Water quality criteria (WQC) play an important role in the environmental management of pollutants in different countries or institutions. It has been found that endocrine disrupting chemicals (EDCs) can potentially alter functions of the endocrine system and consequently cause adverse effects in aquatic organisms. Therefore, the complicated modes of action and mechanisms of EDCs should be carefully considered in WQC studies. For example, the research regarding the WQC derivation of EDCs should prioritize sensitive aquatic species in consideration of the WQC derivation. Second, the chronic toxicity of EDCs should be of utmost concern. In addition, the appropriate effects and endpoints of EDCs should be carefully selected for the WQC derivation. Moreover, it was pointed out that some new methods should be taken into consideration in the WQC studies of EDCs in the near future.
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Article Synopsis
- The study focuses on the effects of copper on water quality criteria (WQC) and explores the limitations of using regression models to predict copper toxicity due to species-specific factors.
- It introduces a backpropagation neural network (BPNN) model optimized with a genetic algorithm, which performs better than traditional mixed linear regression (MNLR) models in predicting toxicity values.
- Results indicate the BPNN model not only enhances predictive accuracy when considering species-specific data but also provides WQC ranges that align with global guidelines, offering a solid foundation for future research in water quality assessment.*
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Water quality criteria (WQC) serve as a scientific foundation for pollutant risk assessment and control in aquatic ecosystems. The development of regionally differentiated WQC tailored to specific regional characteristics has become an emerging trend. However, the current WQC is constrained by a lack of regional species toxicity data.
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