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Prediction of the Air Quality by Artificial Neural Network Using Instability Indices in the City of Tehran-Iran | ||
| AUT Journal of Civil Engineering | ||
| مقاله 9، دوره 4، شماره 4، اسفند 2020، صفحه 517-528 اصل مقاله (1.08 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2019.17018.5609 | ||
| نویسندگان | ||
| Razieh Farhadi1؛ Mojtaba Hadavifar* 2؛ Mazaher Moeinaddini3؛ Mahmood Amintoosi4 | ||
| 1Graduated Student, Environmental Sciences Department, Hakim Sabzevari University, Sabzevar, Iran | ||
| 2Assistant Professor, Environmental Sciences Department, Hakim Sabzevari University, Sabzevar, Iran | ||
| 3Assistant Professor, Environmental Sciences Department, University of Tehran, Tehran, Iran | ||
| 4Assistant Professor, Faculty of Mathematics and Computer Science, Hakim Sabzevari University, Sabzevar, Iran | ||
| چکیده | ||
| Today, air pollution is a serious environmental problem becoming a global concern for human beings Air quality is influenced by emissions, meteorological parameters, and topography. The effect of these parameters can be predicted using statistical methods. In the current study, the data in the period of March 2012 to October 2013 are used. These data have been gathered from the stations of the Department of Environment and Air Quality Control Organization (Azadi and Sharif stations) in Tehran city. The main purpose was to predict the air quality of the next day and emissions of carbon monoxide and suspended particles under the influence of instability indices and meteorological parameters using the Artificial Neural Network. Results of the modeling process showed that the concentration of pollutants is strongly influenced by meteorological parameters. Also, prediction of the PM10 concentration of the next day using meteorological parameters (RMSE=29.03, R=0.76), instability indices and meteorological parameters (RMSE=28.13, R=0.76) were better than those obtained for AQI predicted by meteorological parameters (RMSE=20.81, R=0.50) and instability indices and meteorological parameters (RMSE=19.23, R=0.47). In general, the predicted values of PM10 and CO were better compared to AQI. It can be concluded that an artificial neural network couldn’t load the model properly for AQI compared to PM10. | ||
| کلیدواژهها | ||
| Instability indices؛ Air quality؛ Artificial neural network؛ Multi-layer perceptron؛ Particulate matter | ||
| مراجع | ||
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[1]M. Moeinaddini, A.E. Sari, A.R. Bakhtiari, A.Y.-C. Chan, S.M. Taghavi, D. Connell, et al., Sources and Health Risk of Organic Compounds in Respirable Particles in Tehran, Iran, Polycycl. Aromat. Compd. 34 (2014) 469–492.
[2]M.J. Mohammadi-Zadeh, A. Karbassi, N. Bidhendi, M. Abbaspour, A. Padash, An Analysis of Air Pollutants Emission Coefficient in the Transport Sector of Tehran, Open J. Ecol. 7 (2017) 309–323.
[3]Y. Feng, W. Zhang, D. Sun, L. Zhang, Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification, Atmos. Environ. 45 (2011) 1979–1985.
[4]M. Boznar, M. Lesjak, P. Mlakar, A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain, Atmos. Environ. Part B. Urban Atmos. 27 (1993) 221–230.
[5]A. Chakraborty, Rohit; Saha, Upal; Singh, A. K.; Maitra, Association of atmospheric pollution and instability indices: A detailed investigation over an Indian urban metropolis, Atmos. Res. 196 (2017) 83–96.
[6]S. Golbaz, M. Farzadkia, M. Kermani, Determination of Tehran air quality with emphasis on air quality index ( AQI ); 2008-2009, Iran Occup. Heal. 6 (2010) 62–68.
[7]M.G. Lawrence, The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications, Bull. Am. Meteorol. Soc. 86 (2005) 225–233.
[8]R.C. Miller, Notes on Analysis and Severe Storm Forecasting Procedures of the Air Force Global Weather Centre, (1972) 184.
[9] forecast hourly O3 and NO2 levels in the Bilbao area, Environ. Model. Softw. 21 (2006) 430–446.
[10]W.C. Wang, K.W. Chau, C.T. Cheng, L. Qiu, A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series, J. Hydrol. 374 (2009) 294–306.
[11]M.T. Hagan, M.B. Menhaj, Training feed forward networks with the Marquardt algorithm, IEEE Trans. Neural Networks. 5 (1994) 989–993.
[12]K.P. Moustris, I.C. Ziomas, A.G. Paliatsos, 3-day-ahead forecasting of regional pollution index for the pollutants NO2, CO, SO2, and O3 using artificial neural networks in Athens, Greece, Water. Air. Soil Pollut. 209 (2010) 29–43.
[13]S.A. Abdul-Wahab, W.S. Bouhamra, Diurnal Variations of Air Pollution From Motor Vehicles in Residential Area, Int. J. Environ. Stud. 61 (2004) 73–98.
[14]W. Chen, H. Tang, H. Zhao, Diurnal, weekly and monthly spatial variations of air pollutants and air quality of Beijing, Atmos. Environ. 119 (2015) 21–34.
[15]P.W. Summers, The seasonal, weekly, and daily cycles of atmospheric smoke content in central Montreal, J. Air Pollut. Control Assoc. 16 (1966) 432–438.
[16]A.B. Safavi SY, Study of effective geographical factors the air pollution in Tehran city, Geogr Res J. 58 (2006) 99–112.
[17]A. Afzali, M. Rashid, B. Sabariah, M. Ramli, PM10 pollution: Its prediction and meteorological influence in PasirGudang, Johor, in: IOP Conf. Ser. Earth Environ. Sci., 2014: p. 012100.
[18]H.K. Elminir, Dependence of urban air pollutants on meteorology, Sci. Total Environ. 350 (2005) 225–237.
[19]G. Latini, R.C. Grifoni, G. Passerini, Influence of meteorological parameters on urban and suburban air pollution, Adv. Air Pollut. 11 (2002) 1–10.
[20]I.G. McKendry, Evaluation of Artificial Neural Networks for Fine Particulate Pollution (PM10 and PM2.5) Forecasting, J. Air Waste Manage. Assoc. 52 (2002) 1096–1101.
[21]C. Dueas, M.C. Fernandez, S. Caete, J. Carretero, E. Liger, Assessment of ozone variations and meteorological effects in an urban area in the Mediterranean Coast, Sci. Total Environ. 299 (2002) 97–113.
[22]M.. Grundström, H.W. Linderholm, J.. Klingberg, H. Pleijel, Urban NO2 and NO pollution in relation to the North Atlantic Oscillation NAO, Atmos. Environ. 45 (2011) 883–888.
[23]J.L. Pearce, J. Beringer, N. Nicholls, R.J. Hyndman, N.J. Tapper, Quantifying the influence of local meteorology on air quality using generalized additive models, Atmos. Environ. 45 (2011) 1328–1336.
[24]A.M. Jones, R.M. Harrison, J. Baker, The wind speed dependence of the concentrations of airborne particulate matter and NOx, Atmos. Environ. 44 (2010) 1682–1690.
[25]P. Perez, J. Reyes, Prediction of maximum of 24-h average of PM10 concentrations 30 h in advance in Santiago, Chile, Atmos. Environ. 36 (2002) 4555–4561.
[26]T. Y. Pai, H. H.H. M. Lo, T. J. Wan, L. Chen, P. S. Hung, H. H.H. M. Lo, et al., Predicting air pollutant emissions from a medical incinerator using grey model and neural network, Appl. Math. Model. 39 (2015) 1513–1525.
[27]D. Chen, T. Xu, Y. Li, Y. Zhou, J. Lang, X. Liu, et al., A hybrid approach to forecast air quality during high-PM concentration pollution period, Aerosol Air Qual. Res. 15 (2015) 1325–1337.
[28]N. Haizum, A. Rahman, M. Hisyam, M. Talib, Forecasting of Air Pollution Index with Artificial Neural Network, J. Teknol. Sciences Eng. 63 (2013) 59–64.
[29]S. Nigam, R. Nigam, S. Kapoor, Forecasting Carbon Monoxide Concentration Using Artificial Neural Network Modeling, IJCA Proc. Int. Conf. Curr. Trends Adv. Comput. ICCTAC 2013. ICCTAC (2013) 35–40.
[30]P. Wang, Y. Liu, Z. Qin, G. Zhang, A novel hybrid forecasting model for PM10 and SO2 daily concentrations, Sci. Total Environ. 505 (2015) 1202–1212.
[31]M. Shakerkhatibi, N. Mohammadi, K.Z. Benis, A.B. Sarand, Using ANN and EPR models to predict carbon monoxide concentrations in urban area of Tabriz, 2 (2015) 117–122. | ||
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