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Título

Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise

AutorBorrego, Carlos S.; Viana, Mar CSIC ORCID ; Minguillón, María Cruz CSIC ORCID ; Penza, Michèle
Palabras claveAir quality
Air quality monitoring
Experimental campaign
Intercomparison
Microsensors
Reference methods
Particulate matter
Fecha de publicación1-dic-2016
EditorElsevier
CitaciónAtmospheric Environment 147: 246-263 (2016)
ResumenThe 1st EuNetAir Air Quality Joint Intercomparison Exercise organized in Aveiro (Portugal) from 13th–27th October 2014, focused on the evaluation and assessment of environmental gas, particulate matter (PM) and meteorological microsensors, versus standard air quality reference methods through an experimental urban air quality monitoring campaign. The IDAD-Institute of Environment and Development Air Quality Mobile Laboratory was placed at an urban traffic location in the city centre of Aveiro to conduct continuous measurements with standard equipment and reference analysers for CO, NOx, O3, SO2, PM10, PM2.5, temperature, humidity, wind speed and direction, solar radiation and precipitation. The comparison of the sensor data generated by different microsensor-systems installed side-by-side with reference analysers, contributes to the assessment of the performance and the accuracy of microsensor-systems in a real-world context, and supports their calibration and further development. The overall performance of the sensors in terms of their statistical metrics and measurement profile indicates significant differences in the results depending on the platform and on the sensors considered. In terms of pollutants, some promising results were observed for O3 (r2: 0.12–0.77), CO (r2: 0.53–0.87), and NO2 (r2: 0.02–0.89). For PM (r2: 0.07–0.36) and SO2 (r2: 0.09–0.20) the results show a poor performance with low correlation coefficients between the reference and microsensor measurements. These field observations under specific environmental conditions suggest that the relevant microsensor platforms, if supported by the proper post processing and data modelling tools, have enormous potential for new strategies in air quality control. © 2016 Elsevier Ltd
Versión del editorhttps://doi.org/10.1016/j.atmosenv.2016.09.050
URIhttp://hdl.handle.net/10261/174922
DOI10.1016/j.atmosenv.2016.09.050
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