The first evidence of microplastic occurrence in mine water: The largest black coal mining area in the Czech Republic
矿井水中存在微塑料的第一个证据：捷克共和国最大的黑煤矿区

2.1. Sampling sites and collection
2.1. 采样点和采集

Groundwater samples from mines and wells were collected at four locations in the Moravian-Silesian Region, Czechia, see Fig. 1. The region includes a densely populated agglomeration with approximately one million permanent residents. Moreover, coal mining and heavy industry have impacted the region a great deal.
在捷克摩拉维亚-西里西亚地区的四个地点采集了矿山和水井的地下水样本，见图 1。该地区包括一个人口稠密的集聚区，拥有约100万常住居民。此外，煤炭开采和重工业对该地区产生了很大影响。

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Fig. 1. Sampling site, Map data: ©2023 GeoBasis.
图 1.采样点，地图数据：©2023 GeoBasis。

Mine water was collected from two terminated black coal mines, Jeremenko Mine in Ostrava-Vítkovice and Žofie Mine in Orlová-Poruba. The water is continuously pumped from the mines and discharged into surface waters to prevent flooding and subsidence in the mining region. The pumped waters have a neutral pH and are saline (Arnošt Grmela, 2017; Arnošt et al., 2003). Additionally, water was sampled from two nearby shallow wells for comparison. These were metal wells without any plastic fittings and serve as sources of water for gardening and flushing in private houses located in Paskov.
矿井水是从两个已终止的黑煤矿收集的，即俄斯特拉发-维特科维采的Jeremenko煤矿和Orlová-Poruba的Žofie煤矿。水不断从矿井中抽出并排放到地表水中，以防止矿区发生洪水和沉降。泵送水的pH值为中性，是咸水（Arnošt Grmela，2017 年;Arnošt 等人 ，2003 年）。此外，还从附近的两口浅井中取样取水进行比较。这些是没有任何塑料配件的金属井，用作位于帕斯科夫的私人住宅的园艺和冲洗水源。

Shallow well groundwater samples were obtained using identically prepared containers. Water was sampled directly from the wells using a metal bucket and poured into the prepared sampling bottle. Next, samples were immediately transported to the lab in a cooler for analysis and thus no sample stabilization was required.
浅井地下水样品是使用相同制备的容器获得的。使用金属桶直接从井中取样水，然后倒入准备好的取样瓶中。接下来，样品立即在冷却器中运送到实验室进行分析，因此不需要样品稳定。

2.2. Quality control 2.2. 质量控制

Precautions were taken to prevent contamination from plastic materials during the sampling process and while using sampling tools. We followed the guidelines outlined in the EN ISO 5667–14:2006 standard for sample collection, which focuses on reducing errors during sampling. All staff involved in handling the samples received proper training and were familiar with the sampling protocols.
在取样过程中和使用取样工具时，采取了预防措施以防止塑料材料的污染。我们遵循 EN ISO 5667–14：2006 标准中概述的样本采集指南，该标准侧重于减少采样过程中的错误。所有参与处理样本的工作人员都接受了适当的培训，并熟悉采样规程。

Mine water samples were collected using amber glass storage bottles, which were pre-washed with pure ethanol (VWR; 97%; CAS: 64–17–5) and wrapped in aluminum foil. The operator wore disposable latex gloves and cotton clothing during the process. Mine water samples were directly obtained from the pumped pits and shallow groundwater samples were collected using steel buckets from the wells and transferred to amber glass storage bottles. The samples were immediately sealed and stored at 4 °C for future analysis. In the laboratory, before filtration, all surfaces were rinsed with pure ethanol (VWR; 97%; CAS: 64–17–5) and cleaned with a cotton towel. Only one person was present in the lab and the air conditioner was turned off to minimize potential contamination. All laboratory glassware was rinsed with clean ethanol (VWR; 97%; CAS: 64–17–5) and covered with aluminum foil. Throughout the process, cotton coats and disposable nitrile gloves were used. During filtration, a blank test was conducted by placing dry filter paper (A/C Glass Fiber, 1 μm, 47 mm, PALL Mexico) in an open Petri dish alongside the single sample filtration to assess air contamination.
矿井水样品是使用琥珀色玻璃储存瓶收集的，这些储存瓶用纯乙醇（VWR; 97%;CAS：64-17-5）并用铝箔包裹。操作人员在此过程中佩戴一次性乳胶手套和棉质衣物。矿井水样直接从抽水坑中获取，浅层地下水样使用井中的钢桶收集，然后转移到琥珀色玻璃储存瓶中。将样品立即密封并储存在4°C以备将来分析。在实验室中，在过滤之前，所有表面都用纯乙醇（VWR;CAS：64-17-5），并用棉毛巾清洁。实验室里只有一个人在场，空调被关闭，以尽量减少可能的污染。所有实验室玻璃器皿均用干净的乙醇（VWR;97%;CAS：64-17-5）并用铝箔覆盖。在整个过程中，使用了棉质外套和一次性丁腈手套。在过滤过程中，通过将干燥滤纸（A/C 玻璃纤维，1 μm，47 mm，PALL Mexico）与单个样品过滤一起放入开放式培养皿中进行空白测试，以评估空气污染。

2.3. Microplastic extraction and identification
2.3. 微塑料的提取和鉴定

Water samples, each with a volume of 400 ml, were transferred to clean glass beakers and prepared for filtration. Due to limited filter capacity, each sample was divided into two beakers, with a volume of 200 ml each. In total, 20 samples (40 filters) were analyzed: 10 samples (20 filters) of groundwater from two wells (Well 1, Well 2) and 10 samples (20 filters) of mine water from two mines (Žofie Mine, Jeremenko Mine). All samples were filtered using vacuum filtration through glass fiber filters (A/C Glass Fiber, 1 μm, 47 mm, PALL Mexico). After filtration, the glass fiber filters were carefully transferred to glass Petri dishes using tweezers. Subsequently, the Petri dishes were placed in a desiccator at room temperature for 48 h to dry.
将每个体积为400ml的水样转移到干净的玻璃烧杯中并准备过滤。由于过滤容量有限，每个样品被分成两个烧杯，每个烧杯的体积为 200 ml。总共分析了 20 个样品（40 个过滤器）：来自两口井（1 号井、2 号井）的 10 个样品（20 个过滤器）和来自两个矿井（Žofie 矿、Jeremenko 矿）的 10 个样品（20 个过滤器）。所有样品均通过玻璃纤维滤光片（A/C 玻璃纤维，1 μm，47 mm，PALL Mexico）使用真空过滤进行过滤。过滤后，用镊子小心地将玻璃纤维过滤器转移到玻璃培养皿中。随后，将培养皿置于室温干燥器中48小时以干燥。

After filtration and drying, the samples underwent a preliminary visual analysis using a stereomicroscope (SZX10, Olympus, Japan) with a 60x magnification digital camera (Canon EOS 5000, Canon, Japan). Suspected particles were selected and their properties (color, particle type, size) were assessed for each sample. The total number of suspected particles was optically quantified and photographed.
过滤和干燥后，使用体视显微镜（SZX10，奥林巴斯，日本）和60倍放大数码相机（佳能EOS 5000，佳能，日本）对样品进行了初步的视觉分析。选择可疑颗粒，并评估每个样品的特性（颜色、颗粒类型、大小）。对可疑颗粒的总数进行光学量化和拍照。

The particles were analyzed using ImageJ (Schneider et al., 2012, p. 25) to determine their size, including length, width and area. The threshold function was used to modify the particles and their maximum length, maximum width and total area were measured by outlining the entire particle.
使用 ImageJ（Schneider 等人，2012 年 ，第 25 页）分析颗粒以确定其大小，包括长度、宽度和面积。利用阈值函数对粒子进行修饰，通过勾勒出整个粒子的轮廓来测量粒子的最大长度、最大宽度和总面积。

Then, Fourier transform infrared spectroscopy (μ-FTIR) was performed on a Nicolet iN10 instrument (Thermo Fisher Scientific, USA) with an MCT detector cooled by liquid nitrogen. Each sample, having 2 filters, allowed selecting a maximum of 10 particles for μ-FTIR analysis from each filter. Ten particles were manually chosen from each filter using tweezers and placed on a glass slide for analysis under a microscope. The particles were measured in reflection mode with 64 scans in high quality, covering the spectral range of 400–4000 cm⁻¹. Under the microscope, the shape and color of the particles were primarily determined. The obtained vibrational spectra and the measured particles were compared with 5 spectral libraries of plastic materials and plastic additives. A threshold of over 60% match with the spectra in the library was set to confirm the polymer composition of particles. For matches within the range 50%−70% manual analysis was conducted to compare the characteristic peaks of the identified plastic material. Finally, the experimental data obtained were evaluated and subjected to statistical processing.
然后，在Nicolet iN10仪器（Thermo Fisher Scientific，USA）上进行傅里叶变换红外光谱（μ-FTIR），该仪器带有由液氮冷却的MCT检测器。每个样品有 2 个滤光片，允许从每个滤光片中选择最多 10 个颗粒进行 μ-FTIR 分析。使用镊子从每个过滤器中手动选择10个颗粒，然后放在载玻片上，在显微镜下进行分析。这些颗粒在反射模式下进行了64次高质量扫描，覆盖了400-4000 cm⁻¹的光谱范围。在显微镜下，主要确定了颗粒的形状和颜色。将得到的振动光谱和被测颗粒与塑料材料和塑料添加剂的5个光谱库进行了比较。设定了与谱库中光谱匹配度超过60%的阈值，以确认颗粒的聚合物组成。对于50%−70%范围内的匹配，进行了手动分析，以比较鉴定出的塑料材料的特征峰。最后，对得到的实验数据进行评估和统计处理。

The technique was selected based on methods reported in the available literature (Table 3) and due to the absence of a standardized methodology for analyzing microplastics in groundwater. It is important to mention that ISO 24187 - "Principles for the analysis of microplastics present in the environment" is currently under preparation. Once adopted, this could help standardize both the data obtained and the methodologies used.
该技术的选择基于现有文献中报告的方法（表 3），并且由于缺乏分析地下水中微塑料的标准化方法。值得一提的是，ISO 24187 - “环境中存在的微塑料分析原则”目前正在制定中。一旦获得通过，这将有助于使所获得的数据和所使用的方法标准化。

2.4. Statistical analysis
2.4. 统计分析

Factor Analysis of Mixed Data (FAMD) was employed to examine overall trends in the data, allowing a simultaneous assessment of qualitative and quantitative active variables. The variables used for constructing the model were particle shape, particle color and the number of estimated plastic particles. Additionally, variables like sample (two wells and two mines), site (mine X well) and sampling month were projected to the model.
混合数据因子分析（FAMD）用于检查数据的总体趋势，从而可以同时评估定性和定量的活动变量。用于构建模型的变量是颗粒形状、颗粒颜色和估计的塑料颗粒数量。此外，样本（两口井和两口矿井）、地点（X井）和采样月份等变量也被预测到模型中。

To assess the dependence of the number of estimated plastic particles on the sample, site and sampling month, as well as the combination of the sampling month and sample or site, one-way analysis of variance (ANOVA) was applied. The analyses and visualizations were conducted using the R environment (R Core Team, 2021), specifically utilizing the FactoMineR (Lê et al., 2008) and factoextra packages (Kassambara and Mundt, 2020).
为了评估估计的塑料颗粒数量对样品、地点和采样月份的依赖性，以及采样月份和样品或地点的组合，应用了单因素方差分析（ANOVA）。分析和可视化是使用 R 环境（R Core Team，2021 年）进行的，特别是利用 FactoMineR（Lê 等人 ，2008 年）和 factoextra 包（Kassambara 和 Mundt，2020 年）。

3.1. Microplastics abundance
3.1. 微塑料的丰度

In the well samples, there were 194 suspected particles, of which 149 were analyzed using μ-FTIR. Among these particles, 45 were confirmed as plastic or plastic additives, accounting for 30% of the total. The microplastic abundance was found to be 2.5–20 items/L.
在井样中，有194个疑似颗粒，其中149个使用μ-FTIR进行分析。在这些颗粒中，有45种被确认为塑料或塑料添加剂，占总数的30%。发现微塑料丰度为 2.5-20 件/L。

The mine water samples exhibited a lower microplastics load. Out of 251 suspected particles, 153 were analyzed using μ-FTIR, but only 35 were confirmed as plastic or plastic additives, making up 23% of the total. The microplastic abundance ranged from 2.5 to 17.5 items/L.
矿井水样品的微塑料含量较低。在 251 个疑似颗粒中，有 153 个使用 μ-FTIR 进行了分析，但只有 35 个被确认为塑料或塑料添加剂，占总数的 23%。微塑料丰度范围为2.5至17.5件/升。

The comparison of the abundance of microplastics in mine waters with other regions, cities, or states is currently not feasible due to the lack of similar studies. As for the origin of MPS, in the Czech Republic the presence of microplastics at significant depths below the surface might be linked to mine ventilation. During mine operation, forced air flow with fans creates a negative pressure, allowing fresh air distribution to all parts of the mine. Consequently, microplastics from the surface air could have accumulated in mine shafts to be later released into mine waters while flooding the mine upon termination. (Personal communication, 10/12/2022) Atmospheric transport as a potential cause of microplastic occurrence in underground spaces is supported by findings from Baraza in a cave environment (Baraza and Hasenmueller, 2023). However, other pathways cannot be ruled out. Surface water influx during heavy precipitation events might carry anthropogenic particles, contributing to the microplastic presence. The extensive surface area of deep mines and insufficient mapping of potential underground pathways add to the uncertainty. Infiltration of polymer particles through soil and rock environments is another possible pathway, but this process is influenced by various factors like particle size, polymer composition, hydrophobicity and specific soil types (Guo et al., 2022). Fig. 2 shows a negative Pearson correlation between precipitation and microplastics in mine water (r = −0.61 for Jeremenko and r = −0.68 for Žofie). However, the correlation was not found to be significant. There was also a lower negative correlation to the groundwater samples from wells (r = −0.24 and t = −0.13), which suggests that precipitation might not be the sole factor affecting microplastic abundance in the selected area.
由于缺乏类似的研究，目前无法将矿井水中微塑料的丰度与其他地区、城市或州进行比较。至于MPS的起源，在捷克共和国，地表以下大量深度的微塑料的存在可能与矿井通风有关。在矿井作业期间，通过风扇的强制气流会产生负压，使新鲜空气能够分布到矿井的所有部分。因此，地表空气中的微塑料可能会在矿井中积聚，然后在矿井结束时释放到矿井中，同时淹没矿井。（个人来文，2022年10月12日）大气迁移是地下空间中微塑料发生的潜在原因，得到了 Baraza 在洞穴环境中的发现的支持（Baraza 和 Hasenmueller，2023 年）。但是，不能排除其他途径。在强降水事件期间，地表水的流入可能会携带人为颗粒物，从而导致微塑料的存在。深部矿山的广阔表面积和潜在地下通道的测绘不足增加了不确定性。聚合物颗粒通过土壤和岩石环境渗透是另一种可能的途径，但这一过程受到颗粒大小、聚合物组成、疏水性和特定土壤类型等各种因素的影响（Guo et al.， 2022）。图 2显示了矿井水中降水与微塑料之间的负Pearson相关性（Jeremenko的r = -0.61，Žofie的r = -0.68）。然而，没有发现相关性显着。 与井中地下水样品的负相关也较低（r = −0.24和t = −0.13），这表明降水可能不是影响所选地区微塑料丰度的唯一因素。

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Fig. 2. The abundance of MPs and the precipitation in Moravian-Silesian Region (Czech Republic) during the monitored period.
图 2.监测期间摩拉维亚-西里西亚地区（捷克共和国）的MP丰度和降水量。

3.2. Microplastics characterization - morphology, color and size
3.2. 微塑料表征 - 形态、颜色和大小

The samples contained particles with variations in shape, color and size. Four distinct shapes were identified: fibers, fragments, particles and film. Among all the samples, fibers were the most prevalent, as depicted in Fig. 3; S. 1 and S. 2. This dominance of fibers aligns with findings from previous studies examining groundwater microplastics (Baraza and Hasenmueller, 2023; Esfandiari et al., 2022; Fajaruddin Natsir et al., 2021; Shu et al., 2023; Wu et al., 2022). However, there are studies that emphasize the prevalence of microplastic fragments in groundwater (Cha et al., 2023; Jeong et al., 2023; Kim et al., 2023). This suggests that the type of particles found might be related to the specific utilization of the location.
这些样品含有形状、颜色和大小各不相同的颗粒。鉴定出四种不同的形状：纤维、碎片、颗粒和薄膜。在所有样品中，纤维是最普遍的，如图3所示 ;第1条及第2条。纤维的这种主导地位与之前检查地下水微塑料的研究结果一致（Baraza 和 Hasenmueller，2023 年;Esfandiari 等人 ，2022 年;Fajaruddin Natsir 等人 ，2021 年; Shu et al.， 2023; Wu 等人 ，2022 年）。然而，有研究强调地下水中微塑料碎片的普遍存在（Cha et al.， 2023;Jeong 等人 ，2023 年;Kim 等人 ，2023 年）。这表明发现的颗粒类型可能与该位置的特定利用有关。

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Fig. 3. Distribution of microplastics morphology in water from; a) wells; b) mines; c) total distribution.
图 3.微塑料在水中的形态分布;a） 油井;b） 矿山;c） 总分配。

In contrast, the presence of films in our samples was either infrequent or nonexistent. The irregular shape of fragments and films indicates that they likely originated from the breakdown of larger plastic objects like plastic bags, bottles, or foils. On the other hand, fibers exhibit a regular shape and varying lengths, suggesting their possible sources as synthetic clothing, upholstery, or the breakdown of larger plastic pieces (Chaudhari and Samnani, 2022).
相比之下，我们的样本中薄膜的存在要么很少见，要么根本不存在。碎片和薄膜的不规则形状表明它们可能起源于塑料袋、瓶子或箔纸等较大塑料物体的分解。另一方面，纤维表现出规则的形状和不同的长度，这表明它们可能的来源是合成服装、室内装潢或较大塑料片的分解（Chaudhari 和 Samnani，2022 年）。

Fig. 3 illustrates the distribution of different microplastic morphologies in the samples. Out of the 80 confirmed microplastics, fibers accounted for the largest portion at 59%, followed by particles (20%), fragments (19%) and films (2%). In both mine and well samples, fibers were the most abundant, making up 49% and 67% respectively. However, films were only observed in the mine water samples.
图 3显示了样品中不同微塑料形态的分布。在80种已确认的微塑料中，纤维占最大比例，为59%，其次是颗粒（20%）、碎片（19%）和薄膜（2%）。在矿山和油井样品中，纤维含量最高，分别占49%和67%。然而，仅在矿井水样中观察到薄膜。

Considerable color variability was observed, with a total of 11 distinct colors identified. Based on the data in Fig. 4, the most prevalent colors were as follows: blue (27%), gray (24%), brown (18%), transparent (10%), black (6%), white (4%), pink (4%), red (3%), beige (2%), orange (1%) and silver (1%). Notably, the dominance of the blue color aligns with findings from previous studies by Wu et al. and Zhang et al. However, our study observed a relatively lower prevalence of the transparent color, contrasting with the higher representation reported in their respective studies (Wu et al., 2022; Zhang et al., 2019). In contrast, K et al. reported blue as the second least abundant color (K et al., 2021). The chi-squared test revealed no significant differences in color distribution between the mine water and well samples (p = 0.17).
观察到相当大的颜色变异性，总共识别出 11 种不同的颜色。根据图 4中的数据，最流行的颜色如下：蓝色（27%）、灰色（24%）、棕色（18%）、透明（10%）、黑色（6%）、白色（4%）、粉红色（4%）、红色（3%）、米色（2%）、橙色（1%）和银色（1%）。值得注意的是，蓝色的主导地位与Wu等人和Zhang等人先前的研究结果一致。然而，我们的研究观察到透明色的患病率相对较低，这与他们各自研究中报告的较高代表性形成鲜明对比（Wu 等人 ，2022 年;Zhang 等人 ，2019 年）。相比之下，K 等人报告说蓝色是第二不丰富的颜色（K 等人 ，2021 年）。卡方检验结果显示，矿井水和油井样品的颜色分布无显著差异（p = 0.17）。

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Fig. 4. Color distribution of microplastics in water from; a) wells; b) mines; c) total color distribution.
图 4.微塑料在水中的颜色分布;a） 油井;b） 矿山;c） 总颜色分布。

In well samples, the most prevalent colors were gray (33%), blue (23%) and brown (20%), while no white, silver, red, or orange particles were observed in the selected samples (S. 3a). In the first well, the largest share of particles was gray (29.5%) and brown (29.5%), while the second well had the highest proportion of gray particles at 38%.
在油井样品中，最普遍的颜色是灰色（33%）、蓝色（23%）和棕色（20%），而在选定的样品中没有观察到白色、银色、红色或橙色颗粒（S.3a）。在第一口井中，灰色（29.5%）和棕色（29.5%）的颗粒所占比例最大，而第二口井的灰色颗粒所占比例最高，为38%。

In mine samples, blue particles were the most abundant at 34%, followed by brown (14%) and gray (11%) (S. 3b). The Jeremko mine had the highest proportion of blue particles at 39%, while in Žofie, the color blue appeared most often at 29%.
在矿山样本中，蓝色颗粒的含量最高，为34%，其次是棕色（14%）和灰色（11%）（S.3b）。Jeremko矿的蓝色颗粒比例最高，为39%，而在Žofie，蓝色出现频率最高，为29%。

The analysis of microplastics in the samples focused on their size, specifically length, width and area. The results were categorized into six size groups: 0–50 µm, 50–100 µm, 100–250 µm, 250–500 µm, 500–1000 µm, and above 1000 µm. Additionally, for a more detailed view of the microplastic surface area, four distinct categories were established: 1000–10,000 µm², 10,000–15,000 µm², 15,000–25,000 µm² and 25,000–600,000 µm².
样品中微塑料的分析主要集中在它们的大小上，特别是长度、宽度和面积。结果分为6个尺寸组：0-50 μm、50-100 μm、100-250 μm、250-500 μm、500-1000 μm和1000 μm以上。此外，为了更详细地观察微塑料表面积，建立了四个不同的类别：1000-10,000 μm²、10,000-15,000 μm²、15,000-25,000 μm² 和 25,000-600,000 μm²。

Fig. 5 presents the findings related to microplastics discovered in mine waters. The particles in mine waters exhibited lengths ranging 58–3822 µm, widths spanning 5.7–226.6 µm and areas covering values between 1088 and 116,579 µm². In contrast, microplastics obtained from wells showed different ranges. The length varied 104.7–1798 µm, the width 7.6–692.6 µm and the area was recorded within the interval of 2647.6–520,626 µm².
图 5显示了与在矿井水中发现的微塑料相关的发现。矿井水中的颗粒长度范围为58-3822 μm，宽度范围为5.7-226.6 μm，面积范围在1088-116,579 μm之间²。相比之下，从孔中获得的微塑料显示出不同的范围。长度变化为104.7-1798 μm，宽度为7.6-692.6 μm，面积记录在2647.6-520,626 μm²的区间内。

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Fig. 5. Size distribution of microplastics from mine water and well samples.
图 5.矿井水和油井样品中微塑料的粒径分布。

The most prevalent microplastics were those with a width ranging from 0 to 50 µm, accounting for 50 pieces (62.5%) of all 80 identified microplastics. This group of microplastics prevailed and was the dominant component in the analyzed samples.
最普遍的微塑料是宽度范围为0至50μm的微塑料，占所有80种已确定的微塑料的50块（62.5%）。这组微塑料占主导地位，是分析样品中的主要成分。

Our results were compared with Balesta et al.'s study, which reported 78% of microplastics within the 100–990 µm length range (Balestra et al., 2023). In our research, we observed a similar outcome, with 88.75% of microplastics falling within this specific length range.
我们的结果与 Balesta 等人的研究进行了比较，该研究报告了 78% 的微塑料在 100-990 μm 长度范围内（Balestra 等人 ，2023 年）。在我们的研究中，我们观察到了类似的结果，88.75%的微塑料属于这个特定的长度范围。

3.3. Microplastics characterization - polymer composition
3.3. 微塑料表征 - 聚合物成分

Thirty-one types of microplastics were measured and identified in all samples.
在所有样品中测量和鉴定了 31 种类型的微塑料。

In the analysis of water from wells, plastic-coated paper was the most abundant plastic material type, with 15 identified particles. Other substances found included Polyethylene terephthalate (PET), Polyester (PES) and Tetrafluoroethylene-perfluoro (Propyl Vinyl Ether) Copolymer (TFE-PPVE), each accounting for 7% of the total sample volume.
在对井水的分析中，塑料淋膜纸是最丰富的塑料材料类型，有15个已确定的颗粒。发现的其他物质包括聚对苯二甲酸乙二醇酯 （PET）、聚酯 （PES） 和四氟乙烯-全氟（丙基乙烯基醚）共聚物 （TFE-PPVE），每种物质均占总样品体积的 7%。

In the analysis of water from mines, plastic-coated paper accounted for 27% of the total particles, while polyester (PES), polyethylene terephthalate (PET) and polypropylene (PP) made up 18%, 12% and 9% of the total volume, respectively (see Fig. 6).
在矿井水的分析中，塑料涂层纸占总颗粒量的27%，而聚酯（PES）、聚对苯二甲酸乙二醇酯（PET）和聚丙烯（PP）分别占总体积的18%、12%和9%（见图 6）。

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Fig. 6. Selected vibrational spectra obtained by micro-FTIR; a) paper with polymer coating; b) Polypropylene (PP); c) Polyethylene terephthalate (PET); d) Poly (acrylonitrile-co-acrylic acid) (PANCAA); e) Polypropylene (PP).
图 6.通过micro-FTIR获得的选定振动光谱;a） 带有聚合物涂层的纸张;b） 聚丙烯（PP）;c） 聚对苯二甲酸乙二醇酯（PET）;d） 聚（丙烯腈-共-丙烯酸）（PANCAA）;e） 聚丙烯（PP）。

The identified particles in the samples can originate from various sources, including packaging, industrial waste, or other waterborne contaminants. The high prevalence of plastic-coated paper in all samples strongly suggests its origin in the packaging industry. Chen et al. provide an example, mentioning the widespread use of disposable paper cups and PE-coated packaging as potential sources (Chen et al., 2023).
样品中识别出的颗粒可能来自各种来源，包括包装、工业废物或其他水性污染物。塑料淋布纸在所有样品中的高含量强烈表明其起源于包装行业。Chen et al. 提供了一个例子，提到一次性纸杯和 PE 涂层包装的广泛使用是潜在来源（Chen et al.， 2023）。

3.4. Statistical analysis
3.4. 统计分析

Fig. 7 visualizes the results of FAMD for the variables. The first two dimensions explain a similar amount of variability in the data. The only quantitative variable, the number of estimated plastic particles, showed a strong positive correlation with the first dimension (r = 0.81, p = 0.4 . 10⁻²²). Particle color, sampling month and particle shape were also significantly associated with this dimension (R2 = 0.68, R2 = 0.38 and R2 = 0.28, respectively). The second dimension was associated with particle shape, color and sample (R2 = 0.75, R2 = 0.78 and R2 = 0.11, respectively). The significant association of both sampling months and sampling site is noteworthy since these variables were only projected to the model and not used for its construction. However, the sampling site was not linked to any of the dimensions, as evident when the individuals (i.e., individual particles) were plotted on the first factor plane based on their sampling site (Fig. 8).
图 7 可视化了变量的 FAMD 结果。前两个维度解释了数据中相似的可变性量。唯一的定量变量，即估计的塑性颗粒数量，与第一维（r = 0.81，p = 0.4 . 10⁻²²）呈强正相关。 颗粒颜色、采样月份和颗粒形状也与该维度显著相关（R2 = 0.68、R2 = 0.38和R2 = 0.28）。第二个维度与颗粒形状、颜色和样品相关（R2 = 0.75、R2 = 0.78 和 R2 = 0.11）。值得注意的是，采样月份和采样地点之间的显著关联是值得注意的，因为这些变量只是投影到模型中，并未用于模型的构建。然而，采样点与任何维度都没有关联，当个体（即单个粒子）根据其采样点绘制在第一因子平面上时，这一点很明显（图 8）。

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Fig. 7. FAMD first factor plane, plot of variables.
图 7.FAMD 第一因子平面，变量图。

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Fig. 8. FAMD first factor plane, plot of individual measurements and determined color of the particles.
图 8.FAMD 第一因子平面，单个测量值的图和确定的颗粒颜色。

The results of One-way Analysis of variance corroborated the aforementioned phenomenon. There was no significant difference found in the number of estimated plastic particles determined in mine waters and wells (Fig. 9a). On the other hand, there was a significant difference (p = 0.021) between the samples (Fig. 9b). While the samples from wells were rather similar in the number of plastic particles estimated, samples from mines were found to be much more variable – both between the samples and in the samples. In addition, the sampling month was found to have a significant effect (p = 0.11 . 10⁻⁵) on the number of estimated plastic particles as well as the combination of the sampling month and the sample (p = 0.2 . 10⁻¹⁵, two-way ANOVA).
单因素方差分析结果证实了上述现象。在矿井和油井中测定的估计塑料颗粒数量没有发现显着差异（图 9a）。另一方面，样品之间存在显着差异 （p = 0.021）（图 9b）。虽然来自井的样品在估计的塑料颗粒数量上相当相似，但发现来自矿山的样品的可变性要大得多——无论是在样品之间还是在样品中。此外，还发现采样月份对估计的塑料颗粒数量以及采样月份和样品的组合具有显着影响（p = 0.2 . 10⁻¹⁵，双向方差分析）。

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Fig. 9. Estimated plastic particle number a) per site b) per sample.
图 9.估计的塑料颗粒数 a） 每个部位 b） 每个样品。

3.5. Remediation strategies in water sources
3.5. 水源地修复策略

Concerningly, microplastics are found in significant quantities both in shallow groundwater and deep water. As mentioned previously, it is highly probable that legislation will restrict the presence of microplastics in water sources in the future. Consequently, addressing the issue of their removal from water bodies becomes essential. This calls for supplementing the existing water treatment technologies with processes specifically designed to eliminate microplastics.
令人担忧的是，在浅层地下水和深层水中都发现了大量的微塑料。如前所述，未来立法极有可能限制水源中微塑料的存在。因此，解决将其从水体中移除的问题变得至关重要。这就要求用专门设计的工艺来补充现有的水处理技术，以消除微塑料。

To this end, various remediation technologies for water and wastewater treatment have been investigated, including membrane filtration, adsorption, coagulation-flocculation-sedimentation, bioremediation, and advanced oxidation processes. Next, in their review paper, Lu et al. analyze the advantages and disadvantages of each technique to ensure the efficient removal of microplastics from water sources (Lu et al., 2023).
为此，人们研究了用于水和废水处理的各种修复技术，包括膜过滤、吸附、混凝-絮凝-沉淀、生物修复和高级氧化工艺。接下来，在他们的评论论文中，Lu 等人 分析了每种技术的优缺点，以确保有效去除水源中的微塑料（Lu 等人 ，2023 年）。

Moreover, a hybrid treatment approach using technologies like a membrane bioreactor or a combination of coagulation and membrane filtration is proving to be highly effective in microplastic removal (Adegoke et al., 2023).
此外，使用膜生物反应器等技术或混凝和膜过滤相结合的混合处理方法被证明在去除微塑料方面非常有效（Adegoke 等人 ，2023 年）。