Kinetics and prediction modeling of heavy metal phytoremediation from glass industry effluent by water hyacinth (Eichhornia crassipes)

Abstract

This study aimed to explore the effectiveness of water hyacinth (Eichhornia crassipes) for heavy metals reduction from highly toxic glass industry effluent. Based on a 40 days laboratory-scale experiment, a total of five diluted concentrations of glass industry effluent were selected to cultivate water hyacinth. Furthermore, the kinetics and prediction models were used to analyze the metals reduction data to understand the plant behavior during the phytoextraction of heavy metals. Results suggested that water hyacinth was capable to reduce Cd (91.30%), Cu (93.55%), Fe (92.81%), Mn (93.45%), Pb (89.66%), and Zn (94.44%) metals most efficiently in 25% glass industry effluent concentration, respectively. Moreover, the growth performance of the water hyacinth plant exhibited the maximum fresh plant biomass (254.90 ± 3.75 g), chlorophyll (3.53 ± 0.11 mgg−1 fwt.), and relative growth rate (0.0026 gg−1d−1) using 25% dilution treatment respectively. Additionally, the first-order model gave the best fitting results in terms of the coefficient of determination (R2 > 0.82) and rate constant (k > 0.023 mgL−1d−1). Besides this, prediction modeling using multiple regression helped to construct mathematical equations to predict the heavy metals absorbed by water hyacinth plant tissues which were further supported by model efficiency (> 0.80) and normalized error tools (< 0.02). The results of this report are novel and have the utmost importance for the eco-friendly and sustainable treatment of glass industry effluent.