There is often obvious particle breakage for silica sand under high-stress, which will lead to the bearing capacity reduction and excessive settlement of the foundation. This paper focuses on the particle breakage characteristics of marine silica sand from the East China Sea under high-stress conditions. A series of conventional triaxial tests for silica sand, including consolidated drained (CD) and consolidated undrained (CU) shear tests, were conducted under the confining pressures in the range of 2–8 MPa to investigate the breakage rule during the shearing process. The developments of particle breakage index Br with axial strain ε1 and volumetric strain εv present hyperbolic and linear trends, respectively. A hyperbolic model was adopted to describe the relationship of Br and ε1 and the corresponding model parameters were obtained. The particle breakage index also has a good correlation with the input work per unit volume under various average stresses, regardless of the stress history. Furthermore, the relationship between the fractal dimension and the particle breakage was studied based on the particle size distribution curve. It is concluded that the fractal dimension increases in an up–convex hyperbolic trend with the increase of particle breakage index. The dividing radius for whether the silica sand particles exhibit the fractal features is determined as approximately 0.4 mm. This is anticipated to provide reference and supplementary test data for analyzing sand constitutive models/environments regarding particle crushing.
Enhancing the willingness to pay (WTP) for earthquake insurance for rural houses (EIRH) and transferring earthquake risk is extremely important for rural economic growth, especially in the Xinjiang Uygur Autonomous Region (Xinjiang), where earthquake disasters frequently occur. However, there is a gap between the WTP found in EIRH research and the actual demand present in this field. In this paper, we collect 400 questionnaires from among farmers in Xinjiang and conducted one-on-one empirical interviews with four government managers. Via descriptive statistics, the control variable method, and binary regression analysis, we examine the effects of farmers' basic information, risk perception, and risk transfer on WTP. The results reveal that (1) the WTP of farmers is positively correlated with their education level, and the WTP of farmers with poor disaster resistance (aged 40-49 or incomes below 30,000 RMB) is higher. (2) Among earthquake risk perceptions, awareness is correlated positively with WTP, worry and experience are negatively correlated with WTP, and likelihood and controllability are irrelevant with regard to WTP. (3) Farmers recognize EIRH as a risk transfer mechanism, but they are more dependent on government assistance. (4) The increase in WTP is more obvious when the proportion of government subsidies is in the range of either 30-50% or 70-90%. Thus, we suggest that Xinjiang promotes EIRH by adjusting the proportion of subsidies and strengthening the popularization of disaster risk knowledge, which is of great practical significance to promoting the stable development of the rural economy in Xinjiang.
Abstract Qinghai Province is located in the Qinghai-Tibet Plateau region, with complex and diverse topography and sparse precipitation stations, which makes it difficult to obtain reliable precipitation data. This study proposes a classification and regression model based on a deep learning algorithm, which combines a convolutional neural network (CNN) and a long short-term memory neural network (LSTM), with the CNN extracting the spatial features of multi-source data, the LSTM capturing their temporal dependencies. The regression results are used to determine whether rainfall is occurring and to further calibrate the non-rainfall component of the precipitation forecast results. ERA5, IMERG, CHIRPS and DEM were selected as feature data and rain gauge data as label data. The findings indicate that the proposed CNN-LSTM classification regression model (CLCR) is superior to other models (CNN, CNN-LSTM, LSTM). The Kling-Gupta efficiency (KGE) of the data fused using CLCR was 0.66, which was significantly better than that of the raw rainfall data (0.53, -0.36, 0.34) and other models (0.58, 0.65, 0.63). CLCR also showed more performance in daily precipitation detection than other models and raw precipitation data, with Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR) 0.61, 0.25 and 0.76 respectively. This study generated a high-precision daily rainfall dataset with a precision of 0.01° resolution for 2013-2017 in Qinghai Province, which provides reliable data support for hydrological studies in Qinghai Province.
A systematic study of the movement of PAHs (Polycyclic aromatic hydrocarbons) and their derivatives through air, soil, and water is key to understanding the exchange and transport mechanisms of these pollutants in the environment and for ultimately improving environmental quality. PAHs and their derivatives, such as nitrated PAHs (NPAHs), oxygenated PAHs (OPAHs), brominated PAHs (BrPAHs) and chlorinated PAHs (ClPAHs), were analyzed in air, bulk deposition, soil, and water samples collected from urban, rural, field, and background sites on the eastern coast of China. The goal was to investigate and discuss their spatiotemporal variations, exchange fluxes, and transport potential. The concentrations of PAHs and their derivatives in the air and bulk deposition displayed distinct seasonal patterns, with higher concentrations observed during the winter and spring and lower concentrations during the summer and autumn. NPAHs exhibited the opposite trend. Significant urban-rural gradients were observed for most of the PAHs and their derivatives. According to the air-soil fugacity calculations, 2-3 ring PAHs, BrPAHs, and ClPAHs were found to volatilize from the soil into the air, while 4-7 ring PAHs, OPAHs, and NPAHs deposited from the air into the soil. The air-water fugacity of the PAHs and their derivatives indicated that surface water was an important source for the ambient atmosphere in Qingdao. The characteristic travel distances (CTDs) and persistence (Pov) for atmospheric transport were much lower than that for the water samples, which may be due to the longer half-lives of PAHs and their derivatives in water. NPAHs and ClPAHs with long transport distances and strong persistence in water could lead to a significant impact on marine pollution.
Since Qinghai is located in the high-altitude Qinghai-Tibet Plateau region, the geomorphological types are complex and diverse, and the distribution of ground precipitation observation stations is sparse, improving the accuracy of precipitation data is critical for studying regional ecological change over time. In the paper, we study and construct a multi-source precipitation data fusion model based on neural networks, which consists of back propagation neural network (BPNN) and long short-term memory network (LSTM). The global precipitation measurement (GPM), fifth generation ECMWF atmospheric reanalysis (ERA5), digital elevation model (DEM), and normalized difference vegetation index (NDVI) data are selected as feature data and ground observation station data as label data for model training. The results show that the fused data generated by the BP-LSTM model reduces the root mean square error to 2.48mm and the overall relative bias to 0.25% compared with the original GPM, which is better than ERA5 on data accuracy. The precipitation event capture capability is improved, which is very close to the ERA5 data with strong precipitation event capture capability, and the probability of detection, false alarm rate, and missing event rate are 0.95, 0.53, and 0.04 respectively. Finally, the regional precipitation data is generated by the fusion model with resolution of 0.01°, 1h. The model proposed in the paper incorporates topographic factors and seasonal characteristics to solve the temporal and spatial correlation of precipitation data in Qinghai Province improve the accuracy of precipitation data, and provide reliable data support for the study of regional hydro-ecological spatial and temporal variation patterns.
On-road vehicle emissions have become the main source of urban air pollution and attracted broad attentions. Vehicle emission factor is a basic parameter to reflect the status of vehicle emissions, but the measured emission factor is difficult to obtain, and the simulated emission factor is not localized in China. Based on the synchronized increments of traffic flow and concentration of air pollutants in the morning rush hour period, while meteorological condition and background air pollution concentration retain relatively stable, the relationship between the increase of traffic and the increase of air pollution concentration close to a road is established. Infinite line source Gaussian dispersion model was transformed for the inversion of average vehicle emission factors. A case study was conducted on a main road in Beijing. Traffic flow, meteorological data and carbon monoxide (CO) concentration were collected to estimate average vehicle emission factors of CO. The results were compared with simulated emission factors of COPERT4 model. Results showed that the average emission factors estimated by the proposed approach and COPERT4 in August were 2.0 g x km(-1) and 1.2 g x km(-1), respectively, and in December were 5.5 g x km(-1) and 5.2 g x km(-1), respectively. The emission factors from the proposed approach and COPERT4 showed close values and similar seasonal trends. The proposed method for average emission factor estimation eliminates the disturbance of background concentrations and potentially provides real-time access to vehicle fleet emission factors.
In recent years, despite the fact that the Chinese government is closely monitoring food safety, the perception of food production enterprises is not obvious. The reason is that information asymmetry hinders the effective transmission of regulatory information to food production enterprises. In the present research, a choice test is conducted to explore the preference of decision-makers for the information on government regulations in 224 food production enterprises with violations. It is found out that the decision-makers of food production enterprises have a strong preference for the regulatory information released by local governments. With a preference for reference information, compared to those who violate the law just once, decision-makers in food production companies that have several infractions exhibit a high "reference dependence" mentality. Also, the preference of different decision-maker characteristics shows an evident heterogeneity, as does the preference of various enterprises for the regulatory information about food safety. It is recommended that the government should improve the mechanism of disclosing the information about food safety, and focus on tailoring the information to different types of enterprises.
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