Machine learning (ML) techniques are becoming increasingly valuable for modeling the transport of pollutants in plant systems. However, two challenges (small sample sizes and a lack of quantitative calculation functions) remain when using ML to predict migration in hydroponic systems. For the bioaccumulation of per- and polyfluoroalkyl substances, we studied the key factors and quantitative calculation equations based on data augmentation, ML, and symbolic regression. First, feature expansion was performed on the input data after data preprocessing; the most important step was data augmentation. The original training set was expanded nine times by combining the synthetic minority oversampling technique and a variational autoencoder. Subsequently, the four ML models were applied to the test set to predict the selected output parameters. Categorical boosting (CatBoost) had the highest prediction accuracy (R² = 0.83). The Shapley Additive Explanation values indicated that molecular weight and exposure time were the most important parameters. We applied three symbolic regression models to obtain accurate prediction equations based on the original and augmented data. Based on augmented data, the high-dimensional sparse interaction equation exhibited the highest accuracy (R² = 0.776). Our results indicate that this method could provide crucial insights into absorption and accumulation in plant roots.

In this study, we aimed to investigate the effects of coagulants and dissolved organic matter (DOM) on the biological toxicity of nano-zinc oxide (nZnO) to key microorganisms involved in biological phosphorus removal during sewage treatment. Polyaluminum chloride and polyferric chloride were selected as coagulants, whereas fulvic acid, glucose, and aspartic acid represented the DOM. The mechanisms through which these chemicals influence nZnO toxicity were also investigated. The results show that polyaluminum chloride and polyferric chloride effectively reduced nZnO toxicity in phosphorus-accumulating organisms, demonstrating their detoxification effects. Similarly, fulvic acid and glucose mitigated nZnO toxicity, whereas aspartic acid displayed dual effects: detoxification at low concentrations and enhanced toxicity at high concentrations. These findings highlight the dual role of sewage treatment additives in enhancing traditional pollutant removal and mitigating the nanoparticle-induced inhibition of microbial biochemical processes. This study clarified the interactions between coagulant chemicals, DOM, and nanoparticles in sewage treatment, offering insights into the regulatory mechanisms that improve treatment efficacy and reduce ecological risks.