Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506-2906, USA.
J Air Waste Manag Assoc. 2013 May;63(5):545-56. doi: 10.1080/10962247.2013.768311.
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.
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.
反向扩散建模已被用于确定地面区域源的空气排放通量，包括露天肉牛养殖场。本研究比较了基于高斯的 AERMOD 和美国环保署（EPA）首选的监管扩散模型，以及基于后向拉格朗日随机的 WindTrax 扩散模型，以确定堪萨斯州一个大型肉牛养殖场的 PM10 排放率。还评估了气象数据类型的影响。使用微气象/涡动协方差仪器和锥形元素振荡微天平（TEOM）PM10 监测仪分别测量了 2010 年 5 月至 2011 年 9 月期间的气象条件和养殖场的 PM10 浓度。使用测量的气象条件并假设单位排放通量（即 1μg/m2-sec），每个模型都用于计算 PM10 浓度（称为单位通量浓度）。然后使用测量和计算的单位通量 PM10 浓度反推 PM10 排放通量。对于 AERMOD，结果表明，使用两种不同气象数据集（涡动协方差衍生和 AERMET 生成）评估的 PM10 排放通量基本相同。对于 WindTrax，两种气象数据集（声速仪数据集，由风参数、表面粗糙度和大气稳定性组成的三变量数据集）也产生了基本相同的 PM10 排放通量。从 AERMOD 反推的排放通量比从 WindTrax 反推的排放通量高 32%至 69%。
本工作通过使用 AERMOD 和 WindTrax 的反向扩散建模比较了堪萨斯州一个大型商业肉牛养殖场确定的 PM10 排放率。从 AERMOD 得出的排放通量比从 WindTrax 得出的排放通量高平均 1.3 至 1.6 倍。基于两个模型之间观察到的高度线性关系，可以使用适当的转换因子将一种扩散模型得出的排放通量应用于另一种模型，以模拟扩散。
