Statistical Downscaling of PM2.5 and Gaseous Pollutants in East Asia Based on Graph Neural Network

Abstract

Traditional data assimilation based on numerical models has been utilized for risk assessment and served as a basis for policy decision-making and regulatory establishment. However, data assimilation is constrained by the resolution of the underlying numerical models, presenting limitations in producing high resolution. In this study, we propose a statistical downscaling method to generate 1 km concentration fields for East Asia using a Graph Convolutional Network (GCN) model. The study was conducted in two phases. In Phase 1, the initial concentration fields were derived using the Community Multiscale Air Quality (CMAQ) model, driven by WRF-simulated meteorology and SMOKE-based emission inventories, with further refinement via surface observation data assimilation. In Phase 2, the GCN model was developed to downscale from 27 km to 1 km resolution, using the reanalysis fields from Phase 1, land-use data from WPS, and emission data from EDGAR as input features. The GCN model used semi-supervised learning by masking 70% of surface monitoring stations to separate training and validation data. The model evaluation indicated that the RMSE was 1.28 μg/m³ for PM_2.5, 1.5 ppb for O₃, and 0.8 ppb for NO₂ in China. In the Korean Peninsula, the RMSE was 1.83 μg/m³ for PM_2.5, 2.0 ppb for O₃, and 1.3 ppb for NO₂. The proposed GCN-based statistical downscaling methodology is expected to produce high-quality, high-resolution data that can contribute to risk assessment and policy development.

Acknowledgment

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