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将RLINE纳入AERMOD:移动源应用的更新与评估

Incorporation of RLINE into AERMOD: An update and evaluation for mobile source applications.

作者信息

Owen R Chris, Heist David K, Snyder Michelle G, Miller Rebecca, Kent Laura, Buechlein Melissa, Carr Ed

机构信息

U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Research Triangle Park (EPA-RTP), Durham, NC, USA.

WSP, Durham, NC, USA.

出版信息

J Air Waste Manag Assoc. 2025 Apr;75(4):304-321. doi: 10.1080/10962247.2024.2447458. Epub 2025 Jan 23.

Abstract

The R-LINE model, which was released in 2013 as a stand-alone model for roadway-type applications and was based on a set of newly developed dispersion curves, exhibited favorable model performance in a limited set of evaluations. In 2019, the R-LINE model was incorporated as the RLINE source type in EPA's preferred near-field dispersion model AERMOD. Since its inclusion in AERMOD, the RLINE source type has been tested and compared to other AERMOD source types using multiple data sets and transportation studies. The outcome of these tests is a need to revisit the dispersion parameters used in the original RLINE dispersion curves to address performance issues suggested by comparisons to AREA and VOLUME source types in AERMOD. The work presented here includes corrections to the RLINE vertical wind profiling, harmonization of several aspects of the RLINE formulation with AERMOD's AREA and VOLUME source types (i.e. the addition of terrain and meander weighting), and updates to the RLINE dispersion parameterization based on a computational optimization routine. The updated RLINE source type is compared with AREA and VOLUME estimates for two hot-spot transportation studies. RLINE modeled estimates are also reevaluated with two of the previous evaluation studies and two additional tracer studies. The updated RLINE formulation leads to improved performance in most cases and closer comparison with the AREA and VOLUME sources.: The RLINE source type was recently added by the EPA to the AERMOD model as a "preferred" model option. Thus, the RLINE source type is now available to the air quality modeling community as a modeling option without any approval required. This paper explains recent changes to the model formulation and provides both an updated and expanded model evaluation. For the updated evaluation, we compare the three AERMOD source types (RLINE, AREA, and VOLUME) for two tracer databases used when the RLINE source was initially created (Caltrans 99 and Idaho Falls). We also add model evaluations for two "new" databases (GM Sulfate and Berkeley Freeway Experiment) to expand the assessments of model performance. Additionally, two model intercomparisons are examined, comparing design concentrations for two real-world highway hot-spot projects for RLINE against the AREA and VOLUME sources, which show much better agreement between the three source types with the updated RLINE model. The work is essential for dispersion model practitioners to understand the specifics of RLINE's model formulation as well as its performance against the other two AERMOD source types typically used for modeling roadway emissions.

摘要

R-LINE模型于2013年发布,作为一种用于道路类型应用的独立模型,它基于一组新开发的扩散曲线,在有限的一组评估中表现出良好的模型性能。2019年,R-LINE模型作为RLINE源类型被纳入美国环境保护局(EPA)首选的近场扩散模型AERMOD中。自被纳入AERMOD以来,RLINE源类型已通过多个数据集和交通研究进行了测试,并与其他AERMOD源类型进行了比较。这些测试的结果是需要重新审视原始RLINE扩散曲线中使用的扩散参数,以解决与AERMOD中的面积(AREA)和体积(VOLUME)源类型比较所表明的性能问题。本文介绍的工作包括对RLINE垂直风廓线的修正、使RLINE公式的几个方面与AERMOD的面积和体积源类型相协调(即增加地形和蜿蜒加权),以及基于计算优化程序对RLINE扩散参数化进行更新。针对两项热点交通研究,将更新后的RLINE源类型与面积和体积估算值进行了比较。还使用之前的两项评估研究和另外两项示踪剂研究对RLINE模型估算值进行了重新评估。更新后的RLINE公式在大多数情况下提高了性能,并与面积和体积源进行了更紧密的比较。:RLINE源类型最近被EPA添加到AERMOD模型中,作为一种“首选”模型选项。因此,空气质量建模界现在可以将RLINE源类型作为一种建模选项使用,无需任何审批。本文解释了模型公式的近期变化,并提供了更新和扩展的模型评估。对于更新后的评估,我们针对RLINE源最初创建时使用的两个示踪剂数据库(加州运输部99号数据库和爱达荷瀑布数据库)比较了三种AERMOD源类型(RLINE、AREA和VOLUME)。我们还增加了对两个“新”数据库(通用汽车硫酸盐数据库和伯克利高速公路实验数据库)的模型评估,以扩大对模型性能的评估。此外,还研究了两项模型相互比较,比较了RLINE与面积和体积源针对两个实际公路热点项目的设计浓度,结果表明在更新后的RLINE模型中,三种源类型之间的一致性要好得多。这项工作对于扩散模型从业者了解RLINE模型公式的细节以及它相对于通常用于模拟道路排放的其他两种AERMOD源类型的性能至关重要。

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