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

Marios Panourgias () and Kostas Karatzas ()
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Marios Panourgias: Aristotle University
Kostas Karatzas: Aristotle University

A chapter in Advances and New Trends in Environmental Informatics, 2023, pp 73-89 from Springer

Abstract: 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.

Keywords: Low-cost air quality monitoring devices; Measurement uncertainty; Data quality; Computational intelligence (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-18311-9_5

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DOI: 10.1007/978-3-031-18311-9_5

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