dataesr/scientific-paragraphs-categorization huggingface dataset:
Since many radionuclides encountered in the environment are metallic in nature, bioavailability models such as the BLM are in principle applicable to them. In developing such a model, there is a need firstly to assess the prediction of radionuclide speciation under realistic environmental conditions. Furthermore, since conditions entail exposure of organisms to multiple contaminants, there is a need to assess whether and how the presence of such co-contaminants influences radionuclide speciation. This study evaluates the influence of co-contaminants on the modelled speciation of uranium and thorium in surface waters by applying a set of speciation model frameworks to a collection of real-life examples of surface waters impacted by uranium and thorium contamination. This study places the similarities and differences among the predictions of speciation in the context of the different model frameworks and databases of thermodynamic parameters (binding constants) used.
The latter is particularly pertinent for radionuclides, given the historic effort into producing internally consistent databases of binding constants for use in thermodynamic models (e. g. Grenthe et al. , 2004)  and the number of different databases that consequently exist. We have chosen uranium and thorium as the radioelements of interest given the potential for their release to the environment during mining, milling and processing operations, nuclear fuel production, power station discharges and waste storage/processing, alongside a range of potential co-contaminants (e. g. chromium, nickel, zinc) and other radionuclides (e. g. Garnier-Laplace et al. , 2009; Herlory et al. , 2013; Vanhoudt et al. , 2012). The equilibrium speciation of uranium, including redox transformations and binding to environmentally-relevant colloidal and mineral phases (e.
g. humic substances, iron(III) oxyhydroxides), has been extensively studied (e. g. Amme, 2002; Maher et al. , 2013; O'Loughlin et al. , 2011; Vitorge and Capdevila, 2003)  and can be readily computed using geochemical speciation models  (Denison and Garnier-Laplace, 2005; Vercouter et al. , 2015). Equilibrium constants for uranium complexation with important freshwater ligands (e. g. CO3 2-, organic matter) have been compiled into a number of databases for use with specific speciation models. Thorium speciation in freshwaters has also been studied (e. g. Moulin et al. , 1992). We originally intended to include radium and polonium also. However, initial screening of the model databases showed that none contained any data on radium or polonium complex formation, thus speciation of these elements could not be simulated.
g. Hegazy et al. , 2013). We have performed comparative uranium and thorium speciation calculations using seven different combinations of speciation model and binding constant database. Predictions of uranium speciation have been made in four real-life freshwaters impacted by discharges of uranium and other contaminants. By comparing speciation predictions in the presence and absence of other contaminants, we assess the potential for such contaminants to impact the speciation of uranium in freshwaters. We assess the comparative predictions of the different speciation models and databases, and make recommendations for future consolidation and updating of databases.
In all calculations, iron(III) was allowed to precipitate as HFO if its solubility product was exceeded. The solubility expression given by  Lofts and Tipping (2011), aFe[3+] •aH[+] -2. 49 ≥ 10 -0. 52 , was used to check for HFO precipitation. Precipitated HFO may be allowed to have a chemically active surface that binds ions. WHAM scenario simulations were done with two variants of the solution database: i. The default database (simulations denoted WHAM-d). This database was originally compiled by  Tipping (1994)  and updated for WHAM7 to allow simulation of the formation of the uranyl complexes UO2(CO3)3 4-, UO2(SO4)2 2-, UO2(SO4)3 4-, UO2HPO4 0 , UO2H2PO4 + , UO2H3PO4 2+ , UO2Cl + , UO2Cl2 0 , UO2F + , UO2F2 0 , UO2F3 -and UO2F4 2-, and the thorium complexes Th(CO3)5 6-, Th(OH)2(CO3)3 4-, Th(OH)3CO3 -, ThSO4 2+ , Th(SO4)2 0 and Th(SO4)3 2-.
Precipitated HFO was assumed to have a chemically active surface capable of binding protons and cations, including uranyl (UO2 2+ and UO2OH + ) and thorium (Th 4+ and ThOH 3+ ). Constants for the binding of uranyl and thorium to HFO were calculated using the linear free energy relationship of  Lofts and Tipping (1998)  (see Supplementary Information for details). ii. A modified database allowing formation of the uranyl alkaline earth metal-carbonate complexes MgUO2(CO3)3 2-, CaUO2(CO3)3 2-, Ca2UO2(CO3)3 0 , SrUO2(CO3)3 2-, BaUO2(CO3)3 2-, and Ba2UO2(CO3)3 0 , and the hydroxy-carbonate complexes (UO2)2(OH)3CO3 -, (UO2)3(OH)3CO3 + and (UO2)11(OH)12(CO3)6 2-. (simulations denoted WHAM-m). Binding constants for the alkaline earth metal-carbonate complexes were calculated from the results of  Dong and Brooks (2006) , using log K values for the UO2(CO3)3 4-complex corrected to the experimental ionic strength using the extended Debye-Hückel equation.
Visual MINTEQ (VMIN) and the NICA-Donnan model Visual MINTEQ is a development of the MINTEQA2 model  (Hydrogeologic Inc. and Allison Geoscience Consultants Inc. , 1999). The model combines codes for aqueous speciation, oxidation-reduction equilibria, and mineral equilibria. It includes the NICA-Donnan model  (Benedetti et al. , 1995)  for simulating the complexation of protons and metals to humic substances and the generalised two-layer model (GTLM) for the binding of metals to HFO  (Dzombak and Morel, 1990). The NICA-Donnan implementation in Visual MINTEQ is parameterised for the binding of 23 metal species including uranyl. The GTLM binding constant database as supplied with the model does not contain constants for the binding of uranyl to HFO, therefore we added the binding constants derived by  Mahoney et al.
(2009)  to the database. Version 3. 0 of Visual MINTEQ and the default inorganic speciation database were used for computations. The database is based on the original MINTEQA2 model database, with binding constants updated with values from the NIST thermodynamic database, version 7. 0  (Smith et al. , 2003) , where possible. Simulations are denoted VMIN-d.
Chemical equilibrium of species and surfaces (CHESS) CHESS (Van der Lee, 1998) combines aqueous speciation, oxidation-reduction equilibria and mineral equilibria. The model has the capability to simulate ion exchange and surface complexation on minerals and colloids. CHESS simulations were done using two solution databases: the default chess. tdb database (simulations denoted CHESS-ch) and the ctdpv3_Dong database (simulations denoted CHESS-ct). The default database is derived from the original database for the EQ3/6 speciation model. It contains parameters for ion binding to HFO using the GTLM. It also contains a small number of parameters for ion binding to humic acid; however these do not include parameters for uranyl. The ctdpv3_Dong database is based on a database released in 2004 in the framework of the Common Thermodynamic Database Project (van der  Lee and Lomenech, 2004) , which merged a review of uranyl thermodynamic data performed by  Denison (2004)  and the default Chess database, augmented with binding constants for the complexes MgUO2(CO3)3 2-, CaUO2(CO3)3 2-, Ca2UO2(CO3)3 0 , SrUO2(CO3)3 2- and Sr2UO2(CO3)3 0  (Dong and Brooks, 2006; Geipel et al.
Geochemist's Workbench (GWB) The GWB  (Bethke and Yeakel, 2012)  can simulate solution, oxidation-reduction and precipitation equilibria. It can also simulate ion binding to mineral surfaces, including ion exchangers (clays). Three databases for ion binding to HFO using the GTLM are available; however, none contain binding parameters for uranyl. The model does not have a submodel for ion-binding to humic substances. GWB simulations were done using two solution databases: the Thermo. com. v8. r6+ database (simulations denoted GWB-c) and the Thermo. Minteq database (simulations denoted GWBm). The Thermo. com. v8. r6+ database is derived from the Lawrence Livermore National Laboratory 'combined' database, version 8 release 6. The Thermo. Minteq database is the database used by version 2. 40 of Visual MINTEQ.
PHREEQC PHREEQC can simulate solution, oxidation-reduction and precipitation equilibria, ion exchange reactions and adsorption on mineral surfaces. Simulations were done using two databases: the llnl. dat database (simulations denoted PHREEQC-l) and the minteq. dat database (PHREEQC-m). Both databases contain parameters for the binding of ions to HFO using the GTLM, however neither database contains binding constants for uranyl or thorium. The model does not have a submodel for ion-binding to humic substances, although it is possible to include the Humic Ion-Binding Model in the code  (Liu et al. , 2008; Marsac et al. , 2011). The minteq. dat database has no binding constants for thorium, therefore this combination could not be used to assess thorium speciation.
Tajikistan The chemical compositions of the waters were taken from data published by  Skipperud et al. (2013). Two samples from around the Taboshar uranium mine were selected as scenarios. The first is a water-filled opencast mine hole (sample name Pit Lake) at the mine. The second is a sample from a stream draining from the mine tailings dump, named the 'Yellow Mountain' (sample name Yellow Mountain). Both waters were alkaline. Hardness, calculated from measurements of dissolved magnesium and calcium, was very high (463 and 507 mg CaCO3 dm -3 respectively). Alkalinity was not measured, but simulations using WHAM7, assuming the measured ionic charge deficit in the waters to be due to carbonates, predicted carbonate alkalinities of 207 and 257 mg CaCO3 dm - 3 respectively. Concentrations of U in the samples were the highest in the scenarios, being 2000 µg dm -3 and 1100 µg dm -3 for Pit Lake and Yellow Mountain respectively.
Scope of model/database combinations for uranyl speciation Table  S1  in the Supplementary Information shows the binding constants for the inorganic uranyl solution species (complexes) in each model/database combination. The databases for CHESS-ch, GWB-c and PHREEQC-l are identical not only for the uranyl complexes which may be formed, but also have the same thermodynamic constants for their formation; thus speciation predictions using these model/database combinations should be similar if not identical. The GWB-m database is very similar to the VMIN-d database, being an older version of the same database, however VMIN-d does not have binding constants for the hydroxy-carbonate complexes (UO2)2CO3(OH)3 -and (UO2)3CO3(OH)3 + , which GWB-m does. The binding constants for the alkaline earth metal-uranyl-carbonate complexes CaUO2(CO3)3 2-and Ca2UO2(CO3)3 0 also differ between the two databases.
With the exception of PHREEQC-m, all the model/database combinations simulate formation of the monomeric hydrolysis products UO2(OH)n (2-n) (n = 1,2,3 or 4), the simple carbonate complexes UO2(CO3)n (2-2n) (n = 1,2 or 3), the sulphate complexes UO2(SO4)n (2-2n) (n = 1 or 2), the phosphate complexes UO2HnPO4 (n-1) (n = 0, 1, 2 or 3) and the halide complexes UO2Hn (2-n)    (H is Cl -or F -, n = 1 or 2 for Cl -and n = 1, 2, 3 or 4 for F -). The PHREEQC-m database is generally sparser with respect to the range of complexes simulated compared to the other databases; of the monomeric hydrolysis products, it simulates only UO2OH + , and does not simulate formation of UO2SO4 0 , UO2PO4 -or UO2Cl2 0. An appreciable number of other uranyl species are included in a relatively small number of the databases.
Scope of model/database combinations for thorium speciation Table  S1  in the Supplementary Information shows the binding constants for the inorganic thorium solution species (complexes) in each model/database combination. The WHAM database, which is identical in both the WHAM-d and WHAM-m situations, allows formation of the monomeric thorium hydrolysis products Th(OH)n (4-n)+ (n = 1-4), the carbonate complexes ThCO3 2+ and Th(CO3)5 6-, the hydroxy-carbonate complexes Th(OH)2(CO3)3 4-and Th(OH)3CO3 -, the sulphate complexes Th(SO4)n (4-2n)+ (n = 1 to 3) and the halide complexes ThCl 3+ , ThF 3+ and ThF2 2+. As with uranyl, the databases for CHESS-ch, GWB-c and PHREEQC-l are identical in respect of the thorium complexes that may form and their binding constants. The databases all allow formation of the monomeric thorium hydrolysis products Th(OH)n (4-n)+ (n = 1-4), three polymeric hydrolysis products Thm(OH)n (4m-n)+ with m = 2 and n = 2, m = 4 and n = 8, m = 6 and n = 15.
They also allow formation of a range of complexes with Cl -, F -, SO4 2- and PO4 3-(see Table  S1  for details). However, they have no binding constants for carbonate or hydroxy-carbonate complex formation. VMIN-d and GWB-m also have similar databases, albeit simulating formation of different complexes. Of the hydrolysis products, they simulate only ThOH 3+ , Th(OH)2 2+ and Th2(OH)2 6+ , and of the carbonate and hydroxy-carbonate complexes only Th(CO3)5 6-and Th(OH)3CO3 -. They also simulate ThCl 3+ , ThF 3+ , ThF2 2+ , ThF3 + , ThF4 0 and ThNO3 3+ , but have no constants for complexes with PO4 3-. CHESS-ct simulates a similar range of complexes to CHESS-ch, also including Th(CO3)5 6-and Th(OH)3CO3 -but excluding any complexes with PO4 3-except for the hydroxy-phosphate complex Th(OH)4PO4 3-, which is not found in any of the other databases.
Free and hydrolysed forms were predicted to dominate (~85%) with contributions from carbonate (~10-15%) and hydroxy-carbonate species (~1-3%). The CHESS-ct simulation predicted a much smaller contribution from free and hydrolysed species (~19%), with larger contributions from carbonate (~41%) and hydroxy-carbonate complexes (~39%). Inspection of the model outputs indicated that this was due to the standard log K for the UO2(OH)2 0 species being 1. 44 greater in CHESS-ch, thus favouring formation of UO2(OH)2 0 in CHESS-ch at the expense of UO2CO3 0 and (UO2)2CO3(OH)3 -. Similar dominance of free and hydrolysed species was predicted by GWB-c and PHREEQC-l, as expected given that their databases are identical to that of CHESS-ch. Similar differences were seen when comparing the simulations done using the Geochemist's Workbench.
In the GWB-c simulation, free and hydrolysed species were predicted to dominate (~84%) with carbonate complexes also present (~15%). In the GWB-m simulation, carbonate complexes were predicted to dominate (~75%), with contributions from free and hydrolysed species (~12%), hydroxy-carbonate complexes (~6%) and the silicate complex UO2H3SiO4 + (~8%). The PHREEQC-l simulation predicted a dominance of free and hydrolysed species (~96%) with carbonate complexes (~3%) making up most of the remainder, while in the PHREEQC-m simulation the speciation was predicted to be made up largely of near equal contributions from carbonate complexes (~35%), free and hydrolysed species (~33%) and UO2H3SiO4 + (~29%). In contrast to the Ritord results, the predicted speciation for the Beaverlodge scenario was consistently dominated by carbonate-containing complexes, regardless of the model or database used.
Important differences among the predictions were still seen, particularly in respect to the predicted importance of the alkaline earth-uranyl-carbonate complexes. Where the database employed contained such ternary complexes, these were predicted to be dominant (~84% for WHAM-m, ~92% for Visual MINTEQ, ~97% for CHESS-ct, ~95% for GWB-m). In the remaining simulations carbonate complexes were predicted to dominate (~97-99%) with small amounts (~1-2%) present in the free ion and hydrolysed forms. The simulations for Pit Lake also predicted dominance of alkaline earth-uranyl-carbonate complexes. In the four model/database combinations where they could form (WHAM-m, VMIN-d, CHESS-ct and GWB-m), they were predicted to make up at least 97% of the uranyl. In the CHESS-ch, GWB-c and PHREEQC-l scenarios, uranyl was predicted to be largely present as carbonate complexes split between free and hydrolysed forms (~14%), binary carbonate species (~43-44%) and mixed hydroxyl-carbonate species (~43%).
The Beaverlodge WHAM-d and WHAMm predictions were similar to those made in the absence of DOM/HFO, with organic and HFObound species predicted to be <1%. VMIN-d predicted ~2% organically-complexed uranyl in Beaverlodge. Complexation to DOM and HFO in Pit Lake was negligible in all model/database combinations. In Yellow Mountain, VMIN-d predicted ~1% complexation to DOM, otherwise there were negligible differences compared to the predictions without DOM/HFO.
Predicted occurrence of major thorium species As with uranyl, the predicted occurrence of thorium species across the different model/database combinations, in the absence of DOM and HFO, was variable. A notable pattern across the scenarios was the dominance of two specific complexes, Th(OH)4 0 and Th(OH)3CO3 -. An important component of the variability of the predicted speciation was determined by whether the database used contained binding constants for these complexes. In Beaverlodge, WHAMd/WHAM-m predicted that Th(OH)4 0 dominated (~79%) with a major contribution from Th(OH)3CO3 -(~21%). In the VMIN-d and GWB-m model/database combinations, for which the binding constant for Th(OH)4 0 was absent, Th(OH)3CO3 -completely dominated speciation (~100%). Conversely, CHESS-ch, GWB-c and PHREEQC-l, for which the binding constant for Th(OH)3CO3 -was absent, predicted ~100% Th(OH)4 0.
It is possible that uranyl binding to HFO may be important in pyritic acid mine drainage systems, where oxidation of high concentrations of Fe(II) produced by pyrite dissolution can produce elevated concentrations of HFO which may exert a significant control on metal speciation and transport (e. g. Balistrieri et al. , 2007). The effect of Fe(III) was negligible in the other scenarios, due to its relatively low concentrations (Table  2 ) and the higher hardness and/or pH, which increased uranyl-carbonate association. This limited the formation of HFO and thus of uranyl-HFO adsorbed complexes. Formation of uranyl adsorbed to HFO did not exceed ~1% of total uranyl in the other scenarios. The considerable variations in predicted speciation for each scenario across the different model/database combinations illustrate how differences among the databases clearly influence the computed speciation.
The patterns observed in the thorium predictions are simpler and dominated by Th(OH)4 0 and Th(OH)3CO3 - ; for specific model/database combinations, the predicted speciation is highly dependent on whether either or both of these complexes are allowed to form. Simulations involving DOM/HFO showed contrasting predicted binding behaviour of thorium between WHAM and Visual MINTEQ, with the latter predicting that DOM complexes completely dominated thorium speciation, while the former predicted them to be of minor importance. Further work is clearly needed to better quantify the actual importance of DOM for uranyl and thorium complexation in surface waters, to provide data against which to further assess and improve the models and to constrain their predictions. Coupled with this, there needs to be a raising of awareness among users of geochemical speciation modelling, regarding the importance of assessing database coverage and completeness prior to performing computations.
As noted in the introduction, a further intention of this study was to model radium and polonium alongside uranium and thorium, but this was not possible due to a general lack of binding constants in the model databases. This is a notable data gap given the importance of radium as a decay product of uranium and thorium. Some work on estimating Ra 2+ binding constants exists;  Langmuir and Riese (1985)  estimated binding constants and enthalpies for some simple Ra 2+ complexes (RaOH + , RaCO3 0 , RaCl + , RaSO4 0 ) by extrapolation from the relationship between the corresponding binding constants for other Group II metals (Ca, Sr, Ba) and the effective ionic radius of the cation. However, such estimated constants for solution complexes are not generally incorporated into speciation modelling databases.
Conclusions  Well-characterised field waters, with minor extrapolation where required, were used to compare geochemical model predictions of dissolved uranyl (in four scenarios) and thorium speciation (in three scenarios) and to investigate the influence of cocontaminants on the predicted forms of uranyl and thorium.  Removal of co-contaminants from simulations generally had negligible effects on the predicted speciation, with the exception of the Ritord scenario where simulation using WHAM7 in the absence of iron prevented formation of uranyl adsorbed to HFO. This suggests iron could have a significant influence on uranyl speciation in waters receiving iron-rich inputs such as acid mine drainage.  Speciation predictions were dependent on model capabilities and the coverage of inorganic and organic complexes in the database used.
 Organic complexation, where it could be simulated, was predicted to be important for uranyl in a slightly acidic water of relatively high DOM concentration. Binding of thorium to DOM was predicted to dominate speciation in circumneutral waters when modelled using Visual MINTEQ, however predictions of the same waters using WHAM7 predicted only a minor role for DOM in binding thorium.  Thorium speciation was predicted to be controlled by a small number of specific complexes. Variations in predicted speciation were strongly dependent on the presence/absence of these complexes from the database used.  In circumneutral waters, formation of carbonate complexes, including hydroxycarbonates and alkaline earth metal-carbonates, was predicted to dominate the uranyl speciation.  Differences in the range of carbonate complexes considered in the model databases had an important influence on uranyl speciation predictions in circumneutral waters, where inorganic complexes were predicted to dominate.
Alkaline earth metal-carbonate and hydroxy-carbonate complexes were predicted to be important species in such waters, when they were included in simulations. Where they were absent, simple carbonate species were generally predicted to be important.  Complexation constants for radium and polonium were entirely absent from all the databases and so no assessment of speciation was possible. There is a need for experimental determination of radium and polonium complexation constants in order to properly incorporate these elements into geochemical speciation models.  Model users need to be aware of the differences among model databases before applying models, in particular the status with respect to updates and the incorporation of the most up to date binding constants.
Summary of binding complexes Table  S1. Thermodynamic data a,b,c,d for solution complexes of uranyl and thorium simulated by each model/database combination. Reaction enthalpies for the CHESS-ch/GWB-c and CHESS-ct databases are computed by interpolation of point log K values for 0 o C and 25 o C using the Van't Hoff equation. Underlining denotes where the value in the database is the same as that in the NEA-TDB compilation  (Guillaumont et al. , 2003). b Reaction enthalpy data have been converted from kcal/mol to kJ/mol using a factor of 1 kcal = 4. 184 kJ. Model/database combination WHAM-d a,b WHAM-m a,b VMIN-d, GWB-m CHESS-ch c,d , GWB-c c,d CHESS-ct c,d PHREEQC-l c, c Uranyl carbonate and uranyl hydroxy-carbonate complex formation data have been converted to refer to CO3 2-, not HCO3 2-, as the reacting ligand.
Conversion was done using the parameters for the reaction HCO3 - H + + CO3 2-present in the same database. d Data for formation of phosphate complexes converted to refer to PO4 3-, not HPO4 3-, as the reacting ligand. Conversion was done using the parameters for the reaction HPO4 2-  H + + PO4 3-present in the same database. e Value in database (0. 9 kJ/mol) deemed incorrect so not used.