环境科学
温室气体
排放清单
甲烷排放
大气扩散模型
大气科学
甲烷
无组织排放
风速
气象学
空气污染
环境工程
化学
物理
空气质量指数
生物
有机化学
生态学
作者
Marcel Bühler,Christoph Häni,Christof Ammann,Joachim Mohn,A. Neftel,Sabine Schrade,Michael Zähner,Kerstin Zeyer,Stefan Brönnimann,Thomas Küpper
标识
DOI:10.1016/j.agrformet.2021.108501
摘要
Methane (CH4) emissions from dairy housings, mainly originating from enteric fermentation of ruminating animals, are a significant source of greenhouse gases. The quantification of emissions from naturally ventilated dairy housings is challenging due to the spatial distribution of sources (animals, housing areas) and variable air exchange. The inverse dispersion method (IDM) is a promising option, which is increasingly used to determine gaseous emissions from stationary sources, as it offers high flexibility in the application at reasonable costs. We used a backward Lagrangian stochastic model combined with concentration measurements by open-path tunable diode laser spectrometers placed up- and downwind of a naturally ventilated housing with 40 dairy cows to determine the CH4 emissions. The average emissions per livestock unit (LU) were 317 (±44) g LU−1 d−1 and 267 (±43) g LU−1 d−1 for the first and second campaign, in September – October and November – December, respectively. For each campaign, inhouse tracer ratio measurements (iTRM) were conducted in parallel during two subperiods. For simultaneous measurements, IDM showed average emissions which were lower by 8% and 1% than that of iTRM, respectively, for the two campaigns. The differences are within the uncertainty range of any of the two methods. The IDM CH4 emissions were further analysed by wind direction and atmospheric stability and no differences in emissions were found. Overall, IDM showed its aptitude to accurately determine CH4 emissions from dairy housings or other stationary sources if the site allows adequate placement of sensors up- and downwind in the prevailing wind direction. To acquire reliable emission data, depending on the data loss during measurements due to quality filtering or instrument failure, a measuring time of at least 10 days is required.
科研通智能强力驱动
Strongly Powered by AbleSci AI