Variance Analysis of Signals from Four Electrode Electrochemical Sensors

Ionascu, Marian-Emanuel; Gruicin, Iasmina; Marcu, Marius

doi:10.1109/telfor48224.2019.8971056

Cited by 1 publication

(3 citation statements)

References 8 publications

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“…The calibration methods proposed in this work were designed for the air quality measurement device platform described in [ 24 ], under the premise that low-level data acquisition and filtering is available [ 25 ].…”

Section: Implementation Of Calibration Modelsmentioning

confidence: 99%

“…The air monitoring device, described in Figure 1 , is equipped with electrochemical Alphasense (UK) A4-type sensors (CO, NO 2 , NO, O 3 , and SO 2 ), which are the smallest and cheapest 4-electrode sensors designed for ppb levels; a non-disruptive infrared (NDIR) pyroelectric sensor for CO 2 ; and a light-scattering sensor from PlanTower for PMs [ 25 ], which are placed in a closed case. The case is equipped with a fan and holes to allow good air flow over the sensors.…”

Section: Implementation Of Calibration Modelsmentioning

confidence: 99%

“…In order to reduce this noise, we designed an in-house acquisition board made of reliable circuitry that is capable of assessing any variance and noise introduced by other electronics, such as wireless communication modules or fan start/stop periods [ 25 ].…”

Section: Implementation Of Calibration Modelsmentioning

confidence: 99%

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Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring

Ionascu

Castell

Boncalo

et al. 2021

Sensors

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During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O3 gases and a similar accuracy for the NO2 gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.

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Section: Implementation Of Calibration Modelsmentioning

confidence: 99%

Section: Implementation Of Calibration Modelsmentioning

confidence: 99%