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Título: | H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network |
Autor: | Barceló-Ordinas, José María; Ferrer-Cid, Pau; García Vidal, Jorge; Viana, Mar CSIC ORCID ; Ripoll, Anna CSIC | Palabras clave: | Air quality Low-cost sensors Ozone MOX sensors Calibration of sensors |
Fecha de publicación: | 1-mar-2021 | Editor: | Zenodo | Citación: | Barceló-Ordinas, José María; Ferrer-Cid, Pau; García Vidal, Jorge; Viana, Mar; Ripoll, Anna; 2021; H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network [Dataset]; Zenodo; Version 1; https://doi.org/10.5281/zenodo.4570449 | Resumen: | 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). | Versión del editor: | https://doi.org/10.5281/zenodo.4570449 | URI: | http://hdl.handle.net/10261/285142 | DOI: | 10.5281/zenodo.4570449 | Referencias: | 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 |
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