*Article* **Spatiotemporal Changes in Water Quality Parameters and the Eutrophication in Lake Erhai of Southwest China**

**Kun Chen <sup>1</sup> , Lizeng Duan 2,\*, Qi Liu <sup>2</sup> , Yang Zhang <sup>2</sup> , Xiaonan Zhang <sup>2</sup> , Fengwen Liu <sup>2</sup> and Hucai Zhang 2,\***

> 1 Institute for International Rivers and Eco-Security, Yunnan University, Kunming 650500, China

2 Institute for Ecological Research and Pollution Control of Plateau Lakes,

School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China

**\*** Correspondence: duanlizeng2019@ynu.edu.cn (L.D.); zhanghc@ynu.edu.cn (H.Z.)

**Abstract:** To understand the lake status and reasons of eutrophication at Lake Erhai in recent years, water quality, including water temperature (T), pH, dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP) and chlorophyll-a (Chl-a) from 2016 to 2020 was monitored and analyzed. The results showed no obvious thermocline in the vertical direction at Lake Erhai, while Chl-a demonstrated obvious spatiotemporal distribution characteristics in Lake Erhai. Chl-a concentrations increased to a maximum in summer in August with the low TN:TP value, leading to algal blooms, most notably in the southern lakes. Low pH and DO appeared due to the thermocline of Erhai Lake (August 2016). A large area of algae distribution due to the increase of total phosphorus appeared in the northern lake area of Lake Erhai in December 2016, with a tendency of mesotrophic to light eutrophic in summer by the nutritional evaluation of Lake Erhai, especially in the central lake area and the northern lake area. Pearson's correlation coefficient and principal component analysis showed a significant positive correlation between Chl-a and T (r = 0.34, *p* ≤ 0.01) and TP (r = 0.31 *p* ≤ 0.01) in the mesotrophic Lake Erhai, indicating that TP content was one of the triggering factors for the algal blooming. Based on the spatiotemporal changes in water quality parameters and their relationship with eutrophication, scientific agencies should implement management strategies to protect Lake Erhai, supplemental to the costly engineering measurements.

**Keywords:** Lake Erhai; temperature; Chl-a; seasonal changes; eutrophication

## **1. Introduction**

The study of lakes eutrophication is important to understand the changes in their ecological environment under the influence of natural and human activities [1]. Research on the seasonal vertical distribution characteristics of physical and chemical parameters of plateau lakes helps identify corresponding environmental indicators and assess changes in the ecosystems of plateau lakes, indicating the importance of long-term and continuous monitoring and research [2,3]. The heat distribution of the lake water affects its stratification and mixing, resulting in changes in its physical and chemical parameters [4,5]. T measurement shows that the temperature of lakes has a strong seasonal variation, while stratification shows that they have a seasonal pattern, suggesting that lakes can be classified based on T [6]. The research results indicated that typical eutrophic non-aquaculture water had mean concentrations of Chl-a of higher than 10 µg/L, and significant positive correlations were found between pH, DO and Chl-a. When the mean concentration of Chl-a was less than 10 µg/L, no correlation was found between DO and Chl-a for waters with a high exchange rate or heavily organically polluted natural waters [7].

External nutrients inputs above a critical level may drive shallow lakes to shift from clear water to a turbid state, enhanced eutrophication process, and the biomass of submerged macrophyte and dominance community changed by nutrient input [8,9]. The algal growth was often within a certain range in lakes caused by nutrient input. The study of

**Citation:** Chen, K.; Duan, L.; Liu, Q.; Zhang, Y.; Zhang, X.; Liu, F.; Zhang, H. Spatiotemporal Changes in Water Quality Parameters and the Eutrophication in Lake Erhai of Southwest China. *Water* **2022**, *14*, 3398. https://doi.org/10.3390/ w14213398

Academic Editor: Roohollah Noori

Received: 8 September 2022 Accepted: 24 October 2022 Published: 26 October 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/).

Hou et al. showed that the lake water in Chenghai appeared temperature stratification in summer. Induces nutrient release from lake sediments and promotes eutrophication resulting from cyanobacterial reproduction in the upper thermocline [10]. The eutrophication of lakes is usually affected by total nitrogen or total phosphorus. When different types of exogenous polluted water flows into the lake, the N/P ratio will change, resulting in changes in the nitrogen and phosphorus limitation of the lake. It has been reported that the nutrient status of Jianhu has gradually shifted from N-limited (2000–2010) to P-limited in recent years (2010–2018) [11]. There have a study assessed the actual water situation in the estuarine area of Lake Taihu, China, based on eutrophication levels and status of water quality using the trophic level index (TLI) and water quality index (WQI) methods. In the wet (August 2017) and dry (March 2018) seasons, the average TLI and WQI values in the wet season were worse than that in the dry season (TLI: 57.40, WQI: 65.74), and P may be the main factor in the dry season [12]. of Hou et al. showed that the lake water in Chenghai appeared temperature stratification in summer. Induces nutrient release from lake sediments and promotes eutrophication resulting from cyanobacterial reproduction in the upper thermocline [10]. The eutrophi‐ cation of lakes is usually affected by total nitrogen or total phosphorus. When different types of exogenous polluted water flows into the lake, the N/P ratio will change, resulting in changes in the nitrogen and phosphorus limitation of the lake. It has been reported that the nutrient status of Jianhu has gradually shifted from N‐limited (2000–2010) to P‐limited in recent years (2010–2018) [11]. There have a study assessed the actual water situation in the estuarine area of Lake Taihu, China, based on eutrophication levels and status of water quality using the trophic level index (TLI) and water quality index (WQI) methods. In the wet (August 2017) and dry (March 2018) seasons, the average TLI and WQI values in the wet season were worse than that in the dry season (TLI: 57.40, WQI: 65.74), and P may be the main factor in the dry season [12].

Previous studies on Lake Erhai mostly focused on the analysis of imported phosphorus species in Lake Erhai [13], the role of sediment bioavailability phosphorus in algal growth [14], historical changes in water level and responses to human activities and climate change [15]. Comparatively, this study aimed to analyze the monitoring data of Lake Erhai from 2016 to 2020, investigate the vertical and horizontal distribution characteristics of the physical and chemical parameters in recent years, as well as the seasonal change processes, and analyze their relationship with eutrophication to provide a scientific basis for the environmental restoration and eutrophication mitigation of Lake Erhai. Previous studies on Lake Erhai mostly focused on the analysis of imported phospho‐ rus species in Lake Erhai [13], the role of sediment bioavailability phosphorus in algal growth [14], historical changes in water level and responses to human activities and cli‐ mate change [15]. Comparatively, this study aimed to analyze the monitoring data of Lake Erhai from 2016 to 2020, investigate the vertical and horizontal distribution characteristics of the physical and chemical parameters in recent years, as well as the seasonal change processes, and analyze their relationship with eutrophication to provide a scientific basis for the environmental restoration and eutrophication mitigation of Lake Erhai.

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

#### *2.1. Overview of the Study Area 2.1. Overview of the Study Area*

Lake Erhai is located in the central part of the Dali Bai Autonomous Prefecture in Yunnan (100◦050 E 100◦170 E, 25◦360 N 25◦580 N) and is a shallow plateau lake formed by tectonic and rifting movement (Figure 1). It belongs to the Lancang River Basin, originating from Cibi Lake in Eryuan County. Its water source is from Bagu Mountain via the Mizhi River in the north and Xier River in the west and relies on surface runoff and lake precipitation. The annual water volume of the lake is 13.78 <sup>×</sup> <sup>10</sup><sup>8</sup> <sup>m</sup><sup>3</sup> , with an area of 256.5 km<sup>2</sup> , water storage capacity of 270 <sup>×</sup> <sup>10</sup><sup>8</sup> <sup>m</sup><sup>3</sup> , average water depth of about 10.5 m, deepest depth reaching up to 20.9 m, and a water retention time of about 2.75 years [16]. Lake Erhai has a subtropical plateau monsoon climate, with mild four seasons, low average temperature, large daily range, long daylight hours, and clear dry and rainy seasons. Lake Erhai is located in the central part of the Dali Bai Autonomous Prefecture in Yunnan (100°05′ E 100°17′ E, 25°36′ N 25°58′ N) and is a shallow plateau lake formed by tectonic and rifting movement (Figure 1). It belongs to the Lancang River Basin, originat‐ ing from Cibi Lake in Eryuan County. Its water source is from Bagu Mountain via the Mizhi River in the north and Xier River in the west and relies on surface runoff and lake precipitation. The annual water volume of the lake is 13.78 × 108 m3, with an area of 256.5 km2, water storage capacity of 270 × 108 m3, average water depth of about 10.5 m, deepest depth reaching up to 20.9 m, and a water retention time of about 2.75 years [16]. Lake Erhai has a subtropical plateau monsoon climate, with mild four seasons, low average temperature, large daily range, long daylight hours, and clear dry and rainy seasons.

**Figure Figure 1. 1.** Map Map showing the location of Lake Erhai. showing the location of Lake Erhai.

Erhai Basin has a total population of 883,900, and the urban population is mainly concentrated in the western and southern regions. The western basin of Lake Erhai is the main agricultural area and the high-value areas of pollution discharge in the basin are concentrated in the northern area of Lake Erhai. From 2007 to 2017, the non-point source pollution discharge in Erhai Basin accounted for 94.10% of the total discharge in the basin [17].

#### *2.2. Data Collection and Research Methods*

To comprehensively and systematically investigate Lake Erhai's water quality status and seasonal changes, water samples were collected from January 2016 to June 2020. A total of 10 sites were set up from north to south for fixed-point monitoring, of which sites 1–3 were located in the northern lake area of Lake Erhai, sites 4–7 in the middle lake area and sites 8–10 in the southern lake area. The water quality parameters, including temperature, pH, DO and Chl-a, were measured simultaneously, using YSI multiparameter sonde (product model: EXO2, United States, sampling frequency: 0.2 s) at the sampling site at 1 m interval.

Because water depth in most of the Lake Erhai is less than 5 m, we selected the measured date at 1 m and 5 m depth to discuss and the data at 1 m and 5 m from January, April, August and October 2017 were used to draw a heat map for seasonal comparison (representing winter, spring, summer, and autumn). The depth data at 1 m, 5 m, 10 m and 15 m from January 2016 to June 2020 (averaged data of each month) were used to analyze the change in trends and investigate long-term series change. Sites 2, 7 and 10 (representing the northern, central and southern parts of the lake) were used to assess TN and TP, and analyze the changing trends of different depths and long-term series in the three lake areas.

The water quality evaluation of surface layer mainly used the lake eutrophication level scoring standard of Aizaki Morihiro to score the three sampling site 2, 7 and 10 (Table 1), and used the Carson index method to classify the monitoring site [18].


**Table 1.** Grading and classification standard.

From the six water quality parameters, we selected all corresponding data (at the same depth and time: total 287 groups) at sites 2, 7, and 10 for Pearson correlation analysis to determine the causes of the higher Chl-a content in the water environment of Lake Erhai. At the same time, the 1 m, 5 m and 10 m depth data (at the same depth and time: total 76 groups) at sites 2, 7 and 10 were selected for principal component analysis to investigate the correlation between temperature, water quality parameters and TP and Chl-a. The above analysis was conducted by origin (version: 2021b).

#### **3. Results**

#### *3.1. Spatiotemporal Changes in Water Quality Parameters*

#### 3.1.1. Spatiotemporal Changes in T

According to the meteorological data from 1981 to 2010 (data source: www.nmc.cn (accessed on 1 May 2022), the annual average precipitation at Dali Bai autonomous prefecture is about 1078.9 mm, with monthly average precipitation in the rainy season from May to October greater than 76 mm, accounting for 85–96% of the annual precipitation (Figure 2). Years of meteorological data as the basis for the division of dry and rainy seasons. Precipitation and temperature has obvious distribution characteristics in dry and rainy seasons, of which May-October is the rainy season, with an average temperature >15 ◦C. The dry season is from November to April and has an average temperature <15 ◦C. Thus, the dry season is from January to April and the rainy season is from May to October.

sons.

water bodies [20].

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time when the temperature was higher than the average temperature.

(Figure 2). Years of meteorological data as the basis for the division of dry and rainy sea‐ sons. Precipitation and temperature has obvious distribution characteristics in dry and rainy seasons, of which May‐October is the rainy season, with an average temperature >15 °C. The dry season is from November to April and has an average temperature <15 °C. Thus, the dry season is from January to April and the rainy season is from May to October. Based on the monthly changes in the T, our monitoring data from 2016 to 2020 (Figure 3) showed that the T of Lake Erhai was higher than the average temperature of Dali Bai autonomous prefecture during the same period but lower than the highest temperature in that same period. Since the monitoring time was mainly concentrated during the day‐

According to the research of Taihu Lake, the key factors affecting the growth of algae are lake T, light energy [19]. From the perspective of different lake areas, the T in the northwestern lake area was slightly higher than in its southeast area, especially in January and April 2017. In different lake layers, we observed that the overall T decreased as the water depths increased. The T and local temperature had the same trend in different sea‐

From the temporal change of Lake Erhai's temperature, the T in the rainy season was found to be significantly higher than in the dry season, with the highest temperature rec‐ orded in summer and starting to drop in autumn. The lake T did not change much in the four seasons from 2016 to 2017. From 2017 to 2020, several high‐temperature rainy months were observed. A large temperature difference was observed between the upper and lower layers of the lake (5–10 m) during the rainy August seasons in 2016. Studies have shown that the process of thermal stratification will lead to oxygen deficiency (O2 < 1 mg/L), and the oxygen consumption rate of lakes indicates the more eutrophic nature of

**Figure 2.** Monthly average temperature and precipitation from 1981 to 2010 (Dali Bai autonomous prefecture). **Figure 2.** Monthly average temperature and precipitation from 1981 to 2010 (Dali Bai autonomous prefecture).

Based on the monthly changes in the T, our monitoring data from 2016 to 2020 (Figure 3) showed that the T of Lake Erhai was higher than the average temperature of Dali Bai autonomous prefecture during the same period but lower than the highest temperature in that same period. Since the monitoring time was mainly concentrated during the daytime when the temperature was higher than the average temperature. *Water* **2022**, *14*, 3398 5 of 14

**Figure 3.** Spatiotemporal changes in T. **Figure 3.** Spatiotemporal changes in T.

**Figure 4.** Spatiotemporal changes in pH.

3.1.2. Spatiotemporal Changes in pH Algae growth and bacteria decomposing organic matter affect pH. The study found that high correlation values were obtained for pH and algae cellular growth [21]. From the temporal change of Lake Erhai's pH, the pH of the entire lake was low alkaline, higher than 7.5 per all monitoring months throughout the year (Figure 4). According to the research of Taihu Lake, the key factors affecting the growth of algae are lake T, light energy [19]. From the perspective of different lake areas, the T in the northwestern lake area was slightly higher than in its southeast area, especially in January and April 2017. In different lake layers, we observed that the overall T decreased as the water depths increased. The T and local temperature had the same trend in different seasons.

In regard to seasons varies of pH, it was lowest in winter and started to rise until it was maximum in autumn. The data at different lake areas showed that the pH in the southwestern lake area was slightly higher than in the northeast area. In different lake layers, the pH decreased with increased water depths. The lake's pH has increased from 2016 to 2017. From the summer of 2017 to 2020, the pH of Lake Erhai drops in summer. From the temporal change of Lake Erhai's temperature, the T in the rainy season was found to be significantly higher than in the dry season, with the highest temperature recorded in summer and starting to drop in autumn. The lake T did not change much in the four seasons from 2016 to 2017. From 2017 to 2020, several high-temperature rainy months were observed. A large temperature difference was observed between the upper and lower

Depth change in pH reached a maximum in summer, with low pH appeared in August 2016 between 5–10 m. The low pH value may be caused by the decomposition of organic

matter and algae by microorganisms after the thermocline appears.

layers of the lake (5–10 m) during the rainy August seasons in 2016. Studies have shown that the process of thermal stratification will lead to oxygen deficiency (O<sup>2</sup> < 1 mg/L), and the oxygen consumption rate of lakes indicates the more eutrophic nature of water bodies [20]. the temporal change of Lake Erhai's pH, the pH of the entire lake was low alkaline, higher than 7.5 per all monitoring months throughout the year (Figure 4). In regard to seasons varies of pH, it was lowest in winter and started to rise until it was maximum in autumn. The data at different lake areas showed that the pH in the

southwestern lake area was slightly higher than in the northeast area. In different lake

Algae growth and bacteria decomposing organic matter affect pH. The study found that high correlation values were obtained for pH and algae cellular growth [21]. From

#### 3.1.2. Spatiotemporal Changes in pH layers, the pH decreased with increased water depths. The lake's pH has increased from

**Figure 3.** Spatiotemporal changes in T.

3.1.2. Spatiotemporal Changes in pH

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Algae growth and bacteria decomposing organic matter affect pH. The study found that high correlation values were obtained for pH and algae cellular growth [21]. From the temporal change of Lake Erhai's pH, the pH of the entire lake was low alkaline, higher than 7.5 per all monitoring months throughout the year (Figure 4). 2016 to 2017. From the summer of 2017 to 2020, the pH of Lake Erhai drops in summer. Depth change in pH reached a maximum in summer, with low pH appeared in August 2016 between 5–10 m. The low pH value may be caused by the decomposition of organic matter and algae by microorganisms after the thermocline appears.

**Figure 4. Figure 4.** Spatiotemporal Spatiotemporal changes in pH. changes in pH.

In regard to seasons varies of pH, it was lowest in winter and started to rise until it was maximum in autumn. The data at different lake areas showed that the pH in the southwestern lake area was slightly higher than in the northeast area. In different lake layers, the pH decreased with increased water depths. The lake's pH has increased from 2016 to 2017. From the summer of 2017 to 2020, the pH of Lake Erhai drops in summer. Depth change in pH reached a maximum in summer, with low pH appeared in August 2016 between 5–10 m. The low pH value may be caused by the decomposition of organic matter and algae by microorganisms after the thermocline appears.

#### 3.1.3. Spatiotemporal Changes of DO

The DO in lakes was important to maintain the dynamic balance of the ecological environment of a water body for the survival of aquatic organisms. DO also participates in the transformation of some substances [22]. Relevant studies have shown that the activities of plankton directly or indirectly determine the scope and degree of the hypoxic (less than 4 mg/L) less zone in the Qiandao Lake area. At the same time, the low oxygen area will affect the growth of aquatic organisms [23].

From the seasonal changes of DO in Lake Erhai in 2017, the DO increased slightly from winter to spring (from 7 mg/L to about 8 mg/L), and from spring to summer, the DO in water dropped sharply (from 8 mg/L to 8 mg/L). Dropped to about 4 mg/L) and the DO at a depth of 15 m was as low as 2 mg/L, forming a hypoxic area. From summer to autumn, the DO content in water increases (from 4 mg/L to about 10 mg/L). This may be related to the decrease in algae density in the water and the fact that photosynthesis produces more oxygen than respiration consumes, and photosynthesis is stronger on the lake surface, especially in the northeastern lake area (Figure 5).

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area will affect the growth of aquatic organisms [23].

especially in the northeastern lake area (Figure 5).

duces organic acid species, resulting in a drop in DO and pH.

The DO in lakes was important to maintain the dynamic balance of the ecological environment of a water body for the survival of aquatic organisms. DO also participates in the transformation of some substances [22]. Relevant studies have shown that the ac‐ tivities of plankton directly or indirectly determine the scope and degree of the hypoxic (less than 4 mg/L) less zone in the Qiandao Lake area. At the same time, the low oxygen

From the seasonal changes of DO in Lake Erhai in 2017, the DO increased slightly from winter to spring (from 7 mg/L to about 8 mg/L), and from spring to summer, the DO in water dropped sharply (from 8 mg/L to 8 mg/L). Dropped to about 4 mg/L) and the DO at a depth of 15 m was as low as 2 mg/L, forming a hypoxic area. From summer to autumn, the DO content in water increases (from 4 mg/L to about 10 mg/L). This may be related to the decrease in algae density in the water and the fact that photosynthesis produces more oxygen than respiration consumes, and photosynthesis is stronger on the lake surface,

From the long‐term sequence of 2017–2020, the phenomenon appeared in August in summer when the DO in the lake water at a depth of 1–5 m was significantly greater than that in a depth of 10–15 m, of which 2016 was the most obvious, followed by 2018. This phenomenon may be related to the fact that a large number of algae in the upper layer of photosynthesis is stronger than respiration to increase the oxygen concentration, while the lower layer of algae is decomposed by bacteria due to the shading of the upper layer of algae and lack of light. The bacterial decomposition process consumes oxygen and pro‐

3.1.3. Spatiotemporal Changes of DO

**Figure 5.** Spatiotemporal changes in DO. **Figure 5.** Spatiotemporal changes in DO.

3.1.4. Spatiotemporal Changes in Chl‐a Chl‐a is the main parameter characterizing the existing amount of phytoplankton and is one of the important indicators of lake water quality [24]. A change in Chl‐a concentra‐ tion can reflect the nutritional status of the water body. Previous research results showed significant seasonal changes in Chl‐a in Lake Erhai, with the overall content beginning to rise gradually in April, increasing sharply in July, reaching a peak in October, and drop‐ ping sharply in January the following year [25]. From the long-term sequence of 2017–2020, the phenomenon appeared in August in summer when the DO in the lake water at a depth of 1–5 m was significantly greater than that in a depth of 10–15 m, of which 2016 was the most obvious, followed by 2018. This phenomenon may be related to the fact that a large number of algae in the upper layer of photosynthesis is stronger than respiration to increase the oxygen concentration, while the lower layer of algae is decomposed by bacteria due to the shading of the upper layer of algae and lack of light. The bacterial decomposition process consumes oxygen and produces organic acid species, resulting in a drop in DO and pH.

#### 3.1.4. Spatiotemporal Changes in Chl-a

Chl-a is the main parameter characterizing the existing amount of phytoplankton and is one of the important indicators of lake water quality [24]. A change in Chl-a concentration can reflect the nutritional status of the water body. Previous research results showed significant seasonal changes in Chl-a in Lake Erhai, with the overall content beginning to rise gradually in April, increasing sharply in July, reaching a peak in October, and dropping sharply in January the following year [25].

From the temporal change of Lake Erhai Chl-a, the months with higher Chl-a content appeared from June to September. The distribution characteristics of Chl-a concentration at a depth of 1–5 m in different lake areas in four seasons in 2017 were southern area > northern area > central area (Figure 6). Combined with the long-term series, Chl-a appeared higher in four periods, namely July-September 2016, December 2016, June-August 2017, and August 2018. The content of Chl-a in the summer and winter of 2016 showed that the content in the lower layer was higher than that in the upper layer. In August 2017 and 2018, the Chl-a content of the upper layer was higher than that of the lower layer. This reflects the increasing tendency of algal growth space to the surface layer of 1–5 m in summer.

#### 3.1.5. Temporal Changes in TN and TP

Assessment of changes in TN and TP concentrations with depth at indicated sites of Lake Erhai showed no obvious change trend in TN content with depth, while the TP content slightly decreased with increased depth (Figure 7). In terms of overall season, the TN content was lowest in August 2017, while that of TP was highest. The characteristics of TP concentration in different seasons were as follows: summer > autumn > spring > winter. mer.

From the temporal change of Lake Erhai Chl‐a, the months with higher Chl‐a content appeared from June to September. The distribution characteristics of Chl‐a concentration at a depth of 1–5 m in different lake areas in four seasons in 2017 were southern area > northern area > central area (Figure 6). Combined with the long‐term series, Chl‐a ap‐ peared higher in four periods, namely July‐September 2016, December 2016, June‐August 2017, and August 2018. The content of Chl‐a in the summer and winter of 2016 showed that the content in the lower layer was higher than that in the upper layer. In August 2017 and 2018, the Chl‐a content of the upper layer was higher than that of the lower layer. This reflects the increasing tendency of algal growth space to the surface layer of 1–5 m in sum‐

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**Figure 6.** Spatiotemporal changes in Chl‐a. **Figure 6.** Spatiotemporal changes in Chl-a.

**Figure 7.** Spatiotemporal changes in TN and TP. **Figure 7.** Spatiotemporal changes in TN and TP.

*3.2. Trophic Status Evaluation* A study used the comprehensive trophic state index to evaluate Dianchi Lake, Lake Erhai, and Fuxian Lake in 2017. The results showed that the medians of the comprehensive trophic state index were 63.21, 31.69, and 18.55, respectively. Lake Erhai was evaluated as mesotrophic [26]. From the long-term observation of TN, we noticed a decreasing trend in TN in Lake Erhai, with its value fluctuating between 0.3–0.9 mg/L. A higher value was mainly recorded in February and August. Its content at the surface layer was generally higher than that of the lower layer. From the long-term observation of TP, we noticed significant variation in TP content in Lake Erhai during different months. Higher values were mainly recorded in

According to the evaluation of eutrophic level of surface water at three points in Lake Erhai from 2016 to 2018 (Table 2). The evaluation results showed that Lake Erhai is mainly in a mesotrophic state, while only the northern and central lake regions in 2018 were in a

creased while the TN score decreased, which means that the value of N: P was decreasing,

**Score3**

‐7 17.5 50.9 43.5 37.3 Mesotropher ‐12 45.7 41.6 43.1 43.5 Mesotropher ‐8 47.9 48.5 42.7 46.3 Mesotropher ‐8 47.3 44.9 59.8 50.7 Light eutropher

2016‐7 27.0 53.9 47.7 42.9 Mesotropher 2016‐12 39.5 45.1 44.2 42.9 Mesotropher 2017‐8 42.3 42.3 41.2 41.9 Mesotropher

**Average score**

**Eutrophication Level**

**Score1 TN Score2 TP**

**Table 2.** Eutrophication level evaluation in Lake Erhai.

**Sample Year‐Month Chl‐<sup>a</sup>**

Site 2

Site 7

indicating that the contribution of TP to eutrophication was increasing.

July and December 2016, August 2017 and August 2018, and TP and Chl-a have the same seasonal distribution trend.

#### *3.2. Trophic Status Evaluation*

A study used the comprehensive trophic state index to evaluate Dianchi Lake, Lake Erhai, and Fuxian Lake in 2017. The results showed that the medians of the comprehensive trophic state index were 63.21, 31.69, and 18.55, respectively. Lake Erhai was evaluated as mesotrophic [26]. *Water* **2022**, *14*, 3398 9 of 14

> According to the evaluation of eutrophic level of surface water at three points in Lake Erhai from 2016 to 2018 (Table 2). The evaluation results showed that Lake Erhai is mainly in a mesotrophic state, while only the northern and central lake regions in 2018 were in a light eutrophic state. The north lake and central lakes showed a fast transition to light eutrophic, while the southern lakes showed a slower trend. Moreover, the TP score increased while the TN score decreased, which means that the value of N: P was decreasing, indicating that the contribution of TP to eutrophication was increasing. 2018‐8 50.0 48.0 53.3 50.4 Light eutropher Site 10 2016‐7 33.6 49.3 46.3 43.1 Mesotropher 2016‐12 41.1 46.6 44.5 44.1 Mesotropher 2017‐8 52.6 42.6 42.2 45.8 Mesotropher 2018‐8 47.2 46.2 51.1 48.1 Mesotropher


**Table 2.** Eutrophication level evaluation in Lake Erhai. *3.3. Correlation and Principal Component Analysis* The correlation analysis of Chl‐a at different depths showed (Figure 8a): (1) a positive

#### *3.3. Correlation and Principal Component Analysis* ences of Lake Erhai at depths of 1, 5 and 10 m are relatively low, indicating that the spatial mixing of the lake water is relatively high. The principal component PC1 (38.1%) may

The correlation analysis of Chl-a at different depths showed (Figure 8a): (1) a positive correlation with temperature (r = 0.36, *p* ≤ 0.01) and TP (r = 0.31, *p* ≤ 0.01), (2) a negative correlation with TN (r = −0.31, *p* ≤ 0.01). represent a nutrient factor that promotes water eutrophication. It can be seen that when it is reduced to only three depths, it also has a relatively consistent correlation with Figure 8a.

**Figure 8.** Correlation (**a**) and principal component (**b**) analysis of six water quality **Figure 8.** Correlation (**a**) and principal component (**b**) analysis of six water quality parameters.

parameters. Based on the above analysis results, the temperature and TP content of Lake Erhai demonstrated a significant and positive correlation with Chl‐a content. Using 95% regres‐ sion linear fitting (Figure 9a,b), we found that the linear regression coefficients for tem‐ Chl-a reflects the density of photosynthetic aquatic plants in lake water, and the vertical distribution of aquatic plants in lakes changes regional DO and pH. This explains the decomposition of large amounts of algae at a depth of 10–15 m in August 2016, leading to the formation of anoxic and acidic environments in this area.

perature and TP were (R1 = 0.121, R2 = 0.099; *p >* 0.05). From the scatter plot, when the temperature was lower in winter, the recorded Chl‐a value was higher, which may be related to the sudden increase of TP in December 2016. The increase of TP significantly In our study, temperature and TP in Lake Erhai were the main promoting factors for algal growth in Lake Erhai. TN had a downward trend in summer, and was negatively

reduced the distribution of Chl‐a in the low value area. TP was the main limiting nutrient for elevated Chl‐a concentration. This result is consistent with that of Chl‐a in many stud‐

ies [28–31].

correlated with Chl-a, which was not the limiting factor for algal growth in Lake Erhai in summer. According to the trend of evaluation scores of TN and TP, TP has become one of the main limiting nutrients for aquatic photosynthetic plants in Lake Erhai in summer. This is consistent with Li Donglin's research results in Qilu Lake [27]. From the principal component analysis (Figure 8b), it can be seen that the water quality parameters's differences of Lake Erhai at depths of 1, 5 and 10 m are relatively low, indicating that the spatial mixing of the lake water is relatively high. The principal component PC1 (38.1%) may represent a nutrient factor that promotes water eutrophication. It can be seen that when it is reduced to only three depths, it also has a relatively consistent correlation with Figure 8a.

Based on the above analysis results, the temperature and TP content of Lake Erhai demonstrated a significant and positive correlation with Chl-a content. Using 95% regression linear fitting (Figure 9a,b), we found that the linear regression coefficients for temperature and TP were (R1 = 0.121, R2 = 0.099; *p >* 0.05). From the scatter plot, when the temperature was lower in winter, the recorded Chl-a value was higher, which may be related to the sudden increase of TP in December 2016. The increase of TP significantly reduced the distribution of Chl-a in the low value area. TP was the main limiting nutrient for elevated Chl-a concentration. This result is consistent with that of Chl-a in many studies [28–31]. *Water* **2022**, *14*, 3398 10 of 14

**Figure 9.** Linear regression relationship of T to Chl‐a. (**a**) and TP to Chl‐a. (**b**) **Figure 9.** Linear regression relationship of T to Chl-a. (**a**) and TP to Chl-a. (**b**).

#### **4. Discussion 4. Discussion**

blooms [35].

#### *4.1. Water Quality Parameters 4.1. Water Quality Parameters*

T is an important factor affecting Chl‐a concentration, and it is a key factor for the growth of phytoplanktons [32,33]. Similar to the local monthly average temperature at Lake Erhai, obvious seasonal changes were observed in the lake T. The monthly average air temperature directly affected the T of the lake. However, as a large shallow lake, Lake Erhai has a large specific heat capacity. Since sampling was performed during the day, the recorded surface T of the lake was slightly higher than the local monthly average air temperature. The spatial distribution of Lake Erhai's temperature was mainly affected by wind, the southwest wind was the main wind direction, The wind speed of Lake Erhai was significantly different at different locations of the lake, with the southern lake region > central lake region > northern lake region [34]. The southern lake area of Lake Erhai was the shallowest with strong wind disturbance. The southwest wind blew the surface water of the southern lake area to the northern lake area, so the lower water level at the northern lake area flowed to the southern lake area as a supplement. At the same time, the strong wind in the southern part of Lake Erhai cooled the shallow lake faster. Therefore, the T in the northern lake area was observed to be higher than in the central and southern lake areas. The temperature of Lake Erhai demonstrated little vertical variation. The lake water was completely mixed underthe influence of wind, and there was no obvious thermocline in the T in most months. In the summer of August 2016, thermocline was observed at a T is an important factor affecting Chl-a concentration, and it is a key factor for the growth of phytoplanktons [32,33]. Similar to the local monthly average temperature at Lake Erhai, obvious seasonal changes were observed in the lake T. The monthly average air temperature directly affected the T of the lake. However, as a large shallow lake, Lake Erhai has a large specific heat capacity. Since sampling was performed during the day, the recorded surface T of the lake was slightly higher than the local monthly average air temperature. The spatial distribution of Lake Erhai's temperature was mainly affected by wind, the southwest wind was the main wind direction, The wind speed of Lake Erhai was significantly different at different locations of the lake, with the southern lake region > central lake region > northern lake region [34]. The southern lake area of Lake Erhai was the shallowest with strong wind disturbance. The southwest wind blew the surface water of the southern lake area to the northern lake area, so the lower water level at the northern lake area flowed to the southern lake area as a supplement. At the same time, the strong wind in the southern part of Lake Erhai cooled the shallow lake faster. Therefore, the T in the northern lake area was observed to be higher than in the central and southern lake areas. The temperature of Lake Erhai demonstrated little vertical variation. The lake water was completely mixed under the influence of wind, and there was no

depth of 5–10 m in Lake Erhai. At this time, the wind makes the lake water less mixed. the algae die and settle down and are decomposed by bacteria, resulting in the release of a

likely condition for eutrophication in this month. Studies have shown that the location of the thermocline and the euphotic depth can create a functional niche for diazotrophic cy‐ anobacteria, resulting the upward transport of nitrate into the euphotic zone is reduced by a subjacent thermocline changed of nitrogen and phosphorus limiting factors on algal

From the perspective of the spatiotemporal changes in the lake's pH value, the pho‐ tosynthesis of aquatic plants and algae consuming a large amount of CO2 is the main rea‐ son for the increase in pH value. In terms of seasons, in summer and autumn, the total photosynthetic rate of aquatic organisms was greater than the respiration rate, which re‐ duced the dissolved CO2 content and increased the pH value of the lake water. On the contrary, the light intensity was weakest in winter, and the photosynthesis intensity of aquatic plants and algae was lowest. The photosynthesis rate was lower than the respira‐ tion rate, making the dissolved CO2 content higher and the lake water pH lower. The pH

obvious thermocline in the T in most months. In the summer of August 2016, thermocline was observed at a depth of 5–10 m in Lake Erhai. At this time, the wind makes the lake water less mixed. the algae die and settle down and are decomposed by bacteria, resulting in the release of a large amount of Chl-a, and the DO and pH in the water body decrease, which is the most likely condition for eutrophication in this month. Studies have shown that the location of the thermocline and the euphotic depth can create a functional niche for diazotrophic cyanobacteria, resulting the upward transport of nitrate into the euphotic zone is reduced by a subjacent thermocline changed of nitrogen and phosphorus limiting factors on algal blooms [35].

From the perspective of the spatiotemporal changes in the lake's pH value, the photosynthesis of aquatic plants and algae consuming a large amount of CO<sup>2</sup> is the main reason for the increase in pH value. In terms of seasons, in summer and autumn, the total photosynthetic rate of aquatic organisms was greater than the respiration rate, which reduced the dissolved CO<sup>2</sup> content and increased the pH value of the lake water. On the contrary, the light intensity was weakest in winter, and the photosynthesis intensity of aquatic plants and algae was lowest. The photosynthesis rate was lower than the respiration rate, making the dissolved CO<sup>2</sup> content higher and the lake water pH lower. The pH value was also related to the lake's depth, which was the main reason for the spatial differentiation in pH values. In summer and autumn, the light and intensity of aquatic plants in the shallow lake area of southern Lake Erhai were greater than the respiration intensity, which was the reason for the higher pH value of the southern shallow lake area. In winter, a lower pH value was recorded in the shallow lake area of southern Lake Erhai because the light and intensity of aquatic plants were less than the respiration intensity. In the summer of August 2016, the temperature was higher and the wind was lower, resulting in a decrease in pH value in the area below 5 m. Studies have investigated the relationships between early summer partial pressures of CO<sup>2</sup> and dissolved organic carbon concentration in the surface waters of 27 northern Wisconsin lakes. CO<sup>2</sup> had a strong positive relationship with dissolved organic carbon concentration [36]. Other studies show that bacteria decomposed the organic matter on the bottom of the lake, oxygen was consumed, and a large amount of acidic substances were produced [37].

DO in Lake Erhai showed significant seasonal variation. It was highest in winter and lowest in summer. This could have been related to the low temperature in winter due to strong wind and weak solar radiation, which increased the contact of the water body from the surface to the bottom with the atmosphere, increasing the frequency of oxygen exchange. In summer, the temperature was high and precipitation was heavy. The death and decomposition of algae and aquatic plants consume a lot of oxygen, and the effect of microorganisms in the water below the mixed layer to decompose organic matter in the water is strengthened. As a result, the DO content of the lower water body decreases. We found that the content of Chl-a is affected by lake depth, wind and DO, and studies have shown that when DO increases, aquatic plants increase, resulting in an increase in Chl-a content [38]. Warmer summer temperatures result in high algal biomass. The photosynthesis of the algae on the surface is greater than the respiration to produce oxygen. The algae in the lower layer are decomposed to produce Chl-a, so that the oxygen concentration and Chl-a content have opposite trends in the vertical direction. Combining the above two situations, the DO and Chl-a in Lake Erhai were negatively correlated (r = −0.12). It was reported that DO stratification remarkably influence N species and transformation pathways in different water columns by high frequency sampling during summers in Longjing Lake, China. Results showed that Oxycline (4–11 m) was the major place for N transformations [39]. It was explained that the change of DO in Erhai Lake caused nitrogen to be removed and phosphorus to become a limiting nutrient factor.

Previous studies have investigated the Chl-a concentration in Lake Erhai from 2009 to 2013, and found that the annual average Chl-a concentration in Lake Erhai was 5–10.0 µg/L. Compared with this study, the content of Chl-a in Lake Erhai from 2016 to 2018 showed an increasing trend [40]. In addition, analysis of monitoring data showed that the summer

temperature and TP in 2016–2018 also led to an upward trend in Chl-a. Due to a large number of algal blooms in summer, the temperature of Lake Erhai began to drop in autumn, and the DO content began to rise. After a large number of phytoplankton were decomposed by bacteria the Chl-a concentration also began to decline. This observation is consistent with the use of models to simulate seasonal changes in water quality parameters in Lake Erhai [41]. In December 2016, the lake area in the northwest of Lake Erhai became higher in TP content, even with fast wind speed and high DO content in December. However, the T in the northwestern lake area is higher, and a large amount of phosphorus is conducive to the growth of aquatic plants and algae, resulting in higher Chl-a content in the middle and lower layers (5–15 m) of the lake area in the northwestern Lake Erhai. The phenomenon that Chl-a in the middle and lower layers is higher than that in the surface layer may be related to the fact that the surface temperature is lower than that in the middle and lower layers in December.

## *4.2. Trophic Status and Changes*

From the eutrophication score, it can be seen that the nutrient score changes: northern lake area > central lake area > southern lake area, which may be related to the fact that the outflow river Xi'er is in the south and the main wind direction is southwesterly. The gradual increase of TP drives the increase of Chl-a content, which leads to the change of Lake Erhai from light eutrophic state in summer. The sudden increase in TP in December 2016 also increased the score. The TN score is not high in summer, and phosphorus gradually becomes the main nutritional factor of algae blooming. The buffer capacity and hydrodynamic conditions of Lake Erhai should be improved and the input of exogenous phosphorus in the northwestern lake area should be controlled to prevent the massive growth of algae [42].

## *4.3. Trophic Causes and Effects*

The changes in water quality in Lake Erhai are complex, and the trend of increasing nutrient levels is due to various factors. Although Lake Erhai is still in the mesotrophic, principal component analysis and correlation analysis show that temperature and phosphorus elements promote the growth of algae in the lake, and the excessive growth of algae changes DO and pH. This is consistent with the nutrition level analysis of the Sabalan Dam Reservoir in northwest Iran that implied TN:TP value was lower in summer than in winter, and therefore phosphorus became the main limiting factor of eutrophication [43]. Lake Erhai shows similar nutrient characteristics in summer, in addition that the range and trend of Erhai algae controlled by wind and lake currents.

#### **5. Conclusions**

Water quality parameters of Lake Erhai measured from 2016 to 2020 provided a deepening understanding of the lake's eutrophication features and their changes. The monitoring data analysis shows that there is no obvious thermocline occured in the lake, indicating that Lake Erhai belongs to a shallow lake with a high degree of water mixed state.

The Chl-a in Lake Erhai showed obvious spatiotemporal distribution characteristics. It is higher in the southern lake area in August and higher in the northern lake area in December. A high Chl-a values appeared around August in summer and December in 2016. T (r = 0.36) and TP (r = 0.31) are the main promoting factors for the increase of Chl-a content in Lake Erhai. The phenomenon of low T, DO, pH, and high Chl-a appeared in the middle and lower water depths (<5 m) of the central Lake Erhai in August 2016, which may be caused by the decomposition of a large number of algae by microorganisms. The thermocline and low TN:TP values may cause the risk of water quality deterioration in summer. The abnormal high Chl-a content appeared in Lake Erhai in winter in December 2016 is directly related to the particularly high TP content this month. The nutritional evaluation of the main months with high Chl-a content in Lake Erhai showed that has a trend of mesotrophic to light eutrophic.

**Author Contributions:** Methodology, Software, Writing—Original draft preparation, K.C.; Conceptualization, Supervision, Resources, Writing—review & editing, Foundations acquisition, H.Z.; Investigation, Data Curation, L.D., Q.L., Y.Z., X.Z. and F.L. 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).

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.

**Acknowledgments:** We thank all the graduate students whom participated the field works and laboratory analysis.

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

#### **References**


**Weidong Xu <sup>1</sup> , Lizeng Duan <sup>2</sup> , Xinyu Wen <sup>3</sup> , Huayong Li <sup>4</sup> , Donglin Li <sup>1</sup> , Yang Zhang <sup>2</sup> and Hucai Zhang 2,\***


**Abstract:** Understanding the seasonal variation characteristics and trends in water quality is one of the most important aspects for protecting and conserving lakes. Lake Yangzong water quality parameters and nutrients, including water temperature, dissolved oxygen (DO), pH, conductivity, Chlorophyll-*a*, phycocyanin, total nitrogen (TN) and total phosphorus (TP), were monitored in different seasons from 2015 to 2021. Based on the monitoring data, the temporal and spatial variations of various parameters were analyzed. The results showed that Lake Yangzong is a warm monomictic lake. The Pearson correlation coefficient and correlation analysis showed water quality parameters were significantly correlated and probably affected by temperature. Cyanobacteria were at risk of blooming in spring and autumn. The contents of TN and TP in winter were significantly higher than in summer, especially TN, with both reaching a peak at the epilimnion and hypolimnion in December 2020 (TN = 1.3 mg/L, TP = 0.06 mg/L). We also observed a dual risk of endogenous release and exogenous input. Therefore, strengthening the supervision for controlling eutrophication caused by human activities and endogenous release is urgently needed.

**Keywords:** Lake Yangzong; water quality parameters; temporal and spatial variations; cyanophyte relative quantity index; nutrient reduction

#### **1. Introduction**

Lakes have a variety of functions, including water supply, flood prevention, aquaculture, transport and tourism [1]. As the water exchange of lake water is long, it has weak self-purification abilities [2]. Lakes are easily contaminated because they receive water from surrounding areas. The spatial and temporal changes in the water quality parameters can directly reflect the environmental water conditions of a lake, as variations in water quality parameters can lead to changes in lake trophic status [3]. Therefore, it is of practical significance to strengthen the monitoring and analysis of water quality parameters, especially for preventing and controlling eutrophication and protecting water quality and safety.

Mazhar et al. studied the changes of water bodies in different regions under different conditions. The results show that the river water profile has different laws in different climatic regions, which has an impact on DO dynamics [4]. The Ara Waterway, located in the wet regions, has a higher water quality variation in seasonal scale than that of the Yamuna Waterway, which is in the dry region [5]. In the southern estuarine water ecosystem of the Boseong County in Korea, there is a high Carlson Trophic State Index in the cropping area and land settlements in summer and autumn [6]. Through the groundwater monitoring data in Pakistan, it can be found that excessive groundwater abstraction has caused adverse impacts on groundwater quality [7].

Thermal stratification is the result of geographic location and summer–winter climatological differences and the depth of the lake. The seasonal vertical distribution of nutrients

**Citation:** Xu, W.; Duan, L.; Wen, X.; Li, H.; Li, D.; Zhang, Y.; Zhang, H. Effects of Seasonal Variation on Water Quality Parameters and Eutrophication in Lake Yangzong. *Water* **2022**, *14*, 2732. https:// doi.org/10.3390/w14172732

Academic Editor: Anas Ghadouani

Received: 18 July 2022 Accepted: 29 August 2022 Published: 1 September 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/).

accompanies the thermal structure dynamics [8,9]. The thermal stratification of lakes may lead to a lack of oxygen in the hypolimnion and a boom in algae growth in the epilimnion in summer. This would destroy the balance of aquatic ecosystems and damage water quality.

Algal growth rate and other physiological characterization of living organisms are responding temperature and not vice versa. Chlorophyll-*a* is one of the important parameters of the ecological water system. Its content reflects phytoplankton biomass in water and is the basis of determining eutrophication. Phycocyanin is a kind of phycobiliprotein usually found in the body of cyanophyte, red alga, cryptophyta, and dinoflagellate. In these kinds of algae, red algae are usually found in the matrix of an oligotrophic stream. Cryptomonas mainly live in water with poor or moderate levels of nutrition. It is a common alga in tropical and polar waters, contributing less to the abundance of phytoplankton species. The amount of dinoflagellate is also lower in fresh water. However, cyanobacteria have remarkable heterogeneous structures and functions and a relatively broad ecological niche, with an absorption peak near a wavelength of 620 nm [10]. Cyanobacteria include many species, which are often the dominant species in lakes. The algal toxins contained in it will have a serious impact on organisms [11]. According to a study by Xie et al. [12], there are 13 genera (33.3%) of cyanophytes, 4 genera (10.3%) of dinoflagellate and 1 genus of cryptophyta in the water body of Lake Yangzong in 2013. Based on the above, we can preliminarily determine that cyanobacteria are the main algae in Lake Yangzong. Chlorophyll-*a* is usually used as an indicator to evaluate the trophic status of the water in most previous studies, but the use of phycocyanin is rare. Normally, eutrophication is often accompanied by the outbreak of cyanobacteria bloom; therefore, strengthening the estimation of cyanobacteria quantity is of great significance for preventing eutrophication.

Since cyanobacteria contain both Chlorophyll-*a* and phycocyanin, the ratio of the two pigments in lakes may indicate the relative amount of cyanobacteria in the lake water. The previous Cyanophyte Relative Quantity Index (CRQI) estimation mainly relied on the data from remote sensing rather than the water quality data measured in situ. According to the calculation formula established by Zhang et al. [13], Chlorophyll-*a* and phycocyanin were measured in situ to estimate the relative quantity index of cyanobacteria in this study. This method could avoid the estimation error caused by the separate use of Chlorophyll-*a* as an indicator. Similarly, TN and TP contents are also important conventional indicators for measuring water quality as they are associated with lake water eutrophication which can lead to cyanobacteria bloom and other consequences. 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 [14], such as normalized cyanobacteria outbreaks formed in lakes such as Lake Dian (Dianchi) [15] and Lake Jian. Therefore, the accurate determination and analysis of TN and TP in lakes are of great significance for the management of lakes.

Lake Yangzong is the third deepest lake in the Yunnan province of southwestern China. It has a variety of social and economic functions, and its water quality is of high concern. Previous studies on Lake Yangzong mainly focused on the analysis of phytoplankton biomass and their population structure [12,16,17], the dynamic characteristics and inflow capacity of TN and TP [18,19], the source of arsenic pollution [20,21], water quality change process [22] and environmental risks caused by heavy metal and arsenic pollution [23]. However, studies on water quality parameters were limited to monitoring data for a short time and on a small scale [24]. Therefore, this study combined multi-season, high-frequency, large-scale and continuous water quality monitoring data to provide a scientific basis for the evaluation of the nutritional status of Lake Yangzong and water quality management.

## **2. Material and Methods**

#### *2.1. Physical Geographical Background of Lake Yangzong*

Lake Yangzong is a freshwater lake located in Yiliang county, Yunnan Province (24◦510– 24◦580 N, 102◦550–103◦020 E). Its average water depth is 20 m with a maximum depth of 31 m. The lake has an average altitude of about 1800 m above sea level, with a lake area of 31 km<sup>2</sup> and a water volume of 6.04 <sup>×</sup> <sup>10</sup><sup>8</sup> <sup>m</sup><sup>3</sup> . The catchment of Lake Yangzong belongs to

the northern subtropical monsoon climate zone, covering an area of 192 km<sup>2</sup> . The annual average temperature of Lake Yangzong is 15.2 ◦C, the average maximum temperature is 21.5 ◦C, and the average minimum temperature is 12.4 ◦C [25], with an obvious difference in wet and dry seasons and strong evaporation in dry seasons [26]. The main rivers flowing into the lake include the Yangzong River, Qixing River and Luxichong River in Chengjiang County, Baiyi River, and Tangchi River in Yiliang county [27]. The annual average surface rainfall of Lake Yangzong is 824.7 mm. The rainy season is from May to October, during which precipitation accounts for 85% of the total annual rainfall, forming a typical lowlatitude regional climate. The main water source of the lake is atmospheric precipitation. Rainfall is sufficient in summer and scarce in winter, forming two transition periods in spring and autumn [22].

The basin economy is dominated by industry and supplemented by tourism. For a long time, the vulnerability of the lake's ecosystem has been neglected, despite the effects of human activities. Chemical fertilizers have induced non-point pollution and long-distance water diversion into the lake, and combined with aquaculture, rural domestic sewage, solid wastes and soil and water loss caused by vegetation damage, they are the main sources of pollution of the lake [27]. Lake Yangzong is the water supply for industry, agriculture and tourism.

In recent years, the water supply for real estate development and land replacement around the lake has depended mainly on water extraction. However, because of the catastrophic artificial arsenic pollution in 2008 and subsequent chemical treatments, the water quality of Lake Yangzong was seriously affected. Although the water quality has improved after treatment, the arsenic content was remained at a moderate level (in 2010, it fluctuated around 0.05 mg/L > 0.01 mg/L), and the nutrition level gradually rose [16,18].

#### *2.2. Sampling*

Based on the shape of the lake, three monitoring sites were set up in the southern (S1), middle (S2) and northern (S3) parts of Lake Yangzong (Figure 1). In situ monitoring was performed in April and November of 2015, January, June and November of 2016, April, June, July and September of 2017, December 2020 and August 2021. The whole lake monitoring was carried out in May 2018 and 2019, December 2020 and August 2021 (in total, 17 points were sampled and measured). The sampling sites were marked and located by a GPS satellite navigator, and the water quality parameters, including water temperature (WT), dissolved oxygen (DO) concentration, Chlorophyll-*a* (Chl-*a*) concentration, pH value and conductivity, were measured with a multi-parameter water quality monitoring instrument (YSI6600V2). A vertical line was set to monitor the water quality (including WT, DO, Chl-a, pH, conductivity and phycocyanin) at different depths at each site. The first data were measured about 0.5 m below the water surface, the last data were monitored 0.5 m above the bottom of the lake, and other data were collected at one-meter intervals. To ensure the accuracy of the data, each depth was measured six times.

Similarly, TN and TP samples from the vertical section of the lake water were collected in 1 L brown polyethylene bottles in September, October, November and December 2016, June, July, September and October 2017, March, May, June, August and October 2018, May 2019, December 2020 and August 2021. The samples were sent to a laboratory and placed in a refrigerator at 4 ◦C for chemical analysis within 2 h after sample collection.

#### *2.3. Analysis Methods*

The TP and TN levels were measured using an ultraviolet spectrophotometer (UV-2600) to determine the absorbance at the wavelengths 200 nm, 275 nm (TN) and 700 nm (TP). Then, the absorbance of the standard sample was corrected to obtain the specific TN and TP contents.

**Figure 1.** Study area and sampling sites of Lake Yangzong. **Figure 1.** Study area and sampling sites of Lake Yangzong.

#### Similarly, TN and TP samples from the vertical section of the lake water were col-*2.4. Data Processing*

lected in 1 L brown polyethylene bottles in September, October, November and December 2016, June, July, September and October 2017, March, May, June, August and October 2018, May 2019, December 2020 and August 2021. The samples were sent to a laboratory and placed in a refrigerator at 4 °C for chemical analysis within 2 h after sample collection. Microsoft Excel 2016 was used to assess the recorded data. The vertical profile diagram, column chart and correlation analysis diagram of each parameter were drawn using Origin 2020b (Origin Lab, Ltd., Northampton, MA, USA). In the column chart, the ratio of the total concentration of Chl-*a* to phycocyanin at the vertical section of each monitoring site was calculated using Microsoft Excel 2016.

#### *2.3. Analysis Methods* **3. Results**

#### The TP and TN levels were measured using an ultraviolet spectrophotometer (UV-2600) to determine the absorbance at the wavelengths 200 nm, 275 nm (TN) and 700 nm *3.1. Seasonal Variation of Water Temperature*

(TP). Then, the absorbance of the standard sample was corrected to obtain the specific TN and TP contents. *2.4. Data Processing* Microsoft Excel 2016 was used to assess the recorded data. The vertical profile diagram, column chart and correlation analysis diagram of each parameter were drawn using Origin 2020b (Origin Lab, Ltd., Northampton, MA, USA). In the column chart, the ratio of The temperature of deep water plateau lakes is affected by changes in air temperature [8,28], with changes in temperature leading to the thermal stratification of lakes. Increasing depths of water lakes cause a slow decrease in water temperature at the epilimnion and hypolimnion, leading to a sharp decrease in the thermocline. Similar to other deep-water plateau lakes (high mountains), the water temperature of Lake Yangzong presents a stratification and mixing phenomenon in the vertical profile. The lake is layered in spring, summer and autumn, and mixed in winter (Figure 2).

the total concentration of Chl-*a* to phycocyanin at the vertical section of each monitoring

site was calculated using Microsoft Excel 2016.

**3. Results**

*3.1. Seasonal Variation of Water Temperature*

spring, summer and autumn, and mixed in winter (Figure 2).

The temperature of deep water plateau lakes is affected by changes in air temperature [8,28], with changes in temperature leading to the thermal stratification of lakes. Increasing depths of water lakes cause a slow decrease in water temperature at the epilimnion and hypolimnion, leading to a sharp decrease in the thermocline. Similar to other deepwater plateau lakes (high mountains), the water temperature of Lake Yangzong presents a stratification and mixing phenomenon in the vertical profile. The lake is layered in

**Figure 2.** Vertical profile of the water temperature in Lake Yangzong. **Figure 2.** Vertical profile of the water temperature in Lake Yangzong.

Our findings showed that the temperature change pattern could be divided into four seasons, which included April 2015 and 2017 in spring (between 14.2 °C and 19.8 °C); June 2016, June, July and September 2017 and August 2021 in summer (between 14.1 °C and 26.6 °C); November 2015 and 2016 in autumn (between 15 °C and 19 °C); and January 2016 and December 2020 in winter (between 13.3 °C and 15.1 °C). These grouping modes represented the division of the four seasons. In winter, the lake belonged to the mixed period. Except for slight changes in the epilimnion from 0 m to 2 m (January), the other water depths were evenly mixed. In spring, epilimnion, thermocline and hypolimnion appeared but were not stable and significant. In summer, the water temperature increased dramatically in the epilimnion (water depth: 0 m to 6 m), while the variation in hypolimnion (water depth: ~18 m) was not obvious. There was a significant temperature gradient and a wide range in the thermocline (water depth: 6 m to 18 m). The hierarchical structure was stable during this time. The water temperature dropped obviously from surface to bottom in autumn. Temperature changes dropped into the deep waters. Moreover, a relatively higher temperature formed in the hypolimnion before the end of summer, which lasted until autumn. The thermocline (water depth: 16 m to 20 m) was in a lower depth and had a smaller range, indicating the vanishing stage of thermal stratification. As soon as the temperature of epilimnion dropped to the temperature of hypolimnion, the lake became Our findings showed that the temperature change pattern could be divided into four seasons, which included April 2015 and 2017 in spring (between 14.2 ◦C and 19.8 ◦C); June 2016, June, July and September 2017 and August 2021 in summer (between 14.1 ◦C and 26.6 ◦C); November 2015 and 2016 in autumn (between 15 ◦C and 19 ◦C); and January 2016 and December 2020 in winter (between 13.3 ◦C and 15.1 ◦C). These grouping modes represented the division of the four seasons. In winter, the lake belonged to the mixed period. Except for slight changes in the epilimnion from 0 m to 2 m (January), the other water depths were evenly mixed. In spring, epilimnion, thermocline and hypolimnion appeared but were not stable and significant. In summer, the water temperature increased dramatically in the epilimnion (water depth: 0 m to 6 m), while the variation in hypolimnion (water depth: ~18 m) was not obvious. There was a significant temperature gradient and a wide range in the thermocline (water depth: 6 m to 18 m). The hierarchical structure was stable during this time. The water temperature dropped obviously from surface to bottom in autumn. Temperature changes dropped into the deep waters. Moreover, a relatively higher temperature formed in the hypolimnion before the end of summer, which lasted until autumn. The thermocline (water depth: 16 m to 20 m) was in a lower depth and had a smaller range, indicating the vanishing stage of thermal stratification. As soon as the temperature of epilimnion dropped to the temperature of hypolimnion, the lake became mixed. Among the four seasons, the surface temperature range was 14.49 ◦C to 25.58 ◦C, the maximum temperature difference was 11.09 ◦C, the bottom temperature range was 13.31 ◦C to 16.38 ◦C and the maximum temperature difference was 3.07 ◦C. The whole year's maximum temperature and maximum temperature difference appeared in summer.

The difference among the three monitoring sites was not significant horizontally. In April 2015, the water temperature in the northern part of the lake was higher than the others. In July 2017, the water temperature in the southern part of the lake was higher than the others. In November 2015, according to the strict definition of thermocline [10], the thermocline in the south part of the lake disappeared, and the mixing period started.

#### *3.2. Seasonal Variation Characteristics of Water Quality Profile 3.2. Seasonal Variation Characteristics of Water Quality Profile*

*Water* **2022**, *14*, x FOR PEER REVIEW 6 of 18

#### 3.2.1. Dissolved Oxygen (DO) 3.2.1. Dissolved Oxygen (DO)

mer.

There were obvious seasonal stratification and mixing phenomena in the variation of the DO concentration in Lake Yangzong (Figure 3). There were obvious seasonal stratification and mixing phenomena in the variation of the DO concentration in Lake Yangzong (Figure 3).

mixed. Among the four seasons, the surface temperature range was 14.49 °C to 25.58 °C, the maximum temperature difference was 11.09 °C, the bottom temperature range was 13.31 °C to 16.38 °C and the maximum temperature difference was 3.07 °C. The whole year's maximum temperature and maximum temperature difference appeared in sum-

The difference among the three monitoring sites was not significant horizontally. In April 2015, the water temperature in the northern part of the lake was higher than the others. In July 2017, the water temperature in the southern part of the lake was higher than the others. In November 2015, according to the strict definition of thermocline [10], the thermocline in the south part of the lake disappeared, and the mixing period started.

**Figure 3.** Vertical profile of dissolved oxygen (DO) in Lake Yangzong. **Figure 3.** Vertical profile of dissolved oxygen (DO) in Lake Yangzong.

Similar to the variation trend of water temperature, the higher the temperature differences are, the stronger the stratification stability and DO distribution are. The curve of dissolved oxygen concentration could also be divided into four seasons. The highest value of surface DO was recorded (about 10.69 mg/L) in July 2017. The DO in the vertical direction was similar in winter. The DO stratification phenomenon began in spring, and the concentration of DO decreased with the water depth. Even during this time, the stratification was not stable. The stratification phenomenon was obvious in the middle and north parts of the lake. In summer, an obvious gradient of DO formed within the range of 4 m to 12 m below the water surface, where the content of DO decreased sharply. In this period, the stable stratification of DO was formed, leading to a lack of DO in deeper waters. In autumn, the DO stratification phenomenon began to vanish, and the water above 15 m was evenly mixed. In the horizontal direction, the seasonal changes in the central and Similar to the variation trend of water temperature, the higher the temperature differences are, the stronger the stratification stability and DO distribution are. The curve of dissolved oxygen concentration could also be divided into four seasons. The highest value of surface DO was recorded (about 10.69 mg/L) in July 2017. The DO in the vertical direction was similar in winter. The DO stratification phenomenon began in spring, and the concentration of DO decreased with the water depth. Even during this time, the stratification was not stable. The stratification phenomenon was obvious in the middle and north parts of the lake. In summer, an obvious gradient of DO formed within the range of 4 m to 12 m below the water surface, where the content of DO decreased sharply. In this period, the stable stratification of DO was formed, leading to a lack of DO in deeper waters. In autumn, the DO stratification phenomenon began to vanish, and the water above 15 m was evenly mixed. In the horizontal direction, the seasonal changes in the central and northern regions were more obvious. The change trends of DO in all groups were consistent with the trend of temperature change.

#### 3.2.2. pH Values and Their Variations

The water in Lake Yangzong was alkaline, and the change in pH is obvious (Figure 4).

northern regions were more obvious. The change trends of DO in all groups were con-

The water in Lake Yangzong was alkaline, and the change in pH is obvious (Figure

In spring, summer and autumn, with the stratification of temperature and DO, the pH was also stratified. The same four seasons were analyzed according to the grouping mode of temperature and DO. In winter, the variation range of pH was small, with some differences found at the surface. In January 2016, in the range of 0 m to 2 m, the pH decreased sharply in the southern part of the lake (from 8.91 to 8.07). Conversely, in the northern and central parts of the lake, the pH increased within the top 2 m. In spring, an increase in variation range was observed, with the difference between surface and bottom indicating the stratification of pH. In April 2015, there was a dramatically changing layer in the vertical profile, but in April 2017, the pH gradient at each depth was similar. Since the depths differed in the three monitoring sites, sharp changes in the layers were located at different depths. In summer, except in June 2017, the pH decreased with depth and formed a stable pH stratification. During this period, the changes mainly appeared in the southern part of the lake. In autumn, a variation trend in pH identical to the trend of temperature and DO was observed. In the central part of the lake, the pH value at the epilim-

sistent with the trend of temperature change.

3.2.2. pH Values and Their Variations

nion was relatively low.

4).

**Figure 4.** Vertical pH profile in Lake Yangzong. **Figure 4.** Vertical pH profile in Lake Yangzong.

3.2.3. Conductivity Conductivity refers to the ability to transmit electricity. It is mainly affected by salinity, dissolved solids, temperature and water supply. In Lake Yangzong, seasonal variation in conductivity was obvious, i.e., the vertical change in conductivity in spring and winter was not significant, while it was clearly stratified in summer and autumn (Figure 5). In spring, summer and autumn, with the stratification of temperature and DO, the pH was also stratified. The same four seasons were analyzed according to the grouping mode of temperature and DO. In winter, the variation range of pH was small, with some differences found at the surface. In January 2016, in the range of 0 m to 2 m, the pH decreased sharply in the southern part of the lake (from 8.91 to 8.07). Conversely, in the northern and central parts of the lake, the pH increased within the top 2 m. In spring, an increase in variation range was observed, with the difference between surface and bottom indicating the stratification of pH. In April 2015, there was a dramatically changing layer in the vertical profile, but in April 2017, the pH gradient at each depth was similar. Since the depths differed in the three monitoring sites, sharp changes in the layers were located at different depths. In summer, except in June 2017, the pH decreased with depth and formed a stable pH stratification. During this period, the changes mainly appeared in the southern part of the lake. In autumn, a variation trend in pH identical to the trend of temperature and DO was observed. In the central part of the lake, the pH value at the epilimnion was relatively low.

#### 3.2.3. Conductivity

Conductivity refers to the ability to transmit electricity. It is mainly affected by salinity, dissolved solids, temperature and water supply. In Lake Yangzong, seasonal variation in conductivity was obvious, i.e., the vertical change in conductivity in spring and winter was not significant, while it was clearly stratified in summer and autumn (Figure 5).

Similar to the temperature, DO and pH classification, the changes in conductivity could also be divided into four seasons. In April 2015, January 2016 and April 2017, except for the difference in value, the vertical and horizontal conductivity variation was insignificant. In spring, conductivity slightly increased with depth in the vertical direction, but with a very small amount. The changes at the three monitoring sites were similar. The conductivity in April 2017 (0.458 mS/cm) was higher than in April 2015 (0.447 mS/cm) and had no stratification. In summer, the stable stratification of conductivity began to form. At this stage, there was a sharp increase in the middle part of the vertical profile. In June 2017, the conductivity showed an abnormally high value (up to 0.532 mS/cm). The horizontal distribution difference was small in the same month. Except in July 2017, the surface conductivity of the northern part was higher than the southern and central parts. Among them, the highest conductivity was recorded in June 2017. In autumn, the conductivity was higher than that of summer. Vertically, the conductivity increased sharply within the range of 16 m to 20 m. Horizontally, there was no significant difference among

**Figure 5.** Vertical profile of conductivity in Lake Yangzong. **Figure 5.** Vertical profile of conductivity in Lake Yangzong.

3.2.4. Chlorophyll-*a* (Chl-*a*) Compared with other parameters, the variation in Chl-*a* demonstrated distinct characteristics, and the concentration of Chl-*a* was higher in autumn and winter (Figure 6). Significant changes could be found in different seasons. There were obvious differences in both Chl-*a* value and variation trends in winter and early spring. In spring, the difference in the vertical and horizontal directions was obvious. The concentration of Chl*a* was significantly high in the northern part of the lake in April 2015, and there was a clear peak within the 4 m to 8 m water depth. In the central and northern parts of the lake, there were lower values between the 4 m and 6 m water depths, respectively. In April 2017, the concentration of Chl-*a* first increased and then decreased with an increase in depth. The concentration in the central and northern parts increased sharply within 4 m of the epilimnion, while in the southern part of the lake, the Chl-*a* concentration increased less Similar to the temperature, DO and pH classification, the changes in conductivity could also be divided into four seasons. In April 2015, January 2016 and April 2017, except for the difference in value, the vertical and horizontal conductivity variation was insignificant. In spring, conductivity slightly increased with depth in the vertical direction, but with a very small amount. The changes at the three monitoring sites were similar. The conductivity in April 2017 (0.458 mS/cm) was higher than in April 2015 (0.447 mS/cm) and had no stratification. In summer, the stable stratification of conductivity began to form. At this stage, there was a sharp increase in the middle part of the vertical profile. In June 2017, the conductivity showed an abnormally high value (up to 0.532 mS/cm). The horizontal distribution difference was small in the same month. Except in July 2017, the surface conductivity of the northern part was higher than the southern and central parts. Among them, the highest conductivity was recorded in June 2017. In autumn, the conductivity was higher than that of summer. Vertically, the conductivity increased sharply within the range of 16 m to 20 m. Horizontally, there was no significant difference among the three monitoring sites.

#### 3.2.4. Chlorophyll-*a* (Chl-*a*)

the three monitoring sites.

Compared with other parameters, the variation in Chl-*a* demonstrated distinct characteristics, and the concentration of Chl-*a* was higher in autumn and winter (Figure 6).

December 2020, Chl-*a* demonstrated little change at each depth.

within 6 m of the epilimnion and did not change much in other depth ranges. The Chl-*a* concentration in April 2015 was higher than that of April 2017. In spring, the changes in the lake were disordered, and the concentration of Chl-*a* was higher in the northern part of the lake. In summer, a stable peak formed in the vertical profile. In July 2017, a peak was recorded from 0 m to 5 m at a value of ~5 μg/L. In June 2016 and 2017, the peak and depth decreased more than in July 2017. The stable stratification structure formed at all monitoring sites and all months during this stage, except for the variation in June 2017 in the southern part. In autumn, the Chl-*a* concentration in November 2015 was extremely high and clustered in the range of 2 m to 18 m water depth. The stratification in this stage was obvious, and the variation trend of all monitoring sites was consistent. In winter, the concentration was higher than in early spring. In January 2016, the concentration of Chl-*a* increased within the top 4 m and remained similarly constant at ~6 μg/L below 4 m. In

**Figure 6.** Vertical profile of Chlorophyll-a in Lake Yangzong. **Figure 6.** Vertical profile of Chlorophyll-a in Lake Yangzong.

3.2.5. Phycocyanin Phycocyanin and Chl-*a* have the same function; they can be used to estimate the amount of plankton and the trophic state of the water lakes. The difference between them is that Chl-*a* exists in almost all eukaryotes, while phycocyanin mainly exists in the cyanobacteria. The seasonal distribution of phycocyanin in Lake Yangzong had its own characteristics (Figure 7). Using the four seasonal classifications, the variation trend of phycocyanin was different from that of Chl-*a*. The phycocyanin concentration gradually decreased from spring, was lowest in summer and then gradually increased to its highest in winter. The concentration of phycocyanin was higher in spring than in autumn. In winter and early spring, the phycocyanin concentration was at its highest level and was largely distributed at water depths of 4 m to 17 m. In January 2016, the phycocyanin concentration first increased, then stabilized. In April 2015, the phycocyanin concen-Significant changes could be found in different seasons. There were obvious differences in both Chl-*a* value and variation trends in winter and early spring. In spring, the difference in the vertical and horizontal directions was obvious. The concentration of Chl-*a* was significantly high in the northern part of the lake in April 2015, and there was a clear peak within the 4 m to 8 m water depth. In the central and northern parts of the lake, there were lower values between the 4 m and 6 m water depths, respectively. In April 2017, the concentration of Chl-*a* first increased and then decreased with an increase in depth. The concentration in the central and northern parts increased sharply within 4 m of the epilimnion, while in the southern part of the lake, the Chl-*a* concentration increased less within 6 m of the epilimnion and did not change much in other depth ranges. The Chl-*a* concentration in April 2015 was higher than that of April 2017. In spring, the changes in the lake were disordered, and the concentration of Chl-*a* was higher in the northern part of the lake. In summer, a stable peak formed in the vertical profile. In July 2017, a peak was recorded from 0 m to 5 m at a value of ~5 µg/L. In June 2016 and 2017, the peak and depth decreased more than in July 2017. The stable stratification structure formed at all monitoring sites and all months during this stage, except for the variation in June 2017 in the southern part. In autumn, the Chl-*a* concentration in November 2015 was extremely high and clustered in the range of 2 m to 18 m water depth. The stratification in this stage was obvious, and the variation trend of all monitoring sites was consistent. In winter, the concentration was higher than in early spring. In January 2016, the concentration of Chl-*a* increased within the top 4 m and remained similarly constant at ~6 µg/L below 4 m. In December 2020, Chl-*a* demonstrated little change at each depth.

## 3.2.5. Phycocyanin

Phycocyanin and Chl-*a* have the same function; they can be used to estimate the amount of plankton and the trophic state of the water lakes. The difference between them is that Chl-*a* exists in almost all eukaryotes, while phycocyanin mainly exists in the

cyanobacteria. The seasonal distribution of phycocyanin in Lake Yangzong had its own characteristics (Figure 7). lake was relatively high and stable at water depths ranging from 4 m to 16 m water depth, with small vertical changes.

tration gradually increased from south to north, with little change in the horizontal direction in the remaining months. The concentration in April 2017 was higher than in April 2015. In summer, stratification stably developed, and compared to spring, the range was narrower. In June 2016 and June 2017, the phycocyanin concentration peaked between a water depth of 10 m to 12 m in the central and northern part of the lake, and stratification was obvious. In July 2017, the phycocyanin concentration increased sharply at water depths ranging from 3 m to 8 m and reached a peak at about 6 m water depth and then suddenly decreased. In September 2017, the phycocyanin concentration decreased sharply after reaching a peak at 5 m to 7 m water depth and was highest at water depths ranging from 0 m to 3 m compared to the other months. In autumn, the concentration increased and remained at a middle level. The stratification maintained a similar trend at different monitoring sites. In November 2015 and 2016, the phycocyanin concentration in the entire

*Water* **2022**, *14*, x FOR PEER REVIEW 10 of 18

**Figure 7.** Vertical profile of phycocyanin in Lake Yangzong. **Figure 7.** Vertical profile of phycocyanin in Lake Yangzong.

*3.3. Seasonal Variation Characteristics of CRQI* The relative amount of cyanobacteria in Lake Yangzong was relatively high in spring, summer and winter (Figure 8). Figure 8 shows that from April 2015 to April 2017, the CRQI decreased first and then Using the four seasonal classifications, the variation trend of phycocyanin was different from that of Chl-*a*. The phycocyanin concentration gradually decreased from spring, was lowest in summer and then gradually increased to its highest in winter. The concentration of phycocyanin was higher in spring than in autumn.

increased. In September 2017, the CRQI was the highest among the three monitoring loci, and the maximum value was recorded in the central part of the lake. Comparing April 2015 to April 2017, an increase in CRQI could be observed, especially in the central part of the lake. It also showed that in 2017, the number of cyanobacteria was higher than that of 2016, except in June 2017. The minimum value of the CRQI monitored was recorded in November 2015, with few differences among the three monitoring sites. Compared with November 2015, the CRQI increased in November 2016, most notably in the southern and central parts of the lake. In winter and early spring, the phycocyanin concentration was at its highest level and was largely distributed at water depths of 4 m to 17 m. In January 2016, the phycocyanin concentration first increased, then stabilized. In April 2015, the phycocyanin concentration gradually increased from south to north, with little change in the horizontal direction in the remaining months. The concentration in April 2017 was higher than in April 2015. In summer, stratification stably developed, and compared to spring, the range was narrower. In June 2016 and June 2017, the phycocyanin concentration peaked between a water depth of 10 m to 12 m in the central and northern part of the lake, and stratification was obvious. In July 2017, the phycocyanin concentration increased sharply at water depths ranging from 3 m to 8 m and reached a peak at about 6 m water depth and then suddenly decreased. In September 2017, the phycocyanin concentration decreased sharply after reaching a peak at 5 m to 7 m water depth and was highest at water depths ranging from 0 m to 3 m compared to the other months. In autumn, the concentration increased and remained at a middle level. The stratification maintained a similar trend at different monitoring sites. In November 2015 and 2016, the phycocyanin concentration in the entire lake was relatively high and stable at water depths ranging from 4 m to 16 m water depth, with small vertical changes.

#### *3.3. Seasonal Variation Characteristics of CRQI*

The relative amount of cyanobacteria in Lake Yangzong was relatively high in spring, summer and winter (Figure 8).

**Figure 8.** Cyanophyte relative quantity index from 2015–2017 in Lake Yangzong. Note: The ordinate value in the figure was calculated using the formula CRQI = [PC]/[Chl-*a*], and its unit is cells/μg. **Figure 8.** Cyanophyte relative quantity index from 2015–2017 in Lake Yangzong. Note: The ordinate value in the figure was calculated using the formula CRQI = [PC]/[Chl-*a*], and its unit is cells/µg.

*3.4. TN and TP Contents and Their Correlation with Other Indexes* The TN content in Lake Yangzong demonstrated a certain change in different seasons. The monitoring data showed that the TN content was lower in the summer and autumn of 2016, 2017 and 2018, and higher in winter and spring (Figure 9), which was related to less water in winter and spring. The TN content in August 2021 increased compared to 2018 (about 28%). The TP content decreased from 2016 to 2018 (Figure 10). The TP content in May 2019 did not change significantly from May 2018, but in August 2021 (about 0.04 mg/L), it was significantly higher than that of August 2018 (0.03 mg/L). During May 2018 and May 2019, the TN and TP contents at the Hypolimnion of the Figure 8 shows that from April 2015 to April 2017, the CRQI decreased first and then increased. In September 2017, the CRQI was the highest among the three monitoring loci, and the maximum value was recorded in the central part of the lake. Comparing April 2015 to April 2017, an increase in CRQI could be observed, especially in the central part of the lake. It also showed that in 2017, the number of cyanobacteria was higher than that of 2016, except in June 2017. The minimum value of the CRQI monitored was recorded in November 2015, with few differences among the three monitoring sites. Compared with November 2015, the CRQI increased in November 2016, most notably in the southern and central parts of the lake.

#### lake were significantly higher than at the epilimnion. In May 2018 and 2019, TN and TP *3.4. TN and TP Contents and Their Correlation with Other Indexes*

contents of epilimnion were relatively uniform, but in May 2019, TN and TP contents of hypolimnion were higher in the middle of the lake area. In December 2020, the TN and TP contents in the epilimnion and hypolimnion were extremely high. The TN content at the epilimnion was higher in the south and higher at the hypolimnion in the east. The TP content at the epilimnion was relatively uniform in the southeast and higher in the hypolimnion. In August 2021, the TN and TP contents at the epilimnion were relatively uniform (0.78 mg/L < TN < 1.21 mg/L, 0.02 mg/L < TP < 0.05 mg/L). The TN content at the The TN content in Lake Yangzong demonstrated a certain change in different seasons. The monitoring data showed that the TN content was lower in the summer and autumn of 2016, 2017 and 2018, and higher in winter and spring (Figure 9), which was related to less water in winter and spring. The TN content in August 2021 increased compared to 2018 (about 28%). The TP content decreased from 2016 to 2018 (Figure 10). The TP content in May 2019 did not change significantly from May 2018, but in August 2021 (about 0.04 mg/L), it was significantly higher than that of August 2018 (0.03 mg/L).

hypolimnion was significantly higher in the north than in the south, and the TP at the bottom was higher in the northeast (about 0.14 mg/L). During May 2018 and May 2019, the TN and TP contents at the Hypolimnion of the lake were significantly higher than at the epilimnion. In May 2018 and 2019, TN and TP contents of epilimnion were relatively uniform, but in May 2019, TN and TP contents of hypolimnion were higher in the middle of the lake area. In December 2020, the TN and TP contents in the epilimnion and hypolimnion were extremely high. The TN content at the epilimnion was higher in the south and higher at the hypolimnion in the east. The TP content at the epilimnion was relatively uniform in the southeast and higher in the hypolimnion. In August 2021, the TN and TP contents at the epilimnion were relatively uniform (0.78 mg/L < TN < 1.21 mg/L, 0.02 mg/L < TP < 0.05 mg/L). The TN content at the hypolimnion was significantly higher in the north than in the south, and the TP at the bottom was higher in the northeast (about 0.14 mg/L).

**Figure 8.** Cyanophyte relative quantity index from 2015–2017 in Lake Yangzong. Note: The ordinate value in the figure was calculated using the formula CRQI = [PC]/[Chl-*a*], and its unit is cells/μg.

The TN content in Lake Yangzong demonstrated a certain change in different seasons. The monitoring data showed that the TN content was lower in the summer and autumn of 2016, 2017 and 2018, and higher in winter and spring (Figure 9), which was related to less water in winter and spring. The TN content in August 2021 increased compared to 2018 (about 28%). The TP content decreased from 2016 to 2018 (Figure 10). The TP content in May 2019 did not change significantly from May 2018, but in August 2021 (about 0.04

During May 2018 and May 2019, the TN and TP contents at the Hypolimnion of the lake were significantly higher than at the epilimnion. In May 2018 and 2019, TN and TP contents of epilimnion were relatively uniform, but in May 2019, TN and TP contents of hypolimnion were higher in the middle of the lake area. In December 2020, the TN and TP contents in the epilimnion and hypolimnion were extremely high. The TN content at the epilimnion was higher in the south and higher at the hypolimnion in the east. The TP content at the epilimnion was relatively uniform in the southeast and higher in the hypolimnion. In August 2021, the TN and TP contents at the epilimnion were relatively uniform (0.78 mg/L < TN < 1.21 mg/L, 0.02 mg/L < TP < 0.05 mg/L). The TN content at the hypolimnion was significantly higher in the north than in the south, and the TP at the

*3.4. TN and TP Contents and Their Correlation with Other Indexes*

bottom was higher in the northeast (about 0.14 mg/L).

mg/L), it was significantly higher than that of August 2018 (0.03 mg/L).

**Figure 9.** Change in TN content in Lake Yangzong from 2017 to 2021. (**a**) indicates the distribution of TN content in the whole lake at the surface and bottom. (**b**) represents the mean value of TN content at 5 different depths. content at 5 different depths. In the vertical distribution, the TP and TN contents were significantly higher at 20 m depth than at 0 m, 2 m, 4 m and 10 m depths.

**Figure 10.** Changes in TP content in Lake Yangzong from 2017 to 2021. (**a**) indicates the distribution of TP content in the whole lake at the surface and bottom. (**b**) represents the mean value of TP content at 5 different depths. **Figure 10.** Changes in TP content in Lake Yangzong from 2017 to 2021. (**a**) indicates the distribution of TP content in the whole lake at the surface and bottom. (**b**) represents the mean value of TP content at 5 different depths.

Correlation analysis of various indicators of Lake Yangzong, including water quality parameters, TN and TP contents in October and September 2016, June, July and September In the vertical distribution, the TP and TN contents were significantly higher at 20 m depth than at 0 m, 2 m, 4 m and 10 m depths.

2017, December 2020 and August 2021 (Figure 11) revealed a high correlation coefficient between DO and Chl-*a* contents (*p* = 0.83), and the correlation between temperature and DO, pH and Chl-*a* was also relatively high (*p* ≥ 0.54). However, the correlation between TN and TP was relatively low (*p* = 0.31). Correlation analysis of various indicators of Lake Yangzong, including water quality parameters, TN and TP contents in October and September 2016, June, July and September 2017, December 2020 and August 2021 (Figure 11) revealed a high correlation coefficient between DO and Chl-*a* contents (*p* = 0.83), and the correlation between temperature and DO, pH and Chl-*a* was also relatively high (*p* ≥ 0.54). However, the correlation between TN and TP was relatively low (*p* = 0.31).

**Figure 11.** Correlation (**a**) and principal component (**b**) analysis of various water quality parameters.

There were also differences in the TN and TP compositions at different sites and sea-

sons (Figures 12 and 13).

**Figure 10.** Changes in TP content in Lake Yangzong from 2017 to 2021. (**a**) indicates the distribution of TP content in the whole lake at the surface and bottom. (**b**) represents the mean value of TP

Correlation analysis of various indicators of Lake Yangzong, including water quality parameters, TN and TP contents in October and September 2016, June, July and September 2017, December 2020 and August 2021 (Figure 11) revealed a high correlation coefficient between DO and Chl-*a* contents (*p* = 0.83), and the correlation between temperature and DO, pH and Chl-*a* was also relatively high (*p* ≥ 0.54). However, the correlation between

**Figure 9.** Change in TN content in Lake Yangzong from 2017 to 2021. (**a**) indicates the distribution of TN content in the whole lake at the surface and bottom. (**b**) represents the mean value of TN

In the vertical distribution, the TP and TN contents were significantly higher at 20 m

content at 5 different depths.

content at 5 different depths.

TN and TP was relatively low (*p* = 0.31).

depth than at 0 m, 2 m, 4 m and 10 m depths.

**Figure 11.** Correlation (**a**) and principal component (**b**) analysis of various water quality parameters. **Figure 11.** Correlation (**a**) and principal component (**b**) analysis of various water quality parameters.

There were also differences in the TN and TP compositions at different sites and seasons (Figures 12 and 13). There were also differences in the TN and TP compositions at different sites and seasons (Figures 12 and 13). *Water* **2022**, *14*, x FOR PEER REVIEW 13 of 18

**Figure 12.** Nitrogen composition accumulation diagram in December 2020 and August 2021. **Figure 12.** Nitrogen composition accumulation diagram in December 2020 and August 2021.

The content of particulate nitrogen (PN) and dissolved total nitrogen (DTN) at S1 was significantly higher in December 2020 than in August 2021, and the opposite was

<sup>−</sup>) content changed very little with

<sup>−</sup> content gradually increased with depth.

found at S3. In December 2020, the nitrate nitrogen (NO<sup>3</sup>

**Figure 13.** Phosphorus composition accumulation diagram in December 2020 and August 2021. **Figure 13.** Phosphorus composition accumulation diagram in December 2020 and August 2021.

At S1, in December 2020, the particulate phosphorus (PP) content was significantly higher than in August 2021 and fluctuated greatly with water depth. There was little difference between the two seasons in dissolved total phosphorus (DTP). The content of PP at S3 in August 2021 also fluctuated with water depth. The orthophosphate (PO<sup>4</sup> 3− ) content The content of particulate nitrogen (PN) and dissolved total nitrogen (DTN) at S1 was significantly higher in December 2020 than in August 2021, and the opposite was found at S3. In December 2020, the nitrate nitrogen (NO<sup>3</sup> −) content changed very little with water depth, and in August 2021, the NO<sup>3</sup> − content gradually increased with depth.

of the whole lake in December 2020 was significantly higher than in August 2021. **4. Analysis Discussion** *4.1. Mixing Type of Lake Yangzong* The seasonal temperature stratification and mixing characteristics of Lake Yangzong At S1, in December 2020, the particulate phosphorus (PP) content was significantly higher than in August 2021 and fluctuated greatly with water depth. There was little difference between the two seasons in dissolved total phosphorus (DTP). The content of PP at S3 in August 2021 also fluctuated with water depth. The orthophosphate (PO<sup>4</sup> <sup>3</sup>−) content of the whole lake in December 2020 was significantly higher than in August 2021.

#### could be divided into six types [29]. Since the lake was located in temperate, subtropical **4. Analysis Discussion**

#### mountainous areas, was affected by ocean climate cycled once a year and had a minimum *4.1. Mixing Type of Lake Yangzong*

water temperature ≥4 °C, it was classified as a warm monomictic lake. Lake Yangzong maintained this form from spring to autumn, during which the thermocline switched between present and absent, forming a dynamic cyclical process from stratification to mixing. The seasonal temperature stratification and mixing characteristics of Lake Yangzong could be divided into six types [29]. Since the lake was located in temperate, subtropical mountainous areas, was affected by ocean climate cycled once a year and had a minimum

water temperature ≥4 ◦C, it was classified as a warm monomictic lake. Lake Yangzong maintained this form from spring to autumn, during which the thermocline switched between present and absent, forming a dynamic cyclical process from stratification to mixing.

#### *4.2. Water Quality Parameters*

Water temperature is an important factor in determining the primary productivity of lakes, affecting their physical properties, chemical reaction processes and biological activity. Variations in water temperature and the formation and disappearance of thermocline significantly influenced the levels of the chemical parameters [9,30]. The environmental parameters follow the thermal structure and stratification in response to the climatological condition. According to the seasonal variation in water temperature, April, June, July, September and November could be classified as the stratification period, and January and December as the mixed period.

DO is the molecular oxygen dissolved in the water from the air. It is influenced by temperature, algae growth, biochemical reaction, etc. During the thermal stratification period, algal photosynthetic and atmosphere reaeration efficiency in epilimnion was high. With increasing water depth, photosynthesis weakened. Respiration of the upper aquatic organisms consumed the oxygen produced by photosynthesis, causing a lack of oxygen in the deeper waters. In addition, DO deletion in the hypolimnion is due to geochemical DO consumption during decomposition and stable stratification prevent mixing. Therefore, in this period, DO decreased from the surface to the bottom, especially in summer, DO in the middle layer of the lake decreased sharply and tended to be ~0 (0.34 mg/L) at the bottom layer. According to a study by Kalff Jacob et al. [10], the solubility of DO in freshwater mainly depended on the water temperature. Under constant air pressure, a lower water temperature led to a higher concentration of DO. Therefore, compared with December 2020 and January 2016, the winter season, January had a higher DO (7.925 m/L) at lower temperatures (Figures 2 and 3).

The concentration of CO<sup>2</sup> in water usually affects pH change, limiting the amount of phytoplankton at the surface and the decomposition of organic matter at the bottom. The photosynthesis of algae at the epilimnion consumed a large amount of CO2, causing a reduction in radical acid ions and an increase in pH value. The bottom layer displayed low denitrification, which ultimately reduced the pH. Temperature plays an important role in the growth of algae and the decomposition of organic matter. Therefore, pH level was mostly controlled by photosynthetic activity. The pH value was kept at a high level in the upper profile and a low level in the lower profile. The variation in pH value in Lake Yangzong was in line with the trend. In the mixing period at a lower temperature, the photosynthesis of planktonic algae was weak, the physical and chemical reaction in the lake was not significant, the acidity and alkalinity of the lake changed slightly in the vertical direction and the water was homogeneous in this period.

The vegetation coverage in the catchment of Lake Yangzong was low and showed a steep slope. In the rainy seasons, a large amount of solid matter and industrial and agricultural pollutants were transported into the lake by rivers and surface runoff, which contained large amounts of nutrients. After entering the lake, a portion of the nutrients is consumed by aquatic organisms, with the rest sinking into the bottom of the lake. In addition, the nutrients in the surface sediments migrate upward and are released into the water when the bottom conditions, especially the redox status, change. As a result, the conductivity at the epilimnion was lower, and that of the middle and lower layers increased with depth. From November, Lake Yangzong entered the dry season until the middle of May the following year. During this period, the precipitation was very low, leading to greater evaporation of the lake water than the amount of precipitation, increasing the concentration of salt substances in the lake and increasing the conductivity [31]. The conductivity remained at the same level in the vertical profile. In the study of Deng et al. [32], it was found that the quantity of some cyanobacteria had a good correlation with the electrical conductivity, which was related to the K<sup>+</sup> , Cl− and NO<sup>2</sup> − plasma brought by

agricultural emissions. Large amounts of nutrients and pollutants due to domestic sewage discharge from residential areas, wastewater from factories and fertilizers entered the lake through seepage, increasing its conductivity. This also led to a higher CRQI value in April 2017 than in April 2015 (Figure 8). The pH value and the conductivity were abnormally high in June 2017 (Figures 4 and 5) and might be attributed to the second phase of the arsenic pollution treatment project launched in June 2017. Through the Ferric Salt Coagulation method, a large amount of FeCl<sup>3</sup> was injected into Lake Yangzong [33], which might have led to an abnormally low phycocyanin concentration and CRQI.

The concentration of Chl-*a* and phycocyanin is used as indicators of phytoplankton biomass. The growth of algae was affected by temperature, light, nutrients and other factors. Epilimnion of Lake Yangzong has large hydrodynamic force and is not suitable for algal growth, so the Chl-*a* content is higher at 2.5–9 m. Algae growth positively correlated with temperature and DO in Lake Yangzong (*p* ≥ 0.60). During the thermal stratification of Lake Yangzong, the middle and upper water body temperature was suitable, and the DO was sufficient. At the same time, large amounts of precipitation in Lake Yangzong during rainy seasons with large amounts of pollutants provided sufficient nutrients for the algae to bloom. In the mixing period, the conductivity varied little at different water depths, indicating a homogeneous distribution of nutrients and algae at all depths.

#### *4.3. Trophic Status and the Eutrophication*

Eutrophication of water bodies is an aging phenomenon of water bodies. TN and TP contents are important indicators of eutrophication levels of lakes. However, the correlation between TN and TP in Lake Yangzong was generally low (*p* = 0.31).

The average content of TN in Lake Yangzong above 20 m depth was 0.79 mg/L, and that of TP was 0.04 mg/L, both of which were classified as Grade III water quality according to GB 3838-2002. However, the water nutrition below 20 m belonged to Grade IV (TN = 1.05 mg/L, TP = 0.06 mg/L). A sharp increase in TN and TP contents at the bottom of the lake indicated a significant release of nitrogen and phosphorus from sediments. The TN and TP contents at the hypolimnion were higher in the north than in other places in August 2021, and as the northern part was deeper than the southern part, the surface sediments released TN and TP much easier under anaerobic conditions. In the dry season, the lake water mainly comes from the rivers entering the lake. Therefore, in December 2020, the TN and TP contents at point S1 of Lake Yangzong were significantly high.

The TN and TP content in the water column showed no obvious stratification with changes in water depth, which was different from other parameters. The TN and TP content in August 2021 increased significantly compared to August 2018 (Figures 9b and 10b), indicating the degree of nutrition in Lake Yangzong was still increasing.

#### **5. Conclusions**

Analysis of the vertical and horizontal spatial distribution characteristics of water quality parameters and nutrition clearly showed that Lake Yangzong undergoes complex seasonal changes.

Lake Yangzong was identified as a typical warm monomictic lake. Its water quality parameters showed obvious changes following stratification, excluding winter, indicating that the water quality parameters were strongly influenced by temperature variations. As the lake water temperature change almost followed the change in air temperature, the lake water quality parameters were also highly influenced by changes in air temperature.

In spring and autumn, the CRQI index was higher, indicating a higher risk of cyanobacterial bloom. Though the contents of TN and TP in Lake Yangzong were not high (the TN contents, especially, were still lower than 2.0 mg/L, the threshold value for algae blooming), it returned to higher values in December 2020 (TN = 1.3 mg/L, TP = 0.06 mg/L), causing a rise in the inter-annual variation. These findings suggest that Lake Yangzong is facing a serious algae blooming threat. The contents of different forms of nitrogen and phosphorus have increased at the bottom of the lake, showing that the nitrogen and phosphorus released from sediments were strengthened. Therefore, it is necessary to intensify lake water quality monitoring and control human activities and endogenous release to prevent further deterioration of the water of Lake Yangzong.

**Author Contributions:** Conceptualization—H.Z.; Original draft preparation—W.X.; Supervision, Writing—review and editing, H.Z.; Conceptualization, Supervision, Resources, Writing—review and editing, Foundations acquisition—H.Z.; Investigation, Data Curation, L.D., W.X., X.W., H.L., D.L. and Y.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Special Project for Social Development of Yunnan Province (202103AC100001), Natural Science Foundation of Yunnan Province (2018FH 001-047) and NSFC (41807447).

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.

**Acknowledgments:** We thank all the graduate students who participated in the field works and laboratory analyses.

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

## **References**

