Having a better understanding of air pollutants in railway systems is crucial to ensure a clean public transport. This study measured, for the first time in Brazil, nanoparticles (NPs) and black carbon (BC) on two ground-level platforms and inside trains of the Metropolitan Area of Porto Alegre (MAPA). An intense sampling campaign during thirteen consecutive months was carried out and the chemical composition of NPs was examined by advanced microscopy techniques. The results showed that highest concentrations of the pollutants occur in colder seasons and influenced by variables such as frequency of the trains and passenger densities. Also, internal and external sources of pollution at the stations were identified. The predominance of NPs enriched with metals that increase oxidative stress like Cd, Fe, Pb, Cr, Zn, Ni, V, Hg, Sn, and Ba both on the platforms and inside trains, including Fe-minerals as hematite and magnetite, represents a critical risk to the health of passengers and employees of the system. This interdisciplinary and multi-analytical study aims to provide an improved understanding of reported adverse health effects induced by railway system aerosols. 相似文献
Hydraulic fracturing is an essential technology for the development of unconventional resources such as tight gas. The evaluation of the fracture performance and productivity is important for the design of fracturing operations. However, the traditional dimensionless fracture conductivity is too simple to be applied in real fracturing operations. In this work, we proposed a new model of dimensionless fracture conductivity (FCD), which considers the irregular fracture geometry, proppant position and concentration. It was based on the numerical study of the multistage hydraulic fracturing and production in a tight gas horizontal well of the North German Basin. A self-developed full 3D hydraulic fracturing model, FLAC3Dplus, was combined with a sensitive/reliability analysis and robust design optimization tool optiSLang and reservoir simulator TMVOCMP to achieve an automatic history matching as well as simulation of the gas production. With this tool chain, the four fracturing stages were history matched. The simulation results show that all four fractures have different geometry and proppant distribution, which is mainly due to different stress states and injection schedule. The position and concentration of the proppant play important roles for the later production, which is not considered in the traditional dimensionless fracture conductivity FCD. In comparison, the newly proposed formulation of FCD could predict the productivity more accurately and is better for the posttreatment evaluation.
Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy. 相似文献
Fine characterization of pore systems and heterogeneity of shale reservoirs are significant contents of shale gas reservoir physical property research.The research on micro-control factors of low productivity in the Qiongzhusi Formation(Fm.)is still controversial.The lower Cambrian Qiongzhusi Fm.in the Qujing,Yunnan was taken as the object to investigate the influence of mineral compositions on the phys-ical properties of the reservoir and the heterogeneity of shale,using the algorithm to improve the char-acterization ability of Atomic Force Microscopy(AFM).The results showed that:(1)The pores are mainly wedge-shaped pores and V-shaped pores.The pore diameter of the main pore segment ranges from 5 to 10 nm.Mesopores are mainly developed in the Qiongzhusi Fm.shale in Well QD1,with the average pore diameter of 6.08 nm.(2)Microscopic pore structure and shale surface properties show strong hetero-geneity,which complicates the micro-migration of shale gas and increases the difficulty of identifying high-quality reservoirs.(3)The increase of clay mineral content intensifies the compaction and then destroys the pores.Conversely,brittle minerals can protect pores.The support and protection of brittle minerals to pores space depend on their content,mechanical properties and diagenesis.(4)Compression damage to pores,large microscopic roughness and surface fluctuations and strong pore structure heterogeneity are the reasons for the poor gas storage capacity of the Qiongzhusi Fm.,which will lead to poor productivity in the Qiongzhusi Fm. 相似文献