Reverse dispersion modeling has been used to determine air emission fluxes from ground-level area sources, including open-lot beef cattle feedlots. This research compared Gaussian-based AERMOD, the preferred regulatory dispersion model of the US. Environmental Protection Agency (EPA), and WindTrax, a backward Lagrangian stochastic-based dispersion model, in determining PM10 emission rates for a large beef cattle feedlot in Kansas. The effect of the type of meteorological data was also evaluated. Meteorological conditions and PM10 concentrations at the feedlot were measured with micrometeorological/eddy covariance instrumentation and tapered element oscillating microbalance (TEOM) PM10 monitors, respectively, from May 2010 through September 2011. Using the measured meteorological conditions and assuming a unit emission flux (i.e., 1 microg/m2-sec), each model was used to calculate PM10 concentrations (referred to as unit-flux concentrations). PM10 emission fluxes were then back-calculated using the measured and calculated unit-flux PM10 concentrations. For AERMOD, results showed that the PM10 emission fluxes determined using the two different meteorological data sets evaluated (eddy covariance-derived and AERMET-generated) were basically the same. For WindTrax, the two meteorological data sets (sonic anemometer data set, a three-variable data set composed of wind parameters, surface roughness, and atmospheric stability) also produced basically the same PM10 emission fluxes. Back-calculated emission fluxes from AERMOD were 32 to 69% higher than those from WindTrax.
Implications: This work compared the PM10 emission rates determined from a large commercial cattle feedlot in Kansas by reverse dispersion modeling using AERMOD and WindTrax. Emission fluxes derived from AERMOD were greater than those from WindTrax by mean factors of 1.3 to 1.6. Based on the high linearity observed between the two models, emission fluxes derived from one dispersion model for the purpose of simulating dispersion could be applied to the other model using appropriate conversion factors.