China's dominant maize varieties, developed through breeding improvements, achieve high yields under current climatic conditions but face significant uncertainties in adapting to future climate change, posing risks to food security. This study assessed the impacts of future climate change on these varieties using a calibrated APSIMMaize model under SSP126 (low-emission) and SSP585 (high-emission) scenarios. To enhance interpretability, we integrated crop modeling with explainable machine learning, specifically a Random Forest (RF) surrogate model combined with SHapley Additive exPlanations (SHAP), to identify key climatic drivers of yield variation. Results indicated that under current conditions, varieties like MY73, ZD958, and JK968 exhibit high yields and large cultivation areas. However, future climatic scenarios projected shortened growth periods and widespread yield declines, with national maize yield expected to decrease by 5.4 % under SSP126 and 10.0 % under SSP585. Northeast China would experience the most severe yield reductions, with declines of 10.7 % by the 2050s and up to 19.0 % by the 2080s under SSP585. Mechanism analysis revealed that temperature-related factors negatively influenced maize yield under SSP126, while solar radiation and cold days had positive effects under SSP585, with other variables exerting significant negative impacts. These findings demonstrate the vulnerability of current maize varieties to future climate change and emphasize the necessity for breeding programs to develop stress-tolerant varieties, particularly with enhanced resistance to extreme temperatures. Additionally, by integrating crop modeling and explainable machine learning methods, this research provides a feasible approach for understanding how climate change affects crop production and guiding adaptation strategies to ensure food security.

China is the world's largest soybean consumer, yet persistently low domestic yields and weak economic returns limit supply. Current efforts mainly focus on technology, but the factors influencing yield and profitability under real farm conditions remain poorly understood. Here, we integrate 609 farmer surveys with machine learning and optimization techniques to identify the coupled biophysical and socioeconomic factors across China's two major production regions, using Heihe and Jining as the representative cities in Northeast China and Huang-Huai-Hai Plain. We show that soybean performance is shaped by both biophysical and socioeconomic factors. Education consistently influences yield in both regions, while farm size plays a significant role in Jining. Despite similar yields (2.85 vs. 2.87 Mg ha-1), the determinants differ: In Heihe, performance is mainly influenced by variety, pesticide, herbicide and cropping patterns, whereas in Jining it is driven by precipitation, mechanization, temperature and irrigation. Profitability differs markedly, with higher net returns in Jining (4640 CNY ha-1) compared to Heihe (2030 CNY ha-1), reflecting structural differences in prices and land rental costs. Optimization results suggest that coordinated management improvements could increase profits by 1142 to 1859 CNY ha-1. Overall, the findings highlight that profitability, rather than yield alone, is the primary constraint shaping soybean systems, providing a data-driven framework for region-specific optimization and policy design.

Mineral-associated organic carbon (MAOC) is one of the largest and most persistent soil C reservoirs, with major implications for soil functioning and climate mitigation. However, bulk MAOC is chemically heterogeneous and may contain organic matter with contrasting solubility, chemical composition, source-related signatures, and environmental controls, which remain poorly understood. Here, we used archived surface and subsurface soils from 43 National Ecological Observatory Network sites across North America to isolate MAOC and partition it into soluble fractions, which were released by repeated hydrofluoric acid (HF) extraction followed by water rinsing, and insoluble fractions which comprised the extraction residue. Across sites, the soluble fraction accounted for 11–96% of total MAOC, whereas the insoluble fraction accounted for 5–89%. The soluble fraction exhibited a Langmuir-type accumulation pattern and was enriched in phenolic and carboxyl functional groups, as well as lignin, tannin, and condensed aromatic-like molecules. In contrast, the insoluble fraction increased nonlinearly without an apparent asymptote across the observed MAOC range and was enriched in alkyl C and non-aromatic molecular classes, including lipids, carbohydrates, and proteins. Composition-based estimates indicated stronger plant-associated contributions in soluble MAOC and stronger microbial-associated contributions in insoluble MAOC. Despite these chemical contrasts, radiocarbon values did not differ detectably between the soluble and insoluble MAOC fractions; instead, Δ14C declined with soil depth and increased with mean annual precipitation. Environmental analyses showed that a higher contribution of soluble MAOC to bulk MAOC was associated with deeper soils and wetter, more acidic, Fe/Al-rich conditions. These findings suggest that bulk MAOC contains operationally separable fractions with distinct solubility, accumulation behavior, chemical composition, source-related signatures, and environmental associations. Accounting for this heterogeneity may improve conceptual and process-based models of MAOC dynamics.

Robust quantification of crop status in real-time is essential for agile decision-making. While use of unmanned aerial vehicle (UAV) data appears promising in this vein, the contribution and transferability of various features (e.g. vegetation indices, plant height and texture features) in crop above ground biomass (AGB) prediction remain poorly understood. Here, our objectives were to (1) evaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features, (2) elicit the contribution of various UAV features, (3) assess the transferability of features across growth stages and sites. Four field experiments, incorporating several water and nitrogen treatments across two sites, were assembled for use in AGB prognostics. We invoked four ML algorithms-Random forest (RF), Lasso regression (LR), K-nearest neighbors (KNN) and a stacked ensemble integrating the three methods (SML)-to predict wheat AGB using multiple UAV data and phenological information. Additionally, interpretable ML techniques were employed to elucidate the influence of UAV features on AGB prediction across growth stages. Our results showed that all algorithms exhibited robust performance in predicting wheat biomass, with RMSE values of 1.64, 1.71, 1.71, and 1.57 Mg ha-1 for RF, LR, KNN, and SML, respectively. RF predominantly relied on plant height features, LR leveraged vegetation indices, and KNN prioritized texture features, while SML synthesized the advantages of multiple ML algorithms. Fusion of multiple datasets amplified model prognostic capacity and scalability, with R2 and rRMSE of 0.92 and 22 % when using data from external sites. Features pertaining to vegetation indices and plant height during vegetative growth and around flowering had seminal contributions of model predictions. Texture features significantly reduced the saturation effect during the reproductive stage but diminished the model's transferability during the vegetative stage. Complementarity among data types enhanced effectiveness of ensemble machine learning, which leverages strengths of diverse data to improve the accuracy and robustness of AGB predictions. Future studies could combine multiple sources of remote sensing, such as LiDAR and thermal infrared alongside system modeling, to improve ML accuracy and generalization capability.

Context or problem: Climate change greatly threatens global food security, particularly maize production in the North China Plain (NCP), a vital breadbasket region. It is crucial to develop effective adaptation strategies to counteract the negative effects of climate change on agricultural production. Objective or research question: This study aims to quantify the impacts of climate change on maize production and investigate feasible adaptation strategies to mitigate its adverse effects on yields, shedding light on the role of field crop systems optimization in climate change adaptation. Methods: This study evaluated the impacts of projected climate change on maize yields under a high-emission scenario (SSP585) by coupling a calibrated Agricultural Production Systems sIMulator (APSIM) model with global climate models (GCMs). Three adaptation strategies-changing sowing dates (CSD), optimizing genotypes (OGT), and a combined approach (CSD&OGT)-were proposed and assessed under varying climatic and soil conditions (or Environment). Results and conclusions: Sensitivity analysis revealed that, in addition to radiation use efficiency and the maximum grain number coefficient, key phenological parameters, including thermal time from emergence to the end of juvenility and from flowering to maturity, significantly influenced maize yield. Model simulation results indicated that if no adaptation measures are taken, future climate change could reduce maize grain yields by 1.0-11.8 % in 82 % of NCP regions during the 2050s (2041-2070) and 1.3-18.5 % in 90 % of regions during the 2080s (2071-2100), compared with the baseline (1990-2020). Among the adaptation measures examined, the CSD&OGT strategy demonstrated the greatest potential for mitigating climate-induced yield losses, achieving maize yield increases of 13.3-65.1 % in the 2050 s and 5.5-58.3 % in the 2080 s. Under this strategy, optimal sowing dates of maize have been shifted to later dates, while the optimal cultivars are characterized by longer reproductive growth periods, higher radiation use efficiencies, and greater maximum grain numbers, enabling maize to achieve high yields under future climate conditions. Implications or significance: This study underscores the synergistic benefits of genotype (G), environment (E), and management (M) interactions (G x E x M) in mitigating the adverse impacts of climate change, highlighting the importance of integrating future breeding programs with adaptive agricultural practices to ensure food security under a changing climate.

Achieving sustainable crop productivity while preserving soil health remains a critical challenge in northeastern China. A comprehensive understanding of the influence of agronomic management practices on soil fertility and crop yield is vitally important. Therefore, the present study evaluates the responses of soil organic carbon (SOC), total nitrogen (TN), and crop yield to agronomic management practices in northeastern China through a meta-analysis of 1,865 paired observations from 86 studies. Results showed that soil fertility improved substantially with SOC increasing by 7.52% and TN by 7.02%, which can be attributed to a range of field management practices. Among these, the co-application of organic and chemical fertilizer showed the greatest soil benefits (+26.47% SOC, +23.02% TN), followed by straw returning (+5.76% SOC, +8.08% TN) and conservation tillage (+4.84% SOC, +2.40% TN). The integration of conservative agronomic management practices, i.e., crop rotation, straw returning, conservation tillage, and organic fertilizer amendments, significantly increased crop yield by 4.91%, which was primarily driven by crop rotation (+10.40%), the co-application of organic and chemical fertilizer (+8.39%), and straw returning (+4.85%), while minimum or no tillage reduced yield by 3.33% compared to traditional practices. Notably, the co-application of organic and chemical fertilizer and straw returning increased SOC and TN annually by 1.45%, 0.63%, 0.85%, and 0.30%, respectively. Overall, crop rotation, co-application of organic and chemical fertilizers, and straw returning substantially improved soil quality and crop yield, whereas minimum or no tillage could reduce crop yield under certain conditions, which highlights the need of optimizing agronomic managements for site-specific strategies. This study provides evidence to support climate-smart, sustainable agriculture and inform decision-making by farmers and policymakers.

To elucidate the mechanisms underlying yield formation in response to water and fertilizer management in traditional and modern foxtail millet varieties, a field experiment was conducted using the traditional cultivar Jingu 6 and the modern cultivar Changsheng 13. Three irrigation regimes (rainfed, pre-sowing supplemental irrigation, and full growth-stage irrigation) and two fertilizer levels (high and low) were implemented. Phenotypic traits, photosynthetic parameters, dry matter accumulation and translocation, photosynthate content, and yield were measured. Multivariate statistical analysis was performed to reveal the intrinsic factors responsible for yield differences between the varieties under varying water and fertilizer conditions. The results indicated that the traditional cultivar exhibited low yield potential but high stability, with minimal inter-annual variation and low sensitivity to water and fertilizer inputs. Under rainfed conditions, its yield decreased by 16.98-39.18 %, which was maintained primarily through optimized photoprotective mechanisms and pre-flowering dry matter allocation. In contrast, the modern cultivar showed high yield potential but poor stability, with yield increases ranging from 39.09 % to 272.42 % under conditions of high water and high fertilizer inputs. Its high yield depended on full growth-stage irrigation, achieved mainly through improved plant architecture, enhanced photosynthetic efficiency, and strengthened source-sink coordination. Therefore, traditional cultivars are suitable for rainfed dryland agriculture, whereas modern cultivars require reliable irrigation. Future breeding strategies should integrate the water-saving and stress-tolerance traits of traditional cultivars with the high-yield potential of modern cultivars to develop water-efficient and high-yielding hybrids, which is crucial for building a climate-resilient foxtail millet production system.

Water management significantly impacts methane (CH4) emissions from paddy fields and cadmium (Cd) accumulation in rice grains through often opposing mechanisms, presenting a complex challenge in optimizing practices to simultaneously mitigate both issues. Through comprehensive field observations across four irrigation regimes over three consecutive planting seasons (i.e., the late rice, early rice, and late rice), along with a pot experiment, we developed an innovative strategy that effectively reduces CH4 emissions and Cd levels while maintaining optimal rice yields. The CTFG treatment—an optimized approach combining controlled irrigation (CI) during rice tillering stage with continuous flooding (CF) during rice grain-filling stage—demonstrated remarkable consistent efficacy over the three seasons. Compared to high-yielding irrigation practice, this regime achieved a 33 % reduction in CH4 emissions and a 42 % decrease in Cd content in brown rice, without compromising rice yield. Furthermore, when benchmarked against specialized irrigation regimes, CTFG outperformed a Cd-minimizing regime by reducing CH4 emissions by 39 % and surpassed a CH4-reducing regime by lowering Cd levels in brown rice by 40 %, while maintaining comparable performance in each targeted area. Mechanistic studies revealed that the tillering and grain-filling stages play pivotal roles in regulating CH4 emissions and Cd content, respectively. CI implementation during tillering stage effectively suppressed methanogen activity while enhancing methanotroph populations, thereby significantly reducing CH4 emissions. Conversely, CF during grain-filling stage decreased soil redox potential and promoted sulfate-reducing bacteria, consequently limiting Cd mobility and its subsequent uptake by rice plants. The results of pot experiments further demonstrated the positive effect of CTFG regime in reducing emissions and cadmium levels, thereby confirming the efficacy of this approach. These findings provide valuable scientific insights for developing more sustainable rice production systems through optimized water management strategies. The CTFG approach represents a significant advancement in balancing environmental protection and food safety concerns in rice cultivation.

Context: Water management plays a crucial role in determining rice yield, methane (CH4) emissions from paddy fields, and cadmium (Cd) level in rice grains. Controlled irrigation can significantly reduce CH4 emissions from paddy fields, but may increase Cd level in rice grain and decrease yield through excessive drought. To innovate water management practices is urgent to achieve the synergistic goals of higher yields while lowering both CH4 emissions and Cd level. Objective: The objective of this study was to investigate the effects of optimized irrigation coupled with dense planting on rice yield, CH4 emissions, and grain Cd content, along with their underlying mechanisms. Methods: Therefore, we conducted a three-consecutive-season field experiment, including four treatments: CK (local high-yield irrigation strategy), OPT0 (Optimized irrigation strategy with equivalent planting density to the CK), OPT15, and OPT30 (Optimized irrigation strategy with 15% and 30% increases in planting density of the CK, respectively). Results: Relative to CK, the OPT15 and OPT30 treatments significantly enhanced rice yields by 15% and 26%, respectively, while concurrently reducing CH4 emissions by 50% and 45%, and lowering Cd contents in brown rice by 30% and 33%. Although the OPT0 treatment significantly reduced CH4 emissions and Cd accumulation compared to CK, it caused an 8% yield reduction. Mechanistically, dense planting combined with irrigation optimization improved rice yield by enhancing the number of effective panicles per unit area. Simultaneously, the co-improvement of methanogens and sulfate-reducing bacteria at rice tillering stage under optimized irrigation may reduce CH4 emissions and Cd level. Conclusions: This study demonstrates that dense planting combined with optimized irrigation synergistically enhances rice yield while reducing CH4 emissions and grain Cd levels. Implications: Our findings can offer scientific support and technical solutions for developing food safety, low-carbon, and high-yielding rice cropping systems in Southern China and other similar regions.

Trade connects regions and countries where food is produced and where it is consumed. This leads to virtual water flows (VWF) that move water resources indirectly through food trade. Understanding the patterns and factors that drive these VWFs is essential in a world facing freshwater scarcity. This information can guide the development of food security policies and inform investments in agricultural infrastructure. Here, we investigate the spatiotemporal changes in VWF for grain crops from 1985 to 2020 across three spatial scales in China, and identify the primary drivers of total VWF and crop-specific VWF. At the provincial scale, VWFs embedded in grain trade increased by 82.8

Amid accelerating global land degradation, establishing high-efficiency ecological restoration principles and frameworks is crucial. Here, we explore the application of threshold effects in the ecological restoration process based on field experiments and globally available experimental data from 173 sites. Combining data integration analysis and meta-analysis, we collectively verified the universality of threshold effects in grasslands. The global grasslands’ average nitrogen application threshold is 3.78 g·m−2·yr−1, while the threshold value of degraded grassland (3.65 g·m−2·yr−1) is lower than that of nondegraded grassland (5.90 g·m−2·yr−1). The low nitrogen-driven thresholds are affected by degradation status, climate (precipitation and temperature), and other site conditions, but not fertilization forms. Independent experiments further demonstrated that an increase in soil moisture content can lead to the disappearance of nitrogen threshold effects, revealing that ecological threshold effects are influenced by ecosystem stress factors. Following the significant increase in plant biomass triggered by the nitrogen threshold, the ecosystem undergoes systemic improvement. Soil organic carbon, urease activity, soil microbial diversity, and other soil properties are significantly enhanced. Soil nitrogen cycle-related microbial communities and soil physicochemical attributes are significantly activated. The results indicate that a threshold response pattern may develop before nitrogen saturation is reached, and low nitrogen input can boost productivity and improve the plant-soil-microbe system. Our findings reveal a nonprogressive path of restoration in degraded ecosystems, and thus, restoration based on threshold effects can offer an efficient and safe solution to combat ecological degradation.

This study aimed to clarify the differences in quality between traditional and modern foxtail millet varieties under different irrigation conditions. The traditional variety Jingu 6 and the modern variety Changsheng 13 were used to compare and analyze the effects of rainfed and irrigation treatments on their appearance quality, culinary quality, nutritional quality and volatile metabolites. Significant inter-annual variation was observed in the effect of irrigation on the quality of foxtail millet. In the wet year, irrigation improved appearance and culinary quality but reduced nutritional quality, and the response of the modern variety was greater than that of the traditional variety. During the dry year, irrigation significantly inhibited the appearance and nutritional quality. Additionly, irrigation optimized the culinary characteristics of the traditional variety in drought years but led to a decrease in the culinary quality of the modern variety. An analysis of volatile metabolites further revealed that irrigation reduced flavor differences between varieties by regulating terpenoid biosynthesis, thereby reducing the unpleasant flavor of the traditional variety, and increasing aromatic substance content in the modern variety. This study systematically clarified the internal mechanism of the interaction of irrigation, variety, and climate on the quality of foxtail millet and provided a theoretical basis and practical guidance for the breeding of high-quality varieties and precise water management.

Cultivar evolution through plant breeding is a cornerstone of contemporary food security, but the extent to which genetic adaptation to climatic variability and shocks contributes to yield gains is not well known. Here, we compile 48,797 cultivar-site-year observations from 2001 to 2020, covering the four prominent maize production regions in China with differing shifts in climatic conditions. The data shows that cultivar evolution underlies long-term yield gains, with productivity increasing by 0.3-2.8 Mg ha-1 per decade. Yields in Northeast China (NEC) and North China (NC) are most vulnerable to heat stress during July and August, whereas high or insufficient precipitation during the growing season is a foremost constraint to yield gains in Southwest China (SWC) and Northwest China (NWC), respectively. Cultivar evolution has significant impacts on yield sensitivity to climate, with genotypic sensitivities to heat stress amplifying in NEC and diminishing over time in NC, respectively. In contrast, yield sensitivity to precipitation increases in SWC and NWC as a result of breeding. These results underscore the importance of breeding climate-resilient cultivars that account for contextualised in situ environmental constraints and climatic adversities in obtaining high yield.

The turnover and stabilization of soil organic carbon is the core element of carbon sequestration mechanisms. The addition of biochar could alter the soil environment, thereby manipulating the structure and function of soil microbial communities, and exerting a profound influence on soil carbon sequestration functions and carbon pool dynamics. In this study, the effects of biochar addition on carbon components, carbon pool management index, and carbon cycling functional genes of reclaimed soil from coal mining subsidence areas, were explored through employing pot simulation experiments, high-throughput sequencing, and structural equation modeling techniques. The research target was to identify feasible pathways for carbon sequestration and sink enhancement in reclaimed soil. The results showed that: ① Biochar addition significantly increased the soil total organic carbon (TOC) and particulate organic carbon (POC) contents, while these augments were proportional to the amount of biochar added. The soil TOC content in the 5.0% corn straw biochar (CB) treatment group increased by approximately 139.4%, whereas the increasing proportion of POC content was particularly significant, reaching as high as 259.2%. Different proportions of biochar addition performed varying effects on soil microbial biomass carbon (MBC), with low proportions presenting a promoting effect and high proportions showing significant differentiation. Among them, the CB treatment group had a significant impact on MBC, with the largest increase at a 1.0% addition amount, reaching up to 274.15 mg/kg. However, there was a consistent significant difference in the impact on dissolved organic carbon (DOC). ② The carbon pool management index of reclaimed soil increased by 4.7%, 4.8%, and 24.0% with the addition of rice straw biochar (RB), wheat straw biochar (WB), and corn straw biochar (CB), respectively. Compared with the control treatment group, the absolute abundance of CBBL (Ribulose Bisphosphate Carboxylase Large Subunit Gene) in the RB, WB and CB treatment groups was significantly increased (p<0.05). There were significant differences in the absolute abundance of PMOA (Particulate Methane Monooxygenase Gene) among different biochar additions (p<0.05). ③ Spearman correlation analysis indicated that microbial biomass carbon was an important factor in characterizing soil carbon pool dynamics. The addition of biochar significantly altered the relationships between soil carbon components and carbon sequestration functional genes and the carbon pool management index, enhancing the correlation and closeness between soil microbial communities and carbon sequestration functional genes and the carbon pool management index, with the CB treatment being the most significant. ④ Biochar addition changed the physicochemical properties of reclaimed soil, thereby affecting soil carbon components and soil enzyme activities, and ultimately influencing soil carbon pool dynamics. At the same time, it might affect the structure and function of microbial communities, thereby altering the abundance of carbon sequestration functional genes and regulating soil carbon pool dynamics. This research indicates the adaptation and mechanism of biochar addition on carbon components, microbial community functions, and carbon pool dynamics of reclaimed soil, providing theoretical support for carbon sequestration and sink enhancement in coal mining subsidence reclaimed soil.

Huge removal projects of Spartina alterniflora Loisel. have recently been carried out in China, which is of great significance for mitigating the biological invasion. However, these removal projects might lead to secondary soil and water loss, short-term biodiversity degradation, and ecosystem imbalance. Based on this perspective, this study synthesized the development timeline, invasion causes, advantages and disadvantages of removal projects, and remediation strategies. The results showed that the development timeline of S. alterniflora has gone through five stages: exploration and experimentation, coastal guardian, positive vs negative, ecological killer, biosecurity reclamation. Due to the high reproductive and favorable external environment of S. alterniflora, it has developed into an invasive species that threatens the coastal ecosystems. Moreover, after large-scale removal projects, it might threaten the ecosystem stability in the short term while promote biodiversity in the long term. During the removal projects, there should be developed in adopting a location-specific management approach, employing diverse approaches alongside ecological restoration, reducing costs by optimizing effectiveness, promoting technological innovation and resource reutilization. This study would provide new perspectives for managing S. alterniflora and the ecological restoration of ecosystems affected by similar invasive species.

Heterojunction engineering is a powerful approach for improving the separation efficiency of photogenerated charge carriers. In this study, ultrathin 2D/2D CeVO4/WO3 center dot H2O heterojunction nanosheets are synthesized via electrostatic self-assembly of ultrathin CeVO4 (similar to 1.3 nm) and WO3 center dot H2O (similar to 2.4 nm) nanosheets for greatly enhancing photocatalytic CO2 reduction and H2O oxidation performance. The 2D heterojunction nanosheets process a Z-scheme charge transfer pathway to effectively promote charge separation efficiency and superior redox capabilities. Concurrently, the ultrathin heterojunction configuration substantially reduces the migration distance of charge carriers. These synergistic effects endow the CeVO4/WO3 center dot H2O composite with the marked CH4 and CO production rates of 8.4 mu mol center dot g(-1)center dot h(-1) and 38.5 mu mol center dot g(-1)center dot h(-1) under full-spectrum illumination. The CO generation rate of the ultrathin heterojunction surpasses pure WO3 center dot H2O by similar to 32 times and CeVO4 by similar to 18 times. This study also provides insights into the rational design of heterojunction nanosheet photocatalysts for CO2 reduction with H2O.

Designing efficient dual-functional catalysts for photocatalytic oxygen reduction to produce hydrogen peroxide (H2O2) and photodegradation of dye pollutants is challenging. In this work, we designed and fabricated an Sscheme heterojunction (g-C3N4/ZnO composite photocatalyst) via one-pot calcination of a mixture of ZIF-8 and melamine in the KCl/LiCl molten salt medium. The KCN/ZnO composite produced 4.72 mM of H2O2 within 90 min under illumination (with AM 1.5 filter), which is almost 1.3 and 7.8 times than that produced over KCN and ZnO, respectively. Simultaneously, the KCN/ZnO also showed excellent photodegradation performance for the dye pollutants (Rhodamine B, RhB), with a removal rate of 92 % within 2 h. The apparent degradation rate constant of RhB over KCN/ZnO was approximately 5-8 times that of KCN and ZnO. In the photocatalytic process, photo-generated holes and superoxide radicals are the main active species. Oxygen (O2) was mainly reduced to produce H2O2 via a two-electron (2e-) pathway with superoxide radicals as intermediates and the 2e- oxygen reduction reaction selectivity of KCN/ZnO was close to 69.82 %. Photo-generated holes are mainly responsible for the degradation of RhB. Compared with pure KCN and ZnO, the enhanced photocatalytic activity of the KCN/ ZnO composite is mainly attributed to the following aspects: 1) larger specific surface area and pore volume is beneficial to expose more active sites; 2) stronger light harvesting ability and red-shifted absorption edge bestow the compound a stronger light utilization efficiency; 3) the construction of S-scheme heterostructure between KCN and ZnO improve the photogenerated electron-hole pairs separation ability and bestow photogenerated carriers a higher redox potential.

Clarifying the complex effects of hydropower development on international river ecosystems is essential for balancing development,conservation,sustainable growth,and maintaining stable international relations.Focusing on the Lancang-Mekong River Basin(LMRB),this study used the InVEST model to quantify five key ecosystem services(ESs),including food production(FP),water yield(WY),soil conservation(SC),carbon storage(CS),and habitat quality(HQ),from 1990 to 2020.By examining hydropower development characteristics along both the main and tributary rivers,it reveals the evolution of multidimensional ESs and their trade-offs or synergies across two periods:1990-2005 and 2005-2020.The results showed:① SC,CS,and HQ generally exhibited a"northeast high,southwest low"distribution,while FP showed the opposite,and WY displayed a"low upstream and downstream,high midstream"and"east high,west low"pattern.② There was a complex correlation between the kernel density changes of hydropower station and the multidimensional changes in ESs.Upstream hydropower development increased downstream WY and SC with significant spatial heterogeneity but had minimal impact on CS and HQ.Downstream hydropower development slightly increased WY,SC,and CS,while reducing HQ in localized regions.③ ESs in the LMRB showed a"cooperative with partial trade-off"relationship,with mainstem hydropower development fostering WY-SC,WY-CS,and WY-HQ synergies and enhancing the CS-HQ trade-off.Tributary hydropower development intensified WY-SC,WY-CS,and WY-HQ trade-offs,while promoting WY-FP synergy.This study provides a scientific basis for international river ecosystem protection,sustainable socioeconomic management,and resolving hydropower-related disputes.

In this work, a novel hybrid system was developed to degrade recalcitrant perfluorooctanoic acid (PFOA) in aqueous solution. This system consisted of a Fe3O4-MoS2/ceramsite (FMC) pipe and a cavitation-impinging stream (CIS) reactor. The FMC pipe served as a hydraulic-driven piezocatalytic reactor by the water flow for highly efficient defluorination of PFOA. The FMC pipe effluent was further treated in the CIS reactor to accomplish effective decomposition of PFOA degradation intermediates and total organic carbon (TOC). Under the optimum conditions, the hybrid system achieved complete PFOA removal and higher than 99% of defluorination and TOC removal. The main active species in the piezocatalytic process were h+, e-, center dot OH, center dot O2- and 1O2, of which e- dominated the PFOA degradation process. The removal ratio of PFOA was 92.5% after 8 cycles by the FMC pipe, indicating excellent reusability of the FMC pipe. Overall, this work not only offers a deep insight on PFOA process, but also provides an efficient and reliable approach for decontamination of recalcitrant wastewater.