Dual-metal phytoremediation of cadmium and nickel by wetland macrophytes: Comparative evaluation, metal uptake dynamics, and process optimization.

Abstract

Escalating heavy metal contamination of aquatic environments presents a significant threat to ecosystem integrity and public health, yet existing treatment technologies remain economically and technically inadequate for the efficient removal of low-concentration metal pollutants from wastewater. This study evaluated the phytoremediation potential of two macrophytes, Hymenachne amplexicaulis (Olive Hymenachne) and Pogonatherum paniceum (Bamboo Grass), for the removal of cadmium (Cd) and nickel (Ni) from synthetic wastewater under hydroponic conditions. Experiments were conducted over 21 days under single and combined metal exposures at low Cd concentrations of 0.5-1.0 mg/L and Ni concentrations of 2.0-5.0 mg/L. Physicochemical parameters were monitored to assess rhizosphere interactions, while phytoremediation performance was evaluated using removal efficiency and phytoextraction indices. Response Surface Methodology (RSM) was applied for system optimization. Results showed that planted systems significantly outperformed unplanted controls, which achieved less than 18% metal removal. H. amplexicaulis recorded the highest Cd removal efficiency (93%) at 0.5 mg/L and demonstrated strong bioaccumulation capacity, with bioaccumulation factor (BAF) values up to 89.63. However, it exhibited greater biomass reduction under combined metal stress, indicating greater sensitivity to co-contamination under the tested conditions. In contrast, P. paniceum achieved maximum Ni removal of 60.8% at 2.0 mg/L and maintained greater biomass stability under combined exposure, with BAF values ranging from 1.62 to 35.71. Translocation factor analysis indicated stronger root retention in H. amplexicaulis and more balanced internal metal distribution in P. paniceum. RSM identified exposure time as the most influential factor affecting metal removal efficiency, and the developed models demonstrated strong predictive performance for process optimization.