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Matrix and Media Extrapolation
Published in Keith R. Solomon, Theo C.M. Brock, Dick de Zwart, Scott D. Dyer, Leo Posthuma, Sean M. Richards, Hans Sanderson, Paul K. Sibley, Paul J. van den Brink, Extrapolation Practice for Ecotoxicological Effect Characterization of Chemicals, 2008
Dick De Zwart, Amanda Warne-Lorscheider, Valery Forbes, Leo Posthuma, Willie Peijnenburg, Dik van de Meent
The biotic ligand model is gaining interest in the scientific and regulatory communities for predicting and evaluating metal bioavailability and toxicity of metals, because it takes into account both metal speciation and interactions at receptor and transport sites at the organism–water interface (de Schamphelaere and Janssen 2002). Allen (1999) linked existing water chemistry models such as the CHESS model (Santore and Driscoll 1995) and the WHAM model (Tipping 1994) to ecotoxicological endpoints (e.g., Playle et al. 1993a, 1993b). The resulting BLM (Figure 2.3) incorporates chemical interactions between dissolved organic ligands (humic acids and fulvic acids) and inorganic ligands (Ca++ and Mg++), between toxic metal ions and dissolved organic ligands (humic acids and fulvic acids) and inorganic ligands (OH–, SO4––, CO3–, Cl–, and HCO3–), as well as between cations (Ca++, H+, Na+, and Cu++) and biological binding sites (biotic ligands). This BLM is based on a conceptual model similar to the gill site interaction model (GSIM) originally proposed by Pagenkopf (1983) and the free ion activity model (FIAM) as described by Campbell (1995). The model therefore supports the hypothesis not only that toxicity is related to total or dissolved metal concentration, but also that metal complexation and interaction at the site of action need to be considered. The BLM has been calibrated toward acute ecotoxicity endpoints (LC50 and EC50) for fish and invertebrates and is under revision by the USEPA for integration into the US regulatory framework. To further validate this approach in Europe, an extensive research project has been set up to evaluate the applicability of the BLM not only for acute but also for chronic exposures (de Schamphelaere and Janssen 2002). Acute and chronic models were established and validated 1) in European surface waters of varying physicochemical characteristics (Bossuyt et al. 2004), 2) during a multispecies mesocosm test setup (Schaëfers 2002), and 3) using native organisms collected in European waters (Bossuyt and Janssen 2004).
Responses of Freshwater Biota to Heavy Metal and Metalloid Exposure
Published in Abhik Gupta, Heavy Metal and Metalloid Contamination of Surface and Underground Water, 2020
The biotic ligand model (BLM) developed around the findings that metal toxicity mainly resulted from the activity of the free ionic forms, and complexation of metals by various organic and inorganic ligands and dissolved organic matter (DOM) could reduce the toxic effects (Niyogi and Wood 2004). Water contains dissolved inorganic and organic ligands such as hydroxyl (OH–), bicarbonate (HCO3–), carbonate (CO32–), chloride (Cl–), and sulfide (HS–) ions, and dissolved organic matter (DOM). Water also contains dissolved cations such as H+, Na+, Ca2+, Mg2+, and various heavy metal and metalloid ions. These ions get attached to the organic and inorganic ligands to form complexes. The BLM is based on the premise that water also contains biotic ligands (BLs) which are biological receptors on the surface of an organism, for example, the gill of fish. Dissolved cations including heavy metal ions bind with inorganic, organic, and biotic ligands to form different complexes. When the concentrations of major cations like Ca2+ are high, they can outcompete the metal ions for BL sites and keep the toxicity reduced. It is for this reason that toxicity of many metals are relatively less in waters having higher hardness. Similarly, DOM can bind heavy metals and prevent them from binding to BL sites (Figure 6.1). However, there are some assumptions in BLM which may be said to comprise its limitations. Firstly, BLM takes into account a single metal and a single species at one time and does not cover metal mixtures. Secondly, it considers dissolved phases only, and assumes that the system is at chemical equilibrium (Smith et al. 2015). The BLM was preceded by the gill surface interaction model (GSIM), which was based on the facts that trace metals altered gill function, leading to fish mortality due to respiratory distress; that the metals along with the hydrogen ions present in water formed complexes on the gill surface; and that metals in water varied significantly in their toxicity. The protective action of increased water hardness was due to competitive inhibition of toxic metal species by calcium and magnesium. The GSIM model was applied to Cd, Cu, and Zn. In the case of metal mixtures, GSIM assumed an additive effect of toxicity (Pagenkopf 1983). At the same time, the free ion activity model (FIAM) was also proposed (Morel 1983—as cited in Niyogi and Wood 2004). Several studies also observed that at the acute toxic level of 96-h LC50, fishes exposed to most metals rarely had respiratory distress, although there was inhibition of ion transport in fish gills. For example, Ag+ and Cu2+ blocked Na+ and Cl– uptake, while Cd2+, Co2+, Pb2+, and Zn2+ blocked Ca2+ uptake (Niyogi and Wood 2004).
Incorporation of chemical and toxicological availability into metal mixture toxicity modeling: State of the art and future perspectives
Published in Critical Reviews in Environmental Science and Technology, 2022
Bing Gong, Hao Qiu, Ana Romero-Freire, Cornelis A. M. Van Gestel, Erkai He
For an effective and accurate risk assessment of metal mixtures, appropriate models or tools are required that enable the prediction of mixture effects, which cover both simple and complex mixtures and incorporate mixture interactive effects. Many mechanistically underpinned models based on different perspectives have thus been developed to predict the mixture toxicity of metals considering the interactive effects of mixture components, including: a) thermodynamic equilibrium models (e.g., biotic ligand model (BLM) (Di Toro et al., 2001), electrostatic toxicity model (ETM) (Wang et al., 2008) and WHAM-Ftox approach (Stockdale et al., 2010)); b) process-based approaches (e.g., toxicokinetic-toxicodynamic (TK-TD) model (Jager et al., 2011)); and c) modern analytical technologies (e.g., omics-based approaches (Ankley et al., 2006)). From the thermodynamic equilibrium models, it is suitable to apply the BLM-based approaches to interpret mixture effects, postulating that competition is responsible for metal mixture interactive effects (Niyogi & Wood, 2004). The ETM assumes that metal toxicity and uptake are determined by the ion activity at the surface of the cell membrane. Cations (e.g., Ca2+, Mg2+, and H+) in the bulk solution can reduce the negativity of the electrical potential at the surface of the cell membrane by charge screening and ionic binding (Kinraide, 1998; Wang, Kinraide et al., 2011), which, in turn, can reduce metal ion activities at the membrane surface. Therefore, the ETM modeling approach allows incorporating the effects of various cations simultaneously in modeling mixture toxicity and may provide mechanistic insights (in addition to competitive binding) into mixture interactive effects at the boundary layer surrounding the cell surface. The WHAM-Ftox serves as an innovative bioavailability-based model (Stockdale et al., 2010). It is assumed that the interactive effects between metals and biological surfaces can be reflected by the interactive effects with particulate humic acid (HA) (Stockdale et al., 2014). HA contains various functional groups and can represent the heterogeneous distribution of biotic ligand sites. It should be noted that the mixture toxicity of metals to certain endpoints is predicted by these thermodynamic equilibrium models without considering the influence of time, which is of great significance for quantitative risk assessments (Di Toro et al., 2001; Slaveykova & Wilkinson, 2005).
Ni bioavailability in oat (Avena sativa) grown in naturally aged, Ni refinery-impacted agricultural soils
Published in Human and Ecological Risk Assessment: An International Journal, 2019
Yamini Gopalapillai, Tereza Dan, Beverley Hale
Both functional and mechanistic models exist for identifying the portion of the total metal concentration in soil that is bioavailable. The main goal of this is to achieve the greatest decrease in intraspecies sensitivity, thereby allowing results to be extrapolated to different sites using site-specific chemistry. Environmental availability can be estimated by measuring labile (also called “bioaccessible”) metal concentration (herein referred to as the Labile Metal Model or LMM). Environmental bioavailability can be estimated by measuring or predicting free metal ion activity (Free Ion Activity Model or FIAM) or by predicting metal accumulation at the biotic ligand (Biotic Ligand Model or BLM), which is hypothetical in plants. And toxicological bioavailability can be estimated by measuring tissue metal concentration (Tissue Residue Approach or TRA). All of these models are expected to better relate to plant uptake and/or toxicity than does the TMM. Functional models such as the LMM correlate to some degree with ecotoxicity and/or trophic transfer of trace elements, which has been demonstrated for metals in sediments (Tessier et al.1984) and soils (Menzies et al.2007). The liquid extractant to isolate the labile fraction of the metal is typically a weak salt, a weak acid, or a chelator, each of which simulate the influence of roots on the rhizosphere, which includes exudation of protons and low molecular weight organic acids (Marschner 2012). Neutral salt (such as CaCl2) extractions of Cd, Cu, Ni, Pb, and Zn from soils correlated better with tissue accumulation of these metals than did extraction with strong chelators such as diethylenetriamine pentaacetic acid (DTPA) (Menzies et al.2007). However, the authors acknowledge that for Ni, at least, this conclusion is based on only two datasets. Another functional approach for measuring the labile metal fraction is diffusive gradients in thin films (DGT), which emulate the dynamic models of plant uptake (Zhang and Davison 2006). DGT has been shown to correlate well with concentrations of metals in tissues of plants grown in the same soil, but are more technically challenging than simple extractions. Mechanistic models such as the BLM use solution chemistry to estimate environmental bioavailability and are now regulatory instruments for aquatic toxicity of Cu (USEPA 2007). Such models are in the process of development for Ni and Cd. The BLM integrates a metal's binding affinity for the site of toxicity (the biotic ligand, or BL) as well as the competition between the metal and other dissolved elements in the exposure solution for the BL (Playle 1998; Paquin et al.2002). Thakali et al. (2006a, 2006b) developed a terrestrial BLM (TBLM) for Ni in soils, which was a better predictor (i.e. within a factor of 2) of observed EC50 than either the FIAM or the TMM. The TBLM for Ni has not yet been compared to the LMM or the TRA as predictors of toxicity of higher plants to naturally-aged field soils, which is a data gap that the present article aims to fill, among others.