Tuesday, 8 November 2005 - 9:15 AM
130-3

Training Dust Capture on Vegetation in Humid and Semi-Arid Ecosystems.

Chatten Cowherd Jr.1, Dick L. Gebhart2, and Greg Muleski1. (1) Midwest Research Institutte, 425 Volker Boulevard, Kansas City, MO 64110, (2) ERDC-CERL, PO Box 9005, Champaign, IL 61826

As part of complying with air quality regulations in and around Army installations, this study assesses the capture of dust by vegetation at military forts. Vehicle movement during prolonged and intensive training activities on unpaved surfaces at U.S. Army installations generates significant particulate matter (PM) emissions. The efforts to revegetate soils that have been disturbed by heavy equipment traffic including tanks and other military vehicles are expected to have a two-fold outcome. Besides stabilizing the soil, standing vegetation also serves to inhibit long range transport of military dust to off-post locations. Plume profiling tests show that vegetative groundcover (e.g., trees, shrubs, grasses) bordering an emission source captures fugitive dust at rates that significantly exceed the levels represented in standard air plume dispersion models used for regulatory compliance purposes. New field testing at Fort Riley, Kansas is being conducted to assess the effectiveness of tall native grasses in capturing particulate matter (PM) emissions from military training exercises. Testing was previously performed in 2003 and 2004 near military roads within forest canopies at Fort Leonard Wood, Missouri. The effectiveness of particle capture by representative types/densities of trees was determined, as affected by the types of trees (and underbrush) that provided a range of vegetative characteristics. The results of this study will be used to improve the modeling of dust transport downwind of military training areas. The proposed deposition algorithm is suitable for inclusion in EPA regulatory plume dispersion models. Incorporation of an appropriate deposition term will improve the accuracy of these models by removing a cause of characteristic over-prediction of the air quality impacts of dust emissions from training activities and other open sources.

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