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Studying how populations in various environments differ genetically is crucial for gaining insights into the evolution of biodiversity. In order to pinpoint potential indicators of divergence and adaptation to diverse environments, we conducted a comprehensive analysis of 3,491,868 single nucleotide polymorphisms (SNPs) derived from five populations of Brachymystax lenok. We discovered significant geographic divergence among these 5 populations, which lack evidence of gene flow among them. Our results further demonstrated that the current distribution pattern of Brachymystax lenok are driven by geographical isolation and changes in oceans and rivers. We also performed genome-wide scan and identified the genes evolved to adapt the different environments, including stress response. In general, these results provide genomic support for high-level genetic divergence and the genetic basis of adaptation to different environments.

As earth surface human activities become more frequent, global ecosystem service functions and especially biodiversity maintenance functions are challenged. This study aimed to analyze spatiotemporal changes in Xi‘an section of the northern foothills of the Qinling Mountains from 1990 to 2020. Temporal and spatial changes in habitat quality in the study area were visualized using InVEST model and land use data, and factors affecting habitat quality were analyzed using Geodetector. The results showed that during the study period, the cultivated land, grassland, and water decreased by 16.40%, 74.37%, and 35.39%, respectively, while the area of forest land and construction land increased, among which the construction land increased by 117.70%, the largest increase, and the forest land increased by 8.47%. The main changes in land use are the conversion of cultivated land into forest land and construction land, and the conversion of grassland into forest land and cultivated land. During the period 1990–2020, the average habitat quality index in the study area changed from 0.8617 to 0.8585, showing a slow decreasing trend. The spatial distribution of habitat quality showed a trend of “high in the south, moderate in the north, and low in the northwest”. The high habitat quality was mainly concentrated in the southern forest land, the middle habitat quality was mainly distributed in the northern cultivated land, and the low habitat quality was mainly distributed in the northwest construction land. The land use type has a great influence on habitat quality, and the interaction between any two factors is stronger than that of a single factor. The temporal and spatial variation of habitat quality is influenced by both natural and human factors. This study provides a theoretical basis for ecological protection and nature reserve planning in the Qinling Mountains region.

A total of 18 samples of shallow groundwater around the Hancheng mining area in the Guanzhong Basin were collected from 1–4 May 2018. According to the analysis of hydrochemical data, the Gibbs semi-logarithmic diagram and Piper diagram were used to research the hydrogeochemical behavior of shallow groundwater around the Hancheng mining area in the Guanzhong Basin. The results of the groundwater hydrochemical analyses shown on the Gibbs and Piper plots are as follows: The chemical composition analysis showed that the main cation components were Mg2+, Ca2+, Na+, and K+, the anion components were HCO3−, Cl−, and SO42−. A measure of 89% of the groundwater samples in this area were freshwater, the HCO3− were mainly dolomite, calcite, and gypsum dissolved precipitation resulted. Na+ and Cl− came from the dissolution of halite. Most of the groundwater was of the SO4·Cl-Ca·Mg type, accounting for 61.1%. The main ion chemistry of the shallow groundwater in this area is controlled by rock weathering, and pyrite oxidation is a significant factor affecting the SO42− concentration. These research results will help analyze the formation mechanism of chemical components and provide some basic data for the evolution of mine water in this area in the future.

Soil conservation services play a vital role in regulating ecosystem services to prevent soil erosion and ensure regional ecological security. Therefore, effective evaluation and quantification of soil conservation services in the Three Gorges Reservoir Area (TGRA) are conducive to sustainable management under future global change. In this study, based on a basic database, including land use/cover data, soil data, topographic data, meteorological data, and NDVI (Normalized Difference Vegetation Index) data as the basic databases, to evaluate the temporal and spatial changes of soil conservation services in the TGRA from 1990 to 2015 at a regional-scale level using the general soil loss equation. The results showed that forest ecosystems (including coniferous and broad-leaved mixed forests, coniferous forests, shrub forests, and broad-leaved forests) made a greater contribution (69%) to regulating soil conservation in TGRA, followed by farmland ecosystems (29%). In total TGRA, large spatial variation in soil conservation, such as the highest appeared in the northern hinterland, whereas the lowest was mostly shown in the northwest with relatively frequent human activities and developed industry and agriculture. In general, soil conservation in the TGRA ecosystem gradually increased from 1990 to 2015, with a total increase of 6%. In this period, with the effective implementation of ecological projects, such as the conversion of farmland to forest and natural forest protection, the distributed proportion of forest land area in total TGRA showed a significant increase. In the meantime, the increase of vegetation coverage also helps the restoration of ecosystem structure and function and the improvement of soil conservation services. Our findings will aid our knowledge regarding the ecosystem services of the TGRA and provide implications for future sustainable land management and ecological protection.

Most waste sludge belongs to long sludge-retention-time (SRT) sludge in China, but its outlet is becoming a severe environmental problem nowadays. In this study, the characteristics of mesophilic and thermophilic anaerobic digestion of long SRT sludge was investigated under different total solid content (TSC) of 4%, 6%, and 8% in batch reactors. The results showed that the methane production potential (Po) and methane production rate (Rmax) of thermophilic anaerobic digestion (TAD) were increased by about 60% and 200% compared with that of the mesophilic anaerobic digestion (MAD) at 4% TSC. In TAD groups, the Po of 240.95 LCH4 kgVS-1added and Rmax of 22.17 LCH4 kgVS-1added day-1 were achieved in the reactor of 4% TSC while the inhibition concentration of propionate and free ammonia reached 1022 mg L-1 and 603 mg L-1 in the reactor of 8% TSC. Although the diversity of microbial communities were poor in TAD, Coprothermobacter was enriched in TAD, which could degrade protein efficiently. In addition, the dewaterability of digested sludge in TAD was poorer than that in MAD, and both got worse with the increased TSC. Therefore, it is appropriate for the long SRT sludge to anaerobic digestion under thermophilic condition and lower TSC. So, thermophilic anaerobic digestion with lower TSC might be an alternative approach for the treatment and resource utilization of long SRT sludge.

To enhance the positioning accuracy of standalone GNSS receivers in environments unable to provide precise ephemeris and clock offset, such as undeveloped forest areas that lack network communication and power supply, this study employed the Time Difference Carrier Phase (TDCP) technology to improve the positioning accuracy of Standard Point Positioning (SPP), where the Least-Squares (LS) and the extended Multi-Epoch Least Squares (MELS) method were applied in the position domain filtering for a single GNSS receiver and compare its performance with the existing observation domain filtering method. Firstly, the simulated data sets with various positioning accuracies were used to verify the effectiveness and convergence of the LS filtering methods. The results indicate that the LS filtering method produces a lower root mean square (RMS) error than the original strategy. Secondly, this study uses two kinematic GNSS data sets to evaluate the performance of the observation and position domain filtering, with an emphasis on the MELS method. The numerical experiment results show that the position domain LS filtering method outperforms the other two methods. The open environment experiments result shows that the positioning domain filtering method achieved positioning accuracies of 0.202 m, 0.843 m, and 2.036 m in the E, N, and U directions, respectively, with improvements of 68.0%, 21.6%, and 24.0%, compared to the original algorithm which achieved positioning accuracies of 0.631 m, 1.076 m, and 2.680 m. It also achieved improvements of 24.0%, 4.0%, and 18.3%, respectively, compared to the observation domain filtering method with positioning accuracies of 0.353 m, 0.886 m, and 2.526 m. The forest scenes experiments result shows that the positioning domain filtering method achieved positioning accuracies of 1.308 m, 1.375 m, and 2.133 m in the E, N, and U directions, respectively, with improvements of 42.4%, 36.2%, and 27.6%, compared to original algorithm which achieved positioning accuracies of 1.863 m, 1.873 m, and 2.722 m, and also achieved improvements of 27.0%, 19.4% and 10.6%, respectively, comparing to observation domain filtering method with positioning accuracies of 1.661 m, 1.642 m and 2.359 m. Moreover, the examination of the LS method results based on different epochs reveals that the filtering accuracy increases as more epochs are incorporated into the position domain integration and the enhancement value reaches a few millimeters.

The carbon storage of forest vegetation plays a crucial role in the terrestrial carbon budget. The objective of this study is to elucidate the current biomass carbon storage and sequestration capacity, as well as the future carbon sequestration potential of forest ecosystems in Shaanxi Province of China, thus providing data support and policy references for sustainable forest management and the response of carbon sequestration to climate change. Based on the data obtained from the seventh and ninth forest resource inventories, the regional biomass conversion factors, and carbon measurement parameters, the biomass conversion factor method is employed to estimate the biomass storage and carbon sequestration capacity of forest ecosystems. (1) The total carbon storage of forest lands in Shaanxi Province was 285.20 Tg. The carbon storage of arbor forests, sparse woodlands, scattered forests, four-side trees, shrub woodland, and bamboo forests were 237.09 Tg, 2.93 Tg, 12.30 Tg, 5.98 Tg, 26.35 Tg, and 0.56 Tg, respectively. (2) Over the 10 years from the seventh (2005) to the ninth (2015) forest resource inventories, the carbon storage of forests increased from 207 Tg to 285 Tg, with a total increase of 78.01 Tg (37.65%), demonstrating a significant carbon sink function. (3) From 2015 to 2060, the carbon density of arbor forests will increase from 33.53 Mg/ha to 46.90 Mg/ha, and the carbon storage will increase from 237 Tg to 432 Tg. These results indicate that forests have significant net carbon sequestration capacity and can play an important role in achieving China’s carbon peak and carbon neutrality goals. Aiming for carbon neutrality, improving forest management, along with protecting and utilizing forest resources through technological innovation, will become the driving force for increasing carbon storage in Shaanxi Province in the future.

Mining of mineral resources exposes various minerals to oxidizing environments, especially sulfide minerals, which are decomposed by water after oxidation and make the water in the mine area acidic. Acid mine drainage (AMD) from mining can pollute surrounding rivers and lakes, causing serious ecological problems. Compared with traditional field surveys, unmanned aerial vehicle (UAV) technology has advantages in terms of real-time imagery, security, and image accuracy. UAV technology can compensate for the shortcomings of traditional technology in mine environmental surveys and effectively improve the implementat ion efficiency of the work. UAV technology has gradually become one of the important ways of mine environmental monitoring. In this study, a UAV aerial photography system equipped with a Red, Green, Blue (RGB) camera collected very-high-resolution images of the stone coal mining area in Ziyang County, northwest China, and classified the very-high-resolution images by support vector machine (SVM), random forest (RF), and U-Net methods, and detected the distribution of five types of land cover, including AMD, roof, water, vegetation, and bare land. Finally, the accuracy of the recognition results was evaluated based on the land-cover map using the confusion matrix. The recognition accuracy of AMD using the U-Net method is significantly better than that of SVM and RF traditional machine-learning methods. The results showed that a UAV aerial photography system equipped with an RGB camera and the depth neural network algorithm could be combined for the competent detection of mine environmental problems.

Distribution of nutrients (organic matter, total nitrogen and phosphorus contents) and heavy metal elements(zinc, copper, lead, cadmium, chromium, nickel and arsenic)in the sediments, collected from three selected locations of the Shiwei River, Shenzheng city. Sediment quality guidelines (SQGs) and integrated index analyses method containing pollution index(PI)and potential ecological risk index (PERI),was used to estimate risks of nutrients and heavy metal pollution in the Shiwei River sediments. Furthermore, the environmental dredging depth was calculated through critical-risk-depth method was used in this study.Sediments collected from areas recorded elevated nutrient and heavy metal concentrations,which exceeded SQGs. Also, the higher value of PERI value in the surface sediment suggested that Shiwer River was in considerable ecological risk. Among that, Cd posed the chief ecological risk on the Shiwer River. Based on the critical-risk-depth method,the recommended dredging depth for the Shiwei River should between 134cm and 95cm. [ABSTRACT FROM AUTHOR]