IDEAS home Printed from https://ideas.repec.org/h/spr/prochp/978-3-031-85284-8_3.html
   My bibliography  Save this book chapter

Low-Cost Air Quality Sensor Nodes in a Network Setup: Using Shared Information to Impute Missing Values

In: Advances and New Trends in Environmental Informatics

Author

Listed:
  • Theodosios Kassandros

    (Aristotle University of Thessaloniki)

  • Evangelos Bagkis

    (Aristotle University of Thessaloniki)

  • Kostas Karatzas

    (Aristotle University of Thessaloniki)

Abstract

Low-Cost Air Quality Sensor Nodes (LCAQSN) are being widely deployed across numerous cities worldwide as a new way for assessing air quality. Despite facing challenges related to accuracy and consistency, these nodes offer valuable insights, substantially reducing the costs associated with monitoring air pollutants. A significant hurdle to address is the occurrence of missing values. In this study, we hypothesize that a network of LCAQSN within the same urban environment can effectively retain and utilize shared information to accurately impute missing values, even in cases with substantial gaps in the time-series data of individual nodes. Employing various Machine Learning techniques, our analysis reveals that a network comprising 26 LCAQSN in the Greater Thessaloniki Area, Greece, with 40.93% missing values, can achieve an imputation accuracy of 0.7 R2 on a simulated test set of 10% of missing values. These findings exhibit great promise and unveil numerous opportunities for leveraging LCAQSN networks further, including data fusion and downscaling applications.

Suggested Citation

  • Theodosios Kassandros & Evangelos Bagkis & Kostas Karatzas, 2025. "Low-Cost Air Quality Sensor Nodes in a Network Setup: Using Shared Information to Impute Missing Values," Progress in IS, in: Volker Wohlgemuth & Hamdy Kandil & Amna Ramzy (ed.), Advances and New Trends in Environmental Informatics, pages 35-46, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-85284-8_3
    DOI: 10.1007/978-3-031-85284-8_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:prochp:978-3-031-85284-8_3. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.