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Comparison of Atmospheric Stability Methods for Calculating Ammonia and Methane Emission Rates with WindTrax

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASABE. 56(2): 763-768. (doi: 10.13031/2013.42664) @2013
Authors:   Anita C Koehn, April B. Leytem, David L. Bjorneberg
Keywords:   Air emissions Ammonia Inversion dispersion model Methane.

Abstract. Inverse dispersion models are useful tools for estimating emissions from animal feeding operations, waste storage ponds, and manure application fields. Atmospheric stability is an important input parameter to such models. The objective of this study was to compare emission rates calculated with a backward Lagrangian stochastic (bLS) inverse-dispersion model (WindTrax) using three different methods for calculating atmospheric stability: sonic anemometer, gradient Richardson number, and Pasquill-Gifford (P-G) stability class. Ammonia and methane emission data from a compost yard at a 10,000-cow dairy were used for the comparisons. Overall, average emission rates were not significantly different among the stability methods. Emission rates correlated well between the sonic and other methods (r2 > 0.79, p < 0.001). The slopes of the regression lines between the sonic and Richardson methods were 0.95 and 1.0 for CH4 and NH3, respectively. The regression line slopes for the P-G method were about 1.9 for CH4 and 1.6 for NH3, which means emission rates predicted with the P-G method tended to be 50% to 100% greater than rates predicted with sonic anemometer data. Based on this limited data set, using the gradient Richardson method to represent atmospheric stability resulted in emission rates that more closely matched emission rates from the sonic method. Considering the amount of variability inherent in emissions calculations, a three-dimensional sonic anemometer should be used, if possible, to directly provide the necessary data to calculate parameters representing wind properties, rather than inferring values from other stability classification methods.

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