This study evaluated metal contamination in surface sediments and macroalgae of mangroves and port complexes on the Brazilian equatorial margin. Samples were collected between August 2020 and February 2021 at seven points in a mangrove swamp under the influence of port activity and at two points without port activity. Metal concentrations in the macroalgae and sediments were determined using inductively coupled plasma optical emission spectrometry. All macroalgal species bioaccumulated metals, as demonstrated by their bioaccumulation factors. The geochemical contamination indices indicated that the estuarine complex was influenced by port activity as moderately contaminated by Pb, Cr, Mn, and Fe, and considerably contaminated by Zn and Cu. The enrichment factor confirmed significant mineral enrichment of Zn and Cu in this environment. The concentrations of the metals in the sediment followed the order Fe > Mn > Cr > Zn > Cu > Pb at most collection points. Cladophoropsis membranacea recorded the highest bioaccumulation values for Pb (0.44), Rhizoclonium africanum for Zn (1.08), Cr (0.55), and Fe (0.30), and Bostrychia radicans for Mn (2.22). The bioaccumulation pattern of metals in the most abundant macroalgal species followed the order Bostrychia radicans (Mn > Zn > Cu > Cr > Pb > Fe) and Rhizoclonium africanum (Zn > Mn > Cr > Cu > Pb > Fe).
Research Article
Evaluation of metal contamination in surface sediments and macroalgae in mangrove and port complex ecosystems on the Brazilian Equatorial Margin
https://doi.org/10.21203/rs.3.rs-1505643/v1
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Brazilian Equatorial Margin
mangrove ecosystem
surface sediment contamination
metal accumulation
Globally, coastal regions undergo significant environmental stresses and are under pressure from various forms of anthropogenic activity. In this century alone, 80% of human activity has been concentrated in coastal zones (GRUBER et al. 2003). Intense urbanization, tourism, port use, and industrial activity are examples of large-scale human interventions within coastal regions.
Most mangrove extensions of the South American Eastern margin (approximately 80%) occur along the Brazilian coast (Ferreira and Lacerda 2016). Mangroves cover large areas in Brazil, with a patchy distribution along the 6,800 km coastline. The most extensive mangrove areas are in the north—Amapá State, Pará State, and Maranhão State—with wide tidal ranges and high levels of rainfall, which encourage their development.
As this ecosystem preferentially develops in coastal areas, mangroves continuously undergo numerous interventions. According to Neumann et al. (2015), high rates of population growth and urbanization in coastal regions are likely to persist. This rapid and disorganized urban expansion has a significant impact on mangroves.
Among anthropogenic impacts, we highlight the release of domestic and industrial sewage, mining, indiscriminate use of agricultural fertilizers and pesticides, and harbor activities. These activities are potentially harmful to estuaries and, consequently, mangroves and the organisms that depend on them. Metals are the most abundant contaminants resulting from these activities.
Metals can enter the aquatic environment in the following forms: dissolved (in the water column and interstitial water of the sediments), particulate (linked to the suspended material and bottom sediment) (Yahya, Mohamed and Mohamed 2018), and biotic absorption.
Non-degradable substances, such as metals, are observed at the same time toxicity, persistence, and bioaccumulation in the food chain (Marcovecchio 2000; Marins et al. 2002), which makes it relevant to study the absorption of such elements by primary producers, which are the basis of the trophic chain, such as macroalgae.
Macroalgae are benthic photosynthetic organisms that are closely related to nutrient cycling in the environment where they occur. They act as the basis of the food chain because they are primary producers that play a fundamental role in ecological structure, functioning, and balance while serving as an essential renewable resource within marine ecosystems (Valentin 2010). In mangroves, macroalgae attach themselves to various substrates such as roots, pneumatophores, dead branches, trunks, and muddy surfaces (Dawes et al. 1999).
The use of macroalgae as biomonitoring organisms has been increasing in metal contamination assessments (Billah et al. 2017; Taylor et al. 2017; Reis et al. 2016; Chakraborty et al. 2014). These organisms provide key information due to their high capacity to accumulate toxic substances, which allows their use as a tool for quantitatively assessing their environment. Therefore, they are called biological sponges, whose substance capture is controlled by two factors (Baoli and Congqiang 2004). The first factor is the environment and is influenced by metal concentrations. The second is the biological factor and is influenced by macroalgal affinity for metals.
Such metals can enter these organisms in two ways: adsorption on their extracellular walls (biosorption) or absorption inside the cell (bioaccumulation) (Veglio and Beolchini 1997). Studies have shown that the effect of each metal on algae depends on factors such as the type of metal, concentration, and exposure time (Mamboya et al. 1999) in addition to the chelation and detoxification levels of each alga (Lombardi and Vieira 1998).
In Brazil, studies of macroalgae as biomonitors of metal contamination have been conducted in Rio de Janeiro (Lacerda et al. 1985; Karez et al. 1994; Amado Filho et al. 1999; Machado et al. 2003), Pernambuco (Macedo et al. 2009) and Bahia (Brito et al. 2012). Overall, few studies have reported biomonitoring of metal contamination using macroalgae from mangrove ecosystems. However, the accumulation patterns of metals in the mangroves of the Brazilian equatorial margin and port areas have not yet been determined.
Therefore, studying estuarine environments and mangroves on the equatorial margin of Brazil using sediment and macroalgae is of great importance because the region has the longest continuous band of mangroves in the country. This ecosystem partially includes one of the most important port areas on the north coast of Brazil, which has been recognized for the intensive movement of iron and manganese minerals that occurs in the region.
Port activities are potential sources of contamination in coastal ecosystems (Jahan and Strezov 2019; Buruaem et al. 2012; Shafie et al. 2012; Hortellani and Sarkis, 2008). The contamination that occurs around ports can compromise the ecosystem services provided by mangroves as the organisms that inhabit this environment are food and income sources for many communities around the world.
The present study focused on (1) determining the metal levels (Cu, Zn, Fe, and Mn) in sediment in the study area; (2) investigating the patterns of metal accumulation in the dominant mangrove macroalgal species associated with pneumatophores and tree trunks of mangroves; and (3) determining the geo-accumulation index (Igeo), contamination factor (CF), enrichment factor (EF), and pollution load index (PLI). An assessment of metal concentrations in this region is necessary to identify the vulnerability of these ecosystems to pollution and compile baseline data for future monitoring.
Study area
The study area comprises two large estuarine systems on the Brazilian equatorial margin with recognized importance for the region: the São Marcos estuarine complex (SMEC) and the São José complex (SJEC).
The Itaqui Port Complex (IPC) is located at the SMEC, which has been internationally recognized for its operational capacity. The IPC comprises the Itaqui Organized Terminal (IOT), Ponta da Espera Maritime Terminal (PEMT), and Alumar Private Use Terminal (PUT Alumar). Intensive movement of solid mineral bulk consisting of iron and manganese ores occurs in the PEMT (MTPA, 2018). PUT Alumar is responsible for handling bauxite and alumina loads (MTPA, 2018). The processing of bauxite generates as a by-product the so-called red mud consisting of iron, aluminum, sodium, and calcium oxides, as well as titanium dioxide and metals such as gallium, vanadium, and rare earth.
SJEC was marked by intense urbanization, mainly at points 09 and 08. However, there was little to no industrial activity, especially in the absence of port activity.
Due to the strong fluvial input in both estuarine complexes, the waters are characteristically turbid, with high concentrations of particulate matter, mainly clay and silt, originating from continental and mangrove areas (Silva, 2011).
The region's climate is tropical, marked by well-defined periods of rain and drought, with an average annual rainfall of 1,896 mm/year and an average temperature of 27 °C (https://www.climatempo.com.br). Thus, it is part of the context of humid tropical regions between 15°N and 15°S latitude, whose characteristics are high and constant precipitation (> 1,500 mm/year), temperatures > 20°, and low thermal variation (Nittrouer et al., 1995).
Because of these characteristics, the study area is characterized by the presence of structurally complex mangroves (Rebelo-Mochel 1997), the most abundant of which are Rhizophora mangle Linnaeus (1753), Avicennia germinans (L.) Stearn (1958), and Laguncularia racemosa (L.) CF Gaertn.
Sediment and macroalgae sampling
Sampling was conducted between August 2020 and February 2021 at nine sampling points: P01 to P07 in SMEC and P08 and P09 in SJEC (Fig. 1).
At all points, the in-situ water temperature, pH, salinity, total dissolved solids (TDS), and dissolved oxygen (DO) were determined using a multiparametric probe model HANNA HI 98194.
The intertidal surface sediments (top 5 cm) were collected using a plastic scoop at each sampling point. Three replicates of each sample were conducted in each plot of the study area (n = 54; considering two sample surveys). The sediment samples were dried in the laboratory at 60oC for 48 h and ground for further treatment.
Macroalgae were collected from the trunks and pneumatophores in the intertidal zone at each sampling point. Five pneumatophores were randomly harvested from a single sampling plot and cut in the mudline using a clipper (Billah et al. 2017). Therefore, 90 pneumatophores (45 pneumatophores × 2 samples) were harvested. In the laboratory, the samples were carefully washed to remove adhered particles and identified based on marine macroalgae literature (JHA et al. 2009).
Determination of metal concentrations in macroalgae
In the laboratory, the algae samples were lyophilized at -20 °C for 48 h, crushed, and homogenized for the additional procedures. Extraction of inorganic elements from macroalgae was performed using 0.750 g (dry weight) of the crushed sample. Initially, the lyophilized samples were placed in Teflon tubes (X-press) to which concentrated HNO3 (8 ml) and concentrated HF (2 mL) were added.
The extracts were allowed to rest overnight at room temperature and then placed in a microwave, model Mars X-press (CEM), for 40 min (15 min –ramp and 25 min–wait) at a temperature of 175 °C and power of 1600 W, adapted from EPA 3052. After cooling (30 min), H3BO3 (12 mL) was added to neutralize the HF, and then the tubes were taken back to the microwave for 25 min (15 min, ramp and 10 min, hold) at 170 °C.
The final cooled extract was filtered (Whatman No. 40) and measured at a final volume of 25 mL with 0.5 N HNO3 in a volumetric flask. The elements were determined using inductively coupled plasma optical emission spectrometry (IPC-OES 720). Precision measurements were obtained from the digestion of macroalgae and sediment in triplicate every 10 samples, and accuracy was determined from certified macroalgae and sediment standards Apple Leaves NIST SRM 1515. Recovery rates for metals in the macroalgae were 106% for Zn, 92.7% for Cu, 99.6% for Cr and 104.5% for Fe.
Elementary extraction and determination of metals in sediment
Initially, dried and powdered sediment (0.5 g) was placed in Teflon tubes (X-press) to which HNO3 (9 ml), HF (4 ml), and HCl (2 ml) were added. The extracts were allowed at rest overnight at room temperature and then placed in a microwave, model Mars X-press (CEM), for 40 min (15 min - Ramp and 25 min - waiting) at a temperature of 175 °C and power of 1600 W. These adjustments were adapted from a method described by the United States Environmental Protection Agency (EPA 3052). After cooling (30 min), H3BO3 (25 mL) was added to neutralize the HF, and the tubes returned to the microwave where they remained for 25 min (15 min, ramp and 10 min, hold) at 170 °C. After cooling for 30 min, the final extract was filtered (Whatman No. 40) and measured at a final volume of 50 mL with 0.5 N HNO3 in a volumetric flask. The determination of metals was performed later in the IPC-OES 720 equipment, and NIST 1646a was used as the standard. Recovery rates for metals in the sediment were 98% for Zn, 92% for Cu, 111% for Pb, 95% for Cr, 94% for Mn, and 95% for Fe. All analyses were performed in triplicate.
Granulometric analysis and Pejrup diagram
An interval-programmed Sald-3101 Shimadzu laser diffraction particle analyzer (Table 1) was used to determine the particle size using an aliquot of 2 g of the crude sample.
Table 1 Ranges of granulometric values according to the Wentworth classification (1922)
|
Phi |
Wentworth Classification |
Diameter |
|
0 |
very coarse sand |
2mm - 1mm |
|
1 |
coarse sand |
1mm - 500µm |
|
2 |
medium sand |
500µm - 250µm |
|
3 |
thin sand |
250 µm - 125µm |
|
4 |
very thin sand |
125µm-63µm |
|
5 |
coarse silt |
63µm - 31µm |
|
6 |
medium silt |
31µm - 16µm |
|
7 |
fine silt |
16µm - 8µm |
|
8 |
very fine silt |
8µm - 4µm |
|
9 |
clay |
< 4µm |
The particle size distribution was treated using the SYSGRAN program (version 3.0), producing the following parameters: mean, median, standard deviation, asymmetry, kurtosis, normalized kurtosis, Wentworth classification, selection degree, and kurtosis classification. The statistical parameters of the particle size distributions were calculated from the particle size diameters (fi, mean, median, standard deviation, asymmetry, and kurtosis) using the equations proposed by Folk and Ward (1957).
The values obtained from the particle size analysis were used to construct a Pejrup diagram (1988). This diagram indicates the hydrodynamic conditions of the environment based on the sand, silt, and clay content present in the sampled sediment. A higher percentage of grains in the fine fraction indicates calmer hydrodynamic conditions.
The triangular diagram has four hydrodynamic sections (I to IV) that indicate hydrodynamic conditions: low (I), moderate (II), high (III), and very high (IV). In addition, constant sand content lines are suitable for textural classification. Thus, the triangle was divided into four texture classes, indicating a decrease in the sand content in the sediment. The sand percentages ranged from A to D, with A (90–100%), B (50–90%), C (10–50%), and D (0–100%).
Geo-accumulation index (Igeo), contamination factor (CF), enrichment factor (EF), and pollution load index (PLI)
The geochemical background values for Zn, Cu, Pb, Cr, Mn, and Fe proposed by De Paula Filho et al. (2015) in a study carried out in the Parnaíba Delta were used to calculate the indices. The São Luís Island and Parnaíba sedimentary basins are classified in the same morphological unit type, known as the Barreiras do Tertiary Group (CPRM 2000).
Fe was adopted as a reference element because of its similarity to other trace metals, uniform natural concentration, and its characteristic association with thin solid surfaces (Varol 2011). Thus, it was considered suitable as a normalizing element for the evaluated indices.
Geo-accumulation index (Igeo)
This index quantifies the metal pollution in aquatic sediments (Muller, 1979) using the following formula:
Igeo = Log2 CN/1,5BN
where CN is the concentration of the “N” element measured in the sediment, 1.5 is a correction factor for the background matrix that includes any possible variations in values due to lithogenic effects (Muller 1979), and BN is the background value of the element (Zhiyuan et al. 2011; Ozkan 2012). Below, seven descriptive classes were proposed for the values obtained from the geo-accumulation index (Table 2).
Table 2 Geo-Accumulation Index (Igeo) classes and values for sediment quality
|
Igeo Value |
Igeo Class |
Sediment Quality |
|
0 |
0 |
Uncontaminated |
|
0 – 1 |
1 |
Uncontaminated to Moderately Contaminated |
|
1 – 2 |
2 |
Moderately Contaminated |
|
2 – 3 |
3 |
Moderately to strong contaminated |
|
3 – 4 |
4 |
Strongly contaminated |
|
4 – 5 |
5 |
Strongly to Extremely contaminated |
|
> 5 |
6 |
Extremely contaminated |
Contamination factor (CF)
Hakanson (1980) proposed contamination factor (CF) as a marker to assess the level of metal concentrations in soils. CF corresponds to the ratio between the concentration of metals measured in the sediments and the background value of the metal at a given location (Turekian and Wedepohl, 1961).
Where for the “N” element, CFN is the ratio between the concentration of the element in the sediment (CN) and the background value (BN). Using the classification of Hakanson (1980), we obtain:
FC < 1: Low Contamination
1 ≤ FC < 3: Moderate Contamination
3 ≤ FC < 6: Considerable Contamination
FC ≥ 6: High Contamination
Enrichment factor (EF)
The enrichment factor (EF) corresponds to the standardization of a tested element with a reference element (Fe) that has low occurrence variability. The most common reference elements are Sc, Mn, Ti, and Al (Reimann and De Caritat, 2000; Sutherland, 2000).
Ms and Mb are the concentration of a given metal in the sediment and its background value, respectively. Fes and Feb are the concentration in the sediment and the background value of the reference metal Fe, respectively. The Enrichment Factor has seven categories (Taylor 1964) which are interpreted as:
EF < 1: No Mineral Enrichment;
EF = 1 – 3: Minimum Enrichment
EF = 3 – 5: Moderate Enrichment
EF = 5 – 10: Significant Enrichment;
EF = 10 – 25: Severe Enrichment;
EF = 25 – 50: Very Severe Enrichment;
EF > 50: Extreme enrichment.
EF values ranging between 0.5 and 1.5 suggest the contribution of the crust as a metal source, while values > 1.5 indicate anthropogenic influence (Nowrouzi and Pourkhabbaz 2014).
Pollution load index (PLI)
The Pollution Load Index (PLI) is obtained by multiplying the previously calculated contamination factors (CFs) and subsequent extraction of the root, according to the following equation:
Where: PLI < 1 No Polluted
PLI = 0 Normal levels
PLI > 1 Polluted Environment
Bioconcentration factor (BCF)
Bioconcentration is the accumulation of contaminants in aquatic organisms through non-dietary absorption pathways, for example, from the soluble phase. The bioconcentration factor has been used to assess the potential for metal bioaccumulation (Conti and Cecchetti 2003; Akcali and Kucuksezgin 2011). The calculation corresponds to the ratio of the metal concentration in the macroalgae to that in the sediment (BCFs) according to the following formula:
Threshold effect level (TEL), probable effect level (PEL), effects range low (ERL), and effects range medium (ERM)
TEL and PEL are interpretive toxicity criteria for marine and estuarine sediments that were established by Canadian legislation based on compiled biological and chemical data from laboratory studies, field measurements, and numerous models. TEL indicates the level below which there are no adverse effects on the biological community, and PEL is the level above which adverse effects are expected. The range above the TEL and below the PEL represents a possible adverse effect on the community (Hortellani et al. 2008; De Paula Filho et al. 2021).
ERL and ERM are American criteria established by Long et al. (1995) using chemical and biological data from field studies in marine and estuarine sediments. The ERL is the concentration limit below which it is rarely toxic. The ERM, in turn, is the limit above which there is a high likelihood of toxicity, and the range between the ERL and ERM indicates possible toxicity (Hortellani et al. 2008; Long et al. 1995). The ERL and ERM limits were adopted as reference values by resolutions 344/2004 and 454/2012 of the National Environmental Council (CONAMA, Brazil) for estuarine and marine sediments. The TEL, PEL, ERL, and ERM limits are not applicable to Al, Fe, and Mn.
Table 3 TEL, PEL, ERL and ERM limits for the metals Cd, Cr, Cu, Hg, Ni, Pb and Zn in estuarine and marine sediments. 1Hortellani et al, 2008; 2Long et al. 1995
|
Elements |
1TEL (μg g-1) |
1PEL (μg g-1) |
2ERL (μg g-1) |
2ERM (μg g-1) |
|
Cd |
0.7 |
4.21 |
1.2 |
9.6 |
|
Cr |
52.3 |
160 |
81 |
370 |
|
Cu |
18.7 |
108 |
34 |
270 |
|
Hg |
0.13 |
0.70 |
0.15 |
0.71 |
|
Ni |
15.9 |
42.8 |
20.9 |
51.6 |
|
Pb |
30.2 |
112 |
46.7 |
218 |
|
Zn |
124 |
271 |
150 |
410 |
Statistical Analysis
The Shapiro-Wilk test was used to verify the normality of all data. ANOVA (One-way) and Kruskal-Wallis tests were used to test for significant differences. Spearman's correlation coefficient was used to understand the paired relationships between the measured variables. For all tests, values of p < 0.05 and p < 0.01 were considered to indicate statistical significance. Analyses were performed using SPSS v26 software.
Physical and Chemical Variables
The physical and chemical data obtained during sampling are summarized in Table 4. Water temperature, salinity, and dissolved oxygen showed significant differences between the collection points (ANOVA, p < 0.05) and TDS (Kruskal-Wallis, p < 0.05).
Table 4 Physical and chemical variables of the sampling points (mean + standard deviation). Different superscript letters in the same column indicate significant differences between sampling points according to Tukey’s Post-Hoc test (p < 0.05)
|
Points |
Temperature |
Salinity |
DO |
pH |
TDS |
|
P01 |
33.00 + 1.41b |
28.60 + 0.71d |
7.55 + 1.20c,e |
7.55 + 0.49 |
22.35 + 0.49b,d,e,f,g |
|
P02 |
35.60 + 0.14c |
23.80 + 0.85b |
6.10 + 0.28a,e |
7.30 + 0.00 |
18.95 + 0.64a,e |
|
P03 |
35.10 + 0.00c |
28.30 + 0.00d |
9.40 + 0.00d |
8.15 + 0.00 |
22.80 + 0.00d,e,f,g |
|
P04 |
35.05 + 0.21c |
23.63 + 0.10b |
3.46 + 0.01b |
7.80 + 0.14 |
18.92 + 0.03a,d |
|
P05 |
30.25 + 0.35a |
25.99 + 0.01c |
6.29 + 0.02a,c |
7.78 + 0.00 |
20.30 + 0.00a,f |
|
P06 |
31.76 + 0.11a,b |
23.09 + 0.04b |
2.27 + 0.01b |
7.85 + 0.21 |
18.40 + 0.02a,b |
|
P07 |
31.00 + 0.28a |
17.90 + 0.99a |
5.80 + 0.14a |
7.75 + 0.21 |
14.65 + 0.78a |
|
P08 |
30.00 + 0.00a,d |
26.00 + 0.00c |
1.55 + 0.01b,f |
6.26 + 0.01 |
45.20 + 0.00f,g |
|
P09 |
28.14 + 0.06d |
26.85 + 0.07c,d |
0.47 + 0.02f |
7.70 + 0.00 |
21.10 + 0.00a,g |
The lowest and highest value mean water temperatures were observed in P09 and P02, at 28.14 and 35.60 °C, respectively. P02 (35.60 + 0.14 °C), P03 (35.10 + 0.00 °C), and P04 (35.05 + 0.21 °C) were similar to each other, however, different from all others sampling points (ANOVA, p < 0.05).
The mean salinity ranged from 17.9 and 28.6, and the lowest values were found at the collection points belonging to the SMEC. P07 (17.9) showed statistically significant differences compared to all other sampling points (ANOVA, p < 0.05).
Macroalgae
In the studied area, 03 species belonging to the Chlorophyceae group, Cladophoropsis membranacea Børgesen,1905, Rhizoclonium africanum Kutzing, 1853, and Rhizoclonium tortuosum Kutzing,1845 and 02 belonging to the Rhodophyceae group, Bostrychia radicans Montagne, 1842 and Catenella caespitosa Parke and Dixon, 1976, were identified. R.africanum was present at all sampling points, except in P05. The second most common species found was B. radicans, absent only in P02 and P05. C.caespitosa occurred in P1, P4, P6, P8, and P9, C. membranacea in P1, P3, P4, P5, and P6, while R.tortuosum was found only in P08.
Granulometric analysis and Pejrup diagram
The granulometric analysis recorded mean Phi values between 2.84 and 8.28, showing that the local particle size ranged from medium sand to very fine silt. Sedimentary grains ranged from very poorly to moderately selected, characterizing a variation in particle size between collection points (Table 5).
Most sampling points presented platykurtic or mesokurtic kurtosis with heterogenous value distribution, except for P05 and P08, which recorded leptokurtic kurtosis; these data were concentrated around the mean (Table 5).
Table 5 Particle size analysis of sampled points in the mangroves of São Luís Island, MA
|
Points |
Medio Phi |
Wentworth Classification |
Selection Degree |
Kurtosis |
|
P01 |
2.84 |
Medium Sand |
Poorly selected |
Platykurtics |
|
P02 |
6.55 |
Medium Silt |
Poorly selected |
Platykurtics |
|
P03 |
6.36 |
Medium Silt |
Moderately selected |
Mesokurtics |
|
P04 |
4.21 |
Very Fine Sand |
Very Poorly selected |
Platykurtics |
|
P05 |
8.28 |
Very Fine Silt |
Moderately selected |
Leptokurtics |
|
P06 |
8.10 |
Very Fine Silt |
Moderately selected |
Mesokurtics |
|
P07 |
8.25 |
Very Fine Silt |
Moderately selected |
Mesokurtics |
|
P08 |
3.72 |
Sand Fine |
Poorly selected |
Leptokurtics |
|
P09 |
4.32 |
Very Fine Sand |
Very Poorly selected |
Platykurtics |
Only points P01 and P05 showed significant differences in the particle size (ANOVA, p = 0.05). There was no significant correlation between the metals in the sediment and the particle size.
The Pejrup diagram shows that most points (P03, P04, P05, P06, P07, and P09) were associated with moderate hydrodynamic conditions. Among them, only P04 and P09 showed a predominance of sand deposited under moderate hydrodynamic conditions (groups B and II). P03, P05, P06, and P07 showed a predominance of fine grains deposited under the same hydrodynamic conditions (Groups C, II, D, and II).
P01 was associated with the predominance of sand deposited under low hydrodynamic conditions (Groups B and I). P02 and P08 were the only ones indicated under high hydrodynamics (groups C and III, and B and III, respectively) (Fig. 2).
Metal concentration in sediments
The concentrations of Zn, Cu, Pb, Cr, Mn, and Fe in the surface sediments at the nine sampled points are summarized in Table 6. Based on the mean values, the concentration pattern followed the order Fe > Mn > Cr > Zn > Cu > Pb at all collection points except for P07, whose pattern followed the order Fe > Mn > Zn > Cr > Cu > Pb.
Table 6 Metal concentrations (µg/g) in the surface sediment of the collection points (mean + standard deviation). 1Regional background mean values (De Paula Filho et al. 2015); 2World average values of shale metals (Turekian and Wedepohl 1961)
|
Points |
Zn |
Cu |
Pb |
Cr |
Mn |
Fe |
|
BG1 |
13.40 |
6.80 |
5.90 |
18.00 |
633.00 |
14,000.00 |
|
Global2 |
95.00 |
45.00 |
20.00 |
90.00 |
850.00 |
47,200.00 |
|
P01 |
14.99 + 9.13 |
14.29 + 3.87 |
4.20 + 1.49 |
17.82 + 12.29 |
86.84 + 58.43 |
15,575.97 + 10,611.76 |
|
P02 |
32.84 + 4.09 |
21.05 + 2.27 |
8.78 + 0.05 |
40.26 + 4.56 |
152.65 + 42.81 |
33,507.13 + 3,391.63 |
|
P03 |
57.09 + 55.71 |
16.88 + 7.38 |
13.39 + 2.40 |
39.77 + 32.27 |
233.30 + 193.82 |
14,658.37 + 11,901.80 |
|
P04 |
31.01 + 5.95 |
19.91 + 1.77 |
9.00 + 1.33 |
36.44 + 6.11 |
151.86 + 34.18 |
28,437.13 + 4,911.12 |
|
P05 |
14.16 + 6.47 |
14.13 + 3.53 |
4.50 + 0.33 |
18.02 + 11.14 |
116.28 + 64.53 |
10,953.14 + 6,962.80 |
|
P06 |
28.98 + 12.71 |
17.31 + 5.80 |
6.97 + 3.45 |
33.76 + 12.03 |
153.58 + 53.00 |
34,950.35 + 5,493.24 |
|
P07 |
12.07 + 6.91 |
10.10 + 1.38 |
2.52 + 0.00 |
11.06 + 6.17 |
19.53 + 19.49 |
12,300.30 + 4,437.28 |
|
P08 |
23.93 + 13.74 |
17.38 + 4.06 |
5.97 + 1.18 |
26.80 + 16.06 |
118.98 + 60.23 |
23,323.01 + 13,289.23 |
|
P09 |
15.17 + 8.47 |
13.13 + 3.78 |
3.81 + 0.97 |
15.73 + 10.76 |
70.14 + 38.50 |
11,957.34 + 7,209.23 |
The concentrations obtained presented a normal distribution and homogeneity of variances only for Fe (Shapiro-Wilk, p > 0.05; Levene, p > 0.05). Fe was also the only element that showed a significant difference between P05 and P06 (ANOVA, p < 0.05). The other metals did not show significant differences between collection points.
The highest concentrations of metals were recorded in the sediments of the SMEC port area, with concentrations exceeding the regional background values (Table 6). The lowest and highest mean values of Zn were recorded in P07 (12.07 + 6.91 µg/g) and P03 (57.09 + 55.71 µg/g), respectively. The furthest point from the port area, P07, recorded the lowest mean values for Cu (10.10 + 1.38 µg/g), Pb (2.52 + 0.00 µg/g), Cr (11.06 + 6.17 µg/g) and Mn (19.53 + 19.49 µg/g). The highest mean values of Cu, Cr, Pb, and Mn were recorded in the port areas, P02 (Cu = 21.05 + 2.27 and Cr = 40.26 + 4.56 µg/g) and P03 (Pb = 13. 39 + 2.40 and Mn = 233.30 + 193.82 µg/g).
All registered values of the sampling points were lower than those of TEL, PEL, ERL, and ERM (Fig. 3). Cu was an exception, as its concentrations were higher than the TEL in P02 (Cu = 21.05) and P04 (Cu = 19.91). All points registered concentrations below those established by the national legislation contained in Resolutions 344/2004 and 454/2012 of the National Council for the Environment (CONAMA, Brazil) for estuarine and marine sediments, whose guidelines are based on ERL and ERM values.
Metal concentration in macroalgae
The concentrations of Zn, Cu, Pb, Cr, Mn, and Fe in the macroalgae between the sampling points are summarized in Table 7.
The highest (48.03 µg/g) and lowest (0.37 µg/g) Zn values were detected in B. radicans rhodophyte at P01 and P09, respectively. The species R. africanum and C. caespitosa had the highest (10.72 µg/g) and lowest (1.30 µg/g) Cu values, recorded in P03 and P04, respectively.
The highest concentration (5.61 µg/g) of Pb was recorded in P04, and the lowest (1.00 µg/g) in P06 in the macroalgae C. membranacea. Cr had the lowest concentration detected in the species C. caespitosa (2.06 µg/g) and the highest in the species R. africanum (19.90 µg/g), also recorded in P04 and P06, respectively (Table 7).
The concentrations of Mn and Fe were higher in all species than those of the other quantified elements (ANOVA, p < 0.05). Mn showed a high variation between the highest (825.86 µg/g) and the lowest (13.56 µg/g) values, detected in C. membranacea (P05) and R. africanum (P04). Fe also showed a high rate of variation between the highest (8060.56 µg/g) and lowest (2.26 µg/g) values, detected in C. membranacea (P04) and C. caespitosa (P09) (Table 7).
Table 7 Metal concentration (µg/g) in macroalgae by collection point, in the mangroves of São Luis Island, Maranhão
|
Points |
Macroalgae |
Zn |
Cu |
Pb |
Cr |
Mn |
Fe |
|
P01 |
B. radicans |
48.03 |
8.51 |
2.91 |
15.64 |
460.74 |
4772.68 |
|
C. caespitosa |
28.89 |
1.63 |
N/D |
2.14 |
64.83 |
549.02 |
|
|
C. membranacea |
11.64 |
1.89 |
2.84 |
9.60 |
119.12 |
3748.82 |
|
|
R. africanun |
36.02 |
7.56 |
2.97 |
19.00 |
125.17 |
5501.22 |
|
|
P03 |
R. africanun |
38.81 |
10.72 |
3.53 |
18.97 |
125.86 |
5653.26 |
|
B. radicans |
27.69 |
8.84 |
1.73 |
8.64 |
286.53 |
2761.03 |
|
|
C. membranacea |
21.54 |
8.61 |
4.28 |
15.00 |
272.28 |
6279.84 |
|
|
P04 |
B. radicans |
26.36 |
6.41 |
1.49 |
10.44 |
162,76 |
3093.81 |
|
C. caespitosa |
12.04 |
1.30 |
N/D |
2,06 |
71,23 |
683.93 |
|
|
C. membranacea |
28.95 |
8.14 |
5,61 |
19.17 |
299.66 |
8060.56 |
|
|
R. africanun |
5.18 |
1.49 |
1.58 |
2.81 |
13.56 |
7248.34 |
|
|
P05 |
C. membranacea |
23.60 |
8.74 |
3.38 |
16.71 |
825.86 |
5648.55 |
|
P06 |
R. africanun |
31.08 |
8.32 |
2.27 |
19.90 |
109.08 |
6604.72 |
|
C. membranacea |
21.46 |
6.09 |
1.00 |
16.68 |
467.77 |
7231.25 |
|
|
C. caespitosa |
16.26 |
4.82 |
N/D |
3.52 |
107.41 |
975.58 |
|
|
B. radicans |
20.15 |
5,92 |
1.64 |
11.08 |
195.82 |
3722.59 |
Bioaccumulation Factor in Macroalgae
Figure 4 shows the bioaccumulation factor (BCF), indicating that almost all macroalgae collected in this study were susceptible to bioaccumulation. B. radicans recorded the highest bioaccumulation value for Mn (2.22) and the lowest for Pb (0.31). C. caespitosa, also Rhodophyceae, showed low bioaccumulation values compared to the other species (Cu = 0.15, Pb = 0.001, Cr = 0.09, Mn = 0.64, and Fe = 0.03).
C. caespitosa was significantly different from the chlorophytes C. membranacea and R. africanum (ANOVA, p < 0.05). The BCF for R. tortuosum was impossible to calculate because obtained values did not exceed the detection level.
The pattern of metal bioaccumulation in the macroalgal species was in the following order: Bostrychia radicans (Mn > Zn > Cu > Cr > Pb > Fe), Catenella caespitosa (Zn > Mn > Cu > Cr > Fe > Pb), Cladophoropsis membranacea (Mn > Zn > Cr > Pb > Cu > Fe), and Rhizoclonium africanum (Zn > Mn > Cr > Cu > Pb > Fe).
Geo-accumulation index (Igeo)
The Geoaccumulation index (Igeo) was calculated to quantify metal pollution in the sediment, the values of which are shown in Figure 5. In general, the sampled points presented an Igeo below two, which characterizes the environment as moderately contaminated to non-contaminated.
The points closest to the port areas (P01, P02, P03, and P04) showed moderate contamination (Igeo = 0 – 1) of two or more metals. The highest Igeo values were identified in P03 and P07 for Zn (Igeo = 1.51) and Cu (Igeo = 1.05). However, Mn was below 0 at all collection points, characterizing them as non-contaminated for this element. Although the Igeo values varied between environments, only Fe showed a significant difference (ANOVA, p < 0.05) between points P05 and P06 (Fig. 5).
As for CF, only Mn showed values below one at all sampled points, indicating low contamination of this metal in mangroves. In general, based on the CF values, the environment tended to be moderately contaminated. P02 and P03 exhibited considerable contamination with Cu (CF = 3.10) and Zn (CF = 4.26), respectively. The other elements followed the general trend of moderate contamination. P02, P03, P04, and P06 recorded PLI values above 1, indicating contamination in these environments. The other points had PLI values below 1, indicating zero contamination (Fig. 6).
PLI and CF did not show significant differences between the collection points (ANOVA, p > 0.05; Kruskal-Wallis, p > 0.05).
To assess the contribution of natural and anthropogenic sources to mineral enrichment at the collection points, an enrichment factor was applied, as summarized in Figure 7. In general, the environment showed a tendency towards minimal mineral enrichment (1 < EF < 3). Zn and Cu presented EF values above five at P03, indicating significant enrichment at this point. The EF values for Cu at all collection points, except P03 and P05, were between 1.5 and 3 (minimum enrichment), showing anthropogenic influence for this metal. P03 was statistically different at all points for all metals (Kruskal-Wallis, p < 0.05).
The values of physical and chemical variables obtained in this study agree with those recorded for both SMEC and SJEC by Cavalcanti and Cutrim (2018) and Serejo et al. (2020).
Although many studies point to salinity, pH, and DO variables as relevant to the mobility of metals in estuarine environments (Karbassi et al. 2014; Karbasi and Heidari 2015; Li et al. 2013; Samani et al. 2014), these parameters showed weak positive correlations, although not significant, with the metal values in the sediments. However, among the parameters measured in this study, temperature was moderately to strongly related to Zn (r = 0.686, p = 0.041), Pb (r = 0.751, p = 0.020), and Cr (r = 0.764, p = 0.017) in the sedimentary compartment. This highlights sediment as a potential source of these metals for interstitial and surrounding waters.
It is known that high temperatures provide a high release of metals from the sediment to the liquid medium, which, consequently, are bioavailable for macroalgae (Li et al. 2013; Schuhmacher et al. 1995; Zhao et al. 2013), because such organisms only accumulate metallic ions dissolved in water (Luoma 1983; Luoma et al. 1982).
Only OD showed a significant correlation with metal concentrations in macroalgae. Such correlations were observed between the OD and Cu (r = 0.675; p < 0.05), Cr (r = 0.694; p < 0.05), and Mn (r = 0.716; p < 0.05). This is the same relationship reported by Macedo et al. (2009), who found that the copper content in rhodophyte species is influenced by dissolved oxygen. Studies that address the relationship between the accumulation of metals in macroalgae and OD are scarce, thus compromising our understanding of this interaction.
Some studies have described the influence of DO on metals adsorbed to the sediment (Atkinson et al. 2007; Huang et al., 2017; Kang et al. 2019; Li et al. 2013; Liu et al. 2019). OD values ranging between 7.0 and 9.0 mg/L promote the release of metals from the sediment to the interstitial water (Li et al. 2013), becoming bioavailable for macroalgae, while OD values lower than 7.0 mg/L provide little or no release of metals to water, depending on the metal (Li et al. 2013).
Conversely, OD values lower than 7.0 mg/L provide little or no release of metals to water, depending on the metal (Li et al. 2013); this primarily occurs if such values are observed due to the intense degradation of organic matter (Gerringa 1991), which may explain the high concentrations of metals in P09. At the time of collection, domestic sewage was discharged directly into the sampling location at P09.
Metals in sediment
The metal concentrations obtained in the sediment samples were compared with the world mean concentrations of metals in shale (Turekian and Wedepohl 1961) and the regional average (De Paula Filho et al. 2015) (Table 6). Regarding the global average, the results of this study were well below; however, above the regional average, sometimes on the order of two to three times higher. Mn was the exception, being below global and regional concentrations, corroborating the assertion of non-contamination of the environment by this metal (Table 6).
The global values proposed by Turekian and Wedepohl (1961) are high and therefore may not be representative of different sedimentary basins. Thus, it is necessary to define the use of background values at local or regional levels, according to the content deposited in the sedimentary basin in pre-industrial times (De Paula Filho et al. 2015; De Paula Filho et al. 2021).
Only Fe showed a significant difference among the sampling points (ANOVA, p > 0.05). However, all metals were significantly strongly and positively correlated with each other, indicating common sources and processes (De Paula Filho et al. 2021). Zn (r = 0.917; p < 0.01), Pb (r = 0.950; p < 0.01), and Cr (r = 0.894; p < 0.01) were strongly correlated with Mn, indicating the probable adsorption of these metals to Mn oxide hydroxides (Chakraborty et al. 2014; Udechukwu et al. 2014). In contrast, Cu (r = 0.808; p < 0.01) was more strongly correlated with Fe, indicating the probable adsorption of Cu to Fe oxide hydroxides (Chakraborty et al. 2014).
Mn manifested itself as a metal carrier for the sediment in the estuarine complexes of the Brazilian equatorial margin, probably because of its high oxidation resistance and subsequent flocculation, remaining longer in the water column and thus adsorbing a greater number of metals onto Fe. Mn tends to deposit in the form of oxyhydroxide only when oxygen-rich marine waters enter the estuary (Lacerda et al. 1999).
Santos et al. (2019) verified that significant differences were observed between Mn, Fe, and Zn in SMEC, suggesting different origins and carriers, and Fe and Mn showed strong correlations with each other. Such results are contrary to those of this study, which suggests the relevant capacity of the system to behave in different ways depending on the level of contamination to which it is exposed. However, both studies point to similar behaviors for Cu, Pb, and Cr.
Geochemical indices
Geochemical indices are essential for analyzing contamination at sampling points. Although metals have been shown to have values higher than the regional background at some points, only the indices showed specific information regarding the degree of contamination. In general, the indices indicated moderate contamination of the studied environments.
Igeo ranged between 0 and 2, particularly at points associated with the adjacencies of port areas. P02, P03, P04, and P06 recorded ICP values above 1, indicating contamination in these environments. FC indicated that P02 and P03 were considerably contaminated with Cu (FC = 3.10) and Zn (FC = 4.26), respectively. As for mineral enrichment, only P02 and P06 did not show an anthropogenic influence on metal concentrations (FE < 1.5). P03 showed significant enrichment for Zn and Cu.
Several studies have evaluated metal contamination in port areas worldwide, highlighting these areas as potential sources of Zn, Cu, Cr, and Pb (Buruaem et al. 2012; Hortellani and Sarkis 2008; Jahan and Strezov 2019; Sari et al. 2014; Shafie et al. 2012). The results for the port area of the Brazilian equatorial margin were not different because, in general, the indices showed Zn, Cu, and Cr as the principal contaminants of the sampled environments, originating from anthropogenic sources, particularly Cu.
In port areas, metals such as Pb and Cr can originate from erosion of ship hulls. In turn, Cu can be released by the antifouling paint used on boats and ships (Santos et al. 2019; Shafie et al. 2012).
The strong positive and significant correlations between all sampled points (r > 0.9; p < 0.01) suggest that the estuarine complexes of Maranhão, CESM and CESJ, share similar sources and processes responsible for controlling the mobility and bioavailability of metallic elements. However, although there are similarities, the points located in the CESM seem to suffer from high interference in terms of sources and processes. For example, P02, P03, and P04 exceeded two to three times the regional background values (Table 6), demonstrating relevant levels of contamination, as evidenced by the different geochemical indices. The gradient of metal concentrations observed from P01 to P05 must be associated with the local hydrodynamics, which should trap metals in this region.
Local hydrodynamics seem to contribute to the imprisonment of metals between these points, as there was a gradient in metal concentrations from P01 to P05. Points P02, P03, and P04 are in places with low depth (< 20 m) and currents with speeds of less than 1.1 m/s (González-Gorbeña et al. 2015), which can favor flocculation and deposition processes at such points. In SMEC, high concentrations of metals in low-energy zones have already been reported by Santos et al. (2019), who pointed out that in these zones, metals have more binding capacity to fine sediments and organic material, furthering the flocculation process. This effect was evident when comparing the metal concentrations in the sediment and the results of the Pejrup diagram. P02, P03, and P04 presented low-to-moderate hydrodynamics and the highest concentrations of metals in the sediment (Fig. 2; Table 6).
TEL, PEL, ERL, and ERM
Compared with the TEL, PEL, ERL, and ERM values, the metal concentrations between the points were well below the established limits, except for the Cu concentrations in P02 and P04, which were above the TEL and below the PEL (Fig. 3).
This range between the TEL and PEL draws attention to possible adverse effects on the biological community (Hortellani et al. 2008; De Paula Filho et al. 2021). Therefore, although Cu is an essential element, it can be toxic to aquatic organisms, especially those that live or are in direct contact with sediment when their values are within this range (CCME 1999).
In Brazil, ERL and ERM limits were adopted as reference values by Resolutions 344/2004 and 454/2012 of the National Council for the Environment (CONAMA, Brazil) for estuarine and marine sediments. Therefore, the sampled points comply with national legislation.
De Paula Filho et al. (2021) observed this Cu performance in the Parnaíba Delta, a region located on the Brazilian equatorial margin, whose concentrations exceeded the TEL at 61% of the sampling sites, mainly in areas with more sediment deposition.
Metals in macroalgae
The macroalgae recorded at the sampling points are typical of Brazilian mangrove areas (Fernandes and Alves 2011; Oliveira-Filho 1984; Yokoya et al. 1999).
The lack of rocky substrate, low salinity, and high turbidity of coastal waters make the island of São Luís an environment with low diversity of macroalgae (Oliveira-Filho 1984). These conditions favor the dominance of species adapted to mangrove ecosystems. Macroalgal species grow epiphytically in pneumatophores, rhizophores, and trunks (Phillips et al. 1994; Phillips et al. 1996), as is the case with the species found in this study.
The correlation values between the concentrations of metals in macroalgae and sediments ranged from 0.829 to 1 (p < 0.05), which showed strong significant correlations and demonstrated that macroalgal bioaccumulation is strongly associated with the concentrations of metals found in sediments.
Bioaccumulation factor (BCF)
All macroalgae bioaccumulate in different orders and magnitudes according to their BCF. Although macroalgae share the same habitat and conditions, they exhibit particularities in metal accumulation, such as life span, morphology, contact surface area, growth rate, and selective metal affinities (Chakraborty et al. 2014; Schintu et al. 2010). Among the essential metals, absorption followed the order Mn > Zn > Cu > Fe for B. radicans and C. membranacea, whereas C. caespitosa and R. africanum followed the order Zn > Mn > Cu > Fe.
Among the non-essential metals, the absorption process followed the order of Cr > Pb for both groups of macroalgae. Chlorophyceae biosorbed Cr and Pb (non-essential) at higher concentrations than essential metals, such as Cu and Fe.
The biosorption process occurs in macroalgae because cell wall polysaccharides have functional groups (hydroxyl, sulfate, and carboxyl) that act as ion exchangers and bind to metallic cations. (Trifan et al. 2015). Once adsorbed to the cell wall, metals can precipitate through the excretion of metabolic products by macroalgae or be transported slowly into the cytoplasm through chemisorption (Veglio and Beolchini 1997). This last process is the most harmful, as metals reaching the cytoplasm and bioaccumulation can cause adverse effects on macroalgae, such as oxidative stress, inhibited photosynthesis and chlorophyll production, and deficient macroalgal growth (Baumann et al. 2009; Gledhill et al. 1997; Pinto et al. 2003).
The bioaccumulation of metals is not restricted to microalgae as primary producers because, once absorbed, these metals can be transferred along the food chain and reach humans (Santos et al. 2019).
Thick macroalgae tend to have lower metal concentrations than filamentous macroalgae (Trifan et al. 2015), which explains the reduced accumulation values in C. caespitosa compared to other species. Chlorophyceous showed a better accumulation response at the sampled points, with high observed concentrations of metals, most of which were non-essential metals. This affinity of chlorophytes with metals can be attributed to their growth in direct contact with the sediment and the surrounding water, especially C. membranacea, which adheres to the muddy substrate and retains large amounts of sediment owing to its cushion-like morphology.
The results obtained in this study are similar to those of other studies conducted on other mangroves. Chlorophytes are excellent biomonitors owing to their high capacity to reflect environmental contamination. Chakraborty et al. (2014) verified the efficiency of Ulva lactuca in accumulating high levels of Cu, Fe, and Zn, and Akcali and Kucuksezgin (2011) reported high levels of Cu, Pb, Zn, and Fe.
During the absorption process, macroalgae tend to control the composition and concentration of essential metals when they are available in the environment at levels that only satisfy the needs of metabolism and growth. When these levels exceed those required by the organism, biological regulation is impaired owing to external forcing. Consequently, macroalgae accumulate high levels of essential metals and concomitantly accumulate these non-essential metals (Baoli and Congqiang 2004). The strong positive and significant correlations between metal concentrations in macroalgae and sediment, accompanied by the high values recorded for the bioaccumulation factor, reinforce this statement.
Mn is an essential metal required by macroalgae, and in this study, it was a relevant carrier of metals such as Zn, Pb, and Cr to the sediment. Mn was the most bioaccumulated metal (Table 9), and we realized that it influenced the bioaccumulation of these metals. Hall and Brown (2002) demonstrated that the association between Mn and Cu results in a high absorption of Cd in macroalgae.
The representativeness of C. membranacea in the bioaccumulation of Mn in relation to other species allows it to be chosen as a key species for monitoring this metal and others associated with it. Compared to Chlorophyceae, the two species of Rhodophyceae proved to be better biomonitors for assessing environmental contamination by Fe (Kruskal-Wallis; p < 0.05). Therefore, the use of the four species of macroalgae is complementary and essential for a broader response to contaminants in mangrove ecosystems.
The metal concentrations recorded in the sediments of both estuarine complexes were below the mean values of the global background but above the regional level. This reinforces what past studies have pointed out regarding the need to carry out local and regional surveys of the concentrations of metals deposited in pre-industrial times, as the high global mean values may not be representative of the different sedimentary basins.
The geochemical indices indicate moderate contamination and enrichment among the studied environments, showing significant enrichment of Zn and Cu in the regions under the influence of port activity. This result reinforces the idea that, although there is contamination by metals in other estuarine areas on the Brazilian equatorial margin, port activity remains a relevant source of contaminants in the estuarine environment.
Local hydrodynamics have a significant influence on the metal deposition process in areas close to harbor regions. Cu concentrations were above the TEL and below the PEL, indicating possible adverse effects on the biological community. The results showed different relationships from those reported in previous studies, indicating different environmental responses to their exposure to contaminants.
The absence of significant differences and strong correlations between metals suggests similar sources and processes for the deposition of these elements in the equatorial margin of Brazil. Mn showed a strong positive correlation with Zn, Pb, and Cr, which proved to be excellent metal carriers in this region.
All macroalgae bioaccumulate metals, demonstrating the biomonitor potential of these organisms. The species showed different patterns of bioaccumulation; however, Mn and Zn were the most bioaccumulated. The divergent behavior in the bioaccumulation pattern among macroalgae suggests that monitoring different species is essential for a more accurate response to environmental contamination by different types of metals.
We suggest complementary studies to verify possible seasonal variations in the bioaccumulation patterns of macroalgae associated with mangroves on the Brazilian equatorial margin, primarily because the dynamics of metal bioaccumulation in the macroalgal species found in this area are poorly investigated in the international scene, this being the main difficulty of this work. In this way, we bring preliminary information about the accumulation of metals regarding the species found in our investigation.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Consent for publication
Not applicable
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Author Contributions
All authors contributed to the study conception and design. JC: Conceptualization, methodology, data collection, analysis, writing-original draft and editing. MC: Conceptualization, methodology, writing-original draft, writing-review and editing, and supervision.
Ethical Approval and consent to participate
Not applicable
Availability of data and materials
The authors certify that all data and materials, as well as the software application or custom code, support their published claims and meet field standards.
Acknowledgments
This research was supported by grand given by Foundation for Research and Scientific and Technological Development of Maranhão – Brazil (FAPEMA) through the concession of a Master’s scholarship (BM-00061/20) to the first author. The authors would like to thank the Postgraduate Program in Oceanography (PPGOceano- UFMA) as well as the Laboratory of Phytoplankton of Federal University of Maranhão (UFMA) and Laboratory of Environmental Science (UENF) for technical and structural support.
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