Search

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction.

ABSTRACT Methanethiol (MT) is a sulfur-containing compound produced during dimethylsulfoniopropionate (DMSP) degradation by marine bacteria. The C–S bond of MT can be cleaved by methanethiol oxidases (MTOs) to release a sulfur atom. However, the cleaving process remains unclear, and the species of sulfur product is uncertain. It has long been assumed that MTOs produce hydrogen sulfide (H2S) from MT. Herein, we studied the MTOs in the Rhodobacteraceae family—whose members are important DMSP degraders ubiquitous in marine environments. We identified 57 MTOs from 1,904 Rhodobacteraceae genomes. These MTOs were grouped into two major clusters. Cluster 1 members share three conserved cysteine residues, while cluster 2 members contain one conserved cysteine residue. We examined the products of three representative MTOs both in vitro and in vivo. All of them produced sulfane sulfur other than H2S from MT. Their conserved cysteines are substrate-binding sites in which the MTO–S–S–CH3 complex is formed. This finding clarified the sulfur product of MTOs and enlightened the MTO-catalyzing process. Moreover, this study connected DMSP degradation with sulfane sulfur metabolism, filling a critical gap in the DMSP degradation pathway and representing new knowledge in the marine sulfur cycle field.IMPORTANCEThis study overthrows a long-time assumption that methanethiol oxidases (MTOs) cleave the C–S bond of methanethiol to produce both H2S and H2O2—the former is a strong reductant and the latter is a strong oxidant. From a chemistry viewpoint, this reaction is difficult to happen. Investigations on three representative MTOs indicated that sulfane sulfur (S0) was the direct product, and no H2O2 was produced. Finally, the products of MTOs were corrected to be S0 and H2O. This finding connected dimethylsulfoniopropionate (DMSP) degradation with sulfane sulfur metabolism, filling a critical gap in the DMSP degradation pathway and representing new knowledge in the marine sulfur cycle field.

With economic progression in China, Yellow River Basin serves as a critical economic belt, which has also been recognized as a cradle of Chinese culture. A watershed is a complex structure of social, economic, and natural factors, and the diversity of its components determines its complexity. Studies on the spatiotemporal distribution characteristics and factors influencing the tourism eco-efficiency at the watershed scale are crucial for the sustainable regional socio-economic development, maintaining a virtuous cycle of various ecosystems, and comprehensively considering the utilization and coordinated development of various elements. Based on tourism eco-efficiency, the coordination degree of regional human-land system and the sustainable development levels can be accurately measured. With the tourism eco-efficiency in the Yellow River Basin from 2009 to 2019, the present study considers 63 cities in the Yellow River Basin as the research area by adopting the super-efficiency slacks-based measure (Super-SBM) model. Methods such as trend surface analysis, spatial autocorrelation analysis, elliptic standard deviation analysis, and hot spot analysis were used to explore their spatiotemporal distribution and evolution characteristics. The geographical and temporal weighted regression (GTWR) model was used to determine the factors influencing the tourism eco-efficiency value. The findings are as follows: ①The level of tourism eco-efficiency in the Yellow River Basin is not high, exhibiting a fluctuating upward trend. ②The tourism eco-efficiency in the Yellow River Basin shows significant spatial interdependence and agglomeration. Furthermore, the track of the center of gravity moves from northeast to southwest. ③ The tourism eco-efficiency in the Yellow River Basin is affected by various factors, with the economic development level having the greatest influence.

Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of “double carbon”. In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB.