Comprehensive assessment of eutrophication in Tangxun Lake, a large urban lake in the middle reaches of the Yangtze River

Abstract

Urban lakes play an important role in supporting the ecological environment and human society, yet increasing pollution poses a threat to water quality. Tangxun Lake, a typical Large urban lake in the middle reaches of the Yangtze River, has experienced cultural eutrophication in recent years; however, few studies have comprehensively examined the water quality of this lake. Therefore, we aimed to evaluate the water quality of the Tangxun Lake Basin using the Nemerow pollution index, principal component analysis (PCA), phytoplankton density and diversity, and the comprehensive trophic level index (TLI). Additionally, we analyzed the main influencing factors to provide a reference for the management of the aquatic environment of Tangxun Lake. The Nemerow pollution index evaluation showed that the annual degree of pollution in Tangxun Lake ranged from mild to heavy. The PCA results showed that the main pollution factors affecting water quality were total phosphorus (TP) and total nitrogen (TN). According to the results of TLI, the water in Tangxun Lake was moderately to severely eutrophic. Evaluation of algal cell density showed that Tangxun Lake was mesotrophic, and the phytoplankton diversity index showed that the eutrophication level was high. The degree of pollution in Tangxun Lake varied seasonally and was in the order of spring > winter > summer > autumn. The pollution in inner Tangxun Lake was relatively serious in spring, summer, and autumn, whereas that in outer Tangxun Lake was more serious in winter. This study provides a scientific basis for the comprehensive management of the ecological environment in the Tangxun Lake Basin.

Keywords:

• Tangxun lake
• water quality evaluation
• Nemerow pollution index
• algal density
• comprehensive trophic level index

1. Introduction

As receptors of atmospheric deposition, storm runoff, and the products of human activities, urban lakes are polluted by an increasing number of anthropogenic compounds, resulting in a decline in lake water quality (Xiao et al. 2023), which directly threatens the survival and health of the surrounding residents. Furthermore, the weak self-purification ability of enclosed water makes these lakes more prone to eutrophication, which is characterized by an overgrowth of algae. Eutrophication not only reduces the aesthetic value of lakes but may also lead to environmental hazards (Yue et al. 2023). Thus, water quality assessment is of great importance for basin management (Cheng et al. 2023). Water quality assessment refers to the process of transforming monitoring data into qualitative descriptions according to certain standards and is an objective evaluation of water quality (Cheng et al. 2023). Scientific evaluation of aquatic environments can provide a theoretical basis for the ecological protection and restoration of lakes.

Previous studies have utilized a variety of water quality evaluation methods; however, there is no unified evaluation standard. For example, microorganisms such as phytoplankton are important biological indicators of aquatic ecological health and are widely used to assess aquatic ecosystems (Hu et al. 2023; Zhang et al. 2023). However, different evaluation methods focus on different indicators, yielding different results (Pan et al. 2019). Principal component analysis (PCA) can reduce the deviation in water quality index statistics by simplifying the parameters (Mansi and Sunil 2019). A comprehensive pollution index method based on the entropy weight method reduces the influence of the extreme value on the evaluation results, making the evaluation system more scientific and reasonable (Zhao et al. 2023). Barakat et al. (2016) used a variety of analytical methods to determine that the average concentration of most variables in the Oum Er Rbia River and its tributaries exceeded those recommended by the guide levels allowed by the Moroccan Directive concerning the water quality of river. However, in recent studies on Tangxun Lake, Guan et al. (2023) found that its eutrophication probability was related to industrial pollution, but did not explain the eutrophication degree of Tangxun Lake. During the dry period, Tangxun Lake in the Xunsi River watershed is seriously polluted (Zhou et al. 2022). But the source of contamination was not studied further. The nitrogen (N) and phosphorus (P) content in the surface sediment of Tangxun Lake is generally higher (Luo et al. 2023).

As the largest urban lake in Asia, Tangxun Lake plays an important roles in water conservation, flood control, recreation, and ecological regulation in Wuhan City. With accelerating urbanization in Wuhan, human activities have resulted in increased eutrophication in some urban lakes and a gradual loss of ecosystem services and stability (Wu et al. 2019). However, few studies have evaluated eutrophication in Tangxun Lake. Recently, large lakes have been fragmented by overexploitation and urban construction, further compounding this issue (Wu et al. 2019). From 1995 to 2015, the area of Tangxun Lake decreased by a total of 14.20 km², a shrinkage of 36.61% (Chen et al. 2019). In this study, we aimed to systematically evaluate and analyze the water quality of Tangxun Lake using the Nemerow pollution index, phytoplankton density and diversity, comprehensive trophic level index (TLI) method, and PCA. The results of this study will provide a theoretical reference for the ecological restoration of Tangxun Lake.

2. Materials and methods

2.1. Study area

Tangxun Lake (30°22′–30°30′ N, 114°15′–114°35′ E) is located in southeastern Wuhan City, Hubei Province, China. It is the largest urban lake in Wuhan City, and it is the reserve drinking water source for the city. It is divided into two parts, inner Tangxun Lake (east side) and outer Tangxun Lake (west side), which are connected by culverts. There are six main inflowing rivers. The catchment area of Tangxun Lake is 240.38 km², accounting for 2.83% of the total area of Wuhan. The surface area of the lake is 36.24 km², accounting for 20.7% of the total area of the 38 lakes in Wuhan. Tangxun Lake is located in the northern subtropical monsoon climate zone, and precipitation is concentrated in the summer. The basin is dominated by precipitation and riverine recharge (Figure 1).

Figure 1. Remote sensing image (a) and land use (b) of Tangxun Lake Basin.

2.2. Sample point setting and index determination

Based on the area and shape of Tangxun Lake, seven sampling points were established in the lake (T1–T7) and six sampling points in the inflowing river (G1–G6) (Figure 2). Among them, T1, T4, T5, and T6 were located in inner Tangxun Lake, while T2, T3, and T7 were located in outer Tangxun Lake. Among them, T1 and T2 were the midpoints of the inner and outer Tangxun Lake, and the others were the inlet points of the lake. The water quality of Tangxun Lake was investigated from January to December of 2019. T1 and T2 were assessed monthly, and the rest were assessed once every two months.

Figure 2. The map of study area.

The water quality indicators measured at each point included the pH, Secchi depth (SD), dissolved oxygen (DO), permanganate index (COD_Mn), five-day biochemical oxygen demand (BOD₅), ammonia nitrogen (NH₃-N), Chl-a, total N (TN), and total P (TP). When collecting the water samples, floating surface matter was removed, and lake water was used to wash the instrument. Water samples were collected at a depth of 50 cm using a water sampler and placed in a 2.5 L acid-washed polyethylene bottle. The samples were sent to the laboratory within 3 h. All indicators were analyzed and determined according to the guidelines described in ‘Water and Wastewater Monitoring and Analysis Method (Fourth Edition)’ (State Environmental Protection Agency, China 2002) and ‘Lake Eutrophication Survey Specification (Second Edition)’ (Jin and Tu 1990).

Phytoplankton samples were collected according to the methods described in ‘Chinese freshwater algae system, classification, and ecology’ (Hu and Wei 2006). The qualitative samples were collected using a planktonic biological network with a pore size of 0.064 mm. A 50 mL sample was collected, fixed with 1% formaldehyde solution by volume, and sent to the laboratory for microscopic examination. For quantitative samples, Lugol’s solution was added at a volume of 1.5%, the samples were fixed and allowed to stand for 5 min, and then they were transported back to the laboratory for testing.

The surface sediment samples (0–10 cm) were collected using a Peterson grab sampler. Each surface sample was sealed in a polyethylene self-sealing bag marked with the corresponding sampling point information, placed in a 4 °C incubator, and transported back to the laboratory. The samples were freeze-dried to a constant weight, and debris such as gravel, animals, and plant residue were removed. After grinding, the samples were passed through a 100-mesh sieve. TN in the sediment was determined using alkaline potassium persulfate digestion, and TP was determined using the potassium persulfate oxidation method (Wang 2014). Organic matter (OM) was determined using the potassium dichromate volumetric method according to Editional Board of Water and Wastewater Monitoring and Analysis Methods (Ministry of Environmental Potection of the People’ s Republic of China 2002).

2.3. Evaluation method

2.3.1. Nemerow pollution index

The Nemerow index method considers the average pollution status of various influencing parameters and the most polluting factors, reducing the influence of pollution extremes on the results. The formula is as follows (Wang et al. 2016; Sheng et al. 2022): (1) $$N_{\mathit{\text{ave}}}\hspace{0.17em}=\hspace{0.17em}\frac{1}{m}{\displaystyle {\sum }_{j=1}^{j=m}\mathit{\text{Nj}}}$$(1) (2) $$N=\sqrt{\frac{{\text{Nmax}}^2+N_{\text{ave}}{}^2}{2}}$$(2) where N is the Nemerow pollution index, ${\mathrm{N}}_{\mathrm{j}}$ is the pollution index of j, ${\mathrm{N}}_{\mathrm{\text{max}}}$ is the maximum value of a single pollution index, and ${\mathrm{N}}_{\mathrm{\text{ave}}}$ is the average value of all indices. Grading criteria are listed in Table 1 (Hu and Wei 2006).

Table 1. Nemerow pollution index classification standards.

Pollution level	Pollution index	Pollution degree
I	N ≤ 1	Clean
II	1 < N ≤ 2	Mild pollution
III	2 < N ≤ 3	Medium pollution
IV	3 < N ≤ 5	Heavy pollution
V	5 < N	Serious pollution

2.3.2 Comprehensive trophic level index method

The TLI is used to classify water based on their nutrient status or level of biological productivity. A comprehensive trophic level evaluation was conducted to evaluate the degree of eutrophication using a combination of five representative water quality parameters: Chl-a, SD, TP, TN, and COD_Mn (Wang et al. 2020). The TLIs of the five water quality parameters were calculated based on the weights assigned to each indicator.

The comprehensive trophic level index (TLI (Σ)), was calculated as follows: (3) $$\text{TLI}\left(\Sigma \right)\text{=\hspace{0.17em}}{\displaystyle \sum }_{\text{j\hspace{0.17em}=\hspace{0.17em}1}}^m{\text{W}}_{\text{j}}\cdot \text{TLI(j)}$$(3) where W_j is the correlation weight of the j^th trophic level index; TLI (j) is the trophic level index of the j^th parameter; and m is the number of evaluation parameters (Wu et al. 2021).

Taking Chl-a as the reference parameter, the normalized correlation weight calculation formula for the j^th parameter is as follows: (4) $${\mathrm{W}}_{\mathrm{j}}\text{=}\frac{{\mathrm{r}}_{\text{ij}}^2}{\sum _{\text{j = 1}}^{\mathrm{m}}{\mathrm{r}}_{\text{ij}}^2}$$(4) where r_ij is the correlation coefficient between the j^th parameter and Chl-a. The correlation r_ij and r²_ij between Chl-a and other water quality parameters of lakes (reservoirs) in China are shown in Table 2. Grading criteria are listed in Table 3.

Table 2. Correlation between water quality parameters of Lakes (reservoirs) in China and Chl-a.

	Chl-a	TP	TN	SD	CODMn
rij	1.0000	0.8400	0.8200	−0.8300	0.8300
r^2ij	1.0000	0.7056	0.6724	0.6889	0.6889
Wj	0.2663	0.1879	0.1790	0.1834	0.1834

Table 3. Trophic level of water.

Trophic level	TLI (∑)	Qualitative assessment
Oligotrophic	TLI (∑) < 30	Excellent
Mesotrophic	30 ≤ TLI (∑) ≤ 50	Good
Eutrophic	TLI (∑) > 50	Polluted
Mildly eutrophic	50 < TLI (∑) ≤ 60	Mildly polluted
Moderately eutrophic	60 < TLI (∑) ≤ 70	Moderately polluted
Severely eutrophic	TLI (∑) > 70	Heavily polluted

The TLI was calculated for each evaluation factor using the following formulae (Jin and Tu 1990): (5) $$\text{TLI}\left(\text{Chl}-a\right)\text{=10(2.5\hspace{0.17em}\hspace{0.05em}}\text{+\hspace{0.05em}\hspace{0.17em}1.086\hspace{0.17em}ln\hspace{0.17em}Chl}-\text{a)}$$(5) (6) $$\text{TLI}\left(\text{TP}\right)\text{=10(9.436\hspace{0.05em}\hspace{0.17em}+\hspace{0.17em}\hspace{0.05em}}1\text{.624\hspace{0.17em}ln\hspace{0.17em}TP)}$$(6) (7) $$\text{TLI}\left(\text{TN}\right)\text{=10(5.453\hspace{0.17em}\hspace{0.05em}}+\text{\hspace{0.05em}\hspace{0.17em}1.694\hspace{0.17em}ln\hspace{0.17em}TN)}$$(7) (8) $$\text{TLI}\left(\text{SD}\right)\text{=10(5.118}-1\text{.940\hspace{0.17em}ln\hspace{0.17em}SD)}$$(8) (9) $$\text{TLI}\left({\text{COD}}_{\text{Mn}}\right)\text{=10(0.109\hspace{0.05em}\hspace{0.17em}+\hspace{0.05em}\hspace{0.17em}2.661\hspace{0.17em}ln\hspace{0.17em}}{\text{COD}}_{\text{Mn}}\text{)}$$(9)

2.3.3 Principal component analysis

The PCA method is widely used to assess water quality (Dalal et al. 2010). Under the premise that a single factor involved in the evaluation has a strong correlation, PCA is based on the idea of using dimensionality reduction to simplify and synthesize many pollution factors, thus forming a few comprehensive indicators that can represent the original data to the greatest extent. The principal component factors and comprehensive pollution scores of the monitoring points are analyzed, and the water quality characteristics of the lake are obtained (Table 4) (Zou et al. 2008).

Table 4. Water quality grading standards of principal component analysis.

	Comprehensive principal component	Water quality
1	≤ −3	I
2	−3–-2	II
3	−2–-1	III
4	−1–0	IV
5	> 0	V

2.3.4 Phytoplankton diversity

Different biological species are dominant at different degrees of eutrophication, and the phytoplankton cell density, Shannon–Wiener diversity index (H’) (Dennis and Emmanuel 2019), Margalef richness index (D’) (Kohn and Pielou 1977), and Pielou evenness index (J’) (Shannon 1948) can be used as water quality indicators (Yu et al. 2022) (Table 5).

Table 5. Diversity indices and standards of water nutrient status evaluation.

Trophic level	Cell density (10^4 cells/L)	Shannon–Wiener diversity index (H’)	Pielou evenness index (J’)	Margalef richness index (D’)	Qualitative evaluation
Extremely oligotrophic	50 <	> 3	> 0.8	> 5	Clean
Oligotrophic	50–100
Oligo-mesotrophic	100–900
Mesotrophic	900–4000	2–3	0.5–0.8	4–5	Mild pollution
Meso-eutrophic	4000–8000	1–2	0.3–0.5	3–4	Moderate pollution
Eutrophic	8000–10000	0–1	0–0.3	0–3	Heavy pollution
Hyper-eutrophic	> 10000

2.4 Data processing

All data were processed using Microsoft Excel. Google Earth and ArcGIS 10.7 software were used to construct the study area map. Additionally, Pearson correlation analysis and PCA on seven water quality (pH, DO, COD_Mn, BOD₅, TN, TP and Chl-a) were carried out in SPSS. p < 0.05 indicated a significant correlation. Bar and line graphs were created using Origin 2022 software.

3. Results

3.1. Physicochemical index

3.1.1. Nutrient levels

3.1.1.1 Water nutrient levels

The seasonal variation in the concentration of TP in the water at T1–T7 ranged from 0.120 to 0.270 mg/L (Figure 3), with an average value of 0.211 mg/L, and the levels at all points were higher than the water quality standard (GB3838 − 2002) limit of IV. Spatially, the concentration of TP in outer Tangxun Lake exhibited less seasonal variation than that in inner Tangxun Lake. Temporally, the mean concentration of TP at each point was higher in autumn than in the other seasons.

Figure 3. Seasonal variation of TP and TN in Tangxun Lake.

The concentration of TN in the water varied from 0.89 to 5.81 mg/L, with an average value of 3.23 mg/L, which was higher than the surface water quality standard limit of V. The mean values of TN exhibited the following order: spring > winter > summer > autumn. Spatially, the mean concentration of TN in the inner Tangxun Lake was significantly higher than that in the outer Tangxun Lake (p < 0.05), which was consistent with the results of Zhao et al. (2022).

3.1.1.2 Sediment nutrient levels

The TP in the sediment of Tangxun Lake ranged from 74.741 to 1624.732 mg/kg (Figure 4), with an annual average value of 915.248 mg/kg. The maximum value occurred in the southwestern part (T7), and the minimum value occurred in the western part. The mean annual value of TN in sediment was 483.81 mg/kg, with the maximum value of 1226.98 mg/kg occurring in T7 (southeastern outer Tangxun Lake) and the minimum value of 33.44 mg/kg occurring in northeastern inner Tangxun Lake.

Figure 4. The contents of TP and TN in sediment of Tangxun Lake.

3.1.2. Organic pollution index

The BOD₅ of Tangxun Lake ranged from 2.76 to 8.60 mg/L (Figure 5), with an annual average of 4.80 mg/L. The greatest variation between sites was observed in spring, with a coefficient of variation of 0.43. Seasonally, BOD₅ was the highest in summer and lowest in winter. COD_Mn ranged from 4.07 to 9.97 mg/L, with a yearly average of 6.32 mg/L. Spatially, the fluctuations were greatest in spring and least variable in summer, with coefficients of variation of 0.31 and 0.03, respectively. Overall, COD_Mn was higher in inner Tangxun Lake than in outer Tangxun Lake. Fluctuations in OM pollution in the water were similar to the annual variations in concentrations of TN and TP, and pollution was most serious during the dry season. DO ranged from 7.80 to 15.23 mg/L, with an annual mean of 10.13 mg/L. The mean value of DO was higher in summer than in the other seasons. Spatially, the fluctuations were greatest in summer and least variable in autumn, with coefficients of variation of 0.24 and 0.04, respectively.

Figure 5. Spatial variation trend of dissolved oxygen (DO), five-day biochemical oxygen demand (BOD₅), and permanganate index (COD_Mn) in Tangxun Lake.

3.2. Pollution evaluate

3.2.1. Nemerow pollution index

The evaluation was carried out through six indicators including TP, TN, pH, COD_Mn, BOD₅ and DO. The maximum value of a single pollution index in spring and winter was TN, while the maximum value of a single pollution index in summer and autumn was TP. In PAC, the main pollution factors affecting water quality were also TP and TN.

The Nemerow pollution index was greater than 1 for all points (Figure 6), indicating that all sampling points were polluted to some degree. Pollution was highest in spring, and the Nemerow pollution index was greater than 3, indicating serious pollution. The pollution indices of T2, T3, and T7 were lower than those of the other sites, indicating that the pollution level of outer Tangxun Lake was less severe than that of inner Tangxun Lake in spring. Water quality improved in summer, and only T1 and T3 were heavily polluted. The pollution indices of T1, T4, T5, and T6 exceeded 3 in autumn, indicating heavy pollution. In winter, T1, T4, T5, and T6 were moderately polluted, and T2, T3, and T7 were heavily polluted, showing that the water quality of outer Tangxun Lake was worse than that of inner Tangxun Lake.

Figure 6. Nemerow pollution index in Tangxun Lake.

According to the Nemerow pollution index, seasonally, the degree of pollution in Tangxun Lake was in the order of spring > autumn > winter > summer. In spring and autumn. The pollution level of inner Tangxun Lake was relatively serious, whereas in winter, the pollution of outer Tangxun Lake was more serious.

3.2.2. Comprehensive trophic level index

The comprehensive TLI of Tangxun Lake ranged from 68.46 to 71.45, with a mean value of 70.13 (Table 6), indicating that all points had moderate to heavy eutrophication. The mean values of sampling points in inner Tangxun Lake were higher than those of points in outer Tangxun Lake, indicating that inner Tangxun Lake was more seriously polluted than the outer Tangxun Lake, which was consistent with other evaluation results.

Table 6. Comprehensive trophic level index.

Site	TLI (Chl-a)	TLI (TP)	TLI (TN)	TLI (SD)	TLI (CODMn)	TLI (∑)	Trophic level
T1	73.64	68.16	74.72	78.07	50.24	69.33	Moderate eutrophication
T2	71.24	64.34	74.72	82.40	48.58	68.46	Moderate eutrophication
T3	75.65	66.51	74.57	79.69	49.21	69.63	Moderate eutrophication
T4	74.72	69.02	75.54	85.56	51.61	71.54	Severe eutrophication
T5	72.75	70.93	76.25	76.58	51.90	69.91	Moderate eutrophication
T6	75.48	70.14	75.38	80.55	50.90	70.88	Severe eutrophication
T7	75.16	67.28	73.93	87.98	49.96	71.19	Severe eutrophication

3.2.3 Principal component analysis

Through Kaiser-Meyer-Olkin (KMO) and Bartlett’s sphericity tests, the final KMO value was determined as 0.614 (KMO > 0.5) and p = 0.045 (p < 0.05). According to the PCA results, three principal components (PC1, PC2, and PC3) were determined based on the principle that the initial eigenvalues were greater than 1. The cumulative contribution rate of the three principal components was 90.808%. Specifically, PC1 explained 59.085% of the variance, and the factors with strong positive correlations were TN, TP, BOD, and COD_Mn, with loads of 0.886, 0.966, 0.925, and 0.894, respectively, while pH had a negative correlation. Additionally, PC2 and PC3 explained 19.743% and 12.980% of the variance, respectively. The factor showing a strong positive correlation with PC2 was DO, with a load of 0.789. A strong positive correlation was found between PC3 and Chl-a concentrations.

According to the PCA results, seasonally, the degree of pollution in Tangxun Lake was in the order of: spring > winter > summer > autumn (Table 7). In particular, T1, T4, T5, and T6 had the highest levels of pollution in spring, summer, and autumn, indicating that the pollution level of inner Tangxun Lake was relatively severe in these three seasons, while it was severe in outer Tangxun Lake in winter, which was consistent with the results of the Nemerow pollution index. From the perspective of the entire year, the principal component score of inner Tangxun Lake was higher than that of outer Tangxun Lake, and the degree of pollution was greater in the inner Tangxun lake than in the outer Tangxun lake, which was mainly affected by PC1.

Table 7. Comprehensive evaluation of water quality using principal component analysis.

Site	F1	Rank	F2	Rank	F3	Rank	F	Rank	Water quality
T1	10.526	4	−10.462	6	−5.655	6	0.414	5	V
T2	9.923	5	−9.655	5	−5.392	5	0.394	7	V
T3	9.260	7	−7.242	2	−3.996	3	0.426	4	V
T4	10.769	3	−11.234	7	−5.774	7	0.411	6	V
T5	10.855	2	−7.581	3	−3.834	2	0.535	2	V
T6	11.332	1	−8.526	4	−4.358	4	0.538	1	V
T7	9.314	6	−6.983	1	−3.574	1	0.443	3	V

F1, F2, and F3 represent the scores of PC1, PC2, and PC3, respectively, and F represents the comprehensive score.

3.2.4. Phytoplankton diversity

3.2.4.1 Algal cell density

Tangxun Lake remained at the mesotrophic–meso-eutrophic level (Table 8) throughout the year. The algal cell density ranged from 2025.46 × 10⁴ to 4881.22 × 10⁴ cells/L. Except for T4, which was at the meso-eutrophic level, all sites were at the mesotrophic level.

Table 8. Algal cell density and trophic level in Tangxun Lake.

Site	Algal cell density (10^4 cells/L)	Trophic level
T1	2332.42	Mesotrophic
T2	2025.46	Mesotrophic
T3	3350.23	Mesotrophic
T4	4881.22	Meso-eutrophic
T5	2055.68	Mesotrophic
T6	2147.64	Mesotrophic
T7	2075.25	Mesotrophic

3.2.4.2 Phytoplankton diversity indices

According to the phytoplankton diversity indices, Tangxun Lake had a moderate level of eutrophication, and the degree of water pollution was moderately to heavily polluted (Table 9). The average Shannon–Wiener index was 1.548, and the highest value of 1.71 was found at T3, which was approximately 1.28 times the lowest value. The mean Margalef index was 1.994, indicating heavy pollution. The average Pielou index was 0.435, and the highest value (T3) was approximately 1.26 times than the lowest value (T1).

Table 9. The evaluation results of phytoplankton diversity index.

	T1	T2	T3	T4	T5	T6	T7	Mean value	Trophic level	Qualitative assessment
Shannon–Wiener (H’)	1.34	1.50	1.71	1.54	1.40	1.69	1.67	1.548	Moderately eutrophic	Moderately polluted
Margalef (D’)	2.00	2.02	1.96	1.92	2.02	2.01	2.02	1.994	Eutrophic	Heavily polluted
Pielou (J′)	0.38	0.42	0.48	0.43	0.39	0.47	0.47	0.435	Moderately eutrophic	Moderately polluted

4. Discussion

4.1. Source of pollutants

The Tangxun Lake Basin is densely populated and has a high degree of urbanization, making it the largest urban lake in Asia. The types of land use were mainly construction land and agricultural land in 2019, which accounted for 34% and 23% of the total area of the basin, respectively. Residential sewage discharge, industrial and agricultural wastewater have become the main sources of pollution in Tangxun Lake. However, with the development of the city in recent years, more than 19,000 acres of aquaculture have been removed and residential sewage discharge has gradually dominated. These pollutants mainly enter Tangxun Lake through inflowing rivers. In addition, livestock and poultry waste, sewage treatment plant tailwaters and atmospheric deposition contribute to pollution.

In the spring of 2019, the southern drainage outlet of Tangxun Lake exhibited a distinct stench and the water appeared turbid and yellow, which was one of the factors leading to serious pollution. Therefore, it is necessary to strengthen the monitoring of water quality and pollution discharge. At the same time, DO reached the highest level, and algae and aquatic plants grow vigorously in summer, gradually consuming N and P, leading to relatively lower concentrations. After autumn and winter, the dead aquatic animals and plants due to natural causes accumulate in the lake, deteriorating the water quality and lead to the worst water quality in spring.

Exogenous pollutants can flow into water with rainfall runoff, resulting in an increase in pollutant concentrations (Yang et al. 2012). Rainstorms also play an important role in lake eutrophication (Chen et al. 2022). Studies have shown that the runoff generated by rainfall was the main driving force for wastewater entering Tangxun Lake Basin. Tangxun Lake Basin has a subtropical monsoonal climate. In summer, the total precipitation is 475 mm. There was more precipitation and a higher volume of flow through inflowing rivers into the lake. In the dry season, the wastewater discharged from industrial and agricultural activities entered the lake with inflowing rivers, and low precipitation resulted in higher nutrient concentrations. It corresponded with the results of the Nemerow pollution index, which showed the highest level of pollution in spring and the lowest level in summer.

4.2. Effect of sediment on water quality

The sediment in water stores nutrients, such as N and P. Nutrients and pollutants are enriched in the sediment through sedimentation and adsorption (Sheng et al. 2022). In shallow lakes, disturbance of the sediment by precipitation, adsorption, and biological activities increases the exchange of materials between the overlying water and sediment (Wu et al. 2020). The release of nutrients from the sediment contributes to eutrophication in lakes (Bormans et al. 2015). Alkaline waters have been shown to promote the release of TN and TP from sediment (Jensen and Andersen 1992). The pH of Tangxun Lake was alkaline throughout year, ranging from 7.04 to 9.25, which promoted the release of N and P from sediment, and there was a highly significant correlation between TN and pH in the water (p = 0.009). The release of P is also influenced by redox properties, and it is more readily released when pH > 8. The perennially alkaline water quality of Tangxun Lake was consistent with the increased release of P from the sediment (Boström 1984).

Exogenous pollution discharged from industrial parks and sewage treatment plants near Tangxun Lake deposited nutrients into the lake, which underwent sedimentation. The resuspension of Tangxun Lake sediment was more likely to occur under high temperatures in summer, resulting in the release of nutrients from the sediment into the water. The mean values of TN and TP in the surface sediments of Tangxun Lake were 483.81 mg/kg and 915.25 mg/kg, respectively. The endogenous pollution loads of TP and TN were 11.44 t/a and 323.18 t/a, respectively, which accounted for 9.8% and 16.5% of the total pollution load. Therefore, pollutants in the sediment of Tangxun Lake had a relatively high risk of endogenous pollutant release. This was consistent with the findings of Ma et al. (2020).

4.3. Phytoplankton diversity and water quality assessment

Phytoplankton are an important component of aquatic ecosystems (Qiu et al. 2018), and they are often used to assess water quality because of their sensitivity to the environment (Yu et al. 2022; Xiao et al. 2023). The aquatic community structure of Tangxun Lake was relatively simple, with chlorophyta dominating in spring and autumn, followed by cyanophyta and diatoms. Our results showed that Tangxun Lake exhibited moderate eutrophication, and the water was moderately to heavily polluted. This study showed that the main factor affecting the growth of the phytoplankton community in Tangxun Lake was the downward effect of the food web. Therefore, it is necessary to reduce the population density of the phytoplankton community so that the zooplankton biomass in Tangxun Lake can be improved, thus preventing the occurrence of cyanobacterial blooms (Cao et al. 2022). The zooplankton biomass in the inner Tangxun Lake was much lower than that in the outer Tangxun Lake due to a large number of aquaculture fisheries. It is suggested to increasing the zooplankton biomass in Tangxun Lake and control the fishery. From the perspective of the food chain, the prevention of algae outbreaks is closely related to the reduction of the cultivation of filter – feeding fish, the reduction of the population density of the phytoplankton community, and the increase of zooplankton biomass. It is necessary to implement the strictest water resources management system and strengthen the protection of water resources in Tangxun Lake.

4.4. Pollution load of inflowing Rivers

A large amount of N and P entering lakes via inflowing rivers is an important cause of eutrophication (Huang 2022; Hua et al. 2023). The level of water quality of the six major inflowing rivers was in the order: G2 > G1 > G5 > G6 > G4 > G3. Specifically, G3 was class IV, G4 was class V, and G1, G2, G5, and G6 were in classes inferior V. Furthermore, G3, the confluence system connecting Tangxun Lake to Liangzi Lake, had the highest flow and best water quality among the inflowing rivers. In recent years, many economic development zones and industrial parks have been established near Tangxun Lake, generating large amounts of industrial wastewater that is rich in chemical substances and heavy metals. Meanwhile, fish farms and mussel farms were previously located around Tangxun Lake, serving as a source of fish feed entering the lake. Additionally, a survey conducted in February 2019 revealed that domestic sewage was being discharged into the lake. Therefore, the main sources of pollution in inflowing rivers were residential sewage discharge, industrial wastewater and aquaculture.

The pollution loads entering the lake in spring and autumn were significantly higher than that in summer and winter (p = 0.001) (Figure S1). Spatially, the pollution load of G2 was the largest and was far greater than that of the other inflowing rivers. In contrast, the pollution loads of rivers G3 and G4 were much lower than those of the other rivers. The seasonal variation of the main pollution loads in Tangxun Lake was consistent with that of the nutrient levels in the lake. The main source of runoff pollution was domestic sewage, followed by agricultural non-point source pollution. These sources corresponded with the levels of NH₃-N, TN and TP, which were approximately 542.8098, 807.3368, and 55.384 t/a, respectively (Table S1). However, as outer Tangxun Lake is larger than inner Tangxun Lake, it has a stronger self-purification ability. Additionally, outer Tangxun Lake has two entrances and exits that connect to the Yangtze River. The flowing of NH₃-N, TN, and TP out of the Tangxun lake reached 6.48, 31.08 and 49 t/m.

In Tangxun Lake, TP was mainly enriched in spring and autumn, whereas TN was mainly enriched in spring and winter. There was a significant correlation between the TP, TN and NH₃-N in the inflowing rivers and the amount of rainfall (p < 0.05) (Table S2). The lower the rainfall in spring and winter, the more serious the pollution. Pollution entered Tangxun Lake through inflowing rivers resulting in poor water quality in spring and winter.

4.5. Comparison of water quality evaluation methods

The PCA method synthesizes pollutants into several principal components. Each pollutant was assigned a different weight; the greater the weight, the greater the impact of the pollutant on water quality. Finally, the pollution level of water quality was determined using the score of PCA. In our study, the results of PCA were consistent with those of the Nemerow pollution index. Both showed that the degree of pollution in inner Tangxun Lake was greater than that in outer Tangxun Lake and that pollution was most severe in spring.

A comprehensive TLI can be obtained using an equal weight or weighted average method with multiple environmental variables. Similarly, there was a low correlation between the concentration of Chl-a and other indicators in Tangxun Lake. The evaluation results for phytoplankton diversity were similar to those of other studies, but those for algal cell density were quite different (Ji and Liu 2019).

The evaluation criteria for each method and the weight of the water quality index were different, and the evaluation results also differed (Pan et al. 2019). Using a combination of evaluation methods can render the evaluation of water quality more scientific. By using the Nemerow index and comprehensive TLI methods, water quality can be qualitatively analyzed, and the degree of water pollution can be comprehensively evaluated. Additionally, PCA supports quantitative analysis of the main factors affecting lake water quality. Moreover, determining phytoplankton density and diversity reveals the growth status and dominant species of phytoplankton in water, and can provide a scientific explanation for the occurrence of blooms and changes in water quality (Yu et al. 2023).

5. Conclusions

The results of the water quality evaluation based on different assessment methods showed that:

1. The water of Tangxun Lake exhibited severe eutrophication according to mean values of the comprehensive trophic level index. The pollution in inner Tangxun Lake was more serious than that in outer Tangxun Lake.

2. The sources of endogenous pollution in Tangxun Lake were mainly N and P in the sediment, and the perennial alkalinity of the water promoted the release of P. Residential sewage discharge were the main sources of eutrophication in Tangxun Lake, and rainfall was the main driving factor. There was a significant correlation between TP, TN and NH₃-N in the inflowing rivers and the amount of rainfall (p < 0.05).

3. The phytoplankton in Tangxun Lake were mainly cyanophyta and chlorophyta, which can easily cause blooms. The zooplankton biomass was relatively low, and algal cell density and diversity varied. Evaluation of algal cell density showed that Tangxun Lake was mesotrophic, and the phytoplankton diversity index showed that the eutrophication level was high.

4. According to the degree of eutrophication, it is advice to establish acquatic vegetation community, strengthen the monitoring and management of inflowing rivers, and inhibit the release of nutrients in sediments.

Acknowledgements

The authors would like to acknowledge the members of Laboratory of Aquatic Ecological Restoration Hubei Normal University for their help in sample determination.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

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

Additional information

Funding

This research was funded by the Open Foundation of Hubei Key Laboratory of Pollutant Analysis & Reuse Technology (Hubei Normal University) (PA220103), the Open Foundation of Research Center for Transformation and Development of Resource depletion Cities (Hubei Normal University) (KF2023Z01), the Open Project Funding of Key Laboratory of Intelligent Health Perception and Ecological Restoration of Rivers and Lakes, Ministry of Education, Hubei University of Technology (HGKFYBP03), and the College Students’ Innovative Entrepreneurial Training Plan Program (202310513013, S202310513064).