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Non-point source pollution in agriculture was a global environmental concern. It is an important measure for preventing and controlling targeted agricultural non-point source (ANPS) pollution to determine the critical source areas and key factors by evaluation. This paper reviewed the evaluation indexes and methods of ANPS pollution and their selection at different scales, highlighting the evaluation indexes and their weights involved in the pollution sources, mitigation strategies, and environmental impacts of ANPS. It also explored load estimation of different scales from ANPS pollution. Estimation methods mainly include regional pollution load balance, unit pollution load, and simulation model. Field monitoring can provide an accurate estimation of ANPS pollution loads. Still, it is costly, and it requires intensive labor, leading to scarce monitoring data. Most empirical models in calculating ANPS pollution at watershed scales lacked the process of ANPS pollution entering the water body. The mechanism model was limited by available monitoring data, which was difficult to be applied on a large scale. Quantifying nutrient loads at regional or national scales was challenging, mainly due to model shortcomings and a lack of high-resolution data on agricultural management practices. Therefore, the evaluation of ANPS pollution should formulate systematic technical standards and develop the evaluation model based on information technology. Further implemented measures to prevent and control ANPS pollution should be according to local conditions.

Agricultural non-point source pollution (AGNPS) is considered a key problem affecting global water quality. The main causes of AGNPS are the loss of N, P, and other pollutants in agricultural production activities, the improper treatment of livestock and poultry manure, and rural household garbage and sewage. However, there is a lack of systematic summary on the source of AGNPS. This paper summarizes the sources, current situation, prevention, and control policies of AGNPS, and technical control policy and fertilizer reduction policy, which can contribute to solve AGNPS arising from agricultural production, such as renewable energy policies and control technologies that mitigate AGNPS caused by livestock industry, urban–rural integration policies and domestic sewage biogas treatment have mitigated AGNPS caused by rural life. Therefore, this paper can provide an important basis and guidance for solving AGNPS.

It is the key point to reveal the effect of irrigation water and fertilization conditions on the agriculture non-point pollution in the paddy field. In this study, the estimation model of agricultural non-point source pollution loads at field scale was established on the basis of agricultural drainage irrigation model and combined with pollutant concentration predication model. Based on the estimation model of agricultural non-point source pollution in the field and experimental data, the load of agricultural non-point source pollution in different irrigate amount and fertilization schedule in paddy field was calculated. The results showed that the variation of field drainage varies greatly under different irrigation conditions, and there is an “inflection point” between the irrigation water amount and field drainage amount. The non-point pollution load increased with the increase of irrigation water and showed a significant power correlation. Under the different irrigation condition, the increase amplitude of non-point pollution load with the increase of irrigation water was different. When the irrigation water is smaller, the non-point pollution load increase relatively less, and when the irrigation water increased to inflection point, the non-point pollution load will increase considerably. In addition, there was a positive correlation between the fertilization and non-point pollution load. The non-point pollution load had obvious difference in different fertilization schedule even with same fertilization level, in which the fertilizer pollution load increased the most in the period of turning green to tillering. The results provide some basis for the field control and management of agricultural non-point source pollution.

The contribution of urban non-point source (NPS) pollution to surface water pollution has gradually increased, analyzing the sources of urban NPS pollution is of great significance for precisely controlling surface water pollution. A bibliometric analysis of relevant research literature from 2000 to 2021 reveals that the main methods used in the source analysis research of urban NPS pollution include the emission inventory approach, entry-exit mass balance approach, principal component analysis (PCA), positive matrix factorization (PMF) model, etc. These methods are primarily applied in three aspects: source analysis of rainfall-runoff pollution, source analysis of wet weather flow (WWF) pollution in combined sewers, and analysis of the contribution of urban NPS to the surface water pollution load. The application of source analysis methods in urban NPS pollution research has demonstrated an evolution from qualitative to quantitative, and further towards precise quantification. This progression has transitioned from predominantly relying on on-site monitoring to incorporating model simulations and employing mathematical statistical analyses for traceability. This paper reviews the principles, advantages, disadvantages, and the scope of application of these methods. It also aims to address existing problems and analyze potential future development directions, providing valuable references for subsequent related research.

Non-point source (NPS) pollution, including fertilizer and manure application, sediment erosion, and haphazard discharge of wastewater, has led to a wide range of water pollution problems in the Miyun Reservoir, the most important drinking water source in Beijing. In this study, the Soil and Water Assessment Tool (SWAT) model was used to evaluate NPS pollution loads and the effectiveness of best management practices (BMPs) in the two subwatersheds within the Miyun Reservoir Watershed (MRW). Spatial distributions of soil types and land uses, and changes in precipitation and fertilizer application, were analysed to elucidate the distribution of pollution in this watershed from 1990 to 2010. The results demonstrated that the nutrient losses were significantly affected by soil properties and higher in both agricultural land and barren land. The temporal distribution of pollutant loads was consistent with that of precipitation. Soil erosion and nutrient losses would increase risks of water eutrophication and ecosystem degradation in the Miyun Reservoir. The well-calibrated SWAT model was used to assess the effects of several Best Management Practices (BMPs), including filter strips, grassed waterways, constructed wetlands, detention basins, converting farmland to forest, soil nutrient management, conservation tillage, contour farming, and strip cropping. The removal rates of those BMPs ranged from 1.03 to 38.40% and from 1.36 to 39.34% for total nitrogen (TN) and total phosphorus (TP) loads, respectively. The efficiency of BMPs was dependent on design parameters and local factors and varied in different sub-basins. This study revealed that no single BMP could achieve the water quality improvement targets and highlighted the importance of optimal configuration of BMP combinations at sub-basin scale. The findings presented here provide valuable information for developing the sustainable watershed management strategies.

Agricultural non-point source pollution causes global warming and the deterioration of air and water quality. It is difficult to identify and monitor the emission sources of agricultural pollution due to the large number of farms in China. Many studies focus on the technological aspect of achieving agricultural sustainability, but its socioeconomic aspect is poorly understood. Here, we report how group size (number of farms in a certain region) affects agricultural pollution governance through conducting a social science experiment. We found that when communication was allowed among group members, a small group size facilitated cooperation. Although deviations from the cooperation equilibrium occurred with time in all groups, the smaller the group size, the slower the cooperation equilibrium became frangible. These findings suggest that reducing number of farms and extending the length of farm property rights can benefit the mitigation of agricultural non-point pollution in China. Social science experiments can be a useful tool to understand the socioeconomic aspect of agricultural sustainability.

Agricultural non-point source pollution is threatening water environmental health of the Three Gorges reservoir. However, current studies for precision management of the agricultural non-point source pollution within this area are still limited. The objective of this study was identifying the critical areas and primary sources of agricultural non-point source pollution for precision management. Firstly, the inventory analysis approach was used to estimate the discharge amount of total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) from farmland fertilizer, crop residues, livestock breeding, and daily activities. Afterwards, the deviation standardization method was applied to evaluate the emission intensity of TN, TP, and COD, as well as calculating the comprehensive pollution index (CPI) of each village, based on which the critical areas for agricultural non-point source pollution management could be distinguished. Moreover, the equivalence pollution load method was conducted to identify the primary pollution sources within each critical zone. The above methods were implemented to an emigrant town within the Three Gorges reservoir area named Gufu. Results showed that agricultural non-point source pollution in Gufu town has been alleviated to a certain extent since 2016. Nevertheless, in four areas of the town (i.e., Longzhu, Fuzi, Shendu, and Maicang), the agricultural non-point source pollution still deserved attention and improvement. For the mentioned critical areas, farmland fertilizer and livestock breeding were the primary sources causing agricultural non-point source pollution. The emission amount of TN and TP from farmland fertilizer accounted for 60% and 48% of the total, respectively. And those from livestock breeding were 29% and 46%. Our research could provide definite targets to relieve agricultural non-point source pollution, which had great significance to protect water environment while coordinating regional economic growth after emigrant resettlement.

Solving the contradiction between agricultural economic growth and agricultural environmental problems is a difficult problem in regional environmental governance. Based on the panel data of 31 provinces, municipalities, and autonomous regions in China from 2000 to 2019, spatial Dubin model (SDM) is used to analyze the influence of agricultural economic growth and other factors on planting non-point source pollution. Innovate from the perspective of research objects and research methods, and the research results show (1) In the past 20 years, the amount of fertilizer applied and crop straw yield increased continuously. Through the fertilizer and farmland solid waste discharge of ammonia nitrogen (NH 3 –N), total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD), calculation of the equal-standard discharges of planting non-point source pollution shows that China’s planting non-point source pollution is serious. Among the investigated areas in 2019, the equal-standard discharges of planting non-point source pollution in Heilongjiang Province were the highest and have reached 24.35 × 10 10 m 3 . (2) The global Moran index of 20 years in the study area shows obvious spatial aggregation and diffusion characteristics, and has a significant positive global spatial autocorrelation, indicating that planting non-point source pollution discharges of the study area have potential interdependence in space. (3) SDM time-fixed effect model showed that the equal-standard discharges of planting non-point source pollution had a significant negative spatial spillover effect, and the spatial lag coefficient was − 0.11. Among the influencing factors, agricultural economic growth, technological progress, financial support to agriculture level, consumption capacity, industrial structure, and risk perception have significant spatial spillover effects on planting non-point source pollution. The results of effect decomposition show that the positive spatial spillover effect of agricultural economic growth on adjacent areas is greater than the negative effect on the local area. Based on the analysis of significant influencing factors, the paper provides direction guidance for the formulation of planting non-point source pollution control policy.

Non-point source (NPS) pollution has become a vital contaminant source affecting the water environment because of its wide distribution, hydrodynamic complexity, and difficulty in prevention and control. In this study, the identification and evaluation of NPS pollution risk based on landscape pattern were carried out in the Hanjiang River basin above Ankang hydrological section, Shaanxi province, China. Landscape distribution information was obtained through land use data, analyzing the contribution of “source-sink” landscape to NPS pollution through the location-weighted landscape contrast index. Using the NPS pollution risk index to identify and evaluate the regional NPS pollution risk considering the slope, cost distance, soil erosion, and precipitation erosion affect migration of pollutants. The results showed that (i) the pollution risk was generally high in the whole watershed, and the sub-watersheds dominated by “source” landscapes account for 74.61% of the whole basin; (ii) the high-risk areas were distributed in the central, eastern, and western regions of the river basin; the extremely high-risk areas accounted for 12.7% of the whole watershed; and the southern and northern regions were dominated by forestland and grassland with little pollution risk; (iii) “source” landscapes were mostly distributed in areas close to the river course, which had a great impact on environment, and the landscape pattern units near the water body needed to be further adjusted to reduce the influence of NPS pollution.

Environmental regulation (ER) is essential to preventing agricultural non-point source pollution (ANSP). Prior research has focused on the effect of ER on agricultural pollution (AP), but little is known about the impact of ER following digitization on preventing AP, particularly ANSP. Based on the spatial heterogeneity, the effect of ER was examined using a geographic detector tool with provincial panel data from 2010 to 2020 in rural China. The results show that ER is a driver in preventing ANSP, primarily because of the constraint on farmers’ behavior. Digitization positively affects the prevention of ANSP, as the new impetus for the infrastructure, technology, and capital is supported. The interaction between ER and digitalization forms a driving effect on the prevention of ANSP, indicating that digitalization constitutes the path dependence of farmers’ rule acquisition and perception and addresses the “free riding” dilemma of farmers’ participation, thereby enabling the incentive of ER to make agricultural production green and efficient. These findings indicate that the endogenous factor of digitization allowing ER is essential to preventing ANSP.