A Geographically Weighted Gaussian Process Regression Emulator of the GCHP 13.0.0 Global Air Quality Model

作者：Anthony Y. H. Wong, Sebastian D. Eastham, Erwan Monier, Noelle E. Selin · 年份：2025 · DOI：10.5194/egusphere-2025-2663 · 被引用次数：1 · 研究领域：Air Quality Monitoring and Forecasting、Vehicle emissions and performance、Air Quality and Health Impacts

Abstract. Air quality modelling has been an essential tool to study the impacts of socio-economic changes and policies on air quality and associated social costs due to human health impacts. However, high computational and human resource demands limit the use of state-of-the-art air quality models outside of the atmospheric science community. We address this limitation by training Geographically Weighted Gaussian Process Regressors (GW-GPR) on the outputs of a series of perturbation experiments from the high-fidelity GEOS-Chem High Performance global chemical transport model (GCHP 13.0.0). The Gaussian Process Regressor relates changes in annual mean surface anthropogenic PM2.5 to changes in short-lived air pollutant emissions and atmospheric CH4 and CO2 levels for each GCHP model grid cell. In comparison to existing widely adopted linearized and regionalized approaches, our method can account for sub-regional changes in air pollutant emission patterns and incorporates the non-linear response of secondary air pollutants to precursor and greenhouse gas emissions. We evaluate and demonstrate the utility of our model by predicting the global distribution of PM2.5 in 2050 (relative to 2014) under 4 sets of climate and air pollution control policy scenarios. The emulator reproduces grid cell-level changes in anthropogenic PM2.5 (R2 = 0.94 – 0.99 over the 4 scenarios tested), and associated global changes in premature mortalities at 95 % confidence level, while requiring < 10 se...