Setting water quality criteria in China: approaches for developing species sensitivity distributions for metals and metalloids

Ecological risk assessment of aquatic organisms induced by heavy metals in the estuarine waters of the Pearl River

With the rapid economic development of China's coastal areas and the growth of industry and population, the problem of heavy metal contamination in estuarine waters is increasing in sensitivity and seriousness. In order to accurately and quantitatively describe the current status of heavy metal contamination and identify sensitive aquatic organisms with high ecological risks, five heavy metals in eight estuaries of the Pearl River were monitored at monthly intervals from January to December in 2020, and the ecological risks of aquatic organisms induced by heavy metals were evaluated using Risk quotients (RQ) and species sensitivity distributions (SSD) methods. The results showed that the concentrations of As, Cu, Pb, Hg and Zn in estuaries of the Pearl River were (0.65-9.25) μg/L, (0.07-11.57) μg/L, (0.05-9.09) μg/L, (< 0.40) μg/L and (0.67-86.12) μg/L, respectively. With the exception of Hg in Jiaomen water, the other heavy metals in each sampling site met or exceed the water quality standard of Grade II. The aquatic ecological risks of As, Pb and Hg were generally low in the waters of the Pearl River estuary, but individual aquatic organisms are subject to elevated ecological risks due to Cu and Zn. The content of Zn has a lethal effect on the crustaceans Temora Stylifera, and the content of Cu has a serious impact on the mollusks Corbicula Fluminea and has a certain impact on the crustaceans Corophium sp. and the fish Sparus aurata. Heavy metal levels and joint ecological risks (msPAF) in the Humen, Jiaomen, Hongqimen, and Hengmen estuaries were slightly higher than in other estuaries, and the Yamen estuary had the lowest contration of heavy metals and ecological risk. Research findings can serve as a basis for formulating water quality standards for heavy metals and for protecting aquatic biodiversity in the Pearl River Estuary.

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

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

Same sensitivity with shorter exposure: behavior as an appropriate parameter to assess metal toxicity

The exposure of animals to toxicants may cause a depletion in the energy uptake, which compromises reproduction and growth. Although both parameters are ecologically relevant, they usually need long-term bioassays. This is a handicap for the availability of toxicological data for environmental risk assessment. Short-term bioassays conducted with environmental concentrations, and using relevant ecological parameters sensitive to short-term exposures, such as behavior, could be a good alternative. Therefore, to include this parameter in the risk assessment procedures, it is relevant the comparison of its sensitivity with that of growth and reproduction bioassays. The study aim was the assessment of differences between endpoints based on mortality, behaviour, reproduction, and growth for the toxicity of metals on aquatic animals. We used the ECOTOX database to gather data to construct chemical toxicity distribution (CTD) curves. The mean concentrations, the mean exposure time, and the ratio between the mean concentration and the exposure time were compared among endpoints. Our results showed that behavioral, growth, and reproduction bioassays presented similar sensitivity. The shortest exposure was found in behavioral and reproduction bioassays. In general, the amount of toxicant used per time was lower in growth and reproduction bioassays than in behavioral and mortality bioassays. We can conclude that, for metal toxicity, behavioral bioassays are less time-consuming than growth bioassays. As the sensitivity of behavior was similar to that of growth and reproduction, this endpoint could be a better alternative to longer bioassays.

Linking environmental with biological data: Low sampling frequencies of chemical pollutants and nutrients in rivers reduce the reliability of model results

Linking environmental and biological data using ecological models can provide crucial knowledge about the effects of water quality parameters on freshwater ecosystems. However, a model can only be as reliable as its input data. Here, the influence of sampling frequency of temporal variable environmental input data on the reliability of model results when linked to biological data was investigated using Threshold Indicator Taxa Analysis (TITAN) and species sensitivity distributions (SSDs). Large-scale biological data from benthic macroinvertebrates and matching water quality data including four metals and four nutrients of up to 559 site-year combinations formed the initial data sets. To compare different sampling frequencies, the initial water quality data sets (n = 12 samples per year, set as reference) were subsampled (n = 10, 8, 6, 4, 2 and 1), annual mean values calculated and used as input data in the models. As expected, subsampling significantly reduced the reliability of the environmental input data across all eight substances. For TITAN, the use of environmental input data with a reduced reliability led to a considerable (1) loss of information because valid taxa were no longer identified, (2) gain of unreliable taxon-specific change points due to false positive taxa, and (3) bias in the change point estimation. In contrast, the reliability of the SSD results appeared to be much less reduced. However, closer examination of the SSD input data indicated that existing effects were masked by poor model performance. The results confirm that the sampling frequency of water quality data significantly influences the reliability of model results when linked with biological data. For studies limited to low sampling frequencies, the discussion provides recommendations on how to deal with low sampling frequencies of temporally variable water quality data when using them in TITAN, in SSDs, and in other ecological models.