Statistical Substantiation of the Revising of Readings by the CityAir Station of PM2.5 Concentration Levels in the Atmospheric Boundary Layer of the City
Keywords:
particulate Matter, PM2.5 concentration level, supervised learning, regression models, sensor system revisingAbstract
As a marker characterizing air pollution in the surface layer of the atmosphere of modern cities, the concentration level of particulate matter with a diameter of 2.5 microns or less (Particulate Matter, PM2.5) is often used. The paper discusses the practice of using a relatively cheap optical sensor, which is part of the CityAir station, to measure the concentration of PM2.5 in an urban environment. The article proposes a statistically justified correction of the primary data obtained by CityAir stations on the values of the concentration of suspended particles PM2.5 in the surface layer of the atmosphere of Krasnoyarsk. For the construction of regression models, measurements obtained from E-BAM analyzers located at the same observation posts as the corrected sensors were considered as a reference. For the analysis, primary data was used 1) from 9 automated observation posts of the regional departmental information and analytical system of data on the state of the environment of the Krasnoyarsk Territory (KVIAS); 2) from the 21st CityAir station of the monitoring system of the Krasnoyarsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences. The paper demonstrates that when correcting sensor readings, it is necessary to take into account meteorological indicators. In addition, it is shown that the regression coefficients significantly depend on the season. Supervised learning methods are compared for solving the problem of correcting the readings of inexpensive sensors. Additional information on the results of data analysis, which was not included in the text of the article, is available on the electronic resource https://asm.krasn.ru/.
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2. Kim B., Lim Y., Wan Cha J. Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based maching learning algorithms. Atmospheric Pollution Research. 2022. vol. 13(10). no. 101547.
3. Perrino C., Catrambone M., Pietrodangelo A. Influence of atmospheric stability on the mass concentration and chemical composition of atmospheric particles: A case study in Rome, Italy. Environment International. 2008. vol. 34. pp. 621–628.
4. Perez P., Menares C., Ramirez C. PM2.5 forecasting in Coyhaique, the most polluted city in the Americas. Urban Climate. 2020. vol. 32. no. 100608.
5. Zhang Zh., Wu L., Chen Y. Forecasting PM2.5 and PM10 concentrations using GMCN(1,N) model with the similar meteorological condition: Case of Shijiazhuang in China. Ecological Indicators. 2020. vol. 119. no. 106871.
6. Yang J., Yan R., Nong M., Liao J., Li F., Sun W. PM2.5 concentrations forecasting in Beijing through deep learning with different inputs model structures and forecast time. Atmospheric Pollution Research. 2021. vol. 12(9). no. 101168.
7. Lychenko N.M., Velikanova L.I., Verzunov S.N., Sorokovaya A.V. [Models for predicting the level of atmospheric air pollution in Bishkek]. Vestnik Kyrgyzsko-Rossiyskogo Slavyanskogo universiteta – Bulletin of the Kyrgyz-Russian Slavic University. 2021. vol. 21. no. 4. pp. 87–95. (In Russ.).
8. Vlachogianni A., Kassomenos P., Karppinen A., Karakitsios S., Kukkonen J. Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki. Science of the total environment. 2011. vol. 409. pp. 1559–1571.
9. Iglesias-Gonzalez S., Huertas-Bolanos M.E., Hernandez-Paniagua I.Y., Mendoza A. Explicit Modeling of Meteorological Explanatory Variables in Short-Term Forecasting of Maximum Ozone Concentrations via a Multiple Regression Time Series Framework. Atmosphere. 2020. vol. 11(12). no. 1304.
10. Zhou Q., Jiang H., Wang J., Zhou J. A hybrid model for PM 2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Science of the Total Environment. 2014. vol. 496. pp. 264–274.
11. Aronov P.M. [Estimation of consensus value of interlaboratory measurement results accompanied by a minimum increase in associated uncertainty]. Etalony. Standartnye obrazcy – Standards. Reference materials. 2019. vol. 15. no. 4. pp. 49–52. (In Russ.).
12. Noskov S.I. [Maximum Consistency Method in Regression Analysis]. Izvestija TulGU. Tehnicheskie nauki – Proceedings of TulGU. Technical science. 2021. № 10. С. 380–385. (In Russ.).
13. Badura M., Batog P., Drzeniecka-Osiadacz A., Modzel P. Evaluation of Low-Cost Sensors for Ambient PM 2.5 Monitoring. Journal of Sensors. 2018. vol. 1. no. 5096540.
14. Shen H., Hou W., Zhu Y., Zheng S., Ainiwaer S., Shen G., Chen Y., Cheng H., Hu J., Wan Y., Tao S. Temporal and spatial variation of PM2.5 in indoor air monitored by low-cost sensors. Science of The Total Environment. 2021. vol. 770. no. 145304.
15. Jayaratne R., Liu X., Ahn K.H., Asumadu-Sakyi A., Fisher G., Gao J., Mabon A., Mazaheri M., Mullins B., Nyaku M., Ristovski Z., Scorgie Y., Thai P., Dunbabin M., Morawska L. Low-cost PM2.5 sensors: An assessment of their suitability for variousapplications. Aerosol and Air Quality Research. 2020. vol. 20. no. 3. pp. 520–532.
16. Gao M., Cao J., Seto E. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi’an, China. Environmental Pollution. 2015. vol. 199. pp. 56–65.
17. Wang W., Lung S., Liu Ch. Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network. Sensors. 2020. vol. 20(17). no. 5002.
18. Bi J., Stowell J., et al. Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA. Environmental research. 2020. vol. 180. no. 108810.
19. E-BAM particulate monitor operation manual. Available at: https://metone.com/wp-content/uploads/2022/06/E-BAM-9805-Manual-Rev-G.pdf (accessed: 08.05.2023).
20. Environmental Technology Verification Report. Available at: https://archive.epa.gov/nrmrl/archive-etv/web/pdf/01_vr_metone_bam1020.pdf (accessed: 18.08.2023).
21. Zavoruev V.V., Yakubailik O.E., Kadochnikov A.A., Tokarev A.V. [Air monitoring system of the Krasnoyarsk Scientific Center of the SB RAS]. Regionalnyye problem distantsionnogo zondirovaniya Zemli: Materialy VII Mezhdunarodnoy nauchnoy konferentsii [Regional problems of remote sensing of the Earth: Proceedings of the VII International Scientific Conference]. Krasnoyarsk: SFU. 2020. pp. 70-73. (In Russ.).
22. Stancija monitoringa vozduha CityAir. Jelektronnyj resurs [ Air Monitoring Station CityAir. Electronic resource]. Available at: https://cityair.ru/ru/equipment/ (accessed: 08.05.2023). (In Russ.).
23. Monitoring sostojanija vozduha. Jelektronnyj resurs [Air state monitoring. Electronic resource.] Available at: https://asm.krasn.ru/ (accessed: 08.05.2023). (In Russ.).
24. Tyuki J. Analiz rezul’tatov nabljudenij. Razvedochnyj analiz [Analysis of observation results. Exploratory analysis]. Moscow. 1981. 696 p. (In Russ.).
25. Seber G. Linejnyj regressionnyj analiz [Linear regression analysis]. Moscow. 1980. 456 p. (In Russ.).
26. Demidenko E.Z. Linejnaja i nelinejnaja regressii. [Linear and nonlinear regression]. Moscow: Finansy i Statistika. 1981. 302 p. (In Russ.).
27. Hastie Tr., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition. Springer. 2017. 768 p.
28. Hoerl А.Е., Kennard R.W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970. vol. 12. no. 1. pp. 55–67.
29. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series В: Statistical Methodology. 1996. vol. 58. pp. 267–288.
30. Zou Н., Hastie Т. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series В: Statistical Methodology. 2005. vol. 67. no. 2. pp. 301–320.
31. Vapnik V., Chervonenkis A. Teorija raspoznavanija obrazov (statisticheskie problem obuchenija) [Pattern recognition theory (statistical learning problems)]. M.: Nauka. 1974. 416 p. (In Russ.).
32. Vapnik V. The Nature of Statistical Learning Theory. New- York: Springer Verlag N.Y. 1995. 188 p. DOI: 10.1007/978-1-4757-2440-0.
33. Vapnik V., Golowich S., Smola A. Support Vector Method for Function Approximation Regression Estimation and Signal. Advances in Neural Information Processing Systems. 1996. pp. 281–287.
34. Morgan J.N. Sonquist J.А. Problems in the analysis of survey data, and а proposal. Journal of the American Statistical Association. 1963. vol. 58. pp. 415–434.
35. Breiman L., Friedman J., Olshen R., Stone C. Classification and regression trees. CA: Wadsworth and Brooks/Cole Advanced Books and Software. 1984. 368 p.
36. Breiman L. Bagging Predictors. Machine Learning. 1996. vol. 24. pp. 123–140.
37. Breiman L. Random Forests. Machine Learning. 2001. vol. 45. pp. 5–32.
38. Wallace J., Kanaroglou P. The effect of temperature inversions on ground-level nitrogen dioxide (NO2) and fine particulate matter (PM2.5) using temperature profiles from the Atmospheric Infrared Sounder (AIRS). Science of The Total Environment. 2009. vol. 407. no. 18. pp. 5085–5095.
39. Sanitary rules and norms SanPiN 1.2.3685-21 «Hygienic standards and requirements for ensuring the safety and (or) harmlessness of environmental factors for humans». 2021. Available at: https://docs.cntd.ru/document/573500115 (accessed: 26.01.2024). (In Russ.).
40. Akaike H. A new look at statistical model identification. IEEE Transactions on Automatic Control. 1974. vol. 19. pp. 716–723.
41. Stoica P., Selen Y. Model-order selection: a review of information criterion rules. IEEE Signal Processing Magazine. 2004. vol. 21. no. 4. pp. 36–47.
Published
2024-03-28
How to Cite
Karepova, E., & Petrakova, V. (2024). Statistical Substantiation of the Revising of Readings by the CityAir Station of PM2.5 Concentration Levels in the Atmospheric Boundary Layer of the City. Informatics and Automation, 23(2), 352-376. https://doi.org/10.15622/ia.23.2.2
Section
Mathematical Modeling and Applied Mathematics
Copyright (c) viktoriya sergeevna petrakova, Евгения Карепова
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