J Air Waste Manag Assoc. 2014 Mar;64(3):309-21. doi: 10.1080/10962247.2013.872709.
The U.S. Environmental Protection Agency (EPA) short-distance dispersion model, AERMOD, has been shown to overpredict by a factor of as much as 10 when compared with observed concentrations from continuous releases at the Oak Ridge, TN (OR), and Idaho Falls, ID (IF), field experiments during stable periods when wind speeds often dropped below 1 m/sec. Some of this overprediction tendency can be reduced by revising AERMOD's meteorological preprocessor's parameterizations of the friction velocity, u*, during low-wind stable conditions, thus increasing the calculated sigma(v) and sigma(w) and hence the lateral and vertical dispersion rates. Observations show that as the mean wind speed approaches zero at night, there is always significant sigma(v) and sigma(w) over time periods of 15 to 60 min, while standard Monin-Obukhov Similarity Theory (MOST) predicts that sigma(v) and sigma(w) will approach zero. This paper focuses on the u* estimation methods and the minimum turbulence (sigma(v) and sigma(w)) assumptions in AERMOD (beta option 4) and two widely used U.S. operational dispersion models, AERMOD (v12345) and SCICHEM. The U.S. EPA has provided results of its tests with the OR and IF data, with its base AERMOD version and its December 2012 modified versions, which assume adjustments to the low-wind u* and increases in the minimum sigma(v) parameterization. SCICHEM has relatively small mean bias for both data sets. The revised AERMOD shows much less mean bias, agreeing more with SCICHEM.
Suggestions are made for improvements to dispersion models such as AERMOD to correct overpredictions during light-wind stable conditions. Methods for estimating u*, L, and the minimum turbulence parameters (sigma(v) and sigma(w)) are reviewed and compared. SCICHEM and the current operational version and an optional beta version (December 2012) of AERMOD are evaluated with tracer data from low-wind stable field experiments in Idaho Falls and Oak Ridge. It is seen that the operational version of AERMOD overpredicts by a factor of 2 to 10, while the optional beta version of AERMOD and SCICHEM have much less bias.
美国环境保护署 (EPA) 的短距离扩散模型 AERMOD 在与橡树岭 (TN) 和爱达荷瀑布 (ID) 的现场实验中稳定期连续释放的观测浓度进行比较时，风速经常降至 1 m/sec 以下时，其预测值高出观测值 10 倍。通过修改 AERMOD 的气象预处理程序在低风速稳定条件下的摩擦速度 u* 参数化，可以减少部分预测误差，从而增加计算出的 sigma(v) 和 sigma(w) 以及侧向和垂直扩散率。观测结果表明，随着夜间平均风速接近零，在 15 至 60 分钟的时间段内，sigma(v) 和 sigma(w) 始终存在显著值，而标准的 Monin-Obukhov 相似性理论 (MOST) 预测 sigma(v) 和 sigma(w) 将趋于零。本文重点介绍 AERMOD（beta 选项 4）和两种广泛使用的美国运行扩散模型 AERMOD（v12345）和 SCICHEM 中的 u估计方法和最小湍流（sigma(v)和 sigma(w)）假设。美国环保署提供了其使用橡树岭和爱达荷瀑布数据的测试结果，包括其基本 AERMOD 版本和 2012 年 12 月修改后的版本，这些版本假设对低风速 u进行调整并增加最小 sigma(v) 参数化。SCICHEM 对这两个数据集的平均偏差都相对较小。经过修订的 AERMOD 显示出的平均偏差要小得多，与 SCICHEM 更一致。
建议对 AERMOD 等扩散模型进行改进，以纠正轻风和稳定条件下的过度预测。审查并比较了估计 u*、L 和最小湍流参数（sigma(v)和 sigma(w)）的方法。使用爱达荷瀑布和橡树岭的低风稳定现场实验中的示踪剂数据评估了 SCICHEM 和当前运行版本以及 AERMOD 的可选 beta 版本（2012 年 12 月）。结果表明，AERMOD 的运行版本预测值高出 2 至 10 倍，而可选 beta 版本的 AERMOD 和 SCICHEM 的偏差要小得多。
