National-scale investigation of dual nitrate isotopes and chloride ion in South Korea: Nitrate source apportionment for stream water

Riverine nitrate source identification combining δ15N/δ18O-NO3- with Δ17O-NO3- and a nitrification 15N-enrichment factor in a drinking water source region

Dual nitrate isotopes (δ15N/δ18O-NO3-) are an effective tool for tracing nitrate sources in freshwater systems worldwide. However, the initial δ15N/δ18O values of different nitrate sources might be altered by isotopic fractionation during nitrification, thereby limiting the efficiency of source apportionment results. This study integrated hydrochemical parameters, site-specific isotopic compositions of potential nitrate sources, multiple stable isotopes (δD/δ18O-H2O, δ15N/δ18O-NO3- and Δ17O-NO3-), soil incubation experiments assessing the nitrification 15N-enrichment factor (εN), and a Bayesian mixing model (MixSIAR) to reduce/eliminate the influence of 15N/18O-fractionations on nitrate source apportionment. Surface water samples from a typical drinking water source region were collected quarterly (June 2021 to March 2022). Nitrate concentrations ranged from 0.35 to 3.06 mg/L (mean = 0.78 ± 0.46 mg/L), constituting ∼70 % of total nitrogen. A MixSIAR model was developed based on δ15N/δ18O-NO3- values of surface waters and the incorporation of a nitrification εN (-6.9 ± 1.8 ‰). Model source apportionment followed: manure/sewage (46.2 ± 10.7 %) > soil organic nitrogen (32.3 ± 18.5 %) > nitrogen fertilizer (19.7 ± 13.1 %) > atmospheric deposition (1.8 ± 1.6 %). An additional MixSIAR model coupling δ15N/δ18O-NO3- with Δ17O-NO3- and εN was constructed to estimate the potential nitrate source contributions for the June 2021 water samples. Results revealed similar nitrate source contributions (manure/sewage = 43.4 ± 14.1 %, soil organic nitrogen = 29.3 ± 19.4 %, nitrogen fertilizer = 19.8 ± 13.8 %, atmospheric deposition = 7.5 ± 1.6 %) to the original MixSIAR model based on εN and δ15N/δ18O-NO3-. Finally, an uncertainty analysis indicated the MixSIAR model coupling δ15N/δ18O-NO3- with Δ17O-NO3- and εN performed better as it generated lower uncertainties with uncertainty index (UI90) of 0.435 compared with the MixSIAR model based on δ15N/δ18O-NO3- (UI90 = 0.522) and the MixSIAR model based on δ15N/δ18O-NO3- and εN (UI90 = 0.442).

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.