Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors

• Published in: Signals (Basel) Vol. 5; no. 1; pp. 60 - 86
• Main Authors: Christakis, Ioannis, Sarri, Elena, Tsakiridis, Odysseas, Stavrakas, Ilias
• Format: Journal Article
• Language: English
• Published: Tokyo MDPI AG 01.03.2024
• Subjects: Air pollution; air quality IoT; Calibration; Data transmission; LASSO regression; low-cost sensing systems; Methods; Neural networks; Nitrogen dioxide; nitrogen dioxide (NO2); Optimization; optimization low-cost sensors measurements; Outdoor air quality; ozone (O3); Pollutants; Regression analysis; Sensors; Greece
• ISSN: 2624-6120; 2624-6120
• DOI: 10.3390/signals5010004

Air quality is a subject of study, particularly in densely populated areas, as it has been shown to affect human health and the local ecosystem. In recent years, with the rapid development of technology, low-cost sensors have emerged, with many people interested in the quality of the air in their area turning to the procurement of such sensors as they are affordable. The reliability of measurements from low-cost sensors remains a question in the research community. In this paper, the determination of the correction factor of low-cost sensor measurements by applying the least absolute shrinkage and selection operator (LASSO) regression method is investigated. The results are promising, as following the application of the correction factor determined through LASSO regression the adjusted measurements exhibit a closer alignment with the reference measurements. This approach ensures that the measurements from low-cost sensors become more reliable and trustworthy.