No abstract

Coal mine safety is crucial to the healthy and sustainable development of the coal industry, and coal mine flood is a major hidden danger of coal mine accidents. Therefore, the processing of coal mine water source data is of great significance to prevent mine water inrush accidents. In this experiment, the laser induced fluorescence technology was used to obtain the data information of 7 water sources with the assistance of laser. The laser emission power was set to 100 mw, and the 405 nm laser was emitted to the measured water body to obtain 210 groups of fluorescence spectral data of experimental water samples. The standard normal variable transformation (SNV) and multiple scattering correction (MSC) of the pretreatment algorithm are used to denoise the data and improve the spectral specificity. Due to the excessive calculation of the initial data, principal component analysis (PCA) was used to model and reduce the dimension of seven water samples, so as to obtain small data and maintain the data characteristics of the original information. In order to identify the water inrush type of coal mine water source, the sparrow search algorithm (SSA) is used to optimize the BP neural network in this study. This is because the SSA algorithm has the advantages of strong optimization ability and fast convergence rate compared with particle swarm optimization(PSO) and other optimization algorithms. Experiments show that under the premise of SNV pretreatment, the R 2 of SSA-BP model is infinitely close to 1, MRE is 0.0017, RMSE is 0.0001, the R 2 of PSO-BP model is 0.9995, MRE is 0.0026, RMSE is 0.0019, the R 2 of BP model is 0.9983, MRE is 0.0140, RMSE is 0.0075. Therefore, SSA-BP model is more suitable for the classification of coal mine water sources.

In order to promote sustainable economic development in the areas along the Belt and Road in China, it is of great necessity to reduce the negative impact of air pollutants resulting from industrialization and urbanization on the complex and fragile ecological environments of neighboring areas. First, this study estimated the total-factor air environmental efficiency (TFAEE) of 17 provinces along the Belt and Road in China from 2010 to 2017 using a slacks-based measure (SBM) model. Second, the global and local Moran indices were used to test the spatial correlations between TFAEEs. Finally, the spatial factors and spatial spillover effects influencing the TFAEEs were investigated using the spatial Durbin model with spatiotemporal double fixed effects. The results were shown as follows: (1) The total-factor TFAEEs of the areas along the Belt and Road were low and showed significant regional spatial differences during 2010–2017. (2) There was a positive spatial autocorrelation between the TFAEEs of the areas along the Belt and Road, and the spatial distribution generally clustered into High-High and Low-Low concentrations. (3) Economic development and technological innovation played significantly positive effects on TFAEEs of the areas in the Belt and Road, while energy consumption structure had negative effect on it. In addition, although industrial structure and environmental regulation were negatively correlated with TFAEEs, the coefficients were not significant. (4) The positive spatial spillover effect of the TFAEEs of the areas along the Belt and Road was mainly the result of significant environmental regulations and insignificant economic development factors, while the technological innovations, energy consumption structures and industrial structures showed insignificant negative spatial spillover effects.

Based on DEA theory and window analysis method, this paper empirically measures the ecological efficiency of six resource exhausted cities in Jilin old industrial base from 2012 to 2017, and investigates their regional differences and dynamic evolution characteristics. The results show that: in the sample period, the overall ecological efficiency of Jilin old industrial base is low, but fluctuates slightly; the difference between different urban areas is obvious, while the industrial structure and low level of science and technology inhibit the improvement of ecological efficiency.

Based on SBM-Undesirable model, this paper evaluated air environmental efficiency of 8 provinces of the Silk Road Economic Belt of China during the period of 2010—2017, and investigated the temporal-variation and energy conservation and emission reduction potential. Empirical analysis shows that the overall atmospheric environmental efficiency of Silk Road Economic Belt is still at low point but presents a mild rising trend over the period 2010—2017, regional differences are remarkable, and it has great potential of energy conservation and emission reduction in the future.