Abstract. The spatial-temporal distributions and sources of sand and dust storm (SDS) in East Asia from 2001 to 2006 were investigated on the basis of visibility and PM 10 data from the routine SDS and weather monitoring networks run by CMA (China Meteorological Administration). A power functional relationships between PM 10 and visibility was found among various regions generally with a good correlation (r 2 =0.90), especially in Asian SDS source regions. In addition, three SDS occurrence centers, i.e. western China, Mongolia and northern China, were identified with the Mongolia source contributing more dust to the downwind areas including Korea and Japan than other two sources. Generally, high PM 10 concentrations were observed in most areas of northern China. The highest value was obtained in the center of western China with a spring daily mean value of 876 µgm −3 , and the value in other source regions exceeds 200 µgm −3 . These data sets together with the satellite observations in China form the main observation database for the evaluation and data assimilation of CUACE/Dust system -an operational SDS forecasting system for East Asia.
Abstract.A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment -Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under-and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The forecast results with DAS usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of forecast model.
Ichthyophthiriasis is a widespread disease in aquaculture and causes mass mortalities of fish. The development of new antiprotozoal agents for the treatment of Ichthyophthirius multifiliis infections is of increasing interest. The aim of the present study was to investigate the efficacy of 30 medicinal plants against I. multifiliis. The results showed that the methanol extracts of Magnolia officinalis and Sophora alopecuroides displayed the highest antiprotozoal activity against theronts, with 4-h LC(50) values estimated to be 2.45 and 3.43 mg L(-1), respectively. Concentrations of 2.5, 5.0, 10.0, and 20.0 mg L(-1) of M. officinalis extracts resulted in tomont mortality of 9.7, 43.7, 91.3, and 100% at 20 h, respectively. From 40 to 320 mg L(-1) of S. alopecuroides extracts, tomont mortality increased from 29.7 to 100%. Antiprotozoal efficacy against settled tomonts (2 and 10 h) was also applied; the results indicated that encysted I. multifiliis tomonts were less susceptible to these plant extract treatments. In vivo experiments demonstrated that high concentrations of M. officinalis and S. alopecuroides extracts could kill tomonts, and M. officinalis significantly reduced its reproduction (P < 0.05). These results suggested that the methanol extracts of M. officinalis and S. alopecuroides have the potential to be used as an eco-friendly approach for the control of I. multifiliis.
Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility and dust loading retrieval from the Chinese geostationary satellite FY-2C. The results show that a major improvement to the capability of CUACE/Dust in forecasting the short-term variability in the spatial distribution and intensity of dust concentrations has been achieved, especially in those areas far from the source regions. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The assimilation results usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful for the unification of observation and numerical modeling results.
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