Computational Calibration

The computational calibration of AQ low cost sensors consist of the development of different calibration models for the correction of a sensor signal under field conditions. Our goal is to make use of low cost sensor readings (and in some cases additional reference meteorological data) in order to develop Computational Intelligence oriented models that improve the overall performance. For doing so, we require the side by side operation of a sensor node to reference instruments for a time period sufficient to depict seasonal changes. We make use of reference measurements to develop, train and evaluate models that will be applied for "boosting" the performance of low cost AQ sensor nodes under real world operational conditions.

An example of the computational improvement of the measurement uncertainty according to the Data Quality Objectives defined in the EU Air Quality Directive (2008/50/EC) for a number of low cost AQ monitoring nodes and for NO2

Computational improvement of the measurement uncertainty according to the Data Quality Objectives (Bagkis et al., 2021) for PM10

Some of our related publications

Kassandros Th., Bagkis E., Karatzas K. (2021), Data fusion for the improvement of Low-Cost Air Quality Sensors, ITM 2021: International Technical Meeting on Air Pollution Modelling and its Application, 18 - 22 October 2021, Barcelona, Spain. To be published at Air Pollution Modelling and its Application volume XXVIII, Springer
Bagkis E., Kassandros Th., Karatzas K. (2021), Performance evaluation and measurement uncertainty improvement of commercially available particulate matter low-cost sensors via data-driven computational methods, European Aerosol Conference 2021, 30 August - 3 September 2021, An interactive live virtual event, hosted by the UK and Ireland Aerosol Society (oral presentation ).
Bagkis E., Kassandros T., Karteris M., Karteris A., Karatzas K. (2021). Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device. Atmosphere 12, 251. https://doi.org/10.3390/atmos12020251
Cofta P., Karatzas Κ., Orlowski C. (2021), A conceptual model of measurement uncertainty in IoT sensor networks. Sensors, 21(5), 1827; https://doi.org/10.3390/s21051827
Kassandros Th., Karatzas K. (2020), Towards a robust ensemble modelling approach to improve Low-Cost Air Quality Sensors performance Environmental Informatics -New perspectives in Environmental Information Systems: Transport, Sensors, Recycling (Kamilaris A., Wohlgemuth V., Karatzas K., Athanasiadis I., eds.), pp. 154-164. Adjunct Enviroinfo2020 proceedings, Shaker Verlang, Kassel, Germany. ISBN: 978-3-8440-7628-8 ISBN: 978-3-8440-7628-8
Borrego C., Costa A.M., Ginja J., Amorim M., Karatzas K., Sioumis Th., Katsifarakis N., Konstantinidis K., De Vito S., Esposito E., Smith P., André N., Gérard P., Francis L.A.,. Castell N., Viana M., Minguillón M.C., Reimringen W., Otjes R.P., v.Sicard O., Pohle R., Elen B., Suriano D., Pfister V., Prato M., Dipinto S., Penza M. (2018), Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise– part II, Atmospheric Environment 193(127-142), https://doi.org/10.1016/j.atmosenv.2018.08.028
Zimianitis P., Karatzas K. (2020), Analysis and modeling of low-cost air quality sensor data towards their computational improvement Environmental Informatics -New perspectives in Environmental Information Systems: Transport, Sensors, Recycling (Kamilaris A., Wohlgemuth V., Karatzas K., Athanasiadis I., eds.), Adjunct Enviroinfo2020 proceedings, Shaker Verlang, Kassel, Germany, in press. 175-181
Pinho P., Lopes S., Panourgias M., Reis J., Karatzas K. (2020), Intercomparison between IoT air quality monitoring devices for PM10 concentration estimations. Environmental Informatics -New perspectives in Environmental Information Systems: Transport, Sensors, Recycling (Kamilaris A., Wohlgemuth V., Karatzas K., Athanasiadis I., eds.), Adjunct Enviroinfo2020 proceedings, Shaker Verlang, Kassel, Germany, pp 139-144, ISBN: 978-3-8440-7628-8
Esposito Ε., De Vito S., Salvato Μ., Fattoruso Γ., Castell Ν., Karatzas K., Di Francia G. (2017), Is on field calibration strategy robust to relocation? In: Proceedings of the 17^th International Symposium on Olfaction and Electronic Nose, (2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose), 28 - 31 May 2017 - Montreal, Canada. DOI: 10.1109/ISOEN.2017.7968904
Sioumis Th., Epitropou V. and Karatzas K. (2017), Development and testing of a low cost air quality microsensor device, Sixth International Conference on Environmental Management, Engineering, Planning and Economics (CEMEPE 2017) and SECOTOX Conference, Proceedings (Kungolos A., Laspidou C., Aravossis K. Samaras P., Schramm K.W. and Marnellos G., eds. Grafima Pub., ISBN: 978-618-5271-15-2), pp. 521-531
Borrego C., Costa A.M., Ginja J., Amorim M., Karatzas K., Sioumis Th., Katsifarakis K., Konstantinidis K., De Vito S., Esposito E., Smith P., André N., Gérard P., Francis L.A.,. Castell N., Viana M., Minguillón M.C., Reimringen W., Otjes R.P., v.Sicard O., Pohle R., Elen B., Suriano D., Pfister V., Prato M., Dipinto S., Penza M. (2016), Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise, Atmospheric Environment 147, pp. 246-263, http://dx.doi.org/10.1016/j.atmosenv.2016.09.050
Borrego C., Ginja C.J., Amorim M., Karatzas K. (2015), EuNetAir Air Quality Joint-Exercise Intercomparison: Assessment of Microsensors Versus Reference Methods. Proceedings of the Fourth Scientific Meeting EuNetAir, pp. 36-39, DOI 10.5162/4EuNetAir2015/10, http://www.ama-science.org/proceedings/details/2125

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