The healthy synergistic development of the Food-Water-Energy-Ecosystem (FWEE) nexus is crucial for maintaining basin ecological security and economic growth. However, achieving synergistic FWEE development within inland river basins remains a significant challenge to attaining Sustainable Development Goals (SDGs). Therefore, this study proposes an integrated analytical framework designed for the synergistic management of multiple FWEE-related SDGs. Using the Aral Sea Basin as a case study, it develops models for each relevant SDGs and designs twenty-seven scenarios. These scenarios vary in water resource availability, food demand, and irrigation efficiency. A bi-level fuzzy multi-objective programming approach is applied to manage the interlinkages between different SDGs. The results show that (1) agricultural irrigation efficiency significantly impacts overall system benefits. As irrigation efficiency improves, the amount of water allocated to the ecological sector increases significantly. Hence, expanding drip irrigation coverage or enhancing water conservancy facilities is crucial for the basin’s sustainable development. (2) Optimizing the planting structure of wheat and maize can further help reduce water use in agriculture. (3) The power sector has low water use but provides significant system benefits for each unit of water. Therefore, stronger cooperation among upstream and downstream countries is crucial to secure water for power generation. These findings improve policymakers’ understanding of the connections between SDGs objectives within the FWEE nexus and offer solutions for promoting sustainable development.

Predicting streamflow in ungauged catchments remains a key challenge in hydrology, particularly in determining the optimal number of donor catchments (N) for model training. Using the U.S. CAMELS dataset (671 catchments) and a random forest model, we systematically evaluate the impact of N on prediction accuracy under two donor-selection strategies: geographic proximity (Geo) and attribute-based similarity (Similarity). Results show a clear but nonlinear relationship between prediction accuracy and N. The optimal number of donor catchments (OPN) varies significantly across regions, reflecting spatial heterogeneity in hydrological similarity. The Geo strategy generally achieves high accuracy with relatively small N values (< 20) and exhibits strong spatial coherence, whereas the Similarity strategy requires larger donor sets but shows greater robustness across varying donor scales. These findings demonstrate that donor-set size should be adaptively determined rather than fixed a priori, providing practical guidance for regionalization and data-driven prediction in ungauged catchments.

Changes in atmospheric humidity affect plant photosynthesis and transpiration processes, thereby altering vegetation growth conditions and crop yields. Therefore, high spatial and temporal resolution atmospheric relative humidity data are particularly crucial when studying the impacts of climate change on agricultural production. In this study, based on daily relative humidity data from ground observation stations in China from 1951 to 2020, we employed a Random Forest (RF) spatial interpolation framework to construct a 1 km & times; 1 km spatial resolution daily relative humidity raster dataset covering the period 1951-2020. This framework incorporated longitude, latitude, elevation, and spatial autocorrelation variables (spatial proximity values and distance) as covariates, effectively capturing nonlinear relationships and spatial dependencies. Cross-validation indicated that after the dense coverage of observation stations in 1956, the DISO between interpolated data and observation station data stabilized around 0.5, demonstrating high overall consistency. Using the constructed dataset, we analyzed the multiscale spatiotemporal variation characteristics of relative humidity in China from the perspective of agricultural spatial zoning, based on linear trend estimation, the Mann-Kendall mutation test, and wavelet analysis. From 1956 to 2020, China's annual mean relative humidity showed a significant downward trend of -0.26% decade- 1, with the most pronounced decline observed in the PHMYRR. The relative humidity of most agricultural zones underwent abrupt changes during the 1990s or early 21st century, exhibiting significant periodic variations with periods of approximately 7 and 37 yr. The regional variation of relative humidity in China was demarcated by 120 degrees E, 45 degrees N-95 degrees E, 30 degrees N. To the east of this line, relative humidity typically showed larger decreases, while west of it, the decrease was relatively minor. The results of the study provide an important dataset for the long-term series of climate change in China, as well as scientific conclusions and theoretical guidance for the understanding of climate fluctuation processes and industrial and agricultural production activities in agricultural zones.

The increasing frequency and intensity of climate extremes are fundamentally reshaping the terrestrial carbon cycle, yet their impacts remain poorly constrained due to limitations in observational coverage and coarse-resolution datasets. Here, we quantify the effects of multiple extreme events—including heatwaves, droughts, extreme precipitation, and cold extremes—on terrestrial carbon sequestration using weather station–scale observations across the Northern Hemisphere, combined with machine learning–derived estimates of gross primary production (GPP) and ecosystem respiration (Reco). We show that droughts and heatwaves exert the strongest and most widespread impacts, frequently co-occurring as compound events that amplify carbon losses. In contrast, the impacts of extreme precipitation and cold extremes—often underrepresented in previous studies—are non-negligible and strongly dependent on seasonal timing. Notably, the timing of extremes critically modulates ecosystem responses, with distinct mechanisms governing spring versus summer events. By leveraging station-scale observations, this study reveals systematic discrepancies between observed and reanalysis-derived extremes and provides a more precise characterization of ecosystem responses. These findings highlight the need to integrate data-driven and process-based approaches to improve predictions of carbon–climate feedbacks under increasing climate variability.

Most respiratory pathogens exhibit distinct seasonal and periodic outbreak patterns driven by climatic factors. However, predictive models that jointly consider climate, air quality index (AQI), and socioeconomic variables are lacking. We retrospectively analyzed targeted or metagenomic next-generation sequencing data from 153,544 respiratory samples collected from 1,880 centers across 30 provinces in China between September 2022 and September 2024. Monthly positivity rates were matched with geographic, climatic, AQI, and GDP data. CO(0.098 ± 0.016), HCHO(0.096 ± 0.021), O3(0.102 ± 0.019), sunshine hours(0.103 ± 0.028), wind speed(0.114 ± 0.024), and GDP(0.095 ± 0.019). were identified as the key geographical factors for the positivity across most respiratory pathogens via mean Gini index reduction, and a gradient boosting decision tree(GBDT) model was trained and benchmarked against other AI methods using the DISO metric. This model accurately simulated the epidemiological trends from September 2022 to September 2024 and outperformed alternative models with the lowest DISO metric of 0.12 in influenza A, 0.21 in SARS-CoV-2, 0.25 in RSV. The GBDT model was used to predict the short-term epidemic of 10 respiratory pathogens between October and December 2024. The predictions showed consistent trends with the external validation cohort for RNA viruses including SARS-CoV-2 and influenza A virus, but differed for bacterial pathogens. Integrating air quality, climatic, and socioeconomic data yields robust predictions of respiratory infection dynamics in the short-term by the GBDT model, bolstering public health surveillance and offering a framework potentially applicable to other infectious diseases.

BACKGROUND:Previous studies have explored the relationships between dengue fever (DF) and its impact factors, using various models. However, few have considered the spatial heterogeneity of these relationships in simulating DF variations. METHODS:This study analyzed monthly DF incidence and rate data across 34 provincial-level administrative divisions (PLADs) in China from 2004 to 2019, along with climatic and socioeconomic variables. Nine impact factors were included: six climatic variables - minimum temperature (Tmin), mean temperature (Tmean), maximum temperature (Tmax), relative humidity (RH), precipitation (PRCP), and the El Niño-Southern Oscillation (ENSO) - and three socioeconomic variables - population density (PD), per capita regional gross domestic product (pcGDP) and number of foreign visitor arrivals (NFVAs). We introduced a modeling system that incorporates the spatial heterogeneity of these factors and integrates four artificial intelligence (AI) algorithms: support vector machine (SVM), artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM). This system was implemented under a novel framework - grid-by-grid, multi-algorithms, optimal combination (GGMAOC) - to explore the spatial heterogeneity of both algorithms and impact factors. RESULTS:From 2004 to 2019, DF in China showed a significant upward trend, an average annual increase of 1618 cases. Among the impact factors, socioeconomic variables exhibited stronger associations, with correlation coefficients exceeding 0.77. In Yunnan, the ANN model performed best (DISO=0.18), with PDLag3 contributing over 31%. In Guangdong, the RF model was optimal (DISO=0.26), with TmaxLag2 contributing over 67%. Performance varied across PLADs, which highlights the spatial heterogeneity of both impact factors and algorithmic responses. Importantly, the proposed GGMAOC framework substantially outperformed the traditional stepwise regression approach. CONCLUSION:Our findings reveal that impact factor patterns vary across PLADs, emphasizing spatial heterogeneity and the need for PLAD-specific modeling. The GGMAOC framework identifies key impact factors and optimal models for DF dynamics, offering high predictive confidence and broad applicability to other diseases with spatially heterogeneous impact factors. Furthermore, the successful model construction in GD and YN and the identification of challenges in TW modeling further support the necessity and innovation of adopting a spatially heterogeneous modeling framework in infectious disease modeling as proposed in this study.

Future variations of global vegetation are of paramount importance for the socio-ecological systems. However, up to now, it is still difficult to develop an approach to project the global vegetation considering the spatial heterogeneities from vegetation, climate factors, and models. Therefore, this study first proposes a novel model framework named GGMAOC (grid-by-grid; multi-algorithms; optimal combination) to construct an optimal model using six algorithms (i.e., LR: linear regression; SVR: support vector regression; RF: random forest; CNN: convolutional neural network; and LSTM: long short-term memory; transformer) based on five climatic factors (i.e., Tmp: temperature; Pre: precipitation; ET: evapotranspiration, SM: soil moisture, and CO2). The optimal model is employed to project the future changes in leaf area index (LAI) for the global and four sub-regions: the high-latitude northern hemisphere (NH), the mid-latitude NH, the tropics, and the mid-latitude southern hemisphere. Our results indicate that global LAI will continue to increase, with the greening rate expanding to 2.25 times in high-latitude NH by 2100 against the 1982-2014 period. Moreover, RF shows strong applicability in the global and NH models. In this study, we introduce an innovative model GGMAOC, which provides a new optimal model scheme for environmental and geoscientific research.

Central Asia (CA) faces escalating threats from increasing temperature, glacier retreat, biodiversity loss, unsustainable water use, terminal lake shrinkage, and soil salinization, all of which challenge the balance between ecological integrity and socio-economic development essential for achieving Sustainable Development Goals. However, a comprehensive understanding of priority areas from a multi-dimensional perspective is lacking, hindering effective conservation and development strategies. To address this, we developed a comprehensive assessment framework with a tailored indicator system, enabling a spatial evaluation of CA’s priority areas by integrating biodiversity, ecosystem services (ESs), and human activities. Combining zonation and geographical detectors, this approach facilitates spatial prioritization and examines ecological and socio-economic heterogeneity. Our findings reveal a heterogeneous distribution of priority areas across CA, with significant concentrations in eastern mountainous regions, river valleys, and oasis agricultural lands. We identified 184 key districts crucial for ecological and societal sustainability. Attribution analysis shows that natural factors like soil types, precipitation, and evapotranspiration significantly shape these areas, influencing human activities and the distribution of biodiversity and ESs. Multi-dimensional analysis indicates existing protected areas cover only 15 % of the top 30 % priority areas, revealing substantial conservation gaps. Additionally, a 38 % overlap between ESs and human activities, along with 63.25 % congruence in integrated areas, underscores significant human impacts on ecological systems and their dependency on ESs. Given CA’s limited resources, it is crucial to implement measures that strengthen conservation efforts, align ecological preservation with socio-economic demands, and enhance resource efficiency through sustainable integrated land and water resource management.

OBJECTIVE:The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern in recent years. Machine learning methods have proven effective in enhancing prediction accuracy. However, existing approaches may lack interpretability regarding underlying mechanisms. Therefore, we aim to employ an interpretable machine learning approach utilizing nationwide cross-sectional data to predict pre-diabetic risk and quantify the impact of potential risks. METHODS:The LASSO regression algorithm was used to conduct feature selection from 30 factors, ultimately identifying nine non-zero coefficient features associated with pre-diabetes, including age, TG, TC, BMI, Apolipoprotein B, TP, leukocyte count, HDL-C, and hypertension. Various machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANNs), Decision Trees (DT), and Logistic Regression (LR), were employed to compare predictive performance. Employing an interpretable machine learning approach, we aimed to enhance the accuracy of pre-diabetes risk prediction and quantify the impact and significance of potential risks on pre-diabetes. RESULTS:From the China Health and Nutrition Survey (CHNS) data, a cohort of 8,277 individuals was selected, exhibiting a disease prevalence of 7.13%. The XGBoost model demonstrated superior performance with an AUC value of 0.939, surpassing RF, SVM, DT, ANNs, Naive Bayes, and LR models. Additionally, Shapley Additive Explanation (SHAP) analysis indicated that age, BMI, TC, ApoB, TG, hypertension, TP, HDL-C, and WBC may serve as risk factors for pre-diabetes. CONCLUSION:The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression of pre-diabetes to T2DM.

This paper presents an optimal control strategy and cost-effectiveness analysis for a dynamic model of cystic echinococcosis. A novel, time-varying model of echinococcosis was developed by integrating current prevention and control measures with the impact of health education. The model's basic reproduction number under constant control is calculated, and its global dynamic behavior is analyzed. Using the optimal control theory, the optimal solution for the time-varying control model is derived. Model parameters were estimated based on actual data and control measures from Baiyin City, Gansu Province, China, and the resulting numerical fit proved satisfactory. Additionally, a numerical simulation of the optimal solution was performed for 24 proposed prevention and control measures, along with an analysis of cost-effectiveness. The results validated the effectiveness of Baiyin City's measures and indicated that implementing an integrated program of health education and treatment enhances the efficacy of prevention and control strategies. Findings further suggest that while health education or treatment alone can reduce echinococcosis transmission, a combined approach is more efficient and cost-effective. Specifically, a comprehensive prevention and control program - including human health education, safe disposal of infected livestock carcasses, preventing dogs from consuming infected organs, dog deworming, sheep immunization and human treatment - is identified as the most cost-effective and effective strategy. A sensitivity analysis of key strategy parameters was also conducted. This study provides valuable theoretical support for the economically viable and effective prevention and control of cystic echinococcosis.

The winter sea ice over the Barents-Kara Sea (BKS) is reducing at an alarming rate, which impacts the Arctic shipping routes and the local ecosystems as well as the climate system across other regions. Consequently, it is imperative to project the winter ice-free state in this region to adequately prepare for future climate change and its impacts. However, most models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) show higher climatological values, weaker decreasing trends, and lower interannual variabilities in winter BKS sea ice concentration compared to observation. Additionally, it can be also seen that there exist great uncertainties in the projections of different models on whether the BKS region will be ice-free in future winters and the ice-free period, which spans almost the entire 21st century. Therefore, the study adopts two approaches developed from distance between indices of simulation and observation (DISO) method to project the winter ice-free period of the BKS under different emission scenarios. The results indicate that under SSP1–2.6, the winter BKS will not be ice-free by 2100. For SSP2–4.5, SSP3–7.0, and SSP5–8.5, the winter BKS is projected to be ice-free during 2076–2086, 2063–2068, and 2049–2061, respectively. By employing the DISO method, the projection uncertainty is reduced and the results highlight the urgency of reducing greenhouse gas emissions to delay the winter ice-free period over the BKS. These projections are intended to provide a reference for policymakers.

Existing evidence suggested that the risk of tuberculosis (TB) infection was associated to the variations in temperature and PM2.5. A total of 9,111 cases of TB were reported in Ningxia Hui Autonomous Region, China from 2013 to 2015 on a daily basis, and 57.2% of them were male. The TB risk was more prominent for a lower temperature in males (RR of 1.724, 95% CI: 1.241, 2.394), the aged over 64 years (RR of 2.241, 95% CI: 1.554, 3.231), and the high mobility occupation subpopulation (RR of 2.758, 95% CI: 1.745, 4.359). High concentration of PM(2.5 )showed a short-term effect and was only associated with an increased risk in the early stages of exposure for the female, and aged 36-64 years group. There were 15.06% (1370 cases) of cases of TB may be attributable to the temperature, and 2.94% (268 cases) may be attributable to the increase of PM2.5 exposures. Low temperatures may be associated with significantly increase in the risk of TB, and high PM2.5 concentrations have a short-term association on increasing the risk of TB. Strengthening the monitoring and regular prevention and control of high risk groups will provide scientific guidance to reduce the incidence of TB.

Under the background of climate change and global warming, extreme drought events in China are becoming increasingly frequent. Drought is one of the primary natural causes of damage to China's agriculture, economy, and environment, making timely, accurate, and high-resolution drought monitoring particularly crucial. The global standardized precipitation - evapotranspiration index database (SPEIbase) is a widely accepted and used global-scale drought monitoring product. However, limited by its spatial resolution of 0.5 degrees, it is difficult to describe the local spatio-temporal structure of drought. How to improve its spatial resolution while maintaining spatio-temporal consistency is one of the current research hotspots. Based on the response of vegetation growth status to drought, this paper proposes a simple and feasible SPEI prediction method, which improves the resolution of SPEIbase from 0.5 degrees to 1 km. Sixteen remote sensing inversion indices, reflectance and elevation data related to drought were selected from Google Earth Engine (GEE) as features. After preprocessing such as gridding and sample balancing, a random forest regression model was constructed to achieve high spatial resolution prediction of SPEI. SPEI with time scales of 1, 3, 6, 9, 12 and 24 months in July 2020, August 2019 and August 2018 in China was selected for experiments. The accuracy of 1 km resolution SPEI was evaluated through metrics such as root mean square error (RMSE), Pearson correlation coefficient (PCC) and determination coefficient (R2). At the same time, it was compared with the existing 1 km resolution SPEI dataset and the site-scale SPEI values. The results show that the method in this paper can obtain accurate prediction results more stably. The PCC and R2 of different months and multiple time scales are all higher than 0.9 and 0.8, and the RMSE is lower than 0.4, showing a good application prospect. Despite the good consistency between the Proposed SPEI and SPEIbase with the site-scale SPEI values, there is still significant room for improvement.

Since the arid regions of Central Asia (ACA) are located in the interior of Eurasia, water resources play a vital role in the stability of its ecosystem and economic development. Based on the terrestrial water storage anomaly (TWSA) of the Gravity Recovery and Climate Experiment (GRACE), we analyze the observed characteristics of the TWSA over the ACA during 2003–2014. Results indicate that the terrestrial water storage (TWS) in the region showed an overall declining trend from 2003 to 2014, and the autumn TWS in this region is the smallest compared to other seasons and exhibits a strong decreasing trend at least −4.5 cm/decade. This means water resources over the ACA are scarcer and more vulnerable in autumn. The Distance between Indices of Simulation and Observation (DISO) method is employed to evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating the autumn TWSA over the ACA. Compared with observational results, the autumn TWSA values captured by CMIP6 models are larger and the declining TWS trends are weaker. Using the optimal CMIP6 models, the statistical downscaling method constrains the projection results of autumn TWSA values over the ACA using the GRACE datasets. It shows autumn TWS will continue to decrease in most parts of the ACA in the future, and water scarcity will be the most severe in Tajikistan and southwestern Kazakhstan. Under SSP126, Tajikistan's TWSA is projected to decrease by 11.0 cm in the long term. This study reveals the current situation and possible future changes in TWS over the ACA in autumn, providing references for water resource management and sustainable development policies in this area to avoid losses caused by water scarcity.

AbstractThe countries of Central Asia are collectively known as Uzbekistan, Kyrgyzstan, Turkmenistan, Tajikistan and Kazakhstan. Central Asian countries have experienced significant warming in the last century as a result of global changes and human activities. Specifically, the five Central Asian countries’ populations and economies have increased, with Turkmenistan showing the fastest growth rates in GDP and per capita GDP. Farmland change, forestry activities, and grazing are examples of land use/land cover change and land management in Central Asia. Land degradation was primarily caused by rangeland degradation, desertification, deforestation, and farmland abandonment. The raised temperature, accelerated melting of glaciers, and deteriorated water resource stability resulted in an increase in the frequency and severity of floods, droughts, and other disasters. The increase of precipitation cannot compensate for the aggravation of water shortage caused by temperature rise in Central Asia. The ecosystem net primary productivity was decreasing over the past years, and the organic carbon pool in the drylands of Central Asia was seriously threatened by climate change. Grassland contributed the most to the increase of ecosystem service values in recent years. Most ecosystem functions decreased between 1995 and 2015, while they are expected to increase in the future (except for water regulation and cultural service/tourism). Global climate change does pose a clear threat to the ecological diversity of Central Asia.

Abstract. Evaluating, ranking, and clustering (ERC) stand as fundamental tasks in scientific research, each requiring a mathematical foundation. This study presents an ERC system anchored in the CCHZ-DISO (Chen, Chen, Hu, and Zhou-Distance between Indices of Simulation and Observation) system. Previous research underscores the optimality achieved by the CCHZ-DISO system (Hu et al., 2022). Since the inception of CCHZ- DISO-series research by Hu et al. (2019), DISO has found extensive applications across various domains including geography, hydrology, and economics. Analogous to the CCHZ-DISO system's construction, the ERC system employs the Euclidean distance to perform evaluating, ranking, and clustering tasks. Furthermore, illustrative examples are provided to elucidate the application of the ERC system. In fact, the ERC system unified the evaluating, ranking, and clustering tasks in one simple equation which is more flexible and simpler than the present system. It will have a more widely application than CCHZ-DISO in diverse scientific domains.

The prevention and control of the spread of Cystic Echinococcosis is an important public health issue. Health education has been supported by many governments because it can increase public awareness of echinococcosis, promote the development of personal hygiene habits, and subsequently reduce the transmission of echinococcosis. In this paper, a dynamic model of echinococcosis is used to integrate all aspects of health education. Theoretical analysis and numerical model fitting were used to quantitatively analysed by the impact of health education on the spread of echinococcosis. Theoretical findings indicate that the basic reproduction number is crucial in determining the prevalence of echinococcosis within a given geographical area. The parameters of the model were estimated and fitted by using data from the Ningxia Hui Autonomous Region in China, and the sensitivity of the basic reproduction number was analysed by using the partial rank correlation coefficient method. These findings illustrate that all aspects of health education demonstrate a negative correlation with the basic reproduction number, suggesting the effectiveness of health education in reducing the basic reproduction number and mitigating the transmission of echinococcosis, which is consistent with reality. Particularly, the basic reproduction number showed a strong negative correlation with the burial rate of infected livestock ($ b $) and the incidence of infected livestock viscera that is not fed to dogs ($ q $). This paper further analyzes the implementation plan for canine deworming rates and sheep immunity rates, as well as the transmission of infected hosts over time under different parameters $ b $ and $ q $. According to the findings, emphasizing the management of infected livestock in health education has the potential to significantly reduce the risk of echinococcosis transmission. This study will provide scientific support for the creation of higher quality health education initiatives.