Jiangxi Province， as one of the first national ecological civilization pilot zones in China， holds a significant responsibility in ecological protection and sustainable development. Ecological vulnerability assessment is of great guiding value for ecological protection and restoration in Jiangxi Province. Based on the sub-watershed and raster evaluation units， combined with remote sensing image data， land use data， soil data， meteorological data， socio-economic data， etc.， an ecological vulnerability assessment framework was established using the ecological sensitivity-resilience-pressure （SRP） model to evaluate the spatiotemporal dynamics of ecological vulnerability in Jiangxi Province from 2000 to 2020， and the driving factors of ecological vulnerability changes were revealed using an interpretable machine learning model （XGBoost-SHAP）. The results indicate that： ① The areas with relatively low ecological vulnerability of Jiangxi Province were primarily distributed in the northeastern， northwestern， and southern mountainous regions， while areas with higher vulnerability were concentrated in the plains and riverbanks where human activities are intensive， such as the Poyang Lake plain area. The overall distribution was primarily characterized by mild and light vulnerability. ② In the years 2000， 2010， and 2020， the average ecological vulnerability index values were 0.224， 0.219， and 0.206， respectively， indicating a downward trend in the ecological vulnerability index. Among these， the areas where the ecological vulnerability index decreased accounted for 75.75% of the total area. ③ The changes in soil erosion intensity， FVC， percentage of soil erosion above moderate， and land use were key factors driving ecological vulnerability changes， with relative importance weights of 34.66%， 25.99%， 10.83%， and 10.63%， respectively. Moreover， the contributions of these factors exhibited significant spatial variation. The research findings can provide theoretical support for ecological environment protection in Jiangxi Province， while also offering important references and insights for the application of machine learning methods in the study of ecological vulnerability.

Straw return is a crucial agricultural practice for enhancing soil carbon accumulation and fertility， but it may induce nutrient limitation on soil microbes， potentially affecting CO2 emissions. Phosphorus （P）， a key nutrient， plays an essential role in this context， yet how P availability and straw return regulate the CO2 emissions of black soil remains poorly understood. We utilized soil samples from a long-term fertilization experiment in Gongzhuling black soil under a non-fertilized treatment. Nine gradients of P addition （0， 5， 10， 15， 30， 50， 80， 100， and 150 mg·kg-1 Na2HPO4 solution， on a P basis）， combined with straw addition， were carried out in a 28-day incubation experiment， during which their dynamic CO2 emissions were monitored to examine the effects of P and straw addition on CO2 emissions from the soil to identify the driving factors. The results indicated that in the absence of straw addition， the cumulative CO2 emissions from the tested black soil ranged from 311.0 to 386.5 mg·kg-1 （on a carbon basis）. As P addition rates increased， the cumulative CO2 emissions exhibited a nonlinear pattern， initially decreasing and then increasing， with the lowest value occurring at 15 mg·kg-1 P addition rate， which was 18.1% lower than that in the no-phosphorus treatment. Under straw addition， the cumulative CO2 emissions from the tested black soil ranged from 721.9 to 855.5 mg·kg-1， which was on average 2.3 times higher than those in the absence of straw. As P addition increased， the CO2 emissions showed a linear increase， peaking at 100 mg·kg-1 P addition rate， which was 18.5% higher than that in the no-phosphorus treatment. Correlation analysis revealed that， in the absence of straw， cumulative CO2 emissions were significantly positively correlated with dissolved organic carbon （DOC） and dissolved inorganic nitrogen （DIN）. Under straw addition， cumulative CO2 emissions were significantly positively correlated with DOC， DIN， and microbial biomass carbon （MBC）. P addition significantly altered the contents of DOC， DIN， MBC， and metabolic quotient （qCO2）， thereby regulating CO2 emissions. In conclusion， in the absence of straw， moderate P addition can alleviate carbon decomposition loss in the tested black soil. However， with straw addition， P addition might promote carbon decomposition， increasing CO2 emissions from the tested black soil. In practical agricultural production， applying phosphate fertilizer should be rationally regulated based on soil P availability to avoid excessive application， thereby achieving the dual objectives of carbon sequestration and CO2 emission reduction.

Nature reserves are effective avenues for providing ecosystem services and conserving biodiversity. Quantitative assessments of ecosystem services and their driving factors are crucial for conservation management and planning in nature reserves. Taking Saihanwula National Nature Reserve as the study area， this study quantified four ecosystem services， including water yield， carbon sequestration， habitat quality， and soil conservation， spanning from 2000 to 2020. We explored the influence of different driving factors on the spatio-temporal patterns of ecosystem services by using the Geodetector and Geographically Weighted Regression （GWR） models. Furthermore， we identified ecosystem service clusters based on the Self-Organizing Map （SOM） neural network and proposed management recommendations accordingly. The results showed that each ecosystem service in Saihanwula National Nature Reserve exhibited spatial heterogeneity. From 2000 to 2020， water yield and soil conservation significantly enhanced， increasing by 89.8% and 126.1%； carbon storage enhanced by 5.2%； and habitat quality remained essentially unchanged， with a change of only 0.7%. Precipitation and land use type were the main ecosystem service driving factors， and the combined effects of various driving factors were greater than those of a single factor. We identified three types of ecosystem service bundles， including the ecological core service bundle， ecological transition service bundle， and ecological fragile service bundle. We then proposed diversified zoning management recommendations for nature reserves from the perspectives of conservation， planning， and management. The results of this study can provide a scientific basis for the zoning management and optimization of Saihanwula National Nature Reserve.

The seasonal fluctuations in water levels significantly influence the wet-dry transitions in the water-level-fluctuating zone （WLFZ） of lakes. Bacteria， as an important microbial group in the biogeochemical cycles of this zone， profoundly affect the ecological functions of the area. A thorough investigation of the changes in soil bacterial communities during water level fluctuations is crucial for understanding the ecological functions of the WLFZ and its responses to environmental changes. Using high-throughput sequencing technology， the seasonal variation of bacterial communities in the Poyang Lake WLFZ from 2019 to 2020 was systematically analyzed. The results indicated that Proteobacteria， Acidobacteriota， and Chloroflexi were the dominant phyla in the WLFZ， while Actinobacteriota， Firmicutes， and Bacteroidota exhibited significant differences between submerged and exposed states. During flooding， bacterial diversity significantly decreased， and the influence of stochastic processes on community assembly increased. Additionally， bacterial co-occurrence networks under flooded conditions displayed higher complexity， modularity， and keystone species abundance. In exposed states， bacterial diversity correlated significantly positively with soil moisture content （P<0.01）， with TN and TP identified as primary drivers of community composition. Under flooded conditions， NH4+-N， TN， TP， and TOC were significantly correlated with bacterial diversity （P<0.01）， while soil pH and TOC were the key factors affecting community structure. The predicted functions of the bacterial community such as nitrogen fixation， carbohydrate degradation， aromatic compound degradation， and methane metabolism exhibited distinct seasonal shifts driven by water-level fluctuations. These findings enhance our understanding of how soil bacterial communities in lake WLFZ adapt structurally and functionally to seasonal hydrological changes.

The Yellow River Basin is an important water source in China， and heavy metal contamination in groundwater of the Yellew River Basin has achieved significant attention. This study analyzed the spatiotemporal variations， pollution levels， and sources of six heavy metals （Fe， Mn， As， Cu， Zn， and Pb） in 152 groundwater drinking water sources in the Yellow River Basin from 2018 to 2022 and assessed human health risks，which provided a scientific basis for groundwater pollution management and health risk management. The results showed that the over-standard rates of Fe， Mn， As， Cl-， ammonia nitrogen， and F- were 0.62%， 2.17%， 0.17%， 0.56%， 0.06%， 0.16%， and 0.33%， respectively. The average concentrations of Fe and Mn generally decreased from upstream to downstream； As peaked in the midstream； while Cu， Zn， and Pb were highest downstream. The heavy metal pollution index （HPI） indicated that 91.89% and 4.20% of the monitored data were at low and moderate pollution levels， respectively. Principal component analysis showed that Fe， Mn， and As in groundwater in the Yellow River Basin were mainly affected by natural factors， such as mineral oxidation and water-rock interaction， followed by human activities. Cu， Zn， and Pb were mainly affected by human factors， such as industrial， transportation， and agricultural activities. Correlation analysis showed that hydrochemical parameters ammonia nitrogen and Cl- were associated with the migration and transformation of Mn， Fe， and As. The health risk assessment results showed that heavy metals had no significant potential non-carcinogenic risk or carcinogenic risk to human body， but under extreme conditions， both the non-carcinogenic risk value （HQ） and carcinogenic risk value （CR） of As exceeded the standard values in different age groups， and As contributed more than 84% to total CR in three age groups. Therefore， it is recommended to improve the quality of groundwater drinking water sources by strengthening the source control measures and preventive measures and to strengthen the risk prevention and control of As to reduce its risk to human health.

Tire wear particles （TWP）， a major source of microplastics （MPs）， are persistent environmental pollutants with notable toxicity. Although TWP pollution in soils has garnered increasing attention， most research has concentrated on aquatic environments， leaving gaps in understanding its effects on plants and co-occurring soil pollutants. This study examined the impact of TWP on plant growth and soil pollutants， specifically antibiotic resistance genes （ARGs）， using the kidney bean （Phaseolus vulgaris L.） as a model. The findings should enhance predictions and develop mitigation strategies for soil TWP risks. Pot experiments and qPCR analysis were conducted to assess the effects of TWP at concentrations of 0.1% and 1% on kidney bean growth and soil ARGs abundance， while evaluating the role of soil properties in regulating these interactions. TWP exposure impaired seedling growth， reducing root development by 31.35%-49.03% and fresh weight by 31.40%-48.33%. At 0.1% TWP， ARGs abundance in rhizosphere and non-rhizosphere soils increased significantly by 13.58% and 14.83%， respectively. At 1% TWP， the enhancement of ARGs abundance weakened， and ARGs in non-rhizosphere soil significantly decreased compared to that in the control. However， higher TWP concentrations led to a significant increase in high-risk genes such as aadE and tetO， indicating that TWP pollution intensified the ARGs risk in soil. Correlation and redundancy analyses revealed that TWP inhibited plant growth and increased the pollution level and health risks of soil ARGs by increasing soil conductivity and depleting nutrients like dissolved organic carbon. This study provides critical insights into the effects of TWP residues on plant growth and soil ARGs. This study's findings aim to offer a scientific basis for the assessment of ecological risks posed by the combined contamination of TWP and ARGs in agricultural soils.

Studying the spatial patterns of ecosystem services and their driving factors is crucial for strengthening ecological management and promoting sustainable environmental development. This research focuses on Beijing as the study area. The InVEST model was applied to analyze the spatial correlation， trade-offs， and synergies of habitat quality， carbon storage， water yield， and soil retention from 2000 to 2020. The analysis utilized methods such as spatial autocorrelation， cold/hot spot analysis， and bivariate spatial autocorrelation analysis. Additionally， the XGBoost-SHAP model was employed to identify the key factors affecting ecosystem services. The results showed that： ① The high-value areas of habitat quality were mainly concentrated in regions with higher terrain and less interference from human activities. Carbon storage exhibited a spatial distribution trend that was high in the northwest and low in the southeast. The high-value areas of water yield were concentrated in urban areas， while the high-value areas of soil conservation were primarily distributed in the southwest and were more scattered in the north. ② Global spatial autocorrelation analysis indicated that the global Moran's I indices for the four ecosystem services all passed the significance test and demonstrated significant high-value aggregation characteristics. ③ There was a significant synergistic relationship between habitat quality， carbon storage， and soil conservation. However， there was a trade-off between water yield and these factors. ④ The XGBoost regression model showed good prediction performance on both the training set and the test set， with the predictive performance on the training set being better than that on the test set. The SHAP model analysis indicated that elevation was the key driving factor affecting the four ecosystem services. Slope significantly affected habitat quality， carbon storage， and soil conservation. Population density mainly affected habitat quality and water yield， while annual precipitation had an important influence on water yield and soil conservation. The research results can provide scientific support for optimizing the spatial patterns of ecosystem services and formulating ecological protection strategies in Beijing.

As a major global carbon emitter， China's provinces and municipalities contribute more than 90% of the country's carbon emissions， while the rest is mainly emitted by special administrative regions， trans-regional emission sources， and airspace and sea areas. How to accurately predict the carbon emissions of different provinces and municipalities and formulate emission reduction policies is the basis for realizing the national dual-carbon target and high-quality synergistic economic development. Taking Shaanxi Province， located in Northwest China， as an example， a top-down and bottom-up integrated RR-STIRPAT-LEAP model is developed using relevant cross-section data from 2000 to 2021， and the prediction accuracy is improved by optimizing the weights of sub-models. On this basis， the carbon emissions of Shaanxi Province from 2022 to 2060 are forecasted， and five joint scenarios are designed to simulate the dual-carbon pathway of Shaanxi Province in combination with the carbon sink absorption model. The ReliefF algorithm is used to analyze the important potential drivers of carbon emission reduction. The results found that the prediction accuracy of the RR-STIRPAT-LEAP-Shaanxi model was significantly better than that of a single model， and the optimized model error was 0.24%. It was predicted that Shaanxi Province will reach its peak in 2030， and the emissions （in terms of tons） will be 419.09 million tons （Mt）. Under the joint scenario， macro-control-EMT-F Shaanxi Province will achieve carbon neutrality by 2060， with an emission of -25.69 million tons， indicating that ecological carbon sinks played an important role in achieving carbon neutrality. Comparison of carbon emission changes under different joint scenarios revealed that upgrading the energy structure and improving energy efficiency were the key drivers of Shaanxi Province's low-carbon transition and that the implementation of macroeconomic and sectoral energy consumption control strategies could reduce more carbon emissions. ReliefF showed that Shaanxi Province's carbon emission reduction focused on the following industrial sectors in order： industry > power generation > agriculture > residential sector > transportation， storage， and postal services > construction > other services. Among them， agriculture was not only an important source of carbon emissions but also an important carbon sink， and its potential for emission reduction should not be ignored. After comprehensively analyzing the short and medium to long-term carbon emission pathways and carbon emission reduction drivers， this study provides a pathway map for the synergistic development of Shaanxi Province， which will provide a scientific basis for government policymakers and relevant enterprises to formulate low-carbon and high-quality economic development plans.

In order to investigate whether the addition of organic fertilizers could reduce the uptake and accumulation of polystyrene nanoplastics （PS-NPs） in the roots and stalks of cotton plants， a pot experiment was conducted using different amounts of organic fertilizers （0 g·kg-1 and 10 g·kg-1） combined with a fixed amount of PS-NPs （100 mg·kg-1）. Fluorescently labeled PS-NPs with a particle size of 200 nm were used as tracers. The results demonstrated that cotton roots absorbed PS-NPs and transferred them to the stalks. Quantitative analysis revealed that most of the particles were retained in the roots under the treatment of nanoplastics + organic fertilizer （MOF1）， with the fluorescence intensity of PS-NPs transferred to the stalks accounting for only 60.08% of that in the roots. Compared to those in the blank control （CK）， the SPAD value， stem dry matter mass， and leaf dry matter mass of cotton plants in the nanoplastic-only （MOF0） treatment were significantly reduced by 6.94%， 37.29%， and 22.36%， respectively. In contrast， the leaf area in the MOF1 treatment increased significantly by 10.12%. Additionally， plant height， stem thickness， leaf area， SPAD value， root dry matter mass， and leaf dry matter mass in the MOF1 treatment were all significantly higher than those in the MOF0 treatment， increasing by 9.28%， 13.99%， 10.12%， 7.82%， 21.05%， and 21.47%， respectively. Furthermore， compared to those in the CK treatment， the activities of superoxide dismutase （SOD）， peroxidase （POD）， and catalase （CAT） in the roots of MOF0-treated plants were significantly elevated by 30.91%， 11.61%， and 40.00%， respectively， while malondialdehyde （MDA） mass molar concentration decreased by 12.24%. For MOF1-treated plants， the activities of SOD and CAT in the roots were significantly increased by 44.51% and 100%， respectively， and MDA mass molar concentration was reduced by 26.43%. In conclusion， organic fertilizer effectively reduced the accumulation of PS-NPs in cotton plant stalks by 43.78%. It also significantly mitigated the transfer of PS-NPs from roots to stalks， thereby reducing the overall accumulation of PS-NPs in cotton plants.

This study integrates multi-source datasets from 24 counties （cities， and districts） in the Henan section of the Yellow River Basin to construct a composite rural ecosystem health （REH） assessment framework encompassing "resource-environment-social-economy" dimensions. Leveraging spatial Gini coefficient analysis， dominant factor identification， and Geographic Detector modeling， the spatiotemporal evolution， spatial differentiation patterns， and driving mechanisms of REH were systematically investigated. The results showed that： ① The comprehensive REH index in the study area ranged from 0.284 5 to 0.590 1， indicating moderate overall health levels with progressive improvement trends. Spatially， a distinct west-high， central-stable， east-low gradient emerged， characterized by environmental and social subsystem advancements offset by declining resource and economic subsystem performance. ② REH classification identified four primary categories （healthy， sub-healthy， unhealthy， and pathological） and seven subcategories. Healthy-type areas （20.83%） clustered in the western region， sub-healthy zones （37.50%） dominated central areas， while unhealthy （33.33%） and pathological-type systems concentrated in northern/eastern regions， notably in Wuzhi and Wen County. ③ Ecosystem service value per unit area emerged as the strongest single explanatory factor. Notably， socioeconomic drivers exhibited increasing influence on REH dynamics in recent years， with interactive factor effects demonstrating significantly higher explanatory power than individual factors. In summary， differentiated regulatory measures are proposed based on the above results： The development of healthy counties should be prioritized and "characteristic industries+ecological protection" should be continuously promoted. Sub-healthy counties should focus on improving the quality of arable land and promoting agricultural tourism. Unhealthy counties must highlight "cultural protection+comprehensive improvement，" and pathological counties should aim to implement targeted ecological restoration projects and socioeconomic revitalization programs to address systemic vulnerabilities.