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

Erickson, Melinda L.; Elliott, Sarah; Christenson, Catherine; Krall, Aliesha L.

doi:10.1029/2018wr023106

Cited by 40 publications

(27 citation statements)

References 69 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Libera

Pedretti

Tateo

et al. 2020

Water Resources Research

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This work proposed a novel method to elucidate the controls of As mobility in complex aquifers based on an unsupervised machine learning algorithm, Self-Organizing Map (SOM), and process-based geochemical modeling. The approach is tested in the shallow aquifers of the Venetian Alluvial Plain (VAP) near Venice, Italy, where As concentrations seasonally and locally exceed recommended drinking water limits. SOM was fed using information from two geochemical surveys on eight VAP boreholes, and continuous reading of hourly groundwater head levels and weekly geochemical analyses from three VAP boreholes between mid-October 2017 and end of January 2018. The SOM analysis is consistent with redox-controlled dissolution-precipitation hydrous ferric oxides (HFOs) as a key control of As mobility in the aquifer. Dissolved As is positively correlated to Fe and NH þ 4 and negatively to the oxidizing-reducing potential (ORP). Negative correlation between As and groundwater head levels suggests a redox control by rainfall-driven recharge, which adds oxidants to the aquifer while progressively attenuating As. This mechanism is tested using process-based geochemical modeling, which simulates different transport modalities of oxidants entering the aquifer. Starting from reducing aquifer conditions, the model reproduces correctly the observed ORP and the trends in As and Fe, when the function describing the occurrence of oxidizing events scales according to the temporal occurrence of rainfall events. Heterogeneity can strongly control the local-scale effectiveness of recharge as a natural As attenuating factor, requiring a different model analysis to be properly assessed and to be developed in a follow-up study.

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Section: Introductionmentioning

confidence: 99%

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

Libera

Pedretti

Tateo

et al. 2020

Water Resources Research

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“…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 ).…”

Section: Introductionmentioning

confidence: 99%

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

Smith¹,

Dupont²,

McLean³

2019

Groundwater Monitoring Rem

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Arsenic in groundwater in excess of the drinking water limit is usually from natural sources. The release of As, however, can be amplified by anthropogenic inputs of carbon, including those associated with the remediation of chlorinated solvents such as trichloroethylene (TCE). Large laboratory columns (15.2 cm diameter, 183 cm long) packed with aquifer solids from a TCE contaminated site near Hill Air Force Base (Utah) were fed with groundwater containing TCE and were biostimulated, bioaugmented, and monitored for TCE dechlorination over 7.5 years. This is a basin‐fill aquifer with As bearing geologic features. The objective of this study was to evaluate the biogeochemical changes that affect As solubilization that occurred after 7.5 years of feeding the columns with either whey or two formulations of Newman Zone® emulsified oil, and a no carbon addition control. The columns were analyzed in 10.16 cm sections for pore water and sediment quality parameters, parameters descriptive of redox conditions, and As geochemistry. The whey treatment resulted in 52.9% (±1.36%) (average ± standard deviation) of the total As in the solids leaching from the columns. The oil treatments promoted loss of 20.9% (±6.40%) of the total As. Arsenic solubility was associated with strongly reducing conditions developed with whey addition, leading to dissolution of crystalline Fe oxides and release of As. Arsenic was attenuated within the oil treated columns with As associated with carbonates in the lower layers. A consequence of adding whey, resulting in the desired full dechlorination of TCE in this aquifer, is the mobilization of As in groundwater.

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“…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.…”

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confidence: 99%

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

Koch¹,

Berger²,

Henriksen³

et al. 2019

Preprint

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Abstract. Machine learning provides a great potential to model hydrological variables at a spatial resolution beyond the capabilities of traditional physically-based modelling. This study features an application of Random Forests (RF) to model the depth to the shallow water table, for a wintertime minimum event, at 50 m resolution over a 15,000 km2 large domain in Denmark. In Denmark, the shallow groundwater poses severe risks of groundwater induced flood events affecting both, urban and agricultural areas. The risk is especially critical in wintertime, when the shallow groundwater is close to terrain. In order to advance modelling capabilities of the shallow groundwater system and to provide estimates at scales required for decision making, this study introduces a simple method to unify RF and physically-based modelling. Results from the national water resources model in Denmark (DK-model) at 500 m resolution are employed as covariate in the RF model. Thereby, RF ensures physical consistency at coarse scale and fully exhausts high-resolution information from readily available environmental variables. The vertical distance to the nearest waterbody was rated the most important covariate in the trained RF model followed by the DK-model. The validation test of the trained RF model was very satisfying with a mean absolute error of 79 cm and a coefficient of determination of 0.55. The resulting map underlines the severity of groundwater flooding risk in Denmark, as the average depth to the shallow groundwater is 1.9 m and approximately 29 % of the area is characterised with a depth less than 1 m during a typical wintertime minimum event. This study brings forward a novel method to assess the spatial patterns of covariate importance of the RF predictions which contributes to an increased interpretability of the RF model. Quantifying uncertainty of RF models is still rare for hydrological applications. Two approaches, namely Random Forests Regression Kriging (RFRK) and Quantile Regression Forests (QRF) were tested to estimate uncertainties related to the predicted groundwater levels. This study argues that the uncertainty sources captured by RFRK and QRF can be considered independent and hence, they can be combined to a total variance through simple uncertainty propagation.

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

Cited by 40 publications

References 69 publications

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

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

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

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

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