Field Calibration of a Low-Cost Air Quality Monitoring Device in an Urban Background Site Using Machine Learning Models

Field calibration of low-cost air quality (AQ) monitoring sensors is essential for their successful operation. Low-cost sensors often exhibit non-linear responses to air pollutants and their signals may be affected by the presence of multiple compounds making their calibration challenging. We investigate different approaches for the field calibration of an AQ monitoring device named ENSENSIA, developed in the Institute of Chemical Engineering Sciences in Greece. The present study focuses on the measurements of two of the most important pollutants measured by ENSENSIA: NO2 and O-3. The measurement site is located in the center of Patras, the third biggest city in Greece. Reference instrumentation used for regulatory purposes by the Region of Western Greece was used as the evaluation standard. The sensors were installed for two years at the same locations. Measurements from the first year (2021) from seven ENSENSIA sensors (NO2, NO, O-3, CO, PM2.5, temperature and relative humidity) were used to train several Machine Learning (ML) and Deep Learning (DL) algorithms. The resulting calibration algorithms were assessed using data from the second year (2022). The Random Forest algorithm exhibited the best performance in correcting O-3 and NO2. For NO2 the mean error was reduced from 9.4 ppb to 3 ppb, whilst R-2 improved from 0.22 to 0.86. Similar results were obtained for O-3, wherein the mean error was reduced from 13 to 4.3 ppb and R-2 increased from 0.52 to 0.69. The Long-Short Term Memory Network (LSTM) also showed good performance in correcting the measurements of the two pollutants.

Automated summary

This study investigates different approaches for the field calibration of the low-cost air quality monitoring device ENSENSIA in Greece. The Random Forest algorithm exhibited the best performance in correcting O-3 and NO2, reducing mean error and improving R-2 values. The Long-Short Term Memory Network (LSTM) also showed good performance in correcting the measurements of the two pollutants.