On the basis of three mountain sites (Mount Tai, Hua and Huang) newly founded in east‐central China and several other sites from the Acid Deposition Monitoring Network in east Asia (EANET) and WMO World Data Centre for Greenhouse Gases (WDCGG), we investigate seasonal cycle of ozone over east Asia and its budgets in east‐central China by using a regional chemical transport model (NAQPMS). The observations show a striking ozone pattern of two sharp peaks in May‐June and September–October at three mountain sites in east‐central China which are higher than those observed at other mountain sites in Europe and North America. Ozone budgets analysis by the model confirms that maximum of net photochemical productions reaches 31.8, 15.1, and 11.4 ppbv/d at Mount Tai, Hua, and Huang, respectively. The net photochemical production dominates the formation of ozone maximums at Mount Tai and Hua in June, and the importing transport also plays a comparable importance at Mount Huang. In comparison with those in the western North Pacific, east‐central China shows stronger net photochemical productions, which are comparable to anthropogenic sources regions in Europe and North America.

Abstract. An online air pollutant tagged module has been developed in the Nested Air Quality Prediction Model System (NAQPMS) to investigate the impact of local and regional sources on the air pollutants in Beijing during the Campaign of Air Quality Research in Beijing 2006(CAREBeijing-2006). The NAQPMS model shows high performance in simulating sulfur dioxide (SO 2 ), particulate matter (PM 10 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) with overall better agreements with the observations at urban sites than rural areas. With the tagged module, the air pollutant contributions from local and regional sources to the surface layer (about 30 m) and the upper layer (about 1.1 km) in Beijing are differentiated and estimated. The air pollutants at the surface layer in Beijing are dominated by the contributions from local sources, accounting for 65 % of SO 2 , 75 % of PM 10 and nearly 90 % of NO 2 , respectively, comparatively, the 1.1 km layer has large source contributions from the surrounding regions (e.g., southern Beijing), accounting for more than 50 % of the SO 2 and PM 10 concentrations. County scale analysis is also performed and the results suggest that Tianjin is the dominant source of SO 2 in Pinggu County, and Langfang, Hebei is the most important regional contributor to PM 10 in Beijing. Moreover, the surrounding regions show larger impact on SO 2 , PM 10 and NO 2 in the eastern counties of Beijing (e.g., Pinggu, Tongzhou and Daxing) than those in western Beijing, which is likely due to the Beijing's semi-basin topography and the summer monsoon. Our results indicate that the efforts to control the air pollutants in Beijing should focus on controlling both local and regional emissions.

Abstract. Mineral dust particles play an important role in the Earth system, imposing a variety of effects on air quality, climate, human health, and economy. Accurate forecasts of dust events are highly desirable to provide an early warning and inform the decision-making process. East Asia is one of the largest dust sources in the world. This study applies and evaluates four widely used regional air quality models to simulate dust storms in northeastern China. Three dust schemes in the Weather Research and Forecasting model with Chemistry (WRF-Chem) (version 3.9.1), two schemes in both CHIMERE (version 2017r4) and CMAQ (version 5.2.1), and one scheme in CAMx (version 6.50) were applied to a dust event during 4–6 May 2015 in northeastern China. Most of these models were able to capture this dust event with the exception of CAMx, which has no dust source map covering the study area; hence, another dust source mask map was introduced to replace the default one for the subsequent simulation. Although these models reproduced the spatial pattern of the dust plume, there were large discrepancies between predicted and observed PM10 concentrations in each model. In general, CHIMERE had relatively better performance among all simulations with default configurations. After parameter tuning, WRF-Chem with the Air Force Weather Agency (AFWA) scheme using a seasonal dust source map from Ginoux et al. (2012) showed the best performance, followed by WRF-Chem with the UOC_Shao2004 scheme, CHIMERE, and CMAQ. The performance of CAMx had significantly improved by substituting the default dust map and removing the friction velocity limitation. This study suggested that the dust source maps should be carefully selected on a regional scale or replaced with a new one constructed with local data. Moreover, further study and measurement of sandblasting efficiency of different soil types and locations should be conducted to improve the accuracy of estimated vertical dust fluxes in air quality models.

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The plasmasphere is located above the ionosphere with low-energy plasma, which is an important component of the solar-terrestrial space environment. As the link between the ionosphere and the magnetosphere, the plasmasphere plays an important role in the coupling process. Therefore, it is of great significance to study the electron content variation of the plasmasphere for the solar-terrestrial space environment. Nowadays, the topside global positioning system (GPS) observations on Low Earth Orbit (LEO) satellites provide a unique opportunity to estimate and study variations in the plasmasphere. In this paper, the plasmaspheric total electron content (PTEC) is estimated, and its long-term variations are studied from topside GPS observations onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC). The PTEC in the daytime is higher than that in the nighttime, with the peak between 14:00 and 17:00 in the magnetic local time, while the minimum value of PTEC in the belt appears between 3:00 and 6:00 in the magnetic local time before sunrise. For seasonal variations, the PTEC is the highest in spring of the northern hemisphere and the lowest in summer of the northern hemisphere regardless of the state of the solar activity. The long-term variation in PTEC is further analyzed using 11-year COSMIC GPS observation data from 2007 to 2017. A high correlation between PTEC and the F10.7 indices is found. Particularly in the geomagnetic high-latitude region during the daytime, the correlation coefficient reaches 0.93. The worst case occurs during the nighttime in the geomagnetic middle-latitude region, but the correlation coefficient is still higher than 0.88. The long-term variations of plasmaspheric TEC are mainly related to the solar activity.