Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China.
Environ Sci Pollut Res Int. 2024 Aug;31(39):51774-51789. doi: 10.1007/s11356-024-34656-1. Epub 2024 Aug 10.
In recent years, the concentrations of ozone and the pollution days with ozone as the primary pollutant have been increasing year by year. The sources of regional ozone mainly depend on local photochemical formation and transboundary transport. The latter is influenced by different weather circulations. How to effectively reduce the inter-regional emission to control ozone pollution under different atmospheric circulation is rarely reported. In this study, we classify the atmospheric circulation of ozone pollution days from 2014 to 2019 over Central China based on the Lamb-Jenkinson method and the global analysis data of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) operation. The effectiveness of emission control to alleviate ozone pollution under different atmospheric circulation is simulated by the WRF-Chem model. Among the 26 types of circulation patterns, 9 types of pollution days account for 79.5% of the total pollution days and further classified into 5 types. The local types (A and C type) are characterized by low surface wind speed and stable weather conditions over Central China due to a high-pressure system or a southwest vortex low-pressure system, blocking the diffusion of pollutants. Sensitivity simulations of A-type show that this heavy pollution process is mainly contributed by local emission sources. Removing the anthropogenic emission of pollutants over Central China would reduce the ozone concentration by 39.1%. The other three circulation patterns show pollution of transport characteristics affected by easterly, northerly, or southerly winds (N-EC, EC, S-EC-type). Under the EC-type, removing anthropogenic pollutants of East China would reduce the ozone concentration by 22.7% in Central China.
近年来，臭氧浓度和以臭氧为首要污染物的污染天数呈逐年上升趋势。区域臭氧的来源主要取决于本地光化学形成和跨境传输。后者受不同天气环流的影响。在不同大气环流条件下，如何有效地减少区域间排放以控制臭氧污染，这方面的研究很少。本研究基于 Lamb-Jenkinson 方法和欧洲中期天气预报中心（ECMWF）再分析（ERA5）全球分析数据，对 2014 年至 2019 年期间华中地区臭氧污染日的大气环流进行了分类。WRF-Chem 模式模拟了不同大气环流条件下的减排措施对缓解臭氧污染的有效性。在 26 种环流类型中，9 种污染日占总污染日的 79.5%，并进一步分为 5 种类型。本地类型（A 型和 C 型）的特征是由于高压系统或西南涡低压系统，地面风速较低，天气稳定，阻碍了污染物的扩散。对 A 型的敏感性模拟表明，这一重度污染过程主要是由本地排放源造成的。去除华中地区的人为污染物排放将使臭氧浓度降低 39.1%。其他三种环流模式表现出受东风、北风或南风（N-EC、EC、S-EC 型）影响的传输特征。在 EC 型下，去除华东地区的人为污染物将使华中地区的臭氧浓度降低 22.7%。
