Statistical modeling of the effects of wind speed, air temperature and relative humidity on the concentration of carbon monoxide in the urban atmosphere
https://doi.org/10.24057/2071-9388-2024-3012
Abstract
The high carbon monoxide content in the urban atmosphere is one of the most important indicators of poor air quality in megacities such as Moscow. This study is to evaluate the importance of wind speed, air temperature, and relative air humidity for predicting the concentrations of carbon monoxide for the day ahead using a simplified one-dimensional quasistationary statistical model. It is shown that the concentration of carbon monoxide in the Moscow atmosphere is determined by a combination of internal (previous days CO concentration) and external (meteorological conditions) factors. The variation of carbon monoxide concentration at one station differs from the variation at another station due to the differences in local conditions. Taking into account wind speed and air temperature increases the predictive value of the onedimensional quasi-stationary statistical model for most of the stations. In contrast to wind, relative air humidity decreases the predictive value of the model for most of the stations. This means that meteorological factors considered in this study could have different effects on predicting carbon monoxide concentration in the case of Moscow. The data from the Balchug weather station, located in the city center, offers a more accurate CO concentration forecast for most Moscow stations compared to the VDNKh weather station. For a more complete description of the influence of meteorological conditions on the predicted low concentration of gases, it is useful to take into account the model wind direction, surface air pressure, and the intensity of mixing in the urban boundary layer.
Keywords
statistical forecasting,
regression-autoregression model,
urban air,
atmospheric pollution,
carbon monoxide,
meteorological factors
Supporting agencies: The authors are grateful to the “Mosecomonitoring” for providing data, reviewers and editor for constructive comments. This work was supported by a part of State Task registration number 1021032424681-6- 1.5.10;1.5.8;1.6.19.
About the Authors
Gleb G. Alexandrov
A.M. Obukhov Institute of Atmospheric Physics Russian Academy of Sciences
Russian Federation
Russian Federation
Pyzhevsky per, 3, Moscow, 119017
Alexander S. Ginzburg
A.M. Obukhov Institute of Atmospheric Physics Russian Academy of Sciences
Russian Federation
Russian Federation
Pyzhevsky per, 3, Moscow, 119017
References
1. Agirre-Basurko E., Ibarra-Berastedi G., Madariaga I. (2006). Regression and multilayer perception-based models to forecast O3 and NO2 levels in Bilbao area. Environmental Modelling & Software, 21(4), 430-446, DOI:10.1016/j.envsoft.2004.07.008.
2. Arya S.P. (1990). Air pollution meteorology and dispersion. Oxford: Oxford University Press.
3. Baklanov A., Hanninen O., Stordal L.H. et al. (2007). Integrated systems for forecasting urban meteorology, air pollution, and population exposure. Atmospheric Chemistry and Physics, 7(3), 855-874, DOI:/10.5194/acp-7-855-2007.
4. Benavides, J., Snyder, M., Guevara, M., Soret, A., Pérez García-Pando, C., Amato, F., & Jorba, O. (2019). CALIOPE-Urban v1. 0: coupling R-LINE with a mesoscale air quality modelling system for urban air quality forecasts over Barcelona city (Spain). Geoscientific Model Development, 12(7), 2811-2835, DOI:10.5194/gmd-12-2811-2019.
5. Berlyand M.E. (1985). Forecast and regulation of atmospheric pollution. Leningrad: Gidrometeoizdat.
6. Berlyand M.E. (1991). Statistical methods of air pollution forecasting. In: Prediction and Regulation of Air Pollution. Atmospheric Sciences Library, vol 14. Dordrecht: Springer, 159-201, DOI:10.1007/978-94-011-3768-3_6.
7. Bornstein R.D. and Johnson D.S. (1977). Urban – rural wind velocity differences. Atmospheric Environment, 11(7), 597-604, DOI:10.1016/0004-6981(77)90112-3.
8. Comrie A. C. and Diem J. E. (1999). Climatology and forecast modeling of ambient carbon monoxide in Phoenix, Arizona. Atmospheric Environment, 33(30), 5023-5036, DOI:10.1016/S1352-2310(99)00314-3.
9. Czerwińska J. and Wielgosiński G. (2020). The effect of selected meteorological factors on the process of» Polish smog» formation. Journal of Ecological Engineering, 21(1), 180-187, DOI:10.12911/22998993/112764.
10. Demchenko P.F., Ginzburg A.S., Aleksandrov G.G., Gorchakov G.I., Zavalishin N.N., Yudin N.I., Vereskov A.I., Zakharova P.V. and Lezina E.A. (2015). Statistical modeling of average daily concentration of pollutants in the atmosphere over Moscow megalopolis by the multiple regression method. Russian Meteorology and Hydrology, 40, 658-666, DOI:10.3103/S1068373915100039.
11. Dias-Robles L.A., Ortega J.C., Fu J.S., Reed G.D., Chow J.C., Watson J.G. and Moncada-Herrera J.A. (2008). A Hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile. Atmospheric Environment, 42(35), 8331 – 8340, DOI:10.1016/j.atmosenv.2008.07.020.
12. Đurić M and Vujović D. (2020). Short-term forecasting of air pollution index in Belgrade, Serbia. Meteorological Applications, 27(5), 1946, DOI:10.1002/met.1946.
13. Elansky N.F., Belikov I.B., Skorokhod A.I., Lokoshchenko M.A. and Trifanova A.V. (2015). On contents of trace gases in the atmospheric surface layer over Moscow. Izvestiya, Atmospheric and Oceanic Physics, 51(1), 30-41, DOI:10.1134/S000143381501003X.
14. Gardner M.W and Dorling S.R. (1998). Artificial Neural Networks (the MaltilayerPrecepton) – a Review of applications in the atmospheric sciences. Atmospheric Environment, 32(14-15), 2627-2636, DOI:10.1016/S1352-2310(97)00447-0.
15. Ginzburg A.S., Semenov V.A., Semutnikova E.G., Aleshina M.A., Zakharova P.V. and Lezina E.A. (2020). Impact of COVID-19 Lockdown on Air Quality in Moscow. Doklady Earth Sciences, 495, 862–866, DOI:10.1134/S1028334X20110069.
16. Golitsyn G.S., Grechko E.I., Dzhola A.V., Emilenko A.S., Kopeikin V.M., Rakitin V.S., Safronov A.N., Fokeeva E.V., Wang G. and Wang P. (2015). Studying the pollution of Moscow and Beijing atmospheres with carbon monoxide and aerosol. Izvestiya, Atmospheric and Oceanic Physics, 51, 1-11, DOI:10.1134/S0001433815010041.
17. Gorchakov G.I., Zotkin E.V., Karpov A.V., Ul’yanenko A.V., Semutnikova E.G. and Lezina E.A. (2006). Variations in gaseous pollutants in the air basin of Moscow. Izvestiya, Atmospheric and Oceanic Physics, 42, 156-170, DOI:10.1134/S0001433806020046.
18. Gorchakov G.I., Karpov A.V., Kolesnikova A.B., Baikova E.S., Semutnikova E.G. and Zadorozhnaya O.S. (2010a). Weekly cycle of air pollution in Moscow: quantitative characteristics and refinement of the method of statistical forecasting of impurity concentrations. Optics of the atmosphere and the ocean, 23(9), 784-792, (in Russian with English summary).
19. Gorchakov G.I., Anoshin B.A., Karpov A.V., Kolesnikova A.B. and Semoutnikova E.G. (2010b). Statistical prediction of the pollution of the urban atmosphere. 1. Statistical regularities of the interdiurnal variations of the carbon monoxide and nitrogen oxide concentrations. Atmospheric and Oceanic Optics, 23(4), 309-316, DOI:10.1134/S102485601004010X.
20. Gorchakov G.I., Anoshin B.A., Karpov A.V., Kolesnikova A.B. and Semoutnikova E.G. (2010c). Statistical prediction of the urban atmosphere contamination. 2. Forecasting method of the interdiurinal and intradiurnal concentration variability of the carbon monoxide and nitrogen oxides. Atmospheric and Oceanic Optics, 23(5), 396-403, DOI:10.1134/S102485601005009X.
21. Grechko E.I., Dzhola A.V., Rakitin V.S., Fokeeva E.V.and Kuznetsov R.D. (2009). Variation of the carbon monoxide total column and atmospheric boundary layer parameters in the center of Moscow. Atmospheric and Oceanic Optics, 22(3), 203-208, DOI:10.1134/S1024856009020110.
22. Kuznetsova I.N. (2012). The effect of meteorology on air pollution in Moscow during the summer episodes of 2010. Izvestiya, Atmospheric and Oceanic Physics, 48(5), 504-515, DOI:10.1134/S0001433812050052.
23. Kuznetsova I.N., Tkacheva Yu.V, Shalygina I.Yu. and Nakhaev M.I. (2021). Forecasting a meteorological indicator of pollutant dispersion in surface air. Hydrometeorological Research and Forecasting, 3(381), 131-149, (in Russian with English summary), DOI:10.37162/2618-9631-2021-3-131-149.
24. Li R., Wang Z., Cui L., Fu H., Zhang L., Kong L. and Chen J. (2019). Air pollution characteristics in China during 2015–2016: Spatiotemporal variations and key meteorological factors. Science of the total environment, 648, 902-915, DOI:10.1016/j.scitotenv.2018.08.181.
25. Liu Y., Zhou Y. and Lu J. (2020). Exploring the relationship between air pollution and meteorological conditions in China under environmental governance. Scientific reports, 10(1), 14518, DOI:10.1038/s41598-020-71338-7.
26. Poggi J.M and Portier B. (2011). PM10 forecasting using clusterwise regression. Atmospheric Environment, 45(38), 7005-7014, DOI:10.1016/j.atmosenv.2011.09.016.
27. Rakitin V.S., Elansky N.F., Skorokhod A.I., Dzhola A.V., Rakitina A.V., Shilkin A.V., Kirillova N.S. and Kazakov A.V. (2021). Long-term tendencies of carbon monoxide in the atmosphere of the Moscow megapolis. Izvestiya, Atmospheric and Oceanic Physics, 57(1), 116-125, DOI:10.1134/S0001433821010102.
28. Revokatova, A. P., Surkova, G. V., Kirsanov A.A. and Rivin G.S. (2012). Forecast of air pollution in the Moscow region using the COSMO-ART model. Bulletin of Moscow University Series 5 Geography, 4, 25-32, (in Russian with English summary).
29. Ruan H. L., Deng W. S., Wang Y., Chen J. B., Hong W. L., Ye S. S. and Hu Z. J. (2021). Carbon monoxide poisoning: a prediction model using meteorological factors and air pollutant. BMC proceedings, 15, 1-9, DOI:10.1186/s12919-021-00206-7.
30. Vilfand R.M., Kuznetsova I.N., Shalygina I.Yu., Zvyagintsev A.M., Nakhaev M.I., Zakharova P.V. and Lapchenko V.A. (2014). Monitoring and forecasting of air quality in the Moscow region. Biosphere, 6(4), 339-351, (in Russian with English summary), DOI:10.24855/biosfera.v6i4.178.
31. Zavalishin N.N., Ginzburg A.S. and Alexandrov G.G. (2018). Statistical forecasting method for atmospheric air pollution in megapolises: Moscow case study. International conference and early career scientist’s school on environmental observations, modeling and information systems: ENVIROMIS-2018, 302-305, (in Russian with English summary).
Review
For citations:
Alexandrov G.G., Ginzburg A.S. Statistical modeling of the effects of wind speed, air temperature and relative humidity on the concentration of carbon monoxide in the urban atmosphere. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2024;17(3):19-34. https://doi.org/10.24057/2071-9388-2024-3012
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