Urban air quality simulation is an important tool to understand the impacts of air pollution. However, the simulations are often computationally expensive, and require extensive data on pollutant sources. Data on road traffic pollution, often the predominant source, can be obtained through sparse measurements, or through simulation of traffic and emissions. Modeling chains combine the simulations of multiple models to provide the most accurate representation possible, however the need to solve multiple models for each simulation increases computational costs even more. In this paper we construct a meta-modeling chain for urban atmospheric pollution, from dynamic traffic modeling to air pollution modeling. Reduced basis methods (RBM) aim to compute a cheap and accurate approximation of a physical state using approximation spaces made of a suitable sample of solutions to the model. One of the keys of these techniques is the decomposition of the computational work into an expensive one-time offline stage and a low-cost parameter-dependent online stage. Traditional RBMs require modifying the assembly routines of the computational code, an intrusive procedure which may be impossible in cases of operational model codes. We propose a non-intrusive reduced order scheme, and study its application to a full chain of operational models. Reduced basis are constructed using principal component analysis (PCA), and the concentration fields are approximated as projections onto this reduced space. We use statistical emulation to approximate projection coefficients in a non-intrusive manner. We apply a multi-level meta-modeling technique to a chain using the dynamic traffic assignment model LADTA, the emissions database COPERT IV, and the urban dispersion-reaction air quality model SIRANE to a case study on the city of Clermont-Ferrand with over 45, 000 daily traffic observations, a 47, 000-link road network, a simulation domain covering $$180\,\text {km}^2$$ . We assess the results using hourly NO $$_2$$ concentration observations measured at stations in the agglomeration. Computational times are reduced from nearly 3 h per simulation to under 0.1 s, while maintaining accuracy comparable to the original models. The low cost of the meta-model chain and its non-intrusive character demonstrate the versatility of the method, and the utility for long-term or many-query air quality studies such as epidemiological inquiry or uncertainty quantification.

中文翻译：

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城市空气质量模拟链的元模型

城市空气质量模拟是了解空气污染影响的重要工具。然而，模拟通常在计算上是昂贵的，并且需要关于污染物源的大量数据。道路交通污染数据（通常是主要污染源）可以通过稀疏测量或通过模拟交通和排放来获得。建模链结合了多个模型的仿真，以提供最准确的表示，但是为每个仿真解决多个模型的需求甚至进一步增加了计算成本。在本文中，我们构建了从动态交通模型到空气污​​染模型的城市大气污染元模型链。缩减基数方法（RBM）的目的是使用由模型解的合适样本构成的近似空间来计算物理状态的廉价且精确的近似。这些技术的关键之一是将计算工作分解为昂贵的一次性离线阶段和低成本的依赖参数的在线阶段。传统的RBM需要修改计算代码的汇编例程，这是一种侵入式过程，在操作模型代码的情况下可能是不可能的。我们提出一种非侵入式降阶方案，并研究其在整个运营模型链中的应用。使用主成分分析（PCA）构造简化的基础，并将浓度场近似地投影到该缩减的空间上。我们使用统计仿真以非介入方式近似投影系数。我们使用动态交通分配模型LADTA，排放数据库COPERT IV和城市弥散反应空气质量模型SIRANE对链进行多级元建模技术，以克莱蒙费朗市为例每日45,000次交通观察，47,000条链接的道路网，覆盖$$ 180 \，\ text {km} ^ 2 $$的模拟域。我们使用在集聚站进行的每小时NO $$ _ 2 $$浓度观测评估结果。计算时间从每次模拟的近3小时减少到0.1秒以下，同时保持了与原始模型相当的精度。元模型链的低成本及其非侵入性证明了该方法的多功能性，