PUBLICATION
Using machine learning models to predict the dose-effect curve of municipal wastewater for zebrafish embryo toxicity
- Authors
- Zhu, M., Fang, Y., Jia, M., Chen, L., Zhang, L., Wu, B.
- ID
- ZDB-PUB-250204-6
- Date
- 2025
- Source
- Journal of hazardous materials 488: 137278137278 (Journal)
- Registered Authors
- Keywords
- Machine learning, Toxicity prediction, Wastewater, Zebrafish embryos
- MeSH Terms
-
- Dose-Response Relationship, Drug
- Zebrafish*/embryology
- Embryo, Nonmammalian*/drug effects
- Wastewater*/toxicity
- Water Pollutants, Chemical*/toxicity
- Machine Learning*
- Animals
- PubMed
- 39899932 Full text @ J. Hazard. Mater.
Citation
Zhu, M., Fang, Y., Jia, M., Chen, L., Zhang, L., Wu, B. (2025) Using machine learning models to predict the dose-effect curve of municipal wastewater for zebrafish embryo toxicity. Journal of hazardous materials. 488:137278137278.
Abstract
Municipal wastewater substantially contributes to aquatic ecological risks. Assessing the toxicity of municipal wastewater through dose-effect curves is challenging owing to the time-consuming, labor-intensive, and costly nature of biological assays. This study developed machine learning models to predict wastewater dose-effect curves for zebrafish embryos. The influent and effluent samples from 176 wastewater treatment plants in China were analyzed to collect water quality data, including information on seven chemical parameters and the toxic effects on zebrafish embryos at eight relative enrichment factors (REFs) of wastewater. Using Spearman's rank correlation coefficient and the max-relevance and min-redundancy algorithm, the parameters of ammonium nitrogen content and toxic effect values at REFs of 2 and 25 (REF2 and REF25), were identified as crucial input features from 15 variables. Decision tree, random forest, and gradient-boosted decision tree (GBDT) models were developed. Among these, GBDT exhibited the best performance, with an average R2 value of 0.91 and an average mean absolute percentage error (MAPE) of 27.91 %. Integrating the dose-effect curve pattern into the machine learning model considerably optimized the GBDT model, reaching a minimum MAPE of 14.74 %. The developed model can accurately determine the dose-effect curves of actual wastewater, reducing at least 75 % of the experimental workload. These findings provide a valuable tool for assessing zebrafish embryo toxicity in municipal wastewater management. This study indicates that combining environmental expertise and machine learning models allows for a scientific assessment of the potential toxic risks in wastewater, providing new perspectives and approaches for environmental policy development.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping