Location: Soil, Water & Air Resources ResearchTitle: Effects of various representations of temporally and spatially variable agricultural processes in air quality dispersion modeling Author
Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 6/1/2011
Publication Date: 8/11/2011
Citation: Moore, K., Martin, R., Wojick, M., Pfeiffer, R.L., Prueger, J.H., Hatfield, J.L. 2011. Effects of various representations of temporally and spatially variable agricultural processes in air quality dispersion modeling [abstract]. American Society of Agricultural and Biological Engineers Annual International Meeting. Paper No. 1111847. Interpretive Summary:
Technical Abstract: Agricultural activities that are both temporally and spatially variable, such as tillage and harvesting, can be challenging to represent as sources in air quality dispersion modeling. Existing models were mainly developed to predict concentrations resulting from a stationary and continuous source with a known emission strength. These assumptions break down when applied to agricultural activities which are both temporally and spatially variable. This study investigates how predicted concentrations from these dispersion models fluctuate depending on how the source activity is represented. This issue becomes especially important when applied to historic events such as the use of inverse modeling to estimate emission rates. This study uses data collected from multiple field-scale fall and spring tillage activities under different meteorological conditions to explore the differences in predicted plume shapes and the variability in predicted concentrations of different PM fractions. AERMOD, the current EPA-recommended regulatory dispersion model, is utilized in conjunction with on-site meteorological observations. Investigated variables include full-hour versus partial-hour source activity, full-field versus hour-by-hour active source areas, and initial vertical dimension of the plume. Preliminary modeling results for the first two variables show a significant difference in both predicted plume shape and concentrations.