Monte Carlo source detection of atmospheric emissions and error functions analysis

Computers and Geosciences

Volumn 36, Issue 7, 2010, Pages 902-909

  Cervone, Guido ^a   Franzese, Pasquale ^a  

^a GEORGE MASON UNIVERSITY   (United States)

Author keywords

Atmospheric emissions; Dispersion model; Error functions; Monte Carlo; Source detection

Indexed keywords

ATMOSPHERIC DISPERSION MODELS; ATMOSPHERIC EMISSION; ATMOSPHERIC RELEASE; DISPERSION MODELS; DOMAIN SIZE; ERROR FUNCTION; FIXED POINTS; GAUSSIAN DISPERSION MODELS; GAUSSIANS; GROUND MEASUREMENTS; ITERATIVE ALGORITHM; MODEL PREDICTION; MONTE CARLO; MONTE CARLO ALGORITHMS; NUMERICAL SIMULATION; PLUME MODEL; RANDOM NOISE; SENSOR MEASUREMENTS; SOURCE DETECTION; STATISTICAL ANALYSIS;

AIR POLLUTION; ALGORITHMS; ATMOSPHERIC SPECTRA; ATMOSPHERICS; COMPUTER SIMULATION; DISPERSIONS; ERROR DETECTION; ITERATIVE METHODS; MONTE CARLO METHODS;

ATMOSPHERIC MOVEMENTS;

ALGORITHM; ATMOSPHERIC PLUME; BENCHMARKING; COMPUTER SIMULATION; CONCENTRATION (COMPOSITION); DETECTION METHOD; ERROR ANALYSIS; GAUSSIAN METHOD; GROUND-BASED MEASUREMENT; MONTE CARLO ANALYSIS; OPTIMIZATION;

EID: 77953543708     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2010.01.007     Document Type: Article

References (25)

1. Allen C.T., Young G.S., Haupt S.E. Improving pollutant source characterization by better estimating wind direction with a genetic algorithm. Atmospheric Environment 2007, 41:2283-2289.
2. Arya P.S. Air Pollution Meteorology and Dispersion 1999, Oxford University Press, Oxford, UK, 310pp.
3. Barad, M., Haugen, D., 1958. Project Prairie Grass. A Field Program in Diffusion. Geophysical Research Paper, No. 59, vol. I, Report AFCRC-TR-58-235(I), Air Force Cambridge Research Center, 299pp.
4. Cervone G., Franzese P., Ezber Y., Boybeyi Z. Risk assessment of atmospheric emissions using machine learning. Natural Hazards and Earth System Science 2008, 8:991-1000.
5. Cervone G., Franzese P., Keesee A.P. Algorithm quasi-optimal (AQ) learning. WIREs: Computational Statistics 2010, 2:218-236.
6. Cervone, G., Michalski, R., Kaufman, K., Panait, L., 2000. Combining machine learning with evolutionary computation: recent results on lem. In: Proceedings of the Fifth International Workshop on Multistrategy Learning (MSL-2000), Guimaraes, Portugal, pp. 41-58.
7. Chang J.C., Franzese P., Chayantrakom K., Hanna S.R. Evaluations of CALPUFF, HPAC, and VLSTRACK with two mesoscale field datasets. Journal of Applied Meteorology 2003, 42:453-466.
8. Chang J.C., Hanna S.R. Air quality model performance evaluation. Meteorology and Atmospheric Physics 2004, 87:167-196.
9. Chow, F., Kosović, B., Chan, T., 2006. Source inversion for contaminant plume dispersion in urban environments using building-resolving simulations. In: Proceedings of the 86th American Meteorological Society Annual Meeting, Atlanta, GA, pp. 12-22.
10. Delle Monache L., Lundquistand J., Kosović B., Johannesson G., Dyer K., Aines R., Chow F., Belles R., Hanley W., Larsen S., Loosmore G., Nitao J., Sugiyama G., Vogt P. Bayesian inference and Markov Chain Monte Carlo sampling to reconstruct a contaminant source on a continental scale. Journal of Applied Meteorology and Climatology 2008, 47:2600-2613.
11. Enting I. Inverse Problems in Atmospheric Constituent Transport 2002, Cambridge University Press, Cambridge, UK, 392pp.
12. Gelman A., Carlin J., Stern H., Rubin D. Bayesian Data Analysis 2003, Chapman & Hall/CRC, London, UK, 668pp.
13. Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning 1989, Addison Wesley, Reading, MA, 412pp.
14. Hammersley J., Handscomb D. Monte Carlo Methods 1964, Fletcher and Son Ltd, Norwich, UK, 160pp.
15. Hanna S.R., Chang J.C., Strimaitis G.D. Hazardous gas model evaluation with field observations. Atmospheric Environment 1993, 27A:2265-2285.
16. Haupt S.E. A demonstration of coupled receptor/dispersion modeling with a genetic algorithm. Atmospheric Environment 2005, 39:7181-7189.
17. Haupt S.E., Young G.S., Allen C.T. Validation of a receptor/dispersion model coupled with a genetic algorithm using synthetic data. Journal of Applied Meteorology 2006, 45:476-490.
18. Haupt S.E., Young G.S., Allen C.T. A genetic algorithm method to assimilate sensor data for a toxic contaminant release. Journal of Computers 2007, 2:85-93.
19. Hourdin F., Issartel J.P. Sub-surface nuclear tests monitoring through the CTBT xenon network. Geophysical Research Letters 2000, 27:2245-2248.
20. Metropolis N., Ulam S. The Monte Carlo method. Journal of the American Statistical Association 1949, 44(247):335-341.
21. Mosteller F., Tukey J.W. Data Analysis and Regression: A Second Course in Statistics, Addison-Wesley Series in Behavioral Science 1977, Addison-Wesley, Reading, MA, 588pp.
22. Pudykiewicz J. Application of adjoint tracer transport equations for evaluating source parameters. Atmospheric Environment 1998, 32:3039-3050.
23. Robert C., Casella G. Monte Carlo Statistical Methods 2004, Springer, New York, 536pp.
24. Rubinstein R., Kroese D. Simulation and the Monte Carlo Method 2007, John Wiley and Sons, Hoboken, NJ, 374pp.
25. Senocak I., Hengartner N., Short M., Daniel W. Stochastic event reconstruction of atmospheric contaminant dispersion using Bayesian inference. Atmospheric Environment 2008, 42:7718-7727.