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Lawrence Berkeley National Laboratory

Title: Research Project on CO2 Geological Storage and Groundwater Resources: Water Quality Effects Caused by CO2 Intrusion into Shallow Groundwater
Author: Birkholzer, Jens
Publication Date: 12-18-2008
Publication Info: Lawrence Berkeley National Laboratory
Permalink: http://escholarship.org/uc/item/82c0q660

# Research Project on CO 2 

# Geological Storage and Groundwater Resources

Water Quality Effects Caused by CO 2 

# Intrusion into Shallow Groundwater

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Technical Report, October 2008

Contact: Jens Birkholzer Earth Sciences Division, Lawren ce Berkeley National Laboratory 1 Cyclotron Rd., MS 90-1116; 510-486-7134; jtbirkholzer@lbl.gov

Contributors: Jens Birkholzer, John Apps, Liange Zheng, Yingqi Zhang, Tianfu Xu, Chin-Fu Tsang EPA Program Managers: Bruce K obelski, Anhar Karimjeei 

## Executive Summary

One promising approach to reduce greenhouse gas emissions is injecting CO 2 into suitable geologic formations, typically depl eted oil/gas reservoirs or salin e formations at depth larger than 800 m. Proper site sele ction and management of CO 2 storage projects will ensure that the risks to human health and the environment are low. However, a risk remains that CO 2 could migrate from a deep storage formation, e.g. via local high-permeability pathways such as permeable faults or degraded wells, and arrive in shallow groundwater resources. The ingress of CO 2 is by itself not typically a c oncern to the water qua lity of an underground source of drinking water (USDW), but it will change the geochemical conditions inthe aquifer and will cause secondary effects mainly induced by changes in pH, in particular the mobilization of hazardous inorganic constituents present in the aquifer mi nerals. Identification and assessment of these potential effects is necessary to analyze risk s associated with geol ogic sequestration of CO 2 . This report describes a systematic evaluation of the possible water quality changes in response to CO 2 intrusion into aquifers currently used as sources of potable water in the United States. Our goal was to develop a general understanding of the potential vul nerability of United States potable groundwater resour ces in the event of CO 2 leakage. This goal was achieved in two main tasks, the first to develop a comprehensive geoc hemical model representi ng typical conditions in many freshwater aquifers (Sectio n 3), the second to conduct a sy stematic reactive-transport modeling study to quantify the effect of CO 2 intrusion into shallow aquifers (Section 4). Via reactive-transport modeling, the amount of hazar dous constituents potentially mobilized by the ingress of CO 2 was determined, the fate and migration of these constituents in the groundwater was predicted, and the likelihood that drinki ng water standards might be exceeded was evaluated. A variety of scenar ios and aquifer conditions was considered in a sensitivity evaluation. The scenarios and conditions simulated in Section 4, in particular those describing the geochemistry and mineralogy of potable aqui fers, were selected based on the comprehensive geochemical model developed in Section 3.
An important step in developing the comprehens ive geochemical model was the identification of those mineral hosts containing hazardous cons tituents, which control their equilibrium concentration in potable groundwaters. This step was accomplished through an extensive review of the literature, especially that relating to ore and coal mineralogy, through which plausible mineral hosts containing hazardous constituents, either as essential co mponents or in solid solution, were provisionally identified. The postulated presence of these mineral hosts in aquifer rocks was then examined through a thermodynamic analysis of more than 38,000 potable groundwaters from the USGS NWIS database, using a distribution-of-species code to test for their thermodynamic saturation in solution. Mineral hosts meeting the criterion of thermodynamic saturation were assumed to contro l the aqueous concentr ations of hazardous constituents and any subsequent changes due to the ingress of CO 2 . Considering a representative groundwater under reducing conditions (a condition me t in the large majority of all evaluated groundwaters), the most likely solubility-contro lling mineral host for lead is galena, while arsenic would typically be contro lled by the arsenopyrite component as a solid solution in pyrite. Other likely mineral hosts for a re ducing groundwater are as follows: barium is likely to beii saturated with respect to barite; cadmium is either in solid solution in sphalerite and/or is present as greenockite or cadmoselite; me rcury is likely to be presen t as cinnabar and possibly as tiemannite, but its presence in solid solution in pyr ite, perhaps in minor amounts, cannot be ruled out; antimony is present as the gud mundite component in solid so lution in pyrite, and probably as kermesite, especially in neutral to acid conditions; selenium could be present as either clausthalite, tiemannite, or cadmoselite; uranium is present as uraninite and/or coffinite; and zinc is present as sphalerite and/or hemimorphite.
Characterization of thermodynamic controls necessitated the iterative improvement and augmentation of a large supporting thermodynamic da tabase comprising the solubility products of minerals of intere st, and of complexes that affect the concentrations of the hazardous constituents in aqueous soluti on. A comprehensive literature an alysis was conducted for this purpose. The resulting thermodynamic data used fo r geochemical modeling were then tested, and in some instances improved via calibration agains t field observations from the NWIS database, based on the resulting thermodyna mic saturations of the more than 38,000 groundwater samples. In addition, the bulk mineralogy of typical aquifer host rocks had also to be determined, accomplished through review of the scientific literature. The bulk mineralogy was combined with the mineralogy of the previously identifi ed mineral hosts for several hazardous inorganic constituents, with their respec tive concentrations estimated through an evaluation of natural abundances in soils and sediments. While these a bundances are typically ve ry small compared to those of the major minerals (order of 10-7 to >1 0-4), all hazardous constituents targeted for study occur ubiquitously in soils, sediments, and a quifer rocks, indicating that their potential mobilization in response to CO 2 intrusion can be a widespread one in domestic aquifers.
A preliminary assessment of CO 2 -related water quality changes was conducted by calculating the expected equilibrium concentrations of hazardous inorganic constituents as a function of the partial pressure of CO 2 . P(CO 2 ) was varied from 10 -4 bar to 10 bar, lead ing to a corresponding pH variation between 8.9 and 5.6. Equilibrium concentrations were determined using a distribution-of-species code, assuming a represen tative groundwater in reducing conditions and incorporating the thermodynamic controls identified in above-m entioned database evaluation. Results indicate that the concentrations of some of the studied constituents, for example barium, cadmium, antimony, and zinc, are sensitive to changes in P(CO 2 ), but that only arsenic, and to a lesser extent lead and zinc, have the pote ntial for exceeding th eir respective maximum contaminant levels (as defined by the U.S. E nvironmental Protection Agency) at elevated CO 2 concentrations. Therefore, in the systematic reactive-transport modeling study discussed below, primary emphasis was placed on the evaluation of the response and transport of arsenic and lead under intruding CO 2 . The reactive-transport simulato r TOUGHREACT was then used to predict the transport of gaseous and dissolved CO 2 within a shallow aquifer, as well as the geochemical changes caused by the presence of CO 2 , in particular those changes leadi ng to the mobilization of lead and arsenic and their migration in the groundwater (Section 4). Various sensitivity studies were conducted for different hydrological, geochemical, and mineralogical conditions, (1) to account for a range of conditions found in domestic potable aquifers a nd their host rocks, and (2) toiii evaluate the uncertainty associated with geochemical processes and model parameters. Equilibrium runs were performed prior to modeling CO 2 intrusion to establish the initial chemical composition of the groundwater at e quilibrium with the selected mineralogy. This initiation step also establishes equilibrium betw een mineral solubilities and adsorption sites, essentially “loading up” the latte r. Thus, the selection of sol ubility-controlling minerals for hazardous inorganic constituents not only define d the initial aqueous concentration in the groundwater, but also the amount of contaminants initially sorbed onto clays and iron oxides.
Starting with the equilibrated init ial conditions, the impact of CO 2 intrusion was then modeled for a synthetic hydrogeological si tuation in a representative pot able aquifer while considering continuous ingress of CO 2 for a time period of 100 years. CO 2 was assumed to have escaped from a deep geologic sequestratio n site via a preferential path way, entering the shallow aquifer locally at a constant rate. A confined a quifer was considered, meaning that excess CO 2 not dissolving into groundwater accumu lates at the aquifer top and sp reads out latera lly, potentially causing widespread pH changes and mobilization of contaminants. (Excess CO 2 in an unconfined aquifer would escape into the va dose zone, causing no further harm to the groundwater quality.) A suite of “representative” aquifer mineralogies was simulated representing a broad range of host rock co mpositions found throughout the United States.
Our simulation results suggest that CO 2 ingress into a shallow aquifer can lead to significant mobilization of lead and arseni c, contaminating the groundwater near the intrusion location and further downstream. While substantial increases in aqueous concentrations are predicted to occur compared to the initial water composition, th e maximum contaminant level for arsenic in groundwater has been exceeded in only a fe w simulation cases, whereas the maximum contaminant level for lead has not been exceeded at all. Notice that the mobilization of lead and arsenic, from minerals and from adsorption s ites, causes only small changes to the initial contaminant mass in the solid phase, even after a time period of 100 years of CO 2 intrusion. Very small amounts of lead and arsenic contained in the aquifer solid phase can provide a long-lasting source of contamination.
As demonstrated in a comprehe nsive sensitivity study, many conditi ons and processes impact the severity and spatial distribution of gr oundwater contamination in response to CO 2 intrusion. Our simulations indicate that adsorption/desorpti on via surface complexation is arguably the most important process controlling the fate of hazardous constituents mobilized by CO 2 leakage when sorbing minerals are present. In a simulati on case without adsorption/ desorption taken into account, the resulting long-term contamination of the aquifer is less significant near the region where CO 2 enters, while aqueous concentrations keep increasing with travel distance as acidic water migrates further and more dissolution occurs. The relative importance of dissolution/precipitation versus adsorption/desorptio n is controlled by ma ny factors, including adsorption parameters and aquifer mineralogy, reaction kinetics, aqueous complexation processes, and mineral solubility constants. For example, the role that adsorption/desorption plays in the mobilizatio n of hazardous constituents depends strongly on the number of adsorption sites. A sensitivity run with lower site density re veals that early increases in aqueous lead and arsenic are much less significant, because desorption is less relevant in mobilizing contaminants.iv Moderate to strong sensitivities in the resulti ng lead and arsenic concentrations have been obtained with respect to other geochemical para meters (such as the solubilities of hazardousconstituent-bearing minerals, the kinetic rates fo r precipitation and diss olution, and the aqueous complexation constants) and conditions (such as the redox environment, with the simulated behavior in a oxidized aquifer be ing very different from an aquifer in a reducing environment). Less relevant appears to be the initial volume fr action of trace- element minerals that contain lead and arsenic as essential components. As long as these trace minerals have not been completely dissolved (a very slow process), the contamination of an aquifer via dissolution and/or desorption is fairly independent of the initial mineral volume. With respect to the representative aquifer mineralogies assumed in our study (ranging from almo st pure arenites to dolomitic limestone), the most important effects resulted from differences in clay content, which affect the adsorption/desorption behavior, as we ll as from differences in the presence of carbonate minerals, which can mode rate the pH changes caused by CO 2 intrusion.
Sensitivity studies have also been conducted with respect to the assumed flow and CO 2 leakage conditions in the aquifer. It turns out that an in crease in the leakage rate does not necessarily lead to increases in the maximum contaminant concen trations in the aquife r. Once the groundwater reaches its solubility limit with respect to CO 2 , which occurs at relatively small intrusion rates, there is no further increase in total inorga nic carbon and acidity. C onsequently, no further mobilization of hazardous constituents occurs once local equilibrium has been achieved. Excess CO 2 remains in the gas phase and ri ses to the top of the aquifer, thereby bypassing most of the groundwater body. The resulting imp act on aqueous contamination is influenced by the general pattern of the CO 2 plume (e.g., the spreading of the plume at the top of the aquifer in the case of a confined aquifer) and the subsequent migration of CO 2 -charged and contaminated water further downstream. Similar effects on the dissolution char acteristics can be seen when the groundwater flow rate changes, as more or less gr oundwater undersaturated with respect to CO 2 will flow past the location where CO 2 enters the aquifer.
As stated before, the goal of this study was to better understand, in a global sense, the vulnerability of representative and re levant aquifer types in the case of CO 2 intrusion, thereby providing sound scientific inform ation to regulators and the public. It is understood, however, that even the most comprehensive sensitiv ity study cannot encompass all possible aquifer conditions and CO 2 intrusion scenarios. Neither can it account for all relevant uncertainties. While the geochemical evaluation conducted in S ection 3 was extremely comprehensive, it was also pointed out that several assumptions, approximations, and compromises were necessary in this effort, mostly because of incomplete characterization of important parameters such as dissolution and dissociation consta nts. We recommend that our study should now be followed up with site-specific model predic tions, and that laboratory or field experiments should be performed to test uncertain model assumptions and parameters. Such experiments would involve exposing initially undisturbed grou ndwater in laboratory samples or in situ in the field to elevated levels of CO 2 . We also recommend testing the de veloped geochemical models using measured data from natural analogue sites, such as those described in Section 5 of this report.v Table of Contents

1. Motivation..................................................................................................................... ..... 1

2. Work Scope and Research Plan....................................................................................... 4

3. Geochemical Model Definition......................................................................................... 9 3.1 Basic Methodology......................................................................................................... 9

3.2 Identification of Mineral Hosts Cont rolling the Concentration of Hazardous Constituents .................................................................................................................. 1 5
3.3 Evaluation of Potable Groundwater Qua lity in Relation to Groundwater Model Development................................................................................................................. 38
3.3.1 Background ............................................................................................................... 38
3.3.2 Concentration of Major Constituents in Potable Groundwater ............................... 40
3.3.3 Concentrations of Hazardous Inorganic Constituents in Potable Groundwater ............................................................................................................. 46
3.3.4 Detection Limits of Hazardous Inorg anic Constituents in Groundwater................. 55
3.3.5 Analytical Artifacts Affecting Meas urements of Hazardous Inorganic Constituents in Groundwater.................................................................................... 57 3.4 Thermodynamic Analysis of Potable Groundwater...................................................... 62
3.4.1 Calculation of Activities and Saturation Indices ...................................................... 63
3.4.2 Modification and Augmentation of the Thermodynamic Data Supporting the Codes EQ3/6 and TOUGHREACT ........................................................................... 64
3.4.3 Evaluating the Integrity of Water Quality Analyses ................................................. 69
3.4.4 Evaluation of Groundwat er Redox State .................................................................. 84
3.4.5 Evaluation of Solubility Controls for Hazardous Constituents ................................ 88

3.4.5.1 Discussion of Resu lts from Run 0..............................................................................................9 1
3.4.5.2 Discussion of Results from Run 1............................................................................................112

3.4.6 Variation of Lead, Cadmium, and Zinc C oncentration with Redox Potential........ 123
3.4.7 Summary of Thermodynamic Controls ................................................................... 134vi 3.5 The Variation of Hazardous Constituent Con centrations with Partial Pressure of Carbon Dioxide in Groundwaters............................................................................... 137
3.5.1 Introduction............................................................................................................. 137
3.5.2 Input Specifications................................................................................................. 137
3.5.3 Results from Equilibrium Calculations................................................................... 139
3.5.4 Uncertainties Associat ed with Equilibrium Calculations....................................... 144

3.5.4.1 Estimation of Aggregat e Uncertain ties ....................................................................................144
3.5.4.2 Discussion Relating to Uncertain ties........................................................................................156

3.5.5 System Variations with P(CO 2 ) and the Impact of Complexation .......................... 158

3.5.5.1 Variation of pH, Eh and P(H 2 S) with P(CO 2 ) ..........................................................................158
3.5.5.2 Nature of Aqueous Complexation and its Impact on Mine ral Solub ility .................................159

3.5.6 Conclusions from Equilibrium Calculations .......................................................... 167 3.6 Representative Aquifer Host Rock Mineralogies ....................................................... 169
3.6.1 Background ............................................................................................................. 169
3.6.2 Approach at Mineralogy Characterization............................................................. 171

3.6.2.1 Distribution of Major and Accessory Mi nerals ........................................................................171
3.6.2.2 Distribution of Trace-E lement Mine rals ..................................................................................176

3.6.3 Representative Mineralogies .................................................................................. 179

3.6.3.1 Coastal Plain Aquife r Host Rock .............................................................................................179
3.6.3.2 Coastal Plain Aquifer Host Ro ck - Oxidizing Conditions........................................................180
3.6.3.3 St Peter Sandstone............................................................................................................. .......181
3.6.3.4 Dolomitic Limestone............................................................................................................ ....181
3.7 Summary Results and Implications for Reactive Transport Model of CO 2 Intrusion...................................................................................................................... 184

4. Systematic Geochemical Modeling.............................................................................. 195

4.1 TOUGHREACT Simulator......................................................................................... 195
4.1.1 Thermodynamic Data and Kinetic Data ................................................................. 197
4.1.2 Improved Adsorption Models.................................................................................. 198vii 4.2 Model Setup................................................................................................................ 199 4.3 Model Sequence and Sensitivity Cases ...................................................................... 203

4.4 Initial Geochemical Conditions for Base Model with Coastal Plain Sandstone......... 205

4.5 Results for CO 2 Intrusion into Coastal Plain Sandstone............................................. 215
4.5.1 Prediction of CO 2 Plume Evolution and Changes in Acidity ................................. 215
4.5.2 Prediction of Pb and As Concentrations ................................................................ 219 4.6 Sensitivity Analyses.................................................................................................... 229
4.6.1 Sensitivity to Adsorption ......................................................................................... 229

4.6.1.1 Sensitivity Run withou t Adsorptio n .........................................................................................229
4.6.1.2 Sensitivity Run with Lo wer Site De nsity .................................................................................233
4.6.1.3 Sensitivity to Surface Comp lexation Cons tant.........................................................................237

4.6.2 Sensitivity to Kinetic Rate Parameter ..................................................................... 239

4.6.2.1 Sensitivity to Kinetic Rate Parameter for Galena.....................................................................239
4.6.2.2 Sensitivity to Kinetic Rate Pa rameter for Ar senopyrite ...........................................................241

4.6.3 Sensitivity to PbS(aq) Dissociation Constant ......................................................... 243
4.6.4 Sensitivity to Mineral Solubility.............................................................................. 245

4.6.4.1 Sensitivity to Solub ility of Galena ...........................................................................................2 45
4.6.4.2 Sensitivity to Solubility of Arsenopy rite..................................................................................247

4.6.5 Sensitivity to Initial Mineral Volume Fraction ....................................................... 248

4.6.5.1 Sensitivity to Initial Volume Fraction of Galena......................................................................248
4.6.5.2 Sensitivity to Initial Volume Fr action of Arse nopyrite ............................................................249

4.6.6 Sensitivity to Initial Partial Pressure of CO 2 .......................................................... 250
4.6.7 Sensitivity to Initial Fluid Pressure (Depth of Aquifer).......................................... 254
4.6.8 Sensitivity to Groundwater Flow Velocity .............................................................. 256
4.6.9 Sensitivity to CO 2 Leakage Rate ............................................................................. 259 4.7 Results for Other Mineralogies................................................................................... 263
4.7.1 St Peter Sandstone .................................................................................................. 263viii 4.7.2 Dolomitic Limestone ............................................................................................... 271 4.8 Oxidizing Conditions.................................................................................................. 277
4.9 Three-Dimensional (3D) Simulation of CO 2 Intrusion............................................... 282
4.9.1 Results from 3D Simulation of Base Model ............................................................ 283
4.9.2 Sensitivity to Groundwater Flow Velocity—3D Model .......................................... 293
4.9.3 Sensitivity to CO 2 Leakage Rate — 3D Model........................................................ 302 4.10 Summary of Modeling Results ................................................................................... 311

5. Analogues...................................................................................................................... . 320 5.1 Natural Analogues ...................................................................................................... 320
5.1.1 San Vittorino, Italy.................................................................................................. 320
5.1.2 Florina Basin, Greece............................................................................................. 321
5.1.3 Albani Hills, Italy.................................................................................................... 322
5.1.4 Central Italy ............................................................................................................ 323
5.1.5 Mammoth Mountain, California, USA.................................................................... 324
5.1.6 Paradox Basin, Utah, USA ..................................................................................... 325
5.2 Landfill Gas Impacts................................................................................................... 325

6. Overall Conclusions and Recommendations.............................................................. 326

7. Acknowledgments ......................................................................................................... 330

8. References..................................................................................................................... . 330

Appendices A through Fix 

## List of Figures

Fig. 1.1. Schematic illustrating potential groundwater quality changes in response to CO 2 leakage from deep storage sites........................................................................................................ .................1

Fig. 2.1. Main research steps and info rmation flow between them....................................................................6

Fig. 3.1. Flow chart describing research steps provid ing necessary information as input to reactive transport models............................................................................................................... ..................14 Fig. 3.2. Concentrations (per gram of sediment) of selected trace elements and Fe in the pyritic and reactive fractions of organic-rich marsh sediments from Atchafalaya Bay with depth below the sediment-water interface (after Huer ta-Diaz and Morse, 1 992). Dashed lines are illustrative only, and included to emphasize trends in the partitioning of trace metals between the reactive and pyritic components. ................................................................................... 23 Fig. 3.3. The degree of trace metal pyritization (DTM P) in terms of the degree of pyritization (DOP) for As, Cd, Hg, Pb and Zn in organic- rich marsh sediments from Atchafalaya Bay (after Huerta-Diaz and Morse, 1992)............................................................................................ .....24 Fig. 3.4. The correlation between the molar concentr ation of Se and As, determined by laserablation ICP-MS, in pyrite from Ohio 5/6/7 b ituminous coal (from Kolker et al., 2002)..................33 Fig. 3.5. Histograms showing the concentration distributions of various chemical constituents, as well as pH and calculated Eh in more than 38,000 analyses of potable groundwaters in the United States. ............................................................................................................. ..................44 Fig. 3.6. Histograms showing the concentration distributions of selected hazardous constituents in more than 38,000 analyses of potable groundwaters in the United States. MCL signifies the maximum contaminant level for each co nstituent. SDWR stan ds for secondary drinking wate r regula tion...................................................................................................... .............48 Fig. 3.7. Spatial distribution of hazardous constituen ts concentrations in soils and surficial sediments of (a) Pb and (b) As (c) Ba and (d) U in the forty-eight contiguous states, from National Geochemical Survey Database (USGS, 2008). ...................................................................52 Fig. 3.8. Spatial distribution of samples with elevated aqueous concentration (Log mg/L) of (a) Pb, (b) As (c) Ba and (d) U in the forty-eight c ontiguous states. The pl otted concentrations were drawn from more than 38,000 groundwat er quality analyses downloaded from the National Water Information Sy stem Database (NWIS).....................................................................55 Fig. 3.9. Frequency distribution of Pb analyses in groundwaters using the analytical method PLM43 (ICP-MS). For reference, the pred icted equilibrium concentration for Pb +2 is 0.0001 mg/L (or -4 in log mg/L), calculated assuming that galena co-exists with pyrite and goethite in a typical reducing groundwater. ................................................................................ 60 Fig. 3.10. Frequency distribution of As analyses in groundwaters using the analytical methods PLM10 and PLM4 0 (ICP-MS). ...................................................................................................... ...62 Fig. 3.11. Histogram showing the pH distribution of more than 35,000 groundwater analyses selected from the NWIS Database. No correction for charge imbalance...........................................74 Fig. 3.12. Histogram showing the distribution of percentage charge imbalance of more than 35,000 groundwater analyses selected from the NWIS Database..................................................................74x Fig. 3.13. Histogram showing the calculated calcite saturation indices of more than 35,000 groundwater analyses selected from the N WIS Database. No correction for charge imbalance...................................................................................................................... .....................75 Fig. 3.14. Histogram showing the calculated dolomite saturation indices of more than 35,000 groundwater analyses selected from the N WIS Database. No correction for charge imbalance...................................................................................................................... .....................76 Fig. 3.15. Histogram showing the calculated gypsum saturation indices of more than 35,000 groundwater analyses selected from the NWIS Database. No correction for charge imbalance...................................................................................................................... .....................77 Fig. 3.16. Histogram showing the distribution of calculate d siderite saturation indices of more than 35,000 groundwater analyses se lected from the NWIS Databa se. No correction for charge imbalance...................................................................................................................... .....................78 Fig. 3.17. Histogram showing the distribution of calculated Log P(CO 2 ) of more than 35,000 groundwater analyses selected from the NWIS Database. No correction for charge imbalance...................................................................................................................... .....................79 Fig. 3.18. Histogram showing the distribution of calculated Log P(H 2 S) distribution of more than 35,000 groundwater analyses selected from the NWIS Da tabase. No correction for charge im balance. .............................................................................................................. ................80 Fig. 3.19. Histogram showing the distribution of calculated calcite saturation indices of more than 35,000 groundwater analyses selected from the NWIS Database. Charge imbalance corrected through adjustment of HCO 3 - concentration. .....................................................................81 Fig. 3.20. Histogram showing the distribution of calculate d siderite saturation indices of more than 35,000 groundwater analyses selected from the NWIS Database. Charge imbalance corrected through adjustment of HCO 3 - concentration. .....................................................................82 Fig. 3.21. Histogram showing the distribution of calculated Log P(CO 2 ) of more than 35,000 groundwater analyses selected from the N WIS Database. Charge imbalance corrected through adjustment of HCO 3 - concentration. .....................................................................................82 Fig. 3.22. Calculated Eh, based on the distribution of Fe(III) and Fe(II), and the assumption that Fe(III) is in equilibrium with goeth ite, plotted against measured pH. ...............................................87 Fig. 3.23. Frequency Distribution of Saturation Indices of (a) Pyrite, (b) Magnetite, (c) Siderite and (d) Chlorite. Saturation Indices are calculated assuming Eh to be defined by Fe(III)/Fe(II) in solution in the presence of goethite, usin g a selected set of grounwaters from the NWIS. .......................................................................................................................... ......................93 Fig. 3.24. Frequency Distribution of Saturation Indices of Clinochlore. Clinochlore saturation is not significantly affected by variation in Eh. ..................................................................................... ......94 Fig. 3.25. Frequency Distribution of Saturation Indices of (a) Arsenopyrite, (b) Scorodite, and (c) Symplesite. The Saturation Indices are calcula ted using a selected set of groundwaters from the NWIS and assuming Eh to be defined by Fe(III)/Fe(II) in solution....................................95 Fig. 3.26. Frequency Distribution of Saturation Indi ces of (a) Barite and (b) Witherite. The Saturation Indices are calculated for a selected set of groundwaters from the NWIS. Eh is defined by Fe(III)/Fe(II) in solution, which ge nerally does not significantly affect either barite or wither ite saturation. ................................................................................................ .............96 Fig. 3.27. Frequency Distribution of Saturation Indices of (a) CdS (Greenockite), (b) Cadmoselite and (c) Otavite. The Saturation Indices ar e calculated using a selected set of groundwaters from the NWIS. Eh is defined by Fe(III)/Fe(II) in solution, which generally does not significantly aff ect otavite saturation............................................................................... ....98xi Fig. 3.28. Frequency Distribution of the Saturation Index of (a) Cinnabar, (b) Tiemannite, and (c) Quicksilver. The Saturation Indices are calculat ed using a selected set of groundwaters from the NWIS and assuming Eh to be defi ned by Fe(III)/Fe(II) in solution..................................100 Fig. 3.29. Frequency Distribution of Saturation Indices of (a) Galena, (b) Clau sthalite, (c) Cerussite, (d) Hydrocerussite (e) Plumbonacrite (f) Anglesite and (g) Mimetite in a selected set of groundwaters from the NWIS. The Saturation Indices are calculated assuming Eh to be defined by Fe(III)/Fe(II) in solution. Ce russite and anglesite saturations are not significantly affected by variations in Eh..................................................................................... ....103 Fig. 3.30. Frequency Distribution of Saturation Indi ces of (a) Gudmundite, (b) Berthierite, (c) Stibnite, (d) Antimonselite and (e) Kermesite. The Saturation Indices are calculated using a selected set of groundwaters from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution. .................................................................................................... .............106 Fig. 3.31. Frequency Distribution of Saturation Indices of (a) Ferroselite, (b) Cadmoselite, (c) Tiemannite, (d) Clausthalite, and (e) Antimonselite. The Saturation Indices are calculated using a selected set of groundwa ters from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution.......................................................................................... .......109 Fig. 3.32. Frequency Distribution of the Saturation In dex of (a) Uraninite, (b) Coffinite and (c) Uranophane. The Saturation Indices are calculated using a selected set of groundwaters from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution.................................110 Fig. 3.33. Frequency Distribution of the Saturation Indi ces of (a) Sphalerite, (b) Smithsonite, (c) Hydrozincite and (d) Hemimorphite. The Saturation Indices are calculated , using a selected set of groundwaters from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution. .................................................................................................... .............111 Fig. 3.34. Frequency Distribution of Saturation Indices of (a) Arsenopyrite, (b) Scorodite, and (c) Symplesite. The Saturation Indices are calcul ated using a selected set of groundwaters from the NWIS, and assuming Eh to be defined by the coexistence of pyrite and goethite. ...........113 Fig. 3.35. Frequency Distribution of the Saturation In dex of Barite. The Saturation Indices are calculated for a selected set of groundwaters from the NWIS. Eh is defined by the coexistence of pyrite and goeth ite, but does not significantly affect barite saturation.....................114 Fig. 3.36. Frequency Distribution of Saturation In dices of (a) CdS (Greenockite) and (b) Cadmoselite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a sel ected set of groundwaters from the NWIS...............115 Fig. 3.37. Frequency Distribution of the Saturation Inde x of (a) Cinnabar, (b) Tiemannite and (c) Quicksilver. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a sel ected set of groundwaters from the NWIS...............116 Fig. 3.38. Frequency Distribution of Saturation Indices of (a) Galena, (b) Clausthalite and (c) Cerussite. Eh is defined by the coexistence of pyrite and goethite, but does not significantly affect cerussite sa turation...................................................................................... ......117 Fig. 3.39. Frequency Distribution of Saturation Indi ces of (a) Gudmundite, (b) Stibnite, (c) Antimonselite and (d) Kermesite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS.................................................................................................................. ...............119 Fig. 3.40. Frequency Distribution of Saturation Indices of (a) Ferroselite, (b) Cadmoselite, (c) Tiemannite, (d) Clausthalite and (e) Antimons elite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS.................................................................................................. ....121xii Fig. 3.41. Frequency Distribution of the Saturation Index of Uraninite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of groundwate rs from the NWIS. .................................................................................122 Fig. 3.42. Frequency Distribution of the Saturation Index of (a) Sphalerite, and (b) Hemimorphite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected se t of groundwaters fr om the NWIS. .....................................122 Fig. 3.43. Calculated Pb Concentration in Groundwate r as a Function of Eh and saturated with respect to Galena and Cerussite in the Presen ce of Goethite and Pyrite. Cerussite = cer; galena = gal; goe = goet hite; py = pyrite. ..................................................................................... ...125 Fig. 3.44. Frequency Distribution of Saturation Indices of Galena in Groundwater Samples analyzed for Pb using PLM43 and PLM48. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite. ...............127 Fig. 3.45. Frequency Distribution of Saturation Indices of Clausthalite in Groundwater Samples analyzed for Pb using PLM43 and PLM48. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite. ...............128 Fig. 3.46. Frequency Distribution of Saturation Indices of (a) Pyrite and (b) Calcite in Groundwater Samples analyzed for Pb using PLM43 and PL M48. The Saturation Indices are calculated assuming Eh to be defined by Fe(III)/Fe(II) in solution in the presence of goethite........................128 Fig. 3.47. Calculated Cd Concentration in Groundwate r as a Function of Eh and saturated with respect to CdS and Otavite in the Presence of either Pyrite or Goethite. CdS = CdS; goe = goethite; ota = otav ite; py = pyrite........................................................................................... ........130 Fig. 3.48. Frequency Distribution of Saturation Indices of CdS in Groundwater Samples analyzed for Cd using PLM43 and PLM47. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh is defined by the coexistence of pyr ite and goethite. .........................131 Fig. 3.49. Frequency Distribution of Saturation Indices of Cadmoselite in Groundwater Samples analyzed for Cd using PLM43 and PLM47. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite. ...............132 Fig. 3.50. Calculated Zn Concentration in Groundwater as a function of Eh and Saturated with respect to either Sphalerite or Hemimorphite in the Presence of Go ethite and Pyrite. goe = goethite; py = pyrite; sph = sphale rite; hemi = he mimorphite.....................................................134 Fig. 3.51. Variation of trace metal concentrations in e quilibrium with host minerals as a function of CO 2 partial pressure taking into account both sulfide and selenide complexation, and with solubility products calculated using thermochemical data and adjusted to ensure consistency with hazardous constituent concentr ations in NWIS waters (see Table 3.14). (SS) refers to solid solution. CV013, CV014, CV016, XV017, CV018, FL007, PH0S3, PLM10, PLM40, PLM43, PLM47 and PLM48 are analytical methods cited in the NWIS database. See Table A2 in Appendix A. .......................................................................................... 143 Fig. 3.52. Histogram showing the temperature (oC) distribution of more than 35,000 groundwater analyses selected from the NWIS Database.....................................................................................15 5 Fig. 3.53. Classification of arenaceous sediments in terms of detrital framework grains (quartz, feldspars and lithic fragments) and a detrital (c lay) interstitial matrix (after Tucker, 1991). The spot on the 7.5-wt% matrix triangle re presents the base-case aquifer host rock introduced in Section 3.6.3.1. The diamond represents the St Peter Sandstone described in Section 3.6.3.3. For comparison, the star illustrates the Gulf Coast Lithic Arkose described in Xu et al. (2006).................................................................................................. ..........173 Fig. 3.54. Representation of the tectoni c provenances of framework grains of arenaceous rocks (after Tucker, 1991). TE = trailing edge or passive continental margin; SS = strike slip continental margin; CA = converging arc marg in; BA = back arc basin and FA = fore arcxiii basin. The black spot indicates the base-cas e aquifer host rock introduced in Section 3.6.3.1. The diamond represents the St Peter Sandstone described in Section 3.6.3.3. For comparison, the star illustrates the Gulf Coas t Lithic Arkose described in Xu et al. (2005)......................................................................................................................... ......................174

Fig. 4.1. Simplified model setup for in itial geochemica l model ....................................................................200 Fig. 4.2. Time evolution of total inorganic carbon (TIC) (left) and pH (right) at CO 2 intrusion location ....................................................................................................................... .....................216 Fig. 4.3. Time evolution of gas saturation degree at CO 2 intrusion location..................................................216 Fig. 4.4. Spatial distribution of saturation degree of CO 2 gas after 100 years of intrusion ............................217 Fig. 4.5. Spatial distribution of total inorganic carbon (T IC) (mol/L) after 100 years of intrusion ...............217 Fig. 4.6. Spatial distribution of pH af ter 100 years of intrusion.....................................................................21 8 Fig. 4.7. Total inorganic carbon (left) and pH (right) along the x-axis at y = 0 m, for Coastal Plain Sandstone...................................................................................................................... ...................219 Fig. 4.8. Calcite volume fraction change profiles along x-axis at y = 0 m, for Coastal Plain Sandstone...................................................................................................................... ...................219 Fig. 4.9. Spatial distribution of total aqueous Pb after 100 years of intrusion ...............................................220 Fig. 4.10. Total aqueous Pb concentration prof ile along x-axis at y = 0 m......................................................221 Fig. 4.11. Surface complex of Pb on the st rong (left) and weak site (ri ght) of illite along x-axis at y = 0 m for Coastal Plain Sandstone.............................................................................................. .....222 Fig. 4.12. Relative change of total Pb surface complexe s concentration along the x-axis at y = 0 m for Coastal Plai n Sandstone. ................................................................................................... .........222 Fig. 4.13. Galena volume fraction change profiles along x-axis at y = 0 m, for Coastal Plain Sandstone...................................................................................................................... ...................224 Fig. 4.14. Spatial distribution of total aqueous As concentration after 100 years of intrusion.........................225 Fig. 4.15. Total aqueous As concentration profiles alon g the x-axis at y = 0 m, for Coastal Plain Sandstone...................................................................................................................... ...................225 Fig. 4.16. Surface complex Ill _HAsO 3 - (left) and Ill _H 2 AsO 3 (right) profiles along the x-axis at y = 0 m, for Coastal Plain Sandstone............................................................................................. .....226 Fig. 4.17. Relative change of total As surface comple xes concentration (left) and arsenopyrite volume fraction change (right) along the x-axis at y = 0 m for Coastal Plain Sandstone. ...............226 Fig. 4.18. Galena volume fraction change profiles along x-axis at y = 0 m 0 at different times in a sensitivity run without adsorption/desorption (left), and comparison of galena volume fraction change profile along x at y=0 after 100 years of CO 2 intrusion with base model (right). ....................................................................................................................... .......................230 Fig. 4.19. Aqueous Pb concentration profile along x at y=0 at different times in a sensitivity run without adsorption/desorption (left), and comp arison of model results for total aqueous Pb concentration profile along x at y=0 after 100 years of CO 2 intrusion with base model (right). ....................................................................................................................... .......................231 Fig. 4.20. Aqueous As concentration profile along x at y=0 at different time in a sensitivity run without adsorption/desorption (left) and compar ison of model results of As concentration profile along x at y=0 after 100 years of CO 2 intrusion with base model (right).............................231 Fig. 4.21. Comparison of model results for sensitivity case without adsorption/desorption and base model, showing aqueous Pb (left) and As (right) concentration profiles along x at y=0 after 1 year of CO 2 intrusion............................................................................................................232xiv Fig. 4.22. Galena volume fraction change profiles along x-axis at y = 0 m 0 at different times in a sensitivity run with lower site density for illite and comparison of galena volume fraction change profile along x at y=0 after 100 years of CO 2 intrusion with base model (right).................235 Fig. 4.23. Aqueous Pb concentration profile along x at y=0 at different times in a sensitivity run with lower site density on illite.............................................................................................. ..........235 Fig. 4.24. Time evolution of total aqueous Pb concentration at the intrusion location (left) and comparison of model results for total aqueou s Pb concentration profile along x at y=0 after 100 years of CO 2 intrusion in sensitivity an d base model (right). ...........................................236 Fig. 4.25. Aqueous As concentration profile along x at y=0 at different time in a sensitivity run with lower site density and comparison of model re sults of As concentration profile along x at y=0 after 100 years of CO 2 intrusion with base model (right). ........................................................236 Fig. 4.26. Computed As concentration after 100 years of CO 2 intrusion in a sensitivity run (logK = - 3) and base model (logK = -2.12) ............................................................................................... .....238 Fig. 4.27. Surface complex Ill _HAsO 3 - (left) and Ill _H 2 AsO 3 (right) profiles along the x-axis at y = 0 m, in a sensitivity run (logK = -3) and base model (logK = -2.12)............................................238 Fig. 4.28. Computed galena volume fraction change (left) and (right) total adsorbed Pb change after 100 years of CO 2 intrusion with different kinetic-rate co nstant.......................................................240 Fig. 4.29. Sensitivity of computed Pb concentration after 100 years of CO 2 intrusion to kinetic-rate constant (k) for simulations w ithout considerin g sorption...............................................................241 Fig. 4.30. Computed As concentration after 100 years of CO 2 intrusion in sensitivity runs with different kinetic-rate constant (k)............................................................................................ .........242 Fig. 4.31. Sensitivity of computed As concentration after 100 years of CO 2 intrusion to rate constant (k) for simulations w ithout considerin g sorption...............................................................242 Fig. 4.32. Computed total adsorbed Pb change (left) and galena volume fraction change (right) after 100 years of CO 2 intrusion with different logK PbS(aq) .......................................................................244 Fig. 4.33. Computed total aqueous Pb concentration after 100 years of CO 2 intrusion with different logK PbS(aq) ............................................................................................................................... ..........244 Fig. 4.34. Computed total adsorbed Pb (left) and aqueous Pb concentration (right) after 100 years of CO 2 intrusion in sensitivity run (logK galena = -13.97) and base model (logK galena = - 14.4).......................................................................................................................... .......................246 Fig. 4.35. Computed aqueous As concentration after 100 years of CO 2 intrusion in a sensitivity run (logK arsenopyrite = 55.16) and base run (logK arse nopyrite = 55.66)...........................................247 Fig. 4.36. Sensitivity of total aqueous Pb concentration after 100 years along the x-axis at y = 0 m to the initial volume fraction of galena. ...................................................................................... .....248 Fig. 4.37. Sensitivity of total aqueous As concentration after 100 years along the x-axis at y = 0 m to the initial volume fr action of ar senopyr ite................................................................................. ..249 Fig. 4.38. Sensitivity of Pb concentration to initial partial pressure of CO 2 . The figure on the right is the same as the one on the le ft but in semi -log scale. ......................................................................252 Fig. 4.39. Total Pb concentration on adsorption sites (left) and concentration of ill w _OPb + (right) along x-axis at y = 0 m in th e base and sens itivity ru n. ...................................................................252 Fig. 4.40. Galena volume fraction change (left) and pH (right) along x-axis at y = 0 m in the base and sensitiv ity run............................................................................................................ ................253 Fig. 4.41. Sensitivity of As concentration to initial partial pressure of CO 2 . ...................................................253 Fig. 4.42. Sensitivity of CO 2 gas saturation degree (left) and total inorganic carbon (TIC) to the depth of aquifer............................................................................................................... .................255xv Fig. 4.43. Sensitivity of pH to the depth of aquifer..................................................................................... .....255 Fig. 4.44. Sensitivity of Pb (left) and As concentr ation (right) to the de pth of aquifer....................................255 Fig. 4.45. Spatial distribution of saturation of CO 2 in a sensitivity run with flow rate of 1m/year after 100 years of intrusion ................................................................................................... ...........256 Fig. 4.46. Spatial distribution of TIC in a sensitivity run with flow rate of 1m/year after 100 years of intrusion ...................................................................................................................... .....................257 Fig. 4.47. Comparison of CO 2 gas saturation degree (left) and TIC (right) along x-axis at y = 0 m in the sensitivity case an d the base run .......................................................................................... ......257 Fig. 4.48. Evolution of pH at different times (left) along the x-axis at y = 0 m where groundwater flow velocity is 1m/year, and sensitivity of pH to groundwater flow velocity after 100 years (r ight).................................................................................................................. ....................258 Fig. 4.49. Total aqueous Pb concentration at different ti mes (left) along the x- axis at y = 0 m where groundwater flow velocity is 1m/year, and se nsitivity to groundwater flow velocity after 100 years (right).............................................................................................................. .................258 Fig. 4.50. Total aqueous As concentration at different times (left) along x-axis at y = 0 m where groundwater flow velocity is 1m/year and se nsitivity to groundwater flow velocity after 100 years (right).............................................................................................................. .................258 Fig. 4.51. Spatial distribution of saturation for CO 2 in a sensitivity run with CO 2 intrusion rate of 6 × 10 -4 kg/s after 100 years of intrusion ..........................................................................................260 Fig. 4.52. Spatial distribution of TIC in a sensitivity run with CO 2 intrusion rate of 6 × 10 -4 kg/s after 100 years of intrusion ................................................................................................... ...........260 Fig. 4.53. Comparison of CO 2 gas saturation degree (left) and dissolved CO 2 mass fraction (right) along x-axis at y = 0 m in th e sensitivity and base run. ...................................................................261 Fig. 4.54. pH evolution at different times (left) along the x-axis at y = 0 m for CO 2 intrusion rate of 6×10 -4 kg/s, and sensitivity of pH to CO 2 intrusion rate. .................................................................261 Fig. 4.55. Total aqueous Pb concentration at different times (left) along the x-axis at y = 0 m for CO 2 intrusion rate of 6×10 -4 kg/s, and sensitivity of Pb concentration to CO 2 intrusion rate. .......................................................................................................................... ........................262 Fig. 4.56. Total aqueous As concentration at different times (left) along the x-axis at y = 0 m for CO 2 intrusion rate of 6×10 -4 kg/s, and sensitivity of As concentration to CO 2 intrusion rate. .......................................................................................................................... ........................262 Fig. 4.57. pH profiles (left) along the x-axis at y = 0 m at different times for St Peter sandstone, and comparison of pH computed for Coastal Plain Sandstone and St Peter Sandstone (right) ..............266 Fig. 4.58. Volume fraction of calcite (left) and dolo mite (right) along x-axis at y = 0 m ................................267 Fig. 4.59. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times for St Peter Sandstone, and comparison of computed Pb concentration for Coastal Plain Sandstone and St Peter Sandstone w ith adsorption/deso rption (right).............................................268 Fig. 4.60. Comparison of total Pb concentration on adsorption sites in mol/L for Coastal Plain Sandstone and St Peter Sandstone (left), and relative change in concentration on adsorption sites for St Peter Sandston e at 50 and 100 y ears (right) .................................................269 Fig. 4.61. Total aqueous As concentration profiles (left) along x-axis at y = 0 m at different times for St Peter Sandstone, and comparison of computed As concentration for Coastal Plain Sandstone and St Pete r Sandstone (right) ....................................................................................... .269 Fig. 4.62. Comparison of total As concentration on adsorption sites in mol/L for Coastal Plain Sandstone and St Peter Sandstone (left), and relative change in concentration on adsorption sites for St Peter Sandston e at 50 and 100 y ears (right) .................................................270xvi Fig. 4.63. Total aqueous Pb (left) and As (right) concen tration profiles (left) along x-axis at y = 0 m at different times for St Peter Sandstone obtained by a simulation without adsorption/desorption.......................................................................................................... .............270 Fig. 4.64. pH profiles (left) along x-axis at y = 0 m at different times for Dolomitic Limestone, and comparison of computed pH with Co astal Plain Sandst one (right). ................................................275 Fig. 4.65. Comparison of calcite volume fraction change for Coastal Plain Sandstone and Dolomitic Limestone. ..................................................................................................................... ..................275 Fig. 4.66. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times for Dolomite Limestone, and comparison of computed Pb concentration with Coastal Plain Sandst one (right)........................................................................................................ .............275 Fig. 4.67. Comparison of total Pb concentration on adsorption sites (left) and change (right) for Coastal Plain Sandstone and Dolomitic Limestone. ........................................................................276 Fig. 4.68. Comparison of surface complex ill w _OPb + (left) and sme s _oPb + for Coastal Plain Sandstone and Dolomitic Limestone. ............................................................................................. .276 Fig. 4.69. Total aqueous As concentration profiles (left) along x-axis at y = 0 m at different times for Dolomitic Limestone, and comparison of computed As concentration with Coastal Plain Sandst one (right)........................................................................................................ .............276 Fig. 4.70. pH profiles along x-axis at y = 0 m, fo r oxidized Coastal Pl ain Sandstone. ....................................280 Fig. 4.71. Surface complex of Pb on the st rong (left) and weak site (ri ght) of illite along x-axis at y = 0 m for oxidized Coasta l Plain Sand stone. ...................................................................................2 81 Fig. 4.72. Relative change of total Pb concentration on adsorption sites (left) and cerussite volume fraction change (right) profiles, along x-axis at y = 0 m (left) for oxidized Coastal Plain Sandstone...................................................................................................................... ...................281 Fig. 4.73. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times for oxidized C ................................................................................................................. .................281 Fig. 4.74. Close-up view of spatial distribution of CO 2 gas saturation after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s. .......................................................285 Fig. 4.75. Profiles CO 2 gas saturation at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.............................285 Fig. 4.76. Close-up view of spatial distribution of Total Inorganic Carbon (TIC) after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s. ...............................................286 Fig. 4.77. Profiles of concentration of Total Inorganic Carbon (TIC) at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s. ......................................................................................................................... ......286 Fig. 4.78. Close-up view of spatial distribution of pH in 3D model after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s. ................................................................287 Fig. 4.79. Profiles of pH at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s. .................................................287 Fig. 4.80. Close-up view of spatial distribution of total aqueous Pb concentration after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s...........................................288 Fig. 4.81. Profiles of total aqueous Pb concentration at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s........................................................................................................................... ........................288 Fig. 4.82. Close-up view of spatial distribution of total aqueous As concentration after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s...........................................289xvii Fig. 4.83. Profiles of total aqueous As concentration at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s........................................................................................................................... ........................289 Fig. 4.84. Spatial distribution of Total Inorganic Carbon (TIC) after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s. ..........................................................................290 Fig. 4.85. Spatial distribution of pH after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s. ........................................................................................................................2 90 Fig. 4.86. Spatial distribution of total aqueous Pb concentration after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s. ..........................................................................291 Fig. 4.87. Spatial distribution of total aqueous As concentration after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s. ..........................................................................291 Fig. 4.88. Total aqueous Pb concentration along x-axis at y = 0 m and different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous Pb concentration along x-axis at y = 0 m for 2D model................................................................................................................... .................292 Fig. 4.89. Total aqueous As concentration along x-axis at y = 0 m and different aquifer depth (z = 0, -5 and -10 m) for 3D model and total aqueous As concentration along x-axis at y = 0 m for 2D model................................................................................................................... .................292 Fig. 4.90. Spatial distribution of CO 2 gas saturation after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y........................................................................................................ .............295 Fig. 4.91. Profiles of CO 2 gas saturation at different lo cations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y............................................................................................... .....295 Fig. 4.92. Spatial distribution of total inorganic carbon (TIC) after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.................................................................................................. ........296 Fig. 4.93. Total inorganic carbon (TIC) y-z profile at different locations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.................................................................................2 96 Fig. 4.94. Spatial distribution of pH after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y. ......................................................................................................................... ......................297 Fig. 4.95. Profiles of pH at different locations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y............................................................................................................. ................297 Fig. 4.96. Spatial distribution of total aqueous Pb concentration after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y............................................................................................... .....298 Fig. 4.97. Profiles of total aqueous Pb concentration at different locations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.................................................................................2 98 Fig. 4.98. Spatial distribution of total aqueous As concentration after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y............................................................................................... .....299 Fig. 4.99. Profiles of total aqueous As concentration at different locations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.................................................................................2 99 Fig. 4.100. Total aqueous Pb concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous Pb concentration along x-axis for 2D model (both models have a flow rate of 1 m/y). Top figure shows x-axis profiles at y = 0 m, bottom figure shows prof iles y = 50 m................................................................................................. ........300 Fig. 4.101. Total aqueous As concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous As concentration along x-axis for 2D model (both models have a flow rate of 1 m/y). Top figure shows x-axis profiles at y = 0 m, bottom figure shows profile s at y = 50 m.............................................................................................. .......301xviii Fig. 4.102. Spatial distribution of CO 2 gas saturation after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 -4 kg/s. ..............................................................................................304 Fig. 4.103. Profiles of CO 2 gas saturation at different lo cations after 100 years of CO 2 leakage in the 3D model with CO 2 intrusion rate of 6×10 -4 kg/s. ...........................................................................304 Fig. 4.104. Spatial distribution of TIC after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 -4 kg/s...............................................................................................................305 Fig. 4.105. Profiles of Total Inorganic Carbon (TIC) at different locations after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 6×10 -4 kg/s. ....................................................305 Fig. 4.106. Spatial distribution of pH after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 -4 kg/s. ......................................................................................................................... ..306 Fig. 4.107. Profiles of pH at different locations after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 6×10 -4 kg/s. ......................................................................................................306 Fig. 4.108. Spatial distribution of total aqueous Pb concentration after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 -4 kg/s. .............................................................................307 Fig. 4.109. Profiles of total aqueous Pb concentration at different locations after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 6×10 -4 kg/s. ....................................................307 Fig. 4.110. Spatial distribution of total aqueous As concentration after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 -4 kg/s. .............................................................................308 Fig. 4.111. Profiles of total aqueous As concentration at different locations after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 6×10 -4 kg/s. ....................................................308 Fig. 4.112. Total aqueous Pb concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous Pb concentration along x-axis for 2D model (both models have a CO 2 intrusion rate of 6×10 -4 kg/s). Top figure shows x-axis profiles at y = 0 m, bottom figure show s profiles y = 50 m. ...................................................................................3 09 Fig. 4.113. Total aqueous As concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous As concentration along x-axis for 2D model (both models have a CO 2 intrusion rate of 6×10 -4 kg/s). Top figure shows x-axis profiles at y = 0 m, bottom figure show s profiles y = 50 m. ...................................................................................3 10

Fig. 5.1. Map of the Albani Hills region showing major drainage basins and locations of water samples from Chiodini an d Frondini (2 001)....................................................................................32 3xix 

## List of Tables

Table 3.1. Characteristics (in weight percent, dry basis) of coals studied by Kolker et al. (2002) .....................20 Table 3.2. Sequential selective leaching procedure to ch aracterize the distribu tion of trace elements in the constituent componen ts of coal (after Ko lker et al ., 2002) ......................................................20 Table 3.3. Distribution (in %) of iron and selected trace elements in coal (from Kolker et al., 2002) ...............21 Table 3.4. Molar ratio of trace metals (Me) in relation to As in pyrite compared to their molar abundance ratio in a bulk sample of Ohio 5/6/7 Coal. R 2 denotes correlation...................................22 Table 3.5. Sequential selective leaching procedure to ch aracterize the distribu tion of trace elements in marine sediments (after Hu erta-Diaz and Mo rse, 1992)................................................................22 Table 3.6. Potential mineral hosts for hazardous inor ganic constituents under reducing conditions..................37 Table 3.7. Values of analytical concentrations of chemical constituents at maximum frequency and associated calculated values of selected species in more th an 38,000 potable groundwaters................................................................................................................... ...................44 Table 3.8. Hazardous inorganic constitu ents with EPA-specified Maximum Contaminant Levels (MCLs) ......................................................................................................................... .....................49 Table 3.9. Percentage of samples with measurable concentrations of hazardous inorganic constituents in groundwater samp les analyzed with the most sensitive analytical method ...............56 Table 3.10. Scope of modifications and augmenta tions to thermodynamic database...........................................67 Table 3.11. Errors introduced through inadequate preser vation of sample integr ity deficiencies. .......................71 Table 3.12. Input constraints for EQ3/6 species distribution runs conducted on groundwater analyses from the NWIS Database......................................................................................................... ..........88 Table 3.13. Summary of minerals identified as controlling the thermodynamic activity of selected hazardous constituents in potable ground waters..............................................................................135 Table 3.14. Input specifications for base case EQ3 runs to calculate the equilibrium concentrations of hazardous inorganic constituents in groun dwater at 25°C as a function of P(CO 2 ). SS = Solid So lution. ................................................................................................................ .................141 Table 3.15. Uncertainties associated with geochemical model assumptions ......................................................145 Table 3.16. Uncertainties in geochemi cal model parameters..............................................................................148 Table 3.17. Uncertainties in chemical analys es of potable gr oundwaters...........................................................150 Table 3.18. Uncertainties relating to environmental variability in sampled NWIS groundwater compositions ................................................................................................................... .................152 Table 3.19. Estimated Uncertainties contributing to the Aggregate Uncertainty in Log K of the Solubility Product of the Hazardous Constituent Mineral Host 1 ......................................................154 Table 3.20. Variation of key dependent compositional parameters in groundwaters with variation in CO 2 partial pressure .............................................................................................................. ...........158 Table 3.21. Concentration of aqueous species of hazardous constituents in aqueous solution in equilibrium with specified host minerals as a Function of CO 2 partial pressure..............................160 Table 3.22. Matrix clay mineralogy........................................................................................................ ............175 Table 3.23. Representation of the mineralogy of hazardo us inorganic substances in an arenaceous aquifer host rock .............................................................................................................. ................178 Table 3.24. Representative mineralogies of aquifer host rocks used in reactive chemical transport simulations.................................................................................................................... ...................183xx Table 4.1. Hydrodynamic parameters ....................................................................................................... ........201 Table 4.2. Summary of se nsitivity cases.................................................................................................. .........204 Table 4.3. Mineral volume fractions, possible secondary minerals, and kinetic properties for a Coastal Plain Sandstone. Kinetic parame ters from Xu et al. (2006)................................................207 Table 4.4. Initial total aqueous concentration of major constituents obtained by initial equilibrium run for Coastal Plain Sandstone and analyti cal concentration at the maximum frequency in the histogram of 38,500 potable groundwaters and saturation indices of some major minerals....................................................................................................................... .....................209 Table 4.4a. Comparison of normalized saturation indices of some major minerals from the initial equilibrium run for Coastal Plain Sandstone with those calculated for potable groundwater with analytical concentrations at the maximum frequency in 38,500 potable groundwaters................................................................................................................... .................212 Table 4.5. Initial chemical composition of trace elemen ts of groundwater from the TOUGHREACT equilibrium simulati on in comparison ........................................................................................... ..213 Table 4.6. Concentrations of a queous species of Pb....................................................................................... ..214 Table 4.7. Speciation of aque ous species of As ........................................................................................... .....214 Table 4.8. Mass distribution (mol/L solution) of Pb and As on solid (as an essential component in minerals or adsorbed on mineral surfaces) an d liquid phase in two-phase zone at initial (t=0) and final time (t=100 years)............................................................................................. .......228 Table 4.9. Mass distribution (%) of Pb and As on solid (as essential an component in minerals or adsorbed on mineral su rfaces) and liquid phase in two-phas e zone at initial (t=0) and final time (t=100 years)............................................................................................................. ...............228 Table 4.10. Concentration (in mol/L) of some Pb aqueous species calculated by different dissociation constants of PbS(aq) at initial condition with logP(CO 2 ) = -2.3 ......................................................244 Table 4.11. Concentration (in mol/L) of some Pb aqueous species after initial equilibrium in base model and in the sensitivity run ............................................................................................... ........246 Table 4.12. Initial chemical composition (mol/L) of groundwater in equilibrium with partial pressures of -2.3 (base model) and -1.5 (sensitivity run) in Log P(CO 2 ).........................................................251 Table 4.13. Mineral volume fractions, possible secondary minerals, and kinetic properties for a St Peter Sa ndst one................................................................................................................ ................264 Table 4.14. Initial chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain Sandstone and St Peter Sandstone ............................................................................................... ....265 Table 4.15. Mineral volume fractions, possible secondary minerals, and kinetic properties for a Dolomitic Limestone ............................................................................................................ ...........272 Table 4.16. Initial chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain Sandstone and Dolomitic Limestone. ............................................................................................. .273 Table 4.17. Mineral volume fractions, possible secondary minerals, and kinetic properties for an oxidized Coastal Plain Sandstone aquifer....................................................................................... .278 Table 4.18. Chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain Sandstone aquifer under reducing an d oxidizing and conditions.....................................................279xxi 

## List of Acronyms and Abbreviations

AA Atomic Adsorption Spectroscopy
As Arsenic ASR Aquifer Storage and Recovery
Ba Barium
Cd Cadmium EPA Environmental Protection Agency ICP-MS Inductively Coupled Pl asma with Mass Spectroscopy Hg Mercury LBNL Lawrence Berkeley National Laboratory NAA Neutron Activation Analysis NASCENT Natural Analogs for the Storage of CO 2 in the geological ENvironmenT
NURE National Uranium Resource Evaluation
MCL Maximum Contaminant Level MSW Municipal Solid Waste NGS National Geochemical Survey
NWIS National Groundwat er Information System
Pb Lead Sb Antimony SDWR Secondary Drinking Water Regulation
Se Selenium
SI Saturation Index
SS Solid Solution TIC Total Inorganic Carbon
TDS Total Dissolved Solids USDW United States Drinking Water
U Uranium XPS X-ray Photo electron Spectroscopy XAS X-ray Absorption Spectroscopyxxii 

## List of Referenced Minerals and Their Chemical Formulas

Name Formula
Albite-low NaAlSi 3 O 8 Akdalaite (Al 2 O 3 ) 4 ·(H 2 O) Alstonite BaCa(CO 3 ) 2 Anglesite PbSO 4 Anhydrite CaSO 4 Ankerite Ca(Mg 0.3 Fe 0.7 )(CO 3 ) 2 Antimonselite Sb 2 Se 3 Antimony Sb Apatite Ca 5 (OH)(PO 4 ) 3 Arsenic As
Arsenic Oxide As 2 O 5 Arsenolite As 2 O 3 Arsenopyrite FeAsS
Barite BaSO4
Berthierite FeSb 2 S 4 Berzelianite Cu 2 Se
Cadmoselite CdSe
Calcite CaCO 3 Cerussite PbCO 3 Chlorite Mg 2.5 Fe 2 . 5 Al 2 Si 3 O 10 (OH) 8 Clinochlore Mg 5 Al 2 Si 3 O 10 (OH) 8 Cinnabar HgS
Claudetite II As 2 O 3 Clausthalite PbSe
Cotunnite PbCl 2 Crandallite CaAl 3 (PO 4 ) 2 (OH) 5 ·(H 2 O)
Dawsonite NaAlCO 3 (OH) 2 Dolomite CaMg(CO 3 ) 2 E-Fe 2 O 3 Fe 2 O 3 Ferrihydrite Fe 10 O 14 (OH) 2 Ferrarisite Ca 5 (AsO 3 OH) 2 (AsO 4 ) 2 .9H 2 Oxxiii Name Formula
Ferroselite FeSe 2 FeS,am FeS
Fieldlerite Pb 3 (OH) 2 Cl 4 Galena PbS Goethite FeO(OH) Green Rust Fe(II) 4 Fe(III) 2 (OH)(SO 4 ).8H 2 O
Greenockite CdS Greigite Fe 3 S 4 Gudmundite FeSbS
Guerinite Ca 5 (AsO 3 OH) 2 (AsO 4 ) 2 .9H 2 O Haidingerite Ca(AsO 3 OH).H 2 O
Hematite Fe 2 O 3 Hornesite Mg 3 (AsO 4 ) 2 .8H 2 O Hydrocerussite Pb(OH) 2 .(PbCO 3 ) 2 Illite K 0.6 Mg 0.25 Al 1.8 Al 0.5 Si 3.5 O 10 (OH) 2 Johnbaumite Ca 5 (AsO 4 ) 3 (OH)
Johnbaumite-Pb Pb 5 (AsO 4 ) 3 (OH) K-feldspar KAlSi 3 O 8 Kaolinite Al 2 Si 2 O 5 (OH) 4 Kermesite Sb 2 S 2 O Kerogen-OS (-CHOH) 18 (-CHCOOH) 5 (-CHCOOCH2CH3) 6 (-CHSH) 10 Kullerudite NiSe 2 Lanarkite PbO.PbSO 4 Laurionite PbClOH
Lead Pb Lead Hydroxide Pb(OH) 2 Lepidocrocite FeO(OH) Litharge PbO
Lorretoite Pb 7 O 6 Cl 2 Mackinawite FeS Maghemite Fe 2 O 3 Magnesite MgCO 3 Marcasite FeS 2xxiv Name Formula
Massicot PbO
Matlockite PbO.PbCl 2 Mendipite (PbO) 2 .PbCl 2 Meta-cinnabar HgS
Mimetite Pb 5 (AsO 4 ) 3 (Cl) Naumannite Ag 2 Se Oligoclase CaNa 4 Al 6 Si 14 O 40 Opal-CT SiO 2 Orpiment As 2 S 3 Paralaurionite PbOHCl Pararealgar AsS
Penfieldite Pb 2 OHCl 3 Pharmacolite CaHAsO 4 .2H 2 O
Pharmacosiderite KFe 4 (AsO 4 ) 3 (OH) 4 .6H 2 O Picropharmacolite Ca4Mg(HAsO 4 ) 2 (AsO 4 ) 2 .11H 2 O
Pitticite Fe 2 (AsO 4 )(SO 4 )(OH).6H 2 O
Plumbonacrite Pb 10 O(OH) 6 (CO 3 ) 6 Pyrite FeS 2 Pyromorphite Pb 5 (PO 4 ) 3 (Cl) Quartz SiO2 Realgar AsS Schwertmannite Fe(III) 16 O 16 (OH) 12 (SO 4 ) 2 .12H 2 O
Scorodite FeAsO 4 .2H 2 O
Senarmonite Sb 2 O 3 Siderite FeCO 3 Sphalerite ZnS
Stibnite Sb 2 S 3 Symplesite Fe 3 (AsO 4 ) 2 .8H 2 O
Smectite-Ca Ca 0.145 Mg 0.26 Al 1.77 Si 3.97 O 10 (OH) 2 Smectite-Na Na 0.290 Mg 0.26 Al 1.77 Si 3.97 O 10 (OH) 2 Smectite-low-Fe-Mg Ca 0.02 Na 0.15 K 0.2 Fe ++ 0.29 Fe +++ 0.16 Mg 0.9 Al 1.25 Si 3 . 75 H2O 2 Sphalerite ZnS Talc Mg 3 Si 4 O 10 (OH) 2xxv Name Formula
Talmessite Ca 2 Mg(AsO 4 ) 2 .2H 2 O Tiemannite HgSe
Tooeleite Fe 6 (AsO 3 ) 4 (SO 4 )(OH) 4 .4H 2 O
Uraninite UO 2 Valentinite Sb 2 O 3 Weilite CaHAsO 4 Yukonite Ca 7 Fe 11 (AsO 4 ) 9 O 10 .24H 2 O1 of 352 

# 1. Motivation

Proper site selection and management of CO 2 storage projects will ensu re that the risks to human
health and the environment are low. However, a risk remains that CO 2 could migrate from a deep storage formation to arrive in shallow groundw ater resources. Figure 1.1 shows schematically such a scenario, where a local high-permeability pathway—such as a permeable fault or an open
abandoned well—allows for CO 2 to escape from depth and reac h a shallow aquifer used for water supply. The buoyant CO 2 will undergo phase transition along this path and will typically reach the shallow strata as a gas, which w ill partially or completely dissolve into the groundwater. This process is by itself not typi cally a concern to the water quality of an underground source of drinking water (USDW), but it will change the geochemical conditions in the aquifer and will cause secondary e ffects, mainly by changes in pH.

Fig. 1.1. Schematic illustrating potential groundwater quality changes in response to CO 2 leakage from deep storage sites2 of 352 As CO 2 migrates into a freshwater aquifer and dissolves into the aqueous phase, the total
concentration of dissolved carbonate increases and cau ses a decrease in pH. The higher acidity of the water may lead to enhan ced dissolution of many minerals , among these some that may contain hazardous constituents such as lead (Pb) or arsenic (As) as essential components. For example, Pb is an essential component in galena, which is lead sulfide, and As is an essential component in arsenopyrite, which is iron arsenic sulfid e. If such minerals are present and dissolve, the increasing concentration of hazardous constituents can cause detrimental effects to groundwater quality, possibly to the extent that maximum allowable levels are exceeded. Another concern related to decreasing pH is the possible mobilization of hazardous constituents on adsorption sites (e.g., clays, iron oxyhydoxides, or sorbed mate rial on sand coats) or ion exchange sites (e.g., smectitic cl ays). For example, dissolution of iron oxyhydroxides in response
to CO 2 injection may have released sorbed toxic metals into the saline water at the Frio Pilot Test site (Kharaka et al., 2006). The problem of m obilizing such hazardous constituents (Pb, As, and others such as barium (Ba) , cadmium (Cd), mercury (hg), antimony (Sb), selenium (Se), uranium(U)) in response to CO 2 intrusion is relevant for most freshwater aquifers, because these elements occur—in trace amounts—ubiquitously in soils, sediments, and aquifer rock throughout
the world.

To date, the potential ge ochemical effects of CO 2 intrusion into USDWs, especially those currently exploited as sources of potable water, have not been systema tically assessed. Wang and Jaffe (2004) conducted reactive-transport simu lations considering the migration of CO 2 into an aquifer with significant volume fr action of galena present as an essential mineral component. Their results suggest strongly incr eased Pb concentration levels be cause of galena dissolution, in the case of a poorly buffered aquifer to levels higher than the maximum contaminant level (MCL) for Pb defined by the U.S. Environmental Protection Agency (EPA). It should be noted, however, that the authors intended their study as an illustrative example, to demonstrate the potential effects of CO 2 intrusion on water quality; their simu lations do not necessarily represent realistic conditions expected to occur if CO 2 were to penetrate a potable water aquifer. This is because their reactive-transport simulations involved very simplified geochemical systems and were based on extreme mineralogical conditions. T hus, while the simulation results of Wang and3 of 352 Jaffe (2004) point to a potential concern that need s to be addressed, it is not clear to date how severe and widespread the problem would be in realistic cases.

To adequately analyze the potential vulnerability of our groundwater resources in the event of large-scale deployment of carbon capture and st orage, the geochemical consequences of CO 2 migration into current and future potable wate r sources requires further evaluation. A multiyear research project was initiated in October 2006 to address these issu es, the first phase of which is summarized in this annual report. The objective of this first phase is to investigate the water quality changes in response to CO 2 intrusion through development of a geochemical model that describes the complete chemistry of a range of specified shallow aquifer systems. This model is then used in a series of reactive-transport modeli ng simulations to evaluate transient responses to
CO 2 intrusion. The goal is to better understand, in a global se nse, the vulnerability of representative and relevant a quifer types in the case of CO 2 intrusion, thereby providing sound scientific information to regul ators and the public. It is unders tood, however, that evaluation of specific sites will require additi onal simulation analysis consider ing the site-specific conditions, ideally to be supported by labor atory and field experiments.4 of 352 

# 2. Work Scope and Research Plan

This report describes a systematic evaluation of the possible water quality changes in response to
CO 2 intrusion into aquifers currently used as sour ces of potable water in the United States. Such waters generally contain total dissolved solids concentrations far less that the maximum permitted 10,000 mg/L permitted in a USDW. Our goa l is to develop a principal understanding that would allow us to evaluate which aquifer systems and regions of the United States may be
vulnerable in case of CO 2 intrusion. In particular, we need to understand, for a range of relevant aquifer conditions, the potential for hazardous consti tuents (such as heavy metals or As) to be mobilized by the ingress of CO 2 and the distribution of these constituents in time and space. Ultimately, we need to determine the likelihoo d for drinking water standards to be exceeded. These issues are addressed by conducting reactive transport simulations for a variety of CO 2 - release scenarios and aquifer conditions. A reactive transport model was developed for quantifying the effect of CO 2 intrusion into a shallow aquifer, simulating the multiphase flow of
CO 2 and water, and predicting the fate and migr ation of trace hazardous constituents in the groundwater (Section 4).

As in any numerical simulation study, reliable model predictions can only be achieved when model parameters are both realis tic and representative, and when all relevant processes are adequately incorporated. In particular, the reactive transport model needs to (1) account adequately for the complexity in natural geochemical systems, (2) consider the relevant geochemical processes, (3) represent a wide ra nge of geochemical cond itions with respect to initial water chemistry and minera logy, and (4) incorporate variability and uncertainty in relevant hydrogeological and geochemical parameters. To ensure adequate model configuration , we have
selected the conditions modeled in the reactive transport simulations—e.g., initial abundance and distribution of these hazardous constituents, aquifer mineralogy, initial water chemistry, oxidation state—based on development of a comp rehensive geochemical model, which included the rigorous evaluation of more than 38,000 groundwater qua lity analyses from aquifers throughout the United States, using a version of the EQ3/6 code (Section 3). This geochemical model development allowed us to draw some i mmediate quantitative c onclusions regarding the5 of 352 potential vulnerability of groundwaters in the Unite d States, for example with respect to the pH or Eh buffering capacity of most samples. Th e geochemical model also allowed preliminary estimates of the potential concentrations of hazar dous constituents in equilibrium with elevated partial pressures of CO 2 following intrusion. However, in order to explore spatial changes in aquifer chemistry under transient conditions following CO 2 intrusion, we employed the reactive transport simulations in which was incorporated a suitable surface complexation model to describe adsorption processes.

Based on the above discussion, the research work as described in this report has two main parts: (1) the development of the aquifer geochemical model detailed in Section 3, and (2) the systematic reactive transport simu lations detailed in Section 4. Figure 2.1 depicts the information flow between these two parts. The key information ga thered in Part (1), a nd provided to Part (2),
is listed below:

- Review, evaluation, and generation of thermodyna mic and solubility data for all relevant minerals and aqueous species, and their incorp oration into the reactive transport simulator

- Geochemical evaluation of a large number of water quality analyses with measurable concentrations of hazardous inorganic constituen ts; determination of chemical potentials and thermodynamic controls for various relevant hazardous constituents; and evaluation of mineral assemblages influencing pH and Eh bu ffering capacity, as well as background levels
of CO 2 concentration

- Review of representative mineralogies for prin cipal aquifers typically exploited for potable water; and classification of these aquifers w ith respect to expected geochemical behavior; development of selected mineralogies for si mulation study (considering major minerals and clays)

- Identification of minor minerals that typically have small volume fraction, but are critical because they contain hazardous chemical constitu ents such as Pb and/or As; and specification of possible volume fraction ranges for these minor minerals

- Review and evaluation of relevant therm odynamic parameters in support of a surface complexation model to describe adsorption a nd desorption, and ion exchange processes.6 of 352 The first step in Part (2) is to set up the reactive transport simulator in a single cell configuration with the above-described inputs, and then run the simulator until a secular equilibrium state is reached. This secular-equilibrium state establishes the initial distribution of the hazardous constituents in the aquifer, which is a necessary prerequisite for reactive transport simulations where the initial state is perturbed following CO 2 intrusion. The initial secular geochemical equilibrium is generated using the same geoc hemical simulator TOUGHREACT (Xu et al., 2006) employed for the reactive transport simu lations (Section 4). The secular equilibrium calculation considers that the substance may be in itially distributed (1) in the aqueous phase as ionic, molecular, and complex species (possibly occurring with differing oxidation states), (2) as adsorbed species on the surfaces of the minerals composing the aquifer rock, (3) as species in ion exchange sites, (4) in trace amounts in solid solu tion, and (5) as discrete minerals in which the substance is an essential component. As a check for the model and its thermodynamic parameters, the simulated initial concentrations of various species in the water are compared back to the range determined in th e database evaluation (Section 4.4).

3⁄4 Development of Geochemical Model – What is the initial concentration of hazardous constituents in aquifers?
– Which trace minerals control the initial concentration of such constituents? – What is the predicted concentration of hazardous constituents as a function of P(CO2)? – What types of mineralogy (main minerals) should be considered to study a range of more or less vulnerable aquifers? – What are the expected volumetric fractions of such trace minerals? 3⁄4 Development of Geochemical Model – What is the initial concentration of hazardous constituents in aquifers?
– Which trace minerals control the initial concentration of such constituents? – What is the predicted concentration of hazardous constituents as a function of P(CO2)? – What types of mineralogy (main minerals) should be considered to study a range of more or less vulnerable aquifers? – What are the expected volumetric fractions of such trace minerals?

3⁄4 Systematic Reactive Transport Model Analysis – How are hazardous constituents initially distributed in the aquifer (aqueous phase, adsorbed, on ion exchange sites, in trace minerals)? – What is the possible impact of CO 2 intrusion on water quality, considering a wide range of hydrogeological and geochemical conditions? – What are the controlling processes or parameters? 3⁄4 Systematic Reactive Transport Model Analysis – How are hazardous constituents initially distributed in the aquifer (aqueous phase, adsorbed, on ion exchange sites, in trace minerals)? – What is the possible impact of CO 2 intrusion on water quality, considering a wide range of hydrogeological and geochemical conditions? – What are the controlling processes or parameters?

Fig. 2.1. Main research steps and information flow between them7 of 352 The final step in Part (2) is to conduct the reactive transport simulations for a series of simulation cases. Starting with the specified initial geoche mical conditions, these simulations predict the impact of CO 2 intrusion into a fresh-water aquifer and determine the related changes to the host rock mineralogy and water chemistry. Various geoc hemical sensitivity cases are evaluated to (1) account for the wide range of c onditions found in domestic potable aquifers and their host rocks, and (2) to encompass the uncertainty associated with geochemical processes and model parameters. In addition, we have varied certain hydrogeological aquifer conditions (such as the depth of the freshwater resource and the groundwater flow rate ), have simulated different CO 2 release scenarios (intrusion rate of CO 2 ), and have conducted 2D versus 3D modeling to understand the impact of dimensionality. While including a wide range of relevant mineral phases in the reactive transport model, including their potential interactio ns, we have focused on two major hazardous constituents—Pb and As—whe n evaluating the detrimental effects of CO 2 intrusion. Based on our modeling re sults, we can provide a better understanding of the expected vulnerability of relevant domestic aquifer types in the case of CO 2 intrusion. As mentioned before, our broad sensitivity study, intended to provide results for a wide range of representative conditions, is a starting point for an essential next step, to conduct a detailed geochemical evaluation of selected storage si tes incorporating the site-speci fic conditions and parameters.

Note that all geochemical modeling studies conduct ed in this report assume the intrusion of pure
CO 2 into potable drinking water. It is possible, however, that leaking CO 2 may carry organic compounds leached at depth; they may also cont ain co-injected trace contaminants, such as H 2 S. The conditions for this to occur, and the potential ramifications, are explored in future project phases.

We also note that bacterial processes are not e xplicitly accounted for in our reactive transport modeling. Bacteria take advantage of non-equilib rium states to extract energy from electron transfer reactions where corresponding abioti c reactions, which would otherwise bring the chemical system into redox equilibrium, are kinetically hindered by a large activation energy
barrier. The intrusion of CO 2 into potable groundwater aquifers will not materially increase the opportunity for bacteria to extract energy from the system, and potential bacterial mechanisms8 of 352 that might have some small impact, such as th ose affecting sulfide mine ral dissolution, have not been examined. Instead, we implicitly account fo r potential bacteria l activity thr ough sensitivity
studies of the kinetics of redox reactions involvi ng the dissolution respectively of galena and the solid solution of arsenopyrite in pyrite.

In addition to geochemical modeling, we also re port on natural and man-made natural analogues providing corroborative information about the pote ntial water quality changes in response to CO 2 intrusion into groundwater (Section 5).9 of 352 

# 3. Geochemical Model Definition

3.1 Basic Methodology

In this section, a series of steps is described with the goal of developing a geochemical model for use in reactive chemical transport simulations described in Section 4. The fate of inorganic hazardous constituents in the presence of elevated CO 2 levels, such as after intrusion of leaking
CO 2 from deep geological storage, ultimately depends on their initial abundance and distribution in the aquifer. Initially, they are distributed (1 ) in the aqueous phase as ionic, molecular, and complex species, (2) as adsorbed species on the surfaces of the minerals composing the aquifer rock, (3) as species in ion exchange sites (prima rily in smectitic clays), and (4) in trace amounts in solid solution in detrital minerals, weatheri ng products, chemical precipitates (e.g., carbonates and sulfates) and diagenetic minerals, and (5) possibly as discrete minerals in which the constituent is an essential component. These in itial distributions must be characterized and quantified as a necessary prerequisite for conduc ting reactive transport simulations evaluating
the mobilization of such constituents in the case of CO 2 intrusion. This requirement is met through the compilation of pertinent thermodynamic parameters governing their distribution in the aquifer, e.g., the thermodynamic properties of both the minerals and their dissolved components in aqueous solution, ion exchange coefficients, adsorption coefficients, and coefficients defining solid solutio ns. Also required are the electrolyte, site complexation, ion exchange, and solid solution models appropriate to the problem at hand.

The steps taken in developing the geochemical m odel included (1) a comprehensive review of the literature in order to identify mineral hosts controlling the concentrations of hazardous constituents in groundwaters, (2) development of an extensive ther modynamic database for relevant minerals and aqueous species and site complexation constants, (3) rigorous geochemical evaluation of more than 38,000 gr oundwater quality analyses fr om potable water aquifers throughout the United States, (4) evaluation of th e natural abundances of hazardous constituents in sedimentary formations hosting potable wate r, and (5) a survey of the sedimentology and10 of 352 hydrology of the principal potable water aquifers throughout the United States. The key questions addressed are as follows:

1. What minerals might be present that contro l the aqueous concentr ations of hazardous constituents in potable groundwaters?
2. How is the aqueous concentration of each h azardous constituent determined? Is the aquifer water saturated with respect to a par ticular mineral that contains that constituent as an essential component, or is it in equili brium with that constituent only as a minor, non-essential component associated w ith other rock-forming minerals?
3. What would be the mass (or volumetric) fract ions of solubility-controlling minerals postulated to be present in the aquifer rocks?

4. What is the framework and matrix mineralogy of typical aquifers exploited for potable water? What types of representative minera l compositions should be considered to study a range of more or less vulnerable aquifers?

Figure 3.1 illustrates schematically the interrelated tasks required to address these questions, and their relationship to tasks performed with the reactive transport model analysis in Section 4. Question (1) is addressed in Section 3.2 through a review of the modes of occurrence for the chosen hazardous constituents in sedimentary rock s, including coal, for which an extensive and highly relevant literature is available. Question (2) is addressed in part through the geochemical evaluation of a large number of groundwater samples from the USGS National Groundwater Information System (NWIS) database (Sections 3.3 and 3.4). The technical objective of the groundwater evaluation is to identify the ther modynamic controls governi ng the concentrations of hazardous inorganic constituents in pot able groundwaters samples taken throughout the United States. Identifying the thermodynamic controls is an indirect way of determining whether (and which) solubility-controlli ng minerals (i.e., galena possibl y controlling the aqueous Pb concentrations) are present in the aquifer from which the sample is recovered. The distributionof-species code EQ3/6 (Wolery et al., 1993) was used in this study to calculate the saturation indices of minerals containing hazardous constituents as essential components in a selected set of suitable groundwater analyses downloaded from the NWIS database (Section 3.3.1).11 of 352 The reason for taking this indirect approach is that the mass fractions of the relevant hazardous constituents (i.e., the solubility -controlling phases) in the aquife r host rocks are very low (on the
order of 10 -4 - 10 -6 or less), and few studies have been conducted th at have unequivocally confirmed the presence of their mineral hosts, particularly in potable water aquifers. Direct means of identifying their presence and spatial di stribution are only possibl e with high-resolution imaging techniques or other highl y complex methodologies, an effo rt that is rarely undertaken and certainly not in a systematic manner. Theref ore, in this study, we pos tulate the presence of solubility-controlling phases in the aquifer rocks through geochemical evaluation of groundwater chemical analyses containing specific data for several hazardous inorganic constituents of interest, and corroborated through independent studi es reported in the li terature (Section 3.4).

Question (3) is addressed by consulting the Na tional Geochemical Survey Database (USGS, 2008) in which the analyzed concentrations of hazardous constituents present in surficial soils and sediments are tabulated (Section 3.3.3). This information was used as a proxy for corresponding concentrations in aquifer host rocks, expressed in mass fraction or volume fraction, and is needed because the thermodynami c analysis described above only identifies which phases may control the concentration of a hazardous constituent in solution, but cannot define the quantities present. (Saturation in dices of minerals are intensive thermodynamic variables—i.e., variables that are independent of mass.)

Finally, to answer Question (4), a variety of i ndependent literature sources was used to obtain representative mineralogies for representative aquifer host rocks (Section 3.6). Inte gration of the bulk mineralogy with the minera logy characterizing hazardous i norganic constituent mineral hosts, and normalizing to volume fractions, yiel ds input suitable for TOUGHREACT. For the geochemical model to be realistic, further info rmation is also required regarding the rock physical properties such as por osity and specific surface areas.

Once the total mineralogy has been identified and quantified, a geochemical secular equilibrium
model is constructed an d tested for consistency with groundwater chemical analyses (Section 4.4). The model must have attained a steady-state kinetic or secular equilibrium with respect to the major rock forming primary and secondary mi nerals, and equilibrium with respect to all12 of 352 minor and trace minerals that could define the aqueous equilibrium concentration of the hazardous constituents under study. This equilibrium state needs to be established not only with respect to the minerals contai ning hazardous inorganic constituent s as essential components, but also with respect to those cons tituents on adsorption sites on primary and secondary host rock minerals, on ion exchange sites in clays, and op tionally in solid solution in secondary minerals. The geochemical model code needed for this pur pose requires a site co mplexation model with suitable adsorption constants for each inorganic constituent, for the surfaces of each mineral incorporated in the model, an ion-exchange mode l with suitable ion-exchange coefficients, and a
suitable solid-solution model for trace substituti on of inorganic substances in specified host minerals. In principle, the distribution of each hazardous constituent between the constituent host minerals can be compared with corresponding field data obtained in answer to Question (1), as a basis for partial model validation. Note that incorporat ion of all mechanisms for immobilization of hazardous constituents in/on solid phases of the host rock is not always necessary. For example, it was found that Pb +2 substitution into ion exchange sites in clays was not a significant sink for Pb in groundwater, when considering the the geochemical model for potable water aquifers generated in this study.

Results from the secular equilibrium geochemical model used in Section 4.4 to generate the simulated aqueous-phase composition at the initial state (prior to CO 2 intrusion) can be compared with the concentration ranges observed in NWIS groundwaters. If the chemical system has been adequately defined, the thermodynamic da ta used to construct the model are accurate, and (last, but not least) thermodynamic equilibr ium is closely approached in the field, then a reasonable agreement should be obtained. The mode l can therefore be checked for consistency against the known ambient conditions and constitute s a further partial validation of the model.

The final step shown in the flowchart in Figure 3.1 is to conduct the reac tive transport simulations predicting the water qu ality impact in response to CO 2 intrusion from an underlying storage reservoir (Sections 4.5 through 4.10). Starti ng with the equilibrated initial conditions, the reactive transport model allows for mobilization of relevant hazardous constituents from mineral dissolution as well as from adsorption and ion ex change sites. In such a model, homogeneous13 of 352 (i.e. within a single phase, most commonly the a queous phase) and heterogeneous (i.e. between different phases) chemical kinetics, gas-a queous-phase mass transfer, and aqueous-phase transport play a role. The reactive-transport simula tor used in this study, for the reactive transport simulations as well as for conducting secular equilibrium runs, is TOUGHREACT (Xu et al., 2005), described in Section 4.1. Th e relevant thermodynamic data incorporated into this simulator have been derived from a comprehens ive review of the published literature, mostly based on laboratory experiments, some estimate d. Extensive revision and augmentation of the thermodynamic data were necessary for the purpose of this study (Section 3.4.2).

Uncertainties associated with the geochemical model remain, primarily because the chemical thermodynamic and kinetic data to model such complex systems, even under ambient conditions, are incomplete or subject to large uncertainties. The physical and chemical response of a potable groundwater aquifer to elevated CO 2 pressures has received only limited study in the laboratory,
and then under conditions that are of limited re levance. It is possibl e that some physical processes and chemical reactions operati ve in the presence of high pressure CO 2 have not been identified, and therefore the modeling could su ffer from unrecognized omissions. Furthermore, the physical characteristics spec ified for aquifer conditions in this study are generalized, and aquifer host-rock heterogeneity is not taken in to account. To remedy the related uncertainties, further laboratory investigations and field characterization are desirable. In the course of this study, uncertainties and knowledge ga ps that could affect the fi ndings of this evaluation are identified, and are summarized in Sections 3.7 and 4.10 of this report.14 of 352 Note: HIC = Hazardous Inorganic Constituent, DL = De tection Limit, MCL = Maximum Contaminant Level
Fig. 3.1. Flow chart describing research steps providing n ecessary information as input to reactive transport models15 of 352 

## 3.2 Identification of Mineral Host s Controlling the Concentration
of Hazardous Constituents

A comprehensive review of the literature wa s conducted to characteri ze the phase relations expected for relevant hazardous constituents in shallow potable water aquifers, and to identify mineral component(s) best suited as references in the thermodynamic eval uation of water quality data. Emphasis is placed on phase relations expe cted under reducing conditions, as appears to be the case with the majority of groundwaters for which water quality data are available (Section 3.4.4).

The selection of trace elements was based on their immediate relevance to the study, i.e., considering the work of Wang and Jaffe (2004) re garding lead (Pb) and recognizing a recent study by Focazio et al. (2006), which showed that arsenic (As) and uranium (U) concentrations sometimes exceeded maximum permissible concentrations in domestic wells. Other elements studied include barium (Ba), cadmium (Cd), me rcury (Hg), antimony (Sb), selenium (Se), and zinc (Zn). These elements, all of which except Zn are considered potentially hazardous, were
selected because their chemical behavior is fair ly well known, or can be correlated with other elements whose chemistry is well understood for the conditions of the st udy. While Zn is not particularly important as a hazardous constituent, its behavior is somewhat similar to Cd, but it is found at higher and more easily detectable concentrations, a nd can therefore serve as an interpretive tracer.

Most of the selected elements, the exceptions be ing Ba and U, participate in the formation of chalcogenides, either as a reactive aqueous meta l cation or complex in solution (i.e., Cd, Hg, Pb and Zn) or as a co-precipitant that substitutes or proxies for sulfide (S(-II)—i.e., As(-I), Sb(-I?) and Se(-II)). Ba(II) precip itates primarily as a sulfate, so metimes as a carbonate, whereas U in the reduced (IV) state commonly precipi tates as the oxide, uraninite (UO 2 ) and/or as the silicate, coffinite (USiO 4 ), or more rarely as th e titanate, brannerite (UTi 2 O 6 ).16 of 352 Reducing conditions in hydrothermal waters are enhanced through hydrolysis by SO 2 , which disproportionates below about 450 ° C, according to the reaction:

4SO 2 + 4H 2 O = 7H + + 3SO 4 -2 + HS and by hydrolysis of Fe(II) containing minerals, thus:

3(FeO) ss + H 2 O = Fe 3 O 4 + H 2 (aq)

These reactions contribute to th e aqueous precipitation of chalc ogenides at temperatures between

≈ 400 and 0 ° C.

At temperatures above 450 ° C, the chalcophilic elements, Cd, H g, Pb and Zn, tend to substitute in their oxide components in rock-forming minerals , except under unusual conditions such as the extreme reduction induced by hydrolysis by the FeO component of mafic minerals. For example,
Pb +2 together with Al +3 tends to participate in coupled substitution for K + with Si +4 in potash feldspars (Stevenson and Martin, 1986) in a manner similar to that of Ba +2 , which is of similar ionic radius (i.e., (Ba,Pb)-Al 2 Si 2 O 8 for K-AlSi 3 O 8 ), to preserve charge balance, or partitions as chloride complexes in residual hypersaline brines following solidification of acidic magmas. In contrast, As and Sb in the (V) state are likely to follow phosphate with substitution in apatite or other metamorphic phosphates (Wise and Loh, 1976) . Selenium in the (IV ) or (VI) states probably mimics S(IV) and S(VI), but further re view is required to cl arify its behavior at elevated temperatures (see Smith, 1963). The chem istry of U follows similarly with substitution of the oxidized form, U(VI), as in magmatic apatite.

During subaerial weathering, many of the magmati cally formed minerals are unstable in the presence of water and decompose, liberating the tr ace elements in their hi ghest oxidation states. As, S, Sb, and Se tend to form oxy-anionic species, whose salts are soluble, and therefore tend to remain in solution, or are adsorbed on secondary minerals whose surfaces are positively charged, e.g., ferric oxides and oxy-hydroxides. Uranium is al so released in its highest oxidation state, as
UO 2 +2 , and Hg as Hg +2 . Detrital and secondary products of weathering will undergo further alteration either immediately following shallo w burial (authigenesis) , or following prolonged17 of 352 deeper burial, where progressively increasing temperatures lead to further alteration and precipitation of secondary minera ls (diagenesis). A critical aspe ct of both auth igenesis and diagenesis is the role that en trapped organic matter plays as an electron donor in facilitating the reduction of As(V), S(VI), Sb(V), Se(IV,VI) and U(VI). Under authigenic conditions, bacteria utilize organic matter in the reduction of these sp ecies, together with Fe (III) and Mn(III and IV) to Fe(II) and Mn(II), respectively. Therefore, ox ides such as hematite, goethite and ferrihydrite, which initially dominate iron minerals, are partia lly or completely reduced to secondary iron sulfides under subsurface conditions. Reduction of iron to sulfide is complicated by the formation of metastable sulfide intermediates such as greigite (Fe 3 S 4 ) and mackinawite (FeS). During the formation of metastab le precursor iron sulfides or pyrite, Hg, As, Sb and Se are sequestered and incorporated in solid solution, probably in a disordered state. Because the
concentration of iron overwhelms the concentra tions of these elements, they are substantially partitioned, possibly in metastable equilibrium, into the iron sulfide, the residue being adsorbed onto thiol groups in the organic matter as Hg is believed to behave (Benoit et al., 1999).

With deeper burial and higher temperatures , organic matter undergoes thermolysis and maturation at temperatures between ~75–120 ° C (Tissot and Welte, 1978), which leads to the
destruction of unstable thiol and amino groups on the organic matter, while accelerating the reduction of Fe(III) and Mn(III and IV) and S(VI). Diagenetic recr ystallization of metastable iron sulfides to pyrite can lead to significant reductio n in the extent of solid solution and expulsion of Hg, As, Sb and Se. Maturation of organic matter al so causes the release of previously adsorbed trace metals to the groundwater, potentially assi sted through complexation with carboxylates and amines. Increased acidity, due to the release of CO 2 during maturation, also enhances the solubility of trace metals. Finally, increased sali nity, either from trapped connate seawater or from the dissolution of evaporites, likewise incr eases the concentration of Cd, Hg, Pb and Zn through chloride complexing. Thus, several mechan isms are operative that lead to enhanced concentration of trace metals in deep groundwater, which can facilitate th eir transport to lower temperature regions. In aggregat e, these processes can result in supersaturation and secondary precipitation of discrete phase s where the affected elements are essential components. Circumstantial evidence in support of this phenomenon is presented below.18 of 352 Deep burial with temperatures rising to ~300 ° C can lead to further destruction of primary minerals, with the associated release of trace elements. Sec ondary pyrite formed under these
conditions contains enhanced conc entrations of Hg, As, Sb, and Se in solid solution, because the capacity to accept these nonstoichiom etric elements in solid solution increases with temperature, and larger quantities become available due to a lteration of the primary minerals. With erosional unroofing of deeply buried sediments and falling temperature, retrograde metamorphism takes place, and the initial metastable pyrite solid solution becomes unstable. Supersaturation with respect to secondary minerals containing the tr ace elements as essential components can then occur. However, it is more likely that ex-sol ution of secondary phases will occur within pyrite crystals, and therefore, the trace metals will re main sequestered by the py rite, rather than being released to the groundwater and precipitating as discrete stoichiometric minerals. The scenarios just described are supported by fi eld observations, but many of the mechanisms involved are not well understood, even in a qualitative sense. Ne vertheless, a general understanding of the processes involved is essential for understa nding how individual hazardous inorganic constituents are likely to behave fo llowing the ingress of high-pressure CO 2 into shallow groundwaters.

In the following paragraphs, we speculate on the behavior of each hazardous inorganic constituent and identify mineral component(s ) as likely candidates in the thermodynamic evaluation of water quality data presented in Section 3.4. The thermodynamic activity of these components, defined in terms of corresponding mine ral saturation indices (SI), can then be used to establish whether they might actually be presen t as discrete minerals in the aquifer host rocks components (i.e., if SI ≥ 0). If, however, SI ≤ 0, then that component is either present in solid solution in another mineral, or some other com ponent is saturated with respect to a discrete mineral. The definition of SI is presented in Section 3.4.1.

Little information is available in the literature concerning the distribution of the hazardous inorganic constituents of interest in shallow aquifer host rocks. However, extensive studies have been undertaken over many years to characterize th e distributions of such constituents in the various mineral components of coals—e.g., see Fi nkelman (1981) and Kolker et al. (2002). The19 of 352 reason for such an interest is that many trace elements ar e environmentally hazardous, and potential coal pretreatment pr ocesses to minimize their release to the atmosphere following combustion is of more than passing interest. It is obvious that coals differ markedly in composition from a typical shallow aquifer hos t rock. A typical arenaceous aquifer rarely contains more than 2 wt% of orga nic matter, in contrast to coal, which in some instances exceeds 95 wt%. Also, the “ash” content of exploitable coal rarely exceeds 30 wt.%, whereas nearly 100 wt.% of an aquifer host rock consists of inor ganic material. However, the sustained reducing environment induced by organic matter in co al beds, and the duration of burial, allow sequestration and enrichment of hazardous cons tituents in discrete mineral hosts, such as chalcogenides, which are more readily identified and characterized in coal than in arenaceous sedimentary rocks. Furthermore, because of the incentive to study the distribution and mode of
occurrence of hazardous constituents in coals fo r environmental reasons, a significant literature
selection is available that can be used opportunistically to help define a comparable host mineralogy for hazardous constituents in potable wa ter aquifers. It goes without saying that such an interpretation must be undertaken with full cognizance of the impact that the substantial compositional differences between coal and arenaceous sedimentary rocks have on the
distributions of hazardous c onstituents between the various mineralogical components comprising each material.

The Unites States Geological Survey recentl y conducted a comprehensive multi-element characterization of selected coals (Kolker et al., 2002). This study is invaluable in providing a qualitative basis for geochemical model deve lopment for the present project. Relevant information is summarized in Tables 3.1, 3.2 and 3.3, which show, respectively, the bulk composition and character of three coals, the sequ ential selective-leaching procedure involved in the separation of the principal components of th e coals, and finally, the distribution of the selected inorganic constituents and iron in the three coals, as determined by the sequential selective leaching procedure. The pertinence of th ese findings in relation to the distributions of individual trace elements in shallow aquifer hos t rocks is provided in the following discussion.20 of 352 Table 3.1. Characteristics (in weight percent, dry basis) of coals studied by Kolker et al. (2002)

Ohio #5, #6, #7 beds Wyodak, Cordero Mine, Gillette, WY North Dakota, Falkirk Mine, Underwood, ND
Ash 9.93 8.13 14.64 Pyritic Sulfur 1.42 0.05 0.28 Organic Sulfur 1.24 0.37 0.56 Sulfate Sulfur 0.02 0.01 0.14 Moisture 2.33 25.81 35.88 Rank Bituminous Sub-bituminous Lignite

Table 3.2. Sequential selective leaching procedure to charact erize the distribution of trace elements in the constituent components of coal (a fter Kolker et al., 2002)

# Procedure Elements Extracted from: 1 1N ammonium acet ate (CH3COONH3) exchangeable sites, wa ter soluble compounds, some carbonates 2 3N hydrochloric acid (HCl) carbonates, iron oxides, mono-sulfides, certain chelated organic compounds 3 Conc. hydrofluoric acid (HF; 48 percent) silicates 4 2N (1:7) nitric acid (HNO3) disulfides, especially pyrite

The trace-element distributions in the principal components of coal (i.e., ion exchange sites, carbonates, sulfates, silicates, sulf ides, and chelating sites in the organic fraction), together with other supporting information (s uch as the mineralogical co mposition of the “ash” and assumptions regarding the adsorption characteris tics of both inorganic and organic components), can be extrapolated to approximate their distributions in se dimentary rocks of shallow aquifers. Although such a calculation can only be of qualitative value, it is nevertheless a helpful guide for planning similar quantitative evaluations of aquifer host rocks. Furthermore, it can also serve as a check on geochemical models of trace element behavi or in aquifer host rocks, which of necessity are based almost entirely on laboratory m easurements involving idealized systems.21 of 352 Table 3.3. Distribution (in %) of iron and selected trace elements in coal (from Kolker et al., 2002)
Coal Type Element Ohio 5/6/7 Wyodak North Dakota
Fe 80 10 10 Pyrite Fe-oxides 1 Silicates 10 75 10 Pyrite Fe-oxides 1 Silicates 10 65 15 Pyrite Fe-oxides 1 Silicates As 60 25 5 10 Pyrite Arsenates Silicates Organic 2 15 40 15 30 Pyrite Arsenates Silicates Organic 15 50 15 20 Pyrite Arsenates Silicates organic Ba 5 90 5 Barite Silicates Organic (5%) 80 5 15 Barite Silicates Organic (5%) 2 85 15 Barite Silicates

Cd 20 35 40 Sphalerite Pyrite Organic (5%) 2 70 10 15 Sphalerite Pyrite organic -

-

- ND ND ND Hg 60 20 10 Pyrite Organic Exchangeable 60 20 20 Pyrite Organic Exchangeable 80 10 Pyrite Organic

Pb 35 40 10 15 Galena Pyrite Silicates Organic 70 10 20 Galena Pyrite Silicates 85 5 10 Galena Pyrite Silicates

Sb 20 60 15 Silicates Organic Pyrite 35 45 15 Silicates Organic Pyrite 40 50 10 Silicates Organic Pyrite Se 50 50 Pyrite Organic 20 75 Pyrite Organic (5%) 2 25 70 Pyrite Organic (10%) 2 U 20 5 70 Silicates HCl Soluble 3 Organic 4 30 50 15 Silicates HCl Soluble 3 Organic (10%) 2,4 45 45 15 Silicates HCl Soluble 3 Organic 4
Zn 30 30 10 30 Sphalerite Pyrite Silicates Organic (5%) 2 50 25 10 15 Sphalerite Pyrite Silicates Organic 65 5 10 20 Sphalerite Pyrite Silicates Organic

1 May also include oxidized pyrite and mono-sulfides 2 Number in parentheses after Organic % indicates percent in exchange sites. 3 Species unknown, may be asso ciated with organic chelat es, and/or with zircon. 4 Small amounts may be associated with zircon.22 of 352 Despite the undoubted value of the re sults regarding the distributions of trace elements in coals, it should be noted that the sequential select ive leaching procedure employed in the study introduces artifacts that could misrepresent the tr ue distribution of trace elements in the unmined
in situ coals. In particular, the lo w-temperature ashing of the coal , conducted at about 200°C, can
lead to the oxidation of redox sens itive elements, such as the presen ce of As as arsenate indicated in Table 3.3. Even preparation of the coal sample s, such as their comminution prior to selective leaching, can result in partial oxida tion, if conducted under atmospheric air.

Table 3.4. Molar ratio of trace metals (Me) in relation to As in pyrite compared to thei r molar abundance ratio in a bulk sample of Ohio 5/6/7 Coal. R 2 denotes correlation.

Trace Element Molar Ratio (Me:As) in pyrite R 2 Molar Ratio (Me:As) in bulk coal Relative Enrichment Enrichment In Pyrite

As 1.000 1.00 1.000 1.000 18.1 1
Cd 0.0014 0.71 0.003714 0.377 6.82 Hg 0.0112 0.65 0.003777 2.97 53.8
Pb 0.392 0.73 0.1849 2.12 38.4
Sb 0.0471 0.63 0.6707 0.702 12.7
Se 0.964 0.92 0.0949 10.2 185
Zn 0.0441 0.13 1.352 0.0362 0.66

1 Ratio of As concentration in pyrite (321 ppm) to hat in coal (17.8 ppm)

Table 3.5. Sequential selective leaching procedure to character ize the distribution of tr ace elements in marine sediments (after Huerta-Diaz and Morse, 1992)

# Fraction Procedure Elements Extracted from: 1 Reactive Digestion of a freeze-dried sediment sample for 16 hours with 1M HCI. Monosulfides, amorphous and crystalline iron, and manganese oxyhydroxides, carbonates, and hydrous aluminosilicates 2 Silicate Two consecutive leachings with 10M HF for 1 hour and 16 hours, respectively, followed by addition of boric acid to redissolve precipitated fluorides. Clay minerals

3 Pyrite Digestion of the silicate fraction residue with con centrated nitric acid for 2 hours. Pyrite and associated trace metals23 of 352 Of the extensive suite of trace elements studied, five (As, Cd, Hg, Pb and Zn) are pertinent to the current study. The distributions of these elements and of Fe between the reactive and pyritic fractions (as defined in Table 3.5 ) are shown in Figure 3.2, which il lustrates the depth profiles in organic-rich marsh sediments from Atchafalaya Bay, Mississipp i. This sediment contained connate water with a lower salinity ( ≤ 5 ‰) than other sedimentary environments studied by the investigators, and is therefore considered to be more relevant to the present evaluation.

Fig. 3.2. Concentrations (per gram of sedi ment) of selected trace elements an d Fe in the pyritic and reactive fractions of organic-rich marsh sediments from Atchafalaya Bay with depth below the sediment-water interface (after Huerta-Diaz and Morse, 1992). Dashed lines are illustrative only, and included to emphasize trends in the partitioning of trace meta ls between the reactive and pyritic components.24 of 352 Of particular interest is the observed partitioning of the tra ce metals between the reactive and pyritic components as a function of the extent of pyritization. Huerta-Diaz and Morse (1992) examined their behavior in terms of two para meters, the degree of trace-metal pyritization (DTMP):

(%) 100 Re Pyrite Me DTMP Pyrite Me active Me − = × −+ − (3.1)

and the degree of pyritization (DOP), defined as:

(%) 100 Re Pyrite Fe DOP Pyrite Fe active Fe − = × −+ − (3.2)

When DTMP is plotted against DOP, a measure of the degree of preferential partitioning of each trace element in pyrite is obtained. This is shown in Figure 3.3 for samples taken from the organic-rich marsh sediments fr om Atchafalaya Bay, Mississippi.

Fig. 3.3. The degree of trace metal pyritization (DTMP) in te rms of the degree of pyritization (DOP) for As, Cd, Hg, Pb and Zn in organic-rich marsh sediments fro m Atchafalaya Bay (after Huerta-Diaz and Morse, 1992).25 of 352 In the following paragraphs, we make reference to the information taken from Kolker et al. (2002) and from Huerta-Diaz and Morse (1992) in discussions of the likely behavior of each selected hazardous inorganic constituent as it relates to solid solution substitution in pyrite or to the formation of discrete phases forming separa tely from co-existing pyrite. While reactive transport modeling in Section 4 is applied w ith a focus on Pb and As, we provide here a summary evaluation of the behavior of severa l other potentially relevant hazardous inorganic
constituents.

Arsenic

. An extensive literature testifies to the presence of As substituting for S as As(-I) in authigenic, diagenetic, and hydrot hermal pyrite (Blanchard et al ., 2006; Reich et al., 2005). The abundance of As in relation to the quantity of sulf ide (-II) sulfur in pyrit e is generally small (As

≤ 0.05), although As substitution in FeS 2 at higher temperatures is more extensive. Reich and Becker (2006) cite concentrati ons as high as 19 wt% observed in natural pyrite and marcasite samples. These investigators, on the basis of theo retical calculations, show that metastable As substitution in pyrite up to As ~ 0.05 is thermodynamically favored over the corresponding substitution in marcasite, a less stable polymorph of pyrite. At higher mass fractions, a sulfurrich arsenopyrite (FeAsS), which possesses the marcasite structure, would coexist with the metastable arsenian pyrite.

Saturation with respect to arsenopyrite in th e presence of arsenian pyrite in typical lowtemperature shallow potable water aquifers is unlikely, since the abundance of As is normally insufficient (see Section 3.4.5.2, Figure 3.34a). Indee d, only one example has been cited in the literature (Utsunomiya et al., 2003). However, Reic h and Becker (2006) show that at 300 K, the ultimate state of thermodynamic equilibrium would be a pure end-member pyrite coexisting with sulfur-rich arsenopyrite. This state is not norma lly achieved in nature. Prolonged annealing of arsenian pyrite l eads instead to exso lution of arsenopyrite domains (F leet et al., 1989; Simon et al., 1999a,b; Palenik et al., 2004) that remain isol ated within the pyrite. At low solid-solution concentrations, i.e., As ~ 0.01, unm ixing appears to be too sluggish to be observable in field specimens (Savage et al., 2000). Arsenic mobili zation will therefore be sensitive only to environmental changes that induce pyrite dissolu tion, although such change s, when induced by26 of 352 oxidizing conditions, could be accel erated by the presence of As in pyrite (Blanchard et al., 2007).

The As content in authigenic pyrite as a functi on of depth in brackish and marine sediments shows that almost all As is sequestered by th e pyrite soon after burial (Huerta-Diaz and Morse, 1992), as shown in Figures 3.2 and 3.3. The partitioning of As in coal also supports the belief that As substitutes in autogenously generated pyrite (Finkelman, 1981; Ko lker et al., 2002), and that, at most, only about 15% resi des in coexisting silicates in coal. A significant concentration of As is, however, associated with organic matter probably complexed with thiol functional groups (Table 3.3). In contrast, the small organic cont ent of arenaceous aquifers, usually less than one percent, would precl ude such material being a signi ficant host for As under normal circumstances. Therefore, the contribution of As adsorption on organic material to its overall distribution in aquifer host rock s can be ignored for the purpos e of reactive transport modeling
described in Section 4.

Barium

. The chemistry of barium in groundwaters is relatively simple, because it does not undergo any change in oxidation state in reduc ing groundwaters, nor does it form any minerals with reduced species of As, S, Sb, and Se. It is present primarily as barite (BaSO 4 ) in subbituminous and lignitic coals (Table 3.3). In bitu minous coal, it is present dominantly in the silicate component. The concentration of Ba in the latter is about one order of magnitude less
than in the other two coals. Th e reason for its presence in th e silicate component suggests the depletion of sulfate through reducti on to sulfide. In the absence of sulfate, it can substitute as
Ba +2 –Al +3 for K + - Si +4 in potash feldspars, and occupy excha nge sites in clays, both occurrences of which have been noted by Finkelman (1981). In coal, Ba can also substitute for Ca in crandallite (CaAl 3 (PO 4 ) 2 (OH) 5 ) and can also form the carbonates alstonite (BaCa(CO 3 ) 2 ) and witherite (BaCO 3 ) (Finkelman, 1981). Because few, if any, potable groundwaters are seriously
deficient in SO 4 -2 , the primary candidate for Ba solubility control is barite , although witherite (BaCO 3 ) and crandallite might also be options in rare sulfate-deficient environments.

Cadmium

. Studies of the mineralogy of non-ferrous ore deposits univers ally recognize that cadmium is sequestered as a solid solution in sphalerite (ZnFeS) (P iatak, 2004), where Cd27 of 352 concentrations can reach 5 wt%. Cadmium for i ndustrial purposes is re covered primarily during the roasting and acid treatment of sphalerite, th e principal zinc ore. In coal, Cd reports in secondary sphalerite in concen trations up to 2-wt% (Gluskot er and Lindahl, 1973; Finkelman, 1981). Indeed, Gluskoter and Lindahl (1973) noted th at the Zn:Cd ratio in bulk coal samples was essentially the same as found in the contained spha lerite. Kolker et al. (2002) confirm the strong concentration of Cd in sphalerite, but they noted that significant fractions are also partitioned in pyrite and organic material (Table 3.3). This is partially corroborated by laser ablation ICP-MS (Inductively Coupled Plasma–Mass Spectroscopy) analyses of tr ace elements in pyrite from Ohio 5/6/7 coal presented in the same report, where Cd s hows a modest enrichment over background concentrations (Table 3.4). However, Huerta-Diaz and Morse ( 1992) show that Cd is essentially excluded from precipitating pyrite in ma rine and brackish sediments, as illustrated in Figures 3.2 and 3.3.

Experimental investigations by Framson and Leck ie (1978) show that neither Fe substitution in greenockite, nor Cd substitution in FeS ta kes place to any significant extent at 25 ° C. This
behavior is similar to that of Zn, as is noted further below under the discussion for zinc. Given
the exclusion of both Cd and Zn from precipitating pyrite, it is r easonable to assume that the CdS component is likely to be incorporated in spha lerite in reducing ground waters. Experiments by Bostick et al. (2000) also show that Cd +2 can oxidize pyrite to el emental sulfur and iron hydroxides with co-precipitation of CdS. This obs ervation is consistent with the experimental findings and thermodynamic predictions by Framson and Leckie (1978) that no ferrous sulfide can precipitate before all excess Cd +2 has precipitated from solution, and that Cd would precipitate as CdS in sediments primarily thro ugh surface exchange with ferrous monosulfide substrates. Thus, it is possible th at the association of Cd with pyrite in coal may have involved redox reactions, leading to physical incorporation of a discrete CdS phase as an impurity. Cd can also substitute for Pb in galena—e.g., see Bet hke and Barton (1971)—but the extent of solid
solution at 25 ° C, << 0.005 mol fraction, precludes signifi cant sequestrative competition with sphalerite.28 of 352 Both Framson and Leckie (1978) and Davies-Colley et al. (1985) infer that Cd precipitates in the form of essentially pure (unspeci fied) CdS, or greenockite, in shallow marine sediments where high HS- activities are gene rated through bacterial reduction of sulfate. The latter authors also found that Cd activity appeared to be controlled by a poorly orde red, more soluble form of CdS than greenockite. No reference is made to th e possible co-precipitati on of Cd with Zn in sphalerite. Further clarification regarding the as sociation of Zn with Cd under authigenic and diagenetic conditions in the field will be required to clarify the relationship.

For the purpose of this study, we assume that Cd is taken up primarily in solid solution in sphalerite. However, as is noted under the follo wing section on selenium, it is possible that Cd could also precipitate as cad moselite (CdSe) under conditions where sphalerite has been destabilized by a somewhat higher oxidation state, as suggested by the existence of cadmoselite
and other ore minerals in calcite veins associated with altered (hematite-stained) amphibolites in Argentina (Paar et al., 2002). Howe ver, at the type locality, cadmo selite is report ed to coexist with cadmian sphalerite, ferroselite and pyrite (Bur’yanova et al., 1957). The existence of ferroselite suggests an extremely rich selenium environment, and that the cadmoselite is
stabilized for that reason rather than being due to th e destabilization of sp halerite. Clearly, phase relations in Se-rich assemblage s require further study. See also the discussion on zinc, below.

Mercury . During authigenesis, precipita ting pyrite sequesters Hg, as is illustrated by field
observations of Huerta-Diaz a nd Morse (1992) involving auth igenic pyrite precipitation in brackish water sediments (Figures 3.2 and 3 .3), which shows a 50-fold enrichment over background. Similar observations have been ma de in studies of coal—e.g., Finkelman (1981), Kolker et al. (2002), Diel et al. (2004), Kolker et al. (2006)—where Hg can be enriched in pyrite 50 or more times over background concentrations (T able 3.4). In coals atypically rich in Hg, samples have been found to contain isolated part icles of cinnabar (Fin kelman, 1981; Brownfield et al., 2005), or finely disseminated particle s of tiemannite (HgSe) (Tewalt et al., 2001). Clausthalite (PbSe) also appears to act as a host for Hg in coal, as one sample observed contained 0.54wt% Hg, representing a 10,000 fold enrichment over the background c oncentration in the coal (Hower and Robertson, 2003). Galena can also act as a host for Hg. Finally, Barbanson et al.29 of 352 (1985) and Martinez Garcia et al. (1991) describe a variety of sphalerite containing up to 7% substitution of zinc by mercur y. This variety has been found in the mines of Argayon, Cotos, Rubios, Tresviso, and Hozarco in Spain. Electroch emical studies (e.g., Giannetti et al., 2006) and spectroscopic studies of Hg adsorption on the pyri te surface (e.g., Ehrhardt et al., 2000), using Xray photo electron spectroscopy (XPS), and Behra et al. (2001), using XPS and X-ray absorption spectroscopy (XAS)), both affirm that Hg is st rongly adsorbed by functi onal groups on the pyrite surface, which under acid conditions were identified as ≡ S -1 -Hg-OH or ≡ S -1 -Hg-Cl. However, it is not clear whether the Hg +2 becomes incorporated as a solid solution in the pyrite or concentrates in isolated HgS domains. We ta ke a provisional approach by assuming that Hg enters into solid solution in pyrite in a reducing aquifer, but that substitution in galena or sphalerite, or formation as discrete cinnabar or tiemannite minerals, might be plausible. It is possible that, like As substitution in pyrite, HgS substitution is metastable, and that long-term recrystallization of pyrite leads to its eventu al expulsion from the pyrite lattice. Thus, conclusions arrived at through a preliminary literature review mu st remain tentative, pending further thermodynamic anal ysis in Section 3.4.5.

Lead . Mineralogical studies of coal suggest that galena is the dom inant mineral containing Pb as an essential component (Kolker et al., 2002). However, Finkelman (1981) found that over 50% of coals in the Appalachian Basi n and 10% outside the Basin contai ned PbSe (clausthalite) rather than galena, although it was estimated that at most no more than 50% of the Pb could be present in that form. Kolker et al. (2002) show that Pb does not appear to report in pyrite in significant amounts despite the preponderance of this minera l, except perhaps in bituminous coals from Ohio (Table 3.3). In such coals, Pb is enriched in pyrite by a factor of nearly 40 (Table 3.4). However, Finkelman (1981) suggests that Pb enrich ment in pyrite might be due to the presence of impurities such as galena, which may well have been incorporated in a manner similar to that of CdS though redox reactions on the pyrite surface. This supposition is reinforced by the strong correlation displayed between Pb and Sb in pyrite samples from Ohio 5/6/7 coal, where Sb:Pb
~0.13 and R 2 =0.91 and in As bearing pyrite from the Roudny gold deposit of the Bohemian Massif (Zacharias et al., 2004), where Sb:Pb ~ 1.5 with a correlation coefficient of 0.93 in one generation of pyrite. The latter authors believe that the stoichiometric ratio of Pb to Sb reflects30 of 352 the possible presence of “crypto -inclusions” of jamesonite (Pb 4 FeSb 6 S 14 ). A similar but somewhat weaker correlation between Pb and Sb (R 2 = 0.66) is also observed in arsenian pyrite associated with gold mineralizat ion in neoproterozoic sediments of the Saldania Belt in South Africa (Belcher et al., 2004). Huer ta-Diaz and Morse (1992) show that Pb is almost always quantitatively excluded from authigenic pyrite, in conformity with the observed behavior of Cd under the same conditions (Figures 3.2 and 3.3). Thus, we can conclude that galena is the predominant host mineral for Pb except where excess selenium stabilizes clausthalite, and that any association of Pb with pyrite is probabl y due to the presence of foreign inclusions.

Antimony . Information on the mode of occurrence of antimony is spar se. In principle, it would be expected that Sb would behave somewhat similarly to As. Gudmundite (FeSbS) is an isostructural analogue of arse nopyrite, but occurs infrequently in Sb-rich epigenetic mineralization (Williams-Jones and Normand, 1997). Li mited field evidence also indicates that Sb does report in epigenetic arse nian pyrite, although an expected correlation between As and Sb is not always observed (Z acharias et al., 2004). This finding is consistent with the complete lack
of a correlation between As and Sb concentrations in a diverse set of pyrite samples associated
with coals in the Black Warrior Basin of Northwes tern Alabama (Diel et al ., 2004). In contrast, a correlation between As and Sb in nine samples of pyrite from Ohio 5/6/ 7 suggests a sympathetic association (R 2 = 0.63), although Sb uptake appears to be less efficient than would be predicted
on the basis of the As/Sb ratio in the bulk coal (Table 3.4). A sympathetic correlation between As and Sb (R 2 = 0.70) is also observed in arsenian pyrite associated with gold mineralization in Neoproterozoic sediments of the Saldania Belt in South Africa (Belcher et al., 2004). The somewhat less efficient uptake of py rite from Ohio 5/6/7 may be due to a larger anionic radius of Sb(-I), when substituting for S(-II), or because of the slightly highe r stability of Sb(III) compared with As(III) in aqueous solution. The sometimes strong correlation between Pb and Sb in pyrite, noted under the discussion for Pb, above, further sugge sts that Sb solid solution in pyrite may be restricted, and that Sb is incorporated as nanopa rticulate inclusions. The mode of occurrence of Sb in coals (Table 3.5) also indi cates that significant fractions of Sb are retained by the organic and silicate components, in agreement with a review of earlier studi es by Finkelman (1981).31 of 352 Therefore, it appears that Sb chemistry differs sufficiently that a correlative chemical behavior with As in pyrite cannot be presumed.

The most commonly observed Sb host mineral under reducing conditions is stibnite (Sb 2 S 3 ). However, Williams-Jones and Normand (1997) point ou t that stibnite is destabilized in favor of kermesite (Sb 2 S 2 O) below 150oC. According to these authors, the vast majority of occurrences of kermesite are of supergene origin. There are several well-documented occurrences of kermesite having been formed through hypogene processes. In the event that pyrite is destabilized, any released Sb is therefore likely to precipitate as stibnite above 150oC and as kermesite below that temperature, because both possess stability fiel ds extending to more oxidizing conditions than that of pyrite. Stibnite and ke rmesite also become more stable with decreasing pH and/or H 2 S activity (Williams-Jones and Normand, 1997).

The recently discovered, rare antimony an alogue of orpiment, antimonselite (Sb 2 Se 3 ) (Chen et al., 1993), has been reported in epithermal deposits associated with galena, sphalerite, and pyrite, marcasite, uraninite, coffinite and organic matte r (Min et al., 2000), and with tiemannite, clausthalite, and berzelianite (Cu 2 Se) (Vikre, 2005). Antimonselite has also been observed to form a solid solution series with stibnite (Liu et al., 2005). Because the antimonselite stability field also extends to significantly higher redox pot entials than pyrite, its formation could result
from the liberation of both Sb and Se upon oxidation of the latter . Accordingly, the presence of antimonselite in sedimentary ro cks should not be discounted.

In the absence of more definitive information, we tentatively conclude th at Sb preferentially substitutes for S in the pyrite lattice in a manner si milar to that of As, but that stibnite, kermesite, and antimonselite might also host Sb, especially under less reducing conditions where pyrite is no longer stable.

Selenium . Selenium substitutes for sulfur in the pyr ite lattice, and a limited solid solution is observed between pyrite and its se lenium metastable isostructura l analogue, dzharkenite (FeSe 2 ) (Yakovleva et al., 2003). Limited fi eld evidence from Western phosp hatic shales (Ryser et al., 2005) suggests that dzharkenite may form prefer entially in the presence of excess Se under32 of 352 diagenetic conditions. However, the more stable form of FeSe 2 is ferroselite, which possesses the marcasite structure. An examination of the behavior of Se in relation to As in pyrite in Ohio bituminous coal shows a remarkable coherence between the two, as shown in Figure 3.4. A stoichiometric ratio close to unity is revealed, with R 2 = 0.92 (Table 3.4), despite the ten-fold greater abundance of As. Finkelman (1981) also not ed a parallel behavior between As and Se in pyrite from Upper Freeport coal, although in a nother sample of the same coal, a negative
correlation was observed. Whether th e correlation in pyrite in coal from Ohio is fortuitous or reflects an underlying energetically favored c oupled substitution is not known. However, the
mode of occurrence of se lenium in coals suggests that excess Se will preferentially co-precipitate with Pb as clausthalite (Finkelman, 1981) or me rcury as tiemannite (Tewalt et al., 2001) before saturation with respect to either dzharkenite or ferroselite. Neither dzharkenite nor ferroselite have been observed in coal, an indication that th e relative abundance of Se in coal is insufficient to saturate with respect to either of these pha ses. As noted at the conclusion of Section 3.4.5.1, the coexistence of selenian pyrite with dzharken ite in Western phosphatic sh ales (Ryser et al., 2005) is probably unusual, since it occurs where th e ambient concentration of Se is up to three orders of magnitude higher than the average crustal abundance. In the event that such an association is observed, Se shoul d, depending on availability, pref erentially co-pre cipitate heavy metal selenides such as tiemannite, clausthalite , and cadmoselite, in preference to cinnabar, galena and greenockite or cadmian sphalerite, which is generally consistent with reported field occurrences—e.g., Bur'yanova et al. (1957).

Laboratory studies show an extensive solid solution series between ZnS and ZnSe at temperatures at least as low as 160 ° C (Wright et al., 1965). Because of the relative abundance of Zn in relation to Se in nature, it is not expe cted that sufficient excess Se would permit the stabilization of the selenium analogue of sphaleri te, stilleite (ZnSe), except where more oxidizing conditions would cause the decomposition of sphale rite in favor of stilleite and zinc carbonates or silicates. Such a mechanism might also expl ain the occasional occurrence of the selenium analogue of greenockite, cadmoselite (CdSe), in sedimentary strata under reducing conditions despite the potential for Cd sequestration by s phalerite. The thermodynamic analysis (Section 3.4.5.2) likewise suggests that the aqueous concentr ation of Cd could be controlled by33 of 352 cadmoselite in preference to greenockite or ha wleyite. Unfortunately, the general lack of thermodynamic data and information on the solid solution behavior of Se in FeS 2 polymorphs and of Cd, Hg, Pb and Zn in FeS 2 polymorphs in relation to their monosulfides and monoselenides precludes a quantitative assessment at this time. We will therefore assume that ferroselite (for which thermodynamic data are av ailable) can be treate d as a solid solution component in pyrite under reducing groundwater conditions, while monitoring the potential for saturation control by the Cd, Hg, and Pb m onoselenides (cadmoselite, tiemannite, and clausthalite) respectively.

Fig. 3.4. The correlation between the molar concentration of Se and As, determined by laser-ablation ICP-MS, in pyrite from Ohio 5/6/7 bituminous coal (from Kolker et al., 2002)

Uranium

. Extensive evidence indicates that U is mobilized in intermediate to acid igneous intrusive and particularly extrusive rocks, under oxic to mildly reducing conditions as the uranyl ion (UO 2 +2 ). The source of UO 2 +2 is probably apatite, which is a known host for U in igneous rocks. However, uranium in sedimentary rocks is invariably associated with organic matter34 of 352 (Abdel-Gawad and Kerr, 1961; Szalay, 1964; Mohan et al., 1982; Drew Ilger et al., 1987) or with accessory detrital minerals such as zircon (ZrSiO 4 ). Humic acids within the organic matter sequester UO 2 +2 through complexation on carboxyl f unctional groups (R-COOH) (Szalay, 1964; Drew Ilger et al., 1987), primarily as monodentate complexes, i.e.,

R-COOH + UO 2 +2 = R-COOUO 2 + + H+

or

R-COOH + UO 2 +2 + H 2 O = R-COOUO 2 OH + 2H +

(e.g., see Nefedov et al., 1998) leading to enri chment factors over cont acting groundwaters by a factor as high as 10,000 (Szalay, 1964). Evidence s uggests that a signifi cant portion of the U
remains adsorbed as metastable UO 2 +2 . However, field evidence from many sedimentary uranium deposits indicates that some adsorbed UO 2 +2 is reduced from the (VI) to the (IV) state, through reduction by HS - , resulting from either biogenic or abiogenic activity (Min et al., 2000),

e.g.,

4UO 2 +2 + H 2 S + 4H 2 O = 4UO 2 +SO 4 -2 +10H +

although direct microbial reduction of U(VI) to U(IV) has also been observed under anaerobic conditions (Suzuki et al., 2005). Autogenous radiol ysis of water and/or organic matter—e.g., see Levinson (1977) and Dubessy et al. (1988)—a nd reduction by reactive hydrogen free radicals (H ′ ) could also be operative, possibly with conc omitant oxidation of excess organic material (Min et al., 2000), leading to the formati on of uraninite and/or coffinite, thus:

UO 2 +2 + 4H  ̊ = U +4 + 2H 2 O

U +4 +2H 2 O = UO 2 (uraninite) +4H +

U +4 + SiO 2 (aq) + 2H 2 O =UO 2 (coffinite) +4H +

UO 2 + SiO 2 (aq) = USiO 4 (coffinite)35 of 352 Both uraninite (mostly as pitchblende, a poorly ordered and partially oxidi zed form of uraninite) and coffinite have been reporte d to occur in association with lignitic, humic, and bitumic material in sedimentary rocks, and in coal beds in a few cases (Drew Ilger et al., 1987), despite significant concentrations of U. When found together, they appear to have been co-generated (Min et al., 2000). Lack of detect ion of either or both minerals may result from their occurrence in a colloidal or nanoparticul ar state (Min et al., 2000).

U(IV) has also been reported occasionally in sedimentary rocks as brannerite (UTi 2 O 6 ) (Finkelman and Klemic, 1976; Dutkiewicz et al., 2003), which probably forms through the
interaction of dissolved TiO 2 with uraninite, thus:

UO 2 + 2TiO 2 (aq) = UTi 2 O 6 (brannerite)

The possible presence of brannerite in potabl e water aquifers should not be discounted, particularly when thermodynamic data is availabl e to calculate its solubi lity (Helean et al., 2003). However, the very low solubiliti es of potential sources of TiO 2 (aq), such as anatase, rutile or sphene, which are 3–4 orders of magnitude less soluble than the silica polymorphs, precludes rapid formation of this mineral, explaining it s normal occurrence primar ily in Paleozoic and proterozoic sediments (Finkelman and Klemic, 1976; Dutkiewicz et al., 2003). Uranium is also found in zircon, but because of its extremely low so lubility and indefinite persistence in nature, any contained U is essentially isolated from groundwater, and therefore would be of no concern in the present study.

The reported distribution of U in coal, as shown in Table 3.3, is consistent with the previously cited findings regarding its association with orga nic material. A significant fraction of the U is HCl-soluble, i.e., it is readily desorb ed from carboxyl functional groups as UO 2 +2 , whereas the remainder is present in the silicate fraction (as coffinite?) and in the organic fraction, where its mode of occurrence is uncertain, but probably in the form of organic or mineral colloids. Finkelman (1981) has reviewed an extensive earlier lite rature on the occurrence of U in coal, and reaches essentially similar conclu sions regarding its distribution.36 of 352 We therefore assume that U is present primaril y as uraninite, or as coffinite, in groundwaters under reducing conditions.

Zinc . Both field and experi mental evidence uniformly indicate that sphalerite (ZnS) is the predominant host for zinc under reducing conditi ons. Furthermore, Framson and Leckie (1978), both on experimental and theoretical grounds, argue that Zn is unlikely to substitute in iron sulfides, citing Barton and Toulmin (1966) , who found that substitution of Zn +2 in pyrrhotite, even at temperatures as high as 850 ° C, is only 0.006 mol fraction. This argument is further substantiated by field observations. In the sediment ary environment, Zn part itions negligibly into authigenically formed pyrite, as shown in Fi gures 3.2 and 3.3 (Huerta- Diaz and Morse, 1992). Measurements of Zn concentrations in pyrite from Ohio 5/6/7 coal indicates that Zn is the only trace element that is depleted in pyrite compared with its concentration in bulk coal (Table 3.4). The partitioning of Zn into spha lerite dominates its behavior in coal (Table 3.3). Finkelman (1981), on the basis of a review of the earlier literature and his own mineralogical studies, also concluded that sphalerite is the primary mineral host for Zn. Thus, we can assume that sphalerite controls Zn solubility in gr oundwaters under reducing conditions.

Summary

In Table 3.6, we summarize the expected occurr ences of minerals, which might determine the activities of the selected hazardous inorganic constituents in reducing groundwaters. Where thermodynamic data are available, solubility produc ts for these minerals have been incorporated in the EQ3/6 Data0.dat database, and in the corresponding database in TOUGHREACT, to permit calculation of potential thermodynamic contro ls of the selected hazardous constituents in groundwaters, respectively under static equilibrium and dynamic conditions.

It should be emphasized that the chemical be havior of the selected hazardous inorganic constituents in groundwaters is a ffected by many processes, which in clude the past history of the aquifer, reactive chemical processes involvin g advective transport fr om distant sources, segregation due to changing host rocks, oxidation states and salinities, and the current oxidation state of the aquifer at the time of sampling. Because of the comple xity of the chemical systems37 of 352 involved, the preceding review is intended as a preliminary surv ey, and is subject to future refinement. However, despite this cautionary note, it is believed that the selected suite of minerals presented in Table 3.6 provides a suitabl e starting point for geochemical modeling thermodynamic controls in Section 3.4.5. The thermodynamic properties of the identified minerals and associated aqueous species of th e hazardous constituents (Appendices C, D, and E) were used in compiling solubility product and dissociation constant data, respectively, for incorporation in the EQ3/6 code. The code wa s then used in evalua ting potable groundwater quality analyses to find out whether the id entified minerals might be controlling the
concentrations of the hazardous constituents in so lution. This procedure is described in the next
section.

Table 3.6. Potential mineral hosts for hazardous inorganic constituents under reducing conditions

Potential Mineral Controls Hazardous Inorganic Constituent Solid Solution Component Discrete Mineral

As (FeAsS) py Arsenopyrite (FeAsS)
Ba - Barite (BaSO 4 ); Witherite (BaCO 3 ); [Crandallite (CaAl 3 (PO 4 ) 2 (OH) 5 )] Cd (CdS) sph Greenockite(CdS); Cadmoselite (CdSe) Hg (HgS) py Cinnabar (HgS); Tiemannite (HgSe) Pb - Galena (PbS); Clausthalite (PbSe) Sb (FeSbS) py Stibnite (Sb 2 S 3 ); Kermesite (Sb 2 S 2 O); Antimonselite (Sb 2 Se 3 ); Gudmundite (FeSbS) Se (FeSe 2 ) py Ferroselite (FeSe 2 ); [Dzharkenite (FeSe 2 )]; Antimonselite (Sb 2 Se 3 ); Cadmoselite (CdSe); Clausthalite (PbSe); Tiemannite (HgSe) U - Uraninite (UO 2 ); Coffinite (USiO 4 ) [Brannerite (UTi 2 O 6 )] Zn - Sphalerite (ZnS); Hemimorphite (Zn 4 Si 2 O 7 (OH) 2 ·H 2 O) Note: py = pyrite, sp = sphalerite38 of 352 

## 3.3 Evaluation of Potable Groundwat er Quality in Relation to Groundwater Model Development

3.3.1 Background

This section describes the geochemical evaluati on of a large number of water-quality analyses
with measurable concentrations of hazardous inor ganic constituents, with the main objective of identifying minerals containing specified hazardous constituents that saturate these waters. Many groundwaters that are recovered fo r use as drinking wate r in the United States are analyzed, not only for major dissolved chemical components, but also for trace concen trations of hazardous inorganic (and organic) constituen ts. Where comprehensive chemical analyses are available, the thermodynamic controls that govern the concentr ations of the hazardous inorganic constituents in solution can be identified through comparison of the saturation indices of candidate minerals identified in the previous sect ion. This is accomplished in th is study through the use of the distribution-of-species code EQ3/6 (Wolery et al., 1993), which calculates the thermodynamic
activities of all dissolv ed components. The thermodynamic activ ities of all dissolved components
are then used to calculate the saturation indices of minerals that are potentially in equilibrium with the groundwater, including those where the hazardous constituent is an essential component. Other useful information is obtained from the thermodynamic evaluation (e.g., aquifer buffering capacity, redox pote ntial, background levels of CO 2 concentration), which, together with independent information relating to the mineralogical and physical properties of the aquifer host rocks, can be used to support development of a comprehensive geochemical
model.

The USGS National Water Information System (NWIS) Database cont ains approximately a quarter of a million chemical analyses of groundw aters of which a small fraction meets the needs of this project. Data mining of the NWIS wa s kindly conducted by Jonathon C. Scott, USGS, Oklahoma City, searching the database for all available data on well water specifically used for potable purposes. Search criter ia were defined as follows:39 of 352 1. The groundwaters had to be recovered from wells for drinking water purposes.
2. The chemical analysis had to include at l east one of the following elements: As, Ba, Cd, Hg, Pb, Sb, Se, U, Zn.
3. The trace metal analyses had to be accompan ied by complete chemical analyses of the water, i.e., pH, Na, K, Mg, Ca, Cl, SO 4 , CO 3 or alkalinity, and if possible Fe(II), Fe(III) or Fe(T), Si or SiO 2 (aq), Al.
4. Additional information was given that helped define oxidation state, e.g., Eh, dissolved oxygen concentration, or information that woul d help with the calcu lation of potential redox pairs such as CH 4 /CO 2 , H 2 S/H 2 SO 4 , As(III)/As(V), Sb(III)/Sb(V).
5. Electrical conductivity was measured.
In addition, the following supplementary data fo r each well site was extracted, when available:

1. The geographic coordinate of the well site
2. The geologic formation penetrated
3. The well screen intervals
4. The groundwater temperature
5. Information pertaining to the mineralogy or ch emical composition of well core or chips.

Using the above search criteria, data files were extracted from the USGS National Water Quality Information System (NWIS) containing 38,610 water-quality analyses with 703,352 distinct measurements (total) at 22,437 unique data-collection sites nati onwide. This large database was uploaded into a data management tool (based on the Matlab software) deve loped specifically for this study. The following sections describe the analyses conducted and discuss results. Section 3.3.2 shows the concentration histograms of the ma jor chemical constituen ts of the examined water quality analyses and certain other parameters of interest. Section 3 .3.3 shows concentration histograms for the hazardous inorganic constituen ts selected for study, and illustrates spatial distributions for As, Ba, Pb and U. The raw data presented in Section 3.3.3 are subject to limitations arising from the analytical methods em ployed in the detection of trace concentrations of hazardous constituents. Discussion of applicab le detection limits is given in Section 3.3.4, and the impact of analytical artifact s evident in some of the histograms is discussed in Section 3.3.5.40 of 352 

### 3.3.2 Concentration of Major Consti tuents in Potable Groundwater

The downloaded NWIS database of more than 38,000 analyses was first characterized in terms of the distributions of the majo r constituents contained in all groundwaters, i.e., Na, K, Mg, Ca, Fe(total), HCO 3 - , SO 4 , Cl and pH. In addition, analyses of Al, a minor species, with a typical concentration of less than 0.01 mg/L, were al so compiled, because of its importance in geochemical modeling. SiO 2 (aq) was omitted from the datase t initially downloaded from the NWIS database, but was computed in the followi ng step. The suite of chemical analyses was processed using the EQ3/6 code (s ee Section 3.4.5) in order to eval uate several additional species that are best represented in a form not readily obtainable from the analytical data, and which are required for comparison with the simulated groundwater composition generated by
TOUGHREACT under initial conditions of secula r equilibrium. The species included Fe(II),
total HCO 3 - and Eh. SiO 2 (aq) was set at saturation with respect to opal-CT, which is characteristic of many shallow groundwaters (Abercrombie et al., 1994; Apps, 2004). Total
HCO 3 - was calculated from the entered “free” HCO 3 - or alkalinity, see Section 3.4.3 below. The Eh was imposed through an EQ3/6 input specif ication that both pyrit e and goethite are at equilibrium at a given pH. In addition, the calculated concentration of Al was also retrieved from the output for comparison with the analyses of Al in the NWIS samples. The calculated value was based on the assumption that the groundwaters were also in equilibrium with a low Fe, Mg
smectite.

The resulting multiple sets of input data were st ripped of analyses reporting ”greater than”, “less than”, “zero”, “presence verified but not quantifie d” concentrations, and the remainder, with the exception of pH, were converted to uniform con centration units of mg/L and transformed to a logarithmic scale. The remaining analyses for each constituent were divided into uniform bins, representing incremental concentration ranges on a logarithmic scale, and plotted as histograms. The output parameters from the EQ3/6 species dist ributions were similarly plotted as histograms. The results are given in Figures 3.5(a)–(m) below, and the appropriate modal value are
summarized in Table 3.7.41 of 352 Note that the analytical methods used for the qua ntification of major speci es in solution, with the exception of Al and Fe, are sufficiently sensitiv e to permit satisfactory measurement of their
concentrations. The omission of unquantified analyses therefor e has negligible bias on the results, particularly when it is recognized that it is the mode of the distribu tion that is of interest
in this evaluation of the chem ical analyses. This condition does not apply, however, to trace
element concentrations at or near the limits of detection for the analytical method used, especially with regard to Al and Fe, and to most of the hazardous inorganic constituents identified for study in Section 3.3.3. For th ese constituents, the omission of unquantified analyses introduces a similarly unquantifiable positive skew to the plotted histograms. No techniques have been identified th at would easily correct for this bias. The strategy adopted in this study is to independently develop a geochemical model, and compare the model with field observations as an aid to establishing the degree of bias and acceptability of the field data. It should be noted, however, that a positive bias can be inherently conservative, in that the bias could over-predict ambient concentrations of hazardous constituents in groundwaters in equilibrium with elevated partial pressures of CO 2 .

Table 3.7 also includes additional parameters of interest for wh at is effectively the most commonly encountered or modal groundwater composition among those potable groundwater samples downloaded from the NWIS database. This modal groundwater is just slightly supersaturated with respect to calcite, and displays a relatively elevated P(CO 2 ) (see Section 3.4.3 for further discussion of P(CO 2 ) in potable groundwaters). The composition represents of a groundwater of the Ca-Mg bicarbonate type. Th e “analytical” charge imbalance for this groundwater is excellent, being sign ificantly less than the acceptabl e value of 5% of total charge. Note, however, that this composite charge imbalan ce is not representative of the average charge imbalance of the 38,000 groundwater analyses evalua ted in this report. As is shown in Section 3.4.3, about two-third of the stud ied groundwaters meet the 5% ch arge imbalance criterion for acceptability. The ionic strength is very low as expected, and is a useful benchmark for comparison with other aqueous systems.42 of 352 (a) (b)

(c) (d)

(e) (f) Fig. 3.5. Part 143 of 352 (g) (h)

(i) (j)

(k) (l) Fig. 3.5. Part 244 of 352 (m) Fig. 3.5. Histograms showing the concentration distributions of various chemical constituents, as well as pH and calculated Eh in more than 38,000 analyses of potable groundwaters in the United States.

Table 3.7. Values of analytical concentrations of chemical constituents at maximum frequency and associated calculated values of selected species in more than 38,000 potable groundwaters.

Parameter Unit Condition Value Na mg/L measured 12.6 K mg/L measured 1.6 Mg mg/L measured 20.0 Ca mg/L measured 63.1 Fe mg/L measured ≈ 1.3 Fe(total) mg/L calculated 1.3 Al mg/L measured ≈ 0.002 Al(total) mg/L calculated 0.0002
SiO 2 (aq) mg/L calculated 75.0 Cl mg/L measured 15.8
HCO 3 - mg/L calculated 316
SO 4 mg/L measured 20.0 pH Log (mol/L) measured 7.3
Eh Volt calculated -0.18 SI (calcite) - calculated +0.09 P(CO 2 ) Log (bar) calculated -1.86 Ionic Strength Mol/kg H 2 O calculated 0.0078 Charge Imbalance % of total charge calculated -1.7545 of 352 The two constituents likely to show the greatest discrepancy between field observation and modeled values are Al and Fe (Figure 3.5i vs. 3.5j for Al, and Figure 3.5k vs. 3.5l for Fe). In the case of Al, only the more recently developed an alytical method employing inductively coupled plasma with mass spectroscopy (ICP-MS) is su fficiently sensitive to allow quantitative measurements in groundwaters. Fi gure 3.5i shows the distribution of analytical concentrations for Al using the PLM43 method (Appendix A), wh ich employs ICP-MS. The detection limit for Al varies when using this method, the most sensitive being 0.001 and 0.002 mg/L. Of all 1,213 analyses reported, 696, or 57%, reported detectable values of Al. This implies that the most
common Al concentration lies close to the det ection limit at circa 0.002 mg/L. However, even when taking into account all potential Al complexi ng in groundwaters, especially with respect to silica complexes (Tagirov and Schott, 2001), the cal culated concentration is still one order of magnitude less at ≈ 0.0002 mg/L. The reason for the discrepa ncy probably lies in the precipitation of a metastable amorphous hydrated Al-Si silicate caused by the dissolution of unstable detrital feldspars (e.g., see Paces, 1978), and which has not yet been incorporated in the geochemical
model.

Iron analyses show a very broad range of valu es in analyzed groundwaters as shown by Figure 3.5k. This range reflects in part the potential varia tion in oxidation state of groundwaters in relation to the Fe(III)/Fe(II) coupl e, but also artifacts introduced by exposure of the groundwater sample to air with consequent oxidation and precipitation of Fe(III). As a consequence, the reported analytical con centrations of Fe(T) ca nnot be considered who lly trustworthy. As is discussed further below (Section 3.4), the evalua tion of the concentra tion distributions of hazardous inorganic constituents strongly suggests that most groundwaters are reducing, which is consistent with redox control by the pyrite/goeth ite redox buffer, or by other buffers controlling the redox potential within a similar range of values.46 of 352 

### 3.3.3 Concentrations of Hazardous In organic Constituents in Potable
Groundwater

The NWIS database was mined for chemical anal yses of As, Ba, Cd, Hg, Pb, Sb, Se, U and Zn. Similar to the procedure employed for major constituents, the resulting multiple datasets processed as follows. Analyses re porting ”greater than,” “less th an,” “zero,” “presence verified but not quantified” concen trations were deleted, while the re mainder were converted to uniform concentration units of mg/L a nd transformed to a logarithmic scale. (Note: The mg/L unit roughly corresponds to 1,000 ppb for the conditions considered.) The remaining population of analyses for each element was divided into unifo rm bins, representing incremental concentration ranges on a logarithmic scale, and plotted as histograms. Note that the elimination of unquantified analyses potentially introduces a positiv e skew to the resulting histogram, as noted in Section 3.3.2, above, which can bear on the ultimat e interpretation of the data in terms of the geochemical model developed in Chapter 3. This issue is discussed with respect to certain specific hazardous constituents under thei r respective sub-headings in Section 3.4.5.

The resulting histograms are given in Fi gures 3.6(a)–(i). For comparison, the maximum contaminant level (MCL) permitted (U.S. Environmental Protection Agency, 2004) is also plotted, except for Zn. See Table 3.8 for a list of the hazardous elements and respective MCLs in drinking water. (If MCLs are exceeded in more than 10% of tap water samples, water providers need to take corrective actions.) It is evident from Figure 3.6 th at, but for a few outliers, for the majority of groundwater samples in the database, most of the elements meet MCL requirements under current conditions. A significant minor per centage of groundwaters containing As and U exceed the MCL for those elements, as noted by Focazio et al. (2006).47 of 352 (a) Pb (b) As

(c) Ba (d) Cd

(e) Hg (f) Sb Fig. 3.6 (Part 1)48 of 352 (g) Se (h) U

(i) Zn
Fig. 3.6. Histograms showing the concentration distributions of selected hazardous constituents in more than 38,000 analyses of potable groundwaters in the United States. MCL signifies the maximum contaminant level for each constituent. SDWR stan ds for secondary drinki ng water regulation.
The histograms reported here are plotted without regard to analytical method or accuracy. All histograms are characterized by isolated bins with anomalously high populations. These bins contain those analyses with “r ound number” concentrations, su ch as 0.001 mg/L or 0.002 mg/L, considered to be stemming from analytic al artifacts, as disc ussed in Section 3.3.5.49 of 352 Table 3.8. Hazardous inorganic constituents with EPA-specified Maximum Contaminant Levels (MCLs)

Contaminant MCL (in mg/L) MCL (in ppb) As, arsenic 0.01 10 Ba, barium 2 2000 Cd, cadmium 0.005 5 Hg, mercury 0.002 2 Pb, lead 0.015 15 Sb, antimony 0.006 6 Se, selenium 0.05 50 U, uranium 0.015 15

The majority of the histograms in Figure 3.6 show concentrations rangi ng over three to five orders of magnitude. Apart from the analytical artifacts (see Section 3.3.5), the large spread might be attributed to several causes:

1. The element concentration in the aqueous pha se may not be controlled by a mineral of which the element is an essential component , meaning a saturation state has not been reached. The aqueous concentration is instead determined by the element concentration in solid solution, on ion exchange sites, or on surface complexati on sites, or any distribution
between all three.
2. Variations in the bulk chemistry of the syst em may affect either the total mass of the element in solution, or may impact mass action controls.
3. Different minerals of which the element is an essential component control the
concentration in solution.
4. One or more of the components of solubili ty-controlling minerals may vary naturally over a large range of concentrations, due to, fo r example, variations in the oxidation state.
5. Large ranges in pH or the pr esence of complexants may lead to large ranges in saturation concentration of the element in the aqueous phase.
6. Some samples might be contaminated thr ough some form of accidental anthropogenic
intervention.50 of 352 The large aqueous concentration ranges displayed by the selected trace elements highlights the need for a thermodynamic analysis of the data. By calculating the thermod ynamic potentials, it is possible to establish which minerals may control the solubi lity of an element. If such calculations show that no mineral containing th at element as an esse ntial component contro ls solubility, then other minerals may have to be considered, i.e., those minerals which incorporate the element in solid solution, on exchange sites, or on surface complexation sites. The thermodynamic aspects of this problem are discussed further in S ection 3.4.5. The lack of sa turation control of a hazardous inorganic constituent by a pure minera l of fixed stoichiometry containing that constituent as an essential component could lead to variability in aqueous concentrations that reflects the bulk composition of the aquifer host rock s, as appears to be the case with respect to As under oxidizing conditions (Sme dley and Kinniburgh, 2008).

Every stable element in the periodi c table, including the hazardous trace elements of interest in this study, possesses a natural crustal abundan ce (e.g., see Section 3.6, Table 3.17). Geological and geochemical processes can lead to concentra tion or depletion of certain elements, depending on their chemical properties and the extent and magnitude of the processes involved. No processes, whether natural or ar tificial, are 100 percent effective in separating and concentrating a given element. Therefore, residues of any elemen t can always be found in all natural materials. As a consequence, all elements targeted for st udy in this report occur ubiquitously in soils, sediments, and aquifer rocks thr oughout the world. In th e United States, metal concentrations in soils and surficial sediments vary greatly with region, and over ranges of concentration that approach four orders of magnitude. This is illu strated for Pb, As, Ba, and U in distribution maps
taken from the USGS National Geochemical Su rvey database (USGS, 2008) (Figure 3.7).51 of 352 (a)

(b)

(c) Fig. 3.7. (Part 1)52 of 352 (d) Fig. 3.7. Spatial distribution of hazardous constituents concentr ations in soils and surficial sediments of (a) Pb and (b) As (c) Ba and (d) U in the forty-eight co ntiguous states, from National Geochemical Survey Database (USGS, 2008).
To check whether the aqueous concentrations mi ght mirror the local natural surficial soil and sediment abundances of these same elements in the aquifer host rocks, they were plotted on a map of the 48 contiguous stat es, as illustrated in Figures 3.8. The respective aqueous and soil/sediment distributions for each element show a weak correlation, which can be a result of natural and anthrophogenic impacts. We should note that all groundwaters would exhibit small (mostly below MCLs), but non-zero concentrat ions of hazardous inorganic constituents if measured with sufficiently sensitive analytical t echniques, as a corollary of the assertion that
these constituents also occur ub iquitously in soils, sediments, and aquifer rocks throughout the
United States and elsewhere in the world. Diffe rences between the aqueous and soil/sediment distributions may be caused by sediment transpor t, climatic variations leading to differing degrees of constituent retention in soils, or localized anthropogenic modifications, such as the environmental dispersion of Pb from vehicular e xhausts or battery recyc ling facilities, river and lake contamination with mercury from chemi cal plants producing so dium hydroxide, or the application of phosphate fertilizers or g ypsum soil amendments containing elevated concentrations of uranium. In each case, elevated surficial concentrations of these elements, and others might be observed in the absence of subsurface aquifer contamination. Modification of aquifer chemistry due to surficial anthropoge nic contamination woul d depend on many factors such as leaching and transport rates, recharge locations, subsurface hydrogeologic conditions, the53 of 352 chemical properties of the transported elements, and withdrawal rates from potable aquifers. Such complexities can be elucidated only throug h case-by-case examination of specific aquifers, and not through a ge neralized review.

Close examination of Figures 3.7 and 3.8 reveals clearly that the weak correlation between aqueous and soil/sediment distributions is in pa rt caused by inconsistencies in sampling density and use of analytical methods. The water quality analyses are sparsely di stributed in many parts of the country, while differing analytical me thods on solid and aqueous samples were inconsistently applied from state to state and re gionally. For example, as pointed out in Appendix B1, the “hot spots” of measurable Pb concentr ations in Figure 3.8(a) ar e clearly correlated to states or regions where large numbers of high- quality analyses (i.e., with low enough detection limits) have been conducted. In contrast, states or regions with no or only few detectable Pb concentrations in Figure 3.8(a) correspond to locati ons with either no measurements or analyses
with insufficient detection limits.

A better correlation appears evident between di fferent hazardous constituents in the aqueous samples than between aqueous and solid samples for a given hazardous constituent. For example, evidence for a “hot spot” of elevated Pb concentr ations in the states of Illinois and Iowa is duplicated for As and barium. The origin and o ccurrence of these observed anomalies has been well documented by Goldhaber et al. (2003).

The distribution of U in Figure 3.8 appears to differ from Pb, As , and Ba. This may reflect the proximity of weathering igneous granitic rocks, wh ich are commonly enriched in U. It is likely that the dissolved species is UO 2 ++ , since minerals containing this cation are relatively soluble, thereby preventing saturation control. The regiona l variation in metal concentration may also reflect variations in aquifer chemistry rather than lack of saturation control. Only a thermodynamic analysis can help resolve this unc ertainty, which points to the need of conducting a distribution-of-sp ecies analysis of the select ed groundwaters (Section 3.4.5).54 of 352 (a)

(b)

(c) Fig. 3.8. (Part 1)55 of 352 (d)

Fig. 3.8. Spatial distribution of samples with elevated aqueous concentration (Log mg/L) of (a) Pb, (b) As (c) Ba and (d) U in the forty-eight contiguous states. The plotted concentrations were drawn from more than 38,000 groundwater quality analyses downloaded from the National Water Information System Database (NWIS).



### 3.3.4 Detection Limits of Hazardous Inor ganic Constituents in Groundwater

The NWIS database has been compiled over a numb er of years, during which time newer, more accurate, and more precise analytical methods ha ve supplanted earlier methods. Several of the earlier analytical methods were insufficiently sensitive to provide quantitative measurements of
the concentrations of several of the hazardous constituents under investigation in this report. Even the most sensitive methods currently in us e are not necessarily suffi ciently sensitive in all cases to provide quantitative measurements. Some idea of the limitations of the methods can be gleaned from the percentage of analyses that were successful in providing a quantitative measurement. This percentage also provides a m easure of the concentration range expected for a given constituent. For example, if the percentage of measurable values is 50%, we can expect that the most frequent concentration of that cons tituent is at or near the detection limit. If the value is only 20%, then it can be expected that the most frequent concentration is significantly below the detection limit, although an approximate idea of its va lue might be estimated from extrapolation of the observed tail providing the upper-bound of the dispersion profile.56 of 352 In Table 3.9, we have summarized the percentage of samples with measurable concentrations (above the detection limits) compared to all sa mples taken for the most sensitive analytical methods. For each analytical method, a range of de tection limits arises, due to variations in sample quality, background contamination, and othe r factors that might influence analytical precision and accuracy. A complete tabulation of all analytical methods used, their various detection limits, and the number of analyses recording measurements below and above the limits of detection are given in Appendix A in Tables A1(a)–(i). The different analytical methods are referred to using code names, e.g., PLM43, whic h refers to metal analyses on unfiltered water conducted with ICP-MS, one of the more sensitive methods in routine use. Descriptions of the analytical methods are provided in Appendix A in Table A2. Tabl e 3.9 suggests that measurable concentrations are frequently observed in the groundwater samples, ranging from as low as 17% of all samples analyzed using PLM47 for Cd to as high as 96%, 97% and 94% of samples analyzed for barium, uranium, and zinc, respecti vely. These findings are a valuable adjunct in interpreting the thermodynamic analysis of water quality data in Section 3.4.

Table 3.9. Percentage of samples with measurable concen trations of hazardous i norganic constituents in groundwater samples analyzed with the most sensitive analytical method

Contaminant Analytical Method Code* Reported Range of Detection Limits (mg/L) Percentage of Samples above Detection Limit (%)
Arsenic As PLM10 PLM40 0.00012 0.0002 – 0.0009 92 87 Barium Ba PLM43 0.0002 – 0.001 96 Cadmium Cd PLM43 PLM47 0.00004 – 0.001 0.00004 – 0.001 24 17 Mercury Hg CV013 0.0001 – 0.001 12

Lead Pb PLM43 PLM48 0.0001 – 0.004 0.00006 – 0.001 49 82 Antimony Sb PLM43 0.00005 – 0.002 18 Selenium Se PLM40 0.0003 – 0.002 58 Uranium U FL006 FL007 0.0004 0.00001 69 97 Zinc Zn PLM43 0.0006 – 0.010 94

* See Tables A1 and A2 in Appendix A.57 of 352 

### 3.3.5 Analytical Artifacts Affecting Measurements of Hazardous Inorganic
Constituents in Groundwater

The distributions of several of the histograms s hown in Figure 3.6 reflec t artifacts introduced by the differing accuracy and sensi tivity of the analytical methods used, rather than to some underlying chemical control affecting groundwater composition. This issue appears to be most prominent in analyses for Cd, Hg, Sb and Se, as evident by the presence of isolated bins with anomalously high populations, although the effect is also noticeable in th e case of As and Pb. The elements Ba, U, and Zn appear to be relati vely unaffected, because most analytical methods used over time have been sufficiently sensitive to record quantitative values of concentration.

As noted in the preceding secti on, the analytical methods used, th eir various detection limits, and the number of analyses recordi ng measurements below and above the limits of detection, and descriptions of the analytical methods are gi ven in Appendix A. The NWIS database provides the respective detection limits only for those sample s with concentrations reported as “less than”; in all other cases, measurable concentrations are reported in the database without further mentioning of the specific detec tion limit of the analytical method. Using Pb as an example (see Table A1(e)), we have obtained almost 2,000 samples with Pb c oncentrations analyzed using method PLM43. About 1,000 of these are non-dete cts, with 700 reporting less than 0.001 mg/L (1 ppb) and 280 reporting less than 0.0001 mg/L (0.1 ppb), indicating that different ICP-MS techniques with different detection limits have been utilized. The other half of about 1,000 samples reports measurable concentrations. The database, however, does not report the specific detection limits for these samples. In Appendix B 2, we make a further attempt to evaluate the significance of analytical artifacts for the elements of interest.

An example in which almost all analyses could be artifacts is illustrated by the distribution of Cd analyses. Inspection of Figure 3.6(d) shows that th ere are essentially two sets of measurements corresponding to log concentration ranges of -4.6 to -3.0 (log mg/L) and -3.0 to -1.0 (log mg/L), respectively. Each set of analyses starts with an initial high frequency, apparently corresponding to the detection limit for most of the analyses, which then falls off with increasing concentration58 of 352 until a relatively low frequency is recorded approximately one log unit above the detection limit. The question arises whether any of these data , even those provided by the most sensitive methods, can be used to determine thermodynam ic controls for Cd. Table A1(c) shows that
cadmium has been measured with methods PLM 43 or PLM47 in many cases, with detection limits as low as 0.00004 mg/L. In the former me thod, 24% of the analyses yielded detectable values, whereas the latter yielded 17%. These low levels of detection inde pendently suggest that current analytical methods for Cd may be of marg inal utility, and that id entification of minerals controlling the solubility of cadmium from the evaluation of groundwater data could be problematic.

We may compare these detection limits to the expected maximum concentration of cadmium in groundwater when in equilibrium with a solubility -controlling mineral. As discussed in Section 3.4.5 below, geochemical calculations using the most current thermodynamic data for greenockite (CdS), which woul d yield the maximum Cd con centration in solution for a representative reducing potable groundwater, show that the pred icted concentration should be
about 2×10 -4 mg/L in the presence of a pyrite/goeth ite redox couple (see Figure 3.48), or five
times the detection limit of the mo st sensitive analytical methods currently in use. This suggests
that the saturation concentration should be dete ctable, provided that th e analytical artifacts described above do not interfere, and that the pr edicted concentration is correct. However, the uncertainty associated with the predicted conc entration is about ± 0.5 log units. Furthermore,
Cd +2 is likely to substitute as a minor component in either sphalerite (ZnS) or galena, because of its low absolute abundance in crustal rocks, and therefore its concentration in groundwaters could be significantly lower than the predic ted saturation concentra tion of greenockite.

Of particular concern is the adequacy of an alytical methods for Pb and As, because the interpretation of thermodynamic controls gover ning these elements are at the center of the current project for modeling the impact of CO 2 intrusion on ambient concentrations in potable groundwaters. For example, Figures 3.6(a) indicate s that similar artifacts are operative for the less sensitive analytical methods measuring Pb, i.e., those with detection limits of 0.001 mg/L and above. The analytical method PLM43, howev er, for which a detection limit of 0.0001 mg/L59 of 352 (or 0.1 ppb) is reported in a large number of cases (see Table A1(e)), seems to be of sufficient sensitivity to measur e reliable values.

In Figure 3.9, to check this assumption, we have plotted a histogram with measured concentrations using only method PLM43. For reference, the predicted equilibrium log concentration (mg/L) for Pb +2 is –4.0 (i.e., 0.0001 mg/L or 0.1 ppb), calculated assuming that galena coexists with pyrite and goethite in a typical reducing groundwat er (Figure 3.44) (using the solubility product for galena, K s, based on thermochemical measurements described in Appendix D in Table D1). This concentration is incrementally higher than the lowest detection limit of analytical method PLM48 (0.00006 mg/L), whic h appears to be situated close to a local maximum in the frequency of Pb concentration values. This would indicate that, indeed, the aqueous concentration of Pb in these samples is like ly to be controlled by the presence of galena. This supposition is reinforced by the observation that of the two methods, in 49% of the analyses by PLM43, measurable values of Pb were de tected with a detec tion limit of 0.0001 mg/L, whereas with PLM48, 82% yielded measurable va lues with a detecti on limit of 0.00006 mg/L. Unfortunately, reported analyses using the less sensitive method in PLM43 (with a detection limit of 0.001 mg/L) appear to disp lay the same characteristics as those analytical methods for Cd and Hg, showing isolated bi ns with anomalously high populat ions. It would therefore be
desirable to eliminate from further consideration those reported analyses using the less sensitive method. However, as discussed above, the detec tion limit for each analysis with measurable concentrations is not reported in the NWIS da tabase, and therefore, the two methods cannot be clearly discriminated.

In the case of As, a similar eval uation to that shown for cadmium or Pb cannot be conducted: as in the majority of potable ground waters, the concentration of As in solution is determined in reducing groundwaters not by a mineral of fixed stoichiometry, where As is an essential component, but by solid solution substitution of th e FeAsS (arsenopyrite) component in pyrite. Consequently, the concentration of As in solu tion can vary, depending on the local abundance of As in the aquifer host rock and an equilibrium state established between the arsenopyrite solid solution, adsorption sites on host rock mi neral surfaces, and the aqueous phase. A60 of 352 thermodynamic analysis conducted elsewhere in this report (Section 3.4) conf irms that no solid phase where As is an essentia l component controls the concen tration of As in the potable groundwaters evaluated in this report The fr equency histogram for arsenopyrite saturation indices shows that SI values are negative with a maximum frequency at SI = -3 (see Figure 3.34). Furthermore, no other As minerals were identifie d that showed higher SI values than those of arsenopyrite. Thus, the evidence is quite convincing that the As concentration in solution is controlled by the binary (metastable) solid solu tion of the FeAsS compone nt in pyrite in the majority of groundwaters. A comparison can be made between predicted concentrations and those actually measured by developing a thermodynamic model describing the FeS 2 -FeAsS binary solid solution, and calculating the metastab le equilibrium As concentrations in typical groundwaters from that model. Such an analysis would also require determining the magnitude of the solid solution in pyrite samples from representative potable water aquifers. Absent the current feasibility of such an exercise, it is cons idered sufficient in this study to estimate the As activity in equilibrium with th e arsenopyrite (FeAsS) solid solutio n component and use this value in subsequent equilibrium and r eactive-transport modeling studies.

Fig. 3.9. Frequency distribution of Pb analyses in groundwaters using the analytical method PLM43 (ICP-MS). For reference, the predicted equilibrium concentration for Pb +2 is 0.0001 mg/L (or -4 in log mg/L), calculated assuming that galena co-exists with pyrite and goethite in a typical reducing groundwater.61 of 352 Below, we examine the ability of the most sensitiv e analytical methods to arrive at quantitative As determinations in the potab le groundwaters evaluated in th is study. The most sensitive analytical methods reported are PLM10 and PL M40, both of which utilize ICP-MS, and which have reported detection limits of 0.00012 a nd between 0.0002 and 0.0009 mg/L respectively (Appendix A, Table A1(a)). For the former anal ytical method, the percentage of detectable
concentrations out of the total number of determina tions is 92%, whereas for the latter, it is 87%, indicating that both methods ar e sufficiently sensitive for quant itative detection of As in the majority of potable waters studied. Figure 3.10 illu strates the concentration frequency histogram for As using these two analytical methods. It is evident that the maximum concentration frequency occurs at Log As =-2.5 (mg/L), or about 0.003 mg/L, which confirms that PLM10 and PLM40, in particular, are suitable analytical methods.

In conclusion, it can be stated that for several of the inorga nic hazardous c onstituents under investigation, past and even current analytical methods of measuring gro undwater concentrations are insufficiently sensitive for the purpose. This is clearly the case for cadmium, possibly also for mercury, antimony, and selenium. Reported values cl ose to the detection lim it may be subject to spurious readings, the frequenc y of which appears to decline in a log-linear manner with increasing concentration (Appendix B2). An explan ation for this behavior is at present not known, but may be dependent on the analytical met hod used. Eliminating the spurious values is not presently feasible, because there is no basis for separating spurious from real values. A partial solution is to el iminate from consideration all analys es obtained using th ose methods that, on the basis of geochemical considerations, ar e clearly out of range. This approach gives reasonable results in the cases of Pb and As, for example. Barium, uranium, and zinc are of less concern, as most analytical met hods used over time have been su fficiently sensitive to record quantitative values of concentration. Further discussion on the geochemical evaluation of thermodynamic controls in relation to the plausi bility of analytical m easurements of certain hazardous constituents is given in Section 3.4.62 of 352 Fig. 3.10. Frequency distribution of As analyses in groundwaters using the analytical methods PLM10 and PLM40 (ICP-MS).



## 3.4 Thermodynamic Analysis of Potable Groundwater

This section describes the identification of th e thermodynamic controls that may influence the
concentration and distribution of the selected hazardous inorganic constituents in the groundwater. We start in Section 3.4.1 with a description of the procedure for calculating the
saturation indices of selected mineral hosts fo r hazardous constituents for the large number of groundwater quality analyses available from the NW IS database. Before this procedure can be applied, however, necessary modifications mu st be made to the thermodynamic database (Section 3.4.2), the integrity of th e chemical analyses must be examined by evaluating charge imbalance (Section 3.4.3), and the groundwater redox state must be defined (Section 3.4.4). Results from the thermodynamic analysis are give n in Section 3.4.5. Finally, the effect of redox
state on the saturation concen tration of Cd, Pb, and Zn is examined in Section 3.4.6.

We selected the distribution-of-species code, EQ3/6 V.7.2b (Wolery et al., 1993), to calculate the saturation indices, as well as other parameters of interest such as ion activity ratios, which are related to the chemical potential s of components in the aqueous phase, Eh, charge balance, and the distribution of aqueous complexes in soluti on. This code has an associated database of63 of 352 solubility products for solid pha ses and dissociation constants of aqueous species, data0.ymp.R4. The database was selected primarily because it had undergone several fairly recent revisions, especially with respect to the co rrection of errors in solubility products for silica polymorphs and rock-forming silicate minerals (Apps, 2004). Neve rtheless, we realized that the database
contained substantial amounts of obsolete and e rroneous data, in some cases because data had been drawn from incompatible secondary data sources that were not checked for internal consistency. Extensive revisions were therefore undertaken in this study to rectify all relevant deficiencies, as further disc ussed in Section 3.4.2 below.

To conduct thermodynamic analysis of a large number of groundwater data sets (more than 38,000 water-quality analysis for a full set), we developed automated procedures for selecting and entering species concentrations in the input file of EQ3/6. The procedure involves entry of chemical analyses of each c onstituent in the EQ3 input f ile. When possible redundancies occurred, preferred analyses were chosen according to a hierarchical set of preferences. EQ3 was then executed, selected parameters were extr acted and compiled, and th e process was repeated for the next chemical analysis until all or a sele cted subset of samples was evaluated. If, for some reason, convergence was not obtained, the diagnostic s were extracted and recorded for further
review. The entire calculation was very rapid; i.e., the complete database of selected groundwater analyses from the National Water In formation Database coul d be processed in a
few hours.



### 3.4.1 Calculation of Activities and Saturation Indices

The saturation state is rela ted to the Gibbs free energy,
r Δ G , associated with the formation of the mineral, thus

0 ln ln r a RT RT G a = = Ω Δ (3.1)64 of 352 where R is the gas constant ( units), 

### a and 

### a 0 are, respectively, the mean molar activity of the participating species in the aqueous phase and the mean molar activity of the species in equilibrium with the mineral, and Ω is saturation state, given by:

1

o a IAP K a υ ⎛ ⎞ Ω= = ⎜ ⎟ ⎝ ⎠ (3.2)

where I AP is the Ion Activity Product, K is the equilibrium constant, and 

### υ is the number of
atoms in the molecular unit. In this study, we follow the us ual convention of defining the saturation index, SI, as:


### . . log S I = Ω (3.3)



### 3.4.2 Modification and Augmentation of the Thermodynamic Data Supporting the Codes EQ3/6 and TOUGHREACT

A critical requirement for meaningful interp retation and evaluation ofhazardous-inorganicspecies behavior in groundwaters is a realisti c thermodynamic description of the complete chemical system. The minimum thermochemical parameters needed for such a description
include Δ G f , P r , T

r 0

, S P r , T

r 0

, C P r 0 and V P

r , T

r 0 (these parameters are, respectively, the Gibbs free energy and entropy at the reference pressure and temperat ure (298.15 K, 1 bar), heat capacity at 1 bar as a function of temperature, and the molar volume at the referen ce temperature and pressure) for pure solid phases (i.e., minerals), the correspondi ng partial molal properties of dissolved species, i.e., Δ G f , P r , T

r 0

, S P r , T

r 0

, C P r , T

r 0 and V P r , T

r 0 , and an electrolyte model to describe the thermodynamic activities of the dissolved species in so lution as a function of ionic strength.

The distribution-of-species code, EQ3/6, used in this study for thermodynamic analyses, contains a database, Data0.dat, listing so lubility products and dissociation constants for minerals and aqueous species. The version of the Data0.dat used in this st udy was qualified for use under the65 of 352 U.S. Department of Energy’s Yucca Mountain Proj ect, and is not fully suited for the demanding requirements of the current study. Much of the cont ained information is eith er irrelevant or has not been revised in the light of more recent data in the published literature. Data of critical importance to interpreting the beha vior of the hazardous constituen ts selected for this study were never incorporated. We have therefore reviewed and, where necessary, revised or augmented the thermodynamic database. For consistency, we ha ve conducted similar modifications to the database employed by TOUGHREACT, the reactive tr ansport simulator utilized in Section 4. Because potable groundwaters are dilute and their ambient temperatures and pressures are relatively close to defined standard state conditions, a number of simplifications and approximations are possible in the derivation of thermodynami c data for this study. When necessary, simple corrections can be made to account for deviations in temperature or ionic strength. The methods used and a pproximations made in the relevant derivations are discussed at greater length in Appendix C.

Augmentation of the thermodynamic data for solid phases consists of the following steps:

1. Review the literature to find informati on on the thermodynamic parameters of solid phases of interest, based on calorimetric, ther mochemical, phase equilibrium or solubility measurements, and crystallographic data. Wher e necessary, raw data must be processed to retrieve the needed parameters.
2. Reconcile data to ensure that all thermodynamic data are internally c onsistent, i.e., are
referenced to the same standards.
3. Enter corrected data in the thermodynamic processing code, SUPCRT (Johnson et al., 1992).
4. Generate discrete log solubility pr oduct values, logKs, at 0, 25, 60, and 100 ° C, using
SUPCRT.
5. Process the SUPCRT output data in a format suitable for entry of solid phases in the Data0.dat database of EQ3/6.

The processing of thermodynamic data for aqueous species is analogous. However, to maintain consistency with the SUPCRT format for aque ous species, parameters for the HKF (Helgeson-66 of 352 Kirkham-Flowers) “Equation of State (EoS)” thermodynamic model (Tanger and Helgeson, 1988) were also calculated, where feasible:

1. Review of the literature to find informati on on the thermodynamic parameters of aqueous species of interest, based on calorimetric, th ermochemical, or solubility measurements. Where necessary, reported dissociation constant s are processed to retrieve the needed parameters.
2. Reconcile data to ensure internal consis tency, i.e., reference data to the same thermodynamic standards.
3. Augment the basic data derived in Step 1 to ca lculate S, and the C1 and C2 parameters of the HKF EoS. The a1–a4 parameters of the HKF EoS are not needed for the purposes of this study.
4. Enter corrected data in SUPCRT.
5. Generate discrete log dissociation cons tant values, logKdiss, at 0, 25, 60, and 100 ° C, using SUPCRT.
6. Process SUPCRT output data in a format su itable for entry in the Data0.dat aqueous species database of EQ3/6.
The entire scope of the thermodynamic data enhancements is indicated in Table 3.10.

It should be noted that the in clusion of certain minerals in the database is made with the understanding that the perturbatio n of aquifer systems—either re sulting from the ingress of oxygenated groundwater, aggressive anaerobic bact erial reduction, or from the intrusion of CO 2 , or other anthropogenic perturbation s—leads to the precipitation of metastable minerals that are finely crystalline or amorphous and which possess s ubstantial specific surface areas. The large specific surface areas form impor tant reactive adsorption sites that adsorb trace elements, including those hazardous constituents under study. In reactive tr ansport modeling, the precipitation of metastable pha ses, and concurrent changes in specific surface area and adsorption capacity, can be accounted for usi ng appropriate equations to describe the heterogeneous kinetics.67 of 352 Table 3.10. Scope of modifications and augmentations to thermodynamic database

Rock forming and accessory minerals

• Minerals participating in redox equilibria:

− Ferric oxides and oxyhydroxides (ferrihydr ite, goethite, lepi docrocite, hematite, ε− Fe2O3, maghemite)

− Iron sulfides (pyrite, mackinaw ite, greigite, amorphous FeS)

− Mixed oxide/sulfates (shw ertmannite, green-rust)

− Silicates containing Fe(II)/Fe(III), e.g. chlorite, smectite, illite

• Carbonates (dolomite, ankerite, siderite, magnesite)

• Silica polymorphs
Minerals that contain a hazardous constituen t as an essential component and are likely to be stable under moderate to strongly reducing groundwater conditions

• Arsenopyrite, arsenian pyrite, realgar, orpiment, symplesite (As)

• Barite, witherite (Ba)

• Greenockite, cadmoselite (Cd)

• Cinnabar, tiemannite (Hg)

• Galena, clausthalite (Pb)

• Stibnite, kermesite, gudmundite, berthierite, antimonselite, (Sb)

• Ferroselite, cadmoselite, tiemannite, clausthalite, antimonselite (Se)

• Uraninite, coffinite, brannerite (U)

• Sphalerite (Zn)
Minerals that contain a hazardous constituen t as an essential component and are likely to be stable under oxidizing groundwater conditions

• Scorodite, etc. (As). For a co mplete list, see Appendix C.

• Barite, witherite (Ba)

• Otavite, (Cd)

• Quicksilver, calomel, etc. (Hg). See note.

• Carbonates (cerussite, hydrocerussite, plumbonacrite, etc.) (Pb). For complete list, see Appendix C.

• Senarmonite, valentinite (Sb). See note.

• Other? (Se). See note

• Uranophane, boltwoodite, weeksite, soddyite, etc. (U)

• Hemimorphite, hydrozincite, smithsonite (Zn) Aqueous carbonate species for major rock-fo rming components, e.g., Mg, Ca, Fe(II). Aqueous carbonate species for Ba, Cd, Hg, Pb, U(IV,VI), Zn Arsenite (As(III)) and arsenate (As(V)) species and complexes, antimonite (Sb(III)) and antimonate (Sb(VI)) species and complexes; se lenite (Se(IV)) and selenate (Se(VI)) species and complexes Sulfide and selenide complexes of As, Cd, Hg, Pb, Sb, Se, Zn and Fe(II)
Note: Not all potential solubility-controlling minerals have been listed or identified. Some minerals may not have been recognized, or the thermodynamic properties have not been determined. Some minerals may substitute in solid solution as a minor component in a more abundant therm odynamically stable phase under the considered redox conditions. In some cases, thermodynamic data were estima ted with varying degrees of precision and confidence, using various estimation techniques as described in Appendices C, D, and E.68 of 352 A possible complication is the common nonattainment of thermodynamic equilibrium between species participating in redox re actions in the aqueous phase. Th is problem is well known, and has been investigated previously (Lindberg and Runnells, 1984; Stefansson, 2003). Problems of interpretation in the current study also extend to equilibria involving aqueous species of As (0, III, V); Hg(0, II); Sb (II, IV, VI ); Se (II, IV, VI) and U (IV, VI). Furthermore, some of the species in solution form complexes with S(II) (whi ch is also capable of several higher oxidation states, principally IV and VI) an d with Fe(II) (which can also be oxidized to Fe(III)). Added complexity arises through the precipitation of phases with components in differing oxidation states, e.g., ferric arsenite (t ooeleite), and ferrous arsenate (symplesite). Both EQ3/6 and TOUGHREACT can incorporate nonattainment of homogeneous equilibrium. In both codes, the respective reduced and oxidized forms can be tr eated as separate nonreac ting species. In the case of TOUGHREACT, the kinetics of transformation from oxidized to reduced forms or vice versa
can be included in the model.

Finally, it should be recognized th at hazardous constituents may co-p recipitate to form minerals where two hazardous constituents are present as essential components. Examples are cadmoselite (SCdSe) or mimetite (Pb 5 (AsO 4 ) 3 (Cl)). A priori evaluation rega rding the ranges and pH and Eh conditions where these minerals might be stable is difficult, the easy way out being to incorporate the solubility products of all of these minerals in the thermodynamic database of any model of the groundwater system.

Appendix C provides a detailed di scussion of the thermodynamic data for both solid phases and aqueous species that were compiled for this stu dy. The calculations performed to generate both the associated thermodynamic data and the rele vant solubility prod ucts and dissociation constants are explained. In the di scussion of results, it is noted where results are provisional, and
how the inclusion of revised or otherwise mi ssing data might impact the current findings.69 of 352 

### 3.4.3 Evaluating the Integrity of Water Quality Analyses

One of the first tasks in evalua ting the groundwater data using EQ3/ 6 is to evaluate the integrity of the chemical analyses. A rule of thumb for te sting the quality of a ch emical analysis is to check the charge balance according to the following equation:

100 cations anions
cations anions − × + 

## ∑ ∑ ∑ ∑ (3.4)

The analysis is normally considered acceptable if the charge imbalance is ≤ 5%. However, the EQ3/6 evaluation demonstrates that imbalances were quite substantial for many of the chemical analyses extracted from the NWIS database. Th e primary reason is probably the application of analytical data that fail to take into account the effects of CO 2 degassing and possible precipitation of calcium carbonate resulting from inadequate cust odial preservation of sample integrity following field sampling and analysis, prior to laboratory determination of alkalinity. Most groundwaters are very dilute, so that the bicarbonate concentration contributes significantly to the charge balance. The bicarbonate concentra tion is related to the pa rtial pressure of carbon
dioxide as follows:

HCO 3 - = CO 2 (g) + OH - 2

3 CO P OH K HCO −

− ⎡ ⎤ ⎡ ⎤ ⎣ ⎦ ⎣ ⎦

= ⎡ ⎤ ⎣ ⎦ (3.5)

where the brackets [ ] represent activities of the enclosed species. When the equilibrium partial pressure of CO 2 is calculated from chemical analyses of most groundwaters whose integrity is assured, it is found that it is significantly high er than that found in the atmosphere, i.e., Log P(CO 2 ) varies between –3 and –1 co mpared with –3.5 for atmos pheric conditions (see Figure 3.17, below). Groundwater samples exposed to the at mosphere will therefore tend to degas, which usually leads to a corres ponding upward shift in pH, supe rsaturation with respect to calcium carbonate, and eventual precipitation of calcium carbonate. This can be expressed by the following reaction:70 of 352 2HCO 3 - + Ca +2 = CO 2 (g) + CaCO 3 (s) + H 2 O

If the pH of a sample is measured in the fi eld before degassing occurs , whereas the water is analyzed for bicarbonate content or alkalinity after de gassing, a significant charge imbalance can be expected when calculating the distribution of species in the sa mple using the reported values for pH and carbonate content. Charge imbalan ce can furthermore be caused if the resulting supersaturation induces precipita tion of calcium carbonate prior to measurement of alkalinity. Less significant charge imbalances may also resu lt from failure to account for the temperature difference between the groundwater and that at which analyses are conducted.

The possible errors introduced as a result of unaccounted-for degassing and precipitation are illustrated in Table 3.11 for a typical groundwater containing a low concentration of dissolved solids, where it is assumed that the groundwater is in equilibrium with resp ect to calcite before sampling and analysis. In this table, seven cha nged states of the original sample, given in Column 3, are considered, together with certain assumptions made in interpreting the chemical analyses and their impact on calculated para meters. The “true” groundwater composition is described in Column 3 in terms of the measured analytical parameters (pH, HCO 3 - [alkalinity] and Ca) and the calculated parameters (calc ite saturation index SI[calcite), Log P(CO 2 ) and charge imbalance), all listed in Column 1. The changed states are summarized in Columns 4–10,
and are described as follows:

• Column 4. The groundwater is allowed to warm up to normal room conditions, i.e., T = 25oC, resulting in a minor lowering of pH and slig ht supersaturation with respect to calcite.

• Column 5. The groundwater is exposed to the atmos phere at 25oC and allowed to degas CO 2 until Log P(CO 2 ) = -3.5 (that of the atmosphere). The pH changes by approximately one unit,
HCO 3 - falls, and the sample becomes significantly supersaturated with respect to calcite.

• Column 6. The degassed groundwater in Column 5 is allowed to re-e quilibrate through precipitation of calcite. Both HCO 3 - and Ca fall substantially, and SI(calcite) returns to zero.

• Column 7. The effect on the calculated parameters is shown by assuming a pH measured at 25oC in the field (Column 4) and a laboratory alkalini ty determination after CO 2 degassing71 of 352 (Column 5). SI(calcite) increases slightly, Log P(CO 2 ) is higher than that in the formation (Column 3), and the charge imbalance rises to +6% of the total, a value that would, according to standard criteria, make the overa ll analysis marginally unacceptable.

• Column 8. The effect of accepting a field alkalinity determination and a laboratory pH after degassing CO 2 is shown. SI(calcite) is str ongly supersaturat ed, and Log P(CO 2 ) is nearly one log unit lower than the true value in the formation, even though the charge imbalance remains relatively small.

• Column 9. The field pH measurement is accepted toge ther with an alkalinity determination after calcite precip itation, but assuming that Ca was meas ured on an aliquot acidified in the field prior to sample degassing a nd precipitation of calci te, as is standard practice. This leads to a relatively modest apparent undersaturation with respect to calcite and slightly lower P(CO 2 ), but a drastic and normally unaccep table charge imbalance of +27%.

• Column 10. The field pH measurement is accepted t ogether with the alkalinity determination
after CO 2 degassing. SI(calcite) and Log P(CO 2 ) are moderately affected, compared with the true values in the field, and char ge imbalance increases by about +6%.

Table 3.11. Errors introduced through inadequate preservation of sample integrity deficiencies.

Parameter Unit Ground water with calcite at 10°C Ground water raised to 25°C Degassed to P(CO 2 ) (atm) Degassed to P(CO 2 ) (atm) & calcite pptn Field pH; lab HCO 3 - Lab pH; degassed to P(CO 2 ) atm. Fld HCO 3 - Field pH Lab Ca After calcite pptn Field pH HCO 3 - after degas.

pH - 7.739 7.611 8.694 8.548 7.611 8.694 7.611 7.611

HCO 3 - mg/L 227.29 227.29 197.06 114.81 197.81 227.29 114.81 197.06

Ca ++ mg/L 37.669 37.669 37.669 6.39 4 37.669 37.699 37.669 37.669
SI (calcite) - 0.000 0.093 +1.132 0.000 +0.038 +1.130 -0.184 +0.036
Log P(CO 2 ) Bar -2.5 -2.286 -3.5 -3.5 -2.346 -3.372 -2.581 -2.348
Charge imbalance % of total 0.000 0.000 0.000 0.000 +6.00 -5.73 +27.4 +6.16

Other constituents (mg/L): Na + , 45.8; K + , 5.04; Mg ++ , 2.01; Cl - , 7.14; SO 4 -2 , 18.40; SiO 2 (aq), 60.97.72 of 352 A complication arising from low concentrations of dissolved solids typical of potable groundwaters is that the pH may be poorly buffered and subject to effects of contamination and instrumental drift. Thus, both pH measuremen ts and alkalinity (or bicarbonate or carbonate) analyses may be subject to error. Unfortunate ly, the two parameters are interdependent, and therefore it is not possible to discriminate directly whether th ey may be erroneous.

Analytical chemists are aware of the fugitive nature of CO 2 and the tendency of pH to drift with time. Therefore, efforts are often made to me asure both pH and alkalinity in the field, with additional backup analyses of these parameters (and/or bica rbonate or carbonate) in the laboratory. Because environmental conditions in the field are less well controlled than in the laboratory, field measurements can be less accu rate, though more representative of actual conditions prior to sampling. Chemical analyses of groundwaters stored in the NWIS database commonly report both field and laboratory measurements for these parameters, sometimes additionally on filtered or unfiltered samples. Therefore it is necessary to choose those measurements of pH, alkalinity, or bicarbonate/car bonate that are most likely to provide accurate results during subsequent EQ3/6 calculations.

The problem of selecting an ap propriate value for alkalinity is further complicated by the multiplicity of different analytic al methods used to measure alkalinity, bicarbonate, or total
carbonate concentrations over th e last decades. The NWIS data base contains no less than 20 listed analytical procedures for alkalinity, 12 for HCO 3 - and 17 for CO 3 -2 . Their persistence in the NWIS database has a long history. Over time, USGS and EPA have attempted to standardize procedures and have supplante d earlier methods with improved techniques (Alexander et al., 1988). However, the legacy of earlier techniques remains embedded in the NWIS database. To guide the choice of analytical methods, we deve loped a hierarchical 32- step protocol for the
selection of HCO 3 - (and CO 3 -2 ) or alkalinity data from the database. All data was transformed to equivalent HCO 3 - (mg/L) for entry in the EQ3 input file.

Earlier versions of the EQ3 code (Wolery, 1983) possessed the option of entering the titration alkalinity directly as an entr y in the input file, but this op tion was subsequently discontinued (Wolery, 1992, p. 21-27). The proposed soluti on advocated by Wolery (1993) involved the73 of 352 calculation of the equilibr ium concentration of CO 2 (aq) in the presence of HCO 3 - , then summing
the molar concentration of CO 2 (aq) and the incremental titration equivalents of HCO 3 - and CO 3 -2

, respectively for each analysis, and finally enteri ng the appropriate total concentration of HCO 3 - for each analysis. This solution, however, would have been excessively time consuming. Instead, we opted to transform the measured alkalinity, or titration concentration of HCO 3 - , to
HCO 3 - (mg/L), and enter it in the input file as “free” HCO 3 - , i.e., the concentration of that specific species, regardless of the concentrations of other complexes of the HCO 3 - basis species in solution. This automatically accounted for CO 2 (aq) and CO 3 2- in the final species distribution,
but also double counted th e concentrations of aque ous complexes with HCO 3 - , such as
NaHCO 3 (aq) or CaHCO 3 + . Because the latter complexes repr esent only a small fraction of total
HCO 3 - , the error introduced by this ap proximation is generally small.

We performed EQ3/6 runs on all 38,597 sample s to evaluate the significance of sample degassing. As an additional constraint, we specifie d that Eh is determined by the coexistence of pyrite and goethite, a reasonable assumption for most anoxic groundwaters. (This assumption is
evaluated further in the Sec tion 3.4.5). Out of the total number of samples, 35,069 runs converged, and the initial pH , charge imbalance, P(CO 2 ) and P(H 2 S), as well as calcite, dolomite and siderite (ferrous carbonate) saturation indices, were recorde d. The resulting outputs, plotted in the form of histograms of the frequency of occurrence, are of considerable interest, not just for evaluating concerns about charge imbalance, but al so with respect to characterizing the average geochemical conditions of most groundwaters.

Figure 3.11 shows the pH range of all groundwaters. The acid “tail” is surprisingly persistent, and may reflect shallow groundwater contaminati on with organic acids, or relatively high CO 2 partial pressures. The charge imbalance (Fig. 3 .12) peaks at a slightly negative value, which would be expected if aqueous complexes of HCO 3 - had been overcounted in the approximation
of total HCO 3 - concentration. The positive skew to the histogram has not been investigated, and remains to be explained.74 of 352 Fig. 3.11. Histogram showing the pH distribution of more than 35,000 groundwater analyses selected from the NWIS Database. No correction for charge imbalance.

Fig. 3.12. Histogram showing the distribution of percentage charge imbalance of more than 35,000 groundwater analyses selected from the NWIS Database.

The frequency histogram for the cal cite saturation index (Fig. 3.13) displays a peak where SI is slightly above zero, indicating supersaturation w ith respect to calcite. The substantial population75 of 352 of values approximately centered on SI ~ 0 s uggests that the majority of groundwaters are saturated with respect to calcite. The cause of the secondary maximum at SI ~ -3 was not investigated. The pH of a groundwater saturated w ith respect to calcite is likely to be more strongly buffered and therefore be more resistant to change under increasing partial pressures of
CO 2 . As a consequence, the concentr ations of those hazardous inorga nic constituents that are in part determined by ambient pH would be less imp acted than would be the case where calcite is absent. It should be noted, however, that if calcite is present only in trace amounts, the ingress of high-pressure CO 2 could lead to the complete dissoluti on of calcite, and the system would no longer be buffered by its presence.

Fig. 3.13. Histogram showing the calculated calcite saturation i ndices of more than 35,000 groundwater analyses selected from the NWIS Database. No correction for ch arge imbalance.

Many dolomitic limestones are sources of drinking water in the eastern United States, and it is
therefore of interest to examine the state of sa turation of all groundwaters with respect to this carbonate. Figure 3.14 shows that almost SI(dolomite) peaks at significantly less than zero, which is consistent with reported occurren ces of dedolomitization in shallow groundwaters (Edmunds et al., 1982; Back et al., 1983; Plummer et al., 1990; James et al., 1993; Aravena et al.,76 of 352 1995; sacks and Tihansky, 1996; Hopkins and Putnam, 2000; Smedley and Edmunds, 2002; Pacheco and Szocs, 2006). Therefore, it is to be expected that groundwat ers will not come into thermodynamic equilibrium with those represen tative host-rock mineralogies containing dolomite, i.e., St Peter Sandstone and Dolom itic Limestone (Section 3.6.3) in TOUGHREACT simulations described in Section 4 of this repor t, unless complete dedolo mitization occurs during the groundwater initialization step.

Fig. 3.14. Histogram showing the calculated dolomite saturation indices of more than 35,000 groundwater analyses selected from the NWIS Database . No correction for charge imbalance.

Because several authors cited above have attrib uted dedolomitization to concomitant dissolution of gypsum (CaSO 4 .2H 2 O) or anhydrite (CaSO 4 ), it is worth checking to establish whether the selected water quality analyses are saturated wi th respect to calcium sulfate in one form or another. Figure 3.15 illustrates the SI distribution for gypsum. It is clear that few, if any potable groundwaters are saturated with respect to this mineral, and therefore buffering of the sulfate concentration or any influence it might have in defining the redox state via the SO 4 -2 /HS - buffer is negligible. Instead, it is more likel y that barite may indirectly control SO 4 -2 activity (Section 3.4.4). It should also be noted that in the calculated equilibriu m distribution of species using77 of 352 EQ3/6, the partial pressure of H 2 S is defined though its relation to the activity of SO 4 -2 in solution, thus:

H 2 S(g) + 2O 2 (g) = 2H + + SO 4 -2

where O 2 (g) and H + are respectively set by the specified by Eh and pH. See further discussion regarding P(H 2 S) below.

Fig. 3.15. Histogram showing the calculated gypsum saturation indices of more than 35,000 groundwater analyses selected from the NWIS Databa se. No correction for charge imbalance.

Figure 3.16 indicates that most groundwaters are not saturated with re spect to the ferrous carbonate, siderite, which is consis tent with relatively infrequent observations of its occurrence in shallow groundwater aquifers. Relaxation of the control of redo x state through the coexistence of goethite and pyrite would likely decrease the saturation state even further with respect to siderite saturation. The finding that siderite may not be a common constituent of potable groundwater aquifers has three implications regard ing the modeling of hazardous inorganic constituent behavior in potable aquifers being invaded by high-pressure CO 2 . First, its inclusion in the model mineralogy of the aquifer host rocks is not critical; second, it need not be78 of 352 considered as a potential pH buffer; and third, consideration of a system whose redox state is buffered by a siderite/pyrite assemblage can be ignored in modeling general cases.

Fig. 3.16. Histogram showing the distribution of calculated si derite saturation indices of more than 35,000 groundwater analyses selected from the NWIS Database. No correction for charge imbalance.

The distribution of partial pressures of CO 2 in Figure 3.17 shows that, in a large majority of groundwaters, they are up to 2.5 orders of magn itude higher than those of the atmosphere (Log P(CO 2 ) = -3.5), clearly indicating the potential of these groundwaters to degas if exposed to air. Therefore, it is preferable to assume that Log P(CO 2 ) is buffered at circa –2.0 (the approximate modal value in the more than 35,000 groundwater samples examined), rather than imposing an external Log P(CO 2 ) = -3.5, as was done by Wang and Jaffe (2004) in their preliminary study of the effect of high-pressure CO 2 intrusion.

Elevated P(CO 2 ) in the subsurface environment is co mmonly attributed to the effects of microbial oxidation of organic matter. Indeed, this is most probably the case in shallow subaqueous unconsolidated sediments undergoing active authigenesis. However, elevated P(CO 2 ) can also result from thermogenesis of organic matter and decarboxylation, or from deeper metasomatic processes, such as translocation of CO 2 to shallower environments occurring79 of 352 through diffusion/and or advective transport. Furt hermore, we conducted test calculations with EQ3 of groundwater saturated with respect to representative di agenetic mineral assemblages including pyrite and goet hite, calcite and siderite at 25°C, yielding P(CO 2 ) levels comparable to
those observed without recourse to th e external introduction of biogenic CO 2 . Therefore it remains an open question whether biogenic CO 2 modifies the diagenetic mineral assemblage, or whether the diagenetic assemblage specifies a P(CO 2 ) that is elevated with respect to that of the atmosphere, regardless of the presence of organic matter.

Fig. 3.17. Histogram showing the distribution of calculated Log P(CO 2 ) of more than 35,000 groundwater analyses selected from the NWIS Databa se. No correction for charge imbalance.

The P(H 2 S) distribution (partial pressure of H 2 S) in Figure 3.18 is fictive, in that it is the result of assuming that the coexisting pair of minerals, py rite and goethite, define s the redox state at a given pH for a given SO 4 -2 concentration. In reality, the distri bution is primarily a reflection of
the distribution of SO 4 -2 in the population of groundwater analyses. However, the calculated range of concentrations is consistent with the fact that only in rare cases would H 2 S be detectable in solution, and that most anoxic gr oundwaters would be free of odor. P(H 2 S) is a measure of the hydrogen sulfide activity in solution, and has an important bearing on th e extent of sulfide complexing of heavy metals in solution, in cluding Cd, Hg, Pb, and Zn, and under some80 of 352 circumstances can also affect the behavior of As and Sb. It also define s the activities of these metals with respect to the solubility products of their sulfides. Thus this parameter is an important dependent variable in describing sy stem behavior, just as are pH, Eh and P(CO 2 ).

Fig. 3.18. Histogram showing the distribution of calculated Log P(H 2 S) distribution of more than 35,000 groundwater analyses selected from the NWIS Da tabase. No correction for charge imbalance.

In recognizing that the above results may in part be distorted by th e potential loss of CO 2 , we conducted an alternative set of EQ3 runs with adjustment of the concentration of HCO 3 - to eliminate charge imbalance. Figures 3.19 through 3.21 correspond to Figures 3.13, 3.16 and 3.17, but include this correction. Examination of Figure 3.19 shows that by retaining a fixed pH, the saturation index for calcite increases, thereb y shifting the SI calcite to higher values. This effect varies with pH, bu t in aggregate, it leads to a compre ssion of the range and an upscale shift of both primary and secondary maxima, the seconda ry maximum being more strongly affected.

The histogram for siderite satu ration indices (Fig. 3.20) transfor ms into a nearly symmetrical peak about SI = 0. This result might lead to the conclusion that most groundwaters are saturated with respect to siderite. However, it may also be an artifact arising from the original derivation of the thermodynamic properties of goethite and sideri te, which define the solubility product values81 of 352 incorporated in the EQ3/6 database. Because the [Fe +2 ]/[H + ] 2 activity ratio is determined by the redox-controlling pair goethite/pyrite, the SI pr ofile for siderite is actually defined by the corresponding distribution for Log P(CO 2 ) (Fig. 3.21). Examination of the Log P(CO 2 ) profile clearly indicates that this para meter has been unduly biased upscal e to partial pressures that are unrealistically high for shallow aquifers. Thus, while correction for charge imbalance using
HCO 3 - is justified to account for CO 2 degassing, it is inappropriate to apply such a correction in all cases. Unfortunately, there is no simple means by which the correction for charge imbalance may be applied without the inadvertent introduction of other undesired biases.

Fig. 3.19. Histogram showing the distribution of calculated calcite saturation indices of more than 35,000 groundwater analyses selected from the NWIS Database. Charge imbalance corrected through adjustment of HCO 3 - concentration.82 of 352 Fig. 3.20. Histogram showing the distribution of calculated si derite saturation indices of more than 35,000 groundwater analyses selected from the NWIS Database. Charge imbalance corrected through adjustment of HCO 3 - concentration.

Fig. 3.21. Histogram showing the distribution of calculated Log P(CO 2 ) of more than 35,000 groundwater analyses selected from the NWIS Database. Charge imbalance corrected through adjustment of HCO 3 - concentration.83 of 352 The purpose of the foregoing discussion was to eval uate the significance of analytical artifacts primarily associated with inadvertent degassin g of groundwater samples prior to alkalinity
determinations and associated errors introduced by secondary precipita tion of calcium carbonate, and other factors relating to the measurement of pH. By so doing, we hoped to develop a procedure whereby such artifacts might be removed, or at least, mitigated during the subsequent processing of the more than 38,000 groundwater analyses with the EQ3/6 species distribution
code. It was not the intent of this analysis to define the pa rameters required for simulating CO 2 intrusion in a potable water aquifer and its impact on hazardous inorganic constituent concentrations in the aquifer. However, the study raised a number of issues relating to the broader issue of geochemical model validation.

The reference to Figure 3.12 show s that if the criterion for acce ptance of a groundwater chemical analysis were a charge imbalance of no more th an 5%, then two-thirds of the 35,000 analyses
downloaded from the NWIS database and su ccessfully run by EQ3 would be considered acceptable. Table 3.11 shows that errors in the m easurement of ambient pH might arise through inappropriate sample handling, ev en though the sample would pass the charge imbalance screen. Attempts to rectify such by restoring lost CO 2 through charge balancing on HCO 3 - were only partially successful. Thus , it appeared that no simple stra tegy could be adopt ed that would account for analytical errors without introdu cing unwanted biases in the evaluation. The following is a partial list of pot ential errors affecting the gr oundwater chemical analyses: (1) permitting degassing of CO 2 , (2) permitting precipitation of dissolved constituents, (3) failure to analyze all principal constituents in solution, (4) acidification of a sample containing suspended material, (5) permitting access of oxygen, thereby oxidizing reduc ed species, (6) inadvertent contamination both before and after sample co llection, (7) inadequate calibration of pH electrodes, (8) contamination of sample by refere nce electrode electrolyte, and (9) interferences
and biases associated with individual analytical met hods for specific elements.

It became evident that the develo pment of a systematic strategy fo r correcting chemical analyses containing discrepant measurements would require more time and effort than could be devoted within this study. Furthermore, se lecting only those analyses mee ting certain criteria might not84 of 352 necessarily improve the quality of the subse quent evaluation, because analyses might be
eliminated that could otherwis e contribute significantly to the statistics of small sample populations involving such hazardous constituents as antimony, which is infrequently analyzed in groundwaters. We decided, therefore, to incl ude all analyses regard less of quality. Such a decision is likely to increase the statistical disp ersion of calculated satura tion indices of minerals controlling the solubilities of hazardous inorganic constituents, but it is unlikely to affect their identification, or to introduce systematic biases, as it is the mode of the distribution that is the critical parameter of interest. It would clearly be interesting to pursue this issue on a broader scale, as it has important implications regardi ng geochemical model validation and the degree of
confidence attached to the results of field studies.



### 3.4.4 Evaluation of Groundwater Redox State

Estimating the redox potential of groundwaters is critic al to interpretation of most hazardous constituents targeted in this study. As noted before, As, Sb, Se, and U can occur in differing
oxidation states that are stable in the presence of water. Ar senic, Cd, Hg, Sb, Pb and Zn commonly form insoluble sulfides under redu cing conditions, which become unstable under oxidizing conditions, where sulfur is oxidized from th e -II to the 0 or the VI state. As, Sb and Se can also form compounds with Fe(II), which is destabilized under oxid izing conditions to the Fe(III) state. Furthermore, some of the hazardous constituents can form compounds with each other, e.g., selenides of As, Cd, Pb or Zn, or arsenates of Pb. Thus, a variety of solubilitycontrolling minerals can form, depending main ly on both the pH a nd redox state of the groundwater.

Unfortunately, although the redox state, or Eh, of a groundwater is easily measured using a Pt electrode, this type of measurement is usually subject to large and unpr edictable errors (Baas Becking et al., 1960; Lindberg and Runnels, 1984; Fish, 1993; Stefansson, 2003). Furthermore, not all species of an element that can occur in different oxidation states are necessarily in redox equilibrium, as has been observed with respec t to As(III)/As(V) (Che rry et al., 1979; Johnston and Singer, 2007), Sb(III)/Sb(V) and Se(II)/(IV)/Se(VI) and S(II )/S(VI) (Sato, 1992). Although85 of 352 the distribution-of-species code EQ3/6 can account for a lack of homogeneous equilibrium within the aqueous phase, and th e transformation of species from one redox state to another can be treated kinetically in TOUGHREACT, such a refinement is beyond th e scope of this study and not utilized in the analysis presented here.

Instead of relying on direct Eh measurements , which, as noted above, are of questionable reliability, we use an indirect method to estima te redox state. Equilibr ation between Fe(II) and Fe(III) occurs relatively rapidly in the aqueous phase (Grundl and Macalady, 1989), and therefore advantage can be taken of measurements of these species in solution to calculate the Eh of solution at a given pH. While the concentratio n of Fe(III) in solution is usually very low and not normally measured, its concentration and activ ity can be calculated if equilibrium with a ferric oxide or oxyhydroxide is assumed in the aquife r. This assumption is reasonable, especially under oxidizing conditions. With an increasingly re duced state, more and more of the Fe(III) in the ferric phase would be reduced to Fe(II), which can be incorporat ed in the octahedral layers of secondary smectites or chlorites. Under even more highly reducing conditions, iron can be sequestered and precipitated as a sulfide, usually as pyrite. Even tually, a state would be reached where all Fe(III) containing minera ls are dissolved, and the redox state can no longer be defined by the Fe(II)/Fe(III) activity ratio.

Over 20,000 analyses in the database extracted fro m the NWIS contain measurable Fe analyses. These provide a basis for calculating the probable Eh of a significant fraction of all samples. As part of the NWIS database search, additiona l analytical data on redox pairs such as SO 3 -2 /HS - , Mn +3 /Mn +2 , HCO 3 - /CH 4 (aq), NO 3 - /NH 4 + , etc., redox measurements, and total dissolved oxygen were also solicited when included in groundwater analyses containing an alyses of hazardous inorganic constituents. Such additional information can be used to augment Eh values calculated using Fe analyses and to estimate the state of equilibrium or disequilibrium, but this was not attempted in the present study. Un like the Fe(III)/Fe(II) couple, the other redox pairs are slow to achieve homogeneous equilibrium, with the po ssible exception of the Mn(III)/Mn(II) couple,. They are therefore useful in establishing whether authigenic (biogenic) processes are active in a potable groundwater environment, or whether the groundwater has attained a measure of86 of 352 homogeneous equilibrium. Although of considerab le importance (and further study would be merited), this subject was not explored further, owing to the relative paucity of suitable datasets in the groundwater quality analyses downloaded from the NWIS database.

The following approach was taken when using Fe analyses to estimate Eh. It was initially assumed that goethite was present in equilibriu m with the groundwater for the given chemical composition and pH. The total measured Fe c oncentration was then inserted as the Fe +2 concentration, and the Eh was computed based on the Fe(III)/Fe(II) ratio. The calculated total iron concentration was then compared with the measured total iron concentration according to the following three criteria, where 

### Fe ( o / p ) is the calculated total iron concentration and 

### Fe ( i / p ) is the measured input concentration of total Fe:

(a) If Fe ( o / p )

− Fe ( i / p ) Fe ( o / p ) = 0 Leave unchanged, and record Eh.

(b) If 05 0 p o Fe p i Fe p o Fe

. ) / ( ) / ( ) / ( ≥ − , but < 0.95 Subtract Fe(i/p) from Fe(o/p) and enter the difference as Fe +2

. Re-run and record Eh.
(c) If F e ( o / p )

− F e ( i / p ) Fe ( o / p ) = 1 Leave unchanged, and record Eh.

Case (a) arises when the calculated output concen tration is essentially identical to the input concentration, i.e., when the contribution of Fe(II I) to the total Fe concen tration is negligible. Case (c) represents the situation when the measured concentration is small in relation to the calculated concentration of Fe(III) in equilibrium with goethite. Case (b) arises when the concentrations of Fe(II) and Fe (III) are approximately equivalent, and the approximations that total iron is predominantly Fe(II), as in Case (a), or predominantly Fe(III), as in Case (b), cannot be applied, and the concentration of Fe(II) must be calculated explicitly. The applicable range

where 0.05 ≤ F e ( o / p )

− F e ( i / p ) Fe ( o / p ) < 0. 95 is established to ensure stability in the calculations,

while also recognizing the anal ytical uncertainty in the m easured Fe concentrations.

An additional specification is that the measured SO 4 -2 concentration is en tered as the “free” concentration, i.e., the total concentration of sulfate species is as analyzed, and sulfide87 of 352 concentrations are added in to achieve equilibriu m with the sulfate at the pH and Eh conditions specified for each run. This adjustment reflects th e fact that the analytical procedures usually analyze for sulfate alone rather than total sulfur.

The calculated Eh values were then plotted agai nst pH to produce a typical Eh–pH diagram, as illustrated in Figure 3.22. The plot shows that most of the groundwaters, for which measured Fe analyses are available, fall within a range of moderately to strongly reducing conditions. The upper bound is constrained by the an alytical limitations for measur ing Fe(total) in solution and falls within the goethite stability field. The lower stability limit is bounded by the decomposition of water and can fall within the stability field of pyrite. Quantitative estimates of Eh are therefore constrained by the sensitivity of analytical met hods for measuring dissolved iron. Consequently, a bias might be introduced through this analysis, which must be taken into consideration in interpreting the thermodynamic controls affecting the behavior of hazardous inorganic constituents in groundwater (Section 3.4.5).

Fig. 3.22. Calculated Eh, based on the distribution of Fe(III) and Fe(II), and the assumption that Fe(III) is in equilibrium with goethite, plotted against measured pH.88 of 352 

### 3.4.5 Evaluation of Solubility Controls for Hazardous Constituents

A series of five EQ3/6 runs were made in wh ich groundwater analyses ex tracted from the NWIS database were subject to differing initial constrai nts. The purpose of these runs was to determine
whether concentrations of the se lected hazardous inorganic cons tituents were controlled by certain minerals in the aquifers. A further two runs were conducted on a limited subset of waters containing Pb, analyzed using PLM43 and PLM4 8 and Cd, analyzed using PLM43 and PLM47. These denote analytical methods employing ICP- MS, which is the most sensitive technique currently available in general use and most likel y to yield valid measurements of Pb and Cd concentrations (see Appendix A). A description of the runs is given in Table 3.12. As pointed out before, each run involves an automated procedur e with multiple EQ3/6 simulations for a large number of groundwater analyses.

Table 3.12. Input constraints for EQ3/6 species distribution runs conducted on groundwater analyses from the NWIS Database.

Run Input Constraints
0 Eh defined by goethite saturation and total dissolved iron distributed between Fe(II) and Fe(III). No correction for charge imbalance. 01 Eh defined by goethite saturation and total dissolved iron distributed between Fe(II) and Fe(III). Charge imbalance corrected through adjustment of HCO 3 - concentration. 1 Eh at the measured pH buffered by the pyrite/goethite redox couple. No correction for charge imbalance. 2 Eh at the measured pH buffered by the pyrite/goethite redox couple. Charge imbalance corrected through adjustment of HCO 3 - concentration. 3 Eh at the measured pH buffered by the pyrite/goethite redox couple. Charge imbalance corrected through adjustment of H + concentration. 4 Pb analyzed using PLM43 and PLM48 and Cd analyzed using PLM43 and PLM47. Eh defined by goethite saturation and total dissolved iron distributed between Fe(II) and Fe(III). No correction for char ge imbalance. 5 Pb analyzed using PLM43 and PLM48 and Cd analyzed using PLM43 and PLM47. Eh at the measured pH buffered by the pyrite/goethite re dox couple. No correction for charge imbalance.89 of 352 The rationale for conducting these different runs is as follows.

1. Considerable uncertainty atte nds the characteriza tion of the redox state of the analyzed groundwaters. It is surmised that most are anoxic and have equili brated under reducing conditions. However, a minor fraction of the sampled groundwaters may be oxic, although recognition that those waters stabili zed under such conditions can be made only indirectly through examinati on of their total chemistry.

2. Two alternative approaches ar e taken to better constrain the oxidation state: (a) The
oxidation state can be determined if either th e Fe(II) and Fe(III), or total Fe have been analyzed in solution, while it is assumed that the groundwater is saturated with respect to goethite. This assumption is reasonable, but goethite saturati on cannot be presumed in all cases. Fe(II)/Fe(III) buffering might be accomp lished through the presence of other ferric oxides or oxyhydroxides, which would modify the calculated Eh. Furthermore, acid stabilization of the groundwater for subseque ntly analyzing Fe(II) in solution must be made prior to exposure to air, to prevent oxidation and precipitat ion of Fe(III) (Singer and Stumm, 1968, 1970). Unfortunately, not all analyses from the NWIS database report either Fe(II) and Fe(III), or total Fe. Therefore, the entire dataset is not available for evaluation by this method. (b) The redox state is controlled by the pyrite/goethite redox couple in all cases. This constraint would obvi ously be inapplicable to oxic groundwaters. Furthermore, not all reducing groundwat ers are necessarily buffered by the pyrite/goethite couple. Other couples, fo r example pyrite/magnetite, pyrite/hematite, pyrite/siderite, pyrite/pyrrhotite and pyrite/ chlorite, are possible. See Section 3.4.5.1
below.

3. The thermodynamic analyses conducted usin g EQ3/6 are carried out assuming that homogeneous equilibrium with respect to redox conditions ha s been established in all cases. However, groundwaters of relati vely young age are known not to be in homogeneous equilibrium, as noted in Sect ion 3.4.3, above. As a working hypothesis, it is assumed that most of the groundwaters sampled are in homogeneous equilibrium.90 of 352 4. The partial oxidation of sulfides can lead to a metastable equilibrium state with respect to native sulfur. For example, the oxidation of galena proceeds according to the following
half-cell reaction:

PbS = S rhombohedral + Pb +2 + 2e under anoxic conditions where neither hydr ogen peroxide (Sato, 1992) nor bacterial populations are available to promote the oxidati on of sulfur to sulfat e. We assume that the metastable persistence of native sulf ur does not occur over the long time frames available for sulfide equilibration in groundwat ers, because natural radioactive decay of ubiquitous uranium will induce a low ambient H 2 O 2 flux to catalyze sulfur oxidation.

5. The majority of analyses display unacceptably large charge imbalances, i.e., > ± 10%. The probable cause is CO 2 loss followed by carbonate precip itation before analysis of alkalinity or bicarbonate c oncentration, but after pH meas urement. A secondary cause could be measurement of pH after analysis of alkalinity or bicar bonate concentration CO 2 degassing. Evaluations presented in Section 3.4.4 indi cate that these causes are operative. No simple strategy exists, however, for iden tifying and correcting for both causes on a case-by-case basis.

6. As noted in Sections 3.3.4 and 3.3.5, the an alyses of some hazardous inorganic constituents are suspect, becau se the analytical methods used are insufficiently sensitive
to detect the low concentrati ons that would equilibrate with host minerals under reducing conditions. Because it is not easily determin ed whether the groundwater was exposed to reducing conditions, it is generally not pos sible to state unequivocally whether a particular analysis is invalid. Thus, it wa s decided to accept all available hazardous inorganic constituent analyses for preliminary evaluation.

7. Given the uncertainties listed above, it was decided that by making a multiplicity of runs testing different hypothetical conditions, and examining the results, it would be possible to determine the most plausible thermodynamic controls affecting th e concentrations of the selected hazardous inorganic constituents . The runs are therefore exploratory in nature, and aimed towards narrowi ng the scope of the problem.91 of 352 The output histograms of the saturati on indices of selected minerals from the series of runs were examined closely. The output of Run 0 is compar able with that of Run 01, and likewise, the output of Run 1 is comparable with those of Ru ns 2 and 3, meaning that no significant reduction in statistical dispersion has been obtained through charge balancing. Instead, a bias is introduced through either an increase in the calculated value of P(CO 2 ) as a consequence of charge balancing on HCO3-, or through effects cause d by an upward shift in pH through charge balancing on pH. Because these biases could not be justified by independent lines of inquiry and statistical dispersion is not improved, and also because no differences in interpretation are obtained, we decided to omit further discussion of runs 01, 2 and 3. The following discussion of results is therefore restricted to Runs 0 and 1, where charge balancing is ignored. The findings from Runs 4 and 5, which also omit charge ba lancing, are included in Section 3.4.5, below, where the effect of redox potential on Cd, Pb and Zn aqueous concentrations is considered.

3.4.5.1 Discussion of Resu lts from Run 0

Histograms showing the calculated saturation indices for the minerals expected to control the selected hazardous inorganic constituents under reducing conditions (see Section 3.2), as defined by goethite saturation and the conc entration of iron in solution, are illustrated in Figures 3.25 through 3.33 below. However, because the redox potential is defined independently of other
minerals assumed to contain Fe in the reduced (II) state, we start a discussion of results by examining how many of the sampled groundwaters might actually have b een close to or at saturation with respect to such minerals. In Figure 3.23, we illust rate the histograms of saturation indices for pyrite, magnetite, siderite, and chlor ite. The pyrite SI histogram attains a maximum at SI ~ +5, with a broad plateau extending to SI ~ -20. This suggests th at some groundwaters are near or at saturation with respect to this minera l. Both the spread in values and the lengthy tail extending to SI ~ -70 are open to several interpretations. The pronounced skew towards negative values suggests a range of oxidatio n states in the subset of samples for which Fe analyses exist. The sharp cutoff at SI ~ +10 reflects a natural limitation in realistic aque ous concentrations of Fe, but also implies a considerable analytic al uncertainty associated with quantitative measurements for SI(pyrite). A nother explanation is that the assumed homogeneous equilibrium92 of 352 with respect to the SO 4 -2 /HS - couple is not applicab le to many of the groundwater samples, or that some or all of the aqueous Fe(II) oxidized and precipitated prior to analysis. Such questions should be investigated further. De spite the substantial unc ertainties associated with this analysis, it is nevertheless plausible to assume that many of the samples had equilibrated with pyrite, the uncertainties all suggesting a higher propor tion than is indicated in Figure 3.23a.

The spread in SI values for magnetite (Figure 3.23b) is much more restricted than for pyrite, although a negative skew is again observed. The frequency of SI values peaks at about zero, which is strongly suggestive that a magnetite/goe thite or magnetite/hematite buffer might also participate in controllin g the oxidation state of many of the groundwaters. In contrast, siderite SI values in Figure 3.23c peak at about -2, indicati ng that the presence of this mineral is less common, and that the likelihood that it could buffe r the oxidation state w ith either goethite or hematite is small. Note that this figure differs from that of Figure 3.16, which also displays a SI frequency histogram for siderite, because the latt er was determined assuming the coexistence of pyrite and goethite and a calculated activity of Fe +2

. Another possible redox buffer involves the coex istence of chlorite with goethite, hematite, magnetite, or possibly pyrite. The stability relati ons between chlorite and the cited iron-bearing minerals are complicated, because this mineral is a solid solution where Mg +2 substitutes for Fe +2 in the structure, whose end members are respecti vely identified by the mi nerals clinochlore and daphnite. The calculated SI for chlorite, where Mg +2 /(Mg +2 + Fe +2 ) = 0.5, indicates significant supersaturation with SI ~ 5, as shown in Fi gure 3.23d. However, the calculations assume saturation with respect to opal-CT, which would supersaturate the groundw ater with respect to quartz. Correction for silica saturation to qua rtz and normalization of the SI to unit Fe stoichiometry results in SI(chlor ite) ~ +1, which is reasonably close to saturation, given the potential uncertainties associated with the de rivation of the thermodynamic properties for this
mineral. It should be noted that the precipitation of Fe in pyrite us ually results in depletion of Fe in the chlorite, and shifting of the chlorite composition towards end-member clinochlore.93 of 352 (a) (b)

(c) (d)

Fig. 3.23. Frequency Distribution of Saturation Indices of (a) Pyrite, (b) Magnetite, (c) Siderite and (d) Chlorite. Saturation Indices are calculated assuming Eh to be defined by Fe(III)/Fe(II) in solution in the presence of goethite, using a selected set of groundwaters from the NWIS.

The calculated SI histogram for clinochlore, Fi gure 3.24, shows a somewhat similar profile to chlorite, but is shifted to a somewh at lower degree of supersaturation ( ≈ 3). Allowing for
corrections for SIO 2 (aq) and possible sympathetic errors in the thermodynamic properties of both clinochlore and chlorite (Appendix C), it is r easonable to suggest that clinochlore might94 of 352 preferentially coexist with pyrite when the latter occurs in the absence of ferric minerals in potable water aquifers. Further investigation of this association would be warranted.

Fig. 3.24. Frequency Distribution of Saturation Indices of Clinoc hlore. Clinochlore saturation is not significantly affected by variation in Eh.

We now follow with a discussion of the calcula ted SI frequency histog rams of minerals that might control the activities of th e hazardous inorganic constituents selected for evaluation. They are given in the alphabetical orde r of their chemical symbols, i. e., As, Ba, Cd, Hg, Pb, Sb, Se, U and Zn. Candidate minerals have been pr eviously identified in Section 3.2.

Arsenic. Figures 3.25a, b, and c show the histogram s of saturation indices for arsenopyrite (FeAsS), scorodite (FeASO 4 .2H 2 O), and symplesite (Fe(II) 3 (AsO 4 ) 2 ·8H 2 O), respectively. The histogram for arsenopyrite Figure 3.2 5a displays a bimodal distribu tion. The smaller peak on the right hand side has a broad maximum at SI ~ 0, suggesting arsenopyrite solid solution in pyrite,
as is often observed in the field. The broad dist ribution of values about SI(max) ~ -50 relates to conditions where As(V) species predominate unde r oxidation states t oo high to support the presence of pyrite. The scorodite and symplesite histograms, in contrast, show no evidence that95 of 352 any groundwaters attain saturation w ith respect to either mineral. This finding is in conflict with the postulated existence of a scorodite-like pha se in groundwaters subject to oxidation, as suggested by Thornberg and Sahai (2004). Q a

(a) (b)

(c)

Fig. 3.25. Frequency Distribution of Saturation Indices of (a) Arsenopyrite, (b) Scorodite, and (c) Symplesite. The Saturation Indices are calculated using a se lected set of groundwat ers from the NWIS and assuming Eh to be defined by Fe(III)/Fe(II) in solution.96 of 352 Barium. Figures 3.26a and b display SI histograms for barite (BaSO 4 ) and witherite (BaCO 3 ).
These two minerals are isostructural with the analogous Pb minerals, anglesite (PbSO 4 ) and cerussite (PbCO 3 ), which are compared separately, below. Because barium is present only in one oxidation state in natural groundwaters, solubility control is essentially i ndependent of Eh, unless all sulfate in the system is reduced to sulfide. Such conditions can arise in coal beds where Ba is
observed associated with silicates or phosphates, e.g., crandallite ((Ca, Ba)Al 3 (PO 4 ) 2 (OH) 5 ·H 2 O) (Finkelman, 1981; Brownfield et al., 2005), or dur ing hydrolysis by diabase, when the presence
of witherite is favored. From Fi gure 3.26a, it is evident that bari um solubility is controlled by the presence of barite where SI(max) ~ 0, rather th an by witherite, which is undersaturated (SI(max)

≈ -3).

(a) (b)

Fig. 3.26. Frequency Distribution of Saturation Indices of (a) Ba rite and (b) Witherite. The Saturation Indices are calculated for a selected set of groundwaters from the NWIS. Eh is defined by Fe(III)/Fe(II) in solution, which generally does not significantly a ffect either barite or witherite saturation.97 of 352 Cadmium. A broad distribution of SI’s for CdS (cadmi um sulfide, or greenockite) is illustrated in Figure 3.27a. The peak reflects (a) the dispersi on of analytical values in the calculation of SI(CdS), (b) false positive readings due to the a pplication of insufficiently sensitive methods, (c) uncertainty in the calcula ted solubility product of CdS, and fi nally (d) uncertainties associated with the identities and thermodynamic properties of Cd sulfide and selenide complexes in solution in the EQ3/6 distribution. The sharp peak distribution at SI ~ 0 st rongly suggests that at least some of the samples are saturated with respect to greenockite. However, as noted in Section 3.4.5.2 below, this apparent saturation can be an artifact resulting from the omission of selenide complexing due to the absence of Se analyses in some of the groundwaters for which Cd analyses have been made. Therefore, the possibility that CdS exists only in solid solution with sphalerite cannot be excluded on th e basis of the present analysis.

Figure 3.27b illustrates the calcula ted SI distribution for cadmoselite (CdSe), an example where two separate trace hazardous constituents are e ssential components of the same mineral. The
distribution is much narrower fo r the selenide than for the co rresponding sulfide, because the Se(II) state is stable over a wider range of oxidation potentials defi ned in this case by Fe(III)/Fe(II). However, it is interesting to note that SI(max) ~ 0, suggesting that this mineral could actually control Cd(II) activity in the majority of potable groundwaters.

The SI histogram for otavite (CdCO 3 ) is given in Figure 3.27c. Otavite is generally believed to be the most stable phase containing Cd as an essential component under oxidizing conditions (Carroll et al., 1998; McGrath et al., 2007). The histogram indica tes that almost all groundwaters analyzed for Cd, and passing the screen for R un 0, are undersaturated with respect to this
mineral.98 of 352 (a) (b)

(c)

Fig. 3.27. Frequency Distribution of Saturation Indices of (a) CdS (Greenockite), (b) Cadmoselite and (c) Otavite. The Saturation Indices are calculated using a selected set of groundwa ters from the NWIS. Eh is defined by Fe(III)/Fe(II) in solution, which gene rally does not significantly affect otavite saturation.99 of 352 Mercury. The distribution of SI’s for cinnabar, tiemannite (HgSe), a nd quicksilver are illustrated in Figure 3.28a–c. The distribution for cinnabar is similar to that of CdS, but a significant fraction is supersaturated in t hose groundwater samples that were analyzed for Hg, with SI(max) being achieved at SI ~ +1. The SI distribution for tiemannite is ill defined, because of the limited number of analyses conducted for both Hg and Se. However, most of the available determinations fall between SI = 0 and –1, suggesti ng that this phase might form, especially in
Se rich environments.

In conformity with observations for CdS regard ing interpretations conc erning the saturation of this phase in groundwaters, sim ilar arguments can be presented for caution in accepting the corresponding findings for cinnabar and tiemann ite. The peaks reflect (a) the dispersion of analytical values in the calculation of SI for ci nnabar and tiemannite, (b) false positive readings caused by the application of insufficiently sens itive methods, (c) uncertain ty in the calculated solubility products of ci nnabar and tiemannite, and finally (d) uncertainty regarding the identities and thermodynamic properties of Hg sulfide and selenide complexes in solution in the EQ3/6 distribution (see Appendices D and E).

The findings presented here are in consistent with field observati ons by Benoit et al (2003) on shallow organic-rich sediments, and by Huer ta-Diaz and Morse (1992) on shallow marine sediments, who show, respectively, that Hg activity is unsaturate d with respect to cinnabar, and that Hg is preferentially incorporated in precipi tating pyrite, rather than forming a discrete solid phase where Hg is an essential component. Howe ver, both field environments reflect transient authigenic conditions where metastable phases are likely controlling the aqueous solubility of Hg, and where long-term evolution of the system, as postulated by the Ostwald Rule of stages, will lead to recrystallization of pyrite and expulsion of Hg from the lattice involving the formation of metastable phases. Both cinnabar and tiemannite have been observed in coal (Tewalt et al., 2001; Brownfield et al., 2005; Kolker et al., 2006), and it is therefore probable that, given sufficient time, the natural environment evolves and saturation is achieved with respect to either or both minerals in shallow reducing groundwaters.100 of 352 Also included in Figure 3.28 is the SI histog ram for quicksilver, which shows a bimodal distribution. The secondary peak at SI ~ -1 probably reflects the true saturation state of quicksilver under reducing conditions. The supersat uration where SI(max) ~ +9 is indicative of artifacts caused by an erroneously elevated oxi dation state calculated from the Fe(III)/Fe(II) ratio. Comparison should be made with the da ta provided by Run 1, in Section 3.4.5.2 below.

(a) (b)

(c)
Fig. 3.28. Frequency Distribution of the Saturation Index of (a) Cinnabar, (b) Tiemannite, and (c) Quicksilver. The Saturation Indices are calculated using a se lected set of groundwat ers from the NWIS and assuming Eh to be defined by Fe(III)/Fe(II) in solution.101 of 352 Lead. The thermodynamic control of Pb by galena (PbS) was assumed in a recent study to evaluate the impact of elevated P(CO 2 ) on hazardous inorganic constituent concentrations in groundwaters (Wang and Jaffe, 2005), and provide d a motivation for the current project. Therefore, the general behavior of Pb in groundw ater, and of galena in particular, deserves special attention. Figures 3.29a through 3.29f illust rate the SI distributions for galena and clausthalite (PbSe), eith er of which might control the Pb ac tivity in reducing groundwaters, and for five other minerals that might potentially co ntrol the Pb activity in oxic aquifers: cerussite (PbCO 3 ), hydrocerussite (Pb 3 (CO 3 ) 2 (OH) 2 ), plumbonacrite (Pb 10 (CO 3 ) 6 O(OH) 6 ), anglesite (PbSO 4 ) and mimetite (Pb 5 (AsO 4 ) 3 Cl). Another relative ly insoluble Pb mineral, pyromorphite (Pb 5 (PO 4 ) 3 Cl), the phosphate analogue of mimetite, was not examined, because no special effort was made to acquire and evaluate phosphate analyses in the NWIS groundwater quality data.

The distribution of galena SI’s suggests that a significant fr action of the groundwaters is saturated with respect to this mineral, as a cl early defined maximum fre quency in SI is observed at SI ~ 0 in Figure 3.29a. In common with cadmoseli te, clausthalite displays a tight dispersion in SI with SI(max) ~ 0. Also, as noted above, the stability fiel d for Se(II) extends to higher oxidation states at a given pH than does S(II), thereby restricting the broader distribution shown by galena. The SI distribution pr ofile for clausthalite strongly suggests th at this phase may coexist with or supplant galena in potable water aquifers. The dominance of clausthalite over galena in Appalachian coal deposits is suggestive that this might be the case.

The findings regarding Pb carbonate minerals, and anglesite and mimetite, indicate that, with the exception of a very small fraction of groundwater s saturated with respect to cerussite, these
minerals all show moderate to substantial degrees of undersaturati on, and appear to be implausible candidates for control of Pb activity in all groundwaters studied. However, either cerussite or hydrocerussite satu ration would be expected, depe nding on pH, if galena in an aquifer were to be oxidized. Taken together, th ese observations suggest that most groundwaters are sufficiently reducing to stabilize galena.102 of 352 (a) (b)

(c) (d)

Fig. 3.29. (Part 1)103 of 352 (e) (f)

(g)

Fig. 3.29. Frequency Distribution of Saturation Indices of (a) Galena, (b) Claustha lite, (c) Cerussite, (d) Hydrocerussite (e) Plumbonacrite (f) Anglesite and (g ) Mimetite in a selected set of groundwaters from the NWIS. The Saturation Indices are calculated assuming Eh to be defined by Fe(III)/Fe(II) in solution. Cerussite and anglesite saturations are not significantly affected by variations in Eh.

Finally, it should be mentioned th at a significant array of natu rally occurring, but obscure Pb silicates formed mainly under epithermal conditi ons have been described in the literature. Nothing is known of their solubilities or othe r thermodynamic properties, and thus it is104 of 352 impossible to state that any one of these minerals might also c ontrol Pb activity in groundwaters,
where oxidation has destabilized e ither galena or clausthalite. Although remote, the possibility exists that one of these minerals, as with he mimorphite controlling Zn, might likewise control
Pb.

Antimony. Histograms of the saturation indices of selected antimony minerals, which might control the activity of Sb in potable groundwaters, are presen ted in Figure 3.30a-e. Gudmundite (FeSbS) is the antimony analogue of arsenopyrite and, like its As counterpart, is believed to enter in solid solution in pyrite. Gudmundite, berthierite (FeSb 2 S 4 ), and stibnite (Sb 2 S 3 ) are antimony minerals found in epithermal ore deposits of an timony, the latter being the primary and most abundant antimony ore mineral. Antimonselite (Sb 2 Se 3 ), the selenium analogue of stibnite, is rarely observed in nature, but may control Sb activity in potable water aquifers. Kermesite (Sb 2 S 2 O), or red antimony, is commonly found as a supergene alteration product in antimony ores, but occasionally occurs as a primar y mineral (Williams-Jones and Normand, 1997).

Interpretation of Sb activity control by candida te minerals is complicated by uncertainties regarding the validity of some of the Sb analyses in groundwaters, by the sparsity of data, and by uncertainties regarding the valid ity of the oxidation state defi ned by Fe(III)/Fe(II) where Fe analyses are of uncertain quality. From the distribu tion of measured concentr ations illustrated by Figure 3.6f, we might infer that meaningful con centrations of Sb in NWIS groundwaters fall between –5 and -3 (in Log mg/L), and that concentr ations above this range are artifacts resulting from the application of insufficiently sensitive an alytical methods. However, as noted elsewhere, carte blanche rejection of questionable analyses cannot be justified through speculation alone,
and therefore all available Sb measurements are used in the present analysis. In the following discussion, we recognize the limitations imposed by both the analytical methods and the sparsity
of measurements.105 of 352 (a) (b)

(c) (d)

Fig. 3.30 . (Part 1)106 of 352 (e)

Fig. 3.30. Frequency Distribution of Saturation Indices of (a) Gudmundite, (b) Berthierite, (c) Stibnite, (d) Antimonselite and (e) Kermesite. The Saturation Indices are calculated using a selected set of groundwaters from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution.

Figures 3.30a, b, c, and d, showing the SI histog rams for gudmundite, berthierite, stibnite, and antimonselite respectively, all suggest that Sb activity in groundwater s is unlikely to be controlled by their presence as discrete phases, si nce the majority of SI’s suggest moderate to extreme undersaturation. However, for all four mi nerals, local maxima are observed at SI ~ 0. Under rare situations where strongly reducing cond itions prevail, one of these minerals could be present, coexisting with pyrite or another iron sulfide phase at a given pH (Williams-Jones and Normand, 1997). Further analysis to substantiate this conjecture is presently beyond the scope of this study. In contrast to the antimony minerals referred to above , a significant population of SI’s for kermesite is in positive territory. It is therefore probable that this mineral controls Sb activity in potable groundwaters.107 of 352 Selenium. Selenium ranks as particularly important, not only as a hazardous constituent in its own right, but because of its potential as Se( -II) to complex strongly with other hazardous constituents in solution and to form extremely insoluble selenides. In Figure 3.31, we assemble SI histograms of all potential activity controlling selenides under consideration, i.e., ferroselite (FeSe 2 ), cadmoselite, tiemannite, clausthalite, and antimonselite.

The SI histogram for ferroselite displays a bimoda l distribution with SI(max) at SI ~ -17 and –3.
A similar distribution is also displayed in Run 1 (Figure 3. 40), where SI(max) ~ -16 and –7.5. The less negative peak probably represents Se substitution for S in pyr ite and indicates that insufficient Se is present in most potable water aquifer host rocks for saturation with respect to ferroselite. Spectroscopic studies have revealed the presence of dzharkenite, a ferroselite polymorph coexisting with selenian pyrite in shales associated with Western phosphate deposits (Ryser et al., 2005). However the average concen tration of Se in these shales approaches 300 mg/kg, which is over three orders of magnitude greater than is believed to occur in most domestic potable water aquifer hos t rocks (see Section 3.6). An explanation for the peak centered about SI ~ -17 is not presently forthcoming.

Cadmoselite, tiemannite and claustha lite all appear to be at or cl ose to saturation, suggesting that these selenides could co-exist with or supplant the corresponding su lfide, provided that the Gibbs Phase Rule is obeyed at equilibrium. The very limited SI data available for antimonselite suggests that this mineral, like it s sulfide analogue, stibnite, is unlikely to be present and control Sb activity in NWIS potable groundwaters. Howeve r, it is interesting to speculate whether Se analogues of kermesite with formulas such as Sb 2 SSeO or Sb 2 Se 2 O, although never observed or described, might be present in small quantities. Fu rther study to define their stability fields as functions of Sb and Se abundance and pH and Eh is merited.108 of 352 (a) (b)

(c) (d)

Fig. 3.31. (Part 1)109 of 352 (e)

Fig. 3.31. Frequency Distribution of Saturation Indices of (a ) Ferroselite, (b) Cadmoselite, (c) Tiemannite, (d) Clausthalite, and (e) Antimonselite. The Saturation Indices are calculated using a selected set of groundwaters from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution.

Uranium. Figure 3.32 gives SI histograms for uraninite (UO 2 ), coffinite (USiO 4 ) and a typical and fairly common oxidation product of uraninite, uranophane (Ca(UO 2 ) 2 SiO 3 (OH) 2 .5H 2 O). A relatively sharp peak in the fre quency histogram for uraninite (UO 2 ) is observed at SI ~ +1. A
similar result is obtained for coffinite at SI ~ +1.5. Bearing in mind that SiO 2 (aq) had been set to be in equilibrium with respect to opal C-T, and that the SiO 2 (aq) activity for this silica polymorph is about 5 times higher than for the most stable polymorph, quartz, these results suggest that U activity in solution is probably c ontrolled by either uraninite, coffinite, or both. The reason for the observed modest degree of supers aturation is not known, and further review to investigate the cause is warranted. This finding is consistent with field observations of sediment hosted uranium deposits, where the co-existence of the two minerals is not uncommon (AbdelGaward and Kerr 1961; Drew Ilger et al., 1987). Furthermore, th e finding is strongly indicative that most groundwaters are su fficiently reducing to stabili ze one or both minerals. The histograms of uraninite and coffi nite can be compared with th at for uranophane, where U is present in the (VI) instead of the (IV) state. It is obvious that all groundwaters examined are110 of 352 unsaturated with respect to this mineral, further substantiating the conclusion that the groundwaters are sufficiently reducing to stabilize U +4 instead of UO 2 +2

.

(a) (b)

(c)

Fig. 3.32. Frequency Distribution of the Saturation Index of (a) Uraninite, (b) Coffinite and (c) Uranophane. The Saturation Indices are calculated us ing a selected set of groundwat ers from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution.111 of 352 Zinc. Figures 3.33a, b, c, and d provide the SI hi stograms for sphalerite (ZnS), smithsonite (ZnCO 3 ), hydrozincite (Zn 5 (CO 3 ) 2 (OH) 6 ) and hemimorphite (Zn 4 Si 2 O 7 (OH) 2 .H 2 O).

(a) (b)

(c) (d)

Fig. 3.33. Frequency Distribution of the Saturation Indices of (a) Sphalerite, (b) Smithsonite, (c) Hydrozincite and (d) Hemimorphite. The Saturatio n Indices are calculated , using a selected set of groundwaters from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution.112 of 352 The histogram for sphalerite shows a maximum at SI ~ +2. This observation suggests that the Zn concentration in reducing groundwaters could be controlled by sphalerite. The reason for the positive SI deviation from zero is not clear. Interpretation is further complicated by the observation that the SI frequency for hemimorphite, SI ~ +2. The SiO 2 (aq) activity in the analysis is defined by opal-CT, which is approx imately 0.7 log units higher than that for quartz saturation. For groundwaters satura ted with respect to quartz, the saturation index would be approximately zero. This suggests that control of Zn activity in groundwaters may be determined by either sphalerite and/or hemimorphite. Contro l of Zn activity by smithsonite or hydrozincite appears to be unlikely in all but significantly alkaline groundwaters.

3.4.5.2 Discussion of Results from Run 1

A spread of Eh values is observed in Run 0 above, depending on pH and the availability of Fe(II), Fe(II) plus Fe(III), or Fe(total) analys es, together with the assumption of goethite saturation in all analyses. Run 1, in contrast , defines the Eh at a given pH through the coexistence of goethite and pyrite, which constitutes an Eh buffer. It is thus assumed that all groundwaters are reducing, even though it is probabl e that a minor fraction might actually be oxidizing. This concession to expediency in the absence of feasible methods for characterizing the oxidation state of groundwaters other than through the method adopted for Run 0 should be borne in mind when considering the follo wing analysis of thermodynamic controls.

Arsenic . SI histograms for arsenopyrite, scorodite, a nd symplesite are given in Figures 3.34a, b and c. They differ substantially from those ge nerated under Run 0, because Eh is now tightly constrained, and the variability induced by transitions in oxidation state from As(III) to As(V) is no longer a factor. The SI profile for arsenopyrite peaks at SI ~ -3.5, sugge sting that As activity is controlled primarily by arsenopyrite in solid solution in pyrite (ars enian pyrite). Neither scorodite nor symplesite could be present in the aquifers from which the sampled population of potable groundwaters are taken, sinc e both are strongly undersaturated.113 of 352 (a) (b)

(c)

Fig. 3.34. Frequency Distribution of Saturation Indices of (a) Arsenopyrite, (b) Scorodite, and (c) Symplesite. The Saturation Indices are calculated using a se lected set of groundwat ers from the NWIS, and assuming Eh to be defined by the coexistence of pyrite and goethite.

Barium. The histogram for barite, Figure 3.35, is si milar to that of Figure 3.26a. The profile clearly shows a negative skew rather than being symmetrical. The latter would be expected if all groundwaters were saturated with respect to barite, thus suggesting a bias towards undersaturation in the populati on of sampled groundwaters. Pres entation of the corresponding114 of 352 histogram for witherite is omitted, since it would be essentially identical to that displayed under
barium in Run 0.

Fig. 3.35. Frequency Distribution of the Saturation Index of Barite. The Saturation Indices are calculated for a selected set of groundwaters from the NWIS. Eh is defined by the coexistence of pyrite and goethite, but does not significantly affect barite saturation.

Cadmium. The CdS SI histogram, Figure 3.36a, displays a trimodal distributi on with SI maxima at ~ -3, -0.5, and +1. This complex distribution is probably in part an artifact caused by the bimodal distribution of analyzed Cd concentrat ions (Figure 3.6d). The analysis conducted in Appendix B2 suggests that SI(max) ~ -0.5 is more credible, and is supported by further selection and evaluation of only those ground water analyses conducte d using the most sensitive analytical methods under Runs 4 and 5 (see Section 3.4.6, be low). The cause of the peak where SI(max) ~ -3 might be explained by the presence of co mpetitive selenide complexing depressing the activity of Cd +2 in those groundwater samples also reportin g Se. If this supposition is true, then saturation with respect to CdS is not achieved, and the Cd +2 activity reflects a solid solution in sphalerite and/or galena. However, the possibility remains that greenockite could be saturated at potable water aquifer temperatures, because solid solution substitution in sphalerite and/or galena would decrease with falling temperature. Furthe r examination of the water quality analyses, and evaluation of the temperature-dependent behavior of Cd substitution in sphalerite and/or galena,115 of 352 are needed to resolve this matter. The SI hist ogram for cadmoselite in Figure 3.36b is relatively well defined, with SI(max) at ~ 0, although a small scattering of SI values occur between SI = -4 and –1. Saturation suggests that Cd +2 activity may actually be cont rolled by the presence of cadmoselite. The findings are consistent with those of Run 0 in Figure 3.27b.

(a) (b)

Fig. 3.36. Frequency Distribution of Saturation Indices of (a) CdS (Greenockite) and (b) Cadmoselite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS.

Mercury . Figures 3.37a-c show the SI histograms for cinnabar, tiem annite and quicksilver. Peak frequencies in SI for all three minerals are ge nerally consistent with the findings of Run 0 (Figure 3.28). The supersaturation revealed in the histogram for cinnabar, where SI ~ 1.0, indicates its likely presence in aquifers, whereas tiemannite coul d also be present occasionally, as SI(max) ~ -0.5, and therefore close to saturation. The observe d saturation with respect to quicksilver (SI(max) ~ 0) is inconsistent with the reducing conditions imposed by the goethite/pyrite buffer. Taken together, the supers aturation with respect to cinnabar and saturation with respect to quicksilver suggest an error in calculated Hg +2 activity by approximately one log unit. Several alternative explanations are possible, and further refinement of the analysis in this report is needed. However, uncer tainties associated w ith the thermodynamic parameters used in the analysis (see discussion under mercury in the preceding Section 3.4.5.1) preclude drawing a116 of 352 definitive conclusion at this time. If a systematic error in Hg +2 activity were confirmed, then SI (tiemannite) would fall to ~ -1.5, and would be un likely to be present in other than Se-rich
environments.

(a) (b)

(c)

Fig. 3.37. Frequency Distribution of the Saturation Index of (a) Cinnabar, (b) Tiemannite and (c) Quicksilver. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS.117 of 352 Lead. SI histograms for galena, clau sthalite and cerussite are presented in Figures 3.38a, b, and c. The histogram for galena is represented by a ragged single peak with a maximum at about SI ~ +1.0. In contrast to the corresponding histogram for Run 0 (Figure 3.29a), the dispersion is far smaller owing to the uniform treatment whereby all analyses are saturated with respect to goethite and pyrite. The maximum frequency for clau sthalite is at SI ~ -0.5, which compares well with Figure 3.29b from Run 0. Both analyses support th e belief that either galena, clausthalite, or
both could control the Pb +2 activity in potable groundwaters.

(a) (b)

(c) Fig. 3.38. Frequency Distribution of Saturatio n Indices of (a) Galena , (b) Clausthalite and (c) Cerussite. Eh is defined by the coexistence of pyrite and goethite, bu t does not significantly affect cerussite saturation.118 of 352 The SI histogram for cerussite i ndicates that this mineral does not control Pb activity in most groundwaters under reducing conditions. However, the small sec ondary peak at SI ~ 0 suggests that a small percentage, ~ 5%, of potable groundw aters could be saturated with respect to this mineral. It should be noted that, were this to be the case, the groundwat er would be oxidizing, and the assumption of goethite/pyrite e quilibrium would no l onger be applicable.

Antimony . SI histograms for gudmundite, stibnite, an timonselite and kermesite are presented in Figures 3.39a, b, c, and d. The very sparse da taset represented in the corresponding Figures 3.30a, b, c, and e is augmented in Run 1, because a significant number of Sb analyses were eliminated in Run 0, due to the absence of an alyses for Fe in the same groundwater sample. Nevertheless, the results are largely compar able and the conclusions reached under the discussion for antimony in Section 3.4.5.1 are genera lly applicable. The bimodal distributions, especially evident with respect to stibnite and kermesite, result from the bimodal distribution in
Sb concentration observable in Figure 3.6f. As discussed elsewh ere in this report (Section 3.3.5, Appendix B2), the more positive peaks in SI are artifacts caused by the application of insufficiently sensitive analytical methods, and th erefore reference should be made to the more negative peak. The histogram fo r gudmundite, Figure 3.39a, is mo re clearly defined than its counterpart, Figure 3.30a, due to th e constraints imposed on Eh by th e pyrite/goethite couple. All SI values are negative and peak at SI(max) ~ -5. The result is consistent with the likely solid solution of the gudmundite component in pyrite, in analogy with th e corresponding arsenopyrite
solid solution. The stibnite and antimonselit e histogram shows SI(max) ~ -13 and -20, respectively, indicative that neithe r mineral is likely to be presen t in potable water aquifers. The peak value in SI frequency for kermesite occurs at SI ~ -0.5, which again suggests its potential occurrence as a mineral controlling the activity of Sb(III) in solution, especially in aquifers deficient in pyrite.119 of 352 (a) (b)

(c) (d)

Fig. 3.39. Frequency Distribution of Saturation Indices of (a ) Gudmundite, (b) Stibnite, (c) Antimonselite and (d) Kermesite. The Saturation Indices are calculated assu ming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS.

Selenium. SI histograms for all selenium minerals are again assembled in Figures 3.40. The ferroselite SI histogram gives a bimodal distri bution, potentially attribut ed to the omission of analyses of species in solution that complex sele nium, i.e., Cd, Hg, Pb, Zn, thus giving rise to120 of 352 elevated HSe - activities in the groundwaters. However, this artifact requires further confirmation. The profile at SI(max) ~ -15 proba bly represents the true state of saturation with respect to ferroselite in the evaluated set of NWIS ground waters. With the exception of antimonselite, the remaining minerals all appear to be close to or at saturation, and therefore could be effective in controlling SE(II) activity in potable groundwaters.

(a) (b)

(c) (d) Fig. 3.40 Part 1121 of 352 (e)

Fig. 3.40. Frequency Distribution of Saturation Indices of (a ) Ferroselite, (b) Cadmoselite, (c) Tiemannite, (d) Clausthalite and (e) Antimonselite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of groun dwaters from the NWIS.

Uranium . Results for uranium in Figure 3.41 are simila r to those of Run 0. Uraninite shows SI ~ +1, whereas coffinite shows SI ~ +1.5. In correcting for SiO 2 (aq) from saturation with respect to opal-CT and quartz, coffinite SI would be approximately +0.7, which suggests that the occurrence frequency of coffinite would be comparable to that of uraninite, consistent with field observations of sediment-hosted uranium deposits (Abdel-Gaward and Kerr, 1961; Drew Ilger et al., 1987).

Zinc. The single peak in SI in the histogram for sphalerite, Figure 3.42, is consistent with the conservative assumptions of Run 1, leading to SI ~ +1, which is comparable with that given by Run 0. The negative skew sugge sts that not all groundwaters ar e saturated with respect to sphalerite. The histogram for hemi morphite is also given, even t hough this mineral is more likely to be present under more oxidizi ng conditions where pyrite is no longer stable. The peak SI frequency occurs at SI ~ -2, s uggesting that hemimorphite is le ss likely to be found coexisting with sphalerite and pyrite, even though the re lative stabilities for a ll three minerals are comparable.122 of 352 (a) (b)

Fig. 3.41. Frequency Distribution of the Saturation Index of Uraninite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS.

(a) (b)

Fig. 3.42. Frequency Distribution of the Saturation Index of (a) Sphalerite, and (b) Hemimorphite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS.123 of 352 

### 3.4.6 Variation of Lead, Cadmium, and Zinc Concentration with Redox
Potential

A wealth of information has been presented in the previous ections (3.4.5.1 and 3.4.5.2), and apparent conflicts between the alternative distribu tions of SI’s for the same mineral have been observed, as different specified equilibrium cond itions are compared, especially in relation to defining the redox potential, or Eh, of the system. As is noted at the outse t of this section, the purpose of conducting alternative runs is to gain insight into the chemical controls affecting the collective set of all grou ndwaters selected for geochemical analys is in the face of uncertainties in the quality of the analyses, uncertainties in the id entities of aqueous speci es and minerals that may control the activities of hazardous inorganic constituents in solution, uncertainties in the quality of thermodynamic data used to calculate the mineral solubility products and aqueous species dissociation constants in the EQ3/6 Data0.dat database, and uncertainties defining the redox potential of the groundwaters when in situ . Despite the uncertainties, the results generally show a consistent pattern, allowing the investigator to use expert judgment in reaching a number
of reasonable conclusions.

The principal finding drawn from a comparison of th e results obtained in Runs 0 and 1 is that the majority of groundwaters are re ducing, and that the assumption that they are buffered by the pyrite/goethite couple is proba bly a good approximation, even t hough more refined evaluations considering other natural redox buffe rs are justified in future stud ies. The large variations in redox state generated through the assumption of goeth ite equilibrium with re spect to total Fe in solution causes substantial and unrealistic scatter in data relating to certain sulfide minerals, especially pyrite, galena, kermesite, and CdS. Furthermore, a comparison between cinnabar and quicksilver saturation profiles suggests that the unrealistically high levels of quicksilver saturation are the result of treating a solution in equilibrium with cinnabar, and containing abundant Hg sulfide complexes in solution, that are not taken into account because an erroneously elevated redox potential has been specified. Likewise, the extreme degrees of undersaturation of arsenates, as well as the lo w concentrations of Cd, Pb, and U, and their undersaturation with respect to alteration products under mildly reducing conditions—all suggest124 of 352 that the groundwaters were reduc ing and that the Fe analyses in these groundwaters were erroneously low. This is hardly surprising, give n that neutral reduced-s tate groundwaters, if exposed to air, will immediately lead to oxida tion and precipitation of any Fe(II) present. Thus, we believe that the results presented for Run 1 are more credible and can be relied upon with greater confidence than those of Run 0. However, th is assertion is made with the caveat that a small proportion of the groundwat ers with an imposed pyrite/goe thite redox buffer may in fact have been drawn from an aquifer wher e oxidizing conditions actually prevailed.

Some of the issues involved in variations between reducing and oxidizing conditions are discussed with respect to Pb, cadmium, and zinc in the following paragraphs.

Lead

. Figure 3.43 illustrates some of the contro ls that determine Pb concentration in groundwaters. The solubilitie s of galena and cerussite in a re presentative groundwater with a pH = 7.6 (a pH typical of the groundw aters studied), and slightly s upersaturated with respect to calcite, are calculated as a function of Eh, which is treated as an independent parameter. Coexisting values of As, Cd, Hg, Sb, Se, and Zn were set to plausible average values in NWIS groundwaters, as shown in Figure 3.6, recognizing ar tifacts due to applica tion of insufficiently sensitive analytical methods. Th e figure is a vertical section through an Eh–pH, or Pourbaix diagram, at a specified pH. Where Eh < -0. 23 V, the Pb concentration is defined by the coexistence of pyrite and galena . The Pb concentration remains essentially invariant, because most of the available S(VI) in solution is redu ced to S(-II). At Eh ~ -0.23 V, pyrite becomes unstable and decomposes to goethit e, at the invariant point wher e galena, goethite, and pyrite coexist. Between Eh ~ -0.23 and ~ -0.18 V, galena and goethite coexist, a nd the Pb concentration increases steeply until saturation is achieved w ith respect to cerussite, and galena becomes unstable. At Eh ~ -0.18 V, cerussite, galena and goethite coexist, wherea s at higher Eh, the Pb
concentration is defined by cerussite alone in the presence of goethite. Because no further redox reactions are involved in the system under consid eration, the Pb concentration thereafter remains essentially invariant with increasing Eh.125 of 352 Fig. 3.43. Calculated Pb Concentration in Groundwater as a F unction of Eh and saturated with respect to Galena and Cerussite in the Presence of Goethite and Pyrite . Cerussite = cer; galena = gal; goe = goethite; py = pyrite.

Also included in Figure 3.43 are the minimum detection limits for the analytical methods PLM43 and PLM48 at 0.0001and 0.00006 mg/L, respectively. Th ese two methods are currently the most sensitive used routinely in the measurement of Pb in groundwaters. Pb wa s detected in nearly 50% of the analyses using me thod PLM43 and in over 80% usi ng the more sensitive method
PLM48.

The solubility product of galena, K(gal), is defined by the dissolution reaction:

PbS + H + = Pb +2 + HS Values reported in the literature for logK(gal) vary over two orders of magnitude, from -12.78 to –14.85, the highest value being based on an experiment al determination of galena solubility, and the lowest value, currently specified in Data 0.dat of EQ3/6 (Appendices A and D), being based on thermochemical properties of galena. This larg e variation in reported va lues would have had a pronounced impact in determining whether the pr esence of Pb in solution could have been detected were galena to coexist with pyrite. However, detailed investigations summarized in126 of 352 Appendices D and E revealed the underlying cause for the discrepancies in LogK(gal), and allowed reconciliation of experimental determinations of galena solubility with those predicted using thermochemical determinations of Δ G f , P r , T

r 0 (galena). A major factor in achieving reconciliation came with the recognition that the saturated concentration of Pb in solution is determined not only by the presen ce of known monomeric complexe s, but also by the presence of previously unrecognized oligomeric sulfide and selenide complexes. These additional complexes contributed significantly to elevating the concentration of Pb to such an extent that it should be just detectable using the most sensit ive analytical methods. Furthermore, it became clear that Pb concentrations in equilibrium w ith galena depend not only on the ambient activity
of HS - , but also on that of HSe - , the latter being dependent on sa turation with respect to some other coexisting solid selenide mineral such as cadmoselite, clausthalite, or tiemannite, for example.

Figure 3.44 shows the calculated frequency of SI’s for galena in groundwaters analyzed for Pb by analytical methods PLM43 and PLM48, while assuming that logK (gal) = –14.85 (Appendix D, Table D2) and that Eh is specified eith er by (a) the Fe(III)/Fe(II) couple and goethite saturation, or (b) by the goethite/pyrite couple. The two representations illustrate, respectively, the minimum and maximum likely calculated freque ncies of galena saturation. Both histograms indicate that SI(max) ~ +0.5, s uggesting galena satura tion in the subset of NWIS groundwaters
examined. It should be noted th at all thermodynamic calculations are made assuming that the temperature of the groundwater is 25oC. In fact, most shallow groundwaters have temperatures closer to 10oC, and correction for temperature would result in a correction to logK(gal) of approximately –1.0. Given that the uncertainties asso ciated with the evaluation could be as high as ±1.0 in SI, overall agreement is excellent.127 of 352 (a) (b)

Fig. 3.44. Frequency Distribution of Saturation Indices of Galena in Groundwater Samples analyzed for Pb using PLM43 and PLM48. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite.

Because of the role that Se plays in comple xing Pb in solution, and the fact that the corresponding selenide analogue of galena might control the Pb concentration, SI histograms for clausthalite (Pb selenide) were al so calculated using the same analytical data for Pb as used in the calculation of the correspond ing galena histograms. These ar e illustrated in Figure 3.45. Both histograms indicate that SI(max) ~ -1. When comp ared with galena (Fig ure 3.44), these findings suggest that clausthalite is unde rsaturated in the suite of gr oundwaters examined. However, the
overall uncertainties in both th ermochemical data for claustha lite, estimates of sulfide and selenide Pb complexation constants, and othe r factors relating to the groundwater quality analyses preclude making such an assertion with confidence.

The frequency distribution of SI’s for pyrite in the selected groundwater samples used in the construction of Figures 3.44 and 3.45 is presented in Figure 3.46a (where Eh is again defined by Fe(III)/Fe(II) in solution in the presence of goethite). Only a small fraction of samples are saturated with respect to pyrite, and as noted above, it is not clear whether these results are meaningful. Figure 3.46b also shows that most of the groundwaters anal yzed for Pb by PLM43128 of 352 and PLM48 are saturated with respect to calc ite, consistent with the groundwater composition
used to calculate the Pb concentration in Figure 3.43.

(a) (b) Fig. 3.45. Frequency Distribution of Saturation Indices of Clau sthalite in Groundwater Samples analyzed for Pb using PLM43 and PLM48. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite.

(a) (b)

Fig. 3.46. Frequency Distribution of Saturation Indices of (a ) Pyrite and (b) Calcite in Groundwater Samples analyzed for Pb using PLM43 and PLM48. The Satura tion Indices are calculated assuming Eh to be defined by Fe(III)/Fe(II) in solution in the presence of goethite.129 of 352 Cadmium . Figure 3.47 illustrate s the variation in cadmium concentration in solution as a function of Eh for the same groundw ater, with pH = 7.6, as used in the analysis of Pb solubility in the preceding section. Below Eh ~ -0.22 V, Cd S (greenockite) coexists with pyrite. Between Eh ~ -0.22 and ~ -0.17 V, CdS coexists with go ethite. Above Eh ~ -0.17 V, CdS decomposes, and Cd concentration is controlled by otavite (CdCO 3 ), and is independent of Eh. It should be noted that the leveling off of Cd concentration at Eh < -0.22 V is due to the conversion of all available S(VI) in solution into S(-II) species, i.e., HS - and H 2 S(aq). The satu ration control of Cd in solution by otavite has been shown to occur under field conditions, as demonstrated by Carroll et al. (1998) and McGrath et al. (2007). White et al. (2004) have also shown that Cd is readily leached from soils by d ilute acid treatment, s uggesting the presence of Cd as a carbonate.
This is in contrast to the leac hing behavior of Zn, which suggests stabilization as a silicate (see below).

As noted in Appendix B2, most of the reported analyses for Cd in groundwaters appear to be artifacts, due to insufficient se nsitivity of past an alytical methods. The detection limit by the most sensitive analytical methods used routinel y in the analysis of Cd in groundwaters, PLM43 and PLM47, is 0.00004 mg/L, as included in Fi gure 3.47. Only 24% of the analyses using PLM43 and 17% of the analyses usin g PLM47 detected Cd in solution.

Independent determinations of greenockite solu bility logK(CdS), represented by the reaction

CdS + H + = Cd +2 + HS have been reported in the literature, as noted in Appendix D, and differ by nearly two orders of magnitude, i.e., logK(CDS) = - 14.36 and –15.91, respectively. The smaller value is currently specified in Data0.dat of EQ3/6. As noted above, the discrepancies were reconciled as described in Appendices D and E, and a LogK(CdS) was ch osen based on a thermochemical determination
of Δ G f , P r , T

r 0 (CdS). If CdS were to coexist with pyrite, Cd in solution would be about one order of magnitude above the detection limit of PLM43 and PLM47 at 0.00004 mg/L. In the absence of pyrite, Cd concentrations in equilibrium with CdS should be even more easily detectable as saturation with respect to otavite is approached. A few of the Cd an alyses fall within the range of130 of 352 otavite solubility, i.e., circ a Log Cd ~ -1.5 (mg/L), as s hown in Figure 3.6d, and probably represent conditions in ox idized aquifers, in anal ogy with cerussite saturation with respect to Pb (Figure 3.43). The calculated solubility is therefor e about one order of magn itude greater than the apparent marginal detectability of Cd in solution, but correction for lower ambient temperatures
than the 25oC reference and other factors c ould easily allow for reconciliation of this discrepancy.

Fig. 3.47. Calculated Cd Concentration in Groundwater as a Function of Eh and saturated with respect to CdS and Otavite in the Presence of eith er Pyrite or Goethite. CdS = CdS; goe = goethite; ota = otavite; py = pyrite.

Figure 3.48 illustrates the SI histograms for CdS wher e Eh is defined either by Fe(III)/Fe(II) in the presence of goethite (Figure 3.48a), or by the coexistence of goethite and pyrite (Figure 3.48b). Both figures show SI(max) ~ 0, indi cating saturation. However, Figure 3.48b also displays a bimodal distribution with a secondary peak at SI(max) ~ -3. An explanation for this secondary peak is not readily available. It cannot be caused by an insufficiently sensitive analytical method, because the histogram is ba sed on only those analyses for Cd using PLM43 and PLM47, both of which have the same detecti on limits. A more likely explanation is that the two peaks arise from the omission of Se analyses from those samples comprising the peak where131 of 352 SI(max) ~ 0. In this case, the lower peak, wher e selenide complexing is taken into account, would indicate that CdS must be a component of either sphalerite or ga lena in solid solution. However, this would imply that CdS saturati on would be about two log units higher than calculated in Figure 3.48, in which selenide complexation is accounted for.

SI frequency histograms for cadmoselite are give n in Figure 3.49. Both indicate SI(max) ~ 0, and the results are relatively unamb iguous. Furthermore, the population of analyses permitting calculation of SI(cadmoselite) in Figure 3.49b is approximately equi valent to that populating the secondary peak in Figure 3.48b. From this compar ison, one is drawn to th e tentative postulate that, with respect to the NWIS groundwaters st udied and analyzed for Cd using PLM43 and PLM47, CdS is unsaturated in solution, and th at the phase controlling Cd solubility is cadmoselite. Clearly, further review of the com position of the affected groundwater analyses, thermodynamic data refinement, and additional co rroboration is required before this finding can be accepted.

(a) (b)

Fig. 3.48. Frequency Distribution of Saturation Indices of CdS in Groundwater Samples analyzed for Cd using PLM43 and PLM47. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite.132 of 352 (a) (b)

Fig. 3.49. Frequency Distribution of Saturation Indices of Cadm oselite in Groundwater Samples analyzed for Cd using PLM43 and PLM47. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite.

Zinc

. It is instructive to compare the behavior of Zn in solution with that of Cd under similar conditions, since their sulfides are crystallographical ly similar, i.e., sphalerite is the structural analogue of hawleyite, and wurtzite is the analogue of greenockite (CdS). However, in contrast to Cd, the detection and quantif ication of Zn in groundwaters is readily achieved with current analytical methods (Figure 3.6i). For example, although the minimum detection limit using the most sensitive analytical method routinely av ailable for Zn, PLM43, is higher at 0.0006 mg/L compared to 0.00004 mg/L for Cd, Zn was detected in 94% of the samples analyzed, in contrast with only 24% for Cd. Furthermore, Figure 3. 6i does not show any obvious indications of analytical artifacts, so cl early suggested by the corre sponding Figure 3.6d for Cd.

Figure 3.50 shows the calculated Zn concentration as a function of Eh in the same groundwater as that for Cd in Figure 3.47. The coexistence of sphalerite with pyrite and goethite occurs at a Zn concentration of approximately 0.002 mg/L, wh ich is in contrast with the corresponding three-phase coexistence where Cd concentr ation is 0.0002 mg/L. Unde r oxidizing conditions, sphalerite is transformed to hemimorphite (Zn 4 Si 2 O 7 (OH) 2 .(H 2 O)) (McPhail et al., 2006). That a133 of 352 silicate such as hemimorphite is likely to immobilize Zn is su bstantiated by le aching studies on
soils distal to sulfide mineralization in Austra lia, where no more than 50% Zn is mobilized by acid leaching of the <65μm fraction, but Hf/aqua regia is required to solubilize the remainder (White et al., 2004). This is in contrast to C d, where almost all in the <65 μm fraction was mobilized by acid leaching alone. The solubility product for hemimorphite at 25oC was retrieved from preliminary data presented by McPhail et al. (2003), and utilized in the construction of Figure 3.50. The limitation of the minimum Zn con centration to approximately log Zn = -10 is
due also to the conversi on of all S(VI) to S(-II) species. The concentration of Zn in solution where goethite, sphalerite, and hemimorphite coexist is calculat ed to be 0.004 mg/L.

As noted in Appendix D, two independent determ inations of sphalerite solubility logK(sph), represented by the reaction (ZnS + H + = Zn +2 + HS - ) are in substantial agreement, i.e., logK(sph) = -11.44 and 11.46, respectively, the higher value be ing currently specifi ed in Data0.dat of EQ3/6. The value derived from thermochemical m easurements was used to calculate LogK(sph), and the potential participation of neutral oligomeric neutral sulf ide and selenide complexes was
taken into account as descri bed in Appendices D and E.
The SI frequency histogram for sphalerite in Figure 3.33a gives SI(max) = +1, where Eh is calculated on the basis of Fe(III)/Fe(II) ratio in a solution saturated with respect to goethite. Figure 3.42a shows a corresponding histogram gi ving SI(max) = +1, where Eh is calculated assuming a goethite/pyrite buffer. The maximum fr equency of Zn concentration is circa Log Zn = -1.5 (mg/L), which is about one log unit higher than the calculated concentr ations illustrated in Figure 3.50 for both sphalerite and hemimorphite , and is consistent with the SI frequency diagrams cited above. This discrepanc y suggests that either the predicted Δ G f , P r , T

r 0 (sph) and Log K(hemi) have imposed excessive stability on th ese minerals in soluti on, or, in the case of sphalerite, that the stabilities of the sulfide and selenide complexes are greater than predicted on the basis of the evaluations given in Appendices D and E. Because of the complexity of the system and uncertainties associated with the thermodynamic parameters used, together with significant variations in water quality, a more rigorous evaluation will be needed than that presented in this scoping study. Given the cumulative uncertainties inherent in this analysis, the134 of 352 close correspondence between predicted and observ ed degrees of saturation with respect to sphalerite and hemimorphite is very good.

Fig. 3.50. Calculated Zn Concentration in Groundwater as a fu nction of Eh and Saturated with respect to either Sphalerite or Hemimorphite in th e Presence of Goethite and Pyrite. goe = goethite; py = pyrite; sph = sphalerite; hemi = hemimorphite.

In conclusion, the theoretical calculations provi de a basis for explaining field observations and substantiate the proposition that sphalerite controls Zn con centrations in groundwaters under moderate to strongly reducing conditions, and hemimorphite unde r mildly reducing to oxidizing
conditions.



### 3.4.7 Summary of Thermodynamic Controls

Conclusions as to which minerals thermodynami cally control the activi ties of the selected hazardous constituents in NWIS potable groundwate rs in the United States are summarized in Table 3.13. Most groundwaters, at least 75% of all samples from the NWIS data base, are moderately to strongly reducing, with the lower bound to the redox state most likely buffered by pyrite, or other ferrous sulfide, and another mineral containing iron in either the Fe(II) or Fe(III)135 of 352 state. A small fraction of groundwat ers, estimated to be perhaps as low as 5 percent, may be mildly reducing or even oxidizing, as suggested by the behavior of certain hazardous constituents such as As, Cd and Pb that they might. Table 3.13 recognizes the distin ction between these two categories of groundwater, as define d broadly by their redox states.

The claims presented in Table 3.13 are generali zations that apply to potable groundwaters of average composition and pH. They are used to construct a geochemical model of general applicability for addressing problems related to groundwater contamination, as demonstrated in Section 4 of this report. Overall model uncertainty is difficult to estimate; rigorous site-specific
characterization would be necessa ry to further evaluate model uncertainty. Model uncertainties are evaluated in greater detail in the following Section 3.5. Th e selection of water quality data meeting more stringent criteria, a more critical review of thermodynamic data already used in geochemical model analysis, and incorporation of additional aqueous species relevant to the modeling effort could limit model un certainty in future studies.

Table 3.13. Summary of minerals identified as controlling the thermodynamic activity of selected hazardous constituents in potable groundwaters

Thermodynamic Control on Groundwater Activity Con- stituent Moderately - Strongly Reducing, i.e., Pyrite/Goethite Buffer Mildly Reducing - Oxidizing Comments

As (1) Arsenopyrite in solid solution in pyrite (arsenian pyrite) (2) Arsenopyrite Adsorption on host rock mineral surfaces, especially iron oxides, or through substitution for SO 4 -2 in anionic clays such as green rust No mineral in which As is an essential component, other than arsenopyrite, has been identified controlling As activity in solution in the groundwaters examined.

Ba Barite Barite Barium activity is controlled by barite alone, regardless of oxidation state. No other Ba minerals controlling Ba activity have been identified.136 of 352 Thermodynamic Control on Groundwater Activity Con- stituent Moderately - Strongly Reducing, i.e., Pyrite/Goethite Buffer Mildly Reducing - Oxidizing Comments

Cd (1) CdS (greenockite) substitution in either galena or sphalerite (2) Cadmoselite Otavite Further evaluation is required to resolve ambiguities. Greenockite could saturate at potable water aquifer temperatures, as solid solution substitution in sphalerite and/or galena decreases with falling temperature. The observed saturation with respect to cadmoselite suggests that this phase may control Cd +2 activity in the evaluated NWIS groundwaters for which Cd and Se analyses are available. Equilibrium concentrations in solution are so low, however, that current analytical methods are marginally effective in providing quantitative measurements. Under oxidizing conditions, cadmium activity is controlled by otavite. Otavite cannot be present in the majority of potable groundwaters analyzed for Cd in the NWIS database, because Cd concentrations would be easily detected by current state-of-the art analytical methods. This observation indirectly supports the conclusion that most groundwaters are reducing. Hg (1) HgS solid solution in pyrite (2) Cinnabar (3) Tiemannite Native Mercury

Pb (1) Galena (2) Clausthalite Cerussite A small fraction, ≈ 5%, of groundwater s is sufficiently oxidizing that Pb activity is defined with respect to cerussite saturation. This observation indirectly supports the conclusion that most gr oundwaters are reducing. Pb mineralogy under mildly reducing – oxidizing conditions is complex, and it is possible that secondary Pb silicates might also control Pb activity in groundwaters under these conditions. Sb (1) Gudmundite solid solution in pyrite. (2) Kermesite (red antimony) Kermesite (red antimony).

Se (1) Cadmoselite (2) Tiemannite (3) Clausthalite (4) Dzharkenite solid solution in pyrite None recognized Solid solution substitution of the dzharkenite (FeSe 2 ) component in pyrite, when present, could also be responsible for defining Se(-II) activity. No potential selenite of selenate minerals are recognized as activity controlling under mildly reducing - oxidizing conditions. U (1) Uraninite and/or (2) Coffinite None recognized Uranophane is strongly undersaturated in all cases when the redox state of the groundw ater was specified by the goethite-Fe(III)/Fe(II) Eh buffer at a given pH. Zn Sphalerite Hemimorphite137 of 352 

## 3.5 The Variation of Hazardous Cons tituent Concentrations with Partial Pressure of Carbon Dioxide in Groundwaters

3.5.1 Introduction

Given certain assumptions and approximations , changes in the groundwater equilibrium concentrations of hazardous inorganic constituents caused by changes in the partial pressure of
CO 2 can be calculated using a dist ribution-of-species code. In th is section, we present such calculations, performed with the distribution-of -species simulator EQ3/6, and discuss their significance and limitations. The equilibrium calcu lations reflect the direct calibration of the model to the pertinent field data for the hazardou s constituents and give preliminary estimates of the potential magnitude of the impact of CO 2 intrusion on groundwater quality. Further, the
results indicate which hazardous constituents are sensitive to CO 2 intrusion and which are not.

In the following subsections, we first discuss the inputs to the ther modynamic model (Section 3.5.2) and then present the base case results from the equilibrium calculations (Section 3.5.3). In Section 3.5.4, we evaluate the ov erall uncertainties associated w ith the model and the chemical analyses, and show the impact that such uncerta inties may have on th e results. Finally, we examine how aqueous complexation impacts the total concentrations of each constituent in groundwaters with changing P(CO 2 ) (Section 3.5.5).



### 3.5.2 Input Specifications

Before providing further detail s regarding this thermodynamic an alysis, it is important to understand the relationship between (1) the th ermodynamic model of the geochemistry of groundwaters used to predict equilibrium concentr ations of given hazard ous constituents under specified equilibrated CO 2 partial pressures, and (2) the observed concentrations of these same constituents in NWIS groundwaters. The th ermodynamic model was developed entirely independently of any of the chemical analys es of NWIS groundwaters. It was based on the existing thermochemical database in EQ3/6, wh ich was revised and augmented using recent literature on calorimetrically or experimental ly derived solubility products (Section 3.4.2;138 of 352 Appendices C, D, and E). This independent th ermodynamic model was then used in Section 3.4.5 to evaluate the chemical analyses and to determine to what extent the groundwaters were saturated with respect to minerals that might control the activities of the selected hazardous constituents. The candidate minerals are presen t at such low abundances, however, that their direct detection is practically impossible with out using advanced spectroscopic techniques. Therefore, with rare exceptions, we can only surmise their presence through thermodynamic
evaluation.

In the evaluation of solubility controls for h azardous constituents presented in Section 3.4.5, the modes of the histograms of the logarithm of th e SI of each candidate mineral are determined.
The calculated mode differs from the ideal value of zero by ± some incremental value, reflecting a “closing error” between mode l prediction and actual field obs ervation. This deviation is a measure of the overall uncertainty of all parame ters that go towards construction of the model, including the uncertainti es and variabilities asso ciated with the 38,000 chemical analyses of the NWIS groundwaters as well as uncertainty as to whether the candidate mineral is a plausible thermodynamic control. It is desirable to ensure th at the calculated model variations in hazardous
constituent concentrations as a function of P(CO 2 ) are consistent with the field observations. This was done here by adjusting in th e model the Log K values of th e dissolution constants of the minerals plausible mineral candidates so that th e calculated log SI’s of the mode for each are now zero, rather than showing a clos ing error. In other words, the model is now calibrated to the selected modes of the histograms of the SI’s calculated from NWIS groundwater analyses.

Table 3.14 summarizes the imposed input constr aints for the base cas e runs, including the
modifications made in the calibration. The list of minerals identified as controlling hazardous
constituent concentrations is based on the th ermodynamic evaluations summarized in Section 3.4.7. Thermochemical data, including the measured solubilities of most of these minerals, are available (Appendix C, D, and E), and calorimetr ically or experimental ly derived solubility products at 25°C were entered in the EQ3/6 Data0.dat database wh ere needed. Calculations were performed using a typical groundwater compositi on. The redox state was defined by the assumed equilibrium between pyrite and goethite at a given pH to simulate reducing conditions typical of139 of 352 shallow confined aquifers. The groundwater was al so assumed to be saturated with respect to calcite, opal-CT, and low Fe, Mg smectite. (The effect of these minerals on the solubilities of the hazardous inorganic constituents is negligible.) Electrical neutrality was achieved by adjustment of pH. Also, as mentioned before, previous ly unrecognized metal sulfide and selenide complexation in solution is taken in to account in the Data0.dat database.

Calibration adjustments to the log K values of the solubility products of minerals that control the concentrations of Pb, Hg, Se, U and Zn in NW IS groundwaters were made to cinnabar, galena, and tiemannite, uraninite and sphalerite, respectiv ely, as indicated in Column 4 of the Table 3.14, the purpose being to ensure consistency between the modal concentrations of these constituents and the thermodynamic model. Corrections were also made to the solubility products of arsenopyrite and gudmundite, to reflect the assump tion that both As and Sb are substituting in solid solution in pyrite as FeAsS and FeSbS co mponents. The magnitude of each correction is
based on the calculated mode of the SI distribution determined through the analysis of NWIS groundwaters given in Section 3.4.5.2. No adjustments were made to the log K values of barite or CdS (greenockite). In the former, there app ears to be substantial agreement between model predictions and observed concentrations of Ba. Regarding th e latter, considerable ambiguity remains regarding the saturation state in NWIS groundwaters, and calibration was not attempted.



### 3.5.3 Results from Equilibrium Calculations

Results from the calculations of the saturation concentrations for each hazardous constituent as a function of P(CO 2 ) are presented graphically in Figure 3.51. The figure shows the most likely expected concentrations (black lines) as well as a prediction of the uncertainty band (shaded areas) based on the uncertainty an alsyis described in Section 3.5.4. Also included in the figure are the relevant MCLs (or SWDL for Zn), the de tection limits for the most sensitive analytical methods, and the estimated ±2 σ variation about the mean of the log concentration of each hazardous constituent at log P(CO 2 ) = -2, i.e., the average natu ral partial pressure of CO 2 in NWIS groundwaters (see Figur e 3.17). The estimated ±2 σ variation about the mean is assumed
to be ≈ ±1.5 log(mg/L), and is based on the observed log-normal distributions of Ba, U and Zn in140 of 352 NWIS potable groundwaters, all of whic h gave quantitative measurements for ≥ 90% of all samples (see Table 3.17 below). For Pb and Se, the lower “tail” of the distribution falls below the limits of detection for the most sensitive analyt ical methods used, whereas for Cd. Hg and Sb,
the estimated mean value falls below the corresponding mo st sensitive detection limits. For Cd, Hg, and Sb, therefore, the variation in natu ral groundwaters was estimated assuming that the percentage of detectable va lues represents an observed upper “tail” of the log-normal
distribution.

We shall start discussing the gene ral trends observed for most lik ely results shown as black lines in Figure 3.51. The concentrations of some of th e hazardous constituents are quite sensitive to changes in P(CO 2 ), whereas others remain relatively una ffected. For example, Hg, Se, and U are insensitive to changes. Ba is slightly affect ed, but the maximum concentration at elevated P(CO 2 ) (~ 1.1 mg/L at 10 bar) remains below the MC L (maximum contaminant level) of 2 mg/L. Cd, Pb, and Zn show marked increases after log P(CO 2 ) rises above -1. However, only in the
case of lead is the maximum c oncentration at elevated P(CO 2 ) (~ 0.02 mg/L at 10 bar) higher than the MCL of 0.015 mg/L. Arsenic concentra tion changes sympathetically with increasing P(CO 2 ), with a maximum concentration (~ 0.46 at 10 bar) significantly hi gher than the MCL of 0.01 mg/L. Sb initially increases in parallel with As, then saturate s with respect to kermesite at Log P(CO 2 ) > -2, and thereafter remains at a concentr ation level significantly below the MCL of 0.006 mg/L. The reasons for these differing be haviors can be understood by reference to variations in pH and Eh, and to variations in the nature and dist ributions of complexes of each element in aqueous solution. These variati ons are discussed further in Section 3.5.5.141 of 352 Table 3.14. Input specifications for base case EQ3 runs to calcu late the equilibrium concentrations of hazardous inorganic constituents in groundwater at 25°C as a function of P(CO 2 ). SS = Solid Solution.
Adjustments 2 Aqueous Basis Species Concentration (mg/L) Mineral Saturation 1 Specified Change Explanation H + - - Charge balance Electrical neutrality Na + 45.8 - K + 5.04 - Mg +2 2.01 - Ca +2 Set by mineral saturation calcite

Fe +2 Set by mineral saturation pyrite

Al +3 Set by mineral saturation low Fe,Mg smectite SiO 2 (aq) Set by mineral saturation opal-CT

O 2 (g) Set by mineral saturation goethite

HCO 3 - Set by P(CO 2 ) saturation log P(CO 2 ) -4, –3, -2, -1, 0, +1 Variation from background due to CO 2 intrusion Cl - 7.14 - SO 4 -2 18.4 - H 2 AsO 4 - Set by mineral saturation arsenopyrite SS Change Log K from +58.66 by -3.00 to +55.66 Calibration. Change also accounts for solid solution

Ba +2 Set by mineral saturation barite Log K = -9.97 (no change) No calibration needed

Cd +2 Set by mineral saturation CdS (greenockite) Log K = -14.09 (no change) No calibration needed
Hg +2 Set by mineral saturation cinnabar Change Log K from -38.08 to -37.50 Calibration

Pb +2 Set by mineral saturation galena Change Log K from -14.87 to -14.40 Calibration
Sb(OH) 3 (aq) Set by mineral saturation gudmundite SS, kermesite Change Log K from +38.76 by -5.00 to +33.76 Calibration. Change also accounts for solid solution

SeO 3 -2 Set by mineral saturation tiemannite Change Log K from -43.27 to -44.10 Calibration

UO 2 +2 Set by mineral saturation uraninite Change Log K from -4.83 to –3.83 Calibration

Zn +2 Set by mineral saturation sphalerite Change Log K from -11.27 to -10.00 Calibration

1. Mineral saturation is specified, which determines the aq ueous concentration of the basis species identified in column 1 through the law of mass action. 2. Adjustments specified in the input file of EQ3. All adjustments w ith the exception of H + and HCO 3 - involve corrections to Log K values of mineral solubility products in order to calibrate the thermodynamic geochemical model with the mode of the corresponding mineral saturation indices in the sampled NWIS groundwaters. Charge balancing is assumed through adjustment of H + by the EQ3 code algorithm. HCO 3 - is specified through successive incremental adjustment of log P(CO 2 ) to explore the effect on hazar dous constituent concentrations of the full range of P(CO 2 ) anticipated in the subsurface environment.142 of 352 Figure 3.51 Part 1.143 of 352 Fig. 3.51. Variation of trace metal concentrations in equ ilibrium with host minerals as a function of CO 2 partial pressure taking into account both sulfide and selenide complexation, and with solubility products calculated using thermochemical data and adjusted to ensure consistency w ith hazardous constituent concentrations in NWIS waters (see Table 3.14). (SS) refers to solid solution. CV013, CV014, CV016, XV017, CV018, FL007, PH0S3, PLM10, PLM40, PLM43, PLM47 and PLM48 are analytical methods cited in the NWIS database. See Table A2 in Appendix A.144 of 352 

### 3.5.4 Uncertainties Associated wi th Equilibrium Calculations

3.5.4.1 Estimation of Aggregate Uncertainties

The overall uncertainty related to predictions of hazardous constitu ent concentrations in potable groundwaters as a function of P(CO 2 ) is difficult to estimate, as so many factors affect the analysis and interpretation. Br iefly, four components are i nvolved: (1) th e validity of assumptions relating to the geochemical mode l, (2) uncertainties in geochemical model parameters, (3) uncertainties rela ting to the chemical analyses of NWIS groundwaters, and (4) uncertainties relating to the natural variability in sampled NWIS grou ndwater compositions.
Tables 3.15-3.18 list the various uncertainties unde r these four categories, and their potential impact on hazardous constituent behavior is discussed.

The treatment of uncertainty in this section is at best semi-quantitative. Most of the listed estimates of uncertainty are ba sed on professional judgment, a nd not on quantitative evaluation, the reason being that it would otherwise take an inordinate amount of time and effort to accomplish such a task, even if technically feasible . The term “uncertainty” is assumed to be the standard deviation of the mean, i.e., ± σ , except where otherwise noted. The listed uncertainties in Table 3.15-3.17 are then selectively us ed to calculate the aggregate uncertainties associated with the calibrated log K values of the solubility pr oducts of the hazardous constituent host minerals.
This information is summarized in Table 3.19.

Uncertainties listed in Table 3.18 ar e not utilized in the calculati on of aggregate uncertainties, because these natural-variability uncertainties are accounted for in the estimated ±2 σ variation
observed in hazardous element concen trations at the modal logP(CO 2 ) ≈ -2 . Furthermore, it is the purpose of the analysis to compare the aggregate geochemical model uncertainty in
calculated hazardous concentrations with the observ ed apparent natural va riability in order to
obtain some measure as to whether or not the natural variability exceed s modeled uncertainty.145 of 352 Table 3.15. Uncertainties associated with geochemical model assumptions
No. Item Principal Concern Impact on Findings Justification for Current Assumptions 1 Specified mineral hosts control hazardous constituent concentrations. Identification of mineral hosts was made through indirect thermodynamic comparison of predicted mineral solubilities with those in 38,000 potable groundwaters. The presence of mineral hosts in the subject aquifers has not been verified. If selected minerals have been incorrectly identified, then the predicted response to changing P(CO 2 ) could differ markedly, and the present interpretation would be invalid The correlation between predicted solubilities and the modes of the compositions for all selected constituents in 38,000 groundwaters is consistent with thermodynamic control by the selected minerals. 2 Mineral hosts are pure substances, except where otherwise noted. In the case of Cd, Sb and Se, it is not entirely clear whether pure substances or solid solutions control these constituent concentrations. If the hazardous constituent were a minor component in solid solution in a mineral other than that identified, the thermodynamic response to P(CO 2 ) changes would differ from that in the present model. Identified pure substances containing Cd, Sb and Se, where relevant, are consistent with observed concentrations in groundwaters. Both field observations and thermodynamic analyses are consistent with the assumption that As is present in solid solution in pyrite. 3 Oligomeric sulfide and selenide complexes of Cd, Hg, Pb, and Zn are present in solution and impact equilibrium concentrations of these constituents. The exact compositions of the S and Se oligomers have not been determined. Selenide speciation is assumed to be analogous to that of sulfide speciation, based on correlation between Hg-S(-II) and Se(-II) species. If incorrect, both the concentrations and the thermodynamic response to changes in P(CO 2 ) would differ from that in the present model for the listed species and Se. The assumption that oligomeric sulfide and selenide complexes of Cd, Hg, Pb, and Zn are present in solution, and that the thermodynamic properties of selenide complexes correlate with those of sulfide complexes is necessary to achieve consis tency between model predictions of hazardous constituent concentrations and field observations. 4 The pyrite/goethite pair buffers Eh at a given pH. Goethite is metastable with respect to hematite, i.e., pyrite/hematite might be operative in some aquifers. Other buffers might be operative, e.g., pyrite/magnetite, pyrite/siderite, pyrite/chlorite, etc. A systematic study as to the ranges of conditions under which these alternative buffers might operate, and how they would impact re sults has not been conducted. It is further assumed, but not proven, that the Eh buffer is in thermodynamic equilibrium with the aqueous phase. Variations in Eh at a given pH due to control by alternative buffers could impact mineral host solubilities, possibly by ±1 to 2 log units. Most of the alternative Eh – pH buffers control Eh at a given pH in a range that lies within the overall uncertainty of the calculations. Groundwater analyses are generally consis tent with the assumption that the pyrite/goethite buffer closely represents conditions in most potable water aquifers. An alternative assumption that Eh was defined by goethite saturation in the presence of measured Fe +2 concentration at a given pH did not yield results consistent with hazardous constituent concentrations saturated with respect to the specified host mineral hosts.146 of 352 No. Item Principal Concern Impact on Findings Justification for Current Assumptions 5 Reversible chemical equilibrium is assumed with respect to selected mineral hosts. Some geochemical systems, especially those involving redox reactions, are notoriously slow at achieving chemical equilibrium (see No. 4, above). This is especially pertinent where reactions involve transformations between S(-II) and S(VI). If partial oxidation in the field leads to dissolution of sulfides of Cd, Hg, Pb, and Zn with formation of native sulfur or other intermediate species instead of SO 4 -2 , the stability fields of these sulfides would be extended to higher oxidation states. This would in turn lead to a broader distribution of observed concentrations. The statistical dispersion of the SIs of the identified mineral hosts is suggestive that not all groundwaters are in thermodynamic equilibrium. The agreement between predicted and modalvalues with respect to redoxsensitive mineral hosts indicate, however, that substantial equilibrium has been achieved in many groundwaters under buffered redox (i.e., pyrite/goethite) conditions. 6 Temperature and pressure is assumed to be 25oC and one bar, respectively. The temperatures of most groundwaters within the 48 contiguous states fall between 5oC and 30oC with a mode at aroud 12oC (see Figure 3.52). Hydrostatic pressures in potable water aquifers are unlikely to exceed 10 bars. Correction of log K for the dissolution reaction of a host mineral from 25oC to 10oC ranges from ≈ –2.0 to ≈ +0.75 for the host minerals under consideration. Pressure changes of only one bar would have negligible impact on thermodynamic equilibria and can be ignored. With respect to most hazardous constituent complexes, the values of their dissociation constants at other than 25o are unavailable, and therefore corrections for temperature cannot be made at present. With the exception of uraninite, none of the corrections of host mineral solubility products for temperature would improve the correlation between model predictions and field observations. Such corrections are generally within the overall uncertainty of the model and the field data used for comparison.

7 Activity coefficients of some sulfide and selenide complexes of hazardous constituents in supporting electrolytes in experiments were assumed to be unity. Supporting electrolytes were sometimes seawater, or 1 N perchlorate solutions (e.g., Hg selenide complexes). Failure to correct for ionic strength in such solutions could lead to errors in associated dissociation constants of as high as ±1 log unit. Activity coefficientcorrections, especially for illdefined oligomeric species, are difficult to implement. In general, corrections to calculated dissociation constants would be less than ±0.5 log units, and even less for oligomeric neutral species, and are therefore within the uncertainty of the model predictions. 8 TDS is assumed to be zero. Corrections for ionic strength are not made. Errors thereby introduced are very small, as most potable waters have very low ( ≤ 1000 mg/L) TDS. Impact on hazardous constituent concentrations is negligible.147 of 352 No. Item Principal Concern Impact on Findings Justification for Current Assumptions 9 All hazardous constituent mineral hosts are coarsely crystalline. Some minerals, especially those formed as secondary precipitates, could be amorphous or very finely crystalline. Amorphous or finely crystalline phases are more soluble than their coarsely crystalline counterparts. Failure to account for increased solubility of hazardous constituent host minerals could lead to predictions of hazardous constituent concentrations 1 – 2 orders of magnitude lower than actually occurs. Hazardous constituent mineral hosts are assumed to have coarsened through the mechanism of Ostwald ripening over perhaps millions of years, and therefore mineral crystallinity or surface free energy effects are expected to be negligible.
10 Silica activity in solution is controlled by saturation with repect to opal-CT. Not all waters are saturated with respect to opal-CT; many are expected to be saturated with respect to quartz. Choice of silica mineral affects predictions of coffinite saturation. The assumption that silica issaturated with respect to opalCT in shallow aquifers is reasonable, an d consistent with field observations of such systems undergoing diagenesis. 11 Aluminum in solution is controlled by saturation with repect to low Fe,Mg smectite. Aluminium concentration is under-predicted when compared with field observations. In general, the impact of incorrectly simulating Al concentrations on predictions of the concentrations of hazardous constituents is small. Because the underlying problem concerning Al concentration discrepancies has not yet been resolved, selection of low Fe,Mg smectite is appropriate in the context of the present geochemical model. 12 Calcium in solution is controlled by saturation with respect to calcite. Not all groundwaters are saturated with respect to calcite. Calcite will buffer pH changes due to changing P(CO 2 ) more strongly than would be the case if it were absent. The evaluation of NWIS groundwaters indicated that most are saturated with respect to calcite.

13 Concentrations of major species in groundwater. Concentrations were fixed and chosen to be representative of the modes of major species analyses in NWIS groundwaters. Variations could impact predictions of hazardous constituent concentrations The sulfate concentration will affect predictions of Ba concentration in groundwaters by ≈ ±0.3 log unit, as it is assumed that barite is the host mineral. The sulfate concentration will also affect the redox potential through equilibrium with the pyrite/goethite couple by a similar uncertainty. Other species will have negligible effect, as none participate directly in mass action reactions defining hazardous constituent concentrations. Selection of representative values for the major species is appropriate to ensure model verisimilitude with field observations.148 of 352 Table 3.16. Uncertainties in geochemical model parameters
No. Parameter Estimated Uncertainty Impact on Findings 1 Mineral solubility products of hazardous constituent mineral hosts. The primary uncertainty is associated with thermochemical (calorimetric) determinations of Δ

H f

o , which display standard deviations of ≈ ±0.5 - 1.0 kcal/mol in typical cases. Errors associated with the Δ G f o of aqueous basis species are generally small, i.e., ≈ ±0.2 kcal/mol, although the uncertainty associated with some basis species could be larger, e.g., for Fe +2 , Fe +3

, HSe In general, uncertainties associated with mineralsolubility products could lead to over- or underpredicting the concentration of a hazardous constituent by ≈ ±1 order of magnitude.

2 Dissociation constants of sulfide and selenide complexes of hazardous constituents. Experimentally determin ed uncertainties of dissociation constants of sulfide complexes are usually less than ±0.5 log unit. However, with the exception of Hg selenide complexes, all other selenide complex dissoci ation complexes have been estimated with potential uncertainties that could be as high as ±2 log units. Uncertainties associated with the correct identification and stoichiometry of sulfide and selenide complexes can lead to significant errors in estimated concentrations of some hazardous constituents, especially with respect to Cd, Hg, Pb, Se and Zn. Uncertainties are most acute where selenide complexes dominate over corresponding sulfide complexes, as is the case with the solubility of cinnabar (Hg,) and tiemannite (Se). The solubilities of CdS (greenockite), galena (P b) and sphalerite (Zn) are affected only incrementally by selenide complexation. Therefore the uncertainties in predicted concentrations of Hg and Se are likely to be much larger, i.e., ±2 log units, than for Cd, Pb and Zn, i.e., ±0.5 log units. 3 Arsenopyrite and gudmundite solid solution proxies. Field evidence suggests that the abundances of As and Sb in potable wa ter aquifers are normally insufficient to cause saturation with respect to either arsenopyrite or gudmundite, although, with respect to Sb, it is probable that kermesite saturates under more acid conditions induced by higher P(CO 2 ). Therefore, it was assumed that both As and Sb exist in solid solution in pyrite as arsenopyrite and gudmundite components, respectively, and their solid solution saturations were calibrated against the estimated modes of the concentrations of these elements in potable NWIS groundwaters. Uncertainties are approximately ±1 log unit for arsenopyrite SS, and ±2 log unit for gudmundite SS. The uncertainties in arsenopyrite SS and gudmundite SS likewise affect calculated concentrations of As and Sb in solution, except where the latter saturates with respect to kermesite.

4 Activity coefficient estimates. The a parameter in the extended form of the Debye Huckel expression is estimated for almost all sulfide and all se lenide complexes of hazardous constituents. Because potable waters have low total dissolved solids concentrations, uncertainties associated with the a parameter have minor and inconsequential effect on calculated concentrations of hazardous constituents in solution.149 of 352 No. Parameter Estimated Uncertainty Impact on Findings 5 Rock-forming mineral solubility products. Uncertainties range from very low in the case of silica polymorphs (±0.1 log unit) to significant, as is the case with several clay minerals ( ≥ ±1.5 log unit) depending on mineral stoichiometry. The rock forming minerals equilibrate with the groundwater and buffer pH and Eh. They therefore define the environmental conditions, which in turn affect the solubilities of the hazardous constituent mineral hosts. The pH and Eh of most groundwaters are buffered within a fairly narrow range. The impact ofuncertainties in the solubility products of rockforming minerals is not explicitly evaluated in this study, but general consistency with field observations suggests that these uncertainties have a small effect on predicted hazardous constituent concentrations in most cases.150 of 352 Table 3.17. Uncertainties in chemical anal yses of potable groundwaters

No. Parameter Estimated Uncertainty Comments Impact on Findings

Basic Chemistry 1 pH ±0.1 pH unit Errors introduced through improper custodial handling could increase the uncertainty to ±0.5 pH units or greater Associated uncertainty could result in errors in the calculated SI of hazardous constituent host minerals and hazardous constituent concentrations of as large as ±1.0 unit, although for most analyses, the error is likely to be much smaller, and the solubilities of several constituents are insensitive to pH. 2 Major cations (Na, K, Mg, Ca) ±5 % These analyses are usually quite reliable, especially those made during the last 15 years. Impact on the calculated SI of hazardous constituent host minerals and hazardous constituent concentrations is expected to be small, i.e., < ±0.1 unit. 3 Major anions (Cl, SO 4 ) ±5 % These analyses are usually quite reliable, especially those made during the last 15 years. Impact on the calculated SI of hazardous constituent host minerals and hazardous constituent concentrations is expected to be small, i.e., < ±0.1 unit. 4 Total HCO 3 - ±10 % Errors introduced through improper custodial handling could increase the uncertainty to ±30% or greater. The calculated extent of carbonate complexing by Ba, Cd, Pb, U and Zn in groundwaters would be affected, but because carbonate complexes are not predominant, except for Pb at >0.1 bar P(CO 2 ), the impact on calculated SI of host minerals is expected to be minor, i.e., << ±0.1 unit. 5 Minor cations (e.g., Al, Fe +2

, Fe +3 ) ±10 % When at the limits of detection, uncertainties could exceed ±50%. Errors introduced through improper custodial handling could increase the uncertainty to the point where the analytical result is essentially meaningless. Uncertainties associated with measurements of Fe +2 in potable waters could lead to very large errors (>> 5 units) in the calculated SI of hazardous constituent host minerals when used to calculate Eh.Uncertainties associated with measurement of Al in the calculated SI of hazardous constituent host minerals are likely to be small, i.e., < ±0.1 unit. 6 Minor Anions (e.g., F, PO 4 ) ±10 % When at the limits of detection, uncertainties could exceed ±50%. Errors introduced through improper custodial handling could increase the uncertainty to the point where the analysis is essentially meaningless. Impact on calculated hazardous constituent concentrations is expect ed to be minor, as no analyses of minor anions were used in the calculation of either SIs of hazardous constituent host minerals or hazardous constituent concentrations.

7 SiO 2 (aq) ±5 % Analyses are usually fairly reliable. Uncertainties in the cal culated SI of hazardous constituent host minerals and hazardous constituent concentrations, associated with measurement of SiO2(aq), are likely to be small, i.e., < ±0.1 unit. 8 Charge imbalance ±30 % The calculated charge imbalance of many potable NWIS groundwaters is larger than generally considered acceptable. The root causes of such large imbalances are difficult to determine. Charge imbalance was ignored in the calculation of SIs of hazardous constituent host minerals. However, the large statistical dispersion in values of charge imba lance suggests cumulative uncertainties in the chemical analyses of individual constituents , especially pH and HCO 3 - , or to the omission of analyses of one or more major species.151 of 352 No. Parameter Estimated Uncertainty Comments Impact on Findings

Hazardous Constituents 9 As ±10 % For the most part, only the most sensitive analytical method, i.e. PLM40 with a detection limit of ≈ 0.0002 mg/L, yields 87% quantitative measurements. The potential error in SI, due to analytical uncertainty, is ≈ 0.1 unit for the most sensitive analytical method.

9 Ba ±5 % Modern analytical methods, e.g., PLM43 and PLM47 with a detection limit of 0.0002 mg/L, yield >95% quantitative measurements. The potential error in SI, due to analytical uncertainty, is <0.1 units for the most sensitive analytical methods.

10 Cd ±50 % Only the most sensitive analytical methods, i.e. PLM43 and PLM47 with a detection limit of ≈ 0.00004 mg/L, yield meaningful measurements, of which only 24 and 17% respectively are quantitative. The potential error in SI, due to analytical uncertainty, is ≈ 0.2 unit for the most sensitive analytical method.

11 Hg ±70 % Most anal ytical methods have detection limits of ≈ 0.0001 mg/L, but only 0 - 14% yield quantitative measurements. The potential error in SI, due to analytical uncertainty, is ≈ 0.1 unit for the most sensitive analytical method.
12 Pb ±20 % Only the most sensitive analytical methods, i.e. PLM43 and PLM48 with a detection limit of ≈ 0.0001 mg/L and ≈ 0.00006 mg/L, yield meaningful meas urements, with 49% and 82% giving quantitative data. The potential error in SI, due to analytical uncertainty, is ≈ 0.1 unit for the most sensitive analytical methods.

13 Sb ±50 % Only the most sensitive analytical method, i.e. PLM43 with a detection limit of ≈ 0.00005 mg/L, yields meaningful measurements, of which only 18% are quantitative. The potential error in SI, due to analytical uncertainty, is ≈ 0.2 unit for the most sensitive analytical method.

14 Se ±30 % Only the most sensitive analytical methods, i.e. PLM10 and PLM40 with detection limits of 0.0008 and 0.0003 mg/L respectively, yield meaningful measurements, of which 75 and 54% are quantitative. The potential error in SI, due to analytical uncertainty, is > 0.1 unit for the most sensitive analytical method.

14 U ±20 % The most sensitive analytical methods, i.e., PHOS3 with a detection limit of ≈ 0.0004 mg/L and FL007 with a detection limit of ≈ 0.00001 mg/L respectively, yield 67% and 97% quantitative measurements. The potential error in SI, due to analytical uncertainty, is ≈ 0.1 unit for the most sensitive analytical methods.

16 Zn ±5 % Modern analytical methods, e.g., PLM10 and PLM43, both with a detection limit of ≈ 0.0006 mg/L respectively, yield 82% and 94% quantitative measurements. The potential error in SI, due to analytical uncertainty, is <0.1 units for the most sensitive analytical methods.152 of 352 Table 3.18. Uncertainties relating to environmental variab ility in sampled NWIS groundwater compositions
No. Parameter Estimated Variability Impact on Findings 1 Temperature and pressure The temperatures of most groundwaters within the 48 contiguous states probably fall between 5 oC and 30oC (see Figure 3.52). Hydrostatic pressures in potable water aquifers likely range from 1 to 10 bar. Variations in log K for the dissolution reaction of a host mineral between 5 and 25o range from

≈ –1.0 to ≈ +2.5 for the host minerals under consideration. Pressure changes of up to 10 bars would have negligible impact on thermodynamic equilibria. 2 Oxidation state Oxidation st ate could range from highly reducing, i.e., close to the decomposition of water with respect to the evolution of H 2 , to oxidizing, i.e., in e quilibrium with air. Such large variations in oxidation state would lead to changes in the identities of mineral hosts controlling hazardous constituent saturations. Variations in SI of specified mineral hosts are likely to range over ± 2 – 3 log units. A general consistency of SI distri butions in NWIS potable groundwaters suggests that most fall into a narrower range of oxidation states. 3 Natural variations in hazardous constituent concentrations Examination of hazardous constituent distributions of concentration in soils and sediments in the Na tional Geochemistry Database shows that the standard deviation of an approximated log normal distribution averages about 0.3. Therefore at 3 standard distributions (accounting for 99.7% of all), the range is approximately ±1 log unit. If the same constituent distributions apply to potable water aquifer host rocks, then whether or not a mineral host controls the aqueous concentration of a hazardous constituent would depend on whether the concentration distribution in the aquifer host rock permits saturation. Estimating the natural va riability of hazardous constituent concentrations in potable waters from the corresponding distributions in aquifer host rocks is difficult to establish without detailed analysis, as many geochemical factors are involved. The measured distributions, based on chemical an alyses are confounded with analytical uncertainties, and are therefore broader than would be found in nature. If the concentration distributions of Ba, U and Zn in NWIS potable waters are examined, it appears that natural variability does not exceed ± 1.2, ±2.0 and ± 1.5 log units, respectively. It is expected that the other hazardous constituent concentrations would fall into similar ranges, but they cannot be accurately estimated because the mean of the distribution is at or below the detection limit. 4 Host rock mineralogy Aquifer host rocks range in mineralogy from essentially pure arenites through impure arkoses to dolomitic limestones. Some shallow unconfined aquifers may be hosted in unconsolidated sediments. Under reducing conditions, the stability of these identified mineral hosts is relatively unaffected by these differing mineralogical environments. The pH of groundwaters in partial equilibrium with the mineral assemblages may vary by as much as ±2 pH units, which, under oxidizing conditions could destabilize the mineral hosts found under these conditions, e.g., cerussite or otavite, and perhaps hemimorphite for Pb, Cd and Zn, respectively. Most of the hazardous constituent concentrations in NW IS potable groundwaters strongly suggest equilibrium with mineral hosts under reducing conditions in the presence of a pyrite/goethite or similar buffer. Observed concentration distributions of several hazardous constituents also suggest a positive skew, which may be due to equilibration under oxidizing conditions with respect to higher-solubility oxidized mineral hosts at varying pH values, or the skew may be due to analytical artifacts. We conclude that under reducing conditions, mineralogical variati ons could cause small changes, i.e., ≤ 1 log unit in concentration. A very minor population of NWIS groundwaters has been determined to reside under mildly reducing to oxidizing conditions. This lead to an increase in equilibrium concentration over reducing conditions by +2 to 3 log units, or greater.153 of 352 No. Parameter Estimated Variability Impact on Findings 5 Extent to which system is at equilibrium Few studies have been conducted to rigorously analyze the extent to which sedimentary systems have attained equilibrium with pore waters. Generally, when equilibrium between detrital silicat es and the groundwater is not attained, diagenetic processes reflect a progressive approach to equilibrium, which may involve the production of metastable secondary minerals such as opal-CT and smectites. As noted above (Table 3.3, Item 5) the lack of thermodynamic equilibrium between rock-forming minerals and groundwaters generally does not materially affect the aqueous concentrations of hazar dous constituents. With respect to hazardous constituent mineral hosts, it is expected that most will closely approach equilibrium in reducing groundwater provided that the system is not perturbed through the ingress of oxygen. One issue of concern, however, relates to attainment of equilibrium between SO 4 -2 and H 2 S(aq) . Redox equilibrium in this reaction may not be achieved, and this should be borne in mind when conducting model analyses. Non attainment of equilibrium between these species also impacts perturbations in equilibrium between sulfide mineral hosts and the groundwater due to changing pH, where increased solubility can lead to only partial oxidation of S(-II) to native sulfur rather than S(VI). Non-attainment of equilibrium between aqueous sulfur species could lead to significant dispersion in the calculated SIs of sulfide mineral hosts. However, the statistical dispersion of the calculated SIs for sphalerite (ZnS) is not significantly different from that of barite (BaSO 4 ) or uraninite (UO 2 ). Because the SI was calculated using the measured SO 4 -2 concentration, and calculation of H 2 S(aq) was based on assumed equilibrium between SO 4 -2 and H 2 S(aq), this observation lends credence to the belief that non-attainment of equilibrium between aqueous sulfur species is not a significant issue in the sampled NWIS potable groundwaters.

6 Anthropogenic contaminants Anthropogenic contamination by certain hazardous constituents, e.g., As, Hg, Pb, Se, in surficial soils and sedime nts could be the source of subsequent groundwater contamination, due to solubilization and infiltration. Clear indications of such processes are usually localized, and inadvertent exploitation of potable waters that have been contaminated in that manner are rare. It is not expected that anthropogenic contamination of groundwaters materially affects the compositions of the NWIS groundwaters selected for this study.154 of 352 Table 3.19. Estimated Uncertainties contributing to the Aggregate Uncertainty in Log K of the Solubility Product of the Hazardous Constituent Mineral Host 1

Item As Ba Cd Hg Pb Sb Se U Zn
Host mineral Arsenopyrite SS Barite CdS Cinnabar Galena Gudmundite SS Tiemannite Uraninite Sphalerite Relevant Uncertainty Contri butions from Table 3.15 Item 6 0.85 0.26 0.7 1. 7 0.7 0.85 1.8 0.6 0.3 Item 7 0 0 0 0.5 0 0 0.5 0 0 Item 11 0.3 0.3 0.3 0. 3 0.3 0.3 0 0. 0.3 Relevant Uncertainty Contri butions from Table 3.16 Item 1 0.5 0.3 0.23 0. 48 0.35 0.5 0.5 0.3 0.26 Item 2 0 0 0.5 1.0 0.5 0 1.0 0 0 Item 3 1.0 0 0 0 0 1.0 0 0 0 Relevant Uncertainty Contri butions from Table 3.17 Item 1 0.1 0 0 0 0.1 0.1 0.1 0 0 Item Haz. Const. 0.1 0.1 0.2 0.1 0.1 0.2 0.1 0.1 0.1 Aggregate Uncertainty

σ ( +) 1.17 0.44 0.66 1.26 0. 69 1.18 1.23 0.68 0.65

σ (

- ) 2 1.49 0.51 0.96 2.11 0. 99 1.50 2.18 0.32 0.71
Log K + σ ( +) 56.83 -9.53 -13.43 -36.24 - 13.71 34.97 -46.28 3.15 -9.35 Log K 3 55.66 -9.97 -14.09 -37.50 - 14.40 33.76 -44.10 3.83 -10.00 Logk - σ ( -) 54.17 -10.48 -14.09 -39.61 - 15.39 32.26 -42.87 4.15 -10.71

1 Values refer to one σ , i.e., one standard deviation from the mean.
2 The larger negative uncertainty takes into account the bias introduced by assuming a model temperature that is about 10-15oC higher than average ambient aquifer temperatures.
3 Best-estimate value used in equilibrium calculation. See Table 3.14.
Because the geochemical model is calibrated against field measurements, as discussed in Section 3.5.2, the major uncertainty associated with iden tification of the specifi c redox buffers defining oxidation state is implicitly taken into account. Uncer tainties in log K of the solubility products
of the mineral hosts due to differences between the modeled and observed temperatures are also partially compensated by the ca libration step. A biased uncer tainty arising from model calculations made at 25oC is, however, included to account for the modal ambient temperature of ~12oC observed in potable wate r aquifers, as shown in Figur e 3.52. This figure gives the frequency histogram for the temperatures of th e NWIS groundwaters evaluated in this study. For convenience, uncertainties associated with sulfide and selenide complexation are folded into the uncertainty associated with the log K values of the solubility products. Although this approach is not strictly correct, it is adopted in order to faci litate calculation of the overall model uncertainty. The aggregate uncertainty in Table 3.19 is estimated by taking the square root of the sum of the155 of 352 squares of the uncertainties associated with e ach parameter, an approach that is commonly adopted as a convenient technique for estimating this value. Note that the upper and lower
uncertainties differ to reflect the bias introduced by assuming a 25oC temperature.

Fig. 3.52. Histogram showing the temperature (oC) distribution of more than 35,000 groundwater analyses selected from the NWIS Database.
In order to generate an uncerta inty envelope for th e equilibrium calculati ons, the computed upper and lower values of Log K for the solubility products of the respectiv e mineral hosts for each
element were used to calculate the concentrat ions of the respective hazardous constituents at discrete intervals of log P(CO 2 ). The calculations were performed by varying the log K value separately for each hazardous constituent, whil e fixing all other log K values for the other
hazardous constituents at the calibrated value. The results are plotted in Figure 3.51 in the form of a grey envelope, which delineates the ± σ limits of uncertainty about the calibrated value. Note
that the total concentrations of Cd, Hg, Pb and Zn are aff ected in varying degrees by complexation with selenide, as would result from the ± σ variation in log K for tiemannite (HgSe). These variations are not taken into account in the presen t analysis, but further study of this phenomenon is merited in future more rigorous studies. The a ssumption that the Se concentration in solution is determined by tiem annite saturation has the unintended consequence156 of 352 of swamping out variations in Hg aqueous concentration imposed by the - σ variation in log K for cinnabar, because Hg selenide complexation acc ounts for a substantial fr action of the total Hg
concentration in solution. Hence an artifact is cr eated in the form of a highly asymmetric grey uncertainty envelope about the calibrated value displayed by Hg in Figure 3.51.

3.5.4.2 Discussion Relating to Uncertainties

Inspection of the graphs pres ented in Figure 3.51 in most cas es shows excellent agreement between the calculated equilibrium concentr ation of a given constituent at log P(CO 2 ) = -2 and
the measured or estimated mean concentra tion in NWIS groundwaters at the same P(CO 2 ). In other words, the predicted con centration from the geochemical model agrees reasonably well with the observed data from groundwater analyses. This agreement is expected as a consequence of the calibration process, which is applied to al l constituents, except Ba and Cd, as described in Section 3.5.2. Furthermore, the calcul ated aggregate uncertainties at ±1 σ , illustrated with the grey shading, are on average about half the ±2 σ range estimated independently for the variability
in hazardous constituent concentrations in NWIS potable groundwaters. Thus, despite the obvious crudity in estimating aggregate uncertain ty, it is comparable with the statistical dispersion actually observed in potable groundwater.

Significant discrepancies arise, how ever, with respect to the calcula ted concentrations of Cd, Hg, and Sb when compared with the corresponding fi eld estimates of the mean concentrations in NWIS potable groundwaters. This requires further comment. In all three, the initia l calibration
was based on the selection of th e mode of the SI for the minerals CdS (Figure 3.36a), cinnabar (Figure 3.37a) and gudmundite soli d solution (Figure 3.39a), bearin g in mind the fact that the analyses most trustworthy are those made with the most sensitive analytical methods currently
available. The evaluation did not take into acco unt the most likely statistical distribution based on the percentage of quantitative measurements versus non-detects in NWIS waters using the most sensitive analytical method for a given constitu ent, as is evaluated in this section. The fact that only 24(17)% have detects for Cd, only 12% for Hg, and only 18% for Sb (see Table 3.9, for all samples analyzed with the most sensitive anal ytical methods) sugge sts that many low-157 of 352 concentration samples for these constituents are no t accounted for. In other words, the real mean values of the log concentrations for Cd, Hg and Sb falls below the detection limits of the most sensitive analytical methods cu rrently in routine use. An a ttempt was made in Figure 3.51 to show an adjusted real concen tration range in NWIS potable groundwaters, assuming the the percentage of detectable values represents an observed upper “tail” of lognormally distributed values. However, the calibrati on of log K values for the geochemical model was conducted without taking this bias into account; thus the pres ently predicted concen trations of these constituents as a function of P(CO 2 ) are overstated, and are therefor e inherently conservative. A reappraisal of the thermodynamic controls affecting the concentr ations of Cd, Hg and Sb in potable groundwaters is clearly necessary. Such co rrections would lead to the conclusion that
these three constituents would be even less likely to adversely impact water quality upon CO 2 intrusion than the present equili brium concentrations indicate.

If the revised estimated mean concentration of Cd in NWIS goundwaters was taken into account,
then the modal value of SI for CdS would be ≈ -1.5, rather than -0.5, indi cating that CdS is even less likely to be present as a discrete phase, an d is most probably present in solid solution in sphalerite or possibly galena, although saturati on with respect to cadmo selite still remains a plausible option in some groundwaters. It should be noted that the SI for CdS is unlikely to be depressed significantly be selenide complexing of Cd +2 , as such species appear to be relatively minor contributors (see Table 3.21 under CdS (g reenockite)). Figure 3.51 shows that the predicted versus estimated mean concen tration of Hg in solution at log P(CO 2 ) = -2 differs by about 2 log units. Such a large correction w ould depress the modal SI for cinnabar and quicksilver to ≈ -2 in both cases. Saturation with respect to tiemannite is likely to be similarly
affected. The extent of undersaturation could be offset by assuming that the log K value of the solubility product is -38.08 (Appendix D, Table D1 ), based on calorimetric data, rather than the
calibrated value of -37.5. A correction of th is magnitude would still leave cinnabar undersaturated, and is consistent with several field observations suggesting that Hg normally partitions preferentially in pyr ite (see Section 3.2). With re spect to Sb, Figure 3.51 suggests a correction of approximately -1.5 lo g units to the calibrated value of gudmundite in solid solution.
Such a correction would defer saturation w ith respect to kermesite until log P(CO 2 ) exceeds -0.5.158 of 352 

### 3.5.5 System Variations with P(CO 2 

### ) and the Impact of Complexation

The underlying reasons for the response of hazardous constituent concentrations in potable groundwaters to increasing P(CO 2 ) can be better understood th rough an examination of the induced variations in pH, Eh and P(H 2 S), and the progressive redi stribution of participating aqueous complexes in solution in response to these changes. Th is examination is conducted in the following sections.

3.5.5.1 Variation of pH, Eh and P(H 2 S) with P(CO 2 )

Species distributions were made at 25 ° C over a range of CO 2 partial pressures from log P(CO 2 ) = -4 to +1, reflecting the full range of partial pressu res either in natural sh allow groundwaters, or in those intruded by CO 2 escaping from an underlying deeply bur ied aquifer. The variation in the equilibrium partial pressure of CO 2 leads to variations in the concentration of CO 2 (aq), which, being a weak acid, hydrolyses and lowers pH, as shown in Table 3.20. The pyrite/goethite phase boundary also defines the Eh at any specified pH, where Eh is inversely related to pH.

Table 3.20. Variation of key dependent compositional parameters in groundwaters with variation in CO 2 partial pressure
Log P(CO 2 ) (bar) pH Eh (V) LogP(H 2 S) (bar)
-4 8.906 -0.314 -8.713
-3 8.136 -0.257 -8.815
-2 7.378 -0.200 -8.912
-1 6.684 -0.149 -8.999
0 6.051 -0.103 -9.075
+1 5.574 -0.069 -9.127

The calculated values of log P(H 2 S) are also listed in Table 3.15 as a function of Log P(CO 2 ). As noted under Section 3.4.5.1, P(H 2 S) is a measure of the hydrogen sulfide activity in solution, and has an important bearing on the extent of sulfide complexing of heavy metals in solution, including Cd, Hg, Pb and Zn. The calculated log P(H 2 S) at log P(CO 2 ) = -2 , the modal value159 of 352 calculated for NWIS groundwaters illustrated in Figure 3.17, is –8.91, which corresponds closely to the calculated NWIS groundwater modal valu e for this species, -8.95, in Figure 3.18. It is interesting to note that the variation in P(CO 2 ) over five orders of magnitude, from log P(CO 2 ) = -4 to +1, results in only a minor variation in log P(H 2 S), i.e., from –8.7 to –9.1 for the specified base case conditions given in Table 3.20.

3.5.5.2 Nature of Aqueous Complexation and its Impact on Mineral Solubility

The shift in pH and Eh contributes to the changing solubilities of the trace-element host minerals and their stability in relation to other potential mineral hosts. It is, therefore, instructive to investigate the underlying reasons why the concentr ations of some elements are sensitive to changes in the partial pressure of CO 2 , whereas others are not, and why. Table 3.21 lists how the principal aqueous species in equilibrium with the host minerals vary with P(CO 2 ) for each of the elements studied. All species are listed that cons titute greater than 0.001% of the total speciation at any value of log P(CO 2 ) between -4 and +1.

For most minerals, the changing so lubility as a function of P(CO 2 ) can be ascribed primarily to a change in pH, and not to increased complexati on by carbonate species. For Hg, As, Sb, and Se, neutral and anionic species are predominant, and carbonate species play no part. Enhanced solubility due to carbonate complexation occurs with Ba, Cd, and Pb. However, only with Pb does carbonate complexation, as PbCO 3 (aq), dominate the aque ous speciation. The molal percentage of PbCO 3 (aq) increases only incrementally over a 5-order of magnitude change in both total Pb in solution and P(CO 2 ). With Ba and Cd, the predom inant carbonate species is the bicarbonate, MHCO 3 - (M = Ba or Cd), but it never beco mes the major specie s contributing to aqueous solubility with either element. Uraniu m solubility is dominated by the neutral U(IV) species UO 2 (aq). Although the U(VI) species UO 2 +2 complexes strongly with carbonate, this species is present in inconsequential concentr ations under reducing conditions, and therefore the carbonate complexes, UO 2 (CO 3 ) 3 -4 , UO 2 (CO 3 ) 2 -2 and UO 2 CO 3 (aq), are present only in minor amounts, even at the highest P(CO 2 ) studied.

.160 of 352Table 3.21.Concentration of aqueous species of hazardous constituents in aqueous solution in equilibrium with specified host minerals as aFunction ofCO2partial pressure.Arsenopyrite Solid Solution ComponentLog P(CO2) (bar)-4 -3 -2 -1 0 +1ConcentrationSpeciesmol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol %H3AsO3(aq) 1.2617E-09 63.60 1.0346E-08 91.12 7.8402E-08 98.30 4.7269E-07 99.64 2.2437E-06 99.91 6.1197E-06 99.97H2AsO3-7.1869E-10 36.23 1.0059E-09 8.86 1.3530E-09 1.70 1.6980E-09 3.579E-01 1.9655E-09 8.752E-02 1.9302E-09 3.153E-02AsS(OH)(SH)- 3.0122E-12 1.518E-01 2.6413E-12 2.326E-02 2.2651E-12 2.840E-03 <0.001 <0.001 <0.001HAsO4-23.6050E-13 1.817E-02 2.2218E-13 1.957E-03 <0.001 <0.001 <0.001 <0.001MgAsO4-5.2383E-14 2.640E-03 <0.001 <0.001 <0.001 <0.001 <0.001CaAsO4-4.5689E-14 2.303E-03 <0.001 <0.001 <0.001 <0.001 <0.001MgHAsO4(aq) 1.2901E-14 6.503E-04 <0.001 <0.001 <0.001 <0.001 <0.001CaHAsO4(aq) 1.0005E-14 5.043E-04 <0.001 <0.001 <0.001 <0.001 <0.001FeAsO4(aq) <0.001 <0.001 <0.001 4.1048E-12 8.653E-04 3.9021E-11 1.738E-03 1.6940E-10 2.767E-03BariteLog P(CO2) (bar)-4 -3 -2 -1 0 +1ConcentrationSpeciesmol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol %Ba+29.0983E-07 97.35 9.7134E-07 97.78 1.1677E-06 96.97 1.6138E-06 94.71 2.6912E-06 89.92 6.4937E-06 77.82BaCO3(aq) 1.4885E-08 1.59 4.4642E-09 4.494E-01 1.5267E-09 1.268E-01 7.6410E-10 4.484E-02 5.6868E-10 1.900E-02 1.0489E-09 1.257E-02BaHCO3+9.8517E-09 1.05 1.7525E-08 1.76 3.4959E-08 2.90 8.9372E-08 5.24 3.0107E-07 10.06 1.8491E-06 22.16BaCl+4.6233E-11 4.947E-03 4.8169E-11 4.849E-03 5.4081E-11 4.491E-03 6.6273E-11 3.889E-03 9.1111E-11 3.044E-03 1.5387E-10 1.844E-03BaOH+ 1.9472E-11 2.083E-03 <0.001 <0.001 <0.001 <0.001 <0.001161 of 352Table 3.21.Continued.CdS (Greenockite)Log P(CO2) (bar)-4 -3 -2 -1 0 +1ConcentrationSpeciesmol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol %Cd(OH)2(HS)-2.3468E-08 97.03 4.0056E-09 87.40 7.1093E-10 56.15 1.4793E-10 19.51 3.6015E-11 1.48 1.2793E-11 3.338E-02CdS(aq) 5.1286E-10 2.12 5.1286E-10 11.19 5.1286E-10 40.51 5.1286E-10 67.63 5.1286E-10 21.01 5.1286E-10 1.34Cd(OH)2(HSe)-1.6923E-10 6.997E-01 2.8885E-11 6.303E-01 5.1265E-12 4.049E-01 1.0667E-12 1.407E-01 2.5971E-13 1.064E-02 <0.001CdSe(aq) 3.5481E-11 1.467E-01 3.5481E-11 7.742E-01 3.5481E-11 2.80 3.5481E-11 4.68 3.5481E-11 1.45 3.5481E-11 9.259E-02Cd+2<0.001 3.4753E-14 7.583E-04 1.5300E-12 1.208E-01 5.1586E-11 6.80 1.3747E-09 56.32 2.0165E-08 52.62CdHCO3+<0.001 <0.001 1.3916E-13 1.099E-02 8.6793E-12 1.14 4.6725E-10 19.14 1.7446E-08 45.53CdCl+<0.001 <0.001 2.0816E-14 1.644E-03 6.2233E-13 8.207E-02 1.3672E-11 5.601E-01 1.4037E-10 3.663E-01CdCO3(aq) <0.001 <0.001 <0.001 5.5262E-14 7.288E-03 6.5727E-13 2.693E-02 7.3700E-12 1.923E-02CdOH+<0.001 <0.001 <0.001 1.4958E-14 1.973E-03 8.0528E-14 3.299E-03 3.0067E-13 7.846E-04CdSO4(aq) <0.001 <0.001 <0.001 <0.001 5.4951E-14 2.251E-03 3.3405E-13 8.717E-04CinnabarLog P(CO2) (bar)-4 -3 -2 -1 0 +1ConcentrationSpeciesmol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol %HgSe(aq) 2.1380E-09 68.12 2.1380E-09 68.13 2.1380E-09 68.13 2.1380E-09 68.13 2.1380E-09 68.13 2.1380E-09 68.13HgS(aq) 1.0000E-09 31.86 1.0000E-09 31.87 1.0000E-09 31.87 1.0000E-09 31.87 1.0000E-09 31.87 1.0000E-09 31.87HgS2-24.2968E-13 1.369E-02 <0.001 <0.001 <0.001 <0.001 <0.001HgS(HS)-2.5805E-13 8.222E-03 3.4863E-14 1.111E-03 <0.001 <0.001 <0.001 <0.001162 of 352Table 3.21.Continued.GalenaLog P(CO2) (bar)-4 -3 -2 -1 0 +1ConcentrationSpeciesmol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol %PbCO3(aq) 2.6250E-13 1.918E-02 3.3164E-12 2.417E-01 4.1534E-11 2.94 5.0714E-10 26.59 6.0318E-09 74.20 6.7635E-08 84.71Pb+21.7060E-14 1.243E-03 7.5210E-13 5.327E-02 2.5440E-11 1.33 6.8289E-10 8.40 1.0222E-08 12.80PbS(aq) 1.2589E-09 91.97 1.2589E-09 91.75 1.2589E-09 89.16 1.2589E-09 65.99 1.2589E-09 15.49 1.2589E-09 1.58Pb(CO3)2-24.5836E-14 3.348E-03 1.7084E-13 1.245E-02 6.9837E-13 4.946E-02 3.9345E-12 2.063E-01 3.0843E-11 3.794E-01 5.4452E-10 6.820E-01PbSe(aq) 1.0965E-10 8.01 1.0965E-10 7.99 1.0965E-10 7.77 1.0965E-10 5.75 1.0965E-10 1.35 1.0965E-10 1.373E-01Pb(OH)+7.1397E-15 5.216E-04 5.3504E-14 3.899E-03 3.9085E-13 2.768E-02 2.4377E-12 1.278E-01 1.3123E-11 1.614E-01 4.8999E-11 6.137E-02PbCl+<0.001 <0.001 <0.001 8.8924E-14 4.662E-03 1.9536E-12 2.403E-02 2.0057E-11 2.512E-02Gudmundite Solid Solution ComponentLog P(CO2) (bar)-4 -3 -2 -1 0 +1ConcentrationSpeciesmol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol %Sb(OH)3(aq) 1.2617E-11 99.72 1.0346E-10 99.95 1.1415E-09 99.99 1.3935E-09 100.00 1.6553E-09 100.00 1.8374E-09 100.00Sb(OH)4-3.5686E-14 2.820E-01 4.9945E-14 4.825E-02 9.7811E-14 8.568E-03 2.4850E-14 1.783E-03 7.1924E-15 4.345E-04 2.8552E-15 1.554E-04163 of 352Table 3.21.Continued.TiemanniteLog P(CO2) (bar)-4 -3 -2 -1 0 +1ConcentrationSpeciesmol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol %ZnSe(aq) 1.4454E-08 85.46 1.4454E-08 86.20 1.4454E-08 86.33 1.4454E-08 86.35 1.4454E-08 86.36 1.4454E-08 86.36HgSe(aq) 2.1380E-09 12.64 2.1380E-09 12.75 2.1380E-09 12.77 2.1380E-09 12.77 2.1380E-09 12.77 2.1380E-09 12.77PbSe(aq) 1.0965E-10 6.483E-01 1.0965E-10 6.539E-01 1.0965E-10 6.548E-01 1.0965E-10 6.550E-01 1.0965E-10 6.551E-01 1.0965E-10 6.551E-01CdSe(aq) 3.5481E-11 2.098E-01 3.5481E-11 2.116E-01 3.5481E-11 2.119E-01 3.5481E-11 2.120E-01 3.5481E-11 2.120E-01 3.5481E-11 2.120E-01FeSe(aq) 6.0651E-12 3.586E-02 3.0077E-12 1.794E-02 1.5312E-12 9.145E-03 8.4127E-13 5.026E-03 5.0044E-13 2.990E-03 3.5737E-13 2.135E-03Cd(OH)2(HSe)-1.6923E-10 1.00 2.8885E-11 1.722E-01 5.1265E-12 3.062E-02 1.0667E-12 6.373E-03 2.5971E-13 1.552E-03 9.2254E-14 5.512E-04UraniniteLog P(CO2) (bar)-4 -3 -2 -1 0 +1ConcentrationSpeciesmol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol %UO2(aq) 4.0476E-09 99.92 4.0476E-09 99.99 4.0476E-09 100.00 4.0476E-09 100.00 4.0476E-09 99.99 4.0476E-09 99.82UO2(CO3)3-32.8238E-15 6.971E-05 6.5828E-15 1.626E-04 4.1055E-14 1.014E-03 4.1055E-14 1.014E-03 4.2100E-13 1.040E-02 5.5480E-12 1.368E-01UO2(CO3)2-26.8029E-16 1.679E-05 5.1132E-15 1.263E-04 1.9863E-14 4.907E-04 1.9863E-14 4.907E-04 1.2075E-13 2.983E-03 1.5798E-12 3.896E-02UO2CO3(aq) 6.4301E-19 1.587E-08 1.6382E-17 4.047E-07 4.0298E-16 9.956E-06 4.0298E-16 9.956E-06 8.9561E-15 2.212E-04 1.7907E-13 4.416E-03UO2+2.0264E-17 5.002E-07 1.9186E-16 4.739E-06 1.7552E-15 4.336E-05 1.7552E-15 4.336E-05 1.3367E-14 3.302E-04 8.5589E-14 2.111E-03HUO3-3.3530E-12 8.277E-02 5.7230E-13 1.414E-02 1.0158E-13 2.509E-03 1.0158E-13 2.509E-03 2.1140E-14 5.222E-04 5.1510E-15 1.270E-04164 of 352Table 3.21.Continued.SphaleriteLog P(CO2) (bar)-4 -3 -2 -1 0 +1ConcentrationSpeciesmol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol %ZnS(aq) 3.1623E-07 95.59 3.1623E-07 95.28 3.1623E-07 88.66 3.1623E-07 28.97 3.1623E-07 1.44 3.1623E-07 7.635E-02ZnSe(aq) 1.4454E-08 4.37 1.4454E-08 4.36 1.4454E-08 4.05 1.4454E-08 1.32 1.4454E-08 6.583E-02 1.4454E-08 3.490E-03ZnOH+9.2743E-11 2.804E-02 6.9500E-10 2.094E-01 5.0770E-09 1.42 3.1665E-08 2.90 1.7047E-07 7.764E-01 6.3648E-07 1.537E-01ZnS(HS)-1.2125E-11 3.665E-03 1.6382E-12 4.936E-04 <0.001 <0.001 <0.001 <0.001Zn+29.4721E-12 2.863E-03 4.2567E-10 1.283E-01 1.8690E-08 5.24 6.2637E-07 57.38 1.6471E-05 75.02 2.3312E-04 56.28ZnO(aq) 5.6732E-12 1.715E-03 7.1675E-12 2.160E-03 8.9764E-12 2.517E-03 1.0960E-11 1.004E-03 <0.001 <0.001ZnCO3(aq) 2.7953E-12 8.450E-04 3.5315E-11 1.064E-02 4.4229E-10 1.240E-01 5.4004E-09 4.947E-01 6.4231E-08 2.925E-01 7.2023E-07 1.739E-01ZnHCO3+2.6012E-13 7.863E-05 1.9493E-11 5.874E-03 1.4240E-09 3.992E-01 8.8814E-08 8.14 4.7813E-06 21.78 1.7852E-04 43.10ZnSO4(aq) <0.001 9.5241E-12 2.870E-03 3.4878E-10 9.779E-02 8.5090E-09 7.795E-01 1.3598E-07 6.193E-01 8.2664E-07 1.996E-01ZnCl+<0.001 <0.001 4.3321E-12 1.215E-03 1.2951E-10 1.186E-02 2.8454E-09 1.296E-02 2.9213E-08 7.053E-03Zn(OH)Cl(aq) <0.001 2.4503E-13 7.383E-05 1.7271E-12 4.842E-04 1.0107E-11 9.259E-04 <0.001 <0.001165 of 352 Arsenic and Sb behave similarly to each other, where aqueous species are dominated by neutral oxy-complex molecules of each element in the (III) state. Traces of As(V) oxy-anionic and neutral species are pr esent, including H 2 AsO 4 - and complexes of Mg +2 , Ca +2 and Fe +3 . The omission of corresponding species in the Sb system does not mean that they are absent, only that thermodynamic data are unavailable and no evid ence has so far indicated they might be important. Both As and Sb complex with sulf ide, but under the conditions of the current simulation, such complexation is negligible. Selenium complexation is also found to be negligible. Neither sulfide nor selenide complexes of either Ba or U are known.

At low P(CO 2 ), Cd speciation is dominated by Cd(OH) 2 (HS)- (relative concentration = 97 mol%) at P(CO 2 ) = 0.0001 bar, but declines progr essively with increasing P(CO 2 ) until it becomes inconsequential at P(CO 2 ) = 10 bar. CdS(aq), a minor species at low P(CO 2 ), increases
to a maximum relative concentr ation of 68 mol% at P(CO 2 ) =0.1 bar, but falls with further increase in P(CO 2 ). CdSe(aq) and Cd(OH) 2 (HSe) - are present as minor species, CdSe(aq) following CdS(aq) and reaching a maximum relative concentration of about 5 mol% at P(CO 2 ) = 0.1 bar. With increasing P(CO 2 ), Cd +2 increases progressively to a maximum relative concentration at P(CO 2 ) = 1 bar, then begins to decline as CdHCO3 + , whose relative importance increases progressively with increasing P(CO 2 ), and becomes predominant at P(CO 2 ) = 10 bar,
with a relative concentration of 53 mol%. The complex interaction between sulfide, selenide, bicarbonate and the uncomplexed Cd cation lead s to complex CdS (greenockite) solubility behavior with respect to P(CO 2 ), as is illustrated in Figure 3.51.

Hg speciation is almost completely dominated by neutral oligomeric species represented by HgS(aq) and HgSe(aq), and the solubility of cinna bar is consequently esse ntially independent of P(CO 2 ). The presence of selenide in solution effectively incr eases cinnabar solubility by approximately 50 mol%.

The behavior of galena sol ubility as a function of P(CO 2 ) is dominated primarily by the oligomeric species PbS(aq) at low P(CO 2 ), but with increasing P(CO 2 ), the carbonate complex, PbCO3(aq), becomes increasingly impor tant until it predominates at P(CO 2 ) > 1 bar. PbSe(aq) follows PbS(aq) in behavior, but increases ga lena solubility by only about 10 mol%. Pb+2166 of 352 increases progressively to a maximum rela tive concentration of 13 mol% at P(CO 2 ) = 10 bar. By analogy with Cd +2 , it is possible that the relative concen tration of this species would decline at even greater P(CO 2 ), as carbonate species become predominan t. The increase in galena solubility with P(CO 2 ) is due to the formation of the Pb(CO3 )(aq) complex, and occurs when Log P(CO 2 )
exceeds -1.

In specifying the input parameters, we recognize th at cadmoselite, tiemannite and clausthalite are all potential candidates fo r controlling Se(II) activity in solution, but it is not clear which is most plausible. Tiemannite was selected, because of the availability of thermochemical data for Hg selenide complexes (Mehra and Gubeli, 1971). A further constraint is imposed by Gibbs Phase Rule for equilibrium assemblages, which requires that only one selenide can be selected, unless saturation with respect to one or more sulfides of Cd, Hg, Pb, and Zn is suppressed. With the selection of tiemannite, and incorporation of selenide complexes, the saturation index for ferroselite is depressed to ~ -15 (Section 3.4.5.2). This suggests that Se substitution for S in pyrite is probably small. Although Ryser et al. (2 005) have identified the a ssociation of selenian pyrite and dzharkenite, a ferrose lite polymorph in shales asso ciated with Western phosphate deposits in the United States, we believe that such an occurrence is unusual and is not representative of potable aquifer host rocks in general. Western phosphate shales are exceedingly rich in Se, averaging about 275 mg/kg rock. This contrasts with the average abundance of Se
measured in surficial sediments and soils in the United States of no more than 1 mg/kg of material (Section 3.6). Western shal es therefore represent a case of extreme selenium enrichment by a factor of perhaps as much as 1,000, where it must be presumed that all Cd, Hg, Pb, and perhaps some or all of the Zn are stabilized as selenides rather than sulfides, and there is sufficient Se in excess to precipitate FeSe 2 . Selenium speciation in solution saturated with respect to tiemannite is completely dominated by complexation with Zn and Hg as the oligomer ic species ZnSe(aq) and HgSe(aq). Because sphalerite is relatively soluble in groundwater s, Zn complexing predominates, constituting 86 mol% of Se species in solution, the balan ce being made up primarily by HgSe(aq). Under alkaline conditions where P(CO 2 ) = 0.0001 bar, the Cd species Cd(OH) 2 (HSe)
– begins to make167 of 352 an appearance, but only to the exte nt of about 1 mol% in relative concentration. Other selenide species, including CdSe(aq), PbSe(aq), and FeSe(a q), never reach significant concentrations in
the current simulation.

The solubility of sphalerite at low P(CO 2 ) is determined primarily by the oligomeric species represented by ZnS(aq), which predominates in solution (~ 90+ mol%) until P(CO 2 ) exceeds 0.01 bar, thereafter falli ng to insignificant concentrations as P(CO 2 ) reaches 10 bar. The Se analogue, ZnSe(aq), behaves similarly, but constitute s no more than 5 mol% of the total relative concentration. With increasing P(CO 2 ), uncomplexed Zn +2 and ZnHCO 3 - become predominant, representing 56 and 43 mol% respectively of the re lative concentration at 10 bar. The behavior of Zn species with respect to sphalerite saturation is somewhat analogous to that of Cd species with respect to CdS (greenock ite) saturation, but wi th the important distinction that a Zn analogue to the species Cd(OH) 2 (HS)
– has not been identified. The variation of sphalerite solubility with P(CO 2 ) actually shows greater affinity with that of galena, where both are insensitive to P(CO 2 )
until it exceeds ~ 0.01 bar as carbonate co mplexes progressively overwhelm the sulfide complexes.

Finally, it should be noted that othe r species, including complexes of Cl - and SO 4 -2 with Ba, Pb, Zn, or Cd are evident, even at the low concentr ations of these species in the representative groundwater composition used in the analysis. Hi gher concentrations of either species could have a marked impact on the distributions repo rted in Table 3.20, which highlights the fact that complex geochemical systems must be fully desc ribed to account for all possible interactions.



### 3.5.6 Conclusions from Equilibrium Calculations

Figure 3.51 shows that the most serious water quality issue resulting from CO 2 intrusion may be the enhanced dissolution of pyr ite with consequent solubilizat ion of arsenic, with a high probability that the MCL would be exceeded. At plausible P(CO 2 ) levels representative of maximum possible values in sh allow groundwaters, Ba, Pb and Zn may approach or exceed regulatory concentration limits. For those elements, however, model uncertainties, kinetic factors, and post-withdrawal water treatment methods all suggest diligence rather than alarm. Of168 of 352 the remaining elements, the calculated concentra tions of Cd, Hg and Sb are somewhat higher than are believed to occur naturally, but analy tical methods are insufficiently sensitive for accurate quantificati on. None are expected to exceed MCLs with increasing P(CO 2 ). Hg, Se and U concentrations are unaffected by CO 2 intrusion.

From the foregoing discussion and the results depict ed in Figure 3.51, it is apparent that of the hazardous constituents investigated, only As and to a lesser extent Pb and Zn have the potential to exceed MCLs (or SDWRs) through imposition of elevated partial pressures of CO 2 . The focus of subsequent reactive transport simulations in Section 4 is on the mobilization and transport of As and Pb in potable water aquifers following intrusion of CO 2 from deep storage.169 of 352 

## 3.6 Representative Aquifer Host Rock Mineralogies

3.6.1 Background

This section provides a basis for defining the mineralogy of repres entative aquifers exploited for potable water, with the objective of illustra ting a range of geochemical responses to CO 2 intrusion (e.g., a well buffered aquifer vs. a poor ly buffered aquifer). The resulting suite of realistic “representative” mineralogies are used in the simulation study in Section 4 to evaluate
the effect of CO 2 intrusion on the chemical composition of the groundwater, and particularly on the potential changes in the co ncentrations of hazardous constituents. Our attempt to correlate aquifer mineral composition with host rock and groundwater chemical anal yses is part of an overall strategy to develop a geochemical mode l capable of represen ting any potable water aquifer.

Previous modeling of heavy metal mobilization a nd transport in potable water aquifers assumed grossly simplified mineral compositions (Wang and Jaffe, 2004), consisting of 50 wt.% galena
and 50 wt.% of either calcite or of quartz. In subsequent wor k, we formulated more realistic aquifer mineralogies (Birkholzer et al., 2008; Xu et al., 2007), based on extensive studies of Gulf Coast oil field arenaceous aquifers and associated confining shales (Xu et al., 2005), as well as on deep carbonaceous sandstone from the Ohio Ba sin (Zerai et al., 2006 ; Xu et al., 2006). To simulate to potential mo bilization of these contaminants under elevated CO 2 partial pressures, we assumed that a 1% volume fraction of either gale na or arsenopyrite was present, in addition to the known major minerals. However, as discu ssed before, these volume fractions of trace minerals such as galena or arsenopyrite in pota ble water aquifer host rock s are still unrealistically high. Furthermore, the Gulf Coast aquifer minera logy, which can be classi fied as a lithic arkose (see Figure 3.53 below), was based on that of deep arenaceous horiz ons in the Frio Formation of Texas, which is not necessarily representative of shallow potable water aqui fers over most of the contiguous 48 U.S. states.

Below, we develop “representative” mineralogi es for domestic shallow potable aquifers. These mineralogies are (1) an impure sandstone typical of those that are found underlying the Atlantic170 of 352 Coastal Plain, the Gulf Coast, and deposited in the Central Valley of California; (2) a relatively pure arenite, characteristic of the St. Peter sandstone underlyi ng the Illinois basin; and (3) a typical dolomitic limestone, found underlying ex tensive tracts of the Appalachians. These mineralogies are a compromise in representing broad ranges of aquifer host rock compositions found throughout the United States, with focus on aquifers that may overlie future CO 2 storage sites and are expected to be vulnerable to CO 2 intrusion. This compromise is a necessity in a global assessment of groundwater vulnerability, b ecause it would be impo ssible to explore fully the impact on hazardous metals transport under the diverse range of aquifer mineralogies that probably exist throughout the United States, ev en if such mineralogies had been fully
characterized.

In practice, such a restri ction is not generally a critical issu e. The main reason for this is the nature of the thermodynamic controls affectin g the partitioning of heavy metals between the solids of which they form an essential component and the aqueous phase (i .e., groundwater). As discussed in Section 3.4.1, equilibrium between the solid and the aqueous phase is governed by the relationship specified in Equation (3.1). Th is relationship can be redefined in terms of activity ratios of ionic species or in terms of chemical potentials, both of which are intensive parameters, i.e., they are independent of mass. This means that as long as a particular mineral is present, it does not matter how much is present. Likewise, if the mi neralogy of a host rock is in thermodynamic equilibrium, the groundwater compos ition would not be affected by the relative concentrations of the minerals are present.

Composition-dependent differences may arise, however, upon perturbation of the geochemical system, where the reaction path leading to re-establishment of thermodynamic equilibrium is defined by chemical kinetic and transport processes. Such a pe rturbation would arise from CO 2 intrusion where, for example, a certain mineral might dissolve entirely, or a secondary mineral might precipitate in one aquifer, but would not occur in another containing the same mineralogy, but with differing mass fractions. Also, the rate of dissolution or precipitation would be mass- or rather surface-dependent. Mass-dependent differen ces could become important to water quality, where pH or Eh buffering minerals are present in low concentrations, or where the activities of171 of 352 hazardous inorganic constituents are determined by trace concentrations of minerals containing
the constituents as esse ntial components. Although such kine tic issues must be recognized in simulating aquifer responses to CO 2 intrusion, we believe as a gene ral rule that only substantial variations in aquifer host rock mineralogy would l ead to major differences in the evolution of the system due to CO 2 intrusion. Thus, selection of the thr ee mentioned mineralogies should cover a broad range of likely geochemical environmen ts. Furthermore the methodology developed here has flexibility, by allowing simulations to be con ducted for site-specific conditions with actually measured aquifer mineralogies, physical properties , temperatures, and other relevant properties.



### 3.6.2 Approach at Mineralogy Characterization

3.6.2.1 Distribution of Major and Accessory Minerals

The Groundwater Atlas of the Unite d States, published by the Unite d States Geological Survey (U.S. Geological Survey, 1992-1997), was consulte d to identify the exte nt and importance of major aquifer host rock types in the Unites States . The atlas is subdivided into 12 segments, of
which four were selected for further evaluatio n: Segment 1 (California, Nevada), Segment 9 (Iowa, Michigan, Minnesota, Wisconsin), Segment 10 (Illinois, Indiana, Kentucky, Ohio, Tennessee), and Segment 11 (Delaware, Maryland, New Jersey, North Carolina, Pennsylvania, Virginia, West Virginia).

Although confined aquifers most likely to be affected by leakage from future underlying CO 2 storage reservoirs were of partic ular interest, the review showed that by far the largest source of drinking water in the four identified regions wa s from unconfined aquifers, which would be less likely to retain and trap intruding CO 2 . In Segments 9 and 10, the sources of many potable waters are locally shaley limestone and dolomites, typically less critical with regards to the impacts of
CO 2 intrusion. A host rock mineral composition th at is predominantly composed of carbonates would mitigate groundwater compositional changes in response to CO 2 intrusion because of the buffering effect of the carbonates. The main re maining potable water sources in Segment 9 are relatively pure sandstones of Cambrian-Ordovic ian age, principally th e St. Peter-Prairie du Chien-Jordan, Ironton-Gatesville, and Mount Simon aquifers. In Segment 11, arenaceous172 of 352 aquifers of the North-Atlantic Coastal Plain are the principal sources of drinking water, notably the Chesapeake, Castle Hayne-Aquia, Severn-M agothy, Peedee-Upper Cape Fear, and Potomac aquifers. The predominant sour ce of underground drinking water in the Segment 1 states of California and Nevada is the Central Valley Aquifer System.

Major aquifers vary substantially in mineralogy, both vertically and latera lly, reflecting temporal and areal facies and depositional changes duri ng palaeogeographical evolution. Furthermore, subsequent diagenetic and metasomatic processe s also vary in areal extent and intensity, depending on postburial history a nd subsequent exhumation. Thus, th e identification of a specific aquifer does not result in a unique characterization of its minera logy. Furthermore, variations characteristically overlap with other formati ons of similar provenance.To characterize the mineralogy, we used the principal formations identif ied from the review of the four segments in
a series of literature searches , with the objective of identifyi ng mineralogical analyses. Results from these searches were disappointing; few pape rs yielded critical quantitative information of the type desired, and those that did were not considered representative.

After failing to locate suitable representative mineralogical analyses of major aquifers in the regions studied, a different approach was taken to characterize the principal mineral composition of arenaceous rocks, as discussed in the follow ing paragraphs. Less attention was paid to the
characterization of carbonate aquifer mineralogy, because the aqueous buffering capacity is insensitive to large mass and compositional variati ons in the essential minerals composing such rocks, i.e., calcite and dolomite, and therefore significant mass and compositional variations in
these minerals are not critical.

Recently published textbooks on sedimentary petrology (i.e., Greensmith, 1989; Tucker, 1991; Blatt, 1992; Boggs, 2003) were reviewed to find a classification that would define the mineralogical characteristics of arenaceous rocks in a manner consistent with geochemical model development and the objectives of the project. A further requireme nt was that the classification would provide a framework for incorporating and exploring variations in the mineralogy of any chosen domestic potable-aquifer host rock. Although the authors of the cited textbooks pointed out a long-term problem regarding inconsiste nt, conflicting, and conf using classification173 of 352 schemes, a general consensus has emerged that a scheme broadly based on the classic text of Pettijohn et al. (1987) may be most reasonable.

This scheme by Pettijohn et al. (1987), illustrated in Figure 3.53, as based on Tucker (1991, Fig. 2.57, p.49), considers only the detrital material comprising the rock, ignoring subsequent postdepositional mineralogical change s arising from diagenesis a nd metamorphism. The mineralogy of the rock “framework,” or large particles, is represented on a triangula r diagram in terms of three major components: quartz, feldspar, and lith ic fragments, while in the plane perpendicular to the triangular diagram, the increasing fraction of included “matrix” or interstitial fine-grained (<30 μ m) material is considered. In further refining a selection of a specific mineralogy for the purposes of model development, use was also made of Figure 3.54, also from Tucker (1991, Fig. 2.62, p. 53), where the petrologic characteristics of arenaceous sediments derived under differing
tectonic conditions ar e plotted onto the triangular diag ram of the principal framework
constituents.

Fig. 3.53. Classification of arenaceous sedime nts in terms of detrital framework grains (quartz, feldspars and lithic fragments) and a detrital (clay) interstitial matr ix (after Tucker, 1991). The spot on the 7.5-wt% matrix triangle represents the base-case aquifer host rock introduced in Section 3.6.3.1. The diamond represents the St Peter Sandstone described in Section 3.6.3.3. For comparison, the star illustrates the Gulf Coast Lithic Arkose described in Xu et al. (2006).174 of 352 Fig. 3.54. Representation of the tectonic prov enances of framework grains of arenaceous rocks (after Tucker, 1991). TE = trailing edge or passive continental margin; SS = strike slip continental margin; CA = converging arc margin; BA = back arc basin and FA = fore arc basi n. The black spot indicates the base-case aquifer host rock introduced in Section 3.6.3.1. The diamond represents the St Peter Sandstone described in Section 3.6.3.3. For comparison, the star illustrates the Gulf Coast Lithic Arkose described in Xu et al. (2005). After defining the composition of the arenite in te rms of the Pettijohn et al. (1987) sandstone classification scheme, a furthe r characterization of the basi c mineralogy is required. This characterization includes (1) identifying the nature and composition of detrital feldspars, (2) the composition of the lithic fragments, and (3) the composition of the clay minerals in the matrix. Because the provenances of most detrital feld spars were exposed granitic to granodioritic batholiths, their composition is presumed to be primarily feldspar and relatively sodic plagioclases, i.e., oligoclase. For simplicity, it is assumed that lithic fragments likewise are represented by granitic or granod ioritic mineralogy. Clay mine rals occupy the matrices of arenaceous rocks. The clay mineralogy is ba sed on the average clay-mineral composition of some shales and mudstones reported in the literature (Boggs, 2003, Table 7.6, p. 289). Table 3.22 summarizes this mineralogy, and how it is redistribut ed to conform to limita tions in model input.175 of 352 Table 3.22. Matrix clay mineralogy.

Clay Mineral Concentration, Wt.% Model Distribution, Wt.% Kaolinite 14.4 22.9
Smectite 10.7 19.5
Illite 34.2 40.9
Chlorite 8.2 16.7 Mixed-layer illite/smectite 13.0 -
Kaolinite/chlorite 17.0 -
Montmorillonite/mixed -layer clays 2.3 -
Total 99.8 100.0

The actual characterization of the mineralogy under the three categor ies listed above is somewhat arbitrary, and reflects the lack of critical evaluations necessary to permit generalization. Because each clay mineral differs in its site complexation characteristics for each hazardous constituent in solution, variations in cl ay content and composition can markedly affect their partitioning between the host rock and migrating pore water in aquifers , and likewise strongly affect the mobilizat ion of metals caused by CO 2 intrusion, both of which is demonstrated by reactive transport modeling in Section 4. It is therefore an important consideration that the clay mineralogy be evalua ted for any site that becomes a candidate for a more detailed evaluation. Through the use of spr eadsheets, the mineralogies developed in this study can be recalculated for any specific set of site-specific conditions when required. See
Section 3.6.3 for the four selected mineralo gies defined based on the above approach.

The final step in defining the mineralogy invol ves the inclusion of mi nor, albeit critical secondary minerals that commonly form during au thigenesis and diagenes is, and occasionally through epigenesis. These include (1) organic matter (primarily kerogen), (2) carbonates and sulfates, both of which act as cement, and (3) iron oxides and sulfides, the former acting as adsorbents and the latter acting as hosts for the hazardous inorgani c chemical elements that form the subject of this study. The concentrations of these constituents in arenaceous sedimentary rocks are usually small, and quantitative surv eys defining their observed concentration ranges176 of 352 have not been made. We therefore estimated suita ble values, all of which are probably somewhat higher than average, but not unrealistically so.

We must emphasize that the mineralogical descri ptions of the aquifer host rocks, which are defined further in the following sections, are ba sed on field observations and are almost never assemblages at thermodynamic equilibrium. It is inevitable that incompatible assemblages are defined, which can never achieve thermodynamic equilibrium without mineralogical changes. Preliminary simulations of aquifer chemistry prior to CO 2 intrusion occasi onally necessitated ad
hoc adjustments, and therefore minor differences between the initial and simulation mineralogies occur, of necessity to ensure initial st eady state, if not thermodynamic equilibrium.

3.6.2.2 Distribution of Trace-Element Minerals

The specification of both the identity and th e concentrations of trace minerals containing hazardous metals as essential components is based on two sources of information: (1) identification of the minerals likely to act as hosts through a review of published scientific literature bearing on the subjec t (Section 3.2), and evaluation of potential host minerals controlling the saturation con centration of hazardous constitu ents in NWIS groundwaters (Sections 3.3 and 3.4), and (2) sp ecification of their concentra tions in soils and sediments estimated from information supplied by the National Geochemical Survey (NGS) Database, maintained by the United States Geological Su rvey. This second step is described in the following paragraphs.

The NGS database was searched to find the aver age concentrations of As, Ba, Cd, Hg, Pb, Sb, Se, U, and Zn in all analyzed soils and sediments from the con tiguous forty-eight states. Several analytical methods are reported in the database. Unfortunately, not all methods actually measure the total concentration, but rather a partial amount depending on the effectiveness of treatment used in liberating a particular element for th e analytical method. In this respect, neutron activation is the preferred method, because it does actually measure total concentration. (However, not all elements under considera tion were analyzed by this method.) The concentration, in ppm, was rounded up to the nearest significant fi gure, and it was then assumed177 of 352 that the source rock from which the sediment or soil was derived contains the same concentration of that element in a specified mineral where the element is an essential component. The resulting concentrations are given in Table 3.23.

The concentrations of the selected elements we re further checked against two other referenced sources of elemental abundances; (a) calculated crustal abundances based on the averages of values presented by Turekian (1970), Wanke et al. (1984), Weaver and Tamey (1984), Shaw et al. (1985), Taylor and McLennan (1985), Erns haw and Greenwood (1997), Lide (2005), and (b) abundances in domestic coals (Kuhn et al., 1981; Golightly and Simon, 2 006; Bragg et al., 1998; USGS, 2001), both of which are also included in Table 3.23. The latter source is subject to deviation from expected norms in potable aquifers, because of the overwhelming content of organic matter, which over geologic time could lead to enhanced seque stration of chalcophilic elements. However, because most of the elemen ts under consideration ar e chalcophilic, their relative abundances are likely to be minimally fr actionated. Furthermore, as noted in Section 3.2, an incentive exists for investigating the nature and distribution of hazar dous trace elements in coals that are discharged into the atmosphe re during combustion. As a consequence, many minerals containing trace elements have been identified in coal, which are easier to observe and characterize than in typical aquifer host rocks.

With the exception of cadmium and selenium, the elemental abundances characterizing the selected elements are generally consistent w ith corresponding average crustal abundances and are therefore believed to be reasonably re presentative for most aquifer host rocks. The assumption that each hazardous element is concentr ated in one mineral alone is conservative, since each element is expected to be distri buted over exchange and adsorption sites in the mineral assemblage, co-precipitated with second ary minerals, and in solid solution in primary detrital minerals. Indeed, several of the select ed constituents may be present entirely in substitution as minor components in other minerals , rather being present in minerals listed in Table 3.23, where the constituent is an essential component. Under such circumstances, calculated equilibrium concentrations in the aq ueous phase would represent the expected upper limit under reducing conditions.178 of 352 Table 3.23. Representation of the mineralogy of hazardous inorga nic substances in an arenaceous aquifer host rock
Concentration, ppm National Geochemica l Survey Database (Mainly Sediments and Soils) Element

ICP Part. ICP AA NAA NURE Average Crustal Abundance (1) Coal (2) Values selected for this Study Host Mineral Formula

As 10 1.3 5.3 10 4.7 1.69 6.8 10 Arsenopyrite (SS) FeAsS

Ba 549 - - 500 522 539 237 500 Barite BaSO 4 Cd 2 0.09 - 5 5 0.131 0.98 5 Greenockite CdS Hg - 0.04 0.03 0.04 500 0.0617 0.17 0.04 Cinnabar HgS Pb 18 10 - 20 12 13.9 29 20 Galena PbS Sb - 1 0.6 0.5 2 0.2 11.1 0.5 Gudmundite (SS) Kermesite FeSbS

Sb 2 S 2 O Se 0.241 0.2 0.25 1 0.067 2.6 0.25 Tiemannite FeSe 2 U 100 - - 3 - 1.91 2.3 3 Uraninite UO 2 Zn 64 29 100 58 72 832 100 Sphalerite ZnS Fe 25400 - - See notes 24,500 50,000 19,600 See notes Pyrite, goethite, chlorite, etc. FeS 2 , etc.

S - - - See Notes - 468 39,000 See Notes Pyrite, barite greenockite, galena, etc. FeS 2 , etc.

Notes: ICP: Inductively coupled plasma emission spectroscopy AA: Atomic absorption spectroscopy NAA: Neutron activation analysis NURE: National Uranium Resource Evaluation, a program utilizing various analytical methods (1): Average of five reported values (2): Data from Kuhn et al. (1980), except for Cd (Golightly & Simon, 2006) and Hg (USGS, 2001) Concentrations of Fe and S were not selected, as th ey are defined by the mine ralogies specified for the aquifer host rocks. Both arsenopyrite and gudmundite are expected to be present in solid solution (S S) in pyrite. Greenockite might be in solid solution in sphalerite, but it is a ssumed for the purpose of su bsequent reactive transport modeling that it is present as a pure substance.

Note that all representative mineralogies de veloped in Section 3.6.3 (with the exception of an aquifer in oxidizing conditions), utilized for the reactive transport simulation studies in Section 4, use the same set of trace mi nerals given in Table 3.23. In other words, we make the assumption that trace minerals are present where th e hazardous inorganic constituent is either a major component or incorporated in solid solution. Ba sed on the discussion in Sections 3.2 and 3.3.3, the presence of such trace minerals is a reasonable possibility in many aquifers. The179 of 352 concentration of these trace minerals, however, ar e very small, much smaller than was assumed in previous studies (Wang and Jaffe, 2004; Birkholzer et al., 2008; Xu et al., 2007).



### 3.6.3 Representative Mineralogies

3.6.3.1 Coastal Plain Aqui fer Host Rock

Most arenaceous sediments of the northeast and s outheast Atlantic seaboard, as well as the Gulf Coast, fall into the subarkose classification, and are characteristic of traili ng edge (TE) or passive continental margins (Figure 3.53). Sediments of the Central Valley of California we re laid down in a fore arc (FA) basin, but th eir mineralogy is characterized by weathering products of the
Sierra batholith. A decision was therefore made to choose a fr amework mineralogy that is a compositional compromise between a subarkose and a quartz arenite, consisting of 85 wt.% quartz, 10 wt.% feldspar, and 5 wt.% lithic fragme nts, as identified by the solid circle on Figure 3.53. Additionally, the clay content in the matrix was arbitrarily chosen to be 7.5 wt.% of the total, i.e., the framework comp rises the remaining 92.5 wt.% of the rock, intermediate in composition between an arenite and a greywacke.

We then defined the nature and composition of detrital feldspars, the composition of the lithic fragments, and the composition of the clay minerals in the matrix. The feldspars were divided equally between K-feldspar and oligoclase, refl ecting a granite-granodiorite provenance. The lithic fragments consist of equal parts quartz, K-feldspar, and oligoclase. The clay mineralogy was based on the average clay mineral composition, appropriate ly modified to account for thermodynamic limitations of the mode l, as described under Section 3.6.2.

In the final step, the trace minerals defined in Section 3.6.2.2 were added to the basic mineralogy. The resulting mineralogy is summari zed in Table 3.24. Notice that the expected
realistic volume fractions of Pb -bearing galena or As-bearing ar senopyrite are far smaller than the previously used volu me fractions of 50% or 1%, respec tively. The specified concentrations can be modified, if warranted, to reflect defi nitive determinations from any specific site.180 of 352 3.6.3.2 Coastal Plain Aquifer Host Rock - Oxidizing Conditions

Many potable water aquifers are unconfined, in which case continuous r echarge with oxygenated water may eventually lead to oxidation of residua l organic matter and secondary sulfides, such as pyrite and sulfides containing hazardous inorganic constituents. Furthermore, any ferrous iron present in detrital and secondary clays would also be oxidized to the ferric state, although not necessarily with any modification to their stability or crystal stru cture (Fialips et al., 2002). The recharge of aquifers by oxygenated groundw aters is a common ph enomenon, leading on occasion to the formation of a migrating re dox front (McMahon and Chapelle, 2008). Pervasive
in situ oxidation can occur when penetration is rapi d, particularly in response to anthropogenic extraction of potable water (Thornberg and Sahai 2004) or injection for th e purpose of aquifer storage and recovery (ASR) (Arthur et al., 2005). Such oxygenation can l ead to the mobilization of heavy metals, particularly As, Cd, Pb and U, with adverse consequences to water quality. In fact, oxidative dissolution may result in a higher degree of heavy-metal dissolution than a decrease in pH due to CO 2 intrusion. In fact, the oxidative dissolution can lead, under arid conditions, to the almost quantitative solubilization of all As present, as the natural abundance of As in many rocks, if partitioned completely into the groundwater, would be insufficient to achieve saturation with respect to any arsenate minerals after all Pb present has been coprecipitated as mimetite.

As discussed in Section 3.4, the mineral host limiting Pb concentration in solution under oxidizing conditions would be cerussite, which w ould lead to equilibrium Pb concentrations approximately four orders of magnitude greater that under reducing cond itions, where galena is likely to coexist with pyrite. Because of the like lihood of greatly enhanced solubility of cerussite in the presence of elevated CO 2 partial pressures, it is cons idered important to conduct one simulation case evaluating the potential behavi or of an oxidized aquifer affected by CO 2 intrusion.

Table 3.24 contains a listing of a modified mine ralogy of a representative Coast Plain Aquifer host rock, where all minerals likely to buffer the redox state under reduci ng conditions have been eliminated, and only cerussite is presen t as hazardous inorganic host mineral.181 of 352 3.6.3.3 St Peter Sandstone

The principal arenaceous aquifer rocks of Segm ents 9 and 10, e.g., the St Peter Sandstone, are reworked sands associated with a relatively st able cratonic environmen t, virtually devoid of residual feldspathic or lithic fragments and cont aining only a very small percentage of clay matrix. They therefore fall near or within th e classification of quart z arenite (Figure 3.53). Because of their limited buffer capacity, extensive distribution, and the fact that they overlie much of the Illinois Basin, which is a likely candidate for the storage of CO 2 in deep underlying saline aquifers, it was decided th at a representative mineralogy for the St Peter’s Sandstone should be formulated for the reactive transpor t modeling purposes. Minera logical data, cited in
the literature for St Peter Sandstone in Illi nois (Thiel, 1935; Odom et al., 1977; Hoholick, 1980; Hoholick et al., 1984; Barnes et al., 1992; Girard and Barnes, 1995; Pitman et al., 1997) were used to develop the representative mineralogica l composition as listed in Table 3.24. Noteworthy
is the addition of dolomite to the carbonate mi neralogy. Otherwise, the minerals present are similar, but in significantly differing proportions to those of the Coas tal Plain Aquifer. The framework mineralogy selected fo r St Peter Sandstone falls with in the pure arenite field, as indicated by the diamond symbol in Figur e 3.53. The matrix mineralogy consisting of carbonates, clays, goethite, pyrite and kerogen, in total, comprise only 2 wt.% of the rock, of which clays constitute about 1 wt.%. The specif ic mineral hosts for the hazardous inorganic
constituents were retained with the concen trations as those speci fied in Section 3.6.1.2.

3.6.3.4 Dolomitic Limestone

Wang and Jaffe (2004) pointed to the fact that the presence of carbonates tends to buffer the pH of the groundwater, even in the presence of elevated CO 2 partial pressures up to 10 bars. However, even though most arenaceous aquifers contain traces to minor concentrations of carbonates (principally calcite), the continuous ingress of CO 2 , leaking from an underlying storage reservoir, could eventually dissolve all the calcite in the vi cinity of the point of ingress. With the buffering capacity gone, the pH would fall and further enhan ce the solubility of minerals containing hazardous inorganic c onstituents as essen tial components.182 of 352 Many potable groundwaters are drawn from aquife rs consisting predominantly of limestones, some quite shaley or dolomitic, especially in regions aligned with the west flanks of the Appalachians and the Cumberland Plateau of Tennessee, such as the upper Knox Limestone. Representative dolomitic limestones contain up to 20 wt.% dolom ite. In our study, the mineralogy of an impure dolomitic limestone wa s formulated to represent the extreme case where complete dissolution due to (even long-term) ingress of CO 2 would be implausible. The clay composition of the dolomitic limestone differ s significantly from those of the other rocks specified for study, not only in quantity, but also in kind. Of the total cl ay concentration of 16 wt.%, 7 wt.% is illite and 5wt.% is kaolinite, chlorite and smectite making up the balance in equal proportions. The clay mineralogy is dictated partly by the diagenetic history of dolomitic limestone, where it is assumed that both dolomitization and illitization of smectitic clays were the result of thermogenic processes caused by d eep burial. The contra sting clay mineralogy impacts the adsorption of hazardous constituents, as adsorption sites on illite differ from those of smectite in magnitude and character, which is demonstrated in Section 4.7.2. This mineralogy is given in Table 3.24. The specific mineral hosts fo r the hazardous inorganic constituents were
retained with the concentrations of those specified in Section 3.6.2.2.183 of 352Table 3.24.Representative mineralogies of aquifer host rocks used in reactive chemical transport simulationsCoastal Plain Aquifer(Arenite) Reducing Env.St Peter Sandstone Dolomitic LimestoneCoastal Plain Aquifer (Arenite)Oxidizing Env.Mineral Element gfw V, cm3/mol Wt. % Vol Fraction Wt. % Vol Fraction Wt. % Vol Fraction Wt. % Vol FractionQuartz 60.084 22.688 80.1650.7766692.756209 0.926778 5.009065 0.05144173 79.28271411 0.79592773K-feldspar 278.33 108.87 6.1650.061874.881906 0.0505281 0.000000 0.00000000 6.097148787 0.06340635Oligoclase 1327.094 502.477 6.1750.099980.244095 0.00244551 0.000000 0.00000000 6.107038728 0.06147562Kaolinite 258.159 99.52 1.720.017010.097638 0.00099595 5.009065 0.05251716 1.701069897 0.01743424Smectite-Ca 365.394 132.51 1.460.013580.244095 0.0023423 2.003626 0.01976175 1.443931424 0.01392168Illite 383.899 138.94 3.070.02850.390552 0.00374012 7.012692 0.0690266 3.036211967 0.02921461Chlorite 634.653 210.26 1.250.01060.195276 0.00171185 2.003626 0.01805338 0.000000000 0.000000000Kerogen-OS 1881.97 1738.37 0.11250.0026660.097638 0.00238641 1.001813 0.02516736 0.000000000 0.000000000Pyrite 119.977 23.94 10.005120.097638 0.00051551 0.500907 0.00271834 0.000000000 0.000000000Dolomite 184.40 64.365 0.0000000.0000.488191 0.00450893 15.027196 0.14265542 0.000000000 0.000000000Calcite 100.087 36.934 1.60.015140.097638 0.00095337 60.108785 0.60326492 0.988994126 0.00970287Goethite 88.852 20.82 10.06010.097638 0.00060538 2.003626 0.01276885 0.988994126 0.0061612Apatite P 502.31 158.957 0.2702870.0021945 0.263904 0.00220978 0.270778 0.002330470.267313153 0.00224898Barite Ba 233.39 52.1 0.08496 0.00048944 0.027654 0.00016334 0.028374 0.00017227 0.084032816 0.00049873Arsenopyrite As 162.83 26.272 0.002100 9.0484E-06 0.002122 9.0594E-06 0.002177 9.5542E-06 0.000000000 0.000000000Greenockite Cd 144.48 26.42 0.000621 3.0327E-06 0.000628 3.0363E-06 0.000644 3.2022E-06 0.000000000 0.000000000Cinnabar Hg 232.66 28.416 0.000004 1.4622E-08 0.000005 1.4640E-08 0.000005 1.5439E-08 0.000000000 0.000000000Galena Pb 239.27 31.49 0.002232 7.8433E-06 0.002255 7.8529E-06 0.002314 8.2817E-06 0.000000000 0.000000000Cerussite Pb 267.21 31.49 0.000000 0.00000000 0.000000 0.00000000 0.000000 0.00000000 0.00255086 7.9922E-06Gudmundite Sb 121.75 73.352 0.000024 3.8866E-07 0.000068 3.8913E-07 0.000070 4.1038E-07 0.000000000 0.000000000Tiemannite Se 213.77 30.003 0.000033 1.2256E-07 0.000033 1.2271E-07 0.000034 1.2941E-07 0.000000000 0.000000000Uraninite U 270.03 24.616 0.000329 8.0056E-07 0.000332 8.0154E-07 0.000341 8.4531E-07 0.000000000 0.000000000Sphalerite Zn 96.98 23.83 0.014335 9.4066E-05 0.014485 9.418E-05 0.014862 9.9324E-05 0.000000000 0.000000000Total - - - 100. 1 100 1 100 1 100 1184 of 352 

## 3.7 Summary Results and Implicatio ns for Reactive Transport
Model of CO 2 

## Intrusion

Section 3 of this report covers the developmen t of necessary input for the reactive chemical transport simulations of high-pressure CO 2 intrusion into shallow potable water aquifers, which are described in Section 4. A comprehensive geochemical model was developed representing typical conditions in many freshwater aquifers in the United States. An important step in developing this geochemical model was to use a distribution-of-species code, EQ3/6, for the evaluation of the thermodynamic controls influe ncing the initial aqueous concentration and CO 2 - related mobilization of selected hazardous inor ganic constituents. By evaluating the chemical analyses of more than 38,000 groundwaters from th e USGS NWIS database, minerals hosts were identified containing one or more of the hazardous constituents, either as essential components or in solid solution. These hosts, upon e quilibration with groundwater, ar e able to assume partial or complete control of the aqueous trace-metal co ncentrations and their changes upon ingress of
CO 2 , depending on the nature of the mineralogy of the aquifer host rock. A representative traceelement mineralogy was developed for the solubi lity-controlling minera ls identified above, including their expected (typically very small) concentrations in the aquifer solid phase, and was subsequently used for the reactiv e transport model in Section 4.

Identification of thermodynamic controls necess itated the development and testing of a large supporting thermodynamic database comprising the so lubility products of minerals of interest, and of complexes that could aff ect the concentrations of the h azardous constituen ts in aqueous solution. In addition, the bulk mineralogy of the aquifer host rocks also had to be specified, which was accomplished through review of the sc ientific literature. The bulk mineralogy was combined with the trace element mineralogy involving the hazardous inorganic constituents,
whose abundance were defined in terms of the natural abundances of the hazardous constituents
in soils and sediments in the United States. Th e integrated mineralogy was normalized in terms
of molar volume fractions and passed through for TOUGHREACT simulations described in Section 4. In addition, thermodynamic data, in th e form of mineral so lubility products and185 of 352 aqueous complex dissociation consta nts used in the data base of EQ3/6, were also passed through and formatted for the corresponding TOUGHREACT database.

Prior to the complex reactive-transport modeling in Section 4, a preliminary assessment of CO 2 - related water quality changes was conducte d by calculating the expected equilibrium concentrations of hazardous inorganic constituen ts as a function of the partial pressure of CO 2 in water with a typical potable groundwater compos ition, defined based on the above geochemical
evaluation. Results indicate that the concentrations of some of the constituents are quite sensitive to changes in P(CO 2 ), whereas others remain relatively una ffected. This assessment was used in Section 4 to focus the reactive transport simulati ons on those constituents that have the clear potential to exceed maximum contaminant levels through imposition of elevated partial pressures
of CO 2 . The foregoing paragraphs cannot do justice in summarizing the ma ny tasks that formed part of the overall geochemical evaluation in Sectio n 3. Nor can they touch on the assumptions, approximations, and compromises that were necessa ry to bring such a complex study to fruition in the time available. In the following paragrap hs, we summarize in greater detail the several tasks accomplished in Section 3 of this report, a nd indicate further steps that would be desirable in refining the geochemical modeling with the objective of quantifying and decreasing current
uncertainties. These tasks are described in the following order: (1) lite rature review providing preliminary identification of thermodynamic controls for hazardous inorganic constituents, (2)
review and modification of an extensive ther modynamic database for relevant minerals and aqueous species and site complexation cons tants, (3) evaluation of more than 38,000 groundwater quality analyses from potable wate r aquifers throughout the United States, with final specification of solubil ity-controlling minerals for hazar dous inorganic constituents, (4)
evaluation of the natural abundances of these ha zardous constituents in sedimentary formations hosting potable water, (5) determ ination of typical aquifer hos t-rock mineralogies, and (6) calculation of equilibrium concentrations as a function of partial pressure of CO 2 .186 of 352 (1) Preliminary Assessment of Solubility- Controlling Minerals via Literature Review

Thermodynamic equilibrium of a given hazardous inorganic constituent can be established between the aqueous phase and (a) adsorption sites on mineral surfaces (e.g., iron oxides, silicates, sulfides, carbonates, etc.), (b) on functional groups on organic matter, (c) by ion exchange sites in clays, zeolite s, etc., (d) in solid solution in primary and secondary minerals, and (e) in discrete minerals where the element in question is an essential component. Because of the extremely low solubilities of many of the minerals falling under Category (d) and (e), such minerals are most likely to act as the pr imary thermodynamic controls determining the concentrations of the constituents in groundwat ers. Not many studies are reported in the literature that would help identify such solubi lity-controlling minerals for the relevant hazardous constituents and the range of geochemical conditi ons observed in United States aquifers. We therefore based our initial assessment of possible minerals controlling aque ous concentrations in groundwater on the extensive litera ture describing the mineralogy of coals and marine sediments (Section 3.2). (For the final assessment, we c onducted a rigorous thermodynamic evaluation of more than 38,000 groundwater analyses from potab le aquifers throughout the United States, using the distribution-of-s pecies code, EQ3/6.)

Because coal is burned, and hazardous inorganic constituents can be released in volatile or particulate form to the atmosphere, there is cons iderable interest in th e mode of occurrence of such constituents, and several quite elaborate studies have been undertaken over the past 40 years to establish where and how they are distributed in coal . (Although coal can hardly be considered representative of a quifer host rocks, its component pa rts, which crudely classify the potential host materials listed above, are found in both, although their respective mass fractions differ greatly.) The chemistry of each component has been determined through application of a sequential procedure using progressively more aggressive lixiviants, supplemented by suitable analytical and diagnostic procedures to charac terize the elemental and mineralogical composition together with microscopic techniques. A limite d number of similar studies have also been undertaken to examine the sedimentology of surfic ial marine sediments during authigenesis, the goal being to determine the immobilization of heavy metals through reduction and co-187 of 352 precipitation with pyrite. Finally, the descriptive literature on the metallo graphy of polymetallic ore deposits is a helpful adjunct in identifying anticipated phase and solid solution relationships among ore minerals, especially those most likely to form in minute and usually undetectable concentrations in potable water aquifers.

Based on the literature re view, a list of potential mineral ho sts was generated for the selected hazardous inorganic constituents and average geochemical conditions. This list was used as a suitable starting point for the rigorous thermodyna mic evaluation conducted in a second step (see Task (3) below).

(2) Modification and Augmentation of Thermodynamic Data

The geochemical models used in the current study require solubility produc ts of solid phases and dissociation constants for complexes. Both parame ters are defined by equations relating the solid phases or complex to basis species in solution. The respective solubili ty products and complex
dissociation constants can be calc ulated as a function of temperat ure and pressure, provided that sufficient thermodynamic parameters are availa ble. To ensure a realistic thermodynamic description of the complete geoc hemical system, we reviewed the available literature and, if necessary, revised and augmented the thermod ynamic databases associated with EQ3/6, the distributions-of-species code, a nd TOUGHREACT, the reactive transport simulator. This process was conducted for the hazardous inorganic constituents, as well as for a variety of additional rock forming and accessory minerals (Section 3.4.2).

In many cases, changes to the existing therm odynamic databases for EQ3/6 and TOUGHREACT were necessary not only to ensure that the geoc hemical model realistically represented aquifer conditions, but also to eliminat e obsolete and/or erroneous da ta. Most importantly, several significant errors and omissions were discovered th at entailed a complete revision of mineral solubility and aqueous sulfide and selenide complexation data for Cd, Hg, Pb and Zn. The revision was undertaken in an expedient, though not completely rigorous manner. The raw data from a substantial number of refereed papers related to sulfide and selenide mineral solubilities will eventually require a complete re-analysis to meet archival standards. Such a re-analysis,188 of 352 however, is not expected to significantly impact the findings in this report. All changes and revisions in the thermodynamic databases are described in great de tail in Appendix C.

For many of the minerals and some of the aque ous species, the SUPCRT92 code was used to calculate solubility products a nd complex dissociation constants as a function of pressure and temperature from given thermodynamic parameters . However, some constants were also taken directly from the literature, most of which were specified at 25oC and 1 atmosphere pressure. Because of the magnitude of the task in comp iling data for all the constituents under study, certain approximations and assumptions were made as documented in Appendices C, D, and E, which could introduce minor inconsistencies. These inconsistencies are believed to be small, and well within the overall uncertainty of the model.

The modified and augmented thermodynamic databa se was used in Section 3.4.5 to evaluate the groundwater quality data from the NWIS database and to identify minerals that control the
activities of the selected hazar dous constituents (see summary below). It was observed that some solubility-controlling minerals exhibited moderate discrepancies between the ideal saturation
index value of zero and the calculated modes of the 38,000 chemical analyses. This closing error
between the actual field observa tion and the thermodynamic model prediction was reconciled by further adjusting the log K values of the dissolu tion constants so that th e calculated mode for
each hazardous constituent was shifted to zero (Section 3.5.2). This was done in both the EQ3/6 and the TOUGHREACT databases prior to modeling the geochemical impact of CO 2 intrusion. In other words, a geochemical model calibrated to the NWIS groundwater analyses was used for the equilibrium calculations conducted in Sec tion 3.5 as well as for the reactive transport
simulations conducted in Section 4.

(3) Evaluation of Groundwater Quality Data from the NWIS Database and Identification of Thermodynamic Controls

Over 38,000 groundwater analyses were retrieved from the NWIS database, with the main objective of identifying thermodynamic controls that govern the concentrations of hazardous inorganic constituents in groundwaters. Prior to conducting the geochemical calculations using189 of 352 the distribution-of-species code, EQ3/6, the concentr ation distributions of these constituents were examined for artifacts caused by the use of insu fficiently sensitive analytical methods. In some cases, these resulted in spurious distributions in concentrati ons of elements whose ambient
concentrations were near or at th e limits of detectability. This app lied in particular to Cd, Pb, Sb, and possibly Hg. The analytical methods and thei r ability to measure co nstituent concentrations in groundwaters are listed in Appe ndix A. A discussion of the an alytical artifacts introduced by the use of insufficiently se nsitive analytical methods is given in Appendix B2.

A preliminary examination of all water quality an alyses was also conducted to assess analytical integrity. This examination showed that the majo rity of analyses possessed charge imbalances that were comparably large a ccording to recognized standard s. Although the principal cause could be attributed to inadequate cu stodial handling, leading to sample CO 2 degassing and probable precipitation of carbonate, no single corre ction method could be identified that would
eliminate these errors w ithout introducing others. Therefore all analyses were used in subsequent geochemical evaluations, but with due considerati on being paid to the limitations of the data.

A major challenge with the evaluation of the wa ter quality analyses was finding a way to define the redox state of the groundwaters, given the abse nce of reliable field measurements for this parameter. Two approaches were taken: (1) Eh was calculated based on the Fe(III)/Fe(II) couple at a given pH, using analytical data for dissolved iron together with the assumption that the groundwater was saturated w ith respect to goethite, and (2) Eh was defined at a given pH by the pyrite/goethite buffer. Two sets of runs were conducted with EQ3/6, in which the more than 38,000 analyses were speciated assuming the calc ulated Eh. The calculated saturation indices were the examined with respect to all those minerals containing a hazardous constituent as an essential component. Runs were conducted at 25oC and 1 atmosphere pressure rather than at ambient temperatures (which were probably lowe r), because the NWIS data did not report the ambient temperature and because some of the th ermodynamic data used in the databases of the code was specified only at 25oC . This assumption introduces an uncertainty in the results, which is smaller than the overall uncertainty associated with the water quality analyses and smaller than the natural variability in the large population of groundwaters studied.190 of 352 Several iterative refinements and augmentations to the thermodynamic data were made in successive runs, during which obvious errors we re identified and track ed down. In total, approximately one million species distributions were made. The results of the analysis turned out to be in good agreement with th e data, giving a str ong indication that the model assumptions are robust. Solubility-controlling minerals were id entified with reasonable confidence where the predicted saturation indices of many of the candidate host minera ls were close to or at zero, and the identified minerals correlated well with the limited quantity of information regarding their occurrence in the field, especially with respect to the coal data. Results were also consistent with the detectability of the hazardous constituents in groundwaters as determined by the analytical method used and its sensitivity.

Perhaps the most important observation stemmi ng from the analysis using two methods of independently computing the groundwater Eh wa s that average minera l solubilities were
consistent with conditions close to or defined by the pyrite/goeth ite buffer, thereby indirectly substantiating the argument that the majority of groundwaters comp rising the data set examined are moderately to strongly reducing. Only a small number of analyses, such as those consistent with cerussite saturation, are indicative of more oxidizing conditions. The information derived from the database analysis also showed that the average pH of all groundwaters was about 7.6, and that the calculated average ambient Log P(CO 2 ) was about –2.5, one unit higher than that of the atmosphere, and that most aquifers tested were saturated with respect to calcite.

For a representative groundwater at reducing conditions, th e most likely mineral hosts controlling the concentration of the hazardous i norganic constituents studied are as follows. Arsenic is present as the arsenopyrite component in solid solution in pyrite. Ba is saturated with respect to barite. Cd is either in solid solution in sphalerite and/or is present as greenockite or cadmoselite. Hg is present as cinnabar and possibly as tiemannite, but its presence in solid solution in pyrite, perhaps in mi nor amounts, cannot be ruled out. Pb is present as galena, and/or possibly as clausthalite. Sb is present as the gudmundite component in so lid solution in pyrite, and probably as kermesite, especially in neutral to acid conditions. Se could be present as either clausthalite, tiemannite, or cadmoselite. U is pres ent as uraninite and/or coffinite. and zinc is191 of 352 present as sphalerite and/or hemimorphite. Some of the uncertainty associated with the specific identification of one particular mineral can be attr ibuted to variations in the redox potential and pH between aquifers (owing to the presence of differing redox buffers from the standard),
variations in the absolu te abundances of the hazardous constit uents in the aquifer host rocks, and
the fact that the Gibbs Phase Ru le with zero degrees of freedom at the specified pressure and temperature would not be violated in some cas es were both cited minerals to be present.

The mean saturation indices of some of the mineral hosts, identified as controlling the concentrations of certain hazardous inorgani c constituents, deviate positively from zero. Examples include uraninite and coffinite for uran ium, and sphalerite and/or hemimorphite or
zinc. Such deviations could be caused by any of a number of reasons relating to the thermodynamic properties of the participating solid phases and aqueous speci es in solution, to systematic deviations in the cal culated redox potential, or to di fferences between the reference temperature and mean formation temperature. Furt her targeted investigations are warranted to identify the underlying causes of the saturati on differences between calculated and field measurements (as defined by the saturation index) , and to decrease ambiguity in a few cases.
Where the mean saturation indices of some of the mineral hosts deviate negatively from zero, it is likely that a solid solution may exist in another mineral host. For example, arsenopyrite, gudmundite, or dzarkenite (ferroselite) component s can represent As, Sb, and Se substitution in pyrite. The geochemical model does not currently account for such solid so lutions, and therefore the magnitude of such occupanc ies cannot be directly calculated. Therefore, approximations were made in the current study to offset the omission of a solid solution model for arsenopyrite and gudmundite. Future model refinements would enhance the overall accuracy of geochemical
simulations.

The dispersion revealed in the sa turation index histograms for the minerals displayed in Section 3.4.4, 3.4.5, and 3.4.6 are a measure of the total uncer tainty and variability of the analyzed systems. Uncertainties in geochemical model parameters, as well as the natural variations expected in groundwaters of diffe ring provenances, contribute to this dispersion. A systematic examination of the uncertainties of each paramete r, and their impact on th e overall dispersion of192 of 352 specific mineral saturation indice s, is feasible but time consuming. Such an analysis would provide much insight as to wh at extent each parameter cont ributes to geochemical model uncertainty. The evaluation presente d in this report can therefore be a point of departure for the study of general issues relating to the validity of geochemical m odels in both a scientific and regulatory environment.

We emphasize that the approach of determining thermodynamic controls chosen in this study is an indirect one; it does not prov ide a direct confirmation of the existence of the minerals hosting the inorganic constituents. Direct confirmation is achievable only through verification of their presence through mineralogical, spectroscopic, or separation proc edures. Such procedures are extremely challenging, because the quantities pr esent are minute, and particle sizes may be extremely small. We recommend conducting such pro cedures in future site-specific studies to demonstrate the validity of the genera l geochemical model developed here.

(4) Natural Abundances of th e Trace Elements of Interest

The reactive transport model utilized in Sect ion 4 requires specification of the volumetric fractions of the solubility-controlling mineral hosts for hazardous inorganic constituents identified above. It is important that the specified con centrations of these ha zardous constituents in the aquifer host rocks are representative of those found in the United States. A search of the
literature showed that such information was not generally available, and therefore use was made of the National Geochemistry Database giving av erage elemental abundances in surficial soils and sediments in the United States, to act as a proxy for abundances in aquifer host rocks. Several alternative analytical methods were used in the compilation of the National Geochemistry Database, each having differing de tection levels and char acteristics. Average values were selected from among those different analytical methods deemed most representative. A comparison was also made between the selected average concentrations and the average crustal abundances of those same elements repor ted in the literature, an d it was found that they were for the most part comparable. Average abundances of hazardous inorganic constituents ranged from < 10 -7 for Hg to >10 -4 for Ba (Section 3.6.2.2). While th e above abundances are very small compared to the major minerals, all hazardous constituents targeted for study in this report193 of 352 occur ubiquitously in soils, sediments, and aqui fer rocks throughout the wo rld, indicating that the problem of mobilization of these constituents in response to CO 2 intrusion can be a widespread one in United States aquifers (Section 3.3.2).

(5) Typical Aquifer Host Rock Mineralogies

The mineralogy of the host rock of a potable water aquifer can have a significant influence on the water quality, and similarly impact the nature of compositional changes due to the ingress of high pressure CO 2 . In this report, one goal was to characterize the mineralogies of a selected group of host rocks that would be considered re presentative of the majo rity of confined and unconfined potable water aquifers in the Unite d States. Another consid eration was that the selected aquifers should also be those most likely to be affected by intruding CO 2 from deep saline storage reservoirs. At the outset of the study, it was presumed that the mineralogies of candidate aquifers would be suffi ciently well characterized that the appropriate information need only be lifted from suitable literature sources. It turned out that such information is not widely available, nor is it of sufficient quality or detail to be used for the simu lations contemplated in this study. Representative mineralogies therefor e were created using standard sedimentological classifications, and these were augmented with th e trace quantities of the minerals identified as being at equilibrium with the selected haza rdous inorganic constituents in the NWIS groundwaters (Section 3.6). The sp ecification of different mineralogical compositions was facilitated by the utilization of ExcelTM spreadsheets. If necessary, site-specific mineralogies can be entered and suitable inputs for the TOUGHREA CT or other simulators generated to suit specific needs.

The created mineralogical asse mblages, when contacted by the aqueous phase, are not necessarily able to come to equilibrium without progressive destruction of one or more minerals, because the mineralogy is commonly overspeci fied, and the Gibbs phases rule is violated. However, this condition is also typical of real sedimentary assemblages and is characteristically responsible for observed authig enic and diagenetic processe s. Therefore, the modeled groundwater composition generate d after attempting a simulated equilibration in reactive transport modeling invariably represents a quasi- rather than true equilibrium state. Despite this194 of 352 limitation, the simulated groundwater composition w ith specified initial concentrations of conservative components, such as chloride ion and Cl - , should fall within the range of groundwater compositions downloaded from the NWIS database (see Section 4.4).

(6) Concentrations of Hazardous Inorgan ic Constituents as a Function of P(CO 2 )

A preliminary assessment was made of how the partial pressure of CO 2 affected the equilibrium concentrations of hazardous inorganic constituen ts in water with a typical potable groundwater composition, assuming reducing conditions define d by the pyrite/goethite buffer (Section 3.5). The assessment incorporated both sulfide and selenide complexing of chalcophilic elements. Log P(CO 2 ) was varied between –4 and +1, leading to a corresponding pH variation between 8.9 and
5.6. Of the nine hazardous constitu ents studied, only As, and to a le sser extent Pb and Zn, are of have the potential for exceeding maximum contaminant levels at elevated P(CO 2 ). In Section 4 of this report, primary emphasis is placed on the evaluation of the response and transport of As and Pb under intruding CO 2 .195 of 352 

# 4. Systematic Geochemical Modeling

This section describes a system atic modeling study conducted with a reactive transport simulator to quantify the effect of CO 2 intrusion into a shallow aquifer. The goal was to determine the potential amount of hazardous constituents that can be mobilized by the ingress of CO 2 , to predict the fate and migration of these constituents in the groundwater, and to evaluate the likelihood that drinking water standards might be exceeded. A variety of scenarios and aquifer conditions was considered in a sensitivity evaluation. These scenarios and conditions, in particular those describing the geochemistry and mineralogy of potable aquifers, have been selected based on the geochemical model defini tion described in the previous section.



## 4.1 TOUGHREACT Simulator

We conduct our geochemical modeling with TOUGHREACT, a simulation program for nonisothermal multiphase reactive transport in variably saturated media (Xu et al., 2006). TOUGHREACT has been widely applied to many geologic systems and environmental problems, including geothermal systems, diag enetic/weathering processes, subsurface waste disposal, acid mine drainage remedi ation, contaminant transport, and CO 2 geologic storage (e.g., Xu et al., 2005). The simulator was developed by introducing reactive chemistry into the existing framework of a nonisothermal, multiphase, multicom ponent fluid and heat flow code (Pruess et al., 1999).

TOUGHREACT considers a wide range of thermo-physical-chemi cal conditions of pressure, temperature, water saturation, ioni c strength, pH, and Eh. The major processes for fluid and heat flow are: (1) fluid flow in li quid and gas phases under pressure, vi scous, and gravity forces, (2) interactions between flowing phases represente d by characteristic curves (relative permeability and capillary pressure), (3) heat transfer by conduction and convection, and (4) diffusion of water vapor and noncondensable ga ses. Transport of aqueous and gaseous species by advection and diffusion is considered in both liqui d and gas phases (Xu et al., 2005; 2006).196 of 352 A specific fluid property module within TOUGHREACT for CO 2 sequestration includes a comprehensive description of the thermodyna mics and thermophysical properties of a H 2 O–NaCl
-CO 2 system. Flow processes can be modeled isothermally (as done in this study) or nonisothermally, and phase conditions may include a single (aqueous or CO 2 -rich) phase, as well as two-phase mixtures. Depending on th e temperature-pressure conditions, CO 2 may be in supercritical or gaseous form. Aqueous chemical complexation and gas (CO 2 ) dissolution/exsolution are assumed to be at local equilibrium. For the conditions assumed in this study, the intrusion of gaseous CO 2 into a freshwater aquifer is modeled as a two-phase flow system, with dissolution of CO 2 into water occurring instantaneously until equilibrium
concentration is reached. Excess CO 2 migrates within the aquife r, driven mainly by buoyancy
forces.

The geochemical model implemented in TOUGHREACT considers a variety of equilibrium and kinetic chemical reactions (Xu et al., 2005; 20 06). Aqueous complexation, acid-base, redox, and cation exchange are considered under equilibriu m assumptions. The activity of aqueous species is equal to the product of the activity coeffici ent and the molal concentration. Aqueous species activity coefficients with the exception of CO 2 (aq) are calculated from the extended DebyeHückel equation (Helges on et al., 1981). The CO 2 (aq) activity and CO 2 (gas) fugacity coefficients are determined as functions of pressure, temperatur e, and salinity, with deta ils given in Xu et al. (2004). The thermodynamic data incorporated in TOUGHREACT have been derived from a comprehensive review of the published literature. Originally based on the equilibrium constants for aqueous species and minerals given in the EQ3/6 V7.2b database (Wolery, 1992), many substitutions and changes to this data base ha ve been incorporated into TOUGHREACT over the years in response to revised or improved information on the gi ven thermodynamic properties (Xu et al., 2005, 2006). In addition to these change s, Xu et al. (2005) have incorporatedthe thermodynamic properties of several phases re levant to geologic sequestration of CO 2 , among these various carbonate phases. Mineral dissol ution and precipitation can be modeled in TOUGHREACT, subject to either local equilibrium or kinetic conditions. For kinetically
controlled mineral dissolution and precipitation, a general form of ra te law is used, based on that used by Steefel and Lasaga ( 1994). The TOUGHREACT version with the geochemical model197 of 352 described in Xu et al. (2005; 2006) was the starting point for th e modeling studies performed in this study. However, further modifications we re made, as briefly summarized below.



### 4.1.1 Thermodynamic Data and Kinetic Data
Because of the unusual requirements of the curre nt study, the thermodynamic data incorporated
within the TOUGHREACT version described in Xu et al. (2005; 2006) are insufficient for adequately modeling the complex chemistry associat ed with the behavior of hazardous inorganic constituents in groundwaters. Ther efore, the available literature was reviewed and the database used for the reactive transport simulation has been significantly modified and, if necessary, the thermodynamic database in TOUGHREACT was re vised and augmented within this study, as described in Section 3.4.2 and Appendix C. Specific consideration was given to the thermodynamic data of the metal sulfide minera ls and aqueous complexes (see Appendix D for details), as well as metal selenide minerals and aqueous complexes (s ee Appendix E). Further modification of thermodynamic parameters was n ecessary to reconcile differences between the measured and predicted solubil ity controls in the more than 38,000 groundwater analyses from
the NWIS database. The modifications made to “calibrate” the geochemical model are described
in Section 3.5.1.

Mineral dissolution and precipita tion rates are a product of the ki netic-rate constant and the reactive surface area (Xu et al., 2006). Mineral r eactive surface areas (except those for illite, kaolinite, smectite and goethite) used in this study are taken from Xu et al. (2006, 2007), which are based on the work of Sonnenthal et al. (2 005). The values develo ped in Xu et al. (2006, 2007) are typically two orders of magnitude smaller than m easured surface areas based on surface roughness evaluation, because only part of the total mineral surface is involved in the chemical reaction, due to coati ngs, reduced exposure to the a queous phase, and channeling of
reactive flow. The reactive surface areas of illite , kaolinite, smectite and goethite are taken from published BET surface areas (Bradbury and Baeyen s, 2005; Gu and Evans, 2007; Lackovic et al., 2003; Muller and Sigg, 1991) in co nformity with the surface area used for surface complexation reaction involved in adsorption/desorption. Kinetic rate parameters for most minerals are taken from Xu et al. (2006, 2007), wh ich are based on work by Palandr i and Kharaka (2004), with the198 of 352 exception of galena and arsenopyrite. Kinetic rate parameters for ga lena are taken from Zhang et al. (2003). Those for arsenopyrite are assumed to be the same as those for galena, based on their structural similarity. All parameters are given in Table 4.3 and other ta bles throughout Section 4 that detail the basic mineralogies used in the simulations. The imp act of uncertainy in the kinetic rate parameters has been evaluated in a sensitivity anal ysis (Section 4.6.2).



### 4.1.2 Improved Adsorption Models
Adsorption via surface complexation has been widely studied (Dzombak and Morel, 1990; Lutzenkirchen, 2006) and is a key process with resp ect to the fate and transport of heavy metals (e.g., Bradl, 2004) or other hazar dous constituents such as As (Goldberg, 2002; Goldberg et al., 2005; Manning and Goldberg, 1997). We have th erefore incorporated into TOUGHREACT a model considering adsorption via surface comp lexation, using nonelectrostatic, constant capacity, and double layer models (Dzombak and Morel, 1990). The surface complexation model implemented in TOUGHREACT was verified by comparison with other codes such as PHREEQC (Parkhurst and Appelo, 1999) and CRUNCH (Steefel, 2001).

Adsorption of heavy metal (and As) ions on minerals is influenced by a va riety of factors, the most important of which are pH, type, and speciation of metal ion involved, as well as competition among different ions (Bradl, 2004). In this study, we consid er goethite, illite, kaolinite, and smectite as principal adsorbents, mainly because these minerals have strong adsorption capacities with respect to Pb and As

. A literature analysis was conducted to determine relevant surface-complexation reactions and thei r thermodynamic constants for these cations and minerals, as summarized in Appendix F.

Usually, the surface complexation reaction is com posed of the formation of outer-sphere and inner-sphere complexes. The formation of outer-s phere complexes is relatively rapid and usually treated as an equilibrium pro cess, which is known as steady- state approximation (Selim and Amacher, 1996). The formation of inner-sphere complexes is relatively slow, and is often calculated by a kinetic law. If the formation of outer-sphere and inner-sphere complexes occurs sequentially, the overall rate is determined by the formation of the inner-sphere complexes; if the199 of 352 formation of outer-sphere and inner-sphere comple xes occurs in parallel reactions, the overall rate is determined by the formation of the oute r-sphere complexes. Because in our model, the formation of outer-sphere complex and inner-sphere complex are treated as parallel reactions, all surface complexation reactions are treated as an e quilibrium process. It is worth noting that the time range to attain equilibrium of sorption reactions is from s econds to days while that for mineral dissolution is from days to years or ev en thousands of years (S elim and Amacher, 1996). Therefore, considering the simulatio n period of interest in current study (100 ye ar), it is safe to assume that sorption via surface complexation can be modeled as an equilibrium process, while
mineral dissolution is ki netically controlled.



## 4.2 Model Setup

The basic model setup for most of the simulations presented here is depicted in Figure 4.1. The hydrogeological configuration considered is rela tively simple. We consider a confined aquifer
with a uniform vertical thickne ss of 10 m at a reference dept h of 50 m. The simulation domain comprises an area of this aquifer 500 m in lengt h and 200 m in width. Wate r flows from left to right with a flow velocity of 10 m per year (tra nsport velocity). Hydrostatic pressure is assumed; i.e., the initial fluid pressure is 5 bar. Hydrogeologic proper ties, as given in Table 4.1, are homogeneous, isotropic, and cons tant over time. Carbon dioxide is assumed to migrate from a deeper geological storage site into the aquifer, representing, for example, local leakage through a fault zone. Specifics of these leak age conditions at depth are not im portant in the co ntext of this study; only the rate of CO 2 leakage and the area over which it ar rives at the freshwater resource
are relevant. We assume in the base model a continuous CO 2 leakage rate of 7.5×10 -5 kg/s, which corresponds to 2.36 tonnes per year, ente ring from below over an area of 100 m 2 (10 m × 10 m) at x = 105 m and y = 0 m.

With the intrusion of 2.36 tonnes per year of CO 2 , a small, two-phase zone evolves where water and gaseous CO 2 coexist (see simulation results in Sec tion 4.5). However, most of the gaseous
CO 2 quickly dissolves into the groundwater. With time, the CO 2 -charged groundwater flows downstream from the intrusion location, with the result that the acidity of the affected200 of 352 groundwater increases, along with the potential for mobilizing hazardous inorganic constituents. The simplified model setup is represented, in mo st simulation cases, as a two-dimensional (2D) horizontal depth-averaged model. A sensitivity case simulating a fully three-dimensional (3D) setup is described in Section 4.9.

The potential rate of CO 2 leakage into an aquifer may vary wi dely in field situations. We have selected the above rate leakage rate of 7.5×10 -5 kg/s, roughly based on the amount of CO 2 that can dissolve into the groundwater without fo rming a large two-phase zone of gaseous CO 2 and water. (The chosen leakage rate was based on a simple back-of-th e-envelope analysis, using as input the volume of water a ffected in the aquifer at gi ven flow velocity and the CO 2 solubility at given pressure and temperat ure conditions.) Excess CO 2 (i.e., gaseous CO 2 not dissolving into water) would likely migrate upwards and, in an unc onfined aquifer, would escape into the vadose zone without further impacting gr oundwater quality. In a confined aquifer, which is the situation assumed in this study, excess CO 2 would accumulate under the confining layer, largely bypassed by the migrating groundwater. We have condu cted sensitivity st udies varying the CO 2 leakage rate, as described in Sections 4.6.9 and 4.9.3.

Groundwater flow with 10 m per year 200 m

Gaseous CO 2 intrusion at a rate of 5x10 -5 kg/s (1.58 tonnes/yr) for 100 year simulation perio d 500 m Confined Shallow Aquifer at 50 m Depth

10 m 

### x z y

Groundwater flow with 10 m per year 200 m

Gaseous CO 2 intrusion at a rate of 5x10 -5 kg/s (1.58 tonnes/yr) for 100 year simulation perio d 500 m Confined Shallow Aquifer at 50 m Depth

10 m 

### x z y

Gaseous CO2 intrusion at a rate of 7.5 × 10 -5 kg/s (2.36 tonnes/yr) for 100 year simulation period 105 m

Fig. 4.1. Simplified model setup for initial geochemical model201 of 352 Table 4.1. Hydrodynamic parameters

Porosity 0.3 Intrinsic permeability 1×10 -12 m 2 (hydraulic conductivity of ≈ 0.86 m/day) Relative permeability (van Genuchten-Mualem model) 2 0.457 1/ 0.457 * * 1 1 rl kS S ⎧ ⎫ ⎪ ⎪ ⎛ ⎞ ⎡ ⎤

= −− ⎨ ⎬ ⎜ ⎟ ⎣ ⎦ ⎝ ⎠ ⎪ ⎪ ⎩ ⎭

with * ( 0.3) /(1.0 0.3) l S S =− − where S l is the liquid saturation degree Gas relative permeability 

# ( ) ( ) 2 2 ˆ ˆ 1 1 rg k SS =− − with 

### ( ) ( ) ˆ 0.3 / 1 0.3 0.05 l S S

= − −− Capillary pressure function 1 0.457 1/ 0.457 * 5 1 1 5.1 10 cap P S −

−

− ⎛ ⎞ ⎡ ⎤

=− − ⎜ ⎟

⎣ ⎦ ⎝ ⎠ × with * / 0.999 l S S = and subject to restriction 7 10 0 cap P −≤ ≤
Molecular diffusion coefficient 10 -9 m 2 /s

Due to its simplicity, the hydrogeological situati on assumed in this study does not account for effects that may affect the system behavior in fi eld situations. These effects include, for example, the impact of internal heterogeneity, which could lead to fast preferential transport in the aquifer, or the impact of vertical anisotropy in the aquifer’s permeability (e.g., due to bedding), which could affect the vertical spreading of the CO 2 plume. We chose an idealized hydrogeological situation in this simulation study to bring out mo re clearly the geochemical changes in response
to intrusion of CO 2 and to more easily interpret related sensitivities; in other words, we focused on a very complex geochemical system rather than a very complex hydrogeological system. It is
understood that our simulations ar e idealized representations that can only serve as a guide to actual field behavior, which can vary strongly from place to place. As discussed in Section 6, we recommend conducting site-specific model predictions in future studies—ideally combined with adequately designed field experiments—where th e local hydrogeological conditions would be represented in great detail.202 of 352 In the base model, the background CO 2 partial pressure was set to 0.002 bar (which corresponds to a Log P(CO 2 ) value of -2.7). This is about on e order of magnitude higher than CO 2 levels in the atmosphere, consistent with the fact that most groundwaters have elevated P(CO 2 ) levels due to the decay of organic matter. The selected background concentration of 0.002 bar falls into the range of values calculated from the database analysis described in Section 3.4.3 (Figure 3.17). Appelo and Postma (1994) re port partial pressures of CO 2 dissolved in natural groundwaters ranging from 0.0002 bar to 0.01 bar, depending on the host-rock mineralogy. Background partial pressure of CO 2 in the groundwater may be important in the context of this study, because the higher initial P(CO 2 ) values would correlate to lower initia l pH in the water, and thus the pH change would be less than expected with intrusion of CO 2 . The initial CO 2 partial pressure furthermore impacts the initial distribution of Pb, Cd, and As in the aqueous phase or as adsorbed species. See Section 4.6.6 for a sensitiv ity study with respect to initial CO 2 concentration.

Note that the base model assumes reducing co nditions with a redox potential of about -0.23 at pH around 7.6, defined by the coexistence of pyrite and goethite. The thermodynamic analysis of potable groundwater reported in Section 3.4 has demonstrated that the majority of groundwater samples represent waters from reducing environm ents. To evaluate the implications of oxidizing conditions, we conducted one simu lation study with a redox potenti al of approximately +0.37 V (Section 4.8).

In this simulation study, we focus on the behavior of Pb and As as the hazardous constituents that may be mobilized by the intrusion of CO 2 . Of the hazardous constituents studied in this report, the concentration of Ba, Cd, Sb, and Zn are also affected by the acidic c onditions after
CO 2 leakage (Section 3.5). Pb and As have been se lected here because these two are most likely to exceed MCLs in potable waters as a result of CO 2 intrusion (see Section 3.5.)

The chemical reactions considered in this modeling study include aqueous complexation, mineral dissolution/precipitation, and adsorption/deso rption via surface complexation, the latter considering goethite, illite, kaolinite, and smectit e as principal adsorbents. Ion exchange is not explicitly considered, since earl ier analysis suggested a less si gnificant effect on the chemical
evolution of As and Pb. Solid solutions ar e not explicitly considered, because the203 of 352 TOUGHREACT simulator offers currently onl y a very simplified solid-solution model. Approximations are made in the current modeling wo rk to offset the omission of an explicit solid solution model. As pointed out in Section 3.5, correc tions were made to the solubility products of arsenopyrite and gudmundite to refl ect the assumption that both As and Sb are likely to occur in solid solution in pyrite as FeAsS and FeSbS components. The magnitude of each correction was
based on the calculated maximum SI frequency determined through the analysis of the more than 38,000 NWIS groundwaters given in Section 3.4.5.2.



## 4.3 Model Sequence and Sensitivity Cases

All simulations were performed using a two-step procedure. First, equilibrium runs were conducted with a specified mineralogical compos ition to establish the initial (steady state) chemical composition of the groundwater at equilibrium condition with minerals and adsorbed
matter before the intrusion of CO 2 (see, for example, results described in Section 4.4). Second, reactive transport simulations were conducted for the hydrogeological environment shown in Figure 4.1, with the initial aqueous chemical co mposition obtained from the equilibrium run.
CO 2 intrusion starts at time zero and continues fo r the entire simulation period of 100 years. In other words, it is assumed that the CO 2 leakage remains undetected and that no remedial
measures are undertaken.

Various geochemical sensitivity cases are eval uated to (1) account for the wide range of conditions found in domestic potable aquifers a nd their host rocks, and (2) to encompass the uncertainty associated with geochemical proce sses and model parameters. In addition, we have varied certain hydrogeological aquifer conditions, su ch as the depth of th e freshwater resource, the groundwater flow rate and CO 2 intrusion rates. We have also evaluated the fidelity of the 2D model by comparing the results to those of the 3D model.

Most of the sensitivity cases described in this report assume the basic mineralogy of a Coastal Plain Sandstone aquifer, as described in Sectio n 3.6.3.1. This mineralogy is representative of an impure sandstone—with a composition consisting of mostly quartz plus some feldspar, lithic204 of 352 fragments, and clays—expected to represent one of the more vulnerable aquifers, because the pH buffering capacity to CO 2 intrusion is limited. We describe in Section 4.4 the initial geochemical conditions derived for this type of aquifer a nd discuss how these conditions compare with the measured concentrations of major constituents in potable groundwater described in Section 3.3.2. We then provide the simulation results for CO 2 intrusion into a Coastal Plain Sandstone, starting with the base model (Section 4.5) and follo wed by various sensitivity cases (Section 4.6).

Table 4.2. Summary of sensitivity cases

Base Model Coastal Plain Sand stone Mineralogy Section 4.5 Sensitivity to Adsorption Section 4.6.1 Sensitivity to Kinetic Rate Parameters Section 4.6.2 Sensitivity to PbS(aq) Associ ation Constant Section 4.6.3 Sensitivity to Mineral Solubility Section 4.6.4 Geochemical
Processes and
Parameters Sensitivity to Initial Mineral Volume Fraction Section 4.6.5 Sensitivity to Background CO 2 Concentration Section 4.6.6 Sensitivity to Depth of Aquifer Section 4.6.7 Sensitivity to Groundwater Flow Rate Section 4.6.8 Model Setup,
Initial and Boundary
Conditions Sensitivity to CO 2 Leakage Rate Section 4.6.9
St. Peter Sandstone Min eralogy Section 4.7.1 Other Mineralogies Dolomitic Limestone Mi neralogy Section 4.7.2 Oxidizing
Conditions Coastal Plain Sandstone Mineralogy Section 4.8

Dimensionality of Model 3D Model Section 4.9

Returning to the base model setup, we repor t (in Section 4.7) simulations using other mineralogical compositions. These include two end-member cases: (a) one having the mineralogical characteristics of St Peter Sandstone , which is an almost pu re arenite and expected
to be most vulnerable to CO 2 intrusion, and (b) another having the mineralogy of a Dolomitic Limestone aquifer, where the pH is well buffe red and expected to be less vulnerable to CO 2 intrusion. A modified mineralogy representing a Coastal Plain Sandst one in an oxidizing environment is used in Section 4.8. All specified mineral compositions c ontain trace amounts of205 of 352 minerals containing hazardous metals or As as essential components, as detailed in Section 3.6.2.2. A complete list of all sensitivity cases is given below. Some other relevant factors and their potential importa nce are qualitatively di scussed in Section 4.10.



## 4.4 Initial Geochemical Conditions for Base Model with Coastal
Plain Sandstone

The base model simulates the groundwater disturbance by CO 2 intrusion into a Coastal Plain Sandstone aquifer, as described in Section 3.6.3. 1. The base model setup, introduced in Section 4.2, assumes a two-dimensional depth-averaged model domain with homogeneous properties.
Gaseous CO 2 intrudes from the bottom into the sha llow, confined aquifer groundwater with groundwater flowing at 10 m per y ear. Reducing conditions are assumed.

The basic mineralogy of Coastal Plain Sandsto ne aquifer, including the hazardous-constituentbearing minerals, is listed in Table 4.3. With respect to mi nerals containing hazardous substances, we consider galena (Pb), arsenopyrit e solid solution (As), barite (Ba), greenockite (Cd), cinnabar (Hg), gudmundite solid solu tion (Sb), tiemannite (Se), uraninite (UO 2 +2 ), and sphalerite (Zn). However, in our current simula tion study, we focus only on the evolution of Pb
and As. The other listed hazardous constituents are included in the reactive transport simulations, but are not further reported here, because ei ther their MCLs are not exceeded in reducing groundwaters, or they are believ ed to be insensitive to CO 2 intrusion (Section 3.5). The selection of these minerals, as well as their solubili ties, is based on the thermodynamic evaluation
conducted in Section 3.4.5 and is consistent w ith the equilibrium calculations conducted in Section 3.5. Notice the relatively small volume fraction of the hazardous-constituent-bearing minerals in Table 4.3, which is a realistic repr esentation based on the re view of trace element concentrations in soils and sediments given in Section 3.6.2.2.

Table 4.3 also gives the selected parameters for the rate law used for kinetically controlled mineral dissolution and precipiat ion. TOUGHREACT (Xu et al., 2006) uses a general form of206 of 352 rate expression, which is based on transition stat e theory (TST) (Lasaga et al., 1994; Steefel and Lasaga, 1994):



### ( ) 1 / r kA Q K η θ ⎡ ⎤

= − ⎣ ⎦ (4.1)

where r is the kinetic rate (positive values indicate dissolution, negative values indicate precipitation), k is the rate cons tant (moles per unit mineral surf ace area and unit time) which is temperature dependent, A is the spec ific reactive surf ace area per kg H 2 O, K is the equilibrium
constant for the mineral–water reaction written for the destruction of one mo le of mineral, and Q is the reaction quotient. The parameters θ and η must be determined by experiments, but are commonly set equal to unity when experimental qua ntification is unavailable. The kinetics rate constant can be summed from three mechanisms (Lasaga et al., 1994):

25 25

25 11 11 exp exp 198.15 198.15
1 1 exp 198.15 H

OH nu H

n nu H a a H

OH

n OH a H E E kk k RT RT

E k R T α

α ⎡ ⎤⎡ ⎤

− − ⎛⎞ ⎛⎞ = −+ − ⎢ ⎥⎢ ⎥ ⎜⎟ ⎜⎟ ⎝⎠ ⎝⎠ ⎣ ⎦⎣ ⎦ ⎡ ⎤

− ⎛ ⎞ + − ⎢ ⎥ ⎜ ⎟ ⎝ ⎠ ⎣ ⎦ (4.2)

where subscripts nu, H, and OH indicate neutral, acid, and base mechanisms, respectively, E is the activation energy, k 25 is the rate constant at 25 °C, R is gas constant, T is absolute temperature, a is the activity of the species, and n is a power term (constant). Notice that parameters θ and η (see Eq. (4.2)) are assumed to be the same for each mechanism. TOUGHREACT also provides a general form of species-dependent rate constant, which is detailed in Xu et al. (2006).

Prior to modeling the reactive transport pr ocesses related to the intrusion of CO 2 (with results reported in Section 4.5), an equili brium run is performed to establis h the initial conditions for the reactive transport model. We discuss results fr om this equilibrium run below, to evaluate consistency between the simulated initial state of the aqueous phase composition and the concentration ranges revealed by the evaluation of groundwater from the NWIS database, as described in Section 3.3. A reasonable agreement would demonstrate that our reactive transport207 of 352 model, along with its input ge ochemical properties and thermodyna mic data, has been adequately defined. Results are given in Tables 4.4 and 4.5, listing the initial chemical compositions with respect to major and trace consti tuents. Additional information is provided in Tables 4.6 and 4.7, giving the aqueous speciation of Pb a nd As for comparison with Table 3.15.

Table 4.3. Mineral volume fractions, possible secondary minerals, and kinetic properties for a Coastal Plain Sandstone. Kinetic parameters from Xu et al. (2006).

Parameters for Kinetic Rate Law
Neutral Mechanism Acid Mechanism Base Mechanism Mineral Volume
Fraction
of solid A (cm 2 /g)
k 25 (mol/m 2 /s) E a (KJ /mol) k 25 E a n(H + ) k 25 E a n(H + )

Primary : Quartz 0.77 9.8 1.02 × 10 -14 87.7 K-feldspar 0.0618 9.8 3.89 × 10 -13 38 8.71 × 10 -11 51.7 0.5 6.31 × 10 -12 94.1 -0.823 Oligoclase 0.06 9.8 1.45 × 10 -12 69.8 2.14 × 10 -10 65 0.457 Kaolinite 0.017 1.95×10 5 6.92 × 10 -14 22.2 4.89 × 10 -12 65.9 0.777 8.91 × 10 -18 17.9 -0.472
Smectite-Ca 0.0136 5.64×10 5 1.66 × 10 -13 35 1.05 × 10 -11 23.6 0.34 3.02 × 10 -17 58.9 -0.4
Illite 0.0285 6.68×10 5 1.66 × 10 -13 35 1.05 × 10 -11 23.6 0.34 3.02 × 10 -17 58.9 -0.4
Chlorite 0.0106 9.8 3.02 × 10 -13 88 7.76 × 10 -12 88 0.5 Kerogen-os 2.67 × 10 -3 9.8 3.02 × 10 -13 88 7.76 × 10 -12 88 0.5 Pyrite 0.00512 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Calcite 0.0151 Assume d at equilibrium Goethite 0.00601 1.47×10 5 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Apatite 2.2 × 10 -3 12.9 2.34 × 10 -7 43.54 1 Barite 4.89 × 10 -4 12.9 2.34 × 10 -7 43.54 1 Arsenopyrite 9.05 × 10 -6 12.9 2.34 × 10 -7 43.54 1 Greenockite 3.03 × 10 -6 12.9 2.34 × 10 -7 43.54 1 Cinnabar 1.46 × 10 -8 12.9 2.34 × 10 -7 43.54 1 Galena 7.84 × 10 -6 12.9 2.34 × 10 -7 43.54 1 Gudmundite 3.88 × 10 -7 12.9 2.34 × 10 -7 43.54 1 Tiemannite 1.22 × 10 -7 12.9 2.34 × 10 -7 43.54 1 Uraninite 8.005 × 10 -7 12.9 2.34 × 10 -7 43.54 1 Sphalerite 9.4 × 10 -5 12.9 2.34 × 10 -7 43.54 1 Secondary: Dolomite 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Magnesite 9.8 4.57 × 10 -10 23.5 4.17 × 10 -7 14.4 1 Ankerite 9.8 1.26 × 10 -9 62.76 6.46 × 10 -4 36.1 0.5 Dawsonite 9.8 1.26 × 10 -9 62.76 6.46 × 10 -4 36.1 0.5 Smectite-Na 151.6 1.66 × 10 -13 35 1.04 × 10 -11 23.6 0.34 3.02 × 10 -17 58.9 -0.4 Pyromorphite 12.9 2.34 × 10 -7 43.54 1 Ferroselite 12.9 2.34 × 10 -7 43.54 1 Clausthalite 12.9 2.34 × 10 -7 43.54 1208 of 352 Table 4.4 shows the initial total aqueous concentr ation of major constituents, plus some other geochemical properties, obtained from the initia l equilibrium run. These are compared with the respective values obtained at maximum frequency in histograms of the major constituents in more than 38,500 potable groundwaters evaluated in the NWIS database analysis, as given in Table 3.7. The water composition calculated in the initial equilibrium run with Coastal Plain Sandstone is characterized by slig htly higher Na and K, slightly lower Ca and Fe, and fairly
similar concentrations of SiO 2 , SO 4 -2 , and Cl - . The observed differences are quite moderate, considering the fact that the reported peak hi stogram values in Table 3.7 typify very broad concentration distributions, char acterized by the range and variety of geochemical conditions encountered. The water compositions show slightly different values for the partial pressure of
CO 2, pH, and Eh. The difference with respect to Ca is in part caused by the fact that the water composition calculated for the Coastal Plain Sa ndstone had lower alkalinity and higher pH, assuming the water is in equilibrium with calcite —whereas the measured concentrations at peak value are slightly oversaturated with respect to calcite (based on a EQ3 run using the analytical peak concentrations). There are, however, consid erable differences with respect to Mg and Al (respectively 40 times and 2,000 times smalle r in the simulated concentration). These discrepancies are caused by some limitations in the current geochemical model, which we discuss briefly in the following paragraphs.

Normalized saturation indices of a suite of minerals commonl y encountered in sedimentary
environments are listed in Table 4.4a for initial secular equilibrium conditions generated for the
Coastal Plain Sandstone and for modal chemical analyses of NWIS potable water composition.
The saturation indices are normalized to unit sili ca stoichiometry for silicates and unit carbonate stoichiometry for carbonates. This normaliza tion procedure affords a better basis for intercomparison of mineral saturations, because th e saturation index is an extensive parameter that can vary depending on how the chemical form ula of a mineral is written. Both the Coastal Plain Sandstone aquifer initial condition and th e potable water compositions are concordant in displaying significant supersaturation with respect to quartz, the former due to kinetic constraints and the latter due to specification of saturati on with respect to opal-CT. Both also display significant undersaturation with re spect to dolomite, consistent w ith field observations indicating209 of 352 that de-dolomitization can occur in shallow freshw ater aquifers. Otherwise, the saturation indices
of most silicates and carbonates in dicate a relatively close approach to saturation, and fall within the range of uncertainty expected from the level of accuracy in log K values of mineral solubility products.

Table 4.4. Initial total aqueous concentration of major constituents obtained by initial equilibrium run for Coastal Plain Sandstone and analytical concentration at the maximum frequency in the histogram of 38,500 potable groundwaters and saturation indices of some major minerals
Species Total Aqueous Concentration (mol/L) Total Aqueous Concentration (mg/L) Modal values of Analytical Concentrations at (mg/L)
Eh -0.215 (volt) -0.18 (volt) pH 7.61 7.3 Ca 9.00E-04 36.0 63.1 Mg 2.17E-05 0.52 20 Na 1.97E-03 45.36 12.6 K 2.68E-04 10.47 1.6 Fe 5.63E-06 0.31 1.3 Si 9.31E-04 55.9 75
TIC as HCO 3 - 3.33E-03 203.1 316
SO 4 -2 1.95E-04 18.7 20 Al 4.19E-11 1.1E-06 0.002 Cl 2.07E-04 7.3 15.8 P(CO 2 ) (log bar) -2.3 -2.3 -1.86 ionic strength (mol/L) 0.0051 0.0078

Noteworthy in both cases is the relatively close a pproach to saturation with respect to K-feldspar, in contrast to significant unders aturation with respect to low al bite. However, the degree of supersaturation of Ca- and Na-smectites in the potable waters suggests deficiencies in the thermodynamic data for these minerals. Furthermor e, the very large level of supersaturation predicted for chlorite (Mg 2.5 Fe 2.5 Al 2 Si 3 O 10 (OH) 8 ) in the potable water s uggests that either the computed solubility product for this mineral wa s inadequate, or that some other Mg-bearing mineral is controlling the Mg activity at metastab ly elevated levels in potable groundwaters. In this regard, note that talc (Mg 3 Si 4 O 10 (OH) 2 ) is close to saturation. It is geochemically210 of 352 unreasonable to expect talc to occur in most shallow arenaceous aquifers, but the presence of an otherwise unrecognized poorly cr ystallized magnesian-rich phyllo silicate metastably saturating such aquifers, especially in aquifers undergoing dedolomitization, cannot be discounted.

The observed apparent chlorite supersaturati on may also be attributed to the higher Al concentration reported in potab le groundwaters than predicted from thermodynamic calculations. A partial explanation for this phenomenon is given in Section 3.3.2, where it is noted that the precipitation of metastable amorphous hydrated alum inum silicates caused by the dissolution of unstable detrital feldspars is not accounted for, a process recognized by Paces (1978) several decades ago. Both the kinetics of feldspar a lteration and the associated precipitation of amorphous hydrated aluminum sili cates are poorly understood, an d further insight into the dissolution/precipitation mechanisms involved is needed before this process can be incorporated satisfactorily in geochemical models. However, r ecognition of this process may also explain the apparent chlorite supersaturation noted above.

The very large discrepancy between the modeled Al concentration in the Coastal Plain Sandstone aquifer initial condition and the modal value of analytical concen trations in NWIS groundwaters, as shown in Table 4.4 is primarily caused by th e model deficiency described above. However, a further contribution, also discussed in Sec tion 3.3.2, is the omission from the TOUGHREACT database of alumino-silicate complexes described by Tagirov and Schott (2001). These complexes can increase the total Al concentration in solution by a factor of ten. In conclusion, we may infer that the primary cause for th e discrepancies between modeled and observed aqueous concentrations in both Mg and Al is the failure to account for secondary precipitation of amorphous or cryptocrystalline Mg- and Al-hydroxy- silicates. It is beyond the scope of this study to investigate this matter furt her, particularly when it is recognized that failure to take this
issue into account has an in substantial impact on the ther modynamic and kinetic controls affecting the concentrations of hazardous inor ganic constituents in groundwaters, the primary subject of investigation. Ther e are several contributing reasons why the observed Mg and Al discrepancies are not relevant in the context of this st udy, as explained below.211 of 352 First, the activities of all of the hazardous constituents under study are determined by the solubilities of their mineral hosts, which have be en identified as sulfides or selenides, carbonates and a silicate. None involve the participation of minerals containing alumina. Second, the pH of the aqueous phase is normally buffered primarily by carbonate minerals, espe cially by calcite. In the absence of carbonates, aluminosilicates, especi ally clays, may play an important role in buffering pH, but it is the well-defined amphoteric equilibrium between aqueous hydroxyalumina species that determines buffering capaci ty, rather than the solubility product of
aluminosilicates. It should be noted that the sol ubility products of most aluminosilicate minerals incorporated in both EQ3/6 and TOUGHREACT thermodynamic databases are derived from calorimetric measurements, rather than from direct measurement of their solubilities. In geochemical modeling of aqueous systems in whic h aluminosilcates play a part, the system is specified by Gibbs phase rule, and the concentrati on of Al in solution, defined in relation to the basis species, Al +3 in EQ3/6, or AlO 2 - in TOUGHREACT, is a depe ndent variable that is generally ignored, unless, as in the present study, an attempt is made to validate model predictions against fiel d observation. Finally, Eh is specif ied primarily through equilibria between pyrite and goethite at a given pH. Dissolv ed Al does not play a role in defining the redox state of the system, yet it is the redox state that is generally critic al in specifying the
activities of the studied hazardous constituents when in equilibrium with their mineral hosts. In summary, aluminum speciation in solution does not play a direct role in modifying the thermodynamics or kinetics of the activities of hazardous constituents in solution, and its amphoteric behavior in buffering pH is also not at issue.

Table 4.5 lists the initial aqueous concentrati on of all hazardous inorganic constituents in groundwater. The predicted total a queous Pb concentration is 1.3×10 -9 mol/L (2.68×10 -4 mg/L), which corresponds to a local maximum in the histogr am of measured Pb concentrations from the NWIS database for potable groundwaters (see Fi gure 3.6a in Section 3.3.3). The predicted total aqueous As concentration is 4.42×10 -8 mol/L (3.31×10 -3 mg/L), which falls close to the maximum frequency of As concen tration observed in NWIS analys es (see Figure 3.6b in Section 3.3.3). Similarly good agreement between the simulate d initial concentrati ons and the range of values reported from the database is also obser ved for the other hazardous inorganic constituents.212 of 352 Notice, furthermore, that the initial aqueous concentrations are fully consistent with the trace metal concentrations displayed in Figure 3.51 (at the selected log P(CO 2 ) level of -2.3), where equilibrium concentrations as a function of CO 2 partial pressure have been determined from EQ3/6 calculations. The above comparisons, for both major and trace elements, demonstrate that the geochemical conditions in the base model at initial eq uilibrium state are reasonably consistent with the average conditions observed in a large number of United States aquifers.

Table 4.4a. Comparison of normalized saturation indices of some major minerals from the initial equilibrium run for Coastal Plain Sandstone with those calcu lated for potable groundw ater with analytical concentrations at the maximum frequency in 38,500 potable groundwaters

Normalized Saturation Index 1 Mineral Initial condition, Coastal Plain Sandstone At maximum frequency in potable NWIS groundwaters Comments

Primary : Quartz +0.71 +0.84 Opal-CT - 0.00 Saturation specified K-feldspar -0.11 +0.16 Albite_lo

w -0.90 -0.48

Kaolinite -1.23 +0.25 Talc - +0.24SmectiteCa -0.02 +0.75

Smectitelow-FeMg - 0.00 Saturation specified in NWIS groundwater

Illite +0.02 +0.03 Chlorite +0.00 +2.21 Pyrite 0.00 0.00 Saturation specified Calcite 0.00 +0.09 Saturation specified in simulation
Goethite 0.00 0.00 Saturation specified Secondary: Dolomite -1.77 -1.07 Magnesite -1.91 -4.19 Ankerite -0.35 +0.08 Siderite -0.58 -0.05Nasmectite -0.11 +0.61

1 Normalized to unit SiO 2 for silicates, and unit CO 3 for carbonates213 of 352 Table 4.5. Initial chemical composition of trace elements of groundwater from the TOUGHREACT equilibrium simulation in comparison
Species Total Aqueous Concentration (mol/L) Total Aqueous Concentration (mg/L)

Pb 1.30E-09 2.69E-04 As 4.42E-08 3.31E-03 Ba 1.12E-06 1.53E-01 Cd 1.76E-09 1.98E-04 Hg 2.53E-09 5.08E-04 Sb 3.57E-10 4.34E-05 Se 1.69E-08 1.33E-03 U 4.48E-09 1.07E-03 Zn 3.40E-07 2.22E-02

With respect to aqueous speciation, the concentrati ons of Pb aqueous species listed in Table 4.6 are consistent with the findings discussed in S ection 3.5.4 and the values presented in Table 3.15.
At the selected CO 2 partial pressure of –2.3 (in log10), th e most important Pb aqueous complex is PbS(aq), which accounts for about 85% of th e total aqueous Pb. PbSe(aq) accounts for about
14% and PbCO 3 (aq) for about 1%. Table 4.7, which show s aqueous As specia tion, indicates that arsenite (As III) is the dominant species at the selected initial c onditions and mainly exists as a complex of H 3 AsO 3 (aq), while arsenate (As V) can be ne glected. These results are consistent with those derived from EQ3/6 calculations (Table 3.16).214 of 352 Table 4.6. Concentrations of aqueous species of Pb Species Concentration (mol/L) Percentage PbCO 3 (aq) 1.50E-11 1.15% Pb(HS) 2 (aq) 7.93E-18 0.00% Pb (HS) 3 - 9.63E-26 0.00%
PbCl + 8.67E-16 0.00%
PbCl 2 (aq) 5.50E-19 0.00%
PbCl 3 - 5.50E-23 0.00%
PbCl 4 -2 8.57E-27 0.00%
PbOH + 1.75E-13 0.01% Pb(OH) 2 (aq) 2.07E-15 0.00% Pb (OH) 3 - 9.25E-19 0.00% Pb (CO 3 ) 2 -2 1.15E-14 0.00% PbO(aq) 2.58E-15 0.00% PbHCO 3 + 3.59E-13 0.03%
Pb +2 2.06E-13 0.02% Pb(HSe) 2 (aq) 6.56E-24 0.00% Pb(HSe) 3 - 4.76E-38 0.00% PbS(aq) 1.11E-09 84.80% PbSe(aq) 1.83E-10 13.99% Total Pb 1.30E-09 100%

Table 4.7. Speciation of aqueous species of As Species Concentration (mol/L) Percentage H 3 AsO 3 (aq) 4.28E-08 96.79% H 2 AsO 3 - 1.41E-09 3.19% H 2 AsO 4 - 1.77E-13 0.00% H 3 AsO 4 (aq) 6.89E-19 0.00% HAsO 4 -2 1.63E-12 0.00% H 3 As 3 S 6 (aq) 7.84E-46 0.00% H 2 As 3 S 6 - 8.36E-42 0.00% HAs 3 S 6 -2 4.20E-42 0.00% H 3 As 3 Se 6 (aq) 3.93E-77 0.00% H 2 As 3 Se 6 - 1.39E-73 0.00% HAs 3 Se 6 -2 5.75E-75 0.00%
HAsS 2 (aq) 6.51E-20 0.00% AsSe(OH)(HSe) - 1.63E-19 0.00% AsS(OH)(HS) - 8.83E-12 0.02% Total As 4.42E-08 100%215 of 352 

## 4.5 Results for CO 2 

## Intrusion into Coas tal Plain Sandstone

4.5.1 Prediction of CO 2 

### Plume Evolution and Changes in Acidity

In our simulation scenario, CO 2 gas enters the aquifer from th e bottom at x = 105 m and y = 0 m,
over a horizontal area of 10 m × 10 m. The dissolution of CO 2 into water leads to the formation of various aqueous carbonate complexes and therefore the total amount of dissolved CO 2 is given by the concentration of total inorga nic carbon (TIC). The intrusion of CO 2 leads to an increase of TIC (Figure 4.2 left) and a corre sponding decrease of pH (Figure 4.2 right). Note that the
variation in concentration of TI C is the combined effect of CO 2 intrusion, dissolution of CO 2 in water, and chemical reactions (mainly as calcite dissolution in the current simulation). While initially all CO 2 dissolves almost instantaneously into the groundwater, a gas phase evolves after around 3 years close to the intrusion area (Figure 4.3). Both water and gaseous CO 2 are present in this area, forming a small two-phase zone that remains present until the end of the simulation period at 100 years (Figure 4.4). The CO 2 gas dissolves into the gr oundwater and is transported downstream (Figure 4.5). The plume of dissolved CO 2 (total carbonate) migrates primarily along the groundwater flow direction, while lateral spreading of dissolved CO 2 is minor, penetrating only up to 20 m in the y-axis di rection (direction perpendicular to groundwater flow). The spatial distribution of pH shown in Fi gure 4.6 closely follows the distri bution of TIC. The two-phase zone has the highest partial pressure of CO 2 and thus largest TIC concentration, and therefore, the pH in the two-phase zone is the lowest.

Notice in this context that the TOUGHREACT transport model has been set up without consideration of hydrodynamic (long itudinal or transverse) disper sion, which would induce more spreading. The process of hydrodynamic dispersion is often represented by a Fickian diffusion analogue (convection–dispersion eq uation) that has some flaws and limitations (Xu and Pruess, 2001). A Fickian model for dispersion is no t used in TOUGHREACT. Instead, hydrodynamic dispersion is introduced through spatial resolution. The discreti zation used for the transport simulations employs a grid size of 10 m in th e horizontal directions, implying that both the longitudinal and transverse dispersivities are on the order of about 10 m. In our study, modeling216 of 352 without explicit consideration of hydrodynamic dispersion means th at less spreading and dilution
of the dissolved CO 2 plume occurs, thus maximizing (at least locally) the impacts of CO 2 intrusion on the geochemistry of the system. If hydrodynamic dispersion wa s significantly larger than assumed here, the area affected by dissolved CO 2 would increase, but the maximum
concentrations would be reduced due to enhanced dilution of the plume.

0 20 40 60 80 100 Time (year) 0 0.04 0.08 0.12 0.16TIC (mol/L)

0 20 40 60 80 100 Time (year) 5.5

6 6.5

7 7.5

8pH

Fig. 4.2. Time evolution of total inorganic carbon (TIC) (left) and pH (right) at CO 2 intrusion location

0 20 40 60 80 100 Time (year) 0 0.02 0.04 0.06 0.08

0.1Gas saturation degree

Fig. 4.3. Time evolution of gas saturation degree at CO 2 intrusion location217 of 352 XY 100 200 300 400 50 100 150 200

SG 0.075 0.070 0.065 0.060 0.055 0.050 0.045 0.040 0.035 0.030 0.025 0.020 0.015 0.010 0.005

Fig. 4.4. Spatial distribution of saturation degree of CO 2 gas after 100 years of intrusion

XY 100 200 300 400 50 100 150 200

TIC 0.15 0.14 0.13 0.12 0.11 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01

Fig. 4.5. Spatial distribution of total inorganic carbon (TIC) (mol/L) after 100 years of intrusion218 of 352 XY 100 200 300 400 50 100 150 200

pH 7.49 7.37 7.24 7.12 7.00 6.88 6.75 6.63 6.51 6.39 6.26 6.14 6.02 5.90 5.77

Fig. 4.6. Spatial distribution of pH after 100 years of intrusion

Figure 4.7 shows profiles of TIC (left) and pH (right) at four tim es after the start of intrusion of
CO 2 , i.e., at 5, 20, 50, and 100 years. The profiles are ta ken along the direction of flow (x-axis) at y = 0 m. The plume of dissolved CO 2 migrates with the groundwat er flow over time, extending further and further downstream until a steady stat e is reached within the model domain. The evolution of pH closely follows th e evolution of TIC, with pH decreasing as the concentration of TIC rises. After about 50 years, the re gion of decreased pH extends from the CO 2 intrusion point downstream to the boundary of the model domain. Notice that the lowest pH is around 5.87 outside of the two-phase zone, which is higher th an the expected pH in a system without any buffering capacity. (For a nonbuffered system, a pH around 4.5 would be expected). The moderation in pH decrease is caused primarily by the dissolution of calcite. Figure 4.8 shows the volumetric change in calcite cause d by dissolution. Almost all of the calcite initially present near the two-phase zone has dissolved after 100 years. This significant dissolution of calcite buffers the pH response to the increasing CO 2 levels in the water; however , this buffering effect would cease locally if CO 2 intrusion were to continue after 100 y ears, since calcite would eventually be depleted.219 of 352 0 100 200 300 400 500 Distance (m) 0.0x10 0 4.0x10 -2 8.0x10 -2 1.2x10 -1 1.6x10 -1TIC (mol/L) 5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) 5.6

6 6.4 6.8 7.2 7.6

8pH 5 yr 20 yr 50 yr 100 yr

Fig. 4.7. Total inorganic carbon (left) and pH (right) along the x-axis at y = 0 m, for Coastal Plain Sandstone.

0 100 200 300 400 500 Distance (m) -100

-80 -60 -40 -20

0 20Calcite volume fraction change (%)

5 yr 20 yr 50 yr 100 yr

Fig. 4.8. Calcite volume fraction change profiles along x-axis at y = 0 m, for Coastal Plain Sandstone.



### 4.5.2 Prediction of Pb and As Concentrations

The processes that control the changes in aqueous Pb and As concentration in response to CO 2 intrusion include desorption/adsorption from the surface of minerals, dissolution/precipitation of relevant minerals (galena for Pb and arse nopyrite in solid solution for As), and the
dissociation/association of a queous complexes. The resulti ng concentrations are moreover affected by complex interac tion between these processes

.220 of 352 Figure 4.9 shows the contour map of total aque ous Pb concentration after 100 years, which resembles closely the spatial distribution of the pH decrease. The highest concentration occurs along the y=0 profile. Transport in the longitudinal direction is dominant. Figure 4.10 shows the total aqueous Pb concentration pr ofile along the x-axis at y = 0 m for different times. The peak value of aqueous Pb is 3.4×10 -8 mol/L in the small two-phase zone at the intrusion location after 5 years, which then decreases to 1.8×10 -8 mol/L after 50 years. As time progresses, the peak moves incrementally downstream, a nd after 100 years falls to 1.6×10 -8 mol/L. The aqueous Pb concentration downstream approach es a stable value around 1.1×10 -8 mol/L. This value is significantly higher (about one or der of magnitude) than the initial concentration, but remains below the MCL (maximum contamination level), which is 7.24 × 10 -8 mol/L (15 ppb). The observed Pb concentration profiles reflect a combination of dissolution/precipitation and adsorption/desorption effects, as discussed below.



### XY 100 200 300 400 50 100 150 200

Pb 1.5E-08 1.4E-08 1.3E-08 1.2E-08 1.1E-08 1.0E-08 9.1E-09 8.2E-09 7.2E-09 6.3E-09 5.4E-09 4.4E-09 3.5E-09 2.5E-09 1.6E-09

Fig. 4.9. Spatial distribution of total aqueous Pb after 100 years of intrusion221 of 352 0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

Fig. 4.10. Total aqueous Pb concentration profile along x-axis at y = 0 m.

Our results for the base-case model suggest that adsorption and desorption of Pb +2 ion from mineral surfaces are more important to the evol ution of aqueous Pb concentrations than the precipitation and dissolution of galena. Figur e 4.11 exemplifies the complexity of these processes by illustrating the change in the surf ace complex concentrations for illite. The surface complex concentration is expressed in mol/L solution. Desorption and adsorption constitute, respectively, the net effects of decreasing and increasing the concentration for all relevant surface complexes. Illite is the most important adsorbent of Pb. Ill s _OPb + is a surface complex of Pb on illite (Appendix F), which represents the ma ss of Pb on the strong site at the surface of illite, while Ill w _OPb + represents the mass of Pb on the w eak site at the surface of illite. The
increase of Ill s _OPb + (Figure 4.11 left) compared to the initia l levels indicates that aqueous Pb is adsorbed on the strong site, whereas the decrease of Ill w _OPb + (Figure 4.11 right ) indicates that aqueous Pb is released from the weak site on illite. Another important surface complex that
adsorbs Pb is sme s _OPb + , which represents the mass of Pb on the strong site at the surface of
smectite.

Figure 4.12 shows the relative chan ge of total Pb concentration on adsorption sites (which is a
summation of the concentration of all Pb surface complexes), calculated as:

t ini

ini S S S − (4.3)222 of 352 where S t is the total Pb concentration on adsorption sites at a given time t , and S ini is the initial total Pb concentration on adsorption sites (bot h given in mol/L solution). The figure indicates that significant desorption of Pb occurs in the tw o-phase zone (depleting the initially sorbed Pb by about 60% after 100 years), thereby increasi ng the aqueous Pb concentration. Less effective desorption occurs further downstream, although the Ph there is similarly small. The overall effect of desorption is smaller because the aqueous Pb mobilized in the two-phase zone migrates downstream, inhibiting further desorption.

0 100 200 300 400 500 Distance (m) 0.0x10 0 4.0x10 -8 8.0x10 -8 1.2x10 -7 1.6x10 -7 2.0x10 -7ills_OPb+(mol/L) 5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) 10 -6 2x10 -6 3x10 -6 4x10 -6 5x10 -6 6x10 -6illw_OPb+(mol/L)

5 yr 20 yr 50 yr 100 yr

Fig. 4.11. Surface complex of Pb on the strong (left) and weak site (right) of illite along x-axis at y = 0 m for Coastal Plain Sandstone.

0 100 200 300 400 500 Distance (m) -80 -60 -40 -20

0 20Total Pb sorption sites change (%)

100 yr

Fig. 4.12. Relative change of total Pb surface complexes concen tration along the x-axis at y = 0 m for Coastal Plain Sandstone.223 of 352 Section 3.5 illustrated how the so lubility of galena increases w ith the partial pressure of CO 2 and presented the expected equilibrium concentra tions of Pb and other inorganic hazardous constituents at various P(CO 2 ) levels. It is important to point out, however, that these calculations (in Section 3.5.3) repres ent an equilibrium state, whic h does not take into account transient changes in adsorption/desorption and precipitation/dissolution. The ingress of CO 2 immediately disturbs both adsorption and solubili ty equilibria, but whereas adsorption reactions are always assumed to re-equilibrate instantane ously, the dissolution preci pitation of a hazardous mineral host is kinetically controlled and typica lly much slower. As we can observe from the transient runs conducted here, the relatively sudden release of Pb from adsorption sites increases the aqueous Pb concentration so significantly that the solution becomes in fact supersaturated with respect to galena (even though the groundwa ter becomes more acidic, suggesting a higher galena solubility), and galena precipi tation occurs in the two-phase zone.

Figure 4.13 exemplifies this process, giving the rela tive change in the galena volume fraction as
a function of time. The volume fr action change is calculated as:

t ini

ini V V V − (4.4)

where V t is the volume fraction of mineral at given time t , and V ini is the initial volume fraction of mineral. Negative values indi cate dissolution, positive values indicate precipitation. Notice that galena continues to precipitate in the two- phase zone over the entire simulation period of 100 years. At later stages, howev er, galena may start dissolving, because the Pb on adsorption sites will have eventually depleted.

As pointed out in Section 3.5, sulfide and seleni de aqueous complexation play a very important
role in the behavior and evoluti on of several of the hazardous c onstituents, most notably Pb, but also cadmium, mercury, and zinc. Because very st able neutral complexes form in solution, the impact that elevated CO 2 levels have on increasing the solubility of galena (PBS), for example, is reduced compared to a case where no complexation is considered, due to the competition between chalcogenide complexes and carbonate co mplexes. While not reported here, we have224 of 352 conducted reactive transport simulations without accounting for sulfide and selenide complexes
in solution. These resulted in higher overall aqueous Pb concentrations after CO 2 intrusion compared to the base model, because both desorption and galena dissolution caused Pb mobilization in the two-phase zone.

0 100 200 300 400 500 Distance (m) -0.1

0 0.1 0.2 0.3 0.4Galena volume fraction change (%)

5 yr 20 yr 50 yr 100 yr

Fig. 4.13. Galena volume fraction change profiles along x-axis at y = 0 m, for Coastal Plain Sandstone.

Figures 4.14 and 4.15 (left) show, re spectively, the spatial distribution of As concentration at 100 years and the evolution of As concentration in a profile along the x-axis. The peak concentration
of As is 1.29 × 10 -7 mol/L, with the As concentration stabilizing at 1.19 × 10 -7 mol/L downstream, slightly le ss than the MCL (1.33 × 10 -7 mol/L or 10 ppb). The increases in As
concentration are constrained to a narrow area downstream of the CO 2 intrusion point.

As was the case with Pb, the observed As c oncentrations are signi ficantly affected by adsorption/desorption processes. In the base mode l, illite is the most important adsorbent for both Pb and As, but as conditions change, other minerals may become more relevant, depending on the thermodynamic solubility, specific adso rption site density, and volume fraction of minerals present in the aquifer host rock. The ad sorption/desorption of As exhibits even more complexity than that of Pb. For example, H 2 AsO 3 (aq) is adsorbed on illite as three types of surface complexes: ill _H 2 AsO 3 , ill _HAsO 3 - , and ill _AsO 3 2- . Desorption or adsorption is the net
effect of increase or decrease of As concentrat ion on all three surface complexes, as well as225 of 352 surface complexes on other minerals. For example, a decrease in ill_HAsO 3 - (Figure 4.16, left) indicates the release of initially adsorbed As into water, whereas an increase in ill _H 2 AsO 3 (Figure 4.16, right) indi cates that aqueous As is adsorbe d. The relative change of total As surface-complex concentration shown in Figure 4.17 (l eft) indicates that desorption occurs in the two-phase zone, thus increasing th e aqueous As concentration, wh ereas adsorption occurs further downstream, thus limiting further increases in aqueous As.



### XY 100 200 300 400 50 100 150 200

As 1.2E-07 1.2E-07 1.1E-07 1.1E-07 1.0E-07 9.7E-08 9.2E-08 8.7E-08 8.1E-08 7.6E-08 7.1E-08 6.5E-08 6.0E-08 5.5E-08 4.9E-08

Fig. 4.14. Spatial distribution of total aqueous As concentration after 100 years of intrusion

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

Fig. 4.15. Total aqueous As concentration profiles along the x- axis at y = 0 m, for Coastal Plain Sandstone.226 of 352 0 100 200 300 400 500 Distance (m) 0 0.01 0.02 0.03ill_HAsO3-(mol/L) 5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) 0 0.01 0.02 0.03 0.04ill_H2AsO3(mol/L)

5 yr 20 yr 50 yr 100 yr

Fig. 4.16. Surface complex Ill _HAsO 3 - (left) and Ill _H 2 AsO 3 (right) profiles along the x-axis at y = 0 m, for Coastal Plain Sandstone.

0 100 200 300 400 500 Distance (m) -0.04 0 0.04 0.08 0.12Total As sorption sites change (%)

100 yr

0 100 200 300 400 500 Distance (m) -0.04 -0.03 -0.02 -0.01 0Arsenopyrite volume fraction change (%)

5 yr 20 yr 50 yr 100 yr

Fig. 4.17. Relative change of total As surface complexes conc entration (left) and arse nopyrite volume fraction change (right) along the x-axis at y = 0 m for Coastal Plain Sandstone.

Figure 4.17 (right) shows the vol ume fraction change of arsenopyr ite in solid solution. In contrast to the Pb observations, where the meta l-hosting mineral (galena) actually precipitates
after intrusion of CO 2 , As is mobilized by arse nopyrite dissolution in th e two-phase zone and, to a lesser degree, in the downstream region. Thus, in the case of As, the decrease of pH in the twophase zone causes both desorptio n of As from the mineral su rface (Figure 4.17 left) and dissolution of arsenopyrite (Figure 4.17 right), generating a concentration increase almost to the227 of 352 MCL. Further increases of As concentrations downstream of the two-phase zone are limited, because the water arriving from upstream already ha s elevated As concentrations. As a result, the dissolution of arsenopyrite becomes less effective, and aqueous As sorbs back to the mineral surfaces (Figure 4.17 left). That the dissolution of arsenopyrite is a rather slow process becomes evident in Figure 4.17 (right), where the maxi mum volumetric change of arsenopyrite after 100 years is only about 0.03% of its initial volume frac tion. This suggests that the rather small initial volume of arsenopyrite in solid solution can be a long-lasting source of aquifer contamination.

The differences in the two-phase-zone beha vior of Pb and As in response to CO 2 intrusion— desorption combined with galena precipitation in the case of Pb compared to desorption and arsenopyrite dissolution in the cas e of As—point to the complex ities involved in reactive transport modeling and the interplay between th e various processes involved. For example, as shown in Figure 3.51 (see Section 3.5.3), the so lubility of arsenopyrite increases more significantly with the partial pressure of CO 2 than the solubility of galena, because sulfide and selenide complexation effectivel y inhibit PbCO3(aq) complexati on. This is one of the main reasons why the desorption of As does not cause supersaturation with respect to arsenopyrite, while the desorption of Pb causes supersaturation w ith respect to galena. However, variations in the kinetic rates or changes in other geochemical parameters coul d affect the observed trends.

The following tables show how Pb and As are initi ally distributed in the aquifer between solid and aqueous phases, and how this initial di stribution changes during the 100 years of CO 2 intrusion. Table 4.8 expresses the mass (mol/L solu tion) of Pb and As in solid (as an essential component in a mineral and adsorbed on minera l surfaces) and aqueous pha ses in the two-phase
zone near the CO 2 intrusion point, at t = 0 and t = 100 years. Table 4.9 gives the same information, but relative to th e initial (t = 0) total mass of Pb and As, respectively.

Initially, most Pb resides in the solid phase: about 99% as an essential component in galena and about 1% on adsorption sites, while the relative amount of Pb in the aqueous phase is very low (around 2×10 -4 %). After 100 years, the total mass of Pb on adsorption sites has decreased by
about two-thirds as a re sult of desorption, while Pb as an esse ntial component in galena increases marginally as a result of the galena precipita tion. In contrast, the mass of Pb dissolved in the228 of 352 aqueous phase has increased by almost one order of magnitude compared to the initial value. In absolute numbers, however, the relative amount of Pb in the aqueous phase is still marginal compared to the solid phases. This points out th at there is enough Pb in the aquifer rock to provide a long-lasting source fo r aqueous contamination. Tota l mass of Pb in all phases decreases by about 0.3%, indicating that part of the dissolved Pb is transported downstream.

Arsenic in the solid phase, both on adsorption sites and in arsenopyrite, decreases only marginally during the 100 years of CO 2 intrusion, while the mass of As dissolved in the aqueous phase increases by a factor of about three. Note that adsorbed mass accounts for the majority of As in the solid phase (about 98% adsorbed compar ed to about 2% as an essential component in arsenopyrite).

Table 4.8. Mass distribution (mol/L solution) of Pb and As on solid (as an essential component in minerals or adsorbed on mineral surfaces) and liquid phase in tw o-phase zone at initial (t=0) and final time (t=100 years)

Solid Phase Species Aqueous Phase (mol/L solution) Mineral (mol/L solution) Surface (mol/L solution) Total Mass (mol/L solution) Time

Pb 1.30E-09 0.000578 5.62E-06 0.000583 t=0 Pb 1.41E-08 0.000580 1.97E-06 0.000582 t=100 year
As 4.42E-08 0.000795 0.0383625 0.039157 t=0 As 1.29E-07 0.000794 0.0383584 0.039153 t=100 year

Table 4.9. Mass distribution (%) of Pb and As on solid (as essential an component in minerals or adsorbed on mineral surfaces) and liquid phase in two-phase zone at initial (t=0) and final time (t=100 years)

Solid Phase Species Aqueous Phase Mineral Surface Total Time

Pb 2.23E-04 99.036 0.964 100 t=0 Pb 2.41E-03 99.336 0.338 99.68 t=100 year
As 1.13E-04 2.029 97.971 100 t=0 As 3.30E-04 2.029 97.960 99.989 t=100 year229 of 352 

## 4.6 Sensitivity Analyses

In this section, we discuss the results of a series of sensitiv ity analyses with respect to geochemical processes, model setup, and ini tial and boundary conditions. The base model is described in Section 4.2, and the modeling results concerning this case are given in Section 4.5. These results suggest that ad sorption/desorption and mineral dissolution/precipitation act together as the most important processes that control the e volution of aqueous Pb and As. Previous work on the subject by Wang and Jaffe (2004) did not consider adsorption/desorption. Therefore, to explore the role of adsorption/desorption, we conduc ted a sensitivity analysis as described below, evaluating the geochemical effects of CO 2 intrusion with or without consideration of adsorption/desorption. Further sensitivity analyses were performed on other key chemical parameters, including solubility paramete rs, the volume fraction of minerals containing hazardous constituents, and surface complexation c onstants. Finally, sensitivity analyses were performed on the hydrodynamic assumpti ons of the base model: background CO 2 concentration, aquifer depth, groundwater flow rate, and CO 2 intrusion rate.



### 4.6.1 Sensitivity to Adsorption

4.6.1.1 Sensitivity Run without Adsorption

In the base model, adsorption and desorption pr ocesses are included in the simulation through a surface complexation model. In this section, we present results for a simulation without adsorption/desorption, and compare these with the base model. A simulation run without adsorption allows quantitative evaluation of the ro le of adsorption/desorption on the evolution of the aqueous concentration of hazardous constituents. Because of this change, the initial distribution of hazardous constituents in the aqui fer is different. Specifically, no initial mass is sorbed onto mineral surfaces, which is othe rwise released with the ingress of CO 2 (see previous section). Thus, in the sensitivity run without adso rption, dissolution of galena is the only process mobilizing Pb and increasing the aqueous Pb c oncentrations, while dissolution of arsenopyrite (in solid solution) is the only process producing changes in aqueous As concentrations.230 of 352 In the base run, desorption in the two-phase zone releases Pb into the aq ueous phase; the solution there becomes supersaturated with respect to galena, and consequently galena precipitates (Figure 4.18 right). In the sensitivity run, the increased acidity caused by the ingress of CO 2 in turn causes dissolution of galena (Figure 4.18 left). Spatial distribut ions of the resulting aqueous
Pb concentration at different times are shown in Figure 4.19. The Pb dissolved close to the intrusion zone migrates downstream, where galena continues to dissolve because of the lowered pH. As a result, the initially relatively small aqueous Pb concentrations in the two phases continue to build up with trav el distance (Figure 4.19, left); how ever, the trend observed in the profiles is very moderate, and it is not clear wh ether Pb concentrations above the MCL could be reached much further downstream, should the simula tion interval be sufficiently long. During the 100-year period shown here, the resulting Pb c oncentration is smaller in the sensitivity run compared to the base case (Figure 4.19 right), i ndicating that desorption has the potential to release more Pb than dissolution of galena.

0 100 200 300 400 500 Distance (m) -0.016 -0.012 -0.008 -0.004 0Galena volume fraction change (%)

5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) -0.1

0 0.1 0.2 0.3 0.4Galena volume fraction change (%)

100 yr sorption 100 yr no sorption

Fig. 4.18. Galena volume fraction change profiles along x-axis at y = 0 m 0 at different times in a sensitivity run without adsorption/desorption (left), and comparison of galena volume fraction change profile along x at y=0 after 100 years of CO 2 intrusion with base model (right).231 of 352 0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr sorption 100 yr no sorption MCL

Fig. 4.19. Aqueous Pb concentration profile along x at y=0 at different times in a sensitivity run without adsorption/desorption (left), and comparison of model results for total aqueous Pb concentration profile along x at y=0 after 100 years of CO 2 intrusion with base model (right).
The omission of adsorption/desorption processe s is similarly import ant for As. Without adsorption, the As concentrations increase significantly with trav el distance (Figure 4.20, left). The trend seen in the profiles suggests that the MCL for As would be exceeded further downstream, and that the maximum concentration of As would be limited by the solubility of arsenopyrite (see Section 3.5.3). This is in contrast to the base mode l, where adsorption stabilizes the aqueous As to a relatively uniform level th at is smaller than the MCL (Figure 4.20 right). That the base-model results show much higher con centration values in the two-phase zone near
the CO 2 intrusion location is caused by the significant mobilization of initially sorbed As.

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

100 yr sorption 100 yr no sorption MCL

Fig. 4.20. Aqueous As concentration profile along x at y=0 at different time in a sensitivity run without adsorption/desorption (left) and comparison of model results of As concentration profile along x at y=0 after 100 years of CO 2 intrusion with base model (right).232 of 352 In the TOUGHREACT model, adso rption/desorption processes ar e calculated assuming a local equilibrium, which means that the surface complex responds instantaneously to pH changes. This is a reasonable assumption, because adsorption reac tions are fast compared to the kinetically controlled dissolution/precipitation of hazardous mi neral hosts. As a result, desorption is the dominant process affecting aqueous Pb and As in the short term (see Figure 4.21 for the situation after 1 year of CO 2 intrusion).

0 100 200 300 400 500 Distance (m) 1.2x10 -9 1.4x10 -9 1.6x10 -9 1.8x10 -9 2.0x10 -9 2.2x10 -9 2.4x10 -9Total aqueous Pb concentration (mol/L)

1 year sorption 1 year no sorption

0 100 200 300 400 500 Distance (m) 4.0x10 -8 5.0x10 -8 6.0x10 -8 7.0x10 -8 8.0x10 -8Total aqueous As concentration (mol/L)

1 year sorption 1 year no sorption

Fig. 4.21. Comparison of model results for sensitivity case without adsorption/desorption and base model, showing aqueous Pb (left) and As (right) concentration profiles along x at y=0 after 1 year of CO 2 intrusion.

We may conclude that adsorpti on/desorption processes are extrem ely important when predicting changes in aqueous concentratio ns of Pb and As (for the gi ven mineralogical composition). Incorporation of these processes ha s two main effects: (1) initially sorbed matter will be released as pH changes, leading to increased concentratio n of Pb and As near th e intrusion zone, and (2) adsorption of dissolved or desorbed mass tends to stabilize its downstream concentration. Compared to the relatively slow dissolution rate of galena or arsenopyrite, desorption acts much more rapidly, leading to increased local Pb or As concentrations.233 of 352 4.6.1.2 Sensitivity Run with Lower Site Density

The role adsorption/desorption plays in the m obilization of hazardous constituents depends strongly on the total number of adsorption sites, which is the product of adsorption-site density and specific surface area of mineral. In the base run, illite is the most important mineral that
adsorbs or desorbs both Pb and As. In the ba se model, the specific surface area (66.8 m 2 /g) and site density (1.3×10 -6 mol/m 2 for strong sites and 2.27×10 -6 mol/m 2 for weak sites) for this mineral are taken from Gu and Evans (2007). Howe ver, Lackovic et al. (2003) pointed out that surface properties vary widely for illites fr om different sources. To study the effect of uncertainties of surface properties of illite on the evolution of Pb and As after CO 2 intrusion, we use the published site density (2×10 -7 mol/m 2 for strong sites and 6×10 -7 mol/m 2 for weak sites) from Lackovic et al. (2003) in the sensitivity run.

In the current simulation, the chemical evolu tion of Pb depends on the dynamic balance of galena dissolution/precipitation and adsorption/desorption from mi neral surfaces. If these two processes are considered separately, the increase in acidity triggers desorption as well as galena dissolution. In a coupled system, however, depe nding on the respective geochemical parameters, desorption may release sufficient Pb to the aqueous phase to precipitate ga lena, or in contrast, when galena dissolution may release sufficient Pb to the aqueous phase, such that adsorption takes place. In the base model, galena precipitates (Figure 4.22 right) because desorption of Pb
from illite surface releases sufficient Pb such that the solution becomes supersaturated with respect to galena. In th e sensitivity run, because the site density on illite is much smaller, the mass of Pb released from adsorption sites decreases correspondingly, the solution becomes undersaturated with respect to galena, and thus dissolution of this mineral occurs (Figure 4.22 left).

Figure 4.23 shows the spatial distribution of aque ous Pb at different times, exhibiting a very complex transient behavior. The aqueous Pb concentration actually decreases during the first two years, although galena dissolves and desorption occurs. This is because dissociation of important aqueous complexes affects the earl y-time aqueous concentration of Pb. Remember that at initial conditions, Pb +2 and PbCO 3 (aq) account for only 0.02% and 1% of the total aqueous Pb,234 of 352 respectively, while PbS(aq) acc ounts for about 85% (see Table 4.4). When the solution becomes more acidic, the complexation of PbCO 3 (aq) tends to generate an increase of aqueous Pb, whereas the dissociation of PbS(aq) tends to work against an increase of aqueous Pb, both of which are assumed to be equilibrium reactions in TOUGHREACT. The dissociation of PbS(aq)
does not increase the total a queous Pb, but releases Pb +2 , which is adsorbed on mineral surfaces, leading to a decrease in total aqueous Pb. Since this decrease is initially not fully compensated by the dissolution of galena, we observe a decreas e of total aqueous Pb concentration in the twozone (Figure 4.23 left), which migrates dow nstream with time (Figure 4.23 right, 5 and 20 years). As time progresses, the long-term effect of dissolution of galena eventually surpasses the short-term effect of dissociation of PbS(aq). We observe an increase in aqueous Pb concentration with peaks in the two-phase zone and an elevated Pb concentra tion downstream, which is about
two times the initia l concentration.

Figure 4.24 (left) shows the time evolution of tota l aqueous Pb concentration measured at the
CO 2 intrusion location. The early concentration decrease during the first two years is followed by a gradual increase up to the peak concentration observed in the pr ofile plots. This early-time transient behavior is controlled by the comple x interaction of the di ssociation of PbS(aq), adsorption of Pb +2 onto mineral surfaces, dissolution of galena, transport of all Pb species downstream, all of which are affected by a gr adually increasing part ial pressure of CO 2 . (The partial pressure of CO 2 increases slowly until a two-phase zone forms after about 3 years—see Figures 4.2 and 4.3.) Figures 4.24 (right) shows th e total aqueous Pb c oncentration after 100 years for the sensitivity case (in comparison with the base model). The base run leads to much higher Pb concentrations, because desorption is mo re effective and more Pb is released into
solution.

The results for As indicate a simpler behavi or than for Pb. Without complex aqueousdissociation processes occurri ng, arsenopyrite dissolves in res ponse to pH changes, while adsorption occurs in the two-phase region and desorption takes pl ace further downstream. Because of the lower site density on illite in the sensitivity run, less As is released from adsorption sites, and therefore a lower As con centration is observed in the two-phase zone.235 of 352 Subsequently, less aqueous As is transported downstream, and the aqueous As concentration is smaller compared to the base model (Figure 4.25).

0 100 200 300 400 500 Distance (m) -0.02 -0.016 -0.012 -0.008 -0.004 0Galena volume fraction change (%)

5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) -0.1

0 0.1 0.2 0.3 0.4Galena volume fraction change (%)

100 yr base run 100 yr sensitivity run

Fig. 4.22. Galena volume fraction change profiles along x-axis at y = 0 m 0 at different times in a sensitivity run with lower site density for illite and comparison of galena volume fraction change profile along x at y=0 after 100 years of CO 2 intrusion with base model (right).

0 100 200 300 400 500 Distance (m) 6.0x10 -10 8.0x10 -10 10 -9 1.2x10 -9 1.4x10 -9Total aqueous Pb concentration (mol/L)

0.27 yr 1 yr 2 yr

0 100 200 300 400 500 Distance (m) 0x10 0

10 -9 2x10 -9 3x10 -9 4x10 -9Total aqueous Pb concentration (mol/L) 5 yr 20 yr 50 yr 100 yr

Fig. 4.23. Aqueous Pb concentration profile along x at y=0 at different times in a sensitivity run with lower site density on illite.236 of 352 012345 Time (year) 0x10 0

10 -9 2x10 -9 3x10 -9 4x10 -9Total aqueous Pb concentration (mol/L)

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr base run 100 yr sensitivity run MCL

Fig. 4.24. Time evolution of total aqueous Pb concentration at the intrusion location (left) and comparison of model results for total aqueous Pb concentration profile along x at y=0 after 100 years of CO 2 intrusion in sensitivity and base model (right).

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

100 yr base run 100 yr sensitivity run MCL

Fig. 4.25. Aqueous As concentration profile along x at y=0 at different time in a sensitivity run with lower site density and comparison of model results of As concentration profile along x at y=0 after 100 years of CO 2 intrusion with base model (right).

The sensitivity run with lower site density reveals to what extent adsorpti on affects the chemical
evolution of Pb and As in relati on to the total number of sites on a mineral surface. Overall, decreasing site density lowers aqueous Pb a nd As concentrations. However, the transient chemical evolution of Pb is affected by a dynamic balance between mineral dissolution, adsorption, and dissociation of aqueous complexes.237 of 352 4.6.1.3 Sensitivity to Surface Complexation Constant

Illite is considered the most important adsorbent of As in this study, and th e following surface complexation reaction

3 2 33

_ _ Ill OHAsO H O H Ill OH H AsO − +

++ + U (4.5)

is most relevant. In the base model, the surface complexation constant (logK) in reaction (4.3) is taken to be -2.12 (Manning and Goldberg, 1997). However, according to Goldberg (2002), the value of the surface complexation constant (logK) may be as low as -3. With this in mind, we conducted a sensitivity run using logK = -3.

Figure 4.26 indicates that the evol ution of aqueous As is quite se nsitive to the change in the complexation constant. A decreased complexati on constant corresponds to less effective adsorption of As and thus a higher aqueous As concentration. It should be noticed that adsorption/desorption is determined as the ne t effect of surface complexation on different mineral surfaces for different surface complexes. Thus, variation of the c onstant for one surface complexation reaction changes not only the concen tration of its own surface complex, but also the concentration of other surface complexe s. For example, when the constant of 3

_ Ill OHAsO − changes from -2.12 to -3, the concentration of 3

_ Ill OHAsO − decreases (Figure 4.27 left), while the

concentration of 2 3

_ Ill OH AsO increases (Figure 4.27 right). The f act that more As is adsorbed
when the constant for reaction 4.3 changes is the result of variation in As related surface complexes on the illite, as well as on other minerals.238 of 352 0 100 200 300 400 500 Distance (m) 4.0x10 -8 8.0x10 -8 1.2x10 -7 1.6x10 -7Total aqueous As concentration (mol/L)

100 yr base run logk = -2.12 100 yr sensitivity run logk = -3 MCL

Fig. 4.26. Computed As concentration after 100 years of CO 2 intrusion in a sensitivity run (logK = -3) and base model (logK = -2.12)

0 100 200 300 400 500 Distance (m) 0 0.01 0.02 0.03 0.04ill_HAsO3-(mol/L)
100 yr base run logK = -2.12 100 yr sensitivity run logK = -3

0 100 200 300 400 500 Distance (m) 0 0.01 0.02 0.03 0.04ill_H2AsO3(mol/L)

100 yr base run logK = -2.12 100 yr sensitivity run logK = -3

Fig. 4.27. Surface complex Ill _HAsO 3 - (left) and Ill _H 2 AsO 3 (right) profiles along the x-axis at y = 0 m, in a sensitivity run (logK = -3) and base model (logK = -2.12).239 of 352 

### 4.6.2 Sensitivity to Kine tic Rate Parameter

4.6.2.1 Sensitivity to Kinetic Ra te Parameter for Galena

In the current model, the dissolution of galena is mainly caused by the increase of acidity as CO 2 intrudes into the aquifer. A l iterature search for nonoxidative rates of ga lena dissolution was conducted. Published values, determined by laborat ory experiments, vary over several orders of magnitude, with most values obtained at pH less than 4. Zhang et al. (2003) conducted an experiment on dissolution kinetics of galena in 1 mol/L NaCl solutions at pH 0.43–2.45 and 25–
75 o C. The kinetic-rate constant for gale na dissolution is given by the equation: 7 2.34 10 H k α + − = ×
at 25 o C. Acero et al. (2007) reported a galena dissolution kinetic-rate constant as:

23 3 5.7 0.4 0.43 0.05 10 RT H k ea
+ − ± −± ± = for pH below 2. Apart from the large variation in measured rates from laboratory experiments, discrepancies between fi eld estimates and laboratory measurements of reaction rates have also been observed, with di fferences of up to four orders of magnitude (Brantley and Velbel, 1993). These discrepancies may result from a lack of understanding of the reaction mechanism, experimental errors, differe nces in solution chemistry, inaccuracies in
reactive surface area and estimates of water-miner al contact area, temperature variability, or biological activity (Brantley and Velbel, 1993). Note that the kinetic rate (r) is a product of kinetic-rate constant (k) a nd reactive surface ar ea (A) (see Eq. 4.1). The magnitude of the reactive surface area is also highly uncertain, whic h further aggravates the uncertainty associated
with the kinetic rate.

Sensitivity simulations have been performed to part ially address the impact of large uncertainties of the kinetic rate. Below, we have varied the kinetic-rate constant by two orders of magnitude. Because the kinetic rate is a product of the kine tic-rate constant and th e reactive surface area, a two-order of magnitude variation of the reactive surface area would lead to the same variation of the kinetic rate; therefore, we have not conducted a separate se nsitivity simulation varying the reactive surface area. In the current simulation, the precipitation rate is assumed to be the same
as the dissolution rate.240 of 352 As discussed in Section 4.2 for th e base run, galena precipitat es rather than dissolves upon
intrusion of CO 2 . It follows that in the sensitivity run, a lower kinetic rate leads to less precipitation of galena (Figure 4.28 left). Consequently, more aqueous Pb remains in the aqueous phase and the concentration of Pb in the two-ph ase zone is slightly higher (Figure 4.28 right). Overall, the simulated concentrations of aqueous Pb after 100 years of CO 2 intrusion are rather
insensitive to the variation of the kinetic rate, because desorptio n is the dominant process that
controls the evolution of aqueous Pb concentration, effectivel y dampening the effect of mineral precipitation. In order to illustrate how the kinetic rate would affect the aqueous Pb concentration with less dominant sorption, we also performed two sensitivity simulations varying the kinetic rate constant in the absence of sorption. Without sorption, galena dissolves, causing corresponding increases in aqueous Pb concentrations. An increase in the kinetic-rate constant by two orders of magnitude l eads to more effective dissolution, whic h results in a slight increase of aqueous Pb concentration after 100 years of CO 2 intrusion (see Figure 4.29)

0 100 200 300 400 500 Distance (m) -0.1

0 0.1 0.2 0.3 0.4 0.5Galena volumefraction change (%)

100 yr base run K 25 H = 2.34x10 -7 100 yr sensitivity run K 25 H = 2.34x10 -5 100 yr sensitivity run K 25 H = 2.34x10 -9

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

base run K 25 H =2.34x10 -7 sensitivity run K 25 H =2.34x10 -5 sensitivity run K 25 H =2.34x10 -9 MCL

Fig. 4.28. Computed galena volume fraction change (left) and (right) total adsorbed Pb change after 100 years of CO 2 intrusion with different kinetic-rate constant.241 of 352 0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

base run K 25 H =2.34x10 -7 sensitivity run K 25 H =2.34x10 -5 sensitivity run K 25 H =2.34x10 -9 MCL

Fig. 4.29. Sensitivity of computed Pb concentration after 100 years of CO 2 intrusion to kinetic-rate constant (k) for simulations without considering sorption.

4.6.2.2 Sensitivity to Kinetic Rate Parameter for Arsenopyrite

Although the kinetic rates of arse nopyrite oxidation have been wide ly studied (McKibben et al., 2008; Walker et al., 2006; Yu et al., 2007), nonoxi dative rates for arsenopyr ite dissolution have been rarely reported, and do not permit parameters estimation for the purpose of this study. In the current model, the kinetic-rate co nstant for arsenopyrite is assumed to be the same as for galena (Zhang et al., 2003). We conducted a sensitivity anal ysis varying the kineti c-rate constant for arsenopyrite by two orde rs of magnitude.

Figure 4.30 shows the sensitivity of the resulting aqueous As con centration to the kinetic-rate constant. As discussed in the Section 4.2, wh ile desorption of As from sorption sites and dissolution of arsenopyrite both rele ase As into the aqueous phase, the contribution of dissolution is comparably small in the base run. A decr ease of the kinetic-rate constant makes the contribution of dissolution even smaller, only marg inally affecting the total release of As. An
increase of the rate constant results in increa sed mobilization of As, which shows in a slightly higher aqueous As concentration. Ov erall, the model results are rath er insensitive to the kinetic rate constant. As with Pb, the dominant proces s affecting aqueous concentration is adsorption/ desorption rather than precip itation/dissolution. For the purpos e of compariso n, sensitivity242 of 352 simulations were conducted for a case without so rption (Figure 4.31). For this case, a two-orderof-magnitude higher kinetic rate leads to a dram atic increase in aqueous As concentration.

0 100 200 300 400 500 Distance (m) 4x10 -8 6x10 -8 8x10 -8

10 -7 1x10 -7 1x10 -7Total aqueous As concentration (mol/L)

base run K 25 H =2.34x10 -7 sensitivity run K 25 H =2.34x10 -9 sensitivity run K 25 H =2.34x10 -5 MCL

Fig. 4.30. Computed As concentration after 100 years of CO 2 intrusion in sensitivity runs with different kineticrate constant (k).

0 100 200 300 400 500 Distance (m) 10 -8 10 -7 10 -6 10 -5Total aqueousAs concentration (mol/L)

base run K 25 H =2.34x10 -7 sensitivity run K 25 H =2.34x10 -9 sensitivity run K 25 H =2.34x10 -5

MCL

Fig. 4.31. Sensitivity of computed As concentration after 100 years of CO 2 intrusion to rate constant (k) for simulations without considering sorption.243 of 352 

### 4.6.3 Sensitivity to PbS(aq) Dissociation Constant

As discussed in Sections 4.4 and 3.5.3, PbS(aq) is the most important aqueous complex at ambient (low) partial pressure of CO 2 partial pressure; it accounts for about 85% of the total aqueous Pb at a logP(CO 2 ) of -2.3. For reasons reported in pr eceding sections, the existence of Pb sulfide complexes can strongly retard solubility changes of galena when P(CO 2 ) increases.
This result is based on a disso ciation constant (logK) of -5.5 for the following reaction:

2 ( ) log 5.5 PbS aq H HS Pb K + −+ + + =− U (4.6)

There is some uncertainty associated with the magnitude of the dissociation constant, as is discussed in Appendix D. Therefor e, a sensitivity study to evalua te changes in the magnitude of this constant is called for, which was conducted by changing logK PbS(aq) by one order of magnitude up and down.

Table 4.10 shows the concentration of some Pb aqueous species obtained from the initial equilibrium run. Variation of logK PbS(aq) affects the initial concentra tion of PbS(aq) and also the total Pb concentration, while other important species like Pb +2 and PbCO 3 (aq) remain the same. When the initially equilibrated system is disturbed as a result of CO 2 intrusion, important processes involved in the evolution of aqueous Pb concentra tion are association/dissociation of PbS(aq), desorption/adsorption of Pb +2 , precipitation of galena, and dissolution of pyrite. Note that precipitation of galena and dissolution of pyr ite are both kinetically c ontrolled. In the short term (within the first 2 years), diss olution of pyrite does not produce enough HS - . Therefore, PbS(aq) dissociates (see Section 4.6.1.1), and the desorption of Pb +2 is consequently retarded. In the long term (after 5 y ears), dissolution of pyrit e produces sufficient HS - so that PbS(aq) associates, and the desorption of Pb +2 is accelerated. As a result, the sensitivity case with logK PbS(aq) =-6.5 leads to greater desorption after 100 years of CO 2 intrusion (see Figure 4.32 left), while less galena precipitates, because the association of PbS(aq) retards the precipitation of galena (Figure 4.32 right). In summary, the case with logK PbS(aq) =-6.5 results in more desorption, less galena precipitatio n, and higher Pb concentrat ion, both initially and after CO 2 intrusion (see Figure 4.33). The opposite behavi or can be seen for the case with logK PbS(aq) =-4.5.244 of 352 Table 4.10. Concentration (in mol/L) of some Pb aqueous species calculated by different dissociation constants of PbS(aq) at initial condition with logP(CO 2 ) = -2.3
Species logK PbS(aq) =-5.5 logK PbS(aq) =-6.5 logK PbS(aq) =-4.5
PbCO 3 (aq) 1.50E-11 1.50E-11 1.50E-11
Pb +2 2.06E-13 2.06E-13 2.06E-13 PbS(aq) 1.11E-09 1.11E-08 1.11E-10 PbSe(aq) 1.83E-10 1.83E-10 1.83E-10
Total Pb 1.30E-09 1.13E-08 3.03E-10

0 100 200 300 400 500 Distance (m) -100

-80 -60 -40 -20

0 20Total Pb sorption sites change (%)

100 yr logK PbS(aq) =-5.5 100 yr logK PbS(aq) =-6.5 100 yr logK PbS(aq) =-4.5

0 100 200 300 400 500 Distance (m) 10 -1 0x10 0

10 -1 2x10 -1 3x10 -1 4x10 -1Galena volume fraction change (%)

100 yr logK PbS(aq) =-5.5 100 yr logK PbS(aq) =-6.5 100 yr logK PbS(aq) =-4.5

Fig. 4.32. Computed total adsorbed Pb change (left) and galena volume fraction change (right) after 100 years of CO 2 intrusion with different logK PbS(aq) .

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr logK PbS(aq) =-5.5 100 yr logK PbS(aq) =-6.5 100 yr logK PbS(aq) =-4.5 MCL

Fig. 4.33. Computed total aqueous Pb concentration after 100 years of CO 2 intrusion with different logK PbS(aq) .245 of 352 

### 4.6.4 Sensitivity to Mineral Solubility

4.6.4.1 Sensitivity to Solubility of Galena

The solubility of hazardous-constituent-bearing mi nerals, with galena and arsenopyrite (in solid solution) being the focus in the current model, is the most critical parameter for dissolutionrelated increases in aqueous concentrations of Pb or As. With this in mind, we conducted a sensitivity run in which the solubility of gale na is changed. In TOU GHREACT, the dissolution reaction of galena is written as:

2 log 14.4 PbS H HS Pb K + −+ + + =− U (4.7)

The value of logK (-14.4) for reaction (4.5) has been selected ba sed on the thermodynamic analysis conducted in Section 3 (Table 3.13). Howe ver, as discussed in detail in Appendix D, published values for logK, derived either from solubility measurements or from thermochemical measurements, show significant discrepancies, wh ich were resolved throu gh consideration of the neutral complex PbS(aq). However, thermochemical data used in the calibr ation of logK (galena)
have uncertainties of ± 1 kcal/mol, which translate into an uncertainty in logK of about 0.5. The MINTEQ (Allison et al., 1991 ) database cites a logK value fo r reaction (4.5) of -13.97. In the sensitivity run, we arbitrarily change logK of reaction 4.5 to this value.

As mentioned in Section 4.2, our simulations include two steps: (1) an equilibrium run, establishing the initial equilibrium conditions before CO 2 intrusion and (2) a reactive transport run, simulating the chemical evolution after CO 2 intrusion. Increasing the solubility of galena changes the initial distribution of Pb in th e aqueous phase and cha nges the amount of Pb
adsorbed on mineral surfaces. Table 4.11 shows that the initial total aqueous Pb concentration in the sensitivity run is higher (by a factor of 2.5) than that in the base model. What should be
mentioned here is that the initial Pb concentra tion on adsorption sites for a given mineral surface (for example, illite) is affect ed by the initial aqueous concen tration of the corresponding ion (Pb +2 ) as well as other i ons (for example, H 3 AsO 3 (aq)) that participate in surface complexation reactions on this mineral surface (for example, ill s _OPb + ). In this sensitivity run, the initial total246 of 352 number of Pb adsorption sites is higher, be cause the initial aqueous concentration of Pb +2 is higher. Consequently, more desorp tion occurs in the se nsitivity run (see Figure 4.34 left), and the simulated total aqueous Pb concentration is signif icantly higher than that in the base run (see Figure 4.34 right), but still does not exceed the MCL.

Table 4.11. Concentration (in mol/L) of some Pb aqueous species after initial equilibrium in base model and in the sensitivity run
Species Base model Sensitivity run PbS(aq) 1.11E-09 2.98E-09 PbSe(aq) 1.83E-10 1.78E-10
PbCO 3 (aq) 1.77E-11 4.77E-11
Pb +2 2E-13 5.5E-13 Total aqueous Pb 1.30E-09 3.21E-09
Total adsorbed Pb 5.09E-6 1.36E-5

0 100 200 300 400 500 Distance (m) 0.0x10 0 4.0x10 -6 8.0x10 -6 1.2x10 -5 1.6x10 -5total Pb sorption sites (mol/L)

100 yr base run logK galena = -14.4 100 yr sensitivity run logK galena = -13.97

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr base run logK galena = -14.4 100 yr sensitivity run logK galena = -13.97

MCL

Fig. 4.34. Computed total adsorbed Pb (left) and aqueous Pb concentration (right) after 100 years of CO 2 intrusion in sensitivity run (logK galena = -13.97) and base model (logK galena = -14.4)247 of 352 4.6.4.2 Sensitivity to Solubility of Arsenopyrite

As pointed out before, the initial As concentrati on is likely controlled by arsenopyrite in solid solution in pyrite in most groundwaters, based on the evaluation of thermodynamic controls in Section 3.4. Since TOUGHREACT cannot explicitly treat the dissolution of solid solutions, arsenopyrite is instead incorporated in the m odel as a stand-alone pseudophase, possessing the same properties and stoichiometry as the pure e nd-member of arsenopyrite, but with a solubility product lowered to reflect the real ity that the As is present in solid solution. Considering the
uncertainties in the calibration of this solubility product—using measured groundwater analysis from the NWIS database (Section 3.4)—we conducte d a sensitivity run in which the base model solubility is reduced by a one-half order of magnitude (from logK = 55.66 to logK = 55.16). Decreasing the solubility of ar senopyrite has two effects. First, the initial to tal aqueous As concentration is lower, which also causes the to tal As concentration on adsorption sites to be smaller. In the base model, the initia l total aqueous As c oncentration is 4.4×10 -8 mol/L and the
initial total As concentratio n on adsorption sites is 3.83×10 -2 mol/L, whereas the respective values in the sensitivity run are 1.4×10 -8 mol/L and 3.78×10 -2 mol/L. Second, the total aqueous
As concentration af ter intrusion of CO 2 is smaller than in the base model, because both desorption from mineral surfaces and dissolu tion of arsenopyrite are less significant.

0 100 200 300 400 500 Distance (m) 0x10 0 4x10 -8 8x10 -8 1x10 -7 2x10 -7Total aqueous As concentration (mol/L)

100 yr base run logK arsenopyrite = 55.66 100 yr sensitivity run logK arsenopyrite = 55.16 MCL

Fig. 4.35. Computed aqueous As concentration after 100 years of CO 2 intrusion in a sensitivity run (logK arsenopyrite = 55.16) and base run (logK arsenopyrite = 55.66)248 of 352 

### 4.6.5 Sensitivity to Initial Mi neral Volume Fraction

4.6.5.1 Sensitivity to Initial Volume Fraction of Galena

In the base model, the initial volume fraction of galena is set to 7.84 × 10 -6 , based on the extensive discussion of minor secondary mine rals described in Section 3.6.2.2. To account for the possible variability and to understand the impact of an increase in volume fraction, we conducted a sensitivity run with an initial volume fraction for galena of 7.84 × 10 -3 . While this is three orders of magnitude larger than in the base model, the volume fracti on is still much smaller
than the unrealistic 50% volume fr action for galena assumed in earlier studies by Wang and Jaffe (2004). The increase in initial volume fraction of galena means not only that a larger amount of galena is available for dissolution, but also that the dissolution ki netics change, through an assumed increase in reactive surface area. However, as shown in Figure 4.36, the total aqueous Pb concentrations at 100 years are not sensitive to the volume fraction of galena. As discussed in Section 4.5.2, the dominant process for increasing a queous Pb concentrations is desorption rather than galena dissolution, which explain why gale na volume changes, within reasonable ranges reflecting natural variability, have no significant impact.

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr V galena =7.84 x 10 -6 100 yr V galena =7.84 x 10 -3 MCL

Fig. 4.36. Sensitivity of total aqueous Pb concentration after 10 0 years along the x-axis at y = 0 m to the initial volume fraction of galena.249 of 352 4.6.5.2 Sensitivity to Initial Volume Fraction of Arsenopyrite

The volume fraction of arsenopyrite used in the model is based on the mean abundance of As in soils and sediments, as reported by the National Geochemistry Survey database of the USGS, and used as a proxy for As abundance in aquifer host rocks (Section 3.6.2.2). It is furthermore assumed, as pointed out before, that arsenopyrite o ccurs in solid solution in pyrite. The volume fraction of arsenopyrite is therefore that of the FeAsS component in pyrite with an assumed ideal partial molar volume of unity. To account for vari ability and uncertainty in the magnitude of the volume fraction of arsenopyrite, we have increase d the value chosen in the base model by two orders of magnitude, from about 9 × 10 -6 to about 9 × 10 -3 . The increase in initial volume fraction of arsenopyrite means not only that a larger am ount of arsenopyrite is av ailable for dissolution, but also that the dissolution kinetics change, through an assumed increas e in reactive surface area. Figure 4.37 shows that the total aqueous As concentrations are moderately sensitive to the volume fraction of arsenopyrite. Increasing the volume fraction of arsenopyrite leads to faster dissolution of arsenopyrite and, thus, the sensitiv ity results arrive at higher aqueous As concentrations after 100 years. The reason that As concentrations are affected by a change in the volume fraction of its solubility-controlling minera l host, while Pb concentrations are not (see earlier section), has been given before. Pb con centration increases are caused by desorption (while galena actually precipitates instead of di ssolving), whereas As con centration increases are generated from both desorption a nd dissolution of arsenopyrite.

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

100 yr V arsenopyrite =9x10 -6 100 yr 100 yr V arsenopyrite =9x10 -4 MCL

Fig. 4.37. Sensitivity of total aqueous As concentration after 100 years along the x-axis at y = 0 m to the initial volume fraction of arsenopyrite.250 of 352 

### 4.6.6 Sensitivity to Initial Partial Pressure of CO 2

In the base model, the initial chemical composition is in equilibrium with a specified CO 2 partial pressure around 0.005 bar (which corresponds to a Log P(CO 2 ) value of -2.3) . The specified value falls into the medium range of measured CO 2 partial pressures calculated from the groundwater database analysis (Section 3.4.3, Fi gure 3.17). The high end of these measured CO 2 partial pressures is on the order of –1.5 in Log P(CO 2 ). To evaluate the impact of the higher initial partial pressure of CO 2 in the groundwater, we conducted a sensitivity study using this high-end value of -1.5. First, a equilibrium run wa s carried out to establis h the initial chemical composition, and then a reactive transport simu lation was conducted, modeling the ingress of
CO 2 into the equilibrated system.

Table 4.12 shows the initial chemical composition in equilibrium with a Log P(CO 2 ) value to be
–1.5. The increase of initial partial pressure of CO 2 results in a higher bicarbonate concentration and a lower pH. Consistent with the equilibrium analysis in Sec tion 3.5 and the trends shown in Figure 3.51, the total aqueous Pb concentration increases sligh tly compared to the base model,
due to the increase in PbCO 3 (aq), while the initial As concentr ation in equilibrium with a Log P(CO 2 ) value of –1.5 is significantly higher. In fact, the equilibrium concentration for As is approximately 1.4 × 10 -7 mol/L, which exceeds the MCL of 1.33 × 10 -7 mol/L (or 10 ppb). It is possible that samples with elevated ambient CO 2 concentrations could be responsible for some occurrences of high As concentrations in groundwaters, as shown in Figure 3.6(b).

We shall now compare the Pb concentration prof iles for base model and sensitivity run at 100 years of CO 2 intrusion, shown in Fi gures 4.34. In the base model (initial Log P(CO 2 ) = -2.3), the Pb concentration increases with the ingress of CO 2 from 1.3×10 -10 mol/L to about 1.57×10 -8 mol/L in the two-phase zone near th e intrusion location, and to about 1.1×10 -8 mol/L downstream. In the sensitivity run (initial Log P(CO 2 ) = -1.5), the aqueous Pb concentration increases from initially 1.4×10 -9 mol/L to about 1.55×10 -8 mol/L near the intrusion location and
to 1.15×10 -8 mol/L downstream. Thus, in absolute values, the situation after CO 2 intrusion is251 of 352 almost identical in both cases; onl y the starting point is slightly di fferent, owing to differences in
the initial Pb concentr ation (Figure 4.38).

Table 4.12. Initial chemical composition (mol/L) of groundwater in equilibrium with partial pressures of -2.3 (base model) and -1.5 (sensitivity run) in Log P(CO 2 )

Species Partial pressure of CO 2 Log P(CO 2 ) = -2.3 Partial pressure of CO 2 Log P(CO 2 ) = - 1.5 Eh -0.215 (volt) -0.195 (volt) pH 7.61 7.36
Ca 9.00E-04 4.98E-04 Mg 2.17E-05 9.62E-05
N a 1.97E-03 1.97E-03
K 2.68E-04 3.85E-04
Fe 5.63E-06 2.31E-05
Si 9.31E-04 9.18E-04
TIC 3.33E-03 1.26E-02
SO 4 -2 1.95E-04 1.92E-04
HS - 1.44E-08 1.36E-08
Al 4.19E-11 2.92E-11
Cl 2.07E-04 2.07E-04
P 1.78E-06 6.72E-06
Pb 1.30E-09 1.40E-09
As 4.42E-08 1.43E-07
Ba 1.04E-07 1.04E-07 Hg 8.37E-10 9.77E-10
Sb 2.10E-09 2.05E-09
U 7.18E-11 4.18E-11
Zn 1.15E-08 1.22E-08
Se 1.72E-09 1.86E-09
Cd 1.80E-09 1.29E-09

As mentioned before, the increase in Pb concentration caused by CO 2 intrusion is mainly controlled by two processes: adsorption/desorp tion of Pb on adsorption sites and galena precipitation/dissolution. Although the aqueous Pb concentrat ions after 100 years are almost252 of 352 identical in the two simulation cases, the underl ying processes affecting them differ somewhat. The total Pb concentration on adsorption sites is lower in the sensitivity run (see Figure 4.39 left), because of the sma ller concentration of ill w _OPb + (Figure 4.39 right). Less desorption in the sensitivity run induces less precipitation of galena (see Figure 4.40 left), while the computed pH is similar in both cas es (Figure 4.40 right).

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr initial Pco 2 =10 -2.3 100 yr initial Pco 2 =10 -1.5 MCL

0 100 200 300 400 500 Distance (m) 10 -9 10 -8 10 -7Total aqueous Pb concentration (mol/L)

100 yr initial Pco 2 =10 -2.3 100 yr initial Pco 2 =10 -1.5 MCL

Fig. 4.38. Sensitivity of Pb concentration to initial partial pressure of CO 2 . The figure on the right is the same as the one on the left but in semi-log scale.

0 100 200 300 400 500 Distance (m) 10 -6 2x10 -6 3x10 -6 4x10 -6 5x10 -6 6x10 -6Total Pb sorption sites (mol/L)

100 yr initial Pco 2 =10 -2.3 100 yr initial Pco 2 =10 -1.5

0 100 200 300 400 500 Distance (m) 0x10 0

10 -6 2x10 -6 3x10 -6 4x10 -6 5x10 -6 6x10 -6illw_OPb+(mol/L)

100 yr initial Pco 2 =10 -2.3 100 yr initial Pco 2 =10 -1.5

Fig. 4.39. Total Pb concentration on adsorption s ites (left) and concentration of ill w _OPb + (right) along x-axis at y = 0 m in the base and sensitivity run.253 of 352 0 100 200 300 400 500 Distance (m) -0.1

0 0.1 0.2 0.3 0.4Galena volume fraction change (%)

100 yr Pco 2 =10 -2.6 100 yr Pco 2 =10 -1.5

0 100 200 300 400 500 Distance (m) 5.6

6 6.4 6.8 7.2 7.6

8pH 100 yr initial Pco 2 =10 -2.6 100 yr initial Pco 2 =10 -1.5

Fig. 4.40. Galena volume fraction change (left) and pH (right) along x-axis at y = 0 m in the base and sensitivity

run.

Figure 4.41 shows concentration profiles of total a queous As after 100 years in the sensitivity run (initial Log P(CO 2 ) = -1.5) and the base run (initial Log P(CO 2 ) = -1.5). The increase in initial Log P(CO 2 ) results in increases of comparable magnit ude in both the initial As concentration and
the final As concentration after intrusion of CO 2 . Thus, the main difference between the two simulation cases is the initial As concentration, wh ich is much higher in the sensitivity run using a Log P(CO 2 ) of -1.5. As mentioned above, the initial As concentration is al ready higher than the MCL; the ingress of CO 2 leads to a further in crease (Figure 4.41).

0 100 200 300 400 500 Distance (m) 0x10 0

10 -7 2x10 -7 3x10 -7 4x10 -7Total aqueous As concentration (mol/L)

100 yr Pco 2 = 10 -2.3 100 yr Pco 2 = 10 -1.5

MCL

Fig. 4.41. Sensitivity of As concentration to initial partial pressure of CO 2 .254 of 352 

### 4.6.7 Sensitivity to Initial Fluid Pressure (Depth of Aquifer)

In the base model, the initial pre ssure in the aquifer is set to 5 bar, corresponding to an aquifer at a depth of about 50 m. Becau se the solubility of CO 2 in water is a function of CO 2 partial pressure, a change in the initial aquife r pressure controls , upon intrusion of CO 2 , how much of the gaseous CO 2 dissolves in the groundwat er (and may affect pH) vers us how much remains in gas phase and migrates under buoyant forces. In a shallower aquifer, the maximum amount of
dissolved CO 2 would be smaller than in a deeper a quifer, and vice versa. To understand the implications of this dependence, we conducted a sensitivity run assuming CO 2 intrusion at 3 bar initial pressure (i.e., eq uivalent to an aquifer depth of about 30 m). All other model assumptions and parameters are identical to the base model.

Figure 4.42 shows a s lightly larger two-phase z one forms in the sensitiv ity run with a slightly higher gas saturation (Figur e 4.42 left), while less CO 2 is dissolved in the water (Figure 4.42 right). As a result, the pH obtained in the sensitivity run (fluid pre ssure of 3 bar) is higher than in the base run (Figure 4.43). Consequently, the Pb and As concentrations (Figure 4.40) observed after 100 years of CO 2 intrusion are slightly lower in the sensitivity run due, in the case of Pb, not only to the higher pH, but also to less complexing of dissolved CO 2 with Pb +2 . In summary, the geochemical impact of CO 2 intrusion into a drinking water aq uifer is moderately affected by initial fluid pressure (or aquifer depth), with sm aller effects expected in a shallower aquifer. A drinking water aquifer would have to be significantly deeper (resulting in a higher maximum
amount of dissolved CO 2 ), and a CO 2 would have to leak into the aquifer at a higher rate (resulting in an increase in dissolved CO 2 up to or exceeding the maximum amount) to be exhibit stronger effects from CO 2 intrusion and related pH changes.255 of 352 0 100 200 300 400 500 Distance (m) 0 0.02 0.04 0.06 0.08

0.1CO2gas saturation degree 100 yr depth = 50 m 100 yr depth = 30 m

0 100 200 300 400 500 Distance (m) 0x10 0 4x10 -2 8x10 -2 1x10 -1 2x10 -1TIC (mol/L) 100 yr depth = 50 m 100 yr depth = 30 m

Fig. 4.42. Sensitivity of CO 2 gas saturation degree (left) and total inorganic carbon (TIC) to the depth of aquifer.

0 100 200 300 400 500 Distance (m) 5.6

6 6.4 6.8 7.2 7.6

8pH 100 yr depth = 50 m 100 yr depth = 30 m

Fig. 4.43. Sensitivity of pH to the depth of aquifer.

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr depth = 50 m 100 yr depth = 30 m MCL

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueousAs concentration (mol/L)

100 yr depth = 50 m 100 yr depth = 30 m MCL

Fig. 4.44. Sensitivity of Pb (left) and As concentr ation (right) to the depth of aquifer.256 of 352 

### 4.6.8 Sensitivity to Groundwater Flow Velocity

In all simulation cases conducted so far, we ha ve assumed a groundwater flow velocity of 10 m/year (transport velocity). A sensitivity r un with lower flow rate of 1 m/year was performed to study the effect of the groundwater flow conditi ons on the evolution of hazardous constituents. When the flow velocity decreases (while the CO 2 intrusion rate remains the same), an enlarged two-phase zone is observed compared to the ba se model (see Figure 4.45). The two-phase zone is larger because less fresh water flows past the intrusion zo ne and thus less CO 2 can dissolve in the water. The highest CO 2 gas saturation degree is 0.14 in th e sensitivity run, whereas in the base run it is 0.08 (Figure 4.47 left). The correspondi ng area with elevated TIC is larger in the sensitivity run compared to the base run (Figure 4.46 and Figure 4.47 right). Because groundwater flow is much slower, the enhanced TIC front has not adva nced sufficiently to intersect the right boundary of the simulated zone after 100 years. (Note that the larger two-phase
zone observed in this simulation case raises th e question as to whether the assumption of depthaveraged processes remains valid, as the gaseous CO 2 region is more pronounced. We refer to
Section 4.9 for a three-dimensional te st case to address this issue.)

XY 100 200 300 400 50 100 150 200

SG 0.14 0.13 0.12 0.11 0.10 0.09 0.08 0.07 0.06 0.05 0.05 0.04 0.03 0.02 0.01

Fig. 4.45. Spatial distribution of saturation of CO 2 in a sensitivity run with flow rate of 1m/year after 100 years of intrusion257 of 352 XY 100 200 300 400 50 100 150 200

TIC 0.21 0.19 0.18 0.16 0.15 0.14 0.12 0.11 0.10 0.08 0.07 0.06 0.04 0.03 0.02

Fig. 4.46. Spatial distribution of TIC in a sensitivity run with fl ow rate of 1m/year after 100 years of intrusion

0 100 200 300 400 500 Distance (m) 0 0.04 0.08

0.1 0.2CO2gas saturation degree 100 yr flow rate = 10 m/y 100 yr flow rate = 1 m/y

0 100 200 300 400 500 Distance (m) 0.0x10 0 5.0x10 -2 10 -1 1.5x10 -1 2.0x10 -1 2.5x10 -1TIC (mol/L) 100 yr flow rate = 10 m/y 100 yr flow rate = 1 m/y

Fig. 4.47. Comparison of CO 2 gas saturation degree (left) and TIC (right) along x-axis at y = 0 m in the sensitivity case and the base run

When the groundwater flow velocity decreases to 1 m/year, diffusi on rather than just advection becomes important in controlli ng the transport of a queous species. Figure 4.48 (left) shows the evolution of pH at different times. As a result of diffusion, the low pH profile moves not only downstream with the groundwater flow , but also in the upstream dire ction (right-hand side of the figure). Elevated aqueous Pb and As concentra tions correspond to the areas where pH is low (Figure 4.49 and 4.50). We may conclude that for a case with low groundwater velocity, the impact of CO 2 intrusion can be more significan t, but is also more localized.258 of 352 0 100 200 300 400 500 Distance (m) 5.5

6 6.5

7 7.5

8pH 5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) 5.5

6 6.5

7 7.5

8pH 100 yr flow rate = 10 m/y 100 yr flow rate = 1 m/y

Fig. 4.48. Evolution of pH at different times (left) along the x-axis at y = 0 m where groundwater flow velocity is 1m/year, and sensitivity of pH to groundwate r flow velocity after 100 years (right)

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr flow rate = 10 m/y 100 yr flow rate = 1 m/y MCL

Fig. 4.49. Total aqueous Pb concentration at different times (l eft) along the x-axis at y = 0 m where groundwater flow velocity is 1m/year, and sensitivity to groun dwater flow velocity after 100 years (right)

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

100 yr flow rate = 10 m/y 100 yr flow rate = 1 m/y MCL

Fig. 4.50. Total aqueous As concentration at different times (left) along x-axis at y = 0 m where groundwater flow velocity is 1m/year and sensitivity to groundw ater flow velocity after 100 years (right).259 of 352 

### 4.6.9 Sensitivity to CO 2 

### Leakage Rate

The base model assumes a gaseous CO 2 intrusion rate of 7.5 × 10 -5 kg/s, which corresponds to
2.36 tonnes of CO 2 per year. This rate was sel ected based on the amount of CO 2 that can dissolve in the aquifer without forming a large two-phase zone. To understand the implications of more
CO 2 leaking into the aquifer, we pe rformed a sensitivity run with a CO 2 intrusion rate of 6 × 10 -4 kg/s. When the CO 2 intrusion rate increases, we observe a much larger two-phase region (see Figure 4.51 and Figure 4.53 left) and a correspondingly la rger region with elevated TIC (see Figure 4.52 and Figure 4.53, right). As a resu lt, in this two-phase zone, the CO 2 partial pressure is larger than in the base run and the pH is lower (Figure 4.54, right). This pH decrease has implications for the mobilization of hazardous constituents, giving rise to significant increases in aqueous Pb concentrations, (Figure 4.55, right), a nd moderate increases in As concentrations downstream (Figure 4.56, right). Anot her reason for the significant increase of total aqueous Pb concentration is that the Pb carbonate aqueous complex, PbCO 3 (aq), becomes the most important aqueous species containing Pb. An increase of the carbonate conc entration gives rise to an increase of Pb carbonate aqueous complex and thus elevates the total aque ous Pb concentration.

Even more so than in the previous section, the large two-phase zone forming in this sensitivity case implies that the assumption of depth-averag ed processes may not be valid for the model being studied. Because of buoyancy, CO 2 would tend to accumulate under the confining layer at the top of the aquifer, possibl y bypassing much of the groundwater flow in the aquifer. See Section 4.9 for a three-dimensional simulation of this sensitivity case.260 of 352 XY 100 200 300 400 50 100 150 200

SG 0.18 0.17 0.15 0.14 0.13 0.12 0.11 0.09 0.08 0.07 0.06 0.05 0.04 0.02 0.01

Fig. 4.51. Spatial distribution of saturation for CO 2 in a sensitivity run with CO 2 intrusion rate of 6 × 10 -4 kg/s after 100 years of intrusion



### XY 100 200 300 400 50 100 150 200

TIC 0.22 0.20 0.19 0.17 0.16 0.14 0.13 0.12 0.10 0.09 0.07 0.06 0.04 0.03 0.02

Fig. 4.52. Spatial distribution of TIC in a sensitivity run with CO 2 intrusion rate of 6 × 10 -4 kg/s after 100 years of intrusion261 of 352 0 100 200 300 400 500 Distance (m) 0 0.04 0.08 0.12 0.16

0.2CO2gas saturation degree

100 yr CO 2 intrusion rate = 7.5x10 -5 kg/s 100 yr CO 2 intrusion rate = 6x10 -4 kg/s

0 100 200 300 400 500 Distance (m) 0 0.05

0.1 0.15

0.2 0.25TIC (mol/L) 100 yr CO 2 intrusion rate = 7.5x10 -5 kg/s 100 yr CO 2 intrusion rate = 6x10 -4 kg/s

Fig. 4.53. Comparison of CO 2 gas saturation degree (left) and dissolved CO 2 mass fraction (right) along x-axis at y = 0 m in the sensitivity and base run.

0 100 200 300 400 500 Distance (m) 5.5

6 6.5

7 7.5

8pH 5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) 5.5

6 6.5

7 7.5

8pH 100 yr CO 2 intrusion rate = 7.5x10 -5 kg/s 100 yr CO 2 intrusion rate = 6x10 -4 kg/s

Fig. 4.54. pH evolution at different times (left) along the x-axis at y = 0 m for CO 2 intrusion rate of 6×10 -4 kg/s, and sensitivity of pH to CO 2 intrusion rate.262 of 352 0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr CO 2 intrusion rate = 7.5x10 -5 kg/s 100 yr CO 2 intrusion rate = 6x10 -4 kg/s

MCL

Fig. 4.55. Total aqueous Pb concentration at different ti mes (left) along the x- axis at y = 0 m for CO 2 intrusion rate of 6×10 -4 kg/s, and sensitivity of Pb concentration to CO 2 intrusion rate.

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueousAs concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueousAs concentration (mol/L)

100 yr CO 2 intrusion rate = 7.5x10 -5 kg/s 100 yr CO 2 intrusion rate = 6x10 -4 kg/s MCL

Fig. 4.56. Total aqueous As concentration at different ti mes (left) along the x- axis at y = 0 m for CO 2 intrusion rate of 6×10 -4 kg/s, and sensitivity of As concentration to CO 2 intrusion rate.263 of 352 

## 4.7 Results for Other Mineralogies

The base run and all sensitivity cases conducted thus far have all assumed the basic mineralogy of a Coastal Plain Sandstone aquifer, as in troduced in Section 3.6.3.1. This mineralogy is representative of an impure arenite, with a co mposition mostly consisting of quartz plus some feldspars, lithic fragments, and clays. Below, we evaluate other basic mineralogies, otherwise simulating the base model setup described in Secti on 4.2. The first is repres entative of an almost pure arenite typical of the mineralogy of a St Peter Sandstone aquifer, and the second is the mineralogy of a dolomitic limestone aquifer. The St Peter Sandstone, which consists primarily of quartz sand, has a poor pH bufferi ng capacity and is presumed to be the type of aquifer most
vulnerable to mobilization of hazardous inorganic constituents through CO 2 intrusion. The dolomitic limestone, which is dominated by calci te and dolomite, should have good pH buffering capacity and is assumed to be least vulnerabl e to the mobilization of hazardous constituents during CO 2 intrusion. All basic mineral compositions contain the same volumetric fractions of trace minerals containing metals or As as essential components (Section 3.6.2.2).



### 4.7.1 St Peter Sandstone
The mineralogy of the St Peter Sandstone, Illinoi s, as presented in Section 3.6.3.3, is an example of an aquifer with limited pH buffering capabilit y. The most obvious feature of this mineralogy is that it consists mainly of quartz sand with small amounts of calcite and dolomite, both of which are important pH buffering minerals. The cl ay matrix content is very small. Table 4.13 lists the mineralogical composition and gives the ki netic properties for minerals in this aquifer. An equilibrium run reveals the initial chemical composition in equilibrium with St Peter
Sandstone before intrusion of CO 2 (Table 4.14), which has a lower pH and Eh compared with the Coastal Plain Sandstone aquifer assumed in the ba se model. Slightly larger initial As aqueous concentrations are obtained, while the initial Pb c oncentration is virtually identical. Overall, the
differences in the initial c onditions are not significant.264 of 352 Table 4.13. Mineral volume fractions, possible secondary minerals, and kinetic properties for a St Peter Sandstone

Parameters for Kinetic Rate Law
Neutral
Mechanism Acid Mechanism Base Mechanism Mineral Volume
Fraction
of solid A (cm 2 /g)

k 25 (mol/m 2 /s) E a (KJ /mol) k 25 E a n(H + ) k 25 E a n(H + )

Primary : Quartz 0.926778 9.8 1.023 × 10 -14 87.7 K-feldspar 0.0505281 9.8 3.890 × 10 -13 38 8.710 × 10 -11 51.7 0.5 6.310 × 10 -12 94.1 -0.823 Oligoclase 0.00244551 9.8 1.445 × 10 -12 69.8 2.138 × 10 -10 65 0.457 Kaolinite 0.00099595 1.95×10 5 6.918 × 10 -14 22.2 4.898 × 10 -12 65.9 0.777 8.913 × 10 -18 17.9 -0.472
Smectite-ca 0.0023423 5.64×10 5 1.660 × 10 -13 35 1.047 × 10 -11 23.6 0.34 3.020 × 10 -17 58.9 -0.4
Illite 0.00374012 6.68×10 5 1.660 × 10 -13 35 1.047 × 10 -11 23.6 0.34 3.020 × 10 -17 58.9 -0.4
Chlorite 0.00171185 9.8 3.02 × 10 -13 88 7.762 × 10 -12 88 0.5 Kerogen-os 0.00238641 9.8 3.02 × 10 -13 88 7.762 × 10 -12 88 0.5 Pyrite 0.000515 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 dolomite 0.0045 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Calcite 0.00095337 Assumed at equilibrium Goethite 0.00060538 1.47×10 5 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Apatite 0.00220978 12.9 2.34 × 10 -7 43.54 1 Barite 0.00016334 12.9 2.34 × 10 -7 43.54 1 Arsenopyrite 9.0594E-06 12.9 2.34 × 10 -7 43.54 1 Greenockite 3.0363E-06 12.9 2.34 × 10 -7 43.54 1 Cinnabar 1.4640E-08 12.9 2.34 × 10 -7 43.54 1 Galena 7.8529E-06 12.9 2.34 × 10 -7 43.54 1 Gudmundite 3.89 × 10 -7 12.9 2.34 × 10 -7 43.54 1 Tiemannite 1.22 × 10 -7 12.9 2.34 × 10 -7 43.54 1 Uraninite 8.015 × 10 -7 12.9 2.34 × 10 -7 43.54 1 Sphalerite 9.42 × 10 -5 12.9 2.34 × 10 -7 43.54 1 Secondary: Dolomite 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Magnesite 9.8 4.571 × 10 -10 23.5 4.169 × 10 -7 14.4 1 Ankerite 9.8 1.260 × 10 -9 62.76 6.457 × 10 -4 36.1 0.5 Dawsonite 9.8 1.260 × 10 -9 62.76 6.457 × 10 -4 36.1 0.5 Na-smectite 151.6 1.660 × 10 -13 35 1.047 × 10 -11 23.6 0.34 3.020 × 10 -17 58.9 -0.4 Pyromorphite 12.9 2.34 × 10 -7 43.54 1 Ferroselite 12.9 2.34 × 10 -7 43.54 1 Clausthalite 12.9 2.34 × 10 -7 43.54 1265 of 352 Table 4.14. Initial chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain Sandstone and St Peter Sandstone

Species Base Model with Coastal Plain Sandstone St Peter Sandstone Eh -0.215 (volt) -0.2 (volt) pH 7.61 7.45
Ca 9.00E-04 1.94E-03 Mg 2.17E-05 4.23E-05
N a 1.97E-03 1.97E-03
K 2.68E-04 4.17E-04
Fe 5.63E-06 8.45E-06
Si 9.31E-04 6.35E-04
TIC 3.33E-03 2.38E-03
SO 4 -2 1.95E-04 1.97E-04
HS - 1.44E-08 1.37E-08
Al 4.19E-11 6.58E-11
Cl 2.07E-04 2.07E-04
P 1.78E-06 1.04E-06
Pb 1.30E-09 1.30E-09
As 4.42E-08 5.44E-08
Ba 1.12E-06 1.27E-06
Cd 1.76E-09 1.41E-09 Hg 2.53E-09 2.52E-09
Sb 3.57E-10 4.19E-10
Se 1.69E-08 1.67E-08
U 4.48E-09 3.15E-09
Zn 3.40E-07 3.51E-07

Intrusion of CO 2 into the St Peter Sandstone aquifer gives rise to geochemical changes similar to those observed in the base model. With gaseous CO 2 dissolving into the aqueous phase, the pH drops and mobilization of hazardous constituents occurs. Figure 4.57 (left) shows the simulated pH in profiles along the x-axis at y=0 m for diff erent times. Note that the pH in the two-phase zone close to the intrusion location is significan tly smaller at 100 years than at 50 years. Slight266 of 352 decreases in pH can also be seen along the first 100 m downstream of the two-phase zone. This further pH drop is caused by the pa rtial loss of pH buffer capability as the initially small volumes
of calcite and dolomite minerals dissolve and eventually become depleted in the two-phase area and the immediate downstream region.

Figure 4.58 (left) shows the calcite volume fraction changes at diffe rent times. After 20 years, a small region of complete calcite depletion develops in the tw o-phase zone, which means that calcite ceases acting as a pH buffer. With time, this region extends further downstream. The dissolution of dolomite (Fi gure 4.59 right) is less ra pid; dolomite is fully depleted only in a small region, after about 100 years. In the two-phase region, where both calcite and dolomite are fully depleted at the end of the simu lation period, the pH decreases to about 4.5 at 100 years (Figure 4.57). In the region immediately downstream, where calcite is depleted, but dolomite is still present, the pH value is slight ly lower than further downstream. As shown in Figure 4.57 (right), the regions with complete dissolution of calcite /dolomite correspond to the regions where the St
Peter Sandstone and the Coastal Plain Sands tone differ most with respect to pH.

0 100 200 300 400 500 Distance (m) 4 5 6 7 8pH

5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) 4 5 6 7 8pH 100 yr Coastal Plain Sandstone 100 yr St Peter Sandstone

Fig. 4.57. pH profiles (left) along the x-axis at y = 0 m at di fferent times for St Peter sandstone, and comparison of pH computed for Coastal Plain Sandstone and St Peter Sandstone (right)267 of 352 0 100 200 300 400 500 Distance (m) -100

-80 -60 -40 -20

0 20Calcite volume fraction change (%)

5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) -100

-80 -60 -40 -20

0 20Dolomite volume fraction change (%)

5 yr 20 yr 50 yr 100 yr

Fig. 4.58. Volume fraction of calcite (left) and do lomite (right) along x-axis at y = 0 m

Figures 4.55 (left) shows the resulting Pb concentrations for the St Peter Sa ndstone at different
times after CO 2 intrusion, exhibiting a complex tr ansient behavior. Early on, the Pb concentrations increase strongly in the two-pha se zone, as observed in the base model, but decrease at later stages. From the observed evol ution of pH, which decr eases at later stages because buffering minerals become depleted, one would have expected the opposite behavior. Analysis of the adsorption characteristics for Pb reveals that the trend of decreasing Pb concentrations in the two-phase zone is cause d by a corresponding depletion of Pb on adsorption sites (Figures 4.56). Similar to the base model, th e early increase in aqueous Pb is mainly caused by desorption of initially sorbed Pb. Since the St Peter sandstone contains less clay mineral than the Coastal Plain Sandstone, the in itial amount of sorbed Pb is smaller and thus becomes fully depleted in the two-phas e zone after about 50 year s, taking away a signific ant source of Pb in solid phase.

Figures 4.55 (right) shows a comparison of Pb concentrations af ter 100 years of CO 2 intrusion
for the St Peter Sandstone and Coastal Plain Sandstone, respectively. Th e sensitivity run has
smaller Pb concentrations in and near the tw o-phase zone, because of the above-discussed depletion of sorbed Pb. In contrast, the area fu rther downstream shows slightly higher aqueous
Pb concentrations for the St Peter Sandstone , apparently caused by the less effective readsorption of Pb migrating in downstream directi on. We may conclude that the differences in Pb268 of 352 concentrations observed in the se nsitivity run compared to the base model are mostly related to differences in the adsorption/desorption characteristi cs (the clay content is different), while the differences in the pH buffer char acteristics play a lesser role.

A different behavior is seen in the As results. The As concentr ation in the groundw ater increases strongly in the two-phase zone between 50 years to 100 years (Figure 4.61 left), consistent with the further decrease of pH during this period. Th e obvious difference to th e results observed for Pb is that there is enoug h As initially sorbed on mineral sites such that depletion of sorbed matter is not an issue. In fact, the pl ot showing the total As concentr ation on adsorpti on sites at 100 years in Figure 4.62 (left) is a straight line (f or both the St Peter Sandstone and Coastal Plain Sandstone) because the spatial variation is too sm all to be detected. That significant desorption of As occurs in the two-phase zone between 50 and 100 years is evident in Figure 4.62 (right), which shows the change in sorbed As compar ed to the initial conditions. The resulting differences between the sensitivity case and the base model (Figure 4.61 right) are consistent with the observed differences in pH within and near the two-phase zone. The St Peter Sandstone aquifer arrives at higher As concentration th an the Coastal Plain Sandstone because (1) As desorbs more strongly and (2) dissol ution of arsenopyrite is enhanced.

0 100 200 300 400 500 Distance (m) 0.0x10 0 2.0x10 -8 4.0x10 -8 6.0x10 -8 8.0x10 -8Total aqueous Pb concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 0.0x10 0 2.0x10 -8 4.0x10 -8 6.0x10 -8 8.0x10 -8Total aqueous Pb concentration (mol/L)

100 yr Coastal Plain Sandstone 100 yr St Peter Sandstone MCL

Fig. 4.59. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times for St Peter Sandstone, and comparison of co mputed Pb concentration for Coastal Plain Sandstone and St Peter Sandstone with adsorption/desorption (right)269 of 352 0 100 200 300 400 500 Distance (m) 0.0x10 0 2.0x10 -6 4.0x10 -6 6.0x10 -6Total Pb sorption sites (mol/L)

100 yr Coastal Plain Sandstone 100 yr St Peter Sandstone

0 100 200 300 400 500 Distance (m) -100

-80 -60 -40 -20

0 20Total Pb sorpiton sites change (%)

50 yrs 100 yrs

Fig. 4.60. Comparison of total Pb concentration on adsorption sites in mol/L for Coastal Plain Sandstone and St Peter Sandstone (left), and relativ e change in concentration on adsorption sites for St Peter Sandstone at 50 and 100 years (right)

0 100 200 300 400 500 Distance (m) 0.0x10 0 10 -7 2.0x10 -7 3.0x10 -7 4.0x10 -7Total aqueous As concentration (mol/L)

5 yr 20 yr 50 yr 100 yr

MCL

0 100 200 300 400 500 Distance (m) 0.0x10 0 10 -7 2.0x10 -7 3.0x10 -7 4.0x10 -7Total aqueous As concentration (mol/L)

100 yr Coastal Plain Sandstone 100 yr St Peter Sandstone MCL

Fig. 4.61. Total aqueous As concentration profiles (left) along x-axis at y = 0 m at different times for St Peter Sandstone, and comparison of co mputed As concentration for Coastal Plain Sandstone and St Peter Sandstone (right)270 of 352 0 100 200 300 400 500 Distance (m) 0.0x10 0 10 -2 2.0x10 -2 3.0x10 -2 4.0x10 -2Total As sorption sites (mol/L)

100 yr Coastal Plain Sandstone 100 yr St Peter Sandstone

0 100 200 300 400 500 Distance (m) -0.3 -0.2 -0.1

0 0.1Total As sorption sites change (%)

50 yrs 100 yrs

Fig. 4.62. Comparison of total As concentration on adsorption sites in mol/L for Coastal Plain Sandstone and St Peter Sandstone (left), and relativ e change in concentration on adsorption sites for St Peter Sandstone at 50 and 100 years (right)

0 100 200 300 400 500 Distance (m) 10 -9 2.0x10 -9 3.0x10 -9 4.0x10 -9Total aqueous Pb concentration (mol/L)

5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7 1.6x10 -7Total aqueous As concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

Fig. 4.63. Total aqueous Pb (left) and As (right) concentration profiles (left) along x-axis at y = 0 m at different times for St Peter Sandstone obtained by a simulation without adsorption/desorption271 of 352 

### 4.7.2 Dolomitic Limestone

A dolomitic limestone aquifer is considered in th is section as an example of a mineralogy in which complete dissolution of buffering carbonat es due to even long-term ingress of CO 2 would be implausible. A brief introduction to the sl ightly impure Dolomitic Limestone assumed below is given in Section 3.6.3.4, while Table 4.15 list s its mineralogical composition and provides kinetic parameters. The most obvious feature of the Dolomitic Limestone mineralogy is that it contains large amounts of calcite (>60% volume fraction) and dolomite (>14% volume fraction), both of which strongly buffer pH.

An equilibrium run provides the initial chemic al composition in equilibrium with Dolomitic Limestone, prior to intrusion of CO 2 (Table 4.16). Compared to the initial chemical composition for the Coastal Plain Sandstone, the Dolomitic Lime stone aquifer has a slightly higher initial pH and a lower Eh. Identical initial Pb and slightly lower As concentrations are obtained compared with those of the Coastal Plain Sandstone aquifer.

Figure 4.64 shows profiles of simu lated pH for the Dolomitic Limestone at different times during ingress of CO 2 (left figure) and compares the 100-year profile to the Coastal Plain Sandstone aquifer from the base model (right figure). Both mineralogies have an almost identical pH profile, which indicates that: (1) the slight diffe rence in initial pH of the groundwater does not affect the pH after CO 2 intrusion; and (2) the actual volume fraction of buffering minerals is not relevant so long as the minerals are present. However, because the Dolomitic Limestone aquifer contains much more calcite and dolomite than the Coastal Plain Sandstone, the relative volume changes caused by dissolution of these minerals are different. For example, only 1% of the
calcite fraction dissolves in the se nsitivity case during 100 years of CO 2 intrusion, compared to almost 100% in the Coastal Plain aquifer (Figur e 4.65). It can be anticip ated that the Coastal Plain Sandstone would be depleted of calcite shortly after 100 years, whereas the Dolomitic Limestone would buffer pH for thousands of years.272 of 352 Table 4.15. Mineral volume fractions, possible secondary minerals, and kinetic properties for a Dolomitic Limestone

Parameters for Kinetic Rate Law
Neutral
Mechanism Acid Mechanism Base Mechanism Mineral Volume
Fraction
of solid A (cm 2 /g)

k 25 (mol/m 2 /s) E a (KJ /mol) k 25 E a n(H + ) k 25 E a n(H + )

Primary : Quartz 0.05144173 9.8 1.023 × 10 -14 87.7 K-feldspar 0.00000000 9.8 3.890 × 10 -13 38 8.710 × 10 -11 51.7 0.5 6.310 × 10 -12 94.1 -0.823 Oligoclase 0.00000000 9.8 1.445 × 10 -12 69.8 2.138 × 10 -10 65 0.457 Kaolinite 0.05251716 1.95×10 5 6.918 × 10 -14 22.2 4.898 × 10 -12 65.9 0.777 8.913 × 10 -18 17.9 -0.472
Smectite-ca 0.01976175 5.64×10 5 1.660 × 10 -13 35 1.047 × 10 -11 23.6 0.34 3.020 × 10 -17 58.9 -0.4
Illite 0.0690266 6.68×10 5 1.660 × 10 -13 35 1.047 × 10 -11 23.6 0.34 3.020 × 10 -17 58.9 -0.4
Chlorite 0.01805338 9.8 3.02 × 10 -13 88 7.762 × 10 -12 88 0.5 Kerogen-os 0.02516736 9.8 3.02 × 10 -13 88 7.762 × 10 -12 88 0.5 Pyrite 0.00271834 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 dolomite 0.14265542 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Calcite 0.60326492 Assumed at equilibrium Goethite 0.01276885 1.47×10 5 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Apatite 0.00233047 12.9 2.34 × 10 -7 43.54 1 Barite 0.00017227 12.9 2.34 × 10 -7 43.54 1 Arsenopyrite 9.5542E-06 12.9 2.34 × 10 -7 43.54 1 Greenockite 3.2022E-06 12.9 2.34 × 10 -7 43.54 1 Cinnabar 1.5439E-08 12.9 2.34 × 10 -7 43.54 1 Galena 8.2817E-06 12.9 2.34 × 10 -7 43.54 1 Gudmundite 4.1038E-07 12.9 2.34 × 10 -7 43.54 1 Tiemannite 1.2941E-07 12.9 2.34 × 10 -7 43.54 1 Uraninite 8.4531E-07 12.9 2.34 × 10 -7 43.54 1 Sphalerite 9.9324E-05 12.9 2.34 × 10 -7 43.54 1 Secondary: Dolomite 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Magnesite 9.8 4.571 × 10 -10 23.5 4.169 × 10 -7 14.4 1 Ankerite 9.8 1.260 × 10 -9 62.76 6.457 × 10 -4 36.1 0.5 Dawsonite 9.8 1.260 × 10 -9 62.76 6.457 × 10 -4 36.1 0.5 Na-smectite 151.6 1.660 × 10 -13 35 1.047 × 10 -11 23.6 0.34 3.020 × 10 -17 58.9 -0.4 Pyromorphite 12.9 2.34 × 10 -7 43.54 1 Ferroselite 12.9 2.34 × 10 -7 43.54 1 Clausthalite 12.9 2.34 × 10 -7 43.54 1273 of 352 Table 4.16. Initial chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain Sandstone and Dolomitic Limestone.
Species Coastal Plain Sandstone Dolomitic Limestone Eh -0.215 (volt) -0.22 (volt) pH 7.61 7.68
Ca 9.00E-04 6.61E-04 Mg 2.17E-05 1.69E-05
N a 1.97E-03 1.97E-03
K 2.68E-04 2.39E-04
Fe 5.63E-06 4.86E-06
Si 9.31E-04 9.72E-04
TIC 3.33E-03 3.85E-03
SO 4 -2 1.95E-04 1.95E-04
HS - 1.44E-08 1.45E-08
Al 4.19E-11 4.23E-11
Cl 2.07E-04 2.07E-04
P 1.78E-06 1.88E-06
Pb 1.30E-09 1.30E-09
As 4.42E-08 4.32E-08
Ba 1.12E-06 5.11E-07
Cd 1.76E-09 1.95E-09 Hg 2.53E-09 2.57E-09
Sb 3.57E-10 3.27E-10
Se 1.69E-08 1.41E-08
U 4.48E-09 5.28E-09
Zn 3.40E-07 3.31E-07

With the pH differences almost negligible, the effect s of galena precipitation or arsenopyrite dissolution should be fairly sim ilar between the two sensitivity cases. However, the resulting
concentrations of Pb and As do show small but noticeable differences. The reason is that the274 of 352 adsorption/desorption characteristics differ because the content of illite and smectite differs
between the Dolomitic Limestone and the Coastal Plain Sandstone mineralogies.

4.66 shows the spatial distribution of total aqueous Pb concentra tions at different times for Dolomitic Limestone (left) and compares them with those for the Coastal Plain Sandstone (right). The sensitivity case has a slightly higher Pb concentrati on in the two-phase region and marginally lower Pb concentration downstream. The weak site on illite is the principal surface complex that releases Pb, while the strong site on smectite is the princi pal surface complex that adsorbs Pb. Desorption is stronge r in the two-phase zone for the Dolomitic Limestone than for the Coastal Plain Sandstone (Fi gure 4.67 and Figure 4.68 left), beca use the former contains more
illite than the latter. The Dolomitic Limestone al so contains more smectite, which explains why
more Pb sorbs back onto the st rong site on smectite in the downstream region (Figure 4.68 right). Overall, the sensitivity run show s a very small amount of Pb in the downstream region, whereas
the base model exhibits a mi nor amount of desorption. While hard to observe in Figure 4.68 (right), the relative changed of total Pb sorbed is +0.15% for th e sensitivity case compared to -
0.2% for the base model.

Figure 4.69 shows the spatial distri bution of total aqueous As con centrations at different times for the Dolomitic Limestone (left) and a comparis on of the 100-year resu lts with those for the Coastal Plain Sandstone (right). Th e sensitivity case shows higher aqueous As concentration than
the base model. This result is somewhat counter intuitive when consider ing the large amounts of pH buffering minerals present in the Dolomitic Limestone. However, as pointed out before, the pH evolution after ingress of CO 2 is largely identical for both mineralogies, because the actual volume fraction of buffering minerals is not releva nt as long as these minerals remain present.
The difference in the As concentrations is rath er caused by the larger illite content in the Dolomitic Limestone, which leads to stronger de sorption in the two-phase zone and subsequent transport of elevated aqueous As in downstream direction.275 of 352 0 100 200 300 400 500 Distance (m) 5.5

6 6.5

7 7.5

8pH 5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) 5.5

6 6.5

7 7.5

8pH 100 yr Coastal Plain Sandstone 100 yr Dolomitic Limestone

Fig. 4.64. pH profiles (left) along x-axis at y = 0 m at diffe rent times for Dolomitic Limestone, and comparison of computed pH with Coasta l Plain Sandstone (right).

0 100 200 300 400 500 Distance (m) -100

-80 -60 -40 -20

0 20Calcite volume fraction change (%)

100 yr Coastal Plain Sandstone 100 yr Dolomitic Limestone

Fig. 4.65. Comparison of calcite volume fraction change for Coastal Plain Sandstone and Dolomitic Limestone.

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

100 yr Coastal Plain Sandstone 100 yr Dolomitic Limestone MCL

Fig. 4.66. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times for Dolomite Limestone, and comparison of computed Pb concentration with Coastal Plain Sandstone (right)276 of 352 0 100 200 300 400 500 Distance (m) 0.0x10 0 2.0x10 -6 4.0x10 -6 6.0x10 -6 8.0x10 -6 10 -5 1.2x10 -5Total Pb sorption sites (mol/L)

100 yr Coastal Plain Sandstone 100 yr Dolomitic Limestone

0 100 200 300 400 500 Distance (m) -8.0x10 1 -6.0x10 1 -4.0x10 1 -2.0x10 1 0.0x10 0 2.0x10 1Total Pb sorption sites change (%)

100 yr Coastal Plain Sandstone 100 yr Dolomitic Limestone

Fig. 4.67. Comparison of total Pb concentration on adsorptio n sites (left) and change (right) for Coastal Plain Sandstone and Dolomitic Limestone.

0 100 200 300 400 500 Distance (m) 0.0x10 0 2.0x10 -6 4.0x10 -6 6.0x10 -6 8.0x10 -6 10 -5 1.2x10 -5illw_OPb+(mol/L)
100 yr Coastal Plain Sandstone 100 yr Dolomitic Limestone

0 100 200 300 400 500 Distance (m) 0.0x10 0 2.0x10 -7 4.0x10 -7 6.0x10 -7 8.0x10 -7 10 -6smes_OPb+(mol/L)

100 yr Coastal Plain Sandstone 100 yr Dolomitic Limestone

Fig. 4.68. Comparison of surface complex ill w _OPb + (left) and sme s _oPb + for Coastal Plain Sandstone and Dolomitic Limestone.

0 100 200 300 400 500 Distance (m) 4.0x10 -8 8.0x10 -8 1.2x10 -7 1.6x10 -7Total aqueous As concentration (mol/L)

5 yr 20 yr 50 yr 100 yr MCL

0 100 200 300 400 500 Distance (m) 4x10 -8 8x10 -8 1x10 -7 2x10 -7Total aqueousAs concentration (mol/L)

100 yr Coastal Plain Sandstone 100 yr Dolomitic Limestone MCL

Fig. 4.69. Total aqueous As concentration profiles (left) along x-axis at y = 0 m at different times for Dolomitic Limestone, and comparison of computed As concentration with Coastal Plain Sandstone (right)277 of 352 

## 4.8 Oxidizing Conditions

Section 3.6.3.2 introduces the mineralogy of a Coastal Plain Sand stone under oxidizing conditions. Oxidizing conditions may result from continuous recharge with oxygenated water in unconfined aquifers, or in response to anthropogenic extrac tion of potable water. In this section, we investigate the behavior of an oxidized aquifer affected by CO 2 intrusion. Notice, however, that oxygenation itself can lead to the mobilization of heavy me tals as a result of oxidative dissolution, an effect that is often more relevant than the pH decrease caused by CO 2 intrusion.

As discussed in Section 3.6.3.2, the mineralogical composition of an oxidized aquifer differs from an aquifer under reducing conditions. The most significant differences with respect to the Coastal Plain Sandstone are that (1) pyrite is oxid ized and that (2) cerussite instead of galena would be the mineral controlling Pb concentrat ion. Table 4.17 lists the modified mineralogical composition of a representative Coastal Plain Sandstone under oxidizing conditions. All minerals likely to buffer the redox stat e under reducing conditions have been eliminated, and only cerussite is present as a host mineral for Pb. We focus in this sensitivity case only on the potential for Pb contamination; the behavior of As under analogous conditions may be studied in a future analysis.

An equilibrium run reveals the initial chemical composition in equilibrium with the oxidized Coastal Plain Sandstone, be fore the intrusion of CO 2 . Well-oxidized soils ha ve redox potentials of +0.4 to +0.8 V, while extrem ely reduced soils may have poten tials of -0.1 to -0.5 V (Dragun, 1988). In the equilibrium run, Eh is specified by the redox couple of O 2 /H 2 O, giving an Eh value of +0.37 V. An Eh value of +0.37 V represen ts an end case of groundwater redox condition, which probably only exists near th e recharge area of an aquifer where the water table is direct contact with air. The obj ective of conducting a simulation with Eh of +0.37 V is to establish the maximum effect of redox potential on the mobili zation of Pb. Table 4.18 compares the chemical composition of groundwater for th e Coastal Plain Sandstone a quifer under both reducing and oxidizing conditions. The latter composition is characterized by vanishingly small concentrations for Fe(II) and HS - ; pH is similar. The most relevant diffe rence, of course, is the initial aqueous278 of 352 Pb concentration in the oxidized aquifer, which is more than three orders of magnitude larger than under reducing conditions. In fact, the initial Pb concentration of 1.36 × 10 -6 mol/L in the oxidized aquifer is significantly above the MCL of 7.24 × 10 -8 mol/L (or 15 ppb). This difference is due to cerussite, rather than galena, controll ing the aqueous Pb concen tration under oxidizing conditions (see Section 3.4.6). Note th at the following reaction define s cerussite solu bility in the presence of CO 2 . According to the PHREEQC (Parkhurst and Appelo, 1999) database, one has:

2 2 3 3

( ) log 13.13 PbCO S Pb CO K + − + =− U (4.8)

Table 4.17. Mineral volume fractions, possible secondary minerals, and kinetic properties for an oxidized Coastal Plain Sandstone aquifer.

Parameters for kinetic rate law
Neutral mechanism Acid mechanism Base mechanism Mineral Volume
fraction
of solid A (cm 2 /g)
k 25 (mol/m 2 /s) E a (KJ /mol) k 25 E a n(H + ) k 25 E a n(H + )

Primary : Quartz 0.796 9.8 1.023 × 10 -14 87.7 K-feldspar 0.0634 9.8 3.890 × 10 -13 38 8.710 × 10 -11 51.7 0.5 6.310 × 10 -12 94.1 -0.823 Oligoclase 0.0614 9.8 1.445 × 10 -12 69.8 2.138 × 10 -10 65 0.457
Kaolinite 0.0174 151.6 6.918 × 10 -14 22.2 4.898 × 10 -12 65.9 0.777 8.913 × 10 -18 17.9 -0.472
Ca-smectite 0.0139 151.6 1.660 × 10 -13 35 1.047 × 10 -11 23.6 0.34 3.020 × 10 -17 58.9 -0.4
Illite 0.029 151.6 1.660 × 10 -13 35 1.047 × 10 -11 23.6 0.34 3.020 × 10 -17 58.9 -0.4
Calcite 0.0097 Assume d at equilibrium
Goethite 0.00616 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Apatite 2.2 × 10 -4 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1
Barite 4.9×10 -4
Cerussite 7.99 × 10 -6 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1 Secondary: Magnesite 9.8 4.571 × 10 -10 23.5 4.169 × 10 -7 14.4 1
Ankerite 9.8 1.260 × 10 -9 62.76 6.457 × 10 -4 36.1 0.5
Dawsonite 9.8 1.260 × 10 -9 62.76 6.457 × 10 -4 36.1 0.5 Pyromorphite 12.9 2.52 × 10 -12 62.76 2.34 × 10 -7 43.54 1
Na-smectite 151.6 1.660 × 10 -13 35 1.047 × 10 -11 23.6 0.34 3.020 × 10 -17 58.9 -0.4279 of 352 Table 4.18. Chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain Sandstone aquifer under reducing and oxidizing and conditions.

Species Reducing Conditions (Base Model) Oxidizing Conditions
Eh -0.215 (volt) 0.37 (volt) pH 7.61 7.78
Ca 9.00E-04 1.33E-03 Mg 2.17E-05 1.52E-14
N a 1.97E-03 1.35E-02
K 2.68E-04 1.27E-05
Fe 5.63E-06 6.50E-17
Si 9.31E-04 1.02E-03
TIC 3.33E-03 2.27E-03
SO 4 -2 1.95E-04 6.65E-03
HS - 1.44E-08 2.18E-16
Al 4.19E-11 1.08E-09
Cl 2.07E-04 2.08E-05
P 1.78E-06 1.52E-06
Pb 1.30E-09 1.36E-06

Except for the difference in mi neralogy and initial chemical conditions, the simulation of CO 2 intrusion into the oxidized Coas tal Plain Sandstone was conducte d for a model setup similar to the base model (Section 4.2). Although oxidizing conditions woul d typically exist in very shallow aquifers, we did not change the aquifer depth.

Figure 4.70 shows the evolution of pH along the x-axis at y=0, which is almost identical with the base model under reducing conditions . The pH decrease in the two-phase zone near the intrusion
location induces the release of Pb from adsorption sites. Figure 4.71, for example, shows a decrease in the concentration of surface comple xes on illite in the two-phase zone for both the strong (left) and weak sites (r ight). Within 100 years of CO 2 intrusion, the total mass of Pb on adsorption sites drops by about 6% in the two- phase zone (Figure 4.72 left). The elevated aqueous Pb concentrations, mostly induced by desorption in the two-phase zone, migrate280 of 352 downstream with the groundwater flow, where a small amount of Pb adsorbs onto surface complexes (note the minor increase in adsorbed ma ss downstream of the intr usion zone in Figure 4.72 left). Note that the solution becomes oversaturated with respec t to cerussite in both the twophase zone and downstream of it, which causes Pb to precipitate out as cerussite (note the few percent increase in the cerussite volume fraction in Figur e 4.72, right). Thus, while cerussite controls the initial equilibrium concentrations of Pb within the groundwater and on adsorption sites, the increase in aqueous Pb in response to CO 2 intrusion is not actually caused by cerussite dissolution, but rath er by desorption of initia lly sorbed matter. This could be different if adsorption processes were less releva nt in the aquifer, e .g., if less clay minera ls were present or the site density was lower.

Figure 4.73 shows the resulting evolution of a queous Pb concentrations along the x-axis. The peak in the two-phase zone observed at 5 year s is caused by the strong desorption occurring with the change in pH. Because surface complexation is a relatively fast process, considered as an equilibrium reaction in the current simulations, desorption ceases when the pH in two-phase zone stops decreasing. After 100 years, the downstream Pb concentrations have attained a fairly uniform concentration, which is about 30 times la rger than the initial Pb concentration. Both
initial and final conditions have Pb concentrations significan tly larger than the MCL of 7.24 × 10 -

8 mol/L (15 ppb).

0 100 200 300 400 500 Distance (m) 5.5

6 6.5

7 7.5

8pH 5 yr 20 yr 50 yr 100 yr

Fig. 4.70. pH profiles along x-axis at y = 0 m, for oxidized Coastal Plain Sandstone.281 of 352 0 100 200 300 400 500 Distance (m) 3.50x10 -2 3.55x10 -2 3.60x10 -2 3.65x10 -2 3.70x10 -2 3.75x10 -2ills_OPb+(mol/L)

5 yr 20 yr 50 yr 100 yr

0 100 200 300 400 500 Distance (m) 2.14x10 -2 2.16x10 -2 2.18x10 -2 2.20x10 -2 2.22x10 -2illw_OPb+(mol/L)

5 yr 20 yr 50 yr 100 yr

Fig. 4.71. Surface complex of Pb on the strong (left) and weak site (right) of illite along x-axis at y = 0 m for oxidized Coastal Plain Sandstone.

0 100 200 300 400 500 Distance (m) -8 -6 -4 -2 0 2Total Pb surface complex change (%)

100 yr

0 100 200 300 400 500 Distance (m) 0 2 4 6Cerussite volume fraction change (%) 5 yr 20 yr 50 yr 100 yr

Fig. 4.72. Relative change of total Pb concentration on adso rption sites (left) and cerussite volume fraction change (right) profiles, along x-axis at y = 0 m (left) for oxidized Coastal Plain Sandstone.

0 100 200 300 400 500 Distance (m) 0x10 0 4x10 -5 8x10 -5 1x10 -4 2x10 -4Total aqueous Pb concentration (mol/L)

5 yr 20 yr 50 yr 100 yr

Fig. 4.73. Total aqueous Pb concentration profiles (left) along x- axis at y = 0 m at different times for oxidized C282 of 352 

## 4.9 Three-Dimensional (3D) Simulation of CO 2 

## Intrusion

A two-dimensional depth-averaged model is used in all simulations described in the previous sections. Thus, the vertical vari ation in gaseous and aqueous CO 2 , as well as the vertical variation in the resulting geochemi cal changes, has been neglected so far. This raises questions regarding the limitations of the 2D assumption. For example, if a 3D model with vertical variation were used, how different would be the spatial distribution and magnitude of CO 2 in the gas phase and dissolved in the aqueous phase? Ho w would these differences affect the pH as well as the aqueous concentrations of Pb and As? Would the observed discrepancies between 2D and 3D modeling be less relevant for the base model described in Section 4.5, where most of the gaseous CO 2 readily dissolves in water, compared to the sensitivity cases described in Sections 4.6.8 and 4.6.9, where much larger CO 2 gas plumes are observed suggesting that buoyancy is
more relevant?

To answer these questions, a three-dimensional model was de veloped using the same model setup and similar model dimensions as in the 2D simulations. Like the 2D model, the 3D domain is 500 m long and has a uniform thickness of 10 m. However, the vertical ex tent of the model is now discretized using five model layers of 2 m thickness each. To increase computational efficiency, the lateral extent of the 3D mode l is reduced to 100 m, which was shown to be
sufficient in the 2D simulations. CO 2 enters the model domain from below. Three simulation
cases are considered: The first one uses a model setup identical to the 2D base model described in Section 4.5, excep t that vertical va riability is now considered (Section 4.9.1). The second case
is identical to the 2D sensitiv ity case described in Section 4.6. 8, featuring a reduced groundwater flow velocity (Section 4.9.2). The third case is identical to the 2D case described in Section 4.6.9, where a higher CO 2 leakage rate is assumed (Section 4.9.3). Note that in all cases, a confined aquifer has been assumed, so that gaseous CO 2 cannot escape into the overlying vadose zone. This is a conservative assumption because excess CO 2 (i.e., gaseous and buoyant CO 2 not dissolving into the grou ndwater), which would otherwise escap e into the vadose zone, is forced to remain in the aquifer.283 of 352 

### 4.9.1 Results from 3D Simulation of Base Model

The CO 2 leakage rate assumed in th e base-case simulation is 7.5×10 -5 kg/s, which is about 2.4 tonnes per year. As pointed out in Section 4.2, th is leakage rate was chosen roughly based on the
amount of CO 2 that can dissolve into the groundwater at a flow velocity of 10 m per year. Thus, buoyancy effects should be less relevant in this case, and the 2D model simplification should be a reasonable surrogate for the more complex 3D simulations. We will demonstrate this in the following discussion of 3D simula tion results, starting with a se t of figures with a close-in viewing of the intrusion zone and immediate vicinity.

Figures 4.74 and 4.75 show CO 2 gas saturation after 100 years of leakage, both in a 3D contour plot and in several y-z cross-se ctions. Consistent with the 2D results described in Section 4.5.1, the two-phase zone predicted by the 3D model is rela tively small, extendi ng about 30 m in the direction of groundwater flow (x-direction) and about 10 m perpendicula r to it (y-direction). Vertically, the two-phase zone extends almost to the top of the aquifer, but does not accumulate there, as most of the gaseous CO 2 effectively dissolves into th e groundwater. Plots of dissolved
CO 2 (which is given by the concentration of total inorganic carbonat e (TIC)) are shown in Figures 4.76 and 4.77. Qualitatively, the 3D result s are again similar to those obtained from the
2D model. An elevated concentration of TIC is observed in the two- phase region near the intrusion location and further downstream, resultin g from slow advective transport with the groundwater, while lateral spreading of dissolved CO 2 is minor. TIC contours indicate a fairly
uniform distribution of dissolved CO 2 with depth.

While qualitatively similar, there are some differ ences between 2D and 3D results. For example, the peak CO 2 gas saturation and the peak concentrations of dissolved CO 2 as predicted from the 3D model, observed near the intrus ion point at the base of the a quifer, are higher than those in the 2D model. As a result, the lowest pH va lue calculated in the 3D model is around 5.5 (see Figure 4.78 and 4.79), which is more acidic than in the 2D model (pH is equal to 5.65). However, outside of the immediate intrusi on location, the predicted pH changes from CO 2 intrusion are in fair agreement between the 3D and 2D models. Reasonable agreement between284 of 352 3D and 2D can also be seen in the predicted Pb and As concentrations, which correlate well with the groundwater volume experiencing pH changes (Figure 4.80 to 4.83). Elevated Pb and As have peak values near the intrusion location and slightly smaller values in the downstream direction. Except for the upperm ost portion of the aquifer, a queous Pb and As are well equilibrated over the de pth of the aquifer.

Figures 4.84 through 4.87 provide, respectively, contours of TIC, pH, Pb, and As from the 3D model, showing the entire length (i n the x-direction) of the mode l domain rather than focusing on the intrusion location. These figures reiterate qual itatively what was observed before in the 2D results. Transport in the longitudi nal direction is domina nt, with little lateral spreading. They also show that, while there is some vertical variab ility further downstream, the observed geochemical behavior is relatively uniform with depth.

A direct and quantitative comparison of predicte d 3D and 2D results is provided in Figures 4.88 and 4.89. Figure 4.88 shows to tal aqueous Pb concentration al ong the x-axis at y = 0 m for different aquifer depths (top at z = 0 m, middle at z = -5 m, and bottom at z = -10 m) in the 3D model, together with the depth-averaged re sults from the 2D model. The highest Pb
concentrations are observed at the bottom and in the middle of the aquifer, much higher than those observed at the top. As discussed above, this is because gaseous CO 2 dissolves before reaching the aquifer top. The 2D concentration pr ofiles resemble those of the 3D simulations, but deviate from them by about 20-30% when comp aring the aquifer bottom and top. Figure 4.89 shows the comparison of As concentrations fo r 3D and 2D models. Results from the depthaveraged 2D model are very similar to those pred icted at the bottom and in the middle of the 3D
domain.285 of 352 -5Z

90
100
110
120
130 X

0 10 20 30 40

Y Y

X Z

SG 0.091 0.085 0.079 0.073 0.067 0.061 0.055 0.048 0.042 0.036 0.030 0.024 0.018 0.012 0.006

Fig. 4.74. Close-up view of spatial distribution of CO 2 gas saturation after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.

Fig. 4.75. Profiles CO 2 gas saturation at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.286 of 352 -5Z 90
100
110
120
130 X

0 10 20 30 40

Y Y

X Z

TIC 0.22 0.20 0.19 0.17 0.16 0.14 0.13 0.12 0.10 0.09 0.07 0.06 0.04 0.03 0.02

Fig. 4.76. Close-up view of spatial distribution of Total Inorganic Carbon (TIC) after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.

Fig. 4.77. Profiles of concentration of Total Inorganic Carbon (TIC) at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.287 of 352 -5Z 90 100 110 120 130 X

0 10 20 30 40

Y Y Z

X
pH 7.48 7.35 7.22 7.09 6.96 6.83 6.70 6.57 6.44 6.31 6.18 6.05 5.92 5.79 5.66

Fig. 4.78. Close-up view of spatial distribution of pH in 3D model after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.

Fig. 4.79. Profiles of pH at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.288 of 352 -5Z 90
100
110
120
130 X

0 10 20 30 40

Y Y Z

X

Pb 2.7E-08 2.5E-08 2.3E-08 2.2E-08 2.0E-08 1.8E-08 1.6E-08 1.5E-08 1.3E-08 1.1E-08 9.4E-09 7.6E-09 5.9E-09 4.2E-09 2.4E-09

Fig. 4.80. Close-up view of spatial distribution of total aqueous Pb concentration after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.

Fig. 4.81. Profiles of total aqueous Pb concentration at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.289 of 352 -5Z 90
100
110
120
130 X

0 10 20 30 40

Y Y

X Z

As 1.4E-07 1.3E-07 1.2E-07 1.2E-07 1.1E-07 1.1E-07 9.9E-08 9.3E-08 8.7E-08 8.1E-08 7.5E-08 6.9E-08 6.3E-08 5.6E-08 5.0E-08

Fig. 4.82. Close-up view of spatial distribution of total aqueous As concentration after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.

Fig. 4.83. Profiles of total aqueous As concentration at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.290 of 352 -5Z 0

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50 Y Y Z

X

TIC 0.22 0.20 0.19 0.17 0.16 0.14 0.13 0.12 0.10 0.09 0.07 0.06 0.04 0.03 0.02

Fig. 4.84. Spatial distribution of Total Inorganic Carbon (TIC) after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.

-5Z 0

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400 X

50 Y Y Z

X pH 7.48 7.35 7.22 7.09 6.96 6.83 6.70 6.57 6.44 6.31 6.18 6.05 5.92 5.79 5.66

Fig. 4.85. Spatial distribution of pH after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.291 of 352 -5Z 0

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50 Y Y Z

X
Pb 2.7E-08 2.5E-08 2.3E-08 2.2E-08 2.0E-08 1.8E-08 1.6E-08 1.5E-08 1.3E-08 1.1E-08 9.3E-09 7.6E-09 5.8E-09 4.1E-09 2.3E-09

Fig. 4.86. Spatial distribution of total aqueous Pb concentration after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.

-5Z

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X
As 1.4E-07 1.3E-07 1.2E-07 1.2E-07 1.1E-07 1.1E-07 9.9E-08 9.3E-08 8.7E-08 8.1E-08 7.5E-08 6.9E-08 6.3E-08 5.6E-08 5.0E-08

Fig. 4.87. Spatial distribution of total aqueous As concentration after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 7.5×10 -5 kg/s.292 of 352 0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

3D y=0 z=0 3D y=0 z=-5 3D y=0 z=-10 2D MCL

Fig. 4.88. Total aqueous Pb concentration along x-axis at y = 0 m and different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous Pb concentration along x-axis at y = 0 m for 2D model.

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7 1.6x10 -7Total aqueous As concentration (mol/L)

3D y=0 z=0 3D y=0 z=-5 3D y=0 z=-10 2D MCL

Fig. 4.89. Total aqueous As concentration along x-axis at y = 0 m and different aquifer depth (z = 0, -5 and -10 m) for 3D model and total aqueous As concentration along x-axis at y = 0 m for 2D model.

We may conclude that the 2D results provide a reasonable approximation of the threedimensional system behavior. The differences ob served between 3D and 2D are well within the range stemming from uncertainties in many of the geochemical parameters. Of course, this is a conclusion specific to the considered case, which has a small zone of gaseous CO 2 and is therefore not dominated by buoyancy effects. Furthermore, we point out that the293 of 352 TOUGHREACT transport model has been set up such that lateral and vertical mixing is limited (i.e., by setting hydrodynamic dispersion to zero, or by c hoosing homogenous rather than spatially distributed parameters). More vertical mixing in the 3D model w ould lead to stronger equilibration over the thickness of the aquifer, thus further improving the agreement between 2D
and 3D models.



### 4.9.2 Sensitivity to Groundwate r Flow Velocity—3D Model

Our 2D simulation of the sensitivity to groundwate r flow velocity case indicates that a much larger two-phase zone with gaseous CO 2 and water evolves when the groundwater flow rate decreases to 1 m/y (Section 4.6.8). In such a case, buoyancy forces should be more relevant than in the base model, suggesting th at a 3D model representation ma y be necessary. Figures 4.90 and
4.91 show the 3D model results of CO 2 gas saturation af ter 100 years of CO 2 intrusion in an aquifer with a groundwater flow ra te of 1 m/yr. With less wate r flowing toward s the intrusion zone, dissolution of CO 2 is less effective. As a result, most of the leaking CO 2 remains in the gas phase, migrates upward until it r eaches the upper closed boundary of the confined aquifer, and then spreads out laterally. A strongly three-dime nsional behavior can be seen, with significant vertical variability, which is not captured by the 2D model results s hown in Section 4.6.8.

Figures 4.92 and 4.93 provide contours of dissolved CO 2 (given as total inorganic carbon, TIC) as predicted by the 3D model. CO 2 has dissolved into the aque ous phase wherever gaseous CO 2 is in contact with water. Thus the region of elevated TIC corres ponds roughly with the CO 2 plume volume, with high levels of TIC near th e intrusion location and at the aquifer top. However, some advective and diffu sive transport of dissolved CO 2 also widens the region somewhat, with elevated TIC observed mainly in the downstream direction from the intrusion
location.

The spatial distributions of pH, total aqueous Pb, and total aque ous As concentration shown in Figures 4.94 through 4.99 correlat e well with the TIC contours. Due to the buoyancy of the CO 2 gas, contamination occurs mainly near the top of aquifer. In th is region, increased acidity and294 of 352 elevated contaminant concentrations extends far from the intrusion location, both downstream and upstream as well as laterally. In the lower portions of the aquifer, contamination occurs primarily near the lo cation of intrusion.

A direct and quantitative comparison of predicted 3D and 2D results for this sensitivity case is provided in Figures 4.100 and 4.101. Figure 4.100 s hows the total aqueous Pb concentration along the x axis for different aquifer depths (top at z = 0 m, middle at z = -5 m, and bottom at z = -10 m) in the 3D model, together with the dept h-averaged results from the 2D model. The top figures are for x-axis profiles at y = 0 m; i.e., they intersect the CO 2 intrusion location. The bottom figures are at y = 50 m, away from the intrusion location. While the predicted maximum concentrations are similar, the 2D results clearl y underestimate the extent of contamination at the top of the aquifer and do not capture the spatial characteristics of contamination in the lower aquifer body. Figure 4.101 for As shows similar discre pancies. We conclude that this sensitivity case clearly demonstrates the necessity of usi ng a 3D model, especia lly in aquifers where advective velocity is low. The 2D results can, at best, provide a first-or der approximation of the system behavior.295 of 352 -5Z 0

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50 Y Y

X Z

SG 0.21 0.19 0.18 0.17 0.15 0.14 0.12 0.11 0.10 0.08 0.07 0.06 0.04 0.03 0.01

Fig. 4.90. Spatial distribution of CO 2 gas saturation after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.

Fig. 4.91. Profiles of CO 2 gas saturation at different lo cations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.296 of 352 -5Z 0 50 100 150 200 250 300 350 400 X

50 Y Y

X Z

TIC 0.21 0.20 0.18 0.17 0.15 0.14 0.13 0.11 0.10 0.09 0.07 0.06 0.04 0.03 0.02

Fig. 4.92. Spatial distribution of total inorganic carbon (TIC) after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.

Fig. 4.93. Total inorganic carbon (TIC) y-z profile at different locations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.297 of 352 -5Z 0 50 100 150 200 250 300 350 400 X

50 Y Y Z

X

pH 7.48 7.35 7.22 7.10 6.97 6.84 6.71 6.58 6.45 6.32 6.20 6.07 5.94 5.81 5.68

Fig. 4.94. Spatial distribution of pH after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.

Fig. 4.95. Profiles of pH at different locations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.298 of 352 -5Z 0 50 100 150 200 250 300 350 400 X

50 Y Y Z

X

Pb 3.6E-08 3.4E-08 3.1E-08 2.9E-08 2.6E-08 2.4E-08 2.2E-08 1.9E-08 1.7E-08 1.5E-08 1.2E-08 1.0E-08 7.7E-09 5.3E-09 3.0E-09

Fig. 4.96. Spatial distribution of total aqueous Pb concentration after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.

Fig. 4.97. Profiles of total aqueous Pb concentration at different locations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.299 of 352 -5Z 0 50 100 150 200 250 300 350 400 X

50 Y Y Z

X

As 1.2E-07 1.1E-07 1.1E-07 1.0E-07 9.8E-08 9.3E-08 8.8E-08 8.3E-08 7.8E-08 7.4E-08 6.9E-08 6.4E-08 5.9E-08 5.4E-08 4.9E-08

Fig. 4.98. Spatial distribution of total aqueous As concentration after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.

Fig. 4.99. Profiles of total aqueous As concentration at different locations after 100 years of CO 2 intrusion in 3D model with flow rate of 1 m/y.300 of 352 0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

3D y=0 z=0 3D y=0 z=-5 3D y=0 z=-10 2D y=0 MCL

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

3D y=50 z=0 3D y=50 z=-5 3D y=50 z=-10 2D y=50 MCL

Fig. 4.100. Total aqueous Pb concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous Pb concentration along x-axis for 2D model (both models have a flow rate of 1 m/y). Top figure shows x-axis profiles at y = 0 m, bottom figure shows profiles y = 50 m.301 of 352 0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

3D y=0 z=0 3D y=0 z=-5 3D y=0 z=-10 2D y=0 MCL

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

3D y=50 z=0 3D y=50 z=-5 3D y=50 z=-10 2D y=50 MCL

Fig. 4.101. Total aqueous As concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous As concentration along x-axis for 2D model (both models have a flow rate of 1 m/y). Top figure shows x-axis profiles at y = 0 m, bottom figure shows profiles at y = 50 m.302 of 352 

### 4.9.3 Sensitivity to CO 2 

### Leakage Rate — 3D Model

An increased CO 2 leakage rate (by a factor of 8) has shown that a large CO 2 gas plume is generated, as evident in the 2D results (S ection 4.6.9), suggesting that buoyancy forces are relevant and that a 3D model should provide a better simulation. Figures 4.102 and 4.103 show
model results of CO 2 gas saturation, as predicted by the 3D model, for the situation after 100 years of CO 2 leakage into the aquifer. As expected, the buoyancy of CO 2 leads to accumulation of a gaseous plume at the top of the aquifer a nd induces significant vert ical variation in gas saturation. Note that the groundw ater flow direction affect s the distribution of the CO 2 plume at the aquifer top, which extends much further downstream than upstream.

Figures 4.104 and 4.105 show contours of dissolved CO 2 (total inorganic carbon). Two principal factors affect the spatial distribution of TIC: (1) the di stribution of the CO 2 plume, and (2) the advective-diffusive transport of dissolved CO 2 with the groundwater flow. The dissolved CO 2 concentrations thus follow the general pattern of the CO 2 plume (e.g., the spreading of the plume at the top), but also show a significant migration of CO 2 -charged water from the intrusion location further downstream (evident at the bottom of the aquifer along the x-axis). The spatial distributions of pH, total aqueous Pb, and tota l aqueous As concentration (Figure 4.106 to 4.111)
follow similar trends as TIC. Areas with increased acidity as well as high Pb and As correspond to areas with elevated TIC. Overall, a strongly three-dimensional behavior can be seen in these plots, with significant vertical va riability, which is not well represented by the 2D model results
shown in Section 4.6.9.

A direct and quantitative comparison of predicted 3D and 2D results for this sensitivity case is provided in Figures 4.112 and 4.113. The figures show, respectivel y, total aqueous Pb and As concentration along the x-axis for different aquife r depths (top at z = 0 m, middle at z = -5 m, and bottom at z = -10 m) in the 3D model, togeth er with the depth-averaged results from the 2D model. The top figures are for profil es at y = 0 m, intersecting the CO 2 intrusion location. The bottom figures are for profiles at y = 50 m. Interestingly, the 3D and 2D results are not too different for the top figures; ther e is no significant vert ical variation in the 3D results measured303 of 352 along the x-axis. One reason is that the groundwater above the intrusion location appears to be completely saturated with CO 2 , meaning that the TIC does not increase further even though gaseous CO 2 continues to transit upward from the bo ttom to the top of the aquifer. Carbon dioxide is thus relatively well e quilibrated in the vertical dir ection near the in trusion location, and vertically uniform geochemical conditions are observed. This uniformity does not deviate significantly further downstream, because the CO 2 -charged and possibly contaminated water travels sufficiently far within the 100-year s imulation time to reach the downstream model boundary. In other words, contamination via transport of dissolved CO 2 in the lower portion of the aquifer is comparable with contamination wh ere extensive spreading of the gas plume occurs at the top of aquifer. Howeve r, as Figures 4.106 through 4.111 sugge st, vertical variability of TIC, pH, Pb, and As is much st ronger for all locations offset from that immediately downstream
of the intrusion location (i.e., away from y = 0 m) , indicating that indeed a 3D model is preferred for this sensitivity case. This is clearly evident from the x-axis profiles at y = 50 m in Figures 4.112 and 4.113, where the 2D results are in good agr eement with the 3D concentrations at the top of the aquifer (where gaseous CO 2 accumulates), but strongly overpredict those in the lower aquifer portions.304 of 352 -5Z 0

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50 Y Y Z

X

SG 0.25 0.23 0.22 0.20 0.18 0.17 0.15 0.13 0.12 0.10 0.08 0.07 0.05 0.03 0.02

Fig. 4.102. Spatial distribution of CO 2 gas saturation after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 -4 kg/s.

Fig. 4.103. Profiles of CO 2 gas saturation at different lo cations after 100 years of CO 2 leakage in the 3D model with CO 2 intrusion rate of 6×10 -4 kg/s.305 of 352 -5Z 0

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50 Y Y Z

X

TIC 0.22 0.21 0.19 0.18 0.16 0.15 0.13 0.12 0.10 0.09 0.08 0.06 0.05 0.03 0.02

Fig. 4.104. Spatial distribution of TIC after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 -4 kg/s.

Fig. 4.105. Profiles of Total Inorganic Carbon (TIC) at different locations after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 6×10 -4 kg/s.306 of 352 -5Z 0

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X

pH 7.48 7.35 7.22 7.09 6.96 6.83 6.70 6.57 6.44 6.31 6.18 6.05 5.92 5.79 5.66

Fig. 4.106. Spatial distribution of pH after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 - 4 kg/s.

Fig. 4.107. Profiles of pH at different locations after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 6×10 -4 kg/s.307 of 352 -5Z 0

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Pb 3.75E-08 3.50E-08 3.25E-08 3.00E-08 2.75E-08 2.51E-08 2.26E-08 2.01E-08 1.76E-08 1.51E-08 1.26E-08 1.01E-08 7.66E-09 5.17E-09 2.69E-09

Fig. 4.108. Spatial distribution of total aqueous Pb concentration after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 -4 kg/s.

Fig. 4.109. Profiles of total aqueous Pb concentration at different locations after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 6×10 -4 kg/s.308 of 352 -5Z 0

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As 1.27E-07 1.22E-07 1.16E-07 1.10E-07 1.05E-07 9.94E-08 9.39E-08 8.84E-08 8.29E-08 7.73E-08 7.18E-08 6.63E-08 6.07E-08 5.52E-08 4.97E-08

Fig. 4.110. Spatial distribution of total aqueous As concentration after 100 years of CO 2 intrusion in 3D model with CO 2 leakage rate of 6×10 -4 kg/s.

Fig. 4.111. Profiles of total aqueous As concentration at different locations after 100 years of CO 2 intrusion in the 3D model with CO 2 leakage rate of 6×10 -4 kg/s.309 of 352 0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

3D y=0 z=0 3D y=0 z=-5 3D y=0 z=-10 2D y=0 MCL

0 100 200 300 400 500 Distance (m) 0x10 0 2x10 -8 4x10 -8 6x10 -8 8x10 -8Total aqueous Pb concentration (mol/L)

3D y=50 z=0 3D y=50 z=-5 3D y=50 z=-10 2D y=50 MCL

Fig. 4.112. Total aqueous Pb concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous Pb concentration along x-axis for 2D model (both models have a CO 2 intrusion rate of 6×10 -4 kg/s). Top figure shows x-axis profiles at y = 0 m, bottom figure shows profiles y = 50 m.310 of 352 0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

3D y=0 z=0 3D y=0 z=-5 3D y=0 z=-10 2D y=0 MCL

0 100 200 300 400 500 Distance (m) 4.0x10 -8 6.0x10 -8 8.0x10 -8 10 -7 1.2x10 -7 1.4x10 -7Total aqueous As concentration (mol/L)

3D y=50 z=0 3D y=50 z=-5 3D y=50 z=-10 2D y=50 MCL

Fig. 4.113. Total aqueous As concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D model and total aqueous As concentration along x-axis for 2D model (both models have a CO 2 intrusion rate of 6×10 -4 kg/s). Top figure shows x-axis profiles at y = 0 m, bottom figure shows profiles y = 50 m.311 of 352 

## 4.10 Summary of Modeling Results

Systematic reactive transport simulations have been conducted to evaluate the effect of CO 2 intrusion into domestic shallow potable aq uifers. A synthetic si mplified hydrogeological
situation was considered as desc ribed in Section 4.2. The model st arts with the basic assumption
that CO 2 stored in deep geologic units can escape via preferential pa thways, such as a fault zone, and enter the shallow aquifer locally from below at a constant rate. The reactive transport model is used to predict the trans port of gaseous and dissolved CO 2 within the shallow aquifer as well as the geochemical changes introduced by the presence of CO 2 , in particular the mobilization of potentially hazardous constituents and their migr ation in the groundwater. Various sensitivity
studies have been conducted for different hydrological, geochemical, and mineralogical conditions, to (1) account (partially) for the range of conditions found in domestic potable aquifers and their host rocks, and (2) to evalua te the uncertainty associ ated with geochemical processes and model parameters. The base model setup and the sensitivity scenarios have been selected based on the geochemical mode l definition described in Section 3.

With respect to hazardous cons tituents, the focus in this m odeling study is on Pb and As
mobilization. It is assumed in all simulation cases that small volume fractions of minerals containing hazardous constituents are present in th e aquifers, including those containing Pb and As as essential components. Based on the discussion in Sections 3.3.3 and 3.6.2.2, the presence
of such trace minerals can be considered a reasonable possibility in all aquifers . The minerals
that are considered to control th e aqueous concentrations of Pb and As are, respectively, galena and arsenopyrite (the latter in solid solution in pyrite). These we re identified in Section 3.4 as likely solubility-controlling mineral hosts under re ducing conditions, a conclusion obtained from thermodynamic evaluation of more than 35,000 gr oundwater analyses in the NWIS database.

Equilibrium runs were c onducted prior to modeling CO 2 intrusion to establish the initial chemical composition of the groundwater at e quilibrium with the selected mineralogy. This initiation step also establishes equilibrium betw een mineral solubilities and adsorption sites, essentially “loading” these up. Thus, the select ion of solubility-con trolling minerals for312 of 352 hazardous inorganic constituents not only defines the initial aqueous concentration in the groundwater, but also affects the am ount of contaminants initially sorbed onto clays or goethite.

Starting with the equilibrated init ial conditions, the impact of CO 2 intrusion was then modeled with the reactive transport simulator TOUGHREA CT, for a simulation time period of 100 years with continuous ingress of CO 2 . Our simulation results suggest that CO 2 ingress into a shallow aquifer can lead to significant and long-lasti ng mobilization of Pb and As, contaminating the groundwater near the intrusion location and furt her downstream of it. However, while strong increases in aqueous concentrations are predic ted to occur compared to the initial water composition, the MCL for As in groundwater has b een exceeded in only a few simulation cases,
whereas the MCL for Pb has not been exceeded at all. According to our sensitivity study, many conditions and factors impact the severity and spatial distributi on of groundwater contamination in response to CO 2 intrusion. Below, we summarize the important implications from the sensitivity study and discuss the relative im portance of various c onditions and factors.

Base Model

The base model described in Sections 4.2 and 4.5 assumes CO 2 entering a shallow aquifer of 10
m thickness at an annual rate of about 2.4 tonnes. The aquifer is in a reducing environment. The host rock mineralogy, referred to as Coastal Pl ain Sandstone, is representative of an impure sandstone aquifer. According to the m odel predictions, the local ingress of CO 2 results in a small two-phase zone and an elongated elevated dissolved CO 2 zone along the flow direction. This leads to moderate increases in acidity within the two-phase zone as we ll as in the downstream area, followed by mobilization of Pb and As. The mobilization of Pb and As depends on the complex transient interaction between adsorp tion/desorption onto/from mineral surfaces and dissolution/ precipitation of relevant minerals.

In the case of Pb, desorption of initially sorb ed mass is the main contributor to the observed increase in aqueous concentrations. In fact, de sorption releases enough Pb from adsorption sites that the solution becomes supersatur ated with respect to galena, a nd precipitation of this mineral occurs. The resulting aqueous conc entrations of Pb are about one order of magnitude higher than313 of 352 the initial concentrations , but lower than the MCL (less than 3 ppb compared to the MCL of 15 ppb) within the small two-phase zone and the elongated downstream plume. As pointed out in Section 3.5, sulfide and selenide aqueous complexation play a very important role in the
behavior and evolution of Pb in response to CO 2 intrusion. Because very stable neutral complexes form in solution, th e impact that elevated CO 2 levels have on incr easing the solubility of galena (PbS), for example, is reduced co mpared to the case where no complexation is considered. While not reported here, we have conducted reactive transpor t simulations without accounting for sulfide and selenide complexes in solution. These resu lted in higher overall aqueous Pb concentrations after CO 2 intrusion compared to the base model, because both desorption and galena dissolution caused Pb mobilization in the two-phase zone.

Contrary to what is observed for Pb, desorption processes do not release sufficient As into
solution to inhibit the dissoluti on of arsenopyrite. Therefore, desorption of As from mineral surface and dissolution of arsenopyrite occur simultaneously and lead to a substantial increase of aqueous As concentrations. The maximum con centration almost reaches the MCL (10 ppb) within the small two-phase zone and is slightly lowe r (about 9 ppb) than the MCL within the elongated area of increased acidity down stream of the intrusion location.

The mobilization of Pb and As, fr om minerals and from adsorption sites, causes only very small changes to the initial contaminan t mass in the solid phase, even after a time period of 100 years
of CO 2 intrusion. For example, although the initial volume fraction of arse nopyrite in the aquifer is very small, on the order of 10 -5 of the total mineral volume, this mineral is far from being depleted after 100 years of increased acidity. Very small amounts of Pb and As contained in the aquifer solid phase can provide a lo ng-lasting source of contamination.

Importance of Adsorption/Desorption Processes
Our simulations indicate that adsorption/deso rption via surface complexation is arguably the most important process controlling the fate of hazardous constituents mobilized by CO 2 leakage when sorbing minerals are present. Adsorb ed species are modeled in the TOUGHREACT simulations via a surface complexation model that takes into account the competitive314 of 352 complexation of As and Pb on several clays a nd goethite. Our simulation results suggest that desorption of initially sorbed As and dissolution of arsenopyrite act together in mobilizing As near the region of CO 2 intrusion, whereas the desorption of Pb from mineral surfaces is so significant that galena precipita tes instead of dissolving. The oppos ite behavior is seen further downstream. There, adsorption removes significan t amounts of Pb and As from the solution, dampening further increases of contaminant concentrations.

The importance of adsorption is clearly dem onstrated in Section 4.6.1.1, where a simulation without adsorption is compared to the base mo del considering adsorpti on. Without adsorption, the resulting long-term contamination of an aquifer would be less significant near the region
where CO 2 enters, whereas aqueous concentrations w ould keep increasing w ith travel distance (eventually exceeding MCLs), as acidic water mi grates further and more dissolution occurs. Thus, without adsorption, an important pro cess mitigating the long-term impact of CO 2 intrusion would be neglected. Furthermore, whereas adso rption/desorption reactions are always assumed to equilibrate instantaneously, the dissolution or precipitation of galena or arsenopyrite is a kinetically controlled, slow pro cess, and, therefore, restoration of a complete equilibrium state following perturbation of the system can take years or decades or even longer, depending on the parameters of the system. Thus, desorption of in itially sorbed matter is the dominant process in mobilizing contaminants in the short term.

The relative importance of dissolution/precipitati on versus adsorption/desorption is controlled by many factors including adsorption parameters and aquifer mineral ogy, but also re action kinetics, aqueous complexation processes, and solubility constants. For example, the role that adsorption/desorption plays in the mobilization of hazardous constituents depends highly on the total number of adsorption sites, which is the product of adsorption site density and specific surface area of the sorbing minerals. A sensitiv ity run (Section 4.6.1.2) with lower site density reveals that the increases in aqueous Pb and As are much less significant, because desorption is less relevant in mobilizing contaminants.315 of 352 Different Host Rock Mineralogies
A suite of “representative” host rock minera logies was simulated in a sensitivity study representing broad ranges of aquifer host rock compositions found throughout the United States (Section 4.7). In addition to the Coastal Plain Sandstone aquifer considered in the base case, we evaluated two other representative cases: (1) a mineral composition representative of a St Peter Sandstone and (2) a mineral composition representative of a Dolomitic Limestone. The St Peter Sandstone is composed primarily of quartz sa nd and has a poor pH bu ffering capacity, whereas the dolomitic limestone is dominated by calcite and dolomite, and has a good pH buffering capacity. Note that the host rock mineralogies differ with respect to major and accessory minerals, including clays and other adsorbents, but all have the same (small) volumetric fractions of trace minerals, including thos e containing Pb and As as esse ntial components. The following
conclusions can be drawn from the comparison of different host rocks:

• Even small amounts of calcite or dolomite help moderate the pH changes caused by CO 2 intrusion. The typical range of pH observed in our simulations is between 5.5 and 6, whereas values as low as 4.5 could be expected in th e absence of buffering from aquifer minerals. Buffer capacity is effective as long as these mi nerals have not completely dissolved. In other words, the actual volumetric fraction of carbona te minerals is less relevant for buffer capacity, as long as the minerals remain pres ent in the host rock. Th e St Peter Sandstone represents the most vulnerable aquifer, b ecause the small initial amount of carbonate minerals is depleted after some time and the pH buffer capacity is lost. The dolomitic limestone is less vulnerable from the perspectiv e of long-term effects, because it contains large amounts of carbonate minerals, whic h can can buffer pH for a long time.

• While buffer capacity is relevant, differences in adsorption/desorption behavior have equal or more important effects on the resulting contaminant concentrations. The host rocks considered in this study differ with respect to clay minerals and clay volumetric fractions. The dolomitic limestone, for example, results in higher As concentrations after 100 years of
CO 2 intrusion, because it contains more clay minera ls and thus releases more initially sorbed As into the groundwater, despite the fact that carbonate minerals provide long-term buffering capacity.316 of 352 Oxidizing Versus Reducing Conditions
Although most groundwaters analyzed in Section 3 suggest reducing conditions, one sensitivity
run was conducted to evaluate th e potential for Pb contaminatio n in an oxidized aquifer. The mineralogical composition of an oxidized aqui fer was developed in Section 3.6.3.2, with the most relevant differences compared to the base case that pyrite is oxidized and cerussite instead of galena controls the aqueous concentration of Pb. The simulated behavior of the oxidized aquifer is very different from an aquifer in a reducing environment. Because of the much higher solubility of cerussite, the initial aqueous Pb co ncentration is already considerably higher than the MCL, even without considering CO 2 leakage into the aquifer. This confirms the well-known fact that oxidative dissolution can lead to signi ficant mobilization of hazardous constituents, an
effect that is often more relevant than the decrease in pH caused by CO 2 intrusion. A further increase in Pb concentration is predicted as CO 2 enters the oxidized aqui fer. As discussed in Section 4.8, Pb is mobilized near the CO 2 intrusion region primarily from desorption. Further downstream, Pb precipitates out as cerussite, stabilizing the downstream Pb concentrations.

Sensitivity to Other Geochemi cal Parameters and Conditions

• It is known that the kinetic rate of mineral dissolution/pr ecipitation is highly uncertain. Sensitivity simulations have been conducted va rying the kinetic rate by two orders of magnitude. However, the simulated concentrations of aqueous Pb and As after 100 years of
CO 2 intrusion are rather insensitive to a variatio n of the kinetic rate, due to the dominance of adsorption/desorption processes. Strong sensitv ities were observed for simulation case in which sorption processes were neglected, with aqueous concentrations of As increasing significantly with an increase in the kinetic rate.

• The solubilities of hazardous-constituent-bearing minerals and the association constant used in the aqueous complexation of Pb are critic al geochemical parameters that are not well defined in all cases. In exploratory sensitiv ity runs we conducted, these parameters were varied based on the variability of values found in the literature. Changes in the solubilities of galena and arsenopyrite (by about a half-order of magnitude) give rise to strong differences in the resulting aqueous concentrations of Pb and As. As discussed in Sections 4.6.4, these317 of 352 differences result in part from the different dissolution beha vior, but more importantly are caused by changes in the adsorptio n/desorption characteristics (the initial distribution of Pb and As in the aqueous phase and adsorbed on mineral surfaces changes with a variation of solubility). A variation in the PbS(aq) associa tion constant also lead s to significant changes in aqueous Pb concentrations after CO 2 intrusion.

• The initial volume fraction of galena was in creased by three orders of magnitude in a sensitivity run, to understand the consequences of natural variabil ity in trace-element minerals (Section 4.6.5.1). However, the cha nges in aqueous Pb concentrations were negligibly small in this sensitivity case, ma inly because Pb mobilization is dominated by desorption processes, not by galena dissolution. In contrast to what was observed for Pb, an increase in the arsenopyrite vol ume fraction (by two orders of magnitude) gives rise to a moderate increase in the aqueous As concentrat ions, mainly because the dissolution kinetics are assumed to change with varia tion in the reacti ve surface area.

• One sensitivity run was conducte d to evaluate the impact of a comparatively high initial partial pressure of CO 2 in the groundwater (Section 4.6.6). This has minor impact on the resulting aqueous Pb concentra tions, but causes significant incr eases in As concentrations,
both at initial conditions and after intrusion of CO 2 .

Sensitivity to Hydrogeologic Conditions and CO 2 Leakage Rate

• The maximum amount of CO 2 dissolved in water is a functi on of fluid pressure. Thus, in a shallower aquifer, fewer CO 2 -related pH changes and, conse quently, smaller increases in aqueous Pb and As concentrations would be ex pected. A sensitivity run using an initial fluid pressure of 3 bar (30 m head), instead of 5 ba r (50 m head) as in the base model, showed moderate impact, slightly decreasing the change in pH as well as the resulting contaminant concentrations compared to the base case (Section 4.6.7).

• A sensitivity run with a flow rate of 1 m per year (instead of 10 m per year in the base model) was conducted to study the e ffect of groundwater flow c onditions on the evolution of hazardous constituents. One major effect of a re duced flow rate is that dissolution of CO 2 is318 of 352 less effective, because a smaller volume of groundwater undersaturate d with respect to CO 2 flows past the location where CO 2 enters the aquifer. As a result, most of the leaking CO 2 remains in the gas phase, migrates upward to wards the top of the confined aquifer, and spreads out laterally. Initially modeled as a 2D depth-averaged pr ocess (Section 4.6.8), it became clear that this strongly buoyancy-driven behavior should be modeled in 3D (Section 4.9.2). Except for the uppermost portion of the aquifer, where contamination spreads widely following the dissolution of the gaseous CO 2 plume, the area affected by contamination is smaller than in the base case, because the tran sport of aqueous species is driven by diffusion rather than by advection (i.e., downstream tran sport of TIC, Pb, and As is minimal). The maximum concentrations are moderately higher than in the base case, because of less
effective dilution.

• Similar 3D effects occur in a se nsitivity case with increased CO 2 leakage rate (Section 4.9.3).
Since the rate of CO 2 entering from below exceeds the dissolution capacity of the groundwater, the gaseous plume rises to the top of the aquifer, thereby bypassing most of the groundwater body. The resulting impact on aqueous contamination is influenced by the general pattern of the CO 2 plume (e.g., the spreading of the plume at the top of the aquifer) and the subsequent migration of CO 2 -charged and contaminated water further downstream. Maximum concentrations are moderately higher than in the base case, mostly at the top of the aquifer.

• The above cases showing the evolution of larger CO 2 plumes demonstrate that the rate of
CO 2 entering an aquifer has only a minor effect on the maximum contaminant concentrations. Once the groundwater reaches its solubility limit, which occurs at relatively small intrusion rates, there is no further increase in total inor ganic carbon and acidity. Consequently, no further mobilization of hazardous constituents occurs once local equilibrium has been achieved. Excess CO 2 remains in the gas phase and migrates elsewhere, thereby changing the spatial distribution of possible groundwater contamination, but the contamination level remains essentially the same.319 of 352 Discussion of Conservatis ms and Mitigating Effects
Some assumptions made in the model setup and in the geochemical model represent conservative choices. Below, we list these choices and discus s the expected implications on the geochemical system.

• A confined aquifer was consid ered, meaning that excess CO 2 not dissolving in to groundwater accumulates at the aquifer top, potentially causi ng widespread pH changes and mobilization
of contaminants. Excess CO 2 in an unconfined aquifer woul d escape into the vadose zone, causing no further harm.

• Contaminant transport was modeled without consideration of hydrodynamic dispersion, meaning that less spreading a nd dilution of the dissolved CO 2 plume occurs. This tends to
maximize the contaminant concentr ations predicted by the model.

• CO 2 continuously enters the aqui fer for an extended period of time (100 years), implying that
the CO 2 leakage remains undetected and no remedial measures are undertaken.

• While the reactive transport simulations demonstrate the potential for increased concentration of Pb and As, it is not clear that the probl em would remain after pumping and standard treatment of the water for potable purposes. Water recovery from the aquifer would lead to degassing of CO 2 , probably causing supersaturation wi th respect to calcite, which would likely induce co-precipitation of these contaminants.320 of 352 

# 5. Analogues

Both natural and man-made analogues of CO 2 intrusion into groundwater may help us to better understand corresponding changes in wate r quality. The natural leakage of CO 2 (plus additional acid gases in some cases) from geologic reservoirs into groundwaters is di scussed in Section 5.1, with emphasis on the resulting groundwater chem ical compositions (based on Lewicki et al., 2006, 2007). While changes in grou ndwater chemistry were obser ved to be related to CO 2 (and sulfur gas) leakage as caused by acidification a nd interaction with host rocks along flow paths, waters remained potable in mo st cases. Section 5.2 describes the geochemical impacts of CO 2 releases from a landfill into a shallow aquifer, causing cadmium levels in the groundwater to
increase to levels above the maximum contam inant level of 5 ppb (McGrath et al., 2007)



## 5.1 Natural Analogues

5.1.1 San Vittorino, Italy

The San Vittorino plain, located 100 km northeast of Rome in central Italy, is a sparsely populated intramontane basin that is used pr imarily for agriculture. Carbonate rocks are prevalent in the region (e.g., bedrock limest ones and surface travertines). Numerous CO 2 -rich mineral springs, gas vents, and sinkholes, many of which pose hazards to local infrastructure, are located throughout the plain and are believed to be associated with migration of CO 2 from depth along major faults in the area. In an effo rt to understand the influence of this CO 2 leakage on groundwater chemistry and sinkhole formation, NASCE NT (Natural Analogs for the Storage of
CO 2 in the geological ENvironmenT) (2005) conducte d soil gas, bubble gas, dissolved gas, and groundwater geochemistry surveys throughout the plain. They found that anomalously high soil gas concentrations (up to 70% CO 2 , 80 ppm He, and 2% CH 4 ) were closely correlated with the
locations of sinkholes. Gas bubbles in springs, sinkholes, and wells had concentrations of CO 2 from 36 to 85%, CH 4 from 150 to 2100 mg L -1 , He from 6 to 400 mg L -1 , and N 2 from 7 to 60%.
H 2 S was also present, but unquant ified. Dissolved gases had CO 2 concentrations from <0.2 to 0.8 L gas per L water at ambient pressu re and temperature. The highest CO 2 concentrations were321 of 352 close to the solubility limit of CO 2 in water at 1 atm pressure. Chemical analysis of waters collected from springs, wells, and sinkholes indi cated that most are classified as Ca-HCO 3 waters, while a few are classified as Ca-Mg-HCO 3 waters. The total dissol ved constituents varied widely between samples, with Total Dissolv ed Solids (TDS) rangi ng from 330 to 1843 mg L -1 and hardness ranging from 307 to 1843 mg L -1 as CaCO 3 . The concentrations of major elements were well correlated in samples. Concentrations ranged from 89 to 550 mg L -1 for Ca, 17 to 97 mg L -1 for Mg, 10 to 795 mg L -1 for SO 4 , 43 to 1945 mg L -1 for B, and 230 to 2900 mg L -1 for Sr. Some waters were also enriched in Mn, Al, Zn, Pd, and Rb. Samp les with the highest
concentrations of dissolved const ituents were located in the north-central part of the plain and also had the highest CO 2 concentrations in both dissolved and excluded gases. The leakage of
CO 2 (and H 2 S) typically resulted in a 5 to 10 fold incr ease in most major and trace elements in springs. Despite elevated concen trations of dissolved chemical constituents in groundwaters, these waters are potable, according to current inte rnational groundwater standards for major ions.

Correlations between dissolved chemical constituen ts in waters were interpreted to reflect the dissolution of carbonate rocks along flow paths of groundwaters th at have become acidified by
CO 2 and H 2 S rising along high permeability pathways in fault zones. The formation of sinkholes was found to be closely associated with gr oundwaters having anomal ously high dissolved chemical constituents. These features were likely formed by the dissolution of fine-grained rocks by acidified groundwaters and collapse triggere d by earthquakes. NASCENT (2005) developed and deployed a continuous groundwater chemistry m onitoring station in the San Vittorino plain, designed to detect anomalous changes that might serve as precursors to sinkhole formation.



### 5.1.2 Florina Basin, Greece

Two aquifers are present in the Florina basin. The first is a confined aquifer in clastic sedimentary rocks capped by silts and clays that extends throughout most of the basin and hosts
the CO 2 reservoir. The second is an unconfined a quifer hosted in kars tic carbonates on the eastern and southern margins of the basin. To evaluate the effects of CO 2 leakage from depth on groundwater quality in the Florina basin, Beaubien et al. (2004) conducted an investigation of the322 of 352 chemistry of waters from wells and springs th roughout the basin. They analyzed major element concentrations in 132 samples and trace element concentrations in 17 of these samples, and found that total dissolved solids ranged from 150 to 4230 mg L -1 and hardness ranged from 70 to 1950 mg L -1 as CaCO 3 . Waters collected from shallo w wells and springs were Ca-HCO 3 type, while those collected from deep boreholes were Mg-HCO 3 type. Most water samples were potable. However, samples collected close to the CO 2 field had higher total dissolved chemical constituents and hardness, and consequently we re of poorer drinking wa ter quality. Beaubien et al. (2004) interpreted the spatial trends in the chemical compositions of groundwaters as reflecting the interaction of CO 2 -rich groundwaters with host rocks along flow paths.



### 5.1.3 Albani Hills, Italy

Three main hydrogeologic units have been iden tified in the Albani Hi ll region (Boni et al., 1995). From depth to the surface, these include: (1) a calcareous-siliceous-marly basal complex constituting a multilayered aquifer hosted in fr actured carbonates interbedded with confining clay layers, (2) a clayey -marly intermediate complex that acts as a groundwater flow boundary due to its low permeability, and (3) the Albani Hills volcanic complex, which displays wide variation in permeability and acts as a multilay ered aquifer with radial groundwater flow. Chiodini and Frondini (2001) c onducted a geochemical study of groundwaters collected from 293 wells and 63 springs in the re gion (Figure 5.1), and found high CO 2 partial pressures (average = 0.2 bar), indicating a high CO 2 leakage rate into the regional aquifers. HCO 3 is the major anion in most waters, with only a few samples characterized by high Cl and SO 4 concentrations. The SO 4 -rich waters in the northern part of the study area are associated with an influx of deep sulfur-rich gases into shallow gr oundwaters, while the Cl-ri ch waters are located on the coastal plain and are associated with fo ssil seawater. The relativ e concentrations of cations (e.g., Na/K ratios, Figure 3.1) in waters reflect the interaction of CO 2 -rich groundwaters with host rocks. In particular, many samples have high HCO 3 , Ca, and Mg contents, which can be explained by the influx of CO 2 -rich fluids from depth and subsequent interaction with sedimentary rocks.323 of 352 Fig. 5.1. Map of the Albani Hills region showing major dr ainage basins and locations of water samples from Chiodini and Frondini (2001).



### 5.1.4 Central Italy

Throughout Tyrrhenian Central It aly, widespread nonvolcanic CO 2 degassing (likely originating from a crustally-contaminated mantle or a mixtur e of magmatic and crustal components) occurs
from vent and diffuse so il gas emissions and from CO 2 -enriched groundwaters (Chiodini et al., 1999). From the Tyrrhenian Sea to the Apennine Mountains, buried structur al highs act as gas traps. Widespread CO 2 leakage occurs at the surface throughout the region in the form of diffuse and vent gas emissions or by dissolution in groundwaters and s ubsequent release at surface324 of 352 springs. Chiodini et al. (1999, 2000, 2001) conducte d geochemical investigations of regional aquifers and found that CO 2 partial pressure values for springs are up to four or ders of magnitude greater than that of the atmos phere. They also found that in ge ographic regions characterized by thick regional carbonate-e vaporite aquifers, most or part of the deeply derived gas is dissolved by the aquifers, whereas in regions characteri zed by small aquifers, these aquifers cannot dissolve a large quantity of the CO 2 , and extensive vent and soil CO 2 emissions occur at the surface. Chiodini et al. (2000) used mass-ba lance equations coupled with carbon isotopic analyses and hydrologic data to es timate fluxes of deeply derived CO 2 of up to 0.29 g m -2 d -1 into the aquifers. For waters with high CO 2 partial pressures, the CO 2 flux lost to the atmosphere is of the same order of magnitude as the influx of deep CO 2 .
Based on the analysis of major dissolved chem ical constituents in 52 springs, 65% were classified as Ca(Mg)-HCO 3 in composition, 10% as Ca-Mg-SO 4 in composition, 10% as Na-Cl in composition, and the remaining as intermedia te in composition (Chiodini et al., 2000). A major process controlling the ch emical composition of most groundwaters was the dissolution of calcite, while the dissolution of gypsum or halite wa s important in the springs of a few aquifers. Total dissolved inorganic carbon concentra tions in springs ranged from 0.00244 to 0.05080 mol/kg.



### 5.1.5 Mammoth Mountain, California, USA

Mammoth Mountain in eastern California has numerous CO 2 -rich cold springs located on its lower flanks, primarily associated with leakage from the high-pressure ga s reservoir beneath the volcano (Evans et al., 2002). Ev ans et al. (2002) conducted a geoc hemical study of these springs to quantify the magmatic carbon discharge in co ld groundwaters. Based on measured spring flow rates and concentrations and isotopic compositi ons of dissolved inorganic carbon in spring discharges, they found that Mammoth discharges 2×10 4 t yr -1 of magmatic carbon (as CO 2 ); this discharge varies seasona lly from 30 to 90 t d -1 . While most springs cont ain high concentrations
of dissolved CO 2 , they are otherwise dilute, with sp ecific conductance ranging from 100 to 300

μ S cm -1

.325 of 352 

### 5.1.6 Paradox Basin, Utah, USA

A number of natural springs and well bores discharge CO 2 -rich groundwater along the Little
Grand Wash and Salt Wash faults in the Paradox basin of Utah. Samples collected from springs along the Little Grand Wash and Salt Wash faults and from Crystal Geyser were analyzed to determine the contents of major dissolved chemi cal constituents in groundwaters (Shipton et al., 2004). Concentrations (in mmol L -1 ) were as follows: 2.37-20.83 for Ca, 7.86 to 9.30 for Mg, 167.82-236.38 for Na, 7.62 to 10.44 for K, 0.00 to 0.14 for Fe, 0.00 to 0.02 for Mn, 0.01 to 0.16 for Sr, 46.40-75.70 for HCO 3 , 91.15-204.52 for Cl, and 24.33-30.81 for SO 4 . Waters were high in total dissolved solids, which ranged from 13,555 to 21,188 mg L -1 . Shipton et al. (2004) determined that the chemical compositions of groundwaters were primarily controlled by the dissolution of calcite and evaporites along groundwater flow paths.



## 5.2 Landfill Gas Impacts

McGrath et al. (2007) report on a study evaluating the potential impact of carbon dioxide contained in municipal solid waste (MSW) landfill gas on the release of cadmium from uncontaminated native soils. Elevated aque ous cadmium was detected in groundwater monitoring wells downgradient of the landfill but not in the landf ill leachate or monitoring wells located upgradient. Based on soil leaching experiments and geochemical modeling, the authors
concluded that the observed incr eases in aqueous cadmium in the field were likely caused by the
CO 2 dissolution into the groundwater and the related increase of ac idity, causing mobilization of
cadmium from the natural soils. Both the so il leaching experiments and the modeling results showed strong increases in cadmium concentra tions with increasing pa rtial pressure of CO 2 . Naturally occurring otavite (o r cadmium carbonate) was reported to be the primary mineral controlling cadmium solubility, which suggests oxidizing conditions. In response to these findings, the landfill operator initiated a soil vapor extraction system in the vadose zone overlying the aquifer. Since the n, aqueous cadmium concentrations in the formerly impacted
wells have fallen to below detect ion limits (McGrath et al., 2007).326 of 352 

# 6. Overall Conclusions and Recommendations

This report describes a systematic evaluation of the possible water quality changes in response to
localized CO 2 intrusion into aquifers currently used as sources of potable water in the United States. Our goal was to develop a general understanding of the pot ential vulnerabil ity of United States potable groundwater re sources in the event of CO 2 leakage from deep geologic sequestration reservoirs. This goal was achieved in two main tasks, the first to develop a comprehensive geochemical model representing t ypical conditions in many freshwater aquifers (Section 3), the second to conduct a systematic reactive-transport modeli ng study to quantify the
effect of CO 2 intrusion into shallow aquifers for a va riety of scenarios and aquifer conditions (Section 4). To ensure adequate model setup, the scenarios and conditions modeled in the reactive transport simulations—in particular thos e describing the geoche mistry and mineralogy of potable aquifers—were selected based on the comprehensive geochemical model developed in
Section 3. Detailed summaries of results from th e two tasks are given, resp ectively, in Sections
3.7 and 4.10.

An important step in developing the comprehe nsive geochemical model in Section 3 was the
identification of those mineral hosts containi ng hazardous constituents, which control their equilibrium concentration in potable groundwat ers. Thermodynamic cont rols were evaluated using geochemical data for 38,000 potable gr oundwaters extracted from the USGS NWIS database, using a distribution-of -species code to te st for their therm odynamic saturation in
solution. This necessita ted the iterative improvement and augmentation of a large supporting thermodynamic database comprising the solubility products of minerals of interest, and of complexes that affect the concen trations of the haza rdous constituents in aqueous solution. Considering a representative groundwater under reduc ing conditions (a condition met in the large majority of all evaluated groundwat ers), the most likely solubility-controlling mineral host for Pb is galena, while As would typically be cont rolled by the arsenopyrite component as a solid solution in pyrite. These mineral hosts are assumed to partially or completely control the initial aqueous concentrations of hazardous constituen ts as well as subsequent changes due to the ingress of CO 2 . The thermodynamic controls obtained for other hazardous constituents are327 of 352 summarized in Section 3.7, together with other necessary input for the reactive transport
simulations such as the evaluation of relevant aquifer conditions (e.g., oxidation state, initial CO 2 concentrations in the aquifer) and the definition of the bulk/trace-element mineralogy in typical aquifer host rocks.

Starting with the comprehensive geochemical model, a preliminary assessment of CO 2 -related water quality changes was conducted by calculating the expected equilibrium concentrations of hazardous inorganic constituents as a f unction of the partia l pressure of CO 2 . P(CO 2 ) was varied
from 10 -4 bar to 10 bar, leading to a corres ponding pH variation between 8.9 and 5.6. Equilibrium concentrations were determined us ing a distribution-of-species code, assuming a representative groundwater in reducing conditions and incor porating the thermodynamic controls
identified in above-mentioned database evaluation. Results indicate that the concentrations of some of the studied constituents, for example Ba, Cd, Sb, and Zn, are sensitive to changes in P(CO 2 ), but that As, and to a lesser extent Pb a nd Zn, have shown the potential for exceeding their respective MCLs at elevated CO 2 concentrations. In the systematic reactive-transport modeling study discussed below, primary emphasis was placed on the evaluation of the response and transport of As and Pb under intruding CO 2 . The reactive-transport simulator TOUGHREACT was used to predict the transport of gaseous
and dissolved CO 2 within a shallow aquifer, as well as induced geochemical changes caused by the presence of CO 2 , in particular those changes leading to the mobilization of Pb and As and their migration in the groundwater (Section 4). Our simulation results suggest that CO 2 ingress into a shallow aquifer can lead to significan t and long-lasting mobilization of Pb and As, contaminating the groundwater near the intrus ion location and further downstream. However, while substantial increases in aqueous concentrat ions are predicted to occur compared to the initial water composition, the MCL for As in groundwater has been exceeded in only a few simulation cases, whereas the MCL for Pb has not been exceeded at all.

Various sensitivity cases were simulated for different hydrological, geochemical, and mineralogical conditions, (1) to account for a range of conditions found in domestic potable aquifers and their host rocks, and (2) to evalua te the uncertainty associ ated with geochemical328 of 352 processes and model parameters, results of which are summarized in Section 4.10. Adsorption/desorption via surface complexation is arguably the most important process controlling the fate of hazardous constituents mobilized by CO 2 leakage when sorbing minerals are present. In a simulation case without adsorp tion/desorption taken into account, the resulting long-term contamination of the aquifer is less significant near the region where CO 2 enters, whereas aqueous concentrations keep increasi ng with travel distance (eventually exceeding MCLs), as acidic water migrates further and more dissolution occurs. The relative importance of dissolution/precipitation versus adsorption/desorp tion is controlled by many factors, including adsorption parameters and aquifer mineralogy, reaction kinetics, aqueous complexation processes, and mineral solubility constants. For example, the role that adsorption/desorption plays in the mobilization of hazardous constitu ents depends highly on the total number of adsorption sites. A sensitivity run with lower site density reveals that ea rly increases in aqueous Pb and As are much less significant, because desorption is less relevant in mobilizing
contaminants.

Moderate to strong sensitivities in the resulting Pb and As concentrations have been obtained for several other geochemical parameters (such as the solubilities of hazardous-constituent-bearing minerals, the kinetic rates for precipitation and dissolution, and the aqueous complexation constants) and conditions (such as the redox envi ronment, with the simulated behavior in a oxidized aquifer being very different from an aquifer in a reducing environment). With respect to the typical aquifer mineralogies assumed in our study (ranging from almo st pure arenites to dolomitic limestone), the most important effects resulted from differences in clay content, which affect the adsorption/desorption behavior, as we ll as from differences in the presence of carbonate minerals, which can mode rate the pH changes caused by CO 2 intrusion. An increase in the leakage rate does not necessarily lead to increases in the maximum contaminant concentrations in the aquifer. Once the groundwater reaches its solubility limit with respect to
CO 2 , which occurs at relatively small intrusion rates, there is no further change in total inorganic carbon and acidity. Consequently, no further mob ilization of hazardous cons tituents occurs once local equilibrium has been achieved. Excess CO 2 remains in the gas phase and rises to the top of the aquifer, thereby bypassing most of the gr oundwater body. The resul ting impact on aqueous329 of 352 contamination is then influenced by the general pa ttern of the CO 2 plume (e.g., the spreading of the plume at the top of the aquifer in the case of a confined aquifer) and the subsequent migration
of CO 2 -charged and contaminated water further downstream.

As stated before, the goal of this study was to better understand, in a global sense, the vulnerability of representative and re levant aquifer types in the case of CO 2 intrusion, thereby providing sound scientific inform ation to regulators and the public. It is understood, however, that even the most comprehensive sensitiv ity study cannot encompass all possible aquifer
conditions and CO 2 intrusion scenarios. Neither can it account for all relevant uncertainties. While the geochemical evaluation conducted in S ection 3 was extremely comprehensive, it was also pointed out that several assumptions, approximations, and compromises were necessary in this effort, mostly because of incomplete characterization of important parameters such as
dissolution and dissociation consta nts. We recommend that our study should now be followed up with site-specific model predic tions, and that laboratory or field experiments should be performed to test uncertain model assumptions and parameters. Such experiments would involve exposing initially undist urbed groundwater in laboratory samples or in situ in the field to
elevated levels of CO 2 . One could also attempt to test th e developed geochemi cal models using measured data from natural analogue sites, such as those described in Section 5.

Our studies suggest that the fate of inorganic ha zardous constituents in the presence of elevated
CO 2 levels ultimately depends on their initial abundance and distribution in the aquifer minerals. For example, contaminants released from adso rption sites are expected to cause short-term perturbation in aqueous heavy-me tal concentrations, while contaminants in host minerals would be mobilized in slower, longer duration pro cesses. As pointed out before, the required
information on hazardous constituents and their di stribution in the mineral hosts is not easy to come by, because relevant mineral hosts in the a quifer rocks are difficult to identify where the
concentrations are too small to be detected with standard mineralogical characterization methods. Therefore, an indirect approach was develo ped to determine thei r initial abundance and distribution, based on evaluation of geochemical da ta that are more easily measurable (i.e., by using a large number of groundwater analyses to test for saturation with respect to postulated330 of 352 mineral hosts in conjunction with a supporting literature review, a nd analyses of trace elements in soils and sediments). While the approach demonstrated a high level of consistency, we recommend corroborating our findings through dire ct measurements of the form, distribution,
and association of trace hazardous constituents within associat ed rock samples, which would involve, for example, synchroton-based microsp ectroscopic analysis at extremely high spatial
resolution.



# 7. Acknowledgments

We are grateful to Guoxiang Zhang and Dan Hawk es of LBNL for constr uctive review of this manuscript. We thank the US Environmental Pr otection Agency, Office of Water and Office of Air and Radiation, for funding this study under an Interagency Agreement with the U.S. Department of Energy at the Lawrence Berkel ey National Laboratory, Contract No. DE-AC02- 05CH11231. Special thanks go to Jonathan Scott of the U.S. Geological Survey, Oklahoma City, Oklahoma, who provided indispensable contributi ons to this report by ex tracting and supplying over 38,000 groundwaters from the NWIS database.



# 8. References

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# Research Project on CO 2 

# Geological Storage and Groundwater Resources

Water Quality Effects Caused by CO 2 

# Intrusion into Shallow Groundwater

Appendices A-FPage A1 of 10 

# Appendix A: Lists of Analytical Methods for Groundwater Analysis

Table A1. Summary of Groundwater Analyses Data Available for Trace Elements (see list of analytical methods in Table A2)

(a) Summary of Arsenic (As) Analyses

Analytical Method Code PCode Total No. of Analyses Detection Limit (mg/L) No. below Detn Limit No. at or above Detn Limit Percentage at or above Detn. Limit (%) Notes

GF042 01000 135 0.001 124 11 8.15 GF044 01002 2 0.001 1 1 50.00 GF073 01002 9 0.005 1 8 88.89 PL120 01002 2 0.001 1 1 50.00 PLA06 01000 5 0.002 4 1 20.00 GF085 01000 652 0.001 0.002 0.004 175 91 1 385

59.05 GF096 01002 157 0.002 0.003 0.004 0.008 60 2 2 1 92

58.60 GF031 01002 69 0.001 44 25 36.23 HY010 01000 5522 0.001 0.005 0.01 2309 1 9 3203

58.00 HY013 01002 137 0.001 81 56 40.88 PLA07 01002 117 0.002 0.004 33 66 99 84.62 PLM10 01000 76 0.00012 6 70 92.11 PLM27 01000 174 0.002 0.001 0.01 104 24 2 144

82.76 PLM40 01000 2782 0.0002 0.0003 0.0004 0.0009 201 72 1 87 2421

87.02 Unknown 11610 >0.1 0.01-0.1 0.01 0.005-0.01 0.005 0.004 0.003-0.004 0.003 0.002 0.001 0.000001- 0.001 0 82 131 3366 11 593 36 1 10 94 2586 149 14 3688

31.77 40 presence verified but not quantified.
788 analyzed but not detected
1 greater than 1.e6 mg/LPage A2 of 10 (b) Summary of Barium (Ba) Analyses

Analytical Method Code PCode Total No. of Analyses Detection Limit (mg/L) No. below Detn Limit No. at or above Detn Limit Percentage at or above Detn. Limit (%) Notes

AA003 01005 117 0.10 70 47 40.17 GF046 01005 16 - 0 16 100.00 PLA06 01005 24 0.0005 1 23 95.83 PLM27 01005 177 0.1 18 159 89.83 PLA11 01005 5178 0.0009 0.001 0.002 0.003 0.006 0.1 6 9 100 3 1 6 5053

97.59 PLM43 01005 1252 0.0002 0.001 2 29 1206 96.33 15 Presence verified but not quantified PLM47 01007 87 0.0002 1 86 98.85 GF033 01007 18 - 0 18 100.00 PLA15 01007 13 - 0 13 100.00 PLA07 01007 120 - 0 120 100.00 PLA06 01007 24 - 0 24 100.00 unknown 7337 0.0001 0.0002 0.001 0.002 0.005 0.01 0.025 0.03 0.04 0.05 0.06 0.07 0.1 0.2 0.3 0.4 0.5 10 2875 4462

60.82Page A3 of 10 (c) Summary of Cadmium (Cd) Analyses

Analytical Method Code PCode Total No. of Analyses Detection Limit (mg/L) No. below Detn Limit No. at or above Detn Limit Percentage at or above Detn. Limit (%) Notes

AA005 01025 39 0.01 38 1 2.56 CX002 01025 39 0.001 0.004 0.01 31 1 1 6

15.38 GF086 01025 2421 0.00001 0.001 1 2405 15 0.62 CX013 01027 27 0.001 19 8 29.63 GF097 01027 67 0.001 67 0 0.00 PLA06 01025 23 0.0005 22 1 4.35 PLA07 01027 118 0.0005 114 4 3.39 PLA11 01025 2403 0.001 0.002 0.003 0.005 0.008 0.009 0.01 1964 12 18 7 49 1 4 348

14.48 PLM27 01025 177 0.001 0.002 31 145 1 0.56 PLM43 01025 3290 0.00004 0.001 others 1385 1093 15 797

24.22 PLM47 01027 64 0.00004 53 11 17.19 Unknown 01025 or 01027 12201 0 0-0.0002 0.0002 0.0005 0.0006 0.001 0.001-0.005 0.005 0.005-0.01 0.01 0.02 225 89 229 1339 117 2653 2628 484 11 940 1095 319

2.6 1118 Analyzed for but not detected

953 Presence verified but not quantified

(d) Summary of Mercury (Hg) Analyses

Analytical Method Code PCode Total No. of Analyses Detection Limit (mg/L) No. below Detn Limit No. at or above Detn Limit Percentage at or above Detn. Limit (%) Notes

CV013 71890 2534 0.0001 0.0002 0.0003 0.001 2210 14 1 6 303

11.96 CV014 71890 112 0.0001 0.0002 78 24 10 8.93 CV016 71900 111 0.0001 0.0002 94 1 16 14.41 CV017 71900 58 0.0001 58 0 0.00 CV018 71900 69 0.0001 0.0002 36 25 8 11.59Page A4 of 10 (e) Summary of Lead (Pb) Analyses

Analytical Method Code PCode Total No. of Analyses Detection Limit (mg/L) No. below Detn Limit No. at or above Detn Limit Percentage at or above Detn. Limit (%) Notes

AA019 01049 74 0.1 71 3 4.05 CX007 01049 501 0.001 0.004 0.005 0.01 300 1 54 11 135

26.95 CX017 01049 40 0.005 22 18 45.00 GF031 01051 47 0.001 29 18 38.30 GF056 01051 1 0.005 1 0 0.00 GF078 01049 1 0.005 1 0 0.00 GF089 01049 2876 0.001 0.002 0.1 2531 1 1 341

11.86 2 empty values

GF100 01051 94 0.001 0.005 36 1 57 60.64 PLA06 01049 23 1.001 0.002 16 5 2 8.70 PLA07 01051 123 0.001 0.002 39 76 8 6.50 PLA11 01049 1657 0.001 0.01 0.02 0.03 0.05 0.1 1 1368 9 15 7 40 215

12.98 2 empty values

PLM43 01049 1970 0.0001 0.0001- 0.001 0.001 0.002 0.004 285 6 701 7 1 970

49.24 PLM48 01051 100 0.00006 0.001 6 12 82 82.00 Unknown 12374 0 0.000001 0.00001 0.00001- 0.0001 0.0001- 0.001 0.001 0.001-0.01

0.01 0.01-0.1 0.1 0.1-1 >1 187 55 4 24 81 417 3932 (3337 of them are 0.005) 3466 499 271 18 11 438

3.54 971 Analyzed for but not detected

2000 Presence verified but not quantifiedPage A5 of 10 (f) Summary of Antimony (Sb) Analyses Analytical Method Code PCode Total No. of Analyses Detection Limit (mg/L) No. below Detn Limit No. at or above Detn Limit Percentage at or above Detn. Limit (%) Notes

GF032 01095 1 0.001 1 0 0.00 GF040 01097 115 0.001 115 0 0.00 PLM48 01097 54 0.0002 0.0006 0.0009 49 5

9.26 HY009 01095 01097 516 0.001 432 84 16.28 PLM43 - 1274 0.00005 0.0001 0.0002 0.0003 0.0004 0.0006 0.0008 0.001 0.002 >=0.003 105 1 219 148 1 1 2 555 7 2 233

18.29 Trivial No. of other misc. detn limits

Unknown 01095 790 Misc: 0.00005-0.3 761 29 3.67

(g) Summary of Selenium (Se) Analyses

Analytical Method Code PCode Detection Limit (mg/L) No. below Detn Limit No. at or above Detn Limit Percentage at or above Detn. Limit (%) Notes

GF060 01145 0.001 16 6 27.27 PLM10 01145 0.0008 17 52 75.36 GF091 01145 0.001 0.002 0.006 0.014 297 41 6 4 80

18.69 PLM27 01145 0.01 153 21 12.07 HY011 01145 0.001 0.002 0.003 0.005 0.01 3240 61 1 3 10 752

18.49 PLM40 01145 0.0003 0.0004 0.0005 0.0007 0.002 365 250 230 160 1 1411

58.38 Unknown 01145 0 <0.001 0.001 0.001-0.01 0.01 0.01-0.1 >=0.1 192 112 932 248 2170 53 53 1108

22.76Page A6 of 10 (h) Summary of Uranium (U) Analyses

Analytical Method Code PCode Total No. of Analyses Detection Limit (mg/L) No. below Detn Limit No. at or above Detn Limit Percentage at or above Detn. Limit (%) Notes

PHOS1 22703 218 0.0004 72 146 66.97 PHOS2 22703 4 0.09 1 3 75.00 PHOS3 22703 141 0.00001 10 131 7.09 PHOS4 22703 1 0.00024 1 0 0.00 PHOS5 22703 162 0.00 8 149

4.94 5 of them are “Presence verified but not detected” PHOS6 22703 61 0.001 44 2

3.28 15 of them are “Presence verified but not detected” PLM43 22703 1310 0.004 0.003 0.002 0.001 0.00016 0.00007 0.00004 0.00002 1 1 4 315 1 1 31 43 913

69.70 PLM48 28011 62 - 0 62 100. FL001 22703 163 0.001 120 43 26.38 FL002 75990 122 - 0 122 100.00 FL003 28011 28011

75993 56 0.001 21 35

60.00 FL004 22703 4 - 0 4 100.00 FL006 22703 59 0.0004 18 41 69.49 FL007 80020 33 0.00001 1 32 96.97 Unknown 848 0.00001 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0011 0.007 0.013 0.02 11 56 38 1 12 16 20 3 2 1 63 6 618

72.88Page A7 of 10 (i) Summary of Zinc (Zn) Analyses

Analytical Method Code PCode Total No. of Analyses Detection Limit (mg/L) No. below Detn Limit No. at or above Detn Limit Percentage at or above Detn. Limit (%) Notes

AA039 01090 187 0.01 75 112 59.89 AA071 01092 125 0.01 26 99 79.20 PLM48 01092 89 0.002 9 80 89.89 GF084 01090 1 0 1 100.00 PLA06 01090 83 0.001 0.002 0.01 2 16 1 64

77.11 PLA07 01092 141 0.001 0.002 4 14 123 87.23 PLA15 01092 17 0.006 0.031 0.04 6 11

64.71 PLM10 01090 11 0.0006 2 9 81.82 PLM27 01090 175 0.01 120 55 31.43 PLA11 01090 5407 0.003 0.004 0.005 0.006 0.007 0.009 0.01 0.014 0.02 0.024 0.06 0.08 587 8 7 5 1 5 11 1 342 7 6 1 4426

81.86 PLM43 01090 1554 0.0006 0.001 0.001-0.01 0.0101 4 87 7 1 1455

93.63 Unknown 01090 01092 14304 0 0-0.0001 0.001 0.001-0.01 0.01 0.02 0.03 0.04 0.05 0.05-0.1 0.1 >0.1 64 21 9 392 1378 1924 8 13 2010 65 103 16 8301(1225 are zeros)

58.03Page A8 of 10 Table A2. List and Explanation of Analytical Methods from USGS NWIS Database Used in Table A

Analytical Method Code Short Info Long Info Database Citation Method Number Method Owner

AA003 Barium, wf, direct AAS Barium in filtered water by direct AAS TWRI 5-A1/1989, p. 89 I-1084-85:USGS USGSNWQL AA005 Cadmium, wf, direct AAS Cadmium in filtered water by direct AAS TW RI 5-A1/1989, p. 127 I- 1135-85:USGS USGSNWQL

AA019 Lead, wf, direct AAS Lead in filtered water by di rect AAS TWRI 5-A1/1989, p. 247 I-1399-85:USGS USGSNWQL AA039 Zinc, wf, direct AAS Zinc in filtered water by di rect AAS TWRI 5-A1/1989, p. 507 I-1900-85:USGS USGSNWQL AA071 Zinc, wu, USGS digest, AAS Zinc recoverable from unfiltered water by dilute HCl (USGS) digestion and direct AAS TWRI 5-A1/1989, p. 507 I-3900-85:USGS USGSNWQL

CV013 Mercury, wf, auto CVAAS Mercury in filtered water by automatedsequential CVAAS TWRI 5-A1/1989, p. 289 I-2462-85:USGS USGSNWQL

CV014 Mercury, wf, CV-AFS Mercury in Filtered Water by Cold VaporAtomic Fluorescence Spectrometry WRI 01-4132 I-2464-01:USGS USGSNWQL

CV016 Mercury, wu, CVAAS (NWQL) Mercury recoverable from unfiltered water by CVAAS TWRI 5-A1/1989, p. 285 I-3462-85:USGS USGSNWQL

CV017 Mercury, wu, CVAAS (NWQL) Mercury recoverable from unfiltered water by CVAAS TWRI 5-A1/1989, p. 285 I-3462-85:USGS USGSOCFL

CV018 Mercury, wu, CV-AFS Mercury in Unfiltered Water by Cold Vapor-Atomic Fluorescence Spectrometry WRI 01-4132 I-4464-01:USGS USGSNWQL

CX002 Cadmium, wf, APDC & MIBK, AAS Cadmium in filtered water by chelationextraction with APDC and MIBK, and AAS TWRI 5-A1/1989, p. 125 I-1136-85:USGS USGSOCFL

CX007 Lead, wf, APDC & MIBK, AAS Lead in filtered water by chelationextraction with APDC and MIBK, and AAS TWRI 5-A1/1989, p. 245 I-1400-85:USGS USGSOCFL

CX013 Cadmium, USGS digest,extract AAS Cadmium recoverable from unfiltered waterby dilute HCl (USGS) digestion, chelationextraction with APDC and MIBK, and AAS TWRI 5-A1/1989, p. 125 I-3136-85:USGS USGSOCFL

CX017 Lead, USGS digest, extract AAS Lead recoverable from unfiltered water bydilute HCl (USGS) digestion, chelationextraction with APDC and MIBK, and AAS TWRI 5-A1/1989, p. 245 I-3400-85:USGS USGSOCFL

FL001 U (natural), wf, fluorometric Dissolved fluorometric USGSNWQL

FL002 U (natural) 2SPE, wf,fluorimetry Fluorimetry, Uranium, FIL USGSNWQL

FL003 U (natural), water, fluorometric U, Fluorometric, TOT USGSNWQL

FL004 U (natural), wf, FL-DIR GR-W U-FIL, fluorometric method--direct (FLDIR.), ground water (GR-W) USGSNWQL

FL006 U (natural), wf, direct fluorom U-FIL, fluorometric method--direct (FLUOR-direct) USGSNWQL

FL007 Uranium, wf,extract fluorometric U-FIL, fluorometric method--extracted sample (FLUOR-ext) USGSNWQL

GF031 Elements, drinking water, GFAA EPA 200.9 determination of total recoverable trace elements in drinking water by GFAA 200.9:USEPA USGSNWQL

GF032 Antimony, drinking wtaer, GFAA GFAA, Antimony, dissolved, EPA 200.9, Drinking Water 200.9:USEPA USGSNWQL

GF040 Antimony, wu, GFAA ANTIMONY TOTA L (GFAA) 204.2:USEPA USGSOCFL GF042 Arsenic, wf, GFAA ARSENIC DISSOLVED (GFAA) 206.2:USEPA USGSOCFL GF044 Arsenic, wu, GFAA ARSENIC TOTAL GFAA 206.2:USEPA USGSOCFL GF046 Barium, wf, GF BARIUM DISSOLVED (GF) 208.2:USEPA USGSOCFL GF056 Lead, LL, wu, GFAA LEAD TOTAL LL (GFAA) 239.2:USEPA USGSOCFLPage A9 of 10 GF060 Selenium, wf, GFAA (Ocala) SELENIUM DISSOLVED (GFAA) 270.2:USEPA USGSOCFL

GF073 Arsenic and selenium, wu, GFAA Graphite furnace atomic absorption I-0000-91:USGS USGSNWQL

GF078 Lead, LIS, GFAAS Lead in low ionic strength water by GFAAS TWRI 5-A1/1989, p. 249 I-1401-85:USGS USGSNWQL

GF084 Zinc, LIS, GFAAS Zinc in low i onic strength water by GFAAS TWRI 5- A1/1989, p. 509 I-1901-85:USGS USGSNWQL GF085 Arsenic, wf, GFAAS Arsenic in Filtered Wa ter by GFAAS OF 98-639 I-2063-98:USGS USGSNWQL GF086 Cadmium, wf, GF-AAS Cadmium in filtered water, by GF-AAS OF 93-125, p. 53 I-2138-89:USGS USGSNWQL GF089 Lead, wf, GF-AAS Lead in f iltered water, by GF-AAS OF 93- 125, p. 87 I-2403-89: USGS USGSNWQL GF091 Selenium, wf, GFAAS (NWQL) Selenium in Filtered Water by GFAAS OF 98-639 I-2668-98:USGS USGSNWQL

GF096 Arsenic, wu, GFAAS Arsenic Recoverable from Unfiltered Water by GFAAS OF 98-639 I-4063-98:USGS USGSNWQL

GF097 Cadmium, wu, GF-AAS Cadmium in unfiltered water, by GF-AAS OF 93-125, p. 53 I-4138-89:USGS USGSNWQL GF100 Lead, wu, GF-AAS Lead recoverable from unfiltered water, by GF-AAS OF 93-125, p. 87 I-4403-89:USGS USGSNWQL

HY010 Arsenic, wf, auto hydride AAS Arsenic in filtered water by automated sulfuric acid potassium persulfate digestion or ultraviolet radiation, hydride generation, and tube furnace AAS TWRI 5-A1/1989, p. 77 I-2062-85:USGS USGSNWQL

HY011 Selenium, wf, auto hydride AAS Selenium in filtered water by automated hydride generation and AAS TWRI 5-A1/1989, p. 403 I-2667-85:USGS USGSNWQL

HY013 Arsenic, wu, digest & hydrideAAS Total arsenic in unfiltered water by automated sulfuric acid potassium persulfate digestion or ultraviolet radiation, hydride generation, and tube furnace AAS TWRI 5-A1/1989, p. 77 I-4062-85:USGS USGSNWQL

PHOS1 Laser phosphorim, dir (Eberline) Laser-induced phosphorimetry, direct (analysis by Eberline Services) USGSNWQL

PHOS2 Laser phosphorimetry, dir (NWQL) Laser-induced phosphorimetry, direct (analysis by NWQL) USGSNWQL

PHOS3 Laser phosphorim, ext (Eberline) Laser-induced phosphorimetry, extracted (analysis by Eberline Services) USGSNWQL

PHOS4 PHOS5 Uranium, wf, dir phosphorescence Uranium, Water, Filtered, by Direct Laser Induced Phosphorescence USGSNWQL

PHOS6 Uranium, wf, ext phosphorescence Uranium, Water, Filtered, by Extraction and Laser Induced Phosphorescence USGSNWQL

PL120 Metals, wu, ICP-OES or ICP-MS Metals Recoverable from Unfiltered Water ICP-OES or ICP-MS OF 98-165 I-4471-97:USGS USGSNWQL

PLA06 Trace elements, wf, ICP/T (Ocala Ocala Lab analysis of dissolved trace elements by Inductively Coupled Plasma-- Atomic Emission Spectrometry (ICP/T) 200.7:USEPA USGSOCFL

PLA07 Trace elements, wu,ICP/T (Ocala) Ocala Lab analysis of total trace elements by Inductively Coupled Plasma--Atomic Emission Spectrometry (ICP/T) 200.7:USEPA USGSOCFL

PLA11 Metals, wf, ICP-AES Metals, filtered water, Inductively coupled plasma-atomic emmission spectrometry OF 93-125, p. 101 I-1472-87:USGS USGSNWQL

PLA15 Metals, wu, ICP-AES Meta ls, unfiltered water, Inductively coupled plasma-atomic emission spectrometry OF 98-165 I-4471-97:USGS USGSNWQL

PLM10 Collision/reaction cell ICPMS Elements in Natural-Water, Biota, Sediment, and Soil Samples Using Collision/Reaction Cell Inductively Coupled Plasma-Mass Spectrometry USGS TMR 5-B1 :USGS USGSNWQL

PLM27 Elements, wf, ICP-MS w/o digest Trace elements in water and wastes by inductively coupled plasma - mass spectrometry without digestion EPA-600/R-94/111 200.8:USEPA USGSOKWC

PLM40 Metals, water, ICP-MS Metals in Wate r by ICP-MS OF 92-634 I-2477-92:USGS USGSNWQLPage A10 of 10 PLM43 Metals, wf, ICP-MS Metals, filtered water, Inductively coupled plasma-mass spectrometry OF 92-634 I-2477-92:USGS USGSNWQL

PLM47 Metals, wu, ICP-MS Meta ls, unfiltered water, Inductively coupled plasma-mass spectrometry OF 98-165 I-4471-97:USGS USGSNWQL

PLM48 Metals, wu, ICP-MS Meta ls, unfiltered water, Inductively coupled plasma-mass spectrometry OF 98-165 I-4471-97:USGS USGSNWQLPage B1 of 6 

# Appendix B: Discussion of Spatial Distributions and Analytical

Artifacts in Groundwater Analyses from NWIS Database
B1. Spatial Distribution of Groundwater Analyses from NWIS Database

With reference to discussion in Section 3.3.3, we fu rther analyze here the sp atial distributions of measured aqueous concentrations of lead in groundwater analyses from the NWIS database (Figure 3.8(a) and Figure B.1(a)). The question is why the measurable lead concentrations are so sparsely distributed over many parts of the countr y. A possible reason is that less water quality analyses for hazardous inorganic constituents have been conducted in certain parts of the country or that analyses have been conducted using anal ytical methods with insufficient detection limits. For evaluation, we have plotted in Figure B1(b) th e spatial distribution of all samples that have
been declared “non-detects” for lead, with the co lor scheme indicating the detection limit of the analytical method used (in Log mg /L). In Figure B1(c), we fu rthermore plot the location of samples that have been declared in the NWIS data base as “presence verified, but not quantified”.

Figures B1(a) through B1(c) together indicate that both possible r easons stated above are valid. Clearly, there are many states and regions where al most no water analyses for lead have been conducted, or at least these analyses have not been reported in the NWIS database. There are furthermore several states and regions with many lead analyses in the NWIS database, but the majority of those with insufficient detection limits. A good example are the states of Iowa and Illinois, where numerous samples have detecti on limits of about 0.01 mg/L, two orders of magnitude above the expected equilibrium l ead concentration of 0.0001 mg/L for a typical reducing groundwater where galena coexists with pyrite and goethite (S ection 3.3.5). The “hot spots” of measurable lead concentrations in Figure B1(a) are clearly co rrelated to states or regions where large numbers of high-quality anal yses (i.e., with low enough detection limits) have been conducted. In contrast, states or regions with no or only few detectable lead concentrations in Figure 3B1(a) correspond to loca tions with either no measurements or analysesPage B2 of 6 with insufficient detection limits. We may concl ude that almost all groundwaters in the United States would exhibit small (mos tly below MCLs), but non-zero lead concentrations if measured with appropriate analytical techniques, which is consistent with the above finding that lead occurs ubiquitously in soils, sediments and aqui fer rocks throughout the United States. While not reported here, the same conclusions can be drawn for the other ha zardous inorganic constituents of interest in this study.Page B3 of 6 (a)

(b)

(c)
Fig. B1 . Spatial distribution of (a) samples with measured aqueous concentration (Log mg/L) of lead, (b) samples with non-detects for lead with color scheme indicating the detection limit of the analytical method (in Log mg/L), and (c) samples with text stating “presence verified, but not quantified”. The plots were drawn from more than 38,000 groundwa ter quality analyses downloaded from the National Water Information System Database (NWIS).Page B4 of 6 

# B2. Characterization of Analytical Artifacts in the Measurement of Concentrations of Hazardous Inorganic Constituents

Section 3.3.5 discusses the possibi lity and the implications of an alytical artifacts introduced by the differing accuracy and sensitivity of analy tical methods. The Cd concentrations in Figure 3.6(d) were mentioned there as an example where almost all analyses may have been affected. The figure shows essentially two sets of measurem ents, with each set starting an initially high frequency, apparently corresponding to the detect ion limit for some of the analyses, which then falls off with increasing concentration until a rela tively low frequency is recorded. This is, of course, very different from a normal or possibly log-normal distribution of concentrations to be expected in natural groundwaters.

The analogy of the observations in Figure 3.6( d) with radioactive decay suggests a similar process involving Poisson statistics. This is illustrated by Figures B2(a) and B2(b), which display the normalized analytical frequenc y versus log concentration for tw o selected analytical methods (i.e., PLA11 with a detection limit of 0.001 mg/L and up, and PLM43 with a detection limit of 0.00002 mg/L and up, see Table A1). A linear regr ession of the plots indicates a very good correlation between frequency and concentration, with R 2 = 0.98 for both analytical methods. The normalized frequency of “non-detects” for each method is plotted as the negative value of the detection limit, and is included in the regression analysis.

The theoretical basis for these correlations ha s not been evaluated, bu t probably relates to
contamination or unknown interferences in the anal ytical method, as replicate analyses for these methods do not show such large spurious variat ions. Clearly, further study of this phenomenon is warranted, especially in relation to the anal ytical sensitivity of the method and its suitability for detecting actual concen trations expected in a given ground water sample.

Similar evaluations of selected analytical methods for other elements yield similar correlations. For example, Figure B3 is a plot of the nor malized analytical frequency versus linear concentration for Hg analyses using the CV013 an alytical method with a detection limit no lower than 0.0001 mg/L, suggesting the majo rity of reported Hg analyses are also artifacts. AfterPage B5 of 6 discounting for potential artifacts, the remaining chemical analyses are of marginal utility in predicting thermodynamic controls for mercury c oncentrations in ground waters, and therefore a theoretical interpretation of me rcury behavior may be the onl y recourse for predicting the response of dissolved mercury to elevated partial pressures of CO 2 . Figures B4(a) and B4(b) show similar artifacts are operative for lead when less sensitive analytical methods are utilized.

(a) y = -0.3274x - 0.5116 R 2 = 0.982

-4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5

0

-2 0 2 4 6 8 10 Cd Concentration (mg/L x 10^-3)Log Normalized Frequency (-))

(b) y = -0.1813 x - 0.5 802 R 2 = 0.9832

-2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2

0

-3 -2 -1 0 1 2 3 4 5 6 7 Cd Concentration (mg/L x 10^5)Log Norma lized Frequency (-)

Fig. B2. Log normalized frequency of chemical analyses as a function of cadmium concentration in ground waters at and above the detection limit for the analytical method. (a) PLA11; (b) PLM43.Page B6 of 6 y = -0.2796x - 0.7661

R 2 = 0.9536

-4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5

0

-4 -2 0 2 4 6 8 10 12 Hg Concentration (mg/L x 10^-4)Normalized Log Frequency (-)

Fig. B3. Log normalized frequency of chemical analyses as a function of mercury concentration in ground waters at and above the detection limit for the CV013 analytical method.

(a) y = -0.5094x - 0.5497 R 2 = 0.9909

-3 -2.5 -2 -1.5 -1 -0.5

0

-2 -1 0 1 2 3 4 5 Pb Concentration (mg/L x 10^2)N orm alize d Lo g Freq uen cy (-)

(b) y = -0.2483x - 0.6746 R 2 = 0.9537

-3 -2.5 -2 -1.5 -1 -0.5

0

-2 0 2 4 6 8 10 Pb Con centration (mg/L x 10 ^3)Normaliz ed Log Frequ enc y (-)

Fig. B4. Log normalized frequency of chemical analyses as a function of lead concentration in ground waters at and above the detection limit for the analytical method. (a) PLA11; (b) CX007.C1 of 32 

# Appendix C: Thermodynamic Database Augmentation

C1. Introduction

A substantial augmentation of solubility products of solid phases and dissociation constants of aqueous species is required to adequately mo del the complex chemistry associated with the behavior of hazardous inorganic constituents in ground waters. The augmentation is not limited merely to those primary minerals and sec ondary alteration products where the hazardous constituent is an essential component; attention must also be paid to minerals that interact with the hazardous constituents through co-preci pitation, ion exchange, and/or adsorption. Furthermore, the database used for EQ3/ 6 simulations, Data0.dat.YMP, suffers from obsolescence. The solubility pr oducts of many common rock-formi ng minerals listed in the database require revision to more accurately re flect advances since the original compilations used in the database were published.

Appendix C is subdivided into several sections dealing respectively w ith additional aqueous speciation and minerals for arse nic, lead, antimony and selenium. Corrections are also provided
for Fe +2 and Fe +3 aqueous species, and an expanded databa se is provided for metastable iron oxides, oxy-hydroxides, and sulfides. Supplementa ry information for carbonates and chlorite is also included, because of their re levance in participating as pH or Eh buffers. The appendix concludes with a section in which data are pres ented on minerals that had to be included on a case-by-case basis in order to ensure that the geochemical model reflected reality.


# C2. Arsenic

C2.1 Background
The presence of arsenic in groundwater has long been a serious problem in many parts of the world, particularly in the Ganges delta of Bangladesh (Smedley, 2003), where farmers draw drinking water from shallow aquifers that are subject to seasonal oxida tion and mobilization of arsenate. In the United States, several area s are also characterized by elevated arsenic concentrations in ground waters, notably in north western Nevada, where th e municipal water ofC2 of 32 the city of Fallon has unacceptably high levels (S medley, 2003) and the Ri o Grande Rift of New Mexico (Bexfield and Plummer, 2003). The city of Albuquerque is forced to treat its water supplies to mitigate excessive concentrations of As. Other regions of the country, such as the Upper Michigan Peninsula and parts of Mi nnesota (Thornberg and Sahai 2004) and New Hampshire (Utsunomiya et al., 2003), are also affected by high As levels in groundwater. Increasing use of aquifer storag e and recovery techniques (ASR ) has also lead to arsenic contamination of the recovered water (Arthur et al., 2005). A significant proportion of the sampled more than 38,000 well water samples taken from the NWIS database already possess As levels exceeding current MCLs. The introduction of high pressure CO 2 into aquifers with such elevated ambient As concentrations could aggrav ate an already marginal water quality in some areas. Therefore, the evaluation of As chemistry in groundwaters is very important, and takes precedence over some other hazardous constituen ts in relation to the present problem.

The chemistry of As in surficia l waters and shallow subsurface groundwaters is extraordinarily complex. As occurs naturally in at least five oxi dation states (-3, 0, +2, +3 and +5). Both +3 and +5 states form hydroxy-anions in aqueous soluti on with varying degrees of protonation. Both
are noted for reactions with Fe in both +2 and +3 states. As(V) adsorption onto hydrated ferric oxides (HFO) stabilizes the latter in a metastable or colloidal or nanoparticulate form, which can persist for tens to hundreds of years. In the presence of Ca +2 , Ca-As-Fe-OH macro complexes form in solution, which can aggregate to form gla ssy or amorphous minerals such as pitticite or yukonite. As an added complication, it is well known that the transfor mation of aqueous As(III) to As(V) and vice versa is sluggish (Cherry et al., 1979). Aqueous As concentrations in ground waters are therefore likely to be dictated not only by the formation of metastable phases and adsorption-stabilized colloi ds, but also by kinetically hindered redox transformations.

A substantial literature has evolved in which the thermodynamic and kinetic properties of As species have been studied and elucidated, ma inly because of the interest in groundwater treatment, and the need to remediate contaminat ion from current and former mining operations. The literature has been searched and thermodynami c data compiled (or in some cases estimated) for 24 aqueous species and complexes, and 15 mi nerals or potential stabilizing precipitates.
Most of these data have been formatted for entry in the EQ3 Data0.dat and TOUGHREACT thermodynamic databases in the form of relevant dissociation constants or solubility products atC3 of 32 25°C. Additional calculations were also conduc ted using SUPCRT92 (Johnson et al., 1992) to generate solubility and dissociation constant data wherever feasible.

One aspect of As chemistry, which has not been adequately addresse d in this report , concerns the substitution of As for S in pyrite. One of the pr incipal causes of As co ntamination in aquifers occurs through the oxidation of arsenian pyrite. Reich and Becker (2006) have formulated a theoretical solid-solution model quantifying As s ubstitution for S in both pyrite and marcasite. Time constraints prevented the incorporation of this model in the TOUGHREACT simulator. Instead, an empirical correction factor was applied to both arsenopyrite and gudmundite solubility products to account for their dilution fr om unit activity due to so lid solution in pyrite. This was accomplished by reconciling field observa tions of both As and Sb concentrations in NWIS potable groundwater samples with thermodyna mic predictions of their concentrations, as
described in Section 3.4.

In the following two sections, methods are de scribed for building an expanded internally consistent database of thermodynamic properties of aqueous As species and solid As phases, respectively, based on an earlier stud y by Nordstrom and Archer (2003).



## C2.2 Aqueous Arsenic Species.

C2.2.1 Calculation of HKF Equation of State Parameters for Aqueous Arsenate and Arsenite Species

The thermodynamic properties of aqueous arsenic species as derived by Nordstrom and Archer (2003) were used as a point of departure. They are summarized in Table C1. Entropy, S P r , T

r 0 , of two species, HAsO 3 -2 , and AsO 3 -3 , needed to be estimated. This was accomplished through a correlation plot of the entropies of several oxyani on species as listed in Table C2, and illustrated in Figure C1.C4 of 32 Table C1. Thermodynamic properties of Arsenic Solid Phas es and Aqueous Species (from Nordstrom and Archer, 2003)

Phase 0 T P f r

r G

,

, Δ cal mol -1 Δ H f
, P r , T

r 0 cal mol -1 S P r , T

r 0 cal mol -1 K -1 C P r , T

r 0 cal mol -1 K -1 As 0 0 8.516 5.839 S 0 0 7.600 5.402 O 2 (g) 0 0 49.003 7.016 H 2 (g) 0 0 31.208 6.889 H 2 O(l) -56687 -68315 16.709 17.995 As 2 O 3 (cub) -137749 -157091 25.664 23.155 As 2 O 3 (mon) -137794 -156709 27.096 23.179 As 2 O 5 -185220 -219309 25.201 27.701 S 2 (g) 18953 30681 54.510 7.761 AsS(a) -7481 -7600 15.033 11.233 AsS(b) -7385 -7409 15.177 11.233 As 2 S 3 (a) -20292 -20507 39.149 38.958 As 2 S 3 (am) -18356 -15989 47.801

Phase 0 T P f r

r G

,

, Δ cal mol -1 Δ G f
, P r , T

r 0 cal mol -1 S P r , T

r 0 cal mol -1 K -1 C P r , T

r 0 cal mol -1 K -1
H 2 S(aq) -6661 -9610 28.920 39.914 HS - (aq) 2880 -4216 15.010 -21.989 As 3 S 4 (SH) 2 - -30019 AsS(OH)(SH) - -58413 H 3 AsO 3 (aq) -152971 -177428 46.804 H 2 AsO 3 -1 -140454 -170827 26.957 HAsO 3 -2 -121272 (-1.839) H 3 AsO 4 (aq) -183258 -215930 43.755 H 2 AsO 4 - -180127 -217835 26.859 HAsO 4 -2 -170586 -217115 -2.729 AsO 4 -3 -154484 -212765 -42.139 AsO 3 -3 -100813 (-46.715)

Table C2. Standard Partial Molal Entropies, S P r , T

r 0

, of Selected Oxyanions

Charge S P r , T

r 0 , cal mol -1 K -1

AsO 4 -3 AsO 3 -3 PO 4 -3 PO 3 -3 SO 4 -2 CH 3 COO 0 43.755 46.804 43.6 42.7 1 26.859 26.957 21.6 31.7 30 20.6 2 -2.729 -8 4 4.5 3 -42.139 -53C5 of 32 y = -7.2936x 2 - 8.4074x + 44.15 R 2

= 0.9801

-60 -40 -20 0 20 40 60

0 0.5 1 1.5 2 2.5 3 3.5

Charge

Fig. C1. Regression analysis of standard partial molal entropies, S P r , T

r 0

, of selected oxyanions as a function of charge.

A regression analysis through al l data points yielded the equati on listed in Figure C1, with a regression coefficient, R 2 , = 0.9801. The calculated values of S P r , T

r 0 at Z = -2 and -3 were used as

estimates for HAsO 3 -2 and AsO 3 -3 . The heat capacities, C P r , T

r 0 of all species were then calculated using equations derived by Shock et al. (1997; Table 6), which ar e for hydrogen bearing anions

C P r , T

r 0

= 1.65 S P r , T

r 0

− 46.8

and for hydrogen-free anions where z j = -3

C P r , T

r 0

= 0.60 S P r , T

r 0

− 92.8

Calculation of the HKF equation of state parameters, c 1 , c 2 and the Born coefficient, ω P

r , T

r , then follows with application of the following equa tions (Shock and Helgeson, 1988; Shock et al., 1989). For charged species, the Born coefficient can be calculated from:C6 of 32 ω

j , P

r , T

r = η Z j η Z j
r e , j , P r , T

r − 1 3.08 ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥

where η = N 0 e 2 2 = 1.66027 × 10 5 A 0 cal mol -1 , with N 0 Avogadro’s number and e the absolute
electronic charge with a value of 4.80298 × 10 − 10 esu

. r e , j , P r , T

r is the effective electrostatic radius of

the ion

r e , j , P r , T

r = Z j 2 η Y

P

r , T

r − 100 

# ( )

S P r , T

r 0

− α z

where Y P

r , T

r has the value

− 5.802 × 10 − 5 K − 1 and refers to the Born function

Y = 1

ε ∂ ln ε ∂

T ⎛ ⎝ ⎜ ⎞ ⎠

⎟

P

and α z is a charge dependent correlation parameter

α

z = 71.5 Z j

For neutral aqueous inorganic species, the effective Born coefficient is

ω

e = − 1514.4 S P r , T

r 0 + 0.34 × 10 5

Calculation of c 1 proceeds through the following steps. The heat capacity of an ion is assumed to consist of two components, thus

C P 0 = Δ C P , n 0 + Δ C P , s 0

representing respectively the nonsolvation and solv ation contributions. The Born coefficient can

be used to calculate Δ C P , s 0 , thus

Δ C P r , T

r , s 0

= ω TXC7 of 32 where X is the standard partial molal heat ca pacity Born Function (Helgeson and Kirkham, 1974)

X = 1

ε ∂ 2 ln ε ∂

T 2 ⎛ ⎝ ⎜ ⎞ ⎠

⎟

P − ∂ ln ε ∂

T ⎛ ⎝ ⎜ ⎞ ⎠

⎟

P 2 ⎛ ⎝ ⎜ ⎜ ⎞ ⎠

⎟ ⎟

which at P
r , T
r has the value of 3.09 K − 2 × 10 − 7

.

Δ C P , n 0 can then be obtained by differe nce. Furthermore, we have

Δ C P , n 0

= c 1 + c 2 1 T

− θ ⎛ ⎝ ⎜ ⎞ ⎠

⎟ 2

where c 2 × 10 − 4 = 0.2037 P P r , T

r 0

− 3.0346 and θ = 228 K

.
The general form of the HKF equation of state is given by



## ( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ +

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− − − Δ = Δ
r

r r o Tr o f o T T T T T c T T S G G

Tr ln 1 Pr, Pr, 

## ( ) ( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− −

− ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

− ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− − ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

− − θ θ θ θ θ θ θ
r r

r T T T T T T T T c ln 1 1

2 2

+ a 1 P

− P
r 

## ( ) + a 2 ln Ψ + P Ψ + P
r ⎛ ⎝ ⎜ ⎞ ⎠

⎟ + a 3 P

− P
r T

− θ ⎛ ⎝ ⎞ ⎠ + a 4 1 T

− θ ⎛ ⎝ ⎞ ⎠ ln Ψ + P Ψ + P
r ⎛ ⎝ ⎜ ⎞ ⎠

⎟



## ( ) 

# [ ] ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

− − + ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− +

− + 1 1 1 1
Pr, Pr, Pr Pr, ε ω ω ε ε ω Tr r Tr Tr Tr k T T Y ;

V o = a 1 + a 2 1 Ψ + P ⎛ ⎝ ⎞ ⎠ + a 3 + a 4 1 Ψ + P ⎛ ⎝ ⎞ ⎠ ⎛ ⎝ ⎞ ⎠ 1 T

− θ ⎛ ⎝ ⎞ ⎠

− ω Q + 1

ε − 1 ⎛ ⎝ ⎞ ⎠ ∂ ω ∂

P ⎛ ⎝ ⎞ ⎠ T ;



## ( ) ( ) ( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+

Ψ + Ψ +

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− −

− + =

r r o P P P a P P a T T
T c c C ln 2

4 3 3 2 2 1 θ θ

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

− − ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ + + 2 2 1 1 2 T T T TY TX

P ∂ ω ∂

ε ∂ ∂ω ωC8 of 32 where and [ k ] is a switch function equal to 1 for ch arged species and 0 for neutral species (Sverjensky et al., 1997), and [ Q ], [ Y ], and [ X ] are given by (Tanger and Helgeson, 1988)

Q ≡ − ∂ 1 ε 

### ( ) ∂

P ⎛ ⎝ ⎜ ⎞ ⎠
T = 1

ε 2 ∂ ε ∂

P ⎛ ⎝ ⎞ ⎠ T ;

Y ≡ − ∂ 1 ε 

### ( ) ∂

T ⎛ ⎝ ⎜ ⎞ ⎠
P = 1

ε

2 ∂ ε ∂

T ⎛ ⎝ ⎞ ⎠ P ;

X ≡ ∂ Y ∂

T ⎛ ⎝ ⎞ ⎠ P = 1

ε 2 ∂ 2 ε ∂

T 2 ⎛ ⎝ ⎜ ⎞ ⎠ P − 2

ε ∂ε ∂

T ⎛ ⎝ ⎞ ⎠ P

2 ⎛ ⎝ ⎜ ⎞ ⎠

⎟

and can be computed from equations expre ssing the dielectric prope rties of water to 1000 ° C and 5 kbar (Johnson and Norton, 1992).

However, the parameters a 1 , a 2 , a 3 and a 4 relate only to corrections for pressure. Because
potable ground waters are obtained from relatively shallow wells, correction of Δ G f
, j 0 for pressure would be trivial and al most certainly within the uncer tainties associated with the
derivations under discussion.C9 of 32 

### C2.2.2 Calculation of HKF Equation of State Parameters for Metal Complexes with Aqueous Arsena te and Arsenite Species
Langmuir et al. (2006) present esti mated values of the association constants of a number of metal arsenate complexes. Those complexes relevant to the problem at hand are listed in Table C3.

Table C3. Association Constant and Gibbs Free Energy of Formation of Selected Arsenate Complexes (after Langmuir et al., 2006) and Derived HKF Equation of State Parameters
Complex log K
assoc at 25 ° C Δ G f
, P r , T

r 0 cal mol-1 S P r , T

r 0 cal mol -1 K -1 C P r , T

r 0 cal mol -1 K -1 c 1 cal mol -1 K -1 c 2 cal K mol -1 x 10 -4 ω P r , T

r cal mol -1

x 10 -5 Z

Arsenite Species
H 4 AsO 3 + -0.29 -116510 -81.203 +1 H 3 AsO 3 (aq) -152971 46.80 30.43 20.6012 3.1635 -0.3688 0 H 2 AsO 3 - -9.17 -140454 26.96 -2.32 15.9835 -3.5072 1.2132 -1 HAsO 3 -2 -23.27 -121272 -1.84 -49.83 7.0916 -13.1859 3.2706 -2 AsO 3 -3 -38.27 -100813 -46.71 -120.83 -13.3179 -27.6474 5.5714 -3 Arsenate Species
H 3 AsO 4 (aq) -183258 43.75 25.40 18.0776 2.1384 -0.3226 0 H 2 AsO 4 - -2.30 -180127 26.86 -2.48 15.9024 -3.5402 1.2146 -1 HAsO 4 -2 -9.29 -170590 -2.73 -51.30 6.3549 -13.4851 3.2840 -2 AsO 4 -3 -21.09 -154489 -42.14 -118.08 -12.3472 -27.0882 5.5021 -3 MgH 2 AsO 4 + -0.78 -290706 25.86 42.18 32.2896 5.5571 0.1524 1 MgHAsO 4 (aq) -6.43 -282997 -19.38 -6.64 8.1099 -4.3879 0.6336 0 MgAsO 4 - -14.75 -271645 -74.45 -73.42 -11.5410 -17.9910 2.7487 -1 CaH 2 AsO 4 + -1.24 -313693 45.36 41.23 29.0125 5.3636 -0.1429 1 CaHAsO 4 (aq) -6.60 -306380 1.19 -7.59 4.6829 -4.5814 0.3220 0 CaAsO 4 - -14.87 -295096 -52.80 -74.37 -15.1173 -18.1845 2.4209 -1 AlH 2 AsO 4 +2 0.77 -300826 -0.73 15.02 25.0924 0.0246 1.0989 2 AlHAsO 4 + -2.00 -297046 -48.43 -33.80 -1.8770 -9.9204 1.2773 1 AlAsO 4 (aq) -6.99 -290238 -105.95 -100.58 -34.8667 -23.5235 1.9445 0 MnHAsO 4 (aq) -5.54 -230206 -1.66 -5.40 6.3638 -4.1353 0.3651 0 MnAsO 4 - -14.96 -217353 -55.80 -72.18 -13.4157 -17.7384 2.4663 -1 FeH 2 AsO 4 + 0.38 -205421 33.56 39.62 29.7151 5.0356 0.0358 1 FeHAsO 4 (aq) -5.75 -197057 -11.26 -9.20 5.4762 -4.9094 0.5105 0 FeAsO 4 - -14.03 -185759 -65.90 -75.98 -14.2337 -18.5124 2.6193 -1 FeH 2 AsO 4 +2 1.74 -200131 10.05 28.92 31.7358 2.8560 0.9358 2 FeHAsO 4 + 0.57 -198535 -37.03 -19.90 4.6789 -7.0890 1.1047 1 FeAsO4(aq) -2.19 -194769 -93.93 -86.68 -28.3985 -20.6920 1.7624 0 ZnHAsO 4 (aq) -6.08 -210169 -12.21 -6.63 7.1149 -4.3859 0.5249 0 CdHAsO 4 (aq) -5.58 -194212 -2.93 -4.80 6.8921 -4.0131 0.3843 0 PbH 2 AsO 4 + -0.77 -187925 63.06 34.82 22.7867 4.0579 -0.4109 1 PbHAsO4(aq) -6.25 -180447 19.86 19.86 19.8614 1.0112 0.0392 0C10 of 32 Using the information supplied by Nordstrom and Archer (2003), Δ G f
, j 0 can be calculated, as also listed in Table C3. A necessary requirem ent for calculating the HKF equation of state parameters is quantification of S P r , T

r 0 and C P r , T

r 0 , neither of which have been measured or

estimated. Calculation of S P r , T

r 0 is facilitated by the observation by Sverjensky et al. (1997) that
the standard partial molal entropies of association Δ S assoc ., P r , T

r 0 of metal sulfate complexes

M Z + 2 + SO 4 2

− = MSO 4 Z

of a given charge, Z , are essentially identical, as indicated in Figure C2.

Fig. C2. Standard partial molal entropies of association Δ S assoc ., P r , T

r 0 of metal sulfate complexes (after Sverjensky et al., 1997)

These correlations were summarized by the auth ors in the form of the following equation:

Δ S assoc ., P r , T

r , y = 1 0

= α Z , SO 4 2

− S M Z + 2 0 + β Z , SO 4 2

−

where α Z , SO 4 2

− = − 0.055 Z + 0.055 and β Z , SO 4 2

− = 13.84 Z + 18.16.

Recognizing that the entropies of oxyanions of iden tical charge are nearly identical, as shown in Table C2 and Figure C1, above, the assumption was made that Δ S assoc ., P r , T

r 0 for metal arsenate complexes with charges equivalent to those of metal complexes would be equivalent. Thus, forC11 of 32 example, values of Δ S assoc ., P r , T

r 0 for CaHAsO 4 (aq) are equivalent to CaSO 4 (aq). The estimated

values of S P r , T

r 0 all metal complexes listed were calcula ted using the above equation, and are presented in Table C3.

The estimation of C P r , T

r 0 for metal arsenate complexes coul d not be accomplished by reference to the existing papers describing parameter estimati ons for the HKF equation of state. The normal procedure is to use correlation plots relating C P r , T

r 0 to S P r , T

r 0 for given families of ionic or molecular species, e.g., Schock and Helgeson (1988, Eq. 91) and Shock et al. (1989, Eq. 24). However, in this case, no such correlation plots have been de veloped, and publ ished plots are neither relevant nor adaptable for to arsenate complexes. Perusal of limited C P r , T

r 0 data available for metal complexes of oxyanions presented in Sverjensky et al. (1997) indicates that the

Δ C P , assoc ., P r , T

r 0 for these species are all of similar magnitude, i.e., circa 50 cal mol -1 K, suggesting the formation of outer sphere complexes, as proposed many years ago by Garrels and Christ (1965, Figure 4.4, p. 100). Table C4 presents the results of this anal ysis. Among those listed complexes is MnSO 4 (aq). However, Sverjensky et al. (1997), referring to supporting literature, have asserted that the complexation of Fe +2 Mn +2 and Zn +2 involves the release of one or more
molecules of water from the inne r coordination sphere of central cations in such complexes, and indeed, this may be consistent with the elevated Δ C P , assoc ., P r , T

r 0 values for CH 3 COO - complexes of
Fe +2 and Zn +2 . Compensating for one mol of water woul d result in a correcti on of –18.0 cal mol 1 K -1 to Δ C assoc ., P r , T

r 0 for CH 3 COOFe + and CH 3 COOZn + leading to equivalent values of 65 and

67.33 cal mol -1 K -1 , respectively, which is more in accord with Δ C assoc ., P r , T

r 0 for the oxyanion
complexes listed in Table C4. Note, however, that Δ C assoc ., P r , T

r 0 for the reaction between CH3COOH(aq) and either Fe +2 or Zn +2 is also approximately 50 cal mol -1 K -1 . Bearing in mind the tentative nature of the analysis, we have made a provisi onal assumption that C P r , T

r 0 for all
metal arsenate complexes listed in Table C3 can be estimated assuming that Δ C assoc ., P r , T

r 0 = 50 cal
mol -1 K -1 for all association reactions.C12 of 32 Table C4. A Comparison of Heat Capacities of Dissociation of selected Oxyanions

Reaction Participating Species C P r , T

r 0 Δ C assoc ., P r , T

r 0

K + -3.0 SO 4 -2 -64.38 K + + SO 4 -2 =KSO 4 - KSO 4 - -10.9 56.48

K + -3.0 HSO 4 - 5.3 K + + HSO 4 - =KHSO 4 (aq) KHSO 4 (aq) 57.72 49.42

Mn +2 -4.1 SO 4 -2 -64.38 Mn +2 +SO 4 -2 = MnSO 4 (aq) MnSO 4 (aq) -20.6 47.88

Ca +2 -7.53 CO 3 -2 -69.5 Ca +2 +CO 3 -2 = CaCO 3 (aq) CaCO 3 (aq) -29.6 47.43

Na + 9.06 HSiO 3 - -21 Na + + HSiO 3 - = NaHSiO 3 (aq) NaHSiO 3 (aq) 24.61 36.55

Ca +2 -7.63 HSiO 3 - -21 Ca +2 + HSiO 3 - = CaHSiO 3 + CaHSiO 3 + 32.87 61.5
Average Value 49.9 Fe +2 -7.9 CH 3 COO - 6.2 Fe +2 + CH 3 COO - = CH 3 COOFe + CH 3 COOFe + 81.3 83.0

Zn +2 -5.33 CH 3 COO - 6.2 Zn +2 + CH 3 COO - = CH 3 COOZn + CH 3 COOZn + 86.2 85.33

Fe +2 -7.9 CH 3 COOH 40.56 Fe +2 + CH 3 COOH = CH 3 COOFe + + H + CH 3 COOFe + 81.3 48.64

Zn +2 -5.33 CH 3 COOH 40.56 Zn +2 + CH 3 COOH = CH 3 COOZn + + H + CH 3 COOFe + 86.2 51.07

Calculation of the HKF equa tion of state parameters, c 1 , c 2 and the Born coefficient, ω P

r , T

r , follows the procedures outlined above. Th e respective values are listed in Table C3.



### C2.2.3 Derivation of Thermodynamic Prop erties of Arsenic Solid Phases and Minerals
The following two tables summarize information on arsenic solid phases. Table C5 gives the crystallographic information necessary to calculate molar volumes of the solid phases, which are used in thermodynamic calculations to correct for the effects of pressure, and Table C6 providesC13 of 32 thermodynamic data for the solid phases. Th is information, in conjunction with the corresponding aqueous basis species information, is used to calculate the solid phase solubility products.

The thermodynamic properties of some of the pha ses or minerals listed in Table C6 are incomplete. Information available in the litera ture may be sufficient to estimate preliminary
values of Δ G P r , T

r 0 for the minerals in question, but time c onstraints prevented further evaluation.
In some cases, Δ G P r , T

r 0 values were taken directly, or calcu lated from solubility product data reported in the literature , rather than by re-eva luating raw data and us ing revised thermodynamic data for aqueous species published by Nordstrom and Archer (2003) to re calculate solubility product and Δ G P r , T

r 0 values. The tabulated data are not entirely consistent internally, and are therefore preliminary. However, the uncertainties introduced by taking this expedient approach are well within the uncertainties of the geochemi cal model developed in Section 3 of the report, and have no impact on the resulting findings and conclusions.

A very recent paper by Perfetti et al. (2008) gives revised thermodynamic data for arsenopyrite. In this paper, Δ G P r , T

r 0 for arsenopyrite is given as –32,612 cal/mol, which differs by +1,231 cal/mol from the value reported in Table C6, whic h was cited from an ear lier paper by Pokrovski et al. (2002). Using this revised value for the calculation of solubility produc ts would decrease
the extent of undersatur ation of arsenopyrite reported in S ection 3.4.5.2, Figure 3.34a, by approximately one unit in SI. This change w ould necessitate a smaller calibration correction (-1.8 vs. –3.0) to match the modeled As concen tration in groundwaters in equilibrium with an arsenopyrite solid solution in pyrite, to that actu ally observed, as presented in Section 3.5.2, Table 3.13. This revised correction appears to be more realistic, although further corroborating
evidence is needed.C14 of 32Table C5.Crystallographic Properties of Arsenic Solid PhasesCell ConstantsName Formulaao(Å) bo(Å) co(Å)αβγZSpace Group ReferenceArsenolite As2O311.07343 11.07343 11.07343 90 90 90 16 *Fd3m Ballirano and Maras (2002)Claudetite II As2O37.99 4.645 9.115 90 78.3 90 4 P2_1/n Pertlik (1975)Arsenic As 3.7597 3.7597 10.4412 90 90 120 6R3m Schiferl and Barrett (1969)Arsenic Oxide As2O58.64 8.45 4.62 90 90 90 4 P212121Hansen (1977)Ferrarisite Ca5(AsO3OH)2(AsO4)2.9H2O 8.294 6.722 11.198 106.16 92.94 99.21 P-1 Catti et al. (1980)Guerinite Ca5(AsO3OH)2(AsO4)2.9H2O 17.63 6.734 23.47 90 90.6 90 5 P2_1/n Catti and Ferraris G (1974)Haidingerite Ca(AsO3OH).H2O 6.904 16.161 7.935 90 90 90 8 Pcnb Ferraris et al. (1972)Hornesite Mg3(AsO4)2.8H2O 10.2573 13.4211 4.7526 90 105.076 90 2 C2/m Rojo et al. (1996)Johnbaumite Ca5(AsO4)3(OH) 9.70 - 6.93 2 P 63/m or P63Dunn et al. (1980)Johnbaumite-Pb Pb5(AsO4)3(OH)Mimetite Pb5(AsO4)3(Cl) 10.211 - 7.4185 2 P6_3/m Yonshan et al. (1991)Pyromorphite Pb5(PO4)3(Cl) 9.9764 - 7.3511 2 P6_3/m Dai and Hughes (1989)Orpiment As2S311.475 9.577 4.256 90 90.68 90 4 P2_1/n Mullen and Nowacki (1972)Pharmacolite CaHAsO4.2H2O 5.9745 15.434 6.2797 90 114.83 90 4 IaFerraris et al. (1971)Pharmacosiderite KFe4(AsO4)3(OH)4.6H2O 7.9816 7.9816 7.9816 90 90 90 2 P-43m Buerger et al. (1967)Picropharmacolite Ca4Mg(HAsO4)2(AsO4)2.11H2O 13.547 13.5 6.71 99.85 96.41 91.6 2 P-1 Cattiet al. (1981)Pitticite Fe2(AsO4)(SO4)(OH).6H2ORealgar AsS 9.327 13.563 6.59 90 106.46 90 16 P2_1/n Kyono et al. (2005)Pararealgar AsS 9.909 9.655 9.502 90 97.29 90 16 P2_1/c Bonazzi et al. (1995)Scorodite FeAsO4.2H2O 8.942 10.075 10.339 90 90 90 Pbca Xu et al. (2007)Scorodite FeAsO4.2H2O 10.325 8.953 10.038 90 90 90 Pbca Kitahama et al. (1975)Symplesite Fe3(AsO4)2.8H2O 7.97 9.41 4.72 99.917 97.383 105.967 1 P-1 Wolfe (1940)Talmessite Ca2Mg(AsO4)2.2H2O 5.874 6.943 5.537 97.3 108.7 108.1 1P-1 Catti M, et al. (1977)Tooeleite Fe6(AsO3)4(SO4)(OH)4.4H2O 8.9575 6.4238 9.7912 90 96.032 90 1 C2/m Morin et al. (2007)Weilite CaHAsO47.0591 6.8906 7.2006 97.43 103.55 87.75 4 P-1 Ferraris and Chiari (1970)Yukonite Ca7Fe11(AsO4)9O10.24H2OArsenopyrite FeAsS 5.744 5.675 5.785 90 112.28 90 4 P-1 Morimoto and Clark (19610C15 of 32Table C6.Thermodynamic Properties of Arsenic Solid PhasesCPr0cal mol-1K-1Mineral name FormulaΔGf,Pr,Tr0cal mol-1RefΔHf,Pr,Tr0cal mol-1RefSPr,Tr0cal mol-1K-1RefVPr,Tr0cm3mol-1Refab×103c×10−5Ref(C3RH2.25) Ca3(AsO4)2.2.25H2O -862066 88.361 3.2400 -4.68E+00 5(C3RH3) Ca3(AsO4)2.3H2O -904227 96.911 3.2400 -4.68E+00 5(C3RH3.67) Ca3(AsO4)2.3.67H2O -942878 2 104.549 3.2400 -4.68E+00 5(C3RH4.25) Ca3(AsO4)2.4.25H2O -976338 2 111.161 3.2400 -4.68E+00 5(C4RH5) Ca4(OH)2(AsO4)2.4H2O -1176881 2 119.981 4.3200 -6.24E+00 5(F2R1.5SH) Fe4(AsO4)3(SO4)(OH) 106.309 31.0062 -5.92E+00 5(FR1.5HX) Fe2(HAsO4)3.xH2O(FRX) Fe2(AsO4)x(HAsO4)y(R) As2O5-185220 1 -219309 1 25.201 1 50.781 7 27.701 0.000 0.000 4Arsenic As 0 1 0 1 8.516 1 13.049 8 5.137 2.602 -0.089Arsenopyrite FeAsS -33843 11 16.370 11 26.420 8 18.047 4.780 -7.543Arseniosiderite Ca2Fe3(AsO4)3O2.3H2O -887437 111.33 6 195.618 8 134.327 30.060 -8.450 5Arsenolite As2O3-137749 1 -157091 1 25.664 1 50.724 8 8.37 48.6 0.000 4Ca-arsenate Ca3(AsO4)2.xH2O -887437 49.761 100.070 8 61.700 13.900 -15.010Claudetite As2O3-137794 1 -156709 1 27.096 1 47.257 8 23.197 9.326 -6.300Ferrarisite Ca5(AsO3OH)2(AsO4)2.9H2O -1866157 2 189.017 6 354.655 8 223.462 5.400 -7.800 5Guerinite Ca5(AsO3OH)2(AsO4)2.9H2O -1864962 2 184.458 6 335.580 8 223.462 5.400 -7.800 5Haidingerite Ca(AsO3OH).H2O -366396 2 35.591 6 66.991 8 40.476 1.080 -1.560 5Hornesite Mg3(AsO4)2.8H2O -887437 114.241 6 190.194 8 149.441 5.220 -4.440 5Johnbaumite Ca5(AsO4)3(OH) -1216375 2 94.817 6 170.031 103.457 5.400 -7.800 5Johnbaumite-Pb Pb5(AsO4)3(OH) -635516 3 123.721 6 204.215 90.357 32.000 0.000 5Mimetite Pb5(AsO4)3(Cl) -630019 10 -713432 10 143.164 10 204.215 8 119.981 4.3200 -6.2400 5Orpiment As2S3-20292 1 -20507 1 39.149 1 70.503 8 22.73 13.82 0.605 9As2S3,am As2S3-18356 1 -15989 1 47.801 1 70.503 22.73 13.82 0.605 9Pharmacolite CaHAsO4.2H2O 43.177 6 79.363 8 51.876 1.080 -1.560 5Pharmacosiderite KFe4(AsO4)3(OH)4.6H2O 162.563 6 306.084 8 184.967 42.605 -9.100 5Picropharmacolite Ca4Mg(HAsO4)2(AsO4)2.11H2O 199.568 6 361.370 8 244.772 10.385 -7.720 5Pitticite Fe2(AsO4)(SO4)(OH).6H2O 103.29 6 100.070 123.518 12.406 -2.370 5Realgar AsS -7481 1 -7600 1 15.033 1 29.743 8 12.94 -0.0133 1.396 9Pararealgar AsS -7385 1 -7409 1 15.177 1 30.368 8 12.94 -0.0133 1.396 9Scorodite FeAsO4.2H2O -306991 49.761 69.117 8 48.396 9.300 -1.780Scorodite,am FeAsO4.2H2O -303130 38.313 6 69.117 48.396 9.300 -1.780 5Symplesite Fe3(AsO4)2.8H2O -872736 119.583 6 198.385 8 155.261 6.210 -2.250 5Talmessite Ca2Mg(AsO4)2.2H2O 70.333 6 120.084 8 84.021 3.900 -4.600 5Tooeleite Fe6(AsO3)4(SO4)(OH)4.4H2O 177.878 6 337.409 8 161.252 66.906 -9.470 5Weillite CaHAsO427.344 6 51.858 8 29.076 1.080 -1.560 5Yukonite Ca7Fe11(AsO4)9O10.24H2O -940771 515.389 6 100.070 609.140 109.860 -30.400 5Notes: 1. Nordstrom and Archer (2003); 2. Bothe and Brown (1999); Lee and Nriagu (2007); 4. Kelley (1960); 5. Calculated usingthe summation of oxide components. This work; 6. Calculated using the procedure described byHolland (1989). This work; 7. Jansen (1977); 8. Calculatedfrom cell parameter data. 9. Pokrovski et al. (1996); 10. Flis etal. (2007),0TPrrS,recalculated using elemental data from NIST; 11. Pokrovski et al. (2002).C16 of 32 

# C3. Lead

C3.1 Introduction
Incorporation of a wide r range of lead solid and aqueous species in the ongoing evaluation of groundwater analyses, and in model development, is important for two reasons. First, the ingress of oxygen into aquifers that were formerly reducing, and where lead was presumably immobilized as the sulfide, galena, could indu ce solubilization of l ead exceeding MCLs, even more so than would be induced through pH lo wering through the ingress of high-pressure CO 2 . It is important to know whether the precipitati on of secondary lead salts might restrain lead solubility under those conditions, especially with respect to those ground waters downloaded from the NWIS database, some of which exhibit le ad concentrations that are supersaturated with respect to galena. Secondly, the addition of re levant aqueous lead complexes, especially those of carbonate, sulfide and selenide, will aid in a mo re precise calculations of lead concentrations in solutions saturated with respect to minerals co ntaining lead as an essential component. These additional species might also affect the predicte d sorption and ion exchange of lead on aquifer minerals, depending on the nature of the problem at hand. In this appendix, we present thermodynamic properties of lead minerals a nd other phases where lead is an essential component, and for all aqueous species except for lead sulfide and selenide complexes, which are discussed in Appendices D and E of this report.



## C3.2 Aqueous Lead Species
An earlier unpublished compilation of internally consistent thermodynamic properties of a variety of lead carbonates, chlorides and sulf ates, and a corresponding compilation relating to lead complexes in solution, was used to ca lculate their respective solubility products and
dissociation constants in the Data0.dat databa se of EQ3 and the corresponding TOUGHREACT database. All respective constants were calcul ated using SUPCRT92, in which estimated HKFequation-of-state parameters for the aqueous speci es were included. In addition to replacing seven extant aqueous species and eleven solid ph ases in the database, three additional aqueous species, and thirteen additiona l solid phases were incorporated. Table C7 lists the thermodynamic properties of aqueous lead specie s together with their calculated HKF-equationof-state parameters.C17 of 32 

## C3.3 Lead Solid Phases and Minerals
The following two tables summarize information on lead solid phases. Table C8 gives the crystallographic information necessary to calculat e molar volumes of minera ls, which are used in thermodynamic calculations to correct for the effects of pressure, and Table C9 provides thermodynamic data for the solid phase. This in formation, in conjunction with the corresponding aqueous basis species information is used to ca lculate the solid phase solubility products. The listed solid phases also include those known to very insoluble. An example is the Pb phosphate, pyromorphite, Pb 5 (PO 4 ) 3 (Cl), in the presence of which lead concentrations are known to be depressed to low levels. The application of phosphate compounds as a means of controlling Pb mobility in contaminated soils has been suggest ed. The corresponding lead arsenate, mimetite,
Pb 5 (AsO 4 ) 3 (Cl), is also known to be very insoluble, and it is possible that mimetite could limit
the saturation concentration of both As and Pb in ground waters. Therefore this mineral is also
included as well as the arsenic an alogue of apatite, johnbaumite, Pb 5 (PO 4 ) 3 (OH).

It should be noted that many of the data compiled in Tables C7, C8, and C9 were evaluated and reconciled over a decade ago. Time did permit only limited retrieval of new information published in the archival literature since that time. A casual perusal of the literature indicates that no substantive changes have occurred since th e original work was compiled; rather, the data has been refined and augmented. Therefore, the application of mo re recent data, where available, would not change the findings of this report in any significant manner, and would be within the limits of geochemical model uncertainty . The advantage in usi ng the current database is that it is internally consis tent, and that the solubility produc t and dissociation constant data have been uniformly treated, as all calculations were performed using SUPCRT (Johnston et al., 1992). However, a complete review of the data in the light of more recent published data is definitely recommended. 

## C3.4 Acknowledgements
We would like to acknowledge the contributions of Mr. Motomi Oshika, a guest scientist at the Berkeley Lab during 1992-3, who assisted in th e evaluation of the thermodynamic properties of lead minerals, under a FY 1992 allocation of Explor atory R&D Funds to one of the authors (John Apps) for the project “Synthesizing Optical PbSO 4 for Advanced Scintillation Applications”.C18 of 32Table C7.Thermodynamic Properties ofLead Aqueous SpeciesAqueousSpeciesΔGf,Pr,Tr0ΔHf,Pr,Tr0SoCpobfVoCga1uaax10a2uaax10−2a3v.aaa4w.aa10−4c1baac2waa×10−4waz×10−5ChargeH+0 0 0 0 0 0 0 0 0 0 0 0 +1HCO3--140282l-164898m23.53m-8.46 24.6 7.5621 1.1505 1.2346 -2.8266 12.93965 -4.7579 1.2733 -1CO3-2-126191m-161385m-11.95m-69.5n-5.02 2.8524 -.39844 6.4142 -2.6143 -3.3206 -17.1917 3.3914 -2Pb+2-5710h220e4.2e-12.74 -15.6 -0.0051 -7.7939 8.8134 -2.4568 8.6624 -5.6216 1.0788 2Pb(OH)+-52089 -57652*21.34 4.54 7.029 2.804 -0.9348 6.1174 -2.7404 10.9315 -2.1098 0.2284 1Pb(OH)2(aq) -95060 -111214*41.57 33 48.127 8.4485 12.8476 .07206 -3.3101 22.8390 3.6875 -0.2895 0Pb(OH)3--137641 -166209*40.06 15.1792 40.06 7.5921 10.7680 1.4938 -3.2241 24.4800 0.0574 1.0223 -1PbCO3(aq) -141819 -156632*40.72 29 46.880 8.2735 12.3922 0.9392 -3.2913 20.6134 2.8727 -0.2767 0Pb(CO3)2-2-272746 -301163*70.01 -14.492 83.41 13.906 26.173 -4.5397 -3.8610 17.5025 -5.9866 2.1523 -2PbCl+-39046 -43260.*28.0 3.400*2.2870 -2.9703 8.5760 -2.6502 21.8 1.8542 0.16800 1PbCl2-71190 -78281.*45.0 22.400*4.7542 3.9456 3.9447 -2.9421 22.2616 3.2801 -0.2550 0PbCl3--102147 -115066*52.1 41.400*7.7573 12.3640 -1.6927 -3.2901 39.698 5.521 0.9679 -1PbCl4-2-133254 -153342*54.7 60.399*10.7757 20.8252 -7.3588 -3.6399 57.0825 7.5985 2.2060 -2Cl--31379h-39933e13.56 -29.44d17.79d4.0320 4.8010 5.5630 -2.847 -4.40 -5.714 1.4560 -1OH- -3.7595h-54977e-2.56e-32.79d-4.18d1.2527 0.0738 1.8423 -2.7821 4.15 -10.346 1.7246 -1SO4-2-177930h-217400e 4.50e-64.38d13.88d8.3014 -1.9846 -6.2122 -2.6970 1.64 -17.998 3.1463 -2HSO4--180630r-212500r30.0r5.3 35.2 6.9788 9.2590 2.1108 -3.1618 20.0961 -1.9550 1.1748 -1Notes a: cal mol-1; b: cal mol-1K-1; c:cm3mol-1; d: Tanger and Helgeson; e: CODATA(1978); *:supcrt; f: from electrolytes in Table 10 unless otherwise indicated; h: calculated fromΔHSoPTandSOshown using standard entropiues of the elements from CODATA(1978); g: From electrolytes in Table 9 unless otherwise indicated; l: Helgeson, Kirkham, and Flowers (1981);m: Berg and Vanderzee (1978); r:consistent with log KrPT,ΔHrPt ANDΔSrpt from dissociation reaction; u: Cal Mol-1xbar-1; v: cal K mol-1xbar-1; w: cal K mol-1; z: from Table 1, 2,and 3; aa: calculated in a manner consistentwith table 8 unless otherwise indicatedC19 of 32Table C8.Crystallographic Properties of Lead Solid PhasesNotes a. Palache et al. (1951); b.Did not calculate; c. Cesbron and Schubnel (1968); d. Boivin et al. (1968), Tridot et al. (1969); e. Kuzel (1973).Unit Cell Parameters Z Space GroupReferenceMineral Name Formulaaobocoαβγ4Cotunnite PbCl2Kx 7.67 9.15 4.590 9090 PnamPalache et al. (1951)Å 7.68 9.17 4.514Laurionite PbOHCl Å 9.72 4.06 7.1190 9090 PcmnPalache et al. (1951)Kx 9.7 4.05 7.13PbO.PbCl2.nH2O Å 9.57 - -90 9090Lorettoite Pb7O6Cl24Palache et al. (1951)Mendipite Pb3O2Cl2Kx 9.50 11.87 5.8790 9090 PnmaPalache et al. (1951)Å 9.52 11.89 5.884 P212121Paralaurionite Pb(OH)Cl Kx 10.77 3.97 7.1890 117.2213/60 C21/mPalache et al. (1951)Å 10.79 3.98 7.19Penfieldite Pb2OHCl3Å 11.28 x 48.654 P6/mCesbron and Schubnel (1968);Fieldlerite Pb3(OH)2Cl4Kx 16.59 8.00 7.1990 102.2090 P21/aPalache et al. (1951)Å 16.62 8.02 7.204Anglesite PbSO4Kx 8.45 5.38 6.9390 9090 PnmaPalache et al. (1951)Å 8.47 5.39 6.944Lanarkite Pb2(SO4)O Kx 13.73 5.68 7.0790 116.2290 C2/mÅ 13.76 5.69 7.082Sulfate Dibasque 2PbO.PbSO4Å 7.814 5.803 8.03590 102.6490 2 P21or P21/mBoivin et al. (1968)(Second Source) Å 8.06 5.79 7.171033PbO.PbSO4Å 10.30 6.37 7.4587.2 75.079.2 4 P1 or p1Lanarkite PbO.PbSO4Å 13.751 5.6969 7.0699− 115.82- 2 P3PbO.PbSO4.H2O Å 10.30 6.37 7.4587.2 75.079.2 4 P1 or p1Boivin et al. (1968)2ndSource 4PbO.PbSO4Å 11.5234 11.7138 7.315290 91.0490 P21/aBoivin et al. (1968)11.443 11.666 7.31690 90.8290HydrocerussitePlumbonacriteMatlockiteLeadLithargeMassicotC20 of 32Table C9.Thermodynamic Properties of Lead Solid PhasesCPr0Coefficients, cal.mol-1.k-1Mineral Name Formula G.F.W.g. .mol-1ΔGf,Pr,Tr0cal.mol-1Ref.ΔHf,Pr,Tr0kcal.mol-1Ref.SPr,Tr0cal.mol-1.k-1Ref.VPr,Tr0cm3.mol1Ref.a b x 103c x 10-5Ref.Lead Pb 207.2 0.00.015.48418.1535.2907.772.80-0.720.230.00Litharge PbO 223.199 -45244-5244016.21423.98310.38510.7944.774.0-0.6100.00Massicot PbO 223.199 -44910 8,9,11 -51940 16.42 23.983 23 9.05 6.40 0.00 14Lead Hydroxide Pb(OH)2241.215 -108100 8,9,11,12 -1233008,9,11 26.69 26 41.2 26 17.71 12.24 0.00 26Cotunnite PbCl2278.106 -75050 -85800 20 32.50 47.84 15.96 8.00 0.00 14Anglesite PbSO4303.258 -194353 -219870 35.51 47.95 10.96 31.0 4.2Cerussite PbCO3267.209 -150370 -168000 31.3 40.747 12.39 28.60 0.00- (PbO)2.Pb(OH)2687.613 58.9 26 90.33 15 38.91 20.24 0.00 26Matlockite PbO.PbCl2501.305 -126557 26 -144700 8,9 47.93 26 68.89 26.56 12.0 0.00 26Lorretoite Pb7O6Cl2Mendipite (PbO)2.PbCl2724.505 -176860 6 -199800 8,9 65.79 100.305 37.16 16.0 0.00 26- (PbO)3.PbCl2947.704 -253400 8,9 47.76 20.0 0.00 26Lanarkite PbO.PbSO4526.457 -246700 8,9,12 -276658 53.374 75.1 21.778 33.87 3.473 3- (PbO)2.PbSO4749.656 -294000 9 -328189 70.326 107.1 26 34.830 39.28 2.940 3- PbO.(PbCO3)2757.618 -351600 -395342 26 74.097 79.469 35.165 61.970 -0.621Laurionite PbClOH 259.660 -93470 9 -109402 26 28.83 42.26 16.835 10.12 0.00 26Paralaurionite PbOHCl 259.660 -92340 -108361 26 28.53 26 41.36 26 16.835 10.12 0.00 26Penfieldite Pb2OHCl3537.766 61.22 89.71 32.795 18.12 0.00 26Fieldlerite Pb3(OH)2Cl4797.427 93.22 26 141.285 26 49.63 28.24 0.00 26- (Pb(OH)2)3.PbCl21001.770 69.09 44.72 0.00 26Hydrocerussite Pb(OH)2.(PbCO3)2775.633 -408328 -456000 21 86.083 112.894 45.735 61.970 -0.621- (PbO)3.PbSO4990.871 -341200 8 -388800 8,9 81.4 8,9 139.7 26 42.74 43 4.2 26Plumbonacrite Pb10O(OH)6(CO3)62550.098 -1270767 -1448776 26 272.629 357.584 144.590 190.680 -2.448Notes 1. Solans-Huguet and Font-Altaba (1967); 2. Derriche and Perrot (1976); 3. Kellogg (1989). 4. Esdaile (1967). 5.Kellogg and Basu (1960) Abdul-Samad etal. (1982). 6. Humphreys et. al.(1980). 7. Loza et. al. (1984). 8. Wagman et al. (1968); 9.Wagman et. al. (1982); 10. Chase et. al. (1985); 11. Weast et al. (1984); 12. Dekock (1986). 14. Kelley (1960); 15. Tolkachev et. al.(1958). 16. Brasseur (1940); 17. Toptygina et. al. (1984).18. Kuzel (1973) 19. Cesbron and Schubnel (1968). 20. Shelton (1970). 21. Ball and Casson (1977). 22. Taylor and Lopata (1984).23. Hill (1985). 24. Howie and Moser (1968), density referred to Clark and Tyler(1939). 25. SUPCRT. 26. This work.C21 of 32 

# C4. Antimony and Selenium

C4.1 Aqueous Antimony and Selenium Species
Table C10 gives the thermodynamic properties for aqueous species of Sb(II I). Sb(V) species,
SbO 2 + and Sb(OH) 6 - , were also included in the geochemical model using extant data in the EQ3
Data0.dat database. The relevant selenite and selenate complexes included in the model were also extant data in the EQ3 Data0.dat database . The properties of selenide species used or derived in this study are discussed in Appendix E.



## C4.2 Antimony and Selenium So lid Phases and Minerals.
Trace concentrations of selenides of heavy metals can play an important role in defining the solubility of hazardous constituents selected for study in this report. In Table C11, the crystallographic properties of se lected selenides and antimony mine rals are summarized with the purpose of calculating their molar volumes, whic h are given, together with other thermodynamic properties in Table C12.C22 of 32Table C10.Thermodynamic Properties of Antimony Aqueous SpeciesAqueousSpeciesΔGf,Pr,Tr0cal mol-1SPr,Tr0cal mol-1K-1CPr,Tr0calmol-1K-1VPr,Tr0cm3mol-1a1dx 10cal mol-1bar-1a2ax 10-2cal mol-1a3ecal K mol-1bar-1a4fx 10-4cal K mol1c1cal mol-1K-1c2x 10-4cal K mol-1 ωPr,Trcal mol-1Sb(OH)3(aq) -154010 49.43 39.0954 9.1415 14.5397 0.0630 -3.3801 28.6838 4.9284 -0.038Sb(OH)4-1-195038 42.28 22.96 44.58 8.1966 12.2325 0.9671-3.284728.65151.6426 0.9815H2Sb2S4(aq) -35488 69.36 71.3 70.7 11.4268 20.1196 -2.1236 -3.6107 47.5025 11.4892 -0.038HSb2S4-1-28583 50.70 32.56 50.78.991314.17280.2068 -3.364933.0864 3.5984 0.8540Sb2S4-2-15622 42.38 -30.48 44.66 8.7546 13.5948 0.4332 -3.3410 12.3553 -9.2424 2.6016Note: Data after Akinfiniev etal. (1994) and Bessinger (2000)Table C11.Crystallographic Properties of Antimony and Selenium MineralsCell ConstantsName Formulaao(Å) bo(Å) co(Å)αβγZSpace Group ReferenceNaumannite Ag2Se 4.333 7.062 7.764 90 90 90 4 *P2_12_12_1 Wiegers (1971)Berzelianite Cu2Se 5.765 5.765 5.765 90 90 90 4 Fm3m Heyding and Murray (1976)Antimonselite Sb2Se311.794 3.986 11.648 90 90 90 4 PnmaCaracas and Gonze (2005)Clausthalite PbSe 6.1243 6.1243 6.1243 90 90 90 4 Fm3m Wyckoff (1963)Tiemannite HgSe 6.084 6.084 6.084 90 90 90 4 F-43m Earley (1950)Ferroselite FeSe24.804 5.784 3.586 90 90 90 2 Pnnm Pickardt et al. (1975)Stibnite Sb2S311.3107 3.8363 11.2285 90 90 90 4 Pnma Bayliss and Nowacki (1972)Antimony Sb 4.50661 4.50661 4.50661 57.108 57.108 57.108 20 R-3m Wyckoff (1963)Senarmonite Sb2O311.1519 11.1519 11.1519 90 90 90 16 Fd-3m Svensson (1975)Valentinite Sb2O34.911 12.464 5.412 90 90 90 4 Pccn Svensson (1974)Berthierite FeSb2S411.401 14.148 3.758 90 90 90 4 Pnam Lemoine et al. (1991)Gudmundite FeSbS 6.02 5.93 6.02 90 67.87 90 4 P1_1/c Buerger (1939)Kermesite Sb2S2O 8.147 10.709 5.785 102.78110.63 101.00 4 P-1 Bonazzi et al. (1987)C23 of 32Table C12.Thermodynamic Properties of Selenides and Antimony MineralsCPr0cal mol-1K-1Mineral name FormulaΔGf,Pr,Tr0cal mol-1RefΔHf,Pr,Tr0cal mol-1RefSPr,Tr0cal mol-1K-1RefVPr,Tr0cm3mol-1Refab×103c×10−5RefNaumannite Ag2Se -11998 -10397 35.813 35.768 19.479 - -Berzelianite Cu2Se -17089 -15607 30.914 28.846 19.460 - -Cadmoselite CdSe -33830 -34600 19.900 33.727 11.190 2.23 - 3Clausthalite PbSe -23566 -2409223.827 34.583 11.29 2.39 - 3Tiemannite HgSe -9130 -10397 23.995 33.905 11.70 3.70 - 3Ferroselite FeSe2-19598 -21511 13.011 30.003 16.871 - -Kullerudite NiSe2-25096 -26052 24.138 32.206 18.028 - -Antimony Sb 0. 0. 10.773 18.188 6 2.490 5.930 1.880Senarmonite Sb2O3-151400 27.370 52.220 6 22.530 12.030 -3.179Valentinite Sb2O3-149600 29.430 49.874 6 24.840 10.300 -3.197Stibnite Sb2S3-35846 43.499 73.352 6 28.750 3.927 -1.264Antimonselite Sb2Se3-30019 -30497 48.911 82.441 6 28.38 5.00 - 3Gudmundite FeSbS -109433 5 -109060 5 106.000 5 29.971 6 126 - - 5Berthierite FeSb2S4-61176 58.560 91.261 6 46.590 0.2630 -3.860Kermesite Sb2S2O 8 8 8 66.282 6Notes 1. Zotov et al. (2003); 2. Xiong(2003); 3. Mills (1974);4. Robie (1967); 5. WilliamsJones and Normand (1997); 6. Calculated from cell parameters. See text; 7. Bonazzi et al. (1987); 8.Thermodynamic properties for kermesite require calculation based on data reported byWilliams Jones and Normand (1997), which was in turn based on earlier work by Babcan (1976) and usedin this study to calculate Log K(kermesite) directly.C24 of 32 

# C5. Iron

C5.1 Revision of Fe 2+ 

## and Fe 3+

Uncertainties relating to the thermodynamic properti es of the ferrous and ferric ions in aqueous solution have bedeviled precise modeling of ge ochemical systems involving these species for some time, e.g. see Nordstrom (1984) and Re ardon and Beckie (1987). Recent work on the solubility of siderite (Preis and Gamsjager, 2002) provides support for the validity of the properties of Fe 2+ in the survey by Parker and Khodakovski i (1995). We therefore incorporated revised properties of Fe 2+ and Fe 3+ in SUPCRT92 and recalculated the solubility products of all participating minerals whose thermodynamic prope rties were derived from calorimetry. These included chlorite, daphnite, pyrite, goethite and hematite, as well as appropriate corrections to the solubility products of siderite and ankerite.

Calculations for generating th e HKF EoS parameters for Fe 2+ and Fe 3+ were conducted with the aid of an Excel spreadsheet. In order to indepe ndently check the correctn ess of the transcribed equations, calculations were rep eated for both species using the C P r , T

r 0 and S P r , T

r 0 cited by Shock et al. (1997). The derived values for the Born coefficient, ω P

r , T

r , and c 1 , which depends on the value of , did not match closely with those cite d by Shock et al. (1997). Agreement was obtained only if r e , j , P r , T

r for each species was assumed to have a discrete value of 2.64 and 3.56 A o

, respectively. Furthermore, instead of using

α z = 71.5 Z j ,

the original intercepts were used for the regressed data for Δ S n , j o

− Z j Δ S

s , H + abs o (cal mol -1 K -1 ) for aqueous species with Z j = 1, 2 or 3 against Z j 2 r e , j ( A o ), where α z = 72, 141 and 211 cal mol -1 K 1 , respectively. After appropriate substitution, close agreement was obtained, as shown in Table

C13.C25 of 32 Table C13. Comparison of HKF EoS Parameters for Fe 2+ and Fe 3+

Shock et al. (1997) This Work Parameter Fe 2+ Fe 3+ Fe 2+ Fe 3+

c 1 , cal mol -1 K -1 14.7860 19.0459 14.7863 19.0462

c 2 , cal K mol -1 x 10 -4 -4.6437 -6.8233 -4.6438 -6.8234

ω

P r , T

r , cal mol -1 x 10 -5 1.4382 2.5812 1.4382 2.5812

In the present study, the calculated values for r e , j , P r , T

r given in Shock and Helgeson (1988) were used in the calculation of ω P

r , T

r and c1. The a1 through a4 HKF equation of state parameters for Fe2+ and Fe3+ (Shock et al., 1997) were re tained. The thermodynamic properties for both species are summarized in Table C14.

Table C14. Thermodynamic Parameters for Fe 2+ and Fe 3+

Parameter Fe 2+ Fe 3+

Δ G f
, P r , T

r 0 , cal mol -1 -21637 -3891

Δ H f
, P r , T

r 0 , cal mol -1 -21511 -11711
S

P r , T

r 0 , cal mol -1 K -1 -24.283 -66.539

C P

r , T

r 0 , cal mol -1 K -1 -7.37 -18.85

V o , cm 3 mol -1 -22.2 -37.0

a 1 , cal mol -1 xbar -1 -0.7867 -2.4256

a 2 , cal mol -1 -9.6869 -13.6961

a 3 , cal K mol -1 xbar -1 9.5479 11.1141

a 4 , cal K mol -1 -2.3780 -2.2127

c 1 , cal mol -1 K -1 15.2738 20.0173

c 2 , cal K mol -1 x 10 -4 -4.5359 -6.8743

ω

P r , T

r , cal mol -1 x 10 -5 1.4574 2.6358



## C5.2 Iron Minerals
Iron minerals are a particularly important factor in modelin g the behavior of hazardous constituents in shallow groundwaters. Not on ly do they participate in buffering the redox potential of the system, but also by virtue of their adsorptive properties and their commonly very large specific surface areas (i.e., very fine crysta llinity), they can be a significant reservoir of
adsorbed hazardous constituents. Ir on minerals of interest to the problem at hand consist of three broad classes: (1) Ferric/ferrous oxides and oxy-hydroxides, (2) si licates containing ferrous iron, particularly phyllites, and (3) sulfides. The r ecent literature was reviewed, and crystallographicC26 of 32 and thermodynamic data compiled, for a number of pertinent minerals, and the resulting
information is summarized in Tables C15 and C16.

Because the point of zero charge (PZC) of ferric oxides/hydroxides li es in the pH range of circa 8.5, these minerals are generally positively charged over the pH range of the majority of shallow potable groundwaters, and would remain so upon intrusion of high-pressure CO 2 . The net positive charge attracts and adsorbs anionic species from solution. They are therefore likely to adsorb oxy-anionic species of As(III), As(V), Sb(III), Sb(V) and Se(II), Se(IV) and Se(VI). Other potential adsorbents incl ude negatively charged sulfide a nd selenide complexes of heavy metals (Appendix D and Appendix E), although it is qui te likely that sulfide and selenide species could be destabilized through oxi dation of S(-II) and Se(-II) to higher oxidation states, depending on the overall equilibrium state.

Two of the minerals listed are of particular inte rest: ferrihydrite and gr een rust. Ferrihydrite, a nanocrystalline or amorphous hydroxide, tr aditionally given the formula, Fe(OH) 3 , is commonly used in laboratory studies to qua ntify adsorption of charged or molecular species from solution, e.g., see the classic text on surface complexa tion modeling by Dzomback and Morel (1990). Until very recently, the structure of ferrihydr ite was unknown. Indeed, it was commonly believed that ferrihydrite might consist of two se parate phases (on the basis of X-ray diffraction studies) and thus referred to as “2-line” and “6-line” ferrihydrite. All this change d in 2007, when Michel and his co-workers (M ichel et al., 2007a, 2007b; Penn, 2007), using advanced X-ray diffraction techniques and sophistic ated methods of interpretation, were able to resolve the structure unambiguously as being is ostructural with akdalaite, Al 10 O 14 (OH) 2 , more commonly known as tohdite (Shpanov et al ., 1970; Tilley and Eggleton, 1994). With ferrihydrite now known to possess the stoichiometry, Al 10 O 14 (OH) 2 , calorimetric data for a series of ferrihydrites with differing specific surface areas and quantities of adsorbed water, (Majzlan et al., 2004) could be re-evaluated, and the enthalpy of formation, Δ H f
, P r , T

r 0 , for coarsely crystalline ferrihydrite calculated. Figure C3 shows a plot of measured Δ H f
, P r , T

r 0 , vs. excess water, extrapolated to zero excess water, the value at th e intersection of the regression trendline with the y-axis being Δ H f
, P r , T

r 0 reported in Table C16.C27 of 32 y = -69.081x - 1017.4 R 2 = 0.9993

-2000 -1800 -1600 -1400 -1200 -1000

-800

-4 -2 0 2 4 6 8 10 12 14
Excess Water

Fig. C3. Correlation of calorimetrically dete rmined enthalpy of formation of ferrihydrite samples in relation to their content of excess water. Data from Majzlan et al. (2004)

Green rust is a mineral consisting of stacked la yers separated by coordina ted water molecules of Fe(II) tri-octahedral sheets, one of the basic building bloc ks of phyllosilicates or clay minerals.
The octahedral sheet can be e ither of the gibbsite type, Al 2 (OH) 6 , or of the brucite type Mg 3 (OH) 6 , representing di- and tri-octahedral sheets, respectively. Partial in situ oxidation of the Fe(II) to Fe(III) in a tri-octahedral sheet causes the octahedral sheet to develop a net positive charge, which is satisfied through the uptake of an ionic species in the in ter-layer sites. The charge-balancing anion is commonly SO4-2, but any of the oxy-anionic species of As(III), As(V), Sb(III), Sb(V), Se(II), Se(IV) and Se(VI), as well as HCO3- or CO3-2 can also occupy sites. Because of their structural similarity to clays, such phases are sometimes referred to as “anionic” clays in distinction to regular “cationic” clays. In Tabl es C15 and C16, the properties of a typical idealized anionic clay , green rust, is given, where the stoichiometry of the waters of hydration were adjusted to fully sa turate the interlayer positions.C28 of 32Table C15.Crystallographic Properties of Iron Minerals, excluding those containing ArsenicCell ConstantsName Formulaao(Å) bo(Å) co(Å)αβγZSpaceGroupReferenceGoethite FeO(OH)4.5979 9.9510 3.0178 90 90 90 4 Pbnm Yang et al. (2006)Lepidocrocite FeO(OH)12.4 3.87 3.06 90 90 90 4 Bbmm Wyckoff (1963)Hematite2 Fe2O35.038 13.772 6 R-3c Blake et al. (1966)Maghemite Fe2O38.33 90 90 90 12 Fd3m Pecharroman et al. (1995)E-Fe2O3Fe2O35.095 8.789 9.437 90 90 90 8 Pna21Tronc et al. (1998)Marcasite FeS24.4446 5.4246 3.3864 90 90 90 2 Pnnm Rieder et al. (2007)Pyrite FeS25.4166 90 90 90 4 Pa3 Bayliss (1977)Ferrihydrite Fe10O14(OH)2~5.95 ~9.06 90 90 90 1 P63mc Michel et al. (2007)Mackinawite FeS3.6735 5.0328 90 90 90 2 P4/nmm Lennie et al. (1995)Greigite Fe3S49.876 90 90 90 8 Fd3m Skinner et al. (1964)Schwertmannite Fe(III)16O16(OH)12(SO4)210.66(4) 6.04(1) 90 90 90 1 P4/m (?) Bigham et al. (1994)Green Rust Fe(II)4Fe(III)2(OH)(SO4).8H2O5.524 11.011 0.5P3m1Simon et al. (2003)Table C16.Thermodynamic Properties of Iron Phases (Excluding those containing Arsenic and Carbon)CPr0cal mol-1K-1Mineral name FormulaΔGf,Pr,Tr0cal mol-1RefΔHf,Pr,Tr0cal mol-1RefSPr,Tr0cal mol-1K1RefVPr,Tr0cm3mol-1Refab×103c×10−5RefGoethite FeO(OH) -117065 5 14.269 4 19.866 9 0.289 55.736 0.750 4Lepidocrocite FeO(OH) -114747 5 15.559 4 22.108 10 14.283 14.465 -1.847 4Hematite Fe2O3178155 20 -197720 20.940 20 30.384 11 23.490 18.600 -3.550 20Maghemite Fe2O3-173972 5 22.228 4 29.007 12 25.526 15.557 -4.508 4E-Fe2O3Fe2O3-171558 6 -190727 6 22.228 6 31.811 13 25.526 15.557 -4.508 21Marcasite FeS2-37859 23 -40511 23 12.882 23 24.584 26 17.880 1.320 -3.050 25Pyrite FeS2-38293 20 -41000 20 12.650 20 23.926 14 17.880 1.320 -3.050 20Ferrihydrite Fe10O14(OH)2-799730 6,7 -1017400 101.800 3 167.280 15 124.560 93.000 -17.800 2Mackinawite FeS -22300 22 -21900 24 15.4 24 20.450 16 12.000 - - 24FeS,am FeS -21300 22 -20900 24 15.4 24 12.000 - - 24Greigite Fe3S4-69400 22 -70300 24 47.1 24 72.511 17 42.000 - - 24Schwertmannite Fe(III)16O16(OH)12(SO4)2.12H2O -2661124 6,8 - 254.950 3 413.335 18 395.855 136.399 -26.000 2Green Rust Fe(II)4Fe(III)2(OH)(SO4).8H2O -1330280 1 - 192.870 3 350.466 19 220.065 20.6806 -5.370 2Notes 1. Hansen et al. (1994), modified to account for 8H2O of water; 2. Calculated using the summation of oxide components; 3. Calculated using the procedure described by Holland (1989). Thiswork. 4. Majzlan et al. (2003a); 5. Majzlan et al. (2003b); 6.Majzlan et al. (2004); 7. Data reinterpreted. See text; 8.Modified to account for 12H2O of water; 9. Yang et al. (2006); 10.Wyckoff (1963); 11. Blake et al. (1966); 12. Pecharroman et al.(1995); 13. Tronc et al. (1998); 14. Bayliss (1977); 15. Michel et al. (2007); 16. Lennie et al. (1995); 17. Skinner et al. (1964);18. Bigham et al. (1994); 19. Simon et al. (2003); 20. As reported by Helgeson et al. (1978); 21. Assumed the same as for maghemite, in conformity with assumption forSPr,Tr0by Majzlan et al.(2004); 22. Berner (1967); 23. Robie and Hemingway (1995); 24.Estimates provided by Anderko and Shuler (1998); 25. Assumedto be equivalent to pyrite; 26. Rieder et al. (2007)C29 of 32 

# C6. Carbonates

An essential requirement for reactiv e transport simulations involving CO 2 sequestration is the need for accurate thermodynamic data of partic ipating carbonate phases. Recent studies have revealed respectively the possible existence of s ubstantial errors in the Gibbs free energy of formation of magnesite and dolomite (Rock et al., 2001). Furthermore, the experimental consistency of siderite solubility data (Preis and Gamsjager, 2002) has b een recently established, and by inference, validated the thermodynamic data for ferrous and ferric ions recommended by Parker and Khodakovskii (1995). These modificati ons also require a re determination of the thermodynamic properties of the ankerite solid so lution utilized in earlie r work (Xu et al., 2003).

The thermodynamic properties of dolomite and magne site were calculated, using the Gibbs free energy values determined by Rock et al. (2001), together with the respective entropies provided by Robie and Hemingway (1995). These, together with entropy data for end-member ankerite, CaFe(CO3) 2 , from Holland and Powell (1998), and the pr operties of siderite from Preis and Gamsjager (2002) as well as calcite from the SU PCRT92 database, were used to calculate the properties of end-member ankerite and the ankerite ideal binary solid solution with the limiting composition: CaMg 0.3 Fe 0.7 CO 3 ) 2 , using the lattice energy calcul ations reported by Chai and Navrotsky (1996). The thermodynamic data for carbona tes, either calculated as described above
or utilized in the present study, are summa rized in Table C17.C30 of 32Table C17.Thermodynamic Properties of Selected Carbonate MineralsCPr0cal mol-1K-1MineralnameFormulaΔGf,Pr,Tr0cal mol-1RefΔHf,Pr,Tr0cal mol-1RefSPr,Tr0cal mol-1K-1RefVPr,Tr0cm3mol-1Refab×103c×10−5RefCalcite CaCO3-2698801,2-2885521,222.150136.934124.980 5.240 -6.200 1Magnesite MgCO3-2407914-2608166 15.557 328.018119.731 12.539 -4.748 1Siderite FeCO3-1622615-179732522.818529.378111.630 26.800 0.000 1Dolomite CaMg(CO3)2-5133414-552250637.089364.365141.557 23.952 -9.884 1Ankerite Ca(Mg0.3Fe0.7)(CO3)2-4575536-494243643.627665.594646.070 18.011 -10.437 6Notes 1. Helgeson et al. (1978); 2.Helgeson (1990), Gibbs free energies calculated from solubility data reported by Plummerand Busenberg (1982) [Enthalpies adjusted for the correction in theGibbs free energy]; 3. Robie and Hemingway (1995); 4. Rock et al. (2001); 5. Preis and Gamsjager (2002); 6. This work. See text.C31 of 32 

# C7. Chlorite

In prior simulations (Xu et al., 2002), chlorite was represented as a 1:1 mixture of clinochlore and daphnite. The solubility products used were those derived originally from the thermodynamic evaluation of phase equilibria by Helgeson et al. (1987). More recent and comprehensive evaluations, e.g., those conducted by Holland and Powell (1998), indicate that a revision in the solubility products of clinochlor e and daphnite is called for. Although clinochlore and daphnite are only minor components in most sedimentary arenaceous rocks, a revision is justified because they compete with secondary carbonates for magnesium and ferrous iron, and they can buffer the redox potential when coexisting with pyrite.

The calculated thermodynamic properties for clin ochlore and daphnite we re taken from Holland and Powell (1998). The thermodynami c properties of chlorite, Mg 2.5 Fe 2.5 Al 2 [Si 3 O 10 ](OH) 8 , were calculated assuming ideal solid solution betwee n the end members. These properties were
entered into the SUPCRT92 database , and the chlorite solubility products were calculated. The thermodynamic data used in the curr ent study are given in Table C18.

Table C18. Thermodynamic Parameters for Chlorite Minerals

Parameter Units Clinochlore Mg 5 Al 2 [Si 3 O 10 ](OH) 8 Ref. Daphnite Fe 5 Al 2 [Si 3 O 10 ](OH) 8 Ref. Chlorite Mg 2.5 Fe 2.5 Al 2 [Si 3 O 10 ](OH) 8 Ref.

Δ G f
, P r , T

r 0 cal mol -1 -1975051 4 -1562107 4 -1768992 4
Δ

H

f
, P

r , T

r 0 cal mol -1 -2134288 1 -1709845 1 -1922066 4
S P

r , T

r 0 , cal mol -1 K -1 98.112 1 130.258 1 115.562 4

V

P r , T

r 0 , cm 3 mol -1 207.110 1 213.420 1 210.260 4

C

P r 0 Coefficients

a 166.500 2 176.210 2 171.355 3

b × 10 3 42.100 2 43.760 2 42.930 3

c × 10 − 5 cal mol -1 K -1 -37.470 2 -33.820 2 -35.645 3

Notes 1. Holland and Powell (1998); 2. Helgeson et al. ( 1978); 3. Interpolated between c linochlore and daphnite; 4. Thi s work.


# C8. Miscellaneous

The mineral hemimorphite proved to be an important thermodynamic control on the
concentration of dissolved zinc in oxidized groundwaters, and mi ght also control in part theC32 of 32 concentration in reducing gr oundwaters containing sphalerite. Thermodynamic data are extremely limited, essentially confined to preliminary study by McPhail et al. (2006), who measured the solubility at 50 and 80oC and co mputed the solubility product at the same temperatures. In Figure C4, a linear extrapolatio n of Log K(hemimorphite) is made as a function of reciprocal temperature to 25oC. The resultin g value, Log K = 23.981, incorporated in the EQ3/6 and TOUGHREACT thermodynamic databases.

y = 7.6212x - 1.5817

19.5

20 20.5

21 21.5

22 22.5

23 23.5

24 24.5

2.8 2.9 3 3.1 3.2 3.3 3.4 1/ T x 10 0 0 ( K^- 1)LogK (hemimophite)

Fig. C4. A linear extrapolation of the solubility product of hemi morphite to 25oC with reciprocal temperature, 1/K. Data from McPhail et al. (2006).D1 of 22 

# Appendix D: Solubilities of Sulfides of Cadmium, Mercury, Lead and Zinc at 25°C

D1. Background

The present study requires the calculation of the solubilities of minera ls containing hazardous inorganic constituents as essential components under conditions that might occur in shallow potable water aquifers. The majority of th e more than 38,000 groundwaters downloaded from
the NWIS database and evaluated during the course of the presen t investigation appear to be reducing, and it is probable that pyrite or some other iron sulfide is present. Under such conditions, log P H2S (bar) ranges from approximately –8 to –10, and among the minerals potentially controlling the solubility of Zn, Cd , Hg and Pb in solutio n are the corresponding sulfides sphalerite, greenockite, cinnabar and galena . The divalent cations of these four elements can also complex with sulfide species (HS - and H 2 S) and selenide species (HSe - and H 2 Se) in solution, potentially enhancing both sulfide and selenide mineral solubilities.

Among the sulfide and presumably selenide comp lexes in solution at equilibrium with the respective sulfides and seleni de minerals are a series of neutral species of undefined stoichiometry, but circumstantial evidence suggests that they may be oligomers possessing
formulas Me x S x , where x = 1,2,3,4... Experimental evidence al so suggests that these species are predominant under equilibrium conditions with the me tal sulfide mineral, and thus must be taken into account if thermochemical data pertaining to sulfide mineral equilibria with respect to the aqueous phase are to be reconcile d and mineral sulfide equilibria are to be modeled realistically. For this reason, both the identities of sulfide complexes of the ca tions of and their respective dissociation constants needed to be incorporat ed in the thermodynamic databases of both EQ3/6 and TOUGHREACT codes. Without such data, si mulations of reactive chemical transport of these metals in the presence of elevated P CO2 would not be representative.

In this appendix, we review the issues concer ning identification of the relevant sulfide complexes, and how their dissociation constants, K diss , relate to the solubility products, K s , of theD2 of 22 corresponding sulfide minerals, us ing galena (PbS) as an exampl e. In the companion Appendix E, we review issues relating to the corresponding selenide comp lexes. Then, on the basis of a literature review, we choose and desc ribe internally consis tent data sets of sulfide and selenide species dissociation constants and associated sol ubility products for inco rporation in the EQ3/6
and TOUGHREACT databases.

The procedure followed was first to gene rate a correlation plot for Log K(MeS) s and Log K(MeS) aq for sulfide species (Me = metal). A correlation plot was also generated comparing solid phase sulfide and selenide species, together with Hg sulfide and selenide species (Appendix E). The latter plot then allowed prediction of unknown neutral and char ged selenide complexes of the relevant heavy metals based upon the iden tities and literature valu es of aqueous sulfide complexes of Cd, Hg Pb and Zn, and neutral sulfide complexes estimated from the first correlation plot. The resulting data were then incorporated in the EQ3/6 database and EQ3 run using a representative groundwater composition, based on the distribution of heavy metal concentrations observed in NWIS groundwaters, taking into account possible analytical artifacts. Alternative hypotheses were tested until a satisfactory reconcilia tion of all parameters was achieved, and the calculated saturation indices fo r those heavy metal sulfides and selenides expected to be present, based on observed modes of occurrence, fell close to 0, i.e., within ± 2 units. The interdependence of all parameters tightly restricted the ranges over which each parameter could be varied independently. Ther efore, although the results presented in this appendix and the following Appendix E leave room for refinement, it is expected that such refinements would not alter substantially the findings presented in this report.



# D2. Issues

In both the EQ3/6 and TOUGHREACT codes, mine ral solubility is defined in terms of a dissolution equation relating the mi neral to the “basis” species in solution, e.g., for galena, the
reaction is:

PbS + H + = Pb +2 + HS - (D1)

where Pb +2 and HS - are the basis species in this case, a nd the solubility product is defined asD3 of 22 K
s = Pb + 2 

# [ ] HS − 

# [ ]
PbS 

## [ ] H + 

# [ ] (D2)

with [ ] denoting thermodynamic ac tivities. The activity of the so lid phase is unity for a pure substance. In addition, knowledge of the potential complexes of the basis species in solution is required. In the example given, th ese complexes modify the total aqueous concentrations of both
Pb +2 and HS - . Elsewhere in this report (Section 3.5), we show that apart from sulfide and selenide complexes, other signi ficant aqueous species affecting the saturation concentration of Pb in shallow potable aquifers include PbCO 3 (aq), PbOH + , Pb(CO 3 ) 2 -2 and PbCl + in addition to
Pb +2 . The corresponding dissociation constants for these complexes ar e also defined in terms of the basis species. At higher sulfide concentrat ions, not normally encountered in potable water aquifers, significant quantities of sulfide complexes with Pb +2 might also form, e.g., Pb(HS) 2 (aq) and Pb(HS) 3 - , as shown by Hemley (1953) and Giordano and Barnes (1979). It is under such conditions that experiments are sometimes c onducted, either to measure mineral sulfide solubilities, or to characteriz e the dissociation constants of relevant sulfide complexes in solution, where the saturated mineral is used as a thermodynamic control.

The solubility product, defined by Eqn. (D2) can also be calculated from independent knowledge of the Gibbs free energies, Δ G f , P r , T

r 0

, of the participating species, ju st as the measured solubility

can be used to calculate Δ G f , P r , T

r 0 of the solid phase. Δ G f , P r , T

r 0 of the solid phase or mineral can be obtained from calorimetric, thermo- or electro-chemical, or phase equilibrium studies. For example, Δ G f , P r , T

r 0 ( galena) has been determined from electrochemical (galvanic cell) measurements (Kiukkola and Wagner, 1957), ther mochemical measurements (Stubbles and Birchenall, 1959), and by high-te mperature oxide-melt solution calorimetry (Deore and Navrotsky, 2006). In Table D1, the solubility products of four sulfide phases are cross-compared with the corresponding values of Δ G f , P r , T

r 0 derived from various property measurements.D4 of 22Table D1.Gibbs Free Energy of Formation and SolubilityProducts of Selected Trace Metal SulfidesZnS (sphalerite) CdS (greenockite)HgS (cinnabar) PbS (galena)Source NotesΔGf,Pr,Tr0kcal mol-1logKsat 25°CΔGf,Pr,Tr0kcal mol-1logKsat 25°CΔGf,Pr,Tr0kcal mol-1logKsat 25°CΔGf,Pr,Tr0kcal mol-1logKsat 25°CRobie andHemingway (1995)-47.71±0.36-11.27±0.26-34.92±0.31-14.09±0.23-9.73±0.65-38.08±0.48 -23.14±0.48-14.87±0.35Deore andNavrotsky (2006)-48.29±0.96-11.69±0.70-34.73±0.99-13.95±0.73-23.09±1.04-14.84±0.76Dyrssen andKremling (1990)-47.25±0.93-44.55±0.44-10.93±0.68-8.95±0.32(wurtzite)-33.97±0.93 -13.39±0.68-10.85±0.55-11.67±0.55-38.9±0.4(black)-39.5±0.4(red)-21.91±0.78-13.97±0.57Daskalakis andHelz (1993)-48.005-11.482Shea and Helz(1989)-20.29-12.78Daskalakis andHelz (1992)-33.93±0.35-13.36±0.26Paquette and Helz(1995)-7.85-36.7Notes:1.Bold face values are given or deriveddirectly from the source reference.2.logKsis in reference to the dissolution reaction: MS(s) + H+= M+2+ HS-. Where M is Zn, Cd, Hg or Pb.3.ΔGf,Pr,Tr0values referenced to Deore and Navrotsky (2006) were calculated fromΔHf,Pr,Tr0assuming thatTΔSf,Pr0corresponds to that assumed by Robieand Hemingway (1995)4.Uncertainties in derived values are calculated assuming no uncertainty in values of the aqueous species, and are thereforeminimum values.5.ΔGf,Pr,Tr0values given by Robie and Hemingway (1995) are from Schaefer and Gokcen (1982) for sphalerite; Adami and King (1964) for greenockite;Pankratz and King (1987) for cinnabar; Stubbles and Birchenall (1959) and Kiukkola and Wagner (1957) for galena.D5 of 22 Most Δ G f , P r , T

r 0 values calculated from solubility measurements in Table D1 are less negative than those determined using other methods. However, the thermochemical determinations summarized by Robie and Hemingway (1995) agr ee with the calorimetric determinations by Deore and Navrotsky (2006) within experimental error. Thus, it appears that solubility measurements may not adequately describe th e speciation or thermodynamic behavior of the system. Tossell (1999), in his paper advocating ab initio quantum mechanical methods to calculate the stabilities of Hg complexes in solution, summarized the serious limitations of corresponding experimental methods:

(1) “The speciation models are often ambiguous, with different researchers obtaining apparently different sets of species and formation constants.” (2) “... chemical components[,] whose activitie s or concentrations cannot be varied experimentally[,] are indeterminable, e.g., the pa rticipation of water in such species cannot be determined in aqueous solution. Spectral st udies could, in principle, determine which species were present, as well as their concentrations if relative spectral intensities for the different species could be asse ssed. However, in many cases th e species concentrations are so low that no usable sp ectra can be obtained.”

Tossell, in particular, singled out Hg sulfide specia tion as a case in point, but such problems also extend to the other metal sulfide species consider ed in this appendix. In a more recent review, Rickard and Luther (2006) reitera te the limitations of current e xperimental methods, noting that “One of the astonishing things the uninitiated reader will discover... is the large number of sulfide complexes that have b een proposed and even modeled w ith little or no evidence to support their existence in the first place.” Given the current uncer tainties in the literature, it is prudent to regard the following analysis with cauti on, even though the re sults presented are sufficiently robust to allow their use in the eval uation of thermodynamic c ontrols on heavy metal solubilities in pot able groundwaters.

Explanations for the mismatch between the calc ulated and measured a queous solubilities of chalcogenides in general has been accommodated by postulating equilibrium with a neutral species in solution, possessing th e same stoichiometry as that of the solid phase, thus:

MeS(s) = MeS(aq) (D3)D6 of 22 where [MeS(aq)]/[MeS(s)] = K int , the intrinsic solubility pro duct (Dyrssen, 1985, 1988; Dyrssen and Wedborg, 1989, 1991; Benoit et al., 1999; Rickard, 2006). The precise nature of the complex is unknown. Dyrssen and Wedborg (1991) argue convincingly that it could not be a monomer. They point out that HgS(aq) is equi valent to the hydrated species, Hg(OH)(SH)(aq), and one cannot be distinguished from the other using conventional speciation studies (Tossell, 1999; vide supra ), i.e.,

HgS(aq) + H 2 O = Hg(OH)(SH)(aq); Log K is undetermined. (D4)

Although the two stoichiometries cannot be distinguished in solubility studies, it is reasonable to assume that the complex is composed of (OH) - and SH - rather than S -2 and H 2 O. Dyrssen and Wedborg (1991) showed, using correspondence plots for Hg stability cons tants, and theory relating to calculation of mixed ligand complexes (Anfalt et al ., 1968), that the calculated dissociation constant for the latter species is

Hg(OH)(SH)(aq) + H + = HgSH + + H 2 O; Log K = +3.4. (D5)

This dissociation constant is comparable w ith those of other hydrol ytic reactions, e.g.,

Hg(OH)2(aq) + H + = HgOH + + H 2 O; Log K = +2.6 (D6)

HgCl(OH)(aq) + H + = HgCl + + H 2 O; Log K = +3.1 (D7)

However, the measured dissociation constant for

HgS(aq) + H + = HgSH + ; Log K ≈ -8.9 (D8)

differs by approximately 12 log units. Similar incompatibilities were also recognized in computing the intrinsic solubility products for ZnS and CdS, based respectively on the work by Gubeli and Ste-Marie (1967) a nd by Ste-Marie et al. (1964).

More recently, Rickard (2006), in studying the solubi lity of mackinawite (F eS), also identified and determined experimentally the intrin sic solubility product for this mineral

FeS(mackinawite) = FeS(aq) (D9)D7 of 22 He concluded that the FeS(aq) complex was an oligomer, citing earlier electrochemical studies by Buffle et al. (1988) and Theberge and Luth er (1997) of FeS(aq) in equilibrium with amorphous FeS. The exact stoichiometry of FeS( aq) is uncertain. Theberge and Luther (1997) concluded that polymerization oc curred as a two-step process, in which the octahedrally coordinated hex-aquo Fe +2 ion reacted with HS - , thus,

Fe(H 2 O) 6 +2 + HS - = Fe(H 2 O) 5 (HS) + + H 2 O (D10)

followed by polymerization to form a trimer

3Fe(H 2 O) 5 (HS) + + 3HS - = Fe 3 S 3 (H 2 O) 6 (aq) + 3H 2 S + 9H 2 O (D11)

Implicit in this reaction is a tr ansformation from octahedral to tetrahedral coordination in the ferrous ion, and formation of a closed six-memb er ring representing a ba sic building block in both cubic FeS and in mackinawite (where Fe(II) is tetrahedrally coordinated in both). However, it should be noted that the probability of the simultaneous reaction of si x aqueous entities is extremely unlikely. Therefore Eqn. D11 must be considered merely a summary reaction that incorporates a series of intermediate steps, incl uding the formation of a precursor dimer, and that it is probable that equilibria be tween the postulated trimer and its precursors is established at saturation with respect to the solid phase.

A further comparison can be made with sulfide complexes of As and Sb, which form ionizing oligomeric complexes H 3 As 3 S 6 (aq) and H 2 Sb 2 S 4 (aq), respectively. These complexes apparently form to the exclusion of simpler monomeric spec ies. Although both As and Sb are present in these complexes in the (III) stat e, a parallelism with the fo rmation of postulated analogous oligomers with divalent (II) metals is suggested by the limited experiment al evidence regarding
the formation of Fe 3 S 3 (H 2 O) 6 (aq).

Similar arguments could be advanc ed in defining polymer formation with respect to Zn and Cd sulfide complexes, where both Zn and Cd are te trahedrally coordinated in both isostructural polymorphs, i.e., cubic sphalerite and hawley ite, and hexagonal wurtz ite and greenockite, respectively. Hg(II) is tetrahedrally coordinate d in cubic met cinnabar, but in cinnabar it is octahedrally coordinated. A si milar coordination change is al so noted in the sulfides of manganese (MnS). For example, in alabandite, Mn is octahedrally coordinated, whereas inD8 of 22 ramsbergite, it is tetrahedrally coordinated in an anti-wurtzite structure. Thus, it is unclear whether Hg(II) aqueous sulfide complexes are oc tahedrally or tetrahedrally coordinated. Because metacinnabar is less stable than cinnabar, it is more likely that Hg(II) is octahedrally coordinated in sulfide complexes. With octahedral coordination, as also occurs with respect to Pb in galena, it would be logical to infe r that the aqueous complexes are dimers,
Me 2 S 2 (H 2 O) x (aq), or tetramers, Me 4 S 4 (H 2 O) y (aq) (where Me is either Hg or Pb), forming single or double four-member rings respectively. Howeve r, Rozen et al. (2003), on the basis of mass spectrometry of freeze dried aqueous samples, i ndicated that the species had the formula Pb 3 S 3 .



# D3. Estimation of the Dissociation Constants of Neutral Sulfide Complexes

If a comparison is made between the sulfide solu bility products and the dissociation constants of the neutral oligomeric sulfide complexes for the four metals for which data are available (Fe(II), Zn(II), Cd(II) and Hg(II)), conventionally written as

MeS(s) + H + = Me +2 + HS - (D12)

and

1/x[Me x S x (aq) + xH + = xMe +2 + xHS - ]

i.e.,

MeS(aq) + H + = Me +2 + HS - (D13)

where x is probably 2, 3 or 4 (depending on the nature of the metal ligand) , a surprisingly good correlation between the two is obtained, as show n by regression trendline (a) in Figure D1.D9 of 22 Table D2. A Comparison between the Solubility Product of a Sulfide Mineral, and the Apparent Dissociation Constant of the Corresponding Neutral Species in Solution at 25°C
Mineral Composition Log K (MeS) s Source Log K (MeS )aq Source

Mackinawite FeS -3.50 1 +2.20 1 Sphalerite ZnS -11.27 2 -5.63 3
Greenockite CdS -14.09 2 -7.24 3 Cinnabar HgS -38.08 2 -28.08 4
Galena PbS -14.87 2 -8.06 5

Notes: 1. Rickard (2006) 2. Derived from thermochemical data provided by Robie and Hemingway (1995) 3. Dyrssen and Wedborg (1991) from data reported by Gubeli and Ste-Marie (1967) and Ste-Marie et al. (1964) 4. Dyrssen and Wedborg (1989) 5. This work.

However, despite the impressive correlation between Log K(MeS) s and Log K(MeS) aq , incorporation of the dissociat ion constants of neutral sulf ide and corresponding selenide complexes in the database did not satisfactorily resolve the unrealistic predictions of sulfide and
selenide mineral saturation indi ces in NWIS potable waters.D10 of 22 Fig. D1. Correlation between the solubility product of a sulfide mineral, and the apparent dissociation constant of the corresponding neutral species in solution at 25°C.

An alternative approach at estimating the correl ation between the solid a nd aqueous sulfides and selenide species was then examined. Dyr ssen (1988), citing earlier work (Dyrssen, 1985), attempted to correlate the stability constants for sulfide complexes of a range of heavy and
transition metals with the corres ponding aqueous extraction constants, K
e x , for dithizone
dissolved in carbon tetrachloride:

Me n+ + n H 2 Dz(org) = Me(Hdz) n (org) + n H + (D14)

He used studies by Ste-Marie et al. (1964) and Gubeli and St e-Marie (1967) on Cd and Zn sulfide complexes, respectively, to calibrate the correlation equation:

log K ( M e S ) aq = − 0.95log K
ex − A (D15)

where log K ( M e S ) aq represents Eq. (D13), and A = 3.02 . Table D3 summarizes the respective parameters cited in Dyrssen (1988, 1989) together w ith other data used in the current analysis. When the data given in Table D3 are plotted, both Pb and Zn deviated significantly from theD11 of 22 observed trend, although the data for Cd and Hg yielded satisfactory result s for both sulfides and selenides (greenockite and cadmoselite, and cinn abar and tiemannite, respectively). Arbitrary revisions of the values of log K ( M e S ) aq to –5.5 and –3.5 for Pb and Zn, respectively, not only

achieved better coherence with log K ( M e S ) aq values for other metals, but also yielded satisfactory saturation indices for galena, claustha lite, and sphalerite for the representative NWIS potable waters. The resulting regression trendline (b) is shown in Figure D1. For the purposes of this study, the log K ( M e S ) aq values for Cd, Pb and Zn summari zed in Table D3 with the noted corrections for Pb and Zn are provisionally ac cepted for use in the databases for EQ3/6 and TOUGHREACT, and applied for the prediction of neutral selenide complexes of Cd, Pb and Zn. With respect to Hg, the value for log K ( M e S ) aq is chosen that was used in the construction of regression trendline (a) in Figure D1. This value is based on the intrinsic solubility product for HgS estimated by Dyrssen and Wedborg (1991), log K int − 10 . This value gave somewhat better agreement with postulated saturation with respect to cinnabar in NWIS potable waters.

Table D3. Estimated Values of Neutral Sulfide Stability Constants, aq MeS K )

( , based on the Corresponding Dithizone Extraction Constants, ex K , compared with Sulfide Solubility Products, S MeS K )

(

Metal log K
ex 1 log K ( M e S ) aq 4 S MeS K )

( Mn(II) -5.4 +2.1 -0.02 Fe(II) -3.4 +0.2 -7.90 3 Ni(II) -0.8 -2.3 -9.50 3 Cu(II) 10.0 -12.5 -22.50 3 Zn(II) 2.0 -4.9 2 -11.27 Cd(II) 1.9 -4.8 -14.09 Hg(II) 26.8 -28.5 -38.08 Pb(II) 0.4 -3.4 2 -14.87
Notes: 1. As cited by Dyrssen (1988) from Irving (1977) 2. Adjusted to -3.5, -5.5 respectiv ely for Zn and Pb. See text. 3. Values cited by Dyrssen (1989). All other values for Log K(MeS), as given in Table D1. 4. As cited by Dyrssen (1989)

Despite the satisfactory agreement ac hieved through the correlation of log K ( M e S ) aq with log K
ex proposed and used by Dyrssen in several of his papers, agreement is poor with respect to FeS
from the more recent work of Ri ckard (2006). Furthermore, if log K ( M e S ) aq for Mn is correlated

with log K ( M eS )
s using Δ G f , P r , T

r 0 by Robie and Hemingway (1995), correlation of Mn(II) withD12 of 22 the rest of the data set is also poor. Arbitrary correction of log K ( M n S ) aq to account for the

relative difference between log K
ex for Fe(II) and Mn(II) does, however, restore a more restricted correlation, if the outlier for Hg is excluded and the values obt ained by Rickard (2006) replace those given by Dyrssen (1989), as indicated for re gression trendline (c) in Figure D1. Clearly, such speculations do little to improve the rigor of the analysis and are included merely to suggest further lines of inquiry.

In the following paragraphs, we briefly describe the data sets for sulfide complexes of Zn, Cd, Hg and Pb with reference to the compilations in Tables D1 and D2, and to selected speciation models for each metal in the published literature.


# D4. Greenockite (CdS)

The data for Δ G f , P r , T

r 0 for greenockite indicates ≈ 1.00 kcal/mol discrepancy between those values obtained by calorimetry or thermochemical me asurements, and those derived from solubility measurements. Davies-Colley et al . (1985), in comparing measured Cd +2 concentrations in relatively high p(HS - ) anaerobic estuarine sediments with those based on the assumed presence of greenockite precipitate, found that greenockite solubility was exceeded by about one log unit.
The basis for their calculations was Δ G f , P r , T

r 0 (greenockite) = -34.8 kcal/mol from an earlier publication by Robie et al (1978) , which is similar to that given by Robie and Hemingway (1995). The sulfide speciation used in their calculati ons also differs from that given by Daskalakis and Helz (1992). Da vies-Colley et al. (1985) attribut e the discrepancy to the possible presence of polysulfide complexes, or that the solubility-control ling sulfide was a poorly ordered CdS phase. Given the inherent un certainties in the interpretation of field data, and lack of knowledge as to the nature of immobile Cd +2 in the sediments, i.e., whether as a discrete solid phase, or in solid solu tion with sphalerite, or whether adsorbed on thiol groups on organic material, it is perhaps surprising that such good agreement was obtai ned. Table D4 presents the internally consistent data set for Cd sulfide complexes from the study by Daskalakis and Helz (1993). The Log K value for the dissociation of CdS(aq) is derived from data presented by Dyrssen and Wedborg (1991) as summarized in Ta ble D4, and the solubility product is based on thermochemical data given by Robie and Hemingway (1995).D13 of 22 Table D4. Complexes of HS- with Cd+2 after data by Daskal akis and Helz (1993), CdS(aq) after Dyrssen and Wedborg (1991) and Greenockite Solubility Product derived from Thermochemical Data given by Robie and Hemingway (1995), all at 25°C
Reaction Log K Cd(HS) 3 - = Cd +2 + 3 HS - -16.44 Cd(HS) 4 -2 = Cd +2 + 4 HS - -17.89 Cd(OH) 2 (HS) - + 2 H + = Cd +2 + 3 HS - + 2 H 2 O +2.47 CdS(aq) + H + = Cd +2 + HS - -4.8 CdS(greenockite) + H + = Cd +2 + HS - -14.09



# D5. Cinnabar (HgS)

Discrepancies in derived values of Δ G f , P r , T

r 0 (cinnabar) are substantial, ranging over almost 4 kcal/mol, and the corres ponding values of log K s show a range of approxi mately 3 units (Table D1). The uncertainty is aggravated by questi ons concerning the identification of Hg sulfide species in solution and the magnitude s of their dissociation constants, c.f. Tossell (1999). We
have identified and calcul ated the relevant dissociation consta nts using the results presented by Schwarzenbach and Widmer (1963) and modified by Paquette and Helz (1997) on the solubility
of red cinnabar in buffered 0.7 M KCl solution at 25°C. In orde r to retrieve the dissociation constants from their data, a sol ubility product for cinnabar must be assumed, and the first
dissociation constant for H 2 S(aq) specified. The solubi lity product derived from
Δ G f , P r , T

r 0 (cinnabar), given by Robie and Hemingw ay (1995) (Log K = -38.08), and the

dissociation constant for H 2 S(aq) specified by Bessinger (2000) (Log K = -6.980) were used in the calculation. Corrections to zero ionic strength standard state conditions were not applied, as activity coefficient corrections are self compensa ting for two of the thr ee species, and correction for the third would not be very accurate given the limited amount of available data. Results of the calculations are presented in Table D5.D14 of 22 Table D5. Complexes of HS- with Hg+2 after data by Schwartzenbach and Widmer (1963) and modified by Paquette and Helz (1997), HgS(aq) after Dyr ssen and Wedborg (1989) and Cinnabar Solubility Product derived from Thermochemical Data given by Robie and Hemingway (1995), all at 25°C
Reaction Log K Hg(HS) 2 (aq) = Hg +2 + 2 HS - -39.65 HgS(HS) - + H + = Hg +2 + 2 HS - -32.67 HgS 2 -2 + 2 H + = Hg +2 + 2 HS - -23.91 HgS(aq) + H + = Hg +2 + HS - -28.08 HgS(cinnabar) + H + = Hg +2 + HS - -38.08

Incorporation of the neutral HgS( aq) complex in the analysis is essential, as use of the cited solubility product for cinnabar and the dissociati on constants of the Hg sulfide aqueous species alone would predict unrealistically low Hg concen trations in groundwaters. This difficulty was also recognized by Benoit et al. (1999), who incorporated HgS(aq ) in their study of dissolved mercury behavior in sediments from the Florida Everglades and the Pat uxent River Estuary of Maryland. Our value for Log K = -28.08 can be compared with their value of –26.5 and their assumed Log solubility product of cinnabar of – 37.0 instead of –38.08 used here. Correction for the higher solubility product woul d yield a value of –27.6, which is in substantial agreement with our own value for this parameter.

It should be noted that Benoit et al. (1999) did not assume that dissolved mercury had at any time equilibrated with cinnaba r as a discrete solid ph ase in the sediments unde r study. Rather, they considered cinnabar as a potentia l solid solution component in a matrix of iron monosulfide or pyrite. Their treatment was analogous to their assumption that Hg +2 complexes with a “solid solution” of thiol groups on organic material. However, although saturation with respect to cinnabar was never observed, their model predicted quite well the variability of Hg concentration in sediment pore waters with dissolved sulfide concentration, and was independently correlated with expected methyl mercury speciation. Th eir model could therefore be considered a reasonable paradigm for testing the correlation of aqueous Hg concentrations with cinnabar saturation, even extrapolated to the much lowe r dissolved sulfide concen trations expected in potable groundwaters.D15 of 22 

# D6. Galena (PbS)

Issues relating to the solubility of galena in groundwaters are discussed at somewhat greater length than in previous sections for other sulf ide minerals, because of its relevance in this project. Reported values for the solubility prod uct of galena (PbS), as defined by the dissolution equation

PbS + H + = Pb +2 + HS - (D16)

vary over two orders of magnitude, as shown in Table D1. The value utilized by Wang and Jaffe (2004) in their simulations of galena dissoluti on in shallow potable water aquifers was taken from a database compiled for the MINTEQ code (Felmy et al., 1984; Hydrogeologic and Allison Geoscience Consultants, 1999), but also used by the PHREEQ-C code (Parkhurst and Appelo, 1999). This database (minteq.v4.dat 85 2005-02-02 16:13:00Z dlpark), gives a logK s value of
-13.97 at 25 ° C for the above reaction. The source of th is value can be traced to Dyrssen and Kremling (1990), who in a study of trace metal beha vior in anoxic Baltic waters attempted to derive Log K s values for a number of metal sulfides. The values presented are based on the product of two reaction constants, K 1 , and K sO , representing respectively the reactions:

H + + S 2- = HS - (D17)

and

M a S b (s) = aM n+ + bS 2- (D18)

where M represents the metal with charge +2 (n=2 , a=b=1) or +1 (n=1, a=2, b=1). Because of the high degree of uncertainty in K 2 (published values of LogK 1 vary between 12 and
18.51 ± 0.56), the authors utilized published values of K sO together with corresponding values for
K 1 from the original sources to calculate the product (K 1 K sO ) for the reaction:

M a S b (s) + bH + = aM n+ + bHS - (D19)D16 of 22 which for galena is represented by the reaction descri bed in Eq. (D1).

The PHREEQ-C code is also capable of usin g another database used by the WATEQ code (wateq4f.dat 431 2005-08-23 17:29:36Z dlpark ). This database gives a logK s = –12.780 at 25 ° C for the reaction in Eq. (D1), which is 1.2 log units higher than the value cited by Dyrssen and Kremling (1990). The cited value is a correction by Shea and Helz (1989) for Pb complexation by chloride ion based on results from an earlie r experimental study by Uhler and Helz (1984). The latter investigator s enhanced the solubil ity of galena through Pb +2 complexing with ethylene diamine acetic acid (EDTA) in a solution of di sodium EDTA and 0.1 M NaCl. The technique overcomes a serious difficulty in analyzing solution compositions where one or more of the principal chemical components ar e at or below the detection li mits of available analytical methods, yet introduces additional complex ity and uncertainty associated with the thermodynamic parameters needed to interpret the behavior of the chelating agents used in the study. The technique is also dependent on kn owledge of the identities and thermodynamic properties of lead sulfide complexes in solution. Finally, derivation of K s depends on appropriate ionic strength corrections to permit extrapolation to standard state conditions. Despite much progress in making such correcti ons, current techniques still leave room for improvement.

The EQ3/6 code (Wolery et al., 1992) used in th is study utilizes anothe r thermodynamic database data0.ymp.R4 (downloaded N.S. 3/3/05 DT N: SN0410T0510404.002), which was qualified for use under the Yucca Mountain Project and supporte d by the U.S. Department of Energy. The database gives a logK s = -14.8544 at 25 ° C for the reaction in Eq. (1), which is 0.95 log units lower than the value cited by Dyrssen and Kremli ng (1990). This value was calculated from the thermodynamic properties for galena reported in He lgeson et al. (1978), who in turn cited Mills (1974). The reported value for Δ G f , P r , T

r 0 , -23.12 kcal/mol, is close to the -23.14 kcal/mol reported by Robie and Hemingway (1995), which is in turn based on thermochemical measurements by Stubbles and Birchenall (1959) . Stubbles and Birchenall (1959) conducted thermochemical measurements on lead/lead sulfide equilibria be tween 585 and 920oC, necessitating a substantial extrapolation to the 25oC reference temperature. Uhler and Helz (1984), in referring to the same work, note further that, apart from the magnitude of the extrapolation, additional uncertainties arise from phase changes in both elemental lead and sulfur between 585 and 25oC, contributingD17 of 22 to an overall uncertainty in excess of 2 kcal/mol in Δ G f , P r , T

r 0 (galena), or > ± 1.5 units in LogK s . However, this uncertainty is much larger th an that estimated by Robie and Hemingway (1995) for the same data (Table D1).

Barrett and Anderson (1982, 1988) meas ured the solubility of both ga lena and sphalerite (ZnS) at temperatures between ≈ 25 and 95oC in sodium chloride brines ranging between 3 and 5 M. These investigators then independe ntly calculated the solubilities of galena and sphalerite to 300oC in sodium chloride brines between 1 and 5 M at pH = 4 and m s(r) = 0.001. They also compared their measured solubilities with th e independently calculated values, and obtained a reasonable match (calculated values were be tween 0.3 and 0.7 log units too low). Their calculations were based on logK s (galena) cited in Bowers et al. (1985), which appears to have been derived from the sources cited by Helges on et al. (1976). The calculations took into account Pb chloride complexing quantified by Seward (1984) a nd Ruaya and Seward (1986), and activity coefficient ( γ i ) corrections were based on the b- dot method described by Helgeson (1969). Barrett and Anderson (1988) c ontrasted their results using the logK s (galena) cited in Bowers et al. (1985) with those obtained by Uhle r and Helz (1984), noting the almost two order of magnitude discrepancy, and pointing out that using the latter would result in calculated solubilities much greater than their measured values. However, they also note that their own experimental measurements are subject to errors arising from inadequate corrections for liquid junction potentials in their measurements of pH. Furthermore, their calcu lated solubilities are subject to the same uncertainties associated with corrections fo r chloride and sulfide complexing by Pb +2 . Inadequate corrections for activity coeffi cients of the ionic species in high-ionic strength sodium chloride brines are also of c oncern to the derived results of Uhler and Helz (1984), as noted above.

In conclusion, the discrepancies between K s (galena) derived from solubility measurements and
those based on thermochemical measurements are so significant that meaningful predictions of galena solubility in shallow potable groundwaters at ambient temperatures are not possible
unless the conflict is resolved. This conflict appears to be caused by failure to account for the presence of the oligomeric complex PbS(aq), by analogy with the resolved discrepancies between measured and calculated sphalerite, gree nockite and cinnabar sol ubilities, given in the preceding sections. We therefore adopted the strategy described in the introduction of thisD18 of 22 Appendix, i.e., Log K s (galena) would be based on thermochemical data, and the dissociation constant of PbS(aq) would be estimated based on the regression analysis between Log K(MeS) s and log K ( M e S ) aq illustrated in Figure D1. The value chosen for Log (PbS)aq, -5.5, is consistent, within the overall uncertainty of the analysis, with independent measurements by Rozan et al. (2003), who give a value for Log K THERM = 16.83 ± 0.33 for PbS(aq), 36.38±0.47 for Pb 2 S 2 (aq),
62.49±0.61 for Pb 3 S 3 , and 135.6±0.8 for Pb 6 S 6 , respectively, for the reaction

xPb +2 + xS -2 = Pb x S x (aq) (D20)

Normalizing these Log K THERM values to unit stoichiometry, as well as recasting the reaction in
terms of HS - and as a dissociation yields L ogK ( PbS )( aq ) = +0.31, -1.67, -4.46 and -6.08, respectively. Bearing in mind that Rozan et al. (2003), on the basis of mass spectrometry determinations of freeze-dried samples, found that the species Pb 3 S 3 (aq) appeared to predominate, it can be seen that there is r easonable agreement between the normalized log K value for that species, -4.46, and the value of –5.5 provisionally accepted for the average of all oligomeric PbS(aq) species. Note, however , that the foregoi ng calculation assumed Log K THERM = 16.83 ± 0.33 for the reaction

S -2 + H + = HS - (D21)

Values ranging from 18.51 ± 0.56 (Schoonen and Barnes, 1988) to ≈ 14.0 have been reported in the literature (Dyrssen, 1989) . Therefore the uncertainties in the derived values for ) )( ( aq

PbS LogK from Rozan et al. (2003) are substantial.

The dissociation constants for other Pb sulfid e species in solution are derived from the association constants reported by Giordano and Barnes (1979) at 30°C, which are essentially identical to earlier work by Heml ey (1953) at 25°C, and were trea ted analogously to those for Hg sulfide complexes using solubility products derived from Δ G f , P r , T

r 0 (galena), given by Robie and Hemingway (1995) (Log K = -14.870), and the dissociation constant for H 2 S(aq) specified by Bessinger (2000) (Log K = -6.98). The re levant data are given in Table D6.D19 of 22 Table D6. Complexes of HS- with Pb +2 after data by Giordano and Barnes (1979), PbS(aq) computed in this Appendix, and Galena Solubility Product derived from Thermochemical Data given by Robie and Hemingway (1995), all at 25°C
Reaction Log K Pb(HS) 2 (aq) = Pb +2 + 2 HS - -14.058 Pb(HS) 3 - = Pb +2 + 3 HS - -15.258 PbS(aq) + H + = Pb +2 + HS - -5.5 PbS(galena) + H + = Pb +2 + HS - -14.870



# D7. Sphalerite (ZnS)

Excellent agreement is obtained for the calculated values of Δ G f , P r , T

r 0 for sphalerite derived from the data by Daskalakis and Helz (1993), Deore and Navrotsky (2006), and Schaefer and Gokcen (1982). The value of Δ G f , P r , T

r 0 derived from data by Dyrssen and Kremling (1990) is somewhat higher, but otherwise in reasona ble agreement. Barrett and Anderson (1988), using a value of Log K s for sphalerite of –11.40, found that the meas ured solubility at 25 °C was 0.4 log units higher in 3 M NaCl brine, i.e., Log Ks ≈ –11.00, consistent with that of –10.93 reported by Dyrssen and Kremling (1990). Bearing in mind that Barrett and Anderson’s measured solubility measurements are difficult to interpret, as noted further in Section D5 of this appendix, the consistency of their results with other solub ility measurements, and with thermochemical
determinations of Δ G f , P r , T

r 0 suggests that reasonable confidence can be attached to the internally consistent dataset for sulfide speciation and log K s for sphalerite, obtained by Daskalakis and Helz (1992), and presented in Ta ble D7, below. An uncertainty of about ± 1 log unit is expected in predictions of sphalerite saturation indices. The Log K value for the dissociation of ZnS(aq) is derived from data presented by Dyrssen and Wedborg (1991) as summarized in Table D7, and the solubility product is based on thermochemi cal data given by Robie and Hemingway (1995).D20 of 22 Table D7. Complexes of HS- with Zn+2 after data by Daskal akis and Helz (1992), ZnS(aq) after Dyrssen and Wedborg (1991) and Sphalerite Solubility Product derived from Thermochemical Data given by Robie and Hemingway (1995), all at 25°C
Reaction Log K Zn(HS) 4 -2 = Zn +2 + 4 HS- -14.640 ZnS(HS) - + H + = Zn +2 + 2 HS - -6.842 ZnS(HS) 2 -2 + H + = Zn +2 + 3 HS - -6.152 ZnS(aq) + H + = Zn +2 + HS - -3.5 ZnS(sphalerite) + H + = Zn +2 + HS -11.27



# D8. Recommendations for Further Refinement of Thermodynamic Data Pertinent to Sulfide and Selenide Solubilities

A pragmatic approach was taken in the foregoing analysis to reconcile thermochemical data for
solid sulfide and selenide minerals with measur ed aqueous solubilities, and further reconcile field measurements of heavy metal concentrat ions in potable groundwaters with postulated
saturation indices for the sulfide and selenide minera ls. For reasons of time, an in-depth analysis
was not conducted.

In their review of metal sulfide complexes and clusters, Rickard and Luther (2006) cite studies suggesting the existence of severa l other charged and uncharged oligomeric metal clusters of Fe, Cu, Zn, Cd, Ag, Au, Pb and Hg. Supporting eviden ce is for the most part equivocal, and further
definitive studies are needed to resolve current ambiguities regarding cluster stoichiometries and
stabilities. The same authors also cite recen t work by Zhang and Millero (1994), Al-Farawati and van den Berg (1999), and Rozan et al. ( 2003) all of whom used various voltammetric techniques to identify sulfide speciation in marine waters for a range of elements, including those of interest in this report. Unfortunately, tim e did not permit evaluati on of their findings in relation to the work presented here. However, some preliminary checks indicate consistency with the results presented in this appendix. Furt her analysis of the data presented in these and other papers cited by Rickard and Luther (2006) , as well as the work of ongoing investigations must be conducted as part of a broader strategy to refine th e geochemical model for potable

waters.D21 of 22 The following are aspects of the problem that were insufficiently addressed.

(1) The aqueous species MeS(aq) represents a di stribution of oligomeric complexes, which would vary depending on both intensive and extensive variables in the system. Molecular dynamic modeling and/or spectroscopic studies will be required to establish the identities of species and their thermodynamic relations. Recent studies of sulfide complexation in marine waters using voltammetric methods ( vide supra ) must be reviewed and compared with earlier work cited in this appendi x and account taken of oligom eric species where their thermodynamic properties have been quantified.

(2) Experimental measurements of sulfide and seleni de mineral solubilities were conducted in a variety of supporting electr olytes. Ionic strength corrections to the activities of species were not uniformly treated or evaluated.

(3) Corrections to mineral solubilities by assumi ng the presence of neutral sulfide complexes
were made without a re-evalu ation of the raw data. Cons equently, corrections to the distributions between species such as MeS(aq) and species such as Me(HS) 2 (aq) were
accounted for in a consistent manner.

(4) Improved correlation plots between sulfide a nd selenide species might be achieved by consideration of experimental work on sele nide complexes of Mn and Ag by Mehra and Gubeli (1970a and 1970c).

(5) Changes in the distributions of sulfide and selenide minerals as a func tion of heavy metal abundances and redox potential in natural system s should be examined further to establish the phase relations of selenide minerals in relati on to corresponding su lfide minerals in potable water aquifers.

(6) In making tests of the validity of calculated saturation indices of sulfides and selenides in potable groundwaters, the e ffect of temperature was ig nored. Potable groundwater temperatures could range down to as low as 5oC, which could shift the calculated saturation index by as much as one log unit. In situ te mperatures of the studied NWIS potable waters should be retrieved and temperature corrections made for the dissociation constants of all participating aqueous species and all part icipating mineral solubility products.D22 of 22 (7) The stability constants of mixed sulfide/se lenide complexes should be estimated and incorporated in the thermodynami c database used in the evalua tion of saturation indices of sulfide and selenide minerals in potable groundwaters.E1 of 7 

# Appendix E: Dissociation Constants for Selenides of Arsenic, Cadmium, Mercury, Lead and Antimony

E1. Background

During the course of the current investigation, it became apparent that the concentrations of several hazardous inorganic const ituents in groundwaters could be controlled by their selenides, rather than their corresponding sulfides. This su pposition is reinforced by field evidence for the existence of clausthalite (PbSe) occurring in ov er 50% of coals in th e Appalachian Basin and 10% outside the Basin (Finkelman, 1981), and the o ccasional occurrence of tiemannite (HgSe) in coal (Tewalt et al., 2001). Furthermore, both cadmoselite (CdSe) and antimonselite (Sb 2 Se 3 ) have been observed in sedimentary formations under conditions that might suggest their presence in potable groundwater aquifers.

Preliminary calculations of the saturation indices for metal (Me) selenides, using solubility products based on thermodynamic data in reported in the published literatu re, for the reaction:

Me a Se b (s) + bH + = aMe n+ + bHSe - (E1)

also indicated that all of th e cited selenides would be extr emely supersaturated in those groundwaters from the NWIS database reporting Se together with Cd, Hg, Pb or Sb. In fact, the calculated saturation indices were so unrealistically high ( ≈ +5 to +10), that it was clear that that the thermodynamic database used in the EQ3/6 c ode for the evaluation was seriously deficient and failed to represent the behavior of selenide mineral solubilities. this deficiency would compromise the objective of the study to corr ectly identify and quantify the thermodynamic controls governing the concentr ations of the cited constitu ents in potable groundwaters.

An obvious omission in the Data0.dat database were the selenide complex dissociation constants corresponding to those of sulfide complexes with As +3 , Cd +2 , Hg +2 , Pb +2 , Sb +3 , Zn +2 and Fe +2

, which were incorporated on the basis of a review of their di ssociation constants given in Appendix D. A perusal of the literature lead to the discovery of two reviews regarding the solubilities of selenium phases and selenium speciation in solution, Cowan (1988) and Seby et al.E2 of 7 (2001). Both reviews cited the same sources re garding selenide complexation with metal ions, which were limited to studies by Mehra and G ubeli (1970a,b, 1971) concerning Ag, Mn and Hg selenide complexation. No other studies rela ting to selenide complexation with metals in aqueous solution appear to have been published since date of the latest review. Only the information by Mehra and Gubeli (1971) concerning Hg selenide complexing is relevant to the current study. In the following pa ragraphs, we discuss how the av ailable data were adapted to account for selenide complexation with As +3 , Cd +2 , Hg +2 , Pb +3 , Sb +3 , Zn +2 and Fe +2

.


# E2. Hg Selenide Complexes in Aqueous Solution

Mehra and Gubeli (1971) independently determin ed the Log solubility product of HgSe, according to the reaction

HgSe(s) = Hg +2 + Se -2 (E2)

to be –56.6±0.2. Assuming Log K for the reaction

HSe- = H + + Se -2 (E3)

is –14.0±1.0 (Seby et al., 2001), it follows that Log K(tiemannite) for the reaction

HgSe(s) + H + = Hg +2 + HSe - (E4)

is –42.6. This value is comparable with the valu e, -43.27, calculated from thermochemical data reported by Vaughan and Craig (1978), and the same as that reported by Mills (1974), which has been incorporated in the EQ3/6 Data0.da t, V4.2.YMP, and accepted in this report.

The data presented by Mehra and Gubeli (1971) also allows calculation of th e intrinsic solubility of HgS (tiemannite) under acid conditions (i.e., at pH < 3.8), as their measurements were
conducted in 1 Normal NaClO 4 solution assumed to be in equilibrium with solid HgSe. Assuming the reaction

HgSe(s) =HgSe(aq) (E5)E3 of 7 is in equilibrium with a total log concentrati on of Hg = -7.83, as reported by Mehra and Gubeli (1971), then Log K int = − 7.83 . Given that Log K(tiemannite) for the reaction (E 2) is –43.27, the
dissociation constant for the reaction

HgSe(aq) + H + = Hg +2 + HSe - (E6)

is LogKHgSe ( aq ) = − 35.43

.
Instead of postulating the existe nce of HgSe(aq) as a proxy for oligomeric species in solution, Mehra and Gubeli (1971) assumed the existence of a complex of the form Hg(HSe)(OH)(aq), which, through subtraction of one H 2 O, assumes the same form as HgSe(aq). The two species are therefore equivalent, and it w ould be impossible to discrimina te between the two on the basis of the experimental results alone.

Mehra and Gubeli (1971) also postulated the ex istence of two other species in solution, Hg(HSe) 2 (OH) - and Hg(HSe) 2 (OH) 2 -2 , which are equivalent to HgSe(HSe) - and HgSe 2 -2

, respectively.

Association constants for each were defined thus:

β

21 = Hg HSe 

## ( ) 2 OH 

## ( ) − 

# [ ]
Hg + 2 

# [ ] HSe − 

# [ ] 2 OH −



# [ ] = Hg complex 

# [ ] K 2

2
K sp • K W H + 

# [ ] Se − 2 

# [ ] (E7)

where L og β 21 = 52.8 ± 0.3 , and

β

22 = Hg HSe 

## ( ) 2 OH 

## ( ) 2 − 2 

# [ ]
Hg + 2 

# [ ] HSe − 

# [ ] 2 OH −



# [ ] 2 = Hg tot 

## [ ] K 2 2
K sp • K W 2 Se − 2 

# [ ] (E8)

where L og β 22 = 61.0 ± 0.3 . The value of the second ionization constant, L ogK 2 = − 11.6 , had been determined earlier by Mehra and Gubeli (1970a). Log K sp = 56.6 ± 0.2 is the solubility product of HgSe, tacitly assumed to be tiemannite. It was also calculated from the value reported
for the reaction

HgSe = Hg +2 + Se -2 (E9)E4 of 7 Log K w is the dissociation constant for water, which was not specified. The log K 2 value is
inconsistent with that recomm ended by Seby et al. (2001) (log K 2 = -14.0 ±1.0), the elevated value being possibly caused by inadvertent contamination by pol yselenides generated through
oxidation of HSe - by traces of oxygen (Wood, 1958), even though the study was conducted under an inert atmosphere of nitrogen. Because a 1 M NaClO 4 solution was used as the supporting electrolyte, a more realistic value for log K W is –13.7, a value used by Dyrssen (1988) in correcting for the ionic stre ngth of seawater. Finally, Log K sp differs from that calculated from thermochemical data by approximately 0.7 log unit, which might be due to a finer crystallinity of the material used in their solubility determina tions. Because this discrepancy is within the overall uncertainty of the calcula tions, no correction was applied to account for this difference. Application of appropriate corrections yields revised values for the dissociation constants, which are presented in Table E1.

Table E1. Dissociation Constants of Complexes of HSe- with Hg+2 and the Tiemannite Solubility Product in 1 M NaClO4 Solution at 25oC (after data by Mehra and Gubeli, 1971).
Reaction Log K HgSe(aq) + H + = Hg +2 + HSe - -35.43 HgSe(HSe) - + H + = Hg +2 + 2 HSe - -39.10 HgSe 2 -2 + 2 H + = Hg +2 + 2 HSe - -28.80 HgSe(s) + H + = Hg +2 + HSe - -43.27

Corrections were inconsistently applied to the reported dissociation constants for HgSe(HSe) - and HgSe 2 -2 . The corrected value for Log K(HgSe(HSe) - ) should have been –34.30. However,
had this value been used instead of that cite d in Table E1, the calcu lated Log K values of selenide complexes given in this appendix would have differed very little from those actually calculated and used in subsequent calculations in this report. W ith respect specifically to Hg selenide complexation, the differences are also inconsequential, and well within the overall uncertainty of the model results, as the HgSe(HSe) - species is always less than 10 -6 % of the total Hg in groundwaters. It should also be noted that the corrections are preliminary, and that a
more careful re-evaluatio n of the findings by Mehra and Gubeli (1971) is required. Furthermore, it is very probable that both HgSe(HSe) - and HgSe 2 -2 species are oligomeric, and this should also
be taken into account in re-interpreting the data.E5 of 7 

# E3. Estimated Dissociation Constants of Selenide Complexes of As +3 

# , Cd +2 

# , Pb +3 

# , Sb +3 

# , Zn +2 

# and Fe +2 

# in Aqueous Solution

In the absence of any information pe rtaining to selenide complexes for Cd +2 , Pb +3 , Sb +3 , Zn +2 and
Fe +2 , values of their dissociation constants we re estimated from those of the corresponding sulfide complexes using a linear correlation equation in which were plotted solubility product pairs for metal sulfide and selenide minerals, offset values of the dissociation constant pairs for
H 2 S(aq) and H 2 Se(aq), and HS - and HSe - , respectively, and dissociation constants for Hg sulfide and selenide aqueous complex pair s. The respective sulfide and se lenide solubility products are calculated from thermochemical, electrochemical or calorimetric data, but not solubility measurements. The data used in this plot and the sources of information are given in Table E2.
Note that metacinnabar and marcas ite solubility products are used in place of those for cinnabar and pyrite, respectively, in order to compare sulfide and selenide minerals with identical crystal

structures.

The initial regression was conducted on all species together, correcting H 2 Se(aq) and HSe species values by an identical amount sufficient to bring them into colinearity with the trend of the solid phases. The aqueous species were then restored to the original values, and then the
value for the dissociation constant for HSe - adjusted to ensure that the slopes of the two lines were identical. This entailed adjustment from Log K(HSe - ) = -14.0 ±1.0 to –14.67, which is well within the uncertainty of value assigned by (Seby et al., 2001). The reason for the offset is not known, but might be ascribed to the effects of so lvation and electrostricti on in the aqueous phase acting on these molecular and ioni c species, e.g., see Shock and Helgeson (1988). The analysis and interpretation is quite speculative, and may be modified upon more comprehensive review.E6 of 7 Table E2. Log K Values of Sulfide and Selenide Aqueous Complex and Mineral Pairs used to generate a Correlation Plot to Estimate the Dissociation Constants of Unknown Metal Selenide Complexes and Effective Solubility Products of Metal Selenides

Sulfide Selenide Species Log K Source Species Log K Source Aqueous HS - -16.52 1 HSe - -14.67 4 H 2 S(aq) -6.99 2 H 2 Se(aq) -3.81 5 HgS(aq) -28.08 3 HgSe(aq) -35.43 4 HgS(HS) - -32.67 3 HgSe(HSe) - -39.10 4 HgS 2 -2 -23.91 3 HgSe 2 -2 -28.80 4 Mineral CdS (greenockite) -14.09 6 Cadmoselite -18.89 7 Meta-cinnabar -37.78 6 Tiemannite -43.27 7 Galena -14.87 6 Clausthalite -20.78 7 Marcasite -49.87 7 Ferroselite -55.45 7

Notes: 1. Dyrssen (1988); 2. Bessinger (2000); 3. Paquette and Helz (1997); 4. this appendix; 5. Seby et al. (2001); 6. Appendix D; 7. EQ3/6 Data0.ymp.R4 database (see Appendix D)

The data summarized in Table E2 and results fro m the regression analysis are shown in Figure E1. The linear regression of the data is good (R 2 = 0.993). The regressi on equation was used to
estimate the dissociation constants for the corresponding selenide analogues of sulfide complexes of As(III), Cd(II), Pb(I I), Sb(III), Zn(II) and Fe(II), which are presented in Table E3. These estimated values were incorporated in the thermodynamic da tabases of EQ3/6 and TOUGHREACT, and used to calculate saturation i ndices of selenide mine rals in those NWIS groundwaters that were analyzed for Se.E7 of 7 y = 1.1396x - 1 .5358 R 2

= 0.9911 y = 1 .1396x + 4.1557 R 2

= 1

-80 -70 -60 -50 -40 -30 -20 -10

0

-60 -50 -40 -30 -20 -10 0 Log K(sulfide)Log K(selenide)

Fig. E1. Linear regression of dissociation constants of Hg sulfide and selenide complex pairs, and solubility product pairs for metal selenides and sulfides. “aq.” refers to aqueous species.

Table E3. Estimated Log K Values of Selenide Aqueous Complexes of As+3, Cd+2, Pb+3, Sb+3, Zn+2 and Fe+

Reaction Log K Arsenic H 3 As 3 Se 6 (aq) + 1.5O 2 (g) + 9H 2 O = 3H 2 AsO 4 - + 6HSe - + 9H + -23.96 H 2 As 3 Se 6 - + 1.5O 2 (g) + 9H 2 O = 3H 2 AsO 4 - + 6HSe - + 8H + -19.63 HAs 3 Se 6 -2 + 1.5O 2 (g) + 9H 2 O = 3H 2 AsO 4 - + 6HSe - + 7H + -10.64 AsSe(OH)(SeH) - + 0.5O 2 (g) + 2H 2 O = H 2 AsO 4 - + 2HSe - + 2H + -6.91 Cadmium CdSe(aq) + H + = Cd +2 + HSe -10.24 Cd(HSe) 3 - = Cd +2 + 3HSe - -23.47 Cd(HSe) 4 -2 = Cd +2 + 4HSe - -25.12 Cd(OH) 2 (HSe) - + 2H + = Cd +2 + 3HSe - + 2H 2 O -1.98 Lead PbSe(aq) + H + = Pb +2 + HSe - -11.04 Pb(HSe) 2 (aq) = Pb +2 + 2HSe - -20.76 Pb(HSe) 3 - = Pb +2 + 3HSe - -22.15 Antimony H 2 Sb 2 Se 4 (aq) + 6H 2 O = 2Sb(OH) 3 + 4HSe - + 4H + -70.63 HSb 2 Se 4 - + 6H 2 O = 2Sb(OH) 3 + 4HSe - + 3H + -64.88 Sb 2 Se 4 -2 + 6H 2 O = 2Sb(OH) 3 + 4HSe - + 2H + -54.08 Zinc ZnSe(aq) + H + = Pb +2 + HSe - -8.76 Zn(HSe) 4 -2 = Zn +2 + 4HSe - -21.42 ZnSe(HSe) - + H + = Zn +2 + 2HSe - -12.56 ZnSe(HSe) 2 -2 + H + = Zn +2 + 3HSe - -11.78 Iron FeSe(aq) + H + = Fe +2 + HSe - -2.28F1 of 16 

# Appendix F: Improved Adsorption Models in TOUGHREACT

F1. Introduction

Many minerals such as metal oxides, hydroxides and layered silicates (Al 2 O 3 , TiO 2 , FeOOH,
SiO 2 , kaolinite, etc.) exhibit electrica lly charged surfaces in the pres ence of natura l waters. These surfaces contain ionizable functional groups (e.g., s ilanol groups in hydrated silica, Si-OH) being responsible for chemical reactions at the surf ace. Adsorption via surface complexation has been widely studied (Dzombak and Morel, 1990; Lu tzenkirchen, 2006) and is a key process with respect to the fate and transport of heavy metals (e.g., Bradl, 2004) or other hazardous constituents such as arsenic (Goldberg, 2002; Goldberg et al., 2005; Manning and Goldberg, 1997). We have therefore incorporated into TOUGHREACT a model considering adsorption via surface complexation, using different surface comple xation models such as the non-electrostatic model, the double layer model, and the constant capacity model (Dzomb ak and Morel, 1990). The surface complexation models implemented in TOUGHREACT were verified by comparison
with other codes such as PHREEQC (Parkhurst and Appel o, 1999) and CRUNCH (Steefel, 2001). This Appendix include s two main parts. In the first pa rt, we described the mathematical formulation and numerical implementation of surface complexation adopted in TOUGHREACT, while in the second part, we review the surf ace complexation reactions and their thermodynamic data used in the reactive transpor t simulations conducted in Section 4.


# F2. Surface Complexation Models

F2.1 Surface Electric Potential Model

The sorption of solutes on solid surfaces can be described as a chemical reaction between the aqueous species and the specific surface sites (surface complexation). These surface reactions include proton exchange, cation binding and an ion binding via ligand exchange at surfaceF2 of 16 hydroxil sites (represented here as XOH to avoid confusion with other chemical species). For example, the sorption of a me tal could be represented as:

+ + -1 + z z X OH + = + X OM M H (F1)
At equilibrium, the sorption reactions can be described by the mass law equation:



## [ ] 1

z z H
app M X OM a K XOM a − + +

+ ⎡ ⎤ ⎣ ⎦

= (F2)

where 

### K app is referred to as the apparent equilibrium constant, because it includes surface charge effects and hence is dependent on the extent of surface ionization (Dzombak and Morel, 1990),

a is the thermodynamic activity of the aqueous species, and the terms in brackets represent the concentration of surface complexes (mol/kg).

In aqueous complexation reactions, the electric charge is assumed to be homogeneously distributed in the solution. However, surface re actions take place on a fixed charged surface
which creates an electrostatic fi eld. An additional ener getic term accounting for the work needed for the aqueous species to travel across the surface electric field is required:

0 ads intr coul intr =0 = intr 0 = + = +( - )= - zF GGG G GG G ψ ψ ψ ψ ΔΔΔ Δ ΔΔ Δ (F3)

where ads G Δ is the free enthalpy change of the overall adsorption reaction,
r G

int Δ and coul G Δ are the free enthalpy change due to chemical bonding and to the electrosta tic work, respectively, z is the charge of the surface species, F the Faraday's constant (96485 C/mol), and 0 ψ is the mean surface potential ( V ). Since

G = -RTlnK Δ , (F4)
Equation (F3) can be rewritten as:

0 zF R T app int 

### = e K K ψ , (F5)

where R is the gas constant (8.314 J/mol/K), T is the absolute temperature (K), and K int

is the intrinsic equilibrium constant which does not depend on the surface charge.F3 of 16 

## F2.2 Diffuse Layer Model

The diffuse layer model has been described in great detail by Dzombak and Morel (1990) and was applied to adsorption of metals on iron oxide surfaces. In the diffuse layer model, the solidwater interface is composed of two layers: a layer of surface-bound complexes and a diffuse layer of counter ions in solution. The surface charge is calculated from the total surf ace species adsorbed on the layer:

s N

p k k k=1 F = z y A σ 

## ∑ (F6)

According to the Gouy-Chapman theory, the surface charge density p σ (c/m 2 ) is related to the potential at the surface (volt) by:

3 0 10 sinh 1/2 d 0 = (8RT I (zF /2RT) ) ε ψ σ ε × , (F7)

where R is the molar gas constant (8.314 J mol -1 K -1 ), T is the absolute temperature (K), ε is the relative dielectric constant of water ( ε = 78.5 at 25 o C), 0 ε is the permittivity of free space (8.854 × 10 -12 C V -1 m -1 or 8.854 × 10 -12 C 2 J -1 m -1 ), and c is the molar electrolyte concentration (M). Equation (F7) is only valid for a symmetrical electrolyte (Z=ionic charge).

It is common to use the linear ized version of Equation (F7) fo r low values of the potential:

0 0 p σ εε κψ = , (F8)

where 1/ κ

(m) is the double-layer thickness defined as:

1/2 1000 0 2 1 RT

= ( ) 2 I

F ε ε
κ ⋅ (F9)

Equations (F7) can be simplified by rewritten 3 10 1/2 0 (8RT I ) ε ε × for 25 o C :

1/2 0.1174 sinh p d = I (zF /2RT) ψ σ (F10)F4 of 16 Therefore, in the diffuse-layer model, the value of the capacitance C relating the surface charge and the potential can be calculate d based on theoretical considerat ions (Equation (F8)) instead of being an experimental fitting parameter.



## F2.3 Constant Capacitance Model

Similar to the diffused-layer, the constant capac itance model is based on the assumption that all the species are adsorbed in the same layer an d a diffuse layer of counterions constitutes the transition to homogenous solution. However, differently from the diffuse-layer model, the relationship between the surf ace charge and the potential is assumed to be linear:

= C σ ψ (F11)

where C is a constant value to be obtaine d from fitting experimental data.



## F2.4 Mathematical Formulation of Adsorption Reactions

Lets us consider the follow ing surface desorption reaction:

H

- M + XOH = X OM + z 1

- z + +

with the equilibrium constant of the reaction given as: 

## [ ] [ ] 0

int zF M RT

H XOH a K e XOM a ψ

+ − ⎛ ⎞ ⎜ ⎟ ⎝ ⎠ = (F12)

Usually, the neutral surface complex XOH is included in the N C

aqueous primary species. The new unknown is the potential term

0 F R T

ψ ψ − =  (F13)

The concentration of a surface complex, y j , (mol/m 3 ), can be expressed in terms of the concentration of the primary sp ecies and the potential term:F5 of 16 

## 1 C y y j ji ji N z -1 Y i j i j i=1 = e y c N K 

## j= ... ψ ν ν

γ 

## ∏  (F14)

where K j is the intrinsic equilibrium consta nt of the desorption reaction.

In order to solve for the potential term, an ad ditional equilibrium equation is needed. For the double diffuse layer model, we obtain Equation (F15), if the Guy-Chapman double-layer theory is used to relate surface charge and the potential (Equation (F7)). If the linearized Equation (F8) is used, we obtain Equation (F16) . If a constant capacity model is used, we obtain Equation (F17).

3 10 sinh 0 s N 1/2 0 k k k=1 F (8RT I ( /2) z ) y A ε ψ

ε × −− = 

## ∑  (F15)

2

0 0 s N

k k k=1 F z y A RT ψ

εε κ + = 

# ∑  (F16)

2 0 s N

k k k=1 F z y ACRT ψ + = 

## ∑  (F17)



## F2.5 Implementation in TOUGHREACT

When surface complexation is considered, the total concentration of the primary species j at time zero (initial conditions) is assu med to be known, given by:

00 0 0 0 0 p q x s N N N N

C j j kj k mj m nj n sj s k=1 m=1 n=1 s=1 T = c c c c c j = 1... N νν ν ν ++ ++



# ∑∑ ∑ ∑ (F18)

where superscript 0 represents time zero; c denotes concentration; subscripts j, k, m, n and s are the indices of the primary species, aqueous comp lexes, minerals at equilibrium, minerals under kinetic constraints, and surface complexes, respectively; N c , N x , N p , N q and N s are the number of corresponding species and minerals; and ν kj , ν mj , ν nj and ν sj are stoichiometric coefficients of the primary species in the aqueous complexes, equili brium, kinetic minerals, and surface complexes, respectively.F6 of 16 After a time step t Δ , the total concentration of primary species j ( j T ) is given by


# ( ) 0 p q x s N N N N

C j j kj k mj m nj n n sj s k=1 m=1 n=1 s=1 T = c c c c r t c j = 1... N νν ν ν + + + −Δ+



# ∑∑ ∑ ∑ (F19)

where r n is the kinetic rate of mineral dissolution (negative for precipitation, units used here are mole per kilogram of water per time), for which a general multi-me chanism rate la w can be used (Xu et al., 2006). 0 j T and j T are related through generation of j among primary species as follows

0 C j 1,...N a N
j j lj l l=1 T T r t ν −= Δ = 

## ∑ (F20)

where l is the aqueous kinetic reaction (including biodegradation) index, N a is total number of kinetic reactions among primary species, and r l is the kinetic rate which is in terms of one mole of product species.

By substituting Equations (F18) and (F19) in to Equation (F20), and denoting residuals as c j F (which are zero in the limit of convergence), we have

0

0

0 ( ) Primary species
( ) equilibrium aqueous complexes

( ) equilibrium x

p c j j j
N
kj k k k=1
N

mj m m m=1 = c c F

cc

cc ν

ν

−

+ −

+ − 

# ∑
∑

1 minerals

kinetic minerals

kinetics among primary species q

a N
nj n n=1
N
lj l l rt

rt

ν

ν

= − Δ

− Δ

+ 

# ∑
∑

0 ( ) surface complexes

0 s N
sj s s s=1

C cc

j = 1... N ν

−

= 

# ∑ (F21)

According to mass-action equations, the concentrations of aqueous complexes c k can be expressed as functions of concen trations of the primary species c j . Kinetic rates r n and r l are functions of c j . The expression for r n is given in Xu et al. (2006), and r l will be presented later.F7 of 16 No explicit expressions relate equilibrium mineral concentrations c m to c j . Therefore, N P additional mass-action equations (o ne per mineral) are needed. Surf ace complexes ar e expressed as the product of primary species and an additional potential term ψ  . Additional equilibrium equations (Equation (F16) or (F17) depending on the surface complexation model) have to be solved together with Equation (F21) and the N p mass-action equations fo r equilibrium minerals.

The final Jacobian matrix has Nc+Np+ N ψ rows and Nc+Np+ N ψ columns, as follows:

ccc jjj

i i

ppp jjj

i i

i i Nc Np N
FFF Nc c p FFF Np c p FFF N c p ψ ψ ψ ψ

ψ

ψ

ψ ψ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂  



 (F22)

x c N Nq Ns j cc k nn n s ji kj ik nj in sj is k=1 n=1 s=1 i i ii F c kA c J c c cc δ νν νν νν ∂ Ω = =+ + + ∂ 

# ∑∑ ∑ (F23)

c Ns j c
s jss s=1 F J zc

ψ ν ψ ∂

= = ∂ 

# ∑ 

 (F24)

s N

c s s is s=1 i i c F J z c c ψ ψ ν ∂

= =− ∂ 

# ∑ 

 (F25)

or

2

0 s N

c s s is s=1 i i c F F J z c A RT c ψ ψ ν εε κ ∂

= = ∂ 

# ∑ 

 (F26)

or

2 s N

c s s is s=1 i i c F F J z c ACRT c ψ ψ ν ∂

= = ∂ 

# ∑ 

 (F27)

3 1 2 10 cosh s N 1/2 0 k k k k=1 F F J (8RT I ( /2) z z y ) A ψ ψψ ε ψ ε ψ ∂ = =− × − − ∂ 

## ∑ 

  

 (F28)

orF8 of 16 2

0 1 s N

sss s=1 F F J zzc A RT ψ ψψ ψ εε κ ∂ == + ∂ 

# ∑ 

 

 (F29)

or

2 1 s N

sss s=1 F F J zzc ACRT ψ ψψ ψ ∂ == + ∂ 

## ∑ 

 

 (F30)



## F2.6 Verification in Comparison with Other Codes

The surface complexation calculation by TOUGHREA CT has been verified against other codes such as PHREEQC (Parkhurst and Appelo, 1999) and CRUNCH (Steefel, 2001). The example of zinc sorption at low site density presented by Dzombak and Morel (1990) has been used by PHREEQC and CRUNCH to verify their surface complexation calculation. Zinc sorption on hydrous ferric oxide is simulated assuming two type s of sites, weak and st rong. Protons and zinc ions compete for the two types of binding site s, and equilibrium is described by mass-action equations. The double layer model is used to calculate the speciation of Zn 2+ in aqueous and surface complexes. The example considers the va riation in sorption of zinc on hydrous ferric oxides as a function of pH fo r low zinc concentration (10 -7 M) and high zinc concentrations (10 -4 M) in 0.1 M sodium nitrate electrolyte.

Figure F1 shows the calculation results of Dz ombak and Morel (1990). Table 1 compares the calculated equilibrium speciation of Zn 2+ calculated by TOUGHREACT and CRUNCH. Both
simulators arrive at almost identical result s. Figures F2 shows th e calculated equilibrium speciation of Zn 2+ as a function of pH cal culated by TOUGHREACT.F9 of 16 Fig. F1. Calculated equilibrium speciation as a function of pH for zinc in a 90-mg/L HFO suspension: The top figure shows results for Total Zn=10 -7 M, I = 0.1 M; the bottom figure shows results for Total Zn = 10 - 4 M, I = 0.1 M (Dzombak and Morel, 1990).F10 of 16 Table F1. Calculated equilibrium speciation for zinc by TOUGHREACT and by CRUNCH

pH Zn 2+ Fe s OZn + Fe w OZn +

TOUGHREACT CRUNCH TOUGHREACT CRUNCH TOUGHREACT CRUNCH 5 9.94E-08 9.94E-08 5.51E-10 6.03E-10 2.31E-11 2.52E-11 6 6.48E-08 6.28E-08 3.38E-08 3.57E-08 1.43E-09 1.51E-09 7 2.60E-09 2.61E-09 9.34E-08 9.34E-08 3.99E-09 3.99E-09 8.062 1.40E-10 1.38E-10 9.58E-08 9.58E-08 4.09E-09 4.09E-09

6

7

8

9

10

11 456789 pH-log C Zn 2+

3

4

5

6

7

8 456789 pH-log C Zn 2+ Fe s oZn +

Fe w oZn +

Fig. F2. Calculated equilibrium speciation as a function of pH for zinc. The top figure shows results for Total Zn=10 -4 M, I = 0.1 M; the bottom figure shows results for Total Zn=10 -7 M, I = 0.1 M.F11 of 16 

# F3. Surface Complexation Reactions and Parameters Used in Section 4

The adsorption of heavy metal (and arsenic) ions on minerals is influenced by a variety of factors, the most important bei ng pH, type and speciation of meta l ion involved, as well as heavy metal competition (Bradl, 2004). In this study, we c onsider goethite, illite, kaolinite, and smectite as principal adsorbents, mainly because these mi nerals have strong adsorption capacity, and we
focus on lead and arsenic as relevant hazardous constituents

. A literature analysis was conducted to determine the relevant surface complexation reactions and their ther modynamic constants for these cations and minerals, as summarized below.

Adsorption of Pb(II) on goethite was investigat ed under varying conditions. Kovacevic et al. (2000) experimentally studied th e adsorption of lead species on goethite. Experiments were carried out for total Pb species concentrations of 2.5 × 10 -4 mol/L, ph of 3–6, and ionic strength
of 2 × 10 -3 mol/L. The calculated surface complexation co nstant (logK) is -0.9 for a reaction expressed in the following form:

2 SOPb H Pb SOH ++ +

+ + U (F31)

where S indicates surface, and SOPb + and SOH are surface complexes.

Muller and Sigg (1991) investigated the adsorption of Pb(II) on goeth ite in dilute (i.e., 2.41 × 10 -

7 mol/L) Pb (II) solutions over a pH range 6.6–8.2. Their published surface complexation constant (logK) is 0.52 for the monodentate su rface complex (Equation F31) and 6.27 for the bidentate surface complex. Published surface co mplexation constants by Muller and Sigg (1991) are adopted in our simulations here, since they were obtained under conditions similar to the simulation setup in terms of Pb(II) concentrat ion and pH. Since PbCO3( aq) is used as the primary species in our simulation instead of Pb (II), the reaction described in Equation (F31) occurs together with the following reaction between PbCO3(aq) and Pb(II):

2 3 3 ( ) log 3.06 Pb HCO H PbCO aq K + −+ + + =− U (F32)
Note that the log K value of Equation (F32) was taken from the EQ3/6 V7.2b data base (Wolery, 1993).F12 of 16 Combining Equations (F31) and (F32), we obtain the surface complexation reaction shown in Table F2, where log K is -2.56. Note that the surface complex is expressed as _ Goe OPb + instead

of SOPb +

, in order to distinguish it from other surface complexes of Pb(II).

Hizal and Apak (2006) studied lead(II) adsorption individually on kaolinite-b ased clay minerals. They performed experiments on three samples and calculated surface complexation constants and site densities. In our study, the surface complexation constant is taken to be the average of the value for these three samples, which is -1 .87 for Equation (F31). Co mbining Equations (F31) and (F32), the log K value is -4.95.

Using batch adsorption experiments, Gu and Evan s (2007) investigated the adsorption of Pb(II)
onto illite as a function of pH and ionic strength. (Surface comp lexation of Pb on illite in a 0.01
mol/L NaNO 3 electrolyte is considered in our mode l because the majority of the potable groundwaters recorded in the NWIS database (USGS, 2008) have an ionic strength around 0.01M.) The investigators provided surface comp lexation constants for complexes on both edge and basal sites. Here, we refer to these as constants for strong sites (Ill s _OPb + ) and weak sites (Ill w _OPb + ), respectively (see Table F2).

Bradbury and Baeyens (2005) summarized surface complexation constants on smectite for more than ten metals. They presented linear free en ergy relationships (LFER) for strong and weak
sites. The LFER correlated th e surface complexation constants with aqueous complexation constants of the corresponding aqueous comp lexes. The calculated surface complexation constants for Pb(II) on montmorillonite, a member of the smectite group, for strong and weak sites using the LFERs (see Table 6 in Bradbur y and Baeyens, (2005) are adopted in our simulation (see Table F2).

All the surface complexation reactions for lead used in the present simulation are listed in Table F2, while Table F3 summarizes these reactions using Pb +2 as primary species.

Adsorption of arsenite species on minerals can be describe d by three surface complexation
reactions:

232 33 SH AsO H O SOH H AsO + + U (F33)F13 of 16 3 2 33 SHAsO H O H SOH H AsO − +

++ + U (F34)

2 3 2 33 2 SHAsO H O H SOH H AsO − + ++ + U (F35)

where S denotes the surface of any mineral.

Surface complexation constants (see Table F3) for adsorption of arsenite on goethite are taken from Dixit and Hering (2003) by combining two reactions:

3 232 3 _ _ 3 log 39.93 Goe OH AsO H O Goe OH AsO H K − + + + + =− U (F36)

3 3 33 3 log 34.74 AsO H H AsO K − + + = U (F37)
Goldberg (2002) published the surface complexation constants (log K ) for Equations (F36) and (37), which are -4.68 and 2.27, respectively. Th ese constants are similar to those reported by Dixit and Hering (2003).

Goldberg (2002) also presented the surface comple xation constants for the adsorption of arsenite on illite, kaolinite, and smectite. However, Manni ng and Goldberg (1997) gave a more complete description for adsorption of arsenite on these th ree minerals. In our study, we use the published surface complexation constants for Equation s (F33), (F34) and (F35) from Manning and Goldberg (1997) (see Table F4). Table F4 summa rizes the surface complexation reactions of As
used in Section 4.

Specific surface area and site density are th e other two important parameters affecting adsorption. Table F5 summarizes some publis hed values for specific surface area and site density, while Table F6 li sts those values used in the base model.F14 of 16 Table F2. Surface complexation of Pb on different mine rals using PbCO3(aq) as primary species

Adsorbent Surface Complexes Reactions Log k int Reference

goethite Goe_OPb+ Goe _OPb + + HCO 3 - U Goe_OH + PbCO 3 (aq) -2.56
goethite (Goe _O) 2 Pb (Goe _O) 2 Pb + H + + HCO 3 - U 2Goe_OH + PbCO 3 (aq) 3.18 (Muller and Sigg, 1991)

kaolinite Kao_OPb+ Kao_OPb + + HCO3 - U Kao_OH + PbCO3(aq) -4.95 (Hizal and Apak, 2006)

illite Ill s _OPb+ Ill s _OPb + + HCO3 - U Ill s _OH + PbCO3(aq) -4.43

illite Ill w _OPb+ Ill w _OPb + + HCO3 - U Ill w _OH + PbCO3(aq) 0.78 (Gu and Evans, 2007)

smectite Sme s _OPb+ Sme s _OPb + + HCO3 - U Sme s _OH + PbCO3(aq) -4.18

smectite Sme w _OPb+ Sme w _OPb + + HCO3 - U Sme w _OH + PbCO3(aq) -1.78 (Bradbury and Baeyens, 2005)

glauconite Gla_OPb + Gla_OPb + + HCO 3 - U Gla _OH + PbCO 3 (aq) -6.4 (Smith et al., 1996)

Table F3. Surface complexation of Pb on different minerals using Pb +2 as primary species.

Adsorbent Surface Complexes Reactions Log k int Reference

goethite Goe_OPb+ Goe _OPb + + H + U Goe_OH + Pb +2 0.5 goethite (Goe _O) 2 Pb (Goe _O) 2 Pb+2H + U 2Goe_OH + Pb +2 6.24 (Muller and Sigg, 1991)

kaolinite Kao_OPb+ Kao_OPb + + H + U Kao_OH + Pb +2 -1.89 (Hizal and Apak, 2006)

illite Ill s _OPb+ Ill s _OPb + + H + U Ill s _OH + Pb +2 -1.37 illite Ill w _OPb+ Illw_OPb + + H + U Illw_OH + Pb +2 3.84 (Gu and Evans, 2007)

smectite Sme s _OPb+ Sme s _OPb + + H + U Sme s _OH + Pb +2 -1.12 smectite Sme w _OPb+ Sme w _OPb + + H + U Sme w _OH + Pb +2 1.28 (Bradbury and Baeyens, 2005) glauconite Gla_OPb + Gla_OPb + + H + U Gla _OH + Pb +2 -3.34 (Smith et al., 1996)F15 of 16 Table F4. Surface complexation of H 3 AsO 3 on different minerals

Adsorbent Surface Complexes Reactions Log k int Reference
goethite Goe _H 2 AsO 3 Goe _H 2 AsO 3 + H 2 O U Goe _OH + H 3 AsO 3 -5.19 goethite Goe _HAsO 3 - Goe _HAsO 3 - + H 2 O + H + U Goe_OH + H 3 AsO 3 2.34 (Dixit and Hering, 2003)

kaolinite Kao_H 2 AsO 3 Kao _H 2 AsO 3 + H 2 O U Kao _OH + H 3 AsO 3 -8.23 kaolinite Kao _HAsO 3 - Kao_HAsO 3 - + H 2 O + H + U Kao _OH + H 3 AsO 3 0.664

kaolinite Kao _OAsO 3 2- Kao_AsO 3 2- + H 2 O + 2H + U Kao _OH + H 3 AsO 3 13.67 (Manning and Goldberg,

1997)

illite Ill_H 2 AsO 3 Ill _H 2 AsO 3 + H 2 O U Ill_OH + H 3 AsO 3 -9.07 illite Ill _HAsO 3 - Ill _HAsO 3 - + H 2 O + H + U Ill _OH + H 3 AsO 3 -3.000 illite Ill_AsO 3 2- Ill_AsO 3 2- + H 2 O + 2H + U Ill_OH + H 3 AsO 3 10.3 (Manning and Goldberg,

1997) smectite Sme _H 2 AsO 3 Sme _H 2 AsO 3 + H 2 O U Sme _OH + H 3 AsO 3 -8.89 smectite Sme _HAsO 3 - Sme _HAsO 3 - + H 2 O + H + U Sme _OH + H 3 AsO 3 4.650

smectite Sme _AsO 3 2- Sme _AsO 3 2- + H 2 O + 2H + U Sme _OH + H 3 AsO 3 13.700 (Manning and Goldberg,

1997)

albite~low Alb _OH 2 AsO 3 Alb _H 2 AsO 3 + H 2 O U Alb _OH + H 3 AsO 3 -7.58 albite~low Alb _OHAsO 3 - Alb _OHAsO 3 - + H 2 O + H + U Alb _OH + H 3 AsO 3 2.43

albite~low Alb _OAsO 3 2- Alb _OAsO 3 2- + H 2 O + 2H + U Alb _OH + H 3 AsO 3 14.8 (Manning and Goldberg,

1997)

Table F5. Summary of published specific surface area and site densities
Site density (mol/m 2 ) Absorbent Specific surface area (m 2 /g) Strong site Weak site Reference hematite 5.4 0.00219 0.219 Dzombak and morel (1990) goethite 54 3.32e-6 Dixit and Hering (2003) goethite 14.7 1.76e-6 3.22e-6 Muller and Sigg (1991) kaolinite 19.53 1.02 e-8 Hizal and Apak (2006) kaolinite 14.73 2.2e-6 3.0e-6 Lackovic, et al. (2003) kaolinite 9.1 2.68e-7 Manning and Goldberg (1997)

illite 66.8 1.3e-6 2.27e-6 Gu and Evans (2007) illite 123 2e-7 6e-7 Lackovic, et al. (2003) illite 24.2 4.79e-8 Manning and Goldberg (1997) Na-smectite 56.38 4.77e-8 9.54e-7 Bradbury and Baeyens (2005)

smectite 18.6 1.1e-6 Manning and Goldberg (1997) albite~low 5.9 2.9e-5 Manning and Goldberg (1997) glauconite 48.7 1.6e-6 Smith et al. (1996)F16 of 16 Table F6. Specific surface area and site densities used in the base model
Site density (mol/m 2 ) Absorbent Specific surface area (m 2 /g) Strong site Weak site Reference goethite 14.7 1.76e-6 3.22e-6 Muller and Sigg, (1991) kaolinite 14.73 2.2e-6 3.0e-6 Lackovic et al. (2003)

illite 66.8 1.3e-6 2.27e-6 Gu and Evans (2007) Na-smectite 56.38 4.77e-8 9.54e-7 Bradbury and Baeyens (2005)