Multi-scale Modeling of Nutrient Pollution in the Rivers of China

A novel framework reveals anthropogenic stressors of phosphorus polluted river-lake connection water system in Poyang lake basin of China

Poyang lake (PYL), as China's largest freshwater lake, has been suffering from increasing total phosphorus (TP) pollution associated with rapid basinal socio-economic development. However, anthropogenic phosphorus stressors were rarely examined in PYL basin due to its large-scale and complex river-lake connection water system, hampering phosphorus pollution control efforts. In this study, water pollution stress from multiple anthropogenic activities is quantitatively examined in PYL basin based on a newly developed framework coupling grey water footprint (GWF) analysis with the SPARROW model. Results show that the phosphorus source-sink process in PYL basin has been well simulated by SPARROW model quantifying an overall TP delivery rate of 0.39, with catchments closer to PYL showing higher delivery rate of phosphorus (up to over 0.8). The GWF analysis demonstrates that anthropogenic phosphorus sources have imposed much higher pollution stress on PYL than its inflowing rivers, with twelve catchments nearby PYL identified as critical source areas of TP contamination. Agricultural farming, livestock & poultry production, and urban household are recognized as the dominant anthropogenic stressors burdening water environment of PYL. Based on these, policy recommendations are provided for advancing control of the phosphorus pollution stressors. The methodology is effective in refined examination of water pollution sources, which is expected to be applied in other watersheds providing informative diagnosis of water issues especially in lakes.

Spatio-temporal evolution mechanism and dynamic simulation of nitrogen and phosphorus pollution of the Yangtze River economic Belt in China

Excess nitrogen and phosphorus inputs are the main causes of aquatic environmental deterioration. Accurately quantifying and dynamically assessing the regional nitrogen and phosphorus pollution emission (NPPE) loads and influencing factors is crucial for local authorities to implement and formulate refined pollution reduction management strategies. In this study, we constructed a methodological framework for evaluating the spatio-temporal evolution mechanism and dynamic simulation of NPPE. We investigated the spatio-temporal evolution mechanism and influencing factors of NPPE in the Yangtze River Economic Belt (YREB) of China through the pollution load accounting model, spatial correlation analysis model, geographical detector model, back propagation neural network model, and trend analysis model. The results show that the NPPE inputs in the YREB exhibit a general trend of first rising and then falling, with uneven development among various cities in each province. Nonpoint sources are the largest source of land-based NPPE. Overall, positive spatial clustering of NPPE is observed in the cities of the YREB, and there is a certain enhancement in clustering. The GDP of the primary industry and cultivated area are important human activity factors affecting the spatial distribution of NPPE, with economic factors exerting the greatest influence on the NPPE. In the future, the change in NPPE in the YREB at the provincial level is slight, while the nitrogen pollution emissions at the municipal level will develop towards a polarization trend. Most cities in the middle and lower reaches of the YREB in 2035 will exhibit medium to high emissions. This study provides a scientific basis for the control of regional NPPE, and it is necessary to strengthen cooperation and coordination among cities in the future, jointly improve the nitrogen and phosphorus pollution tracing and control management system, and achieve regional sustainable development.

The impact of emissions controls on atmospheric nitrogen inputs to Chinese river basins highlights the urgency of ammonia abatement

Excessive nitrogen (N) deposition affects aquatic ecosystems worldwide, but effectiveness of emissions controls and their impact on water pollution remains uncertain. In this modeling study, we assess historical and future N deposition trends in Chinese river basins and their contributions to water pollution via direct and indirect N deposition (the latter referring to transport of N to water from N deposited on land). The control of acid gas emissions (i.e., nitrogen oxides and sulfur dioxide) has had limited effectiveness in reducing total N deposition, with notable contributions from agricultural reduced N deposition. Despite increasing controls on acid gas emissions between 2011 and 2019, N inputs to rivers increased by 3%, primarily through indirect deposition. Simultaneously controlling acid gas and ammonia emissions could reduce N deposition and water inputs by 56 and 47%, respectively, by 2050 compared to 2019. Our findings underscore the importance of agricultural ammonia mitigation in protecting water bodies.

Multi-evidences investigation into spatiotemporal variety, sources tracing, and health risk assessment of surface water nitrogen contamination in China

A comprehensive understanding of nitrogen pollution status, especially the identification of sources and fate of nitrate is essential for effective water quality management at the local scale. However, the nitrogen contamination of surface water across China was poorly understood at the national scale. A dataset related to nitrogen was established based on 111 pieces of literature from 2000 to 2020 in this study. The spatiotemporal variability, source tracing, health risk assessment, and drivers of China's surface water nitrogen pollution were analyzed by integrating multiple methods. These results revealed a significant spatiotemporal heterogeneity in the nitrogen concentration of surface water across China. Spatially, the Haihe River Basin and Yellow River Basin were the basins where surface water was seriously contaminated by nitrogen in China, while the surface water of Southwest Basin was less affected. Temporally, significant differences were observed in the nitrogen content of surface water in the Songhua and Liaohe River Basin, Pearl River Basin, Southeast Basin, and Yellow River Basin. There were 1%, 1%, 12%, and 46% probability exceeding the unacceptable risk level (HI＞1) for children in the Songhua and Liaohe River Basin, Pearl River Basin, Haihe River Basin, and Yellow River Basin, respectively. The primary sources of surface water nitrate in China were found to be domestic sewage and manure (37.7%), soil nitrogen (31.7%), and chemical fertilizer (26.9%), with a limited contribution from atmospheric precipitation (3.7%). Human activities determined the current spatiotemporal distribution of nitrogen contamination in China as well as the future development trend. This research could provide scientifically reasonable recommendations for the containment of surface water nitrogen contamination in China and even globally.

Multidirectional Fate Path Model to Connect Phosphorus Emissions with Freshwater Eutrophication Potential

Excessive anthropogenic phosphorus (P) emissions put constant pressure on aquatic ecosystems. This pressure can be quantified as the freshwater eutrophication potential (FEP) by linking P emissions, P fate in environmental compartments, and the potentially disappeared fraction of species due to increase of P concentrations in freshwater. However, previous fate modeling on global and regional scales is mainly based on the eight-direction algorithm without distinguishing pollution sources. The algorithm fails to characterize the fate paths of point-source emissions via subsurface pipelines and wastewater treatment infrastructure, and exhibits suboptimal performance in accounting for multidirectional paths caused by river bifurcations, especially in flat terrains. Here we aim to improve the fate modeling by incorporating various fate paths and addressing multidirectional scenarios. We also update the P estimates by complementing potential untreated point-source emissions (PSu). The improved method is examined in a rapidly urbanizing area in Taihu Lake Basin, China in 2017 at a spatial resolution of 100 m × 100 m. Results show that the contribution of PSu on FEP (62.6%) is greater than that on P emissions (58.5%). The FEP is more spatially widely distributed with the improved fate modeling, facilitating targeted regulatory strategies tailored to local conditions.

Spatial-temporal change of river thermal environment and anthropogenic impact in China

River water temperature is important and closely related to river ecosystem, concerning fishery industry, human health, and the land-sea exchange of nutrients, especially for great powers with a good deal of heat emission from once-through cooling systems of thermal power plants. However, the changes in river water temperature under the joint action of climate change and human activity such as the heat emission have not been well investigated for rising powers, hampering environmental policy making for sustainable development. Therefore, we have taken advantage of a recently-developed land surface model including river water temperature calculation with anthropogenic thermal discharge and zonal statistics to quantitatively make out the river water temperature variation and the man-made influence over the past thirty years (1981-2010) in China for the first time. Results show that the estimated water temperature in major rivers is generally close to the observation with the r2 of 0.83, though the underestimation exists in some rivers. The river water in the Pearl River Basin was the warmest with the mean temperature of 19.2 °C and the others in order were in the Southeast Basin, the Huaihe River Basin, the Yangtze River Basin, the Haihe River Basin, the Yellow River Basin, the Southwest Basin, the Song-Liao River Basin, and the Continental Basin, ranging from 16.7 °C to 6.3 °C. The Huaihe River Basin had the fastest mean increase rate of ca. 0.27 °C decade-1, while the slowest mean increase rate of ca. 0.13 °C decade-1 existed in the Pearl River Basin. At the subbasin scale, a meridionally-distributed hot spot zone (along the 115°E) of the increasing water temperature has been identified, where the trends ranged from 0.2 °C decade-1 to 0.5 °C decade-1. Air temperature exerted a major control on the spatial pattern of climatological water temperature, while both air temperature and downwelling solar flux played a leading role in the distribution of water temperature change trends. Although anthropogenic thermal emission heated the rivers locally, the impacts in the Song-Liao River, the Haihe River, the Huaihe River, and the middle and lower reaches of Yellow River and Yangtze River had been raised up to ca. 4.5 °C, when comparing with those controlled by climate change only. In general, these results show an acceptable level of river water temperature simulation in the land surface model, and could provide a scientific reference for the assessment on riverine thermal environment under the climate change and social impact in China.

Keeping Nitrogen Use in China within the Planetary Boundary Using a Spatially Explicit Approach

Nitrogen (N) supports food production, but its excess causes water pollution. We lack an understanding of the boundary of N for water quality while considering complex relationships between N inputs and in-stream N concentrations. Our knowledge is limited to regional reduction targets to secure food production. Here, we aim to derive a spatially explicit boundary of N inputs to rivers for surface water quality using a bottom-up approach and to explore ways to meet the derived N boundary while considering the associated impacts on both surface water quality and food production in China. We modified a multiscale nutrient modeling system simulating around 6.5 Tg of N inputs to rivers that are allowed for whole of China in 2012. Maximum allowed N inputs to rivers are higher for intensive food production regions and lower for highly urbanized regions. When fertilizer and manure use is reduced, 45-76% of the streams could meet the N water quality threshold under different scenarios. A comparison of "water quality first" and "food production first" scenarios indicates that trade-offs between water quality and food production exist in 2-8% of the streams, which may put 7-28% of crop production at stake. Our insights could support region-specific policies for improving water quality.

How much nutrient reaches a stream: Insights from a hybrid model and implications for watershed nitrogen export and removal

Excess nitrogen (N) discharged into streams and rivers degrades freshwater quality and threatens ecosystems worldwide. Land use patterns may influence riverine N export, yet the effect of location on N export and removal is not fully understood. We proposed a hybrid model to analyze N export and removal within the watersheds. The proposed model is satisfied for the riverine N modelling. The KGE and R2 are 0.75 and 0.72 in the calibration period which are 0.76 and 0.61 in the validation period. Human-impacted land use may modify the N yield in the watershed, and the net N export from built-up to the in-stream system was highest in the urbanized sub-watersheds (0.81), followed by the agricultural sub-watersheds (0.88), and forested sub-watersheds (0.96). Agricultural activities make a large contribution to the N exports in the watersheds, and the mean N input from the agricultural land use to in-stream were 2069-4353 kg km-2 yr-1. Besides, the excess inputs of N by overapplication of fertilizer and manure during the agricultural activities may increase legacy N in soil and groundwater. Biological processes for the riverine N removal may be controlled by the available substrate in the freshwater system, and temperature sensitivity of denitrification is highest in the flood seasons, especially for the human-impacted sub-watersheds. The riverine biological processes may be limited by other competitions. Our model results provide evidence that quantity and location of specific land use may control biogeochemistry within watersheds. We demonstrate the need to understand nutrient export and removal within watersheds by improving the representation of spatial patterns in existing watershed models, and we consider this study to be a new effort for the spatially explicit modeling to support land-use based N management in watersheds.

Enhancing phosphorus source apportionment in watersheds through species-specific analysis

Phosphorus (P) is a pivotal element responsible for triggering watershed eutrophication, and accurate source apportionment is a prerequisite for achieving the targeted prevention and control of P pollution. Current research predominantly emphasizes the allocation of total phosphorus (TP) loads from watershed pollution sources, with limited integration of source apportionment considering P species and their specific implications for eutrophication. This article conducts a retrospective analysis of the current state of research on watershed P source apportionment models, providing a comprehensive evaluation of three source apportionment methods, inventory analysis, diffusion models, and receptor models. Furthermore, a quantitative analysis of the impact of P species on watersheds is carried out, followed by the relationship between P species and the P source apportionment being critically clarified within watersheds. The study reveals that the impact of P on watershed eutrophication is highly dependent on P species, rather than absolute concentration of TP. Current research overlooking P species composition of pollution sources may render the acquired results of source apportionment incapable of assessing the impact of P sources on eutrophication accurately. In order to enhance the accuracy of watershed P pollution source apportionment, the following prospectives are recommended: (1) quantifying the P species composition of typical pollution sources; (2) revealing the mechanisms governing the migration and transformation of P species in watersheds; (3) expanding the application of traditional models and introducing novel methods to achieve quantitative source apportionment specifically for P species. Conducting source apportionment of specific species within a watershed contributes to a deeper understanding of P migration and transformation, enhancing the precise of management of P pollution sources and facilitating the targeted recovery of P resources.

High resolution spatiotemporal modeling of long term anthropogenic nutrient discharge in China

High-resolution integration of large-scale and long-term anthropogenic nutrient discharge data is crucial for understanding the spatiotemporal evolution of pollution and identifying intervention points for pollution mitigation. Here, we establish the MEANS-ST1.0 dataset, which has a high spatiotemporal resolution and encompasses anthropogenic nutrient discharge data collected in China from 1980 to 2020. The dataset includes five components, namely, urban residential, rural residential, industrial, crop farming, and livestock farming, with a spatial resolution of 1 km and a temporal resolution of monthly. The data are available in three formats, namely, GeoTIFF, NetCDF and Excel, catering to GIS users, researchers and policymakers in various application scenarios, such as visualization and modelling. Additionally, rigorous quality control was performed on the dataset, and its reliability was confirmed through cross-scale validation and literature comparisons at the national and regional levels. These data offer valuable insights for further modelling the interactions between humans and the environment and the construction of a digital Earth.

Water pollution scenarios and response options for China

China has formulated several policies to alleviate the water pollution load, but few studies have quantitatively analyzed their impacts on future water pollution loads in China. Based on grey water footprint (GWF) assessment and scenario simulation, we analyze the water pollution (including COD, NH3-N, TN and TP) in China from 2021 to 2035 under different scenarios for three areas: consumption-side, production-side and terminal treatment. We find that under the current policy scenario, the GWF of COD, NH3-N, TN, and TP in China could be reduced by 15.0 % to 39.9 %; the most effective measures for GWF reduction are diet structure change (in the consumption-side area), and the wastewater treatment rate and livestock manure utilization improvement (in the terminal treatment area). However, the GWF will still increase in 8 provinces, indicating that the current implemented policy is not universally effective in reducing GWF across all provinces. Under the technical improvement scenario, the GWF of the four pollutants will decrease by 54.9 %-71.1 % via improvements in the current measures related to current policies and new measures in the production-side area and the terminal treatment area; thus, GWF reduction is possible in all 31 provinces. However, some policies face significant challenges in achieving full implementation, and certain policies are only applicable to a subset of provinces. Our detailed analysis of future water pollution scenarios and response options to reduce pollution loads can help to inform the protection of freshwater resources in China and quantitatively assess the effectiveness of policies in other fields.

High-frequency data significantly enhances the prediction ability of point and interval estimation

Accurate prediction of dissolved oxygen (DO) dynamics is crucial for understanding the influence of environmental factors on the stability of aquatic ecosystem. However, limited research has been conducted to determine the optimal frequency of water quality monitoring that ensures continuous assessment of water health while minimizing costs. To address these challenges, the present study developed a hybrid stochastic hydrological model (i.e., ARIMA-GARCH hybrid model) and machine learning (ML) models. The objective of this study is to identify the best-performing model and establish the optimal monitoring frequency. Results revealed that high-frequency DO monitoring data exhibit greater variability compared to low-frequency data. Moreover, the ARIMA-GARCH model demonstrates promising potential in predicting DO concentrations for low-frequency monitoring data, surpassing ML models in performance. Furthermore, increasing the monitoring frequency significantly improves the prediction accuracy of models, regardless of whether point (with lower R2 values of 0.64 and 0.51 for daily detection than these of every 15 min (0.96 and 0.99) at CHQ and LHT, respectively) or interval predictions (with RIW higher values of 2.00 and 1.55 for daily detection higher than these of 0.02 and 0.16 in every 15 min at CHQ and LHT, respectively) are considered. Additionally, a 4 hourly monitoring frequency was found to be optimal for water quality assessment using each model. These findings identify the superior performing of the ARIMA-GARCH model and highlight the crucial role of monitoring frequency in enhancing DO prediction and improving model performance.

Exploring wastewater nitrogen and phosphorus flows in urban and rural areas in China for the period 1970 to 2015

China has experienced rapid population growth and increasing human N and P discharge from point sources. This paper presents a new spatial and temporal model-based, province-scale inventory of N and P in wastewater using detailed information on the location and functioning of 4436 WWTPs covering China for the period 1970-2015. China's nutrient discharge to surface water increased 22-fold from 177 to 3908 Gg N yr-1 and 29-fold from 20 to 577 Gg P yr-1 in urban areas between 1970 and 2015. The ten strongly urbanized and industrialized provinces along the Eastern coast contributed 43 % of China's total N and P discharge to surface water in 2015. At present, the contribution of rural areas to total wastewater discharge (2082 Gg N yr-1 and 434 Gg P yr-1) is 35 % for N and 43 % for P. The model approach and sensitivity analysis of this study indicate that policies aiming at improving water quality need to consider these regional differences, i.e., improvement of the wastewater treatment technology level in Eastern regions and increasing both the sewage connection and wastewater treatment in Central and Western regions.

Evaluation of three prevalent global riverine nutrient transport models

Global riverine nitrogen (N) and phosphorus (P) transport models offer important insights into basin nutrient cycling. However, appropriate model selection for a given research objective remains ambiguous. This study conducted a meta-analysis to evaluate the performance and applicability of three prevalent global riverine nutrient transport models: Global NEWS, IMAGE-GNM, and WorldQual. According to performance criteria (satisfactory: R2 > 0.50 and NSE > 0.50), the Global NEWS model performs satisfactorily in simulating dissolved organic nitrogen (DON; n = 101, R2 = 0.58, NSE = 0.57) and dissolved organic phosphorus loads (DOP; n = 80, R2 = 0.59, NSE = 0.59). The model falls short in simulating dissolved inorganic nitrogen (DIN; n = 644, R2 = 0.56, NSE = - 0.80) and dissolved inorganic phosphorus loads (DIP; n = 450, R2 = 0.33, NSE = - 0.12). The IMAGE-GNM model shows satisfactory accuracies in simulating riverine total nitrogen (TN; n = 831, R2 = 0.56, NSE = 0.53) and total phosphorus (TP; n = 902, R2 = 0.59, NSE = 0.48) concentrations, particularly in European basins. The WorldQual model presented unsatisfactory performance in simulating riverine TN (n = 11, R2 = 0.76, NSE = 0.34) and TP (n = 13, R2 = 0.71, NSE = - 0.25) concentrations. Using a two-segment linear model, we recommend the Global NEWS model for basins larger than 2.2 × 104 km2 for DIN and 3.2 × 104 km2 for DIP. The IMAGE-GNM model is best suited for basins with long-term datasets and high latitudes (TN > 21 years and > 53.8 °N; TP > 22 years and > 54.5 °N). For model improvements, both the Global NEWS and WorldQual models could benefit from enhanced in-stream nutrient retention/release modules. The Global NEWS model could be further improved with a better chemical weathering module. For the IMAGE-GNM model, refining the soil erosion module is warranted to enhance model performance. Addressing legacy nutrient effects is crucial for all three models. This study provides valuable guidance for selecting and improving nutrient transport models based on specific research needs.

Estimating Yangtze River basin's riverine N2O emissions through hybrid modeling of land-river-atmosphere nitrogen flows

Riverine ecosystems are a significant source of nitrous oxide (N2O) worldwide, but how they respond to human and natural changes remains unknown. In this study, we developed a compound model chain that integrates mechanism-based modeling and machine learning to understand N2O transfer patterns within land, rivers, and the atmosphere. The findings reveal a decrease in N2O emissions in the Yangtze River basin from 4.7 Gg yr-1 in 2000 to 2.8 Gg yr-1 in 2019, with riverine emissions accounting for 0.28% of anthropogenic nitrogen discharges from land. This unexpected reduction is primarily attributed to improved water quality from human-driven nitrogen control, while natural factors contributed to a 0.23 Gg yr-1 increase. Notably, urban rivers exhibited a more rapid N2O efflux ( [Formula: see text] ), with upstream levels nearly 3.1 times higher than rural areas. We also observed nonlinear increases in [Formula: see text] with nitrogen discharge intensity, with urban areas showing a gradual and broader range of increase compared to rural areas, which exhibited a sharper but narrower increase. These nonlinearities imply that nitrogen control measures in urban areas lead to stable reductions in N2O emissions, while rural areas require innovative nitrogen source management solutions for greater benefits. Our assessment offers fresh insights into interpreting riverine N2O emissions and the potential for driving regionally differentiated emission reductions.

Gridded livestock density database and spatial trends for Kazakhstan

Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSKD) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSKD database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000-2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.

Vertical distribution analysis and total mass estimation of nitrogen and phosphorus in large shallow lakes

Analysing the vertical distribution of nutrient salts and estimating the total mass of lake nutrients is helpful for the management of lake nutrient status and the formulation of drainage standards in basins. However, studies on nitrogen (N) and phosphorus (P) in lakes have focused on obtaining measures of N and P concentrations, but no understanding exists on the vertical distribution of N and P in the entire water column. The present study proposes algorithms for estimating the total masses of N/P per unit water column (ALGO-TNmass/ALGO-TPmass) for shallow eutrophic lakes. Using Lake Taihu as an example, the total masses of nutrients in Lake Taihu in the historical period were obtained, and the algorithm performance was discussed. The results showed that the vertical distribution of nutrients decreased with increasing depth and exhibited a quadratic distribution. Surface nutrients and chlorophyll-a concentrations play important roles in the vertical distribution of nutrients. Based on conventional surface water quality indicators, algorithms for the vertical nutrient concentration in Lake Taihu were proposed. Both algorithms had good accuracy (ALGO-TNmass R2 > 0.75, RMSE <0.57; ALGO-TPmass R2 > 0.80, RMSE ≤0.50), the ALGO-TPmass had better applicability than the ALGO-TNmass, and had good accuracy in other shallow lakes. Therefore, deducing the TPmass using conventional water quality indicators in surface water, which not only simplifies the sampling process but also provides an opportunity for remote sensing technology to monitor the total masses of nutrients, is feasible. The long-term average total mass of N was 11,727 t, showing a gradual downward trend before 2010, after which it stabilised. The maximum and minimum intra-annual total N masses were observed in May and November, respectively. The long-term average total mass of P was 512 t, showing a gradual downward trend before 2010, and a slow upward trend thereafter. The maximum and minimum intra-annual total masses of P occurred in August and February or May, respectively. The correlation between the total mass of N and meteorological conditions was not obvious, whereas some influence on the total mass of P was evident, particularly water level and wind speed.

A raster-based estimation of watershed phosphorus load and its impacts on surrounding rivers based on process-based modelling

Quantifying phosphorus (P) load from watersheds at a fine scale is crucial for studying P sources in lake or river ecosystems; however, it is particularly challenging for mountain-lowland mixed watersheds. To address this challenge, we proposed a framework to estimate the P load at the grid scale and assessed its risk to surrounding rivers in a typical mountain-lowland mixed watershed (Huxi Region in Lake Taihu Basin, China). The framework coupled three models: the Phosphorus Dynamic model for lowland Polder systems (PDP), the Soil and Water Assessment Tool (SWAT), and the Export Coefficient Model (ECM). The coupled model performed satisfactory for both hydrological and water quality variables (Nash-Sutcliffe efficiency >0.5). Our modelling practice revealed that polder, non-polder, and mountainous areas had P load of 211.4, 437.2, and 149.9 t yr^-1, respectively. P load intensity in lowlands and mountains was 1.75 and 0.60 kg ha^-1 yr^-1, respectively. A higher P load intensity (>3 kg ha^-1 yr^-1) was mainly observed in the non-polder area. In lowland areas, irrigated cropland, aquaculture ponds and impervious surfaces contributed 36.7%, 24.8%, and 25.8% of the P load, respectively. In mountainous areas, irrigated croplands, aquaculture ponds, and impervious surfaces contributed 28.6%, 27.0%, and 16.4% of the P load, respectively. Rivers with relatively high P load risks were mainly observed around big cities during rice season, owing to a large contribution of P load from the non-point source pollution of urban and agricultural activities. This study demonstrated a raster-based estimation of watershed P load and their impacts on surrounding rivers using coupled process-based models. It would be useful to identify the hotspots and hot moments of P load at the grid scale.

Urbanization and agriculture intensification jointly enlarge the spatial inequality of river water quality

Rivers are severely polluted by multiple anthropogenic stressors. An unevenly distributed landscape pattern can aggravate the deterioration of water quality in rivers. Identifying the impacts of landscape patterns on the spatial characteristics of water quality is helpful for river management and water sustainability. Herein we quantified the nationwide water quality degradation in China's rivers and analyzed its responses to spatial patterns of anthropogenic landscapes. The results showed that the spatial patterns of river water quality degradation had a strong spatial inequality and worsened severely in eastern and northern China. The spatial aggregation of agricultural/urban landscape and the water quality degradation exhibits high consistency. Our findings suggested that river water quality would further deteriorate from high spatial aggregation of cities and agricultures, which reminded us that the dispersion of anthropogenic landscape patterns might effectively alleviate water quality pressures.

Impact of COVID-19 lockdown on water quality in China during 2020 and 2022: two case surges

The COVID-19 severely affected the world in 2020. Taking the two outbreaks in China in 2020 and 2022 as examples, the spatiotemporal changes in surface water quality levels and COD_Mn and NH₃-N concentrations were analyzed, and the relationships between the variations in the two pollutants and environmental and social factors were evaluated. The results showed that during the two lockdowns, due to the total water consumption (including industrial, agricultural, and domestic water) decreased, the proportion of good water quality increased by 6.22% and 4.58%, and the proportion of polluted water decreased by 6.00% and 3.98%, the quality of water environment has been improved significantly. However, the proportion of excellent water quality decreased by 6.19% after entering the unlocking period. Before the second lockdown period, the average COD_Mn concentration exhibited a "falling, rising, and falling" trend, while the average NH₃-N concentration changed in the opposite direction. The correlation analysis revealed that the increasing trend of pollutant concentrations was positively correlated with longitude and latitude, and weakly correlated with DEM and precipitation. A slight decrease trend in NH₃-N concentration was negatively correlated with the population density variation and positively correlated with the temperature variation. The relationship between the change in the number of confirmed cases in provincial regions and the change in pollutant concentrations was uncertain, with positive and negative correlations. This study demonstrates the impact of lockdowns on water quality and the possibility of improving water quality through artificial regulation, which can provide a reference basis for water environmental management.

Study of the characteristics of pollutants in rural domestic sewage and the optimal sewage treatment process: a Chengdu Plain case study

Domestic sewage is an important source of surface water pollution in the rural areas of developing countries, especially in the rural areas of China. In recent years, with the strategy of rural revitalization, China has paid increasing attention to the treatment of rural domestic sewage. Therefore, 16 villages in the Chengdu Plain were selected for the study, and seven indicators were analyzed and evaluated, including pH, five-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), suspended solids (SS) and total nitrogen (TN), in the water samples at the inlet and outlet of the wastewater treatment plant. The concentration of each pollutant in the rural scattered domestic sewage of the Chengdu Plain in Southwest China was obtained, and the concentration of each pollutant in domestic sewage was higher than that in summer. In addition, the preferred process for removing each pollutant was obtained by studying the effects of the treatment process, season and hydraulic retention time on the removal efficiency of each pollutant. The research results provide valuable references for the planning and process selection of rural domestic sewage treatment.

Phosphorus - The main limiting factor in riverine ecosystems in China

River receive substantial nutrient inputs, and serve as the main channel for nitrogen and phosphorus to enter the lake, their nutrient control is of great significance to the alleviation of lake eutrophication. While nutrient limitation affects the primary productivity of water ecosystems and the biodiversity of aquatic communities, identifying the limiting factors in riverine ecosystems across China remains elusive. Here, we explore which nutrients have a stronger effect on nutritional balance and aquatic ecosystems in China's rivers based on the total nitrogen (TN) and total phosphorus (TP) observations from 1412 sampling sites in 2018. This study supports the following three main conclusions. Though the percentages of the sites with TN or TP exceeding the limits varied as per different mesotrophic targets, and TP (53.7 %) contributed more to nutrient enrichment than TN (46.3 %). In addition, the spatial distribution characteristics of river nutrients were high in the north (arid zone) and low in the south (humid zone) in China. According to four classification criteria of N:P ratio, 70.8 % of the sampling sites were attributed to phosphorus limiting, much higher than the sites with nitrogen limiting (4.1 %). TN and TP have a synergistic effect on river nutrients, while TP has a stronger regulation framework. Our results reveal that the nutrients in China's rivers are mainly phosphorus limiting, which implies that phosphorus-oriented best management practices are more likely to maintain the nutrient balance of rivers towards healthy aquatic ecosystems. Synopsis: Phosphorus is the key factor that affecting the stability and nutrient balance of riverine ecosystem.

Modeling nutrient flows from land to rivers and seas - A review and synthesis

Water quality modeling facilitates management of nutrient flows from land to rivers and seas, in addition to environmental pollution management in watersheds. In the present paper, we review advances made in the development of seven water quality models and highlight their respective strengths and weaknesses. Afterward, we propose their future development directions, with distinct characteristics for different scenarios. We also discuss the practical problems that such models address in the same region, China, and summarize their different characteristics based on their performance. We focus on the temporal and geographical scales of the models, sources of pollution considered, and the main problems that can be addressed. Summary of such characteristics could facilitate the selection of appropriate models for resolving practical challenges on nutrient pollution in the corresponding scenarios globally by stakeholders. We also make recommendations for model enhancement to expand their capabilities.

On the coordination in diversity between water environmental capacity and regional development in the Three Gorges Reservoir area

Water environment capacity has drew the attention of policymakers and stakeholders to sustainable development, and its dynamic changes are ultimately impacted by population, capital, and industrial clusters under regional development. Previous research, however, has not been able to completely comprehend it. In this paper, the authors use the Coupling Coordination Degree model and the Geodetector model to study the temporal and spatial evolution of water environment capacity and its driving mechanism based on regional development represented by regional function including urbanization function, ecological function, and agricultural function using the Three Gorges Reservoir area on county scale as a case study from 2000 to 2015. The results showed that (1) compared with 2000, 2005, and 2010, the water environment capacity of the whole reservoir area in 2015 was significantly improved. (2) The urban functions of each district and county are increasing in different years, and the dynamic changes of ecological and agricultural functions are obviously different. (3) The water environment capacity of districts and counties in the head area. There are significant disparities in the relationship between water environment capacity and regional function in various regions. Differences in water environment capacity are largely influenced by ecological function and the interaction driver of the proportion of agricultural function and urban function, which are typically the biggest of all the components. This suggests that regional development is a top priority in order to improve the operability of the water environmental capacity through more regulation, rules, and planning.

Comparing critical source areas for the sediment and nutrients of calibrated and uncalibrated models in a plateau watershed in southwest China

Controlling non-point source pollution is often difficult and costly. Therefore, focusing on areas that contribute the most, so-called critical source areas (CSAs), can have economic and ecological benefits. CSAs are often determined using a modelling approach, yet it has proved difficult to calibrate the models in regions with limited data availability. Since identifying CSAs is based on the relative contributions of sub-basins to the total load, it has been suggested that uncalibrated models could be used to identify CSAs to overcome data scarcity issues. Here, we use the SWAT model to study the extent to which an uncalibrated model can be applied to determine CSAs. We classify and rank sub-basins to identify CSAs for sediment, total nitrogen (TN), and total phosphorus (TP) in the Fengyu River Watershed (China) with and without model calibration. The results show high similarity (81%-93%) between the identified sediment and TP CSA number and locations before and after calibration both on the yearly and seasonal scale. For TN alone, the results show moderate similarity on the yearly scale (73%). This may be because, in our study area, TN is determined more by groundwater flow after calibration than by surface water flow. We conclude that CSA identification with the uncalibrated model for TP is always good because its CSA number and locations changed least, and for sediment, it is generally satisfactory. The use of the uncalibrated model for TN is acceptable, as its CSA locations did not change after calibration; however, the TN CSA number changed by over 60% compared to the figures before calibration on both yearly and seasonal scales. Therefore, we advise using an uncalibrated model to identify CSAs for TN only if water yield composition changes are expected to be limited. This study shows that CSAs can be identified based on relative loading estimates with uncalibrated models in data-deficient regions.

Environmental DNA metabarcoding reveals the impacts of anthropogenic pollution on multitrophic aquatic communities across an urban river of western China

Anthropogenic activities are intensively affecting the structure and function of biological communities in river ecosystems. The effects of anthropogenic pollution on single-trophic community have been widely explored, but their effects on the structures and co-occurrence patterns of multitrophic communities remain largely unknown. In this study, we collected 13 water samples from the Neijiang River in Chengdu City of China, and identified totally 2352 bacterial, 207 algal, 204 macroinvertebrate, and 33 fish species based on the eDNA metabarcoding to systematically investigate the responses of multitrophic communities to environmental stressors. We observed significant variations in bacterial, algal, and macroinvertebrate community structures (except fish) with the pollution levels in the river. Network analyses indicated a more intensive interspecific co-occurrence pattern at high pollution level. Although taxonomic diversity of the multitrophic communities varied insignificantly, phylogenetic diversities of fish and algae showed significantly positive and negative associations with the pollution levels, respectively. We demonstrated the primary role of environmental filtering in driving the structures of bacteria, algae, and macroinvertebrates, while the fish was more controlled by dispersal limitation. Nitrogen was identified as the most important factor impacting the multitrophic community, where bacterial composition was mostly associated with NO₃^--N, algal spatial differentiation with TN, and macroinvertebrate and fish with NH₄⁺-N. Further partial least-squares path model confirmed more important effect of environmental variables on the relative abundance of bacteria and algae, while macroinvertebrate and fish communities were directly driven by the algae-mediated pathway in the food web. Our study highlighted the necessity of integrated consideration of multitrophic biodiversity for riverine pollution management, and emphasized the importance of controlling nitrogen inputs targeting a healthy ecosystem.

Nitrogen in the Yangtze River Basin: Pollution Reduction through Coupling Crop and Livestock Production

Livestock production poses a threat to water quality worldwide. A better understanding of the contribution of individual livestock species to nitrogen (N) pollution in rivers is essential to improve water quality. This paper aims to quantify inputs of dissolved inorganic nitrogen (DIN) to the Yangtze River from different livestock species at multiple scales and explore ways for reducing these inputs through coupling crop and livestock production. We extended the previously developed model MARINA (Model to Assess River Input of Nutrient to seAs) with the NUFER (Nutrient flows in Food chains, Environment, and Resource use) approach for livestock. Results show that DIN inputs to the Yangtze River vary across basins, sub-basins, and 0.5° grids, as well as across livestock species. In 2012, livestock production resulted in 2000 Gg of DIN inputs to the Yangtze River. Pig production was responsible for 55-85% of manure-related DIN inputs. Rivers in the downstream sub-basin received higher manure-related DIN inputs than rivers in the other sub-basins. Around 20% of the Yangtze basin is considered as a manure-related hotspot of river pollution. Recycling manure on cropland can avoid direct discharges of manure from pig production and thus reduce river pollution. The potential for recycling manure is larger in cereal production than in other crop species. Our results can help to identify effective solutions for coupling crop and livestock production in the Yangtze basin.

A high-resolution nutrient emission inventory for hotspot identification in the Yangtze River Basin

A high-resolution nutrient emission inventory can provide reliable and accurate identification of priority control areas, which is crucial for efficient decisions on water quality restoration. However, the inventories widely used in large-scale modeling are usually based on provincial inputs, which induce the challenges of lacking localized parameters and missing localized characteristic when provincial scale inputs are converted to finer scales with the down-scale methods. Based on elaborate investigations and statistical data at the county scale with multi-scale data conversion, the China Emission Inventory of Nutrients (CEIN) was developed with a spatial resolution of a 0.1° grid and sub-basin scales. The Yangtze River Basin was used as a case study to illustrate the potential applications of CEIN. The emissions of total nitrogen (TN) and total phosphorus (TP) of Yangtze River Basin is 0.43 Mt and 0.04 Mt for point sources, 11.09 Mt and 4.64 Mt for diffuse sources in 2017. The hotspot analysis for 2606 sub-basins indicated that cropland is the key source of nutrient emissions, accounting for 58.88% and 79.15% of TN and TP, respectively. Industrial sewage and freshwater aquaculture accounted for 27.39% (TN) and 21.98% (TP) of the point sources, which is substantial due to their direct discharge into surface waters. The current results also reveal that, in contrast to CEIN, the previously used common emission factors based on GDP per capita produced considerable overestimations of 2.37 and 2.65 times the actual TN and TP emissions, respectively. Additional advantages of the CEIN have been demonstrated in identifying priority control areas more accurately with reduced bias and quantifying the effects of policies at much smaller scales. For example, the CEIN helps to distinguish hotspots, which was neglected when identifying sources at the level-III sub-basin scale, and indicates that the management of fractional areas (TN: 16.97%; TP: 13.44%) provides the highest nutrient emissions control (TN: 44.34%; TP: 48.65%) for the entire basin. The evaluation of China's toilet revolution policy demonstrates that achieving equitable access to safe sanitation has resulted in a reduction of 7240 t of TN and 833 t of TP, which is extremely critical for rural water quality and health.

Improved estimation of nitrogen dynamics in paddy surface water in China

Paddy surface water is the direct source of artificial drainage and surface runoff leading to N loss from rice paddy fields. Quantifying the N dynamics in paddy surface water on a large scale is challenging because of model deficiencies and the limitations of field measurements. This study analyzed the N dynamics and the influencing factors in paddy surface water in the three main Chinese rice-growing regions: Northeast Plain, Yangtze River Basin, and Southeast Coast. An improved first-order kinetic model was proposed to evaluate the total nitrogen (TN) dynamics at a countrywide scale by improving the calculation method of the initial TN concentration (C₀) and providing the optimum value of attenuation coefficient (k). The results show that: (1) the average reduction rate of TN concentration on the 7th day after fertilization increased with the growth period (85%, 90%, and 95% during the basal, tillering, and panicle fertilization periods, respectively); (2) the attenuation coefficient k for the growth periods was ranked as follows: panicle fertilization period > tillering fertilization period > basal fertilization period. The Yangtze River Basin had the highest average k value (0.31-0.34), followed by the Southeast Coast (0.24-0.41) and Northeast Plain (0.22-0.30); and (3) the improved first-order kinetic model performed well in the N dynamics estimation (R² > 0.6). High TN concentration with high fertilizer application amounts and precipitation caused the Yangtze River Basin to have a high N runoff loss risk. The proposed universal model realizes the simulation of N dynamics from a single site to multi-sites while greatly saving multi-site monitoring costs. This study provides a basis for effectively optimizing N management and preventing N loss in rice paddies.

Spatial characteristics of nutrient budget on town scale in the Three Gorges Reservoir area, China

Accurately quantifying nutrient budget is an essential step toward sustainable nutrient management in large watersheds increasingly disturbed by human activity. A town-scale nutrient budget framework based on the Soil and Water Assessment Tool was developed for 2010-2012 in the Three Gorges Reservoir area in China (TGRA). Moran's I spatial correlation test and Geodetector spatial heterogeneity test were employed to systematically analyze the spatial characteristics of the resulting nutrient budget. The Moran's I value of total nitrogen (TN) and total phosphorus (TP) gradually increased from input to output in the range of 0.091-0.232 and 0.102-0.484, respectively. Towns with higher TN and TP inputs were largely concentrated in the main urban area of Chongqing because of its high population density. By contrast, towns with higher TN and TP outputs were concentrated in the head of the TGRA. The Moran's I values of the TN and TP retention coefficients (R) were 0.433 and 0.524, respectively, demonstrating clear spatial consistency. Towns with a "High-high" spatial consistency pattern and positive R value were concentrated in the tail and hinterland, while those with a "Low-low" spatial consistency pattern and negative coefficient value were located mainly in the head of the TGRA. This phenomenon was mostly caused by differences in regional elevation, the normalized difference vegetation index, and soil erosion factor. The interaction effect between any two of these three factors on nutrient retention (Geodetector q-value) was greater than 60%. Therefore, future nutrient management should be based on a full understanding of regional biophysical conditions, especially in large areas. These findings provide a new perspective on fine nutrient management.

A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory

Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins.

Multi-pollutant assessment of river pollution from livestock production worldwide

Livestock production is often a source of multiple pollutants in rivers. However, current assessments of water pollution seldomly take a multi-pollutant perspective, while this is essential for improving water quality. This study quantifies inputs of multiple pollutants to rivers from livestock production worldwide, by animal types and spatially explicit. We focus on nitrogen (N), phosphorus (P), and Cryptosporidium (pathogen). We developed the MARINA-Global-L (Model to Assess River Inputs of pollutaNts to seAs for Livetsock) model for 10,226 sub-basins and eleven livestock species. Global inputs to land from livestock are around 94 Tg N, 19 Tg P, and 2.9 × 1021 oocysts from Cryptosporidium in 2010. Over 57% of these amounts are from grazed animals. Asia, South America, and Africa account for over 68% of these amounts on land. The inputs to rivers are around 22 Tg Total Dissolved Nitrogen (TDN), 1.8 Tg Total Dissolved P (TDP), and 1.3 × 1021 oocysts in 2010. Cattle, pigs, and chickens are responsible for 74-88% of these pollutants in rivers. One-fourth of the global sub-basins can be considered pollution hotspots and contribute 71-95% to the TDN, TDP, and oocysts in rivers. Our study could contribute to effective manure management for individual livestock species in sub-basins to reduce multiple pollutants in rivers.

Mitigating phosphorus pollution from detergents in the surface waters of China

Phosphorus (P) from detergents contributes to water pollution and eutrophication. Understanding the impacts of detergent use on P inputs to surface waters and their main drivers is vital for supporting Sustainable Development Goals on clean water. This study aims to quantify past and future trends in P inputs to surface waters from detergent use in China. We modify the Model to Assess River Input of Nutrient to seAs (MARINA) model to assess the effects of past policies and explore options for the future on mitigating detergents P losses in China. The total consumption of detergents tripled from 2000 to 2018. However, P inputs to surface waters from detergent use decreased by 35% during these years. Although P losses vary across regions, most losses occurred in rural areas. Clearly, the P-free detergent policy which was initiated in the year 2000 has been effective. Without this policy, the detergent P losses would likely have increased fourfold during 2000-2018. In the future, detergent P inputs to surface waters in China may be further reduced to very low levels (95% reduction relative to 2018) by a combination of completely P-free detergents, an increasing urbanized population connected to sewage systems, and improving P removal in sewage treatment systems. Our results enhance the understanding of P pollution in surface waters from detergents and, illustrate the effectiveness of measures to control detergent P losses.

Impacts of anthropogenic disturbances on microbial community of coastal waters in Shenzhen, South China

During the urbanization, human activities have brought great changes to marine biodiversity and microbial communities of coastal water. Shenzhen is a coastal city that has developed rapidly over the past four decades, but the microbial communities and metabolic potential in offshore water are still not well characterized. Here, 16S rRNA gene V4-V5 sequencing was conducted to determine the microbial components from coastal waters in twenty selected areas of Shenzhen. The results showed a significant difference on the microbial composition between the western and eastern waters. Samples from western coast had more abundant Burkholderiaceae, Sporichthyaceae, Aeromonadaceae, and Methylophilaceae compared to eastern coast, and at the genus level, Candidatus Aquiluna, Aeromonas, Arcobacter, Ottowia and Acidibacter were significantly higher in western waters. There was also a notable difference within the western sample group, suggesting the taxa-compositional heterogeneity. Moreover, analysis of environmental factors and water quality revealed that salinity, pH and dissolved oxygen were relatively decreased in western samples, while total nitrogen, total phosphorus, chemical oxygen demand, and harmful marine vibrio were significantly increased compared to eastern waters. The results suggest the coastal waters pollution is more serious in western Shenzhen than eastern Shenzhen and the microbial communities are altered, which can be associated with anthropogenic disturbances.

Dietary shifts and nitrogen losses to water in urban China: the case of Shanghai

China's extraordinary economic development has provided the country's growing population with easier access to animal food products, especially in densely populated urban agglomerations. Increased consumption of such products translates in a higher amount of nitrogen (N) excreted in the form of human manure. Depending on the connection to a sewerage system, or lack thereof, and the N removal efficiency from wastewater treatment plants (WWTPs), a share of the excreted N gets ultimately discharged to water bodies, causing eutrophication. In heavily urbanised areas, N losses from household food consumption account for a dominant portion of total N losses to water. In this study, we firstly estimate dietary N intake, excretion and consequent N losses to water from the residents of Shanghai in 2012. We then explore different scenarios to 2030, in terms of further dietary modifications and different levels of development of the city's sewerage system and WWTPs. In 2012, Shanghai's residents excreted a total of 148.4 Gg N, 54% of which ultimately reached the city's water bodies in diffused N form. The urban population contributed for the majority of the N losses (93%) and showed a higher per capita N load, due to limited N removal efficiency from WWTPs and the significant portion (27%) of residents not connected to the sewerage and directly discharging their excreta to water. The vast majority of the scarce rural population were not connected to the sewerage system and showed a much lower per capita N load, mainly due to the common practice of recycling excreta for agricultural practices. We identify two main approaches to reduce dietary N losses: (1) improving N removal efficiency and sewerage connection rates towards the levels of OECD countries; (2) managing the increase of dietary N intake by promoting healthy and sustainable consumption, as recommended by recent dietary guidelines. According to our scenario analysis, technological improvements can potentially achieve a more significant reduction of total N losses and are easier to implement. Managing demand of animal food and consequent N intake would only stabilise N losses around 2012's levels. On the other hand, a dramatic increase of animal food consumption could have detrimental effects on the city's water bodies, more so if the expected population growth will not be met by an adequate development of a more capillary sewerage system. This study provides valuable insights on dietary N losses in one of China's most developed mega cities, strongly advocating for the necessity of improving N removal efficiency from WWTPs and reducing the percentage of urban residents directly discharging their waste to water bodies.

Characterizing the river water quality in China: Recent progress and on-going challenges

Food production systems, urbanization, and other anthropogenic activities dramatically alter natural hydrological and nutrient cycles, and are primarily responsible for water quality impairments in China's rivers. This study compiled a 16-year (2003-2018) dataset of river water quality (161,337 records from 2424 sites), watershed/landscape features, and meteorological conditions to investigate the spatial water quality patterns and underlying drivers of river impairment (defined as water quality worse than Class V according to China's Environmental Quality Standards for Surface Waters, GB3838-2002) at a national scale. Our analysis provided evidence of a distinct water quality improvement with a gradual decrease in the frequency of prevalence of anoxic conditions, an alleviation of the severity of heavy metal pollution, whereas the cultural eutrophication has only been moderately mitigated between 2003 and 2018. We also identified significant spatial variation with relatively poorer water quality in eastern China, where 17.2% of the sampling sites registered poor water quality conditions, compared with only 4.6% in western China. Total phosphorus (TP) and ammonia-nitrogen (NH₃-N) are collectively responsible for >85% of the identified incidences of impaired conditions. Bayesian modelling was used to delineate the most significant covariates of TP/NH₃-N riverine levels in six large river basins (Liao, Hai, Yellow, Yangtze, Huai, and Pearl). Water quality impairments are predominantly shaped by anthropogenic drivers (82.5% for TP, 79.5% for NH₃-N), whereas natural factors appear to play a secondary role (20.5% for TP, 17.5% for NH₃-N). Two indicator variables of urbanization (urban areal extent and nighttime light intensity) and farmland areal extent were the strongest predictors of riverine TP/NH₃-N levels and collectively accounted for most of the ambient nutrient variability. We concluded that there is still a long way to go in order to eradicate eutrophication and realize acceptable ecological conditions. The design of the remedial measures must be tailored to the site-specific landscape characteristics, meteorological conditions, and should also consider the increasing importance of non-point source pollution and internal nutrient loading.

Mitigation of Multiple Environmental Footprints for China's Pig Production Using Different Land Use Strategies

Pig production contributes considerably to land use and greenhouse gas (GHG) and reactive nitrogen (Nr) emissions. Land use strategies were widely proposed, but the spillover effects on biological flow are rarely explored. Here, we simultaneously assessed the carbon (C), nitrogen (N), and cropland footprints of China's pig production at the provincial scale in 2017. The environmental impacts of land use strategies were further evaluated. Results show that one kg live-weight pig production generated an average of 1.9 kg CO₂-equiv and 59 g Nr emissions, occupying 3.5 m² cropland, with large regional variations. A large reduction in GHG (58-64%) and Nr (12-14%) losses and occupied cropland (10-11%) could be achieved simultaneously if combined strategies of intensive crop production, improved feed-protein utilization efficiency, and feeding co-products were implemented. However, adopting a single strategy may have environmental side-effects. Reallocating cropland that pigs used for feed to plant food alternatives would enhance human-edible energy (3-20 times) and protein delivery (1-5 times) and reduce C and N footprints, except for rice and vegetables. Reallocating cropland to beef and milk production would decrease energy and protein supply. Therefore, a proper combination of land use strategies is essential to alleviate land use changes and nutrient emissions without sacrificing food supply.

Spatial Planning Needed to Drastically Reduce Nitrogen and Phosphorus Surpluses in China's Agriculture

China's fertilization practices contribute greatly to the global biogeochemical nitrogen (N) and phosphorus (P) flows, which have exceeded the safe-operating space. Here, we quantified the potentials of improved nutrient management in the food chain and spatial planning of livestock farms on nutrient use efficiency and losses in China, using a nutrient flow model and detailed information on >2300 counties. Annual fertilizer use could be reduced by 26 Tg N and 6.4 Tg P following improved nutrient management. This reduction N and P fertilizer use would contribute 30% and 80% of the required global reduction, needed to keep the biogeochemical N and P flows within the planetary boundary. However, there are various barriers to make this happen. A major barrier is the transportation cost due to the uneven distributions of crop land, livestock, and people within the country. The amounts of N and P in wastes and residues are larger than the N and P demand of the crops grown in 30% and 50% of the counties, respectively. We argue that a drastic increase in the recycling and utilization of N and P from wastes and residues can only happen following relocation of livestock farms to areas with sufficient cropland.

Modeling the Contribution of Crops to Nitrogen Pollution in the Yangtze River

Agriculture contributes considerably to nitrogen (N) inputs to the world's rivers. In this study, we aim to improve our understanding of the contribution of different crops to N inputs to rivers. To this end, we developed a new model system by linking the MARINA 2.0 (Model to Assess River Input of Nutrient to seAs) and WOFOST (WOrld FOod STudy) models. We applied this linked model system to the Yangtze as an illustrative example. The N inputs to crops in the Yangtze River basin showed large spatial variability. Our results indicate that approximately 6,000 Gg of N entered all rivers of the Yangtze basin from crop production as dissolved inorganic N (DIN) in 2012. Half of this amount is from the production of single rice, wheat, and vegetables, where synthetic fertilizers were largely applied. In general, animal manure contributes 12% to total DIN inputs to rivers. Three-quarters of manure-related DIN in rivers are from vegetable, fruit, and potato production. The contributions of crops to river pollution differ among sub-basins. For example, potato is an important source of DIN in rivers of some upstream sub-basins. Our results may help to prioritize the dominant crop sources for management to mitigate N pollution in the future.

Decrease of both river flow and quality aggravates water crisis in North China: a typical example of the upper Yongding River watershed

Due to unevenly distributed water resources, semi-arid regions are particularly prone to severe water shortage and quality degradation. In this study, based on long-term hydrological database (1935-2015), and the latest available water quality data sets (2011-2016), we analyzed the water crisis and its driving forces in the upper Yongding River watershed, a typical water shortage area in North China. The results showed that human induced excessive water consumption is responsible for the significantly decreased river flow over the past eight decades. Although the capacity of the watershed wastewater treatment has improved, current water quality does not meet the requirements of the national water management goals, because of the excessive nitrogen and COD_Cr (chemical oxygen demand), which mainly come from the wastewater and feedlots discharge. Due to the decreased river flow, current Yongding River is unable to dilute and assimilate pollutions. The analysis of river pollutant load illustrated that more than 60 % of the nitrogen in the river water system is diverted for reservoir storage, and more than 50 % of the COD_Cr and TP are diverted for irrigation, thereby, increasing the risk of reservoirs eutrophication and threatening food safety. Besides, the high Cl^- (388.2 ± 322.5 mg/L) and SO₄^2- (470.6 ± 357.7 mg/L) imply that the upper river water are not suitable for drinking and irrigation purposes, and a potential risk of salinization if the river flow continues to decrease. We conclude that water resources over extraction and quality degradation are the main driving factors of the Yongding River water crisis.

Hydrotransport-Oriented Zn, Cu, and Pb Behavior Assessment and Source Identification in the River Network of a Historically Mined Area in the Hokuroku Basin, Northeast Japan

Aquatic ecosystems continuously receive potentially hazardous heavy metals from natural and anthropogenic sources. Focusing on the origin of heavy metals, this study aims to estimate the load contribution of tributaries from individual watershed and human drainage and to dissect the source of heavy metals, as commonly required for environmental impact assessment. Using integrated water dynamics, Geographic Information System (GIS), and chemical analysis, we identified and evaluated the heavy metal sources of the Kosaka river system in Hokuroku basin, which is a historically mined area in Northeast Japan, both in the high-water and low-water seasons. The migration and diffusion behaviors of heavy metals along with hydro-transport were analyzed, and the effects of mining activities on regional water quality both in the high-water and low-water seasons were clarified. The results indicate that Zn pollution was obvious in the Kosaka River network, especially in the downstream area. The spatial heterogeneity of heavy metal outflows from tributary watersheds was obvious, and the variations had strong correlations with mine site locations. The heavy metal flows in the mainstream increased sharply in the vicinity downstream of the Kosaka refinery drainage outlets. Compared to the low-water season, the influences of human drainage were slighter in high-water season, with lower contribution rates due to the dilution effect of the greater water discharge. Downscale sampling is effective to identify pollutant sources in regional basins.