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

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...

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Detalhes bibliográficos
Autores: Barceló-Ordinas, José María, Ferrer-Cid, Pau, García Vidal, Jorge, Viana, Mar, Ripoll, Anna
Formato: conjunto de datos
Fecha de publicación:2021
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/285142
Acesso em linha:http://hdl.handle.net/10261/285142
Access Level:acceso abierto
Palavra-chave:Air quality
Low-cost sensors
Ozone MOX sensors
Calibration of sensors
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dc.title.none.fl_str_mv H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network
title H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network
spellingShingle H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network
Barceló-Ordinas, José María
Air quality
Low-cost sensors
Ozone MOX sensors
Calibration of sensors
title_short H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network
title_full H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network
title_fullStr H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network
title_full_unstemmed H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network
title_sort H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network
dc.creator.none.fl_str_mv Barceló-Ordinas, José María
Ferrer-Cid, Pau
García Vidal, Jorge
Viana, Mar
Ripoll, Anna
author Barceló-Ordinas, José María
author_facet Barceló-Ordinas, José María
Ferrer-Cid, Pau
García Vidal, Jorge
Viana, Mar
Ripoll, Anna
author_role author
author2 Ferrer-Cid, Pau
García Vidal, Jorge
Viana, Mar
Ripoll, Anna
author2_role author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Barceló-Ordinas, José María [000-0002-9738-2425]
Ferrer-Cid, Pau [0000-0003-2112-8516]
García Vidal, Jorge [0000-0001-5969-1182]
Viana, Mar [0000-0002-4073-3802]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Air quality
Low-cost sensors
Ozone MOX sensors
Calibration of sensors
topic Air quality
Low-cost sensors
Ozone MOX sensors
Calibration of sensors
description 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 here 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 a reference station, followed by a deployment in a non-urban 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 non-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 non-urban reference stations for two weeks. All data presented in this repository 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 sources (reference stations and low-cost sensors).
publishDate 2021
dc.date.none.fl_str_mv 2021
2022
2022
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/285142
url http://hdl.handle.net/10261/285142
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/688110
Barceló-Ordinas, José María; Ferrer-Cid, Pau; García-Vidal, Jorge; Ripoll, Anna; Viana, Mar. Distributed multi-scale calibration of low-cost ozone sensors in wireless sensor networks. Sensors 19 (11) 2503 (2019). https://doi.org/10.3390/s19112503 . http://hdl.handle.net/10261/200321
Ripoll, Anna CSIC; Viana, Mar; Padrosa, M.; Querol, Xavier; Minutolo, A.; Hou, K. M.; Barceló-Ordinas, José María; García-Vidal, Jorge. Testing the performance of sensors for ozone pollution monitoring in a citizen science approach. Science of the Total Environment 651: 1166-1179 (2019). https://doi.org/10.1016/j.scitotenv.2018.09.257 . http://hdl.handle.net/10261/170478
Jose M. Barcelo-Ordinasa; Messaud Doudou; Jorge Garcia-Vidal; Nadjib Badach. Self-calibration methods for uncontrolled environments in sensor networks: A reference survey. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2019.01.008
Ferrer-Cid, Pau; Barceló-Ordinas, José María; García-Vidal, Jorge; Ripoll, A.; Viana, Mar. A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms. IEEE INTERNET OF THINGS JOURNAL 6: 9563- 9571 (2019). http://dx.doi.org/10.1109/JIOT.2019.2929594 . http://hdl.handle.net/10261/209843
Ferrer-Cid, Pau; Barceló-Ordinas, José María; García Vidal, Jorge; Ripoll, Anna; Viana, Mar. Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. IEEE Internet of Things Journal 7 (4): 3124-3132 (2020). https://doi.org/10.1109/JIOT.2020.2965283 . http://hdl.handle.net/10261/217105
https://doi.org/10.5281/zenodo.4570449
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dc.publisher.none.fl_str_mv Zenodo
publisher.none.fl_str_mv Zenodo
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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spelling H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring networkBarceló-Ordinas, José MaríaFerrer-Cid, PauGarcía Vidal, JorgeViana, MarRipoll, AnnaAir qualityLow-cost sensorsOzone MOX sensorsCalibration of sensorsThe 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 here 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 a reference station, followed by a deployment in a non-urban 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 non-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 non-urban reference stations for two weeks. All data presented in this repository 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 sources (reference stations and low-cost sensors).Peer reviewedZenodoEuropean CommissionBarceló-Ordinas, José María [000-0002-9738-2425]Ferrer-Cid, Pau [0000-0003-2112-8516]García Vidal, Jorge [0000-0001-5969-1182]Viana, Mar [0000-0002-4073-3802]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222021info:eu-repo/semantics/datasethttp://purl.org/coar/resource_type/c_ddb1text/csvhttp://hdl.handle.net/10261/285142reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/688110Barceló-Ordinas, José María; Ferrer-Cid, Pau; García-Vidal, Jorge; Ripoll, Anna; Viana, Mar. Distributed multi-scale calibration of low-cost ozone sensors in wireless sensor networks. Sensors 19 (11) 2503 (2019). https://doi.org/10.3390/s19112503 . http://hdl.handle.net/10261/200321Ripoll, Anna CSIC; Viana, Mar; Padrosa, M.; Querol, Xavier; Minutolo, A.; Hou, K. M.; Barceló-Ordinas, José María; García-Vidal, Jorge. Testing the performance of sensors for ozone pollution monitoring in a citizen science approach. Science of the Total Environment 651: 1166-1179 (2019). https://doi.org/10.1016/j.scitotenv.2018.09.257 . http://hdl.handle.net/10261/170478Jose M. Barcelo-Ordinasa; Messaud Doudou; Jorge Garcia-Vidal; Nadjib Badach. Self-calibration methods for uncontrolled environments in sensor networks: A reference survey. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2019.01.008Ferrer-Cid, Pau; Barceló-Ordinas, José María; García-Vidal, Jorge; Ripoll, A.; Viana, Mar. A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms. IEEE INTERNET OF THINGS JOURNAL 6: 9563- 9571 (2019). http://dx.doi.org/10.1109/JIOT.2019.2929594 . http://hdl.handle.net/10261/209843Ferrer-Cid, Pau; Barceló-Ordinas, José María; García Vidal, Jorge; Ripoll, Anna; Viana, Mar. Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. IEEE Internet of Things Journal 7 (4): 3124-3132 (2020). https://doi.org/10.1109/JIOT.2020.2965283 . http://hdl.handle.net/10261/217105https://doi.org/10.5281/zenodo.4570449Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2851422026-05-22T06:33:51Z
score 15,198674

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