Geologic-sourced arsenic is common in Minnesota groundwater. Drinking-water managers, well owners, and well contractors need to know where and why high arsenic in groundwater is likely to occur in wells in order to take measures to protect public health. The USGS is assessing the spatial distribution of high arsenic groundwater in Minnesota, and identifying factors affecting arsenic mobilization.
Project Objectives:
The overall study objective is to better delineate and help explain groundwater arsenic (As) concentrations in Minnesota over space and time.
Specific objectives are:
- Evaluate methods used by well drillers to collect groundwater samples for arsenic analysis
- Evaluate temporal arsenic concentration variability over one year in newly constructed wells
- Assess the relation between arsenic concentration and hydrogeologic, geochemical, and well-construction factors using statistical models
We have made progress on project objectives, as published in three journal articles.
We evaluated methods used by well drillers to collect groundwater samples for arsenic analysis. We found that water sampling protocols—filtration, timing, sample point—influence variability in arsenic concentrations in new drinking water wells. Variability in measured arsenic concentration at a well was reduced when samples were (1) filtered, (2) collected from household plumbing instead of from the drill rig pump, or (3) collected several months after well construction (instead of within 4 weeks of well installation). Particulates and fine aquifer sediments entrained in groundwater samples, or other artifacts of drilling disturbance, can cause undesirable variability in geochemical measurements, including arsenic measurements. Establishing regulatory protocols requiring sample filtration and/or collection from household plumbing could improve the reliability of information provided to well owners and to secondary data users.
We developed a boosted regression tree (BRT) statistical model to assess the relation between arsenic concentration and hydrogeologic, geochemical, and well-construction factors. We found that 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 results show 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.
We evaluated the effects of geochemical changes after well installation and operation on arsenic concentrations. We examined changes in arsenic and other geochemical constituents over one year in water from 250 new domestic water wells. Our aquifers are late Quaternary-age glacial aquifers or fractured crystalline bedrock aquifers. During the study, arsenic concentrations increased in wells in glacial aquifers, and redox conditions changed toward more reducing. In bedrock aquifer wells, there was no significant change in arsenic concentrations, and conditions became more oxic. The arsenic concentration variability we identified has important implications for water treatment, and for programs that require testing of new wells, such as in Minnesota and New Jersey. Information on how and why concentrations of arsenic vary at new wells provides context on what constitutes a representative sample in situations where testing is required or desired. Measurement and mechanistic characterization of human-induced geochemical changes associated with drilling, installing, pumping, and sampling of drinking water wells can improve guidance to well owners and policy makers on when to sample wells.
The research is funded by the State of Minnesota Clean Water Fund through the Minnesota Department of Health and the USGS Cooperative Matching Fund. The work was also supported by the National Science Foundation Graduate Research Fellowship Program and an internship provided through the Graduate Research Internship Program (GRIP).
Additional Publications
Erickson, Melinda L. and Barnes, Randal J. 2005, Arsenic concentration variability in public water system wells in Minnesota, USA, Applied Geochemistry, Volume 21, Issue 2, February 2006, Pages 305–317, doi:10.1016/j.apgeochem.2005.12.005
Erickson, Melinda L. and Barnes, Randal J., 2005, Glacial Sediment Causing Regional-Scale Elevated Arsenic in Drinking Water, Groundwater, Volume 43, Issue 6, pages 796–805, DOI: 10.1111/j.1745-6584.2005.00053.x
Erickson, Melinda L. and Barnes, Randal J, 2005. Well characteristics influencing arsenic concentrations in groundwater, Water research, Volume 39, Issue 16, Pages 4029-4039, doi:10.1016/j.watres.2005.07.026 and Erickson, Melinda L., 2005, Erratum to: Erickson, ML, Barnes, RJ, October 2005. Well characteristics influencing arsenic concentrations in ground water. Water Res. 39 (16) 4029–4039. Water Research, Volume 39, Issue 20, Pages 5277-5278
Sarah Nicholas, Melinda L Erickson, Laurel G Woodruff, Alan R Knaeble, Matthew A Marcus, Joshua K Lynch, and Brandy M Toner, 2017. Solid-phase arsenic speciation in aquifer sediments: a micro-X-ray absorption spectroscopy approach for quantifying trace-level speciation. Geochimica et Cosmochimica Acta., 211, pp 228-255, http://dx.doi.org/10.1016/j.gca.2017.05.018
Below are data or web applications associated with this project.
Groundwater arsenic data and ASCII grids for predicting elevated arsenic in northwestern and central Minnesota using boosted regression tree methods
Below are publications associated with this project.
Machine-learning predictions of high arsenic and high manganese at drinking water depths of the glacial aquifer system, northern continental United States
Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States
Months-long spike in aqueous Arsenic following domestic well installation and disinfection: Short- and long-term drinking water quality implications
Machine learning predictions of pH in the Glacial Aquifer System, Northern USA
Stability of inorganic and methylated arsenic species in laboratory standards, surface water and groundwater under three different preservation regimes
Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization
How or when samples are collected affects measured arsenic concentration in new drinking water wells
Predicting geogenic arsenic in drinking water wells in glacial aquifers, north-central USA: Accounting for depth-dependent features
Below are news stories associated with this project.
Below are partners associated with this project.
- Overview
Geologic-sourced arsenic is common in Minnesota groundwater. Drinking-water managers, well owners, and well contractors need to know where and why high arsenic in groundwater is likely to occur in wells in order to take measures to protect public health. The USGS is assessing the spatial distribution of high arsenic groundwater in Minnesota, and identifying factors affecting arsenic mobilization.
Project Objectives:
The overall study objective is to better delineate and help explain groundwater arsenic (As) concentrations in Minnesota over space and time.
Specific objectives are:
- Evaluate methods used by well drillers to collect groundwater samples for arsenic analysis
- Evaluate temporal arsenic concentration variability over one year in newly constructed wells
- Assess the relation between arsenic concentration and hydrogeologic, geochemical, and well-construction factors using statistical models
We have made progress on project objectives, as published in three journal articles.
We evaluated methods used by well drillers to collect groundwater samples for arsenic analysis. We found that water sampling protocols—filtration, timing, sample point—influence variability in arsenic concentrations in new drinking water wells. Variability in measured arsenic concentration at a well was reduced when samples were (1) filtered, (2) collected from household plumbing instead of from the drill rig pump, or (3) collected several months after well construction (instead of within 4 weeks of well installation). Particulates and fine aquifer sediments entrained in groundwater samples, or other artifacts of drilling disturbance, can cause undesirable variability in geochemical measurements, including arsenic measurements. Establishing regulatory protocols requiring sample filtration and/or collection from household plumbing could improve the reliability of information provided to well owners and to secondary data users.
We developed a boosted regression tree (BRT) statistical model to assess the relation between arsenic concentration and hydrogeologic, geochemical, and well-construction factors. We found that 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 results show 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.
We evaluated the effects of geochemical changes after well installation and operation on arsenic concentrations. We examined changes in arsenic and other geochemical constituents over one year in water from 250 new domestic water wells. Our aquifers are late Quaternary-age glacial aquifers or fractured crystalline bedrock aquifers. During the study, arsenic concentrations increased in wells in glacial aquifers, and redox conditions changed toward more reducing. In bedrock aquifer wells, there was no significant change in arsenic concentrations, and conditions became more oxic. The arsenic concentration variability we identified has important implications for water treatment, and for programs that require testing of new wells, such as in Minnesota and New Jersey. Information on how and why concentrations of arsenic vary at new wells provides context on what constitutes a representative sample in situations where testing is required or desired. Measurement and mechanistic characterization of human-induced geochemical changes associated with drilling, installing, pumping, and sampling of drinking water wells can improve guidance to well owners and policy makers on when to sample wells.
The research is funded by the State of Minnesota Clean Water Fund through the Minnesota Department of Health and the USGS Cooperative Matching Fund. The work was also supported by the National Science Foundation Graduate Research Fellowship Program and an internship provided through the Graduate Research Internship Program (GRIP).
This illustration compares the construction characteristics of two water wells. Note that the distance from the top of the well screen to the confining unit, or aquitard, is much shorter for the well on the right, as is the length of the screen in the underlying aquifer unit. Placing a well screen farther beneath the confining unit and/or using a longer-length screen, as shown for the well on the left, can decrease the likelihood of elevated arsenic concentrations in domestic well water. (Credit: Modified from Figure 1 in Erickson & Barnes, 2005, reprinted with permission.) Additional Publications
Erickson, Melinda L. and Barnes, Randal J. 2005, Arsenic concentration variability in public water system wells in Minnesota, USA, Applied Geochemistry, Volume 21, Issue 2, February 2006, Pages 305–317, doi:10.1016/j.apgeochem.2005.12.005
Erickson, Melinda L. and Barnes, Randal J., 2005, Glacial Sediment Causing Regional-Scale Elevated Arsenic in Drinking Water, Groundwater, Volume 43, Issue 6, pages 796–805, DOI: 10.1111/j.1745-6584.2005.00053.x
Erickson, Melinda L. and Barnes, Randal J, 2005. Well characteristics influencing arsenic concentrations in groundwater, Water research, Volume 39, Issue 16, Pages 4029-4039, doi:10.1016/j.watres.2005.07.026 and Erickson, Melinda L., 2005, Erratum to: Erickson, ML, Barnes, RJ, October 2005. Well characteristics influencing arsenic concentrations in ground water. Water Res. 39 (16) 4029–4039. Water Research, Volume 39, Issue 20, Pages 5277-5278
Sarah Nicholas, Melinda L Erickson, Laurel G Woodruff, Alan R Knaeble, Matthew A Marcus, Joshua K Lynch, and Brandy M Toner, 2017. Solid-phase arsenic speciation in aquifer sediments: a micro-X-ray absorption spectroscopy approach for quantifying trace-level speciation. Geochimica et Cosmochimica Acta., 211, pp 228-255, http://dx.doi.org/10.1016/j.gca.2017.05.018
- Data
Below are data or web applications associated with this project.
Groundwater arsenic data and ASCII grids for predicting elevated arsenic in northwestern and central Minnesota using boosted regression tree methods
This data release contains: (1) ASCII grids of predicted probability of elevated arsenic in groundwater for the Northwest and Central Minnesota regions, (2) input arsenic and predictive variable data used in model development and calculation of predictions, and (3) ASCII files used to predict the probability of elevated arsenic across the two study regions. The probability of elevated arsenic was - Publications
Below are publications associated with this project.
Machine-learning predictions of high arsenic and high manganese at drinking water depths of the glacial aquifer system, northern continental United States
Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT moAuthorsMelinda L. Erickson, Sarah M. Elliott, Craig J. Brown, Paul Stackelberg, Katherine Marie Ransom, James E. Reddy, Charles A. CravottaMachine learning predicted redox conditions in the glacial aquifer system, northern continental United States
Groundwater supplies 50% of drinking water worldwide and 30% in the United States. Geogenic and anthropogenic contaminants can, however, compromise water quality, thus limiting groundwater availability. Reduction/oxidation (redox) processes and redox conditions affect groundwater quality by influencing the mobility and transport of common geogenic and anthropogenic contaminants. In the glacial aquAuthorsMelinda L. Erickson, Sarah M. Elliott, Craig J. Brown, Paul Stackelberg, Katherine Marie Ransom, James E. ReddyMonths-long spike in aqueous Arsenic following domestic well installation and disinfection: Short- and long-term drinking water quality implications
Exposure to high concentration geogenic arsenic via groundwater is a worldwide health concern. Well installation introduces oxic drilling fluids and hypochlorite (a strong oxidant) for disinfection, thus inducing geochemical disequilibrium. Well installation causes changes in geochemistry lasting 12 + months, as illustrated in a recent study of 250 new domestic wells in Minnesota, north-central UnAuthorsMelinda L. Erickson, Elizabeth D. Swanner, Brady A. Ziegler, Jeffrey R. HavigMachine learning predictions of pH in the Glacial Aquifer System, Northern USA
A boosted regression tree model was developed to predict pH conditions in three dimensions throughout the glacial aquifer system of the contiguous United States using pH measurements in samples from 18,386 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and, whenAuthorsPaul Stackelberg, Kenneth Belitz, Craig J. Brown, Melinda L. Erickson, Sarah M. Elliott, Leon J. Kauffman, Katherine Marie Ransom, James E. ReddyStability of inorganic and methylated arsenic species in laboratory standards, surface water and groundwater under three different preservation regimes
Geogenic arsenic (As) adversely affects drinking water quality in geologically diverse aquifers across the globe. Although the species of As significantly affects its fate, transport, toxicity, and As treatment technology efficacy, reported effectiveness of As species preservation methods varies widely with preservation methods and natural water geochemistry. Our study 1) evaluates the shelf lifeAuthorsSarah Stetson, Melinda L. Erickson, Jeffrey Brenner, Emily C. Berquist, Christopher J. Kanagy, Susan Melissa Whitcomb, Caitlyn Margaret LawrenceArsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization
Chronic exposure to geogenic arsenic via drinking water is a worldwide health concern. However, effects of well installation and operation on arsenic concentrations and mobilization are not well understood. This knowledge gap impacts both reliable detection of arsenic in drinking water and effective public health recommendations to reduce exposure to arsenic. This study examines changes in arsenicAuthorsMelinda L. Erickson, Helen F. Malenda, Emily C. Berquist, Joseph D. AyotteHow or when samples are collected affects measured arsenic concentration in new drinking water wells
Naturally occurring arsenic can adversely affect water quality in geologically diverse aquifers throughout the world. Chronic exposure to arsenic via drinking water is a human health concern due to risks for certain cancers, skin abnormalities, peripheral neuropathy, and other negative health effects. Statewide in Minnesota, USA, 11% of samples from new drinking water wells have arsenic concentratAuthorsMelinda L. Erickson, Helen F. Malenda, Emily C. BerquistPredicting geogenic arsenic in drinking water wells in glacial aquifers, north-central USA: Accounting for depth-dependent features
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 witAuthorsMelinda L. Erickson, Sarah M. Elliott, Catherine Christenson, Aliesha L. Krall - News
Below are news stories associated with this project.
- Partners
Below are partners associated with this project.