Can We Reasonably Predict Chronic Species Sensitivity Distributions from Acute Species Sensitivity Distributions?

Community-level risk assessments on organophosphate esters in the sediments from the Bohai Sea of China based on multimodal species sensitivity distributions coupled with the equilibrium partitioning method

Organophosphate esters (OPEs), increasingly used as alternatives to brominated flame retardants, are ubiquitous in the global aquatic environment. Despite their potential toxicological impact on ecosystems, community-level risk assessments for OPEs in sediments remain scarce. This study investigated OPE occurrences and composition characteristics in the Bohai Sea's sediments and appraised both individual and joint ecological risks posed by characteristic OPE homologs using ten commonly used species sensitivity distribution (SSD) models, integrating acute-to-chronic conversion and phase equilibrium partitioning. OPEs were detected across all sediment samples, with total concentrations ranging from 0.213 ng/g dry weight (dw) to 91.1 ng/g dw. The predominant congeners included tri-n-butyl phosphate (TnBP), triisobutyl phosphate (TiBP), tri(2-ethylhexyl) phosphate, tris(2-chloroethyl) phosphate (TCEP), tris(1-chloro-2-propyl) phosphate (TCPP), tris(1, 3-dichloro-2-propyl) phosphate (TDCIPP), and triphenylphosphine oxide. Best-fit SSD models varied among TnBP, TiBP, TCEP, TCPP, and TDCIPP, demonstrating Sigmoid, Burr III, Sigmoid, Burr III, and Burr III, respectively. The same parametric model demonstrated variability in the fitting process for different OPE congeners, which also happened to the fitting results of ten parametric models for the same specific characteristic congener, underscoring the necessity of employing multiple models for precise community-level risk assessments. Hazard concentrations for a 5% cumulative probability were 0.116 mg/L, 2.88 mg/L, 1.30 mg/L, 1.44 mg/L, and 1.85 mg/L for each respective congener. The resulting risk quotients (RQ) and overall hazard index (HI) were selected as criteria to assess the individual and joint ecological risks of OPEs in sediments from the Bohai Sea, respectively. RQ and HI were both below 0.1, indicating a low risk to the local ecosystems. Multi-model SSD analysis could provide refined data for community-level risk evaluation, offering valuable insights for the development of evidence-based environmental standards and pollution control strategies.

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.

Aquatic and sediment ecotoxicity data of difenoconazole and its potential environmental risks in ponds bordering rice paddies

Difenoconazole has a widespread agricultural use to control fungal diseases in crops, including rice. In edge-of-field surface waters the residues of this lipophilic fungicide may be toxic to both pelagic and benthic organisms. To allow an effect assessment we mined the regulatory and open literature for aquatic toxicity data. Since published sediment toxicity data were scarce we conducted 28 d sediment-spiked toxicity test with 8 species of benthic macroinvertebrates. Ecotoxicological threshold levels for effects were assessed by applying the species sensitivity distribution approach. Based on short-term L(E)C50's for aquatic organisms from water-only tests an acute Hazardous Concentration to 5% of the species (HC5) of 100 µg difenoconazole/L was obtained, while the HC5 based on chronic NOEC values was a factor of 104 lower (0.96 µg difenoconazole/L). For benthic macroinvertebrates the chronic HC5, based on 28d-L(E)C10 values, was 0.82 mg difenoconazole/kg dry weight sediment. To allow a risk assessment for water- and sediment-dwelling organisms, exposure concentrations were predicted for the water and sediment compartment of an edge-of-field pond bordering rice paddies treated with difenoconazole using the Chinese Top-Rice modelling approach, the Chinese Nanchang exposure scenario and the Equilibrium Partitioning theory. It appeared that in the vast majority of the 20 climate years simulated, potential risks to aquatic and sediment organisms cannot be excluded. Although the HC5 values based on laboratory toxicity data provide one line of evidence only, our evaluation suggests population- and community-level effects on these organisms due to chronic risks in particular.

Derivation of aquatic predicted no-effect concentration and ecological risk assessment for triphenyl phosphate and tris(1,3-dichloro-2-propyl) phosphate

Triphenyl phosphate (TPhP) and tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) are common organophosphate esters (OPEs), which are used as additives in various industries. These compounds have been widely detected in aquatic environment, raising concerns about their adverse effects on aquatic organisms. In order to protect aquatic ecosystems, a total of 7 species were selected for acute and chronic toxicity tests in this study. The results indicated that TPhP and TDCIPP exhibited varying degrees of toxicity to aquatic organisms. The 96-h LC50 values ranged from 1.088 mg/L to 1.574 mg/L for TPhP and from 2.027 mg/L to 17.855 mg/L for TDCIPP. The 28-d LC10 values ranged from 0.023 mg/L to 0.177 mg/L for TPhP and from 0.300 mg/L to 1.102 mg/L for TDCIPP. The tested toxicity data, combined with collected toxicity data, were used to investigate the predicted no-effect concentration in water (PNECwater) of TPhP and TDCIPP by species sensitivity distribution (SSD) method. The results revealed PNECwater values of 6.35 and 38.0 μg/L for TPhP and TDCIPP, respectively. Furthermore, the predicted no-effect concentrations in sediment (PNECsed) were derived as 110 μg/kg dry weight (dw) for TPhP and 424 μg/kg dw for TDCIPP using the equilibrium partitioning (EqP) approach. Based on the toxicity data and PNECs, the ecological risk of these two chemicals in surface waters and sediments worldwide over the last decade were evaluated. The results indicated that TDCIPP posed negligible risk in aquatic ecosystems. However, TPhP showed potential risk in sediments, as indicated by the hazard quotients (HQs) exceeding 0.1. The results of joint probability curves (JPC) indicated that the probabilities of exceeding hazardous concentration for 1 % of species for TPhP in water and sediment were 0.33 % and 5.2 %, respectively. Overall, these findings highlight the need for continued monitoring and assessment of the presence and potential impacts of TPhP and TDCIPP in aquatic ecosystems.

Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning

The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluate these hazards, especially for chronic risk. In this study, machine learning (ML) was employed to construct quantitative structure-activity relationship (QSAR) models, enabling the prediction of chronic toxicity to aquatic organisms by leveraging the molecular characteristics of pollutants, namely, the molecular descriptors, fingerprints, and graphs. The limited dataset size hindered the notable advantages of the graph attention network (GAT) model for the molecular graphs. Considering computational efficiency and performance (R2 = 0.78; RMSE = 0.77), XGBoost (XGB) was used for reliable QSAR-ML models predicting chronic toxicity using small- or medium-sized tabular data and the molecular descriptors. Further kernel density estimation analysis confirmed the high accuracy of the model for pollutant concentrations ranging from 10-3 to 102 mg/L, effectively aligning with most environmental scenarios. Model interpretation showed SlogP and exposure duration as the primary influential factors. SlogP, representing the distribution coefficient of a molecule between lipophilic and hydrophilic environments, had a negative effect on the toxicity outcomes. Additionally, the exposure duration played a crucial role in determining the chronic toxicity. Finally, the chronic toxicity data of bisphenol A validated the robustness and reliability of the model established in this research. Our study provided a robust and feasible methodology for chronic ecological risk evaluation of various types of pollutants and could facilitate and increase the use of ML applications in environmental fields.

Spatial variation in the sensitivity of freshwater macroinvertebrate assemblages to chemical stressors

Assessing spatial variation in the chemical sensitivity of natural assemblages will enhance ecological relevance and reduce uncertainty in ecological risk assessments and the derivation of environmental quality standards (EQSs). However, the majority of species in natural communities have not undergone toxicity testing for any chemical, which poses a major challenge when assessing their sensitivity. We investigated spatial variation and patterns in the sensitivity of 4084 freshwater macroinvertebrate assemblages across England to 5 general-acting chemicals (heavy metals) and 13 specifically acting chemicals (insecticides) using a novel hierarchical species sensitivity distribution method based on taxonomic relatedness. Furthermore, we explored how river typology relates to spatial variation in assemblage sensitivity to chemicals and the potential impacts of such variation on current EQSs. Our findings revealed that, whereas assemblages with similar taxonomic compositions exhibit comparable sensitivity distributions, assemblages with different taxonomic compositions could have very similar or very different sensitivity distributions. The variation in assemblage sensitivity was greater for specifically acting chemicals than for general-acting chemicals and exhibited spatial clustering patterns. These spatial clustering patterns varied depending on the chemical, and the regions where assemblages were most sensitive to metals were generally not the same as the regions where assemblages were most sensitive to insecticides. Spatial variation in assemblage sensitivity was related to river typology with sensitive assemblages being more common than expected in lowland calcareous (or mixed geology) rivers within very small to small catchments. Comparing spatial variation in assemblage-specific chemical sensitivity to EQSs, we found that the operational EQSs in England would protect most study assemblages (i.e., > 99.5 %), although a small proportion of assemblages may face potential risks associated with azinphos-methyl, copper, and malathion. In many cases the EQSs were very precautionary, potentially requiring expensive control measures or restricting beneficial chemical use with no additional environmental benefit. The development of spatially defined EQSs, possibly based on river types, could be developed to target areas that require the highest level of protection and thus strike a balance between the benefits of chemical use and environmental protection.

Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is still limited, and their potential for addressing a wide range of chemicals is unknown. We prioritized chemical-related parameters for chemical toxicity characterization to inform ML model development based on two criteria: (1) each parameter's relevance to robustly characterize chemical toxicity described by the uncertainty in characterization results attributable to each parameter and (2) the potential for ML-based approaches to predict parameter values for a wide range of chemicals described by the availability of chemicals with measured parameter data. We prioritized 13 out of 38 parameters for developing ML-based approaches, while flagging another nine with critical data gaps. For all prioritized parameters, we performed a chemical space analysis to assess further the potential for ML-based approaches to predict data for diverse chemicals considering the structural diversity of available measured data, showing that ML-based approaches can potentially predict 8-46% of marketed chemicals based on 1-10% with available measured data. Our results can systematically inform future ML model development efforts to address data gaps in chemical toxicity characterization.

Analysis and derivation of the marine water quality criteria of phenol for Korean seas

Marine water quality criteria (WQC) have to be determined prior to the derivation of water quality based effluent limitations (WQBELs) for hazardous and noxious substances (HNS) discharged from marine industrial facilities. In this study, we carried out toxicity tests using ten native marine organisms and analyzed international toxicity data and data tested in this study to derive the WQC of phenol for Korean seas. By converting acute values to chronic ones with ACRs (acute-chronic ratios) of each trophic level according to well-verified method, we derived provisional WQC (0.96 mg/L) of phenol for Korean seas for the first time. The procedure to derive marine WQC and results of this study could provide the essential information for the establishment of national marine WQC and WQBELs for HNS discharged from marine industrial facilities.

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.

Derivation and validation of soil cadmium thresholds for the safe farmland production of vegetables in high geological background area

Excessive dietary intake of cadmium (Cd) poses toxicity risks to human health, and it is therefore essential to establish accurate and regionally appropriate soil Cd thresholds that ensure the safety of agricultural products grown in different areas. This study investigated the differences in the Cd accumulation in 32 vegetable varieties and found that the Cd content ranged from 0.01 to 0.24 mg·kg^-1, and decreased in the order of stem and bulb vegetables > leafy vegetables > solanaceous crops > bean cultivars. A correlation analysis and structural equation model showed that pH, soil organic matter, and the cation exchange capacity had significant effects on Cd accumulation in the vegetables and explained 72.1 % of the variance. In addition, species sensitivity distribution (SSD) curves showed that stem and bulb vegetables were more sensitive to Cd than other types of vegetables. Using the Burr Type III function for curve fitting, we derived Cd thresholds of 6.66, 4.15, and 1.57 mg·kg^-1 for vegetable soils. These thresholds will ensure that 20 %, 50 %, and 95 % of these vegetable varieties were risk-free, respectively. The predicted threshold of soil Cd was more than twice that of China's current National Soil Quality Standard (GB 15618-2018) for Cd values. Therefore, soil scenarios and cultivars should be considered comprehensively when determining farmland soil thresholds. The present results provide a new model for setting soil Cd criteria in high geological background areas.

Sulfate sensitivity of aquatic organism in soft freshwaters explored by toxicity tests and species sensitivity distribution

Elevated concentrations of sulfate in waterways are observed due to various anthropogenic activities. Elevated levels of sulfate can have harmful effects on aquatic life in freshwaters: sulfate can cause osmotic stress or specific ion toxicity in aquatic organisms, especially in soft waters where Ca²⁺ and Mg²⁺ concentrations are low. Formerly, chronic toxicity test data in soft water have been scarce. The chronic and acute sulfate toxicity tests conducted with aquatic organisms from 10 families across various trophic levels in this study multiplied the number of tests conducted in soft freshwater conditions and enabled derivation of the species sensitivity distribution (SSD) and sulfate hazardous concentrations for soft freshwaters. The cladoceran Daphnia longispina and freshwater snail Lymnaea stagnalis were the most sensitive to sulfate among the studied species. Harmful effects on the reproduction of D. longispina were observed at 49 mg SO₄ /L while growth of L. stagnalis was inhibited at 217 mg SO₄ /L. Most studied organisms tolerated high sulfate concentrations: the median of chronic effective concentrations (EC10 or LC10) was 1008 mg/L for all the species tested in this study. Based on the species sensitivity distribution of the studied species the hazardous concentration for 5 % of aquatic organism (HC5) in soft waters was 117-194 mg SO₄/L. Different data set combinations were used to demonstrate the data variability in SSD-based HC5 estimates. The lowest values were produced from combining biotest results from the present study and earlier literature, while the highest values were calculated from the present study only. The derived chronic no-effect concentrations (PNEC) varied between 39 and 65 mg SO₄/L.

An impact-based environmental risk assessment model toolbox for offshore produced water discharges

We present a novel approach to environmental risk assessment of produced water discharges based on explicit impact and probability, using a combination of transport, fate and toxicokinetic-toxicodynamic models within a super-individual framework, with a probabilistic element obtained from ensemble simulations. Our approach is motivated by a need for location and species specific tools which also accounts for the dynamic nature of exposure and uptake of produced water components in the sea. Our approach is based on the well-established fate model DREAM, and accounts for time-variable exposure, considers body burden and effects for specific species and stressors, and assesses the probability of impact. Using a produced water discharge in the Barents Sea, with early life stages of spawning haddock, we demonstrate that it is possible to conduct a model-based risk assessment that highlights the effect of natural variations in environmental conditions. The benefits, limitations and potential for further improvements are discussed.

The applicability of species sensitivity distributions to the development of generic doses for phytosanitary irradiation

Ionizing radiation is used as a phytosanitary treatment to prevent the introduction of pests through trade. Generic doses are a valuable means to increase the number of pest-commodity combinations that can be treated using phytosanitary irradiation. Generic doses allow for the treatment of the entire taxa for which the dose has been approved, allowing for the treatment of untested species. As such, the approval of a generic dose requires substantial supporting data and careful consideration of the risks involved. We adopt the Species Sensitivity Distribution (SSD) framework, already in widespread use in the field of ecotoxicology and environmental risk assessment, to evaluate generic doses for phytosanitary irradiation treatments. Parametric SSDs for Curculionidae and Tephritidae were developed using existing data on efficacious phytosanitary irradiation treatments. The resulting SSDs provided estimates of the taxa coverage expected by the generic dose, along with the margin of uncertainty. The SSD analysis lends support to the existing 150 Gy generic dose for Tephritidae and a proposed 175 Gy generic dose for Curculionidae. The quantitative estimates of risk produced by the SSD approach can be a valuable tool for phytosanitary rule making, improving the process for generic dose development and approval.

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.

Ecotoxicity characterization of chemicals: Global recommendations and implementation in USEtox

Chemicals emitted to the environment affect ecosystem health from local to global scale, and reducing chemical impacts has become an important element of European and global sustainability efforts. The present work advances ecotoxicity characterization of chemicals in life cycle impact assessment by proposing recommendations resulting from international expert workshops and work conducted under the umbrella of the UNEP-SETAC Life Cycle Initiative in the GLAM project (Global guidance on environmental life cycle impact assessment indicators). We include specific recommendations for broadening the assessment scope through proposing to introduce additional environmental compartments beyond freshwater and related ecotoxicity indicators, as well as for adapting the ecotoxicity effect modelling approach to better reflect environmentally relevant exposure levels and including to a larger extent chronic test data. As result, we (1) propose a consistent mathematical framework for calculating freshwater ecotoxicity characterization factors and their underlying fate, exposure and effect parameters; (2) implement the framework into the USEtox scientific consensus model; (3) calculate characterization factors for chemicals reported in an inventory of a life cycle assessment case study on rice production and consumption; and (4) investigate the influence of effect data selection criteria on resulting indicator scores. Our results highlight the need for careful interpretation of life cycle assessment impact scores in light of robustness of underlying species sensitivity distributions. Next steps are to apply the recommended characterization framework in additional case studies, and to adapt it to soil, sediment and the marine environment. Our framework is applicable for evaluating chemicals in life cycle assessment, chemical and environmental footprinting, chemical substitution, risk screening, chemical prioritization, and comparison with environmental sustainability targets.

Can Chemical Toxicity in Saltwater Be Predicted from Toxicity in Freshwater? A Comprehensive Evaluation Using Species Sensitivity Distributions

Species sensitivity distributions (SSDs) play an important role in ecological risk assessment. Estimating SSDs requires toxicity data for many species, but reports on saltwater species are often limited compared to freshwater species. This limitation can constrain informed management of saltwater quality for the protection of marine ecosystems. We investigated the relationships between the parameters (i.e., mean and standard deviation [SD]) of freshwater and saltwater log-normal SSDs to determine how accurately saltwater toxicity could be estimated from freshwater toxicity test data. We estimated freshwater and saltwater SSDs for 104 chemicals with reported acute toxicity data for five or more species and compared their means, SDs, and hazardous concentrations for 5% of the species (HC5) derived from the acute SSDs. Standard major axis regression analyses generally showed that log-log relationships between freshwater and saltwater SSD means, SDs, and HC5 values were nearly 1:1. In addition, the ratios of freshwater-to-saltwater SSD means and HC5 values for most of the 104 chemicals fell within the range 0.1-10. Although such a strong correlation was not observed for SSD SDs (r²  < 0.5), differences between freshwater and saltwater SSD SDs were relatively small. These results indicate that saltwater acute SSDs can be reasonably estimated using freshwater acute SSDs. Because the differences of the means and SDs between freshwater and saltwater SSDs were larger when the number of test species used for SSD estimation was lower (i.e., five to seven species in the present study), obtaining toxicity data for an adequate number of species will be key to better approximation of a saltwater acute SSD from a freshwater acute SSD for a given chemical. Environ Toxicol Chem 2022;41:2021-2027. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Emerging trends in nanoparticle toxicity and the significance of using Daphnia as a model organism

Nanoparticle production is on the rise due to its many uses in the burgeoning nanotechnology industry. Although nanoparticles have growing applications, there is great concern over their environmental impact due to their inevitable release into the environment. With uncertainty of environmental concentration and risk to aquatic organisms, the microcrustacean Daphnia spp. has emerged as an important freshwater model organism for risk assessment of nanoparticles because of its biological properties, including parthenogenetic reproduction; small size and short generation time; wide range of endpoints for ecotoxicological studies; known genome, useful for providing mechanistic information; and high sensitivity to environmental contaminants and other stressors. In this review, we (1) highlight the advantages of using Daphnia as an experimental model organism for nanotoxicity studies, (2) summarize the impacts of nanoparticle physicochemical characteristics on toxicity in relation to Daphnia, and (3) summarize the effects of nanoparticles (including nanoplastics) on Daphnia as well as mechanisms of toxicity, and (4) highlight research uncertainties and recommend future directions necessary to develop a deeper understanding of the fate and toxicity of nanoparticles and for the development of safer and more sustainable nanotechnology.

Comparison of Species Sensitivity Distributions for Sediment-Associated Nonionic Organic Chemicals Through Equilibrium Partitioning Theory and Spiked-Sediment Toxicity Tests with Invertebrates

Equilibrium partitioning (EqP) theory and spiked-sediment toxicity tests are useful methods to develop sediment quality benchmarks. However, neither approach has been directly compared based on species sensitivity distributions (SSDs) to date. In the present study, we compared SSDs for 10 nonionic hydrophobic chemicals (e.g., pyrethroid insecticides, other insecticides, and polycyclic aromatic hydrocarbons) based on 10-14-day spiked-sediment toxicity test data with those based on EqP theory using acute water-only tests. Because the exposure periods were different between the two tests, effective concentrations (i.e., median effective/lethal concentration) were corrected to compare SSDs. Accordingly, we found that hazardous concentrations for 50% and 5% of species (HC50 and HC5, respectively) differed by up to a factor of 100 and 129 between the two approaches, respectively. However, when five or more species were used for SSD estimation, their differences were reduced to a factor of 1.7 and 5.1 for HC50 and HC5, respectively, and the 95% confidence intervals of HC50 values overlapped considerably between the two approaches. These results suggest that when the number of test species is adequate, SSDs based on EqP theory and spiked-sediment tests are comparable in sediment risk assessments. Environ Toxicol Chem 2022;41:462-473. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Estimating species sensitivity distributions on the basis of readily obtainable descriptors and toxicity data for three species of algae, crustaceans, and fish

Estimation of species sensitivity distributions (SSDs) is a crucial approach to predicting ecological risks and water quality benchmarks, but the amount of data required to implement this approach is a serious constraint on the application of SSDs to chemicals for which there are few or no toxicity data. The development of statistical models to directly estimate the mean and standard deviation (SD) of the logarithms of log-normally distributed SSDs has recently been proposed to overcome this problem. To predict these two parameters, we developed multiple linear regression models that included, in addition to readily obtainable descriptors, the mean and SD of the logarithms of the concentrations that are acutely toxic to one algal, one crustacean, and one fish species, as predictors. We hypothesized that use of the three species' mean and SD would improve the accuracy of the predicted means and SDs of the logarithms of the SSDs. We derived SSDs for 60 chemicals based on quality-assured acute toxicity data. Forty-five of the chemicals were used for model fitting, and 15 for external validation. Our results supported previous findings that models developed on the basis of only descriptors such as log K OW had limited ability to predict the mean and SD of SSD (e.g., r 2 = 0.62 and 0.49, respectively). Inclusion of the three species' mean and SD, in addition to the descriptors, in the models markedly improved the predictions of the means and SDs of SSDs (e.g., r 2 = 0.96 and 0.75, respectively). We conclude that use of the three species' mean and SD is promising for more accurately estimating an SSD and thus the hazardous concentration for 5% of species in cases where limited ecotoxicity data are available.