Effectiveness of inter-regional collaborative emission reduction for ozone mitigation under local-dominated and transport-affected synoptic patterns.

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.

Machine-learning-based corrections of CMIP6 historical surface ozone in China during 1950-2014.

Due to a lack of long-term observations in China, reports on historical ozone concentration are severely limited. In this study, by combining observation, reanalysis and model simulation data, XGBoost machine learning algorithm is used to correct the surface ozone concentration from CMIP6 climate model, and the long-term and large-scale surface ozone concentration of China during 1950-2014 is obtained. The long-term evolutions and trends of ozone and meteorological effects on interannual ozone variations are further analyzed. The results reveal that CMIP6 historical simulations have a large underestimation in ozone concentrations and their trends. The XGB-derived ozone are closer to observations, with R2 value of 0.66 and 0.74 for daily and monthly retrievals, respectively. Both the concentrations and exceedances of ozone in most parts of China have shown increasing trends from 1950 to 2014. The daily mean ozone concentration without climate change effects is estimated to be 117 ppb in the year 1950 averaged over China. It indicates that the increase in anthropogenic emissions of China has a significant contribution to ozone enhancement between 1950 and 2014. The higher ozone growth rates of XGB retrievals than those from the model indicate a regional surface ozone penalty due to the warming climate. The relatively significant increment in ozone are estimated in the Central and Western China. Seasonally, the ozone enhancement is largest in spring, indicating a shift in seasonal variation of ozone. Given the uncertainty in simulating historical ozone by climate model, we show that machine learning approaches can provide improved assessment of evolution in surface ozone, along with valuable information to guide future model development and formulate future ozone pollution prevention and control policies.