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H2020 project CAPTOR dataset: Raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network

TitleH2020 project CAPTOR dataset: Raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network
Publication TypeJournal Article
Year of Publication2021
AuthorsBarcelo-Ordinas, JM, Ferrer-Cid, P, Garcia-Vidal, J, Viana, M, Ripoll, A
JournalData in Brief
KeywordsCalibration of sensors, Low-cost sensors, Machine learning algorithms, Metal-oxide sensors, Pollution maps, Tropospheric ozone

The H2020 CAPTOR project deployed three testbeds in Spain, Italy and Austria with low-cost sensors for the measurement of tropospheric ozone (O3). The aim of the H2020 CAPTOR project was to raise public awareness in a project focused on citizen science. Each testbed was supported by an NGO in charge of deciding how to raise citizen awareness according to the needs of each country. The data presented in this document correspond to the raw data captured by the sensor nodes in the Spanish testbed using SGX Sensortech MICS 2614 metal-oxide sensors. The Spanish testbed consisted of the deployment of twenty-five nodes. Each sensor node included four SGX Sensortech MICS 2614 ozone sensors, one temperature sensor and one relative humidity sensor. Each node underwent a calibration process by co-locating the node at an EU reference air quality monitoring station, followed by a deployment in a sub-urban or rural area in Catalonia, Spain. All nodes spent two to three weeks co-located at a reference station in Barcelona, Spain (urban area), followed by two to three weeks co-located at three sub-urban reference stations near the final deployment site. The nodes were then deployed in volunteers' homes for about two months and, finally, the nodes were co-located again at the sub-urban reference stations for two weeks for final calibration and assessment of potential drifts. All data presented in this paper are raw data taken by the sensors that can be used for scientific purposes such as calibration studies using machine learning algorithms, or once the concentration values of the nodes are obtained, they can be used to create tropospheric ozone pollution maps with heterogeneous data sources (reference stations and low-cost sensors).