Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors

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aut.relation.issue9
aut.relation.journalSensors
aut.relation.volume24
dc.contributor.authorAli, Sharafat
dc.contributor.authorAlam, Fakhrul
dc.contributor.authorPotgieter, Johan
dc.contributor.authorArif, Khalid Mahmood
dc.date.accessioned2024-05-06T00:12:35Z
dc.date.available2024-05-06T00:12:35Z
dc.date.issued2024-05-04
dc.description.abstractLow-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.
dc.identifier.citationSensors, ISSN: 1424-8220 (Print), MDPI, 24(9). doi: 10.3390/s24092930
dc.identifier.doi10.3390/s24092930
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10292/17514
dc.publisherMDPI
dc.relation.urihttps://www.mdpi.com/1424-8220/24/9/2930
dc.rights© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject0301 Analytical Chemistry
dc.subject0502 Environmental Science and Management
dc.subject0602 Ecology
dc.subject0805 Distributed Computing
dc.subject0906 Electrical and Electronic Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.titleLeveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors
dc.typeJournal Article
pubs.elements-id547096
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