Evaluation of two common source estimation measurement strategies using large-eddy simulation of plume dispersion under neutral atmospheric conditions
Evaluation of two common source estimation measurement strategies using large-eddy simulation of plume dispersion under neutral atmospheric conditions
复制标题
使用中性大气条件下羽流扩散的大涡模拟来评估两种常见的源估计测量策略
DOI：
10.5194/amt-2022-25
复制
发表时间：
2022
影响因子：
3.8
通讯作者：
M. Krol
中科院分区：
文献类型：
--
作者：
A. Raz̆njević;C. V. van Heerwaarden;M. Krol
抽象的。本研究使用大涡模拟（LES）来评估两个广泛使用的观测技术，估计点源排放。我们评估了垂直于风向的汽车测量和常用的其他示踪剂方法33A（OTM33A）的使用。LES研究模拟了从点源释放到平坦地形上的静止、均匀和中性大气表面层的羽流。这一选择的动机是我们的野心，以验证观测方法在受控条件下，他们预计将表现良好，因为不确定性的来源被最小化。三个羽流不同的释放高度进行了采样的方式，模仿采样，根据汽车横断面和固定OTM 33A方法。随后，将源强度估计值与模拟中使用的真实源强度进行比较。估计的源强度的标准偏差衰减成正比的平均断面数的平方根的倒数，显示出统计独立的单个样本。分析表明，对于汽车横断面测量，至少需要对15个重复测量系列进行平均，以获得在真实源强度的40%以内的源强度。对于建议在距离源200米以内进行测量的OTM33A分析，源强度的估计值在靠近源的地方具有类似的值，这是由于靠近源的湍流混合造成的羽流分散不充分造成的。此外，OTM33A方法得到的源强度大大高估。这种高估是由所提出的OTM33A色散系数驱动的，该色散系数对于该特定情况来说太大。这表明OTM33A色散常数的推导条件可能受到长度尺度超过表面层尺度的运动的影响。最后，我们的模拟表明，由于风切变的影响，时间平均的羽流中心线的位置可能会与羽流排放高度不同。如果使用高斯羽流模型（GPM）来解释测量结果，则这种不匹配可能是额外的误差源。在汽车样带测量的情况下，正确的源估计需要在GPM中调整源高度。
Abstract. This study uses large-eddy simulations (LES) to evaluate two widely-used observational techniques that estimate point source emissions. We evaluate the use of car measurements perpendicular to the wind direction and the commonly used Other Tracer Method 33A (OTM33A). The LES study simulates a plume from a point source released into a stationary, homogeneous and neutral atmospheric surface layer over flat terrain. This choice is motivated by our ambition to validate the observational methods under controlled conditions where they are expected to perform well since the sources of uncertainties are minimized. Three plumes with different release heights were sampled in a manner that mimics sampling according to car transects and the stationary OTM33A method. Subsequently, source strength estimates are compared to the true source strength used in the simulation. Standard deviations of the estimated source strengths decay proportionally to the inverse of the square root of the number of averaged transects, showing statistical independence of individual samples. The analysis shows that for the car transect measurements at least 15 repeated measurement series need to be averaged to obtain a source strength within 40 % of the true source strength. For the OTM33A analysis, which recommends measurements within 200 m from the source, the estimates of source strengths have similar values close to the source, which is caused by insufficient dispersion of the plume by turbulent mixing close to the source. Additionally, the derived source strength is substantially overestimated with the OTM33A method. This overestimation is driven by the proposed OTM33A dispersion coefficients, which are too large for this specific case. This suggests that the conditions under which the OTM33A dispersion constants were derived, were likely influenced by motions with length scales beyond the scale of the surface layer. Lastly, our simulations indicate that, due to wind-shear effects, the position of the time-averaged centerline of the plumes may differ from the plume emission height. This mismatch can be an additional source of error if a Gaussian plume model (GPM) is used to interpret the measurement. In case of the car transect measurements, a correct source estimate then requires an adjustment of the source height in the GPM.