Artificial intelligence–based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L

• Published in: Environmental science and pollution research international 2023-03, Vol.30 (14), p.40206-40217
• Main Authors: Aasim, Muhammad, Ali, Seyid Amjad, Aydin, Senar, Bakhsh, Allah, Sogukpinar, Canan, Karatas, Mehmet, Khawar, Khalid Mahmood, Aydin, Mehmet Emin
• Format: Article
• Language: English
• Subjects: Aquatic ecosystems; Aquatic environment; Aquatic plants; Aquatic Pollution; Artificial Intelligence; Atmospheric Protection/Air Quality Control/Air Pollution; Bioaccumulation; bioaccumulation factor; Biodegradation, Environmental; Biological magnification; Cadmium; Ceratophyllum demersum; Earth and Environmental Science; Ecosystem; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Exposure; exposure duration; Floating plants; Heavy metals; human health; Humans; Macrophytes; Metals, Heavy - analysis; Multilayer perceptrons; neural networks; Optimization; Performance measurement; Phytoremediation; Plants; prediction; Regression analysis; Regression coefficients; Research Article; Waste Water Technology; Water; Water Management; Water Pollutants, Chemical - analysis; Water Pollution Control
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-022-25081-3

Water bodies or aquatic ecosystem are susceptible to heavy metal accumulation and can adversely affect the environment and human health especially in underdeveloped nations. Phytoremediation techniques of water bodies using aquatic plants or macrophytes are well established and are recognized as eco-friendly world over. Phytoremediation of heavy metals and other pollutants in aquatic environments can be achieved by using Ceratophyllum demersum L. — a well-known floating macrophyte. In vitro regenerated plants of C. demersum (7.5 g/L) were exposed to 24, 72, and 120 h to 0, 0.5, 1.0, 2.0, and 4.0 mg/L of cadmium (CdSO 4 ·8H 2 O) in water. Results revealed significantly different relationship in terms of Cd in water, Cd uptake by plants, bioconcentration factor (BCF), and Cd removal (%) from water. The study showed that Cd uptake by plants and BCF values increased significantly with exposure time. The highest BCF value (3776.50) was recorded for plant samples exposed to 2 mg/L Cd for 72 h. Application of all Cd concentrations and various exposure duration yielded Cd removal (%) between the ranges of 93.8 and 98.7%. These results were predicted through artificial intelligence–based models, namely, random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). The tested models predicted the results accurately, and the attained results were further validated via three different performance metrics. The optimal regression coefficient ( R 2 ) for the models was recorded as 0.7970 (Cd water, mg/L), 0.9661 (Cd plants, mg/kg), 0.9797 bioconcentration factor (BCF), and 0.9996 (Cd removal, %), respectively. These achieved results suggest that in vitro regenerated C. demersum can be efficaciously used for phytoremediation of Cd-contaminated aquatic environments. Likewise, the proposed modeling of phytoremediation studies can further be employed more comprehensively in future studies aimed at data prediction and optimization. Graphical Abstract