Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China.

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Spatio-temporal characteristics and multi-scale risk identification of pollution load based on sensitivity analysis in small watersheds located in Tuojiang River Basin, China.

High-quality development of water resources supports high-quality socio-economic development. High-quality development connects high-quality life, and clarifying the key management contents of small watersheds plays an important role in building ecologically clean small watersheds and promoting regional production and life. Previous research on pollution loads has focused on examining the impact of various external drivers on pollution loads but still lacks research on the impact of changes in pollution sources themselves on pollution loads. In this study, sensitivity analysis was used to determine the impact of changes from different sources on the total pollution loads, which can recognize the critical pollution sources. We first employed the pollutant discharge coefficient method to quantify non-point source pollution loads in the small watershed in the upstream Tuojiang River basin from 2010 to 2021. Then, combination sensitivity analysis with Getis-Ord Gi* was used to identify the critical sources and their crucial areas at the global, districts (counties), and towns (streets) scales, respectively. The results indicate: (1) The pollution loads of COD, NH3-N, TN, and TP all show a decreasing trend, reducing by 18.3%, 16.2%, 18.6%, and 28.1% from 2010 to 2021, respectively; (2) Livestock and poultry breeding pollution source is the most critical source for majority areas across watershed; (3) High-risk areas are mainly concentrated in Jingyang district and its subordinate towns (streets). There is a trend of low-pollution risk areas transitioning to high-pollution risk areas, with high-risk areas predominantly concentrated in the southeast and exhibiting a noticeable phenomenon of pollution load spilling around. This study can promote other similar small watersheds, holding significant importance for non-point source pollution control in small watersheds.

[Spatial and Temporal Scale Effects of Landscape Pattern on Riverine Nitrogen and Phosphorus Nutrients in the Qingshan Lake Watershed].

The influences of landscape pattern on water quality are dependent on spatial-temporal scales. However, the effects of landscape composition, landscape configuration, and landscape slope metrics on seasonal water quality at different spatial scales remain unclear. Based on the total nitrogen, total phosphorus, nitrate-N, and ammonium-N data from 26 sampling sites in the Qingshan Lake watershed, this study coupled landscape pattern analysis, redundancy analysis, and partial redundancy analysis to quantify the spatiotemporal scale effects of landscape pattern on riverine nitrogen （N） and phosphorus （P） concentrations. The results showed that： ① The explanatory ability of landscape pattern at the sub-watershed scale on riverine N and P concentrations was 6.8%-8.4% higher than that at the buffer scale, and this effect was more obvious in the dry season. ② At the sub-watershed scale, the percentage of forestland and the interspersion and juxtaposition degree of residential land had a greater influence on riverine N and P concentrations. At the buffer scale, the slope of farmland and residential land and the aggregation degree of forestland patches were the key factors affecting riverine N and P concentrations. ③ The contribution rate of landscape configuration to riverine N and P concentration variations （20.1%-36.5%） was the highest. The sensitivity of the effect of landscape configuration on riverine N and P concentrations to seasonal changes was the highest, and the effect of landscape slope on riverine N and P concentrations had the highest sensitivity to spatial scale changes. Therefore, landscape pattern-regulated non-point source pollution should be considered from a multi-scale perspective. These results can provide scientific basis for the formulation of landscape pattern optimization measures aiming at non-point source pollution control.

The use of the self-organizing map (SOM) methodology to study the dissolved organic matter (DOM) in different water body types.

This study investigated the characteristics of dissolved organic matter (DOM) in two distinct water bodies, through the utilization of three-dimensional fluorescence spectroscopy coupled with self-organizing map (SOM) methodology. Specifically, this analysis concentrated on neurons 3, 14, and 17 within the SOM model, identifying notable differences in the DOM compositions of a coal subsidence water body (TX) and the MaChang Reservoir (MC). The humic substance content of DOM TX exceeded that of MC. The origin of DOM in TX was primarily linked to agricultural inputs and rainfall runoff, whereas the DOM in MC was associated with human activities, displaying distinctive autochthonous features and heightened biological activity. Principal component analysis revealed that humic substances dominated the DOM in TX, while the natural DOM in MC was primarily autochthonous. Furthermore, a multiple linear regression model (MLR) determined that external pollution was responsible for 99.11% of variation in the humification index (HIX) of water bodies.

Assessing nonpoint source pollution risk in watersheds using a water-functioning zone approach.

A thorough understanding of the geographical and spatial attributes of nonpoint source pollution risk in watersheds is crucial for conducting nonpoint source pollutant studies and implementing effective scientific administration strategies. The inclusion of a water-related functioning zone was considered during the nonpoint source's pollution risks assessment procedure. Nevertheless, there has not been a thorough investigation into the potential risk of nonpoint sources of pollution to adequately safeguard the quality of water in watersheds having varying capacities to handle contaminants in the water. This research presents an innovative approach for assessing the risk of nonpoint sources contamination. This allows for a quantitative evaluation of the effect of discharges of pollution from a sub-catchment on the quality of water bodies nearby. The nonpoint source losses of nutrients process, as modeled by the Water and Soil Assessment Tool, had been used to assess the hazard of nonpoint source contamination in Le 'a River Watersheds. This assessment happened on both yearly and monthly scales. The findings indicated that the risk of nonpoint source contamination exhibits both seasonal and regional variations and is significantly impacted by the ability of the fluid ecosystem. Elevated nonpoint sources pollutants do not always equate to elevated pollutant dangers. On the other hand, a small amount of nutrients in the nonpoint sources does not indicate an insignificant degree of susceptibility to region risk. Furthermore, by utilizing a risk assessment method that considers the capacity of the water's environment, it is possible to identify variations in risk levels that may be overlooked when solely considering nonpoint sources contaminant losses, and fluid functioning zone. This approach allows for precise regulation of nonpoint sources of pollution administration.

Linking terrestrial biogeochemical processes and water ages to catchment water quality: A new Damköhler analysis based on coupled modeling of isotope tracers and nitrate dynamics.

Catchment-scale nitrate dynamics involve complex coupling of hydrological transport and biogeochemical transformations, imposing challenges for source control of diffuse pollution. The Damköhler number (Da) offers a dimensionless dual-lens concept that integrates the timescales of exposure and processing, but quantifying both timescales in heterogeneous catchments remains methodologically challenging. Here, we propose a novel spatio-temporal framework for catchment-scale quantification of Da based on the ecohydrological modeling platform EcH2O-iso that coupled isotope-aided water age tracking and nitrate modeling. We examined Da variability of soil denitrification in the heterogeneous Selke catchment (456 km2, central Germany). Results showed that warm-season soil denitrification was of catchment-wide significance (Da >1), while its high spatial variations were co-determined by varying exposure times and removal efficiencies (e.g., channel-connected lowland areas are hotspots). Moreover, Da seasonally shifted from processing-dominance to transport-dominance during the wet-spring season (from >1 to <1), implying important linkages between summer terrestrial denitrification and subsequent winter river water quality. Under the prolonged 2018-2019 droughts, denitrification removal generally reduced, resulting in further accumulation in agricultural soils. Moreover, the space-time responses of Da variability indicated important implications for catchment water quality. The older water in lowland areas exhibited extra risks of groundwater contamination, whilst agricultural areas in the hydrologically responsive uplands became sensitive hotspots for export and river water pollution. Importantly, the lowland pixels intersecting river channels exhibited high removal efficiencies, as well as high resilience to the disturbances (wet-spring Da shifted to >1 under drought conditions). The proposed catchment-wide Da framework is implied by mechanistic modeling, which is transferable across various environmental conditions. This could shed light on understanding of catchment N processes, and thus providing site-specific implications of non-point source pollution controls.

Agricultural watershed conservation and optimization using a participatory hydrological approach.

Maximizing the impact of agricultural wastewater conservation practices (CP) to achieve total maximum daily load (TMDL) scenarios in agricultural watersheds is a challenge for the practitioners. The complex modeling requirements of sophisticated hydrologic models make their use and interpretation difficult, preventing the inclusion of local watershed stakeholders' knowledge in the development of optimal TMDL scenarios. The present study develops a seamless modeling approach to transform the complex modeling outcomes of Hydrologic Simulation Program Fortran (HSPF) into a simplified participatory framework for developing optimized management scenarios. The study evaluates seven conservation practices in the Pomme de Terre watershed in Minnesota, USA, focusing on sediment and phosphorus pollutant load reductions incorporating farmers' opinions to guide practitioners toward implementing cost-effective CPs. Results show reduced tillage and filter strips are the most cost-effective practices for non-point source pollution reduction, followed by conservation cover perennials. The integration of SAM with HSPF is crucial for sustainable field-scale implementation of conservation practices through enhanced involvement of amateur-modeling stakeholders and farmers directly connected to fields.

Evaluation of agricultural non-point source pollution using an in-situ and automated photochemical flow analysis system.

Off-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4+-N, NO3--N, PO43- are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4+-N, NO3--N, and PO43-, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture.

Spatial distribution of pesticide use based on crop rotation data in La Plata River basin: a case study from an agricultural region of Uruguay.

The intensive global use of pesticides presents an escalating threat to human health, ecosystems, and water quality. To develop national and local environmental management strategies for mitigating pollution caused by pesticides, it is essential to understand the quantities, timing, and location of their application. This study aims to estimate the spatial distribution of pesticide use in an agricultural region of La Plata River basin in Uruguay. Estimates of pesticide use were made by surveying doses applied to each crop. This information was spatialized through identifying agricultural rotations using remote sensing techniques. The study identified the 60 major agricultural rotations in the region and mapped the use and application amount of the nine most significant active ingredients (glyphosate, 2,4-dichlorophenoxyacetic acid, flumioxazin, S-metolachlor, clethodim, flumetsulam, triflumuron, chlorantraniliprole, and fipronil). The results reveal that glyphosate is the most extensively used pesticide (53.5% of the area) and highest amount of use (> 1.44 kg/ha). Moreover, in 19% of the area, at least seven active ingredients are applied in crop rotations. This study marks the initial step in identifying rotations and estimating pesticide applications with high spatial resolution at a regional scale in agricultural regions of La Plata River basin. The results improve the understanding of pesticide spatial distribution based on data obtained from agronomists, technicians, and producers and provide a replicable methodological approach for other geographic and productive contexts. Generating baseline information is key to environmental management and decision making, towards the design of more robust monitoring systems and human exposure assessment.

Can the best management practices resist the combined effects of climate and land-use changes on non-point source pollution control?

Climate and land-use changes have an overlying impact on non-point source (NPS) pollution in river basins. However, the control effect of Best Management Practices (BMPs) for NPS pollution is not yet clear under future scenarios. The Soil and Water Assessment Tool (SWAT) model was coupled with the entropy-weighted method, global climate patterns and land-use data to explore the dynamic variations in total nitrogen (TN) and total phosphorus (TP) loads in the Jing River Basin during the baseline (2000-2020) and future periods (2021-2065), evaluate the pollution reduction effectiveness of individual and combined BMPs, and propose practical BMP configurations. Results indicate that a future trend of urban land expansion, particularly in the economic scenario (LU_SSP585), leads to weakened environmental ecosystems, while the sustainable scenario (LU_SSP126) exhibits more balanced land development. The MIROC-ES2L model demonstrates higher Taylor skill scores, forecasted significant increases in precipitation, maximum, and minimum temperatures under the SSP585 scenario. Spatial heterogeneity in TN and TP loads is notable, showing an upward trajectory in the future. The interaction between land-use and climate change has complex effects on TN and TP loads, with land-use-induced TN changes being relatively small (4.6 %) and TP changes substantial (24.3 %). The spatial distribution, under overlying effects, leans towards the influence of climate change, emphasizing its dominant role in TN and TP load variations. Distinct differences exist in the reduction of NPS pollution loads among different BMPs, with combined BMPs demonstrating superior effectiveness. The environmental-cost effectiveness trends of BMPs remain consistent across various future scenarios. RG (Return agricultural land to grass), RG + TT (Terracing), and RG + FR10 (Fertilizer reduction: 10 %) + GW (Grassed waterway) + FS (Filter strip) + TT emerge as the most effective single, double, and multiple BMP combinations, respectively. The results offer valuable insights for preventing and mitigating future NPS pollution risks, optimizing land-use layouts, and enhancing watershed management decisions.

[Spatial scale effects of landscape patterns on non-point source pollution: A case study of Taizi River Basin in Northeast China]. / éžç‚¹æºæ±¡æŸ“å¯¹æ™¯è§‚æ ¼å±€å“åº”çš„ç©ºé—´å°ºåº¦æ•ˆåº”­­ä»¥ä¸œåŒ—太子河流域为例.

River water quality is influenced by natural processes and human activities. Multi-scale landscape patterns can affect river water quality by altering the generation and transport processes of pollutants at different spatial scales. Taking Taizi River Basin in Northeast China as an example, we analyzed the relationship between landscape patterns and non-point source pollution in rivers based on water quality monitoring data and land use data by using correlation analysis and redundancy analysis methods. We aimed to determine the key spatial scales for the responses of landscape patterns to non-point source pollution and identify the key landscape indices influencing river non-point source pollution. The results showed that water quality of Taizi River Basin had seasonal differences, with better water quality during the flood season than non-flood season. Spatially, total nitrogen (TN) and total phosphorus (TP) were higher at the confluence points of tributaries and downstream areas. The impact of landscape patterns on non-point source pollution was stronger during the non-flood season than the flood season, while the influence on TN was stronger than on TP. At the spatial scale of within 500 m buffer zone during the flood season and at the sub-watershed scale during the non-flood season, landscape patterns showed the highest explanatory power for the variations of TN and TP. At the type level, built-up land, cropland, and bare land were positively correlated with TN and TP, while forest was negatively correlated with TN and TP, which were the key types influencing non-point source pollution. At the landscape level, patch density, percentage of like adjacencies, and contagion index were key indicators affecting watershed water quality. Lower patch density was associated with better connectivity and aggregation of "sink" landscapes, leading to better purification effects on TN, but more pronounced retention effects on TP. Conversely, higher landscape diversity and denser pattern of multiple types would cause the deterioration of water quality. Our results suggested that rational allocation of landscape types within the watershed and riparian buffer zones, appropriately enriching landscape diversity, and optimizing landscape aggregation and connectivity would be effective measures for improving water quality and achieving sustainable ecological management.

A modified approach of the agricultural grey water footprint considering the nitrogen fixation effect of crops in China.

The rapidly growing demand for food in human societies has led to the extensive use of fertilizers, significantly contributing to water pollution. Grey water footprints (GWF) serve as a crucial method for measuring Non-point Source (NPS) pollution, particularly in agriculture. Traditional assessments of agricultural GWF neglect biologically fixed nitrogen and the use of organic fertilizers. This research proposed a modified method to assess the GWF of Chinese agriculture from 2000 to 2020, considering the impact of Nitrogen fixation in crops and the use of organic fertilizer. We also analyzed the determinants of Agricultural Nitrogen Fixation Intensity (ANFI) using the Logarithmic Mean Divisia Index (LMDI) method to better understand factors influencing agricultural GWF. Our findings include (1) Grain cereals (e.g., maize, rice, and wheat) significantly contribute to nitrogen fixation in crop organs, accounting for 87.7%, whereas the other six economic crops contribute the rest of 12.3%. Human wastes account for Nitrogen emissions for 1.40%, and emissions by livestock product, red meat contributes 16.26%, while white meat, eggs, and milk collectively contribute 82.34%. (2) Across China, there is an overestimation of GWF by 22.4 hundred million m3 per year, about 5.13% of the total GWF measured by traditional methods. It appears that the overestimation of GWF in plain regions with more arable land tends to be somewhat more pronounced compared to plateau and coastal municipalities. Biotechnological advancements in the capacity of nitrogen fixation for key crops (e.g., maize, wheat, rice) can alleviate agricultural water pollution. The modified methodology provides a robust scientific basis for a more precise application of GWF assessments, highlighting the substantial overestimation by traditional methods in China.

Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian Network approach.

Lake eutrophication caused by nitrogen and phosphorus has led to frequent harmful algal blooms (HABs), especially under the unknown challenges of climate change, which have seriously damaged human life and property. In this study, a coupled SWAT-Bayesian Network (SWAT-BN) model framework was constructed to elucidate the mechanisms between non-point source nitrogen pollution in agricultural lake watersheds and algal activities. A typical agricultural shallow lake basin, the Taihu Basin (TB), China, was chosen in this study, aiming to investigate the effectiveness of best management practices (BMPs) in controlling HABs risks in TB. By modeling total nitrogen concentration of Taihu Lake from 2007 to 2022 with four BMPs (filter strips, grassed waterway, fertilizer application reduction and no-till agriculture), the results indicated that fertilizer application reduction proved to be the most effective BMP with 0.130 of Harmful Algal Blooms Probability Reduction (HABs-PR) when reducing 40% of fertilizer, followed by filter strips with 0.01 of HABs-PR when 4815ha of filter strips were conducted, while grassed waterway and no-till agriculture showed no significant effect on preventing HABs. Furthermore, the combined practice between 40% fertilizer application reduction and 4815ha filter strips construction showed synergistic effects with HABs-PR increasing to 0.171. Precipitation and temperature data were distorted to model scenarios of extreme events. As a result, the combined approach outperformed any single BMP in terms of robustness under extreme climates. This research provides a watershed-level perspective on HABs risks mitigation and highlights the strategies to address HABs under the influence of climate change.

Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.

Pollution load estimation and influencing factor analysis in the Tuhai River Basin in Shandong Province of China based on improved output coefficient method.

Estimating the pollution loads in the Tuhai River is essential for developing a water quality standard scheme. This study utilized the improved output coefficient method to estimate the total pollution loads in the river basin while analyzing the influencing factors based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Findings indicated that the projected point source pollution loads for total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (AN) would amount to 3937.22 ton, 335,523.25 ton, and 13,946.92 ton in 2021, respectively. Among these, COD pollution would pose the greatest concern. The primary contributors to the pollution loads were rural scattered life, large-scale livestock and poultry breeding, and surface runoff. Per capita GDP emerged as the most influential factor affecting the pollution loads, followed by cultivated land area, while the urbanization rate demonstrated the least impact.

Yield, Quality, and Nitrogen Leaching of Open-Field Tomato in Response to Different Nitrogen Application Measures in Northwestern China.

The overuse of fertilizers in open-field tomato leads to soil deterioration through nutrient leaching and increases the risk of agricultural non-point source contamination. Currently, the combined effects of different fertilization methods on soil nitrogen leaching and tomato production are still unclear. Therefore, the most effective fertilization method for open-field tomato should be discovered by examining how different fertilization methods affected tomato yield and quality, nitrogen use efficiency (NUE), and soil nitrogen leaching. Compared with CK (no fertilization), fertilization significantly increased the yield, total sugar (TS), total soluble solids (TSS), and vitamin C (vC) contents of fruits (p < 0.05), and OPT (optimal fertilization, controlled release nitrogen application, 240 kg ha-1) had the largest effect on increasing yield, quality, and net profit. However, when the fertilizer application rate reached 375 kg ha-1, these indices decreased. Nitrogen leaching concentrations, leaching amount, and titratable acids (TAs) increased with increased nitrogen application rates. Compared with other treatments, OPT reduced the total leaching amounts of total nitrogen (TN), nitrate nitrogen (NO3--N), and ammonia nitrogen (NH4+-N) by 30.09-51.79%, 24.89-50.03%, and 30-65%, respectively. Principal component analysis (PCA) showed that OPT achieved the highest overall score in terms of yield, quality, and nitrogen leaching conditions. The partial least squares path modeling (PLS-PM) further reveals that applications of high amounts of nitorigen have a positive effect on soil nitrogen leaching. The amount of nitrogen leaching vegetatively affects tomato yield and quality, while plant uptake of nitrogen positively affects tomato production. These findings confirm the importance of using controlled-release fertilizers and reducing nitrogen inputs to control nitrogen leaching and enhance open-field tomato yields.

[Nitrate Pollution Characteristics and Its Quantitative Source Identification of Major River Systems in China].

Accurate source identification/apportionment is essential for optimizing water NO3--N pollution control strategies. This study conducted a meta-analysis based on data from 167 rivers across China from 2000 to 2022 to analyze the spatial and temporal variation patterns of nitrate pollution in seven major river systems and to quantitatively identify the source composition of riverine nitrate. The average ρ（NO3--N） in the seven major river systems was （4.54±3.99） mg·L-1， with 9.6% of river ρ（NO3--N） exceeding 10 mg·L-1. The riverine ρ（NO3--N） in eastern China were higher than that in western China， and the highest concentration was observed in the Haihe River system. Additionally， tributaries experienced more serious NO3--N pollution than that in the main stream. The ρ（NO3--N） in most river systems in the dry season was higher than that in the wet season， except in the Yellow River system. There was significant nitrification in the Pearl River system， the middle and lower reaches of the Yellow River system， the middle reaches of the Liaohe River system， the Songhua River system， and the Haihe River system， whereas there was significant denitrification in the Yangtze River system， the Huaihe River system， and the lower reaches of the Pearl River system. Based on the dual stable isotopes-based MixSIAR model， the major NO3--N source was sewage/manure （ > 50%） in the Yangtze River system， Haihe River system， Liaohe River system， and Southeast River system. Soil nitrogen was the main NO3--N source in the Songhua River system （56.4%）， and the contribution of fertilizer nitrogen， soil nitrogen， and sewage/manure to NO3--N pollution in the Pearl River system， Huai River system， and Yellow River system was 20%-40%. The contribution rate of sewage/manure to NO3--N in the tributaries was higher than that in the main stream， whereas the contribution rate of soil nitrogen to NO3--N in the main stream was higher than that in the tributaries. The contribution rate of soil nitrogen， fertilizer nitrogen， and atmospheric deposition nitrogen to nitrate nitrogen in the wet season was higher than that in the dry season， whereas the contribution rate of sewage/manure to NO3--N pollution in the dry season was higher than that in the wet season. Therefore， point source pollution such as domestic and production sewage discharge should be controlled in the Haihe River system， the Yangtze River system， the Liaohe River system， the tributaries and the downstream main stream areas of Yellow River system， and the downstream area of the Pearl River system， whereas non-point source pollution caused by the loss of fertilizer and soil nitrogen should be controlled in the Huaihe River system， the Songhua River system， the middle reaches of the main stream area of the Yellow River system， and the middle and upper reaches of the Pearl River system. The results can provide a scientific basis for the effective control of nitrate pollution in the river systems in China.

[Factors Influencing Willingness of Farmers to Pay for Agricultural Non-point Source Pollution Control Based on Distributed Cognitive Theory].

The analysis of the willingness of individual farmers to cover costs is an important basis for measuring the economic value of agricultural non-point pollution management， and determining the ecological and economic value of rural surface pollution control is a necessary measure to internalize the externalities of agricultural production. Based on the analysis of the hierarchy of factors influencing the cognition of farmers， this study constructed a theoretical framework based on distributed cognition theory to analyze their willingness to pay for agricultural non-point source pollution control from the perspective of individual farmers. On the basis of this framework， we used the Double-Hurdle model to empirically test the overall process of farmers'willingness to pay and their willingness to pay the amount for agricultural non-point source pollution control by combining 531 microscopic research datapoints in Guanzhong， Shaanxi Province. The results showed that： ① the number of farmers with willingness to pay for agricultural non-point source pollution control was 267， accounting for 50.30% of the total sample， and the average value of willingness to pay was 1 469.77 yuan·hm-2； the total economic value of agricultural non-point source pollution control in Shaanxi Province in 2020 was estimated to be 5.791 billion yuan based on the expected value of the willingness to pay level of the research sample. ② Farmers'willingness to pay for agricultural non-point source pollution control was influenced by the combined effects of personal， regional， and cultural forces， and the effects of each dimension were similar； farmers' willingness to pay for agricultural non-point source pollution control was mainly influenced by the cultural force factor， and the effects of personal and regional forces were very limited. ③ The results of the regressions by income level showed that personal and cultural strengths had a significant impact on the willingness to pay among the low-income group but did not contribute to the increase in the willingness to pay.

Characterization of road-deposited sediment wash-off and accurate splitting of initial runoff pollution in heterogeneous urban spaces.

Particulate materials arising from road-deposited sediments (RDS) are an essential target for the control and management of surface runoff pollution. However, the heterogeneity of urban spaces hinders the identification and quantification of particulate pollution, which is challenging when formulating precise control measures. To elucidate the factors that drive particulate pollution in heterogeneous urban spaces, the accumulation of RDS on dry days and the total suspended solids during six natural rainfall events were investigated across three urban-rural spatial units (central urban, central suburban, and remote suburban). The underlying surface type (asphalt or cement roads) and particle size composition jointly determined the spatial heterogeneity in the static accumulation and dynamic output loads of RDS during rainfall. These two factors explained 59.6% and 18.9% of the spatial heterogeneity, respectively, according to principal component analysis. A novel CPSI exponential wash-off equation that incorporates particle size composition and underlying surface type was applied. It precisely described the spatial heterogeneity of RDS wash-off loads, the estimated values exhibiting event mean concentration errors of 10.8-18.2%. When coupled with the M(V) curve, this CPSI exponential wash-off equation more precisely split the initial volume of runoff: a lower total volume (17.6-38.0%) was shown to carry a higher proportion of the load (70.0-93.7%) compared to the traditional coupled exponential wash-off equation (volume: 31.6-49.0%, load: 37-90%). This study provides a new approach to characterizing RDS wash-off processes and splitting initial runoff in heterogeneous spaces.

Assessing ammonium pollution and mitigation measures through a modified watershed non-point source model.

Watershed water quality modeling is a valuable tool for managing ammonium (NH4+) pollution. However, simulating NH4+ pollution presents unique challenges due to the inherent instability of NH4+ in natural environment. This study modified the widely-used Soil and Water Assessment Tool (SWAT) model to simulate non-point source (NPS) NH4+ processes, specifically incorporating the simulation of land-to-water NH4+ delivery. The Jiulong River Watershed (JRW) is the study area, a coastal watershed in Southeast China with substantial sewage discharge, livestock farming, and fertilizer application. The results demonstrate that the modified model can effectively simulate the NPS NH4+ processes. It is recommended to use multiple sets of observations to calibrate NH4+ simulation to enhance model reliability. Despite constituting a minor proportion (5.6 %), point source inputs significantly contribute to NH4+ load at watershed outlet (32.4∼51.9 %), while NPS inputs contribute 15.3∼17.3 % of NH4+ loads. NH4+ primarily enters water through surface runoff and lateral flow, with negligible leaching. Average NH4+ land-to-water delivery rate is about 2.35 to 2.90 kg N/ha/a. High delivery rates mainly occur at agricultural areas. Notably, proposed NH4+ mitigation measures, including urban sewage treatment enhancement, livestock manure management improvement, and fertilizer application reduction, demonstrate potential to collectively reduce the NH4+ load at watershed outlet by 1/4 to 1/3 and significantly enhance water quality standard compliance frequency. Insights gained from modeling experience in the JRW offer valuable implications for NH4+ modeling and management in regions with similar climates and significant anthropogenic nitrogen inputs.