Agricultural Non-point Source Research Articles

Effects of Agricultural Insurance on Water Pollution in China

Water pollution is the most serious environmental problems in China now, water pollution directly affects people's quality of life and the sustainable development of agriculture, and the biggest source of water pollution is agricultural non-point source pollution, so we need to pay more attention to the environmental impact of agricultural production and reduce agricultural non-point source pollution. This paper examines the environmental impact of agricultural insurance on water pollution. Using annual data from 30 provinces across China between 2011 and 2019, this paper employs a two-way fixed-effects model to examine the correlation between agricultural insurance development and agricultural pollutant emissions. Additionally, it takes agricultural carbon emissions and agricultural output value as dependent variables to comprehensively investigate the environmental and economic impacts of agricultural insurance. The research results indicate that the development of agricultural insurance has a significant effect on reducing agricultural water pollution and carbon emissions. The pollutant reduction effect is more significant in the major grain-producing areas. The positive environmental effect of agricultural insurance is based on the fact that the development of agricultural insurance is conducive to reducing the use of pesticides and chemical fertilisers and does not come at the expense of increased agricultural output.

The dynamic relation between fertilization, precipitation and the diffusion of agricultural non-point pollution in Hainan Island

With the increasing demand for crops, the excessive application of fertilizers has gradually become a significant factor affecting water quality. Therefore, studying the relationship between agricultural fertilizer runoff and water quality is crucial for the sustainable development of agricultural production. The present study sets up a new dynamic model of nitrogen fertilizer loss, introducing variables such as precipitation, fertilization application during dry and rainy seasons, and their lagged and interaction terms. The paper analyzed the issue of fertilizer runoff under the complex interactions of various factors, including spatial and temporal scales and climatic conditions, and explored the relationship between agricultural activities and water quality changes in the context of sustainable development. Due to Hainan Island’s independent river system, which is free from transboundary pollution, and its low level of industrial pollution, it provides an excellent sample for assessing the impact of agricultural non-point source pollution on water quality. Based on watershed monitoring data from Hainan Island, this study draws the following conclusions: 1. Precipitation exhibits a pronounced seasonal influence on total nitrogen concentration. 2. Nitrogen fertilizer enters into water bodies through precipitation, resulting in a lag effect on total nitrogen concentration. 3. The influence of grain and tropical crops on nitrogen loss is less significant, while cash crops will trigger the nitrogen overloading of rivers in scenarios where they account for a high proportion of the planting structure of the sown area.

Application of Nitrate–Ammonium Nitrogen Fertilization Reduced Nitrogen Loss in Surface Runoff and Infiltration by Improving Root Morphology of Flue-Cured Tobacco

Nitrogen loss in water from farmland has become an environmental issue. Nitrogen fertilizer is the main cause of agricultural non-point source pollution in the Lake Basin, Yunnan. However, it is unclear how different nitrogen fertilizer forms affect water loss from farmland and how the root systems of crops respond. We established five nitrogen fertilizer treatments (100–0% [T1], 75–25% [T2], 50–50% [T3], 25–75% [T4], and 0–100% [(T5)] nitrate–ammonium) and performed an investigation to determine nitrogen loss in water and root morphological parameters of tobacco in Mile County and Chengjiang County. Compared with in the T1, T4, and T5 treatments, the total nitrogen loss in surface runoff was reduced by 4.67%, 11.85% and 9.56% in the T2 treatment and 27.32%, 23.20%, and 31.43% in the T3 treatment, respectively. Similar results were observed for the nitrogen loss due to infiltration. The root biomass was negatively correlated with nitrogen loss. There was greater root biomass, root surface area, and root spatial distribution in T2 and T3 compared with in T1, T4, and T5. These results indicate that 50–50% nitrate–ammonium nitrogen fertilizer can facilitate the root growth of tobacco and reduce nitrogen loss, which provides a reference for agricultural sustainable development.

Temporal and spatial analysis of fertilizer application intensity and its environmental risks in China from 1978 to 2022

Fertilizers are an essential input in agriculture as they can enhance crop yields. However, their use also poses significant environmental risks. To thoroughly explore the intensity of fertilizer use and its potential threats to the ecological environment, this study analyzed the environmental risks of fertilizer use from a temporal and spatial perspective based on fertilizer application data in China from 1978 to 2022. Additionally, the contribution of fertilizer application in Chinese farmland to greenhouse gas N2O emissions was quantified using IPCC emission factor methodology. The results indicated that fertilizer application intensity and N2O emissions in China initially increased and then decreased from 1978 to 2022. Despite the implementation of various fertilizer control measures at the policy level, such as the Zero Growth of Fertilizer Action in 2015 and the Efficiency-Increasing Action for Reducing Fertilizer Use in 2022, the intensity of fertilizer application in China still exceeded international safety standards by 1.33-fold in 2022, reaching 298.79 kg/hm2. Furthermore, N2O emissions amounted to 50.17 × 104t, accounting for 16% of China's total agricultural greenhouse gas emissions that year. Correlation and regression analyses demonstrated that with increasing fertilizer application, crop production exhibits an inverted U-shaped growth trend, indicating limited effectiveness of high-intensity fertilizer use in increasing crop yields. These findings highlight the profound greenhouse effect resulting from the use of agricultural nitrogen fertilizer. Therefore, this study proposed technical and policy-level mitigation measures to address the issues caused by excessive fertilizer application, aiming to provide insights for controlling agricultural non-point source pollution and preserving the agroecological environment.

The Impact of Tea Farmers’ Cognition on Green Production Behavior in Jingmai Mountain: Chain Mediation by Social and Personal Norms and the Moderating Role of Government Regulation

China’s agricultural sector faces significant challenges, including fragmented farming practices, limited farmer knowledge of sustainable production, and outdated pest control technologies. These issues result in improper fertilization, pesticide application, and disposal of agricultural inputs, contributing to agricultural non-point source pollution and hindering the transition to a green economy. Thus, promoting green production behavior among farmers is critical for achieving carbon peaking, carbon neutrality, and harmonious coexistence between humans and nature. However, the existing literature on this topic is still relatively scarce. This study aims to investigate the impact of farmers’ cognition on their green production behavior (GPB). Considering the role of policy, this study also examines the moderating effect of government regulation in this relationship. An analysis of 306 survey responses from tea farmers in Jingmai Mountain, Pu’er City, Yunnan Province, reveals that farmers’ cognition exerts a significant and positive impact on GPB. Social norms and personal norms serve as chain mediators in the relationship between farmers’ cognition and GPB. Moreover, government regulation moderates the influence of farmers’ cognition on social norms, further amplifying its impact on them. This study advances the theoretical understanding of farmers’ behavior and offers practical insights for fostering the sustainable development of the tea industry.

Economic and Environmental Effects of Farmers’ Green Production Behaviors: Evidence from Major Rice-Producing Areas in Jiangxi Province, China

This study examines the economic and environmental impacts of green production practices among farmers. It aims to contribute to sustainable agricultural development, mitigate agricultural non-point source (NPS) pollution, and align environmental protection with economic growth. This paper utilizes survey data from 1345 farm households in the main rice production areas of Jiangxi Province, China, using the example of reduced fertilizer application (RFA) among rice farmers. This study constructs a slack-based measure data envelopment analysis (DEA—SBM) model with undesirable outputs to measure environmental effects and applies an endogenous switching regression model (ESRM) to test the economic and environmental effects of farmers’ adoption of green production technologies. We found the following: (1) The RFA behavior of farmers has a significant positive impact on their net profit per hectare (NPH), helping farmers increase their income, with the increase ranging from 2.05% to 6.54%. (2) Farmers’ RFA behavior has a significant positive impact on agricultural green productivity (AGP), contributing to the improvement of the environment, ranging from 44.09% to 45.35%. (3) A heterogeneity analysis found inconsistencies in the income-enhancing and environmental-enhancing effects at different quantiles of NPH and AGP. Therefore, attention should be placed on improving the agricultural product quality supervision system under the market circulation mechanism, creating land scale conditions conducive to the promotion and application of fertilizer reduction technologies and promoting the implementation of externality internalization compensation systems.

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.

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.

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.

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.

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.

Study on Non-Point Source Pollution Prevention and Control System in Nansi Lake Basin Based on System Dynamics Approach

Agriculture, as an important activity on which human beings depend for their livelihood, brings serious environmental problems while meeting the needs of human survival, among which agricultural non-point source (NPS) pollution is one of the most urgent environmental problems. This study quantitatively assessed the loading characteristics spatial and temporal evolution patterns of two agricultural NPS pollutants, chemical oxygen demand (COD) and ammonia nitrogen (NH3-N), from 2010 to 2020 in the Nansi Lake Basin as an example, and constructed a system dynamics (SD) simulation model to simulate and analyze agricultural NPS pollution under different development and treatment scenarios, based on an investigation of the regional prevention and control strategy of agricultural NPS pollution and the technological system. The results show that the current status of agricultural NPS pollution load in the Nansi Lake Basin is poor, and the level of pollution load is high, showing obvious geographical differences. In terms of temporal changes, the pollution loads of the two pollutants showed a decreasing trend from 2010 to 2020, among which the pollution load of NH3-N showed the largest change. Spatially, the spatial distribution of each type of pollutant has some similarities, with smaller pollution loads in Jining and Zaozhuang and relatively larger pollution loads in Heze and Ningyang. The main source of COD pollution in the Nansi Lake Basin is rural life, with an emission proportion of 52.85%, and the main sources of NH3-N pollution from agricultural NPS pollution in the area are rural life and livestock and poultry farming, with emission proportions of 47.55% and 35.36%, respectively. Under the status quo continuum scenario, the pollution load values for COD are consistently higher than those for NH3-N, so the relative impact of COD is greater. In this study, the principles and methods of SD in system science are adopted to deal with the agricultural NPS pollution of Nansi Lake Basin, and the evolution of its behavioral characteristics are simulated, forecasted, and predicted, and policy experiments are conducted, with a view to providing references for the prevention and control of agricultural NPS pollution in Nansi Lake Basin and further research.

A Harmony-Based Approach for the Evaluation and Regulation of Water Security in the Yellow River Water-Receiving Area of Henan Province

Water security, as a crucial component of national security, plays a significant role in maintaining regional stability and ensuring the healthy and rapid development of the economy and society. The Yellow River water-receiving area of Henan Province (YRWAR-HN) is selected as the research area in this study. Firstly, a comprehensive evaluation index system is constructed based on the actual water security problems of the research area, and the single index quantification–multiple indices syntheses–poly-criteria integration method (SMI-P) is introduced to quantify the water security degree of 14 cities in the YRWAR-HN from 2010 to 2021. Then, the obstacle degree model is used to identify the key obstacle indexes that restrict the improvement of water security. Finally, the harmonious behavior set optimization method is adopted to carry out the regulation of water security, and the improvement path of water security in the YRWAR-HN is formulated. The results indicate the following: (1) the water security degree of the YRWAR-HN shows a fluctuating upward trend, increasing from 0.4348 (2010) to 0.6766 (2021), a significant rise of 55.61%. The water security level improves from the relatively unsafe level to the relatively safe level. Hebi City exhibits the fastest rate of water security improvement, while Xinxiang City shows the slowest rate. (2) The density of the river network (X1) and the proportion of investment in water conservancy and environmental protection in the total investment (X15) are the two indexes with the highest obstacle degree, with the average obstacle degrees being 15.09% and 10.79%, respectively. (3) The combination of the composite regulation scenario and improvement Path 2 is the optimal regulation strategy for water security in the YRWAR-HN. From the implementation process, Luoyang, Sanmenxia, Jiyuan, Xuchang, and Shangqiu may prioritize improving their flood and drought disaster defense capabilities and emergency response capabilities, continuously enhancing the flood prevention and disaster reduction system. Zhengzhou, Kaifeng, Xinxiang, Jiaozuo, Anyang, Hebi, Pingdingshan, and Zhoukou may prioritize resolving the regional water supply and demand conflicts, balancing development and conservation, actively seeking transboundary and external water transfers, and strengthening the capacity for water conservation and intensive utilization. Puyang City may prioritize enhancing its comprehensive water environment management capabilities, increasing investment in water conservancy and the environment, improving production processes, reducing pollutant emissions, and mitigating agricultural non-point source pollution.

Watershed landscape characteristics and connectivity drive river water quality under seasonal dynamics

Landscape characteristics and connectivity are pivotal determinants of river water quality within the context of water quality management. They play particularly important roles in the spread of non-point source pollution. However, previous research has not investigated their combined effects, especially in the topographically and ecologically volatile Loess Plateau, China. This study applied machine learning and positive matrix factorization techniques to water quality monitoring data for the Wuding River basin, China, from 2017 to 2021 to assess the seasonal impacts of environmental factors on river water quality. The results showed that there were spatial and seasonal variations in pollution sources, and specific thresholds for the key landscape indices that influence water quality were identified. Landscape composition and configuration have the greatest influence on the water quality parameters during the dry season, whereas connectivity and landscape configuration are more critical during the wet season. The connectivity contributions to chemical oxygen demand, the potassium permanganate index, and total phosphorus in the wet season were 19.07%, 27.47%, and 5.08% greater than in the dry season, respectively. Connectivity also made a significant contribution to the composite water quality index, accounting for 33.46% in the dry season and 36.22% in the wet season. The positive matrix factorization model identified agricultural non-point sources and mixed pollutants as the main factors affecting water quality during the dry season, whereas erosion and mixed pollution sources dominated the wet season. Critical thresholds were established for the landscape indices that affected water quality, such as a landscape shape index below 18.5 and a grassland proportion above 65%. The results can be used to develop more effective water quality management strategies and illustrate the essential role played by connectivity when tailoring water quality management strategies to seasonal variations.

Glufosinate-ammonium increased nitrogen and phosphorus content in water and shaped microbial community in epiphytic biofilm of Hydrilla verticillata

Glufosinate-ammonium (GLAM) can be released into adjacent water bodies with rainfall runoff and return water from farmland irrigation. However, impacts of GLAM on aquatic organisms remain unclear. In this study, changes in water quality, plant physiological parameters and epiphytic microbial community were investigated in wetlands with Hydrilla verticillata exposed to GLAM for 24 days. We found GLAM addition damaged cell and reduced chlorophyll a content in Hydrilla verticillata leaves, and increased ammonium and phosphorus in water (p < 0.001). The α-diversity increased in bacterial community but decreased in eukaryotic community with GLAM exposure. Neutral community models explained 62.3 % and 55.0 % of the variance in bacterial and eukaryotic communities, respectively. Many GLAM micro-biomarkers were obtained, including some clades from Proteobacteria, Bacteroidete, Actinobacteriota, Phragmoplastophyta, Annelida and Arthropoda. Redundancy analysis revealed that GLAM concentration was positively correlated to Flavobacterium, Gomphonema and Closterium but negatively to Methyloglobulus and Methylocystis. Network analysis revealed that 15 mg/L GLAM disturbed the interactions among phytoplankton, protozoa, metazoan and bacteria and reduced the stability of the microbial communities compared to 8 mg/L GLAM. GLAM shaped the nitrogen and phosphorus cycle related bacterial genes. This study highlights that herbicides are non-neglectable factors affecting the efficiency of aquatic ecological restoration in agricultural areas to control agricultural non-point source pollution.

Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization

As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R2 = 0.93), while the SVM was superior in the wet season (R2 = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified CODMn and NH3-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China.

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

Evaluating the Impacts of Fertilization and Rainfall on Multi-Form Phosphorus Losses from Agricultural Fields: A Case Study on the North China Plain

Excessive fertilizer application increases the risk of eutrophication and agricultural non-point source pollution (ANPS) in rivers near farmland. However, the processes and mechanisms of runoff and phosphorus losses, particularly in the interflow, under various fertilizer treatments and rainfall scenarios are not well understood. This study used orthogonal experimental methods to investigate the combined effects of fertilization schemes and rainfall intensity on multi-form phosphorus runoff losses and to establish statistical relationships and regression models between phosphorus losses and environmental factors in surface runoff and interflow. The results indicated that (1) the optimized fertilization scheme, compared with conventional fertilization, enhanced pak choi (Brassica rapa) growth while reducing phosphorus runoff losses. By reducing phosphorus fertilization by 35.7%, total phosphorus losses decreased by 29.3%, 34.2%, and 29.8% under light, moderate, and heavy rainfall, respectively. (2) Different fertilizer applications and rainfall intensities had varying effects on phosphorus losses through different pathways. Fertilizer application was the primary factor affecting phosphorus losses in surface runoff, while rainfall intensity mainly influenced phosphorus losses through interflow. (3) Surface runoff was the dominant pathway for phosphorus losses from farmland (&gt;92.0%), with particulate phosphorus (&gt;89.4%) being the predominant form. However, under high-intensity and long-duration rainfall, interflow became a significant pathway for phosphorus losses. This study highlights the importance of optimized fertilization in reducing phosphorus losses and improving fertilizer efficiency in agricultural fields. The findings will help develop strategies to mitigate ANPS and soil nutrient losses in the North China Plain.

Effects of Paddy Rain-Flood Storage on Rice Growth Physiological Indices and Nitrogen Leaching under Organic Planting in Erhai Lake Basin.

In order to address the increasingly prominent issues of water resource protection and agricultural non-point source pollution in the Erhai Lake Basin, this study conducted a two-year field experiment in Gusheng Village, located in the Erhai Lake Basin. In 2022, two irrigation treatments were set up: conventional flooding irrigation (CK) and controlled irrigation (C), with three replicates for each treatment. In 2023, aiming to enhance the utilization rate of rainwater resources and reduce the direct discharge of dry-farming tailwater from upstream into Erhai Lake. The paddy field was used as an ecological storage basin, and the water storage depth of the paddy field was increased compared to the depth of 2022. Combined with the deep storage of rainwater, the dry-farming tailwater was recharged into the paddy field to reduce the drainage. In 2023, two water treatments, flooding irrigation with deep storage and controlled drainage (CKCD) and water-saving irrigation with deep storage and controlled drainage (CCD) were set up, and each treatment was set up with three replicates. The growth and physiological index of rice at various stages were observed. Nitrogen leaching of paddy field in surface water, soil water, and groundwater under different water treatments after tillering fertilizer were observed. The research results show that the combined application of organic and inorganic fertilizers under organic planting can provide more reasonable nutrient supply for rice, promote dry matter accumulation and other indices, and also reduce the concentration of NH4+-N in surface water. Compared with CK, the yield, 1000-grain weight, root-to-shoot ratio, and leaf area index of C are increased by 4.8%, 4.1%, 20.9%, and 9.7%, respectively. Compared with CKCD, the yield, 1000-grain weight, root-to-shoot ratio, and leaf area index of CCD are increased by 6.5%, 3.8%, 19.6%, and 21.9%, respectively. The yield in 2023 is 19% higher than that in 2022. Treatment C can increase the growth indicators and reduce the net photosynthetic rate to a certain extent, while CCD rain-flood storage can alleviate the inhibition of low irrigation lower limit on the net photosynthetic rate of rice. Both C and CCD can reduce nitrogen loss and irrigation amount in paddy fields. CCD can reduce the tailwater in the Gusheng area of the Erhai Lake Basin to Erhai Lake, and also can make full use of N, P, and other nutrients in the tailwater to promote the formation and development of rice. In conclusion, the paddy field rain-flood storage methodology in the Erhai Lake Basin can promote various growth and physiological indicators of rice, improve water resource utilization efficiency, reduce direct discharge of tailwater into Erhai Lake, and decrease the risk of agricultural non-point source pollution.

Lanthanum ferrite modified filling materials and its enhancement for phosphorus and COD removal: Performance and mechanism

Previous studies have developed numerous adsorption composite materials demonstrating substantial removal efficiencies for phosphorus (P) and chemical oxygen demand (COD). However, most of materials targeted single contaminant and were inconvenient to apply in real water bodies owing to size limitation. In the current study, three composite materials‑lanthanum ferrite-modified Quartz balls/Maifan stones/Honeycomb ceramics-were synthesized using one step co-precipitation method. Structural and morphological changes of pre- and post-adsorption were examined through Scanning Electron Microscope - Energy Dispersive Spectrometer (SEM-EDS) and X-ray diffraction (XRD). Kinetics, thermodynamics, and isotherm models were simulated for the analysis of P and COD adsorption. X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FTIR) were employed to elucidate the adsorption mechanisms. The results demonstrated that these materials achieved P maximal removal rate of 99.56 % and COD maximal removal rate up to 94.82 %. The second-order kinetic model for P adsorption was more excellent than that of the first-order model, and the intra-particle diffusion model more accurately described the COD adsorption. Both the Langmuir and Freundlich were used to fit the adsorption isotherm data for P and COD. XPS and FTIR revealed that the adsorption mechanisms for P were ligand exchange and chemical precipitation. Whereas the adsorption mechanisms for COD were ligand exchange and physical adsorption. The materials in the actual farmland drainage performed excellent and effectively reduced the concentration of P and COD up to 68.58 % and 53.98 %, respectively. The lanthanum ferrite-modified fillers provide accessible, universal and bifunctional option for controlling agricultural non-point source pollution.