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Towards Wearable Air Quality Monitoring Systems - Initial Assessments on Newly Developed Sensors

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Wearable sensors have gained enormous attention because of their capability to do real-time monitoring of health as well as the environment. A wearable sensing device mainly relies on flexible sensors incorporated within it that undergo mechanical deformations during utilization. The applications of wearable sensors in various sectors like homeland security, defense, regular monitoring of environment and human health, etc. are motivating researchers to develop the most reliable and highly efficient flexible sensors. In recent years, two-dimensional (2D) transition metal dichalcogenide (TMDC)-based flexible sensors have been rigorously developed for gas detection due to their special attributes like high surface-to-volume ratio, tunable bandgap, robust mechanical strength, high conformability on different substrates, and low power consumption. The most important is the ability to operate at room temperature, which is not easily attainable in metal oxides and other gas-sensing materials. The room temperature detection of gases is essential in some vital applications, like continuous health monitoring and medical diagnosis. Therefore, the key focus of this review is on the recent progress and future aspects of room temperature operable energy-efficient flexible gas sensors fabricated by utilizing 2D-TMDCs. The properties of 2D-TMDCs favorable for a flexible sensor, sensing mechanism, and different approaches to optimize the performance parameters of flexible gas sensors are reviewed. In addition, various strategies and underlying challenges encountered through the integration of the sensing material into e-skin/textile-oriented gas sensors along with promising solutions for these challenges are discussed. Future prospects of flexible gas sensors with respect to their performance, biocompatibility, and durability for realization at the commercial level have also been deliberated.
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The World Health Organization (WHO) recognizes the environmental, behavioral, physiological, and psychological domains that impact adversely human health, well-being, and quality of life (QoL) in general. The environmental domain has significant interaction with the others. With respect to proactive and personalized medicine and the Internet of medical things (IoMT), wearables are most important for continuous health monitoring. In this work, we analyze wearables in healthcare from a perspective of innovation by categorizing them according to the four domains. Furthermore, we consider the mode of wearability, costs, and prolonged monitoring. We identify features and investigate the wearable devices in the terms of sampling rate, resolution, data usage (propagation), and data transmission. We also investigate applications of wearable devices. Web of Science, Scopus, PubMed, IEEE Xplore, and ACM Library delivered wearables that we require to monitor at least one environmental parameter, e.g., a pollutant. According to the number of domains, from which the wearables record data, we identify groups: G1, environmental parameters only; G2, environmental and behavioral parameters; G3, environmental, behavioral, and physiological parameters; and G4 parameters from all domains. In total, we included 53 devices of which 35, 9, 9, and 0 belong to G1, G2, G3, and G4, respectively. Furthermore, 32, 11, 7, and 5 wearables are applied in general health and well-being monitoring, specific diagnostics, disease management, and non-medical. We further propose customized and quantified output for future wearables from both, the perspectives of users, as well as physicians. Our study shows a shift of wearable devices towards disease management and particular applications. It also indicates the significant role of wearables in proactive healthcare, having capability of creating big data and linking to external healthcare systems for real-time monitoring and care delivery at the point of perception.
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The use of low-cost air quality sensors has proliferated among non-profits and citizen scientists, due to their portability, affordability, and ease of use. Researchers are examining the sensors for their potential use in a wide range of applications, including the examination of the spatial and temporal variability of particulate matter (PM). However, few studies have quantified the performance (e.g., accuracy, precision, and reliability) of the sensors under real-world conditions. This study examined the performance of two models of PM sensors, the AirBeam and the Alphasense Optical Particle Counter (OPC-N2), over a 12-week period in the Cuyama Valley of California, where PM concentrations are impacted by wind-blown dust events and regional transport. The sensor measurements were compared with observations from two well-characterized instruments: the GRIMM 11-R optical particle counter, and the Met One beta attenuation monitor (BAM). Both sensor models demonstrated a high degree of collocated precision (R² = 0.8–0.99), and a moderate degree of correlation against the reference instruments (R² = 0.6–0.76). Sensor measurements were influenced by the meteorological environment and the aerosol size distribution. Quantifying the performance of sensors in real-world conditions is a requisite step to ensuring that sensors will be used in ways commensurate with their data quality.
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The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality.
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In this work the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques, are compared. A cluster of either metal oxide or electrochemical sensors for nitrogen monoxide and carbon monoxide together with miniaturized infra-red carbon dioxide sensors was operated. Calibration was carried out during the two first weeks of evaluation against reference measurements. The accuracy of each regression method was evaluated on a five months field experiment at a semi-rural site using different indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. In addition to the analyses for ozone and nitrogen oxide already published in Part A [1], this work assessed if carbon monoxide sensors can reach the Data Quality Objective (DQOs) of 25% of uncertainty set in the European Air Quality Directive for indicative methods. As for ozone and nitrogen oxide, it was found for NO, CO and CO2 that the best agreement between sensors and reference measurements was observed for supervised learning techniques compared to linear and multilinear regression.
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The performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques are compared. A cluster of ozone, nitrogen dioxide, nitrogen monoxide, carbon monoxide and carbon dioxide sensors was operated. The sensors were either of metal oxide or electrochemical type or based on miniaturized infra-red cell. For each method, a two-week calibration was carried out at a semi-rural site against reference measurements. Subsequently, the accuracy of the predicted values was evaluated for about five months using a few indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. The study assessed if the sensors were could reach the Data Quality Objective (DQOs) of the European Air Quality Directive for indicative methods (between 25 and 30% of uncertainty for O3 and NO2). In this study it appears that O3 may be calibrated using simple regression techniques while for NO2 a better agreement between sensors and reference measurements was reached using supervised learning techniques. The hourly O3 DQO was met while it was unlikely that NO2 hourly one could be met. This was likely caused by the low NO2 levels correlated with high O3 levels that are typical of semi-rural site where the measurements of this study took place.
Conference Paper
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Air quality monitoring is extremely important as air pollution has a direct impact on human health. Low-cost gas sensors are used to effectively perceive the environment by mounting them on top of mobile vehicles, for example, using a public transport network. Thus, these sensors are part of a mobile network and perform from time to time measurements in each others vicinity. In this paper, we study three calibration algorithms that exploit co-located sensor measurements to enhance sensor calibration and consequently the quality of the pollution measurements on-the-fly. Forward calibration, based on a traditional approach widely used in the literature, is used as performance benchmark for two novel algorithms: backward and instant calibration. We validate all three algorithms with real ozone pollution measurements carried out in an urban setting by comparing gas sensor output to high-quality measurements from analytical instruments. We find that both backward and instant calibration reduce the average measurement error by a factor of two compared to forward calibration. Furthermore, we unveil the arising difficulties if sensor calibration is not based on reliable reference measurements but on sensor readings of low-cost gas sensors which is inevitable in a mobile scenario with only a few reliable sensors. We propose a solution and evaluate its effect on the measurement accuracy in experiments and simulation.
Protocol of evaluation and calibration of low-cost gas sensors for the monitoring of air pollution
  • L Spinelle
  • M Aleixandre
  • M Gerboles
L. Spinelle, M. Aleixandre and M. Gerboles, "Protocol of evaluation and calibration of low-cost gas sensors for the monitoring of air pollution", JRC Technical Reports (Report EUR 26112 EN), 2013.