Journal
DATA IN BRIEF
Volume 36, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.dib.2021.107127
Keywords
Low-cost sensors; Metal-oxide sensors; Tropospheric ozone; Calibration of sensors; Machine learning algorithms; Pollution maps
Categories
Funding
- H2020 project CAPTOR
- National Spanish project [PID2019-107910RBI0 0, 2017SGR990]
- Secretaria d'Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu
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The H2020 CAPTOR project deployed three testbeds in Spain, Italy, and Austria with low-cost sensors for measuring tropospheric ozone. The project aimed to raise public awareness through citizen science. Each testbed was supported by an NGO to decide on awareness-raising strategies based on country-specific needs.
The H2020 CAPTOR project deployed three testbeds in Spain, Italy and Austria with low-cost sensors for the measurement of tropospheric ozone (O-3). 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). (C) 2021 The Author(s). Published by Elsevier Inc.
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