Objective Synoptic Weather Classification on Air Pollution during Winter Seasons in Hangzhou

Using the 2015-2018 Hangzhou city PM2.5,PM10,SO2,CO,NO2 and O3 mass concentration data,ERA5 reanalysis data and ground observation data,through the PCT classification method,the objective analysis of the winter air pollution weather situation in Hangzhou was obtained.The results showed that the winter air quality concentration in Hangzhou continued to be high from 2015 to 2018,and the air pollution was the most significant.Through objective classification,it is concluded that the main weather conditions affecting the region in winter are divided into 6 types,namely high pressure control,high pressure bottom control equalizing field,L-shaped high pressure control,high pressure front control equalizing field,low pressure control,low pressure front control Equalizing field.Among them,when high pressure control,high pressure bottom control equalizing field,L high pressure control,low pressure control are affected by local sources,the impact of external sources has a greater impact on the air quality in Hangzhou,and air pollution is prone to occur;before low pressure When the pressure equalization field is controlled by the Ministry and the pressure equalization field is controlled by the high pressure front,the local wind and precipitation in Hangzhou are relatively high,which is not conducive to the accumulation of air pollutants.The probability of occurrence of air pollution is small,and air pollution is not easy to occur.

Updating of the hierarchical rock mass rating(HRMR) system and a new subsystem developed for weathered granite formations

The RMR system is still very much applied in rock mechanics engineering context. It is based on the evaluation of six weights to obtain a final rating. To obtain the final rating a considerable amount of information is needed concerning the rock mass which can be difficult to obtain in some projects or project stages at least with accuracy. In 2007 an alternative classification scheme based on the RMR, the Hierarchical Rock Mass Rating(HRMR) was presented. The main feature of this system was the adaptation to the level of knowledge existent about the rock mass to obtain the classification of the rock mass since it followed a decision tree approach. However, the HRMR was only valid for hard rock granites with low fracturing degrees. In this work, the database was enlarged with approximately 40% more cases considering other types of granite rock masses including weathered granites and based on this increased database the system was updated. Granite formations existent in the north of Portugal including Porto city are predominantly granites. Some years ago a light rail infrastructure was built in the city of Porto and surrounding municipalities which involved considerable challenges due to the high heterogeneity levels of the granite formations and the difficulties involved in their geomechanical characterization. In this work it is intended to provide also a contribution to improve the characterization of these formations with special emphasis to the weathered horizons. A specific subsystem applicable to the weathered formations was developed. The results of the validation of these systems are presented and show acceptable performances in identifying the correct class using less information than with the RMR system.

Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.