The coupling and coordination development of the environment and economy (CC2E) is one of the most vital issues to sustainable development. This paper adopted the coupling coordination model, projection pursuit algorithm, and random forest model to explore the spatial-temporal evolution and influencing factors of the CC2E in the Yangtze River Delta from 2015 to 2019, respectively. The results showed that: (1) The degree of coupling coordination (DCC) of the CC2E in most cities of the Yangtze River Delta has risen from primary coordination to intermediate coordination. (2) In the spatial perspective, the distribution of DCC is correlated with geographical location. The value of DCC in the western region was significantly lower than that of the eastern cities. (3) The influencing factors results showed that the GDP in the economic subsystem and the annual average concentration of PM2.5 in the environmental subsystem were the most influencing factors of DCC in the Yangtze River Delta. The established index system of CC2E and the measurements of CC2E provide a new idea for how to achieve sustainable development. Meanwhile, this study can provide recommendations for formulating the environmental protection and economic development policy.

Unmanned aerial vehicle (UAV) experiments, multiple datasets from ground-based stations and satellite remote sensing platforms, and backward trajectory models were combined to investigate the characteristics and influential mechanisms of the air pollution episode that occurred in Nanjing during 3–4 December 2017. Before the experiments, the position of the detector mounted on a UAV that was minimally disturbed by the rotation of the rotors was analyzed based on computational fluid dynamics (CFD) simulations. The combined analysis indicated that the surface meteorological conditions—high relative humidity, low wind speed, and low temperature—were conducive to the accumulation of PM_2.5. Strongly intense temperature inversion layers and the low thickness of the atmospheric mixed layer could have resulted in elevated PM_2.5 mass concentrations. In the early stage, air pollution was affected by the synoptic circulation of the homogenous pressure field and low wind speeds, and the pollutants mainly originated from emissions from surrounding areas. The aggravated pollution was mainly attributed to the cold front and strong northwesterly winds above 850 hPa, and the pollutants mostly originated from the long-distance transport of emissions with northwesterly winds, mainly from the Beijing‒Tianjin‒Hebei (BTH) region and its surrounding areas. This long-distance transport predominated during this event. The air pollution level and aerosol optical depth (AOD) were positively correlated with respect to their spatial distributions; they could reflect shifts in areas of serious pollution. Pollution was concentrated in Anhui Province when it was alleviated in Nanjing. Polluted dust, polluted continental and smoke aerosols were primarily observed during this process. In particular, polluted dust aerosols accounted for a major part of the transport stage, and existed between the surface and 4 km. Moreover, the average extinction coefficient at lower altitudes (<1 km) was higher for aerosol deposition.

Image fusion makes the fused image more reliable and intelligible, and more suitable for human vision and computer detection, classification, recognition and understanding. This paper proposes a pixel-level image fusion method for merging two source images of the same scene using wavelet transform and gray-level features (GLF). First, a three-level discrete two-dimensional wavelet transform is used to decompose the two source images into low-frequency image components and horizontal, vertical, and diagonal high-frequency components. Then, the spatial frequency correlation coefficient is used to determine the pixel fusion rule to apply to each of the low-frequency images, and the correlation coefficient of the GLF is used to determine the pixel fusion rule to apply to each of the high-frequency images. Finally, the fused image is reconstructed using inverse wavelet transform. The results of the experiments conducted indicate that the proposed method is more effective than relevant conventional methods.

Here a classification method for ground-based visible images is proposed based on a transfer convolutional neural network (TCNN). This approach combines the ability of deep learning (DL) and transfer learning (TL). A sample database containing all ten cloud types was used; this database was expanded four-fold using enhancement processing. AlexNet was chosen as the basic convolutional neural network (CNN), with the ImageNet database being used for pre-transfer. The optimal method, once determined by layer-by-layer fine-tuning, was used to test the classification effects for ten cloud types. The proposed method achieved 92.3% recognition accuracy for all ten ground-based cloud types.

Motion blur in images is usually modeled as the convolution of a point spread function (PSF) and the original image represented as pixel intensities. The knife-edge function can be used to model various types of motion-blurs, and hence it allows for the construction of a PSF and accurate estimation of the degradation function without knowledge of the specific degradation model. This paper addresses the problem of image restoration using a knife-edge function and optimal window Wiener filtering. In the proposed method, we first calculate the motion-blur parameters and construct the optimal window. Then, we use the detected knife-edge function to obtain the system degradation function. Finally, we perform Wiener filtering to obtain the restored image. Experiments show that the restored image has improved resolution and contrast parameters with clear details and no discernible ringing effects.

The improvement of port cluster eco-efficiency is of great significance to constructing a world-class shipping hub and the high-quality development of regional economy. This study adopts the Super-EBM (Super-efficiency Epsilon-Based Measure) model to evaluate the eco-efficiency of the Yangtze River Delta port cluster in China, and the GML (Global Malmquist-Luenberger) index, spatial hot spot analysis, gravity center migration model, and the Theil index are combined to reveal the spatial-temporal evolution. The results show that the average eco-efficiency of the Yangtze River Delta port cluster is 0.686, with 55.6% of the ports being below the average, which is directly related to the low scale efficiency. Mainly driven by technical efficiency improvement, the overall eco-efficiency has a growth rate of 8.7% from 2010 to 2019. Moreover, considerable spatial divergence has formed in the port cluster, and the eco-efficiency gravity center has always been in the south of Jiangsu. The overall eco-efficiency gap has widened by 19.92%, and the gap within the region, particularly within Zhejiang, is the major source. To improve the overall eco-efficiency of the port cluster, policymakers should strengthen the technological spillover of ecologically efficient ports in clean production and mechanism reform, while optimizing the resource consolidation system of ports with relatively low eco-efficiency.