A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms

This paper shows the result of the calibration process of an Internet of Things platform for the measurement of tropospheric ozone (O-3). This platform, formed by 60 nodes, deployed in Italy, Spain, and Austria, consisted of 140 metal-oxide O-3 sensors, 25 electro-chemical O-3 sensors, 25 electro-chemical NO2 sensors, and 60 temperature and relative humidity sensors. As ozone is a seasonal pollutant, which appears in summer in Europe, the biggest challenge is to calibrate the sensors in a short period of time. In this paper, we compare four calibration methods in the presence of a large dataset for model training and we also study the impact of a limited training dataset on the long-range predictions. We show that the difficulty in calibrating these sensor technologies in a real deployment is mainly due to the bias produced by the different environmental conditions found in the prediction with respect to those found in the data training phase.