Understanding spatiotemporal shifts in vegetation and their climatic and anthropogenic regulatory factors can offer a crucial theoretical basis for environmental conservation and restoration. In this article, the normalized difference vegetation index (NDVI) of the Miaoling area from 2000 to 2020 is studied using a trend analysis and the Mann–Kendall mutation test (MK test) to review the vegetation’s dynamic changes. Our study uses the Hurst index, a partial correlation analysis, and a geographic detector to investigate the contributions of climate change and human activities to regional vegetation changes and their drivers. We found that Miaoling’s annual average NDVI was between 0.66 and 0.83 in 2000–2020, with a mean of 0.766. The overall trend was slow upward (0.0009/year), and 53.82% of the region continued to grow and gradually increased from west to east in the spatial domain, among which the karst regional NDVI distribution area and its growth rate were higher than those of non-karst sites. Based on correlations between climatic factors and NDVI, precipitation seasonality (coefficient of variation, CV) had the strongest correlation (positive correlation) with NDVI, while vapor pressure deficit (VPD) had a negative correlation with NDVI. In the interaction, human activities played a dominant role in the influence of NDVI on the vegetation of Miaoling. The night light index had the most explanatory power on the NDVI (q = 0.422), and the interaction between anthropogenic factors and other factors dominated its explanatory power. This study has academic and practical importance for the management, protection, and sustainable development of karst basins.

To enhance the permeability, hydrophilicity, and antifouling properties of poly(vinyl chloride) (PVC) membranes, in this study, poly(ether glycol) methyl ether methacrylate (PEGMA) was grafted onto poly(ether sulfone) (PES) to first synthesize PES‐g‐PEGMA. Then, PES‐g‐PEGMA was blended with PVC (with blend ratios of PVC/PES‐g‐PEGMA that varied from 10 : 0 to 7 : 3) through the non‐solvent‐induced phase separation method to fabricate PVC/PES‐g‐PEGMA blend membranes. Shear viscosity and Fourier transform infrared–attenuated total reflection experiments showed that PVC/PES‐g‐PEGMA was a partially compatible system and that it was more compatible than a PVC/PES blending system. The contact angle, equilibrium water content, and X‐ray photoelectron spectroscopy measurements confirmed that the PVC/PES‐g‐PEGMA blend membranes had a stronger hydrophilicity than the pure PVC membranes. Additionally, the results of this experiment also show that the PVC/PES‐g‐PEGMA blend membranes exhibited a higher permeability and superior antifouling properties than the pure PVC membranes, and the optimum blend ratio of the PVC/PES‐g‐PEGMA blend membranes was 7 : 3. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41726.

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In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.

The fragile karst habitat is extremely sensitive to human activities such as large-scale engineering construction. To explore the influence of the construction and operation of the GH (Guiyang-Huangguoshu) highway on the vegetation within a certain range and the response of NDVI to climate factors, Landsat data were used to synthesize annual NDVI maps using the maximum value compositing method. Trend, correlation, and coefficient of variation analyses were performed. The results demonstrate that: (1) During the construction and operation periods, NDVI showed an overall upward trend, and the NDVI value and growth rate in the contrast area were greater than those in the core area; (2) the correlation between temperature and vegetation cover along the GH highway was stronger than that between precipitation and vegetation; (3) construction of the GH highway has had a significant impact on the surrounding vegetation, with the impact on vegetation ecology along the road mainly concentrated within the 2 km range. The increase of artificial surfaces along the road has had a great impact on the NDVI, and the vegetation cover change in the core area is more significant than that in the contrast area; and (4) the overall disturbance of the GH highway project to the surrounding ecology was mainly observed in the form of low and medium fluctuations. This study aims to provide a reference for environmental assessment and management in karst areas.