Work

Inversion of a Near‐Real‐Time China Gridded Hourly SO 2 Emission Inventory Using Deep Learning Combined With 3D‐Variational Assimilation

Year: 2025

Type: article

Abstract: Accurate emission inventories are essential for effective air quality prediction and management. Traditional bottom‐up methods and top‐down data assimilation approaches struggle to balance accuracy, timeliness, and usability. This study presents a novel inversion method combining deep learning (DL) and 3D‐Variational (3DVar) assimilation, termed DL‐3DVar. Based on the prior emissions and SO 2 concentration observations, this method was applied to invert China's grided hourly SO 2 emission inventory during the 2023 Chinese New Year (CNY). The results show that DL‐3DVar estimated an hourly SO 2 emission increment of 1.95 × 10 6 kg at midnight on CNY's Eve relative to the prior emissions, primarily due to extensive fireworks. Forecast experiments showed that using DL‐3DVar inverted emissions reduced the root‐mean‐square error (RMSE) by 52.9% and increased the correlation coefficient (CORR) by 331% compared to the prior emissions. This high‐temporal‐resolution emission inventory can improve pollutant forecasting and provide robust support for air pollution control policymaking.

Source: Geophysical Research Letters

DOI: 10.1029/2024gl114375