IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i13p5457-d381233.html
   My bibliography  Save this article

The Impact of Technological Processes on Odorant Emissions at Municipal Waste Biogas Plants

Author

Listed:
  • Marta Wiśniewska

    (Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, 20 Nowowiejska Street, 00-653 Warsaw, Poland)

  • Andrzej Kulig

    (Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, 20 Nowowiejska Street, 00-653 Warsaw, Poland)

  • Krystyna Lelicińska-Serafin

    (Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, 20 Nowowiejska Street, 00-653 Warsaw, Poland)

Abstract

Municipal waste treatment is inherently associated with odour emissions. The compounds characteristic of the processes used for this purpose, and at the same time causing a negative olfactory sensation, are organic and inorganic sulphur and nitrogen compounds. The tests were carried out at the waste management plant, which in the biological part, uses the methane fermentation process and is also equipped with an installation for the collection, treatment, and energetic use of biogas. The tests include measurements of the four odorant concentrations and emissions, i.e., volatile organic compounds (VOCs), ammonia (NH 3 ), hydrogen sulphide (H 2 S), and methanethiol (CH 3 SH). Measurements were made using a MultiRae Pro portable gas detector sensor. The tests were carried out in ten series for twenty measurement points in each series. The results show a significant impact of technological processes on odorant emissions. The types of waste going to the plant are also important in shaping this emission. On the one hand, it relates to the waste collection system and, on the other hand, the season of year. In addition, it has been proved that the detector used during the research is a valuable tool enabling the control of technological processes in municipal waste processing plants.

Suggested Citation

  • Marta Wiśniewska & Andrzej Kulig & Krystyna Lelicińska-Serafin, 2020. "The Impact of Technological Processes on Odorant Emissions at Municipal Waste Biogas Plants," Sustainability, MDPI, vol. 12(13), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:13:p:5457-:d:381233
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/13/5457/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/13/5457/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hubert Byliński & Andrzej Sobecki & Jacek Gębicki, 2019. "The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process," Sustainability, MDPI, vol. 11(16), pages 1-12, August.
    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. Marta Wiśniewska & Andrzej Kulig & Krystyna Lelicińska-Serafin, 2022. "Odour Load of Selected Elements of the Technological Line at a Municipal Waste Biogas Plant," Energies, MDPI, vol. 15(7), pages 1-19, March.
    2. Izabela Konkol & Robert Tylingo & Szymon Mania & Adam Cenian, 2023. "Odour Perception Using a Sniffing Team at a Municipal Solid Waste Treatment Plant: A Case Study," Sustainability, MDPI, vol. 15(16), pages 1-17, August.
    3. Marta Wiśniewska & Andrzej Kulig & Krystyna Lelicińska-Serafin, 2021. "Odour Nuisance at Municipal Waste Biogas Plants and the Effect of Feedstock Modification on the Circular Economy—A Review," Energies, MDPI, vol. 14(20), pages 1-22, October.

    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. Sławomir Francik & Adrian Knapczyk & Artur Knapczyk & Renata Francik, 2020. "Decision Support System for the Production of Miscanthus and Willow Briquettes," Energies, MDPI, vol. 13(6), pages 1-24, March.
    2. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    3. Kowalczyk-Juśko, Alina & Pochwatka, Patrycja & Zaborowicz, Maciej & Czekała, Wojciech & Mazurkiewicz, Jakub & Mazur, Andrzej & Janczak, Damian & Marczuk, Andrzej & Dach, Jacek, 2020. "Energy value estimation of silages for substrate in biogas plants using an artificial neural network," Energy, Elsevier, vol. 202(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:12:y:2020:i:13:p:5457-:d:381233. 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.