This paper introduces a miniaturized personal electronic nose (39 mm × 33 mm), which is managed through an app developed on a smartphone. The electronic nose (e-nose) incorporates four new generation digital gas sensors. These MOx-type sensors incorporate a microcontroller in the same package, being also smaller than the previous generation. This makes it easier to integrate them into the electronics and improves their performance. In this research, the application of the device is focused on the detection of atmospheric pollutants in order to complement the information provided by the reference stations. To validate the system, it has been tested with different concentrations of NOx including some tests specifically developed to study the behavior of the device in different humidity conditions. Finally, a mobile application has been developed to provide classification services. In this regard, a neural network has been developed, trained, and integrated into a smartphone to process the information retrieved from e-nose devices.

/pdf/electronic-nose-with-digital-gas-sensors-connected-via-4v3mnvujer.pdf

Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements

Abstract Humans spend the majority of their time indoors, where they are potentially exposed to hazardous pollutants. Within this context, over the past few years, there has been an upsurge of low-cost sensors (LCS) for the measurement of indoor air pollutants, motivated both by recent technological advances and by increased awareness of indoor air quality (IAQ) and its potential negative health impacts. Although not meeting the performance requirements for reference regulatory-equivalent monitoring indoors, LCS can provide informative measurements, offering an opportunity for high-resolution monitoring, emission source identification, exposure mitigation and managing IAQ and energy efficiency, among others. This article discusses the strengths and limitations that LCS offer for applications in the field of IAQ monitoring; it provides an overview of existing sensor technologies and gives recommendations for different indoor applications, considering their performance in the complex indoor environment and discussing future trends.

https://doi.org/10.1080/05704928.2022.2085734

Review of low-cost sensors for indoor air quality: Features and applications

Hydrothermal synthesis of copper (׀׀) oxide-nanoparticles with highly enhanced BTEX gas sensing performance using chemiresistive sensor.

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

/pdf/data-driven-techniques-for-low-cost-sensor-selection-and-1boeki8i.pdf

Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring

IoT-Based Air Quality Monitoring in Hair Salons: Screening of Hazardous Air Pollutants Based on Personal Exposure and Health Risk Assessment

The objective of this study was to examine the sensor response characteristics of three commercial Internet of Things (IoT) compatible metal oxide (MOx) sensors in preparation for the development of field-scale sensor networks for the real-time monitoring of volatile organic compounds (VOCs) in indoor environments located in proximity to brownfield sites. Currently, there is limited examination of such sensor responses to relevant mixtures of target VOCs, such as the common petrochemicals benzene, toluene, ethylbenzene, and xylene (BTEX), as well as chlorinated aliphatic hydrocarbon (CAH) contaminants such as tetrachloroethylene (PCE) and trichloroethylene (TCE) which are frequently associated with deterioration of indoor air quality. To address this, a study of three commercial metal oxide (MOx) sensors (SGP30, BME680, and CCS811) was undertaken to examine the sensor response characteristics of individual components as well as mixtures of each of the target BTEX and CAH chemicals over relevant indoor air concentrations within the operating range of the MOx sensors (0–6000 ppb). Our investigation revealed similar response patterns to those previously reported for the thick film MOx sensor to most individual target VOCs, however, response trends for mixtures were more difficult to discern. In general, the MOx sensors we examined demonstrated similar magnitude responses to the CAHs as BTEX compounds indicating reliable detection of CAHs.

Real-Time Sensor Response Characteristics of 3 Commercial Metal Oxide Sensors for Detection of BTEX and Chlorinated Aliphatic Hydrocarbon Organic Vapors

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Real-Time Sensor Response Characteristics of 3 Commercial Metal Oxide Sensors for Detection of BTEX and Chlorinated Aliphatic Hydrocarbon Organic Vapors