Deciphering the nitrate sources and processes in the Ganga river using dual isotopes of nitrate and Bayesian mixing model

Nitrate sources and transformation in surface water and groundwater in Huazhou District, Shaanxi, China: Integrated research using hydrochemistry, isotopes and MixSIAR model

Global water resources affected by excessive nitrate (NO3-) have caused a series of human health and ecological problems. Therefore, identification of NO3- sources and transformations is of pivotal significance in the strategic governance of widespread NO3- contaminant. In this investigation, a combination of statistical analysis, chemical indicators, isotopes, and MixSIAR model approaches was adopted to reveal the hydrochemical factors affecting NO3- concentrations and quantify the contribution of each source to NO3- concentrations in surface water and groundwater. The findings revealed that high groundwater NO3- concentration is concentrated in the southwestern region, peaking at 271 mg/L. NO3- concentration in the Wei River and Yuxian River exhibited an increase from upstream to downstream, but in the Shidi River and Luowen River, its concentration was highest in the upstream. Groundwater NO3- has noticeable correlation with Na+, Ca2+, Mg2+, Cl-, HCO3-, TDS, EC, and ORP. In surface water, NO3- level is significantly correlated with NH4+ and ORP. Major sources of NO3- in surface and groundwater comprise manure & sewage and soil nitrogen. Source contribution for surface water was calculated by MixSIAR model to obtain soil nitrogen (57.7%), manure & sewage (23.8%), chemical fertilizer (12%), and atmospheric deposition (6.4%). In groundwater, soil nitrogen and manure & sewage accounted for 19% and 63.8% of nitrate sources, respectively. Both surface water and groundwater exhibited strong oxidation, with nitrification the primary process. It is expected that this study will provide insights into the dynamics of NO3- and contribute to the development of effective strategies for mitigating NO3- contaminant, leading to sustainable management of water resources.

Nitrate transformation and source tracking of Yarlung Tsangpo River using a multi-tracer approach combined with Bayesian stable isotope mixing model

Excessive levels of nitrate nitrogen (NO3--N) could lead to ecological issues, particularly in the Yarlung Tsangpo River (YTR) region located on the Qinghai Tibet Plateau. Therefore, it is crucial to understand the fate and sources of nitrogen to facilitate pollution mitigation efforts. Herein, multiple isotopes and source resolution models were applied to analyze key transformation processes and quantify the sources of NO3-. The δ15N-NO3- and δ18O-NO3- isotopic compositions in the YTR varied between 1.23‰ and 13.64‰ and -7.88‰-11.19‰, respectively. The NO3--N concentrations varied from 0.08 to 0.86 mg/L in the dry season and 0.20-1.19 mg/L during the wet season. Nitrification remained the primary process for nitrogen transformation in both seasons. However, the wet season had a widespread effect on increasing nitrate levels, while denitrification had a limited ability to reduce nitrate. The elevated nitrate concentrations during the flood season were caused by increased release of NO3- from manure & sewage (M&S) and chemical fertilizers (CF). Future endeavors should prioritize enhancing management strategies to improve the utilization efficiency of CF and hinder the direct entry of untreated sewage into the water system.

Anthropogenic impacts and quantitative sources of nitrate in a rural-urban canal using a combined PMF, δ15N/δ18O-NO3-, and MixSIAR approach

Nitrate (NO3-) pollution in irrigation canals is of great concern because it threatens canal water use; however, little is known about it at present. Herein, a combination of positive matrix factorization (PMF), isotope tracers, and Mixing Stable Isotope Analysis in R (MixSIAR) was developed to identify anthropogenic impacts and quantitative sources of NO3- in a rural-urban canal in China. The NO3- concentration (0.99-1.93 mg/L) of canal water increased along the flow direction and was higher than the internationally recognized eutrophication risk value in autumn and spring. The inputs of the Fuhe River, NH4+ fertilizer, soil nitrogen, manure & sewage, and rainfall were the main driving factors of canal water NO3- based on principal component analysis and PMF, which was supported by evidence from δ15N/δ18O-NO3-. According to the chemical and isotopic analyses, nitrogen transformation was weak, highlighting the potential of δ15N/δ18O-NO3- to trace NO3- sources in canal water. The MixSIAR and PMF results with a <15% divergence emphasized the predominance of the Fuhe River (contributing >50%) and anthropogenic impacts (NH4+ fertilizer plus manure & sewage, >37%) on NO3- in the entire canal, reflecting the effectiveness of the model analysis. According to the MixSIAR model, (1) higher NO3- concentration in canal water was caused by the general enhancement of human activities in spring and (2) NO3- source contributions were associated with land-use patterns. The high contributions of NH4+ fertilizer and manure & sewage showed inverse spatial variations, suggesting the necessity of reducing excessive fertilizer use in the agricultural area and controlling blind wastewater release in the urban area. These findings provide valuable insights into NO3- dynamics and fate for sustainable management of canal water resources. Nevertheless, long-term chemical and isotopic monitoring with alternative modeling should be strengthened for the accurate evaluation of canal NO3- pollution in future studies.

Multiple isotopes decipher the nitrogen cycle in the cascade reservoirs and downstream in the middle and lower Yellow River: Insight for reservoir drainage period

Intensive anthropogenic activities, such as excessive nitrogen input and dam construction, have altered the nitrogen cycle in the global river system. However, the focus on the source, transformation and fate of nitrogen in the Yellow River is still scarce. In this study, the multiple isotopes (δ15N-NO3-, δ18O-NO3-, δ15N-NH4+ and δ15N-PN) were deciphered to explore the nitrogen cycling processes and the driving factors in the thermally stratified cascade reservoirs (Sanmenxia Reservoir: SMXR and Xiaolangdi Reservoir: XLDR) and Lower Yellow River (LYR) during the drainage period of the XLDR. In the SMXR, algal bloom triggered the assimilation process in the upper layer before the SMX Dam, followed by remineralization and subsequent nitrification processes in the lower water layers. The nitrification reaction in the XLDR progressively increased along both longitudinal and vertical directions to the lower layer of the XLD Dam, which was linked to the variation in the water residence time of riverine, transition and lentic zones. The robust nitrification rates in the lower layer of the lentic zone coincided with the substantial depletion of nitrate isotopic composition and enrichment of both δ15N-PN and δ15N-NH4+, indicating the longer water residence time not only promoted the growth of the nitrifying population but also facilitated the remineralization to enhance NH4+ availability. In the LYR, the slight nitrate assimilation, as indicated by nitrate isotopic composition and fractionation models, was the predominant nitrogen transformation process. The Bayesian isotope mixing model results showed that manure and sewage was the dominant nitrate source (50 %) in the middle and lower Yellow River. Notably, the in-reservoir nitrification was a significant nitrate source (27 %) in the XLDR and LYR. Our study deepens the understanding of anthropogenic activities impacting the nitrogen cycle in the river-reservoir system, providing valuable insight into water quality management and nitrogen cycle mechanisms in the Yellow River.

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.