Abstract
In the modern world, attention is increasingly drawn to the pressing problem of atmospheric air pollution, which is a serious threat to human health. Worldwide, China, India, Indonesia and some of the countries in Europe, including Bulgaria, are the most polluted countries. To help solve these issues, a very large number of scientific studies have been devoted, including the study, analysis and forecasting of atmospheric air pollution with particulate matter PM10. In this study the PM10 concentrations in the town of Smolyan, Bulgaria are examined and mathematical models with high performance for prediction and forecasting depending on weather conditions are developed. For this purpose, the powerful method of multivariate adaptive regression splines (MARS) is implemented. The examined data cover a period of 9 years - from 2010 to 2018, on a daily basis. As independent variables, 7 meteorological factors are used - minimum and maximum daily temperatures, wind speed and direction, atmospheric pressure, etc. Additional predictors also used are lagged PM10 and meteorological variables with a delay of 1 day. Three time variables are included to account for time. Multiple models are created with interactions between predictors up to the 4th order. The obtained best MARS models fit to over 80% of measured data. The models are used to forecast PM10 concentrations for 7 days ahead of time. This approach could be applied for real predictions and development of computer and mobile applications.
This work has been accomplished with the financial support of the MES by the Grant No. D01-221/03.12.2018 for NCDSC, part of the Bulgarian National Roadmap on RIs.
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Gocheva-Ilieva, S.G., Ivanov, A.V., Voynikova, D.S., Stoimenova, M.P. (2020). Modeling of PM10 Air Pollution in Urban Environment Using MARS. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2019. Lecture Notes in Computer Science(), vol 11958. Springer, Cham. https://doi.org/10.1007/978-3-030-41032-2_27
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DOI: https://doi.org/10.1007/978-3-030-41032-2_27
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