IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i17p4760-d262673.html
   My bibliography  Save this article

Comparison of Different Enhanced Coagulation Methods for Azo Dye Removal from Wastewater

Author

Listed:
  • Xinhao Luo

    (School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
    The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China)

  • Chen Liang

    (School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
    The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China)

  • Yongyou Hu

    (School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
    The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China)

Abstract

Printing and dyeing wastewater (PDW) is considered to be one of the most difficult industrial wastewaters to treat because of its large quantities, high pH values, and high color and toxicity, which may endanger the lives of animals and humans. In this study, we assessed the chemical decolorization process of Congo Red in azo dyes using response surface methodology (RSM), and the effect of different enhanced coagulation pretreatment processes (ECPPs) on the microbial community structure of PDW using high-throughput sequencing technology. We concluded that, based on the initial concentration and pH of Congo Red, different decolorants can be selected for decolorization reactions. In addition, it was found that the microbial community of the wastewater after three different ECPP treatments was similar to the raw wastewater and the oxidation ditch wastewater from a treatment plant. We also found that the ECPPs with polymeric iron sulfate had the smallest effect on the microbial community. In practical applications, these findings provide a reference for an established link between the physicochemical and biochemical treatment of PDW.

Suggested Citation

  • Xinhao Luo & Chen Liang & Yongyou Hu, 2019. "Comparison of Different Enhanced Coagulation Methods for Azo Dye Removal from Wastewater," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4760-:d:262673
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/17/4760/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/17/4760/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wilkinson, Leland & Friendly, Michael, 2009. "The History of the Cluster Heat Map," The American Statistician, American Statistical Association, vol. 63(2), pages 179-184.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sara Yasipourtehrani & Vladimir Strezov & Tao Kan & Tim Evans, 2021. "Investigation of Dye Removal Capability of Blast Furnace Slag in Wastewater Treatment," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    2. Aiya Chantarasiri, 2020. "Klebsiella and Enterobacter Isolated from Mangrove Wetland Soils in Thailand and Their Application in Biological Decolorization of Textile Reactive Dyes," IJERPH, MDPI, vol. 17(20), pages 1-21, October.
    3. Wudneh Ayele Shewa & Martha Dagnew, 2020. "Revisiting Chemically Enhanced Primary Treatment of Wastewater: A Review," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    4. Nhung T. Tuyet Hoang & D. Duc Nguyen, 2023. "Improving the Degradation Kinetics of Industrial Dyes with Chitosan/TiO 2 /Glycerol Films for the Sustainable Recovery of Chitosan from Waste Streams," Sustainability, MDPI, vol. 15(8), pages 1-16, April.
    5. Zainab Naseem & Muhammad Naveed & Hafiz Naeem Asghar & Mansoor Hameed, 2022. "Metal Resistant Enterobacter cloacae ZA14 Enhanced Seedling Vigor and Metal Tolerance through Improved Growth, Physiology and Antioxidants in Tomato ( Solanum lycopersicum ) Irrigated with Textile Eff," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    6. Ewa Okoniewska, 2021. "Removal of Selected Dyes on Activated Carbons," Sustainability, MDPI, vol. 13(8), pages 1-13, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Miriam Aparicio, 2021. "Resiliency and Cooperation or Regarding Social and Collective Competencies for University Achievement. An Analysis from a Systemic Perspective," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 8, ejser_v8_.
    2. Fabio Salamanca-Buentello & Mary V Seeman & Abdallah S Daar & Ross E G Upshur, 2020. "The ethical, social, and cultural dimensions of screening for mental health in children and adolescents of the developing world," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-25, August.
    3. Nicodemo, Catia & Satorra, Albert, 2020. "Exploratory Data Analysis on Large Data Sets: The Example of Salary Variation in Spanish Social Security Data," IZA Discussion Papers 13459, Institute of Labor Economics (IZA).
    4. Wittek, Peter, 2013. "Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 193-201.
    5. Lorentz, Harri & Kumar, Mukesh & Srai, Jagjit Singh, 2018. "Managing distance in international purchasing and supply: a systematic review of literature from the resource-based view perspective," International Business Review, Elsevier, vol. 27(2), pages 339-354.
    6. Romildo Brito Neto & Celso Santos & Kevin Mulligan & Lucia Barbato, 2016. "Spatial and temporal water-level variations in the Texas portion of the Ogallala Aquifer," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(1), pages 351-365, January.
    7. Shah Jahan Miah & Huy Quan Vu & Damminda Alahakoon, 2022. "A social media analytics perspective for human‐oriented smart city planning and management," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 119-135, January.
    8. Francesco Pasanisi & Gaia Righini & Massimo D’Isidoro & Lina Vitali & Gino Briganti & Sergio Grauso & Lorenzo Moretti & Carlo Tebano & Gabriele Zanini & Mabafokeng Mahahabisa & Mosuoe Letuma & Muso Ra, 2021. "A Cooperation Project in Lesotho: Renewable Energy Potential Maps Embedded in a WebGIS Tool," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    9. Diaz-Balteiro, L. & Alfranca, O. & Voces, R. & Soliño, M., 2023. "Using google search patterns to explain the demand for wild edible mushrooms," Forest Policy and Economics, Elsevier, vol. 152(C).
    10. Terrill L. Frantz, 2018. "Blockmap: an interactive visualization tool for big-data networks," Computational and Mathematical Organization Theory, Springer, vol. 24(2), pages 149-168, June.
    11. Yan Wang & Peng Jia & Luping Liu & Cheng Huang & Zhonglin Liu, 2020. "A systematic review of fuzzing based on machine learning techniques," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-37, August.
    12. Magdalena Jastrzębska & Urszula Wachowska & Marta K. Kostrzewska, 2020. "Pathogenic and Non-Pathogenic Fungal Communities in Wheat Grain as Influenced by Recycled Phosphorus Fertilizers: A Case Study," Agriculture, MDPI, vol. 10(6), pages 1-15, June.
    13. Chengcheng Huang & Guoqiang Wang & Xiaogu Zheng & Jingshan Yu & Xinyi Xu, 2015. "Simple Linear Modeling Approach for Linking Hydrological Model Parameters to the Physical Features of a River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3265-3289, July.
    14. Francesca Conte & Pierluigi Vitale & Agostino Vollero & Alfonso Siano, 2018. "Designing a Data Visualization Dashboard for Managing the Sustainability Communication of Healthcare Organizations on Facebook," Sustainability, MDPI, vol. 10(12), pages 1-14, November.
    15. Chia-Chun Yen & Weng Shih Kun Liu & Chuen-Lin Tien & Tian-Jong Hwu, 2024. "The Impacts of Government Subsidies on Public Transportation Customer Complaints: A Case Study of Taichung City Bus Subsidy Policy," Sustainability, MDPI, vol. 16(8), pages 1-24, April.
    16. Bin Liu & Longyun Fang & Fule Liu & Xiaolong Wang & Junjie Chen & Kuo-Chen Chou, 2015. "Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-20, March.
    17. Mark Paddrik & Richard Haynes & Andrew E. Todd & Peter A. Beling & William T. Scherer, 2014. "The Role of Visual Analysis in the Regulation of Electronic Order Book Markets," Staff Discussion Papers 14-02, Office of Financial Research, US Department of the Treasury.
    18. Pawel Zukowski & Paweł Okal & Konrad Kierczynski & Przemyslaw Rogalski & Sebastian Borucki & Michał Kunicki & Tomasz N. Koltunowicz, 2023. "Investigations into the Influence of Matrix Dimensions and Number of Iterations on the Percolation Phenomenon for Direct Current," Energies, MDPI, vol. 16(20), pages 1-19, October.
    19. Roy Costilla & Ivy Liu & Richard Arnold & Daniel Fernández, 2019. "Bayesian model-based clustering for longitudinal ordinal data," Computational Statistics, Springer, vol. 34(3), pages 1015-1038, September.
    20. “Jimmy” Xu, Zhenning & Ramirez, Edward & Liu, Pan & Frankwick, Gary L., 2024. "Evaluating underlying factor structures using novel machine learning algorithms: An empirical and simulation study," Journal of Business Research, Elsevier, vol. 173(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4760-:d:262673. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.