Distribution Characteristics of Microplastics in Domestic Sewage Waters: A Case Study in Guilin City, China

Distribution Characteristics of Microplastics in Domestic Sewage Waters: A Case Study in Guilin City, 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 Distribution Characteristics of Microplastics in Domestic Sewage Waters: A Case Study in Guilin City, China Meiyuan Lu, Huimei Shan, Hongbin Zhan, Yuxin Shi, Yunquan Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5460558/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Microplastics (MPs) resulting from plastic fragmentation with a size less than 5 mm have become one of the main pollutants endangering the water environment. Therefore, it is necessary to know about the abundance and size distribution in sewage waters as well as the influences of water quality on MPs. In this study, water samples are collected from 20 sewage outlets in Guilin, China, to analyze the abundance and morphology of the MPs and their hydrochemical characteristics. Multivariate statistical analyses are conducted to identify the major factors related to the MPs’ distribution in sewage waters. Results show that MPs in sewage water are mainly composed of fiber and film, and about 67.8% is in the size of <0.3 mm. The abundance is in the range of 6 (±1)–47 (±3) items/L. The correlation analysis presents that the abundance of MPs is weakly correlated with hydrochemical parameters and metal ions due to the complexity of the abundance data. The redundancy analysis indicates that the MPs’ morphology distribution is significantly affected by NO 3 -N, Zn, Ca, and Cu contents, and the MPs’ size distribution is mainly related to Zn, Ca, and Cu contents. The study highlights the occurrence characteristics and environmental influencing factors of the MPs in sewage water, which may be significant for future studies on the pollution control of MPs. Microplastics Distribution Sewage water Relationship analysis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Microplastics (MPs) refer to plastic particles, fibers, or fragments with a particle size (or one-dimensional length) less than 5 mm (Thompson et al., 2004 ). MPs can exist in the environment for decades to centuries a long time by the characteristics of refractory degradation, small size, large specific surface area, strong hydrophobicity, etc. Moreover, it is easy to adsorb organic pollutants and heavy metals, which will migrate and spread over long distances and cause great harm to the ecosystem (Zhang et al., 2021b ). Therefore, it is important to identify the source, transport, and fate of MPs for environmental protection and public health. MPs can easily enter the natural water environment through stormwater runoff, atmospheric sedimentation, sewage pipes, sewage treatment plant effluent, etc. MPs have been detected in various environmental media, including the atmosphere (Sun et al., 2022 ), domestic wastewater (Atugoda et al., 2021 ; De Falco et al., 2019 ; Galvão et al., 2020 ), rivers (Buwono et al., 2021 ; Liu et al., 2019 ), lakes (Feng et al., 2021 a), Ocean (Isobe et al., 2015 ; Xu et al., 2022 ), sediment (Liet al., 2022 ), and soil (Scheurer et al., 2018; Zhou et al., 2020 ), etc. Among them, domestic wastewater is assumed to contain high amounts of MPs and is recognized as the major source of MPs in aquatic environments. After MPs are released from sewage pipes into rivers, rivers become the main route for MPs' transportation and further carry MPs to enter the ocean from inland areas. This will greatly threaten water species and even harm local communities that rely on these bodies of water for drinking water (Wanget al., 2017 ; Van Emmerik et al., 2020). Due to the complexity and ever-changing characteristics of the natural environment, the occurrence and distribution of MPs are related to a variety of environmental factors (Wen et al., 2018 ). Generally, water quality conditions or solution environments are significantly influenced by the presence and levels of MPs, ions, pH, and salinity in water. For example, Liu et al. ( 2019 ) reported that the removal of MPs in wastewater was accompanied by the reduction of pollutants such as TN, TP, and COD in wastewater. Aryani et al. ( 2021 ) reported that high concentrations of MPs in water led to a decrease in total ammonia. Liu et al. ( 2022 ) found that the increase in temperature and the decrease in salinity were beneficial to improving the adsorption of heavy metals by MPs. Li et al. ( 2022 ) found that calcium ions and NH 4 + can promote the adsorption of MPs on aquifer media, and the adsorption of MPs on aquifer media tended to be gradually inhibited with the increase of anion (SO 4 2− ) strength. The above research explains that the abundance and distribution of MPs are closely related to the physiochemical properties of the environment. Therefore, it is vital to analyze the water quality parameters that determine the existence of MPs in the aquatic environment. However, the hydrochemical and environmental effects of MPs have been rarely investigated in recent years (Bayo et al., 2020 ). Such information is critical for future management initiatives aimed at improving river water quality and reducing the pollution of MPs. As a typical karst landform area, Guilin has a complex groundwater system and weak self-purification ability. The characteristics of karst landform make it easier for pollutants to spread rapidly through underground rivers, and the close relationship between surface water and groundwater intensifies this phenomenon (Hasenmueller et al., 2023 ). Once the MPs enter the aquatic environment, they may quickly penetrate the groundwater system and spread in a wide area, making the pollution diffusion difficult to control (Chen et al., 2022 ; Gong et al., 2023 ). Guilin is not only famous for its unique natural landscape, but also with the acceleration of urbanization in recent years, the rapid growth of population has caused great pressure on the domestic sewage discharge system of the city. The pollution of MPs in domestic sewage is becoming more serious in the process of urban expansion. The existing sewage treatment facilities may not be able to effectively filter and remove these tiny particles, resulting in their direct discharge into natural water bodies. Due to the direct discharge of sewage outlets, the long-term accumulation of MPs in the water body may have an irreversible negative impact on the aquatic ecosystem, which not only threatens the ecological balance but also may endanger public health. Although research on MPs’ pollution in water has increased rapidly in recent years, research on Guilin, a unique karst geological environment, is still relatively scarce. At present, most researches focus on the size, morphology, and distribution characteristics of MPs (Zhang et al., 2021a , 2021b ; Shu et al., 2023 ). However, investigations into the relationship between MPs in domestic wastewater and environmental factors (such as hydrochemical parameters, metal ions, etc.) are still limited. Considering the unique geological conditions and important position as a tourist city of Guilin, it is particularly urgent to investigate the environmental risk associated with MPs’ pollution. Herein, to better understand the existence of MPs in domestic sewage waters and the impact of hydrochemical parameters on MPs, Guilin City, China, is selected as the study area to sample sewage water and analyze the relationship between the abundance of MPs and hydrochemical parameters. Meanwhile, the impacts of hydrochemical parameters and metal ions on MPs are identified by using the Redundancy Analysis (RDA) method. This can provide theoretical guidance for future water resource management to comprehensively evaluate the overall pollution situation of water bodies, to improve water quality, and to reduce MPs in the water environment. 2. Materials and methods 2.1 Sampling From July to August 2023, a series of sewage water samples were collected from the sewage water outlets of Guilin City, and their locations are shown in Fig. 1 . The samples of S1-S11 are collected near Guangjiang River (GR area), S12-S14 are collected near Fuyi Water (FW area), S15-S18 are collected near Guijiang River (LS area), and S19-S20 are collected near Pingdeng River (PR area), respectively. Detailed information on water samplings is listed in Table S1 (Supplementary Materials). Sewage water samples are collected at a water depth of about 20 cm and are placed in 2 L plastic buckets that have been rinsed with sample water. The samples are kept in a box filled with ice until they are transported to the laboratory for analysis. At each sampling point, three parallel water samples are collected to ensure the representativeness of the water samples. 2.2 Hydrochemical analysis Hydrochemical parameters of sewage water samples, including pH, Ec (µS/cm), total phosphorus (TP, mg/L), ammonia nitrogen (NH 3 -N, mg/L), nitrate (NO 3 -N, mg/L), total nitrogen (TN, mg/L), chloride (Cl − , mg/L), sulfate (SO 4 2− , mg/L) and metal ions (K, Na, Ca, Mg, Fe, Al, Mn, Pb, Zn, Cd, Cu, and As, mg/L) are evaluated. Specifically, the pH and Ec are measured by portable multi-parameter digital analysis (Hach-HQ30d, CO, USA), the concentrations of TN, TP, NH 3 -N, and NO 3 -N are measured using a UV spectrophotometer (UV-2355, UNICO, Shanghai), the concentrations of anions (Cl − and SO 4 2− ) are measured using Ion chromatograph (Dionex ICS-10000IC, USA). The concentration of metal ions in aqueous solutions is determined by an inductively coupled plasma optical emission spectrometer (Optima 7000DV, Platinum Elmer Instruments, Inc. Waltham, MA, USA), and the relative standard deviation is less than 5%. 2.3 MPs identification Before testing MPs, the water sample needs to be treated in the following steps. Firstly, refer to previous studies, stainless steel screens with particle sizes of 5 mm, 1 mm, and 0.3 mm are used to filter sewage samples, respectively (Masura et al., 2015 ). Secondly, the filtrates are treated with 30% H 2 O 2 solutions at 65 o C and 80 rpm for 72 hours to digest biological materials (Hidalgo-Ruz et al., 2012 ; Zhao et al., 2024 ). Finally, the digested solutions are filtered through a vacuum pump (glass fiber filter membranes, 47 mm Ø , 0.45 µm apertures), and the filter membranes are placed in clean Petri dishes. The dishes with samples are placed under a stereomicroscope (Phenix, SMZ-180, China) to observe and record the size and morphology of MPs at a magnification of 40 times. To avoid selection bias, the National Oceanic and Atmospheric Administration (NOAA) guidelines are utilized to classify and quantify the abundance of MPs in water samples. The methods of observing MPs have been standardized as shown below: (1) there is no apparent cellular or biological structure; (2) the fibers have the same thickness down and must not taper at the ends; (3) the ribbon’s fibers are not segmented and appear bent; (4) the particles are not glossy (Mohamed Nor et al., 2014). The types of MPs include plastic fragments (compact color), pellets (compact color), filaments/fibers (compact color), plastic films (transparent color), foamed plastics, granules, and Styrofoam (Hidalgo-Ruz et al., 2012 ). 2.4 Quality control and data analysis Samples are collected based on the latest quality assurance and quality control standards, and strict control measures are implemented (Adomat et al., 2021; Koelmans et al., 2019 ). To minimize experimental errors, glass containers and stainless-steel vessels used in the water sampling, treatment, and filtration phases are rinsed with deionized water before and after use. In all the experiments, each medium is used with three blank controls. Filtered distilled water is used as a blank in the laboratory and treated according to the same procedure used for the samples (Zhu et al., 2021 ). The sampling sites are mapped using ArcGIS 10.8 (ESRI, Redlands, CA, USA). To quantify the variability of MPs as they are affected by hydrochemical parameters and metal ions, the Pearson correlation analysis is used to compare multiple groups. A significant level of 5% (p < 0.05) is set for all analyses. Canoco 5.0 software (Ithaca, NY, USA) is used to perform a redundancy analysis (RDA) to examine the associations among MPs, hydrochemical parameters, and metal ions. 3. Results and discussion 3.1 Hydrochemical characteristics The hydrochemical results are shown in Table 1 . Comparing the water quality indexes of four areas (GR area, FW area, PR area, and LS area) in Guilin City, the pH of the study area is neutral and weakly alkaline; this indicates that domestic sewage does not cause acid-base pollution in the water body. This result is consistent with the study of rural domestic sewage in southwest China by Xie et al. ( 2018 ). Specifically, the pH values of the GR area range from 6.59–7.84 with a mean value of 7.31; the pH values of the LS area range from 7.45–7.79 with a mean value of 7.63; the pH values of the FW area range from 7.21–7.92 with a mean value of 7.48; and the pH values of the PR area range from 7.29–7.79 with a mean value of 7.54, respectively. The coefficient of variation (CV) is often used to reflect the variation degree of each observed value and to analyze the dispersion degree and stability of variables. When the CV value is less than 0.1, it is a weak variation; between 0.1 and 1.0, it is a medium variation; and greater than 1.0 is a strong variation (Sefie et al., 2018 ). The CV values of NH 3 -N and TP in the GR area are 1.04 and 1.06, respectively, indicating their strong variations and the sources of nitrogen and phosphorus in the water bodies of this region are unevenly distributed and may be affected by human activities. Specifically, S3 shows the maximum values of NH 3 -N and TP (0.495 and 0.380 mg/L, respectively), and S7 shows the maximum values of TN and NO 3 -N (1.357 and 4.239 mg/L, respectively). The possible reason is that these two sampling points are located next to residential areas, where a small amount of laundry detergent is used by people containing elements such as N and P. Especially daily sewage is directly discharged without treatment, further aggravating nitrogen and phosphorus release into the water. Another possible pollution source may be the flushing water produced by raising poultry and livestock, and the livestock and poultry feces and urine containing high concentrations of N and P (Hu et al., 2017 ). The CV value of Ec in the FW area is 1.06, which is a strong variation. The concentration of Ec at point S13 in the FW area is very high, reaching 355 µS/cm, which may be related to the fact that this point is located in a region close to the township. The discharge of domestic sewage in urban areas is large, and the agricultural and traffic runoff around the town also carries pollutants (such as chloride and nitrate) into the nearby water bodies (Peterse et al., 2024 )In addition, water in urban water bodies tends to flow very slowly and is poor at self-purifying compared to suburban water bodies. Because there are many pollution sources, the water body cannot effectively dilute or degrade the incoming pollutants, resulting in a high content of dissolved solids. The CV values of NO 3 -N, Cl − , and Ec in the PR area are 1.04, 1.38, and 1.18, respectively. Their large variations indicate that the sources of nitrogen, salts, and Ec in the water bodies of this region are unevenly distributed. In particular, the concentrations of NO 3 -N, Cl − and Ec at S20 are very high, reaching 2.735 mg/L, 408.369 mg/L, and 1873 µS/cm, respectively, which may be related to the proximity of this point to the expressway. Some studies have shown that the application of salt on expressways and roads reduces the freezing point of water, thus melting ice and snow, but it also produces saline water that runs off the road into the surrounding environment (Dailey et al., 2014 ). Table 1 Hydrochemical analysis results Regions Parameters Max Min Mean Std. deviation CV GR area NH 3 -N 0.495 0.026 0.128 0.134 104.347 TN 1.357 0.739 0.986 0.173 17.501 TP 0.380 0.010 0.135 0.144 106.832 NO 3 -N 4.239 0.816 2.183 1.144 52.434 Cl − 8.994 1.715 3.867 2.312 59.774 SO 4 2− 9.332 1.466 3.977 2.244 56.438 pH 7.84 6.59 7.314 0.395 5.401 Ec 219.6 66.8 136.991 50.793 37.077 LS area NH 3 -N 0.057 0.025 0.038 0.014 35.771 TN 0.596 0.49 0.543 0.053 9.672 TP 0.027 0.008 0.017 0.008 47.303 NO 3 -N 2.46 0.868 1.294 0.778 60.126 Cl − 2.001 0.885 1.477 0.545 36.965 SO 4 2− 6.594 2.465 4.034 1.973 48.922 pH 7.790 7.450 7.630 0.175 2.295 Ec 349 64 158.50 133.425 84.180 FW area NH 3 -N 0.023 0.013 0.017 0.005 29.605 TN 0.850 0.394 0.648 0.232 35.864 TP 0.023 0.006 0.014 0.008 63.085 NO 3 -N 2.028 0.816 1.333 0.625 46.887 Cl − 3.803 1.481 2.543 1.173 46.141 SO 4 2− 7.026 2.266 3.879 2.726 70.267 pH 7.920 7.210 7.48 0.384 5.138 Ec 355 48.40 159.2 170.058 106.820 PR area NH 3 -N 0.039 0.028 0.033 0.008 23.218 TN 0.775 0.672 0.724 0.073 10.067 TP 0.033 0.023 0.028 0.007 25.254 NO 3 -N 2.735 0.405 1.570 1.648 104.940 Cl − 408.369 4.378 206.374 285.664 138.421 SO 4 2− 8.233 2.135 5.184 4.312 83.178 pH 7.790 7.290 7.540 0.354 4.6989 Ec 1873 166.8 1019.9 1206.466 118.293 3.2 MPs distribution characteristics 3.2.1 Abundance of MPs MPs are detected in all the sewage waters, and their abundance is presented in Fig. 2 . The range of MPs abundance is 6 (± 1)–47 (± 3) items/L, and the distribution of MPs shows significant spatial differences because of the various hydraulic conditions and human activities. The abundance of MPs in the GR area increases at first and then decreases from upstream to downstream. In this area, the upstream water flow is fast, the water retention time is short, and MPs are easily washed downstream, so the abundance of MPs in the upstream is low. The decrease in downstream abundance may be related to the sedimentation of MPs because as the current velocity decreases, the larger MPs’ particles are more likely to settle in the water, leading to a decrease in downstream abundance (Kukkola et al., 2023 ). The highest abundance of MPs in this area is located in S7 (47 ± 3 items/L), which is collected at the sewage outlet of a sewage treatment plant. It is reported that although sewage treatment plants can usually achieve a removal rate of up to 90% for MPs, the number of MPs in the influent is still large, and the number of MPs in the discharged wastewater is considerable (Sun et al., 2019 ; Liu et al., 2021 ). According to previous studies, MPs are more abundant in densely populated areas with more frequent human activities (Shi et al., 2022 ). The highest abundance of MPs in the FW area is located in S13 (27 ± 2 items/L), and the highest abundance of MPs in the LC area is located in S15 (40 ± 3 items/L). This may be due to the fact that the highest abundance of MPs in these two areas occurs in the center of the town, where transportation and daily life may generate and discharge more plastic waste; human activities may largely contribute to the high abundance of MPs (Abbasi et al., 2019 ; Li et al., 2023 ). 3.2.2 Morphology of MPs Figure 3 shows the two-shape characteristics of MPs in sewage samples obtained by visualized analysis. It can be seen that fibers and films are widely observed in the sewage water samples. Specifically, fiber is the most universal shape type of MPs in this sewage water, and its appearance is slender and long. Many debris residues are attached to the fiber surface, and a few filaments are observed on the plastic surface, meaning that the pollutants are adsorbed on the rough surface of the fiber, which may be attributed to the long-term oxidation of plastic particles in the aqueous solutions (Guo et al., 2018 ; Ding et al., 2022 ). There are no foam and particle-shaped MPs in sewage, mainly because those particles are mainly present in beach sediments instead of surface water or near-shore sediments. Similar results are also reported by Faure et al. ( 2012 ). Besides, fibers are the primary shape species in the sewage water samples, ranging 95% of all the MPs. This result is similar to the morphology distribution of MPs in most domestic sewage treatment plants in China (Mintenig et al., 2017 ; Li et al., 2018 ). This may be largely ascribed to laundry wastewater containing fibers (Corami et al., 2020 ). Meanwhile, atmospheric sedimentation, overland runoff, and fishing tools are also potential sources of fiber plastics (Imbulana et al., 2024 ; Miao et al., 2023 ). Many studies have reported that fiber is the predominant shape of MPs in river ecosystems since some fiber can enter the water body via various sources. For example, the clothes washing process in daily life can produce a large number of MPs, leading to the content ranging from 124 mg/kg to 308 mg/kg discharged into the river through the municipal sewage systems (De Falco et al., 2019 ). Notable, since the outbreak of COVID-19 in 2019, the production and use of personal protective equipment, mainly including disposable masks, have increased dramatically, and these disposable masks are inevitably being exposed to the environment. It is estimated that around 129 billion pieces of used masks worldwide each month in 2020 are discharged into the ocean, and mask waste could contribute 76–276 items/L MPs after exposure to the aquatic environment, leading to the emergence of an increasing number of fiber MPs ( Liu et al., 2022 ). Films of MPs accounted for 5%, and their potential sources are sanitary sewage containing plastic membranous and agricultural mulching membranous in the study area. Besides, the development of industrialization also increases the use of industrial film, which may be another potential source (Chen H et al., 2022 ; Xiong et al., 2018 ). 3.2.3 Sizes of MPs To further evaluate the size distribution of MPs in the surface water, the MPs of 20 sewage water samples in the study area are classified into four size classes, including class Ⅰ (< 0.3mm), class Ⅱ (0.3–0.5 mm), class Ⅲ (0.5–1.0 mm), and class Ⅳ (1.0–5.0 mm), and the results is shown in Fig. 4 . It shows that the MPs account for the largest proportion in class Ⅰ (< 0.3 mm), ranging from 65.8–67.8%, and class Ⅱ (0.3–0.5 mm) accounts for 18.9–20.9%. This is similar to the size distribution of MPs reported in the South Sea and the Pearl River in China (Cai et al., 2022 ; Yan et al., 2019 ). For the other classes, the size proportion of MPs decreases as the plastic particle size increases. The overall plastic size proportion is consistent with that reported in the Dongting and Hong Lakes in China (Wang et al., 2018 ). A great number of MPs with smaller dimensions may be due to the larger plastic waste being decomposed into small particles. When plastic wastes enter the aquatic system, they can interact with biotic and abiotic (ultraviolet irradiation and weathering) factors, and the molecular structure of MPs can be destroyed, thus degrading large plastics into small-sized ones (Chubarenko et al., 2020 ). Considering that fiber is the major shape of MPs in the sewage water samples, meaning that plastic wastes may undergo intense weathered and then be transported into rivers with surface runoff and further experience strong sand erosion in the river. 3.3 Relationship analysis 3.3.1 Relationship of MPs abundance with hydrochemical parameters Correlation analysis (CA) is a multivariate statistical method that measures the close correlation between different variable factors by analyzing two or more related variable elements (Sedgwick, 2012 ). It can initially estimate the degree of correlation between multiple indicators (Kothari et al., 2021 ). In this study, a correlation matrix is computed to evaluate the degree of a linear association between any two of the parameters, and the degree of correlation is presented as a coefficient ( R ) as follows (Asuero et al., 2006 ): where x and y represent variables, ‾ x represents the arithmetic mean of the variable x , ‾ y represents the arithmetic mean of the variable, and n represents the number of observations. The value of R commonly ranged from + 1 to -1. If the value of R is + 1, there is the strongest positive linear correlation between the two parameters compared. The closer of R -value to + 1, the more positive the relationship is. If the value of R is -1, it reveals the strongest negative linear correlation. Besides, the larger absolute R -value usually means the stronger the correlation between different elements and the higher the possibility of homology (Li et al., 2020 ). As shown in Fig. 5 , the abundance of MPs shows a moderately positive correlation with NO 3 -N with an R- value of 0.404, shows weakly positively correlated with TN, TP, SO 4 2− , and Na with R values of 0.220, 0.245, 0.287, and 0.202, respectively, and shows weakly correlated with NH 3 -N, pH, As, and Mn, with R values of 0.177, 0.071, 0.133, and 0.010, respectively. Several studies have shown that the correlation between MPs’ abundance and certain water chemical parameters may be weak. This is because MPs’ abundance is affected by multiple factors, including chemical parameters. In domestic sewage, due to the complex pollution sources, the abundance of MPs may be more affected by the source (such as sewage treatment system, precipitation, runoff) (Mahon et al., 2017 ) and environmental conditions (such as hydrodynamics and sedimentation) (Radford et al., 2024 ) so the correlation with a single hydrochemical parameter is often not significant. Compared with other parameters, there is a strong correlation between the abundance of MPs and NO 3 -N in this study, indicating that MPs may participate in nitrification or denitrification in some form (Li et al., 2022 ; Parrish et al., 2019). Different from these hydrochemical parameters, metal ions show a weak correlation with the abundance of MPs, which is consistent with the finding of Nguyen et al. ( 2023 ). One possible explanation for the weak correlation is that the MPs in the study area are relatively pristine, meaning that they have undergone little weathering or degradation processes that could enhance their ability to absorb metals (Holmes et al., 2014 ; Wang et al., 2020 ). Similar to the study reported by Somasundaram et al. (2023), no parameter is found to be significantly correlated with MPs’ abundance through the Pearson correlation test. Besides, the distribution of MPs varies upstream and downstream of rivers and in urban areas due to multiple sources and the influence of the surrounding environment (Conowall et al., 2023 ; Janakiram et al., 2023 ; Jin et al., 2023 ). The diversity of urban ecosystems poses unique challenges to understanding microplastic migration (Yan et al., 2021 ). Identifying key factors affecting the abundance of MPs is complex because they are diverse and interrelated. 3.3.2 Relationship of MPs size and morphology with hydrochemical parameters Although some evidence suggests a link between MPs and water quality data, Kataoka et al. ( 2019 ) have indicated that, given the temporal and spatial variability of the river, the linear associations between water quality parameters and MPs abundance may be problematic. Therefore, redundancy analysis (RDA) is used to further demonstrate the percentage contribution of the associations between water quality parameters and MPs’ abundance. RDA is another tool for assessing correlations across multiple variable sets, allowing an in-depth understanding of multi-variable correlations by leveraging redundant information (Wang et al., 2018 ). RDA is a direct extension of multiple regression and can model the effect of an explanatory matrix M ( n × p ) on a response matrix N ( n × m ) (Legendre et al., 2011 ). It can be conducted by the following two steps: firstly, multiple regression is conducted through the linear Eq. ( 2 ): where each object in N is regressed on the explanatory variables in M , resulting in a matrix of fitted values N fit . Secondly, a principal components analysis (PCA) is applied to the fitted matrix N fit to reduce dimensionality. Obtaining a matrix Z containing the canonical axes corresponding to the linear combinations of the explanatory variables in the space of M . The linearity of the combinations of the M variables is a fundamental property of RDA. Once RDA has been calculated, additional statistics can be calculated to interpret the explanatory power of the included variables and to assess the significance of the observed relationships. These statistics include the P-statistic, contribution (%), and pseudo-F, etc. Among them, the P-statistic corresponds to an overall test of the significance of an RDA by comparing the computed model to a null model. It can also be used to sequentially test the significance of each canonical axis. Contribution (%) indicates the contribution value of the parameter. Pseudo-F is used to ensure that the test has good horizontal accuracy. In this study, RDA is used to determine the relationship between the hydrochemical parameters, metal ions, and the size as well as morphology of MPs. The contribution rate of each variable to the environment can be maintained independently through RDA, and the statistical characteristics of a single variable can still be described in the case of different combinations of variables. The environmental explanatory variables are hydrochemical indicators (Ec, pH, TN, TP, NO 3 -N, NH 3 -N, Cl − , and SO 4 2− ) and metal ions (K, Na, Ca, etc.), which are represented by blue arrows. The response variable is the size and morphology of MPs in the sewage water, indicated by the red arrow. The length of the arrow in the figure represents the proportion of the explanatory variable. Figure 6 a shows that the first two RDA axes of the hydrochemical indicators, metal ions, and the size of MPs explain 49.52% and 73.82% of the total variance, respectively. It shows that for MPs with sizes of < 0.3 mm and 0.3–0.5 mm, only NH 3 -N, TN, TP, and Na are positively correlated; this correlation may be due to the similar sources of MPs and nutrients (e.g., wastewater and non-point source pollution) (Xu et al., 2023 ). For MPs with a size of 0.5-1.0 mm, there are positive correlations with pH, SO 4 2− , Al, Ca, Mg, and Cu. MPs of this size may be more stable in an alkaline and mineral-rich environment. This indicates that they may be more likely to accumulate in water with high ion content and more easily combined or attached to positively charged minerals (such as Ca, Mg, and Al) (Ta et al., 2020). MPs with a size of 0.5-1.0 mm have negative correlations with NH 3 -N, TP, and TN. This may mean that these smaller MPs are less distributed in eutrophic water bodies and may be more easily decomposed in such environments. For MPs with a size of 1.0–5.0 mm, there is a positive correlation with metal ions and anions, which indicates that these larger-size MPs tend to exist in an environment with higher salt content and heavy pollution. This may be because the larger-size MPs usually make it easier to adsorb these ions because of their larger surface area and mass, and they are more difficult to be transported by the current, so they are more likely to be deposited at the bottom of the water, especially in areas with high content of heavy metal ions (Gao et al., 2019 ). A negative correlation with TP, TN, NH 3 -N, and NO 3 -N may indicate that they exist in a small proportion of domestic wastewater. This phenomenon may be related to the fact that larger MPs’ particles are easier to settle in these environments and more difficult to suspend in eutrophic water. Figure 6 b shows that the first two RDA axes of the hydrochemical indicators, metal ions, and the morphology of MPs explain 74.21% and 100% of the total variance, respectively. It can be seen that the fibers are positively correlated with NO 3 -N, TP, TN, Na, Mn, As, and SO 4 2− . The positive correlation between fiber and nutrients such as NO 3 -N, TP, and TN indicates that MPs are more likely to accumulate in eutrophic water bodies. This may harm eutrophic water bodies, exacerbating algae growth and water ecological imbalance. The longer arrow line of NO 3 -N indicates that this parameter has a greater influence on fiber distribution, while the slightly shorter arrow line of TP and TN has a slightly lesser influence. The strong correlations observed between various forms of nitrogen and MPs indicate a potential disturbance process associated with nitrogen recurrence (Li et al., 2022 ). The positive correlation between fiber and metal ions such as As and Mn indicates that these MPs may be more easily captured or deposited in heavily metal-polluted areas. This also suggests that fiber may act as a carrier for heavy metals, increasing the migration capacity and bioavailability of these harmful metals in water bodies, thereby posing a threat to aquatic ecosystems. The positive correlation between fiber and pollutants such as nitrogen, phosphorus, and metal ions suggests that they may act as carriers of other pollutants in the environment, facilitating their wider distribution in water bodies. MPs can adsorb these pollutants, allowing them to enter organisms through the food chain, thereby posing potential hazards to the ecosystem. The negative correlation between fiber, K, Cl − , and Ec indicates that the concentration of fiber in high-salinity sewage water is low. It means that fibers are more susceptible to degradation, sedimentation, or reactions with other substances in environments with high salinity where fibrous MPs may decompose faster and are less likely to be retained and diffused. Besides, the films are positively correlated with Al, Zn, Ca, Cu, and Cd. This shows that the sources of these elements are related to the production and use of plastics, and these metals are used as additives or catalysts in some plastic production processes (Turner et al., 2021; Godoy et al., 2019 ). The existence of these metal elements within the ecosystem may influence the sedimentation, distribution, and biodegradability of MPs and potentially result in the enrichment of these metals by some organisms. The films are negatively correlated with Pb, Fe, and Mg, indicating that the deposition and sources of these metals are significantly different from those of MPs. This may also reflect the physiochemical characteristics of thin film MPs under specific environmental conditions. Anon ( 2019 ) has shown that under various pH values and ionic strengths, MPs may have different binding abilities with some metal ions, resulting in different correlations. The positive and negative correlation between MPs, hydrochemical parameters, and metal ions not only elucidates MPs' behavior and characteristics in the environment but also offers insights into the intricate dynamics of environmental contamination. This analysis facilitates the identification of MPs' and their pollutants' impact on the ecosystem, thereby providing a reference point for further research and environmental protection. 3.4 Environmental implications The contamination of aquatic environments by MPs represents a significant global environmental concern. In this study, multivariate statistical analyses are employed to explore potential correlations between MPs and hydrochemical parameters (including conventional anions and cations, metal ions, etc.) in sewage water. The results of the multivariate statistical analyses indicate that there is a weak correlation between the abundance of MPs and the hydrochemical parameters. However, there is a stronger correlation with sewage water when different compositions of MPs are considered, such as size and morphology. In particular, the films and MPs with larger particle sizes (1.0–5.0 mm) exhibit higher correlations with metal ions in the sewage water. To further explore the potential risks of hydrochemical parameters and metal ions on the distribution of MPs, future studies should expand the scope of experiments, consider the impact of factors such as the color, shape, and different particle sizes of MPs on the water environment, and consider more water indicators, such as rainy season, dry season, and water turbidity, etc. 4. Conclusion The findings of this research show that microplastic pollution is found at domestic sewage outlets in Guilin of China. The range of microplastic abundance is 6(± 1)–47(± 3) items/L. The class of < 0.3 mm dominates the size of the MPs. The results of CA reveal that the abundance of MPs is weakly correlated with hydrochemical parameters and metal ions. The possible reason is that the MPs are relatively pristine in the study area, meaning that they have undergone little weathering or degradation processes that enhance their ability to absorb metals. The results of the RDA indicate that there are correlations between MPs of different morphology and sizes, and the hydrochemical parameters and metal ions. NO 3 -N has a greater influence on the distribution of microplastic fiber; Zn, Ca, and Cu have a greater influence on the distribution of microplastic film. For MPs with sizes of < 0.3 mm and 0.3–0.5 mm, NH 3 -N, TN, TP, and Na are positively correlated; there are positive correlations with pH, SO 4 2− , Al, Ca, Mg, and Cu. MPs of this size may be more stable in an alkaline and mineral-rich environment; for MPs with a size of 1.0–5.0 mm, there is a positive correlation with metal ions and anions, it indicates that these larger MPs particles tend to exist in the environment with higher salt content and heavy pollution. In general, the correlation between hydrochemistry parameters and metal ions and MPs’ abundance is weak. However, there are stronger correlations between these parameters and MPs' size and morphology. These findings indicate that water quality parameters may influence the nature of MPs, underscoring the necessity for comprehensive analyses of these variables to comprehend their interactions with the aquatic environment. Declarations Author Contribution MY L wrote the main manuscript text.HM S supervised the project and acquired funding for the study.YQ L developed the research concept and methodology.YX S performed data analysis and contributed to validation and formal analysis.HB Z conducted investigation experiments.SX P conducted a review. Acknowledgment: This research is funded by the National Natural Science Foundation of China (42167026), the Natural Science Foundation of Guangxi (2022GXNSFBA035600), and the Guilin University of Technology Program (GLUTQD 2016047). References Anon, 2019. Interactions between microplastics and phthalate esters as affected by microplastics characteristics and solution chemistry[J]. Chemosphere , 214: 688-694. Abbasi S, Keshavarzi B, Moore F, et al., 2019. Distribution and potential health impacts of microplastics and microrubbers in air and street dusts from Asaluyeh County, Iran[J]. Environmental Pollution , 244: 153-164. Adomat Y, Grischek T, 2021. Sampling and processing methods of microplastics in river sediments - A review[J]. Science of The Total Environment , 758: 143691. Aryani D, Khalifa M A, Herjayanto M, et al., 2021. Correlation of Water Quality with Microplastic Exposure Prevalence in Tilapia (Oreochromis niloticus)[J]. Asuero A G, Sayago A, González A G, 2006. The Correlation Coefficient: An Overview[J]. Critical Reviews in Analytical Chemistry , 36(1): 41-59. Atugoda T, Vithanage M, Wijesekara H, et al., 2021. Interactions between microplastics, pharmaceuticals and personal care products: Implications for vector transport[J]. Environment International , 149: 106367. Bayo J, Olmos S, López-Castellanos J, 2020. Microplastics in an urban wastewater treatment plant: The influence of physicochemical parameters and environmental factors[J]. Chemosphere , 238: 124593. Buwono N R, Risjani Y, Soegianto A, 2021. Distribution of microplastic in relation to water quality parameters in the Brantas River, East Java, Indonesia[J]. Environmental Technology & Innovation , 24: 101915. Cai M, Liu M, Qi H, et al., 2022. Transport of microplastics in the South China Sea: A review[J]. Gondwana Research , 108: 49-59. Chen H, Chen Y, Xu Y, et al., 2022. Different functional areas and human activities significantly affect the occurrence and characteristics of microplastics in soils of the Xi’an metropolitan area[J]. Science of The Total Environment , 852: 158581. Chen W, Zhang Z, Zhu Y, et al., 2022. Distribution, sources and transport of polycyclic aromatic hydrocarbons (PAHs) in karst spring systems from Western Hubei, Central China[J]. Chemosphere , 300: 134502. Chubarenko I, Efimova I, Bagaeva M, et al., 2020. On mechanical fragmentation of single-use plastics in the sea swash zone with different types of bottom sediments: Insights from laboratory experiments[J]. Marine Pollution Bulletin , 150: 110726. Conowall P, Schreiner K M, Minor E C, et al., 2023. Variability of microplastic loading and retention in four inland lakes in Minnesota, USA[J]. Environmental Pollution , 328: 121573. Corami F, Rosso B, Bravo B, et al., 2020. A novel method for purification, quantitative analysis and characterization of microplastic fibers using Micro-FTIR[J]. Chemosphere , 238: 124564. Dailey K R, Welch K A, Lyons W B, 2014. Evaluating the influence of road salt on water quality of Ohio rivers over time[J]. Applied Geochemistry , 47: 25-35. De Falco F, Di Pace E, Cocca M, et al., 2019. The contribution of washing processes of synthetic clothes to microplastic pollution[J]. Scientific Reports , 9(1): 6633. Ding R, Ouyang F, Peng D, et al., 2022. A case study of distribution and characteristics of microplastics in surface water and sediments of the seas around Shenzhen, southern coastal area of China[J]. Science of The Total Environment , 838: 156063. Fadare O O, Okoffo E D, 2020. Covid-19 face masks: A potential source of microplastic fibers in the environment[J]. Science of the Total Environment , 737: 140279. Faure F, Corbaz M, Baecher H, et al., 2012. Pollution due to plastics and microplastics in Lake Geneva and in the Mediterranean Sea[J]. Archives Des Sciences . Feng S, Lu H, Yao T, et al., 2021. Microplastic footprints in the Qinghai-Tibet Plateau and their implications to the Yangtze River Basin[J]. Journal of Hazardous Materials , 407: 124776. Galvão A, Aleixo M, De Pablo H, et al., 2020. Microplastics in wastewater: microfiber emissions from common household laundry[J]. Environmental Science and Pollution Research , 27(21): 26643-26649. Gao F, Li J, Sun C, et al., 2019. Study on the capability and characteristics of heavy metals enriched on microplastics in marine environment[J]. Marine Pollution Bulletin , 144: 61-67. Godoy V, Blázquez G, Calero M, et al., 2019. The potential of microplastics as carriers of metals[J]. Environmental Pollution , 255: 113363. Gong X, Tian L, Wang P, et al., 2023. Microplastic pollution in the groundwater under a bedrock island in the South China sea[J]. Environmental Research , 239: 117277. Guo X, Yin Y, Yang C, et al., 2018. Maize straw decorated with sulfide for tylosin removal from the water[J]. Ecotoxicology and Environmental Safety , 152: 16-23. Hasenmueller E A, Baraza T, Hernandez N F, et al., 2023. Cave sediment sequesters anthropogenic microparticles (including microplastics and modified cellulose) in subsurface environments[J]. Science of The Total Environment , 893: 164690. Hidalgo-Ruz V, Gutow L, Thompson R C, et al., 2012. Microplastics in the Marine Environment: A Review of the Methods Used for Identification and Quantification[J]. Environmental Science & Technology , 46(6): 3060-3075. Holmes L A, Turner A, Thompson R C, 2014. Interactions between trace metals and plastic production pellets under estuarine conditions[J]. Marine Chemistry , 167: 25-32. Hu Y, Cheng H, Tao S, 2017. Environmental and human health challenges of industrial livestock and poultry farming in China and their mitigation[J]. Environment International , 107: 111-130. Imbulana S, Tanaka S, Oluwoye I, 2024. Quantifying annual microplastic emissions of an urban catchment: Surface runoff vs wastewater sources[J]. Journal of Environmental Management , 360: 121123. Isobe A, Uchida K, Tokai T, et al., 2015. East Asian seas: A hot spot of pelagic microplastics[J]. Marine Pollution Bulletin , 101(2): 618-623. Janakiram R, Keerthivasan R, Janani R, et al., 2023. Seasonal distribution of microplastics in surface waters of the Northern Indian Ocean[J]. Marine Pollution Bulletin , 190: 114838. Jin X, Fu X, Lu W, et al., 2023. The effects of riverside cities on microplastics in river water: A case study on the Southern Jiangsu Canal, China[J]. Science of The Total Environment , 858: 159783. Kataoka T, Nihei Y, Kudou K, et al., 2019. Assessment of the sources and inflow processes of microplastics in the river environments of Japan[J]. Environmental Pollution , 244: 958-965. Koelmans A A, Mohamed Nor N H, Hermsen E, et al., 2019. Microplastics in freshwaters and drinking water: Critical review and assessment of data quality[J]. Water Research , 155: 410-422. Kothari V, Vij S, Sharma S, et al., 2021. Correlation of various water quality parameters and water quality index of districts of Uttarakhand[J]. Environmental and Sustainability Indicators , 9: 100093. Kukkola A, Runkel R L, Schneidewind U, et al., 2023. Prevailing impacts of river management on microplastic transport in contrasting US streams: Rethinking global microplastic flux estimations[J]. Water Research , 240: 120112. Legendre P, Oksanen J, ter Braak C J F, 2011. Testing the significance of canonical axes in redundancy analysis[J]. Methods in Ecology and Evolution , 2(3): 269-277. Li L, Li F, Deng M, et al., 2022. Microplastics distribution characteristics in typical inflow rivers of Taihu lake: Linking to nitrous oxide emission and microbial analysis[J]. Water Research , 225: 119117. Li Q, Yuan M, Chen Y, et al., 2023. The neglected potential source of microplastics from daily necessities: A study on protective mobile phone cases[J]. Journal of Hazardous Materials , 441: 129911. Li S, Yang M, Wang H, et al., 2022. Adsorption of microplastics on aquifer media: Effects of the action time, initial concentration, ionic strength, ionic types and dissolved organic matter[J]. Environmental Pollution , 308: 119482. Li X, Chen L, Mei Q, et al., 2018. Microplastics in sewage sludge from the wastewater treatment plants in China[J]. Water Research , 142: 75-85. Li X, Han X, Vogt R D, et al., 2022. Polyethylene terephthalate and di-(2-ethylhexyl) phthalate in surface and core sediments of Bohai Bay, China: Occurrence and ecological risk[J]. Chemosphere , 286: 131904. Li Y, Chen H, Teng Y, 2020. Source apportionment and source-oriented risk assessment of heavy metals in the sediments of an urban river-lake system[J]. Science of The Total Environment , 737: 140310. Liu K, Wang X, Fang T, et al., 2019. Source and potential risk assessment of suspended atmospheric microplastics in Shanghai[J]. Science of The Total Environment , 675: 462-471. Liu Q, Wu H, Chen J, et al., 2022. Adsorption mechanism of trace heavy metals on microplastics and simulating their effect on microalgae in river[J]. Environmental Research , 214: 113777. Liu W, Zhang J, Liu H, et al., 2021. A review of the removal of microplastics in global wastewater treatment plants: Characteristics and mechanisms[J]. Environment International , 146: 106277. Liu Z, Wang J, Yang X, et al., 2022. Generation of environmental persistent free radicals (EPFRs) enhances ecotoxicological effects of the disposable face mask waste with the COVID-19 pandemic[J]. Environmental Pollution , 301: 119019. Mahon A M, O’Connell B, Healy M G, et al., 2017. Microplastics in Sewage Sludge: Effects of Treatment[J]. Environmental Science & Technology , 51(2): 810-818. Masura J, Baker J, Foster G, et al., 2015. Laboratory Methods for the Analysis of Microplastics in the Marine Environment[J]. NOAA Technical Memorandum NOS-OR&R-48. Miao C, Zhang J, Jin R, et al., 2023. Microplastics in aquaculture systems: Occurrence, ecological threats and control strategies[J]. Chemosphere , 340: 139924. Mintenig S M, Int-Veen I, Löder M G J, et al., 2017. Identification of microplastic in effluents of waste water treatment plants using focal plane array-based micro-Fourier-transform infrared imaging[J]. Water Research , 108: 365-372. Mohamed Nor N H, Obbard J P, 2014. Microplastics in Singapore’s coastal mangrove ecosystems[J]. Marine Pollution Bulletin , 79(1): 278-283. Nguyen T L H, Duong T L, Nguyen T H T, et al., 2023. Microplastics and trace metals in river sediment: Prevalence and correlation with multiple factors[J]. Science of The Total Environment , 895: 165145. Parrish K, L. Fahrenfeld N, 2019. Microplastic biofilm in fresh- and wastewater as a function of microparticle type and size class[J]. Environmental Science: Water Research & Technology , 5(3): 495-505. Peterse I F, Hendriks L, Weideveld S T J, et al., 2024. Wastewater-effluent discharge and incomplete denitrification drive riverine CO 2 , CH 4 and N 2 O emissions[J]. Science of The Total Environment , 951: 175797. Radford F, Horton A A, Felgate S, et al., 2024. Factors influencing microplastic abundances in the sediments of a seagrass-dominated tropical atoll[J]. Environmental Pollution , 357: 124483. Scheurer M, Bigalke M, 2018. Microplastics in Swiss Floodplain Soils[J]. Environmental Science & Technology . Sedgwick P, 2012. Pearson’s correlation coefficient[J]. BMJ , 345. Sefie A, Aris A Z, Ramli M F, et al., 2018. Hydrogeochemistry and groundwater quality assessment of the multilayered aquifer in Lower Kelantan Basin, Kelantan, Malaysia[J]. Environmental Earth Sciences , 77(10): 397. Shi H, Yu D, Yin L, et al., 2022. Source-sink process of microplastics in watershed-estuary-offshore system[J]. Journal of Cleaner Production , 338: 130612. Shu X, Xu L, Yang M, et al., 2023. Spatial distribution characteristics and migration of microplastics in surface water, groundwater and sediment in karst areas: The case of Yulong River in Guilin, Southwest China[J]. Science of The Total Environment , 868: 161578. Somasundaram R, Radhakrishnan N, 2023. Establishment the relationship between water quality parameter and micro plastic concentration for Adyar and Cooum estuary[J]. Environmental Quality Management , 33(1): 121-133. Sun J, Dai X, Wang Q, et al., 2019. Microplastics in wastewater treatment plants: Detection, occurrence and removal[J]. Water Research , 152: 21-37. Sun J, Peng Z, Zhu Z R, et al., 2022. The atmospheric microplastics deposition contributes to microplastic pollution in urban waters[J]. Water Research , 225: 119116. Ta A T, Babel S, 2020. Microplastic contamination on the lower Chao Phraya: Abundance, characteristic and interaction with heavy metals[J]. Chemosphere , 257: 127234. Thompson R C, Olsen Y, Mitchell R P, et al., 2004. Lost at Sea: Where Is All the Plastic?[J]. Science , 304(5672): 838-838. Turner A, Filella M, 2021. Hazardous metal additives in plastics and their environmental impacts[J]. Environment International, 156: 106622. Van Emmerik T, Schwarz A, 2020. Plastic debris in rivers[J]. WIREs Water , 7(1): e1398. Wang L, Liu M, Tao W, et al., 2018. Pollution characteristics and health risk assessment of phthalate esters in urban soil in the typical semi-arid city of Xi’an, Northwest China[J]. Chemosphere , 191: 467-476. Wang Q, Zhang Y, Wangjin X, et al., 2020. The adsorption behavior of metals in aqueous solution by microplastics effected by UV radiation[J]. Journal of Environmental Sciences , 87: 272-280. Wang W, Ndungu A W, Li Z, et al., 2017. Microplastics pollution in inland freshwaters of China: A case study in urban surface waters of Wuhan, China[J]. Science of The Total Environment , 575: 1369-1374. Wang W, Yuan W, Chen Y, et al., 2018. Microplastics in surface waters of Dongting Lake and Hong Lake, China[J]. Science of The Total Environment , 633: 539-545. Wen X, Du C, Xu P, et al., 2018. Microplastic pollution in surface sediments of urban water areas in Changsha, China: Abundance, composition, surface textures[J]. Marine Pollution Bulletin , 136: 414-423. Xie Y, Liu Z, Gou X, et al., 2018. Analysis on quality of domestic sewage and investigation of villages ’willingness from the rural areas of southwest China[J]. Environmental Engineering , 36(08): 165-169+188. Xiong X, Zhang K, Chen X, et al., 2018. Sources and distribution of microplastics in China’s largest inland lake – Qinghai Lake[J]. Environmental Pollution , 235: 899-906. Xu H, Nakano H, Tokai T, et al., 2022. Contamination of sea surface water offshore the Tokai region and Tokyo Bay in Japan by small microplastics[J]. Marine Pollution Bulletin , 185: 114245. Xu N, Jiang L, Zhang Y, et al., 2023. Microplastic pollution in the offshore sea, rivers and wastewater treatment plants in Jiangsu coastal area in China[J]. Marine Environmental Research , 188: 105992. Yan M, Nie H, Xu K, et al., 2019. Microplastic abundance, distribution and composition in the Pearl River along Guangzhou city and Pearl River estuary, China[J]. Chemosphere , 217: 879-886. Yan Z, Chen Y, Bao X, et al., 2021. Microplastic pollution in an urbanized river affected by water diversion: Combining with active biomonitoring[J]. Journal of Hazardous Materials , 417: 126058. Zhang L, Liu J, Xie Y, et al., 2021a. Occurrence and removal of microplastics from wastewater treatment plants in a typical tourist city in China[J]. Journal of Cleaner Production , 291: 125968. Zhang L, Xie Y, Zhong S, et al., 2021b. Microplastics in freshwater and wild fishes from Lijiang River in Guangxi, Southwest China[J]. Science of The Total Environment , 755: 142428. Zhao K, Zhou S, Wang K, et al., 2024. Distribution characteristics and pollution risk assessment of microplastics in urban rivers: A case study in Yitong River, China[J]. J ournal of Water Process Engineering , 61: 105277. Zhou B, Wang J, Zhang H, et al., 2020. Microplastics in agricultural soils on the coastal plain of Hangzhou Bay, east China: Multiple sources other than plastic mulching film[J]. Journal of Hazardous Materials , 388: 121814. Zhu J, Zhang Q, Huang Y, et al., 2021. Long-term trends of microplastics in seawater and farmed oysters in the Maowei Sea, China[J]. Environmental Pollution , 273: 116450. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5460558","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":384665368,"identity":"3ddabd9c-4010-4849-811d-f691894a80e5","order_by":0,"name":"Meiyuan Lu","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Meiyuan","middleName":"","lastName":"Lu","suffix":""},{"id":384665370,"identity":"fc30f5cd-03e1-48ce-a645-16bf4ba7c9bd","order_by":1,"name":"Huimei Shan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACCQYGZgjJfIDhAVgogWgtbAlAxQZEawEBHgPitMjPbn72uLDNIpqfnefzi8S2Pwz87DkGDD934NZicOeYufHMNoncmc282ywS2wwYJHveGDD2nsGjRSLBTJoXqGXDYd5tBiAtBjdyDJgZ2/A4bEb6N7CW/Yd5noG12BPSwnAjB2oLMw/zA7AtEgS0AJ1RJs1zTiJ3xmE2M4aEc8Y8EmeeFRzsxe+wbdI8ZXW5/f2HH3/4UCYnx9+evPHBT3wOQwJswDhi4AGxDhCnARilH4hVOQpGwSgYBSMLAAD2A0rmbwIljAAAAABJRU5ErkJggg==","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Huimei","middleName":"","lastName":"Shan","suffix":""},{"id":384665373,"identity":"2e69cd98-01ca-4510-846e-7e5fe7cf4e42","order_by":2,"name":"Hongbin Zhan","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Hongbin","middleName":"","lastName":"Zhan","suffix":""},{"id":384665375,"identity":"d99f3241-273f-402f-95ec-e008b2f0fbcc","order_by":3,"name":"Yuxin Shi","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Shi","suffix":""},{"id":384665376,"identity":"d59cf179-5e2f-4dd6-b193-174b379c91e8","order_by":4,"name":"Yunquan Liu","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yunquan","middleName":"","lastName":"Liu","suffix":""},{"id":384665377,"identity":"345a72be-4728-496e-98f2-67f8d5a724a7","order_by":5,"name":"Sanxi Peng","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sanxi","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2024-11-15 12:38:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5460558/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5460558/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71938354,"identity":"6532171b-034c-4afa-a01b-5c5e39282e5c","added_by":"auto","created_at":"2024-12-20 00:52:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":806075,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and sampling locations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5460558/v1/b30f58ec52a7bf414947cf29.png"},{"id":71938350,"identity":"664e40f5-16d3-4828-bbe3-089f870fb5a5","added_by":"auto","created_at":"2024-12-20 00:52:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87386,"visible":true,"origin":"","legend":"\u003cp\u003eAbundance distribution of MPs in the domestic sewage water samples.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5460558/v1/18bddc77c1fdff55f60f912c.png"},{"id":71939462,"identity":"574b8210-fb73-4651-ab8d-da5edc9025c6","added_by":"auto","created_at":"2024-12-20 01:08:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":618442,"visible":true,"origin":"","legend":"\u003cp\u003ePhotographs of MPs items, where a, b, and c are identified as fiber, and d and f are identified as film.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5460558/v1/af75ffd8efcdf3d2dabce2f8.png"},{"id":71938342,"identity":"159ce127-a76c-4a08-8d7b-f6d66ce13fb0","added_by":"auto","created_at":"2024-12-20 00:52:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":694862,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of MPs morphology (a), size (b).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5460558/v1/6bf6ae49c86a0d26fae425d7.png"},{"id":71938364,"identity":"22722969-51f8-41a3-b83e-c9bdd87f4e0d","added_by":"auto","created_at":"2024-12-20 00:52:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":475616,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation matrix between MPs abundance and hydrochemical parameters.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5460558/v1/140702a6711debd630105924.png"},{"id":71938356,"identity":"9d1ec269-b300-415b-ac96-a7711eecbd2a","added_by":"auto","created_at":"2024-12-20 00:52:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":64479,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis results of (a) the sizes of MPs and (b) the shapes of MPs with hydrochemical parameters in the domestic sewage water.\u003c/p\u003e\n\u003cp\u003eNote: The length of the arrow in the \u0026nbsp;\u0026nbsp;figure represents the proportion of the explanatory variable. The angle \u0026nbsp;\u0026nbsp;between the arrows representing the variables indicates the correlation \u0026nbsp;\u0026nbsp;between the variables. When the angle is acute, it indicates a positive \u0026nbsp;\u0026nbsp;correlation between the variables, and when the angle is obtuse, it indicates \u0026nbsp;\u0026nbsp;a negative correlation between the variables. The farther the variables are, \u0026nbsp;\u0026nbsp;the weaker the correlation between the variables.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5460558/v1/a7c26f4b290b5a3c4ba683b5.png"},{"id":71940309,"identity":"7e944c5f-495b-4898-8fd2-42e8162c3ff5","added_by":"auto","created_at":"2024-12-20 01:24:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3671173,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5460558/v1/cd0f4366-9c18-4b9f-a2c1-2a2ab515b427.pdf"},{"id":71938336,"identity":"608b731d-c2e8-431f-9fd2-d2f4c859c543","added_by":"auto","created_at":"2024-12-20 00:52:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30037,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5460558/v1/85131b340bb7cc4464171265.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distribution Characteristics of Microplastics in Domestic Sewage Waters: A Case Study in Guilin City, China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMicroplastics (MPs) refer to plastic particles, fibers, or fragments with a particle size (or one-dimensional length) less than 5 mm (Thompson et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). MPs can exist in the environment for decades to centuries a long time by the characteristics of refractory degradation, small size, large specific surface area, strong hydrophobicity, etc. Moreover, it is easy to adsorb organic pollutants and heavy metals, which will migrate and spread over long distances and cause great harm to the ecosystem (Zhang et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Therefore, it is important to identify the source, transport, and fate of MPs for environmental protection and public health. MPs can easily enter the natural water environment through stormwater runoff, atmospheric sedimentation, sewage pipes, sewage treatment plant effluent, etc. MPs have been detected in various environmental media, including the atmosphere (Sun et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), domestic wastewater (Atugoda et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; De Falco et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Galv\u0026atilde;o et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), rivers (Buwono et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), lakes (Feng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003ea), Ocean (Isobe et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), sediment (Liet al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and soil (Scheurer et al., 2018; Zhou et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), etc. Among them, domestic wastewater is assumed to contain high amounts of MPs and is recognized as the major source of MPs in aquatic environments. After MPs are released from sewage pipes into rivers, rivers become the main route for MPs' transportation and further carry MPs to enter the ocean from inland areas. This will greatly threaten water species and even harm local communities that rely on these bodies of water for drinking water (Wanget al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Van Emmerik et al., 2020).\u003c/p\u003e \u003cp\u003eDue to the complexity and ever-changing characteristics of the natural environment, the occurrence and distribution of MPs are related to a variety of environmental factors (Wen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Generally, water quality conditions or solution environments are significantly influenced by the presence and levels of MPs, ions, pH, and salinity in water. For example, Liu et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that the removal of MPs in wastewater was accompanied by the reduction of pollutants such as TN, TP, and COD in wastewater. Aryani et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that high concentrations of MPs in water led to a decrease in total ammonia. Liu et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that the increase in temperature and the decrease in salinity were beneficial to improving the adsorption of heavy metals by MPs. Li et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that calcium ions and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e can promote the adsorption of MPs on aquifer media, and the adsorption of MPs on aquifer media tended to be gradually inhibited with the increase of anion (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) strength. The above research explains that the abundance and distribution of MPs are closely related to the physiochemical properties of the environment. Therefore, it is vital to analyze the water quality parameters that determine the existence of MPs in the aquatic environment. However, the hydrochemical and environmental effects of MPs have been rarely investigated in recent years (Bayo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such information is critical for future management initiatives aimed at improving river water quality and reducing the pollution of MPs.\u003c/p\u003e \u003cp\u003eAs a typical karst landform area, Guilin has a complex groundwater system and weak self-purification ability. The characteristics of karst landform make it easier for pollutants to spread rapidly through underground rivers, and the close relationship between surface water and groundwater intensifies this phenomenon (Hasenmueller et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Once the MPs enter the aquatic environment, they may quickly penetrate the groundwater system and spread in a wide area, making the pollution diffusion difficult to control (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Guilin is not only famous for its unique natural landscape, but also with the acceleration of urbanization in recent years, the rapid growth of population has caused great pressure on the domestic sewage discharge system of the city. The pollution of MPs in domestic sewage is becoming more serious in the process of urban expansion. The existing sewage treatment facilities may not be able to effectively filter and remove these tiny particles, resulting in their direct discharge into natural water bodies. Due to the direct discharge of sewage outlets, the long-term accumulation of MPs in the water body may have an irreversible negative impact on the aquatic ecosystem, which not only threatens the ecological balance but also may endanger public health. Although research on MPs\u0026rsquo; pollution in water has increased rapidly in recent years, research on Guilin, a unique karst geological environment, is still relatively scarce. At present, most researches focus on the size, morphology, and distribution characteristics of MPs (Zhang et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Shu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, investigations into the relationship between MPs in domestic wastewater and environmental factors (such as hydrochemical parameters, metal ions, etc.) are still limited. Considering the unique geological conditions and important position as a tourist city of Guilin, it is particularly urgent to investigate the environmental risk associated with MPs\u0026rsquo; pollution.\u003c/p\u003e \u003cp\u003eHerein, to better understand the existence of MPs in domestic sewage waters and the impact of hydrochemical parameters on MPs, Guilin City, China, is selected as the study area to sample sewage water and analyze the relationship between the abundance of MPs and hydrochemical parameters. Meanwhile, the impacts of hydrochemical parameters and metal ions on MPs are identified by using the Redundancy Analysis (RDA) method. This can provide theoretical guidance for future water resource management to comprehensively evaluate the overall pollution situation of water bodies, to improve water quality, and to reduce MPs in the water environment.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sampling\u003c/h2\u003e \u003cp\u003eFrom July to August 2023, a series of sewage water samples were collected from the sewage water outlets of Guilin City, and their locations are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The samples of S1-S11 are collected near Guangjiang River (GR area), S12-S14 are collected near Fuyi Water (FW area), S15-S18 are collected near Guijiang River (LS area), and S19-S20 are collected near Pingdeng River (PR area), respectively. Detailed information on water samplings is listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (Supplementary Materials). Sewage water samples are collected at a water depth of about 20 cm and are placed in 2 L plastic buckets that have been rinsed with sample water. The samples are kept in a box filled with ice until they are transported to the laboratory for analysis. At each sampling point, three parallel water samples are collected to ensure the representativeness of the water samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Hydrochemical analysis\u003c/h2\u003e \u003cp\u003eHydrochemical parameters of sewage water samples, including pH, Ec (\u0026micro;S/cm), total phosphorus (TP, mg/L), ammonia nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N, mg/L), nitrate (NO\u003csub\u003e3\u003c/sub\u003e-N, mg/L), total nitrogen (TN, mg/L), chloride (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, mg/L), sulfate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, mg/L) and metal ions (K, Na, Ca, Mg, Fe, Al, Mn, Pb, Zn, Cd, Cu, and As, mg/L) are evaluated. Specifically, the pH and Ec are measured by portable multi-parameter digital analysis (Hach-HQ30d, CO, USA), the concentrations of TN, TP, NH\u003csub\u003e3\u003c/sub\u003e-N, and NO\u003csub\u003e3\u003c/sub\u003e-N are measured using a UV spectrophotometer (UV-2355, UNICO, Shanghai), the concentrations of anions (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) are measured using Ion chromatograph (Dionex ICS-10000IC, USA). The concentration of metal ions in aqueous solutions is determined by an inductively coupled plasma optical emission spectrometer (Optima 7000DV, Platinum Elmer Instruments, Inc. Waltham, MA, USA), and the relative standard deviation is less than 5%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MPs identification\u003c/h2\u003e \u003cp\u003eBefore testing MPs, the water sample needs to be treated in the following steps. Firstly, refer to previous studies, stainless steel screens with particle sizes of 5 mm, 1 mm, and 0.3 mm are used to filter sewage samples, respectively (Masura et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Secondly, the filtrates are treated with 30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e solutions at 65\u003csup\u003eo\u003c/sup\u003eC and 80 rpm for 72 hours to digest biological materials (Hidalgo-Ruz et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Finally, the digested solutions are filtered through a vacuum pump (glass fiber filter membranes, 47 mm \u003cem\u003e\u0026Oslash;\u003c/em\u003e, 0.45 \u0026micro;m apertures), and the filter membranes are placed in clean Petri dishes. The dishes with samples are placed under a stereomicroscope (Phenix, SMZ-180, China) to observe and record the size and morphology of MPs at a magnification of 40 times.\u003c/p\u003e \u003cp\u003eTo avoid selection bias, the National Oceanic and Atmospheric Administration (NOAA) guidelines are utilized to classify and quantify the abundance of MPs in water samples. The methods of observing MPs have been standardized as shown below: (1) there is no apparent cellular or biological structure; (2) the fibers have the same thickness down and must not taper at the ends; (3) the ribbon\u0026rsquo;s fibers are not segmented and appear bent; (4) the particles are not glossy (Mohamed Nor et al., 2014). The types of MPs include plastic fragments (compact color), pellets (compact color), filaments/fibers (compact color), plastic films (transparent color), foamed plastics, granules, and Styrofoam (Hidalgo-Ruz et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Quality control and data analysis\u003c/h2\u003e \u003cp\u003eSamples are collected based on the latest quality assurance and quality control standards, and strict control measures are implemented (Adomat et al., 2021; Koelmans et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To minimize experimental errors, glass containers and stainless-steel vessels used in the water sampling, treatment, and filtration phases are rinsed with deionized water before and after use. In all the experiments, each medium is used with three blank controls. Filtered distilled water is used as a blank in the laboratory and treated according to the same procedure used for the samples (Zhu et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sampling sites are mapped using ArcGIS 10.8 (ESRI, Redlands, CA, USA). To quantify the variability of MPs as they are affected by hydrochemical parameters and metal ions, the Pearson correlation analysis is used to compare multiple groups. A significant level of 5% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) is set for all analyses. Canoco 5.0 software (Ithaca, NY, USA) is used to perform a redundancy analysis (RDA) to examine the associations among MPs, hydrochemical parameters, and metal ions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Hydrochemical characteristics\u003c/h2\u003e\n \u003cp\u003eThe hydrochemical results are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Comparing the water quality indexes of four areas (GR area, FW area, PR area, and LS area) in Guilin City, the pH of the study area is neutral and weakly alkaline; this indicates that domestic sewage does not cause acid-base pollution in the water body. This result is consistent with the study of rural domestic sewage in southwest China by Xie et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Specifically, the pH values of the GR area range from 6.59\u0026ndash;7.84 with a mean value of 7.31; the pH values of the LS area range from 7.45\u0026ndash;7.79 with a mean value of 7.63; the pH values of the FW area range from 7.21\u0026ndash;7.92 with a mean value of 7.48; and the pH values of the PR area range from 7.29\u0026ndash;7.79 with a mean value of 7.54, respectively.\u003c/p\u003e\n \u003cp\u003eThe coefficient of variation (CV) is often used to reflect the variation degree of each observed value and to analyze the dispersion degree and stability of variables. When the CV value is less than 0.1, it is a weak variation; between 0.1 and 1.0, it is a medium variation; and greater than 1.0 is a strong variation (Sefie et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The CV values of NH\u003csub\u003e3\u003c/sub\u003e-N and TP in the GR area are 1.04 and 1.06, respectively, indicating their strong variations and the sources of nitrogen and phosphorus in the water bodies of this region are unevenly distributed and may be affected by human activities. Specifically, S3 shows the maximum values of NH\u003csub\u003e3\u003c/sub\u003e-N and TP (0.495 and 0.380 mg/L, respectively), and S7 shows the maximum values of TN and NO\u003csub\u003e3\u003c/sub\u003e-N (1.357 and 4.239 mg/L, respectively). The possible reason is that these two sampling points are located next to residential areas, where a small amount of laundry detergent is used by people containing elements such as N and P. Especially daily sewage is directly discharged without treatment, further aggravating nitrogen and phosphorus release into the water. Another possible pollution source may be the flushing water produced by raising poultry and livestock, and the livestock and poultry feces and urine containing high concentrations of N and P (Hu et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe CV value of Ec in the FW area is 1.06, which is a strong variation. The concentration of Ec at point S13 in the FW area is very high, reaching 355 \u0026micro;S/cm, which may be related to the fact that this point is located in a region close to the township. The discharge of domestic sewage in urban areas is large, and the agricultural and traffic runoff around the town also carries pollutants (such as chloride and nitrate) into the nearby water bodies (Peterse et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e)In addition, water in urban water bodies tends to flow very slowly and is poor at self-purifying compared to suburban water bodies. Because there are many pollution sources, the water body cannot effectively dilute or degrade the incoming pollutants, resulting in a high content of dissolved solids.\u003c/p\u003e\n \u003cp\u003eThe CV values of NO\u003csub\u003e3\u003c/sub\u003e-N, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and Ec in the PR area are 1.04, 1.38, and 1.18, respectively. Their large variations indicate that the sources of nitrogen, salts, and Ec in the water bodies of this region are unevenly distributed. In particular, the concentrations of NO\u003csub\u003e3\u003c/sub\u003e-N, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and Ec at S20 are very high, reaching 2.735 mg/L, 408.369 mg/L, and 1873 \u0026micro;S/cm, respectively, which may be related to the proximity of this point to the expressway. Some studies have shown that the application of salt on expressways and roads reduces the freezing point of water, thus melting ice and snow, but it also produces saline water that runs off the road into the surrounding environment (Dailey et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHydrochemical analysis results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eGR area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eLS area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.922\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eFW area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003ePR area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e408.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1019.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1206.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 MPs distribution characteristics\u003c/h2\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Abundance of MPs\u003c/h2\u003e\n \u003cp\u003eMPs are detected in all the sewage waters, and their abundance is presented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The range of MPs abundance is 6 (\u0026plusmn;\u0026thinsp;1)\u0026ndash;47 (\u0026plusmn;\u0026thinsp;3) items/L, and the distribution of MPs shows significant spatial differences because of the various hydraulic conditions and human activities. The abundance of MPs in the GR area increases at first and then decreases from upstream to downstream. In this area, the upstream water flow is fast, the water retention time is short, and MPs are easily washed downstream, so the abundance of MPs in the upstream is low. The decrease in downstream abundance may be related to the sedimentation of MPs because as the current velocity decreases, the larger MPs\u0026rsquo; particles are more likely to settle in the water, leading to a decrease in downstream abundance (Kukkola et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The highest abundance of MPs in this area is located in S7 (47\u0026thinsp;\u0026plusmn;\u0026thinsp;3 items/L), which is collected at the sewage outlet of a sewage treatment plant. It is reported that although sewage treatment plants can usually achieve a removal rate of up to 90% for MPs, the number of MPs in the influent is still large, and the number of MPs in the discharged wastewater is considerable (Sun et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to previous studies, MPs are more abundant in densely populated areas with more frequent human activities (Shi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The highest abundance of MPs in the FW area is located in S13 (27\u0026thinsp;\u0026plusmn;\u0026thinsp;2 items/L), and the highest abundance of MPs in the LC area is located in S15 (40\u0026thinsp;\u0026plusmn;\u0026thinsp;3 items/L). This may be due to the fact that the highest abundance of MPs in these two areas occurs in the center of the town, where transportation and daily life may generate and discharge more plastic waste; human activities may largely contribute to the high abundance of MPs (Abbasi et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Morphology of MPs\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the two-shape characteristics of MPs in sewage samples obtained by visualized analysis. It can be seen that fibers and films are widely observed in the sewage water samples. Specifically, fiber is the most universal shape type of MPs in this sewage water, and its appearance is slender and long. Many debris residues are attached to the fiber surface, and a few filaments are observed on the plastic surface, meaning that the pollutants are adsorbed on the rough surface of the fiber, which may be attributed to the long-term oxidation of plastic particles in the aqueous solutions (Guo et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ding et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). There are no foam and particle-shaped MPs in sewage, mainly because those particles are mainly present in beach sediments instead of surface water or near-shore sediments. Similar results are also reported by Faure et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eBesides, fibers are the primary shape species in the sewage water samples, ranging 95% of all the MPs. This result is similar to the morphology distribution of MPs in most domestic sewage treatment plants in China (Mintenig et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). This may be largely ascribed to laundry wastewater containing fibers (Corami et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Meanwhile, atmospheric sedimentation, overland runoff, and fishing tools are also potential sources of fiber plastics (Imbulana et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Miao et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Many studies have reported that fiber is the predominant shape of MPs in river ecosystems since some fiber can enter the water body via various sources. For example, the clothes washing process in daily life can produce a large number of MPs, leading to the content ranging from 124 mg/kg to 308 mg/kg discharged into the river through the municipal sewage systems (De Falco et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Notable, since the outbreak of COVID-19 in 2019, the production and use of personal protective equipment, mainly including disposable masks, have increased dramatically, and these disposable masks are inevitably being exposed to the environment. It is estimated that around 129 billion pieces of used masks worldwide each month in 2020 are discharged into the ocean, and mask waste could contribute 76\u0026ndash;276 items/L MPs after exposure to the aquatic environment, leading to the emergence of an increasing number of fiber MPs ( Liu et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Films of MPs accounted for 5%, and their potential sources are sanitary sewage containing plastic membranous and agricultural mulching membranous in the study area. Besides, the development of industrialization also increases the use of industrial film, which may be another potential source (Chen H et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xiong et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 Sizes of MPs\u003c/h2\u003e\n \u003cp\u003eTo further evaluate the size distribution of MPs in the surface water, the MPs of 20 sewage water samples in the study area are classified into four size classes, including class Ⅰ (\u0026lt;\u0026thinsp;0.3mm), class Ⅱ (0.3\u0026ndash;0.5 mm), class Ⅲ (0.5\u0026ndash;1.0 mm), and class Ⅳ (1.0\u0026ndash;5.0 mm), and the results is shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. It shows that the MPs account for the largest proportion in class Ⅰ (\u0026lt;\u0026thinsp;0.3 mm), ranging from 65.8\u0026ndash;67.8%, and class Ⅱ (0.3\u0026ndash;0.5 mm) accounts for 18.9\u0026ndash;20.9%. This is similar to the size distribution of MPs reported in the South Sea and the Pearl River in China (Cai et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yan et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). For the other classes, the size proportion of MPs decreases as the plastic particle size increases. The overall plastic size proportion is consistent with that reported in the Dongting and Hong Lakes in China (Wang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). A great number of MPs with smaller dimensions may be due to the larger plastic waste being decomposed into small particles. When plastic wastes enter the aquatic system, they can interact with biotic and abiotic (ultraviolet irradiation and weathering) factors, and the molecular structure of MPs can be destroyed, thus degrading large plastics into small-sized ones (Chubarenko et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Considering that fiber is the major shape of MPs in the sewage water samples, meaning that plastic wastes may undergo intense weathered and then be transported into rivers with surface runoff and further experience strong sand erosion in the river.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Relationship analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Relationship of MPs abundance with hydrochemical parameters\u003c/h2\u003e\n \u003cp\u003eCorrelation analysis (CA) is a multivariate statistical method that measures the close correlation between different variable factors by analyzing two or more related variable elements (Sedgwick, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). It can initially estimate the degree of correlation between multiple indicators (Kothari et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, a correlation matrix is computed to evaluate the degree of a linear association between any two of the parameters, and the degree of correlation is presented as a coefficient (\u003cem\u003eR\u003c/em\u003e) as follows (Asuero et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e represent variables, \u0026oline;\u003cem\u003ex\u003c/em\u003e represents the arithmetic mean of the variable \u003cem\u003ex\u003c/em\u003e, \u0026oline;\u003cem\u003ey\u003c/em\u003e represents the arithmetic mean of the variable, and \u003cem\u003en\u003c/em\u003e represents the number of observations. The value of \u003cem\u003eR\u003c/em\u003e commonly ranged from +\u0026thinsp;1 to -1. If the value of \u003cem\u003eR\u003c/em\u003e is +\u0026thinsp;1, there is the strongest positive linear correlation between the two parameters compared. The closer of \u003cem\u003eR\u003c/em\u003e-value to +\u0026thinsp;1, the more positive the relationship is. If the value of \u003cem\u003eR\u003c/em\u003e is -1, it reveals the strongest negative linear correlation. Besides, the larger absolute \u003cem\u003eR\u003c/em\u003e-value usually means the stronger the correlation between different elements and the higher the possibility of homology (Li et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the abundance of MPs shows a moderately positive correlation with NO\u003csub\u003e3\u003c/sub\u003e-N with an \u003cem\u003eR-\u003c/em\u003evalue of 0.404, shows weakly positively correlated with TN, TP, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, and Na with \u003cem\u003eR\u003c/em\u003e values of 0.220, 0.245, 0.287, and 0.202, respectively, and shows weakly correlated with NH\u003csub\u003e3\u003c/sub\u003e-N, pH, As, and Mn, with \u003cem\u003eR\u003c/em\u003e values of 0.177, 0.071, 0.133, and 0.010, respectively. Several studies have shown that the correlation between MPs\u0026rsquo; abundance and certain water chemical parameters may be weak. This is because MPs\u0026rsquo; abundance is affected by multiple factors, including chemical parameters. In domestic sewage, due to the complex pollution sources, the abundance of MPs may be more affected by the source (such as sewage treatment system, precipitation, runoff) (Mahon et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and environmental conditions (such as hydrodynamics and sedimentation) (Radford et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) so the correlation with a single hydrochemical parameter is often not significant.\u003c/p\u003e\n \u003cp\u003eCompared with other parameters, there is a strong correlation between the abundance of MPs and NO\u003csub\u003e3\u003c/sub\u003e-N in this study, indicating that MPs may participate in nitrification or denitrification in some form (Li et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Parrish et al., 2019). Different from these hydrochemical parameters, metal ions show a weak correlation with the abundance of MPs, which is consistent with the finding of Nguyen et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). One possible explanation for the weak correlation is that the MPs in the study area are relatively pristine, meaning that they have undergone little weathering or degradation processes that could enhance their ability to absorb metals (Holmes et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar to the study reported by Somasundaram et al. (2023), no parameter is found to be significantly correlated with MPs\u0026rsquo; abundance through the Pearson correlation test. Besides, the distribution of MPs varies upstream and downstream of rivers and in urban areas due to multiple sources and the influence of the surrounding environment (Conowall et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Janakiram et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jin et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The diversity of urban ecosystems poses unique challenges to understanding microplastic migration (Yan et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Identifying key factors affecting the abundance of MPs is complex because they are diverse and interrelated.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.3.2 Relationship of MPs size and morphology with hydrochemical parameters\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eAlthough some evidence suggests a link between MPs and water quality data, Kataoka et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) have indicated that, given the temporal and spatial variability of the river, the linear associations between water quality parameters and MPs abundance may be problematic. Therefore, redundancy analysis (RDA) is used to further demonstrate the percentage contribution of the associations between water quality parameters and MPs\u0026rsquo; abundance. RDA is another tool for assessing correlations across multiple variable sets, allowing an in-depth understanding of multi-variable correlations by leveraging redundant information (Wang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). RDA is a direct extension of multiple regression and can model the effect of an explanatory matrix \u003cem\u003eM\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026times;\u003cem\u003ep\u003c/em\u003e) on a response matrix \u003cem\u003eN\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026times;\u003cem\u003em\u003c/em\u003e) (Legendre et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). It can be conducted by the following two steps: firstly, multiple regression is conducted through the linear Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere each object in \u003cem\u003eN\u003c/em\u003e is regressed on the explanatory variables in \u003cem\u003eM\u003c/em\u003e, resulting in a matrix of fitted values \u003cem\u003eN\u003c/em\u003e\u003csub\u003efit\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003eSecondly, a principal components analysis (PCA) is applied to the fitted matrix \u003cem\u003eN\u003c/em\u003e\u003csub\u003efit\u003c/sub\u003e to reduce dimensionality. Obtaining a matrix \u003cem\u003eZ\u003c/em\u003e containing the canonical axes corresponding to the linear combinations of the explanatory variables in the space of \u003cem\u003eM\u003c/em\u003e. The linearity of the combinations of the \u003cem\u003eM\u003c/em\u003e variables is a fundamental property of RDA. Once RDA has been calculated, additional statistics can be calculated to interpret the explanatory power of the included variables and to assess the significance of the observed relationships. These statistics include the P-statistic, contribution (%), and pseudo-F, etc. Among them, the P-statistic corresponds to an overall test of the significance of an RDA by comparing the computed model to a null model. It can also be used to sequentially test the significance of each canonical axis. Contribution (%) indicates the contribution value of the parameter. Pseudo-F is used to ensure that the test has good horizontal accuracy.\u003c/p\u003e\n \u003cp\u003eIn this study, RDA is used to determine the relationship between the hydrochemical parameters, metal ions, and the size as well as morphology of MPs. The contribution rate of each variable to the environment can be maintained independently through RDA, and the statistical characteristics of a single variable can still be described in the case of different combinations of variables. The environmental explanatory variables are hydrochemical indicators (Ec, pH, TN, TP, NO\u003csub\u003e3\u003c/sub\u003e-N, NH\u003csub\u003e3\u003c/sub\u003e-N, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) and metal ions (K, Na, Ca, etc.), which are represented by blue arrows. The response variable is the size and morphology of MPs in the sewage water, indicated by the red arrow. The length of the arrow in the figure represents the proportion of the explanatory variable.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea shows that the first two RDA axes of the hydrochemical indicators, metal ions, and the size of MPs explain 49.52% and 73.82% of the total variance, respectively. It shows that for MPs with sizes of \u0026lt;\u0026thinsp;0.3 mm and 0.3\u0026ndash;0.5 mm, only NH\u003csub\u003e3\u003c/sub\u003e-N, TN, TP, and Na are positively correlated; this correlation may be due to the similar sources of MPs and nutrients (e.g., wastewater and non-point source pollution) (Xu et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). For MPs with a size of 0.5-1.0 mm, there are positive correlations with pH, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Al, Ca, Mg, and Cu. MPs of this size may be more stable in an alkaline and mineral-rich environment. This indicates that they may be more likely to accumulate in water with high ion content and more easily combined or attached to positively charged minerals (such as Ca, Mg, and Al) (Ta et al., 2020). MPs with a size of 0.5-1.0 mm have negative correlations with NH\u003csub\u003e3\u003c/sub\u003e-N, TP, and TN. This may mean that these smaller MPs are less distributed in eutrophic water bodies and may be more easily decomposed in such environments.\u003c/p\u003e\n \u003cp\u003eFor MPs with a size of 1.0\u0026ndash;5.0 mm, there is a positive correlation with metal ions and anions, which indicates that these larger-size MPs tend to exist in an environment with higher salt content and heavy pollution. This may be because the larger-size MPs usually make it easier to adsorb these ions because of their larger surface area and mass, and they are more difficult to be transported by the current, so they are more likely to be deposited at the bottom of the water, especially in areas with high content of heavy metal ions (Gao et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). A negative correlation with TP, TN, NH\u003csub\u003e3\u003c/sub\u003e-N, and NO\u003csub\u003e3\u003c/sub\u003e-N may indicate that they exist in a small proportion of domestic wastewater. This phenomenon may be related to the fact that larger MPs\u0026rsquo; particles are easier to settle in these environments and more difficult to suspend in eutrophic water.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb shows that the first two RDA axes of the hydrochemical indicators, metal ions, and the morphology of MPs explain 74.21% and 100% of the total variance, respectively. It can be seen that the fibers are positively correlated with NO\u003csub\u003e3\u003c/sub\u003e-N, TP, TN, Na, Mn, As, and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e. The positive correlation between fiber and nutrients such as NO\u003csub\u003e3\u003c/sub\u003e-N, TP, and TN indicates that MPs are more likely to accumulate in eutrophic water bodies. This may harm eutrophic water bodies, exacerbating algae growth and water ecological imbalance. The longer arrow line of NO\u003csub\u003e3\u003c/sub\u003e-N indicates that this parameter has a greater influence on fiber distribution, while the slightly shorter arrow line of TP and TN has a slightly lesser influence. The strong correlations observed between various forms of nitrogen and MPs indicate a potential disturbance process associated with nitrogen recurrence (Li et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The positive correlation between fiber and metal ions such as As and Mn indicates that these MPs may be more easily captured or deposited in heavily metal-polluted areas. This also suggests that fiber may act as a carrier for heavy metals, increasing the migration capacity and bioavailability of these harmful metals in water bodies, thereby posing a threat to aquatic ecosystems. The positive correlation between fiber and pollutants such as nitrogen, phosphorus, and metal ions suggests that they may act as carriers of other pollutants in the environment, facilitating their wider distribution in water bodies. MPs can adsorb these pollutants, allowing them to enter organisms through the food chain, thereby posing potential hazards to the ecosystem.\u003c/p\u003e\n \u003cp\u003eThe negative correlation between fiber, K, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and Ec indicates that the concentration of fiber in high-salinity sewage water is low. It means that fibers are more susceptible to degradation, sedimentation, or reactions with other substances in environments with high salinity where fibrous MPs may decompose faster and are less likely to be retained and diffused. Besides, the films are positively correlated with Al, Zn, Ca, Cu, and Cd. This shows that the sources of these elements are related to the production and use of plastics, and these metals are used as additives or catalysts in some plastic production processes (Turner et al., 2021; Godoy et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The existence of these metal elements within the ecosystem may influence the sedimentation, distribution, and biodegradability of MPs and potentially result in the enrichment of these metals by some organisms. The films are negatively correlated with Pb, Fe, and Mg, indicating that the deposition and sources of these metals are significantly different from those of MPs. This may also reflect the physiochemical characteristics of thin film MPs under specific environmental conditions. Anon (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) has shown that under various pH values and ionic strengths, MPs may have different binding abilities with some metal ions, resulting in different correlations.\u003c/p\u003e\n \u003cp\u003eThe positive and negative correlation between MPs, hydrochemical parameters, and metal ions not only elucidates MPs\u0026apos; behavior and characteristics in the environment but also offers insights into the intricate dynamics of environmental contamination. This analysis facilitates the identification of MPs\u0026apos; and their pollutants\u0026apos; impact on the ecosystem, thereby providing a reference point for further research and environmental protection.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Environmental implications\u003c/h2\u003e\n \u003cp\u003eThe contamination of aquatic environments by MPs represents a significant global environmental concern. In this study, multivariate statistical analyses are employed to explore potential correlations between MPs and hydrochemical parameters (including conventional anions and cations, metal ions, etc.) in sewage water. The results of the multivariate statistical analyses indicate that there is a weak correlation between the abundance of MPs and the hydrochemical parameters. However, there is a stronger correlation with sewage water when different compositions of MPs are considered, such as size and morphology. In particular, the films and MPs with larger particle sizes (1.0\u0026ndash;5.0 mm) exhibit higher correlations with metal ions in the sewage water. To further explore the potential risks of hydrochemical parameters and metal ions on the distribution of MPs, future studies should expand the scope of experiments, consider the impact of factors such as the color, shape, and different particle sizes of MPs on the water environment, and consider more water indicators, such as rainy season, dry season, and water turbidity, etc.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe findings of this research show that microplastic pollution is found at domestic sewage outlets in Guilin of China. The range of microplastic abundance is 6(\u0026plusmn;\u0026thinsp;1)\u0026ndash;47(\u0026plusmn;\u0026thinsp;3) items/L. The class of \u0026lt;\u0026thinsp;0.3 mm dominates the size of the MPs. The results of CA reveal that the abundance of MPs is weakly correlated with hydrochemical parameters and metal ions. The possible reason is that the MPs are relatively pristine in the study area, meaning that they have undergone little weathering or degradation processes that enhance their ability to absorb metals.\u003c/p\u003e \u003cp\u003eThe results of the RDA indicate that there are correlations between MPs of different morphology and sizes, and the hydrochemical parameters and metal ions. NO\u003csub\u003e3\u003c/sub\u003e-N has a greater influence on the distribution of microplastic fiber; Zn, Ca, and Cu have a greater influence on the distribution of microplastic film. For MPs with sizes of \u0026lt;\u0026thinsp;0.3 mm and 0.3\u0026ndash;0.5 mm, NH\u003csub\u003e3\u003c/sub\u003e-N, TN, TP, and Na are positively correlated; there are positive correlations with pH, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Al, Ca, Mg, and Cu. MPs of this size may be more stable in an alkaline and mineral-rich environment; for MPs with a size of 1.0\u0026ndash;5.0 mm, there is a positive correlation with metal ions and anions, it indicates that these larger MPs particles tend to exist in the environment with higher salt content and heavy pollution.\u003c/p\u003e \u003cp\u003eIn general, the correlation between hydrochemistry parameters and metal ions and MPs\u0026rsquo; abundance is weak. However, there are stronger correlations between these parameters and MPs' size and morphology. These findings indicate that water quality parameters may influence the nature of MPs, underscoring the necessity for comprehensive analyses of these variables to comprehend their interactions with the aquatic environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMY L wrote the main manuscript text.HM S supervised the project and acquired funding for the study.YQ L developed the research concept and methodology.YX S performed data analysis and contributed to validation and formal analysis.HB Z conducted investigation experiments.SX P conducted a review.\u003c/p\u003e\u003ch2\u003eAcknowledgment:\u003c/h2\u003e \u003cp\u003eThis research is funded by the National Natural Science Foundation of China (42167026), the Natural Science Foundation of Guangxi (2022GXNSFBA035600), and the Guilin University of Technology Program (GLUTQD 2016047).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnon, 2019. Interactions between microplastics and phthalate esters as affected by microplastics characteristics and solution chemistry[J]. \u003cem\u003eChemosphere\u003c/em\u003e, 214: 688-694.\u003c/li\u003e\n \u003cli\u003eAbbasi S, Keshavarzi B, Moore F, et al., 2019. Distribution and potential health impacts of microplastics and microrubbers in air and street dusts from Asaluyeh County, Iran[J]. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 244: 153-164.\u003c/li\u003e\n \u003cli\u003eAdomat Y, Grischek T, 2021. Sampling and processing methods of microplastics in river sediments - A review[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 758: 143691.\u003c/li\u003e\n \u003cli\u003eAryani D, Khalifa M A, Herjayanto M, et al., 2021. Correlation of Water Quality with Microplastic Exposure Prevalence in Tilapia (Oreochromis niloticus)[J].\u003c/li\u003e\n \u003cli\u003eAsuero A G, Sayago A, Gonz\u0026aacute;lez A G, 2006. The Correlation Coefficient: An Overview[J].\u003cem\u003e\u0026nbsp;Critical Reviews in Analytical Chemistry\u003c/em\u003e, 36(1): 41-59.\u003c/li\u003e\n \u003cli\u003eAtugoda T, Vithanage M, Wijesekara H, et al., 2021. Interactions between microplastics, pharmaceuticals and personal care products: Implications for vector transport[J]. \u003cem\u003eEnvironment International\u003c/em\u003e, 149: 106367.\u003c/li\u003e\n \u003cli\u003eBayo J, Olmos S, L\u0026oacute;pez-Castellanos J, 2020. Microplastics in an urban wastewater treatment plant: The influence of physicochemical parameters and environmental factors[J]. \u003cem\u003eChemosphere\u003c/em\u003e, 238: 124593.\u003c/li\u003e\n \u003cli\u003eBuwono N R, Risjani Y, Soegianto A, 2021. Distribution of microplastic in relation to water quality parameters in the Brantas River, East Java, Indonesia[J]. \u003cem\u003eEnvironmental Technology \u0026amp; Innovation\u003c/em\u003e, 24: 101915.\u003c/li\u003e\n \u003cli\u003eCai M, Liu M, Qi H, et al., 2022. Transport of microplastics in the South China Sea: A review[J]. \u003cem\u003eGondwana Research\u003c/em\u003e, 108: 49-59.\u003c/li\u003e\n \u003cli\u003eChen H, Chen Y, Xu Y, et al., 2022. Different functional areas and human activities significantly affect the occurrence and characteristics of microplastics in soils of the Xi\u0026rsquo;an metropolitan area[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 852: 158581.\u003c/li\u003e\n \u003cli\u003eChen W, Zhang Z, Zhu Y, et al., 2022. Distribution, sources and transport of polycyclic aromatic hydrocarbons (PAHs) in karst spring systems from Western Hubei, Central China[J]. \u003cem\u003eChemosphere\u003c/em\u003e, 300: 134502.\u003c/li\u003e\n \u003cli\u003eChubarenko I, Efimova I, Bagaeva M, et al., 2020. On mechanical fragmentation of single-use plastics in the sea swash zone with different types of bottom sediments: Insights from laboratory experiments[J]. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e, 150: 110726.\u003c/li\u003e\n \u003cli\u003eConowall P, Schreiner K M, Minor E C, et al., 2023. Variability of microplastic loading and retention in four inland lakes in Minnesota, USA[J]. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 328: 121573.\u003c/li\u003e\n \u003cli\u003eCorami F, Rosso B, Bravo B, et al., 2020. A novel method for purification, quantitative analysis and characterization of microplastic fibers using Micro-FTIR[J]. \u003cem\u003eChemosphere\u003c/em\u003e, 238: 124564.\u003c/li\u003e\n \u003cli\u003eDailey K R, Welch K A, Lyons W B, 2014. Evaluating the influence of road salt on water quality of Ohio rivers over time[J]. \u003cem\u003eApplied Geochemistry\u003c/em\u003e, 47: 25-35.\u003c/li\u003e\n \u003cli\u003eDe Falco F, Di Pace E, Cocca M, et al., 2019. The contribution of washing processes of synthetic clothes to microplastic pollution[J]. \u003cem\u003eScientific Reports\u003c/em\u003e, 9(1): 6633.\u003c/li\u003e\n \u003cli\u003eDing R, Ouyang F, Peng D, et al., 2022. A case study of distribution and characteristics of microplastics in surface water and sediments of the seas around Shenzhen, southern coastal area of China[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 838: 156063.\u003c/li\u003e\n \u003cli\u003eFadare O O, Okoffo E D, 2020. Covid-19 face masks: A potential source of microplastic fibers in the environment[J]. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, 737: 140279.\u003c/li\u003e\n \u003cli\u003eFaure F, Corbaz M, Baecher H, et al., 2012. Pollution due to plastics and microplastics in Lake Geneva and in the Mediterranean Sea[J]. \u003cem\u003eArchives Des Sciences\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eFeng S, Lu H, Yao T, et al., 2021. Microplastic footprints in the Qinghai-Tibet Plateau and their implications to the Yangtze River Basin[J]. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, 407: 124776.\u003c/li\u003e\n \u003cli\u003eGalv\u0026atilde;o A, Aleixo M, De Pablo H, et al., 2020. Microplastics in wastewater: microfiber emissions from common household laundry[J]. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, 27(21): 26643-26649.\u003c/li\u003e\n \u003cli\u003eGao F, Li J, Sun C, et al., 2019. Study on the capability and characteristics of heavy metals enriched on microplastics in marine environment[J]. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e, 144: 61-67.\u003c/li\u003e\n \u003cli\u003eGodoy V, Bl\u0026aacute;zquez G, Calero M, et al., 2019. The potential of microplastics as carriers of metals[J]. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 255: 113363.\u003c/li\u003e\n \u003cli\u003eGong X, Tian L, Wang P, et al., 2023. Microplastic pollution in the groundwater under a bedrock island in the South China sea[J]. \u003cem\u003eEnvironmental Research\u003c/em\u003e, 239: 117277.\u003c/li\u003e\n \u003cli\u003eGuo X, Yin Y, Yang C, et al., 2018. Maize straw decorated with sulfide for tylosin removal from the water[J]. \u003cem\u003eEcotoxicology and Environmental Safety\u003c/em\u003e, 152: 16-23.\u003c/li\u003e\n \u003cli\u003eHasenmueller E A, Baraza T, Hernandez N F, et al., 2023. Cave sediment sequesters anthropogenic microparticles (including microplastics and modified cellulose) in subsurface environments[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 893: 164690.\u003c/li\u003e\n \u003cli\u003eHidalgo-Ruz V, Gutow L, Thompson R C, et al., 2012. Microplastics in the Marine Environment: A Review of the Methods Used for Identification and Quantification[J]. \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e, 46(6): 3060-3075.\u003c/li\u003e\n \u003cli\u003eHolmes L A, Turner A, Thompson R C, 2014. Interactions between trace metals and plastic production pellets under estuarine conditions[J]. \u003cem\u003eMarine Chemistry\u003c/em\u003e, 167: 25-32.\u003c/li\u003e\n \u003cli\u003eHu Y, Cheng H, Tao S, 2017. Environmental and human health challenges of industrial livestock and poultry farming in China and their mitigation[J]. \u003cem\u003eEnvironment International\u003c/em\u003e, 107: 111-130.\u003c/li\u003e\n \u003cli\u003eImbulana S, Tanaka S, Oluwoye I, 2024. Quantifying annual microplastic emissions of an urban catchment: Surface runoff \u003cem\u003evs\u003c/em\u003e wastewater sources[J]. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, 360: 121123.\u003c/li\u003e\n \u003cli\u003eIsobe A, Uchida K, Tokai T, et al., 2015. East Asian seas: A hot spot of pelagic microplastics[J]. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e, 101(2): 618-623.\u003c/li\u003e\n \u003cli\u003eJanakiram R, Keerthivasan R, Janani R, et al., 2023. Seasonal distribution of microplastics in surface waters of the Northern Indian Ocean[J]. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e, 190: 114838.\u003c/li\u003e\n \u003cli\u003eJin X, Fu X, Lu W, et al., 2023. The effects of riverside cities on microplastics in river water: A case study on the Southern Jiangsu Canal, China[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 858: 159783.\u003c/li\u003e\n \u003cli\u003eKataoka T, Nihei Y, Kudou K, et al., 2019. Assessment of the sources and inflow processes of microplastics in the river environments of Japan[J]. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 244: 958-965.\u003c/li\u003e\n \u003cli\u003eKoelmans A A, Mohamed Nor N H, Hermsen E, et al., 2019. Microplastics in freshwaters and drinking water: Critical review and assessment of data quality[J]. \u003cem\u003eWater Research\u003c/em\u003e, 155: 410-422.\u003c/li\u003e\n \u003cli\u003eKothari V, Vij S, Sharma S, et al., 2021. Correlation of various water quality parameters and water quality index of districts of Uttarakhand[J]. \u003cem\u003eEnvironmental and Sustainability Indicators\u003c/em\u003e, 9: 100093.\u003c/li\u003e\n \u003cli\u003eKukkola A, Runkel R L, Schneidewind U, et al., 2023. Prevailing impacts of river management on microplastic transport in contrasting US streams: Rethinking global microplastic flux estimations[J]. \u003cem\u003eWater Research\u003c/em\u003e, 240: 120112.\u003c/li\u003e\n \u003cli\u003eLegendre P, Oksanen J, ter Braak C J F, 2011. Testing the significance of canonical axes in redundancy analysis[J]. \u003cem\u003eMethods in Ecology and Evolution\u003c/em\u003e, 2(3): 269-277.\u003c/li\u003e\n \u003cli\u003eLi L, Li F, Deng M, et al., 2022. Microplastics distribution characteristics in typical inflow rivers of Taihu lake: Linking to nitrous oxide emission and microbial analysis[J]. \u003cem\u003eWater Research\u003c/em\u003e, 225: 119117.\u003c/li\u003e\n \u003cli\u003eLi Q, Yuan M, Chen Y, et al., 2023. The neglected potential source of microplastics from daily necessities: A study on protective mobile phone cases[J]. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, 441: 129911.\u003c/li\u003e\n \u003cli\u003eLi S, Yang M, Wang H, et al., 2022. Adsorption of microplastics on aquifer media: Effects of the action time, initial concentration, ionic strength, ionic types and dissolved organic matter[J]. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 308: 119482.\u003c/li\u003e\n \u003cli\u003eLi X, Chen L, Mei Q, et al., 2018. Microplastics in sewage sludge from the wastewater treatment plants in China[J]. \u003cem\u003eWater Research\u003c/em\u003e, 142: 75-85.\u003c/li\u003e\n \u003cli\u003eLi X, Han X, Vogt R D, et al., 2022. Polyethylene terephthalate and di-(2-ethylhexyl) phthalate in surface and core sediments of Bohai Bay, China: Occurrence and ecological risk[J]. \u003cem\u003eChemosphere\u003c/em\u003e, 286: 131904.\u003c/li\u003e\n \u003cli\u003eLi Y, Chen H, Teng Y, 2020. Source apportionment and source-oriented risk assessment of heavy metals in the sediments of an urban river-lake system[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 737: 140310.\u003c/li\u003e\n \u003cli\u003eLiu K, Wang X, Fang T, et al., 2019. Source and potential risk assessment of suspended atmospheric microplastics in Shanghai[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 675: 462-471.\u003c/li\u003e\n \u003cli\u003eLiu Q, Wu H, Chen J, et al., 2022. Adsorption mechanism of trace heavy metals on microplastics and simulating their effect on microalgae in river[J]. \u003cem\u003eEnvironmental Research\u003c/em\u003e, 214: 113777.\u003c/li\u003e\n \u003cli\u003eLiu W, Zhang J, Liu H, et al., 2021. A review of the removal of microplastics in global wastewater treatment plants: Characteristics and mechanisms[J]. \u003cem\u003eEnvironment International\u003c/em\u003e, 146: 106277.\u003c/li\u003e\n \u003cli\u003eLiu Z, Wang J, Yang X, et al., 2022. Generation of environmental persistent free radicals (EPFRs) enhances ecotoxicological effects of the disposable face mask waste with the COVID-19 pandemic[J]. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 301: 119019.\u003c/li\u003e\n \u003cli\u003eMahon A M, O\u0026rsquo;Connell B, Healy M G, et al., 2017. Microplastics in Sewage Sludge: Effects of Treatment[J]. \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e, 51(2): 810-818.\u003c/li\u003e\n \u003cli\u003eMasura J, Baker J, Foster G, et al., 2015. Laboratory Methods for the Analysis of Microplastics in the Marine Environment[J]. NOAA Technical Memorandum NOS-OR\u0026amp;R-48.\u003c/li\u003e\n \u003cli\u003eMiao C, Zhang J, Jin R, et al., 2023. Microplastics in aquaculture systems: Occurrence, ecological threats and control strategies[J]. \u003cem\u003eChemosphere\u003c/em\u003e, 340: 139924.\u003c/li\u003e\n \u003cli\u003eMintenig S M, Int-Veen I, L\u0026ouml;der M G J, et al., 2017. Identification of microplastic in effluents of waste water treatment plants using focal plane array-based micro-Fourier-transform infrared imaging[J]. \u003cem\u003eWater Research\u003c/em\u003e, 108: 365-372.\u003c/li\u003e\n \u003cli\u003eMohamed Nor N H, Obbard J P, 2014. Microplastics in Singapore\u0026rsquo;s coastal mangrove ecosystems[J]. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e, 79(1): 278-283.\u003c/li\u003e\n \u003cli\u003eNguyen T L H, Duong T L, Nguyen T H T, et al., 2023. Microplastics and trace metals in river sediment: Prevalence and correlation with multiple factors[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 895: 165145.\u003c/li\u003e\n \u003cli\u003eParrish K, L.\u0026nbsp;Fahrenfeld N, 2019. Microplastic biofilm in fresh- and wastewater as a function of microparticle type and size class[J].\u003cem\u003e\u0026nbsp;Environmental Science: Water Research \u0026amp; Technology\u003c/em\u003e, 5(3): 495-505.\u003c/li\u003e\n \u003cli\u003ePeterse I F, Hendriks L, Weideveld S T J, et al., 2024. Wastewater-effluent discharge and incomplete denitrification drive riverine CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 951: 175797.\u003c/li\u003e\n \u003cli\u003eRadford F, Horton A A, Felgate S, et al., 2024. Factors influencing microplastic abundances in the sediments of a seagrass-dominated tropical atoll[J]. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 357: 124483.\u003c/li\u003e\n \u003cli\u003eScheurer M, Bigalke M, 2018. Microplastics in Swiss Floodplain Soils[J]. \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSedgwick P, 2012. Pearson\u0026rsquo;s correlation coefficient[J]. \u003cem\u003eBMJ\u003c/em\u003e, 345.\u003c/li\u003e\n \u003cli\u003eSefie A, Aris A Z, Ramli M F, et al., 2018. Hydrogeochemistry and groundwater quality assessment of the multilayered aquifer in Lower Kelantan Basin, Kelantan, Malaysia[J].\u003cem\u003e\u0026nbsp;Environmental Earth Sciences\u003c/em\u003e, 77(10): 397.\u003c/li\u003e\n \u003cli\u003eShi H, Yu D, Yin L, et al., 2022. Source-sink process of microplastics in watershed-estuary-offshore system[J]. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 338: 130612.\u003c/li\u003e\n \u003cli\u003eShu X, Xu L, Yang M, et al., 2023. Spatial distribution characteristics and migration of microplastics in surface water, groundwater and sediment in karst areas: The case of Yulong River in Guilin, Southwest China[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 868: 161578.\u003c/li\u003e\n \u003cli\u003eSomasundaram R, Radhakrishnan N, 2023. Establishment the relationship between water quality parameter and micro plastic concentration for Adyar and Cooum estuary[J]. \u003cem\u003eEnvironmental Quality Management\u003c/em\u003e, 33(1): 121-133.\u003c/li\u003e\n \u003cli\u003eSun J, Dai X, Wang Q, et al., 2019. Microplastics in wastewater treatment plants: Detection, occurrence and removal[J]. \u003cem\u003eWater Research\u003c/em\u003e, 152: 21-37.\u003c/li\u003e\n \u003cli\u003eSun J, Peng Z, Zhu Z R, et al., 2022. The atmospheric microplastics deposition contributes to microplastic pollution in urban waters[J]. \u003cem\u003eWater Research\u003c/em\u003e, 225: 119116.\u003c/li\u003e\n \u003cli\u003eTa A T, Babel S, 2020. Microplastic contamination on the lower Chao Phraya: Abundance, characteristic and interaction with heavy metals[J]. \u003cem\u003eChemosphere\u003c/em\u003e, 257: 127234.\u003c/li\u003e\n \u003cli\u003eThompson R C, Olsen Y, Mitchell R P, et al., 2004. Lost at Sea: Where Is All the Plastic?[J]. \u003cem\u003eScience\u003c/em\u003e, 304(5672): 838-838.\u003c/li\u003e\n \u003cli\u003eTurner A, Filella M, 2021. Hazardous metal additives in plastics and their environmental impacts[J]. Environment International, 156: 106622.\u003c/li\u003e\n \u003cli\u003eVan Emmerik T, Schwarz A, 2020. Plastic debris in rivers[J]. \u003cem\u003eWIREs Water\u003c/em\u003e, 7(1): e1398.\u003c/li\u003e\n \u003cli\u003eWang L, Liu M, Tao W, et al., 2018. Pollution characteristics and health risk assessment of phthalate esters in urban soil in the typical semi-arid city of Xi\u0026rsquo;an, Northwest China[J]. \u003cem\u003eChemosphere\u003c/em\u003e, 191: 467-476.\u003c/li\u003e\n \u003cli\u003eWang Q, Zhang Y, Wangjin X, et al., 2020. The adsorption behavior of metals in aqueous solution by microplastics effected by UV radiation[J]. \u003cem\u003eJournal of Environmental Sciences\u003c/em\u003e, 87: 272-280.\u003c/li\u003e\n \u003cli\u003eWang W, Ndungu A W, Li Z, et al., 2017. Microplastics pollution in inland freshwaters of China: A case study in urban surface waters of Wuhan, China[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 575: 1369-1374.\u003c/li\u003e\n \u003cli\u003eWang W, Yuan W, Chen Y, et al., 2018. Microplastics in surface waters of Dongting Lake and Hong Lake, China[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 633: 539-545.\u003c/li\u003e\n \u003cli\u003eWen X, Du C, Xu P, et al., 2018. Microplastic pollution in surface sediments of urban water areas in Changsha, China: Abundance, composition, surface textures[J]. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e, 136: 414-423.\u003c/li\u003e\n \u003cli\u003eXie Y, Liu Z, Gou X, et al., 2018. Analysis on quality of domestic sewage and investigation of villages \u0026rsquo;willingness from the rural areas of southwest China[J]. \u003cem\u003eEnvironmental Engineering\u003c/em\u003e, 36(08): 165-169+188.\u003c/li\u003e\n \u003cli\u003eXiong X, Zhang K, Chen X, et al., 2018. Sources and distribution of microplastics in China\u0026rsquo;s largest inland lake \u0026ndash; Qinghai Lake[J]. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 235: 899-906.\u003c/li\u003e\n \u003cli\u003eXu H, Nakano H, Tokai T, et al., 2022. Contamination of sea surface water offshore the Tokai region and Tokyo Bay in Japan by small microplastics[J]. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e, 185: 114245.\u003c/li\u003e\n \u003cli\u003eXu N, Jiang L, Zhang Y, et al., 2023. Microplastic pollution in the offshore sea, rivers and wastewater treatment plants in Jiangsu coastal area in China[J]. \u003cem\u003eMarine Environmental Research\u003c/em\u003e, 188: 105992.\u003c/li\u003e\n \u003cli\u003eYan M, Nie H, Xu K, et al., 2019. Microplastic abundance, distribution and composition in the Pearl River along Guangzhou city and Pearl River estuary, China[J]. \u003cem\u003eChemosphere\u003c/em\u003e, 217: 879-886.\u003c/li\u003e\n \u003cli\u003eYan Z, Chen Y, Bao X, et al., 2021. Microplastic pollution in an urbanized river affected by water diversion: Combining with active biomonitoring[J]. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, 417: 126058.\u003c/li\u003e\n \u003cli\u003eZhang L, Liu J, Xie Y, et al., 2021a. Occurrence and removal of microplastics from wastewater treatment plants in a typical tourist city in China[J]. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 291: 125968.\u003c/li\u003e\n \u003cli\u003eZhang L, Xie Y, Zhong S, et al., 2021b. Microplastics in freshwater and wild fishes from Lijiang River in Guangxi, Southwest China[J]. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 755: 142428.\u003c/li\u003e\n \u003cli\u003eZhao K, Zhou S, Wang K, et al., 2024. Distribution characteristics and pollution risk assessment of microplastics in urban rivers: A case study in Yitong River, China[J]. J\u003cem\u003eournal of Water Process Engineering\u003c/em\u003e, 61: 105277.\u003c/li\u003e\n \u003cli\u003eZhou B, Wang J, Zhang H, et al., 2020. Microplastics in agricultural soils on the coastal plain of Hangzhou Bay, east China: Multiple sources other than plastic mulching film[J]. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, 388: 121814.\u003c/li\u003e\n \u003cli\u003eZhu J, Zhang Q, Huang Y, et al., 2021. Long-term trends of microplastics in seawater and farmed oysters in the Maowei Sea, China[J]. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 273: 116450.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Microplastics, Distribution, Sewage water, Relationship analysis.","lastPublishedDoi":"10.21203/rs.3.rs-5460558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5460558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroplastics (MPs) resulting from plastic fragmentation with a size less than 5 mm have become one of the main pollutants endangering the water environment. Therefore, it is necessary to know about the abundance and size distribution in sewage waters as well as the influences of water quality on MPs. In this study, water samples are collected from 20 sewage outlets in Guilin, China, to analyze the abundance and morphology of the MPs and their hydrochemical characteristics. Multivariate statistical analyses are conducted to identify the major factors related to the MPs’ distribution in sewage waters. Results show that MPs in sewage water are mainly composed of fiber and film, and about 67.8% is in the size of \u0026lt;0.3 mm. The abundance is in the range of 6 (±1)–47 (±3) items/L. The correlation analysis presents that the abundance of MPs is weakly correlated with hydrochemical parameters and metal ions due to the complexity of the abundance data. The redundancy analysis indicates that the MPs’ morphology distribution is significantly affected by NO\u003csub\u003e3\u003c/sub\u003e-N, Zn, Ca, and Cu contents, and the MPs’ size distribution is mainly related to Zn, Ca, and Cu contents. The study highlights the occurrence characteristics and environmental influencing factors of the MPs in sewage water, which may be significant for future studies on the pollution control of MPs.\u003c/p\u003e","manuscriptTitle":"Distribution Characteristics of Microplastics in Domestic Sewage Waters: A Case Study in Guilin City, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-20 00:52:42","doi":"10.21203/rs.3.rs-5460558/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e945168f-682f-4a73-bc68-426740cd5865","owner":[],"postedDate":"December 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-20T01:00:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-20 00:52:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5460558","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5460558","identity":"rs-5460558","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}