Evaluation of Wind Erosion Emissions Factors for Air Quality Modeling

The exposure to airborne particulates and surface deposition are important pathways for many human health assessments. Our primary focus is on air quality modeling issues associated with the emission of airborne particulates from contaminated surfaces via wind erosion and the subsequent deposition o...

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Veröffentlicht in:Soil Science Society of America journal 2011-07, Vol.75 (4), p.1285-1294
Hauptverfasser: Sullivan, David A, Ajwa, Husein A
Format: Artikel
Sprache:eng
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Zusammenfassung:The exposure to airborne particulates and surface deposition are important pathways for many human health assessments. Our primary focus is on air quality modeling issues associated with the emission of airborne particulates from contaminated surfaces via wind erosion and the subsequent deposition onto other areas. In this context, the analysis is directed at long-term impacts associated with wind-erosion-induced movement from a contaminated area onto residential and other property. For this type of application, detailed inputs on soil moisture, armoring, and other factors are typically not known as a function of time. The defined scope and data limitations generally render more detailed and data-intensive approaches impractical compared with more simplified empirical models. We compare and contrast selected empirical models to show the central tendency as a function of the type of applications (flat–regional and flat–local) as examples. This review demonstrates the importance of applying empirical wind erosion equations in a manner that is consistent with their development and with due consideration of site-specific features and the specific air quality analysis under evaluation. Of importance to this review are: the scale of analysis, flat vs. pile (elevated) sources, and an appropriate representation of uncertainty. Without consideration of such factors within dispersion modeling analyses of airborne particulate concentrations or surface deposition rates, the exposure analysis can be substantially biased.
ISSN:0361-5995
1435-0661
DOI:10.2136/sssaj2010.0132