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Forecasting of air pollution with time series and multiple regression models in Sofia, Bulgaria

    Nikolay Stoyanov Affiliation
    ; Antonia Pandelova Affiliation
    ; Tzanko Georgiev Affiliation
    ; Julia Kalapchiiska Affiliation
    ; Bozhidar Dzhudzhev Affiliation

Abstract

Air pollution is one of the serious environmental problems. The high concentrations of particulate matter can have a serious impact over human health and ecosystems, especially in highly urbanized areas. In this regard, the present study employs a combined ARIMA-Multiple Linear Regression modelling approach for forecasting particulate matter content. The capital city of Bulgaria is used as case study. A regression analysis techniques are used to study the relationship between particulate matter concentration and basic meteorological variables – air temperature, solar radiation, wind speed, wind direction, atmospheric pressure. The adequacy of the models has been proven by examining the behavior of the residues. The synthesized time series model can be used for forecasting, monitoring and controlling the air quality conditions. All analyzes and calculations were performed with statistical software STATGRAPHICS.

Keyword : Integrated Autoregressive Moving Average (ARIMA), multiple linear regression, air pollution, PM10, meteorological variables

How to Cite
Stoyanov, N., Pandelova, A., Georgiev, T., Kalapchiiska, J., & Dzhudzhev, B. (2023). Forecasting of air pollution with time series and multiple regression models in Sofia, Bulgaria. Journal of Environmental Engineering and Landscape Management, 31(3), 176–185. https://doi.org/10.3846/jeelm.2023.19467
Published in Issue
Aug 2, 2023
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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