Artificial neural network application for forecasting the nitrogen oxides in the atmosphere at the microclimate conditions: example of Iğdır city in Turkey

Year 2020, , 27 - 38, 15.03.2020

Aysun Altıkat

https://doi.org/10.31015/jaefs.2020.1.5

Abstract

The aim of this research is forecasting the NOx, NO2 and NO concentration levels with different artificial neural networks structures (ANNs) and determining the best ANNs structure for forecasting emissions. For this aim, it was used one learning function and, six different transfer function pairs with three different neuron numbers. The MATLAB software helped constructing ANNs models. In addition, the air pollutants and meteorological factors were used as input parameters simultaneously at the ANNs. The end of the research, NOx, NO and NO2's concentration levels were modelled with high accurate levels. The R2 values of the NOx, NO and NO2 were calculated as 0.998, 0.995 and 0.997, respectively. The best results were obtained from ANNs structures which used Logarithmic sigmoid - Symmetric sigmoid transfer functions with 20 and 30 neuron number for forecasting of the NOx and NO concentration levels, respectively. In addition, the forecasting of NO2 emission rate, the best results were determined from the ANNs structure used Logarithmic sigmoid - Linear transfer function with 30 neuron number. According to sensitivity analyses and correlation tests, it was concluded that O3, SO2, wind direction, wind speed, and relative humidity inputs were more effective on the NO2, NO and NOx concentrations than the other inputs. Finally, it can be said that with the use of both air pollutants and meteorological factors as input parameters simultaneously the artificial neural network models can be simulated the concentration level of NO, NOx and NO2 with high accuracy.

Keywords

Air pollutions, Meteorological factor, ANN, Transfer function, Learning function

References

• Chaudhuri, S., Acharya, R., (2012) Artificial neural network model to forecast the concentration of pollutants over Delhi: skill assessment of learning rules. Asian J. Water Environ. Pollut. 1, 71-81. [Google Scholar Link].
• Chen, J., Lu, J., Avise, J.C., DaMassa, J.A., Kleeman, M.J., Kaduwela, A.P. (2014) Seasonal modeling of PM2.5 in California's San Joaquin Valley. Atmos. Environ. 92, 182-190. [Google Scholar Link].
• Chen, Y., Shi, R., Shu, S., Gao, W. (2013) Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis. Atmos. Environ. 74, 346-359. [Google Scholar Link].
• Cheng, S.Y., Li, L., Chen, D.S., Li, J.B. (2012). A neural network-based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling. Journal of Environmental Management, 112,404–414. [Google Scholar Link].
• Corporation, H.P. (2013) Forecasting SO2 pollution incidents by means of Elman artificial neural networks and ARIMA models. Abstr. Appl. Analysis 4, 1728-1749. [Google Scholar Link]
• Djalalova, I., Monache, L.D., Wilczak, J. (2015) PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model. Atmos. Environ. 108, 76-87. [Google Scholar Link].
• Domannska, D., Woktylak, M. (2012) Application of fuzzy time series models for forecasting pollution concentrations. Expert Syst. Appl. 39, 7673-7679. [Google Scholar Link].
• Elangasinghe, M.A., Singhal N., Dirks, K.N., Salmond J.F. (2014) Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmospheric Pollution Research, 5,696- 708. [Google Scholar Link].
• Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J. (2015) Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 107, 118-128. [Google Scholar Link].
• Gantt, B., Meskhidze, N., Zhang, Y., Xu, J. (2010) The effect of marine isoprene emissions on secondary organic aerosol and ozone formation in the coastal United States. Atmos. Environ., 44, 115-121. [Google Scholar Link].
• Gao, X.L., Hu, T.J., Wang, K. (2014) Research on motor vehicle exhaust pollution monitoring technology. Appl. Mech. Mater., 620, 244-247. [Google Scholar Link].Gardner, M.W., Dorling, S.R. (1998) Artificial neural networks (the multilayer perceptron) a review of applications in the atmospheric sciences. Atmospheric Environment, 32, 2627–2636. [Google Scholar Link].
• Hrust,L.,Klaic,Z.B.,Krizan,J.,Antonic,O.,Hercog,P.(2009) Neural network forecasting of air pollutants hourly concentrations using optimized temporal averages of meteorological variables and pollutant concentrations. Atmospheric Environment 43,5588–5596. [Google Scholar Link].
• Jian, L., Zhao, Y., Zhu, Y.P., Zhang, M., Bertolatti, D. (2012) An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Sci. Total Environ., 426, 336-345. [Google Scholar Link].
• Jiang,N., Hay,J.E., Fisher,G.W., (2005) Effects of meteorological conditions on concentrations of nitrogen oxides in Auckland. Weather and Climate, 24,15–34. [Google Scholar Link].
• Kurt, A., Oktay, A.B. (2010) Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Systems with Applications, 37,7986–7992. [Google Scholar Link].
• Lal, S, Patil, R.S. (2001) Monitoring of atmospheric behavior of NOx from vehicular traffic. Environmental Monitoring and Assessment, 68:37–50. [Google Scholar Link].
• Nagendra S.M.S., Khare, M. (2006) Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecological Modelling;190, 99–115. [Google Scholar Link].
• Ozel, G., Cakmakyapan, S. (2015). A new approach to the prediction of PM10 concentrations in Central Anatolia Region, Turkey. Atmos. Pollut. Res., 6, 735-741. [Google Scholar Link].
• Pai, T.Y., Hanaki, K., Chiou, R.J. (2013) Forecasting hourly roadside particulate matter in Taipei county of Taiwan based on first-order and one-variable grey model. Cleane Soil Air Water, 41, 737-742. [Google Scholar Link].
• Pauzi, H.M., Abdullah, L. (2015) Neural network training algorithm for carbon dioxide emissions forecast: a performance comparison. Lect. Notes Electr. Eng., 315, 717-726. [Google Scholar Link].
• Perez, P. (2012) Combined model for PM10 forecasting in a large city. Atmospheric Environment, 60, 271–276. [Google Scholar Link].
• Russo, A., Lind, P., Raischel, F., Trigo, R., Mendes, M. (2015). Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales. Atmos. Pollut. Res., 6 [Google Scholar Link].
• Russo, A., Soares, A.O. (2014) Hybrid model for urban air pollution forecasting: a stochastic spatio-temporal approach. Math. Geosci., 46, 75-93 [Google Scholar Link].
• Samia, A., Kaouther, N., Abdelwahed, T. (2012) A hybrid ARIMA and artificial neural networks model to forecast air quality in urban areas: case of Tunisia. Adv. Mater. Res., 518-523, 2969-2979 [Google Scholar Link].
• Singh, K.P., Gupta, S., Kumar, A., Shukla, S.P. (2012). Linear and nonlinear modeling approaches for urban air quality prediction. Science of the Total Environment, 426,244–255 [Google Scholar Link].
• Urbanski, S.P., Hao, W.M., Nordgren, B. (2011) The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty. Atmos. Chem. Phys., 11, 12973-13000 [Google Scholar Link].
• Wang, J., Wang, Y., Liu, H., Yang, Y., Zhang, X., Li, Y., Zhang, Y., Deng, G. (2013). Diagnostic identification of the impact of meteorological conditions on PM2.5 concentrations in Beijing. Atmos. Environ., 81, 158-165 [Google Scholar Link].
• Wang, L., Zhang, N., Liu, Z., Sun, Y., Ji, D., Wang, Y. (2014) The influence of climate factors, meteorological conditions, and boundary-layer structure on severe haze pollution in the Beijing-Tianjin-Hebei Region during January 2013. Adv. Meteorol.1-14 [Google Scholar Link].
• Wu, Q., Xu, W., Shi, A., Li, Y., Zhao, X., Wang, Z., Li, J., Wang, L. (2014) Air quality forecast of PM10 in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) system: emission and improvement. Geosci. Model Dev., 7, 2243-2259 [Google Scholar Link].
• Wu, W., Zha, Y., Zhang, J., Gao, J., He, J. (2014) A temperature inversion-induced air pollution process as analyzed from Mie LiDAR data. Sci. Total Environ., 480, 102-108 [Google Scholar Link].
• Yahya, K., Zhang, Y., Vukovich, J.M. (2014) Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: multiple-year assessment and sensitivity studies. Atmos. Environ., 92, 318-338 [Google Scholar Link].
• Yan Chan, K.Y., Jian, L. (2013). Identification of significant factors for air pollution levels using a neural network-based knowledge discovery system. Neurocomputing, 99,564–569 [Google Scholar Link].
• Zhang, Y., Seigneur, C., Bocquet, M., Mallet, V., Baklanov, A. (2012) Real-time air quality forecasting, part I: history, techniques, and current status. Atmos. Environ., 60, 632-655 [Google Scholar Link].