Extended biotic ligand model for predicting combined Cu-Zn toxicity to wheat (Triticum aestivum L.): Incorporating the effects of concentration ratio, major cations and pH

Evaluation of heavy metal speciation distribution in soil and the accumulation characteristics in wild plants: A study on naturally aged abandoned farmland adjacent to tailings

Heavy metal composite pollution is widespread in the surrounding environment of tailings ponds in arid and semi-arid regions, leading to the abandonment of substantial agricultural land. This study investigates the speciation distribution and plant accumulation characteristics of heavy metals in abandoned farmland with different durations of natural aging. The aim is to comprehend the local heavy metal behavior pattern in the soil-plant system and offer insights for environmental remediation. Our findings reveal that Cd stands out as the primary heavy metal pollutant in this area. The mobility ranking of heavy metals is Cd > Pb > Zn > Cu, with Cd and Pb mobility decreasing along the basin. Notably, active Pb exhibits a higher affinity for soil binding compared to other metals. The predominant plant species in the region are primarily small shrubs, herbaceous plants, and semi-shrubs that demonstrate tolerance to drought and salt. Most plant samples showed elevated levels of Cd, Pb, and Zn, surpassing the maximum tolerance levels for dietary minerals in livestock. This elevated metal content poses potential threats to the health of local livestock and wildlife, yet it is also considered a potential for phytoremediation. Selected dominant plant species from the current study include Kalidium foliatum & gracile which shows potential as a Cd accumulator and indicator. Neotrinia splendens and Reaumuria songarica demonstrate potential as Cd excluders, with the latter exhibiting higher tolerance to Cd (62.9 mg/kg). Additionally, our observations indicate that different plant parts exhibit distinct responses to heavy metals, and Zn synergistically influences the aerial part accumulation of Cd. This study holds significant importance in understanding the complex behavior patterns of multi-metal pollutants in the natural environment. The identification of native plants with remediation potential is valuable for phytoremediation of environment pollution in mining area.

Environmental conditions and plant physiology modulate Cu phytotoxicity in field-contaminated soils

Foliar Cu concentration has been widely used as a biomarker of plant growth in phytotoxicity bioassays. This relation has helped find plant processes altered by Cu in dose-response experiments (a bivariate approach). However, when plants are grown in field conditions, their responses can vary in function of multiple variables, such as the environment, plant physiology, and other elements in plant (plant ionome). These sources of variability are commonly unreported, which could limit bioassays' utility. Thus, the present study aimed to assess and integrate the mentioned sources of variability on Cu phytotoxicity. Lettuce was used as plant model. Lettuces were grown in growth chambers with contrasting light and air humidity conditions and on two different field-contaminated soils (sandy and loamy soils). Results showed that environmental conditions significantly affected foliar Cu and plant growth, but this effect differed in the two studied soils. Foliar Cu was not a good biomarker of plant growth. In contrast, integrating the potential phytotoxicity effect with the plant's nutritional status allowed a better understanding of plant growth. We remarked on using a structural equation modeling approach (SEM) to integrate plant physiology and plant ionome as moderators of plant growth. Results showed that plant growth was primarily related to plant nutritional status rather than Cu phytotoxicity. Also, the foliar Cu concentration would affect plant nutritional status due to photosynthesis-related plant processes and cation balance. Finally, this research invites to state and include sources of variability when assessing phytotoxicity. This way, it is possible to advance toward understanding complex linked processes occurring in field conditions.

Identification and quantification of the combined phytotoxicity of one element with various valences: Cr(III) and Cr(VI) for barley root elongation as an example

There is uncertainty in quantifying the toxic effects of total chromium (Cr) in the environment by modeling the toxicity of individual Cr(III) or Cr(VI). In the present study, the effects of Cr(III) and Cr(VI) on barley root elongation were investigated in a hydroponic system where Cr(III) and Cr(VI) combination dose-response experiments under monotoxicity concentration, single-dose addition, and fixed concentration ratios were designed to identify and quantify their combined phytotoxicity of one element with various valences. The results show that the calculated mixed toxicity unit values for 50% inhibition (TU_mix50) ranged from 1.06 to 1.45, indicating the weak antagonism effects of Cr(III) and Cr(VI) on barley root toxicity. Also, the single-dose group experiment has proved that the EC₅₀ of Cr(VI) was increased from 71.2 μM to 119.9 μM with Cr(III) addition, which suggested that Cr(III) has antagonism on the toxicity of Cr(VI). While EC₅₀ of Cr(III) was not affected by Cr(VI) addition. After introducing the expansion coefficient of Cr(III) on Cr(VI) toxicity, both the extended concentration addition model (e-CA) based on the log-logistic and Weibull equations and the extended independent action model (e-IA) could more accurately predict the barley root elongation under Cr(III) and Cr(VI) interaction. The e-CA model based on the Weibull equation had almost the best correlation coefficient (R²) and lowest root mean square error (RMSE) between the measured and predicted values. Finally, the combined toxicity and antagonism of the same element with co-existing different valences simultaneously were successfully and firstly identified and quantified in the present study.

Predicting cytotoxicity of binary pollutants towards a human cell panel in environmental water by experimentation and deep learning methods

Biological assays are useful in water quality evaluation by providing the overall toxicity of chemical mixtures in environmental waters. However, it is impossible to elucidate the source of toxicity and some lethal combination of pollutants simply using biological assays. As facile and cost-effective methods, computation model-based toxicity assessments are complementary technologies. Herein, we predicted the human health risk of binary pollutant mixtures (i.e., binary combinations of As(III), Cd(II), Cr(VI), Pb(II) and F(I)) in water using in vitro biological assays and deep learning methods. By employing a human cell panel containing human stomach, colon, liver, and kidney cell lines, we assessed the human health risk mimicking cellular responses after oral exposures of environmental water containing pollutants. Based on the experimental cytotoxicity data in pure water, multi-task deep learning was applied to predict cellular response of binary pollutant mixtures in environmental water. Using additive descriptors and single pollutant toxicity data in pure water, the established deep learning model could predict the toxicity of most binary mixtures in environmental water, with coefficient of determination (R²) > 0.65 and root mean squared error (RMSE) < 0.22. Further combining the experimental data on synergistic and antagonistic effects of pollutant mixtures, deep learning helped improve the predictive ability of the model (R² > 0.74 and RMSE <0.17). Moreover, predictive models allowed us identify a number of toxicity source-related physiochemical properties. This study illustrates the combination of experimental findings and deep learning methods in the water quality evaluation.

The transfer of trace metals in the soil-plant-arthropod system

Essential and non-essential trace metals are capable of causing toxicity to organisms above a threshold concentration. Extensive research has assessed the behaviour of trace metals in biological and ecological systems, but has typically focused on single organisms within a trophic level and not on multi-trophic transfer through terrestrial food chains. This reinforces the notion of metal toxicity as a closed system, failing to consider one trophic level as a pollution source to another; therefore, obscuring the full extent of ecosystem effects. Given the relatively few studies on trophic transfer of metals, this review has taken a compartment-based approach, where transfer of metals through trophic pathways is considered as a series of linked compartments (soil-plant-arthropod herbivore-arthropod predator). In particular, we consider the mechanisms by which trace metals are taken up by organisms, the forms and transformations that can occur within the organism and the consequences for trace metal availability to the next trophic level. The review focuses on four of the most prevalent metal cations in soil which are labile in terrestrial food chains: Cd, Cu, Zn and Ni. Current knowledge of the processes and mechanisms by which these metals are transformed and moved within and between trophic levels in the soil-plant-arthropod system are evaluated. We demonstrate that the key factors controlling the transfer of trace metals through the soil-plant-arthropod system are the form and location in which the metal occurs in the lower trophic level and the physiological mechanisms of each organism in regulating uptake, transformation, detoxification and transfer. The magnitude of transfer varies considerably depending on the trace metal concerned, as does its toxicity, and we conclude that biomagnification is not a general property of plant-arthropod and arthropod-arthropod systems. To deliver a more holistic assessment of ecosystem toxicity, integrated studies across ecosystem compartments are needed to identify critical pathways that can result in secondary toxicity across terrestrial food-chains.

Modeling of selenite toxicity to wheat root elongation using biotic ligand model: Considering the effects of pH and phosphate anion

It has not been well understood that the binding affinity and potential toxicity of different chemical forms of selenite (Se(IV)), which are predominant forms of selenium with plant availability. The influences of pH and major anions on Se(IV) toxicity to wheat root elongation were determined in solutions and modeled based on the biotic ligand model (BLM) and free ion activity model (FIAM) concepts. Results showed that EC50[Se(IV)]_T values increased from 164 to 273 μM as the pH raised from 4.5 to 8.0, indicating the increase of pH induced weakened Se(IV) toxicity. The EC50{SeO₃^2-} values increased from 0.019 to 71.3 μM while the EC50{H₂SeO₃} values sharply decreased from 2.08 μM to 0.760 nM with the pH increasing from 4.5 to 8.0. The effect of pH on Se(IV) toxicity could be explained by the changes of Se(IV) species in different pH solutions as H₂SeO₃, HSeO₃^- and SeO₃^2- were differently toxic to wheat root elongation. The toxicity of Se(IV) decreased with increasing H₂PO₄^- activity but not for SO₄^2-, NO₃^- and Cl^- activities, indicating that only H₂PO₄^- had a competitive effect with Se(IV) on the binding sites. A site-specific BLM was developed to count in effects of pH and H₂PO₄^-, and stability constants of H₂SeO₃, HSeO₃^-, SeO₃^2- and H₂PO₄^- to the binding sites were obtained: log [Formula: see text]  = 4.96, log [Formula: see text]  = 3.47, log [Formula: see text]  = 2.56 and log [Formula: see text]  = 2.00. Results implied that BLM performed much better than FIAM in the wheat root elongation prediction when coupling toxic species H₂SeO₃, HSeO₃^-, SeO₃^2-, and the competitions of H₂PO₄^- for the binding sites while developing the Se(IV)-BLM.

A thorough screening based on QTLs controlling zinc and copper accumulation in the grain of different wheat genotypes

Excess trace metals may cause damage to human health due to the consumption of food grain grown in contaminated soils. This study was designed to understand the genetic mechanisms of copper (Cu) and zinc (Zn) accumulation in wheat grain under stressed environments. The differences of Cu/Zn contents in the grain among 246 wheat varieties were analyzed, and the wheat varieties with low or high accumulation of Cu and Zn in the safe range were also screened out. The accumulation of Cu and Zn in grains of "Chushanbao" was lowest, which could be used as a novel germplasm for wheat breeding under heavy metal stress. We found that Cu contents of wheat grain were significantly and positively correlated with Zn. The quantitative trait loci (QTLs) for grain Cu content (GCuC) and grain Zn content (GZnC) were detected by genome-wide association study (GWAS). Twenty-three loci affecting GCuC were identified on chromosomes 1A, 1D, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B 4D, 5A, 6D, 7A, and 7B, explaining 2.6-5.8% of the phenotypic variation. Sixteen loci associated with the GZnC on 11 different chromosomes 1B, 2B, 2D, 3A, 3D, 4A, 4B, 5A, 5D, 6B, and 7D were detected, which could explain 2.7~6.6% of phenotypic variance. We also determined five associated loci on chromosomes 2B, 2D, 3A, 4B, and 5A were in pleiotropic regions affecting both GCuC and GZnC. This study would help in better understanding the molecular basis of Cu/Zn accumulation in wheat grain, and the associated markers may be useful for marker-assisted selection (MAS) breeding program.

Extension of a biotic ligand model for predicting the toxicity of metalloid selenate to wheat: The effects of pH, phosphate and sulphate

It has not been well understood that the influences of pH and accompanying anions on the toxicity of selenate (Se(VI)). The influences of pH and major anions on Se(VI) toxicity to wheat root elongation were determined and modeled based on the biotic ligand model (BLM) and free ion activity model (FIAM) concepts. Results showed that EC50[Se(VI)]_T values increased from 162 to 251 μM as the pH values increased from 4.5 to 8.0, indicating that the pH increases alleviated the Se(VI) toxicity. The EC50{SeO₄^2-} values increased from 133 to 203 μM while the EC50{HSeO₄^-} values sharply decreased from 210 to 0.102 nM with the pH increasing from 4.5 to 8.0. The effect of pH on Se(VI) toxicity could be explained by the changes of Se(VI) species in different pH solutions as SeO₄^2- and HSeO₄^-were differently toxic to wheat root elongation. The toxicity of Se(VI) decreased with the increasing activities of H₂PO₄^- and SO₄^2- but not for NO₃^- and Cl^- activities, indicating that only H₂PO₄^- and SO₄^2- had competitive effects with Se(VI) on the binding sites. An extended BLM was developed to consider effects of pH, phosphate and sulphate, and stability constants of SeO₄^2-, HSeO₄^-, H₂PO₄^- and SO₄^2- to the binding sites were obtained: log [Formula: see text]  = 3.45, log [Formula: see text]  = 5.98, log [Formula: see text]  = 2.05, log [Formula: see text]  = 1.85. Results implied that BLM performed much better than FIAM in the wheat root elongation prediction when coupling with toxic species SeO₄^2- and HSeO₄^-, and the competitions of H₂PO₄^- and SO₄^2- for the binding sites while developing the Se(VI)-BLM.

Predicting copper toxicity in zebrafish larvae under complex water chemistry conditions by using a toxicokinetic-toxicodynamic model

Multiple water chemistry parameters influence metal toxicity in natural waters and accurate quantification of those influences may accelerate the development of site-specific water quality criteria (WQC) and further execute metal risk assessment for better protection of aquatic biota. Here, we investigated the effects of water chemistry parameters on copper (Cu) toxicity of larval zebrafish (Danio rerio) and then incorporated the effects of key parameters in a Toxicokinetic and Toxicodynamic (TK-TD) model. Further, the proposed TK-TD model was used to predict Cu toxicity in laboratory artificial waters as well as natural water samples. The predictive performance of the TK-TD model was evaluated in comparison to the biotic ligand model (BLM). The results showed that increasing Ca, Mg, pH, and fulvic acid (FA) levels significantly mitigated Cu toxicity in larvae, while K and Na levels had no significant effect on Cu toxicity. A predictive TK-TD model based on these data described 91 % of Cu accumulation and 87 % of survival of larvae exposed to Cu under 0, 2.5, 5, 10 mg/L FA. Compared with BLM, TK-TD model predicted better Cu accumulation and toxicity for an independent dataset in low DOC concentration (<10.95 mg L^-1) of 9 sites in Haihe river (Tianjin, China) media during 96 h exposure. The BLM under-predicted the acute Cu toxicity to larvae when compared with observed values. In high DOC concentration (13.12-17.78 mg L^-1) among three field sites, BLM and TK-TD model both under-predicted the acute Cu toxicity to larvae when compared with observed values. Our research provides a TK-TD approach for predicting Cu toxicity under complex water chemistry conditions and deriving Cu-WQC in different scenarios where there exist limits for using the BLM.

Bioavailability and phytotoxicity of rare earth metals to Triticum aestivum under various exposure scenarios

It is a daunting challenge to predict toxicity and accumulation of rare earth metals (REMs) in different exposure scenarios (e.g., varying water chemistry and metal combinations). Herein, we investigated the toxicity and uptake of La and Ce in the presence of various levels of Ca, Mg, Na, K, and at different pH values, as well as the combined effects of La and Ce in wheat Triticum aestivum. Major cations (Ca²⁺ and Mg²⁺) significantly mitigated the toxicity and accumulation of La³⁺/Ce³⁺. Toxicity and uptake of La, Ce, and La-Ce mixtures could be well quantified by the multi-metal biotic ligand model (BLM) and by the Langmuir-type uptake model with the consideration of the competitive effects of Ca²⁺ and Mg²⁺, with more than 85.1% of variations explained. The derived binding constants of Ca, Mg, La, and Ce to wheat root were respectively 3.87, 3.59, 6.97, and 6.48 on the basis of toxicity data, and 3.23, 2.84, 6.07, and 5.27 on the basis of uptake data. The use of the alternative WHAM-F_tox approach, requiring fewer model parameters than the BLM but with similar Akaike information criterion (AIC) values, successfully predicted the toxicity and accumulation of La/Ce as well as toxicity of La-Ce mixtures, with at least 76.4% of variations explained. However, caution should be taken when using this approach to explain the uptake of La-Ce mixtures. Our results provided promising tools for delineating REMs toxicity/uptake in the presence of other toxicity-modifying factors or in mixture scenarios.

Predicting the combined toxicity of binary metal mixtures (Cu-Ni and Zn-Ni) to wheat

In order to investigate and model toxicity and interactions between metals in mixtures, inhibition of wheat root elongation in response to additions of single-metals of copper (Cu), zinc (Zn), and nickel (Ni) and of binary mixed-metal combinations of Cu-Ni and Zn-Ni was tested, using water culture experiments under different Mg concentrations and pH values. A biotic ligand model (BLM) of single-metal Cu, Zn, and Ni was established. The results showed that the toxicity of Cu, Zn or Ni in isolation decreased with increasing Mg concentration whereas the effects of pH on Cu, Zn, or Ni toxicity were related not only to free Cu²⁺, Zn²⁺, and Ni²⁺ concentrations, but also to inorganic metal complexes. In binary mixtures, the two metals in the Cu-Ni mixture showed a weakly antagonistic effect, whereas the two metals in the Zn-Ni mixture showed greater antagonism. Using data from single-metal Cu, Zn, and Ni BLMs, combined with the toxicity index and the overall amounts of metal ions bound to the biotic ligands, one simple model was developed. This model consisted of the toxic unit (TU_M, no competition included) and two extended BLMs, BLM-TU_f (f as a function of TU, including competition between Mg²⁺ and metal ions) and BLM-f_mix (including the competition between Mg²⁺ and metal ions, as well as between free metal ions). They were then used to predict the joint toxicity of Cu-Ni and Zn-Ni binary mixtures to wheat. Both of the extended BLMs could provide more accurate predictions of toxic effects of Cu-Ni and Zn-Ni than TU_M. BLM-f_mix performed best for the Zn-Ni binary mixture (r² = 0.93; root-mean-square error, RMSE = 9.87). On the other hand, for the Cu-Ni mixture, the predictive effect based on BLM-TU_f (r² = 0.93; RMSE = 9.60) was similar to that of BLM-f_mix (r² = 0.93; RMSE = 9.56). The results provide a theoretical basis for the evaluation and remediation of soils contaminated with mixtures of heavy metals.

Coherent toxicity prediction framework for deciphering the joint effects of rare earth metals (La and Ce) under varied levels of calcium and NTA

With the development of modern technologies, the exploitation and application of rare earth metals (REMs) have increased parallelly. Consequently, more REMs are entering into the environment and therefore there is a pressing need to assess their potential environmental hazards. Here, a standard toxicity test with wheat (Triticum aestivum) was conducted to investigate the single and mixture toxicity of La and Ce in solutions with different levels of calcium and nitrilotriacetic acid (NTA) and results were deciphered by different modeling approaches. Both La and Ce caused adverse effect to wheat, but the presence of Ca and NTA alleviated their toxicity. The obtained EC₅₀ for [La] or [Ce] changed by more than 28-fold and by 4-fold, respectively, with the increase of Ca or NTA. The biotic ligand model (BLM) explained approximately 93% variation of single La or Ce toxicity. The binding constants obtained were 4.14, 6.67, and 6.59 for logK_CaBL, logK_LaBL, and logK_CeBL respectively. The electrostatic toxicity model (ETM) was proved as effective as the BLM, with R² = 0.93 for La and R² = 0.92 for Ce. For La-Ce mixtures, parameters from single toxicity approaches were applied successfully to predict the mixture toxicity with concentration addition (CA) model based on the BLM or ETM theory (R² = 0.92 and RMSE = 8.56; R² = 0.90 and RMSE = 9.6, respectively). Thus, the results obtained in this study prove that both ETM and BLM theories are appropriate to predict single and mixture REMs toxicity, providing coherent and promising tools for the risk assessment of REM pollution.

Development of a coupled model of quantitative ion character-activity relationships-biotic ligand model (QICARs-BLM) for predicting toxicity for data poor metals

The biotic ligand model (BLM) is proposed as a tool to quantitatively evaluate biological toxicity of metals considering both metal speciation and the influence of environmental conditions. The model assumes that biological sites bind to metals as biotic ligands (BLs) and obtains a series of BLM parameters including conditional binding constants (K). However, developing a BLM for each metal and biology takes a lot of experimentation. In the present study, relationships between metal ionic characters and BLM parameter K were respectively investigated for three terrestrial organisms. The results showed that ionization potential was the most strongly related to log K for barley (R2 = 0.845, p <  0.01) and earthworm (R2 = 0.881, p <  0.01), and electronegativity index most significantly related to log K for lettuce (R2 = 0.835, p <  0.01). Based on these relationships, a set of quantitative ion character-activity relationships (QICARs) were developed for predicting log K of metals. Then the QICAR were coupled with BLM and a novel QICAR-BLM was constructed. Finally, the QICAR-BLM was applied to predict EC50 of other unknown-toxicity metals for selected species, and compensate for the lack of toxicity data for a large number of metals in soil.

The prediction of combined toxicity of Cu-Ni for barley using an extended concentration addition model

Environment pollution often occurs as an obvious combined effect involving two (or more) elements, and this effect changes with the concentrations of the different elements. The effects on barley root elongation were studied in hydroponic systems to investigate the toxicity of Cu-Ni combined at low doses and at a fixed concentration ratio. For low doses of Cu-Ni, the addition of Ni (<0.5 μM) to Cu significantly decreased Cu toxicity for barley, but the addition of Cu (<0.25 μM) had no significant effect on Ni toxicity. At a fixed concentration ratio, according to the single effective concentration (EC) (barley root elongation inhibitory concentration) values of Cu and Ni, five sets of Cu-Ni fixed ratios were used: ECn(Cu)+ECm(Ni) (n + m = 100) (ECn and EC_m indicate toxicity unit value for n% and m% inhibition of barley root length, respectively). The calculated toxicity unit value for 50% inhibition of root length ranged from 0.44 to 0.98 (i.e., <1), indicating a synergistic effect. To consider the interactions between the metal ions, the extended concentration addition model (e-CA) was established by integrating the Cu-Ni interaction into the concentration addition model (CA), and the data of two groups (the low doses of Cu-Ni and at a fixed concentration ratio) were respectively fitted. The e-CA accurately predicted the root length of barley under the Cu-Ni combined action. The correlation coefficient (r) and the root-mean-square error (RMSE) between predicted and observed values were 0.97 and 6.6 (low-dose group) and 0.96 and 8.12 (fixed-ratio group), respectively, and e-CA significantly improved the prediction accuracy compared to the traditional CA model without consideration of the Cu-Ni competition (r = 0.89, RMSE = 14.16). The results provided a theoretical basis for evaluation and remediation of soil contaminated with heavy metal composites.

Rhizosphere interactions between copper oxide nanoparticles and wheat root exudates in a sand matrix: Influences on copper bioavailability and uptake

The impact of copper oxide nanoparticles (CuONPs) on crop production is dependent on the biogeochemistry of Cu in the rooting zone of the plant. The present study addressed the metabolites in wheat root exudates that increased dissolution of CuONPs and whether solubility correlated with Cu uptake into the plant. Bread wheat (Triticum aestivum cv. Dolores) was grown for 10 d with 0 to 300 mg Cu/kg as CuONPs in sand, a matrix deficient in Fe, Zn, Mn, and Cu for optimum plant growth. Increased NP doses enhanced root exudation, including the Cu-complexing phytosiderophore, 2'-deoxymugineic acid (DMA), and corresponded to greater dissolution of the CuONPs. Toxicity, observed as reduced root elongation, was attributable to a combination of CuONPs and dissolved Cu complexes. Geochemical modeling predicted that the majority of the solution phase Cu was complexed with citrate at low dosing or DMA at higher dosing. Altered biogeochemistry within the rhizosphere correlated with bio-responses via exudate type, quantity, and metal uptake. Exposure of wheat to CuONPs led to dose-dependent decreases in Fe, Ca, Mg, Mn, and K in roots and shoots. The present study is relevant to growth of a commercially important crop, wheat, in the presence of CuONPs as a fertilizer, fungicide, or pollutant. Environ Toxicol Chem 2018;37:2619-2632. © 2018 SETAC.

Predicting cadmium and lead toxicities in zebrafish (Danio rerio) larvae by using a toxicokinetic-toxicodynamic model that considers the effects of cations

Protons and cations may affect metal accumulation in aquatic organisms and further influence metal toxicity. The effects of K⁺, Na⁺, Ca²⁺, Mg²⁺, and H⁺ on the accumulation and toxicity of Cd and Pb in zebrafish larvae after 24 h exposure were examined. We found that Na⁺, Ca²⁺, Mg²⁺, and H⁺ exerted significant effects on both the accumulation and toxicity of Cd, and Ca²⁺, Mg²⁺, and H⁺ also affected both the accumulation and toxicity of Pb significantly. Subsequently, stability constants for the binding of Pb²⁺, Cd²⁺, K⁺, Ca²⁺, Mg²⁺, Na⁺, and H⁺ to biotic ligand were estimated with the Langmuir model and biotic ligand model (BLM). Using the BLM-estimated binding constants calculated with toxicity data, a refined toxicokinetic-toxicodynamic (TK-TD) model considering cation competition effects was used to predict Cd and Pb accumulation and survival rates in zebrafish larvae with varying cation concentrations. Results showed that the developed TK-TD model could successfully predict Cd and Pb toxicity to zebrafish larvae as a function of major competitive cations. The TK-TD model incorporated cation competition effects is a promising tool to quantify and assess the metal risk in natural water.