The form in which a trace element or other component of seawater is present, and its tendency to react, depends on its activity (Clegg and Whitfield, 1991). This is the product of its concentration (usually mol per kg seawater), and an activity coefficient (γ) which is a complex function of temperature, pressure, and salinity (or, more generally, solution composition). Many of the important reactions in seawater involve acid-base equilibria, which introduces pH as a further variable.

The Pitzer model (Pitzer, 1991) is a set of equations to calculate activity coefficients (and hence all forms of chemical equilibria) in aqueous solutions as functions of composition and concentration, temperature, and pressure. The equations for water and solute activities, and thermal and volumetric properties, are derived from a single expression for the excess Gibbs energy of the solutions. The equations contain sets of parameters (which are functions of temperature and pressure) of two kinds: “pure” solution parameters whose values are determined by fitting to data for solutions containing single electrolytes (e.g., NaCl, MgSO₄); and “mixture” parameters whose values are determined from measurements containing, typically, two different electrolytes with a common ion. (e.g., NaCl and Na₂SO₄).

The data needed to build a model of a complex mixture, such as seawater, include: solvent and solute activities, apparent molar enthalpies and heat capacities (yielding the variation of the model parameters with temperature), apparent molar volumes and compressibilities (the variation of the parameters with pressure), salt solubilities, liquid/liquid phase partitioning, equilibrium partial pressures of volatile solutes, and others. The model is described in detail by Pitzer (1991), and its application to the chemistry of natural waters by Clegg and Whitfield (1991). For any solution, the major effort in constructing a Pitzer model is the determination of the parameter values; a process that often includes the resolution of differences between inconsistent sets of data, and obtaining approximate values of parameters for which there are no data.

The principal Pitzer chemical speciation model of seawater is that of Millero and co-workers at the University of Miami (Millero and Roy, 1997; Millero and Pierrot, 1998; Waters and Millero, 2013). Using many of the same data sources, Clegg and Whitfield (1995) also developed a seawater model, including the protonation of dissolved ammonia. The model of Millero and co-workers is applicable primarily to major ions in seawater (from 0 to 50°C, and 0 to >40 salinity) containing the species H⁺, Na⁺, K⁺, Mg²⁺, Ca²⁺, Sr²⁺, Cl⁻, Br⁻, OH⁻, HCO3−, B(OH)4−, HSO4−, SO42−, CO32−, CO₂, B(OH)₃, and H₂O. The use of the model to calculate pH, and some of the problems concerning pH scales that still need to be addressed, are discussed by Waters and Millero (2013). There is, as yet, no comprehensive evaluation of the uncertainties in model-calculated speciation arising from uncertainties in the thermodynamic measurements upon which they are based. The data for trace metal activities and complexation in seawater—which are important for understanding their behavior and fate—are best described as patchy, and the effects of pressure on activities are only well characterized for aqueous solutions of some of the major seawater electrolytes (e.g., NaCl). This limits the accuracy of calculations of chemical speciation in the subsurface ocean.

The parameters that comprise the Pitzer models developed for seawater are drawn from many sources, include data up to a century old, and are often not optimized for solutions of seawater but rather for modeling brines, e.g., Harvie et al. (1984). To our knowledge, the number of new studies yielding the activities and other data needed to improve the model has been in decline for many years, though the need to quantitatively understand ocean biogeochemistry, especially carbonate chemistry, has increased significantly. Furthermore, there is no comprehensive evaluation relating the capabilities of speciation models, and the measurements upon which they are based, to current and future needs in chemical oceanography as exemplified in current programs, such as the international GEOTRACES Program (Henderson et al., 2007).

The matrix of Pitzer parameters for major ions in seawater—of the composition noted above but excluding Sr²⁺ and boric acid—is considerable: 36 sets of cation-anion interactions, and potentially 210 ternary or “mixture” parameters that express the interactions between two dissimilar ions of one charge type, and one of the opposite charge type. Some of these can be neglected if all the interacting species are at very low concentration. Nevertheless, the large numbers of interactions and the fact that they can vary with both temperature and pressure emphasizes the need to (1) assess the completeness and reliability of the model, (2) validate its basis in measured thermodynamic properties (including analyses to relate the uncertainties in the output quantities of the model to those of the fundamental data—for each interaction—upon which it is based); and (3) establish the sets of measurements still needed to adequately characterize the behavior of seawater, estuarine and other natural waters encountered worldwide. The treatment of uncertainties could adopt the methods applied to the IAPWS 1995 equation of state for water (Feistel et al., 2016).

When Pitzer equations are used to calculate activity coefficients, the stability constants used for the chemical equilibria are thermodynamic constants K, which are functions of temperature and pressure only. However, many practical applications make use of stoichiometric constants K^* (sometimes also called conditional constants), which are expressed in terms of concentrations and are thus functions of temperature, pressure and solution composition. This dependence on solution composition—via the activity coefficients γ—limits the practical value of stoichiometric constants. Taking the dissociation of HF as an example, and using square brackets to represent concentrations: (1)K∗=[H+][F−]/[HF]=K·(γHF/γH+ γF−)

Values of the stoichiometric constant K^* measured in normal seawater can be used only for solutions of seawater composition and for the salinity, or salinities, for which K^* has been determined. Any variations from seawater composition—such as might be found in enclosed seas, pore waters, and some estuaries—will lead to changes in the values of the three activity coefficients in the equation above and therefore a change in K^*.

Models based on the Pitzer equations are used to calculate chemical speciation at equilibrium, although there is of course no guarantee that a particular system is at equilibrium. In marine environments, the different redox couples that are present are, in general, not in equilibrium with each other. The Pitzer equations can be used to examine this phenomenon by calculating the equilibrium speciation in the two oxidation states of a redox couple: combining measured total concentrations and the standard potential for the couple allows the degree of disequilibrium to be established. Such calculations can also be used to establish the relative oxidizing or reducing power of different redox couples at in situ conditions. A knowledge of the equilibrium speciation may also contribute to the analysis of rate processes in places where complexation of a metal ion affects the rate of oxidation (Santana-Casiano et al., 2005).

The Pitzer equations are applicable to reactions involving relatively simple chemical species whose chemical composition and structure are well defined. Natural organic matter (NOM) in natural waters, including seawater, falls outside this definition since it is a polydisperse material comprising a mixture of many different molecular structures (Koch et al., 2005). Modeling of NOM in seawater is currently restricted to its complexation of trace metals; the available information is derived from competitive ligand exchange cathodic stripping voltammetry (CLE-CSV) titrations (section 6.1), which are summarized as the “concentrations” and “stability constants” of one or more ligand classes. This operational summary cannot be applied in conditions that depart from those used in the titration, although in many cases a broad agreement between different studies has been observed. An alternative strategy worthy of investigation is the use of modeling approaches for freshwater NOM, following extensive laboratory studies of extracted material. Three modeling approaches have been developed: the Windermere Humic Acid Model (WHAM) (Tipping et al., 2011); the Stockholm Humic Model (SHM) (Gustafsson, 2001); and the NICA-Donnan model (Koopal et al., 2005). All three approaches explicitly recognize the polydisperse nature of NOM, and also explicitly treat the development of charge due to ionization and complexation reactions. The model codes developed for these approaches in fresh waters use simple extended Debye-Hückel equations for the calculation of activity coefficients. These equations are not adequate for use in seawater. However, the WHAM formulation has recently been combined with a Pitzer model (Ulfsbo et al., 2015), providing the basis to test this approach for NOM modeling in seawater.

The basis of any speciation model that aims to predict activity coefficients accurately is the background electrolyte that determines the physicochemical properties of seawater, and the chemical environment experienced by trace species. On a molar concentration basis, seven chemical elements account for 99.9% of the dissolved species in seawater. Models of the seawater electrolyte normally include the 11 elements whose concentrations exceed 1 μmol kg⁻¹ and constitute a constant or near-constant proportion of salinity. These are, in order of descending concentration, chlorine (Cl), sodium (Na), magnesium (Mg), sulfur (S), calcium (Ca), potassium (K), (inorganic) carbon (C), bromine (Br), boron (B), strontium (Sr), and fluorine (F).

The definition of the core speciation model also includes the ranges of the three master variables temperature, salinity, and pressure. A temperature range of 0–50°C and a salinity range of 0–50 correspond to the ranges of many speciation models, although the most complete data collection is usually at 25°C. Information on pressure dependence is often limited, so the core speciation model that the Working Group will consider is initially for 1 atmosphere pressure.

The practical scales used by chemical oceanographers to measure pH are summarized by Waters and Millero (2013). The complexities of these scales, and the difficulty of defining pH, arise because neither the concentration nor the activity of H⁺ can be measured directly and independently of other quantities (Dickson, 1984). On the total scale, the H⁺ associated with SO42− (as HSO4−) is included in the pH, so that: (2)pHT=−log10([H+]+[HSO4−])          = −log10{[H+](1 + [SO42−]/KHSO4*)}

The seawater pH scale also includes the H⁺ bound to F⁻ (as HF), so that, (3)pHSWS​​=  ​−log10([H+]+[HSO4−]+[HF])             =​ −log10{[H+](1 + [SO42−]/KHSO4*+ [F−]/KHF*)}     

In the above equations the stoichiometric dissociation constants K^*_HSO4 (for reaction HSO4− ↔ H⁺ + SO42−) and K^*_HF (HF ↔ H⁺ + F⁻) vary with temperature, pressure, and salinity. More generally, they vary with chemical composition which affects pH in natural waters whose composition differs from that of normal seawater.

The total and seawater pH scales have been adopted for the following practical reason: the artificial seawater solutions used for the characterization of pH buffers and pH indicators must contain a relatively high concentration of sulfate in order to match the major ionic composition of natural seawater. However, glass and hydrogen pH electrodes respond only to the free H⁺ in solution, while analytical hydrogen ion concentrations obtained from measurements on these scales include both H⁺ and HSO4−. This difference is significant: −log₁₀[H⁺] and pH_T at salinity 35 and 25°C differ by 0.128 pH units, while the difference between pH_T and pH_SWS is only 0.005 units. The accuracy with which the free H⁺ concentration can be determined from pH_T and pH_SWS is currently limited by the accuracy with which K^*_HSO4 is known (Waters and Millero, 2013). Waters and Millero recommend further work to resolve discrepancies between measured and modeled activity coefficients in seawater-like solutions containing sulfate.

Figure 2 shows concentrations of the principal species controlling pH in a salinity 35 seawater at 25°C, calculated using an ion-interaction speciation model (Clegg and Whitfield, 1995). It is clear, from the relatively high concentrations of carbonate and borate, that seawater pH is largely controlled by equilibria involving these species.

The chemical species in the model include the weak acids that are involved in defining the pH and alkalinity of seawater. These are clearly illustrated in the open-ended definition of total alkalinity A_T (Dickson, 1981): (4)AT=[HCO3−]+ 2[CO32−]+[B(OH)4−]+[OH−]+[HPO42−]               + 2[PO43−]+[SiO(OH)3−]+[NH3]+….                −[H+]−[HSO4−]−[HF]−[H3PO4]−…

The core chemical species in the model have been selected based on a significant contribution (>1 μmol kg⁻¹) to A_T in the open ocean. This includes all the species in Equation (4), with the exception of the weak base ammonia.

A number of software packages are freely available for carrying out calculations on the CO₂ system, and are based on stoichiometric constants parameterized as a function of salinity, temperature, and pressure (Orr et al., 2015). However, the use of salinity as a master variable carries the drawback that these calculations are not applicable to waters of different compositions. This is a particular problem at lower salinities where major ion composition of the seawater/freshwater mixture does not correspond simply to diluted seawater. Compared with seawater, river waters generally have high Mg, Ca, and borate concentrations relative to Na, Cl, and SO₄. Furthermore, river waters are typically high in organic ligands of various molecular sizes. This material can contribute to alkalinity, as noted above, and its decomposition in estuaries can cause substantial changes in the CO₂ system (Cai, 2011; Chen et al., 2013; Yang et al., 2013).

The interpretation of measured alkalinity, and its use in calculations of CO₂ equilibria in natural waters, requires identification of all the relevant weak acids (i.e., those that contribute more than ca. 1 μmol kg⁻¹ to the alkalinity), and their degrees of dissociation at the temperature, salinity, and pressure of interest. This presents an analytical challenge in many estuarine and brackish waters where natural organic matter can make a significant contribution to alkalinity (Hernandez-Ayon et al., 2007; Kulinski et al., 2014). Complete modeling of the CO₂ system in such waters will also require a treatment of the acid-base chemistry of this material. Overall, there is a great deal to be gained by integrating calculations of the carbon dioxide system, and pH, into a Pitzer model of chemical speciation.

An aspect of ocean acidification that has received a great deal of attention is the concomitant increase in the solubility of calcium carbonate, and its consequences for calcifying marine organisms. A large number of experiments have been carried out exposing different calcifying (and non-calcifying) marine organisms to reduced pH. These experiments show a range of results, and that, at least in the short term, not all calcifying organisms are negatively affected (Dupont and Poertner, 2013). It is, however, clear that oceanic pH is falling and that organisms that precipitate the more soluble forms of calcium carbonate will be greatly affected. Corals, for example, precipitate aragonite, which is the most soluble pure form of calcium carbonate, although high magnesium calcites can have even higher solubilities (Haese et al., 2014). Projections show that aragonite will become undersaturated in large parts of the ocean during the next century. As for the CO₂ system equilibria in solution, the solubility products of calcite and aragonite are currently available as functions of salinity, temperature, and pressure (Millero, 2007). Incorporation of these equilibria into a Pitzer framework would enable improved calculations of the solubility products of calcite and aragonite in waters whose composition differs from standard seawater.

The recognition of ocean acidification as “the other CO₂ problem” has highlighted the need for precise and accurate pH measurements in seawater. Two methods are currently recognized as meeting these requirements: glass electrodes calibrated in artificial seawater buffers; and indicator spectrophotometry (Dickson et al., 2007), also calibrated against buffer solutions. The most widely used buffer for this purpose is “tris” (2-amino-2-hydroxymethyl-1,3-propanediol), although alternative buffers, such as 2-aminopyridine, morpholine (tetrahydro-l,4-isoxazine) and bis (2-amino-2-methyl-l.3-propanediol) can be used when the pH departs significantly from the normal seawater range (Dickson, 1993). The pH values assigned to these buffers are available as functions of salinity and temperature, and have been derived by least-squares fitting pH values determined from a large number of potentiometric measurements in cells without liquid junctions (DelValls and Dickson, 1998). As yet, these measurements cover a limited salinity range (20–40). An alternative approach to fitting the buffer pH values to semi-empirical functions of salinity and temperature, is to derive Pitzer parameters for the interactions of these two buffers with the components of seawater. This would then allow these important pH buffers to be incorporated into the planned unified model of seawater, and also facilitate the treatment of low salinity buffer solutions.

Indicator spectrophotometry has gained wide acceptance as the method of choice for measurements of seawater pH. These measurements require the stoichiometric stability constant of the indicator dye at the salinity and temperature of the measurement. This is currently achieved by laboratory measurements of these constants under a range of conditions, followed by least squares fitting of the data to semi-empirical functions of salinity and temperature (Liu et al., 2011). As with the pH buffers, an alternative approach to fitting the indicator pH values to semi-empirical functions of salinity and temperature is to derive Pitzer parameters for the interactions of the relevant indicators with the components of seawater. This would allow these indicators to be incorporated into the planned unified seawater model. The most important indicators are m-cresol purple, phenol red and thymol blue, which are used for pH measurements; and also bromocresol green and bromocresol purple which are used for the determination of alkalinity by the single point method.

It is now recognized that the bioavailability of iron is a key factor in determining primary production and/or phytoplankton community structure in large areas of the ocean (Turner and Hunter, 2001). The thermodynamically stable oxidation state in oxic waters, Fe(III), is poorly soluble with a solubility of the order of 10⁻¹¹ mol L⁻¹ in seawater in the absence of organic ligands (Liu and Millero, 2002). However, strong organic complexation can maintain significantly higher concentrations. While organic complexation by (as yet) not fully characterized ligands dominates the speciation of Fe(III), there is continuing interest in defining the inorganic complexation and solubility as essential supporting information. There is also considerable interest in Fe(II), both as a component of anoxic waters, and as metastable Fe(II) produced by photochemical reduction in surface waters. The speciation of this metastable Fe(II) and its effects on Fe oxidation rates are key factors determining the bioavailability of Fe in sunlit surface waters.

While much attention has been focused on Fe in recent years, several other metals are essential for biological processes. A recent review gives the generalized ranking of trace metal content in phytoplankton as Fe ≈ Zn > Mn ≈ Ni ≈ Cu >> Co ≈ Cd (Twining and Baines, 2013). While all these metals act as micronutrients, some can also have toxic effects at elevated concentrations (Cd, Ni, Cu, Zn, see also section 7.2). Four of these metals (Fe, Zn, Cu, and Cd) are obligatory core parameters on GEOTRACES cruise sections, which are providing extensive new information on these metals, both in terms of dissolved (<0.2 μm) concentrations and CLE-CSV titration data (see section 6.1). Chemical speciation modeling will provide an excellent tool to help extract full value from these unique global datasets.

Manganese is present as insoluble MnO₂ at equilibrium in oxic seawater. However, relatively slow oxidation rates result in dissolved Mn concentrations at the nM level. This slow oxidation has led to the use of Mn as a tracer for releases from anoxic environments, where Mn is present as dissolved Mn(II), and from hydrothermal systems where significant amounts of reduced Mn are released into the surrounding waters (German et al., 1999). The speciation of dissolved Mn has been considered to be dominated by weakly complexed Mn(II), but recent work indicates that Mn(III) may also be present at significant concentrations (Luther et al., 2015).

In surface waters, Al has been used as a tracer for dust input, which is a major source of Fe to the ocean. The strong correlation between dissolved Al and silicic acid has prompted suggestions of biological control of Al distributions (van Hulten et al., 2014). However, for example in the Mediterranean Sea, the correlation between Al and Si could be explained purely by vertical transport of biogenic particles (Rolison et al., 2015).

The lanthanides series are a powerful set of tracers because of their coherent and predictable behavior. Lanthanides have been used in many studies to investigate redox conditions (Liu et al., 1988; Sholkovitz and Schneider, 1991), particulate exchange and scavenging processes (Andersson et al., 2008), water mass transport (Scher and Martin, 2004; Haley et al., 2008) and identification of benthic sources of pore fluid (Abbott et al., 2015). More recently, the lanthanides' widespread use in high-technology processes and products is increasing their environmental occurrence. For example, lanthanides are now being used as tracers of wastewater-derived contaminants in the marine environment (Hatje et al., 2014, 2016).

The close relationship between the depth profiles of Cd and phosphate concentrations makes Cd an attractive candidate as a proxy for phosphate concentrations in palaeoceanography, although the relationship shows significant variation (de Baar et al., 1994).

The most widely used method for characterizing the organic complexation of trace metals in seawater is currently Competitive Ligand Exchange (CLE), although direct electrochemical measurements by Anodic Stripping Voltammetry (ASV) are also used, e.g., Sinoir et al. (2016). CLE involves the addition of a competing ligand which has two properties: it forms a complex that competes for trace metals with the natural unknown ligands present in the sample; and it forms a complex with the target trace metal that adsorbs on a mercury electrode and can thus be detected by Cathodic Stripping Voltammetry (CSV). The methods of fitting the CLE/CSV and ASV titration data accurately have been improved significantly in recent times (Pizeta et al., 2015). However, to enable accurate CLE characterization of the complexation of the trace metal with unknown ligands, it is essential to quantify the complexation of the metal by the added competing ligand, as well as competition reactions with other metals.

For CLE measurements, the most commonly used added ligands are SA (salicylaldoxime), which complexes with Cu and Fe (Campos and van den Berg, 1994; Buck et al., 2007; Abualhaija and van den Berg, 2014); NN (1-nitroso-2-naphthol), which complexes with Fe (Aldrich and van den Berg, 1998); APDC (aminopyrrolidinedithiocarbamate), which complexes with Zn (van den Berg, 1985); and DHN (dihydroxynaphthalene), which complexes with Fe (van den Berg, 2006). Other relevant ligands are TAC [2-(2-thiazolylazo)-p-cresol], which has been used as competing ligand for the determination of Fe organic complexation in seawater (Croot and Johansson, 2000; Sander et al., 2015); and DMG (dimethyglyoxime), which has been used for determinations of Co and Ni complexation (van den Berg et al., 1991; Ellwood and van den Berg, 2001).

In addition to modeling the chemistry of the added ligands, attention needs to be paid to the buffers added to control pH in the CSV measurements. Optimal buffers are those near the natural pH of the water. While the carbonate system acts as the major pH buffer in seawater, sample deaeration undertaken during the CSV analysis disrupts this function, so that an additional buffer is needed. As well as buffering close to the natural pH of seawater, the added buffer must have only weak interactions with the trace metals being studied, so that it does not act as an additional competing ligand. The two buffers that are most commonly used are EPPS [4-(2-hydroxyethyl)-1-piperazinepropanesulphonic acid] and HEPES [4-(2-hydroxyethyl) piperazine-1-ethanesulphonic acid], however phosphate buffer is becoming more popular as it can be cleaned very efficiently to remove any traces of organic ligands and trace metals. A quantitative understanding of these buffers' chemistry in seawater would allow the buffering effect to be calculated more accurately, thus providing a better definition of the chemical conditions of the complexation titrations.

Model organic chelators are used in culture media to ensure that the inorganic trace metal concentrations stay constant during culturing. Furthermore, the use of these chelators, in conjunction with chemical equilibrium modeling, allows the researcher to manipulate the inorganic concentration of a given trace metal while maintaining the inorganic concentrations of the remaining bioactive trace elements unchanged. This design is especially suitable for controlled physiological studies investigating the response of phytoplankton to limiting or toxic trace metal concentrations. The most common general model chelator in culture studies is EDTA (ethylenediaminetetraacetic acid; Price et al., 1988; Sunda et al., 2005). However, the chelator of choice may vary depending on the research question. For example, if Cu is the metal of interest, one might use DTPA (diethylene triamine pentaacetic acid) instead of EDTA, given that DTPA is a stronger chelator of Cu than EDTA.

Limiting growth in cultures by manipulation of a specific trace metal is often challenging, and requires the use of well-chosen metal chelators. For example, siderophores, such as DFB (desferrioxamine B), and DFE (desferrioxamine E), are often used to significantly reduce the concentrations of inorganic Fe in the culture media, and thus induce Fe limitation in phytoplankton with extremely low Fe requirements (Strzepek et al., 2011). Similarly, Cu specific chelators, such as Cyclam (1,4,8,11-tetraazacyclotetradecane) and Cyclen (1,4,7,10-tetraazacyclododecane) are frequently used to induce Cu limitation in phytoplankton cultures (Semeniuk, 2014). In addition, these organic ligands are frequently used in uptake experiments (Semeniuk et al., 2015), as they are believed to mimic naturally occurring strong organic ligands of Fe and Cu in seawater. Naturally occurring thiols, such as glutathione, cysteine and thiourea are known to strongly bind Cu and other chalcophilic metals (Laglera and van den Berg, 2003). Chemical modeling that includes these organic chelators will greatly enhance our understanding of the chemical speciation of the trace metals in laboratory growth and uptake media, as well as in situ oceanic conditions.

Estuaries are where the river water meets seawater, and where the mixture interacts with the land, oceans, atmosphere, sediments, and biota. The freshwater end members are typically richer in major nutrients (nitrate, nitrite, ammonia, phosphate, and silicate) compared with ocean waters, hence biological productivities are generally high in estuaries. River waters subject to anthropogenic influence frequently have high concentrations of micronutrients and toxic elements (Liu et al., 2010), while permafrost degradation affects the chemistry of Arctic rivers and coastal seas through the mobilization of organic matter (Semiletov et al., 2016). Furthermore, submarine groundwaters with unique chemical compositions discharge into many estuaries and coastal waters (Zhang and Mandal, 2012). In the case of eutrophication, the pH and dissolved oxygen concentration are both lowered following organic matter breakdown, with consequences for acid-base and redox reactions. Moreover, when riparian tidal freshwater lands and oligohaline marshes are inundated by seawater, competition by major cations, such as Mg and Ca can result in the release of trace metals from particulate matter (Wang et al., 2012; Zhang and Mandal, 2012).

The United Nations Environmental Programme (UNEP, 2013) has highlighted the phenomenon of “global chemical intensification”: in many parts of the world, the anthropogenic inputs of metals to the ocean are estimated to be far greater than non-anthropogenic inputs, and the way in which this affects global biogeochemical cycles and ecosystem diversity is as yet poorly understood. Islam and Tanaka (2004) and Davies (1978) noted that the 10 most significant contaminant elements in marine waters are, in order of decreasing toxicity Hg, Cd, Ag, Ni, Se, Pb, Cu, Cr, As, and Zn. Four of these (Cd, Ni, Cu, and Zn) are classified as key micronutrients (Twining and Baines, 2013), and Se and Cr can in addition act as micronutrients.

Specific examples of contaminant discharge include run-off from metal mines, their associated mine tailings, and also naturally occurring acid rock drainage springs. The metals that are commonly found at elevated concentrations (micromolar to millimolar) in mine runoff include Fe, Cu, Zn (Brown et al., 2005; Braungardt et al., 2007), and depending on the ore formations, may also include Cd, Ni, Ag, Au, As. High acidity (pH < 1) is associated with the oxidation of iron pyrites and the subsequent formation of sulfuric acid. High acidity also mobilizes metals and prevents their removal by precipitation and scavenging.

Industrial discharges are also potential sources of inorganic contaminants, including those from metallurgical industry (metals), fertilizer industry (N, P), and desalination plants (metals). Furthermore, domestic waste water treatment operations discharge inorganic contaminants into receiving waters, and the growth of aquaculture in coastal zones is associated with increasing pollution by inorganic compounds.

The need to model chemistry in anoxic pore waters, and to understand the chemical processes occurring at the oxic/anoxic boundary, focuses attention on redox reactions, and on the chemistry of reduced oxidation states. The most abundant of these are NH₃, Fe(II), Mn(II), and sulfide. The most challenging of these species for chemical modeling is sulfide. In the last 2 decades, metal sulfide speciation in aquatic systems has become of great interest, due to the discovery of nanomolar levels of sulfide in oxic seawater (Luther and Tsamakis, 1989). Sulfides become stable in oxic conditions by forming complexes with trace elements, especially with Hg, Cu (Dyrssen, 1988; Luther and Tsamakis, 1989); and Pb (Bura-Nakic et al., 2007). These metal (M)—sulfide species in oxic waters include simple MHS⁺ (or M(HS)20) complexes, but also higher-order unprotonated clusters (multinuclear oligomers) with high stability constants (Rozan et al., 2000). In the case of Cu and Zn, the resulting metal-sulfide species are resistant to oxidation in oxic waters and include a mixture of dissolved metal-sulfide complexes and active metal-sulfide nanoparticles (Rozan et al., 1999; Sukola et al., 2005).

Since hot hydrothermal vents expel fluid at temperatures between 100 and 450°C, depending on vent activity and depth, these fluids are highly reactive and far from thermodynamic equilibrium with the surrounding seawater. Besides their high temperature, they exhibit extreme pH values (<3 or >9), are highly reduced and may have a very different ionic composition to that of standard seawater. In fact, the fluid composition is defined by the underlying bedrock, pressure and temperature, and may reflect phase separation. Once the venting fluids encounter cold oxygenated seawater, precipitation reactions occur instantly, resulting in the commonly seen black (or white) smokers. While the chemical signature of hot hydrothermal vents can be followed in the hydrothermal plume over thousands of kilometers, the temperature will only be >300°C for seconds or millimeters after discharge. Most chemical reactions with the seawater will occur at temperatures between 4 and 300°C. Taking samples that are representative of in-situ conditions is almost impossible as samples will undergo spontaneous degassing upon the release of pressure, and cooling. Thus, to fully understand the reactions and processes occurring at depth in the presence of high temperature and pressure, it is essential to model the speciation of the fluid at in situ conditions.

Shallow vents are of great importance for the supply of micro- and macronutrients to the photic zone. In contrast to hot vents, the majority of shallow vents are characterized by diffuse venting, making them an interesting field of research and a natural laboratory to link speciation with biological uptake and toxicity (Klevenz et al., 2012; Kleint et al., 2015). For the vast majority of chemical tracers enriched in vent fluids, net fluxes to the oceans are modified as these tracers are incorporated into Fe-rich polymetallic sulfide and oxyhydroxide particles that sink to the seafloor at or close to mid ocean ridges (Mottl and McConachy, 1990; German et al., 1991; Kadko, 1993). Since diffuse vent fluids have undergone modification in the sub-seafloor and are composed of more than 90% seawater, they have had time to partially equilibrate with ambient seawater. However, often they are still exposed to reducing conditions. The mixing zone for diffuse venting is in the order of meters. Beyond that mixing zone, minor ions still undergo reactions that need to be modeled to understand the chemistry and biological uptake in the vicinity of these diffuse vents.

Polar regions are subject to intensive research activities, as they are particularly sensitive to rising temperatures and increasing atmospheric CO₂ concentrations, whilst at the same time playing a key role in global biogeochemical cycles and climate. Ocean acidification adds another stressor to these rapidly changing ecosystems (Orr et al., 2005). Polar regions experience extremes in primary productivity. In the Southern Ocean, low productivity is common in extensive regions with low iron supply, while high productivity is observed in regions with substantial iron supply from sediments (South Georgia) or ice melt (Nielsdottir et al., 2012). Whereas, iron supply over a large part of the Arctic is considered sufficient to sustain primary productivity (Klunder et al., 2012), the macro-nutrient concentrations may become exhausted following ice retreat. The freezing of sea ice results in brines with high salinity (100 or more), with freezing points well below that of normal seawater (ca. −1.8°C). These are conditions that are not included in chemical speciation models for seawater. In particular, the carbonate system in the polar oceans is not adequately described at sub-zero temperatures and salinities >50. In order to improve our mechanistic understanding of the dynamics of polar carbonate chemistry, and to allow quantification of CO₂ fluxes across the atmosphere–ice–seawater interfaces, Pitzer based chemical speciation models covering these conditions will be valuable.

Salt lakes are systems with very high ionic strength, the record being held by Don Juan Pond (Antarctica), which contains 3.72 mol kg⁻¹ CaCl₂ and 0.5 mol kg⁻¹ NaCl (Marion, 1997). The major focus in modeling of salt lakes and brines is the accurate prediction of precipitation equilibria as a function of temperature and composition (e.g., Harvie et al., 1984; Vanèina et al., 1986, 1997; Greenberg and Møller, 1989), with two major areas of application. The first is in understanding the evolution of past environments. An example is the modeling of evaporite sequences (layers of different salts accumulated over time) to infer the temperatures, concentrations, and compositions in the water body at the time of deposition. This type of work can be used to link the evolution of the water body to long term climate variations. The second area concerns the modeling of future scenarios. Interest here is focused on water bodies that are saturated, or close to saturation, by one or more salts in response to changes in inflow/outflow and evaporation. Such changes can be due to human activities. The Dead Sea, for example, is decreasing in volume, resulting in salt precipitation.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.