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首页> 外文期刊>Agricultural and Forest Meteorology >Uncertainty in using dispersion models to estimate methane emissions from manure lagoons in dairies
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Uncertainty in using dispersion models to estimate methane emissions from manure lagoons in dairies

机译:使用分散模型来估算牛奶泻湖甲烷排放的不确定性

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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.
机译:奶制乳头的粪便对甲烷的排放作出重大贡献,这是一个主要的温室气体的排放;然而,这些排放中存在高度的不确定性。在本文中,我们将分散模型与独特的采样策略相结合,涉及泻湖周围多个点的静止测量,以估算位于两个奶牛群中的粪便泻湖的甲烷排放,其中一个在加利福尼亚州的南部和其他加利福尼亚州。然后,我们通过用两个色散模型来解释我们的测量,估计与这种方法的结果相关联的不确定性,数字欧拉模型(EN)和后向拉格朗日随机(BLS)模型。从这两个模型推断的排放范围是与这些模型的配方的差异有关的不确定性。我们还通过在模型估计和测量之间引导残差来估计来自每个模型的排放估计的95%置信区间。两种模型解释了两种乳渣测量的甲烷浓度方差的85%以上。对于加州南部的乳制品(1066奶牛),EN模型推断的排放率的95%置信区间为282 kg / d至482 kg / d。 BLS模型的相应间隔为174kg / d至246kg / d。来自en模型的最佳拟合值约为BLS模型的1.9倍。对于加州中央乳制品(3200枚挤奶奶牛),两种型号的最佳排放率约为10%。由ZH模型推断的排放率从3198 kg / d到5312 kg / d,从2943kg / d到4977kg / d的范围。我们的结果与甲烷排放符合来自乳制品牛群的信息,并在这两个奶牛队的粪便管理实践。这些结果表明这种测量技术很容易展开和有效地量化与粪便泻湖的甲烷排放相关的不确定性。

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