Ecological risk and pollution assessment of heavy metals in Jubail coastal sediments, Arabian Gulf: A multiscale assessment

2.1 Study area

This study was conducted across offshore, midshore, and inshore zones around Jubail City on the eastern coast of Saudi Arabia (Fig. 1). The region is heavily industrialized, hosting petrochemical complexes, desalination plants, shipping ports, and oil refineries. Given the semi-enclosed nature of the Arabian Gulf, limited water circulation, and high evaporation rates, the area is particularly prone to localized HM enrichment and sediment pollution. To capture spatial variability, 32 surface-seabed sediment samples were collected in December 2023 from strategically selected locations that reflected varying levels of industrial influence, including sites near discharge points, shipping lanes, desalination outfalls, and comparatively pristine offshore reference sites (Fig. 1). Sampling coordinates were recorded using differential GPS for precise spatial mapping.

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Fig. 1.

The location map of the study area shows inshore, midshore, and offshore sampling sites along the Jubail coast in the Arabian Gulf.

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2.2 Sediment sampling

Sediments were collected using a Van Veen grab sampler and a stainless-steel box corer, both of which are commonly employed in marine sediment studies for minimally disturbed sample retrieval. Each deployment aimed to recover sediments from the upper 0–30 cm of the seabed, the layer most reactive in metal binding. Samples compromised by coarse gravel closure were discarded and re-collected to preserve representativeness. Immediately after collection, sediments were transferred into acid-washed polyethylene containers using plastic spatulas to minimize contamination, labelled by texture and color, and kept at −4°C onboard until lab transport. After each deployment, sampling gear was rinsed with seawater and acid-cleaned to prevent cross-sample contamination.

2.3 Lab preparation and chemical analysis

Sample preparation followed the US EPA/CE-81-1 guidelines for marine sediments. Samples were air-dried for 72 h, gently disaggregated, and passed through a 63 µm sieve to isolate the fine fraction (Que et al., 2024). Fine sediments were stored in acid-cleaned polypropylene vials prior to digestion. Approximately 0.15 g of homogenized sample was weighed into Teflon microwave vessels and digested with a mixture of HNO₃, HCl, and HF at 170°C for 50 min, followed by a 30 min hold, using a Milestone ETHOS UP microwave system. Digests were cooled, neutralized with boric acid, and diluted to 50 mL with ultrapure water. Metal quantification was done via ICP-MS (NexION 300D, PerkinElmer, USA) at KACST–NTI. Analysis included major (Fe, Mn, Sr, Ti) and trace metals (Cr, Co, Ni, Cu, Zn, As, Se, Mo, Ag, Cd, Pb, Hf, Sn). Calibration used certified multi-element standards and reference material SRM 2702 – Inorganics in Marine Sediment, with blanks, duplicates (10%), and spike recoveries in every batch. Precision was kept within ±5%, recoveries ranged 90% to 105%, and method detection limits ranged from 0.01 to 0.05 mg/kg.

2.4 Sediment texture characterization

The relationship between sediment grain size and metal adsorption was investigated by measuring the sand, silt, and clay fractions using a laser diffraction instrument (Malvern Mastersizer 3000). Pretreatment included the use of hydrogen peroxide to remove organic matter and sodium hexametaphosphate as a dispersant. Sediments were classified according to ASTM D2487 standards. Studies have highlighted that fine particles often carry elevated metal concentrations due to their higher surface area and reactive phases (Que et al., 2024).

2.5 Pollution indices and risk assessment

To evaluate contamination and ecological risk, the following indices were calculated:

• CF: assessed the degree of individual metal enrichment relative to background values (Hakanson, 1980) as follows;

  (1)
  $CF=CoCb\dots \dots$

• EF: normalized metal concentrations against Fe, used as a conservative lithogenic reference (Sutherland, 2000), using this equation.

  (2)
  $EF=\text{M}\times \text{X}sample\text{M}\times \text{X}background\dots$

• Geo-accumulation Index (Igeo): classified contamination levels following Müller (1969).

  (3)
  $Igeo=\log \text{2}Cn\text{1}.\text{5}\times Bn\dots \dots .$

• Pollution Load Index (PLI): provided an integrated measure of overall site contamination (Tomlinson et al., 1980) through Eq. (4).

  (4)
  $PLI=CF1\times CF\text{2}\times CF\text{3}\times CF\text{4}\dots \dots \dots .\times CF\text{1}n\dots \dots .$

• PERI: estimated ecological risk intensity following Hakanson’s (1980) framework.

  (5)
  $PERI=i=\text{1}nEri\dots$

where

Then

We used global average shale values (Turekian & Wedepohl, 1961) and regional baselines for the Arabian Gulf (Naser, 2013) as background references. Recent studies continue to apply this multiparameter framework (Faisal et al., 2025; Salah-Tantawy et al., 2025) to assess HM contamination in sediments and validate source apportionment. Furthermore, to assess ecological risks, sediment metal concentrations were compared against internationally recognized SQGs, including TELs, PELs, and PECs (MacDonald et al., 2000).

The symbols applied in this study are defined as follows: M and C_o denote the analyzed metals, whereas X and C_b represent the levels of the normalizer element. Iron (Fe) was employed as the normalizing element owing to its high abundance in the Earth’s crust and its relatively low mobility under most natural conditions compared with other trace elements. Consequently, Fe provides a reliable reference for assessing natural background levels of metals. C_n refers to the measured concentration of HMs in soils, while B_n indicates the geochemical background concentration of HMs in shale (Taylor and McLennan, 1995). A correction constant of 1.5 was introduced in the calculations to minimize the influence of natural variability in background concentrations (Hakanson, 1980).

The ecological risk associated with metal contamination was further evaluated using a suite of indices. The potential ecological risk factor (Eᵣᶦ) quantifies the risk posed by metal i in the sediment (Hakanson, 1980). The toxic-response factor (Tᵣᶦ) reflects both the intrinsic toxicity of the metal and its relative sensitivity in the environment (Zhang et al., 2016). The CFᵢ, as defined above, was incorporated into the ecological risk assessment to provide a quantitative measure of metal enrichment. Collectively, these indices enable a comprehensive evaluation of both contamination intensity and its associated ecological consequences (Hakanson, 1980; Cai et al., 2019).

Sediment quality was further interpreted in relation to widely applied SQGs. The TEL is the concentration below which adverse biological effects are unlikely to occur. In contrast, the PEL represents concentrations above which adverse effects are expected to occur more frequently. The PEC denotes concentrations above which adverse effects are highly probable (MacDonald et al., 2000). These benchmarks provide a consistent framework for assessing ecological risks across different environments (Long et al., 1995; MacDonald et al., 2000). The classification schemes and reference values of the indices described above are summarized in Tables 1(a-d), which serve as the basis for evaluating the degree of contamination and potential ecological risk (Hakanson, 1980; Tomlinson et al., 1980; MacDonald et al., 2000).

Statistical analyses were also conducted to interpret spatial patterns, examine relationships among metals, and identify potential sources of contamination. Descriptive statistics, including mean, standard deviation, and coefficient of variation, were calculated for all metals. Pearson correlation analysis was used to evaluate relationships between metals and sediment fractions, indicating possible shared sources. PCA helped differentiate between lithogenic and anthropogenic inputs, while Cluster Analysis (CA) grouped sampling sites based on their similarities in contamination. All statistical analyses were performed using IBM SPSS v26.

PCA was performed using a varimax rotation with Kaiser normalization to maximize the variance explained by each factor and to facilitate clearer interpretation of variable groupings. Components with eigenvalues greater than 1.0 were retained according to the Kaiser criterion, and only factor loadings ≥ 0.6 were considered significant in defining the association between metals and principal components. These criteria ensured that the extracted components captured the dominant geochemical patterns with minimal redundancy and improved interpretability.

3.1 Sediment texture and granulometry

The sediments from offshore and inshore sampling sites were predominantly sandy-silt with minor clay fractions. Grain size analysis revealed that offshore sites were dominated by sand (65–80%), whereas inshore sediments closer to industrial discharge zones contained higher proportions of silt and clay (30–45%) (Tables 2a and b). Poorly graded sands characterized coastal sites, whereas offshore sediments exhibited higher silt and clay content, especially near South Jana and North Karan Islands. This spatial distribution is typical of semi-enclosed marine systems (Fig. 2), where fine particles tend to settle in sheltered areas with lower hydrodynamic energy (ASTM D2487, 2011). Fine-grained sediments are known to have higher metal adsorption capacity due to larger specific surface area and higher organic matter content (Forstner & Wittmann, 2012; He et al., 2020).

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Fig. 2.

Grain size distribution of surface seabed sediments at different site groups illustrates higher silt and clay fractions nearshore. Four soil classifications of (a) sand, (b) gravel, (c) clay, and (d) silt were plotted as given in Table 2a.

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3.2 Heavy metal concentrations

Table 3 summarizes the average, minimum, and maximum concentrations of major and trace metals across all sampling sites. HM concentrations varied across the study area. Iron (Fe) was the most abundant metal (mean: 5163 mg/kg), followed by manganese (Mn: 148 mg/kg) and chromium (Cr: 82 mg/kg). Potentially toxic metals such as arsenic (As: 24.8 mg/kg), lead (Pb: 4.8 mg/kg), and cadmium (Cd ≤ 2.4 mg/kg) mainly were below National Oceanic and Atmospheric Administration, NOAA, PECs, although localized exceedances of As, Ag, Cd, and Co were detected at sites P9, P11, P14, and P29. Notably, As exceeded 33 mg/kg PEC in five sites, suggesting moderate ecological concern (Long et al., 1995). Elevated Zn and Cu were found inshore, reflecting inputs from industrial effluents and urban runoff, while Fe, Mn, Ni, and Co primarily reflected lithogenic contributions from natural sediment sources. Spatial distributions of lithogenic and anthropogenic metals are illustrated in Figs. 3 and 4. Moreover, Fig. 5 shows the boxplots of key metals (As, Cd, Pb, Zn, Cu), highlighting variability across sites and potential exceedances. Notably, As and Cd displayed greater variability and occasional elevations, whereas Pb and Zn remained low and stable. This pattern reflects localized anthropogenic input over a background of predominantly lithogenic metals.

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Fig. 3.

Spatial distribution maps of lithogenic metal concentrations, for (a) As, (b) Zn, (c) Pb, and (d) Cu, in surface sediments demonstrate localized hotspots near industrial zones.

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Fig. 4.

Spatial distribution maps of anthropogenic metal concentrations for (a) Fe, (b) Mn, (c) Cr, and (d) Ni, in surface sediments display localized hotspots near industrial zones.

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Fig. 5.

Boxplots of key metals As, Cd, Pb, Zn, and Cu show variability and potential exceedances.

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3.3 Spatial variations and site-based ranking of metal contamination

To better integrate site-specific variations, metal contamination levels across the 32 sampling sites were compared using CFs and the PLI. The calculated CFs for ten metals (Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Pb) and corresponding PLI values have been presented in Table 4. PLI values ranged from 0.12 to 0.80, indicating generally low to moderate contamination across the study area.

The highest contamination was observed at Sites P11, P14, P13, P12, and P16, with PLI values ranging from 0.64 to 0.80, suggesting localized enrichment likely influenced by anthropogenic sources, such as port operations and effluent discharge. The most dominant contaminants at these sites were As, Co, and Ni, which exhibited CF values exceeding 1.0, indicating moderate contamination levels. In contrast, the lowest PLI values (≤0.20) were recorded at Sites P28, P24, P20, and P35, reflecting background or near-natural conditions.

Spatially, contamination followed a declining gradient from the inner harbor and industrially influenced zones toward the open marine stations, suggesting dilution and sediment mixing effects. The ranking clearly delineates potential contamination hotspots and supports prioritization for ecological risk evaluation and management.

To further differentiate site contamination levels, the measured metal concentrations were compared with the US EPA mean baseline levels for uncontaminated soils (EPA, 2015; ATSDR, 2022). Sites P11–P16 surpassed the baselines for As, Co, and Ni, confirming localized anthropogenic enrichment, while most offshore sites remained below the EPA thresholds

3.4 Spatial heterogeneity and dominant metal patterns

The integration of site-specific contamination indices reveals distinct spatial patterns in HM distribution, providing new insights beyond the overall concentration trends discussed earlier (Table 4). Sites such as P11–P16, located closer to industrial outfalls and navigation channels, consistently showed elevated CFs for As, Co, and Ni, reflecting contributions from metallurgical effluents and ship maintenance activities. Arsenic dominance in several sites (e.g., P11, P13) suggests possible inputs from fuel combustion and antifouling paints, aligning with patterns reported in other coastal sediment studies (Swarnakar et al., 2024).

Moderate enrichment in Co and Ni at the mid-channel sites may also be associated with hydrodynamic trapping and organic matter binding, as these metals often exhibit particle-reactive behavior. The low-PLI sites, particularly P28–P24, exhibited metal levels close to geochemical background values, confirming the limited anthropogenic influence in offshore areas. By ranking sites and identifying dominant metals, this analysis provides a spatially explicit framework for evaluating the intensity of contamination and its ecological implications. Such an approach strengthens sediment quality assessment by pinpointing priority zones for monitoring and remediation.

The classification of sites using US-EPA mean soil baseline values supports the PLI-based ranking and reinforces the identification of As-, Co-, and Ni-dominated hotspots. The agreement between these two independent approaches underscores the robustness of the spatial assessment framework.

3.5 Integrated contamination, source apportionment, and ecological risk implications

To comprehensively assess the contamination status and ecological implications of HMs in Jubail coastal sediments, multiple indices, including the CF, EF, Geoaccumulation Index (Igeo), PLI, and PERI, were integrated with multivariate statistical analyses (Tables 5-8; Figs. 6-8).

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Fig. 6.

The correlation heatmap displays the lithogenic vs. anthropogenic grouping of metals.

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Fig. 7.

PCA Biplot for HM source identification indicates natural and anthropogenic metal sources.

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Fig. 8.

Comparison of PLI and PERI for inshore, midshore, and offshore sites.

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Across all sites, most metals (Cr, Ni, Cu, Pb, Co, Mn, Fe) exhibited CF < 1 and low Igeo values, confirming their primarily lithogenic origin with minimal anthropogenic enrichment. In contrast, cadmium (Cd) and arsenic (As) were distinctly elevated, with CF values of 5.03 and 1.82 and EFs of 46.01 and 2.36, respectively. The levels of EF for Cd indicate anthropogenic inputs, likely linked to petrochemical effluents, desalination brine discharges, and port-related activities. In contrast, the enrichment of As is attributed to antifouling coatings, fuel combustion, and industrial runoff.

The PLI values ranged from 0.48 to 0.85 across all sampling zones, remaining below the pollution threshold (PLI = 1.0), which classified the sediments as generally unpolluted (Tomlinson et al., 1980). However, inshore areas recorded higher PLI values (0.85), reflecting localized industrial influence and reduced hydrodynamic dispersion. This spatial gradient was reflected in the PERI results, with inshore sediments showing moderate ecological risk (PERI = 225.7), midshore sediments indicating low risk (PERI = 138.2), and offshore sediments indicating minimal risk (PERI = 93.6). Cadmium contributed a total ecological risk (ERᵢ = 151), while As contributed moderately (ERᵢ = 18.2). The remaining metals exhibited ERᵢ < 40, confirming that localized enrichment of Cd and As drives the ecological risk in this system.

Statistical correlations and source apportionment analyses (Pearson’s correlation and PCA) revealed two distinct metal groupings. The PCA model retained two principal components with eigenvalues > 1.0, cumulatively explaining 78.4% of the total variance. The use of varimax rotation with Kaiser normalization improved interpretability, distinguishing a lithogenic component (Fe, Mn, Cr, Ni, Co) from an anthropogenic component (Cd, As, Cu, Zn, Pb). High loadings of Cd and As on the second component, combined with elevated PERI scores in inshore sediments, confirm that industrial and port activities are the primary sources of contamination. These results closely correspond to patterns observed in other Gulf coastal zones, such as Abu Ali Island, where Cd and As similarly dominate anthropogenic signatures (Mahboob et al., 2022).

Ecological and spatial implications derived from the PERI and PCA analyses indicate that although overall contamination levels remain low to moderate, hotspots of Cd and As enrichment near industrial discharge zones present clear ecological concern. These elements are highly mobile and bioavailable in fine-grained sediments, increasing their potential to accumulate in benthic invertebrates such as polychaetes, bivalves, and crustaceans (Mohamed et al., 2024). Continuous sediment resuspension and particle ingestion enhance metal bioaccumulation, facilitating trophic transfer to demersal fish and filter-feeding organisms.

Benthic macrofauna, which is essential for sediment oxygenation and nutrient cycling, may experience sublethal stress under elevated Cadmium exposure (ERᵢ = 151), potentially leading to biodiversity loss and altered biogeochemical processes. Similar Cd-linked ecological effects have been reported in other Gulf sediments impacted by industrial effluents and refinery discharges (Naser, 2013). Although the integrated PERI values suggest generally low regional risk, the disproportionate contribution of Cd highlights the need for continued biological monitoring using bioassay and biomarker-based approaches to validate theoretical risk indices (Mohamed et al., 2024).

Spatially, elevated PERI scores and anthropogenically influenced PCA loadings co-occurred at sites adjacent to industrial discharges, shipping lanes, and desalination. In contrast, lithogenic elements (Fe, Mn, Cr) associated with natural geogenic processes were strongly loaded onto the secondary component, reflecting background sediment contributions from coastal erosion and resuspension. This spatial correspondence between PCA groupings and ecological risk metrics validates the integrated multi-index approach, which links contamination sources, geochemical behavior, and ecological implications.

Although the PERI and related indices suggest moderate ecological risk in localized zones, they provide a quantitative framework for estimating potential risk, but not actual biological impact. Accordingly, the present findings should be viewed as precautionary indicators of potential ecological stress rather than definitive measures of biological harm. Integrating future bioassay data and benthic community analyses will be critical to confirm the sediment–biota linkages implied by the current geochemical results.

3.6 Unique perspectives and implications for coastal management

The results of this study offer a shift from region-wide descriptive trends to site-specific process understanding. Whereas Swetha et al. (2025) concluded that the Arabian Gulf exhibits low-to-moderate contamination with industrial hotspots, our findings reveal that micro-environmental variations in sediment texture and hydrodynamic energy strongly govern metal enrichment and ecological risk at the sub-kilometer scale. The identification of fine-grained sediment traps near discharge outlets as preferential zones for As and Cd accumulation highlights the mechanistic linkage between sediment dynamics, providing a predictive framework for future coastal management and restoration.

3.7 Temporal scope and baseline nature of the study

This investigation was designed as a spatially explicit baseline assessment of HM contamination in Jubail’s coastal sediments rather than a temporal monitoring study. The sampling campaign, conducted in December 2023, coincided with hydrographically stable conditions characterized by minimal storm or tidal disturbance. This timing was intentional to minimize short-term variability associated with sediment resuspension and to establish a reliable reference dataset under relatively steady environmental conditions. Although this approach provides a robust spatial characterization of contamination and ecological risk, we acknowledge that metal deposition and bioavailability can vary seasonally, influenced by fluctuations in hydrodynamic energy, industrial discharge cycles, and variations in runoff intensity. Therefore, while the present findings effectively define baseline contamination patterns, they should be interpreted as representing a snapshot of steady state within the annual cycle. Subsequent multi-seasonal and multi-year monitoring will be valuable for capturing dynamic temporal trends and refining risk assessments under variable environmental conditions.

It is important to note that the current assessment serves as a geochemical baseline rather than a comprehensive ecotoxicological evaluation. While the indices applied (CF, EF, Igeo, PLI, and PERI) provide strong predictive insights into contamination intensity and potential ecological risk, they do not substitute for direct biological testing. No bioassay or organismal data were collected as part of this initial survey, and therefore, the inferred ecological risks should be interpreted as indicative rather than confirmatory. Future investigations will incorporate sediment bioassays and biomarker-based assessments to validate these theoretical risk predictions and enhance understanding of biological exposure pathways within the Jubail coastal ecosystem.

3.8 Management and monitoring implications

In response to the enrichment of cadmium (Cd) and arsenic (As) in inshore sediments and the moderate ecological risk levels observed, several management actions are recommended to mitigate contamination and enhance policy relevance.

• (1)

  Monitoring intervals: A biannual to annual monitoring schedule (every 6–12 months) is proposed for industrial and near-port sediment zones, with extended intervals (approximately every 24 months) for mid- and offshore areas where contamination indices (PLI < 1.0 and PERI < 150) indicate lower risk. Such periodic monitoring aligns with current regional recommendations for heavy-metal surveillance in coastal ecosystems (Swetha et al., 2025).

• (2)

  Effluent and discharge control: Given the strong anthropogenic signal of Cd and As linked to petrochemical, desalination, and port operations, implementation of pre-treatment and heavy-metal removal systems (e.g., adsorption, precipitation, or filtration technologies) prior to discharge is recommended. Industrial effluent guidelines should specify concentration limits and require routine reporting. Similar strategies have proven effective in coastal industrial zones worldwide (El-Sharkawy et al., 2025).

• (3)

  Hotspot remediation: For sites exhibiting “moderate” ecological risk (PERI > 200), targeted mitigation should be prioritized. Sediment capping, dredging, or in-situ stabilization using low-impact materials may reduce contaminant mobility and bioavailability, as supported by sustainable remediation case studies.

• (4)

  Adaptive management and stakeholder engagement: An adaptive management framework is recommended, whereby updated sediment data guide management decisions in each monitoring cycle. This approach should involve coordination among industry operators, port authorities, environmental agencies, and local stakeholders to enhance transparency and ensure compliance (El-Sharkawy et al., 2025).

• (5)

  Integration of indices in regulatory frameworks: The PLI and PERI employed in this study could serve as early-warning indicators for sediment quality management. Trigger levels (e.g., PLI > 1.0 or PERI > 300) may be incorporated into environmental monitoring protocols to signal when management actions are required. Similar index-based monitoring systems have been successfully implemented in other Gulf regions (Swetha et al., 2025).