Non-point Source Research Articles

Combined effects of flood, drought and land use dominate water quality and nutrient exports in Jialing River basin, SW China

Climate change and the associated increase in hydroclimatic extremes necessitate a deeper understanding of the resulting water quality responses. This study investigates the combined impacts of hydroclimatic extremes and land uses on water quality of the Chinese Jialing River, of which the middle and downstream areas experienced a flood in 2021 and a severe drought in 2022. Water Quality Index (WQI) and nutrient loads were assessed using daily data from 22 monitoring stations across the Jialing River and its two tributaries, the Qujiang River and Fujiang River, over 2021–2022. The results indicate a slight upward trend in water quality, as reflected by the WQI, for the tributaries from 2021 to 2022, while a declining trend was observed in the mainstream. Floods had a more pronounced impact on water quality than droughts, particularly on nutrient concentrations, and both dilution and flushing effects were observed as discharge increased in the Jialing River and its tributaries. Notably, water quality deterioration was most pronounced in the downstream areas with land uses dominated by cropland and built-up area, where intensified rainfall distributed and exacerbated nutrient losses in the rainy seasons. Nutrient fluxes, including Chemical Oxygen Demand (COD), Total Phosphorus (TP), and Total Nitrogen (TN), were closely linked to discharge, with hydroclimatic extremes therefore significantly affecting nutrient exports. This study elucidates the complex interactions between land use, extreme weather and water quality in the Jialing River Basin. Our findings underscore the need to strengthen the management of non-point source pollutants in the downstream areas of the Jialing River to address the challenges posed by anticipated increases in extreme rainfall in the near future.

Spatial variability, source identification, and partitioning of groundwater constituents in a typical lakeside plain on Yungui Plateau

The fragile ecological balance of lakeside plains on plateau makes groundwater hydrochemical composition highly susceptible to human disturbances. This research investigates a typical plain on Yunnan Plateau to quantitatively identify the potential sources of groundwater pollution in lakeside plains on plateau by combining receptor models with risk assessment models. Receptor models show there are three potential pollution sources for groundwater including water-rock interactions, agricultural pollution, and domestic sewage/manure discharge. Groundwater hydrochemical composition is predominantly shaped by natural water-rock interactions, accounting for an average of 50.12 % of the influence. This is followed by agricultural non-point source pollution, which contributes 10.79 %, and the discharge of domestic sewage and manure, which accounts for 10.61 %. Groundwater in the current plain could pose a minor non-carcinogenic risk for long-term drinking, with the total hazard index (THI) for adults varying between 0.015 and 1.089, and for children between 0.024 and 1.737. Notably, 4.35 % of groundwater samples have a THI exceeding the safe threshold of 1. NO3- (72 %) is the primary contributor to non-carcinogenic risks, accounting for 72 %, trailed by Mn (13 %), NH4+ (9 %), Fe (6 %) and NO2- (1 %). The non-carcinogenic risks are predominantly attributed to agricultural non-point source pollution (71.6 %) and the discharge of domestic sewage and manure (26.9 %). A conceptual model has been developed to encapsulate the dynamics of groundwater quality formation in lakeside plains on plateau. This research will aid residents in safeguarding against groundwater contamination and enhancing agricultural practices in present plain and similar lakeside plain worldwide.

The Isotopic Composition of Selected Phosphate Sources (δ18O-PO4) from the Area of the Vistula and Bug Interfluve (Poland)

Phosphorus belongs to the crucial bioelements that cause eutrophication, and phosphates, easily assimilated by organisms, are widespread in the environment. Phosphates can be of natural or anthropogenic origin and can derive from various point or non-point sources. Knowledge about the origin of nutrients is necessary to effectively manage, protect, and revitalize water resources. To recognize various phosphate sources in the study area of our research, i.e., the Vistula and Bug interfluve (SE Poland), we used the oxygen isotopic signature of phosphate ions (δ18O-PO4), which has been successfully used in recent decades as a tracer of phosphorus cycling in water studies. We measured the δ18O-PO4 of dissolved inorganic phosphates (DIPs) extracted from various phosphate sources. The obtained results are as follows: For springs, the δ18O-PO4 value varied from +14.8‰ to +18.5‰; for riverine samples, from +10.3‰ to +18.6‰, which were significantly location-dependent; while waste water treatment plant effluents ranged from +12.4‰ to +15.6‰. Two tested drainage water samples had similar isotopic compositions (+16.7‰ and +17.3‰). In the case of two analyzed bedrock samples, the δ18O-PO4 values, which were similar (+20.5‰ and +21.7‰), are close to the existing data on sedimentary bedrocks derived from similar geological periods. The obtained results can be helpful in future research aimed at identifying phosphate sources and P cycling in the studied area.

From Pollution to Preservation: Impacts of the Namami Gange Project on the Ganga River Ecosystem in the Varanasi Urban Area

The Namami Gange project was started by the Government of India, in 2014 as a mission to rejuvenate the Ganga River from acute pollution and environmental degradation, achingly throwing a plight on this holy waterway. This research paper explores the impact of the Namami Gange project on the holy river Ganga in Varanasi Urban Area, Uttar Pradesh. The study is based on a multi-disciplinary approach with the help of water quality monitoring, sources of pollution, and assessment of the measures followed under the Namami Gange Program. For monitoring the water quality, the parameters adopted in the present research are temperature, pH, conductivity, DO (dissolved oxygen), BOD (biochemical oxygen demand), FC (fecal coliform), and TC (total coliform), before and after the Namami Gange program implementation. Results show a visible improvement in water quality with major reductions in BOD and coliform levels, reflecting better oxygenation and reduced organic pollution. In this direction, the work pertaining to the establishment of STPs (Sewage Treatment Plants) and upgrading of existing infrastructure has been extremely crucial for mitigating the inflows of untreated sewage and industrial effluents into the river. The research also investigated the program's ecological effects on the restoration of aquatic habitats and biodiversity recolonization in a few river reaches. Public awareness campaigns and community participation programs were also completed with huge responses from the local population and pilgrims to enhance the sense of stewardship, which leads to greater sustainability and reduced pollution. The research, however, portrays that some of the lingering challenges pertain to continuous monitoring, STP maintenance, and non-point sources of pollution. More importantly, this would call for a holistic invasive management approach so that these positive outcomes as potentials of the Namami Gange program do not go astray. Overall, the paper provides valuable insights into the efficacy of large-scale environmental interventions and their potential to revive and preserve vital water resources like the Ganga River in Varanasi.

Towards the development of a ‘land-river-lake’ two-stage deep learning model for water quality prediction and its application in a large plateau lake

Accurate prediction of water quality that is essential for effective lake management requires a comprehensive understanding of influential factors in the ’land-river-lake’ continuum. We develop a novel deep learning model employing a Long Short-Term Memory (LSTM) model for predicting water quality, and apply it for a large plateau lake, Lake Dianchi. The model comprises two stages: the first establishes a correlation between land and rivers by predicting nutrient loads, incorporating socioeconomic and meteorological features. The second establishes a correlation between feeding rivers and the lake by predicting nutrient concentrations, taking water quality, meteorological conditions, riverine import, and agricultural Non-Point Source (NPS) loads into consideration. Compared to single-stage deep learning models developed using classical methods, the two-stage model reduces Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), indicating enhanced prediction accuracy. Shapley Additive Explanation (SHAP) analysis is further employed to identify key contributing features. Precipitation emerges as the primary feature in the first stage, while in the second stage, disparities in the most contributing features for two zones of the lake are revealed due to dam isolation and water diversion projects. In the main zone, the primary predicting features are temperature and agricultural NPS features, while in the dam-isolated zone, Secchi depth is the key predictor, highlighting spatial–temporal variations in driving features. Our model contributes a novel deep learning framework and valuable insights for water quality management in large lakes, emphasizing the importance of considering the dynamic interplay of features or factors in the ’land-river-lake’ system.

Synergistic adsorption and degradation of sulfonylurea herbicides by biochar-supported nano zero-valent iron composites in in-situ soil remediation

The extensive and continuous application of sulfonylurea herbicide is crucial for weed management but has resulted in widespread non-point source pollution. In this study, nano zero-valent iron/biochar (nZVI/BC) was synthesized by using FeCl3-impregnated corn stalks under anoxic pyrolysis conditions, which was applied in the in-situ remediation of bensulfuron methyl (BSFM) and sulfometuron methyl (SMTM) polluted soil. The results show that nZVI/BC with a 2.7 ratio of Fe/C had the highest efficiency in removing sulfonylurea herbicides. The removal efficiencies of BSFM and SMTM by nZVI/BC were negatively correlated to initial concentrations of sulfonylurea herbicides and positively correlated to nZVI/BC addition amount in the soil. Moreover, nZVI/BC could remove 53 % of BSFM and 72 % of SMTM at the concentration of 5 mg/kg after 21 d, which was much higher than that of BC. Notably, the removal efficiency of BSFM by nZVI/BC reached 36 % within 7 d, while the removal efficiency of SMTM by nZVI/BC was up to 68 % even within 3 d. The adsorption of herbicides by nZVI/BC was mainly driven by chemical processes involving multilayer adsorption. Furthermore, the removal mechanisms of BMTM and SMTM by nZVI/BC also involved C = O and C–O groups and oxidation of nZVI to FeO and Fe3O4 along with the cleavage of the sulfonylurea bridge. This study provides a promising application of nZVI/BC in in-situ remediating sulfonylurea herbicide- polluted soil.

Investigating soil physicochemical factors influencing trace element contamination at the semi-urban-rural home gardening interfaces on the Fiji Islands

Due to its ecological and public health implications, home gardening soil pollution is challenging. However, the physicochemical factors of trace element pollution in semi-urban-rural home gardening soil interfaces in Fiji are unclear. Self-organizing map (SOM), chemometrics, compositional data analysis (CDA), and soil quality indices were used to evaluate spatial patterns, contamination characteristics, sources, and factors affecting trace element contamination in 55 soil samples from semi-urban and rural Fiji. The average contents of diethylenetriaminepentaacetic acid (DTPA)-extractable forms of trace element levels (mg/kg) increased in rural areas as Fe (55.7) > Mn (40.4) > Zn (9.4) > Cu (5.9) and semi-urban areas as Fe (55.2) > Zn (35.9) > Mn (37.1) > Cu (16.1). Rural soils have less ecological risks to home gardening than semi-urban soils. SOM and CDA analysis showed four spatial clusters: clusters 1 and 3 are natural geogenic in rural regions while clusters 2 and 4 are human-induced non-point sources in semi-urban areas. Principal component analysis (PCA) and hierarchical cluster analysis showed that semi-urban Cu-Zn was more affected by manufacturing emissions or fertilization, whereas rural Fe-Mn was more likely to be lithogenic. The research found that pH and organic matter significantly affect Cu and Zn pollution in semi-urban soils (p < 0.05). For rural and semi-urban soils, trace element subsets explained 44 %–87 % of soil contamination changes using the stepwise regression model. These findings aid to establishing a primary database of eco-environmental risks and facilitate comprehensive strategies for assessing soil contamination and potential threats to food safety.

The adsorption mechanism and optimal dosage of walnut shell biochar for chloramphenicol

Biochar derived from biomass pyrolysis has proven to be an excellent material for pesticide adsorption and can be used as soil amendment for pesticide non-point pollution. However, the adsorption and desorption mechanisms for certain biochar and pesticide are still unclear. In this study, we investigated the properties of biochar derived from walnut (Juglans regia L.) shell (WSB), and used batch equilibrium method to investigate the adsorption and desorption behavior for chlorantraniliprole (CAP). The physical-chemical analysis showed that there were mainly lignin charcoal of alkyl carbon, methoxyl carbon, aromatic carbon, and carboayl carbon as the primary carbon compounds of WSB. The π - π electron donor acceptor interaction, electrostatic interaction, and hydrogen bond were the primary adsorption mechanisms of the WSB adsorption. Batch equilibrium study under 298 K showed that WSB application in the soil significantly improved the adsorption ability for CAP, and the adsorption behavior was a mono-layer adsorption process as Langmuir model fitted the adsorption isotherm data better than the Freundlich model. While Freundlich model analysis showed that WSB addition to the soil changed the isothermal adsorption line from the S style to the L style. The spontaneous degree reaction of sorbents from strong to weak was in the following order: 5%-WSB >7%-WSB >10%-WSB >1%-WSB >3%-WSB > soil > WSB, and the maximum application effect was achieved at 5 % (m/m) WSB dosage mixed with the soil. Therefore, we considered that WSB addition in soil increased its CAP adsorption capacity, and 5 % (m/m) WSB application was the best choice for CAP pollution control. These data will contribute to the adsorption mechanism and the optimal use dosage of WSB for CAP pollution control.

Analysis of the Distribution and Influencing Factors of Antibiotic Partition Coefficients in the Fenhe River Basin

Affected by point and non-point source pollution, the Fenhe River Basin faces significant environmental challenges. This study aimed to analyze the distribution characteristics and influencing factors of antibiotics in the water and sediments of the Fenhe River Basin. Samples were collected from 23 sites within the basin, and 26 antibiotics from five different classes were detected and analyzed using high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS). The water–sediment partition coefficient (Kp) was calculated, and spatial analysis was conducted using geographic information system (GIS) technology. The results showed that 25 antibiotics were detected in the water, with concentrations ranging from 130 to 1615 ng/L, and 17 antibiotics were detected in the sediments, with concentrations ranging from 121 to 426 μg/kg. For quinolones (QNs), except for ofloxacin, all others could be calculated with overall high values of Kp ranging from 692 to 16,106 L/kg. The Kp values for QNs were generally higher in the midstream, with considerable point source pollution from industries and non-point source pollution from developed agriculture. The distribution of Kp is closely associated with risk. This study found that the Kp values of the antibiotics were influenced by various factors such as temperature, water flow, and the physicochemical properties of sediments. Correlation analysis revealed significant relationships between Kp and parameters such as river width, water depth, water quality (total nitrogen, total phosphorus, and chemical oxygen demand), and sediment pH and clay content.

A framework integrating affinity propagation algorithm and spatial bivariate analysis for enhanced identification and localisation of soil heavy metals pollution sources.

The accurate identification of pollutant sources and their spatial distribution is crucial for mitigating soil heavy metals (SHMs) pollution. However, the receptor model struggles to effectively categorize pollutant sources and pinpoint their locations and dispersion trends. We propose a novel comprehensive framework that combines a receptor model, random forest (RF), affinity propagation (AP) algorithm, and bivariate local indicator of spatial association (BLISA), to optimize the traditional approach for tracing SHMs sources in industrial regions. We apportioned SHMs sources using a receptor model combined with RF, while BLISA combined with AP methods were employed to accurately locate the source areas and identify their dispersion tendencies. The results revealed that SHMs originated from mixed sources of equipment manufacturing agglomeration and agricultural activities (59.0%), geological background (30.5%), and emissions from heavily-polluting industries (10.5%). The pollution sources of soil Cd and Pb were located near specific industries, showing characteristics of multi-site concurrent pollution diffusion influenced by their proximity to industrial sites. The spatial distribution of Cr, Cu, and Zn sources was concentrated in high-density urban industrial areas, transitioning from point to nonpoint sources, with diffusion patterns influenced by the spatial agglomeration effect of industries. Our enhanced framework accurately identifies the location of SHMs sources and their dispersion tendencies, thereby improving regional soil pollution management.

Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.

The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple points and non-point sources. Monitoring chlorophyll-a (Chl-a) concentrations, a proxy for algal biomass is essential for assessing the trophic status of the river and managing its ecological health. This study aimed to improve the accuracy and reliability of Chl-a estimation in the Nakdong River using machine learning models (MLMs) and simultaneous use of multiple remotely sensed datasets. This study compared the performances of four MLMs: multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and eXetreme Gradient Boosting (XGB) using three different input datasets: (1) two remotely sensed datasets (Sentinel-2 and Landsat-8), (2) standalone Sentinel-2, and (3) standalone Landsat-8. The results showed that the MLP model with multiple remotely sensed datasets outperformed other MLMs with 0.43 - 0.86 greater in R2 and 0.36 - 5.88 lower in RMSE. The MLP model demonstrated the highest performance across the range of Chl-a concentrations and predicted peaks above 20 mg/m3 relatively well compared to other models. This was likely due to the capacity of MLP to handle imbalanced datasets. The predictive map of the spatial distribution of Chl-a generated by MLP well captured the areas with high and low Chl-a concentrations. This study pointed out the impacts of imbalanced Chl-a concentration observations (dominated by low Chl-a concentrations) on the performance of MLMs. The data imbalance likely led to MLMs poorly trained for high Chl-a values, producing low prediction accuracy. In conclusion, this study demonstrated the value of multiple remotely sensed datasets in enhancing the accuracy and reliability of Chl-a estimation, mainly when using the MLP model. These findings would provide valuable insights into utilizing MLMs effectively for Chl-a monitoring.

The Temporal and Spatial Analysis and Assessment of Water Quality in the Tuo River (Suzhou Section) based on the CCME-WQI Method

To investigate the spatiotemporal variations and disparities in water quality within the Suzhou section of the Tuo River, as well as to identify the key pollution factors and influencing elements, this study utilized monitoring data from five water quality monitoring stations along the Tuo River in Suzhou, collected between 2020 and 2022. After analyzing the spatiotemporal variations in water quality indicators, the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) was employed to assess the river's water quality status. The study also examined the causes of water pollution and proposed appropriate pollution control measures. The research results indicate that:(1) Total Nitrogen (TN), Chemical Oxygen Demand (COD), Permanganate Index (CODMn), and Fluoride (F) were identified as the main pollution factors, with COD and TN being the primary pollutants exceeding standard limits, with exceedance rates of 55.66% and 37.14%, respectively;(2)From 2020 to 2022, the concentrations of CODMn, COD, and TN were significantly higher during the flood season compared to the non-flood season, suggesting that non-point source pollution is the major contributor to the pollution load;(3)Temporally, the study area exhibited significant seasonal variations in water quality, with water quality being better during the non-flood season than during the flood season. Spatially, the study area showed notable spatial differences in water quality, with the S1 monitoring station recording the poorest water quality, while the S3 station exhibited the best;(4)The water quality of the Tuo River was significantly influenced by precipitation, with increased rainfall during the flood season exacerbating urban runoff, agricultural non-point source pollution, and internal pollution within the river channels.

Does institutional location matter? The effect of agency type on resource distribution for nonpoint water pollution

ABSTRACT With the rejuvenated emphasis on nonpoint pollution under the Water Quality Act (WQA) of 1987, states seek ways to address nonpoint pollution sources. States can use Clean Water State Revolving Funds (CWSRF) to address nonpoint needs; these funds are administered by state agencies, and funding decisions are typically made by agency personnel. While some states have aggressively funded nonpoint projects, other states have been much more reticent. Moreover, states have created different administrative structures to administer these funds. Does the nature of the structure impact the distribution of loans? Utilizing a dataset spanning state-level data from 1988 to 2016, we test a novel negative binomial cross-sectional time-series model employing the number of CWSRF loans directed to nonpoint projects as the dependent variable. We find that states where water quality management is buried within a ‘mini-EPA’ agency are the least likely to allocate CWSRF loans for nonpoint source issues, whereas states with ‘super’ agencies or combined environmental/health agencies allocate a higher number of CWSRF loans to nonpoint source projects.

Spatiotemporal drivers of agricultural non-point source pollution: A case study of the Huang-Huai-Hai Plain, China

Agricultural non-point source pollution (ANPSP) poses a severe threat to ecological environments, especially in China's major grain-producing regions. Despite the increasing attention, existing studies often overlook the spatial heterogeneity and driving mechanisms of ANPSP within different functional regions. This study addresses this research gap by constructing a bottom-up regional inventory of ANPSP for the Huang-Huai-Hai Plain (HHHP) and applying the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyse the spatio-temporal patterns of ANPSP from 2000 to 2020. Spatial econometric models were further applied to examine the spatial spillover effects of driving factors from the perspective of Major Function-oriented Zoning (MFZ). The results show that while ANPSP emissions in the HHHP have generally increased over the past two decades, a slight decrease has been observed since 2015. Grain yield capacity and cropping intensity were identified as the primary drivers of ANPSP growth, particularly in urbanised zones (UZs) and main agricultural production zones (MAPZs). The study also highlights significant spatial heterogeneity in the impact of driving factors on ANPSP across different MFZs, with marked differences in both the direct and spatial spillover effects of these factors. This underlines the need for differentiated environmental protection policies tailored to the functions and characteristics of each region. By integrating the LMDI decomposition method with spatial econometric models, this study offers a new framework for understanding the ANPSP dynamics within the context of MFZs, providing policymakers with valuable insights for designing effective, regionally coordinated governance strategies.

Exploring the temporal toxicity signature: A baseline evaluation of the heavy metal concentration in estuarine core sediments in the coastal region of cauvery delta, bay of bengal.

Globally, the concentration of heavy metals and sediment toxicity analysis are significant liabilities to aquatic environments. This scrutiny outlines the sediment textures, heavy metals and toxicity status associated with environmental pollution indices in the core sediment of the Cauvery and Vettar estuaries, East coast of India. The impact of rapid industrialization, urbanization, harbour activities and agricultural activities influences on the twain estuary is a significant concern to designate the environment. The contamination status of the sediments affects the potential biodiversity, ecological risks and human health. A total of two core sediments were recovered from the Cauvery and Vettar estuaries in March 2023 to decipher the environmental pollution status. Meticulous observation of the textural studies underscores the prevalence of sand content in Cauvery, and Vettar sediments consist of predominate clay content and minor silt contents. Furthermore, the organic matter is augmented in the Vettar River due to the higher input of waste disposal, seaweeds and algae due to the surrounding landmass. Twain core sediments argue that heavy metal concentration is decreasing in order as Fe > Zn > Ni > Pb > Cu > Cr by using portable X-ray fluorescence (pXRF) spectrometry. Remarkable results of environmental pollution indices such as Igeo, Ef, Cf, Cd and mCd state very highly polluted, extreme enrichments, high contamination and very high degree of contamination. Furthermore, the potential ecological risk indices such as PLI, SQGs, and PERI argue polluted, medium to high toxicity and moderate adverse ecological risk to the estuarine regions. Statistical analysis of the heavy metal affirms the enrichment of Fe metals may derive from lithogenic and/or anthropogenic influences, and the other studied metals such as Cu, Ni, Zn, Pb and Cr may be influenced by the anthropogenic activities in the aspect of point and non-point pollution sources. This could result from both estuaries undergoing higher pollution, in which the Vettar estuary is a considerable environmental risk zone compared to the Cauvery river due to the impact of industrial effluents and rapid urbanization activities. This finding underscores the urgent need for enhanced estuarine sediment quality study and comprehensive assessment of sediment toxicity, regulating the beneficial acumen for the government to follow the suitable remediation on the embellish policy of river and marine environments.

The Impact of Green Finance on Agricultural Non-Point Source Pollution: Analysis of the Role of Environmental Regulation and Rural Land Transfer

This study evaluates the impact of green finance on agricultural non-point source pollution control and emission reduction in 30 Chinese provinces from 2005 to 2022. Utilizing the entropy value method and the unit survey inventory method, the research measures the levels of green finance development and agricultural non-point source pollution. It employs a mediation effect model to empirically assess the pollution control efficacy of green finance and to elucidate the mechanisms underlying its influence. The findings indicate that green finance development significantly curtails agricultural non-point source pollution emissions. This conclusion is still valid after a series of robustness tests. The results of mechanism analysis show that environmental regulation and land transfer are important channels for green finance to reduce agricultural non-point source pollution. However, the slowing effect of green finance is stronger in provinces where the economic development level is still in the catch-up zone. Consequently, this study suggests strengthening green finance infrastructure in rural areas, coordinating green finance and environmental regulation policies, optimizing land transfer systems to promote scale management, and developing differentiated green finance policies based on regional economic development levels. These measures aim to augment the role of green finance in pollution treatment and emission reduction, thereby optimizing the green financial system, advancing environmental protection, and fostering sustainable development in China’s agricultural sector.

Using surface runoff to reveal the mechanisms of landscape patterns driving on various forms of nitrogen in non-point source pollution

Non-point source (NPS) pollution directly threatens river water quality, constrains sustainable economic development, and poses hazards to human health. Comprehension of the impact factors on NPS pollution is essential for scientific river water quality management. Despite the landscape pattern being considered to have a significant impact on NPS pollution, the driving mechanism of landscape patterns on NPS pollution remains unclear. Therefore, this study coupled multi-models including the Soil and Water Assessment Tool (SWAT), Random Forest, and Partial Least Squares Structural Equation Modeling (PLS-SEM) to construct the connection between landscape patterns, NPS pollution, and surface runoff. The results suggested that increased runoff during the wet season enhances the link between landscape patterns and NPS pollution, and the explained NPS pollution variation by landscape pattern increased from 59.6 % (dry season) to 84.9 % (wet season). Furthermore, from the impact pathways, we find that the sink landscape pattern can significantly and indirectly influence NPS pollution by regulating surface runoff during the wet season (0.301*). Meanwhile, the sink and source landscape patterns significantly and directly impact NPS pollution during different seasons. Moreover, we further find that the percentage of paddy land use (Pad_PLAND) and grassland patch density (Gra_PD) metrics can significantly predict the dissolved total nitrogen (DTN) and nitrate nitrogen (NO3−-N) variation. Thus, controlling the runoff migration process by guiding the rational evolution of watershed landscape patterns is an important development direction for watershed NPS pollution management.

Innovative approach for assessing nitrogen loss risk to surface waters from crop production in a watershed scale through nitrogen surplus index method

Although various measures are applied to control farmland nitrogen loss at the field scale, effective control strategies at the watershed scale are still lacking due to poor understanding of nitrogen loss risks across different scales. Therefore, an innovative approach to assess the risk of nitrogen loss to surface waters from crop production at the watershed scale was established, with nitrogen surplus, a more accurate indicator, as a major source factor introduced to the traditional index method. Furthermore, this approach was applied in the Baiyangdian Lake Watershed, a typically agricultural-dominated watershed in China, and the control strategies for farmland nitrogen loss reduction were investigated with scenario analysis. The major results included: (1) Areas with high (2–4 for risk to river, 1.6–3.2 for risk to lake) and very high (>4 for risk to river, >3.2 for risk to lake) risks of nitrogen loss in crop production were relatively small. For example, in the wheat-maize rotation system, these areas accounted for only 2.1–3.5 % and 0.9–1.8% of the total study area, respectively. Additionally, the critical source areas of nitrogen loss were concentrated around the receiving water bodies. (2) The nitrogen surplus in wheat season increased the nitrogen loss to surface waters. Compared to the maize season, the area with high and very high risk for nitrogen loss to the nearby river in wheat-maize rotation system increased by 58.3% and 141.4%. Furthermore, the areas with high and very high risk for nitrogen loss to the lake in wheat-maize rotation system were 129.6% and 220.6% more than that during maize season. (3) Nitrogen loss risk to the nearby rivers was correlated with nitrogen surplus, but nitrogen loss risk to the lake was related to the transport coefficient. (4) The optimized scenario for nitrogen loss reduction in crop production was the combination of nitrogen surplus being less than 20 kg/ha and 500 m banning area around the river with no fertilizer application, which reduced the nitrogen loss risk to the nearby river and to the lake by 94–95% and 89–95%. The results of this study provide a scientific basis for nutrient resource management and non-point source pollution control.

Research on the mechanisms of 2D road runoff pollution migration and the influence of pipeline overflow onto roads

In this paper, a novel numerical model capable of high-resolution, accurate simulation of the accumulation, wash-off, and migration of nonpoint source (NPS) pollutants on roads is proposed, effectively addressing the challenge of limited pipe network data for high-density urban building communities. This approach is based on a 1D-2D hydrodynamic and water quality dynamic bidirectional coupling model: GAST-SWMM. The calculation accuracy of the GAST two-dimensional road NPS wash-off model is validated via comparison with experimental data. The obtained Nash-Sutcliffe efficiency (NSE) is greater than 0.8. Moreover, the model was used to simulate the NPSs in a densely populated urban region of Xi'an, China, lacking building community pipeline data. The NPS pollutant transport and fate under the influence of both road runoff and the building community hydrodynamic water quality during rainfall events with a specific return period were examined. The proposed model can effectively and accurately replicate the accumulation and removal of NPS pollutants on a two-dimensional road and their dynamic interaction with the drainage network. With increasing rainfall return period, the peak time of the surface contaminant total load is postponed. The maximum surface pollutant load durations during rainfall events with 2-, 10-, and 50-year return periods are 60, 75, and 80 min, respectively. During the peak surface pollutant load time, the overflow pollutant fraction can exceed 85% for a 50-year rainfall return period. The simulation method presented in this paper accurately captures the spatial and temporal variations in NPS pollutants in densely populated urban areas, even when pipe network data for building communities are lacking. This method offers valuable technical assistance for urban environmental management and water quality protection.

Characterization of Silica Sand-Based Pervious Bricks and Their Performance under Stormwater Treatment

The acceleration of urbanization has disrupted natural water cycles, resulting in increased impervious urban surfaces and non-point source pollution from stormwater runoff. Addressing urban stormwater recharge has become crucial. This study introduces a novel silica sand-based permeable filtration material, investigating its surface characteristics, pore structure, permeability, and pollutant interception capabilities. The results demonstrate that hydrophilic binder coating modification of the permeable surface sand aggregate, combined with hydrophilic inorganic additives, having a porous structure with an average pore size of less than 50 μm and a porosity between 15% and 35%, significantly enhances surface hydrophilicity, achieving a permeation rate of up to 6.8 mL/(min·cm²). Moreover, it shows exceptional filtration and anti-clogging properties, achieving over 98% suspended solids interception and strong resistance to fouling. Dynamic biofilm formation experiments using simulated rain and domestic wastewater explore biofilm morphology and function on silica sand filtration well surfaces. Mature biofilms sustain COD removal efficiency exceeding 70%, with levels consistently below 50 mg/L, NH4+ decreasing to 2 mg N/L, and total nitrogen maintained below 10 mg N/L. The system features anoxic, anoxic, and aerobic zones, fostering synergistic organic matter and nitrogen removal by diverse microorganisms, enhancing pollutant mitigation. Silica sand-based permeable filtration material effectively mitigates urban stormwater runoff pollutants—suspended solids, organic matter, and nitrogen—offering an innovative solution for sponge city development and rainwater resource management.