*Article* **Release of Endogenous Nutrients Drives the Transformation of Nitrogen and Phosphorous in the Shallow Plateau of Lake Jian in Southwestern China**

**Yang Zhang , Fengqin Chang \*, Xiaonan Zhang , Donglin Li , Qi Liu , Fengwen Liu and Hucai Zhang \***

Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650504, China

**\*** Correspondence: changfq@ynu.edu.cn (F.C.); zhanghc@ynu.edu.cn (H.Z.)

**Abstract:** Eutrophication remediation is an ongoing priority for protecting aquatic ecosystems, especially in plateau lakes with fragile ecologies and special tectonic environments. However, current strategies to control the phosphorus (P) and nitrogen (N) levels in eutrophication sites have been mainly guided by laboratory experiments or literature reviews without in-field analyses of the geochemical processes associated with the hydrological and eutrophic characteristics of lakes. This study analyzed the water quality parameters of 50 sites at Lake Jian in May 2019, a moderate eutrophication shallow plateau lake, based on dissolved/sedimentary nitrogen, phosphorous and organic matter, grain size, C/N ratios and stable isotope ratios of δ <sup>13</sup>C or δ <sup>15</sup>N in sediments. The results showed that the average total nitrogen (TN) and total phosphorus (TP) concentrations in the lake water were 0.57 mg/L and 0.071 mg/L, respectively. The TN and TP contents of surface sediment ranged from 2.15 to 9.55 g/kg and 0.76 to 1.74 g/kg, respectively. Stable isotope and grain source analysis indicated that N in sediments mainly existed in organic matter form and P mainly occurred as inorganic mineral adsorption. Endogenous pollution contributed to >20% of TN. Furthermore, our findings showed that phosphorus was the nutrient that limited eutrophication at Lake Jian, unlike other eutrophic shallow lakes. In contrast, the nutrient levels in the sediment and input streams belonged entirely to the N-limitation state. The distinctness in release intensity of N and P could modify the N/P limitation in the lake, which affects algae growth and nutrient control. These results demonstrated that reducing exogenous nutrients might not effectively mitigate lake eutrophication due to their endogenous recycling; thus, detailed nutrient monitoring is needed to preserve aquatic ecosystems.

**Keywords:** plateau lake; eutrophication; nutrient limitation transformation

## **1. Introduction**

Eutrophication, i.e., the excessive richness of nutrients in a lake or other water body, is one of the principal ecological disasters during the evolution of lakes [1]. Overloaded N, P and other biogenic elements from exogenous pollution in a water body directly cause an abnormal increase in the primary productivity of aquatic ecosystems, leading to eutrophication [2]. Nutrient control of the eutrophicated lake is one of the remediation strategies based on the nitrogen and phosphorus limitation of algae growth [3]. Previous studies demonstrated that in severely affected eutrophicated lakes, the nutrient limitation theory might not work [3]. However, some scholars believe that it is necessary to control all nutrient elements [2]. In recent years, considering the costs and excessive amount of nitrogen or phosphorus causing algae growth, the single nutrient limitation strategy has been proposed as an attempt to remediate the eutrophication of lakes [4].

Lake sediments are important potential reservoirs of N, P and biogenic elements that accumulate in lake water through physical diffusion, convection, resuspension, and other geobiochemical procedures after discharge [5]. Their recycling can also affect lake

**Citation:** Zhang, Y.; Chang, F.; Zhang, X.; Li, D.; Liu, Q.; Liu, F.; Zhang, H. Release of Endogenous Nutrients Drives the Transformation of Nitrogen and Phosphorous in the Shallow Plateau of Lake Jian in Southwestern China. *Water* **2022**, *14*, 2624. https://doi.org/10.3390/ w14172624

Academic Editor: Bommanna Krishnappan

Received: 14 March 2022 Accepted: 26 July 2022 Published: 26 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

water quality, resulting in endogenous pollution. Meanwhile, long-term accumulation and degradation of organic matter (OM) produced by terrestrial materials, phytoplankton, and aquatic plant residues are also the main sources for the release of N and P [6,7]. Before effectively solving endogenous pollution, eutrophication might become more serious, even with exogenous pollution cut off [8].

Despite large amounts of investments, including >USD 10 billion annually and globally, to control N and P influx in lakes, the eutrophication problem is still spreading and growing, leading to recurrent cyanobacteria blooming even after remediation [3]. This phenomenon indicates that the current nutrient influx controls are insufficient. The main reason could be that we lack a full understanding of the internal cycling nutrients and geological or biogeochemical conditions of lakes. The previous eutrophication control paradigm was based mainly on laboratory or field experimental results, which tended to simulate the effects of external input without considering biogeochemical processes and geological background in the catchment of lakes [4,6,7]. Thus, more effort is needed to improve our understanding of the nutrient dynamics in lakes and the recharge procedures from catchment to achieve the desired eutrophication mitigation solutions, especially in plateau lakes with fragile ecological environments.

During the past decade, there has been a growing consensus that the concentration of nutrients in the upper sedimentary or water samples provides a basic general understanding of catchment development and the distribution of nutrients in a specific area because their concentrations do not account for the local geological types and other bio-geochemical controls [9]. Variations in grain size and composition of the sediment samples must be considered when documenting spatial variations in elemental concentrations. Samples rich in chemically reactive fine-grained (<63 µm) sediments are likely to contain higher concentrations than a sample dominated by sand, even if both originated from the natural rock unit or contaminated soils [10]. Moreover, isotopes are also useful tools for tracking the sources of nutrients and estimating their inner cycle processes [11]. To address the recycling issues and sedimentary variations in lakes, many researchers propose the use of multi-geochemical indexes, including C, N and P isotopes and grain size, as well as nutrient fractions, to determine the concentration of nutrients [12].

Lake Jian is an ecologically and environmentally protected region in northwest Yunnan that plays an important role in maintaining the regional ecological and environmental function and biodiversity [13,14]. Recently, developments in the catchment have drastically reduced the volume of Lake Jian, causing the lake to gradually wither [15]. Moreover, the nutrient element content has rapidly increased through agricultural production and other anthropogenic impacts, which have caused eutrophication and reduced the environmental function of lakes [16,17]. Most previous studies have focused on paleolimnology and paleoclimatology [18], ecological responses [13,16] and persistent environmental pollutants [19,20]. No comprehensive investigations have been conducted to reconstruct the reasons for eutrophication and estimate exogenous and endogenous pollution. Therefore, current nutrient levels, especially the levels of N and P in lake catchment systems, the source of pollutants and the internal reaction of biogenic elements, must be accurately and comprehensively explored.

Herein, 50 water and sediment samples from Lake Jian and its catchment rivers were analyzed to investigate the potential source and inner cycle of nutrient dynamics, specifically isotopes and grain size, in relation to lake trophic status and relative importance of N and P limitation.

## **2. Materials and Methods**

#### *2.1. Overview of Lake Jian*

Lake Jian is a plateau fault lake located in the southern Hengduan Mountains in Dali prefecture, northwest Yunnan, Southwest China (Figure 1). It covers an area of 6.23 km<sup>2</sup> , with an average water depth of 2.3 m. As one of the important upstream sources of Southeast Asian fluvial systems, more than five rivers originate from the basin but have

only one outlet, the Heihui River, which eventually joins the Yangbi River to become the principal branch of the Lantsang (Mekong) river and crosses six countries. Lake Jian has a maximum volume of 45.32 <sup>×</sup> <sup>10</sup><sup>6</sup> <sup>m</sup><sup>3</sup> , reflecting a decrease of more than 60% in recent years [14]. The mean air temperatures in winter and summer are 15 ◦C and 28 ◦C, respectively, and the mean annual rainfall is 786 mm. east Asian fluvial systems, more than five rivers originate from the basin but have only one outlet, the Heihui River, which eventually joins the Yangbi River to become the principal branch of the Lantsang (Mekong) river and crosses six countries. Lake Jian has a maximum volume of 45.32 × 10<sup>6</sup> m<sup>3</sup> , reflecting a decrease of more than 60% in recent years [14]. The mean air temperatures in winter and summer are 15 °C and 28 °C, respectively, and the mean annual rainfall is 786 mm.

with an average water depth of 2.3 m. As one of the important upstream sources of South-

Lake Jian is a plateau fault lake located in the southern Hengduan Mountains in Dali prefecture, northwest Yunnan, Southwest China (Figure 1). It covers an area of 6.23 km<sup>2</sup>

,

*Water* **2022**, *14*, x FOR PEER REVIEW 3 of 15

**Figure 1.** Location and geographic information (**A**), watershed system and digital elevation model of the catchment (**B**), lake isobaths (**C**) and sampling site (**B**: rivers and streams; **C**: lake water and sediment) of Lake Jian. **Figure 1.** Location and geographic information (**A**), watershed system and digital elevation model of the catchment (**B**), lake isobaths (**C**) and sampling site ((**B**) rivers and streams; (**C**) lake water and sediment) of Lake Jian.

#### *2.2. Sampling and Detecting 2.2. Sampling and Detecting*

**2. Materials and Methods** *2.1. Overview of Lake Jian*

In May 2019, 50 surface sediment (each 0.5 cm thick), surface water and bottom water were sampled from Lake Jian, and 10 water samples from main streams and rivers into its catchment were collected using a gravity corer and condensate trap (Figure 1). All the samples were kept in brown polyethylene bottles and frozen in a refrigerator. In May 2019, 50 surface sediment (each 0.5 cm thick), surface water and bottom water were sampled from Lake Jian, and 10 water samples from main streams and rivers into its catchment were collected using a gravity corer and condensate trap (Figure 1). All the samples were kept in brown polyethylene bottles and frozen in a refrigerator.

The nutrient element indices in water, including total nitrogen (TN) and total phosphorus (TP), were determined by the alkaline potassium persulfate digestion-UV spectrophotometric method (GB 11894-89) and ammonium molybdate spectrophotometric method (GB 11893-89) formulated by the State Environmental Protection Administration of China (SEPAC). The results were taken from the average values determined at three parallel times, and the measurement errors were less than 1%. Water quality parameters, including depth, temperature, chlorophyll a (Chl-a), pH, and dissolved oxygen (DO), were measured using 6600-V2 YSI during sampling. TN and TP in the surface sediment were measured by sulfuric acid digestion-Kjeldahl determination (GB 7173-87) and the Mo-Sb colorimetric method (GB 7852-87), following the standard of SEPAC with resulting measurement errors <5%. Samples for grain-size analysis were pre-treated using H2O<sup>2</sup> and HCl to remove organic matter and carbonates. Grain size distribution between 0.02 μm and 2000 μm was measured using a Malvern Mastersizer 2000 laser grain-size analyzer, before the samples were deionized and dispersed by Na(PO3)6. Three types of bulk (<4 mm, 4–64 mm and >64 mm) size fractions were analyzed, and the analysis was focused on the fine-grained fraction (closely related to element enrichment in the surface soils). OM in the surface sediment was measured by the loss on ignition method. C/N, δ15N, and δ13C in the surface sediment were detected with a Thermo MAT-253, with analytical errors The nutrient element indices in water, including total nitrogen (TN) and total phosphorus (TP), were determined by the alkaline potassium persulfate digestion-UV spectrophotometric method (GB 11894-89) and ammonium molybdate spectrophotometric method (GB 11893-89) formulated by the State Environmental Protection Administration of China (SEPAC). The results were taken from the average values determined at three parallel times, and the measurement errors were less than 1%. Water quality parameters, including depth, temperature, chlorophyll a (Chl-a), pH, and dissolved oxygen (DO), were measured using 6600-V2 YSI during sampling. TN and TP in the surface sediment were measured by sulfuric acid digestion-Kjeldahl determination (GB 7173-87) and the Mo-Sb colorimetric method (GB 7852-87), following the standard of SEPAC with resulting measurement errors <5%. Samples for grain-size analysis were pre-treated using H2O<sup>2</sup> and HCl to remove organic matter and carbonates. Grain size distribution between 0.02 µm and 2000 µm was measured using a Malvern Mastersizer 2000 laser grain-size analyzer, before the samples were deionized and dispersed by Na(PO3)6. Three types of bulk (<4 mm, 4–64 mm and >64 mm) size fractions were analyzed, and the analysis was focused on the fine-grained fraction (closely related to element enrichment in the surface soils). OM in the surface sediment was measured by the loss on ignition method. C/N, δ <sup>15</sup>N, and δ <sup>13</sup>C in the surface sediment were detected with a Thermo MAT-253, with analytical errors of less than 0.2%.

#### of less than 0.2%. *2.3. Statistical Analysis*

When considering the geochemical background and sources of nutrients, their relationship with grain size composition of samples were used for calculation [9]. Generally, higher concentrations of major elements in the earth's crust were observed in chemically reactive fine-grained (<64 µm) sediments, even in the samples obtained from natural and anthropogenic conditions [10]. Thus, the nutrients strongly correlated with the fine-grained fraction were chosen as exogenous sources to identify elements that could account for

the differences in grain size and composition [21]. The LTS robust regression model was defined by a logarithmic regression [22], and geochemical conditions in the catchment area and possible sources were estimated by the regression analyses in Lake Jian (details in Section 3.5). The N-, and P-limitations of lake eutrophication for algae growth complied with the formulation of the "Redfield ratio", e.g., N-limitation, N/P < 10; P-limitation, N/P > 20 [23].

For δ <sup>15</sup>N and δ <sup>13</sup>C analyses, δ notation was used to represent isotopic ratio differences between samples and standard materials. The formulas are expressed as follows:

$$\delta^{13}\text{N} = \frac{\left(\text{Rsp}\_{\text{N}} - \text{Rst}\_{\text{N}}\right)}{\text{Rst}\_{\text{N}}} \times 1000\% \text{o} \tag{1}$$

$$\delta^{15}\text{C}\_{\text{org}} = \frac{(\text{Rsp}\_{\text{Corg}} - \text{Rst}\_{\text{Corg}})}{\text{Rst}\_{\text{CC}\_{\text{org}}}} \times 1000\,\% \tag{2}$$

Here, δ <sup>15</sup>N and δ <sup>13</sup>C represent the differences (‰) from the Vienna PDB standard and atmospheric N2, and Rst and Rsp represent the stable isotope ratio of δ <sup>13</sup>C or δ <sup>15</sup>N in standard samples. Based on the mass conservation hybrid model and linear mixed model, the contribution of N and OM from different sources could be estimated as follows [24]:

$$\boldsymbol{\delta}^{13}\mathbf{N} = \sum\_{\mathbf{x}=1}^{n} \mathbf{W}\_{\mathbf{x}} \times \boldsymbol{\delta}^{13}\mathbf{N}\_{\mathbf{x}} \tag{3}$$

$$\delta^{13}\mathbf{C}\_{\text{org}} = \sum\_{\mathbf{x}=1}^{\text{n}} \mathbf{W}\_{\text{x}} \times \delta^{13}\mathbf{C}\_{\text{org}} \tag{4}$$

Here, δ <sup>15</sup>Nx and δ <sup>13</sup>Corgx represent the corresponding isotope ratio detected in different end members of samples [25,26]. In this study, the main sources of the sedimentary OM were considered to be plankton (with typical stable compositions of C/N: 5~8, δ <sup>13</sup>C: −32~−23‰ and δ <sup>15</sup>N: 5~8‰), macrophytes (10~30, <sup>−</sup>27~−20‰, <sup>−</sup>15~20‰), soil (8~15, −32~−9‰, 2~5‰), terrestrial C3 plants (15~40, −32~−22‰, −6~5‰), terrestrial C4 plants (15~40, −16~−9‰, −6~5‰), and sewage (6.6~13, −26.7~−22.9‰, 7~25‰). The principal sources of sedimentary N were as follows: agricultural fertilizer (with δ <sup>15</sup>N from <sup>−</sup>4~4‰), domestic sewage (10~20‰), soil erosion (3~8‰), terrestrial OM (with avg. of 2‰), and endogenous OM (6.5‰) [25]. The W<sup>x</sup> was the contribution probability of different pollutant sources, computed by an iterative calculation model, which is as follows:

$$\mathbf{W}\_{\mathbf{x}} = \frac{\left[\left(\frac{100}{\mathrm{i}}\right) + (\mathbf{n}\_{\mathrm{S}} - 1)\right]!}{\left(\frac{100}{\mathrm{i}}\right)! (\mathbf{n}\_{\mathrm{S}} - 1)!} \,\mathrm{}\tag{5}$$

Here, i represents the increment coefficient of the calculation model, and nS represents the number of N sources. Isotope sources were calculated using IsoSource [24]. Statistical analysis, one-way analysis of variance (ANOVA) and Pearson correlation (PC) were implemented using PAST v4.0 [27].

#### **3. Results and Discussion**

#### *3.1. Nutrient Level and Water Quality of Lake Jian*

The spatial distributions of mean TN (range: 0.05–0.99 mg/L, average: 0.57 mg/L) and TP (range: 0.003–0.173 mg/L, average: 0.071 mg/L) contents in surface water were as follows: western areas > central part > eastern areas (Figure 2). Moreover, the mean contents of TN and TP in the sites around the lakeside were higher than in the lake basin. Similarly, the contents of TN and TP in the bottom water of Lake Jian also showed broad variations, which ranged between 0.09 and 0.76 mg/L (0.29 mg/L) and 0.003 and 0.140 mg/L (0.087 mg/L). Generally, the highest TN and TP values were observed at

the western end of the lake near the entrance of the Yongfeng River, but the minimum values were detected in the central and eastern lake areas. Additionally, higher mean concentrations of TN and TP were observed at the surface water compared with bottom water (Table 1). Except for some sites in the western part of Lake Jian, the whole lake was in a state of middle eutrophication, belonging to Level III water quality (SEPAC standard). Significant differences in nutrient levels from the entering rivers and streams were observed. TN (2.81 mg/L) and TP (0.428 mg/L) levels in the Yongfeng River, which crosses a population center carrying domestic sewage, were significantly higher than in the Huilong River (TN: 1.93 mg/L, TP: 0.065 mg/L), Mei River (0.52 mg/L, 0.062 mg/L) and Jinlong River (1.58 mg/L, 0.016 mg/L) and streams entering the basin (0.14–0.51 mg/L, 0.015–0.049 mg/L), and traversing rural villages and farms in the catchment basin. High nutrient element levels were observed in the influx river, which was consistent with that accumulated in the lake area near an estuary, explaining the spatial discrepancy of nutrient elements in water to some extent. Nutrient elements of only one outlet, the Haiwei River, were 1.55 mg/L and 0.052 mg/L, respectively. *Water* **2022**, *14*, x FOR PEER REVIEW 6 of 15

**Figure 2.** Environmental indexes of Lake Jian, including TN/TP in surface water (**A**,**F**), bottom water (**B**,**G**) and surface sediment (**C**,**H**), spatial distribution of C/N ratios (**D**), OM contents (**I**), δ<sup>13</sup>C (**E**), δ <sup>15</sup>N (**J**), fine-grain size (**K**) and sand fraction (**L**) composition. **Figure 2.** Environmental indexes of Lake Jian, including TN/TP in surface water (**A**,**F**), bottom water (**B**,**G**) and surface sediment (**C**,**H**), spatial distribution of C/N ratios (**D**), OM contents (**I**), δ <sup>13</sup>C (**E**), δ <sup>15</sup>N (**J**), fine-grain size (**K**) and sand fraction (**L**) composition.

The spatial variability in TP, TN and OM concentrations in the surface sediment of

Generally, the distribution of TN in surface sediment is similar to that of OM, with a significant inward decreasing trend from the lakeshore (4.68–9.55 g/kg) to the deepest basin (2.15–4.03 g/kg). The mean concentration of TP decreased from the western lake margin (1.31 g/kg) to the middle (1.17 g/kg) and eastern parts of the lake (1.16 g/kg). In Lake Jian, the ratio of C/N was characterized by obvious spatial variation, with a range from 10.76 to 16.53, and an average value of 12.59. Higher C/N ratios were found in the sediment samples near Yongfeng River estuary (16.53) and the Mei River (15.03). Meanwhile,

Jian ranged from 4.25% to 21.30% (averaging 11.57%), with higher values observed on the western and eastern shores (>14.05%). There were lower contents of OM in the middle of

the lake basin.

*3.2. Spatial Distribution of Environmental Parameters in Surface Sediments*


**Table 1.** Nutrient element levels, water quality indices and geochemical information.

Note: <sup>a</sup> [28]; <sup>b</sup> [16].

#### *3.2. Spatial Distribution of Environmental Parameters in Surface Sediments*

The spatial variability in TP, TN and OM concentrations in the surface sediment of Lake Jian is shown in Figure 2 (additional details in Table 1). OM concentrations in Lake Jian ranged from 4.25% to 21.30% (averaging 11.57%), with higher values observed on the western and eastern shores (>14.05%). There were lower contents of OM in the middle of the lake basin.

Generally, the distribution of TN in surface sediment is similar to that of OM, with a significant inward decreasing trend from the lakeshore (4.68–9.55 g/kg) to the deepest basin (2.15–4.03 g/kg). The mean concentration of TP decreased from the western lake margin (1.31 g/kg) to the middle (1.17 g/kg) and eastern parts of the lake (1.16 g/kg). In Lake Jian, the ratio of C/N was characterized by obvious spatial variation, with a range from 10.76 to 16.53, and an average value of 12.59. Higher C/N ratios were found in the sediment samples near Yongfeng River estuary (16.53) and the Mei River (15.03). Meanwhile, the C/N ratio was lower in the lake basin at its deepest portions (10.76~11.08), compared with other areas (11.17~13.05). The spatial distribution of δ <sup>13</sup>C and δ <sup>15</sup>N of surface sediment from Lake Jian is presented in Figure 2; their values ranged from −30.08 to −24.08‰ (averaging −28.28‰) and from 2.45 to 5.00 ‰ (3.84 ‰), respectively. ∆ <sup>13</sup>C exhibited a great spatial gradient, with its highest values recorded at the central lake bay area and lowest values recorded in the estuary of the western lake, while there was an overall decreasing trend from the eastern (−27.60‰) to the western part of the lake (−28.68‰). In contrast, δ <sup>15</sup>N values showed regular spatial variation by isobath, with the most depleted δ <sup>15</sup>N values occurring at the lakeshore (3.91–5.00‰), compared to the central basin (2.45–3.69‰).

The spatial variability in grain size fraction components is shown in Figure 2 (additional details in Table 1). To sort the particle size fractions, the percentage of clay components in the surface sediment samples of Lake Jian fell between 26.80 and 72.28%, with an average of 51.32%. There were obvious spatial differences between the central region and those close to the lake shore. The spatial change characteristics showed an increasing trend from the lake shore (49.46%) to the central region (56.73%). The silt component ranged between 27.50 and 67.38% (averaging 46.98%). The characteristics of spatial variation show that there was a decreasing trend from the lake shore to the lake center, i.e., the eastern lake area (54.78%) > the western lake area (48.32%) ≥ the central lake basin (41.75%). The percentage of sand components was between 0 and 9.68% (1.70%). Obvious high values were observed at the west lake bay near the entrance of the Jinlong River. Some high-value areas were also observed from the east bay to the central lake area. The mean contents of sand in Lake Jian were as follows: the western lake area (2.22%) > the central lake area (1.52%) > the eastern lake area (1.20%).

#### *3.3. Source of Sedimentary OM and N 3.3. Source of Sedimentary OM and N* In this study, five OM sources (plankton, macrophytes, sewage, soil OM, terrestrial

central lake area (1.52%) > the eastern lake area (1.20%).

*Water* **2022**, *14*, x FOR PEER REVIEW 7 of 15

In this study, five OM sources (plankton, macrophytes, sewage, soil OM, terrestrial C3 and terrestrial C4) and N sources (agricultural fertilizer, domestic sewage, exogenous release, soil erosion, terrestrial input) were chosen to evaluate sources of pollutants (Figure 3). C3 and terrestrial C4) and N sources (agricultural fertilizer, domestic sewage, exogenous release, soil erosion, terrestrial input) were chosen to evaluate sources of pollutants (Figure 3).

the C/N ratio was lower in the lake basin at its deepest portions (10.76~11.08), compared with other areas (11.17~13.05). The spatial distribution of δ13C and δ15N of surface sediment from Lake Jian is presented in Figure 2; their values ranged from −30.08 to −24.08‰ (averaging −28.28‰) and from 2.45 to 5.00 ‰ (3.84 ‰), respectively. Δ13C exhibited a great spatial gradient, with its highest values recorded at the central lake bay area and lowest values recorded in the estuary of the western lake, while there was an overall decreasing trend from the eastern (−27.60‰) to the western part of the lake (−28.68‰). In contrast, δ15N values showed regular spatial variation by isobath, with the most depleted δ15N values occurring at the lakeshore (3.91–5.00‰), compared to the central basin (2.45–3.69‰). The spatial variability in grain size fraction components is shown in Figure 2 (additional details in Table 1). To sort the particle size fractions, the percentage of clay components in the surface sediment samples of Lake Jian fell between 26.80 and 72.28%, with an average of 51.32%. There were obvious spatial differences between the central region and those close to the lake shore. The spatial change characteristics showed an increasing trend from the lake shore (49.46%) to the central region (56.73%). The silt component ranged between 27.50 and 67.38% (averaging 46.98%). The characteristics of spatial variation show that there was a decreasing trend from the lake shore to the lake center, i.e., the eastern lake area (54.78%) > the western lake area (48.32%) ≥ the central lake basin (41.75%). The percentage of sand components was between 0 and 9.68% (1.70%). Obvious high values were observed at the west lake bay near the entrance of the Jinlong River. Some high-value areas were also observed from the east bay to the central lake area. The mean contents of sand in Lake Jian were as follows: the western lake area (2.22%) > the

**Figure 3.** Scatterplots of δ<sup>15</sup>N/CN ratios (**A**), δ<sup>15</sup>N/δ<sup>13</sup>C (**B**) and δ<sup>13</sup>C/CN ratios (**C**), site-special range of δ<sup>15</sup>N (**D**) (isotopes boundary data from [25,26]), as well as the contribution of N (**E**) and organic matter sources (**F**). **Figure 3.** Scatterplots of δ <sup>15</sup>N/CN ratios (**A**), δ <sup>15</sup>N/δ <sup>13</sup>C (**B**) and δ <sup>13</sup>C/CN ratios (**C**), site-special range of δ <sup>15</sup>N (**D**) (isotopes boundary data from [25,26]), as well as the contribution of N (**E**) and organic matter sources (**F**).

Scatterplots of the δ <sup>15</sup>N and C/N (Figure 3A) in Lake Jian showed that sedimentary OM was mainly divided into two sources, macrophytes and soil OM. In addition, the results of δ <sup>13</sup>C vs. δ <sup>15</sup>N analyses (Figure 3B) showed that sedimentary OM sediments were primarily derived from terrestrial C3 plants and soil OM. The site-specific distribution of δ <sup>13</sup>C and C/N in Lake Jian was within the ranges of soil OM (Figure 3C). Based on the background range of source-specific environments (Figure 3D), δ <sup>15</sup>N in Lake Jian was mainly introduced from agricultural fertilizer and soil erosion.

In the increment coefficient of a calculation model for sources analysis, the mean and median values from the typically stable composition were used as the calculating δ <sup>13</sup>C values of plankton (−27.5‰), macrophytes (−23.5‰), sewage (−24.8‰), soil OM (−27.5‰), terrestrial C3 (−27.0‰) and terrestrial C4 (−12.5‰). Similarly, the C/N ratios of plankton, macrophytes, sewage, soil OM, terrestrial C3 and terrestrial C4 were 6.5, 20.0, 9.8, 11.5, 27.5 and 27.5, respectively [25]. Meanwhile, the δ <sup>15</sup>N values of plankton (6.5‰), macrophytes (2.5‰), sewage (16.0‰), soil OM (3.5‰), terrestrial C3 (−0.5‰) and terrestrial C4 (−0.5‰) were set as the source information into the IsoSource database [25,26]. For the multiple linear regression analysis in this study, an increase in the models was set as 1%, and the output data calculated were all reliable under the tolerance index of 3 ‰. The results shown in Figure 3 indicate that the contribution of plankton in sedimentary OM was 25.4–68.8% (average: 48.5%), 1.5–16.7% (9.7%) for macrophytes, 2.3–25.5% (15.7%) for sewage, 0.6–18.8% (6.7%) for soil OM, 8.5–40.7% (16.6%) for terrestrial C3 plant and 0.1–7.7% (2.8%) for terrestrial C4 plant. For the N sources calculation model, δ <sup>15</sup>N mean values of agricultural fertilizer, domestic sewage, exogenous release, soil erosion, and terrestrial input were 0.0‰, 15.0‰, 6.5‰, 5.5‰ and 2.0‰, respectively. The calculated mean contribution of agricultural fertilizer, domestic sewage, exogenous release, soil erosion, terrestrial input accounted for 28.4% (range: 19.3–44.8%), 7.5% (3.9–11.6%), 16.8% (9.6–22.7%), 19.3% (11.5–24.6%), and 28.0% (21.9–31.1%), respectively.

#### *3.4. Exogenous Pollution due to N and P*

The PC results showed that the correlation of TN (coefficient of correlation R = 0.70, *p* < 0.001) and TP (R = 0.99, *p* < 0.001) between surface water and bottom water samples in Lake Jian were significant (Figure 4A), indicating that the exchange and mixing of the upper and lower water body were sufficient. Considering a strong disturbance, the stable condition of solid nutrient elements in the surface sediment was destroyed, significantly exacerbating the migration and release of nutrient elements, especially in the shallow Lake Jian [29]. Higher dissolved nutrient element contents in the bottom water were found in Lake Jian, which was 1.2–1.4 times that in surface water (Figure 4C,D). *Water* **2022**, *14*, x FOR PEER REVIEW 9 of 15

**Figure 4.** Relationship of TN and TP in surface water/bottom water (**A**) and bottom water/surface sediment (**B**), the contents levels of TP (**C**) and TN (**D**) in Lake Jian, with difference (%) in nutrient concentration between median value and site-specific data in surface sediment (**C**,**D**). **Figure 4.** Relationship of TN and TP in surface water/bottom water (**A**) and bottom water/surface sediment (**B**), the contents levels of TP (**C**) and TN (**D**) in Lake Jian, with difference (%) in nutrient concentration between median value and site-specific data in surface sediment (**C**,**D**).

However, we found that the release intensity of N and P was remarkably different, and the fluctuation degree of TN (4.85 ± 1.02 g/kg, with a maximum distance of 21.03%) content was much higher than that of TP (1.24 ± 0.07, 6.07%) in surface sediment (Figure 4C,D). Under the same disturbed conditions and with similar input backgrounds, the results suggest that the deposition process of TP was more stable in Lake Jian. Furthermore, compared with nutrient element fluxes in the whole lake, the relative level of P content (10.10−326.76 × 10<sup>3</sup> times the amount in the water body) in surface sediment was exceptionally higher than that of N (7.72~59.17 × 10<sup>3</sup> times). Moreover, TP contents in the surface sediment and bottom water were more positively correlated (R = 0.44) compared with TN (0.24), especially in lakeside areas (P: 0.78, N: 0.23), indicating that the deposition process was relatively stable and there was no obvious endogenous release process (Figure 4B). On the other hand, the ANOVA result of N showed that the deposition state of solid N and dissolved N in lake water were two distinct processes (*p* < 0.001), and the mean relative level of N in surface sediment was nearly six times lower than that of P, indicating that the release of N could be more pronounced. *3.5. Endogenous Load and Different Forms of Nutrient Elements Pollutants* Except for the influence of deposition and gravity, vertical divergence in dissolved nutrient elements could be affected by the migration process near the sediment boundary by a series of bio-geochemical and geophysical processes [30,31]. The nutrient elements in the surface sediment were weakly correlated with TN (R = 0.20) and TP (R = 0.47) in the bottom water. All these results indicated that an increase in the concentrations of N and P in bottom sediments led to only a slight increase in the concentrations of these elements in waters and a slow release and migration from sediments into the lake water [32]. Recently, endogenous pollution of nutrient elements through release and migration has become one of the most important problems for pollution remediation and protection [33,34]. Previous studies have shown that exogenous pollution in lake systems was serious and difficult to control. In major eutrophic lakes worldwide, the endogenous load of nutrient elements exceeded 20–40% [35]. Noticeably, the TN (3.83–5.87 g/kg) and TP (1.16–1.31 g/kg) content in the surface sediment of Lake Jian were extremely high compared with other eutrophic lakes in China [36,37], indicating that endogenous pollution was likely to occur. The distribution difference between dissolved nutrient elements in water and sedimentary solid nutrient elements revealed that the release/migration process occurred at the sediment interface caused by endogenous pollution.

The pattern of endogenous pollution in a lake ecosystem is modulated by the distribution of distinct forms of nutrients and by environmental background limitations. Generally, under natural conditions, the different forms of N can be divided into organic ni-

age, industrial smelting, and other anthropogenic processes [38]. Additionally, through the absorption and transformation processes of microorganisms and aquatic animals and plants, organic nitrogen is abundant in the ecosystem, and could be in the form of biological debris and biological chain [39,40]. Both the degradation of organic nitrogen and the

However, we found that the release intensity of N and P was remarkably different, and the fluctuation degree of TN (4.85 ± 1.02 g/kg, with a maximum distance of 21.03%) content was much higher than that of TP (1.24 ± 0.07, 6.07%) in surface sediment (Figure 4C,D). Under the same disturbed conditions and with similar input backgrounds, the results suggest that the deposition process of TP was more stable in Lake Jian. Furthermore, compared with nutrient element fluxes in the whole lake, the relative level of P content (10.10–326.76 <sup>×</sup> <sup>10</sup><sup>3</sup> times the amount in the water body) in surface sediment was exceptionally higher than that of N (7.72~59.17 <sup>×</sup> <sup>10</sup><sup>3</sup> times). Moreover, TP contents in the surface sediment and bottom water were more positively correlated (R = 0.44) compared with TN (0.24), especially in lakeside areas (P: 0.78, N: 0.23), indicating that the deposition process was relatively stable and there was no obvious endogenous release process (Figure 4B). On the other hand, the ANOVA result of N showed that the deposition state of solid N and dissolved N in lake water were two distinct processes (*p* < 0.001), and the mean relative level of N in surface sediment was nearly six times lower than that of P, indicating that the release of N could be more pronounced.

## *3.5. Endogenous Load and Different Forms of Nutrient Elements Pollutants*

The pattern of endogenous pollution in a lake ecosystem is modulated by the distribution of distinct forms of nutrients and by environmental background limitations. Generally, under natural conditions, the different forms of N can be divided into organic nitrogen and inorganic nitrogen. Inorganic nitrogen mainly includes nitrate nitrogen, ammonia nitrogen, and nitrite nitrogen, introduced by agricultural activities, domestic sewage, industrial smelting, and other anthropogenic processes [38]. Additionally, through the absorption and transformation processes of microorganisms and aquatic animals and plants, organic nitrogen is abundant in the ecosystem, and could be in the form of biological debris and biological chain [39,40]. Both the degradation of organic nitrogen and the process of digestion/denitrification that involves inorganic nitrogen could lead to the migration and release of N in sediments [41]. However, under natural conditions, solid inorganic P in sediments produced by human activities were found to be mainly composed of ferric hydroxides, iron-manganese oxidizing material, and the AL–OM complex, which could reduce and decompose only in an anaerobic environment [42,43]. In comparison, the endogenous pollution of P was more limited than that of N, and irrespective of the effects of pH, temperature, visibility, dissolved oxygen, and other hydro-chemical environmental factors, or physical factors, such as biological disturbance intensity and wind/wave disturbance, it had more severe impacts on N release [43,44].

Nutrient elements and grain size could be influenced by additional factors, including provenance, degree of weathering, diagenesis, and biogenic production, requiring site-specific empirical data [45]. The main streams and their tributaries drain different catchments composed of variable bedrock geology (Figure 5C). Most river courses are predominantly of the Carboniferous system (gravelly coarse-grained and quartz sandstone), with minor basic metamorphics [21]. The upper reach of the Jinglong River was influenced by many tributaries draining Pleistocene, Jurassic, and Triassic sandstone and shales. The upper courses of the Huanglong and Huilong Rivers predominantly drain from Neogene and Quaternary formations. Overall, similar provenances in near-source areas did not influence the composition of minerals and phyllosilicates, which are typically the main carriers of trace and inorganic nutrient elements [21]. Areas of different provenance in our dataset did, nonetheless, display two opposing trends of N/FG and P/FG ratios, although we observed minor excursions from the general trend for FG, excluding the influence of samples from lake inlets and high-pollution outliers (Figure 5A,B). This result suggests that N concentrations in the surface sediment showed a relatively low sensitivity to provenance changes. Moreover, the occurrence form, combined with fine-grained minerals, was not the main speciation of sedimentary inorganic nitrogen contaminants. In contrast, high positive correlations of TP and FG in the surface sediment are observed in Lake Jian, indicating that the occurrence form combined with fine-grained minerals was the main speciation

of P, i.e., ferric hydroxides, iron–manganese oxidizing material inorganic fine-grained complexes [43]. indicating that the occurrence form combined with fine-grained minerals was the main speciation of P, i.e., ferric hydroxides, iron–manganese oxidizing material inorganic finegrained complexes [43].

*Water* **2022**, *14*, x FOR PEER REVIEW 10 of 15

process of digestion/denitrification that involves inorganic nitrogen could lead to the migration and release of N in sediments [41]. However, under natural conditions, solid inorganic P in sediments produced by human activities were found to be mainly composed of ferric hydroxides, iron-manganese oxidizing material, and the AL–OM complex, which could reduce and decompose only in an anaerobic environment [42,43]. In comparison, the endogenous pollution of P was more limited than that of N, and irrespective of the effects of pH, temperature, visibility, dissolved oxygen, and other hydro-chemical environmental factors, or physical factors, such as biological disturbance intensity and

Nutrient elements and grain size could be influenced by additional factors, including provenance, degree of weathering, diagenesis, and biogenic production, requiring sitespecific empirical data [45]. The main streams and their tributaries drain different catchments composed of variable bedrock geology (Figure 5C). Most river courses are predominantly of the Carboniferous system (gravelly coarse-grained and quartz sandstone), with minor basic metamorphics [21]. The upper reach of the Jinglong River was influenced by many tributaries draining Pleistocene, Jurassic, and Triassic sandstone and shales. The upper courses of the Huanglong and Huilong Rivers predominantly drain from Neogene and Quaternary formations. Overall, similar provenances in near-source areas did not influence the composition of minerals and phyllosilicates, which are typically the main carriers of trace and inorganic nutrient elements [21]. Areas of different provenance in our dataset did, nonetheless, display two opposing trends of N/FG and P/FG ratios, although we observed minor excursions from the general trend for FG, excluding the influence of samples from lake inlets and high-pollution outliers (Figure 5A,B). This result suggests that N concentrations in the surface sediment showed a relatively low sensitivity to provenance changes. Moreover, the occurrence form, combined with fine-grained minerals, was not the main speciation of sedimentary inorganic nitrogen contaminants. In contrast, high positive correlations of TP and FG in the surface sediment are observed in Lake Jian,

wind/wave disturbance, it had more severe impacts on N release [43,44].

**Figure 5.** Bivariate plots of N (**A**) and P (**B**) vs. fine grain (FG), as well as Tukey's boxplots showing the geometrical form of absorbed mineral particle size based on the LTS robust regression, and geological stratigraphic map of the catchment of Lake Jian (**C**).

The results of biogenic element distribution showed that relative OM and TN levels in the surface sediment of Lake Jian were significantly higher than those of other lakes in Yunnan Province [25]. In addition to nutrient element levels, other factors, such as vegetation coverage and a well-developed river system, also increased TOC contents. Specifically, OM and TN contents in Lake Jian (R = 0.95) exhibited a significant linear correlation (Figure 6D), suggesting that more organic nitrogen was stored in Lake Jian. The situation was different with the PC results of TP and OM (0.36), which revealed that inorganic P was the main occurrence form of P in Lake Jian, which was then verified by traceability assessment. The OM in >75% of the sediment samples derived mainly from endogenous processes (plankton: 25.4–68.8%, averaging 52.53%; macrophytes: 3.7–16.6%, 8.99%), and only samples from entering river estuaries were related to domestic sewage (2.3–25.5% 17.47%), agricultural irrigation (soils OM: 0.6–14.4%, 7.37%), and crop planting (terrestrial C3: 8.5–40.7%, 18.35%; terrestrial C4: 0.1–6.0%, 3.1%). The source estimation of N showed that more than 16.8% of the sediment samples were derived from the sourcing process, which was remarkable for a lake with a low eutrophication level. Furthermore, we found that the level and distribution of N in surface sediment were less related to its material source (R−N/δ <sup>15</sup>N = 0.23) and occurrence form (R−N/<sup>δ</sup> <sup>13</sup>C = 0.28). ANOVA demonstrated that the sedimentary state of N was an independent process (*p* < 0.001). All this evidence indicates that the processes of N migration and release were determined by the current N level of the lake.

**Figure 5.** Bivariate plots of N (**A**) and P (**B**) vs. fine grain (FG), as well as Tukey's boxplots showing the geometrical form of absorbed mineral particle size based on the LTS robust regression, and ge-

The results of biogenic element distribution showed that relative OM and TN levels in the surface sediment of Lake Jian were significantly higher than those of other lakes in Yunnan Province [25]. In addition to nutrient element levels, other factors, such as vegetation coverage and a well-developed river system, also increased TOC contents. Specifically, OM and TN contents in Lake Jian (R = 0.95) exhibited a significant linear correlation (Figure 6D), suggesting that more organic nitrogen was stored in Lake Jian. The situation was different with the PC results of TP and OM (0.36), which revealed that inorganic P was the main occurrence form of P in Lake Jian, which was then verified by traceability assessment. The OM in >75% of the sediment samples derived mainly from endogenous processes (plankton: 25.4–68.8%, averaging 52.53%; macrophytes: 3.7–16.6%, 8.99%), and only samples from entering river estuaries were related to domestic sewage (2.3–25.5% 17.47%), agricultural irrigation (soils OM: 0.6–14.4%, 7.37%), and crop planting (terrestrial C3: 8.5–40.7%, 18.35%; terrestrial C4: 0.1–6.0%, 3.1%). The source estimation of N showed that more than 16.8% of the sediment samples were derived from the sourcing process, which was remarkable for a lake with a low eutrophication level. Furthermore, we found that the level and distribution of N in surface sediment were less related to its material source (R−N/δ15N = 0.23) and occurrence form (R−N/δ13C = 0.28). ANOVA demonstrated that the sedimentary state of N was an independent process (*p* < 0.001). All this evidence indicates that the processes of N migration and release were determined by the current N

ological stratigraphic map of the catchment of Lake Jian (**C**).

level of the lake.

**Figure 6.** Ratio of TN and TP (**A**), correlation between TN and δ<sup>15</sup>N/δ<sup>13</sup>C (**B**), TN/TP range (**C**), and correlation between nutrient elements in surface sediment and bottom water (**D**) in Lake Jian. **Figure 6.** Ratio of TN and TP (**A**), correlation between TN and δ <sup>15</sup>N/δ <sup>13</sup>C (**B**), TN/TP range (**C**), and correlation between nutrient elements in surface sediment and bottom water (**D**) in Lake Jian.

#### *3.6. Transformation of N/P-Limitation Driven by Endogenous Pollution*

Intriguingly, we found that the algal growth pattern of the Lake Jian water body was limited mainly by P (N/P ratios ranged from 5.7 to 64.2 times, averaging 26.5 times), while the nutrient elements levels in the sediment belonged entirely to the N-limitation state (2.1–7.3 times, 3.8 times). Generally, the strong exogenous pollution of N in the sediment, caused by its multiple unstable migrated forms and pathways, led to the enrichment of P [46,47]. These processes result in more dissolved N released into water, changing nutrient limitation. The hydrological results and lake monitoring records also support this conclusion (Figure 6A). There was also a great difference between the inflow of rivers and streams (5.8–13.2 times, 10.1 times) and the output river (29.5–45.3 times), meaning that whatever the river input via irrigation agriculture or city sewage, P was the dominating nutrient element and eutrophication was limited mainly by N. However, after the water entered Lake Jian, the exogenous pollution and dynamic processes changed the relative levels and structure of N/P, altering nutrient element limitation. Compared with previous monitoring data, the status of nutrient elements in Lake Jian has gradually changed from N-limitation (2000–2010) to P-limitation (2010–2018) in recent years [15,28,48]. This phenomenon was affected by the differences in cyclic processes and occurrences between N and P [49]. Due to the bio-accumulation of algae and aquatic plants, a large amount of external N in the lake is bounded by OM, enriching the sediments with biological death, completing the process of re-concentration of N in the lacustrine system [50]. Inevitably, combined with OM degradation and improved lake productivity, these processes re-intensify endogenous pollution and the enrichment condition of N, ultimately causing the transformation of nutrient element limitation.

We hypothesized that except for the influence of exogenous pollution, the primary cause of nutrient elements limiting their transformation in Lake Jian was their geophysical structure and environmental conditions in the catchment area. As a shallow lake, oxygen in the water-sediment interface in Lake Jian could be sufficiently supplied by air–water exchange and dynamic disturbance, alleviating anaerobic conditions by OM degradation [3]. Normally, the unstable states of P from anthropogenic activities that enter sediments by dynamic processes are principally excited as Al-Fe binding states, Ca-OM binding states, and residual P complexes [3,30]. Among them, hydrolysis and reduction of the Al-Fe-P complex, including ferric hydroxide and phosphoric iron, are the pathways for inorganic P release [46,49]. Under aerobic conditions, these materials are stable and immobile, inhibiting the migration and release of P.

A series of previous studies indicated that the external loss process of N (denitrification process) was strictly limited by oxygen content, and the loss process of P (dynamic deposition) was weakened, resulting in the formation of an N limitation state in shallow lakes, due to stronger water disturbance [3,37,49]. However, we observed dramatic OM levels and poor DO states at some sites, which were closely related to swamp formation processes at Lake Jian. In recent years, due to the impact of land reclamation and climate change, the lake area has decreased by more than 40% [14], resulting in most lakeside areas being exposed directly to the impact of the surrounding runoff. The original lakeside ecosystem has gradually evolved into a swampy area with dense vegetation. Despite high vegetation coverage having significantly alleviated the nutrient elements imported from external sources, it also played an important role in improving the transparency and oxygen content of the water body, limiting the occurrence of endogenous pollution processes, especially P release, which depend upon an anaerobic environment. However, due to the lack of exogenous pollution controls in early rising times, the huge algae masses produced by eutrophication and aquatic plants adsorbed innumerable nutrient elements in the lake sediments. Through the biological enrichment process, pollutants are enriched and transferred, especially the production of organic nitrogen, which could complete the migration and release process through various pathways, resulting in dissolved nitrogen frequently existing in the lake. Under an aerobic environment, the obstruction of gasification and efflux processes, such as denitrification, were also aggravated, finally achieving a P-limited state.

### **4. Conclusions**

Based on the influence of environmental conditions, we observed several differences in the observed release/migration intensity of N and P in the sediments of Lake Jian, which resulted in the transformation of nutrients as limiting elements. We call this phenomenon the "pump diaphragm effect," which is the self-enrichment process of N, leading to the swamping of shallow plateau lakes. More remarkably, the water exchange cycle period was relatively long because of the closed environment and structural characteristics, which rendered the ecosystem of plateau lakes fragile and water self-restoration processes slow. Once the environmental background begins to fluctuate, the ecological and environmental health functions of plateau lakes could be irreversibly damaged. Our research results provide a sound theoretical basis and supporting data for improving the treatment of shallow, swamped plateau lakes. While decreasing/eliminating endogenous pollution, attention should also be focused on nutrient element circulation processes in the lake itself. More importantly, strategies that can control the nitrogen and phosphorus levels should be more carefully formulated due to complex and variable lake conditions; thus, the best strategy may be to implement the "one lake with one governance" strategy to fully understand the special characteristics of lakes and establish protocols for long-term eutrophication detection, assessment and management.

**Author Contributions:** Conceptualization, Y.Z. and H.Z; Formal analysis, D.L.; Funding acquisition, F.C. and H.Z.; Methodology, Y.Z.; Project administration, X.Z.; Resources, H.Z.; Software, Q.L. and F.L.; Supervision, F.C. and H.Z.; Writing—original draft, Y.Z.; Writing—review and editing, H.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Yunnan Provincial Government Scientist workshop and the Special Project for Social Development of Yunnan Province (Grant No. 202103AC100001, Funder: Hucai Zhang).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Special thanks are given to the team of the Key Laboratory of Plateau Lake Ecology and Global Change and the Institute for Ecological Research and Pollution Control of Plateau Lakes, who provided support with the sample collection and analysis.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Jing Qi <sup>1</sup> , Le Deng <sup>1</sup> , Yongjun Song <sup>1</sup> , Weixiao Qi 2,\* and Chengzhi Hu 1,3**


**\*** Correspondence: wxqi@mail.tsinghua.edu.cn

**Abstract:** The responses of phytoplankton to nutrients vary for different natural bodies of water, which can finally affect the occurrence of phytoplankton bloom. However, the effect of high alkalinity characteristic on the nutrient thresholds of natural alkaline lake is rarely considered. Bioassay experiments were conducted to investigate the nutrient thresholds and the responses of phytoplankton growth to nutrients for the closed plateau Chenghai Lake, Southwest China, which has a high pH background of up to 9.66. The growth of the phytoplankton community was restricted by phosphorus without obvious correlation with the input of nitrogen sources. This can be explained by the nitrogen fixation function of cyanobacteria, which can meet their growth needs for nitrogen. In addition, nitrate nitrogen (NO<sup>3</sup> -N) could be utilized more efficiently than ammonia nitrogen (NH<sup>4</sup> -N) for the phytoplankton in Chenghai Lake. Interestingly, the eutrophication thresholds of soluble reactive phosphorus (SRP), NH<sup>4</sup> -N, and NO<sup>3</sup> -N should be targeted at below 0.05 mg/L, 0.30 mg/L, and 0.50 mg/L, respectively, which are higher than the usual standards for eutrophication. This can be explained by the inhibition effect of the high pH background on phytoplankton growth due to the damage to phytoplankton cells. Therefore, the prevention of phytoplankton blooms should be considered from not only the aspect of reducing nutrient input, especially phosphorus input, but also maintaining the high alkalinity characteristic in natural alkaline lake, which was formed due to the geological background of saline-alkali soil.

**Keywords:** nutrient threshold; alkaline lake; pH; phytoplankton blooms

## **1. Introduction**

Harmful phytoplankton blooms in natural water have aroused great concern due to their negative effects on water quality and aquatic ecosystems globally [1,2]. This poses a serious threat to the safety of drinking water, food webs, and the overall sustainability of freshwater ecosystems [3,4]. It has been reported that megafauna may be endangered by cyanotoxins released by harmful phytoplankton [5]. In addition, the expansion of phytoplankton blooms could be triggered by climate change and eutrophication [6]. It is widely believed that the reduction of nutrient input is fundamental for the control of harmful phytoplankton blooms. However, the responses of phytoplankton to nutrients vary in different natural bodies of water [3,7]. Research indicates that the thresholds for regime shifts between turbid-water and clear-water conditions in shallow lakes vary depending on basins and climates [8]. Therefore, it is important to determine the nutrient thresholds in water bodies, especially for natural water with special water quality backgrounds.

Nutrient thresholds are regarded as the critical levels of nutrients that control population shifts, such as the sudden and long-term dominance of phytoplankton blooms. The determination of nutrient thresholds is a quantifiable and meaningful approach [9]. N and P are the main material bases for phytoplankton growth, and their relationship with

**Citation:** Qi, J.; Deng, L.; Song, Y.; Qi, W.; Hu, C. Nutrient Thresholds Required to Control Eutrophication: Does It Work for Natural Alkaline Lakes? *Water* **2022**, *14*, 2674. https:// doi.org/10.3390/w14172674

Academic Editors: Hucai Zhang and Jingan Chen

Received: 21 July 2022 Accepted: 26 August 2022 Published: 29 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

phytoplankton biomass is one of the important aspects in studying eutrophication [10]. It is well known that addition of N and P can not only stimulate phytoplankton growth, but also promote phosphorus release from sediment or nitrogen fixation from atmosphere [11,12]. The critical values of total nitrogen (TN) and total phosphorus (TP) for eutrophication of lakes were reported to be 0.8 mg/L and 0.05 mg/L, respectively [13]. It has been proved that the understanding and utilization of ecological thresholds is the key to the successful management of water environments [14]. The control of nutrients below threshold levels is more practical, achievable, and cost-effective than reducing them to historical levels [15].

Due to the great differences in the environment and geographical location at different altitude, plateau lakes have unique hydrological chemistry characteristics, such as high alkalinity [16]. Chenghai Lake is a typical representative of plateau lakes in southwest China with high alkalinity characteristic, which was naturally formed due to the geological background of saline-alkali soil [17,18]. Increasing salinization due to climate change might negatively affect inland water sources [19]. According to the water quality conditions of Chenghai Lake in 2018–2019, the average pH value is up to 9.42. In addition, the average values of TN and TP are 1.12 and 0.06 mg/L, respectively, which have exceeded the generally recognized concentrations for eutrophication occurrence [10,13,20–22]. However, the current cyanobacteria biomass in Chenghai Lake is still at a slight bloom level without large-scale phytoplankton blooms [18]. Therefore, the nutrient thresholds of natural alkaline lake might be higher than other lakes, which needs to be clarified. It has been reported that pH range has some inhibition or promotion effects on phytoplankton in various environmental backgrounds [23,24]. However, the effect of high alkalinity characteristic on the nutrient thresholds of natural alkaline lake is rarely considered. Therefore, it is important to determine the nutrient thresholds and explore the effect of high alkalinity, which are fundamental for the prevention of phytoplankton blooms in natural alkaline lakes.

Based on the aforementioned considerations, this study aims to: (1) determine the nutrient thresholds of NH4-N, NO3-N, and SRP in Chenghai Lake; (2) investigate the binding and synergistic interactions between NH4-N, NO3-N, and SRP; (3) explore the effect of high alkalinity characteristic on nutrient thresholds of natural alkaline lake.

#### **2. Materials and Methods**

#### *2.1. Study Area and Field Method*

Chenghai Lake is located in the Yunnan Plateau, southwestern China (26◦270–26◦380 N; 100◦380–100◦410 E) with an altitude of 1503 m (Figure 1). The lake covers an area of about 72.9 square kilometers and has an average water depth of 23.7 m, and an annual average water temperature of 17.8 ◦C. Chenghai is a typical closed-type deep-water lake, surrounded by mountains on the east, west and north, and the terrain is flat on the south [25].

Monthly sampling was conducted from June 2018 to May 2019 for all 15 sampling sites (Figure 1). The in-situ data of conductivity (EC), pH, and temperature (WT) were measured on site with a hand-held multi-parameter meter (MYRON L 6P, California, USA). TN, NH4-N, NO3-N, TP, and total dissolved phosphorus (TDP) were analyzed according to standard methods [25]. The phytoplankton samples were settled for 48 h after being fixed with Lugol iodine solution (2%) [26]. Cell density and community composition were determined under microscope with a Sedgwick-Rafter counting chamber [27].

**Figure 1.** Location of the study area and sampling sites.

#### *2.2. Nutrient Limitation Bioassay Experiments*

Water samples from site H were used for nutrient threshold and addition bioassay experiments. Zooplankton were removed by screening with a 200-µm grid to minimize the effects of grazing [28]. The water samples were cultivated in a lighted incubator within 2 ◦C of the in situ temperature. The light intensity was maintained at 100 µmol photon m−<sup>2</sup> s <sup>−</sup><sup>1</sup> with a 14:10-h light-dark cycle. The initial physical, chemical and biological properties of the water sample used for nutrient limitation bioassay experiments are given in Table S1. For all the experiments, SRP, NO3-N, and NH4-N were added as K2HPO4·3H2O, NaNO3, and NH4Cl, respectively. The experiment was carried out over 15 days to ensure that the phytoplankton had sufficient time to adapt and grow.

The nutrient threshold bioassay experiment was conducted to explore the nutrient thresholds of SRP, NO3-N, and NH4-N. Various concentrations of SRP (0.017, 0.02, 0.03, 0.04, 0.05, 0.06, 0.08, 0.10, 0.50, 1.00 mg/L P) and fixed NO3-N (10 mg/L N) were used in the SRP threshold experiment. Various concentrations of NO3-N (0.04, 0.10, 0.20, 0.30, 0.40, 0.50, 0.80, 1.00, 1.50, 2.00 mg/L N) and fixed P (5 mg/L N) were used in the NO3-N threshold experiment. Various concentrations of NH4-N (0.04, 0.10, 0.20, 0.30, 0.40, 0.50, 0.80, 1.00, 1.50, 2.00 mg/L N) and fixed P (5 mg/L N) were used in the NH4-N threshold experiment.

A separate nutrient addition bioassay experiment was conducted with treated water samples containing individual or combined SRP, NO3-N, and NH4-N, which could assess the individual or combined effects of these nutrients on the growth of phytoplankton. A total of 30 treatments for three scenarios were designed in this experiment (Table 1).

The growth rate (*µ*) for all treatments was calculated according to the exponential growth equation [9]: *µ* = ln(*X*2/*X*1)/(*T*2-*T*1), where *X*<sup>1</sup> is the phytoplankton density at the initial incubation time point (*T*1), and *X*<sup>2</sup> is the phytoplankton density at the last time point (*T*2). The Monod kinetic equation was used to calculate the maximum growth rate (*µ*max) and half-saturation constant (*K*u) [29]. The nutrient threshold can be estimated according to the change points on the response curves.

The difference in growth response among various treatments was analyzed by oneway ANOVA [30]. The Tukey's least significant difference procedure was used to compare multiple treatments after the event [31]. Statistical analysis was conducted using the SPSS 13.0 statistical software package (SPSS Inc., Chicago, IL, USA), and the significance level of the test used was *p* < 0.05 [32].


**Table 1.** Basic Schemes for Nutrient Addition Bioassay Experiment.

## *2.3. Effect of pH Range on the Growth of Phytoplankton*

According to the water quality conditions of Chenghai Lake in 2018–2019, the average pH value is up to 9.42 with the highest value of 9.66. The effect of pH range on nutrient thresholds was investigated with 3 different gradients of pH value (9.17, 8.50, and 7.50). The initial pH value of the water samples from Chenghai Lake used in this study was 9.17, and all the other gradients of pH value were adjusted before cultivation. The treatments were cultivated under a light intensity of 100 µmol photon m−<sup>2</sup> s <sup>−</sup><sup>1</sup> with a 14:10-h light-dark cycle. The pH of each treatment was controlled during the whole experimental period. The phytoplankton density was recorded regularly every day.

#### **3. Results and Discussion**

#### *3.1. Seasonal Variation of Phytoplankton Density and Water Quality*

The water quality parameters for each sampling point of Chenghai Lake from June 2018 to May 2019 are shown in Figure 2. As we can see from the monthly changes, most water quality parameters showed seasonal variations. As shown in Figure 2b, the phytoplankton density was relatively lower in autumn and spring. The highest phytoplankton density of 6.46 <sup>×</sup> <sup>10</sup><sup>7</sup> cells/L was found in the northern part of Chenghai Lake in May. The nutritional status for Chenghai Lake was mainly maintained at a slight bloom level according to the phytoplankton density [33].

**Figure 2.** Monthly variation of phytoplankton density and water quality indexes in Chenghai Lake for all the 15 sampling sites. (**a**) Sampling sites delineation in Chenghai Lake. The spatial and temporal distribution characteristics of (**b**) phytoplankton density, (**c**) TN, (**d**) TP, (**e**) NH<sup>4</sup> -N, and (**f**) NO<sup>3</sup> -N in Chenghai Lake.

Nitrogen and phosphorus are the most important nutrients for phytoplankton growth. The TN value showed a continuous upward trend during the monthly sampling, varying from 0.30 to 1.99 mg/L. The highest TN content of 1.99 mg/L also appeared in the north of Chenghai Lake in May. What's more, the TP value was in the range from 0.02 to 0.11 mg/L. Most of the measured TN and TP values were higher than the commonly reported critical values for eutrophication [10,13,20–22]. In addition, NH4-N and NO3-N are also provided to illustrate the variation of different nitrogen sources (Figure 2e,f). The highest NH4-N and NO3-N values were determined to be as high as 0.43 mg/L and 0.59 mg/L, respectively. Therefore, the reduction of external nutrients is essential to ensure acceptable water quality, which can finally reduce the internal nitrogen and phosphorus load [15,34].

#### *3.2. Nutrient Thresholds Required to Control Eutrophication in Chenghai Lake*

Nitrogen and phosphorus are important nutrients that limit the growth of phytoplankton, and it is particularly important to formulate nutrient thresholds to control the occurrence of blooms [35]. The bioassay experiment is an effective tool to explore the

growth response of phytoplankton under different nutrient concentrations [36]. To examine the nutrient thresholds of SRP, NO3-N, and NH4-N, the growth rate response of phytoplankton to different nutrient concentrations was explored. Figure 3 shows the growth curves fitted by nonlinear regression for SRP, NO3-N, and NH4-N, respectively. Figure 3a shows that the growth rate of phytoplankton increased with SRP addition from 0 to 0.05 mg/L P, while it remained constant with the further increase in SRP addition from 0.05 to 1.0 mg/L P. The change point on the response curve was found to be 0.05 mg/L P, which indicated that the growth of phytoplankton would be no longer restricted by P when SRP enrichment exceeded 0.05 mg/L P. Therefore, the threshold of SRP could be determined to be 0.05 mg/L P. Similarly, eutrophication thresholds of NH4-N and NO3-N were found to be 0.3 mg/L N and 0.5 mg/L N, respectively (Figure 3b,c).

**Figure 3.** Growth kinetics of phytoplankton in response to (**a**) SRP, (**b**) NH<sup>4</sup> -N, and (**c**) NO<sup>3</sup> -N concentrations.

The maximum growth rates (*µ*max) for SRP, NH4-N, and NO3-N were found to be 0.124, 0.162, and 0.272 d−<sup>1</sup> , respectively, according to the Monod equation [13]. The halfsaturation constant (*K*u) for SRP, NH4-N, and NO3-N were determined to be 0.009, 0.029, and 0.030 mg/L, respectively. The higher *µ*max/*K*u ratio of NO3-N compared with NH4-N could demonstrate the higher utilization efficiency of NO3-N in Chenghai Lake.

The growth responses of phytoplankton to increasing concentrations of SRP, NH4-N, and NO3-N are shown in Figure 4. The addition of NH4-N and NO3-N alone showed little effect on the growth of phytoplankton compared with the control group. As we can see from Figure 4a, the growth rate reached a peak when the concentration of NH4-N reached 1.0 mg/L N together with P addition. This indicated that phosphorus could be the main limiting factor for the growth of phytoplankton. As for NO3-N (Figure 4b), the growth rate of phytoplankton remained almost unchanged with the increase in concentration from 0.5 mg/L N to 4.0 mg/L N which was higher than the control group. However, the growth rates of phytoplankton could be promoted by increased SRP concentration with or without nitrogen sources (Figure 4c). These results indicated that the growth of the phytoplankton community in Chenghai Lake was restricted by phosphorus without obvious correlation with the input of nitrogen sources. Similar results have been reported in Meiliang Bay of Taihu Lake, and phosphorus was also found to restrict the growth of phytoplankton [20]. This can be explained by the nitrogen fixation function of cyanobacteria, which can meet their growth needs for nitrogen [37].

Figure 5 shows the growth rate of phytoplankton in response to individual or combined NH4-N, NO3-N, and SRP additions according to the nutrient addition bioassay experiment. N addition alone showed little effect on the growth rate of phytoplankton compared with the control. A significant stimulatory effect can be found with the combined addition of NO3-N and SRP. This can directly prove the stimulatory effect of NO3-N on phytoplankton growth, which is consistent with the results of nutrient threshold bioassay experiments. In addition, the stimulatory effect of SRP addition compared with the control can be further enhanced with the combined addition of NH4-N or NO3-N. However, the growth rate of phytoplankton with combined addition of "2 mg/L SRP + 5.0 mg/L NH4-N + 5.0 mg/L NO3-N" was not higher than the group with "2 mg/L SRP + 10.0 mg/L NH4-N" or "2 mg/L SRP + 10.0 mg/L NO3-N". This indicated that the form of nitrogen source could

not show similar stimulatory effect on the growth of phytoplankton under the sufficient concentration of phosphorus.

**Figure 4.** Phytoplankton growth responses to various concentrations of (**a**) NH<sup>4</sup> -N, (**b**) NO<sup>3</sup> -N, and (**c**) SRP additions.

**Figure 5.** Comprehensive effects of NH<sup>4</sup> -N, NO<sup>3</sup> -N, and SRP on phytoplankton growth.

#### *3.3. Effect of High Alkalinity Background on Nutrient Thresholds*

According to the above results of nutrient limitation bioassay experiments, the nutrient thresholds of Chenghai Lake are higher than other reported lakes. Only a slight level of bloom was found in Chenghai Lake with a high-nutrient environment. These results indicate that the growth of phytoplankton might be influenced by other factors in addition to nutrients. The pH range in natural water bodies has been reported to show inhibition or promotion effects on phytoplankton [23,24]. Combined with the high alkalinity characteristic in Chenghai Lake [18], the effects of pH range on the growth of phytoplankton in Chenghai Lake were also explored in this study.

As we can see from Figure 6, the phytoplankton density increased with decrease in the pH value from 9.17 to 7.50 during the cultivation. These results indicated that a relatively lower water body pH could promote the growth of phytoplankton, while the high pH background of Chenghai Lake (pH = 9.17) could limit the growth of phytoplankton. It has been reported that a high pH condition in freshwater could not promote the growth and reproduction of cyanobacteria [38]. Most phytoplankton species cannot grow properly under high alkalinity conditions, especially when pH value exceeds 9 [39,40]. Thus, the relatively higher nutrient thresholds of Chenghai Lake can be explained by the inhibition effects of the high pH background on phytoplankton growth. High pH background might alter the transport processes of membrane and the metabolic functions of cells and change the relative composition of amino acids in cellular, which can finally affect the growth of phytoplankton [40]. As a means to prevent and control phytoplankton blooms, many researchers have undertaken pH adjustment as a method in actual lake management [41–43]. Therefore, it is necessary to maintain the high alkalinity characteristic of Chenghai Lake, which is helpful for the prevention of phytoplankton bloom.

**Figure 6.** Effect of different pH levels on phytoplankton growth in Chenghai Lake.

#### **4. Conclusions**

The results in this study indicated that Chenghai Lake was always maintained at a slight bloom level according to the phytoplankton density. Phosphorus was found to be the main limiting factor for the growth of phytoplankton. In addition, the utilization efficiency of NO3-N was higher than that of NH4-N in Chenghai Lake. The eutrophication thresholds of SRP, NH4-N, and NO3-N were determined to be 0.05 mg/L, 0.30 mg/L, and 0.50 mg/L, respectively. The higher nutrient thresholds can be explained by the high pH range in Chenghai Lake, which would inhibit phytoplankton growth. In addition to the reduction of nutrient input, the maintenance of high alkalinity characteristic is also necessary for the prevention of phytoplankton blooms in natural alkaline lake.

**Supplementary Materials:** The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/w14172674/s1, Table S1: Physical and chemical indexes of water samples collected at site H.

**Author Contributions:** Conceptualization, J.Q. and W.Q.; methodology, J.Q. and L.D.; software, L.D.; validation, J.Q. and L.D.; formal analysis, J.Q.; investigation, J.Q., L.D., and Y.S.; resources, C.H.; data curation, J.Q.; writing—original draft preparation, J.Q.; writing—review and editing, W.Q. and C.H.; supervision, J.Q. and W.Q; project administration, J.Q. and W.Q.; funding acquisition, J.Q. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was financially supported by the Funds for National Key R&D Program of China (Grant No. 2018YFE0204101), and the National Natural Science Foundation of China (Grant No. 52170014, and 51808531).

**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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