Development of interspecies correlation estimation (ICE) models to predict the reproduction toxicity of EDCs to aquatic species

Deriving ecological risk thresholds for soil molybdenum in China based on interspecies correlation estimation and quantitative ion character-activity relationship models

Soil molybdenum (Mo) levels can reach ecologically hazardous levels. China has not yet established the relevant thresholds, posing challenges for environmental management. Therefore, we present our data relevant to Mo toxicity for several important species. By normalizing soil properties, we obtained a correlation model of Mo toxicity to Hordeum vulgare, as well as 31 models for the toxicity of other elements including Cu and Ni to invertebrates and microbial processes. Using interspecies correlation estimation (ICE) extrapolation, the sensitivity coefficient (0.12-0.71) for five plants were found. For invertebrates and microbial processes lacking Mo data, we used regression analysis to establish Mo toxicity models based on the soil quantitative ion character-activity relationships (s-QICAR; R2 =0.70-0.95) and known toxicities of other metal elements to invertebrate and microbial processes. Furthermore, combining species sensitivity distribution calculations, the HC5 values for protecting 95% of soil species from Mo in three typical soil scenarios in China were calculated. After correction, the predicted no-effect concentrations were 6.8, 4.8, and 3.4 mg/kg, respectively. This study innovatively combined ICE and s - QICAR to derive soil Mo thresholds. Our results can provide a basis for decision-making in the assessment and management of soil Mo pollution.

An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus

The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following models to predict the concentration response curve concerning the joint reproductive toxicity of mixtures of three bisphenol analogues (BPA, BPF, BPAF) on the rotifer Brachionus calyciflorus: concentration addition (CA), independent action (IA), and two deep neural network (DNN) models. One applied mixture molecular descriptors as input variables (DNN-QSAR), while the other applied the ratios of chemicals in the mixtures (DNN-Ratio). Descriptors related to molecular mass were found to be of greater importance and exhibited a proportional relationship with toxic effects. The results indicate that the range of correlation coefficients (R2) between predicted and measured values for various mixture rays by CA and IA models is 0.372 to 0.974 and - 0.970 to 0.586, respectively. The R2 values for DNN-Ratio and DNN-QSAR were 0.841 to 0.984 and 0.834 to 0.991, respectively, demonstrating that models developed by DNN significantly outperform traditional models in predicting the joint toxicity of mixtures. Furthermore, DNN-QSAR not only predicts mixture toxicity but also provides accurate toxicity predictions for BPA, BPF, and BPAF, with R2 values of 0.990, 0.616, and 0.887, respectively, while DNN-Ratio yields values of 0.920, 0.355, and - 0.495. The study also found that the joint effects of mixtures are primarily influenced by the total concentration of the mixtures, and an increase in total concentration shifts the joint effects towards addition. This study introduces a novel approach to predict joint toxicity and analyze the influencing factors of joint effects, providing a more comprehensive assessment of the ecological risk posed by mixtures.

Ecological risk assessment of commonly used antibiotics in aquatic ecosystems along the coast of China

The consistent input of antibiotics into aquatic environments may pose risks to various creatures and ecosystems. However, risk assessment of pharmaceuticals and personal care products (PPCPs) in aquatic environments is frequently limited by the lack of toxicity data. To investigate the risk of commonly used antibiotics to various aquatic creatures, we focused on the distribution patterns and temporal dynamics of antibiotics in the coastal estuary area of China and performed a comprehensive ecological risk assessment for four antibiotics: erythromycin (ERY), tetracycline (TCN), norfloxacin (NOR) and sulfamethoxazole (SMX). An interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) combined model was applied to predict the toxicity data of untested aquatic species, and an accurate ecological risk assessment procedure was developed to evaluate the risk level of PPCPs. The results of risk quotient assessments and probabilistic risk assessments (PRAs) suggested that four objective antibiotics in the Chinese coastal estuary area were at a low risk level. These antibiotics posed a high risk in antibiotic-related global hot spots, with probabilistic risk values for ERY, NOR, SMX, and TCN of 81.33 %, 27.08 %, 21.13 %, and 15.44 %, respectively. We applied an extrapolation method to overcome the lack of toxicity data in ecological risk assessment, enhanced the ecological reality of water quality criteria derivation and reduced the uncertainty of risk assessment for antibiotics.

Prediction of HC5s for phthalate esters by use of the QSAR-ICE model and ecological risk assessment in Chinese surface waters

Due to their endocrine-disrupting effects and the risks posed in surface waters, in particular by chronic low-dose exposure to aquatic organisms, phthalate esters (PAEs) have received significant attention. However, most assessments of risks posed by PAEs were performed at a selection level, and thus limited by empirical data on toxic effects and potencies. A quantitative structure activity relationship (QSAR) and interspecies correlation estimation (ICE) model was constructed to estimate hazardous concentrations (HCs) of selected PAEs to aquatic organisms, then they were used to conduct a multiple-level environmental risk assessment for PAEs in surface waters of China. Values of hazardous concentration for 5% of species (HC5s), based on acute lethality, estimated by use of the QSAR-ICE model were within 1.25-fold of HC5 values derived from empirical data on toxic potency, indicating that the QSAR-ICE model predicts the toxicity of these three PAEs with sufficient accuracy. The five selected PAEs may be commonly measured in China surface waters at concentrations between ng/L and μg/L. Risk quotients according to median concentrations of the five PAEs ranged from 3.24 for di(2-ethylhexhyl) phthalate (DEHP) to 4.10 × 10-3 for dimethyl phthalate (DMP). DEHP and dibutyl phthalate (DBP) had risks to the most vulnerable aquatic biota, with the frequency of exceedances of the predicted no-effect concentration (PNECs) of 75.5% and 38.0%, respectively. DEHP and DBP were identified as having "high" or "moderate" risks. Results of the joint probability curves (JPC) method indicated DEHP posed "intermediate" risk to freshwater species with a maximum risk product of 5.98%. The multiple level system introduced in this study can be used to prioritize chemicals and other new pollutant in the aquatic ecological.

Predicted no-effect concentration for eight PAHs and their ecological risks in seven major river systems of China

The initial step in the assessment of the ecological risk of pollutants is to determine the predicted no-effect concentration (PNEC). However, ecological risk assessments of eight carcinogenic polycyclic aromatic hydrocarbons (PAHs), including dimethylbenz[a]anthracene (DMBA), methylcholanthrene (MCA), benzo(a)anthracene (BaA), chrysene (CHR), benzo(b)fluoranthene (BbF), benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), and dibenzo(a,h)anthracene (DBA), are rarely conducted due to the lack of their PNECs based on test data. In this study, quantitative structure-activity relationship (QSAR) models and interspecies correlation estimation (ICE) models were combined to predict the acute toxicity of these eight target PHAs. A Kolmogorov-Smirnov analysis for species sensitivity distributions (SSDs) of native and all species was conducted. There was no significant difference between the predictions for native Chinese species and the predictions for all species by the QSAR-ICE models. In addition, the feasibility of the QSAR-ICE models was demonstrated by comparing the SSD curves constructed by measured toxicity data of BaP and those predicted by the QSAR-ICE models. The PNECs of the eight PAHs were estimated based on the SSDs and acute to chronic ratio (ACR) method; these data were 0.071 μg/L, 0.033 μg/L, 0.049 μg/L, 0.114 μg/L, 0.019 μg/L, 0.021 μg/L, 0.038 μg/L and 0.054 μg/L for DMBA, DBA, BaP, MCA, BaA, CHR, BbF, BkF, respectively. The higher PNECs of the alkylated PAHs suggested their lower ecological risks. Based on the mixed risk quotient (mRQ) of PAHs through the concentration addition (CA) model, high ecological risk watersheds, such as the Songhua River (mRQ = 1.95), the Liao River (mRQ = 4.59), and the Huai River (mRQ = 1.93), were identified.

Generational effects and abnormalities in craniofacial chondrogenesis in zebrafish (Danio rerio) embryos upon maternal exposure to estrogen endocrine disrupting chemicals

Bisphenol A (BPA) and diethyl phthalate (DEP) are estrogenic endocrine disrupting chemicals (EEDCs). The present study reconfirmed that the angle of the ceratohyal cartilage (CH) in embryos were larger from maternal BPA and E2, but smaller from DEP compared to the control. However, it is still unknown whether both the BPA and DEP chemicals disrupted the action of E2 and thereby influence the estrogen signaling pathways. Additionally, it remains unclear whether they also disrupted certain related genes in the migratory pathways of neural crest cells (NCCs) in their offspring. The present data showed that nuclear estrogen receptors and membrane estrogen receptors have different disrupted profiles among female zebrafish exposed to BPA (F-BPA), and DEP (F-DEP), and external E2 (F-E2). However, certain related genes in the migratory pathways of NCCs in embryos from F-BPA and F-E2 such as the sox10, chm1, and tgfbr1a mRNA expressions showed a positive relationship compared with CH angles; the gene expressions of sox9a, smad3, and col2a1a and the CH angles of embryos exhibited an opposite relationship upon F-DEP treatments. Thus, we suggested that the genes involved in NCCs migration were potentially induced by the residual maternal DEP contents. Two sets of genes, chm1/tgfb3 and chm1/gper1, exhibited an identical profile in the ovary and its offspring at 2 h of post fertilization upon F-E2 and F-BPA treatments, respectively. We suggested that the maternal mRNA from female to embryos were transferred before the maternal-to-zygotic transition stage.

Advancements, Challenges, and Future Directions in Aquatic Life Criteria Research in China

Aquatic life criteria (ALC) serve as the scientific foundation for establishing water quality standards, and in China, significant strides have been made in the development of freshwater ALC. This comprehensive review traces the evolution of China's WQC, focusing on the methodological advancements and challenges in priority pollutants selection, test organism screening, and standardized ecotoxicity testing protocols. It also provides a critical evaluation of quality assurance measures, data validation techniques, and minimum data requirements essential for ALC assessments. The paper highlights China's technical guidelines for deriving ALC, and reviews the published values for typical pollutants, assessing their impact on environmental quality standards. Emerging trends and future research avenues are discussed, including the incorporation of molecular toxicology data and the development of predictive models for pollutant toxicity. The review concludes by advocating for a tiered WQC system that accommodates China's diverse ecological regions, thereby offering a robust scientific basis for enhanced water quality management.

Acute Toxicity Assessment and Prediction Models of Four Heavy Metals

Water quality criteria (WQC) are developed to protect aquatic organisms. Toxicity data of local fish are essential to improve the applicability of WQC derivatives. However, the paucity of local cold-water fish toxicity data limits the development of WQC in China. Brachymystax lenok is a representative Chinese-endemic cold-water fish, which plays an important role in the characterization of metal toxicity in the water environment. Whereas, the ecotoxicological effects of copper, zinc, lead and cadmium, as well as its potential as a test species for the metal WQC, remain to be investigated. In our study, acute toxicity tests of copper, zinc, lead and cadmium were performed on this fish according to the OECD method and 96 h-LC₅₀ values were calculated. The results showed that the 96 h-LC₅₀ values of Cu²⁺, Zn²⁺, Pb²⁺ and Cd²⁺ for B. lenok were 134, 222, 514 and 734 μg/L, respectively. Toxicity data for freshwater species and Chinese-native species were collected and screened, and the mean acute values of each metal for each species were ranked. The results showed that the accumulation probability of zinc by B. lenok was the lowest and less than 15%. Thus, B. lenok was sensitive to Zn and can be considered as the test cold-water fish for derivation of Zn WQC. In addition, B. lenok in comparison with warm-water fish, we found that cold-water fish are not always more sensitive to heavy metals than warm-water fish. Finally, the models for toxic effects prediction of different heavy metals on the same species were constructed and evaluated the reliability of the model. We suggest that the alternative toxicity data provided by the simulations can be used to derive WQC for metals.

The Application of Reference Dose Prediction Model to Human Health Water Quality Criteria and Risk Assessment

Oral reference dose (RfD) is a key parameter for deriving the human health ambient water quality criteria (AWQC) for non-carcinogenic substances. In this study, a non-experimental approach was used to calculate the RfD values, which explore the potential correlation between toxicity and physicochemical characteristics and the chemical structure of pesticides. The molecular descriptors of contaminants were calculated using T.E.S.T software from EPA, and a prediction model was developed using a stepwise multiple linear regression (MLR) approaches. Approximately 95% and 85% of the data points differ by less than 10-fold and 5-fold between predicted values and true values, respectively, which improves the efficiency of RfD calculation. The model prediction values have certain reference values in the absence of experimental data, which is beneficial to the advancement of contaminant health risk assessment. In addition, using the prediction model constructed in this manuscript, the RfD values of two pesticide substances in the list of priority pollutants are calculated to derive human health water quality criteria. Furthermore, an initial assessment of the health risk was performed by the quotient value method based on the human health water quality criteria calculated by the prediction model.

Original and improved interspecies correlation estimation models in China for potential application in water quality criteria

Fluoranthene (FLU) has gained much attention in recent years because of its continuous discharge in natural waters and toxicity to aquatic ecosystems. However, it is difficult to control and manage FLU pollution because of the lack of a rational and scientific water quality criteria (WQC) of FLU. To solve these data gaps, the US EPA established an interspecies correlation estimation (ICE) model, which can be utilized to develop the SSD and HC₅ (hazardous concentration, 5th percentile). Moreover, an improved model was developed using a combination of North American ICE models supplemented with China-specific species. In this study, to verify the applicability of the two ICE models, measured acute toxicity data for FLU were obtained from 9 acute toxicity tests using indigenous Chinese aquatic species from different taxonomic levels. Original and improved ICE-based SSD curves, which were generated using 3 surrogate species (Daphnia magna, Oncorhynchus mykiss, and Lepomis macrochirus), were compared with SSD curves based on measured data. The results showed that HC₅ was 1.838, 1.062, and 0.570 mg/L for the original ICE, improved ICE, and measured data, respectively. The improved ICE-based HC₅ value for FLU was within twofold of the HC₅ value based on measure data, while the original ICE-based HC₅ value was threefold higher than the HC₅ value based on measure data. This indicated that the improved ICE had better predictability in extrapolating data with acceptable deviation than the original ICE. Furthermore, their differences between HC₅ derived from two SSD curves were not significant. Generally, the improved ICE model was verified as a valid approach for generating SSDs with limited toxicity data and for deriving WQC for FLU.

Reference dose prediction by using CDK molecular descriptors: A non-experimental method

Reference dose (RfD) is an estimate of a daily dose that individual can be exposed chronically without obvious deleterious effects during a lifetime. In the area of toxicology, researchers always use the traditional approach by employing NOAEL/LOAEL or the benchmark dose (BMD) and other dose-response approaches to estimate RfD. These methods have, despite their typicalness, certain limitations. In this study, we present a novel method of the estimation of reference dose without experiments. The information of the organic chemicals is available from the Integrated Risk Information System (IRIS) of USEPA. Molecular descriptors for each molecular structure were calculated by an integrated platform, and the chemicals were classified into four categories based on molecular similarity: 128 contained benzene rings, 47 were heteroaromatics, 104 contained halogen substituents and 44 were halogenated aliphatic hydrocarbons. The predictive model of RfD was constructed by the multiple linear stepwise regression (MLR) method. Approximately 95% and 82% of the data points differ by less than 10-fold and 5-fold between the predicted values and the true values respectively. The non-experimental method improves the estimation efficiency and has a certain reference value to predict.

A QSAR-ICE-SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity

Alkylphenols (APs) are ubiquitous and generally present in higher residue levels in the environment. The present work focuses on the development of a set of in silico models to predict the aquatic toxicity of APs with incomplete/unknown toxicity data in aquatic environments. To achieve this, a QSAR-ICE-SSD model was constructed for aquatic organisms by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the hazardous concentrations (HCs) of selected APs. The research indicated that the keywords "alkylphenol" and "nonylphenol" were most commonly studied. The selected ICE models were robust (R2: 0.70-0.99; p-value < 0.01). All models had a high reliability cross- validation success rates (>75%), and the HC5 predicted with the QSAR-ICE-SSD model was 2-fold than that derived with measured experimental data. The HC5 values demonstrated nearly linear decreasing trend from 2-MP to 4-HTP, while the decreasing trend from 4-HTP to 4-DP became shallower, indicates that the toxicity of APs to aquatic organisms increases with the addition of alkyl carbon chain lengths. The ecological risks assessment (ERA) of APs revealed that aquatic organisms were at risk from exposure to 4-NP at most river stations (the highest risk quotient (RQ) = 1.51), with the highest relative risk associated with 2.9% of 4-NP detected in 82.9% of the sampling sites. The targeted APs posed potential ecological risks in the Yongding and Beiyun River according to the mixture ERA. The potential application of QSAR-ICE-SSD models could satisfy the immediate needs for HC5 derivations without the need for additional in vivo testing.

Water quality criteria for lanthanum for freshwater aquatic organisms derived via species sensitivity distributions and interspecies correlation estimation models

The increasing exploitation and application of rare earth elements (REEs) may induce hazardous risks to freshwater aquatic organisms. Due to the lack of water quality criteria (WQC) and sufficient reliable toxicity data, little information is available on the ecological risk of REEs in surface water. In this study, lanthanum (La) toxicity data were collected from published toxicological studies, and the data quality was assessed using a toxicological data reliability assessment tool. To obtain more toxicity data, Daphnia magna, Cyprinus carpio, and Dania rerio embryos were selected as surrogate species, and an interspecies correlation estimation (ICE) model was used to predict the toxicity of La for untested species. The species sensitivity distributions (SSDs) of La toxicity and WQC were investigated. Differences were observed in the hazardous concentrations for 5% of species (HC₅), but no statistically significant differences were noted in the SSD curves between the measured acute toxicity data and the predicted data. For the SSDs constructed from the measured toxicity data, the ICE-predicted toxicity data and all acute data supplemented with the ICE-predicted data, the acute WQC values of La were 88, 1022 and 256 μg/L, respectively. According to the SSD and corresponding HC₅ of chronic toxicity data, the chronic WQC was 14 μg/L. The results provide a scientific reference for establishing WQC for freshwater aquatic organisms and ecological risk assessments of REEs.

Tiered ecological risk assessment of nonylphenol and tetrabromobisphenol A in the surface waters of China based on the augmented species sensitivity distribution models

The ecological risks of nonylphenol (NP) and tetrabromobisphenol A (TBBPA) have received continued attention owing to their large consumption, frequently detection, adverse effects on the reproductive fitness, and lack of risk assessment technical systems. The geometric mean of the median concentrations of NP in the 22 surface waters was 0.278 μg/L, and TBBPA in the seven surface waters was 0.014 μg/L in China. The species sensitivity distribution (SSD) models were augmented by extrapolated reproductive toxicity data of native species to reduce uncertainty. The SSD models and the hazardous concentrations for 5% of species exhibited good robustness and reliability using the bootstrap method and minimum sample size determination. The acute and reproductive predicted no-effect concentrations (PNECs) were derived as 9.88 and 0.187 μg/L for NP, and 56.6 and 0.0878 μg/L for TBBPA, respectively. The risk quotients indicated that 11 of 22 locations for NP, and 3 of 7 locations for TBBPA were at high ecological risk levels based on the reproductive PNECs. Furthermore, the higher tier ecological risk assessment (ERA) based on potential affected fraction and joint probability curves indicated that the ecological risks in the four of above locations needed further concern. The ERA based on both the acute and reproductive toxicity is essential for assessing the ecological risks of NP and TBBPA, otherwise using acute PNECs only may result in an underestimation of ecological risk. The developed tiered ERA method and its framework can provide accurate, detailed, quantitative, locally applicable, and economically technical support for ERA of typical endocrine-disrupting chemicals in China.

Ecological risk assessment for difenoconazole in aquatic ecosystems using a web-based interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) model

Difenoconazole is a typical triazole fungicide that can inhibit demethylation during ergosterol synthesis. Due to its wide use, difenoconazole is frequently detected in surface water, paddy water, agricultural water, and other aquatic environments. Presently, an assessment of the ecological risk posed by difenoconazole in aquatic ecosystems is lacking. Here, a web-based interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) model was first applied to assess the ecological risk of difenoconazole in aquatic environments. Meanwhile, maximum acceptable concentration (MAC), maximum risk-free concentration (MRFC), and risk quotient (RQ) values were used to evaluate the potential risk of difenoconazole to aquatic organisms. Our results showed that an aquatic MAC value of 0.31 μg/L was acceptable for difenoconazole in aquatic environments. Further, the detected concentration of difenoconazole was lower than the MRFC value of 0.09 μg/L indicating no risk to aquatic organisms. Assessment data suggested that difenoconazole exhibited potential risks to eight studied aquatic ecosystems (including surface water, paddy water, and agricultural water) in different countries (RQ > 1), indicating that difenoconazole overuse could cause adverse effects to aquatic organisms in these aquatic ecosystems. Thus, restricted use and rational use of difenoconazole are recommended.

Prediction of chemical reproductive toxicity to aquatic species using a machine learning model: An application in an ecological risk assessment of the Yangtze River, China

The endocrine disrupting chemicals (EDCs) have been at the forefront of environmental issues for over 20 years and are a principle factor considered in every ecological risk assessment, but this kind of risk assessment faces difficulties. The expense, time cost of in vivo tests, and lack of toxicity data are key limiting factors for the ability to conduct ecological risk assessments of EDCs to aquatic species. In this study, a machine learning model named the support vector machine (SVM) was used to predict the reproductive toxicity of EDCs, and the performance of the models was evaluated. The results showed that the SVM model provided more accurate toxicity prediction data compared with the interspecies correlation estimation (ICE) model developed by previous study to predict the reproductive toxicity. The application of the predicted toxicity data was an important supplement to the observed data for the ecological risk assessment of EDCs in the Yangtze River, where estrogens and phenolic compounds have been found at some sampling sites in the middle and lower reaches. The results showed that the ecological risk of estrone, 17β-estradiol, and ethinyl estradiol were significant. This study revealed the application potential of machine learning models for the prediction of reproductive toxicity effects of EDCs. This can provide reliable alternative toxicity data for the ecological risk assessments of EDCs.

Effective extrapolation models for ecotoxicity of benzene, toluene, ethylbenzene, and xylene (BTEX)

Benzene homologues have significant toxic effects to aquatic organisms. In this study, the acute toxicity data of benzene, toluene, ethylbenzene and xylene (BTEX) were collected and screened, and the toxicity extrapolation model of paired BTEX was established. The results showed that except the correlation between benzene and xylene was not strong due to insufficient data, the linear correlation of the other five paired BTEX was good (p < 0.01), and R² fitted by the four out of five paired BTEX was greater than 0.85. The cross validation showed that ethylbenzene-xylene model was optimal, and for most species (81.8%), the established five BTEX models had a prediction error of less than 10%. Also, these extrapolation models were validated by experimental results of Pseudorasbora parva. The difference between the predicted and measured values of the acute toxicity of BTEX was less than 1 fold, which indicated that the extrapolation model had high accuracy.