Liu Haobing, Xu Xiaodan, Rodgers Michael O, Xu Yanzhi Ann, Guensler Randall L
a School of Civil and Environmental Engineering, Georgia Institute of Technology , Atlanta , GA , USA.
J Air Waste Manag Assoc. 2017 Jul;67(7):763-775. doi: 10.1080/10962247.2017.1287788. Epub 2017 Feb 6.
MOVES and AERMOD are the U.S. Environmental Protection Agency's recommended models for use in project-level transportation conformity and hot-spot analysis. However, the structure and algorithms involved in running MOVES make analyses cumbersome and time-consuming. Likewise, the modeling setup process, including extensive data requirements and required input formats, in AERMOD lead to a high potential for analysis error in dispersion modeling. This study presents a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix, a high-performance emission modeling tool, with the microscale dispersion models CALINE4 and AERMOD. MOVES-Matrix was prepared by iteratively running MOVES across all possible iterations of vehicle source-type, fuel, operating conditions, and environmental parameters to create a huge multi-dimensional emission rate lookup matrix. AERMOD and CALINE4 are connected with MOVES-Matrix in a distributed computing cluster using a series of Python scripts. This streamlined system built on MOVES-Matrix generates exactly the same emission rates and concentration results as using MOVES with AERMOD and CALINE4, but the approach is more than 200 times faster than using the MOVES graphical user interface. Because AERMOD requires detailed meteorological input, which is difficult to obtain, this study also recommends using CALINE4 as a screening tool for identifying the potential area that may exceed air quality standards before using AERMOD (and identifying areas that are exceedingly unlikely to exceed air quality standards). CALINE4 worst case method yields consistently higher concentration results than AERMOD for all comparisons in this paper, as expected given the nature of the meteorological data employed.
The paper demonstrates a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix with the CALINE4 and AERMOD. This streamlined system generates exactly the same emission rates and concentration results as traditional way to use MOVES with AERMOD and CALINE4, which are regulatory models approved by the U.S. EPA for conformity analysis, but the approach is more than 200 times faster than implementing the MOVES model. We highlighted the potentially significant benefit of using CALINE4 as screening tool for identifying potential area that may exceeds air quality standards before using AERMOD, which requires much more meteorology input than CALINE4.
MOVES和AERMOD是美国环境保护局推荐用于项目层面交通一致性和热点分析的模型。然而，运行MOVES所涉及的结构和算法使得分析繁琐且耗时。同样，AERMOD中的建模设置过程，包括大量的数据要求和所需的输入格式，导致在扩散建模中存在较高的分析误差可能性。本研究提出了一种用于线源扩散建模的分布式计算方法，该方法将高性能排放建模工具MOVES - Matrix与微尺度扩散模型CALINE4和AERMOD集成在一起。MOVES - Matrix是通过在车辆源类型、燃料、运行条件和环境参数的所有可能迭代中反复运行MOVES来创建一个巨大的多维排放率查找矩阵而准备的。AERMOD和CALINE4在分布式计算集群中使用一系列Python脚本与MOVES - Matrix相连。这个基于MOVES - Matrix构建的简化系统生成的排放率和浓度结果与使用MOVES结合AERMOD和CALINE4时完全相同，但该方法比使用MOVES图形用户界面快200多倍。由于AERMOD需要详细的气象输入，而这很难获得，本研究还建议在使用AERMOD之前（以及识别极不可能超过空气质量标准的区域），使用CALINE4作为筛选工具来识别可能超过空气质量标准的潜在区域。CALINE4最坏情况方法在本文的所有比较中产生的浓度结果始终高于AERMOD，鉴于所采用气象数据的性质，这是预期的。
本文展示了一种用于线源扩散建模的分布式计算方法，该方法将MOVES - Matrix与CALINE4和AERMOD集成在一起。这个简化系统生成的排放率和浓度结果与传统的将MOVES与AERMOD和CALINE4一起使用的方式完全相同，AERMOD和CALINE4是美国环保署批准用于一致性分析的监管模型，但该方法比实施MOVES模型快200多倍。我们强调了在使用AERMOD之前使用CALINE4作为筛选工具来识别可能超过空气质量标准的潜在区域的潜在显著好处，AERMOD比CALINE4需要更多的气象输入。
