Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources

Thorson, Jacob; Collier-Oxandale, Ashley; Hannigan, Michael P.

doi:10.3390/s19173723

Cited by 31 publications

(14 citation statements)

References 58 publications

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“…Advancements in machine learning techniques show how LCS can be used for source identification and attribution in regions where little quantitative information currently exists on dominant emission sources (Hagan et al, 2019;Thorson et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Bittner

Cross²,

Hagan³

et al. 2021

Preprint

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Abstract. Low-cost gas and particulate sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a 1-year deployment to rural Malawi using collocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modelling approaches to calibrate the electrochemical gas sensors: k-Nearest Neighbor (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, Ox, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated; they also appeared to be the most transferable approach when applied to field data collected in Malawi. We compared calibrated CO observations to remote sensing data in two regions in Malawi and found qualitative agreement in spatial and annual trends. However, the monthly mean surface observations were 2 to 4 times higher than the remote sensing data, possibly due to proximity to small-scale combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using collocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH > 70 %) conditions and influence from emissions from nearby biomass cookstoves. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi; however, overall data recovery was limited by insufficient power and access to technical resources at deployment sites. Future low-cost sensor deployments to rural Sub-Saharan Africa would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regulatory grade regional infrastructure.

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Section: Discussionmentioning

confidence: 99%

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Bittner

Cross²,

Hagan³

et al. 2021

Preprint

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“…Yet other researchers noted that a higher density of nodes in the monitored area improves monitoring [16][17][18]. Based on publications in recent years, the current trend is the use of low-cost sensors connected to wireless networks.…”

Section: Related Workmentioning

confidence: 99%

“…The authors described a procedure for calibrating sensors to improve the functioning of the monitoring system. The authors in [17] described their research-the development of a low-cost, multi-sensor node for measuring air pollution, as well as protocols to optimize the collection of data from sensors in a WSN. An overview of state-of-art uses for low-cost sensors in environmental monitoring was presented in [18].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Urban Air Pollution Detection Systems

Shakhov

Materuhin

Sokolova

et al. 2022

Sensors

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Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.

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“…Currently, the three most popular types of low-cost air quality sensors are electrochemical sensors (EC), metal-oxide sensors (MOS) and photoionization detectors (PID) [ 22 , 23 ]. Since the objective is to achieve the widest possible distribution of air monitoring sensors in cities, their price is an essential factor.…”

Section: Introductionmentioning

confidence: 99%

An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis

Sales-Lérida

Bello

Sánchez‐Alzola

et al. 2021

Sensors

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Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring.

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Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources

Cited by 31 publications

References 58 publications

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Optimizing Urban Air Pollution Detection Systems

An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis

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