Pollution Characteristics and Ecological Risk Assessment of Organochlorine Pesticides and Polychlorinated Biphenyls in the Maoming Coastal Zone, China

Abstract

Coastal zones, as critical ocean–land–atmosphere ecotones, face significant ecological threats from persistent organic pollutants like organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs). However, there are still obvious deficiencies in the understanding of the pollution characteristics and ecological risks of OCPs and PCBs in the coastal environment of South China, especially in western Guangdong. Due to the absence of prior research on these pollutants in the Maoming area, we measured the grain sizes from 157 sediment samples and the concentrations of PCBs and OCPs from 11 key locations to assess their environmental occurrence and risks. As analyzed by the GC-MS system, OCP levels range from 0.39 to 50.20 ng/g (mean 10.25 ng/g), while PCB concentrations range from 1.6 to 92.59 ng/g. Through the analysis of pollutant data and analysis of similar areas, we found that OCPs and PCBs in the Maoming coastal zone primarily originate from fishing port operations, ship antifouling paints, and historical legacy pollutants. In addition, the distribution of pollution is significantly controlled by hydrodynamic conditions and the semi-enclosed geomorphological characteristics of the bay. As grain size increases, the correlation with pollutant concentrations shifts from positive to negative. This trend reveals that finer-grained sediments in low-energy environments accumulate significantly higher levels of pollution compared to their coarser counterparts in more dynamic settings. Compared to other coastal regions globally, the study area demonstrates relatively lower pollution intensity. Dual assessments using Sediment Quality Guidelines (SQGs) and Sediment Quality Standards (SQSs) indicate a generally low probability of adverse biological effects, with elevated risk localized to sites near port activities. This study provides a scientific basis for the prevention and control of OCP and PCB pollution in the Maoming coastal zone and also provides a reference for pollution assessment in similar areas.

1. Introduction

The coastal zone is a critical interface between terrestrial and marine ecosystems, sustaining key economic activities such as fisheries, aquaculture, port operations, shipping industries, and tourism resources. However, with the intensification of socio-economic activities, environmental degradation in coastal regions has become increasingly severe, exerting profound adverse effects on local ecological systems. These impacts include biodiversity loss, degradation of ecosystem services, and so on [1,2,3]. Therefore, the coastal zone occupies a crucial position at the intersection of land and sea, and its environmental pollution problems cannot be ignored.

Persistent organic pollutants (POPs), particularly organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs), pose a severe and long-term threat to global ecosystems and human health. Characterized by high chemical stability, toxicity, and lipophilicity, OCPs are highly efficient broad-spectrum insecticides, with significant environmental persistence and a strong propensity for bioaccumulation [4,5,6,7]. Among OCPs, hexachlorocyclohexanes (HCHs) and dichloro-diphenyl-trichloroethanes (DDTs) are particularly concerning due to their significant ecological and health risks [8]. Despite the prohibition of PCBs, their historical products and improper disposal continue to represent significant environmental sources. These legacy reservoirs progressively release persistent compounds into the environment through diverse pathways [9]. Crucially, sediments in ecosystems act as a major sink for both OCPs and PCBs, while also serving as a potential secondary source of pollution to the water column [10].

OCPs and PCBs have been widely documented in coastal sediments across various global regions [11,12,13]. Pollution is often elevated in semi-enclosed bays and areas proximal to historical pollution sources, where factors such as hydrodynamic conditions, sediment grain size, and total organic carbon content significantly influence their distribution and persistence [14,15,16]. The Maoming coastal zone, featuring a significant fishery hub (Bohe Port) and the largest mainland ecological lagoon in China (Shuidong Bay), represents a critical yet insufficiently examined region. Its semi-enclosed geomorphology and intense anthropogenic pressures create a potential high-risk area for OCP and PCB accumulation. However, the current lack of systematic research on the characteristics, sources, and associated ecological risks of these pollutants in this region leaves the pollution risks poorly defined.

Here, we aim to fill this knowledge deficiency by conducting a systematic assessment of OCP and PCB pollution in the Maoming coastal zone. The specific objectives were to (1) analyze the concentrations and compositional profiles of 24 OCP and 28 PCB congeners in surface sediments; (2) identify their potential pollution sources using compositional and diagnostic ratio analysis; (3) elucidate the key environmental factors governing their spatial distribution; and (4) evaluate the potential ecological risks. Through a systematic evaluation of pollution characteristics and their potential environmental impacts, this study provides critical empirical data and analytical insights that can inform future coastal zone management strategies and ecological conservation policies.

2. Regional Background

Maoming is located in the southwestern Guangdong Province (21°29′8″–21°59′4″ N, 110°50′36″–111°27′11″ E), within the East Asian monsoon climate zone, which provides ample sunlight, thermal energy, and hydrological resources [17]. The area features a mainland coastline of approximately 180 km and includes several natural harbors, such as Shuidong Bay, Bohe Port, Jida Bay, and Shapa Bay.

Both Bohe Port and Shuidong Bay are located in the southeastern part of Dianbai District, Maoming City, exemplifying typical semi-enclosed sandbar–lagoon coastal systems [18,19]. Serving as a critical marine fishery hub and port logistics center in Maoming, Bohe Port is distinguished by its abundant fishery resources, advanced port logistics infrastructure, and considerable ecological significance. Notably, it ranks among the three major fishing ports in Guangdong Province, primarily supporting fishery production and functioning as a key operational base for the marine fishing industry in Maoming. The newly developed port area of Bohe accommodates petrochemical and ethylene industrial complexes, alongside mariculture facilities [20,21]. Amidst rapid economic development and intensifying anthropogenic activities, Bohe Port is confronted with escalating environmental pressures. Concurrently, Shuidong Bay, recognized as China’s largest mainland ecological lagoon and a unique urban inner bay, supports diverse ecosystems and abundant biodiversity. In recent years, the Dianbai District has implemented ecological restoration initiatives, including mangrove rehabilitation and the “Beautiful Bay” construction project, leading to measurable improvements in Shuidong Bay’s environmental conditions. Nevertheless, challenges such as severe siltation, obstructed tidal channels, diffuse rural pollution sources, and declining biodiversity remain prevalent and require continued attention [22].

Geologically, the study area overlies Cambrian metamorphic bedrock, with Quaternary sedimentary deposits typically ranging from 20 to 30 m in thickness, indicative of long-term geological stability [23]. Ecologically, both Bohe Port and Shuidong Bay face substantial anthropogenic pressures. Marine pollution assessments identify these areas as major contributors to coastal pollution in Maoming, with petroleum hydrocarbons, chemical oxygen demand, sulfides, and phosphorus collectively accounting for over 82% of the total pollutant load [21]. Although heavy metal pollution in sediments of Bohe Port currently poses a relatively low overall ecological risk [21], insufficient wastewater treatment capacity and substantial discharges from aquaculture operations remain critical environmental challenges, contributing significantly to coastal pollution in the region [24].

Undoubtedly, conducting comprehensive marine disaster risk assessments, implementing systematic hazard investigations, and strengthening governance mechanisms in bay areas are imperative priorities for coastal environmental management. These efforts are particularly crucial for enhancing disaster prevention capacity and emergency response systems in the face of sudden marine pollution incidents, such as those that may arise from the Maoming Petrochemical Base. As key development zones, Bohe Port and Shuidong Bay necessitate integrated ecological protection and management measures for their fishery waters to systematically address existing and emerging environmental pollution challenges.

3. Materials and Methods

3.1. Samples

Surface sediment samples collected from a depth of 20–50 cm were obtained from 11 coastal areas in Maoming, China. Grain size analysis was performed on a total of 157 samples to characterize the overall sedimentary environment. For pollutant analysis, 14 representative samples were selected from key locations within semi-enclosed bays and port areas—Bohe Port (BH01, BH03, BH04), Shuidong Bay (SD01, SD02, SD03, SD04), Jida Bay (JD11, JD12, JD13, JD14, JD15), Wangcun Port (WC02), and Shapa Bay (SP01) (Figure 1). This selective sampling focused on bays and ports because prior regional and global studies indicate that such hydrodynamic settings are preferential sinks for persistent hydrophobic pollutants like OCPs and PCBs. In the field, all samples were homogenized using a stainless steel shovel, stored in pre-cleaned brown glass bottle, and transported under refrigerated conditions (0–4 °C) to the laboratory, where they were freeze-dried for 24 h prior to further analysis.

Figure 1. The schematic diagram of sampling points in the Maoming coastal zone.

The collected sediment samples displayed distinct regional characteristics in their physical composition and appearance. Samples from Shuidong Bay consisted of organic-rich, dark brown muddy sand with a distinct odor and containing abundant shell fragments. Those from Bohe Port presented as dark brown, well-sorted clay with notable fluidity and a putrid odor, while sediments from Jida Bay were characterized as black, poorly sorted sandy mud with a clayey matrix, limited fluidity, and a distinctly foul smell.

3.2. Methods

3.2.1. Surface Sediments Grain Size Test Method

Grain size analysis was performed at the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) using a Malvern Mastersizer 3000 laser diffraction grain size analyzer produced by Malvern, UK. Sample pre-treatment was performed according to the following protocol: to each 0.5 g sediment sample, 15–20 mL of 30% H₂O₂ was added, thoroughly mixed, and left to stand for 12 h to remove organic matter. Subsequently, 15 mL of a 1:2 HCl solution was added, thoroughly mixed, allowed to react for 24 h, and periodically agitated to dissolve biogenic calcium carbonate shells, authigenic iron oxides, and hydroxides. The residue was then centrifuged 3 times with ultrapure water to remove residual reagents. Prior to analysis, sediment samples were dispersed by adding 10 mL of 36 g/L (NaPO₃)₆ solution and shaking thoroughly. All analyses were performed under the conditions of a shading range of 15–21% and a stirrer speed of 2500 rpm. The measurements were repeated three times and averaged, so the results were more reliable. After machine testing, grain size parameters, including mean grain size (Mz), sorting coefficient (Sc), skewness (Sk), and kurtosis (Kg), were calculated from the measured data using the formulas proposed by Folk and Ward [25]. Among them was $\mathrm{M}\mathrm{e}\mathrm{a}\mathrm{n} \mathrm{g}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n} \mathrm{s}\mathrm{i}\mathrm{z}\mathrm{e} (\mathrm{M}\mathrm{z})={\displaystyle \frac{{\mathsf{\phi }}_{16}+{\mathsf{\phi }}_{50}+{\mathsf{\phi }}_{84}}{3}}$, where a larger Mz corresponds to a smaller grain size.

3.2.2. Concentration Test of OCPs and PCBs in Surface Sediments

The concentration analysis of OCPs and PCBs was completed at the Marine Element and Isotope Platform of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) using a Trace 1310-TSQ triple quadrupole GC-MS system produced by Thermo Fisher Scientific, Waltham, MA, USA. Given the typically low concentrations of OCPs in sediments, direct analysis is often unfeasible, necessitating effective sample preconcentration [26]. Freeze-dried sediments were homogenized and sieved through an 80-mesh sieve. Subsamples were weighed into pre-folded barrel filter paper and extracted with 100 mL of dichloromethane for 48 h. Sulfur interference was removed using pre-treated copper sheets that had been sequentially cleaned with diluted HCl, deionized water, acetone, and dichloromethane. The extracts were rotary-evaporated to 5 mL, solvent-exchanged with 15 mL n-hexane, and concentrated to 2 mL before being transferred to sample vials. For cleanup, extracts were further concentrated to ~1 mL under gentle nitrogen and subjected to chromatographic separation using a glass column (8 mm inner diameter) packed from bottom to top with 3 cm of deactivated neutral alumina, 3 cm of deactivated silica gel, and 1 cm of anhydrous sodium sulfate. After column conditioning with n-hexane, samples were loaded and eluted with 20 mL n-hexane/dichloromethane (1:1). The final eluate was concentrated to 0.5 mL under nitrogen for GC-MS analysis [27,28].

The analytical method quantified 24 OCP congeners and 28 PCB congeners [29,30]. Quality assurance included method blanks, duplicate samples, and matrix spike recovery tests. Although trace levels of target analytes were occasionally detected in blank samples, their concentrations were negligible compared to those in actual sediment samples. Method validation results showed mean recoveries of 67.32% for OCPs and 142.17% for PCBs. The elevated recovery for PCBs likely reflects significant matrix enhancement effects common in complex sediment extracts analyzed by GC-MS. Despite this, the precision of duplicate analyses was satisfactory, indicating that the reported uncorrected values reliably reflect relative concentrations and spatial trends. Therefore, to avoid introducing correction factors based on a single spike level and to maintain consistency with a substantial body of sediment pollution literature that reports uncorrected data, all reported concentration data are presented as uncorrected measured values.

3.2.3. Pollution Assessment Methods of OCPs and PCBs in Surface Sediments

Sediment Quality Guidelines (SQGs) and Sediment Quality Standards (SQSs) methods were used to evaluate the ecological risk of OCPs and PCBs in surface sediments of Maoming. SQGs are primarily derived from scientific research and toxicity data, serving as reference thresholds for ecological risk assessment. In contrast, SQSs are regulatory standards specified by the government or regulatory agencies. The ecological risk assessment by these two methods can not only evaluate the potential harm of pollutants to biological communities by comparing the concentration of pollutants with the biological effect threshold but also judge whether the pollution exceeds the standard, as well as the degree of exceeding the standard, by comparing with the regulatory standards.

SQGs serve as an effective tool for assessing the quality of freshwater, bay, and marine sediments. Based on extensive experimental data, Long et al. [31] proposed the Effects Range-Low (ERL) and Effects Range-Median (ERM) for determining the potential ecological risk of organic pollutants in estuarine and marine sediments, which have since been widely adopted as benchmark levels for sediment quality assessment. When the pollutant concentration is below the ERL, the toxic side effects on organisms are not obvious (risk probability < 10%); when the pollutant concentration exceeds the ERM, the toxic side effects on organisms may have a certain degree of negative ecological effects (risk probability > 50%); when the pollutant concentration is between ERL and ERM, the probability of biological harmful effects is between 10% and 50%, and only occasionally are negative effects produced.

According to the Chinese marine sediment quality standard [32], the first quality standard of PCBs is 20 ng/g, the second quality standard is 200 ng/g, and the third quality standard is 600 ng/g. For OCPs, DDTs are used as the reference indicator, with corresponding limits of 20 ng/g (first standard), 50 ng/g (second standard), and 100 ng/g (third standard). Because the existing sediment evaluation standards in China are relatively simple, this study additionally employed the sediment quality standard method issued by Quebec, Canada, in 2006 to conduct a more comprehensive evaluation of the surface sediments of Bohe Port and selected coastal zones of Maoming [33].

4. Results

4.1. Surface Sediments Grain Size Test Results

4.1.1. Surface Sediment Grain Size Distribution Characteristics

Analysis of surface sediment samples from the Maoming coastal zone revealed four grain-size fractions: clay, silt, sand, and gravel (Figure 2). Spatially, clay particles were primarily concentrated in Shuidong Bay, Bohe Port, Shapa Bay, and Jida Bay, with a minor presence in Wangcun Port and negligible levels elsewhere. Silt content demonstrated significant spatial variability (0–64.33%, mean = 6.05%), with elevated levels co-occurring in the same bays showing high clay deposition. Sand constituted the dominant fraction (5.9–100%, mean = 88.24%), exhibiting an inverse distribution pattern to clay, with lower proportions (<10%) found in bay areas and most other sites exceeding 90% sand content. Gravel distribution was more localized (0–28.16%, mean = 3.69%), peaking in discrete zones of Wangcun Port, Yanjingling, and Shapa Bay. Regarding sediment make-up, sand is the main component in the entire study area, while the proportion of silt and gravel is similar and less, while clay only accounts for a trace amount.

Figure 2. Clay, silt/sand/gravel ternary diagram of coastal sediment samples.

4.1.2. Surface Sediment Grain Size Parameters Distribution Characteristics

The grain-size parameters of surface sediments in the study area are summarized in Table 1. The Mz ranges from −0.61 φ to 4.80 φ (mean = 2.13 φ), reflecting a sediment assemblage dominated by sand. Sc vary from −0.77 to 2.83 (mean = 0.57), indicating sorting qualities spanning from excellent (<0.35) through good (0.50–0.71) to moderate (0.71–1.00). Most samples exhibited near-symmetrical Sk, with values ranging from −0.52 to 0.33, suggesting a dominance of medium-grained fractions. Kg displays notable variability (0.69–2.56), with the majority of samples classified as a minority as leptokurtic.

Table 1. Statistics of grain size parameters of sediments in the coastal zone of Maoming.

Sampling Area	Points	Statistic	Mid-Value/φ	Mean Grain Size (Mz)/φ	Sorting Coefficient (Sc)	Skewness (Sk)	Kurtosis (Kg)	Clay/%	Sand/%	Silt/%	Gravel/%
WC	10	Mean	1.47	1.56	0.27	−0.08	1.09	0.91	5.51	86.64	6.95
		Min	−0.56	−0.49	−0.77	−0.42	0.81	0.00	0.00	48.33	0.00
		Max	4.07	4.60	1.36	0.20	1.50	8.44	43.23	99.12	25.37
AN	5	Mean	2.37	2.35	0.75	0.03	1.21	0.30	2.10	97.57	0.03
		Min	1.43	1.43	0.32	−0.20	0.96	0.00	1.15	94.53	0.00
		Max	2.73	2.68	0.93	0.15	1.82	1.45	3.92	98.86	0.10
YJ	11	Mean	0.81	0.78	−0.05	0.02	1.01	0.00	0.21	93.86	5.93
		Min	−0.44	−0.35	−0.65	−0.27	0.77	0.00	0.00	77.21	0.00
		Max	2.57	2.57	1.07	0.30	1.23	0.00	0.54	100.00	22.38
ZG	13	Mean	1.86	1.85	0.55	0.00	0.96	0.05	0.74	98.69	0.52
		Min	1.16	1.19	−0.07	−0.10	0.69	0.00	0.00	92.85	0.00
		Max	2.60	2.58	0.97	0.11	1.09	0.64	3.55	100.00	5.61
SD	10	Mean	3.04	3.05	0.97	−0.05	1.00	4.39	15.38	80.23	0.00
		Min	2.10	2.10	0.22	−0.18	0.94	0.00	0.00	15.04	0.00
		Max	6.47	6.31	1.92	0.03	1.18	20.63	64.33	100.00	0.01
YY	4	Mean	2.28	2.27	0.86	0.02	0.95	0.00	0.36	99.57	0.07
		Min	2.08	2.07	0.70	0.01	0.94	0.00	0.00	98.30	0.00
		Max	2.38	2.38	0.93	0.03	0.97	0.00	1.43	100.00	0.28
LM	5	Mean	1.80	1.74	0.51	0.09	1.00	0.00	0.26	99.30	0.43
		Min	1.42	1.39	0.22	0.00	0.94	0.00	0.00	96.56	0.00
		Max	2.24	2.24	0.84	0.33	1.17	0.00	1.32	100.00	2.10
BH	5	Mean	4.65	4.80	1.32	−0.22	1.48	18.26	37.09	44.64	0.01
		Min	1.50	1.82	−0.46	−0.52	0.85	1.66	3.93	5.95	0.00
		Max	7.56	7.82	2.83	−0.06	2.56	42.59	62.06	94.33	0.07
JH	24	Mean	1.41	1.40	0.28	0.02	1.05	0.03	1.19	96.55	2.24
		Min	−0.07	−0.07	−0.45	−0.31	0.77	0.00	0.00	86.19	0.00
		Max	2.46	2.45	0.97	0.22	1.61	0.40	6.60	99.84	11.76
JD	27	Mean	2.40	2.40	0.67	0.03	1.01	3.28	9.43	86.21	1.08
		Min	0.05	0.04	−0.36	−0.51	0.71	0.00	0.00	13.25	0.00
		Max	6.57	6.62	2.12	0.26	1.27	26.89	61.59	100.00	8.32
SP	43	Mean	1.12	1.17	0.09	−0.05	1.01	1.91	5.75	84.15	8.20
		Min	−0.63	−0.61	−0.70	−0.49	0.76	0.00	0.00	11.29	0.00
		Max	6.48	6.65	2.23	0.24	1.43	24.98	63.72	100.00	28.16

4.2. Concentrations of OCP and PCB Pollution

PCBs and OCPs were ubiquitously detected across all sampling stations (Table 2 and Table 3). The compositions of OCPs varied spatially (Table 2). DDT and its metabolites (DDE, DDD) constituted the predominant fraction of OCPs at stations in Shuidong Bay (mean contribution: 66.7%), Bohe Port (mean: 64.5%), and Jida Bay (mean: 72.7%). Dicofol was also a notable component at these sites. In contrast, both the absolute concentrations and the proportion of DDT-related compounds were lower at Wangcun Port and Shapa Bay. The total PCB concentrations showed marked spatial heterogeneity (Table 3). Station JD13 (92.59 ng/g) and JD14 (54.28 ng/g) were clear outliers, with PCBs 9.0 and 5.3 times higher than the average of the remaining 12 stations (10.24 ng/g), respectively. Among the 24 OCPs, 11 congeners had detection frequencies exceeding 50%. Elevated concentrations were observed for dicofol, p,p′-DDE, o,p′-DDT, p,p′-DDD, and p,p′-DDT, with p,p′-DDD displaying the highest mean concentration (range: 0.01–28.09 ng/g; mean: 2.67 ng/g). Similarly, 14 of the 28 PCB congeners showed detection rates > 50%, with PCB123 and PCB118 showing particularly high concentrations. PCB118 emerged as the most prevalent congener (range: 0.07–31.53 ng/g; mean: 7.93 ng/g), indicating its significant contribution to overall PCB pollution in the studied area.

Table 2. Concentration levels (ng/g) of 24 OCP monomers in 14 station samples.

Monomer	SD01	SD02	SD03	SD04	BH01	BH03	BH04	JD11	JD12	JD13	JD14	JD15	WC02	SP01
HCB	0.17	0.10	0.01	0.10	0.02	0.01	0.02	0.00	0.00	0.16	0.48	0.02	0.00	0.08
α-HCH	0.00	0.01	0.00	0.04	N.D.	0.01	N.D.	N.D.	N.D.	0.06	0.12	N.D.	N.D.	0.03
β-HCH	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.
γ-HCH	0.02	0.01	0.00	0.03	0.01	0.01	0.01	0.00	N.D.	0.07	1.70	N.D.	N.D.	0.03
Heptachlor	N.D.	0.00	N.D.	N.D.	N.D.	0.01	N.D.	N.D.	N.D.	N.D.	0.17	0.00	N.D.	0.04
δ-HCH	0.01	N.D.	N.D.	0.02	N.D.	N.D.	N.D.	N.D.	N.D.	0.03	0.02	N.D.	N.D.	0.03
Aldrin	0.02	0.01	0.03	0.03	0.01	N.D.	N.D.	N.D.	0.01	N.D.	N.D.	0.03	N.D.	0.01
Dicofol	3.57	1.43	0.80	6.22	0.33	1.21	0.17	0.07	0.02	3.86	2.20	0.31	0.01	0.05
Epoxy heptachlor B	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	0.02	N.D.	N.D.	0.05
Trans-chlordane	0.01	0.01	N.D.	0.09	0.00	N.D.	N.D.	N.D.	N.D.	0.05	0.02	N.D.	N.D.	0.04
Cis-chlordane	0.01	0.01	N.D.	0.02	0.00	N.D.	N.D.	N.D.	N.D.	0.05	0.02	N.D.	N.D.	0.04
α-Endosulfan	N.D.	0.02	0.00	0.08	N.D.	N.D.	N.D.	N.D.	N.D.	0.09	0.03	N.D.	0.01	0.05
p,p’-DDE	10.88	5.77	1.00	0.39	0.27	3.40	0.06	0.25	0.01	8.56	1.53	2.08	0.02	0.07
Dieldrin	N.D.	0.05	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	0.03	0.06
Endrin	0.06	0.15	0.05	0.38	0.10	0.21	N.D.	0.05	0.02	N.D.	N.D.	N.D.	N.D.	0.06
o,p’-DDT	1.66	0.57	0.12	0.62	0.20	1.12	0.16	0.20	0.05	2.93	0.68	1.61	N.D.	0.09
p,p’-DDD	28.09	1.11	0.28	0.14	0.20	3.30	0.06	0.42	0.01	2.35	0.63	0.71	0.01	0.02
β-Endosulfan	0.02	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	0.03
p,p’-DDT	5.66	2.94	0.53	0.82	0.53	3.77	0.12	0.60	0.08	10.02	2.39	6.31	0.06	0.11
Endrin Ketone	N.D.	N.D.	N.D.	N.D.	0.33	0.37	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	0.13
Endosulfan sulfate	0.01	0.01	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	0.03
Methoxychlor	0.01	N.D.	N.D.	0.07	N.D.	N.D.	N.D.	N.D.	N.D.	0.21	N.D.	N.D.	N.D.	0.03
Mirex	0.01	0.06	0.01	0.02	0.01	0.01	0.00	0.00	N.D.	0.05	0.03	0.00	N.D.	0.03
Endrin Aldehyde	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	0.23	N.D.	0.18	N.D.	N.D.	N.D.	N.D.	N.D.
∑OCPs	50.20	12.25	2.84	9.08	2.03	13.42	0.83	1.59	0.39	28.50	10.03	11.07	0.15	1.10

Note: N.D. indicates no detection.

Table 3. Concentration levels (ng/g) of 28 PCB monomers in 14 station samples.

Monomer	SD01	SD02	SD03	SD04	BH01	BH03	BH04	JD11	JD12	JD13	JD14	JD15	WC02	SP01
PCB8	0.24	0.58	0.67	0.44	0.04	0.06	0.55	0.45	0.82	0.48	0.06	0.03	0.39	0.43
PCB18	1.20	0.05	1.47	0.34	0.16	0.15	1.56	0.31	0.34	1.40	0.06	0.66	0.44	0.63
PCB28	0.11	0.14	0.11	0.64	0.48	0.38	0.18	0.04	0.06	0.66	0.15	0.03	0.08	0.08
PCB52	0.07	0.12	0.11	0.31	0.22	0.08	0.23	0.08	0.28	1.24	0.24	0.41	0.13	0.31
PCB44	N.D.	N.D.	N.D.	0.35	N.D.	N.D.	N.D.	0.07	0.13	0.39	N.D.	0.14	0.08	0.12
PCB66	N.D.	0.03	0.11	0.92	N.D.	N.D.	N.D.	N.D.	N.D.	0.34	0.14	N.D.	N.D.	0.04
PCB101	0.07	0.13	0.29	0.37	N.D.	0.21	0.12	0.06	0.08	0.52	0.21	N.D.	0.16	0.16
PCB81	N.D.	0.10	N.D.	1.62	N.D.	N.D.	N.D.	N.D.	N.D.	1.13	0.29	N.D.	N.D.	N.D.
PCB77	0.07	0.10	0.06	0.40	0.12	0.31	N.D.	N.D.	N.D.	1.64	0.34	0.15	N.D.	N.D.
PCB123	0.42	4.72	3.19	0.74	4.90	0.44	N.D.	0.60	0.07	15.63	11.22	2.26	N.D.	N.D.
PCB118	0.28	25.39	7.17	0.68	3.91	0.30	N.D.	0.25	0.07	15.21	31.53	2.45	N.D.	N.D.
PCB153	0.25	0.41	0.08	0.81	0.38	0.37	0.11	0.04	N.D.	3.80	1.59	0.33	0.09	N.D.
PCB114	0.19	2.33	0.66	0.31	2.00	0.25	0.05	0.18	0.08	5.42	0.49	0.32	0.11	0.08
PCB105	0.17	0.42	0.51	0.42	1.96	0.36	0.09	0.10	0.08	0.64	0.66	1.60	0.12	0.08
PCB138	N.D.	N.D.	N.D.	1.96	N.D.	N.D.	N.D.	N.D.	N.D.	5.08	N.D.	N.D.	N.D.	N.D.
PCB187	N.D.	0.09	N.D.	1.52	0.16	N.D.	N.D.	N.D.	N.D.	2.38	0.38	N.D.	N.D.	N.D.
PCB126	0.37	0.10	N.D.	0.38	N.D.	N.D.	N.D.	N.D.	N.D.	0.87	1.45	0.51	N.D.	0.09
PCB128	0.26	N.D.	N.D.	0.74	N.D.	N.D.	N.D.	N.D.	N.D.	1.72	0.33	0.21	N.D.	0.30
PCB167	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	0.90	N.D.	N.D.	N.D.	N.D.
PCB156	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	11.93	N.D.	N.D.	N.D.	N.D.
PCB180	N.D.	N.D.	N.D.	0.65	0.37	0.31	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	0.07
PCB157	N.D.	N.D.	N.D.	N.D.	0.34	0.30	N.D.	N.D.	N.D.	11.76	N.D.	N.D.	N.D.	0.28
PCB169	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.
PCB170	0.12	0.40	0.25	2.56	0.77	N.D.	0.22	N.D.	N.D.	5.95	1.68	N.D.	N.D.	N.D.
PCB189	N.D.	N.D.	N.D.	10.39	N.D.	N.D.	N.D.	N.D.	N.D.	1.09	1.15	N.D.	N.D.	N.D.
PCB195	0.25	N.D.	N.D.	N.D.	0.65	0.47	N.D.	N.D.	N.D.	1.44	2.32	0.58	N.D.	0.07
PCB206	N.D.	0.12	N.D.	0.50	N.D.	N.D.	N.D.	N.D.	N.D.	0.97	N.D.	N.D.	N.D.	0.04
∑PCBs	4.07	35.23	14.69	27.05	16.46	4.01	3.11	2.18	2.01	92.59	54.28	9.68	1.60	2.77

Note: N.D. indicates no detection.

5. Discussion

5.1. Sediment Pollution Sources and Hydrodynamic Environment Analysis

5.1.1. Sediment Pollution Sources Identification and Residual Behavior Analysis

As a large industrial and agricultural country, the pollution levels of PCBs and OCPs in surface water in the southeast coastal areas of China are usually high [34]. Atmospheric dry and wet deposition, urban runoff, and industrial waste discharge are important sources of PCBs and OCPs in water [35].

The spatial distribution and compositional profiles of OCPs and PCBs in the coastal sediments of the Maoming area provide critical insights into their sources and environmental behaviors. The data reveal that PCB pollution hotspots (JD13, ∑PCBs = 92.59 ng/g; JD14, ∑PCBs = 54.28 ng/g) and elevated OCP levels (SD01, ∑OCPs = 50.20 ng/g) are predominantly associated with specific sediment characteristics: dark color (from black to reddish-brown), sandy-muddy texture, poor fluidity, and noticeable odor. These characteristics often correlate with higher organic matter content, which plays a crucial role in the accumulation of hydrophobic organic pollutants like PCBs and OCPs due to their strong adsorption capacity [36]. This observation aligns with findings from the Huaihe River, where sediment total organic carbon (TOC) was identified as a key controlling factor in OCP distribution [36].

In addition, industrial DDTs are composed of about 70% p, p’-DDT and about 15% o, p’-DDT, and o, p’-DDT is more easily degraded than p, p’-DDT [37,38]. The biodegradation products of DDT are DDD and DDE, and the ratio between these metabolites serve as indicators for inferring degradation conditions and distinguishing recent versus historical DDT inputs. Under anaerobic conditions, DDT is predominantly transformed into p, p’-DDD via microbial reductive dechlorination; under aerobic conditions, it is converted into p, p’-DDE through microbial dechlorination and dehydrogenation [39]. The ratio of DDD/DDE can thus reflect the prevailing degradation environment: a value greater than 1 suggests dominant anaerobic degradation, whereas a value below 1 indicates aerobic degradation predominance. Furthermore, if (DDD + DDE)/∑DDTs > 0.5, the DDT composition is primarily derived from historical residues; otherwise, recent DDT inputs are likely present [40].

It can be seen that the DDD/DDE ratios at stations SD01 and JD11 exceed 1 (Table 4), indicating that DDT degradation in these locations occurs predominantly under anaerobic conditions. In contrast, at other stations, the ratio is below 1, suggesting aerobic degradation as the dominant pathway. The lower oxygen levels implied by anaerobic degradation at SD01 and JD11 may reflect sediment environments with restricted oxygen exchange or elevated organic loading, often associated with pronounced water pollution. Furthermore, the ratios (DDD + DDE)/∑DDTs at stations SD01, SD02, SD03, and BH03 exceed 0.5, indicating that DDTs in these areas are mainly derived from historical residues. At the remaining stations, ratios below 0.5 indicate potential ongoing inputs of DDT. Such recent pollution may be attributed to the use of dicofol, a pesticide known to contain DDT impurities [41,42], which was detected at notable concentrations in our samples (e.g., 6.22 ng/g at SD04, 3.86 ng/g at JD13). This pattern aligns with observations in the Pearl River Delta, where dicofol use was identified as a significant ongoing source of DDT pollution [43]. Near the port area, ship antifouling paint may be the main reason for the concentration of DDTs near the fishing port [41]. As both Bohe Port and Jida Port are fishing ports, they accommodate vessels of varying sizes, from small fishing boats to larger ships. This suggests that DDT pollution concentrations are closely linked to the scale of the port and vessel traffic.

Table 4. The ratio of DDD/DDE and (DDD + DDE)/∑DDTs in surface sediments of the Maoming coastal zone.

Station	SD01	SD02	SD03	SD04	BH01	BH03	BH04	JD11	JD12	JD13	JD14	JD15	WC02	SP01
DDD/DDE	2.58	0.19	0.28	0.36	0.72	0.97	0.97	1.70	0.80	0.27	0.41	0.34	0.71	0.33
(DDD + DDE)/∑DDTs	0.84	0.66	0.66	0.26	0.39	0.58	0.29	0.45	0.15	0.45	0.41	0.26	0.34	0.28

The significant concentrations of specific PCB congeners, particularly PCB118 and PCB123, provide clues to their industrial origins, as these compounds are recognized markers of industrial thermal processes [44]. In our study area, elevated levels of these compounds, especially at stations JD13 (PCB118 = 15.21 ng/g, PCB123 = 15.63 ng/g) and JD14 (PCB118 = 31.53 ng/g, PCB123 = 11.22 ng/g), may be associated with historical and potentially ongoing industrial activities in the Maoming region, which hosts a large-scale petrochemical complex. It has been estimated that a significant portion of unintentionally produced PCBs in China originates from high-temperature industrial processes like coking [45]. The proximity of these pollution hotspots to port areas (Bohe Port and Jida Port) further suggests contributions from shipping activities, including the use and release of PCB-containing materials and antifouling paints on vessels.

The elevated concentrations of POPs in the inner bay areas are attributed to a pollution accumulation mechanism associated with semi-enclosed geomorphology. While sewage outfalls (including those in Bohe Town and Jida Port [24]) represent potential pollution sources, the critical controlling factor is the restricted water exchange and weak hydrodynamic energy characteristic of these bays. As evidenced by the prevalence of fine-grained sediments in these areas (Section 4.1), the resulting low-energy depositional environment promotes the deposition and long-term accumulation of particle-sorbed pollutants. Consequently, even with moderate pollutant inputs, this geomorphological setting facilitates the progressive enrichment of POPs in bay sediments.

5.1.2. Impact of Hydrodynamic Conditions on Pollutant Distribution and Concentration

Shuidong Bay features a classic sandbar–lagoon landform [46]. Its geomorphic evolution is dominated by tidal dynamics, which establish a reciprocating flow pattern within the main tidal channel, with ebb velocities typically exceeding flood velocities [47]. This regime, combined with wave-driven alongshore currents, promotes significant sediment transport, delivering external sediments into the lagoon during the flood tide and exporting sediments to the ebb-tide delta area during the ebb tide [46]. The resulting hydrodynamic energy gradient—weaker inside the bay and stronger near the mouth—fundamentally controls the deposition of fine-grained sediments and associated pollutants in the inner bay. Sediment grain size exhibited a clear gradient: samples from the inner bay (SD01–SD03) were finer (clay and silt dominated), reflecting weaker hydrodynamics, while those near the bay mouth (SD04–SD05) were coarser (sand dominated), indicating stronger hydrodynamic energy. Correspondingly, OCP concentrations at inner bay stations (SD01: 50.20 ng/g, SD02: 12.25 ng/g) were significantly higher than those at the bay mouth station (SD04: 9.08 ng/g), with PCBs showing a similar trend at SD02 (SD02: 35.23 ng/g, SD04: 27.05 ng/g). This clearly demonstrates that the weak hydrodynamic conditions in the inner bay facilitate the formation of a “pollution sink”, where fine-grained sediments accumulate over time and exhibit stronger adsorption capacity for organic pollutants. This phenomenon is further corroborated by studies of surface sediments in Zhelin Bay, which revealed that the correlation between sediment grain size and OCP concentrations gradually shifts from positive to negative as grain size increases, indicating that larger grain sizes are less conducive to the accumulation and enrichment of OCPs in sediments (Figure 3) [48].

Figure 3. (A) The correlation diagram between PCB concentration and mean diameter. (B) The correlation diagram between OCP concentration and mean diameter.

Bohe Port is located in the southeast of Dianbai District, which is a semi-open bay surrounded by Donggeling, Liantouling Peninsula, Dafangji Island, and Xiaofangji Island [21]. The local bathymetry features deep water near the coast, relatively weak tidal energy, and limited terrestrial sediment input. The sandy seabed covers most of the sea area, and the sediment concentration of the water body is very low [49]. Bohe Port belongs to a weak tidal sea area. Tidal currents exhibit a rotational pattern in the west and a reciprocating flow in the east, with velocities generally decreasing from the surface to the seabed. This hydrodynamic regime results in a distinct sediment grain-size gradient: coarser sediments (coarse sand and silt) dominate the deeper channel and outer waters, while finer sediments (medium to fine sand) are found in the shallower inner bay and nearshore areas [50]. Compared to other regions, the surface sediments in Bohe Port are comparatively finer (Figure 4). Sediments at BH01 consisted predominantly of sand (94.33%). From the inner bay (BH03) to the bay mouth (BH04), sediment grain size coarsened, with clay content decreasing and silt/sand increasing. This pattern reflects hydrodynamic gradients: weaker conditions inside the bay (BH03) allow deposition of finer materials, while strong currents near the mouth (BH04) transport and deposit coarser particles during the ebb tide. Consequently, higher pollutant concentrations at BH03 than at BH04 demonstrate the preferential accumulation of pollutants in low-energy, fine-grained sedimentary environments, a process amplified by nearby anthropogenic inputs.

Figure 4. Percent accumulation histogram of sediment grain size content in the coastal zone of Maoming.

5.2. Comparative Analysis of OCP and PCB Pollution Levels

The PCB concentrations in surface sediments of the Maoming coastal zone were compared with those reported for marine sediments from various domestic and international regions (Table 5). Excluding the notable hotspots JD13 and JD14, the PCB concentrations in other regions of Maoming are generally comparable to other Chinese coastal waters and are lower than most foreign industrial ports, such as San Diego, USA [51], and important ports in the Gulf of Korea [52]. The pollution characteristics of the Maoming coastal zone are similar to those of the Pearl River Delta and the Guangzhou section of the Pearl River, which are affected by industrial activities and historical pollution accumulation. In the process of port and ship operation, the discharge of toxic waste liquid, solid waste, and fuel oil increases the pollution load of PCBs in sediments [53]. In general, the residual content of PCBs in surface sediments across most of the surveyed areas of the Maoming coastal zone remains relatively low.

Table 5. Comparison of PCB content in marine sediments at home and abroad.

Area	Year	Number of Monomers	Content (ng/g)	Reference
Maoming coastal zone	2024	28	1.6~92.59	This study
Pearl River coastal	2020	37	0.024~5.11	[54]
The South China Sea	2019	68	0.77~4.8	[55]
Hangzhou Bay	2019	68	5.15~11.67	[55]
North Sea offshore	2017	10	1.05~7.89	[56]
Xiangshan Bay	2014~2017	10	N.D.~3.5	[57]
Pearl River Estuary Shenzhen Sea Area	2014	7	0.35~1.035	[45]
Pearl River Delta	2012	7	19.8~111	[53]
Yellow River	2011~2012	12	0.0023~0.0148	[58]
East China Sea	2010~2011	24	0.0243~0.3433	[59]
Yellow Sea	2010~2011	24	0.099~3.13	[59]
Yangtze River Estuary	2007	22	5.08~19.64	[60]
Pearl River Basin	2003~2004	20	2.83~21.7	[61]
Daya Bay sea area	2002~2004	12	2.4~14.1	[62]
The Guangzhou segment of the Pearl River	1997	25	48.27~485.45	[63]
Arctic Svalbard Islands	2019	18	0.417	[64]
Persian Gulf	2015	22	2.95~7.95	[65]
American San Diego	2014	26	22.5~1387	[51]
Kuwait coastal waters	2013/2014	25	1.25~78.4	[66]
Antarctic	2013	68	3.78~47.74	[55]
Prydz Bay, Antarctic	2008~2013	22	0.87~5.84	[67]
Gulf of Korea	1997~2002	22	0.088~199	[52]

The concentrations of OCPs and DDTs in surface sediments of the Maoming coastal zone were lower than those reported for sediments from various domestic and international regions (Table 6). For instance, the average DDT pollution level in this study was lower than that documented in the coastal wetlands of Shanwei, Zhanjiang Bay, and Zhelin Bay and the estuarine wetland of Bohai Bay. The elevated OCP and DDT levels observed in Zhelin Bay have been attributed primarily to historical residues and long-term weathering of pesticide-derived compounds [48]. The DDTs in the estuarine wetland of Bohai Bay mainly come from the recently imported industrial DDTs [68]. Compared with the Maoming study area, the Yangtze River Estuary and its adjacent waters—located at the confluence of the Yangtze River and Qiantang River—are characterized by high freshwater discharge and rapid flow velocities, conditions that limit the retention and deposition of particulate-bound pollutants. Therefore, compared with the Maoming coastal zone, the pollutant concentrations in the Yangtze River Estuary are relatively low. The content of OCPs in the surface sediments of the Prydz Bay in Antarctica is similar to that in other regions of Antarctica and the inaccessible lakes, estuaries, and bays of the Qinghai-Tibet Plateau in China [67]. Because there is no near-source pollution caused by human activities, the concentration of pollutants is lower than that of the Maoming coastal zone. Furthermore, DDT pollution in the Maoming coastal zone is lower than that in Kyeonggi Bay, South Korea.

Table 6. Comparison of OCP and DDT contents in marine sediments at home and abroad (ng/g).

Area	Year	∑OCPs		∑DDTs		Reference
		Content	Average	Content	Average
Maoming coastal zone	2024	0.39~50.20	10.25	0.09~46.28	8.25	This study
Nearshore of Guangxi North Bay	2019	—	—	N.D.~3.7	—	[41]
Shanwei Nearshore	2018	—	—	22.04~105.76	59.98	[69]
Pearl River estuary	2017	—	—	1.83~6.98	4.03	[70]
North Sea offshore	2017	—	—	N.D.~3.82	0.89	[56]
Zhanjiang Bay	2017	N.D.~189.52	32.17	N.D.~173.19	26.9	[71]
Zhelin Bay	2013	14.14~306.88	78.37	13.88~300.51	74.18	[48]
Bohai Bay estuary wetland	2011	—	—	98.32~129.10	113.71	[68]
Yangtze River Estuary	2007	0.46~4.99	2.3	0.27~4.06	1.96	[60]
Prydz Bay, Antarctic	2008~2013	0.79~7.9	2.35	0.45~6.5	1.7	[67]
Bay of Bengal	1998	—	—	0.04~4.79	—	[16]
Kyeonggi Bay, South Korea	1995	—	—	1.7~32	9.4	[15]
Casco Bay, USA	1991	—	—	<0.20~20.42	—	[72]

5.3. Ecological Risk Assessment

5.3.1. Sediment Quality Guidelines

The biological toxic effects of OCPs (mainly DDTs) and PCBs in the surface sediments of Bohe Port and some coastal areas of Maoming are evaluated by SQGs, as shown in Figure 5. Among the 14 sampling stations, DDTs at 7 stations fell between ERL and ERM. At station SD01, DDT levels exceeded the ERM, while at the other stations concentrations were below the ERL. This indicates that the DDTs at stations SD02, SD03, SD04, BH03, JD13, JD14, and JD15 have a 10–50% probability of causing adverse ecological effects [31]. At SD01, the probability exceeds 50%, suggesting a higher likelihood of negative ecological effects [69]. For PCBs, concentrations at 4 of the 14 stations were between ERL and ERM, with the others below ERL. This indicates that PCBs at stations SD02, SD04, JD13, and JD14 carry a 10–50% probability of biologically harmful effects [31]. Given the potential for bioaccumulation and biomagnification of these pollutants in aquatic food webs [65], continued monitoring and management of DDTs and PCBs are warranted, even in areas currently classified as low risk. The management and control of DDT and PCB pollution in low-risk areas also need to be strengthened.

Figure 5. Quality criteria evaluation of DDTs and PCBs in surface sediments of the Maoming coastal zone.

5.3.2. Sediment Quality Standards

Based on the Chinese marine sediment quality standard [32], the PCB concentrations at ten sampling stations (SD01, SD03, BH01, BH03, BH04, JD11, JD12, JD15, WC02, SP01) met the first quality standard (suitable for the most sensitive marine uses, including fisheries and nature reserves), while the remaining four stations (SD02, SD04, JD13, JD14) met the second quality standard (applicable to general industrial and scenic areas) (Figure 6). Furthermore, DDT concentrations at all stations complied with the stringent first quality standard (Figure 6). The observation that all measured pollutant levels remain within the permissible thresholds for their respective functional zoning categories suggests a relatively low pollution risk, which remains within a manageable range.

Figure 6. PCB and DDT concentration histogram of each station.

The sediment quality standard method issued by Canada contains five thresholds (Table 7): Rare Effect Concentration (REL), Threshold Effect Level (TEL), Occasional Effect Concentration (OEL), Probable Effect Level (PEL), and Frequent Effect Concentration (FEL) [33].

Table 7. Marine sediment quality evaluation standard.

Compound	REL (ng/g)	TEL (ng/g)	OEL (ng/g)	PEL (ng/g)	FEL (ng/g)
DDT	0.33	1.2	3.8	4.8	10
PCBs	12	22	59	190	490

The results show that the DDT content at stations BH04, JD12, WC02, and SP01 are below the REL, posing no ecological risk to organisms. At stations SD03, BH01, and JD11, the DDT content falls between the REL and TEL, indicating a minimal probability of adverse effects on organisms. For stations SD02, SD04, and JD14, the DDT content ranges between the TEL and OEL, meaning the likelihood of adverse effects on organisms is limited to occasional occurrences. In contrast, the DDT content at stations SD01, BH03, and JD15 exceeds the PEL and approaches the FEL, posing a significant ecological risk to organisms [33]. Therefore, enhanced local monitoring is recommended in these areas. The DDT content at station JD13 exceeds the FEL, indicating adverse effects on most organisms and necessitating immediate control measures. Regarding PCBs, the content at stations SD01, BH03, BH04, JD11, JD12, JD15, WC02, and SP01 are below the REL, posing no ecological risk, while stations SD03 and BH01 have PCB levels between the REL and TEL, suggesting only a rare probability of adverse effects. For stations SD02, SD04, and JD14, PCB concentrations fall between the TEL and OEL, with adverse effects limited to occasional occurrences, whereas station JD13 exhibits PCB levels between the OEL and PEL, potentially posing ecological risks [33]. Notably, no stations exceed the PEL, indicating that PCB pollution in the study area does not require sediment remediation.

In summary, the dual assessment based on SQGs and SQSs reveals a spatially variable ecological risk profile. While PCB levels across the study area pose a minimal immediate risk, the exceedance of the FEL for DDTs at station JD13 indicates a potential threat to local benthic communities, warranting prioritized attention. It is important to note that even where guidelines are not exceeded, the persistent and bioaccumulative nature of these pollutants necessitates a precautionary perspective regarding long-term ecosystem health. Consequently, the overall contamination in the Maoming coastal zone can be characterized as predominantly low risk at a broad scale, yet with identifiable, localized areas of concern that merit targeted monitoring and management.

6. Conclusions and Prospects

This study provides a comprehensive assessment of OCP and PCB pollution in the Maoming coastal zone, elucidating their spatial patterns, potential sources, and associated ecological risks. The key findings are summarized as follows:

(1) Pollution Characteristics and Implications: OCPs and PCBs were ubiquitously detected in surface sediments. The predominant OCP pollutions were identified as p, p’-DDT, DDE, DDD, and dicofol. It points to a complex pollution history involving both weathered historical residues and potential ongoing inputs. For PCBs, congeners PCB118 and PCB123, recognized markers of industrial thermal processes, were the major contributors, underscoring the lingering influence of regional industrial and port activities on the coastal environment.

(2) Contamination Pattern and Its Drivers: While the overall pollution levels of OCPs and PCBs in the Maoming coastal zone are generally lower than those reported for many other industrialized estuaries, significant hotspots were identified, particularly in proximity to port facilities (JD13, JD14). Critically, this distribution is not random but is fundamentally controlled by a synergistic interplay of anthropogenic pressure and natural environmental filters. The pollution primarily originates from historical residues, ship antifouling paints, industrial activities, and discharges of domestic and industrial wastewater. Spatial patterns are strongly influenced by hydrodynamic conditions and sediment characteristics, with higher accumulation observed in areas characterized by fine-grained sediments, limited water exchange, and semi-enclosed geomorphology.

(3) Ecological Risk Assessment: Evaluations based on SQGs and SQSs indicate a generally low ecological risk across the study area. However, localized elevated risks were identified at specific stations (JD13 for DDTs), necessitating targeted attention.

(4) Limitations and Forward Look: The pollution source in this study apportionment relies on diagnostic ratios and relative studies, which carry inherent uncertainty compared to definitive tracer methods. The lack of total organic carbon data also limits a fully mechanistic interpretation of sediment–pollutant interactions. Future studies incorporating isotopic tracing and detailed organic geochemistry would further clarify pollution sources and sequestration processes.

To protect the marine environment, we recommend the implementation of tailored management strategies based on the ecological value and pollution status of each zone by (i) establishing intensified, long-term monitoring programs focused on identified port-adjacent hotspots and inner-bay sedimentary sinks; (ii) incorporating hydrodynamic and sedimentological models into coastal zoning plans to predict and mitigate future pollution accumulation risks.