Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada.
J Air Waste Manag Assoc. 2020 Apr;70(4):425-435. doi: 10.1080/10962247.2020.1728424. Epub 2020 Feb 26.
Lagrangian stochastic dispersion models are sometimes run in backward mode to estimate air emissions from different types of sources including area sources. The forward-in-time and backward-in-time Lagrangian stochastic (fLS and bLS) dispersion models may not result in the same estimates. The two models were compared under different atmospheric conditions in micro-scale applications. They are equivalent in a steady-state and horizontally homogeneous atmosphere in many features including estimating concentration at a point, using surface receptor, and prerunning the models. Although bLS shows better computational efficiency, it has a larger uncertainty in results due to the use of surface receptors. In a non-steady-state wind field, the two models show opposite transition trends when the wind fields experience a step change. Under sinusoidal-varying winds, the two models show different shapes of the predicated concentration curves. The normalized differences of the mean concentrations mainly increase with the receptor height when the source-receptor distance is fixed. A controlled methane release experiment was conducted to investigate the behaviors of the two models driven by real wind fields. The correlation coefficient between model-predicted concentrations is 0.95. The model-predicted (forward model) and measured concentrations show similar trend with a correlation coefficient of 0.70. The bLS model estimates larger peak concentrations than that fLS model under the same emission rate. The best-fitted results of the fLS and bLS models give recovery ratios of 1.1558 and 0.9675, respectively, which are better than that using a constant 15-min averaged wind (0.7922).: There are large uncertainties and difficulties in quantification of fugitive air emissions from area sources such as landfills, agriculture, and industry sections. Lagrangian stochastic dispersion model is a versatile tool for these applications with the capability of near-field description and good efficiency. Backward models are usually used to estimate emission rates from area sources due to high computing efficiencies. But they may not result in the same estimate as the forward models due to factors involving model realization and input parameters. It is necessary to investigate the discrepancies to select the best model with minimal uncertainty in the results.
拉格朗日随机扩散模型有时会以反向模式运行，以估计不同类型的源（包括区域源）的空气排放。向前和向后的拉格朗日随机（fLS 和 bLS）扩散模型可能不会得出相同的估计值。在微尺度应用中，在不同的大气条件下对这两个模型进行了比较。在稳态和水平均匀大气中，它们在许多方面是等效的，包括在一个点上估计浓度、使用表面受体和预先运行模型。虽然 bLS 显示出更好的计算效率，但由于使用表面受体，结果的不确定性更大。在非稳态风场中，当风场经历阶跃变化时，两个模型显示出相反的过渡趋势。在正弦变化的风场下，两个模型显示出不同形状的预测浓度曲线。当源-受体距离固定时，归一化平均浓度的差异主要随受体高度增加而增加。进行了一项受控的甲烷释放实验，以研究由真实风场驱动的两个模型的行为。模型预测浓度之间的相关系数为 0.95。模型预测（正向模型）和测量浓度的相关性为 0.70。在相同的排放率下，bLS 模型估计的峰值浓度大于 fLS 模型。fLS 和 bLS 模型的最佳拟合结果分别给出了 1.1558 和 0.9675 的恢复比，优于使用 15 分钟平均风速的常数（0.7922）。
从垃圾填埋场、农业和工业等区域源逸散的空气排放的量化存在很大的不确定性和困难。拉格朗日随机扩散模型是这些应用的通用工具，具有近场描述的能力和良好的效率。由于计算效率高，反向模型通常用于估计区域源的排放率。但是，由于涉及模型实现和输入参数的因素，它们可能不会得出与正向模型相同的估计值。有必要研究差异，以选择结果不确定性最小的最佳模型。
