Probabilistic Species Sensitivity Distributions Research Articles

Critical Evaluation and Meta-Analysis of Ecotoxicological Data on Per- and Polyfluoroalkyl Substances (PFAS) in Freshwater Species.

Despite the increasing concern regarding the ecological risks posed by per- and polyfluoroalkyl substances (PFAS), a lack of comprehensive understanding of their actual ecotoxicity remains. Through a meticulous examination of 91 peer-reviewed studies investigating effects at a population level and constructing probabilistic species sensitivity distributions (PSSDs), we present a state-of-the-science hazard assessment of PFAS in freshwater species. Using data subsets containing suboptimal data led to an overestimation of the predicted no-effect concentrations (PNECs) of PFAS. We report PNECs of perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonates (PFSAs) in freshwater to be 4.8-2000 μg/L and 0.4-8.9 μg/L, respectively, derived from high-quality data. Statistical analyses revealed that both functional groups and carbon chain length significantly influenced (p < 0.05) the variations in toxicity observed among different PFAS. This study underscores the importance of obtaining high-quality PFAS ecotoxicity data to comprehend associated hazards. The PNECs of PFAS derived in this study are higher compared to those of micro/nanoplastics and persistent organic pollutants. Our research offers valuable insights into prioritizing the regulation of more toxic PFAS.

Meta-analysis of the hazards of microplastics in freshwaters using species sensitivity distributions

The environmental hazards of microplastics have raised concerns about their potential ecological risks. However, our understanding of the true risks may be limited because most laboratory studies used pristine microplastics. Here, we analyzed the available literature about ecotoxicological effects of microplastics, including weathered microplastics in particular, on freshwater biota and performed probabilistic species sensitivity distributions. The predicted no-effect concentrations for pristine microplastics were lower than those for weathered microplastics, both in mass concentration (6.1 and 4.8 × 102 μg/L) and number concentration (2.6 × 104 and 2.0 × 106 part/m3). In addition, the toxicological studies on microplastics contains often inconsistent and inconclusive information due to the complexity of the microplastics and the employed exposure conditions. The available data for Daphnia magna and Danio rerio was analyzed in detail to understand the effects of microplastic size, shape and polymer type on their ecotoxicity. Microplastic size was the biggest driving factor, followed by shape and polymer type. There was a tendency for increasing toxicity with smaller size, however, a high variability of effect data was observed for small microplastics. This study provided further insights into the effect thresholds for ecological risk assessment of microplastics and the effects of microplastic characteristics on toxicity.

Advantages of model averaging of species sensitivity distributions used for regulating produced water discharges.

Produced water (PW) generated by Australian offshore oil and gas activities is typically discharged to the ocean after treatment. These complex mixtures of organic and inorganic compounds can pose significant environmental risk to receiving waters, if not managed appropriately. Oil and gas operators in Australia are required to demonstrate that environmental impacts of their activity are managed to levels that are as low as reasonably practicable, for example, through risk assessments comparing predicted no-effect concentrations (PNECs) with predicted environmental concentrations of PW. Probabilistic species sensitivity distribution (SSD) approaches are increasingly being used to derive PW PNECs and subsequently calculating dilutions of PW (termed "safe" dilutions) required to protect a nominated percentage of species in the receiving environment (e.g., 95% and 99% or PC95 and PC99, respectively). Limitations associated with SSDs include fitting a single model to small (six to eight species) data sets, resulting in large uncertainty (very wide 95% confidence limits) in the region associated with PC99 and PC95 results. Recent advances in SSD methodology, in the form of model averaging, claim to overcome some of these limitations by applying the average model fit of multiple models to a data set. We assessed the advantages and limitations of four different SSD software packages for determining PNECs for five PWs from a gas and condensate platform off the North West Shelf of Australia. Model averaging reduced occurrences of extreme uncertainty around PC95 and PC99 values compared with single model fitting and was less prone to the derivation of overly conservative PC99 and PC95 values that resulted from lack of fit to single models. Our results support the use of model averaging for improved robustness in derived PNEC and subsequent "safe" dilution values for PW discharge management and risk assessment. In addition, we present and discuss the toxicity of PW considering the paucity of such information in peer-reviewed literature. Integr Environ Assess Manag 2024;20:498-517. © 2023 Commonwealth Scientific and Industrial Research Organisation. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Probabilistic environmental risk assessment of microplastics in marine habitats

Microplastics are ubiquitous in the environment and given the large number of published hazard and exposure studies, quantitative environmental risks assessments of microplastics become feasible. We present here the first environmental risk assessment for marine waters based only on measured concentrations. The Thevariability and uncertainty of the measured data was accounted for in the exposure assessment, while probabilistic species sensitivity distributions were used for hazard assessment, from which a probability distribution was extracted for the predicted no-effect concentration (PNEC). By dividing the exposure distribution by the PNEC-distribution, we were able to calculate probabilistic risk characterisation ratios for each water body in which measurements were performed. Results show a good coverage of the world's major water bodies by measured exposure concentrations (MECs), while the hazard assessment could be improved by aligning the type of particles tested in hazard studies (size, form, polymer) to those actually found in the oceans. Overall, the mean predicted no-effect concentration (PNEC) is 3.84·106 part m−3, with Oryzias melastigma being the most sensitive species (calculated mean NOEC of 3.90·106 part m−3). Interestingly, the only type of dose descriptor that could be extracted from the literature for particles above 10−20 μm was the highest observed no effect concentration (HONEC), which indicates a very low or null toxicity of these larger MPs towards marine organisms. The mean MEC is 1.5·103 part m−3, the highest concentrations being measured in the Atlantic and Pacific Ocean. Although there is a very small overlap of the probability distribution associated with the RCR (0.00002 % of the data points), the mean RCR is 4·10-4 and therefore risks are unlikely given the available data. However, as increasing amounts of plastic reach the environment, RCRs can be expected to increase in the future.

A Meta-analysis of Ecotoxicological Hazard Data for Nanoplastics in Marine and Freshwater Systems.

There is emerging concern about the potential health and environmental impacts of nanoplastics in the environment. Information on exposure has been lacking, but a growing amount of ecotoxicological hazard data is now available, allowing a hazard assessment to be conducted for nanoplastics in freshwater and marine systems. Based on a critical evaluation of published studies and the construction of probabilistic species sensitivity distributions (PSSDs), we present a comprehensive, state‐of‐the‐art understanding of nanoplastic ecotoxicity. Different freshwater and marine datasets were constructed based on different data quality levels, and for each of the datasets, PSSDs were built for both mass‐ and particle number‐based concentrations. Predicted no‐effect concentrations (PNECs) were then extracted from the PSSDs. We report PNECs at 99 and 72 μg L–1, respectively, for the freshwater and marine dataset after the removal of data measured in the presence of sodium azide (NaN3), which is considered to be a major interfering factor in the ecotoxicity testing of nanoplastics. By comparing the PNECs, we found that nanoplastics are less toxic than microplastics and many engineered nanomaterials. In addition, the effects of size and polymer type on toxicity were also statistically tested. We observed no significant difference in ecotoxicity for nanoplastics of different sizes, whereas polystyrene nanoplastics were significantly more toxic than all other tested nanoplastics. In conclusion, the results we present provide a comprehensive description of nanoplastic ecotoxicity based on current knowledge. The results constitute a fundamental step toward an environmental risk assessment for nanoplastics in freshwater and marine systems. Environ Toxicol Chem 2020;39:2588–2598. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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.

Crystalline phase-dependent toxicity of aluminum oxide nanoparticles toward Daphnia magna and ecological risk assessment

Aluminum oxide nanoparticles (Al2O3 NPs) can be found in different crystalline phases, and with the emergence of nanotechnology there has been a rapid increase in the demand for Al2O3 NPs in different engineering areas and for consumer products. However, a careful evaluation of the potential environmental and human health risks is required to assess the implications of the release of Al2O3 NPs into the environment. Thus, the objective of this study was to investigate the toxicity of two crystalline phases of Al2O3 NPs, alpha (α-Al2O3 NPs) and eta (η-Al2O3 NPs), toward Daphnia magna and evaluate the risk to the aquatic ecology of Al2O3 NPs with different crystalline phases, based on a probabilistic approach. Different techniques were used for the characterization of the Al2O3 NPs. The toxicity toward Daphnia magna was assessed based on multiple toxicological endpoints, and the probabilistic species sensitivity distribution (PSSD) was used to estimate the risk of Al2O3 NPs to the aquatic ecology. The results obtained verify the toxic potential of the NPs toward D. magna even in sublethal concentrations, with a more pronounced effect being observed for η-Al2O3 NPs. The toxicity is associated with an increase in the reactive oxygen species (ROS) content and deregulation of antioxidant enzymatic/non-enzymatic enzymes (CAT, SOD and GSH). In addition, changes in MDA levels were observed, indicating that D. magna was under oxidative stress. The most prominent chronic toxic effects were observed in the organisms exposed to η-Al2O3 NPs, since the lowest LOEC was 3.12 mg/L for all parameters, while for α-Al2O3 NPs the lowest LOEC was 6.25 mg/L for longevity, growth and reproduction. However, the risk assessment results indicate that, based on a probabilistic approach, Al2O3 NPs (alpha, gamma, delta, eta and theta) only a very limited risk to organisms in surface waters.

Environmental hazard assessment for polymeric and inorganic nanobiomaterials used in drug delivery

BackgroundThe increasing development and use of nanobiomaterials raises questions about their potential adverse effects on the environment after excretion and release. Published ecotoxicological data was searched for five polymeric nanobiomaterials [chitosan, polylactic acid (PLA), polyacrylonitrile (PAN), polyhydroxyalkanoates (PHA), and poly(lactic–glycolic acid) (PLGA)] and one inorganic nanobiomaterial [hydroxyapatite (HAP)] to evaluate the environmental hazards for freshwater and soil using a meta-analysis. If enough data was available, a probabilistic species sensitivity distribution (pSSD) and from this a predicted no effect concentration (PNEC) was calculated. If only one data point was available, a PNEC was calculated based on the most sensitive endpoint. Each material was classified either as “nano” or “non-nano”, depending on the categorization in the original articles. When the original article specified that the material consisted of nanoparticles, the material was classified as nano; when nothing was mentioned, the material was classified as “non-nano”.ResultsFor PLA, PHA and PLGA, no published data on ecotoxicity was found and therefore no hazard assessment could be conducted. In soils, HAP was found to have the lowest PNEC with 0.3 mg/kg, followed by PAN and chitosan. In freshwater, chitosan was found to have the lowest PNEC with 5 µg/l, followed by nano-chitosan, HAP and PAN.ConclusionCompared with other common pollutants, even the most sensitive of the selected nanobiomaterials, chitosan, is less toxic than engineered nanomaterials such as nano-ZnO and nano-Ag, some common antibiotics, heavy metals or organic pollutants such as triclosan. Given the current knowledge, the nanobiomaterials covered in this work therefore pose only little or no environmental hazard.

Toward an ecotoxicological risk assessment of microplastics: Comparison of available hazard and exposure data in freshwaters.

Microplastics have been detected in freshwaters all over the world in almost all samples, and ecotoxicological studies have shown adverse effects of microplastics on organisms. However, no risk assessment of microplastics has been performed specifically in freshwater so far. The aim of the present study was therefore to review all exposure and ecotoxicity data available for microplastics in freshwaters and to perform a preliminary probabilistic risk assessment. The exposure probability distribution was based on 391 concentrations measured in Asia, Europe, and North America. Because exposure data are mainly available in particle number–based metrics but results from hazard studies are mostly mass‐based, the hazard results were converted into particle number concentrations. A statistical analysis of the hazard data showed that there was no significant influence of particle shape or type of polymer on the no‐observed‐effect concentration. The predicted‐no‐effect concentration (PNEC) was calculated as the fifth percentile of the probabilistic species sensitivity distribution, based on 53 values from 14 freshwater species, to have a mode of 7.4 × 105 particles · m−3 (25th and 75th quantiles of 6.1 × 105 and 1.3 × 106 particles · m−3, respectively). The exposure probability distribution was divided by the PNEC probability distribution to calculate risk characterization ratios (RCRs), with modes of 1.3 × 10−6 in North America, 3.3 × 10−6 in Europe, and 4.6 × 10−3 in Asia. Probability distributions associated with the RCRs showed that ecological risks cannot be entirely excluded in Asia, where 0.4% of the RCR values were above 1. Environ Toxicol Chem 2019;38:436–447. © 2018 SETAC

Environmental risk assessment of engineered nano-SiO2 , nano iron oxides, nano-CeO2 , nano-Al2 O3 , and quantum dots.

Many research studies have endeavored to investigate the ecotoxicological hazards of engineered nanomaterials (ENMs). However, little is known regarding the actual environmental risks of ENMs, combining both hazard and exposure data. The aim of the present study was to quantify the environmental risks for nano-Al2 O3 , nano-SiO2 , nano iron oxides, nano-CeO2 , and quantum dots by comparing the predicted environmental concentrations (PECs) with the predicted-no-effect concentrations (PNECs). The PEC values of these 5 ENMs in freshwaters in 2020 for northern Europe and southeastern Europe were taken from a published dynamic probabilistic material flow analysis model. The PNEC values were calculated using probabilistic species sensitivity distribution (SSD). The order of the PNEC values was quantum dots < nano-CeO2 < nano iron oxides < nano-Al2 O3 < nano-SiO2 . The risks posed by these 5 ENMs were demonstrated to be in the reverse order: nano-Al2 O3 > nano-SiO2 > nano iron oxides > nano-CeO2 > quantum dots. However, all risk characterization values are 4 to 8 orders of magnitude lower than 1, and no risk was therefore predicted for any of the investigated ENMs at the estimated release level in 2020. Compared to static models, the dynamic material flow model allowed us to use PEC values based on a more complex parameterization, considering a dynamic input over time and time-dependent release of ENMs. The probabilistic SSD approach makes it possible to include all available data to estimate hazards of ENMs by considering the whole range of variability between studies and material types. The risk-assessment approach is therefore able to handle the uncertainty and variability associated with the collected data. The results of the present study provide a scientific foundation for risk-based regulatory decisions of the investigated ENMs. Environ Toxicol Chem 2018;37:1387-1395. © 2018 SETAC.

Are engineered nano iron oxide particles safe? an environmental risk assessment by probabilistic exposure, effects and risk modeling

Nano iron oxide particles are beneficial to our daily lives through their use in paints, construction materials, biomedical imaging and other industrial fields. However, little is known about the possible risks associated with the current exposure level of engineered nano iron oxides (nano-FeOX) to organisms in the environment. The goal of this study was to predict the release of nano-FeOX to the environment and assess their risks for surface waters in the EU and Switzerland. The material flows of nano-FeOX to technical compartments (waste incineration and waste water treatment plants) and to the environment were calculated with a probabilistic modeling approach. The mean value of the predicted environmental concentrations (PECs) of nano-FeOX in surface waters in the EU for a worst-case scenario (no particle sedimentation) was estimated to be 28 ng/l. Using a probabilistic species sensitivity distribution, the predicted no-effect concentration (PNEC) was determined from ecotoxicological data. The risk characterization ratio, calculated by dividing the PEC by PNEC values, was used to characterize the risks. The mean risk characterization ratio was predicted to be several orders of magnitude smaller than 1 (1.4 × 10−4). Therefore, this modeling effort indicates that only a very limited risk is posed by the current release level of nano-FeOX to organisms in surface waters. However, a better understanding of the hazards of nano-FeOX to the organisms in other ecosystems (such as sediment) needs to be assessed to determine the overall risk of these particles to the environment.

Keystone indices probabilistic species sensitivity distribution in the case of the derivation of water quality criteria for copper in Tai Lake.

An alternative method for species sensitivity distribution (SSD) that considers the interaction between species and the community importance is illustrated in this article. First, a food web based on the relationship between predator and prey was constructed, and the keystone indices of species were obtained based on the food web. Then, the probability density distribution of the sensitivity for each species was derived from all of the available endpoints of each species. Finally, the species sensitivity distribution for ecosystem was constructed by sampling a specific number of values from the probability density distribution of the sensitivity for each species. Data of copper toxicity to the aquatic organisms in Tai Lake were selected to derived site-specific water quality criteria (WQC). Ninety-seven endpoints of acute toxicity for 47 species and 188 endpoints of chronic toxicity for 29 species were included, and the acute and chronic WQC developed by keystone indices probabilistic species sensitivity distribution (K-PSSD) were 4.982μg/L and 0.965μg/L, respectively. Results showed that the aquatic organisms of Tai Lake might be underprotected. Compared with the SSD, the K-PSSD coped with the interactions between species, the community importance, and the intraspecies and interspecies variation more effectively and was better at depicting the tendency and information of raw data. The K-PSSD was especially applicable to site-specific WQC and provided an alternative or supplement to the SSD.

Probabilistic modeling of the flows and environmental risks of nano-silica

Nano-silica, the engineered nanomaterial with one of the largest production volumes, has a wide range of applications in consumer products and industry. This study aimed to quantify the exposure of nano-silica to the environment and to assess its risk to surface waters. Concentrations were calculated for four environmental (air, soil, surface water, sediments) and two technical compartments (wastewater, solid waste) for the EU and Switzerland using probabilistic material flow modeling. The corresponding median concentration in surface water is predicted to be 0.12μg/l in the EU (0.053–3.3μg/l, 15/85% quantiles). The concentrations in sediments in the complete sedimentation scenario were found to be the largest among all environmental compartments, with a median annual increase of 0.43mg/kg·y in the EU (0.19–12mg/kg·y, 15/85% quantiles). Moreover, probabilistic species sensitivity distributions (PSSD) were computed and the risk of nano-silica in surface waters was quantified by comparing the predicted environmental concentration (PEC) with the predicted no-effect concentration (PNEC) distribution, which was derived from the cumulative PSSD. This assessment suggests that nano-silica currently poses no risk to aquatic organisms in surface waters. Further investigations are needed to assess the risk of nano-silica in other environmental compartments, which is currently not possible due to a lack of ecotoxicological data.

Probabilistic environmental risk assessment of five nanomaterials (nano-TiO2, nano-Ag, nano-ZnO, CNT, and fullerenes)

The environmental risks of five engineered nanomaterials (nano-TiO2, nano-Ag, nano-ZnO, CNT, and fullerenes) were quantified in water, soils, and sediments using probabilistic Species Sensitivity Distributions (pSSDs) and probabilistic predicted environmental concentrations (PECs). For water and soil, enough ecotoxicological endpoints were found for a full risk characterization (between 17 and 73 data points per nanomaterial for water and between 4 and 20 for soil) whereas for sediments, the data availability was not sufficient. Predicted No Effect Concentrations (PNECs) were obtained from the pSSD and used to calculate risk characterization ratios (PEC/PNEC). For most materials and environmental compartments, exposure and effect concentrations were separated by several orders of magnitude. Nano-ZnO in freshwaters and nano-TiO2 in soils were the combinations where the risk characterization ratio was closest to one, meaning that these are compartment/ENM combinations to be studied in more depth with the highest priority. The probabilistic risk quantification allows us to consider the large variability of observed effects in different ecotoxicological studies and the uncertainty in modeled exposure concentrations. The risk characterization results presented in this work allows for a more focused investigation of environmental risks of nanomaterials by consideration of material/compartment combinations where the highest probability for effects with predicted environmental concentrations is likely.