Paper
11 September 2003 Data-model comparison of field landmine soil chemical signatures at Ft. Leonard Wood
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Abstract
Chemical signatures from buried landmines vary widely due to landmine and environmental conditions. The simulation model T2TNT was developed to evaluate the nature of chemical transport in the soil surrounding a buried landmine. This model uses landmine chemical emission, soil physics, soil-chemical interaction, and surface weather data to estimate surface and subsurface concentrations to help understand the phenomenology of landmine trace chemical detection. While T2TNT compares favorably to controlled laboratory experiments for a buried source of DNT, field data-model comparisons are needed to further increase confidence in T2TNT predictions. The only multi-season landmine soil residue data are from a long-term monitoring project at the DARPA-developed Ft. Leonard Wood Site in Missouri, USA. About 1000 soil residue samples had been taken over six sampling events spanning 21 months since landmine burial. This effort compares the soil residue data from two landmine types to T2TNT model predictions. A one-dimensional model was used to represent the situation and used actual weather data from the site during this period, landmine flux data specific for the mines buried, and temperature and moisture-content dependent degradation rates. Spatial and temporal predictions of chemical concentrations in the soil compare favorably with the soil residue data from Ft. Leonard Wood, increasing confidence in the utility of T2TNT estimates of landmine signature chemicals for other locations.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James M. Phelan and Stephen W. Webb "Data-model comparison of field landmine soil chemical signatures at Ft. Leonard Wood", Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003); https://doi.org/10.1117/12.487420
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Cited by 5 scholarly publications.
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KEYWORDS
Land mines

Data modeling

Chemical analysis

Mining

Soil science

Chemical detection

Fourier transforms

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