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


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.
Published in Issue
Aug 2, 2023
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Aarnio, M. A., Kukkonen, J., Kangas, L., Kauhaniemi, M., Kousa, A., Hendriks, C., Yli-Tuomi, T., Lanki, T., Hoek, G., Brunekreef, B., Elolähde, T., & Karppinen, A. (2016). Modelling of particulate matter concentrations and source contributions in the Helsinki Metropolitan Area in 2008 and 2010. Boreal Environment Research, 21(5–6), 445–460.

Abderrahim, H., Chellali, M. R., & Hamou, A. (2016). Forecasting PM10 in Algiers: Efficacy of multilayer perceptron networks. Environmental Science and Pollution Research, 23, 1634–1641.

Abdullah, S., Ismail, M., & Fong, S. Y. (2017). Multiple linear regression (MLR) models for long term PM10 concentration forecasting during different monsoon seasons. Journal of Sustainability Science and Management, 12(1), 60–69.

Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control (Rev. ed.). Holden-Day.

Chaloulakou, A., Grivas, G., & Spyrellis, N. (2003). Neural network and multiple regression models for PM10 prediction in Athens: A comparative assessment. Journal of Air & Waste Management Association, 53, 1183–1190.

Doncheva, M., & Boneva, G. (2013). Particulate matter air pollution in urban areas in Bulgaria. Journal of Environmental Protection and Ecology, 14(2), 422–429.

Ejohwomu, O. A., Oshodi, O. S., Oladokun, M., Bukove, O. T., Emekwuru, N., Sotunbo, A., & Adenuga, O. (2022). Modelling and forecasting temporal PM2.5 concentration using ensemble machine learning methods. Buildings, 12(1), 46.

Galindo, N., Varea, M., Gil-Moltó, J., Yubero, E., & Nicolás, J. (2011). The influence of meteorology on particulate matter concentrations at an urban mediterranean location. Water Air and Soil Pollution, 215, 365–372.

Gocheva-Ilieva, S. G., Ivanov, A. V., Voynikova, D. S., & Boyadzhiev, D. T. (2014). Time series analysis and forecasting of air pollution in a small urban area: SARIMA approach and factor analysis. Stochastic Environmental Research and Risk Assessment, 28(4), 1045–1060.

Hoi, K. I., Yuen, K. V., & Mok, K. M. (2009). Prediction of daily averaged PM10 concentrations by statistical time-varying model. Atmospheric Environment, 43(16), 2579–2581.

Honore, C., Rouıl, L., Vautard, R., Beekmann, M., Bessagnet, B., Dufour, A., Elichegaray, C., Flaud, J.-M., Malherbe, L., Meleux, F., Menut, L., Martin, D., Peuch, A., Peuch, V.-H., & Poisson, N. (2008). Predictability of European air quality: Assessment of 3 years of operational forecasts and analyses by the PREV’AIR system. Journal of Geophysical Research, 113, D04301.

Kammal, M., Jailani, R., & Shauri, R. L. A. (2006). Prediction of ambient air quality based on neural network technique. In 4th Student Conference on Research and Development (pp. 115–119). IEEE.

Li, X., Hussain, S. A., Sobri, S., & Md Said, M. S. (2021). Overviewing the air quality models on air pollution in Sichuan Basin, China. Chemosphere, 271, 129502.

Liping, X., & Yaping, S. (2005). Modelling of traffic flow and air pollution emission with application to Hong Kong Island. Environmental Modelling & Software, 20(9), 1175–1188.

Mancini, S., Francavilla, A., Graziuso, G., & Guarnaccia, C. (2022). An application of ARIMA modelling to air pollution concentrations during covid pandemic in Italy. Journal of Physics: Conference Series, 2162, 012009.

Roadknight, C. M., Balls, G. R., Mills, G. E., & Palmer-Brown, D. (1997). Modeling complex environmental data. IEEE Transactions Neural Network, 8(4), 852–862.

Stoimenova, M. P. (2016). Stochastic modeling of problematic air pollution with particulate matter in the city of Pernik, Bulgaria. Ecologia Balkanica, 8(2), 33–41.

Subbiah, S. S., & Kumar, S. (2022). Deep learning based load forecasting with decomposition and feature selection technics. Journal of Scientific & Industrial Research, 81, 505–517.

Ul-Saufie, A. Z., Yahya, A. S., & Ramli, N. A. (2011). Improving multiple linear regression model using principal component analysis for predicting PM10 concentration in Seberang Prai, Pulau Pinang. International Journal of Environmental Sciences, 2(2), 403.

Viotti, P., Liuti, G., & Di Genova, P. (2002). Atmospheric urban pollution: Applications of an artificial neural network (ANN) to the city of Perugia. Ecological Modelling, 148(1), 27–46.

Wahid, H., Ha, Q. P., & Duc, H. N. (2011). Computational intelligence estimation of natural background ozone level and its distribution for air quality modelling and emission control. In Proceedings of 28th International Symposium on Automation and Robotics in Construction (pp. 1157–1163), Seoul, Korea.

Wei, P., Xie, S., Huang, L., Zhu, G., Tang, Y., & Zhang, Y. (2006). Prediction of PM2.5 concentration in Guangxi region, China based on MLR-ARIMA. Journal of Physics: Conference Series, 2006, 012023.

Ye, Z. (2019). Air pollutants prediction in Shenzhen based on ARIMA and Prophet method. E3S Web of Conferences, 136, 05001.

Zhang, H., Zhang, S., Wang, P., Qin, Y., & Wang, H. (2017). Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China. Journal of the Air & Waste Management Association, 67(7), 776–788.