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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.

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.

Fertiliser use in agriculture increases the non-point pollution of waters with nitrogen (N) and phosphorus (P). Wetland buffer zones (WBZs) are wetland ecosystems between agricultural lands and water bodies that protect surface waters from non-point source pollution. We assessed how vegetation harvesting within WBZs impacts their N and P removal efficiency, nutrient uptake by plants and their biomass quality. We surveyed vegetation of a spontaneously rewetted fen along a small river in Poland, and analysed plant biomass, its nutrient contents and nutrient-leaching potential and the water chemistry. Total N removal reached 34–92% and total P removal 17–63%. N removal was positively related to the initial N concentration, regardless of mowing status. We found a high N removal efficiency (92%) in the harvested site. Vegetation of mown sites differed from that of unmown sites by a higher water-leached carbon and P contents in the biomass. We found that vegetation harvesting may stimulate the overall N removal, but may increase potential biomass decomposability, which eases the recycling of plant-incorporated nutrients back to WBZ. Thus, mowing should always be followed by the removal of biomass. Neglecting already mown WBZs may temporarily lower their nutrient removal efficiency due to potentially faster decomposition of plant biomass.

Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor have been used to detect emissions from large point sources. Emissions from over 400 sources have been quantified individually based on OMI observations, accounting for about a half of total reported anthropogenic SO2 emissions. Here we report a newly developed emission inventory, OMI-HTAP, by combining these OMI-based emission estimates and the conventional bottom-up inventory, HTAP, for smaller sources that OMI is not able to detect. OMI-HTAP includes emissions from OMI-detected sources that are not captured in previous leading bottom-up inventories, enabling more accurate emission estimates for regions with such missing sources. In addition, our approach offers the possibility of rapid updates to emissions from large point sources that can be detected by satellites. Our methodology applied to OMI-HTAP can also be used to merge improved satellite-derived estimates with other multi-year bottom-up inventories, which may further improve the accuracy of the emission trends. OMI-HTAP SO2 emissions estimates for Persian Gulf, Mexico, and Russia are 59%, 65%, and 56% larger than HTAP estimates, respectively, in year 2010. We have evaluated the OMI-HTAP inventory by performing simulations with the Goddard Earth Observing System version 5 (GEOS-5) model. The GEOS-5 simulated SO2 concentrations driven by both HTAP and OMI-HTAP were compared against in situ measurements. We focus for the validation on year 2010 for which HTAP is most valid and for which a relatively large number of in situ measurements are available. Results show that the OMI-HTAP inventory improves the agreement between the model and observations, in particular over the US, with the normalized mean bias decreasing from 0.41 (HTAP) to -0.03 (OMI-HTAP) for year 2010. Simulations with the OMI-HTAP inventory capture the worldwide major trends of large anthropogenic SO2 emissions that are observed with OMI. Correlation coefficients of the observed and modelled surface SO2 in 2014 increase from 0.16 (HTAP) to 0.59 (OMI-HTAP) and the normalized mean bias dropped from 0.29 (HTAP) to 0.05 (OMI-HTAP), when we updated 2010 HTAP emissions with 2014 OMI-HTAP emissions in the model.

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.

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.

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.

Food security is the lifeline of national security. It is not only an important cornerstone for world peace, stability, and development, but also the core driving force for promoting the green development of agriculture. This paper utilizes the empirical data of 30 provinces in China from 2010 to 2022 to explore how agricultural non-point source pollution (ANSP) affects food security (FS), and also discusses the moderating effect of environmental regulations (ER). Empirical research shows that the intensification of agricultural non-point source pollution will lead to a decline in the level of food security, especially in the western areas, grain-producing areas, and grain-balanced areas. Furthermore, environmental regulations have a positive moderating effect on the impact of agricultural non-point source pollution on food security. From the perspective of the spatial spillover effect, the inhibitory effect of agricultural non-point source pollution on food security has a spillover effect in space and will radiate to the development of food security in adjacent areas. The research suggests that the local government should enhance its attention and supervision over agricultural non-point source pollution, integrate agricultural technology with environmental supervision, optimize the allocation of agricultural resources, and attach importance to the protection of the agricultural ecological environment, so as to better ensure food security and achieve green agricultural development.

Based on the panel data of 31 provinces in China from 2000 to 2019, a spatial simultaneous equation model is used to study the two-way interaction and spatial spillover between non-point source pollution and agricultural green development. The results show that (1) non-point source pollution is the most significant hindering factor for agricultural green development, agricultural green development can reduce non-point source pollution, and non-point source pollution and agricultural green development have significant spatial spillover effects respectively. (2) Yield development target is an important internal factor influencing the relationship between agricultural green development transition and non-point source pollution. Environmental regulation and agricultural R&D stock promote agricultural green development but also aggravate non-point source pollution. Production scale cannot promote agricultural green development but can help reduce non-point source pollution. (3) Urbanization and agricultural trade dependence both promote the green development of agriculture, while farmers' income and agricultural machinery strength increase and reduce non-point source pollution respectively. To promote the agricultural non-point source pollution treatment and green development, we should strengthen the protection of agricultural resources and the monitoring of agricultural environment and change the production relations of small farmers.

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.