Toxicity Of Binary Mixtures Research Articles

Equations for estimating binary mixture toxicity: 3-methyl-2-butanone with a series of electrophiles.

Mixture toxicity was determined for 32 binary combinations. One chemical was the non-reactive, non-polar narcotic 3-methyl-2-butanone (always chemical A) and the other was a potentially reactive electrophile (chemical B). Bioluminescence inhibition in Allovibrio fischeri was measured at 15-, 30-, and 45-minutes of exposure for A, B, and the mixture (MX). Concentration-response curves (CRCs) were developed for each chemical and used to develop predicted CRCs for the concentration addition (CA) and independent action (IA) mixture toxicity models. Also, MX CRCs were generated and compared with model predictions using the 45-minute data. Classification of observed mixture toxicity used three specific criteria: 1) predicted IA EC50 vs. CA EC50 values at 45-minutes, 2) consistency of 45-minute MX CRC fit to IA, CA, or otherwise at three effect levels (EC25, EC50 and EC75), and 3) the known/suspected mechanism of toxicity for chemical B. Mixture toxicity was then classified into one of seven groupings. As a result of the predicted IA EC50 being more toxic than the predicted CA EC50, IA represented the greater toxic hazard. For this reason, non-sham MXs having toxicity consistent with CA were classified as being "coincident" with CA rather than mechanistically-consistent with CA. Multiple linear regression analyses were performed to develop equations that can be used to estimate the toxicity of other 3M2B-containing binary mixtures. These equations were developed from the data for both IA and CA, at each exposure duration and effect level. Each equation had a coefficient of determination (r2) above 0.950 and a variance inflation factor <1.2. This approach can potentially reduce the need for mixture testing and is amenable to other model systems and to assays that evaluate toxicity at low effect levels.

Evaluation of Single and Binary Mixture Toxicity of Organic UV-Filters Using H295R Cells

Evaluation of Single and Binary Mixture Toxicity of Organic UV-Filters Using H295R Cells

Toxicity of Diuron, Diquat and Terbutryn to Cyanobacterial Mats

This study investigated acute toxicity of Diuron, Diquat and Terbutryn to cyanobacterial mats collected from Wadi Gaza, Palestine. Toxicity was measured by the reduction of population growth of cyanobacterial mats exposed to various herbicide concentrations in the range of 0-15.44 μmole L-1. Optical density of cyanobacterial suspension was recorded using a spectrophotometer at 680 nm from time zero up to 8 days and taken as an indicator of growth. EC50 and ET50 values were estimated using linear regression equations. Results showed that cyanobacterial mats adapted and grew fast under laboratory conditions. The toxicity of Diuron, Terbutryn and Diquat reached 89.35, 76.02 and 71.24%, respectively. Toxicity of binary mixtures (Diuron and Diquat) decreased from 77.35% to 65.33% due to the reduction of Diuron concentration in the mixture. EC50 values for Diuron, Terbutryn and Diquat were 0.009, 0.031and 0.381 μmole L-1 whereas ET50 values were 32.32, 35.89 and 23.45 h, respectively. EC50 values of binary mixtures (B1-B3) were 0.004, 0.01 and 0.012 toxicity units. Tertiary mixtures (T1-T7) had a wide range of EC50 (0.043-2.9E-07) TUs. The mixture toxicity index (MTI) had positive and negative value in mixtures, indicating synergetic and antagonistic effects. Application of the above-mentioned herbicides may severely damage the ecosystem. Our results are the first of its kind in the region and may be considered as a guideline for future studies.

Toxicity toward freshwater and marine water organisms of the cytostatic drugs bleomycin and vincristine and their binary mixture

Antineoplastic drugs are not effectively removed by wastewater treatment plants, ending up in surface waters. Since these drugs can interfere with the structure and functions of DNA, they pose a potential threat to aquatic biota. Unfortunately, many chemotherapeutic agents have not been studied in an environmental context. Additionally, there is a significant lack of information about the impact of anticancer drugs on marine organisms compared to freshwater species, and most studies only focus on the toxicity of single compounds rather than considering their occurrence as complex mixtures in the environment. Therefore, the aim of this study was to evaluate the ecotoxicity of two commonly used cytostatics, bleomycin and vincristine, toward six biomodels: Pseudokirchneriella subcapitata, Phaeodactylum tricornutum, Brachionus plicatilis, Brachionus calyciflorus, Thamnocephalus platyurus, and Artemia franciscana. These selected aquatic organisms are representatives of both freshwater and marine environments and belong to different trophic levels. The pharmaceuticals were investigated both individually and in combination. Binary mixture toxicity predictions were performed according to the Response Additivity and Independent Action models. Additionally, the toxicity data obtained from these experiments were utilized for risk assessment in the context of the drugs' environmental occurrence. The results indicated that freshwater species were generally more sensitive to both tested compounds than marine organisms, with T. platyurus being the most sensitive. Based on the tests performed on this biomodel, bleomycin was categorized as extremely toxic, while vincristine was considered moderately toxic. Neither of the applied models suitably predicted binary mixture toxicity, as the combination of drugs showed additive, synergistic, and antagonistic effects, suggesting that single compound toxicity data are insufficient for predicting the aquatic toxicities of cytostatics mixtures. The environmental risk of vincristine ranged from low to high, and for bleomycin varied from moderate to high, depending on the matrices examined. Therefore, further research on drug removal is recommended.

Emerging pollutants in textile wastewater: an ecotoxicological assessment focusing on surfactants.

Water and several chemicals, including dyestuffs, surfactants, acids, and salts, are required during textile dyeing processes. Surfactants are harmful to the aquatic environment and induce several negative biological effects in exposed biota. In this context, the present study aimed to assess acute effects of five surfactants, comprising anionic and nonionic classes, and other auxiliary products used in fiber dyeing processes to aquatic organisms Vibrio fischeri (bacteria) and Daphnia similis (cladocerans). The toxicities of binary surfactant mixtures containing the anionic surfactant dodecylbenzene sulfonate + nonionic fatty alcohol ethoxylate and dodecylbenzene sulfonate + nonionic alkylene oxide were also evaluated. Nonionic surfactants were more toxic than anionic compounds for both organisms. Acute nonionic toxicity ranged from 1.3mg/L (fatty alcohol ethoxylate surfactant) to 2.6mg/L (ethoxylate surfactant) for V. fischeri and from 1.9mg/L (alkylene oxide surfactant) to 12.5mg/L (alkyl aryl ethoxylated and aromatic sulfonate surfactant) for D. similis, while the anionic dodecylbenzene sulfonate EC50s were determined as 66.2mg/L and 19.7mg/L, respectively. Both mixtures were very toxic for the exposed organisms: the EC50 average in the anionic + fatty alcohol ethoxylate mixture was of 1.0mg/L ± 0.11 for V. fischeri and 4.09mg/L ± 0.69 for D. similis. While the anionic + alkylene oxide mixture, EC50 of 3.34mg/L for D. similis and 3.60mg/L for V. fischeri. These toxicity data suggested that the concentration addition was the best model to explain the action that is more likely to occur for mixture for the dodecylbenzene sulfonate and alkylene oxide mixtures in both organisms. Our findings also suggest that textile wastewater surfactants may interact and produce different responses in aquatic organisms, such as synergism and antagonism. Ecotoxicological assays provide relevant information concerning hazardous pollutants, which may then be adequately treated and suitably managed to reduce toxic loads, associated to suitable management plans.

Acute toxicity of binary mixtures for quorum sensing inhibitors and sulfonamides against Aliivibrio fischeri: QSAR investigations and joint toxic actions

Quorum sensing inhibitors (QSIs), as a kind of ideal antibiotic substitutes, have been recommended to be used in combination with traditional antibiotics in medical and aquaculture fields. Due to the co-existence of QSIs and antibiotics in environmental media, it is necessary to evaluate their joint risk. However, there is little information about the acute toxicity of mixtures for QSIs and antibiotics. In this study, 10 QSIs and 3 sulfonamides (SAs, as the representatives for traditional antibiotics) were selected as the test chemicals, and their acute toxic effects were determined using the bioluminescence of Aliivibrio fischeri (A. fischeri) as the endpoint. The results indicated that SAs and QSIs all induced S-shaped dose-responses in A. fischeri bioluminescence. Furthermore, SAs possessed greater acute toxicity than QSIs, and luciferase (Luc) might be the target protein of test chemicals. Based on the median effective concentration (EC50) for each test chemical, QSI-SA mixtures were designed according to equitoxic (EC50(QSI):EC50(SA) = 1:1) and non-equitoxic ratios (EC50(QSI):EC50(SA) = 1:10, 1:5, 1:0.2, and 1:0.1). It could be observed that with the increase of QSI proportion, the acute toxicity of QSI-SA mixtures enhanced while the corresponding TU values decreased. Furthermore, QSIs contributed more to the acute toxicity of test binary mixtures. The joint toxic actions of QSIs and SAs were synergism for 23 mixtures, antagonism for 12 mixtures, and addition for 1 mixture. Quantitative structure–activity relationship (QSAR) models for the acute toxicity QSIs, SAs, and their binary mixtures were then constructed based on the lowest CDOCKER interaction energy (Ebind-Luc) between Luc and each chemical and the component proportion in the mixture. These models exhibited good robustness and predictive ability in evaluating the toxicity data and joint toxic actions of QSIs and SAs. This study provides reference data and applicable QSAR models for the environmental risk assessment of QSIs, and gives a new perspective for exploring the joint effects of QSI-antibiotic mixtures.

Predictive binary mixture toxicity modeling of fluoroquinolones (FQs) and the projection of toxicity of hypothetical binary FQ mixtures: a combination of 2D-QSAR and machine-learning approaches.

All sorts of chemicals get degraded under various environmental stresses, and the degradates coexist with the parent compounds as mixtures in the environment. Antibiotics emerge as an additional concern due to the bioactive nature of both the parent compound and degradation products and their combined exposure to the environment. Therefore, environmental risk assessment of antibiotics and their degradation products is very much necessary. In this direction, we made use of in silico new approach methodologies (NAMs) and machine-learning algorithms. In this study, we have developed a robust and predictive mixture-quantitative structure-activity relationship (QSAR) model with promising quality and predictability (internal: MAETrain = 0.085, QLOO2 = 0.849, external: MAETest = 0.090, and QF12 = 0.859) for predicting the toxicity of the mixtures of a class of antibiotics and their degradation products. To obtain the predictive model, toxicity data of 78 binary fluoroquinolone mixtures in E. coli (endpoint: log 1/IC50 in molar) have been utilized. We have used only 0D-2D descriptors to efficiently encode the structural features of mixture components without any additional complexities. The optimization of the class of mixture descriptors has been performed in this study by using three different mixing rules (linear combination of molecular contributions, the squared molecular contributions, and the norm of molecular contributions). Different machine-learning approaches namely, random forest (RF), ada boost, gradient boost (GB), extreme gradient boost (XGB), support vector machine (SVM), linear support vector machine (LSVM), and ridge regression (RR) have been employed here apart from the conventional partial least squares (PLS) regression to optimize the modeling approach. A rigorous validation protocol has been used for assessing the goodness-of-fit, robustness, and external predictability of the models. Finally, the toxicity of possible untested mixtures of different photodegradation products of fluoroquinolones has been predicted using the best model reported in this study.

Binary mixtures of imidacloprid and thiamethoxam do not appear to cause additive toxicity in fathead minnow larvae (Pimephales promelas).

Introduction: Considerable use of neonicotinoid insecticides has resulted in their detection in surface waters globally, with imidacloprid (IM) and thiamethoxam (TM) frequently found together. Neonicotinoids are selective agonists for invertebrate nicotinic acetylcholine receptors (nAChR) leading to paralysis and death. While not overtly toxic to vertebrates, growing evidence suggests that chronic exposure to individual neonicotinoids can cause adverse health effects in fish. This work examined whether chronic exposure to binary mixtures of imidacloprid (IM) and thiamethoxam (TM) would be more toxic to fathead minnow (Pimephales promelas) larvae than either insecticide alone. Materials and Methods: Embryos were exposed to a 1:1 mixture of IM and TM (0.2, 2, 20, 200 or 2,000μg/L of each pesticide) or a 1:5, 1:10, or 1:20 mixture of IM and TM (0.02μg/L of IM with 0.1, 0.2, or 0.4μg/L of TM) for a total of 8days. Survival, developmental toxicity, embryonic motor activity, and startle escape responses were quantified. Results: Survival and growth were reduced, and hatching induced by exposure to a 1:1 mixture containing > 2μg/L of each insecticide, but not following exposure to mixtures containing environmentally-relevant concentrations. Acute exposure to a 1:1 mixture did not alter embryonic motor activity; however, chronic exposure to these mixtures resulted in a slight but significant decrease in embryonic movements. Only 1:1 mixtures of high concentrations of IM and TM altered the startle escape response by increasing latency of response; however, a significant proportion of fish exposed to 1:1 mixtures had altered latency and burst speed. Taken together, these behavioral indicators of nAChR activation suggest that in mixtures, neonicotinoids could interfere with nAChR signaling despite their low affinity for the nAChR. Conclusion: Our findings suggest that toxicity of binary mixtures of IM and TM is primarily driven by IM, and that mixtures of IM with TM do not appear to cause significant additive toxicity when compared with our previous studies evaluating each neonicotinoid alone. Given the limited toxicological data available for mixtures of neonicotinoid insecticides in fish, further study is required to better understand the ecological risks these insecticides may pose to aquatic ecosystems.

A novel method for predicting the emergence of toxicity interaction in ternary mixtures

The environment is teeming with a wide variety of pollutants, but the complexity and diversity of their combinations make it difficult to fully assess their toxicity interaction. A novel toxicity interaction prediction method (TIPM) based on the three-dimensional (3D) surface form of the concentration addition (CA) deviation model (dCA) was proposed to predict the emergence of toxicity interaction in ternary mixtures. Doxycycline hyclate (DH), bromoacetic acid (BAA) and iodoacetic acid (IAA) were used as target pollutants. The toxicity of binary and ternary mixtures designed by the direct equipartition ray design method (EquRay) and the uniform design ray method (UD-Ray) against Escherichia coli (E. coli) was determined by using a time-dependent microplate toxicity analysis (t-MTA) method. The toxicity interaction within mixtures was characterized qualitatively and quantitatively using dCA 3D surface modeling and the emergence of DH-MAA-IAA toxicity interaction was predicted by TIPM. The results showed that the dCA 3D surface model could well characterize the toxicity interactions of the mixtures, and toxicity interaction was closely related to the components’ concentration ratio (pi). TIPM could predict the emergence of DH-MAA-IAA toxicity interactions well based on the relationship. Due the model is only related to the toxicity interactions and pi value of a mixture, so it can be suggested to predict toxicity interaction within the more complex multicomponent mixtures, which provides a novel approach for the environmental risk assessment and prediction of hazardous substances.

Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees

We have reported here a quantitative read-across structure-activity relationship (q-RASAR) model for the prediction of binary mixture toxicity (acute contact toxicity) in honey bees. Both the quantitative structure-activity relationship (QSAR) and the similarity-based read-across algorithms are used simultaneously for enhancing the predictability of the model. Several similarity and error-based parameters, obtained from the read-across prediction tool, have been put together with the structural and physicochemical descriptors to develop the final q-RASAR model. The calculated statistical and validation metrics indicate the goodness-of-fit, robustness, and good predictability of the partial least squares (PLS) regression model. Machine learning algorithms like ridge regression, linear support vector machine (SVM), and non-linear SVM have been used to further enhance the predictability of the q-RASAR model. The prediction quality of the q-RASAR models outperforms the previously reported quasi-SMILEs-based QSAR model in terms of external correlation coefficient (Q2F1 SVM q-RASAR: 0.935 vs. Q2VLD QSAR: 0.89). In this research, the toxicity values of several new untested binary mixtures have been predicted with the new models, and the reliability of the PLS predictions has been validated by the prediction reliability indicator tool. The q-RASAR approach can be used as reliable, complementary, and integrative to the conventional experimental approaches of pesticide mixture risk assessment.

Toxicity of binary mixtures of copper, lead, and glyphosate on neuronal cells

Regulations for contaminants are primarily based on isolated exposures to an individual toxicant. This practice does not account for interactions that may happen between toxicants. The assessment of chemical mixtures ought to be routinely studied to better understand the risks to human health, yet no standard protocol exists. This study provides a framework focusing on the complex interactions between individual chemical toxicants and how that can affect a potential regulatory shift. The work evaluates the binary exposures of environmental contaminants, lead(II) acetate, copper(II) nitrate, and glyphosate, against a neuronal brain cell model (SH-SY5Y) by comparing the concentration addition (CA) model to experimental cytotoxicity values. Binary mixture effects were identified and interpreted using the interaction index and isobologram. Isobolograms were developed to visualize the comparative data. Against the concentration addition model, the EC50 values of binary mixtures composed of copper-lead and EC50 values of binary mixtures of copper-glyphosate showed antagonistic responses with interaction indices greater than one. The EC50 values of binary mixtures composed of lead-glyphosate produced additive effects with an interaction index within the 95% confidence interval of one. These results, along with mechanism elucidations, illustrate the complexity of mixture toxicology and the need for further studies emphasizing comparisons between prediction modeling and experimental results.

“Data fusion” quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species

Antibiotics are often found in the environment as pollutants. They are usually found as mixtures in the environment and may produce toxicity against different ecological species due to joint exposure in the sub-optimal range. Sometimes the degradation products of parent chemicals also interact with it and cause mixture toxicity. In this study, we have developed three different mixture-Quantitative Structure-Activity Relationship (mixture-QSAR) models for three different bacterial species (Vibrio fischeri, Escherichia coli, and Bacillus subtilis). The toxicity data were collected from a previous experimental report in the literature, which comprised binary and ternary mixtures of sulfonamides (SAs), sulfonamide potentiators (SAPs), and tetracyclines (TCs). We have also explored the interspecies modeling to find inter-correlation among the toxicity of these studied organisms and have developed quantitative structure activity-activity relationship (QSAAR) models by employing the “data fusion” quantitative read-across structure-activity–activity relationship (q-RASAAR) and partial least squares (PLS) regression algorithms. All the models are strictly validated using both internal and external validation tests as suggested in the OECD guidelines. Three different mixing rules have been used in this study for descriptor computations to incorporate the additive and interaction effects among the mixture components. To the best of our knowledge, this is the first report of interspecies mixture toxicity models which can predict the cellular toxicity of binary and ternary mixtures against any of the three above-mentioned organisms.

Determination of fumigant toxicity of single, binary and tertiary mixtures of three essential oils against Frankliniella occidentalis Pergande (Thysanoptera: Thripidae)

Determination of fumigant toxicity of single, binary and tertiary mixtures of three essential oils against Frankliniella occidentalis Pergande (Thysanoptera: Thripidae)

Acute toxicity assessment of nine organic UV filters using a set of biotests

UV filters in environmental compartments are a source of concern related to their ecotoxicological effects. However, little is known about UV filters’ toxicity, particularly those released into the environment as mixtures. Acute toxicity of nine organic UV filters benzophenone-1, benzophenone-2, benzophenone-3, 4-methoxy benzylidene camphor, octocrylene, ethylhexyl methoxycinnamate, 2-ethylhexyl salicylate, homosalate, and butyl methoxydibenzoylmethane was determined. UV filter solutions were tested as single, binary, and ternary mixtures of various compositions. Single solutions were tested using a set of bio tests, including tests on saline crustaceans (Artemia franciscana), freshwater crustaceans (Daphnia magna), marine bacteria (Aliivibrio fischeri), and freshwater plants (Lemna minor). The tests represent different stages of the trophic chain, and hence their overall results could be used to risk assessment concerning various water reservoirs. The toxicity of binary and ternary mixtures was analyzed using the standardized Microtox® method. Generally, organic UV filters were classified as acutely toxic. Octocrylene was the most toxic for Arthemia franciscana (LC50 = 0.55 mg L–1) and Daphnia magna (EC50 = 2.66–3.67 mg L–1). The most toxic against freshwater plants were homosalate (IC50 = 1.46 mg L–1) and octocrylene (IC50 = 1.95 mg L–1). Ethylhexyl methoxycinnamate (EC50 = 1.38–2.16 mg L–1) was the most toxic for marine bacteria. The least toxic for crustaceans and plants were benzophenone-1 (EC50 = 6.15–46.78 mg L–1) and benzophenone-2 (EC50 = 14.15–54.30 mg L–1), while 4-methoxy benzylidene camphor was the least toxic for marine bacteria (EC50 = 12.97–15.44 mg L–1). Individual species differ in their sensitivity to the tested organic UV filters. An assessment of the toxicity of mixtures indicates high and acute toxicity to marine bacteria after exposition to a binary mixture of benzophenone-2 with octocrylene, 2-ethylhexyl salicylate, or homosalate. The toxicity of mixtures was lower than single solutions predicting antagonistic interaction between chemicals.Graphical abstract

O79 | Toxicity of individual and binary mixtures of perfluoroalkyl compounds in freshwater algae Raphidocelis subcapitata

Journal of Veterinary Pharmacology and TherapeuticsVolume 46, Issue S1 p. 67-67 ORAL PRESENTATIONS O79 | Toxicity of individual and binary mixtures of perfluoroalkyl compounds in freshwater algae Raphidocelis subcapitata First published: 11 June 2023 https://doi.org/10.1111/jvp.13245Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume46, IssueS1Special Issue: 15th International Congress of the European Association for Veterinary Pharmacology and Toxicology held Bruges, Belgium, July 2–5, 2023June 2023Pages 67-67 RelatedInformation

Assessment of Experimental and Predicted Toxicity of Pesticides and Heavy Metals Binary Mixtures to Rhizobium Species

Assessment of Experimental and Predicted Toxicity of Pesticides and Heavy Metals Binary Mixtures to Rhizobium Species

Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures

As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.

Toxicity of Binary Metal Mixtures to the Tropical Ostracod Strandesia Trispinosa.

The ecotoxicity of metals is generally assessed individually, in part because current knowledge does not allow for the accurate prediction of the toxicity of metal mixtures to aquatic organisms. The objective of this study was to investigate the toxic effects of binary combinations of metal salts (copper sulphate-CuSO4, cadmium chloride-CdCl2, mercury chloride-HgCl2 and manganese sulphate-MnSO4) on the tropical ostracod Strandesia trispinosa through acute toxicity tests. To this end, ostracods were exposed to each individual metal salt as well as to their combinations by applying a full factorial design. The model that best explained the effects of the mixtures CuSO4 x CdCl2, CuSO4 x HgCl2 and CuSO4 x MnSO4 on the survival of S. trispinosa was Concentration Addition, whereas this was Independent Action for the CdCl2 x HgCl2 mixture. The observed synergistic interactions are likely to result in unacceptable risks to aquatic ecosystems under real field conditions. This is especially the case if CuSO4 predominates the metal mixture, as observed for its combination with mercury and manganese.

Calibration of an acute toxicity model for the marine crustacean, Artemia franciscana, nauplii to support oil spill effect assessments

Oil spill risk and impact assessments rely on time-dependent toxicity models to predict the hazard of the constituents that comprise crude oils and petroleum substances. Dissolved aromatic compounds (ACs) are recognized as a primary driver of aquatic toxicity in surface spill exposure scenarios. However, limited time-dependent toxicity data are available for different classes of ACs to calibrate such models. This study examined the acute toxicity of 14 ACs and 3 binary AC mixtures on Artemia franciscana nauplii at 25 °C. Toxicity tests for 3 ACs were also conducted at 15 °C to evaluate the role of temperature on toxicity. The ACs investigated represented parent and alkylated homocyclic and nitrogen-, sulfur- and oxygen-containing heterocyclic structures with octanol-water partition coefficients (log Kow) ranging from 3.2 to 6.6. Passive dosing was used to expose and maintain concentrations in toxicity tests which were confirmed using fluorometry, and independently validated for 6 ACs using GC–MS analysis. Mortality was assessed at 6, 24, and 48 h to characterize the time course of toxicity. No mortality was observed for the most hydrophobic AC tested, 7,12-dimethylbenz[a]anthracene, due to apparent water solubility constraints. Empirical log LC50 s for the remaining ACs were fit to a linear regression with log Kow to derive a critical target lipid body burden (CTLBB) based on the target lipid model. The calculated 48 h CTLBB of 47.1 ± 8.1 μmol/g octanol indicates that Artemia nauplii exhibited comparable sensitivity to other crustaceans. A steep concentration-response was found across all compounds as evidenced by a narrow range (1.0–3.1) in the observed LC50 /LC10 ratio. Differences in toxicokinetics were noted, and no impacts of temperature-dependence of AC toxicity were found. Toxicity data obtained for individual ACs yielded acceptable predictions of observed binary AC mixture toxicity. Results from this study advance toxicity models used in oil spill assessments.

Antibiotic Toxicity Isolated and as Binary Mixture to Freshwater Algae Raphidocelis subcapitata: Growth Inhibition, Prediction Model, and Environmental Risk Assessment.

Antibiotics in aqueous environments can have extremely adverse effects on non-targeted organisms. However, many research projects have only focused on the toxicological evaluation of individual antibiotics in various environments. In the present work, individual and binary mixture toxicity experiments have been conducted with the model organism Raphidocelis subcapitata (R. subcapitata), and a mixture concentration-response curve was established and contrasted with the estimated effects on the basis of both the concentration addition (CA) and the independent action (IA) models. In addition, different risk assessment methods were used and compared to evaluate the environmental risk of binary mixtures. The toxic ranking of the selected antibiotics to R. subcapitata was erythromycin (ERY) &gt; sulfamethoxazole (SMX) &gt; sulfamethazine (SMZ). In general, the conclusion of this study is that the adverse effects of binary mixtures are higher than the individual antibiotics. The CA model and RQSTU are more suitable for toxicity prediction and risk assessment of binary mixtures. This study reveals the potential ecological risks that antibiotics and their mixtures may pose to water ecosystems, thus providing scientific information for environmental quality regulation.