ESTIMATION OF THE INTENSITY OF THE SUCCESSIVE
INSTANTANEOUS EMISSIONS RELEASED BY
AN AIR POLLUTION POINT SOURCE
D. Parra-Guevara1, Yu. N. Skiba2
Institute of Atmospheric Sciences and Climate Change
National Autonomous University of Mexico
Circuito Exterior, Ciudad Universitaria
CDMX, C. P. 04510, MEXICO
A new method for assessing the intensity of successive
instantaneous emissions of an air pollutant from a point source is
presented. The intensity parameters were obtained using a time series of the
pollutant concentration detected at a monitoring site. A well-posed
atmospheric dispersion model is used to estimate the transport of the
pollutant from the source. The parameter estimation method is formulated as
an optimization problem. The optimal point determines the intensity of
instantaneous emissions and is calculated as the solution of a positive
system of linear equations. Particular solutions of the dispersion model are
used to adjust the matrix and the right-side of the linear system. The
capabilities of the parameter estimation method are demonstrated using
one-dimensional synthetic numerical examples.
You will need Adobe Acrobat reader. For more information and free download of the reader, please follow this link.
References
[1] T. Chai, R. Draxler and A. Stein, Source term estimation using air concentration
measurements and a Lagrangian dispersion model - experiments
with pseudo and real Cesium-137 observations from the Fukushima nuclear
accident, Atmospheric Environment, 106 (2015), 241-251.
[2] I.G. Enting, Inverse Problems in Atmospheric Constituent Transport,
Cambridge University Press, Cambridge (2002).
[3] C.A. Gough, M.J. Chadwick, B. Biewald, J. Kylenstierna, P.D. Bailey and
S. Cinderby, Developing optimal abatement strategies for the effects of
sulphur and nitrogen deposition at European scale, Water, Air and Soil
Pollution, 85, No 4 (1995), 2601-2606.
[4] J. Hadamard, Lectures on Cauchy’s Problem in Linear Partial Differential
Equations, Dover Publications (1923).
[5] S. Lang, Linear Algebra, Springer, New York (1987).
[6] D.G. Luenberger, Linear and Nonlinear Programming, Addison-Wesley,
Mass. (1984).
[7] G.I. Marchuk, Mathematical Models in Environmental Problems, Elsevier,
New York (1986).
[8] J.H. Mathews and K.D. Fink, Numerical Methods Using Matlab, Pearson,
New Jersey (2004).
[9] H. Nagai, G. Katata, H. Terada and M. Chino, Source term estimation
of 131I and 137Cs discharged from the Fukushima Daiichi Nuclear Power
Plant into the atmosphere. In: S. Takahashi (Ed.), Radiation Monitoring
and Dose Estimation of the Fukushima Nuclear Accident, Springer, Tokyo
(2014), 155-173.
[10] D. Parra-Guevara, Y.N. Skiba and D. Pe˜na-Maciel, Controlling the forcing
of the linear transport equation to meet air quality norms at every point.
International Journal of Applied Mathematics, 30, No 6 (2017), 527-545;
DOI: 10.12732/ijam.v30i6.6.
[11] D. Parra-Guevara and Y.N. Skiba, Elements of the mathematical modelling
in the control of pollutants emissions, Ecological Modelling, 167, No 3
(2003), 263-275.
[12] A. Ralston and P. Rabinowitz, A First Course in Numerical Analysis,
Dover, New York (2001).
[13] J.H. Seinfeld and S.N. Pandis, Atmospheric Chemistry and Physics: From
Air Pollution to Climate Change, Wiley, New York (1998).
[14] Y.N. Skiba and D. Parra-Guevara, Industrial pollution transport. Part I:
Formulation of the problem and air pollution estimates. Env. Modeling and
Assessment, 5, No 3 (2000), 169-175.
[15] K. Wark, W.T. Davis and C.F. Warner, Air Pollution: Its Origin and
Control, Addison-Wesley, California (1999).