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Mathematical Geosciences

, Volume 46, Issue 1, pp 75–93 | Cite as

Hybrid Model for Urban Air Pollution Forecasting: A Stochastic Spatio-Temporal Approach

  • Ana RussoEmail author
  • Amílcar O. Soares
Article

Abstract

Air pollution is usually driven by a complex combination of factors in which meteorology, physical obstacles, and interactions between pollutants play significant roles. Considering the characteristics of urban atmospheric pollution and its consequent impacts on human health and quality of life, forecasting models have emerged as an effective tool to identify and forecast air pollution episodes. The overall objective of the present work is to produce forecasts of pollutant concentrations with high spatio-temporal resolution and to quantify the uncertainty in those forecasts. Therefore, a new approach was developed based on a two-step methodology. Firstly, neural network models were used to generate short-term temporal forecasts based on air pollution and meteorology data. The accuracy of those forecasts was then evaluated against an independent set of historical data. Secondly, local conditional distributions of the observed values with respect to the predicted values were used to perform spatial stochastic simulations for the entire geographic area of interest. With this approach the spatio-temporal dispersion of a pollutant can be predicted, while accounting for both the temporal uncertainty in the forecast (reflecting the neural networks efficiency at each monitoring station) and the spatial uncertainty as revealed by the spatial variograms. Based on an analysis of the results, our proposed method offers a highly promising alternative for the characterization of urban air quality.

Keywords

Air quality Neural networks Stochastic simulation PM10 Uncertainty 

Notes

Acknowledgements

The authors acknowledge the Instituto de Meteorologia and Agência Portuguesa do Ambiente for the meteorological and environmental data, respectively. The authors also acknowledge the Fundação para a Ciência e Tecnologia from the Science, Technology and Superior Education Ministry, for supporting this research through grant SFRH/BD/27765/2006.

References

  1. Agirre-Basurko E, Ibarra-Berastegi G, Madariaga I (2006) Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environ Model Softw 21:430–446 CrossRefGoogle Scholar
  2. APA—Agência Portuguesa do Ambiente (2008) Evolução da qualidade do ar em Portugal entre 2001 e 2005. Report Google Scholar
  3. Atkinson PM, Lloyd CD (2001) Ordinary and indicator kriging of monthly mean nitrogen dioxide concentrations in the United Kingdom. In: Monestiez P et al (eds) GeoENV VIII-geostatistics for environmental applications. Kluwer Academic, Norwell, pp 33–44 CrossRefGoogle Scholar
  4. Bilonick RA (1983) Risk qualified maps of hydrogen ion concentration for the New York state area for 1966–1978. Atmos Environ 17:2513–2524 CrossRefGoogle Scholar
  5. Bilonick RA (1985) The space–time distribution of sulfate deposition in the northeastern united states. Atmos Environ 19:1829–1845 CrossRefGoogle Scholar
  6. Cobourn W, Dolcine L, French M, Hubbard M (2000) A comparison of nonlinear regression and neural network models for ground-level ozone forecasting. J Air Waste Manage Assoc 50:1999–2009 CrossRefGoogle Scholar
  7. Cressie N, Kaiser MS, Daniels MJ, Aldworth J, Lee J, Lahiri SN, Cox LH (1999) Spatial analysis of particulate matter in an urban environment. In: Gomez-Hernandez J, Soares A, Froidevaux R (eds) GeoENV II—geostatistics for environmental applications. Kluwer Academic, Dordrecht, pp 41–52 CrossRefGoogle Scholar
  8. Demuzere M, Trigo R, Arellano V, van Lipzig N (2009) The impact of weather and atmospheric circulation on O3 and PM10 levels at a rural mid-latitude site. Atmos Chem Phys 9:2695–2714 CrossRefGoogle Scholar
  9. Dutot AL, Rynkiewicz J, Steiner FE, Rude J (2007) A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions. Environ Model Softw 22:1261–1269 CrossRefGoogle Scholar
  10. EC—European Community (2006) Development of a methodology to assess population exposed to high levels of noise and air pollution close to major transport infrastructure. Final report. Entec UK limited Google Scholar
  11. EEA—European Environment Agency (2011) Air quality in Europe. Technical report No 12/2011 Google Scholar
  12. EEA—European Environment Agency (2010) The European environment–state and outlook 2010: synthesis. European Environment Agency, Copenhagen Google Scholar
  13. Franco C, Soares A, Delgado J (2006) Geostatistical modelling of heavy metal contamination in the topsoil of Guadiamar river margins (Spain) using a stochastic simulation technique. Geoderma 136(3–4):852–864 CrossRefGoogle Scholar
  14. Gardner M, Dorling S (2000) Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmos Environ 34:21–34 CrossRefGoogle Scholar
  15. Gardner M, Dorling S (1999) Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmos Environ 33:709–719 CrossRefGoogle Scholar
  16. Gomez-Hernandez J, Journel AG (1993) Joint sequential simulation of multi-Gaussian fields. In: Soares A (ed) Geostatistics TROIA’92. Kluwer Academic, Dordrecht, pp 85–94 Google Scholar
  17. Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford Univ. Press, New York Google Scholar
  18. 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 CrossRefGoogle Scholar
  19. Horta A, Soares A (2010) Direct sequential co-simulation with joint probability distributions. Math Geosci 42:269–292 CrossRefGoogle Scholar
  20. INE—Instituto Nacional de Estatística. www.ine.pt. Accessed 17 July 2012
  21. Kolehmainen M, Martikainen H, Ruuskanen J (2001) Neural networks and periodic components used in air quality forecasting. Atmos Environ 35:815–825 CrossRefGoogle Scholar
  22. Kyriakidis P, Journel A (1999) Geostatistical space–time models: a review. Math Geol 31(6):651–685 CrossRefGoogle Scholar
  23. Lal B, Tripathy SS (2012) Prediction of dust concentration in open cast coal mine using artificial neural network. Atmos Pollut Res 3:211–218 CrossRefGoogle Scholar
  24. Lamb RG (1983) A regional scale (1000 km) model of photochemical air pollution, part I: theoretical formulation. EPA 600/3-83-035. US Environmental Protection Agency, Research Triangle Park, NC Google Scholar
  25. Luecken DJ, Hutzell WT, Gipson GL (2006) Development and analysis of air quality modeling simulations for hazardous air pollutants. Atmos Environ 40:5087–5096 CrossRefGoogle Scholar
  26. Monestiez P, Meiring W, Sampson PD, Guttorp P (1997) Modelling non-stationary spatial covariance structure from space–time monitoring data. Ciba Foundation symposium. 01/1997; 210:38-48; discussion 48-51, 68-78. Google Scholar
  27. Monteiro A, Vautard R, Borrego C, Miranda A (2005) Long-term simulations of photo oxidant pollution over Portugal using the CHIMERE model. Atmos Environ 39(17):3089–3101 CrossRefGoogle Scholar
  28. Morris RE, Meyers TC (1990) User’s guide for the Urban Airshed Model. In: User’s manual for UAM (CB-IV). EPA-450/4-90-007A, vol. I. US Environmental Protection Agency, Research Triangle Park Google Scholar
  29. Nunes C, Soares A (2005) Geostatistical space–time simulation model. Environmetrics 16:393–404 CrossRefGoogle Scholar
  30. Pereira MJ (1999) Air quality modelling and simulation. PhD Dissertation, Instituto Superior Técnico, Lisbon, Portugal Google Scholar
  31. Perez P, Reyes J (2002) Prediction of maximum of 24-h average of PM10 concentrations 30h in advance in Santiago. Chile Atmos Environ 36:4555–4561 CrossRefGoogle Scholar
  32. 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 CrossRefGoogle Scholar
  33. Russo A, Trigo R, Soares A (2008) Stochastic modelling applied to air quality space–time characterization. In: Soares A, Pereira MJ, Dimitrakopoulos R (eds) GeoENV VI-geostatistics for environmental applications. Springer, Berlin, pp 83–93 CrossRefGoogle Scholar
  34. Soares A (2001) Sequential direct simulation and co-simulation. Math Geol 33(8):911–926 CrossRefGoogle Scholar
  35. Soares A, Pereira MJ (2007) Space–time modelling of air quality for environmental-risk maps: a case study in South Portugal. Comput Geosci 33(10):1327–1336 CrossRefGoogle Scholar
  36. Sokhi RS, San José R, Kitwiroon N, Fragkou E, Pérez JL, Middleton DR (2006) Prediction of ozone levels in London using the MM5–CMAQ modelling system. Environ Model Softw 21:566–576 CrossRefGoogle Scholar
  37. Srivastava RM (1992) Reservoir characterization with probability field simulation. SPE Paper 24753 Google Scholar
  38. Trigo R, DaCamara C (2000) Circulation weather types and their impact on the precipitation regime in Portugal. Int J Climatol 20:1559–1581 CrossRefGoogle Scholar
  39. Trigo R, Palutikof J (1999) Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach. Clim Res 13:45–59 CrossRefGoogle Scholar
  40. Turias I, González F, Martin M, Galindo P (2007) Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar region, Spain: a multiple comparison strategy. Environ Monit Assess 143:131–146 CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2013

Authors and Affiliations

  1. 1.Instituto Dom Luiz, Faculdade de Ciências da Universidade de LisboaUniversidade de LisboaLisboaPortugal
  2. 2.CERENA, Instituto Superior TécnicoUniversidade Técnica de LisboaLisboaPortugal

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