Development and Evaluation of the Online Hybrid Model CAMx-LPiG

Piccoli, Andrea; Agresti, Valentina; Lonati, Giovanni; Pirovano, Guido · 2025 · Crossref

DOI: 10.3390/atmos16050604

archive: archived pipeline: cataloged verified

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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 pollutants like NO2 and PM10. Standard Eulerian Chemical Transport Models (CTMs) lack the spatial resolution to capture sub-grid scale phenomena such as street-level dispersion, while standalone local models often fail to accurately integrate with regional background concentrations or handle complex atmospheric chemistry. To bridge this gap, the authors developed CAMx-LPiG, an online hybrid model that integrates a new sub-grid module, Linear Plume in Grid (LPiG), into the Comprehensive Air Quality Model with Extensions (CAMx). This approach aims to provide a multiscale simulation from regional to urban scales, ensuring coherent chemical states 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 sources, such as road segments, rather than point sources. The method treats each road link as a linear source and simulates its dispersion using a "synthetic puff" that mimics the smoke surface generated by the road. This formulation incorporates a Super Gaussian approach to describe road source characteristics and includes options for simplified (GREASD) or more complete (IRON) atmospheric chemistry. The model was evaluated against measured NO2 concentrations in Milan, Italy, for January 2017. The study utilized a bottom-up emission modeling chain to estimate traffic emissions at the road-link level, allowing for high-resolution analysis of the local contribution of road traffic to atmospheric pollution. The results demonstrate that CAMx-LPiG successfully introduces road traffic-induced gradients in NO2 concentrations at sub-grid resolution, a capability lacking in standard CAMx simulations. The hybrid model significantly reduced bias compared to stand-alone CAMx simulations, providing a more accurate representation of urban air quality. By explicitly simulating the dispersion and chemical transformation of emissions from individual road links, the model captures the localized impact of traffic that is otherwise diluted in coarser grid resolutions. The online integration ensures that the chemical interactions between local emissions and the regional background are handled consistently throughout the simulation. The significance of this work lies in providing a robust, efficient, and easily implementable tool for assessing the environmental impacts of urban mobility policies. As cities transition toward electric vehicles and implement stricter air quality regulations, reliable quantification of these impacts is crucial for policymakers. CAMx-LPiG offers a flexible solution that enhances the accuracy of air quality modeling in urban areas without requiring extensive additional input data, such as detailed urban canopy geometry. This advancement supports better decision-making for air quality management and the evaluation of abatement strategies in densely populated regions.

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• mental model of traffic