Monitoring Of Metal Pollution Research Articles

A hyperspectral metal concentration inversion method using attention mechanism and graph neural network

Soil heavy metal contamination has emerged as a global environmental concern, posing significant risks to human health and ecosystem integrity. Hyperspectral technology, with its non-invasive, non-destructive, large-scale, and high spectral resolution capabilities, shows promising applications in monitoring soil heavy metal pollution. Traditional monitoring methods are often time-consuming, labor-intensive, and expensive, limiting their effectiveness for rapid, large-scale assessments. This study introduces a novel deep learning method, SpecMet, for estimating heavy metal concentrations in naturally contaminated agricultural soils using hyperspectral data. The SpecMet model extracts features from hyperspectral data using convolutional neural networks (CNNs) and achieves end-to-end prediction of soil heavy metal concentrations by integrating attention mechanisms and graph neural networks. Results demonstrate that the OR-SpecMet model, which utilizes raw spectral data, achieves optimal prediction performance, significantly surpassing traditional machine learning methods such as multiple linear regression, partial least squares regression, and support vector machine regression in estimating concentrations of lead (Pb), copper (Cu), cadmium (Cd), and mercury (Hg). Moreover, training specialized OR-SpecMet models for individual heavy metals better accommodates their unique spectral-concentration relationships, enhancing overall estimation accuracy while achieving a 20.3 % improvement in predicting low-concentration mercury. The OR-SpecMet method showcases the superior performance and extensive application potential of deep learning techniques in precise soil heavy metal pollution monitoring, offering new insights and reliable technical support for soil pollution prevention and agricultural ecosystem protection. The code and datasets used in this study are publicly available at: https://github.com/zhang2lei/metal.git.

Chemosensor material as a metal–organic framework with potassium-based perylene tetracarboxylic acid for copper and lead detection

Heavy metal ions stand out as highly toxic pollutants, playing a pivotal role in various human diseases. Consequently, the development of advanced detection methods becomes imperative for global public health. A proven and effective strategy for metal ion detection involves the use of fluorescent chemosensors. In this context, we synthesized metal–organic frameworks (MOFs) based on perylene to create potassium-based MOFs functioning as an efficient detection probe for metal ion detection. Proton nuclear magnetic resonance (NMR) spectral analysis revealed only two distinct types of hydrogen signals are observed from K4PTC, and no hydrogen signals originating from any residual K4PTC are detected. Similarly, In the 13C NMR spectra, the most downfield signal observed at 176.9 ppm is conveniently assigned to the carbonyl carbon of the K4PTC. The probe displayed diminished absorbance and fluorescence intensity upon the introduction of Cu2+ and Pb2+, suggesting K4PTC’s potential as a sensor for detecting these metal ions. The above findings indicate that loading metal ions into the K4PTC system weakens the oxygen-potassium interaction, signifying the interaction between the metal ions and our probe. Interestingly, the limit of detection (LOD) for our sensor material is highly effective for detecting these metal ions. Consequently, this research serves as a significant reference for the sensitive monitoring of metal pollutants in the environment.

Plants as effective bioindicators for heavy metal pollution monitoring

This study investigated the bioindicator potential of Amaranthus retroflexus L., Plantago lanceolata L., Rumex acetosa L., and Trifolium pratense L. including the use of Lolium multiflorum L. as a reference species, for heavy metal pollution monitoring, in particular Zinc (Zn), Cadmium (Cd), Nickel (Ni), and Lead (Pb). Controlled heavy metal contamination was applied through irrigation with metal nitrate solutions two levels of contamination (low and high). The study also focused on analyzing heavy metals concentration in plant tissues and related physiological responses. Distinct physiological responses to heavy metal stress were observed among the investigated species, highlighting unique variations in their reactions. Hydrogen peroxide, malondialdehyde content, and enzymatic activities emerged as reliable indicators of plant stress induced by heavy metal solutions. P. lanceolata displayed elevated Zn concentrations in both roots and leaves (3271 ± 337 and 4956 ± 82 mg kg−1). For Pb, L. multiflorum and P. lanceolata showed highest root concentrations (2964 ± 937 and 1605 ± 289 mg kg−1), while R. acetosa had higher leaf concentration (1957 ± 147 mg kg−1). For Ni, L. multiflorum had the highest root concentration (1148 ± 93 mg kg−1), and P. lanceolata exhibited the highest leaf concentration (2492 ± 28 mg kg−1). P. lanceolata consistently demonstrated the highest Cd concentrations in both roots (126 ± 21 mg kg−1) and leaves (163 ± 12 mg kg−1). These results provide valuable insights for selecting effective bioindicator species to establish control strategies for heavy metal pollution.

Control of Sediment Grainsize on Lead (Pb) Content in the Reef Sediment Systems: A Case Study of Panjang Island, Banten Bay, Indonesia

Sediments are known to accumulate pollutants from terrestrial and coastal waters, and can be used as an indicator to monitor metal pollution in the biosphere and the effects of anthropogenic events in the environment. Eight samples of reef surface sediments were collected from the north and east coasts of Panjang Island. Panjang Island, located to the north of Banten Bay, was chosen for this study because of its proximity to many industrial areas. Nevertheless, the island supports a natural ecosystem, including coral reefs, seagrass, and mangroves. The samples were analyzed for the grainsize, component analysis, and Pb concentrationusing AAS flame method. The result indicated Pb concentration in the grain-supported surface sediment in Panjang Island is varied. The detected Pb concentrations were 0-28.68 mg/kg in dry weight. The study indicated that the different concentration of Pb value is mainly controlled by the different of sediment grainsize. The highest Pb accumulation occurs in the areas with very fine sediment grainsize, which have better adsorption capabilities for heavy metals. This study also suggests that the medium sand fraction may accumulate different Pb values. The result indicated that, according to the classification of ANZECC/ARMCANZ, all sediment samples in the study area is in the low risk of toxicant level. Additionally, the SQG-Q (Sediment Quality Guidelines) quotient analysis indicates that Pb concentration in the study area posed a low-moderate impact to adverse biological effect.

Ecofriendly ratiometric colorimetric determination of mercury(II) ion in environmental water samples using gallic acid-capped gold nanoparticles

Today, the monitoring and determination of heavy metal pollutants in the environment is an essential requirement for the environmental and research communities. Mercury ion is one of the most hazardous heavy metals, and scientists are trying to develop new methods for its detection. In this study, a new colorimetric sensor based on aggregation gallic acid-capped gold nanoparticles (GA-AuNPs) for the determination of mercury ions in environmental water samples was presented. The green synthesized GA-AuNPs exhibited a sharp surface plasmon resonance peak at 515 nm. The addition of mercury ions changed the surface properties of GA-AuNPs, resulting in the formation of a new peak near 670 nm due to the aggregation of GA-AuNPs, and an obvious color change from red to purple occurred. Thus, mercury ions were detected based on the change in the absorbance ratio (A670/A515). The developed sensor can determine the mercury ions in the concentration range of 78.0 nM to 8.3 µM with a detection limit of 5.5 nM. Based on the Environmental Protection Agency (EPA) and the World Health Organization (WHO) reports, the amount of Hg2+ ions in fresh water should be between 10.0 and 30.0 nM. The results indicate that the developed sensor can detect and determine trace amounts of Hg2+ ions in environmental water samples.

The use of marine sponge species as a bioindicator to monitor metal pollution in Red Sea, Saudi Arabia

The existing data on trace elements of benthic sea organisms is scarce. Yet, the pressing issue of environmental contamination has spurred a surge in the use of organisms as biomonitors. In this study, sediment cores were sampled with the sponges, and metal concentrations were determined in both samples using ICP-MS. The mean concentrations of metals in benthic sediments and sponge species analyzed in this study differed significantly (Sediment >Phorbas species > Negombata magnifica > Callyspongia species > Amphimedon chloros). This could be due to the varying capacity of each sponge species to accumulate a particular metal by different means. Negombata magnifica and Phorbas species appear to be indicators, accumulators, or hyper-accumulators of Cu and Mn, while Callyspongia species is an indicator, accumulator, or hyper-accumulator of Cu only due to bioconcentration factor > 1 for the aforementioned metals. Concentrations of Cu and As in sediment were below the Effects Range Median but above the Effects Range Low threshold, hence the need to give more attention to these metals. This research provides a baseline dataset for designing monitoring strategies on this ecosystem and using sponge species for biomonitoring.

Deep Learning Based Spatial Distribution Estimation of Soil Pb Using Multi-Phase Multispectral Remote Sensing Images in a Mining Area

Extensive investigation and monitoring of lead (Pb) content of soil is significant for ensuring hazard-free agricultural production, protecting human health, and ecosystem security, especially in a mining area. One temporal period of a hyperspectral image is usually used to estimate the spatial distribution of Pb and other heavy metals, but hyperspectral images are usually difficult to obtain. Multispectral remote-sensing images are more accessible than hyperspectral images. In this study, a deep learning-based model using 3D convolution is proposed to estimate the Pb content from the constructed multi-phase, multispectral remote-sensing images. Multi-phase multispectral remote-sensing images were stacked to generate a data set with more spectral bands to reduce the atmospheric absorptive aerosol effect. At the same time, a neural network based on 3D convolution (3D-ConvNet) was proposed to estimate Pb content based on the constructed data set. Compared with partial least-squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVMR), and gradient-boosting regression (GBR), experimental results showed the proposed 3D-ConvNet has obvious superiority and generates more accurate estimation results, with the prediction dataset coefficient of determination (R2) and the mean normalized bias (MNB) values being 0.90 and 2.63%, respectively. Therefore, it is possible to effectively estimate heavy metal content from multi-phase, multispectral remote-sensing images, and this study provides a new approach to heavy metal pollution monitoring.

Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation.

The issue of agricultural pollution has become one of the most important environmental concerns worldwide because of its relevance to human survival and health. Microbial remediation is an effective method for treating heavy metal pollution in agriculture, but the evaluation of its effectiveness has been a difficult issue. Machine learning (ML), a widely used data processing technique, can improve the accuracy of assessments and predictions by analyzing and processing large amounts of data. In microbial remediation, ML can help identify the types of microbes, mechanisms of action and adapted environments, predict the effectiveness of microbial remediation and potential problems, and assess the ecological benefits and crop growth after remediation. In addition, ML can help optimize monitoring programs, improve the accuracy and effectiveness of heavy metal pollution monitoring, and provide a scientific basis for the development of treatment measures. Therefore, ML has important application prospects in assessing the effectiveness of microbial remediation of heavy metal pollution in agriculture and is expected to be an effective pollution management technology.

Development and validation of the DGT technique using the novel cryogel for measuring dissolved Hg(II) in the estuary

The complex seawater matrix has significantly influenced the determination of estuarine dissolved Hg(II), hindering its monitoring and risk assessment in maricultural areas. In this work, SiO2-SH-DGT assembled by the sulfhydryl-modified silica cryogel (SiO2-SH cryogel) as the novel binding phase was developed to tackle this problem. The uniform dispersion of the cryogel into binding gel was advantageous for achieving remarkable and comparable capacity, which endowed the estimated diffusion coefficient (D) to be 1.39–3.08 times of the existing research. The SiO2-SH-DGT performance was independent of pH (3–9), ionic strength (10–800 mM), fulvic acid at low content, and seawater matrix (Na+, K+, Ca2+, Cl−), but the high content of Mg2+ did interfere with the Hg(II) accumulation, which manifested as competitive adsorption and diffusion. Therefore, the calibrated model was established by calibrating accumulated mass (M′) and diffusion coefficient (D′) based on the Mg2+ concentration, its high accuracy was further verified in the lab. Finally, SiO2-SH-DGT was deployed in the three typical aquaculture areas in Beibu Gulf, field trials achieved the actual Hg(II) level to be 1.52–5.38 ng/L with consideration of the diffusion boundary layer. The finding could provide new thought and technical support for metal pollution monitoring in estuary maricultural areas.

Spatial Distribution and Potential Ecological Risk Assessment of Trace Metals in Reclaimed Mine Soils in Abuakwa South Municipal, Ghana

ABSTRACT Environmental contamination with trace metals has become an issue of concern to most developing or emerging countries such as Ghana. The study examined the concentrations of arsenic (As), chromium (Cr), nickel (Ni), and lead (Pb) in 40 reclaimed soil samples for their ecological risks assessment and spatial distribution after mining operations in the Abuakwa South Municipal area in Ghana. The mean concentrations of arsenic (0.08 mg/kg), chromium (0.44 mg/kg), nickel (0.27 mg/kg), and lead (1.74 mg/kg) measured were all below the stipulated limit provided by WHO. Using the mean concentrations of these trace metals, pollution indices such as geo-accumulation index (Igeo), contamination factor (CF), ecological risk factor (Er), degree of contamination (Cd), pollution load index (PLI), and potential ecological risk index (PER) were calculated and the results obtained categorized the site as uncontaminated. Spatial distribution maps showed that Cr and Pb were more visible in the study area. This suggests that the study area is well reclaimed and does not pose an ecological risk. The findings presented in this work give data support for trace metal contamination and risk assessment in Abuakwa South. This can have some theoretical implications for the prevention of trace metal pollution and future monitoring studies.

Overview assessment of risk evaluation and treatment technologies for heavy metal pollution of water and soil

Heavy metals are highly toxic elements which are persistent in the natural environment, resulting in magnification throughout the food chain, adversely affecting both human and environmental health. Consequently, heavy metal pollution has received widespread research attention. This review assesses the current progress in heavy metal pollution monitoring, management and mitigations, which show a lack of systematic criteria for risk evaluation. Risk assessment and multivariate analysis methods are also discussed. Assessment of risk evaluation and multivariate statistical analysis are the basic step for the treatment technologies choosing. The technique used is always site-specific, and it often combines different strategies. Only by comprehensively considering the contaminated time, the concentration and nature of the contaminant, the soil/water and site characteristics, the contaminant's availability, and the existence of specific regulations can appropriate restoration and treatment plans be formulated. Combinations of GIS and modeling appear to be most effective for analysis of heavy metal pollution risk. In recent years, many physico, chemical and bio technologies have been used to remove heavy metal pollution from water and soil, each having inherent advantages and limitations. So far, no single method is universally effective for the removal of all heavy metals. Combining different technologies can lead to greater efficiency and may provide a reasonable method to conduct inter-governmental, cross-border heavy-metal prevention and monitoring, for the control of anthropogenic pollution sources. Efficient and commercially viable scaled-up heavy metal treatment technologies will yield large benefits to human and environmental health, as well as economic output. This review provides an overview of the currently available methods, to inform researchers and other stakeholders in the prevention of heavy metal pollution of water and soil.

Five‐wheel framework for system‐based monitoring of heavy metal pollution in rivers

AbstractSectorial approach for monitoring heavy metal pollution in rivers has failed to report realistic pollution status and associated ecological and human health risks. The increasing spread of heavy metals from different sources and emerging risks to human and environmental health call for reexamining heavy metal pollution monitoring frameworks. Also, the sources, spread, and load of heavy metals in the environment have changed significantly over time, requiring consequent modification in the monitoring frameworks. Therefore, studies on heavy metal monitoring in rivers conducted in the last decade were evaluated for experimental designs, research frameworks, and data presentations. Most studies (∼99%) (i) lacked inclusiveness of all environmental compartments; (ii) focused on “one pollutant – one/two compartment” or sometimes “one pollutant – one compartment – one effect” approach; and (iii) remained “data‐rich but information poor.” An ecological approach with integrative system thinking is proposed to develop a holistic approach for monitoring river pollution. It is visualized that heavy metal monitoring, risk analyses, and water management must incorporate tracking pollutants in different environmental compartments of a river (water, sediment, and floodplain/bank soil) and consider correlating it with riverbank land use. The systems‐based pollution monitoring and assessment studies will reveal the critical factors that drive heavy metals pollutant movement in ecosystems and associated potential risks to the environment, wildlife, and humans. Also, water quality and pollution indexing tools would help better communicate complex pollution data and associated risks among all stakeholders. Therefore, integrating systems approaches in scientific‐ and policy‐based tools would help sustainably manage the health of rivers, wildlife, and humans.

Analysis of Crassostrea gasar transcriptome reveals candidate genes involved in metal metabolism

Oysters have been extensively employed for monitoring of metal pollution in dynamic aquatic ecosystems. Therefore, the use of specific biomarkers can assist in discriminating the ecotoxicological implications of different elements in such complex environments. In this study, we revisited the sequencing data of gills and digestive glands transcripts in the mangrove oyster Crassostrea gasar and generated a reference transcriptome assembly from multiple assemblers, seven in total. Overall, we were able to identify a total of 11,917 transcripts, with 86.6% of them being functionally annotated and 1.4 times more than the first annotation. We screened the annotated transcripts to identify genes potentially involved in metals' transport, storage, and detoxification. Our findings included genes related to Zn distribution in cells (Zn transporters - ZIP, ZnT), metallothionein (MT-I and MT-IV), GSH biosynthesis, Ca+ transporter (NCX and ATP2B), and Cu distribution in cells (ATP7, ATOX1, CCS, and laccase-like). These results provided a reference transcriptome for additional insights into the transcriptional profile of C. gasar and other bivalves to better understand the molecular pathways underpinning metal tolerance and susceptibility. The study also provided an auxiliary tool for biomonitoring metal contamination in dynamic environments as estuaries.

Long Term Monitoring of Heavy Metal Pollutants in Sediment of Southern Iraq

The levels of seven heavy metals (Cd, Co, Cu, Fe, Ni, Pb and Zn) were determined in the sediment samples from different water bodies southern Iraq. Distribution of these heavy metals in sediment showed variations in concentration with sampling site, it was undetectable for Cd and Pb for all sites, while the highest concentration was 2.207 µg/g for Ni in the upper and the middle part of Shatt Al-Arab River. Co, Cu, Fe, Ni and Zn ranged from 0.083, 0.402, 1.829, 0.214 and 0.307 µg/g, respectively in Al-Chibayish Marsh to 0.486, 0.863, 2.186, 2.207 and 1.343 µg/g, respectively in the middle part of Shatt Al-Arab River. The geoaccumulation Index (Igeo) was calculated for the metals in the sediment of this study and for all other findings since 2001, to evaluate the levels of sediment pollution with heavy metals. The values of Igeo indicated that the sediment of southern Iraq were mostly unpolluted with heavy metals, except for Cd which was the polluting element in almost all of the previous studies.

An integrated biomarker approach: Non-monotonic responses to cadmium exposure in the suckermouth catfish Hypostomus plecostomus

Environmental risk assessment in aquatic ecosystems typically uses biomarkers to detect interactions between potential hazards and biological systems. Next to knowing environmental contaminant levels in tissues and the environment, it is important to link to potentially deleterious effects at higher levels of biological organization such as biochemistry, physiology, and overall health status. In this laboratory study we assessed the toxicity of waterborne cadmium (Cd) over an exposure range of 0 - 100 µg l-1 for nine days to the loricariid suckermouth catfish Hypostomus plecostomus. We evaluated the integrated response of the fish at the biochemical to physiological level by means of a suite of tissue biomarkers of exposure and effects, including Cd concentrations in gills, liver metallothioneins (MT) and cholinesterase activity (ChE) in brain, before and after the inhibition of the alkaloid eserine, as well as whole-fish resting oxygen consumption rates and ingestion rate. Tissue biomarkers (MT and ChE) showed a non-monotonic relationship, with maximum/minimum responses at intermediate doses. i.e. 10 and 50 µg l-1, whereas biomarker responses of fish exposed at 100 µg l-1 more closely resembled biomarker responses seen at lower concentrations (< 10 µg l-1). Conversely, the oxygen consumption rate peaked at 100 µg l-1, suggesting a higher metabolic cost for higher metal exposure, with no significant correlation with fish body condition and food intake. Integrated Biomarker Response (IBR) values peaked at the intermediate exposure concentration of 50 µg l-1 Cd. The non-monotonic dose-response of the biochemical biomarkers of exposure, together with the higher metabolic rates of fish exposed to 50 - 100 µg l-1 of Cd and the non-significant effects on the more relevant physiological and histological variables suggests that H. plecostomus is capable of biochemically and physiologically regulating moderately high Cd concentrations, thus representing a suitable indicator organism to monitor metal pollution by Cd.

Use of limpets as alternative to mussels in metal pollution monitoring; application in the Canary Islands

Metal pollution monitoring programs make use of organisms, such as mussels, as biomonitors to evaluate and compare the metal pollution status of coastal areas worldwide. Despite the widespread distribution of mussels of the genus Mytilus, there are places where these organisms are absent or where their abundance is insufficient for biomonitoring purposes, such as in the Canary Islands (Spain). This study considers the use of limpets of the genus Patella as alternative/complementary species in metal pollution monitoring, and compares the bioaccumulation capacity of mussels and limpets collected simultaneously during several sampling campaigns at 11 sampling sites along the continental Spanish Atlantic coastline. Results show that there are some differences in bioaccumulation capacity between limpets and mussels, with limpets showing in average higher concentrations of Cd, Cr, Ni, As and Cu, and lower concentrations of Zn, while concentrations of Hg and Pb were similar for both types of organisms. Significant correlations between metal concentration in mussels and limpets were found for most metals, with the notable exception of Cd, that showed a very different bioaccumulation pattern in both types of organisms. Limpet to mussel metal concentration ratios were derived that can be used to compare the results of metal biomonitoring using either limpets or mussels, and limpets were successfully used in a monitoring survey in the Canary Islands.

Growth and Reproduction of the Earthworm After Exposure to Eisenia fetida Sub Lethal Concentration from Remilitine and Lead Mixture

Earthworms play an important role in metal pollution monitoring and are widely recognized in terrestrial ecosystems. In the present study, adult Eisenia fetida (Saving) were exposed to soils contaminated with the metal lead and the fungicide Remiltine combination at a sub-lethal concentration (50+500 ppm) for ten weeks to evaluate the worm body growth after 28, 49 and 70 days of exposure, the reproductive parameters (cocoon production and the juvenile number per cocoon) were after 49 and 70 days of exposure. Results showed that the mean values of the worm's body weight exposed to the lead-Remiltine mixture over 70 days estimate a significant difference as compared to the control site (F=6.06 P&lt;0.05) as the control worms reported higher weights. Furthermore, the t-test revealed a significantly higher juvenile number in the control compared to the treated worms (F=13.43 P&lt;0.05). However, at the end of the experiment of 70 days, no significant difference was reported in cocoon production (F = 0.06 P&gt;0.05).

Discussion on the Status and Development of Heavy Metal Pollution Monitoring in Soil

Discussion on the Status and Development of Heavy Metal Pollution Monitoring in Soil

Bacterial community response to chronic heavy metal contamination in marine sediments of the East China Sea

Marine sediments act as a sink for various heavy metals, which may have profound impact on sedimentary microbiota. However, our knowledge about the collaborative response of bacterial community to chronic heavy metal contamination remains little. In this study, concentrations of seven heavy metals (As, Cd, Cr, Cu, Hg, Pb, and Zn) in sediments collected from the East China Sea were analyzed and Illumina Miseq 16 S rRNA sequencing was applied to characterize the structure of bacterial community. Microbiota inhabiting sediments in the East China Sea polluted with heavy metals showed different community composition from relatively pristine sites. The response of bacterial community to heavy metal stress was further interrogated with weighted correlation network analysis (WGCNA). WGCNA revealed ten bacterial modules exhibiting distinct co-occurrence patterns and among them, five modules were related to heavy metal pollution. Three of them were positively correlated with an increase in at least one heavy metal concentration, hubs (more influential bacterial taxa) of which were previously reported to be involved in the geochemical cycling of heavy metals or possess tolerance to heavy metals, while another two modules showed opposite patterns. Our research suggested that ecological functional transition might have occurred in East China Sea sediments by shifts of community composition with sensitive modules majorly involved in the meaningful global biogeochemical cycling of carbon, sulfur, and nitrogen replaced by more tolerant groups of bacteria due to long-term exposure to low-concentration heavy metals. Hubs may serve as indicators of perturbations of benthic bacterial community caused by heavy metal pollution and support monitoring remediation of polluted sites in marine environments.

Trace metal accumulation in tissues of wedge clams from sandy habitats of the Bulgarian Black Sea coast

The aim of the present study was to carry out an initial screening of trace metals bioaccumulation in soft tissues of the wedge clam (Donax trunculus Linnaeus, 1758) from different localities of the Bulgarian Black Sea coastal area and to evaluate the bioindicator potential of this species. Wedge clams were collected in June and September 2020 from sublittoral sandy habitats at different localities of the Bulgarian Black Sea coast. Soft tissues of individual clams were digested with nitric acid followed by ICP-OES analytical determination. The content of trace metals in the wedge clams differed significantly amongst localities. Higher metal content was present in wedge clams from Sveti Vlas, Shkorpilovtsi, Slanchev Bryag, Ahtopol and Kranevo. The highest values of lead (Pb) (2.51 mg/kg) and cadmium (Cd) (0.32 mg/kg) were found in samples from Sveti Vlas and the highest concentration of copper (Cu) (34.12 mg/kg), iron (Fe) (269.52 mg/kg) and nickel (Ni) (0.32 mg/kg) were detected in wedge clams from Shkorpilovtzi. Maximum content of chromium (Cr) (0.58 mg/kg) was present in samples from Slanchev Bryag, together with high values of Fe. The highest concentration of zinc (Zn) (18.04 mg/kg) together with high values of Cr and Fe were measured in wedge clams from Irakli. In conclusion, the wedge clams from the localities known to have higher coastal inflows and touristic pressures, i.e. Varna, Shkorpilovtzi, Sveti Vlas, Slanchev Bryag and Ahtopol accumulated significantly higher metal elements in their tissues. Only few significant seasonal differences in the concentration of metal elements in wedge clams were present and the observed seasonal variations were probably connected to the hydrological parameters of the ecosystems. The wedge clam D. trunculus is a suitable bioindicator for assessment and monitoring of metal pollution in the Bulgarian Black Sea environment.