Riverine NO3 Research Articles

Unveiling riverine N2O dynamics along urbanization gradients by integrating hydrological, biogeochemical and microbial processes

Human-disturbed rivers are globally significant sources of atmospheric nitrous oxide (N2O). Yet, the underlying mechanisms of urbanization impact on riverine N2O dynamics are not well understood. This study unveiled the effects of urbanization on N2O dynamics by integrating hydrological, biogeochemical and microbial processes in a river with various urbanization intensities. Riverine NO3− concentration enhanced with increasing urban land percentage, primarily because of the increased proportional contribution of sewage & manure source. The 15N site preference and relevant isotopic evidences revealed that the proportion of denitrification derived N2O increased from 60% to 76%, with the urban land percentage increasing from < 5% to > 22%, which was caused by decreases in flow velocity and dissolved oxygen saturation, increases in NO3− concentration and N2O-denitrifying genes. The non-negligible contribution of nitrification to N2O production (∼ 40%) in lower-urbanized river stretches may be attributed to aerobic conditions and lower impermeable riparian zone facilitating the occurrence of in-river nitrification and the access of in-soil nitrification to river. Urbanization-mediated decreases in flow velocity and dissolved oxygen and increases in nitrogen availability and denitrification process resulted in an increase in N2O concentration and flux, with N2O concentration approximately four times higher in higher-urbanized river reaches (50.7 ± 26.3 nmol/L) than in lower-urbanized river reaches (14.4 ± 2.5 nmol/L). In addition, increased proportional contribution of sewage & manure source also provides the possibility for exogenous N2O inputs with urban expansion. These findings contribute to deepening our understanding of how urbanization drives N2O dynamics in river systems.

A hybrid deep learning approach to predict hourly riverine nitrate concentrations using routine monitored data

With high-frequency data of nitrate (NO3–N) concentrations in waters becoming increasingly important for understanding of watershed system behaviors and ecosystem managements, the accurate and economic acquisition of high-frequency NO3–N concentration data has become a key point. This study attempted to use coupled deep learning neural networks and routine monitored data to predict hourly NO3–N concentrations in a river. The hourly NO3–N concentration at the outlet of the Oyster River watershed in New Hampshire, USA, was predicted through neural networks with a hybrid model architecture coupling the Convolutional Neural Networks and the Long Short-Term Memory model (CNN-LSTM). The routine monitored data (the river depth, water temperature, air temperature, precipitation, specific conductivity, pH and dissolved oxygen concentrations) for model training were collected from a nested high-frequency monitoring network, while the high-frequency NO3–N concentration data obtained at the outlet were not included as inputs. The whole dataset was separated into training, validation, and testing processes according to the ratio of 5:3:2, respectively. The hybrid CNN-LSTM model with different input lengths (1d, 3d, 7d, 15d, 30d) displayed comparable even better performance than other studies with lower frequencies, showing mean values of the Nash-Sutcliffe Efficiency 0.60–0.83. Models with shorter input lengths demonstrated both the higher modeling accuracy and stability. The water level, water temperature and pH values at monitoring sites were main controlling factors for forecasting performances. This study provided a new insight of using deep learning networks with a coupled architecture and routine monitored data for high-frequency riverine NO3–N concentration forecasting and suggestions about strategies about variable and input length selection during preprocessing of input data.

Coupled processes involving ammonium inputs, microbial nitrification, and calcite dissolution control riverine nitrate pollution in the piedmont zone (Qingshui River, China)

Rivers in agricultural countries widely suffer from diffuse nitrate (NO3−) pollution. Although pollution sources and fates of riverine NO3− have been reported worldwide, the driving mechanisms of riverine NO3− pollution associated with mineral dissolution in piedmont zones remain unclear. This study combined hydrogeochemical compositions, stable isotopes (δ18O–NO3−, δ15N–NO3−, δ18O–H2O, and δ2H–H2O), and molecular bioinformation to determine the pollution sources, biogeochemical evolution, and natural attenuation of riverine NO3− in a typical piedmont zone (Qingshui River). High NO3− concentration (37.5 ± 9.44 mg/L) was mainly observed in the agricultural reaches of the river, with ~15.38 % of the samples exceeding the acceptable limit for drinking purpose (44 mg/L as NO3−) set by the World Health Organization. Ammonium inputs, microbial nitrification, and HNO3-induced calcite dissolution were the dominant driving factors that control riverine NO3− contamination in the piedmont zone. Approximately 99.4 % of riverine NO3− contents were derived from NH4+-containing pollutants, consisted of manure & domestic sewage (74.0 % ± 13.0 %), NH4+-synthetic fertilizer (16.1 % ± 8.99 %), and soil organic nitrogen (9.35 % ± 4.49 %). These NH4+-containing pollutants were converted to HNO3 (37.2 ± 9.38 mg/L) by nitrifying bacteria, and then the produced HNO3 preferentially participated in the carbonate (mainly calcite) dissolution, which accounted for 40.0 % ± 12.1 % of the total riverine Ca2+ + Mg2+, also resulting in the rapid release of NO3− into the river water. Thus, microbial nitrification could be a new and non-negligible contributor of riverine NO3− pollution, whereas the involvement of HNO3 in calcite dissolution acted as an accelerator of riverine NO3− pollution. However, denitrification had lesser contribution to natural attenuation for high NO3− pollution. The obtained results indicated that the mitigation of riverine NO3− pollution should focus on the management of ammonium discharges, and the HNO3-induced carbonate dissolution needs to be considered in comprehensively understanding riverine NO3− pollution in piedmont zones.

Implication of self-organizing map, stable isotopes combined with MixSIAR model for accurate nitrogen control in a well-protected reservoir

Nitrogen pollution and eutrophication in reservoirs is a global environmental geochemical concern. Occasional algal blooms still exist in reservoirs that have undergone pollution treatment. The lack of quantitative evidence of nitrogen sources and fate limits long-term stable ecological safety management. This work applied an approach integrated zonal mapping, stable isotopes (δ18OH2O, δ15Nnitrate, δ18Onitrate, and δ13C-DIC) and a Bayesian isotope model to analyze regional and seasonal differences in the contribution and sources of nitrogen to a well-protected reservoir. The values of δ18Onitrate and the positive relationship between NO3− and δ13C-DIC suggested that nitrification was the primary NO3− production in the rivers. While Denitrification was present at only a few sites. Results of the MixSIAR model coupled the NO3−/Cl− indicator revealed that the domestic sewage contributed high riverine NO3− loading (68.6 ± 10.6 %) in the dry season. In the wet season, the main nitrate sources of upper watershed were ammonia and carbamide fertilizers (47.5 % and 40.3 %). While the domestic sewage was still the major contributor of downstream region (a dense residential area), indicating possible problems with rainwater and sewage drainage networks. The results implied that the colleting and treatment of sewages were the priority in downstream region, and non-point source pollution control and wastewater treatment plant upgrading were essential to control nitrate pollution in the two upstream regions. These findings provide new insights into precise nitrogen pollution traceability and identification of treatment priorities in the sub-region, and promote the management other well-protected watershed in similar need of further nitrogen contamination control.

A spatially explicit interpretable machine-learning method to track dissolved inorganic nitrogen pollution in a coastal watershed

Sustainable water management requires an in-depth understanding of dissolved inorganic nitrogen (DIN) in watersheds. However, it remains challenging in the context of intensifying human-induced and naturally impacted stresses in a changing environment. A spatially explicit interpretable machine-learning method was proposed based on the annual variation in land use/cover, Catchment Land Surface Model, Noah land surface model, population dynamics, and nightlight in a coastal Chinese watershed. The proposed model was effective for delineating DIN processes and sources in watersheds. The d (index of agreement), R2 (coefficient of determination), |PBIAS| (percent bias), and KGE (Kling-Gupta efficiency)during the training period were 0.91–0.98, 0.76–0.96, 0.38–3.12, and 0.73–0.87, respectively. The d, R2, |PBIAS|, and KGE during the testing period were 0.85–0.94, 0.56–0.79, 3.59–6.18, and 0.65–0.86, respectively. NH4+-N in the watersheds may be strongly related to the effects of urbanization within the watersheds while the agricultural activities may modify the patterns of NO3–-N in the watersheds. The NH4+-N was highly related to the urbanization effects, which contributed 20 %–30 % of the riverine NH4+-N in the North River. In contrast, agricultural activities may modify patterns of NO3–-N in watersheds, and agricultural activities in the West River contributed 50 %–70 % riverine NO3–-N in the watershed. Moreover, urbanization may alter the water content, soil properties, and regional climate patterns within the watersheds, while repeated DIN input through agricultural activities in the agricultural watersheds may change the nitrogen processes in the soil and groundwater. Paired with agricultural activities, dams or reservoirs may amplify NH4+-N to watersheds with modifying the advection and diffusion of DIN from agricultural activities, and increasing sediments in the watersheds. This study demonstrated the potentials of the proposed method in tracking DIN pollution and provided new insights into nutrient management in the watersheds under intensifying human-nature interactions.

From Soil to River: Revealing the Mechanisms Underlying the High Riverine Nitrate Levels in a Forest Dominated Catchment.

Elevated riverine nitrate (NO3-) levels have led to increased eutrophication and other ecological implications. While high riverine NO3- levels were generally ascribed to anthropogenic activities, high NO3- levels in some pristine or minimally disturbed rivers were reported. The drivers of these unexpectedly high NO3- levels remain unclear. This study combined natural abundance isotopes, 15N-labeling techniques, and molecular techniques to reveal the processes driving the high NO3- levels in a sparsely populated forest river. The natural abundance isotopes revealed that the NO3- was mainly from soil sources and that NO3- removal processes were insignificant. The 15N-labeling experiments also quantitatively showed that the biological NO3- removal processes, i.e., denitrification, dissimilatory NO3- reduction to ammonium (DNRA), and anaerobic ammonia oxidation (anammox), in the soils and sediments were weak relative to nitrification in summer. While nitrification was minor in winter, the NO3- removal was insignificant relative to the large NO3- stock in the catchment. Stepwise multiple regression analyses and structural equation models revealed that in summer, nitrification in the soils was regulated by the amoA-AOB gene abundances and NH4+-N contents. Low temperature constrained nitrification in winter. Denitrification was largely controlled by moisture content in both seasons, and anammox and DNRA could be explained by the competition with nitrification and denitrification on their substrate (nitrite-NO2-). We also revealed the strong hydrological control on the transport of soil NO3- to the river. This study effectively revealed the mechanisms underlying the high NO3- levels in a nearly pristine river, which has implications for the understanding of riverine NO3- levels worldwide.

Unexpectedly high nitrate levels in a pristine forest river on the Southeastern Qinghai-Tibet Plateau

River nitrate (NO3–) pollution is a global environmental issue. Recently, high NO3– levels in some pristine or minimally-disturbed rivers were reported, but their drivers remain unclear. This study integrated river isotopes (δ18O/δ15N-NO3– and δD/18O-H2O), 15N pairing experiments, and qPCR to reveal the processes driving the high NO3– levels in a nearly pristine forest river on the Qinghai-Tibet Plateau. The river isotopes suggested that, at the catchment scale, NO3– removal was prevalent in summer, but weak in winter. The pristine forest soils contributed more than 90 % of the riverine NO3–, indicating the high NO3– backgrounds. The release of soil NO3– to the river was “transport-limited” in both seasons, i.e., the NO3– production/stock in the soils exceeded the capacity of hydrological NO3– leaching. In summer, this regime and the NO3–-plentiful conditions in the soils associated with the strong NO3– nitrification led to the high riverine NO3– levels. While the in-soil nitrification was weak in winter, the leaching of legacy NO3– resulted in the consistently high NO3– levels. This study provides insights into the reasons for high NO3– levels in pristine or minimally-disturbed rivers worldwide and highlights the necessity to consider NO3– backgrounds when evaluating anthropogenic NO3– pollution in rivers.

Tracking Riverine Nitrate Sources and Transformations in the Yiluo River Basin by Nitrogen and Oxygen Isotopes

The impacts of natural processes and anthropogenic input on riverine nitrate (NO-3) could be identified by NO-3 concentrations and nitrogen and oxygen isotope ratios (δ15N-NO-3 and δ18O-NO-3); however, the effects of variable land use on riverine NO-3 sources and transformations remain unclear. In particular, the human impacts on riverine NO-3 in mountain areas are still unknown. The Yihe River and Luohe River were used to elucidate this question due to their spatially heterogeneous land use. Hydrochemical compositions, water isotope ratios (δD-H2O and δ18O-H2O), and δ15N-NO-3 and δ18O-NO-3 values were utilized to constrain the NO-3 sources and transformations affected by different land use types. The results indicated that ① the mean nitrate concentrations in the Yihe River and Luohe River waters were 6.57 and 9.29 mg·L-1, the mean values of δ15N-NO-3 were 9.6‰ and 10.4‰, and the average δ18O-NO-3 values were -2.2‰ and -2.7‰, respectively. Based on the analysis of δ15N-NO-3 and δ18O-NO-3 values, the NO-3 in the Yihe and Luohe Rivers were derived from multiple sources, and nitrogen removal existed in the Luohe River, but the biological removal in the Yihe River was weak. ② The contributions of different nitrate sources were calculated using a Bayesian isotope mixing model (BIMM) based on δ15N-NO-3 and δ18O-NO-3 values of river water in the mainstream and tributaries with spatial locations. The results revealed that sewage and manure had major impacts on riverine nitrate in the upper reaches of both the Luohe River and Yihe River, where forest vegetation was widely distributed. However, the contributions from soil organic nitrogen and chemical fertilizer were higher in the upper reaches than in downstream ones. The contributions of sewage and manure still increased in the downstream reaches. Our results confirmed the primary impacts of point sources, e.g., sewage and manure, on riverine nitrate in the studied area, and the contributions of nonpoint sources, e.g., chemical fertilizer, had not increased as the agricultural activities elevated the downstream. Therefore, more attention should be paid to point source pollution treatment, and the high-quality development of ecological civilization in the Yellow River Basin should be maintained.

Traceability of nitrate polluted hotspots in plain river networks of the Yangtze River delta by nitrogen and oxygen isotopes coupling bayesian model

The adverse effects of increased nitrate (NO3−) pollution especially from the non-point source on the hydrosphere and anthroposphere are becoming more prominent. The non-point-derived NO3− in the rivers supplying the upstream threatens the aquatic ecosystem of Taihu Lake. Here, dual-stable isotopes (δ15N and δ18O) of NO3− were applied to the Bayesian model (SIAR) for quantitative source identification of reactive nitrogen (Nr) in a mixed agricultural and urban region along the complex river network of the Yangze River delta. The results showed that the NO3− concentrations in the rivers ranged from 1.09 to 4.44 mg L−1 and decreased from the highly urbanized areas to the lakeside rural areas. The specific isotopic characteristics of four sources (atmospheric deposition, AD; chemical fertilizer, CF; manure and sewage, MS; and soil leachate, SL) by the SIAR isotope model indicated that the MS source made the greatest contribution (46.56%) to the total NO3− load, followed by SL (27.86%), CF (23.77%), and AD (1.81%). The highly urbanized areas and the hybrid areas, which contained a mix of industrialized, populated, and agricultural areas, were identified as hotspot areas with heavy Nr pollution, responsible for spatial patterns of δ15N–NO3- and δ18O–NO3-. These hotspot areas were characterized by a less well-developed sewage pipeline system with high Nr emissions from cash crops. The changes in wastewater treatment level, the agricultural production structure, and meteorological changes were the main factors of spatial variation of Nr concentration and source in the upstream Taihu Lake Basin. The variation in Nr concentration across Taihu Lake would respond to these anthropogenic-driven Nr loads. These findings suggest that MS was the predominant source had the strongest effect on the overall riverine NO3− source which was the primary problem that needed to be solved.

Backgrounds as a potentially important component of riverine nitrate loads

Nitrate (NO3−) is a major trigger for river eutrophication. While efforts have been made to understand the anthropogenic NO3− pollution in rivers, the role of background NO3− in determining NO3− loads remains to be studied. In this study, we used dual-isotopes (δ15N/δ18O-NO3−) and an isotope-mixing model to reveal the natural and anthropogenic processes regulating the NO3− loads in a forest river that acts as a headwater source for the China's South to North Water Transfer Project. Even though the basin is sparsely populated, the mean NO3−-N concentration (0.6 ± 0.2 mg/L) was much higher than the median concentration of global rivers (0.3 ± 0.2 mg/L). Meanwhile, the δ15N-NO3− was extremely depleted (as low as −14.4‰). The correlations between the NO3− concentrations and isotopes indicate that the nitrification of different sources (i.e., soil organic nitrogen, chemical fertilizer, manure, and sewage) dominates the NO3− loads. Soil organic nitrogen accounted for c.a. 60% of the riverine NO3− in the high-flow season, which alone exceeds China's national standard. This finding clearly shows that high NO3− loads in rivers could not all be ascribed to direct anthropogenic inputs, and background NO3− could be critical triggers. Therefore, when evaluating the NO3− pollution of rivers, the background NO3− concentrations must be considered along with the actual NO3− loads. In the low-flow season, the contribution from manure and sewage (c.a. 34%) increases. This study highlights the potentially important role of background NO3− in regulating riverine NO3− loads, providing important implications for understanding high riverine NO3− loads worldwide.

Multiple Isotopes Reveal a Hydrology Dominated Control on the Nitrogen Cycling in the Nujiang River Basin, the Last Undammed Large River Basin on the Tibetan Plateau.

The Tibetan Plateau is sensitive to climate change, but the feedbacks of nitrogen (N) cycling to climate conditions on this plateau are not well-understood, especially under varying degrees of anthropogenic disturbances. The Nujiang River Basin, the last undammed large river basin on the Tibetan Plateau, provides an opportunity to reveal the feedbacks at a broad river basin scale. The isotopic compositions revealed that the conservative mixing of multiple sources controlled the nitrate (NO3-) loadings during the low-flow season, while biological removal processes (assimilation and denitrification) occurred in the high-flow season. During the high-flow season, soil sources, sewage, and atmospheric precipitation contributed 76.3%, 15.6%, and 8.1% to the riverine NO3-. In the low-flow season, the contribution of soil sources decreased while that of sewage increased. The relationship between d-excess and δ15N-NO3- suggests that the hydrological conditions largely controlled the N cycling dynamics in the basin, causing the high spatiotemporal heterogeneity of the riverine NO3- sources and transformation mechanisms. During the high-flow season, the precipitation and evaporation patterns controlled the in-soil processes and soil leaching. In contrast, in-stream nitrification became more evident during the low-flow season, which was related to the long water residence time. This study illustrates hydrology dominated control on N cycling over a large basin scale, which has implications for understanding the N cycling dynamics in the Tibetan Plateau.

Long-term riverine nitrogen dynamics reveal the efficacy of water pollution control strategies

Identification of long-term water quality trends in response to watershed anthropogenic interventions is crucial for developing and adapting water pollution control strategies. This study represents the first use of the Weighted Regressions on Time, Discharge, and Season (WRTDS) model to evaluate trends and sources of riverine nitrogen (N) levels over the 1980–2019 period in the Yongan River watershed of eastern China. The WRTDS model showed satisfactory accuracies for predicting daily riverine total N (TN), NH4+ and NO3− concentrations/loads (R2 > 0.55, n = 366). Modeled flow-normalized riverine NH4+ concentration increased by 789% from 1980 to 2009 and then decreased by 63% in 2010–2019. This changing trend for riverine NH4+ concentration was mainly attributed to a 43% decrease of wastewater NH4+ discharge load in 2010–2019 due to establishment of three new WWTPs in urban areas and enhanced rural domestic sewage collection/treatment. Although chemical N fertilizer use decreased by 49% and domestic animal numbers decreased by 73% in 2000–2019, flow-normalized riverine TN and NO3− concentrations progressively increased by 161% and 232% in 1980–2019, respectively. The paradox between decreasing N inputs and increasing riverine TN/NO3− concentrations is attributed to inputs of legacy N from soil and groundwater. This is supported by the 3.8-fold increase of riverine NO3− concentration in 1980–2019 (86% increase in 2000–2019) following 10-days with no-precipitation (representing groundwater contributions to baseflow) and a 4.1-fold increase of riverine NO3− concentration in 1980–2019 (91% increase in 2000–2019) following the first rainstorm after 10-days of no-precipitation (representing soil flushing). These results document that point-source pollution control efforts were effective, whereas benefits from nonpoint-source pollution control were masked by inputs from legacy N pollution. The WRTDS model was demonstrated to be a useful tool for assessing long-term riverine N pollution dynamics and sources, thereby providing decision-makers with critical information to guide watershed N pollution control strategies.

Identifying the source and transformation of riverine nitrates in a karst watershed, North China: Comprehensive use of major ions, multiple isotopes and a Bayesian model

Nitrate (NO3−) contamination of surface water is a globally concern, especially in karstic regions affected by intensive agricultural activities. This study combines hydrochemistry, and environmental isotopes (δ2HH2O, δ18OH2O, δ15NNO3, and δ18ONO3) with a Bayesian isotope mixing model (Simmr) to reduce the uncertainty in estimating the contributions of different pollution sources. Samples were collected from 32 surface water sites in the Yufu River (YFR) watershed, North China, in September and December 2019. The results revealed that NO3−-N was the predominant form of inorganic nitrogen that caused the deterioration of water quality in the watershed, accounting for approximately 58% of the total nitrogen (TN). The hydrochemical compositions and nitrate isotopes indicated that NO3− mainly originated from soil nitrogen (SN), ammonium fertilizer (AF), but nitrate fertilizer (NF), manure and sewage (M&S) and atmospheric precipitation (AP) were limited. The isotopic composition of nitrate in the upper reaches of the watershed was mainly affected by microbial nitrification, while the mixture of multiple sources was the dominant nitrogen transformation process in the mid-lower reaches of the watershed. Simmr model outputs revealed that SN (56.5%) and AF (29.5%) were the primary contributor to riverine NO3− pollution, followed by NF (7.1%), MS (3.6%), and AP (3.4%) sources. Moreover, an uncertainty index (UI90) of the isotope mixing showed that SN (0.73) and AF (0.67) had the highest values, followed by NF (0.22), M&S (0.22) and AP (0.10). Chemical fertilizer and SN collectively contributed >50% of nitrate during the two sampling campaigns. These results indicated that reducing the application of nitrogen fertilizers and rational irrigation are the keys to alleviate of NO3− pollution. The study is helpful in understanding the source and transformation of riverine NO3− and effectively reducing NO3− pollution in karst agricultural rivers or watersheds.

Bending of the concentration discharge relationship can inform about in-stream nitrate removal

Abstract. Nitrate (NO3-) excess in rivers harms aquatic ecosystems and can induce detrimental algae growths in coastal areas. Riverine NO3- uptake is a crucial element of the catchment-scale nitrogen balance and can be measured at small spatiotemporal scales, while at the scale of entire river networks, uptake measurements are rarely available. Concurrent, low-frequency NO3- concentration and streamflow (Q) observations at a basin outlet, however, are commonly monitored and can be analyzed in terms of concentration discharge (C–Q) relationships. Previous studies suggest that steeper positive log (C)–log (Q) slopes under low flow conditions (than under high flows) are linked to biological NO3- uptake, creating a bent rather than linear log (C)–log (Q) relationship. Here we explore if network-scale NO3- uptake creates bent log (C)–log (Q) relationships and when in turn uptake can be quantified from observed low-frequency C–Q data. To this end we apply a parsimonious mass-balance-based river network uptake model in 13 mesoscale German catchments (21–1450 km2) and explore the linkages between log (C)–log (Q) bending and different model parameter combinations. The modeling results show that uptake and transport in the river network can create bent log (C)–log (Q) relationships at the basin outlet from log–log linear C–Q relationships describing the NO3- land-to-stream transfer. We find that within the chosen parameter range the bending is mainly shaped by geomorphological parameters that control the channel reactive surface area rather than by the biological uptake velocity itself. Further we show that in this exploratory modeling environment, bending is positively correlated to percentage of NO3- load removed in the network (Lr.perc) but that network-wide flow velocities should be taken into account when interpreting log (C)–log (Q) bending. Classification trees, finally, can successfully predict classes of low (∼4 %), intermediate (∼32 %) and high (∼68 %) Lr.perc using information on water velocity and log (C)–log (Q) bending. These results can help to identify stream networks that efficiently attenuate NO3- loads based on low-frequency NO3- and Q observations and generally show the importance of the channel geomorphology on the emerging log (C)–log (Q) bending at network scales.

The effects of clean energy production and urbanization on sources and transformation processes of nitrate in a subtropical river system: Insights from the dual isotopes of nitrate and Bayesian model

Source appointment and control of water pollution caused by nitrate in river are the key to water quality management as well as the basis for achieving aquatic environment sustainability. However, the effects of clean energy production (hydropower generation) and urbanization on the nitrate accumulation and transformation remain unclear. For that, this study quantified and identified the nitrate sources and key processes in a subtropical river, which has been impounded and urbanized in varying degree. The variations of dual isotopes and water chemistry indicated that NO3− in agriculture and forest land mainly from the chemical fertilizer and soil N, while manure and sewage were the main riverine NO3− sources. Nitrification was the dominate process while influenced greatly by inputs of manure and sewage, especially in urban area. Progressive denitrification has been found in the upper reaches of the Beixi River where the river impounded by dams for hydropower generation, with an estimated contribution of 36% (1%–68%). Results from Bayesian model showed that fertilizer and soil N contributed more than half of the NO3− in the wet season as a result of rainfall and surface runoff; while manure and sewage were the dominate NO3− sources in the dry season due to the urbanization. Land use including agriculture, dam construction, and urbanization have characterized the nitrate sources and pollution in the Jiulong River, and cascade dam impoundment could enlarge nitrate retention. We concluded that the combined use of dual isotopes of NO3−, hydrochemicals and the SIAR model offers an efficient approach to understand the nitrogen transformation and quantify the contributions of nitrogen source in river waters.

Seasonality of nitrogen sources, cycling, and loading in a New England river discerned from nitrate isotope ratios

Abstract. Coastal waters globally are increasingly impacted due to the anthropogenic loading of nitrogen (N) from the watershed. To assess dominant sources contributing to the eutrophication of the Little Narragansett Bay estuary in New England, we carried out an annual study of N loading from the Pawcatuck River. We conducted weekly monitoring of nutrients and nitrate (NO3-) isotope ratios (15N / 14N, 18O / 16O, and 17O / 16O) at the mouth of the river and from the larger of two wastewater treatment facilities (WWTFs) along the estuary, as well as seasonal along-river surveys. Our observations reveal a direct relationship between N loading and the magnitude of river discharge and a consequent seasonality to N loading into the estuary – rendering loading from the WWTFs and from an industrial site more important at lower river flows during warmer months, comprising ∼ 23 % and ∼ 18 % of N loading, respectively. Riverine nutrients derived predominantly from deeper groundwater and the industrial point source upriver in summer and from shallower groundwater and surface flow during colder months – wherein NO3- associated with deeper groundwater had higher 15N / 14N ratios than shallower groundwater. Corresponding NO3- 18O / 16O ratios were lower during the warm season, due to increased biological cycling in-river. Uncycled atmospheric NO3-, detected from its unique mass-independent NO3- 17O / 16O vs. 18O / 16O fractionation, accounted for &lt; 3 % of riverine NO3-, even at elevated discharge. Along-river, NO3- 15N / 14N ratios showed a correspondence to regional land use, increasing from agricultural and forested catchments to the more urbanized watershed downriver. The evolution of 18O / 16O isotope ratios along-river conformed to the notion of nutrient spiraling, reflecting the input of NO3- from the catchment and from in-river nitrification and its coincident removal by biological consumption. These findings stress the importance of considering seasonality of riverine N sources and loading to mitigate eutrophication in receiving estuaries. Our study further advances a conceptual framework that reconciles with the current theory of riverine nutrient cycling, from which to robustly interpret NO3- isotope ratios to constrain cycling and source partitioning in river systems.

Impacts of Multiple Environmental Changes on Long‐Term Nitrogen Loading From the Chesapeake Bay Watershed

AbstractExcessive nutrient inputs from land, particularly nitrogen (N), have been found to increase the occurrence of hypoxia and harmful algal blooms in coastal ecosystems. To identify the main contributors of increased N loading and evaluate the efficacy of water pollution control policies, it is essential to quantify and attribute the long‐term changes in riverine N export. Here, we use a state‐of‐the‐art terrestrial–aquatic interface model to examine how multiple environmental factors may have affected N export from the Chesapeake Bay watershed since 1900. These factors include changes in climate, carbon dioxide, land use, and N inputs (i.e., atmospheric N deposition, animal manure, synthetic N fertilizer use, and wastewater discharge). Our results estimated that ammonium (NH4+) and nitrate (NO3−) export increased substantially (66% for NH4+ and 123% for NO3−) from the 1900s to the 1990s and then declined (32% for NH4+ and 14% for NO3−) since 2000. The temporal trend of dissolved organic nitrogen (DON) export paralleled that of dissolved inorganic N, while particulate organic nitrogen export was relatively constant during 1900–2015. Precipitation was the primary driver of interannual variability in N export to the Bay. Wastewater discharge explained most of the long‐term change in riverine NH4+ and DON fluxes from 1900 to 2015. The changes in atmospheric deposition, wastewater, and synthetic fertilizer were responsible for the trend of riverine NO3−. In light of our model‐based attribution analysis, terrestrial non‐point source nutrient management will play an important role in achieving water quality goals.

Dual-isotope-based source apportionment of nitrate in 30 rivers draining into the Bohai Sea, north China

Excessive nitrate (NO3−) in rivers can lead to water quality deterioration, and can also be directly input into estuaries and oceans, thus posing a serious threat to the stability of their ecosystems. In this study, the concentration, isotopes and sources of NO3− in 30 rivers discharging into the Bohai Sea were comprehensively investigated. The mean concentration of NO3−-N was 2.24 ± 2.11 mg L−1, with obvious seasonal and spatial variations. In total, 104.24 kt of NO3−-N was discharged into the Bohai Sea annually, to which the Yellow River Basin and Liao River Basin made the largest contributions. The range of δ15N–NO3− was −1.1‰ to +33.2‰ (mean value, +11.4 ± 5.0‰), with no significant seasonal or spatial differences; the mean value of δ18O–NO3− was +9.4 ± 7.2‰, with much higher values seen in June. Based on the MixSIAR model, manure (24.3 ± 7.5%) and sewage (19.1 ± 14.5%) were the primary sources of NO3− in the 30 rivers, followed by NO3− fertilizers (16.3 ± 12.5%), soil N (15.5 ± 11.9%), atmospheric deposition of NO3− (13.5 ± 5.7%) and NH4+ fertilizers (11.4 ± 8.9%). This finding highlights the vital roles of sewage and manure management in riverine NO3−. Using a mathematical method, the contributions of various sources to each river were simulated. The results indicated that management of the Yellow River, Daliao River, Liao River, and Xiaoqing River is more urgently needed than that of other rivers to control Bohai NO3− pollution. We believe that this finding will provide guidance for scientific management of NO3− pollution in these 30 rivers and the Bohai Sea.

Nitrogen Dynamics of Inundated Sediments in an Emerging Coastal Deltaic Floodplain in Mississippi River Delta Using Isotope Pairing Technique to Test Response to Nitrate Enrichment and Sediment Organic Matter

Nitrate (NO3–) enrichment in rivers over the last four decades has potentially shifted how coastal ecosystems process nitrogen (N). Shifts in N dynamics may be particularly significant in coastal deltaic floodplains when sediments are inundated during river flood stage as this may change the fate of NO3– transported to the coastal ocean. We evaluated the relative importance of denitrification, dissimilative nitrate reduction to ammonium (DNRA), and anaerobic ammonium oxidation (anammox) in intact sediment cores of Wax Lake Delta (WLD) using continuous flow-through system. We manipulated this experimental system with two concentrations of riverine NO3– (lower concentration at 5 μM and ambient concentration at 100 μM) to test how nitrate-enriched waters may modify N cycling in coastal deltaic floodplains. Inundated sediments in a coastal deltaic floodplain removed bio-reactive N as a function of sediment organic matter (SOM) concentrations, indicating how N dynamics vary as a result of deltaic succession. Direct denitrification was the dominant N pathway in inundated sediments in WLD, which was several times greater than coupled nitrification-denitrification, DNRA, and anammox. Gross denitrification (direct denitrification + coupled nitrification-denitrification) rates generally increased with SOM concentrations and ranged from 4 to 12 μmol N m–2 h–1 under lower NO3– enrichment (5 μM) and 68 to 276 μmol N m–2 h–1 under higher NO3– enrichment (100 μM). Most of these changes in N fluxes with SOM and NO3– enrichment resulted from the increase in direct denitrification rates rather than coupled nitrification-denitrification rates. DNRA rates varied from 0 to 65 μmol N m–2 h–1, and the relative significance of DNRA increased as a percentage of total NO3– reduction (%DNRA) with increasing ratio of organic carbon to NO3– concentrations (OC/NO3–). Anammox rates increased among sites as SOM increased, but rates were lower from 0.0 to 38 μmol N m–2 h–1. The dominance of direct denitrification and increased anammox rates with SOM concentrations indicate that dynamic deltaic floodplains can permanently remove bio-reactive N in the proximal zone of river deposition of tidal freshwater wetlands.

Climatic and anthropogenic driving forces of the nitrogen cycling in a subtropical river basin

To date, basin-scale understanding of nitrogen (N) cycling is lacking, which undermines riverine N pollution control efforts. Applying a multiple-isotopic approach, this study provided insights into the impacts of climate and anthropogenic activities on the N cycling at a basin scale. The isotopic compositions of the river water were regulated by a simple mixing process in winter, while unconservative processes (nitrification and denitrification) occurred in warm seasons. Denitrification dominated the N transformations in summer, while coupled nitrification-denitrification in soils after fertilization was responsible for the isotopic fractionations in spring and autumn. While at least 58.7% of the nitrate (NO3−) was removed from the basin, the NO3− loadings in the river remained high, suggesting that the ecosystem services could not balance the anthropogenic pollution. After correcting the isotopic fractionations, the sources of the riverine NO3− were quantified by a Markov chain Monte Carlo isotope mixing model. The contributions of point sources versus non-point sources changed dynamically with the precipitation and fertilization patterns. In summer and autumn, the soil organic N and chemical fertilizer dominated the riverine NO3−, with total contributions of 75.9% and 74.6%, respectively. The contributions from sewage and manure significantly increased during spring (47.9%) and winter (50.2%). Overall, the annual NO3− fluxes were from SON (28.7%), CF (28.1%), DS (18.2%), MA (23.9%), and AP (1.1%). In addition, we presented the large uncertainties in source apportionment that arose from the ignorance of isotope fractionations, highlighting the importance of considering the effect of isotopic fractionations in N source apportionment studies.