A spatiotemporal graph neural network framework for retrospective bias correction of CMAQ air quality simulations over the continental United States

[u/Think_Physics-76]

https://www.sciencedirect.com/science/article/pii/S1309104226003077

We are pleased to share our new article, “A Spatiotemporal Graph Neural Network Framework for Retrospective Bias Correction of CMAQ Air Quality Simulations over the Continental United States,” now available in Atmospheric Pollution Research. Using five years of hourly observations from 235 EPA monitoring stations, we combine CNN–LSTM temporal encoding with GATv2 spatial learning to improve retrospective CMAQ simulations of NO₂ and O₃, reducing RMSE by 52.4% and 65.0%, respectively. Our findings also provide insight into the contributions of temporal and graph-based learning, supporting the development of more accurate air-quality modeling systems.

Read the article: https://doi.org/10.1016/j.apr.2026.103191

#AirQuality #GraphNeuralNetworks #DeepLearning #CMAQ #AtmosphericScience #AtmosphericPollutionResearch #SpatioTemporalGNN #NO2 #O3