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Article

Assessment of Metal Pollution and Its Environmental Impact on Spanish Mediterranean Coastal Ecosystems

by
María Pachés
1,
Remedios Martínez-Guijarro
2,
Inmaculada Romero
2 and
Daniel Aguado
1,*
1
CALAGUA—Unidad Mixta UV-UPV, Institut Universitari d’Investigació d’Enginyeria de l’Aigua i MediAmbient—IIAMA, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
2
GEA-Institut Universitari d’Investigació d’Enginyeria de l’Aigua i Medi Ambient—IIAMA, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2023, 11(1), 89; https://doi.org/10.3390/jmse11010089
Submission received: 14 December 2022 / Revised: 27 December 2022 / Accepted: 28 December 2022 / Published: 3 January 2023
(This article belongs to the Special Issue Chemical Contamination on Coastal Ecosystems)
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Abstract

:
This study evaluated the presence and significance of the concentrations and spatial distribution of seven metals (Cr, Fe, Ni, Cu, Zn, Cd, Hg, and Pb) along the Spanish Mediterranean coast. The concentrations were determined in surface sediments (taken at a mean depth of 8.6 m) and biota (wild mussels (Mytilus galloprovincialys) and clams (Donax trunculus)). The results show different metal pollution patterns in the sediments of the northern and southern water bodies along 476 km of the Valencia Community coastline. The lithogenic sedimentary concentrations are higher in the northern area. According to the sediment quality guidelines, most of the metal contents along the coast do not constitute a potential risk to aquatic organisms, and only the Hg in the sediments of the two water bodies and the Ni in the sediments of one of the water bodies studied could have low-level effects. Due to their metabolism, mussels’ metal content was always higher than that of clams (taken at the same location) and thus were better indicator organisms of this type of pollution. Despite the low concentrations found, the results show overall sediment and bivalve pollution by metals. Periodic sampling campaigns are recommended to monitor the long-term tendency of these persistent toxic pollutants.

1. Introduction

The Mediterranean Sea ecosystem has always been threatened by human pressures and has even been characterized as a sea “under siege” [1]. Today, the human/anthropogenic impact on these littoral ecosystems is still high and is manifested by changes in its hydro-morphological characteristics and high pollutant loads, among others, which may affect the whole aquatic ecosystem and could cause conflicts between coastal users. Of these pollutants, metals are of concern to the scientific and seafood consuming communities because of their toxicity, persistence, and accumulation in the various environmental compartments (sediment, water, and atmosphere) [2].
Metals represent a threat to natural habitats, in both the long and short term, with toxic and chronic effects on organisms that could lead to a loss of biodiversity in the ecosystems affected [3] and, also in anthropogenic habitats such as harbours. Harbours are known as confined ecosystems that are among the most coastal areas most affected by a wide range of environmental problems, i.e., the discharge of sewage waste, petroleum and its derivatives, and antifouling paints, together with dredging activities, which may affect both the dredged and the disposal site [4].
The release of metals into the environment that end up in the aquatic ecosystems settled in the sediment has a great impact and can affect the entire food chain, from producer to consumer [5].
Some metals are essential in the biological systems (e.g., Cu, Fe, Zn), carrying out important functions, although high intakes may cause adverse health problems [6]. Others (e.g., Cd, Pb, and Hg) are non-essential, but may compete with essential metals for transport and utilisation and are highly toxic to organisms. Some of these elements could eventually cause harm to living organisms and pose health risks to humans through the consumption of fish and seafood, so the public administration governments and scientific communities have focused on studying the environmental impact of these pollutants to ensure effective environmental management outcomes [7,8,9].
Two criteria are widely accepted in assessing this environmental impact: (i) evaluating the metal concentration on the corresponding matrix, and (ii) analysing the biological effects on bioindicator organisms. Regarding the first, the metal fractions in the environmental matrices of sediment and biota are analysed to determine the pollutant load on the coastal ecosystem. Since metals exhibit extremely low solubility, they are rapidly fixed on the solid material in the suspension and sink to the bottom [10], so that the concentrations of these elements in sediments could be several orders of magnitude higher than those on the overlying waters [11]. Low concentrations of metals in the water of marine ecosystems add to the saline effect and make it difficult or even impossible to employ certain analytical techniques to determine their concentration [12,13,14]. Sediment samples absorb chemicals over time and organisms often concentrate these pollutants in their tissues (bio-concentration), facilitating the detection of specific substances and reflecting the state of contamination [13,15].
The second criterion involves assessing the impact of metals using bioindicators. For this purpose, bivalves belong to the first-choice species to be used as bioindicators for environmental pollution. Apart from clams (Donax spp.), Mytilus spp. is one of the organisms most frequently used as a bioindicator in pollution monitoring programs [16,17]. These filtering organisms reveal geographic distribution patterns and temporary trends in coastal pollution and are ubiquitous, abundant, sedentary, and have a relatively long life-cycle. They also resist hypoxic conditions, environmental stress, contamination, and are consumed by other organisms at higher trophic levels [18].
To assess the risks of contamination and ecotoxicological effects of metals in the aquatic environment, it is necessary to apply sediment quality indicators [19], for which several Sediment Quality Guidelines (SQGs) have been compiled to assess metal contamination and deal with environmental concerns [20,21]. The guidelines evaluate the degree to which the sediment-associated chemical status could adversely affect aquatic organisms, and thus are useful for screening sediment contamination by comparing contaminant concentrations with the appropriate quality guidelines. The SQGs may be used to identify and designate sediments with high, moderate, and low probabilities of being associated with adverse effects on aquatic organisms and are a valuable tool for assisting sediment assessors and managers responsible for the interpretation of sediment quality [22].
The main objective of this research project was to assess the environmental impact of metal pollution on the coastline of the Valencia Community (VC) considering the type of water bodies (spatial/geographic context, and whether they were or not under the additional pressure of the existence of a harbour) and applying the two most widely accepted criteria, i.e., the concentration level and the biological effects on the organisms.

2. Materials and Methods

2.1. Study Area

The study was carried out along the 476 km long coastline of the Valencia Community (Figure 1) which combines areas of great ecological value (Columbretes and Peñon de Ifach) with other highly anthropized areas (Valencia, Alicante, Benidorm, etc.) and areas under pressure from agriculture and industrial activities. The study coast was divided into two water types based on a holistic feature, taking into account the annual mean salinity value, coastal geomorphology, coastal transport services, prevailing winds, rainfall, fluvial areas, continental inputs, and wetland zones. The outcome of the analysis revealed that the San Antonio Cape divides the VC into two main areas [23].
In the area north of the Cape, the coastline is regular and almost straight with a predominance of sand deposits, while the river basins are larger and receive continental inputs from rivers and irrigation channels, while the wetlands possess oligohaline waters.
In contrast, high and low cliffs predominate to the south of Cape San Antonio; prevailing winds are south-easterly [24]; and the river basins are smaller, with predominantly dry riverbeds en route to the sea [25]. As the wetlands to the south are generally hypersaline with some brackish water, the southern coastline has a limited continental influence, while the northern coastline receives moderate influence due to the freshwater input.
Each area was divided into water bodies (a total of 24 water bodies, 6 of which contain a commercial, recreational and/or fishing harbor (H), and the remaining 18 were natural coastal waters (N)) to establish a homogenous monitoring network. As can be seen in Figure 1, the northern area is composed of water bodies 001 to 011 (including four port zones: 0041, 0081, 0101, and 0102), while the southern area is formed of 012 to 019 (including port zone 0161). It is important to note that in the water bodies with a harbour, the samples (sediment and biota) were not taken inside the harbour (i.e., they were not from the sheltered basin of the harbour). The different names given to these water bodies make it clear that they are under additional environmental pressure, e.g., the presence of a harbour with its associated maritime traffic and port activities.

2.2. Sample Collection

Several sampling campaigns were carried out from 2005 to 2015, taking samples from both biota (mussels and clams) and sediments along the coastline. Sediment sampling was carried out by trained personnel using 20 mL methacrylate corers. Two to four replicates were taken from surface sediments in each zone (at a mean depth of 8.6 m), which were then mixed into an individual composite sample. The upper layer of collected sediment was approximately 6 to 10 cm thick. The corers were then placed in a portable refrigerator, taken to the laboratory as quickly as possible, and stored at −20 °C until analysis.
Samples of biota, mussels, and clams were collected according to their natural availability. One to four bivalve samples were taken when available. Mussels (mean shell length 33 ± 7 mm) were collected by hand from the rocky areas or breakwaters. These samples were collected in a 2 L plastic bottle (40–50 specimens per sample). Clams (mean shell length 21 ± 3 mm) were taken from sandy beach areas using a rake. For the clam samples, a 0.5 L bottle was filled with 100–150 organisms at each sample site.

2.3. Analytical Techniques

2.3.1. Analytical Procedure for Sediments Samples

The sediments were freeze-dried (ilShin FD5510 freeze dryer) and dry sieved. Metals were determined by acid digestion with nitric acid (65% Merck, Suprapur) and hydrogen peroxide (30% Merck, Suprapur) in a Milestone Ethos 1 microwave, according to EPA Method [26], to obtain the total metal content solution.
Analyses of cadmium (Cd), copper (Cu), lead (Pb), chromium (Cr), nickel (Ni), iron (Fe), and zinc (Zn) were performed by Graphite Furnace Atomic Absorption Spectrometry or Flame Atomic Absorption Spectrometry (AAnalyst 800 Perkin Elmer with Zeeman correction), according to the metal concentration. Mercury (Hg) was determined by the cold vapour technique on an Atomic Absorption Spectrometer with autosampler (AS 90) and a flow injection system (FIAS 100). All the processes were performed following the APHA [27].
Data were analysed as total metal concentrations on a dry weight basis and are expressed in acid-soluble mg kg−1 dry weight (mg kg−1 dw). The detection limits (LOD) and quantification limits (LOQ) expressed in acid soluble µg kg−1 dw were: LOD, Cr 5.57; Cd 1.23; Ni 10.55; Pb 11.61; Hg 13.21; Cu 15.92; Zn and Fe expressed both in mg kg−1 dw were 1.02 and 50.0, respectively; LOQ, Cr 18.56; Cd 4.08; Ni 35.16; Pb 38.70; Hg 44.02; Cu 53.07; and Zn and Fe expressed both in mg kg−1 dw were 3.41 and 166.6, respectively.
To evaluate the performance of the total analytical process, the reference sediment samples were prepared using a sediment sample with metal values below the detection limit. Known concentrations of metals were added to the sample and then analysed to determine the recovery percentages. These percentages were determined by comparing sediment samples spiked with the target metal to unspiked samples. The recovery percentages were 80.3–105.2 for Cr, 88.8–108.0 for Cd, 84.9–103.4 for Ni, 95.5–106.7 for Pb, 100.9–107.9 for Hg, 86.6–98.1 for Cu, 82.9–100.2 for Zn, and 98.1–105.3 for Fe.
Routine duplicate digestions and analyses of the samples were carried out to test analytical reproducibility. Procedural blanks were analysed for every ten samples to check for contamination.

2.3.2. Analytical Procedure for Biota Samples

The bivalves’ soft tissue was separated from the shells with a plastic knife, rinsed with Milli-Q, pooled, and freeze-dried (ilShin FD5510 Freeze Dryer). The lyophilised tissues were pulverised by a mixer mill (Retsch MM301) with balls and container jar, both of zirconium oxide. A subsample of the pulverised sample was digested ([26]) with nitric acid (65% Merck, Suprapur) and hydrogen peroxide (30% Merck, Suprapur) in a microwave (Milestone Ethos 1). Digested samples were diluted to 25 mL with Milli-Q water. Metal concentrations are expressed in acid-soluble mg kg−1 dw. These concentrations are for about one-fifth of the mass of mussels’ wet tissue and about one-seventh the mass of clams’ wet tissue.
Zn was analysed by a Flame Atomic Absorption Spectrometry (Perkin Elmer 3300 Atomic Absorption Spectrometer) with an air-acetylene flame. Analyses of Cd, Cu, Pb, Cr, and Ni were performed by Graphite Furnace Atomic Absorption Spectrometry (Perkin Elmer, 4100ZL, with Zeeman background correction). Hg was determined by the cold vapour technique, employing an Atomic Absorption Spectrometer (Perkin Elmer 3300) with an autosampler (AS 90) and a flow injection system (FIAS 100). Data were analysed as total metal concentrations on a dry weight basis and are expressed in acid-soluble mg kg−1 dry weight (mg kg−1 dw). The detection limits expressed in acid-soluble µg kg−1 dw were: Cr 4.0; Cd 2.0; Ni 12.0; Pb 15.0; Hg 9.0; Cu 23.0 and Zn expressed in mg kg−1 dw were 0.39. The LOQ were Cr 13.3; Cd 6.7; Ni 40.0; Pb 50.0; Hg 30.0; Cu 76.7 and Zn expressed in mg kg−1 dw 1.3. Analytical quality control for biota was performed with ERM-CE278 certified reference material. The recovery percentages were high for all metals and in the range of 98–101.
The routine procedure involves duplicate digestion and metal measurement to test analytical reproducibility. To identify background contamination (possible sources of intralab contamination by reagents and material used) procedural blanks were analysed for every ten samples. To avoid possible contamination, all glassware, plasticware, and equipment was washed prior to use by soaking in 20% HNO3 for 24 h and rinsed with Milli-Q water.

2.3.3. Sediment Quality Guidelines (SQGs)

The sediment quality-assessment guidelines provide two values for several contaminant substances (trace metals, total polychlorinated biphenyls PCBs, pesticides, and polynuclear aromatic hydrocarbons (PAHs)). These two values define concentration ranges that are rarely, occasionally, or frequently associated with adverse effects. Sediment Quality Guideline values (Table 1) were established by Long et al. [28] to estimate the biological effects of sediments for the protection of organisms living in or near sediments [29,30]. This SQG involves the ELR, (the lower 10th percentile in the data identified as effects of low range) and the ERM (the 50th percentile of the effects data distribution). Other SQGs values (Table 1) set by Macdonald et al. [31] contain the TEL (threshold effect level) and PEL (probable effect level) values. ERL and TEL (low-range values) are concentrations below which adverse effects are not expected, while ERM and PEL are concentrations above which adverse effects can be expected more often than not [21].

2.3.4. Statistical Analyses

Analysis of variance (ANOVA) was used to determine the significant differences (p < 0.05) among the different water bodies and biota analysed. The factors chosen to assess the environmental impact on sediments were the presence of a harbour within the water body and the geographical gradient, while for biota it was the type of organism (mussel or clam). Before performing the ANOVA, it was verified that the data met the assumptions for the analysis. This task was carried out on SPSS 16.0 software.
Heatmaps were used to plot and visualise the Pearson’s correlation coefficients among the studied metals. The heatmap is a data visualisation technique in which the values of a matrix are plotted as colours in two dimensions, enabling easy visualisation and interpretation of the magnitude of the variable represented. Phyton was used to represent the heatmaps.
Principal Component Analysis (PCA) was performed to examine the multivariate relationship among the studied metals in the sediments and to compress the variance in the data into a few new variables (known as components or latent variables). SIMCA_P 10.0 software (Umetrics, Sweden) was used to fit the PCA model.

3. Results and Discussion

3.1. Metal Content in Sediment

3.1.1. Harbours’ Influence on the Metal Content

The main statistical parameters used to study the metal content in the water bodies with (H) and without (N) the influence of a harbour are summarised in Table 2. As can be seen in this table, of all the metals, Zn, Cr, and Ni are those with the highest content and largest standard deviation (31.0 ± 16.3; 13.5 ± 5.7 and 7.1 ± 4 mg kg−1 dw, respectively, for harbour water bodies), while Cd and Hg have the lowest, registering less than one order of magnitude (0.2 ± 0.0; 0.1 ± 0.1 mg kg−1 dw). The analysis of variance carried out to study differences between these types of water body showed no significant difference between metal concentration between the coastal waters with or without a harbour (Table 2), p-values > 0.05 in all the tests, except for Cd (being lower in water bodies with a harbour).
The lack of difference between water bodies with or without a harbour reveals that the VC coastline has been highly modified by human activities for several centuries, as have many other Mediterranean zones [32] over the last century. The human pressure measured via anthropogenic indexes developed and applied to the VC coast reveal that the anthropogenic pressure mainly comes from activities such as industry, agriculture, and transport, and is not just a consequence of harbour activity [33,34]. In fact, no-harbour water bodies suffer other anthropogenic pressures as a consequence of the high-density population and tourist areas, industrial areas, and intensive agricultural areas with the same intensity as that derived from the harbours. The higher variability in metal content in the non-harbour water bodies (40% higher for Cr 40%, 30% for Zn and 22% for Ni) may reflect the greater diverse anthropogenic pressures suffered in these water bodies, while in harbours, the pressure could be more homogeneous.
The sediment metal concentrations measured in this study are within the range (but towards the bottom of this range) of the values reported in other coastal zones of the Mediterranean Sea. For example, similar concentrations of metals were found in the SW Mediterranean Sea of Algeria [35,36], except for Zn, whose concentrations in this study are three times lower. Slightly higher concentrations were reported in the NE Adriatic Sea of Croatia [37], and for all the metals measured in this study, their concentration in the sediments is close to the lower values reported for the coastal sediments of the Adriatic Sea (Italy), Golfe-Juan Bay (France), North Cyprus, and Greece [38,39,40,41].

3.1.2. Spatial Influence on Metal Content

In the ANOVA, considering the geographical zone (north and south of San Antonio Cape), differences between several metals appear (Table 3; p-values < 0.05). As can be seen in Table 3, Cr, Cu, Ni, Cd and Zn show differences between the geographical/spatial zones (p-values < 0.05; df = 95). These elements exhibit the same pattern. The northern coast registers higher values than the southern coast. For example, Cr and Zn contents in the north are double those in the south, and Ni increases by up to 144% in the north. For other metals, such as Hg and Pb, there are no geographical differences. The Hg content obtained for both northern and southern areas is the same along the coastline and is close to the quantification limit of the technique. The metal concentrations along the combined northern and southern areas agree with those reported by other authors for the Mediterranean coast [42].
To visualise the differences between the North and South, Figure 2 shows a boxplot including all the concentrations measured for each metal in the northern and southern water bodies. To interpret and understand these differences between the north and south zones, the geomorphologic condition must be considered. In the north of the VC, river basins are larger and receive continental run-off containing a high proportion of pollutants from rivers and irrigation channels that run to the coastline [25]. The northern area of the VC is under intensive agricultural pressure in which Zn, which is present in fungicides (zinc oxide, zinc sulphate, and zinc oxysulfates) or even in pesticides, Cu, present in fungicides and Cd in fertilisers are widely used [43,44]. The intensive agricultural production system in the north [45,46] combined with the manufacturing industry, population density (in cities such as Castellón, Sagunto, Valencia, and Gandia), and livestock farming could be responsible for the larger presence and accumulation of metals in the soil profile and groundwater, reaching the coastal sediments [33,47,48].
It should be noted that in the northern sediments, silt and clay fractions prevail [49] with higher metal adsorption rates than larger particles. In the south, low cliffs and rocky beaches predominate, and the river basins are smaller [25], with rocks and larger particles with little metal adsorption capacity.

3.1.3. Metal Sources

The Pearson correlation coefficient of the studied metals in sediments was calculated separately for both the northern and the southern areas to seek any common sources, patterns, mutual dependence, or similar behaviour during their transport to sediment. Since Fe and Al are so abundant in the natural environment, to the extent that anthropic contributions do not produce significant variations in their concentration, they are widely used as a reference to determine the origin of metal pollutants [50]. The Fe content was therefore also included in the analysis.
The coefficients are displayed in heatmaps in Figure 3 and Figure 4 to simplify visualisation of the bivariate correlation structure. As can be seen, there are statistically significant correlations between all metals and Fe, except for Pb and Hg in the north and Pb, Hg, and Cd in the southern zones. This correlation with Fe indicates that the metal content comes from a natural common source which can be classified as lithogenic, since the Iberian and Sierra de Irta mountain ranges are close to the coastline. The larger values in the northern zones (Table 3) could also be related to these mountain ranges in which sedimentary rocks prevail (such as shale, limestone, and sandstone), so that the natural contribution in this area has a significant influence on the final concentration.
However, the Pb and Hg content in the north is not natural but anthropogenic (reflected in the lack of correlation with Fe and the absence of any natural sources in this area). According to the PRTR-Spain [47], urban and industrial waste and wastewater management could be the main sources of Pb and Hg in these littoral areas, while atmospheric transport plays a central role in the supply of material of these elements to the ocean, especially in shelf seas and semi-enclosed seas such as the Mediterranean [51].
This Cd is not correlated with any of the elements except only slightly with Hg. This may indicate both either a different entry channel or a different mobilisation pattern. Most of the elements have very low solubility, and so they are quickly fixed on the solid material in suspension in the water column. These particles sink and are finally deposited on the sediments. However, Cd mobilisation is higher than other elements and depends on salinity, since the ionic strength of coastal water promotes a higher Cd release [52].
To minimise the environmental impact of some of the metals from natural sources in the ecosystems, their concentration and evolution must be tracked over time. Discharges must be monitored and limited to the medium to avoid the synergistic effects and pollution that could have adverse effects on organisms.
A PCA was fitted to the metal concentration in the sediments to study the multivariate correlation structure of all the metals analysed and facilitate an environmental assessment. The results are plotted in Figure 5 and reveal that only two components explain 84.6% of the total variance. The first component (explains 64.7% of the variance) is determined mainly by Zn, Cr, and Ni with 0.428, 0.427, and 0.425 weight, respectively, and is highly correlated with Fe. The second component (explains 19.9% of the variance) is determined by Hg, with a weight of 0.738, and Pb and Cu, which are in the middle zone of these two components. The first component seems to be related to the natural origin of the metals with a strong correlation with Fe [50,53,54,55,56,57], while the second is influenced by their anthropogenic source.
When the water bodies are projected onto the loadings, they can be seen to be distributed along the axes (Figure 5b). The score plot (Figure 5b) shows how the sediments of the different water bodies relate to each other (i.e., similar pollution patterns in the sediments of the water bodies—in terms of the seven metals analysed—they are projected closely to each other in the score plot). As can be seen in this Figure, the northern water bodies (001, 002 and 003) make the greatest contribution to the first component, indicating both the high concentrations of these metals and that their main origin is lithogenic. On the other hand, all the water bodies with harbours are placed in the middle of the first component (metals of non-lithogenic origin) and some of them are clearly in the positive range of the second component (metals of anthropogenic origin). However, the water body with the highest anthropogenic pressure is 017 (Santa-Pola-Guardamar del Segura in Alicante), which is in the south, due to its characteristic hydrodynamic system of its coastline morphology, which hampers dilution and favours the concentration of pollutants.
It is also interesting to note (Figure 5b) that the sediments in 005 and 007 water bodies in the north present a metal pollution pattern more similar to the less polluted southern water bodies than those of northern areas.

3.2. Metal Content in Biota

The main statistical parameters of metal concentrations in mussels (Mytilus galloprovincialis) and clams (Donax trunculus) were analysed to assess the impact of metal in biota in non-harbour (N) and harbour (H) water bodies (Table 4). As can be seen, essential metals had the highest concentration Zn (104.0 ± 44.7; 112.6 ± 61.3 mg kg−1 dw) and Cu (9.22 ± 4.11; 11.15 ± 5.62 mg kg−1 dw). Since these elements act as structural or catalytic components, organisms need to filter and accumulate them internally at a faster rate than non-essential metals to ensure their important biochemical role in the organism’s life [57,58]. The least abundant metals were the toxic metals such Cd, Pb, and Hg, which had median concentration values of 0.24 ± 0.18 and 0.19 ±0.17 mg kg−1 dw; 0.69 ± 0.40 and 0.68 ± 0.29 mg kg−1 dw; 0.17 ± 0.15 and 0.21 ± 0.21 mg kg−1 dw, in non-harbour (N) and harbour (H) water bodies, respectively.
The concentrations measured for most metals in the biota in this study along the Valencia coastline are slightly lower than those reported by Joksimović et al. [59] for the Mediterranean Sea (Italian, Croatian and Spanish coasts), but notably lower for Cd and Pb which are particularly toxic metals (Cd 90% lower, and Pb 83%). Meanwhile for Cu and Zn, which are essential elements for life but become toxic upon exceeding a certain threshold, the values are within the range reported (Cu, 6.25–15.25 mg kg−1 dw and slightly lower for Zn 160–221.1 mg kg−1 dw).
The analysis of variance on non-harbour (N) and harbour (H) water bodies revealed no significant differences between the two types of water bodies (p-values > 0.05; df = 193) for all elements except Cd and Cu. The higher Cd concentration values were recorded in natural water bodies, while the highest Cu concentration values were found in water bodies with harbours.
However, when the organism is taken into account (since clams and mussels have different absorption rates and feeding strategies), larger differences appear (Table 5). There are statistically significant differences (p-values < 0.05; df = 193) between mussels and clams for all the metals studied except Pb, and Hg. Mussels always had higher concentrations than clams for Cr (25%), Cd (20%), Ni (50%) and Zn (56%), although clams had higher Cu values (56%).
This could be due to the fact that mussels have high feeding rates as an adaptation strategy to oligotrophic conditions (low phytoplankton, nutritious detrital and mineral particles), besides the absence of any internal concentration regulatory mechanisms for many chemicals [60]. However, the greater nutrient enrichment of the coastline due to anthropogenic activities satisfies the mussels’ nutritional needs once the metal content has been stored. As clams are able to regulate their ingestion rates by reducing clearance rates, and not so much by rejecting excess particles as pseudofaeces [61], lower inner levels are achieved. These results are in agreement with those of Chong and Wang [62], who determined absorption efficiencies 3.6–4.6 times higher for Cd, Cr, and Zn in mussels than in clams for a standard body size of 1 g.
Values obtained for metals in mussels in this study are in the range of those found by Guendouzi et al. [35] for the Southwestern Mediterranean Algerian coast. For clams, their metals concentrations are comparable with those reported for the Adriatic Sea [63], except for Pb, which is almost 1.5 times higher in the VC region. Nevertheless, the Pb content is in agreement with that found in other Mediterranean zones (Aegean Sea) [64] and lower when compared to other coastal environments worldwide [65].

3.3. Sediment Quality Guidelines

After recording the differences in metal concentration in sediments and quantifying the organisms’ inner concentrations, it is important to determine whether these pollution levels could have biological effects and pose an ecological risk to the whole ecosystem. The adverse effects on the sediments were evaluated by comparison with the available benchmark Sediment Quality Guidelines (SQG), which evaluate the degree to which the sediment-associated chemical status could adversely affect aquatic organisms.
Figure 6 shows the assessment of the biological effects using the guide by Long et al. [28], in which ELR (effects low range) and ERM (effects median range) determine the final classification. We also applied the SQG by Macdonald et al. [31], which contains the TEL (threshold effect level) and PEL (probable effect level) values to define the final state.
As can be seen in Figure 6, when both SQGs are applied for Zn, Cu, Pb, Cd, and Cr, all the coastal zones show a metal concentration below the ERL or TEL of both sediment quality guidelines. These levels under the ERL or TEL limits mean that no adverse effects can be expected due to metal concentrations in aquatic organisms in contact with the sediments. Although there were some statistical differences between the water bodies (Table 3; northern areas are more polluted than the southern) none of these differences were large enough to raise the adverse effects by a degree.
It should be noted that Macdonald’s guide was first published for continental waters, in which salinity is lower and pollution sediment–organism transfer could be different to that of sea water. Although this guide is widely used for sea water in aquatic systems with high salinity values (Western Mediterranean Sea = 37.5 gL−1), the results should be taken as an approach [66,67].
As Hg yields almost the same classification for both quality guides, all the coastline is under the ERL and TEL except for two water bodies, one in the north and the other in the south, above these values. The northern (006(H)) has a harbour and those in the south (016 and 017) do not, but directly receive the Segura River discharge. As previously indicated, these water bodies have a characteristic hydrodynamic system that fosters accumulation processes that could affect organisms.
The SGQ applied to assess the toxicological risk due to the metal level in the sediments of other Mediterranean zones with similar metal levels yielded similar classification [35]. Other Mediterranean coastal areas (such as those in the Adriatic Sea, North Cyprus, and Greece, as well as in harbours) with higher metal concentrations exceed the ERL (and TEL) for one or several metals [38,39,40,41,68].
However, none of the water bodies reached the median range effect or the probable effect level for any of the metals analysed, and thus, they do not pose a severe risk to the benthic ecosystem. Despite this fact, monitoring coastal pollution is still an urgent priority for sustainable management of littoral habitats.

4. Conclusions

Metal concentration in sediment and bivalves organisms along de VC has been evaluated to determine if the type of water body (geographical location and the presence or not of a harbour) has an important role in the environmental impact.
The average metal content in the sediments of the 24 stations studied in east Mediterranean coastal waters of Spain decreased in the following order: Zn > Cr > Ni > Pb > Cu > Cd > Hg.
Different metal pollution patterns were found in the sediments along the VC coast between the northern and southern water bodies. These differences were mainly due to metals of lithogenic origin (Cr, Ni, Cu, Zn) that showed higher levels in the northern area due to the closeness of high mountain ranges and the larger basins. These northern coastal zones receive more continental inputs than the southern zones, and this, besides the sediment typology (sandy sediments with considerable fractions of silt and clay), yields higher values of metals.
The results have shown no statistically significant differences in metal pollution between water bodies with or without a harbour (with all the associated maritime traffic and port activities), although differences were detected between the bivalve species studied. In mussels, the metal concentration values decreased in the following order Zn > Cu > Ni > Cr > Pb > Cd > Hg, while in clams, the sequence is Zn > Cu > Cr > Ni > Pb > Hg > Cd. Mussels always had higher concentrations than clams for Cr, Cd, Ni, and Zn, probably due to clams’ capacity to reduce ingestion rates. Thus, mussels are better metal pollution indicator organisms than clams, due to their better filtration and accumulation capacity and better reflection of metal content in water. Since none of these species have a filter control mechanism for Pb or Hg, there were no differences between them regarding the concentration levels of these two pollutants.
The presence of metals in sediments could affect aquatic organisms living close to these sediments, and their impact can be assessed by the Sediment Quality Guidelines. The SQGs used in this study were Long et al. [28] and Macdonald et al. [31]. Along 476 km of coast, only two water bodies were identified (by the two quality guidelines applied) as having low range effects due to the Hg concentration. According to Macdonald et al. [31], one of the water bodies presented threshold effect levels of Ni and could cause occasional adverse effects to the benthic organisms. The remaining metals studied and detected along this coast do not constitute any potential risk to aquatic organisms.

Author Contributions

Conceptualization, M.P. and D.A.; methodology, M.P. and R.M.-G.; software, M.P. and D.A.; validation, M.P. and R.M.-G.; formal analysis, M.P., D.A. and R.M.-G.; investigation, M.P. and R.M.-G.; resources, I.R.; data curation, M.P. and R.M.-G.; writing—original draft preparation, M.P., D.A. and R.M.-G.; writing—review and editing, D.A.; visualization, D.A.; supervision, D.A. and I.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research work was supported by the Generalitat Valenciana as part of the studies involved in the Water Framework Directive.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data cannot be made available because of the policy of the Generalitat.

Conflicts of Interest

The authors declare no conflict of interest.

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Jmse 11 00089 g001 550
Figure 1. Study area: 24 water bodies defined along the 476 km of the Valencia coastline (Spain).
Figure 1. Study area: 24 water bodies defined along the 476 km of the Valencia coastline (Spain).
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Jmse 11 00089 g002 550
Figure 2. Boxplot of metal content in the sediments of the northern and southern areas of the Valencia coastline.
Figure 2. Boxplot of metal content in the sediments of the northern and southern areas of the Valencia coastline.
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Jmse 11 00089 g003 550
Figure 3. Heatmap of metal correlation coefficients of the sediments of the water bodies in the northern area. n = 56. Correlation coefficients above |0.4| are statistically significant at 0.01 and above |0.3| at 0.05.
Figure 3. Heatmap of metal correlation coefficients of the sediments of the water bodies in the northern area. n = 56. Correlation coefficients above |0.4| are statistically significant at 0.01 and above |0.3| at 0.05.
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Jmse 11 00089 g004 550
Figure 4. Heatmap of metal correlation coefficients of the sediments in water bodies in the southern area. n = 40. Correlation coefficients above |0.4| are statistically significant at 0.01 and above |0.3| at 0.05.
Figure 4. Heatmap of metal correlation coefficients of the sediments in water bodies in the southern area. n = 40. Correlation coefficients above |0.4| are statistically significant at 0.01 and above |0.3| at 0.05.
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Jmse 11 00089 g005 550
Figure 5. PCA results for the multivariate statistical analysis of the 7 metals in the sediments of the 24 Valencia water bodies: (a) loading-plot (b) score-plot. To facilitate identification of the water bodies, those including a port are labelled with (H).
Figure 5. PCA results for the multivariate statistical analysis of the 7 metals in the sediments of the 24 Valencia water bodies: (a) loading-plot (b) score-plot. To facilitate identification of the water bodies, those including a port are labelled with (H).
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Figure 6. SQG result for all water bodies ([M]: Metal concentration; blue: [M] ≤ ERL and [M] ≤ TEL; orange: ERL ≤ [M] ≤ ERM and TEL≤ [M] ≤ PEL; red [M] ≥ ERM and [M] ≥ PEL).
Figure 6. SQG result for all water bodies ([M]: Metal concentration; blue: [M] ≤ ERL and [M] ≤ TEL; orange: ERL ≤ [M] ≤ ERM and TEL≤ [M] ≤ PEL; red [M] ≥ ERM and [M] ≥ PEL).
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Table
Table 1. Sediment Quality Guideline values (mg kg−1, dry weight). (ERL) Effects of Low Range and (ERM) Effects of Median Range; (TEL) Threshold Effect Level and (PEL) Probable Effect Level. [*] Long et al. [28]; [**] Macdonald et al. [31].
Table 1. Sediment Quality Guideline values (mg kg−1, dry weight). (ERL) Effects of Low Range and (ERM) Effects of Median Range; (TEL) Threshold Effect Level and (PEL) Probable Effect Level. [*] Long et al. [28]; [**] Macdonald et al. [31].
CrCdNiPbHgCuZnAs
ERL811.220.946.70.15034.0150.08.2[*]
ERM370.09.651.6218.00.710270.0410.070.0
TEL52.30.6815.930.20.1318.7124.07.24[**]
PEL160.04.2142.8112.00.70108.0271.041.6
Table
Table 2. Statistical parameters for the measured metals in natural vs. harbour water bodies in VC (N: no harbour presence in these water bodies; H: harbour existence in these water bodies).
Table 2. Statistical parameters for the measured metals in natural vs. harbour water bodies in VC (N: no harbour presence in these water bodies; H: harbour existence in these water bodies).
mg kg−1 dwMeanStandard DeviationMinimumMaximumSig.
CrN15.69.62.437.50.303
H13.55.72.823.6
CdN0.190.060.100.360.013
H0.160.040.080.23
NiN8.05.11.318.30.399
H7.14.01.821.6
PbN5.42.81.712.60.059
H6.62.62.311.4
HgN0.040.050.0050.270.667
H0.070.070.010.34
CuN3.21.71.17.90.400
H3.72.21.212.0
ZnN30.516.36.866.70.884
H31.011.716.669.7
Table
Table 3. Statistical parameters for the measured metals in the sediments of northern and southern water bodies of the VC (N: north; S: south).
Table 3. Statistical parameters for the measured metals in the sediments of northern and southern water bodies of the VC (N: north; S: south).
mg kg−1 dwMeanStandard DeviationMinimumMaximumSig.
CrN20.38.66.837.50.000
S9.95.32.423.6
CdN0.210.070.100.360.000
S0.160.040.080.28
NiN11.04.44.721.60.000
S4.52.71.312.3
PbN6.02.61.712.60.339
S5.42.91.712.2
HgN0.050.050.010.340.889
S0.050.060.010.27
CuN3.71.81.212.00.038
S2.91.81.17.9
ZnN40.813.119.469.70.000
S20.49.26.842.4
Table
Table 4. Statistical parameters for the measured metals in biota in non-harbour (N) vs. harbour (H) water bodies in VC.
Table 4. Statistical parameters for the measured metals in biota in non-harbour (N) vs. harbour (H) water bodies in VC.
mg kg−1 dwMeanStandard DeviationMinimumMaximumSig.
CrN0.950.470.152.840.3109
H1.030.510.202.21
CdN0.240.180.020.780.0157
H0.190.170.020.84
NiN1.150.750.186.070.4540
H1.300.920.243.75
PbN0.690.400.143.390.07
H0.680.290.221.65
HgN0.170.150.010.90.0908
H0.210.210.011.09
CuN9.224.112.2020.36<0.05
0.022
H11.155.624.1234.82
ZnN104.044.752.6253.4>0.05
0.344
H112.661.352.3344.9
Table
Table 5. Statistical parameters for the measured heavy metals in biota, differentiating between clams (C) vs. mussels (M) in all the studied water bodies.
Table 5. Statistical parameters for the measured heavy metals in biota, differentiating between clams (C) vs. mussels (M) in all the studied water bodies.
mg kg−1 dwMeanStandard DeviationMinimumMaximumSig.
CrC0.90.40.22.10.011
M1.21.20.410.3
CdC0.070.030.020.180.000
M0.390.130.160.84
NiC0.80.30.21.90.000
M1.60.90.66.1
PbC0.70.20.21.20.181
M0.70.50.13.4
HgC0.150.150.031.09
M0.220.190.010.90
CuC12.63.64.920.40.000
M7.13.92.234.8
ZnC75.07.052.388.60.000
M137.954.752.6344.9
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Pachés, M.; Martínez-Guijarro, R.; Romero, I.; Aguado, D. Assessment of Metal Pollution and Its Environmental Impact on Spanish Mediterranean Coastal Ecosystems. J. Mar. Sci. Eng. 2023, 11, 89. https://doi.org/10.3390/jmse11010089

AMA Style

Pachés M, Martínez-Guijarro R, Romero I, Aguado D. Assessment of Metal Pollution and Its Environmental Impact on Spanish Mediterranean Coastal Ecosystems. Journal of Marine Science and Engineering. 2023; 11(1):89. https://doi.org/10.3390/jmse11010089

Chicago/Turabian Style

Pachés, María, Remedios Martínez-Guijarro, Inmaculada Romero, and Daniel Aguado. 2023. "Assessment of Metal Pollution and Its Environmental Impact on Spanish Mediterranean Coastal Ecosystems" Journal of Marine Science and Engineering 11, no. 1: 89. https://doi.org/10.3390/jmse11010089

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