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Tracing nitrate sources and transformations using △17O, δ15N, and δ18O-NO3 in a coastal plain river network of eastern China

  • Author(s): Ji, X;
  • Shu, L;
  • Li, J;
  • Zhao, C;
  • Chen, W;
  • Chen, Z;
  • Shang, X;
  • Dahlgren, RA;
  • Yang, Y;
  • Zhang, M
  • et al.
Abstract

Identifying the sources and transformations of riverine nitrate plays a critical role in mitigating nitrogen enrichment of river networks. Several previous studies have used δ18O-NO3− to quantitatively assess riverine nitrate contributed by atmospheric nitrate and terrestrial sources, but their results have great uncertainty due to the wide range of δ18O-NO3− values and isotopic fractionation during nitrogen-cycling processes in terrestrial environment. The nitrate 17O anomaly (△17O-NO3−), as an unambiguous tracer of atmospheric nitrate, is a promising tool to effectively separate atmospheric nitrate from microbially produced nitrate. However, to our knowledge, △17O-NO3− approach has not been previously applied to identify nitrate pollution sources in plain river networks of eastern China. In this study, we used a multiple isotope approach (δD/δ18O-H2O and δ15N/δ18O/△17O-NO3−) for the first time to quantitatively identify sources and transformations of riverine nitrate in a hypereutrophic coastal plain river network namely Wen-Rui Tang River located in eastern China, which is a region receiving high inputs of atmospheric nitrogen deposition. The △17O-NO3− values in precipitation and river water during the study period (April–June of 2021) varied from 14.83‰ to 31.39‰ and from − 2.82‰ to 9.66‰, respectively. The δD/δ18O-H2O values revealed that river water mainly originated from recent precipitation with little evaporation. Moreover, the δ15N/δ18O-NO3− values indicated that microbial nitrification, not denitrification, was the predominant nitrogen-cycling process in the watershed. Based on a Bayesian mixing model (Stable Isotope Analysis in R, SIAR) using δ15N/△17O-NO3−, municipal sewage was identified as the dominant nitrate source (50.5 ± 11.7%), followed by soil nitrogen (23.8 ± 13.7%), atmospheric nitrate deposition (14.3 ± 2.9%), and nitrogen fertilizer (11.4 ± 8.7%). Finally, an uncertainty analysis for nitrate source apportionment demonstrated that the greatest uncertainty was associated with soil nitrogen, followed by municipal sewage, nitrogen fertilizer, and atmospheric nitrate deposition. This study provides important scientific information on riverine nitrate source apportionment to guide pollution control/remediation strategies and highlights the benefits of utilizing △17O-NO3− to enhance nitrate source apportionment.

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