Microplastics (MPs), as emerging contaminants, are widely present in rivers and accumulate in hyporheic zone sediments through processes such as aggregation and sedimentation. They pose ecological and biological risks, significantly influencing bacterial communities. However, research on the impact of MPs on bacterial communities in riverine hyporheic zone sediments remains relatively limited. This study investigated the distribution characteristics of MPs in the sediments of the Beiluo River hyporheic zone and explored the relationships between environmental factors, bacterial communities, and MPs based on the field investigation. The results showed that the average abundance of MPs was 18.41 ± 18.94 items·g-1, exhibiting moderate pollution. The dominant polymer type of MPs was Polyethylene terephthalate (PET, 44%), with particles smaller than 30 μm (52%) constituting the majority. The properties of MPs, particularly polymer types and particle size (<30 μm), are key drivers of bacterial community structure in the hyporheic zone. Bacterial responses to MPs exhibit clear polymer specificity. Acidobacteriota and Methylomirabilota show positive correlations with PET but negative correlations with Polypropylene (PP) and Polytetrafluoroethylene (PTFE). Furthermore, MP diversity is significantly negatively correlated with bacterial α-diversity. Partial least squares path modeling further indicates that sediment properties indirectly influence bacterial communities by regulating MP diversity and polymer composition, while MP diversity significantly suppresses both the structure and diversity of these communities. This study provides critical insights into the ecological risk assessment of MPs in the hyporheic zone sediments of the Beiluo River and reveals the potential impacts of MPs on bacterial communities.

Excessive nitrate pollution poses a significant threat to aquatic ecosystems, particularly in river hyporheic zones where microbially mediated nitrogen cycling critically governs water quality. In this study, we integrated metagenomic analysis and indoor soil column simulations to investigate microbial responses and nitrogen-cycling regulatory mechanisms across four nitrate concentrations and three river hyporheic zone depths. The results demonstrated that exogenous nitrate input reduced microbial diversity and altered the abundance of nitrogen-cycling functional genes, with changes primarily occurring in the shallow and middle layers. Under low-nitrate concentrations, the community possesses multiple nitrogen transformation potentials, such as nitrogen fixation, nitrification, and assimilatory nitrate reduction to ammonium (ANRA), which results in the accumulation of ammonium nitrogen (NH4 +-N) in the deep layer. As nitrate levels increased, denitrifying taxa such as Thauera and Dechloromonas became enriched, accompanied by elevated abundances of denitrification genes (norB and norC) and dissimilatory nitrate reduction to ammonium (DNRA) genes (nirB and nirD). The nitrate concentration, dissolved oxygen (DO), and redox potential (Eh) emerged as the key environmental drivers shaping microbial community composition and functional gene distribution. These findings provide critical insights for nitrogen pollution mitigation and river water quality management.

Macroinvertebrates are among the most sensitive and informative biological indicators of river ecosystem health, yet the environmental mechanisms shaping their distribution remain unclear in human-impacted watersheds. The respective and combined contributions of catchment land-use and hyporheic exchange are poorly quantified, despite their complementary roles in regulating water quality and benthic habitat. In this study, 15 sites in the Weihe River Basin were surveyed during autumn 2023 to examine spatial patterns of land use and hyporheic exchange flux (Q(v)) and their links with macroinvertebrate communities and water physicochemical parameters. A causal framework based on a Directed Acyclic Graph (DAG) guided the integration of Variance Partitioning Analysis (VPA), Generalized Additive Models (GAMs), and Structural Equation Modeling (SEM) to quantify independent and joint effects and to identify pathways shaping community composition. A total of 53 macroinvertebrate species (1,354 individuals) were identified. The community was dominated by pollution-tolerant taxa (55% relative abundance), with Chironomidae as the most abundant group (700 individuals). VPA indicated that land use and Q(v) contributed 20.07% and 4.95% to community variation, respectively. Although water physicochemical parameters explained the largest proportion (26.57%), the combined effects of these three drivers accounted for 64.5% of the total variation. GAMs further quantified the relative contributions of these drivers to the Shannon Diversity Index (SDI) and the Hilsenhoff Biotic Index (HBI). Water quality, land use, and Q(v) explained 56.0%, 47.2%, and 7.6% of SDI variation, and 60.2%, 55.6%, and 6.3% of HBI variation, respectively, together accounting for over 86.25% of the total variance, indicating that multi-factor interactions exert a stronger influence on macroinvertebrate communities than individual factors. SEM demonstrated that both land use and Q(v) indirectly influenced macroinvertebrate assemblages through modifications of key water physicochemical variables (p < 0.05), particularly TDS, DO, and TOC. In addition, land-use changes regulated Q(v) via sediment particle-size feedbacks (p < 0.001), with farmland expansion increasing coarse sediment inputs and promoting Q(v), whereas forest and grassland cover stabilized riverbed substrates and reduced Q(v). Overall, the results uncover a sediment-governed cascade in which land-use patterns reshape Q(v) and water-quality conditions, ultimately organizing macroinvertebrate community structure. Clarifying this cross-scale pathway provides a more coherent scientific basis for watershed management, underscoring that maintaining riverine ecological integrity requires coordinated actions on landscape configuration and sediment regimes to indirectly moderate subsurface hydrological conditions.

Distributed hydrological models require sensitivity analyses that explicitly account for spatial heterogeneity, yet such analyses are often constrained by high computational demands. This study presents a two-step, deep learning-assisted spatial sensitivity analysis (SSA) framework to identify dominant parameters across multiple spatial scales. Using the Soil and Water Assessment Tool (SWAT) in the Jinghe River Basin as a case study, the Morris method was first applied with a spatially lumped strategy to screen influential parameters. Subsequently, SSA was conducted using the Sobol' method with multilayer perceptron (MLP) surrogates to evaluate parameter sensitivities under both subbasin- and hydrologic response unit (HRU)-scale parameterizations. The surrogate models emulated SWAT with high accuracy for 195 subbasin-scale and 2559 HRU-scale parameters, enabling efficient estimation of Sobol' sensitivity indices. Results reveal pronounced spatial heterogeneity in parameter sensitivities. Sensitivity hotspots are consistently concentrated in gauge-proximal subbasins, while HRU-scale analysis further resolves localized controls. Robustness analyses demonstrate that the identified sensitivity patterns remain stable across different performance metrics, NSE-constrained posterior parameter distributions, alternative observation configurations, and varying numbers of screened parameters. These findings confirm the reliability of the proposed two-step framework and highlight its capability to efficiently diagnose dominant hydrological controls in high-dimensional distributed models. The methodology provides a transferable and computationally feasible approach for sensitivity assessment, supporting scale-aware calibration and improving the interpretability and predictive reliability of distributed hydrological simulations.

Monitoring and understanding of groundwater variability is critical for water resource management but remains highly challenging in vulnerable arid regions with limited ground observations. This study explores improved characterization for groundwater storage anomalies (GWSA) and recharge dynamics by leveraging GRACE observations, land surface models, key climate variables, and in-situ well records, combined with complementary modeling strategies. Multiple GRACE-derived terrestrial water storage anomaly (TWSA) solutions were examined to support a reliable observational basis for the analysis. The GRACE-Noah model indicated that the long-term and seasonal trends in GWSA are highly synchronized with TWSA, emphasizing groundwater's central role in terrestrial water storage, while snowmelt was also found to influence groundwater dynamics through its impact on surface water and soil moisture. The well-configured artificial neural network (ANN) model demonstrated superior performance, proving to be effective for capturing connections between key hydroclimate factors and groundwater. Results revealed a significant decline averaging -4.34 mm/yr in groundwater storage since 2002 (-4.51 mm/yr from GRACE-Noah and -4.17 mm/yr from GRACE-ANN), highlighting ongoing stress on groundwater resources. The spatiotemporal variations in groundwater recharge and depletion across basins are characterized, revealing the depletion patterns around the Tianshan Mountains and seasonal dynamics driven by snowmelt and evapotranspiration. By integrating GRACE observations with complementary modeling strategies, this study contributes to improving the characterization of groundwater depletion and recharge dynamics in Northwest China, advancing knowledge of groundwater system behavior under changing climatic conditions, further providing new insights for effective groundwater resource assessment in arid regions.

Dissolved organic matter (DOM) in river sediments crucially influences the speciation and ecological risk assessment of heavy metals (HMs), while HM migration modulates DOM heterogeneity, thereby impacting aquatic carbon cycling. This study investigates the interactions between DOM and HMs (vanadium (V), chromium (Cr), arsenic (As), manganese (Mn), zinc (Zn)) in riparian sediments, examining their spatiotemporal distribution, source characteristics, and coupling mechanisms across seasonal and sediment depth gradients (0 cm, 0-15 cm, 15-30 cm). Fluorescence spectroscopy identified two major DOM components: humus-like (C1) and protein-like (C2 + C3) fractions. Dissolved Organic Carbon (DOC) concentrations increased with depth (6.33 +/- 3.57 mg/L, 8.58 +/- 2.82 mg/L, 8.78 +/- 3.32 mg/L for surface, subsurface, deep layers respectively). HM vertical distribution exhibited clear differentiation: Zn concentrations decreased with depth, while Mn, Cr, V, and As accumulated in deeper layers. Pollution Load Index (PLI) analysis revealed significant contamination by As and Mn in the dry season, and by V, Zn, As, and Mn in the wet season, with PLI values exceeding 1 at most sampling sites. During the dry season, both humus-like and protein-like DOM components were regulated by Mn, Cr, V, and As; in contrast, wet-season protein-like DOM was primarily influenced by V, As, and Mn. Geographical and socioeconomic factors significantly impacted HM accumulation in sediments during the dry season, indirectly modifying DOM composition and properties. These findings underscore the complex dual controls of natural processes and human activities on DOM and HM biogeochemical cycling in aquatic sediments across depth gradients, providing targeted insights for river basin pollution management.

Hyporheic exchange (HE) is a critical process driving groundwater-surface water interactions in riverine ecosystems and plays a key role in regulating hydrogeochemical cycling. However, previous studies have primarily focused on reach-scale processes, leaving the watershed-scale spatial heterogeneity of HE and its hydrochemical consequences insufficiently understood. This study systematically quantified the spatial patterns of HE and associated hydrochemical responses across an entire semi-arid river basin, using in-situ temperature profiles, sediment properties, and hydrochemical analyses in the Weihe River Basin. HE exhibited a pronounced longitudinal gradient, with fluxes ranging from 17.71 to 316.04 mm d-1, transitioning from strong and highly variable upwelling in the upper and middle reaches to weak and stable downwelling in the downstream plains. These contrasting HE regimes generated distinct hydrochemical signatures. Upwelling transported Ca-HCO3-rich groundwater into the hyporheic zone, leading to elevated major-ion concentrations (porewater TDS 531.29 mg L-1) conditions, whereas downwelling introduced oxygenated, low-salinity river water that enhanced dilution and redox buffering within the hyporheic zone. Pronounced variations in nitrogen were observed between upwelling and downwelling HE regimes across the basin. Surface-water TN concentrations were higher in upwelling reaches (5.99 mg L-1) than in downwelling reaches (2.94 mg L-1), while porewater TN remained low throughout. Surface water quality in downwelling zones was significantly better than in upwelling zones (p < 0.05). PCA identified four components explaining 86.8% of the total variance. Natural environmental processes, including evaporite weathering, evapotranspiration, and cation exchange, dominated the evolution of major-ion hydrochemistry, whereas nitrogen and organic carbon patterns were primarily influenced by anthropogenic inputs. By modifying vertical flow paths, residence times, and redox conditions, HE mediates the transport and reactive mixing of solutes across the surface water-hyporheic interface, shaping spatial contrasts in water chemical composition and quality trajectories of rivers. This watershed-scale integration of HE spatial heterogeneity and hydrochemical sensitivity provides new mechanistic insight into river-corridor functioning and offers a framework for managing groundwater-surface water interactions in semi-arid basins.

Hyporheic zones in erosion-prone loess watersheds are key interfaces for solute and contaminant transport and biogeochemical transformation. Heavy metals can alter sediment microbial communities, while microorganisms mediate nutrient and organic-matter cycling. However, the relative roles of metal gradients and sediment environmental conditions in structuring bacterial and fungal communities in these systems remain poorly resolved. This study investigated 16 sites across the Jinghe River Basin on the Loess Plateau during the hydrologically stable dry season, combining sediment metal and environmental measurements with high-throughput sequencing. The results showed that 88% of the sites had low pollution loads, whereas moderate pollution was confined to a confluence site and the lower downstream reach. Arsenic exhibited the highest potential ecological risk, with a risk factor about 15 times that of the second-ranked metal, V. Bacterial richness in downstream was 20% higher than upstream, fungal species richness was higher upstream than downstream. Fungal genera exhibited associations with more metal species, while significant correlations between bacteria and metals were predominantly limited to Pb and Zn. PLS-SEM showed that total metal concentrations were strongly associated with ecological risk but only weakly with microbial attributes, while sediment conditions represented the main pathway associated with microbial distribution, accounting for nearly half of bacterial and fungal community variation. These findings support ecological assessment and targeted management that integrate metal and nutrient source control with protection of hyporheic habitats in loess watersheds.

Microplastics (MPs) can adsorb antibiotics to form complex pollutants that seriously threaten the health of freshwater ecosystems. However, few studies have examined the combined ecotoxicity of MPs and multiple antibiotics in natural aquatic environments. In this study, we examined the effects of combined exposure to antibiotics and MPs on aquatic community structure in natural settings in the Beiluo River using eDNA analysis. The results revealed that antibiotic pollution in the Beiluo River mainly originates from animal husbandry and agriculture, whereas MPs mainly originate from agriculture and urban sewage. The community structure of aquatic organisms was significantly correlated with the concentrations of antibiotics and MPs (Mantel's test r ​= ​−0.58 to 0.90, p ​< ​0.05). The combined effects of antibiotics and MPs explained 24.36 ​% and 45.14 ​% of the variation in cyanobacteria and phytoplankton (VPA). Key antibiotics including tetracycline hydrochloride (r ​= ​0.89), OTC hydrochloride (r ​= ​0.87), and lincomycin hydrochloride (r ​= ​0.90) showed particularly strong associations with phytoplankton communities. The effect of MPs on antibiotic toxicity was highly dependent on the antibiotic type and MP particle size. Small-particle-size MPs (<500 ​μm) mainly affected planktonic communities, while large-particle-size MPs (>500 ​μm) predominantly impacted zoobenthos. The results of this study enhance our understanding of the complex combined effects of MPs and antibiotics on aquatic organisms, emphasizing the necessity of informed scientific management of these emerging contaminants.

Nitrogen cycling is a critical process for maintaining the ecological function and water quality stability of river ecosystems. However, under increasing anthropogenic disturbances, its transformation pathways and ecological response mechanisms have become increasingly complex. The water-sediment interface, as a biogeochemically active zone for nitrogen transformations, is influenced by both environmental factors and biological processes. Yet, the drivers of its multi-pathway nitrogen cycling remain unclear, particularly under multi-modal and multi-factor interaction scenarios. This study employed co-occurrence network analysis, random forest modeling, and coupled matrix and tensor factorization (CMTF) to identify biological-environmental associations, screen key taxa and environmental factors influencing different nitrogen cycling pathways, and explore the latent core drivers and mechanisms underlying multi-path nitrogen transformation processes. The co-occurrence network indicated that nitrogen transformations in surface water are more dynamically and jointly regulated by rapid physicochemical fluctuations and multi-trophic interactions. The random forest results showed denitrification and nitrogen fixation were strongly responsive to salinity, NH4+ and NO3-, while phytoplankton and zooplankton primarily influenced organic nitrogen transformation, assimilation, and ANRA pathways. Microbial communities mainly participated in inorganic nitrogen transformation processes. CMTF further resolved three major ecological mechanisms: (1) reductive processes associated with nitrogen fixation and the DNRA pathway primarily driven by environmental factors, (2) nitrification processes jointly governed by microbial communities, oxygenated and nutrient conditions, (3) organic nitrogen transformation and ANRA processes co-regulated by biological activity and environmental factors. This study elucidates the differential ecological drivers of nitrogen cycling pathways in river systems, enhances the understanding of multi-pathway nitrogen dynamics in complex ecosystems, and provides theoretical insights for watershed nitrogen pollution control and ecosystem management.

The dynamics of soil erosion play a critical role in shaping the spatial and temporal distribution of suspended sediment concentration (SSC) and particle size in rivers. However, the responses of SSC and particle size to soil erosion remain unknown under different seasons and terrains. This study examined soil erosion, SSC, and particle size in the Beiluo River Basin across various seasons and terrains. The results revealed the soil erosion was higher in summer (18.10 t & centerdot;ha(-1)) than in autumn (0.20 t & centerdot;ha(-1)), whereas the SSC exhibited the opposite trend, peaking (9158.0 mg & centerdot;L-1) in the hilly gully area during autumn. Suspended sediment particle sizes were predominantly concentrated in the <10 mu m and 10-50 mu m ranges. The relationship between SSC and soil erosion modulus was particularly strong in autumn (r = 0.929**) in the tableland gully area. Additionally, the response of SSC to soil erosion varied with particle size. Specifically, in summer, the strongest response was observed for coarser sediments (>100 mu m, r = 0.729**) in the tableland gully area, whereas in autumn, fine sediments (<10 mu m) exhibited a more pronounced response (r = 0.769**) in the tableland gully area. These findings enhance our understanding of the interactions among soil erosion, SSC, and particle size, offering valuable insights for the development and implementation of soil and water conservation measures.

Soil drought and atmospheric drought can have devastating impacts on ecosystems and society. In this study, by employing copula models, correlation analysis, and structural equation modeling, we reveal the occurrence characteristics and driving factors of soil-atmosphere compound drought events from 1980 to 2023. We found a significant negative correlation between soil moisture (SM) and vapor pressure deficit (VPD), and their joint distribution showed a bimodal pattern. This highlights the need to examine extreme events. In addition, the joint occurrence probability of extremely low SM and extremely high VPD was substantially higher than the expected probability under the assumption of independence. This indicates that extremely low SM and extremely high VPD do not occur independently, but usually occur as compound events. Compound extreme drought events start in mid-June on average, at day of year (DOY) 165, with a significant trend toward an earlier start. They end in late July, at DOY 206, showing a noticeable delay. Additionally, the start and end of compound extreme droughts show notable latitudinal differentiation. The frequency, duration, and exposed area of compound extreme droughts have all increased significantly, and the intensity of these events has intensified, particularly on the Loess Plateau and in the middle and lower reaches of the Yangtze River. The compound extreme drought of 2022 was the most severe in the past 44 years. The geographic centroid of these droughts shows a trend of southward migration. The contribution of compound extreme drought duration to severity exceeded the contributions of SM and VPD. These findings advance our understanding of contemporary compound extreme droughts and are critical for accurately assessing the impacts of extreme climate events under ongoing climate change.

Understanding the structure of zooplankton communities in water contaminated with per- and polyfluoroalkyl substances (PFAS) is essential to the conservation of aquatic biodiversity. This study focused on the Weihe River and systematically characterized the PFAS pollution. By employing environmental DNA metabarcoding, multivariate statistics, and Partial Least Squares Path Modeling (PLS-PM), we systematically analyzed the associations between PFAS and zooplankton within the context of water parameters. The results showed that short-chain PFAS were the dominant PFAS compounds in the Weihe River (accounting for 70.89% of ΣPFAS), and that both PFAS and the zooplankton community exhibited similar spatial patterns. PLS-PM identified a key pathway: water chemistry promoted PFAS accumulation, which in turn exerted taxon-specific effects. Short-chain PFAS were primarily associated with Cercozoa, and path analysis indicated negative relationships, whereas long-chain PFAS were correlated with Ciliophora and Rotifera. Specific taxon within Ciliophora showed potential as bioindicators. Additionally, higher community relative abundance was associated with reduced diversity loss under anthropogenic stress, indicating a potential buffering response. Overall, short-chain PFAS, in combination with water parameters, were associated with higher ecological risk to zooplankton communities. This study highlights the importance of including indirect pathways and taxon-specific responses into risk assessments of emerging contaminants.

The transport and fate of microplastics in deep hyporheic sediments remain poorly understood. This study investigated microplastic distribution and driving mechanisms in the Beiluo River, China. Five sampling sites and sediment samples from six depth intervals (0–60 cm) were analyzed using microplastic identification, a one-dimensional heat advection–diffusion model, 16S rRNA sequencing, and metagenomic analysis. Microplastic abundance decreased from 312 items·kg−1 in surface sediments (0–10 cm) to 140 items·kg−1 in deep sediments (50–60 cm), with 0.05–0.5 mm particles accounting for more than 70% of total microplastics. Downwelling dominated water exchange, and exchange intensity showed a significant correlation with 0.05–0.5 mm microplastic abundance (r = 0.91, p < 0.01), indicating the key role of hydraulic processes in microplastic transport. Firmicutes and Actinobacteria showed significant positive and negative correlations with small-sized microplastics (r = 0.67 and − 0.72, respectively; p < 0.05). Functional gene analysis detected alkB1_2 and β-oxidation pathway genes in all samples, indicating microbial potential for polyethylene and polypropylene degradation. This study addresses the knowledge gap regarding microplastic migration mechanisms in deep hyporheic zones by integrating hydrological and microbial perspectives. A conceptual framework integrating hydraulic transport and microbial responses is proposed to describe microplastic fate and potential transformation pathways.

Annually, the low-lying Niger Delta region of Nigeria, one of the country's most flood-prone areas, experiences severe flooding that causes extensive damage to infrastructure, ecosystems, and socio-economic stability. Hence, accurate flood susceptibility assessment and risk mapping are crucial for supporting effective mitigation and planning, which is the focus of this study. To address this, an integrated Geographic Information Systems (GIS) and Frequency Ratio (FR) approach is used to delineate flood hazard zones and identify high-risk areas across the region. Notably, this represents the first application of the FR model across the unified Niger Delta, encompassing its six core states. The lack of documented flood-susceptibility assessments in this economically vital region underscores a significant research gap. Ten multicollinearity-free flood-conditioning factors, including rainfall, distance to river, drainage density, land use/land cover, elevation, slope, NDVI, soil type, curvature, and topographic wetness index, were analysed to generate a flood susceptibility map classified into five levels. The results indicate that 71.79% (26,297.21 Km2) of the region falls within the moderate to very high susceptibility category. In comparison, 28.21% (10,335.46 Km2) exhibits low to very low susceptibility, with Bayelsa, Rivers, and Delta States identified as the most at risk. The model achieved a reliability rate of 83.16% based on the ROC-AUC analysis, confirming its predictive accuracy. Overall, the findings provide critical, data-driven insights for policymakers and urban planners, supporting a shift from reactive disaster response to proactive flood risk management, and offering a transferable, robust framework for flood mitigation and sustainable development in similar flood-prone regions.

Per-and poly-fluoroalkyl substances(PFAS)have garnered significant global attention due to their widespread presence and potential environmental and health risks.However,research on the occurrence and environmental behavior of PFAS across different media remains limited.We analyzed the occurrence,distribution,sources,and ecological risks of 32 PFAS across multiple media in the Weihe River,China.The concentrations of PFAS ranged from 5.89 to 472.84 ng/L in the pore water and from 9.93 to 459.50 ng/L in surface water,exhibiting significant spatial variability(P<0.05).In contrast,the PFAS concentration range in the sediments was 0.74-1.81 ng/g dry weight,with no pronounced spatial variation in solid-phase PFAS(P>0.05).Vertically,concentrations in 33.00%of pore water samples exceeded those in surface water,showing a heterogeneous vertical distribution with enrichment at depths of 40-60 cm.The physical-chemical characteristics of PFAS and the hydrological and sedimentary processes at the basin scale were responsible for PFAS partitioning between the aquatic environment and sediments.Four major sources were identified through integrated source apportionment:industrial and domestic wastewater(58.25%),aqueous film-forming foam(18.07%),combined input from household pollution and metal plating(8.70%),and stormwater runoff and landfill leachate(14.98%).The ecological risk assessment revealed negligible risks from short-chain PFAS in surface water and pore water,whereas long-chain PFAS posed low to moderate ecological risks.Furthermore,the discharge of PFAS from the Weihe River to the Yellow River was estimated up to 708.20 kg/a.This study provides critical data informing strategies for mitigating PFAS pollution in rivers across typical arid and semi-arid areas of China.

Microbial communities at the soil-stream-sediment interface provide important ecological functions and services. However, per- and polyfluoroalkyl substances (PFAS), as globally pervasive emerging contaminants, pose a substantial challenge to chemical safety and microbial biodiversity across environmental compartments. This study evaluated the impacts of PFAS contamination in distinct environmental media on bacterial, microeukaryotic, and fungal communities along the soil-stream-sediment continuum. Microbial and PFAS data were collected from six monitoring sites along the lower Beiluo River, in the Loess Plateau, China. PFAS concentrations ranged from 7.50 to 18.46 ng/L in water, 0.21 to 2.49 ng/g dry weight (dw) in soil, and 0.22 to 1.27 ng/g dw in sediment. Microbial assemblages exhibited media-specific patterns; soil and sediment groups shared high similarity in bacterial and microeukaryotic profiles, whereas microeukaryotes and fungi were more similar between water and sediment. Non-metric multidimensional scaling indicated that microbial community distributions were spatially aligned with the PFAS pollution patterns. Pearson and Mantel tests revealed that both traditional long-chain and alternative short-chain PFAS disrupt microbial community stability. Linear mixed-effects models (LMMs) confirmed that bacterial communities displayed greater sensitivity to PFAS than microeukaryotic and fungal communities, as evidenced by more significant perturbations. Redundancy analysis further suggested that both PFAS and nutrients plays a crucial role in shaping microbial community structure across the continuum. This study highlights PFAS impacts on micro-ecosystems and underscores the need for enhanced monitoring and management of riverine systems.

Increasing concerns regarding aquatic ecological health and eutrophication driven by urbanization and human activities have highlighted the need to understand primary productivity (PP) dynamics in aquatic ecosystems. This study investigated the spatial distribution of PP across the Weihe River Basin, China using inverse distance weighting and analyzed the influence of different land uses and water physical-chemical parameters on PP using Mantel test and Spearman analysis. Significantly spatial heterogeneity in PP concentrations, ranging from 0.458 to 3262.807 mg C/(m2·d), was observed with high-PP sites clustered in the middle-lower reaches dominated by farmland-construction land mosaics. Core drivers included light availability (Secchi depth and sunlight duration) and phytoplankton biomass (chlorophyll-a (Chl-a)), while water temperature exhibited threshold-dependent effects. Total organic carbon played dual roles, promoting PP concentrations in low-Chl-a regions, but suppressing it under high-Chl-a regions. Dual-scale buffer analysis (500 and 1000 m buffer zones) revealed PP heterogeneity stemed from interactive land use configurations, rather than isolated types. Balanced construction land-to-farmland ratio (0.467–2.890) elevated PP concentrations in human-dominated basins (the main stem of the Weihe River and Jinghe River), whereas excessive agricultural homogenization reduced PP likely due to fertilizer saturation and algal self-shading. Ecologically sensitive basins (the Beiluohe River Basin) demonstrated distinct patterns, in which PP concentration was regulated through natural-agricultural synergies. These results deepened the understanding of land use effects on aquatic PP, providing a theoretical basis for optimizing land use strategies to reconcile eutrophication control with ecological productivity in human-stressed basins.