Occurrence Characteristics and Ecological Risk Assessment of Microplastics in the Water Body of Lake Gahai, Tibetan Plateau of China

Occurrence Characteristics and Ecological Risk Assessment of Microplastics in the Water Body of Lake Gahai, Tibetan Plateau of China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Occurrence Characteristics and Ecological Risk Assessment of Microplastics in the Water Body of Lake Gahai, Tibetan Plateau of China Guohong WU, Yanrong TAN, Wenye CHEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9134685/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Microplastics (MPs) pollution has emerged as a significant global concern. Research on the sources, distribution, and ecological risks of MPs in the lakes of the Tibetan Plateau remains relatively scarce, thereby hindering efforts to clarify the current status and transport patterns of MPs in these aquatic systems. This study examines the surface water of Lake Gahai—a representative permanent freshwater lake located on the northeastern margin of the Tibetan Plateau—to analyze the abundance, size distribution, and polymer types of microplastics. By integrating existing literature, we discuss the primary transport pathways of MPs in the study area and quantitatively assess ecological risks using the pollution risk index (H), pollution load index (PLI), and potential ecological risk index (PRI). Results indicate that the abundance of microplastics (MPs) in the surface water of Lake Gahai varied from 3.33 to 82.00 items/L, with an average abundance of 30.07 ± 24.70 items/L. The majority of MPs were found within the size range of 20–50µm, constituting over 64.42% of the total. A total of 35 polymer types were identified, predominantly including polyethylene (PE), polyethylene terephthalate (PET), polyamide (PA), polylactic acid (PLA), polytetrafluoroethylene (PTFE), polyvinyl chloride (PVC), polypropylene (PP), acrylate polymers, and polyvinyl alcohol. The ecological risk assessment revealed that the pollution load index (PLI) of the lake's surface water was low, while both the hazard quotient (H) and the pollution risk index (PRI) reached the highest severity level. These findings enhance the understanding of the sources and ecological risks associated with MPs in the aquatic environment of the Tibetan Plateau and provide valuable data and theoretical support for future research on MPs pollution. Tibetan Plateau lakes surface water microplastics occurrence characteristics ecological risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Plastic products are extensively utilized in various sectors, including packaging, construction, automobiles, electrical appliances, and agriculture, owing to their high quality and low cost (Zhu et al., 2019 ). Global plastic production surged from 2 million tons annually in 1950 to 400 million tons in 2022, with projections indicating it may reach 800 million tons by 2050. In contrast, the plastic recycling rate has stagnated at a low level, with a recovery rate of approximately 9% (Houssini et al., 2025 ). That means over 90% of plastic products are discarded annually, which poses significant risks of long-term accumulation in both human living spaces and the natural environment. In June 2024, a study conducted by Chinese researchers revealed that each gram of wear and tear on common kitchen cleaning sponges releases up to 6.5 million microplastics (MPs) (Su et al., 2019). This finding became a trending topic on the internet in China, reigniting public discourse regarding the pollution hazards associated with plastic products. Microplastics are defined as plastic debris and particles with a diameter of less than 5 mm. Microplastics are defined by their small size, extensive specific surface area, and significant ability to adsorb pollutants (Han et al., 2021). During their decomposition, they release toxic substances, including plastic additives (Baldwin et al., 2016 ). Their persistence in the natural environment (Allen et al., 2019 ) presents a threat to both ecosystem functioning and human health (Chen et al., 2026 ). In December 2022, China's environmental authorities released the Key New Pollutants Control List (2023 Edition), which explicitly identifies microplastics as one of the new pollutants. Current research on environmental microplastics in China primarily concentrates on marine ecosystems or densely populated inland regions (Sun et al., 2023 ). The Qinghai-Tibet Plateau, due to its geographical location, high altitude, and other factors, has historically encountered minimal human disturbance, rendering it one of the more pristine areas on Earth. Nevertheless, recent studies indicate that microplastics are now extensively distributed throughout the plateau's rivers, lakes, sediments, soils, atmosphere, and glacial/snow environments (Wang et al., 2023 ).Plateau lakes, characterized by their closed and static nature, are particularly susceptible to the convergence and accumulation of microplastics (Feng et al., 2021 ). Microplastics infiltrate wetland environments, including lakes, through mechanisms such as atmospheric deposition and rainfall runoff. Once introduced, they not only act as vectors for microorganisms, thereby enhancing the transport capacity of pathogenic microbes (Chen et al., 2023 ), but also affect microbial composition and metabolic activity. This, in turn, alters the richness and diversity of bacterial communities, significantly impacting nutrient and energy flow within wetland ecosystems (Li et al., 2022 ). Microplastics can be absorbed by plants, which hinders their uptake of organic nutrients and alters the habitat of wetland vegetation, thereby inhibiting plant growth (Xu et al., 2023). Additionally, microplastics can generate cascading effects across various trophic levels, impacting the entire ecosystem. For instance, microplastic pollution can influence the population structure and abundance of plankton; a decline in plankton may disrupt the food chain for fish and other organisms, leading to far-reaching consequences for the food web of the entire ecosystem (Pan et al., 2022 ). Reports on microplastics in lakes on the Qinghai-Tibet Plateau remain relatively limited. The existing research is insufficient to elucidate the current pollution status and transport patterns of microplastics in the lake waters of the plateau. This study concentrates on Gahai Lake, a representative permanent freshwater lake situated on the northeastern edge of the Qinghai-Tibet Plateau. This research aims to conduct a comprehensive investigation into the occurrence characteristics of microplastics in rivers and lakes within this pristine yet fragile ecosystem by scientifically designing water sample collection points. It seeks to explore the source pathways of microplastics in these lakes while simultaneously assessing the ecological risks they pose to the plateau's aquatic environment. The findings are intended to provide a scientific foundation for future research on microplastics in plateau lakes, as well as for the management and control of these pollutants. 2 Materials and Methods 2.1 Overview of the Study Area Gahai Lake is situated in Luqu County within the Gannan Tibetan Autonomous Prefecture of Gansu Province. It is positioned on the eastern fringe of the Qinghai-Tibet Plateau, at the transition zone leading to the Longnan mountainous region and the Loess Plateau. This region experiences a continental monsoon climate characteristic of the Qinghai-Tibet Plateau. Due to the influence of westerly circulation and the topographical features of the plateau, the study area receives a relatively high amount of rainfall, with an average annual precipitation of 654.2 mm, primarily occurring during the summer and autumn months.The average altitude of the study area is approximately 3,500 meters above sea level. The annual average temperature is 3.5°C. The mean annual wind speed is 1.6 m/s, with easterly winds predominating throughout the year, followed by northeasterly winds. The annual effective accumulated temperature totals 1,254.2°C. The average maximum depth of frozen soil reaches 73.7 cm. The average annual duration of sunshine is 2,237.1 hours, and the annual solar radiation measures 520.0 kJ/cm²·a. The average annual evaporation varies between 1,100 mm and 1,200 mm. Gahai Lake is located within the Zhouqu River basin, a tributary of the Tao River. At an elevation of 3,470 m, it is the largest freshwater lake on the Gansu Province plateau. The lake's water is sourced from multiple rivers and springs, including the Qiongmuqiequ, Wu'ekuqu, Wengniqiu, Dongcaiqiu, Duomuqiequ, and Geqiongkuqu, which receive meltwater from the Guo'ermang Liang and the northern slopes of the Xiqing Mountains, converging at Youqing Qingkuhe and Gengqing Qingkuhe. Furthermore, an artificial channel on the western side of the lake diverts a portion of the Zhongqu River's flow into it. Currently, the lake spans approximately 2,700 hm², with an average depth of about 2.0 m and a maximum depth of approximately 5.0 m. Its water storage capacity is estimated at 0.48 × 10⁸m³. Gahai Town, home to the lake, is a predominantly pastoral region. It encompasses three villages: Xiuwa, Gaxiu, and Jiacang, with a combined population of 5,207 residents. National Highway G213 traverses the town, facilitating convenient transportation access. 2.2 Sample Site Layout and Collection Twelve sampling sites were established to ensure representativeness, taking into account the topographic conditions of the study area, the extent of primary human activities, the distribution of main inlets and outlets, water flow direction, and prevailing wind direction (Fig. 1 ). These sites were allocated across four distinct zones: the northern section of the lake, the western section, the southern section, and the lake center. Three sampling sites (G1–G3) were established in the northern region of the lake, adjacent to villages, a highway, and a scenic area, thereby representing locations with higher levels of human activity. Three additional sites (G4–G6) were located in the western part of the lake, near the lake's outlet. In the southern region, three sites (G7–G9) were identified as the primary water supply area for Gahai Lake, characterized by minimal human activity. The final three sampling sites (G10–G12) were positioned at the center of the lake. Surface water samples (0–20 cm depth) of 3 L were collected from each site on April 27, July 30, and October 3, 2025, using a stainless steel bucket. The collected water was filtered through a 20µm stainless steel sieve. Particles retained on the sieve were rinsed three times into aluminum foil bags with ultrapure water. The samples were promptly transported to the laboratory and stored in a refrigerator at 4°C. Prior to sampling, all equipment was cleaned by rinsing three times with ultrapure water and subsequently wrapped in aluminum foil for storage. Before each sampling event, three separate 100 mL aliquots of ultrapure water were collected in pre-cleaned aluminum foil bags to serve as blank controls (CK1, CK2, CK3). No rainfall occurred in the three days preceding each sampling event, and no strong winds were present on the sampling days. 2.3 Microplastic Detection in Samples The specific procedure was as follows: The water sample was weighed and recorded before being subjected to vacuum filtration through a steel membrane with a pore size of 13µm. The resulting membrane was placed into a sample vial, to which 30% H₂O₂was added to facilitate the removal of organic matter. The vial underwent ultrasonication for 30 minutes and was subsequently allowed to stand for 24 hours, enabling the hydrogen peroxide to fully react with the organic matter. The digestion solution was then vacuum-filtered again using a steel membrane with a pore size of 13µm. The obtained membrane was placed in a sample vial, immersed in an ethanol solution, and ultrasonicated for 30 minutes to disperse the particles from the membrane into the ethanol solution. After removing the membrane from the ethanol solution, it was rinsed multiple times with ethanol. The ethanol solution, containing the dispersed particles, was concentrated in an infrared drying oven to a final volume of 150µL. Subsequently, the concentrate was drop-cast onto a high-reflection glass slide. Once the ethanol had completely evaporated, analysis was conducted using Laser Direct Infrared Imaging (LDIR). The particle analysis mode was selected, a method was established utilizing the microplastic spectral library, and an automated test method was configured (match > 0.65, particle size range 20–500µm) for measurement. 2.4 Quality Control All solvents utilized in the analysis were filtered through a 0.45µm pore size polytetrafluoroethylene (PTFE) membrane prior to use. All experimental consumables were composed of glass and were rinsed three times with ethanol before being dried for use. The blank controls established detected 5, 10, and 9 microplastic particles, respectively. To determine microplastic abundance, the blank contamination values were subtracted, yielding the final abundance values. 2.5 Ecological Risk Assessment This study utilized the Hazard Index (H), Pollution Load Index (PLI), and Potential Ecological Risk Index (PRI) to quantitatively evaluate the ecological risk associated with the surface water of Gahai Lake. The calculation formulas for these indices, along with the classification standards for microplastic ecological risk levels (Table 1 ), were derived from existing literature (Sun Xuechun et al., 2023; Liu Xiaowei et al., 2024; Wang Kelin et al., 2026). The plastic polymer hazard scores (Table 2 ) employed in the computation of the H, PRI, and PLI indices were informed by Lithner et al. ( 2011 ). The reference value for microplastic abundance in water, utilized in the PLI calculation, was determined from the average abundance of microplastics observed in the blank controls of this study. Table 1 Risk classification of microplastics Index classification Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ H 1000 - PLI 30 - PRI 1200 Note: The H and PLI indices categorize risk levels as follows: Level I indicates low risk (minor pollution), Level II signifies medium risk (mild pollution), Level III denotes high risk (moderate pollution), and Level IV represents extreme risk (severe pollution). In contrast, the PRI index classifies ecological risk into five levels: I, II, III, IV, and V. Table 2 Hazard score of microplastics polymer type NO. Polymer Hazard score 1 Acrylates 1 2 Acrylonitrile Butadiene 15761 3 Acrylonitrile Butadiene Styrene(ABS) 10517 4 Ethylene Vinyl Acetate (EVA) 6 5 Ethylene acrylic acid(EAA) 6 6 Methyl methacrylate-Butadiene-Styrene(MBS) 7017 7 Polyethylene (PE) 11 8 Polybutadiene 20001 9 Polycaprolactone 0 10 Polycarbonate (PC) 1155 11 Polyethylene Terephthalate (PET) 4 12 Polylactic acid (PLA) 0 13 Polymethylmethacrylate (PMMA) 1021 14 Polytetrafluoroethylene (PTFE) 0 15 Polyurethane (PU) 3140 16 Polyvinylchloride (PVC) 10001 17 Polypropylene (PP) 1 18 Polystyrene (PS) 30 19 Rubber 11400 20 Polyacetal(POM) 20131 21 Polyamide (PA) 50 22 Polymerized Styrene Butadiene Rubber (SBR) 11400 23 Styrene-butadiene styrene 6687 24 Styrene-isoprene styrene triblock copolyme(SIS) 20 Note: For polymers composed of multiple monomers, the polymer hazard score is calculated as the average of the hazard scores of the constituent monomers. 2.6 Data Processing and Analysis Microsoft Excel 2010 facilitated the preliminary statistical processing of microplastic abundance, particle size, and type. IBM SPSS Statistics 24.0 was employed to assess the normality and homogeneity of variance of the data. Differences among data groups were analyzed using Analysis of Variance (ANOVA) or non-parametric tests. ArcGIS 10.8 software was utilized to generate the spatial distribution map of sampling sites, while other figures were produced using Origin 2024. Microplastic abundance in water samples is expressed as mean ± standard deviation, with units reported as "items/L." 3 Results and Analysis 3.1 Microplastic Abundance Microplastics were identified in all 39 water samples analyzed. In April, the abundance of microplastics across various sampling zones varied from 3.33 to 82.00 items/L, yielding an average of 23.94 ± 22.21 items/L. In July, the abundance ranged from 6.67 to 90.67 items/L, with an average of 27.20 ± 28.06 items/L. In October, the abundance varied from 9.33 to 78.67 items/L, resulting in an average of 39.08 ± 24.03 items/L. Analysis of variance (Table 3 ) indicated that, over time, there were no significant differences in average microplastic abundance across the various sampling months. Nevertheless, in October, the microplastic abundance in the surface water of the northern sampling zone was significantly greater than that observed in April and July (P < 0.05). Spatially, in October, the microplastic abundance in the northern sampling zone's surface water was significantly higher than that in the lake center sampling zone (P < 0.05). In the other months, no significant spatial differences in microplastic abundance among the sampling zones were detected. Table 3 Microplastic abundance in each sampling area during different months MonthSampling site April July October G1–G3 18.33 ± 23.43 Ab(3.33 ~ 45.33) 16.56 ± 8.84 Ab(10.33 ~ 26.67) 64.78 ± 20.42 Aa(41.33 ~ 78.67) G4–G6 15.55 ± 12.22 Aa(6.00 ~ 29.33) 17.11 ± 4.07 Aa(12.67 ~ 20.67) 36.56 ± 13.74 ABa(25.67 ~ 52.00) G7–G9 38.33 ± 39.07 Aa(6.67 ~ 82.00) 31.56 ± 42.53 Aa(6.67 ~ 80.67) 31.55 ± 32.08 ABa(9.33 ~ 68.33) G10–G12 23.56 ± 4.86 Aa(19.00 ~ 28.67) 43.56 ± 40.94 Aa(16.67 ~ 90.67) 23.44 ± 9.48 Ba(17.00 ~ 34.33) Average 23.94 ± 22.21a 27.20 ± 28.06a 39.08 ± 24.03a Note: Distinct uppercase letters signify significant differences in microplastic abundance among various sampling zones within the same month, whereas different lowercase letters denote significant differences in microplastic abundance within the same sampling zone across different months. The numbers in parentheses indicate the range of abundance. 3.2 Microplastic Particle Size The distribution of microplastic particle sizes (Fig. 2 ) in the surface water of Gahai Lake predominantly concentrated in the range of 20–50µm, comprising 64.42% to 86.61% of the total. This was followed by the 50–100µm range, which accounted for 7.79% to 26.18%. Microplastics sized 200–500µm represented the smallest proportion, with some months or specific sampling zones showing no detection of particles within this size range. On a temporal scale (Table 4 ), the quantity of microplastics measuring 20–50µm in the northern sampling zone exhibited a significant increase in October compared to April and July (P < 0.05). Furthermore, in October, the quantity of microplastics sized 50–100µm in the northern sampling zone was also significantly greater than that observed in April (P < 0.05). In contrast, on a spatial scale, no significant differences were noted in the distribution of microplastics across various size fractions among the different sampling zones during any of the sampling months. Table 4 Statistical count of microplastic particle sizes across seasons and sampling areas Month Sampling site [20–50µm) [50–100µm) [100–200µm) [200–500µm) April G1–G3 47.33 ± 64.16Ba 4.34 ± 3.79Ba 3.33 ± 2.08Aa 0.67 ± 1.15Aa G4–G6 41.00 ± 34.53Aa 4.67 ± 1.00Aa 1.67 ± 2.08Aa 0.00 ± 0.00Aa G7–G9 89.00 ± 98.85Aa 15.34 ± 12.06Aa 7.00 ± 3.61Aa 4.00 ± 4.00Aa G10–G12 58.33 ± 12.49Aa 10.67 ± 4.58Aa 1.67 ± 1.53Aa 0.33 ± 0.58Aa July G1–G3 32.00 ± 10.02Ba 13.00 ± 12.74ABa 4.33 ± 4.04Aa 0.33 ± 0.58Aa G4–G6 40.66 ± 11.68Aa 7.00 ± 3.51Aa 3.00 ± 1.00Aa 0.67 ± 1.15Aa G7–G9 69.33 ± 94.40Aa 18.00 ± 24.83Aa 7.33 ± 7.57Aa 0.33 ± 0.58Aa G10–G12 112.66 ± 100.83Aa 15.34 ± 15.95Aa 2.33 ± 4.04Aa 1.00 ± 1.73Aa October G1–G3 155.66 ± 54.79Aa 32.00 ± 10.21Aa 7.00 ± 2.65Aa 0.00 ± 0.00Aa G4–G6 79.00 ± 19.35Aa 26.34 ± 18.56Aa 4.33 ± 3.06Aa 0.67 ± 1.15Aa G7–G9 78.33 ± 83.62Aa 12.00 ± 9.29Aa 4.33 ± 3.21Aa 0.33 ± 0.58Aa G10–G12 58.33 ± 22.11Aa 8.34 ± 5.69Aa 4.00 ± 2.00Aa 0.33 ± 0.58Aa Note: Different uppercase letters indicate significant differences in the abundance of microplastics of the same size fraction in the same sampling zone across different months. 3.3 Types of Microplastic Polymers The analysis of matching results showed the detection of 35, 29, and 33 types of microplastic polymers in the surface water of Gahai Lake in April, July, and October, respectively. In Fig. 3 , the Venn diagram illustrates that although the types of microplastic polymers varied across different sampling zones within the same month, the shared types consistently comprised over 82.78%, with unique types being minimal (< 0.94%). Additionally, a statistical examination of microplastic polymer types in Gahai Lake's surface water identified nine primary types: Acrylates, Polyethylene (PE), Polyethylene terephthalate (PET), Polylactic acid (PLA), Polytetrafluoroethylene (PTFE), Polyvinyl chloride (PVC), Polypropylene (PP), Polyvinyl alcohol, and Polyamide (PA), with the remaining types collectively representing about 20% of the total quantity, as depicted in Figs. 4 and 5 . Note The numbers in parentheses represent the proportion of microplastic quantity. 3.4 Ecological Risk Index The Pollution Load Index (PLI) results depicted in Fig. 6 revealed that microplastics in surface water exhibited PLI values ranging from 1.50 to 5.92 across all sampling sites, indicating a state of slight pollution. The average PLI values for April, July, and October were 2.94, 3.10, and 3.80, respectively, with the highest average PLI value of 4.99 observed in the northern sampling zone during October. The Hazard Index (H) varied from 247.33 to 3376.14 in the study area, with all sites falling into either high-risk (30.56%) or significant-risk (69.44%) categories. The highest average H value of 2288.58 was recorded in the southern sampling zone in April. The Potential Ecological Risk Index (PRI) ranged from 556.50 to 107,192.45 for each sampling site, with only one site classified as having LevelⅢpotential ecological risk, while the remaining sites exhibited the highest potential ecological risk level (LevelⅤ). Figure 6 Ecological risk assessment of microplastics 4 Discussion 4.1 Analysis of Microplastic Occurrence Characteristics and Transport Pathways Microplastics, as emerging contaminants, have garnered significant attention in global environmental and health research due to their pollution status and ecological risks. A comprehensive analysis of recent domestic microplastic studies was conducted, focusing on those with sieve pore sizes matching those used in this study for accurate comparison. Results indicated that the abundance of microplastics in Gahai Lake's surface water (3.33–82.00 items/L) was comparable to Jiuzhaigou Valley lakes (20.27–58.80 n/L), lower than Poyang Lake tributaries (43–276 n/L) with higher population density, yet higher than lakes and rivers on the Qinghai-Tibet Plateau (0–5.56 n/L) at greater altitudes and more remote locations. The lack of a standardized classification for microplastic particle size results in varying definitions of size fractions among different studies. Approximately 50% of studies show that the smallest defined size fraction does not always represent the highest proportion of microplastics. Studies conducted on lakes in the Qinghai-Tibet Plateau revealed that microplastics larger than 100µm accounted for around 51%–80% of the total. In this investigation, 90% of the microplastics were found within the 20–100µm range, with the smallest size fraction (20–50µm) being the most prevalent (> 64.42%), indicating significant disparities from prior investigations. This study identified up to 35 distinct microplastic polymer types, significantly surpassing the quantities reported in previous research. Notably, the six polymers typically dominant in earlier studies—PP, PE, PS, PA, PET, and PVC—constituted a relatively low proportion in this investigation. The discrepancies in particle size distribution and the variety of polymer types, when compared to prior studies, may be attributed to the timing of the research rather than alterations in the pollution characteristics of the water body itself. Most previous studies were conducted between 2019 and 2022. As microplastic research has advanced, improvements in identification methods and the expansion and refinement of standard microplastic spectral libraries have markedly increased detection efficiency and the ability to identify complex microplastic polymer types. Previous research has established a notable correlation between microplastic levels and urbanization as well as population density (Yu Lu et al., 2025). The study area, situated in Luqu County, is characterized by a total population of around 34,000 individuals and a population density of approximately 6.48 people per square kilometer, categorizing it as sparsely populated (Wang Lucang et al., 2017). Despite this classification, the Statistical Bulletin on National Economic and Social Development of Luqu County for 2023 reported that the county welcomed 2.3899 million domestic and international tourists in the same year. Consequently, the peak tourist season from May to August, coupled with the abrasion of tires from vehicles, could potentially elevate the presence of microplastics (Feng Sansan et al., 2021). Notably, the percentage of microplastics ranging from 50 to 100µm in size in the northern sampling zone notably surged in July, possibly linked to tourist-related activities. However, overall, there was no significant disparity in microplastic quantities in the surface water of Gahai Lake between July and either April or October, indicating that heightened tourist influx did not markedly influence microplastic levels in the lake's surface waters. In October, microplastic abundance in the northern sampling zone, which is situated near villages and roads, increased significantly compared to April and July. This increase may be closely associated with rainfall and wind patterns (Xu et al., 2022 ), as local precipitation primarily occurs during the summer and autumn months, while August and September experience the lowest average wind speeds of the year. Rainfall events, coupled with relatively stable atmospheric conditions, facilitate the deposition of microplastics. Atmospheric transport serves as a critical pathway for microplastics to reach remote regions, including the Antarctic, Arctic, and high mountain areas (Luo et al., 2025 ). Smaller microplastic particles are lighter and more readily transported over long distances through the atmosphere, moving from low-altitude areas with high population density to elevated regions. The predominance of microplastics in the 20–50µm size range in this study indirectly supports the notion that atmospheric transport may represent a significant source of microplastics in plateau regions (Zhang et al., 2026 ). Table 5 Research results on the occurrence characteristics of microplastics in water environments No. Study Year Study Area Research Objects Sampling Depth Collection Method Identification Method Match Degree Abundance Amount of Polymer Types Main Particle Size Main Polymer Types Reference 1 2020 Chaohu Lake Lake 20–30 cm 50 µm stainless steel sieve Raman spectrometer Unknown 0.25–3.20 n/L 7 Main Polymer Types and others 50–200 µm PP,PE,PS,PET,PVC Chen et al.,2022 2 2021 Huixian Wetland Basin Lake,River 20–30 cm 50 µm stainless steel sieve Fourier transform infrared spectroscopy > 0.70 5.47–34.91 n/L 4 Main Polymer Types and others 0.45–500 µm PET,PS,PP,PE Chen et al.,2023 3 2023- 2024 Poyang Lake Lake,River Surface water 20 µm stainless steel sieve Fourier transform infrared spectroscopy Unknown 43–276 n/L 8 20–100 µm PE,PP,PS,PVC Peng et al.,2025 4 2024 Xingyun Lake Basin Surface water Unknown 200 µm stainless steel sieve Micro-infraredspectrometer > 0.70 3.33–7.66 n/L 6 0.2–0.5 mm RY,PES Wang et al.,2026 5 2018 Tibet Plateau Lake,River 0–10 cm 50 µm stainless steel sieve Laser micro-raman spectrometer > 0.70 2.25–7.75 n/L 11 Main Polymer Types and others 50–200 µm PA,PU,PET,PVC,PP Yang et al.,2025 6 2019 Qinghai-Tibet Plateau Lake,River 0–10 cm 20 µm stainless steel sieve Raman spectrometer Unknown 0–1.92 n/L 4 Main Polymer Types and others 100–500 µm PP,PE,PS Feng et al.,2021 7 2019 Qinghai-Tibet Plateau Lake,River 0–10 cm 20 µm stainless steel sieve Raman spectrometer > 0.80 0–2 n/L 8 100–500 µm PP,PE,PS,PET Feng et al.,2021 8 2020 Inland Lakes in Tibet Plateau Lake 0–30 cm 100 µm stainless steel sieve Raman spectrometer > 0.70 0.17–3.30 n/L 6 Main Polymer Types and others 0.1–2 mm PP,PET,PA,PS,PE Liu et al.,2022 9 2020 Yamzhog Yumco Lake Lake 0–40 cm 50 µm stainless steel sieve Fourier transform infrared spectroscopy > 0.70 0.09 n/L 10 0.5–2 mm PET,PA,RY Yu et al.,2025 10 2020 Tibet Plateau Lake,River 0–10 cm 20 µm stainless steel sieve Raman spectrometer > 0.80 0.1–1.8 n/L 7 Main Polymer Types and others 100–250 µm PP,PE,PS,PET,PVC Feng et al.,2021 11 2020 Tibet Plateau Lake 0–10 cm 20 µm stainless steel sieve Raman spectrometer > 0.80 0.2–0.5 n/L 4 Main Polymer Types and others 100–500 µm PE,PP Feng et al.,2021 12 2020 Qinghai-Tibet Plateau Lake 0–30 cm 100 µm stainless steel sieve Raman spectrometer > 0.70 0.3–1.7 n/L 8 Main Polymer Types and others 0.1–1 mm PE,PP Han et al.,2024 13 2020 Tibet Plateau Lake Surface water 31 µm stainless steel sieve Raman spectrometer Unknown 0.35–5.56 n/L 4 100–500 µm PP,PET Liang et al.,2024 14 2021 Tibet Plateau Lake,River 0–20 cm 20 µm stainless steel sieve Raman spectrometer > 0.75 0.15–1 n/L 10 unknown PP,PE,PA,PS Feng et al.,2024 15 2023 Eastern of Qinghai-Tibet Plateau Lake Surface water 0.45 µm filter membrane Fourier transform infrared spectroscopy Unknown 0–228 n/L 9 0.45–100 µm PE,PP,PET,PA Li et al.,2025 16 2023 Jiuzhaigou Lake 0–20 cm 10 µm filter membrane Fourier transform infrared spectroscopyr and Raman spectrometer Unknown 20.27–58.80 n/L 4 0.1–0.5 mm PET,PE,PP,PS Zhou et al.,2025 17 2026 Eastern of Tibet Plateau Lake 0–20 cm 20 µm stainless steel sieve Laser infrared imaging spectrometer > 0.65 3.33–82.00 n/L 35 20–50 µm PE,PET,PLA,PTFE,PVC,PP,PA,Acrylates,Polyvinyl alcohol This Study 4.2 Ecological Risk Assessment of Microplastics The Qinghai-Tibet Plateau, referred to as the "Asian Water Tower," is a crucial global freshwater source due to its glacial meltwater, lakes, and rivers. Microplastic pollution in this region poses unique dispersion risks (Zhang et al., 2026 ). A compilation of data from studies on microplastic ecological risk assessment (Table 6 ) revealed that, apart from slight pollution in groundwater within the Xingyun Lake Basin and water bodies in the upper Min River, the Pollution Load Index (PLI) for microplastics in Gahai Lake's surface water consistently indicated slight pollution, aligning with most research outcomes. The PLI evaluates ecological risk based on microplastic levels (Liu et al., 2024 ), indicating that the majority of surface waters in China face low to moderate ecological risks concerning microplastic levels. This observation is in agreement with Gan's findings (2025). The Hazard Index (H) is derived from the hazard scores associated with various microplastic polymer types, whereas the Potential Ecological Risk Index (PRI) incorporates both the abundance of microplastics and the toxicity of different polymer types (Liu et al., 2024 ). In this study, the H and PRI values for the surface water of Gahai Lake were significantly higher than those reported in previous research. This discrepancy can be attributed to the diverse range of polymer types identified in this study, including several with elevated hazard scores. Sun (2023) also observed that omitting certain polymers from ecological risk index calculations, due to the absence of hazard scores for those polymers, could result in an underestimation of microplastic ecological risk. Based on the classification criteria for monomers and their associated hazard levels in polymers established by Lithner(2011), this study identified nine polymers categorized as the highest hazard level (Ⅴ): Acrylonitrile Butadiene, Acrylonitrile Butadiene Styrene (ABS), Methyl methacrylate-Butadiene-Styrene (MBS), Polybutadiene, Polyvinylchloride (PVC), Rubber, Polyacetal (POM), Polymerized Styrene Butadiene Rubber (SBR), and Styrene-butadiene styrene. The hazard scores for these polymers range from 6687 to 20131. As a result, the H and PRI indices calculated in this study are notably elevated. Table 6 Research results on the ecological risk assessment of microplasticsin water environments No. Study Year Study Area Research Objects Sampling Depth Main Polymer Types PLI H PRI Reference 1 2022 Wuhan city Urban Lakes 0 ~ 20 cm PE、PP、PVC、PS、PET、PC、PMMA No data No data 601.5 ~ 8 954 Wang et al.,2023 2 2023 Yichang city Reservoirs Surface water PP、PE 1.00-2.70 4.21–17.55 7.27–79.05 Liu et al.,2024 3 2024 XingyunLakeBasin Groundwater Unknown RY、PES 72.6 3.2 ± 2.5 No data Wang et al.,2026 4 2019 Salt Lakes on the Tibet Plateau Sediments 0 ~ 2 cm PES、PEI、PA、PP、PS、PESTUR、PE-PP 1.52 92.3 No data Sun et al.,2023 5 2020 Tibet Plateau Lakes Surface water PE、PET、PS、PP 0.265–1.063 No data 0.352–2.589 Lianget al.,2024 6 2021 Tibet Plateau Rivers 0 ~ 20 cm PP、PA、PE、PS、PET、PAN、PC、PVC、PTFE、Ruber 2.73–6.88 1.48–27.15 45.89-546.73 Fenget al.,2024 7 2021 Tibet Plateau Qinghai Lake 5 ~ 20 cm PE、PP、PE、PC、PS、PVC 1.00-2.04 846.8 2453 Liet al.,2023 Namco Lake 5 ~ 20 cm PE、PP、PE、PC、PS、PVC 182 241 8 2023 Easternof TibetPlateau Rivers Surface water PE、PP、PET、PVC、PC、PS、NR、PES、PA About 10to 30 About 10to 250 About 250to 6000 Liuet al.,2025 9 2023 Yarlung Zangbo River Rivers 0 ~ 20 cm PP、PE、PVC、PA、PS、PET 1.65 About 250 About 700 Miaoet al.,2025 Sediments 0 ~ 10 cm 2.41 About 400 About 2600 10 2026 Easternof TibetPlateau Gahai Lake 0 ~ 20 cm PE、PET、PLA、PTFE、PVC、PP、PA、Acrylates、Polyvinyl alcohol 1.50 ~ 5.92 1312.57 ± 89.32 11346.60 ± 3909.23 ThisStudy Declarations Author Contribution Guohong WU wrote the main manuscript and Yanrong Tan performed data analysis and prepared the figures and tables and Wenye Chen contributed to data collection and data analysis.All authors reviewed the manuscript. Data Availability All data supporting the findings of this study are available within the paper and its supplementary information. References Allen S, Allen D, Phoenix VR, Roux GL, Jiménez PD, Simonneau A, Binet S, Galop D (2019) Atmospheric transport and deposition of microplastics in a remote mountain catchment. Nature Geoscience. https://doi.org/10.1038/s41561-019-0335-5 Baldwin AK, Corsi SR, Mason SA (2016) Plastic Debris in 29 Great Lakes Tributaries: Relations to Watershed Attributes and Hydrology. Environmental science & technology. https://doi.org/10.1021/acs.est.6b02917 Chen X, Zhang ZW, Liu YZ, Li J, Jiang ZG (2022) Compositional and distributional characteristics of microplastics in watersamples of Chaohu Lake. Research of Environmental Sciences. DOI: 10.13198/j.issn.1001-6929.2022.09.10 Chen Y, Zhang LS, Zhou RY, Wei ZX, Zhong S, Liu JY (2023) Spatial and temporal distribution chatacteristics and influencing factors of microplastics in Huixian karst wetland. 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DOI10.1016/j.scitotenv.2011.04.038 Liu XW, Li WM, Yan K, Xiao M, Li X, Wang FW,Li YC, Gao YK,Ding S, Fang ZJ, Li H (2024) The characterstics and risk assessment of microplastics pollution in cascade reservoirs of the Huangbai River. China Environmental Science. DOI: 10.19674/j.cnki.issn1000-6923.20240828.001 Liu X, Zhong B, Li NY, Wu WM, Wang XF, Li XX, Yang Z, Mei XT, Yi SL, He YX (2025) Notable ecological risks of microplastics to Minjiang River ecosystem over headwater to upstream in Eastern Qinghai-Tibetan Plateau. Water Research. https://doi.org/10.1016/j.watres.2025.123137 Liu ZQ (2022) Distribution and risk assessment of microplastics in water environment of typical inland lake in Tibetan Plateau. Dissertation, Xi’an University of Technology Luo X, Zhang YL,Kang SC, Chen RS, Gao TG, Allen S (2025) Atmospheric emissions of microplastics entrained with dust from potential source regions. Journal of Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2025.137509 Miao LZ, Chen YY, Adyel T M, Luo D, Zheng QQ, Huang Y, Su LB, Qian YX, Deng XY, Yao Y, Kong M, Hou J (2025) Microplastics in the third pole of the world: Abundance and ecological risk assessment. Journal of Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2025.137642 Pan Y, Long Y Y, Hui J,et al.2022. Microplastics can affect the trophic cascade strength and stability of plankton ecosystems via behavior-mediated indirect interactions[J].Journal of Hazardous Materials,430,128415. Peng HK (2025) Study on the distribution characteristics and ecologicalrisk assessment of microplastics in Poyang Lake typicalwastershed. Dissertation, Jiangxi University of Science and Technology Su Y, Yang CQ,Wang SF, Li HM, Wu YY, Xing BS, Ji R (2024) Mechanochemical Formation of Poly(melamine-formaldehyde) Microplastic Fibers During Abrasion of Cleaning Sponges. Environmental science & technology. https://pubs.acs.org/doi/ 10.1021/acs.est.4c00846 Sun XC, Hou SG, Huang RH, Zhang WB, Zou XQ, Liu K (2023) Characteristics, sources and ecological risks of microplastics in Telashi Lake in Hoh Xil. Acta Scientiae Circumstantiae. DOI: 10.13671/j.hjkxxb.2022.0188 Xu LB, Hu M, Jia WQ, Zhang MJ, Tang Q, Tian XD, Huang Y (2022) Distribution and transport of atmospheric microplastics and the environmental impacts: A review (in Chinese). Chin Sci Bull. DOI: 10.1360/TB-2021-1236 Wang LC, Li W, Li KX (2017) The characteristics and layout optimization of rural settlements’spatial distribution of alpinepastoral area—A case study of Luqu county of Gannan prefecture. JOURNAL OF HUMAN SETTLEMENTS IN WEST CHINA. DOI: 10.13791/j.cnki.hsfwest.20170116 Wang T, Qu LY, Luo DH, Ji XL, Ma ZL, Wang ZG, Dahlgren RA, Zhang MH, Shang X (2023) Microplastic pollution characteristics and its future perspectives in the Tibetan Plateau. Journal of hazardous materials. https://doi.org/10.1016/j.jhazmat.2023.131711 Wang FT, Bao K, Qi X (2023) Occurrence characteristics and ecological risk assessment of microplastics in surface water of Wuhan lakes inYangtze River Basin. Chinese Journal of Environmental Engineering. DOI: 10.12030/j.cjee.202308065 Wang KL, Yang YD, Hou L, Wang XX, Wu SW (2026) Microplastic Pollution in Groundwater of the Xingyun Lake Plateau Watershed:Characterization, Sources and Risk Assessment. Environmental Science. DOI: 10.13227/j.hjkx.202508120 Xu MY (2023) Research Progress on Microplastic Pollution, Migration and Transformation in Wetland Ecosystems. WETLAND SCIENCE & MANAGEMENT. DOI: 10.3969/j.issn.1673-3290.2023.04.19 Yang L,Kang SC,Zhang YL, Wang ZQ, Luo X, Guo JM, Gao TG (2025) Microplastic characteristics in rivers and lakes across the Tibetan Plateau. Journal of Mountain Science. https://doi.org/10.1007/s11629-024-9376-3 Yu L, Luo W, Jiang N, Zhao P, Ga BL (2025) Comparison of two stratified sampling methods for microplastics in lake watercolumn. Acta Scientiae Circumstantiae. DOI: 10.13671/j.hjkxxb.2025.0150 Zhang ZH, Chen JY, Yu HY, Yin QX, Fu XH, Yin HW, Bu D (2026) Research progress of microplastics of different environmental media in the plateau area. ENVIRONMENTAL CHEMISTRY. DOI: 10.7524/j.issn.0254-6108.2025052802 Zhou M,Luo CY,Zhang JW, Li RX, Chen JL, Ren P, Tang YL, Suo ZR, Chen K (2025) Potential risk of microplastics in plateau karst lakes: Insights from metagenomic analysis. Environmental Research. https://doi.org/10.1016/j.envres.2025.120984 Zhu YG, Zhu D, Xu T, Ma J (2019) Impacts of(micro)plastics on soil ecosystem: Progress and perspective. Journal of Agro-Environment Science. DOI: 10.11654/jaes.2018-1427 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9134685","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610167319,"identity":"1f752019-562f-4de2-8d0c-7e086699c288","order_by":0,"name":"Guohong WU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYHACNoYEA4l6fmbmww+I1/KhwCJBsp0tzYBoLYwzPlQkGJznUZAgSr3BjfRrj3kMJPKMD/MwGDDU2EQToSWn3BiopdjsMO+BBwzH0nIbCGkxu5GTJg3UwrjtMF+CAWPDYRK0bG4GkcRpST8mOcNAInEDM7Fa7M+8YZP4YCBhLHEYGMgJxPhFsj39mUTCnzo5/v7Dhx98qLEhrIWBgQcpAhMIKwcB9gfEqRsFo2AUjIKRCwANTz2WUyXduAAAAABJRU5ErkJggg==","orcid":"","institution":"Gansu Academy of Forestry","correspondingAuthor":true,"prefix":"","firstName":"Guohong","middleName":"","lastName":"WU","suffix":""},{"id":610167320,"identity":"f887166d-ec06-434a-83f6-64d4529cd891","order_by":1,"name":"Yanrong TAN","email":"","orcid":"","institution":"Gansu Academy of Forestry","correspondingAuthor":false,"prefix":"","firstName":"Yanrong","middleName":"","lastName":"TAN","suffix":""},{"id":610167321,"identity":"75e0959d-79ee-4693-a226-6d6f8eaf4e64","order_by":2,"name":"Wenye CHEN","email":"","orcid":"","institution":"Gansu Academy of Forestry","correspondingAuthor":false,"prefix":"","firstName":"Wenye","middleName":"","lastName":"CHEN","suffix":""}],"badges":[],"createdAt":"2026-03-16 07:55:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9134685/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9134685/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105315615,"identity":"14e5586c-f7dd-4200-b4c9-9f7e4dac1838","added_by":"auto","created_at":"2026-03-24 16:11:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":661151,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of sampling sites of this study\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9134685/v1/8605be775945d8a7b3983c17.png"},{"id":105315632,"identity":"07e79dfa-bc9c-4749-84d0-a372aff15e8c","added_by":"auto","created_at":"2026-03-24 16:11:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":239394,"visible":true,"origin":"","legend":"\u003cp\u003eMicroplastic particle size distribution by season and sampling area\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9134685/v1/a22cf1c4dde63290030e57f8.png"},{"id":105315643,"identity":"3bdda195-afb7-4d0e-bbf2-ea3a31b01ef0","added_by":"auto","created_at":"2026-03-24 16:11:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":301479,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of microplastic polymer types by month and sampling area\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9134685/v1/1ec7c7073248b19b8e599424.png"},{"id":105315644,"identity":"3b91d19c-05ad-48d6-ad07-d6f9f103249d","added_by":"auto","created_at":"2026-03-24 16:11:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2813877,"visible":true,"origin":"","legend":"\u003cp\u003ePredominant microplastic polymer types by month\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9134685/v1/4da492b3d2de71bb01b0ce4a.png"},{"id":105315638,"identity":"d6312c5b-f0d4-48f5-8091-9bfe1fec4ed6","added_by":"auto","created_at":"2026-03-24 16:11:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":142703,"visible":true,"origin":"","legend":"\u003cp\u003eEcological risk assessment of microplastics\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9134685/v1/1c621758526eb95c6bccd3ce.png"},{"id":105315610,"identity":"e6724fbb-428a-4b25-95a5-b4c96d9e835f","added_by":"auto","created_at":"2026-03-24 16:11:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4493,"visible":true,"origin":"","legend":"\u003cp\u003eEcological risk assessment of microplastics\u003c/p\u003e","description":"","filename":"fig.png","url":"https://assets-eu.researchsquare.com/files/rs-9134685/v1/31b18a55bf0a706196d535ba.png"},{"id":105315733,"identity":"12618fb0-e4a8-41f0-af87-9de7063e9344","added_by":"auto","created_at":"2026-03-24 16:12:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4917922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9134685/v1/2c871e1a-58d4-4306-a77a-42c4277c1770.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Occurrence Characteristics and Ecological Risk Assessment of Microplastics in the Water Body of Lake Gahai, Tibetan Plateau of China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePlastic products are extensively utilized in various sectors, including packaging, construction, automobiles, electrical appliances, and agriculture, owing to their high quality and low cost (Zhu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Global plastic production surged from 2\u0026nbsp;million tons annually in 1950 to 400\u0026nbsp;million tons in 2022, with projections indicating it may reach 800\u0026nbsp;million tons by 2050. In contrast, the plastic recycling rate has stagnated at a low level, with a recovery rate of approximately 9% (Houssini et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). That means over 90% of plastic products are discarded annually, which poses significant risks of long-term accumulation in both human living spaces and the natural environment. In June 2024, a study conducted by Chinese researchers revealed that each gram of wear and tear on common kitchen cleaning sponges releases up to 6.5\u0026nbsp;million microplastics (MPs) (Su et al., 2019). This finding became a trending topic on the internet in China, reigniting public discourse regarding the pollution hazards associated with plastic products. Microplastics are defined as plastic debris and particles with a diameter of less than 5 mm. Microplastics are defined by their small size, extensive specific surface area, and significant ability to adsorb pollutants (Han et al., 2021). During their decomposition, they release toxic substances, including plastic additives (Baldwin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Their persistence in the natural environment (Allen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) presents a threat to both ecosystem functioning and human health (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). In December 2022, China's environmental authorities released the Key New Pollutants Control List (2023 Edition), which explicitly identifies microplastics as one of the new pollutants.\u003c/p\u003e \u003cp\u003eCurrent research on environmental microplastics in China primarily concentrates on marine ecosystems or densely populated inland regions (Sun et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Qinghai-Tibet Plateau, due to its geographical location, high altitude, and other factors, has historically encountered minimal human disturbance, rendering it one of the more pristine areas on Earth. Nevertheless, recent studies indicate that microplastics are now extensively distributed throughout the plateau's rivers, lakes, sediments, soils, atmosphere, and glacial/snow environments (Wang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).Plateau lakes, characterized by their closed and static nature, are particularly susceptible to the convergence and accumulation of microplastics (Feng et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Microplastics infiltrate wetland environments, including lakes, through mechanisms such as atmospheric deposition and rainfall runoff. Once introduced, they not only act as vectors for microorganisms, thereby enhancing the transport capacity of pathogenic microbes (Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but also affect microbial composition and metabolic activity. This, in turn, alters the richness and diversity of bacterial communities, significantly impacting nutrient and energy flow within wetland ecosystems (Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Microplastics can be absorbed by plants, which hinders their uptake of organic nutrients and alters the habitat of wetland vegetation, thereby inhibiting plant growth (Xu et al., 2023). Additionally, microplastics can generate cascading effects across various trophic levels, impacting the entire ecosystem. For instance, microplastic pollution can influence the population structure and abundance of plankton; a decline in plankton may disrupt the food chain for fish and other organisms, leading to far-reaching consequences for the food web of the entire ecosystem (Pan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReports on microplastics in lakes on the Qinghai-Tibet Plateau remain relatively limited. The existing research is insufficient to elucidate the current pollution status and transport patterns of microplastics in the lake waters of the plateau. This study concentrates on Gahai Lake, a representative permanent freshwater lake situated on the northeastern edge of the Qinghai-Tibet Plateau. This research aims to conduct a comprehensive investigation into the occurrence characteristics of microplastics in rivers and lakes within this pristine yet fragile ecosystem by scientifically designing water sample collection points. It seeks to explore the source pathways of microplastics in these lakes while simultaneously assessing the ecological risks they pose to the plateau's aquatic environment. The findings are intended to provide a scientific foundation for future research on microplastics in plateau lakes, as well as for the management and control of these pollutants.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Overview of the Study Area\u003c/h2\u003e \u003cp\u003eGahai Lake is situated in Luqu County within the Gannan Tibetan Autonomous Prefecture of Gansu Province. It is positioned on the eastern fringe of the Qinghai-Tibet Plateau, at the transition zone leading to the Longnan mountainous region and the Loess Plateau. This region experiences a continental monsoon climate characteristic of the Qinghai-Tibet Plateau. Due to the influence of westerly circulation and the topographical features of the plateau, the study area receives a relatively high amount of rainfall, with an average annual precipitation of 654.2 mm, primarily occurring during the summer and autumn months.The average altitude of the study area is approximately 3,500 meters above sea level. The annual average temperature is 3.5\u0026deg;C. The mean annual wind speed is 1.6 m/s, with easterly winds predominating throughout the year, followed by northeasterly winds. The annual effective accumulated temperature totals 1,254.2\u0026deg;C. The average maximum depth of frozen soil reaches 73.7 cm. The average annual duration of sunshine is 2,237.1 hours, and the annual solar radiation measures 520.0 kJ/cm\u0026sup2;\u0026middot;a. The average annual evaporation varies between 1,100 mm and 1,200 mm.\u003c/p\u003e \u003cp\u003eGahai Lake is located within the Zhouqu River basin, a tributary of the Tao River. At an elevation of 3,470 m, it is the largest freshwater lake on the Gansu Province plateau. The lake's water is sourced from multiple rivers and springs, including the Qiongmuqiequ, Wu'ekuqu, Wengniqiu, Dongcaiqiu, Duomuqiequ, and Geqiongkuqu, which receive meltwater from the Guo'ermang Liang and the northern slopes of the Xiqing Mountains, converging at Youqing Qingkuhe and Gengqing Qingkuhe. Furthermore, an artificial channel on the western side of the lake diverts a portion of the Zhongqu River's flow into it. Currently, the lake spans approximately 2,700 hm\u0026sup2;, with an average depth of about 2.0 m and a maximum depth of approximately 5.0 m. Its water storage capacity is estimated at 0.48 \u0026times; 10⁸m\u0026sup3;.\u003c/p\u003e \u003cp\u003eGahai Town, home to the lake, is a predominantly pastoral region. It encompasses three villages: Xiuwa, Gaxiu, and Jiacang, with a combined population of 5,207 residents. National Highway G213 traverses the town, facilitating convenient transportation access.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample Site Layout and Collection\u003c/h2\u003e \u003cp\u003eTwelve sampling sites were established to ensure representativeness, taking into account the topographic conditions of the study area, the extent of primary human activities, the distribution of main inlets and outlets, water flow direction, and prevailing wind direction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These sites were allocated across four distinct zones: the northern section of the lake, the western section, the southern section, and the lake center.\u003c/p\u003e \u003cp\u003eThree sampling sites (G1\u0026ndash;G3) were established in the northern region of the lake, adjacent to villages, a highway, and a scenic area, thereby representing locations with higher levels of human activity. Three additional sites (G4\u0026ndash;G6) were located in the western part of the lake, near the lake's outlet. In the southern region, three sites (G7\u0026ndash;G9) were identified as the primary water supply area for Gahai Lake, characterized by minimal human activity. The final three sampling sites (G10\u0026ndash;G12) were positioned at the center of the lake.\u003c/p\u003e \u003cp\u003eSurface water samples (0\u0026ndash;20 cm depth) of 3 L were collected from each site on April 27, July 30, and October 3, 2025, using a stainless steel bucket. The collected water was filtered through a 20\u0026micro;m stainless steel sieve. Particles retained on the sieve were rinsed three times into aluminum foil bags with ultrapure water. The samples were promptly transported to the laboratory and stored in a refrigerator at 4\u0026deg;C. Prior to sampling, all equipment was cleaned by rinsing three times with ultrapure water and subsequently wrapped in aluminum foil for storage. Before each sampling event, three separate 100 mL aliquots of ultrapure water were collected in pre-cleaned aluminum foil bags to serve as blank controls (CK1, CK2, CK3). No rainfall occurred in the three days preceding each sampling event, and no strong winds were present on the sampling days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Microplastic Detection in Samples\u003c/h2\u003e \u003cp\u003eThe specific procedure was as follows: The water sample was weighed and recorded before being subjected to vacuum filtration through a steel membrane with a pore size of 13\u0026micro;m. The resulting membrane was placed into a sample vial, to which 30% H₂O₂was added to facilitate the removal of organic matter. The vial underwent ultrasonication for 30 minutes and was subsequently allowed to stand for 24 hours, enabling the hydrogen peroxide to fully react with the organic matter. The digestion solution was then vacuum-filtered again using a steel membrane with a pore size of 13\u0026micro;m. The obtained membrane was placed in a sample vial, immersed in an ethanol solution, and ultrasonicated for 30 minutes to disperse the particles from the membrane into the ethanol solution. After removing the membrane from the ethanol solution, it was rinsed multiple times with ethanol. The ethanol solution, containing the dispersed particles, was concentrated in an infrared drying oven to a final volume of 150\u0026micro;L. Subsequently, the concentrate was drop-cast onto a high-reflection glass slide. Once the ethanol had completely evaporated, analysis was conducted using Laser Direct Infrared Imaging (LDIR). The particle analysis mode was selected, a method was established utilizing the microplastic spectral library, and an automated test method was configured (match\u0026thinsp;\u0026gt;\u0026thinsp;0.65, particle size range 20\u0026ndash;500\u0026micro;m) for measurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Quality Control\u003c/h2\u003e \u003cp\u003eAll solvents utilized in the analysis were filtered through a 0.45\u0026micro;m pore size polytetrafluoroethylene (PTFE) membrane prior to use. All experimental consumables were composed of glass and were rinsed three times with ethanol before being dried for use. The blank controls established detected 5, 10, and 9 microplastic particles, respectively. To determine microplastic abundance, the blank contamination values were subtracted, yielding the final abundance values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Ecological Risk Assessment\u003c/h2\u003e \u003cp\u003eThis study utilized the Hazard Index (H), Pollution Load Index (PLI), and Potential Ecological Risk Index (PRI) to quantitatively evaluate the ecological risk associated with the surface water of Gahai Lake. The calculation formulas for these indices, along with the classification standards for microplastic ecological risk levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), were derived from existing literature (Sun Xuechun et al., 2023; Liu Xiaowei et al., 2024; Wang Kelin et al., 2026). The plastic polymer hazard scores (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) employed in the computation of the H, PRI, and PLI indices were informed by Lithner et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The reference value for microplastic abundance in water, utilized in the PLI calculation, was determined from the average abundance of microplastics observed in the blank controls of this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk classification of microplastics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eclassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eⅤ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026thinsp;~\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u0026thinsp;~\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026thinsp;~\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u0026thinsp;~\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u0026thinsp;~\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300\u0026thinsp;~\u0026thinsp;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e600\u0026thinsp;~\u0026thinsp;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;1200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: The H and PLI indices categorize risk levels as follows: Level I indicates low risk (minor pollution), Level II signifies medium risk (mild pollution), Level III denotes high risk (moderate pollution), and Level IV represents extreme risk (severe pollution). In contrast, the PRI index classifies ecological risk into five levels: I, II, III, IV, and V.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard score of microplastics polymer type\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolymer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcrylates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcrylonitrile Butadiene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcrylonitrile Butadiene Styrene(ABS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthylene Vinyl Acetate (EVA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthylene acrylic acid(EAA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethyl methacrylate-Butadiene-Styrene(MBS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolyethylene (PE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolybutadiene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolycaprolactone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolycarbonate (PC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolyethylene Terephthalate (PET)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolylactic acid (PLA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolymethylmethacrylate (PMMA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolytetrafluoroethylene (PTFE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolyurethane (PU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolyvinylchloride (PVC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolypropylene (PP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolystyrene (PS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRubber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolyacetal(POM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolyamide (PA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolymerized Styrene Butadiene Rubber (SBR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStyrene-butadiene styrene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStyrene-isoprene styrene triblock copolyme(SIS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: For polymers composed of multiple monomers, the polymer hazard score is calculated as the average of the hazard scores of the constituent monomers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data Processing and Analysis\u003c/h2\u003e \u003cp\u003eMicrosoft Excel 2010 facilitated the preliminary statistical processing of microplastic abundance, particle size, and type. IBM SPSS Statistics 24.0 was employed to assess the normality and homogeneity of variance of the data. Differences among data groups were analyzed using Analysis of Variance (ANOVA) or non-parametric tests. ArcGIS 10.8 software was utilized to generate the spatial distribution map of sampling sites, while other figures were produced using Origin 2024. Microplastic abundance in water samples is expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, with units reported as \"items/L.\"\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and Analysis","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Microplastic Abundance\u003c/h2\u003e \u003cp\u003eMicroplastics were identified in all 39 water samples analyzed. In April, the abundance of microplastics across various sampling zones varied from 3.33 to 82.00 items/L, yielding an average of 23.94\u0026thinsp;\u0026plusmn;\u0026thinsp;22.21 items/L. In July, the abundance ranged from 6.67 to 90.67 items/L, with an average of 27.20\u0026thinsp;\u0026plusmn;\u0026thinsp;28.06 items/L. In October, the abundance varied from 9.33 to 78.67 items/L, resulting in an average of 39.08\u0026thinsp;\u0026plusmn;\u0026thinsp;24.03 items/L.\u003c/p\u003e \u003cp\u003eAnalysis of variance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicated that, over time, there were no significant differences in average microplastic abundance across the various sampling months. Nevertheless, in October, the microplastic abundance in the surface water of the northern sampling zone was significantly greater than that observed in April and July (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Spatially, in October, the microplastic abundance in the northern sampling zone's surface water was significantly higher than that in the lake center sampling zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the other months, no significant spatial differences in microplastic abundance among the sampling zones were detected.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMicroplastic abundance in each sampling area during different months\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthSampling site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u0026ndash;G3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.33\u0026thinsp;\u0026plusmn;\u0026thinsp;23.43 Ab(3.33\u0026thinsp;~\u0026thinsp;45.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84 Ab(10.33\u0026thinsp;~\u0026thinsp;26.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.78\u0026thinsp;\u0026plusmn;\u0026thinsp;20.42 Aa(41.33\u0026thinsp;~\u0026thinsp;78.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u0026ndash;G6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.55\u0026thinsp;\u0026plusmn;\u0026thinsp;12.22 Aa(6.00\u0026thinsp;~\u0026thinsp;29.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07 Aa(12.67\u0026thinsp;~\u0026thinsp;20.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.56\u0026thinsp;\u0026plusmn;\u0026thinsp;13.74 ABa(25.67\u0026thinsp;~\u0026thinsp;52.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u0026ndash;G9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.33\u0026thinsp;\u0026plusmn;\u0026thinsp;39.07 Aa(6.67\u0026thinsp;~\u0026thinsp;82.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.56\u0026thinsp;\u0026plusmn;\u0026thinsp;42.53 Aa(6.67\u0026thinsp;~\u0026thinsp;80.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.55\u0026thinsp;\u0026plusmn;\u0026thinsp;32.08 ABa(9.33\u0026thinsp;~\u0026thinsp;68.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u0026ndash;G12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86 Aa(19.00\u0026thinsp;~\u0026thinsp;28.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.56\u0026thinsp;\u0026plusmn;\u0026thinsp;40.94 Aa(16.67\u0026thinsp;~\u0026thinsp;90.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.44\u0026thinsp;\u0026plusmn;\u0026thinsp;9.48 Ba(17.00\u0026thinsp;~\u0026thinsp;34.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.94\u0026thinsp;\u0026plusmn;\u0026thinsp;22.21a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.20\u0026thinsp;\u0026plusmn;\u0026thinsp;28.06a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.08\u0026thinsp;\u0026plusmn;\u0026thinsp;24.03a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Distinct uppercase letters signify significant differences in microplastic abundance among various sampling zones within the same month, whereas different lowercase letters denote significant differences in microplastic abundance within the same sampling zone across different months. The numbers in parentheses indicate the range of abundance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Microplastic Particle Size\u003c/h2\u003e \u003cp\u003eThe distribution of microplastic particle sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in the surface water of Gahai Lake predominantly concentrated in the range of 20\u0026ndash;50\u0026micro;m, comprising 64.42% to 86.61% of the total. This was followed by the 50\u0026ndash;100\u0026micro;m range, which accounted for 7.79% to 26.18%. Microplastics sized 200\u0026ndash;500\u0026micro;m represented the smallest proportion, with some months or specific sampling zones showing no detection of particles within this size range. On a temporal scale (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the quantity of microplastics measuring 20\u0026ndash;50\u0026micro;m in the northern sampling zone exhibited a significant increase in October compared to April and July (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, in October, the quantity of microplastics sized 50\u0026ndash;100\u0026micro;m in the northern sampling zone was also significantly greater than that observed in April (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, on a spatial scale, no significant differences were noted in the distribution of microplastics across various size fractions among the different sampling zones during any of the sampling months.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical count of microplastic particle sizes across seasons and sampling areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSampling site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[20\u0026ndash;50\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[50\u0026ndash;100\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[100\u0026ndash;200\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[200\u0026ndash;500\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u0026ndash;G3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.33\u0026thinsp;\u0026plusmn;\u0026thinsp;64.16Ba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79Ba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG4\u0026ndash;G6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.00\u0026thinsp;\u0026plusmn;\u0026thinsp;34.53Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG7\u0026ndash;G9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.00\u0026thinsp;\u0026plusmn;\u0026thinsp;98.85Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.34\u0026thinsp;\u0026plusmn;\u0026thinsp;12.06Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG10\u0026ndash;G12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.33\u0026thinsp;\u0026plusmn;\u0026thinsp;12.49Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u0026ndash;G3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.02Ba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.00\u0026thinsp;\u0026plusmn;\u0026thinsp;12.74ABa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG4\u0026ndash;G6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.66\u0026thinsp;\u0026plusmn;\u0026thinsp;11.68Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG7\u0026ndash;G9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.33\u0026thinsp;\u0026plusmn;\u0026thinsp;94.40Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.00\u0026thinsp;\u0026plusmn;\u0026thinsp;24.83Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG10\u0026ndash;G12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.66\u0026thinsp;\u0026plusmn;\u0026thinsp;100.83Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.34\u0026thinsp;\u0026plusmn;\u0026thinsp;15.95Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u0026ndash;G3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.66\u0026thinsp;\u0026plusmn;\u0026thinsp;54.79Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.21Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG4\u0026ndash;G6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.00\u0026thinsp;\u0026plusmn;\u0026thinsp;19.35Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.34\u0026thinsp;\u0026plusmn;\u0026thinsp;18.56Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG7\u0026ndash;G9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.33\u0026thinsp;\u0026plusmn;\u0026thinsp;83.62Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.29Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG10\u0026ndash;G12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.33\u0026thinsp;\u0026plusmn;\u0026thinsp;22.11Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00Aa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58Aa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Different uppercase letters indicate significant differences in the abundance of microplastics of the same size fraction in the same sampling zone across different months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Types of Microplastic Polymers\u003c/h2\u003e \u003cp\u003eThe analysis of matching results showed the detection of 35, 29, and 33 types of microplastic polymers in the surface water of Gahai Lake in April, July, and October, respectively. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the Venn diagram illustrates that although the types of microplastic polymers varied across different sampling zones within the same month, the shared types consistently comprised over 82.78%, with unique types being minimal (\u0026lt;\u0026thinsp;0.94%). Additionally, a statistical examination of microplastic polymer types in Gahai Lake's surface water identified nine primary types: Acrylates, Polyethylene (PE), Polyethylene terephthalate (PET), Polylactic acid (PLA), Polytetrafluoroethylene (PTFE), Polyvinyl chloride (PVC), Polypropylene (PP), Polyvinyl alcohol, and Polyamide (PA), with the remaining types collectively representing about 20% of the total quantity, as depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThe numbers in parentheses represent the proportion of microplastic quantity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Ecological Risk Index\u003c/h2\u003e \u003cp\u003eThe Pollution Load Index (PLI) results depicted in Fig.\u0026nbsp;6 revealed that microplastics in surface water exhibited PLI values ranging from 1.50 to 5.92 across all sampling sites, indicating a state of slight pollution. The average PLI values for April, July, and October were 2.94, 3.10, and 3.80, respectively, with the highest average PLI value of 4.99 observed in the northern sampling zone during October. The Hazard Index (H) varied from 247.33 to 3376.14 in the study area, with all sites falling into either high-risk (30.56%) or significant-risk (69.44%) categories. The highest average H value of 2288.58 was recorded in the southern sampling zone in April. The Potential Ecological Risk Index (PRI) ranged from 556.50 to 107,192.45 for each sampling site, with only one site classified as having LevelⅢpotential ecological risk, while the remaining sites exhibited the highest potential ecological risk level (LevelⅤ).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;6 Ecological risk assessment of microplastics\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Analysis of Microplastic Occurrence Characteristics and Transport Pathways\u003c/h2\u003e \u003cp\u003eMicroplastics, as emerging contaminants, have garnered significant attention in global environmental and health research due to their pollution status and ecological risks. A comprehensive analysis of recent domestic microplastic studies was conducted, focusing on those with sieve pore sizes matching those used in this study for accurate comparison. Results indicated that the abundance of microplastics in Gahai Lake's surface water (3.33\u0026ndash;82.00 items/L) was comparable to Jiuzhaigou Valley lakes (20.27\u0026ndash;58.80 n/L), lower than Poyang Lake tributaries (43\u0026ndash;276 n/L) with higher population density, yet higher than lakes and rivers on the Qinghai-Tibet Plateau (0\u0026ndash;5.56 n/L) at greater altitudes and more remote locations.\u003c/p\u003e \u003cp\u003eThe lack of a standardized classification for microplastic particle size results in varying definitions of size fractions among different studies. Approximately 50% of studies show that the smallest defined size fraction does not always represent the highest proportion of microplastics. Studies conducted on lakes in the Qinghai-Tibet Plateau revealed that microplastics larger than 100\u0026micro;m accounted for around 51%\u0026ndash;80% of the total. In this investigation, 90% of the microplastics were found within the 20\u0026ndash;100\u0026micro;m range, with the smallest size fraction (20\u0026ndash;50\u0026micro;m) being the most prevalent (\u0026gt;\u0026thinsp;64.42%), indicating significant disparities from prior investigations.\u003c/p\u003e \u003cp\u003eThis study identified up to 35 distinct microplastic polymer types, significantly surpassing the quantities reported in previous research. Notably, the six polymers typically dominant in earlier studies\u0026mdash;PP, PE, PS, PA, PET, and PVC\u0026mdash;constituted a relatively low proportion in this investigation. The discrepancies in particle size distribution and the variety of polymer types, when compared to prior studies, may be attributed to the timing of the research rather than alterations in the pollution characteristics of the water body itself. Most previous studies were conducted between 2019 and 2022. As microplastic research has advanced, improvements in identification methods and the expansion and refinement of standard microplastic spectral libraries have markedly increased detection efficiency and the ability to identify complex microplastic polymer types.\u003c/p\u003e \u003cp\u003ePrevious research has established a notable correlation between microplastic levels and urbanization as well as population density (Yu Lu et al., 2025). The study area, situated in Luqu County, is characterized by a total population of around 34,000 individuals and a population density of approximately 6.48 people per square kilometer, categorizing it as sparsely populated (Wang Lucang et al., 2017). Despite this classification, the Statistical Bulletin on National Economic and Social Development of Luqu County for 2023 reported that the county welcomed 2.3899\u0026nbsp;million domestic and international tourists in the same year. Consequently, the peak tourist season from May to August, coupled with the abrasion of tires from vehicles, could potentially elevate the presence of microplastics (Feng Sansan et al., 2021). Notably, the percentage of microplastics ranging from 50 to 100\u0026micro;m in size in the northern sampling zone notably surged in July, possibly linked to tourist-related activities. However, overall, there was no significant disparity in microplastic quantities in the surface water of Gahai Lake between July and either April or October, indicating that heightened tourist influx did not markedly influence microplastic levels in the lake's surface waters.\u003c/p\u003e \u003cp\u003eIn October, microplastic abundance in the northern sampling zone, which is situated near villages and roads, increased significantly compared to April and July. This increase may be closely associated with rainfall and wind patterns (Xu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as local precipitation primarily occurs during the summer and autumn months, while August and September experience the lowest average wind speeds of the year. Rainfall events, coupled with relatively stable atmospheric conditions, facilitate the deposition of microplastics. Atmospheric transport serves as a critical pathway for microplastics to reach remote regions, including the Antarctic, Arctic, and high mountain areas (Luo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Smaller microplastic particles are lighter and more readily transported over long distances through the atmosphere, moving from low-altitude areas with high population density to elevated regions. The predominance of microplastics in the 20\u0026ndash;50\u0026micro;m size range in this study indirectly supports the notion that atmospheric transport may represent a significant source of microplastics in plateau regions (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Research results on the occurrence characteristics of microplastics in water environments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResearch\u003c/p\u003e \u003cp\u003eObjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSampling Depth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCollection Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIdentification Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMatch Degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAbundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAmount of Polymer Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMain Particle Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMain Polymer Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChaohu Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u0026ndash;30 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRaman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.25\u0026ndash;3.20 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7 Main Polymer Types and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u0026ndash;200 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePP,PE,PS,PET,PVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eChen et al.,2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuixian Wetland Basin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake,River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u0026ndash;30 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFourier transform\u003c/p\u003e \u003cp\u003einfrared spectroscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.47\u0026ndash;34.91 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4 Main Polymer Types and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.45\u0026ndash;500 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePET,PS,PP,PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eChen et al.,2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023-\u003c/p\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoyang Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake,River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFourier transform\u003c/p\u003e \u003cp\u003einfrared spectroscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43\u0026ndash;276 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20\u0026ndash;100 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePE,PP,PS,PVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePeng et al.,2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXingyun Lake Basin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e200 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMicro-infraredspectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.33\u0026ndash;7.66 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2\u0026ndash;0.5 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRY,PES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eWang et al.,2026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake,River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;10 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLaser micro-raman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.25\u0026ndash;7.75 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11 Main Polymer Types and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u0026ndash;200 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePA,PU,PET,PVC,PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYang et al.,2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQinghai-Tibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake,River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;10 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRaman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u0026ndash;1.92 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4 Main Polymer Types and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u0026ndash;500 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePP,PE,PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFeng et al.,2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQinghai-Tibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake,River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;10 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRaman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u0026ndash;2 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u0026ndash;500 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePP,PE,PS,PET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFeng et al.,2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInland Lakes in Tibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;30 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRaman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17\u0026ndash;3.30 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6 Main Polymer Types and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u0026ndash;2 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePP,PET,PA,PS,PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLiu et al.,2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYamzhog Yumco Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;40 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFourier transform\u003c/p\u003e \u003cp\u003einfrared spectroscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.5\u0026ndash;2 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePET,PA,RY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYu et al.,2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake,River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;10 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRaman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u0026ndash;1.8 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7 Main Polymer Types and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u0026ndash;250 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePP,PE,PS,PET,PVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFeng et al.,2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;10 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRaman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u0026ndash;0.5 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4 Main Polymer Types and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u0026ndash;500 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePE,PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFeng et al.,2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQinghai-Tibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;30 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRaman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3\u0026ndash;1.7 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8 Main Polymer Types and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u0026ndash;1 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePE,PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eHan et al.,2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRaman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.35\u0026ndash;5.56 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u0026ndash;500 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePP,PET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLiang et al.,2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake,River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;20 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRaman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u0026ndash;1 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePP,PE,PA,PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFeng et al.,2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEastern of Qinghai-Tibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45 \u0026micro;m filter membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFourier transform\u003c/p\u003e \u003cp\u003einfrared spectroscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u0026ndash;228 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.45\u0026ndash;100 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePE,PP,PET,PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLi et al.,2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJiuzhaigou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;20 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 \u0026micro;m filter membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFourier transform\u003c/p\u003e \u003cp\u003einfrared spectroscopyr and Raman spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.27\u0026ndash;58.80 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u0026ndash;0.5 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePET,PE,PP,PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eZhou et al.,2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEastern of Tibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;20 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 \u0026micro;m stainless steel sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLaser infrared imaging spectrometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.33\u0026ndash;82.00 n/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20\u0026ndash;50 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePE,PET,PLA,PTFE,PVC,PP,PA,Acrylates,Polyvinyl alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eThis Study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Ecological Risk Assessment of Microplastics\u003c/h2\u003e \u003cp\u003eThe Qinghai-Tibet Plateau, referred to as the \"Asian Water Tower,\" is a crucial global freshwater source due to its glacial meltwater, lakes, and rivers. Microplastic pollution in this region poses unique dispersion risks (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). A compilation of data from studies on microplastic ecological risk assessment (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed that, apart from slight pollution in groundwater within the Xingyun Lake Basin and water bodies in the upper Min River, the Pollution Load Index (PLI) for microplastics in Gahai Lake's surface water consistently indicated slight pollution, aligning with most research outcomes. The PLI evaluates ecological risk based on microplastic levels (Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), indicating that the majority of surface waters in China face low to moderate ecological risks concerning microplastic levels. This observation is in agreement with Gan's findings (2025).\u003c/p\u003e \u003cp\u003eThe Hazard Index (H) is derived from the hazard scores associated with various microplastic polymer types, whereas the Potential Ecological Risk Index (PRI) incorporates both the abundance of microplastics and the toxicity of different polymer types (Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, the H and PRI values for the surface water of Gahai Lake were significantly higher than those reported in previous research. This discrepancy can be attributed to the diverse range of polymer types identified in this study, including several with elevated hazard scores. Sun (2023) also observed that omitting certain polymers from ecological risk index calculations, due to the absence of hazard scores for those polymers, could result in an underestimation of microplastic ecological risk.\u003c/p\u003e \u003cp\u003eBased on the classification criteria for monomers and their associated hazard levels in polymers established by Lithner(2011), this study identified nine polymers categorized as the highest hazard level (Ⅴ): Acrylonitrile Butadiene, Acrylonitrile Butadiene Styrene (ABS), Methyl methacrylate-Butadiene-Styrene (MBS), Polybutadiene, Polyvinylchloride (PVC), Rubber, Polyacetal (POM), Polymerized Styrene Butadiene Rubber (SBR), and Styrene-butadiene styrene. The hazard scores for these polymers range from 6687 to 20131. As a result, the H and PRI indices calculated in this study are notably elevated.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResearch results on the ecological risk assessment of microplasticsin water environments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResearch\u003c/p\u003e \u003cp\u003eObjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSampling Depth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMain Polymer Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWuhan city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban Lakes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;20 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePE、PP、PVC、PS、PET、PC、PMMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e601.5\u0026thinsp;~\u0026thinsp;8 954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWang et al.,2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYichang city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReservoirs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP、PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00-2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.21\u0026ndash;17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.27\u0026ndash;79.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLiu et al.,2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXingyunLakeBasin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroundwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRY、PES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWang et al.,2026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSalt Lakes on the Tibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePES、PEI、PA、PP、PS、PESTUR、PE-PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSun et al.,2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLakes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePE、PET、PS、PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.265\u0026ndash;1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.352\u0026ndash;2.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLianget al.,2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;20 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP、PA、PE、PS、PET、PAN、PC、PVC、PTFE、Ruber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.73\u0026ndash;6.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.48\u0026ndash;27.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.89-546.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFenget al.,2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTibet Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQinghai Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u0026thinsp;~\u0026thinsp;20 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePE、PP、PE、PC、PS、PVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00-2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e846.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLiet al.,2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNamco Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u0026thinsp;~\u0026thinsp;20 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePE、PP、PE、PC、PS、PVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEasternof TibetPlateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePE、PP、PET、PVC、PC、PS、NR、PES、PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAbout 10to 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbout 10to 250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAbout 250to 6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLiuet al.,2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYarlung Zangbo River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;20 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePP、PE、PVC、PA、PS、PET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbout 250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAbout 700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMiaoet al.,2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;10 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbout 400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAbout 2600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEasternof TibetPlateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGahai Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;20 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePE、PET、PLA、PTFE、PVC、PP、PA、Acrylates、Polyvinyl alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.50\u0026thinsp;~\u0026thinsp;5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1312.57\u0026thinsp;\u0026plusmn;\u0026thinsp;89.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11346.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3909.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eThisStudy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGuohong WU wrote the main manuscript and Yanrong Tan performed data analysis and prepared the figures and tables and Wenye Chen contributed to data collection and data analysis.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its supplementary information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllen S, Allen D, Phoenix VR, Roux GL, Jim\u0026eacute;nez PD, Simonneau A, Binet S, Galop D (2019) Atmospheric transport and deposition of microplastics in a remote mountain catchment. 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Environmental Research. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2025.120984\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2025.120984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu YG, Zhu D, Xu T, Ma J (2019) Impacts of(micro)plastics on soil ecosystem: Progress and perspective. Journal of Agro-Environment Science. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11654/jaes.2018-1427\u003c/span\u003e\u003cspan address=\"10.11654/jaes.2018-1427\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Tibetan Plateau lakes, surface water, microplastics, occurrence characteristics, ecological risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-9134685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9134685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroplastics (MPs) pollution has emerged as a significant global concern. Research on the sources, distribution, and ecological risks of MPs in the lakes of the Tibetan Plateau remains relatively scarce, thereby hindering efforts to clarify the current status and transport patterns of MPs in these aquatic systems. This study examines the surface water of Lake Gahai\u0026mdash;a representative permanent freshwater lake located on the northeastern margin of the Tibetan Plateau\u0026mdash;to analyze the abundance, size distribution, and polymer types of microplastics. By integrating existing literature, we discuss the primary transport pathways of MPs in the study area and quantitatively assess ecological risks using the pollution risk index (H), pollution load index (PLI), and potential ecological risk index (PRI). Results indicate that the abundance of microplastics (MPs) in the surface water of Lake Gahai varied from 3.33 to 82.00 items/L, with an average abundance of 30.07\u0026thinsp;\u0026plusmn;\u0026thinsp;24.70 items/L. The majority of MPs were found within the size range of 20\u0026ndash;50\u0026micro;m, constituting over 64.42% of the total. A total of 35 polymer types were identified, predominantly including polyethylene (PE), polyethylene terephthalate (PET), polyamide (PA), polylactic acid (PLA), polytetrafluoroethylene (PTFE), polyvinyl chloride (PVC), polypropylene (PP), acrylate polymers, and polyvinyl alcohol. The ecological risk assessment revealed that the pollution load index (PLI) of the lake's surface water was low, while both the hazard quotient (H) and the pollution risk index (PRI) reached the highest severity level. These findings enhance the understanding of the sources and ecological risks associated with MPs in the aquatic environment of the Tibetan Plateau and provide valuable data and theoretical support for future research on MPs pollution.\u003c/p\u003e","manuscriptTitle":"Occurrence Characteristics and Ecological Risk Assessment of Microplastics in the Water Body of Lake Gahai, Tibetan Plateau of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 16:09:04","doi":"10.21203/rs.3.rs-9134685/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T01:03:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T10:54:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T07:15:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244322134198295923499763943126821350921","date":"2026-03-21T11:15:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313839785255793453119830534596285930330","date":"2026-03-21T08:08:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T08:58:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-19T08:35:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T18:02:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Geochemistry and Health","date":"2026-03-16T07:41:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8b2684fd-4292-43fd-85d6-1aa21cbae489","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T14:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 16:09:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9134685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9134685","identity":"rs-9134685","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}