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Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II

TitleAssessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2018
AuthorsBorrego, C., Ginja J., Coutinho M., Ribeiro C., Karatzas K., Sioumis T., Katsifarakis N., Konstantinidis K., De Vito S., Esposito E., Salvato M., Smith P., André N., Gérard P., Francis L.A., Castell N., Schneider P., Viana M., Minguillón M.C., Reimringer W., Otjes R.P., von Sicard O., Pohle R., Elen B., Suriano Domenico, Pfister Valerio, Prato Mario, Dipinto S., and Penza Michele
JournalAtmospheric Environment
Keywordsair monitoring, air pollutant, Air quality, air quality control, Air quality monitoring, air quality standard, Air quality standards, ambient air, article, Artificial intelligence, assessment method, atmospheric modeling, Calibration, clinical assessment, controlled study, Data handling, detection method, Experimental campaign, experimental study, Learning systems, Low costs, machine learning, measurement method, Measurement uncertainty, Microsensors, numerical method, Particles (particulate matter), particulate matter, Quality control, Reference method, sensor, Uncertainty analysis, urban area

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r2 = 0.13–0.83), NO2 (r2 = 0.24–0.93), O3 (r2 = 0.22–0.84), PM10 (r2 = 0.54–0.83), PM2.5 (r2 = 0.33–0.40) and SO2 (r2 = 0.49–0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements. © 2018 Elsevier Ltd


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Citation KeyBorrego2018127