Aquatic Toxicity Research Articles

Developing a Passive Dosing Method for Acute Aquatic Toxicity Tests of Cationic Surfactant Benzalkoniums (BACs).

Benzalkonium chlorides (BACs) have been of environmental concern due to their widespread use and potential harm. However, challenges arise in defining and controlling the exposure concentration (Cw) in aquatic toxicity tests involving BACs with a long alkyl chain (i.e., #C > 14). To address this, a novel passive dosing method was introduced in the 48 h-acute ecotoxicity test on Daphnia magna and compared to the conventional solvent-spiking method in terms of Cw stability and toxicity results. Among 13 sorbent materials tested for their sorption capacity, poly(ether sulfone) (PES) membrane was an optimal passive dosing reservoir, with equilibrium desorption of BACs to water achieved within 24 h. The Cw of BACs remained constant in both applied dosing methods during the test period. However, the Cw in solvent-spiking tests was lower than the nominal concentration for long-chain BACs, particularly at low exposure concentrations. Notably, the solvent-spiking tests indicated that the toxicity of BACs increased with alkyl chain length from C6 to 14, followed by a decline in toxicity from C14 to 18. In contrast, the passive dosing method displayed similar or slightly increasing toxicity levels of BACs from C14 to C18, indicating higher toxicity of C16 and C18-BACs than that inferred by the solvent spiking test. These findings emphasize the potential of applying this innovative passive dosing approach in aquatic toxicity tests to generate reliable and accurate toxicity data and support a comprehensive risk assessment of cationic surfactants.

Phylogeny predicts sensitivity in aquatic animals for only a minority of chemicals

There are substantial gaps in our empirical knowledge of the effects of chemical exposure on aquatic life that are unlikely to be filled by traditional laboratory toxicity testing alone. One possible alternative of generating new toxicity data is cross-species extrapolation (CSE), a statistical approach in which existing data are used to predict the effect of a chemical on untested species. Some CSE models use relatedness as a predictor of chemical sensitivity, but relatively little is known about how strongly shared evolutionary history influences sensitivity across all chemicals. To address this question, we conducted a survey of phylogenetic signal in the toxicity data from aquatic animal species for a large set of chemicals using a phylogeny inferred from taxonomy. Strong phylogenetic signal was present in just nine of thirty-six toxicity datasets, and there were no clear shared properties among those datasets with strong signal. Strong signal was rare even among chemicals specifically developed to target insects, meaning that these chemicals may be equally lethal to non-target taxa, including chordates. When signal was strong, distinct patterns of sensitivity were evident in the data, which may be informative when assembling toxicity datasets for regulatory use. Although strong signal does not appear to manifest in aquatic toxicity data for most chemicals, we encourage additional phylogenetic evaluations of toxicity data in order to guide the selection of CSE tools and as a means to explore the patterns of chemical sensitivity across the broad diversity of life.

A Comparative Study of Phase I and II Hepatic Microsomal Biotransformation of Phenol in Three Species of Salmonidae: Hydroquinone, Catechol, and Phenylglucuronide Formation.

The in vitro biotransformation of phenol at 11 °C was studied using pre-spawn adult rainbow (Oncorhynchus mykiss) (RBT), brook (Salvelinus fontinalis) (BKT), and lake trout (Salvelinus namaycush) (LKT) hepatic microsomal preparations. The incubations were optimized for time, cofactor concentration, pH, and microsomal protein concentration. Formation of Phase I ring-hydroxylation and Phase II glucuronidation metabolites was quantified using HPLC with dual-channel electrochemical and UV detection. The biotransformation of phenol over a range of substrate concentrations (1 to 180 mM) was quantified, and the Michaelis-Menten kinetics constants, Km and Vmax, for the formation of hydroquinone (HQ), catechol (CAT), and phenylglucuronide (PG) were calculated. Species differences were noted in the Km values for Phase I enzyme production of HQ and CAT, with the following rank order of apparent enzyme affinity for substrate: RBT > BKT = LKT. However, no apparent differences in the Km for Phase II metabolism of phenol to PG were detected. Conversely, while there were no apparent differences in Vmax between species for HQ or CAT formation, the apparent maximum capacity for PG formation was significantly less in LKT than that observed for RBT and BKT. These experiments provide a means to quantify metabolic activation and deactivation of xenobiotics in fish, to compare activation and deactivation reactions across species, and to act as a guide for future predictions of new chemical biotransformation pathways and rates in fish. These experiments provided the necessary rate and capacity (Km and Vmax) inputs that are required to parameterize a fish physiologically based toxicokinetic (PB-TK) model for a reactive chemical that is readily biotransformed, such as phenol. In the future, an extensive database of these rate and capacity parameters on important fish species for selected chemical structures will be needed to allow the effective use of predictive models for reactive, biotransformation chemicals in aquatic toxicology and environmental risk assessment.

From primers to pipettes: An immersive course introducing high school students to qPCR for quantifying chemical defense gene expression.

We created a 2-week, dual-module summer course introducing high school students to environmental toxicology by teaching them quantitative polymerase chain reaction (qPCR) as a way to quantify gene expression of chemical defense proteins in response to exposure to environmental pollutants. During the course, students are guided through the various stages of a successful qPCR experiment: in silico primer design and quality control, total RNA extraction and isolation, cDNA conversion, primer test PCR, and evaluation of results via agarose gel electrophoresis or UV/Vis spectra. The course combines lectures, discussions, and demonstrations with dry and wet laboratory sections to give students a thorough understanding of the scope, utility, and chemical principles of qPCR. At the end of the course, the students are taught how to analyze qPCR data and are encouraged to discuss their findings with other classmates to evaluate their hypotheses and assess possible sources of error. This course was designed to be easily adaptable to multiple test species, chemical exposures, and genes of interest. To explore both terrestrial and aquatic toxicology, the students use honey bees (Apis mellifera) and mosquitofish (Gambusia affinis) as test organisms, as well as ABC-type efflux transporters, antioxidant enzymes, and cytochrome P450 enzymes as endpoints for assessing gene expression. We share this course setup and applied protocols to encourage others to design and offer similar courses that give high school students a hands-on introduction to a broad swath of environmental toxicology research and an opportunity to develop scientific skills necessary for university-level research.

Isolation and characterisation of two epithelial-like cell lines from the gills of Chrysophrys auratus (Australasian snapper)and Oncorhynchus tshawytscha (Chinook salmon) and their use in aquatic toxicology.

In vitro gill models are becoming increasingly important in aquatic toxicology, yet the fish gill invitrome is underrepresented, encompassing approximately 0.1% of extant species. Here, we describe the establishment and characterisation of two gill-derived, epithelial-like cell lines isolated from fish species of significant importance to New Zealand: Chrysophrys auratus (Australasian snapper) and Oncorhynchus tshawytscha (Chinook salmon). Designated CAgill1PFR (Chrysophrys auratus, gill 1, Plant & Food Research) and OTgill1PFR (Oncorhynchus tshawytscha, gill 1, Plant & Food Research), these cell lines have each been passaged greater than each 70 times over several years and are considered spontaneously immortalised. Both cell lines required serum for growth and exhibited differential responses to basal media formulations. CAgill1PFR was sensitive to low temperatures (4°C) but replicated at high temperatures (30°C), whereas OTgill1PFR was sensitive to high temperatures but remained viable at low temperatures, mirroring the natural environment of their host species. Immunostaining revealed expression of epithelial cell markers cytokeratin and E-cadherin, alongside positivity for the mesenchymal cell marker, vimentin. CAgill1PFR was more sensitive to the environmental toxin 3,4 dichloroaniline than OTgill1PFR through measurements of metabolic activity, membrane integrity, and lysosomal function. Furthermore, CAgill1PFR produced less CYP1A activity, indicative of ongoing biotransformation processes, in response to beta-naphthoflavone than OTgill1PFR. These cell lines expand the toolbox of resources and emphasise the need for species-specific aquatic toxicology research.

Regression Quantitative Structure-toxicity Relationship of Pesticides on Fishes

Pesticide usage reaches several million metric tons annually worldwide, and the effects of pesticides on non-target species, such as various fishes in aquatic environments, have resulted in serious concerns. Predicting pesticide aquatic toxicity to fish is of great significance. In this paper, 20 molecular descriptors were successfully used to develop a regression quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity logLC50 of a large data set consisting of 1106 pesticides on fishes by using a general regression neural network (GRNN) algorithm. The optimal GRNN model produced correlation coefficients R of 0.8901 (rms = 0.6910) for the training set, 0.8531 (rms = 0.7486) for the validation set, and 0.8802 (rms = 0.6903) for the test set, which are satisfactory compared with other models in the literature, although a large data set of toxicity logLC50 was used in this work.

Physiological and transcriptome level responses of Microcystis aeruginosa and M. viridis to environmental concentrations of triclosan

The toxicity of triclosan (TCS) to various aquatic organisms has been demonstrated at environmental concentrations. However, the effects and mechanisms of TCS on toxic cyanobacteria remains largely unexplored. This study investigated the physiological and molecular variations in two representative toxic Microcystis species (M. aeruginosa and M. viridis) under exposure to TCS for 12 d. Our findings demonstrated that the median effective concentration (EC50) of TCS for both Microcystis species were close to the levels detected in the environment (M. aeruginosa: 9.62 μg L−1; M. viridis: 27.56 μg L−1). An increased level of reactive oxygen species (ROS) was observed in Microcystis, resulting in oxidative damage when exposed to TCS at concentrations ranging from 10 μg L−1 to 50 μg L−1. The photosynthetic activity of Microcystis had a certain degree of recovery capability at low concentrations of TCS. Compared to M. aeruginosa, the higher recovery capability of the photosynthetic system in M. viridis would be mainly attributed to the increased ability for PSII repair and phycobilisome synthesis. Additionally, the synthesis of microcystins in the two species and the release rate in M. viridis significantly increased under 10–50 μg L−1 TCS. At the molecular level, exposure to TCS at EC50 for 12 d induced the dysregulation of genes associated with photosynthesis and antioxidant system. The upregulation of genes associated with microcystin synthesis and nitrogen metabolism further increased the potential risk of microcystin release. Our results revealed the aquatic toxicity and secondary ecological risks of TCS at environmental concentrations, and provided theoretical data with practical reference value for TCS monitoring.

Aquatic toxicity, bioaccumulation potential, and human estrogen/androgen activity of three oxo-Liquid Organic Hydrogen Carrier (oxo-LOHC) systems

The Liquid Organic Hydrogen Carrier (LOHC) technology offers a technically attractive way for hydrogen storage. If LOHC systems were to fully replace liquid fossil fuels, they would need to be handled at the multi-million tonne scale. To date, LOHC systems on the market based on toluene or benzyltoluene still offer potential for improvements. Thus, it is of great interest to investigate potential LOHCs that promise better performance and environmental/human hazard profiles. In this context, we investigated the acute aquatic toxicity of oxygen-containing LOHC (oxo-LOHC) systems. Toxic Ratio (TR) values of oxo-LOHC compounds classify them baseline toxicants (0.1 < TR < 10). Additionally, the mixture toxicity test conducted with D. magna suggests that the overall toxicity of a benzophenone-based system can be accurately predicted using a concentration addition model. The estimation of bioconcentration factors (BCF) through the use of the membrane-water partition coefficient indicates that oxo-LOHCs are unlikely to be bioaccumulative (BCF < 2000). None of the oxo-LOHC compounds exhibited hormonal disrupting activities at the tested concentration of 2 mg/L in yeast-based reporter gene assays. Therefore, the oxo-LOHC systems seem to pose a low level of hazard and deserve more attention in ongoing studies searching for the best hydrogen storage technologies.

Combining QSAR and SSD to predict aquatic toxicity and species sensitivity of pyrethroid and organophosphate pesticides

ABSTRACT The widespread use of pyrethroid and organophosphate pesticides necessitates accurate toxicity predictions for regulatory compliance. In this study QSAR and SSD models for six pyrethroid and four organophosphate compounds using QSAR Toolbox and SSD Toolbox have been developed. The QSAR models, described by the formula 48 h-EC50 or 96 h-LC50 = x + y * log Kow, were validated for predicting 48 h-EC50 values for acute Daphnia toxicity and 96 h-LC50 values for acute fish toxicity, meeting criteria of n ≥10, r 2 ≥0.7, and Q 2 >0.5. Predicted 48 h-EC50 values for pyrethroids ranged from 3.95 × 10−5 mg/L (permethrin) to 8.21 × 10−3 mg/L (fenpropathrin), and 96 h-LC50 values from 3.89 × 10−5 mg/L (permethrin) to 1.68 × 10−2 mg/L (metofluthrin). For organophosphates, 48 h-EC50 values ranged from 2.00 × 10−5 mg/L (carbophenothion) to 3.76 × 10−2 mg/L (crufomate) and 96 h-LC50 values from 3.81 × 10−3 mg/L (carbophenothion) to 12.3 mg/L (crufomate). These values show a good agreement with experimental data, though some, like Carbophenothion, overestimated toxicity. HC05 values, indicating hazardous concentrations for 5% of species, range from 0.029 to 0.061 µg/L for pyrethroids and 0.030 to 0.072 µg/L for organophosphates. These values aid in establishing environmental quality standards (EQS). Compared to existing EQS, HC05 values for pyrethroids were less conservative, while those for organophosphates were comparable.

Borrowing Historical Information Across All Concentration Groups with an Application of Potency Estimation in Aquatic Toxicity Assessment

AbstractAquatic toxicity tests assess the negative impact of toxicants on survival, reproduction, and growth of organisms. Since the tests are often repeated in the same laboratory, borrowing historical experimental outcomes that are congruent would increase the precision of potency estimation with current data. In this paper, we extend the historical borrowing from control only to historical data from all concentration levels in toxicity assessments with current data using the calibrated power prior. We assume the transportability (i.e., identical concentration–response relationship) between historical and current experimental outcomes while penalizing the historical data borrowing in each concentration group independently with a modified distribution-informed calibration process. This modified calibration process prevents the over/underfitting issues by sufficiently exploring the distribution of congruence level and allows researchers to determine the level of historical information borrowing, either conservatively or aggressively, from a data-driven perspective. The effectiveness and flexibility of our proposed method are demonstrated via simulation studies. As shown in the simulation study, in scenarios when the transportability assumption fails, the proposed method still tremendously aids in the precision of toxicity assessment. The proposed method is applied in a $$\textit{Ceriodaphnia dubia}$$ Ceriodaphnia dubia test.

Modeling the geochemical evolution of mine waters during mixing

This study focuses on assessing the hydrogeochemical processes influencing the mobility of dissolved metal and metalloid species during mine effluent mixing. Field samples were collected to characterize effluents at an active gold mine located in the Abitibi Greenstone belt in western Québec, Canada. Controlled laboratory mixing experiments were further performed with real effluents. In situ physicochemical parameters, concentrations of major dissolved ions and trace elements were analyzed. Mineralogical analyses were also performed on precipitates from the laboratory mixtures. The data were used for statistical analyses and for modeling the geochemical evolution of effluents using PHREEQC with the wateq4f.dat database (with modifications). The results suggest that the formation of secondary minerals such as schwertmannite, Fe(OH)3, and jarosite could significantly affect the concentrations of trace elements in effluents. The precipitation of secondary minerals immobilized trace elements through coprecipitation and sorption processes. The main limitations of the modeling approach used here include the evaluation of the ion balance for low pH samples with high Fe and Al concentrations and the omission of biological processes. The approach provides insights into the geochemical evolution of mine effluents and could be adapted to several mining sites as a tool for improving water management.

Antagonistic interaction between caffeine and ketamine in zebrafish: Implications for aquatic toxicity

The coexistence of caffeine (CF) and ketamine (KET) in surface waters across Asia has been widely reported. Previous studies have implied that CF and KET may share a mechanism of action. However, the combined toxicity of these two chemicals on aquatic organisms remains unclear at environmental levels, and the underlying mechanisms are not well understood. Here we demonstrate that KET antagonizes the adverse effects of CF on zebrafish larvae by modulating the gamma-aminobutyric acid (GABA)ergic synapse pathway. Specifically, KET (10–250 ng L−1) ameliorates the locomotor hyperactivity and impaired circadian rhythms in zebrafish larvae induced by 2 mg L−1 of CF, showing a dose-dependent relationship. Additionally, the developmental abnormalities in zebrafish larvae exposed to CF are mitigated by KET, with an incidence rate reduced from 26.7% to 6.7%. The competition between CF and KET for binding sites on the GABA-A receptor (in situ and in silico) elucidates the antagonistic interactions between the two chemicals. Following a seven-day recovery period, the adverse outcomes of CF exposure persist in the fish, whereas the changes observed in the CF + KET groups are significantly alleviated, especially with KET at 10 ng L−1. Based on these results, it is imperative to further assess the environmental risks associated with CF and KET co-pollution. This pilot study underscores the utility of systems toxicology approaches in estimating the combined toxicity of environmental chemicals on aquatic organisms. Moreover, the nighttime behavioral functions of fish could serve as a sensitive biomarker for evaluating the toxicity of psychoactive substances.

Climate impact, toxicological risks and economic efficiency of conventional and new weed control methods in sugar beet

The aim of this study was to assess the toxicological risks, the climate impact and the economic efficiency of conventional and new weed control methods with reduced herbicide use. For this purpose, i) resource consumption, CO2 emissions, number of field work days, costs and labor hours were calculated for 11 weed control methods; ii) the toxicological risk for non-target organisms was modeled for 4 methods with reduced herbicide use and for conventional spraying with SYNOPS-GIS. The results showed that no method is fully environmentally friendly. Although the use of the FarmDroid hoeing robot is toxicologically harmless, it is disadvantageous in terms of CO2 emissions and overall costs compared to broadcast spraying. Equipping a FarmDroid with spot spraying would reduce the toxicological risk by 90% and CO2 emissions by 5% compared to conventional broadcast spraying. The costs would be only slightly higher than for conventional surface spraying. Although the new CONVISO method has the highest aquatic toxicity, it is the most favorable in terms of climate friendliness, energy consumption, terrestrial toxicity and cost-effectiveness.

Multi-task aquatic toxicity prediction model based on multi-level features fusion

IntroductionWith the escalating menace of organic compounds in environmental pollution imperiling the survival of aquatic organisms, the investigation of organic compound toxicity across diverse aquatic species assumes paramount significance for environmental protection. Understanding how different species respond to these compounds helps assess the potential ecological impact of pollution on aquatic ecosystems as a whole. Compared with traditional experimental methods, deep learning methods have higher accuracy in predicting aquatic toxicity, faster data processing speed and better generalization ability. ObjectivesThis article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity. MethodsThe model integrates molecular fingerprints and molecule graphs to characterize molecules, enabling the simultaneous prediction of acute toxicity for the same organic compound across four distinct fish species. Furthermore, to validate the advantages of multi-task learning, we independently construct prediction models, named ATFPGT-single, for each fish species. We employ cross-validation in our experiments to assess the performance and generalization ability of ATFPGT-multi. ResultsThe experimental results indicate, first, that ATFPGT-multi outperforms ATFPGT-single on four fish datasets with AUC improvements of 9.8%, 4%, 4.8%, and 8.2%, respectively, demonstrating the superiority of multi-task learning over single-task learning. Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. Moreover, ATFPGT-multi utilizes attention scores to identify molecular fragments associated with fish toxicity in organic molecules, as demonstrated by two organic molecule examples in the main text, demonstrating the interpretability of ATFPGT-multi. ConclusionIn summary, ATFPGT-multi provides important support and reference for the further development of aquatic toxicity assessment. All of codes and datasets are freely available online at https://github.com/zhaoqi106/ATFPGT-multi.

High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review.

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.

Exploring structure/property relationships to health and environmental hazards of polymeric polyisocyanate prepolymer substances-3. Aquatic exposure and hazard of aliphatic diisocyanate-based prepolymers.

The water extractability and acute aquatic toxicity of seven aliphatic diisocyanate-based prepolymer substances were investigated to determine if lesser reactivity of the aliphatic isocyanate groups, as well as increased ionization potential of the expected (aliphatic amine-terminated) polymeric hydrolysis products, would influence their aquatic behavior compared to that of previously investigated aromatic diisocyanate-based prepolymers. At loading rates of 100 and 1,000mg/L, only the substances having log Kow ≤9 exhibited more than 1% extractability in water, and a maximum of 66% water extractability was determined for a prepolymer having log Kow = 2.2. For the more hydrophobic prepolymer substances (log Kow values from 18-37), water extractability was negligible. High-resolution mass spectrometric analyses were performed on the water-accommodated fractions (WAF) of the prepolymers, which indicated the occurrence of primary aliphatic amine-terminated polymer species having backbones and functional group equivalent weights aligned to those of the parent prepolymers. Measurements of reduced surface tension and presence of suspended micelles in the WAFs further supported the occurrence of these surface-active cationic polymer species as hydrolysis products of the prepolymers. Despite these characteristics, the water-extractable hydrolysis products were practically non-toxic to Daphnia magna. All of the substances tested exhibited 48-h EL50 values of >1,000mg/L, with one exception of EL50 = 157mg/L. The results from this investigation support a grouping of the aliphatic diisocyanate-based prepolymers as a class of water-reactive polymer substances having predictable aquatic exposure and a uniformly low hazard potential, consistent with that previously demonstrated for the aromatic diisocyanate-based prepolymers.

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.

Which distribution to choose for deriving a species sensitivity distribution? Implications from analysis of acute and chronic ecotoxicity data

Species sensitivity distributions (SSDs) estimated by fitting a statistical distribution to ecotoxicity data are indispensable tools used to derive the hazardous concentration for 5 % of species (HC5) and thereby a predicted no-effect concentration in environmental risk assessment. Whereas various statistical distributions are available for SSD estimation, the fundamental question of which statistical distribution should be used has received limited systematic analysis. We aimed to address this knowledge gap by applying four frequently used statistical distributions (log-normal, log-logistic, Burr type III, and Weibull distributions) to acute and chronic SSD estimation using aquatic toxicity data for 191 and 31 chemicals, respectively. Based on the differences in the corrected Akaike’s information criterion (AICc) as well as visual inspection of the fitting of the lower tails of SSD curves, the log-normal SSD was generally better or equally good for the majority of chemicals examined. Together with the fact that the ratios of HC5 values of other alternative SSDs to those of log-normal SSDs generally fell within the range 0.1–10, our findings indicate that the log-normal distribution can be a reasonable first candidate for SSD derivation, which does not contest the existing widespread use of log-normal SSDs.

Discovery of novel benzotriazole ultraviolet stabilizers in surface water

The comprehensive understanding of the occurrence of benzotriazole UV stabilizers (BZT-UVs) in environmental surface water is imperative due to their widespread application and potential aquatic toxicity. We conducted an analysis of 13 traditional BZT-UVs in surface water samples collected from Taihu Lake (TL, n = 23) and Qiantang River (QR, n = 22) in China. The results revealed that 5‑chloro-2-(3,5-di-tertbutyl-2-hydroxyphenyl)-benzotriazole (UV-327) was consistently the predominant BZT-UV in water samples from TL (mean 16 ng/L; detection frequency 96 %) and QR (14 ng/L; 91 %). Furthermore, we developed a characteristic fragment ion-based strategy to screen and identify unknown BZT-UVs in collected surface water, utilizing a high-resolution mass spectrometer. A total of seven novel BZT-UVs were discovered in water samples, and their chemical structures were proposed. Four of these novel BZT-UVs were further confirmed with standards provided by industrial manufacturers. Semi-quantitative analysis revealed that among discovered novel BZT-UVs, 2-(2‑hydroxy-3‑tert‑butyl‑5-methylphenyl)-benzotriazole was consistently the predominant novel BZT-UV in TL (mean 4.1 ng/L, detection frequency 70 %) and QR (2.8 ng/L, 77 %) water. In TL water, the second predominant novel BZT-UV was 2-(3-allyl-2‑hydroxy-5-methylphenyl)-2H-benzotriazole (mean 3.9 ng/L, <LOD–11 ng/L), while in QR water, it was 3-(2H-benzotriazol-2-yl)-5-(1,1-dimethylethyl)-4‑hydroxy-benzotriazole (mean 2.1 ng/L, <LOD–3.6 ng/L). Subsequent studies are needed to elucidate the environmental source, behaviors, and aquatic toxicity of these newly identified BZT-UVs in surface water.

Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE)

Exposure to toxic chemicals threatens species and ecosystems. This study introduces a novel approach using Graph Neural Networks (GNNs) to integrate aquatic toxicity data, providing an alternative to complement traditional in vivo ecotoxicity testing. This study pioneers the application of GNN in ecotoxicology by formulating the problem as a relation prediction task. GRAPE’s key innovation lies in simultaneously modelling 444 aquatic species and 2826 chemicals within a graph, leveraging relations from existing datasets where informative species and chemical features are augmented to make informed predictions. Extensive evaluations demonstrate the superiority of GRAPE over Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models, achieving remarkable improvements of up to a 30% increase in recall values. GRAPE consistently outperforms LR and MLP in predicting novel chemicals and new species. In particular, GRAPE showcases substantial enhancements in recall values, with improvements of ≥ 100% for novel chemicals and up to 13% for new species. Specifically, GRAPE correctly predicts the effects of novel chemicals (104 out of 126) and effects on new species (7 out of 8). Moreover, the study highlights the effectiveness of the proposed chemical features and induced network topology through GNN for accurately predicting metallic (74 out of 86) and organic (612 out of 674) chemicals, showcasing the broad applicability and robustness of the GRAPE model in ecotoxicological investigations. The code/data are provided at https://github.com/csiro-robotics/GRAPE.