Nitrate dynamics and source apportionment on the East China Sea shelf revealed by nitrate stable isotopes and a Bayesian mixing model

Unveiling active nitrate and nitrite cycling in a eutrophic coastal bay, southern China from a dual isotope perspective

Increased nutrient loading in coastal waters poses a threat to marine ecosystems. To develop effective management strategies, a clearer understanding of nitrogen cycle dynamics for the main species is crucial for understudied urbanized areas. By employing stable isotopes of nitrate (δ15N and δ18O) and the rarely reported nitrite isotopes, we found a decoupling between physical mixing and microbial transformative processes in the Xiamen Bay. During the dry season, dominated by endmember mixing, the SIAR (Stable Isotope Analysis in R) model identifies manure (50%) as the primary nitrate source, followed by fertilizer, sewage, and rainfall. Microbial processes govern nitrogen cycling during the wet season, as evidenced by the relatively low ε value (∼2.4‰) using the Rayleigh fractionation model. This likely reflects distinct environmental conditions in coastal waters compared to the open ocean, such as limited light and iron availability. Nitrite isotope ratios implicate ammonia oxidation and nitrite oxidation as the primary drivers of nitrite variability during the wet season. This suggests that seasonal nitrite accumulation in summer may result from a decoupling of these processes in response to temperature fluctuations. Theoretical calculations of the nitrite reservoir, based on key parameters like temperature and substrate concentration, further support this argument. Our findings highlight the highly dynamic nature of nitrate and nitrite cycling in coastal environments. This underscores the need for further research in these understudied coastal systems, particularly in the context of intensifying human activities and climate change.

Artificial regulation affects nitrate sources and transformations in cascade reservoirs by altering hydrological conditions

Reservoirs are recognized as pivotal zones within the nitrate cycle, substantially modifying the source-sink dynamics of nitrate in fluvial systems. However, the impact of hydrological alterations induced by artificial regulation on nitrate sources and transformation processes in cascade hydropower reservoirs remains insufficiently comprehended. Consequently, we investigated the spatiotemporal characteristics of nitrate concentrations, δ15NNO3, δ18ONO3, δD, Δ17OH2O, relative water column stability (RWCS), runoff, and associated environmental factors within cascade reservoirs along the Wujiang River in Southwest China. The Bayesian stable isotope mixing model (MixSIAR) identified manure and sewage (M&S) as the predominant source of untreated nitrate (43.5 %) in the cascade reservoirs, with soil organic nitrogen (SON, 27.2 %), chemical fertilizer (CF, 18.5 %), and atmospheric precipitation (AP, 10.8 %) following in significance. However, evidence derived from δ15NNO3 and Δ17O isotopic values indicates that the contribution of nitrate produced by nitrification within the water body has been overlooked, accounting for 59 % in January, 40 % in July, and 24 % in October. Furthermore, the coupling of nitrification and assimilation emerged as the predominant process for nitrate transformation within the cascade reservoirs. This process was primarily regulated by RWCS and runoff (p < 0.01), indicating a substantial hydrological influence on the nitrogen cycle. This research quantifies the contribution of nitrate originating from the nitrification process within cascade reservoirs, while also addressing the limitations inherent in the traditional MixSIAR model. Additionally, it advances our comprehension of the impacts of hydrological conditions and thermal stratification on the nitrate cycle in reservoirs worldwide.

Tracing nitrogen sources and transformation characteristics in a large basin with spatially heterogeneous pollution distribution

This study used dual stable isotopes to examine nitrate sources and geographical distribution in the Liao River Basin (LRB), one of China's seven major river basins. During a normal hydrological season in April 2021, water samples were taken from the main streams of the Liao River (MLR), Shuangtaizi River (STR), Hun River (HR), Taizi River (TZR), and Daliao River (DLR). Monitoring results indicated that 93% of the water samples had a total nitrogen level exceeding the Class IV limit (1.5 mg/L) of the 'Environmental Quality Standards (EQS) for surface water', indicating a serious nitrogen pollution status. 71.3% of the total nitrogen on average was in the form of nitrate. The scatterplots of δD-H2O and δ18O-H2O showed that water in TZR and DLR were mainly affected by precipitation, while MLR, STR and HR were additionally impacted by evaporation and groundwater. The overall δ15N and δ18O of NO3- varied from 7.7‰ to 17.9‰ and 0.6‰-11.2‰, respectively. The correlations between δ15N-NO3- and δ18O-NO3-, along with attribution results from the Bayesian isotopic mixing model, indicated a predominant role of manure/sewage (MS) pollution in affecting river nitrate, accounting for 78% of total nitrate in MLR and 72% in DLR. A positive correlation between δ15N-NO3- and δ18O-NO3- in MLR indicated the occurrence of denitrification process. Overall, attribution results showed that the primary nitrate sources varied in different river systems within such a large basin, mainly due to spatially varied land use and human activities. Tailored nitrogen management strategies should be implemented to address the main anthropogenic pressures.

Key biogeochemical processes and source apportionment of nitrate in the Bohai Sea based on nitrate stable isotopes

Excessive nitrate input is one of the primary factors causing nearshore eutrophication. This study applied the nitrate stable isotope techniques to analyse the biogeochemical processes and sources of nitrate in the Bohai Sea (BHS). The results showed that intensive NO3- assimilation probably occurred at surface in summer, while nitrification primarily occurred in the Yellow River diluted water. In autumn, regional assimilation and nitrification were still identified. For avoiding the interference from assimilation, the isotopic fractionations were further calculated as correction data for the quantitative analysis of nitrate sources. The river inputs were identified as the primary source of nitrate in the BHS in summer and autumn, accounting for >50 %, and the atmospheric deposition was the secondary source. This study provides quantitative data for evaluating the significance of river inputs to the nearshore nitrate, which will be beneficial to policy formulation on the BHS eutrophication control.

Identification of groundwater nitrate sources in an urban aquifer (Alborz Province, Iran) using a multi-parameter approach

High concentrations of NO3̄ in water resources are detrimental to both human health and aquatic ecosystems. Identification of NO3̄ sources and biogeochemical processes is a crucial step in managing and controlling NO3̄ pollution. In this study, land use, hydrochemical data, dual stable isotopic ratios and Bayesian Stable Isotope Mixing Models (BSIMM) were integrated to identify NO3̄ sources and estimate their proportional contributions to the contamination of the Karaj Urban Aquifer (Iran). Elevated NO3̄ concentrations indicated a severe NO3̄ pollution, with 39 and 52% of groundwater (GW) samples displaying the concentrations of NO3̄ in exceedance of the World Health Organization (WHO) standard of 50 mg NO3̄ L-1 in the rainy and dry seasons, respectively. Dual stable isotopes inferred that urban sewage is the main NO3̄ source in the Karaj Plain. The diagram of NO3̄/Cl‾ versus Cl‾ confirmed that municipal sewage is the major source of NO3̄. Results also showed that biogeochemical nitrogen dynamics are mainly influenced by nitrification, while denitrification is minimal. The BSIMM model suggested that NO3̄ originated predominantly from urban sewage (78.2%), followed by soil organic nitrogen (12.2%), and chemical fertilizer (9.5%) in the dry season. In the wet season, the relative contributions of urban sewage, soil nitrogen and chemical fertilizer were 87.5, 6.7, and 5.5%, respectively. The sensitivity analysis for the BSIMM modeling indicates that the isotopic signatures of sewage had the major impact on the overall GW NO3̄ source apportionment. The findings provide important insights for local authorities to support effective and sustainable GW resources management in the Karaj Urban Aquifer. It also demonstrates that employing Bayesian models combined with multi-parameters can improve the accuracy of NO3̄ source identification.

Identification of nitrate accumulation mechanism of surface water in a mining-rural-urban agglomeration area based on multiple isotopic evidence

The accumulation of nitrate (NO3-) in surface waters resulting from mining activities and rapid urbanization has raised widespread concerns. Therefore, it is crucial to develop a nitrate transformation information system to elucidate the nitrogen cycle and ensure sustainable water quality management. In this study, we focused on the main river and subsidence area of the Huaibei mining region to monitor the temporal and spatial variations in the NO3- content. Multiple isotopes (δD, δ18O-H2O, δ15N-NO3-, δ18O-NO3-, and δ15N-NH4+) along with water chemistry indicators were employed to identify the key mechanisms responsible for nitrate accumulation (e.g., nitrification and denitrification). The NO3- concentrations in surface water ranged from 0.28 to 7.50 mg/L, with NO3- being the predominant form of nitrogen pollution. Moreover, the average NO3- levels were higher during the dry season than during the wet season. Nitrification was identified as the primary process driving NO3- accumulation in rivers and subsidence areas, which was further supported by the linear relationship between δ15N-NO3- and δ15N-NH4+. The redox conditions and the relationship between δ15N-NO3- and δ18O-NO3-, and lower isotope enrichment factor of denitrification indicated that denitrification was weakened. Phytoplankton preferentially utilized available NH4+ sources while inhibiting NO3- assimilation because of their abundance. These findings provide direct evidence regarding the mechanism underlying nitrate accumulation in mining areas, while aiding in formulating improved measures for effectively managing water environments to prevent further deterioration.

Basin-wide tracking of nitrate cycling in Yangtze River through dual isotope and machine learning

The nitrate (NO3-) input has adversely affected the water quality and ecological function in the whole basin of the Yangtze River. The protection of water sources and implementation of "great protection of Yangtze River" policy require large-scale information on water contamination. In this study, dual isotope and Bayesian mixing model were used to research the transformation and sources of nitrate. Chemical fertilizers contribute 76 % of the nitrate sources in the upstream, while chemical fertilizers were also dominant in the midstream (39 %) and downstream (39 %) of Yangtze River. In addition, nitrification process occurred in the whole basin. Four machine learning models were used to relate nitrate concentrations to explanatory variables describing influence factors to predict nitrate concentrations in the whole basin of Yangtze River. The anthropogenic and natural factors, such as rainfall, GDP and population were chosen to take as predictor variables. The eXtreme Gradient Boosting (XGBoost) model for nitrate has a better predictive performance with an R2 of 0.74. The predictive models of nitrate concentrations will help identify the nitrate distribution and transport in the whole Yangtze River basin. Overall, this study represents the first basin-wide data-driven assessment of the nitrate cycling in the Yangtze River basin.