1. INTRODUCTION
Nitrate contamination of drinking water sources is a serious global
problem (Denk et al., 2017; Torres-Martínez et al., 2021). The World
Health Organization stipulates that the nitrate nitrogen content in
drinking water should not exceed 10 mg/L (WHO,2017), and if the nitrate
content in drinking water exceeds the standard, it can easily lead to
diseases such as methemoglobinemia in infants and children and gastric
cancer in adults (Nestler et al., 2011; Bu et al., 2017). In addition,
high concentrations of nitrate in surface waters can lead to
eutrophication (Bailey et al., 2016). There are two predominant sources
of nitrogen pollution in aquatic environments: natural and
anthropogenic. The natural source originates from atmospheric deposition
and soil organic nitrogen, whereas the anthropogenic source is the
result of human activities, including the use of fertilizers, domestic
sewage discharge, livestock manure, industrial effluent, and leaching
from solid waste. The crucial to the study of nitrate pollution is to
identify the contribution of human activities. Nitrogen undergoes a
variety of physical, biological, and chemical reactions from pollution
sources to water, and accurately tracing the source of nitrogen remains
a scientific challenge when it is transported and transformed in the
environment (Stoliker et al., 2016; Wang et al., 2020; Cao et al.,
2021).
After decades of development, great progress has been made in the
research on the source of nitrate pollution and transport and
transformation patterns in surface water and groundwater. As early as
the 1960s, there was research on nitrate pollution in the world. Early
studies generally chose hydrochemical and statistical methods. Nitrogen
isotopes were first applied to quantify the contributions of nitrogen
pollution sources in 1971, but the results were questioned due to the
neglect of factors such as isotope fractionation (Kohl et al., 1971). In
order to distinguish the sources of overlapping nitrate pollution areas,
Amberger first measured the oxygen isotopes in nitrate (Amberger et al.,
1987). The introduction of the Bayesian isotope mixing models has solved
the problem of determining the contribution of multiple potential
nitrogen sources to nitrate pollution in water bodies. Commonly used
models include SIAR (Parnell et al., 2010), MixSIAR (Moore et al.,
2008), among others. In the first application of the SIAR model to the
source apportionment of nitrate in water, Xue quantified the
contributions of atmospheric deposition, soil nitrogen, nitrate
fertilizers, ammonium fertilizers, and fecal and domestic wastewater to
nitrate pollution and its contribution rates (Xue et al., 2012). Since
then, a large number of studies have started to use hydrochemical
combined with nitrogen and oxygen isotopes to quantify the sources of
nitrate in watershed water (Zhang et al., 2018; Yi et al., 2020; Guo et
al., 2022). Furthermore, some studies have attempted to improve the
accuracy of quantifying nitrate source apportionment, including the
introduction of multiple isotopes such as 34S and13C (Torres-Martínez et al., 2020; Ren et al., 2022).
In addition, uncertainty analysis of isotope mixing models has gradually
begun to be applied (Ji et al., 2017; Zhang et al., 2018; Shang et al.,
2020; Ji et al., 2022). The analytical process described above is well
established for analyzing the nitrate sources of one water body alone.
However, in a watershed scale, surface water and groundwater typically
interact, and focusing solely on one water body may lead to substantial
inaccuracies. Therefore, a comprehensive approach that takes into
account both surface water and groundwater is necessary. Additionally,
financial constraints often result in relatively low sample densities
when conducting research at the watershed scale. It is particularly
important to maximize the accuracy of identifying nitrate sources in a
watershed using limited resources.
The study area of this research, the Chuzhou Hushan Hydrological
Experimental Watershed, is located in the southwestern part of the
Jianghuai Hilly area, and is a typical agricultural small watershed. It
was established as one of the primary hydrological experimental
watersheds in China based on the International Hydrological Programme
(IHP) after the country’s founding. This study aims to: (1) quantify the
contributions of different sources of nitrate using a Bayesian isotope
mixing model, and (2) quantify the uncertainty of the model results and
analyze the impact of input variability on the model outputs. The
findings provide scientific guidance for controlling nitrate pollution
in the watershed.