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Abstract Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human–machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human–machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human–machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human–machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

Abstract To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients’ readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients.

China’s oases are the center of human life and economic development for tens of millions of inhabitants in drylands. However, oases have experienced a significant expansion of cropland during the past three decades, which has resulted in rapidly increasing agricultural water demand and increasing water shortages in the region. Although crop switching is recognized as a promising strategy for improving the sustainability of agricultural production, little is known about how much it can improve the sustainability of oasis cropping systems and whether there are trade-offs among multiple sustainability goals. Here, we used a crop-water-use model, data on crop production and nutrient content, observational data on streamflow, and oasis land-use data to assess the impacts of different crop switching scenarios on water demand, nutrient supply, and income. The results showed that oasis agricultural water demand increased from 28.5 km3 to 41.1 km3 between 1987 and 2017, with 74.6 % of the oasis counties experiencing increasing agricultural water stress. By replacing all crops with those having the lowest water demand in each respective county, the demand for irrigation can be reduced by 23.1 %, and the demand for groundwater reduced by as much as 72.8 %. Crop switching scenarios that also consider crop species diversity, in which replacing the highest water demand crops with alternative crops (those with the lowest water demands, highest protein production, or highest calorie production) in each county can effectively reduce irrigation water demand (8.5–12.9 % for streamflow and 32.7–43.3 % for groundwater), while increasing protein (21.3–35.8 %) and calorie (27.5–42.7 %) production as well as income (1.4–2.4 %). Our research provides potential solutions to effectively alleviate the conflict between agricultural development and the environment in oasis regions.

Abstract Background There is a causal link between childhood socioeconomic status and health status in adulthood and beyond. It's vital to comprehend the relationship between childhood socioeconomic status and mental health among older Chinese individuals from the current generation who have undergone significant social changes in China. This understanding is critical to foster healthy demographic and social development in China. Methods Using data from the 2020 China Family Panel Studies, we investigate the relationship between childhood socioeconomic status and depression in older adults. Additionally, we examine the mediating role of adult socioeconomic status and subjective well-being. Results 1) Childhood socioeconomic status of Chinese older adults differences by region of residence, while depression levels differences by gender, region of residence, and marital status. 2) Adult socioeconomic status mediated the relationship between childhood socioeconomic status and depression in older adults. 3) Adult socioeconomic status and subjective well-being had a chain-mediated role in the relationship between childhood socioeconomic status and depression in older adults. Conclusions In terms of childhood socioeconomic status, older adults in urban regions were significantly higher than those in rural regions. As for depression level, female older adults were more depressed than males; married older people have the lowest depression levels, while unmarried and widowed older people have higher depression levels; older adults in rural regions had higher depression levels than those in urban regions. Evidence from our study further suggests that childhood socioeconomic status can suppress the depression level in older adults through adult socioeconomic status; it can also further reduce the depression level in older adults through the chain mediation of adult economic status affecting subjective well-being. As depression is more prevalent among older individuals with a lower childhood socioeconomic status, it is vital to prioritize the extensive impact of childhood socioeconomic status as a distal factor and investigate "upstream" solutions to enhance childhood socioeconomic status and reduce the gap during the early years of life.

Abstract Compound droughts with low soil moisture (SM) and high vapor pressure deficit (VPD) pose significant threats to terrestrial carbon sink and agricultural production. However, the frequency and intensity of compound droughts and their adverse impacts on the carbon cycle remain highly uncertain. Here, we define and identify vegetation compound droughts (VCDs) when low SM and high VPD severely limit and adversely affect vegetation carbon uptake. We find frequent and severe VCDs and their considerable adverse impacts on carbon uptake in mid- and low-latitude regions, particularly in drylands. Risks of VCDs have been greatly underestimated as the widely adopted quantile-based approach identifies only 11% of VCDs and 26% of global GPP anomalies due to VCDs. The frequency and intensity of VCDs and their adverse impacts on carbon uptake are projected to increase further, irrespective of whether the CO2 fertilization effect on vegetation growth and photosynthesis is considered or not. These findings improve our understanding of current and future risks of VCDs and underline the importance of adaptation measures to cope with the adverse impacts of ever-increasing compound droughts.

Abstract Resistance and resilience are widely used to characterize ecosystem drought stability. Tradeoff between resistance and resilience have been reported, but its long‐term trends remain uncertain at global scale. Based on remotely sensed vegetation indices, we assessed the spatiotemporal dynamics of drought resistance and resilience. Result revealed that there was a significant decline in the tradeoff between resistance and resilience, corresponding to a substantial increase in the proportion of areas with high resistance‐high resilience or low resistance‐low resilience. In the South Sahel, South Africa and Central China, the increased precipitation and vegetation coverage contribute to enhanced drought stability constructed by resistance and resilience; while rising temperature, decreased water availability and deforestation lead to declined stability in northeastern North America, South America and the Congo region. Increases in the areas with low resistance‐low resilience resulting from declining tradeoffs warn of increased regional ecosystem vulnerability.

Abstract Facing the dual threats of climate and socioeconomic changes, how Tibet can seize the opportunity for ecological restoration to enhance environmental quality while improving the relationship between humans and nature is of great significance for regional sustainable development. Situated in an ecologically vulnerable area, the cognitive structure of Tibetan residents regarding resources and policies is a crucial reference for evaluating whether environmental governance is sustainable. We aimed to detect what impact ecological restoration in Tibet had on the cognitive structures of stakeholders. We distributed questionnaires to Tibetan residents, distinguished experimental groups (EG, n = 325) and control groups (CG, n = 165) by the implementation of ecological restoration projects or not, used a network approach to construct indicators of overall connectivity and evenness for assessing the cognitive structure and explored the influencing factors on cognitive structures. We found that interviewees in areas where ecological restoration was implemented had more positive perceptions of the importance of ecosystem services, the relationship between ecological conservation and well‐being, attitudes towards ecological restoration, and the impact of governance measures. The overall connectivity and evenness of the EG were significantly higher than those of the CG. It showed an increase in positive stakeholder perceptions about policy and nature and a greater emphasis on the balance between environmental protection and productive life. The implementation of ecological restoration enhanced the connection between local people and the environment. Elevation and annual precipitation were the geographic influencing factors that dominated overall connectivity. Based on the cognitive structure of Tibetan residents, the implementation of ecological restoration enhanced connections in social‐ecological systems elements, played a positive role in the human–nature relationship and promoted collective governance of common pool resources. Safeguarding and improving residents' livelihoods and enhancing the regional low‐connectivity cognitive structure under different geographical contexts are important for future governance. Read the free Plain Language Summary for this article on the Journal blog.

Abstract Grassland provides multiple ecosystem services and plays a key role in preventing desert encroachment and maintaining oasis stability. In China, the area of cropland in oases has expanded significantly in recent decades, which results in a rapid increase in agricultural water demand and encroachment on grassland subsistence space. However, our knowledge about how the expansion of cropland affects oasis grasslands remains limited. We used machine learning, temporal segmentation of spectral trajectories, and maximum covariance analysis to generate an annual oasis grassland aboveground biomass (AGB) data set at 30‐m resolution from 1989 to 2021 based on multiple remotely sensed and ground observation data sets, and investigated the dynamics of grassland AGB under cropland expansion in oases. We found that overall oasis grassland AGB increased significantly (0.3 gm−2 yr−1, P

Sustaining or enhancing nature’s contributions to people (NCPs) requires a comprehensive understanding of both nature’s contributions and people’s needs. However, the 2 aspects for water-related NCPs are spatially mismatched. We introduced an assessment framework for water-related NCPs from a spatial flow perspective, considering the local nature’s contributions assessed using the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model, as well as people’s needs in the downstream assessed via a distance decay method. We assessed 3 water-related NCPs’ spatial distribution and correlation on the Loess Plateau from 2000 to 2020, where a large-scale ecological restoration was implemented that may affect downstream people. The results showed that NCP6 (downstream needs from water yield) showed no increasing trend in the majority watersheds over the past 20 years, in contrast to NCP7 (downstream needs from water purification) and NCP8 (downstream needs from soil conservation). There are spatial synergies among NCP6, NCP7, and NCP8. From 2000 to 2020, the spatial synergy between NCP7 and NCP8 increased while decreased between other NCPs. The temporal dynamics of NCP6 and NCP8 showed a trade-off, while NCP6 and NCP7 showed a synergy. NCP7 and NCP8, in turn, showed a transition from synergy to trade-off. Guided by nature’s contributions and people’s needs, we proposed 3 ecological measures: thinning and intermediate cutting measures, control nonpoint source pollution, and soil and water conservation projects to promote ecological restoration. This assessment can offer multifunctional guidance for planning ecological conservation and restoration in the upstream based on people’s needs in the downstream.

Carbon dots doped with boron and nitrogen atoms (B, N-CDs) were designed and prepared by hydrothermal strategy using citric acid, urea and borax as raw materials. The structure and optical properties of B, N-CDs were characterized by various characterization technologies. The results showed that the B, N-CDs exhibited good mono-dispersion, excellent fluorescence stability, and high fluorescence intensity (quantum yield up to 32 %). A dual-functional Fe3+ and pH detection platform based on B, N-CDs was constructed. The Fe3+ sensing method based on B, N-CDs fluorescence quenching showed an excellent linear relationship between 0.044–70 μM (R2 = 0.994), with a detection limit as low as 0.044 μM. Then, this detection method was successfully applied to the high-accuracy detection of Fe3+ in water samples and iron supplement tablets. The study of the quenching mechanism showed that the fluorescence of B, N-CDs was quenched by Fe3+ in two ways: static quenching and dynamic quenching. Moreover, B, N-CDs can also be used as a pH fluorescence sensor in the ranges of pH 2.0–4.8 and 5.2–7.6. The developed sensing system based on B, N-CDs has high selectivity and sensitivity for the detection of Fe3+, and a wide detection range for pH.

Post-activation performance enhancement (PAPE) can significantly improve athletic performance. This study investigated the effects of two different velocity loss (10% VL and 20% VL) protocols on PAPE in 20 m sprint performance among sprint athletes. Twenty-four male sprint athletes (100 m sprint time: 10.96 ± 0.15 s) participated in the study. A randomized crossover experimental design was used to compare the traditional group (TG) and 10% VL and 20% VL interventions. Sprint tests were conducted at 4, 8, 12, and 16 min post-intervention. A two-way repeated measures ANOVA revealed a significant interaction effect between group and time on 20 m sprint performance (F = 2.817, p = 0.037, partial η2 = 0.585). Simple main effects analysis revealed significant improvements at 4 min for the 20% VL group (p < 0.05). Cohen’s d values indicated improvements in 10 m sprint times at 8 min for all groups (TG: effect size (ES) = −0.270, 10% VL: ES = −0.038, 20% VL: ES = −0.279). Improvements in 20 m sprint times were observed at 4 min for the 20% VL group (ES = −0.296) and at 16 min for the 10% VL group (ES = −0.276). In conclusion, the velocity loss-based PAPE protocol (20% VL) demonstrated a superior induction of PAPE effects in sprint athletes at 4 min compared to traditional 1RM-based PAPE protocols. However, no significant differences were observed between the two protocols at 8, 12, and 16 min.

Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau.

Unraveling the complexity of the 17 interacting sustainable development goals (SDGs) is crucial for their achievement. Empirically revealing the dimensions of the SDGs helps generalize the dominant features of SDGs and better understand their drivers. Here, using a database of 166 countries’ progress toward achieving each individual SDG, we found that about 70% of the variability of national SDG performance can be captured by three dimensions: socioeconomic development at the expense of resource and climate, the environment, and development at the expense of equality. Moreover, these dimensions are mainly affected by the economy; as gross domestic product (GDP) per capita increases, the first dimension increases monotonically, the environment dimension decreases and then increases, and the inequality dimension increases and then decreases. Our findings indicate a dim prospect of eventually achieving all SDGs because of the conflicts between economic growth and resource and climate goals under the current development paradigm, highlighting the importance of sustainable transformation.

Abstract The COVID-19 pandemic continues to pose substantial challenges to achieving the Sustainable Development Goals (SDGs). Exploring systematic SDG strategies is urgently needed to aid recovery from the pandemic and reinvigorate global SDG actions. Based on available data and comprehensive analysis of the literature, this paper highlights ongoing challenges facing the SDGs, identifies the effects of COVID-19 on SDG progress, and proposes a systematic framework for promoting the achievement of SDGs in the post-pandemic era. Progress towards attaining the SDGs was already lagging behind even before the onset of the COVID-19 pandemic. Inequitable distribution of food–energy–water resources and environmental crises clearly threaten SDG implementation. Evidently, there are gaps between the vision for SDG realization and actual capacity that constrain national efforts. The turbulent geopolitical environment, spatial inequities, and trade-offs limit the effectiveness of SDG implementation. The global public health crisis and socio-economic downturn under COVID-19 have further impeded progress toward attaining the SDGs. Not only has the pandemic delayed SDG advancement in general, but it has also amplified spatial imbalances in achieving progress, undermined connectivity, and accentuated anti-globalization sentiment under lockdowns and geopolitical conflicts. Nevertheless, positive developments in technology and improvement in environmental conditions have also occurred. In reflecting on the overall situation globally, it is recommended that post-pandemic SDG actions adopt a “Classification–Coordination–Collaboration” framework. Classification facilitates both identification of the current development status and the urgency of SDG achievement aligned with national conditions. Coordination promotes domestic/international and inter-departmental synergy for short-term recovery as well as long-term development. Cooperation is key to strengthening economic exchanges, promoting technological innovation, and building a global culture of sustainable development that is essential if the endeavor of achieving the SDGs is to be successful. Systematic actions are urgently needed to get the SDG process back on track.

Heteroatom-doping carbon dots (CDs) have excellent fluorescence properties. In the paper, nitrogen (N) and phosphorus (P) co-doped CDs (N, P-CDs) were prepared through the hydrothermal method and characterized by TEM, PXRD, FT-IR, 13C NMR, XPS, UV–vis, and fluorescence spectra. The optical properties and drug (doxorubicin) - delivery performance of N, P-CDs were also studied. The results showed that the average size of N, P-CDs was 3.64 nm and the quantum yield was 30 %. The N, P-CDs had down- and up-conversion fluorescence properties, and the luminescence mechanism of the N, P-CDs was explained. The drug loading capacity of N, P-CDs was 39.11 %, and the drug delivery system (N, P-CDs-DOX) had low cytotoxicity, sustained release, and pH-targeted properties. The in vitro release of N, P-CDs-DOX belonged to the Weibull model and Fick diffusion, exhibiting the same release model and mechanism with free DOX. The N, P-CDs could deliver DOX successfully, which was demonstrated by the co-collocation of N, P-CDs and DOX in SH-SY5Y cells through single/two-photon imaging. N, P-CDs could potentially be used in drug-targeted delivery and cell imaging.

Background and Aims: Despite an early insight into the risk factors of uterine fibroids (UFs), there is still controversy about the roles of blood lipids and apolipoprotein on UFs. Therefore, to provide more reliable evidence, we investigated the causal effect of blood lipids and apolipoprotein on the risk of UFs through Mendelian randomization (MR). Method: We utilized the large genome-wide association studies of blood lipids and apolipoprotein as instruments and integrated them in the inverse-variance-weighted (IVW) model in two-sample MR to investigate their causal relationship in large sample sizes. To eliminate potential pleiotropy, we listed a conclusive table of genes related to confounding factors, such as body mass index, waist circumference, blood pressure, glucose, hypertension, the metabolic syndrome and smoking and removed the repeated SNPs in the exposure data.To adjust for multiple comparisons, the statistical significance of the MR effect estimates was evaluated in a false discovery rate correlation of less than 5%. Neither pleiotropy nor heterogeneity was reported in all MR sensitivity analysis, including IVW, MR-Egger, simple median, weighted median, weighted mode and Mendelian Randomization Pleiotropy Residual Sum and Outlier methods. Results: Total triglycerides, very low-density lipoprotein (VLDL) cholesterol, including large, medium and small VLDL particles and free cholesterol in large VLDL cholesterol showed positive association with UFs. Meanwhile, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1, free cholesterol in small and medium VLDL and total cholesterol are not statistically related to the development of UFs. Conclusion: Exposure to various aberrant blood lipids increases the risk of UFs, providing genetic evidence for the vital role of aberrant lipid levels in the pathogenesis of UFs. Meanwhile, our results provided genetic insights suggesting intervention in blood lipids might be a prospective strategy in the early prevention and treatment of UFs.

Increasing aridity and grazing have multifaceted effects on the ecosystem, of which plant traits interact with each other to coregulate the productivity function including above- and below- ground compartments of plant. The ecological interaction network among plant traits could reflect how ecosystem interact with, influence, and respond to environment change. In this study, the interaction network between leaf, root, and biomass function was revealed to indicate systemic ecosystem response to increasing aridity and grazing by sampling in enclosed plots along the aridity gradient and in neighboring grazing plots of China drylands. Systemic and abrupt changes in the network relationship between leaf, root, and biomass were observed with a threshold value of 1-AI (aridity index) ∼ 0.8. The control effect of roots on other traits and the interaction of leaf and root compartments increased in the opposite direction above the threshold. The change in the relative importance of key traits with increasing aridity caused the different grazing effects. Grazing decreased the leaf area in the area with1-AI ∼ 0.6, whereas it increased the effect of roots in the area with 1-AI ∼ 0.8, thus promoting the interaction of functional traits. This result suggests the essential role of the interaction network between plant traits in ecosystem response to environmental change, which could indicate the ecosystem change in a holistic perspective under global change.

Background: Although compliance with the guideline recommendations for heart failure (HF) is associated with improved survival, the effects of medication on clinical practice often fail to meet expectations due to physician and/or patient-related reasons that are unclear. This study analyzed physicians’ compliance with guideline-directed medical therapy (GDMT) based on real-world clinical data and identified risk factors of low compliance. Methods: This study included patients with HF, who were treated at the Affiliated Hospital of North Sichuan Medical College from July 2017 to June 2021. All patients were divided into high compliance, moderate compliance, and low compliance with GDMT groups. The proportion of patients receiving treatment in compliance with GDMT was analyzed, the relationship between compliance with GDMT and clinical outcomes was evaluated, and the risk factors of low compliance were identified. Results: Of all patients with HF included in the study, 498 (23.8%) had low compliance with GDMT, 1413 (67.4%) had moderate compliance with GDMT, and 185 (8.8%) had high compliance with GDMT. The readmission rate of patients in the moderate compliance with GDMT group was significantly higher than that in the high and low compliance groups (p = 0.028). There were no significant differences in the rates of severe cardiovascular disease among the three groups. The mortality rate of patients in the high compliance with GDMT group was significantly higher than that of the other groups (p < 0.001). We found that a history of hypertension; New York Heart Association (NYHA) classification (III and IV vs. I); and abnormal heart rate, high-sensitive troponin T (hsTnT), N-terminal prohormone of brain natriuretic peptide (NT-proBNP), uric acid, and left ventricular diastolic dysfunction (LVDD) were all significantly associated with low compliance with GDMT. Conclusions: The proportion of physicians’ compliance with GDMT in treating patients with HF is low. Risk factors of low compliance include hypertension; NYHA classification (III and IV vs. I); and abnormal heart rate, hsTnT, NT proBNP, uric acid, and LVDD.

Abstract Whether the world can achieve the United Nations Sustainable Development Goals (SDGs) largely depends on the ability of less‐developed areas to cope with multiple socio‐economic changes. The challenges that hinterland areas would face during the realization of SDGs has not yet received enough attentions. In this study, a context‐based assessment of regional food balances was conducted, considering key challenges related to socio‐economic development as well as land use competition under the framework of the shared socioeconomic pathways (SSPs) and the implementation of reforestation. We examined one of the poorest hinterland provinces in China as a case study, projecting its food deficit and exploring the potential threats to and opportunities for SDG realization by 2030, including population growth, urbanization, urban land expansion and reforestation. The projections revealed a crop deficit of 4.9–9.8 million tonnes, corresponding to the food demands of 10.2–20.6 million people. Approximately 76%–81% of this deficit was expected to be caused by increased food demand, rather than reforestation policies. Therefore, for this less‐developed area with low agricultural productivity and large groups of vulnerable people, population growth and urbanization are likely to result in demands for food that cannot be met locally. In addition, large‐scale reforestation projects, while enhancing a number of key ecosystem services, will increase the local food deficit by promoting the abandonment of cropland. This will result in greater reliance on food imports, with potential impacts on SDG realization in other regions. These findings highlight the need for integrated governance across multiple scales to ensure hinterland sustainability.

BackgroundEpidemiological observational studies have investigated the relationship between rheumatoid arthritis(RA) and pre-eclampsia, but no consistent conclusions were obtained due to various limitations. Hence, we conducted a two-sample mendelian randomization analysis to evaluate the potential causal effect of RA on pre-eclampsia.MethodsSummary-level statistics for RA were derived from a large-scale meta-analysis of datasets of genome-wide association studies(GWAS) which involved 14,361 cases and 43,923 controls. Moreover, summary statistics for pre-eclampsia or eclampsia were sourced from the Finn biobank which contained 3,903 cases and 114,735 controls. The inverse variance weighting (IVW) as well as other four effective methods including MR-Egger, weighted median, weighted mode, and simple mode were applied to deduce the potential causal relationships between RA and pre-eclampsia comprehensively.ResultsThe two-sample MR analysis suggested a strong causal relationship between RA and pre-eclampsia[OR,1.05;95%CI, 1.01-1.09;p

Urban tree cover is widely regarded as an environmentally-friendly and effective urban cooling approach. Meteorological variables, including air temperature, wind speed, and humidity, have complex impacts on the cooling efficiency (CE) of urban trees (i.e., the negative ratio of the land surface temperature change to the tree cover percentage change). This means that increasing the urban tree cover to alleviate heat stress is not necessarily suitable for cities with different climates. African cities are confronted with larger heat risks but lack considerations for the effectiveness of urban tree cooling in urban planning. In this study, 40 African major cities with population greater than 500,000 in different climatic regions were selected, and 1459 CEs during each city’s corresponding warmest 3 consecutive months were calculated combined with the availability of meteorological data. The generalized additive models revealed the non-linear impacts of air temperature/temperature dew point difference on CE, which were more evident in arid cities. The CE of urban trees actually increased and then decreased along with the increase of air temperature/temperature dew point difference, and the turning point were 34 °C/26 °C, respectively. African cities would have different frequencies of warm days with an air temperature over 34 °C under different Shared Socioeconomic Pathways within the next 30 years. Specially, the cities around Sahel would suffer up to 40–60% days over 34 °C, which meant their urban tree CE would decrease along with air temperature increase. This study highlighted that in African cities, especially those with arid climate, it was unadvisable to only count on increasing tree cover to alleviate urban heat stress in the warming future, which called for other combined cooling strategies.

Grassland resilience is influenced by climate change and human activities. However, little is known about how grassland resilience has changed, driven by climate change and human activities, on the Mongolian Plateau. We calculated grassland resilience on the Mongolian Plateau from 2000 to 2021 using the kernel normalized difference vegetation index (kNDVI), quantified the trends of grassland resilience using a newly proposed “critical slowing down” indicator with a machine learning algorithm, and compared the driving forces for these changes between Inner Mongolia and Mongolia. The findings of this study demonstrate that heightened levels of precipitation and reduced temperature contribute to the enhanced resilience of grassland ecosystems on the Mongolian Plateau. Conversely, the presence of grazing activities exhibits a detrimental effect on such resilience. In semi-arid regions, approximately 43% of grassland areas are undergoing a discernible decline in resilience. This decline is particularly pronounced in regions characterized by heightened levels of grazing intensity. In addition, resilience declined in 54% of areas with population growth compared with 32% in areas with population decline. Inner Mongolia, with its higher intensity of human activities, has a more serious decline in ecological resilience than Mongolia, indicating that further ecological restoration measures are needed.

On the basis of elaborating on the regional geological background, this paper analyzes the lithological and sedimentary characteristics and explorative prospects of new strata with oil and gas in the southern and surrounding areas of Dayangshu Basin. Based on the latest high-precision airborne gravity and magnetic comprehensive survey data, combined with the latest data from geological explorations, physical surveys, and drilling, and the use of basin structure layering combination methods, we clarified the characteristics of the bottom of the Jurassic–Cretaceous and the occurrence characteristics of the Upper Paleozoic in the study area and revealed the determinative effect of multi-period structures on the most important sedimentary layers. Then, we summarized the accumulation conditions and prediction methods of hydrocarbons and proposed the oil and gas prospects of these deep new strata. The results show that the Liuhe Sag in Dayangshu Basin, the depression in the northeast of Longjiang Basin, and the northern parts of the Taikang swell have good source–reservoir–cap combination conditions and favorable structural characteristics for oil and gas, where there is a high potential for exploration.

Grassland ecosystems are among the largest terrestrial ecosystems in China, and grazing, as an important grassland management method, has direct and indirect impacts on grassland ecosystems. Meta-analyses can be used to systematically evaluate and summarize multiple findings from existing studies, but there have been few comparisons of meta-analysis methods. In this review, we summarize the effects of grazing on grassland plants and soil in the existing meta-analysis studies in China from 38 meta-analysis papers. The results show that they have consistent conclusions, such as grazing reduces the aboveground biomass by approximately half, increases the soil pH, decreases the C:N:P ratio, and reduces the number of topsoil microorganisms, but the conclusions of light and moderate grazing index changes vary greatly from study to study. The belowground biomass was generally found to increase, but it slightly decreased in some cases, and the total biomass generally decreased, but it slightly increased in other cases. Vegetation coverage increased during moderate grazing; the soil moisture content was highest for light grazing, and microbial diversity increased at light to moderate levels of grazing. There are also very inconsistent conclusions due to the different datasets and quantities of samples used in meta-analysis studies, as well as variations in the types and scales of grassland areas. The ranges of changes in other indicators were large, especially for the root-shoot ratio and soil carbon. However, changes in the aboveground biomass were generally stable. We suggest subsequent meta-analyses of grazing should further clarify the classification of grassland types and compare conclusions at different scales. Additionally, standardized network analyses are recommended for field manipulation experiments to further improve the accuracy of meta-analysis and reduce the temporal and spatial limitations of existing data.

The great challenges of sustainable development highlight an urgent need to systematically understand the mechanisms linking humans and nature. Resources and Environmental Sciences are a broad and practical discipline focused on coupled human and natural systems. They aim to study the formation and evolution of resources in the earth system, the drivers of various environmental problems, processes and relationships between resources and the environment, particularly under the combined impacts of natural conditions and human activities. The major resources and environmental problems drive the discipline development; international science programmes guide the direction of the discipline; interdisciplinary and transdisciplinary integration promotes new branches of the discipline; and technological progress results in a research paradigm shift. Facing the critical research requirements of strengthening trans- and interdisciplinarity, breaking through the key technology, targeting major environmental and disaster issues, and supporting sustainable development, nine critical scientific issues should be focused on climate change impact and adaptation, petroleum and mineral resources, water cycle and water resources, soil and land resources, ecosystems, remote sensing and geographic information science, environmental science and technology, disaster risk, and global and regional sustainable development. Suggestions to enhancing funding systems, improve talent cultivation, develop scientific platforms, and strength international cooperation are provided in this study to support scientific policymaking. The promotion of Resources and Environmental Sciences enables a more comprehensive and in-depth understanding of economic development and environmental changes relevant to assure a more sustainable global development.

The balance between the supply and demand of ecosystem services (ESs) is an important prerequisite for maintaining the sustainability of ecological protection and restoration project implementation. However, research related to ecological protection and restoration is insufficient for the study of the demand for ecosystem services. Many ecological protection and restoration projects have been implemented in the Fujian Province, but the ESs and the relationship changes between supply and demand are not clear. In this study, multisource remote sensing and public data and the InVEST model were used to quantitatively assess and map four typical ESs, including food production, water yield, soil retention and carbon sequestration. Hotspot analysis was used to analyze the spatial cluster of the ESs supply–demand ratio. The results showed that: (1) there were trade-offs between supporting and regulating services, particularly between carbon sequestration and water yield services, and the strength of trade-offs or synergies between food production and other services was stronger in protection and restoration areas than in other areas; (2) the supply of ESs in the Fujian Province exceeded the demand, and the supply–demand ratio for ESs decreased from the mountainous regions in the northwest interior to the economically developed regions in the southeast coast; and (3) ecological restoration projects improved the relationship between supply and demand for some ESs, while other areas (except protection and restoration areas) had many low-value clusters of supply–demand ratios, especially regarding water yield and carbon sequestration services. Based on the results, our findings also provide suggestions for ensuring the sustainability of ecological protection and restoration in southeast hilly areas and other similar regions.

Climate change and human activities have caused a wide range of ecological risks in the Qinghai–Tibet Plateau (QTP) over the past two decades, such as land degradation and biodiversity loss. Therefore, it is imperative to assess the ecological security and drivers for its sustainable development. However, there still lacks a spatial understanding of ecological security in the QTP, as well as the geographic driving forces. In this study, a pressure–state–response (PSR) framework and the coupled fuzzy and obstacle degree models were used to evaluate the spatial pattern and factors that modulate ecological security of the QTP. The southeast of the plateau exhibited high pressure and state levels, indicating that population and economic development activities were concentrated in these regions owing to the good natural conditions. The ecological security evaluation value of the QTP is moderately low, with a median value of 47.4 (the full mark is 100). Seven regions with low ecological security were identified where targeted planning and governance measures should be implemented based on the local natural and economic conditions. Population density, net primary productivity index (NPP) of vegetation, and GDP per unit area were the main factors that modulated ecological security in the QTP, with obstacles accounting for 17.52%, 13.20%, and 12.97%, respectively. These results improve our understanding of the major vulnerable areas and main driving forces of ecological security, providing key information for optimization of ecological security pattern in the QTP.

The implementation of strategies to achieve the Sustainable Development Goals (SDGs) is frequently hindered by potential trade-offs between priorities for either environmental protection or human well-being. However, ecosystem services (ES)-based solutions can offer possible co-benefits for SDGs implementation that are often overlooked or underexploited. In this study, we cover this gap and investigate how experts from different countries value the SDGs and relate them with ES. A total of 66 countries participated to the survey, and answers were grouped into three macro-regions: Asia; Europe, North America, and Oceania (ENO); Latin America, Caribbean and Africa (LA). Results show that the most prioritized SDGs in the three macro-regions are usually those related to essential material needs and environmental conditions, such as SDG2 (Zero Hunger), SDG1 (No Poverty), and SDG6 (Clean Water). At a global scale, the number of prioritized synergies between SDGs and ES largely exceeded trade-offs. The highest amount of synergies was observed for SDG1 (No Poverty), mainly with SDG2, SDG3 (Good Health), SDG5 (Gender Equality), and SDG8 (Economic Growth). Other major synergies among SDGs include SDG14-15 (Life below water - Life on land), SDG5-10 (Gender Equity - Reduced Inequality), and SDG1-2 (No poverty - Zero Hunger). At a global scale, SDG15, SDG13, SDG14, and SDG6 were closely related to ES like climate regulation, freshwater, food, water purification, biodiversity, and education. SDG11 (Sustainable Cities) and SDG3 were also relevant in Asia and in LA, respectively. Overall, this study shows the potential to couple future policies that can implement SDGs' strategies while adopting ES-based solutions in different regions of the world.

As the world's top two economies, the United States (U.S.) and China face a number of similar water resources problems. Yet, few studies have been done to systematically compare policies and approaches on water resources management between China and the U.S. This study compares water resources policies of China and the U.S. in the areas of national authority, water supply, water quality, and ecosystem use of the water to draw lessons learned and shed light on water resources management in China, the U.S., and the rest of the world. The lessons learned from the comparison include six aspects. 1) New paradigms of people-water harmony and a water-saving society are urgently needed to address the pressing water crisis and achieve the United Nations Sustainable Development Goals (UN SDGs). 2) A comprehensive, consistent, forward-looking national policy is necessary to achieve sustainable use of water resources. 3) Empowerment of river basin commissions with comprehensive authority over the integrative management of air, land, water, and biological resources in the river basin could significantly enhance the benefits and effectiveness of economic development and environmental protection. 4) Expansion of water exchange through market mechanisms among water users promotes efficient and beneficial water uses. 5) Use of water for ecosystem services should be an integral part of water resources management. China has set up a national blueprint for achieving ecological civilization; maintaining appropriate amounts of flow in rivers and lakes for maintenance of wildlife and fisheries and ecosystems should be institutionalized as part of this national strategy as well. 6) By sharing their rich experiences and lessons in water resources management, economic development, and ecological protection with other countries, China and the U.S. can help the world to achieve global human-water harmony and the UN SDGs.

Ecosystem Services (ES) are common-pool resources that can be valued by people's willingness to pay (WTP). In contrast to place-based WTP research at the community-level, the stakeholders tend to be geographically diverse, and the benefits are not spatially apparent on the national level. Aiming to find the geographical diversity of the WTP for ES at the large scale, this study implemented an online survey of more than 25,000 samples to detect the WTP of Chinese people for water conservation, soil retention, carbon fixation, pollution decomposition, biodiversity conservation, and aesthetic existence of the Tibetan Plateau. The results showed the top limit of payments was 1,080.95 CNY/year/capita on average, and people would like to pay 172.40 CNY/year/capita for water conservation, which is the highest among the six ES. The percent of people “Aged 16–35”, “Government agency staff” and “Know WTP” influenced payments at provincial level. On an individual level, people's knowledge and attitudes directly drove the payment amounts, as well as their ecosystem management decisions. Consequently, geographical diversity of the payment for ES exists in China, and in contrast to the objective social structure and spatial accessibility of ES, people's knowledge and attitudes were the main driving forces of this geographical diversity. These findings suggest that a bottom-up adaptive governance approach is encouraged for managing common pool resources in developing countries.

Under the navigational process of simultaneous beacon-calibrating and target-positioning, the real-time positioning and calibrating accuracy in indoor scenarios is significantly limited. To solve it, a novel Simultaneous Calibration and Localization (SCAL) algorithm framework for indoor scenarios is proposed. The proposed framework is mainly divided into the Target Localization and Beacon Calibration (TLBC) and the Global Optimization (GO) section: in the TLBC section, under the typical distributed beacon layout, a processing mechanism for synchronously locating the target and calibrating beacons is proposed; in GO section, parallel to TLBC section, a global optimization method based on Geometric Dilution of Precision based on Error-propagation (EGDOP) model is further proposed, and it can utilize the geometric trajectory of target and positioning error of target and beacons to efficiently and simultaneously optimize the positioning of target trajectory and beacons from TLBC section. The numerical simulation results show that the improvement of the GO section on the positioning performance is verified. Compared with the SCAL algorithm based on backward processing with inverse trajectory, the proposed framework has improved the accuracy of target and beacon positioning by 39.06% and 58.01%, respectively, and accuracy of positioning reaches 0.2557m and 0.2437m, respectively, in an actual indoor positioning scene.

In order to evaluate the impacts of parameter uncertainty and nonparametric uncertainty on the natural characteristics of a dual-rotor system, a nonparametric probabilistic method based on random matrix theory is proposed. In this paper, a nonparametric Riccati whole transfer model is derived based on the maximum entropy principle and the random matrix theory. It is used to model a dual-rotor system with nonparametric uncertainty, as well as to calculate the natural characteristics of the system. Furthermore, the impacts of parameter uncertainty and nonparametric uncertainty on the natural characteristics at the intermediate support element and at the disk-shaft element are discussed using numerical simulations, and the results are compared with related references. The results show that at the same level of uncertainties, the effect of nonparametric uncertainty is often more significant than that of parameter uncertainty. The effects of uncertainties also increase with the level of uncertainties. The results of this paper provide a theoretical basis for the design of uncertain dual-rotor and multi-rotor systems.

To address the difficulty of extracting the features of composite-fault signals under a low signal-to-noise ratio and complex noise conditions, a feature-extraction method based on phase-space reconstruction and maximum correlation Re’nyi entropy deconvolution is proposed. Using the Re’nyi entropy as the performance index, which allows for a favorable trade-off between sporadic noise stability and fault sensitivity, the noise-suppression and decomposition characteristics of singular-value decomposition are fully utilized and integrated into the feature extraction of composite-fault signals by the maximum correlation Re’nyi entropy deconvolution. Verification based on simulation, experimental data, and a bench test proves that the proposed method is superior to the existing methods regarding the extraction of composite-fault signal features.

This study aimed to utilize the enzymatic hydrolysis of leftover cooked rice (LCR) for fermentative ethanol production. Effect of glucoamylase volumes (V1: 5 U/g, V2: 25 U/g, and V3: 50 U/g) on the performance of LCR hydrolysis was also evaluated. It was found that the highest chemical oxygen demand (COD) of 77.5 g/L and reducing sugar (RS) of 34.6 g/L were achieved at V3. The LCR hydrolyzate obtained from enzymatic hydrolysis was then used as feedstock for ethanol fermentation. Higher ethanol production was obtained when RS increased from 18.7 g/L (V1) to 23.2 g/L (V2). However, lower ethanol production was found when RS further increased to 34.6 g/L (V3) probably because too high RS concentration led to the inhibition on the yeast. The maximum ethanol production and yield were 21.1 g/L and 0.3 g ethanol/g LCR, respectively. The LCR could be a promising substrate for fermentative ethanol production for industrial application.

Abstract Collective memory plays a controlling role in adaptation to potential flood risks, by learning from past disasters. However, with little quantitative empirical data, previous socio‐hydrological models have conceptualized the decaying process of flood memory in an oversimple approach. Here, based on survey data of 683 respondents on Ningxia Floodplain, we confirmed that flood memory decays overtime via two channels: oral communication (communicative memory) and physical recording of information (cultural memory). Using the Universal Decay Model (UDM) proposed by previous researchers provides better fitting of results to the decay of flooding memory (adjusted R2 coefficient are 0.97, 0.90, 0.95 when data of all, rural or urban respondents used, respectively) compared with the original exponential model (adjusted R2 coefficient are 0.91, 0.74, 0.59, corresponding). Then, significantly reduced losses for the same flood sequence predicted by integrating the UDM into a socio‐hydrological model by 16% and the differences between different clusters (urban and rural respondents) can even reach 22.81%. These differences suggest that previous socio‐hydrological models have been too simplistic in their conceptualizations of decaying processes associated with collective memory, which may have limited deeper insights into flood risk management.

Oases are among the most productive and vulnerable ecosystems on drylands as well as central areas of human life and economic development. In China, oasis expansion and degradation are directly related to the survival and development of tens of millions of inhabitants who live within them, profoundly affecting human well-being. However, our understanding of the spatiotemporal dynamics of China’s oases has been limited. This creates a challenge to accurately assess the environmental impacts of oasis dynamics and sustainable oasis development. Here we analyzed Landsat images (5 TM, 7 ETM+, and 8 OIL, ∼61 900 images) over the period 1987–2017, and we produced annual maps of China’s oases (30 m spatial resolution) using trajectories and spectral-temporal segmentation algorithm (LandTrendr) and machine-learning methods (Random Forest). We found that oasis areas increased significantly between 1987 and 2017 (+8.82 Mha). Grasslands and croplands expanded the most, and reclamation of deserts and grasslands made cropland the largest gainer. At the provincial scale, oasis expansion in Inner Mongolia and Qinghai was mainly attributed to grassland restoration. In Xinjiang, Gansu, and Ningxia, reclamation of croplands was also an important factor in addition to grassland restoration. With the expansion of the oasis ∼73.36% of areas showed decreasing trends of soil moisture, and even desertification in some areas. The overexpansion of oases might cause a great challenge to regional water security. Our dataset and results can provide a basis for identifying trade-off areas/regions between development benefits and environmental impacts, helping policymakers to identify high-priority areas for intervention and reasonably plan land-use spatial structure in oasis areas for sustainable development of oases.

The traditional signal of opportunity (SOP) positioning system is equipped with dedicated receivers for each type of signal to ensure continuous signal perception. However, it causes a low equipment resources utilization and energy waste. With increasing SOP types, problems become more serious. This paper proposes a new signal perception unit for SOP positioning systems. By extracting the perception function from the positioning system and operating independently, the system can flexibly schedule resources and reduce waste based on the perception results. Through time-frequency joint representation, time-frequency image can be obtained which provides more information for signal recognition, and is difficult for traditional single time/frequency-domain analysis. We also designed a convolutional neural network (CNN) for signal recognition and a negative learning method to correct the overfitting to noisy data. Finally, a prototype system was built using USRP and LabVIEW for a 2.4 GHz frequency band test. The results show that the system can effectively identify Wi-Fi, Bluetooth, and ZigBee signals at the same time, and verified the effectiveness of the proposed signal perception architecture. It can be further promoted to realize SOP perception in almost full frequency domain, and improve the integration and resource utilization efficiency of the SOP positioning system.

For mass application positioning demands, the current single positioning sensor cannot provide reliable and accurate positioning. Herein, we present batch inverse covariance intersection (BICI) and BICI with interacting multiple model (BICI-IMM) multi-sensor fusion positioning methods, which are based on the batch form of the sequential inverse covariance intersection (SICI) fusion method. Meanwhile, it is proved that the BICI is robust. Compared with SICI, BICI-IMM reduces estimation error variance of the motion model and has less conservativeness. The BICI-IMM algorithm improves the accuracy of local filtering by interacting with multiple models and realizes global fusion estimation based on BICI. The validity of the BICI and BICI-IMM algorithm are demonstrated by two simulations and experiments in the open and semi-open scenes, and its positioning accuracy relations are shown. In addition, it is demonstrated that the BICI-IMM algorithm can improve the positioning accuracy in the actual scenes.

New types of remote sensed land cover datasets provide key evidence for understanding global environmental change. However, low data consistency makes understanding the changes unclear. China has become a hot spot of land cover change in the world due to climate change and a series of human measures, such as ecological engineering, land consolidation, and urbanization. However, due to the inconsistencies in interpretation of signs and thresholds, the understanding of yearly-continued land cover changes in China is still unclear. We aim to produce China’s land cover fraction dataset from 2001 to 2015 by weighted consistency analysis. We compare the Moderate-resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), the Climate Change Initiative Land Cover (CCI-LC) datasets, and a new land cover fraction dataset named China-LCFMCD-CCI, produced with a 1 km resolution. The obvious increased forest areas only accounted for 4.6% of the total forest areas, and were mainly distributed in northeast China. Approximately 75.8% of the grassland and shrubland areas decreased in size, and these areas were relatively concentrated in northeast and south China. The obvious increased areas of cropland (3.7%) were equal to the obvious decreased areas (3.6%), and the increased cropland areas were in northwest China. The change in bare land was not obvious, as the obvious increased areas only accounted for 0.75% of the bare land areas. The results not only prove that the data fusion of the weighted consistency method is feasible to form a land cover fraction dataset, but also helps to fully reveal the trends in land cover fraction change in China.

In recent decades, the monitoring of vegetation dynamics has become crucial because of its important role in terrestrial ecosystems. In this study, a satellite-derived normalized difference vegetation index (NDVI) was combined with climate factors to explore the spatiotemporal patterns of vegetation change during the growing season, as well as their driving forces in China from 2001 to 2012. Our results showed that the growing season NDVI increased continuously during 2001–2012, with a linear trend of 1.4%/10 years (p < 0.01). The NDVI in north China mainly exhibited an increasing spatial trend, but this trend was generally decreasing in south China. The vegetation dynamics were mainly at a moderate intensity level in both the increasing and decreasing areas. The significantly increasing trend in the NDVI for arid and semi-arid areas of northwest China was attributed mainly to an increasing trend in the NDVI during the spring, whereas that for the north and northeast of China was due to an increasing trend in the NDVI during the summer and autumn. Different vegetation types exhibited great variation in their trends, where the grass-forb community had the highest linear trend of 2%/10 years (p < 0.05), followed by meadow, and needle-leaf forest with the lowest increasing trend, i.e., a linear trend of 0.3%/10 years. Our results also suggested that the cumulative precipitation during the growing season had a dominant effect on the vegetation dynamics compared with temperature for all six vegetation types. In addition, the response of different vegetation types to climate variability exhibited considerable differences. In terms of anthropological activity, our statistical analyses showed that there was a strong correlation between the cumulative afforestation area and NDVI during the study period, especially in a pilot region for ecological restoration, thereby suggesting the important role of ecological restoration programs in ecological recovery throughout China in the last decade.

Vegetation is one of the most important components of the terrestrial ecosystem and, thus, monitoring the spatial and temporal dynamics of vegetation has become the key to exploring the basic process of the terrestrial ecosystem. Vegetation change studies have focused on the relationship between climatic factors and vegetation dynamics. However, correlations among the climatic factors always disturb the results. In addition, the impact of anthropogenic activities on vegetation dynamics was indeterminate. Here, vegetation dynamics in 14 provinces in Eastern China over a 10-year period was quantified to determine the driving mechanisms relating to climate and anthropogenic factors using partial correlation analysis. The results showed that from 1999 to 2008, the vegetation density increased in the whole, with spatial variations. The vegetation improvement was concentrated in the Yangtze River Delta, with the vegetation degradation concentrated in the other developed areas, such as Beijing-Tianjin-Hebei Region and the Pearl River Delta. The annual NDVI changes were mainly driven by temperature in Northeast China and the Pearl River Delta, and by precipitation in the Bohai Rim; while in the Yangtze River Delta, the driving forces of temperature and precipitation almost equaled each other. Furthermore, the impact of anthropogenic activities on vegetation dynamics had accumulative effects in the time series, and had a phase effect on the vegetation change trend.

Water erosion has negative effects on agricultural productivity in Africa. The cover management factor (C factor) related to vegetation is the most influential parameter in the estimation of water erosion and reflects key interrelated cover and management information on the soil retention service of water erosion. However, the estimations of C factor on a large-scale display large differences. The Nile River Basin has suffered severe water erosion for several decades, while the soil retention service of water erosion from vegetation is still unclear. This study assesses the spatiotemporal change in soil retention service in the Nile River Basin from 1982 to 2013, based on the Revised Universal Soil Loss Equation (RUSLE) model. The comparison of six kinds of estimation methods showed that four methods could be used, whereas the other two were inappropriate. The average annual soil retention service based on the C factor variance ranged from 934.08–1240.56 (t ha−1 y−1), and spatially increased from south to north. The area of significant increasing and decreasing tendencies were 1.31 × 104–1.94 × 105 km2 and 5.68 × 103–3.81 × 104 km2, respectively. These results provide a reference for the selection of C factor methods and spatiotemporal evidence of the soil retention service of water erosion in the Nile River Basin.

Oceanic glaciers are one of the most sensitive indicators of climate change. However, remotely sensed evidence of land cover change in the oceanic glacial region is still limited due to the cloudy weather during the growing season. In addition, the performance of common machine learning classification algorithms is also worth testing in this cloudy, frigid and mountainous region. In this study, three algorithms, namely, the random forest, back-propagation neural network (BPNN) and convolutional neural network algorithms, were compared in their interpretation of the land cover change in south-eastern Tibet and resulted in three findings. (1) The BPNN achieves the highest overall accuracy and Kappa coefficient compared with the other two algorithms. The overall accuracy was 97.82%, 98.07%, 98.92%, and 94.63% in 1990, 2000, 2007, and 2016, and the Kappa coefficient was 0.958, 0.959, 0.980, and 0.918 in these four years, respectively. (2) From 1990 to 2000, the dominant land cover was ice at the landscape level. The landscape fragmentation decreased and the landscape aggregation increased. From 2000 to 2016, the dominant land cover transformed from ice to vegetation. The vegetation aggregation increased, while the ice aggregation decreased. (3) When the elevation was less than 4 km, the vegetation was usually transformed into bare land; otherwise, the probability of direct transformation between vegetation and ice increased. The findings on the land cover transformation in the oceanic glacial region by multiple classification algorithms can provide both long-term evidence and methodological indications to understand the recent environmental change in the “third pole”.

In recent decades, climate change has affected vegetation growth in terrestrial ecosystems. We investigated spatial and temporal patterns of vegetation cover on the Loess Plateau’s Shaanxi-Gansu-Ningxia region in central China using MODIS-NDVI data for 2000–2014. We examined the roles of regional climate change and human activities in vegetation restoration, particularly from 1999 when conversion of sloping farmland to forestland or grassland began under the national Grain-for-Green program. Our results indicated a general upward trend in average NDVI values in the study area. The region’s annual growth rate greatly exceeded those of the Three-North Shelter Forest, the upper reaches of the Yellow River, the Qinling–Daba Mountains, and the Three-River Headwater region. The green vegetation zone has been annually extending from the southeast toward the northwest, with about 97.4% of the region evidencing an upward trend in vegetation cover. The NDVI trend and fluctuation characteristics indicate the occurrence of vegetation restoration in the study region, with gradual vegetation stabilization associated with 15 years of ecological engineering projects. Under favorable climatic conditions, increasing local vegetation cover is primarily attributable to ecosystem reconstruction projects. However, our findings indicate a growing risk of vegetation degradation in the northern part of Shaanxi Province as a result of energy production facilities and chemical industry infrastructure, and increasing exploitation of mineral resources.

Changes in biodiversity owing to vegetation degradation resulting from widespread urbanization demands serious attention. However, the connection between vegetation degradation and urbanization appears to be complex and nonlinear, and deserves a series of long-term observations. On the basis of the Normalized Difference Vegetation Index (NDVI) and the image’s digital number (DN) in nighttime stable light data (NTL), we delineated the spatiotemporal relations between urbanization and vegetation degradation of different metropolises by using a simplified NTL calibration method and Theil-Sen regression. The results showed clear and noticeable spatiotemporal differences. On spatial relations, rapidly urbanized cities were found to have a high probability of vegetation degradation, but in reality, not all of them experience sharp vegetation degradation. On temporal characteristics, the degradation degree was found to vary during different periods, which may depend on different stages of urbanization and climate history. These results verify that under the scenario of a vegetation restoration effort combined with increasing demand for a high-quality urban environment, the urbanization process will not necessarily result in vegetation degradation on a large scale. The positive effects of urban vegetation restoration should be emphasized since there has been an increase in demand for improved urban environmental quality. However, slight vegetation degradation is still observed when NDVI in an urbanized area is compared with NDVI in the outside buffer. It is worthwhile to pay attention to landscape sustainability and reduce the negative urbanization effects by urban landscape planning.

Soil moisture (SM) is an important variable for the terrestrial surface system, as its changes greatly affect the global water and energy cycle. The description and understanding of spatiotemporal changes in global soil moisture require long time-series observation. Taking advantage of the European Space Agency (ESA) Climate Change Initiative (CCI) combined SM dataset, this study aims at identifying the non-linear trends of global SM dynamics and their variations at multiple time scales. The distribution of global surface SM changes in 1979−2016 was identified by a non-linear methodology based on a stepwise regression at the annual and seasonal scales. On the annual scale, significant changes have taken place in about one third of the lands, in which nonlinear trends account for 48.13%. At the seasonal scale, the phenomenon that “wet season get wetter, and dry season get dryer” is found this study via hemispherical SM trend analysis at seasonal scale. And, the changes in seasonal SM are more pronounced (change rate at seasonal scales is about 5 times higher than that at annual scale) and the areas seeing significant changes cover a larger surface. Seasonal SM fluctuations distributed in southwestern China, central North America and southern Africa, are concealed at the annual scale. Overall, non-linear trend analysis at multiple time scale has revealed more complex dynamics for these long time series of SM.

Numerous global-scale land-cover datasets have greatly contributed to the study of global environmental change and the sustainable management of natural resources. However, land-cover datasets inevitably experience information loss because of the nature of the uncertainty in the interpretation of remote-sensing images. Therefore, analyzing the spatial consistency of multi-source land-cover datasets on the global scale is important to maintain the consistency of time and consider the effects of land-cover changes on spatial consistency. In this study, we assess the spatial consistency of five land-cover datasets, namely, GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO, at the global and continental scales through climate and elevation partitions. The influencing factors of surface conditions and data producers on the spatial inconsistency are discussed. The results show that the global overall consistency of the five datasets ranges from 49.2% to 67.63%. The spatial consistency of Europe is high, and the multi-year value is 66.57%. In addition, the overall consistency in the EF climatic zone is very high, around 95%. The surface conditions and data producers affect the spatial consistency of land-cover datasets to different degrees. CCI LC and GLCNMO (2013) have the highest overall consistencies on the global scale, reaching 67.63%. Generally, the consistency of these five global land-cover datasets is relatively low, increasing the difficulty of satisfying the needs of high-precision land-surface-process simulations.

The agro-pastoral transitional zone (APTZ) in Northern China is one of the most important ecological barriers of the world. The commonly-used method to identify the spatial distribution of ATPZ is to apply a threshold rule on climatic or land use indicators. This approach is highly subjective, and the quantity standards vary among the studies. In this study, we adopted the spatial continuous wavelet transform (SCWT) technique to detect the spatial fluctuation in normalized difference vegetation index (NDVI) sequences, and as such identify the APTZ. To carry out this analysis, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI 1-month data (MODND1M) covering the period 2006⁻2015 were used. Based on the spatial variation in NDVI, we identified two sub-regions within the APTZ. The temporal change of APTZ showed that although vegetation spatial pattern changed annually, certain areas appeared to be stable, while others showed higher sensitivity to environmental variance. Through correlation analysis between the dynamics of APTZ and precipitation, we found that the mean center of the APTZ moved toward the southeast during dry years and toward the northwest during humid years. By comparing the APTZ spatial pattern obtained in the present study with the outcome following the traditional approach based on mean annual precipitation data, it can be concluded that our study provides a reliable basis to advance the methodological framework to identify accurately transitional zones. The identification framework is of high importance to support decision-making in land use management in Northern China as well as other similar regions around the world.

The North Yellow Sea Basin is a Mesozoic and Cenozoic basin. Based on basin-margin facies, sedimentary thinning, size and shape of the basin and vitrinite reflectance, North Yellow Sea Basin is not a residual basin. Analysis of the development of the basin’s three structural layers, self-contained petroleum systems, boundary fault activity, migration of the Mesozoic–Cenozoic sedimentation centers, different basin structures formed during different periods, and superposition of a two-stage extended basin and one-stage depression basin, the North Yellow Sea Basin is recognized as a superimposed basin.