03 - AirSensEUR: An Open-Designed Multi-Sensor Platform for Air Quality Monitoring

M., Gerboles,; Spinelle, Laurent; Kotsev, A.; Signorini, Martina

doi:10.5162/4eunetair2015/03

Cited by 19 publications

(13 citation statements)

References 1 publication

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“…New sensors emerging on the market, such as metal-oxide [ 9 ] and electrochemical [ 10 ] sensors, or those using light-scattering principles [ 5 ], offer new research opportunities. Recent works [ 3 , 15 , 17 ] target the collection of real-time air quality data with the aim of improving the existing infrastructure of fixed air quality monitoring stations in cities by improving temporal and spatial resolutions.…”

Section: Data Correction and Calibration Methodsmentioning

confidence: 99%

“…In order to make use of low-cost sensors, a couple of steps are required [ 7 , 8 , 9 , 10 , 14 , 15 , 17 , 18 , 19 , 20 ]: design a small device using low-cost sensors; calibrate the device in closed controlled chambers in a laboratory using known gas concentrations; assess the effectiveness of the calibration algorithm by placing the units outside in colocation with a reference station; compare the calibrated results with the reference results. …”

Section: Data Correction and Calibration Methodsmentioning

confidence: 99%

“…The sensors used in our devices were evaluated by other studies [ 10 , 14 ], and the reported results showed promising usability of them in a real-world scenario. The contribution of this work is the development and validation of calibration models based on state-of-the-art multivariate linear regression (MLR) models and random forest (RF) algorithms [ 5 , 8 , 14 , 15 , 16 ] such that the low-cost sensor platform meets the DQO.…”

Section: Introductionmentioning

confidence: 99%

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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: Data Correction and Calibration Methodsmentioning

confidence: 99%

Section: Data Correction and Calibration Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…This sensor is part of the platform developed by the Joint Research Centre (JRC) of the European Commission: AirSensEUR. It is an air quality monitoring system developed as an open software/open hardware object and complies with the INSPIRE Directive [ 20 ]. Apart from the Alphasense NO 2 sensor, the AirSensEUR configuration used in this experiment also contained a Membrapor O 3 /M-5 for ozone measurements and dedicated meteorological sensors for temperature, air pressure, and relative humidity.…”

Section: Methodsmentioning

confidence: 99%

Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations

Ratingen

Vonk

Blokhuis

et al. 2021

Sensors

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Low-cost sensor technology has been available for several years and has the potential to complement official monitoring networks. The current generation of nitrogen dioxide (NO2) sensors suffers from various technical problems. This study explores the added value of calibration models based on (multiple) linear regression including cross terms on the performance of an electrochemical NO2 sensor, the B43F manufactured by Alphasense. Sensor data were collected in duplicate at four reference sites in the Netherlands over a period of one year. It is shown that a calibration, using O3 and temperature in addition to a reference NO2 measurement, improves the prediction in terms of R2 from less than 0.5 to 0.69–0.84. The uncertainty of the calibrated sensors meets the Data Quality Objective for indicative methods specified by the EU directive in some cases and it was verified that the sensor signal itself remains an important predictor in the multilinear regressions. In practice, these sensors are likely to be calibrated over a period (much) shorter than one year. This study shows the dependence of the quality of the calibrated signal on the choice of these short (monthly) calibration and validation periods. This information will be valuable for determining short-period calibration strategies.

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“…The development of our device has been largely inspired by the works of Gerboles et al (2015) and Gerboles et al (2016) on the open data/software/hardware multi-sensor AirSensEUR platform. Other relevant open source systems are now available, e.g.…”

Section: Introductionmentioning

confidence: 99%

Antilope, A Portable Low-Cost Sensor System for the Assessment of Indoor and Outdoor Air Pollution Exposure

et al. 2021

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The increasing availability of low-cost sensors and open source projects make it easier than ever for a maker to build his own air quality node. Nonetheless, depending on one’s goal and its related data quality objective, to customize an existing project or to build a specific printed circuit board may still be very useful. In the framework of the Outdoor and Indoor Exposure project, a portable mini-station has been developed, tested and then used in two experiments: exposure assessment and complementary network measurement. The present paper focuses on the description of the equipment that was designed and prototyped, as well as on the tests that were made in the lab and in the field to evaluate its overall performance and that of its different sensors. Finally, we present what we consider to be its main drawbacks and our perspectives for further development and tests.

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03 - AirSensEUR: An Open-Designed Multi-Sensor Platform for Air Quality Monitoring

Cited by 19 publications

References 1 publication

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

Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations

Antilope, A Portable Low-Cost Sensor System for the Assessment of Indoor and Outdoor Air Pollution Exposure

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