AERMOD is designated as U.S. Environmental Protection Agency (EPA)'s preferred air dispersion model for refined transportation project hot-spot analyses beginning in 2020. One of the key challenges in its modeling process is spatially encoding roadway geometry, especially when simulating highways with complex geometric designs. This research proposed an open-source Python package, GTA, which enables conversion of publicly available roadway Geographic Information System (GIS) layers into defined sources, and source-based emission rates from MOtor Vehicle Emissions Simulator (MOVES) output for AERMOD modeling. The research selected a suburban area in Atlanta, and conducted a comprehensive analysis in terms of annual PM2.5 concentration results and the speed of preparing AERMOD input files for highway network modeling both manually and using software developed based on the proposed methodology. The results prove that the proposed methodology significantly expedites the AERMOD input preparation process, and facilitates convenient testing of multiple modeling configurations for multi-scenario or sensitivity analysis.

中文翻译：

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用于将地理信息系统集成到监管扩散模型中以进行精细污染建模的 Python 工具包


AERMOD 被指定为美国环境保护署 (EPA) 从 2020 年开始用于精细交通项目热点分析的首选空气扩散模型。其建模过程中的关键挑战之一是对道路几何形状进行空间编码，特别是在模拟具有复杂结构的高速公路时几何设计。这项研究提出了一个开源 Python 包 GTA，它能够将公开可用的道路地理信息系统 (GIS) 图层转换为定义的源，以及来自 MOtor 车辆排放模拟器 (MOVES) 输出的基于源的排放率，以进行 AERMOD 建模。该研究选择了亚特兰大郊区，对年度 PM2.5 浓度结果以及手动和使用基于该方法开发的软件准备用于公路网建模的 AERMOD 输入文件的速度进行了综合分析。结果证明，所提出的方法显着加快了 AERMOD 输入准备过程，并有助于方便地测试多场景或敏感性分析的多个建模配置。