Evaluation of Water Quality and Pollution Source Analysis of Meihu Reservoir

Abstract

Under the background of increasingly serious global environmental pollution, ensuring the safety of drinking water has become one of the focuses of global attention. In this study, Meihu Reservoir, a drinking water source, was selected as the research object, and the main pollution problems and their sources were revealed through conventional water quality analysis, suitability evaluation of the drinking water source and eutrophication evaluation of the reservoir. Using modern water quality monitoring technology and methods, the paper monitors and analyzes various water quality parameters of the Meihu Reservoir. The results showed that the water quality indexes, except total nitrogen, met the class II–III standard of drinking water, and the comprehensive nutrient state index method (TLI) evaluated the reservoir, and its index met $30\le \mathrm{TLI}(\sum )\le 50$, indicating that the reservoir belongs to the medium nutrition category. Therefore, the water quality of the reservoir has been affected by different degrees of agricultural, domestic and livestock pollution, mainly reflected in the serious excess of the total nitrogen index (the peak has reached 2.99 mg/L). The results of the on-site investigation showed that the main sources of nitrogen in the reservoir included agricultural non-point-source pollution, domestic sewage pollution, domestic garbage pollution and livestock and poultry pollution, accounting for 50.09%, 23.99%, 14.13% and 11.80% of the total load, respectively. On this basis, this paper puts forward some countermeasures for pollution control in order to provide a scientific basis and practical path for water quality protection and improvement of the Meihu Reservoir and other similar reservoirs.

1. Introduction

The quality of drinking water sources is critical to the health of residents. Studies conducted by the World Health Organization (WHO) consistently show that a large number of human diseases are directly linked to unsafe drinking water and inadequate hygiene practices, particularly with reservoirs [1,2]. Ensuring safe drinking water is, therefore, a fundamental prerequisite for human health and development [3,4]. In recent years, with the continuous advancement of industrialization and urbanization, the environmental pressure of water sources has increased significantly, and various pollution sources have appeared frequently, leading to the deterioration of water quality. Research on the water quality of drinking water sources and pollution source control is not only an urgent need to ensure water quality safety but also an important part of ecological environmental protection and sustainable development [5,6,7,8,9,10]. As an important water resource reserve in Cixi City, Meihu Reservoir not only undertakes the task of providing drinking and irrigation water for local residents and agriculture but also plays a key role in maintaining regional ecological balance. However, in recent years, the water quality of the reservoir has gradually deteriorated, facing the threat of pollution.

Water quality problems are manifested in various forms, including excessive nutrients, heavy metal pollution and the emergence of organic pollutants [4]. Excessive nutrients, especially increased concentrations of nitrogen and phosphorus, are one of the main causes of water eutrophication [11,12]. The phenomenon of eutrophication is usually caused by agricultural non-point-source pollution, mainly from the use of fertilizers and pesticides. After being washed by rain, these nutrients enter the water with runoff, resulting in a sharp increase in the concentration of nutrients in the water. Eutrophication will not only lead to the excessive reproduction of algae to form bloom but also consume dissolved oxygen in the water body, seriously affecting the survival of aquatic organisms, and some cyanobacteria will produce toxins during reproduction, posing a threat to the ecosystem and human health [13,14,15]. In addition, heavy metal pollution is another significant water quality problem, mainly from industrial wastewater discharge, mineral exploitation and urban runoff [16,17]. Common heavy metal pollutants include lead, mercury, cadmium, arsenic, etc. These metals can exist in water for a long time, are difficult to degrade and cause great harm to the ecosystem and human health. Studies have shown that the increase in heavy metal concentration in water bodies is directly related to the deterioration of water quality and significantly inhibits the self-purification ability of water bodies [18,19]. Of course, organic pollutants mainly refer to compounds containing carbon and come from a wide range of sources, including industrial production, agricultural activities and domestic sewage [20,21]. Common organic pollutants include volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), pesticide residues and pharmaceuticals. The presence of these organic pollutants in water bodies not only affects water quality but also may cause toxic effects on aquatic organisms and even pose a threat to human health through drinking water routes. In particular, some persistent organic pollutants (POPs), which are biocumulative and persistent, will exist in the ecosystem for a long time and cause continuous pollution [22,23]. It can be seen that each form of pollution poses a potential threat to the health of the water ecosystem and the quality of human life. Therefore, in-depth research on the sources, influencing mechanisms and control strategies of these forms of pollution is of great significance for improving water quality and protecting the ecological environment.

In order to effectively control water quality, it is very important to analyze and identify pollution sources. The sources of water pollution can be various, including agricultural non-point-source pollution, industrial discharge, urban sewage, etc. [5,24]. After the unfiltered pollution source enters the water body, it will show different pollution characteristics in the water body, which help to reveal the nature and source of the pollution source. For example, agricultural runoff often contains high concentrations of nitrogen and phosphorus, while industrial wastewater may contain heavy metals and organic pollutants. By identifying these sources of pollution, targeted measures can be taken to prevent further diffusion and accumulation of pollutants [8,25,26]. Pollution sources can usually be divided into two categories: point-source pollution and non-point-source pollution. Point-source pollution mainly comes from pollution at specific locations, such as industrial wastewater discharge outlets and sewage treatment plants. This type of pollution source is characterized by relatively concentrated pollutant emissions, which are easy to monitor and manage, while non-point-source pollution mainly comes from a wide range of regional pollution, such as agricultural non-point-source pollution. This kind of pollution source is characterized by scattered pollutant discharge, which is difficult to directly monitor and control. After entering the water body, the pollutants will undergo diffusion, settlement, transformation and other processes [27,28,29]. By analyzing the flow path of pollutants, we can also understand the behavior characteristics of pollutants in the water body and its impact on water quality. This process usually requires a combination of hydrological models and geographic information system (GIS) technology to simulate the migration and transformation process of pollutants so as to predict the future distribution of pollutants [30,31]. It can be seen that the systematic analysis of pollution sources can provide a scientific basis for the design and implementation of pollution control measures.

There are many successful cases of reservoir water quality management and pollution control at home and abroad. Hoover Reservoir is faced with the challenge of water pollution, which mainly comes from agricultural and urban runoff. After the implementation of reservoir management measures, including strengthening pollution control in the basin, adopting advanced water quality monitoring technology and raising public awareness, the water quality of the reservoir has been effectively improved, the water quality has been kept within the safe range and the ecological health of the water body has been improved [32,33]. As an important drinking water source in Australia, Sydney Reservoir is also faced with the threat of water quality pollution. After taking comprehensive water quality protection measures, including source control, sewage treatment and ecological restoration, its water quality has been effectively protected and meets the high standard of drinking water requirements [34]. Vester Reservoir also faced the problem of deteriorating water quality, mainly due to agricultural and industrial pollution. The Dutch government implemented ecological restoration measures, including vegetation restoration, ecological buffer zone construction and a water quality monitoring ecological restoration plan, which significantly improved the water quality of the reservoir, restored the ecosystem well and significantly improved the health of the water body [35]. As an important water source in Singapore, Bukit Timah Reservoir also faces the challenge of water quality protection. In the face of this problem, the local government has adopted comprehensive reservoir management measures, including rainwater collection, sewage treatment and the construction of a water quality monitoring system so as to effectively protect the water quality of the reservoir and ensure the safe and stable supply of drinking water [36].

To sum up, it can be found that there have been many achievements in research on water quality monitoring technology and pollution source analysis methods of drinking water sources, but relatively few studies have systematically elaborated on the aspects of water body sampling, water environment quality assessment, pollution source investigation and analysis. In this paper, we analyzed the existing water quality of the Meihu Reservoir and used the comprehensive nutrient state index (TLI) to evaluate the eutrophication and determine the nutrient status of the reservoir. On this basis, the investigation and analysis of reservoir pollution sources not only provide a scientific basis for the management and protection of the Meihu Reservoir but also provide a certain reference for water quality control in other areas.

2. Materials and Methods

2.1. Study Area

The Meihu Reservoir is situated within the Cixi City of Ningbo, Zhejiang Province, China (30°12′41′′ N, 121°13′09′′ E). It serves primarily as a medium-sized, multipurpose reservoir with a focus on irrigation, flood control and water supply, while also integrating functions such as power generation and fish farming. Construction of the reservoir commenced in January 1958, featuring a type of inclined core wall earth dam. The dam has a height of 21 m, a crest length of 252 m and a crest width of 4 m. The reservoir’s total storage capacity is 16.03 million m³, with an effective storage capacity of 12.84 million m³. It covers a catchment area of 23.5 km², with a main stream length of 10 km and a water surface area of 1.35 km². In early 2008, due to the frequent outbreaks of blue-green algae, a sediment dredging project was undertaken, which involved the removal of 280,000 km³ of sludge from the reservoir bottom.

2.2. Monitoring Layout and Testing Items

The distribution and testing of water quality monitoring items in drinking water sources hold significant importance [37,38]. By precisely distributing monitoring points, the continuous monitoring of key indicators, such as microbial pollution, organic matter content, heavy metal content, etc., can be achieved for reservoir water quality; the specific process is shown in Figure 1. These data not only assist in ensuring the safety of residents’ drinking water and the timely detection and resolution of water quality issues but also effectively prevent the risk of source water pollution, thereby safeguarding public health and the environment.

In order to cover the temporal and spatial variability of reservoir water quality parameters, five water sample monitoring points were determined by referring to the “Technical Specifications Requirements for Monitoring of Surface Water and Waste Water (HJ/T 91-2002)” [39] and taking into account the natural properties of reservoir water area, shape and inflow river (Figure 2): the main inlet 1 (ST1), the outlet in front of the dam (ST2), the middle of the dam 1 (ST3), the middle of the dam 2 (ST4) and the main inlet 2 (ST5). Each monitoring point was set with three vertical monitoring lines at the left, center and right planes, with each vertical line establishing two monitoring points at the upper and lower positions.

Water samples were collected at five sites (ST1 to ST5) within the reservoir from January 2021 to September 2023, with sampling conducted on a monthly basis. Surface water samples were collected at a depth of 0.5 m below the water surface, while deep water samples were obtained from 0.5 m above the sediment. The final values were calculated as the average of two samples collected for each water sample. A total of 13 test items were determined by referring to the test indicators of basic items of the lake (reservoir) and supplementary items of the drinking water source as required in [40] and combined with the actual situation of the reservoir.

During the water sample collection process, a depth finder (SM-5A, Speedtech) and a 30 cm Secchi disk were used to measure depth and Secchi depth (SD) in situ [41]. For the determination of chlorophyll-a (Chl-a), water samples were filtered using glass microfiber filters (GF/C Whatman).

2.3. Laboratory Analysis

The total phosphorus (TP) in the sample was determined by the ammonium molybdate spectrophotometric method, and the water sample was digested with 50 g/L ammonium molybdate. The detection limit of TP was 0.01 mg/L. After the total nitrogen (TN) was dissolved in 40 g/L potassium persulfate solution, the detection limit was 0.05 mg/L by ultraviolet spectrophotometry. Ammonia nitrogen (NH₃-N) was determined by salicylate spectrophotometry with a UV-2501 spectrophotometer (Shanghai Yiden N4S) at the wavelength of 655 nm, and the detection limit was 0.01 mg/L. COD_Mn was determined by the acid method, and the detection limit was 0.5 mg/L. BOD₅ was determined by the dilution method, and the detection limit was 2 mg/L. A multi-parameter water quality monitor (YSI EXO2) was used to measure dissolved oxygen (DO) and pH directly [42,43].

The sulfate and anionic surfactants in the samples were determined by methylene blue spectrophotometry (Shanghai Yiden N4S), and the detection limits were 0.005 mg/L and 0.05 mg/L, respectively. Fluoride was determined by Thermo Scientific Dionex Integrion (HPIC) with a detection limit of 0.02 mg/L. Chloride was determined by silver nitrate titration with a detection limit of 0.1 mg/L. Nitrate, iron and manganese were determined by a colorimetric method (Hash Table Spectrophotometer DR3900), and the detection limits were 0.1 mg/L, 0.05 mg/L and 0.01 mg/L, respectively.

2.4. Data Processing

The distribution map of the sample points was created using ArcGIS 10.2 software (Esri, Redlands, CA, USA). Data analysis and processing for the final figures were carried out using Origin 2021 (OriginLab Corporation, Northampton, MA, USA).

2.5. Evaluation and Survey Methods

2.5.1. The Trophic Level Index (TLI) Method

The Trophic Level Index (TLI) is an indicator of overall eutrophication levels used to evaluate reservoir water quality by integrating representative water quality parameters such as Chl-a, SD, TP, TN and COD_Mn [44].

The final TLI value is calculated by multiplying the trophic level indices by their respective weights. The specific formula is as follows:

$$\mathrm{TLI}(\sum )={\displaystyle \sum {\mathrm{W}}_{\mathrm{j}}\cdot \mathrm{TLI}(\mathrm{j})}$$

(1)

where $\mathrm{TLI}(\sum )$ is the comprehensive trophic level index; and ${\mathrm{W}}_{\mathrm{j}}$ is the relative weight of the nutrient status index of the j-th parameter.

Taking Chl-a as the reference parameter, the normalized relative weight of the j-th parameter is calculated according to Formula (2):

$${\mathrm{W}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{r}}_{\mathrm{i}\mathrm{j}}^2}{\mathrm{m}}}$$

(2)

where $r_{ij}$ is the correlation coefficient between the j-th parameter and the reference parameter chl-a; and m is the number of evaluation parameters. The nutrient level index of each index can be calculated from Equations (3)–(7) [45] as follows:

$$\mathrm{TLI}(\mathrm{chl})=10(2.5+1.086\ln \mathrm{chl})$$

(3)

$$\mathrm{TLI}(\mathrm{TP})=10(9.436+1.624\ln \mathrm{TP})$$

(4)

$$\mathrm{TLI}(\mathrm{TN})=10(5.453+1.694\ln \mathrm{TN})$$

(5)

$$\mathrm{TLI}(\mathrm{SD})=10(5.118-1.94\ln \mathrm{SD})$$

(6)

$$\mathrm{TLI}(\mathrm{CO}{\mathrm{D}}_{\mathrm{M}\mathrm{n}})=10(0.109+2.661\ln \mathrm{COD})$$

(7)

These indicators are widely used to monitor and assess the degree of eutrophication of water bodies. chl is an important index of algae biomass in water, and its content can directly reflect the eutrophication degree of water. TN and TP are the main nutrients that lead to the eutrophication of water bodies, and their content can directly measure the nutrient state of a water body. A decrease in SD usually means a large number of suspended solids and algae in the water, which is an obvious feature of eutrophication. COD_Mn reflects the content of organic matter in water, and the increase in organic matter content is also a manifestation of eutrophication.

2.5.2. Pollution Source Survey Method

Given the current situation of the Meihu Reservoir, the investigation of pollution sources mainly considers agricultural non-point-source pollution, domestic pollution and livestock farming pollution.

The assessment of agricultural non-point-source pollution primarily employs empirical Formula (8) [46] to estimate the total amount of nitrogen and phosphorus pollutants entering the reservoir through surface runoff.

$${\mathrm{W}}_{\mathrm{a}}={10}^{-6}\times {\mathrm{C}}_{\mathrm{s}}\times \mathrm{Y}$$

(8)

where W_a is the annual storage amounts of adsorbed pollutants in the sediment of the reservoir basin, t/a; C_s is the concentration of pollutants in sediment; and Y is the sediment loss in the basin, which is calculated by Equation (9).

$$\mathrm{Y}=0.2\times \mathrm{A}$$

(9)

where A is the amount of soil erosion, using the soil erosion equation (USLE) of the Agricultural Research Institute of the United States Department of Agriculture [38,47], as follows:

$$\mathrm{A}=\mathrm{R}\cdot \mathrm{K}\cdot \mathrm{LS}\cdot \mathrm{C}\cdot \mathrm{P}$$

(10)

$$\mathrm{R}={\displaystyle \sum _{\mathrm{i}=1}^{12}\left(-1.5527+0.1792\mathrm{P}\mathrm{i}\right)}=0.1792\mathrm{P}-18.6324$$

(11)

$$\mathrm{K}=\left(164.80-2.31{\mathrm{X}}_{\mathrm{1}}+0.38{\mathrm{X}}_2+2.26{\mathrm{X}}_3+1.31{\mathrm{X}}_4-14.67{\mathrm{X}}_5\right)\times {10}^{-3}$$

(12)

$$\mathrm{LS}=\left(65.41{\mathrm{Sin}}^2\mathrm{S}+4.56\mathrm{Sin}\mathrm{S}+0.065\right)=5.6261\cdot {(\mathrm{L}/22.13)}^{\mathrm{m}}$$

(13)

where A represents the erosion intensity; R represents the erosion factor; P represents the annual rainfall (mm); Pi represents the average monthly rainfall (mm); K represents the soil factor; X1 represents the content of (3~1 mm) coarse gravel, %; X2 represents the content of (0.25~0.05 mm) fine sand, %; X3 represents the content of (0.05~0.01 mm) coarse silt, %; X4 represents the content of (0.01~0.005 mm) fine silt, %; X5 represents the organic matter content, %; LS represents the topographic factor; L represents the length from the point where runoff starts to accumulate sediment or enters the watercourse (m); S represents the average slope length of the runoff; M represents the modulus (specific values can be found in Table S1); C represents the biological factor (specific values can be found in Table S2); and P represents the soil conservation measure factor (specific values can be found in Table S3).

The total soil erosion is calculated according to Formula (14):

$$\mathrm{Q}={\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{A}}_{\mathrm{i}}{\mathrm{S}}_{\mathrm{i}}}$$

(14)

where S_i is the loss area (m²); A_i is the loss intensity (t/hm²); and n is the number of loss areas. In this study, the loss area is regarded as two parts, so n = 2.

The domestic pollution survey and the livestock and poultry breeding pollution survey mainly use the pollution load calculation method to estimate the nitrogen and phosphorus load caused by domestic sewage, domestic garbage and livestock and poultry breeding pollution.

3. Results and Discussion

3.1. Water Quality Analysis of Reservoir

3.1.1. Water Environment Quality

Water environmental pollution mainly refers to the process of water quality deterioration caused by various harmful substances introduced by human activities. Therefore, an accurate pollution assessment can only be conducted after determining reasonable background or reference values.

Selecting appropriate evaluation indicators is crucial to accurately reflect the degree of water pollution. For the Meihu Reservoir, not all detection items are characteristic pollutants. This study focuses on 13 indicators corresponding to the evaluation standards as the evaluation indicators (Table S4), with classification standards referenced from [40].

According to the evaluation results in Table S4, it can be observed that, except for total nitrogen (TN), all other indicators comply with the Class II-III water standards (i.e., meeting the drinking water standards), indicating that the overall water quality of the Meihu Reservoir is good. Additionally, as shown in Figure 3 and Figure 4, the TP content shows a decreasing trend year by year (Class II standard: $\mathrm{TP}\le 0.025\mathrm{mg}/\mathrm{L}$; Class III standard: $\mathrm{TP}\le 0.05\mathrm{mg}/\mathrm{L}$), while the TN variation is not significant, with nitrogen content consistently fluctuating around 2.0 mg/L (Class II standard: $\mathrm{TN}\le 0.5\mathrm{mg}/\mathrm{L}$; Class III standard: $\mathrm{TN}\le 1.0\mathrm{mg}/\mathrm{L}$). However, the TN content in this reservoir meets the Class V water standard (Class V standard: $\mathrm{TN}\le 2.0\mathrm{mg}/\mathrm{L}$), indicating that the TN content in the Meihu Reservoir significantly exceeds the acceptable levels and does not meet the water quality standards required for drinking water sources.

3.1.2. Suitability Evaluation of Drinking Water Source

The primary function of the Meihu Reservoir is water supply, making the evaluation of its water quality as a drinking water source particularly essential. The assessment standards adopted are the secondary standards from [48].

To enhance the reliability of the evaluation, the selection of evaluation factors is based on the principle of reflecting the water quality status as comprehensively as possible. Twenty-one indicators (pH, iron, manganese, copper, zinc, selenium, mercury, arsenic, hexavalent chromium, cadmium, lead, volatile phenol, sulfate, chloride, fluoride, cyanide, nitrate, ammonia nitrogen, COD_Mn, anionic surfactants and total coliforms) were chosen for the evaluation based on the water quality characteristics. The evaluation reference year is 2023, and the comparison method was used for the assessment, where measured values are compared against standard values, with exceedances indicating non-compliance.

It can be found from the evaluation results (Table S5) that in the suitability evaluation of the Meihu Reservoir water source in 2023, all the water quality detection indexes meet the standards. Therefore, the water in the reservoir can meet the water quality standards of drinking water sources when it is directly used as a drinking water source.

3.1.3. Evaluation of Reservoir Eutrophication

Based on the mechanism of eutrophication formation, the trophic level index (TLI) is calculated using Formulas (1)–(7). These formulas utilize the average values of the following parameters: total nitrogen (TN), total phosphorus (TP), transparency (SD), chlorophyll-a (chla) and permanganate index (COD_Mn) [45]. The total score ranges from 0 to 100 points, with the scoring criteria provided in Table S6 and S7. The corresponding relationship values can be referenced in Table S8. Using the aforementioned formulas, the comprehensive trophic level index (TLI) of the Meihu Reservoir was calculated, and the results are presented in

Table 1. Calculation results of nutrient status of Meihu Reservoir (TLI value).

Sampling Time	Chl-a (mg/m3)	TN (mg/L)	TP (mg/L)	SD (m)	CODMn (mg/L)	TLI
January 2021	7.06	1.44	0.02	1.80	1.50	42.68
February 2021	6.34	1.37	0.01	2.00	1.53	41.58
March 2021	6.42	1.77	0.02	2.00	1.65	43.72
April 2021	6.11	1.95	0.01	2.10	1.41	42.73
May 2021	7.78	1.46	0.01	1.50	1.61	43.01
June 2921	7.68	1.65	0.01	1.55	2.17	49.01
July 2021	6.54	3.00	0.01	2.10	2.00	44.79
Auguest 2021	6.36	2.90	0.01	2.00	1.78	48.91
September 2021	6.29	1.90	0.01	2.20	2.46	44.83
October 2021	7.75	1.34	0.01	1.50	1.79	43.49
November 2021	8.91	1.12	0.01	1.00	1.46	44.12
December 2021	7.52	1.10	0.01	1.50	1.42	41.80
January 2022	7.17	1.40	0.02	1.80	1.46	42.65
February 2022	7.23	1.96	0.01	1.80	1.46	43.22
Mach 2022	6.58	2.12	0.01	2.00	1.53	43.23
April 2022	7.21	2.47	0.02	1.80	1.43	44.37
May 2022	6.73	2.19	0.01	2.00	1.64	43.55
June 2022	7.35	1.67	0.02	1.80	1.80	44.18
July 2022	6.13	2.34	0.01	2.20	1.76	43.95
Auguest 2022	6.42	2.08	0.01	2.00	1.99	44.36
September 2022	6.39	1.88	0.02	2.00	2.07	47.72
October 2022	5.91	2.17	0.01	2.50	1.69	43.33
November 2022	6.85	1.57	0.01	1.70	1.84	43.19
December 2022	6.38	2.37	0.01	2.00	1.62	43.78
January 2023	6.41	2.46	0.02	2.00	1.65	46.99
February 2023	6.35	1.89	0.02	2.00	1.16	43.63
March 2023	5.83	1.77	0.01	2.50	1.83	46.44
April 2023	6.45	1.98	0.01	2.10	1.76	47.80
May 2023	7.42	2.16	0.01	1.80	1.50	36.63
June 2023	7.38	1.66	0.02	1.80	1.64	43.58
July 2023	7.75	1.90	0.01	1.50	1.68	44.45
Auguest 2023	7.61	1.37	0.01	1.60	1.86	47.21
September 2023	7.68	1.21	0.01	1.60	1.55	41.73

.

At the same time,

Table 2. Classification of trophic status using the trophic level index (TLI).

TLI Value	Trophic States
$\mathrm{TLI}(\sum )<30$	Oligotropher
$30\le \mathrm{TLI}(\sum )\le 50$	Mesotropher
$\mathrm{TLI}(\sum )>50$	Eutropher
$50<\mathrm{TLI}(\sum )\le 60$	Light eutropher
$60<\mathrm{TLI}(\sum )\le 70$	Middle eutropher
$\mathrm{TLI}(\sum )>70$	Hyper eutropher

gives a comparison table of nutrient classification by nutrient level index (TLI). After comparison, it is found that Meihu Reservoir belongs to the medium nutrient type; Liu et al. and Madyouni et al. also used this method to evaluate the eutrophication of Bao’an Lake and Joumine reservoir in Tunisia, respectively and reached similar conclusions [49,50]. The water body of the reservoir was not seriously trophicated, and the water body changed from mild eutrophication to medium trophication.

3.2. Reservoir Pollution Source Survey

The investigation of pollution sources in the reservoir aims to systematically identify and analyze the types, distribution, and impact levels of pollution sources. By identifying the primary pollution sources, targeted management and protection strategies can be developed to reduce the impact of pollution on the reservoir and ensure the safety of water quality and the health of the ecosystem [5,50].

According to the current water quality evaluation results, it is found that the overall pollution status of the Meihu Reservoir is that the TN content of the water body exceeds the standard. Field investigation shows (see Table S9 for details) that the ecological environment around the reservoir is well preserved, and most areas still maintain a natural state. Ecological fish farming is carried out in the reservoir area, fish feed is not used and there are no industrial and mining enterprises around the reservoir area. Therefore, according to the survey, the main pollution sources in the reservoir area can be divided into three categories: agricultural non-point-source pollution, domestic pollution and livestock and poultry pollution. The specific distribution of the main pollution sources is shown in Figure 5.

3.2.1. Agricultural Non-Point-Source Pollution

Agricultural non-point-source pollution primarily enters surface and groundwater through runoff and infiltration, leading to water quality deterioration, a decline in biodiversity and potential threats to human health [51,52]. Additionally, agricultural non-point-source pollution can cause soil degradation, eutrophication and other environmental issues. Due to its diffuse and complex nature, managing agricultural non-point-source pollution requires a comprehensive application of various agricultural management and environmental protection techniques, including optimized fertilization, the promotion of organic farming and the establishment of ecological buffer zones.

On-site investigations revealed that bayberry is cultivated within the Meihu Reservoir watershed. To ensure the yield and quality of the bayberries, farmers typically apply fertilizers. Additionally, herbicides such as glyphosate and paraquat are used for weed control during the cultivation process. However, not all the fertilizers applied to the roots of the bayberry trees are absorbed, with a significant portion remaining in the soil and being washed into the reservoir by rainwater, thus constituting a major source of nitrogen and phosphorus pollution in the reservoir. Therefore, it is necessary to estimate the total nitrogen and phosphorus pollutants entering the reservoir through surface runoff. Based on Formulas (8)–(14) [46] and considering factors such as rainfall, soil, topography, vegetation, cultivation methods and soil and water conservation measures in the study area, the annual nitrogen and phosphorus pollutant loads entering the reservoir were estimated. The specific results are presented in

Table 3. Calculation results of soil erosion in Meihu Reservoir watershed.

Land-Use Type	R	K	LS	C	P	Erosion per Unit Area (t/hm2)	Area (km2)	Erosion Amount (t)
Cultivated land	163.58	0.17	29.59	0.01	0.17	2.85	1.00	285
Woodland	163.58	0.19	0.71	0.25	0.40	1.80	22.5	4050
Total							23.5	4335

and

Table 4. Calculation results of the inflow of N and P pollutants through surface runoff in Meihu Reservoir (2023).

Land-Use Type	Soil Detection Value mg/kg		Sediment Concentration in Runoff mg/kg		Quantity of N and P in Storage kg/a
	TN	TP	TN	TP	TN	TP
Cultivated land	158.3	3.32	316.6	6.64	90.23	1.89
Woodland	46.6	0.15	93.2	0.3	377.46	1.21
Total			409.8	6.94	467.69	3.10

.

Based on the calculation results in Table 2 and Table 3 and in conjunction with Formula (9), it can be determined that the total sediment load entering the reservoir area from the study region in 2023 is 867 tons. Of this sediment, the concentration of available TN is 409.8 mg/kg, and the concentration of available TP is 6.94 mg/kg. Therefore, the capacity of adsorbed TN entering the reservoir through surface runoff in 2023 is 467.69 kg, and the capacity of adsorbed TP is 3.10 kg.

3.2.2. Domestic Pollution

Domestic pollution typically refers to the pollution caused by human activities in daily life, including waste, wastewater and exhaust emissions. These pollutants contain a large number of harmful substances, such as heavy metals, organic pollutants and pathogenic microorganisms, posing serious threats to air, water and soil environments [53,54]. Domestic pollution not only disrupts the ecological balance and affects biodiversity but also poses potential risks to human health.

After investigation and analysis, the upper reaches of the Meihu Reservoir involve dispersed domestic pollution in two villages (Dashan Village and Meixi Village). Both villages have built sewage treatment facilities, but the operation effect is not ideal, and there are problems such as dispersed discharge of domestic sewage and intermittent operation of centralized sewage treatment facilities. This study mainly reviewed the nitrogen and phosphorus load caused by domestic sewage and domestic garbage pollution.

Domestic Sewage Pollution

The determination of domestic sewage generation volume employs the pollutant discharge coefficient method [55], requiring key data such as per capita daily water consumption, rural population within the reservoir area and sewage discharge coefficients.

The determination of per capita daily water consumption is influenced by three factors: water supply conditions, types of sanitary facilities and the region’s characteristics [56]. The specific values are shown in Table S8. Cixi City falls under category III, where rural areas have mostly achieved household tap water access and use metered billing, with households typically having handwashing sinks and other sanitary facilities. Therefore, the per capita daily water consumption is selected to be 60–100 L/(d·person). Additionally, [57] has set corresponding standards for the maximum daily residential water consumption in rural areas, similar to the aforementioned standards. Considering the standards, regulations, and actual conditions of the reservoir area, the average per capita daily water consumption is chosen to be 80 L/(d·person).

The rural population within the reservoir area mainly includes the permanent and floating population. Based on the statistical data from the on-site investigation, the populations of the two villages are 1100 and 2300, respectively.

The determination of the sewage discharge coefficient is influenced by water usage patterns, discharge methods and living habits. Considering the actual conditions in rural areas of Cixi City, the domestic sewage discharge coefficient is selected to be 0.85.

According to the above values, it can be concluded that the annual domestic sewage discharge of the Meihu Reservoir is 0.85 × 10 5m³/a. On this basis, the domestic sewage discharge coefficient recommended by SEPA was adopted, and the domestic sewage pollutant discharge coefficient TN was 0.73 kg·person⁻¹·year⁻¹; TP was 0.183 kg·person⁻¹·year⁻¹. It can be deduced that the total nitrogen and total phosphorus load caused by domestic sewage pollution in the reservoir area are 1985.6 kg/a and 496.4 kg/a, respectively.

Domestic Garbage Pollution

According to social statistics from Ningbo City, the per capita generation rate of rural domestic waste in the Cixi area is significantly lower than that in urban areas. Based on the actual conditions within the reservoir area, this study selects a per capita generation rate of 0.25 kg/(p·d), leading to an estimated annual domestic waste generation volume of 366.0 tons/year for the Meihu Reservoir area.

In addition, the survey found that in the villages upstream of the reservoir, 1/10 of the domestic waste is directly stacked on the roadside. According to experimental studies, garbage entering the water body can release almost all organic nitrogen and phosphorus within 2–6 months, and accumulated garbage can also release almost all nutrients within a year. According to the population output coefficient, the TN recommended by SEPA is 1.58 kg·person⁻¹·year⁻¹, and TP is 0.16 kg·person⁻¹·year⁻¹; it can be calculated that the TN and TP load caused by domestic waste pollution in the reservoir area are 950.9 kg/a and 95.1 kg/a, respectively.

3.2.3. Livestock Pollution

Livestock and poultry farming pollution typically refers to the environmental contamination caused by waste products generated during the farming process. These wastes include animal manure, urine, feed residues and wastewater containing high amounts of organic matter, nitrogen, phosphorus and pathogenic microorganisms [58]. If these pollutants are discharged without effective treatment, they can contaminate surface and groundwater through runoff or infiltration, leading to water quality degradation and eutrophication and negatively impacting surrounding soil and air quality. Additionally, livestock farming generates greenhouse gases such as methane and ammonia, exacerbating air pollution and climate change [59,60].

According to the field investigation, there is no large-scale poultry farm in the Meihu Reservoir area, but the situation of free-range and small-scale breeding is widespread, resulting in a large number of livestock and poultry feces that are not treated or utilized and are piled up at will. The nutrients, such as nitrogen and phosphorus, will enter the reservoir with the leaching of rainwater. According to the field investigation, the breeding species in the reservoir area are mainly chickens and ducks. In this study, the total number of chickens and ducks raised in the Meihu Reservoir area was 2000, the excretion coefficient of each animal and poultry was 0.28 kg·person⁻¹·year⁻¹ and the TP was 0.12 kg·person⁻¹·year⁻¹, as recommended by the State Environmental Protection Administration. It can be estimated that the TN and TP loads caused by livestock and poultry pollution in the reservoir area are 560 kg/a and 240 kg/a, respectively.

In summary, farmland nitrogen and phosphorus losses caused by excessive use of chemical fertilizers and direct discharge of rural domestic waste, domestic sewage and aquaculture waste into water bodies through surface runoff are the main factors that constitute water body pollution of the Meihu Lake Reservoir. In addition, the bottom of the Meihu Lake Reservoir was desilted at the beginning of 2018 to remove 280,000 m³ of silt. Therefore, pollution source analysis was not carried out in this survey. It can be seen from

Table 5. Summary of pollutant discharge in Meihu Reservoir area (kg/a).

Pollutant Source Type	TN	TP
Agricultural non-point-source pollution	467.69	3.1
Domestic sewage pollution	1985.6	496.4
Domestic garbage pollution	950.9	95.1
Livestock pollution	560	240

and Figure 6 and Figure 7 that the main sources of nitrogen in the reservoir were domestic sewage pollution > domestic garbage pollution > livestock pollution > agricultural non-point-source pollution, accounting for 50.09%, 23.99%, 14.13% and 11.80% of the total load, respectively. Comprehensive prevention and treatment measures must be implemented in these areas.

4. Conclusions

This study mainly takes Meihu Reservoir as the research object and determines the pollution degree of the reservoir through the analysis of the current water quality of the reservoir. On this basis, the main pollution sources of the reservoir are investigated, analyzed and discussed.

(1)

The water quality analysis results of the Meihu Reservoir show that among the 13 evaluation indicators extracted, except for total nitrogen, the rest are in line with Class II-III standards for drinking water, and the control and management of nitrogen sources should be strengthened after reservoir management.

(2)

In the water source suitability evaluation of the Meihu Reservoir, all the water quality detection indicators from January to September 2023 reached the standard. Therefore, the water of the reservoir is directly used as a drinking water source, which can meet the quality standards of drinking water sources.

(3)

The eutrophication evaluation results of the Meihu Reservoir showed that the comprehensive nutrient state index (TLI) of the reservoir was between 30 and 50/month, indicating that the eutrophication degree of the water body was not serious, and the water body changed from mild eutrophication to moderate eutrophication.

(4)

The survey results of pollution sources in the Meihu Reservoir show that the main sources of pollution are non-point-source pollution such as agricultural planting, domestic pollution and livestock and poultry breeding pollution. The main sources of nitrogen in the reservoir were domestic sewage pollution > domestic garbage pollution > livestock pollution > agricultural non-point-source pollution, accounting for 50.09%, 23.99%, 14.13% and 11.80% of the total load, respectively.

To sum up, this research work has certain guiding significance for the daily management and operation of the Meihu Reservoir. However, there are some limitations. As a desilting project was carried out at the bottom of the reservoir in 2018, the analysis did not involve the investigation of sediment pollution sources. In addition, the universality of the analytical investigation method needs to be further demonstrated, which points out the direction for the next research work.