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Title: Optimized Field Sampling and Monitoring of Airborne Hazardous Transport Plumes; A Geostatistical Simulation Approach

Abstract

Airborne hazardous plumes inadvertently released during nuclear/chemical/biological incidents are mostly of unknown composition and concentration until measurements are taken of post-accident ground concentrations from plume-ground deposition of constituents. Unfortunately, measurements often are days post-incident and rely on hazardous manned air-vehicle measurements. Before this happens, computational plume migration models are the only source of information on the plume characteristics, constituents, concentrations, directions of travel, ground deposition, etc. A mobile ''lighter than air'' (LTA) system is being developed at Oak Ridge National Laboratory that will be part of the first response in emergency conditions. These interactive and remote unmanned air vehicles will carry light-weight detectors and weather instrumentation to measure the conditions during and after plume release. This requires a cooperative computationally organized, GPS-controlled set of LTA's that self-coordinate around the objectives in an emergency situation in restricted time frames. A critical step before an optimum and cost-effective field sampling and monitoring program proceeds is the collection of data that provides statistically significant information, collected in a reliable and expeditious manner. Efficient aerial arrangements of the detectors taking the data (for active airborne release conditions) are necessary for plume identification, computational 3-dimensional reconstruction, and source distribution functions. This report describes the applicationmore » of stochastic or geostatistical simulations to delineate the plume for guiding subsequent sampling and monitoring designs. A case study is presented of building digital plume images, based on existing ''hard'' experimental data and ''soft'' preliminary transport modeling results of Prairie Grass Trials Site. Markov Bayes Simulation, a coupled Bayesian/geostatistical methodology, quantitatively combines soft information regarding contaminant location with hard experimental results. Soft information is used to build an initial conceptual image of where contamination is likely to be. As experimental data are collected and analyzed, indicator kriging is used to update the initial conceptual image. The sequential Gaussian simulation is then practiced to make a comparison between the two simulations. Simulated annealing is served as a postprocessor to improve the result of Markov Bayes simulation or sequential Gaussian simulation.« less

Authors:
Publication Date:
Research Org.:
Oak Ridge National Lab., TN (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
789424
Report Number(s):
ORNL/TM-2001/170
TRN: US0111367
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: 21 Nov 2001
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; AIR POLLUTION MONITORING; PLUMES; SAMPLING; ENVIRONMENTAL TRANSPORT; HAZARDOUS MATERIALS SPILLS; AIR POLLUTION MONITORS; AERIAL MONITORING; COMPUTER CALCULATIONS

Citation Formats

Chen, DI-WEN. Optimized Field Sampling and Monitoring of Airborne Hazardous Transport Plumes; A Geostatistical Simulation Approach. United States: N. p., 2001. Web. doi:10.2172/789424.
Chen, DI-WEN. Optimized Field Sampling and Monitoring of Airborne Hazardous Transport Plumes; A Geostatistical Simulation Approach. United States. doi:10.2172/789424.
Chen, DI-WEN. Wed . "Optimized Field Sampling and Monitoring of Airborne Hazardous Transport Plumes; A Geostatistical Simulation Approach". United States. doi:10.2172/789424. https://www.osti.gov/servlets/purl/789424.
@article{osti_789424,
title = {Optimized Field Sampling and Monitoring of Airborne Hazardous Transport Plumes; A Geostatistical Simulation Approach},
author = {Chen, DI-WEN},
abstractNote = {Airborne hazardous plumes inadvertently released during nuclear/chemical/biological incidents are mostly of unknown composition and concentration until measurements are taken of post-accident ground concentrations from plume-ground deposition of constituents. Unfortunately, measurements often are days post-incident and rely on hazardous manned air-vehicle measurements. Before this happens, computational plume migration models are the only source of information on the plume characteristics, constituents, concentrations, directions of travel, ground deposition, etc. A mobile ''lighter than air'' (LTA) system is being developed at Oak Ridge National Laboratory that will be part of the first response in emergency conditions. These interactive and remote unmanned air vehicles will carry light-weight detectors and weather instrumentation to measure the conditions during and after plume release. This requires a cooperative computationally organized, GPS-controlled set of LTA's that self-coordinate around the objectives in an emergency situation in restricted time frames. A critical step before an optimum and cost-effective field sampling and monitoring program proceeds is the collection of data that provides statistically significant information, collected in a reliable and expeditious manner. Efficient aerial arrangements of the detectors taking the data (for active airborne release conditions) are necessary for plume identification, computational 3-dimensional reconstruction, and source distribution functions. This report describes the application of stochastic or geostatistical simulations to delineate the plume for guiding subsequent sampling and monitoring designs. A case study is presented of building digital plume images, based on existing ''hard'' experimental data and ''soft'' preliminary transport modeling results of Prairie Grass Trials Site. Markov Bayes Simulation, a coupled Bayesian/geostatistical methodology, quantitatively combines soft information regarding contaminant location with hard experimental results. Soft information is used to build an initial conceptual image of where contamination is likely to be. As experimental data are collected and analyzed, indicator kriging is used to update the initial conceptual image. The sequential Gaussian simulation is then practiced to make a comparison between the two simulations. Simulated annealing is served as a postprocessor to improve the result of Markov Bayes simulation or sequential Gaussian simulation.},
doi = {10.2172/789424},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Nov 21 00:00:00 EST 2001},
month = {Wed Nov 21 00:00:00 EST 2001}
}

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