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Analysis and Improvement of Two Low-Cost Air Quality Sensor Measurements’ Uncertainty

In: Advances and New Trends in Environmental Informatics

Author

Listed:
  • Marios Panourgias

    (Aristotle University)

  • Kostas Karatzas

    (Aristotle University)

Abstract

Measurements resulting from the operation of two different low-cost air quality monitoring devices (LCAQMD) are used as a basis for a data analytics and modelling procedure towards the improvement of the uncertainty of sensor readings. Α data processing method for missing value and outliers handling, followed by the implementation of computational intelligence-oriented algorithms aimed to the PM10 modelling. Descriptive statistics and correlation coefficients are used for a primary evaluation of data analytics results, while modelling outcomes are compared with the aid of the relative expanded uncertainty, as well as via the model performance evaluation metrics, to determine the most efficient model. Results suggest that the advanced artificial neural network oriented computational intelligence algorithms, may lead to significant improvement of the performance of the two LCAQMD, this being applicable for a certain concentration range (18–65 μg/m3), indicating that additional future work and more advanced computational techniques are required for further improvement of their performance.

Suggested Citation

  • Marios Panourgias & Kostas Karatzas, 2023. "Analysis and Improvement of Two Low-Cost Air Quality Sensor Measurements’ Uncertainty," Progress in IS, in: Volker Wohlgemuth & Stefan Naumann & Grit Behrens & Hans-Knud Arndt & Maximilian Höb (ed.), Advances and New Trends in Environmental Informatics, pages 73-89, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-18311-9_5
    DOI: 10.1007/978-3-031-18311-9_5
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