Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models

Development of an effective QSAR-based hazard threshold prediction model for the ecological risk assessment of aromatic hydrocarbon compounds

The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure-activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC5) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and Vm, displayed excellent performance, as reflected by an optimal goodness of fit (R2adj = 0.918), robustness (Q2LOO = 0.869), and external prediction ability (Q2F1 = 0.760, Q2F2 = 0.782, and Q2F3 = 0.774). The hazard thresholds estimated using the optimal QSAR model were approximately close to the published water quality criteria developed by different countries and regions. The quantitative structure-toxicity relationship demonstrated that the molecular descriptors associated with electrophilicity and topological and electrotopological properties were important factors that affected the risks of AHCs. A new and reliable approach to estimate the hazard threshold of ecological risk assessment for various aromatic hydrocarbon pollutants was provided in this study, which can be widely popularised to similar contaminants with diverse structures.

Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters

Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure-activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.

Nanopolystyrene size effect and its combined acute toxicity with halogenated PAHs on Daphnia magna

Nanoplastics (NPs, diameter <1 μm) not only have toxicity but also change the toxicity of other pollutants in water. To date, the nanopolystyrene (nano-PS) size effect and its combined toxicity with halogenated polycyclic aromatic hydrocarbons (HPAHs) remain unclear. In this study, the single toxicity, combined toxicity, and mode of action of the binary mixture of polystyrene (PS) and HPAH were examined. At the same time, the nano-PS size effect on combined toxicity was also discussed. According to our results, the 48 h acute toxicity test results showed that 30 nm PS was highly toxic (EC50-48 h = 1.65 mg/L), 200 nm PS was moderately toxic (EC50-48 h = 17.8 mg/L), and 1 μm PS was lowly toxic (EC50-48 h = 189 mg/L). The NP toxicity decreased with increasing size. HPAHs were highly toxic substances to Daphnia magna (EC50-48 h = 0.12-0.22 mg/L). The mode of action of PS and HPAHs was antagonistic according to the toxicity unit method (TU), additive index method (AI), and mixture toxicity index method (MTI). The size effect of nano-PS operates via two mechanisms: the inherent toxicity of nano-PS and the sorption of pollutants by nano-PS. The former impacts the combined toxicity more than the latter. In the binary mixed system, the larger the particle size and the higher the proportion of NPs in the system, the less toxic the system was. Linear interpolation analysis can be used to predict the combined toxicity of a mixed system with any mixing ratio.

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.

Environmental exposure and ecological risk of perfluorinated substances (PFASs) in the Shaying River Basin, China

There have been concerns raised about the environmental effects of perfluoroalkyl substances (PFASs) because of their toxicity, widespread distribution, and persistence. Understanding the occurrences and ecological risk posed by PFASs is essential, especially for the short-chain replacements perfluorobutanoic acid (PFBA) and perfluorobutane sulfonic acid (PFBS), which are now becoming predominant PFASs. The lack of aquatic life criteria (ALC), however, prevents an accurate assessment of the ecological risks of PFBA and PFBS. This study thus investigated the occurrence of 15 PFASs at 29 sampling sites in Shaying River Basin (in China) systematically, conducted the toxicity tests of PFBA and PFBS on eight resident aquatic organisms in China, and derived the predicted non-effect concentration (PNEC) values for PFBA and PFBS for two environmental media in China. The results showed that the total PFASs concentrations (ΣPFASs) ranged from 5.07 to 20.32 ng/L (average of 10.95 ng/L) in surface water, whereas in sediment, ΣPFASs ranged from 6.46 to 20.05 ng/g (dw) (average of 11.51 ng/g). The presence of PFBS was the most prominent PFASs in both water (0.372-8.194 ng/L) and sediment (4.54-15.72 ng/g), demonstrating that short-chain substitution effects can be observed in watersheds. The PNEC values for freshwater and sediment were 6.60 mg/L and 8.30 mg/kg (ww), respectively, for PFBA, and 14.04 mg/L, 37.08 mg/kg (ww), respectively, for PFBS. Ecological risk assessment of two long-chain PFASs, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), and two short-chain PFASs, PFBA and PFBS, using the hazard quotient method revealed that Shaying River and other major River Basins in China were at risk of PFOS contamination. This study contributes to a better understanding of the presence and risk of PFASs in the Shaying River and first proposes the ALCs for PFBA and PFBS in China, which could provide important reference information for water quality standards.

Toxic effects of three perfluorinated or polyfluorinated compounds (PFCs) on two strains of freshwater algae: Implications for ecological risk assessments

Perfluorinated or polyfluorinated compounds (PFCs) continue entering to the environmental as individuals or mixtures, but their toxicological information remains largely unknown. Here, we investigated the toxic effects and ecological risks of Perfluorooctane sulfonic acid (PFOS) and its substitutes on prokaryotes (Chlorella vulgaris) and eukaryotes (Microcystis aeruginosa). Based on the calculated EC₅₀ values, the results showed that PFOS was significantly more toxic to both algae than its alternatives including Perfluorobutane sulfonic acid (PFBS) and 6:2 Fluoromodulated sulfonates (6:2 FTS), and the PFOS-PFBS mixture was more toxic to both algae than the other two PFC mixtures. The action mode of binary PFC mixtures on Chlorella vulgaris was mainly shown as antagonistic and on Microcystis aeruginosa as synergistic, by using Combination index (CI) model coupled with Monte Carlo simulation. The mean risk quotient (RQ) value of three individual PFCs and their mixtures were all below the threshold of 10^-1, but the risk of those binary mixtures were higher than that of PFCs individually because of their synergistic effect. Our findings contribute to enhance the understanding of the toxicological information and ecological risks of emerging PFCs and provide a scientific basis for their pollution control.

Optimized Derivation of Predicted No-Effect Concentrations (PNECs) for Eight Polycyclic Aromatic Hydrocarbons (PAHs) Using HC10 Based on Acute Toxicity Data

For persistent organic pollutants, a concern of environmental supervision, predicted no-effect concentrations (PNECs) are often used in ecological risk assessment, which is commonly derived from the hazardous concentration of 5% (HC₅) of the species sensitivity distribution (SSD). To address the problem of a lack of toxicity data, the objectives of this study are to propose and apply two improvement ideas for SSD application, taking polycyclic aromatic hydrocarbons (PAHs) as an example: whether the chronic PNEC can be derived from the acute SSD curve; whether the PNEC may be calculated by HC₁₀ to avoid solely statistical extrapolation. In this study, the acute SSD curves for eight PAHs and the chronic SSD curves for three PAHs were constructed. The quantity relationship of HC₅s between the acute and chronic SSD curves was explored, and the value of the assessment factor when using HC₁₀ to calculate PNEC was derived. The results showed that, for PAHs, the chronic PNEC can be estimated by multiplying the acute PNEC by 0.1, and the value of the assessment factor corresponding to HC₁₀ is 10. For acenaphthene, anthracene, benzo[a]pyrene, fluoranthene, fluorene, naphthalene, phenanthrene, and pyrene, the chronic PNECs based on the acute HC₁₀s were 0.8120, 0.008925, 0.005202, 0.07602, 2.328, 12.75, 0.5731, and 0.05360 μg/L, respectively.

Reliable and Representative Estimation of Extrapolation Model Application in Deriving Water Quality Criteria for Antibiotics

Deriving water quality benchmarks based on the species sensitivity distribution (SSD) is crucial for assessing the ecological risks of antibiotics. The application of extrapolation methods such as interspecies correlation estimation (ICE) and acute-to-chronic ratios (ACRs) can effectively supplement insufficient toxicity data for these emerging contaminants. Acute-to-chronic ratios can predict chronic toxicity from acute toxicity, and ICE can extrapolate an acute toxicity value from one species to another species. The present study explored the impact of two extrapolation methods on the reliability of SSDs by analyzing different scenarios. The results show that, compared with the normal and Weibull distributions, the logistic model was the best-fitting model. For most antibiotics, SSDs derived by extrapolation have high reliability, with 82.9% of R² values being higher than 0.9, and combining ICE and ACR methods can bring a maximum increase of 10% in R² . Based on the results of Monte Carlo simulation, the statistical uncertainty brought by ICE in SSD is 10-40 times larger than that brought by ACR, and combining the two methods could reduce uncertainty. In addition, the sensitivity test showed that whether the toxicity data came from extrapolation or actual measurement, the lower the value of toxicity endpoints was, the greater the bias caused by the corresponding species in every scenario. Combining the two aforementioned extrapolation methods could effectively increase the stability of SSD, with their bias nearly equal to 1. Environ Toxicol Chem 2023;42:191-204. © 2022 SETAC.

The potential ecological risk of veterinary pharmaceuticals from swine wastewater on freshwater aquatic environment

The impact of pharmaceutical residue transport in the aquatic ecosystem has become an increasing subject of environmental interest due to the inherent bioactivity of trace levels of antibiotics and the negative environmental and public health impact. In this study, three veterinary pharmaceuticals including tetracycline, ivermectin, and salicylic acid were investigated in a piggery effluent from Western Cape, South Africa. Three freshwater organisms' taxonomic groups (Pseudokirchneriella subcapitata, Daphnia magna, and Tetrahymena thermophila) were used to determine the ecological risk of different treated piggery effluent concentration range of 1%, 10%, and 20% and a cocktail mixture of veterinary pharmaceuticals of environmental concerns. The average concentration of veterinary pharmaceuticals was in the range of 47.35, 7.19, and 1.46 μg L^-1 for salicylic acid, chloro-tetracycline, and ivermectin, respectively. P. subcapitata exposed to 20% piggery wastewater effluent at 24- and 48-h EC₅₀ showed a toxicity value of 14.2% and 13.6% (v/v), respectively. The study established the ecological risk of the test compounds as low to medium risk for low-level dose and low concentrations of piggery effluent. The relative sensitivity ranking of the taxa drawn is microalgae > protozoa > Cladocera. The study results demonstrated that a high dose of piggery effluent and mixtures of veterinary pharmaceutical can pose a high risk in freshwater ecosystems. PRACTITIONER POINTS: Transport processes of veterinary antibiotics into the environment were investigated. Dilution effect of the veterinary pharmaceutical on the antibiotic levels exists. High dose of piggery effluent presented an ecological risk.

SAHmap: Synergistic-antagonistic heatmap to evaluate the combined synergistic effect of mixtures of three pesticides on multiple endpoints of Caenorhabditis elegans

The environmental pollution caused by toxic chemicals such as pesticides has become a global problem. The mixture of dichlorvos (DIC), dimethoate (DIM), aldicarb (ALD) poses potential risks to the environment and human health. To fully explore the interaction of complex mixtures on Caenorhabditis elegans behavioral toxicity endpoint. This study created a synergistic-antagonistic heatmap (SAHmap) based on the combination index to systematically describe the toxicological interaction prospect of the mixture system. It was shown that the three pesticides and their binary as well as ternary mixture rays have significant concentration-response relationship on three behavioral endpoints of nematodes, From the perspective of synergistic-antagonistic heatmaps, all the mixture rays in the DIC-DIM mixture system showed strong synergism on the three behavioral and lethal endpoints. In the ternary mixture system, the five mixture rays showed different interaction between the behavioral endpoint and the lethal endpoint, and showed slight synergism to two behavioral endpoints as a whole. The emergence of synergism should arouse our attention to these hazardous chemicals. In addition, the use of SAHmap and the significant linear correlation among three behavioral endpoints further improved the efficiency of the study on the behavioral toxicity of pesticide mixtures to Caenorhabditis elegans.

Deriving the predicted no effect concentrations of 35 pesticides by the QSAR-SSD method

The widespread use of pesticides results in their frequent detection in water bodies and other environmental media. Pesticide residues may cause certain risks to the environment and human health, and reliable predicted no effect concentrations (PNEC) must be obtained when assessing environmental risks. Species sensitivity distribution (SSD) is an important method for the derivation of chemical PNECs. Construction of the SSD model requires sufficient toxicity data to various species including at least eight families in three phyla, suitable nonlinear fitting functions and assessment factors (AFs) with certain uncertainty. However, most chemicals could not collect sufficient species toxicity data, while some chemicals had sufficient species toxicity data but could not find suitable fitting functions, thus hindering the construction of effective SSD models. To this end, the established QSAR models were applied to predict toxicity of chemicals to specific species to fill in the toxicity data gaps required for SSD and selecting multiple nonlinear functions to optimize the SSD model. Combined with QSAR and SSD methods, a new method of PNEC derivation was developed and successfully applied to the derivation of PNEC for 35 pesticides. Three QSAR models were used to predict the toxicities of six pesticides with few toxicity data. Nine two-parameter nonlinear functions were used to fit the toxicity-cumulative probability data one by one to determine the optimal SSD models. The hazardous concentrations at the cumulative probability of 5% and 10%, i. e, HC₅ and HC₁₀, respectively, were calculated by the optimal SSD model. The assessment factor used to determine the PNEC of the chemical based on the HC₁₀ was derived from the quantitative correlation between HC₁₀ and HC₅ of pesticides found in this study. When the toxicity data are insufficient, it may be more appropriate to calculate the PNECs of chemicals using HC₁₀ than using HC₅.