TY - JOUR
T1 - Characterization of atmospheric contaminant sources using adaptive evolutionary algorithms
AU - Cervone, Guido
AU - Franzese, Pasquale
AU - Grajdeanu, Adrian
N1 - Funding Information:
This material is partly based upon work supported by the National Science Foundation under Grant AGS 0849191 and by George Mason University Summer Research Funding.
PY - 2010/10
Y1 - 2010/10
N2 - The characteristics of an unknown source of emissions in the atmosphere are identified using an Adaptive Evolutionary Strategy (AES) methodology based on ground concentration measurements and a Gaussian plume model. The AES methodology selects an initial set of source characteristics including position, size, mass emission rate, and wind direction, from which a forward dispersion simulation is performed. The error between the simulated concentrations from the tentative source and the observed ground measurements is calculated. Then the AES algorithm prescribes the next tentative set of source characteristics. The iteration proceeds towards minimum error, corresponding to convergence towards the real source.The proposed methodology was used to identify the source characteristics of 12 releases from the Prairie Grass field experiment of dispersion, two for each atmospheric stability class, ranging from very unstable to stable atmosphere. The AES algorithm was found to have advantages over a simple canonical ES and a Monte Carlo (MC) method which were used as benchmarks.
AB - The characteristics of an unknown source of emissions in the atmosphere are identified using an Adaptive Evolutionary Strategy (AES) methodology based on ground concentration measurements and a Gaussian plume model. The AES methodology selects an initial set of source characteristics including position, size, mass emission rate, and wind direction, from which a forward dispersion simulation is performed. The error between the simulated concentrations from the tentative source and the observed ground measurements is calculated. Then the AES algorithm prescribes the next tentative set of source characteristics. The iteration proceeds towards minimum error, corresponding to convergence towards the real source.The proposed methodology was used to identify the source characteristics of 12 releases from the Prairie Grass field experiment of dispersion, two for each atmospheric stability class, ranging from very unstable to stable atmosphere. The AES algorithm was found to have advantages over a simple canonical ES and a Monte Carlo (MC) method which were used as benchmarks.
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U2 - 10.1016/j.atmosenv.2010.06.046
DO - 10.1016/j.atmosenv.2010.06.046
M3 - Article
AN - SCOPUS:77955843573
SN - 1352-2310
VL - 44
SP - 3787
EP - 3796
JO - Atmospheric Environment
JF - Atmospheric Environment
IS - 31
ER -