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Ultrasonic decomposition of sludge is a popular method for sludge treatment. Potassium permanganate (KMnO4) was added to promote the reduction of sludge and hydrolysis efficiency. The effect of ultrasonic treatment (ULT) combined with potassium permanganate (KMnO4) preprocessing on the disintegration of waste activated sludge (WS) was studied in this survey. The results indicated that the combined pretreatment of KMnO4 and ULT (150 W, 20 min) significantly improved the decomposition efficiency and nutrient release efficiency of sludge. The volatile suspended solids (VSS) were lessened by 15.22%, which was 56.8% larger than that of raw sludge. The soluble proteins, soluble chemical oxygen demand (SCOD), polysaccharides, and volatile fatty acids (VFAs) were improved by 2005.1%, 464%, 669.8%, and 719.9% respectively. The deliquescent organic matter in sludge products also effectively decreased, demonstrating the effective promoting effect of KMnO4 + ULT. Mechanistic studies showed that ULT united with KMnO4 pretreatment could improve the biodegradability of soluble organic matter by generating reactive radicals, effectively disrupt the structure of cell walls, lyse extracellular polymers, and accelerate the liberation of organic matter. The composite sludge decomposition process further mitigates the harm of sludge to the environment and provides a cleaner and more efficient sludge reduction and utilization method.

The temporal and spatial accuracy of precipitation of ensemble numerical forecast systems is an important factor that affects the level of meteorological and hydrological coupled forecasting. This article focuses on the current research of verification of precipitation accuracy and statistical post-processing. The verification of forecast precipitation accuracy mainly focuses on the probabilistic characteristics such deterministic accuracy, the resolution, the forecasting skills and the degree of dispersion. Some mainstream statistical post-processing methods have strong performance of spatial downscaling and error correction, but they commonly have the defect of destroying the temporal and spatial dependent structure of precipitation. A comprehensive statistical post-processing method integrated the three functions is the development direction in the future. At the same time, statistical post-processing methods to improve the certainty and probabilistic accuracy of forecast precipitation need to be systematically identified. Its impact on the spatio-temporal dependence structure also needs to be improved.