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). This platform, formed by 60 nodes, deployed in Italy, Spain, and Austria, consisted of 140 metal-oxide O sensors, 25 electro-chemical O sensors, 25 electro-chemical NO 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.
| Main Authors: | , , , , |
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| Format: | artículo biblioteca |
| Published: |
Institute of Electrical and Electronics Engineers
2019
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| Online Access: | http://hdl.handle.net/10261/209843 |
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| Summary: | This paper shows the result of the calibration process of an Internet of Things platform for the measurement of tropospheric ozone (O). This platform, formed by 60 nodes, deployed in Italy, Spain, and Austria, consisted of 140 metal-oxide O sensors, 25 electro-chemical O sensors, 25 electro-chemical NO 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. |
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