Comprehensive investigation of Lindane removal from aqueous solution using environmentally friendly seagrass biomass: life cycle assessment, adsorption thermodynamics, kinetics, and reusability

Adeola Akeem Akinpelu, Mazen K. Nazal*, Nabeel Abuzaid, Tajudeen A. Oyehan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Removal of Lindane pesticide from the environment is of utmost requirement. Besides, production of adsorbents to remove Lindane releases other pollutants and greenhouse gases into the environment. Therefore, in this study, the application of environmentally friendly low-cost, and readily available Halodule uninervis seagrass powder (SG) as an adsorbent is reported for the removal of Lindane from contaminated water. The environmental footprint of SG production was investigated using life cycle inventory (LCI). To investigate the adsorption parameters and reduce the wastage associated with the one variable at a time (OVAT) conventional investigation approach, response surface method (RSM) and artificial intelligence (AI) approaches were engaged. However, to investigate the isotherm and adsorption kinetics, OVAT parameter optimization was conducted. It is found that 16 h and 24 °C were the optimum time and temperature, respectively, and pH does not affect Lindane removal. Pseudo-second-order kinetics and Freundlich isotherm models were found the best to describe Lindane adsorption onto SG which was seen to be exothermic (− 29.17 kJ mol−1). The spent SG was found to be reusable in the synthetic wastewater with a value of 74% removal efficiency. XPS characterization results indicated that Lindane is physically adsorbed onto SG. LCI data showed that SG adsorbent has promising performance with very low negative environmental impacts. Graphical Abstract: [Figure not available: see fulltext.].

Original languageEnglish
JournalBiomass Conversion and Biorefinery
DOIs
StateAccepted/In press - 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords

  • Artificial intelligence
  • Life cycle assessment
  • Life cycle inventory
  • Random forest
  • Response surface method
  • Seagrass

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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