Wearable system for outdoor air quality monitoring in a WSN with cloud computing: Design, validation and deployment

被引:19
|
作者
Palomeque-Mangut, Sergio [1 ]
Melendez, Felix [1 ]
Gomez-Suarez, Jaime [1 ]
Frutos-Puerto, Samuel [2 ]
Arroyo, Patricia [1 ]
Pinilla-Gil, Eduardo [2 ]
Lozano, Jesus [1 ]
机构
[1] Univ Extremadura, Dept Elect Technol Elect & Automat, Avda Elvas S-N, Badajoz 06006, Spain
[2] Univ Extremadura, Dept Analyt Chem, Avda Elvas S-N, Badajoz 06006, Spain
关键词
Air quality monitoring; Gas sensors; Metal oxide semiconductor; MOS devices; Smartphone; Particulate matter; Wireless sensor network; WSN; Air quality index; AQI; METAL-OXIDE NANOSTRUCTURES; GAS SENSORS; POLLUTION; EXPOSURE;
D O I
10.1016/j.chemosphere.2022.135948
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Breathing poor-quality air is a global threat at the same level as unhealthy diets or tobacco smoking, so the availability of affordable instrument for the measurement of air pollutant levels is highly relevant for human and environmental protection. We developed an air quality monitoring platform that comprises a wearable device embedding low-cost metal oxide semiconductor (MOS) gas sensors, a PM sensor, and a smartphone for collecting the data using Bluetooth Low Energy (BLE) communication. Our own developed app displays information about the air surrounding the user and sends the gathered geolocalized data to a cloud, where the users can map the air quality levels measured in the network. The resulting device is small-sized, light-weighted, compact, and belt-worn, with a user-friendly interface and a low cost. The data collected by the sensor array are validated in two experimental setups, first in laboratory-controlled conditions and then against referential pollutant concentrations measured by standard instruments in an outdoor environment. The performance of our air quality platform was tested in a field testing campaign in Barcelona with six moving devices acting as wireless sensor nodes. Devices were trained by means of machine learning algorithms to differentiate between air quality index (AQI) referential concentration values (97% success in the laboratory, 82.3% success in the field). Humidity correction was applied to all data.
引用
收藏
页数:9
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