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Article

Nitrite Cycling in Freshwater Ecosystems: A Case Study of an Artificial Reservoir in Eastern China Using Nitrite Dual Isotopes Combined with a Geochemical Model

by
Xinwei Li
1,
Xingzhou Zhang
1,
Yuanyuan Yang
1,
Yingying Li
1,
Lujie Jia
1 and
Yangjun Chen
1,2,*
1
School of Marine Science and Fisheries, Jiangsu Ocean University, Lianyungang 222005, China
2
Jiangsu Key Laboratory of Marine Biotechnology, Jiangsu Ocean University, Lianyungang 222005, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(24), 11099; https://doi.org/10.3390/su162411099
Submission received: 27 November 2024 / Revised: 14 December 2024 / Accepted: 17 December 2024 / Published: 18 December 2024
(This article belongs to the Topic Climate Change and Human Impact on Freshwater Water Resources: Rivers and Lakes)
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Abstract

:
Reservoirs are hotspots for emissions of the greenhouse gas nitrous oxide; however, the nitrite cycling processes associated with nitrous oxide production therein remain poorly understood, limiting a better assessment of the potential for reservoirs to emit nitrous oxide. Accordingly, this study presents the application of the natural abundance isotope technique combined with a geochemical model to elucidate the nitrite cycling in the freshwater aquaculture and non-aquaculture zones of a large artificial reservoir in eastern China. We employed nitrite dual isotopes to identify nitrite transformation processes. Additionally, a steady-state model was used to estimate the rates of these processes as well as the residence time of nitrite. Our findings indicate that nitrite production in this reservoir may be primarily driven by ammonia oxidation. However, the pathways of nitrite removal differ notably between the aquaculture and non-aquaculture zones, suggesting a significant impact of the aquaculture activities. The steady-state model calculations revealed that nitrification may be more pronounced in the aquaculture zones compared to the non-aquaculture zones, which may be related to the altered balance of competition for substrates between phytoplankton and microbes induced by aquaculture activities. Moreover, we observed a latitude-dependent increase in the significance of nitrite oxidation in natural environments, highlighting potential implications for regional and global nitrogen cycling. Our study highlights the complexity of the nitrite cycle and emphasizes the roles of both natural and anthropogenic factors in shaping nitrogen dynamics within freshwater reservoirs. This understanding contributes to a more accurate assessment of the greenhouse gas emission potential of reservoirs, offering valuable implications for the adoption of sustainable aquaculture practices to mitigate climate impacts and support global sustainable development goals.

1. Introduction

Nitrogen (N) is an essential nutrient for all living organisms and plays a vital role in various biological processes. The N cycle has a significant impact on the Earth’s ecosystems by regulating the availability of nutrients and supporting primary productivity [1]. In aquatic environments, bioavailable N is primarily in the form of nitrate (NO3−), nitrite (NO2−), and ammonium (NH4+). These inorganic N substances are essential for sustaining aquatic life as they serve as key nutrients for phytoplankton and other primary producers [1]. The interconversion of this inorganic N forms a complex N cycling system [2]. This intricate system ensures that N is continually cycled within ecosystems. Nitrification is a key process in this N cycle system, linking the sources and consumption of fixed N. It generally proceeds through two sequential steps, including the oxidation of ammonia (NH3)/NH4+ to NO2− by NH3-oxidizing archaea/bacteria communities, and the oxidation of the resulting NO2− to NO3− by NO2−-oxidizing bacteria [3]. It is notable that nitrous oxide (N2O), a potent greenhouse gas, is a byproduct of nitrification [3,4]. Given its high global warming potential relative to carbon dioxide [5,6], an understanding of the dynamics of nitrification is becoming increasingly significant in the assessment of N2O emissions from various ecosystems that have been impacted by natural conditions and agricultural activities.
NO2−, as a unique component of the N cycle, is involved in nearly all N transformation processes [2,7]. In oxygenated environments, NO2− primarily originates from assimilatory NO3− reduction and NH3 oxidation [2,7]. The competitive dynamics between these two processes in marine settings are key factors in the formation of primary NO2− maxima (PNM) [8,9,10,11,12,13,14,15,16,17]. Current research suggests that the formation of most global oceanic PNMs may be predominantly controlled by NH3 oxidation, underscoring its importance in the production of NO2− [9]. This finding is further supported by theoretical analyses, which suggest that the production of NO2− via assimilatory NO3− reduction requires more stringent environmental conditions compared to NH3 oxidation [9]. Specifically, phytoplankton release NO2− into ambient waters only when their growth is initially limited by factors like light or trace nutrients [18], and when NO2− accumulation becomes physiologically toxic to the cells [19]. The main consumption processes of NO2− in oxygenated waters are NO2− oxidation and NO2− assimilation [2,7]. The relative strength of these two consumption pathways may vary across latitudinal gradients, reflecting distinct N dynamics in different oceanic regions [9]. Unfortunately, much less is known about the NO2− cycle in freshwater ecosystems, particularly in reservoirs, which are recognized as hotspots for N2O emissions [20,21]. Addressing this knowledge gap is essential to better assess N2O emission patterns from reservoirs and to refine our understanding of their contribution to global N2O fluxes. The natural abundance NO2− isotope technique has emerged as a powerful tool for elucidating N cycling in soils [22], rivers [12,23,24,25], and marine ecosystems [8,9,10,11,12,13,14]. This tool has significantly advanced knowledge of NO2− biogeochemical processes, including the formation of oceanic PNMs [8,9,10,11,12,13,14]. However, data on NO2− dual isotopes, especially in freshwater ecosystems, are scarce, limiting our comprehensive insight into the intricacies of the environmental NO2− cycle.
In this study, the natural abundance isotope technique combined with a steady-state geochemical model was employed to comprehensively investigate the biogeochemical cycling of NO2− in the Shilianghe reservoir. This reservoir is the largest artificial reservoir in eastern China, incorporating both aquaculture and non-aquaculture zones. This study aimed to enhance our understanding of NO2− cycling processes in freshwater ecosystems, particularly under the dual influences of natural environmental conditions and anthropogenic activities (such as aquaculture). By integrating isotopic tracing with quantitative modeling, the study provides valuable insights into the mechanisms driving N transformations and their implications for nutrient dynamics, greenhouse gas emissions, and ecosystem sustainability in reservoirs.

2. Materials and Methods

2.1. Sample Collection

Water samples were collected at 10 stations in the Shilianghe reservoir in October 2023 (Figure 1). There were 5 stations in each of the aquaculture and non-aquaculture zones, with stations S6–S10 in the aquaculture zone of the reservoir (Figure 1). Given the limited water depth of the reservoir (maximum depth of 17 m), water samples were collected from the middle layer at each station. Basic hydrochemical parameters, such as temperature and pH, were measured in situ using a pre-calibrated hand-held rapid measuring device (Model EZ9909SP, Guangzhou Artisan Hall Electronic Co., Guangzhou China). For the measurement of NO2− concentration, samples were filtered in situ and subsequently stored frozen for the analysis in a laboratory. The NO2− isotopic samples were collected in high-density polyethylene bottles and then stored at room temperature with the addition of 6 M sodium hydroxide to prevent significant changes in the isotopic composition of NO2−, particularly the oxygen (O) isotopic composition of NO2− [8,9,10,11,26,27,28,29,30].

2.2. Concentration and Isotopic Measurements

The determination of NO2− concentrations was conducted on a QuAAtro flow analyzer (Seal, Norderstedt, Germany) by the flow injection technique, employing a standard colorimetric method comprising reagent preparation, primary and secondary standard preparation, and blank and refractive index corrections [31].
The N and O isotopic composition of NO2− was determined by the classical azide reduction method [32]. Based on the measured NO2− concentration, the sample volume transferred to a 20 mL headspace vial was calculated to ensure a target injection amount of 20 nmol N2O. The sodium azide/acetic acid buffer solution was prepared freshly on the day of measurement, with a sufficient volume configured for the required sample analysis. It should be noted that, given that the samples were preserved by the addition of sodium hydroxide, the concentration of acetic acid must be adjusted to 7.8 M to achieve optimal reaction efficiency [27,29,32]. The prepared buffer solution was then transferred to a new headspace vial. Both the buffer and the sample were purged with high-purity helium to remove any N2O they may contain. After purging, 0.9 mL of the buffer solution was added to the sample before sealing the vial with an aluminum cap. The vials were then placed in a thermostatic water bath at 30 °C for the reaction. Finally, the reaction was ended by adding 0.5 mL of a 10 M sodium hydroxide solution to the vial [32]. The resulting N2O was analyzed using a Thermo Finnigan Gasbench II system (Thermo Fisher Scientific, Massachusetts, USA) for cryogenic extraction and purification, interfaced with a DELTAplus XP isotope ratio mass spectrometer for stable N and O isotope analyses. NO2− isotope standards (RSIL-N23, RSIL-N7373, and RSIL-N10219, [28]) were subjected to the same preservation and treatment procedures as the samples. The solution volumes and N contents of the samples and standards were kept consistent to minimize the effects of pH dependence and sample size dependence on N and O isotope determinations [30,33]. Repeated measurements of both standards and samples showed that the precision (pooled standard deviation) for δ15NNO2 and δ18ONO2 (δ (‰) = (Rsample/Rstandard − 1) × 1000) determinations were both better than 0.5‰.

2.3. Application of Steady-State Model

This study employed a well-established steady-state model to estimate the biogeochemical cycling rate and residence time of NO2− in the Shilianghe reservoir [12]. This steady-state model assumes that the source and consumption of NO2− are in balance in terms of both mass and isotopes. The calculation was based on the following equations [12]:
FAO + FNR = FNO + FNA = FB
fX = FX/FB
fAO + fNR = fNO + fNA = 1
δ15NNO2, AO × fAO + δ15NNO2, NR × fNR = (δ15NNO2 − 15εk, NO) × fNO + (δ15NNO2 − 15εk, NA) × fNA
δ18ONO2, AO × fAO + δ18ONO2, NR × fNR + δ18ONO2, eq × Feq/FB = (δ18ONO2 − 18εk, NO) × fNO + (δ18ONO2 − 18εk, NA) × fNA + δ18ONO2 × Feq/FB
Feq = k × [NO2−]
τA = [NO2−]/Feq
τB = [NO2−]/FB
where Equations (1)–(3) are mass balance equations that describe the fluxes involved in NO2− cycling processes. Specifically, in Equation (1), FB denotes the total biological flux, corresponding to the overall flux of NO2− sources or consumption. In Equation (2), FX represents the biological fluxes of the different N transformation processes, including NH3 oxidation (AO), assimilatory NO3− reduction (NR), NO2− oxidation (NO), and NO2− assimilation (NA). fX represents the fraction of independent NO2− transformation processes relative to the total biological flux. Equations (4)–(5) define the equilibrium relationships for isotopic signatures of different processes. In these equations, the δ15NNO2 and δ18ONO2 are the measured values, while the δ15NNO2, AO (or δ15NNO2, NR) and δ18ONO2, AO (or δ18ONO2, NR) represent the N and O isotope values of NO2− produced via NH3 oxidation (or assimilatory NO3− reduction), respectively. The parameters 15εk and 18εk represent the kinetic N and O isotope effects during N cycling processes (Table 1). The equilibrium value of δ18ONO2 (δ18ONO2, eq) in Equation (5) is achieved when O isotopic exchange between NO2− and H2O reaches equilibrium (abiotic equilibration) [12,28]. The δ18ONO2, eq can be calculated using the O isotope composition of the ambient water (assumed to be 0‰ in this study) and the equilibrium O isotope effect, derived based on the measured temperature [12]. The flux associated with this equilibrium process is defined as Feq in Equation (6). k is the abiotic equilibration rate constant between NO2− and H2O, calculated from in situ temperature and pH [12]. Equation (7) introduces τA, which represents the residence time of NO2− due to abiotic equilibration, while Equation (8) defines τB, which represents the residence time of NO2− related to biological turnover.
When conducting steady-state model calculations, we evaluated the applicability of the non-steady-state model [9]. The main difference between these two models lies in whether the O isotope exchange between NO2− and H2O occurs simultaneously with the biological processes regulating NO2− [12]. A steady-state model allows abiotic equilibration to occur simultaneously with the production and consumption of NO2−. In contrast, the non-steady-state model follows a sequential process, where NO2− is first produced, then consumed, and finally exchanged with H2O for O isotopes [12]. Additionally, the absence of mass balance constraints makes it difficult for a non-steady-state model to calculate the rates of individual processes, though it can still calculate the residence time of NO2− [9,12]. A previous study on residence times in steady-state and non-steady-state models demonstrated that both models yield comparable results. Nevertheless, the steady-state model, which allows abiotic equilibration to occur simultaneously with the biological production and consumption of NO2−, is considered more realistic in oceanic environments [9,12]. Furthermore, given the longer residence time of the water in the reservoir [21] and the fact that the steady-state model can also be used to calculate the biological fluxes of each process, the steady-state model was employed in this study to perform the calculations. During model calculations, not all f values can be directly derived from the above formulas. Hence, a reasonable estimate of one f value is necessary to calculate the remaining f values [8,9,12]. Based on our previous research in the South China Sea [8] and the measured δ18ONO2, we assumed fAO = 1 to simplify the calculations. Thus, in this study, we set fAO = 1 and employed parameters averaged over the aquaculture and non-aquaculture zones for the model calculations to compare the discrepancies in NO2− cycling between them, and further compared them with other environmental NO2− cycling.

3. Results and Discussions

3.1. Basic Hydrochemical Parameters and Nitrite Dual Isotopes in Shilianghe Reservoir

The average temperature in the aquaculture zone of the Shilianghe reservoir (7.7 °C) was slightly lower than in the non-aquaculture zone (7.9 °C), while the pH levels showed no significant difference between these two zones, indicating a stable aquatic environment across the study region (Table 2). We observed that the highest NO2− concentration in the aquaculture zone was significantly higher than that in the non-aquaculture zone, and, on average, NO2− concentrations in the aquaculture zone were also slightly higher than those in the non-aquaculture zone (Table 2). This difference in nutrient concentrations likely reflects the varying degrees of anthropogenic impact between these two zones.
For the first time, we reported the δ15NNO2 and δ18ONO2 from a reservoir environment, highlighting the differences between freshwater aquaculture and non-aquaculture zones. In the aquaculture zone, δ15NNO2 ranged from −20.3 ± 0.3‰ to 3.7 ± 0.4‰, with a mean of −8.9 ± 13.5‰, while δ18ONO2 ranged from 3.9‰ to 11.6‰, averaging at 7.3 ± 2.8‰. In comparison, the non-aquaculture zone exhibited δ15NNO2 ranging from −16.7 ± 0.5‰ to 5.4‰, with a mean of −4.2 ± 10.5‰, and δ18ONO2 ranging from 6.4 ± 0.2‰ to 12.0 ± 0.5‰, averaging at 8.9 ± 2.2‰. It is evident that, regardless of the zone, all δ18ONO2 values were consistently higher than δ15NNO2. This observation may be attributed to the source signal, but it is also a consequence of the abiotic process of O isotope exchange between NO2− and H2O, which differs from the regulation of δ15NNO2 [8,12]. For both δ15NNO2 and δ18ONO2, this study presented the first observation of lower values in the aquaculture zone compared to the non-aquaculture zone. Based on our first dataset of NO2− dual isotopes from the reservoir, we compared these results with those from other environments (Table 3) [37,38,39,40,41,42,43,44,45]. Reported δ15NNO2 in rivers range from −14.2‰ to 17.2‰ [14,23,24,25], with the upper range being significantly higher than those found in the reservoir (Table 3). The δ18ONO2 was higher in the reservoir than in the river (Table 3). This observation may be related to the longer residence time of the water in the reservoir compared to the river [21], which facilitates more extensive O isotope exchange between NO2− and H2O [12]. Furthermore, a comparison of these findings with the marine environment reveals that δ15NNO2 is extremely low in the ocean [10,11,40,41], which distinctly differs from those in river and reservoir systems (Table 3). Similarly, the marine environment also shows anomalously high δ18ONO2 [10,11]. The current interpretations attribute these extreme values to the enzyme-mediated isotopic exchange reaction between NO2− and NO3− [10,11,40,41], which represents a N transformation process beyond the canonical N cycle processes (e.g., NO3− reduction, NH3 oxidation, NO2− oxidation, NO2− reduction, and NO2− assimilation) that regulate the NO2− cycle [10,11,40,41]. Based on these comparisons, we infer that the enzyme-mediated isotopic exchange reaction is likely absent in river and reservoir ecosystems, as its activity would result in anomalous isotopic signals for NO2− [10,11,40,41]. This implies that the biogeochemical cycling of NO2− in reservoirs, rivers, and soil ecosystems primarily operates within the framework of the canonical N cycle [10,11].

3.2. Sources and Consumption Processes of Nitrite in the Shilianghe Reservoir

To better understand the sources and consumption processes of NO2− using its dual isotopes, it is crucial to first determine the end-member values of the two potential NO2− sources. This step is essential for identifying the processes that regulate the NO2− pool [8,9,12]. Although the δ15N of NH4+ was not directly measured in this study, the reported δ15N of particulate N (ranging from −16.4‰ to 16.0‰) [46,47,48,49,50,51,52] and the N isotope effect during remineralization (3‰) [12,41,42] were used to derive an estimated δ15NNH4 range of −19.4‰ to 13‰. We then calculated the δ15NNO2, AO produced from NH3 oxidation by considering the N isotope effect associated with this process. In this calculation, two distinct scenarios were considered to account for possible δ15NNO2, AO resulting from NH3 oxidation [10,41]. When NH3-oxidizing archaea/bacteria communities are less competitive than phytoplankton, the N isotope effect in the NH3 oxidation process will be fully expressed (14‰–19‰) [53], resulting in δ15NNO2, AO from −38.4‰ to −1‰. In contrast, if NH3 oxidation dominates the fate of NH3/NH4+, as the N isotope effect during NH4+ uptake is close to 0‰ [54,55], the δ15NNO2, AO will closely reflect that of NH4+ itself [10,41], ranging between −19.4‰ and 13‰. Based on our measured δ15NNO2, the second scenario appears to be more plausible. Therefore, we adopt an average δ15NNO2, AO of 1.4 ± 7.8‰ as the end-member value for NO2− derived from NH3 oxidation (Figure 2). For the δ18ONO2, AO from NH3 oxidation, we applied a literature-reported equation [12], assuming δ18O values of dissolved oxygen and H2O to be 24.2‰ and 0‰ [8,9,12], respectively, yielding an estimated δ18ONO2, AO of 3‰ (Figure 2). The δ15NNO2, NR and δ18ONO2, NR derived from assimilatory NO3− reduction were estimated based on the reported δ15N and δ18O of NO3− in surface waters of eastern China [56] and the N and O isotope effects associated with assimilatory NO3− reduction [56]. The δ15NNO3 has been reported to range from 5‰ to 15‰ [57]. Using a N isotope effect of 5‰ during the assimilatory NO3− reduction process, we estimated the δ15NNO2, NR to range from 0‰ to 10‰, with an average of 5.0 ± 7.1‰ (Figure 2). For δ18ONO2, NR, due to the combined effects of the kinetic O isotope effect [56] and branching O isotope effect [28], the calculation is more complex compared to the calculation of δ15NNO2, NR. Assuming δ18ONO3 between 0‰ and 10‰ [56], the δ18ONO2, NR was estimated to range from 20‰ to 30‰, with an average of 25.0 ± 7.1‰ (Figure 2). It is important to note that NO2− will undergo O isotope exchange with H2O, which will modify the δ18O signal of NO2− regardless of its source [12]. When the exchange equilibrium is reached, using reported equations [12] and our measured temperature, we estimated the δ18O value of modified NO2− (δ18ONO2, eq) to be 15.1 ± 0.2‰ (Figure 2). Based on these calculations, we identified the isotopic signatures of the end-member values of the two potential biological sources of NO2− and the impact of abiotic equilibration on δ18ONO2.
Given the distinct differences in the δ18O signal of NO2− from NH3 oxidation and assimilatory NO3− reduction (Figure 2), it is possible to quantitatively assess the contributions of these two processes to NO2− production, as well as the impact of O isotope exchange between NO2− and H2O [8,9,12]. If NO2− is predominantly derived from assimilatory NO3−, the measured data points should lie above the equilibrium line on the plot of δ15NNO2 vs. δ18ONO2. Conversely, if NO2− primarily originates from NH3 oxidation, the data points are expected to fall below the equilibrium line [8,9,12]. Based on the theoretical framework outlined above and the position of our measured data points, the NO2− production in the Shilianghe reservoir may be predominantly controlled by NH3 oxidation, both in the aquaculture and non-aquaculture zones (Figure 2). This may be related to the fact that NO2− production through assimilatory NO3− reduction requires stricter environmental conditions [9], such as limitations of light and iron [18], compared to its production via NH3 oxidation. As reservoirs are generally shallow (Table 2), light inhibition is unlikely to occur, and terrestrial runoff may help alleviate iron deficiency. Therefore, the conditions for NO2− production via assimilatory NO3− reduction are unlikely to be met in reservoirs. Our conclusion that the NO2− production in freshwater reservoirs may be primarily driven by NH3 oxidation aligns with findings from marine environments [8,9,10,11,12,13,14]. This further emphasizes the critical role of NH3 oxidation in NO2− production across diverse ecosystems. Additionally, it supports the notion that reservoirs act as hotspots for N2O emissions [20,21], given that N2O is a byproduct of nitrification [3,4]. These findings will aid in refining the estimation of N2O production fluxes from reservoirs and their contributions to global N2O emissions. For NO2− consumption processes, our supplementary trend lines indicated that NO2− oxidation and NO2− assimilation play significant roles at different sampling stations (Figure 2), which likely reflects the varying competitive dynamics between phytoplankton and NO2−-oxidizing bacteria.

3.3. Nitrite Dynamics in the Shilianghe Reservoir

In this study, we used measured NO2− isotope data and a steady-state model to compare the rates of various biogeochemical processes involved in the production and consumption of NO2− in the aquaculture and non-aquaculture zones of the Shilianghe reservoir. Additionally, we compared these findings with NO2− cycling in other environments.
Currently, our understanding of NO2− biogeochemical turnover rates and residence times in freshwater reservoir ecosystems remains limited. Our initial estimation indicated that the mean rates of fNO, NH3 oxidation rate (FAO), and NO2− oxidation rate (FNO) in the freshwater reservoir aquaculture zone were elevated in comparison to those observed in the non-aquaculture zone (Table 4). Conversely, the fNA and NO2− assimilation rate (FNA) were observed to be lower than those recorded in the non-aquaculture zone. This feature may be closely linked to aquaculture activities. The aquaculture activities within the aquaculture zone may create conditions that disrupt the balance between phytoplankton and bacteria [58]. Specifically, aquaculture species may pose a threat to phytoplankton abundance, thereby facilitating bacteria growth [58]. As bacteria outcompete phytoplankton for essential substrates like NO2−, phytoplankton lose their competitive advantage, resulting in a decline in the assimilation of NO2− by phytoplankton. This shift provides NO2−-oxidizing bacteria with a competitive edge in utilizing NO2−, suggesting that the NO2− oxidation process may be more pronounced and active in the aquaculture zone. Furthermore, δ18ONO2 supports the critical role of NH3 oxidation in both aquaculture and non-aquaculture areas. Combined with isotope data and steady-state model calculations, this clearly indicates that nitrification may be more active in the aquaculture zone compared to non-aquaculture zones. This highlights the potential role of aquaculture activities in shaping N transformation pathways in aquatic ecosystems.
The higher fNO in the aquaculture zone was consistent with observations from polar marine environments [9], whereas the non-aquaculture zone aligns more with scenarios typical of mid-latitude seawater [8,12] (Figure 3). Excluding the aquaculture zones, we observed a clear trend where the role of NO2− oxidation in regulating NO2− pools in natural environments becomes increasingly significant with rising latitude (Figure 3). This pattern may be attributed to the sensitivity of NO2−-oxidizing bacteria to light exposure, as their ability to oxidize NO2− is known to be inhibited by light [3,59,60,61,62]. At higher latitudes, reduced light intensity and longer periods of low-light conditions may create an environment that is more favorable for NO2− oxidation by NO2−-oxidizing bacteria [3,59,60,61,62]. Additionally, the recovery of NO2−-oxidizing bacteria from light-induced inhibition could also play a critical role in shaping this trend, potentially enabling them to maintain higher activity in regions with less intense or shorter photoperiods [3,59,60,61,62]. Nevertheless, the factors influencing NO2− oxidation activity across different latitudes, including its interaction with other environmental variables, require further research to better understand the underlying mechanisms and their implications for N cycling in natural ecosystems.
The residence time of NO2− due to abiotic equilibration (τA) was found to be quite similar between the aquaculture and non-aquaculture zones. This similarity is primarily attributed to the dominance of abiotic equilibration factors, specifically temperature and pH [8,9,12], which did not exhibit significant differences between the two zones. These two parameters, being critical regulators of abiotic equilibration, create consistent conditions across these environments, irrespective of aquaculture activities. When compared to marine environments, the τA of the Shilianghe reservoir is comparable to that observed in the South China Sea [8] and the Arabian Sea [12]. However, it is noticeably longer than that of Arctic and subarctic waters [9]. This disparity can be explained by the significant differences in temperature and pH between these regions [8,9,12], as colder temperatures and distinct pH characteristics in Arctic and subarctic waters accelerate equilibration processes, thereby reducing τA. These findings underscore the importance of regional environmental factors, such as temperature and pH, in shaping the abiotic equilibration dynamics of NO2− across diverse aquatic ecosystems.
Furthermore, in the aquaculture zone, the biological residence time of NO2− (τB) was measured at 72 ± 18 days, whereas in the non-aquaculture zone, it was slightly longer at 85 ± 13 days. Previous studies demonstrated that when τB exceeds τA, the biological turnover rate of NO2− is slower than the rate of O isotope exchange between NO2− and water, resulting in δ18ONO2 approaching δ18ONO2, eq [2,12]. Conversely, when τB is shorter than τA, the biological turnover of NO2− dominates over abiotic processes, leading to δ18ONO2 that deviates from δ18ONO2, eq and instead aligns more closely with the biological end-member signal [2,12]. This relationship explains why the majority of the station data points in this study align closely with the equilibrium line (Figure 2). The consistent proximity of the δ18ONO2 to the equilibrium line indicates that abiotic equilibration plays a dominant role in shaping the isotopic composition of NO2− under the studied condition, providing further insight into the dynamics of NO2− cycling in freshwater ecosystems.
Currently, no comparable data on the τB are available for other freshwater ecosystems. Therefore, we compared the results from this study with data from marine environments (Figure 4) [8,9,12,15,16]. Our analysis reveals that the Shilianghe reservoir, as a freshwater reservoir, exhibits no significant differences in the τB compared to marine environments at similar latitudes and high-latitude polar regions (Figure 4). This finding suggests that NO2− cycling processes in different aquatic ecosystems, such as freshwater and marine ecosystems, may be regulated by similar controlling mechanisms. It implies that microbial activities involved in NO2− cycling, such as NH3 oxidation, NO2− oxidation, and NO2− assimilation, likely follow analogous metabolic pathways, regardless of the ecosystem type. These results underscore the universality of certain core processes in the N cycle, indicating that N cycle dynamics may not be exclusively reliant on specific ecosystem characteristics, but may be instead regulated by fundamental biological and chemical principles. However, it is important to note that the water residence time in reservoirs is inherently longer compared to other freshwater systems, such as rivers [21]. Thus, future research should focus on obtaining data on the τB in river ecosystems to better compare NO2− cycling dynamics between freshwater and marine ecosystems.

4. Conclusions and Implications

In this study, we employed the natural abundance NO2− isotope technique, integrated with a steady-state model, to investigate the NO2−cycling in freshwater ecosystems, focusing on both aquaculture and non-aquaculture zones within the Shilianghe reservoir. Our study identifies the sources and consumption processes of NO2− in the Shilianghe reservoir, emphasizing the impact of aquaculture activities. Furthermore, through a comparison with N cycling processes in aquatic ecosystems at different latitudes, we highlight the importance of NO2− oxidation in natural ecosystems. These findings underscore the importance of integrating sustainable aquaculture practices, which can mitigate greenhouse gas emissions and support efforts to reduce the environmental footprint of human activities in aquatic ecosystems. This aligns with the broader goals of sustainable development and climate resilience. Further research deserves further insight into the environmental controls on the differences in various N cycling processes between aquaculture and non-aquaculture zones, as well as the processes of N exchange between the water column and sediments. In particular, the oxidation of NO2− represents a significant N cycling process, and its pattern of variation in the natural environment would benefit from further combination with laboratory work to yield more robust evidence.

Author Contributions

X.L.: methodology, software, formal analysis, investigation, data curation, writing—original draft preparation. X.Z.: methodology, software, formal analysis, writing—original draft preparation. Y.Y.: methodology, investigation. Y.L.: methodology, software. L.J.: methodology, software. Y.C.: conceptualization, methodology, formal analysis, investigation, data curation, supervision, validation, funding acquisition, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 42306055), Natural Science Foundation of Jiangsu Province (No. BK20230695), and Open-end Funds of Jiangsu Key Laboratory of Marine Biotechnology, Jiangsu Ocean University (No. HS2022001), as well as the College Student Innovation and Entrepreneurship Training Program of Jiangsu Province Higher Education (No. 2022120135).

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Acknowledgments

We would like to thank Min Chen’s group at the College of Ocean and Earth Sciences, Xiamen University, for their assistance with the isotopic measurements.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hutchins, D.A.; Capone, D.G. The marine nitrogen cycle: New developments and global change. Nat. Rev. Microbiol. 2022, 20, 401–414. [Google Scholar] [CrossRef]
  2. Casciotti, K.L. Nitrogen and oxygen isotopic studies of the marine nitrogen cycle. Annu. Rev. Mar. Sci. 2016, 8, 379–407. [Google Scholar] [CrossRef]
  3. Ward, B.B. Nitrification in marine systems. In Nitrogen in the Marine Environment; Capone, D.G., Bronk, D.A., Mulholland, M.R., Carpenter, E.J., Eds.; Elsevier: Amsterdam, The Netherlands, 2008; Volume 2, pp. 199–261. [Google Scholar]
  4. Santoro, A.E.; Buchwald, C.; McIlvin, M.R.; Casciotti, K.L. Isotopic signature of N2O produced by marine ammonia-oxidizing archaea. Science 2011, 333, 1282–1285. [Google Scholar] [CrossRef] [PubMed]
  5. Rodhe, H. A comparison of the contribution of various gases to the greenhouse effect. Science 1990, 248, 1217–1219. [Google Scholar] [CrossRef]
  6. IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V.; Zhai, P.; Pirani, A.; Connors, S.L.; Péan, C.; Berger, S.; Caud, N.; Chen, Y.; Goldfarb, L.; Gomis, M.I.; et al. (Eds.) Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021. [Google Scholar]
  7. Casciotti, K.L. Nitrite isotopes as tracers of marine N cycle processes. Philos. Trans. Royal Soc. A. 2016, 374, 20150295. [Google Scholar] [CrossRef]
  8. Chen, Y.J.; Bardhan, P.; Zhao, X.F.; Zheng, M.; Qiu, Y.; Chen, M. Nitrite cycle indicated by dual isotopes in the northern South China Sea. J. Geophys. Res.-Biogeo. 2021, 126, e2020JG006129. [Google Scholar] [CrossRef]
  9. Chen, Y.J.; Chen, M. Nitrite cycling in warming Arctic and subarctic waters. Geophys. Res. Lett. 2022, 49, e2021GL096947. [Google Scholar] [CrossRef]
  10. Chen, Y.J.; Chen, M.; Chen, J.X.; Fan, L.; Zheng, M.F.; Qiu, Y.S. Dual isotopes of nitrite in the Amundsen Sea in summer. Sci. Total Environ. 2022, 843, 157055. [Google Scholar] [CrossRef]
  11. Chen, Y.J.; Chen, J.X.; Wang, Y.; Jiang, Y.; Zheng, M.F.; Qiu, Y.S.; Chen, M. Sources and transformations of nitrite in the Amundsen Sea in summer 2019 and 2020 as revealed by nitrogen and oxygen isotopes. Acta Oceanol. Sin. 2023, 42, 16–24. [Google Scholar] [CrossRef]
  12. Buchwald, C.; Casciotti, K.L. Isotopic ratios of nitrite as tracers of the sources and age of oceanic nitrite. Nat. Geosci. 2013, 6, 308–313. [Google Scholar] [CrossRef]
  13. Buchwald, C.; Santoro, A.E.; Stanley, R.H.; Casciotti, K.L. Nitrogen cycling in the secondary nitrite maximum of the eastern tropical North Pacific off Costa Rica. Global Biogeochem. Cycles 2015, 29, 2061–2081. [Google Scholar] [CrossRef]
  14. Liu, S.; Ning, X.; Dong, S.; Song, G.; Wang, L.; Altabet, M.A.; Zhu, Z.; Wu, Y.; Ren, J.L.; Liu, C.G.; et al. Source versus recycling influences on the isotopic composition of nitrate and nitrite in the East China Sea. J. Geophys. Res. Oceans. 2020, 125, e2020JC016061. [Google Scholar] [CrossRef]
  15. Olson, R.J. 15N tracer studies of the primary nitrite maximum. Ices J. Mar. Sci. 1980, 39, 203–226. [Google Scholar]
  16. Santoro, A.E.; Sakamoto, C.M.; Smith, J.M.; Plant, J.N.; Gehman, A.L.; Worden, A.Z.; Johnson, K.S.; Francis, C.A.; Casciotti, K.L. Measurements of nitrite production in and around the primary nitrite maximum in the central California Current. Biogeosciences 2013, 10, 7395–7410. [Google Scholar] [CrossRef]
  17. Zakem, E.J.; Al-Haj, A.; Church, M.J.; van Dijken, G.L.; Dutkiewicz, S.; Foster, S.Q.; Fulweiler, R.W.; Mills, M.M.; Follows, M.J. Ecological control of nitrite in the upper ocean. Nat. Commun. 2018, 9, 1–13. [Google Scholar] [CrossRef] [PubMed]
  18. Lomas, M.W.; Lipschultz, F. Forming the primary nitrite maximum: Nitrifiers or phytoplankton? Limnol. Oceanogr. 2006, 51, 2453–2467. [Google Scholar] [CrossRef]
  19. Painter, H.A. A review of literature on inorganic nitrogen metabolism in microorganisms. Water Res. 1970, 4, 393–450. [Google Scholar] [CrossRef]
  20. Li, Y.; Tian, H.; Yao, Y.; Shi, H.; Bian, Z.; Shi, Y.; Wang, S.; Maavara, T.; Lauerwald, R.; Pan, S. Increased nitrous oxide emissions from global lakes and reservoirs since the pre-industrial era. Nat. Commun. 2024, 15, 942. [Google Scholar] [CrossRef] [PubMed]
  21. Deemer, B.R.; Harrison, J.A.; Li, S.; Beaulieu, J.J.; DelAontro, E.; Barros, N.; Bezerra-Neto, J.; Powers, S.M.; Santos, M.A.; Vonk, J.A. Greenhouse gas emissions from reservoir water surfaces: A new global synthesis. BioScience 2016, 66, 949–964. [Google Scholar] [CrossRef]
  22. Lewicka-Szczebak, D.; Jansen-Willems, A.; Müller, C.; Dyckmans, J.; Well, R. Nitrite isotope characteristics and associated soil N transformations. Sci. Rep. 2021, 11, 5008. [Google Scholar] [CrossRef]
  23. Jacob, J.; Sanders, T.; Dähnke, K. Nitrite consumption and associated isotope changes during a river flood event. Biogeosciences 2016, 13, 5649–5659. [Google Scholar] [CrossRef]
  24. Yan, X.L.; Guo, X.L.; Yao, A.Q.; Bao, H.Y.; Li, D.W.; Huang, J.C.; Wu, J.W.; Han, L.L.; Kao, S.J. Deciphering the effects of hydrological, biogeochemical and anthropogenic factors on nitrogen species in a subtropical watershed using multiple isotopes. J. Hydrol. 2024, 630, 130736. [Google Scholar] [CrossRef]
  25. Yan, X.; Wan, X.S.; Liu, L.; Xu, M.N.; Tan, E.; Zheng, Z.; Zou, W.; Tian, L.; Li, D.W.; Trull, T.W.; et al. Biogeochemical Dynamics in a Eutrophic Tidal Estuary Revealed by Isotopic Compositions of Multiple Nitrogen Species. J. Geophys. Res.-Biogeo. 2019, 124, 1849–1864. [Google Scholar] [CrossRef]
  26. Bourbonnais, A.; Altabet, M.A.; Charoenpong, C.N.; Larkum, J.; Hu, H.; Bange, H.W.; Stramma, L. N-loss isotope effects in the Peru oxygen minimum zone studied using a mesoscale eddy as a natural tracer experiment. Global Biogeochem. Cycles 2015, 29, 793–811. [Google Scholar] [CrossRef]
  27. Bourbonnais, A.; Letscher, R.T.; Bange, H.W.; Échevin, V.; Larkum, J.; Mohn, J.; Yoshida, N.; Altabet, M.A. N2O production and consumption from stable isotopic and concentration data in the Peruvian coastal upwelling system. Global Biogeochem. Cycles 2017, 31, 678–698. [Google Scholar] [CrossRef]
  28. Casciotti, K.L.; Bohlke, J.K.; McIlvin, M.R.; Mroczkowski, S.J.; Hannon, J.E. Oxygen isotopes in nitrite: Analysis, calibration, and equilibration. Anal. Chem. 2007, 79, 2427–2436. [Google Scholar] [CrossRef] [PubMed]
  29. Hu, H.; Bourbonnais, A.; Larkum, J.; Bange, H.W.; Altabet, M.A. Nitrogen cycling in shallow low-oxygen coastal waters off Peru from nitrite and nitrate nitrogen and oxygen isotopes. Biogeosciences 2016, 13, 1453–1468. [Google Scholar] [CrossRef]
  30. Kobayashi, K.; Fukushima, K.; Onishi, Y.; Nishina, K.; Makabe, A.; Yano, M.; Wankel, S.D.; Koba, K.; Okabe, S. Influence of δ18O of water on measurements of δ18O of nitrite and nitrate. Rapid Commun. Mass Spectrom. 2021, 35, e8979. [Google Scholar] [CrossRef] [PubMed]
  31. Strickland, J.D.H.; Parsons, T.R. A practical handbook of seawater analysis. Bull. Fish. Res. Board Canada 1972, 167, 1–310. [Google Scholar]
  32. McIlvin, M.R.; Altabet, M.A. Chemical conversion of nitrate and nitrite to nitrous oxide for nitrogen and oxygen isotopic analysis in freshwater and seawater. Anal. Chem. 2005, 77, 5589–5595. [Google Scholar] [CrossRef]
  33. Granger, J.; Boshers, D.S.; Böhlke, J.K.; Yu, D.; Chen, N.; Tobias, C.R. The influence of sample matrix on the accuracy of nitrite N and O isotope ratio analyses with the azide method. Rapid Commun. Mass Spectrom. 2020, 34, e8569. [Google Scholar] [CrossRef]
  34. Buchwald, C.; Casciotti, K.L. Oxygen isotopic fractionation and exchange during bacterial nitrite oxidation. Limnol. Oceanogr. 2010, 55, 1064–1074. [Google Scholar] [CrossRef]
  35. Casciotti, K.L. Inverse kinetic isotope fractionation during bacterial nitrite oxidation. Geochim. Cosmochim. Acta 2009, 73, 2061–2076. [Google Scholar] [CrossRef]
  36. Waser, N.A.; Harrison, P.J.; Nielsen, B.; Calvert, S.E.; Turpin, D.H. Nitrogen isotope fractionation during the uptake and assimilation of nitrate, nitrite, ammonium, and urea by a marine diatom. Limnol. Oceanogr. 1998, 43, 215–224. [Google Scholar] [CrossRef]
  37. Casciotti, K.L.; McIlvin, M.R. Isotopic analyses of nitrate and nitrite from reference mixtures and application to Eastern Tropical North Pacific waters. Mar. Chem. 2007, 107, 184–201. [Google Scholar] [CrossRef]
  38. Casciotti, K.L.; Buchwald, C. McIlvin, M.R. Implications of nitrate and nitrite isotopic measurements for the mechanisms of nitrogen cycling in the Peru oxygen deficient zone. Deep-Sea Res. Pt. I 2013, 80, 78–93. [Google Scholar] [CrossRef]
  39. Casciotti, K.L.; Forbes, M.; Vedamati, J.; Peters, B.D.; Martin, T.S.; Mordy, C.W. Nitrous oxide cycling in the Eastern Tropical South Pacific as inferred from isotopic and isotopomeric data. Deep-Sea Res. Pt. II 2018, 156, 155–167. [Google Scholar] [CrossRef]
  40. Fripiat, F.; Martínez-García, A.; Marconi, D.; Fawcett, S.E.; Kopf, S.H.; Luu, V.H.; Rafter, P.A.; Zhang, R.; Sigman, D.M.; Haug, G.H. The isotope effect of nitrate assimilation in the Antarctic Zone: Improved estimates and paleoceanographic implications. Geochim. Cosmochim. Acta 2019, 247, 261–279. [Google Scholar] [CrossRef]
  41. Kemeny, P.; Weigand, M.A.; Zhang, R.; Carter, B.R.; Karsh, K.L.; Fawcett, S.E.; Sigman, D.M. Enzyme-level interconversion of nitrate and nitrite in the fall mixed layer of the Antarctic Ocean. Global Biogeochem. Cycles 2016, 30, 1069–1085. [Google Scholar] [CrossRef]
  42. Peng, X.; Fawcett, S.E.; Van Oostende, N.; Wolf, M.J.; Marconi, D.; Sigman, D.M.; Ward, B.B. Nitrogen uptake and nitrification in the subarctic North Atlantic Ocean. Limnol. Oceanogr. 2018, 63, 1462–1487. [Google Scholar] [CrossRef]
  43. Peters, B.D.; Babbin, A.R.; Lettmann, K.A.; Mordy, C.W.; Ulloa, O.; Ward, B.B.; Casciotti, K.L. Vertical modeling of the nitrogen cycle in the eastern tropical South Pacific oxygen deficient zone using high-resolution concentration and isotope measurements. Global Biogeochem. Cycles 2016, 30, 1661–1681. [Google Scholar] [CrossRef]
  44. Peters, B.; Horak, R.; Devol, A.; Fuchsman, C.; Forbes, M.; Mordy, C.W.; Casciotti, K.L. Estimating fixed nitrogen loss and associated isotope effects using concentration and isotope measurements of NO3–, NO2–, and N2 from the Eastern Tropical South Pacific oxygen deficient zone. Deep-Sea Res. Pt. II 2018, 156, 121–136. [Google Scholar] [CrossRef]
  45. Smart, S.M.; Fawcett, S.E.; Thomalla, S.J.; Weigand, M.A.; Reason, C.J.; Sigman, D.M. Isotopic evidence for nitrification in the Antarctic winter mixed layer. Global Biogeochem. Cycles 2015, 29, 427–445. [Google Scholar] [CrossRef]
  46. Mu, J.; Ding, S.; Liu, S.M.; Song, G.; Ning, X.; Zhang, X.; Zhang, H. Multiple isotopes decipher the nitrogen cycle in the cascade reservoirs and downstream in the middle and lower Yellow River: Insight for reservoir drainage period. Sci. Total Environ. 2024, 918, 170625. [Google Scholar] [CrossRef] [PubMed]
  47. Liu, K.K.; Kao, S.J.; Wen, L.S.; Chen, K.L. Carbon and nitrogen isotopic compositions of particulate organic matter and biogeochemical processes in the eutrophic Danshuei Estuary in northern Taiwan. Sci. Total Environ. 2007, 382, 103–120. [Google Scholar] [CrossRef] [PubMed]
  48. Kim, S.H.; Kim, M.S.; Lee, D.H.; Shin, K.H. Impact of typhoons on anthropogenic nitrogen sources in Lake Sihwa, South Korea. Mar. Pollut. Bull. 2024, 202, 116324. [Google Scholar] [CrossRef]
  49. Zhang, A.; Lei, K.; Lang, Q.; Li, Y. Identification of nitrogen sources and cycling along freshwater river to estuarine water continuum using multiple stable isotopes. Sci. Total Environ. 2022, 851, 158136. [Google Scholar] [CrossRef] [PubMed]
  50. Zhang, A.; Wang, Y.; Li, Y.; Tan, Y.; Liu, P.; Lv, X.; Lei, K. Multiple isotopes reveal the driving forces of nitrogen cycling from freshwater to brackish water. Sci. Total Environ. 2023, 897, 165396. [Google Scholar] [CrossRef]
  51. Yan, X.; Yang, J.Y.T.; Xu, M.N.; Wang, H.; Dai, M.; Kao, S.J. Nitrogen isotope constraint on the zonation of multiple transformations between dissolved and particulate organic nitrogen in the Changjiang plume. Sci. Total Environ. 2022, 818, 151678. [Google Scholar] [CrossRef] [PubMed]
  52. Grabb, K.C.; Ding, S.; Ning, X.; Liu, S.M.; Qian, B. Characterizing the impact of Three Gorges Dam on the Changjiang (Yangtze River): A story of nitrogen biogeochemical cycling through the lens of nitrogen stable isotopes. Environ. Res. 2021, 195, 110759. [Google Scholar] [CrossRef] [PubMed]
  53. Casciotti, K.L.; Sigman, D.M.; Ward, B.B. Linking diversity and stable isotope fractionation in ammonia-oxidizing bacteria. Geomicrobiol. J. 2003, 20, 335–353. [Google Scholar] [CrossRef]
  54. Hoch, M.P.; Fogel, M.L.; Kirchman, D.L. Isotope fractionation associated with ammonium uptake by a marine bacterium. Limnol. Oceanogr. 1992, 37, 1447–1459. [Google Scholar] [CrossRef]
  55. Liu, K.K.; Kao, S.J.; Chiang, K.P.; Gong, G.C.; Chang, J.; Cheng, J.S.; Lan, C.Y. Concentration dependent nitrogen isotope fractionation during ammonium uptake by phytoplankton under an algal bloom condition in the Danshuei estuary, northern Taiwan. Mar. Chem. 2013, 157, 242–252. [Google Scholar] [CrossRef]
  56. Granger, J.; Sigman, D.M.; Needoba, J.A.; Harrison, P.J. Coupled nitrogen and oxygen isotope fractionation of nitrate during assimilation by cultures of marine phytoplankton. Limnol. Oceanogr. 2004, 49, 1763–1773. [Google Scholar] [CrossRef]
  57. Zhang, X.; Zhang, Y.; Bi, Z.; Shan, Z.; Ren, L.; Li, Q. Distribution and Source Analysis of Nitrate in Surface Waters of China. Environ. Sci. 2020, 41, 1594–1606. [Google Scholar]
  58. Sharpe, H.; Guyondet, T.; Barrell, J.; Belzile, C.; McKindsey, C.W.; Salvo, F.; Lacoursière-Roussel, A. Monitoring bay-scale ecosystem changes in bivalve aquaculture embayments using flow cytometry. PLoS ONE 2024, 19, e0313271. [Google Scholar] [CrossRef] [PubMed]
  59. Guerrero, M.A.; Jones, R.D. Photoinhibition of marine nitrifying bacteria. II. Dark recovery after monochromatic or polychromatic irradiation. Mar. Ecol. Prog. Ser. 1996, 141, 193–198. [Google Scholar] [CrossRef]
  60. Horrigan, S.G.; Carlucci, A.F.; Williams, P.M. Light inhibition of nitrification in sea-surface films. J. Mar. Res. 1981, 39, 557–565. [Google Scholar]
  61. Olson, R.J. Differential photoinhibition of marine nitrifying bacteria: A possible mechanism for the formation of the primary nitrite maximum. J. Mar. Res. 1980, 39, 227–238. [Google Scholar]
  62. Vanzella, A.; Guerrero, M.A.; Jones, R.D. Effect of CO and light on ammonium and nitrite oxidation by chemolithotrophic bacteria. Mar. Ecol. Prog. Ser. 1989, 57, 69–76. [Google Scholar] [CrossRef]
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Figure 1. A map of sampling stations. Stations S6–S10 were situated in the cage aquaculture zone, while S1–S5 were in the non-agriculture zone.
Figure 1. A map of sampling stations. Stations S6–S10 were situated in the cage aquaculture zone, while S1–S5 were in the non-agriculture zone.
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Figure 2. An illustration of biogeochemical processes affecting nitrite dual isotopes in the Shilianghe reservoir. Note that the error bars for the sample measurements are masked by the data symbols.
Figure 2. An illustration of biogeochemical processes affecting nitrite dual isotopes in the Shilianghe reservoir. Note that the error bars for the sample measurements are masked by the data symbols.
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Figure 3. Significance of nitrite oxidation in aquatic ecosystems at various latitudes. Data calculated using same model in marine environment at different latitudes were used for comparison [8,9,12].
Figure 3. Significance of nitrite oxidation in aquatic ecosystems at various latitudes. Data calculated using same model in marine environment at different latitudes were used for comparison [8,9,12].
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Figure 4. The comparison of the residence time of nitrite due to biological turnover in marine [8,9,12,15,16] and freshwater ecosystems. The bar chart includes letters (a, b, ab) to indicate statistical significance. Bars labeled with the same letter are not significantly different, while bars with different letters indicate significant differences (p < 0.05).
Figure 4. The comparison of the residence time of nitrite due to biological turnover in marine [8,9,12,15,16] and freshwater ecosystems. The bar chart includes letters (a, b, ab) to indicate statistical significance. Bars labeled with the same letter are not significantly different, while bars with different letters indicate significant differences (p < 0.05).
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Table 1. The isotope effects used in steady-state model calculations.
Table 1. The isotope effects used in steady-state model calculations.
ProcessTermValueReferences
NO2− oxidation15εk, NO−9‰–−20‰[34,35]
18εk, NO−1‰–−8‰[34]
NO2− assimilation15εk, NA1‰[36]
18εk, NA1‰[36]
O isotopic exchange between NO2− and H2O18εeqCalculated
(15.1 ± 0.2‰)
Table 2. Station depths, hydrochemical parameters, and nitrite concentrations of the Shilianghe reservoir.
Table 2. Station depths, hydrochemical parameters, and nitrite concentrations of the Shilianghe reservoir.
RegionsStationDepth
(m)		Bot. Depth
(m)		Temperature
(°C)		pHNO2−
(μmol/L)
Non-aquaculture zones11.83.56.98.073.21
s28.517.09.27.913.21
s33.16.26.07.873.07
s46.012.09.17.913.14
s53.67.28.17.743.14
Aquaculture zones61.83.58.77.723.14
s71.02.05.08.214.81
s84.18.28.37.763.42
s92.85.68.27.833.07
s103.77.38.57.773.28
Table 3. Dual isotopes of NO2− in different ecosystems.
Table 3. Dual isotopes of NO2− in different ecosystems.
Regionsδ15NNO2 (‰)δ18ONO2 (‰)References
Reservoirs−20.3–5.43.9–12.0This study
Rivers−14.2–17.21.2–9.1[14,23,24,25]
Oceans−91–53.3–63.3[8,9,10,11,12,13,37,38,39,40,41,42,43,44,45]
Table 4. The values of fNO, fNA, FAO, FNO, FNA, τB, and τA calculated by the steady-state model based on the measured temperature, NO2−concentration, δ15NNO2, and δ18ONO2 and assuming fAO = 1.
Table 4. The values of fNO, fNA, FAO, FNO, FNA, τB, and τA calculated by the steady-state model based on the measured temperature, NO2−concentration, δ15NNO2, and δ18ONO2 and assuming fAO = 1.
RegionfNOfNAFAO
(nM d−1)		FNO
(nM d−1)		FNO
(nM d−1)		τB (Days)τA (Days)
Aquaculture waters
(n = 5)		0.83 ± 0.340.17 ± 0.3451.8 ± 13.011.9 ± 19.911.9 ± 19.972 ± 1868
Non-aquaculture waters (n = 5)0.49 ± 0.200.51 ± 0.2037.1 ± 5.817.2 ± 4.619.9 ± 10.485 ± 1369
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Li, X.; Zhang, X.; Yang, Y.; Li, Y.; Jia, L.; Chen, Y. Nitrite Cycling in Freshwater Ecosystems: A Case Study of an Artificial Reservoir in Eastern China Using Nitrite Dual Isotopes Combined with a Geochemical Model. Sustainability 2024, 16, 11099. https://doi.org/10.3390/su162411099

AMA Style

Li X, Zhang X, Yang Y, Li Y, Jia L, Chen Y. Nitrite Cycling in Freshwater Ecosystems: A Case Study of an Artificial Reservoir in Eastern China Using Nitrite Dual Isotopes Combined with a Geochemical Model. Sustainability. 2024; 16(24):11099. https://doi.org/10.3390/su162411099

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Li, Xinwei, Xingzhou Zhang, Yuanyuan Yang, Yingying Li, Lujie Jia, and Yangjun Chen. 2024. "Nitrite Cycling in Freshwater Ecosystems: A Case Study of an Artificial Reservoir in Eastern China Using Nitrite Dual Isotopes Combined with a Geochemical Model" Sustainability 16, no. 24: 11099. https://doi.org/10.3390/su162411099

APA Style

Li, X., Zhang, X., Yang, Y., Li, Y., Jia, L., & Chen, Y. (2024). Nitrite Cycling in Freshwater Ecosystems: A Case Study of an Artificial Reservoir in Eastern China Using Nitrite Dual Isotopes Combined with a Geochemical Model. Sustainability, 16(24), 11099. https://doi.org/10.3390/su162411099

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