Hao Huang

To address the issues of poor Co²⁺ regeneration and limited interfacial electron transfer in heterogeneous catalytic systems, this study proposes the synthesis of highly efficient and stable Co₃O₄/ZnO composites through the pyrolysis–oxidation reaction of Co/Zn MOFs for the degradation of rhodamine B (RhB) using activated peroxymonosulfate (PMS). The results confirmed that the catalyst exhibited a high electron transfer capacity, and the synergistic effect between the bimetals enhanced the reversible redox cycle of Co³⁺/Co²⁺. Under optimal conditions, complete removal of RhB was achieved in just 6 min using the Co₃O₄/ZnO composite, which demonstrated excellent stability after five cycles. Furthermore, the catalyst exhibited a high degradation efficiency in real water samples with a total organic carbon (TOC) removal rate of approximately 65% after 60 min. The electrochemical measurements, identification of active species, and X-ray photoelectron spectroscopy (XPS) analysis revealed that non-radicals (¹O₂ and direct charge transfer) played a major role in the degradation of RhB. Finally, the potential mechanisms and degradation pathways for RhB degradation using this catalyst were systematically investigated. This study opens new avenues for the development of efficient and stable PMS catalysts, and provides insights into the preparation of other emerging metal oxides.

Quantitative simulation of urban waterlogging using computer models is an effective technical means for urban storm water management, especially for predicting and preventing waterlogging. In this study, a city in the northern Anhui Plain, China, was selected as the study site. The Storm Water Management Model was applied to simulate the dynamic changes in the pipeline overload, node overflow, and discharge port runoff characteristics from three perspectives: surface runoff, pipe network transmission, and flow control of low-impact development. The operation of the rainwater pipe network under different return periods and the real-time operation of the rainwater pipe network were simulated to seek solutions to urban waterlogging problems caused by flat terrain and slow drainage. The results revealed that surface runoff is the primary source of rainfall in the study area, with a runoff coefficient of 0.599. The drainage pipe network was optimized by expanding the diameter of the pipe from ≤1.5 mm to ≥2 mm. The water reduction rate was more than 50%, and overload did not occur after optimization. Therefore, sinking green space technology and optimization methods for expanding a pipe diameter can reduce urban waterlogging.

In the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply. It is a feasible direction for precision agricultural subsidies, but it is difficult to collect large-scale parcel data and obtain high spatial resolution or time-series remote sensing images in mass production. It is a beneficial exploration of making use of existing parcel data generated by the ground survey and medium-resolution remote sensing images with suitable time and spatial resolution to extract winter wheat planting areas for large-scale precision agricultural subsidies. Therefore, this paper proposes a new algorithm to extract winter wheat planting areas based on ownership parcel data and medium-resolution remote sensing images for improving classification accuracy. Initially, the segmentation of the image is carried out. To this end, the parcel data is used to generate the region of interest (ROI) of each parcel. Second, the homogeneity of each ROI is detected by its statistical indices (mean value and standard deviation). Third, the parallelepiped classifier and rule-based feature extraction classification methods are utilized to conduct the homogeneous and nonhomogeneous ROIs. Finally, two classification results are combined as the final classification result. The new algorithm was applied to a complex planting area of 103.60km² in central China based on the ownership parcel data and Gaofen-1 PMS and WFV remote sensing images in this paper. The experimental results show that the new algorithm can effectively extract winter wheat planting area, eliminate the problem of salt-and-pepper noise, and obtain high-precision classification results (kappa=0.9279, overall accuracy=96.41, users accuracy=99.16, producers accuracy=93.39, commission errors=0.84, and omission errors=6.61) when the size of ownership parcels matches the spatial resolution of remote sensing images.