Gao Zhiling, Desjardins Raymond L, van Haarlem Ronald P, Flesch Thomas K
Research Branch, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada.
J Air Waste Manag Assoc. 2008 Nov;58(11):1415-21. doi: 10.3155/1047-3289.58.11.1415.
Manure storage tanks and animals in barns are important agricultural sources of methane. To examine the possibility of using an inverse dispersion technique based on a backward Lagrangian Stochastic (bLS) model to quantify methane (CH4) emissions from multiple on-farm sources, a series of tests were carried out with four possible source configurations and three controlled area sources. The simulated configurations were: (C1) three spatially separate ground-level sources, (C2) three spatially separate sources with wind-flow disturbance, (C3) three adjacent ground-level sources to simulate a group of adjacent sources with different emission rates, and (C4) a configuration with a ground level and two elevated sources. For multiple ground-level sources without flow obstructions (C1 and C3), we can use the condition number (K, the ratio of the uncertainty in the calculated emission rate to the uncertainty in the predicted ratio of concentration to emission rate) to evaluate the applicability of this inverse dispersion technique and a preliminary threshold of K <10 is recommended. For multiple sources with wind disturbance (C2) or an even more complex configuration including ground level and elevated sources (C4), a low kappa is not sufficient to provide reasonable discrete and total emission rates. The effect of flow obstructions can be neglected as long as the distance between the source and the measurement location is greater than approximately 10 times the height of the flow obstructions. This study shows that the bLS model has the potential to provide accurate discrete emission rates from multiple on-farm emissions of gases provided that certain conditions are met.
储粪池和畜舍中的牲畜是甲烷重要的农业排放源。为了研究基于反向拉格朗日随机（bLS）模型的逆扩散技术用于量化多种农场源甲烷（CH4）排放的可能性，针对四种可能的源配置和三个受控区域源进行了一系列测试。模拟的配置为：（C1）三个空间上分开的地面源；（C2）三个受风流干扰的空间上分开的源；（C3）三个相邻的地面源，以模拟一组具有不同排放率的相邻源；（C4）一个包含地面源和两个高架源的配置。对于无气流阻碍的多个地面源（C1和C3），我们可以使用条件数（K，即计算排放率的不确定性与预测浓度与排放率之比的不确定性的比值）来评估这种逆扩散技术的适用性，建议K <10的初步阈值。对于受风流干扰的多个源（C2）或包括地面源和高架源的更复杂配置（C4），低kappa不足以提供合理的离散排放率和总排放率。只要源与测量位置之间的距离大于气流阻碍高度的约10倍，气流阻碍的影响就可以忽略不计。本研究表明，只要满足某些条件，bLS模型有潜力提供来自多个农场气体排放源的准确离散排放率。
