Using dual stable isotopes method for nitrate sources identification in Cao-E River Basin, Eastern China

Excess nitrate (NO₃⁻) of water is a worldwide environmental problem. Therefore, identifying the sources and analyzing respective contribution rates are of great importance for improving water quality. The current study was carried out to identify the potential sources of NO₃⁻ pollution in Cao-E River basin, in Eastern China. Surface water samples were collected during the dry season and wet season. Multiple hydrochemical indices, dual NO₃⁻ isotopes (δ¹⁵N–NO₃⁻ and δ¹⁸O–NO₃⁻) and a Bayesian model (stable isotope analysis in R, MixSIAR) were applied to identify NO₃⁻ sources and estimate the proportional contributions of multiple NO₃⁻ sources. During the sampling period, nitrification was a dominant nitrogen transformation process in the study area. The results of the NO₃⁻ isotopes suggested that manure and sewage (M&S), soil nitrogen (SN) and nitrogen fertilizer (NF) were the major contributors to NO₃⁻. Moreover, the results obtained from the MixSIAR model showed that the proportional contributions of atmospheric deposition (AD), NF, M&S and SN to NO₃⁻ were 2.82, 15.45, 44.25, 37.47% and 3.14, 23.39, 31.78, 41.69% in the dry and wet season, respectively. This study provided evidence to further understand the sources, transport, and transformation of N in Cao-E River basin, which deepens the understanding of the management of N contaminant.

1 Introduction

In recent decades, the discharge of point source pollutants through industrial and domestic wastewater, as well as the use of large amounts of fertilizers in agricultural systems, have led to increased nitrate (NO₃⁻) concentrations in river water (Bu et al., 2019; Zhang et al., 2021). For example, the Taihu Lake basin (Vidal et al., 2020), the Yellow River (Xie et al., 2021), the Liao River (Yu et al., 2021), the Amazon River (Bijay and Craswell, 2021) and so on are also polluted to varying degrees. NO₃⁻ is considered a worrying pollutant in river ecosystems because the discharge to rivers exceeds the self-purification capacity of water bodies (Xue et al., 2009). Increased NO₃⁻ concentrations in water can lead to eutrophication and algal blooms, negatively impacting aquatic ecosystems (Cao et al., 2022), entering the ocean through estuaries poses a serious threat to the stability of their ecosystems (Yu et al., 2021). Long-term consumption of drinking water containing high concentrations of NO₃⁻ can cause serious harm to human health and pose risks to human health, such as methemoglobinemia, diabetes, spontaneous abortion, thyroid disease and stomach cancer (Burns, 2011; Danni et al., 2019; Xia et al., 2019).

Determining the NO₃⁻ source in the river basin is essential for effective control and treatment of river NO₃⁻ pollution. The traditional source analysis method of NO₃⁻ in rivers is mainly done by studying regional land use types and combining river water chemistry characteristics. This method is cumbersome and circumscribed. NO₃⁻ source is complex, related to season, flow, rainfall and other factors (Ding et al., 2015), and is also affected by social factors such as exogenous input and a series of biogeochemical reactions that occur during the nitrogen cycle, such as ammoniation, nitrification and denitrification, etc., which are difficult to identify by traditional source analysis methods.

Different NO₃⁻ sources have different isotopic characteristics (Wang et al., 2018). The source of river NO₃⁻ can be traced according to its unique stable isotopic characteristics through the analysis of N and O (¹⁵N-NO₃⁻ and ¹⁸O-NO₃⁻) isotopes of NO₃⁻(Ji et al., 2022). However, NO₃⁻ isotopes (¹⁵N-NO₃⁻ and ¹⁸O-NO₃⁻) can only indicate the source of NO₃⁻, not the proportion of sources of pollution (e.g., atmospheric deposition, soil N reservoir, fertilizer, manure and sewage). Combining bistable isotope information with Bayesian statistics (i.e., MixSIAR) can effectively quantify NO₃⁻ sources with unique isotopic characteristics (Xue et al., 2009). For example, Soto et al. (2019) used NO₃⁻ isotopes and SIAR models to determine NO₃⁻ sources in Assiniboine and Red rivers (Canada). Their results showed that manure and wastewater discharge contributed 62% of NO₃⁻ sources in the Assiniboine River, while inorganic agricultural fertilizers contributed 40% of NO₃⁻ sources in the Red River (Soto et al., 2019). Ji et al. (2022) used the SIAR model to quantify the contribution of NO₃⁻ sources in the Wenruitang River Basin of China, and determined that urban sewage was the main source of NO₃⁻ (58.5–75.7%), followed by nitrogen fertilizer (8.6–20.9%) and soil nitrogen (7.8–20.1%), and atmospheric deposition was (<0.1–7.9%) (Ji et al., 2022). Furthermore, the Bayesian stable isotope mixing model was also applied to reveal the source contributions of nitrate in the Ganga river (Kumar et al., 2023), the western coast of Guangdong Province, South China (Lao et al., 2019), East China Sea (Wang et al., 2023), a rural karst basin in Chongqing, southwestern China (Chang et al., 2022), Han, Rong and Lian river basins (Ye et al., 2021), and the eastern coast of Hainan Island (Chen et al., 2020a). Chen et al. (2020a) uesd the dual isotopes and some ion tracers to study NO₃⁻ sources and watershed denitrification. Their results indicated that nitrification in soil zones was the main NO₃⁻ source in dry winter, the lowest denitrification (10%) occurred in April and the highest denitrification (48%) took place in August (Chen et al., 2020a). Chang et al. (2022) analysed hydrochemistry and dual NO₃⁻ isotopes of water samples from a rural karst basin in Chongqing, southwestern China, their results indicated that the change of land use patterns and enhanced rural tourism activities alter the dominant NO₃⁻ sources in the rural karst river basin (Chang et al., 2022). Characterizing NO₃⁻ sources and biogeochemical processes have been increasingly more common and resultful through use of hydrochemistry and δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻(Chen et al., 2020b; Valiente et al., 2020).

In eastern China, non-point source pollution is of great importance due to its prevalence on water quality impairment with excessive chemical fertilizer application and rapid economic development in recent years (Ji et al., 2017; Chang et al., 2022). Studies have attempted to understand the distribution of different nitrogen forms and their spatial and temporal variations in different pollution types of tributaries or reaches based on catchment characteristics and nitrogen forms in Cao-E River Basin (Jin et al., 2009; Shen et al., 2011). Ji et al. (2017) adopted the environmental isotope (δD-H₂O, δ¹⁸O-H₂O, δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻) analysis and the Markov Chain Monte Carlo (MCMC) mixing model to determine the proportions of riverine NO₃⁻ inputs from four potential NO₃⁻ sources (AD, NF, SN, M&S) in ChangLe River which was tributary of Cao-e River Basin (Ji et al., 2017).

The analysis of the source of NO₃⁻ will help to further understand the impact of environmental changes in the basin on water quality, and provide a data basis for the development of hydrological and water resources research in the Cao-e River Basin. The objectives of this study were: 1) to qualitatively explore the source changes of NO₃⁻ in river water and its possible biological and chemical transformation processes based on the changes of δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻ values according to the water chemical characteristics of the samples; 2) The MixSIAR model was used to quantitatively analyze the contribution rate of each NO₃⁻ source by calculating the nitrogen and oxygen isotope values; 3) According to the pollution sources obtained from the analysis, put forward reasonable suggestions for the treatment and protection of the river basin.

2 Manuscript

2.1 Materials and methods

2.1.1 Study area

The Cao-E River belongs to the Qiangtang River system and is the main tributary of the estuary section of the Qiangtang River, with a total length of 193 km and a basin area of 6,080 km². The basin ranges from 120°30′E−121°15′E and 29°08′N-30°15′N. Originating from Changwu in the Dayan Mountain Range in Wang Village, Shanghu Town, Pan’an County, it flows from south to north through Xinchang, Shengzhou, Shangdu District and Keao District, and flows into Hangzhou Bay at the lower reaches of the Xinsanjiang Gate below the mouth of the Sanjiang River in Shaoxing. Above Shengzhou Pass is the upstream, Shengzhou pass to Shangdu Baiguan is the middle stream, and below the Baiguan is downstream. The upper section is a mountain-stream river, and the middle reaches of Shangdu Lock is a tidal river section, which is affected by the tide of Hangzhou Bay. There are a large number of industrial enterprises distributed in the river basin, and in addition to industrial wastewater discharge, there are also domestic sewage and non-point source pollution of farmland.

2.1.2 Sampling and pre-treatment

A total of 25 river water samples were collected from free-flowing reaches (FFRs) in January 2022 (the dry season), and the same samples were collected in June 2022 (the wet season). Collecting samples in two seasons to study the effects of seasonal changes on NO₃⁻ sources. Considering the river system distribution and hydrological characteristics. One sampling point was laid in the upper reaches of the Cao-e River Basin, 8 sampling points were laid in the middle reaches of the Cao-e River Basin, 5 sampling points were laid in the lower reaches of the Cao-e River, 1 main tributary Qianxi River, 2 Xinchang River, 1 Chengtan River, 1 Huangze River, 1 Yintan Stream, 1 Xiaguan Stream, 1 Xiaoshunjiang, 1 Hangzhou-Ningbo Canal, 1 Dongxiaojiang. The sampling point range covered the whole basin of the Cao-e River. All samples collected in the main stream after the tributaries merged are located 1.52km–6.93 km downstream from the confluence, where the nitrogen from the tributaries has been mixed completed (Fischer et al., 1979), and the location of the sampling points is shown in Figure 1. All water samples were collected at 0.5 m below the water surface, the collected water samples were stored separately in 500 mL polyethylene bottles that were prerinsed with distilled water and were then put into a portable incubator for temporary storage. They were then taken back to the laboratory and analyzed within 24 h.

FIGURE 1

FIGURE 1. Map of the sampling points.

2.1.3 Isotopic and chemical analyses

The river water pH, temperature (T, ˚C), electrical conductivity (EC), dissolved oxygen (DO), and oxidation-reduction potential (ORP/Eh, mV) were measured in situ using a multiparameter portable meter (Hach HQ40d, United States), the precision for these analyses were 0.1, 0.1 °C, 0.01 μS/cm, 0.01 mg/L, 0.1 mV, respectively. The collected water samples were returned to the laboratory on the same day of collection to measure the NH₄⁺, NO₃⁻, and NO₂⁻ concentrations. These parameters were analyzed according to standard methods approved by the National Environmental Protection Agency of China (Administration, 2002). NH₄⁺ was determined by the Nesslerization colorimetric spectrophotometric method, NO₃⁻ was measured by the phenol disulfonic acid ultraviolet spectrophotometric method, NO₂⁻ was measured by the N-(1-naphthyl)-ethylenediamine spectrophotometric method (Zhang et al., 2017), with an ultraviolet and visible spectrophotometer (UV 2450,Shimadzu, Japan). The detection limits for NH₄⁺, NO₃⁻ and NO₂⁻ being 0.02, 0.02, 0.01 mg/L⁻¹, respectively. Concentrations of chloride (Cl⁻) was analyzed using ion chromatography (Dionex ICS-600), the analytical precision was 0.01 mg/L.

The δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻ values were obtained using the chemical conversion method. First, 20 mL of filtered water sample was placed in a 40 mL headspace vial, 0.1 mL of cadmium chloride (CdCl) (20 g/L) solution and 0.8 mL of ammonia chloride (NH₄Cl) (250 g/L) solution were added, 3 to 4 zinc tablets of 3 × 10 cm were wiped clean with alcohol was added, and the headspace vial was placed on a shaker and oscillated at 220 r/min for 15 min. After the full reaction, the zinc tablets were removed, the headspace vial was sealed, and the NO₂⁻ reduction step was completed. Add 1 mL of sodium azide (NaN₃) solution (2 mol/L) and acetic acid (CH₃COOH) (20%) 1:1 mixture to the headspace vial and mix the sample and reagents by vigorous shaking. After that, it was oscillated at 220 r/min for 30 min, and finally 0.6 mL of sodium hydroxide (NaOH) solution (6 mol/L) was added as a terminator (the solution was alkaline and not conducive to azidification reaction) to end the azidation reaction. The NO₃⁻ was converted into N₂O gas by the above chemical process reaction. δ¹⁵N and δ¹⁸O values of N₂O were analyzed by an Isotope Ratio Mass Spectrometer (IRMS, Thermo Fisher MAT 253) equipped with a Gas-Bench ІІ device (Thermo Fisher). The reproducibility was within ±0.2‰ for δ¹⁵N-NO₃⁻ and δ¹⁸O- NO₃⁻.

For the measurements of δ¹⁸O-H₂O, the filtered water sample was transferred into a 2 mL chromatographic bottle, the height of the sample volume is ensured to be > 0.5mm, covered with a hollow cap, and placed on the sample holder. The δ¹⁸O-H₂O values were analyzed by IRMS (Thermo Scientific Delta V Advantage). The analytical precision was ±0.3‰ for δ¹⁸O-H₂O.

The nitrogen and oxygen isotopic analysis followed (Lawniczak et al., 2016) by the chemical conversion of NO₃⁻ and NO₂⁻ to N₂O. International standards (USGS-32 and USGS-34) were applied to calibrate “blank” samples. The stable isotopic rates were expressed in parts per thousand (‰) relative to N₂ in the atmosphere and Vienna Standard Mean Ocean Water for δ¹⁵N and δ¹⁸O, respectively:

${\mathrm{\delta }}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}=\left(\frac{{\mathrm{R}}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}}{{\mathrm{R}}_{\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{d}}}-1\right)\times 1000(1)$

In Eq. 1, δ_sample is the stable isotope ratio in the samples. R_sample and R_standard are the ratios of ¹⁵N/¹⁴Nor ¹⁸O/¹⁶O in the samples and the standards, and the reference standards of N and O are atmospheric nitrogen and Vienna standard mean ocean water (V-SMOW), respectively. Sample analysis had an average precision of±0.2‰ for δ¹⁵N and δ¹⁸O (Breitburg et al., 2018).

2.1.4 Multivariate statistical analysis

Principal component analysis (PCA) simplifies the complexity in high-dimensional data while retaining trends and patterns. It does this by transforming the data into fewer dimensions, which act as summaries of features (Lever et al., 2017). PCA takes a small number of comprehensive indicators to characterize the research objective through dimensionality reduction and obtains the components of eigenvalues greater than 1, defined as the main components (PCs). The Kaiser normalized orthogonal rotation were used to obtain the load of each component. When using the PCA model, the raw data is first converted to a dimensionless standardized form to eliminate the impact of different dimensions (Jenkins and Doney, 2008).

$z_{ij}=\frac{c_{ij}-{\overline{c}}_i}{{\sigma }_i}(2)$

where z_ij is the normalized value; c_ij is the concentration of element i in sample j, i = 1,2,3, … , n, j = 1,2,3, … , m; c_i and σ_i are the mean concentration and standard deviation for element i, respectively.

Then, the PCA model can be expressed as:

$z_{ij}={\sum }_{k=1}^p{g}_{ik}{h}_{kj}(3)$

where k = 1, … , p, represents the different sources of pollution, g_ik represents the concentration of element i in the pollution source k, also known as the factor load and h_kj represents the contribution of the pollution source k to the sample j, called the factor score.

2.1.5 MixSIAR model

In order to reduce the uncertainty of source registration due to the overlap of multisource NO₃⁻ stable isotopes and the fractionation of δ¹⁵N-NO₃⁻ and δ¹⁸O- NO₃⁻ during transition, the contribution ratio of different sources was quantitatively evaluated using Bayesian isotope mixing model (MixSIAR) (Archana et al., 2018). In this study, the MixSIAR model (https://github.com/brianstock/MixSIAR/issues) was applied to estimate proportional contributions of NO₃⁻ sources to river water samples. MixSIAR model is also applicable when multiple pollution sources exist simultaneously, and the uncertainty of the data is taken into account. By defining J isotopes of K sources and N mixtures, the equations were as follows (Xia et al., 2018).

$\mathrm{X}\mathrm{i}\mathrm{j}={\sum }_{\mathrm{k}=1}^{\mathrm{K}}\mathrm{p}\mathrm{k}\left(\mathrm{S}\mathrm{j}\mathrm{k}+\mathrm{C}\mathrm{j}\mathrm{k}\right)+\mathrm{\epsilon }\mathrm{i}\mathrm{j}\mathrm{S}\mathrm{j}\mathrm{k}\sim \mathrm{N}\left(\mathrm{\mu }\mathrm{j}\mathrm{k},{\mathrm{\omega }}_{\mathrm{j}\mathrm{k}}^2\right)\mathrm{C}\mathrm{j}\mathrm{k}\sim \mathrm{N}\left(\mathrm{\lambda }\mathrm{j}\mathrm{k},{\mathrm{\tau }}_{\mathrm{j}\mathrm{k}}^2\right)\mathrm{\epsilon }\mathrm{j}\mathrm{k}\sim \mathrm{N}\left(0,{\mathrm{\sigma }}_{\mathrm{j}}^2\right)(4)$

In Eq. 4, X_ij is the isotope value j of the water sample i (i = 1, 2, 3 … ,N; j = 1,2,3 … ,J); P_k is the proportional contribution of source k, which is calculated using MixSIAR; S_jk is the isotope value j of source k (k = 1, 2, 3 … , K) and followed a normal distribution with mean µ_jk and standard deviation ω²_jk; C_jk is the fractionation factor for isotope j on source k and followed a normal distribution with mean λ_jk and standard deviation τ² j_k; and ε_ij is the residual error for isotope value j in mixed sample and followed a normal distribution with mean = 0 and standard deviation σ².

In the study of predecessors, atmospheric deposition (AD), nitrogen from soil (SN), nitrogen fertilizer (NF), and manure and sewage (M&S) these four sources are generally used to analyze NO₃⁻(Chen et al., 2020b; Chang et al., 2022; Shi et al., 2022). And Ji et al. (2017) used these four sources to analyze NO₃⁻in Changle river which was one of the main tributaries of the Cao-E River (Ji et al., 2017). In this study, we assumed that all riverine NO₃⁻-N contamination derived from these four sources. As shown in Table 1, the mean δ¹⁵N value was 5.0‰ ± 1.5‰ for SN(Diaz and Rosenberg, 2008), which was in the reported ranges summarized by (Tobari et al., 2010). Then, measured the δ¹⁵N of synthetic fertilizers when studying several rivers located in the Loess Plateau (Zhao et al., 2020). The δ¹⁵N range of ammonium bicarbonate/urea/ammonium sulfate was 0.3‰ ± 3.0‰. Finally, the mean δ¹⁵N value of M&S was 11.3‰ ± 0.2‰ (Zhang et al., 2018b).

TABLE 1

TABLE 1. Summary of the δ¹⁵N and δ¹⁸O values of main sources.

2.2 Results and discussion

2.2.1 Characterization of hydrochemistry in dry and wet season

A summary of water quality variables measured at the 25 sampling sites in dry/wet season of the Cao-E River is provided in Table 2. In dry season, NO₃⁻ concentrations of samples collected in the water body ranged from 1.11 to 2.99 mgL⁻¹, with an average concentration of 2.17 mgL⁻¹, the concentration of NO₂⁻ is from 0.004 to 0.08 mgL⁻¹, with an average of 0.03 mgL⁻¹, and the concentration of NH₄⁺ ranges from 0.50 to 0.95 mgL⁻¹, with an average value of 0.68 mgL⁻¹. In wet season, NO₃⁻ concentrations of samples collected in the water body ranged from 0.32 to 5.41 mgL⁻¹, with an average concentration of 3.39 mgL⁻¹, the concentration of NO₂⁻ is from 0.01 to 0.18 mgL⁻¹, with an average of 0.07 mgL⁻¹, and the concentration of NH₄⁺ ranges from 0.19 to 0.85  mgL⁻¹, with an average value of 0.47 mgL⁻¹. The concentration of NO₃⁻ in water in wet season was higher than that in dry season. In general, NO₃⁻ accounted for the highest concentration of inorganic nitrogen (Figure 2) and was the main pollutant, followed by NH₄⁺ and then NO₂ ^-.

TABLE 2

TABLE 2. Descriptive statistics of water quality variables (dry/wet season).

FIGURE 2

FIGURE 2. Triangular phase diagrams of three inorganic nitrogen concentrations; (A) dry season, (B) wet season.

Through principal component analysis of the original data of five water chemical components (pH、DO、NO₃⁻、Cl⁻、T) at 25 sampling points of Cao-E River, the result shows that the contribution of the five PCs is 30.42, 25.39, 19.84, 15.01% and 9.34%, respectively, (Table 3). Figure 3 shows five water chemistry parameters of the first two PCs. The greater the projection of this parameter on the axis, the greater the load of this parameter on the PC. Parameters that are ipsilateral on the x- or y-axis indicate that there is a positive correlation between these parameters, while there is a negative correlation in other cases. In PC 1, Cl⁻ and DO are the larger loads, and there is a positive correlation between them (Figure 3). The strong correlation between DO and NO₃⁻ suggests that the concentration of NO₃⁻ in the Cao-E River Basin may be affected by the redox environment. Ruiza et al. (2003) studied the behaviour of the nitrification system during consecutive changes in DO values. The results showed that DO had no influence on NO₃⁻ accumulation at values of 2.7–5.7 mg/L. The DO concentrations of the basin averaged at 10.15 mg/L in dry season, and at 9.03 mg/L in wet season, suggesting oxidation environment (Ruiz et al., 2003). So NO₃⁻ can exist stably in the basin, not reduced to NO₂⁻, nor affected by redox in water.

TABLE 3

TABLE 3. Eigenvalue, Variance, Cumulative Variance, and loading values in PC1 and PC2 of PCA analysis.

FIGURE 3

FIGURE 3. Principal component analysis of five water chemical parameters at sampling points.

Cl⁻ is a stable tracer that is not affected by changes in NO₃⁻ content (Xia et al., 2016), and it can often be used as an indicator of different pollution sources (KELLMAN and HILLAIRE-MARCEL, 1998; Mengis et al., 1999) due to the good stability of Cl⁻. Potential sources of Cl⁻ in rivers may be industry, domestic sewage and manure, application of agricultural fertilizer, etc. It can be seen from Figure 4 that the NO₃⁻concentration basically varies with the Cl⁻ concentration in the dry season and the wet season is basically consistent. So the correlation analysis between Cl⁻ and NO₃⁻ is carried out, as shown in Figure 5, Cl⁻ have a positive relationship with NO₃⁻ (R² = 0.96 in the dry season, R² = 0.954 in the wet season). Therefore, the source of NO₃⁻ can be roughly revealed based on the source of Cl⁻. Combined with the distribution of factories and soil utilization in the Cao-E River Basin, it is speculated that the source of NO₃⁻ pollution may come from fertilizer, manure or sewage.

FIGURE 4

FIGURE 4. Trend of NO₃⁻ and Cl⁻ concentrations at each sampling point; (A) dry season, (B) wet season.

FIGURE 5

FIGURE 5. Correlation between NO₃⁻ and Cl⁻ concentrations in the study area; (A) dry season, (B) wet season.

2.2.2 Study on the law of NO₃⁻ migration and transformation in water

The use of dual NO₃⁻ isotopes (δ¹⁵N–NO₃⁻ andδ¹⁸O–NO₃⁻) to identify the source of NO₃⁻ in rivers is based on the fact that NO₃⁻ can maintain a certain stability after entering the water body. However, there is a difficulty that possible denitrification in rivers will cause the fractionation of NO₃⁻ to change the composition of NO₃⁻ source isotopes (Paredes et al., 2018; Paredes et al., 2019). Therefore, the migration and transformation of NO₃⁻ in rivers needs to be studied. Recent studies have shown that nitrification is the main process of nitrogen conversion in aquatic systems (Ye et al., 2015).

Denitrification refers to the process of reducing NO₃⁻ to N₂ and N₂O under anaerobic conditions, which can effectively reduce the pollution degree of NO₃⁻ in water (Mayer et al., 2002). In this process, the δ¹⁵N and δ ¹⁸O value of residual NO₃⁻ in the water body increases (Kendall et al., 2007). Relevant studies have shown that denitrification leads to a ratio of δ¹⁵N and δ¹⁸O in aquatic systems of 1: 1 (Ye et al., 2021). The ratio of the two in all samples is not 1:1. And from Figure 6, it can be seen that there is no significant negative correlation between δ¹⁵N and δ¹⁸O (dry season, R² = 0.062; wet season, R² = 0.040). Therefore, denitrification is not the dominant process of nitrogen conversion in the study area during sampling. The DO concentrations of the basin averaged at 10.15 mg/L in dry season, and at 9.03 mg/L in wet season. This also suggested that denitrification was not likely to occur and was not the process responsible for causing the enrichment of δ¹⁵N- NO₃⁻ in study area, since denitrification generally occurs at low DO (<5 mg/L⁻¹) conditions. This is consistent with the results of Xing and Liu(Xing and Liu, 2016).

FIGURE 6

FIGURE 6. Characteristic values of δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻ isotopes in the study area; (A)dry season, (B)wet season.

The relationship between δ¹⁸O-NO₃⁻ and δ¹⁵N-NO₃⁻ further demonstrates the nitrification process. In the nitrification process, one-third of the oxygen atoms of the nitrified NO₃⁻ come from atmospheric oxygen and two out of three oxygen atoms come from H₂O in the environment (Kendall et al., 2007; Xue et al., 2009). Therefore, the theoretical value of δ¹⁸O-NO₃⁻ obtained from nitrification reaction can be calculated from Equation 5.

${\delta }^{18}O-N{O}_3^{-}=\frac{1}{3}{\delta }^{18}O-O_2+\frac{2}{3}{\delta }^{18}O-H_{2}O(5)$

Among them, δ¹⁸O-NO₃⁻ is the theoretical δ¹⁸O value of NO₃⁻ derived from nitrification, δ¹⁸O-H₂O is the δ¹⁸O value of water samples measured in this study, and δ¹⁸O-O₂ is the δ¹⁸O value of atmospheric oxygen, which is generally considered to be 23.5‰(Kendall et al., 2007).

The exchange of oxygen atoms occurs during nitrification reactions, especially in the final nitrification step of nitrate generation, where oxygen atoms are added from water (Wells et al., 2019). The results of the oxidation reaction showed that the δ¹⁸O-NO₃⁻ value was closer to the δ¹⁸O-H₂O value (Sigman et al., 2009; Casciotti et al., 2010). In addition, studies have reported that less than one-sixth of the oxygen atoms in the NO₃⁻ after nitrification come from atmospheric oxygen and the rest from H₂O(Kool et al., 2011). According to Xuan et al. (2020) study, another theoretical formula for δ¹⁸O-NO₃⁻ Equation 6 (Xuan et al., 2020).

${\delta }^{18}O-N{O}_3^{-}=\frac{5}{6}{\delta }^{18}O-O_2+\frac{1}{6}{\delta }^{18}O-H_{2}O(6)$

The function plots of theoretical δ¹⁸O-NO₃⁻ and δ¹⁸O-H₂O are shown in Figure 7 with theoretical lines 1) and 2), respectively, and almost all samples are located between the two theoretical lines. δ¹⁸O-NO₃⁻ and δ¹⁸O-H₂O increased at the same time in the Cao-E River Basin, indicating that nitrification process occurred in the basin. According to the above δ¹⁸O-NO₃⁻, δ¹⁸O-H₂O and δ¹⁵N- NO₃⁻ isotope analysis results, nitrification occurs in almost all surface water as the main NO₃⁻ conversion process.

FIGURE 7

FIGURE 7. Point plot (δ¹⁸O-NO₃⁻ versus δ¹⁸O-H₂O) of surface water and theoretical Eqs 5, 6 to indicate the presence of nitrification in study area; (A)dry season, (B)wet season.

2.2.3 Identification of NO₃⁻ sources by dual isotopes

Different NO₃⁻ sources show different isotopes signals and can be used to qualitatively evaluate the source of NO₃⁻ inputs (Ji et al., 2017). The traditional dual isotope bi-plot method made it easier to identify the main factors influencing NO₃⁻ concentrations in the river system. Typical δ¹⁵N range from −3‰ to +7‰ in precipitation NO₃⁻(Zhang et al., 2019). Nitrogen fertilizer is a commonly used synthetic fertilizer, which is produced by atmospheric N₂ fixation. Therefore, the δ¹⁵N values of these fertilizers are similar, ranging from −6‰ to +6‰ (Gibson et al., 2005; Xue et al., 2009; Yang et al., 2013). The δ¹⁵N-NO₃⁻ deposited through soil nitrogen exhibited a large variation, with values ranging from 0 to +8‰ (Kendall, 1998). δ¹⁵N-NO₃⁻ of M&S are generally high, ranging from +3 to +17‰ (Zhi-Wei et al., 2014). For δ¹⁸O, NO₃⁻ in precipitation and NO₃⁻ fertilizer had the typical value ranging from +25 to +70‰ and +17 to +25‰, respectively (Amberger and Schmidt, 1987; Kendall, 1998), the δ¹⁸O- NO₃⁻ value from nitrogen fertilizer, N soil and M&S microbial nitrification tends to vary between −5‰ and +15‰(Kendall, 1998; MAYER et al., 2001).

As shown in Figure 8, the δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻ values of most water samples were in the range of manure and sewage, soil nitrogen and nitrogen fertilizer, indicating that the main sources of NO₃⁻ in the Cao-E River Basin were these three sources, which was consistent with the results of 3.1 analysis. The isotopic fingerprints of several samples on the three main nitrate sources overlap, which is also important to note. Comparing the source analysis of the wet season and the dry season, it is found that the nitrate sources of soil nitrogen in the wet season are more than those in the dry season, which may be due to more precipitation in the flood period, and the rainwater washes the soil and then flows into the river, resulting in an increase in the nitrate source of soil nitrogen in the Cao-e River Basin.

FIGURE 8

FIGURE 8. Cross plot of δ¹⁵N-NO₃⁻ and δ¹⁸O- NO₃⁻ in surface water with typical ranges of stable isotopic composition; (A)dry season, (B)wet season.

2.2.4 Quantification of the dominant sources of NO₃⁻

In order to further quantitatively estimate the proportional contributions of different potential NO₃⁻ sources to the riverine NO₃⁻ pollution, MixSIAR model was employed. Similarly, we classified the NO₃⁻ sources into four groups, i.e., AD, NF, SN and M&S. In this study, we assumed that all riverine NO₃⁻ contamination derived from these four sources. For the surface water, the fractionation factor C_jk was set based on Yu et al. (2020) (Zhang et al., 2018a). The MixSIAR model was used to calculate the contribution range of each NO₃⁻ source, as shown in Figure 9. The average contribution rate of NO₃⁻ in the dry season of Cao-e River is M&S (44.25%)> SN (37.47%)> NF (15.45%)> AD (2.83%), the average contribution rate of NO₃⁻ in the wet season is SN (41.69%)> M&S (31.78%)> NF (23.39%)> AD (3.14%).

FIGURE 9

FIGURE 9. Proportional contributions of each pollution source, boxplot denotes the fifth, 95th, mode, and mean values; (A)dry season, (B)wet season.

Studies have shown that NO₃⁻ pollution in this basin is mainly due to local direct human activities and soil nitrogen losses (Bowes et al., 2020). As shown in Figure 9, these three sources (i.e., M&S, SN and NF) contribute about 97% of the river’s NO₃⁻. There are many industrial plants in printing and dyeing, chemical industry, tanning, paper making and other industries in the Cao-E River Basin, with a population of 1.53 million living in the basin, which leads to a high M&S contribution rate of 31.78%–44.25%.

The valley plain of the Cao-E River Basin accounts for 18%, hills and mountains account for 82%. The forest coverage rate is as high as 54% (Shen et al., 2011), and water-soluble organic nitrogen in forest soil occupies an important position in soil nitrogen reservoir. For many forests, soil-soluble organic nitrogen levels are more than 100 times higher than NH₄⁺ and NO₃⁻ levels (Kalbitz et al., 2000; Wu et al., 2010). Due to the increase of precipitation during the wet season, water-soluble organic nitrogen enters the Cao-E River with the washing rainwater, and they are converted into NO₃⁻ under nitrification, resulting in a significant increase in the SN contribution rate during the wet season.

There are 593.33 km² mu of arable land in the basin, including 48.67 km² of paddy land and 16.67 km² of dry land, and the contribution rate of NF in the two seasons (15.45%–23.39%) is smaller than that of SN and M&S. The contribution of NF in the wet season is significantly higher than that in the dry season, which may be due to the fertilization amount in the wet season is more than that in the dry season. The precipitation filters the fertilizer, fertilizer flowing into the Cao-E River with rainwater, resulting in a significant increase in the contribution of NF in the wet season.

In this study, there is a large range for the estimation of the probability of contribution to a single source, which indicates a large uncertainty in the allocation results of the model (Figure 8). However, the MixSIAR model can still effectively calculate the proportional contribution of NO₃⁻ sources, which is basically consistent with the analysis results. However, the contribution of AD is not shown in the distribution plot of the contribution rate of a single source, and the analysis results of the MixSIAR model show that the contribution rate of the wet season (3.14%) is slightly greater than that of the dry season (2.28%), which may be due to the increased atmospheric N wet deposition by precipitation (Leeuw et al., 2001) during the wet season.

The uncertainty of the quantitative allocation of NO₃⁻ pollution sources mainly come from two factors: 1) the temporal and spatial changes of NO₃⁻ sources and their isotopic composition in the basin, and 2) the changes in NO₃⁻ isotope values caused by the fractionation process (nitrification, denitrification, assimilation). Therefore, quantitatively determining the initial isotopic composition of different NO₃⁻ sources and estimating the isotope fractionation coefficient of each NO₃⁻ source is an important task to reduce uncertainty. In future studies, in order to reduce the uncertainty of the results, future research can consider the in-situ fractionation factor to improve the Bayesian mixing model applications, more accurate source isotope data, such as small variance ω²_jk, can be used to reduce the uncertainty of the distribution results.

2.3 Conclusion

In this study, water quality parameters, δ¹⁵N–NO₃⁻ and δ¹⁸O–NO₃⁻ and MixSIAR models were used to evaluate the conversion and sources of inorganic nitrogen in the Cao-E River Basin. The results showed that NO₃⁻ was the main pollutant in the inorganic nitrogen form. After water chemical analysis and the distribution characteristics of δ¹⁵N–NO₃⁻ and δ¹⁸O–NO₃⁻, the nitrogen conversion process of river system was mainly nitrification, and there was no obvious denitrification effect. In addition, the results of the dual isotope method combined with MixSIAR model showed that the contribution of M&S in the dry season was relatively high (44.25%), followed by SN (37.47%), NF (15.45%) and AD (2.83%). The contribution of SN during the wet season was relatively high (41.69%), followed by M&S (31.78%), NF (23.39%), and AD (3.14%). According to the analysis results, it is necessary to strengthen sewage treatment and sewage pipe network construction in the Cao-E River Basin in the future, improve the sewage collection rate, and strengthen water and soil conservation in the basin, so as to reduce the discharge of pollutants in the basin and improve the water ecological environment.

In order to further reduce the uncertainty of isotope allocation, the isotopic characteristic range and isotope fractionation factor of the main NO₃⁻ source should be further determined in future studies to more accurately calculate the contribution of NO₃⁻ sources. In addition, this study only considers the sources and transformations of NO₃⁻ in the wet and dry seasons, and the effects of seasonal changes and spatial distribution will be considered in the next study.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

JL: Conceptualization, Investigation, Writing—original draft. QS: Edited Manuscript. KL: Methodology, Supervision, Review. LC: Review, Edited Manuscript. XL: Supervision, Revise. All authors contributed to the article and approved the submitted version.

Funding

This research is financially supported by the National Key Research and Development Program (2021YFC3101700).

Acknowledgments

The authors thank the Chinese Research Academy of Environmental Sciences for their assistance in sample collection.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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