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Influence of Zinc and Humic Acids on Dye Adsorption from Water by Two Composts

Author

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  • Remigio Paradelo

    (CRETUS-Department of Soil Science and Agricultural Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain)

  • Paula García

    (CRETUS-Department of Soil Science and Agricultural Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain)

  • Alba González

    (CRETUS-Department of Soil Science and Agricultural Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain)

  • Khaled Al-Zawahreh

    (Department of Earth Sciences and Environment, Prince El-Hassan bin Talal Faculty for Natural Resources and Environment, The Hashemite University, Zarqa 13133, Jordan)

  • Maria Teresa Barral

    (CRETUS-Department of Soil Science and Agricultural Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain)

Abstract

Searching for alternative low-cost biosorbents for the removal of textile dyes from wastewater is currently an important subject of research. In this work, we have investigated how the presence of other contaminants in textile wastewaters can affect dye adsorption by biosorbents. We tested the adsorption of three dyes of different types: Basic Violet 10 (BV10), Acid Blue 113 (AB113) and Direct Blue 71 (DB71) by two different composts—municipal solid waste compost and pine bark compost—in the presence of Zn (5 mg L −1 ) or dissolved organic matter (100 mg humic acids L −1 ) in batch experiments. Dye adsorption capacity for both composts followed the following sequence: BV10 > AB113 > DB71. In general, dye sorption at the equilibrium was adequately described by the Freundlich model, but not always by the Langmuir model, which did not allow for the estimation of maximum retention capacities in all cases. In general, these were around 1 mg g −1 for DB71, 2 mg g −1 for AB113, and 40 mg g −1 for BV10. Municipal solid waste compost had slightly higher affinity than pine bark compost for the anionic dyes AB113 and DB71, whereas for the cationic dye BV10, pine bark compost presented a much higher adsorption capacity (41.7 mg g −1 versus 6.8 mg g −1 ). The presence of Zn or dissolved organic matter in the solutions at typical wastewater concentrations did not decrease the dye adsorption capacity of the composts. This result is positive both for the real application of composts to real textile wastewaters and for the validity of the results of biosorbent performance obtained with single-dye solutions.

Suggested Citation

  • Remigio Paradelo & Paula García & Alba González & Khaled Al-Zawahreh & Maria Teresa Barral, 2023. "Influence of Zinc and Humic Acids on Dye Adsorption from Water by Two Composts," IJERPH, MDPI, vol. 20(7), pages 1-10, March.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:7:p:5353-:d:1112897
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    References listed on IDEAS

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    1. Baty, Florent & Ritz, Christian & Charles, Sandrine & Brutsche, Martin & Flandrois, Jean-Pierre & Delignette-Muller, Marie-Laure, 2015. "A Toolbox for Nonlinear Regression in R: The Package nlstools," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i05).
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