Analysis of nitrogen flow in the Yellow River Basin over a long time series

Analysis of nitrogen flow in the Yellow River Basin over a long time series | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of nitrogen flow in the Yellow River Basin over a long time series Ying Cui, Ruiping Li, Xu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4962696/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 9 You are reading this latest preprint version Abstract Based on the basic statistical data and related parameters of The Yellow River Basin (YRB) from 2000 to 2019, the nitrogen flow model of the YRB was constructed by using the full nitrogen flow analysis model (FNFA) and the emission coefficient method to analyze the characteristics of the nitrogen inputs and outputs in the YRB. The results revealed that over the past 20 years, both the total nitrogen inputs and outputs in the YRB have shown a significant increasing trend. Specifically, the total nitrogen input rose from 12,806.69 Gg to 18,553.42 Gg, while the total output increased from 9,250.93 Gg to 12,955.0 Gg. Among the various subsystems, the industrial and agricultural sectors were the largest contributors to nitrogen balance, accounting for 28.30% and 26.22% of the total nitrogen input, and 26.22% and 40.48% of the total nitrogen output, respectively. The overall nitrogen utilization efficiency (NUE) across the subsystems required improvement, particularly within the cropland subsystem, which had an NUE ranging from 25.67–36.10%. In contrast, the livestock subsystem exhibited only half the NUE of the cropland subsystem. High emissions and inefficient nitrogen utilization led to a continuous increase in environmental nitrogen loads, with atmospheric nitrogen loads being particularly pronounced. Additionally, the life cycle analysis of industrial nitrogen revealed that a substantial amount of nitrogen was enriched in the atmosphere. These findings can serve as scientific basis and support for regulating nitrogen inflow and outflow within watershed areas, and formulating more rational integrated management strategies for nitrogen. Nitrogen balance Full nitrogen flow analysis Nitrogen use efficiency The Yellow River Basin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Nitrogen (N) is an indispensable element in the composition of living organisms and plays a pivotal role in the Earth's biochemical cycles(Stein & Klotz, 2016 ). Normally, nitrogen inputs and outputs in ecosystems maintain a relative equilibrium. However, since the Industrial Revolution, population growth has led to an increasing demand for nitrogen. The invention of Haber-Bosch nitrogen fixation (HBNF) significantly augmented the production of man-made nitrogen(Wen et al., 2023 ). Currently, anthropogenic production of reactive nitrogen (Nr) surpasses natural output (anthropogenic N production, 210 Tg N/yr; natural N production, 203 Tg N/yr)(Fowler et al., 2013 ). Yet, only a small portion of anthropogenic N is efficiently utilized by humans, with much of it lost or accumulated in the environment(Xia & Yan, 2023 ). Excessive N in the environment has caused a large number of nitrogen pollution problems, including biodiversity loss, eutrophication of water bodies, climate change, photochemical smog, non-point source pollution(Gu et al., 2013 ; Pan et al., 2019 ; X. Wang et al., 2023 ). Consequently, excess Nr has emerged as one of the most pressing environmental challenges of our time. Nitrogen flow estimation can reveal the origin and destinations of nitrogen, playing an important role in quantitatively analyzing the status of systematic nitrogen inputs and outputs, thereby scientifically regulating the direction of regional nitrogen flow(Stuart et al., 2015 ). Academics have made significant research achievements in the field of N flow, with some scholars starting from ecosystems in their natural state, such as forests(Johnson & Turner, 2014 ), soils and groundwater(Shen et al., 2023 ), to estimate the nitrogen balance. Since the invention of the Haber-Bosch ammonia synthesis method, human interference with the nitrogen cycle has intensified, leading to increased attention on the influence of industrial, agricultural, and livestock systems on nitrogen cycle under the leadership of human activities(Beckinghausen et al., 2020 ; Dong et al., 2020 ; Moomaw, 2002 ). In addition to individual systems, scholars have also synthesized multiple systems to analyze N flow structure along the entire food supply chain from production through processing to consumption using a life cycle perspective(Chen et al., 2022 ). At different spatial scale including global(Stevens, 2019 ), continental(Van Egmond et al., 2002 ), national(de Jesus Delmiro Rocha & Neto, 2023 ), urban(Zhao & Yang, 2022 ), and watershed(M. Cui et al., 2023 ), researchers have assessed N balance characteristics. The assessment reveals that economically developed regions are generally characterized by high N inputs. However, for people in less developed parts of Africa, they are facing a food shortage crisis caused by a lack of nitrogen. Unlike previous study areas mentioned above, watershed-scale analyses focused on nitrate and ammonia fluxes and concentrations in river water while only a few scholars have evaluated overall N balance characteristics. As a significant grain producing area and ecological functional area in China, the YRB is a key area for winning the battle against pollution and building a beautiful China. However, with the process of urbanization and industrialization, the YRB has experienced serious nitrogen pollution problems, which seriously restricts the high-quality development of the YRB(Wu et al., 2022 ). In order to better control the problem of nitrogen pollution, numerous studies have been conducted on nitrogen flow in the YRB. In 2017 alone, agricultural sources contributed to a total nitrogen emission of 79,000 tons in the YRB. Nitrogen fertilizer application per unit area exceeded the state-mandated maximum value of 250 kg/hm 2 by reaching approximately 325 kg/hm 2 . Current research primarily focuses on the nitrogen flow within agricultural production systems(Liu et al., 2022 ), livestock and poultry system(Cheng et al., 2007 ), with limited analysis available on long-term variation in nitrogen transport. So, this paper presents a systematic analysis of the long-term evolutionary characteristics of N flows in 12 subsystems within the YRB over the past two decades (2000–2019). The objectives of this study are as follows: (1) developing a comprehensive N flow model based on the material flow analysis method, encompassing N production, utilization, and waste management; (2) characterizing and comparing changes in N fluxes, flow structures, and emission intensities among different subsystems within the YRB region during the period from 2000 to 2019. Materials and methods Study area The Yellow River of China (89°~126°E, 26°~54°N) is the fifth longest river in the world with a total length of 5464 km(S.-Y. Wang et al., 2010 ). It encompasses a basin area of approximately 795,000 km 2 . Originating from the northern foothills of the Bayan Kara Mountains on the Tibetan Plateau, it flows from west to east through nine provinces including Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan and Shandong before finally discharging into the Bohai Sea in Dongying City, Shandong Province (Fig. 1 ). In 2019, the combined GDP of these nine provinces and regions amounted to around 24.74 trillion yuan or about 24.97% of the country's total GDP. The watersheds collectively support a population of 421 million, constituting approximately 30% of the total population in China. Quantification of N flow This study utilized the FNFA, which was developed based on the material flow analysis method, to conduct an in-depth examination of N flows within YRB(Dong et al., 2020 ). The boundary of the YRB was defined both horizontally and vertically. Horizontally, it encompassed the natural watershed boundary where various human activities such as agricultural activities, livestock breeding, and industrial production took place on land surface. Vertically, it extended from below 1000 meters of the near-surface atmosphere to above the bedrock(Zimmerman et al., 1988 ). The YRB was divided into 12 subsystems: cropland system (CP), livestock system (LS), forest system (FR), industry system (ID), aquaculture system (AQ), Urban green system (UG), human system (HM), sewage disposal system (SD), cropland disposal system (GD), surface water system (SW), groundwater system(GW) and atmosphere system (AT). These subsystems were interconnected with continuous transport and transfer of nitrogen between them, while also experiencing stagnation and accumulation within each subsystem. The computational procedure for N input and output is shown in Table S1 -22 (Supplementary Material). The target period of this study spans from 2000 to 2019, with the base year set as 2010. The N flow is defined as the mass flow of N that connects two subsystems, while the cumulative amount of N is determined by calculating the difference between input and output within these subsystems. Adhering to the law of conservation of mass, both the entire system and its modules adhere to the following principle: IN i and OUT j denote the N input and output respectively, and AC k represents the N accumulation. N maintains a relatively smooth state in the whole system, and its accumulation in each subsystem is affected by the inputs from other subsystems on one hand, and the outputs from this subsystem to other subsystems on the other hand. The nitrogen utilization efficiency (NUE) is a commonly used indicator for evaluating the performance of N in relation to food production. It can be employed to assess the NUE of three subsystems (cropland, livestock and aquaculture) in the YRB. In this study, we utilized the ratio of product N content to total input N to reflect and analyze the NUE of food production-related subsystems(Sharma & Bali, 2017 ), was expressed as: Where, N food , N fert , N manu , N BNF , N dep and N irri are denoted as food N, chemical fertilizer N, organic fertilizer N, BNF, N deposition, and irrigation N, respectively. The amount of N contained in irrigation water is introduced to improve the equation(Erisman et al., 2018 ), but the amount of import and export are not contained in the equation. In this study, socioeconomic and other basic data of the YRB from 2000 to 2019 were collected from National Bureau of Statistical of China and Provincial bureau of statistics. The parameters for subsystems are mainly gathered from academic papers and industry reports. Additionally, due to variations in the sizes of the nine provinces within the YRB, the provincial data was ultimately converted to basin-scale data using an area-weighting method at different ratios. Results and discussion Characterization of whole system N flow Over the past 20 years, the annual total nitrogen input, output, and accumulation in the YRB exhibited a significant growth trend (Fig. 2 a). Specifically, the annual total nitrogen input increased from 12,806.69 Gg to 18,553.42 Gg, representing a 1.45-fold increase with some fluctuation. Total nitrogen output rose from 9,250.93 Gg to 12,955.0 Gg, reflecting a 1.40-fold increase, which was slightly lower than the increase in input. Nitrogen accumulation grew from 3,555.76 Gg to 5,598.43 Gg. Regarding input sources, anthropogenic nitrogen was the predominant input, including contributions from HBNF, fossil fuel combustion, fertilization, food or feed usage, and other major processes. This input continued to rise, accounting for 85.69% of the total nitrogen input in 2010. The nitrogen flux resulting from the combustion of fossil fuels has experienced a significant increase over the past 20 years, rising from 427.27 Gg to 2008.26 Gg with an average annual growth rate of 18.50%. The N fixation intensity through this way in the entire YRB was 5.37–25.26 kg N ha − 1 y − 1 , surpassing the national average growth rate of 8.85 kg N ha − 1 y − 1 (S. Cui et al., 2013 ). The amount of N fixed by HBNF, which was utilized in the manufacturing of nitrogen-based fertilizers and other synthetic ammonia products, increased from 2,091.67 Gg in 2000 to a peak maximum of 2,890 Gg in 2013, followed by a gradual decline year after year, reaching 2,494.21 Gg in 2019 (Fig. 2 b). The estimated input strengths of HBNF ranged from 26.31 to 36.35 kg N ha − 1 y − 1 , slightly below the average value in China (48.9 kg N ha − 1 y − 1 )(Luo et al., 2018 ). Atmospheric N deposition and biological nitrogen fixation (BNF) serve as the natural sources of N in the basin, accounting for only 14.31% of total inputs. Among these sources, BNF represents a sustainable and environmentally-friendly method of N fixation, with an expected significant increase N fixation by this way (545.92-703.34 Gg N yr − 1 ). Nitrogen entering the system was recycled multiple times before being released into the atmosphere and water environments (Fig. 3 ). The nitrogen released into the atmosphere was primarily in the forms of NOx (50.18%), NH 3 (47.32%), and N 2 (32.72%), with a small fraction lost as N 2 O (2.51%). Nitrogen released into surface water mainly originated from natural processes such as runoff and atmospheric nitrogen deposition, which accounted for 46.76% and 46.75% of the total nitrogen input into surface water, respectively. Due to limited research on groundwater, this study only estimated the nitrogen content entering groundwater through leaching. The accumulation of nitrogen, determined by the difference between inputs and outputs, increased by 1.57 times over the past 20 years, indicating that the YRB faced escalating nitrogen pollution. Characterization of N flow in major subsystems The main N flow results of 12 subsystems are presented in Fig. 3 and Table 1 . It is observed that anthropogenic perturbations not only enhance the input of N to meet the growing production and consumption demands in the YRB, but also significantly alter the distribution pattern of N within the YRB. At the same time, there are significant variations in N fluxes across different subsystems. The income and expenditure of N in the agricultural and industrial subsystems were both significantly higher than those in other subsystems, accounting for 26.22% and 28.30% of total N input, and 26.23% and 40.48% of total N output, respectively. However, the cropland subsystem accumulated a total of 1311.67 Gg of nitrogen annually, while the industrial subsystem did not contribute to any nitrogen accumulation as part of its process. The atmospheric subsystem exhibited the highest level of accumulation (1669.18 Gg N/yr), accounting for 33.32% of the overall accumulated in 2010. Furthermore, the human subsystem experienced a significant N buildup (556.19 Gg N/yr). Excessive nitrogen accumulation in both the atmosphere and human subsystems can result in nitrogen pollution, posing risks to human health. Therefore, this study solely focuses on analyzing the industry, cropland, livestock, and human subsystems due to their stronger correlations with ecological environment and sustainable development within the YRB. Table 1 N input, output, and accumulate for each subsystem in YRB in 2010 (Gg N/yr). subsystems input output accumulate Cropland (CP) 4364.77 3053.10 1311.67 Livestock (LS) 948.18 946.78 1.40 Forest (FR) 531.34 215.53 315.80 Industry (ID) 4711.64 4711.64 0 Aquaculture (AQ) 50.60 44.79 5.81 Urban green (UG) 3.15 1.36 1.79 Human (HM) 1568.90 1012.72 556.19 Sewage disposal (SD) 57.79 57.79 0 Garbage disposal (GD) 451.49 451.49 0 Surface water (SW) 610.59 335.88 274.71 Groundwater (GW) 422.43 - - Atmosphere (AT) 2928.83 1259.66 1669.18 N flow analysis of industrial subsystem The industrial subsystem functioned as the processing department for N-containing products that are essential to meet human needs, and it demonstrated the highest and most rapid growth rate of N flow among the 12 subsystems. There was no stockpile of N within this subsystem, and the flux of N increased from 3183.06 Gg in 2000 to 5542.30 Gg in 2019, representing a remarkable increase of 74.12% over a span of two decades (Fig. 4 a). In 2010, the exclusive contribution of N from HBNF in the industrial subsystem accounted for 55.79% of the total input within this subsystem (Table 2 ). HBNF played a pivotal role in ensuring food safety and meeting the industrial demand for N(Luo et al., 2018 ). Fossil fuel combustion constituted 25.85% of the N input in the industrial subsystem, serving as a primary driver for the continuous increase in total N input within the subsystem. This ongoing surge in fossil fuel N input signifies an acceleration of the industrialization process in the YRB. Regarding the outflow of N from the industrial subsystem, fertilizer N alone accounted for approximately half of the N output. The majority of these fertilizers were applied to the farmland subsystem in order to enhance crop yields (94.43%), with a smaller portion being allocated to the livestock subsystem. Globally, around 80% of synthetic fixed N is utilized for fertilizers; However, in developed countries like Japan, this proportion is relatively lower at approximately 40–53%(Hayashi et al., 2021 ). In the YRB region, on the other hand, an average proportion of 91.55% was used for fertilizer purposes, indicating a strong dependence on N fertilizer for agricultural production. Other significant pathways through which N flowed out from the industrial subsystem included emissions of N oxides and nitrogen-containing industrial products, accounting for 25.85% and 18.35%, respectively. N flow analysis of cropland subsystem The cropland subsystem constituted the second largest N reservoir, following the industrial subsystem. There was a consistent downward trend in the accumulation of the cropland subsystem, decreasing from 1281.79 Gg in 2000 to 951.73 Gg in 2019, indicating a gradual reduction in its N sink capacity (Fig. 4 b). Nitrogen inputs into this subsystem were classified as natural sources (13.49%) and anthropogenic sources (86.51%). Among the anthropogenic sources, fertilizer emerged as the predominant contributor to N inflow, accounting for 49.79–57.95%. Globally, approximately 80% of synthetic fixed N is utilized in fertilizer production, with percentages ranging from 40–53% in countries like Japan(Hayashi et al., 2021 ). In contrast, the average fertilizer utilization rate in the YRB was 91.55%, highlighting the region's heavy dependence on N fertilizers for agricultural production. In terms of N outflow, there are multiple pathways for N output in cropland subsystem. N oxide emissions account for 28.07% of the total N output, while crops and stalks contribute 27.58% and 13.55%, respectively, by retaining and releasing N within their biomass during harvesting in this subsystem. A portion of the N enters the livestock subsystem; Subsequently, when livestock is processed into meat products, the accumulated N is transferred to consumers. N flow analysis of livestock subsystem The N inputs and outputs to the livestock subsystem were in a state of equilibrium, with the N stock in this subsystem remaining consistently low at 0.15% of the input. The magnitude of N fluxes in this subsystem exhibited a strong correlation with the size of the livestock population each year. For instance, there was a significant reduction in livestock numbers observed in 2007 due to an outbreak of avian influenza disease, resulting in a sharp decline in the N flow within the subsystem (Fig. 4 c). The N source in the livestock subsystem is relatively straightforward, as it enters solely through the livestock diet. Agricultural products consistently dominated the diet, accounting for approximately 70% over many years. Nitrogen outflow exhibits fluctuating growth (872.76-978.76 Gg) with various forms of outflow observed over time. In terms of N flow output, NH 3 volatilisation from livestock excreta (50.97% in 2010) constitutes the primary mode, followed by a runoff carrying livestock excreta into water bodies (28.39%). Both situations have adverse ecological effects, highlighting the need for enhanced supervision and control measures in breeding areas. The primary source of N entering surface water in the YRB is livestock runoff, indicating inadequate management of livestock and poultry excreta. The current rate of returning livestock manure to fields remains relatively low, emphasizing the need to strengthen the connection between livestock farming and agricultural production. The separation of agriculture and livestock has been identified as a significant factor contributing to the low utilization rate of N. Therefore, integrating crop cultivation with livestock farming is essential for addressing N loss and promoting sustainable intensive agriculture. Nitrogen accumulation exhibited a trend of rapid growth to stability, eventually slightly decreasing(Liu et al., 2022 ). N flow analysis of human subsystem The human subsystem was the primary consumer of N, with the total N input into the human subsystem increasing by 1.43 times from 2000 to 2019, primarily due to the rise in N input from natural synthetic products and crop intake (Fig. 4 d). It was observed that industrial and agricultural products continued to be imported into the human subsystem to meet growing consumption demands. Among these, N input into the human subsystem through food intake constituted approximately 33.37% of the total annual input, with a ratio of approximately 7:2:1 for crops, livestock, and aquatic products in food consumption estimation. This indicates that plant-based protein remained as the predominant nutritional source during this period of abundant food supply. The consumption of animal-based food had been steadily increasing over several years, resulting in higher N input requirements and breeding costs. Nitrogen output from the human subsystem mainly consisted of waste from industrial products (44.58%) and excreta (30.71%). Due to improved waste treatment efficiency, there has been a decrease in N loss from human excreta from 312.86 Gg in 2000 to 127.19 Gg in 2019. Population density in the YRB (530 people/km 2 ) exceeded China's national average (132 people/km 2 ), with high-density human activities contributing to pollutant emissions. Importantly, there has been a significant increasing trend in N accumulation within the human subsystem, rising from 433.42 Gg in 2000 to 927.67 Gg in 2019, marking a twofold increase. The enrichment of N within the human body can pose certain health hazards, severe cases have even been associated with gastric and esophageal cancers(Ward et al., 2008 ). Therefore, increased attention is warranted regarding living environments and dietary health. Table 2 2010 Major Sources and Sinks of N by Subsystem Gg N yr − 1 Human Nature Coupling System Main source Main remittance Item N flow Item N flow CP fertilizer 2417.84 crop 842.10 LS agro-food 710.28 NH 3 emission 482.61 FR forest tree 315.65 precipitate 116.89 ID HBNF 2628.78 Fertilizer 2560.58 AQ forage 40.02 aquatic product 26.12 UG deposition 1.50 NH 3 emissions 0.70 HM natural product 864.80 invalidate 451.49 N loss to the environment in YRB Environmental N load studies indicate that the ecological environment in the YRB is under significant pressure (Fig. 5 ). Approximately 30% of the total N input is discharged into the environment, showing a clear upward trend. In terms of distribution, N emissions into the atmosphere constitutes the highest proportion (72.81–81.78%), followed by surface water (10.31–14.86%), with groundwater having the smallest share (7.71–9.24%). Sun et al(Sun et al., 2021 ) reported that NH 3 emissions account for 64.3% of the total reactive N emissions in Australia, and similar results are observed in the YRB, where NH 3 is one of the major gas emission sources, ranging from 36.35–64.58%. Regarding sources, the agricultural subsystem, livestock subsystem, and industrial subsystem makes significant contributions to environmental load, accounting for 31.26–42.68%, 14.34–20.88%, and 12.34–35.30%, respectively. The primary driver of environmental load growth emissions is the in NO X emissions, primarily originating from industrial activities (88.75%). Simultaneously, it can be observed that agricultural activities have significantly reduced their contribution to environmental load due to measures implemented to control fertilizer usage. Overall, the N environmental load exhibited rapid growth in the early period and later stabilized later, increasing from 3724.81 Gg in 2000 to 5693.43 Gg in 2019. In 2010, the combined emissions of NH 3 , NO X , and N 2 O into the atmosphere reached 2928.83 Gg, which are critical components contributing to atmospheric haze formation. It was observed that in the presence of elevated NO X levels, there is a significant increase in the rate of PM2.5 formation from SO 2 and NO X (Zhang et al., 2020 ). Moreover, N 2 O, a potent greenhouse gas with a global warming potential 310 times greater than CO 2 , further emphasizes the importance of comprehending NH 3 , NOx, and N 2 O emissions and the sources for regional haze control and mitigation of greenhouse effects. NH 3 emissions primarily originated from agricultural subsystems (61.84%) and livestock subsystems (34.83%). Industrial activities predominantly contribute to NO X emissions (82.88%), while agricultural activities mainly account for N 2 O emissions (63.03%). Although N input into aquatic systems is relatively low compared to the atmosphere, excessive N in water bodies can lead to various impacts such as deterioration in water quality and loss of biodiversity. These effects become particularly significant under conditions where human activities result in increased N inputs. Apart from atmospheric nitrogen deposition, the primary sources of N entering surface waters are livestock runoff (50.20%) and agricultural runoff (33.46%). Therefore, it is crucial to implement measures that effectively control N emissions from cropland and livestock subsystems while enhancing their management practices. These findings emphasize the significance of comprehensive monitoring and management of N emissions in order to mitigate atmospheric haze and reduce the environmental impacts associated with excessive N enrichment in aquatic systems. Nitrogen use efficiency The analysis revealed significant variations in NUE among the subsystems in the YRB. The farm subsystem exhibited a relatively high NUE (28.77% in 2010) attributed to the substantial presence of effective products in its output items. Conversely, the livestock subsystem, where a considerable amount of nitrogen was lost through excreta, generally displayed lower NUE (15.88%). Although the aquatic subsystem demonstrated the highest NUE (51.62%) among the three subsystems, its N flux remained comparatively low. Although the N utilization efficiency of the farm subsystem increased from 25.67% in 2000 to 36.10% in 2019, it still remains relatively low compared to other countries (Table 3 ). The primary reason for this disparity lies in sub-optimal amounts and application method of N fertilizer. In order to enhance crop yield and achieve higher economic returns, farmers tend to apply large excessive amounts of N fertilizer during cultivation; However, due to inappropriate timing such as applying N fertilizer on the surface layer during the early stages of crop growth when the root uptake is weak, significant nitrogen losses occur(Z. Cui et al., 2014 ). This issue has been alleviated through a series of systematic agricultural surface pollution control policies. Since 2013, the increase in nitrogen fertilizer prices has prompted farmers to adopt more precise nitrogen management practices, resulting in a reduction of nitrogen inputs, indirectly leading to an increase in nitrogen use efficiency. To sustainably meet the N requirements for agriculture, renewable resources such as crop residues, manure and other waste organic waste materials are necessary for providing and maintaining nutrients essential for crop growth. Notably, over the past decade there has been a significant increase in straw returning back into fields compared to manure usage. The livestock subsystem exhibited a range of 12.21–16.69% variation in NUE over the study period (Table 3 ), which was approximately half of the NUE observed in the crop-based food within the cropland subsystem when compared to animal-based food. The high nitrogen levels found in livestock excreta during livestock farming can be attributed to both excessive nitrogenous feed intake and inadequate digestive utilization of nitrogen in feeds, thereby contributing significantly to the low level of NUE within the livestock subsystem(Alejo-Alvarez et al., 2016 ). In 2019 alone, statistics indicate that livestock excreta produced within the watershed amounted to 815.15 Gg accounting for 83.16% of the total N input into this subsystem. Furthermore, due to insufficient pollution treatment measures implemented within the district, haphazard discharge of livestock faeces and effluents from livestock farming exacerbates nitrogen accumulation in the environment. Therefore, enhancing resource utilization efficiency of livestock excreta and implementing rationally regulation on nitrogen input from livestock feeding practices can effectively mitigate surface source pollution within the watershed while simultaneously improving NUE and optimizing dietary structure for individuals. The NUE of the aquaculture subsystem is marginally superior compared to other countries, exhibiting minimal variation over the 20-year period and an average NUE of 51.78% (Table 3 ). It should be noted that fish sourced from natural environments were not included in this study; However, in Japan, for example, the NUE in the aquatic subsystem was surprisingly high at 480%, accounting for wild-caught fish(Hayashi et al., 2021 ). Approximately 92.92% of the N within the aquatic subsystem originated from feed intake, with a relatively efficient absorption and utilization by the fish. A small fraction was lost to the water column without consumption, while the remaining portion was excreted by the fish. Overall, there is a comparatively low level of nitrogen loss throughout this process. Furthermore, it is worth mentioning that higher protein content within fish may also contribute to enhanced NUE within this system. To address the issue of low NUE in the YRB, we propose the following management measures: on the input side, firstly, precision agriculture should be developed. This can be achieved by enhancing nitrogen fertilizer utilization efficiency, optimizing irrigation water management, and implementing soil formula fertilization programs and water-saving irrigation technologies tailored to the local conditions to minimize excess nitrogen loss(Li et al., 2023 ). Secondly, for the livestock and aquatic subsystems, feed management should be optimized to reduce nitrogen loss in feed. On the output side, waste should be utilized rationally by guiding farmers to prioritize returning human and livestock manure as well as crop residues back into fields. This will facilitate recycling of N waste from human livestock, and agricultural subsystems. Additionally, comprehensive cross-industry management measures need to be implemented while considering local conditions. Strengthening regulatory systems is crucial along with investing in and promoting new technologies. Furthermore, governmental departments should formulate policies and regulations that support both nitrogen resource utilization and environmental protection in order to promote green development. While excess N in agricultural fields can contribute somewhat to non-point source pollution, available studies have demonstrated that nitrogen can be effective remediate organic pollutants and certain heavy metals. In summary, improving nitrogen utilization efficiency requires comprehensive management approaches coupled with technological innovation at multiple perspectives and levels so as to achieve sustainable growth within the food industry. Table 3 Comparison of NUE in this study with typical areas Subsystem Region Year NUE(%) Reference CP Europe Union 2000–2020 44.0–48.0 (Oenema et al., 2009 ) China 2010 39.0 (Gu et al., 2017 ) Canada 1996–2016 46.7–50.8 (Karimi et al., 2020 ) This study 2000–2019 25.67–36.10 LS Japan 2000–2015 19.7 (Hayashi et al., 2021 ) China 2010 16.0 (Gu et al., 2017 ) Canada 1996–2016 21.2–22.8 (Karimi et al., 2020 ) This study 2000–2019 12.21–16.69 AQ Japan 2000–2015 34.0 (Hayashi et al., 2021 ) Portugal 2003 41 (Matos et al., 2006 ) This study 2000–2019 50.66–53.14 Life cycle analysis of industrial N flow Industrial production plays a crucial role in the N cycle by converting N from various sources such as HBNF, agricultural production, fossil fuel combustion, livestock farming, and wood into fertilizers, naturally synthesized nitrogen products, industrially synthesized nitrogen products, and wastewater effluents. These converted forms of nitrogen then enter the next stage of the cycle. The Sankey diagram can visually demonstrate how N converges in industrial production and is distributed throughout its flow. Figure 6 presents the analysis results. In order to meet the human demand, the N flux of the industrial subsystem continues to grow at an average annual growth rate of 3.71%. From Fig. 7, it can be observed that the industrial subsystem exhibits two main features: high nitrogen concentration and abundant waste generation. The HBNF serves as the primary input source of Nr for the industrial subsystem contributing significantly more than the other four (55.79%), and this part of nitrogen was mainly used in the production of industrial fertilizers and synthetic nitrogen products. Additionally, agricultural activities contribute significantly (18.22%) to industrial subsystem through crop cultivation and straw output, which cannot be overlooked. Nitrogen entering into industrial production undergoes processing and is utilized by other subsystems. The main utilization of N input in industrial production lies in agricultural practices and human consumption, particularly for the production of agricultural fertilizer that alone accounts for 43.52% of total industrial N output. These fertilizers are primarily derived from HBNF, which is mainly derived from HBNF, which synthesizes ammonia to create various nitrogen-based fertilizer products. Industrial production has led to substantial advancements in nitrogen utilization pathways and benefits for humans; However, only a relatively small fraction flows directly into the human subsystem through natural or industrially synthesized products. A large proportion of N is emitted or transferred to the environment throughout various stages via wastewater exhaust during this process, with emissions continuing to rise steadily over time. For instance, NOx losses alone have escalated from 427.27 Gg in 2000 to 2008.26 Gg in 2019. In response to the ongoing rise in industrial pollutant emissions in the YRB, two types of measures can be implemented for their control: firstly, reducing the production of reactive nitrogen by promoting clean energy sources to minimize fossil fuel combustion. Secondly, converting reactive nitrogen N 2 can be achieved through relevant reaction technology to convert nitrate and ammonium salts to N 2 . Additionally, promoting an industrial circular economy model is crucial for internal recycling of nitrogen through waste utilization and resource sharing within the industrial chain. For example, nitrogen present in industrial wastewater should be utilized as fertilizer for recycled water or converted into raw materials for organic synthesis from nitrogen-rich industrial waste gases. Strengthening regulations and standards on industrial nitrogen emissions is essential while encouraging industries to adopt cleaner production techniques and equipment to limit unnecessary losses of nitrogen. These measures will not only contribute towards reducing environmental pollution, but also enhance resource utilization efficiency and foster sustainable industrial development. Conclusions Accurate estimation of the nitrogen balance within YRB is essential to effectively manage nitrogen pollution and enhance the overall environmental quality. Therefore, this study evaluates nitrogen flow in the YRB over the past 20 years using the FNFA method. The findings are as follows: (1) The annual total nitrogen input in the YRB increased from 12,806.69 Gg in 2000 to 18,553.42 Gg in 2019, indicating a rapid growth rate. Nitrogen input and output are concentrated in several major subsystems, with the agricultural and industrial subsystems being particularly significant. In 2010, the nitrogen inputs to the agricultural and industrial subsystems reached 4,711.64 Gg and 4,364.77 Gg, respectively, with these subsystems contributing substantial amounts of nitrogen to the overall system through the delivery of agricultural and industrial production materials. (2) The environmental load within the YRB has been heavy, increasing from 3,724.81 Gg in 2000 to 5,693.43 Gg in 2019. (3) There are significant differences in nitrogen use efficiency among different production subsystems, with particular attention needed to improve nitrogen use efficiency in the agricultural and livestock subsystems. (4) Lifecycle analysis of industrial nitrogen flow indicates that only a small portion of the nitrogen is utilized in industrial products for human consumption, while the majority ultimately dissipates into the environment. Declarations Disclosure statement No potential conflict of interest was reported by the author(s). Funding This research was funded by the General Program for Humanities and Social Sciences Research, Ministry of Education (Grant No. 19YJCzHO85). Data availability The data set used or analyzed during this study is under study and cannot be shared due to confidentiality. Some of the publicly available datasets are detailed in the manuscript with sources and access. Author contribution Ying Cui: conceptualization, data curation, formal analysis, writing – original draft, writing. Ruiping Li: conceptualization, supervision, resources, funding acquisition, project administration, review & editing. Xu Chen: data curation, supervision, review & validation. Acknowledgements We would like to express our gratitude to the anonymous reviewers for their valuable comments. ORCID Ying Cui https://orcid.org/0009-0009-6898-6826 References Alejo-Alvarez, L., Guzmán-Fierro, V., Fernández, K., & Roeckel, M. (2016). Technical and economical optimization of a full-scale poultry manure treatment process: Total ammonia nitrogen balance. Environmental Technology, 37 (22), 2865–2878. https://doi.org/10.1080/09593330.2016.1167963 Beckinghausen, A., Odlare, M., Thorin, E., & Schwede, S. (2020). From removal to recovery: An evaluation of nitrogen recovery techniques from wastewater. Applied Energy, 263 , 114616. https://doi.org/10.1016/j.apenergy.2020.114616 Chen, D., Wang, C., & Liu, Y. (2022). Investigation of the nitrogen flows of the food supply chain in Beijing-Tianjin-Hebei region, China during 1978–2017. Journal of Environmental Management, 314 , 115038. https://doi.org/10.1016/j.jenvman.2022.115038 Cheng, H., Ouyang, W., Hao, F., Ren, X., & Yang, S. (2007). The non-point source pollution in livestock-breeding areas of the Heihe River basin in Yellow River. Stochastic Environmental Research and Risk Assessment, 21 (3), 213–221. https://doi.org/10.1007/s00477-006-0057-2 Cui, M., Guo, Q., Wei, Y., Yu, X., Hu, J., Tian, L., & Kong, J. (2023). Variations and its driven factors of anthropogenic nitrogen loads in the Yangtze River Economic Belt during 2000–2019. Environmental Science and Pollution Research International, 30 (2), 2450–2468. https://doi.org/10.1007/s11356-022-21943-y Cui, S., Shi, Y., Groffman, P. M., Schlesinger, W. H., & Zhu, Y.-G. (2013). Centennial-scale analysis of the creation and fate of reactive nitrogen in China (1910–2010). Proceedings of the National Academy of Sciences , 110 (6), 2052–2057. https://doi.org/10.1073/pnas.1221638110 Cui, Z., Wang, G., Yue, S., Wu, L., Zhang, W., Zhang, F., & Chen, X. (2014). Closing the N-Use Efficiency Gap to Achieve Food and Environmental Security. Environmental Science & Technology, 48 (10), 5780–5787. https://doi.org/10.1021/es5007127 de Jesus Delmiro Rocha, M., & Neto, I. E. L. (2023). Nitrogen mass balance and uptake velocity for eutrophic reservoirs in the Brazilian semiarid region. Environmental Science and Pollution Research, 30 (42), 95621–95633. https://doi.org/10.1007/s11356-023-29136-x Dong, Y., Xu, L., Yang, Z., Zheng, H., & Chen, L. (2020). Aggravation of reactive nitrogen flow driven by human production and consumption in Guangzhou City China. Nature Communications, 11 (1), 1209. https://doi.org/10.1038/s41467-020-14699-x Erisman, J. W., Leach, A., Bleeker, A., Atwell, B., Cattaneo, L., & Galloway, J. (2018). An Integrated Approach to a Nitrogen Use Efficiency (NUE) Indicator for the Food Production–Consumption Chain. Sustainability, 10 (4), Article 4. https://doi.org/10.3390/su10040925 Fowler, D., Coyle, M., Skiba, U., Sutton, M. A., Cape, J. N., Reis, S., Sheppard, L. J., Jenkins, A., Grizzetti, B., Galloway, J. N., Vitousek, P., Leach, A., Bouwman, A. F., Butterbach-Bahl, K., Dentener, F., Stevenson, D., Amann, M., & Voss, M. (2013). The global nitrogen cycle in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences, 368 (1621), 20130164. https://doi.org/10.1098/rstb.2013.0164 Gu, B., Ge, Y., Chang, S. X., Luo, W., & Chang, J. (2013). Nitrate in groundwater of China: Sources and driving forces. Global Environmental Change, 23 (5), 1112–1121. https://doi.org/10.1016/j.gloenvcha.2013.05.004 Gu, B., Ju, X., Chang, S. X., Ge, Y., & Chang, J. (2017). Nitrogen use efficiencies in Chinese agricultural systems and implications for food security and environmental protection. Regional Environmental Change, 17 (4), 1217–1227. https://doi.org/10.1007/s10113-016-1101-5 Hayashi, K., Shibata, H., Oita, A., Nishina, K., Ito, A., Katagiri, K., Shindo, J., & Winiwarter, W. (2021). Nitrogen budgets in Japan from 2000 to 2015: Decreasing trend of nitrogen loss to the environment and the challenge to further reduce nitrogen waste. Environmental Pollution, 286 , 117559. https://doi.org/10.1016/j.envpol.2021.117559 Johnson, D. W., & Turner, J. (2014). Nitrogen budgets of forest ecosystems: A review. Forest Ecology and Management, 318 , 370–379. https://doi.org/10.1016/j.foreco.2013.08.028 Karimi, R., Pogue, S. J., Kröbel, R., Beauchemin, K. A., Schwinghamer, T., & Henry Janzen, H. (2020). An updated nitrogen budget for Canadian agroecosystems. Agriculture, Ecosystems & Environment, 304 , 107046. https://doi.org/10.1016/j.agee.2020.107046 Li, B., Yan, L., & Zhang, W. (2023). Study on N application and N reduction potential of farmland in China. Environmental Monitoring and Assessment, 195 (10), 1156. https://doi.org/10.1007/s10661-023-11780-y Liu, J., Li, Y., Zheng, Y., Tong, S., Zhang, X., Zhao, Y., Zheng, W., Zhai, B., Wang, Z., Zhang, X., Li, Z., & Zamanian, K. (2022). The spatial and temporal distribution of nitrogen flow in the agricultural system and green development assessment of the Yellow River Basin. Agricultural Water Management, 263 , 107425. https://doi.org/10.1016/j.agwat.2021.107425 Luo, Z., Hu, S., Chen, D., & Zhu, B. (2018). From Production to Consumption: A Coupled Human–Environmental Nitrogen Flow Analysis in China. Environmental Science & Technology, 52 (4), 2025–2035. https://doi.org/10.1021/acs.est.7b03471 Matos, J., Costa, S., Rodrigues, A., Pereira, R., & Sousa Pinto, I. (2006). Experimental integrated aquaculture of fish and red seaweeds in Northern Portugal. Aquaculture, 252 (1), 31–42. https://doi.org/10.1016/j.aquaculture.2005.11.047 Moomaw, W. R. (2002). Energy, Industry and Nitrogen: Strategies for Decreasing Reactive Nitrogen Emissions. AMBIO: A Journal of the Human Environment, 31 (2), 184–189. https://doi.org/10.1579/0044-7447-31.2.184 Oenema, O., Witzke, H. P., Klimont, Z., Lesschen, J. P., & Velthof, G. L. (2009). Integrated assessment of promising measures to decrease nitrogen losses from agriculture in EU-27. Agriculture, Ecosystems & Environment, 133 (3–4), 280–288. https://doi.org/10.1016/j.agee.2009.04.025 Pan, J., Ding, N., & Yang, J. (2019). Changes of urban nitrogen metabolism in the Beijing megacity of China, 2000–2016. Science of The Total Environment, 666 , 1048–1057. https://doi.org/10.1016/j.scitotenv.2019.02.315 Sharma, L., & Bali, S. (2017). A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture. Sustainability, 10 (2), 51. https://doi.org/10.3390/su10010051 Shen, Z., Xin, J., Wu, H., Jiang, Z., Peng, H., Xu, F., He, C., Shi, Q., & Zheng, X. (2023). Kinetic and molecular evidence for DON transformation in the deep vadose zone: Important implications for soil nitrogen budgeting and groundwater nitrate management. Journal of Hydrology, 616 , 128782. https://doi.org/10.1016/j.jhydrol.2022.128782 Stein, L. Y., & Klotz, M. G. (2016). The nitrogen cycle. Current Biology, 26 (3), R94–R98. https://doi.org/10.1016/j.cub.2015.12.021 Stevens, C. J. (2019). Nitrogen in the environment. Science, 363 (6427), 578–580. https://doi.org/10.1126/science.aav8215 Stuart, D., Basso, B., Marquart-Pyatt, S., Reimer, A., Robertson, G. P., & Zhao, J. (2015). The Need for a Coupled Human and Natural Systems Understanding of Agricultural Nitrogen Loss. BioScience, 65 (6), 571–578. https://doi.org/10.1093/biosci/biv049 Sun, Y., Gu, B., Grinsven, H. J. M. van, Reis, S., Lam, S. K., Zhang, X., Chen, Y., Zhou, F., Zhang, L., Wang, R., Chen, D., & Xu, J. (2021). The Warming Climate Aggravates Atmospheric Nitrogen Pollution in Australia. Research. https://doi.org/10.34133/2021/9804583 Van Egmond, K., Bresser, T., & Bouwman, L. (2002). The European Nitrogen Case. AMBIO: A Journal of the Human Environment, 31 (2), 72–78. https://doi.org/10.1579/0044-7447-31.2.72 Wang, S.-Y., Liu, J.-S., & Ma, T.-B. (2010). Dynamics and changes in spatial patterns of land use in Yellow River Basin, China. Land Use Policy, 27 (2), 313–323. https://doi.org/10.1016/j.landusepol.2009.04.002 Wang, X., Xu, M., Lin, B., Bodirsky, B. L., Xuan, J., Dietrich, J. P., Stevanović, M., Bai, Z., Ma, L., Jin, S., Fan, S., Lotze-Campen, H., & Popp, A. (2023). Reforming China’s fertilizer policies: Implications for nitrogen pollution reduction and food security. Sustainability Science, 18 (1), 407–420. https://doi.org/10.1007/s11625-022-01189-w Ward, M. H., Heineman, E. F., Markin, R. S., & Weisenburger, D. D. (2008). Adenocarcinoma of the Stomach and Esophagus and Drinking Water and Dietary Sources of Nitrate and Nitrite. International Journal of Occupational and Environmental Health, 14 (3), 193–197. Wen, L., Lei, M., Zhang, B., Kong, X., Liao, Y., & Chen, W. (2023). Significant increase in gray water footprint enhanced the degradation risk of cropland system in China since 1990. Journal of Cleaner Production, 423 , 138715. https://doi.org/10.1016/j.jclepro.2023.138715 Wu, Z., Jiang, M., Wang, H., Di, D., & Guo, X. (2022). Management implications of spatial–temporal variations of net anthropogenic nitrogen inputs (NANI) in the Yellow River Basin. Environmental Science and Pollution Research, 29 (35), 52317–52335. https://doi.org/10.1007/s11356-022-19440-3 Xia, L., & Yan, X. (2023). How to feed the world while reducing nitrogen pollution. Nature, 613 (7942), 34–35. https://doi.org/10.1038/d41586-022-04490-x Zhang, X., Gu, B., Van Grinsven, H., Lam, S. K., Liang, X., Bai, M., & Chen, D. (2020). Societal benefits of halving agricultural ammonia emissions in China far exceed the abatement costs. Nature Communications, 11 (1), 4357. https://doi.org/10.1038/s41467-020-18196-z Zhao, Y., & Yang, S. (2022). Characteristics of nitrogen flow and its environmental effects in the Yellow River Basin, China. Environmental Technology, 45 , 1–20. https://doi.org/10.1080/09593330.2022.2114015 Zimmerman, P. R., Greenberg, J. P., & Westberg, C. E. (1988). Measurements of atmospheric hydrocarbons and biogenic emission fluxes in the Amazon Boundary layer. Journal of Geophysical Research, 93 (D2), 1407. https://doi.org/10.1029/JD093iD02p01407 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 23 Oct, 2024 Reviews received at journal 18 Oct, 2024 Reviews received at journal 02 Oct, 2024 Reviewers agreed at journal 19 Sep, 2024 Reviewers agreed at journal 12 Sep, 2024 Reviewers invited by journal 12 Sep, 2024 Editor assigned by journal 29 Aug, 2024 Submission checks completed at journal 29 Aug, 2024 First submitted to journal 23 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4962696","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":357894106,"identity":"49537143-2475-422f-b658-9c4e83679be8","order_by":0,"name":"Ying Cui","email":"","orcid":"","institution":"Qufu Normal University （Rizhao Campus）","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Cui","suffix":""},{"id":357894107,"identity":"52ec33c5-4079-4070-ba2b-7ec503d07852","order_by":1,"name":"Ruiping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACPmYgkcAgwcDA3v/wwQcDGzuCWtjgWnjOMBvOKEhLJqwFzpLIYZPm+XCIsYGgFnbeZxIPd1jkyfucPWxsY3CAmYH98NEN+B3GbiaReEai2PB4X+LjHIM7fAw8aWk38GthY5NIbJNI3NhzwNg4x+AZM4MEjxmRWmYkmElbGBxmbCBay3yJHDNpBiK1MFsA/ZK4gedYsmGPQVoyGyG/8PMfY7z5c0dd4vz25oMPfvyxseNnP3wMrxYwAMWFwQGYvQSVw7TINxCldBSMglEwCkYiAAB6hUTUFXcQDwAAAABJRU5ErkJggg==","orcid":"","institution":"Qufu Normal University （Rizhao Campus）","correspondingAuthor":true,"prefix":"","firstName":"Ruiping","middleName":"","lastName":"Li","suffix":""},{"id":357894108,"identity":"0606a7ae-3187-4381-abbd-0d7b22f192d6","order_by":2,"name":"Xu Chen","email":"","orcid":"","institution":"Qufu Normal University （Rizhao Campus）","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-08-23 08:29:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4962696/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4962696/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-024-13505-1","type":"published","date":"2024-12-05T15:58:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65605693,"identity":"feeb9e22-169e-4d0c-9aba-d7af2aeaaf0e","added_by":"auto","created_at":"2024-09-30 12:37:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11469164,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the YRB\u003c/p\u003e","description":"","filename":"Figure1.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-4962696/v1/3ab5d9bd6e53f6456ae7121d.png"},{"id":65606709,"identity":"dd140ec0-ba16-47ef-986a-455296f06443","added_by":"auto","created_at":"2024-09-30 12:45:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16076537,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the N budget of the YRB system from 2000 to 2019 (a) total N revenue, expenditure and accumulation in the system (b) HBNF, BNF, and fossil fuel burning inflow\u003c/p\u003e","description":"","filename":"Figure2.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-4962696/v1/d2d62f6317168e9133cf8ec2.png"},{"id":65605696,"identity":"6d657a05-14a7-4066-858e-f8a7c2ff0d67","added_by":"auto","created_at":"2024-09-30 12:37:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":293348,"visible":true,"origin":"","legend":"\u003cp\u003eN flow analysis for YRB. N inputs and outputs are represented by solid and dashed lines, respectively, with numbers on the lines with arrows representing N fluxes and horizontal boundaries in the dashed box. The numbers in brackets and out of brackets represent N fluxes in 2015 and 1995, respectively (in Gg N yr\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4962696/v1/9b1b4da502bb4b1320b16326.jpg"},{"id":65605694,"identity":"2c273f27-6c66-45f5-a4de-f31dd5db5c86","added_by":"auto","created_at":"2024-09-30 12:37:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3908084,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variation of N income and expenditure in the YRB (in Gg N yr\u003csup\u003e-1\u003c/sup\u003e). (a) Industrial subsystem N income and expenditure, (b) cropland subsystem N income and expenditure, (c) livestock subsystem N income and expenditure, (d) human subsystem N income and expenditure\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4962696/v1/706614be375e6ed2dffc3c2a.png"},{"id":65606710,"identity":"0c756feb-f5b4-46b6-b3cb-3b103f7109e7","added_by":"auto","created_at":"2024-09-30 12:45:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41997099,"visible":true,"origin":"","legend":"\u003cp\u003eN loss to the environment in YRB from 2000 to 2019. (a) By destination (atmosphere, surface water, and groundwater) (b) By source\u003c/p\u003e","description":"","filename":"Figure5.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-4962696/v1/ad8b3301a296ec2ab4122513.png"},{"id":65605695,"identity":"64c302d0-6f1e-4f02-a0b5-30193235cfac","added_by":"auto","created_at":"2024-09-30 12:37:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":628679,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variation of N income and expenditure in the YRB (in Gg N yr\u003csup\u003e-1\u003c/sup\u003e). (a) Industrial subsystem N income and expenditure, (b) cropland subsystem N income and expenditure, (c) livestock subsystem N income and expenditure, (d) human subsystem N income and expenditure\u003c/p\u003e","description":"","filename":"Figure6.tiff.png","url":"https://assets-eu.researchsquare.com/files/rs-4962696/v1/b115e4474ccdc14d4271c981.png"},{"id":65605697,"identity":"69508f91-d33f-42f1-b605-35a0519ef610","added_by":"auto","created_at":"2024-09-30 12:37:54","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":421880,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4962696/v1/1d6b1e489f3ceab9238acd75.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of nitrogen flow in the Yellow River Basin over a long time series","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNitrogen (N) is an indispensable element in the composition of living organisms and plays a pivotal role in the Earth's biochemical cycles(Stein \u0026amp; Klotz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Normally, nitrogen inputs and outputs in ecosystems maintain a relative equilibrium. However, since the Industrial Revolution, population growth has led to an increasing demand for nitrogen. The invention of Haber-Bosch nitrogen fixation (HBNF) significantly augmented the production of man-made nitrogen(Wen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Currently, anthropogenic production of reactive nitrogen (Nr) surpasses natural output (anthropogenic N production, 210 Tg N/yr; natural N production, 203 Tg N/yr)(Fowler et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Yet, only a small portion of anthropogenic N is efficiently utilized by humans, with much of it lost or accumulated in the environment(Xia \u0026amp; Yan, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Excessive N in the environment has caused a large number of nitrogen pollution problems, including biodiversity loss, eutrophication of water bodies, climate change, photochemical smog, non-point source pollution(Gu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; X. Wang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, excess Nr has emerged as one of the most pressing environmental challenges of our time.\u003c/p\u003e \u003cp\u003eNitrogen flow estimation can reveal the origin and destinations of nitrogen, playing an important role in quantitatively analyzing the status of systematic nitrogen inputs and outputs, thereby scientifically regulating the direction of regional nitrogen flow(Stuart et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Academics have made significant research achievements in the field of N flow, with some scholars starting from ecosystems in their natural state, such as forests(Johnson \u0026amp; Turner, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), soils and groundwater(Shen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), to estimate the nitrogen balance. Since the invention of the Haber-Bosch ammonia synthesis method, human interference with the nitrogen cycle has intensified, leading to increased attention on the influence of industrial, agricultural, and livestock systems on nitrogen cycle under the leadership of human activities(Beckinghausen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Moomaw, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In addition to individual systems, scholars have also synthesized multiple systems to analyze N flow structure along the entire food supply chain from production through processing to consumption using a life cycle perspective(Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At different spatial scale including global(Stevens, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), continental(Van Egmond et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), national(de Jesus Delmiro Rocha \u0026amp; Neto, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), urban(Zhao \u0026amp; Yang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and watershed(M. Cui et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), researchers have assessed N balance characteristics. The assessment reveals that economically developed regions are generally characterized by high N inputs. However, for people in less developed parts of Africa, they are facing a food shortage crisis caused by a lack of nitrogen. Unlike previous study areas mentioned above, watershed-scale analyses focused on nitrate and ammonia fluxes and concentrations in river water while only a few scholars have evaluated overall N balance characteristics.\u003c/p\u003e \u003cp\u003eAs a significant grain producing area and ecological functional area in China, the YRB is a key area for winning the battle against pollution and building a beautiful China. However, with the process of urbanization and industrialization, the YRB has experienced serious nitrogen pollution problems, which seriously restricts the high-quality development of the YRB(Wu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In order to better control the problem of nitrogen pollution, numerous studies have been conducted on nitrogen flow in the YRB. In 2017 alone, agricultural sources contributed to a total nitrogen emission of 79,000 tons in the YRB. Nitrogen fertilizer application per unit area exceeded the state-mandated maximum value of 250 kg/hm\u003csup\u003e2\u003c/sup\u003e by reaching approximately 325 kg/hm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrent research primarily focuses on the nitrogen flow within agricultural production systems(Liu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), livestock and poultry system(Cheng et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), with limited analysis available on long-term variation in nitrogen transport. So, this paper presents a systematic analysis of the long-term evolutionary characteristics of N flows in 12 subsystems within the YRB over the past two decades (2000\u0026ndash;2019). The objectives of this study are as follows: (1) developing a comprehensive N flow model based on the material flow analysis method, encompassing N production, utilization, and waste management; (2) characterizing and comparing changes in N fluxes, flow structures, and emission intensities among different subsystems within the YRB region during the period from 2000 to 2019.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy area\u003c/p\u003e\n\u003cp\u003eThe Yellow River of China (89\u0026deg;~126\u0026deg;E, 26\u0026deg;~54\u0026deg;N) is the fifth longest river in the world with a total length of 5464 km(S.-Y. Wang et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). It encompasses a basin area of approximately 795,000 km\u003csup\u003e2\u003c/sup\u003e. Originating from the northern foothills of the Bayan Kara Mountains on the Tibetan Plateau, it flows from west to east through nine provinces including Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan and Shandong before finally discharging into the Bohai Sea in Dongying City, Shandong Province (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2019, the combined GDP of these nine provinces and regions amounted to around 24.74 trillion yuan or about 24.97% of the country\u0026apos;s total GDP. The watersheds collectively support a population of 421 million, constituting approximately 30% of the total population in China.\u003c/p\u003e\n\u003cp\u003eQuantification of N flow\u003c/p\u003e\n\u003cp\u003eThis study utilized the FNFA, which was developed based on the material flow analysis method, to conduct an in-depth examination of N flows within YRB(Dong et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The boundary of the YRB was defined both horizontally and vertically. Horizontally, it encompassed the natural watershed boundary where various human activities such as agricultural activities, livestock breeding, and industrial production took place on land surface. Vertically, it extended from below 1000 meters of the near-surface atmosphere to above the bedrock(Zimmerman et al., \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e). The YRB was divided into 12 subsystems: cropland system (CP), livestock system (LS), forest system (FR), industry system (ID), aquaculture system (AQ), Urban green system (UG), human system (HM), sewage disposal system (SD), cropland disposal system (GD), surface water system (SW), groundwater system(GW) and atmosphere system (AT). These subsystems were interconnected with continuous transport and transfer of nitrogen between them, while also experiencing stagnation and accumulation within each subsystem. The computational procedure for N input and output is shown in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-22 (Supplementary Material).\u003c/p\u003e\n\u003cp\u003eThe target period of this study spans from 2000 to 2019, with the base year set as 2010. The N flow is defined as the mass flow of N that connects two subsystems, while the cumulative amount of N is determined by calculating the difference between input and output within these subsystems. Adhering to the law of conservation of mass, both the entire system and its modules adhere to the following principle:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eIN\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e and \u003cem\u003eOUT\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e denote the N input and output respectively, and \u003cem\u003eAC\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e represents the N accumulation. N maintains a relatively smooth state in the whole system, and its accumulation in each subsystem is affected by the inputs from other subsystems on one hand, and the outputs from this subsystem to other subsystems on the other hand.\u003c/p\u003e\n\u003cp\u003eThe nitrogen utilization efficiency (NUE) is a commonly used indicator for evaluating the performance of N in relation to food production. It can be employed to assess the NUE of three subsystems (cropland, livestock and aquaculture) in the YRB. In this study, we utilized the ratio of product N content to total input N to reflect and analyze the NUE of food production-related subsystems(Sharma \u0026amp; Bali, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), was expressed as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere, \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003efood\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003efert\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003emanu\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003eBNF\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003edep\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003eirri\u003c/em\u003e\u003c/sub\u003e are denoted as food N, chemical fertilizer N, organic fertilizer N, BNF, N deposition, and irrigation N, respectively. The amount of N contained in irrigation water is introduced to improve the equation(Erisman et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), but the amount of import and export are not contained in the equation.\u003c/p\u003e\n\u003cp\u003eIn this study, socioeconomic and other basic data of the YRB from 2000 to 2019 were collected from National Bureau of Statistical of China and Provincial bureau of statistics. The parameters for subsystems are mainly gathered from academic papers and industry reports. Additionally, due to variations in the sizes of the nine provinces within the YRB, the provincial data was ultimately converted to basin-scale data using an area-weighting method at different ratios.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eCharacterization of whole system N flow\u003c/p\u003e \u003cp\u003eOver the past 20 years, the annual total nitrogen input, output, and accumulation in the YRB exhibited a significant growth trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Specifically, the annual total nitrogen input increased from 12,806.69 Gg to 18,553.42 Gg, representing a 1.45-fold increase with some fluctuation. Total nitrogen output rose from 9,250.93 Gg to 12,955.0 Gg, reflecting a 1.40-fold increase, which was slightly lower than the increase in input. Nitrogen accumulation grew from 3,555.76 Gg to 5,598.43 Gg. Regarding input sources, anthropogenic nitrogen was the predominant input, including contributions from HBNF, fossil fuel combustion, fertilization, food or feed usage, and other major processes. This input continued to rise, accounting for 85.69% of the total nitrogen input in 2010. The nitrogen flux resulting from the combustion of fossil fuels has experienced a significant increase over the past 20 years, rising from 427.27 Gg to 2008.26 Gg with an average annual growth rate of 18.50%. The N fixation intensity through this way in the entire YRB was 5.37\u0026ndash;25.26 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, surpassing the national average growth rate of 8.85 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e(S. Cui et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The amount of N fixed by HBNF, which was utilized in the manufacturing of nitrogen-based fertilizers and other synthetic ammonia products, increased from 2,091.67 Gg in 2000 to a peak maximum of 2,890 Gg in 2013, followed by a gradual decline year after year, reaching 2,494.21 Gg in 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The estimated input strengths of HBNF ranged from 26.31 to 36.35 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, slightly below the average value in China (48.9 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)(Luo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Atmospheric N deposition and biological nitrogen fixation (BNF) serve as the natural sources of N in the basin, accounting for only 14.31% of total inputs. Among these sources, BNF represents a sustainable and environmentally-friendly method of N fixation, with an expected significant increase N fixation by this way (545.92-703.34 Gg N yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eNitrogen entering the system was recycled multiple times before being released into the atmosphere and water environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The nitrogen released into the atmosphere was primarily in the forms of NOx (50.18%), NH\u003csub\u003e3\u003c/sub\u003e (47.32%), and N\u003csub\u003e2\u003c/sub\u003e (32.72%), with a small fraction lost as N\u003csub\u003e2\u003c/sub\u003eO (2.51%). Nitrogen released into surface water mainly originated from natural processes such as runoff and atmospheric nitrogen deposition, which accounted for 46.76% and 46.75% of the total nitrogen input into surface water, respectively. Due to limited research on groundwater, this study only estimated the nitrogen content entering groundwater through leaching. The accumulation of nitrogen, determined by the difference between inputs and outputs, increased by 1.57 times over the past 20 years, indicating that the YRB faced escalating nitrogen pollution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCharacterization of N flow in major subsystems\u003c/p\u003e \u003cp\u003eThe main N flow results of 12 subsystems are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It is observed that anthropogenic perturbations not only enhance the input of N to meet the growing production and consumption demands in the YRB, but also significantly alter the distribution pattern of N within the YRB. At the same time, there are significant variations in N fluxes across different subsystems.\u003c/p\u003e \u003cp\u003eThe income and expenditure of N in the agricultural and industrial subsystems were both significantly higher than those in other subsystems, accounting for 26.22% and 28.30% of total N input, and 26.23% and 40.48% of total N output, respectively. However, the cropland subsystem accumulated a total of 1311.67 Gg of nitrogen annually, while the industrial subsystem did not contribute to any nitrogen accumulation as part of its process. The atmospheric subsystem exhibited the highest level of accumulation (1669.18 Gg N/yr), accounting for 33.32% of the overall accumulated in 2010. Furthermore, the human subsystem experienced a significant N buildup (556.19 Gg N/yr). Excessive nitrogen accumulation in both the atmosphere and human subsystems can result in nitrogen pollution, posing risks to human health. Therefore, this study solely focuses on analyzing the industry, cropland, livestock, and human subsystems due to their stronger correlations with ecological environment and sustainable development within the YRB.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eN input, output, and accumulate for each subsystem in YRB in 2010 (Gg N/yr).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003esubsystems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003einput\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eoutput\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaccumulate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropland (CP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4364.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3053.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1311.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock (LS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e948.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e946.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest (FR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e531.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e315.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry (ID)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4711.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4711.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAquaculture (AQ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban green (UG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman (HM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1568.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1012.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e556.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSewage disposal (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGarbage disposal (GD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e451.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e451.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface water (SW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e610.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundwater (GW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e422.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtmosphere (AT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2928.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1259.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1669.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eN flow analysis of industrial subsystem\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe industrial subsystem functioned as the processing department for N-containing products that are essential to meet human needs, and it demonstrated the highest and most rapid growth rate of N flow among the 12 subsystems. There was no stockpile of N within this subsystem, and the flux of N increased from 3183.06 Gg in 2000 to 5542.30 Gg in 2019, representing a remarkable increase of 74.12% over a span of two decades (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eIn 2010, the exclusive contribution of N from HBNF in the industrial subsystem accounted for 55.79% of the total input within this subsystem (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HBNF played a pivotal role in ensuring food safety and meeting the industrial demand for N(Luo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Fossil fuel combustion constituted 25.85% of the N input in the industrial subsystem, serving as a primary driver for the continuous increase in total N input within the subsystem. This ongoing surge in fossil fuel N input signifies an acceleration of the industrialization process in the YRB.\u003c/p\u003e \u003cp\u003eRegarding the outflow of N from the industrial subsystem, fertilizer N alone accounted for approximately half of the N output. The majority of these fertilizers were applied to the farmland subsystem in order to enhance crop yields (94.43%), with a smaller portion being allocated to the livestock subsystem. Globally, around 80% of synthetic fixed N is utilized for fertilizers; However, in developed countries like Japan, this proportion is relatively lower at approximately 40\u0026ndash;53%(Hayashi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the YRB region, on the other hand, an average proportion of 91.55% was used for fertilizer purposes, indicating a strong dependence on N fertilizer for agricultural production. Other significant pathways through which N flowed out from the industrial subsystem included emissions of N oxides and nitrogen-containing industrial products, accounting for 25.85% and 18.35%, respectively.\u003c/p\u003e \u003cp\u003eN flow analysis of cropland subsystem\u003c/p\u003e \u003cp\u003eThe cropland subsystem constituted the second largest N reservoir, following the industrial subsystem. There was a consistent downward trend in the accumulation of the cropland subsystem, decreasing from 1281.79 Gg in 2000 to 951.73 Gg in 2019, indicating a gradual reduction in its N sink capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eNitrogen inputs into this subsystem were classified as natural sources (13.49%) and anthropogenic sources (86.51%). Among the anthropogenic sources, fertilizer emerged as the predominant contributor to N inflow, accounting for 49.79\u0026ndash;57.95%. Globally, approximately 80% of synthetic fixed N is utilized in fertilizer production, with percentages ranging from 40\u0026ndash;53% in countries like Japan(Hayashi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, the average fertilizer utilization rate in the YRB was 91.55%, highlighting the region's heavy dependence on N fertilizers for agricultural production.\u003c/p\u003e \u003cp\u003eIn terms of N outflow, there are multiple pathways for N output in cropland subsystem. N oxide emissions account for 28.07% of the total N output, while crops and stalks contribute 27.58% and 13.55%, respectively, by retaining and releasing N within their biomass during harvesting in this subsystem. A portion of the N enters the livestock subsystem; Subsequently, when livestock is processed into meat products, the accumulated N is transferred to consumers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eN flow analysis of livestock subsystem\u003c/p\u003e \u003cp\u003eThe N inputs and outputs to the livestock subsystem were in a state of equilibrium, with the N stock in this subsystem remaining consistently low at 0.15% of the input. The magnitude of N fluxes in this subsystem exhibited a strong correlation with the size of the livestock population each year. For instance, there was a significant reduction in livestock numbers observed in 2007 due to an outbreak of avian influenza disease, resulting in a sharp decline in the N flow within the subsystem (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe N source in the livestock subsystem is relatively straightforward, as it enters solely through the livestock diet. Agricultural products consistently dominated the diet, accounting for approximately 70% over many years. Nitrogen outflow exhibits fluctuating growth (872.76-978.76 Gg) with various forms of outflow observed over time. In terms of N flow output, NH\u003csub\u003e3\u003c/sub\u003e volatilisation from livestock excreta (50.97% in 2010) constitutes the primary mode, followed by a runoff carrying livestock excreta into water bodies (28.39%). Both situations have adverse ecological effects, highlighting the need for enhanced supervision and control measures in breeding areas.\u003c/p\u003e \u003cp\u003eThe primary source of N entering surface water in the YRB is livestock runoff, indicating inadequate management of livestock and poultry excreta. The current rate of returning livestock manure to fields remains relatively low, emphasizing the need to strengthen the connection between livestock farming and agricultural production. The separation of agriculture and livestock has been identified as a significant factor contributing to the low utilization rate of N. Therefore, integrating crop cultivation with livestock farming is essential for addressing N loss and promoting sustainable intensive agriculture. Nitrogen accumulation exhibited a trend of rapid growth to stability, eventually slightly decreasing(Liu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eN flow analysis of human subsystem\u003c/p\u003e \u003cp\u003eThe human subsystem was the primary consumer of N, with the total N input into the human subsystem increasing by 1.43 times from 2000 to 2019, primarily due to the rise in N input from natural synthetic products and crop intake (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). It was observed that industrial and agricultural products continued to be imported into the human subsystem to meet growing consumption demands. Among these, N input into the human subsystem through food intake constituted approximately 33.37% of the total annual input, with a ratio of approximately 7:2:1 for crops, livestock, and aquatic products in food consumption estimation. This indicates that plant-based protein remained as the predominant nutritional source during this period of abundant food supply. The consumption of animal-based food had been steadily increasing over several years, resulting in higher N input requirements and breeding costs.\u003c/p\u003e \u003cp\u003eNitrogen output from the human subsystem mainly consisted of waste from industrial products (44.58%) and excreta (30.71%). Due to improved waste treatment efficiency, there has been a decrease in N loss from human excreta from 312.86 Gg in 2000 to 127.19 Gg in 2019. Population density in the YRB (530 people/km\u003csup\u003e2\u003c/sup\u003e) exceeded China's national average (132 people/km\u003csup\u003e2\u003c/sup\u003e), with high-density human activities contributing to pollutant emissions. Importantly, there has been a significant increasing trend in N accumulation within the human subsystem, rising from 433.42 Gg in 2000 to 927.67 Gg in 2019, marking a twofold increase. The enrichment of N within the human body can pose certain health hazards, severe cases have even been associated with gastric and esophageal cancers(Ward et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Therefore, increased attention is warranted regarding living environments and dietary health.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e2010 Major Sources and Sinks of N by Subsystem Gg N yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHuman Nature Coupling System\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMain source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMain remittance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN flow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN flow\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2417.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecrop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e842.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eagro-food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e710.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e emission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e482.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eforest tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e315.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprecipitate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHBNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2628.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2560.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eforage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaquatic product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edeposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e emissions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enatural product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e864.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003einvalidate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e451.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eN loss to the environment in YRB\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEnvironmental N load studies indicate that the ecological environment in the YRB is under significant pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Approximately 30% of the total N input is discharged into the environment, showing a clear upward trend. In terms of distribution, N emissions into the atmosphere constitutes the highest proportion (72.81\u0026ndash;81.78%), followed by surface water (10.31\u0026ndash;14.86%), with groundwater having the smallest share (7.71\u0026ndash;9.24%). Sun et al(Sun et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that NH\u003csub\u003e3\u003c/sub\u003e emissions account for 64.3% of the total reactive N emissions in Australia, and similar results are observed in the YRB, where NH\u003csub\u003e3\u003c/sub\u003e is one of the major gas emission sources, ranging from 36.35\u0026ndash;64.58%.\u003c/p\u003e \u003cp\u003eRegarding sources, the agricultural subsystem, livestock subsystem, and industrial subsystem makes significant contributions to environmental load, accounting for 31.26\u0026ndash;42.68%, 14.34\u0026ndash;20.88%, and 12.34\u0026ndash;35.30%, respectively. The primary driver of environmental load growth emissions is the in NO\u003csub\u003eX\u003c/sub\u003e emissions, primarily originating from industrial activities (88.75%). Simultaneously, it can be observed that agricultural activities have significantly reduced their contribution to environmental load due to measures implemented to control fertilizer usage. Overall, the N environmental load exhibited rapid growth in the early period and later stabilized later, increasing from 3724.81 Gg in 2000 to 5693.43 Gg in 2019.\u003c/p\u003e \u003cp\u003eIn 2010, the combined emissions of NH\u003csub\u003e3\u003c/sub\u003e, NO\u003csub\u003eX\u003c/sub\u003e, and N\u003csub\u003e2\u003c/sub\u003eO into the atmosphere reached 2928.83 Gg, which are critical components contributing to atmospheric haze formation. It was observed that in the presence of elevated NO\u003csub\u003eX\u003c/sub\u003e levels, there is a significant increase in the rate of PM2.5 formation from SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003eX\u003c/sub\u003e(Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, N\u003csub\u003e2\u003c/sub\u003eO, a potent greenhouse gas with a global warming potential 310 times greater than CO\u003csub\u003e2\u003c/sub\u003e, further emphasizes the importance of comprehending NH\u003csub\u003e3\u003c/sub\u003e, NOx, and N\u003csub\u003e2\u003c/sub\u003eO emissions and the sources for regional haze control and mitigation of greenhouse effects. NH\u003csub\u003e3\u003c/sub\u003e emissions primarily originated from agricultural subsystems (61.84%) and livestock subsystems (34.83%). Industrial activities predominantly contribute to NO\u003csub\u003eX\u003c/sub\u003e emissions (82.88%), while agricultural activities mainly account for N\u003csub\u003e2\u003c/sub\u003eO emissions (63.03%). Although N input into aquatic systems is relatively low compared to the atmosphere, excessive N in water bodies can lead to various impacts such as deterioration in water quality and loss of biodiversity. These effects become particularly significant under conditions where human activities result in increased N inputs.\u003c/p\u003e \u003cp\u003eApart from atmospheric nitrogen deposition, the primary sources of N entering surface waters are livestock runoff (50.20%) and agricultural runoff (33.46%). Therefore, it is crucial to implement measures that effectively control N emissions from cropland and livestock subsystems while enhancing their management practices. These findings emphasize the significance of comprehensive monitoring and management of N emissions in order to mitigate atmospheric haze and reduce the environmental impacts associated with excessive N enrichment in aquatic systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNitrogen use efficiency\u003c/p\u003e \u003cp\u003eThe analysis revealed significant variations in NUE among the subsystems in the YRB. The farm subsystem exhibited a relatively high NUE (28.77% in 2010) attributed to the substantial presence of effective products in its output items. Conversely, the livestock subsystem, where a considerable amount of nitrogen was lost through excreta, generally displayed lower NUE (15.88%). Although the aquatic subsystem demonstrated the highest NUE (51.62%) among the three subsystems, its N flux remained comparatively low.\u003c/p\u003e \u003cp\u003eAlthough the N utilization efficiency of the farm subsystem increased from 25.67% in 2000 to 36.10% in 2019, it still remains relatively low compared to other countries (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The primary reason for this disparity lies in sub-optimal amounts and application method of N fertilizer. In order to enhance crop yield and achieve higher economic returns, farmers tend to apply large excessive amounts of N fertilizer during cultivation; However, due to inappropriate timing such as applying N fertilizer on the surface layer during the early stages of crop growth when the root uptake is weak, significant nitrogen losses occur(Z. Cui et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This issue has been alleviated through a series of systematic agricultural surface pollution control policies. Since 2013, the increase in nitrogen fertilizer prices has prompted farmers to adopt more precise nitrogen management practices, resulting in a reduction of nitrogen inputs, indirectly leading to an increase in nitrogen use efficiency. To sustainably meet the N requirements for agriculture, renewable resources such as crop residues, manure and other waste organic waste materials are necessary for providing and maintaining nutrients essential for crop growth. Notably, over the past decade there has been a significant increase in straw returning back into fields compared to manure usage.\u003c/p\u003e \u003cp\u003eThe livestock subsystem exhibited a range of 12.21\u0026ndash;16.69% variation in NUE over the study period (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which was approximately half of the NUE observed in the crop-based food within the cropland subsystem when compared to animal-based food. The high nitrogen levels found in livestock excreta during livestock farming can be attributed to both excessive nitrogenous feed intake and inadequate digestive utilization of nitrogen in feeds, thereby contributing significantly to the low level of NUE within the livestock subsystem(Alejo-Alvarez et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In 2019 alone, statistics indicate that livestock excreta produced within the watershed amounted to 815.15 Gg accounting for 83.16% of the total N input into this subsystem. Furthermore, due to insufficient pollution treatment measures implemented within the district, haphazard discharge of livestock faeces and effluents from livestock farming exacerbates nitrogen accumulation in the environment. Therefore, enhancing resource utilization efficiency of livestock excreta and implementing rationally regulation on nitrogen input from livestock feeding practices can effectively mitigate surface source pollution within the watershed while simultaneously improving NUE and optimizing dietary structure for individuals.\u003c/p\u003e \u003cp\u003eThe NUE of the aquaculture subsystem is marginally superior compared to other countries, exhibiting minimal variation over the 20-year period and an average NUE of 51.78% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It should be noted that fish sourced from natural environments were not included in this study; However, in Japan, for example, the NUE in the aquatic subsystem was surprisingly high at 480%, accounting for wild-caught fish(Hayashi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Approximately 92.92% of the N within the aquatic subsystem originated from feed intake, with a relatively efficient absorption and utilization by the fish. A small fraction was lost to the water column without consumption, while the remaining portion was excreted by the fish. Overall, there is a comparatively low level of nitrogen loss throughout this process. Furthermore, it is worth mentioning that higher protein content within fish may also contribute to enhanced NUE within this system.\u003c/p\u003e \u003cp\u003eTo address the issue of low NUE in the YRB, we propose the following management measures: on the input side, firstly, precision agriculture should be developed. This can be achieved by enhancing nitrogen fertilizer utilization efficiency, optimizing irrigation water management, and implementing soil formula fertilization programs and water-saving irrigation technologies tailored to the local conditions to minimize excess nitrogen loss(Li et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Secondly, for the livestock and aquatic subsystems, feed management should be optimized to reduce nitrogen loss in feed. On the output side, waste should be utilized rationally by guiding farmers to prioritize returning human and livestock manure as well as crop residues back into fields. This will facilitate recycling of N waste from human livestock, and agricultural subsystems. Additionally, comprehensive cross-industry management measures need to be implemented while considering local conditions. Strengthening regulatory systems is crucial along with investing in and promoting new technologies. Furthermore, governmental departments should formulate policies and regulations that support both nitrogen resource utilization and environmental protection in order to promote green development. While excess N in agricultural fields can contribute somewhat to non-point source pollution, available studies have demonstrated that nitrogen can be effective remediate organic pollutants and certain heavy metals. In summary, improving nitrogen utilization efficiency requires comprehensive management approaches coupled with technological innovation at multiple perspectives and levels so as to achieve sustainable growth within the food industry.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of NUE in this study with typical areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubsystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNUE(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.0\u0026ndash;48.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Oenema et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Gu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1996\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.7\u0026ndash;50.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Karimi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.67\u0026ndash;36.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Hayashi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Gu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1996\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.2\u0026ndash;22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Karimi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.21\u0026ndash;16.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Hayashi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Matos et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.66\u0026ndash;53.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eLife cycle analysis of industrial N flow\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIndustrial production plays a crucial role in the N cycle by converting N from various sources such as HBNF, agricultural production, fossil fuel combustion, livestock farming, and wood into fertilizers, naturally synthesized nitrogen products, industrially synthesized nitrogen products, and wastewater effluents. These converted forms of nitrogen then enter the next stage of the cycle. The Sankey diagram can visually demonstrate how N converges in industrial production and is distributed throughout its flow. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the analysis results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to meet the human demand, the N flux of the industrial subsystem continues to grow at an average annual growth rate of 3.71%. From Fig.\u0026nbsp;7, it can be observed that the industrial subsystem exhibits two main features: high nitrogen concentration and abundant waste generation. The HBNF serves as the primary input source of Nr for the industrial subsystem contributing significantly more than the other four (55.79%), and this part of nitrogen was mainly used in the production of industrial fertilizers and synthetic nitrogen products. Additionally, agricultural activities contribute significantly (18.22%) to industrial subsystem through crop cultivation and straw output, which cannot be overlooked.\u003c/p\u003e \u003cp\u003eNitrogen entering into industrial production undergoes processing and is utilized by other subsystems. The main utilization of N input in industrial production lies in agricultural practices and human consumption, particularly for the production of agricultural fertilizer that alone accounts for 43.52% of total industrial N output. These fertilizers are primarily derived from HBNF, which is mainly derived from HBNF, which synthesizes ammonia to create various nitrogen-based fertilizer products. Industrial production has led to substantial advancements in nitrogen utilization pathways and benefits for humans; However, only a relatively small fraction flows directly into the human subsystem through natural or industrially synthesized products. A large proportion of N is emitted or transferred to the environment throughout various stages via wastewater exhaust during this process, with emissions continuing to rise steadily over time. For instance, NOx losses alone have escalated from 427.27 Gg in 2000 to 2008.26 Gg in 2019.\u003c/p\u003e \u003cp\u003eIn response to the ongoing rise in industrial pollutant emissions in the YRB, two types of measures can be implemented for their control: firstly, reducing the production of reactive nitrogen by promoting clean energy sources to minimize fossil fuel combustion. Secondly, converting reactive nitrogen N\u003csub\u003e2\u003c/sub\u003e can be achieved through relevant reaction technology to convert nitrate and ammonium salts to N\u003csub\u003e2\u003c/sub\u003e. Additionally, promoting an industrial circular economy model is crucial for internal recycling of nitrogen through waste utilization and resource sharing within the industrial chain. For example, nitrogen present in industrial wastewater should be utilized as fertilizer for recycled water or converted into raw materials for organic synthesis from nitrogen-rich industrial waste gases. Strengthening regulations and standards on industrial nitrogen emissions is essential while encouraging industries to adopt cleaner production techniques and equipment to limit unnecessary losses of nitrogen. These measures will not only contribute towards reducing environmental pollution, but also enhance resource utilization efficiency and foster sustainable industrial development.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAccurate estimation of the nitrogen balance within YRB is essential to effectively manage nitrogen pollution and enhance the overall environmental quality. Therefore, this study evaluates nitrogen flow in the YRB over the past 20 years using the FNFA method. The findings are as follows:\u003c/p\u003e\n\u003cp\u003e(1) The annual total nitrogen input in the YRB increased from 12,806.69 Gg in 2000 to 18,553.42 Gg in 2019, indicating a rapid growth rate. Nitrogen input and output are concentrated in several major subsystems, with the agricultural and industrial subsystems being particularly significant. In 2010, the nitrogen inputs to the agricultural and industrial subsystems reached 4,711.64 Gg and 4,364.77 Gg, respectively, with these subsystems contributing substantial amounts of nitrogen to the overall system through the delivery of agricultural and industrial production materials.\u003c/p\u003e\n\u003cp\u003e(2) The environmental load within the YRB has been heavy, increasing from 3,724.81 Gg in 2000 to 5,693.43 Gg in 2019.\u003c/p\u003e\n\u003cp\u003e(3) There are significant differences in nitrogen use efficiency among different production subsystems, with particular attention needed to improve nitrogen use efficiency in the agricultural and livestock subsystems.\u003c/p\u003e\n\u003cp\u003e(4) Lifecycle analysis of industrial nitrogen flow indicates that only a small portion of the nitrogen is utilized in industrial products for human consumption, while the majority ultimately dissipates into the environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure statement\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; This research\u0026nbsp;was funded by the General Program for Humanities and Social Sciences Research, Ministry of Education (Grant No. 19YJCzHO85).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp; The data set used or analyzed during this study is under study and cannot be shared due to confidentiality. Some of the publicly available datasets are detailed in the manuscript with sources and access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ying Cui: conceptualization, data curation, formal analysis, writing \u0026ndash; original draft, writing.\u003c/p\u003e\n\u003cp\u003eRuiping Li: conceptualization, supervision, resources, funding acquisition, project administration, review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eXu Chen: data curation, supervision, review \u0026amp; validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe would like to express our gratitude to the anonymous reviewers for their valuable comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u0026nbsp; Ying Cui https://orcid.org/0009-0009-6898-6826\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlejo-Alvarez, L., Guzm\u0026aacute;n-Fierro, V., Fern\u0026aacute;ndez, K., \u0026amp; Roeckel, M. (2016). Technical and economical optimization of a full-scale poultry manure treatment process: Total ammonia nitrogen balance. Environmental Technology, \u003cem\u003e37\u003c/em\u003e(22), 2865\u0026ndash;2878. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09593330.2016.1167963\u003c/span\u003e\u003cspan address=\"10.1080/09593330.2016.1167963\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeckinghausen, A., Odlare, M., Thorin, E., \u0026amp; Schwede, S. (2020). From removal to recovery: An evaluation of nitrogen recovery techniques from wastewater. Applied Energy, \u003cem\u003e263\u003c/em\u003e, 114616. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apenergy.2020.114616\u003c/span\u003e\u003cspan address=\"10.1016/j.apenergy.2020.114616\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, D., Wang, C., \u0026amp; Liu, Y. (2022). Investigation of the nitrogen flows of the food supply chain in Beijing-Tianjin-Hebei region, China during 1978\u0026ndash;2017. Journal of Environmental Management, \u003cem\u003e314\u003c/em\u003e, 115038. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jenvman.2022.115038\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2022.115038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, H., Ouyang, W., Hao, F., Ren, X., \u0026amp; Yang, S. (2007). The non-point source pollution in livestock-breeding areas of the Heihe River basin in Yellow River. Stochastic Environmental Research and Risk Assessment, \u003cem\u003e21\u003c/em\u003e(3), 213\u0026ndash;221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00477-006-0057-2\u003c/span\u003e\u003cspan address=\"10.1007/s00477-006-0057-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, M., Guo, Q., Wei, Y., Yu, X., Hu, J., Tian, L., \u0026amp; Kong, J. (2023). Variations and its driven factors of anthropogenic nitrogen loads in the Yangtze River Economic Belt during 2000\u0026ndash;2019. Environmental Science and Pollution Research International, \u003cem\u003e30\u003c/em\u003e(2), 2450\u0026ndash;2468. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-022-21943-y\u003c/span\u003e\u003cspan address=\"10.1007/s11356-022-21943-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, S., Shi, Y., Groffman, P. M., Schlesinger, W. H., \u0026amp; Zhu, Y.-G. (2013). Centennial-scale analysis of the creation and fate of reactive nitrogen in China (1910\u0026ndash;2010). \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e110\u003c/em\u003e(6), 2052\u0026ndash;2057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1221638110\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1221638110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, Z., Wang, G., Yue, S., Wu, L., Zhang, W., Zhang, F., \u0026amp; Chen, X. (2014). Closing the N-Use Efficiency Gap to Achieve Food and Environmental Security. Environmental Science \u0026amp; Technology, \u003cem\u003e48\u003c/em\u003e(10), 5780\u0026ndash;5787. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/es5007127\u003c/span\u003e\u003cspan address=\"10.1021/es5007127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Jesus Delmiro Rocha, M., \u0026amp; Neto, I. E. L. (2023). Nitrogen mass balance and uptake velocity for eutrophic reservoirs in the Brazilian semiarid region. Environmental Science and Pollution Research, \u003cem\u003e30\u003c/em\u003e(42), 95621\u0026ndash;95633. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-023-29136-x\u003c/span\u003e\u003cspan address=\"10.1007/s11356-023-29136-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, Y., Xu, L., Yang, Z., Zheng, H., \u0026amp; Chen, L. (2020). Aggravation of reactive nitrogen flow driven by human production and consumption in Guangzhou City China. Nature Communications, \u003cem\u003e11\u003c/em\u003e(1), 1209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-020-14699-x\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-14699-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErisman, J. W., Leach, A., Bleeker, A., Atwell, B., Cattaneo, L., \u0026amp; Galloway, J. (2018). An Integrated Approach to a Nitrogen Use Efficiency (NUE) Indicator for the Food Production\u0026ndash;Consumption Chain. Sustainability, \u003cem\u003e10\u003c/em\u003e(4), Article 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su10040925\u003c/span\u003e\u003cspan address=\"10.3390/su10040925\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFowler, D., Coyle, M., Skiba, U., Sutton, M. A., Cape, J. N., Reis, S., Sheppard, L. J., Jenkins, A., Grizzetti, B., Galloway, J. N., Vitousek, P., Leach, A., Bouwman, A. F., Butterbach-Bahl, K., Dentener, F., Stevenson, D., Amann, M., \u0026amp; Voss, M. (2013). The global nitrogen cycle in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences, \u003cem\u003e368\u003c/em\u003e(1621), 20130164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rstb.2013.0164\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2013.0164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu, B., Ge, Y., Chang, S. X., Luo, W., \u0026amp; Chang, J. (2013). Nitrate in groundwater of China: Sources and driving forces. Global Environmental Change, \u003cem\u003e23\u003c/em\u003e(5), 1112\u0026ndash;1121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gloenvcha.2013.05.004\u003c/span\u003e\u003cspan address=\"10.1016/j.gloenvcha.2013.05.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu, B., Ju, X., Chang, S. X., Ge, Y., \u0026amp; Chang, J. (2017). Nitrogen use efficiencies in Chinese agricultural systems and implications for food security and environmental protection. Regional Environmental Change, \u003cem\u003e17\u003c/em\u003e(4), 1217\u0026ndash;1227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10113-016-1101-5\u003c/span\u003e\u003cspan address=\"10.1007/s10113-016-1101-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayashi, K., Shibata, H., Oita, A., Nishina, K., Ito, A., Katagiri, K., Shindo, J., \u0026amp; Winiwarter, W. (2021). Nitrogen budgets in Japan from 2000 to 2015: Decreasing trend of nitrogen loss to the environment and the challenge to further reduce nitrogen waste. Environmental Pollution, \u003cem\u003e286\u003c/em\u003e, 117559. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2021.117559\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2021.117559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson, D. W., \u0026amp; Turner, J. (2014). Nitrogen budgets of forest ecosystems: A review. Forest Ecology and Management, \u003cem\u003e318\u003c/em\u003e, 370\u0026ndash;379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foreco.2013.08.028\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2013.08.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarimi, R., Pogue, S. J., Kr\u0026ouml;bel, R., Beauchemin, K. A., Schwinghamer, T., \u0026amp; Henry Janzen, H. (2020). An updated nitrogen budget for Canadian agroecosystems. Agriculture, Ecosystems \u0026amp; Environment, \u003cem\u003e304\u003c/em\u003e, 107046. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agee.2020.107046\u003c/span\u003e\u003cspan address=\"10.1016/j.agee.2020.107046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, B., Yan, L., \u0026amp; Zhang, W. (2023). Study on N application and N reduction potential of farmland in China. Environmental Monitoring and Assessment, \u003cem\u003e195\u003c/em\u003e(10), 1156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-023-11780-y\u003c/span\u003e\u003cspan address=\"10.1007/s10661-023-11780-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, J., Li, Y., Zheng, Y., Tong, S., Zhang, X., Zhao, Y., Zheng, W., Zhai, B., Wang, Z., Zhang, X., Li, Z., \u0026amp; Zamanian, K. (2022). The spatial and temporal distribution of nitrogen flow in the agricultural system and green development assessment of the Yellow River Basin. Agricultural Water Management, \u003cem\u003e263\u003c/em\u003e, 107425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2021.107425\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2021.107425\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, Z., Hu, S., Chen, D., \u0026amp; Zhu, B. (2018). From Production to Consumption: A Coupled Human\u0026ndash;Environmental Nitrogen Flow Analysis in China. Environmental Science \u0026amp; Technology, \u003cem\u003e52\u003c/em\u003e(4), 2025\u0026ndash;2035. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.est.7b03471\u003c/span\u003e\u003cspan address=\"10.1021/acs.est.7b03471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatos, J., Costa, S., Rodrigues, A., Pereira, R., \u0026amp; Sousa Pinto, I. (2006). Experimental integrated aquaculture of fish and red seaweeds in Northern Portugal. Aquaculture, \u003cem\u003e252\u003c/em\u003e(1), 31\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aquaculture.2005.11.047\u003c/span\u003e\u003cspan address=\"10.1016/j.aquaculture.2005.11.047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoomaw, W. R. (2002). Energy, Industry and Nitrogen: Strategies for Decreasing Reactive Nitrogen Emissions. AMBIO: A Journal of the Human Environment, \u003cem\u003e31\u003c/em\u003e(2), 184\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1579/0044-7447-31.2.184\u003c/span\u003e\u003cspan address=\"10.1579/0044-7447-31.2.184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOenema, O., Witzke, H. P., Klimont, Z., Lesschen, J. P., \u0026amp; Velthof, G. L. (2009). Integrated assessment of promising measures to decrease nitrogen losses from agriculture in EU-27. Agriculture, Ecosystems \u0026amp; Environment, \u003cem\u003e133\u003c/em\u003e(3\u0026ndash;4), 280\u0026ndash;288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agee.2009.04.025\u003c/span\u003e\u003cspan address=\"10.1016/j.agee.2009.04.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan, J., Ding, N., \u0026amp; Yang, J. (2019). Changes of urban nitrogen metabolism in the Beijing megacity of China, 2000\u0026ndash;2016. Science of The Total Environment, \u003cem\u003e666\u003c/em\u003e, 1048\u0026ndash;1057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2019.02.315\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2019.02.315\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma, L., \u0026amp; Bali, S. (2017). A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture. Sustainability, \u003cem\u003e10\u003c/em\u003e(2), 51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su10010051\u003c/span\u003e\u003cspan address=\"10.3390/su10010051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, Z., Xin, J., Wu, H., Jiang, Z., Peng, H., Xu, F., He, C., Shi, Q., \u0026amp; Zheng, X. (2023). Kinetic and molecular evidence for DON transformation in the deep vadose zone: Important implications for soil nitrogen budgeting and groundwater nitrate management. Journal of Hydrology, \u003cem\u003e616\u003c/em\u003e, 128782. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2022.128782\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2022.128782\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStein, L. Y., \u0026amp; Klotz, M. G. (2016). The nitrogen cycle. Current Biology, \u003cem\u003e26\u003c/em\u003e(3), R94\u0026ndash;R98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cub.2015.12.021\u003c/span\u003e\u003cspan address=\"10.1016/j.cub.2015.12.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens, C. J. (2019). Nitrogen in the environment. Science, \u003cem\u003e363\u003c/em\u003e(6427), 578\u0026ndash;580. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aav8215\u003c/span\u003e\u003cspan address=\"10.1126/science.aav8215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStuart, D., Basso, B., Marquart-Pyatt, S., Reimer, A., Robertson, G. P., \u0026amp; Zhao, J. (2015). The Need for a Coupled Human and Natural Systems Understanding of Agricultural Nitrogen Loss. BioScience, \u003cem\u003e65\u003c/em\u003e(6), 571\u0026ndash;578. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/biosci/biv049\u003c/span\u003e\u003cspan address=\"10.1093/biosci/biv049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, Y., Gu, B., Grinsven, H. J. M. van, Reis, S., Lam, S. K., Zhang, X., Chen, Y., Zhou, F., Zhang, L., Wang, R., Chen, D., \u0026amp; Xu, J. (2021). The Warming Climate Aggravates Atmospheric Nitrogen Pollution in Australia. Research. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.34133/2021/9804583\u003c/span\u003e\u003cspan address=\"10.34133/2021/9804583\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Egmond, K., Bresser, T., \u0026amp; Bouwman, L. (2002). The European Nitrogen Case. AMBIO: A Journal of the Human Environment, \u003cem\u003e31\u003c/em\u003e(2), 72\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1579/0044-7447-31.2.72\u003c/span\u003e\u003cspan address=\"10.1579/0044-7447-31.2.72\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, S.-Y., Liu, J.-S., \u0026amp; Ma, T.-B. (2010). Dynamics and changes in spatial patterns of land use in Yellow River Basin, China. Land Use Policy, \u003cem\u003e27\u003c/em\u003e(2), 313\u0026ndash;323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landusepol.2009.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.landusepol.2009.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X., Xu, M., Lin, B., Bodirsky, B. L., Xuan, J., Dietrich, J. P., Stevanović, M., Bai, Z., Ma, L., Jin, S., Fan, S., Lotze-Campen, H., \u0026amp; Popp, A. (2023). Reforming China\u0026rsquo;s fertilizer policies: Implications for nitrogen pollution reduction and food security. Sustainability Science, \u003cem\u003e18\u003c/em\u003e(1), 407\u0026ndash;420. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11625-022-01189-w\u003c/span\u003e\u003cspan address=\"10.1007/s11625-022-01189-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWard, M. H., Heineman, E. F., Markin, R. S., \u0026amp; Weisenburger, D. D. (2008). Adenocarcinoma of the Stomach and Esophagus and Drinking Water and Dietary Sources of Nitrate and Nitrite. International Journal of Occupational and Environmental Health, \u003cem\u003e14\u003c/em\u003e(3), 193\u0026ndash;197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen, L., Lei, M., Zhang, B., Kong, X., Liao, Y., \u0026amp; Chen, W. (2023). Significant increase in gray water footprint enhanced the degradation risk of cropland system in China since 1990. Journal of Cleaner Production, \u003cem\u003e423\u003c/em\u003e, 138715. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2023.138715\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2023.138715\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, Z., Jiang, M., Wang, H., Di, D., \u0026amp; Guo, X. (2022). Management implications of spatial\u0026ndash;temporal variations of net anthropogenic nitrogen inputs (NANI) in the Yellow River Basin. Environmental Science and Pollution Research, \u003cem\u003e29\u003c/em\u003e(35), 52317\u0026ndash;52335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-022-19440-3\u003c/span\u003e\u003cspan address=\"10.1007/s11356-022-19440-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia, L., \u0026amp; Yan, X. (2023). How to feed the world while reducing nitrogen pollution. Nature, \u003cem\u003e613\u003c/em\u003e(7942), 34\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/d41586-022-04490-x\u003c/span\u003e\u003cspan address=\"10.1038/d41586-022-04490-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, X., Gu, B., Van Grinsven, H., Lam, S. K., Liang, X., Bai, M., \u0026amp; Chen, D. (2020). Societal benefits of halving agricultural ammonia emissions in China far exceed the abatement costs. Nature Communications, \u003cem\u003e11\u003c/em\u003e(1), 4357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-020-18196-z\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-18196-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, Y., \u0026amp; Yang, S. (2022). Characteristics of nitrogen flow and its environmental effects in the Yellow River Basin, China. Environmental Technology, \u003cem\u003e45\u003c/em\u003e, 1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09593330.2022.2114015\u003c/span\u003e\u003cspan address=\"10.1080/09593330.2022.2114015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmerman, P. R., Greenberg, J. P., \u0026amp; Westberg, C. E. (1988). Measurements of atmospheric hydrocarbons and biogenic emission fluxes in the Amazon Boundary layer. Journal of Geophysical Research, \u003cem\u003e93\u003c/em\u003e(D2), 1407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/JD093iD02p01407\u003c/span\u003e\u003cspan address=\"10.1029/JD093iD02p01407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Nitrogen balance, Full nitrogen flow analysis, Nitrogen use efficiency, The Yellow River Basin","lastPublishedDoi":"10.21203/rs.3.rs-4962696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4962696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on the basic statistical data and related parameters of The Yellow River Basin (YRB) from 2000 to 2019, the nitrogen flow model of the YRB was constructed by using the full nitrogen flow analysis model (FNFA) and the emission coefficient method to analyze the characteristics of the nitrogen inputs and outputs in the YRB. The results revealed that over the past 20 years, both the total nitrogen inputs and outputs in the YRB have shown a significant increasing trend. Specifically, the total nitrogen input rose from 12,806.69 Gg to 18,553.42 Gg, while the total output increased from 9,250.93 Gg to 12,955.0 Gg. Among the various subsystems, the industrial and agricultural sectors were the largest contributors to nitrogen balance, accounting for 28.30% and 26.22% of the total nitrogen input, and 26.22% and 40.48% of the total nitrogen output, respectively. The overall nitrogen utilization efficiency (NUE) across the subsystems required improvement, particularly within the cropland subsystem, which had an NUE ranging from 25.67\u0026ndash;36.10%. In contrast, the livestock subsystem exhibited only half the NUE of the cropland subsystem. High emissions and inefficient nitrogen utilization led to a continuous increase in environmental nitrogen loads, with atmospheric nitrogen loads being particularly pronounced. Additionally, the life cycle analysis of industrial nitrogen revealed that a substantial amount of nitrogen was enriched in the atmosphere. These findings can serve as scientific basis and support for regulating nitrogen inflow and outflow within watershed areas, and formulating more rational integrated management strategies for nitrogen.\u003c/p\u003e","manuscriptTitle":"Analysis of nitrogen flow in the Yellow River Basin over a long time series","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-30 12:37:48","doi":"10.21203/rs.3.rs-4962696/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-23T05:04:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-18T14:57:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-02T04:37:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39436308856196459589493943629329407368","date":"2024-09-19T07:13:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20226879345364723064703186786099140318","date":"2024-09-12T23:22:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-12T14:44:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-30T00:21:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-30T00:21:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-08-23T08:28:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"38080b1a-0626-4cae-bd2a-bd5505f64623","owner":[],"postedDate":"September 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:07:13+00:00","versionOfRecord":{"articleIdentity":"rs-4962696","link":"https://doi.org/10.1007/s10661-024-13505-1","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2024-12-05 15:58:08","publishedOnDateReadable":"December 5th, 2024"},"versionCreatedAt":"2024-09-30 12:37:48","video":"","vorDoi":"10.1007/s10661-024-13505-1","vorDoiUrl":"https://doi.org/10.1007/s10661-024-13505-1","workflowStages":[]},"version":"v1","identity":"rs-4962696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4962696","identity":"rs-4962696","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}