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Attia M, Tsai FTC. Successive bootstrapping deep learning approach and airborne EM-borehole data fusion to understand salt water in the Mississippi River Valley Alluvial Aquifer. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 932:172950. [PMID: 38703842 DOI: 10.1016/j.scitotenv.2024.172950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 05/06/2024]
Abstract
Increasing demands from agriculture and urbanization have decreased groundwater level and increased salinity worldwide. Better aquifer characterization and soil salinity mapping are important for proactive groundwater management. Airborne electromagnetic (AEM) is a powerful tool for aquifer characterization and salinity delineation. However, AEM needs to be interpreted with caution before being used for groundwater quality analysis. This study introduces a framework that utilizes the AEM data for both lithologic modeling and salinity delineation. A resistivity-to-lithology (R2L) model is developed to interpret AEM resistivity to lithology based a depth-dependent multi-resistivity thresholds. Then, a cokriging method is used to integrate AEM data from two different EM systems to predict resistivity at the aquifer. Finally, a resistivity-to-chloride concentration (R2C) model utilizes the resistivity model to estimate chloride concentrations at sand facies. A deep learning artificial neural network (DL-ANN) model is introduced with a successive bootstrapping approach to estimate total dissolved solids first and then use it together with resistivity data to estimate chloride concentration. The methodology was applied to delineating salinity plumes in the Mississippi River Valley alluvial aquifer (MRVA). This study found that the salinity distribution in MRVA is highly correlated with the Jurassic salt basin, salt domes, faulting, seismicity, and river water quality. The result indicates salinity upconing due to excessive pumping.
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Affiliation(s)
- Michael Attia
- Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, Louisiana, 70803, United States of America.
| | - Frank T-C Tsai
- Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, Louisiana, 70803, United States of America.
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Tesoriero AJ, Wherry SA, Dupuy DI, Johnson TD. Predicting Redox Conditions in Groundwater at a National Scale Using Random Forest Classification. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5079-5092. [PMID: 38451152 PMCID: PMC10956438 DOI: 10.1021/acs.est.3c07576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/16/2024] [Accepted: 02/20/2024] [Indexed: 03/08/2024]
Abstract
Redox conditions in groundwater may markedly affect the fate and transport of nutrients, volatile organic compounds, and trace metals, with significant implications for human health. While many local assessments of redox conditions have been made, the spatial variability of redox reaction rates makes the determination of redox conditions at regional or national scales problematic. In this study, redox conditions in groundwater were predicted for the contiguous United States using random forest classification by relating measured water quality data from over 30,000 wells to natural and anthropogenic factors. The model correctly predicted the oxic/suboxic classification for 78 and 79% of the samples in the out-of-bag and hold-out data sets, respectively. Variables describing geology, hydrology, soil properties, and hydrologic position were among the most important factors affecting the likelihood of oxic conditions in groundwater. Important model variables tended to relate to aquifer recharge, groundwater travel time, or prevalence of electron donors, which are key drivers of redox conditions in groundwater. Partial dependence plots suggested that the likelihood of oxic conditions in groundwater decreased sharply as streams were approached and gradually as the depth below the water table increased. The probability of oxic groundwater increased as base flow index values increased, likely due to the prevalence of well-drained soils and geologic materials in high base flow index areas. The likelihood of oxic conditions increased as topographic wetness index (TWI) values decreased. High topographic wetness index values occur in areas with a propensity for standing water and overland flow, conditions that limit the delivery of dissolved oxygen to groundwater by recharge; higher TWI values also tend to occur in discharge areas, which may contain groundwater with long travel times. A second model was developed to predict the probability of elevated manganese (Mn) concentrations in groundwater (i.e., ≥50 μg/L). The Mn model relied on many of the same variables as the oxic/suboxic model and may be used to identify areas where Mn-reducing conditions occur and where there is an increased risk to domestic water supplies due to high Mn concentrations. Model predictions of redox conditions in groundwater produced in this study may help identify regions of the country with elevated groundwater vulnerability and stream vulnerability to groundwater-derived contaminants.
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Affiliation(s)
- Anthony J. Tesoriero
- U.S.
Geological Survey, 601 SW Second Avenue, Suite 1950, Portland, Oregon 97204, United States
| | - Susan A. Wherry
- U.S.
Geological Survey, 601 SW Second Avenue, Suite 1950, Portland, Oregon 97204, United States
| | - Danielle I. Dupuy
- U.S.
Geological Survey, 6000
J Street, Placer Hall, Sacramento, California 95819, United States
| | - Tyler D. Johnson
- U.S.
Geological Survey, 4165
Spruance Road, Suite 200, San Diego, California 92101, United States
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Erickson ML, Brown CJ, Tomaszewski EJ, Ayotte JD, Böhlke JK, Kent DB, Qi S. Prioritizing water availability study settings to address geogenic contaminants and related societal factors. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:303. [PMID: 38400911 PMCID: PMC10894127 DOI: 10.1007/s10661-024-12362-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 01/15/2024] [Indexed: 02/26/2024]
Abstract
Water availability for human and ecological uses depends on both water quantity and water quality. The U.S. Geological Survey (USGS) is developing strategies for prioritizing regional-scale and watershed basin-scale studies of water availability across the nation. Previous USGS ranking processes for basin-scale studies incorporated primarily water quantity factors but are now considering additional water quality factors. This study presents a ranking based on the potential impacts of geogenic constituents on water quality and consideration of societal factors related to water quality. High-concentration geogenic constituents, including trace elements and radionuclides, are among the most prevalent contaminants limiting water availability in the USA and globally. Geogenic constituents commonly occur in groundwater because of subsurface water-rock interactions, and their distributions are controlled by complex geochemical processes. Geogenic constituent mobility can also be affected by human activities (e.g., mining, energy production, irrigation, and pumping). Societal factors and relations to drinking water sources and water quality information are often overlooked when evaluating research priorities. Sociodemographic characteristics, data gaps resulting from historical data-collection disparities, and infrastructure condition/age are examples of factors to consider regarding environmental justice. This paper presents approaches for ranking and prioritizing potential basin-scale study areas across the contiguous USA by considering a suite of conventional physical and geochemical variables related to geogenic constituents, with and without considering variables related to societal factors. Simultaneous consideration of societal and conventional factors could provide decision makers with more diverse, interdisciplinary tools to increase equity and reduce bias in prioritizing focused research areas and future water availability studies.
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Affiliation(s)
- Melinda L Erickson
- U.S. Geological Survey, 2280 Woodale Drive, Mounds View, MN, 55112, USA.
| | - Craig J Brown
- U.S. Geological Survey, 101 Pitkin Street, East Hartford, CT, 06108, USA
| | | | - Joseph D Ayotte
- U.S. Geological Survey, 331 Commerce Way, Pembroke, NH, 03275, USA
| | - John K Böhlke
- U.S. Geological Survey, 12201 Sunrise Valley Dr, Reston, VA, 20192, USA
| | - Douglas B Kent
- U.S. Geological Survey, 345 Middlefield Rd, Menlo Park, CA, 94025, USA
| | - Sharon Qi
- U.S. Geological Survey, 601 SW 2nd Ave. Suite 1950, Portland, OR, 97204, USA
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Lombard MA, Brown EE, Saftner DM, Arienzo MM, Fuller-Thomson E, Brown CJ, Ayotte JD. Estimating Lithium Concentrations in Groundwater Used as Drinking Water for the Conterminous United States. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1255-1264. [PMID: 38164924 PMCID: PMC10795177 DOI: 10.1021/acs.est.3c03315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 11/28/2023] [Accepted: 12/19/2023] [Indexed: 01/03/2024]
Abstract
Lithium (Li) concentrations in drinking-water supplies are not regulated in the United States; however, Li is included in the 2022 U.S. Environmental Protection Agency list of unregulated contaminants for monitoring by public water systems. Li is used pharmaceutically to treat bipolar disorder, and studies have linked its occurrence in drinking water to human-health outcomes. An extreme gradient boosting model was developed to estimate geogenic Li in drinking-water supply wells throughout the conterminous United States. The model was trained using Li measurements from ∼13,500 wells and predictor variables related to its natural occurrence in groundwater. The model predicts the probability of Li in four concentration classifications, ≤4 μg/L, >4 to ≤10 μg/L, >10 to ≤30 μg/L, and >30 μg/L. Model predictions were evaluated using wells held out from model training and with new data and have an accuracy of 47-65%. Important predictor variables include average annual precipitation, well depth, and soil geochemistry. Model predictions were mapped at a spatial resolution of 1 km2 and represent well depths associated with public- and private-supply wells. This model was developed by hydrologists and public-health researchers to estimate Li exposure from drinking water and compare to national-scale human-health data for a better understanding of dose-response to low (<30 μg/L) concentrations of Li.
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Affiliation(s)
- Melissa A. Lombard
- New
England Water Science Center, U.S. Geological
Survey, 331 Commerce Way, Pembroke, New Hampshire 03275, United States
| | - Eric E. Brown
- Centre
for Addiction and Mental Health, University
of Toronto, 80 Workman
Way, Toronto, Ontario, Canada M6J 1H4
| | - Daniel M. Saftner
- Desert
Research Institute, 2215 Raggio Parkway, Reno, Nevada 89512, United States
| | - Monica M. Arienzo
- Desert
Research Institute, 2215 Raggio Parkway, Reno, Nevada 89512, United States
| | - Esme Fuller-Thomson
- Institute
for Life Course and Aging, University of
Toronto, 246 Bloor Street
West, Toronto, Ontario, Canada M5S 1V4
| | - Craig J. Brown
- New
England Water Science Center, U.S. Geological
Survey, 339 Main Street, East Hartford, Connecticut 06108, United States
| | - Joseph D. Ayotte
- New
England Water Science Center, U.S. Geological
Survey, 331 Commerce Way, Pembroke, New Hampshire 03275, United States
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Damaneh JM, Ahmadi J, Rahmanian S, Sadeghi SMM, Nasiri V, Borz SA. Prediction of wild pistachio ecological niche using machine learning models. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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