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With the rapid expansion of global agriculture, phosphorus pollution from nonpoint sources has emerged as a major constraint to water quality improvement. Among the methods used to control nonpoint-source pollution, small water bodies such as ditches and ponds have received increasing attention due to their widespread distribution and low cost. However, most previous studies have focused on specific regions, and the key factors regulating phosphorus retention and their generalizable patterns remain unclear. Additionally, research on the synthesis of global water-body nutrient retention is largely limited to studies from North America and Europe. Here, we compiled a phosphorus retention dataset for small water bodies across China that incorporates research published in Chinese. Using multiple statistical analyses to assess the hydrological processes and external influencing factors on phosphorus removal rate and efficiency, our results show that the median total phosphorus removal rate in ditches and ponds across China was 33.7%. Among all hydrological and external influencing factors, hydraulic retention time (HRT) served as a pivotal parameter in the modulation of phosphorus removal efficiency. Extending HRT is an effective method to enhance phosphorus retention in water bodies, although it also demonstrates diminishing marginal returns. By regulating the HRT of small water bodies, a win–win situation with both pollutant reduction and agricultural production may be achieved, thereby enhancing the resilience of agricultural ecosystems to climate change. This study provides a scientific basis and technical support for achieving the dual goals of sustainable agricultural development and environmental improvement in developing countries—particularly in rice-producing regions.

With the gradual control of point source pollution, the impact of urban nonpoint source pollution on river water quality is becoming more prominent. Regarding the current problem that nonpoint source pollution loads in urban basins are difficult to quantify and the impact on water quality is difficult to analyze, the Licun River basin in Qingdao was selected as the research object. Through the field survey and surface accumulation sampling analysis of the basin, the evaluation model of urban nonpoint source pollution was constructed by revising the land type data of the basin and the urban database of the SWAT model. The results showed that concentration of nitrate in precipitation was most sensitive to the simulation of nitrogen loading; organic P in baseflow was most sensitive to the simulation of phosphorus loading. The Nash–Sutcliffe efficiency coefficient (ENS) and the coefficients of determination (R2) of the SWAT model for runoff, total phosphorus (TP), and total nitrogen (TN) in the simulation validation period meet the model requirements,indicating a good model fit. In addition, the spatial and temporal distribution characteristics of urban nonpoint source pollution of TN and TP in 2021 were analyzed. In July, rainfall-runoff from the Licun River basin was the most polluted.

The carbon emissions and agricultural nonpoint source pollution constraints were incorporated into the input–output index system, and the epsilon-based measure (EBM) super efficiency model and global Malmquist–Luenberger (GML) index were used to measure the cultivated land use efficiency and changes in the total factor productivity (TFP) of cultivated land use in the main grain-producing areas in China from 1993–2016. The results indicate that: (1) from 1993 to 2016, the cultivated land use efficiency in the main grain-producing areas in China showed a tendency to fluctuate and increase, with obvious stage characteristics; however, the overall level was not high. (2) There is a significant difference in the cultivated land use efficiency under the constraints of carbon emissions and nonpoint source pollution in the main grain-producing areas in the different provinces, and low-efficiency provinces have higher input redundancy and undesired output redundancy than high-efficiency provinces. It can be observed that input redundancy and undesired output redundancy have a significant negative effect on cultivated land use efficiency. (3) The TFP of cultivated land use under the constraints of carbon emissions and nonpoint source pollution in China’s main grain-producing areas is estimated by the GML index. The results show that the TFP of cultivated land use in the main provinces in the main grain-producing regions is greater than 1, indicating that the productivity levels of all the provinces in China’s main grain-producing areas are increasing. From the perspective of the power sources in each province, global pure technological change (GPTC) and global scale technological change (GSTC) are the main driving forces for the TFP of cultivated land use, while global pure efficiency change (GPEC) and global scale efficiency change (GSEC) are the bottlenecks for increasing the TFP of cultivated land use.

A localized parameter system is constructed for the Taihu Lake Basin based on the shared socioeconomic pathways (SSPs) to predict the rural population and economy including ANPSP from 2020 to 2050. The results show that: (1) Under the five paths of SSP1–SSP5, the population will continue to decrease, and the scenarios from large to small are SSP3 > SSP1 > SSP2 > SSP4 > SSP5. Agricultural GDP continues to rise under the SSP1, SSP4 and SSP5, and rises first and then slowly declines under the SSP2 and SSP3. (2) The ANPSP caused by living and production will show a downward trend. The scenarios with the highest and lowest rural life pollution are SSP3 and SSP5, and the scenarios with the highest and lowest agricultural production pollution are SSP5 and SSP1, respectively. The total pollution will be reduced, however the reduction rate will gradually slow down, and the pollution caused by rural production and life is difficult to completely avoid. (3) The total nitrogen (TN) and total phosphorus (TP) pollution showed a similar trend. Both types of pollutants are mainly from agricultural production activities, however the proportion of rural domestic pollution is gradually increasing. Agricultural production has a greater impact on TP, while rural life contributes more to TN than TP. (4) SSP1 is the only path that can prevent ANPSP and achieve agricultural economic growth. On the contrary, SSP3 leads to the increase of ANPSP and the decrease of agricultural GDP, which brings the double pressure of economic growth and environmental protection.

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.

Phosphorus (P) is a valuable, nonrenewable resource in agriculture promoting crop growth. P losses through surface runoff and subsurface drainage discharge beneath the root zone is a loss of investment. P entering surface water contributes to eutrophication of freshwater environments, impacting tourism, human health, environmental safety, and property values. Soluble P (SP) from subsurface drainage is nearly all bioavailable and is a significant contributor to freshwater eutrophication. The research objective was to select phosphorus sorbing media (PSM) best suited for removing SP from subsurface drainage discharge. From the preliminary research and literature, PSM with this potential were steel furnace slag (SFS) and a nano-engineered media (NEM). The PSM were evaluated using typical subsurface drainage P concentrations in column experiments, then with an economic analysis for a study site in Michigan. Both the SFS and generalized NEM (GNEM) removed soluble reactive phosphorus from 0.50 to below 0.05 mg/L in laboratory column experiments. The most cost-effective option from the study site was the use of the SFS, then disposing it each year, costing$906/hectare/year for the case study. GNEM that was regenerated onsite had a very similar cost. The most expensive option was the use of GNEM to remove P, including regeneration at the manufacturer, costing $ 1641/hectare/year. This study suggests that both SFS and NEM are both suited for treating drainage discharge. The use of SFS was more economical for the study site, but each site needs to be individually considered.

The accurate monitoring of N and P surface runoff losses from farmland is crucial to control agricultural nonpoint source pollution. A pond constructed with concrete material (CM) is a common collection container used during field experiments in China, but the adsorption characteristics of concrete may cause a considerable underestimation of surface runoff losses from farmland. To characterize any neglected error caused by the collection container material, a laboratory experiment was conducted comparing the N and P contents of runoff samples collected from CM and plastic material (PM) containers. The results indicated that CM containers significantly lowered N and P sample contents compared with PM containers, which was attributed to the adsorption capacity of pollutants by CM containers. This was confirmed by scanning electron microscopy (SEM) images of particles retained in CM containers. In an attempt to alleviate this error, three common water-repellent materials were applied to CM containers that significantly limited the pollutant adsorption of CM containers. Moreover, it was shown that there was no significant difference between the calculated concentration of runoff losses and the total amount of pollutants. To calibrate the observational error from CM containers, stepwise multiple regression models of different forms of N and P pollutants were developed. The results of this study suggest that treating CM containers with water repellent is an effective measure for improving the accuracy of new-built monitor points of agricultural nonpoint source pollutants. In addition, the calibration of observational error from CM containers and delayed sampling is essential to estimate agricultural nonpoint source pollution load via the surface runoff from farmland based on data from monitor points. Graphical abstract

Over the past few decades the quality of many international water bodies has deteriorated, resulting in economic losses from declines in the fishing industry and in tourism, as well as a loss of biodiversity and health impacts from contaminated water. These deterioration has been caused by many factors including nutrient run-off from agriculture, insufficiently treated sewage, drainage of wetlands, coastal erosion, introduction of exotic species, eutrophication and inadequate resource management. One of the most significant sources of degradation has been form excessive discharge of nitrogen and phosphorus compounds (nutrients), due to the poor management practices used in agricultural, domestic and industrial activities. This publication aims to draw the attention of professionals and practitioners working in agricultural and environmental sectors to the experience and successes of the environmentally friendly good agricultural practices being used in the Chesapeake Bay Region of the United States to reduce nutrient loads in water.

Potential risk identification of agricultural nonpoint source pollution (ANPSP) is essential for pollution control and sustainable agriculture. Herein, we propose a novel method for potential risk identification of ANPSP via a comprehensive analysis of risk sources and sink factors. A potential risk assessment index system (PRAIS) was established. The proposed method was used to systematically evaluate the potential risk level of ANPSP of Yichang City, Hubei Province. The potential risk of ANPSP in Yichang City was 18.86%. High-risk areas account for 4.95% and have characteristics such as high nitrogen and phosphorus application rates, large soil erosion factors, and low vegetation coverage. Compared with the identification results of the Diffuse Pollution estimation with the Remote Sensing (DPeRS) model, the area difference of the same risk level calculated by the PRAIS was reduced by 33.9% on average. This indicates that PRAIS has the same level of accuracy as the DPeRS model in identifying potential risks of ANPSP. Thus, a rapid and efficient identification system of potential risks of regional ANPSP was achieved.

Environmental problems associated with pig production have shown a growing trend in China. Subsequently, local governments have imposed bans and restrictions on pig production to control pollution, which has affected the supply of pork in the market. An emission trading system is an effective means to control the pollution of pig production. In this study, the emission trading system for controlling point source pollution is introduced into the control of pig farming nonpoint source pollution. Taking Zhejiang Province as the research area, we selected purposely four representative pig production cities in Hangzhou, Jiaxing, Ningbo, and Quzhou as the survey sites and conducted a face-to-face random sampling survey of scale pig farmers from 2018 to 2019. A Contingent Valuation Method (CVM) was used to evaluate the willingness of scale pig farmers to invest in biogas facilities under the emission trading system, and then, a regression model was constructed to analyze the feasibility of the emission trading system for pig farming pollution control. The empirical study found that the emission trading system can encourage scale pig farmers to use biogas fermentation to treat pollution and make environmental investments in recycling waste resources. Because of the scale economy of pollution treatment, it is proved feasible to introduce the emission trading system into the control of pig farming pollution.