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).Implications: 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.

中文翻译：

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用于微尺度大气扩散的时间向前和向后时间拉格朗日随机色散模型的比较。

拉格朗日随机扩散模型有时以向后模式运行，以估算来自不同类型的源（包括面源）的空气排放。向前和向后时间的拉格朗日随机（fLS和bLS）色散模型可能不会得出相同的估计值。在微观应用中，在不同大气条件下比较了两个模型。在许多情况下，它们在稳态和水平均匀的气氛中都是等效的，包括估算点的浓度，使用表面接收器和运行模型。尽管bLS显示出更好的计算效率，但是由于使用表面受体，其结果不确定性更大。在非稳态风场中，当风场经历阶跃变化时，两个模型显示相反的过渡趋势。在正弦变化风下，两个模型显示出不同的预测浓度曲线形状。当源-受体距离固定时，平均浓度的归一化差异主要随受体高度而增加。进行了受控的甲烷释放实验，以研究由实际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。模型预测的（正向模型）和测得的浓度显示相似的趋势，相关系数为0.70。在相同的发射速率下，bLS模型估计的峰浓度比fLS模型大。fLS和bLS模型的最佳拟合结果分别给出了1.1558和0.9675的回收率，这比使用恒定15分钟平均风速（0.7922）的回收率要好。含义：在量化来自诸如垃圾填埋场，农业和工业部门等区域来源的逃逸性空气排放方面存在很大的不确定性和困难。拉格朗日随机色散模型是具有这些功能的通用工具，具有近场描述能力和良好的效率。由于计算效率高，通常使用向后模型估算区域源的排放率。但是由于涉及模型实现和输入参数的因素，它们可能无法得出与正向模型相同的估计。有必要调查差异以选择结果不确定性最小的最佳模型。和行业部分。拉格朗日随机色散模型是具有这些功能的通用工具，具有近场描述能力和良好的效率。由于计算效率高，通常使用向后模型估算区域源的排放率。但是由于涉及模型实现和输入参数的因素，它们可能无法得出与正向模型相同的估计。有必要调查差异以选择结果不确定性最小的最佳模型。和行业部分。拉格朗日随机色散模型是具有这些功能的通用工具，具有近场描述能力和良好的效率。由于计算效率高，通常使用向后模型估算区域源的排放率。但是由于涉及模型实现和输入参数的因素，它们可能无法得出与正向模型相同的估计。有必要调查差异以选择结果不确定性最小的最佳模型。但是由于涉及模型实现和输入参数的因素，它们可能无法得出与正向模型相同的估计。有必要调查差异以选择结果不确定性最小的最佳模型。但是由于涉及模型实现和输入参数的因素，它们可能无法得出与正向模型相同的估计。有必要调查差异以选择结果不确定性最小的最佳模型。