Identification of sources and transformations of nitrate in the Xijiang River using nitrate isotopes and Bayesian model.

Abstract Coupled nitrogen and oxygen isotopes of nitrate have proven useful in identifying nitrate sources and transformation in rivers. However, isotopic fractionation and low-resolution monitoring limit the accurate estimation of nitrate dynamics. In the present study, the spatio-temporal variations of nitrate isotopes (15N and 18O) and hydrochemical compositions (NO 3 − and Cl−) of river water were examined to understand nitrate sources in the Xijiang River, China. High-frequency sampling campaigns and isotopic analysis were performed at the mouth of the Xijiang River to capture temporal nitrate variabilities. The overall values of δ15N-NO 3 − and δ18O-NO 3 − ranged from +4.4‰ to +14.1‰ and from −0.3‰ to +6.8‰, respectively. The results of nitrate isotopes indicated that NO 3 − mainly originated from soil organic nitrogen (SON), chemical fertilizer (CF), and manure and sewage wastes (M&S). The negative correlation of nitrate isotopic values with NO 3 −/Cl− ratios suggested the importance of denitrification in NO 3 − loss. The results of Bayesian model with incorporation of isotopic fractionation during the denitrification showed that SON and CF contributed to the most (72–73%) nitrate in the wet season; whereas approximately 58% of nitrate was derived from anthropogenic inputs (M&S and CF) in the dry season. The nitrate flux was 2.08 × 105 tons N yr−1 during one hydrologic year between 2013 and 2014, with 86% occurring in the wet season. Long-term fluctuations in nitrate flux indicated that nitrate export increased significantly over the past 35 years, and was significantly correlated with nitrate concentrations. The seasonal pattern of nitrate dynamics indicated the mixing of nitrified NO 3 − and denitrified NO 3 − between surface flow and groundwater flow under different hydrological conditions. Overall, the present study quantitatively evaluates the spatio-temporal variations in nitrate sources in a subtropical watershed, and the high-frequency monitoring gives a better estimate of nitrate exports and proportional contributions of nitrate sources. Graphical abstract Unlabelled Image Highlights • Evaluate spatio-temporal contributions of nitrate sources by Bayesian mixing model. • Identify temporal nitrogen dynamics by high-frequency analysis of nitrate isotopes. • Nitrate concentrations determine the long-term pattern of nitrate flux. [ABSTRACT FROM AUTHOR]