Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep...

Atmospheric chemical transport models (CTMs) have been widely used to simulate spatiotemporally resolved PM_2.5 concentrations. However, CTM results are usually prone to bias and errors. In this study, we improved the accuracy of PM_2.5 predictions by developing an ensemble deep learning framework to fuse model simulations with ground-level observations. The framework encompasses four machine-learning models, i.e., general linear model, fully connected neural network, random forest, and gradient boosting machine, and combines them by stacking approach. This framework is applied to PM_2.5 concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for China from 2014 to 2017, which has complete spatial coverage over the entirety of China at a 12-km resolution, with no sampling biases. The fused PM_2.5 concentration fields were evaluated by comparing with an independent network of observations. The R² values increased from 0.39 to 0.64, and the RMSE values decreased from 33.7 μg/m³ to 24.8 μg/m³. According to the fused data, the percentage of Chinese population residing under the level II National Ambient Air Quality Standards of 35 μg/m³ for PM_2.5 has increased from 46.5% in 2014 to 61.7% in 2017. The method is readily adapted to utilize near-real-time observations for operational analyses and forecasting of pollutant concentrations and can be extended to provide source apportionment forecasts as well.