Estimating the temporal and spatial distribution and threats of bisphenol A in temperate lakes using machine learning models

• Published in: Ecotoxicology and environmental safety Vol. 269; p. 115750
• Main Authors: Wang, Yilin, Zhang, Xiaotian, Guo, Fen, Li, Aopu, Fan, Juntao
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier 01.01.2024
• Subjects: Benzhydryl Compounds; BPA; China; Ecosystem; Environmental Monitoring - methods; Lakes; Phenols; Random forest; Risk assessment; Taihu Lake; Water; Water Pollutants, Chemical - analysis; China; BPA; Random forest; Risk assessment; Taihu Lake
• ISSN: 0147-6513; 1090-2414
• DOI: 10.1016/j.ecoenv.2023.115750

Bisphenol A (BPA) is easily enriched in many human-disturbed watersheds, particularly lakes with poor water mobility, which is posing a threat to aquatic biota. While previous studies have focused on the concentration of BPA in water and its toxicity to aquatic organisms, a small amount of measured data is not enough to reveal the temporal and spatial distribution and threats of BPA, and estimate the ecological risk in watersheds. Therefore, we collected 164 measured BPA data points from Taihu Lake to develop machine learning models using random forest (RF), support vector machine (SVM) and least square regression (LSR) and created month-by-month watershed prediction maps in temperate lakes to estimate the spatiotemporal distribution and threats of BPA. Due to RF's superior robustness to noisy data, the RF model exhibits the best performance among the three algorithms. The RF model showed acceptable predictive performance on the modeling dataset (coefficients of determination and root-mean-square error for the training set were 0.927 and 17.499, respectively, and 0.607, 39.645 for the validation set, respectively). The maps indicated that areas susceptible to anthropogenic activities were more severely polluted by BPA, and rainy climate may favor the migration of BPA to aquatic ecosystems. The model was also applied to predict 42 data points of BPA collected from Dianchi Lake, and the results showed that most predicted data were within a factor of 10 of the measured data, but the prediction accuracy of the model has declined. The ecological risks in the two lakes were evaluated and attention should be paid to the regions with higher risks. Our study provided a novel idea for comprehensive monitoring of an unconventional trace pollutant with endocrine disrupting effects in aquatic ecosystems and analyzing their spatiotemporal distribution, which will contribute to the scientific assessment of the ecological risk of BPA.