Forecasting of air pollution with time series and multiple regression models in Sofia, Bulgaria
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.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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. https://doi.org/10.1007/s11356-015-5406-6
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. https://doi.org/10.1080/10473289.2003.10466276
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. https://doi.org/10.3390/buildings12010046
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. https://doi.org/10.1007/s11270-010-0484-z
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. https://doi.org/10.1007/s00477-013-0800-4
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. https://doi.org/10.1016/j.atmosenv.2009.02.020
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. https://doi.org/10.1029/2007JD008761
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. https://doi.org/10.1109/SCORED.2006.4339321
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. https://doi.org/10.1016/j.chemosphere.2020.129502
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. https://doi.org/10.1016/j.envsoft.2004.08.003
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. https://doi.org/10.1088/1742-6596/2162/1/012009
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. https://doi.org/10.1109/72.595883
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. https://doi.org/10.56042/jsir.v81i05.56794
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. https://doi.org/10.1016/S0304-3800(01)00434-3
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. https://doi.org/10.22260/ISARC2011/0212
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. https://doi.org/10.1088/1742-6596/2006/1/012023
Ye, Z. (2019). Air pollutants prediction in Shenzhen based on ARIMA and Prophet method. E3S Web of Conferences, 136, 05001. https://doi.org/10.1051/e3sconf/201913605001
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. https://doi.org/10.1080/10962247.2017.1292968