Optimization of Phytoremediation of Nickel by Alocasia puber Using Response Surface Methodology

Abstract

The contamination of water by heavy metals is a worldwide environmental problem. Phytoremediation and constructed wetlands have become increasingly popular as more sustainable and environmentally friendly techniques of removing heavy metals from the wastewater. This study, therefore, investigated the phytoremediation of nickel by Alocasia puber (A. puber) in a constructed wetlands (CW) microcosm. This study identified the optimum conditions for nickel (Ni) removal from wastewater using response surface methodology (RSM) with central composite design (CCD). Two operational variables were assessed: exposure time and initial Ni concentration. The optimum conditions for the maximum removal of Ni from water were an exposure time of 10 days and 99.76 mg/L initial Ni concentration. The results indicated that 95.6% removal was achieved under the optimized conditions, with a high correlation coefficient (R2 = 0.97) between the statistical model and the experimental data. Field emission scanning electron microscopy images showed anatomical changes in the A. puber samples due to Ni exposure, and transmission electron microscopy images revealed some internal damages in the A. puber, but visual Ni toxicity symptoms, such as necrosis and chlorosis, were not observed in the A. puber. This study demonstrated that A. puber planted in a constructed wetland microcosm was able to remediate wastewater contaminated with Ni.

A unified framework of response surface methodology and coalescing of Firefly with random forest algorithm for enhancing nano-phytoremediation efficiency of chromium via in vitro regenerated aquatic macrophyte coontail (Ceratophyllum demersum L.)

“…The output parameters attained through experiments can be analyzed by RSM in different ways ranging from the validity of the model to optimizing input factors with a given target (Kumar et al 2018 ). The use of RSM for optimizing the phytoremediation potential of different plants has been documented in recent years for different types of pollutants (Kasman et al 2019 ; Kumar et al 2018 ; Li et al 2021 ; Mohamad Thani et al 2020 ). In the present study, three different input variables were successfully used for optimizing the phytoremediation potential of in vitro-induced C. demersum plantlets against Cr from an aqueous medium.…”

“…Results demonstrated the supremacy of RSM for optimizing input variables and employed for other aquatic plants like Mexican Sword-plant (Kasman et al 2019 ), water hyacinth (Kumar et al 2018 ), Alocasia puber (Hassk.) Schott (Mohamad Thani et al 2020 ), and marine alga ( Gelidium amansii J.V. Lamour) (El-Naggar et al 2018 ) .…”

“…The chance of a larger “Model F-Value” is very rare (0.01%), and generally occurs due to noise. The value of “Prob > F” less than 0.0500 illustrates the significant model and values greater than 0.1000 reflect the not significant model (Kumar et al 2018 ; Mohamad Thani et al 2020 ). The analysis of response surface regression revealed satisfactory R 2 scores for all output parameters.…”

“…However, relatively low R 2 predicted scores were documented (Kasman et al 2019 ), which resulted in variable predicted values for all output parameters. It is noticeable from the results that the performance of the model can be analyzed by checking the difference between actual and predicted R 2 scores of individual output parameters (Mohamad Thani et al 2020 ). Results revealed statistically significant models for all output parameters, which illustrated the precise impact of input variables on all phytoremediation parameters (Kumar et al 2018 ).…”

“…The fitting of experimental data to a polynomial model at the second level is the main disadvantage of RSM (Aydar 2018 ). The use of RSM for phytoremediation studies has been investigated in recent years using different aquatic plants against heavy metals (Ferreira et al 2023 ; Kasman et al 2019 ; Kumar et al 2018 ; Mohamad Thani et al 2020 ).…”