Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 89512, USA.
J Air Waste Manag Assoc. 2011 Jun;61(6):660-72. doi: 10.3155/1047-3289.61.6.660.
The main objective of this study was to investigate the capabilities of the receptor-oriented inverse mode Lagrangian Stochastic Particle Dispersion Model (LSPDM) with the 12-km resolution Mesoscale Model 5 (MM5) wind field input for the assessment of source identification from seven regions impacting two receptors located in the eastern United States. The LSPDM analysis was compared with a standard version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) single-particle backward-trajectory analysis using inputs from MM5 and the Eta Data Assimilation System (EDAS) with horizontal grid resolutions of 12 and 80 km, respectively. The analysis included four 7-day summertime events in 2002; residence times in the modeling domain were computed from the inverse LSPDM runs and HYPSLIT-simulated backward trajectories started from receptor-source heights of 100, 500, 1000, 1500, and 3000 m. Statistics were derived using normalized values of LSPDM- and HYSPLIT-predicted residence times versus Community Multiscale Air Quality model-predicted sulfate concentrations used as baseline information. From 40 cases considered, the LSPDM identified first- and second-ranked emission region influences in 37 cases, whereas HYSPLIT-MM5 (HYSPLIT-EDAS) identified the sources in 21 (16) cases. The LSPDM produced a higher overall correlation coefficient (0.89) compared with HYSPLIT (0.55-0.62). The improvement of using the LSPDM is also seen in the overall normalized root mean square error values of 0.17 for LSPDM compared with 0.30-0.32 for HYSPLIT. The HYSPLIT backward trajectories generally tend to underestimate near-receptor sources because of a lack of stochastic dispersion of the backward trajectories and to overestimate distant sources because of a lack of treatment of dispersion. Additionally, the HYSPLIT backward trajectories showed a lack of consistency in the results obtained from different single vertical levels for starting the backward trajectories. To alleviate problems due to selection of a backward-trajectory starting level within a large complex set of 3-dimensional winds, turbulence, and dispersion, results were averaged from all heights, which yielded uniform improvement against all individual cases.
本研究的主要目的是利用具有 12 公里分辨率的中尺度模式 5（MM5）风场输入的基于受体的反向拉格朗日随机粒子分散模型（LSPDM）的能力来评估来自影响美国东部两个受体的七个区域的源识别。将 LSPDM 分析与使用 MM5 和 Eta 数据同化系统（EDAS）输入的标准混合单粒子拉格朗日综合轨迹（HYSPLIT）单粒子后向轨迹分析进行比较，水平分辨率分别为 12 公里和 80 公里。分析包括 2002 年四个为期 7 天的夏季事件；从反向 LSPDM 运行和从受体-源高度为 100、500、1000、1500 和 3000 米开始的 HYPSLIT 模拟后向轨迹计算了模型域中的停留时间。使用归一化的 LSPDM 和 HYPSLIT 预测停留时间与社区多尺度空气质量模型预测硫酸盐浓度的比值作为基线信息，得出了统计数据。在所考虑的 40 个案例中，LSPDM 在 37 个案例中确定了第一和第二排放区域的影响，而 HYSPLIT-MM5（HYSPLIT-EDAS）在 21（16）个案例中确定了源。与 HYSPLIT（0.55-0.62）相比，LSPDM 产生了更高的整体相关系数（0.89）。与 HYSPLIT 相比，LSPDM 的归一化均方根误差值也有所提高，0.17 对 0.30-0.32。由于缺乏后向轨迹的随机扩散，HYSPLIT 后向轨迹通常倾向于低估近受体源，并且由于缺乏对扩散的处理，HYSPLIT 后向轨迹倾向于高估远距离源。此外，HYSPLIT 后向轨迹在从不同的单个垂直水平开始后向轨迹时，结果的一致性较差。为了缓解由于在复杂的三维风、湍流和扩散中选择后向轨迹起始水平而导致的问题，从所有高度平均结果，这对所有单个案例都产生了均匀的改进。
