Development of a GCN–CALPUFF Hybrid Model for High-Resolution Simulation of PM2.5 and NO2

Urban air pollution is characterized by significant spatio-temporal heterogeneity resulting from the complex interplay between regional long-range transport and localized emission sources, posing a major source of uncertainty in exposure assessment and policy formulation. In particular, fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) are representative pollutants simultaneously influenced by regional background levels and urban traffic/industrial emissions, necessitating the generation of high-resolution concentration fields. While conventional chemical transport models (CTMs) effectively capture regional-scale distribution and transport processes, they are limited in resolving micro-scale variability driven by complex urban terrain, traffic networks, and localized emission characteristics. Conversely, local dispersion models can precisely depict concentration gradients at fine scales but struggle to consistently incorporate background concentrations transported from outside the domain. Thus, hybrid approach that integrates the strengths of both models is essential.

In this study, we propose a hybrid air quality modeling framework that couples a Graph Convolutional Network (GCN) with the CALPUFF dispersion model. Focusing on Seoul, South Korea, in November 2022, the GCN leverages CMAQ data assimilation outputs to estimate high-resolution (1km⨯1h) regional background fields for PM_2.5 and NO₂ across the metropolitan area. By integrating these background fields with CALPUFF simulation results, we simulated PM_2.5 and NO₂ variations at a 100-meter resolution, explicitly accounting for road traffic and localized emission characteristics.

The proposed GCN–CALPUFF hybrid approach overcomes the inherent limitations of single-model frameworks and provides a robust methodology for high-resolution air pollution prediction, with broad applications in urban air quality forecasting, high-resolution exposure and health impact assessments, and evidence-based policy monitoring.

Acknowledgments

"This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)“