Acute Toxicity Assessment and Prediction Models of Four Heavy Metals.

Water quality criteria (WQC) are developed to protect aquatic organisms. Toxicity data of local fish are essential to improve the applicability of WQC derivatives. However, the paucity of local cold-water fish toxicity data limits the development of WQC in China. Brachymystax lenok is a representative Chinese-endemic cold-water fish, which plays an important role in the characterization of metal toxicity in the water environment. Whereas, the ecotoxicological effects of copper, zinc, lead and cadmium, as well as its potential as a test species for the metal WQC, remain to be investigated. In our study, acute toxicity tests of copper, zinc, lead and cadmium were performed on this fish according to the OECD method and 96 h-LC50 values were calculated. The results showed that the 96 h-LC50 values of Cu2+, Zn2+, Pb2+ and Cd2+ for B. lenok were 134, 222, 514 and 734 µg/L, respectively. Toxicity data for freshwater species and Chinese-native species were collected and screened, and the mean acute values of each metal for each species were ranked. The results showed that the accumulation probability of zinc by B. lenok was the lowest and less than 15%. Thus, B. lenok was sensitive to Zn and can be considered as the test cold-water fish for derivation of Zn WQC. In addition, B. lenok in comparison with warm-water fish, we found that cold-water fish are not always more sensitive to heavy metals than warm-water fish. Finally, the models for toxic effects prediction of different heavy metals on the same species were constructed and evaluated the reliability of the model. We suggest that the alternative toxicity data provided by the simulations can be used to derive WQC for metals.

Health risk assessment and development of human health ambient water quality criteria for PBDEs in China.

Polybrominated diphenyl ethers (PBDEs) are not only a class of highly efficient brominated flame retardants (BFRs) but also a class of typical persistent organic pollutants (POPs) that are persistent and widely distributed in various environmental media. This study examined the concentrations of PBDEs in five environmental media (water, soil, air, dust, and food) and two human body media (human milk and blood) in China from 2010 to 2020. In addition, this study conducted multi-pathway exposure health risk assessments of populations of different ages in urban, rural, key regions, and industrial factories using the Monte-Carlo simulation. Finally, the human health ambient water quality criteria (AWQC) of eight PBDEs were derived using Chinese exposure parameters and bioaccumulation factors (BAFs). The results showed that the eastern and southeastern coastal regions of China were heavily polluted by PBDEs, and the variation trends of the ΣPBDEs concentrations in the different exposure media were not consistent. PBDEs did not pose a risk to urban and rural residents in ordinary regions, but the hazard indexes (HIs) for residents in key regions and occupational workers exceeded the safety threshold. Dust exposure was the primary exposure pathway for urban and rural residents in ordinary regions, but for residents in key regions and occupational workers, dietary exposure was the primary exposure pathway. BDE-209 was found to be the most serious individual PBDE congener in China. The following human health AWQC values of the PBDEs were derived: drinking water exposure: 0.233-65.2 µg·L-1; and drinking water and aquatic products exposure: 8.51 × 10-4-1.10 µg·L-1.

Prediction of chemical reproductive toxicity to aquatic species using a machine learning model: An application in an ecological risk assessment of the Yangtze River, China.

The endocrine disrupting chemicals (EDCs) have been at the forefront of environmental issues for over 20 years and are a principle factor considered in every ecological risk assessment, but this kind of risk assessment faces difficulties. The expense, time cost of in vivo tests, and lack of toxicity data are key limiting factors for the ability to conduct ecological risk assessments of EDCs to aquatic species. In this study, a machine learning model named the support vector machine (SVM) was used to predict the reproductive toxicity of EDCs, and the performance of the models was evaluated. The results showed that the SVM model provided more accurate toxicity prediction data compared with the interspecies correlation estimation (ICE) model developed by previous study to predict the reproductive toxicity. The application of the predicted toxicity data was an important supplement to the observed data for the ecological risk assessment of EDCs in the Yangtze River, where estrogens and phenolic compounds have been found at some sampling sites in the middle and lower reaches. The results showed that the ecological risk of estrone, 17ß-estradiol, and ethinyl estradiol were significant. This study revealed the application potential of machine learning models for the prediction of reproductive toxicity effects of EDCs. This can provide reliable alternative toxicity data for the ecological risk assessments of EDCs.

Environmental DNA: An Emerging Tool in Ecological Assessment.

Environmental DNA (eDNA), as a recent research hotspot in environmental science, the use of eDNA in biological monitoring has the advantages of sensitivity and time/labor saving. The eDNA technology combined with scientific advancement has been applied in investigations of target species (such as invasive species, endangered species and other rare species), biomass, and biodiversity. In addition, ecotoxicology studies and environmental pollution impact assessments based on the development of eDNA technology have gradually emerged in recent years. In this article, we summarizes the application of eDNA in ecological assessment, include species diversity assessment and chemical contamination impacts assessment, provide guiding questions for study design. We additionally discuss current challenges associated with eDNA. Finally, looking to the future, we discuss the opportunities of eDNA technology in environmental protein, environmental sample processor and ecogenomic sensors.

Derivation of aquatic life criteria for four phthalate esters and their ecological risk assessment in Liao River.

As a critical family of endocrine disruptors, phthalate esters (PAEs) attracted considerable attentions due to increasingly detected worldwide. Aquatic life criteria (ALC) for PAEs are crucial for their accurate ecological risk assessment (ERA) and have seldom been derived before. Given this concern, the purpose of the present study is to optimize the ALCs of four priority PAEs to estimate their ecological risks in Liao River. Reproductive endpoint was found to be more sensitive than other endpoints. Thus, reproduction related toxicity data were screened to derive ALCs applying species sensitivity distribution (SSD) method. ALCs of DEHP, DBP, BBP and DEP were calculated to be 0.04, 0.62, 4.71 and 41.9 µg L-1, which indicated decreased toxicity in sequence. Then, the derived ALCs of the four PAEs were applied to estimate their ecological risks in Liao River. A total of 27 sampling sites were selected to detect and analyze the exposure concentrations of PAEs. ERA using the hazard quotient (HQ) method was conducted. The results demonstrated that DEHP exhibited higher risks at 92.6% of sampling sites, and risks posed by DBP were moderate at 63.0% sampling sites. However, risks posed by BBP were low at 70.4% of sampling sites, and there were no risks posed by DEP at 96.3% of sampling sites. The results of probabilistic ecological risk assessment (PERA) indicated that probabilities of exceeding effects thresholds on 5% of species were 60.41%, 0%, 0.12%, 14.28% for DEHP, DEP, BBP and DBP, respectively. The work provides useful information to protect aquatic species in Liao River.

Priorization of River Restoration by Coupling Soil and Water Assessment Tool (SWAT) and Support Vector Machine (SVM) Models in the Taizi River Basin, Northern China.

Identifying priority zones for river restoration is important for biodiversity conservation and catchment management. However, limited data due to the difficulty of field collection has led to research to better understand the ecological status within a catchment and develop a targeted planning strategy for river restoration. To address this need, coupling hydrological and machine learning models were constructed to identify priority zones for river restoration based on a dataset of aquatic organisms (i.e., algae, macroinvertebrates, and fish) and physicochemical indicators that were collected from 130 sites in September 2014 in the Taizi River, northern China. A process-based model soil and water assessment tool (SWAT) was developed to model the temporal-spatial variations in environmental indicators. A support vector machine (SVM) model was applied to explore the relationships between aquatic organisms and environmental indicators. Biological indices among different hydrological periods were simulated by coupling SWAT and SVM models. Results indicated that aquatic biological indices and physicochemical indicators exhibited apparent temporal and spatial patterns, and those patterns were more evident in the upper reaches compared to the lower reaches. The ecological status of the Taizi River was better in the flood season than that in the dry season. Priority zones were identified for different hydrological seasons by setting the target values for ecological restoration based on biota organisms, and the results suggest that hydrological conditions significantly influenced restoration prioritization over other environmental parameters. Our approach could be applied in other seasonal river ecosystems to provide important preferences for river restoration.

Ecological status classification of the Taizi River Basin, China: a comparison of integrated risk assessment approaches.

Integrated risk assessment approaches allow to achieve a sound evaluation of ecological status of river basins and to gain knowledge about the likely causes of impairment, useful for informing and supporting the decision-making process. In this paper, the integrated risk assessment (IRA) methodology developed in the EU MODELKEY project (and implemented in the MODELKEY Decision Support System) is applied to the Taizi River (China), in order to assess its Ecological and Chemical Status according to EU Water Framework Directive (WFD) requirements. The available dataset is derived by an extensive survey carried out in 2009 and 2010 across the Taizi River catchment, including the monitoring of physico-chemical (i.e. DO, EC, NH3-_N, chemical oxygen demand (COD), biological oxygen demand in 5 days (BOD5) and TP), chemical (i.e. polycyclic aromatic hydrocarbons (PAHs) and metals), biological (i.e. macroinvertebrates, fish, and algae), and hydromorphological parameters (i.e. water quantity, channel change and morphology diversity). The results show a negative trend in the ecological status from the highland to the lowland of the Taizi River Basin. Organic pollution from agriculture and domestic sources (i.e. COD and BOD5), unstable hydrological regime (i.e. water quantity shortage) and chemical pollutants from industry (i.e. PAHs and metals) are found to be the main stressors impacting the ecological status of the Taizi River Basin. The comparison between the results of the IRA methodology and those of a previous study (Leigh et al. 2012) indicates that the selection of indicators and integrating methodologies can have a relevant impact on the classification of the ecological status. The IRA methodology, which integrates information from five lines of evidence (i.e., biology, physico-chemistry, chemistry, ecotoxicology and hydromorphology) required by WFD, allows to better identify the biological communities that are potentially at risk and the stressors that are most likely responsible for the observed alterations. This knowledge can be beneficial for a more effective restoration and management of the river basin ecosystem.