Air quality modeling at the very local scale within an urban area is performed through a hybrid modeling system (HMS) that combines the CAMx Eulerian model the with AUSTAL2000 Lagrangian model. The enhancements obtained by means of the HMS in the reconstruction of the spatial distribution of fine particles (PM2.5) and elemental carbon (EC) concentration are presented for the case-study of Milan city center in Northern Italy. Modeling results are reported for three receptors (a green area, a residential and shopping area, and a congested crossroad on the inner ring road of the city center) selected in order to represent urban sites characterized by both different features in terms of the surrounding built environment and by different exposure to local emission sources. The peculiarity of the three receptors is further highlighted by source apportionment analysis, developed not only with respect to the kind of emission sources but also to the geographical location of the sources within the whole Northern Italy computational domain. Results show that the outcome of the Eulerian model at the local scale is only representative of a background level, similar to the Lagrangian model’s outcome for the green area receptor, but fails to reproduce concentration gradients and hot-spots, driven by local sources’ emissions.

中文翻译：

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米兰城市规模PM2.5和元素碳源分配的组合欧拉-拉格朗日混合模型系统

通过混合建模系统（HMS）在市区范围内进行空气质量建模，该系统将CAMx欧拉模型与AUSTAL2000拉格朗日模型相结合。在意大利北部米兰市中心的案例研究中，介绍了通过HMS在重建细颗粒（PM2.5）和元素碳（EC）浓度的空间分布方面获得的增强。报告了三种受体的建模结果（绿色区域，住宅和购物区以及市中心内环路上拥挤的十字路口），以代表在建筑周围具有两种不同特征的城市站点环境和暴露于本地排放源的方式不同。源分配分析进一步凸显了这三种受体的特殊性，不仅针对排放源的种类，而且针对整个意大利北部计算域内源的地理位置进行了开发。结果表明，在地方尺度上，欧拉模型的结果仅代表背景水平，与绿色区域受体的拉格朗日模型的结果相似，但是在局部来源的排放驱动下，无法再现浓度梯度和热点。