Forecasting the Short-Term Changes of Surface Ozone and NO2 during a Festival Event Using Stochastic and Neural Network Models

Document Type : Original Research Paper

Authors

1 Department of Information Technology, Kannur University, Kannur, India

2 Department of Physics, Sree Krishna College Guruvayur, Kerala, India- 680102

3 Department of Applied Science and Humanities, Nehru College of Engineering and Research Centre, Pampady, Thrissur, Kerala-680588

4 Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Karnataka, India- 576104

5 Department of Applied Sciences, Govt. Engineering College Kannur, Kerala India-670563

10.22059/poll.2025.377011.2387

Abstract

Air pollution is one of the most destructive environmental issues on the local, regional, and global level. Its negative influences go far beyond ecosystems and the economy, harming human health and environmental sustainability. By these facts, efficient and accurate modelling and forecasting the concentration of air pollutants are vital. Hence, this work explores investigate the time series components of surface ozone (O3) and its precursor nitrogen dioxide (NO2) and develops a model for predicting O3 variations produced by intense fireworks during the Vishu festival over Kannur. Time series methods using Stochastic and Recurrent Neural Network (RNN) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models are considered the most accurate tools for estimating air pollution trends due to their logical flexibility. Model performance is evaluated based on statistical measurements indicating an increasing trend in O3 concentration of 0.11 ppb/year and NO2 of 0.18 ppb/year. Based on the analysis, we found that the SARIMA model shows better accuracy with a Mean Squared Error (MSE) of 0.55 and a Root Mean Squared Error (RMSE) of 0.74. The broader implications of this study highlight the applicability of advanced time series forecasting techniques for air quality monitoring during short-term pollution events.

Keywords

Main Subjects

References

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723.
Antony, E., N S Sreekanth., R K Sunil Kumar., & Nishanth T. (2021). Data Preprocessing Techniques for Handling Time Series data for Environmental Science Studies, International Journal of Engineering Trends and Technology, 69(5), 196-207.
Arputharaj, S., & Samuel Selvaraj, R. (2016). Prediction of surface ozone using arima, International Journal of Current Research, 41643-41646.
Asfaw, A., Simane, B., Hassen, A., & Bantider, A. (2017). Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin. Weather and climate extremes, 19, 29-41.
Attri A.K., Kumar U., & Jain V.K. (2001). Formation of ozone by fireworks. Nature, 411, 1015.
Basurko EA., Anta A., Barron LJR., & Albizu M. (2007) In: Borrego C, Brebbia C (eds) Air pollution XV, WIT transactions on ecology and the environment, 101, 109–118. 
Beldjillali., Hicham., Bachari,, Nour El Islam., & Lamri, Nacef. (2016). Prediction of ozone concentrations according the Box-Jenkins methodology for Assekrem area, Applied Ecology and Environmental Sciences, 4, 48-52.
C. Paoli., G. Notton., M. -L. Nivet., M. Padovani., & J. -L. Savelli. (2011). A Neural Network model forecasting for prediction of hourly ozone concentration in Corsica, 10th International Conference on Environment and Electrical Engineering, 1-4. 
Capilla C. (2016). Int J Sustain Dev Plan, 11(4),558. 
Chandra, S., Ziemke, J. R., & Stewart, R. W. (1999). An 11-year solar cycle in tropospheric ozone from TOMS measurements. Geophysical Research Letters, 26(2), 185-188.
Chowdhury, M. H., Mondal, S., & Islam, J. (2018). Modeling And Forecasting Humidity In Bangladesh: Box-Jenkins Approach. International Journal of Research -GRANTHAALAYAH, 6(4), 50–60. 
Coman A., Ionescu A., & Candau Y. (2008). Hourly ozone prediction for a 24-h horizon using neural networks, Environ Model Software, 23(12), 1407-1421.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74(366), 427–431. 
Duenas C., Ferna´ndez M., Canete S., Carretero J., & Liger E. (2005). Stochastic model to forecast ground-level ozone concentration at urban and rural areas, Chemosphere, 61(10), 1379-1389. 
Gideon Schwarz. (1978). Estimating the Dimension of a Model, Ann. Statist., 6(2), 461 – 464.
Hamzaçebi, Coşkun. (2008). Improving artificial neural networks’ performance in seasonal time series forecasting. Inf. Sci., 178, 4550-4559. 
Hiroo Hata., Kazuya Inoue., Hiroshi Yoshikado., Yutaka Genchi., & Kiyotaka Tsunemi. (2023). Impact of introducing net-zero carbon strategies on tropospheric ozone (O3) and fine particulate matter (PM2.5) concentrations in Japanese region in 2050, Science of The Total Environment, 891, 164442. 
Kumar, K., Yadav, A. K., Singh, M. P., Hassan, H., & Jain, V. K. (2004). Forecasting Daily Maximum Surface Ozone Concentrations in Brunei Darussalam—An ARIMA Modeling Approach. Journal of the Air & Waste Management Association, 54(7), 809–814. 
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., & Shin, Y. (1992). Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root. Journal of Econometrics, 54, 159-178. 
Li, C., Balluz, L. S., Vaidyanathan, A., Wen, X. J., Hao, Y., & Qualters, J. R. (2016). Long-Term Exposure to Ozone and Life Expectancy in the United States, 2002 to 2008. Medicine, 95(7), e2474. 
Ljung, G.M., & Box, G.E. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297-303. 
Mahiyuddin, W. R. W., Jamil, N. I., Seman, Z., Ahmad, N. I., Abdullah, N. A., Latif, M. T., & Sahani, M. (2018). Forecasting Ozone Concentrations Using Box-Jenkins ARIMA Modeling in Malaysia. American Journal of Environmental Sciences, 14(3), 118-128.
Maji, K. J., & Namdeo, A. (2021). Continuous increases of surface ozone and associated premature mortality growth in China during 2015-2019. Environmental pollution (Barking, Essex : 1987), 269, 116183.
Monks P.S., Archibald A.T., Colette A., Cooper O., Coyle M., Derwent R., Fowler D., Granier C., Law K.S., Mills G.E., Stevenson D.S., Tarasova O., Thouret V., & Schneidemesser E. (2015). Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer, Atmos. Chem. Phys., 15, 8889–8973.
Nariaki Sugiura. (1978). Further analysts of the data by akaike’ s information criterion and the finite corrections, Communications in Statistics - Theory and Methods, 7(1), 13-26.
Nishanth, T., Ojha, N., Kumar, M.K.S., & Naja, M. (2012). Influence of solar eclipse of 15 January 2010 on surface O3. Atmospheric Environment, 45, 1752-1758. 
Pawlak I., Fernandes A., Jarosławski J., Klejnowski K., & Pietruczuk A. (2023). Comparison of 24 h Surface Ozone Forecast for Poland: CAMS Models vs. Simple Statistical Models with Limited Number of Input Parameters. Atmosphere. 14(4), 670.  
Phillips, Peter., & Perron, Pierre. (1986). Testing for a Unit Root in Time Series Regression. Cowles Foundation, Yale University, Cowles Foundation Discussion Papers. 75. 
Resmi CT., Nishanth T., Satheesh Kumar MK., Balachandramohan M,. & Valsaraj KT. (2020). Annular Solar Eclipse on 26 December 2019 and its Effect on trace pollutant concentrations and meteorological parameters in Kannur, India: a Coastal City, Asian J Atmos Environ, 14, 289–306. 
Samuel Selvaraj, R., Sachithananthem C.P., & K.Thamizharasan. (2013). Modeling and Predicting Total Ozone Column and Rainfall in Kodaikanal, Tamilnadu By Arima Process, International Journal Of Engineering And Computer Science, 2(8), 2521-2526.
Sen, P.K. (1968) Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63, 1379-1389.
Sepp Hochreiter., & Jürgen Schmidhuber. (1997). Long Short-Term Memory. Neural Comput, 9(8), 1735–1780.  
Xu, W., Lin, W., Xu, X., Tang, J., Huang, J., Wu, H., & Zhang, X. (2016).  Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China – Part 1: Overall trends and characteristics, Atmos, Chem. Phys., 16, 6191–6205. 
Xu, W., Xu, X., Lin, M., Lin, W., Tarasick, D., Tang, J., Ma, J., & Zheng, X. (2018). Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China – Part 2: The roles of anthropogenic emissions and climate variability, Atmos. Chem. Phys., 18, 773–798. 
Zhang, A., Fu, M., Feng, X., Guo, J., Liu, C., Chen, J., Mo, J., Zhang, X., Wang, X., Wu, W., Hou, Y., Yang, H., & Lu, C. (2023). Deep Learning-Based Ensemble Forecasts and Predictability Assessments for Surface Ozone Pollution. Geophysical Research Letters, 50(8), e2022GL102611.
Pollution
Volume 11, Issue 3
July 2025
Pages 576-592

Files

Share

How to cite

Statistics