Inverse Modeling of Urban-Scale Emissions

  • Gary Adamkiewicz
  • Peter S. Wyckoff
  • Menner A. Tatang
  • Gregory J. McRae
Part of the NATO • Challenges of Modern Society book series (NATS, volume 22)


Urban air pollution continues to be a problem worldwide, and there is a critical need to develop cost-effective control strategies. Current strategies are designed using air quality models that describe the formation and transport of photochemical pollutants. Unfortunately, the emissions inventories that are used in airshed modeling and control strategy design have been widely underestimated. New methods are needed to improve the quality of emissions inputs. One approach is to solve the inverse problem using existing ambient data and a photochemical urban airshed model to determine the emission field. However, the high dimensionality of spatially and temporally resolved emissions fields proves to be the primary obstacle in solving this problem.


Proper Orthogonal Decomposition Inverse Modeling Emission Inventory Negative Emission Precursor Emission 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. N. Aubry, R. Guyonnet, and R. Lima. Spatio-temporal analysis of complex signals: Theory and applications. J. Stat. Phys., 64(3/4):683–739, 1991.CrossRefGoogle Scholar
  2. N. Aubry, W.-Y. Lian, and E.S. Titi. Preserving symmetries in the proper orthogonal decomposition. SIAM J. Sci. Compute 14(2):483–505, 1993.CrossRefGoogle Scholar
  3. K. Krischer, R.R. Martinez, I.G. Kevrekidis, H.H. Rotermund, G. Ertl, and J.L. Hudson. Model identification of a spatiotemporally varying catalytic reaction. AIChE J., 39(1):89–98, 1993.CrossRefGoogle Scholar
  4. F.W. Lurmann, W.P.L. Carter, and L.A. Coyner. A surrogate species chemical reaction mechanism for urban scale air quality simulation models. Volumes I and II. ERT Inc., Newbury Park, CA and Statewide Air Pollution Research Center, University of California, Riverside, CA, 1987.Google Scholar
  5. McRae, Gregory J., A.G. Russell and R.A. Harley, CIT Photochemical Airshed Model: Systems Manual and Data Preparation Manual. Carnegie Mellon University and California Institute of Technology, 1992.Google Scholar
  6. National Research Council, Committee on Tropospheric Ozone Formation and Measurement. Rethinking the Ozone Problem in Urban and Regional Air Pollution. National Academy Press, Washington D.C., 1991.Google Scholar
  7. W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 1992.Google Scholar
  8. G. Rowlands and J.C. Sprott. Extraction of dynamical equations from chaotic data. Physica D, 58:251–259, 1992.CrossRefGoogle Scholar
  9. L. Sirovich and R. Everson. Management and analysis of large scientific datasets. Int. J. Supercomp. Appl., 6(1):50–68, 1992.Google Scholar
  10. L.T. Urgell and R.L. Kirlin. Adaptive image compression using Karhunen-Loève transform. Signal Processing, 21:303–313, 1990.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Gary Adamkiewicz
    • 1
  • Peter S. Wyckoff
    • 2
  • Menner A. Tatang
    • 3
  • Gregory J. McRae
    • 1
  1. 1.Department of Chemical EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Sandia National LaboratoriesLivermoreUSA
  3. 3.Universitas IndonesiaDepokIndonesia

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