Bluetooth gas sensing module combined with smartphones for air quality monitoring. (August 2018)

• Record Type: Journal Article
• Authors: Suárez, José Ignacio; Arroyo, Patricia; Lozano, Jesús; Herrero, José Luis; Padilla, Manuel
• Is Part Of: Chemosphere. Volume 205(2018)
• Page Start: 618
• Page End: 626
• Publication Date: 2018-08
• Subjects: Gas sensors -- Machine olfaction -- Smartphones -- Air quality -- Bluetooth; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2018.04.154
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: This study addresses the development of a miniaturized (60 × 60 mm) Wireless Sensing Module (WSM) for environmental application and air quality detection. The proposed prototype has six sensors: one for humidity, one for ambient temperature (SHT21 from Sensirion), and four for gas detection (MiCS-4514, MiCS-5526 and MiCS-5914 from SGX Sensortech). The core of the system is based on a high performance 8-bit microcontroller, model PIC18F46K80, from Microchip. The obtained data values were transmitted to the Smartphone through a Bluetooth communication module and a home-developed Android app. The discrimination capability of the module is tested with 10 volatile organic compounds (acetone, acetic acid, benzene, ethanol, ethyl acetate, ethylbenzene, formaldehyde, toluene, xylene, and dimethylacetamide) and the effect of humidity and drift of the sensors is also studied. Results show that 88.33% and 92.22% success rates in classification stage are obtained using Multilayer Perceptron with BackPropagation Learning algorithm and Radial-Basis based Neural Networks, respectively. Graphical abstract: Image 1 Highlights: A portable (60x60mm) electronic nose with Bluetooth communication has been designed. A home-develop application for Android has been developed for control and monitoring the device, on-line classification and location, etc. 10 different pollutants have been detected and identified by the device using two different neural networks classification. The influence of the ambient humidity has been tested and modelled. It is one of the most reduced size of that founded in the bibliography.