible statistical tools could improve predictions and help identify safer groundwater supply options in the USA, Southeast Asia, and elsewhere.Plain Language Summary This study demonstrates that certain well construction attributes are influential in predicting arsenic (As) concentrations in drinking water wells. Smaller distances between the top of the well screen and overlying aquitard, and shorter well screen lengths, were each associated with higher probabilities of elevated As. Chronic exposure to As via drinking groundwater is a human health concern worldwide, and Minnesota, USA, commonly has elevated As concentrations in well water. This study describes a new, novel, and important finding from an As probability model: Controllable well construction choices (not just location or depth) influence As concentrations in drinking water from wells.

Conceptual Model of Arsenic Mobility in the Shallow Alluvial Aquifers Near Venice (Italy) Elucidated Through Machine Learning and Geochemical Modeling

“…Geogenic (i.e., naturally occurring) arsenic (As) contamination is a well‐known issue that affects unconsolidated and fractured aquifers worldwide (e.g., Choudhury et al, 2018; Desbarats et al, 2017; Erickson et al, 2018; Huang et al, 2018; Jakobsen et al, 2018; Pedretti et al, 2019), including Italian aquifers (e.g., Aiuppa et al, 2003; Carraro et al, 2013; Molinari et al, 2013; Rotiroti et al, 2014; Ungaro et al, 2008). Identifying a valid site‐specific conceptual model able to describe As mobility in groundwater is fundamental for many purposes related to As risk management.…”

Arsenic Release and Attenuation Processes in a Groundwater Aquifer During Anaerobic Remediation of TCE with Biostimulation

“…Specific areas of study have included the Southern High Plains of West Texas (Scanlon et al ), Inner Coastal Plain, New Jersey (Mumford et al. ), Western Snake River Plain, Idaho (Busbee et al ), New England and the Lower Illinois River (Ayotte et al ), and northwest and central Minnesota (Erickson et al ).…”

Modelling of the shallow water table at high spatial resolution using Random Forests

“…Bayesian network). Machine learning has already been applied to model other groundwater related variables such as, nitrate concentration (Nolan et al, 2015;Tesoriero et al, 2015), arsenic concentrations (Erickson et al, 2018;Winkel et al, 2011) or redox conditions in the subsurface (Close et al, 2016;Koch et al, 2019), and the potential to map the depth to the water table is tangible.…”