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Prediction of CO and PM10 in Cold and Warm Seasons and Survey of the Effect of Instability Indices on Contaminants Using Artificial Neural Network: A Case Study in Tehran City

Document Type : Original Article

Authors

1 Environmental Sciences Department, Hakim Sabzevari University, Sabzevar, Iran

2 Environmental Sciences Department, University of Tehran, Tehran, Iran

3 Faculty of Mathematics and Computer Sciences, Hakim Sabzevari University, Sabzevar, Iran

Abstract

Today, air pollution in urban areas is a major issue that have been affecting human health and the environment. Over the years artificial neural network methods has been used for prediction of pollutants concentration in many metropolitans. In the present study data were obtained from department of environment and air quality controlling stations in city of Tehran from March 2012 to October 2013. Prediction of CO and PM10 contaminations during cold and warm seasons under the influence of instability indices and meteorological parameters was done using the artificial neural network. Results of the modeling process showed that the highest correlation coefficient was obtained 0.84 for PM10 in warm season. On the contrary, the highest correlation coefficient of CO in cold season was 0.78. Also, the effect of instability indices on air pollution was investigated. The highest CO concentration occurred during cold seasons (R2= 0.81), while the lowest concentration was in warm season (R2= 0.72). In case of  PM, the highest concentration occurred during warm seasons (R2= 0.84), while the lowest concentration was in cold season (R2=0.75).

Keywords

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References
  1. Barai, S., Dikshit, A. and Sharma, S., 2007. Neural Network Models for Air Quality Prediction: A Comparative Study, in Soft Computing in Industrial Applications. Springer. p. 290-305. Doi: 0.1007/978-3-540-70706-6-27.
  2. Waldrop, J., 2020. “Air Pollution and Health”. The Journal for Nurse Practitioners, 16(5), pp.A5-A6. Doi: 10.1016/j.nurpra.2020.02.017
  3. Künzli, N. and Tager, I. B., 2005. “Air Pollution: From Lung to Heart”. Swiss Med Wkly, 135(47-48), pp.697-702.
  4. Mage, D., Ozolins, G., Peterson, P., Webster, A., Orthofer, R., Vandeweerd, V. and Gwynne, M., 1996. “Urban Air Pollution in Megacities of the World”. Atmospheric Environment, 30(5), pp.681-686. Doi: 10.1016/1352-2310(95)00219-7
  5. Mallet, V. and Sportisse, B., 2008. “Air Quality Modeling: From Deterministic to Stochastic Approaches”. Computers & Mathematics with Applications, 55(10), pp.2329-2337. Doi: 10.1016/j.camwa.2007.11.004
  6. Finardi, S., De Maria, R., D'Allura, A., Cascone, C., Calori, G. and Lollobrigida, F., 2008. “A Deterministic Air Quality Forecasting System for Torino Urban Area, Italy”. Environmental Modelling & Software, 23(3), pp.344-355.                                              Doi: 10.1016/j.envsoft.2007.04.001
  7. Seskin, E. P., Anderson Jr, R. J. and Reid, R. O., 1983. “An Empirical Analysis of Economic Strategies for Controlling Air Pollution”. Journal of Environmental Economics and Management, 10(2), pp.112-124. Doi: 10.1016/0095-0696(83)90020-7
  8. Li, M. and Hassan, R., 2010. Urban Air Pollution Forecasting Using Artificial Intelligence-Based Tools, in Air Pollution, V. Villanyi, Editor., InTech. p.195-220. ISSN: 978-953-307-143-5
  9. Wang, W., Xu, Z. and Lu, J. W., 2003. “Three Improved Neural Network Models for Air Quality Forecasting”. Engineering Computations, 20(2), pp.192-210. Doi: 10.1108/02644400310465317
  10. Gardner, M. W. and Dorling, S., 1998. “Artificial Neural Networks (the Multilayer Perceptron)—a Review of Applications in the Atmospheric Sciences”. Atmospheric Environment, 32(14-15), pp.2627-2636. Doi: 10.1016/S1352-2310(97)00447-0
  11. Brunelli, U., Piazza, V., Pignato, L., Sorbello, F. and Vitabile, S., 2007. “Two-Days Ahead Prediction of Daily Maximum Concentrations of So2, O3, Pm10, No2, Co in the Urban Area of Palermo, Italy”. Atmospheric Environment, 41(14), pp.2967-2995. Doi: 10.1016/j.atmosenv.2006.12.013
  12. de Souza, A., Aristone, F. and Sabbah, I., 2015. “Modeling the Surface Ozone Concentration in Campo Grande (Ms)—Brazil Using Neural Networks”. Natural Science, 7(04), pp.171. Doi: 10.4236/ns.2015.74020
  13. Chelani, A. B., Rao, C. C., Phadke, K. and Hasan, M., 2002. “Prediction of Sulphur Dioxide Concentration Using Artificial Neural Networks”. Environmental Modelling & Software, 17(2), pp.159-166. Doi: 10.1016/S1364-8152(01)00061-5
  14. Kurt, A., Gulbagci, B., Karaca, F. and Alagha, O., 2008. “An Online Air Pollution Forecasting System Using Neural Networks”. Environment International, 34(5), pp.592-598. Doi: 10.1016/j.envint.2007.12.020
  15. Ghazali, S. and Ismail, L. H., 2012. Air Quality Prediction Using Artificial Neural Network. in Proceedings of the International Conference on Civil Environmental Engineering Sustainability, pp: 1-5.
  16. Hrust, L., Klaić, Z. B., Križan, J., Antonić, O. and Hercog, P., 2009. “Neural Network Forecasting of Air Pollutants Hourly Concentrations Using Optimised Temporal Averages of Meteorological Variables and Pollutant Concentrations”. Atmospheric Environment, 43(35), pp.5588-5596. Doi: 10.1016/j.atmosenv.2009.07.048
  17. Cai, M., Yin, Y. and Xie, M., 2009. “Prediction of Hourly Air Pollutant Concentrations near Urban Arterials Using Artificial Neural Network Approach”. Transportation Research Part D: Transport and Environment, 14(1), pp.32-41.  Doi: 10.1016/J.Trd.2008.10.004
  18. Faraji Sabokbar, H., Hosseini, A., Banaitis, A. and Banaitiene, N., 2016. “A Novel Sorting Method Topsis-Sort: An Applicaiton for Tehran Environmental Quality Evaluation”. Business Administration and Management, 19, pp.87-104. Doi: 10.15240/tul/001/2016-2-006
  19. Hosseinpoor, A. R., Forouzanfar, M. H., Yunesian, M., Asghari, F., Naieni, K. H. and Farhood, D., 2005. “Air Pollution and Hospitalization Due to Angina Pectoris in Tehran, Iran: A Time-Series Study”. Environmental Research, 99(1), pp.126-131. Doi: 10.1016/j.envres.2004.12.004
  20. Naddafi, K., Hassanvand, M. S., Yunesian, M., Momeniha, F., Nabizadeh, R., Faridi, S. and Gholampour, A., 2012. “Health Impact Assessment of Air Pollution in Megacity of Tehran, Iran”. Iranian Journal of Environmental Health Science and Engineering, 9(1), pp.1-7. Doi: 10.1186/1735-2746-9-28
  21. Golbaz, S., Farzadkia, M. and Kermani, M., 2010. “Determination of Tehran Air Quality with Emphasis on Air Quality Index (Aqi) 2008-2009”. Iran Occupational Health, 6(4), pp.62-68.
  22. Galway, J. G., 1956. “The Lifted Index as a Predictor of Latent Instability”. Bulletin of the American Meteorological Society, 37(10), pp.528-529. Doi: 10.1175/1520-0477-37.10.528
  23. Miller, R. C., 1975. Notes on Analysis and Severe-Storm Forecasting Procedures of the Air Force Global Weather Central. AWS Tech, Headquarters Air Weather Service, Scott AFB.
  24. Showalter, A. K., 1953. “A Stability Index for Thunderstorm Forecasting”. Bulletin of the American Meteorological Society, 34(6), pp.250-252. ISSN: 0003-0007
  25. Spellman, G., 1999. “An Application of Artificial Neural Networks to the Prediction of Surface Ozone Concentrations in the United Kingdom”. Applied Geography, 19(2), pp.123-136. Doi: 10.1016/S0143-6228(98)00039-3
  26. Warner, B. and Misra, M., 1996. “Understanding Neural Networks as Statistical Tools”. The American Statistician, 50(4), pp.284-293. Doi: 10.1080/00031305.1996.10473554
  27. Venkatesan, C., Raskar, S., Tambe, S., Kulkarni, B. and Keshavamurty, R., 1997. “Prediction of All India Summer Monsoon Rainfall Using Error-Back-Propagation Neural Networks”. Meteorology and Atmospheric Physics, 62(3), pp.225-240. Doi: 10.1007/BF01029704
  28. Derr, V. and Slutz, R., 1994. “Prediction of El-Nino Events in the Pacific by Means of Neural Networks”. AI Applications, 8(2), pp.51-63. ISSN: 1051-8266
  29. Comrie, A. C., 1997. “Comparing Neural Networks and Regression Models for Ozone Forecasting”. Journal of the Air & Waste Management Association, 47(6), pp.653-663. Doi: 10.1080/10473289.1997.10463925
  30. Eleuteri, A., Tagliaferri, R. and Milano, L., 2005. “A Novel Information Geometric Approach to Variable Selection in Mlp Networks”. Neural networks, 18(10), pp.1309-1318. Doi: 10.1016/j.neunet.2005.01.008
  31. Krzywański, J. and Nowak, W., 2017. “Neurocomputing Approach for the Prediction of Nox Emissions from Cfbc in Air-Fired and Oxygen-Enriched Atmospheres”. Journal of Power Technologies, 97(2), ISSN: 2083-4195
  32. Li, B., Yang, G., Wan, R. and Hörmann, G., 2017. “Dynamic Water Quality Evaluation Based on Fuzzy Matter–Element Model and Functional Data Analysis, a Case Study in Poyang Lake”. Environmental Science and Pollution Research, 24(23), pp.19138-19148. Doi: 10.1007/S11356-017-9371-0
  33. Kalinić, H., Mihanović, H., Cosoli, S., Tudor, M. and Vilibić, I., 2017. “Predicting Ocean Surface Currents Using Numerical Weather Prediction Model and Kohonen Neural Network: A Northern Adriatic Study”. Neural Computing and Applications, 28(1), pp.611-620. Doi: 10.1007/S00521-016-2395-4
  34. Agirre-Basurko, E., Ibarra-Berastegi, G. and Madariaga, I., 2006. “Regression and Multilayer Perceptron-Based Models to Forecast Hourly O3 and No2 Levels in the Bilbao Area”. Environmental Modelling & Software, 21(4), pp.430-446. Doi: 10.1016/j.envsoft.2004.07.008
  35. Hagan, M. T. and Menhaj, M. B., 1994. “Training Feedforward Networks with the Marquardt Algorithm”. IEEE Transactions on Neural Networks, 5(6), pp.989-993. Doi: 10.1109/72.329697
  36. Moustris, K. P., Ziomas, I. C. and Paliatsos, A. G., 2010. “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 and Soil Pollution, 209(1), pp.29-43. Doi: 10.1007/s11270-009-0179-5
  37. Mikhailuta, S. V., Taseiko, O. V., Pitt, A., Lezhenin, A. A. and Zakharov, Y. V., 2009. “Seasonal Variations of Air Pollutant Concentrations within Krasnoyarsk City ”. Environmental Monitoring and Assessment 149(1-4), pp.329-41. Doi: 10.1007/s10661-008-0206-9
  38. Manahan, S. E., 2011. Fundamentals of Environmental Chemistry. CRC press. ISSN: 1420052691
  39. Aneja, V. P., Agarwal, A., Roelle, P. A., Phillips, S. B., Tong, Q., Watkins, N. and Yablonsky, R., 2001. “Measurements and Analysis of Criteria Pollutants in New Delhi, India”. Environment International, 27(1), pp.35-42. Doi: 10.1016/S0160-4120(01)00051-4
  40. Mamtimin, B. and Meixner, F. X., 2011. “Air Pollution and Meteorological Processes in the Growing Dryland City of Urumqi (Xinjiang, China)”. Science of the Total Environment, 409(7), pp.1277-1290. Doi: 10.1016/j.scitotenv.2010.12.010
  41. Abdul‐Wahab*, S. A. and Bouhamra, W. S., 2004. “Diurnal Variations of Air Pollution from Motor Vehicles in Residential Area”. International Journal of Environmental Studies, 61(1), pp.73-98. Doi: 10.1080/0020723032000130034
  42. Morikawa, T., Wakamatsu, S., Tanaka, M., Uno, I., Kamiura, T. and Maeda, T., 1998. “C2–C5 Hydrocarbon Concentrations in Central Osaka”. Atmospheric Environment, 32(11), pp.2007-2016. Doi: 10.1016/S1352-2310(97)00509-8
  43. Sahu, L. and Lal, S., 2006. “Distributions of C2–C5 Nmhcs and Related Trace Gases at a Tropical Urban Site in India”. Atmospheric Environment, 40(5), pp.880-891. Doi: 10.1016/j.atmosenv.2005.10.021
  44. Nagendra, S. and Khare, M., 2003. “Diurnal and Seasonal Variations of Carbon Monoxide and Nitrogen Dioxide in Delhi City”. International Journal of Environment and Pollution, 19(1), pp.75-96. ISSN: 0957-4352
  45. Summers, P. W., 1966. “The Seasonal, Weekly, and Daily Cycles of Atmospheric Smoke Content in Central Montreal”. Journal of the Air Pollution Control Association, 16(8), pp.432-438. Doi: 10.1080/00022470.1966.10468496
  46. Witz, S., Larm, A. M., Elvin, B. M. and Moore, A. B., 1982. “The Relationship between Concentration of Traffic-Related Pollutants and Meteorology at a Los Angeles Site”. Journal of the Air Pollution Control Association, 32(6), pp.643-645. Doi: 10.1080/00022470.1982.10465440
  47. Mazaheri Tehrani, A., Karamali, F. and Chimehi, E., 2015. “Evaluation of 5 Air Criteria Pollutants Tehran, Iran”. International Archives of Health Sciences, 2(3), pp.95-100. Doi: 10.7508/iahs.2015.03.001
  48. Demuzere, M., Trigo, R., Vila-Guerau de Arellano, J. and Van Lipzig, N. J. A. C. P. D., 2008. “The Impact of Weather and Atmospheric Circulation on O3 and Pm10 Levels at a Mid-Latitude Site”. 8, pp.21037-21088.
  49. Mansouri, N. U. and Jamshidi Ghasem Abadi, A., 2011. “Determination of the Field Amount of Air Pollution and Psi Index in the Parking Buses in Tehran City”. Human & Environment, 9(4), pp.27-32. ISSN: 1562-5532
  50. Hales, E., 1996. “Severe Weather Forecasting”. Personal SPC notes, Severe Weather Forecast,
  51. George, J., 1960. “Local Forecast Studies-New York City Airports”. Weather Forecasting for Aeronautics, Academic Press, New Yor, pp.625-637.
Iranica Journal of Energy & Environment
Volume 13, Issue 1
January 2022
Pages 71-78
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