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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.

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.

Nitrogen (N) management is essential for food security. The North China Plain is an important food producing region, but also a hotspot of N losses to the environment. This results in water, soil, and air pollution. In this study, we aim to quantify the relative contribution of different crops and animals to N losses, by taking the Quzhou County as a typical example in the North China Plain. We developed and applied a new version of the NUtrient flows in Food Chains, Environment, and Resource use (NUFER) model. Our model is based on updated information for N losses in Quzhou. Our results show that N losses to the environment from crop and animal production in Quzhou were approximately 9 kton in 2017. These high N losses can be explained by the low N use efficiency in food production because of poor N management. For crop production, wheat, maize, and vegetables contributed 80% to N losses. Ammonia emissions and N leaching have dominant shares in these N losses. Pigs and laying hens were responsible for 74% of N losses from animal production. Ammonia emissions to air and direct discharges of manure to water were the main contributors to these N losses. Effective reduction of N losses requires improving the nutrient management in crop (wheat, maize, vegetables) and animal (pigs, laying hens) production. Our work could support the Agricultural Green Development in the North China Plain.

Meeting the United Nations’ (UN’s) 17 Sustainable Development Goals (SDGs) has become a worldwide mission. How these SDGs interrelate, however, is not well known. We assess the interactions between SDGs for the case of water pollution by nutrients in China. The results show 319 interactions between SDGs for clean water (SDGs 6 and 14) and other SDGs, of which 286 are positive (synergies) and 33 are negative (tradeoffs) interactions. We analyze six scenarios in China accounting for the cobenefits of water pollution control using a large-scale water quality model. We consider scenarios that benefit from synergies and avoid tradeoffs. Our results show that effective pollution control requires accounting for the interactions between SDGs. For instance, combining improved nutrient management, efficient food consumption, and climate mitigation is effective for simultaneously meeting SDGs 6 and 14 as well as other SDGs for food, cities and climate. Our study serves as an example of assessing SDG interactions in environmental policies in China as well as in other regions of the world.

Crop and livestock production are essential to maintain food security. In China, crop and livestock production were integrated in the past. Today, small backyard systems are still integrated but the larger livestock farms are landless and largely geographically separated from crop production systems. As a result, there is less recycling of animal manures and there are lower nutrient use efficiencies in the Chinese food production systems. This, in turn, results in considerable losses of nutrients, causing water pollution and harmful algal blooms in Chinese lakes, rivers and seas. To turn the tide, there is a need for agricultural "green" development for food production through reintegrating crop and livestock production. An additional wish is to turn the Chinese water systems "blue" to secure clean water for current and future generations. In this paper, current knowledge is summarized to identify promising interventions for reintegrating crop and livestock production toward clean water. Technical, social, economic, policy and environmental interventions are addressed and examples are given. The paper highlights recommended next steps to achieve "green" agriculture and "blue" water in China.

Atmospheric nitrogen (N) deposition is a vital component of the global N cycle. Excessive N deposition on the Earth’s surface has adverse impacts on ecosystems and humans. Quantification of atmospheric N deposition is indispensable for assessing and addressing N deposition-induced environmental issues. In the present review, we firstly summarized the current methods applied to quantify N deposition (wet, dry, and total N deposition), their advantages and major limitations. Secondly, we illustrated the long-term N deposition monitoring networks worldwide and the results attained via such long-term monitoring. Results show that China faces heavier N deposition than the United States, European countries, and other countries in East Asia. Next, we proposed a framework for estimating the atmospheric wet and dry N deposition using a combined method of surface monitoring, modeling, and satellite remote sensing. Finally, we put forth the critical research challenges and future directions of the atmospheric N deposition. Capsule: A review of quantification methods and the global data on nitrogen deposition and a systematic framework was proposed for quantifying nitrogen deposition.

The Black Sea receives increasing amounts of microplastics from rivers. In this study, we explore options to reduce future river export of microplastics to the Black Sea. We develop five scenarios with different reduction options and implement them to a Model to Assess River Inputs of pollutaNts to seA (MARINA-Global) for 107 sub-basins. Today, European rivers draining into the Black Sea export over half of the total microplastics. In 2050, Asian rivers draining into the sea will be responsible for 34–46% of microplastic pollution. Implemented advanced treatment will reduce point-source pollution. Reduced consumption or more collection of plastics will reduce 40% of microplastics in the sea by 2050. In the optimistic future, sea pollution is 84% lower than today when the abovementioned reduction options are combined. Reduction options affect the share of pollution sources. Our insights could support environmental policies for a zero pollution future of the Black Sea.

Food production in China has been changing fast as a result of socio-economic development. This resulted in an increased use of nitrogen (N) in food production, and also to increased reactive nitrogen (Nr) losses to the environment, causing nitrogen pollution. Our study is the first to quantify future Nr losses from China's food system for the Shared Socio-economic Pathways (SSPs). We show that Nr losses differ largely among SSPs. We first qualitatively described the five SSP storylines for China with a focus on food production and consumption. Next, we interpreted these SSP scenarios quantitatively for 2030 and 2050, using the NUFER (NUtrient Flows in Food chains, Environment and Resources use) model to project the Nr losses from China's food system. The results indicate that Nr losses from future food system in China are relatively low for SSP1 and SSP2, and relatively high for SSP3 and SSP4. In SSP5 Nr losses from China's food system are projected to be slightly lower than the level of today.

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.

Fast socio‐economic development in agriculture and urbanization resulted in increasing nutrient export by rivers, causing coastal eutrophication in China. In addition, climate change may affect hydrology, and as a result, nutrient flows from land to sea. This study aims at a better understanding of how future socio‐economic and climatic changes may affect coastal eutrophication in China. We modeled river export of total dissolved nitrogen (TDN) and phosphorus (TDP) in 2050 for six scenarios combining socio‐economic pathways (SSPs) and Representative Concentration Pathways (RCPs). We used the newly developed MARINA 2.0 (Model to Assess River Inputs of Nutrients to seAs) model. We found that global change can make coastal eutrophication control in China more difficult. In 2050 coastal waters may be considerably more polluted or considerably cleaner than today depending on the SSP‐RCP scenarios. By 2050, river export of TDN and TDP is 52% and 56% higher than in 2012, respectively, in SSP3‐RCP8.5 (assuming large challenges for sustainable socio‐economic development, and severe climate change). In contrast, river export of nutrients could be 56% (TDN) and 85% (TDP) lower in 2050 than in 2012 in SSP1‐RCP2.6 (assuming sustainable socio‐economic development, and low climate change). Climate change alone may increase river export of nutrients considerably through hydrology: We calculate 24% higher river export of TDN and 16% higher TDP for the SSP2 scenario assuming severe climate change compared to the same scenario with low climate change (SSP2‐RCP8.5 vs. SSP2‐RCP2.6). Policies and relevant technologies combining improved nutrient management and climate mitigation may help to improve water quality in rivers and coastal waters of China.

China has been developing fast over the last decades. Intensive human activities in food production and urbanization have led to increasing nitrogen (N) and phosphorus (P) losses to water systems, causing freshwater and coastal water pollution. Reducing water pollution is important in China, especially given its commitments to the Sustainable Development Goals (SDGs) (e.g., SDGs 6 – “clean water and sanitation” and 14 – “life below water”). Nutrient models have been used to analyze the causes of nutrient pollution in water systems and to explore solutions for China. Existing modeling studies focus on either nutrient flows in human activities (e.g., food production) at administrative (e.g., province, country) scales, or on nutrient fluxes from land to water systems at bio-geophysical scales (e.g., river basins). However, a better understanding of how human activities at the administrative scales affect nutrient fluxes at bio-geophysical scales of water systems is needed for China.Nutrient pollution in water systems may continue to increase in the future because of the expected changes in socio-economic development (e.g., population, economy) in China. Climate change may also influence hydrology (e.g., runoff, river discharges), and thus will influence nutrient transport in water systems (e.g., rivers). However, how socio-economic and climatic changes, together, will affect future nutrient pollution in water systems in China is not well studied. Such analysis can be done by implementing the global Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) in nutrient models for China. Moreover, many policies in China attempt to reduce future water pollution by nutrients. However, limited insights are available on the effectiveness of these policies for reducing future water pollution by nutrients.The objective of this PhD thesis is to improve our understanding of nutrient pollution in water systems in China, with a focus on the challenges, trends, and solutions. To this end, I formulated six sub-objectives for my research on nutrient pollution in water systems (sub-objectives 2, 4, and 6) from human activities on land (sub-objectives 1, 3, and 5).Sub-objectives focusing on the challenges:To identify hotspots for nutrient losses from food production (Chapter 2)To compare nutrient loads to Lake Taihu with the critical nutrient loads of the lake (Chapter 3, case study)Sub-objectives focusing on the trends:To analyze how socio-economic development affects future nutrient losses from food production (Chapter 4)To analyze how global change affects future river export of nutrients to seas (Chapter 5)Sub-objectives focusing on the solutions:To explore how current and improved nutrient management affects future nutrient losses from food production (Chapter 6)To explore future scenarios to reduce nutrient pollution in water systems and to meet the SDGs (Chapter 7)To meet the objectives, I developed and applied new versions of the NUFER (NUtrient flows in Food chains, Environment and Resources use) and MARINA (Model to Assess River Inputs of Nutrients to seAs) models. I analyzed nutrient flows in China’s food system for counties, provinces and China using the NUFER model. I analyzed river export of nutrients by 12 large Chinese rivers.The main findings are:In recent past, nutrient losses from food production increased dramatically in China (Chapter 2)Hotspots covered less than 10% of the Chinese land, but contributed by more than half to N and P losses to the environment in 2012 (Chapter 2)To meet critical loads, river export of TDN and TDP to Lake Taihu needs to be reduced by 46–92% (Chapter 3)Opportunities are reducing synthetic fertilizer and improving wastewater treatment (Chapter 3)Nitrogen losses from food production to water systems in the future may increase by up to 65% relative to today if nutrient management does not improve (Chapter 4)Climate change makes water pollution control in China more difficult (Chapter 5)Current policies aimed at zero growth in fertilizer use are not very effective in reducing nutrient pollution from food production in China (Chapter 6)SDG 6&14 can be met with improved nutrient management in agriculture and sewage systems, efficient food consumption, and climate mitigation (Chapter 7)Future scenarios contributing to SDGs 6 and 14 may also contribute to other SDGs (Chapter 7)I reflected on the research in this thesis, drew five main lessons for future nutrient modeling, and discussed the implications of this thesis on future policies (Chapter 8). The main lessons are: 1) linking NUFER and MARINA models can help to better explore solutions to reduce nutrient pollution in water systems at administrative and bio-geophysical scales, 2) preferred spatial scales of modeling depending on the research objectives, 3) indicators can help to better understand and communicate the modeling results, 4) combining different types of scenarios is useful in exploring solutions in environmental modeling, and 5) the modeling approach in this thesis can be used to analyze nutrient pollution in water systems in other world regions.The development of recent environmental policies in China confirms two main findings of this thesis: 1) nutrient pollution in water systems has increased dramatically in China, and 2) direct discharge of animal manure and human waste are the important sources of nutrient pollution in water systems in China. My PhD thesis supports the formulation of future environmental policies for water pollution control in China. The linked NUFER-MARINA approach provides novel insights to nutrient pollution at both administrative (e.g., county, province, and country) and bio-geophysical (e.g., sub-basin) scales. Such insights are useful for formulating policies that are often implemented at administrative scales to reduce water pollution at bio-geophysical scales. The potential options for reducing water pollution proposed in this thesis can be considered by policymakers as a starting point. Further improvement of these options using the participatory approach can be done to account for different perspectives and uncertainties for policy implementation. Quantitative indicators for water pollution in this thesis are helpful in understanding and communicating the modeling results with policymakers and stakeholders. All this hopefully will contribute to reducing water pollution by nutrients, and meeting the SDGs 6 and 14 in China.

Lake Dianchi is severely polluted with nitrogen (N) and phosphorus (P). The effects of implementing environmental policies and technologies on future lake quality are not well understood. We analyse effects of environmental policies and technologies on future river export of nutrients into Lake Dianchi. We develop scenarios for 2050 and analyse these with the existing MARINA-Lakes model (Model to Assess River Inputs of Nutrient to lAkes). The scenarios differ in assumptions about future nutrient management in agriculture, sewage systems and mining. In the SSP3 (Shared Socio-economic Pathway 3) scenario, river export of nutrients to Lake Dianchi is projected to increase 1.4–4.4 times between 2012 and 2050. In the Current Policies scenario, rivers may export fewer nutrients than in SSP3, but this may not avoid eutrophication. Effects of improved nutrient management on river export of nutrients differ among nutrient forms, sub-basins and sources (e.g., urbanization in the north, agriculture in the middle and south). Pollution levels can be reduced below the 2012 level in an Optimistic scenario assuming advanced wastewater treatment, improved nutrient management in agriculture and no mining. However, even this may not completely prevent eutrophication. Preventing eutrophication requires even more efforts, for example, in implementing circular-oriented management options.

Years of poor nutrient management practices in the agriculture industry in the North China Plain have led to large losses of nutrients to the environment, causing severe ecological consequences. Analyzing farm nutrient flows is urgently needed in order to reduce nutrient losses. A farm-level nutrient flow model was developed in this study based on the NUFER (NUtrient flows in Food chains, Environment and Resources use) model, and was used to analyze nitrogen (N) and phosphorus (P) flows, use efficiencies, and losses for nine representative farm types in the North China Plain. Data from 401 farms were evaluated for the years 2012–2015. The analysis showed that mixed farms were more efficient in nutrient utilization than crop-based or landless livestock farms. The efficiencies of N and P used in crop production were highest for mixed dairy farms, reaching 67% for N and 68% for P. Consistently, mixed dairy farms had the lowest N and P surpluses and losses in crop production. Mixed swine farms were 5 and 9% higher in N and P efficiency in livestock production than landless swine farms, respectively. Losses of N and P from the animal manure management chain were 20–42% lower for mixed swine and 69–78% lower for mixed poultry farms than for landless farms of the same animal type. This is at least partially due to more frequent manure removal. Integrated crop-livestock production using livestock wastes as crop fertilizer was shown to be the most sustainable model in nutrient use for the agriculture industry in the North China Plain.

Assessing global water quality issues requires a multi-pollutant modelling approach. We discuss scientific challenges and future directions for such modeling. Multi-pollutant river models need to integrate information on sources of pollutants such as plastic debris, nutrients, chemicals, pathogens, their effects and possible solutions. In this paper, we first explain what we consider multi-pollutant modelling. Second, we discuss scientific challenges in multi-pollutant modelling relating to consistent model inputs, modelling approaches and model evaluation. Next, we illustrate the potential of global multi-pollutant modelling for hotspot analyses. We show hotspots of river pollution with microplastics, nutrients, triclosan and Cryptosporidium in many sub-basins of Europe, North America and South Asia. Finally, we reflect on future directions for multi-pollutant modelling, and for linking model results to policy-making.

The Indus River Basin faces severe water quality degradation because of nutrient enrichment from human activities. Excessive nutrients in tributaries are transported to the river mouth, causing coastal eutrophication. This situation may worsen in the future because of population growth, economic development, and climate change. This study aims at a better understanding of the magnitude and sources of current (2010) and future (2050) river export of total dissolved nitrogen (TDN) by the Indus River at the sub-basin scale. To do this, we implemented the MARINA 1.0 model (Model to Assess River Inputs of Nutrients to seAs). The model inputs for human activities (e.g., agriculture, land use) were mainly from the GLOBIOM (Global Biosphere Management Model) and EPIC (Environmental Policy Integrated Model) models. Model inputs for hydrology were from the Community WATer Model (CWATM). For 2050, three scenarios combining Shared Socio-economic Pathways (SSPs 1, 2 and 3) and Representative Concentration Pathways (RCPs 2.6 and 6.0) were selected. A novelty of this study is the sub-basin analysis of future N export by the Indus River for SSPs and RCPs. Result shows that river export of TDN by the Indus River will increase by a factor of 1.6–2 between 2010 and 2050 under the three scenarios. >90% of the dissolved N exported by the Indus River is from midstream sub-basins. Human waste is expected to be the major source, and contributes by 66–70% to river export of TDN in 2050 depending on the scenarios. Another important source is agriculture, which contributes by 21–29% to dissolved inorganic N export in 2050. Thus a combined reduction in both diffuse and point sources in the midstream sub-basins can be effective to reduce coastal water pollution by nutrients at the river mouth of Indus.

Hai He basin is located in the densely populated North China Plain that is providing food to more than 100 million people. The fast developing agriculture and urbanization in Hai He basin have resulted in discharging nutrient-rich wastewater into lakes and reservoirs, leading to eutrophication and water scarcity such as in Guanting reservoir and Baiyangdian lake. In this study we analyzed future trends in nutrient inputs into Guanting reservoir and Baiyangdian lake by applying the MARINA (Model to Assess River Inputs of Nutrients to seAs) model. We analyzed trends between 2012 and 2050 for a business-as-usual scenario (SSP3) and a scenario based on Current Environmental Policies (CEP). In addition, we assessed future impacts of two important events on river export of nutrients: the 2022 Olympic Winter Games in the Guanting basin (OLY scenario), and the development of Xiong’an in the Baiyangdian basin (URB scenario). Finally, we assumed implementation of advanced technologies to reduce nutrients in rivers (OLY+ and URB+). Our study has five main findings. First, nutrients in Guanting reservoir and Baiyangdian lake were mainly from agriculture in 2012. Second, nutrient export doubles between 2012 and 2050 in SSP3. Third, effective implementation of current environmental policies could reduce the future pollution to levels below that in 2012. Fourth, improved sewage systems associated with the 2022 Winter Olympic Games could not reduce nutrient pollution effectively in Guanting reservoir, indicating that reducing nutrient losses from agriculture may be more effective to improve water quality than urban waste water treatment. Fifth, urbanization in the Baiyangdian basin may increase river export of nutrients to the lake by 28-43% compared to the CEP scenario (URB scenario). Highly effective waste treatment is needed not only in Xiong’an but also in surrounding areas to ensure the availability of clean water (URB+ scenario). Our results could improve our understanding of nutrient management for specific lakes and reservoirs, and highly relevant for policy making for effective environmental policies.

Chinese surface waters are severely polluted by nutrients. This study addresses three challenges in nutrient modeling for rivers in China: (1) difficulties in transferring modeling results across biophysical and administrative scales, (2) poor representation of the locations of point sources, and (3) limited incorporation of the direct discharge of manure to rivers. The objective of this study is, therefore, to quantify inputs of nitrogen (N) and phosphorus (P) to Chinese rivers from different sources at multiple scales. We developed a novel multi-scale modeling approach including a detailed, state-of-the-art representation of point sources of nutrients in rivers. The model results show that the river pollution and source attributions differ among spatial scales. Point sources accounted for 75% of the total dissolved phosphorus (TDP) inputs to rivers in China in 2012, and diffuse sources accounted for 72% of the total dissolved nitrogen (TDN) inputs. One-third of the sub-basins accounted for more than half of the pollution. Downscaling to the smallest scale (polygons) reveals that 14% and 9% of the area contribute to more than half of the calculated TDN and TDP pollution, respectively. Sources of pollution vary considerably among and within counties. Clearly, multi-scale modeling may help to develop effective policies for water pollution.

Intensive agriculture and rapid urbanization have increased nutrient inputs to Lake Taihu in recent decades. This resulted in eutrophication. We aim to better understand the sources of river export of total dissolved nitrogen (TDN) and phosphorus (TDP) to Lake Taihu in relation to critical nutrient loads. We implemented the MARINA-Lake (Model to Assess River Inputs of Nutrients to seAs) model for Lake Taihu. The MARINA-Lake model quantifies river export of dissolved inorganic and organic N and P to the lake by source from sub-basins. Results from the PCLake model are used to identify to what extent river export of nutrients exceeds critical loads. We calculate that rivers exported 61 kton of TDN and 2 kton of TDP to Lake Taihu in 2012. More than half of these nutrients were from human activities (e.g., agriculture, urbanization) in Sub-basins I (north) and IV (south). Most of the nutrients were in dissolved inorganic forms. Diffuse sources contributed 90% to river export of TDN with a relatively large share of synthetic fertilizers. Point sources contributed 52% to river export of TDP with a relatively large share of sewage systems. The relative shares of diffuse and point sources varied greatly among nutrient forms and sub-basins. To meet critical loads, river export of TDN and TDP needs to be reduced by 46-92%, depending on the desired level of chlorophyll-a. There are different opportunities to meet the critical loads. Reducing N inputs from synthetic fertilizers and P from sewage systems may be sufficient to meet the least strict critical loads. A combination of reductions in diffuse and point sources is needed to meet the most strict critical loads. Combining improved nutrient use efficiencies and best available technologies in wastewater treatment may be an effective opportunity. Our study can support the formulation of effective solutions for lake restoration.

Low nitrogen (NUE) and phosphorus (PUE) use efficiencies in food production in China result in large losses of nitrogen (N) and phosphorus (P) to the environment. The Chinese government formulated policies to increase the NUEs and PUEs. Recent policies aim for zero growth in synthetic fertilizer use after 2020 while ensuring food security. In this study we analyzed how current and improved nutrient management in China can affect future nutrient use efficiencies and nutrient losses from food production. The NUEs and PUEs of food production were quantified using the NUFER (NUtrient flows in Food chains, Environment and Resources use) model for 31 provinces and China in 2013, 2020 and 2050. Results show that national NUE (20%) and PUE (24%) in 2013 are low but vary largely among provinces (12–33% for NUE, 10–53% for PUE). The N and P losses to the air (14 Tg year − 1 of N) and waters (12 Tg year − 1 of N, 2 Tg year − 1 of P) are consequently high in 2013. Three scenarios were analyzed for 2020 and 2050 to explore future trends in NUEs and PUEs, assuming Business As Usual ( BAU ) trends, Zero Fertilizer ( ZF ) growth from 2020 , and Improved Nutrient Management ( INM ). In the BAU scenario, the NUEs and PUEs roughly remain at their low 2013 levels, while nutrient inputs to agriculture are increasing. The losses to the air therefore increase by 37% for N and to waters by 40% for N and 48% for P between 2013 and 2050. In the ZF scenario, the NUEs and PUEs are a few percent higher than in BAU . The associated N and P losses to waters are 8–16% lower than in BAU because of increased recycling of manure to cropland, but still higher than in 2013. Improved nutrient management, as assumed in our INM scenario, may increase NUEs and PUEs to 33% and 59% in 2050. Meanwhile, N and P losses to waters in 2050 are 47% and 83% lower than in 2013, and losses of N to the air 20% lower. We conclude that the policy aimed at zero growth in fertilizer use is a good start, but not very effective in reducing nutrient pollution in China. To substantially reduce N and P losses to the environment it is needed to improve nutrient management by not only reducing fertilization without yield losses, but also by improved manure management and animal production with lower nutrient excretion.

In many world regions the availability of clean water is at risk. Pollution of rivers and coastal seas poses a threat to aquatic ecosystems and society. Here, we review representative examples of mathematical models that simulate pollutant flows from land to sea at global and continental scales. We argue that a multi-pollutant modelling approach would help to better understand and manage water quality issues. Pollutants often have common sources and multiple impacts. Most existing spatially explicit models, however, focus on one type of pollution only. A new generation of models is needed to explicitly address the combined exposure of surface waters to multiple pollutants. Such models could serve as a basis for integrated water quantity and water quality assessments.