Data reliability and fault diagnostic for air quality monitoring station based on low cost sensors and active redundancy

Poupry, Sylvain; Medjaher, Kamal; Béler, Cédrick

doi:10.1016/j.measurement.2023.113800

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Specifically, [37] reports on the identification of aging factors in low-cost electrochemical sensors for measurement corrections. Other research works present the application of low-cost sensors for air quality monitoring [38][39][40], and [41] presents the artificial intelligence method of a memory-based precise calibration of a cost-efficient NO 2 sensor, and data reliability and fault diagnostics for an air quality monitoring station based on low-cost sensors and active redundancy, studied in [42].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Regression Approach as a Correction Factor of Measurements of Low-Cost Electrochemical Air Quality Sensors

Christakis,

Tsakiridis,

Sarri

et al. 2024

Applied Sciences

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Air quality directly affects the health of humans. The health implications of poor air quality are recognized by professionals and the public alike and these concerns have driven both the proliferation of formal sensor networks, but also low-cost sensors which can be used in the home. The advancement of technology in recent years has also led to the rapid development of low-cost sensors. Given that citizens are concerned about the air quality of the environment in which they live, they are turning to the supply of low-cost sensors, as they are affordable. The question of the reliability of measurements from low-cost sensors remains an area of research. In this research work, the optimization of ozone (O3) and nitrogen dioxide (NO2) measurements of low-cost electrochemical air quality sensors is investigated by applying nonlinear regression, using a second-order polynomial equation as a correction factor. The proposed correction method is implementable in IoT devices, as it does not require high computational resources. The results show that the measurements are susceptible to correction, with the effect that the corrected values are close to the actual values obtained by the reference instruments of the Department of Environmental Pollution Control Project of Athens (PERPA), a service of the Greek Ministry of the Environment and Energy.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Regression Approach as a Correction Factor of Measurements of Low-Cost Electrochemical Air Quality Sensors

Christakis,

Tsakiridis,

Sarri

et al. 2024

Applied Sciences

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“…The latter is essential for urban areas with diverse pollutant distributions 15 – 17 . Notwithstanding, low-cost sensors encounter reliability limitations 18 – 20 due to instability 21 , fabrication inaccuracies 22 , 23 , and cross-sensitivity to multiple gases 24 – 26 . They are also sensitive to environmental conditions, especially temperature and humidity 27 , 28 .…”

Section: Introductionmentioning

confidence: 99%

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning

Koziel,

Pietrenko-Dabrowska,

Wojcikowski

et al. 2024

Sci Rep

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Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

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Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing

Koziel,

Pietrenko-Dabrowska,

Wojcikowski

et al. 2024

Engineering Science and Technology, an International Journal

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Data reliability and fault diagnostic for air quality monitoring station based on low cost sensors and active redundancy

Cited by 5 publications

References 21 publications

Nonlinear Regression Approach as a Correction Factor of Measurements of Low-Cost Electrochemical Air Quality Sensors

Nonlinear Regression Approach as a Correction Factor of Measurements of Low-Cost Electrochemical Air Quality Sensors

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning

Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing

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