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Article

Traceability and Biogeochemical Process of Nitrate in the Jinan Karst Spring Catchment, North China

by
Kairan Wang
1,2,
Xuequn Chen
1,2,
Zhen Wu
1,2,
Mingsen Wang
1,2,* and
Hongbo Wang
3,*
1
Water Resources Research Institute of Shandong Province, Jinan 250014, China
2
Shandong Provincial Key Laboratory of Water Resources and Environment, Jinan 250014, China
3
School of Municipal and Environmental Engineering, Shandong Jianzhu University, Jinan 250101, China
*
Authors to whom correspondence should be addressed.
Water 2023, 15(15), 2718; https://doi.org/10.3390/w15152718
Submission received: 26 June 2023 / Revised: 26 July 2023 / Accepted: 26 July 2023 / Published: 27 July 2023
(This article belongs to the Special Issue Karst Dynamic System and Its Water Resources Environmental Effects)
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Abstract

:
Accurate identification of nitrate (NO3−) sources is critical to addressing groundwater pollution, especially in highly vulnerable karst aquifers. The groundwater hydrochemistry and δ15NNO3 and δ18ONO3 isotopes were analyzed in samples taken from the Jinan Spring Catchment, which has been affected by urbanization and agricultural activities. The study highlighted the use of hydrochemistry, environmental isotopes, and a multisource linear mixed model for NO3− source identification and apportionment. The results showed that, controlled by carbonate rocks, the hydrochemical types in both rainy and dry seasons were highly consistent, and HCO3·SO4−Ca was the dominant type, accounting for 60%. Except for Ca2+, Mg2+ and HCO3−, the coefficients of variation of other ions were all greater than 0.5 in rainy and dry seasons. The chemical composition of groundwater was mainly controlled by water–rock interaction. Ca2+ and HCO3− were mainly derived from carbonate rock dissolution; K+, Na+, SO42−, NO3− and Cl− were partially derived from atmospheric precipitation. The IsoSource model quantitatively revealed that the majority of the groundwater and surface water was influenced by manure and sewage (M&S) contributing 39.3% and 52.3% in the rainy season, and 37.1% and 56.9% in the dry season, respectively. The NO3− source fraction rates were in the order M&S > SON > AF > CF > AD. In addition, nitrate pollution control measures and suggestions for different areas are put forward. In rural residential areas, the free discharge of livestock manure and sewage should be strictly controlled. In agricultural planting areas, chemical fertilizers and pesticides should be used rationally to prevent non-point source pollution. In urban areas, the centralized treatment of industrial and residential sewage should be strengthened to prevent point source pollution.

1. Introduction

Karst groundwater is an important water resource [1,2,3]. About 25% of the world’s population basically relies on karst groundwater for drinking water and agricultural irrigation [4,5], and one quarter of China’s groundwater resources are distributed in karst areas [6]. Under the background of global climate change, and with the development of regional economies and the advancement of urbanization, the change of agricultural activities and land use has become an important factor affecting groundwater quality [7]. Intensive use of farm manure, chemical fertilizer, pesticides, and arbitrary discharge of industrial wastewater leads to the infiltration of pollutants into karst groundwater system [8,9,10,11], resulting in an increase in NO3−, SO42−, Cl− and other pollutants in groundwater [12,13,14]. Nitrate pollution of water has become a worldwide environmental problem [15,16]. Excessive consumption of nitrates increases the risk of methemoglobin in children and cancer of the stomach and esophagus in adults [17,18,19]. Therefore, identifying the biogeochemical processes that affect the major sources and characteristics of nitrates is an important step in water resource conservation and management practices [20].
In the main discharge areas of karst groundwater systems in Northern China, groundwater quality has deteriorated over 20% of the area [21,22]. In the past 30 years, with the continuous acceleration of industrialization and urbanization, the nitrate content in groundwater of the karst spring catchment in Northern China has increased year by year. Generally, the migration and transformation time of nitrate nitrogen in groundwater is relatively long, is not easily degraded, and treatment and repair is difficult. Since the 1970s, Kohl et al. [23] first used δ15NNO3 to identify nitrate contamination sources in surface water, δ15NNO3 has become the main means to identify nitrate contamination in water. Later, Heaton [24] summarized previous studies and determined the δ15NNO3 isotope characteristic values of three major pollution sources (mineralized soil organic nitrogen sources ranged from 4‰ to 9‰, fertilizer sources ranged from −4‰ to 4‰, and fecal or fecal-containing sewage sources ranged from 9‰ to 20‰). Many studies have been carried out on the use of nitrogen and oxygen isotopes to trace nitrate sources in the world [25,26]. δ15NNO3 has been widely used in the identification of nitrate sources in surface water such as rivers, lakes, and groundwater [27,28,29,30]. With the development of testing technology, δ18ONO3 has also been introduced to trace nitrate contamination together with the δ15NNO3 isotope [31], which further refines the isotopic range of different nitrate contamination sources. At present, the combined hydrochemical characteristics of δ15NNO3 and δ18ONO3, land use, and fertilization status are effective means to identify the source of nitrate in groundwater [32,33]. However, because there are few quantitative studies on the sources of nitrate pollution at present, especially for karst areas with special geological structures, it is more necessary to strengthen the characteristics of nitrate pollution and its migration and transformation rule.
The Jinan Spring Catchment (JSC) is located in the semi-arid and semi-humid monsoon climate zone of the warm temperate zone [34]. The abundant karst groundwater of the carbonate rock fissure is an indispensable resource for industrial and agricultural production and social development in Jinan [35]. The main karst characteristics in this area are the development of a series of karst springs [36], and their dynamic changes are closely related to surface water. In the karst groundwater system of this area, there are strong seepage zones such as karst caves, dry valleys, tectonic fissures and depressions in the recharge area, strong karst development zones or dominant flows such as water-conducting faults and pipe flows in the runoff area, and thin quaternary overlying layers in the discharge area. The filtration and purification effects are quite weak, resulting in a very fragile underground hydrological system. There are a lot of tourism resources in the JSC. While obtaining the economic benefits of tourism, tourism activities have caused adverse effects on the local natural environment. The study of karst groundwater in Jinan began in the late 1970s and early 1980s. Many scholars have studied the hydrogeological conditions, the characteristics of the karst runoff channels, and the characteristics of the water cycle and water environment of the JSC [37,38,39], especially on the hydrochemical characteristics of karst groundwater and water environment evolution. Through the analysis of major elements, trace elements and stable environmental isotopes, some scholars have studied the groundwater recharge sources and hydrogeochemical characteristics of the JSC [40,41,42]. Other scholars have analyzed the variation law of karst groundwater quality and its influencing factors in the JSC [43,44]. The results showed that human activities have greatly changed the distribution characteristics of the groundwater hydrochemical field in the study area. However, not enough attention has been paid to the sources of the main ions in groundwater and the biogeochemical processes that cause the evolution of water chemistry at the spring catchment scale.
The purposes of this study were to (1) analyze the changes in hydrochemistry and dual nitrate isotopes composition, (2) identify the proportional contribution of different nitrate sources, and (3) quantify sources, migration and transformations of nitrate in the karst spring catchment by incorporating hydrochemistry, isotopes, and the IsoSource model. This study provides a scientific basis for the formulation of nitrate pollution control methods in karst groundwater systems. At the same time, it can also provide a reference for related studies in similar regions.

2. Study Area

Jinan is located in the warm temperate continental monsoon climate zone. The annual average temperature is 14.3 °C, and the annual average precipitation is 667 mm. Due to the influence of the monsoon, the precipitation between the seasons is very uneven. In summer, the precipitation from June to August is the highest, with the precipitation ranging from 367 mm to 499 mm. The least precipitation occurs from December to February in winter, with the precipitation ranging from 16.6 mm to 29.7 mm. The rivers that flow through Jinan are the Yellow River, the Yufu River, the Beidasha River, and the Xiaoqing River. The Yellow River flows from the north of Jinan, the Yufu River and the Beidasha River flow into the Yellow River from the mountainous areas in the south, and the Xiaoqing River crosses the urban area from west to east, collecting Jinan spring water and flowing eastward into the Bohai Sea [45].
The JSC is a slightly north-dipping monoclinal tectonic geological body, located in the monoclinal tectonic hydrogeological area of the north wing of the Taishan fault block protrusion, and is also a typical karst development area in North China, covering an area of about 1500 km2 (Figure 1). The Cambrian and Ordovician carbonate strata of Paleozoic are monoclinal, overlying the metamorphic rock series and concealed under the quaternary piedmont strata to the north [46]. Yanshanian gabbro-intrusive rocks are widely distributed in the northern, eastern, and western regions of the study area. In the west, along the Yellow River, the Ordovician is buried under the Carboniferous and Permian, and is distributed in a northwest–southeast direction. This geological structural condition is the main controlling factor for the spatial distribution of the aquifer, groundwater circulation movement, and water abundance in the JSC [47].
The carbonate fracture-karst water-bearing system in the study area is composed of a middle Zhangxia Formation, upper Fengshan Formation, and Ordovician aquifers, and the lithology is limestone, argillaceous limestone, dolomitic limestone, calcareous dolomite, and dolomite. Karst fissure development and good connectivity are conducive to groundwater recharge, runoff, collection, and drainage [48]. From south to north, the study area is divided into an indirect recharge area, a direct recharge-runoff area, and a confluence discharge area. The karst water in the area moves from south to north after being replenished by precipitation and rises and emerges as springs when the gabbro rock is blocked. There is a close hydraulic connection between surface water and groundwater, and the karst aquifer is easily polluted by surface water [49,50].

3. Materials and Methods

3.1. Field Sampling and Analytical Methods

According to the “Water quality-Guidance on sampling techniques” [51], in September 2020 (rainy season) and June 2021 (dry season), water samples were collected at the same 30 sampling sites, including 4 surface water samples and 26 groundwater samples (3 spring and 23 well water samples). Two pre-rinsed, 500 mL polyethylene bottles were filled with filtered (0.45 mm) sample water for cation and anion analyses. The samples for cations were acidified in the field with ultrapure HNO3 to a pH value < 2. Samples for measurement of dual nitrate isotopes were filtered with a 0.22 mm cellulose ester membrane and stored in a 40 mL brown polyethylene bottle per water sample. All of the samples for analyses were kept at 4 °C until analyses [19].
In situ measurements included water temperature (WT), pH, electrical conductivity (EC), and dissolved oxygen (DO) were performed using an HQ40d multi-parameter meter (HACH Corporation, Loveland, Colorado, USA), which has accuracies of 0.1 °C, 0.01 pH unit, 1 μS/cm, and 0.01 mg/L, respectively. All instrument probes were calibrated before use [33]. The NH4+ and NO3− concentrations were determined in situ immediately after collection using a DR3900 spectrophotometer (HACH, Corporation, Loveland, Colorado, USA), which had accuracies of 0.01 mg/L, and 0.01 mg/L, respectively. Major ion analyses were carried out at the Experimental Test Center of the Shandong Lunan Geological Engineering Exploration Institute, China.
The dual nitrate isotopes were analyzed using the bacterial denitrification method [52] and determined by TraceGas and Isotope Ratio Mass Spectrometry (IsoPrime Corporation, Cheadle, UK). Three international standards, including USGS-32, USGS-34, and USGS-35 were used for isotopic value calibration. Method precision was 0.4‰ for δ15NNO3 and 0.2‰ for δ18ONO3. The isotope ratios were reported in δ units and ‰ notation relative to an international standard in which δ15NNO3 and δ18ONO3 values are reported relative to atmospheric N2 and Vienna Standard Mean Ocean Water (VSMOW), respectively. The dual isotopes were analyzed at the Agricultural Environmental Stable Isotope Laboratory of the Chinese Academy of Agricultural Sciences in Beijing.

3.2. Mass-Balance Mixing Model

In general, nitrate in groundwater mainly comes from soil organic nitrogen (SON), chemical/synthetic fertilizer (CF), ammonium fertilizer (AF), manure and sewage (M&S), and atmospheric deposition (AD) [53]. To quantify the contribution of nitrate from the different sources to the total at each site, a mass-balance mixing model (IsoSource) was employed. This model is mainly used in the research of food web and plant moisture [54,55], and has been used widely in aquatic environments [56,57]. In the IsoSource model, resource increment parameters (1%~2%) and mass balance tolerance parameters (0.01‰~0.05‰) were set, and the contribution rates of different contamination sources in water samples were calculated by the iterative method [58]. All possible percentage combinations of different sources were calculated using the Equation (1) [59]:
Q = [(100/i) + (s − 1)]/(s − 1) = [(100/i)+(s − 1)]!/[(100/i)!(s − 1)!]
where Q is the number of combinations, i is the incremental parameter, and s is the number of contamination sources.
This model can examine all possible combinations of potential contributions from each source, and a possible solution is considered when the weighted average of nitrogen and oxygen isotopes from different sources differs less than 0.1‰ from the values in the water samples to be measured.

4. Results and Discussion

4.1. Genesis and Evolution of Hydrochemical Components

4.1.1. Hydrochemical Data Characteristics

The mass concentrations of hydrochemical components of 26 groundwater samples in the JSC during rainy and dry seasons were analyzed, and the statistical results are shown in Table 1. During the dry season, the pH values of the groundwater were 6.85~8.03, showing weak alkalinity. Except for the abnormally low TDS at the G24 sampling point, TDS (Total Dissolved Solids) showed an increasing trend along the groundwater flow direction, and TDS ranged from 119.97 mg/L to 1231.09 mg/L. The maximum values of Ca2+, SO42−, and NO3− were 261.26 mg/L, 629.37 mg/L, and 73.38 mg/L, respectively, at the G4 sampling site, indicating that gypsum dissolution and local groundwater pollution caused by human activities may have a joint effect. The contents of Ca2+, Mg2+, HCO3−, and SO42− in the groundwater during dry season were relatively high. The average concentration of main cations was Ca2+ > Na+ > Mg2+ > K+, and the average concentration of main anion components was HCO3− > SO42− > Cl− > NO3−. The coefficient variation (CV) of K+, Na+, Cl−, SO42−, and NO3− were all greater than 0.5, indicating that these parameters have a large degree of dispersion in space. The CV of Ca2+, Mg2+, and HCO3− were all less than 0.5, indicating that these ions had little change in space.
In the rainy season, the pH values of the groundwater were 7.01~8.93, which was also weakly alkaline. Except for the abnormally low TDS at G14 sampling point, TDS also showed an increasing trend along the groundwater flow direction, and TDS ranged from 185.79 mg/L to 892.74 mg/L. The maximum values of Ca2+, Mg2+, SO42−, and NO3− also appeared at the G4 sampling site, with mass concentrations of 203.13 mg/L, 36.36 mg/L, 346.05 mg/L, and 79.54 mg/L, respectively, indicating serious groundwater pollution near the G4 sampling site. In addition, the variation coefficients of K+, HCO3−, Cl−, and NO3− ions in the rainy season were higher than those in the dry season, while the CV of Na+, Ca2+, Mg2+, and SO42− were lower than those in the dry season, indicating that the hydrochemical field in the JSC was significantly affected by the changes of atmospheric precipitation and hydrodynamic field in different periods.
A Piper diagram is usually used for the classification and evolution of hydrochemical composition. The major ion concentrations within the groundwater and surface water from the study area are plotted on a Piper diagram in Figure 2. The samples in the rainy and dry seasons all fall on the upper left part of the rhomboid; the alkaline earth metals (Ca2+, Mg2+) exceed the alkaline metals (K+, Na+), indicating that the water chemistry in the study area was dominated by alkaline earth metals and weak acids.
According to the statistical data and Piper diagram, the main anion and cation in the water of the JSC were HCO3− and Ca2+, respectively. The hydrochemical types of the samples in rainy and dry seasons of the study area were statistically analyzed according to the Shukalev classification method [48]. The hydrochemical types of the samples were mainly HCO3·SO4−Ca, accounting for 60% of all samples in both the rainy and dry seasons, mainly distributed in the runoff and discharge areas of the study area. In the rainy season, HCO3−Ca accounted for 17% of all samples, and in the dry season, HCO3·SO4·Cl−Ca accounted for 17% of all samples. In addition, some water samples were HCO3−Ca·Mg, HCO3·SO4−Ca·Mg, HCO3·SO4·Cl−Ca·Na, SO4−Ca·Mg, and SO4·Cl−Ca, indicating a complex hydrochemical type system.

4.1.2. Characteristics of Hydrochemical Evolution

The relationships between HCO3−/Na+ and Ca2+/Na+, Mg2+/Na+ and Ca2+/Na+ can be used to determine the influence of water–rock interaction on water chemical composition. The range values of three end-members (Carbonate, Silicate and Evaporite) come from Xu and Liu [60]. Both the groundwater and surface water in the study area were distributed near the carbonate rock, and the distribution of the groundwater was more to the upper right than that of surface water, indicating that the ion composition of the two water bodies was mainly affected by the dissolution of carbonate rocks, and the influence of groundwater was more significant (Figure 3).
A Gibbs diagram is used to characterize the chemical composition and variation of the main ions in water, and to judge whether the groundwater is affected by evaporation and concentration, rock weathering or precipitation, which is an important means to analyze the main control function in the evolution of the groundwater chemical composition. Figure 4 showed that most samples were distributed in the middle of the model during the rainy and dry seasons, TDS was between 100 mg/L and 1000 mg/L, γ(Na+)/[γ(Na+) + γ(Ca2+)] and γ(Cl−)/[γ(Cl−) + γ(HCO3−)]; the values of most samples were between 0 and 0.5. The results showed that HCO3− and Ca2+ were the main anions and cations in the rainy and dry seasons, and the characteristics of the groundwater chemical composition were mainly controlled by rock weathering. From the longitudinal data of time scale, it can be seen that the TDS of rainy and dry seasons have little difference, but the values of γ(Na+)/[γ(Na+) + γ(Ca2+)] and γ(Cl−)/[γ(Cl−) + γ(HCO3−)] in the rainy season were lower than those in the dry season, which reflects that the proportion of HCO3− and Ca2+ in the wet season has an increasing trend compared with the dry season.
Correlation can be used to analyze the similarity of the groundwater chemical components and reveal whether different ions have the same source. The statistical software SPSS was used to calculate the Pearson correlation coefficient of water chemical components, and the correlation matrix of each chemical component was obtained (Table 2). In the dry season, TDS had obvious linear correlation with Na+, Ca2+, Mg2+, and SO42−, and especially with Ca2+ and SO42−, as the correlation coefficients were 0.9 and 0.91, respectively, indicating that the main sources of TDS in dry season were Ca2+ and SO42−. In the rainy season, TDS had obvious linear correlation with Ca2+, Mg2+, Cl−, SO42−, and NO3−, and the correlation with Ca2+ and SO42− were the most significant, with correlation coefficients of 0.83 and 0.8, respectively, indicating that the main sources of TDS in the wet season were also Ca2+ and SO42−. Generally, nitrate in groundwater comes from atmospheric precipitation and soil leaching. However, with the increase in human activities, the discharge of animal feces and domestic sewage, and the use of chemical fertilizers and pesticides, the nitrate content in the groundwater keeps increasing. As can be seen from hydrochemical statistics and correlation coefficient matrix, NO3− and SO42− have become the main ions in the groundwater.
In the rainy season, the correlation coefficient between Na+ and Cl− was 0.7, while in the dry season, the correlation coefficient was only 0.14, indicating that there may be stone-salt minerals in the aquifer of the JSC, which mainly affected by water–rock interaction or leaching in the rainy season, or may be caused by exogenous pollution. At the same time, the correlation between Ca2+ and HCO3− was also significant, indicating that Ca2+ and HCO3− in the groundwater were mainly controlled by calcite dissolution.
The proportion coefficients of some ions in groundwater formed under different genesis or conditions also have obvious differences which can be used to infer the hydrogeochemical process. The chemical composition of groundwater is the result of water–rock interaction in a specific environment. The hydrodynamic characteristics of the groundwater, redox environment, and composition of water-bearing media are important factors affecting the water–rock interaction. According to the mechanism of water–rock interaction, the ratio of anion to cation can indicate the genesis and evolution of groundwater chemical components. The values of [γ(Ca2+) + γ(Mg2+)]/[γ(SO42−) + γ(HCO3−)] in the rainy and dry seasons all fell on or near the 1:1 line, indicating that the formation of hydrochemical components of the JSC was mainly caused by the dissolution of carbonate rocks, and there may also be dissolution of gypsum (Figure 5). According to the geological and hydrogeological conditions, the aquifer in the JSC is mainly composed of limestone and dolomite, and some intercalated gypsum.

4.2. Nitrate Migration and Transformations

4.2.1. Nitrate Distribution Characteristics

Based on ordinary Kriging interpolation using ArcGIS10.7 software, the isoline of NO3−−N concentration in the groundwater of the JSC was drawn (Figure 6); the RMSSE (Root-Mean-Square Standardized Error) of the variance function used in this interpolation method is 0.99. The concentration of NO3−−N in the groundwater at the sampling sites of the upstream recharge area and the downstream discharge area was higher than that at the sampling sites of the runoff area. The high values of NO3−−N concentration appeared in G4, G5, G15, G19, G20, G25, and G26 in the rainy season, and in G4, G5, G10, G12, G13, G15, G16, G19, G20, and G26 in the dry season, and the concentration values were all higher than 45 mg/L. The concentration of NO3−−N at the G5 sampling site was as high as 32.9 mg/L and 34.5 mg/L in the rainy and dry seasons, respectively. According to the analysis of the sampling site types and surrounding environment, the G5 sampling site is a rural civilian well with a groundwater depth of about 7 m and the wellhead protection facilities are not perfect. Domestic sewage and human and animal feces infiltrate into the aquifer with rainfall, resulting in serious pollution. Other sampling sites with high NO3−−N concentration are also located in rural or suburban residential areas, surrounded by cultivated land and factories, which are vulnerable to industrial pollution and fertilizer application.
The sampling sites with excessive NO3−−N concentration basically formed three high-value areas of NO3−−N in space, which were all distributed in the upstream recharge area and downstream discharge area (Table 3). The NO3−−N concentration in the recharge area and runoff area was higher in the dry season than in the rainy season, while the NO3−−N concentration in discharge area remained stable in the seasonal scale. The NO3−−N concentration of sampling sites G10, G12, G16, G20, and G25 varied greatly in the rainy and dry seasons, which was closely related to the lithology of the strata, land use type, and human activities.
Land use type has a direct influence on soil and water pollution [61,62,63,64,65]. The land use types in the study area are complex and changeable. Agricultural production dominates the indirect recharge area and direct recharge area, and the area of arable land and woodland accounts for more than 80% of the total area. The discharge area is the urban area and the land use type is mainly construction land. The mountainous terrain in the southern indirect recharge area and the central direct area covers a wide area with relatively few villages and towns and a low population, and the land use type is dominated by secondary forest, shrubland, and arable land. Along the direction of groundwater flow to the north of the city, towns and villages and population gradually increase to become suitable for living and agricultural activities, and land types are diverse: mainly farmland, villages, and secondary forest.

4.2.2. Identification of Nitrate Sources

Cl− is conservative and not susceptible to outside influence. Its concentration generally changes only when water bodies with different Cl− concentrations are mixed. Therefore, Cl− is an ideal tracer for water pollution sources. The method of Cl− combining to give the NO3−/Cl− ratio is one of the methods commonly used in water chemistry to identify the source of nitrate. A low concentration of Cl− and high NO3−/Cl− value indicate that the NO3− in water mainly comes from chemical fertilizers, while a high concentration of Cl− and low NO3−/Cl− value indicate that the NO3− mainly comes from human and animal feces and domestic sewage. It can be seen from Figure 7 that there was no good linear relationship between NO3− and Cl−, and the correlation is weak, indicating that NO3− and Cl− were from different sources. The samples were mainly distributed in areas with low Cl− and high NO3−/Cl− ratio, indicating that NO3− mainly came from chemical fertilizer. Moreover, the distribution of samples was relatively scattered, indicating that the influence degree of chemical fertilizer, sewage, and organic fertilizer varies greatly in different areas. The sample distributions of the groundwater and surface water were similar, indicating that the two water bodies have similar NO3− sources, and the preliminary judgment is that the main source of NO3− is chemical fertilizer.
The relationship between Cl− and NO3− as well as EC and NO3− can preliminarily determine whether the change of NO3− concentration in water is caused by the mixing process of nitrogen-free form transformation or by denitrification [66]. If the increase in NO3− in water is accompanied by an increase in Cl− and EC, it indicates that NO3− in water may be mainly affected by the mixing process of nitrogen-free form transformation; otherwise, denitrification may occur. As shown in Figure 7, there was no significant correlation between NO3− and Cl−, and between NO3− and EC; however, Cl− and EC still increase with the increase in NO3−, indicating that NO3− was mainly affected by the mixing process of nitrogen-free form transformation, and no obvious denitrification occurs in the JSC.
The rich organic matter in tillage soil is first converted to NH3 by mineralization, and then NH3 is converted to NO3− by nitrosation and nitrification. In general, if there is no significant accumulation of NH4 in the environment, the δ15NNO3 values generated by mineralization and nitrification are consistent with the δ15NNO3 values of the initial reactants, with small isotopic fractionation. Because a large amount of NH4+ accumulates in the environment, the reaction is not complete. On the one hand, it can poison microorganisms in the environment, stopping or slowing down the biochemical reactions. On the other hand, due to isotope kinetic fractionation, the NO3− will be enriched by 14N. Most groundwater samples in the study area contained very little NH4+, and the isotopic fractionation caused by the above reactions can be considered insignificant. As the rainwater contained low NH4+ and was the main form of nitrogen, and NO3− content was also very low, rainwater could not become the main source of NO3− while diluting the groundwater in the study area. The large amount of agricultural fertilizer, industrial sewage, and domestic sewage in the JSC caused serious point source pollution, which was the main reason for the great difference of NO3− content distribution. Potential nitrate sources mainly include AD, CF, AF, SON, and M&S; typical ranges of δ15NNO3 and δ18ONO3 values for different nitrate sources can be obtained from reported studies [67], which can be superimposed on the δ15NNO3 and δ18ONO3 data from the study area as shown in Figure 8. The δ15NNO3 values ranged from 1.05‰ to 14.43‰, and the δ18ONO3 values ranged from −7.92‰ to 22.94‰ in the rainy season. The δ15NNO3 values ranged from −1.42‰ to 15.47‰, and δ18ONO3 values ranged from 0.50‰ to 10.80‰ in the dry season. The results indicate that the main sources of NO3− are livestock feces, and domestic sewage; the mixture of soil organic nitrogen and fertilizer might also occur, and microorganisms participate in nitrification to produce isotope fractionation.

4.2.3. Determination of Denitrification

The existence of denitrification can lead to N isotope fractionation, and identification of denitrification is helpful to eliminate the interference of fractionation on nitrogen source identification. In addition to the isotope technique, the correlation between nitrate concentration and δ15NNO3 and δ18ONO3 values should also be considered to determine whether denitrification has occurred. To determine whether the conditions for denitrification exist, tests including the detection of pH, dissolved oxygen (DO), redox conditions, etc. should be carried out. In addition, a study of the denitrification process can determine the composition of different intermediate isotopes. According to the environmental chemical index of the water in the JSC, the environment of the water in this area is not conducive to denitrification. The δ15NNO3 values in most water samples were higher than +10‰, indicating that the main source of nitrate was manure and sewage (Table 4). The δ15NNO3 and δ18ONO3 showed a positive correlation with the reciprocal concentration of NNO3 (Figure 9). The N and O isotope ratios of most sampling sites were not within the range of 1.3~2.1, and only a few sampling sites were within this range, indicating that there was basically no denitrification in the JSC, and denitrification was not the main factor affecting isotope change.
In the rainy season, the average δ15N value of 26 groundwater samples was 8.04‰, and that of four surface water samples was 11.16‰. In the dry season, the average δ15N value of 26 groundwater samples was 7.60‰, and that of four surface water samples was 12.40‰. The high δ15N values occurred in the area of manure and sewage, indicating that manure and sewage were the main sources of nitrate pollution in the JSC, while other nitrate sources accounted for a relatively small proportion, which may have had a small role in mixing.
Denitrification is an important self-purification process in nitrate-polluted groundwater, and it is widespread because of anoxia in underground environment. Dissolved oxygen (DO) concentration is an index to measure the self-purification ability of water. In the process of denitrification, DO can compete with NNO3 to become an electron acceptor, thus affecting the process of denitrification. Gillham and Cherry [68] considered that the upper limit of the DO concentration for denitrification in the groundwater environment was 2.0 mg/L through statistical analysis in field investigation and study. Desimone and Howes [69] found in the monitoring and analysis of groundwater nitrate pollution that denitrification still existed under the condition of DO concentration at the range of 2~6 mg/L, but the denitrification rate was very small.
According to the DO concentration of water samples measured in this study, it was found that the DO concentration of most water samples was greater than 6.0 mg/L, and only a few water sample concentrations were less than 6.0 mg/L. In the rainy season, the average DO concentration in groundwater was 6.34 mg/L, and that in surface water was 10.38 mg/L. In the dry season, the average DO concentration in groundwater was 6.50 mg/L, and that in surface water was 13.66 mg/L. Generally speaking, the DO concentration in surface water in the study area was higher than that in groundwater and DO concentration in the dry season was higher than that in the rainy season. This is due to the fact that the groundwater is in a relatively closed environment, which gradually consumes the DO in the water as nitrification takes place, while surface water is relatively open and can continuously receive oxygen from the air. Therefore, NNO3 is the main form of nitrogen in the environment with good ventilation of the vadose zone, and NNH4 is the main form of nitrogen in the reductive environment with hypoxia of saturated strata. In this study, it was found that there was a negative correlation between the DO and δ15N values in groundwater, and a positive correlation between the DO and δ15N values in surface water (Figure 10), indicating that a certain amount of nitrate was consumed by weak denitrification in groundwater. The measured isotope data were evaluated using a simplified Rayleigh equation, Equation (2) [70]:
δs = δs,0 + ε·lnf = δs,0 + δ·ln(s/s0)
where:
  • δs = delta value in the substrate (‰);
  • δs,0 = initial delta value in the substrate (‰);
  • ε = enrichment factor (‰, positive or negative);
  • f = fraction of unreacted residual substrate;
  • s = substrate concentration (mg/L);
  • s0 = initial (reference) substrate concentration (mg/L).
In fact, due to a series of biogeochemical reactions, it is difficult to determine the actual concentration of nitrate in groundwater, especially in karst areas, as groundwater has a very fast response to precipitation, and heavy rain or continuous rainfall can make nitrate rapidly infiltrate into groundwater. Therefore, the nitrate concentration in groundwater during the rainy season can be used as the initial concentration of denitrification, and the nitrate concentration in groundwater during the dry season can be used as the concentration of residual substrate. Thus, the enrichment factor was calculated to be −0.47‰.

4.3. Quantitative Estimation of Nitrate Transportation

In this study, the migration and transformation path of nitrate along with groundwater flow is clear, and the sources of nitrate contamination in groundwater can be quantitatively estimated from dual nitrate isotopes. The IsoSource model was used to estimate the probability distribution of the proportional contributions of nitrate from five potential sources, including AD, SON, CF, AF, and M&S. According the results of the IsoSource mixing model, AD, SON, CF, AF, and M&S contributed an average of 3.7%, 27.3%, 6.8%, 22.9%, and 39.3% in the rainy season in groundwater, and 4.7%, 19.2%, 9.4%, 14.4%, and 52.3% in surface water, respectively. In the dry season, AD, SON, CF, AF, and M&S contributed 5.3%, 25.2%, 11.4%, 21%, and 37.1% on average in groundwater, and 7.4%, 14.3%, 10.7%, 10.7%, and 56.9% in surface water, respectively (Table 5). In general, anthropogenic sources of M&S were higher than natural sources, indicating a relatively high risk of nitrate pollution in the study area. SON was the second major source of nitrate after M&S. Thus, the calculation results of the IsoSource model were in good agreement with the numerical analysis of δ15NNO3 and δ18ONO3 isotopes.

5. Conclusions

Coupled analysis of hydrochemistry and dual nitrate isotopes and incorporation of an isotopic mixing model (IsoSource) over a hydrological year explains the spatial and temporal variations of nitrate sources and transformations in a karstic spring catchment. The hydrochemical type was mainly HCO3·SO4−Ca, and many types coexisted, showing a complex hydrochemical type system. With the acceleration of urbanization and the change of land use types, the groundwater hydrochemical conditions in the JSC have changed greatly. In the groundwater and surface water of the study area, NO3− mainly came from M&S, followed by SON. The change of NO3− concentration was mainly affected by the mixing process, nitrification was the main process of nitrate transformation, and denitrification was not a significant isotope fractionation mechanism. The IsoSource results suggest that the majority of the groundwater and surface water was influenced by M&S contributing 39.3% and 52.3% in the rainy season, and 37.1% and 56.9% in the dry season, respectively. The influence of human activities on groundwater in the JSC is more and more obvious. Therefore, in rural residential areas, decentralized family small livestock breeding should be strictly controlled, large-scale and standardized livestock breeding should be developed, and local surface water and groundwater pollution should be prevented from being caused by the arbitrary discharge of livestock manure and sewage. In agricultural planting areas, chemical fertilizers and pesticides should be used rationally, the use of organic fertilizers should be vigorously advocated, and the application of high-efficiency new fertilizers and high-efficiency, low-toxicity and low-residue pesticides should be promoted to prevent non-point source pollution. In urban areas, the centralized treatment of industrial and residential sewage should be strengthened and focus on controlling industrial point source pollution.

Author Contributions

K.W.: conceptualization, methodology, software, writing—original draft, and project administration; X.C.: investigation, data curation, and project administration; Z.W.: investigation and data curation; M.W. and H.W.: supervision, conceptualization, methodology, formal analysis, resources, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Open Research Fund of Water Resources Research Institute of Shandong Province (grant number: SDSKYZX202121-1); the National Key R&D Program of China (grant number: 2021YFC3200504-3); the Open Project of Shandong Engineering Research Center for Environmental Protection and Restoration on Groundwater (grant number: 801KF2022-3); and the Natural Science Foundation of Shandong Provincial (grant number: ZR2019QEE036).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Water 15 02718 g001 550
Figure 1. Hydrogeological sketch and sampling sites of the study area.
Figure 1. Hydrogeological sketch and sampling sites of the study area.
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Figure 2. Piper diagram of water chemistry in (a) Rainy season and (b) Dry season.
Figure 2. Piper diagram of water chemistry in (a) Rainy season and (b) Dry season.
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Figure 3. Relationships between HCO3−/Na+ and Ca2+/Na+, Mg2+/Na+ and Ca2+/Na+.
Figure 3. Relationships between HCO3−/Na+ and Ca2+/Na+, Mg2+/Na+ and Ca2+/Na+.
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Figure 4. Gibbs diagram of groundwater hydrochemistry in the study area.
Figure 4. Gibbs diagram of groundwater hydrochemistry in the study area.
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Figure 5. Relationships between Ca2+ + Mg2+ and SO42− + HCO3−.
Figure 5. Relationships between Ca2+ + Mg2+ and SO42− + HCO3−.
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Figure 6. Contour lines of groundwater NO3−−N concentration in the study area.
Figure 6. Contour lines of groundwater NO3−−N concentration in the study area.
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Figure 7. Relationships of Cl−−NO3−, Cl−−NO3−/Cl− and EC−NO3−.
Figure 7. Relationships of Cl−−NO3−, Cl−−NO3−/Cl− and EC−NO3−.
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Figure 8. δ15NNO3 and δ18ONO3 values of potential NO3− sources and characteristic values of dual nitrate isotopes.
Figure 8. δ15NNO3 and δ18ONO3 values of potential NO3− sources and characteristic values of dual nitrate isotopes.
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Figure 9. Relationships between δ15N and 1/[NNO3], δ18O and 1/[NNO3] in the study area.
Figure 9. Relationships between δ15N and 1/[NNO3], δ18O and 1/[NNO3] in the study area.
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Figure 10. Relationship of DO and δ15NNO3 in the study area.
Figure 10. Relationship of DO and δ15NNO3 in the study area.
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Table
Table 1. Statistical data of hydrochemical parameters in the groundwater samples.
Table 1. Statistical data of hydrochemical parameters in the groundwater samples.
CoordinatespHEC
μS/cm		TDSK+Na+Ca2+Mg2+HCO3−Cl−NO3−SO42−
NEmg/L
Rainy seasonG1116°54′56″36°31′24″7.79716463.863.0857.9977.0616.92220.7739.5413.06133.03
G2116°56′19″36°30′56″7.62851550.610.9230.66129.5015.65234.7135.2529.58174.23
G3117°03′45″36°27′08″7.28742451.702.1418.30109.2616.71283.5126.4132.5985.23
G4117°02′03″36°29′48″7.201251892.743.4121.05203.1336.36304.4327.1079.54346.05
G5117°07′15″36°30′00″7.181044671.464.3518.60146.3528.55309.0730.71145.70118.82
G6117°06′52″36°33′44″7.47755433.321.9111.02124.8215.17339.289.1514.1379.34
G7117°10′25″36°28′46″7.59605369.610.834.8089.7226.28288.169.3125.9559.99
G8117°09′27″36°32′21″7.75608353.710.926.1095.8515.46246.3310.7027.2465.73
G9117°11′19″36°32′11″7.56719420.782.9612.27111.8015.40267.2417.1133.1384.84
G10116°58′26″36°24′17″7.48550337.551.498.5282.0015.69218.4412.4434.3261.87
G11116°53′18″36°32′41″7.59884569.832.3780.8693.0616.72220.7775.309.92168.19
G12116°54′40″36°35′28″7.28795451.470.4824.70105.2117.54313.7240.7521.2170.27
G13117°02′46″36°33′02″7.60644386.670.788.69109.6210.18223.0921.7842.0771.79
G14117°03′30″36°36′01″8.93476185.798.3824.3017.0710.2976.6914.870.7568.23
G15117°06′35″36°37′22″7.271176754.932.0343.97161.7734.61290.4881.7568.86205.02
G16117°03′56″36°38′14″7.921143782.0928.5484.67123.2518.80151.0569.1113.37352.93
G17117°00′55″36°37′47″7.14934571.360.5434.81135.9320.41288.1668.8642.60108.07
G18117°10′27″36°38′28″7.95654386.360.677.3497.1220.75250.9818.7939.6467.90
G19116°55′56″36°37′28″7.011107694.810.4628.12178.3222.62313.7299.6785.65106.83
G20116°53′14″36°36′51″7.24809483.210.7115.67117.1621.29290.4839.1847.6578.38
G21116°51′01″36°37′07″7.61606375.121.0718.6189.1315.81209.1539.1725.1167.95
G22117°00′56″36°38′50″7.05873514.114.1531.06106.9135.19453.1542.277.7942.32
G23117°02′35″36°38′40″7.92803464.223.4649.9849.1842.94153.3790.1815.81131.52
G24117°11′12″36°40′24″7.58639382.541.0513.0592.1116.74188.2336.8533.6682.60
G25117°00′33″36°39′38″7.37872543.441.5032.49125.6422.48269.5762.1645.53104.44
G26117°01′38″36°39′43″7.29972610.621.2941.90136.6623.29290.4875.4351.24121.88
Mean7.53816503.923.0628.06111.8321.23257.5042.0737.91117.59
Std0.39204.7158.695.4821.3337.838.2472.227.066.8778.65
CV0.050.250.311.790.760.340.390.280.640.80.67
Dry
season		G1116°54′56″36°31′24″7.57886559.513.1942.04108.4126.50190.0694.248.86173.51
G2116°56′19″36°30′56″7.291003681.898.2830.74160.2620.92288.7960.6483.7157.71
G3117°03′45″36°27′08″7.20721454.591.6718.73116.1715.36288.7929.4727.3287.62
G4117°02′03″36°29′48″7.3215071231.095.2724.17261.2660.28264.1125.6973.38629.37
G5117°07′15″36°30′00″7.251020701.222.8519.67159.1532.18306.0735.27152.65127.82
G6117°06′52″36°33′44″7.6717491226.051.82155.22227.0117.78264.1117.3314.75650.49
G7117°10′25″36°28′46″7.90608372.181.4118.969.9530.92288.7915.2019.7553.42
G8117°09′27″36°32′21″7.68629395.281.037.26106.4916.21264.1114.1234.5473.31
G9117°11′19″36°32′11″7.48634399.590.777.34101.2119.03288.7914.1930.7870.68
G10116°58′26″36°24′17″7.51659446.961.5913.93105.1221.32261.6424.8562.4073.19
G11116°53′18″36°32′41″7.57888560.642.4760.69100.3819.98197.4688.129.79170.47
G12116°54′40″36°35′28″7.43854543.680.4521.56137.5219.97306.0751.8850.6690.87
G13117°02′46″36°33′02″7.60683357.020.6196.219.4611.49148.1030.7950.9371.34
G14117°03′30″36°36′01″7.71905540.991.0725.05131.8120.37246.8384.4843.05100.75
G15117°06′35″36°37′22″7.261178833.232.3446.67174.4336.07298.6698.9685.34224.58
G16117°03′56″36°38′14″7.401118752.801.6350.54148.7432.13271.5169.5370.37226.50
G17117°00′55″36°37′47″7.24925589.821.1835.15138.2120.24298.6673.6741.01112.17
G18117°10′27″36°38′28″7.73656411.460.627.47104.8121.06269.0519.5043.1868.87
G19116°55′56″36°37′28″7.141110700.830.3827.7176.5223.79306.07110.8679.85109.86
G20116°53′14″36°36′51″7.32875592.640.7213.26150.4722.36288.3650.9691.4197.62
G21116°51′01″36°37′07″7.74615380.701.0117.8590.1116.11214.7440.9225.2468.49
G22117°00′56″36°38′50″6.85897564.6818.1330.62116.7536.82449.2358.201.1563.12
G23117°02′35″36°38′40″8.03804484.204.1948.8251.7943.32155.5093.8920.11138.87
G24117°11′12″36°40′24″7.95203.9119.971.753.4836.102.01111.074.055.368.43
G25117°00′33″36°39′38″7.45828532.962.7348.68100.8022.86229.5574.2633.30123.78
G26117°01′38″36°39′43″7.35929610.121.2337.54136.5123.25288.7977.2453.36121.21
Mean7.49880578.622.6334.97123.8224.32260.9652.2446.63149.77
Std0.27302.25241.53.631.8853.811.2765.7131.727.73152.95
CV0.040.340.421.370.910.430.460.250.610.731.02
Table
Table 2. Correlation coefficient matrix of groundwater chemical composition.
Table 2. Correlation coefficient matrix of groundwater chemical composition.
pHECTDSK+Na+Ca2+Mg2+HCO3−Cl−NO3−SO42−
pH1−0.54−0.540.360.1−0.75−0.35−0.82−0.28−0.52−0.03
EC−0.3910.980.290.490.820.570.370.630.550.74
TDS−0.380.9810.310.490.830.540.330.570.540.80
K+−0.460.160.1710.57−0.06−0.01−0.370.15−0.180.61
Na+0.070.600.510.0110.010.19−0.250.70−0.220.60
Ca2+−0.510.860.900.110.3710.320.590.280.680.49
Mg2+−0.250.530.590.410.520.4910.360.440.330.32
HCO3−−0.760.330.320.480.180.510.321−0.010.32−0.17
Cl−−0.280.290.190.110.840.140.270.0510.150.36
NO3−−0.410.320.34−0.160.170.450.270.270.110.19
SO42−−0.080.870.910.080.590.750.490.03−0.040.081
Note: The lower left is the correlation coefficient matrix in dry season, and the upper right is the correlation coefficient matrix in rainy season.
Table
Table 3. Average NO3−−N concentration of groundwater in different regions in the study area.
Table 3. Average NO3−−N concentration of groundwater in different regions in the study area.
Rainy SeasonDry Season
Indirect recharge area9.83 mg/L11.47 mg/L
Direct recharge area6.73 mg/L11.13 mg/L
Discharge area8.82 mg/L8.74 mg/L
Table
Table 4. NO3 concentrations and δ15NNO3 and δ18ONO3 values in the water.
Table 4. NO3 concentrations and δ15NNO3 and δ18ONO3 values in the water.
SampleRainy SeasonDry Season
δ15N
(‰)		δ18O
(‰)		NNO3
(mg/L)		δ15N
(‰)		δ18O
(‰)		NNO3
(mg/L)
G111.721.62.9511.365.132.00
G26.85−0.656.687.453.0518.90
G39.83−7.927.3610.173.186.17
G47.01−1.4217.966.682.3816.57
G55.52−3.2432.904.710.5034.47
G66.060.423.1912.796.533.33
G71.054.985.862.017.894.46
G84.322.816.155.204.567.80
G97.93−1.717.486.003.626.95
G105.1807.756.702.6814.09
G1112.782.282.249.556.912.21
G1211.441.124.7910.334.5211.44
G137.750.279.506.903.7011.50
G148.5913.830.175.046.499.72
G158.221.5715.5510.193.4919.27
G169.493.033.029.593.3215.89
G1710.07−0.169.629.763.469.26
G185.241.258.956.004.409.75
G199.24−0.8519.348.602.0018.03
G208.48−0.5110.767.101.2620.64
G219.11−0.955.679.273.655.70
G220.3422.941.760.5316.540.26
G2313.86−0.763.5712.462.484.54
G246.70.327.60−1.4215.631.21
G2510.98−0.8310.289.473.107.52
G2611.390.2711.5711.062.7812.05
S114.435.161.4415.477.630.98
S211.182.792.6911.985.691.12
S310.373.212.1711.266.691.02
S48.656.022.8710.9110.801.36
Table
Table 5. Quantitative results of the sources of nitrate contamination.
Table 5. Quantitative results of the sources of nitrate contamination.
SampleRainy SeasonDry Season
AD (%)SON (%)CF (%)AF (%)M&S (%)AD (%)SON (%)CF (%)AF (%)M&S (%)
G12.719.56.814.756.45.31810.413.552.8
G2134.73.527.733.13.729.89.422.634.5
G3329.68.129.629.63.822.3916.848.1
G40.535.32.227.934.13.231.58.625.431.3
G52.729.97.629.929.91.833.85.733.525.2
G61.833.95.329.429.66.4149.910.559.3
G7531.913.134.215.87.228.41729.417.9
G83.531.59.531.823.84.829.61228.125.5
G90.333.71.525.4394.130.610.526.528.3
G101.534.44.732.726.73.431.292531.3
G113.216.37.312.2616.621.412.716.143.2
G122.320.76.115.555.44.820.910.415.748.1
G131.731.95.12437.34.230.110.423.631.7
G1415.618.118.713.6346.22814.526.924.4
G152.6297.121.939.44.1229.416.548
G163.724.2918.244.93.923.79.317.845.3
G171.425.44.219.249.8423.19.417.446
G182.432.96.931.226.64.729.911.625.827.9
G19128.53.121.446.1327.57.720.741.1
G201.230.43.722.941.82.432.26.82533.6
G210.9292.921.845.44.224.39.818.343.5
G2232.115.525.315.811.318.52027.720.713
G231163.112683.416.97.612.759.4
G241.733.75.327.232.216.421.229.524.58.5
G25123.7317.854.53.824.29.118.244.8
G261.721.54.916.255.73.620.38.315.352.6
S15.310.87.88.167.96.48.26.66.172.7
S23.6208.31553.15.716.210.412.155.6
S33.921.8916.4496.517.411.413.151.7
S45.924.212.518.239.21115.514.211.647.7
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Wang, K.; Chen, X.; Wu, Z.; Wang, M.; Wang, H. Traceability and Biogeochemical Process of Nitrate in the Jinan Karst Spring Catchment, North China. Water 2023, 15, 2718. https://doi.org/10.3390/w15152718

AMA Style

Wang K, Chen X, Wu Z, Wang M, Wang H. Traceability and Biogeochemical Process of Nitrate in the Jinan Karst Spring Catchment, North China. Water. 2023; 15(15):2718. https://doi.org/10.3390/w15152718

Chicago/Turabian Style

Wang, Kairan, Xuequn Chen, Zhen Wu, Mingsen Wang, and Hongbo Wang. 2023. "Traceability and Biogeochemical Process of Nitrate in the Jinan Karst Spring Catchment, North China" Water 15, no. 15: 2718. https://doi.org/10.3390/w15152718

APA Style

Wang, K., Chen, X., Wu, Z., Wang, M., & Wang, H. (2023). Traceability and Biogeochemical Process of Nitrate in the Jinan Karst Spring Catchment, North China. Water, 15(15), 2718. https://doi.org/10.3390/w15152718

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