Multisensor and Multimodel Monitoring and Investigation of a Wintertime Air Pollution Event Ahead of a Cold Front Over Eastern China
Xiao‐Ming Hu, Jun Hu, Lan Gao, Changjie Cai, Yujun Jiang, Ming Xue, Tianliang Zhao, Sean Crowell
Abstract
Abstract Given the limitations in pollutant measurements (e.g., coverage, observation errors) and air quality model uncertainties (e.g., with parameterizations and emissions), a multisensor and multimodel approach offers additional benefits compared to a single‐instrument and deterministic approach for monitoring, investigating, and predicting air pollution events. In this study, we use multisensors (including the spaceborne MODIS, OCO‐2, AIRS, and OMPS instruments as well as surface instruments) and multimodels (including WRF‐Chem and WRF‐CO 2 ) to investigate a severe air pollution event on December 9, 2016 over eastern China. During this episode, a strong cold front moved southward. At the leading edge of the front, WRF‐CO 2 simulates an enhanced XCO 2 belt while WRF‐Chem simulates a belt of high PM 2.5 concentration. The XCO 2 and PM 2.5 belts are generally colocated, due to coemission of CO 2 and pollutants (or their precursors). Satellite observations including MODIS AOD, OCO‐2 XCO 2 , OMPS NO 2 , AIRS CO, and surface data confirm the simulated pollution and XCO 2 belts. Later on, the front became distorted due to terrain blocking and mountain channel flows. Both observations and simulations show that the channel winds between Mountains Dabie and Huang transport the haze plume into Jiangxi province, enhancing pollution in the region. It is concluded that the multisensor (including space‐based and ground‐based instruments) and multimodel (e.g., WRF‐CO 2 , WRF‐Chem) approach can be used to collectively monitor and investigate air pollution events, given that emissions of the involved species have generally similar spatial distributions.