Determination of ozone concentration using gene expression programming algorithm (GEP)- Zrenjanin, Serbia

Document Type : Research Paper


1 Department of Mining Engineering, Hamedan University of Technology (HUT), Hamedan, Iran

2 University of Belgrade, Technical Faculty in Bor, Bor, Serbia

3 School of Civil Engineering and Surveying, University of Southern Queensland, Queensland, Australia

4 University of Belgrade, Technical faculty in Bor, Bor, Serbia


As one of the hazardous pollutants, ozone (O3), has significant adverse effects on urban dwellers' health. Predicting the concentration of ozone in the air can be used to control and prevent unpleasant effects. In this paper, an attempt was made to find out two empirical relationships incorporating multiple linear regression (MLR) and gene expression programming (GEP) to predict the ozone concentration in the vicinity of Zrenjanin, Serbia. For this purpose, 1564 data sets were collected, each containing 18 input parameters such as concentrations of air pollutants (SO2, CO, H2S, NO, NO2, NOx, PM10, benzene, toluene, m- and p-xylene, o-xylene, ethylbenzene) and meteorological conditions (wind direction, wind speed, air pressure, air temperature, solar radiation, and relative humidity (RH)). In contrast, the output parameter was ozone concentrate. The correlation coefficient and root mean squared error for the MLR were 0.61 and 21.28, respectively, while the values for the GEP were 0.85 and 13.52, respectively. Also, to evaluate these two methods' validity, a feed-forward artificial neural network (ANN) with an 18-10-5-1 structure has been used to predict the ozone concentration. The correlation coefficient and root mean squared error for the ANN were 0.78 and 16.07, respectively. Comparisons of these parameters revealed that the proposed model based on the GEP is more reliable and more reasonable for predicting the ozone concentrate. Also, the sensitivity analysis of the input parameters indicated that the air temperature has the most significant influence on ozone concentration variations.


[1]. USEPA, "Ozone Pollution",, 2017.
[2].  Susaya,  J.,  Kim,  K.  H.,  Shon,  Z.  H.,  &  Brown,  R.  J.   (2013). Demonstration of long-term increases in tropospheric O3 levels: Causes and potential impacts. Chemosphere, 92(11), 1520-1528.
[3]. Abdul-Wahab, S. A., & Al-Alawi, S. M. (2002). Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environmental Modelling & Software, 17(3), 219-228.
[4]. Mishra, D., & Goyal, P. (2016). Neuro-Fuzzy Approach to forecasting Ozone Episodes over the urban area of  Delhi, India. Environmental Technology & Innovation, 5, 83-94.
[5]. Duan, J., Tan, J., Yang, L., Wu, S., & Hao, J. (2008). Concentration, sources, and ozone formation potential of volatile organic compounds (VOCs) during ozone episode in Beijing. Atmospheric Research, 88(1), 25-35.
[6]. Lengyel, A., Héberger, K., Paksy, L., Bánhidi, O., & Rajkó, R. (2004). Prediction of ozone concentration in ambient air using multivariate methods. Chemosphere, 57(8), 889-896.
[7]. Shao, M., Zhang, Y., Zeng, L., Tang, X., Zhang, J., Zhong, L., & Wang, B. (2009). Ground-level ozone in the Pearl River Delta and the roles of VOC and NOx in its production. Journal of Environmental Management, 90(1), 512-518.
[8]. Sharma, S., Sharma, P., & Khare, M. (2017). Photo-chemical transport modelling of tropospheric ozone: A review. Atmospheric Environment, 159, 34-54.
[9]. Moussiopoulos, N., Sahm, P., & Kessler, C. (1995). Numerical simulation of photochemical smog formation in Athens, Greece—a case study. Atmospheric Environment, 29(24), 3619- 3632.
[10]. Chaloulakou, A., Saisana, M., & Spyrellis, N. (2003). Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Science of the Total Environment, 313(1-3), 1-13.
[11]. Sousa, S. I. V., Martins, F. G., Alvim-Ferraz, M. C. M., &  Pereira, C. (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modelling & Software, 22(1), 97-103.
[12]. Abdi‐Oskouei, M., Carmichael, G., Christiansen, M., Ferrada, G., Roozitalab, B., Sobhani, N., Wade, K., Czarnetzki, A., Pierce, R.B., Wagner, T., & Stanier, C. (2020). Sensitivity of meteorological skill to the selection of WRF‐Chem physical parameterizations and impact on ozone prediction during the  Lake Michigan Ozone Study (LMOS). Journal of Geophysical Research: Atmospheres, 125(5), e2019JD031971.
[13]. Đorđević, P., Mihajlović, I., & Živković, Ž. (2010). Comparison of linear and nonlinear statistics methods applied in the industrial process modeling procedure. Serbian Journal of Management, 5(2), 189-198.
[14]. Arsic, M., Nikolic, D. J., Mihajlovic, I., & Zivkovic, Z. (2014). Monitoring of surface ozone concentrations in the western Banat region (Serbia). Applied ecology and environmental research, 12(4), 975-989.
[15]. Fontes, T., Silva, L. M., Silva, M. P., Barros, N., & Carvalho, A. C. (2014). Can artificial neural networks be used to predict the origin of ozone episodes?. Science of the total environment, 488, 197-207.
[16]. Samadianfard, S., Delirhasannia, R., Kisi, O., &  Agirre-Basurko, (2013). Comparative analysis of ozone level prediction models using gene expression programming and multiple linear regression. Geofizika, 30(1), 43-73.
[17]. Baawain, M. S., & Al-Serihi, A. S. (2014). Systematic approach for the prediction of ground-level air pollution (around an industrial port) using an artificial neural network. Aerosol and air quality research, 14(1), 124-134.
[18]. Geng, F., Tie, X., Xu, J., Zhou, G., Peng, L., Gao, W., ... & Zhao, C. (2008). Characterizations of ozone, NOx, and VOCs measured in Shanghai, China. Atmospheric Environment, 42(29), 6873- 6883.
[19]. Stathopoulou, E., Mihalakakou, G., Santamouris, M., & Bagiorgas, H. S. (2008). On the impact of temperature on tropospheric ozone concentration levels in urban environments. Journal of Earth System Science, 117(3), 227-236.
[20]. Sousa, S. I. V., Martins, F. G., Pereira, M. C., & Alvim-Ferraz, M. M. (2006). Prediction of ozone concentrations in Oporto city with statistical approaches. Chemosphere, 64(7), 1141-1149.
[21]. Bandyopadhyay, G., & Chattopadhyay, S. (2007). Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. International Journal of Environmental Science & Technology, 4(1), 141-149.
[22]. Wang, W., Lu, W., Wang, X., & Leung, A. Y. (2003). Prediction of maximum daily ozone level using combined neural network and statistical characteristics. Environment International, 29(5), 555-562.
[23]. Nishanth, T., Kumar, M. S., & Valsaraj, K. T. (2012). Variations in surface ozone and NO x at Kannur: a tropical, coastal site in India. Journal of Atmospheric Chemistry, 69(2), 101-126.
[24]. Prybutok, V. R., Yi, J., & Mitchell, D. (2000). Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations. European Journal of Operational Research, 122(1), 31-40.
[25]. Zhu, Y., Chen, C., Shi, J., & Shangguan, W. (2020). A novel simulation method for predicting ozone generation in the corona discharge region. Chemical Engineering Science, 227, 115910.
[26]. Arsić, M., Mihajlović, I., Nikolić, D., Živković, Ž., & Panić, M. (2020). Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods. Ozone: Science & Engineering, 42(1), 79-88.
[27]. Mo, Y., Li, Q., Karimian, H., Fang, S., Tang, B., Chen, G., & Sachdeva, S. (2020). A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks. Atmospheric Environment, 220, 117072.
[28]. AlOmar, M. K., Hameed, M. M., & AlSaadi, M. A. (2020). Multi hours ahead prediction of surface ozone gas concentration: Robust artificial intelligence approach. Atmospheric Pollution Research, 11(9), 1572-1587.
[29]. Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027.
[30]. Jodeiri Shokri, B., Dehghani, H., Shamsi, R. (2020). Predicting silver price by applying a coupled multiple linear regression (MLR) and imperialist competitive algorithm (ICA). 1(1):101- 104.
[31]. Jodeiri Shokri, B., Dehghani, H., Shamsi, R. Doulati Ardejani, F. (2020). Prediction of acid mine drainage generation potential of a copper mine tailings using gene expression Programming-a case study. Journal of Mining and Environment, 11(4): 1127- 1140.
[32]. Shakeri, J., Jodeiri Shokri, B., Dehghani, H. (2020). prediction of blast-induced ground vibration using gene expression programming (GEP), artificial neural networks (ANNs), and linear multivariate regression (LMR). Archives of Mining Sciences, 65 (2):317-335.
[33]. Dehghani, H. (2018). Forecasting copper price using gene expression programming. Journal of Mining and Environment, 9(2), 349-360.
[34]. Jodeiri Shokri, B., Ramazi, HR., Doulati Ardejani, F., Moradzadeh, A. (2014) A statistical model to relate pyrite oxidation and oxygen transport within a coal waste pile: case study, Alborz Sharghi, northeast of Iran. Environmental Earth Sciences, 71: 4693-4702.
[35]. Soleimani, M., Jodeiri Shokri, B. (2015). Defining chromite ore production trend by CCD method to reach sustainable development goals in mining sector, Iran. Mineral Economics, 28: 103-115.
[36]. Jodeiri Shokri, B., Ramazi, HR., Doulati Ardejani, F., Sadeghiamirshahidi, MH. (2014). Prediction of pyrite oxidation in a coal washing waste pile applying artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS). Mine Water and the Environment, 33: 146-156.
[37]. Doulati Ardejani, F., Rooki, R., Jodeiri Shokri, B., Eslam Kish, T., Aryafar, A., Tourani, P. (2013). Prediction of rare earth elements in neutral alkaline mine drainage from Razi coal mine, Golestan Province, northeast Iran, using general regression neural network. Journal of Environmental Engineering 139 (6),    896-907.