Co-occurrence of microplastics and heavy metals in sediments of the
Lanzhou section of the Yellow River: Distribution characterizations and comprehensive ecological risk assessment

1、Introduction：

The global plastic waste crisis has emerged as a paramount envi- ronmental challenge, with improperly managed plastics constituting the largest fraction of anthropogenic waste (Liu et al., 2018). As the world's largest producer and consumer of plastics, China recorded an output of 77.72 million tons in 2022 (Ma et al., 2023). Due to the influence of physical, chemical, and biological elements in the environment, plastics fragment into microplastics (MPs, <5 mm) and are more likely to move around, change form, and build up in various ecosystems (Ding et al., 2019). MPs have been detected in virtually all global compartments, though at varying abundance (Deng et al.,2020; Redondo-Hasselerharm et al., 2018), including marine environments (Law and Thompson, 2014), rivers (Di and Wang, 2018), sediments (Peng et al., 2017), and organisms (Moore et al., 2001). Recent evidence indicates that rivers serve as pivotal hubs for the storage and transfer of MPs, acting as key pathways connecting terrestrial and marine systems. However, the persistence of MPs in rivers and the challenges in post-pollution reme- diation have raised significant environmental alarms (Jahromi et al., 2021). Furthermore, studies have reported the detection of MPs with

diverse shapes, colors, and sizes in various commonly consumed fish species and other marine organisms (Tepe et al., 2024). MPs tend to accumulate through the food chain in aquatic environments, potentially causing intestinal damage (Qiao et al.,2019), metabolic dysfunction (Lu et al., 2016), and hematopoietic system impairment (Jing et al., 2022), ultimately increasing human health risks (Lahive et al., 2022). Sedi- ments play a dual role as both “sources” and “sinks” in the transport and transformation of heavy metals (HMs), functioning as significant accu- mulation reservoirs while simultaneously serving as potential release sources (Varol et al., 2025; Yüksel and Ustaolu, 2025). HMs retained in sediments exhibit high toxicity, environmental persistence, and latent bioavailability. Through food chain transfer and biomagnification pro- cesses, these contaminants ultimately pose substantial threats to human health (Jawad Ul et al., 2023; Ustaolu et al., 2024). Consequently, investigating the combined pollution of MPs and HMs is critical for mitigating food chain contamination and reducing potential human health risks.
The rapid development of industry, mining, and urban expansion has led to sediment pollution by MPs and HMs, emerging as a significant environmental issue (Rakib et al., 2022; Wang et al., 2025b). Given the

• Corresponding author at: College of Environment and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, PR China. E-mail address: haojx@mail.lzjtu.cn (J. Hao).
  https://doi.org/10.1016/j.marpolbul.2025.119151
  Received 9 September 2025; Received in revised form 12 December 2025; Accepted 12 December 2025 Available online 8 January 2026
  0025-326X/© 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

intricate nature and wide range of human activities, combined with the varied emission sources of MPs and HMs, urban environments are widely regarded as the primary source for these pollutants into fresh- water systems (Guo et al., 2021; Yin et al., 2021). Moreover, in aquatic systems, MPs and HMs in the water column tend to precipitate in the bottom sediments. Therefore, the distribution of MPs and HMs in sedi- ments serves as a reliable indicator of long-term pollution trends (Zhang et al., 2018).
MPs, characterized by their persistent nature, small size, low density, and large specific surface area, can effectively adsorb hazardous sub- stances through multiple mechanisms including physical interactions, UV-induced surface oxidation, biodegradation processes, and surface biofilm formation. These interactions ultimately lead to the formation of complex environmental pollutants with synergistic effects (Du et al., 2025; Liu et al., 2023; Mishra et al., 2024). Beyond reducing plant biomass, MPs exacerbate HM-induced physiological stress, including impaired photosynthesis and elevated oxidative stress (Huang et al., 2024). The co-exposure of MPs and HMs in aquatic systems enhances HMs toxicity through additive or synergistic interactions, thereby posing significant risks to human health, biodiversity, and the ecological integrity of riverine ecosystems (Mato et al., 2001; Tunali et al., 2020). Specifically, combined exposure to MPs and Cd results in a marked rise in reproductive toxicity, manifested as enhanced cellular necrosis and inflammatory responses (Chen et al., 2025). Furthermore, sediment- bound HMs may be remobilized into pore water under hydrodynamic conditions, causing secondary contamination. Notably, HMs and MPs frequently share common pollution sources and co-occur in the envi- ronment. Their coexistence induces synergistic toxicological effects, substantially amplifying environmental risks (Cao et al., 2021; Zhou et al., 2020). Therefore, it is necessary to conduct in-depth research on these complex pollutants to evaluate their environmental risk potential.
Currently, comprehensive MPs and HMs assessment remains in its early stages. While (Li et al., 2024b) developed an initial two- dimensional index model for combined pollution risk evaluation, sub- sequent modifications by (He et al., 2025), who introduced weight factors, overlooked MP morphology as a key risk parameter. Given that MPs shape directly mediates biological impacts and serves as a potential source indicator (Kaushik et al., 2024), this study innovatively in- tegrates shape-related hazard factors into a two-dimensional composite index model. This integration improves upon existing frameworks, thereby enhancing ecological risk assessment and providing a novel methodology for multi-pollutant risk evaluation.
The Yellow River basin constitutes both China's primary economic corridor and vital ecological preservation zone (Jiang et al., 2020). While pollution by MPs and HMs has been well documented in the Yellow River Delta, headwater regions, and mid-upstream areas (Feng et al., 2024; Islam and Cheng, 2025; Wang et al., 2025a), most studies have focused on single-type contamination, with limited attention given to the co-pollution of MPs and HMs. The Lanzhou section, being the only large urban area in the basin where the main channel flows through the city core, faces increasing composite pollution pressures due to indus- trial wastewater discharge and rapid urbanization. Although previous studies have investigated the individual pollution situations of MPs and HMs in the Lanzhou reach of the Yellow River (Li et al., 2023; Zhou et al., 2024), systematic research on their co-occurrence and combined pollution remains scarce. Therefore, to address these research gaps, this paper selects the Lanzhou section of the Yellow River as a representative urban river system, with the following objectives: (1) to characterize the spatial distribution of MPs and 6 HMs (Cr, Ni, Cu, Zn, As, and Cd) in sediments, (2) to evaluate MPs and HMs pollution status; (3) to assess their two-dimensional comprehensive pollution characteristics; and (4) to examine correlations between MP characteristics (abundance, color, size, and shape) and HM concentrations. This comprehensive approach represents the first integrated assessment of MP-HM composite pollution in this critical river system. The findings will advance our understanding of MPs and HMs pollution in river sediments and inform pollution

control strategies and ecological management for the Yellow River's Lanzhou section.

2. Methods and materials：
   2.1. Research section and sampling
   The Lanzhou section of the Yellow River, spanning 152 km in the upper catchment, follows an east-west trajectory through Xigu District into the urban core, extends along the northern periphery of Yuzhong County, and terminates at Dongwu Gorge (Yuan et al., 2024). It serves as both an important ecological corridor and the core axis of Lanzhou's urban development. The Yellow River's Lanzhou reach maintains consistent flow, remains ice-free annually, and has a relatively low sediment content. The mean annual precipitation and mean annual temperature are 327.7 mm and 9.1 oC respectively (Tang and Guo, 2010). Lanzhou is a major industrial hub in northwest China. Most in- dustrial enterprises within the Yellow River Basin are concentrated along the main river channel and its tributaries. Among them, the Huangshui River, an important tributary of the Yellow River, flows through Qinghai and Gansu provinces and joins the Yellow River at Dachuan Town in Xigu District of Lanzhou, carrying a relatively high pollution load (Shang et al., 2023). Therefore, considering the regional characteristics of sediment distribution along the urban rivers, 12 representative sampling points were selected to cover the Lanzhou section of the Yellow River (Table S2). Surface sediment samples (0–5 cm depth) were collected in November 2024 using a stainless-steel grab sampler. Immediately after collection, samples were transferred to pre- cleaned 2.5 L glass bottles and stored at 4 oC pending subsequent analysis. The spatial distribution of sampling sites is illustrated in Fig. 1.
   2.2. Extraction and identification of MPs and HMs
   MPs extraction and identification: sediment samples were dried in an oven at 60 oC until constant weight was achieved. Subsequently, 50 g of dried sediment was mixed into 150 mL saturated NaCl digestion (ρ = 1.2 g/mL, 25 oC) in a glass beaker. The mixture was stirred for 5 min and then ultrasonicated for 15 min to achieve homogeneity. After homoge- nization, the beaker was sealed with aluminum foil and allowed to settle for 12 h. The supernatant was collected, and the extraction procedure was repeated twice. The combined supernatant was vacuum-filtered through a glass fiber membrane (0.45 μm, Ø 50 mm) using a recircu- lating water vacuum pump. Retained particulates on the membrane were rinsed with 30 % H2O2 to transfer them into a clean beaker, fol- lowed by digestion with 50 mL of 30 % H2O2 at 65 oC (80 rpm, 24 h) to remove organic matter. Finally, the solution was refiltered, and the membrane was air-dried in a labeled Petri dish for further analysis.
   HM extracted and identified: sediment samples were air-dried, me- chanically ground, and sieved through a 100-mesh sieve before diges- tion. Digestion was performed with an HF-HNO3-HCl mixture, followed by quantification of heavy metal concentrations using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7900, Japan). Precisely 0.25 g (±0.1 mg) of sample was digested in PTFE crucibles through sequential acid treatment: initial HCl digestion (10 mL, 90–100 oC) concentrated to 3 mL, followed by HNO3 addition (9 mL) for complete dissolution, HF treatment (5–8 mL, 120 oC, 30 min), and final HClO4 digestion (1 mL, 150–170 oC) to near-dryness. After cooling, the mixture was filtered through a 0.45 μm membrane and diluted to 25 mL with 1 % HNO3 in a volumetric flask for storage and subsequent analysis. Following volumetric preparation, concentrations of Cr, Ni, Cu, Zn, As, and Cd were measured by ICP-MS (Di and Wang, 2018).
   2.3. Quality assurance and quality control
   MPs abundance was expressed as items per kilogram of dry sediment (items/kg). To minimize anthropogenic and atmospheric MP

contamination, all laboratory equipment was triple-rinsed with deion- ized water prior to experiments, while operators wore nitrile gloves and cotton lab coats throughout the procedures. To ensure quality assurance, three replicate samples and two blank controls were included in all analytical procedures. No detectable levels of MPs or HMs were observed in blank samples. The concentrations of HMs in the samples were determined using an Agilent 7900 inductively coupled plasma mass spectrometer (ICP-MS) from Japan. Prior to analysis, the method underwent systematic validation to assess its accuracy, precision, spike recovery rates, and detection limits. The detailed validation data are summarized in Table S1, confirming the reliability and accuracy of the instrumental setup and analytical procedures employed.
2.4. Data analysis
Pearson correlation analysis was employed to examine relationships between MPs characteristics (abundance, color, size, and shape) and heavy metal concentrations in sediments. Statistical analyses were conducted using Microsoft Office Excel 2019, SPSS 26.0 (IBM, USA), and Origin 2021, and spatial distribution maps of geographic features and sampling sites throughout the Lanzhou study area were created using ArcGIS 10.2.
2.5. Risk assessment
2.5.1. Risk assessment of MPs
The Pollution Load Index (PLI), introduced by (Tomlinson et al., 1980), was employed to assess the environmental impact of MPs. This method integrates site-specific pollution indices into a composite regional index, providing a quantitative measure of pollution at both local and watershed scales (Yin et al., 2021).
CFi = Ci /C0 (1)
PLIi = √ (2)
PLI = _ (3)
where CFi denotes the PLI of MPsi; Ci represents the measured abun- dance of MPsi; the background value for C0 was defined as the minimum abundance observed in this study, consistent with the methodology of (Kabir et al., 2021). PLIi is the MPs pollution load index of sampling point i, and PLI is the overall pollution load index for the study area.

Detailed classification criteria are shown in Table S3.
Beyond MPs abundance, the shape of MPs represents a critical determinant of toxicity. To quantify shape-dependent risks, Li et al. (2024b) enhanced the Shape Risk Index (SRI), originally developed by Wang et al., (2024a), by integrating the Toxicity Risk Index (TRI) with environmental parameters (Rangel-Buitrago et al., 2021). The improved framework is expressed as follows:
SRIi = P/C × Si × H (4)
、
SRI = SRIi (5)
where SRI i denotes the toxicity risk index for MPs of shape i; P denotes
the measured abundance of MPs i; C corresponds to the mean abun-
dance across all sampling points; Si is defined as the abundance ratio of
MPs shape i to the total MPs count; H denotes the risk factor for ge-
ometry i; see specific figures in Table S4.
2.5.2. Risk assessment of HMs
To distinguish it from MPs Pollution Load Index (PLI), the Heavy Metal Pollution Load Index is termed MLI in this study (Tomlinson et al., 1980) for the assessment of heavy metals:
CFi = Ci /C0 (6)
MLIi = √ (7)
MLI = MLI1 × MLI2 × … × MLI (8)
where CFi denotes the pollution factor for HMs, Ci represents the measured concentration at each sample location; and C0 refers to the background value of soil elements in Lanzhou City (Table S5) (Li et al., 2023), MLI value exceeding 1 indicates pollution, and below 1 indicates no pollution.
Eri = CFi × T (9)
MRI Eri (10)
i
where Eri represents the individual potential ecological risk index, reflecting the toxicity response coefficient of HMs (Yin et al., 2021) with specific values provided in Table S4, while MRI denotes the

comprehensive ecological risk index for all HMs at a given sampling point.
2.5.3. Comprehensive risk assessment model for MPs–HMs combined pollution
Ecological risk assessment requires consideration of multiple contributing factors. We used a two-dimensional composite pollution index (TPI) (Li et al., 2024b) to assess ecological risks from combined MPs and HMs contamination, incorporating MPs shape as a critical risk parameter. To enable dimensional standardization between the MRI and SRI while preventing single-pollutant bias, we implemented two-factor risk level allocation coefficients to optimize the TPI framework.

(11)
where MAX (MRI, 1/3SRI) refers to maximum value between MRI and 1/3SRI; AVERAGE (MRI + 1/3SRI) signifies average values of TRI and 1/3SRI. The combined MPs and HMs pollution was classified into 15 grades according to Eq. (11). Detailed classification criteria are shown in Table S3.