Prediction of mortality resulted from NO2 concentration in Tehran by Air Q+ software and artificial neural network

  • M. Ebrahimi Ghadi
  • F. QaderiEmail author
  • E. Babanezhad
Original Paper


In this study, for the first time, the combination of Air Q+ software and wavelet neural network was used to predict the mortality rate caused by the increase in NO2 concentration in Tehran. In the combination of these two softwares, the wavelet neural network software was used to predict daily NO2 concentration based on 12 effective parameters, and then the annual concentration of NO2 was calculated using the daily concentration of wavelet neural network output. Then, annual concentration of NO2 was used as the input of Air Q+ software. The mortality rate was calculated by Air Q+ software. In this research, the most appropriate predictive algorithm for neural network was studied and layer recurrent algorithm was the most appropriate algorithm. Then, capability of this network was enhanced to predict future NO2 concentration by wavelet transformation, and wavelet neural network was designed. Also, NO2 concentration is predicted for future 47 months by using of the time series of the previous data and the wavelet neural network. Analyzing the sensitivity of mortality resulted from NO2 concentration was done by using of wavelet neural network and Air Q+ software, and it was concluded that the increase or decrease in the parameters affecting NO2 concentration will affect the mortality rate. This research has identified petrol consumption as the most influential parameter. The conclusion is that by lowering the 10% of petrol consumption, the mortality based on NO2 concentration in ambient air will decrease about 50%.


Prediction Air pollution Artificial neural network Air Q+ NO2 



The authors would like to thank Tehran Weather Bureau and Tehran Municipality.


  1. Alizadeh MJ, Kavianpour MR (2015) Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Mar Pollut Bull 98:171–178. CrossRefGoogle Scholar
  2. Anenberg SC, Belova A, Brandt J et al (2016) Survey of ambient air pollution health risk assessment tools. Risk Anal 36:1718–1736. CrossRefGoogle Scholar
  3. Babanezhad E, Amini Rad H, Hosseini Karimi SS, Qaderi F (2017) Investigating nitrogen removal using simultaneous nitrification-denitrification in transferring wastewater through collection networks with small-diameter pipes. Water Pract Technol 12:396–405. CrossRefGoogle Scholar
  4. Babanezhad E, Qaderi F, Salehi Ziri M (2018) Spatial modeling of groundwater quality based on using Schoeller diagram in GIS base: a case study of Khorramabad, Iran. Environ Earth Sci 77:339. CrossRefGoogle Scholar
  5. Bahrami Asl F, Leili M, Vaziri Y et al (2018) Health impacts quantification of ambient air pollutants using AirQ model approach in Hamadan, Iran. Environ Res 161:114–121. CrossRefGoogle Scholar
  6. Bascom R, Bromberg PA, Costa DL et al (1996) Health effects of outdoor air pollution. Part 2. Committee of the environmental and occupational health assembly of the American Thoracic Society. Am J Respir Crit Care Med 153:477–498. CrossRefGoogle Scholar
  7. Battista G (2017) Analysis of the air pollution sources in the city of Rome (Italy). Energy Proc 126:392–397. CrossRefGoogle Scholar
  8. Beamish LA, Osornio-Vargas AR, Wine E (2011) Air pollution: an environmental factor contributing to intestinal disease. J Crohn’s Colitis 5:279–286CrossRefGoogle Scholar
  9. Bicego M, Baldo S (2016) Properties of the Box–Cox transformation for pattern classification. Neurocomputing 218:390–400. CrossRefGoogle Scholar
  10. Boznar M, Lesjak M, Mlakar P (1993) 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 27:221–230. CrossRefGoogle Scholar
  11. Cabaneros SMS, Calautit JKS, Hughes BR (2017) Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO2 pollution. Energy Proc 142:3524–3530. CrossRefGoogle Scholar
  12. Cai M, Yin Y, Xie M (2009) Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transp Res Part D Transp Environ 14:32–41. CrossRefGoogle Scholar
  13. Chelani AB, Chalapati Rao C, Phadke K, Hasan M (2002) Prediction of sulphur dioxide concentration using artificial neural networks. Environ Model Softw 17:159–166. CrossRefGoogle Scholar
  14. Chen R, Samoli E, Wong CM et al (2012) Associations between short-term exposure to nitrogen dioxide and mortality in 17 Chinese cities: the China air pollution and health effects study (CAPES). Environ Int 45:32–38. CrossRefGoogle Scholar
  15. Contreras-Ochando L, Ferri C (2017) AirVLC: an application for visualizing wind-sensitive interpolation of urban air pollution forecasts. IEEE Int Conf Data Min Work ICDMW 80:1296–1299. CrossRefGoogle Scholar
  16. Elangasinghe MA, Singhal N, Dirks KN, Salmond JA (2014) Development of an ANN: based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmos Pollut Res 5:696–708. CrossRefGoogle Scholar
  17. Faghih Nasiri E, Yousefi Kebria D, Qaderi F (2018) An experimental study on the simultaneous phenol and chromium removal from water using titanium dioxide photocatalyst. Civ Eng J 4:585–593. CrossRefGoogle Scholar
  18. Fattore E, Paiano V, Borgini A et al (2011) Human health risk in relation to air quality in two municipalities in an industrialized area of Northern Italy. Environ Res 111:1321–1327. CrossRefGoogle Scholar
  19. Ferrero E, Alessandrini S, Balanzino A (2016) Impact of the electric vehicles on the air pollution from a highway. Appl Energy 169:450–459. CrossRefGoogle Scholar
  20. Folinsbee LJ (1992) Does nitrogen dioxide exposure increase airways responsiveness? Toxicol Ind Health 8:273–283CrossRefGoogle Scholar
  21. Gardner MW, Dorling SR (1999) Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmos Environ 33:709–719. CrossRefGoogle Scholar
  22. Garson GD (1991) Interpreting neural-network connection weights. ArtifIntell 6:46–51Google Scholar
  23. Ghanbari Ghozikali M, Heibati B, Naddafi K et al (2016) Evaluation of chronic obstructive pulmonary disease (COPD) attributed to atmospheric O3, NO2, and SO2 using Air Q Model (2011–2012 year). Environ Res 144:99–105. CrossRefGoogle Scholar
  24. Goh ATC (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9:143–151. CrossRefGoogle Scholar
  25. Golbaz S, Farzadkia M, Kermani M (2010) Determination of Tehran air quality with emphasis on air quality index (AQI); 2008–2009. Iran Occup Health 6:62–68Google Scholar
  26. Golov N, Rönnbäck L (2017) Big Data normalization for massively parallel processing databases. Comput Stand Interfaces 54:86–93. CrossRefGoogle Scholar
  27. Gryparis A, Forsberg B, Katsouyanni K et al (2004) Acute effects of ozone on mortality from the “Air pollution and health: a European approach” project. Am J Respir Crit Care Med 170:1080–1087. CrossRefGoogle Scholar
  28. Gupta P, Khan MN, da Silva A, Patadia F (2013) MODIS aerosol optical depth observations over urban areas in Pakistan: quantity and quality of the data for air quality monitoring. Atmos Pollut Res 4:43–52. CrossRefGoogle Scholar
  29. Jiang D, Zhang Y, Hu X et al (2004) Progress in developing an ANN model for air pollution index forecast. Atmos Environ 38:7055–7064CrossRefGoogle Scholar
  30. Jiménez-Espadafor FJ, Torres M, Velez JA et al (2012) Experimental analysis of low temperature combustion mode with diesel and biodiesel fuels: a method for reducing NOx and soot emissions. Fuel Process Technol 103:57–63. CrossRefGoogle Scholar
  31. Johansson C, Lövenheim B, Schantz P et al (2017) Impacts on air pollution and health by changing commuting from car to bicycle. Sci Total Environ 584–585:55–63. CrossRefGoogle Scholar
  32. Kamarehie B, Ghaderpoori M, Jafari A et al (2017) Quantification of health effects related to SO2 and NO2 pollutants using Air quality model. J Adv Environ Health Res 5:44–50Google Scholar
  33. Katsouyanni K, Touloumi G, Spix C et al (1997) Short term effects of ambient sulphur dioxide and particulate matter on mortality in 12 European cities: results from time series data from the APHEA project. BMJ 314:1658. CrossRefGoogle Scholar
  34. Kelly FJ, Blomberg A, Frew A et al (1996) Antioxidant kinetics in lung lavage fluid following exposure of humans to nitrogen dioxide. Am J Respir Crit Care Med 154:1700–1705. CrossRefGoogle Scholar
  35. Khaniabadi YO, Polosa R, Chuturkova RZ et al (2017) Human health risk assessment due to ambient PM10 and SO2 by an air quality modeling technique. Process Saf Environ Prot 111:346–354. CrossRefGoogle Scholar
  36. Krzyzanowski M (1997) Methods for assessing the extent of exposure and effects of air pollution. Occup Environ Med Environ Med 54:145–151CrossRefGoogle Scholar
  37. Li S, Feng K, Li M (2017) Identifying the main contributors of air pollution in Beijing. J Clean Prod 163:S359–S365. CrossRefGoogle Scholar
  38. Miri M, Derakhshan Z, Allahabadi A et al (2016) Mortality and morbidity due to exposure to outdoor air pollution in Mashhad metropolis, Iran. The AirQ model approach. Environ Res 151:451–457. CrossRefGoogle Scholar
  39. Naddafi K, Hassanvand MS, Yunesian M et al (2008) Health impact assessment of air pollution in megacity of Tehran, Iran. Iran J Environ Health Sci Eng 9:28. CrossRefGoogle Scholar
  40. Naddafi K, Hassanvand MS, Yunesian M et al (2012) Health impact assessment of air pollution in megacity of Tehran, Iran. Iran J Environ Health Sci Eng 9:28. CrossRefGoogle Scholar
  41. Nikoonahad A, Naserifar R, Alipour V et al (2017) Assessment of hospitalization and mortality from exposure to PM10 using AirQ modeling in Ilam, Iran. Environ Sci Pollut Res 24:21791–21796. CrossRefGoogle Scholar
  42. Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall-runoff modeling. Water Resour Manag 23:2877–2894. CrossRefGoogle Scholar
  43. Okkan U (2012) Wavelet neural network model for reservoir inflow prediction. Sci Iran 19:1445–1455. CrossRefGoogle Scholar
  44. Oliveri Conti G, Heibati B, Kloog I et al (2017) A review of AirQ Models and their applications for forecasting the air pollution health outcomes. Environ Sci Pollut Res 24:6426–6445. CrossRefGoogle Scholar
  45. Ordieres JB, Vergara EP, Capuz RS, Salazar RE (2005) Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua). Environ Model Softw 20:547–559. CrossRefGoogle Scholar
  46. Ozcan HK, Ucan ON, Sahin U et al (2006) Artificial neural network modeling of methane emissions at Istanbul Kemerburgaz–Odayeri landfill site. J Sci Ind Res 65:128–134Google Scholar
  47. Perez P, Reyes J (2001) Prediction of particulate air pollution using neural techniques. Neural Comput Appl 10:165–171. CrossRefGoogle Scholar
  48. Pope CA (2000) Epidemiology of fine particulate air pollution and human health: biologic mechanisms and who’s at risk? Environ Health Perspect 108:713–723. CrossRefGoogle Scholar
  49. Qaderi F, Babanezhad E (2017) Prediction of the groundwater remediation costs for drinking use based on quality of water resource, using artificial neural network. J Clean Prod 161:840–849. CrossRefGoogle Scholar
  50. Qaderi F, Ayati B, Ganjidoust H (2011) Role of moving bed biofilm reactor and sequencing batch reactor in biological degradation of formaldehyde wastewater, Iran. J Environ Health Sci Eng 8:295–306Google Scholar
  51. Qaderi F, Sayahzadeh AH, Azizi M (2018) Efficiency optimization of petroleum wastewater treatment by using of serial moving bed biofilm reactors. J Clean Prod 192:665–677. CrossRefGoogle Scholar
  52. Schenatto K, de Souza EG, Bazzi CL et al (2017) Normalization of data for delineating management zones. Comput Electron Agric 143:238–248. CrossRefGoogle Scholar
  53. Sidney Burrus C, Gopinath RA, Guo H (1998) Introduction to wavelets and wavelet transforms. Prentice-Hall, Inc., HoustonGoogle Scholar
  54. Solgi A, Nourani V, Pourhaghi A (2014) Forecasting daily precipitation using hybrid model of wavelet-artificial neural network and comparison with adaptive neurofuzzy inference system (case study: Verayneh station. Adv Civ Eng, Nahavand). CrossRefGoogle Scholar
  55. Wheida A, Nasser A, El Nazer M et al (2018) Tackling the mortality from long-term exposure to outdoor air pollution in megacities: lessons from the Greater Cairo case study. Environ Res 160:223–231. CrossRefGoogle Scholar
  56. World Health Organization (2013) Health risks of air pollution in Europe: HRAPIE project. 60Google Scholar
  57. World Health Organization Europe (2001) Quantification of the Health Effects of Exposure to Air PollutionGoogle Scholar
  58. Zallaghi E, Goudarzi G, Haddad MN, Marzieh S (2014) Assessing the effects of nitrogen dioxide in urban air on health of west and southwest cities of Iran. Jundishapur J Health Sci. CrossRefGoogle Scholar
  59. Zhang S, Worrell E, Crijns-Graus W (2015) Cutting air pollution by improving energy efficiency of China’s cement industry. Energy Proc 83:10–20. CrossRefGoogle Scholar
  60. Zhang H, Wang Y, Park TW, Deng Y (2017a) Quantifying the relationship between extreme air pollution events and extreme weather events. Atmos Res 188:64–79. CrossRefGoogle Scholar
  61. Zhang X, Zhang X, Chen X (2017b) Valuing air quality using happiness data: the case of China. Ecol Econ 137:29–36. CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2018

Authors and Affiliations

  1. 1.Faculty of Civil EngineeringBabol Noshirvani University of TechnologyBabolIran
  2. 2.Sirjan University of TechnologySirjanIran

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