Assessing the Relocation Robustness of on Field Calibrations for Air Quality Monitoring Devices

E. Esposito, M. Salvato, S. De Vito, G. Fattoruso, N. Castell, K. Karatzas, G. Di Francia

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

The adoption of on field calibration for pervasive air quality monitors, is increasing significantly in the last few years. The sensors data, recorded on the field, together with co-located reference analyzers data, allow to build a knowledge base that is more representative of the real world conditions and thus more effective. However, on field calibration precision may fade in time due to change in operative conditions, due to different drivers. Among these, relocation is deemed among the most relevant. In this work, for the first time, we attempt to assess the robustness of this approach to relocation of the sensor nodes. We try to evaluate the impact on performance of the so called locality issue by measuring the changes in the performance indicators, when a chemical multisensory system operates in a location that differs from the one in which it was on field calibrated. To this purposes, a nonlinear multivariate approach with Neural Networks (NN) and a suitable dataset, provided by NILU (the Norwegian Institute for Air Quality), have been used. The preliminary results show a greater influence of seasonal forcers distribution with respect to the relocation issues.
Original languageEnglish
DOIs
Publication statusPublished - 2018
Event19th AISEM National Conference on Sensors and Microsystems, 2017 - Lecce, Italy
Duration: 1 Jan 2018 → …

Conference

Conference19th AISEM National Conference on Sensors and Microsystems, 2017
CountryItaly
CityLecce
Period1/1/18 → …

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All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Esposito, E., Salvato, M., Vito, S. D., Fattoruso, G., Castell, N., Karatzas, K., & Francia, G. D. (2018). Assessing the Relocation Robustness of on Field Calibrations for Air Quality Monitoring Devices. Paper presented at 19th AISEM National Conference on Sensors and Microsystems, 2017, Lecce, Italy. https://doi.org/10.1007/978-3-319-66802-4_38