Skip to main content

Adaptive Dispersion Modelling and Its Applicattion to Integrated Assessment and Hybrid Monitoring of Air Pollution

  • Chapter
Air Pollution Modeling and Its Application XIV

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Bultijes P., van Loon, M., Segers, A., 2000, Data assimilation: a tool to shape the future, in: Symposium 2000 Eurotrac-2 Abstracts, GSF, Munich

    Google Scholar 

  • Gandin, L.S., 1963, Objective Analysis of Meteorological Fields, Hydrometeoizdat, Leningrad (in Russian)

    Google Scholar 

  • Genikhovich, E., Filatova, E., Ziv, A., 2000, A method for mapping the air pollution in cities with combine use of measured and calculated concentrations. Int. Journ. Envir. Pollut. (in press)

    Google Scholar 

  • Genikhovich, E.L., Graheva, I.G., Groisman, P.Ya., Khurshudyan, L.G., 1999, A new Russian regulatory dispersion model MEAN for calculation of mean annual concentrations and its meteorological preprocessor. Int. Journ. Envir. Pollut. (in press)

    Google Scholar 

  • Haas-Laursen, D.E., Harley, D.E., Prinn, R.G., 1996, Optimizing an inverse method to deduce time-varying emissions of trace gases, Journ. Geophys. Res, D17:22823

    Google Scholar 

  • Houtekamer, P.L., Mitchell, H.L., 1998, Data assimilation using an ensemble Kalman filter technique. Man. Wea. Rev., 126: 796.

    Google Scholar 

  • Kalman, R.E., 1960, A new approach to linear filtering and prediction problems, Trans. ASME, Journ. Basic Eng. 83:146

    Google Scholar 

  • Leondes, C.T., ed., 1976, Control and Dynamic Systems, Acad. Press, NY

    Google Scholar 

  • Runca, E., Melli, P., Spirito, A., 1979, Real Time Forecast of Sulphur Dioxide Concentrations in the Venetian Lagoon Region. P. 1. Advection diffusion model, IIASA, Laxenburg

    Google Scholar 

  • Sasaki Y. 1970. Some basic formalisms in numerical variational analysis. Mon. Wea. Rev. 98: 875

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Kluwer Academic Publishers

About this chapter

Cite this chapter

Genikhovich, E.L., Ziv, A.D., Filatova, E.N. (2004). Adaptive Dispersion Modelling and Its Applicattion to Integrated Assessment and Hybrid Monitoring of Air Pollution. In: Gryning, SE., Schiermeier, F.A. (eds) Air Pollution Modeling and Its Application XIV. Springer, Boston, MA. https://doi.org/10.1007/0-306-47460-3_48

Download citation

  • DOI: https://doi.org/10.1007/0-306-47460-3_48

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-306-46534-5

  • Online ISBN: 978-0-306-47460-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics