Abstract

Manure lagoons in dairies make significant contributions to emissions of methane, a major greenhouse gas; however, there is a high level of uncertainty in these emissions. In this paper, we apply dispersion models in combination with a unique sampling strategy, which involves stationary measurements at multiple points around the lagoons to estimate methane emissions from manure lagoons located in two dairies, one in Southern California and the other in Central California. We then estimate the uncertainty associated with the results from this approach by interpreting our measurements with two dispersion models, a numerical Eulerian model (EN) and a backward Lagrangian stochastic (bLS) model. The range of emissions inferred from these two models is a measure of uncertainty related to differences in the formulation of these models. We also estimate 95% confidence intervals for the emission estimates from each of the models by bootstrapping the residuals between model estimates and measurements. Both models explain more than 85% of the variance of the methane concentrations measured at the two dairies. For the Southern California dairy (1066 milking cows), the 95% confidence interval of the emission rate inferred by the EN model is 282 kg/d to 482 kg/d. The corresponding interval for the bLS model is 174 kg/d to 246 kg/d. The best fit value from the EN model is about 1.9 times that from the bLS model. For the Central California dairy (3200 milking cows), the best emission rates from the two models differ by about 10%. The emission rate inferred by the EN model ranges from 3198 kg/d to 5312 kg/d, and that from the bLS ranges from 2943 kg/d to 4977 kg/d. Our results are consistent with methane emissions derived from information on dairy cow population and manure management practices at these two dairies. These results suggest this measurement technique is easily deployed and effective at quantifying uncertainties associated with methane emissions from manure lagoons.

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