A Novel Approach to Derive the Predicted No-Effect Concentration (PNEC) of Benzophenone-3 (BP-3) Using the Species Sensitivity Distribution (SSD) Method: Suggestion of a New PNEC Value for BP-3

Read-across as a non-animal testing alternative for the generation of risk assessment data can be useful in those cases where quantitative structure-activity relationship (QSAR) models are not available, or are less well developed. This paper provides read-across case studies for the estimation of the aquatic toxicity of five different fragrance substances, and proposes a pragmatic approach for expressing uncertainty in read-across estimates. The aquatic toxicity estimates and their uncertainties are subsequently used to estimate fresh water compartment Predicted No-Effect Concentrations (PNECs), with their two-sided 90% Confidence Intervals (CIs). These PNECs can be used directly in risk assessment. The results of the musk fragrance read-across cases (musk xylene, musk ketone and galaxolide) are compared to experimentally derived PNEC values. The read-across estimates made by using similarity in a hypothesised mechanism of action for (acute) toxicity of musk xylene gave a PNEC of 2μg/L (90% CI 0.0004-13.5μg/L) with the Species Sensitivity Distribution (SSD) approach. This estimated value is 1.8 times above the experimentally-based fresh water PNEC of 1.1μg/L. For musk ketone and galaxolide, the PNEC values based on the SSD approach and employing a toxicity mechanism-based read-across were 2.0 times greater, and 4.9 times below the experimentally derived PNEC values, respectively.

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.

Triclosan (TCS) is a broad-spectrum antimicrobial used in consumer products including toothpaste and hand soap. After being used, TCS is washed or rinsed off and residuals that are not biodegraded or otherwise removed during wastewater treatment can enter the aquatic environment in wastewater effluents and sludges. The environmental exposure and toxicity of TCS has been the subject of various scientific and regulatory discussions in recent years. There have been a number of publications in the past 5 y reporting toxicity, fate and transport, and in-stream monitoring data as well as predictions from aquatic risk assessments. State-of-the-science probabilistic exposure models, including Geography-referenced Regional Exposure Assessment Tool for European Rivers (GREAT-ER) for European surface waters and Pharmaceutical Assessment and Transport Evalutation (PhATE) for US surface waters, have been used to predict in-stream concentrations (PECs). These models take into account spatial and temporal variability in river flows and wastewater emissions based on empirically derived estimates of chemical removal in wastewater treatment and in receiving waters. These model simulations (based on realistic use levels of TCS) have been validated with river monitoring data in areas known to be receiving high wastewater loads. The results suggest that 90th percentile (low flow) TCS concentrations are less than 200 ng/L for the Aire-Calder catchment in the United Kingdom and between 250 ng/L (with in-stream removal) and 850 ng/L (without in-stream removal) for a range of US surface waters. To better identify the aquatic risk of TCS, a species sensitivity distribution (SSD) was constructed based on chronic toxicity values, either no observed effect concentrations (NOECs) or various percentile adverse effect concentrations (EC10-25 values) for 14 aquatic species including fish, invertebrates, macrophytes, and algae. The SSD approach is believed to represent a more realistic threshold of effect than a predicted no effect concentration (PNEC) based on the data from the single most sensitive species tested. The log-logistic SSD was used to estimate a PNEC, based on an HC5,50 (the concentration estimated to affect the survival, reproduction and/or growth of 5% of species with a 50% confidence interval). The PNEC for TCS was 1,550 ng/L. Comparing the SSD-based PNEC with the PECs derived from GREATER and PhATE modeling to simulate in-river conditions in Europe and the United States, the PEC to PNEC ratios are less than unity suggesting risks to pelagic species are low even under the highest likely exposures which would occur immediately downstream of wastewater treatment plant (WWTP) discharge points. In-stream sorption, biodegradation, and photodegradation will further reduce pelagic exposures of TCS. Monitoring data in Europe and the United States corroborate the modeled PEC estimates and reductions in TCS concentrations with distance downstream of WWTP discharges. Environmental metabolites, bioaccumulation, biochemical responses including endocrine-related effects, and community level effects are far less well studied for this chemical but are addressed in the discussion. The aquatic risk assessment for TCS should be refined as additional information becomes available.

Predicted no-effect concentrations determination and ecological risk assessment for benzophenone-type UV filters in aquatic environment

Water pollution by metals and metalloids promotes toxic effects to aquatic biota especially in mining regions. Environmental legislation applied to protect aquatic life from the toxicity of metals relies on the definition of protective values (PVs) for each compound. Among methods used to define PVs, Species Sensitivity Distribution (SSD) curves enable the derivation of the Predicted No Effect concentration (PNEC). In this context, this is one of the first studies to propose the construction of acute and chronic split SSD curves built separately for three groups of freshwater organisms (algae, invertebrates and fish) to derive PNEC values for the 14 metals most commonly observed in iron ore mining tailings. Data used to construct split SSD curves were derived from the USEPA ECOTOX knowledgebase and EnviroTox databases and segregated according to the freshwater organism group and as “acute” or “chronic” toxicity. Then, split SSD curves were built using a minimum of nine species for each group to determine the hazardous concentration to 5% of species (HC5) and PNEC values for each group. Once PNEC were derived, a framework was proposed to calculate the Bioavailabity Factor (BioF) used to adjust values for local bioavailability conditions considering water quality characteristics in different regions. The lowest acute PNEC were observed for algae and invertebrates and corresponded to Silver (Ag). Nearly half of calculated PNEC were below current PVs in practice in Brazil, United States (US), United Kingdom (UK), Canada and European Union (EU). Results reinforce the pertinence of: (i) splitting SSD curves to define PVs for metals; and (ii) taking bioavailability into consideration to correct PNEC for local conditions. In addition, outcomes suggest that it is critical to rethink PVs related to metals for aquatic life protection, mainly in Brazil and Minas Gerais state, a region known for extensive mining activity. Finally, PNEC values obtained in this study may be used for ecological risk assessment studies, especially in areas affected by mining and other activities that result in pollution by metals and metalloids, such as Brazil.

Derivation of predicted no effect concentrations (PNEC) for 2,4,6-trichlorophenol based on Chinese resident species

Bisphenol A (BPA, CAS RN 80-05-7) is a high production volume chemical used as an intermediate in the production of polycarbonate plastic and epoxy resins. During its manufacture and use, some emissions to surface waters are anticipated. Chronic predicted no effect concentrations (PNECs) for aquatic systems are used to support the assessment of potential risks to aquatic organisms in receiving waters. PNECs for a compound are considered protective of populations, communities, and ecosystems. Traditionally, PNECs are derived by taking the lowest no-observed effect concentration (NOEC) from a set of toxicity studies and dividing by an assessment factor (e.g., 10 to 1000). This traditional approach is appropriate for substances with few data, but may not be necessary for substances with many valid studies. For well-studied substances, statistical approaches (i.e., development of Species Sensitivity Distribution or SSD methods) can be used to calculate a PNEC that makes use of the full distribution of available NOEC values. Bisphenol A has an extensive set of aquatic toxicity studies covering diverse taxa including algae, hydra, rotifers, mollusks, crustaceans (both benthic and pelagic), insects, annelids, fish, and amphibians. The full chronic data set was used to calculate PNEC values using four SSD methods: (1) the Hazard Concentration (HC5) approach developed by The Netherlands National Institute of Public Health and the Environment (RIVM), (2) the U.S. Environmental Protection Agency's water quality criteria procedure, (3) SigmaPlot (Systat 2000) commercial software that calculates percentile values, and (4) a distributional method consistent with that used by Environment Canada. Using these approaches, PNEC values for BPA range from 11 to 71 μ g/L. Literature studies suggest that application of an additional assessment factor is unwarranted if an SSD-based PNEC is based on chronic data. SSD-derived PNEC values and the traditionally derived PNEC value of 1.6 μ g/L are then compared to concentrations of BPA that have been measured in North American and European surface waters. Adverse risks to aquatic organisms are not anticipated from measured concentrations of BPA in North American and European surface waters.

In ecological risk assessment, the Predicted No Effect Concentration (PNEC) of a substance is generally derived by one of two methods: either by applying an Assessment Factor (AF) or by using a Species Sensitivity Distribution (SSD). With the AF method, which is the conventional way, the PNEC is determined by dividing the lowest No Observed Effect Concentration (NOEC) by an AF of a certain fixed magnitude. With the SSD method, which is becoming increasingly used in the European Union and the United States, an HC5 value (Hazardous Concentration for 5% of species) is estimated from the NOEC and then divided by an AF to derive the PNEC. This study aimed to explore the most appropriate AF and the most effective application of each method. The performances of the SSD and AF methods were compared on the assumption that the better method is that in which more PNECs are lower than HC5. We concluded that the performance of these methods depends on sample size and variation in species sensitivity. As the sample size increases (i.e., if there are more toxicity data), the performance of each method increases. The performance of the AF method is better when variation in species sensitivity is small (i.e., all species tend to have a similar NOEC), but it declines as variation in sensitivity rises, implying that persisting with either of the methods may misrepresent the ecological risk. Our results suggest that the variation in sensitivity is an important factor affecting the ecological risk and more effort should be paid to understanding why the variation varies depending on chemical substances.

Comparisons of PNEC derivation logic flows under example regulatory schemes and implications for ecoTTC

Predicted no-effect concentration (PNEC) is often used in ecological risk assessment to determine low-risk concentrations for chemicals. In the present study, the chronic data from native saltwater species were used to calculated PNEC values using four methods: log-normal distribution (ETX 2.0), log-triangle distribution (US EPA’s water quality criteria procedure), burr III distribution (BurrliOZ) and traditional assessment factor (AF). The PNECs that were calculated using four methods ranged from 0.08 μg/L to 1.8 μg/L. Three of the SSD-derived PNECs range from 0.94 to 1.8 μg/L, about a factor of two apart. To demonstrate the use of SSD-based PNEC values and comprehensively estimate the regional ecological risk for cadmium in surface water of the Bohai Sea, in the Liaodong Bay, Bohai Bay, and Laizhou Bay, China, the dissolved cadmium concentrations were measured and obtained 753 valid data covering 190 stations from July 2006 to November 2007. Based on three ecological risk assessment approaches, namely hazard quotient (HQ), probabilistic risk quotient and joint probability curve (JPC), the potential ecological risk of cadmium in surface water of the Liaodong Bay, Bohai Bay, and Laizahou Bay were estimated. Overall, the ecological risk of cadmium to aquatic ecosystem in the whole Bohai Sea was at acceptable ecological risk level, the order of ecological risk was Liaodong Bay>Bohai Bay>Laizhou Bay. However, more concerns should be paid to aquatic ecological risk in the Liaodong Bay which is the home of many steel, metallurgy and petrochemical industrial in China.

Environmental risk assessment of zinc in European freshwaters: A critical appraisal

Estimation of population-level benchmark concentrations for protecting aquatic organisms from chemicals is important for value-relevant ecological risk assessments. By employing a species sensitivity distribution (SSD) approach, the authors aimed to derive the population-level hazardous concentration for 5% of species (PHC5) for copper. Based on available information on copper toxicity and population models, the authors estimated population threshold concentrations at which the population size is stable (that is, 0 net population growth) for 13 freshwater species (3 algal, 6 invertebrate, and 4 fish species). The PHC5 for copper was then estimated (6.8 µg/L; 95% confidence interval [CI], 1.8-13.6 µg/L), by fitting a log-normal distribution to the population threshold concentrations obtained. The close overlap between the present study's estimate of the PHC5 and a field-derived threshold concentration suggests that the population-level SSD approach provides a reasonable level of protection for species richness in the natural environment. By contrast, and counterintuitively, the authors' estimate was comparable with the individual-level HC5 reported in the European Union risk assessment. Although the present study cannot determine the underlying reasons for the similar figures, the result provides an indication that the margin between individual-level and population-level benchmarks derived from SSD approaches can be very small. The results therefore suggest that attention is needed to achieve population-level protection using an individual-level SSD approach.

Comparing Sensitivity of Ecotoxicological Effect Endpoints between Laboratory and Field

Quinolones antibiotics in the Baiyangdian Lake, China: Occurrence, distribution, predicted no-effect concentrations (PNECs) and ecological risks by three methods

Making waves: Addressing the over-conservatism of the assessment factor method in aquatic ecological risk assessment.