Ozone Sensitivity in the Yangtze River Delta Region: A Reanalysis With Enhanced Ozone Forecasts via Multi‐Source Data Assimilation
Year: 2025
Type: article
Abstract: Distinguishing ozone‐sensitive areas is crucial for controlling ozone pollution, and model simulations play a key role in distinguishing ozone sensitive areas. WRF‐Chem is one of the state‐of‐the‐art air quality models currently available, but challenges still exist in its simulation of ozone pollution, which are partly due to its strong dependence on the accuracy of initial conditions, boundary conditions, and emissions. To improve the accuracy of ozone predictions, this study assimilated ground‐based monitoring and satellite data to improve initial and boundary conditions during a typical ozone pollution episode in YRD region. As a result, compared with station observations, the R and RMSE of model simulation are greatly improved. Additionally, the emission inventory during this event was inverted and then used to drive the model to re‐simulate this event, and the simulation results are improved. Furthermore, based on the new emissions, we conducted three emission reduction scenario simulations to distinguish ozone sensitivity region. Ozone formation was in a VOC‐limited regime in south Jiangsu during the study period. For north Zhejiang, transitional and NOx‐limited regimes occurred alternately. It is worth noting that the ozone sensitivity regimes derived from the original emission inventory were not consistent with those obtained from the improved emission inventory. A comparison with satellite‐derived OMI HCHO/NO 2 data confirmed that the conclusions based on the updated emission inventory were more consistent. This study highlights that relying on current emission data for policy formulation may result in strategic errors.
DOI: 10.1029/2024jd042958
Authors: Tian Han, Jing Zhang, Yunfei Che, Xiaomin Hu, Xiaoqing Deng, Yuqing Liu, Yifan Wang, Yiwen Lang
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: bronze
Award: G8054155013
OpenAlex ID: W4411375978
