Stormwater wetlands and wet ponds are widely used urban stormwater management systems for nutrient mitigation, yet the hydrodynamic and biogeochemical processes governing their nutrient dynamics remain insufficiently understood. This study developed a three-dimensional Environmental Fluid Dynamics Code (EFDC) model to investigate nutrient transport and transformation processes in a stormwater wetland and a wet pond in Calgary, Alberta, Canada, using two years of field observations for calibration and validation. The model successfully reproduced temporal nutrient variations and system-scale concentrations, providing a basis for process analysis and scenario evaluation. Results showed contrasting nutrient regulation mechanisms between the two systems. Nutrient dynamics in the wetland were primarily controlled by inflow loading, with in-system nutrient storage accounting for only ~5% of annual inflow loading, whereas the wet pond exhibited stronger internal buffering, with in-system storage representing ~50% of annual inflow loading. Temporal nutrient variability was mainly associated with rainfall-driven inflow pulses, sedimentation, and algal uptake during rapid growth periods. Both systems exhibited weak vertical stratification but strong horizontal gradients associated with inflow propagation and hydrodynamic transport. Scenario simulations indicated that the wetland was more responsive to environmental forcing than the wet pond, particularly under changes in temperature and wind conditions. Increased nutrient loading and land-use change produced the largest water-quality responses, while hydraulic residence time and sediment nutrient storage played important roles in regulating phosphorus retention. Overall, the study demonstrates how coupled hydrodynamic and biogeochemical processes govern nutrient retention and resilience in urban stormwater systems.

Neonicotinoids (NEOs) have been widely used for decades but concurrently pose substantial risks to nontarget organisms. Identifying the key pathways of NEOs is crucial for mitigating risks, whereas most multimedia models exhibit limitations in quantifying the fate of chemicals at large scales and over long periods. Here, we developed a novel multimedia model that incorporated stratified agricultural soil compartments, historical accumulation, and an enhanced hydrological module. The new model was applied to 45 tertiary watersheds in the Yangtze River Basin (YRB), focusing on imidacloprid (IMI) as a representative neonicotinoid and spanning a decade (2013-2022). Highest concentrations in agricultural soils, surface water, and sediment pore water were 34.1 ng g-1, 53.5 ng L-1, and 1540 ng L-1, respectively. IMI inputs into the environment were predominantly from paddy and dryland fields, totaling 192 tonnes, with rice and wheat as the main sources. Legacy IMI accounted for 26.3 %, 9.58 %, and 13.3 % of the inputs to agricultural soils, surface water, and sediment, respectively. Approximately 28.9 tonnes of IMI entered surface water via runoff, with 7.86 tonnes (2.44 % of the total input) discharged into the East China Sea. Ecological risks were at moderate to high levels and were expected to intensify with continued input and historical accumulation. This model performed well in terms of identifying agricultural sources, quantifying legacy compounds, and enhancing interwatershed hydrological connectivity, which can provide valuable insights for NEO risk management in complex agricultural watersheds.

Earth's fragile high-altitude ecosystems are facing challenges from nutrient pollution, where data scarcity and complex topography create significant knowledge gaps. This study conducted the first large-scale survey and established a comprehensive inventory of human activities in the Qinghai Lake basin, the largest lake on the Qinghai-Tibet Plateau. We developed a hybrid framework that integrated detailed surveys, process-based modeling, and interpretable machine learning to quantify nitrogen and phosphorus fluxes despite data gaps. Notably, ungauged basins around the lake contribute substantially (34% of phosphorus and 21% of nitrogen) to the lake load, highlighting a serious underestimation of a blind spot in global monitoring networks. Contemporary anthropogenic emissions contributed only 41.12% of TN flux and 45.22% of TP flux. Further machine learning analysis indicates that non-direct human factors (runoff, climate, and geographical conditions) have a dominant influence on nutrient movement. These findings provide a replicable framework for data-poor regions and call for shifting management paradigms from anthropocentric to ecosystem-based approaches across high-altitude areas.

Against the backdrop of global climate change and socioeconomic transition, the coupled dynamics of crop production and water scarcity have posed severe challenges to regional food security. Focusing on the Yangtze River Delta (YRD), this study conducts a comprehensively assessment of future food security risks constrained by water resource limitations and shifting grain supply–demand dynamics, under two combined SSP-RCP scenarios (SSP1–2.6 and SSP3–7.0) across two future time horizons (the 2050s and 2070s), by projecting grain supply and demand that incorporate SSP-driven changes in population structure, dietary preferences, crop sown area, and unit-area yield. Based on the Pressure-Exposure-Resilience (P-E-R) framework, we constructed a Food Security Risk Index (FSRPS) comprising three components—Water Scarcity Index (WSI, pressure), Proportion of Irrigated Grain Production (PIr, exposure), and Grain Self-sufficiency Rate (RATE, resilience). This index was then applied to analyze the spatiotemporal evolution and driving mechanisms of food security risks in the YRD. The results indicate that in the 2017 baseline year, the spatial distribution of food security risks exhibited a distinct north-higher-than-south pattern. Across future scenarios, regional risk will increase significantly compared with the baseline, accompanied by growing spatial heterogeneity. Notably, driven by intensifying water scarcity and high irrigation dependence, the overall risk under the low-emission pathway (SSP1–2.6) — reaching 4.63 in 2050 and 4.12 in 2070 — is higher than that under SSP3–7.0 (4.04 and 3.87, respectively). Spatially, central and northern Jiangsu, Shanghai, and northern Anhui are the core sensitive regions (maximum risk index variation >0.3), whereas Zhejiang and the mountainous areas of southern Anhui remain relatively stable (variation generally below 0.01). Accordingly, two targeted regulation strategies are proposed: under SSP1–2.6, coordinate grain production with water resource carrying capacity; under SSP3–7.0, enhance agricultural climate resilience and implement differentiated zonal governance. This study provides a scientific reference for climate-adaptive food security and water resource management policies in the YRD.

Excessive nitrogen (N) and phosphorus (P) inputs from intensive agriculture and rapid urbanization have become major drivers of persistent eutrophication in large lake basins worldwide, posing significant challenges to water quality management even under strict nutrient control on contemporary anthropogenic input. Using the Poyang Lake basin as a case study, we calculated Net Anthropogenic Nitrogen Input (NANI) and Net Anthropogenic Phosphorus Input (NAPI) for 32 sub-basins during 2020-2023 and estimated corresponding riverine nutrient exports. A multifactor mixed-effects regression model was then employed to characterize the relationship between anthropogenic nutrient inputs and riverine nutrient exports. Based on this framework, we applied the concept of Watershed Potential Load (WPL), defined as the persistent nutrient export at the basin outlet that remains after excluding the effects of annual anthropogenic inputs. The results indicate that NANI has significantly decreased, primarily due to reduced nitrogen fertilizer application, while NAPI increased due to the increased use of phosphate and compound fertilizers. Model-based estimates suggest that approximately 27.2% of total nitrogen and 68.7% of total phosphorus exports were attributed to WPL. Partial least squares structural equation modeling (PLS-SEM) revealed that atmospheric deposition is the predominant source of nitrogen WPL, while legacy phosphorus accumulation is the largest contributor to phosphorus WPL. These findings indicate that reducing short-term anthropogenic nutrient inputs alone may be insufficient, because long-term accumulated nutrient loads sustain elevated baseline exports and weaken management effectiveness.

Soil erosion and water quality pose major challenges to global sustainable development. However, the impacts, relationships, and driving factors of suspended sediment on water quality remain unclear due to limited observations. Here, six years of high-frequency observations were used to examine the impacts and hysteresis patterns of suspended sediment on water quality in soil erosion areas in China. The hysteresis of water quality driven by sediment was analyzed and suggestions for water quality management were provided. The results indicated that the dynamic variability of phosphorus concentration increased by 167% during heavy rainfall events, while high suspended sediment intensified the hysteresis effect of nitrogen. The relationships between turbidity and other water quality constituents reveal that 46.67% of sediment events display a counterclockwise flushing hysteresis in water quality indicators, differing from the conventional relationships between stream discharge and water quality. The dry-wet transition season is particularly susceptible to the hysteresis effect of suspended sediment, delaying nutrient release and increasing the risk of downstream pollution. This study highlights the critical role of suspended sediment and provides a scientific basis for monitoring and early warning of water pollution in soil erosion areas represented by mountainous watersheds.

Legacy nitrogen (N) release is threatening water security globally, yet its hydrological pathways in climate-sensitive regions remain poorly quantified, where refined analyses are essential for preventing water quality degradation. In this study, we integrated gridded N budgeting with hydrological modeling to analyze N dynamics (2010–2023) in the Buha River Basin (BRB), the Tibetan Plateau’s largest lake-inflowing system. We then analyzed the spatiotemporal patterns and drivers of N surplus, and assessed their lagged effects through hydrological pathways. Results showed that the average N surplus increased by 0.04 kg N ha−1 yr−1 and 16.67% of subbasins reached significant levels (P < 0.05). Atmospheric deposition (59.51%) dominated N inputs, whereas leaching (40.99%) dominated N outputs in the BRB. The N surplus exhibited spatial heterogeneity with downstream hotspots resulting from the superposition of extensive atmospheric deposition and high-load livestock manure inputs. Baseflow has become increasingly dominant over direct runoff in driving riverine N (increasing from 59.59% to 65.05%), with its effects amplified by a four-year lagged legacy N. These findings highlight, from a baseflow perspective, the need for preventive N management in plateau watersheds beyond conventional agricultural controls.

Understanding the behavior of the phosphorus (P) phase and pathways is critical for the effective control of nonpoint source pollution. However, the behavior of particulate phosphorus (PP), colloidal phosphorus (CP), and dissolved phosphorus (DP) in different hydrological conditions remains unclear. Here, we combined field monitoring and soil column experiments to investigate P transport across hydrological pathways at the catchment scale and to explore depth-specific migration patterns at the profile scale. Results show that under storm events, surface runoff (SF) contributed over 60 % of TP export, dominated by PP and CP. In contrast, under moderate rainfall, interflow (IF) and baseflow (BF) became the primary pathways, with DP as the main transported fraction. At the profile scale, P fractions exhibited vertical stratification, PP in surface layers, CP enriched at 10-30 cm, and DP prevailing in deeper soils. In purple soils, CP contributed more than 30 % of TP during storm rising limbs, indicating a significant degree of persistence and environmental risk. This study highlights the dual role of CP as both a carrier of transport and a potential source of environmental risk, offering a conceptual and practical basis for more targeted phosphorus pollution control under variable hydrological conditions.

Identifying and quantifying the key pollutants that link CO2 and air pollutants (APs) is critical to achieving effective synergistic effects, yet existing research is very limited. Therefore, this study proposed an "EIQ" comprehensive analysis framework aimed at improving the efficiency of synergistic emission reduction of CO2 and APs. It identified the APs with the best synergistic effect with CO2 from multiple perspectives, and then constructed a quantitative assessment model for CO2 and APs by combining the LMDI and econometric model. We applied the "EIQ" framework to the industrial sector of Guangdong. Economically developed regions exhibit greater synergistic effects between CO2 and APs. NOx shows a high synergistic effect with CO2 compared to other APs during 2012-2021. The contribution of the synergy effect to NOx emission reduction gradually increases over time. CO2 emission reduction significantly affects NOx emission reduction, with every 1 Mt decrease in CO2 reducing NOx by 1873 tons.

Small ponds are ubiquitous components of agricultural landscapes globally, acting as both nutrient sinks and sources that considerably influence downstream water quality. While this functional duality is dynamically controlled by pond attributes, the nonlinear and interactive effects of pond morphology, use types, and sediment nutrients remain underexplored, hindering the development of targeted pond management strategies to mitigate eutrophication. To address this, we developed a multi-scale framework to quantify the integrated impact of small ponds on river water quality in an agricultural catchment. Results demonstrate distinct biogeochemical contrasts between river and pond systems and across pond use types, primarily driven by variations in hydrological regimes, redox conditions, and nutrient accumulation. Sewage ponds exhibited the poorest water quality, while village ponds generally showed worse conditions than aquaculture and irrigation ponds. Across buffer scales, topography and sediment nutrients registered the largest individual importance for river and pond water quality, respectively. Generalized additive models further revealed that increased pond shape (explaining 48.1% of the explained deviance) complexity was associated with lower riverine nitrogen (N) and phosphorus (P) concentrations. Incorporating the interaction effects of pond functional regulators (i.e., pond area, pond-river distance, and sediment nutrients) accounted for >41% of the explanatory contribution and clarified the source-sink duality of ponds. N retention was enhanced in large ponds proximal to rivers with high sediment N content, suggesting the importance of denitrification. Conversely, sediment P-rich ponds with close river proximity posed elevated risks of increasing riverine P concentrations, indicating the pronounced role of sediment nutrients in ponds. Our study offers new insights for informing pond-based and environment-resilient interventions in agricultural regions under a changing climate.

Land-use types significantly influence the quantity and quality of dissolved organic matter (DOM) in soil. However, the long-term effects of land-use practices on soil DOM dynamics remain poorly understood. A two-year field study was conducted in a hilly agricultural region in the upper reaches of the Yangtze River, Southwest China, to investigate the effects of land use on soil DOM. Five land-use types were examined: forest, grassland, orchard, sloping farmland, and paddy field. Dissolved organic carbon (DOC) concentrations and DOM fluorescence indices-fluorescence index (FI) and biological index (BIX) showed significant seasonal variations, generally characterized by an initial increase followed by a decline throughout the year. DOC concentrations were significantly higher in forest, orchard, and paddy soils during the relatively wet year (2023, 750 mm precipitation) compared with the relatively dry year (2022, 563.2 mm precipitation), whereas grassland and sloping farmland soils displayed greater DOC stability under drier conditions. Structural equation modeling (SEM) revealed that soil temperature had a significant effect on DOM concentration and composition in the upper layers, while bulk density played a more prominent role in deeper layers. Empirical models based on soil properties effectively predicted DOM concentrations and fluorescence indices (FI and BIX) across soil depths and land-use types. This study demonstrates that land use significantly affects the seasonal dynamics and drought responses of soil DOM in a hilly agricultural region of Southwest China. These findings improve the understanding of DOM dynamics and provide scientific insights into land management strategies under climate change.

Water is a critical natural resource for maintaining the stability of Earth's ecosystems and the sustainable development of human society and economy. As the basic unit of terrestrial hydrological cycles, small watersheds have water quality conditions that significantly affect residents' drinking water safety, agricultural irrigation quality, and regional hydrological cycle systems. This study focused on the hydrochemical features and formation processes of water bodies, proposing an innovative framework that combined advanced evaluation with predictive modeling to assess drinking water quality in a small watershed within the human settlements of the Chengdu Plain. The results showed that the surface water samples were generally weakly alkaline fresh water, dominated by the Ca-HCO₃ hydrochemical type, with inapparent seasonal variation. The weathering and dissolution of rock minerals were the main controlling factors for the hydrochemical composition. In addition, NO₃- was also affected by human activities, such as agriculture. The improved water quality assessment results, which integrated the analytic hierarchy process (AHP) and entropy weight method (EWM) using the game theory (GT), indicated that the average drinking water quality index (WQI) values of the four phases of water samples were 23.77, 20.82, 16.87, and 23.08, respectively. All samples were of excellent water quality, suitable for domestic use and as drinking water sources. Furthermore, extending the paradigm from static assessment to dynamic prediction. Multiple machine learning methods were used to predict water quality. Among these, the multiple linear regression (MLR) model performed best, with a coefficient of determination (R2) of 0.996, a root mean square error (RMSE) of 0.184, and a mean absolute error (MAE) of 0.126 on the test set. Lasso and Ridge regression identified pH, TDS, and NO3- as the core indicators for prediction. These findings, derived from an integrated assessment and predicting framework, contribute to the scientific exploitation and utilization of watershed water resources and the formulation of related strategies.

Water-quality management requires high-frequency monitoring data, which remains challenging, especially in watersheds exhibit pronounced spatial heterogeneity and sparse monitoring stations. To address this gap, this study proposes a Process-Model-Informed Graph Attention Network (PMIGAT) that integrates in situ observations and process-based variables from a process-based model, and implements intermittent satellite-retrieved water quality data as weak supervision to improve predictions at ungauged reaches. A similarity-guided graph attention module is further introduced to enable targeted transfer of supervisory information from monitored nodes to ungauged reaches based on hydrological and landscape similarity. The proposed method was evaluated for nitrogen simulation in the Hangbu River Basin, China. Results showed that the Kling-Gupta efficiency (KGE) at continuously monitored reaches was 0.66, and the median KGE at sparsely gauged reaches reached 0.60 on dates with satellite retrievals. On ungauged reaches on dates without satellite retrievals, PMIGAT outperformed the process-based model, such as Soil and Water Assessment Tool (SWAT), increasing R² from 0.01 to 0.46 and reducing the mean absolute percentage error (MAPE) from 64% to 26%. Furthermore, the new method also improved the detection of high-concentration events with critical success index increasing from 0.04 to 0.28, and the relative peak error decreasing from 60% to 13%. Ablation analyses indicated that satellite retrievals contributed the largest gains at sparsely gauged reaches, and its synergy with similarity-guided graph attention module strengthened with higher satellite availability, shorter along-river distance to the outlet, and greater land-surface similarity. The method can generate spatiotemporally daily water-quality data despite intermittent monitoring, supporting accurate hotspot identification and watershed management.

Riverine plankton biodiversity underpins freshwater ecosystem functioning, yet the synergistic regulations of hydrological dynamics and water quality gradients remain poorly resolved, particularly regarding potential ecological thresholds. Here, we integrated seasonal eDNA metabarcoding, variation partitioning analysis (VPA), redundancy analysis (RDA), and generalized additive models (GAMs) to disentangle the responses of multi-trophic plankton communities across a temperate river in China. Hydrological and water-quality variables jointly explained 52.81% of the biodiversity variation, with physicochemical factors accounting for a larger fraction than hydrological variables (10.29% vs. 2.95%). Contrary to the classic paradigm of unimodal diversity–productivity relationships observed in riverine systems, total phosphorus (TP) positively correlated with biodiversity (edf = 1.00, p = 0.028), while phytoplankton biomass (Chl-a) exerted a negative effect (edf = 1.00, p < 0.001), indicating a decoupling between resource supply and community evenness in riverine ecosystems. Water temperature (edf = 5.47, p < 0.001) and flow velocity (edf = 4.75, p < 0.001) emerged as the strongest drivers, eliciting complex nonlinear effects without detectable abrupt thresholds. Notably, biodiversity peaked in winter and bottomed in autumn, a pattern inversely related to algal biomass, suggesting that hydrodynamic disturbance and microbial food web interactions buffer against competitive exclusion. Besides, the absence of thresholds implies that riverine ecosystems may undergo progressive regime shifts rather than sudden collapses. Our findings challenge the conventional reliance on single-taxon or water-quality indicators for ecological assessment. We propose that integrating multi-trophic biodiversity metrics with hydrological regimes offers a more resilient framework for managing freshwater ecosystems under global change.

Abstract Anticipating nutrient pollution under changing conditions is urgent for water security. Nonetheless, high-resolution predictive frameworks capturing nonlinear driver responses remain limited. Here, we present a nationwide assessment of China’s water-quality evolution from 2023 to 2100, integrating over 3 million daily records with 41 climatic, landscape, and socioeconomic drivers via regionally tailored Random Forest models (R 2 of 0.88–0.92). Our results reveal a disruptive spatiotemporal shift, with projected NPI ranges from –50.9% to +218.1% under SSP5-8.5. Seasonal patterns restructure toward unimodal peaks, with pollution increases in spring/autumn (up to 28.3%) but decreases in summer (up to 27.0%). Spatial homogenization emerges via a westward/southward shift of pollution centers, with localized increases exceeding 200% from coldspots with low baselines (<0.4 vs >1.0 in hotspots). Landscape configuration dominates (64.5% feature importance) over climatic forcing (7.2%–35.5%), reinforced by minimal climatic projection uncertainty. Strategic land-use planning could be a cornerstone of future water security.

To elucidate water quality evolution and algal responses in sluice-controlled ditches, this study combined in situ monitoring (July-October 2025) in the Chong Lake Watershed of Jianghan Plain (China) with controlled experiments at Changjiang River Scientific Research Institute. This study provided the first evidence of how sluice-induced hydrodynamic changes affect water quality and Spirogyra outbreaks in Jianghan Plain irrigation ditches. In situ monitoring showed that sluice interception significantly altered hydrodynamics, reducing dissolved oxygen (DO) by 18% and increasing chlorophyll-a and total phosphorus by 32% and 12%, respectively, compared to control ditches. Simulation experiments confirmed these trends: under sluice control, suspended solids and DO decreased by 30% and 19%, while ammonia nitrogen and phosphate increased by 8% and 13%; nitrate nitrogen dropped by 20%. Spirogyra dominated both systems but shifted from attached filaments in controls to floating clumps in sluice-controlled ditches, with biomass rising 94%. Pearson correlation linked Spirogyra biomass negatively to DO and positively to ammonia and phosphate. Sluice interception promotes eutrophication and Spirogyra blooms by reducing DO and particulates, which inhibits nitrification and releases soluble phosphate. A flow velocity of 0.05 m & centerdot;s-1 effectively suppresses such outbreaks.

Ditch-pond systems (DPSs) are integral components of agricultural ecosystems, functioning as vital infrastructure for water management and as key pathways for the migration and transformation of non-point source (NPS) pollution. Previous studies have examined the role of DPSs in NPS pollution, but were often limited to isolated processes or individual components. There is a lack of comprehensive reviews that systematically explore both the mechanisms and modeling approaches related to nitrogen (N) transport and removal within DPSs. This study presents a systematic literature review synthesizing recent advances in research on NPS processes in DPSs. Specifically, this study clarifies the hydrological effects of DPSs in agricultural catchments. It further elucidates the mechanisms of N transport and removal in DPSs, highlighting the influence of internal hydrodynamic conditions and the complex interactions among various media, including water, sediment, and vegetation. Meanwhile, this study reviews the modeling approaches for N removal in DPSs, including conceptual and process-based models. Collectively, we identify the limitations of existing models applied to DPSs: (1) unclear layout and interactions within DPSs, and (2) insufficient representation of N transport and removal mechanisms. Thus, we propose future directions for model development, focusing on the integration of biogeochemical and hydrological processes to optimize simulations of N transport and removal. Incorporating key parameters and high-resolution data on DPSs will be essential to improve model accuracy. In addition, future model development should emphasize the integration of human activities, such as irrigation and drainage, to enhance its applicability and feasibility. A better understanding of the functions of DPSs in nutrient cycling and water management is critical for promoting sustainable agroecosystem practices and increasing resilience to environmental changes.

Zero-valent iron (ZVI) has found extensive application in the remediation of contaminated soil and groundwater. However, it is essential to remain vigilant about its potential adverse impacts. This study is based on a real field pollution incident, supplemented by experimental simulations. A chemical plant's leakage of nitrobenzene and ZVI led to contamination at the site, with some of the nitrobenzene transforming into aniline, a compound with increased mobility and toxicity. The peak concentrations of nitrobenzene and aniline in the soil were recorded at 2710 mg/kg and 145 mg/kg, respectively. In groundwater, the highest concentrations of nitrobenzene and aniline reached 132 mg/L and 723 μg/L, respectively, exhibiting similar pollution plume distributions. Laboratory simulations were conducted to examine the transformation process of nitrobenzene in both aqueous and soil media in the presence of ZVI and its oxidation products. These investigations also explored the influence of various factors on the conversion of nitrobenzene and the yield of aniline. The column experiment methodology was employed to investigate the migration and transformation pathways of nitrobenzene in simulated media and actual field soils augmented with iron. Upon introducing iron powder, the maximum concentrations of aniline in quartz sand columns and silty clay columns were observed to be 694 mg/L and 1068 mg/L, respectively. During the reaction process, elemental iron primarily acted as a reductant, converting nitrobenzene to aniline, while being oxidized to Fe3O4 and γ-Fe2O3. This finding provides deeper insights into the mechanism underlying the synergistic transformation of iron and nitrobenzene within the contaminated site's aquifer.