Aveiro Joint Exercise Dataset

Published: 6 February 2019| Version 1 | DOI: 10.17632/b5xxvbd4p2.1
Contributors:
Carlos Borrego, Ana Margarida Costa, João Ginja, Melissa Amorim, Miguel Coutinho, Kostas Karatzas, Th Sioumis, Nikos Katsifarakis, Kostas Konstantinidis, Saverio De Vito, Elena Esposito, Paul Smith, Nicolas André, Pierre Gérard, Laurent Francis, Nùria Castell, Philipp Schneider, Mar Viana, María Cruz Minguillón, Wolfhard Reimringer, René P. Otjes, Oliver Von Sicard, Bart Elen, Domenico Suriano, Valerio Pfister, Mario Prato, Sebastiano Dipinto, Michele Penza

Description

The current datasets comprise of air quality data collected during an AQ monitoring campaign in Aveiro, Portugal, for two weeks in October 2014 (from October 13 until October 27) in order to present the results of an intercomparison of AQ microsensors with reference methods. The two-week experimental campaign was conducted in an urban traffic location in Aveiro city centre. A total of 15 teams originating from various research centres, universities and companies from 12 different countries participated in the campaign. During the intercomparison campaign, the IDAD mobile laboratory was equipped with standard and reference analysers for continuous measurement of atmospheric pollutant concentrations and specific sensors for the measurement of meteorological parameters. Participating teams installed microsensors boxes on it for a co-location deployment experiment using different measuring principles. The dataset is composed by: Reference data (ground truth) - IDAD reference data (@1min rate) including CO, NO, NO2, NOx, O3, SO2, H2S concentrations (21183 data samples); - IDAD reference data (@15min rate) including NO, NO2, NOx, O3 concentrations (15 min averaged data) + CO2, Benzene, Toluene, Ethylbenzene, M&P-Xylene, o-Xylene concentrations (1412 data samples); - IDAD reference data (@1h rate) including all the above mentioned pollutants concentrations plus PM2.5, PM10 concentrations and T, RH, Prec., WV, WD, Press., Rad. data (353 data samples); Microsensors data - 1minute sampled concentrations data provided by University of Cambridge "CAM10" and "CAM11" boxes, ECN "ECN10" and "ECN38" boxes, UCL/CCMOSS, VITO/EveryAware SB boxes; - 1 minute sampled raw uncalibrated data from Siemens microsensor box. - 5 minute sampled concentrations data provided by AUTh-ISAG, NanoEnvi box; - 15 minute sampled data provided by ENEA, AQMesh, VITO/EveryAware SB boxes; - 1 hour data provided by ENEA, NanoEnvi, VITO/EveryAware SB boxes.

Files

Steps to reproduce

Details for result reproduction are described in the two Joint exercise focused papers: C. Borrego, A.M. Costa, J. Ginja, M. Amorim, M. Coutinho, K. Karatzas, Th. Sioumis, N. Katsifarakis, K. Konstantinidis, S. De Vito, E. Esposito, P. Smith, N. André, P. Gérard, L.A. Francis, N. Castell, P. Schneider, M. Viana, M.C. Minguillón, W. Reimringer, R.P. Otjes, O. von Sicard, R. Pohle, B. Elen, D. Suriano, V. Pfister, M. Prato, S. Dipinto, M. Penza, Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise, Atmospheric Environment, Volume 147, 2016, Pages 246-263, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2016.09.050. for a dataset description and simple intercomparison results, and: C. Borrego, J. Ginja, M. Coutinho, C. Ribeiro, K. Karatzas, Th Sioumis, N. Katsifarakis, K. Konstantinidis, S. De Vito, E. Esposito, M. Salvato, P. Smith, N. André, P. Gérard, L.A. Francis, N. Castell, P. Schneider, M. Viana, M.C. Minguillón, W. Reimringer, R.P. Otjes, O. von Sicard, R. Pohle, B. Elen, D. Suriano, V. Pfister, M. Prato, S. Dipinto, M. Penza, Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II, Atmospheric Environment, Volume 193, 2018, Pages 127-142, ISSN 1352-2310, https://doi.org/10.1016 j.atmosenv.2018.08.028. (http://www.sciencedirect.com/science/article/pii/S1352231018305430) for results using machine learning approaches for advanced field calibration, that we kindly ask to cite when using this dataset.

Categories

Air Quality, Machine Learning, Artificial Intelligence Applications, Chemical Sensor, Smart City, Applications of Sensors, Microsensor

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