Laboratory Evaluation and Calibration of Low-Cost Sensors for Air Quality Measurement

Ionascu, Marian-Emanuel; Gruicin, Iasmina; Marcu, Marius

doi:10.1109/saci.2018.8440974

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…The calibration methods proposed in this work were designed for the air quality measurement device platform described in [ 24 ], under the premise that low-level data acquisition and filtering is available [ 25 ].…”

Section: Implementation Of Calibration Modelsmentioning

confidence: 99%

Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring

Ionascu

Castell

Boncalo

et al. 2021

Sensors

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During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O3 gases and a similar accuracy for the NO2 gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.

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Section: Implementation Of Calibration Modelsmentioning

confidence: 99%

Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring

Ionascu

Castell

Boncalo

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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“…There is a number of applications in low-and middle-income countries (e.g. Nagendra et al, 2019;Pope et al, 2018), in studies which included mobile measurements within the urban environment (Ionascu et al, 2018;Jerrett et al, 2017;Miskell et al, 2018), or studies of indoor air quality from multiple sites, such as the SKO-MOBO project conducted in New Zealand, in which the air quality in schools was assessed (Weyers et al, 2018). The greatest advantage though is likely, as their name implies, their lower cost, which made possible the formation of a network of measuring stations (Feinberg et al, 2019;Kotsev et al, 2016;Moltchanov et al, 2015), increasing the spatial resolution and through new data analysis methods improving the mapping of air pollution up to a sub-neighbourhood level (Schneider et al, 2017;Shindler, 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study

et al. 2021

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Abstract. Measurement and source apportionment of atmospheric pollutants are crucial for the assessment of air quality and the implementation of policies for their improvement. In most cases, such measurements use expensive regulatory-grade instruments, which makes it difficult to achieve wide spatial coverage. Low-cost sensors may provide a more affordable alternative, but their capability and reliability in separating distinct sources of particles have not been tested extensively yet. The present study examines the ability of a low-cost optical particle counter (OPC) to identify the sources of particles and conditions that affect particle concentrations at an urban background site in Birmingham, UK. To help evaluate the results, the same analysis is performed on data from a regulatory-grade instrument (SMPS, scanning mobility particle sizer) and compared to the outcomes from the OPC analysis. The analysis of the low-cost sensor data manages to separate periods and atmospheric conditions according to the level of pollution at the site. It also successfully identifies a number of sources for the observed particles, which were also identified using the regulatory-grade instruments. The low-cost sensor, due to the particle size range measured (0.35 to 40 µm), performed rather well in differentiating sources of particles with sizes greater than 1 µm, though its ability to distinguish their diurnal variation, as well as to separate sources of smaller particles, at the site was limited. The current level of source identification demonstrated makes the technique useful for background site studies, where larger particles with smaller temporal variations are of significant importance. This study highlights the current capability of low-cost sensors in source identification and differentiation using clustering approaches. Future directions towards particulate matter source apportionment using low-cost OPCs are highlighted.

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“…Sensor measurement range pertains to minimum and maximum readings of a sensor. For calibrating a sensor, applying these metrics in the laboratory environment is necessary [12].…”

Section: Air Quality Sensing Processmentioning

confidence: 99%

“…In practice, it is not feasible to test all low-cost sensors next to reference stations for field calibration [11]. Therefore, it is necessary to perform reliable laboratory calibration testings [12]. For example, portable lowcost PM sensors were tested under the condition of steady-state particle mass-concentration [13] or the use of air chamber for validation and calibration PM 2.5 [14].…”

Section: Introductionmentioning

confidence: 99%

Low-cost Air Quality Sensing Process: Validation by Indoor-Outdoor Measurements

Motlagh

Zaidan

Fung

et al. 2020

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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Air pollution is a main challenge in societies with particulate matter PM2.5 as the major air pollutant causing serious health implications. Due to health and economic impacts of air pollution, low-cost and portable air quality sensors can be vastly deployed to gain personal air pollutant exposure. In this paper, we present an air quality sensing process needed for lowcost sensors which are planned for long-term use. The steps of this process include design and production, laboratory tests, field tests, deployment, and maintenance. As a case study we focus on the field test, where we use two generations of a portable air quality sensor (capable of measuring meteorological variables and PM2.5) to perform an indoor-outdoor measurement. The study found that all of the measurements shown to be consistent through validation among themselves. The sensors accuracy also demonstrate to be adequate by showing similar readings compared to the nearest air quality reference station.

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Laboratory Evaluation and Calibration of Low-Cost Sensors for Air Quality Measurement

Cited by 6 publications

References 17 publications

Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring

Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring

Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study

Low-cost Air Quality Sensing Process: Validation by Indoor-Outdoor Measurements

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