Adsorption of Lead, manganese, and copper onto biochar in landfill leachate: implication of non-linear regression analysis

Abstract The feasibility of using wood-derived biochar (BC) to remove Pb, Mn, and Cu from landfill leachate was investigated and modeled in this study. BC was produced under the pyrolytic temperature of 740 °C. The effect of contact time, BC dosage and particle size on adsorption of the heavy metals...

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Main Authors: Ali Daryabeigi Zand, Maryam Rabiee Abyaneh
Format: Article
Language:English
Published: BMC 2020-09-01
Series:Sustainable Environment Research
Subjects:
Online Access:http://link.springer.com/article/10.1186/s42834-020-00061-9
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spelling doaj-571aff652d4044f6bc880b70d45379182020-11-25T03:35:50ZengBMCSustainable Environment Research2468-20392020-09-0130111610.1186/s42834-020-00061-9Adsorption of Lead, manganese, and copper onto biochar in landfill leachate: implication of non-linear regression analysisAli Daryabeigi Zand0Maryam Rabiee Abyaneh1School of Environment, College of Engineering, University of TehranSchool of Environment, College of Engineering, University of TehranAbstract The feasibility of using wood-derived biochar (BC) to remove Pb, Mn, and Cu from landfill leachate was investigated and modeled in this study. BC was produced under the pyrolytic temperature of 740 °C. The effect of contact time, BC dosage and particle size on adsorption of the heavy metals onto BC was examined. BC was used in two forms i.e., pulverized (PWB) and crushed (CWB) to evaluate the effect of BC particle size on adsorption characteristics. The kinetics of Pb, Mn, and Cu adsorption onto PWB and CWB were assessed using the pseudo second-order and Elovich models, where both applied models could well describe the adsorption kinetics. Removal efficiencies of the heavy metals were increases by 1.2, 1.4, and 1.6 times, respectively, for Pb, Mn, and Cu, when PWB content of the leachate increased from 0.5 to 5 g L− 1. Equilibrium adsorption capacity of the heavy metals onto BC in leachate system was evaluated using the Langmuir, non-linearized Freundlich, linearized Freundlich, and Temkin isotherms and found to have the following order for PWB: Non-linearized Freundlich > Temkin > Langmuir > Linearized Freundlich. The Langmuir and linearized Freundlich models could not adequately represent adsorption of the heavy metals onto BC, especially for CWB. The highest removal of 88% was obtained for Pb, while the greatest adsorption intensity was found to be 1.58 mg g− 1 for Mn. Using the non-linearized Freundlich isotherm significantly reduced adsorption prediction error. The adsorption affinity of PWB for Pb, Mn, and Cu was greater than that of CWB in all treatments. Wood-derived BC is suggested to be used for the removal of heavy metals from landfill leachate as an economical adsorbent.http://link.springer.com/article/10.1186/s42834-020-00061-9AdsorptionBiocharLandfill leachateHeavy metalsLinearization error
collection DOAJ
language English
format Article
sources DOAJ
author Ali Daryabeigi Zand
Maryam Rabiee Abyaneh
spellingShingle Ali Daryabeigi Zand
Maryam Rabiee Abyaneh
Adsorption of Lead, manganese, and copper onto biochar in landfill leachate: implication of non-linear regression analysis
Sustainable Environment Research
Adsorption
Biochar
Landfill leachate
Heavy metals
Linearization error
author_facet Ali Daryabeigi Zand
Maryam Rabiee Abyaneh
author_sort Ali Daryabeigi Zand
title Adsorption of Lead, manganese, and copper onto biochar in landfill leachate: implication of non-linear regression analysis
title_short Adsorption of Lead, manganese, and copper onto biochar in landfill leachate: implication of non-linear regression analysis
title_full Adsorption of Lead, manganese, and copper onto biochar in landfill leachate: implication of non-linear regression analysis
title_fullStr Adsorption of Lead, manganese, and copper onto biochar in landfill leachate: implication of non-linear regression analysis
title_full_unstemmed Adsorption of Lead, manganese, and copper onto biochar in landfill leachate: implication of non-linear regression analysis
title_sort adsorption of lead, manganese, and copper onto biochar in landfill leachate: implication of non-linear regression analysis
publisher BMC
series Sustainable Environment Research
issn 2468-2039
publishDate 2020-09-01
description Abstract The feasibility of using wood-derived biochar (BC) to remove Pb, Mn, and Cu from landfill leachate was investigated and modeled in this study. BC was produced under the pyrolytic temperature of 740 °C. The effect of contact time, BC dosage and particle size on adsorption of the heavy metals onto BC was examined. BC was used in two forms i.e., pulverized (PWB) and crushed (CWB) to evaluate the effect of BC particle size on adsorption characteristics. The kinetics of Pb, Mn, and Cu adsorption onto PWB and CWB were assessed using the pseudo second-order and Elovich models, where both applied models could well describe the adsorption kinetics. Removal efficiencies of the heavy metals were increases by 1.2, 1.4, and 1.6 times, respectively, for Pb, Mn, and Cu, when PWB content of the leachate increased from 0.5 to 5 g L− 1. Equilibrium adsorption capacity of the heavy metals onto BC in leachate system was evaluated using the Langmuir, non-linearized Freundlich, linearized Freundlich, and Temkin isotherms and found to have the following order for PWB: Non-linearized Freundlich > Temkin > Langmuir > Linearized Freundlich. The Langmuir and linearized Freundlich models could not adequately represent adsorption of the heavy metals onto BC, especially for CWB. The highest removal of 88% was obtained for Pb, while the greatest adsorption intensity was found to be 1.58 mg g− 1 for Mn. Using the non-linearized Freundlich isotherm significantly reduced adsorption prediction error. The adsorption affinity of PWB for Pb, Mn, and Cu was greater than that of CWB in all treatments. Wood-derived BC is suggested to be used for the removal of heavy metals from landfill leachate as an economical adsorbent.
topic Adsorption
Biochar
Landfill leachate
Heavy metals
Linearization error
url http://link.springer.com/article/10.1186/s42834-020-00061-9
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