Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

  • 905 Accesses

  • 46 Citations

Abstract

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NOx), nitrogen dioxide (NO2), nitrogen monoxide (NO), ozone (O3), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM10) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R 2 of more than 0.82 between modeled and observed hourly pollutant levels for CO, NOx, NO2, NO, and PM10. However, predicted O3 levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Borrego C, Monteiro A, Ferreira J, Miranda AI, Costa AM, Carvalho AC, Lopes M (2008) Procedures for estimation of modeling uncertainty in air quality assessment. Environ Int 34:613–620

  2. 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 B 27:221–230

  3. Brunelli U, Piazza V, Pignato L, Sorbello F, Vitabile S (2007) Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy. Atmos Environ 41:2967–2995

  4. Dimov IT (2008) Monte Carlo methods for applied scientists. World Scientific Publishing Co. Pte. Ltd, UK

  5. Faraway J, Chatfield C (1998) Time series forecasting with neural networks: a comparative study using the airline data. Appl Stat 47(2):231–250

  6. Paschalidou A, Karakitsios S, Kleanthous S, Kassomenos P (2011) Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management. Environ Sci Pollut Res 18:316–327

  7. 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

  8. Hooyberghs J, Mensink C, Dumont G, Fierens F, Brasseur O (2005) A neural network forecast for daily average PM10 concentrations in Belgium. Atmos Environ 39:3279–3289

  9. Hrust L, Bencetic Klaic Z, Krizan J, Antonic O, Hercog P (2009) Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations. Atmos Environ 43:5588–5596

  10. Ibarra-Berastegi G, Elias A, Barona A, Saenz J, Ezcurra A, de Argandona JD (2008) From diagnosis to prognosis for forecasting air pollution using neural networks: air pollution monitoring in Bilbao. Environ Model Soft 23:622–637

  11. Jiang D, Zhang Y, Hu X, Zeng Y, Tan J, Shao D (2004) Progress in developing an ANN model for air pollution index forecast. Atmos Environ 38:7055–7064

  12. Karppinen A, Kukkonen J, Elolahde T, Konttinen M, Koskentalo T, Rantakrans E (2000a) A modelling system for predicting urban air pollution: model description and applications in the Helsinki metropolitan area. Atmos Environ 34:3723–3733

  13. Karppinen A, Kukkonen J, Elolahde T, Konttinen M, Koskentalo T (2000b) A modelling system for predicting urban air pollution: comparison of model predictions with the data of an urban measurement network in Helsinki. Atmos Environ 34:3735–3743

  14. Khosravi A, Nahavandi S, Creighton D, Atiya AF (2011) Comprehensive review of neural network-based prediction intervals and new advances. IEEE Trans Neural Netw 22(9):1341–1356

  15. Kolehmainen M, Martikainen H, Ruuskanen J (2001) Neural networks and periodic components used in air quality forecasting. Atmos Environ 35:815–825

  16. Kukkonen J, Partanen L, Karppinen A, Ruuskanen J, Junninen H, Kolehmainen M, Niska H, Dorling S, Chatterton T, Foxall R, Cawley G (2003) Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki. Atmos Environ 37:4549–4550

  17. Lachtermacher G, Fuller JD (1994) Stochastic and statistical methods in hydrology and environmental engineering. In: Hipel KW, McLeod AI, Panu US, Singh VP (eds) Back-propagation in hydrological time series forecasting. Kluwer, Dordrecht

  18. Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Soft 15:101–124

  19. McMichael AJ (2000) The urban environment and health in a world of increasing globalization: issues for developing countries. Bull of the World Health Organ 78:9

  20. Metropolis N, Ulam SM (1949) The Monte Carlo method. J Am Stat Assoc 44:335–341

  21. Molina MJ, Molina LT (2004) Megacities and atmospheric pollution. AirWaste Manag Assoc 54:644–680

  22. Myers RH (1990) Classical and modern regression with applications, 2nd edn. PWS-Kent, Boston

  23. Perez P, Reyes J (2006) An integrated neural network model for PM10 forecasting. Atmos Environ 40:2845–2851

  24. Perez P, Trier A, Reyes J (2000) Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmos Environ 34:1189–1196

  25. Podnar D, Koracin D, Panorska A (2002) Application of artificial neural networks to modeling the transport and dispersion of tracers in complex terrain. Atmos Environ 36:561–570

  26. Reich SL, Gomez DR, Dawidowski LE (1999) Artificial neural network for the identification of unknown air pollution sources. Atmos Environ 33:3045–3052

  27. Ripley BD (1987) Stochastic simulation. Wiley, New York

  28. Rubinstein BY (1981) Simulation and the Monte Carlo method. Wiley, New York

  29. Salazar-Ruiz E, Ordieres JB, Vergara EP, Capuz-Rizo SF (2008) Development and comparative analysis of tropospheric O3 prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US). Environ Model Soft 23:1056–1069

  30. Seinfeld JH, Pandis SN (2006) Atmospheric chemistry and physics: from air pollution to climate change. Wiley, New York

  31. Shahin MA, Jaksa MB, Maier HR (2008) State of the art of artificial neural networks in geotechnical engineering. Electro J Geotech Eng 8:1–26

Download references

Acknowledgments

The authors would like to give special thanks to Tehran AQCC and Iran Meteorological Organization for providing air pollution and meteorological data used in the current study. We also would like to express our thanks to Ms. Maryam Zare Shanhneh for her contributions to this paper.

Author information

Correspondence to Mohammad Arhami.

Additional information

Responsible editor: Michael Matthies

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Arhami, M., Kamali, N. & Rajabi, M.M. Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations. Environ Sci Pollut Res 20, 4777–4789 (2013). https://doi.org/10.1007/s11356-012-1451-6

Download citation

Keywords

  • Urban air pollution
  • Predicting pollutants
  • Artificial neural networks
  • Meteorological variables
  • Monte Carlo simulations
  • Prediction intervals