Does the Choice of NOEC or EC10 Affect the Hazardous Concentration for 5% of the Species?

Incorporation of sediment- and soil-specific aspects in the Criteria for Reporting and Evaluating Ecotoxicity Data (CRED)

In environmental risk assessment either for registration purposes or for retrospective assessments of monitoring data, the hazard assessment is predominantly based on effect data from ecotoxicity studies. Most regulatory frameworks require studies used for risk assessment to be evaluated for reliability and relevance. Historically, the Klimisch methodology was used in many regulatory procedures where reliability needed to be evaluated. More recently, the Criteria for Reporting and Evaluating Ecotoxicity Data (CRED) have been developed for aquatic ecotoxicity studies, providing more detailed guidance on the evaluation and reporting of not only the reliability but also the relevance of a scientific study. Here, we discuss the application of the CRED methodology for assessing sediment and soil ecotoxicity studies, addressing important sediment- and soil-specific criteria that should be included as part of the CRED evaluation system. We also provide detailed recommendations for the design and reporting of sediment and soil toxicity studies that can be used by scientists and researchers wishing to contribute ecotoxicological data for effect assessments carried out within regulatory frameworks. Integr Environ Assess Manag 2024;20:2162-2174. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Trace elements and heavy metal(loid)s triggering ecological risks in a heavily polluted river-reservoir system of central Mexico: Probabilistic approaches

The contamination of trace elements and heavy metal(loid)s in water bodies has emerged as a global environmental concern due to their high toxicity at low concentrations to both biota and humans. This study aimed to evaluate the ecological risk associated with the occurrence and spatial distribution of Mn, Fe, Co, Cd, Ni, Zn, Sb, As, Tl, Cu, Pb, U, and V in the heavily polluted waters of an important river-reservoir system (Atoyac River Basin) in central Mexico, using two-level tired probabilistic approaches: Risk Quotient based on Species Sensitivity Distribution (RQSSD) and Joint Probability Curves (JPCs). The concentrations of these elements varied widely, ranging from 0.055 μg L-1 to 9200 μg L-1 and from 0.056 μg L-1 to 660 μg L-1, in both total and dissolved fractions, respectively. Although geogenic and anthropogenic sources contribute to the presence of these elements in waters, the discharge of untreated or poorly treated industrial wastewater is the main source of contamination. In this regard, the RQSSD results indicated high ecological risk for Mn, Fe, Co, Ni, Zn, and Sb, and medium or low ecological risk for As, Tl, U, and V at almost all sampling sites. The highest RQSSD values were found downstream of a large industrial corridor for Co, Zn, Tl, Pb, and V, with Tl, Pb, and V escalating to higher risk levels, highlighting the negative impact of industrial contamination on biota. The JPC results for these elements are consistent with the RQSSD approach, indicating an ecological risk to species from Mn, Fe, Co, Ni, Zn, and Sb in waters of the Atoyac River Basin. Therefore, the results of this study offer a thorough assessment of pollution risk, providing valuable insights for legislators on managing and mitigating exposure.

Toxicity of ciprofloxacin and ofloxacin to Moina macrocopa and investigation of p-value adjustments for (eco)toxicological studies

Ciprofloxacin (CFX) and ofloxacin (OFX) are commonly found as residual contaminants in aquatic environments, posing potential risks to various species. To ensure the safety of aquatic wildlife, it is essential to determine the toxicity of these antibiotics and establish appropriate concentration limits. Additionally, in (eco)toxicological studies, addressing the issue of multiple hypothesis testing through p-value adjustments is crucial for robust decision-making. In this study, we assessed the no observed adverse effect concentration (NOAEC) of CFX and OFX on Moina macrocopa across a concentration range of 0-400 µg L-1. Furthermore, we investigated multiple p-value adjustments to determine the NOAECs. Our analysis yielded consistent results across seven different p-value adjustments, indicating NOAECs of 100 µg CFX L-1 for age at first reproduction and 200 µg CFX L-1 for fertility. For OFX treatment, a NOAEC of 400 µg L-1 was observed for both biomarkers. However, further investigation is required to establish the NOAEC of OFX at higher concentrations with greater certainty. Our findings demonstrate that CFX exhibits higher toxicity compared to OFX, consistent with previous research. Moreover, this study highlights the differential performance of p-value adjustment methods in terms of maintaining statistical power while controlling the multiplicity problem, and their practical applicability. The study emphasizes the low NOAECs for these antibiotics in the zooplanktonic group, highlighting their significant risks to ecological and environmental safety. Additionally, our investigation of p-value adjustment approaches contributes to a deeper understanding of their performance characteristics, enabling (eco)toxicologists to select appropriate methods based on their specific needs and priorities.

Freshwater ecotoxicity characterization factors for PMT/vPvM substances

This study addresses the gap in freshwater ecotoxicological characterization factors (CFs) for Persistent, Mobile, and Toxic (PMT) and Very Persistent and Very Mobile (vPvM) substances. These CFs are vital for integrating the ecotoxicity impacts of these chemicals into life cycle assessments. Our goals are twofold: first, to calculate experimental freshwater CFs for PMT/vPvM substances listed by the German Environment Agency (UBA); second, to compare these CFs with those from the USEtox database. The expanded UBA list includes 343 PMT/vPvM substances, each representing a unique chemical structure, and linked to 474 REACH-registered substances. This study successfully computed CFs for 244 substances, with 107 overlapping the USEtox database and 137 being new. However, ecotoxicity data limitations prevented CF determination for 97 substances. This research enhances our understanding of freshwater CFs for PMT/vPvM substances, covering 72% of UBA's 343 PMT/vPvM substances. Data scarcity remains a significant challenge, which invariably impedes CF calculations. Notably, the disparities observed between CF values in the USEtox database and those derived in this research largely stem from variations in ecotoxicity data. Consequently, this research underscores the dynamic nature of CFs for substances, emphasizing the need for regular updates to ensure their accuracy and relevance.

Research on aquatic microcosm: Bibliometric analysis, toxicity comparison and model prediction

Recently, aquatic microcosms have attracted considerable attention because they can be used to simulate natural aquatic ecosystems. First, to evaluate the development of trends, hotspots, and national cooperation networks in the field, bibliometric analysis was performed based on 1841 articles on aquatic microcosm (1962-2022). The results of the bibliometric analysis can be categorized as follows: (1) Aquatic microcosm research can be summarized in two sections, with the first part focusing on the ecological processes and services of aquatic ecosystems, and the second focusing on the toxicity and degradation of pollutants. (2) The United States (number of publications: 541, proportion: 29.5%) and China (248, 13.5%) are the two most active countries. Second, to determine whether there is a difference between single-species and microcosm tests, that is, to perform different-tier assessments, the recommended aquatic safety thresholds in risk assessment [i.e., the community-level no effect concentration (NOECcommunity), hazardous concentrations for 5% of species (HC5) and predicted no effect concentration (PNEC)] were compared based on these tests. There was a significant difference between the NOECcommunity and HC5 (P < 0.05). Moreover, regression models predicting microcosm toxicity values were constructed to provide a reference for ecological systemic risk assessments based on aquatic microcosms.

Considerations and data update for the inference of environmental quality standards for two avermectins widely used in salmon aquaculture

We have updated and reviewed toxicity data for Emamectin benzoate (EMB) and Ivermectin (IVER), two in-feed drugs used to treat sea lice in farmed Atlantic salmon, and inferred new Environmental Quality Standards (EQS) using a deterministic approach or Species Sensitivity Distributions (SSDs) based on available data. We used a SSD model averaging approach and inferred a water acute EQS value of 24.9 ng/L (SSD) for EMB, while previously established chronic water EQS of 0.17 ng/L and sediment benthic EQS of 131 ng/kg dry weight remained unchanged. For IVER, both a water acute EQS of 8.04 ng/L and a chronic water EQS of 3.98 ng/L were inferred using SSDs as well as a benthic EQS of 290 ng/kg dry weight using a deterministic approach. In light of the lack of solubility and tendency of both avermectins to sorb to material benthic EQSs remain the most relevant value to consider for regulators.

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.

Methods for estimating no-effect toxicity concentrations in ecotoxicology

A range of new statistical approaches is being developed and/or adopted in ecotoxicology that, when combined, can greatly improve the estimation of no-effect toxicity values from concentration-response (CR) experimental data. In particular, we compare the existing no-effect-concentration (NEC) threshold-based toxicity metric with an alternative no-significant-effect-concentration (NSEC) metric suitable for when CR data do not show evidence of a threshold effect. Using a model-averaging approach, these metrics can be combined to yield estimates of N(S)EC and of their uncertainty within a single analysis framework. The outcome is a framework for CR analysis that is robust to uncertainty in the model formulation, and for which resulting estimates can be confidently integrated into risk assessment frameworks, such as the species sensitivity distribution (SSD). Integr Environ Assess Manag 2024;20:279-293. © 2023 Commonwealth of Australia and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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.

Species sensitivity distributions for two widely used anti-sea lice chemotherapeutants in the salmon aquaculture industry

The main objective of the present study is to construct acute aquatic species sensitivity distributions (SSD) and generate proposed HC₅ values (i.e. the hazardous concentration for which 5 % of species are affected or potentially affected) for two aquaculture anti-sea lice bath pesticides, azamethiphos, and hydrogen peroxide. These values could be used as the basis for the establishment of environmental quality standards (EQS). We have generated SSDs and inferred HC₅ values for mortality and sublethal endpoints using LC50, EC50, and NOEC/LOEC data points separately and for each bath pesticide. Through the examination of literature data on the toxicity of both compounds, we opted to use tests with limited exposure times to ensure environmental relevance for bath pesticides. We also separated life stages for some of the sensitive taxa to account for differences in sensitivities and risk of exposure. The resulting threshold concentrations in environmental seawater are 0.10 μg/L for azamethiphos and 0.15 mg/L for hydrogen peroxide. These suggested azamethiphos and hydrogen peroxide thresholds are comparable to some previously reported EQS values. Further considerations need to be included in how to better use these thresholds in a regulatory context in relation to dispersion patterns. It is also clear that delayed mortality and sublethal effects documented in the literature require further study to fully anticipate the environmental risks of using these two bath pesticides.

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.

Do anti-HIV drugs pose a threat to photosynthetic microorganisms?

We investigated the occurrence and risk assessment of three anti-HIV drugs [(tenofovir (TNF), lamivudine (LMV) and efavirenz (EFV)] in urban rivers from Curitiba (Brazil), as well as the individual and combined effects of their environmental representative concentrations on the freshwater periphytic species Synechococcus elongatus (Cyanobacteria) and Chlorococcum infusionum (Chlorophyta). The three studied drugs, except TNF, were found in 100% of the samples, and concentrations in samples ranged from 165 to 412 ng TNF L^-1, 173-874 ng LMV L^-1 and 13-1250 ng EFV L^-1. Bioassays using artificial contaminated water showed that at environmental concentrations, TNF and LMV did not represent environmental risks to the studied photosynthetic organisms. However, EFV was shown to be toxic, affecting photosynthesis, respiration, and oxidative metabolism. The studied drugs demonstrated interactive effects. Indeed, when submitted to the combination of TNF and LMV, decreased photosynthesis was observed in C. infusionum cells. Moreover, the toxic effects of EFV were amplified in both species when TNF and/or LMV were added to the media. The simultaneous presence of TNF, LMV and EFV in environmental matrices associated with their interactive effects, lead to increased toxicological effects of water contaminated by anti-HIV drugs and thus to an ecological threat to photosynthetic microorganisms.

Towards Sustainable Wastewater Treatment: Bioindication as a Technique for Supporting Treatment Efficiency Assessment

Constructed wetlands (CWs) are a promising alternative for conventional methods of wastewater treatment. However, the biggest challenge in wastewater treatment is the improvement of the technology used so that it is possible to remove micropollutants without additional costs. The impact of wastewater treatment in CWs on toxicity towards Aliivibrio fischeri, Daphnia magna and Lemna minor was investigated. The effects of feeding regime (wastewater fed in five batches per week at a batch volume of 1 L, or twice per week at a batch volume of 2.5 L) and the presence of pharmaceuticals (diclofenac and sulfamethoxazole), as well as the presence of Miscantus giganteus plants in CW columns (twelve of the 24 columns that were planted) were analyzed. A reduction in toxicity was observed in all experimental setups. The effluents from constructed wetlands were classified as moderately toxic (average TU for A. fischeri, D. magna and L. minor was 0.9, 2.5 and 5.5, respectively). The feeding regime of 5 days of feeding/2 days of resting resulted in a positive impact on the ecotoxicological and chemical parameters of wastewater (removal of TOC, N-NH₄ and pharmaceuticals). Extended exposure of Miscantus giganteus to the wastewater containing pharmaceuticals resulted in elevated activity of antioxidant enzymes (catalase and superoxide dismutase) in leaf material.

A critical review of effect modeling for ecological risk assessment of plant protection products

A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).

Application of the Closure Principle Computational Approach Test to Assess Ecotoxicological Field Studies: Comparative Analysis Using Earthworm Field Test Abundance Data

Field studies to determine the effects of chemicals on earthworm communities are generally conducted according to International Organization for Standardization standard 11268-3 (and later comments). However, statistical test procedures suggested in the guideline are frequently criticized, mainly for 2 reasons: 1) Earthworm abundances are count data and often do not fulfill requirements for multiple t tests (normal distribution and homogeneity of variance), and 2) the resulting toxicity metrics of multiple testing procedures (no/lowest-observed-effect concentrations [NOEC/LOEC]) fail to adequately detect the actual level of effects. Recently, a new method to overcome these shortcomings was presented by the introduction of the closure principle computational approach test (CPCAT). We applied this statistical method to assess chemical effects on abundance in a large dataset of 26 earthworm field studies (with up to 3 test chemical application rates) and an additional extended study with 6 application rates. A comparative analysis was provided considering results of well-established multiple testing approaches (Dunnett's test) with particular consideration of the degree of overdispersion found in these data. It was shown that the CPCAT detects substantially more effects in earthworm field tests as statistically significant than standard t test approaches. This lowered the LOEC/NOEC for many chemical treatments to control comparisons. As a consequence, the statistically detected NOECs/LOECs were often set at lower percentage deviations between control and chemical treatment. This is the first time the performance of the CPCAT has been assessed within a comprehensive analysis of earthworm field study data. Environ Toxicol Chem 2021;40:1750-1760. © 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.

Improved toxicity analysis of heavy metal-contaminated water via a novel fermentative bacteria-based test kit

The objective of this study was development of a simple and reliable microbial toxicity test based on fermentative bacteria to assess heavy metal (Hg²⁺, Cu²⁺, Cr⁶⁺, Ni²⁺, As⁵⁺, or Pb²⁺)-contaminated water. The dominant species of test organisms used in this study was a spore-forming fermentative bacterium, Clostridium guangxiense. Toxicity of water was assessed based on inhibition of fermentative gas production of the test organisms, which was analyzed via a syringe method. Overall, the fermentative bacteria-based test kits satisfactorily identified increased toxicity of water as water was contaminated with high amounts of heavy metals; however, levels of inhibition were dissimilar depending on the species of metals. Inhibitory effects of Hg²⁺, Cu²⁺, Cr⁶⁺, and Ni²⁺ were considerably greater than those of As⁵⁺ and Pb²⁺. The 24 h half-maximum effective concentrations (EC₅₀) for Hg²⁺, Cu²⁺, Cr⁶⁺, Ni²⁺, As⁵⁺, and Pb²⁺ were analyzed to be 0.10, 0.51, 1.09, 3.61, 101.33, and 243.45 mg/L, respectively, confirming that Hg²⁺, Cu²⁺, Cr⁶⁺, and Ni²⁺ are more toxic to fermentative gas production than As⁵⁺ and Pb²⁺. The fermentative bacteria-based toxicity test represents an improvement over other existing toxicity tests because of ease of end-point measurement, high reproducibility, and favorable on-site field applicability. These advantages make the fermentative bacteria-based test suitable for simple and reliable toxicity screening for heavy metal-contaminated water.

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

Estimation of species sensitivity distributions (SSDs) is an essential way to estimate the hazardous concentration for 5% of the species (HC5) and thus to derive a "safe" concentration. Here, we examined whether we can reasonably predict SSDs based on chronic no-observed-effect concentration or level (chronic SSDs) from SSDs based on acute median effective/lethal concentration (acute SSDs) by analyzing log-normal SSDs of 150 chemicals. Chronic SSD means were, on average, 10 times lower than acute SSD means. The standard deviations (SDs) of acute and chronic SSDs closely overlapped. Our detailed analysis suggests that the acute SSD SD can be used as an initial estimate of the chronic SSD SD if the number of tested species is ≥10. There were no significant differences in the ratios of chronic to acute SSD means or SDs among three different modes of action. The HC5 of chronic SSDs was, on average, 10 times lower than the acute SSD HC5. We suggest that multiplication of the acute HC5 by a factor of 0.1 is a defensible way to obtain a first approximation of the chronic HC5, particularly when relative ecological risks of chemicals are being evaluated. Further study is needed to develop methods for a more accurate estimation of chronic SSDs.

Effect of interspecific competition on species sensitivity distribution models: Analysis of plant responses to chemical stress

Species Sensitivity Distributions (SSD) are widely used in environmental risk assessment to predict the concentration of a contaminant that is hazardous for 5% of species (HC₅). They are based on monospecific bioassays conducted in the laboratory and thus do not directly take into account ecological interactions. This point, among others, is accounted for in environmental risk assessment through an assessment factor (AF) that is applied to compensate for the lack of environmental representativity. In this study, we aimed to assess the effects of interspecific competition on the responses towards isoproturon of plant species representative of a vegetated filter strip community, and to assess its impact on the derived SSD and HC₅ values. To do so, we realized bioassays confronting six herbaceous species to a gradient of isoproturon exposure in presence and absence of a competitor. Several modelling approaches were applied to see how they affected the results, using different critical effect concentrations and investigating different ways to handle multiple endpoints in SSD. At the species level, there was a strong trend toward organisms being more sensitive to isoproturon in presence of a competitor than in its absence. At the community level, this trend was also observed in the SSDs and HC₅ values were always lower in presence of a competitor (1.12-11.13 times lower, depending on the modelling approach). Our discussion questions the relevance of SSD and AF as currently applied in environmental risk assessment.

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

The calculation of a species sensitivity distribution (SSD) is a commonly accepted approach to derive the predicted no-effect concentration (PNEC) of a substance in the context of environmental risk assessment. The SSD approach usually is data demanding and incorporates a large number of ecotoxicological values from different experimental studies. The probabilistic SSD (PSSD) approach is able to fully consider the variability between different exposure conditions and material types, which is of great importance when constructing an SSD for any chemical, especially for nanomaterials. The aim of our work was to further develop the PSSD approach by implementing methods to better consider the uncertainty and variability of the input data. We incorporated probabilistic elements to consider the uncertainty associated with uncertainty factors by using probability distributions instead of single values. The new PSSD method (named "PSSD+") computes 10 000 PSSDs based on a Monte Carlo routine. For each PSSD calculated, the hazardous concentration for 5% of species (HC₅ ) was extracted to provide a PNEC distribution based on all data available and their associated uncertainty. The PSSD+ approach also includes the option to consider a species weighting according to a typically constituted biome. We applied this PSSD+ approach to a previously published data set on C nanotubes and Ag nanoparticles. The evaluation of the uncertainty factor distributions and species weighting have shown that the proposed PSSD method is robust with respect to the calculation of the PNEC value. Furthermore, we demonstrated that the PSSD+ can handle both small and more comprehensive data sets because the PNEC distributions are a close representation of the data available. Finally, the sensitivity testing toward data set variations showed that the maximum variation of the mean PNEC was of a factor of about 2, so that the method is relatively insensitive to missing data points as long as the most sensitive species is included. Integr Environ Assess Manag 2020;16:211-222. © 2019 SETAC.

Extrapolation Factors for Characterizing Freshwater Ecotoxicity Effects

Various environmental and chemical assessment frameworks including ecological risk assessment and life cycle impact assessment aim at evaluating long-term ecotoxicity effects. Chronic test data are reported under the European Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation for various chemicals. However, chronic data are missing for a large fraction of marketed chemicals, for which acute test results are often available. Utilizing acute data requires robust extrapolation factors across effect endpoints, exposure durations, and species groups. We propose a decision tree based on strict criteria for curating and selecting high-quality aquatic ecotoxicity information available in REACH for organic chemicals, to derive a consistent set of generic and species group-specific extrapolation factors. Where ecotoxicity effect data are not available at all, we alternatively provide extrapolations from octanol-water partitioning coefficients as suitable predictor for chemicals with nonpolar narcosis as mode of action. Extrapolation factors range from 0.2 to 7 and are higher when simultaneously extrapolating across effect endpoints and exposure durations. Our results are consistent with previously reported values, while considering more endpoints, providing species group-specific factors, and characterizing uncertainty. Our proposed decision tree can be adapted to curate information from additional data sources as well as data for other environments, such as sediment ecotoxicity. Our approach and robust extrapolation factors help to increase the substance coverage for characterizing ecotoxicity effects across chemical and environmental assessment frameworks. Environ Toxicol Chem 2019;38:2568-2582. © 2019 SETAC.

Ecotoxicological QSAR modelling of organic chemicals against Pseudokirchneriella subcapitata using consensus predictions approach

The present study provides robust consensus quantitative structure-activity relationship (QSAR) models developed from 334 organic chemicals covering a wide chemical domain for the prediction of effective concentrations of chemicals for 50% and 10% inhibition of algal growth. Only 2D descriptors with definite physicochemical meaning were employed for QSAR model building, whereas development, validation and interpretation were achieved following the strict Organization for Economic Co-operation and Development (OECD) recommended guidelines. Genetic algorithm along with stepwise approach was used in feature selection while the final QSAR models were derived using partial least squares regression technique. The applicability domain of the developed models was also checked. The obtained consensus models were then used to predict 64 organic chemicals having no definite observed responses while the confidence of predictions was checked by the 'prediction reliability indicator' tool. The developed models should be applicable for data gap filling in case of new or untested organic chemicals provided they fall within the domain of the model and can also be implemented to design safer alternatives to the environment.

Toward harmonizing ecotoxicity characterization in life cycle impact assessment

Ecosystem quality is an important area of protection in life cycle impact assessment (LCIA). Chemical pollution has adverse impacts on ecosystems on a global scale. To improve methods for assessing ecosystem impacts, the Life Cycle Initiative hosted by the United Nations Environment Programme established a task force to evaluate the state-of-the-science in modeling chemical exposure of organisms and the resulting ecotoxicological effects for use in LCIA. The outcome of the task force work will be global guidance and harmonization by recommending changes to the existing practice of exposure and effect modeling in ecotoxicity characterization. These changes will reflect the current science and ensure the stability of recommended practice. Recommendations must work within the needs of LCIA in terms of 1) operating on information from any inventory reporting chemical emissions with limited spatiotemporal information, 2) applying best estimates rather than conservative assumptions to ensure unbiased comparison with results for other impact categories, and 3) yielding results that are additive across substances and life cycle stages and that will allow a quantitative expression of damage to the exposed ecosystem. We describe the current framework and discuss research questions identified in a roadmap. Primary research questions relate to the approach toward ecotoxicological effect assessment, the need to clarify the method's scope and interpretation of its results, the need to consider additional environmental compartments and impact pathways, and the relevance of effect metrics other than the currently applied geometric mean of toxicity effect data across species. Because they often dominate ecotoxicity results in LCIA, we give metals a special focus, including consideration of their possible essentiality and changes in environmental bioavailability. We conclude with a summary of key questions along with preliminary recommendations to address them as well as open questions that require additional research efforts. Environ Toxicol Chem 2018;37:2955-2971. © 2018 SETAC.

Subchronic, chronic, lethal and sublethal toxicity of insensitive munitions mixture formulations relative to individual constituents in Hyalella azteca

Insensitive munitions (IMs) are replacing conventional munitions, improving safety from unintended detonation. IMs are deployed in mixture formulations but little is known about their mixture toxicology. We characterized mixture effects of the IM formulations IMX-101 (mixture of 2,4-dinitroanisole [DNAN], 3-nitro-1,2,4-triazol-5-one [NTO], and nitroguanidine [NQ]) and IMX-104 (DNAN, NTO, and hexahydro-1,3,5-trinitro-1,3,5-triazine [RDX]) in subchronic (10 d) and chronic (35 d) water-only tests in Hyalella azteca assessing impacts on survival, growth and reproduction. In 10-d single chemical exposures, DNAN was the most potent constituent, eliciting an LC50 of 16.0 mg/L; the LC50s for NTO and NQ were 891 and 565 mg/L, respectively. RDX did not elicit significant mortality up to 29.5 mg/L, a concentration near its solubility limit. Based on toxic-units (TUs), the toxicity of IMX-101 was driven by the effective concentration of DNAN; however, the presence of NTO, RDX, or both elicited interactive effects causing an approximately 2-fold decrease in lethality for IMX-104. Growth reduction was observed in 10-d exposures to DNAN, IMX-101 and IMX-104, but not for NQ, NTO, or RDX. Longer exposure duration (35 d) to IMX-101, IMX-104, and DNAN resulted in 3-6 times higher sensitivity for lethality and resulted in the most sensitive endpoint for DNAN, RDX, and IMX-101 exposures, decreased reproduction. Slight, but statistically significant, antagonistic responses among IMX-101 constituents were observed for survival and reproduction at 35d. Overall, the results support response-additive summation as a sufficient method to provide conservative hazard assessments of subchronic, chronic, and sublethal IMX-101 and IMX-104 mixture impacts in H. azteca.

Transcriptomics provides mechanistic indicators of mixture toxicology for IMX-101 and IMX-104 formulations in fathead minnows (Pimephales promelas)

Within the US military, new insensitive munitions (IMs) are rapidly replacing conventional munitions improving safety from unintended detonation. Toxicity data for IM chemicals are expanding rapidly, however IM constituents are typically deployed in mixture formulations, and very little is known about their mixture toxicology. In the present study we sought to characterize the mixture effects and toxicology of the two predominant IM formulations IMX-101 and IMX-104 in acute (48 h) larval fathead minnow (Pimephales promelas) exposures. IMX-101 consists of a mixture of 2,4-dinitroanisole (DNAN), 3-nitro-1,2,4-triazol-5-one (NTO), and nitroguanidine (NQ) while IMX-104 is composed of DNAN, NTO, and hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX). DNAN was the most potent constituent in IMX-101 eliciting an LC50 of 36.1 mg/L, whereas NTO and NQ did not elicit significant mortality in exposures up to 1040 and 2640 mg/L, respectively. Toxic unit calculations indicated that IMX-101 elicited toxicity representative of the component concentration of DNAN within the mixture. Toxicogenomic responses for the individual constituents of IMX-101 indicated unique transcriptional expression and functional responses characteristic of: oxidative stress, impaired energy metabolism, tissue damage and inflammatory responses in DNAN exposures; impaired steroid biosynthesis and developmental cell-signaling in NQ exposures; and altered mitogen-activated protein kinase signaling in NTO exposures. Transcriptional responses to the IMX-101 mixture were driven by the effects of DNAN where expression and functional responses were nearly identical comparing DNAN alone versus the fractional equivalent of DNAN within IMX-101. Given that each individual constituent of the IMX-101 mixture elicited unique functional responses, and NTO and NQ did not interact with DNAN within the IMX-101 mixture exposure, the overall toxicity and toxicogenomic responses within acute exposures to the IMX-101 formulation are indicative of "independent" mixture toxicology. Alternatively, in the IMX-104 exposure both DNAN and RDX were each present at concentrations sufficient to elicit lethality (RDX LC50 = 28.9 mg/L). Toxic-unit calculations for IMX-104 mixture formulation exposures indicated slight synergistic toxicity (ΣTU LC50 = 0.82, 95% confidence interval = 0.73-0.90). Unique functional responses relative to DNAN were observed in the IMX-104 exposure including responses characteristic of RDX exposure. Based on previous transcriptomics responses to acute RDX exposures in fathead minnow larvae, we hypothesize that the potentially synergistic responses within the IMX-104 mixture are related to interactive effects of each DNAN and RDX on oxidative stress mitigation pathways.

A multipronged QSAR approach to predict algal low-toxic-effect concentrations of substituted phenols and anilines

Environmental risk assessment procedures require acute and chronic toxicity values of hazardous chemicals. In this respect, the 96-h toxicity bioassays of nitro-, methyl-, methoxy-, chloro-, and nitrile- substituted phenols and anilines to Chlorella vulgaris were performed. Median inhibitory and low-toxic-effect concentrations were reported. Significant correlations between acute and chronic toxicities were found for the chemicals in the data set regardless of mode of action. Consequently, linear models employing theoretical and empirical descriptors were developed for the prediction of NOEC and IC₂₀. The outcome of the study will be beneficial in the risk assessments of organic chemicals and setting water quality standards by the regulators.

Dietary Toxicity Thresholds and Ecological Risks for Birds and Mammals Based on Species Sensitivity Distributions

Species sensitivity distributions (SSDs) are commonly used in regulatory procedures and ecological risk assessments. Yet, most toxicity threshold and risk assessment studies are based on invertebrates and fish. In the present study, no observed effect concentrations (NOECs) specific to birds and mammals were used to derive SSDs and corresponding hazardous concentrations for 5% of the species (HC5 values). This was done for 41 individual substances as well as for subsets of substances aggregated based on their toxic Mode of Action (MoA). In addition, potential differences in SSD parameters (mean and standard deviation) were investigated in relation to MoA and end point (growth, reproduction, and survival). The means of neurotoxic and respirotoxic compounds were significantly lower than those of narcotics, whereas no differences were found between end points. The standard deviations of the SSDs were similar across MoA's and end points. Finally, the SSDs obtained were used in a case study by calculating Ecological Risks (ER) and multisubstance Potentially Affected Fractions of species (msPAF) based on 19 chemicals in 10 Northwestern European estuaries and coastal areas. The assessment showed that the risks were all below 2.6 × 10^-2. However, the calculated risks underestimate the actual risks of chemicals in these areas because the potential impacts of substances that were not measured in the field or for which no SSD was available were not included in the risk assessment. The SSDs obtained can be used in regulatory procedures and for assessing the impacts of contaminants on birds and mammals from fish contaminants monitoring programs.

Comparing the sensitivity of chlorophytes, cyanobacteria, and diatoms to major-use antibiotics

The occurrence of antibiotic residues in the aquatic environment is an emerging concern. In contrast to daphnia and fish, algae are known to be particularly sensitive to antibiotic exposure. However, to date, a systematic evaluation of the sensitivity of different algal species to antibiotics has not been performed. The aim of the present study was therefore to explore the sensitivity of a battery of algal species toward antibiotic exposures. The present study investigated the growth inhibition effects of 3 major-use antibiotics, tylosin, lincomycin, and trimethoprim, on 7 algal species from the chlorophyte, cyanobacteria, and diatom groups. Based on median effective concentration (EC50) values, cyanobacteria (EC50 = 0.095-0.13 μmol/L) were found to be the most sensitive group to lincomycin followed by chlorophytes (EC50 = 7.36-225.73 μmol/L) and diatoms (EC50 > 225.73 μmol/L). Cyanobacteria were also the most sensitive group to tylosin (EC50 = 0.09-0.092 μmol/L), but, for this compound, diatoms (EC50 = 1.33-5.7 μmol/L) were more sensitive than chlorophytes (EC50 = 4.14-81.2 μmol/L). Diatoms were most sensitive to trimethoprim (EC50 = 7.36-74.61 μmol/L), followed by cyanobacteria (EC50 = 315.78-344.45 μmol/L), and chlorophytes (EC50 > 344.45 μmol/L) for trimethoprim. Although these results partly support the current approach to regulatory environmental risk assessment (whereby cyanobacterial species are recommended for use with antibiotic compounds), they indicate that for some antibiotics this group might not be the most appropriate test organism. It is therefore suggested that environmental risk assessments consider data on 3 algal groups (chlorophytes, cyanobacteria, and diatoms) and use test species from these groups, which are consistently found to be the most sensitive (Pseudokirchneriella subcapitata, Anabaena flos-aquae, and Navicula pelliculosa). Environ Toxicol Chem 2016;35:2587-2596. © 2016 SETAC.

Don't be fooled-A no-observed-effect concentration is no substitute for a poor concentration-response experiment

Renowned mathematician and science historian Jacob Bronowski once defined science as "the acceptance of what works and the rejection of what does not" and noted "that needs more courage than we might think." Such would also seem to be the case with no-observed-effect concentrations (NOECs) and no-observed-effect levels in ecotoxicology. Compelling arguments were advanced more than a quarter of a century ago as to why the use of a model to describe the concentration-response relationship was preferable to an isolated metric, with the NOEC singled out as a particularly poor toxicity measure. In the ensuing years numerous articles critical of the NOEC have been written, with some calling for an outright ban on its use. More recently, arguments have been made for the retention of NOECs, with supporters suggesting that this metric is particularly useful in situations where the concentration-response relationship is weak or nonexistent. In addition, it has been claimed that there are situations in ecotoxicology where suitable models are simply not available. These arguments are not correct, and they also have impeded the decades-overdue incorporation of numerous recommendations based on research that NOECs should no longer be used. In the present study the authors counter some of the most recent claims in support of NOECs and provide new insights for 1 class of problem claimed not to be amenable to such modeling. They are confident that similar insights will be developed as further original research in this area is undertaken. Environ Toxicol Chem 2016;35:2141-2148. © 2016 SETAC.