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WHAMC—A Chemical Equilibrium Model and Computer Code for Waters, Sediments, and Soils Incorporating a Discrete Site/Electrostatic Model of Ion-Binding by Humic Substances
Abstract
WHAM (Windermere Humic Aqueous Model) is designed to calculate equilibrium chemical speciation in surface and ground waters, sediments, and soils. The model is suitable especially for problems where the chemical speciation is dominated by organic matter (humic substances). WHAM combines Humic Ion-Binding Model V with a simple inorganic speciation code for aqueous solutions. Precipitation of aluminum and iron oxides, cation-exchange on an idealized clay mineral, and adsorption-desorption reactions of fulvic acid also are taken into account. The importance of ion accumulation in the diffuse layers surrounding the humic molecules is emphasized. Model calculations are performed with a BASIC computer code running on a Personal Computer.
... Dissolved uptake rate constants (k u ) were determined from the slope of the linear relationship between metal influx (μg g -1 d -1 ) and the measured total dissolved and free-ion metal concentrations (μg L -1 d -1 ). Accumulated tracer concentrations in tissues were determined as described by Croteau and Luoma [42]. Briefly, treatments were simultaneously spiked with commercially purchased stable isotope standards (Trace Sciences International) enriched with 65 Cu (99.4%) and 106 Cd (96.5%) to produce a range of dissolved and dietborne exposure conditions (Table 1). ...
... The food used during the exposure treatments consisted of dried and finely ground laboratory cultured freshwater snails (Lymnaea stagnalis) and oligocheates (Lumbriculus variegatus), each pre-exposed to dissolved metals. While this food differs from the natural diet of the two caddisflies, snails and worms accumulate metals from the aqueous phase [42,45], enabling the preparation of food items with internalized labels instead of surface-bound tracers. Two to three milligrams of homogenized tissue mixed in artificial VSW (5-10 ml) were dispersed into the current using a transfer pipette. ...
... All samples and standards were spiked with Ge (8 μL per mL of sample) as an internal standard to account for instrument drift. Accumulated 65 Cu and 106 Cd tracer concentrations were determined using the method described by Croteau and Luoma [42]. Riverine water reference material (SLRS-4) and calibration standards were analyzed throughout each run [38]. ...
Hydropsyche and Arctopsyche are filter-feeding caddisflies (Order: Trichoptera; Family: Hydropsychidae) that are commonly used to monitor metal exposures in rivers. While tissue residue concentrations provide important bioaccumulation data regarding metal bioavailability, they do not provide information regarding the mechanisms of uptake and loss, or exposure history. This study examined the physiological processes that control Cu and Cd uptake and loss using a biokinetic bioaccumulation model. Larvae of each taxon were experimentally exposed to either water or food enriched with stable isotopes ( ⁶⁵ Cu and ¹⁰⁶ Cd). Dissolved Cu uptake (k u ) was similar between species (2.6–3.4 L ⁻¹ g ¹ d ⁻¹ ), but Cd uptake was 3-fold higher in Hydropsyche than Arctopsyche (1.85 L ⁻¹ g ¹ d ⁻¹ and 0.60 L ⁻¹ g ¹ d ⁻¹ , respectively). Cu and Cd efflux rates (k e ) were relatively fast (0.14 d ⁻¹ –0.24 d ⁻¹ ) in both species, and may explain, in part, their metal tolerance to mine-impacted rivers. Food ingestion rates (IR), assimilation efficiency (AE) of ⁶⁵ Cu and ¹⁰⁶ Cd from laboratory diets were also derived and used in a biodynamic model to quantify the relative contribution of dissolved and dietary exposure routes. Results from the biodynamic model were compared to tissue concentrations observed in a long-term field study and indicated that because dissolved Cu and Cd exposures accounted for less than 20% of body concentrations of either taxon, dietary exposure was the predominant metal pathway. An estimation of exposure history was determined using the model to predict steady state concentrations. Under constant exposure conditions (dissolved plus diet), steady state concentrations were reached in less than 30 days, an outcome largely influenced by rapid efflux (k e ).
... WinHumicV is an open-source software and commonly used model for predicting metal ion speciation in surface water in the presence of humic substances (Gustafsson, 1999). This model is based on Humic Ion-Binding Model V, originally created by Dr. Edward Tipping (Tipping, 1994;Tipping & Hurley, 1992). However, while the theory of metal binding is the same as that of Model V, WinHumicV is written in Visual Basic and adapted to the Windows operating system. ...
... Vol:. (1234567890) discrete sites on humic molecules where protons and metals can bind, and the binding is described using equilibrium quotients that depend on the net (usually negative) charge of the humic substance's molecule (Tipping, 1994). The metal species of Zn 2+ and Cd 2+ were evaluated in the absence and presence of dissolved HAs at concentrations of 1, 10, and 50 mg/L. ...
... In this study, pure humic acids were purchased and used in the test solutions; therefore, the percentage of fulvic acid was set to 0% in the model calculation. Moreover, other parameters for model calculations were conducted using the default database sets mentioned in the study by Tipping (1994). ...
Humic substances in aquatic environments can bind metal ions in their carboxylic groups, thereby altering the toxicity and availability of metal ions to aquatic organisms. This study investigated the effects of dissolved humic acids (HAs) on the toxicity and accumulation of waterborne zinc (Zn2+) and cadmium (Cd2+) ions in the larvae of Rheocricotopus species (Chironomidae, Diptera). Chironomid larvae were separately exposed to Zn2+ (0.1 and 1.0 mg/L) and Cd2+ (0.03 and 0.1 mg/L) in the presence of dissolved HAs at concentrations ranging from 0 to 50 mg/L. The metal species and the complexation capacity of dissolved HAs were predicted using the model WinHumicV for speciation modeling. The uptake and accumulation of free [Zn2+] and [Cd2+] in the larval bodies were determined using the ion-selective fluorescence probes; however, the total metal ions were determined through the acid digestion method. WinHunicV model estimated a progressive decrease in the availability of free [Zn2+] and [Cd2+] in the test solutions as the concentration of metal–humic acid complexes increased. The accumulated free [Zn2+] and [Cd2+] in chironomid larvae were found to be statistically significant as the concentrations of dissolved HAs increased (i.e., ≥ 10 mg/L) in both zinc and cadmium treatments. The total Cd2+ accumulation by chironomid larvae exposed to 0.1 mg/L test solution with dissolved HAs did not show notable change. Despite the decrease in free [Zn2+] and [Cd2+] activity due to dissolved HAs, the total accumulated Zn2+and Cd2+ in the larvae were higher than predicted on the basis of free metal ions activity. Thus, the acute exposure study revealed that the presence of dissolved HAs in aquatic environments reduced the toxicity and accumulation of metal ions in chironomid larvae.
... Biotic ligands are biological receptors of an organism (e.g., fish gills) to which metals can bind (Di . Metal speciation and metal-organic matter calculations are estimated by dedicated models (e.g., CHESS for metal speciation-Santore and Driscoll, 1995; WHAM-Model V for metal-OM calculations- Tipping, 1994). According to the studies identified in the current review, BLMs have been applied to study metal toxicity mainly in freshwater environments, including Cu and Ag (silver) toxicity in the freshwater fish fathead minnow (Di (Jou et al., 2009;Liao et al., 2007). ...
... Visual MINTEQ is a freeware chemical equilibrium model for calculating metal speciation, solubility, sorption, and others, in natural waters that runs in most Windows platforms (Gustafsson, 2013). Likewise, the WHAM model (Tipping, 1994) incorporating Humic-Ion Binding Model 7, can be used in oceanic waters to calculate equilibrium free metal ion concentrations, [M] (mol L − 1 ), the amount of metal bound per unit mass of dissolved organic matter (DOM), ν (mol g − 1 ), and their ratio ν/[M] (L g − 1 ), defined as a "local" partition coefficient that is a useful parameter to predict DOM affinity to bind more metal (Tipping et al., 2016). ...
Industrial deep-sea mining will release plumes containing metals that may disperse over long distances; however, there is no general understanding of metal effects on marine ecosystems. Thus, we conducted a systematic review in search of models of metal effects on aquatic biota with the future perspective to support Environmental Risk Assessment (ERA) of deep-sea mining. According to results, the use of models to study metal effects is strongly biased towards freshwater species (83% freshwater versus 14% marine); Cu, Hg, Al, Ni, Pb, Cd and Zn are the best-studied metals, and most studies target few species rather than entire food webs. We argue that these limitations restrain ERA on marine ecosystems. To overcome this gap of knowledge, we suggest future research directions and propose a modelling framework to predict the effects of metals on marine food webs, which in our view is relevant for ERA of deep-sea mining.
... PAF is estimated by combining environmental concentrations (from interpolated measurements or model simulations) with field bioavailability estimates (Klepper et al., 1998). The Clearwater Consensus further suggested using the Windermere Humic Aqueous Model (WHAM) (Tipping, 1994), with freshwater chemical properties commonly available in databases (notably pH, dissolved organic carbon (DOC) and water hardness) as input parameters, to estimate the concentration of trace elements, in the form of free ion and inorganic complexes, necessary to calculate BF and EF. Gandhi and Huijbregts (2010) and Dong et al. (2014) and to define seven freshwater archetypes ranked according to their chemical properties (i.e. ...
... The recent development of a more complex biotic ligand model formalism for predicting the toxic effects of trace element mixtures has further fuelled the debate on this issue. To deal with this complexity, Tipping andLofts, 2015, 2013) suggested using WHAM to predict not only trace element speciation in solution but also trace element binding and toxicity to aquatic and soil organisms using the humic acid profile defined in the WHAM database as a surrogate of biological surfaces. Coupling the WHAM default parameterization with a toxicity function (FTOX), the WHAM-FTOX approach was found to successfully predict the toxic effect of trace element mixtures for a range of aquatic and soil organisms (Balistrieri and Mebane, 2014;Guigues et al., 2016;He and Van Gestel, 2015;Qiu et al., 2016;Yen Le et al., 2015). ...
... PAF is estimated by combining environmental concentrations (from interpolated measurements or model simulations) with field bioavailability estimates (Klepper et al., 1998). The Clearwater Consensus further suggested using the Windermere Humic Aqueous Model (WHAM) (Tipping, 1994), with freshwater chemical properties commonly available in databases (notably pH, dissolved organic carbon (DOC) and water hardness) as input parameters, to estimate the concentration of trace elements, in the form of free ion and inorganic complexes, necessary to calculate BF and EF. Gandhi and Huijbregts (2010) and Dong et al. (2014) developed this approach to calculate generic CTP values for 14 trace elements (i.e., Al(III), Ba, Be, Cd, Co, Cr(III), Cs, Cu, Fe(II), Fe(III), Mn(II), Ni, Pb, Sr, and Zn), and to define seven freshwater archetypes ranked according to their chemical properties (i.e., pH, DOC and water hardness). ...
... The recent development of a more complex biotic ligand model formalism for predicting the toxic effects of trace element mixtures has further fuelled the debate on this issue. To deal with this complexity, Tipping andLofts (2013, 2015) suggested using WHAM to predict not only trace element speciation in solution but also trace element binding and toxicity to aquatic and soil organisms using the humic acid profile defined in the WHAM database as a surrogate of biological surfaces. Coupling the WHAM default parameterization with a toxicity function (F TOX ), the WHAM-F TOX approach was found to successfully predict the toxic effect of trace element mixtures for a range of aquatic and soil organisms (Balistrieri and Mebane, 2014;Guigues et al., 2016;He and Van Gestel, 2015;Qiu et al., 2016;Yen Le et al., 2015). ...
Agricultural recycling of organic waste (OW) derived from urban, agricultural and agroindustrial sources is an essential sustainable development strategy. Yet repeated application of nutrient-laden OW in crop fields can also drastically boost contaminant levels in soil. This review focuses on the consideration of three categories of OW-borne contaminants, namely trace elements, organic contaminants and pathogens (including antibiotic resistance), in environmental assessments, chiefly involving life cycle assessment (LCA) and risk assessment (RA). The in-depth discussion also focuses on gaps between empirical knowledge and the models underlying these frameworks. Potential improvements to fill the identified gaps are proposed, including novel approaches and uses of existing approaches, while also featuring various levels of “readiness.” Finally, a comprehensive theoretical framework to assess OW recycling scenarios, combining complementary approaches and models, is proposed and exemplified.
... Consistent recovery of intrinsic binding affinity of metals to HNP from experimental measurements is systematically tied to adequate modelling of the electrostatic potential distribution from bulk electrolyte solution to inner part of HNP body [2][3][4][5]. Within the equilibrium NICA-Donnan and WHAM/Model VII metal speciation concepts [15][16][17][18], this potential distribution is considered to obey a priori Donnan representation, regardless of the size of the particulate complexant compared to the Debye layer thickness. This approximation, if applied to HNP particles with radius e.g. ...
... The inappropriate [5,19,25]. This finding should be a source of concerns because Donnan electrostatic representation is abundantly adopted in various generic thermodynamic models on metal-to-nanoparticulate organic matter complexation [17] [42]. Applying these models to nanoparticles in the thick double layer regime inherently bias evaluation of the intraparticulate speciation of indium with humics as they underestimate the electrostatic contribution, and thus, arbitrarily overestimate the chemical binding component so as to match experimental data at a given solution ionic strength. ...
Hypothesis
Proper evaluation of the intrinsic stability of metals with humic nanoparticles calls for a robust representation of the particulate electrostatic features. Addressing here the case of trivalent metal association with humics for which a significant electrostatic contribution is expected, we report a robust interpretative approach as an alternative to the conventional but approximative Donnan model applied in the speciation codes.
Experiments
The intrinsic chemical binding affinity of Indium to humic nanoparticles is tackled from equilibrium electroanalytical measurements (Absence of Gradient and Nernstian Equilibrium Stripping) in NaClO4 electrolyte (10-100 mM, pH4) at different metal-to-humics concentration ratios. The electrostatic contribution was evaluated using a Poisson-Boltzmann based-approach where the key electrostatic descriptors of humics are involved.
Findings
The electrochemical results interpreted in the light of so identified non-specific indium binding contribution evidences a dramatic impact of electrostatics on indium complexation by humics, with e.g. in 10 mM electrolyte an intraparticulate Boltzmann metal accumulation factor that is ca. 5 times larger than that reported for cadmium and highly-charged fulvics complexants. A successful comparison between theory and experiments is consistently achieved over the tested electrolyte concentrations with the only adjustment of the radius of the metal accumulation spherical volume. The analysis reveals the necessity to consider full equilibration of charged humics with its intraparticulate counterion atmosphere.
... Dissolved concentrations of the elements K, Mg, Na, Ca, Al, Fe, and Si were analyzed by inductively-coupled plasma optical emission spectroscopy (ICP-OES) on a Varian Vista Ax Pro instrument. Inorganic aluminum (Al i ) was modeled from Al tot using the Windermere Humic Acid Model (WHAM) (Tipping, 1994), calibrated as described in Cory (2006) to Al i measurements made in the Krycklan stream network. Dissolved anions (SO 2− 4 , Cl − , NO − 3 , and F − ) were analyzed on a Dionex DX-300 or DX-320 ion chromatograph system. ...
... However, in our study, brown trout showed a preference for streams which maintained very low Al i even during spring flood, even though Al i concentrations were well below published toxicity thresholds (Fivelstad and Leivestad, 1984;Sadler and Lynam, 1987). One possible explanation for the apparent discrepancy would be if the WHAM model that we used (Tipping, 1994;Cory et al., 2006) underestimated the true proportion of toxic Al i in the presence of high dissolved organic matter concentrations. It has been shown that a substantial part of dissolved organic acidity can be in the strong acid form, which would favor fully dissociated Al binding and enhanced toxicity effects (discussed in Tipping and Carter, 2011;Fakhraei and Driscoll, 2015), and the modeling results are sensitive to this parameter. ...
We used the distribution of stream-dwelling brown trout (Salmo trutta) in a 67 km2 boreal catchment to explore the importance of environmental organizing factors at a range of spatial scales, including whole-catchment characteristics derived from map data, and stream reach chemical and physical characteristics. Brown trout were not observed at any sites characterized by pH < 5.0 during the spring snowmelt episode, matching published toxicity thresholds. Brown trout distributions were patchy even in less acidic regions of the stream network, positively associated with glaciofluvial substrate and negatively associated with fine sand/silty sediments. A multivariate model including only whole-catchment characteristics explained 43% of the variation in brown trout densities, while models with local site physical habitat characteristics or local stream chemistry explained 33 and 25%, respectively. At the stream reach scale, physical habitat apparently played a primary role in organizing brown trout distributions in this stream network, with acidity placing an additional restriction by excluding brown trout from acidic headwater streams. Much of the strength of the catchment characteristics-fish association could be explained by the correlation of catchment-scale landscape characteristics with local stream chemistry and site physical characteristics. These results, consistent with the concept of multiple hierarchical environmental filters regulating the distribution of this fish species, underline the importance of considering a range of spatial scales and both physical and chemical environments when attempting to manage or restore streams for brown trout.
... A number of chemical speciation models for proton and metal binding to humic substances have been developed (Tipping, 1994;Benedetti et al., 1995;Gustafsson, 2001), such as the Windermere Humic Aqueous Model (WHAM) which assumes a discrete site distribution responsible for proton and metal binding (Tipping, 1994;Tipping et al., 2011). Despite of the wide application of these models to metal binding to humic substances and even natural DOM samples, they contain certain simplifications on the binding sites and have been mainly calibrated through the macroscopic experimental data. ...
... A number of chemical speciation models for proton and metal binding to humic substances have been developed (Tipping, 1994;Benedetti et al., 1995;Gustafsson, 2001), such as the Windermere Humic Aqueous Model (WHAM) which assumes a discrete site distribution responsible for proton and metal binding (Tipping, 1994;Tipping et al., 2011). Despite of the wide application of these models to metal binding to humic substances and even natural DOM samples, they contain certain simplifications on the binding sites and have been mainly calibrated through the macroscopic experimental data. ...
Dissolved organic matter (DOM) is one of the most important ligands governing the geochemical cycling of metals in the environment, but recent studies with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) have shown enormous complexity and diversity of DOM composition. How the diverse molecular composition of DOM affects the reactivity of DOM with metals is still largely unknown, which precludes us from developing accurate geochemical models for the fate of metals in the environment. In this study, we combined FT-ICR-MS analysis and theoretical modeling approaches and specifically elucidated the link between molecular composition and the proton and Cu binding ability of DOM, using the Suwannee River fulvic acid (FA) as a model humic substance. Batch adsorption experiments were conducted to generate different extents of molecular fractionation of FA samples by ferrihydrite. FT-ICR-MS analyses were employed to investigate the changes of molecular composition while Cu titration and the Windermere Humic Aqueous Model (WHAM) were used to quantify the variations on the Cu binding capacities of FA samples. We developed a general theoretical modeling approach, which integrated a suite of theoretical modeling methods, including the Vienna Soil-Organic-Matter-Modeler (VSOMM), SPARC Performs Automated Reasoning in Chemistry (SPARC), and the linear free energy relationships (LFER), for molecular modeling based on FT-ICR-MS data. Based on the FT-ICR-MS results, we found that, despite of the complex molecular composition of FA, FA molecules can be divided into three representative groups and each group of molecules had distinct chemical properties. Interestingly, molecules within the same group had similar distributions of molecular properties. Based on the chemical properties of the three groups of FA molecules, we successfully constructed three molecular models of FA using VSOMM, and quantified the distributions of proton and Cu binding constants with SPARC and LFER. Those independently determined binding constants were comparable to the WHAM default proton and Cu binding constants, supporting the validity of our modeling approach. Our modeling results suggested that the molecular complexity of DOM may be simplified with representative groups of molecules based on their binding ability with metals in theoretical modeling. Our modeling approach based on FT-ICR-MS data shed light on developing mechanistical models for metal reactions with DOM based on the molecular data, which is helpful for predicting the geochemical cycling of carbon and metals in the environment
... Other useful models for estimating the charge on organic matter, as documented in the scientific literature, include those proposed by Driscoll et al. [16], the ALCHEMY di-and triprotic model by Schecher and Driscoll [17], the DOM fraction model by Kortelainen [18], and the chemical equilibrium models WHAM and NICA-Donnan by Tipping [19,20] and Kinniburgh et al. [21], respectively. ...
Rising organic charge in northern freshwaters is attributed to increasing levels of Dissolved Natural Organic Matter (DNOM) and shifts in water chemistry. Organic charge concentration may be determined by charge balance calculations (Org.-) or modelled (OAN-) using the conceptual Oliver and Hruška models based on the density of weak acid functional sites (SD) present in DNOM. The charge density (CD) is governed by the SD and protonation and complexation reactions on the functional groups. The models use this SD as a key parameter in empirically fitting the model to Org.-. Utilizing extensive datasets of water chemistry, this study shows that spatial and temporal differences in SD and CD are influenced by variations in the humic-to-fulvic ratio of DNOM and the organic aluminum (Al) complexation, as well as the mole fraction of CD to SD governed by the acidity. Site median SD obtained for 44 long-term monitored acid-sensitive lakes was 11.1 and 13.9 µEq/mg C for the Oliver and the Hruška model, respectively. During the 34 years of monitoring the CD increased by 70%, likely due to rising pH and declining Al complexation with DNOM. Present-day median SD values for the Oliver and Hruška models in 16 low-order streams are 13.8 and 15.8 µEq/mg C, respectively, and 10.8 and 12.5 µEq/mg C, respectively, in 10 high order rivers.
... These two parameters collectively define the entire range of permeabilities within the soil matrix. This approach bears similarity to the method used to address heterogeneous binding sites in the WHAM and SHM organic complexation models (Tipping, 1994;Gustafsson, 2001). ...
To gain insights into phosphorus (P) dynamics in soils and the ability to predict soil responses to varying fertilizer inputs, mass balance models prove to be valuable tools. In this study, a new dynamic mass balance model, PBalD8, was used to describe the change in extracted P in the A horizon of soils subjected to diverse fertilizer treatments over a period of 50 to 60 years in five soil fertility experiments. The model employed a Freundlich equation to describe soil-solution partitioning of P and assumed that acid-lactate-extractable P represented a labile pool of P in instant equilibrium with soil solution P. Additionally, oxalate-extractable inorganic P was presumed to comprise the sum of the labile and stable pools of P, with mass flux to and from the latter described by Fick's first law. The model was evaluated using results from extractions and P K-edge XANES spectroscopy. Notably, organic P, as revealed by P K-edge XANES, did not substantially contribute to long-term changes in soil P content and was therefore excluded from consideration. In general, the model offered reasonable fits to the extracted P concentrations. However, for the P-depleted treatments, a prerequisite was that the P removal through harvest was lower compared to measurements. Conversely, in three of the soils, the modelled fertilizer inputs needed to be reduced to 70 % to 85 % of the known additions. These discrepancies may be attributed to the involvement of deeper soil horizons, including deep crop uptake and mixing with lower soil layers, although other factors such as lateral dispersion and inaccuracies in estimating applied fertilizers cannot be discounted. These results underscore the necessity of gaining a more comprehensive understanding of how deeper soil horizons influence P mass balances in agricultural soils. In one of the soils, Fjärdingslöv, P K-edge XANES results demonstrated the formation of calcium phosphate over time in the highest fertilization treatment, consistent with the model. Additionally, in two soils, Kungsängen and the P-depleted Vreta Kloster soil, the model predicted a significant contribution from mineral weathering. However, the PBalD8 model also projected higher P leaching rates than those observed, suggesting that the model may not fully capture this P output term.
... NICA-Donnan, WHAM and extensions thereof, and freeware chemical models like Visual MINTEQ. [3][4][5][6] The relationship between metal speciation and bioavailability to metal-accumulating microorganisms (e.g. bacteria, microalgae) has further received a lot of attention in literature. 1 In particular, the Biotic Ligand Model (BLM) makes the distinction between total metal activity and activity of free (not complexed) metal M as the determinant bioavailability parameter. ...
Luminescent whole-cell metal biosensors are genetically engineered cells used for the detection of metals in e.g. aqueous solutions. Herein, we detail the quantitative connections between time-response of luminescent bacterial metal sensors and the bioavailability of free and complexed metal species. To that end, we formulate the biophysicochemical dynamics of metal partitioning at a biosensor/solution interface and integrate the required metabolism contribution to cell response. The formalism explains the ways in which cell signal depends on: coupled Eigen kinetics of metal complexation and diffusion of metal species to/from the interface; kinetics of metal excretion, Michaelis-Menten bioaccumulation and ensuing metal depletion from bulk solution; and kinetics of bioluminescence production following intracellular metal sequestration by regulatory metalloproteins. In turn, an expression is derived for the time-dependent cell signal as a function of interrelated (bioavai)lability of metal species and (thermo)dynamic descriptors of extra/intracellular metal complexation. Quantitative criteria are elaborated to identify scenarios where equilibrium modeling of metal speciation is incorrect, bulk metal depletion is operative, metal biouptake kinetics is governed by metal diffusion, or labile metal complexes fully contribute to cell response. Remarkably, in agreement with experiments, the theory predicts time-shifts of bioluminescence peaks with increasing concentration of biosensor and/or metal ligand in solution. We show that these shifts originate from the crosstalk between activation kinetics of cell photoactivity and speciation-dependent kinetics of bulk metal depletion. Overall, the work paves the way for the elaboration of new strategies to exploit the bioluminescence response of metal lux-biosensors at a dynamic level and evaluate metal bioavailability properties in environmental or biological aqueous samples.
... Figure 3 shows an example for zinc at various sites, geographically distributed over The Netherlands (data from Verschoor et al., 2011). Monitoring data included 11 water characteristics that were used as input for chemical speciation calculations using WHAM software (Tipping, 1994) and full-BLM modeling (Heijerick et al., 2002;2005;DeSchamphelaere andJanssen, 2004, 2010;. To visualize the variation over various water types, we constructed SSD-curves for 19 different aquatic organisms, varying from algae to fish (validated toxicity data from Zn-Risk Assessment Report, EU, 2008). ...
Since the soil quality Tool for Risk Identification, Assessment and Display (TRIAD) approach introduced the "three lines of evidence" accounting for chemical, toxicological and ecological stressors to explain adverse effects in biota, the assessment of contaminant risks in the environment has significantly evolved. The concept of chemical speciation, related to water characteristics, boosted the understanding of the role of free-ion activities in the overall accumulation of pollutants in biota. New modeling concepts (e.g. biotic ligand models) and measuring techniques were developed. This in turn triggered widespread research addressing the quantitative role of sediment in the overall water quality, focusing on redox interfaces. For contaminant mixtures in river catchments, complex relations between (bio)availability of compounds, including nutrients, help to explain aquatic toxicity. Variation in ecological patterns and processes across environmental or spatiotemporal gradients occur, which may identify ecological factors that influence contaminant fate and effects. Empirical evidence by meta-analysis and theoretical underpinning by modelling showed relationships between population growth rates and carrying capacities, across chemicals and across species. The potentially affected fraction (PAF) of species may be related to the mean species abundance, an often-used indicator in global change studies. Knowledge gaps remain on how pollutants travel through ecological communities and which species and species-relationships are affected. Outdoor experimental systems that examine the natural environment under controlled conditions may be useful at the higher biological level to investigate the impact of stressors on a variety of species, including mutual interactions.
... The speciation, or chemical form, of Tl (and Cu) in the test waters was calculated using the WHAM v7.0 geochemical speciation code (Tipping, 1994). Inorganic equilibrium constants (aqueous and minerals) were obtained from Markich and Brown (2022). ...
A lack of thallium (Tl) toxicity data for marine organisms has hampered the development of water quality guidelines for protecting marine life and assessing ecological hazard/risk. This study assessed the toxicity (EC10/ EC50) of Tl in natural seawater (salinity 34 psu and pH 8.05) to 26 functionally diverse marine organisms (19 phyla from five trophic levels) from a variety of temperate and tropical coastal marine habitats. EC10 values ranged from 3.0 μg/L (copepod, Acartia tranteri) to 489 μg/L (cyanobacterium, Cyanobium sp.), while EC50 values ranged from 9.7 μg/L to 1550 μg/L. Thallium(I) was the dominant (86-99 %) oxidation state in test waters across the range of EC10 and EC50 values. Thallium toxicity (EC10/EC50) did not differ between temperate and tropical marine organisms. New, reliable, long-term Tl water quality guidelines were derived using species sensitivity distributions (with model-averaging) to protect marine life in Australia (e.g., 3.9 μg/L for 95 % species protection).
... Heterogeneous adsorption sites in soils including clay minerals, iron and aluminum oxides and hydroxides, and organic matter adsorb HMs through outer-sphere complexation (i.e., cation exchange) and inner-sphere complexation mechanisms. Accordingly, various surface complexation models (SCMs) including the diffuse layer model (DLM) (Dzombak and Morel 1990), Windermere Humic Aqueous Model (WHAM) (Tipping 1994), and charge distribution multi-site complexation (CD-MUSIC) model (Hiemstra and van Riemsdijk 1996) have been developed to describe the adsorption and desorption reactions and distribution of ions between solid and solution phases. ...
Biochemical processes in the rhizosphere affect the availability and distribution of heavy metals (HMs) in various forms. Rhizosphere soil (RS) and non-rhizosphere soil (NRS) samples were collected from 10 fields under tarragon (Artemisiadracunculus L.) cultivation to investigate the release kinetics and distribution of HMs including cadmium (Cd), cobalt (Co), copper (Cu), iron (Fe), and zinc (Zn) in five fractions. The cumulative amounts of Cu and Fe released after 88 h were in the following ranges, respectively: 1.31–2.76 and 3.24–6.35 mg kg⁻¹ in RS and 1.41–2.72 and 3.15–5.27 mg kg⁻¹ in NRS. The parabolic diffusion and pseudo-second-order equations provided the best fit to the release kinetics data of Cu and Fe, respectively. The cation exchange model (CEM) based on Gaines–Thomas selectivity coefficients implemented in the PHREEQC program could well simulate the release of Cu and Fe suggesting that cation exchange was the dominant mechanism in the release of Fe and Cu from soils by 0.01 M CaCl2. Cadmium was predominantly found in fraction F2, while other HMs were mainly present in fraction F5. According to the risk assessment code, there was a very high risk for Cd, a medium risk for Co and Cu, a very low risk for Fe, and a low risk for Zn. Correlation analysis showed that soil physicochemical properties were effective in the distribution and transformation of HMs. Significant positive correlations between five fractions indicated that different forms of HMs can potentially transform into each other.
... As discussed above, metal partitioning and transformation in the natural world are highly complex. Several mechanistic models (also called equilibrium and geochemical models), such as MINEQL (Westall et al., 1976), MINTEQA2 (Allison et al., 1991), WHAM (Tipping, 1994), and ORCHESTRA (Meeussen, 2003), have been developed to describe metal partitioning between solid and solution, or metal speciation in solution only. For example, the WHAM model could describe metal sorption on organic matter by nonspecific electrostatic sorption and specific competition sorption (protons and metals compete for binding to two types of sites: carboxylic and phenolic groups) (Tipping, 1998). ...
A predictive understanding of the source-specific (e.g., point and diffuse sources) land-to-river heavy metal (HM) loads and HM dynamics in rivers is essential for mitigating river pollution and developing effective river basin management strategies. Developing such strategies requires adequate monitoring and comprehensive models based on a solid scientific understanding of the watershed system. However, a comprehensive review of existing studies on the watershed-scale HM fate and transport modeling is lacking. In this review, we synthesize the recent developments in the current generation of watershed-scale HM models, which cover a wide range of functionalities, capabilities, and spatial and temporal scales (resolutions). Existing models, constructed at various levels of complexity, have their strengths and weaknesses in supporting diverse intended uses. Additionally, current challenges in the application of watershed HM modeling are covered, including the representation of in-stream processes, organic matter/carbon dynamics and mitigation practices, the issues of model calibration and uncertainty analysis, and the balance between model complexity and available data. Finally, we outline future research requirements regarding modeling, strategic monitoring, and their combined use to enhance model capabilities. In particular, we envisage a flexible framework for future watershed-scale HM models with varying degrees of complexity to accommodate the available data and specific applications.
... Understanding the reactivity of the charged humic substances toward aqueous metal cations requires a correct description of both electrostatic and chemical contributions. Currently, proton and metal cation binding to FA/HA is performed using one of the two leading semi-empirical models NICA (Non-Ideal Competitive Adsorption) [42] and WHAM (Windermere Humic Aqueous Model) [43], both relying on a 'Donnan-like' model to describe the effects of colloidal charge on the non-specific electrostatic metal binding to particle body. However, the application of the Donnan model for humic substances has often been criticized in the literature for its inappropriate description of the electrostatic characteristics, especially when the NOM colloids are nanoparticles-sized [44]. ...
An historical overview covering the field of electroanalytical metal cations speciation in freshwaters is presented here, detailing both the notable experimental and theoretical developments. Then, a critical review of the progress in the last five years is given, underlining in particular the improvements in electrochemical setups and methodologies dedicated to field surveys. Given these recent achievements, a road map to carry out on-site dynamic metal speciation measurements is then proposed, and the key future developments are discussed. This review shows that electroanalytical stripping techniques provide a unique framework for quantitatively assessing metals at trace levels while offering access to both thermodynamic and dynamic features of metal complexation with natural colloidal and particulate ligands.
... Components with scores on both axes are changes in H, labile Al, and NO 3 . Changes in pH are associated with chemical recovery but also with DOM, which contains weak organic acid, while labile Al highly depends on pH since Al speciation is strongly pH-dependent (Tipping, 1994). The change in NO 3 is likely to be a result of changes in the N deposition combined with catchment-processing, which could explain its intermediate position between PC1 and PC2. ...
We present long‐term changes in Norwegian lake water quality across regional gradients in atmospheric pollution, air temperature, hydrology, and vegetation using (a) a national representative lake survey carried out in 1995 and 2019 (ThousandLakes), and (b) an annual lake survey from acid‐sensitive catchments (78 lakes, TrendLakes) from 1990 to 2020. Our analysis encompasses all major chemical constituents, for example, anions and cations, dissolved organic matter (DOM), nutrients, iron (Fe), and silicate (SiO2). During these decades, environmental changes included declines in sulfur (S) and nitrogen (N) deposition, climate warming, and increase in forest biomass. Strong chemical recovery from acidification is found, attributed to large reductions in atmospheric deposition, moderated by catchment processing from land use and climate change. Browning counteracted chemical recovery in some regions, while Ca increased unexpectedly. We suggest that increased weathering, from enhanced terrestrial productivity, is an important driver of increased Ca—substantiated by widespread, substantial increases in SiO2. Light‐ and nutrient‐limitation has become more prevalent, indicated by higher DOM, lower nitrate (NO3), and lower NO3to total phosphorous ratios. Declines in lake NO3 occurred independently of N deposition, suggesting increased catchment N retention, possibly from increased terrestrial productivity. We conclude that decreased air pollution continues to be a dominant driver of long‐term trends in lake chemistry, but climate‐induced increase in terrestrial weathering processes, governed by increased biomass, is likely to have an increasing impact on future lake acidity, nutrient, and light status, that may cascade along the aquatic continuum from rivers to the coast.
... L'étude des mécanismes de sorption des métaux par les surfaces est basée sur MINTEQ (Gustafsson, 2010), WHAM (Tipping, 1994), PHREEQC (Parkhurst and Appelo, 1999 *Obtenue par calcul à partir de la taille des nanoparticules monodispersées. ** Extrapolées à partir de données expérimentales Un autre outil, moins conventionnel et original pour accéder à certaines de ces informations, est la 'sonde' terres rares. ...
L’utilisation des plastiques s’est accompagnée d’un rejet massif de déchets plastiques dans l’environnement. Leur altération par photo-oxydation produit des nanoplastiques (NPs) dont les caractéristiques et les propriétés en font des vecteurs potentiellement importants de métaux. Etant difficiles à échantillonner dans l’environnement, leur étude a été jusqu’à présent réalisée à l’aide de modèle non représentatifs de l’environnement. Il est donc primordial de produire des modèles de NPs plus pertinents afin de mieux appréhender leur comportement et leur impact sur les polluants métalliques. La Py-GCMS permet d’identifier les NPs de polypropylène en présence de matière organique comme dans les matrices environnementales. L’abrasion mécanique des couches d’altération des plastiques photo-oxydés dans l’environnement, nous a permis de produire un modèle plus représentatif. Ces modèles de NPs présentent à leur surface des fonctions capables de complexer les métaux et de contrôler leur stabilité colloïdale. L’utilisation des terres rares et la modélisation thermodynamique, nous a permis de démontrer que l’adsorption des métaux est contrôlée par la formation de complexes mono ou bidentés avec les sites carboxyliques de surface. La formation des complexes mono ou bidentés dépend de la valence des métaux étudiés et des conditions physicochimiques du milieu. Plus globalement, la réactivité des NPs dépend de leur état d’oxydation qui contrôle leur densité de site de surface. De par leurs propriétés de sorption, les NPs peuvent être des acteurs clés de la dynamique des métaux dans des zones polluées par les plastiques comme par exemple: les sols agricoles amendés en déchets plastiques et les décharges.
... Due to the variability and diversity of organic surface functional groups, the organic matter General Introduction surface is typically characterized by a range of values for chemical constants (e.g., proton dissociation) (Sposito, 2008). This variability has also been contemplated for the development of geochemical models that predict and explain the interaction of ions with the organic matter Tipping, 1994). ...
... The measured Fe/OC loading in mineral colloids is 14-39 mmol Fe mol À1 OC (calculated with the lower SUVA value) or 20-55 mmol Fe mol À1 OC (calculated with the higher SUVA value). Generic speciation calculations with the Windermere Humic Aqueous Model (WHAM VII) (Tipping, 1994) predict a Fe binding capacity of fulvic acids to form Fe-OC mononuclear complexes of 0.29 to 18 mmol Fe mol À1 OC at pH 3.5, the pH in the A and the E horizons, assuming equilibrium with soil-Fe (log 10 Ksp = 2.7 = log 10 (Fe 3+ )(H + ) À3 ) (Schwertmann, 1991). This is lower than the observed Fe/OC ratio in mineral F I G U R E 4 The size fractionation of pore water Fe in the soil profile of the Albic Podzol derived from the FlFFF analysis. ...
Iron (Fe) colloids dominate the soil solution Fe and these colloids potentially act as vectors for nutrients and contaminants in soil. The question remains which factors, that is, gradients in soil chemical characteristics with depth or physical processes, cause vertical mobilisation and immobilisation of Fe colloids in soils. This question was addressed by characterising the change in concentration, size and composition of Fe colloids in a podzol profile and by relating these changes to soil properties along the profile. Pore waters were analysed with Flow Field Flow Fractionation (FlFFF‐UV‐ICP‐MS) to overcome the artefacts typically obtained when using filtration for fractionation. Pore water was obtained by centrifugation of field moist samples taken from an Albic Podzol. The samples were taken within a depth of 110 cm, at eight depths corresponding to different horizons. The pore water Fe concentration increased with depth and peaked at 66 μM in the E horizon just above the Bh horizon, beyond which, it sharply decreased to only 9 μM. The pore water Fe concentration correlated strongly with dissolved organic carbon (DOC). The high‐resolution size fractionation analysis with FlFFF suggested that the Fe colloids (<100 nm) in the pore water not only consisted of Fe‐organic carbon (OC) complexes, but also of mineral Fe colloids associated with organic matter (OM), as indicated by the colloid size (>5 nm) and by the relatively large Fe/OC ratio that exceeds the complexation capacity of natural OM. The smallest mineral Fe colloids dominated in the Ah1 horizon, while the larger mineral colloids increased with increasing depth, explaining the rise in total Fe towards the Bh horizon. This suggests that Fe complexation with OM and stabilisation of mineral Fe colloids with OM explain colloidal Fe in the pore water. The adsorption of the OM at the top of the Bs horizons is likely the primary mechanism of DOC retention in the Bh horizon. Below this depth, the concentration of DOC was very low, resulting in low Fe concentration in the pore water. The colloids (<100 nm) are considerably smaller than the soil pore size distribution and increase in size with depth, which suggests that straining was not a significant mechanism for colloid retention. This study demonstrates that OM plays a key role in the transport of Fe colloids in acid sandy soil.
Highlights
Depth profile of pore water Fe in a podzol is studied to investigate colloid migration processes
Pore water colloids are characterised by Flow Field Flow Fractionation
Fe colloids consist of complexes with OM and mainly larger mineral colloids
Natural OM plays a key role in the transport of Fe colloids in acid sandy soil
... WHAM 7 (Tipping, 1994) and PHREEQC 3.0 (Parkhurst and Appelo, 2013) softwares were combined following the method of crossed modeling described in Rigaud et al. (2013) for the determination of (i) aqueous Hg II and MeHg speciations, and (ii) mineral phase equilibrium accounting for redox gradient and dissolved OM complexation. The THERMODDEM default database (https://thermoddem.brgm.fr/) ...
Mercury (Hg) speciation in natural waters is controlled by redox conditions and microbiological activity. Water columns of meromictic lakes have large and stable redox chemical and biological gradients that allow the investigation of many Hg chemical transformations. In this study, Hg speciation (elemental Hg = Hg⁰, methylated Hg = MeHg) and partitioning between truly dissolved (<3 kDa), colloidal (<0.45 μm and >3 kDa), and particulate (>0.45 μm) fractions, were determined throughout a high-resolution water column profile in the ferruginous meromictic Lake Pavin (Massif Central, France) in July 2018. Total Hg concentrations (THg) in water ranged between 0.4 and 8.8 pmol L⁻¹. The particulate phase represented 10–70% of the THg, with a peak found in the mesolimnion associated with the particulate organic carbon maximum. In the mesolimnion, the colloidal fraction represented 12–68% of THg, and the highest value was found at the top of the sulfidic zone, whereas the truly dissolved Hg species (70 ± 9%) dominated in all the rest of the sulfidic zone. MeHg ranged from less than 10% of THg in the oxic mixolimnion to more than 90% in the anoxic monimolimnion. The Hg methylation was most active within the suboxic zone where iron and sulfate reduction are occurring. These results, associated with those of the partition of organic matter (OM), sulfur, and iron, in conjunction with thermodynamic calculations, allow us to present a conceptual scheme for the Hg cycle in the lake. Atmospheric Hg deposited onto surface waters of the lake is partially photo-reduced and returned to the air, another part is scavenged by biogenic particulate matter and conveyed at depth by settling organic material. Water stratification and redox changes create a sequence of reactions from oxic to ferruginous waters where Hg is successively (i) desorbed from particulate OM where mineralization occurs, (ii) adsorbed onto iron-oxy(hydr)oxides, (iii) desorbed where they dissolved, (iv) precipitate as HgS, (v) methylated, and (vi) reduced as Hg⁰ in the deepest part of the lake. In brief, the (micro)biological uptake, OM, iron and sulfur recycling, through associated microbial consortia, control the Hg cycling in the Pavin waters.
... Solution complexation modeling of the REE concentrations in Mississippi River water was performed using the Windermere Humic Aqueous Model VII (WHAM VII), which includes the latest version of the Humic Ion Binding Model VII (Tipping, 1994;Tipping et al., 2011). WHAM VII was chosen for the REE speciation modeling because: (1) it is able to model REE complexation with both organic and inorganic ligands; ...
A closed-system batch reaction experiment was conducted for 270 days to evaluate the effects of interaction between Gulf of Mexico (GOM) seawater and Mississippi River sediments on the system’s dissolved rare earth elements (REE) concentrations and neodymium isotopic compositions (εNd). This study specifically focuses on geochemical reactions involving silicic sediments derived from weathering of the North American continent as they affect the REEs and εNd of seawater along continental margins, in contrast to previous studies that investigated the influence of basaltic rocks and sediments on REEs and εNd in the ocean. Our results show that the dissolution of labile phases of lithogenic Mississippi River sediments leads to an approximately 100-fold increase in dissolved REE concentrations within the first 33 days of the experiment. Secondary mineral precipitation appears to lower the REE concentrations between days 33 and 270 of the experiment, although seawater REE concentrations remain elevated compared to initial values. The two-way elemental transfer involving dissolution and precipitation results in a net increase by a factor of 24 ± 12 (mean ± 1σ) in the dissolved REE concentrations by the end of the experiment (i.e., day 270). The dissolved REE concentration maxima observed after 33 days of the experiment represent the mobilization of approximately 0.37% of the REE content of the operationally defined “exchangeable” fraction of the riverine sediments. The εNd values of the reactive lithogenic components were -9.77 and -9.95, which are similar to the GOM value of -9.81 ± 0.36. Because of the similarity between εNd values, changes in the seawater Nd isotope value throughout the experiment were subtle (mean ± std, reacted seawater εNd of -9.87 ± 0.17). The highest REE concentrations coincided with the most radiogenic εNd (-9.65 ± 0.23; day 33), which suggests that REE concentrations and εNd compositions of the GOM may be buffered by fluxes from sediments in the system. Our results are comparable to previous studies involving basaltic rocks and/or sediments of basaltic composition in that they demonstrate that silicic, river sediments are highly reactive in marine environments with regard to REE mobilization. The experimental results further suggest that “boundary exchange” plays an important role in influencing the εNd of seawater along continental margins dominated by large river systems, although the impacts of boundary exchange will be most profound where ambient seawater and river sediments have distinct Nd isotopic compositions (e.g., basaltic, or Precambrian shield material). Finally, our results indicate that the εNd value of GOM seawater is largely controlled by the lithogenic sediment delivered to the basin by the Mississippi River.
... There are models which helps to analyze and understand the speciation of pollutants based on their chemical reaction nature in the environment. One such model is Windermere Humic Aqueous Model (WHAM) (Tipping, 1994). Using the WHAM Tipping et al. analyzed the different speciation of metal ions in seawater, river and easturine water (Tipping et al., 1998). ...
Preciseness in pollutant analysis and optimizing a process required to remediate wastewater are essential in environmental engineering. The chemometric approach is used to analyze pollutant molecules from actual samples with maximum accuracy quantitatively. Various calibration models like Principal Component Regression, Partial Least Squares, Cluster analysis, Parallel Factor Analysis, and Artificial Neural Networks are employed to compute the pollutant concentration. In this review, the application of chemometrics in aqueous pollutant degradation processes is explained to understand better how accurate and what kind of information can be extracted from the pollutant degradation processes using chemometrics. The reaction rate-determining ability of Multivariate Curve Resolution – Alternating Least Square, a second-order chemometric model, is explained. Understanding the degradation profiles of a mixture of components and analyzing the by-product evolution are benefits of employing chemometrics. This review describes studies where chemometrics and response surface methodology-based techniques are used to gain insights into process optimization and resolve issues on the accurate determination of pollutant concentration profiles. Suitable examples of advanced oxidation methods, namely photocatalytic degradation, and gamma-ray mediated pollutant deterioration, are discussed to understand better the application of Canonical and Ridge analysis. This review gives the readers a good view of various applications of chemometrics in accurate assessment of pollutants in multi-component systems and process optimization of pollutant degradation.
... The development of metal speciation models such as the Free Ion Activity Model (FIAM) (Whitfield and Turner, 1979;Hudson, 2005), The Windermere Humic Acid Model (WHAM) (Tipping, 1994), PHREEQC (Marsac et al., 2011) and Visual MINTEQ (Ytreberg et al., 2011) allowed the prediction of metal speciation based on known water quality and reported dissociation constants. Combining the metal speciation with a better understanding of the fate of metals in the natural environment (Dwane and Tipping, 1998;Van Veen et al., 2002) coupled with ecotoxicology data, resulted in the production of biotic ligand models (BLM) for many metals (e.g. ...
The impact of wastewater treatment works (WwTW) effluent on downstream river water quality is of increasing concern, particularly owing to the presence in effluents of a range of trace substances. In the case of contamination by metals the question of bioavailability has recently been accounted for in setting water quality standards for several metals. In the UK over the past decade the Chemical Investigations Programme (CIP) has generated upstream and downstream river quality data as well as associated WwTW effluent monitoring for over 600 sites, for the main contaminants of regulatory interest under the Water Framework Directive. Data presented here show that at a local level WwTW discharges have little impact for many contaminants. Soluble reactive phosphorus, hexabromocyclododecane (HBCDD), cypermethrin, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) have been shown to be the principal substances where downstream concentrations were at least 10 % larger than the upstream value. Otherwise, poor compliance with riverine water quality standards tends to be associated with contamination at the river catchment scale, with corresponding implications for the nature of remedial actions that are likely to be successful. Compliance with water quality criteria for metals, taking account of bioavailability, is high overall.
... The development of metal speciation models such as the Free Ion Activity Model (FIAM) (Whitfield and Turner, 1979;Hudson, 2005), The Windermere Humic Acid Model (WHAM) (Tipping, 1994), PHREEQC (Marsac et al., 2011) and Visual MINTEQ (Ytreberg et al., 2011) allowed the prediction of metal speciation based on known water quality and reported dissociation constants. Combining the metal speciation with a better understanding of the fate of metals in the natural environment (Dwane and Tipping, 1998;Van Veen et al., 2002) coupled with ecotoxicology data, resulted in the production of biotic ligand models (BLM) for many metals (e.g. ...
... free-ion activity model (FIAM) were introduced to understand the relationship between bioavailability and the toxicity (Morel, 1983;Tipping, 1994). Improvements on these early models resulted in the development of the biotic ligand model (BLM) to predict metal toxicity as a result of variation in the local physiochemical conditions . ...
Zinc is an essential trace metal required for the maintenance of multiple physiological functions. Due to this, organisms can experience both zinc deficiency and toxicity. Hardness is recognized as one of the main modifying physiochemical factors regulating zinc bioavailability. Therefore, the present study analyzed the effect of hardness on zinc toxicity using Daphnia magna. Endpoint parameters were acute‐toxicity, development, reproduction, and expression data for genes involved in metal regulation and oxidative stress. In addition, the temporal expression profiles of genes during the initiation of reproduction and molting were investigated. Water hardness influenced the survival in response to exposures to zinc. A zinc concentration of 50μg/L in soft water (50 mg CaCO3/L) caused 73% mortality after 96h exposure, whereas the same zinc concentration in the hardest water did not cause any significant mortality. Moreover, increasing water hardness from 100 to 200mg CaCO3/L resulted in a reduced number of offspring. Fecundity was higher at first brood for groups exposed to higher Zn concentrations. The survival data was used to assess the precision of the bioavailability models (Bio‐met) and the geochemical model (Visual MINTEQ). As the Bio‐met risk predictions overestimated the Zn toxicity, a competition‐based model to describe the effects of hardness on zinc toxicity is proposed. This approach can be used to minimize differences in setting environmental quality standards. Moreover, gene expression data showed that using the toxicogenomic approach was more sensitive than the physiological endpoints. Therefore, data presented in the study can be used to improve risk assessment for zinc toxicity.
... In both cases, the generation of the required technosols would be extremely difficult, if not impossible. Assuming humification does occur, carboxylate and phenolate functional groups in the HS macromolecule allow for complexes with Mg 2+ , Ca 2+ , Fe 2+ , and Fe 3+ ions and the formation of chelates [198]. Hydrophobic bonds appear to hold the organic component of the humic molecules together and HS seem to behave as micelles in solution [161,162,199]. ...
Humans are dependent upon soil which supplies food, fuel, chemicals, medicine, sequesters pollutants, purifies and conveys water, and supports the built environment. In short, we need soil, but it has little or no need of us. Agriculture, mining, urbanization and other human activities result in temporary land-use and once complete, used and degraded land should be rehabilitated and restored to minimize loss of soil carbon. It is generally accepted that the most effective strategy is phyto-remediation. Typically, phytoremediation involves re-invigoration of soil fertility, physicochemical properties, and its microbiome to facilitate establishment of appropriate climax cover vegetation. A myco-phytoremediation technology called Fungcoal was developed in South Africa to achieve these outcomes for land disturbed by coal mining. Here we outline the contemporary and expanded rationale that underpins Fungcoal, which relies on in situ bio-conversion of carbonaceous waste coal or discard, in order to explore the probable origin of humic substances (HS) and soil organic matter (SOM). To achieve this, microbial processing of low-grade coal and discard, including bio-liquefaction and bio-conversion, is examined in some detail. The significance, origin, structure, and mode of action of coal-derived humics are recounted to emphasize the dynamic equilibrium, that is, humification and the derivation of soil organic matter (SOM). The contribution of plant exudate, extracellular vesicles (EV), extra polymeric substances (EPS), and other small molecules as components of the dynamic equilibrium that sustains SOM is highlighted. Arbuscular mycorrhizal fungi (AMF), saprophytic ectomycorrhizal fungi (EMF), and plant growth promoting rhizobacteria (PGPR) are considered essential microbial biocatalysts that provide mutualistic support to sustain plant growth following soil reclamation and restoration. Finally, we posit that de novo synthesis of SOM is by specialized microbial consortia (or ‘humifiers’) which use molecular components from the root metabolome; and, that combinations of functional biocatalyst act to re-establish and maintain the soil dynamic. It is concluded that a bio-scaffold is necessary for functional phytoremediation including maintenance of the SOM dynamic and overall biogeochemistry of organic carbon in the global ecosystem
... The survey was conducted from November 2016 to January 2017 by the Ministry of the Environment of Japan owing to concerns regarding the ecological risk of nickel (Ni) in river environments (Ministry of the Environment of Japan, 2017). The free-ion concentrations of five trace metals (Ni, copper [Cu], zinc [Zn], cadmium [Cd], and lead [Pb]) in the 45 sites predicted using WHAM software (Tipping, 1994) have been previously reported by Takeshita et al. (2019). ...
The goals of observational dataset analysis vary with the management phase of environments threatened by anthropogenic chemicals. For example, identifying severely compromised sites is necessary to determine candidate sites to implement measures during early management phases. Among the most effective approaches is developing regression models with high predictive powers for dependent variable values using the Akaike information criterion. However, this analytical approach may be theoretically inappropriate to obtain the necessary information in various chemical management phases, such as the intervention effect size of a chemical required in the late chemical management phase to evaluate the necessity of an effluent standard and its specific value. However, choosing appropriate statistical methods based on the data analysis goal in each chemical management phase has rarely been performed. This study provides an overview of the primary data analysis objectives in the early and late chemical management phases. For each objective, several suitable statistical analysis methods for observational datasets are detailed. In addition, examples of linear regression analysis procedures using an available dataset derived from field surveys conducted in Japanese rivers are presented. This article is protected by copyright. All rights reserved.
... The density of the edge sites and the specific surface areas of illite and kaolinite were obtained from previous studies (Gu and Evans, 2008;Gustafsson, 2001;Manning and Goldberg, 1996;Srivastava et al., 2005) and are presented in Table 2. The electrostatic interactions of cations (such as Fe 3+ , Al 3+ , Zn 2+ , and Ca 2+ ) with permanent negatively charged sites was calculated using the Donnan approach (Tipping, 1994). The charge density of clay minerals was fixed at 0.25 mol/kg, which was independent of pH in the model, and the Donnan volume of clay minerals was set as 1 L/kg (Weng et al., 2001). ...
Inorganic P is adsorbed by a variety of soil minerals. For acidic subtropical Alfisols with high contents of both (hydr)oxides and clay minerals, there is an underappreciated understanding of the importance of clay minerals to P species and availability varying with soil pH and P concentration. In this study, the rate and equilibrium of P adsorption in a subtropical Alfisol were investigated by combining soil incubation with three types of P fertilizer with multi-surface modelling. Phosphorus species analysis revealed that most of the P was present in the forms of Al-P and Fe-P in the subtropical Alfisol. The multi-surface model calculation showed that goethite, kaolinite, and illite were the most important soil constituents responsible for the immobilization of P. Clay minerals were as important as Al/Fe (hydr)oxides for P adsorption in the subtropical Alfisol. In addition, there was a highly positive linear correlation between P adsorbed by illite and Olsen P, with a slope of approximately to 1. The model analysis also showed that >99% of the P adsorbed by illite was in the form of -SO2PO22-. These results suggest the important role of illite in controlling P availability and that P adsorption by illite may serve as an indicator of P availability in the subtropical Alfisol.
... In practice, K d values are determined by in situ measurements, sorption-desorption experiments in laboratories and different modelling approaches such as parametric models (Sheppard et al., 2009;Sheppard, 2011), dynamic models (Garcia-Sanchez et al., 2014) and mass-action based on thermodynamic models (Tipping, 1994;Goldberg et al., 2007). ...
The behavior and impact of trace metals discharged into rivers depend on their solid/liquid fractionation and the fate of these two phases according to hydro-sedimentary processes. The solid/liquid fractionation depends on many environmental factors which imply uncertainties of several orders of magnitude in the evaluation of the partition coefficients, whether by geochemical modelling, in situ measurements or sorption-desorption experiments. In this context, this paper presents a model of solid/liquid fractionation of trace metals with suspended sediments in rivers which integrates the hydro-sedimentary conditions and the properties of the watershed. Its comparison with the ¹³⁷Cs concentrations measured in the Rhône River (France) thanks to monitoring shows that the solid/liquid fractionation of ¹³⁷Cs with suspended sediments in rivers depends on chemical processes but also and above all on variations in water flow rate, size and load of suspended sediments and the balance between anthropogenic discharges and the average trace metal content of soils in the watershed.
... The model, indeed, simulates metal interactions with aquatic species, considering all main parameters which affect metal bioavailability (systematically reviewed by Adams et al. (2020)), including hardness, alkalinity and organic matter concentration. At this aim, BLM incorporates elements from three different models applied to freshwater environments: (i) the gill surface interaction model, proposed by Pagenkopf (1983), that considers the biological surface interaction with metal ions complexed by organic matter; (ii) the chemical equilibria in soils and solutions model (CHESS), proposed by Santore and Driscoll (1995), that calculates chemical speciation and ligand affinity strength to predict free metal ions and metal-ligand complexes concentration; and (iii) the Windermere humic aqueous model (WHAM), proposed by Tipping (1994), that describes the interactions occurring between metals and organic matter. ...
Heavy metals are occurring in the aquatic environment as the result of natural or anthropogenic inputs, and depending on concentration, availability and resilience time, they can differently affect the animal wellness. Numerous studies reveal that more than 99% of metals in seawater are complexed with organic ligands suggesting the major role of organic complexation on metal behavior. Moreover, the amphilic character of marine natural organic matter makes this substance a relevant medium for interactions with charged and uncharged metal molecules. Here we review mechanisms and factors that control marine organic matter composition and its interactions with metals. Organic matter–metal complexes modify metal bioavailability and, in turn, change effects on living organisms.
... The speciation and activity for each metal was determined in each treatment using the thermodynamic Windemere Humic Aqueous Model version 7 (WHAM) 44 and the extended Debye-Hückel equation. 27 Input parameters included the measured pH and measured average dissolved metal concentrations (between test start and end) for each treatment, a temperature of 4 C a Toxicity test bioassay conditions also incorporated the culture conditions. ...
Salinity in the Antarctic nearshore marine environment is seasonally dynamic and climate change is driving greater variability through altered sea ice seasons, ocean evaporation rates, and increased terrestrial ice melt. The greatest salinity changes are likely to occur in the nearshore environment where elevated metal exposures from historical waste or wastewater discharge occur. How salinity changes affect metal toxicity has not yet been investigated. This study investigated the toxicity of cadmium, copper, nickel, lead, and zinc, and their equitoxic mixtures across a salinity gradient to the Antarctic marine microalga Phaeocystis antarctica. In the metal-free control exposures, algal population growth rates were significantly lower at salinities <20 PSU or >35 PSU compared to the control growth rate at 35 PSU of 0.60 ± 0.05 doublings per day and there was no growth below 10 or above 68 PSU. Salinity-induced changes to metal speciation and activity were investigated using the WHAM VII model. Percentages of free ion activity and metal-organic complexes increased at decreasing salinities while the activity of inorganic metal complexes increased with increasing salinities. Despite metal speciation and activity changes, toxicity was generally unchanged across the salinity gradient except that there was less copper toxicity and more lead toxicity than model predictions at salinities of 15 and 25 PSU and antagonistic interactions in metal-mixture treatments. In mixtures with and without copper, it was shown that copper was responsible for ∼50% of the antagonism from observed toxicity at salinities below 45 PSU. Across all treatments, using different metal fractions in toxicity models did not improve toxicity predictions compared to dissolved metal concentrations. These results provide evidence that P. antarctica is unlikely to be at a greater risk from metal contaminants as a result of salinity changes.
... The accumulation of metals on the biotic ligand is the pathway by which toxic effects occur in organisms, often interfering with other necessary processes. The chemical speciation calculations are performed with CHESS (Santore and Driscoll 1995), a framework that solves the system of equations associated with chemical equilibria and includes a description of NOM based on reactions incorporated into the Windermere Humic Aqueous Model (WHAM; Ver 1.0, model V; Tipping 1994). Several versions of WHAM have been developed. ...
A review of nickel (Ni) toxicity to aquatic organisms was conducted to determine the primary water quality factors that affect Ni toxicity and to provide information for the development and testing of a biotic ligand model (BLM) for Ni. Acute and chronic data for 66 aquatic species were compiled for the present review. The present review found that dissolved organic carbon (DOC) and hardness act as toxicity‐modifying factors (TMFs) because they reduced Ni toxicity to fish and aquatic invertebrates, and these effects were consistent in acute and chronic exposures. The effects of pH on Ni toxicity were inconsistent, and for most organisms there was either no effect of pH or, in some cases, a reduction in toxicity at low pH. There appears to be a unique pH effect on Ceriodaphnia dubia that results in increased toxicity at pHs above 8, but otherwise the effects of TMFs were consistent enough across all organisms and endpoints that a single set of parameters in the Ni BLM worked well with all acute and chronic toxicity data for fish, amphibians, aquatic invertebrates, and aquatic plants and algae. The unique effects of pH on C. dubia may be due to mixture toxicity involving both Ni and bicarbonate. The implications of this mixture effect on BLM modeling and a proposed set of BLM parameters for C. dubia are addressed in the review. Other than this exception, the Ni BLM with a single set of parameters could successfully predict toxicity to all acute and chronic data compiled in the present review.
... These assemblage models are also called component additive or CA models [6]. Examples for CA models are WHAM [7] which has been developed especially for situations where the chemical speciation is dominated by organic matter or the "generic multisurface sorption model" by Dijkstra et al. [8], which has been devised for the partitioning of heavy metals. ...
The component additive model UNiSeCs II for simulating the physicochemical behaviour of the radionuclides americium, plutonium and selenium in agricultural soils is presented. The model is validated by estimating the distribution coefficients (K d ) of these elements measured in batch experiments from the literature. For all three elements, the resulting average relative deviations from the experimental values are smaller than a factor of 2.5. This indicates that the model has the potential to significantly improve the predictions of radioecological models that normally use tabulated K d values from the IAEA which are known to have large uncertainties. Using UNiSeCs II, the soil solution parameters most important for the partitioning of Am, Pu and Se are identified by single parameter variations.
... The geochemical model Windermere Humic Aqueous Model (WHAM7; Tipping, 1994) was used to speciate Zn in the solution phase of the 0.01 M Ca(NO 3 ) 2 suspensions. Inputs to the model included solution concentrations of cations (Na, Mg, Al, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, Cd, Ba, Pb and U) and the anions (NO − 3 ; PO −3 4 ) in the solution phase of the Ca(NO 3 ) 2 soil suspensions, colloidal (dissolved) fulvic acid, pH, temperature and partial pressure of CO 2 . ...
Zinc (Zn) deficiency is a widespread nutritional problem in human populations, especially in sub-Saharan Africa (SSA). The Zn concentration of crops consumed depends in part on the Zn status of the soil. Improved understanding of factors controlling the phyto-availability of Zn in soils can contribute to potential agronomic interventions to tackle Zn deficiency, but many soil types in SSA are poorly studied. Soil samples (n=475) were collected from a large part of the Amhara Region of Ethiopia, where there is widespread Zn deficiency. Zinc status was quantified by measuring several fractions, including the pseudo-total (aqua regia digestion; ZnTot), available (DTPA (diethylenetriamine pentaacetate) extractable; ZnDTPA), soluble (dissolved in 0.01 M Ca(NO3); ZnSoln) and isotopically exchangeable Zn, using the enriched stable Zn isotope 70Zn (ZnE). Soil geochemical properties were assessed for their influence on Zn lability and solubility. A parameterized geochemical assemblage model (Windermere Humic Aqueous Model – WHAM) was also employed to predict the solid phase fractionation of Zn in tropical soils rather than using sequential chemical extractions. ZnTot ranged from 14.1 to 291 mg kg−1 (median = 100 mg kg−1), whereas ZnDTPA in the majority of soil samples was less than 0.5 mg kg−1, indicating widespread phyto-available Zn deficiency in these soils. The labile fraction of Zn in soil (ZnE as % ZnTot) was low, with median and mean values of 4.7 % and 8.0 %, respectively. Labile Zn partitioning between the solid and the solution phases of soil was highly pH dependent, where 94 % of the variation in the partitioning coefficient of 70Zn was explained by soil pH. Similarly, 86 % of the variation in ZnSoln was explained by soil pH. Zinc distribution between adsorbed ZnE and ZnSoln was controlled by pH. Notably, Zn isotopic exchangeability increased with soil pH. This contrasts with literature on contaminated and urban soils and may arise from covarying factors, such as contrasting soil clay mineralogy across the pH range of the soils used in the current study. These results could be used to improve agronomic interventions to tackle Zn deficiency in SSA.
We set out to study the seasonal variations in porewater phosphorus and lanthanum concentrations in the dated sediment cores from a small eutrophic lake that has been treated with Phoslock, a lanthanum-modified bentonite (LMB) amendment. Three sites were sampled when the hypolimnion was either oxygenated or anoxic: (i) the lake's deepest point, (ii) a littoral site receiving inflows from the catchment, and (iii) a littoral site influenced by nearby septic tanks. Phosphate (PO43--P), lanthanum (La), iron (Fe), dissolved organic carbon (DOC) and sulfate (SO42-) were measured in porewater samples. An inverse diagenetic model was used to quantify fluxes of dissolved elements across the sediment-water interface as well as the net rate of their reactions along the porewater concentration gradients. Results show that porewater P and Fe underwent strong seasonal dynamics, while La did not. P fluxes, 20-fold higher at the deepest site than elsewhere in the basin, were influenced by anoxic conditions in the hypolimnion during summer and winter, suggesting that P mobility remained sensitive to redox fluctuations despite the addition of La. At the deepest site, fluxes of P across the sediment-water interface increased from 1 to 9 × 10-9 μmol cm-2 s-1 between spring and summer, while the rate of P production to the porewater also increased a hundredfold. These increases were concurrent with Fe mobilization. Finally, sediment dating shows that the fraction of P sequestered by La is buried under freshly deposited sediment at a rate of 2-3 mm per year. These results indicate that external P fluxes and erosion control remain crucial to maintain the longevity of the LMB treatment.
Efforts to incorporate bioavailability adjustments into regulatory water quality criteria in the United States have included four major procedures: hardness-based single-linear regression equations, water-effect ratios (WERs), biotic ligand models (BLMs), and multiple-linear regression models (MLRs) that use dissolved organic carbon, hardness, and pH. The performance of each with copper (Cu) is evaluated, emphasizing the relative performance of hardness-based versus MLR-based criteria equations. The WER approach was shown to be inherently highly biased. The hardness-based model is in widest use, and the MLR approach is the US Environmental Protection Agency's (USEPA's) present recommended approach for developing aquatic life criteria for metals. The performance of criteria versions was evaluated with numerous toxicity datasets that were independent of those used to develop the MLR models, including olfactory and behavioral toxicity, and field and ecosystem studies. Within the range of water conditions used to develop the Cu MLR criteria equations, the MLR performed well in terms of predicting toxicity and protecting sensitive species and ecosystems. In soft waters, the MLR outperformed both the BLM and hardness models. In atypical waters with pH <5.5 or >9, neither the MLR nor BLM predictions were reliable, suggesting that site-specific testing would be needed to determine reliable Cu criteria for such settings. The hardness-based criteria performed poorly with all toxicity datasets, showing no or weak ability to predict observed toxicity. In natural waters, MLR and BLM criteria versions were strongly correlated. In contrast, the hardness-criteria version was often out of phase with the MLR and, depending on waterbody and season, could be either strongly overprotective or underprotective. The MLR-based USEPA-style chronic criterion appears to be more generally protective of ecosystems than other models. Environ Toxicol Chem 2023;00:1-35. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Proposed development of a mine within Alaska's Bristol Bay watershed has raised concerns about the potential impact of copper (Cu) on Pacific salmon (Oncorhynchus spp.). We conducted 96-hour flow-through bioassays using low-hardness and low-dissolved organic carbon water to determine the acute lethal toxicity of Cu to sockeye (O. nerka), Chinook (O. tshawytscha), and coho salmon (O. kisutch) fry. We aimed to determine Cu toxicity under field-relevant water quality conditions and to assess three methods of calculating ambient Cu criteria: the USEPA-endorsed biotic ligand model (BLM), a multiple linear regression model, and the hardness-based model currently used by the State of Alaska. The criteria generated by all models were below 20% lethal Cu concentrations by factors ranging from 2.2 to 54.3, indicating that all criteria would be protective against mortality. The multiple linear regression-based criteria were the most conservative and comparable to BLM-based criteria. The median lethal concentrations (LC50s) for sockeye, Chinook, and coho, were 35.2, 23.9, and 6.3 µg Cu/L, respectively. We also used the BLM to predict LC50s for each species. Model-predictions differed from empirical LC50s by factors of 0.7 for sockeye and Chinook salmon, and 1.1 for coho salmon. These differences fell within the acceptable range of ± 2, indicating the model's accuracy. We calculated critical lethal Cu accumulation values for each species to account for differing water chemistry in each bioassay, revealing that coho salmon were most sensitive to Cu, followed by sockeye, and Chinook salmon. Our findings underscore the importance of considering site- and species-specific factors when modeling Cu toxicity. The empirical data presented in this study may enhance Cu risk assessments for Pacific salmon.
Metals are found everywhere in the environment. As such, in practical terms, the ecological risk from metals will never be zero. We are faced, then, with the problem of what level of risk to accept in this case, and this is one of the most difficult problems to solve in risk assessment. In order to carry out a risk assessment, we need to know the hazards. Metal toxicity varies with water chemistry, and it used to be difficult to predict such toxicity; however, this difficulty was overcome by the biotic ligand model (BLM) revolution, and currently, risk assessment involving metals is not conducted without use of the model. In the first part of this chapter, the model is described in detail. Risk assessment is often carried out on a substance-by-substance basis, and therefore the model parameters of the BLM are estimated for each metal individually. These parameters and the metal toxicity values, which are individually estimated, are plotted on a graph in the search for a pattern, and model analysis focused on the source of this pattern provides insight into the toxic effects of the given metal(s). In the second half of the chapter, we explain how the relationship between the parameters and estimated toxicity values for individual metals can give us such insights.KeywordsEcotoxicity of metalsBiotic ligand modelBioavailabilityFree ionMixture effect of metals
The toxic effects of chemical mixtures on ecology have long been a matter of interest, and a great deal of related toxicity testing has already been done. The concentration addition model (CA), which was originally developed in pharmacology to predict the combined effects of drugs, is one of the major models for predicting chemical mixture effects. When the mixture effect is well predicted by the CA, it is considered to be an additive effect. This additivity is closely related to the toxic mechanism of the chemicals, and when the mixture effect is predicted by the CA, the respective toxic mechanisms are assumed to be the same. In this chapter, we first explain why the mixture effect is additive when the toxic mechanisms are the same, using the simple mathematical model of enzyme-substrate kinetics. Then we discuss the statistical model used for testing whether the mixture effect is additive or not. Finally, we consider the toxic effects of metal mixtures. As we saw in the previous chapter concerning the biotic ligand model (BLM), two processes, the binding of the metal to the biotic ligand and metal speciation, are important for predicting the toxicity of individual metals. In this chapter, we will discover that, in the case of metals, the mixture effect is always judged to be non-additive, even if the respective toxic mechanisms are the same and the mixture effect is additive and will discuss why such a strange situation arises, by extending the BLM to predict the toxicity of metal mixtures.KeywordsToxic effect of chemical mixturesConcentration addition (CA) modelLoewe’s additivityBiotic ligand model
Dissolved copper (Cu) can contribute to toxicity in aquatic systems impacted by acid mine drainage (AMD), and its bioavailability is influenced by aqueous complexation with organic ligands that predominantly include fulvic acids (FAs). Because geochemical fractionation of FA that accompanies sorption to hydrous aluminum oxides (HAO) and hydrous iron oxides (HFO) can alter Cu complexation with FA, we investigated FAs isolated from 3 categories of water (Pristine, AMD, and in-situ-fractionated mixtures of Pristine and AMD collected at stream confluences) in 3 mining-impacted alpine watersheds in central Colorado, USA. We also conducted geochemical fractionation of field-collected FA and Suwannee River FA by precipitating HAO and HFO in the laboratory. Spectral properties of the FAs (e.g., UV-VIS absorbance) were altered by geochemical fractionation; and in acute toxicity tests with an aquatic invertebrate (Daphnia magna), Cu was more toxic in the presence of in-situ- and laboratory-fractionated FAs [median lethal concentration (EC50): 19-50 µg Cu L-1 ] than in the presence of non-fractionated FAs (EC50: 48-146 µg Cu L-1 ). After adjusting for the strain-specific sensitivity of our D. magna, we improved the accuracy of Biotic Ligand Model predictions of Cu EC50 values for AMD-related FAs by using an "effective DOC" based on spectral properties that account for among-FA differences in protectiveness against Cu toxicity. However, some differences remained between predicted and measured EC50 values, especially for FAs from AMD-related waters that might contain important metal-binding moieties not accounted for by our measured spectral indices. This article is protected by copyright. All rights reserved. Environ Toxicol Chem 2022;00:0-0. © 2022 SETAC.
Dynamic pH change promoted by biogeochemical reactions in Arctic tundra soils can be a major control on the production and release of CO2 and CH4, which contribute to rising global temperatures. Large quantities of soil organic matter (SOM) in these soils are susceptible to microbial decomposition, leading to pH changes during permafrost thaw. Soil pH buffering capacity (β) modulates the extent of pH change but has not been thoroughly studied and represented in predictive ecosystem scale biogeochemical models in Arctic tundra soils. In this study, we generated titration curves for 21 acidic tundra soils from three Arctic sites across northern Alaska, United States of America. Geochemical and hydrological soil properties were evaluated, and correlations with β were developed. Strong correlations between β and both gravimetric water content (Θg) (R² = 0.847, p < 0.001) and soil water retention (SWR) (R² = 0.849, p = 0.001) indicate that the ability of soil to retain water could be associated with its buffering properties. Correlations between β and soil organic carbon (SOC) and cation exchange capacity (CEC) were also explored, and relationships to SWR are discussed. These correlations were then used with existing soil databases reporting SOC, CEC, and SWR to estimate β across Alaska soils. We further demonstrated the quantitative relationships between β and the simulated rates of biogeochemical reactions and show that lower β leads to higher soil pH and more CH4 production. Our study provides simple proxies for β in Arctic soils and highlights the importance and implications of representing soil buffering in predictive models, thereby enabling quantitative coupling between pH dynamics associated with biogeochemical reactions. Integrating β into predictive models of Arctic biogeochemical cycling may reduce model uncertainty and further our understanding of permafrost SOM degradation accelerated by warming.
Multi-surface modelling (MSM) is an important tool to predict heavy metal partitioning and speciation in soils. However, calcareous clay soils contaminated by smelting activities and mine waste have so far received little attention in MSM studies. In this work, 6 paired soil samples taken nearby former Zn smelters and at further distance were used for quantifying the essential input parameters for MSM including the size of the geochemically reactive pool of heavy metals and the reactivity of soil organic matter (SOM) for metal binding. The reactive heavy metal pool (Cd, Cu, Ni, Pb, and Zn) in these samples was determined by extracting soil with 0.43 M HNO3 and 0.005 M DTPA. For both extraction methods, the contribution of all heavy metals to their total contents was larger in most of the soil samples taken nearby former Zn smelters than in the paired samples from further distance. Furthermore, the amounts of heavy metals extracted with 0.43 M HNO3 were consistently larger than those extracted with 0.005 M DTPA. The sum of the humic acid (HA), fulvic acid (FA) + hydrophobic organic neutral (HON) fraction varied between 6.2 and 43% of total SOM with an average of 24%, which is at the lower end of what is commonly reported in literature. The lower SOM reactivity might be attributed to a lower humification rate of fresh soil organic matter due to heavy metal contamination. The accuracy of the MSM-predicted predictions of solubility of the heavy metals, especially for Ni and Zn, was higher when the results of the DTPA extraction method were used as model input, than when using the results of the HNO3 extraction method, especially when the soil carbonate content was > 4%. Hence, the measurement of the geochemical reactivity of heavy metals by the 0.005 M DTPA extraction method and the reactivity of SOM enable adequate MSM predictions of the solubility of heavy metals in smelter slag-contaminated calcareous clay soils.
Sorption–desorption kinetics have a major impact on the fate of chemicals in soils. Analytical advances enable one to determine better elementary reactions occurring at the molecular level that are part of the overall reaction mechanism, and the extent of multiple sorption processes taking place simultaneously on soil surfaces. Thus, compared to rate parameters previously reported in the literature, those measured with new approaches may be less apparent and more applicable to predict accurately the fate of chemicals in soils using conceptual models.
Population models are increasingly being used to extrapolate individual‐level effects of chemicals, including metals, to population‐level effects. For metals, it is also important to take into account their bioavailability to correctly predict metal toxicity in natural waters. However, to our knowledge, no models exist that integrate metal bioavailability into population modeling. Therefore, our main aims were to (i) incorporate bioavailability of copper and zinc into an Individual Based Model (IBM) of rainbow trout (Oncorhynchus mykiss) and (ii) to predict how survival‐time‐concentration data translate to population‐level effects. For each test water, reduced versions of the General Unified Threshold model of Survival (GUTS‐RED) were calibrated using the complete survival‐time‐concentration data. The GUTS‐RED Individual Threshold (IT) showed the best fit in the different test waters. Little variation between the different test waters was found for two GUTS‐RED‐IT parameters. The GUTS‐RED‐IT parameter “median of distribution of thresholds” (mw) showed a strong positive relation with the Ca2+, Mg2+, Na+ and H+ ion activities. Therefore, mw formed the base of the calibrated GUTS bioavailability model (GUTS‐BLM), which predicted 30‐day LCx values within a two‐fold error. The GUTS‐BLM was combined with an IBM, inSTREAM‐Gen, into a GUTS‐BLM‐IBM. Assuming that juvenile survival was the only effect of copper and zinc exposure, population‐level effect concentrations were predicted to be 1.3 to 6.2 times higher than 30‐day laboratory LCx values, with the larger differences being associated with higher inter‐individual variation of metal sensitivity. The proposed GUTS‐BLM‐IBM model can provide insight into metal bioavailability and effects at the population level and could be further improved upon by incorporating sublethal effects of copper and zinc. This article is protected by copyright. All rights reserved.
The computerized aqueous chemical model of A. H. Truesdell and B. F. Jones, WATEQ, has been greatly revised and expanded to include consideration of ion association and solubility equilibria for several trace metals, Ag, As, Cd, Cu, Mn, Ni, Pb and Zn, solubility equilibria for various metastable and(or) sparingly soluble equilibrium solids, calculation of propagated standard deviation, calculation of redox potential from various couples, polysulfides, and a mass balance section for sulfide solutes. Revisions include expansion and revision of the redox, sulfate, iron, boron, and fluoride solute sections, changes in the possible operations with Fe (II, III, and II plus III), and updating the model's thermodynamic data base using critically evaluated values (81, 50, 58) and new compilations.
Abstract The computerized aqueous chemical model of Truesdell and Jones (2, 3), WATEQ, has been greatly revised and expanded to include consideration of ion association and solubility equilibria several trace metals, Ag, As, Cd, Cu, Mn, Ni, Pb and Zn, solubility equilibria for various metastable and(or) sparingly soluble equilibrium solids, calculation of propagated standard deviation, calculation of redox potential from various couples, polysulfides, and a mass balance section for sulfide solutes. Revisions include expansion and revision of the redox, sulfate, iron, boron, and fluoride solute sections, changes in the possible operations with Fe (II, III, and II + III), and updating the model's thermodynamic data base using critically evaluated values (81, 50, 58) and new compilations (51, 26; R. M. Siebert and C. L. Christ, unpublished data 1976). Mechanical revisions include numerous operational improvements in the computer code.
A revised, updated summary of equilibrium constants and reaction enthalpies for aqueous ion association reactions and mineral solubilities has been compiled from the literature for common equilibria occurring in natural waters at 0-100°C and 1 bar pressure. The species have been limited to those containing the elements Na, K, Li, Ca, Mg, Ba, Sr, Ra, Fe(II/III), Al, Mn (II, III, IV), Si, C, Cl, S(VI) and F. The necessary criteria for obtaining reliable and consistent thermodynamic data for water chemistry modeling is outlined and limitations on the application of equilibrium computations is described. An important limitation is that minerals that do not show reversible solubility behavior should not be assumed to attain chemical equilibrium in natural aquatic systems.
The computer program GEOCHEM is adapted and being developed for soil solutions from the REDEQL2 program. Four categories of important theoretical problems were confronted in connection with the development of GEOCHEM. These problems were the lack of data regarding (a) stability constants of trace metal complexes with many important inorganic and mixed ligands and (b) stability constants of trace metal complexes with naturally occurring organic ligands; (c) solubility product constants for soil clay minerals, and (d) thermodynamic exchange constants and exchanger phase activity coefficients. The resolution of these problems has been discussed. Two representative applications of GEOCHEM in its current form to the calculation of trace metal equilibria in a mixture of irrigation water and a geothermal brine and in the aqueous phase of a sewage sludge amended soil are presented and discussed.
The interaction constant of small Eu concentrations with soil organic matter of different origin is measured at pH = 9 in presence of 0.1 M HCO3 using SCHUBERT’s ion exchange method. Only a 1: 1 complex is formed in the considered soil organic matter concentration range (< 10⁻³ M). The interaction constant increases only slightly (0.3 log K units) with a decrease in the Eu occupancy range from 1 to 10⁻⁴% and tends to a steady value of 1013,9. The interaction constants are identical (10l4± 100.5) for Podzol and Boom Clay humic acid extracts, commercial humic acid (Fluka), and for various organic matter subfractions of a Podzol Soil (humic + fulvic acid). The presence of a 10³ excess of Pb⁺⁺ with respect to Eu does not affect the Eu-interaction constants.
Humic ion-binding Model V is a discrete site/electrostatic model of ion binding by humic matter that describes competition amongst interacting species (protons and metal species) and the effects on binding of ionic strength. Six adjustable parameters are required to describe proton binding. These are nA, (equivalent acid groups per gram), pKA and pKB, (mid-range intrinsic proton dissociation constants), ΔpKA and ΔpKB, (ranges of pK values), and P (empirical constant for electrostatics). For each metal species, two further parameters are required; these are intrinsic equilibrium constants for metal—proton exchanges (pKMHA and pKMHB). Both monodentate and bidentate binding sites for metal species are included, and binding by non-specific accumulation of counterions is taken into account. In this study, Model V parameter values are derived from published results for humic acids (acid—base titrations and binding studies with 15 metals). It is found that an approximate relationship between pKMHA and pKMHB, can be derived, which eliminates an adjustable parameter and allows useful rationalization of the different data sets. Electrostatic effects with humic acids are greater than those with fulvic acids, and the two types of humic material also differ in the affinities of their discrete binding sites for protons and metal ions.
This 10-chapter book discusses components of aquatic systems, natural organic matter, aquatic organic compounds, complexation equilibria, homologous complexants, in situ distribution of chemical species, potentiometric methods, voltammetric methods, and nonelectrochemical methods. The references are from the 1970s and early 1980s. An index and an extensive reference section also are provided.
Humic ion-binding model V is used to interpret competition effects in the binding of trace metal species and alkaline earth cations (Mg2+, Ca2+) by fulvic-type humic substances. Intrinsic equilibrium constants for the alkaline earths are derived from literature data, and the values estimated from direct binding data are shown to be compatible with the results of competition studies involving copper. Within the model, three mechanisms of competition are possible, these being direct competition at discrete sites, competition for counterion accumulation, and reduction in net electrical charge on the humic molecule by alkaline earth cation binding to certain sites, which diminishes the electrostatic contribution to trace metal binding at other sites. The first mechanism is most significant for divalent trace metals having relatively high affinities for humic substances. Features of model V that permit compatibility between competition data and direct binding data are (a) sites with different relative affinities for different metals, (b) the presence of both monodentate and bidentate sites, and (c) the contribution of nonspecific counterion accumulation to alkaline earth binding.
Two fluorescence techniques to study metal-humic interactions are presented. In the first technique, Lanthanide Ion Probe Spectroscopy (LIPS), the humic samples are titrated by Eu3+ ions. The ratio of the intensities of two emission lines of Eu3+, R=I592/I616, is used to estimate the amount of bound and free species of the probe ions. The titration plot is presented as R versus the logarithm of total added Eu3+. In the second technique, fluorescence quenching of the humic material by Cu2+ is used to produce titration curves of intensity versus the logarithm of total added Cu2+. The two techniques are used in conjunction with a model that treats the various ligands in humic substances as continuous distributions of binding sites in which individual ligand concentrations are normally distributed with respect to the individual stability constants for metal binding. The model includes the effects of pH, ionic strength, and competing metal ions. The parameters of the model are estimated by fitting the spectral titration data to the calculated titration plot. Some simulation and experimental data are presented and discussed.
Model V describes the binding of ions by humic substances in terms of complexation at discrete sites, modified by electrostatic attraction and/or repulsion, and also takes account of nonspecific binding due to counterion accumulation. The model operates over wide ranges of pH (3–11) and ionic strength (0.001-1 M). Electrostatic effects on specific binding are described with an empirical relationship involving net humic charge and an electrostatic interaction factor. Accumulation of counterions is described by Donnan-type expressions. The model assumes the presence of eight proton-dissociating groups in the humic material, distinguished by intrinsic pK values. In general, the description of proton dissociation requires seven parameters, but for fulvic-type material only six are needed. The proton-dissociating groups may interact individually with other ions, or pairs of them may form bidentate sites. Binding at the monodentate and bidentate sites is characterized by intrinsic equilibrium constants for cation-proton exchange; there are two such constants (pKMHA and pKMHB) for each cation. Model parameters are derived from published data for fulvic-type material on proton dissociation (eight data sets) and metal binding (twenty-six data sets, eleven metals). In the case of proton dissociation, the greatest variability among samples is in site densities, while intrinsic dissociation constants and electrostatic interaction factors are relatively consistent. With parameters for proton dissociation fixed, adjustments of pKMHA and pKMHB permit reasonable fitting of metal binding data, including pH dependence. There are insufficient available data to evaluate properly ionic strength and competition effects on metal binding, but correct trends are reproduced by the model. Values of pKMHA for metals indicate that binding strength increases in the order Mg2+ < Ca2+ < Mn2+ < Cd2+ < Co2+ < Ni2+ ~ Zn2+ < Pb2+ < Cu2+ < VO2+. The strong correlation between pKMHA and the analogous constant for lactic acid may be useful for estimating values of pKMHA in cases where humic binding data are lacking.
A discrete-site model of ion-binding by humic substances (HS), incorporating a description of electrostatic effects, is evaluated with analytical data for surface waters of acid pH (3.5–6.5). After optimization of the model by adjustment of the binding-site content of the HS, the root-mean-square deviation (RMSD) between measured and calculated concentrations of organically-complexed monomeric aluminium—[Alm-org]—is 1 μM for a range of measured values of 0.1–9.0 μM (108 samples from 12 different locations). The optimization indicates that the dissolved organic matter of natural waters is only about 50% as ‘active’ (in the sense of ion-binding) as isolated HS. The model, optimized for Al-binding, also accounts for the contribution of HS to ionic balance; for 139 samples (from 8 locations) with dissolved organic carbon concentrations in the range 4.6–43.0 mg 1−1, and using measured pH as input for the computations, the mean calculated ratio of cations to anions was 1.03, with a standard deviation of 0.11. A similar result was obtained with an optimized version of the model of B. G. Oliver, E. M. Thurman and R. L. Malcolm (Geochim. cosmochim. Acta47, 2031–2035, 1983). For the same 139 samples, pH values were also calculated, using total measured anion concentration as inputs. The RMSD in pH was 0.35 for all samples, but only 0.18 for the 56 samples of pH ⩽ 4.5. Statistical analyses indicate that inadequacies in model assumptions, including the estimation of concentrations of HS in water samples, account for about one-third of the discrepancy between measured and calculated [Alm-org]; the remaining two-thirds is explained by errors in input data and measured [Alm-org]. In the case of pH prediction, no model inadequacy is apparent, because of the high sensitivity of the calculations to errors in input data.
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