Development and Evaluation of the Online Hybrid Model CAMx-LPiG

Piccoli, Andrea; Agresti, Valentina; Lonati, Giovanni; Pirovano, Guido · 2024 · Crossref

DOI: 10.20944/preprints202405.0858.v1

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Summary

This paper addresses the challenge of accurately simulating urban air quality, specifically the dispersion of road traffic emissions, which are a primary source of nitrogen dioxide (NO2) and particulate matter in cities. Standard Eulerian Chemical Transport Models (CTMs) like CAMx lack the spatial resolution to capture local-scale dispersion phenomena, such as those occurring within street canyons, while standalone local models often fail to accurately account for background concentrations and atmospheric chemistry. To bridge this gap, the authors developed CAMx-LPiG, an online hybrid model that integrates a sub-grid scale module, Linear Plume in Grid (LPiG), into the CAMx framework. This approach aims to provide a multiscale simulation from regional to urban levels, preserving chemical coherence and avoiding the double-counting of emissions common in offline hybrid methods. The LPiG module extends the existing Lagrangian puff scheme of CAMx to handle linear emission sources, such as road segments, rather than just point sources. The methodology involves treating each road link as a linear source and simulating its dispersion using a "synthetic puff" that mimics the smoke surface generated by the road. The model employs a Super Gaussian formulation for puff sampling and allows for two chemistry options: GREASD for simplified NOx and PM chemistry, and IRON for more complete gas-phase chemistry. To reduce computational burden, a preprocessing module was developed to optimize the number of road links considered. The model was evaluated against measured NO2 concentrations for the city of Milan during January 2017, comparing CAMx-LPiG results with stand-alone CAMx simulations. The evaluation demonstrated that CAMx-LPiG successfully introduces road traffic-induced gradients in NO2 concentrations at sub-grid resolution, capturing local variations that the standard CTM misses. Specifically, the hybrid model reduced the bias compared to stand-alone CAMx simulations, indicating improved accuracy in predicting ambient pollutant levels near roadways. The study confirms that the online integration effectively couples local dispersion with regional transport and chemistry, providing a coherent description of the atmospheric state across scales. The significance of this work lies in providing a robust, efficient tool for assessing the environmental impacts of urban mobility policies, such as the transition to electric vehicles. By enabling high-resolution analysis of road traffic contributions without requiring complex post-processing or additional high-resolution meteorological data, CAMx-LPiG supports policymakers in evaluating air quality improvement strategies. The model’s compatibility with existing CAMx workflows makes it accessible for broader application in air quality planning and regulatory compliance under new European directives.

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• mental model of traffic