Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling
Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling
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通过 miniDOAS 测量和逆扩散模型估算放牧地的氨排放量
DOI：
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
A. Neftel
中科院分区：
文献类型：
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作者：
M. Bell;C. Flechard;Y. Fauvel;C. Häni;J. Sintermann;M. Jocher;H. Menzi;A. Hensen;A. Neftel
抽象的。通过应用后向拉格朗日随机模型（bLS）模型结合田间测量的水平浓度梯度，估计了春季奶牛放牧田地的氨（NH3）通量。当牛存在时，通过开放路径miniDOAS（差分光学吸收光谱）仪器在田间边界进行连续浓度测量，并持续6天。排放的NH3的沉积，以清洁补丁上的字段也进行了模拟，允许净排放量和总排放量的估计，其中干沉积速度（VD）预测冠层阻力（Rc）模型开发的本地NH3通量和气象测量。估计的排放量在放牧期间达到峰值，在牛离开田地后下降，而从与温度、风速和湿度的协方差中观察到对排放量的控制，并在田地上进行湿度测量，揭示了一个昼夜的排放概况。由于空间不均匀的排放模式，在顺风受体之间观察到了很大的浓度差异。这可能是由于牛群分布不均匀和放牧密度低造成的，当牛群聚集在某些区域，如水槽周围时，会出现排放热点。通过将模型源区域分成子部分并根据测得的浓度优化单个源区域系数来解释空间复杂性。背景浓度是最大的不确定性来源，根据敏感性/不确定性分析，与本研究得出的排放因子相关的总体不确定性至少为30- 40%。当未模拟沉积时，排放因子可表示为6 ± 2 g NH3奶牛-1天-1，或排泄尿氮的9 ± 3%作为NH3排放;当总排放模型中包括沉积时，排放因子可表示为7 ± 2 g NH3奶牛-1天-1，或排泄尿氮的10 ± 3%作为NH3排放。结果表明，大约14 ± 4%的排放的NH3沉积到田间的斑块中，这些斑块不受尿液或粪便的影响。
Abstract. Ammonia (NH3) fluxes were estimated from a field being grazed by dairy cattle during spring by applying a backward Lagrangian stochastic model (bLS) model combined with horizontal concentration gradients measured across the field. Continuous concentration measurements at field boundaries were made by open-path miniDOAS (differential optical absorption spectroscopy) instruments while the cattle were present and for 6 subsequent days. The deposition of emitted NH3 to clean patches on the field was also simulated, allowing both net and gross emission estimates, where the dry deposition velocity (vd) was predicted by a canopy resistance (Rc) model developed from local NH3 flux and meteorological measurements. Estimated emissions peaked during grazing and decreased after the cattle had left the field, while control on emissions was observed from covariance with temperature, wind speed and humidity and wetness measurements made on the field, revealing a diurnal emission profile. Large concentration differences were observed between downwind receptors, due to spatially heterogeneous emission patterns. This was likely caused by uneven cattle distribution and a low grazing density, where hotspots of emissions would arise as the cattle grouped in certain areas, such as around the water trough. The spatial complexity was accounted for by separating the model source area into sub-sections and optimising individual source area coefficients to measured concentrations. The background concentration was the greatest source of uncertainty, and based on a sensitivity/uncertainty analysis the overall uncertainty associated with derived emission factors from this study is at least 30–40 %. Emission factors can be expressed as 6 ± 2 g NH3 cow−1 day−1, or 9 ± 3 % of excreted urine-N emitted as NH3, when deposition is not simulated and 7 ± 2 g NH3 cow−1 day−1, or 10 ± 3 % of excreted urine-N emitted as NH3, when deposition is included in the gross emission model. The results suggest that around 14 ± 4 % of emitted NH3 was deposited to patches within the field that were not affected by urine or dung.
影响因子：
4.9
作者：
Carslaw, David C.;Ropkins, Karl
通讯作者：
Ropkins, Karl
影响因子：
3.8
作者：
von Bobrutzki, K.;Braban, C. F.;Nemitz, E.
通讯作者：
Nemitz, E.