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Article

Evaluation of Water Quality and Eutrophication of Typical Lakes in Southeast Hubei, China

by
Mingkai Leng
1,
Xiaodong Wu
1,2,3,*,
Xuguang Ge
1,3,
Xiaoqing Yang
1,
Zhi Huang
1,
Haoran Liu
1,
Jiali Zhu
1,
Jinge Li
1,
Mengting Gong
1,
Zhepeng Sun
1 and
Zixiang Li
1
1
College of Urban and Environmental Sciences, Hubei Normal University, Huangshi 435002, China
2
Resource-Exhausted City Transformation and Development Research Center, Hubei Normal University, Huangshi 435002, China
3
Huangshi Key Laboratory of Soil Pollution and Control, Hubei Normal University, Huangshi 435002, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(20), 8964; https://doi.org/10.3390/su16208964
Submission received: 3 September 2024 / Revised: 9 October 2024 / Accepted: 15 October 2024 / Published: 16 October 2024
(This article belongs to the Special Issue Wetlands: Conservation, Management, Restoration and Policy—2nd Edition)
Browse Figures
Versions Notes

Abstract

:
Field surveys and sample analyses were conducted from January 2018 to December 2019 on Daye Lake, Cihu Lake, Baoan Lake, and Xiandao Lake to understand the water quality characteristics of typical lakes in southeast Hubei. A fuzzy comprehensive evaluation was conducted and the comprehensive trophic level index was applied to evaluate the lakes’ water quality. The results showed differences in the regional, spatial, and temporal distributions of physical and chemical indicators in typical lakes in southeast Hubei. The fuzzy comprehensive evaluation showed that the water quality levels in Daye, Cihu, Baoan, and Xiandao Lakes for 2018 and 2019 were IV, IV, III, and II and V, IV, III, and II, respectively, with seasonal variations in water quality occurring during the year. The trophic level index results showed that Cihu Lake was mildly eutrophic in 2018 and moderately eutrophic in 2019, and Daye, Baoan, and Xiandao Lakes were mildly eutrophic, mildly eutrophic, and mesotrophic, respectively. Lake water quality was influenced by land use types, landscape configuration, inflowing rivers, precipitation, and interactions between land use and seasons. This study helps us to understand the trend and causes of lake pollution in Southeast Hubei, which is conducive to watershed management and the control of water quality deterioration, and also has an important role in regulating the sustainable development of industry and agriculture in the watershed.

1. Introduction

Changes in lake water quality are essential to the evolution of lake environments, making them of great interest to countries and academics worldwide [1]. As an important part of the terrestrial hydrosphere and as a participant in the natural water cycle process, lakes can not only control foods, store and supply water, regulate the climate, and protect biodiversity and eco-tourism, but are also closely related to human survival and development. Indeed, lakes are an important part of wetland ecosystems, and they play a crucial role in preserving the socioeconomic conditions and natural environments within watersheds [2]. However, lakes are easily polluted, as they have long water exchange times and a weak self-purification ability [3]. With the rapid development of industrialization in recent years, many lakes have experienced varying pollution problems, including heavy metal pollution and organic pollution [4,5]. The declining water quality in polluted lakes threatens ecological security and significantly harms human production activities [6]. Eutrophication is becoming ever-more prominent as a result of the unregulated discharge of wastewater from lake basins, leading to excessive levels of nitrogen and phosphorus being present in water bodies. This phenomenon has led to the abnormal growth of phytoplankton and the release of harmful algal toxins, resulting in the pollution of water bodies, as well as decreases in the transmittance and oxygen content of water bodies and the mass death of zooplankton, submerged plants, and fish in the water. The carcasses of aquatic organisms are then degraded rapidly by microorganisms, releasing hydrogen sulfide gas, thus leading to the further deterioration of water bodies [7]. Therefore, spatial and temporal changes in lake water quality have gradually attracted people’s attention [8]. It is vital to strengthen the monitoring and analysis of water quality parameters, especially for preventing and controlling eutrophication and protecting water quality and safety [9].
Water quality assessment is key to water environmental protection and management, helping us to fully understand the current situation regarding change trends in water quality and providing necessary technical support for subsequent early warning, forecasting, and planning [10]. Currently, water quality evaluation methods applied by scholars worldwide include the comprehensive pollution index [11,12], the single-factor index method [13], the gray system evaluation method [14,15], the Nemerow pollution index method [16], the comprehensive water quality identification index [17], and the fuzzy comprehensive evaluation method [18]. Since the 1960s, the water quality index has been widely used in various water quality evaluation studies as it can fully utilize information on water quality parameters and efficiently present the overall water quality condition with clear standard values. In recent years, the fuzzy comprehensive evaluation method has been widely used in domestic aquatic environment evaluation; however, among these methods, the maximum membership method is the most popular, as it utilizes evaluation factor weights and the fuzzy comprehensive evaluation matrix to analyze the impacts of evaluation factors on water quality, in line with the careful consideration of the characteristics of the indicators [19]. The comprehensive trophic level method is widely used to evaluate the nutrient statuses of rivers and lakes [20,21]; it is based on the principle that chlorophyll a (Chl-a) is used as a baseline parameter, while the remaining parameters are normalized to derive the corresponding weights to determine the trophic state index using the corresponding formula. The value of the trophic level index (TLI) has a positive proportionality relationship with the degree of eutrophication [22]. Its main advantage is that it can directly derive the impacts of chlorophyll, total nitrogen, total phosphorus, etc., on the eutrophication of the water body; however, it neglects the influence of biological factors on the eutrophication of the water body, and there are some limitations [23]. Thus, this study analyzes the water environment characteristics of four lakes in the southeast Hubei region, Daye Lake, Cihu Lake, Baoan Lake and Xiandao Lake, combining the fuzzy comprehensive evaluation method and the comprehensive trophic level index for tests.
Although scholars have evaluated the water quality statuses of lakes in southeast Hubei, most have only used multiple methods to assess one lake, and few have determined the water quality statuses of different types of lakes in the region. In this study, the water quality data of typical lakes in southeast Hubei in 2018–2019 were used to evaluate the lakes’ water quality and nutrient status using the fuzzy comprehensive evaluation method and the comprehensive trophic level index (TLI). We also analyzed the driving factors behind water quality differences among lakes in combination with the land use statuses of the lake watersheds, which provides a prerequisite for the sustainable development of industry and agriculture in the watersheds and a basis for the management of different types of lakes in the southeast Hubei region.

2. Materials and Methods

2.1. An Overview of the Study Area

Southeast Hubei (113.53°–116.13° E, 29.03°–30.89° N) has a dense network of rivers, and the number of lakes and reservoirs is close to two-thirds of the total number in Hubei Province. This study selected four typical lakes in southeast Hubei: Daye Lake, Cihu Lake, Baoan Lake, and Xiandao Lake. Daye Lake is located in the southeastern part of Daye City, with an area of 64.6 km2 and an average water depth of 1.92 m, and it is classed as an industrial and mining lake [24,25]. Cihu Lake is located in the downtown area of Huangshi City, Hubei Province (30°12′ N, 115°03′ E), with average and maximum depths of 2.7 m and 4.8 m, respectively [26]. Baoan Lake is located in the southern shore area of the middle reaches of the Yangtze River (30°15′ N, 114°43′ E); it has a water area of approximately 45.1 km2 and is mainly surrounded by agricultural land [27]. Xiandao Lake is located in Yangxin County, Huangshi City, Hubei Province (29°47′18″ N, 114°50′07″ E), at the northern foot of Mufu Mountain; it has an average water depth of 25.7 m and a watershed area of 243.00 km2 [28].

2.2. Sampling Point Setting and Indicator Measurement

The number and location of the sample points for each lake were determined according to the lake area. The sampling points were set up far away from the shore. A total of 38 sampling points were set up in this study: 15 in Daye Lake, 10 in Cihu Lake, 6 in Baoan Lake, and 7 in Xindao Lake (Figure 1). Water quality surveys were conducted monthly from January 2018 to December 2019 for each lake.
A plexiglass water collector was used to collect mixed water samples (0.5 m) from the water surface, and the water samples were packed into 2.5 L acid-washed polyethylene bottles with preservative, and quickly transported to the laboratory to be preserved at below 4 °C; this ensured that the analysis of water quality indexes was completed within 24 h. The Secchi depth (SD) was assessed using black and white cycloramic discs to determine the in situ measurements during the collection of water samples, referring to the “Technical Procedures for the Investigation of Lakes” during the sampling operation [29]. The total nitrogen (TN) and total phosphorus (TP) concentration were determined by potassium persulfate oxidation–ultraviolet spectrophotometry and molybdenum–antimony anti-spectrophotometry, respectively; Nessler’s reagent spectrophotometry was used for determining ammonia nitrogen (NH3-N) concentration, the acid method was used for the permanganate index (CODMn), and 90% acetone extraction was used for Chlorophyll-a (Chl-a).

2.3. Evaluation Methods

2.3.1. Fuzzy Comprehensive Evaluation Method

CODMn, NH3-N, TP, and TN were selected as parameters using the “environmental quality standards for surface water” (GB3838-2002 [30]) as the evaluation standard; the specific steps for the fuzzy comprehensive evaluation were based on the relevant literature [31].

2.3.2. Comprehensive TLI

The comprehensive trophic level index (TLI) was calculated as [32,33]:
TLI Σ = ∑ j = 1 m W j · TLI ( j )
Here, Wj is the relative weight of the trophic state index of the jth parameter, and TLI (j) is the trophic state index of the jth parameter. Calculation of TLI values and corresponding weighting reference standards for each indicator [34]. The classification of the nutrient status of lakes is shown in Table 1.

2.4. Data Analysis

Water quality data were statistically analyzed using Excel 2019. Remote sensing images were processed with ArcGIS 10.2 and Envi 5.3.1, and overview maps of the study area and land-use maps were plotted. Principal component analysis of water physicochemical indicators using SPSS 24.0 and plotting principal components using Canoco 5.0 and GraphPad Prism 10.0 were used to perform the difference analysis and mapping.

3. Results

3.1. Chemical and Physical Characteristics of Water Bodies

Spatial and temporal differences in the physical and chemical indicators of water bodies in four typical lakes in southeast Hubei in 2018–2019 were investigated (Figure 2). The SD, CODMn, and Chl-a concentrations in Xiandao Lake differed significantly from those in Daye Lake, Cihu Lake, and Baoan Lake (p < 0.05). In contrast, the differences in SD, CODMn, and NH3-N concentrations were insignificant (p > 0.05) in Daye Lake, Cihu Lake, and Baoan Lake. There were no significant differences (p > 0.05) in TP, NH3-N, and TN concentration in Baoan Lake and Xiandao Lake. Moreover, the annual mean SD values for Xiandao Lake in 2018 and 2019 were 279 cm and 344 cm, respectively, significantly higher than in the other lakes (p < 0.05). In terms of interannual variations, the annual mean TP concentration of Daye Lake and Cihu Lake increased significantly from 0.1151 and 0.0931 to 0.1442 and 0.1188, respectively, from 2018 to 2019 (p < 0.05), and the TP, TN, and Chl-a concentration of Xiandao Lake decreased significantly (p < 0.05). The average TP and TN levels in the two years were ranked as follows: Daye Lake > Cihu Lake > Baoan Lake > Xiandao Lake.

3.2. Comprehensive Evaluation of Water Quality Levels

According to the results of the fuzzy comprehensive evaluation, the weight of each evaluation factor was directly proportional to its impact on water quality. TP had a greater impact on the water quality of Daye Lake in the summer of 2018 and the autumn of 2019, while it was mainly affected by TN in other seasons. The water quality levels in Cihu Lake and Baoan Lake were mainly affected by TP concentration in summer and autumn, while they were more affected by TN concentration in spring and winter (Table 2). The environmental factors with the greatest influence on the water quality of Sendao Lake in 2018–2019 were all related to TN. Among the physicochemical indicators affecting water quality in each lake, nitrogen, and phosphorus had greater weights, so the water quality levels in the lakes in the southeast of Hubei Province were mainly affected by TP and TN (Table 3).
Based on the maximum membership principle, we determined the water quality levels in the lakes (Table 4 and Table 5). In 2018, the annual average water quality level ranged from poor to excellent for Daye Lake (IV), Cihu Lake (IV), Baoan Lake (III), and Xiandao Lake (Class II); in 2019, the average annual water quality level ranged from poor to excellent for Daye Lake (V), Cihu Lake (IV), Baoan Lake (III), and Xiandao Lake (II). The water quality conditions in each lake with seasonal changes were as follows: the water quality of Daye Lake in the winter of 2018 changed to Class V, before changing to Class IV in the fall of 2019; the water quality of Cihu Lake gradually deteriorated after the summer of 2018; the water quality of Baoan Lake fluctuated between Class III and II; and the water quality of Xiandao Lake was best in the winter of 2019, when it was rated Class I.

3.3. An Evaluation of the Comprehensive Trophic Level Index

The results of the comprehensive evaluation of the TLI showed that the annual average trophic state of Daye Lake in 2018–2019 was mildly eutrophic (57.72~61.63), and the most serious moderate eutrophication was found in the fall of 2019; the fluctuating TLI value range in Cihu Lake was 56.66~63.22, with the value rising and then falling with seasonal changes. Both lakes exhibited their highest values in autumn, and the annual average trophic level in 2018 was mildly eutrophic. In 2019, the annual average nutrient level was moderately eutrophic. Baoan Lake, in 2018–2019, experienced small changes in nutrient status with seasonal changes, and the TLI value was 51.61~58.21, making it a mildly eutrophic water body. The TLI value for Xiandao Lake was in the range of 28.08~37.97, and the average nutrient level was mesotrophic in both 2018 and 2019. The highest individual trophic state index values for Daye Lake, Cihu Lake, and Baoan Lake were all related to Chl-a (73.62, 76.08, and 73.91, respectively), and the results of the trophic state evaluation indicated that these three lakes might be heavily polluted. The highest trophic state index of TN for Xiandao Lake (46.25) in the summer of 2019 was mesotrophic (Table 6 and Table 7).

4. Discussion

4.1. An Analysis of Factors Affecting the Water Quality

Principal component analysis (PCA) was performed on data acquired in 2018 and 2019 for six water quality indicators: CODMn, NH3-N, TP, TN, Chl-a, and SD [20]. The KMO and Bartlett’s Sphericity Test results showed that the KMO value was 0.777 and p = 0.000 (p < 0.05) and that parametric data could be used for performing principal component analysis. According to the principle requiring an eigenvalue greater than one, two principal components were extracted: the first component accounted for about 60.148% and the second component accounted for about 20.614% of the total variance in the data set. Together, these two components accounted for about 80.761% of the total variance (Table 8). Therefore, these two principal components could effectively reflect the information contained in the original water quality variables, aligning with the requirements of principal component analysis.
The PCA results of PCA (Table 8) reflected the correlation between each index and its principal component, and the larger the loading coefficient, the closer the correlation. The variance contribution rate of the first principal component (PC1) reached 60.148%, which was more significant than that of the second principal component, and it was the component that most significantly affected water quality. CODMn, SD, and TP had larger loadings on PC1, with all being larger than 0.80. PC2 was larger with TN, Chl-a, and NH3-N, and it was also more extensive.
TP, TN, and NH3-N can characterize the degree of nutrient pollution in water, and P in the water generally originates from domestic sewage and farmland runoff [35]. TN and NH3-N are indicators of the N element, and some studies have pointed out that livestock and poultry breeding and farmland fertilizer are the most essential sources of TN and NH3-N [36]. In addition, excessive TN and TP concentrations encourage the excessive growth of phytoplankton and induce eutrophication in water bodies [37,38]. CODMn and Chl-a have a close relationship with phytoplankton, and previous studies have shown that the concentration of CODMn is positively correlated with the content of organic matter in the water body and that having richer organic matter in water can promote the growth and development of algae [39]. The chlorophyll concentration can also reflect the extent of phytoplankton biomass [40]. After analysis, it can be preliminarily concluded that NH3-N, Chl-a, CODMn, TN, TP, and SD all have obvious influences on the water quality of each lake.

4.2. Impacts of Land Use on Lake Water Quality

The land use status within the watershed significantly impacts a lake’s water quality, and land use types in different seasons have different impacts on water bodies. Land use types in southeast Hubei are mainly dominated by cropland, forest, grassland, water, and impervious surfaces, and scholars have concluded that these land use types have different levels of influence on the water quality statuses of rivers and lakes [41]. For example, anthropogenic disturbance has a high degree of influence on lake water quality [42], whereas forests and grasslands are recognized as land use types that benefit water quality [43,44].
In this study, remote sensing images were used to classify land use in the lakes’ watersheds and calculate each category’s area share. The results showed that Daye Lake’s watershed is mainly dominated by cropland, with an area share of approximately 57.18%, followed by forested land (19.79%), and impervious surfaces are mainly found in Daye City, accounting for approximately 8.84% of land use. Heavy industry and agriculture in the basin are more developed; the industrial wastewater, domestic sewage, and farmland irrigation backwater discharged into the lake lead to the deterioration of water quality; TP and NH3-N concentrations exceeded the maximum permitted level [45]; the investigation of the Daye Lake Basin found that the domestic wastewater treatment plant’s tailwater content of total phosphorus (0.29 ± 0.07 mg/L) was too high, and the wetland basin is harmed by fertilizer farming and sewage discharged into Daye Lake and its watershed rivers, having a more significant impact on water quality. The impervious area of the Cihu Lake basin made up 37.53% of land use, followed by forest and cropland, accounting for 26.00% and 20.79% of the watershed area, respectively. The Cihu Lake basin has a high degree of anthropogenic interference, and the primary source of pollution from urban sewage discharge is not standardized; the exogenous pollution load is large, and the water quality from inflowing rivers is poor, resulting in the deterioration of the water quality of the lake [46]. The Baoan Lake basin land use type is dominated by cropland, accounting for approximately 66.55%, and impervious surfaces and forest, accounting for 6.95% and 6.06%, respectively. Although Baoan Lake’s impervious area is relatively small, there are many intensively cultivated fishponds and much cultivated land in the surrounding area [47]; the proportion of impervious area in the Xiandao Lake watershed is only 1.11%, and the proportion of forested area is as high as 62.69%. The submerged vegetation is rich in variety and quantity, playing a greater role in improving water quality [48,49].
The interaction between land use and seasons also affects the state of the lake’s water environment, and the PCA analysis results show that in spring, Daye Lake and Cihu Lake, which have larger proportions of impervious surfaces, have stronger correlations with CODMn and Chl-a, while the correlations with TP and TN are weaker. In summer and autumn, the correlation between TP and TN and impervious surfaces becomes stronger, but it decreases in winter (Figure 3 and Figure 4). Moreover, TLI values were positively correlated with impervious surfaces and water, while FCE values were positively correlated with forest land in spring in both 2018 and 2019 and positively correlated with cropland and negatively correlated with man-made surfaces in the remaining seasons. The study by Shi et al. also confirms that season, land use status, and water quality are closely related [50]. According to the results of land use classification and the previous analysis, we can see that land use composition (the percentages of various land uses) and landscape configuration (the structure of land use) have a great influence on the water quality and nutrient status level of the lakes in Southeast Hubei [51].

4.3. Impacts of Inflowing Rivers and Precipitation on the Lake’s Water Quality

With accelerating industrialization and the continuous expansion of human activities, the relationship between inflowing rivers and lake water quality has become increasingly close. According to previous studies, the nutrients carried by inflowing rivers directly affect lake water quality, and pollutant distribution patterns in rivers and lakes are essentially the same [52,53]. Polluted rivers entering lakes due to unregulated sewage discharge destabilize lake ecosystems [54,55], and this effect is more prominent in the rainy season because wastewater, rainfall, and urban stormwater collectively carry many pollutants from inlet streams into lakes, resulting in the deterioration of water quality in lakes. In addition, nutrient concentrations in rivers entering a lake are closely related to algal bloom formation, with an increasing inflow of nutrients accompanied by elevated algal biomass in the lake body [56]. After an on-site investigation, some sections of the Dagang River and Niupi River and the main rivers of Daye Lake, were found to be more seriously polluted due to industrial and agricultural pollution, and the water quality was poor (Category 5 water). The Cihu Lake watershed is mainly urban areas, and the water quality of the inflowing rivers is poor, directly affecting the water quality of Cihu Lake. The primary source of pollution in Baoan Lake is the incomplete interception of sewage in the nearby township; in addition, some tailwater and agricultural ditch water discharged into the river, among other issues (Figure 5).
Precipitation is closely related to the trophic states of lakes [57], and pollutants on urban and rural roads and non-road impervious surfaces are constantly pooled and discharged into lakes by precipitation, altering the trophic states of water bodies [58,59]. Our study found that ammonia nitrogen was more closely related to the water quality of Baoan Lake in summer. A similar conclusion was reached in a survey of Castle Peak Lake: total phosphorus and total nitrogen levels in the water body were significantly elevated in conjunction with rainfall on the lake surface [60]. Wang et al. (2016) showed that atmospheric precipitation had a significant effect on the water quality of rivers in Jinzhou City, and when the monthly precipitation was <108 mm, the local water quality improved in line with the increase in atmospheric precipitation; however, when the monthly precipitation was greater than this value, the water quality deteriorated in line with the rise in precipitation [61]. In addition, the effect of heavy precipitation on the pollution loads of river and lake water is significant. An increase in heavy precipitation events leads to a relative increase in the pollution loads of water bodies [62] because the nitrogen and phosphorus loading in plants and soil exceeds their capability to hold these nutrients in the watershed [63]. The deterioration of water quality is aggravated by the fact that the mixed effluent exceeds the conveyance capacity of the intercepting pipeline, resulting in overflows when the rainfall volume is increased [64]. Consequently, Daye Lake, Cihu Lake, and Baoan Lake, which have relatively high levels of anthropogenic disturbance in the watershed, also have relatively high nutrient levels during summer and autumn.

5. Conclusions

Lakes are closely linked to activities of daily living and play an important role in socioeconomic conditions and the natural environment at the watershed scale. This study helps us to understand the trend and causes of lake pollution in Southeast Hubei, and the understanding of pollution sources helps watershed management and control of water quality deterioration, and also plays an important role in regulating the sustainable development of industry and agriculture in the watershed. The findings are as follows: (1) There are spatial and temporal differences in the measured physical and chemical indicators of lake water bodies. (2) The results of fuzzy comprehensive evaluation showed that the annual water quality levels of Daye Lake, Cihu Lake, Baoan Lake, and Xiangdao Lake in 2018 and 2019 were IV, IV, III, and II and V, IV, III, and II, respectively, and there were seasonal variations in water quality within each year. (3) The TLI results show that the annual average trophic status of Daye Lake in 2018–2019 was mildly eutrophic (57.72~61.63). The TLI values of Cihu Lake fluctuated between 56.66 and 63.22, and its annual average trophic levels in 2018 and 2019 were mildly and moderately eutrophic, respectively. The change in the trophic status of Baoan Lake was small, with TLI values ranging from 51.61 to 58.21, making them mildly eutrophic. Finally, Xiandao Lake had a TLI value between 28.08 and 37.97 in 2018–2019, making it mesotrophic. (4) The water quality levels of lakes are affected by the land use composition, the configuration of the watershed and inflowing river landscapes, and precipitation, and it is also affected by the interaction between land use and seasons.

Author Contributions

Conceptualization, M.L., X.W., H.L. and X.Y.; methodology, M.L., Z.H., J.Z. and Z.S.; software, M.L., J.L., Z.L. and X.Y.; validation, M.L., M.G., Z.H. and H.L.; formal analysis, M.L., J.Z., Z.S., M.G. and X.G.; investigation, Z.L., Z.H. and J.L.; resources, X.W. and X.G.; data curation, M.L. and X.W.; writing—original draft preparation, M.L.; writing—review and editing, X.W. and X.G.; supervision, H.L.; project administration, X.W. and X.G. All authors have read and agreed to the published version of the manuscript.

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 Resource-exhausted City Transformation and Development Research Center (Hubei Normal University) (KF2024Y07), 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), graduate innovative research project construction of Hubei Normal University (2023Z038), and the College Students’ Innovative Entrepreneurial Training Plan Program (202310513013, S202310513064).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Distribution of sampling points among four typical lakes in southeast Hubei Province (Letters with numbers represent lake sampling locations).
Figure 1. Distribution of sampling points among four typical lakes in southeast Hubei Province (Letters with numbers represent lake sampling locations).
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Figure 2. Changes in the environmental characteristic parameters of water bodies in southeastern Hubei. (The error bars in the figure are standard deviations; A, B, and C represent the variability in indicator levels between lakes in 2018, and a, b, and c represent the differences in indicator levels between lakes in 2019; ns and * represents whether the same indicator is significantly different between 2018 and 2019 for the same lake).
Figure 2. Changes in the environmental characteristic parameters of water bodies in southeastern Hubei. (The error bars in the figure are standard deviations; A, B, and C represent the variability in indicator levels between lakes in 2018, and a, b, and c represent the differences in indicator levels between lakes in 2019; ns and * represents whether the same indicator is significantly different between 2018 and 2019 for the same lake).
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Figure 3. PCA analysis of land use types and water quality indicators for 2018 ((a–d) stand for spring, summer, autumn, and winter).
Figure 3. PCA analysis of land use types and water quality indicators for 2018 ((a–d) stand for spring, summer, autumn, and winter).
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Figure 4. PCA analysis of land use types and water quality indicators for 2019 ((a–d) stand for spring, summer, autumn, and winter).
Figure 4. PCA analysis of land use types and water quality indicators for 2019 ((a–d) stand for spring, summer, autumn, and winter).
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Figure 5. The main rivers entering typical lakes in southeast Hubei.
Figure 5. The main rivers entering typical lakes in southeast Hubei.
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Table 1. Trophic level classification table for lakes.
Table 1. Trophic level classification table for lakes.
Trophic LevelEvaluation Criteria: TLI(∑)Qualitative Evaluation
OligotrophicationTLI(∑) < 30Great
Mesotrophication30 ≤ TLI(∑) ≤ 50Good
EutrophicationTLI(∑) > 50Polluted
Mild eutrophication50 < TLI(∑) ≤ 60Mildly polluted
Moderate eutrophication60 < TLI(∑) ≤ 70Moderately polluted
Heavy eutrophicationTLI(∑) > 70Heavily polluted
Table 2. Weights of evaluation indicators by season in 2018–2019.
Table 2. Weights of evaluation indicators by season in 2018–2019.
YearsLakeSeasonsCODMnNH3-NTPTN
2018Daye LakeSpring0.11790.20430.26030.4175
Summer0.17620.10210.39780.3239
Autumn0.13940.08700.45260.3209
Winter0.08100.13580.32560.4576
Cihu LakeSpring0.18570.14520.32790.3412
Summer0.19140.10780.38600.3149
Autumn0.15200.09660.44210.3093
Winter0.11490.12790.31900.4382
Baoan LakeSpring0.18340.16550.32470.3263
Summer0.20270.12160.35580.3199
Autumn0.20240.11750.40100.2791
Winter0.13630.14130.29450.4279
Xiandao LakeSpring0.15470.14250.26750.4353
Summer0.19030.07880.19590.5351
Autumn0.22750.07830.26500.4292
Winter0.14610.07020.34920.4345
2019Daye LakeSpring0.07710.16680.36890.3872
Summer0.12910.08870.33730.445
Autumn0.12090.12850.46590.2846
Winter0.0990.13620.33230.4326
Cihu LakeSpring0.13690.11340.34740.4024
Summer0.13160.09480.40810.3655
Autumn0.14110.10430.47790.2766
Winter0.1570.10020.33340.4094
Baoan LakeSpring0.15360.13950.2460.461
Summer0.19360.16280.35140.2922
Autumn0.21050.13350.34860.3074
Winter0.16830.17640.27660.3787
Xiandao LakeSpring0.16950.13090.20360.4959
Summer0.19810.20280.1360.4631
Autumn0.14580.13750.25810.4586
Winter0.17130.15470.22250.4514
Table 3. Average weights of evaluation indicators in 2018–2019.
Table 3. Average weights of evaluation indicators in 2018–2019.
YearsLakeCODMnNH3-NTPTN
2018Daye Lake0.12310.13390.35460.3885
Cihu Lake0.15670.11810.37060.3546
Baoan Lake0.18040.13730.34250.3398
Xiandao Lake0.17960.09380.27250.4540
2019Daye Lake0.10370.13420.37810.384
Cihu Lake0.14130.1030.39850.3571
Baoan Lake0.1810.15280.30420.362
Xiandao Lake0.17330.15820.20410.4644
Table 4. Evaluation results of water quality of typical lakes in southeast China by season in 2018 and 2019.
Table 4. Evaluation results of water quality of typical lakes in southeast China by season in 2018 and 2019.
YearsSeasonsLakeIIIIIIIVVFuzzy
Composite Valuation		Evaluation Results
2018SpringDaye Lake0.02750.15080.24190.36400.21570.3640IV
Cihu Lake0.07240.29410.51910.11440.00000.5190III
Baoan Lake0.02910.35250.48850.12990.00000.4885III
Xiandao Lake0.00000.61740.10270.00000.00000.6174II
SummerDaye Lake0.01000.04860.06570.12010.00570.1200IV
Cihu Lake0.01410.06950.09890.06750.00000.0988III
Baoan Lake0.07940.34080.43740.14230.00000.4373III
Xiandao Lake0.39960.54330.05710.00000.00000.5432II
AutumnDaye Lake0.00900.04600.02980.12190.04340.1218IV
Cihu Lake0.00900.04970.05050.11150.02940.1114IV
Baoan Lake0.08330.38230.30490.22940.00000.3823II
Xiandao Lake0.28300.62780.08920.00000.00000.6277II
WinterDaye Lake0.00780.02910.01730.05260.14320.1431V
Cihu Lake0.01100.04970.01050.12780.05090.1278IV
Baoan Lake0.15270.12490.45100.27140.00000.4509III
Xiandao Lake0.21630.64360.14010.00000.00000.6435II
2019SpringDaye Lake0.01780.05930.14490.10970.66830.6683V
Cihu Lake0.06190.18830.07850.67130.00000.6713IV
Baoan Lake0.09940.19370.36790.33910.00000.3679III
Xiandao Lake0.44720.55280.00000.00000.06000.5528II
SummerDaye Lake0.04290.17480.00000.11870.66360.6636V
Cihu Lake0.01170.04480.00430.16660.02250.1666IV
Baoan Lake0.07710.41890.37910.12500.00000.4189II
Xiandao Lake0.43940.45560.10500.00000.00000.4556II
AutumnDaye Lake0.00000.18230.08440.36940.36390.3694IV
Cihu Lake0.00020.04640.03790.08130.08420.0842V
Baoan Lake0.00000.26150.52840.21010.00000.5284III
Xiandao Lake0.30560.63940.05500.00000.00000.6394II
WinterDaye Lake0.02910.16430.04170.26860.49620.4962V
Cihu Lake0.01010.05350.02300.16340.00000.1634IV
Baoan Lake0.04790.20470.63530.11210.00000.6353III
Xiandao Lake0.51270.48730.00000.00000.00000.5127I
Table 5. Evaluation results of 2019 average water quality of typical lakes in Southeast China.
Table 5. Evaluation results of 2019 average water quality of typical lakes in Southeast China.
YearsLakeIIIIIIIVVFuzzy Composite ValuationEvaluation
Results
2018Daye Lake0.00980.22530.02180.53120.21180.5312IV
Cihu Lake0.05170.22310.26160.46360.00000.4636IV
Baoan Lake0.08810.26880.50390.13920.00000.5039III
Xiandao Lake0.27350.66170.00000.00000.00000.6617II
2019Daye Lake0.00980.17820.04990.22320.53890.5389V
Cihu Lake0.02630.21470.04820.63600.07480.6360IV
Baoan Lake0.06720.26660.57290.09330.00000.5729III
Xiandao Lake0.45210.54790.00000.00000.00000.5479II
Table 6. TLI values of typical lakes in southeastern Hubei by season during the year.
Table 6. TLI values of typical lakes in southeastern Hubei by season during the year.
YearsSeasonsLakeTLI(Chl-a)TLI(TP)TLI(TN)TLI(SD)TLI(CODMn)TLI(∑)Nutrient Level
2018SpringDaye Lake65.367753.577364.093467.704034.678357.7239Mild eutrophication
Cihu Lake70.361550.569653.624466.960635.684156.6623Mild eutrophication
Baoan Lake54.674151.173653.664165.151436.614452.4448Mild eutrophication
Xiandao Lake38.941135.702445.690335.304811.879433.9102Mesotrophication
SummerDaye Lake70.562457.868557.082063.401041.113559.0490Mild eutrophication
Cihu Lake68.257754.326753.421864.713538.309956.8412Mild eutrophication
Baoan Lake69.012851.173651.779965.019036.846855.9436Mild eutrophication
Xiandao Lake42.057826.156844.508033.147010.043532.0025Mesotrophication
AutumnDaye Lake73.615162.235859.295965.729438.605461.0457Moderate eutrophication
Cihu Lake72.677460.794357.566170.100639.171861.1212Moderate eutrophication
Baoan Lake66.387153.055549.404861.548036.707654.5106Mild eutrophication
Xiandao Lake50.114135.500745.402431.554922.070937.9772Mesotrophication
WinterDaye Lake60.045061.879870.515465.873432.317258.2473Mild eutrophication
Cihu Lake67.461455.855263.842666.833432.305858.0696Mild eutrophication
Baoan Lake61.010749.376458.039867.195428.361253.4388Mild eutrophication
Xiandao Lake47.654938.629944.199126.15118.071534.1359Mesotrophication
2019SpringDaye Lake61.470466.017369.588863.145434.692659.1736Mild eutrophication
Cihu Lake71.459755.845660.943763.866234.678358.5042Mild eutrophication
Baoan Lake58.96346.757459.616857.64332.041451.6068Mild eutrophication
Xiandao Lake37.822426.156842.563723.88595.941628.0757Oligotrophication
SummerDaye Lake68.198657.721565.106263.54936.978759.0974Mild eutrophication
Cihu Lake73.552460.206461.13667.496736.497460.9147Moderate eutrophication
Baoan Lake62.801450.649749.908862.738935.093453.1166Mild eutrophication
Xiandao Lake45.542824.24446.251925.21317.698732.8322Mesotrophication
AutumnDaye Lake69.942666.338961.053966.184540.773461.6346Moderate eutrophication
Cihu Lake76.075465.628859.400366.02543.041263.2248Moderate eutrophication
Baoan Lake73.905151.929152.238766.244739.627358.2051Mild eutrophication
Xiandao Lake33.363433.332244.710926.87647.380329.4333Oligotrophication
WinterDaye Lake64.221859.810567.06363.689333.74458.2137Mild eutrophication
Cihu Lake67.8755.103961.164763.52538.219858.0357Mild eutrophication
Baoan Lake60.345348.305355.909863.294333.89752.9789Mild eutrophication
Xiandao Lake37.631827.867841.255434.46726.670730.1868Mesotrophication
Table 7. Annual average TLI values of typical lakes in southeast Hubei.
Table 7. Annual average TLI values of typical lakes in southeast Hubei.
YearsLakeTLI(Chl-a)TLI(TP)TLI(TN)TLI(SD)TLI(CODMn)TLI(∑)Nutrient Level
2018Daye Lake68.552259.255163.557465.616536.896359.5664Mild eutrophication
Cihu Lake69.882055.805657.666967.058136.499958.4096Mild eutrophication
Baoan Lake64.042051.246953.524464.620634.865954.5098Mild eutrophication
Xiandao Lake45.564634.614244.961131.234313.596834.9072Mesotrophication
2019Daye Lake66.452162.904865.983564.10636.687659.812Mild eutrophication
Cihu Lake72.643559.758960.676865.160938.295260.4077Moderate eutrophication
Baoan Lake65.816149.53454.823262.227435.314954.5363Mild eutrophication
Xiandao Lake39.531428.447943.804827.237510.215830.5821Mesotrophication
Table 8. Orthogonal rotation factor load matrix, eigenvalue, and variance contribution rate.
Table 8. Orthogonal rotation factor load matrix, eigenvalue, and variance contribution rate.
Physical and Chemical IndicatorsPC1PC2
TP0.8710.114
CODMn0.848−0.391
SD−0.8020.233
Chl-a0.718−0.563
TN0.7140.554
NH3-N0.680.627
Eigenvalue3.6091.237
Variance contribution60.14820.614
cumulative60.148%80.761%
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Leng, M.; Wu, X.; Ge, X.; Yang, X.; Huang, Z.; Liu, H.; Zhu, J.; Li, J.; Gong, M.; Sun, Z.; et al. Evaluation of Water Quality and Eutrophication of Typical Lakes in Southeast Hubei, China. Sustainability 2024, 16, 8964. https://doi.org/10.3390/su16208964

AMA Style

Leng M, Wu X, Ge X, Yang X, Huang Z, Liu H, Zhu J, Li J, Gong M, Sun Z, et al. Evaluation of Water Quality and Eutrophication of Typical Lakes in Southeast Hubei, China. Sustainability. 2024; 16(20):8964. https://doi.org/10.3390/su16208964

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Leng, Mingkai, Xiaodong Wu, Xuguang Ge, Xiaoqing Yang, Zhi Huang, Haoran Liu, Jiali Zhu, Jinge Li, Mengting Gong, Zhepeng Sun, and et al. 2024. "Evaluation of Water Quality and Eutrophication of Typical Lakes in Southeast Hubei, China" Sustainability 16, no. 20: 8964. https://doi.org/10.3390/su16208964

APA Style

Leng, M., Wu, X., Ge, X., Yang, X., Huang, Z., Liu, H., Zhu, J., Li, J., Gong, M., Sun, Z., & Li, Z. (2024). Evaluation of Water Quality and Eutrophication of Typical Lakes in Southeast Hubei, China. Sustainability, 16(20), 8964. https://doi.org/10.3390/su16208964

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