The present paper reports the heavy metal uptake by water lettuce (Pistia stratiotes L.) from paper mill effluent (PME) with its prediction modeling studies. Lab scale phytoremediation experiments were performed in glass aquariums to grow P. stratiotes in 0% (bore well water as a control), 25%, 50%, 75%, and 100% concentrations of PME. The influence of pH and heavy metal concentration in PME for the effective uptake and accumulation of heavy metal contents (∆Y: mg/kg) in plant tissues was modeled using two-factor multiple linear regression. The results showed that the selected input variables were supportive to develop prediction models with higher linear regression (R2 > 0.72), high model efficiency (ME: 0.92-0.99), low mean average normalizing error (MANE < 0.02), and statistically significant F > Prob values. Kruskal-Wallis one-way post hoc test indicated that the contents of Cd, Cu, Fe, Pb, and Zn in the roots, leaves, and whole plant were affected by PME concentration while the contents of Mn did not. The correlation studies showed that the bioaccumulation of heavy metals was found both element and PME concentration specific. This work represents an effective method to model heavy metal uptake by P. stratiotes from PME. Furthermore, this methodology can also be adopted for predicting effective metal uptake by plant species being used for the phytoremediation of heavy metals from industrial effluents.
Keywords: Bioaccumulation; Heavy metals; Paper mill effluent; Phytoremediation; Pistia stratiotes; Prediction models.