Improving the practicality and reliability of ecological risk assessment of emerging contaminants : development of an integrated framework

Abstract

Driven by the safety concerns of emerging contaminants (ECs) in the aquatic environment, there is an urgent need to conduct ecological risk assessments (ERA) for ECs to support decision-making. However, in practice, applying traditional ERA methodologies to ECs faces major challenges, including the lack of ecotoxicity data, large uncertainty, and exclusion of key endpoints. In this study, an integrated framework was developed to improve the practicality and reliability of ERA for ECs. The up-to-date scientific techniques and findings about the toxicity data prediction, data extrapolation, and predicted no effect concentration (PNEC) derivation were introduced into the current ERA framework. The ecotoxicity data prediction and extrapolation approaches, i.e., quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD), were combined, leveraging their corresponding advantages, as an alternative method to derive short-term PNECs for ECs even without acute toxicity data. Further, to improve the prediction ability and mechanism explanation, the mode of action (MoA)-based QSAR-ICE-SSD models were developed. The models were successfully applied in case studies with their satisfactory predictive ability (most of the bias < 1 order of magnitude) and robustness (statistical uncertainty < 2 orders of magnitude). Moreover, the ICE models were used to augment the robustness and reliability of SSD models, making it possible to derive long-term PNECs for potential endocrine-disrupting chemicals (EDCs) with limited chronic ecotoxicity data. Nonylphenol and tetrabromobisphenol A were selected for the case studies and the reproductive PNECs were derived as 0.187 μg/L and 0.0878 μg/L, respectively. The tiered ERA indicates a high level of long-term ecological risks in the surface waters of China, and using acute PNECs may result in an underestimation of such ecological risks. The species sensitivity weighted distribution (SSWD) models based on the adverse outcome pathway (AOP) networks were constructed and validated, considering the inter/intraspecies variations and integrating nontraditional endpoints of the endocrine-disrupting effects, to improve the ecological relevance and reduce uncertainty in PNEC derivation for typical EDCs. Perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) were selected for the case studies and their PNECs were derived as 18.7 μg/L and 2.52 μg/L, respectively. The current antibiotic ERA ignored an important endpoint, i.e., the development of antibiotic resistance. The ecotoxicity data and minimum inhibitory concentrations of the antibiotics were screened to derive PNECs for the ecological and antibiotic resistance development risks of 36 antibiotics, which ranged from 0.00175–2351 μg/L and 0.037–50 μg/L, respectively. It is apparent that the antibiotic ecological and resistance development risks are geographically widespread in the surface waters of China and β-lactams (penicillin and amoxicillin) presents the highest risk levels. The developed integrated ERA framework provides practical and reliable methods for the ecotoxicity data prediction, data extrapolation, and PNEC derivation for ECs in the aquatic environment according to their toxic effects and the richness of the toxicity data. This work will make significant contributions to the much needed development of ERA of ECs for the long-term protection of environmental quality and ecological safety.