Field Determination and Ecological Risk Assessment of Trace Metals in the Seawater of the Shandong Peninsula, China

Abstract

Coastal marine ecosystems are facing serious ecological risks from metals pollution, threatening biodiversity and human health. The main objective of this study is to evaluate the spatial distributions and ecological risks of dissolved cadmium (Cd), lead (Pb), and copper (Cu) in the Shandong Peninsula coastal areas, China. Two sampling campaigns were conducted at 21 sites in early spring 2025 to measure the concentrations of the three trace metals in the study area using an electrochemical detection system. The results revealed higher metals concentrations in nearshore areas (e.g., port entrances, aquaculture zones, and estuaries). Specifically, the Cd, Pb, and Cu concentrations in the study area ranged from 0 to 0.079 µg L⁻¹, 0.30 to 0.84 µg L⁻¹, and 2.19 to 4.79 µg L⁻¹, with average concentrations of 0.033, 0.55, and 3.18 µg L⁻¹, respectively. The contamination factors (C_f) of the three metals were below 1, indicating low pollution levels and thus meeting China’s Class I seawater quality standard. However, the ecological risk assessment, employing complementary methods, revealed varying interpretations: the risk quotient (RQ), based on species sensitivity distribution and predicted no-effect concentrations (PNECs), indicated low risks associated with Cd and Pb (RQ < 0.1) but a high risk for Cu (RQ > 1) at all sites, attributable to the exceedance of Cu’s protective threshold (0.46 µg L⁻¹), despite its low C_f. These findings highlight the need for continuous monitoring of Cu due to its high ecological impacts. In contrast, the Hakanson potential ecological risk index (ERI), which incorporates toxicity coefficients, suggested overall low risks (ERI < 150) for the combined metals; however, Cd contributed approximately 70% to the ERI due to its high toxicity coefficient, warranting attention despite the low individual Erⁱ values for Cd across the study area. This study provides valuable recent data on metals pollution dynamics in the Shandong Peninsula coastal areas, offering a scientific basis for developing marine pollution control policies and sustainable marine resource management.

1. Introduction

Coastal marine ecosystems play an important role in supporting global biodiversity, regulating biogeochemical cycles, and sustaining socio-economic development [1]. However, these environments are increasingly subjected to anthropogenic pressures, among which metals pollution poses a persistent threat [2,3]. Metals such as Cd, Pb, and Cu are of ecological concern due to their non-biodegradable nature, high toxicity, and tendency to bioaccumulate in aquatic organisms [4,5,6]. Once introduced into marine systems, these metals can disrupt biological processes, alter community structures, and degrade ecosystem services, with potential consequences for human health through the consumption of contaminated seafood [7,8].

Over recent decades, growing industrialization, coastal urbanization, and the expansion of aquaculture have exacerbated the input of metals into China’s coastal waters [9]. The northern Yellow Sea, including the coastal zones of the Shandong Peninsula, has become a region of increasing ecological and economic importance, characterized by intensive marine aquaculture, harbour activities, and complex hydrodynamic regimes [10]. Previous studies in this region have reported varying levels of heavy metal contamination, primarily in sediments and seawater. For instance, in the surface sediments of the southern coast of the Shandong Peninsula, the mean concentrations of Cd, Pb, and Cu ranged from 82.4 µg L⁻¹, 25.0 µg L⁻¹, and 23.1 µg L⁻¹, respectively, with higher values near river estuaries and industrial zones, indicating moderate pollution risks [11]. In the northern Yellow Sea, including Yantai coastal waters, dissolved metal concentrations in seawater have been documented as approximately 0.21–0.51 µg L⁻¹ for Cd, 0.13–1.06 µg L⁻¹ for Pb [12]. Sources such as industrial discharges, agricultural wastes, and aquaculture effluents are degrading water quality, allowing metals to enter the food chain and compromising aquaculture production safety and aquatic product quality. Despite these pressures, the spatial distribution, controlling factors, and ecological risks associated with dissolved metals in this region remain insufficiently explored. Current environmental monitoring efforts often rely on periodic laboratory spectroscopic techniques such as inductively coupled plasma–atomic emission spectrometry (ICP–AES) et al. [13]. These methods provide substantial advantages, including high precision and accuracy in quantifying trace metals, multi-element detection capability, wide linear dynamic range, and robustness against matrix interferences in complex samples like seawater, establishing their reliability as standards for baseline environmental assessments and regulatory compliance. Although these methods are analytically robust, they are constrained by high operational costs, extensive sample processing, and limited capacity to capture short-term spatial variability or real-time fluctuations in metals concentrations [14]. In recent years, portable electrochemical detection systems have been increasingly adopted for trace metals monitoring in estuarine and near-shore waters. These systems enable field measurements, are user-friendly, and allow for the rapid and simultaneous quantification of multiple metals [15,16]. Validation studies have shown that ASV methods exhibit excellent agreement with ICP based techniques, with mean percent differences typically less than 10% for trace metals in water samples and comparable detection limits at ppb levels, supporting their accuracy for environmental applications especially suitable for trace analysis of specific metals such as Cd and Pb [17]. Liang et al. [18], for example, deployed an automated electrochemical platform to quantify dissolved Cu, Pb, and Cd across three major mariculture sites. In this study, a portable anodic stripping voltammetry (ASV) instrument was utilized to perform field measurements of surface seawater in the Shandong Peninsula coastal region, with a focus on early-spring hydrodynamic conditions. In contrast to the research by Liang et al., which offered valuable insights into trace metal contamination in various mariculture areas, our investigation specifically concentrates on the early-spring period, providing a distinct temporal perspective on metal pollution dynamics. Furthermore, we integrated more robust pollution indices, such C_f, RQ, and the ERI, to facilitate a comprehensive and in-depth analysis of ecological risk factors. This methodology enables a more refined understanding of the potential risks associated with dissolved metals, thereby improving the precision of environmental assessments in this region. This approach allowed field, rapid, and continuous measurement of dissolved Cd, Pb, and Cu that provides a solid foundation for subsequent quantitative ecological risk assessment.

Accordingly, the primary objectives of this study are to (1) determine the spatial distribution of dissolved Cd, Pb, and Cu concentrations in the coastal waters of the Shandong Peninsula; (2) assess water quality and pollution levels using contamination factors; (3) evaluate ecological risk through established quantitative models; and (4) explore the relationships between metals distribution and environmental parameters such as salinity, nutrients, and chlorophyll-a. By integrating field-based electrochemical measurements with comprehensive ecological risk assessments, this work addresses a critical knowledge gap in coastal metals pollution research and provides a practical reference for future monitoring and management efforts in northern Chinese marine environments.

2. Field Surveys, Sample Collection Analysis, Study Area, and Data Processing

2.1. Field Surveys, Sample Collection, and Analysis

In this study, two field surveys were conducted aboard the research vessel “Innovation No. 1”. Specifically, 10 and 11 sampling stations in the coastal waters of Yantai and Weihai were surveyed from 19 March to 20 March 2025, and from 22 April to 25 April 2025, respectively The geographical coordinates of the sampling stations were provided by the vessel’s Ferrybox system (4H-JENA engineering GmbH, Jena, Germany). The metals concentrations were field determined using a highly automated electrochemical detection system (HAEDS) [18]. At each sampling station, the suction pipe of the peristaltic pump (Runze Fluid (Nanjing) Co., Ltd., Nanjing, China) was inserted into the water tank directly connected to the external seawater in the cabin laboratory, pumping 200 mL of seawater at a depth of 2–3 m below the surface and then placed in clean 15 mL polypropylene centrifuge tubes, previously soaked in 30% (Volume ratio) hydrochloric acid (Using analytical grade HCl, the diluted HCl mass fraction is 13%) for 48 h and rinsed 3–5 times with ultrapure deionized water. The collected seawater samples were filtered and immediately acidified with high-purity nitric acid (HNO₃, 68%, analytical grade) to achieve a final pH < 2.0, followed by analysis of Cd, Pb, and Cu concentrations using the HAEDS. The HAEDS utilizes differential pulse anodic stripping voltammetry (DPASV) with a glassy carbon working electrode coated with a mercury film, an Ag/AgCl reference electrode, and a platinum auxiliary electrode. The detection limits for dissolved Cd, Pb, and Cu using HAEDS were 0.003 µg L⁻¹, 0.003 µg L⁻¹, and 0.017 µg L⁻¹, respectively. The relative standard deviations (RSD) for Cd, Pb, and Cu concentrations in six seawater samples were 5.37%, 2.05%, and 5.89%, respectively. Comparison of HAEDS determined values in seawater with those obtained by inductively coupled plasma mass spectrometry (ICP-MS) showed errors within an acceptable range, confirming the method’s accuracy and reliability [18]. In addition, other physicochemical parameters, including pH, dissolved oxygen (DO), water temperature (T), and salinity (S), were measured in situ using a YSI ProDSS (Yellow Springs Instruments, Yellow Springs, USA) water quality analyzer. Chlorophyll (Chl-a) concentrations were determined using a UV-Vis spectrophotometer, following the operational procedures outlined in the China Marine Monitoring Specification, with a detection limit of 0.4 μg L⁻¹. Samples intended for NO₃⁻ and PO₄³⁻ analyses were filtered from 0.45 μm syringe filter (Pall Corporation, Fajardo, USA) into 15 mL polypropylene centrifuge tubes and stored at −20 °C. The NO₃⁻ and PO₄³⁻ concentrations were determined using a photometry-based flow injection analyzer (QuAAtro, Bran + Luebbe, Norderstedt, Germany). Ultrapure deionized water (blank), standard solutions, and a standard curve were considered for 40 samples to assess the instrument accuracy. The detection limits of the NO₃⁻ and PO₄³⁻ analysis methods were 0.02 and 0.01 μmol L⁻¹, respectively.

2.2. Study Area

The study area is in the coastal waters of Yantai and Weihai on the Shandong Peninsula (36.6–38.2° N, 120.6–122.8° E), adjacent to the south of North Yellow Sea and the north of South Yellow Sea (Figure 1a,b). The water depth in this marine area ranges from 10 to 30 m. This area is, in fact, influenced by the combined currents of the Bohai Sea and Yellow Sea, resulting in pronounced seasonal tidal variations [19]. The Shandong Peninsula are characterized by excellent coastal water quality, with abundant nutrients, making them suitable for the growth of diverse marine organisms. Furthermore, these coastal areas serve not only as key bases for fishing and marine farming but also as centres for aquaculture activities in North China [20]. However, the rapid development of fisheries and aquaculture in the region, in combination with the intensification of coastal industrialization and urbanization, has led to increasingly severe metals pollution issues [21]. These environmental issues are mainly derived from industrial discharges, agricultural wastes, and aquaculture effluents, degrading water quality and potentially entering marine organisms through the food chain and, consequently, compromising the safety of aquaculture production and the quality of aquatic products. The long-term accumulation of metals poses a serious threat to aquatic species diversity and ecological balance. It is, therefore, urgent to conduct in-depth studies on the distribution patterns of these pollutants and their associated risks to ensure the sustainable development of the ecological environment.

2.3. Water Quality Assessment Methods

The contamination factors (C_f) has been widely employed to evaluate the pollution levels of metals in offshore waters [22,23], reflecting the pollution intensity of an individual metals.

$${\mathrm{C}}_{\mathrm{f}}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{v}\mathrm{y} \mathrm{m}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{l}}}{{\mathrm{C}}_{\mathrm{b}\mathrm{a}\mathrm{c}\mathrm{k}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}}}}$$

(1)

where C_{heavy metal} denote the metals (e.g., Cd, Pb, or Cu) concentration; C_background denotes the background metals concentration in seawater.

The median concentrations of Cd (0.1 μg L⁻¹), Pb (1.08 μg L⁻¹), and Cu (33 μg L⁻¹), revealed by Tian et al. [24], were considered as background concentrations in this study. These values are regionally applicable because our study area in the Shandong Peninsula coastal zones, as well as the broader sampling domain in Tian et al. [24], both encompass nearshore marine environments within the northern Yellow Sea, sharing similar hydrodynamic regimes (e.g., influenced by the Yellow Sea Coastal Current), geochemical characteristics, and baseline nutrient dynamics. This selection avoids overestimation or underestimation of pollution levels by using medians that reflect natural variability in comparable marine environments, rather than site-specific extremes. The pollution levels of the metals were classified into four classes, according to Hakanson’s criteria [22]. These classes included low, moderate, considerable, and very high pollution levels, corresponding to C_f ranges of < 1, 1 ≤ C_f < 3, 3 ≤ C_f < 6, and C_f ≥ 6, respectively.

To comprehensively evaluate the ecological risks posed by dissolved Cd, Pb, and Cu, this study employs two complementary methods: the Species Sensitivity Distribution (SSD)-based Risk Quotient (RQ) and the Hakanson Potential Ecological Risk Index (ERI). The SSD-RQ approach focuses on individual metal risks by deriving probabilistic no-effect thresholds from toxicity data across multiple species, making it suitable for assessing acute, species-level vulnerabilities in marine environments [25,26]. Conversely, the Hakanson ERI integrates toxicity coefficients with multi-metal concentrations to gauge cumulative potential risks, emphasizing long-term ecological impacts and pollution hotspots [22,27]. Using both methods allows for a multi-faceted analysis: RQ identifies high-risk metals based on bioavailability, while ERI quantifies overall ecosystem threat levels. This dual framework enhances reliability, as it accounts for both single-pollutant and synergistic effects, which is essential in dynamic coastal zones like the Shandong Peninsula where metals may interact variably with environmental factors.

The ecological risks of the seawater Cd, Pb, and Cu concentrations were also assessed using the SSD method [25,26]. This method can be employed to quantify the ecological risks posed by specific pollutants to a marine ecosystem. This approach evaluates the ecological risks posed by specific pollutants to the marine ecosystem. It begins with the computation of the predicted no-effect concentration (PNEC) for the metals. The HC₅, corresponding to the 5th percentile of the species sensitivity distribution (SSD), denotes the maximum concentration of heavy metals that protects 95% of species within the ecosystem from adverse effects [28]. The assessment factor (AF), representing the uncertainty factor, was set at 3 [29]. According to Equation (3), the predicted no-effect concentrations (PNEC) of Cd, Pb, and Cu were 1.285, 8.43, and 0.46 µg L⁻¹, respectively. RQ values within the ranges of < 0.1, 0.1 ≤ RQ < 1.0, and RQ > 1.0 indicate low, moderate, and high ecological risks, respectively.

$$\mathrm{R}\mathrm{Q}={\displaystyle \frac{\mathrm{M}\mathrm{E}\mathrm{C}}{\mathrm{P}\mathrm{N}\mathrm{E}\mathrm{C}}}$$

(2)

$$\mathrm{P}\mathrm{N}\mathrm{E}\mathrm{C}={\displaystyle \frac{{\mathrm{H}\mathrm{C}}_5}{\mathrm{A}\mathrm{F}}}$$

(3)

where MEC denotes the measured environmental concentration, representing the actual concentration of a metal in seawater.

The potential ecological risks of the metals in the coastal waters of the Shandong Peninsula were assessed using the Hakanson ERI method, according to the following equation [22,27]:

$$\mathrm{E}\mathrm{R}\mathrm{I}={\sum }_{i=1}^n{\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}={\sum }_{i=1}^n{\mathrm{T}}_{\mathrm{f}}^{\mathrm{i}}\times {\displaystyle \frac{{\mathrm{C}}_{\mathrm{a}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}}}{{\mathrm{C}}_{\mathrm{b}\mathrm{a}\mathrm{c}\mathrm{k}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}}}}$$

(4)

where ERI, E_rⁱ, and T_fⁱ denote the potential risk index, the potential ecological risk factor, and the toxicity coefficient of metal i (i = Cd, Pb, Cu), respectively. The toxicity coefficients of Cd, Pb, and Cu were set at 30, 5, and 5, respectively [30]. C_{average value} denotes the average concentration of a metal in the seawater of the study area; C_background denotes the background concentration of a metal in seawater. In this study, the C_background of Cd, Pb, and Cu were set at 0.1, 1.08, and 33 μg L⁻¹, respectively.

The ecological risk factor of the metal i was classified into four classes, namely low (E_rⁱ < 40), moderate (40 ≤ E_rⁱ < 80), considerable (80 ≤ E_rⁱ < 160), high (160 ≤ E_rⁱ < 320), and very high (E_rⁱ ≥ 320) ecological risks. On the other hand, the ERI values were classified into five classes, namely low (ERI < 150), moderate (150 ≤ ERI < 300), considerable (300 ≤ ERI < 600), and very high (ERI ≥ 320) ecological risks [22].

2.4. Data Analysis

In this study, statistical analyses were performed to evaluate relationships between variables and to assess pollution and ecological risks. Pearson correlation analysis was used to examine linear relationships between dissolved metal concentrations (Cd, Pb, Cu) and environmental parameters (pH, DO, T, S, Chl-a, NO₃⁻, PO₄³⁻). Distribution maps, pollution factor maps, and ecological risk maps were generated in Origin (version 2023) software. The station location analysis was performed using Ocean Data View (V. 5.5.1) (https://odv.awi.de/, accessed on 3 June 2025).

3. Results and Discussion

3.1. Spatial Distributions of the Hydrological Parameters and Nutrients in the Seawater

As shown in Figure 2a, the seawater pH values ranged from 8.2 to 8.47, indicating a slightly alkaline environment. The DO concentrations were relatively high, ranging from 10.05 to 10.61 mg/L, with an average concentration of 10.31 mg/L (Figure 2b), suggesting optimal oxygenation due to the influence of seasonal mixing and moderate biological activities in early spring. The water T varied between 4.9 and 5.2 °C, with an average value of 5.0 °C (Figure 2c). This result reflects the transitional characteristics of late winter to early spring in the northern Yellow Sea. The salinity (per mille, ‰) concentration ranged from 27.59 to 32.15 (Figure 2d). The lowest S values were observed at coastal stations (e.g., S4) because of the effects of freshwater inputs from land-based sources. In contrast, the highest salinity values were found at offshore stations (e.g., S5), suggesting the influence of open-sea water masses. The Chl-a concentrations were low, ranging from 1.20 to 5.30 µg L⁻¹, with an average value of 2.64 µg L⁻¹ (Figure 2e), indicating low to moderate phytoplankton biomass. NO₃⁻ was detected at nearly all stations, with concentrations ranging from 0.075 to 0.26 mg/L (Figure 2f). It is worth noting that the concentration of NO₃⁻ at S21 was below the detection limit. The observed NO₃⁻ concentrations in the study area suggest moderate nitrogen availability levels in the early spring period, possibly due to the effects of terrestrial runoff, winter residual nutrients, or atmospheric inputs [31]. Similarly, the PO₄³⁻ concentrations were generally low, falling below detection limits at most sampling stations, except at S5, S7, S16, S18, S19, and S20 where the concentrations were 0.011, 0.008, 0.003, 0.012, 0.006, and 0.002 mg/L, respectively (Figure 2g, the detection point area circled in the figure was not detected). This finding suggests local PO₄³⁻ inputs or release from sediments. The overall low PO₄³⁻ concentrations suggest limited phosphorus availability across the study area during the early spring period, which might constrain phytoplankton productivity [32].

3.2. Spatial Distributions and Analysis of the Dissolved Metals in the Seawater

As shown in Figure 3a, the Cd concentrations were below the analytical detection limit at the S12 and S15 (the detection point area circled in the figure was not detected). On the other hand, the highest Cd concentration was 0.079 µg L⁻¹ at the S6 sampling station, with an average value of 0.033 µg L⁻¹. Two distinct near-shore enrichment zones were found, namely Sishili Bay (S5–S6) and the southern Weihai sector (S16–S20). Both areas were characterized by the presence of densely distributed industrial facilities, small river outlets, and ship-repair yards, which might result in the discharge of Cd-bearing wastewaters [9]. In contrast, the central coastal region encompassing stations S8 to S13 (Transverse section of S8 connected to S13 in Figure 3a) exhibited comparatively lower Cd concentrations overall, suggesting a general seaward decrease influenced by the dispersive effects of the Yellow Sea Coastal Current.

The results revealed a widespread distribution of the Pb concentrations, varying between 0.30 and 0.84 µg L⁻¹ at the S15 and S14 stations, respectively, with a mean value of 0.55 µg L⁻¹ (Figure 3b). It is worth noting that high Pb concentrations were observed at specific areas, including Zhifu Bay (S1), Weihai Port (S14), and the southern Weihai coast (S19–S21). Indeed, the comparatively higher Pb concentration in Zhifu Bay might be due to the restricted circulation, promoting Pb accumulation from urban runoff and small-scale dock activities. In contrast, the Weihai Port area was strongly influenced by harbour dredging, fuel combustion, and Pb-rich antifouling coatings, while the southern Weihai coast was characterized by intensive fishing, marine traffic, and shoreline construction, enhancing Pb accumulation [33].

As shown in Figure 3c, Cu had the highest absolute concentrations among the three metals, ranging from 2.19 to 4.79 µg L⁻¹, with an average value of 3.18 µg L⁻¹. The highest Cu concentrations (>4 µg L⁻¹) were observed at S19–S20 near the mouth of Weihai Bay, where dense ship-repair facilities and aquaculture activities that rely on Cu-based antifouling paints are found [33]. In addition, the overall concentration of Cu in the Weihai sea area is higher than that in the Yantai Sea area, reflecting the continuing impact of near-shore industrial discharges. On the other hand, the lowest Cu concentrations were found in the S13.

The spatial distributions of the Cd, Pb, and Cu concentrations showed obvious coastal accumulation patterns, forming a continuous enrichment zone from Zhifu Bay to the southern part of Weihai. The highest metals concentrations were observed near harbour entrances and river mouths. These patterns were controlled by multiple factors, including pollution sources (e.g., industrial discharge, harbour activities, and shipping), local water movement (e.g., tides and coastal currents), and geographical features (e.g., enclosed bays and open sea) [34]. The moderate wind-driven mixing in early spring might enhance the transport the metals without reaching deeper waters [35]. These baseline results are important for understanding future changes in metals pollution and evaluating the effectiveness of related pollution control efforts.

3.3. Comparison with Other Coastal Waters

The dissolved metals concentrations in the Shandong Peninsula offshore regions were further compared with those in other coastal regions in China, India, and Vietnam (

Table 1. Comparison of the observed dissolved Cd, Pb, and Cu concentrations in this study with those in other coastal regions (µg L⁻¹).

Regions	Cd Concentration (µg L−1)	Pb Concentration (µg L−1)	Cu Concentration (µg L−1)	References
Laizhou Bay, China	0.096 (0.039–0.16)	2.12 (0.63–4.97)	3.64 (1.93–8.83)	[36]
Laoshan Bay, China	0.11 (0.06–0.47)	0.78 (0.32–2.74)	1.48 (0.51–4.50)	[38]
Dingzi Bay, China	0.36 (0.19–0.56)	1.07 (0.62–1.46)	2.02 (0.87–2.71)	[41]
Xiangshan Bay, China	0.23 (0.03–1.61)	1.70 (0.22–6.00)	2.30 (0.60–8.40)	[6]
Shangchuan Island, China	N.D.-1.12	(0.44–1.37)	(1.60–7.29)	[37]
Shengshan Island, China	N.D.	6.18 (0.62–15.10)	11.52 (3.80–18.66)	[18]
Southeast coast, India	0.11	0.36	5.19	[40]
Red River Delta, Vietnam	0.44 (0.04–2.41)	7.27 (0.80–31.20)	26.91 (0.10–96.00)	[39]
Shandong Peninsula offshore, China	0.033 (N.D.–0.079)	0.55 (0.30–0.84)	3.17 (2.19–4.79)	This study

The values inside and outside parentheses indicate the range and average values of the dissolved metals concentrations, respectively; N.D., not detected.

). The dissolved Cd, Pb, and Cu concentrations in the Shandong Peninsula offshore areas were relatively low, with average concentrations of 0.033 µg L⁻¹ (N.D.-0.079 µg L⁻¹), 0.55 µg L⁻¹ (0.30–0.84 µg L⁻¹), and 3.17 µg L⁻¹ (2.19–4.79 µg L⁻¹), respectively. These concentrations were lower than those revealed in the Laizhou Bay and Shengshan Island regions, China [18,36], such comparisons must account for methodological differences, seasonal effects, and environmental variabilities. The higher Pb and Cu concentrations in the Laizhou Bay and Shengshan Island might be because of industrial activities and local pollution from ship emissions and effluents. In contrast, the Shandong Peninsula offshore areas exhibited relatively lower concentrations of these metals due to the presence of less intensive industrial activities. However, the Shandong Peninsula offshore regions exhibited higher Pb and Cd concentrations than some other regions in China, including the Southeast coast (Pb concentration of 0.36 µg L⁻¹) and Laoshan Bay [6,37,38]. These findings suggest relatively moderate pollution pressures in the Shandong Peninsula offshore areas compared with those in the other areas. Similarly, the metals concentrations in the Shandong Peninsula offshore regions were lower than those revealed in India and Vietnam. The higher Cd, Pb, and Cu concentrations in the Red River Delta of Vietnam might be due to the extent of agricultural runoff and industrial discharges [39]. On the other hand, the higher Pb concentrations in the Southeast coast of India were attributed to emissions from local transportation and motorized vessels [40]. Hence, it can be concluded that the Shandong Peninsula offshore regions were less impacted by anthropogenic activities when compared with those in Vietnam and India. These findings demonstrate the importance of continuous seawater monitoring to implement effective control measures against the intensification of human activities.

3.4. Factors Influencing the Distributions of the Total Dissolved Metals Concentrations

The relationships of the dissolved Cd, Pb, and Cu concentrations with physicochemical parameters (pH, S, T, DO, PO₄³⁻, NO₃⁻, and Chl-a) in the seawaters of the Shandong Peninsula coastal regions were further explored using Pearson correlation analysis (Figure 4). The Cd, Pb, and Cu concentrations exhibited distinct spatial patterns, modulated by specific environmental factors affecting metals speciation and mobility.

The Cd concentrations ranged from undetectable to 0.079 µg L⁻¹. The highest Cd concentrations were observed at the Yantai northern site (S6: 0.079 µg L⁻¹). The Cd concentrations exhibited a strong positive correlation with Chl-a (p < 0.01), may reflect the enhancement effect of phytoplankton biomass on Cd distribution, likely through organic complexation, maintaining Cd in a dissolved state [42]. However, this correlation does not imply causation, and alternative explanations—such as co-variance driven by shared environmental factors like nutrient inputs from terrestrial runoff [39]. In contrast, pH, T, and DO did not exhibit statistically significant correlations with the Cd concentrations (p > 0.05). This finding further demonstrates that biological activity was the main driver of Cd variability in the study area. The Pb concentrations varied from 0.30 to 0.84 µg L⁻¹, peaking at the Weihai southern coastal site (S21: 0.84 µg L⁻¹). The spatial distribution of the Pb concentrations was influenced by anthropogenic inputs. However, the results showed the lack of statistically significant correlations of the Pb concentrations with pH, S, T, DO, PO₄³⁻, NO₃⁻, and Chl-a (p > 0.05), possibly due to dominant external sources or complex interaction effects on this metals element. The Cu concentrations ranged from 2.19 to 4.79 µg L⁻¹, with high levels observed at the Weihai southern offshore site (S20: 4.79 µg L⁻¹). The Cu concentrations exhibited a statistically negative correlation with the pH values (p < 0.05). This finding indicates the occurrence of Cu precipitation or complex formation under high pH values, reducing Cu solubility [43]. On the other hand, the positive correlation between the Cu concentrations and T values (p < 0.05) suggests the enhancement effects of higher temperature on Cu dissolution or desorption from sediments. Whereas the negative correlation between the Cu concentrations and DO (p < 0.05) implies enhanced Cu dissolution under lower oxygen levels by limiting oxidation to insoluble forms [44]. The results also showed a strong and statistically significant positive correlation between the Pb and Cu concentrations (p < 0.01), suggesting common sources or transport mechanisms for these metals elements.

3.5. Water Quality and Ecological Risk Assessments

The Cd, Pb, and Cu pollution levels in the coastal waters of the Shandong Peninsula were further assessed based on the Chinese seawater quality standards. The obtained C_f values of Cd, Pb, and Cu at the different sampling stations ranged from 0 to 0.786, 0.275 to 0.781, and 0.066 to 0.145, respectively (Figure 5a–c), following the decreasing order of Cd > Pb > Cu. In this study, all C_f values were below 1, indicating low metals pollution levels. Hence, the Cd, Pb, and Cu concentrations in this study area were within Class I of the water quality standards.

In this study, the C_f values indicated low Cd, Pb, and Cu pollution levels in the coastal waters of the Shandong Peninsula. However, the associated ecological risks cannot be evaluated solely by the metal’s concentrations but also by the bioavailability and impacts of these pollutants on aquatic organisms. The bioavailable fractions of Cd, Pb, and Cu can negatively affect the growth, reproduction, and behaviour of marine species. Therefore, the associated ecological risks were assessed in this study using the RQ values (Figure 6a–c). The obtained RQ values of Cd and Pb ranged from 0 to 0.061 and 0.036 to 0.100, respectively, indicating low associated ecological risks (RQ < 0.1). On the other hand, the RQ values of Cu ranged from 4.756 to 10.416, indicating high associated ecological risks (RQ ≥ 1.0). These findings highlight that Cu poses significant ecological threats due to its bioavailability.

The potential ecological risks of the metals in the coastal waters of the Shandong Peninsula were evaluated in this study using the ERI and E_rⁱ (Figure 6d). Although the RQ assessment focuses on individual metal risks to species sensitivity, evaluating potential ecological risks through ERI and Erⁱ is essential to consider the cumulative impacts of multiple metals, incorporating their toxicity coefficients and providing a more holistic view of long-term environmental threats. The E_rⁱ values of Cd, Pb, and Cu in Yantai ranged from 2.988 to 23.586, 1.424 to 3.485, and 0.334 to 0.556, respectively, indicating low associated ecological risks (E_rⁱ < 40). Similarly, the E_rⁱ values of Cd, Pb, and Cu in Weihai ranged from 0 to 16.248, 1.375 to 3.904, and 0.331 to 0.725, respectively, indicating low associated ecological risks. The ERI values of the metals in the Shandong Peninsula were all below 150, indicating relatively low potential ecological risks. However, despite the low Erⁱ values for individual metals, Cd contributed more than 70% (calculated as Erⁱ_Cd/ERI_(Cd,Pb,Cu) × 100%, The ERI calculation formula is shown in Equation (4)) to the overall ERI in both the Yantai and Weihai due to its high toxicity coefficient, suggesting a notable potential ecological risk associated with Cd that warrants attention. These results outline the need to prioritize the potential ecological risk of Cd and implement effective environmental regulations to mitigate the risks associated with these metals in the study area.

Comparing the outcomes of the SSD-RQ and Hakanson ERI methods reveals both alignment and nuanced differences, validating the complementary use of these approaches. Both methods consistently indicate low ecological risks for Cd and Pb across the study area (RQ < 0.1; Erⁱ < 40), suggesting minimal immediate or potential threats from these metals under current conditions. However, for Cu, the RQ method highlights high risks (RQ > 1 at all sites), driven by its exceedance of species-protective PNEC values, whereas the ERI classifies Cu as low risk (Erⁱ < 40) due to lower toxicity weighting relative to background levels. This divergence underscores RQ’s emphasis on acute toxicity and bioavailability, contrasting with ERI’s focus on cumulative, normalized pollution severity. The overall low ERI (<150) aligns with the dominance of low individual risks but flags Cd’s disproportionate contribution (Erⁱ up to 23.6, ~70% of ERI), warranting targeted monitoring. Integrating these results provides a balanced risk profile, informing prioritized management strategies such as Cu source control to mitigate biodiversity threats.

4. Conclusions

In this study, a comprehensive field-based assessment of metals pollution and associated ecological risks was conducted in the coastal areas of the Shandong Peninsula. Real-time measurements were conducted using a portable electrochemical detection system across 21 stations during early spring, providing field determination data on the dissolved metals concentrations under near-shore conditions. The results showed relatively low Cd, Pb, and Cu concentrations in the study area, remaining within low pollution thresholds based on national seawater quality standards. The C_f values of the three metals were below 1, indicating relatively low contamination levels. However, the RQ and ERI-based ecological risks revealed low Cd- and Pb-associated risks at most stations. In contrast, Cu exhibited high RQ values (RQ > 1) at all sites, highlighting relatively serious associated ecological risks despite its moderate concentrations in the seawaters. These findings emphasize the importance of considering the bioavailability, toxicological thresholds, and concentrations of metals in marine environmental risk assessments. The spatial distribution analysis results of the three metals revealed distinct coastal enrichment zones, particularly near harbour entrances, aquaculture areas, and river mouths. On the other hand, the correlation analysis results demonstrated the controlling effects of environmental parameters (e.g., T, DO, and Chl-a) on the transport and transformation behaviours of the dissolved metals.

Overall, this study contributes valuable recent data on metal dynamics in the Shandong Peninsula coastal ecosystems, supporting the potential utility of real-time electrochemical monitoring—as validated in prior field applications. The integration of field data with quantitative risk indices can provide a reproducible framework for coastal environmental monitoring, particularly in regions of increasing anthropogenic pressures. Future studies should expand temporal coverage, incorporate metals speciation and bioaccumulation pathways, and explore the impacts of episodic events (e.g., storms and algal blooms) to enhance predictive capacity and support adaptive marine pollution control policies.