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Exploring the effects of different additives on the improvement of newly cultivated farmland in mountainous areas can provide rational soil fertilization plans for regions lacking means of production. We conducted a paddy planting experiment in Ankang City, Shaanxi Province. Six treatments were set up, including sole chemical fertilizer (CK); fertilizer + bacteria agent (NB); chemical fertilizer + alginate bio-organic fertilizer (NO); fertilizer + fulvic acid biomass nutrient solution (NF); chemical fertilizer + acid soil conditioner (NC); fertilizer + silicon–calcium–magnesium–potassium fertilizer (NSi). We collected topsoil samples after paddy harvest, analyzed their physical, chemical, and biological properties, and selected indicators to construct a Total Data Set (TDS) and a Minimum Data Set (MDS). The Soil Quality Index (SQI) was used to evaluate the soil improvement effects after different fertilization regimes. The SQI calculated by the TDS and the MDS showed that the SQI after NF treatment was higher than that under other treatments. The SQI based on the TDS (SQITDS) and the SQI based on the MDS (SQIMDS) were significantly positively correlated with yield. The SQI calculated based on both the TDS and the MDS can objectively reflect the soil fertility quality. The paddy yield and total dry matter accumulation were the highest under the NF treatment, and the SQI was the largest. Thus, the effect of chemical fertilizer combined with fulvic acid biomass nutrient solution on soil fertility was the most significant.

In recent years, side-channel analysis based on deep learning has garnered significant attention from researchers. A pivotal reason for this lies in the fact that deep learning-based side-channel analysis requires minimal preprocessing of side-channel data. The automatic feature extraction property of deep learning methods drastically reduces the workload for researchers, enabling them to focus more on the core issues of side-channel analysis, namely, extracting sensitive information by attacking devices. However, in prior studies, most scholars have concentrated more on the model construction process, with little research focusing on the choice of optimizers.This paper explores a novel deep learning-based optimization algorithm—CPSGD (combined projection stochastic gradient descent). The algorithm comprises two variants, designed, respectively, for unprotected side-channel analysis (CPSGD1) and desynchronized side-channel analysis (CPSGD2), and their convergence has been theoretically proven. Experimental results demonstrate that, while maintaining the neural network structure unchanged, CPSGD1 exhibits the best performance on unprotected datasets compared to other publicly available optimizers, whereas CPSGD2 performs optimally on desynchronized datasets.

Osteoarthritis (OA), a chronic joint disease affecting over 500 million individuals globally, is characterized by the destruction of articular cartilage and joint inflammation. Conventional treatments are insufficient for repairing damaged joint tissue, necessitating novel therapeutic approaches. Mesenchymal stem cells (MSCs), with their potential for differentiation and self-renewal, hold great promise as a treatment for OA. However, challenges such as MSC viability and apoptosis in the ischemic joint environment hinder their therapeutic effectiveness. Hydrogels with biocompatibility and degradability offer a three-dimensional scaffold that support cell viability and differentiation, making them ideal for MSC delivery in OA treatment. This review discusses the pathological features of OA, the properties of MSCs, the challenges associated with MSC therapy, and methods for hydrogel preparation and functionalization. Furthermore, it highlights the advantages of hydrogel-based MSC delivery systems while providing insights into future research directions and the clinical potential of this approach.

Snow is a crucial component of the cryosphere and plays a vital role in the hydrological cycle, energy balance, and ecosystem function of mountainous regions [...]

Although a growing number of studies have suggested the relevance of greenspace exposure to respiratory health, most have explored single greenfield indicators or selected disease outcomes. This systematic review aimed to comprehensively assess the evidence that greenspace exposure is associated with chronic obstructive pulmonary disease (COPD) incidence, prevalence, mortality, and related lung function indicators and to explore the impact of greenspace exposure on COPD-related disease outcomes. We searched the literature published before 28 September 2023, using six electronic databases and snowball searches. After screening 3689 studies, 16 eligible studies were included. These studies examined various greenspace indicators such as the Normalized Difference Vegetation Index (NDVI), canopy coverage, greenspace coverage, and land cover conditions. Most studies have reported the positive effects of greenspace exposure. However, the findings had significant heterogeneity, with large differences in the greenness assessment, COPD identification methods, and population characteristics. The mechanisms of this impact have not been conclusively determined, but we propose several hypotheses to explain the pathways by which greenspace exposure affects COPD. Overall, our findings contribute valuable knowledge for understanding the relationship between greenspace exposure and COPD, informing future research in this field.

The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriched services to users, it also introduces security risks, including sensitive data leakage and misuse, highlighting the importance of data classification and grading as the foundational pillar of security. This paper presents a cloud-edge collaborative banking data open application scenario, focusing on the critical need for an accurate and automated sensitive data classification and categorization method. The regulatory outpost module addresses this requirement, aiming to enhance the precision and efficiency of data classification. Firstly, regulatory policies impose strict requirements concerning data protection. Secondly, the sheer volume of business and the complexity of the work situation make it impractical to rely on manual experts, as they incur high labor costs and are unable to guarantee significant accuracy. Therefore, we propose a scheme UP-SDCG for automatically classifying and grading financially sensitive structured data. We developed a financial data hierarchical classification library. Additionally, we employed library augmentation technology and implemented a synonym discrimination model. We conducted an experimental analysis using simulation datasets, where UP-SDCG achieved precision surpassing 95%, outperforming the other three comparison models. Moreover, we performed real-world testing in financial institutions, achieving good detection results in customer data, supervision, and additional in personally sensitive information, aligning with application goals. Our ongoing work will extend the model’s capabilities to encompass unstructured data classification and grading, broadening the scope of application.

The northeastern margin of the Tibetan Plateau (NE Tibetan Plateau) exhibits active geological structures and has experienced multiple strong earthquakes, with M ≥ 7, throughout history. Particularly noteworthy is the 1920 M81/2 earthquake in the Haiyuan region that occurred a century ago and is documented as one of the deadliest earthquakes. Consequently, analyzing seismic risks in the northeastern margin of the Tibetan Plateau holds significant importance. The b value, a crucial parameter for seismic activity, plays a pivotal role in seismic hazard analyses. This study calculates the spatial b values in this region based on earthquake catalogs since 1970. The study area encompasses several major active faults, and due to variations in b values across different fault types, traditional grid-search methods may introduce significant errors in calculating the spatial b value within complex fault systems. To address this, we employed the hierarchical space–time point–process (HIST-PPM) method proposed by Ogata. This method avoids partitioning earthquake samples, optimizes parameters using Akaike’s Bayesian Information Criterion (ABIC) with entropy maximization, and theoretically allows for a higher spatial resolution and more accurate b value calculations. The results indicate a high spatial heterogeneity in b values within the study area. The northwestern and southeastern regions exhibit higher b values. Along the Haiyuan fault zone, the central rupture zone of the Haiyuan earthquake has relatively higher b values than other regions of this fault zone, which is possibly related to the sufficient release of stress during the main rupture of the Haiyuan earthquake. The b values vary from high in the west to low in the east along the Zhongwei fault. On the West Qinling fault zone, the epicenter of the recent Minxian–Zhangxian earthquake is associated with a low b value. In general, regions with low b values correspond well to areas with moderate–strong seismic events in the past 50 years. The spatial differences in b values may reflect variances in seismic hazards among fault zones and regions within the same fault zone.

Submerged Reefs (SRs) are a kind of artificial fish habitat that can protect coasts and maintain ecological biodiversity. In this study, the flow field of the SR is simulated by solving a Reynolds-averaged Navier–Stokes equation closed with the Realizable k-ε model based on the finite volume method. The turbulent characteristics of SRs under different inflow velocities and submergences in the vicinity of the SR are analyzed. The wake vorticities are the primary turbulent pattern within and around the SR. The back wake and vorticity are chosen as critical indicators to quantitatively assess the hydrodynamic characteristics induced by the SR. The results show: (1) as the main flow passes through the SR, the upwelling is produced in front of the SR and a large-scale wake region is formed behind the SR which contains a clockwise vortex; (2) the length of the wake region formed behind the SR is positively and linearly correlated with both the inflow velocity and submergence; (3) the dipole-type vorticity patterns are induced within the compartment of the SR, where the area and average value of high vorticity have a positive correlation with the flow velocity and a negative correlation with the submergence, respectively.

Psychological anxiety among college students has attracted research interest. Previous studies have shown that greenspaces play a positive role in the recovery of student health. However, limited studies have explored the benefits of restorative environmental greenspace components. Therefore, this study used virtual reality to conduct control variable experiments. Considering the terrain scene, pavement material, and green vision rate as research elements, we monitored the skin conductance level and heart rate variability of 36 college students, as well as the positive and negative affect schedule and perceptual recovery scales, and we found that terrain elements have a significant impact on perceptual recovery, while pavement material has a significant impact on physiological recovery. Significant differences in perceptual recovery scores and changes in negative emotions among the different green vision levels were also observed. According to the regression relationship, the scene’s attractiveness rating was the highest when the scene’s green vision rate was 50%, while at 48%, the positive emotional improvement was the highest, and at 40%, the negative emotional improvement was the greatest.

Numerous studies have confirmed the positive impacts of real forests in areas such as stress relief. However, not everyone can visit forests easily. Virtual technologies offer new ways of experiencing forests for people who are hindered by real-life conditions and provide researchers with a manageable mode of study. Recently, there has been an influx of relevant research; however, themes within the overall perspective of the field remain unclear. This study aimed to review the literature and provide an overview of the potential health benefits of virtual forests. Research themes were extracted, and a conceptual framework for the multidimensional effects of virtual forest exposure was proposed from a holistic perspective. We analyzed studies using descriptive analytical methods through the Scopus, Web of Science, and PubMed databases and snowball searches. In total, 58 studies were included. This review identified four themes and segments throughout the virtual forest exposure process: core elements, experiences, effects, and circulation. We propose a new conceptual framework for a holistic perspective that incorporates technological features that are specific to virtual environments and makes connections to real forests. Finally, perspectives for future research in virtual forest therapy and its practical implications are discussed.

With the recent development from grey infrastructures to green infrastructures, artificial reefs become more popular in coastal protection projects. To investigate the responses of beach profile evolution to the presence of an artificial reef, a non-hydrostatic model is established. Both hydrodynamic and morphodynamic evolution for the beach with and without an artificial reef are compared under regular wave conditions. In addition, the protected beach profile evolution by an artificial reef is discussed under irregular wave conditions. Three key parameters in non-hydrostatic simulation are considered for sensitivity analysis, including maximum wave steepness criterium (maxbrsteep), water depth factor (depthscale), and equilibrium sediment concentration factor (sedcal). The numerical results under regular wave conditions indicate that the artificial reef enhances wave attenuation by inducing wave breaking. In addition, the artificial reef reduces local flow velocity and offshore sediment transport by 51%, therefore decrease the total erosion by 53%. Over the artificial reef, wave skewness and asymmetry go through a drastic change. Under irregular wave conditions, short waves contribute to the wave energy mainly and reflection-induced standing wave effects decline considerably. It demonstrates that the artificial reef can protect the beach from regular and irregular waves by reducing erosion and offshore transport of suspended sediments. Moreover, in the wave breaking area, the increase of maximum wave steepness criterium may give arise to the wave height. The morphological evolution is more sensitive to water depth factor than equilibrium sediment concentration factor, because the former is a controlling factor for beach profile characteristics while the latter forms the sandbar varying irregularly in shape.

With the growing demand for data sharing file formats in financial applications driven by open banking, the use of the OFD (open fixed-layout document) format has become widespread. However, ensuring data security, traceability, and accountability poses significant challenges. To address these concerns, we propose E-SAWM, a dynamic watermarking service framework designed for edge cloud scenarios. This framework incorporates dynamic watermark information at the edge, allowing for precise tracking of data leakage throughout the data-sharing process. By utilizing semantic analysis, E-SAWM generates highly realistic pseudostatements that exploit the structural characteristics of documents within OFD files. These pseudostatements are strategically distributed to embed redundant bits into the structural documents, ensuring that the watermark remains resistant to removal or complete destruction. Experimental results demonstrate that our algorithm has a minimal impact on the original file size, with the watermarked text occupying less than 15%, indicating a high capacity for carrying the watermark. Additionally, compared to existing explicit watermarking schemes for OFD files based on annotation structure, our proposed watermarking scheme is suitable for the technical requirements of complex dynamic watermarking in edge cloud scenario deployment. It effectively overcomes vulnerabilities associated with easy deletion and tampering, providing high concealment and robustness.

To solve the problems of low utilization of biomass and uncertainty and intermittency of wind power (WP) in rural winter, an interval optimization model of a rural integrated energy system with biogas fermentation and electrolytic hydrogen production is constructed in this paper. Firstly, a biogas fermentation kinetic model and a biogas hydrogen blending model are developed. Secondly, the interval number is used to describe the uncertainty of WP, and an interval optimization scheduling model is developed to minimize daily operating cost. Finally, a rural integrated energy system in Northeast China is taken as an example, and a sensitivity analysis of electricity price, gas production, and biomass price is conducted. The simulation results show that the proposed strategy can significantly reduce the wind abandonment rate and improve the economy by 3.8–22.3% compared with conventional energy storage under optimal dispatch.

Previous research has consistently shown that exposure to natural environments provides a variety of health benefits. The purpose of this study is to investigate the restorative benefits of non-virtual environments in field experiments as well as the differences in physiological and psychological effects between different types of restorative sites for stressed young adults. This controlled study design used the Positive and Negative Affect Schedule (PANAS), electroencephalogram (EEG), and heart rate variability (HRV) as psychophysiological indicators of individual affect and stress. We used a “stress imposition-greenspace recovery” pre- and post-test mode to simulate the most realistic short-term recovery experience in the park (Grassplots, Square, Forest, and Lakeside) under relatively free conditions. The experimental results show that all four natural spaces in the park have some degree of recovery. However, there were discernible differences in the restorative effects of four selected natural sites. Lakeside and Forest demonstrated the most robust restorative properties in terms of both negative emotion reduction and positive emotion enhancement. In contrast, Square showed the weakest facilitation of recovery, while Grassplots promoted moderate resilience. Physiologically, we found that the EEG-α% of the Square was significantly lower than the EEG-α% of the Forest (t = −3.56, p = 0.015). This means that stressed young adults were much more relaxed in the forest than in the paved square. The study answers which types of natural spaces, when considered together, would provide greater restorative benefits to stressed young people participating in natural therapies in urban parks. The study’s policy implications include the need to create more green natural spaces, especially forests with multiple plant levels, as well as to improve the restorative nature of urban parks through appropriate landscape space design.

Relation classification is a significant task within the field of natural language processing. Its objective is to extract and identify relations between two entities in a given text. Within the scope of this paper, we construct an artificial dataset (CS13K) for relation classification in the realm of cybersecurity and propose two models for processing such tasks. For any sentence containing two target entities, we first locate the entities and fine-tune the pre-trained BERT model. Next, we utilize graph attention networks to iteratively update word nodes and relation nodes. A new relation classification model is constructed by concatenating the updated vectors of word nodes and relation nodes. Our proposed model achieved exceptional performance on the SemEval-2010 task 8 dataset, surpassing previous approaches with a remarkable F1 value of 92.3%. Additionally, we propose the integration of a ranking-based voting mechanism into the existing model. Our best results are an F1 value of 92.5% on the SemEval-2010 task 8 dataset and a value 94.6% on the CS13K dataset. These findings highlight the effectiveness of our proposed models in tackling relation classification tasks.

Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However, the complex conditions at high altitudes pose additional challenges to the statistical methods. To improve the correction of precipitation measurements in high-altitude areas, we selected the Yakou station, situated at an altitude of 4147 m on the Tibetan plateau, as the study site. In this study, we employed the machine learning method XGBoost regression to correct precipitation measurements using meteorological variables and remote sensing data, including Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Additionally, we examined the transferability of this method between different stations in our study site, Norway, and the United States. Our results show that the Yakou station experiences a large underestimation of precipitation, with a magnitude of 51.4%. This is significantly higher than similar measurements taken in the Arctic or lower altitudes. Furthermore, the remote sensing precipitation datasets underestimated precipitation when compared to the Double Fence Intercomparison Reference (DFIR) precipitation observation. Our findings suggest that the machine learning method outperformed the traditional statistical method in accuracy metrics and frequency distribution. Introducing remote sensing data, especially the GSMaP precipitation, could potentially replace the role of in situ wind speed in precipitation correction, highlighting the potential of remote sensing data for correcting precipitation rather than in situ meteorological observation. Moreover, our results indicate that the machine learning method with remote sensing data demonstrated better transferability than the traditional statistical method when we cross-validated the method with sites located in different countries. This study offers a promising strategy for obtaining more accurate precipitation measurements in high-altitude regions.

Accurate precipitation data are essential for understanding hydrological processes in high mountainous regions with limited observations and highly variable precipitation events. While frequency-corrected precipitation data are expected to aid in understanding hydrological processes, its performance in ungauged high mountain hydrological simulation remains unclear. To clarify this issue, we conducted a numerical experiment that used reanalysis precipitation, frequency-corrected precipitation, and gridded precipitation to drive a distributed cold region hydrological model. We selected an ungauged basin in high mountain Asia (Manas River Basin in China) as the study area and employed a statistical parameter optimization method to avoid subjectivity in parameter selection. Our findings indicate that the frequency information from the few existing stations can aid in correcting the reanalysis precipitation data. The frequency correction approach can reduce the total volume of errors in the reanalysis precipitation data, especially when severe biases occur. Our findings show that frequency-corrected precipitation performs better in modeling discharge, runoff depth, and evaporation. Furthermore, the improvement in precipitation using frequency correction bears clear altitude differences, which implies that having more stations at different altitudes is necessary to measure precipitation accurately in similar areas. Our study provides a feasible flow for future precipitation preparation for similar ungauged high mountain areas. Frequency correction, instead of direct interpolation, may be a viable option for precipitation preparation. Our work has reference implications for future hydrological simulations in similar ungauged high mountains.

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample’s contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.

Moving bed biofilm reactor (MBBR) is considered as a promising technology for wastewater treatment owing to the high biomass retention and low cost. In this study, the performance of using MBBR for partial denitrification (PD) was investigated. Denitrifying biofilm was successfully formed after 40 days with the biomass and nitrite reduction rate of 40.83 mg VSS/g carriers and 51.52 mg N/(gVSS·h), respectively. Morphology analysis by scanning electron microscope (SEM) showed that the biofilm surface was dominant by cocci, filamentous bacteria, and extracellular polymeric substances (EPS). Investigation about the influencing factors of PD found that the optimal COD/NO3−-N and pH for efficient nitrite production (nitrate to nitrite ratio: 96.49%) was 3 and 9, respectively. Moreover, Saccharimonadales was proved to be dominant functional microbes in the constructed PD systems with different influent conditions because its relative abundance exhibited good correlation with the nitrite accumulation. By analyzing the biofilm characteristics under different conditions, PD was observed to mainly occur in the range of 300–700 μm inside the biofilm, where most of the dissolved oxygen was consumed. This study confirmed the feasibility and superior performance of PD-MBBR system.

In brain–computer interface (BCI)-based motor imagery, the symmetric positive definite (SPD) covariance matrices of electroencephalogram (EEG) signals with discriminative information features lie on a Riemannian manifold, which is currently attracting increasing attention. Under a Riemannian manifold perspective, we propose a non-linear dimensionality reduction algorithm based on neural networks to construct a more discriminative low-dimensional SPD manifold. To this end, we design a novel non-linear shrinkage layer to modify the extreme eigenvalues of the SPD matrix properly, then combine the traditional bilinear mapping to non-linearly reduce the dimensionality of SPD matrices from manifold to manifold. Further, we build the SPD manifold network on a Siamese architecture which can learn the similarity metric from the data. Subsequently, the effective signal classification method named minimum distance to Riemannian mean (MDRM) can be implemented directly on the low-dimensional manifold. Finally, a regularization layer is proposed to perform subject-to-subject transfer by exploiting the geometric relationships of multi-subject. Numerical experiments for synthetic data and EEG signal datasets indicate the effectiveness of the proposed manifold network.

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in the simulation of hydrological runoff. Considering the impact of snow cover on runoff in high-altitude mountainous areas, based on remote sensing data and atmospheric reanalysis data, in this paper we established a runoff simulation model with a random forest model and ANN (artificial neural network) model for the Xiying River Basin in the western Qilian region The verification of the measured data showed that the NSE (Nash–Sutcliffe efficiency), RMSE (root mean square error), and PBIAS (percent bias) values of the random forest model and ANN model were 0.701 and 0.748, 6.228 m3/s and 4.554 m3/s, and 4.903% and 8.329%, respectively. Considering the influence of ice and snow on runoff, the simulation accuracy of both the random forest model and ANN model was improved during the period of significant decreases in the annual snow and ice water equivalent in the Xiying River Basin from April to May, after the snow remote sensing data were introduced into the model. Specifically, for the random forest model, the NSE increased by 0.099, the RMSE decreased by 0.369 m3/s, and the PBIAS decreased by 1.689%. For the ANN model, the NSE increased by 0.207, the RMSE decreased by 0.700 m3/s, and the PBIAS decreased by 1.103%. In this study, based on remote sensing data and atmospheric reanalysis data, the random forest model and ANN model were used to effectively simulate hydrological runoff processes in high-altitude mountainous areas without observational data. In particular, the accuracy of the machine learning simulations of snowmelt runoff (especially during the snowmelt period) was effectively improved by introducing the snow remote sensing data, which can provide a methodological reference for the simulation and prediction of snowmelt runoff in alpine mountains.

A method of tripling for a three-level design, which triples both the run size and the number of factors of the initial design, has been proposed for constructing a design that can accommodate a large number of factors by combining all possible level permutations of its initial design. Based on the link between the indicator functions of a triple design and its initial design, the close relationships between a triple design and its initial design are built from properties such as resolution and orthogonality. These theoretical results present a closer look at a triple design and provide a solid foundation for a design constructed using the tripling method, where the constructed designs have better properties, such as high resolution and orthogonality, and are recommended for application in high dimension topics of statistics or large-scale experiments.

Highly efficient designs and uniform designs are widely applied in many fields because of their good properties. The purpose of this paper is to study the issue of design efficiency for asymmetric minimum projection uniform designs. Based on the centered L2 discrepancy, the uniformity of the designs with mixed levels is defined, which is used to measure the projection uniformity of the designs. The analytical relationship between the uniformity pattern and the design efficiency is established for mixed-level orthogonal arrays with a strength of two. Moreover, a tight lower bound of the uniformity pattern is presented. The research is relevant in the field of experimental design by providing a theoretical basis for constructing the minimum number of projection uniform designs with a high design efficiency under a certain condition. These conclusions are verified by some numerical examples, which illustrate the theoretical results obtained in this paper.

Climate change has profoundly impacted elements of land surface at high latitudes and elevations, especially snow and vegetation [...]

Using observational data of soil moisture from the third Tibetan Plateau Experiment for atmospheric science (TIPEX III), the seasonal and diurnal variations characteristics of soil moisture at different depths of 5–160 cm from seven stations were analyzed, with emphasis on the comparative analysis of the differences of soil moisture between different sites and the differences of the synergistic relationship between soil moisture and temperature. The soil moisture was wet in the southeast and dry in the northwest. The studied sites were Lhari, Biru, Nyainrong, Amdo, Nagqu, Baingoin and Seng-ge Kambab in descending order, according to the soil moisture. The seasonal variation of soil moisture at the different sites showed a significant three-peak structure, which was more obvious in the shallow layer than in the deep layer. The first peak occurred from March to May, which was mainly due to the soil thawing in spring. The other two peaks corresponded to the two rainy seasons in the plateau. Soil moisture was the greatest during this rainy period. The diurnal variations of soil moisture and temperature in Amdo, Nagqu, Nyainrong and Baingoin showed a significant positive correlation in the four seasons. The soil moisture and temperature in Lhari and Biru were significantly positively correlated in winter and spring but negatively correlated in summer and autumn. The profiles of the soil moisture with depth varied greatly at different stations in different seasons. The distribution of soil water content at each observational site did not increase or decrease with depth but showed a certain high aquifer, which might be related to the types of the underlying surface and physical properties of soil. During the summer monsoon period, soil moisture in the shallow layer of 5–10 cm was higher at all observational sites. The spatial distribution of soil moisture in the plateau was more heterogeneous than that in the plain area, and only in the central part of the Tibetan Plateau, the soil moisture varied greatly from site to site. This also indicated that it was unreasonable to only use the soil moisture of several stations to represent the overall soil moisture of the region. The results provided a multi-angle observational basis for the validation of satellite data and parameterization of the numerical model of soil moisture over the Tibetan Plateau.

Measuring the soil salinity using visible and near-infrared (vis–NIR) reflectance spectra is considered a fast and cost-effective method. For monitoring purposes, estimating soils with low salinity measured as electrical conductivity (EC) using vis–NIR spectra is still understudied. In this research, 399 legacy soil samples from six regions of Southern Xinjiang, China with low EC values were used. Reflectance spectra were measured in the laboratory on dried and ground soil samples using a portable vis–NIR spectrometer. By using 10-fold cross-validation, three algorithms–partial least-squares regression (PLSR), random forest (RF), and Cubist–were employed to develop statistical models of EC. The model performance evaluation was obtained by the relative importance of variants. In terms of accuracy assessment of soil EC prediction, the results demonstrated that the Cubist model performed better (R2 = 0.67, RMSE = 0.16 mS/cm, RPIQ = 2.28) than both PLSR and RF. Despite similar variants for modelling, the RF model performed somewhat better than that of the PLSR. Additionally, the 610 nm and 790 nm wavelengths only demonstrated significant promise for predicting low soil EC values when used in the Cubist mode. The current research recommends the use of Cubist to estimate the low soil salinity using the vis–NIR reflectance spectra.

Snow cover phenology (SCP) is critical to the climate system. China has the most comprehensive snow cover distribution in the middle and low latitudes and has shown dramatic changes over the past few decades. However, the spatiotemporal characteristics of SCP parameters and their sensitivity to meteorological factors (temperature and precipitation) under different conditions (altitude, snow cover classification, or season) in China are insufficiently studied. Therefore, using improved daily MODIS cloud-gap-filled (CGF) snow-cover-extent (SCE) products, the spatiotemporal characteristics (distribution and variation) and respond to climate of snow cover area (SCA), snow cover start (SCS), snow cover melt (SCM), and snow cover days (SCD) are explored from 2000 to 2020. The results show that in the past 20 years, snow cover in China has demonstrated a trend of decreasing SCA, decreasing SCD, advancing SCS, and advancing SCM, with SCM advancing faster than SCS. The greatest snowfall occurs in January, mainly in northeastern China, northern Xinjiang, and the Tibet Plateau. Spatially, the slope of SCP was mainly within ±0.5 day/year (d/y) Statistics indicated that the area proportion where SCD is significantly reduced is greater than increased; SCD, SCS, and SCM are shortened or advanced in three snow-covered area classifications. Moreover, compared with precipitation, the significantly correlated regions (6–47.2% more than precipitation) and correlation degree (1.23–8.33 times precipitation in significantly correlated snow cover classification) between temperature and SCP in different seasons are larger. For stable snow-covered areas (SSA), SCD are mainly affected by spring temperature below 1500 m and mainly by autumn temperature above 1500 m; the precipitation is more affected in autumn. The correlation of SCP with temperature and precipitation has obvious spatial and seasonal differences and shows characteristic variation with altitude. These results can provide important data support for climate prediction, hydrological research, and disaster warning.

To promote Tibetan meteorological research, the third Tibetan Plateau (TP) Experiment for atmospheric sciences (TIPEX III) has been carried out over the plateau region since 2014, with near-surface heat fluxes measured at different sites. Using the observational data of near-surface heat fluxes measured at 8 plateau stations in TIPEX III, as well as the ECMWF ERA Interim reanalysis data, the land-atmosphere heat transfers over different regions of TP and their responses to the South Asian summer monsoon (SASM) during active/break periods were investigated. Inhomogeneity was found in the land-atmosphere heat transfers over the plateau, with large differences among plateau stations. During the observation period, the daily averaged total heat transfer (the sum of sensible and latent heat flux) varied from 70.2 to 101.2 Wm−2 among the 8 plateau stations, with the sensible heat flux from 18.8 to 60.1 Wm−2 and the latent heat flux from 10.1 to 74.7 Wm−2. These heat transfers were strongly affected by the SASM evolution, but with strong inhomogeneities over the plateau stations. Overall, the more southern station locations exhibited more SASM impacts. The land-atmosphere heat transfers (the total, sensible and latent heat fluxes) were greatly weakened/strengthened during the SASM active/break period at Namco (southeast plateau), Baingoin (central plateau), Lhari (central plateau), and Nagqu (central plateau), which were closely related to the weakened/strengthened radiation conditions. However, the SASM impacts were quite small or even negligible for the other plateau stations, which complicated our conclusions, and further investigations are still needed.

River ice on the Tibetan Plateau has important impacts on the ecosystem and hydrology. High-resolution Synthetic Aperture Radar (SAR) is an important tool for monitoring the thickness of river ice in high-altitude areas without ground data. However, due to the complex topography and narrow width, it remains challenging to monitor the ice thickness of high-order rivers (high-level branches in the plateau river system) on the Tibetan Plateau using SAR. Therefore, this paper focuses on inverting the ice thickness by utilizing dual-polarized C-band radar data. We select a typical watershed in the northeastern Tibetan Plateau, namely, the Babao River basin (including the Babao River and Binggou River), as the study area. The results show the following: (1) Dual-polarized C-band radar data have the potential to monitor the ice thickness of high-order rivers. The RMSEs of the Babao and Binggou Rivers are 0.109 m and 0.258 m, respectively. (2) Ascending and descending orbit radar images perform differently in retrieving the ice thicknesses of rivers with different directions. (3) The thickness of river ice affects the inversion accuracy. (4) Polarization parameters have varying explanatory capacities depending on the river characteristics. Our findings can provide a reference for the subsequent development of highly generalizable river ice inversion equations using dual-polarized radar data.

Cadmium (Cd) pollution in a soil–rice system is closely related to widely concerning issues, such as food security and health risk due to exposure to heavy metals. Therefore, modeling the Cd content in a soil–rice system and identifying related controls could provide critical information for ensuring food security and reducing related health risks. To archive this goal, in this study, we collected 217 pairs of soil–rice samples from three subareas in Zhejiang Province in the Yangtze River Delta of China. All soil–rice samples were air-dried and conducted for chemical analysis. The Pearson’s correlation coefficient, ANOVA, co-occurrence network, multiple regression model, and nonlinear principal component analysis were then used to predict the Cd content in rice and identify potential controls for the accumulation of Cd in rice. Our results indicate that although the mean total concentration of Cd in soil samples was higher than that of the background value in Zhejiang Province, the mean concentration of Cd in rice was higher than that of the national regulation value. Furthermore, a significant difference was detected for Cd content in rice planted in different soil groups derived from different parental materials. In addition, soil organic matter and total Cd in the soil are essential factors for predicting Cd concentrations in rice. Additionally, specific dominant factors resulting in Cd accumulation in rice planted at different subareas were identified via nonlinear principal component analysis. Our study provides new insights and essential implications for policymakers to formulate specific prevention and control strategies for Cd pollution and related health risks.

This project explored the integrated use of satellite, ground observations and hydrological distributed models to support water resources assessment and monitoring in High Mountain Asia (HMA). Hydrological data products were generated taking advantage of the synergies of European and Chinese data assets and space-borne observation systems. Energy-budget-based glacier mass balance and hydrological models driven by satellite observations were developed. These models can be applied to describe glacier-melt contribution to river flow. Satellite hydrological data products were used for forcing, calibration, validation and data assimilation in distributed river basin models. A pilot study was carried out on the Red River basin. Multiple hydrological data products were generated using the data collected by Chinese satellites. A new Evapo-Transpiration (ET) dataset from 2000 to 2018 was generated, including plant transpiration, soil evaporation, rainfall interception loss, snow/ice sublimation and open water evaporation. Higher resolution data were used to characterize glaciers and their response to environmental forcing. These studies focused on the Parlung Zangbo Basin, where glacier facies were mapped with GaoFeng (GF), Sentinal-2/Multi-Spectral Imager (S2/MSI) and Landsat8/Operational Land Imager (L8/OLI) data. The geodetic mass balance was estimated between 2000 and 2017 with Zi-Yuan (ZY)-3 Stereo Images and the SRTM DEM. Surface velocity was studied with Landsat5/Thematic Mapper (L5/TM), L8/OLI and S2/MSI data over the period 2013–2019. An updated method was developed to improve the retrieval of glacier albedo by correcting glacier reflectance for anisotropy, and a new dataset on glacier albedo was generated for the period 2001–2020. A detailed glacier energy and mass balance model was developed with the support of field experiments at the Parlung No. 4 Glacier and the 24 K Glacier, both in the Tibetan Plateau. Besides meteorological measurements, the field experiments included glaciological and hydrological measurements. The energy balance model was formulated in terms of enthalpy for easier treatment of water phase transitions. The model was applied to assess the spatial variability in glacier melt. In the Parlung No. 4 Glacier, the accumulated glacier melt was between 1.5 and 2.5 m w.e. in the accumulation zone and between 4.5 and 6.0 m w.e. in the ablation zone, reaching 6.5 m w.e. at the terminus. The seasonality in the glacier mass balance was observed by combining intensive field campaigns with continuous automatic observations. The linkage of the glacier and snowpack mass balance with water resources in a river basin was analyzed in the Chiese (Italy) and Heihe (China) basins by developing and applying integrated hydrological models using satellite retrievals in multiple ways. The model FEST-WEB was calibrated using retrievals of Land Surface Temperature (LST) to map soil hydrological properties. A watershed model was developed by coupling ecohydrological and socioeconomic systems. Integrated modeling is supported by an updated and parallelized data assimilation system. The latter exploits retrievals of brightness temperature (Advanced Microwave Scanning Radiometer, AMSR), LST (Moderate Resolution Imaging Spectroradiometer, MODIS), precipitation (Tropical Rainfall Measuring Mission (TRMM) and FengYun (FY)-2D) and in-situ measurements. In the case study on the Red River Basin, a new algorithm has been applied to disaggregate the SMOS (Soil Moisture and Ocean Salinity) soil moisture retrievals by making use of the correlation between evaporative fraction and soil moisture.

The reclamation of mine dump is largely centered on the role played by microorganisms. However, the succession of microbial community structure and function in ecological restoration of the mine soils is still poorly understood. In this study, soil samples with different stacking time were collected from the dump of an iron mine in China and the physicochemical characteristics and microbial communities of these samples were comparatively investigated. The results showed that the fresh bare samples had the lowest pH, highest ion concentration, and were the most deficient in nutrients while the acidity and ion concentration of old bare samples decreased significantly, and the nutritional conditions improved remarkably. Vegetated samples had the weakest acidity, lowest ion concentration, and the highest nutrient concentration. In the fresh mine soils, the iron/sulfur-oxidizers such as Acidiferrobacter and Sulfobacillus were dominant, resulting in the strongest acidity. Bacteria from genera Acidibacter, Metallibacterium, and phyla Cyanobacteria, WPS-2 were abundant in the old bare samples, which contributed to the pH increase and TOC accumulation respectively. Acidobacteriota predominated in the vegetated samples and promoted nutrient enrichment and plant growth significantly. The microbial diversity and evenness of the three types of soils increased gradually, with more complex microbial networks, suggesting that the microbial community became more mature with time and microorganisms co-evolved with the mine soils.

High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value > 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.

Suspended particles play a significant role in aquatic systems. However, existing methods to probe suspended particles have several limitations. In this paper, we present a portable prototype to in situ probe individual particles in aquatic suspensions by simultaneously measuring polarized light scattering and fluorescence, aiming to obtain an effective classification of microplastics and microalgae. Results show that the obtained classification accuracy is significantly higher than that for either of these two methods. The setup also successfully measures submicron particles and discriminates two species of Synechococcus. Our study demonstrates the feasibility of simultaneously measuring polarized light scattering and fluorescence, and the promising capability of our method for further aquatic environmental monitoring.

Soil pollution due to toxic elements (TEs) has been a core environmental concern globally, particularly in areas with developed industries. In this study, we sampled 300 surface (0–0.2 m) soil samples from Yuyao City in eastern China. Initially, the geo-accumulation index, potential ecological risk index, single pollution index, and Nemerow composite pollution index were used to evaluate the soil contamination status in Yuyao City. Ordinary kriging was then deployed to map the distribution of the soil TEs. Subsequently, indicator kriging was utilized to identify regions with high risk of TE pollution. Finally, the positive matrix factorization model was used to apportion the sources of the different TEs. Our results indicated that the mean content of different TEs kept the order: Zn > Cr > Pb > Cu > Ni > As > Hg ≈ Cd. Soil pollution was mainly caused by Cd and Hg in the soil of Yuyao City, while the content of other TEs was maintained at a safe level. Regions with high TE content and high pollution risk of TEs are mainly located in the central part of Yuyao City. Four sources of soil TEs were apportioned in Yuyao City. The Pb, Hg, and Zn contents in soil were mainly derived from traffic activities, coal combustion, and smelting. Meanwhile, Cu was mainly sourced from industrial emissions and atmospheric deposition, Cr and Ni mainly originated from soil parental materials, and Cd and As were produced by industrial and agricultural activities. Our study provides important implications for improving the soil environment and contributes to the development of efficient strategies for TE pollution control and remediation.

Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.

Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard–Stone (K–S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21–0.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07–79.6 dS m−1), the spectral reflectance of salinized soil in the MSI data ranged from 0.09–0.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R2 = 0.88, root mean square error (RMSE) = 4.89 dS m−1, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.

Information on spatial, temporal, and depth variability of soil salinity at field and landscape scales is important for a variety of agronomic and environment concerns including irrigation in arid and semi-arid areas. However, challenges remain in characterizing and monitoring soil secondary salinity as it can largely be impacted by managements including irrigation and mulching in addition to natural factors. The objective of this study is to evaluate apparent electrical conductivity (ECa)-directed soil sampling as a basis for monitoring management-induced spatio-temporal change in soil salinity in three dimensions. A field experiment was conducted on an 18-ha saline-sodic field from Alar’s Agricultural Science and Technology Park, China between March, and November 2018. Soil ECa was measured using an electromagnetic induction (EMI) sensor for four times over the growing season and soil core samples were collected from 18 locations (each time) selected using EMI survey data as a-priori information. A multi-variate regression-based predictive relationship between ECa and laboratory-measured electrical conductivity (ECe) was used to predict EC with confidence (R2 between 0.82 and 0.99). A three-dimensional inverse distance weighing (3D-IDW) interpolation clearly showed a strong variability in space and time and with depths within the study field which were mainly attributed to the human management factors including irrigation, mulching, and uncovering of soils and natural factors including air temperature, evaporation, and groundwater level. This study lays a foundation of characterizing secondary salinity at a field scale for precision and sustainable management of agricultural lands in arid and semi-arid areas.

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.

With the rise of corporate social responsibility (CSR) and the great changes of the organization scale of the modern enterprise, it is difficult to maintain an ideal development only by maximizing the equity of shareholders. The co-governance of stakeholders can meet the interests of all stakeholders, which is the embodiment of corporate social responsibility in corporate governance. The creation and sharing of enterprise values are the foundation of the coexistence of stakeholders. A rational allocation of the limited enterprise values among stakeholders plays a crucial role in balancing their relationship and realizing the sustainable development of the enterprise. Based on the application of corporate social responsibility in stakeholder governance, this paper constructs a stakeholder symbiosis evolution model with the help of symbiosis theory. By solving the differential equation, the symbiotic evolution path of stakeholders is analyzed. The dynamic mechanism of stakeholder Symbiosis evolution is analyzed by using the system analysis method and evolution theory. The dynamic mechanism of the population evolution of stakeholders is represented in an original graphical manner to describe the symbiotic evolution process of stakeholders. Lastly, we use the numerical simulation method to simulate different symbiosis models which are reflected by different symbiosis coefficients. It is concluded that the symmetric mutual benefit symbiosis model is the most effective model to reflect corporate social responsibility.

Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.

Atmospheric water vapor plays an important role in the water cycle, especially in arid Central Asia, where precipitation is invaluable to water resources. Understanding and quantifying the relationship between water vapor source regions and precipitation is a key problem in water resource research in typical arid Central Asia, Northern Xinjiang. However, the relationship between precipitation and water vapor sources is still unclear of snow season. This paper aimed at studying the role of water vapor source supply in the Northern Xinjiang precipitation trend, which was investigated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The results showed that the total water vapor contributed from Western Eurasia and the North Polar area presented upward trends similar to the precipitation change trend, which indicated that the water vapor contribution from the two previous water vapor source regions supplied abundant water vapor and maintained the upward precipitation trend from 1980 to 2017 in Northern Xinjiang. From the climatology of water vapor transport, the region was controlled by midlatitude westerlies and major water vapor input from the western boundary, and the net water vapor flux of this region also showed an annual increasing trend. Western Eurasia had the largest moisture percentage contribution to Northern Xinjiang (48.11%) over the past 38 years. Northern Xinjiang precipitation was correlated with water vapor from Western Eurasia, the North Polar area, and Siberia, and the correlation coefficients were 0.66, 0.45, and 0.57, respectively. These results could aid in better understanding the water cycle process and climate change in this typical arid region of Central Asia.

The coordination relationship between land urbanization and population urbanization is crucial for achieving sustainable development under economic transition. Moreover, the balance between land urbanization and population urbanization is essential to guarantee the urbanization process of an entire city. This paper empirically analyzes the interaction between land urbanization and population urbanization in Nanchang from 2002 to 2017 based on the coupling coordination model (CCM). The impacts of the coordination degree on coordinated development are quantified by multivariate linear regression (MLR). The results show the following: (1) The indices of land urbanization and population urbanization in Nanchang showed an upward trend, and therefore the coordination degree in Nanchang increased significantly from 2002 to 2017. (2) The coordinated development of urbanization underwent two stages: disordered and moderately disordered. (3) The urban population proportion and the supporting capability of agricultural production had a positive impact on coordinated development. Meanwhile, the results also show that per capita education expenditures and the per capita public green area had negative impacts on the coordination degree, while economic development and the urban industrial structure were positive contributors to the coordination degree. Finally, this paper proposes that policies should be formulated to achieve coordinated development of urbanization. It can be concluded that the results regarding coordinated development of urbanization can help decision makers formulate effective measures to achieve coordinated development in the future.

To obtain long term accurate high resolution precipitation for the Heihe River Basin (HRB), Weather Research and Forecasting (WRF) model simulations were performed using two different initial boundary conditions, with nine microphysical processes for different analysis parameterization schemes. High spatial-temporal precipitation was simulated from 2000 to 2013 and a suitable set of initial, boundary, and micro parameters for the HRB was evaluated from the Heihe Watershed Allied Telemetry Experimental Research project and Chinese Meteorological Administration data at hourly, daily, monthly, and annual time scales using various statistical indicators. It was found that annual precipitation has gradually increased over the HRB since 2000. Precipitation mostly occurs in summer and is higher in monsoon-influenced areas. High elevations experience winter snowfall. Precipitation is higher in the eastern upstream area than in the western upstream, area; however, the converse occurs in winter. Precipitation gradually increases with elevation from 1000 m to 4000 m, and the maximum precipitation occurs at the height of 3500–4000 m, then the precipitation slowly decreases with elevation from 4000 m to the top over the Qilian Mountains. Precipitation is scare and has a high temporal variation in the downstream area. Results are systematically validated using the in situ observations in this region and it was found that precipitation simulated by the WRF model using suitable physical configuration agrees well with the observation over the HRB at hourly, daily, monthly and yearly scales, as well as at spatial pattern. We also conclude that the dynamic downscaling using the WRF model is capable of producing high-resolution and reliable precipitation over complex mountainous areas and extremely arid environments. The downscaled data can meet the requirement of river basin scale hydrological modeling and water balance analysis.

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.

In this work, a heterogeneous catalytic system consisting of [HO2CMMIm]Cl and ZrOCl2 in isopropanol is demonstrated to be effective for 5-hydroxymethylfurfural (HMF) synthesis with glucose as the feedstock. Various reaction conditions for HMF synthesis by glucose dehydration were investigated systematically. Under optimized reaction conditions, as high as 43 mol% HMF yield could be achieved. Increasing the water content to a level below 3.17% led to the production of HMF with a higher yield, while a lower HMF yield was observed when the water content was increased above 3.17%. In addition, the data also showed that ZrOCl2 could not only effectively convert glucose into intermediate species (which were not fructose, in contrast to the literature) but also catalyze the intermediate species’ in situ dehydration into HMF. [HO2CMMIm]Cl was used to catalyze the intermediate species’ in situ conversion to HMF. The kinetics data showed that a temperature increase accelerated the intermediate species’ dehydration reaction rate. The reaction of glucose dehydration was a strong endothermal reaction.

The change in snow cover under climate change is poorly understood in Tianshan Mountains. Here, we investigate the spatiotemporal characteristics and trends of snow-covered area (SCA) and snow-covered days (SCD) in the Tianshan Mountains by using the cloud-removed MODIS fractional snow cover datasets from 2001–2015. The possible linkage between the snow cover and temperature and precipitation changes over the Tianshan Mountains is also investigated. The results are as follows: (1) The distribution of snow cover over the Tianshan Mountains exhibits a large spatiotemporal heterogeneity. The areas with SCD greater than 120 days are distributed in the principal mountains with elevations of above 3000 m. (2) In total, 26.39% (5.09% with a significant decline) and 34.26% (2.81% with a significant increase) of the study area show declining and increasing trend in SCD, respectively. The SCD mainly decreases in Central and Eastern Tianshan (decreased by 11.88% and 8.03%, respectively), while it increases in Northern and Western Tianshan (increased by 9.36% and 7.47%). (3) The snow cover variations are linked to the temperature and precipitation changes. Temperature tends to be the major factor effecting the snow cover changes in the Tianshan Mountains during 2001–2015.

A series of Lewis acidic metal salts were used for glucose dehydration to 5-hydroymethylfurfural (HMF) in water. Effect of valence state, ionic radii of Lewis acidic cation, and the type of anions on the catalytic performance have been studied systematically. The experimental results showed that the valence state played an important role in determining catalytic activity and selectivity. It was found that a higher glucose conversion rate and HMF selectivity could be obtained over high valent Lewis acid salts, where the ionic radii of these Lewis acidic metal salts are usually relatively small. Analysis on the effect of the anions of Lewis acid salts on the catalytic activity and the selectivity suggested that a higher glucose conversion and HMF selectivity could be readily obtained with Cl−. Furthermore, the recyclability of high valence state Lewis acid salt was also studied, however, inferior catalytic performance was observed. The deactivation mechanism was speculated to be the fact that high valence state Lewis acid salt was comparatively easier to undergo hydrolysis to yield complicated metal aqua ions with less catalytic activity. The Lewis acidic activity could be recovered by introducing a stoichiometric amount of hydrochloric acid (HCl) to the catalytic before the reaction.