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Article

Factors Influencing the Spatial Distribution of Microplastics in Lakes with the Example of Dianchi Lake

by
Chunnuan Deng
1,
Yuejiao Huang
1,
Yao Hu
1,
Quan Zhang
1 and
Hui Zhao
2,*
1
Yunnan Key Laboratory of Plateau Geographical Process and Environmental Change, Faculty of Geography, Yunnan Normal University, Kunming 650500, China
2
Jiangsu Province Hydrogeological and Engineering Geological Survey Brigade, Huai’an 223001, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(16), 2377; https://doi.org/10.3390/w17162377
Submission received: 18 June 2025 / Revised: 8 August 2025 / Accepted: 9 August 2025 / Published: 11 August 2025
(This article belongs to the Special Issue Research on Microplastic Pollution in Water and Soil Environment)
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Abstract

The spatial law and influencing factors of microplastics in lakes are an important part of microplastic research. This study focused on exploring the influences of factors such as the underwater slope, water depth, and pollution source/shore distance of a lake on the spatial distribution of lake MPs (microplastics). The relationships between the underwater slope, other factors, and the concentration of MPs were analyzed. The results showed that the average abundance of MPs in Dianchi Lake was 129 n/m3, and the spatial distribution pattern was higher in the lake center and lower in the lake shore. The correlation analysis showed that MP abundance was not significantly associated with distance to pollution sources or water depth, but it was significantly correlated with slope (p < 0.05) and offshore distance (p < 0.01). There was a significant negative correlation between the abundance of MPs and the underwater slope. The greater the underwater slope, the lower the abundance of MPs; the smaller the underwater slope, the higher the abundance of MPs. There was a significant positive correlation between the abundance of MPs and offshore distance. The abundance of MPs increased with increasing offshore distance. These new discoveries will help us better understand the spatial patterns of MPs in lakes.

1. Introduction

Global plastic production has increased year by year due to the extensive application of plastics, with total production reaching approximately 8.3 billion tons [1]. A substantial portion of plastic waste in the environment has degraded into microplastics [1]. Consequently, microplastic pollution has been listed as one of the top ten environmental issues by the United Nations Environment Program (UNEP) and poses a threat to aquatic ecosystems [2]. Lakes, as critical accumulation zones for microplastics, have become a focal area for microplastic research. Current studies on the distribution and impacts of microplastic pollution in lakes primarily focus on two aspects.
Some research emphasizes the relationship between the occurrence characteristics of microplastics in lakes and the influence of pollution sources. For example, Ballent et al. investigated lakes in Ontario and proposed that black, opaque, and elastic plastics might originate from vehicle tires, while fibrous microplastics could be derived from clothing [3]. Similarly, Egessa et al. examined microplastic contamination in sediments of Lake Victoria’s northern region and found that fibers dominated the sediment microplastics, with 90% of these fibers being attributed to fishery activities [4]. Additionally, Uurasjärvi et al. observed higher microplastic concentrations near sewage treatment plant outfalls, harbors, and snow storage areas compared to other locations [5]. Cable et al. compared microplastic levels in areas receiving tailwater, urban residential zones, and non-urban residential zones, concluding that regions receiving tailwater exhibited elevated microplastic abundances [6]. These studies infer potential pollution sources based on the physical or compositional characteristics of microplastics or compare microplastic occurrence features across regions with differing pollution sources, yet they rarely consider the influence of distance from pollution sources.
Another line of research focuses on analyzing the correlation between the occurrence characteristics of microplastics in urban lakes and their distance from urban pollution sources. These studies first investigate the occurrence characteristics of microplastics in lakes and then conduct correlation analyses between these characteristics and the proximity to urban pollution sources to evaluate the impact of urban pollution on microplastic contamination. For instance, Wang et al. studied 20 urban lakes in Wuhan, China, and reported that the abundance of microplastics in urban lakes was negatively correlated with distance from the city center—i.e., contamination levels decreased with increasing distance [7]. While these studies reveal the influence of urban activities on lake microplastic pollution through distance-correlation analysis, the specific impacts of different pollution sources at varying distances remain unclear.
Although Wang et al. explored the correlation between microplastic abundance in urban lakes and distance from the city center, urban lakes often face simultaneous impacts from multiple pollution sources [7]. On one hand, different pollution sources may exert distinct effects on microplastic contamination in lakes; on the other hand, the same pollution source may affect different regions within a lake differently. Therefore, studies focusing on the correlation between distances from different pollution sources and the distribution of microplastics within lakes are conducive to in-depth analysis of how pollution source distances influence microplastic contamination in lakes, which holds significant implications for the management and control of lake microplastic pollution. This study selected Dianchi Lake, a typical urban lake in China, as a case study to investigate the influence of distances from various pollution sources on the distribution of microplastics in lakes.

2. Materials and Methods

2.1. Study Area and Sample Collection

Dianchi Lake (24°27–25°27 N, 102°29–103°01 E) (Figure 1a) is located in Yunnan Province, China. The area of Dianchi Lake is 330 km2 and is distributed in a crescent shape. Many fishing villages are distributed around the southeast of Dianchi Lake, and more than ten urban wastewater treatment plants (WWTPs) from Kunming are discharged along the north and east of the lake. The terrain of Dianchi Lake changes gradually, and the average water depth is 4.4 m. Dianchi Lake is an ideal study area because there are many sources of MP pollution, and the underwater slope and water depth of Dianchi Lake change gradually.
Sample sites were uniformly distributed in Dianchi Lake (Figure 1a), and samples were collected in April 2019 using a nylon plankton net with a circular opening (0.32m in diameter, 1.40 m in length, and 160 µm mesh size). Two nets were fixed at the end of the boat. The plankton net was dragged at a speed of one section for 10 min (1 section = 1.85 km/h), with a distance of approximately 300 m. The nets were kept under surface water during towing to collect MPs. A digital flowmeter (HYDRO-BIOS, Kiel in Germany) was fixed at the center of the mouth of the net to calculate the water volume collected. The dragging distance and the flow rate passing through the net were calculated by the flowmeter (the dragging distance = the value of the flowmeter ∗ 0.3, the flow rate passing through the net = the dragging distance ∗ the area of the net opening). For each net, the MP samples were stored in a 500 mL water sample bottle.

2.2. Extraction of MPs

After diluting a constant volume to 500 mL, a water sample of 100 mL was taken and passed through a 150 µm stainless-steel mesh. After sieving, the sample was transferred into a 100 mL beaker with a stainless-steel spoon. Then, 50 mL of 30% H2O2 solution was added to the beaker to digest the organic matter. The beaker containing the sample and H2O2 was placed in a water bath for digestion at 60 °C for 72 h. After digestion, the solution in the beaker was filtered through a 0.45 µm membrane. Then, after the filter membrane dried naturally, microscopic observation was carried out.

2.3. Observation and Verification of MPs

Under a stereomicroscope, the microplastic particles on the filter membrane were inspected, identified, and recorded according to the identification standards for microplastic particles. During visual inspection, the MP length was measured using measuring software, and color (including green, blue, transparent, white, red, black, and other colors) and shape (including line, pellet, fragment, film, and others) were recorded. According to the length, the MPs were divided into categories of <0.1 mm, 0.1–0.5 mm, 0.5–1 mm, 1–3 mm, 3–5 mm, and >5 mm, for a total of six intervals. The abundance data used in this study were based on those obtained using microscopy. By calculating the observed quantity of microplastics against the converted volume of the sample water, the concentration of the microplastic sample was obtained in g/m3.
After visual examination, the polymer components were analyzed using Raman spectroscopy (LabRAM HR Evolution, HORIBA Jobin Yvon SAS, Kyoto, Japan). The specific testing process was as follows. The MPs on the filter membrane were detected directly with the Raman spectral range set to 200–3600 cm−1 and the internal wavelength set to 785 nm. The peak of the measured particle was compared to that of a standard plastic sample. If the particle was a microplastic, the matching rate was generated, and the polymer composition of the MPs was obtained.

2.4. Extraction of Underwater Slope and Water Depth

The isobath map of Dianchi Lake (Figure 1b) was used to generate an irregular triangular network (TIN), and then a digital elevation model (DEM) was generated from that. Finally, based on the DEM of Dianchi Lake, the water depth and underwater slope data of each sampling site were extracted using the spatial analysis module of the ArcGIS software platform (Version 10.3).

2.5. The Determination of Lakeshore Distance and Pollution Source Distance

Lakeshore distance was calculated as follows. Considering that Dianchi Lake is shorter from east to west and longer from north to south, the offshore distance of the sample site was calculated by the distance between the sample site and the nearest east or west lake bank.
The distance of the pollution source was calculated after selecting the sources from among the following: the city center, WWTPs near Dianchi Lake, fishing villages, recreation areas of Dianchi Lake, and river entrances. The city center is located in the northeast of Dianchi Lake and includes the seat of the provincial government of Yunnan Province. Since there was more than one discharge point for the same type of pollutant source, three main WWTPs, three main fishing villages, three main scene spots, and four river entrances were selected as different types of pollution sources (their locations are shown in Figure 1a). For one type of pollution source, only one discharge point closest to the sampling site was selected to calculate the distance of the pollution source.

2.6. Data Analysis

Pearson correlation analysis was used to test the correlation between the abundance of MPs and the influencing factors (water depth, lakeshore distance, and distance of the pollution sources). The relationship between the abundance of microplastics (MPs) and the underwater slope gradient was assessed through Spearman’s rank correlation analysis.
SPSS (version 16.0) was used for data statistical analysis, Origin 8.0 was used for mapping, and ArcGIS10.3 was used for geographical data processing and mapping.

3. Results and Analysis

3.1. The Pollution Level and Distribution of MPs in Dianchi Lake

In this study, the abundance of MP pollution in Dianchi Lake (Figure 2) was 36.9–367.0 n/m3, with an average abundance of 128.8 n/m3. To compare the differences in MP abundance between the lake center and the lakeshore, the lake was divided into two areas: the center (sites 2, 5, 8, 11, and 12) and the lakeshore (sites 1, 3, 4, 6, 7, 9, 10, 13, and 14). The average abundance of MPs in the lake center was 188.7 n/m3, and the average abundance in the lakeshore was 95.5 n/m3. The distribution of MPs in Dianchi Lake showed a significant spatial characteristic, which tended to be higher in the lake center and lower in the lakeshore. The maximum abundance was 367 n/m3 located in the lake center (Site 8), which was three times the average concentration. The minimum concentration was 37 n/m3 (site 13), located on the northeastern shore of the lake, close to Kunming City.

3.2. Physical Characteristics and Polymer Composition of MPs

To analyze the size characteristics of MPs, the proportion of MP particles was calculated, as shown in Figure 3a. In Dianchi Lake, there were very few plastic particles bigger than 5 mm, and the average proportion was only 0.8%. The most abundant particle size categories were 0.1–0.5 mm, 0.5–1 mm, and 1–3 mm, and the proportions were 40.9%, 29.65, and 22.3%, respectively. The proportions of these size categories indicated an increasing proportion of MPs in relation to a decrease in particle size. The highest proportion of MP particles was in the 0.1–0.5 mm range. The proportion of small MPs (SMPs) of <1 mm was 73.8%, which was much higher than that of MPs > 1 mm.
The color proportions of MPs in the lake are shown in Figure 3b. Transparent color had the highest proportion, with an average of 54.6%, followed by blue, white, red, and black, with average proportions of 12.6%, 10.2%, 6.7%, and 5.7%, respectively. In terms of shape (Figure 3c), the proportion of lines or strips was the highest, with an average proportion of 69.9%. The proportions of fragments, pellets, and films were 12.0%, 10.5%, and 3.5%, respectively.
A total of 4,056 suspected MP individuals were found under microscopic examination, and 348 of those were randomly selected for polymer identification. A total of 66.1% of the individuals were identified as plastic, and the rest were identified as non-plastic. The results are shown in Table 1. This ratio was similar to that reported in other studies [3,8], which supports the reliability of the results of the microscopic examination in this study. Of the individuals identified, 20.1% were identified as dyes or pigments. Dyes are commonly used in the plastics industry, and the profiles of these substances are highly similar to those of pigments commonly used in plastics and fabrics. Additionally, owing to the characteristics of Raman spectroscopy, the pigment signal is likely to mask the signal of the material itself. Since the substance of plastic particles is detected only by its surface dyes, the plastic component itself cannot be identified. Therefore, to align with other studies [9,10], pigments were classified as plastic polymers in this study. In the present study, 22 individuals were identified as fibers. Most of the fibers were transparent strips, so they were classified as plastic. Detected nitrocellulose (CN) was classified as a non-plastic pollutant [9]. The non-plastic components identified in this study include cotton, glass, nitrocellulose, and quartz.
We found that the main components of MPs in lake surface water were polyethylene terephthalate (PET, 23.3%), polyethylene (PE, 9.5%), polypropylene (PP, 6.9%), pigment plastic components (20.1%), and fiber plastic (6.3%). The PET in the water body was mainly in strip form (as shown in Table 2, there were 34 transparent strips and 9 other-color strips), followed by fragments and thin films (a total of 38). The main shapes of PE and PP were fragments. In addition, 3.2% of polyvinylchloride (PVC) was found, mainly in fragments and films. Small amounts of polyamide (nylon) (PA), polybutene (PB), polystyrene (PS), and polyurethane (PU) were detected in the water.

3.3. Influence of the Distance of the Pollution Source on the Distribution of MPs

To understand the influence of pollution sources on the distribution of MPs in lakes, a correlation analysis between the abundance of MPs in lakes and the distance between different pollution sources was carried out (Table 3).
The results showed that there was no significant correlation between the abundance of MPs in the lake and the distances of the city center, WWTPs, river entrance, recreation areas, and fishing villages. The results indicate that the current distribution of MPs in Dianchi Lake has little relationship with these pollution discharge sources.

3.4. Influence of Offshore Distance on the Distribution of MPs

To understand the influence of offshore distance on the distribution of MPs, the offshore distance of the sample sites was calculated, as shown in Figure 4a. The correlation between the offshore distance and abundance of MPs was then analyzed (Table 4). The Pearson correlation coefficient was r = 0.715 (p < 0.01), indicating that offshore distance was positively correlated with the abundance of MPs. The abundance of MPs increased with increasing offshore distance and decreased with decreasing offshore distance.

3.5. Influence of Water Depth on the Distribution of MPs

To understand the influence of water depth on the spatial distribution of MPs, the water depth data of all sample sites were extracted based on the isobathic line of Dianchi Lake. The water depth values are shown in Figure 4b, and the correlation between water depth and MP abundance was analyzed (Table 4). The abundance of MPs was found to have a similar trend to the change in water depth, but Pearson’s correlation coefficient was not significant (p > 0.05).

3.6. Influence of Underwater Slope on the Distribution of MPs

To understand the influence of the underwater slope on the distribution of MPs, we extracted the underwater slope data of the lake, as shown in Figure 4c. Spearman correlation analysis was conducted on the underwater slope and the abundance of MPs (r = −0.553, p < 0.05, Table 3), which indicated that the underwater slope had a significant negative correlation with the abundance of MPs. The greater the underwater slope, the lower the MP abundance, and the smaller the underwater slope, the higher the MP abundance. For example, the underwater slope of sites with a high abundance of MPs was small at the lake-center sites (sites 2, 5, 8, and 11).

4. Discussion

4.1. The Pollution Level and Spatial Distribution of MPs

In terms of MP pollution levels, the average abundance in Dianchi Lake was 128 n/m3. It was reported that Chiusi Lake and Bolsena Lake had abundances of 2–3 n/m3 [11], indicating that the MP pollution level in Dianchi Lake is higher than in these two lakes. Four lakes—Taihu, Poyang, Dongting, and Honghu Lakes in China—had MP concentrations of 3.4–28.5, 5–34, 0.9–2.8, and 1.25–4.65 n/L, respectively [8,12,13,14]. These lakes were sampled using pumps or motorized samplers. The concentration of MPs in pump sampling can be at least one to three orders of magnitude higher than that in trawl sampling [6,15]. If the concentration of MPs obtained by trawling in Dianchi Lake was increased by two orders of magnitude, the concentration of MP pollution in Dianchi Lake would still be similar to those lakes. Therefore, the present results indicate that the concentration of MP pollution in Dianchi Lake did not decrease because Kunming is less industrialized than cities in eastern China.
In terms of spatial distribution, it was found that the distribution of MPs in the lake presented a trend of being higher in the lake center and lower in the lakeshore, which was similar to the results of some other studies. For example, Xiong et al. also found higher concentrations of MPs in the center of the lake than in other areas [16]. However, most previous studies reported that MP concentrations near lakeshores were higher than those in lake centers [6,8].

4.2. Characteristics and Origin of MPs in Dianchi Lake

In the present study, the proportion of PET (23.3%) was significantly higher than that of PE and PP. In most previous studies, PE and PP accounted for the highest proportion of MPs in lakes, while the proportion of high-density PET was relatively small, as reported by Yuan et al. [13]. However, a few studies have found that PET is the main component in water and sediment [5,7,17], and the results of this study were consistent with the latter results. Although the density of PET is relatively high, it can still exist in large quantities in water, which may be related to disturbance by currents and weathering [7,18].
On the one hand, Dianchi Lake is a shallow lake with a depth of less than 6 m. Lake disturbance is relatively large, and the lake sediments are prone to resuspension. PET deposited in the sediments due to its high density may also be resuspended in water. On the other hand, Yunnan is located in the Yunnan–Guizhou Plateau with strong ultraviolet radiation and long sunshine duration, which accelerates the weathering process and may change the apparent density of plastics [10]. Therefore, more PET may be detected in the water.
In this study, it was found that the individuals identified as PET and fibers were mainly transparent strips. Transparent MPs are commonly used in fishing nets and fishing lines [7,19]. The long-term fishing activities in the past resulted in many damaged or lost fishing nets and fishing lines remaining in the water, which may have led to an increase in the MPs of fishing nets and fishing lines in the water. Strip PET pollution is usually dominated by fibers from clothing [20], followed by nets and fishing lines [21]. However, based on the color and shape characteristics of PET and fiber in the present study, it was speculated that these MPs came primarily from fishing nets or fishing lines, followed by fabric. Consistent with our research results, Yin et al. found that PET (30%) was the main component of MPs in the sediments of East Dongting Lake, the proportion of transparency was 23–67%, and fiber MPs were over 40% [17]. Therefore, it was speculated that these MPs mainly originated from fishing activities, followed by clothing fibers.
We also identified a plastic-like substance as a dye component, consisting mainly of brown and black strips (Table 2). Washing household clothes can produce a large amount of fibers [22], and the fibers of clothes are generally more black and blue [23]. Some MPs still enter the water after being treated in sewage systems, so it is speculated that this part of the MPs may come from fabric.
In terms of MP particle size, plastic particles of <1 mm were the main component, which was consistent with most previous research results [24]. The small size of MPs in great amounts causes greater environmental harm. Small MPs are more easily ingested by aquatic organisms and are more harmful to them. In addition, small MPs have a larger specific surface area; therefore, they more easily absorb persistent organic pollutants and become carriers of pollutants.

4.3. Relationship Between the Distribution of MPs and the Distance of Pollution Sources

In the present study, no significant correlation was found between the distribution of MPs and the distance of pollution sources. In general, the lakeshore is susceptible to nearby pollution sources, and the concentration of MPs decreases with distance from land due to the dilution effect [25]. Therefore, in most previous studies, the concentration of MPs was higher near urban areas than far away from urban areas [17], and the concentration was higher in areas near pollution sources than in areas without pollution sources. Our results were different from those of previous studies. The sources of pollution, such as urban centers, sewage discharges, tourist attractions, fishing villages, and river inlets, had little relationship with the distribution of MPs in this study. In previous studies, sewage discharge from municipal sewage treatment plants was found to be an important source of MPs in lakes, but wastewater treatment plants can reduce a large amount of plastic fibers from homes and fabrics through technological upgrading [26]. The removal efficiency of microplastics by membrane treatment technology in sewage treatment plants can be as high as 99% [27]. According to the field survey, many sewage treatment plants in Kunming City adopt membrane treatment technology, so the MPs in the discharged tail water may be greatly reduced, which results in a decrease in MPs near the sewage discharge outlet. Often, plastic waste is generated around tourist spots and recreational areas. In this study, it was found that tourist sites were not closely related to the distribution of MPs. This may be attributed to the perennial litter clean-up operations in and around Dianchi Lake. Similarly, Hoellein et al. found that the abundance of MPs in Michigan Lake near the city of Chicago was particularly low, which was related to the daily cleaning activities of local volunteers during peak periods, reducing visible large plastics by a significant amount [28].

4.4. Relationship Between the Distribution of MPs and Offshore Distance

In the present study, a significant positive correlation was found between offshore distance and the abundance of MPs. The MP concentration increased with an increase in offshore distance, and vice versa. This was different from the findings of most previous studies, which have generally found higher concentrations of MPs along the shore than in the center of a lake [6,8], or the further away from the lake shore, the lower the abundance of MPs [29]. Kane et al. found that the abundance of MPs in marine sediments did not decrease with an increase in land source distance but was concentrated at a location 50 km away from the coastline [30]. This distribution pattern is believed to be related to the underlying ocean currents. In the present study, the concentration of MPs was positively related to the offshore distance, which may be related to the different hydrodynamic strengths at different offshore distances. Around lake centers, the lake water is deepest and the currents are weak [31], so MPs readily accumulate. In contrast, at the lakeshore location, the lake is shallow, and the lake waves are large [31], so the MPs are prone to transport and migration. In conclusion, different offshore distances may further influence MP migration.

4.5. Relationship Between MP Distribution and Water Depth

In the present study, we found that water depth was not significantly correlated with the abundance of MPs. Similar results have been found in marine environments. For example, Kane et al. found that there was no linear correlation between the abundance of MPs and the water depth in the deep sea, and MPs were concentrated at depths of 600–900 m, which is at the bottom of the ocean [30]. Vaughan et al. also found that there was no significant correlation between water depth and MP distribution in sediment in a shallow lake with an average water depth of 2.5 m [32].

4.6. Relationship Between the Distribution of MPs and Underwater Slope

In the present study, a significant negative correlation was found between the underwater slope and the abundance of MPs. The larger the slope of the lake, the lower the concentration of MPs, and vice versa. Similarly, Kane et al. found that MPs are mainly concentrated 30–50 km from the shore, where the slope is less than that of the continental shelf or continental slope, so MPs accumulate more readily [30]. In addition, some studies have shown that the abundance of MPs is closely related to the particle size of sediments, indicating that the migration and deposition of MPs may be similar to suspended particulate matter [33,34] or fine-grained sediments. The underwater slope of a lake is closely related to the distribution and accumulation of lake sediments [35,36]. Slope is the main factor controlling the physical characteristics of natural sediments [37], and sediment and contaminant accumulation rates tend to be greater in offshore sedimentary basins than in offshore basins [36].
The present results can be explained as follows. First, the central area of Dianchi Lake has a smaller slope than the lake shore, so the lake center is more likely to accumulate sediments containing MP particles than the lake shore. Second, since shallow lakes are prone to producing resuspensions of surface sediments, sediment resuspension in the center of the lake is prone to producing higher concentrations of MP pollution than in the lake shore; thus, it can be inferred that the underwater slope is one of the important factors influencing the distribution of MPs in lake water.

5. Conclusions

Based on the study of the characteristics and distribution of MP pollution in Dianchi Lake, it was found that the main sources of MPs in Dianchi Lake are fishery activities and fabric pollution. Therefore, in lake management, it should be prohibited to discard old fishing nets anywhere in lakes. The spatial distribution of MPs in Dianchi Lake was higher in the lake center and lower in the lakeshore. The spatial distribution has no significant correlation with the distance of pollution sources, including the city center, WWTPs, recreational areas, fishing villages, and river entrances. Water depth had no significant influence on the distribution of MPs. However, the spatial distribution characteristics of MPs in the lake showed a significant correlation with offshore distance and underwater slope. The shorter the offshore distance, the lower the MP abundance, and the longer the offshore distance, the higher the abundance of MPs. The larger the underwater slope, the lower the MP abundance, and the smaller the underwater slope, the higher the abundance.
From this study, we recognize that slope factors of underwater topography have an influence on the spatial distribution of MPs in lakes, but other factors of topography have not been explored; therefore, the further influence of other factors on the spatial distribution of MPs is worthy of further study. Additionally, considering that the use of 160 µm mesh networks may not be sufficient to represent smaller MPs (<150 µm), they are increasingly regarded as having ecological significance. In the research, we classified the detected pigments as microplastic components, which might have led to a certain overestimation of the degree of microplastic pollution. Therefore, in future research, efforts should be made to enhance technological research and break through this limitation.

Author Contributions

Conceptualization, C.D. and H.Z.; methodology, Y.H. (Yuejiao Huang); formal analysis, Y.H. (Yao Hu); investigation, C.D. and Q.Z.; writing—original draft preparation, C.D.; writing—review and editing, C.D. and H.Z.; supervision, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant number: 21866033).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Water 17 02377 g001
Figure 1. Distribution map of sampling sites and pollution source points near Dianchi Lake (a); bathymetric map of Dianchi Lake (b).
Figure 1. Distribution map of sampling sites and pollution source points near Dianchi Lake (a); bathymetric map of Dianchi Lake (b).
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Figure 2. Distribution map of the abundance of MPs in Dianchi Lake.
Figure 2. Distribution map of the abundance of MPs in Dianchi Lake.
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Figure 3. The proportions of particle size (a), color (b), and shape (c) of MPs.
Figure 3. The proportions of particle size (a), color (b), and shape (c) of MPs.
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Figure 4. The relationships between offshore distance (a), water depth (b), underwater slope (c), and MP abundance.
Figure 4. The relationships between offshore distance (a), water depth (b), underwater slope (c), and MP abundance.
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Table 1. Random detection results of suspected MP individuals using Raman spectroscopy.
Table 1. Random detection results of suspected MP individuals using Raman spectroscopy.
CategoryTypeQuantity (n)Percent (%)
Plastic
PET8123.3
PE339.5
PP246.9
PVC113.2
PU20.6
PA51.4
PB10.3
PS30.9
Fiber plastic226.3
Pigment plastic7020.1
Total25272.4
Non-plastic
CN329.2
Cotton267.5
Quartz123.4
Others267.5
Total9627.6
Notes: PET, polyethylene terephthalate; PE, polyethylene; PP, polypropylene; PVC, polyvinylchloride; PU, polyurethane; PA, polyamide (nylon); PB, polybutene; PS, polystyrene; CN, cellulose nitrate.
Table 2. Main shapes of different polymers using Raman spectroscopy.
Table 2. Main shapes of different polymers using Raman spectroscopy.
PolymerLineFragmentFilmTotal
PET4330881
PP516324
PE130233
PVC06511
Pigment (plastic-like)2340770
Table 3. Pearson correlation analysis of MP abundance and the distance of pollution sources.
Table 3. Pearson correlation analysis of MP abundance and the distance of pollution sources.
ValueCity
Center		WWTPRiver
Entrance		Recreation AreasFishing
Village
Abundance of MPsr0.110−0.1570.062−0.120−0.132
p0.7080.5930.8340.6820.653
Table 4. Correlation analysis of MP abundance and offshore distance/water depth/slope.
Table 4. Correlation analysis of MP abundance and offshore distance/water depth/slope.
MPs AbundanceWater DepthSlopeDistance
to Lakeshore
MPs abundance1
Water depth0.3221
Slope−0.553 *−0.593 *1
Distance to lakeshore0.715 **0.515 *0.747 **1
Notes: * p < 0.05, ** p < 0.01.
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Deng, C.; Huang, Y.; Hu, Y.; Zhang, Q.; Zhao, H. Factors Influencing the Spatial Distribution of Microplastics in Lakes with the Example of Dianchi Lake. Water 2025, 17, 2377. https://doi.org/10.3390/w17162377

AMA Style

Deng C, Huang Y, Hu Y, Zhang Q, Zhao H. Factors Influencing the Spatial Distribution of Microplastics in Lakes with the Example of Dianchi Lake. Water. 2025; 17(16):2377. https://doi.org/10.3390/w17162377

Chicago/Turabian Style

Deng, Chunnuan, Yuejiao Huang, Yao Hu, Quan Zhang, and Hui Zhao. 2025. "Factors Influencing the Spatial Distribution of Microplastics in Lakes with the Example of Dianchi Lake" Water 17, no. 16: 2377. https://doi.org/10.3390/w17162377

APA Style

Deng, C., Huang, Y., Hu, Y., Zhang, Q., & Zhao, H. (2025). Factors Influencing the Spatial Distribution of Microplastics in Lakes with the Example of Dianchi Lake. Water, 17(16), 2377. https://doi.org/10.3390/w17162377

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