THERMAL SCIENCE

International Scientific Journal

Danka B. Stojanović

,

Duška N. Kleut

,

Miloš D. Davidović

,

Saverio De Vito

,

Milena V. Jovašević-Stojanović

,

Alena Bartonova

,

Jean-Marie Lepioufle

LOW-PROCESSING DATA ENRICHMENT AND CALIBRATION FOR PM2.5 LOW-COST SENSORS

ABSTRACT
Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PM, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe.
KEYWORDS
PM2.5. low-cost sensors, data enrichment, calibration, low-processing
PAPER SUBMITTED: 2022-11-09
PAPER REVISED: 2022-12-01
PAPER ACCEPTED: 2022-12-05
PUBLISHED ONLINE: 2023-01-07
DOI REFERENCE: https://doi.org/10.2298/TSCI221109221S
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2023, VOLUME 27, ISSUE Issue 3, PAGES [2229 - 2240]
REFERENCES
  1. WHO global air quality guidelines 2021.
  2. Comunian S. et al. Air Pollution and COVID-19: The Role of Particulate Matter in the Spread and Increase of COVID-19's Morbidity and Mortality, Int. J. Environ Res. Public Health 17, (2020.) pp. 4487.
  3. Bai Y et al. Changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China, Environmental research 209 (2022), pp. 112806.
  4. C-T Yang et al, Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control, J. Hazard. Mat. 419 (2021), pp. 126442.
  5. Jovasevic-Stojanovic M. et al., On the use of small and cheaper sensors and devices for indicative citizen-based monitoring of respirable particulate matter, Enviromental pollution 206- 205, (2015), pp. 696-704
  6. Morawska L. et al., Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? , Environment International 116 (2018), pp. 286-299.
  7. Cho H. and Baek Y., Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensor, Sensors 21 (2021), pp. 6162.
  8. Rai A. C. et al., End-user perspective of low-cost sensors for outdoor air pollution monitoring, Science of the Total Environment 607-608 (2017) pp. 607-608.
  9. Giordano M.R. et al., From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors, Journal of Aerosol Science 158 (2021), 105833.
  10. Liang L. and Daniels J., What Influences Low-cost Sensor Data Calibration?- A Systematic Assessment of Algorithms, Duration, and Predictor Selection, Aerosol and Air Quality Research 22 (2022), pp. 220076.
  11. Liang L., Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges, Enviromental Research 197 (2021), pp. 111163.
  12. Wei, P., Ning, Z., Ye, S., Sun, L., Yang, F., Wong, K.C., Westerdahl, D., Louie, P.K.,Impact analysis of temperature and humidity conditions on electrochemical sensor response in ambient air quality monitoring. Sensors 18 (2018), pp. 59.
  13. Badura, M., Batog, P., Drzeniecka-Osiadacz, A., Modzel, P., Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements. SN Appl. Sci. 1 (2019), pp. 622.
  14. Jiao, W., et al.,Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States. Atmos. Meas. Tech. 9 (2016), pp. 5281-5292.
  15. Loh, B.G., Choi, G.H.,Calibration of portable particulate matter-monitoring device using web query and machine learning, Saf. Health Work. 10 (2019), pp. 452-460.
  16. Chojer H. et al., Can data reliability of low-cost sensor devices for indoor air particulate matter monitoring be improved? - An approach using machine learning, Atmospheric Environment 286 (2022), pp. 119251.
  17. Topalovic D. et al., In search of an optimal in-field calibration method of low-cost gas sensors for ambient air pollutants: Comparison of linear, multilinear and artificial neural network approaches, Atmospheric Environment 213 (2019), pp. 640-658.
  18. Schneider P. et al., Toward a Unified Terminology of Processing Levels for Low-Cost Air-Quality Sensors(2019) Environ. Sci. Technol. 53, 15 (2019), pp. 8485-8487.
  19. AllenM. and Cervo D., Multi-domain master data management: Advanced MDM and data governance in practice. Morgan Kaufmann, 2015.
  20. Eamonn J. K. and Pazzani M. J., An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback., KDD-98 Proceedings98(1998), PP. 239.
  21. Knapp, E. D., and Langill j., Industrial Network Security: Securing critical infrastructure networks for smart grid, SCADA, and other Industrial Control Systems. Syngress, 2014.
  22. Rhif M. et al.,Wavelet transform application for/in non-stationary time-series analysis: a review, Applied Sciences 9.7 (2019), pp. 1345.
  23. Wojcikowski M., et al. A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants, Measurement 200 (2022), pp. 111601.
  24. co.citi-sense.eu
  25. Gilliam, J. and Hall, E., Reference and Equivalent Methods Used to Measure National Ambient Air Quality Standards (NAAQS) Criteria Air Pollutants, vol. I U.S. Environmental Protection Agency, Washington, 2016.
  26. Polidori, A., P., V., Feenstra, B., Z., H., Der Boghossian, B., 2016. Field evaluation aqmesh monitor (v.4.0), south coast air quality performance evaluation center (Last accessed, August 2018).
  27. AQMESH Technical Specification www.AQMESH.com/produt/technical-details/ (Accessed October 2017).
  28. Eilers, P., and Marx, B., Flexible Smoothing with B-splines and Penalties, Statist. Sci. 11 (1996), pp. 89-121.
  29. Perperoglou, A. et al. A review of spline function procedures in R. BMC Med Res Methodol 19 (2019)., pp. 46.
  30. Butterworth, S. On the Theory of Filter Amplifiers, Experimental Wireless and the Wireless Engineer. 7 (1930), pp. 536-541.
  31. Badura M. et al, Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements, SN Appl. Sci. 1(2019), pp. 622.
  32. Thomas E.V. and Haaland D.M.. , Comparison of Multivariate Calibration Methods for Quantitative Spectral Analysis, Anal. Chem. 62 (1990), pp. 1091-1099.
  33. Breiman, L. Random Forests, Mach. Learn. 45 (2001), pp. 5-32.
  34. Lepioufle J-M., Marsteen L., Johnsrud M., Error Prediction of Air Quality at Monitoring Stations Using Random Forest in a Total Error Framework, Sensors 21(2021), pp. 2160.
  35. N. Castell et al., Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment International 99 (2017), pp. 293-302.
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Low-processing data enrichment and calibration for PM2.5 low-cost sensors

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