Authors
Leonard Ejimofor Okonkwo, Hannah Ndidiamaka Okorie, Linda Nkechinyere Umegbo, Gerald Walter Ugodi
Published in
Frontiers in chemistry. Volume 14. Pages 1889310. Epub Jul 27, 2026.
Abstract
Heavy-metal contamination of water and wastewater is a persistent environmental challenge because metallic pollutants are non-biodegradable, bioaccumulate through food chains, and pose serious risks to ecosystem and human health even at low concentrations. Conventional treatment technologies are often limited by high cost, secondary waste generation, and operational complexity, creating demand for low-cost, locally available alternatives. In this study, unmodified coconut husk was evaluated as a low-cost biosorbent for the removal of Pb(II), Cu(II), and Zn(II) from single-metal aqueous solutions.
The biosorbent was characterized by Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Batch adsorption experiments were conducted to investigate the effects of initial metal concentration (300-1,500 mg/L), adsorbent dose (0.2-1.0 g), solution pH (2.5-6.5), and contact time (5-120 min) on metal removal. Percentage removal increased with rising pH, adsorbent dose, and contact time, while it declined with increasing initial metal concentration.
Pb(II) consistently exhibited the highest adsorption affinity. Equilibrium data were better described by the Langmuir isotherm (R2 = 0.9808-0.9987) than by the Freundlich model, indicating predominantly monolayer adsorption; all Langmuir separation factor (R_L) values fell within the favourable range (0 < R_L < 1). Adsorption kinetics conformed to the pseudo-second-order model (R2 = 0.9694-0.9989) for all three metals, consistent with a chemisorption rate-limiting mechanism.
These results demonstrate that unmodified coconut husk is a promising, environmentally friendly, and economically accessible biosorbent for heavymetal removal and may have practical relevance for low-cost wastewater treatment in resource-limited settings.
PMID:
42577293
Bibliographic data and abstract were imported from PubMed on 11 Aug 2026.
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