Abstrakti

Formaldehyde is a carcinogenic indoor air pollutant emitted from common wood-based materials. Low-cost sensing of formaldehyde is difficult due to inaccuracies in measuring low concentrations and susceptibility of sensors to changing indoor environmental conditions. Currently gas sensors are calibrated by manufacturers using simplistic models which fail to capture their complex behaviour. We evaluated different low-cost gas sensors to ascertain a suitable component to create a mobile sensing node and built a calibration algorithm to correct it. We compared the performance of 2 electrochemical sensors and 3 metal oxide sensors in a controlled chamber against a photo-acoustic reference device. In the chamber the formaldehyde concentrations, temperature and humidity were varied to assess the sensors in diverse environments. Pre-calibration, the electrochemical sensors (mean absolute error (MAE) = 70.8 ppb) outperformed the best performing metal oxide sensor (MAE = 335 ppb). A two-stage calibration model was built, using linear regression followed by random forest, where the residual of the first stage acted as a input for the second. Post-calibration, the metal oxide sensors (MAE = 154 ppb) improved compared to their electrochemical counterparts (MAE = 78.8 ppb). Nevertheless, the uncalibrated electrochemical sensor showed overall superior performance hence was selected for the mobile sensing node.
Alkuperäiskielienglanti
LehtiIEEE Sensors Journal
Vuosikerta22
Numero12
Sivut11791-11802
Sivumäärä12
ISSN1530-437X
DOI - pysyväislinkit
TilaJulkaistu - 15 kesäk. 2022
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

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