Occurrence and Risk Assessment of Microplastics in a Source Water Reservoir in Middle Reaches of Yellow River

Abstract

As an emerging contaminant, the spatial distribution characteristics of microplastics in source water reservoirs warrant further attention from researchers. In this study, the Luhun Reservoir, which is situated in the middle reaches of the Yellow River, was selected as the object. Field sampling and detection were conducted to ascertain the presence of microplastics in water and sediment. The results indicated that the abundance of microplastics in the water varied from 1.60 to 13.26 items/L, while in the sediment it ranged from 792.38 to 2352.00 items/kg. Polyethylene, polyethylene terephthalate, and polyamides exhibited higher levels in the water, whereas polyamides and polyethylene were more predominant in the sediments. Additionally, the surface layer exhibited the highest abundance of microplastics, followed by the bottom, while the intermediate layer displayed the lowest. As the depth increased, there was a gradual decrease in the proportion of polyethylene and an increase in the proportions of polyethylene terephthalate and polyamides. The risk assessment results showed that the Pollution Risk Index value of the water was 201.79, while the sediment had a value of 184.98, indicating a moderate potential ecological risk. This study provides valuable insights into the spatial distribution patterns of microplastics at different water depths and provides crucial data support for understanding the migration patterns of microplastics in source water reservoirs.

1. Introduction

Plastic products have found their way into various aspects of human life, including packaging, transportation, agriculture, construction materials, electronics, medicine and other fields due to their low-density characteristics, ease of processing and production on a large scale at a low cost [1]. While facilitating convenience in human life, global plastic production reached 367 million tons in 2020, with China accounting for the largest share of 117 million tons (32% of total production), establishing itself as the world’s leading producer of plastic products. In general, the prevalence of environmental pollution problems caused by inadequate plastic waste management has become increasingly common [2]. Various forms of plastic waste may disrupt soil and the aquatic continuum in a variety of ways [3]. The effective management of plastic wastes at their source presents a significant challenge that must be addressed in the future [4].

Last century, plastics were initially detected in the surface water of the ocean [5]. In 2004, Thomas et al. introduced the term “microplastics (MPs)” to refer to plastic pollutants smaller than 5 mm in size [6], which subsequently sparked widespread scholarly attention towards environmental pollution caused by MPs. MPs were extensively detected as an emerging contaminant in various ecosystems including the atmosphere, oceans, freshwater bodies, soil, and even human tissues [7,8,9]. In the past decade, the investigations of MPs in the ocean had emerged as a prominent research topic within the scientific community [10]. The marine environment serves as a significant reservoir for MP accumulation, leading to substantial ecological pollution. However, in comparison to this, the research findings pertaining to MPs in freshwater are relatively limited [11]. Plastic is the product of human industry, and the freshwater environment is disproportionately impacted by anthropogenic activities, so it is imperative to enhance our focus on the issue of MP pollution in freshwater ecosystems. Although our current research on MPs remains limited, emerging evidence suggests their potential ecological and human health implications [7,12].

The availability of freshwater resources is indispensable for the preservation of human, and rivers, lakes, and reservoirs often serve as raw water source for sustaining human life. Particularly in China, reservoirs have progressively emerged as the primary water source for numerous large and densely populated urban areas [13]. Reservoirs are typically characterized by an expansive water surface, a substantial depth, and a sluggish flow velocity, thereby providing favorable conditions for the accumulation of MPs in the water column and sediment deposition [14]. The dam creates a barrier effect that enables the interception of MPs; when the reservoir is released, MPs with smaller particle sizes will be carried away, while larger MPs will be effectively captured in the reservoir [15]. The presence of MPs in reservoirs exhibits a wide range of problems. A study conducted by Pattira et al. revealed that the abundance of MPs in the surface water of the Ubolratana Reservoir varied between 0.025 and 3.36 n/L [16], However, the range in the Danjiangkou Reservoir varied between 0.53 and 24.80 n/L [17]. Due to different sampling methods, the reported abundance of MPs in Qinghai Lake ranges from 50,000 to 758,000 n/km² [18]. The absence of standardized sampling and detection methods remains a persistent challenge for our research. In addition, reservoirs typically exhibit thermal stratification due to their significant water depths [19], resulting in vertical non-uniformity of various water quality parameters, including MPs. Therefore, when conducting investigations, it is imperative to consider the vertical variations and discrepancies of MPs within the reservoir water column as this will greatly aid our comprehension of their migration mechanisms.

Simultaneously, owing to their diminutive size and remarkably high specific surface area, MPs exhibit a substantial adsorption capacity for organic pollutants, antibiotics, heavy metals, and other toxic substances in water [20,21]. And, the aging process of MPs in water is a continuous phenomenon during their migration, leading to an enhanced capacity for adsorbing pollutants [22]. All of these factors contribute to the synergistic pollution of the water environment, exacerbating the degree of water pollution. The lack of investigation reports on MPs in water sources remains a significant obstacle, rendering it impossible to ascertain the baseline value of MPs in water sources, let alone establish a threshold standard for their presence [23]. Several factors contribute to this situation, including the limited duration of our research on MPs as an emerging contaminant; the inconsistent and non-standardized methods employed for sampling and detecting MPs in the environment; and the absence of quantitative descriptions regarding the adverse effects of MPs on human health [24,25]. Therefore, it appears that a collaborative effort among numerous researchers is necessary to further elucidate the mechanisms and hazards of MPs.

In this study, a source water reservoir named Luhun Reservoir located in the middle reaches of Yellow River was selected as the research object. By collecting water samples from different water layers and sediment samples at each sampling point, we conducted a comprehensive statistical analysis of the abundance, size, shape, color, and polymer types of MPs. Furthermore, a thorough risk assessment of MP pollution in this reservoir was performed. To the best of our knowledge, this study represents the first investigation into the occurrence of microplastics in source water reservoirs within the middle reaches of the Yellow River. This research significantly contributes to enhancing our understanding of microplastic pollution characteristics in water sources. Furthermore, we aim to raise awareness among researchers regarding the ecological risks associated with microplastics in water sources and collectively address the formidable challenges posed by these emerging contaminants on water sources.

2. Materials and Methods

2.1. Field Sampling

Luhun Reservoir is situated in Song County, Henan Province, within the longitude range of 112.06° E to 112.20° E and latitude range of 34.10° N to 34.21° N. It is one of the water sources for the cities of Luoyang and Zhengzhou, and includes other functions such as irrigation, power generation, aquaculture, and tourism. The control basin area of Luhun Reservoir is 3492 km², constituting about 60% of the total expanse within the Yi River basin. The reservoir boasts a storage capacity of 1.22 billion m³, accompanied by an average water depth measuring 9.50 m and a maximum water depth reaching 31.00 m. A total of 10 sampling points were established in this investigation, with 6 sampling points (sampling points 1–6) designated within the main reservoir area, and an additional 4 sampling points (sampling points 7–10) strategically positioned along the upstream river, as illustrated in Figure 1. The latitude and longitude information for all sampling points is presented in Table S1.

Surface, intermediate, and bottom water samples as well as sediment samples were collected at sampling points 1 to 6. Water and sediment samples were taken at sampling points 7 to 10. A stainless steel bucket was used to collect 5 L of surface water, while a hand-held electric pump was employed to extract 5 L of water from the corresponding location for intermediate and bottom water samples. The collected water was immediately filtered on board using a stainless steel screen with a diameter of 54 μm. Substances trapped in the screen were rinsed with on-board ultra-pure water into brown glass bottles and sealed for preservation purposes. Approximately 1 kg of sediment samples were obtained at each sampling point using a Peterson-type stainless steel grab bucket and stored in aluminum foil bags. All collected samples were subsequently transported to the laboratory for further experimentation.

2.2. Sample Pretreatment

The pretreatment methods recommended by the National Oceanic and Atmospheric Administration (NOAA) [26]. All water samples were filtered again through a stainless steel screen with a diameter of 54 μm in laboratory and the retained material was thoroughly rinsed with absolute ethyl alcohol into a 200 mL glass beaker, respectively. These beakers were subsequently dried in an oven set at 50 °C, and then 30% hydrogen peroxide was added and placed in a water bath at 50 °C, where they were left for 48 h for digestion. The solution was ultimately subjected to filtration using a vacuum filter and a 0.45 μm glass fiber filter membrane, with the resulting membrane being carefully preserved in a glass culture dish for subsequent detection.

Firstly, for sediment samples, large stones or leaves were meticulously removed using stainless steel tweezers and mixed. Subsequently, 100 g of sediment was accurately weighed and transferred into a 1000 mL glass beaker. Following this, 500 mL of saturated NaCl solution (ρ = 1.2 g/cm³) was added to the beaker and thoroughly mixed. The mixture was then left undisturbed for a duration of 24 h and transferred the supernatant to another beaker. The extraction step was repeated three times consecutively. The supernatant was filtered through a stainless steel screen with a diameter of 54 μm and substance trapped on the screen was then rinsed into a glass beaker employing anhydrous ethanol and subjected to drying in an oven at 50 °C. The following steps were the same as those for water samples. In addition, 100 g of sediment was used to measure the moisture content in each sediment sample using oven drying at 105 °C for 12 h.

2.3. Detection Method of MPs

The suspected MPs on the filtered membranes were observed using an optical microscope (Motic, BA310Digital, Motic Industrial Group Ltd., Xiamen, China) connected to computer by a camera. Image acquisition software (Motic Images Plus 3.0 ML, Motic Industrial Group Ltd., Xiamen, China) was used to collect the suspected MPs on the membranes. The amount, size, morphology, and color of the suspected MPs were recorded. The location of suspected MPs was determined by the membrane’s grid. Then the polymer types of the MPs were detected using a confocal Raman microscopy spectrometer (gora-Lite, Ideaoptics Co. Ltd., Shanghai, China) and the results were analyzed with the corresponding software (gora. Dawn v1.0). The experimental parameters of this spectrometer were set to a wavenumber range of 60 to 3500/cm, with a resolution of less than 5/cm and a laser excitation wavelength of 785 nm. The Raman spectral data were processed using MATLAB software (v2020a) and compared against the standard spectral library of plastics. Upon achieving a matching degree exceeding 70%, the suspected material was identified as a specific polymer type of MPs [27]. After detection, the suspected MPs ultimately identified as MPs were counted for further analysis. The microscopic images of the detected MPs are presented in Figure S1.

The abundance of MPs in the samples was initially determined through microscopic visual observation. A subset of suspected MPs underwent Raman spectroscopy analysis for further confirmation. Subsequently, the abundance data obtained from the initial visual observation were adjusted to account for the accuracy of randomly selected suspects as true MPs [28,29]. For example, the abundance of MPs in water was determined through microscopic observation. Subsequently, 440 out of the 1224 suspected MPs were randomly selected for Raman spectroscopy analysis, revealing that 62% of them were identified as MPs. The data obtained from visual observation were appropriately adjusted based on the results. To determine the abundance of MPs in sediments, we employed the same calculation methodology as used for water samples.

2.4. Ecological Risk Assessments

The contamination risk assessment of MPs in Luhun Reservoir was conducted by employing the risk assessment methods utilized in relevant studies [28,30,31]. The MPs Pollution Load Index (PLI) is a standardized monitoring method utilized to evaluate pollution levels across various sites within an aquatic system, thereby quantifying the abundance of MPs present in the studied water body. Points are considered contaminated when the PLI exceeds 1, and the calculation formulas are as follows [32].

PLI_i = C_i/C₀

(1)

PLI_Z = (PLI₁ × PLI₂ × PLI₃⋯⋯PLI_n)^1/n

(2)

Here, i represents a sampling point; n is the number of sample points; C_i is the abundance of MPs at sample point i; C₀ is the minimum abundance of MPs at different water layers and sediment; The Pollution Loading Index at a single sampling point is represented by PLI_n; PLI_Z is the pollution load index of MPs in the calculated zone.

Considering the diverse toxicity risks associated with various types of MPs, it becomes evident that risk assessments solely based on abundance possess inherent limitations. In this study, we employed the Pollution Risk Index (PRI), which integrates the PLI and Hazard Index (H), to evaluate the extent of MP contamination in the environment [28,33]. The assessment methodology is presented in

Table 1. Pollution levels and risk categories of MPs.

PLI		PRI	Risk Category	H	Risk Category
>1	Polluted	<150	Low	<10	I
		150–300	Medium	10–100	II
		300–600	Considerable	100–1000	III
		600–1200	High	1001–10,000	IV
		>1200	Danger	>10,000	V

, while the relevant formulae are provided below.

H_i = ∑P_n × S_n

(3)

H_Z = (H₁ × H₂ × H₃⋯⋯H_n)^1/n

(4)

PRI_i = H_i × PLI_i

(5)

PRI_Z = (PRI₁ × PRI₂ × PRI₃⋯⋯PRI_n)^1/n

(6)

where H_i is the hazard index of MPs in point i; P_n is the mass fraction of each type of MPs at the sampling point; and S_n is the hazard score of different polymers (Table S2). Hz represents the MP pollution hazard index of Luhun Reservoir as a whole, while PRI_i denotes the MP pollution risk index of point i. Finally, PRI_Z is the pollution risk index of Luhun Reservoir as a whole.

2.5. Quality Control

To ensure the accuracy of experimental data, cotton lab garments and nitrile gloves were worn throughout the experiment. The containers utilized were made of glass and stainless steel. The experimental water used was ultrapure water prepared in the laboratory and filtered through a 0.45 μm membrane prior to usage. In order to prevent airborne MP contamination, our sample pretreatment experiments were conducted within a fume hood, with all containers and samples securely wrapped in aluminum foil between each trial. Furthermore, we implemented a blank control experiment using ultrapure water, which confirmed the absence of MPs on the mesh filter membrane.

3. Results

3.1. Abundance of the MPs

The abundance of MPs in the water samples ranged from 1.60 to 13.26 n/L, as depicted in Figure 2a. The overall abundance of MPs in the water samples from the Yi River was comparatively lower than that observed in Luhun Reservoir. Specifically, the abundance of MPs at the four sampling points upstream (7–10) were 4.60, 2.85, 1.60, and 1.80 items/L, respectively, with an average abundance of 2.72 items/L. Sampling points 7 and 8 were situated in the urban region of Songxian County, exhibiting a higher abundance of MPs in their water compared to the two upstream sampling points. This observation suggests that anthropogenic activities have contributed to the introduction of exogenous MPs into the river. The inflow of river water into the reservoir facilitates the introduction of MPs into the main reservoir area. Additionally, the slow flow rate and extended hydraulic retention time of the reservoir exacerbate the accumulation of MPs, aligning with findings from previous studies on MP pollution in other reservoirs [34,35].

Currently, a plethora of reports have been published on the distribution characteristics of MPs in the surface water of reservoirs. However, there remains a dearth of research data pertaining to the vertical distribution of MPs at various depths within the water column [28]. Figure 2a shows the difference in the abundance of microplastics between different water layers. The highest abundance of MPs in surface water was observed at sample point 6, with a value of 13.26 items/L, while the lowest abundance was found at point 3, with a value of 5.88 items/L. In the intermediate water layer, the recorded abundance of MPs at point 5 reached the highest value observed, measuring 9.00 items/L. Conversely, point 3 exhibited the lowest abundance with a measurement of 2.6 items/L. Point 4 exhibited the highest abundance of MPs with a value of 12.12 items/L in the bottom layer, while point 3 displayed the lowest abundance (4.05 items/L). The mean abundance of MPs at sampling points 1 to 6 was 6.81, 8.44, 5.18, 12.05, 11.64, and 10.47 items/L, respectively. Points 1 and 2, situated within the core areas of tourist boat tours, exhibited a significant influence from tourism on MP presence in the surface water. In the region between points 4 and 6, activities such as fishery farming and fishing nets contribute to elevated levels of MPs. Common factors contributing to these phenomena include deteriorated fishing nets, human fishing activities, and upstream introduction.

It was observed that the abundance of MPs in surface water exhibited the highest levels overall, followed by bottom water, while the intermediate water layer displayed the lowest values. Specifically, the average abundance of MPs in surface, intermediate, and bottom water layers was determined as 11.00 items/L, 5.44 items/L, and 7.36 items/L, respectively. The results of the study were compared with those of reservoirs in other regions, as presented in Table S3. It is evident that MP pollution exhibits a wide distribution. The abundance of MPs in Luhun Reservoir was found to be significantly higher than that observed in Aras Reservoir [36], Feilaixia Reservoir [37], Chitian Reservoir [34], and Ubolratana Reservoir [16], and the range of abundance was similar to that in Danjiangkou Reservoir [38] and Liujiaxia Reservoir [28]. The findings revealed a relatively high abundance of MP pollution in the water in Luhun Reservoir.

As shown in Figure 2b, the abundance of MPs in the sediment samples from Luhun Reservoir ranged from 792.38 to 2352.00 items/kg (dry weight), with an average abundance of 1456.68 items/kg. A number of patterns were observed among the six sediment samples collected from the primary reservoir area. The sampling sites were categorized into two groups: the first group consisted of points 1, 2, and 3, while the second group included points 4, 5, and 6. The abundance of MPs within the first group exhibited a descending order as follows: 1 > 2 > 3; similarly, within the second group, it showed a decreasing trend as follows: 4 > 5 > 6. Notably, both groups displayed a comparable gradual decline in abundance. The gradual decrease in the first group may be attributed to the proximity of the dam body from 3 to 1, which is known to exert a certain intercepting effect on MPs [15,39]. The findings suggested that the abundance of MPs in sediment was influenced by both the interception effect of the dam itself and the frequency of human activities. However, the abundance of MPs in sediments at points 4 and 5 was also relatively high, which appears to contradict the findings of previous studies. Our analysis focuses on this specific area due to its significance in fisheries and aquaculture within the Luhun Reservoir, where waterborne MPs continuously settled and eventually accumulated in the sediments. This observation aligns with the previously mentioned elevated levels of MPs found in the intermediate and bottom layers of water in this region. In comparison to the sediment MP abundance observed in other regions’ reservoirs (Table S3), Luhun Reservoir exhibits a higher abundance of MPs than Liujiaxia Reservoir [28], Aras Reservoir [36], Chitian Reservoir [34], and Ubolratana Reservoir [16], but lower than that of Nandoni Reservoir [40] and Jiayan Reservoir [41].

In recent years, there has been an increasing concern about emerging contaminants such as MPs, leading to a growing number of measures aimed at protecting water sources. Luhun Reservoir serves primarily as a drinking water source and is not surrounded by large-scale industrial activities; however, it still fulfills functions including agricultural irrigation, power generation, fishery farming, and tourism. Simultaneously, the upstream area faces more pollution sources which contribute to the influx of MPs into the reservoir through rivers. This is one of the factors contributing to higher levels of MP contamination in the Luhun Reservoir compared to some other reservoirs in China.

3.2. Polymer Composition of the MPs

A total of 10 polymer types were detected in all water and sediment samples including Polyethylene terephthalate (PET), Polyethylene (PE), Polyamide (PA), Polyvinyl chloride (PVC), Polyphenylene Oxide (PPO), Polypropylene (PP), Polymethyl methacrylate (PMMA), Polystyrene (PS), Polyoxymethylene (POM), and Polytetrafluoroethylene (PTFE). The composition of polymer types in the samples collected from the Luhun Reservoir and the upstream river exhibited variations. As depicted in Figure 3a, the predominant polymer types detected in the water samples from the Luhun Reservoir and the upstream rivers were PE (38%), PET (23%), PA (20%), PPO (12%), and PP (4%). Additionally, a minor fraction of other polymer types accounted for 3%. The polymer types detected in the sediment samples exhibited a similar composition to those observed in the water samples, as depicted in Figure S2. Predominant polymer types identified in the sediment samples included PA (34%), PE (24%), PPO (12%), PP (13%), and PET (11%), and other polymer types collectively accounted for 6%.

The dominant polymer types exhibited variations in studies conducted on other reservoirs; for instance, PE emerged as the most prevalent polymer in the Three Gorges Reservoir following the flood [30]; PE was dominant in the Jiayan Reservoir [41]; and the main constituents of the MPs detected in the Chitian Reservoir were PP and PA [34]; the most prevalent polymer in all seasons and environmental mediums (water and sediments) in the Ubolratana Reservoir was PP [16]. The predominant polymer types detected in the water of the Luhun Reservoir were PE, PET, and PA. Similarly, PE and PA were found to be the most abundant polymers in the sediment. The exceptional physical and chemical properties of PE contribute to its extensive range of applications, encompassing the fabrication of films, packaging materials, pipes, wires and cables, as well as various everyday essentials. The prevalence of PE in the reservoir area can be attributed to recurrent human activity. PET, PA and PP are essential raw materials for textile articles in daily life that generate a significant amount of fibers during regular use and cleaning. Furthermore, the fishing nets employed in fishery farming at the Luhun Reservoir also contribute to the presence of PE and PA as they age.

As shown in Figure 3b, PET and PE were the most dominant polymer types in surface, intermediate and bottom water. With increasing depth, the proportion of PE decreases from 47% in the surface layer to 34% in the bottom layer, while the contribution of PET increases from 16% in the surface layer to 26% in both intermediate and bottom layers. In contrast, other polymer types, such as PPO, exhibited a non-linear trend with depth, initially declining and then ascending. Conversely, the percentage of PP demonstrated a gradual increase with increasing depth. The density of PE, approximately 0.92 g/cm³, decreases with increasing depth, while PET and PA have densities of 1.38 g/cm³ and 1.15 g/cm³, respectively, slightly greater than that of water resulting in an increase in percentage with depth. PPO and PP have densities of 1.07 g/cm³ and 0.91 g/cm³, respectively; however, due to the limited number of samples included in this study, it is not possible to generalize their pattern.

3.3. Morphology of the MPs

The MPs from the Luhun Reservoir were categorized into four distinct types based on their morphology: fibers, fragments, particles, and films. The results presented in Figure 4a demonstrate that fibers were the predominant type of MPs found in both the main reservoir area and the upstream river samples. In the reservoir area, fibers accounted for 61% of water samples and 44% of sediment samples, while in the river water and sediment samples, they constituted 57% and 71%, respectively. These findings highlight the widespread occurrence of fibers within the study area of the Luhun Reservoir. The proportion of fragments in the water and sediment samples collected from the reservoir area was 12% and 25%, respectively, while in the water and sediment samples obtained from the upper river, it was 18% and 16%, respectively. Moreover, particles accounted for 18% and 15% of the water and sediment samples from the reservoir area, whereas in those from the upstream river, they constituted 19% and 11%. Notably, compared to their presence in water samples, particles were relatively less abundant in sediments. Additionally, films exhibited the lowest occurrence among all types of MPs identified both in water samples collected from the reservoir area (8%) as well as those obtained from the upstream river (6%). Furthermore, films comprised only a small percentage of sediments sampled from both areas: 16% for reservoir sediments and merely 2% for upstream river sediments. Consequently, films were found to be least prevalent within our study conducted at Luhun Reservoir. The predominant MP shape in the Luhun Reservoir was fibers, as observed earlier, aligning with findings from studies conducted in other reservoirs such as Aras Reservoir [36], Liujiaxia Reservoir [28] and Nandoni Reservoir [40].

The residential areas are located upstream of the Luhun Reservoir, where fibers originating from textile usage and laundering activities in daily life are discharged into the river alongside sewage and wastewater, ultimately accumulating within the reservoir. Fishing operations occur within the primary reservoir area, with deteriorating fishing nets serving as an additional source of fibrous MPs. Simultaneously, fibers present in soil and atmosphere can infiltrate the reservoir environment through surface runoff and atmospheric deposition [28]. Films are derived from agricultural mulch and plastic bags, which undergo weathering and crushing processes. Particles are derived from industrial plastic granules found in various household products, such as toothpaste and shampoo. Fragments primarily result from the natural aging and crushing of bottles and plastic bags.

The results presented in Figure 4b indicate that fiber-shaped MPs constitute the predominant proportion in surface, intermediate, bottom, and sediment samples. The proportion of fibers exhibits a decreasing trend with increasing depth, declining from 65% in surface water to 44% in sediment. The proportion of fragments in surface, intermediate, bottom, and sediment samples was 11%, 11%, 15%, and 25%, respectively. Notably, there was an increasing trend in the percentage of fragments with depth. The particles in the intermediate water samples reached a maximum of 22%, which subsequently decreased to 15% in the sediment samples. Notably, films exhibited the highest percentage (16%) among all sediment samples. While the proportion of fibers gradually declined with increasing depth, fragments and films displayed an increasing trend. This phenomenon may be attributed to variations in surface area across different MP shapes. It was reported that microorganisms and algae can adhere to MPs in water, resulting in alterations in their mass density [42]. Fragments and films exhibit a larger surface area compared to fibers, rendering them more susceptible to attachment by microorganisms and algae. Consequently, there is an observed increase in the proportion of fragments and films found within bottom water and sediments.

3.4. Color of the MPs

A total of nine color categories were identified across all samples, including black, blue, yellow, green, red, purple, grey, brown and transparent. The predominant color observed in the water and sediment samples was transparent. As depicted in Figure 4c, a significant proportion (67%) of the MPs present in the water samples from the Luhun Reservoir exhibited transparent color. The percentage of yellow, black and blue MPs in the water samples were 12%, 9% and 8%, respectively. The analysis of data collected from each sampling point in the Luhun Reservoir revealed a 100% detection rate for transparent and yellow MPs, while the detection rates for black and blue MPs were found to be 83% and 95%, respectively. Furthermore, no significant differences were observed between the horizontal and vertical planes.

The sediment samples from the Luhun Reservoir exhibited a predominant presence of transparent color MPs, constituting 69% as depicted in Figure 4d. Additionally, the percentages of yellow, black, and blue MPs in the sediment samples were determined to be 9%, 8%, and 10%, respectively. The detection rate of suspected MPs in sediment samples was 100% for transparent, black, and yellow MPs, while blue MPs had a detection rate of 90%. Comparison between the color distribution of MPs in sediment and water samples revealed that transparent MPs were slightly more prevalent in sediments. However, sediments contained fewer colors of MPs than water samples. Transparent MPs account for the largest proportion due to their extensive usage in everyday plastic products, such as plastic bags and bottles. However, other plastics are susceptible to discoloration caused by ultraviolet light exposure or microbial activity [28]. The presence of yellow MPs may also be attributed to the aging process of transparent or white plastics caused by exposure to UV radiation, oxidation, and other environmental factors [43]. The majority of black and blue MPs are predominantly derived from fishing nets and debris discarded by incoming vessels.

3.5. Size of MPs

The proportions of MPs in water samples within the five size intervals were 2%, 22%, 36%, 23%, and 16% as depicted in Figure 5a. Notably, the highest proportion of MPs was observed in the range of 100 to 500 μm, while the lowest proportion was found in the range of 20 to 50 μm. Similarly, Figure 5b illustrates a comparable distribution pattern of MPs in sediment samples with proportions ranging from 11% to 12%. It is noteworthy that the stainless steel screen utilized in our sampling protocol possesses a diameter of 54 μm; however, the results reveal the presence of MPs smaller than 50 μm. This discrepancy may arise from either the attachment of smaller MPs to larger particles within natural aquatic environments or the agglomeration of numerous smaller MPs resulting in an increased size, thereby enabling their entrapment by our field sampling screen. During sample pretreatment experiments, these diminutive MPs disengage or disperse and consequently become detectable.

As illustrated in Figure 5c, the proportion of MPs within the size range of 20 to 50 μm exhibited an increasing trend with depth, rising from 2% in surface water samples to 14% in sediment samples. Similarly, the percentage of MPs sized between 50 and 100 μm increased from 20% in surface water to 29% in sediment. With the increasing depth, the proportion of MPs in the interval 100 to 500 μm gradually increased from 32% in the surface water samples to 41% in the sediment samples. The proportions of MPs in the size ranges of 500 to 1000 μm and 1000 to 5000 μm exhibited a gradual decline from 24% and 22% in surface water to 11% and 5% in sediment, respectively. It is evident from the aforementioned results that the dominant MPs present in surface, intermediate, bottom water, as well as sediment, were primarily within the range of 100 to 500 μm. Furthermore, there was an increasing trend observed for MPs within the size ranges of 20 to 50 μm and 100 to 500 μm with increasing depth; conversely, there was a gradual decrease observed for MPs within the size ranges of 500 to 1000 μm and 1000 to 5000 μm.

The distribution of MP sizes varied with depth, and the proportion of MPs in larger size intervals (larger than 500 μm) gradually decreased with increasing depth until reaching a minimum concentration within the sediment. Conversely, the percentage of MPs in the 100 to 500 μm exhibited an incremental trend with depth. The speculated reason for this phenomenon may be attributed to the morphology of MPs and microorganisms. MPs were predominantly observed as fibers (larger than 500 μm), while particles and fragments were more prevalent in the 20 to 50 μm and 100 to 500 μm. The buoyancy force acting on the fiber MPs within the larger particle size range will be enhanced, while microbial attachment will induce changes in both the mass and the density of the MPs. Consequently, this phenomenon leads to a gradual increase in the proportion of MPs within the smaller particle size range with increasing depth.

The size distribution of MPs at sampling points 1 to 8 and 10 was predominantly in the range of 100 to 500 μm, with the smallest proportion observed in the range of 1000 to 5000 μm. Conversely, at point 9, MPs were primarily distributed in the size ranges of 20 to 50 μm and 500 to 1000 μm. The proportion of MPs ranging from 1000 to 5000 μm gradually increased from 11% to 23% in points 7 to 10, which represented the upstream sampling locations. This phenomenon can be attributed to anthropogenic activities in the upstream area of the Luhun Reservoir, where high population density and subsequent plastic waste generation intensify downstream. As a result, common large plastic items such as bags, bottles, and foams accumulate along the river course, contributing to the enrichment of larger-sized MPs in the reservoir.

3.6. Risk Assessment of MPs Pollution

Reservoirs, being a crucial sink for MPs in freshwater systems, necessitate enhanced assessment of the ecological risks associated with MPs, particularly in source water reservoirs that directly impact public safety by affecting the quality of drinking water. The potential risk posed by MPs is influenced by their abundance and polymer composition [31,44]. The risk assessment indices, including PLI, H, and PRI, were employed in this study to evaluate the risk of MPs in the surface, intermediate, and bottom waters as well as sediments. It was observed that all water sampling points in the Luhun Reservoir exhibited a PLI greater than 1, indicating the presence of MP contamination (Figure 6a). Considering the correlation between PLI and MP abundance, we hypothesized that the PLI results at different depths would reflect a similar pattern to the distribution of MP abundance across these depths. The study data substantiated this hypothesis, revealing higher surface water PLI levels compared to bottom water, while intermediate water PLI values were predominantly lower. The PLI of water in the river area was consistently lower compared to that of surface water in the main reservoir area. The PLI values of all sediment sampling points are presented in Figure 6d, indicating contamination by MPs if the PLI exceeds 1. Among these points, point 10 exhibited the highest sediment PLI value, suggesting the most severe MP contamination. Conversely, point 6 demonstrated the lowest PLI value and exhibited the least abundance among all the sediment sampling points, further confirming that MP abundance determines the PLI value.

Different types of MP polymers exhibit varying levels of toxicity risks and hazard indices, leading to differential environmental harm. As depicted in Figure 6b, the variations in H values at each sampling point were attributed to the different types of MP polymers present. The range of H values for the surface water samples ranged from 20.50 to 141.22. Surface water at sampling points 3 and 10 exhibited H values within the range of 100 to 1000, indicating a risk level III. For the remaining sampling points, surface water displayed H values ranging from 10 to 100, corresponding to a risk level II. In terms of the intermediate layer, each sampling point showed an H value range of 28.77 to 166.75 with sampling point 6 having an intermediate H value of 166.75 and a risk level III classification; whereas for sampling points 1 to 5, their intermediate H values fell within the range of 10–100 with a risk level II classification. Lastly, in the bottom layer, each sampling point had an H value range between 22.42 and 140.50 with sampling point 4 exhibiting an elevated value of 140.50 resulting in a risk level III classification; while for points 1 to 3, 5 and 6 their bottom layer water displayed H values ranging from 10 to 100 indicating a risk level II. The overall H value of MPs in the water samples from the Luhun Reservoir was 55.43, indicating a class II risk level. The sediment H-values at sampling points 1 to 10 ranged from 11.77 to 1323.13. Sediment H-values at sampling points 2, 3, and 6 fell within the range of 10 to 100, corresponding to a class II risk level. Sampling points 1, 5, and 8 to 10 exhibited sediment H-values ranging from 100 to 1000 with a risk level of III. Sampling points 4 and 7 had sediment H-values of 1151.72 and 1323.13, respectively, indicating a risk level of IV. The presence of PVC in the sediment samples at points 4 and 7 contributes to elevated H-values, indicating a higher risk level of MPs at these locations (Figure 6e). Moreover, the overall H-value for MPs in water samples was determined to be 104.50, classifying the overall risk level as II.

The PRI values of surface water at sampling points 1 to 10 ranged from 67.75 to 826.20 (Figure 6c). By referring to Table 1, it is evident that the PRI of surface water at points 7 to 9 were all below 150, indicating a low ecological risk associated with MPs. Surface water at points 1 and 10 exhibited a medium risk level. Points 3 to 5 demonstrated PRI values ranging from 300 to 600, signifying a considerable risk level. On the other hand, points 2 and 6 displayed high-risk levels with PRI values ranging from 600 to 1200. The range of PRI values for the intermediate layer at points 1 to 6 ranged from 46.75 to 267.09. The PRI for the intermediate water at points 1, 3, and 5 were all below 150, indicating a low potential ecological risk associated. In contrast, the PRI value for the intermediate water at point 6 was measured at 562.78. The PRI values for bottom water at points 1 to 6 ranged from 70.61 to 1064.29, indicating a wide range of pollution levels. Specifically, the PRI values for bottom water at points 2 and 3 were below 150, suggesting a low potential ecological risk. The PRI at points 1 and 5 in bottom water indicated a moderate level of risk. Points 4 and 6 in bottom water exhibited a value of 1064.29 and 308.10, signifying a high level of risk. The PRI_Z value of the Luhun Reservoir water was 201.79 (Figure 6c) which indicates a moderate potential ecological risk.

The PRI values for the sediments at points 1 to 10 ranged from 11.77 to 1323.13, indicating a wide range of pollution levels. Specifically, the PRI values for the sediments at points 1 to 3 and 6 were below 150, suggesting a low potential ecological risk associated with MPs. On the other hand, sediments at points 8 to 10 exhibited PRI values ranging from 150 to 300, indicating a medium risk level. Sediments at points 4 and 5 had high PRI values in the range of 600 to 1200, signifying a significant ecological risk posed by MPs. Notably, point 7 had an extremely high PRI value of 1323.13, highlighting an extreme potential ecological risk associated with MPs in this particular location (Figure 6f). Furthermore, analysis of the Luhun Reservoir sediment revealed a PRI_Z value of 184.98 within the medium risk range, emphasizing moderate potential ecological risks posed by MPs in this reservoir.

3.7. Correlation between Water Quality Factors and the MPs

Spatial variations in the MPs were observed, with certain studies indicating that environmental factors exert a more pronounced influence on their distribution compared to human factors [45]. Spearman correlation analyses were conducted in this study to examine the relationship between MPs and water quality parameters measured in the corresponding samples, including turbidity, dissolved oxygen, pH, potential, and conductivity (Figure 7). The abundance of MPs did not exhibit a significant correlation with any of the five examined water quality parameters, which contrasts with previous findings reported for the Wuding River [46] and Jajroud River [47]. It is postulated that the limited sample size obtained in this experiment may have hindered an accurate reflection of the potential relationship between MPs and water quality parameters. Additionally, uncertainties associated with field sample collection and water quality testing could introduce errors in the measured data, thereby impeding the utilization of water quality parameters for identifying potential MPs.

3.8. Limitations

In the course of investigating the distribution characteristics of MPs in the Luhun Reservoir, we identified certain limitations and deficiencies in the experimental implementation. Due to time and cost constraints, we were unable to achieve complete coverage of the entire reservoir. Instead, we selected six representative central sites in the main reservoir area and four sites in the upstream channel for sampling. However, it is important to acknowledge that this limited number of sampling points may introduce potential bias into our data, rendering it less representative of the entire reservoir. Because of the seasonal variability of the sampling site, the distribution characteristics of MPs in the Luhun Reservoir may exhibit divergent outcomes across different seasons. Consequently, it is important to acknowledge that this study alone cannot comprehensively and accurately depict the complete distribution pattern of MPs in the Luhun Reservoir. Therefore, we intend to conduct multiple long-term seasonal samplings of the Luhun Reservoir in order to comprehensively elucidate the spatiotemporal distribution patterns of MPs within the reservoir. For the detection of MPs, we initially employed microscopic Raman technology due to experimental constraints and cost limitations. However, Raman spectroscopy is highly influenced by the detection environment. Therefore, in order to enhance the accuracy of MP polymer detection, we will incorporate both Raman and Fourier-transform infrared (FTIR) technologies in our subsequent investigations. In the section dedicated to MP detection, due to time constraints and other factors, it was not feasible to detect every suspected MP particle. Instead, a random selection method based on the percentage of each sampling point was utilized for faster sample analysis. Nevertheless, this approach may compromise data accuracy. We are committed to refining this methodology further in order to improve data precision and reliability.

4. Conclusions

The present study demonstrated the detection of MPs in both water and sediment samples collected from the Luhun Reservoir and its upstream area. The abundance of MPs in the water ranged from 1.60 to 13.26 items/L, while that in the sediment ranged from 792.38 to 2352.00 items/kg. In terms of polymer types, PE, PET, and PA exhibited higher percentages in the water samples, whereas PA and PE were more prevalent in the sediment samples. Fibers dominated the reservoir with the highest percentage falling within the range of 100 to 500 μm. The water samples obtained from different depths within the main reservoir area were categorized into surface, intermediate, and bottom layers based on their depth profiles. Overall, the results indicated that MP abundance was greatest at the surface layer followed by the bottom layer; meanwhile, MP abundance was lowest at the intermediate layer. With increasing depth, there was a gradual decrease in the proportion of PE along with an increase in the proportions of PET and PA polymers observed. Furthermore, fibers showed a decreasing trend with increasing depth while fragments displayed an opposite pattern; additionally, proportions of MPs within the size ranges of 20 to 50 μm and 100 to 500 μm increased as the depth increased. Moreover, the PRI_Z values for water and sediments were determined to be 201.79 and 184.98, respectively, indicating a moderate potential ecological risk associated with the MPs present therein. This study provides further insights into understanding the distribution patterns of MPs across different water depths within reservoirs as well as the factors influencing their distribution.