# Sediment quality and ecological risk assessment in Mediterranean harbors of Occitanie, France: implications for sustainable dredged material management

Garry Dorleon, Sylvain Rigaud, Isabelle Techer

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Garry Dorleon, Sylvain Rigaud, Isabelle Techer. Sediment quality and ecological risk assessment in Mediterranean harbors of Occitanie, France: implications for sustainable dredged material management. Marine Pollution Bulletin, 2025, 217, 10.1016/j.marpolbul.2025.118097. hal-05055004

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# Sediment quality and ecological risk assessment in Mediterranean harbors of Occitanie, France: implications for sustainable dredged material management

Garry Dorleon *, Sylvain Rigaud, Isabelle Techer

Nîmes Université, Unité Propre de Recherche Transdisciplinaire CHROME, Nîmes, France

## ARTICLE INFO

**Keywords:**

- Dredging operations
- Geochemical characterization
- Potentially toxic elements
- Organic contaminants
- Ecological risk assessment
- Pollution source identification

## ABSTRACT

The Mediterranean coastal region of Occitanie (France) hosts numerous harbors with economic and touristic activities requiring periodic dredging that may release potentially toxic elements and organic pollutants into the marine environment. Monitoring sediment quality using Sediment Quality Guidelines (SQGs) is essential to assess contamination risks. This study summarizes the geochemical and physical characteristics of sediments from ten harbors of the Occitanie region and evaluates associated ecological risks to inform management strategies. Results are based on measurements required by the French regulation and showed that studied harbors are mostly composed of fine-grained sediments rich in organic matter (32 % on average), those accumulate few contaminants, mainly copper (38-123 mg/kg), zinc (66-156 mg/kg), and lead (15-48 mg/kg), as well as organic pollutants including polycyclic aromatic hydrocarbons (0.23-2.1 mg/kg), and polychlorinated biphenyls (0.05-0.3 mg/kg). Statistical analysis revealed strong correlations between sediment grain size and contaminants distribution. Ecological risk assessment indicated varying but low contamination levels. Copper emerged as the main contaminant, exceeding the threshold effect concentration in 77 % of samples and the probable effect concentration in 5 %. The mean probable effect quotient index suggested moderate ecological risks, with a 33-58 % probability of adverse effects on benthic organisms, emphasizing the need for regular monitoring to prevent potential increases in contamination. Results underlined the interest of adaptive sediment management approach, integrating chemical analyses, bioaccumulation studies, and toxicity testing. While French regulatory thresholds provide disposal guidelines, further studies are needed to refine risk assessments and support sustainable remediation strategies.

## 1. Introduction

The exponential growth of global maritime trade has profoundly transformed coastal harbors over the centuries, leading to their development as essential nodes for economic activities (Guo et al., 2019; Marnot, 2014). Since the 20th century, marinas have mushroomed along the coasts of more developed countries, and the commercial harbors have grown into big industrial complexes supposed to meet the ever-growing needs of modern society (Kaddar and Cohen-Hattab, 2023; Wilson et al., 2015). As reported by Fromard et al. (2004) and updated by Puig et al. (2020), this rapid industrialization of the coasts has raised several important questions, from technological to environmental. Among others, sedimentation, siltation, and anthropogenic pollution represent the majors, hence making periodic dredging indispensable for

harbor managers.

Dredging, that corresponds to the removal of sediments at the sea bottom, is a critical intervention in harbor maintenance as it ensures its navigability and supports economic and touristic activities (Crocetti et al., 2022; Soetan et al., 2023). Aside from these practical applications, dredging can also act as a remedial action by contributing to the improvement of environmental quality in harbor ecosystems (Alishirazi et al., 2023). Dredging world activities produce huge volumes every year. For example, France produces >50 million m³ of dredged sediment every year (Harrington et al., 2016), while Europe produces 300 million m³ (Van Bunderen et al., 2019), China 100 million m³ (Bose and Dhar, 2022), and the USA 300 million m³ yearly (Patmont et al., 2018). The nature of their physical and chemical composition shows large variability and is dependent on local hydrodynamic conditions and on

<footnote>* Corresponding author at: UPR CHROME, Site Hoche - Université de Nîmes, 1 place du président Doumergue, 30000 Nîmes, France.
E-mail address: garry.dorleon@unimes.fr (G. Dorleon).</footnote>
peculiar geological features of any watershed (Reczyński et al., 2020; Obodai et al., 2022). However, most of these sediments can indeed been contaminated with potentially toxic elements (PTEs) such as arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), and organic pollutants like PAHs, PCBs, and TBT coming from the harbors activities or from the watershed (Ambrosino et al., 2023; Carvalho et al., 2024; Ferraro et al., 2023; Wang et al., 2022). Proper management of such materials is very important in order to reduce environmental hazards and try to meet the regulatory requirements (Dorleon et al., 2023).

In response, most developed countries have currently established Sediment Quality Guidelines (SQGs), those are used for the regulation and management. Examples include the United States, which uses SQGs developed by the Environmental Protection Agency (EPA) and the National Oceanic and Atmospheric Administration (NOAA) (Long et al., 1995; USEPA, 2005); Canada, with its guidelines published by the Canadian Council of Ministers of the Environment (CCME, 2002); and the Netherlands, which has a tiered system distinguishing between target and intervention values (VROM, 2000). Australia applies the ANZECC/ARMCANZ guidelines to assess sediment quality (ANZECC/ARMCANZ, 2000), while France relies on national thresholds (N1, N2, and N3) to guide management decisions, particularly for dredged sediment disposal (AIDA, 2024). China has implemented national standards (e.g., GB 18668-2002) through its Ministry of Ecology and Environment, classifying sediments in marine and freshwater environments (MEP, 2002). Brazil, under the supervision of its National Environment Council (CONAMA), established sediment quality reference values through Resolution No. 454/2012 to support both the assessment and management of sediment contamination in inland and coastal waters (CONAMA, 2012). Generally speaking, SQGs provide useful tools giving indications of the potential biological impacts of sediment contamination, though they do not represent an absolute toxicity measure of sediments (Alexakis et al., 2023; Liu et al., 2023). Several techniques have been developed to deduce the threshold value of SQGs, including the equilibrium partitioning model (Di Toro et al., 1991), the effects range approaches (Long et al., 1995), the effect level, Threshold Effect Level/Probable Effect Level, TEL/PEL approach (Macdonald et al., 1996), logistic regression models (Engler et al., 2005), and consensus-based methods (MacDonald et al., 2000). Among these, SQGs derived from the consensus approach (Threshold Effect Concentration/Probable Effect Concentration, TEC/PEC) are generally more commonly used (Bateni et al., 2022; MacDonald et al., 2000), and they are widely recognized for their robustness in predicting contamination effects in marine and estuarine environments (Long et al., 2006; MacDonald et al., 2000). Threshold effect concentration (TEC) identifies the contaminant concentration below which adverse effects on sediment-dwelling organisms are unlikely to occur, and the probable effect concentration (PEC) indicates the concentration above which possible adverse effects can be observed on sediment-dwelling organisms (MacDonald et al., 2000). Therefore, Sediment Quality Guidelines (SQGs) are widely applied in the early stages of regional pollution hazard assessments due to their simplicity, rapid implementation, and ease of interpretation (Dung et al., 2013; MacDonald et al., 2000). In addition to these guidelines, statistical methods offer valuable tools for evaluating complex ecotoxicological processes by revealing relationships, interdependencies among variables, and their respective contributions. Previous studies have frequently employed methods such as Pearson correlation coefficients (Liu et al., 2023), cluster analysis (Ferraro et al., 2022; Lisiak-Zielińska et al., 2021), principal component analysis (Lisiak-Zielińska et al., 2021; Liu et al., 2023), and positive matrix factorization (Li et al., 2019) to achieve this goal.

In France, sediment management guidelines are anchored in a regulatory framework that defines threshold levels to guide the dredging operations and helps to decide their administrative regime. These levels, called N1 and N2 and defined in a French national decree since June 2000, refer to a sediments quality based on physico-chemical

indicators including a list of metals and organic components (Dorleon et al., 2023). The N1 N2 values recalled in the French 2020 and 2024 decrees, are obligatory used to assess contamination levels and to guide administrative decisions on the dredging operations and on the sediment reuse or disposal (AIDA, 2024) and therefore play a fundamental role in the assessment and decision-making processes associated with environmental impact assessments of dredging operations (Dorleon et al., 2023). Recently, a new threshold value (so-called the N3 threshold in this article for clarity, although the French regulation does not assign an official name to this level) was introduced in the regulation to help the decision whether dredged materials can be authorized for sea disposal or not. This N3 threshold has put more emphasis on enhanced environmental protection but at the same time poses technical and economic challenges, notably as regards sediment characterization, treatment, and sea disposal (JORF n°0083 of April 9, 2024). As many others sediment quality assessments, the French regulatory relies on total or pseudo-total metal concentrations. This approach does not adequately reflect the bioavailability or ecological toxicity of metals under natural conditions. Only if the N2 threshold is exceeded, complementary ecotoxicological analyses are expected and must be performed case by case. In this context, integrative approaches involving microbiological and ecotoxicological indicators, such as enzymatic activities, microbial community structure, or bioassays, are increasingly recognized as essential for assessing real-world impacts (Carvalho et al., 2024; Sabadini-Santos et al., 2022). As highlighted in the literature (Ankley et al., 1996), the use of total concentration of metal cannot be considered as sufficient to assess toxicity. Geochemical fractionation techniques (Aly-Eldeen et al., 2023), such as sequential extractions (Tessier et al., 1979) and the analysis of acid volatile sulfides (AVS) (Di Toro et al., 1990), offer valuable insights into metal speciation and binding phases, which directly influence their environmental behavior and give informations about toxicity. However, these methods remain underused in routine monitoring and are largely absent from current regulatory frameworks in France. While chemical characterization of sediment contaminants is robust and widely applied, it often fails to capture the complexity of ecological risks, particularly in dynamic coastal and harbor ecosystems (Carvalho et al., 2022).

Indeed, the Mediterranean coast is one of the key area in terms of socio-economic and ecological importance for the France Occitanie region, under intense anthropogenic pressures (Chouba et al., 2024). Harbors are centers of human activities but also hotspots of pollutants from various sources such as urban development, industries, and agricultural runoff (Araújo et al., 2019; Arienzo et al., 2013). Although dredging is important for the maintenance of economic activities in such coastal areas, the historical records of sediment contamination and ecological risks are hardly scrutinized. In this context, the main objective of this study is to improve understanding on the sediment quality in harbors along the Occitanie coastline (southern France) and to support the development of a common sustainable dredging strategy. Drawing on a newly compiled regional database (QUALISED) based on sediment quality data recorded by ten representative harbor's managers (Dorleon et al., 2024), this research adopts an integrated geochemical and statistical approach. Specifically, the objective will be addressed in three steps: (1) the characterization of the general properties of harbors' sediments, including the distribution of physical patterns, of potentially toxic elements (PTEs) and organic contaminants; (2) the investigation of the influence of sediments physical properties on pollutant variability using multivariate statistical analyses, that will help to discuss potential sources of pollution still in light in existing literature; and (3) the evaluation of the ecological risk associated with sediment contamination, especially regarding benthic ecosystems. By combining these complementary approaches, the study provides valuable insights into contamination patterns and risk levels, contributing to a better-informed and environmentally responsible management of dredged sediments in coastal environments.
# 2. Materials and methods

## 2.1. Study area

The study area concerns the Mediterranean coastal area of the Occitanie region (Fig. 1). The Occitanie region in Southern France presents 220 km of coastline and 240 km of canals, and is one of the top spots for water-based tourism in France and Europe. Five departments border it from West to East with the Pyrénées Orientales (66), Aude (11), Hérault (34), Gard (30) and Bouches-du-Rhône (13). This coastal area hosts 30 harbors/marinas in 26 coastal communes. The size of the marinas varies from 123 to 5000 boat places. These harbors stand different maritime traffic and other industrial processes allocated in the surrounding areas due to logistic reasons. A complete description of this coastal area is given in Chouba et al. (2024). On this studied area, 10 harbors were selected based on their geographical situation along the studied coastline and on their size and typology (commercial, fishing, marina, diversity of the fleets ranging from small vessels to larger professional boats) in order to provide a representativeness of the region. The availability of sediment quality data recorded in the framework of previous dredging projects and manager's agreement to use them were also parameters of the selection. These date were collected over the past fifteen years. As a result, the 10 selected sites, not pointed out but illustrated in Fig. 1, give a good representation of the region and an analysis of 10 out of 30 existing harbors in this region can be considered significant and indicative enough to identify the sediment characteristics and sediment management applied in harbors. In response to confidentiality concerns expressed by one of the studied harbors, and for reasons of consistency and fairness, all ten harbors have been anonymized in the study. As such, they are referred to as Harbor 1 to Harbor 10, and their geographical coordinates are not disclosed. The geochemical and geophysical data on sediments from the ten harbors were originally compiled in Dorleon et al. (2024). That study introduced an open-access dataset and emphasized the application of FAIR principles (Findable, Accessible, Interoperable, and Reusable) within a conceptual framework for harbor sediment data sharing. Building on this foundation, the present work provides a comprehensive scientific analysis aimed at evaluating sediment quality and assessing potential ecotoxicological risks. This approach offers novel insights into contamination sources and contributes to informed decision-making for sustainable sediment management and reuse strategies.

## 2.2. Database: available data collection and processing

We used the data presented in the QUALISED database by Dorleon et al. (2024) to achieve the objective of this study. Briefly, these data consist of an inventory of the physical and geochemical parameters of sediments sampled from harbors in the Occitanie region before or during dredging activities performed between 2006 and 2021 (Table 1) and obtained from harbor managers. This database has been deposited in the Recherche Data Gouv repository under DOI: 10.57745/CLDJVQ, in durable format ".csv", and metadata documentation is provided for reuse. Whenever possible, standardized vocabularies have been used in order to increase interoperability and reusability of the dataset. Data validation included verifying sampling locations, analytical protocols, and units. Observations were selected based on GPS coordinates of sampling points and reliable sampling dates within the 2006–2021 period. Samples analyses were performed by Eurofins Laboratory using AFNOR-certified analytical protocols (Table 1). A total of 146 sediment samples were included in the database used in this study, each characterized by 23 variables aligned with regulatory requirements for pre-dredging assessments. These variables include dry matter content (DM), specific gravity (density), pH, grain size distributions (fine <63 µm fraction, termed mud, 0.63 µm–2 mm fraction or sand, and >2 mm fraction or gravel), organic matter and nutrient contents (total phosphorus, Kjeldahl nitrogen), potentially toxic elements (PTEs: Al, As, Cd,

Cr, Cu, Hg, Ni, Pb, Zn), organic contaminants (PAHs, PCBs, TBT), and leachable salt contents (fluorides, chlorides, sulfates) (Table 1).

## 2.3. Statistical analysis

Correlation and principal components analyses (PCA) were used to check for significant relationships among geophysical and geochemical composition parameters in the studied sediment samples. The various statistical methods were performed with a 95 % confidence interval (significance *p* < 0.05). Cluster analysis was used to identify spatial variability among the studied sites. Euclidean distance was used as dissimilarity matrix, whereas Ward's method was used as a linkage method (Miyamoto et al., 2016). Prior to performing statistical analyses, the normality of data distributions was systematically assessed using the Shapiro–Wilk test. As most variables did not meet the assumption of normality, non-parametric methods were selected to ensure the robustness of the statistical treatment. Specifically, Spearman's rank correlation coefficient was used to evaluate relationships between variables, while the Mann–Whitney *U* test was employed for comparing groups. These approaches are appropriate for datasets with non-Gaussian distributions and limited sample sizes, and they provide reliable insights into the variability and structure of sediment characteristics. All statistical analyses were done using R studio software (version R4.3.1).

## 2.4. Sediment contamination and ecological risk assessment methods

### 2.4.1. Sediment quality guidelines (SQGs)

SQGs are used in this study to evaluate sediment toxicity, i.e. the potential threats posed by contaminated sediment to aquatic life (Bavi et al., 2023; Liu et al., 2023). The observed concentrations of PTEs (As, Cd, Cr, Cu, Zn, Pb, Ni, Hg), and organic contaminants (PAHs and PCBs) in studied sediments were compared with TEC and PEC values (MacDonald et al., 2000) (Table 2).

### 2.4.2. Probability of ecological effect (QPECm)

To assess the coupling effects of the considered PTEs and organics contaminants on the biological toxicity and the probability of biotoxic ecological effect, a parameter called the mean PEC quotient (QPECm) was applied (Ingersoll et al., 2001). It was calculated based on the individual pollutant (PTEs, PAHs, and PCBs) concentration as well as the concentration of the corresponding PEC (Li et al., 2022; MacDonald et al., 2000; Vivien et al., 2014):

$$Q_{PECm} = \left( \sum_{i=1}^{n} \left( \frac{C_i}{PEC_i} \right) \right) / n$$

where $C_i$ is the target i pollutant concentration in the sediment (mg/kg). In this study the mean i concentration will be considered for each harbor; $PEC_i$ is the probable effect concentration of the i pollutant; n represents the numbers of pollutants (here $n = 10$). The interpretation of QPECm is: QPECm <0.1 non-adverse effect with a probability <14 % ecological risk effect, 0.1 < QPECm < 1 slightly adverse effect with a probability 15–29 % ecological risk effect, 1 < QPECm < 5 moderate effect with a probability 33–58 % ecological risk effect, QPECm > 5 High effect with a probability 75–81 % ecological risk effect (Goher and Khedr, 2024).

# 3. Results and discussion

## 3.1. Sediments properties and implication for dredging, and sediment management

### 3.1.1. General characteristics of sediments

The general characteristics of Occitanie harbor sediments vary significantly among and between sites (Fig. 2). However, from a physical
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Location of the study area, the Mediterranean coastal area of the Occitanie region (South of France). In response to confidentiality concerns expressed by one of the studied harbors, all ten harbors have been anonymized in this study. As such, their geographical coordinates are not disclosed. (free Geographic Information System (GIS) QGIS 3.34.4-Prizren. https://www.qgis.org/fr/site/forusers/index.html#download).</figcaption></figure>
<table><caption>**Table 1**
Parameters for data description (DW: dry weight).</caption><thead><tr><th>Parameters</th><th>Variables name</th><th>Unit</th><th>Analytical protocols</th></tr></thead><tbody><tr><td>General characteristics</td><td></td><td></td><td></td></tr><tr><td>Hydrogen potential</td><td>pH</td><td>–</td><td>EN 12457-2</td></tr><tr><td>Organic matter content</td><td>OM</td><td>%</td><td>NF ISO 10694</td></tr><tr><td>Total nitrogen content</td><td>Total_N</td><td>g/kg DW</td><td>NF EN ISO 13342</td></tr><tr><td>Total Phosphorus</td><td>Total_P</td><td>mg/kg DW</td><td>NF EN ISO 11 885</td></tr><tr><td>Dry Matter content</td><td>DM</td><td>%</td><td>NF EN 13-040</td></tr><tr><td>Specific gravity</td><td>Density</td><td>g/cm³</td><td>NF ISO 11464</td></tr><tr><td>Sand content (0.63 µm–2 mm fraction)</td><td>Sand</td><td>%</td><td>NF X 31-107 ISO 13320:2009</td></tr><tr><td>Fine fraction (&lt;63 µm in the &lt;2000 µm fraction)</td><td>Mud</td><td>%</td><td></td></tr><tr><td>Aluminum</td><td>Al</td><td>mg/kg DW</td><td>NF EN ISO 11 885</td></tr><tr><td>Leachable fraction</td><td></td><td></td><td></td></tr><tr><td>Chloride</td><td>Cl</td><td>mg/kg DW</td><td>EN 12457-2</td></tr><tr><td>Fluoride</td><td>F</td><td>mg/kg DW</td><td>EN 12457-2</td></tr><tr><td>Sulphate</td><td>SO4</td><td>mg/kg DW</td><td>EN 12457-2</td></tr><tr><td>Potentially toxic elements (PTEs)</td><td></td><td></td><td></td></tr><tr><td>Cadmium</td><td>Cd</td><td>mg/kg DW</td><td>NF EN ISO 11 885</td></tr><tr><td>Chromium</td><td>Cr</td><td>mg/kg DW</td><td>NF EN ISO 11 885</td></tr><tr><td>Arsenic</td><td>As</td><td>mg/kg DW</td><td>NF EN ISO 11 885</td></tr><tr><td>Copper</td><td>Cu</td><td>mg/kg DW</td><td>NF EN ISO 11 885</td></tr><tr><td>Nickel</td><td>Ni</td><td>mg/kg DW</td><td></td></tr><tr><td>Lead</td><td>Pb</td><td>mg/kg DW</td><td></td></tr><tr><td>Zinc</td><td>Zn</td><td>mg/kg DW</td><td>NF EN ISO 11 885</td></tr><tr><td>Mercury</td><td>Hg</td><td>mg/kg DW</td><td>NF EN ISO 1483</td></tr><tr><td>Organic contaminants</td><td></td><td></td><td></td></tr><tr><td>Polycyclic aromatic hydrocarbons</td><td>PAH</td><td>mg/kg DW</td><td>XP X 33-012</td></tr><tr><td>Polychlorinated biphenyls</td><td>PCB</td><td>mg/kg DW</td><td></td></tr><tr><td>Organotin compounds</td><td></td><td></td><td></td></tr><tr><td>Tributyltin</td><td>TBT</td><td>µg/kg DW</td><td>NF EN ISO 23161</td></tr></tbody></table>

point of view a certain degree of homogeneity can but underlined with most harbors (8 out of 10) being characterized by an average mud content (%fine particles <63 µm) exceeding 60 %, underscoring the predominance of fine-grained deposits across the region. Six harbors (Harbor 1, 4, 5, 6, 8 and 10,) showed mud fractions around 60–65 %, and two harbors (Harbor 2 and Harbor 3) pointed out compositions dominated by >70 % of this fraction (82 % and 70 % respectively). Only 2 harbors (Harbor 7 and Harbor 9) exhibited coarser sediments, with mean mud contents of 46 % and 37 % respectively, with locally very low values <30 %. High mud fractions reflected typical conditions of sheltered coastal environments, known to favor the retention and accumulation of fine particles (Lira et al., 2020; Souza et al., 2022). Similar sedimentary conditions promoting fine particle have also been reported in other enclosed harbors areas, such as Sepetiba Bay in Brazil (Carvalho et al., 2021; Carvalho et al., 2022) and the Venice Lagoon in Italy (Bloom et al., 2004). Differences among harbors can be explained taking into

<table><caption>**Table 2**
TEC and PEC values for various substances. Threshold effect concentration (TEC) identifies the contaminant concentration below which adverse effects on sediment-dwelling organisms are unlikely to occur. Probable effect concentration (PEC) indicates the concentration above which possible adverse effects can be observed on sediment-dwelling organisms (MacDonald et al., 2000).</caption><thead><tr><th>Substance</th><th>TEC (mg·kg<sup>-1</sup>)</th><th>PEC (mg·kg<sup>-1</sup>)</th></tr></thead><tbody><tr><td>As</td><td>9.79</td><td>33.0</td></tr><tr><td>Cd</td><td>0.99</td><td>4.98</td></tr><tr><td>Cr</td><td>43.4</td><td>111.0</td></tr><tr><td>Cu</td><td>31.6</td><td>149.0</td></tr><tr><td>Zn</td><td>121.0</td><td>459.0</td></tr><tr><td>Pb</td><td>35.8</td><td>128.0</td></tr><tr><td>Ni</td><td>22.7</td><td>48.6</td></tr><tr><td>Hg</td><td>0.18</td><td>1.06</td></tr><tr><td>HAP</td><td>0.00161</td><td>0.0228</td></tr><tr><td>PCB</td><td>0.0598</td><td>0.676</td></tr></tbody></table>

account a combination of factors specific to each harbor, including catchments characteristics, fine sediment inputs, basin morphology, and exposure to marine influences (Chakraborty et al., 2015; Ali et al., 2021). For instance, Harbor 2 is directly connected to the catchment area and receives inputs from agricultural catchments dominated by vineyards, which may enhance the delivery of fine particles through runoff (Du Laing et al., 2009; Chouba et al., 2024). Otherwise, a relatively higher exposition of an harbor or some of its basins to marine dynamics and/or a sampling point much more distant from the canals could provide reasons for lowest fine fraction, sediment resuspension and transport favoring coarser deposits in such contexts (Wilkes et al., 2019). This could be the case for Harbor 7 and Harbor 9. Given the absence of direct hydrodynamic data (e.g., current measurements or modeling) for each harbor, this hypothesis about the influence of local circulation cannot be strengthened and contribution remains difficult to quantify. Specific environmental drivers can however support our observations. These include terrestrial sediment supply, wind exposure, harbor geometry, and geomorphological setting, all of which can influence sediment deposition and distribution (Castro et al., 2006; Silva et al., 2004; Wyrick et al., 2014). To better understand the role of watershed-derived inputs, we referred for instance to Chouba et al. (2024) work that provided detailed information on land use, anthropogenic activities, and industrial pressures across the drainage basins of some of the studied harbors.

Regarding the sediment water content (whole-rock water content DM%) it appeared to be highly heterogeneous, with no apparent correlation to the % mud content. This observation suggests that water content variability may primarily result from differences in dredging, treatment, and storage conditions of the samples, those are not always well-documented. Aluminum (Al), commonly used as a geochemical marker for fine particles, ranged from 7624 to 32,960 mg/kg. The highest concentrations were observed in Harbor 1, Harbor 2 and Harbor 10, confirming a strong association between clay minerals and fine sediments (Förstner and Salomons, 1980; Kuang et al., 2023). In this relation, Harbor 7 which has a low level of fine particles shows indeed a low Al content. Organic matter (OM) is an important constituent of the sediment. Indeed, the average OM content ranged from 14.31 % (Harbor 4) to 50.17 % (Harbor 6) and the regional average observed (31.7 %) is well above the range of usual values for coastal marine sediments (Tessier et al., 2011; Gao et al., 2021). In the studied area, total nitrogen (N) and phosphorus (P) contents in the sediments varied on average from 0.59 to 3.06 g/kg and from 0.46 to 1.41 g/kg respectively, the highest values being measured respectively in Harbor 2 (6.25 g/kg) and Harbor 5 (8.46 g/kg), linked to high OM and fine grain fractions, as observed by Kong et al. (2025).

The sediment leachable fraction has been analyzed for salts, including chloride (Cl), sulfate (SO₄), and fluoride (F). It exhibited huge variation between harbors, reflecting the interplay of local geological
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Distribution of physical and geochemical characteristics of 146 sediment samples collected between 2006 and 2021 in 10 harbors of the Mediterranean coastal area of Occitanie (southern France), according to their nomenclature in the QUALISED database. Total N: total nitrogen concentrations; Total P: total phosphorus concentrations; DM: dry matter content; OM: organic matter content; Sand: sand content (proportion of particles sediment >63 µm over fraction <2 mm); Mud: mud content (proportion of particles sediment <63 µm over fraction <2 mm); pH: hydrogen potential; leachable chloride (Cl), bulk density (Density), Al: aluminum contents; fluoride (F) and sulfate (SO₄) contents. (DW: dry weight). Whiskers equal 1.5 times the interquartile range. Grubbs test for sediments data indicated no outlier in the Qualised database.</figcaption></figure>

differences, hydrodynamic conditions, and anthropogenic influences, as well as the geochemical background of each site (Rodrigues et al., 2020; Cai et al., 2023). Chloride (Cl) content varied significantly from 99 mg/ kg (Harbor 5) to 42,500 mg/kg (Harbor 2) with a mean value at 15,220 mg/kg. Mean Sulfate (SO₄) was at 2769 mg/kg with values fluctuated between 0.81 mg/kg in Harbor 7 to 10,964 mg/kg in Harbor 5. Fluoride (F) content varied from 0.08 mg/kg (Harbor 7) to 13.34 mg/kg (Harbor 5) with a mean value at 5 mg/kg suggesting localized contamination, likely from the watersheds.

The correlation results related to the general characteristics of the studied harbor sediments were statistically significant at *p* < 0.05, unless otherwise specified, and revealed several key patterns those were primarily governed by grain size and particle composition (Table 3). Grain size, dry matter content (DM), and density were strongly interrelated, emphasizing the critical role of fine particles in controlling both water retention and sediment compaction, an expected pattern given that water is normally the least dense component of sediment (Zhang et al., 2022). Specifically, grain size was significantly and negatively
<caption>**Table 3**</caption>

Pearson Correlation Matrix of physical and geochemical characteristics of 146 sediment samples collected between 2006 and 2021 in 10 harbors of the Mediterranean coastal area of Occitanie (southern France), according to their nomenclature in the QUALISED database. Total N: total nitrogen concentrations; Total P: total phosphorus concentrations; DM: dry matter content; OM: organic matter content; Sand: sand content (proportion of particles sediment >63 µm over fraction <2 mm); Mud: mud content (proportion of particles sediment <63 µm over fraction <2 mm); pH: hydrogen potential; leachable chloride (Cl), bulk density (Density), Al: aluminum contents; fluoride (F) and sulfate (SO₄) contents. (DW: dry weight).

<table><thead><tr><th></th><th>pH</th><th>Cl</th><th>F</th><th>SO₄</th><th>Al</th><th>Sand</th><th>Mud</th><th>Density</th><th>Total_N</th><th>Total_P</th><th>DM</th><th>OM</th></tr></thead><tbody><tr><td>pH</td><td>1,000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cl</td><td>-0,392</td><td>1,000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>F</td><td>-0,548</td><td>0,802</td><td>1,000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SO₄</td><td>0,159</td><td>0,359</td><td>0,216</td><td>1,000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Al</td><td>-0,572</td><td>0,347</td><td>0,490</td><td>-0,094</td><td>1,000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sand</td><td>0,166</td><td>-0,144</td><td>-0,167</td><td>0,208</td><td>-0,047</td><td>1,000</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mud</td><td>-0,514</td><td>0,686</td><td>0,728</td><td>0,271</td><td>0,431</td><td>0,050</td><td>1,000</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Density</td><td>0,466</td><td>-0,573</td><td>-0,599</td><td>-0,207</td><td>-0,330</td><td>0,179</td><td>-0,554</td><td>1,000</td><td></td><td></td><td></td><td></td></tr><tr><td>Total_N</td><td>-0,372</td><td>0,755</td><td>0,719</td><td>0,436</td><td>0,387</td><td>-0,034</td><td>0,672</td><td>-0,565</td><td>1,000</td><td></td><td></td><td></td></tr><tr><td>Total_P</td><td>-0,319</td><td>0,619</td><td>0,649</td><td>0,283</td><td>0,175</td><td>-0,036</td><td>0,606</td><td>-0,539</td><td>0,530</td><td>1,000</td><td></td><td></td></tr><tr><td>DM</td><td>0,398</td><td>-0,600</td><td>-0,638</td><td>-0,125</td><td>-0,247</td><td>0,181</td><td>-0,530</td><td>0,621</td><td>-0,523</td><td>-0,645</td><td>1,000</td><td></td></tr><tr><td>OM</td><td>-0,350</td><td>0,634</td><td>0,605</td><td>0,275</td><td>0,400</td><td>0,077</td><td>0,586</td><td>-0,305</td><td>0,674</td><td>0,407</td><td>-0,216</td><td>1,000</td></tr></tbody></table>

correlated with DM ($r = -0.53, p < 0.05$) and density ($r = -0.55, p < 0.05$). As described, Al was positively correlated ($r = 0.43, p < 0.05$) with grain size, being a main component of the clay minerals fraction (Stein et al., 1994; Chen et al., 2017). Organic matter (OM), total nitrogen (N), and total phosphorus (P) concentrations were closely linked to the sediment's retention capacity and specific surface area, both of which increased with decreasing grain size (Zhou et al., 2022; Gao et al., 2021). Accordingly, finer sediments exhibited significant enrichment in OM ($r = 0.59, p < 0.05$), N ($r = 0.67, p < 0.05$), and P ($r = 0.61, p < 0.05$). A positive correlation ($r = 0.53, p < 0.05$) was also observed between total N and total P in the studied sediments, supporting the hypothesis that these nutrients were influenced by similar geochemical processes (Trolle et al., 2009; Yang et al., 2017; Zhang et al., 2024a, 2024b). The variables of Cl and F were closely linked and also with grain size ($r = 0.69$ and $r = 0.73$, respectively; $p < 0.05$), reflecting their conservative behavior in seawater and dependence upon pore water content (Cai et al., 2023; Rodrigues et al., 2020). In contrast, the relationship of sulfate, SO₄, was weaker and not statistically significant ($r = 0.27, p > 0.05$), indicating less conservatism likely due to partial release from leachable phases or transformation by pyritization processes in the sediments (Chang et al., 2022; Choudhary et al., 2024).

### 3.1.2. Pollutants contents and implication for dredging, and sediment management

The observed concentration of PTEs and organic contaminants varied widely from one harbor to another but also inside a same harbor (Fig. 3). Considering PTEs, Cu and Zn were identified as dominant elements with content means values widely disparate amongst harbors, ranging from 38 to 123 and 66 to 156 mg/kg respectively. The highest Cu concentrations were pointed in Harbor 2, Harbor 8, Harbor 10 and Harbor 1 sediments, also identified for their very fine texture. These high contents are expected to be mostly due to antifouling paints (Turner, 2010), industrial runoff (Rodgers et al., 2020), and metallic corrosion (Lagerström et al., 2020). Copper oxide in antifouling paints is recognized as a significant source of metal contamination in port environments (Costa et al., 2016; Turner, 2010; Lagerström et al., 2020). Its presence is linked to recent regulations those have banned TBT in antifouling paints in France since 1982 for ships <25 m and in Europe since January 1, 2008. The new paints formulations can contain up to 50 % copper by dry weight (Costa et al., 2016), particularly in recreational boating contexts where they are widely used. In semi-enclosed harbors with limited hydrodynamic exchange, recurrent inputs from hull maintenance, paint leaching, and direct application in shipyards may lead to the accumulation of bioavailable copper in surface sediments. The high clay content in these sediments plays a significant role in enhancing the retention of this pollutant, through mechanisms such as adsorption, complexation, and cation exchange commonly associated

with clay minerals (Martínez-Guijarro et al., 2019; Robledo Ardila et al., 2024; Zhang et al., 2014, 2024a, 2024b, 2021). Copper contamination in Mediterranean harbors has been documented in previous studies and is often attributed to the geographical setting of the sites, as well as the influence of marine currents and harbor-related activities (Chouba et al., 2024). The presence of this pollutant is most likely linked to anthropogenic inputs from both local sources and surrounding coastal area (Araújo et al., 2019; Arienzo et al., 2013; Tessier et al., 1979, 2011). Similarly, highest Zn concentrations were also recorded in the sediments, with levels reaching 156 mg/kg at Harbor 8 and 136 mg/kg in Harbor 2 and Harbor 1. These elevated Zn levels are primarily linked to the use of metal-based coatings, galvanization processes, and corrosion-resistant paints commonly applied to harbor infrastructures and vessel hulls (Svensson et al., 2022). The likely industrial origins of this contamination are linked to land-based activities identified within the drainage basins of the affected harbors, as described in Chouba et al. (2024). These include small-scale shipbuilding and repair yards, wastewater treatment facilities, hydrocarbon storage and handling terminals, as well as agro-industrial installations located in the vicinity of port areas (Rodrigues et al., 2020; Zhang et al., 2022). Specifically, the catchment areas of Harbor 8 and Harbor 2 encompass marinas, maintenance workshops, and boatyards, facilities that are well-documented sources of zinc emissions due to runoff, hull cleaning, and degradation of antifouling paints (Lagerström et al., 2020; Birch and Rochford, 2010). The combination of these anthropogenic pressures and the limited hydrodynamic renewal typical of semi-enclosed harbor basins likely contributes to the persistence and accumulation of Zn in the local sediments (de Souza et al., 2021; Gao et al., 2021).

Along with Cu and Zn, high Pb, Cr and Ni contents were measured in sediments. Pb contents ranged from 15 to 48 mg/kg. Once more, highest values were measured in fine-texture sediments of Harbor 8 (48.2 mg/kg) and Harbor 2 (44.4 mg/kg), and at a mean extent in Harbor 3, Harbor 5 and Harbor 10. While historical sources of Pb contamination, such as the degradation of old antifouling paints, plumbing materials, and industrial discharges predated regulatory bans, may still contribute to background levels (Chouba et al., 2024), the contribution of leaded fuels is expected to be likely negligible today, particularly in frequently dredged areas where older deposits have been removed. However, recent inputs of Pb could be associated with the widespread shift from organotin-based antifouling paints (e.g., tributyltin, TBT) to formulations incorporating copper, zinc, and lead compounds. These replacement products, now commonly used in recreational boating and port maintenance, represent a potentially ongoing source of trace metal contamination in harbor environments (Turner, 2010; Lagerström et al., 2020; Ytreberg et al., 2017). This dual legacy, stemming from both past industrial activities and contemporary maritime practices, likely explains the persistence of Pb in surface sediments of several harbors in the
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Distribution of values of potentially toxic elements (PTEs), organic (HAP and PCB) and organotin (TBT) compounds of 146 sediment samples collected between 2006 and 2021 in 10 harbors of the Mediterranean coastal area of Occitanie (southern France), according to their nomenclature in the QUALISED database. Cd: cadmium; Cr: chromium; As: arsenic; Cu: copper; Ni: nickel; Pb: lead; Zn: zinc; Hg: mercury content by type of sediment (harbor); (DW: dry weight). Whiskers equal 1.5 times the interquartile range. French threshold levels for dredging and sediment management N1 (green line), N2 (blue line) and N3 (red line) are reported. It is important to note that the so-called N3 threshold is a designation used in this article, as the French regulation does not assign an official name to this level. This threshold solely serves to determine the authorization for sea disposal of dredged materials. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
region. The data showed that sediment composition and distribution across harbors were strongly influenced by grain size, anthropogenic inputs, and management history, with implications for nutrient dynamics, contamination risks, and sediment stability (Chakraborty et al., 2015; Zhang et al., 2022). A key factor in interpreting sediment quality was the dredging history, namely the location, depth, and timing of past operations, which can affect sample representativeness and partly explain observed heterogeneity (Rodrigues et al., 2020; Gao et al., 2021). Recently dredged sites may expose deeper, older layers with legacy contamination, whereas long-quiet sites may reflect more recent inputs. However, due to limited and inconsistent data on dredging activities, interpreting contamination trends stratigraphically remained challenging. This highlights the need to systematically consider dredging history in future monitoring to improve sediment quality assessments and track contamination over time (Martins et al., 2012; Zhang et al., 2023).

Considering other pollutants, more homogeneous values were measured for Cr and Ni among harbors with at mean 25 mg/kg of Cr and 18 mg/kg of Ni in the sediments, except in Harbor 1 and Harbor 2 characterized by higher Cr contents of respectively 35 mg/kg and 37 mg/kg. These elevated values may reflect localized anthropogenic inputs, particularly from metalworking processes such as plating and welding. In addition, small-scale shipyard and boat maintenance activities, documented in the drainage basins of some harbors including Harbor 2, could contribute to Cr and Ni enrichment through the release of residues from hull treatment, antifouling paint removal, or mechanical repairs (Araújo et al., 2019; Chouba et al., 2024).

Lastly, As, Cd and Hg were also detected in the sediments. Although their absolute concentrations appeared lower than those of other trace metals, these elements are among the most toxic and environmentally persistent, and their potential impact should not be underestimated. Arsenic was measured with a quite identical content in the harbors with contents around 10 mg/kg, except in sediment of Harbor 5 and Harbor 8 where they reached respectively values of 17.4 mg/kg and 14.3 mg/kg. Compared to the average upper continental crust (UCC) value for As (1.5 mg/kg; Taylor and McLennan, 1985), these concentrations are notably elevated. They maybe reflect inputs from pesticide use, coal combustion, and industrial wastes (Nsenga Kumwimba et al., 2016), main sources recognized for this contaminant, however without a specific study the industrial context of the studied it is not possible to get further in this hypothesis. Cadmium concentration ranged from 0.23 to 0.32 mg/kg, with the highest value recorded at Harbor 2. These levels may be attributed to industrial discharges, the use of phosphate fertilizers, and battery waste in the watershed (Chouba et al., 2024). Here again, measured Cd values exceed the UCC baseline of 0.1 mg/kg, suggesting enrichment. Mercury concentrations were relatively low overall, but peaked at Harbor 8 (0.36 mg/kg), potentially attributed to past or ongoing industrial effluents (Chouba et al., 2024). This value also stands well above the UCC average for Hg (0.05 mg/kg; Taylor and McLennan, 1985). These comparisons underscore the importance of contextualizing absolute concentrations. The discussion of origins requires however a specific work and mainly tests and index uses, such as the calculation of geochemical indices, enrichment factor (Tessier et al., 2011) and geoaccumulation index (Müller, 1979). This approach was not undertaken in this study that is not specifically addressed to pollutant origins identification. Their application can be recommended in future targeted investigations using consistent baseline values and normalization methods.

In summary, the contamination of the sediments in PTEs can be described as governed by the occurrence of many toxic elements among which Zn, Cu, Pb, Cr, Ni, As, Cd and Hg. There is significant heterogeneity both between and within the harbors. Some harbors, such as Harbor 2, Harbor 1, Harbor 8, and Harbor 10, are consistently characterized by systematically higher PTEs concentrations, reflecting localized sources of contamination or specific environmental conditions in these areas.

Overall, there is also a great variability in the mean concentrations of organic pollutant contamination in harbor sediments among the studied locations, with alarming maximum values. Higher mean values of PAH were measured at Harbor 6 (2.05 mg/kg), at Harbor 8 (1.51 mg/kg) and at Harbor 2 (1.43 mg/kg). The lowest averages were found at Harbor 1 (0.23 mg/kg) and at Harbor 4 (0.28 mg/kg). Still, the highest maximum values of PAH concentration, measured in Harbor 8 at 3.59 mg/kg and in Harbor 6 at 2.95 mg/kg, likely reflecting contamination from petroleum-derived compounds such as fuel residues, tarry materials, and oil-based maintenance products (Chouba et al., 2024; Carvalho et al., 2022). While no major oil spills have occurred in the study area in recent years to our knowledge, chronic and diffuse inputs from small leisure boats, marina operations, and harbor activities may contribute significantly to hydrocarbon accumulation in sediments over time. Such inputs, though individually minor, can become environmentally relevant in semi-enclosed harbor environments with limited water renewal, where petrogenic PAHs tend to persist and accumulate (Turner et al., 2014; Ytreberg et al., 2022). We therefore consider small-scale recreational boating as a plausible diffuse source of contamination in these sites. For the case of PCB, their highest average concentration, at 0.28 mg/kg, and maximum, at 0.32 mg/kg, was found in Harbor 8, while Harbor 2 and Harbor 3 showed moderate PCB levels likely linked with hydraulic oils and industrial lubricants. The rest of the other harbors, such as Harbor 1, Harbor 10, and Harbor 9, showed average values for PCBs lower than 0.05 mg/kg. In general, the concentrations of PCB detected in the harbors of southern France were comparable to those detected in the deep (>620 m) sediments of the Mediterranean (Mandalakis et al., 2014) and in the coastal waters of Spain (Eljarrat et al., 2005; Solé et al., 2013). These PCB concentrations are indeed lower than in more industrialized and urbanized areas, such as southern Italy (Cardellicchio et al., 2007) the East China Sea (Li et al., 2016) or the Baltic Sea (Sobek et al., 2015).

Finally, regarding tributyltin (TBT), highest average concentrations were recorded in Harbor 10 (168 µg/kg) and Harbor 2 (116 µg/kg), while the lowest averages values were found in Harbor 9 (11 µg/kg) and Harbor 6 (14 µg/kg). Punctually and locally, maximum levels of TBT contamination were observed in Harbor 7 (506 µg/kg), Harbor 2 (146 µg/kg), and Harbor 8 (116 µg/kg). These contents can be attributed to antifouling paints although TBT has been banned in Europe and replaced progressively by copper oxide (Cu₂O) as mentioned above, following growing concerns over its ecotoxicity and subsequent regulatory bans (Alzieu, 2000; Yebra et al., 2004; Chouba et al., 2024). Despite this regulation, several studies have shown that this compound can still be detected in harbor sediments, particularly in enclosed marina areas where water exchange is limited (Turner, 2010; Hoch, 2001; Birchenough et al., 2002). This persistence is largely due to historical accumulation, low sediment turnover, and potentially illegal or unintentional continued use on small leisure boats (Ytreberg et al., 2017). In addition, TBT gradually degrades into less toxic organotin species, such as dibutyltin (DBT) and monobutyltin (MBT), under the influence of environmental conditions including redox potential, microbial activity, and temperature (de Carvalho Oliveira and Santelli, 2010). Although DBT and MBT were not quantified in the present study, their presence in future analyses could provide useful insight into the aging and transformation of legacy TBT contamination. This shift underscores the importance of considering not only legacy contaminants such as TBT, but also the evolving profiles of pollution driven by regulatory changes and current maritime practices. The Heterogeneity observed in both mean and peak TBT concentrations among sites reflects the combined influence of local factors, such as maritime traffic, maintenance operations, and sediment management practices, on the spatial distribution of organotin compounds. These results evidently highlight the substantial contribution of anthropogenic pressures to sediment quality and reinforce the urgent need for integrated monitoring, targeted management, and remediation efforts aimed at reducing the cumulative impact of both historical and emerging contaminants.
The concentrations observed for each pollutant in relation to the N1, N2 and N3 threshold values were compared between sites and the implications in terms of sediment management were discussed. In this approach, the results on PTE analysis in all studied harbors indicate values lower than the N1 threshold for all elements except copper in all the studied harbor. Cu values reached and exceed the N1 and even the N2 threshold in all cases: 70 % of samples show contents higher than the N2 threshold (Fig. 3). In this context, it is crucial to emphasize that the analysis focuses on the dispersion of the data rather than averages. For any given sampling campaign, if even a single sample exceeds the N2 threshold while all others remain below, it is this single sample that will dictate the management approach to be implemented. This ensures that even isolated exceedances are accounted for, as they represent critical scenarios that can significantly impact decision-making processes. In line with the need for sustainable sediment management strategies, the approach proposed by Wasserman et al. (2013) highlights the importance of integrating both environmental and economic considerations in the planning of dredging operations. Their study demonstrates that effective management of contaminated sediments requires not only a thorough understanding of contamination levels but also a careful evaluation of treatment and disposal costs, environmental risks, and long-term impacts. This integrated perspective supports the idea that sediment quality assessment should inform not only risk evaluation but also cost-effective and environmentally sound decision-making processes in dredged material management.

In light of French regulatory guidelines for the management of dredged sediments, the interpretation of measured concentrations through the N1/N2 threshold framework provides valuable insight into potential environmental risks. When the observed concentration of a potentially toxic element (PTE) exceeds the N2 threshold, further investigation is required to assess the risks associated with dredging operations, regardless of the concentrations of other PTEs. Based on our results, such additional studies appear necessary for most harbors, where Cu concentrations exceed N2. For Harbor 6 and Harbor 9, where Cu concentrations fall between N1 and N2, complementary assessments (e.g., sediment ecotoxicity, chemical speciation) may still be recommended to evaluate environmental risk and reuse potential.

This regulatory framework also applies to organic pollutants. While PCB levels in sediments were mostly below the N1 threshold, one value exceeded N2 in Harbor 8 (Fig. 3), indicating the need for targeted ecotoxicological testing prior to any sediment handling. TBT and PAH concentrations above the N1 threshold (100 µg/kg) were also recorded in most harbors (Fig. 3). TBT contamination was detected in sediments from 50 % of the study sites, with only Harbor 9 displaying values consistently below N1. These findings are consistent with those of Chouba et al. (2024), who observed TBT accumulation in vessel maintenance areas of Harbor 7 and Harbor 2.

In addition, Table 4 summarizes major contaminants of studied harbor sediments, their possible sources, and adequate management approaches. Major contaminants include heavy metals such as Cu, Pb, Zn, Hg and organic pollutants including PAHs, polycyclic aromatic hydrocarbons; PCBs, polychlorinated biphenyls and TBT, tributyltin, coming mainly from industrial discharges, shipyard activities, past practices, antifouling paints, and urban and agricultural runoff (Carvalho et al., 2022). Of greater importance, the upstream watersheds contribute by transporting pesticides, nutrients, and other organic and inorganic pollutants downstream, resulting in increased sediment pollution in lower-order harbors (Burton and Johnston, 2010). Although diagnostic ratios and geochemical indices (e.g., Enrichment Factor, Geoaccumulation Index for metals; PAH diagnostic ratios) were not applied in this study due to the lack of standardized geochemical normalization data across historical sources, the probable sources of contamination were inferred through a comparative approach supported by literature from similar harbor settings (Carvalho et al., 2021, 2022). This contextual interpretation provides a consistent and scientifically grounded framework for discussing contaminant origins. Future

<caption>Table 4</caption>

Summary of pollution trends by site, and implications for dredged sediment management. In response to confidentiality concerns expressed by one of the studied harbors, all ten harbors have been anonymized in this study. As such, they are referred to as Harbor 1 through Harbor 10, and their geographical coordinates are not disclosed. Potential sources based on literature (Carvalho et al., 2021; Carvalho et al., 2022; Chouba et al., 2024).

<table><thead><tr><th>Harbor</th><th>Key pollutant with high levels</th><th>Potential sources</th><th>Management implications</th></tr></thead><tbody><tr><td>Harbor 1</td><td>Cu, TBT</td><td>Urban runoff, antifouling paints, and industrial residues.</td><td>Preventive monitoring through storm water filtering and environmentally friendly ship paint, and perform routine sediment quality testing.</td></tr><tr><td>Harbor 2</td><td>Cu, Pb, TBT, HAP</td><td>Paint residues, metal plating, antifouling coatings, and stormwater and agricultural runoff.</td><td>Selective dredging, with on-site treatment particularly for TBT and organic residues, improved stormwater drainage systems, and promote eco-friendly antifouling technologies, Perform periodic sediment dredging, and evaluate TBT removal options, conduct bioremediation pilots for HAP, promote eco-friendly antifouling technologies</td></tr><tr><td>Harbor 3</td><td>Cu, HAP, TBT</td><td>Run-off from shipyards, infrastructures along the coast and minor industries.</td><td>Perform routine sediment testing to monitor Cu levels, adopt low-impact dredging strategies, and implement urban planning to minimize pollution.</td></tr><tr><td>Harbor 4</td><td>Cu</td><td>Low level contamination by urban runoff and vessel traffic</td><td>Implement regular sediment monitoring, strengthen wastewater treatment systems, use adsorption materials for Zn and Cu dredged sediment treatment, enforce stricter pesticide management in the watershed</td></tr><tr><td>Harbor 5</td><td>Cu, Zn, HAP, TBT</td><td>Agricultural runoff, pesticides, antifouling paints and ship maintenance activities.</td><td>Routine dredging with low-impact methods to control contamination spread, Priority to bioremediation processes to treat hydrocarbons, and promote eco-friendly antifouling technologies. Urgent TBT remediation through chemical degradation and secure storage or treatment of sediments, and environmentally friendly ship paint.</td></tr><tr><td>Harbor 6</td><td>Cu, TBT, HAP</td><td>HAP contamination could result from stormwater runoff, urban drainage, or leakage from storage tanks and antifouling paints.</td><td>Immediate remediation of hotspots, chemical stabilization, dredging with confined disposal, and target treatment using chemical or biological methods.</td></tr><tr><td>Harbor 7</td><td>Cu, Hg, Zn, TBT</td><td>TBT contamination from antifouling paints and historical practices.</td><td>Reinforced sediment monitoring programs to prevent future accumulations and identify residual sources.</td></tr><tr><td>Harbor 8</td><td>Cu, PCB, HAP, TBT</td><td>Industrial discharges, shipyard activities, metal corrosion, historical contamination, antifouling paints.</td><td></td></tr><tr><td>Harbor 9</td><td>Cu, HAP</td><td>Limited industrial activity; potential historic contamination.</td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 4 (continued)**</caption><thead><tr><th>Harbor</th><th>Key pollutant with high levels</th><th>Potential sources</th><th>Management implications</th></tr></thead><tbody><tr><td>Harbor 10</td><td>Cu, HAP, TBT</td><td>Shipyard runoff, coastal infrastructure maintenance, and minor industrial activities.</td><td>Periodic dredging, bioremediation trials, conduct periodic sediment dredging, and evaluate TBT hotspots.</td></tr></tbody></table>

studies based on targeted sampling campaigns and harmonized analytical protocols (Tessier et al., 2011) will allow the application of such diagnostic tools (Carvalho et al., 2022), further refining the distinction between natural and anthropogenic contributions.

The set of limit values used to manage dredged sediments in France does not classify materials definitively; in most of the studied harbors, further assessment is needed to clearly identify the potentially toxicity of

some sediments. Implementing an integrated strategy that combines advanced statistical modeling with continuous monitoring programs is essential for evaluating contaminant dispersion patterns and forecasting potential bioaccumulation risks within aquatic ecosystems and the food chain.

### 3.1.3. Correlation analysis between sediment characteristics and pollutants

The spatial distribution of trace metals and organic contaminants in the harbor sediments showed clear links with key sediment characteristics, particularly grain size and organic matter content. Finer sediments were generally associated with higher concentrations of metals such as Cu, Zn, Pb, Ni, and Cr, which is consistent with their known affinity for clay minerals and organic particles (Förstner and Wittmann, 2012; Liu et al., 2019). Organic matter also plays a crucial role in metal retention, either through complexation, adsorption, or co-precipitation processes (Armynot du Châtelet et al., 2009; Couvidat et al., 2016). To support these observations, a Pearson correlation analysis was performed (Fig. 4). Only statistically significant relationships at *p* < 0.05

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Correlation matrix between geochemical characterization data collected for 146 sediment samples in 10 harbors in Occitanie. Paths are also colored by their sign (blue for positive and red for negative). The proximity of the points is determined using multidimensional clustering. The significance level of the correlation coefficient *r* is 0.108, for *a* = 1 %. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
are discussed below. Copper and Zinc exhibited strong positive correlations with both grain size (r = 0.52 and r = 0.48, respectively; p < 0.05) and organic matter (r = 0.40 and r = 0.35; p < 0.05), likely due to their common use in antifouling paints and similar behavior in sediment matrices (Rodrigues et al., 2020; Turner, 2010). Arsenic, on the contrary, is an exception to this pattern. It exerts a poor positive correlation with grain size (r = 0.13, not significant), and a weak negative relation with organic matter (r = -0.12, not significant), suggesting a distinct geochemical pathway. Its poor association with Cr (r = 0.37, p > 0.05) and Cu (r = 0.24, p > 0.05), may reflect its oxyanion behavior, limiting its interaction with sulfides or organic phases (Smedley and Kinniburgh, 2002). Further analysis among metals reveals additional patterns (Fig. 4). The strongest correlations (all at p < 0.05) included Cr-Ni (r = 0.69), Cr-Pb (r = 0.72), Cu-Zn (r = 0.76), Pb-Zn (r = 0.76), and Pb-Ni (r = 0.64), suggesting shared anthropogenic sources or co-binding with fine sediments and organic matter (Birch, 2017; Niu et al., 2021). Cadmium showed moderate correlations with Cr (r = 0.60) and Pb (r = 0.53) (p < 0.05), which may suggest partial affinity for organic fractions despite Cd's recognized anthropogenic origin (Zhaoyong et al., 2015). Aluminum, a proxy for fine aluminosilicate content, correlated significantly with Cr (r = 0.66) and Ni (r = 0.54) (p < 0.05), reinforcing its role in controlling metal distribution. Mercury displayed lower and more variable correlations; its highest was with Pb (r = 0.59, p < 0.05), possibly due to a shared tendency to bind with organic matter (Eggleton and Thomas, 2004). Mercury's specific behavior, including its volatility and bioavailability, makes it less predictable and more sensitive to redox and organic conditions.

The analyses underline the role of fine-grained sediments and organic matter as prime controllers in the distribution of metals within polluted environments. However, the weaker correlations for As and Hg highlight their distinct geochemical pathways and potential alternative sources. In contrast, organic pollutants such as TBT and PAHs showed minimal correlation with sediment characteristics, further supporting the hypothesis that their distribution is largely controlled by localized maritime activities (Ytreberg et al., 2017; Barral-Fraga et al., 2020). These results reinforce the importance of combining sedimentological observations with statistical analyses and geochemical literature, in order to reliably interpret contamination sources and guide port sediment management.

## 3.2. Principal component analysis (PCA)

Principal Component Analysis (PCA) was applied as a complementary tool to assess the spatial variability of sediment contamination across the studied harbors, taking into account both natural sediment characteristics and anthropogenic influences. As observed in the studied sediments, and in agreement with the findings of Förstner and Wittmann (2012) and Chapman et al. (1998), finer-grained sediments and higher organic matter content tend to enhance the retention of trace metals and organic contaminants. This is primarily attributed to their greater specific surface area and the presence of reactive mineral phases.

The PCA was performed to synthesize these relationships and to visualize the clustering behavior between contaminants and sediment properties across sites. The initial analysis included ten variables, with the first two principal components accounting for approximately 80% of the total variance (Fig. 5), thus providing a robust summary of the dominant geochemical patterns.

Component 1 (F1), which explains 56% of the variance, reflects a gradient of contamination linked to sediment texture and organic matter content. Negative F1 scores are associated with coarser, drier sediments exhibiting lower contaminant concentrations, while positive F1 scores correspond to fine-grained, organic-rich sediments enriched in Cu, Hg, PAHs, and TBT. These associations are consistent with known pollutant retention mechanisms in fine sediments (Calmano et al., 1993; Liu et al., 2019). Component 2 (F2), accounting for 24% of the variance, separates two contaminant clusters. Positive F2 values are associated with OM, N,

P, PAH, and SO₄, indicating inputs related to organic contamination and nutrient enrichment (Valković et al., 2007). Negative F2 values correspond to elevated levels of PTEs, particularly As, suggesting a different contamination pattern, likely linked to localized industrial discharges (T. Zhang et al., 2023; Y. Zhang et al., 2020). Interestingly, Cl and F plotted near the center of the PCA space, suggesting more conservative behavior and stable marine input sources. SO₄, in contrast, is more closely aligned with F2, reflecting its sensitivity to redox conditions and biogeochemical processes. Spatially, PCA revealed site-specific contamination profiles (Fig. 5). Harbors such as Harbor 9 and Harbor 7, with negative scores on both axes, are characterized by relatively low levels of contamination, suggesting the occurrence of relatively clean geochemical conditions. In contrast, Harbor 8, positioned in the upper-right quadrant, showed strong positive F1 and F2 scores and was associated with fine sediments and high concentrations of PTEs and organic contaminants. Other harbors such as Harbor 10 and Harbor 4 showed intermediate patterns, reflecting mixed sediment composition and localized anthropogenic pressure.

Two dominant contamination trends have been emphasized in Fig. 6. The red arrow characterizes the overall contamination gradient due to the finer sediments having higher values for organic and PTE concentrations, while the blue arrow characterizes the organic pollution gradient related to nutrients and PAHs. Both trends put into evidence that, besides the fact that the contribution of sediment texture is very important in contamination characteristics, the contribution of anthropogenic activities is also very different between harbors. Harbors were classified into four groups based on PCA clustering (Fig. 6): Group 1: Harbor 8, positioned in the upper-right quadrant, exhibits the highest contamination levels; Group 2: Harbor 5, Harbor 10, and Harbor 1 show intermediate contamination levels; Group 3: Harbor 2, Harbor 6, and Harbor 3 also display intermediate contamination but with slightly higher variability; Group 4: Harbors such as Harbor 9, Harbor 4, and Harbor 7 cluster in the lower-left quadrant, indicating minimal contamination. The above visualization underlines the need for site-specific monitoring and remediation actions at the most polluted areas (such as Harbor 8), while at less impacted sites, preservation-oriented approaches could be performed.

The concentrations observed in Mediterranean harbor sediments fall within the range typical of low to moderately polluted sediments (Tessier et al., 2011) Valkovic et al. (2001) reported increasing concentrations of Cu, Zn, As, and Pb across six categories of coastal sediment samples, ranging from bays and beaches to marinas and ship service areas. Given the predominance of fine particles and the clay-rich mineralogical composition of many of the sediments analyzed (Aknaf et al., 2022), higher trace element concentrations were expected in harbors such as Harbor 2, Harbor 6, and Harbor 5. However, the coarse-grained sediment from the Harbor 7 also showed considerable PTEs loading (Table 5), indicating a strong local anthropogenic influence that overrides grain-size control. Similarly, PCBs concentrations detected in the studied harbors were found to be comparable to values measured in deep Mediterranean basins (>620 m) (Mandalakis et al., 2014) and Spanish coastal waters (Eljarrat et al., 2005; Solé et al., 2013), and remain lower than those observed in more industrialized coastal zones such as southern Italy (Cardellicchio et al., 2007), the East China Sea (Adeleye et al., 2016), or the Baltic Sea (Sobek et al., 2015). These patterns reflect both regional environmental context and localized pressures, including port operations, untreated sewage discharges, and runoff from agricultural or urban areas (Bartalini et al., 2020).

These results point to the composition of sediment and pollutants in harbors due to natural and anthropogenic factors, which would also form the basis of effective management strategies, mostly targeted monitoring and remediation measures for highly contaminated sites, while preservationist approaches might be more appropriate for lesser-impacted sites.
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Projection of the geochemical composition parameters of 146 sediment samples collected between 2006 and 2021 in 10 harbors of the Mediterranean coastal area of Occitanie (southern France), according to their nomenclature in the database QUALISED. Observations on the plane defined by the first two components of principal component analysis (PCA) are presented. The variables are represented by arrows. The coordinates of each variable indicate the correlation coefficients on the first two principal components. The variables are better represented in the plane when the arrow points are close to the circle. When they are correctly represented, the correlations between parameters are stronger and the angle between their directions is smaller. The first and second axes of PCA explained 80 % of the total variance. Chloride (Cl), sulfate (SO4), fluoride (F), organic matter (OM), copper (Cu), cadmium (Cd), mercury (Hg), chromium (Cr), aluminum (Al), nickel (Ni), lead (Pb), zinc (Zn), arsenic (As), polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs), tributyltin (TBT) content, bulk density (Density), total nitrogen (Total_N) and total phosphorus (Total_P) content, Sand: sand content (>63 µm; <2 mm); Mud: mud content (<63 µm), dry matter (DM) content and pH.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Distribution of harbor scores along the PCA axes - F1 vs F2. The harbors have been plotted along each axis to represent the variation in contaminant and grain size composition. Red arrow-an overall contamination gradient driven by finer sediments and higher organic and PTE concentration. Blue vector-an organic pollution gradient, reflecting nutrients and PAHs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

**Table 5**

Comparison of the levels of PTEs (As, Cd, Cr, Cu, Pb, Hg, Ni, Zn) and organic contaminants (HAP, PCB) in harbor sediments of Occitanie with SQGs values (Threshold effect concentration, TEC; Probable effect concentration, PEC). % S >: the percentage of harbor sediments in Occitanie higher than SQGs. NG: no guideline.

<table><thead><tr><th rowspan="2">SQGs</th><th colspan="2">The present study</th><th colspan="2">TEC</th><th colspan="2">PEC</th></tr><tr><th>Min</th><th>Max</th><th>TEC</th><th>% S > TEC</th><th>PEC</th><th>% S > PEC</th></tr></thead><tbody><tr><td>Cd</td><td>0.10</td><td>0.80</td><td>0.99</td><td>0</td><td>4.98</td><td>0</td></tr><tr><td>Cr</td><td>4.58</td><td>68</td><td>43.34</td><td>1</td><td>111</td><td>0</td></tr><tr><td>As</td><td>2.23</td><td>24.20</td><td>9.79</td><td>55</td><td>33</td><td>0</td></tr><tr><td>Cu</td><td>0.10</td><td>761</td><td>31.6</td><td>77</td><td>149</td><td>5</td></tr><tr><td>Ni</td><td>4.41</td><td>29.10</td><td>22.7</td><td>15</td><td>48.6</td><td>0</td></tr><tr><td>Pb</td><td>0.20</td><td>236</td><td>35.8</td><td>27</td><td>128</td><td>0</td></tr><tr><td>Zn</td><td>2.53</td><td>592</td><td>121</td><td>25</td><td>459</td><td>0</td></tr><tr><td>Hg</td><td>0.05</td><td>0.60</td><td>0.18</td><td>11</td><td>1.06</td><td>0</td></tr><tr><td>HAP</td><td>0.10</td><td>10.10</td><td>1.61</td><td>21</td><td>22.8</td><td>0</td></tr><tr><td>PCB</td><td>0.10</td><td>1.60</td><td>0.0598</td><td>25</td><td>0.676</td><td>1</td></tr><tr><td>TBT</td><td>1</td><td>724.38</td><td>NG</td><td>-</td><td>NG</td><td>-</td></tr></tbody></table>

## 3.3. Ecological risk assessment for benthic ecosystems

### 3.3.1. Sediment quality guidelines - individual effect

To allow us to predict the probability of some biological effects occurring on the aquatic organisms living near the polluted sediments, the sediment quality guidelines (SQGs) developed for marine, estuarine ecosystems, and surface water bodies were applied in this study (MacDonald et al., 2000; Goher and Khedr, 2024). The analysis of contaminant concentrations in sediment of the studied harbors shows

distinct patterns of the ecological risk, considering TEC and PEC threshold exceedances (MacDonald et al., 2000) (Table 5). Indeed, TEC values were exceeded in 77 % of the sites for Cu, reflecting widespread contamination with a high probability of ecological effects. Also, 55 % of the sites exceeded the TEC threshold value for As, denoting localized but remarkable concerns. The range of 10 % to 27 % in moderate exceedances for Ni, Pb, Zn, Hg, PAH, and PCB represents a lower but not negligible risk. Further monitoring is thus justified. In the case of Cd and Cr, these elements showed, consistently, values below TEC, being considered of minor ecological concern. Considering PEC values, exceedances are scarce. Indeed, only 5 % of the samples have values that surpassed the PEC threshold for Cu, exclusively in Harbor 1 and Harbor 2 harbors, pointing to a probable ecological impact. The very low percentage (1 %) of samples that exceeded the PEC threshold for PCB, mostly in Harbor 3 harbor, underlined that even for persistent organic pollutants, risks are generally isolated yet probably significant. Taken individually, only Cu and PCB may be associated with probable ecological impacts in these specific harbors. The results emphasize the need for targeted interventions in high-risk sites while maintaining regular monitoring for moderate contaminant levels observed across other locations.

### 3.3.2. Probability of ecological effect (QPECm) - cumulative effect

The QPECm values for the harbors (calculated using the average value of each contaminant) are as follows: Harbor 8 (3.6 ± 0.6), Harbor 5 (2.8 ± 0.6), Harbor 2 (2.7 ± 0.6), Harbor 1 (2.5 ± 0.6), Harbor 10 (2.4 ± 0.6), Harbor 3 (2.3 ± 0.6), Harbor 7 (1.9 ± 0.6), Harbor 4 (1.85 ± 0.6), Harbor 9 (1.8 ± 0.6), and Harbor 6 (1.7 ± 0.6) (Fig. 7). These
<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Ecological risk assessment results. QPECm: probability of ecological effect for benthic organisms. The classification of QPECm is: QPECm <0.1 non-adverse effect with a probability <14 % risk, 0.1 < QPECm <1 slightly adverse effect with a probability 15–29 % risk, 1 < QPECm <5 moderate effect with a probability 33–58 % risk, QPECm >5 High effect with a probability 75–81 % risk.</figcaption></figure>

QPECm values illustrate that all harbors fall in the range of moderate ecologic impact (QPECm 1.7 to 3.6, representing a 33–58 % chance of ecologic impact) (Goher and Khedr, 2024). Harbor 8 exhibits the highest contamination, driven by Cu (26 ± 8 %), Ni (16 ± 2 %), Zn (10 ± 1 %), and significant contributions from Hg (6 ± 2 %), PAH (2 ± 2 %), and PCB (4 ± 4 %), that is directly associated to the very high local contamination previously identified in this harbor. Harbor 5 also reflects a moderate danger, in which Cu (26 ± 8 %), Ni (16 ± 2 %), and As (14 ± 4 %) have a significant impact, and therefore reflects an imperative necessity for focused tracking of these individual compounds. Moderate hazard values for Harbor 2 and Harbor 10, illustrate Cu, Ni, and Zn to be critical in defining contaminant profiles, with little presence of PCB (4 ± 4 %) and PAH (2 ± 2 %) residues, and reflects most probably origin in maritime and urban sources. Lower QPECm values have been detected in Harbor 1, Harbor 10, Harbor 3, and Harbor 7. The distribution seen in the observations reflects strong metal influence, with presence of Cr (11 ± 1 %), Pb (9 ± 2 %), and Cd (2 ± 1 %), and organic pollutants contribution is minimum, signifying a minor level of anthropogenical impact. At last, the lowest QPECm values have been detected in Harbor 9, Harbor 6, and Harbor 4. However, despite these locations having minimum ecological danger in the evaluated scheme, it is advisable to undertake periodical checking for Cu (26 ± 8 %), Ni (16 ± 2 %), and As (14 ± 4 %) in order to curtail the rise in danger levels. In general, this analysis outlines the need for further research on bioaccumulation studies to detect long-term effects, sediment remediation in priority areas, and regulatory measures on pollutant discharges in order to safeguard the benthic ecosystem for the sustainable management of coastal zones. Similar results have been reported for other harbor sediments in the Mediterranean (Mamindy-Pajany et al., 2011), for five sampling sites respectively in the eastern Adriatic Sea (Pelikan et al., 2022) and the inner canals of Shanghai harbor (L. Liu et al., 2020).

It is well established that sediment quality guideline values (SQGs) and associated indices, such as TEC and PEC thresholds, are based on distinct conceptual frameworks and aim to assess environmental risk

from different perspectives. While TEC and PEC offer a pragmatic first-tier screening approach based on concentration thresholds linked to ecological effects (MacDonald et al., 2000), they do not account for critical factors such as metal speciation, bioavailability, or site-specific toxicity. As a result, these guideline-based assessments may provide an incomplete or potentially misleading picture of ecological risk when used in isolation. To improve the robustness of sediment quality evaluations, it is recommended to complement these indices with more integrative tools. For example, the Risk Assessment Code (RAC) focuses on the proportion of metals associated with labile geochemical phases and can indicate the potential for metal remobilization and bioavailability (Aly-Eldeen et al., 2023). Similarly, the Risky Pollution Index (RPI) offers a combined measure of concentration, toxicity, and ecological sensitivity (Jiang et al., 2013). Although less commonly applied, such indices help to contextualize sediment contamination beyond total concentrations. Importantly, divergent outcomes between different assessment methods should be critically examined. If risk classification results are inconsistent or contradictory, they may lead to flawed interpretations and inappropriate management decisions. Therefore, the strengths and limitations of each approach must be carefully considered before application, and a multi-method strategy is advisable. To achieve more accurate and ecologically meaningful risk assessments, we suggest the future integration of additional testing, including toxicity bioassays, benthic community structure analyses, bioaccumulation studies, and detailed geochemical fractionation. The development of composite indices that integrate chemical concentration, fractionation behavior, and toxic-response factors would provide a more holistic understanding of metal behavior and associated risks in sedimentary environments.

## 4. Conclusion

This study assessed the quality of sediments from ten Mediterranean harbors in the Occitanie region (France) and evaluated associated
ecological risks, with a focus on supporting environmentally sound dredging and management practices. The study provided 7884 data points based on 146 sediment samples inventoried from ten different harbors archives. It helps to observe significant variability in the composition and spatial distribution of chemical and organic components amongst the coast. This variability lies inherently in the synergy between the natural and the manmade components.

Sediments were predominantly fine-grained and enriched in organic matter, properties known to favor the accumulation of potentially toxic elements (PTEs) such as Cu, Zn, and Pb, as well as organic pollutants including PAHs, PCBs, and TBT. Although most contaminant concentrations remained below French regulatory thresholds (N1–N2), several hotspots exceeded these limits, particularly for Cu and PAHs, indicating localized pollution requiring further investigation or remediation.

Statistical analyses, including Principal Component Analysis (PCA) and correlation studies, confirmed strong relationship between sediment grain size, organic content, and pollutant accumulation. The QPEcm ecological risk index indicated a moderate risk across most sites, with an estimated 33–58 % probability of adverse effects on benthic communities. These findings support the need for site-specific monitoring and adaptive management.

From a regulatory and operational perspective, this study highlights the importance of integrating chemical monitoring with biological and ecotoxicological assessments. While TEC/PEC thresholds provide a useful initial screening, they should be complemented by bioassays, bioaccumulation studies, and chemical speciation to improve risk characterization. In addition, the application of more integrated indices such as the Risk Assessment Code (RAC) and Risky Pollution Index (RPI) is recommended for future research.

Although the sediments are only weakly contaminated according to regulations, the results suggest that dredged sediments from several harbors in Occitanie are not suitable for open-water disposal without prior treatment or ecotoxicological validation due to certain specific compounds. A multi-tiered management approach is therefore recommended, including regular monitoring, selective dredging, bioremediation, chemical stabilization, and enhanced sediment reuse strategies. Promoting eco-friendly alternatives, such as less toxic antifouling paints, improved stormwater treatment, and reduced pesticide use in watersheds, should also be prioritized to mitigate further inputs.

High-risk zones require targeted remediation, while low-risk sites can be preserved through ongoing surveillance. Finally, although this study primarily focused on chemical characterization, future research should incorporate diagnostic indicators (e.g., PAH ratios, geochemical markers) to refine source attribution and support sustainable, ecosystem-based sediment management in Mediterranean port environments.

### CRediT authorship contribution statement

**Garry Dorleon:** Writing - review & editing, Writing - original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Sylvain Rigaud:** Writing - review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. **Isabelle Teher:** Writing - review & editing, Validation, Supervision, Project administration, Investigation, Funding acquisition, Data curation, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

Thanks are due to the harbor authorities of the Occitanie region, and Nicolas Fauconnier regularly involved in assisting harbors in their dredging administrative authorizations, for their help during data collection. This work was performed within the framework of a thesis supported by the UPR CHROME from the University of Nîmes and funded by the Occitanie region. The authors are also indebted to all contributors providing data and would like to thank all the staff in the CHROME laboratory and particularly Stephane Mussard for their encouragement and useful pieces of advice.

### Data availability

The database QUALISED used in this paper are available on the following Data Repository site: https://doi.org/10.57745/CLDJVQ.

### References

* Adeleye, A.O., Jin, H., Di, Y., Li, D., Chen, J., Ye, Y., 2016. Distribution and ecological risk of organic pollutants in the sediments and seafood of Yangtze Estuary and Hangzhou Bay, East China Sea. Sci. Total Environ. 541, 1540–1548. https://doi.org/10.1016/j.SCITOTENV.2015.09.124.

* AIDA, 2024. Arrêté du 27 mars 2024 fixant les prescriptions générales applicables aux dragages. In: Ministère de la Transition écologique, de la Biodiversité, de la Forêt, de la Mer et de la Pêche.

* Aknaf, A., Akodad, M., Layachi, M., Baghour, M., Oudra, B., Vasconcelos, V., 2022. The chemical characterization and its relationship with heavy metals contamination in surface sediment of Marchica Mediterranean Lagoon (North of Morocco). Environ. Sci. Pollut. Res. 29 (3), 4159–4169. https://doi.org/10.1007/S11356-021-15641-4/TABLES/3.

* Alexakis, E., Ambrosino, M., El-Saadani, Z., Khatita, A.A., Mingqi, W., Palarea-Albaladejo, J., Cicchella, D., 2023. Geochemical Speciation, Ecological Risk and Assessment of Main Sources of Potentially Toxic Elements (PTEs) in Stream Sediments From Nile River in Egypt. Mdpi.Com. https://doi.org/10.3390/w1513208.

* Ali, L., Rashid, A., Khattak, S.A., Gao, X., Jehan, S., Javed, A., 2021. Geochemical modeling, fate distribution, and risk exposure of potentially toxic metals in the surface sediment of the Shyok suture zone, northern Pakistan. Int. J. Sediment Res. 36, 656–667. https://doi.org/10.1016/j.ijsr.2021.02.006.

* Alishirazi, M., Salmak, S., Gitipour, S., 2023. A comprehensive assessment to offer optimized remediation method for mercury contamination in Musa Bay by using hybrid fuzzy AHP-VIKOR approach. Environ. Geochem. Health 45 (11). https://doi.org/10.1007/s10653-023-01745-y.

* Aly-Eldeen, M.A., Shreadah, M.A., Abdel Ghani, S.A., 2023. Distribution, bioavailability, and ecological risk assessment of potentially toxic heavy metals in El-Burullus Lake sediments, Egypt. Mar. Pollut. Bull. 191, 114984. https://doi.org/10.1016/j.marpolbul.2023.114984.

* Alzieu, C., 2000. Environmental impact of TBT: the French experience. Sci. Total Environ., TBT Costs and Benefits- A 258, 99–102. https://doi.org/10.1016/S0048-9697(00)00510-6.

* Ambrosino, M., El-Saadani, Z., Khatita, A.A., Mingqi, W., Palarea-Albaladejo, J., Cicchella, D., 2023. Geochemical speciation, ecological risk and assessment of main sources of potentially toxic elements (PTEs) in stream sediments from Nile River in Egypt. Water 15, 2308. https://doi.org/10.3390/w1513208.

* Ankley, G.T., Di Toro, D.M., Hansen, D.J., Berry, W.J., 1996. Technical basis and proposal for deriving sediment quality criteria for metals. Environ. Toxicol. Chem. 15, 2056–2066. https://doi.org/10.1002/etc.5620151202.

* ANZECC/ARMCANZ, 2000. Australian and New Zealand Guidelines for Fresh and Marine Water Quality. Australian and New Zealand Environment and Conservation Council & Agriculture and Resource Management Council of Australia and New Zealand.

* Araújo, D.F., Ponzevera, E., Briant, N., Knoery, J., Bruzac, S., Sireau, T., Brach-Papa, C., 2019. Copper, zinc and lead isotope signatures of sediments from a Mediterranean coastal bay impacted by naval activities and urban sources. Appl. Geochem. 111, 104440. https://doi.org/10.1016/J.APGEOCHEM.2019.104440.

* Arienzo, M., Masuccio, A., Ferrara, L., 2013. Evaluation of sediment contamination by heavy metals, organochlorinated pesticides, and polycyclic aromatic hydrocarbons in the Berre coastal lagoon (Southeast France). Arch. Environ. Contam. Toxicol. 65 (3), 396–406. https://doi.org/10.1007/S00244-013-9915-3/TABLES/5.

* Armynot du Châtelet, É., Bout-Roumaazeilles, V., Riboulleau, A., Trentesaux, A., 2009. Sediment (grain size and clay mineralogy) and organic matter quality control on living benthic foraminifera. Rev. Micropaleontol. 52 (1), 75–84. https://doi.org/10.1016/J.REVMIC.2008.10.002.

* Barral-Fraga, L., Barral, M.T., MacNeill, K.L., Martíná-Prieto, D., Morin, S., Rodríguez-Castro, M.C., Tuulaikhuu, B.-A., Guasch, H., 2020. Biotic and abiotic factors influencing arsenic biogeochemistry and toxicity in fluvial ecosystems: a review. Int. J. Environ. Res. Public Health 17, 2331. https://doi.org/10.3390/ijerph17072331.

* Bartalini, A., Muñoz-Arnanz, J., Baini, M., Panti, C., Galli, M., Giani, D., Fossi, M.C., Jiménez, B., 2020. Relevance of current PCB concentrations in edible fish species
from the Mediterranean Sea. Sci. Total Environ. 737, 139520. https://doi.org/10.1016/j.JSCITOTENV.2020.139520.

Bateni, F., Mehdinia, A., Lundin, L., Seyed Hasthroudi, M., 2022. Distribution, Source and Ecological Risk Assessment of Polycyclic Aromatic Hydrocarbons in the Sediments of Northern Part of the Persian Gulf. https://doi.org/10.1016/j.chemosphere.2022.133859.

Bavi, H., Hosein, M., Gharaie, M., Moussavi-Harami, R., Zand-Moghadam, H., Mahboubi, A., Mohammad, Tohidi, R., Bavi, H., Gharaie, M.H.M., Moussavi-Harami, R., Mahboubi, A., Zand-Moghadam, H., Tohidi, M.R., 2023. Spatial dispersion hot spots of contamination and human health risk assessments of PTEs in surface sediments of streams around porphyry copper mine, Iran. Springer 45 (6), 3907-3931. https://doi.org/10.1007/s10653-022-01471-x.

Birch, G.F., 2017. Determination of sediment metal background concentrations and enrichment in marine environments - a critical review. Sci. Total Environ. 580, 813-831. https://doi.org/10.1016/j.scitotenv.2016.12.028.

Birch, G.F., Rochford, L., 2010. Stormwater metal loading to a well-mixed/stratified estuary (Sydney Estuary, Australia) and management implications. Environ. Monit. Assess. 169, 531-551. https://doi.org/10.1007/s10661-009-1195-z.

Birchenough, A.C., Barnes, N., Evans, S.M., Hinz, H., Krönke, I., Moss, C., 2002. A review and assessment of tributyltin contamination in the North Sea, based on surveys of butyltin tissue burdens and imposex/intersex in four species of neogastropods. Mar. Pollut. Bull. 44, 534-543. https://doi.org/10.1016/S0025-326X(01)00275-2.

Bloom, N.S., Moretto, L.M., Scopepe, P., Ugo, P., 2004. Seasonal cycling of mercury and monomethyl mercury in the Venice Lagoon (Italy). Mar. Chem. 91, 85-99. https://doi.org/10.1016/j.marchem.2004.06.002.

Bose, B.P., Dhar, M., 2022. Dredged sediments are one of the valuable resources: a review. International Journal of Earth Sciences Knowledge and Applications 4 (2).

Burton, G.A., Johnston, E.L., 2010. Assessing contaminated sediments in the context of multiple stressors. Environ. Toxicol. Chem. 29, 2625-2643. https://doi.org/10.1002/etc.332.

Cai, P., Cai, G., Yang, J., Li, X., Lin, J., Li, S., Zhao, L., 2023. Distribution, risk assessment and quantitative source apportionment of heavy metals in surface sediments from the shelf of the northern South China Sea. Mar. Pollut. Bull. 187, 114589. https://doi.org/10.1016/j.marpolbul.2023.114589.

Calmano, W., Hong, J., Förstner, U., 1993. Binding and mobilization of heavy metals in contaminated sediments affected by pH and redox potential. Water Sci. Technol. 28, 223-235.

Cardelicchio, N., Buccolieri, A., Giandomenico, S., Lopez, L., Pizzulli, F., Spada, L., 2007. Organic pollutants (PAHs, PCBs) in sediments from the Mar Piccolo in Taranto (Ionian Sea, Southern Italy). Mar. Pollut. Bull. 55 (10-12), 451-458. https://doi.org/10.1016/j.MARPOLBUL.2007.09.007.

Carvalho, A.C.B., Moreira, V. de A., Vicente, M. de C., Bernardes, M.C., Bidone, E.D., Sabadini-Santos, E., 2021. Evolution of the sources contribution of aliphatic hydrocarbons and their fate in Sepetiba Bay, Rio de Janeiro, Brazil. Estuar. Coast. Shelf Sci. 261, 107548. https://doi.org/10.1016/j.ecss.2021.107548.

Carvalho, A.C.B., Moreira, V.A., Vicente, M. de C., Bidone, E.D., Bernardes, M.C., Sabadini-Santos, E., 2022. Sterol and PAHs fingerprint analysis of organic matter at Southeast Brazilian Bay. Mar. Pollut. Bull. 181, 113899. https://doi.org/10.1016/j.marpolbul.2022.113899.

Carvalho, A., Cravo-Laureau, C., Moreira, V., Baldy, A., Vicente, M. de C., Bidone, E., Bernardes, M., Sabadini-Santos, E., Duran, R., 2024. Microbial Indicators and Enzymatic Biomarkers for Risk Assessment in Oil-Contaminated Sediments of Tropical Coast. https://doi.org/10.2139/ssrn.4792623.

Castro, H., Ramalheira, F., Quintino, V., Rodrigues, A.M., 2006. Amphipod acute and chronic sediment toxicity assessment in estuarine environmental monitoring: an example from Ria de Aveiro, NW Portugal. Mar. Pollut. Bull. 53, 91-99. https://doi.org/10.1016/j.marpolbul.2005.09.029. Recent Developments in Estuarine Ecology and Management.

CCME, 2002. Canadian Sediment Quality Guidelines for the Protection of Aquatic Life. Canadian Council of Ministers of the Environment.

Chakraborty, P., Sarkar, A., Vudamala, K., Naik, R., Nath, B.N., 2015. Organic matter - a key factor in controlling mercury distribution in estuarine sediment. Mar. Chem. 173, 302-309. https://doi.org/10.1016/j.marchem.2014.10.005. SCOR WG 139: Organic Ligands - A Key Control on Trace Metal Biogeochemistry in the Ocean.

Chang, J., Li, Y., Lu, H., 2022. The morphological characteristics of authigenic pyrite formed in marine sediments. J. Mar. Sci. Eng. 10, 1533. https://doi.org/10.3390/jme10101533.

Chapman, P.M., Wang, F., Janssen, C., Persoone, G., Allen, H.E., 1998. Ecotoxicology of metals in aquatic sediments: binding and release, bioavailability, risk assessment, and remediation. Can. J. Fish. Aquat. Sci. 55, 2221-2243. https://doi.org/10.1139/f98-145.

Chen, B., Liu, Jian, Qiu, J., Zhang, X., Wang, S., Liu, Jinqing, 2017. Spatio-temporal distribution and environmental risk of sedimentary heavy metals in the Yangtze River Estuary and its adjacent areas. Mar. Pollut. Bull. 116, 469-478. https://doi.org/10.1016/j.marpolbul.2016.11.055.

Chouba, C., Pringault, O., Domeau, A., Delpoux, S., Causse, L., Marie, M., Freydier, R., Montigny, C., 2024. In: Chouba, C., Pringault, O., Domeau, A., Delpoux, S., Causse, L., Marie, M., Freydier, R., Montigny, C. (Eds.), Added Value of Water Column Spatiotemporal Monitoring to Assess Variability of Trace Metal Elements and Organotin Compounds in Mediterranean Harbours, Journal of Marine Science and Engineering. Mdpi.Com. https://doi.org/10.3390/jme12030399, 2024•mdpi. Com.

Choudhary, S., Syed Mohammad, S., Mohan, R., Tiwari, M., 2024. Source, bioavailability, and toxicity of metals in modern fjord sediments, west Spitsbergen, and their influence on sediment-associated biota. Front. Mar. Sci. 11. https://doi.org/10.3389/fmars.2024.1429998.

CONAMA, 2012. Resolução nº 454, de 1º de novembro de 2012. Conselho Nacional do Meio Ambiente.

Costa, L.D.F., Mirlean, N., Wasserman, J.C., Wallner-Kersanach, M., 2016. Variability of labile metals in estuarine sediments in areas under the influence of antifouling paints, southern Brazil. Environ. Earth Sci. 75. https://doi.org/10.1007/s12665-016-5355-5, 580-581-512.

Couvaid, J., Benzazoua, M., Chatain, V., Bouzahzah, H., 2016. Environmental evaluation of dredged sediment submitted to a solidification stabilization process using hydraulic binders. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-016-6869-9.

Crocetti, P., González-Camejo, J., Li, K., Foglia, A., Eusebi, A.L., Fatone, F., 2022. An overview of operations and processes for circular management of dredged sediments. In Waste Management 146. https://doi.org/10.1016/j.wasman.2022.04.040.

de Carvalho Oliveira, R., Santelli, R.E., 2010. Occurrence and chemical speciation analysis of organotin compounds in the environment: a review. Talanta 82, 9-24. https://doi.org/10.1016/j.talanta.2010.04.046.

de Souza, A.M., Rocha, D.S., Guerra, J.V., Cunha, B.A., Martins, M.V.A., Geraldes, M.C., 2021. Metal concentrations in marine sediments of the Rio de Janeiro Coast (Brazil): a proposal to establish new acceptable levels of contamination. Mar. Pollut. Bull. 165, 112113. https://doi.org/10.1016/j.marpolbul.2021.112113.

Di Toro, D.M., Mahony, J.D., Hansen, D.J., Scott, K.J., Hicks, M.B., Mayr, S.M., Redmond, M.S., 1990. Toxicity of cadmium in sediments: the role of acid volatile sulfide. Environ. Toxicol. Chem. 9, 1487-1502. https://doi.org/10.1002/etc.5620091208.

Di Toro, D.M., Zarba, C.S., Hansen, D.J., Berry, W.J., Swartz, R.C., Cowan, C.E., Pavlou, S.P., Allen, H.E., Thomas, N.A., Paquin, P.R., 1991. Technical basis for establishing sediment quality criteria for nonionic organic chemicals using equilibrium partitioning. Environ. Toxicol. Chem. 10 (12), 1541-1583. https://doi.org/10.1002/etc.5620101203.

Dorleon, G., Rigaud, S., Techer, I., 2023. Management of Dredged Marine Sediments in Southern France: Main Keys to Large-scale Beneficial Re-use. https://doi.org/10.21203/RS.3.RS-3587148/V1.

Dorleon, G., Techer, I., Rigaud, S., 2024. Geochemical characterization data of harbors dredged sediments in the Occitanie region (southern France). Data Brief 54, 110509. https://doi.org/10.1016/j.DIB.2024.110509.

Du Laing, G., Rinklebe, J., Vandecasteele, B., Meers, E., Tack, F.M.G., 2009. Trace metal behaviour in estuarine and riverine floodplain soils and sediments: a review. Sci. Total Environ. 407, 3972-3985. https://doi.org/10.1016/j.scitotenv.2008.07.025. Thematic Papers: Selected papers from the 2007 Wetland Pollutant Dynamics and Control Symposium.

Dung, T.T.T., Cappuyns, V., Swennen, R., Phung, N.K., 2013. From geochemical background determination to pollution assessment of heavy metals in sediments and soils. Rev. Environ. Sci. Biotechnol. 12 (4), 335-353. https://doi.org/10.1007/s11157-013-9315-1/TABLES/2.

Eggleton, J., Thomas, K.V., 2004. A review of factors affecting the release and bioavailability of contaminants during sediment disturbance events. Environ. Int. 30, 973-980. https://doi.org/10.1016/j.envint.2004.03.001.

Eljarrat, E., De La Cal, A., Larrazabal, D., Fabrellas, B., Fernandez-Alba, A.R., Borrull, F., Marce, R.M., Barcelo, D., 2005. Occurrence of polybrominated diphenylethers, polychlorinated dibenzo-p-dioxins, dibenzofurans and biphenyls in coastal sediments from Spain. Environ. Pollut. 136 (3), 493-501. https://doi.org/10.1016/J.ENVPOL.2004.12.005.

Engler, R., Long, E., Swartz, R., Toro, D.Di., 2005. Chronology of the development of sediment quality assessment methods in North America. https://pubs.usgs.gov/p/ublication/70178169.

Ferraro, A., Parisi, A., Barbone, E., Race, M., Mali, M., Spasiano, D., Fratino, U., 2022. Characterising contaminants distribution in marine-coastal sediments through multivariate and nonparametric statistical analyses: a complementary strategy supporting environmental monitoring and control. Environ. Monit. Assess. https://doi.org/10.1007/s10661-022-10617-4.

Ferraro, A., Marino, E., Trancone, G., Race, M., Mali, M., Pontoni, L., Fabricino, M., Spasiano, D., Fratino, U., 2023. Assessment of environmental parameters effect on potentially toxic elements mobility in foreshore sediments to support marine-coastal contamination prediction. Mar. Pollut. Bull. 194, 115338. https://doi.org/10.1016/j.marpolbul.2023.115338.

Förstner, U., Salomons, W., 1980. Trace metal analysis on polluted sediments: part 1: assessment of sources and intensities. Environ. Technol. Lett. 1, 494-505. https://doi.org/10.1080/0959338009384006.

Förstner, U., Wittmann, G.T.W., 2012. Metal Pollution in the Aquatic Environment. Springer Science & Business Media.

Fromard, F., Vega, C., Proisy, C., 2004. Half a century of dynamic coastal change affecting mangrove shorelines of French Guiana. A case study based on remote sensing data analyses and field surveys. Mar. Geol. 208 (2-4), 265-280. https://doi.org/10.1016/j.MARGEO.2004.04.018.

Gao, L., Li, R., Liang, Z., Wu, Q., Yang, Z., Li, M., Chen, J., Hou, L., 2021. Mobilization mechanisms and toxicity risk of sediment trace metals (Cu, Zn, Ni, and Pb) based on diffusive gradients in thin films: a case study in the Xizhi River basin, South China. J. Hazard. Mater. 410, 124590. https://doi.org/10.1016/j.jhazmat.2020.124590.

Goher, M.E., Khed, A.I., 2024. Novel Combined Toxicity Indices (CTI) to Assess the Ecological Risk of Metals in Sediments Dependent on Fractions and Total Metal Content, Application on Qarun Lake Sediment, Case Study. https://doi.org/10.1016/j.watcyc.2024.02.002.

Guo, Q., Pu, R., Tapley, K., Cheng, J., Li, J., Jiao, T., 2019. Impacts of coastal development strategies on Long-term coastline changes: a comparison between Tampa Bay, USA and Xiangshan Harbor. China. Papers in Applied Geography 5 (1-2), 126-139. https://doi.org/10.1080/23754931.2019.1654405.
Harrington, J., Murphy, J., Coleman, M., Jordan, D., Debuigne, T., Szacuri, G., 2016. Economic modelling of the management of dredged marine sediments. Publications. https://doi.org/10.7494/geol.2016.42.3.311.

Hoch, M., 2001. Organotin compounds in the environment — an overview. Appl. Geochem. 16, 719–743. https://doi.org/10.1016/S0883-2927(00)00067-6.

Ingersoll, C.G., MacDonald, D.D., Wang, N., Crane, J.L., Field, L.J., Haverland, P.S., Kemble, N.E., Lindskog, M., Severn, C., Smorong, D.E., 2001. Predictions of sediment toxicity using consensus-based freshwater sediment quality guidelines. Arch. Environ. Contam. Toxicol. 41, 8–21. https://doi.org/10.1007/s002440010216.

Jiang, M., Zeng, G., Zhang, C., Ma, X., Chen, M., Zhang, J., Lu, L., Yu, Q., Hu, L., Liu, L., 2013. Assessment of heavy metal contamination in the surrounding soils and surface sediments in Xiawangang River, Qingshuitang District. PLOS ONE 8, e71176. https://doi.org/10.1371/journal.pone.0071176.

JORF n°0083 of April 9, 2024. Arrêté du 27/03/24 fixant les prescriptions générales applicables aux dragages ou aux rejets y afférent relevant de la rubrique 4.1.3.0 de la nomenclature annexée à l'article R. 214-1 du code de l'environnement en application des articles L. 214-1 à L. 214-3 | AIDA. https://aida.ineris.fr/reglementation/arrete-270324-fixant-prescriptions-generales-applicables-dragages-rejets-afferent.

Kaddar, G., Cohen-Hattab, K., 2023. 'Riding the wave': the development of marinas around the world and the founding of the marina in Tel Aviv, Israel. J. Tour. Hist. 15, 167–181. https://doi.org/10.1080/1755182X.2023.2205384.

Kong, X., Hu, N., Wang, W., Li, Z., Wang, Z., Wang, H., Sheng, Y., 2025. Estuarine fine particles enhance nitrogen elevation and nutritional imbalance in coastal and offshore waters. J. Hydrol., 133179 https://doi.org/10.1016/j.jhydrol.2025.133179.

Kuang, Z., Wang, H., Han, B., Rao, Y., Gong, H., Zhang, W., Gu, Y., Fan, Z., Wang, S., Huang, H., 2023. Coastal sediment heavy metal(loid) pollution under multifaceted anthropogenic stress: insights based on geochemical baselines and source-related risks. Chemosphere 339, 139653. https://doi.org/10.1016/j.chemosphere.2023.139653.

Lagerström, M., Ytreberg, E., Wiklund, A.-K.E., Granhag, L., 2020. Antifouling paints leach copper in excess – study of metal release rates and efficacy and efficacy of a salinity gradient. Water Res. 186, 116383. https://doi.org/10.1016/j.watres.2020.116383.

Li, D., Adeleye, A.O., Jin, H., Di, Y., Chen, J., Ye, Y., 2016. Distribution and ecological risk of organic pollutants in the sediments and seafood of Yangtze Estuary and Hangzhou Bay, East China Sea. Sci. Total Environ. 541, 1540–1548. https://doi.org/10.1016/j.scitotenv.2015.09.124.

Li, T., Sun, G., Yang, C., Liang, K., Ma, S., Huang, L., Luo, W., 2019. Source apportionment and source-to-sink transport of major and trace elements in coastal sediments: combining positive matrix factorization and sediment trend analysis. Sci. Total Environ. 651, 344–356. https://doi.org/10.1016/j.scitotenv.2018.09.198.

Li, X., Bing, J., Zhang, J., Guo, L., Deng, Z., Wang, D., Liu, L., 2022. Ecological risk assessment and sources identification of heavy metals in surface sediments of a river-reservoir system. Sci. Total Environ. 842, 156683. https://doi.org/10.1016/J.SCITOTENV.2022.156683.

Lira, C.C.S., Medeiros, P.H.A., Neto, I.E.L., 2020. Modelling the impact of sediment management on the trophic state of a tropical reservoir with high water storage variations. An. Acad. Bras. Ciênc. e, 92, e20181169. https://doi.org/10.1590/0001-3765202020181169.

Lisiak-Zielińska, M., Borowiak, K., Budka, A., Kanclerz, J., Janicka, E., Kaczor, A., Zyromski, A., Biniak-Pieróg, M., Podawca, K., Mleczek, M., Niedzielski, P., 2021. How polluted are cities in central Europe? - heavy metal contamination in Taraxacum officinale and soils collected from different land use areas of three representative cities. Chemosphere 266, 129113. https://doi.org/10.1016/J.CHEMOSPHERE.2020.129113.

Liu, X., Zhang, J., Shi, W., Wang, M., Chen, K., Wang, L., 2019. Priority pollutants in water and sediments of a river for control basing on benthic macroinvertebrate community structure. Water 11, 1267. https://doi.org/10.3390/w11061267.

Liu, L., Miao, J., Pan, L., Li, Z., Sun, Y., 2020. Characterization of sediment toxicity in Shanghai Harbor using toxicity tests and digital gene expression analysis based on clams Ruditapes phillipinarum. Ecotoxicol. Environ. Saf. 204, 111065. https://doi.org/10.1016/j.ECOENV.2020.111065.

Liu, Y., Jiang, R., Kuang, W., Lin, C., Sun, X., J.-C.-M. P., & 2023, undefined, 2023. Characterization, Source Identification, Risk Assessment of Potentially Toxic Elements (PTEs) in the Surface Water and Sediment of the Beibu Gulf. Elsevier, China. https://www.sciencedirect.com/science/article/pii/S0025326X23003363.

Long, E.R., MacDonald, D.D., Smith, S.L., Calder, F.D., 1995. Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments. Environ. Manag. 19 (1), 81–97. https://doi.org/10.1007/BF02472006/METRICS.

Long, E.R., Ingersoll, C.G., MacDonald, D.D., 2006. Calculation and uses of mean sediment quality guideline quotients: a critical review. Environ. Sci. Tech. 40 (6), 1726–1736. https://doi.org/10.1021/ES058012D.

Macdonald, D.D., Carr, R.S., Calder, F.D., Long, E.R., Ingersoll, C.G., 1996. Development and evaluation of sediment quality guidelines for Florida coastal waters. Ecotoxicology 5 (4), 253–278. https://doi.org/10.1007/BF00118995/METRICS.

MacDonald, D.D., Ingersoll, C.G., Berger, T.A., 2000. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol. 39 (1), 20–31. https://doi.org/10.1007/s002440010075.

Mamidy-Pajany, Y., Hamer, B., Roméo, M., Géret, F., Galgani, F., Durmisì, E., Huel, C., Marmier, N., 2011. The toxicity of composed sediments from Mediterranean ports evaluated by several bioassays. Chemosphere 82 (3), 362–369. https://doi.org/10.1016/J.CHEMOSPHERE.2010.10.005.

Mandalakis, M., Polymenakou, P.N., Tselepides, A., Lampadariou, N., 2014. Distribution of aliphatic hydrocarbons, polycyclic aromatic hydrocarbons and organochlorinated

pollutants in deep-sea sediments of the southern Cretan margin, eastern Mediterranean Sea: a baseline assessment. Chemosphere 106, 28–35. https://doi.org/10.1016/J.CHEMOSPHERE.2013.12.081.

Marnot, B., 2014. Les ports français et la mondialisation au xixe siècle ou l'émergence de trois modèles de croissance régionales. Monde(s) 6 (2), 195-222. https://doi.org/10.3917/MOND.142.0195.

Martínez-Guijarro, R., Paches, M., Romero, I., Aguado, D., 2019. Enrichment and contamination level of trace metals in the Mediterranean marine sediments of Spain. Sci. Total Environ. 693, 133566. https://doi.org/10.1016/j.scitotenv.2019.07.372.

Martins, R., Azevedo, M.R., Mamede, R., Sousa, B., Freitas, K., Rocha, F., Quintino, V., Rodrigues, A.M., 2012. Sedimentary and geochemical characterization and provenance of the Portuguese continental shelf soft-bottom sediments. J. Mar. Syst. 91, 41–52. https://doi.org/10.1016/j.jmarsys.2011.09.011.

MEP (Ministry of Environmental Protection, China), 2002. GB 18668-2002: Marine Sediment Quality Standard.

Miyamoto, S., Abe, R., Endo, Y., Takeshita, J.I., 2016. Ward method of hierarchical clustering for non-Euclidean similarity measures. In: Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015, pp. 60–63. https://doi.org/10.1109/SOCPAR.2015.7492784.

Müller, G., 1979. Schwermetalle in den Sedimenten des Rheins-Veränderungen seit 1971.

Niu, L., Li, J., Luo, X., Fu, T., Chen, O., Yang, Q., 2021. Identification of heavy metal pollution in estuarine sediments under long-term reclamation: ecological toxicity, sources and implications for estuary management. Environ. Pollut. 290, 118126. https://doi.org/10.1016/j.envpol.2021.118126.

Nsenga Kumwimba, M., Zhu, B., Wang, T., Muyembe, D.K., 2016. Distribution and risk assessment of metals and arsenic contamination in man-made ditch sediments with different land use types. Environ. Sci. Pollut. Res. 23, 24808–24823. https://doi.org/10.1007/s11356-016-7690-1.

Obodai, J., Amaning Adjei, K., Duncan, A.E., Nii Odai, S., 2022. Potentially Toxic Elements (PTEs) contamination and ecological risk of sediment in the upper course of the Ankoora River, Ghana. Environ. Monit. Assess. 194, 446. https://doi.org/10.1007/s10661-022-10120-w.

Patmont, C., LaRosa, P., Narayanan, R., Forrest, C., 2018. Environmental dredging residual generation and management. Integrated Environmental Assessment and Management 14 (3). https://doi.org/10.1002/ieam.4032.

Pelikan, J., Majnarić, N., Maurić Maljković, M., Pikeljk, K., Hamer, B., 2022. Physico-chemical and ecotoxicological evaluation of marine sediments contamination: a case study of Rovinj Coastal Area, NE Adriatic Sea, Croatia. Toxics 10 (8), 478. https://doi.org/10.3390/TOXICS10080478/S1.

Puig, M., Raptis, S., Wooldridge, C., Darbra, R.M., 2020. Performance trends of environmental management in European ports. Mar. Pollut. Bull. 160, 111686. https://doi.org/10.1016/J.MARPOLBUL.2020.111686.

Reczyński, W., Szarlowicz, K., Jakubowska, M., Bitusik, P., Kubica, B., 2020. Comparison of the sediment composition in relation to basic chemical, physical, and geological factors. Int. J. Sediment Res. 35, 307–314. https://doi.org/10.1016/j.ijsrc.2020.01.002.

Robledo Ardila, P.A., Alvarez-Alonso, R., Árcega-Cabrera, F., Durán Valsero, J.J., Morales García, R., Lamas-Cosío, O., Oceguera-Vargas, I., DelValls, A., 2024. Assessment and review of heavy metals pollution in sediments of the Mediterranean Sea. Appl. Sci. 14, 1435. https://doi.org/10.3390/app14041435.

Rodgers, K., McLellan, I., Peshkur, T., Williams, R., Tonner, R., Knapp, C.W., Henriquez, F.L., Hursthouse, A.S., 2020. The legacy of industrial pollution in estuarine sediments: spatial and temporal variability implications for ecosystem stress. Environ. Geochem. Health 42, 1057–1068. https://doi.org/10.1007/s10653-019-00316-4.

Rodrigues, M., Cravo, A., Freire, P., Rosa, A., Santos, D., 2020. Temporal assessment of the water quality along an urban estuary (Tagus estuary, Portugal). Mar. Chem. 223, 103824. https://doi.org/10.1016/j.marchem.2020.103824.

Sabadini-Santos, E., de Almeida Moreira, V., de Carvalho, A.C.B., Nascimento, J.R., Lopez, J.V., Fontana, L.F., Silveira, A.E.F., Bidone, E.D., 2022. Chapter 14 - applying enzymatic biomarkers of the in situ microbial community to assess the risk of coastal sediment. In: Pratà Singh, R., Manchanda, G., Bhattacharjee, K., Panosyan, H. (Eds.), Microbial Symptom-Trophy-Mediated Eco-Enterprising, Developments in Applied Microbiology and Biotechnology. Academic Press, pp. 305–335. https://doi.org/10.1016/B978-0-323-99900-7.00008-0.

Silva, S., Ré, A., Pestana, P., Rodrigues, A., Quintino, V., 2004. Sediment disturbance off the Tagus Estuary, Western Portugal: chronic contamination, sewage outfall operation and runoff events. Mar. Pollut. Bull. 49, 154–162. https://doi.org/10.1016/j.marpolbul.2004.02.004.

Smedley, P.L., Kinniburgh, D.G., 2002. A review of the source, behaviour and distribution of arsenic in natural waters. Appl. Geochem. 17, 517–568. https://doi.org/10.1016/S0883-2927(02)00018-5.

Sobek, A., Sundqvist, K.L., Assefa, A.T., Wiberg, K., 2015. Baltic Sea sediment records: unlikely near-future declines in PCBs and HCB. Sci. Total Environ. 518–519, 8–15. https://doi.org/10.1016/J.SCITOTENV.2015.02.093.

Soetan, O., Nie, J., Viteritto, M., Feng, H., 2023. Evaluation of sediment dredging in remediating toxic metal contamination — a systematic review. Environmental Science and Pollution Research 30 (27). https://doi.org/10.1007/s11356-023-27489-x.

Solé, M., Manzanera, M., Bartolomé, A., Tort, L., Caixach, J., 2013. Persistent organic pollutants (POPs) in sediments from fishing grounds in the NW Mediterranean: Ecotoxicological implications for the benthic fish Solea sp. Mar. Pollut. Bull. 67 (1–2), 158–165. https://doi.org/10.1016/J.MARPOLBUL.2012.11.018.

Souza, J.R., Sielski, L.H., Krause, M., Souza, B.S., Brandão, G.P., Albino, J., Carneiro, M. T.W.D., 2022. The influence of beach geology and morphodynamics on chemical
pollution assessments following a mining accident. Mar. Pollut. Bull. 174, 113230.
https://doi.org/10.1016/j.marpolbul.2021.113230.

Stein, R., Grobe, H., Wahsner, M., 1994. Organic carbon, carbonate, and clay mineral distributions in eastern central Arctic Ocean surface sediments. Mar. Geol. 119, 269–285. https://doi.org/10.1016/0025-3227(94)90185-6, 4th International Conference on Paleooceanography (ICP IV).

Svensson, N., Norén, A., Modin, O., Karlfeldt Fedje, K., Rauch, S., Strömvall, A.-M., Andersson-Sköld, Y., 2022. Integrated cost and environmental impact assessment of management options for dredged sediment. Waste Manag. 138, 30–40. https://doi.org/10.1016/j.wasman.2021.11.031.

Taylor, S., McLennan, S., 1985. The Continental Crust: Its Composition and Evolution. Dep. Geosci. Fac. Publ.

Tessier, A., Campbell, P.G.C., Bisson, M., 1979. Sequential extraction procedure for the speciation of particulate trace metals. Anal. Chem. 51, 844–851. https://doi.org/10.1021/ac50043a017.

Tessier, E., Garnier, C., Mullot, J.U., Lenoble, V., Arnaud, M., Raynaud, M., Mounier, S., 2011. Study of the spatial and historical distribution of sediment inorganic contamination in the Toulon bay (France). Mar. Pollut. Bull. 62 (10), 2075–2086. https://doi.org/10.1016/j.MARPOLBUL.2011.07.022.

Trolle, D., Zhu, G., Hamilton, D., Luo, L., McBride, C., Zhang, L., 2009. The influence of water quality and sediment geochemistry on the horizontal and vertical distribution of phosphorus and nitrogen in sediments of a large, shallow lake. Hydrobiologia 627, 31–44. https://doi.org/10.1007/s10750-009-9713-0.

Turner, A., 2010. Marine pollution from antifouling paint particles. Mar. Pollut. Bull. 60, 159–171. https://doi.org/10.1016/j.marpolbul.2009.12.004.

Turner, R.E., Overton, E.B., Meyer, B.M., Miles, M.S., McClenachan, G., Hooper-Bui, L., Engel, A.S., Swenson, E.M., Lee, J.M., Milan, C.S., Gao, H., 2014. Distribution and recovery trajectory of Macondo (Mississippi Canyon 252) oil in Louisiana coastal wetlands. Mar. Pollut. Bull. 87, 57–67. https://doi.org/10.1016/j.marpolbul.2014.08.011.

USEPA, 2005. Equilibrium Partitioning Sediment Benchmarks (ESBs): Metal Mixtures for the Protection of Benthic Organisms. U.S. Environmental Protection Agency.

Valkovic, V., Orescanin, V., Mikulic, N., Obhodas, J., 2001. Geochemical map of island krk in adriatic sea: elements determined by XRF. J. Trace Microprobe Tech. 19 (3), 393–408. https://doi.org/10.1081/TMA-100105054.

Valković, V., Obhodas, J., Crnjar, M., 2007. Concentration of some elements in the Adriatic coastal sea sediments. Case study: the Kvarner Bay. X-Ray Spectrom. 36 (1), 11–19. https://doi.org/10.1002/XRS.928.

Van Bunderen, C., Snellings, R., Vandewalle, L., Cizer, Ö., 2019. Early-age hydration and autogenous deformation of cement paste containing flash calcined dredging sediments. Construct. Build Mater. 200. https://doi.org/10.1016/j.conbuildmat.2018.12.090.

Vivien, R., Tixier, G., Lafont, M., 2014. Use of oligochaete communities for assessing the quality of sediments in watercourses of the Geneva area (Switzerland) and Artois-Picardie basin (France): proposition of heavy metal toxicity thresholds. Ecohydrology & Hydrobiology 14 (2), 142–151. https://doi.org/10.1016/j.ECOHYD.2014.03.003.

VROM, 2000. Circular on Target Values and Intervention Values for Soil Remediation. Ministry of Housing, Spatial Planning and the Environment, Netherlands.

Wang, W., Lin, C., Jiang, R., Liu, Y., Sun, X., Lin, H., Chen, J., 2022. Distribution, source identification and environmental risk assessment of potentially toxic elements (PTEs) in the surface sediment of Sanmen Bay, Zhejiang Province, China. Mar. Pollut. Bull. 174, 113237. https://doi.org/10.1016/j.marpolbul.2021.113237.

Wasserman, J.C., Barros, S.R., Lima, G.B.A., 2013. Planning dredging services in contaminated sediments for balanced environmental and investment costs. J. Environ. Manage. 121, 48–56. https://doi.org/10.1016/j.jenvman.2013.02.024.

Wilkes, M.A., Gittins, J.R., Mathers, K.L., Mason, R., Casas-Mulet, R., Vanzo, D., Mckenzie, M., Murray-Bligh, J., England, J., Gurnell, A., Jones, J.I., 2019. Physical and biological controls on fine sediment transport and storage in rivers. WIREs Water 6, e1331. https://doi.org/10.1002/wat2.1331.

Wilson, A.M.W., Mugerauer, R., Klinger, T., 2015. Rethinking marine infrastructure policy and practice: insights from three large-scale marina developments in Seattle. Mar. Policy 53, 67–82. https://doi.org/10.1016/j.marpol.2014.11.020.

Wyrick, J.R., Senter, A.E., Pasternack, G.B., 2014. Revealing the natural complexity of fluvial morphology through 2D hydrodynamic delineation of river landforms. Geomorphology 210, 14–22. https://doi.org/10.1016/j geomorph.2013.12.013.

Yang, Y., Gao, B., Hao, H., Zhou, H., Lu, J., 2017. Nitrogen and phosphorus in sediments in China: a national-scale assessment and review. Sci. Total Environ. 576, 840–849. https://doi.org/10.1016/j.scitotenv.2016.10.136.

Yebra, D.M., Kiil, S., Dam-Johansen, K., 2004. Antifouling technology—past, present and future steps towards efficient and environmentally friendly antifouling coatings. Prog. Org. Coat. 50, 75–104. https://doi.org/10.1016/j.porgcoat.2003.06.001.

Ytreberg, E., Strand, J., Eklund, B., Ytreberg, E., Lagerström, M., 2017. Total tin and organotin speciation in historic layers of antifouling paint on leisure boat hulls. Environ. Pollut. 220, 1333–1341. https://doi.org/10.1016/j.envpol.2016.11.001.

Ytreberg, E., Hansson, K., Hermansson, A.L., Parsmo, R., Lagerström, M., Jalkanen, J.-P., Hassellöv, I.-M., 2022. Metal and PAH loads from ships and boats, relative to other sources, in the Baltic Sea. Mar. Pollut. Bull. 182, 113904. https://doi.org/10.1016/j.marpolbul.2022.113904.

Zhang, C., Yu, Z., Zeng, G., Jiang, M., Yang, Z., Cui, F., Zhu, M., Shen, L., Hu, L., 2014. Effects of sediment geochemical properties on heavy metal bioavailability. Environ. Int. 73, 270–281. https://doi.org/10.1016/j.envint.2014.08.010.

Zhang, Y., Li, H., Yin, J., Zhu, L., 2020. Risk Assessment for Sediment Associated Heavy Metals Using Sediment Quality Guidelines Modified by Sediment Properties *. https://doi.org/10.1016/j.envpol.2020.115844.

Zhang, Y., Li, H., Yin, J., Zhu, L., 2021. Risk assessment for sediment associated heavy metals using sediment quality guidelines modified by sediment properties. Environ. Pollut. 275, 115844. https://doi.org/10.1016/j.envpol.2020.115844.

Zhang, M., Sun, X., Hu, Y., Chen, G., Xu, J., 2022. The influence of anthropogenic activities on heavy metal pollution of estuary sediment from the coastal East China Sea in the past nearly 50 years. Mar. Pollut. Bull. 181, 113872. https://doi.org/10.1016/j.marpolbul.2022.113872.

Zhang, T., Chen, Z., Zhang, Z., Zhou, S., Meng, J., Chen, Z., Zhang, J., Cui, J., Chai, B., 2023. Spatial and temporal dynamic response of abundant and rare aerobic denitrifying bacteria to dissolved organic matter in natural water: a case study of Lake Baiyangdian, China. Environmental Research 224, 115524. https://doi.org/10.1016/j.ENVRES.2023.115524.

Zhang, Y., He, G., Xiao, F., Yang, Y., Wang, F., Liu, Y., 2024a. Geochemical characteristics of deep-sea sediments in different Pacific Ocean regions: insights from fractal modeling. Fractal Fract. 8, 45.

Zhang, L., Wu, Y., Li, J., Ni, Z., Ren, Y., Lin, J., Huang, X., 2024b. Hydrodynamics and dissolved organic matter components shaped the fate of dissolved heavy metals in an intensely anthropogenically disturbed estuary. Sci. Total Environ. 934, 173293. https://doi.org/10.1016/j.scitotenv.2024.173293.

Zhaoyong, Z., Abuduwaili, J., Fengqing, J., 2015. Heavy metal contamination, sources, and pollution assessment of surface water in the Tianshan Mountains of China. Environ. Monit. Assess. 187 (2), 1–13. https://doi.org/10.1007/S10661-014-4191-X/TABLES/6.

Zhou, N., Zhang, G.L., Liu, S.M., 2022. Nutrient exchanges at the sediment-water interface and the responses to environmental changes in the Yellow Sea and East China Sea. Mar. Pollut. Bull. 176, 113420. https://doi.org/10.1016/j.marpolbul.2022.113420.