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For the purpose of providing reliable and robust air quality predictions, an operational air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3, and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multi-scale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May-12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate, nitrate, ammonium, organic aerosols (OAs), and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new operational air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors, and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (IOA = 0.60; MB = -13.54) and PM2.5 (IOA = 0.71; MB = -2.43) as compared to the BASE RUN for PM10 (IOA = 0.51; MB = -27.18) and PM2.5 (IOA = 0.67; MB = -9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of -0.27 (BASE RUN) was greatly enhanced to -0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the operational air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3, and PANs in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLH) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free parameter scheme, dust episode prediction, and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the three-dimensional variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean operational air quality forecasting system. [ABSTRACT FROM AUTHOR]

Phellinus baumii is a mushroom used as a folk medicine for a variety of human diseases in several Asian countries. Recently we have reported for the first time about the antidiabetic effect of the crude exopolysaccharides (EPS) produced from submerged mycelial culture of P. baumii in streptozotocin (STZ)-induced diabetic rats. The diabetic rats study revealed that orally administrated P. baumii EPS lowered the blood glucose levels and stimulated insulin excretion in diabetic rats, and consequently restored the functions of pancreas, liver, and kidney, suggesting that the EPS might be useful for the management of human diabetes mellitus. We undertook proteomic analyses for plasma, pancreas, liver, and kidney of the rats to search for novel biomarkers for monitoring diabetes before and after EPS treatments. In this article, we describe the production of EPS in submerged culture of P. baumii and studies of their hypoglycemic activity. We also explore the issue of proteomic analyses for mining biomarkers of diabetes. [ABSTRACT FROM AUTHOR]