Pollution Characteristics and Risk Assessment of Microplastics and Plasticizers Around a Typical Chemical Industrial Park

Abstract

Although the risks of microplastics (MPs) and plasticizers have received attention, plastic chemical parks, as an important source of them, lack adequate research. A river in eastern China that connects to Hangzhou Bay and receives wastewater from the plastics industry was targeted for investigation. The highest concentrations of MPs in water and sediment samples were found at the effluent (2250 ± 469 items/L and 3245 ± 430 items/kg, respectively). The WWTP effluent significantly increased the concentrations of MPs in the downstream water and sediments, which were 2.77 and 2.57 times higher than those in the upstream water, respectively. It was estimated that 2.24 × 10¹³ MPs entered the environment annually through wastewater discharge. The di(2-ethylhexyl) phthalate (DEHP) concentration was the highest at the effluent (32.6 ± 7.9 μg/L in water and 2.7 ± 3.4 μg/g in sediment), and the downstream DEHP concentrations were 3.37 and 2.41 times higher than those in the upstream water and sediment, respectively. All three risk assessment models showed that the WWTP discharge outlet had the highest risk of MPs. DEHP in 87.5% of sediment samples posed a medium risk to algae.

1. Introduction

Microplastics (MPs) have emerged as a novel environmental pollutant, attracting widespread attention due to their global distribution and potential ecological and health risks [1]. The substantial global demand for plastic products has driven a rapid increase in plastic production, which is projected to reach 33 billion tons by 2050 [2]. MPs have a large specific surface area and hydrophobicity, which enable them to effectively adsorb a variety of environmental pollutants and become an important carrier of pollutants in the ecosystem [3]. MPs enter the aquatic environment through various pathways such as wastewater discharge, agricultural runoff, and atmospheric deposition. They not only cause physiological damage to aquatic organisms but also pose potential threats to human health through bioaccumulation in the food chain [4,5]. Ingestion of MPs by zebrafish has been reported to transfer to other organs, resulting in delayed larval development and severe malformations, including spinal and ocular abnormalities [6]. The longer the MP fiber consumed, the more severe the intestinal damage and metabolic disorders it causes [7]. MPs can release toxic chemicals such as plasticizers after aging, which can cause different degrees of damage to the digestive and respiratory systems of organisms after ingestion [8,9]. Studies have detected different concentrations of MPs in human feces, placenta, and blood, further confirming the risk of MPs entering the human circulation system through the food chain [9,10,11]. Phthalic acid esters (PAEs), commonly used as additives in plastic manufacturing, account for approximately 95% of plasticizer applications in plastic products (polyvinyl chloride, polymethyl methacrylate, polyamide, polystyrene, and polyester) to enhance plasticity and low-temperature resistance [12,13,14]. However, since most PAEs do not form covalent bonds with polymer matrices, they are readily released into the environment as plastics age and degrade [15]. Among PAEs, di(2-ethylhexyl) phthalate (DEHP), the most widely used plasticizer, is recognized as a contaminant with severe potential risks to the environment and organisms due to its teratogenic and carcinogenic properties [16,17]. In the plastic production sector, DEHP is the most consumed among all PAEs, followed by dibutyl phthalate and diisobutyl phthalate, collectively accounting for approximately 80% of China’s PAE production [18]. DEHP is widely present in healthcare products, medical supplies, building materials, household items, and packaging materials, making it one of the most toxic PAEs found in aquatic environments due to its extensive sources [19]. Owing to the intensive use of PAEs, populations in developing countries like China and Russia exhibit significantly higher DEHP exposure levels than those in developed countries, a phenomenon closely linked to the regional distribution and usage intensity of the plastic industry [20,21].

Chemical regions producing plastics are important potential sources of MPs and PAEs. Approximately 95% of plastics originate from petrochemical sources, with the global petrochemical sector estimated to have released 14.4 billion MPs in 2021 [22,23]. The cutting and abrasion of plastic pellets and fibers during manufacturing inevitably lead to MPs’ release into the environment [24,25]. For example, MPs’ concentrations in plastic industrial wastewater can reach 1.1 kg/m³, and directly discharging ineffectively treated wastewater serves as a critical input source of MPs to natural environments [26]. The intensive use of additives in industrial production is a primary cause of environmental pollution. DEHP levels in rivers receiving plastic industrial wastewater reach 240–5680 ng/L, significantly higher than in non-industrial surrounding areas [27]. Additionally, wastewater treatment plants (WWTPs), due to their inability to fully remove MPs and PAEs from effluents, are recognized as important sources of these contaminants in the environment [28,29].

Hangzhou Bay is one of the important economic zones and industrial bases in eastern China, and many plastic and petrochemical enterprises are distributed around it. Plasticizer and MPs pollution in industrial areas is often orders of magnitude different from that in other areas [30,31]. As the source of the production of plastic products and additives, the harm of large-scale production to humans and the environment is worrisome. Although existing studies have preliminarily revealed the environmental behavior and health risks of MPs and DEHP, significant gaps remain in systematic research on the occurrence characteristics of these contaminants in multimedia environments (river water and sediments) within typical plastic chemical parks. Therefore, the objectives of this study were to investigate the occurrence and distribution of MPs and DEHP in river water and sediments of a typical Chinese plastic chemical park and analyze the impacts of industrial wastewater discharges on the riverine environment; pollution load index (PLI), polymer hazard index (PHI), potential ecological risk index (PERI), and risk quotient (RQ) were used to assess the risks of MPs and DEHP in the environment. By revealing the pollution characteristics of industrial-source MPs and plasticizers, this study aimed to provide a scientific basis for pollution control in the plastic chemical park, a revision of water environmental quality standards, and the construction of risk early-warning systems.

2. Materials and Methods

2.1. Sample Collection

Hangzhou Bay is an important part of the East China Sea, featuring rich biodiversity and significant ecological functions. However, due to the increase in surrounding industrial activities, especially the wastewater discharge from the chemical industry, the water quality and ecosystem of Hangzhou Bay are facing serious threats. This study took the sewage-receiving river of a typical plastic chemical industrial park connected to Hangzhou Bay in eastern China as the research object. More than 65 plastic production and upstream and downstream chemical enterprises have gathered around the river, including the largest plastic pellet production enterprises in Eastern China (accounting for 15% of the total production capacity) and plastic fiber production enterprises (accounting for 20% of the total production capacity). The main products include synthetic resins, plastics, synthetic rubbers, and synthetic fibers, with an annual production of over 42,000 tons of plastic additives (including antioxidants and plasticizers). A WWTP in the region handles surrounding industrial wastewater, with an annual treatment capacity of 9.95 million tons, and discharges treated effluent directly into the receiving river.

To systematically evaluate the impact of industrial wastewater on the riverine environment, 8 sampling points were set along the receiving river (Figure 1). Upstream control areas (sampling points 1–3) were located outside the influence range of the WWTP effluent, serving as background references unaffected by direct industrial wastewater pollution. The effluent area (sampling point 4) was adjacent to the WWTP effluent, directly receiving the treated effluent. The downstream impact area (sampling points 5–8) was arranged equidistantly along the wastewater flow direction, considering field accessibility, to reflect contaminant migration and diffusion characteristics.

Sampling was conducted in September 2024. To ensure data representativeness, weekly sampling was performed throughout September, with samples from each sampling point collected four times and mixed in equal proportions as a composite sample before analysis. Water samples were collected using methanol-cleaned stainless steel buckets at 0.5 m below the water surface, avoiding sediment disturbance, with 2 L of surface water taken at each point. Surface sediments (5–20 cm depth) were collected using grab samplers, with 1 kg of sediment per sampling point. All samples were immediately sealed in brown glass bottles, stored at 4 °C, and transported to the laboratory for pretreatment and analysis within 24 h.

2.2. Isolation and Identification of MPs

Sediment samples were first processed using a freeze dryer (SCIENTZ, 10N, Ningbo, China), followed by manual removal of impurities larger than 5 mm. Given that most plastics have a density < 1.4 g/cm³, MPs were extracted using a separatory funnel and saturated potassium formate solution (1.54 g/mL) via flotation. The floated liquid was digested with 30% H₂O₂ overnight, and the digested solution was filtered through a PTFE membrane (0.22 µm). Water samples underwent similar treatment, with filtration through a 0.22 µm membrane. After repeating the flotation and digestion processes on the filter membrane, the MP-containing solution was enriched on the membrane for subsequent detection.

Micro-Raman spectroscopy (HORIBA, LabRAM HR Evolution, Palaiseau, France) was employed to identify MPs. To address the time-consuming nature of fully characterizing all MPs on the membrane, the method of Han, et al. [32] was adapted; the total number of MPs was extrapolated from selected regions using the formula:

$$N_D=\sum _{i=1}^3{N}_i{\displaystyle \frac{S_{\mathit{Total}}}{3S_{\mathit{Projected} \mathit{areas}}}}$$

(1)

where N_D represents the total number of MPs on the entire membrane (units: items), N_i is the number of MPs in each projected region (units: items), S_Total is the membrane area (mm²), and S_{Projected areas} is the area of each selected region (mm²). The final particle count for each sample was calculated using the ratio of three selected regions (900 µm × 700 µm) to the total membrane area. The Raman system used a 532 nm laser wavelength, scanned from 100 to 3200 cm⁻¹ with a laser power of 50 mW and an exposure time of 5 s. Detected MPs’ sizes were classified into four categories: <50 μm, 50–500 μm, 500–1000 μm, and 1000–5000 μm. Representative MPs detected in this study are shown in Figure 2.

2.3. Detection of DEHP

The detection of DEHP in water samples followed the method of Li, Han, Su, Hou, Liu, Zhao, Hua, Shi, Meng and Wang [4]. Water samples were first filtered through a 0.22 µm membrane, followed by solid-phase extraction for pretreatment. The HLB solid-phase extraction cartridges were activated sequentially with 5 mL of methanol and 5 mL of ultrapure water at a flow rate of 2 mL·min⁻¹. Water samples were passed through the HLB cartridges at 5 mL·min⁻¹ for extraction. After extraction, the cartridges were dried with nitrogen for 20 min, and target contaminants were eluted using 10 mL of a dichloromethane–n-hexane mixture (1:1, v/v). The eluate was concentrated to 1 mL by nitrogen blowing and analyzed by GC-MS (Agilent, G7000A, Santa Clara, CA, USA).

For sediment samples, a 1:4 (w/v) sediment–dichloromethane mixture was ultrasonicated for 20 min, centrifuged at 2600 rpm for 10 min, and this extraction process was repeated three times. Supernatants were combined, concentrated to near-dryness using a rotary evaporator (HEIDOLPN, Hei-VAP, Schwabach, Germany), redissolved in 2 mL of n-hexane, filtered through a 0.22 µm membrane, and analyzed by GC-MS with an Agilent VF-5 ms capillary column (30 m × 0.25 mm × 0.25 µm). The GC oven program was set as follows: initial temperature 50 °C held for 1 min, increased to 200 °C at 15 °C/min (held for 1 min), then to 280 °C at 8 °C/min (held for 3 min). Quantification ions were 149 m/z, and auxiliary quantification ions were 169 m/z.

Building on studies by Mohammadi, et al. [33] and Lo, et al. [34], this study adapted methods to investigate DEHP adsorbed on MPs, considering their reported role as contaminant carriers in the environment. Since manually selecting small-sized MPs was impractical, sediment samples were freeze-dried and sieved through a 300 µm mesh. Suspected MP particles larger than 300 µm were picked with tweezers, gently rinsed with distilled water, and identified. Confirmed MPs were extracted with 3 × 10 mL dichloromethane in an ultrasonic device (KUDOS, SK2200H, Shanghai, China) at a frequency of 50–60 Hz for 15 min. The combined dichloromethane extracts were centrifuged at 2500 rpm for 20 min, filtered through a PTFE (0.22 µm) membrane, and the eluate was dried via rotary evaporation. Prior to analysis, residues were redissolved in 1 mL of a methanol–water (50:50, v/v) mixture.

2.4. Risk Assessment of MPs

In this study, the PLI, PHI, and PERI were used to assess MPs’ risks, with risk classification criteria outlined in

Table 1. The risk assessment levels of MPs and DEHP.

Pollution Load Index	Category	Polymer Hazard Index	Category	Potential Ecological Risk Index	Category	Risk Quotient	Category
<10	I	0–10	I	<150	Minor	<0.1	Low
-	-	10–100	II	150–300	Medium	-	-
10–20	II	100–1000	III	300–600	High	0.1–1	Medium
20–30	III	1000–1500	IV	600–1200	Danger	1–10	High
>30	IV	>1500	V	>1200	Extreme danger	>10	Very high

[4]. The PLI evaluates risks by comparing the pollution level at sampling points with background values, a widely adopted method for regional risk assessment [35]. The calculation method for the PLI is depicted in Equations (2) and (3):

$${\mathit{CF}}_i={\displaystyle \frac{C_i}{C_{\mathit{oi}}}}$$

(2)

$$\mathit{PLI}=\sqrt{{\mathit{CF}}_i}$$

(3)

In this study, CF_i was defined as the ratio of MPs’ concentrations at each sampling point (C_i) to the background MPs’ concentration (C_Oi). Due to the lack of detailed reported data for the study area, we referenced the approach of Li, Han, Su, Hou, Liu, Zhao, Hua, Shi, Meng and Wang [4], using the lowest detected concentrations of each sample type as background values: 155 items/L for water and 410 items/kg for sediment.

The PHI model referred to the hazard score of plastic polymers provided by Lithner, et al. [36]. Specifically, the PHI calculates risks by first determining the toxicity score of plastics based on their polymer type and chemical composition, then integrating the proportion of each polymer in the sample. The calculation of the PHI is shown in Equation (4):

$$PHI=\sum P_n\times S_n$$

(4)

where $P_n$ is the percentage of each polymer type in a single sample, and $S_n$ is the score of the polymer compound that constituted MP particles [36]. The evaluation results were divided into index levels I–V.

The PERI was used to assess the potential risks of polymers at different sampling points, as described in Equations (5)–(7). This method evaluates hazards based on the risk grades and categories of different polymers [36,37].

$$C_f^i =C_i/C_n^i$$

(5)

$$T_r^i ={\sum }_{i=1}^n{\displaystyle \frac{P_n}{C_i}}\times S_n$$

(6)

$$E_r^i =T_r^i\times C_f^i$$

(7)

In Equations (5)–(7), ($C_f^i$) represents the quotient of MPs’ concentration at each location ($C_i$) and the background concentration ($C_n^i$). The toxicity coefficient ($T_r^i$) reflects toxicity level and biological sensitivity. This coefficient is computed as the sum of the proportion of each type of polymer in the total sample ($P_n$/$C^i$) multiplied by the hazard score of each plastic polymer ($S_n$), based on its potential environmental and biological impact [38]. The details of each plastic are shown in

Table 2. The information and hazard score of detected MPs.

Polymer	Monomer	Density (g/cm3)	Main Applications	Score
Polyethylene (PE)	Ethylene	0.91–0.96	Shopping bags, cosmetics bottles	11
Polypropylene (PP)	Propylene	0.85–0.94	Ropes, food packaging, pipelines	1
Polyvinyl chloride (PVC)	Vinyl chloride	1.41	Disposable plastic bags, pipes, flooring	10,551
Polystyrene (PS)	Styrene	1.05	Expanded polystyrene, CDs, building materials	30
Polyethylene glycol terephthalate (PET)	Ethylene glycol	1.38	Drink bottles, clothes, food packaging	4
Polyacrylonitrile (PAN)	Acrylonitrile	1.15	Clothes, tents, medical equipment	11,521
Polycarbonate (PC)	Phosgene	1.2	Engineered plastic parts, electronics	610
Polyamide (PA)	Caprolactam	1.10–1.15	Textiles, mechanical components	50

Note: The value for the score of each polymer is taken from Lithner, Larsson and Dave [36].

.

2.5. Risk Assessment of DEHP

DEHP has been reported to adsorb onto suspended particles due to its hydrophobicity, thereby causing DEHP-bound particles to settle into sediments [39]. Given the more stable nature of sediments compared to water, we selected sediments to assess the toxic risks of MPs to aquatic organisms. The Risk Quotient (RQ), defined as the ratio of the environmental exposure concentration of a contaminant to its toxicity threshold, was used to characterize the hazard posed by contaminants to ecological environments. DEHP risks were calculated following the European Risk Assessment Technical Guidance Document [40] to estimate the ecological risks of MPs to three sensitive aquatic species: algae, crustaceans, and fish. The formula is as follows:

$$RQ={\displaystyle \frac{MC}{PNEC}}$$

(8)

where MC is the measured concentration of DEHP in sediment samples, and PNEC (predicted no-effect concentration) is derived by dividing the median lethal concentration (LC₅₀) or median effect concentration (EC₅₀) by an assessment factor (AF) of 1000. Toxicity data for DEHP to aquatic organisms (algae, crustaceans, fish) were sourced from the U.S. EPA ECOTOX database, as shown in

Table 3. Toxicity data and relevant information of DEHP to the three representative aquatic species.

Population	Species	Toxicity Data (μg/L)	AF	PNECsediment (μg/g)
Algae	Pseudokirchneriella subcapitata	96 h, population, EC50 = 100	1000	3.25
Crustaceans	Mytilus edulis	21 d, mortality, NOEC = 42	50	13.6
Fish	Gasterosteus aculeatus	28 d, mortality, NOEC = 300	50	32.5

NOEC: no observed effect concentration; AF: assessment factor; EC₅₀: median effect concentration; LC₅₀: median lethal concentration [40].

[40].

2.6. Quality Control and Assurance

To prevent potential laboratory contamination, nitrile gloves and laboratory coats were worn during sampling and analytical procedures. Laboratory doors and windows were kept closed, and unnecessary movement was minimized to reduce airflow-induced contamination. All solutions used for experiments and cleaning were filtered through PTFE (0.22 µm) membranes and wrapped in aluminum foil prior to use. Laboratory glassware for sample preparation and extraction was rinsed with acetone and n-hexane. Three parallel samples were set for each sample. All experimental data were visualized using Origin 2021. Before use, all consumables were carefully cleaned three times with deionized water, air-dried, and wrapped in aluminum foil. To ensure the accuracy of MPs detection, an s-shaped scanning path was adopted to cover all regions of the filter membrane. To ensure the reliability of the quantitative and qualitative analysis of MPs, positive control experiments were selected to verify the reliability of the quantitative and qualitative analysis. The MP standard of 10 μm PS (YIYUAN, China) was chosen because its density is in the medium range among these MPs and can represent the recycling of MPs with different densities. PS standard products were diluted to 1000 items/L to represent the MPs’ concentration in the samples of this study. The processing and testing procedures were consistent with natural samples. The recovery rate was 88.5 ± 6.2%, indicating that the detection method used in this study has good applicability and accuracy.

3. Results and Discussion

3.1. Pollution Characteristics of MPs and DEHP in Different Samples

3.1.1. Abundance and Pollution-Type Characteristics of MPs in Water and Sediments

The abundance of MPs detected is shown in Figure 3a, illustrating a significant impact of the WWTP effluent on river MPs’ concentrations. In upstream river water and sediments unaffected by the direct WWTP effluent, average MPs’ concentrations were 188 items/L and 470 items/kg, respectively. Besides industrial discharges, potential sources of MPs in rivers include the decomposition of plastic waste, domestic drainage, and atmospheric deposition [41]. The highest MPs’ concentrations occurred at the two sampling points near the WWTP effluent (W-4 and S-4), with abundances of 2250 ± 469 items/L and 3240 ± 430 items/kg, respectively. Downstream river water and sediments influenced by the WWTP effluent had average concentrations of 521 items/L and 1208 items/kg, 2.77-fold and 2.57-fold higher than upstream values, indicating that WWTP discharges were the primary driver of elevated downstream MPs’ concentrations. WWTPs should adopt a more advanced membrane filtration process to improve the removal rate of MPs and effectively reduce the number of MPs discharged. For example, a membrane bioreactor process has been reported to have a removal rate of more than 99.9% [42].

A study in South Korea also demonstrated that wastewater treatment plants significantly contributed to downstream MPs pollution, finding higher MP contamination in water, sediments, and fish downstream compared to upstream areas [43]. Additionally, a study along China’s Yellow River reported 100% detection rates of MPs in water, sediment, and fish gut samples, with downstream MPs 2–3 times higher than upstream [44]. When compared to domestic and international reports (Table 4), MPs pollution concentrations in this study are comparable to those reported in China’s Xiangjiang River [45] and significantly higher than regions such as South Korea [43], Iran [46], and China’s Qinghai-Tibet Plateau [4], with a maximum difference of five orders of magnitude, highlighting the unique pollution characteristics of chemical parks.

Although existing studies have shown that the removal efficiency of MPs in conventional WWTPs can achieve 40–99%, the total mass of MP pollutants discharged into receiving environments remains concerning due to the enormous scale of wastewater treatment [47,48]. Based on the detected concentrations and calculated emission loads (995 million tons per year) of MPs in the WWTP effluent samples from this study, the annual input of MPs into the environment via wastewater discharge was estimated at 2.24 × 10¹³ particles. This value is significantly higher than the 5.60 × 10¹² particles annually emitted by a Chinese petrochemical plastics manufacturer [30], and the 2.37 × 10¹⁰ particles released yearly by a municipal WWTP serving 650,000 residents [49]. These findings highlight the substantial contribution of the chemical park to environmental MPs pollution. We suggest optimizing treatment processes and establishing effluent concentration limits to construct a comprehensive prevention and control system spanning from source management to end-of-pipe treatment.

In water and sediment samples, PET, PP, and PE consistently represented the dominant proportions of detected MPs (Figure 4). This is because these three polymers serve as core products of plastics manufacturers and are widely used in packaging (PP/PE films), textiles (PET fibers), and daily commodities (PE containers), making them primary sources of environmental MPs [50,51]. This compositional pattern aligns with observations from global water bodies (Table 4), where PE, PP, and PET were identified as major MP types in China’s Xiangjiang River [45], South Korea [43], Iran [46], and China’s Qinghai-Tibet Plateau [4]. Notably, the combined proportion of these three polymers in upstream water and sediments was 62.4% and 63.0%, respectively, while in downstream areas influenced by WWTP discharges, their total percentages significantly increased to 77.0% (river water) and 81.4% (sediments), reflecting the pronounced impact of WWTP effluents on MPs’ composition. Plastic pellets (PET, PP, PE) and synthetic fibers (PET, Polyacrylonitrile) produced in large quantities by the petrochemical industry are likely important MP sources, as cutting and abrasion during manufacturing inevitably release MP particles that enter the environment through incomplete treatment [24,25,52]. A German study further corroborated the critical role of industrial sources, estimating annual MPs’ emissions of 2.7 tons via industrial wastewater [26].

Table 4. Comparison of MPs pollution characteristics in other regions.

	Location	Abundance	Main Morphologies	Main Size (μm)	Main Types	References
Water	Tanchon stream, Korea	5.3–87.3 items/m3	Fragment	<1000	PE (41%), PP (22%)	[43]
	Xiang Jiang River, China	2173–3998 items/L	Fiber, fragment	<10	PP, PET, PE	[45]
	Helmand River, Iran	15.4–51.8 items/ m3	fiber (46%), fragment (43.5%)	100–500	PS, PE	[46]
	The main rivers on the Qinghai-Tibet Plateau, China	2825–11,865 items/ m3	Fiber (71.5%)	<500 (58.9%)	PP, PET	[4]
	A river that receives wastewater, China	155 ± 20–2250 ± 469 items/L	Fiber (36.9%)	500–1000 (30.5%)	PE (25.1%), PP (24.3%)	This study
Sediment	Tanchon stream, Korea	493.1 ± 136.0 (upstream), 380.0 ± 144.2 items/kg (downstream)	Fragment	<1000	PE (49%), PP (18%)	[43]
	The main rivers on the Qinghai-Tibet Plateau, China	59.1–438.0 items/kg	Fiber	<500 (73.4%)	PP, PET	[4]
	Vembanad Lake, India	35 ± 49.5–1414 ± 182 items/kg	Fragment	<500	PE, PP	[53]
	The Yellow River, China	90–750 items/kg	Fiber (88.7%)	<500 (36.8%)	PET (36%), PE (27%)	[44]
	A river that receives wastewater, China	410 ± 57–3245 ± 430	Fragment (42.0%)	<50 (36.0%)	PET (29.2%), PE (25.1%)	This study

Table 4. Comparison of MPs pollution characteristics in other regions.

	Location	Abundance	Main Morphologies	Main Size (μm)	Main Types	References
Water	Tanchon stream, Korea	5.3–87.3 items/m3	Fragment	<1000	PE (41%), PP (22%)	[43]
	Xiang Jiang River, China	2173–3998 items/L	Fiber, fragment	<10	PP, PET, PE	[45]
	Helmand River, Iran	15.4–51.8 items/ m3	fiber (46%), fragment (43.5%)	100–500	PS, PE	[46]
	The main rivers on the Qinghai-Tibet Plateau, China	2825–11,865 items/ m3	Fiber (71.5%)	<500 (58.9%)	PP, PET	[4]
	A river that receives wastewater, China	155 ± 20–2250 ± 469 items/L	Fiber (36.9%)	500–1000 (30.5%)	PE (25.1%), PP (24.3%)	This study
Sediment	Tanchon stream, Korea	493.1 ± 136.0 (upstream), 380.0 ± 144.2 items/kg (downstream)	Fragment	<1000	PE (49%), PP (18%)	[43]
	The main rivers on the Qinghai-Tibet Plateau, China	59.1–438.0 items/kg	Fiber	<500 (73.4%)	PP, PET	[4]
	Vembanad Lake, India	35 ± 49.5–1414 ± 182 items/kg	Fragment	<500	PE, PP	[53]
	The Yellow River, China	90–750 items/kg	Fiber (88.7%)	<500 (36.8%)	PET (36%), PE (27%)	[44]
	A river that receives wastewater, China	410 ± 57–3245 ± 430	Fragment (42.0%)	<50 (36.0%)	PET (29.2%), PE (25.1%)	This study

3.1.2. Size and Morphology Characteristics of MPs Pollution in Water and Sediments

The particle size distributions of MPs in water and sediment samples exhibited significant differences (Figure 3c). In water bodies, the proportion of MPs in the 500–1000 μm size range (30.6%) was significantly higher than that in sediments (19.2%). Conversely, the average proportion of MPs smaller than 50 μm in sediments (36.0%) was slightly higher than in water samples (28.6%), indicating that sediments are more prone to enriching small-sized MPs. Reports from the Helmand River in Iran [46], the Qinghai-Tibet Plateau in China [4], and Vembanad Lake in India have shown the dominance of MPs <500 μm [53]. Notably, in WWTP effluent samples (W-4 and S-4), the proportions of <50 μm MPs were 45.2% and 52.9%, respectively, higher than in other samples. This phenomenon was attributed to the relatively high interception efficiency of conventional WWTP units for large-sized MPs, which are unable to completely remove particles <50 μm [32,54].

Influenced by the WWTP effluent, the average proportions of <50 μm MPs in downstream water and sediments increased by 28.9% and 61.8% compared to upstream areas, respectively, demonstrating that WWTP effluents are a key factor affecting the particle size distribution of MPs in receiving waters. A similar pattern was observed in a U.S. study, where small-sized MPs significantly increased downstream of four WWTPs [55]. It is worth noting that MPs <20 μm have been reported to accumulate in the kidneys and intestines of organisms [56]. The increased proportion of small-sized MPs in downstream rivers due to WWTP discharges may amplify their ecological risks by enhancing bioavailability and long-distance migration potential.

The morphology of MPs in water and sediment samples also displayed notable differences (Figure 3d). Fibers were the dominant morphology in water (36.9%), significantly higher than in sediments (24.8%). As shown in Figure 2, fibers were representative morphologies for PET and PAN. Films accounted for 10.3% in water, also exceeding their proportion in sediments (5.9%). In contrast, fragments dominated sediments at 42.1%, far surpassing their presence in water (25.3%). These differences are directly related to the physical properties of each morphology: fibers and films, with lower density, tend to float on the water surface, while fragments, with higher density and regular shapes, are more likely to accumulate in sediments [57]. As reported in other regions (Table 4), such as in the River water samples of Xiang Jiang Riverh and the main rivers on the Qinghai-Tibet Plateau in China and Helmand River in Iran, fiber represented the main MPs’ morphology [4,45,46]. Sediments from Tanchon stream in South Korea and Vembanad Lake in India both showed the dominance of fragment MPs [43,53].

The proportion of fibers in the samples (W-4 and S-4) at the effluent discharge outlets was significantly higher than that at other points. This is because the primary MPs (mainly fibers and particles) produced by plastic manufacturing enterprises due to mechanical wear during the granulation and cutting processes enter the environment through production wastewater, becoming the main source of fiber and particle MPs [24,25]. PP and PE MPs in river water and sediment mainly present in the form of particles with different sizes (Figure 2).

Regarding the sources of film-like MPs in the samples, PET and PE films are common MPs in WWTP and are believed to mainly come from industrial wastewater and plastic products used in daily life [58]. In addition to WWTPs, riverine MP film pollution may also stem from agricultural mulch films and other sources, as indicated by studies showing multiple contribution pathways to fluvial MPs contamination [59].

3.1.3. Occurrence of DEHP in Water and Sediments

As shown in Figure 5a, the contamination pattern of DEHP mirrored that of MPs. The highest DEHP concentrations in both water and sediments were observed at the WWTP effluent (W-4 and S-4), reaching 32.6 ± 7.9 μg/L and 2.7 ± 3.4 μg/g, respectively, with downstream areas consistently showing higher levels. This spatial distribution pattern provides evidence for using DEHP as an indicator of MPs pollution. A survey in China’s Yangtze River demonstrated that the abundance of all MPs increased linearly with the DEHP/∑₁₆PAEs ratio (R² = 0.29–0.49), and DEHP, as the most abundant homolog in sediments, ranged from 25.0 to 161.3 ng/g [60].

DEHP is the most common and widely used plasticizer in global plastic products, exhibits high detection frequencies and concentrations in water environments worldwide, and has been listed as a priority pollutant by both the United States and China [60,61]. Other studies have shown elevated DEHP contamination in water bodies receiving industrial wastewater: for example, a Mexican study reported average and maximum DEHP concentrations of 12.5 μg/L and 19.4 μg/L in a river accepting both industrial and domestic wastewater [62], while Iranian raw wastewater and urban runoff showed DEHP concentrations ranging from 21.16 to 37.08 μg/L [63], values close to those in this study. Regarding sedimentary DEHP concentrations, a survey of major watersheds in South Korea found that industrial areas had an average DEHP concentration of 4050 ng/g dry weight (dw) in sediments, compared to only 38 ng/g dw in non-industrial areas, further confirming the significant impact of industrial activities on DEHP pollution [64].

In this study, downstream DEHP concentrations influenced by discharged wastewater were significantly higher than upstream values, with average concentrations in downstream water and sediments being 3.4 and 2.4 times higher than upstream, respectively. The results indicate that industrial wastewater discharge is a major source of riverine DEHP pollution. In plastic production, DEHP is the most consumed of all PAEs, accounting for about 55.0% of PAEs’ production in China [18]. After being used by these enterprises to produce plastic products, DEHP may enter the environment in the form of raw material residues, intermediates, or wastes [65]. Besides wastewater, DEHP has also been reported to originate from the release of plastic products such as daily-use plastics and packaging [18]. Future research could further investigate the spatial distribution and temporal variation in DEHP pollution, particularly considering pollution source interactions among different enterprises within industrial parks, to enable source-level control of DEHP.

3.1.4. Concentration Analysis of DEHP Adsorbed on MPs

As an effective carrier of various pollutants in the aquatic environment, the enrichment concentration of MPs was significantly higher than that of the surrounding environmental media [66]. As shown in Figure 5b, the concentrations of DEHP adsorbed on MPs in water and sediment samples were 1.0 ± 0.3–5.2 ± 0.9 μg/g and 2.2 ± 0.2–14.5 ± 3.3 μg/g, respectively. Notably, the average DEHP concentration adsorbed on MPs in downstream water samples was 2.23 times higher than in upstream areas. The highest concentration of DEHP adsorbed on MPs in water samples was found at W-7 (5.2 ± 0.9 μg/g), rather than the discharge port of WWTP (W-4). This phenomenon may be attributed to the continuous DEHP adsorption of MPs during the migration process caused by water flow. Similarly, DEHP concentrations on MPs in downstream sediments were 2.1 times higher than upstream, with the maximum detected at site S-4 (14.5 ± 3.4 μg/g), consistent with the distribution pattern of DEHP in the environmental matrix. This may result from the slower migration of MPs in sediments, causing their adsorbed concentrations to correlate with ambient DEHP levels. Of particular significance, DEHP concentrations on sediment MPs were 5.5–14.6 times higher than in the surrounding environment, further confirming the strong enrichment capacity of MPs for pollutants in sediments. The adsorption of PAEs such as DEHP on MPs has been reported to be mainly controlled by the partition effect and π–π interactions [67].

Studies worldwide have also identified the carrier effect of MPs on additives. In coastal WWTP areas of Hong Kong, bisphenol A enrichment on MPs’ surfaces reached 9.52–4481 ng/g [34]. A landfill study in Bushehr Port, Iran, reported seasonal fluctuations in PAEs’ concentrations on MPs (14.64–157.98 μg/g), with the highest values in August and lowest in June [33]. More notably, the pollutant enrichment capacity of MPs is not limited to organic additives: a Shanghai landfill study found that their enrichment factors for antibiotic resistance genes were significantly higher than those in leachate [68], while a mariculture study revealed that the abundance of culturable resistant bacteria on MPs’ surfaces could be 100–5000 times higher than in water [69]. Collectively, these findings highlight the universal role of MPs as transboundary and multi-pollutant carriers across environmental media. It is alarming that MPs magnify the exposure risk of pollutants. In the future, it is urgent to assess the environmental behavior and ecological impact of MPs from the perspective of the synergistic effect of multiple pollutants.

3.2. Risk Assessment of MPs and DEHP in Different Samples

3.2.1. Risk Assessment of MPs in Water and Sediment

The risk assessment results for MPs in water and sediment samples using three models are shown in Figure 6a–c. The PLI values for water and sediments ranged from 1.0–3.8 and 1.0–2.8, respectively, which were classified as the lowest risk according to the three models (Table 1). However, the traditional PLI only considers MPs’ concentrations, potentially underestimating their actual risks. The PHI values for water and sediments reached 909–1638 and 612–1524, corresponding to risk grades III–V. The PERI incorporates background values and hazard indices and better reflects the true risks of MPs. According to PERI risk grade ranges, 87.5% of water and sediment samples fell into the highest grade V (>1200). Notably, downstream PERI values for water and sediments were 3.0 and 2.7 times higher than upstream values, respectively, indicating that WWTP discharges not only increased MPs’ concentrations in downstream areas but also elevated their potential ecological risks. Similarly, a MPs study in China’s Yellow River showed that downstream PHI and PERI values increased by 3.0 and 3.9 times compared to upstream areas, respectively [44].

The water sample at the WWTP effluent (S-4) exhibited peak risk parameters (PLI = 3.8, PHI = 1638, PERI = 23,786), with its corresponding sediment sample (W-4) also reaching the maximum PLI (2.8) and PERI (4276). This is because the WWTP effluent had significantly higher concentrations of overall MPs and a greater proportion of highly toxic polymers. According to the calculation formulas for PHI and PERI, the hazard scores of polyvinyl chloride (polyvinyl chloride, 10,551) and (PAN, 11,521) are far higher than those of other polymers (Table 2), and the proportion of these polymers in samples significantly influences risk assessment results [36]. Future risk assessment systems should incorporate additional MP indicators, such as specific toxicological values, to enable a more precise risk evaluation of MPs.

3.2.2. Risk Assessment of DEHP in Water and Sediment

As shown in Figure 6d, according to the RQ risk grade classification, 87.5% of samples in this study posed a medium risk (0.1–1) to algae, attributed to their higher sensitivity to DEHP. Notably, the ecological risk of DEHP exhibits significant regional differentiation: a study on DEHP risk assessment in sediments of major rivers and lakes on China’s Qinghai-Tibet Plateau showed that 95% of sampling sites presented a low risk to algae, directly linked to the region’s low industrialization and minimal plastic usage [4].

DEHP has been reported as the most frequently detected and highest-risk pollutant among environmental PAEs. A study indicated that DEHP in surface sediments of the Yangtze River posed the highest risk to sensitive aquatic organisms (fish, invertebrates, and algae) among all PAEs [60]. Similarly, sediments in the Haihe River showed 100% high-risk levels for DEHP, highlighting the need for targeted regional assessments [70]. In terms of taxon sensitivity, only 25% of sites showed a moderate risk to crustaceans, while all sites showed a low risk to fish (RQ < 0.1). This difference was due to the group specificity of PNEC for different aquatic species. Given that MPs in sediments are less mobile and can continuously release DEHP, cleaning plastic waste from industrial park riverbeds can effectively mitigate biological hazards caused by DEHP leaching from aged MPs.

4. Conclusions

A typical chemical park for plastic production in East China was taken as the research area of this study, systematically revealing the occurrence characteristics and ecological risks of MPs and DEHP in the wastewater receiving rivers and highlighting the significant impacts of the WWTP on water and sediment pollution. The results showed that the effluent of the WWTP was the core area of pollution, especially leading to significant increases in the concentrations of MPs and DEHP in the downstream river. The concentration of MPs in the downstream water and sediment was 2.77 and 2.57 times higher than that upstream, respectively. The water samples were dominated by fibers (36.9%), and the sediments were dominated by debris (42.1%). Pollutants discharged by the WWTP may enter Hangzhou Bay through the river and pose a potential threat to Marine life. As a key pollution source, WWTP wastewater can significantly change the composition of MPs in rivers and increase the proportion of PP, PET, and PE downstream. It is worth noting that MPs significantly enriched DEHP, and the DEHP concentration on MPs was up to 14.6 times higher than that in the environment, highlighting the potential harm of MPs as pollutant carriers to ecosystems. Although the results of the concentration-based PLI assessment showed a lower risk, the results of the polymer-toxicity-based PHI and PERI assessments showed a higher risk for water and sediment, which was directly related to the occurrence of PVC, PAN, and other highly toxic polymers. DEHP in sediments presented a moderate ecological risk to the RQ of algae, suggesting that more attention should be paid to its long-term cumulative effect. In the future, plastic manufacturers should pay attention to the improvement and upgrading of wastewater treatment processes to improve the removal efficiency of MPs and DEHP and reduce the emission of pollutants from the source. There are still limitations in this study, the first of which was the lack of seasonal patterns of pollutant concentrations. Secondly, there was no in-depth analysis of the combined toxic mechanism of MPs and DEHP and the specific harm to organisms or humans. We should promote the alternative development of environmental protection additives and establish a multimedia environmental risk early-warning system to achieve dynamic monitoring and precise prevention and control of compound pollution.