Northwest China Research Articles

Two new species of genus Karschia Walter, 1889 (Solifugae, Karschiidae) from northwest China

Two new species of the solifugae genus Karschia Walter, 1889, are described from northwest China. A detailed morphological description, diagnostic features, and illustrations of Karschia (Karschia) latuis sp. nov. and Karschia (Karschia) wenquan sp. nov. are provided. The current distribution of all the known Karschia Walter, 1889 species from China is mapped.

Feasibility Study on the Construction of Underground Reservoirs in Coal Goaf—A Case Study from Buertai Coal Mine, China

The construction of underground reservoirs in coal goaf is a new technology aimed to realize the sustainable development of coal mining-water storage-surface ecology in arid areas of northwest China. The key to the feasibility of this technology is that underground coal mining cannot affect the near-surface aquifer, and the amount of water entering the underground reservoir must meet the needs of the coal mine. Taking Buertai Coal Mine, one of the largest underground coal mines in the world, as an example, this article used similar simulation, numerical simulation and in-situ test methods to study the height of the water-conducting fracture zone of overlying strata and water inflow of underground reservoirs. The results show that, under the repeated mining of the 22- and 42-coal seams, the maximum height of the water-conducting fracture zone is 178 m, and the distance between the near-surface aquifer and the 42 coal is about 240 m, so the mining has little effect on the near-surface aquifer. During the mining period of the 22-coal seam, the groundwater of the Zhidan and Zhiluo Formations was mainly discharged vertically, while the groundwater of the Yanan Formation was mainly a horizontal flow during the period of the 42-coal mining. In this way, the total water inflow of Buertai Coal Mine reaches 500 m3/h, which not only meets the needs of the mine, but also, the rest of the water can irrigate about 98 hectares of farmland nearby. Underground reservoirs in coal goaf could achieve sustainable development of coal mining, groundwater storage and surface ecology in semi-arid areas.

Relationship Between Salt Accumulation and Soil Structure Fractals in Cotton Fields in an Arid Inland Basin

The relationship between soil structure and salt accumulation is unclear; thus, experiments on salt accumulation under different soil structures were conducted in cotton fields in arid areas of northwest China. Thirty-nine sets of soil samples were collected from the 0 to 180 cm profile of three experimental areas. The total salt content of the soil extracts and the particle size distribution of the soil samples were determined using a JENCO TDS and a laser particle size analyzer, respectively, and the fractal dimension of the soil structure was obtained using fractal theory. Pearson’s correlation analysis and Tukey’s test (p < 0.01) were used to analyze the correlation between soil salinity, soil particle size distribution, and fractal dimensions in the three profiles. The results showed soil salinity accumulation was affected mutually by soil texture and soil structure, and soil salinity tended to accumulate in fine-grained soil. The soil fractal dimension (D) could indicate soil texture and quantify soil salinity content. When the sand content was more than 50%, there was a significant positive correlation between the soil fractal dimension and soil salinity (correlation coefficient R = 0.943). The results provide valuable insights into cotton production in arid areas.

Assessment of PM2.5 pollution features and health advantages in Northwest China

Assessment of PM2.5 pollution features and health advantages in Northwest China

Association of plasma homocysteine with cardiometabolic multimorbidity: a cross-sectional study in northwest China

Association of plasma homocysteine with cardiometabolic multimorbidity: a cross-sectional study in northwest China

Quantifying the Geomorphological Susceptibility of the Piping Erosion in Loess Using LiDAR-Derived DEM and Machine Learning Methods

Soil piping erosion is an underground soil erosion process that is significantly underestimated or overlooked. It can lead to intense soil erosion and trigger surface processes such as landslides, collapses, and channel erosion. Conducting susceptibility mapping is a vital way to identify the potential for soil piping erosion, which is of enormous significance for soil and water conservation as well as geological disaster prevention. This study utilized airborne radar drones to survey and map 1194 sinkholes in Sunjiacha basin, Huining County, on the Loess Plateau in Northwest China. We identified seventeen key hydrogeomorphological factors that influence sinkhole susceptibility and used six machine learning models—support vector machine (SVM), logistic regression (LR), Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), random forest (RF), and gradient boosting decision tree (GBDT)—for the susceptibility assessment and mapping of loess sinkholes. We then evaluated and validated the prediction results of various models using the area under curve (AUC) of the Receiver Operating Characteristic Curve (ROC). The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. The GBDT model had the best predictive accuracy (AUC = 0.94) and model migration performance (AUC = 0.93), and it could find sinkholes with high and very high susceptibility levels in loess areas. This suggests that the GBDT model is well suited for the fine-scale susceptibility mapping of sinkholes in loess regions.

Optimal vegetation coverage from the perspective of ecosystem services in the Qilian Mountains

Abstract The Qilian Mountains (QLMs) serve as a vital ecological security barrier in the northwest China, determining the optimal fractional vegetation cover (FVC) of different land-use types is crucial for ecological restoration and protection. Based on the normalized vegetation index, meteorological data, soil data and land-use data, the InVEST model, bivariate spatial autocorrelation and elastic analysis, we calculated the water conservation, soil conservation and carbon storage, analyzed the spatio-temporal distribution characteristics of FVC and ecosystem services (ES) and the correlation between them. This study aims to evaluate the optimal FVC of the different land-use types and vegetation restoration’s effect on it in the QLMs. The results showed that the FVC and ES showed an increasing trend from 2000 to 2020, with a spatial distribution pattern of high values in the southeast and low values in the northwest. There was a significant and positive correlation between FVC and ecosystem service functions (p < 0.05), FVC had the most obvious effect on soil conservation, followed by water conservation. The optimal FVC varied by land-use type, with its value between 0.4 and 0.48 for forest land, 0.24 and 0.30 for grassland and 0.19 to 0.20 for unused land. In the eastern of the QLMs, nearly 70% of the forests and 50% of the grasslands exceeded the optimal vegetation coverage, while 36% of the grasslands and 62% of the unused lands were under-covered in the western of the QLMs. The findings suggest that differentiated ecological restoration and management measures should be formulated according to the status of vegetation restoration, to enhance the comprehensive capacity of ecosystem services in the QLMs.

Effects of Irrigation Amount and Salinity Levels on Maize (Zea mays L.) Growth, Water Productivity and Carbon Emissions in Arid Region of Northwest China

To address the growing scarcity of freshwater resources, the use of saline water for agricultural irrigation is gaining increasing attention. This study presents findings from a two-year field experiment conducted during the 2023 and 2024 maize-growing seasons in northwestern China. The objective of the experiment was to evaluate the main and interactive effects of saline irrigation water on soil water–salt dynamics, maize growth, photosynthetic characteristics, water productivity, and carbon emissions. The experiment involved nine treatments with three irrigation amounts: 4500 m3 hm−2 (W1), 5625 m3 hm−2 (W2), and 6750 m3 hm−2 (W3), combined with three water salinity levels: 0.85 g L−1 (S1, freshwater), 3 g L−1 (S2), and 5 g L−1 (S3). Results indicated that both irrigation amount and salinity level significantly affected water–salt dynamics, with more soil accumulating in the 0–100 cm soil layer under saline irrigation water; however, this effect diminished with higher irrigation amounts. The maximum leaf area index and plant height were affected by both the irrigation amount and salinity level, as well as their interaction. Photosynthetic capacity declined with increasing salinity of the irrigation water, ultimately reducing grain yield and irrigation water use efficiency. Compared to freshwater (S1), the average maize grain yield under S2 and S3 treatments decreased by 6.28% and 15.43% in 2023 and by 7.82% and 17.48% in 2024, respectively. Additionally, for the same irrigation amount, higher salinity of the irrigation water (S2, S3) significantly reduced total soil CO2 emissions, with reductions of 10.08% and 27.53% in 2023, and 11.97% and 28.01% in 2024, respectively. In summary, to optimize the utilization of saline water, enhance maize yield, and improve soil carbon sequestration, we recommend maintaining the salinity of irrigation water below 3 g L−1, and using an irrigation amount of 6750 m3 hm−2 (W3S2) for optimal outcomes in the study area.

Relationship between stratigraphic overlap and sedimentary facies evolution of the Junggar Basin, Northwest China

The interaction between the topography of the slope zone on the edge of the basin and the distribution of sediments is crucial for accurately predicting sediment distribution, but few studies emphasize the impact of stratigraphic overlap on the spatial evolution of sedimentary facies. The tectonic movement and sedimentary environment of Hala’alat Mountain in the northwest margin of the Junggar Basin are complex, and the sedimentary model of the whole Cretaceous system is still unclear. This article uses lithology, logging, and seismic data to explain the evolution process and sedimentary model of the Cretaceous system. The significant overlap of Cretaceous strata in the research area has a significant impact on the distribution of sedimentary facies zones and the development of sedimentary systems. Furthermore, this article explores the genesis mechanism of the Cretaceous stratigraphic overlap phenomenon, clearly defines the boundary range of stratigraphic overlap, and deeply analyzes how sedimentary facies zones are distributed and their subsequent evolution trends after the formation of overlap. The results demonstrate that there are apparent stratigraphic overlap phenomena in the second member (K1q2) and the third member (K1q3) of the Cretaceous Qingshuihe Formation in this area. The lithology at the bottom of the formation is grey sand conglomerate with finer grain size in the upper part, and the sedimentary facies are transitioned from fan delta facies to shore shallow lake beach bar facies. In member 1 (K1h1), member 2 (K1h2) and member 3 (K1h3) of the Hutubi Formation, due to the gradual slope and relatively stable sedimentary environment, the distance of stratigraphic overlap becomes shorter, the stratigraphic overlap phenomenon is no longer apparent and the sedimentary facies zone does not change. The study not only reveals the intrinsic relationship between stratigraphic overlap and the distribution of sedimentary facies zones, but also deepens the understanding of the dynamic evolution laws of sedimentary systems. The study on the overlap sedimentary mechanism of the Hala’alat Mountain in the northwest margin of Junggar Basin has reference significance.

Automatically Detected CSES Ionospheric Precursors Before Part of the Strong Aftershocks of the 23 January 2024 Wushi MS 7.1 Earthquake in Northwest China

Earthquake prediction is still a large challenge worldwide so far. In this paper, an automatic detection method was put into service immediately after the Wushi MS 7.1 earthquake on 23 January 2024 to weekly detect possible CSES (China Seismo-Electromagnetic Satellite) precursory information before impending aftershocks. An electron perturbation with an enhanced magnitude of 38.3% was first detected on 24 January 2024 at night orbit 33175 and the corresponding variations in different plasma parameters measured at this orbit presented a typical feature of electron depletion or plasma bubble with an abrupt decrease and then an increase after one minute. The Kp index was also checked during this period and the values reached 3.7 once on 23 and 24 January, which indicates that these ionospheric variations probably originated from solar activities instead of three strong aftershocks with a magnitude more than five in the following three days. However, uncertainties still exist. Then, an electron perturbation with amplitude of 24.6%, as well as an O+ one of 27.3%, was successfully searched automatically at the same revisiting orbit 33251 on 3 February 2024 in a magnetically quiet period. These two plasma variations, as well as ones of other ionospheric parameters, were characterized by highly synchronous properties, which increase the availability as seismic precursors. However, no obvious variations were observed at other revisiting orbits or other orbits near the aftershock areas during this period. An aftershock with magnitude of MS 5.3 and the strongest one of MS 5.8 took place on 24 and 25 February, respectively, 20 days after and 1000 km away.

Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China

ABSTRACTThe reliability of seasonal snow cover information is constrained by limitation of in situ observations and uncertainties in remote sensing data and model simulations in alpine region, thus posing important challenges to understanding the climate system and water resource management in alpine region. Here, the assimilation of daily cloud‐free Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference snow index (NDSI) product into an intermediate complexity snow mass and energy balance model—Flexible Snow Model (version FSM2_MO)—was implemented. The aim is to improve the model simulations of seasonal snow cover (snow‐covered extent; SCE, snow depth; SD, snow water equivalent; SWE, and snowmelt runoff; SMR) in the alpine region (a case of the upper‐middle reaches of the Heihe River basin, Northwest China). The results indicate comprehensive improvement in the simulation of SCE, SD, and SMR in the study area through data assimilation, with the ability to significantly reduce prior biases of the FSM2_MO. Based on the independent daily cloud‐free Advanced Very High Resolution Radiometer (AVHRR) SCE product, the updated SCE simulation (i.e., data assimilation) showed a reduction in mean absolute error (MAE) from 10.46% to 7.16%, root mean square error (RMSE) from 16.14% to 12.26%, and an increase in Pearson's correlation coefficient (CC) from 0.18 to 0.67 compared with the open loop simulation (i.e., without assimilation). The evaluation results of SD observation data showed that data assimilation improved SD simulation compared with the open loop run (OL). And utilising the monthly discharge observations at the Yingluoxia hydrological station, data assimilation slightly improved the SMR simulation. The updated SMR simulation achieved a CC of 0.91, Nash‐Sutcliffe efficiency coefficient (NSE) of 0.73, and Kling‐Gupta efficiency coefficient (KGE) of 0.76. Moreover, the Landsat 8‐derived snow cover map and Sentinel‐1‐derived SD also indicated that the updated simulation effectively filled in the missing snow cover and removed the superfluous snow cover predicted by the OL simulation in terms of spatial distribution.

Clinical application of chromosome microarray analysis and karyotyping in prenatal diagnosis in Northwest China

IntroductionKaryotyping and chromosome microarray analysis (CMA) are the two main prenatal diagnostic techniques currently used for genetic testing. We aimed to evaluate the value of chromosomal karyotyping and CMA for different prenatal indications.MethodsA total of 2084 amniocentesis samples from pregnant women who underwent prenatal diagnosis from 16 to 22 + 6 weeks of gestation between January 2021 and December 2022 were retrospectively collected. The pregnant women were classified according to different prenatal diagnostic indications and underwent CMA and karyotype analysis. Clinical data were collected, and the results of the CMA and karyotype analysis were statistically analyzed to compare the effects of the two diagnostic techniques.ResultsThe total detection rate of abnormal chromosomes was significantly higher using CMA than karyotype analysis. The detection rate of abnormal chromosomes using CMA was significantly higher than that using karyotyping for ultrasound abnormalities, high-risk serologic screening, adverse pregnancy history, positive noninvasive prenatal test (NIPT) screening, and ultrasound abnormalities combined with adverse pregnancy history indications. Among the fetuses with inconsistent results between the two testing methods, 144 had an abnormal CMA but a normal karyotype, with the highest percentage of pregnant women with ultrasound abnormalities at 38.89% (56/144). CMA had the highest detection rate for structural abnormalities combined with soft-index abnormalities among all ultrasound abnormalities. The highest detection rate of copy number variants in the group of structural abnormalities in a single system was in the genitourinary system (3/29, 10.34%).ConclusionCMA can improve the detection rate of chromosomal abnormalities in patients with ultrasound abnormalities, high-risk serologic screening, adverse maternal history, positive NIPT screening, and ultrasound abnormalities combined with adverse maternal history and can increase the detection rate of chromosomal abnormalities in karyotypic normality by 6.91% (144/2,084), this result is higher than similar studies. However, karyotype analysis remains advantageous over CMA regarding balanced chromosomal rearrangement and detection of low-level chimeras, and the combination of the two methods is more helpful in improving the detection rate of prenatal chromosomal abnormalities.

Quantitative Assessment of the Peeling on Earthen Heritage Sites Using on Improved Rank Sum Ratio

ABSTRACT The peeling disease has caused the volume of the earthen site to recede over the years, seriously jeopardizing the longevity of the earthen sites. Accurately evaluating the degree of development of peeling disease is the key to carrying out conservation work. This paper organically combines the Decision Making Trial and Evaluation Laboratory, the multi-correlation coefficient, and the anti-entropy weight method in a weighted average combination, and assigns weights to the weight indicators of the rank sum ratio method. The improved RSR evaluation method comprehensively, objectively, and accurately evaluates the degree of peeling disease development at 18 earthen sites in the arid region of northwest China. The results show that 2, 7, and 8 earthen sites have very high, high, and moderate grades of peeling disease development, respectively. Comparison of the evaluation results of the regression values of peeling features using ridge regression verifies the accuracy of the evaluation results of the improved rank sum ratio approach. The improved rank sum ratio method proposed in this paper for the development of peeling disease at earthen sites is a robust assessment system. It lays the foundation for conservationists of earthen sites to make conservation decisions in a scientific manner.

An improved optical trapezoid model to identify wetlands and irrigation water ponds in the arid irrigation district using Google Earth Engine

ABSTRACT In arid regions, wetlands support ecosystem with high biodiversity and productivity. Meanwhile, irrigation during the fallow period often results in the temporary formation of water ponds in the cropland, which plays a critical role in agricultural productivity. The prompt identification of wetlands and irrigation water ponds in irrigation districts is important for the protection of local wetland ecosystem and the effective management of irrigation water. Utilizing the OPtical TRApezoid Model (OPTRAM) for soil moisture estimation, we have proposed an improved version of OPTRAM aimed to identify wetlands and irrigation water ponds in irrigation district using Sentinel-2 data through Google Earth Engine platform. Different criteria were used for the identification of wetland and irrigation water pond, given the greater diversity associated with wetlands. Parameters for the wet edge determined from OPTRAM were used to identify wetlands, while an additional threshold was added to the model and calibrated in accordance with the irrigated area to identify irrigation water ponds. The improved OPTRAM was applied in Hetao Irrigation District (HID) of Northwest China from 2016 to 2023, where autumn irrigation applied in late autumn after crop harvesting was considered. The identified distributions of wetland and autumn irrigation were validated with observations from field survey and statistical data, and were also compared with other remote sensing products. Results show that the proposed model is effective in identifying both wetlands and irrigation ponds. Considering the wetlands, the distances from identified boundaries to the actual boundaries obtained from field surveys were calculated, revealing the mean absolute error of 6.99 m, alongside a validation accuracy ratio of 0.94 for points located within wetlands. For autumn irrigation extent, the overall accuracy is 0.90 based on the observations from field survey, with mean absolute relative errors for irrigated areas across four sub-irrigation districts recorded as 20.55%, 8.10%, 12.83%, and 11.38%, respectively, when compared with statistical data. The majority of wetlands within HID are small in size and scattered, with different trends observed in the wetland areas of sub-irrigation districts and Wuliangsuhai Lake. Autumn irrigated croplands are mainly concentrated in Jiefangzha sub-irrigation district while being scattered across other sub-irrigation districts, depicting an overall decreasing trend in the autumn irrigated area. In summary, the proposed model performed well in identifying both wetlands and irrigation ponds, and can offer valuable support for the conservation of wetland ecosystem and irrigation management.

Regional efficiency analysis of fresh food cold chain logistics in China based on three-stage DEA model

PurposeAssessing the efficiency of fresh food cold chain logistics as accurately as possible is essential for industry development planning. This study was designed to analyze the efficiency of fresh food cold chain logistics in China.Design/methodology/approachA three-stage data envelopment analysis (DEA) model was used to analyze the efficiency of fresh food cold chain logistics in 30 provinces of China from 2013 to 2019. The stochastic frontier analysis (SFA) model in the second stage was used to eliminate the influence of external environmental factors and random disturbances on efficiency analysis results.Findings(1) The overall actual efficiency of fresh food cold chain logistics in China is unsatisfactory, with an average technical efficiency of 0.382 over the 7-year period. (2) The national average technical efficiency and average scale efficiency were overestimated by 29.9% and 40.0%, respectively, compared with the actual values. (3) The efficiency of fresh food cold chain logistics does not align with the level of regional economic development. (4) Distinct regional variations exist in the efficiency of fresh food cold chain logistics in China, with higher efficiencies observed in Northwest China and the Central Yangtze River regions, and the lowest efficiencies in the northeast regions.Originality/valueThis study applies a three-stage DEA model to assess the development and regional differences of fresh food cold chain logistics in China, enriching the application of models and empirical analysis in this field. By analyzing the situation in China, it provides ideas and references for other developing countries to develop cold chain logistics.

Spatio-Temporal Analysis of Net Anthropogenic Phosphorus Inputs (NAPIs) and Their Impacts in Ningxia Hui Autonomous Region Using Monte Carlo Simulations and Sensitivity Analysis

This study employed the Net Anthropogenic Phosphorus Inputs (NAPI) model to assess the impact of human activities on phosphorus input in a watershed, analyzing county-level statistical data and NAPI model parameters from 1991 to 2020. The Monte Carlo method was used for a quantitative analysis of the model parameters’ effects on each NAPI component and the overall simulation results. The sensitivity index method identified each component’s sensitive parameters. The study found that the lowest NAPI value was 454 kg/(km2·a) in 1991 and the highest was 1336 kg/(km2·a) in 2003. NAPI in Ningxia showed an overall upward trend from 1991 to 1999, a slight decrease from 1999 to 2003, and a slight increase from 2003 to 2020, with fertilizer being the main contributing factor, accounting for 77.4% of the total input. On a spatial scale, NAPI in Ningxia was significantly correlated with land use patterns, showing higher values in the northern and southern regions compared to the central part. The NAPI values derived from Monte Carlo simulations with appropriate parameters ranged from −24.83% to 31.49%. The study highlighted the net food and feed imports component as having the highest uncertainty, impacting simulation results within a range of −23.89% to 53.98%. It was observed that the larger a component’s proportion in the NAPI model, the more sensitive its parameters, with the phosphorus fertilizer (Pfer) component’s parameters being notably more sensitive than those of the food/feed phosphorus input and the non-food phosphorus input (Pnf) components. These findings can inform phosphorus pollution control policies in Northwest China, while the selection of sensitive parameters provides a useful reference for future NAPI research in other regions.

Enhancing Soil Salinity Evaluation Accuracy in Arid Regions: An Integrated Spatiotemporal Data Fusion and AI Model Approach for Arable Lands

Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model’s fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R2) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R2 of 0.767 and an RMSE of 1.677 g·kg−1. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas.

Nutrition, Flavor, and Microbial Communities of Two Traditional Bacterial Douchi from Gansu, China

Douchi has attracted attention for its unique taste and rich health functions. This study investigated the nutrition, flavor and correlation between the flavor and microorganisms of two traditional bacterial douchi from the province of Gansu in northwest China. The findings reveal significant variations in nutrition, flavor compounds, and the microbiota between Longnan and Qingyang douchi. Three dominant bacterial genera (Carnobacterium, Ignatzschineria, and Bacillus) and one dominant bacterial genus (Pichia) were found in the QY douchi, while four bacterial genera (Bacillus, Ignatzschineria, Proteus, and Providencia) and three fungal genera (Pichia, Candida, and Rhodosporidium) were dominant in samples of the LN douchi. For flavor substances, a total of 48 volatile components were detected in Longnan douchi and 41 in Qingyang douchi. Using the relative odor activity value (ROAV), we identified five key flavor compounds in Longnan douchi and four key flavor compounds in Qingyang douchi. The correlation analysis showed that there were certain positive or negative correlations between the key microorganisms and the flavor of the two traditional bacterial douchi. The results of this study can serve as a theoretical reference for improving the quality and flavor of traditional douchi.

Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China

Abstract To reveal the diagenetic sequence of reservoir rocks in the central part of the deep depression basin, the Wuerhe Formation in Junggar Basin was taken as an example to conduct the detailed studies on its sedimentary facies, diagenetic sequence, and the micropore structure evolution rules based on the comprehensive data from a super deep exploration well C-6 (approximately 7,000 m in depth). First, an arid environment fan delta sedimentary model of the Wuerhe Formation was established, and its sedimentary evolution law was clarified as a gradual transition from a fan delta front to a fan delta plain during the water-regression process until the lake dried up. Then, the diagenesis types and evolution sequence of the Wuerhe Formation, and the influence degrees of the compaction, cementation, and dissolution on the rock formation process were clarified. Finally, the diagenesis and pore evolution model was established, and the greatest impact factors of the late reservoir densification were clarified. Based on this research, the diagenesis and pore evolution processes of the deep rocks in the studied deep central sag were ultimately revealed to provide useful guidance for the deeply buried oil and gas reservoir exploration.

Vegetation greening mitigates the positive impacts of climate change on water availability in Northwest China

Climate change alters water availability (precipitation minus evapotranspiration, i.e., water yield, WY), especially in arid regions like Northwest China (NWC) where water resources are limited. Despite large-scale implementation of ecological restoration projects has contributed to greener vegetation, this may exacerbate dryland water stress, which in turn interferes with climate change impacts on WY. To better understand this issue we used the random forest model to differentiate the effects of climate change and human activities on WY. We also employed the structural equation model to explore how vegetation greening influenced climate-induced WY trends. The results showed that the multi-year average WY in NWC was 2162.21 t and demonstrated a rising trend from 1982 to 2018, primarily driven by climate change. In contrast, anthropogenic activities had minimal effects on WY but were effective in amplifying the warming and wetting effects of climate (6.64 %-7.98 %) and counteracting unfavorable climate stress effects. Vegetation greening exacerbated soil water depletion, leading to increased evapotranspiration and mitigating the positive effects of climate change on WY. Our results are crucial for an effective comprehension of the role of vegetation in regulating terrestrial water fluxes in arid regions, providing a theoretical basis for ecological restoration.