Occurrence and Characterization of Acrylate-Based Self-Polishing Copolymer Anti-Fouling Paint Particles (SPC-APPs) in the Sediments of the Yangtze River Estuary

Abstract

Acrylate-based self-polishing copolymer antifouling paint particles (SPC-APPs) are persistent micropollutants that act as carriers for biocidal heavy metals, posing significant ecological hazards to aquatic ecosystems. Despite their toxicity, the occurrence, characterization, and metal-leaching risks of SPC-APPs in estuarine environments remain largely understudied. This study investigated the contamination characteristics of SPC-APPs in surface sediments from the Yangtze River Estuary, a hotspot of shipping activity. A multi-technique analytical protocol was employed, combining density separation with scanning electron microscopy–energy-dispersive spectroscopy (SEM-EDS), inductively coupled plasma mass spectrometry (ICP-MS), and pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) to characterize the morphology, quantify particle abundance, and assess the correlation between SPC-APPs and sedimentary heavy metals. SPC-APPs were ubiquitously detected across all sampling sites, with abundances ranging from (0.82 ± 0.15) × 10³ to (3.65 ± 0.42) × 10³ particles g⁻¹ dry sediment. A distinct distribution property (South Branch > North Branch > offshore shoal) was identified, primarily driven by shipping density and hydrodynamic sorting. Morphologically, particles exhibited irregular, abraded surfaces, with EDS confirming Cu (1.76~5.63 wt%) and Zn (0.27~3.65 wt%) as major metallic components. Py-GC/MS analysis identified specific mass fragments (m/z 41, 69, 87) as diagnostic markers. Strong positive correlations were observed between SPC-APP abundance and sediment Cu (r = 0.82, p < 0.01) and Zn (r = 0.76, p < 0.01) concentrations, indicating that these particles are a primary source of metal contamination. Ecological risk assessment based on sediment quality benchmarks showed that Cu in the South Branch reached 82~91% of the probable effect concentration (PEC), highlighting potential risks to benthic organisms. This study provides critical baseline data on the distribution and speciation of SPC-APPs, underscoring their role as vectors for toxic metals and the need for targeted pollution control in high-shipping-intensity estuarine regions.

1. Introduction

Antifouling paints are used as protective coatings on ship hulls to prevent the attachment of marine biofouling [1]. Ship hull coating systems typically consist of three layers—the primer, tie coat, and topcoat—which together ensure surface durability and service life. The primer enhances adhesion and corrosion resistance, while the tie coat can strengthen the bonding between the primer and the topcoat [2] The topcoat, also known as antifouling paint, is formulated with organometallic fungicides and organic copolymers. Following application, biocides leach gradually into surrounding seawater, preventing the attachment of fouling organisms [3]. Antifouling technologies have undergone three generations of antifouling paint upgrades: (1) early soluble antifouling coatings based on rosin or rosin derivative-crosslinked CuO; (2) tin acrylate polymers, which have been mandatorily banned by the International Maritime Organization (IMO) since 2008 due to their negative environmental implications [4,5,6]; and (3) currently widely used acrylate copolymers with copper, zinc, or silane on side chains replacing organotin [7,8].

Acrylate-based self-polishing copolymer antifouling paint particles (SPC-APPs) are widely used on commercial and ocean-going vessels for their long-lasting antifouling performance and self-renewing surface properties [9]. During navigation, these coatings continuously degrade into microscale particles under water scouring, wave impact, and mechanical wear [10]. As persistent micropollutants, SPC-APPs readily accumulate in estuarine sediments due to their similar density to sediment matrices [11]; there, they may act as long-term reservoirs of biocidal metals and organic additives. Their occurrence, spatial distribution, morphology, elemental composition, and association with heavy metals are critical for evaluating ecological risks and developing targeted management strategies [12]. Furthermore, SPC-APPs’ accumulation in sediments directly influences benthic exposure levels, potentially triggering adverse effects in deposit-feeding organisms and disrupting benthic food webs [13]. Thus, understanding SPC-APPs dynamics in estuarine systems has become a priority in marine ecotoxicology, particularly in regions subjected to intense shipping pressure.

The Yangtze River Estuary is a major shipping hub connecting the Yangtze River Basin and the East China Sea, with annual vessel traffic exceeding 1.2 million, including a high proportion of ocean-going ships. It also supports ecologically sensitive habitats such as the Chinese Sturgeon Nature Reserve and Jiuduansha Wetland, which play irreplaceable roles in nutrient cycling and biological reproduction [14]. Given the high shipping intensity and complex hydrodynamics, SPC-APPs inputs are expected to generate distinct contamination patterns in local sediments. However, most existing studies have focused on other coastal zones or foreign ports (e.g., the East China Sea coast, Pearl River Estuary, and Callao, Peru [11,14,15]), leaving a critical knowledge gap regarding the occurrence of SPC-APPs in the Yangtze River Estuary. This limits accurate risk assessment and the formulation of effective pollution control measures.

Accurate identification and characterization of SPC-APPs depend on advanced analytical techniques. Elemental analyses, including scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM-EDS), X-ray fluorescence (XRF), inductively coupled plasma mass spectrometry (ICP-MS), and neutron activation analysis (NAA), provide key information on metal composition [16]. EDS, in particular, enables microscale elemental mapping and is widely used to identify antifouling paint components such as Cu, Zn, Pb, and Sn [17,18,19]. These elemental profiles not only help trace pollution sources but also support forensic investigations of vessel-related contamination [20,21,22]. Moreover, the presence of toxic metals in SPC-APPs highlights their potential as vectors of metal toxicity, with elevated concentrations often found in marinas and shipyards [23,24]. Torres et al. [13] further emphasized that SPC-APPs-related metal leaching could pose cumulative risks to aquatic ecosystems, calling for comprehensive risk assessment in high-shipping areas.

For chemical composition analysis, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) are the most common techniques. FTIR is suitable for particles > 20 μm, while Py-GC/MS offers broader applicability across nano- to millimeter-sized particles [25,26,27]. Dibke et al. [28] successfully detected acrylate-based APPs (down to 1 μm) in the German Bight using Py-GC/MS. However, the adaptability of these methods to complex estuarine sediments which are rich in humus, clay, and organic matter remains unclear, potentially leading to underestimation or misidentification of SPC-APPs.

Based on these regional and methodological gaps, four key research needs are identified: (1) lack of baseline data on SPC-APPs abundance, content, and morphology in the Yangtze River Estuary; (2) unclear drivers of spatial distribution, particularly the combined effects of shipping intensity and hydrodynamics; (3) insufficient understanding of quantitative relationships between SPC-APPs and heavy metals; and (4) inadequate optimization of analytical methods for complex sediment matrices.

To address these issues, this study investigates the occurrence of SPC-APPs in surface sediments from the South Branch, North Branch, and offshore shoal of the Yangtze River Estuary. The main objectives are to clarify contamination characteristics, identify spatial patterns, and establish an optimized analytical protocol for SPC-APPs’ detection in complex estuarine sediments. This research is expected to fill critical knowledge gaps, improve understanding of SPC-APPs’ environmental behavior, and provide scientific support for ecological risk assessment and pollution control in the region.

2. Materials and Methods

2.1. Sampling

Sediment samples were collected in June 2023 from the Yangtze River Estuary and its adjacent waters, with a total of 12 sampling sites established based on three core criteria: (1) covering different shipping density gradients (South Branch: 120~150 vessels day⁻¹, North Branch: 40~60 vessels day⁻¹, offshore shoal: <10 vessels day⁻¹); (2) representing distinct hydrodynamic conditions (main runoff channel, tributary, and tidal shoal); and (3) avoiding direct land-based industrial drain outlet to ensure shipping-derived pollution dominance. Sampling sites covered the South Branch, North Branch, and offshore shoal areas (Figure 1, Table S1).

Sampling was conducted from a small fishing vessel using a gravity corer (1 L capacity) to collect the top 0~5 cm of surface sediment. Triplicate samples were taken at each site to ensure representativeness; on site, large macrofauna, shell fragments, and gravel debris were removed using pre-cleaned stainless-steel forceps. The collected sediment samples were immediately placed into pre-cleaned and baked glass jars, stored in a dark refrigerator at 4 °C, and transported back to the laboratory within 6 h for subsequent processing.

2.2. Sample Handling

First, the refrigerated samples were dried to a constant weight in an oven at 50 °C (duplicate drying to constant weight, weight difference < 0.001 g). The dried samples were ground in an agate mortar to minimize metal contamination, followed by sieving through a 1 mm nylon mesh to isolate the fine-grained sediment fraction (<1 mm) and eliminate incompletely crushed gravel, shell fragments, and other coarse impurities. The undersize fraction was collected and thoroughly homogenized via the quartering method to ensure a homogeneous particle size distribution.

An aliquot of 5.0 g (accurate to 0.001 g) of the homogenized sample was accurately weighed into a 50 mL polytetrafluoroethylene (PTFE) centrifuge tube, followed by the addition of 30 mL anhydrous ethanol. Ultrasonic extraction was conducted using an ultrasonic cleaner (power: 250 W, frequency: 40 kHz) at ambient temperature (25 ± 2 °C) for 30 min. During extraction, the centrifuge tube was manually inverted every 10 min to ensure adequate contact between the sample and extractant, thereby preliminarily removing lipid-soluble interfering substances (e.g., lipids, oils). After sonication, the tube was centrifuged at 8000 rpm for 15 min at 4 °C using a high-speed refrigerated centrifuge. The supernatant was discarded, and 30 mL ultrapure water (ddH₂O, 18.2 MΩ·cm) was added to the residual sediment. The aforementioned ultrasonic centrifugation cycle was repeated to eliminate water-soluble impurities (e.g., salts, small-molecule organics).

Finally, the precipitate obtained after ethanol–water double washing was dried to a constant weight in a thermostatic drying oven at 50 °C. The dried sample was sealed and stored in a desiccator for subsequent isolation, purification, and qualitative/quantitative analysis of SPC-APPs. Throughout the entire pretreatment process, all equipment (PTFE centrifuge tubes, agate mortars, nylon sieves, stainless-steel forceps) was soaked in 10% (v/v) nitric acid (HNO₃) for 24 h, rinsed three times with ultrapure water, and dried to constant weight before use in order to eliminate exogenous contamination.

2.3. Preparation of Standard Reference Anti-Fouling Paint Particles (SRF-APPs)

The substrate material was a 5083 aluminum alloy panel with dimensions of 120 mm × 50 mm × 5 mm. Its surface was first sandblasted to Sa 2.5 (ISO 8501-1:2007) [29] then rinsed alternately with anhydrous ethanol and acetone three times (each rinse for 5 min) and finally air-dried thoroughly in a fume hood before weighing (denoted as m₀ = 79.82 g).

The substrate was coated with a commercial acrylate-based self-polishing copolymer (SPC) anti-fouling paint (AkzoNobel Interswift 6600, Amsterdam, The Netherlands), which contains Cu and Zn at mass fractions of 11.1% and 8%, respectively (provided by AkzoNobel, Amsterdam, The Netherlands). The paint was applied to the substrate using a film applicator (RK Print Coat Instruments, Litlington, UK) with three reciprocating strokes; the coated substrate was then placed in an oven at 50 °C for 48 h to dry and cure, after which it was weighed (denoted m₁ = 82.38 g). The dry film thickness was measured at 5 random positions using a MINTEST 6006 thickness gauge (Shanghai Time Instruments Co., Ltd., Shanghai, China), resulting in an average thickness of 250 ± 5 μm. The volume (V) of the coating was calculated as follows: V = substrate area × average thickness = (120 mm × 50 mm) × 0.25 mm = 1500 mm³ = 1.5 cm³.

The dried and cured coating was manually abraded using 500-mesh fine sandpaper, followed by gentle grinding in an agate mortar to obtain SRF-APPs. The particles were sieved through a 150-mesh (100 μm) nylon screen using a sieve shaker for 5 min; the undersize fraction was collected and stored in a sealed aluminum foil container. The density (ρ) of SRF-APPs was calculated using the following formula (corrected for consistent mass data):

ρ = (m₁ − m₀)/V = (82.38 g − 79.82 g)/1.5 cm³ = 1.706 g cm⁻³

2.4. Density Separation for the SPC-APPs Concentration

Density separation was employed to concentrate SPC-APPs, relying on the principle that SPC-APPs float while higher-density impurities sink. To select the optimal solution, triplicate experiments compared saturated potassium iodide (KI, 1.73 g cm⁻³), sodium chloride (NaCl, 1.12 g cm⁻³), and calcium chloride (CaCl₂, 2.15 g cm⁻³). Self-polishing resin-based paint particles (SRF-APPs) served as surrogates for SPC-APPs. Two indices were evaluated: (1) recovery rate (%) = (mass of recovered SRF-APPs/mass of added SRF-APPs) × 100% (target: 90~110%) [30] and (2) impurity co-flotation rate (%) = (mass of floating impurities/total mass of floating matter) × 100% (target: ≤10%).

To verify the rationality of KI density selection, additional density measurements were conducted on SRF-APPs with different aging degrees (fresh and laboratory-aged SRF-APPs), resulting in a density range of 1.65~1.75 g cm⁻³ (Table S2). This confirms that the saturated KI solution (1.73 g cm⁻³) matches the environmental SPC-APPs’ density range.

To determine impurity co-flotation, 10 mg of SRF-APP was mixed with 0.5 g of standard marine sediment (GBW07309) [31], which was pre-verified to be free of SPC-APPs via microscopic examination. The mixture underwent density separation, and the floating fraction was filtered and dried at 50 °C for 12 h to determine the total floating mass (m_total). SRF-APPs were then manually picked out under a stereomicroscope and weighed (m_recovered). Impurity mass (m_impurity) was calculated as m_total − m_recovered.

For sample analysis, 0.5 g of dried, 150-mesh sieved sediment was mixed with 30 mL of the optimal density solution, vortexed for 1 min, and centrifuged at 5000 rpm for 6 min (4 °C). The supernatant was filtered through a 0.45 μm PES membrane, and the tube was rinsed three times with ultrapure water. The combined residues on the membrane were dried at 50 °C for 12 h for subsequent analysis.

2.5. Digestion of Organic Matter Interference

Organic matter (e.g., humic acid) adhering to SPC-APPs’ surfaces cannot be completely removed by density separation and may interfere with subsequent analysis. Thus, a digestion step was introduced to degrade these interfering organics while preserving the SPC-APPs’ structural integrity. To screen the optimal digestion solution, humic acid (analytical grade) was used as the target degradant. Comparative experiments were conducted with five digestion systems: 6 mol L⁻¹ NaOH, 30% (v/v) H₂O₂, Fenton’s reagent (0.5 g Fe₃O₄ + 30% H₂O₂), 30% H₂O₂ + 6 mol L⁻¹ HNO₃ (v:v = 2:1, 3:1, 4:1), and 6 mol L⁻¹ HNO₃ [30]. The optimal solution was defined as the one achieving >95% humic acid degradation without damaging the SRF-APPs’ structure (verified by SEM observation).

Subsequently, the digestion conditions (temperature and time) were optimized using a mixture of 5 mg SRF-APPs and 10 mg humic acid in 20 mL of the optimal solution, testing temperatures of 40 °C and 60 °C for durations of 3, 6, 12, and 24 h. After density separation, SPC-APPs recovered from sediment samples were digested under the optimal conditions. The mixture was then transferred to a 50 mL PTFE centrifuge tube and centrifuged at 8000 r min⁻¹ for 15 min at 4 °C. The supernatant was discarded, and the precipitate was rinsed with 30 mL of ultrapure water followed by repeated centrifugation to remove residual reagents. Finally, the cleaned precipitate was dried to constant weight in a 40 °C drying oven, sealed, and stored in a desiccator for subsequent characterization.

2.6. Characterization of the SPC-APPs

2.6.1. Optical Microscope Analysis

To determine SPC-APPs’ abundance, 1 mg of the digested and dried subsample (Section 2.5) was mixed with 1 mL ultrapure water, vortexed for 1 min to disperse uniformly, and transferred to a pre-cleaned Sedgwick–Rafter counting chamber (Hausser Scientific Co., Horsham, PA, USA). Counting and photography were performed under an optical microscope (BX53M, Olympus, Hamburg, Germany) at 200× magnification. Particles were classified into SPC-APPs, microplastics (MPs), algal cells, biological debris, and other particles based on morphological characteristics (size, shape, color) and motility (non-motile for SPC-APPs/MPs). Three replicate counts were performed per sample, and the average value was used as the final abundance.

2.6.2. SEM-EDS Analysis

Digested/dried samples were placed on an aluminum stub (Φ25 × 5 mm) affixed with black carbon tape, then sputter-coated with platinum (Pt) at 20 mA for 200 s to a thickness of ~10 nm to improve conductivity without masking surface features. SEM imaging and EDS analysis were performed using a JSM-7600F scanning electron microscope equipped with an energy-dispersive spectrometer (JEOL, Tokyo, Japan) under the following conditions: vacuum level of 5.1 × 10⁻⁵ Pa, accelerating voltage of 10 keV, working distance of 8 mm, magnification range of ×70 to ×330, and probe current of 8~9 nA. Imaging was conducted in low-magnification mode using secondary electron imaging.

To distinguish particle-specific elemental signals from background noise (carbon tape, Pt coating), the Trumap function (AZtec software 6.0, Oxford Instruments, Abingdon, UK) was used to map elemental distributions in particles and their surrounding areas (real-time spectral mapping with peak deconvolution to minimize overlapping peak artifacts). Based on Trumap results, point-and-identify technology (detection limit: 0.1 wt%) was used to determine the elemental composition of particle interiors. EDS spectra were accepted only after accumulating sufficient counts (data collection for 10 min, input rate > 1000 cps) to ensure reliability. Elemental composition was compared with the manufacturer’s Material Safety Data Sheet (MSDS) of the commercial SPC paint to confirm SPC-APPs’ identity. For morphological quantitative analysis, 100 particles per region were analyzed using ImageJ software 1.54r (National Institutes of Health, Bethesda, MD, USA) to determine average particle size and roundness (shape factor).

2.6.3. ICP-MS Analysis

Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine Cu and Zn concentrations in the sediment samples. Approximately 50 mg of digested/dried sediment sample was accurately weighed into a microwave digestion vessel, and a mixed acid solution (7 mL 65% HNO₃ + 2 mL 40% hydrofluoric acid (HF) + 1 mL 37% hydrochloric acid (HCl)) was added. The sample was pre-digested at 120 °C for 30 min on a graphite heating plate in a fume hood to reduce reaction intensity.

Formal microwave digestion was performed using the following program: (1) heat to 130 °C over 5 min, hold for 3 min; (2) heat to 150 °C over 3 min, hold for 10 min; (3) heat to 180 °C over 3 min, hold for 30 min. After digestion, the vessel was cooled to 60 °C in a fume hood, and 2 mL of 5% (w/v) boric acid solution was added to complex residual HF (to avoid corrosion of the ICP-MS nebulizer). The solution was concentrated to ~1 mL by heating at 120 °C then diluted to 25 mL with ultrapure water and mixed uniformly.

Metal concentrations were measured using an Agilent 7800 ICP-MS instrument (Agilent Technologies, Santa Clara, CA, USA) with three parallel determinations per sample. A procedural blank (digestion reagents only) and a certified reference material (CRM: GBW07309, marine sediment) were processed in parallel to verify accuracy (allowable error: ±5%). The instrumental parameters were as follows: radio frequency (RF) power = 1.05 kW, plasma gas flow rate = 15 L min⁻¹, nebulizer gas flow rate = 0.85 L min⁻¹, auxiliary gas flow rate = 1.4 L min⁻¹, pump speed = 15 r min⁻¹, stabilization time = 15 s, uptake time = 30 s, and nebulizer wash time = 15 s. Cu and Zn concentrations in SRF-APPs were measured using the same procedure.

2.6.4. Py-GC/MS Analysis

Py-GC/MS analysis was performed using a pyrolyzer (EGA/PY-3030D, Frontier, Fukushima-shi, Japan) coupled with a gas chromatograph–mass spectrometer (GC-MS, QP2020, Shimadzu, Kyoto, Japan). Chromatographic separation was achieved using an Rtx-5MS capillary column (30 m × 0.25 mm × 0.25 μm, Shimadzu, Japan), with helium as the carrier gas (purity ≥ 99.999%) at a flow rate of 1.0 mL min⁻¹. The m/z scanning range was 29–600. Pyrolysis conditions were as follows: pyrolysis temperature = 550 °C, pyrolysis time = 10 s. GC temperature program: hold at 40 °C for 2 min, increase to 320 °C at 20 °C min⁻¹, hold at 320 °C for 14 min (total run time: 30 min).

For qualitative analysis, pretreated samples (Section 2.5) were analyzed by Py-GC/MS; generated characteristic ion peaks (m/z) were compared with the NIST 2020 Mass Spectral Library (National Institute of Standards and Technology, Gaithersburg, MD, USA) and SRF-APPs’ pyrolysis spectra to identify acrylate-based fragments.

For quantitative analysis, a standard curve of “SRF-APPs injection amount (dry weight) vs. characteristic acrylate fragment peak area” was constructed (R² ≥ 0.99). SPC-APPs’ concentrations in sediment samples were calculated by correlating the peak area of the characteristic fragment with the standard curve. Potential preprocessing errors (particle adsorption, incomplete pyrolysis, and solvent residue) were evaluated via recovery tests, with error control measures implemented as described in Section 3.6.

2.6.5. Ecological Risk Assessment Method

This study adopts the consensus-based sediment quality criteria established by Mac Donald et al. [32] based on freshwater ecosystems to evaluate the toxicity of heavy metals in sediments. It includes the Threshold Effect Concentration (TEC) and the Probable Effect Concentration (PEC), as shown in

Table 1. Heavy metal concentration (mg kg⁻¹) of biological toxicity effects.

Items	Cd	As	Pb	Cr	Cu	Zn	Ni	Hg
TEC	0.99	9.79	35.80	43.40	31.60	121.00	22.70	0.18
PEC	4.98	33.00	128.00	111.00	149.00	459.00	48.60	1.06

If the toxicity is lower than the TEC, it is considered that there will be no toxic effect on benthic organisms; if it is higher than the PEC, it is considered that it is very likely to have a toxic effect on benthic organisms. For the type of combined pollution, the average probable effect concentration quotient Q can be used to predict the toxicity of heavy metal combined pollution in sediments, and the calculation formula is as follows:

$$Q={\displaystyle \frac{1}{n}}\Sigma {\displaystyle \frac{c_n}{PE{C}_n}}$$

In the formula, Q is the average probable effect concentration quotient; C_n is the measured value of pollutant n (mg·kg⁻¹); PEC_n is the probable effect threshold of pollutant n (mg·kg⁻¹). When Q < 0.5, the sediment is considered basically non-toxic; when Q > 0.5, the sediment is considered to have certain toxicity. Cu and Zn were selected as target metals due to their strong correlation with SPC-APPs and status as core biocidal components in antifouling paints.

2.7. Statistical Analysis

All data processing and statistical analyses were performed using SigmaPlot 10.0 (Systat Incorporation, San Jose, CA, USA) and Microsoft Excel 2021. Specific statistical methods were applied based on research objectives: (1) a one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test was used to compare differences in SPC-APPs content, abundance, metal concentrations, and morphological parameters among the South Branch, North Branch, and offshore shoal; (2) Pearson correlation analysis was conducted to explore linear relationships between SPC-APPs abundance and sediment metal concentrations; (3) variance inflation factor (VIF) analysis was used to assess multicollinearity between shipping density and hydrodynamic conditions; (4) stepwise regression analysis was performed to quantify the contribution of driving factors to SPC-APPs distribution; and (5) descriptive statistics (mean, standard deviation, range) were used to characterize SPC-APPs content, abundance, and digestion efficiency data.

Statistical significance was set at p < 0.05. All numerical results are presented as mean ± standard deviation (SD, n = 3). Charts (spatial distribution maps, correlation scatter plots, standard curves) were generated using SigmaPlot 10.0 with consistent formatting for clarity.

2.8. Quality Assurance and Quality Control

A rigorous QA/QC protocol was implemented throughout sampling, pretreatment, and instrumental detection to ensure data accuracy, reproducibility, and reliability. For contamination prevention, all labware was pretreated with 10% (v/v) HNO₃ soaking (24 h), ultrapure water rinsing (3 times), and drying; sample processing was performed in a Class 100 laminar flow hood, with personnel wearing lint-free coats and powder-free gloves and sterile disposable pipette tips used to avoid cross-contamination. For process quality control, samples were stored at 4 °C in the dark immediately after collection; sample homogenization was verified by triplicate total suspend solid (TSS) measurements (the relative standard deviation, RSD < 5%). Method blanks (ddH₂O) were processed alongside samples to assess background contamination; no particles were observed in blanks via optical microscopy, confirming the integrity of the results. SRF-APPs recovery tests were conducted in triplicate (90~110% for density separation, 95~105% for digestion).

For instrumental calibration and validation, ICP-MS was calibrated with Cu/Zn CRMs (5 gradients, R² ≥ 0.999), with blank and CRM (GBW07309) analyzed every 10 samples (allowable error ±5%); Py-GC/MS was calibrated with SRF-APPs, with retention time stability verified (RSD < 2%) and solvent blanks run between samples. SEM-EDS was calibrated with standard copper foil per batch. For data reliability, all samples were analyzed in triplicate (RSD < 10% for SPC-APPs, <5% for Cu/Zn); invalid data were excluded and samples re-analyzed. The minimum detection limits (MDLs) (3 × blank standard deviation) were 0.01 mg g⁻¹ dry sediment for SPC-APPs and 0.01 μg g⁻¹ dry sediment for metals.

Additional accuracy verification included the following: (1) ICP-MS comparison with CRM GBW07309: Cu recovery 96.5% ± 2.3%, Zn recovery 97.2% ± 1.8%, Pb recovery 95.8% ± 2.5%, Cd recovery 94.3% ± 3.1%, Cr recovery 96.1% ± 2.7%, Ni recovery 95.5% ± 2.4%; (2) Py-GC/MS SRF-APPs spiked recovery: 95.3% ± 3.1%; (3) inter-method comparison with Valdiviezo-Gonzales et al. [3] data [Table S7].

3. Results and Discussion

3.1. Optimization of Density Separation for SPC-APPs

The core objective of this section was to screen a density solution that achieves high recovery of SRF-APPs (surrogate for SPC-APPs) while minimizing matrix interference (i.e., effective sedimentation of high-density impurities and low co-floatation of light organic matter). Triplicate comparative experiments were conducted using three saturated solutions (NaCl, CaCl₂, KI), with recovery rate as the primary evaluation index (

Table 2. Comparison of separation efficiencies of different density solutions for SRF-APPs.

Density Solution Type	Solution Density (g cm−3)	SRF-APPs Recovery Rate (%, x ± SD, n = 3)	Impurity Co-Flotation Rate (%)	Evaluation of Separation Effect
NaCl	1.12	12.3 ± 1.5	8.2 ± 0.6	Ineffective, SRF-APPs sedimented
CaCl2	2.15	99.5 ± 0.3	35.7 ± 2.1	severe co-floatation of impurities
KI	1.73	98.6 ± 0.5	5.3 ± 0.4	Optimal, low impurity interference

Note: x represents the mean value, SD represents the standard deviation; impurity co-floatation ratio = (mass of floating impurities/mass of total floating matter) × 100%; evaluation criteria: recovery rate ≥ 95% and impurity co-floatation ratio ≤ 10%.

).

As shown in Table 1, the saturated NaCl solution (1.12 g cm⁻³) failed to separate SRF-APPs effectively, with a recovery rate of only 12.3% ± 1.5%. This was because its density was significantly lower than that of SRF-APPs (1.706 g cm⁻³) and laboratory-aged SRF-APPs (1.651~1.695 g cm⁻³, Table S2), leading to SRF-APPs’ sedimentation. Although the saturated CaCl₂ solution (2.15 g cm⁻³) achieved nearly complete SRF-APP floatation (recovery rate 99.5% ± 0.3%), its high density caused 35.7% ± 2.1% of light organic matter (e.g., biological debris) to co-float, which would reduce the purity of subsequent SPC-APP extracts and interfere with qualitative/quantitative analysis.

In contrast, the saturated KI solution (1.73 g cm⁻³) had a density slightly higher than SRF-APPs, enabling stable suspension of SRF-APPs with a high recovery rate (98.6% ± 0.5%). Meanwhile, its impurity co-floatation ratio was only 5.3% ± 0.4%, as dense inorganic impurities (e.g., quartz sand, gravel) settled completely. This result fully met the pre-established optimization criteria, confirming saturated KI solution as the optimal density separation medium.

Compared with existing studies, the saturated KI system in this study had distinct advantages. Ren et al. [30] used a KI solution (1.73 g cm⁻³) for tire and road wear particle (1.70 g cm⁻³) separation, achieving a recovery rate of 98.1% ± 1.3%. Fiore et al. [33] adopted a KI-I₂ solution (1.80 g cm⁻³) to extract the microplastics in biological tissues with high floatation efficiency but high cost and strong oxidizability, which could damage the polymer structure of microplastics. In contrast, the saturated KI solution in this study not only ensured high separation efficiency and low matrix interference but also had stable chemical properties that did not disrupt the SPC-APPs’ structure (verified by SEM observation in Section 3.4), providing high-purity samples for subsequent digestion and detection.

3.2. Optimization of Digestion System and Conditions for SPC-APPs

The digestion step aimed to remove organic matter (e.g., humic acid) adsorbed on the SPC-APPs’ surfaces while preserving particle structural integrity. Humic acid (analytical grade) was used as a surrogate for sedimentary organic matter, and the optimization was conducted in two stages: digestion solution screening and temperature–time condition optimization.

3.2.1. Screening of Optimal Digestion Solution

Five digestion systems, including three H₂O₂-HNO₃ ratios, were compared, with “humic acid degradation rate” and “SPC-APPs’ structural integrity” as dual evaluation indices (

Table 3. Comparison of degradation efficiency and particle protection capabilities of different digestion solutions.

Digestion Solution Type	Humic Acid Degradation Rate (%, x ± SD, n = 3)	Structural Integrity of SPC-APPs2	Comprehensive Evaluation
NaOH (6 mol L−1)	100.0 ± 0.0	Severely damaged	Unsuitable
HNO3 (6 mol L−1)	72.3 ± 1.4	Intact	Insufficient degradation efficiency
H2O2 (30 wt%)	75.6 ± 2.3	Intact	Insufficient degradation efficiency
Fenton’s reagent (0.5 g Fe3O4 + 30% H2O2)	92.1 ± 1.8	Intact (with surface-adsorbed impurities)	Prone to introducing metallic contamination
H2O2 (30 wt%) + HNO3 (6 mol L−1) (v:v = 2:1)	92.5 ± 1.6	Intact	Insufficient degradation efficiency
H2O2 (30 wt%) + HNO3 (6 mol L−1) (v:v = 3:1)	99.8 ± 0.2	Intact	Optimal
H2O2 (30 wt%) + HNO3 (6 mol L−1) (v:v = 4:1)	99.9 ± 0.1	Slight surface oxidation	Risk of particle damage

Note: structural integrity was determined by SEM (magnification ×1500); evaluation criteria: degradation rate ≥ 98%, no particle damage, and no metal contamination.

). The structural integrity of SPC-APPs was verified by SEM imaging.

As shown in Table 2, NaOH completely dissolved the acrylate polymer backbone (Figure 2c), indicating that strong alkaline conditions are unsuitable for preserving SPC-APP structures during organic matter removal. In contrast, single HNO₃ or H₂O₂ exhibited insufficient degradation efficiency for humic acid (72.3 ± 1.4% and 75.6 ± 2.3%, respectively), failing to eliminate organic interference effectively. This limited efficiency may be attributed to the refractory nature of humic acid, which requires both oxidative cleavage of aromatic structures and acid-promoted dissolution of metal–organic complexes. Fenton’s reagent achieved a relatively high degradation rate (92.1 ± 1.8%), but the residual Fe₃O₄ nanoparticles adsorbed onto SPC-APPs’ surfaces (Figure 2d) introduced significant Fe contamination, which would interfere with subsequent Cu and Zn analysis by ICP-MS. This drawback has rarely been emphasized in previous Fenton-based microplastic pretreatment studies, highlighting the particularity of SPC-APP pretreatment for subsequent trace metal analysis.

The H₂O₂-HNO₃ mixed solution (v:v = 3:1) achieved nearly complete humic acid degradation (99.8 ± 0.2%) through synergistic effects: H₂O₂ provides strong oxidation to break down organic macromolecules, while HNO₃ promotes the dissolution of humic acid–metal complexes and enhances the overall oxidative potential of the system. This synergistic effect is consistent with the mechanism reported by Huang et al. [34]. SEM observations confirmed that the SPC-APPs retained their intact morphology after digestion, with no evidence of polymer dissolution or particle fragmentation, indicating that the mixed oxidant effectively removes organic coatings without damaging the acrylate matrix. The 4:1 ratio showed similar degradation efficiency but caused slight surface oxidation (Figure 2b), making the 3:1 ratio the optimal choice. This balance between high organic degradation efficiency and structural preservation is critical for ensuring the accuracy of subsequent SPC-APP identification and quantification.

3.2.2. Optimization of Digestion Temperature and Time

Based on the optimal digestion solution (H₂O₂-HNO₃, v:v = 3:1), temperature (40 °C, 60 °C) and time (3~24 h) were optimized to balance degradation efficiency and particle integrity (

Table 4. Optimization of temperature–time conditions for the optimal digestion solution (H₂O₂ + HNO₃).

Digestion Temperature (°C)	Digestion Time (h)	Humic Acid Degradation Rate (%, x ± SD, n = 3)	Structural Integrity of SPC-APPs	Comprehensive Evaluation
40	3	65.2 ± 3.1	Intact	Unsuitable
40	6	78.5 ± 2.5	Intact	Unsuitable
40	12	86.3 ± 1.9	Intact	Unsuitable
40	24	89.7 ± 1.6	Intact	Unsuitable
60	3	92.5 ± 1.3	Intact	Basically suitable
60	6	97.8 ± 0.6	Intact	Suitable
60	12	99.8 ± 0.2	Intact	Optimal
60	24	99.9 ± 0.1	Slight oxidation (surface pores)	Unsuitable

Note: Evaluation criteria: degradation rate ≥ 98% and no particle damage.

).

At 40 °C, the humic acid degradation rate was consistently below 90% even after 24 h, failing to meet the interference removal requirement. At 60 °C, the degradation rate increased with time, being 92.5% ± 1.3% at 3 h, 97.8% ± 0.6% at 6 h, and stabilizing at 99.8% ± 0.2% at 12 h. Extending digestion to 24 h did not significantly improve degradation efficiency but caused slight oxidation of SPC-APPs (surface pores observed by SEM). Thus, the optimal digestion conditions were determined to be 60 °C for 12 h.

Compared with existing methods, the established digestion system is efficient and mild. Ren et al. [30] used single H₂O₂ (30%) at 70 °C for 24 h, causing 15% ± 2% oxidative damage to tire wear particles. Huang et al. [34] adopted microwave-assisted HNO₃ digestion, which requires expensive equipment and is not suitable for batch processing. The 60 °C and 12 h condition in this study ensures complete organic matter removal while maximizing SPC-APPs’ integrity. Additionally, repeated ultrapure water washing–centrifugation (Section 2.5) removed residual digestion reagents verified by ion chromatography; there was <0.1 mg L⁻¹ NO₃⁻, and H₂O₂ was not detected, thus avoiding interference with subsequent ICP-MS and Py-GC/MS analyses.

3.3. Abundance of SPC-APPs in Sediments and Its Correlation with Shipping Density and Flow Velocity

Optical microscopy (200× magnification) was used to identify and count SPC-APPs in 36 sediment samples (12 sites × 3 replicates) from the Yangtze River Estuary. Particles were classified based on morphology (size, shape, color) and motility, successfully distinguishing SPC-APPs from microplastics (MPs), algal cells, and biological debris. SPC-APPs were detected at all sampling sites, indicating widespread pollution in the study area, with abundances ranging from (0.82 ± 0.15) × 10³ to (3.65 ± 0.42) × 10³ particles g⁻¹ dry sediment and an average of (2.13 ± 0.28) × 10³ particles g⁻¹ dry sediment. A one-way ANOVA confirmed significant spatial differences in SPC-APPs’ abundance (p < 0.05), which were closely regulated by two core environmental factors: shipping density and flow velocity (Figure 3, Table S3).

To clarify the independent and combined effects of these two factors on SPC-APPs’ abundance, multicollinearity and stepwise regression analyses were conducted. Multicollinearity testing showed variance inflation factor (VIF) values of 1.23 for shipping density and 1.18 for flow velocity (both < 5, Table S4), confirming no significant collinearity between the two factors and ensuring their independent explanatory power for SPC-APPs’ distribution. Stepwise regression analysis further quantified their contribution rates: shipping density accounted for 68% of the variation in SPC-APPs’ abundance, while flow velocity contributed 25%, with the combined model explaining 93% of the total variation (R² = 0.93), highlighting the dominant role of these two factors in regulating SPC-APPs’ accumulation in sediments.

Shipping density, as the primary driving factor, directly determines the input intensity of SPC-APPs. The South Branch, a core navigation channel housing major ports such as Shanghai Port and Nantong Port, has the highest shipping density (120~150 vessels day⁻¹, data from Yangtze River Maritime Safety Administration). Continuous peeling of antifouling coatings from ship hulls during navigation provides a stable and substantial source of SPC-APPs, resulting in the highest regional abundance. In contrast, the North Branch has a relatively lower shipping density (40~60 vessels day⁻¹), leading to reduced exogenous input of SPC-APPs and consequently lower abundance compared to the South Branch. The offshore shoal area, far from main navigation routes, has sparse ship activities (<10 vessels day⁻¹), resulting in minimal SPC-APP input and the lowest sedimentary abundance. This positive correlation between SPC-APPs’ abundance and shipping intensity is consistent with previous studies. Valdiviezo-Gonzales et al. [11] confirmed a direct positive correlation between antifouling paint particle (APP) abundance and shipping intensity; Soroldoni et al. [19] and Abreu et al. [35] further identified high-shipping-density areas (e.g., shipyards, navigation channels) as hotspots for APP accumulation, verifying that shipping activity is the primary source of such particles.

Flow velocity, as a key hydrodynamic regulating factor, influences the migration and deposition of SPC-APPs. The South Branch, as the main runoff channel of the Yangtze River Estuary, has a gentle average flow velocity (0.8 m s⁻¹, measured on-site), which is conducive to the settlement of SPC-APPs with the suspended sediment matrix, promoting particle accumulation. In contrast, the offshore shoal is strongly disturbed by open-sea tides (tidal range 2.5~3.0 m), leading to intense water mixing and high flow velocity. Under such hydrodynamic conditions, SPC-APPs struggle to settle and are easily transported to distant marine areas, resulting in low sedimentary abundance. The North Branch, affected by moderate flow velocity (0.95~1.02 m s⁻¹) and freshwater dilution, shows intermediate SPC-APP abundance between the South Branch and offshore shoal, reflecting the regulatory role of flow velocity in balancing particle input and migration.

3.4. Morphological and Elemental Composition Characteristics of SPC-APPs

3.4.1. Morphological Characteristics

SEM imaging (Figure 4) showed that SPC-APPs in sediments were mainly irregular blocky or flaky, with particle sizes concentrated around 10 μm. Most particles had rough surfaces and edge abrasion marks, which are typical of aged antifouling paint particles in aquatic environments. These abrasion features are caused by physical processes such as water scouring (flow velocity 0.5~1.2 m s⁻¹) and particle collision during migration.

Morphological quantitative analysis (Figure 5) showed significant regional differences: the South Branch’s average particle size was 12.3 ± 2.5 μm, that of the North Branch was 10.1 ± 2.1 μm, and that of the offshore shoal was 8.7 ± 1.8 μm. Roundness (shape factor) in the South Branch was 0.65 ± 0.12, in the North Branch was 0.58 ± 0.11, and in the offshore shoal was 0.52 ± 0.10. The smaller size and lower edge smoothness (ES, Table S5) in the offshore shoal indicate more intense physical wear during long-distance migration.

Comparative analysis with fresh SRF-APPs (smooth surface, regular flake shape, average size 15.2 ± 3.1 μm, ES 0.98 ± 0.01) and literature data confirmed the aging process of SPC-APPs. Kim et al. [36] reported that freshly peeled ship paint particles have smooth surfaces and no abrasion, while aged particles in aquatic environments gradually form rough surfaces and fragmented edges due to physical erosion. This morphological change affects SPC-APPs’ migration (smaller, abraded particles are more easily transported by water flow) and element release (increased specific surface area promotes chemical reactions).

3.4.2. Elemental Composition Characteristics

During EDS point scanning focusing on particle core regions, an accelerating voltage of 15 kV was used to analyze the elemental composition of SPC-APPs, with Trumap function (AZtec software) to exclude background interference from carbon tape and Pt coating (Figure 6).

Figure 6 presents SEM-EDS Trumap elemental mapping of SPC-APPs extracted from Yangtze River Estuary sediments, showing the spatial distribution of four key elements: carbon (C, red), oxygen (O, magenta), Cu (blue), and Zn (green). The colors represent the presence of each element. The C and O maps show a uniform, dense distribution across the field of view, consistent with the acrylate-based polymer matrix of SPC-APPs. In contrast, Cu and Zn exhibit a heterogeneous, spot-like distribution, with distinct hotspots of higher intensity that precisely overlap with the locations of individual SPC-APPs’ particles. This co-localization of Cu and Zn within the polymer matrix confirms the identity of the particles as SPC-APPs, which are known to contain these biocidal metals as core components.

The results (Figure 7) showed that SPC-APPs had characteristic “high Cu, high Zn” elemental profiles, with the average Cu mass fractions ranging from 2.05% to 5.10% and Zn from 0.41% to 3.07%. Compared with fresh SRF-APPs (Cu: 12.5%, Zn: 9.3%), the Cu and Zn contents of SPC-APPs decreased by 50~84% and 54~97%, respectively. This reduction is attributed to the aging process: (1) physical abrasion damages the polymer skeleton, exposing internal Cu/Zn components to water, while (2) chemical oxidation (dissolved oxygen, acidic/alkaline water) accelerates ester bond hydrolysis of the acrylate polymer, promoting Cu/Zn ion dissolution and release. This is consistent with Fischer et al. [37], who reported that aged antifouling paint particles have >60% lower heavy metal content than fresh particles.

The elemental content of SPC-APPs showed a regional pattern of “South Branch > North Branch > offshore shoal”, consistent with the distribution of abundance. This is due to the coupling effect of shipping density and hydrodynamic conditions: the South Branch has a high supply rate of fresh SPC-APPs (from ship peeling) that exceeds the heavy metal release rate, resulting in high Cu/Zn contents, while the offshore shoal has long-term particle migration and intense hydrodynamic disturbance, leading to thorough aging and low heavy metal contents. Additionally, the high C content (up to 60.05%) in offshore shoal SPC-APPs indicates adsorption of sediment organic matter, confirming long-term retention in this region.

The “high Cu, high Zn” characteristic of SPC-APPs distinguishes them from other particles (MPs: mainly C, O; biological debris: C, O, N; quartz sand: Si, O), confirming Cu and Zn as reliable indicator elements for SPC-APPs’ identification. This provides a theoretical basis for subsequent ICP-MS quantitative analysis.

3.5. Metal Concentrations

ICP-MS was used to determine concentrations of Cu, Zn, Pb, Cd, Cr, and Ni in sediments, with procedural blanks and certified reference material (GBW07309) for quality control. The results (Figure 8, Table S6) showed that sediment Cu concentrations ranged from 28.5 to 89.6 mg kg⁻¹ dry sediment, Zn from 65.2 to 156.8 mg kg⁻¹ dry sediment, Pb (5.2~10.3 mg kg⁻¹), Cd (0.08~0.21 mg kg⁻¹), Cr (25.3~42.6 mg kg⁻¹), and Ni (17.8~31.2 mg kg⁻¹). Regional distributions of Cu and Zn were consistent with SPC-APPs’ abundance, being in the following order: South Branch > North Branch > offshore shoal (p < 0.05). Other metals showed no significant regional differences (p > 0.05).

Pearson correlation analysis (Figure 9) showed significant positive correlations between SPC-APPs’ abundance and sediment Cu concentration (r = 0.82, p < 0.01) and Zn concentration (r = 0.76, p < 0.01). While no significant correlations were observed with Pb (r = 0.27, p > 0.05), Cd (r = 0.23, p > 0.05), Cr (r = 0.21, p > 0.05), or Ni (r = 0.18, p > 0.05). This confirms that SPC-APPs are an important source of sediment Cu and Zn in the study area, while other metals primarily originate from non-shipping sources.

Comparative analysis with other studies showed that the sediment Cu/Zn concentrations in the Yangtze River Estuary are moderate, consistent with Wang et al. [38] (Yangtze River Estuary basin: Cu 25.3~92.7 mg kg⁻¹, Zn 60.5~162.3 mg kg⁻¹), slightly lower than Hangzhou Bay [39] (Cu 35.8~105.2 mg kg⁻¹, Zn 72.3~178.5 mg kg⁻¹) due to less intensive industrial activities, and lower than the Venice Lagoon [40] (Cu 120~250 mg kg⁻¹, Zn 180~320 mg kg⁻¹) but higher than the Port of Callao, Peru [11] (Cu 15.2~45.6 mg kg⁻¹, Zn 40.3~95.8 mg kg⁻¹). These differences are driven by regional shipping intensity, industrial development, and pollution control policies.

3.6. Py-GC/MS Qualitative and Quantitative Analysis of SPC-APPs

Py-GC/MS was used for qualitative identification and quantitative analysis of acrylate-based SPC-APPs, with SRF-APPs as the standard reference material. The method reliability was verified by parallel samples (n = 3) and recovery tests.

3.6.1. Qualitative Identification

The qualitative results (Figure 10) revealed the presence of three sets of typical characteristic ions in the sediment samples, namely m/z 41, m/z 69, and m/z 87, each with distinct chemical origins and diagnostic value. The m/z 41 ion corresponds to the allyl cation (C₃H₅⁺), generated by the pyrolytic cleavage of the acrylate polymer backbone. It serves as a universal core marker for distinguishing acrylate-based particles from other types of antifouling paint particles (e.g., chlorinated rubber or vinyl-based). The m/z 69 ion is a characteristic pyrolysis cation (C₄H₅O⁺) of methyl acrylate monomers, whose detection directly confirms the presence of methyl acrylate units. The m/z 87 ion corresponds to the characteristic pyrolysis cation (C₄H₇O₂⁺) of ethyl acrylate monomers, further clarifying the monomer composition of the target particles. These characteristic ions were consistent with SRF-APPs pyrolysis spectra and the NIST 2020 Mass Spectral Library, achieving accurate qualitative identification of SPC-APPs in complex sediment matrices [41].

3.6.2. Quantitative Analysis

For quantitative analysis, a standard curve of “injection amount (dry weight)-peak area of characteristic acrylate ion (m/z = 41)” was plotted using SRF-APPs as the reference material (Figure 11). The curve exhibited a good linear relationship (R² = 0.9949).

Quantitative results (

Table 5. Quantitative analysis results of SPC-APPs in sediments from different sampling sites in the Yangtze River Estuary.

Sampling Site ID	Region	SCP-APPs’ Concentration (mg g−1 Dry Sediment)	Regional Average Concentration (mg g−1 Dry Sediment)	Relative Standard Deviation (RSD, %)
S-1	South Branch	0.26 ± 0.01	0.28 ± 0.02	3.2–6.5
S-2	South Branch	0.24 ± 0.01
S-3	South Branch	0.32 ± 0.02
S-4	South Branch	0.29 ± 0.01
N-1	North Branch	0.17 ± 0.01	0.18 ± 0.02
N-2	North Branch	0.16 ± 0.01
N-3	North Branch	0.20 ± 0.01
N-4	North Branch	0.19 ± 0.01
Sh-1	Shoal	0.09 ± 0.005	0.09 ± 0.01
Sh-2	Shoal	0.10 ± 0.005
Sh-3	Shoal	0.08 ± 0.004
Sh-4	Shoal	0.10 ± 0.005

) showed that the SPC-APP content in sediments of the study area ranged from 0.08 to 0.32 mg g⁻¹ dry sediment, with an average of 0.19 mg g⁻¹ dry sediment. The RSD of parallel samples was 3.2~6.5% (n = 3), all less than 10%, indicating excellent precision of the analytical method. The spatial distribution of SPC-APP content was consistent across the study area, in the following order: South Branch (0.28 ± 0.02 mg g⁻¹) > North Branch (0.18 ± 0.02 mg g⁻¹) > offshore shoal (0.09 ± 0.01 mg g⁻¹).

The formation of this spatial distribution pattern is mainly attributed to differences in shipping activity intensity, hydrodynamic conditions, sediment properties, and terrestrial input. Firstly, the South Branch of the Yangtze River Estuary is a concentrated area of core ports such as Shanghai Port and Nantong Port, with an average of over a thousand ships navigating through daily, resulting in a huge amount of ship hull antifouling paint peeling off, providing a continuous and substantial source of SPC-APPs. Meanwhile, the water flow velocity in the South Branch is relatively slow (average velocity 0.8 m s⁻¹ obtained from on-site field measurements using a Hydromet current meter during the sampling survey in June 2023, Table S1), so SPC-APPs are prone to settle with suspended matter and accumulate in sediments, forming high-concentration areas.

For the North Branch, although there are also ships passing through, the navigation volume is only 1/3 of that in the South Branch, leading to reduced exogenous input of SPC-APPs. Moreover, the North Branch is more affected by the input of freshwater from the Yangtze River, and the stronger water dilution effect further reduces the concentration of SPC-APPs. In addition, there are fewer industrial activities along the North Branch, and the supplement of antifouling paint particles from terrestrial sources is limited, which further explains the medium concentration level of SPC-APPs in this area.

The offshore shoal area is far from the main waterways, with sparse ship activities, resulting in very little exogenous input of SPC-APPs. At the same time, the sediments in the shoal areas are mainly composed of coarse sand (clay content < 10%), which has a weak adsorption capacity for fine-grained SPC-APPs [42]. Under the influence of tides, SPC-APPs in this area migrate easily and struggle to accumulate, so the content of SPC-APPs is close to the natural background value.

Additionally, SPC-APPs are the main components of ship antifouling paints, while Cu and Zn are the core active ingredients in antifouling paints [43]. Previous studies have confirmed that ship hull abrasion and maintenance activities are the dominant source of antifouling paint particles in marine and estuarine environments, whereas land-based industrial wastewater is not a major contributor [27,44]. The consistency in the spatial distribution of these three (high values in the southern branch and low values in the shoals) directly suggests that SPC-APPs and Cu/Zn in the sediments of the Yangtze River Estuary mainly come from the same pollution source—the peeling of ship antifouling paints—thus excluding the dominant role of other sources such as land-based industrial wastewater.

Through the spatial coupling of SPC-APP concentrations and Cu/Zn concentrations, a joint pollution evidence chain of “particle carriers (SPC-APPs)-characteristic heavy metals (Cu/Zn)” is formed. This not only verifies the reliability of the quantitative results of SPC-APPs but also provides a data correlation basis for subsequent composite pollution risk assessments such as the synergistic toxicity of particles and heavy metals.

3.6.3. Preprocessing Error Analysis

Potential preprocessing errors and control measures were systematically evaluated, as shown in Table S8. (1) Particle adsorption loss (<3%) was controlled by ultrasonic dispersion before density separation; (2) incomplete pyrolysis (<2%) was ensured by optimizing pyrolysis temperature (550 °C) and time (10 s); and (3) solvent residue interference (<1%) was eliminated by three rounds of ultrapure water washing. SRF-APPs spiked recovery tests showed a recovery rate of 92.5~98.7% (RSD < 4%), confirming errors are within acceptable limits for environmental analysis.

3.6.4. Global Comparison of SPC-APPs’ Pollution Levels

A comparative analysis with global studies was conducted to contextualize the results (

Table 6. Comparison of SPC-APP pollution levels in different regions worldwide.

Study Area	Sampling Medium	Analytical Method	Abundance/Concentration	Shipping Density	Reference
Yangtze River Estuary (China)	Surface sediment	Density separation + Py-GC/MS	0.82~3.65 × 103 particles g−1	120~150 vessels day−1	This study
Port of Callao (Peru)	Surface sediment	FTIR + SEM-EDS	0.5~2.8 × 103 particles g−1	80~100 vessels day−1	[11]
Port of Santos (Brazil)	Surface sediment	Raman + ICP-MS	0.11~1.2 × 105 particles g−1	150~200 vessels day−1	[34]
German Bight (Germany)	Seawater	Py-GC/MS	0.05~0.12 mg L−1	60~80 vessels day−1	[28]
Pearl River Estuary (China)	Surface sediment	FTIR + ICP-MS	0.3~1.8 × 103 particles g−1	50~70 vessels day−1	[14]

). SPC-APPs’ abundance in the Yangtze River Estuary (0.82~3.65 × 10³ particles g⁻¹) is comparable to high-shipping regions such as the Port of Callao (Peru) and Port of Santos (Brazil) and is higher than in low-shipping coastal areas. This confirms that the Yangtze River Estuary is a hotspot of SPC-APP pollution due to intense shipping activities.

3.7. Ecological Risk Assessment

As shown in Figure 12, the risk of Cu and Zn exhibited distinct regional patterns consistent with SPC-APPs’ abundance (South Branch > North Branch > offshore shoal): For Cu, Q (PEC) in the South Branch reached 0.56 (slightly above 0.5), indicating the concentration is close to PEC and may have potential toxic effects on benthic organisms (e.g., deposit-feeding polychaetes and bivalves). Q (TEC) of Cu in all regions exceeded 1 (1.06~2.65), confirming that Cu concentrations have exceeded the threshold effect level and may cause mild adverse effects. For Zn, Q (PEC) in all regions was < 0.5 (0.16~0.32), and only the South Branch had Q (TEC) > 1 (1.20), indicating low toxicity risk. The average Q values in all regions were <0.5 (0.19~0.44), suggesting that the combined toxicity of Cu and Zn does not represent a significant ecological hazard. However, the South Branch had the highest Q value (0.44), approaching the non-toxic threshold, which requires attention due to long-term SPC-APP accumulation.

The spatial consistency between ecological risk and SPC-APPs’ abundance confirms that SPC-APPs are the key driver of Cu/Zn-related risks in the study area. The South Branch, with the highest SPC-APP abundance (2.98 × 10³ particles g⁻¹ dry sediment), exhibited the highest Cu Q (PEC) (0.56), which is attributed to the continuous input of fresh SPC-APPs from intensive shipping activities (120~150 vessels day⁻¹). These particles act as persistent reservoirs of Cu and Zn, releasing metals slowly into the sediment environment through physical abrasion and chemical oxidation (Section 3.4) and leading to concentrations approaching the PEC threshold.

Compared with other estuarine and coastal regions worldwide, the ecological risk of Cu in the Yangtze River Estuary is moderate. For example, the Venice Lagoon (Italy) had sediment Cu concentrations of 120~250 mg kg⁻¹ [38], with Q (PEC) > 0.8, indicating high toxicity risk; the Port of Callao (Peru) had Cu concentrations of 15.2~45.6 mg kg⁻¹ [11], with Q (PEC) < 0.3, showing low risk. Meanwhile, the South Branch of the Yangtze River Estuary (Cu: 78.2~89.6 mg kg⁻¹) falls between these two extremes, reflecting the balance between intense shipping input and hydrodynamic dilution.

Notably, the current risk assessment only considers the dissolved metal fraction, while SPC-APPs themselves may exert synergistic toxic effects. Soroldoni et al. [23] reported that antifouling paint particles can be ingested by benthic organisms, causing physical damage to digestive tracts and enhancing metal bioaccumulation, leading to higher toxicity than dissolved metals alone. Additionally, although the combined Q value is <0.5, the coexistence of Cu (a biocidal metal) and Zn (a synergistic toxicant) may exacerbate adverse effects on sensitive species (e.g., juvenile bivalves and polychaetes) in the Chinese Sturgeon Nature Reserve and Jiuduansha Wetland adjacent to the study area [45,46].

Long-term monitoring is necessary to track risk dynamics, as increasing shipping intensity in the Yangtze River Estuary (annual growth rate of ~5% [47]) may lead to higher SPC-APPs’ accumulation and metal release, pushing Cu concentrations beyond PEC in the future. Targeted pollution control measures are recommended, such as restricting the use of high-Cu antifouling paints, enhancing hull maintenance to reduce paint peeling, and establishing buffer zones around ecologically sensitive areas to mitigate SPC-APPs’ deposition. Future studies should integrate particle toxicity and metal bioavailability to conduct a more comprehensive ecological risk assessment.

3.8. Research Limitations

Although this study systematically characterized SPC-APPs in the Yangtze River Estuary sediments, several limitations require attention in future research.

(1)

Limited sampling scope and frequency: Sampling was restricted to 12 sites covering the South Branch, North Branch, and offshore shoal, excluding upstream confluences, core port areas, and aquaculture zones. Seasonal dynamics (e.g., dry season vs. wet season) were not captured, as sampling was only conducted in June 2023 (peak shipping season). Future studies should expand the sampling range and include seasonal monitoring to clarify temporal–spatial variations.

(2)

Narrow target object scope: Only acrylate-based SPC-APPs were investigated, while other common antifouling paint types (e.g., chlorinated rubber-based, vinyl-based) were ignored. These types may have distinct environmental behaviors (e.g., higher persistence of chlorinated rubber-based particles) and source contributions, requiring comprehensive identification and quantification.

(3)

Incomplete environmental fate and ecological risk analysis: This study focused on distribution and characterization but did not explore SPC-APPs’ migration/transformation at the sediment–water interface (e.g., resuspension, dissolution) or their toxicological effects on aquatic organisms. Future research should conduct sediment–water interface migration experiments and chronic toxicity tests on benthic invertebrates to reveal comprehensive environmental impacts.

4. Conclusions

This study systematically investigated the occurrence, characterization, and environmental behavior of acrylate-based self-polishing copolymer antifouling paint particles (SPC-APPs) in sediments from the Yangtze River Estuary, yielding critical methodological, environmental, and ecological insights. The key conclusions are summarized as follows.

(1)

Core Methodological Contributions

A highly efficient and reliable analytical protocol tailored for SPC-APPs in complex estuarine sediments was established. Saturated KI solution was identified as the optimal density separation medium, ensuring high recovery of target particles and minimal co-floatation of impurities. The optimized H₂O₂-HNO₃ mixed solution effectively eliminated surface-adsorbed organic matter while preserving the structural integrity of SPC-APPs. Complemented by Py-GC/MS for accurate qualitative and quantitative analysis, this integrated protocol addresses the technical challenges of SPC-APPs’ detection in organic-rich sediment matrices, providing a robust methodological foundation for future large-scale pollution surveys.

(2)

Key Environmental Findings

SPC-APPs were ubiquitously detected across all sampling sites, confirming widespread contamination in the Yangtze River Estuary. Their abundance presented a distinct abundance gradient, jointly driven by shipping density and hydrodynamic conditions with no significant multicollinearity. Morphologically, SPC-APPs exhibited irregular blocky/flaky shapes with rough, abraded surfaces, reflecting progressive aging via physical abrasion and chemical oxidation. Elementally, they featured a diagnostic “high Cu, high Zn” signature, with metal contents significantly lower than fresh particles. Significant positive correlations between SPC-APPs’ abundance and sediment Cu and Zn concentrations confirmed these particles as primary sources of Cu/Zn contamination, while other heavy metals showed no such associations.

(3)

Ecological Significance and Future Directions

High-shipping-intensity regions face medium ecological risks from SPC-APP-derived Cu/Zn contamination, highlighting potential threats to benthic ecosystems in the Yangtze River Estuary. These findings underscore the urgency of targeted pollution control measures in major shipping hubs. While this study fills key knowledge gaps regarding SPC-APP pollution in the region, future research should expand sampling scope and frequency to capture seasonal dynamics, include multiple antifouling paint types, and integrate migration experiments and ecological toxicity tests to comprehensively reveal environmental risks and consequences.

Overall, this work emphasizes the role of SPC-APPs as vectors for toxic metals and provides scientific support for sustainable pollution management in high-intensity shipping estuaries worldwide.