# Metal retention in two semi-arid mangroves under contrasting environmental pressures

Hélène Plihon, Rozane Marins, Stéphane Mounier

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## To cite this version:

Hélène Plihon, Rozane Marins, Stéphane Mounier. Metal retention in two semi-arid mangroves under contrasting environmental pressures. Estuarine, Coastal and Shelf Science, 2025, 326, pp.109564. 10.1016/j.ecss.2025.109564. hal-05331561

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# Metal retention in two semi-arid mangroves under contrasting environmental pressures

Hélène Plihon<sup>a,b,*</sup>, Rozane Marins<sup>a</sup>, Stéphane Mounier<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Quenching

Sulfur

Nitrogen

Complexation

Organic matter

Metals

NE Brazil

## ABSTRACT

This study examines metal interactions in sediments from two semi-arid mangroves in North East Brazil, Jaguaribe (impacted) and Pacoti (preserved). Sediment cores (up to 80 cm) were collected from upstream, middle, and downstream areas, with an additional core near shrimp farm effluents in Jaguaribe. Granulometric parameters (% fine particles, Al, Fe), organic matter (OM) characteristics (Loss on ignition, %C, %N), total sulfur (TS%), and metals contents (Zn, Co, Cr, Hg) were analyzed every 5 cm. Metal excess inventories and OM complexation constants (K) were determined. In Jaguaribe, Hg and Zn strongly correlate with fine particles (R > 0.9), OM (R > 0.87), and N (R > 0.82), while Co primarily associates with fine particles (R = 0.8). In Pacoti, Zn and Co concentrations are low, preventing clear pattern identification, and Hg correlates with sulfur (R = 0.84) rather than OM and N. These results suggest that in Jaguaribe, Hg and Zn interact with nitrogen-rich OM, influenced by anthropogenic effluents, whereas in Pacoti, they associate with sulfur compounds such as pyrite, a typical feature of preserved mangroves. Cr shows no correlation with sediment parameters but exceeds Brazilian limits of 75 mg kg⁻¹ (Jaguaribe: 184 mg kg⁻¹, Pacoti: 216 mg kg⁻¹). Co and Zn metal inventories are increasing downstream in Jaguaribe with highest levels near the shrimp farm, suggesting a higher risk of erosion-induced metal release. Despite OM accumulation, weak Cu complexation (LogK~4.95) suggests limited metal retention, challenging the assumption of mangroves as metal sinks. High Cr inventories in both mangroves warrant further investigation.

## 1. Introduction

Mangrove forests are critical ecosystems located at the interface between land and ocean in tropical and subtropical regions (Kristensen et al., 2008; Castro et al., 2018). When intersected by a river, these forests are classified as riverine mangroves, which are exposed to regular tidal flooding and high microbial activity. As a result, riverine mangrove sediments are often oxygen-depleted but enriched with nutrients, either from riverine inputs or generated within the ecosystem itself (Marchand et al., 2011). This nutrient enrichment, combined with anaerobic conditions that slow decomposition, allows for significant carbon accumulation in mangrove sediments, making these ecosystems important carbon sinks (Kristensen et al., 2008) known as "blue carbon".

In addition to their role in carbon storage, mangrove sediments are supposed key sites for metal sequestration (Lacerda et al., 2020; Marchand et al., 2016), functioning as both chemical and physical traps. Chemically, their high organic matter (OM) content facilitates metal

binding (Castro et al., 2022), while sulfides produced from sulfate reduction in deeper layers have high affinity for metals. Physically, mangrove sediments trap metals by retaining suspended particles via their root systems (Cerri et al., 2025) and by accumulating fine-grained sediments, which provide a high reactive surface for metal adsorption (Harbison, 1986).

Metals in these environments are often complexed with dissolved organic matter (DOM), inorganic materials, colloids, and particulate OM (Harbison, 1986). Metal complexation is influenced by factors such as the type of OM, the specific metal species, and environmental conditions like pH, competition with other ions, and redox conditions (Ondrasek et al., 2019; Louis et al., 2014). High molecular weight molecules, such as humic substances (HS), exhibit a higher binding potential for metals compared to proteins, and they are predominantly present in sediment cores (Xu et al., 2019). Humic substances are large, not elucidated molecules bearing various complexing functional groups, which display a wide range of affinities for metal ions (Tadini et al., 2020). Metals

<footnote><sup>a</sup> Instituto de Ciências do Mar, Universidade Federal do Ceará, Av. Abolição 3207, 60165-081, Fortaleza, CE, Brazil</footnote>

<footnote><sup>b</sup> Univ. Toulon, Aix Marseille Univ., CNRS/INSU, IRD, MIO, Mediterranean Institute of Oceanography, CS 60584, 83041, Toulon, France</footnote>

<footnote>* Corresponding author. Instituto de Ciências do Mar, Universidade Federal do Ceará, Av. Abolição 3207, 60165-081, Fortaleza, CE, Brazil. E-mail address: helene-plihon@etud.univ-tln.fr (H. Plihon).</footnote>
typically interact with carboxylic and phenolic groups (Perdue et al., 1985; Wei et al., 2022a). Phenolic groups, which can also include nitrogen and sulfur functionalities, exhibit stronger binding properties than carboxylic (Croué et al., 2003). These sites are less abundant in HS than carboxylic groups but are especially prevalent in mangroves due to i) nitrate pollution from shrimp farming or wastewater and ii) natural high sulfur content resulting from sulfate reduction (Canfield and Thamdrup, 2009; Ferreira et al., 2007). Sulfate reduction often begins at approximately 30 cm depth in mangrove sediments, where oxygen availability is significantly reduced due to soil flooding by water and diminution of bioturbation and roots systems that bring oxygen (Ferreira et al., 2007; Moura and Lacerda, 2018). Under these anaerobic conditions, microorganisms utilize alternative electron acceptors for respiration and organic matter degradation. Sulfate reduction is one of the most important respiration reaction in mangroves where sulfate ions (SO₄²⁻) are reduced to sulfides (HS⁻, S²⁻). The reaction between HS⁻ and Fe²⁺ leads to the early formation of pyrite (FeS₂), a stable compound that can sequester metals (Zhang et al., 2024; Fromard et al., 2018), and negligible amounts of FeS (Fromard et al., 2018). Pyrite is a stable mineral compound that sequesters metals, and its ability to fix metals follows the adsorption constant order: Co²⁺ > Cd²⁺ > Mn²⁺ > Ni²⁺ > Ca²⁺ > Zn²⁺ (Kornicker and Morse, 1991). Moreover, sulfides produced in these anaerobic conditions can become incorporated into organic matter through a process known as sulfurization (Werne et al., 2004) promoting metal retention more effectively than organic matter with depleted sulfur content (Smith et al., 2002). This process leads to a typical pattern in mangroves sediments where metal enrichment is higher in the deeper sediment layers, where sulfate reduction is more intense (Marchand et al., 2016; Harbison, 1986).

Harbison (1986) (Harbison, 1986) identified three key factors that enhance metal retention in mangrove sediments: (i) high fine-particle content, (ii) high OM content, and (iii) sulfide production in deeper layers which is directly related to redox conditions. However, in impacted mangroves, disturbances in OM content and quality, such as a reduced carbon-to-nitrogen (C:N) ratio due to elevated nitrogen inputs, may disturb this metal retention dynamics. This study examines the variability of key drivers governing metal-sediment interactions by comparing two mangroves, Pacoti, preserved, and Jaguaribe, more impacted, to evaluate whether these classical retention mechanisms are maintained. Additionally, both sites are located in a semi-arid climate in northeast (NE) Brazil, which differs from humid tropical mangroves in terms of biogeochemical processes. For instance, while redox conditions are similar during the wet season, the dry season induces sediment cracking, allowing deeper oxygen penetration (Nobrega et al., 2013). This results in unexpected oxic conditions, a phenomenon not typically observed in humid tropical mangroves (Nobrega et al., 2013). Sequenced metals under reduced conditions such as metals bound to sulfides are then released (Fromard et al., 2018). Studying metal complexation in sediments is crucial, as it directly influences trace metal speciation, thereby controlling their toxicity and bioavailability (Garraud et al., 2023; Nasnodkar and Nayak, 2017; Shao et al., 2020). When metals are stabilized within the sediment matrix, they remain largely unavailable for biological uptake and do not enter the food chain. However, if complexed with low-molecular-weight OM, metals can become more bioavailable and toxic (Shi et al., 2018).

Inside each mangrove, this study examines metal-sediment interactions and metal storage along an upstream-to-downstream gradient. Predictions of sea level rise indicate that mangroves will progressively migrate inland, potentially resulting in the loss of sediment from downstream areas (Godoy and Lacerda, 2015). This approach aims to assess potential spatial variations in metal retention and to determine whether metal storage exhibits a preferential distribution.

The trace metals analyzed—zinc (Zn), cobalt (Co), chromium (Cr), and mercury (Hg)—were selected for their environmental relevance and detectable concentrations with Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Cold Vapour Atomic Fluorescence

Spectroscopy (CVAFS) for Hg. Chromium was notably selected due to the lack of prior studies in these mangroves. Other metals commonly associated with anthropogenic sources, lead (Pb), copper (Cu) and nickel (Ni), were analyzed. However, as their concentration was below the detection limits of ICP-OES due to the extraction protocol used in this study, they were excluded from further discussion. Iron (Fe) and Aluminium (Al) are examined as granulometric parameters. Complexation constant of Cu²⁺ with sediment OM is quantified using a quenching method (Abbad et al., 2025). We hypothesize that the tidal gradient inside a mangrove and the land use (shrimp farms, agriculture) between two mangroves shape sediment properties (e.g., granulometry, OM content) and anaerobic pathways through distinct inputs from tides and upstream watershed, thereby influencing metal-sediment interactions. To validate the hypothesis, the study aims to: (i) assess vertical profiles of sediments parameters (%C, %S, fine particles) to identify depth-related variations between the two mangrove systems and along the tidal gradient; (ii) examine correlations between metal concentrations and sediment characteristics, including organic and inorganic components; (iii) quantify the complexation strength between metals and OM; and (iv) evaluate the overall capacity of sediments to store these trace metals. The results of this work will enhance our understanding of metal fate in mangroves, especially in terms of land use impacts and the consequence of mangrove displacement regarding metal storage. The identification of contamination sources or enrichment factors lies outside the scope of this work and will be addressed in future studies.

## 2. Methodology

### 2.1. Study area

This study investigates the mangroves of Pacoti and Jaguaribe, located in Ceará, NE Brazil, within a semi-arid climate. Both mangrove ecosystems are characterized by a mesotidal regime, which limits their inland expansion. Situated within their respective estuaries, the Pacoti and Jaguaribe mangroves are classified as riverine systems. Maia et al. (2012) (Maia and Coutinho, 2012) classify the mangrove communities of the Jaguaribe and Pacoti rivers under the same estuarine community structure, based on factors such as species number, density, and height.

#### 2.1.1. Pacoti mangrove

The Pacoti River (3° 49' 21.5" S, 38° 24' 23.4" W) originates in the Baturité Mountains and flows 150 km to its estuary, located near the municipalities of Aquiraz (72,628 inhabitants) and Eusébio (46,033 inhabitants), which have an average population density of 150 inhabitants per km², and are adjacent to Fortaleza (over 2 millions inhabitants). The river supports 150 ha of mangrove forest within a total basin area of 1800 km², although the coastal basin downstream of the last reservoir is only 132 km². The Pacoti River estuary is distinctive due to its unique configuration, which culminates in a lagoon with a narrow opening to the sea. The river near the coast is approximately 150 m wide, while the lagoon can extend up to 600 m. The lagoon is largely filled with sand from nearby dunes and the sea, which facilitates the deposition of particles downstream. Most of the estuary is protected by the "APA do Estuário Pacoti," a conservation area established in 2000 to mitigate damage to the mangroves, despite the upstream damming by the Pacoti and Gavião Reservoirs, which were constructed in 1981 to supply water to Fortaleza (Lacerda et al., 2007). The damming of the Pacoti River has reduced the freshwater input into the estuary by a factor of 2.8 compared to pre-damming conditions. Prior to the establishment of the protected area, the mangroves were used for salt production from the 1960s to the 1980s, which resulted in the transformation of part of the mangrove into a hypersaline tidal flat (Lacerda et al., 2007). However, our sampling site is located within a well-preserved mangrove, exhibiting characteristics typical of an undisturbed ecosystem (Rodrigues et al., 2024). This area can be
considered preserved (Rodrigues et al., 2024), and its watershed is less impacted by anthropogenic activities compared to that of the Jaguaribe River (Santos et al., 2023). The dominant mangrove species are *Rhizophora mangle* (52.4 %), followed by *Laguncularia racemosa* (26.5 %) (Maia and Coutinho, 2012).

### 2.1.2. Jaguaribe mangrove

The Jaguaribe mangrove (4° 26' 45.1"S, 37° 47' 19.4"W) is situated 150 km from the Pacoti mangrove, near the cities of Aracati and Fortim, which together have a population of approximately 100,000. It is the major watershed in NE Brazil occupying 50 % of Ceará state. The Jaguaribe River originates in Joaninha hills stretches 610 km and has a total basin area of 75,669 km², supporting 729 ha of mangroves. It is a greater and larger river than Pacoti. However it has a relatively lower surface of mangrove than Pacoti with a river to mangrove ratio of 1:1 and 1:5 for Pacoti. These ratios were calculated using mangrove surface data from Lacerda et al. (2007) (Lacerda et al., 2007) and Godoy (2014) (Godoy and Lacerda, 2014) and river surface area estimated via Google Earth. The evaluation using the Normalized Difference Vegetation Index (NDVI) to compare the overall health of the mangrove forests between Pacoti and Jaguaribe (Marins et al., 2020) suggests that Pacoti mangrove has a higher canopy biomass than the Jaguaribe site. The climate in Jaguaribe is slightly more arid compared to Pacoti, with an average annual rainfall of 764 mm compared to around 1000 mm in Pacoti (Godoy and Lacerda, 2014). The estuary extends circa 36 km, and near the coast, the Jaguaribe River is about 500 m wide. Dominant mangrove species is *Avicenia germinans* (44 %) followed by *Rhizophora mangle* (35.5 %) (Maia and Coutinho, 2012). The Jaguaribe River is more heavily impacted by human activities compared to the Pacoti River (Marins et al., 2020). Due to its large estuary, Jaguaribe has been a strategic location for human settlement and economic activities since the Portuguese colonization, overcoming deforestation (Pantalena and Maia, 2014). The Jaguaribe watershed encompasses four major dams—Castanhão, Oros, Lima Campos, and Cedro—constructed between 1906 and 2004 to support water supply for human consumption and other activities. Castanhão and Oros have storage capacities of 2.1 and 6.7 million cubic meters, respectively. The damming of the Jaguaribe River has reduced freshwater input into the estuary by a factor of 3–6 compared to pre-damming conditions (Dias et al., 2013). Additionally, the region is a significant site for shrimp farming, with between 12 % and 20 % of Brazil’s total shrimp production occurring in the Jaguaribe region, covering approximately 3040 ha in the middle and upper estuary (Lacerda et al., 2024). Effluents from shrimp farming, rich in agricultural chemicals such as algicides and nitrates, are directly discharged into the river (Eschrique et al., 2010; Costa et al., 2013), contributing to eutrophication and algal blooms that damage mangrove vegetation (Lacerda et al., 2021).

The Jaguaribe River is also affected by untreated wastewater from the nearby cities of Aracati and Fortim, as well as runoff from upstream husbandry (Lacerda et al., 2008). Emission factors for pollutants such as nitrates, phosphates, and metals (e.g., Cu and Hg) are higher from shrimp farms compared to wastewater and livestock operations. However, despite the high emission factor per unit area in shrimp farming, its relative area is much smaller—approximately 100 times less than that of agricultural fields and urban areas—resulting in lower annual pollutant inputs to the river (Lacerda et al., 2006a, 2011). Although the watershed as a whole is primarily impacted by agricultural and urban effluents, the mangroves themselves experience significant local contamination from adjacent shrimp farming. The direct discharge of effluents from shrimp farm canals into the mangrove systems is a notable source of pollution, despite the relatively smaller footprint of shrimp farms in the broader watershed context.

Both the Pacoti and Jaguaribe mangroves have experienced recent area expansions, driven by increased sedimentation within their watersheds. Between 1988 and 2010, the Jaguaribe estuary gained approximately 24.15 ha of mangroves on newly formed islands (Godoy

and Lacerda, 2013), while the Pacoti mangrove expanded by 73 ha between 1958 and 2010 (Lacerda et al., 2007), representing respective increases of 3.4 % and 47.8 % in surface area. This enhanced sedimentation is linked to a reduction in sediment export capacity in both rivers, primarily due to decreased rainfall and the presence of upstream dams.

### 2.2. Sampling collection

Field trips were conducted in April and May 2023 in Jaguaribe and Pacoti mangrove respectively. In each mangrove, sampling was performed in three distinct zones—upstream, middle, and downstream—corresponding to positions along the river’s course through the mangrove (Fig. 1a and b). For Pacoti, 2 L of surface river water were collected in pre-washed plastic bottles during the rising tide, and the following parameters were measured: pH, temperature, oxygen concentration ($O_2$ %), and salinity (see Table A1 in the supplementary materials). A similar procedure was applied at the Jaguaribe mangrove, with additional water samples collected along the river profile at seven points (W1 to W7) from downstream to upstream, as well as an additional point near a shrimp tributary (W8). Sediment cores, up to 80 cm, were extracted using PVC tubes at each site and were sealed immediately after collection to prevent leakage. In Pacoti, two cores were collected at each midstream and downstream site: one from the riverside and one from the inland side, with approximately 50 m between the two. In Jaguaribe, an additional core was collected near shrimp farm effluents within a degraded mangrove area (Fig. 1a), where the only input is the canal diverting effluents from the shrimp farms. A total of five and four cores were collected from the Pacoti and Jaguaribe mangroves, respectively. Upon returning to the laboratory, the PVC tubes were opened using an electric saw, and the core material was collected near the center to avoid possible contamination. For textural and geochemical analyses, each core was sectioned into 5 cm intervals, with millimetric roots removed. Accordingly, the four cores from the Jaguaribe mangrove were subsampled into 56 samples, while the five cores from the Pacoti mangrove yielded 56 samples, resulting in a total of 112 sediment samples. Each sample was then divided into two subsamples, which were hermetically sealed. One subsample was preserved for granulometric analysis, while the other was subjected to centrifugation at 7000 rpm for 10 min, followed by freeze-drying using a Liofilizador L101 (Liobras) at -53 °C and 77 µHg.

## 2.3. Analytical methods

### 2.3.1. Granulometry

Granulometric analysis of Pacoti and Jaguaribe sediments (N = 112) was carried out at the LAMA laboratory of INRAE in Lyon using a Cilas 1190 particle size analyzer, employing the Fraunhofer method with laser diffraction. The analysis covered a particle size range from 0.04 µm to 2.500 µm and was performed on wet sediment samples. For Jaguaribe cores, measurements were made without ultrasonic waves (UW), whereas for Pacoti cores, ultrasonic dispersion was applied due to the higher presence of aggregates in the sediments. The ultrasonic dispersion was applied for an initial 20 s and maintained throughout the measurement process. To ensure data consistency and reliability, triplicate analyses were conducted every three measurements. Furthermore, a daily control analysis of Rhône sediments, a well-established reference for the laboratory, was performed with UW to verify instrument accuracy. During the analysis, sample obscuration ranged between 10 % and 20 %. In this article, granulometry refers to the combined percentage by volume of fine particles, clays and silts (<63 µm).

### 2.3.2. pH

For sediment pH, a homogenized dry sediment mixture was prepared in a 5:1 v/m ratio (distilled water in mL to sediment in g) following the methods of Wang et al. (2018) (Wang et al., 2018). After 30 min of agitation, the pH of the sediment samples (N = 112) were measured
<figure><img src="image_5.png" /><figcaption>Fig. 1. Sampling strategy for a Jaguaribe mangrove and b Pacoti mangrove. Red dots indicate sediment core (up to 80 cm depth) sampling locations (JU: Jaguaribe Upstream, JM: Jaguaribe Middle, JD: Jaguaribe Downstream, JS: Jaguaribe Shrimp farms; PU: Pacoti Upstream, PMR: Pacoti Middle River, PMI: Pacoti Middle Inland, PDR: Pacoti Downstream River, PDI: Pacoti Downstream Inland). Blue dots represent surface water sampling points (Jaguaribe: W1–W7 along the gradient to the sea, W8 in the Cumbe Channel, originating from the shrimp farms. Pacoti: WU, WM, and WD for upstream, middle, and downstream, respectively). Pictures modified from Google Earth.</figcaption></figure>

using a digital pH meter and conductivity meter, both daily calibrated.

### 2.3.3. Loss on ignition

Loss On Ignition (LOI) was determined by igniting 2 g of freeze-dried sample at 450 °C for 4 h (N = 112). The difference between the dry weight and the ignited weight indicates the OM content, expressed as a mass percentage (% LOI) (equation (1)) (Nelson and Sommers, 1996). According to Hoogsteen et al. (2015) (Hoogsteen et al., 2015) a correction factor of 0.03 is applied to clays and silts to account for the residual presence of bound water associated with these minerals (equation (2)), which persists after uncompleted lyophilization or rehumidification via atmospheric exposure during sample preparation (Srisuma et al., 2024). The dehydratio120n of clay minerals during loss on ignition involves the dissipation of unbound water, interlayer water, and ultimately the loss of hydroxyl (or lattice OH) water retained within the clay and silts structure (Grim, 1953; Mook and Hoskin, 1982).

$$LOI_{\text{measured}} = (\text{Initialmass} - \text{Finalmass})/\text{Initialmass} \times 100 \quad (1)$$

$$LOI_{\text{corrected}} = LOI_{\text{measured}} - 0.03 \times (\text{Clay}(\%) + \text{Silts}(\%)) \quad (2)$$

### 2.3.4. Total carbon, total nitrogen and total sulfur

Two liters of water from each location are filtered using 0.7 µm glass microfiber filters (Whatman, diameter 47 mm) that have been pre-fired at 450 °C overnight. The filters are weighed before and after filtration. After the filtration process, Milli-Q water is used to rinse away any salts. The glass filters are then dried overnight at 30 °C. Sediments (N = 112) and glass filters (N = 11) are analyzed using the Flash 2000 Organic Elemental Analyzer from ThermoFisher Scientific to determine their total carbon (TC), nitrogen (TN), and sulfur (TS) contents. Triplicates were made for each sediments layer. For CNS analysis, V2O5 is added specifically to identify sulfur. Calibration is made using NCS soil reference 338 40026 (Thermo Scientific).

### 2.3.5. Scanning electron microscopy (SEM) analysis

SEM imaging was conducted at the MAPIEM laboratory of Toulon University on dry sediment samples previously coated with carbon. Images were acquired using a Zeiss Supra 40 VP microscope equipped

with an InLens detector, operating at 10 kV under high vacuum (10⁻⁵ mbar) with a 30 µm diaphragm. SEM imaging was performed on 20 % of the sediment samples (N = 25), selected from the layers with the highest sulfur content, with the aim of identifying pyrite.

### 2.3.6. Metals analysis

Hg was directly determined in dry sediments using CVAFS with an AMA-254 analyzer (detection limit; 0,01 ng). Calibration (R² = 1.00) was achieved using the NIST 2976 Trace Elements and Methylmercury in Mussel Tissue standard (1 ppm), with concentrations ranging from 2 ng g⁻¹ to 30 ng g⁻¹. Cr, Co, Fe, Al and Zn, were quantified using ICP-OES with a PerkinElmer Optima 3000XL, following digestion of 100 mg of sample within 10 mL of aqua regia using UltraWave (ThermoFisher Scientific) (N = 112). For ICP-OES analysis, an internal standard (indium, ¹¹⁵In) was added to each sample to achieve a final concentration of 1 ppm. In each mineralization sequence, a blank and a reference material from river sediments (LGC 6187, LGC group) were analyzed. Calibration (R² = 1.00) was performed using a 100 ppm CPA Chem standard solution, certified to ISO 17025, ISO 9001, and ISO 17034. Calibration curves ranged from 0 to 5 ppm for Al and Fe and from 0 to 1 ppm for Cr, Co and Zn. Control solutions were prepared from a 100 ppm Aristar 84791.180 standard at 1 and 4 ppm for Al, Fe and at 0.1 and 0.2 ppm for Cr, Co and Zn. Recovery rates were required to fall within the 90–110 % range. If recovery rates fell outside of this range, the corresponding batch of samples was re-measured. The relative standard deviation (RSD) for triplicate measurements of all elements was maintained at <15 %. Table 1 presents the detection limits, certified values of the standard, and recovery rates for all assessed elements, including those excluded from further analysis (Cu, Ni, Pb) due to concentrations in aqua regia sample extracts below ICP-OES detection limits. Of the 112 samples, 10 % were processed in triplicate, including mineralization and ICP-OES analysis, to ensure analytical reproducibility.

Metal excess inventory ($I_{xs}$) of a sediment profile was calculated according to the equation (Machado et al., 2002): $I_{xs} = \sum_{i=0}^{45} M_i \times \rho_i \times t$ where $M_i$ is the metal excess (reference-deducted) concentration in ng. g⁻¹ (Hg) and µg.g⁻¹ (Zn, Co and Cr), $\rho_i$ is the bulk density in g.cm⁻³, and
<caption>**Table 1**</caption>

Recovery, certified values and detection limits (expressed as three times the standard deviation determined from blank replicates analyses, N = 10). (–) stands for ‘not available’.

<table><thead><tr><th>Elements</th><th>ICP-OES detection limit<br>(µg L<sup>-1</sup>)</th><th>LGC 6187 certified value<br>(µg L<sup>-1</sup>)</th><th>Recovery<br>(%)</th></tr></thead><tbody><tr><td>Al</td><td>2</td><td>(–)</td><td>(–)</td></tr><tr><td>Co</td><td>4</td><td>(–)</td><td>(–)</td></tr><tr><td>Cu</td><td>10</td><td>290</td><td>91.5</td></tr><tr><td>Cr</td><td>5</td><td>290</td><td>90.1</td></tr><tr><td>Fe</td><td>1</td><td>82,500</td><td>102.8</td></tr><tr><td>Mn</td><td>50</td><td>3430</td><td>98.5</td></tr><tr><td>Ni</td><td>5</td><td>90</td><td>92.3</td></tr><tr><td>Pb</td><td>10</td><td>215</td><td>92.6</td></tr><tr><td>Zn</td><td>20</td><td>1195</td><td>97.2</td></tr></tbody></table>

t is the thickness (5 cm) of sediment-depth interval, for each depth layer i. To ensure homogenization, the excess metal storage is then represented for a depth of 45 cm. Bulk density is determined by weighing a known volume of a freeze-dried sample, following the formula established by Blake et al., 1965) (Blake et al., 1965): *BulkDensity* = *Massoffreezedriedsample*/*Volumeofthesample*. Due to the absence of defined geological backgrounds for the studied areas in the literature, reference values were selected based on the best available data and numerical values and are presented in Supplementary Materials (Table A2) with associated aluminum concentrations for subsequent normalization procedures. For Zn and Co, reference concentrations correspond to the mean values obtained from all sampling points in the Pacotí mangrove, considered preserved for these elements regarding concentrations found in this study and previous one (Nilin et al., 2013; Souza and Silva, 2016). Reference value for Hg is based on Marins et al. (2004) (Marins et al., 2004), who established Hg reference levels for Ceará coast. For Cr, concentrations observed in both mangroves fall within the same range, making it difficult to distinguish between a diffuse contamination and a regional geogenic source, especially considering the elevated levels. Therefore, the reference concentration for Cr was taken from bottom sediments (Ø < 2 mm) of a core collected from the Ceará continental shelf (Aguiar et al., 2014).

### 2.3.7. Fluorescence quenching

To assess the ability of OM in sediments to complex with metals, Cu²⁺-induced fluorescence quenching is a widely used method. This approach relies on the property that the fluorescence of natural OM decreases (quenches) upon complexation with Cu²⁺ (Ryan and Weber, 1982). Cu²⁺ is preferred for such analyses due to its strong quenching effect on OM fluorescence signals. For this analysis, 2 g of sediment was added to 40 mL of a 0.01 M NaOH solution (N = 112). The samples were shaken overnight, then centrifuged (13,000 rpm; RCF 17,572; T = 20 °C; t = 10 min). The resulting supernatants were filtered using a 0.45 µm cellulose acetate filter. Alkaline solubilization is among the most commonly used techniques for extracting humic acid-like substances, facilitating access to the majority of OM compounds (Wei et al., 2022b). The extracted OM solution was then diluted with Milli-Q water to obtain a final OM concentration of approximately 10 ppm for titration. A 0.01 M sodium perchlorate solution was used to fix ionic strength. For fluorescence quenching titration, 150 mL of the OM solution was progressively spiked with 15 incremental additions of copper perchlorate solution (Cu(ClO₄)₂) at concentrations ranging from 10⁻⁴ to 10⁻¹ M. It is important to note that the Cu(II) concentrations applied here exceed typical environmental levels (Chen et al., 2018) but allow for clearer observation of the interaction characteristics between DOM and metal ions. To maintain a constant pH of 6 during titration, small amounts of HCl or NaOH 0.1 M solution were added as needed. For each Cu²⁺ addition, a 1.5 mL aliquot was collected for three-dimensional (3D) fluorescence analysis. 3D fluorescence spectroscopy was used to investigate the binding properties of DOM and Cu²⁺. Fluorescence

excitation-emission matrices (EEMs) were obtained with excitation and emission wavelengths ranging from 200 to 600 nm in 5 nm increments. The 3D fluorescence data were processed using PARAllel FACtor analysis (PARAFAC), a multiway analytical technique. Rayleigh scattering was corrected using a 20 nm window, and Raman scattering correction was performed using the Zepp method (Zepp et al., 2004). The experimental fluorescence intensity (I<sub>exp</sub>) was plotted as a function of log [Cu²⁺]. The model curve was generated by optimizing the parameters K (complexation constant) and L (number of binding sites) under the assumption of a one-ligand aprotic model. Optimization was performed by minimizing the sum of squared differences between I<sub>exp</sub>, the experimentally measured intensity, and I<sub>theo</sub> = f(K,L), the theoretically calculated intensity. Triplicate analyses were performed on 10 % of the samples using the quenching method to ensure analytical reproducibility.

### 2.3.8. Statistical analysis

All statistical tests were conducted using RStudio version 4.1.2. In the text, all confidence intervals and standard errors are calculated at 95 %. The correlation coefficients (R) presented in the correlation matrix are derived from Pearson's correlation analysis.

## 3. Results and discussion

### 3.1. Sulfur occurrence in depth related to granulometry and carbon content

Framboidal pyrite, a typical form of pyrite (Fe2S), is found in mangrove sediments as a result of anaerobic conditions and was observed through SEM in the samples (Fig. 2a and b). Vertical profiles of sediment parameters (%C, %S, fine particles) were plotted for both mangroves, for each core (Fig. 3; Fig. 4). For Jaguaribe, content of total sulfur increases, up to 2.06 %, in the zone enriched in OM indicating sulfide precipitation during processes of sulfate reduction which take place in conditions of anoxia (Otero et al., 2009) (Fig. 3). A prominent form of sulfur observed at these depths is framboidal pyrite (Fig. 2a and b), which has been identified in both Jaguaribe and Pacotí cores. Sulfur peaks were observed at increasing depths from upstream to downstream (Fig. 3). In the upstream section, a slight increase was noted around 15–25 cm, with sulfur concentrations remaining low (below 0.03 %). In the middle section, the peak shifted to 25–50 cm (Fig. 3b), while in the downstream section, the peak extended to 50–65 cm, with an additional smaller peak at 35 cm (Fig. 3c). The deepest sulfur peak was found in the shrimp core, extending beyond 65 cm (Fig. 3d). Due to the consistently low sulfur values (below 0.03 %) in the upstream Jaguaribe core, this area is not considered an anaerobic zone with significant pyrite production, especially in comparison to other cores where sulfur concentrations exceed 0.5 %.

Sulfur, carbon and %fine particles present the same distribution along profiles (Fig. 3). In the upstream section, below 35 cm, a thick sand layer is present with minimal OM (Fig. 3a), which likely explains the absence of sulfur production. Sulfate reduction requires anaerobic conditions and sufficient OM (Marchand et al., 2016; Harbison, 1986), and sand layers, due to their high porosity, tend to be more oxygenated and carbon depleted. A similar phenomenon is observed in the middle core above 50 cm (Fig. 3b).

In the downstream and shrimp cores (Fig. 3c and d), where granulometry is more homogeneous, sulfur production remains undisturbed with increasing depth, closely correlating with the carbon peak and highlighting the critical role of OM in sulfide production. Shrimp core shows a high content of sulfur, up to 2.06 % in the bottom of the core. Sediments close to shrimp farms are known to be anaerobic due to the combination of high nutrient inputs and fine particles (up to 90 % in the bottom) that retains water, both coming from shrimp effluents (Nobrega et al., 2013). Water stays longer and deeper promoting anaerobic conditions and sulfur formation (Marchand et al., 2011). We hypothesize
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Framboidal pyrite observed by Scanning Electron Microscopy (SEM) in dry sediments previously coated with carbon **a** upstream Pacoti in the layer 5-10 cm **b** Jaguaribe shrimp core at 65-70 cm.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Sulfur (% mass), Carbon (% mass) depth profiles and granulometry trend (% fine particles) for **a** Upstream Jaguaribe **b** Middle Jaguaribe **c** Downstream Jaguaribe **d** Shrimp core Jaguaribe. To maintain consistency in scale, only variations in granulometry are represented, rather than the actual values. The standard errors for carbon and sulfur reach maximum values of 0.1 and 0.05, respectively, rendering the observed peak significantly distinct from the overall trend. The gray area indicates the region of sulfur prominence.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Sulfur (% mass), Carbon (% mass) depth profiles and granulometry trend (% fine particles) for **a** Upstream Pacoti **b** Middle river Pacoti **c** Middle inland Pacoti **d** Downstream river Pacoti **e** Downstream inland Pacoti. To maintain consistency in scale, only variations in granulometry are represented, rather than the actual values. The highest %C (5.8 at 25–30 cm) in the upstream Pacoti core is indicated in text only; the y-axis was limited to 4 % for scale comparison. The standard errors for carbon and sulfur reach maximum values of 0.2 and 0.05, respectively, rendering the observed peak significantly distinct from the overall trend. The gray area indicates the region of sulfur prominence.</figcaption></figure>
that the granulometric heterogeneity between the Jaguaribe cores leads to a varying sulfate reduction activity across depth leading to distinct anaerobic profiles.

Total sulfur content is comparable between Pacoti (mean: 0.49 % [0.10; 1.55]) and Jaguaribe (mean: 0.47 % [0; 2.06]) (Table 2). Pacoti shows less variation in sulfur content across the core compared to Jaguaribe (Figs. 3 and 4). In Pacoti, sulfur levels remain around 0.5 % even at the surface of the upstream and downstream river cores, with a maximum peak of 1.55 % observed in the upstream core at a depth of 25 cm (Fig. 4a). Sulfur exhibits multiple small peaks at varying depths without a clear trend along the upstream-to-downstream axis. A peak is observed at 25 cm in the upstream section (Fig. 4a), a slight increase occurs between 10 and 30 cm in the middle river section (Fig. 4b), and two peaks are found at 15 cm and 35 cm in the middle inland section (Fig. 4c). For the downstream river section, a peak is seen at 15 cm, with a slight increase between 40 and 55 cm (Fig. 4d), while an area of sulfur production is observed between 30 and 45 cm in the downstream inland section (Fig. 4e). Compared to Jaguaribe, the sulfur production areas in Pacoti are not as clearly defined and occur at shallower depths, typically between 30 and 40 cm, whereas in Jaguaribe, sulfur peaks can extend to depths of up to 80 cm (Fig. 3d).

Granulometric trend shows less variability than Jaguaribe and is not directly associated with decrease or increase of sulfur (Fig. 4). Bioturbation rates in the Pacoti mangrove (25–50 burrows.m⁻²) are an order of magnitude higher than those in the Jaguaribe mangrove (2.3–3.3 burrows.m⁻²) (Otero et al., 2020; Ferreira et al., 2022). This increased bioturbation likely enhances water circulation, which may re-oxygenate newly produced sulfides (Marchand et al., 2011; Ferreira et al., 2007). Instead of Jaguaribe, decrease of sulfur in Pacoti mangrove is not associated with sandy layers but could be related to re-oxygenated conditions due to bioturbation leading to a more irregular and superficial sulfur production pattern. However, similar to Jaguaribe, sulfur peaks in Pacoti are often associated with carbon peaks (Fig. 4). Sulfur production in both mangroves is linked to OM presence and reduced conditions, which are favored by fine particles in Jaguaribe, but likely diminished by bioturbation in Pacoti. Previous studies (Marchand et al., 2011; Ferreira et al., 2007) indicate that in homogeneous mangrove sediments an anaerobic zone typically develops below 30 cm, resulting in distinct processes between the surface (above 30 cm) and deeper sediments (below 30 cm). Below 30 cm, the reduced presence of live roots limits oxygen input, thereby promoting sulfate reduction. Consequently, the Jaguaribe core exhibits a more characteristic anaerobic

profile than Pacoti, with pronounced sulfur content occurring in a broad sediment layer below 30 cm. Despite Pacoti having a significantly higher average carbon content (2.16 % ± 0.23) compared to Jaguaribe (1.19 % ± 0.17) (t-test; α = 0.05, N = 112) (Table 2), the sulfur content in both mangroves is similar. This suggests that, although Pacoti has more carbon available for sulfate reduction, the conditions are not as anaerobic as in Jaguaribe, likely due to a lower occurrence of fine particles and the higher bioturbation rate in Pacoti, which limits the extent of sulfate reduction.

## 3.2. Trace metal retention differs between preserved and impacted mangroves

For Jaguaribe, Hg, Zn, and Co exhibit a strong correlation with granulometric parameters, such as the percentage of fine particles, as well as Al and Fe content with R > 0.8 (Fig. 5a). Additionally, Zn and Hg show a strong correlation with OM parameters, including LOI, %C, and %N with R > 0.8, whereas the correlation for Co is weaker with respectively R = 0.76, R = 0.75, R = 0.65 for LOI, %C and %N. These patterns are further supported by the Principal Component Analysis (PCA; Fig. A1, Supplementary Materials), in which %fine particles, % LOI, %C, %N, Al, Fe, Zn, Co and Hg exhibit similar directional loadings along PC1, which explains 61 % of the total variance. The PCA thus confirms that these variables co-vary and highlights their combined influence. Fine particles, such as clays, are known to form negatively charged argillo-humic complexes, which can interact with positively charged metal cations. Clays can also interact directly with metals in the absence of OM. Fine particles are more effective at adsorbing metals than larger particles, owing to their negative charge, large surface area, high cation exchange capacity, and pore volume, which allow metals to remain in solution longer, providing time for adsorption (Harbison, 1986; Uddin, 2017). Therefore, metals can interact with fine particles both organically and inorganically. In sediments, cobalt also exhibits interactions with inorganic components, such as sulfur (forming CoS) and manganese oxides, as well as organic particles (Taillefert et al., 2002; Krumholz et al., 2003). This dual affinity may explain the weaker correlation observed in this study between cobalt and organic parameters (Fig. 5a). Cobalt interacts with fine particles, both organic and inorganic, contributing to its significant presence in Jaguaribe sediments, especially in the shrimp core, reaching up to 181.33 mg kg⁻¹ (Table 2).

Hg and Zn are known to interact with sulfur (Hallberg et al., 1980);

<caption>**Table 2**</caption>

Average concentrations of metals, carbon (C%), nitrogen (N%), and sulfur (S%) for each core. * indicates that the shrimp core parameters are significantly higher than other Jaguaribe cores (t-test; α = 0.05, N = 56). ** indicates that the shrimp core parameters are significantly higher than Pacoti cores which all show no significant variations among each other (t-test; α = 0.05, N = 72). For each parameters, minimum and maximum are presented below the average.

<table><thead><tr><th>Site</th><th>Cr (mg.kg<sup>-1</sup>)</th><th>Co (mg.kg<sup>-1</sup>)</th><th>Hg (µg.kg<sup>-1</sup>)</th><th>Zn (mg.kg<sup>-1</sup>)</th><th>C (%)</th><th>N(%)</th><th>S(%)</th></tr></thead><tbody><tr><td>Jaguaribe</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Upstream</td><td>232 [58.91; 453.94]</td><td>11 [0; 49.07]</td><td>2.9 [0.49; 5.56]</td><td>6 [0; 21.38]</td><td>0.40 [0.03; 0.71]</td><td>0.03 [0; 0.06]</td><td>0.01 [0; 0.03]</td></tr><tr><td>Middle</td><td>198 [47.30; 384.21]</td><td>38 [0; 107.5]</td><td>8.4 [1.18; 13.68]</td><td>31 [0; 57.64]</td><td>1.13 [0.08; 2.06]</td><td>0.08 [0; 0.14]</td><td>0.49 [0.09; 0.96]</td></tr><tr><td>Downstream</td><td>138 [79.85; 359.28]</td><td>68 [1.4; 108.28]</td><td>9.2 [6.20; 11.18]</td><td>39 [17.24; 44.67]</td><td>1.18 [0.53; 1.65]</td><td>0.08 [0.04; 0.10]</td><td>0.24 [0.02; 0.85]</td></tr><tr><td>Shrimp</td><td>163 [20.04; 303.52]</td><td>132*** [104.51; 181.33]</td><td>12.3*** [10.33; 13.35]</td><td>67*** [30.91; 79.96]</td><td>1.86* [1.35; 2.46]</td><td>0.11* [0.09; 0.14]</td><td>1.02*** [0.97; 2.06]</td></tr><tr><td>Pacoti</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Upstream</td><td>250 [85; 377.98]</td><td>22 [0; 82.90]</td><td>10.1 [7.65; 19.26]</td><td>11 [0; 33.27]</td><td>2.76 [1.43; 5.8]</td><td>0.11 [0.06; 0.22]</td><td>0.64 [0.4; 1.55]</td></tr><tr><td>Middle river</td><td>207 [32.97; 490.70]</td><td>17 [0; 35.45]</td><td>7.7 [5.0; 9.52]</td><td>6 [0; 16.56]</td><td>2.57 [1.99; 2.83]</td><td>0.12 [0.09; 0.14]</td><td>0.38 [0.15; 0.51]</td></tr><tr><td>Middle inland</td><td>173 [62.74; 415.37]</td><td>12 [0; 40.30]</td><td>6.3 [3.41; 8.57]</td><td>7 [0; 15.64]</td><td>1.86 [1.07; 2.35]</td><td>0.08 [0.05; 0.10]</td><td>0.38 [0.10; 0.65]</td></tr><tr><td>Downstream river</td><td>215 [25.82; 446.74]</td><td>34 [0; 59.96]</td><td>9.6 [4.44; 17.85]</td><td>21 [9.55; 34.93]</td><td>1.75 [0.44; 3.40]</td><td>0.09 [0.03; 0.17]</td><td>0.55 [0.19; 1.02]</td></tr><tr><td>Downstream inland</td><td>256 [127.43; 391.73]</td><td>11 [0; 48.38]</td><td>9.4 [6.3; 14.74]</td><td>10 [0; 27.98]</td><td>2.11 [0.99; 3.51]</td><td>0.1 [0.06; 0.15]</td><td>0.46 [0.22; 0.67]</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Pearson correlation coefficient between physicochemicals characteristics (%C, %N, pH...) and metals content (Zn, Hg, Co, Cr) in sediments for a Jaguaribe (4 cores, N=56) b Pacoti (5 cores, N=56). Coefficient correlation is considered as false if p<0.05.</figcaption></figure>

however in Jaguaribe, they exhibit a stronger correlation with %C and % N ($R > 0.8$), better than with sulfur $R = 0.63$, 0.65 respectively (Fig. 5a), all along the upstream-downstream axis (see Fig. A3 in supplementary materials). This pattern is further supported by the PCA (Fig. A1 in supplementary materials), where PC1 (61 % of the variance) is primarily driven by fine particles, %C, %N, Zn and Hg, which exhibit similar loadings along this axis. Sulfur, by contrast, loads on both PC1 and PC2 (14 % of the variance). This indicates that, although sulfur may interact with metals, its covariation with Hg and Zn is weaker than that of OM and %N. Both Hg and Zn seem to be primarily associated with nitrogen rich OM. However, it is difficult to determine the primary driver of this interaction among fine particles, organic matter content, and nitrogen content. In Jaguaribe, OM with high nitrogen content is influenced by anthropogenic activities: Jaguaribe has around 200 times more nitrogen per $m^3$ of water than Pacoti with 41 % being contributed by shrimp farming (Dias et al., 2013; Lacerda et al., 2008; Mounier et al., 2018). Hg and Zn in estuarine environments is typically found in organic colloidal form (Simpson et al., 2014; Luan and Vadas, 2015; Wang et al., 2017)

and likely arrives already complexed with nitrogen-rich OM, which enters the system through the Jaguaribe River, particularly from shrimp farm canals known for their high nitrogen content (Lacerda et al., 2006a) and significant source of Hg (Lacerda et al., 2011). Particulate Hg deposits in pond bottom sediments and are washed away during the complete drainage of ponds (Lacerda et al., 2011). Particles seem to have low mobility and settle close to the tributary as higher content of Zn, Hg, Co are found in the shrimp core, which also shows the highest carbon and nitrogen content (Fig. 6d, Table 2). The PCA further highlights this pattern, with the shrimp core samples positioned at the extreme values of metals and organic matter parameters, indicating that this core simultaneously accumulates high concentrations of trace metals and organic matter (Fig. A1, Supplementary Materials). Considering the effluent from shrimp farms alone, its spatial influence appears limited, with local effects primarily observed near tributary discharges (Lacerda et al., 2006a, 2011). This pattern aligns with rapid sedimentation near the effluent, consistent with previously reported higher accumulation rates close to shrimp farms compared to less impacted

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Mercury depth profiles (ng per g) in Jaguaribe dry sediments cores a Upstream Jaguaribe (green) b Middle Jaguaribe (blue) c Downstream Jaguaribe (black) d Shrimp core Jaguaribe (red). The standard errors reach maximum values of 0.3, rendering the observed peak significantly distinct from the overall trend. The gray area indicates the region of sulfur prominence.</figcaption></figure>
areas of the mangrove (Perez et al., 2020). Marchand et al. (2011) (Marchand et al., 2011) also noted that the influence of shrimp farms in New Caledonia is restricted to mangrove areas immediately adjacent to the discharge point. However, the consistent correlation between nitrogen and metals throughout the mangrove (Fig. A3, Supplementary Materials) indicates that these interactions, which differ from traditional sediment-metal relationships, are not confined to the shrimp farm area. The entire watershed is affected by anthropogenic activities, with upstream agriculture contributing the largest nitrogen inputs due to its substantially larger spatial extent—approximately 100 times that of shrimp farms (Lacerda et al., 2006a, 2008, 2011). This widespread land use dilutes the impact of shrimp farm effluents on fine particles concentrations while introducing additional agrochemical contaminants (Milhome et al., 2009) and maintaining elevated nitrogen and metal inputs across the mangrove.

The metal-nitrogen interaction appears to influence mercury distribution, as the maximum Hg concentration is not located within the anaerobic zone as expected, but above it in the downstream and shrimp cores (Fig. 6c and d). In the shrimp core, the highest Hg concentration—representing the maximum value observed across the Jaguaribe mangrove—is found above the anaerobic zone, between 30 and 60 cm depth (Fig. 6d). This is especially relevant, as metals occurring closer to the surface (up to 30 cm) are more susceptible to remobilization and enter the food chain due to increased biological activity (bioturbation, root exudates) and other factors that alter redox conditions and enhance metals mobility (Marchand et al., 2011, 2016; Ferreira et al., 2007). Mercury, in particular, is known for its potential to bioaccumulate in organisms and is present in sediments in small amounts, with the majority being remobilized (Moura and Lacerda, 2018; UN Environment Programme, 2019).

In Pacoti, Zn, Co, and Hg do not exhibit a strong correlation with granulometric parameters (Fig. 5b) with R < 0.7 for all parameters. Unlike Jaguaribe, Hg is more correlated with sulfur (R = 0.84), rather than nitrogen (R = 0.72) or carbon (R = 0.63). The PCA shows that %S and Hg covary strongly along the primary axis, which explains 41 % of the total variance, indicating a close association between these two variables in the dataset (Fig. A2, see Supplementary Materials). No significant differences in metal interactions were observed along the upstream-downstream gradient, indicating a consistent pattern throughout the Pacoti mangrove (Fig. A4 in supplementary materials). Sulfide-metal interaction pattern is more typical of mangroves, where chalcophile elements (Hg, Zn) accumulate in association with sulfides (Marchand et al., 2016; Harbison, 1986). This suggests that mercury is more likely to interact with sulfur, either organic or inorganic, in the anaerobic zone of sulfur production (Lei et al., 2019). In contrast to the Jaguaribe core, Hg peaks are observed in the sulfur production area, although this region is situated at a shallow depth (see Fig. A5 in supplementary materials). Thus, similar to the Jaguaribe core, the highest Hg concentrations are found at mid-depth, approximately between 30 and 40 cm. Co and Zn do not exhibit strong correlations with any parameters, likely due to their low concentrations in Pacoti sediments (Table 2) (Nilin et al., 2013; Souza and Silva, 2016). Variations in metal concentrations are not pronounced, making it difficult to discern clear patterns. Although mercury is found in small concentrations, it was measured with a more sensitive instrument capable of detecting low levels.

All cores from each mangrove exhibit a consistent pattern of metal-sediment interactions, with the patterns clearly distinct between Jaguaribe (N = 4 cores, 56 samples) and Pacoti (N = 5 cores, 56 samples). In Jaguaribe, Zn and Hg preferentially associate with nitrogen-rich OM, while in Pacoti, Hg is primarily linked to sulfur peaks. It is not the nitrogen content or sulfur itself—since both mangroves have similar nitrogen and sulfur levels (Table 2)—but rather the nature of the OM that impacts metal occurrence as Jaguaribe has a significant lower C/N ratio (Table 2) (Student's t-test, α = 0.05, N = 112). This is particularly relevant given that OM with lower C/N ratios is more prone to

degradation (Garraud et al., 2023), rendering associated metals less stable compared to those bound to sulfur compounds in Pacoti sediment. However, Marchand et al. (2011) (Marchand et al., 2011) demonstrate that metal-sulfide interactions can still occur in mangroves impacted by shrimp farming. This study highlights the varying behavior of mangroves worldwide, even when exposed to similar types of pollution.

### 3.3. Chromium interacting with inorganic particles

The correlation matrix (Fig. 5a and b) reveals that Cr does not interact with any of the parameters considered in this analysis (granulometry, OM, etc.) for both mangroves. This suggests that chromium exhibits a distinct behavior within sediment cores compared to other metals. The PCA further supports this observation: in Jaguaribe, Zn, Hg, and Co are strongly associated along PC1, which explains 61 % of the total variance, whereas Cr loads primarily on PC2, which accounts for 14 % of the variance, indicating that its variation is largely independent from that of the other trace metals (Fig. A1, see Supplementary Materials). Unlike most metals, Cr is known to be independent of total sulfur content (Lacerda et al., 1991) and does not have a strong affinity for OM (Garraud et al., 2023). Although the Student's t-test (α = 0.05, N = 112) indicates a significant difference between the two mangroves, their Cr concentrations remain within the same range, with mean values of 216, 3 mg kg⁻¹ for Pacoti [25.8; 490.7] and 180.4 mg kg⁻¹ for Jaguaribe [20, 0; 453.9] (Table 2; Fig. A6 in Supplementary Materials). Few studies have addressed Cr content in the sediments of Jaguaribe and Pacoti. Oliveira, (2012) (Oliveira, 2012) reported a mean Cr content of 24.5 mg kg⁻¹ in surface sediments (<10 cm) of the Jaguaribe estuary and 78.5 mg kg⁻¹ in the estuarine plume with maximum values reaching up to 175 mg kg⁻¹ in the plume area, showing considerable spatial variability in Cr distribution within the estuary. Limited Cr retention in the upper Jaguaribe (Oliveira, 2012), leads to Cr accumulation in the estuary primarily in the mangrove regions (Table 2), where it is relatively homogeneously distributed, and in the estuarine plume. We hypothesize that Cr preferentially deposits under low hydrodynamic conditions across the broader Jaguaribe watershed. Identifying the precise sources of Cr input remains challenging, as it likely results from a combination of natural origins (Aguiar et al., 2014; Oliveira, 2012) and anthropogenic sources, including its presence in shrimp feed and urban runoff (Neto et al., 2006; Shi et al., 2021). Jaguaribe and Pacoti have the same range of Cr, either showing a natural background or a similar contamination source despite these mangroves being distant of approximately 300 km. To our knowledge, there are no studies on Cr content in Pacoti sediments. Chromium appears to exhibit unique behavior, likely related to inorganic particles, regardless of their size, leading to a homogeneous distribution throughout the mangrove sediments. Interestingly, Cr exhibits different behaviors in the river and in sediments. Cavalcante et al. (2024) (Cavalcante et al., 2024) reported a positive correlation (R = 0.85) between Cr and DOM in the Jaguaribe river. In solution, Cr has been shown to react with soluble organic compounds released during the partial decomposition of OM, which can mobilize Cr to the overlying waters. A fraction of this dissolved Cr may infiltrate the sediments during flood periods (Harbison, 1986; Bonatti, 1971). As infiltration water percolates into deeper anoxic sediment layers, stable Cr oxides associated with manganese, iron, and aluminum are likely to precipitate (Bonatti, 1971; Pfeiffer et al., 1980; Ajouyed et al., 2010), leading to the gradual accumulation of Cr. Then, Cr may form complexes with high molecular weight organic substances, such as humic acids, in mangrove sediments, which immobilize it in a similar manner to sulfides (Marchand et al., 2016). However, it appears that in Jaguaribe and Pacoti sediments, Cr is not associated with OM and may be only present in the form of immobile Cr oxides when it enters the sediments. There is no trend in depth following Cr occurrence. Cr, despite being more concentrated, may be less toxic in sediment form than when it is associated with OM in the river. In reduced sediments, such as mangroves, Cr is predominantly present in its trivalent form Cr(III), which is less toxic
and less mobile than Cr(VI) (Ajouyed et al., 2010). A next study will aim to investigate the origin of the unexpectedly high Cr content in the sediments.

### 3.4. Weak Cu complexation suggests limited metal retention in mangrove sediments

Both Pacoti and Jaguaribe mangroves exhibit similar complexation constants LogK for Cu and OM with values of 4.96 [4.4; 5.9] and 4.95 [4.3; 5.75], respectively (Fig. 7). These values are lower than the complexation constants of 7.5–9.2 found in estuaries (Fischer et al., 1984). For some cores, a significant trend is observed between surface (0–30 cm) and deeper sediments (>30 cm). In Jaguaribe, in the middle and shrimp areas, and in Pacoti, in the middle inland core, the K value decreases significantly with depth (p values < 0.01) (Fig. 7a), while in Pacoti, K increases with depth, significantly for the middle river (p values < 0.001) and downstream inland areas (p values < 0.05) (Fig. 7b). This difference may highlight the distinct OM composition found in the two mangroves, such as the different C, N, S proportion (Table 2).

The number of available binding sites for a 10 mg.L⁻¹ of C (L) is around 10⁻¹⁰ mol.L⁻¹ for both mangroves, which is lower than the average observed in estuarine and river waters (10⁻⁷ to 10⁻⁵ mol.L⁻¹)

(Ryan and Weber, 1982; Fischer et al., 1984). Mounier et al. (2001) (Mounier et al., 2001) reported values of 10⁻⁶ to 10⁻⁵ mol.L⁻¹ for mangrove waters. This relatively low L value suggests that the OM in mangrove sediments has limited capacity for retaining metals, potentially leading to lower metal storage despite higher concentrations of Zn and Cu in Jaguaribe waters (Lacerda et al., 2011; Da et al., 2013).

Filters from suspended matter sampling were below detection limits for all metals, providing only data on sediment load per liter of river water. To evaluate metal retention, we compared metal concentrations in riverine and deposited sediments (Table 3), only for Jaguaribe as no studies on metal content in Pacoti river suspended particulate matter were found. For calculation of Table 3, for Hg and Cr, we used the particle weight per liter from this study to estimate concentrations in mg per kg from the known concentration in mg per liter from literature. Cr estimation is based on dissolved estimated concentrations as there is no measurement from particulates. For Cu and Zn, we used direct measurements in mg per kg from literature. Results show that the average content of mercury and copper in the sediments is consistent with the average concentrations found in river particles. Copper is a well-known pollutant introduced by shrimp farming and agricultural activities, where it is used as an algaecide or pesticide, and is transported to aquatic systems through particulate copper during basin drainage and soil leaching (Lacerda et al., 2006b, 2009). For a point close to shrimp

<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Logarithm of the complexation constant K in sediments obtained with fluorescence quenching for a Jaguaribe mangrove b Pacoti mangrove. Solid gray bar represents the mean value for the surface layer (<30 cm), while the gray dashed bar corresponds to the deeper layer (>30cm). The boundary between surface and depth is set at 30 cm [3,17]. Error bars represent the standard error of complexation constants measured in sediments. ns refers to a p-value > 0.05, * : p-value ≤ 0.05, ** : p-value ≤ 0.01, *** : p-value≤0.001. P-values indicate differences only within each core between surface and depth layers.</figcaption></figure>
<caption>**Table 3**</caption>

Estimated concentrations of Cr, Cu, Zn (mg·kg⁻¹) and Hg (µg·kg⁻¹) in river water and sediments. Estimates are based on literature and the filters weigh of this study. Cr estimates are based on dissolved fractions due to the absence of particulate data. Values are compared against prevention values established by CONAMA (National Environmental Council of Brazil). (–) stands for not unavailable data.

<table><thead><tr><th>Metal</th><th>River particulate metal (µg L<sup>-1</sup>)</th><th>River particulate metal (mg kg<sup>-1</sup>)</th><th>Content in sediments (mg kg<sup>-1</sup>)</th><th>CONAMA prevention values in sediments (mg kg<sup>-1</sup>)</th><th>References</th></tr></thead><tbody><tr><td>Cr</td><td>[0.06; 0.92]</td><td>[0.04; 0.61]</td><td>180.4 [20.04; 453.94]</td><td>75</td><td>(Cavalcante et al., 2024)</td></tr><tr><td>Cu</td><td>(–)</td><td>[5.0; 30] up to 542 at 600m from shrimp farms effluents</td><td>0–30</td><td>60</td><td>This study (Santos et al., 2023; Cavalcante et al., 2024; Lacerda et al., 2006b)</td></tr><tr><td>Zn</td><td>(–)</td><td>[0; 6200]</td><td>37.3 [0; 80.0]</td><td>300</td><td>(Dias, 2007)</td></tr><tr><td>Hg</td><td>[0.1 10<sup>-3</sup>; 15 10<sup>-3</sup>]</td><td>[7.5·10<sup>-6</sup>; 10·10<sup>-3</sup>]</td><td>10·10<sup>-3</sup></td><td>500·10<sup>-3</sup></td><td>This study (Santos et al., 2023)</td></tr></tbody></table>

farms effluent high content has been found in water up to 542 mg kg⁻¹ (Table 3) which is not found back in sediments which can be explained by the low K complexation between OM and Cu. Marins et al. (2020) (Marins et al., 2020) shows that for small fluxes of nutrients, mangroves retain 95 %, whereas for larger fluxes, only 41 % is retained. As for nutrients, this study highlights the limited capacity of mangroves to retain metals under high pollution fluxes. Mercury content found in shrimp farms products such as aquafeed and fertilizer is around 0.1–90.3 µg kg⁻¹ (Lacerda et al., 2011) which is above the content found in water or sediments likely due to a dilution effect (Table 3). For chromium, however, the sediment concentration is much higher than that observed in the river (Table 3), suggesting that chromium may have a background source or that it primarily settles from particulate chromium. For zinc, sediment concentrations are lower than those in the river, where concentrations can reach up to 6200 mg kg⁻¹, with a mean in Jaguaribe sediments of 37.3 mg kg⁻¹ and a maximum of 80 mg kg⁻¹ (Table 3). Given that Zn interacts with OM in Jaguaribe sediments, this may reflect the poor OM complexation capacity and low site density for zinc retention in sediments (Fig. 7a). As a result, much of the zinc may flow out into the estuary. Further studies are needed to compare metal concentrations in river waters and sediments, particularly to improve our understanding of metal retention in mangrove ecosystems.

According to CONAMA (National Environmental Council of Brazil) (CONAMA and National Environmental Council of Brazil, 2009), prevention values are 300 mg kg⁻¹, 75 mg kg⁻¹, 60 mg kg⁻¹ and 500 µg

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Metal excess inventory for Zn, Co, Cr (g per m²) and Hg (mg per m²) fora Jaguaribe cores (upstream, middle, downstream, shrimp) b Pacoti cores (upstream, middle river, middle inland, downstream river, downstream inland). Error bars represent the propagated standard error of the metal stock, derived from the standard error of metal concentrations used in the stock calculation. * : p-value ≤ 0.05, ** : p-value ≤ 0.01, *** : p-value≤0.001. For Pacoti cores, all p-values are >0.05, for clearification of the graph, it is not indicated. All p-values for Pacoti cores exceeded 0.05 and are therefore omitted for clarity.</figcaption></figure>
$kg^{-1}$ for Zn, Cr, Cu and Hg respectively. In the case of chromium, both Jaguaribe and Pacoti show mean concentrations of 180.4 mg kg$^{-1}$ and 216.3 mg kg$^{-1}$, which are higher than the prevention value.

### 3.5. Metal excess inventory

Jaguaribe exhibits significant variation in metal storage along the mangrove, with the Shrimp core showing significantly the highest concentrations of Co and Zn (respectively $p < 0.001$ and $p < 0.01$) (Fig. 8a). This is likely due to the finer granulometry and nitrogen rich OM content, coupled with anaerobic conditions. Shrimp effluents are also a source for metals such as Zn, Hg and Cu (Lacerda et al., 2006b, 2011; Mounier et al., 2001). Globally, Zn and Co significantly increase from upstream to downstream (Fig. 8a), indicating that these metals are more likely to be transported to the ocean through erosion. Additionally, Thays et al. (Santos et al., 2024) reported that, in a similar mangrove system, Zn and Co exhibited weak associations with the solid phase, indicating their high potential for remobilization by tidal action, even in the absence of direct erosive processes. In contrast, Cr displays an opposite spatial trend, with significantly higher concentrations upstream ($p value < 0.001$), where sandy and inorganic particles prevail. Unlike Co and Zn, Cr is strongly bound to the solid phase (Oliveira, 2012; Santos et al., 2024), and given its upstream accumulation, it is less susceptible to mobilization by either erosion or tidal dynamics. Pacoti demonstrates a relatively homogeneous distribution of metals across the mangrove with no significant differences in metal storage between upstream and downstream and from the river to inland (Fig. 8b). It has significantly the same content of Hg and Cr but lower content of Zn and Co than Jaguaribe (t-test; $\alpha = 0.05$, $N = 9$) which may be attributed to two main factors: (i) different interactions with sediments, as discussed in this article, and (ii) lower anthropogenic input.

Although a differential storage of metals is observed between and within the mangroves, we cannot talk about accumulation of metals for Hg and Zn in comparison to other mangrove systems. A mean Zn inventory of 20.13 $g m^{-2}$ and 144.00 $g m^{-2}$ has been reported for Guanabara Bay and Sepetiba Bay (Brazil), respectively (Machado et al., 2002; Gomes et al., 2009). In contrast, Zn inventories in this study range from 0 to 9.42 $g m^{-2}$, and Hg inventories remain low ranging from 0 to 1.10 $mg m^{-2}$. For comparison, Machado et al. (2002) (Machado et al., 2002) reported an Hg inventory of 25.63 $mg m^{-2}$ in Guanabara Bay. No data on metal excess inventory in mangroves was found on Co and Cr which does not allow comparison.

The assessment of metal excess in sediments has several limitations. First, the number of sampling cores is relatively small given the spatial extent of the mangroves (4 cores for Jaguaribe and 5 cores for Pacoti). Second, this study represents a snapshot at a single time point. Mangrove sediments are dynamic, particularly in semi-arid systems where prolonged dry seasons can alter redox conditions, allowing oxygen penetration through cracks, modifying metal speciation, and affecting storage (Nobrega et al., 2013). Our field campaign was conducted during the wet season; sampling during the dry season could provide insights into potential seasonal metal release or retention. Third, analytical constraints limited our assessment to metal excesses of Cr, Zn, Co, and Hg, potentially overlooking contributions from other metals such as Ni or Pb, which may follow different upstream-downstream patterns. For instance, Cr exhibits behavior distinct from other metals, and downstream accumulation can only be confidently assessed for Zn and Co in Jaguaribe. Finally, uncertainties remain regarding reference values used for calculating metal storage. These values are derived from multiple literature sources (Table A2, Supplementary Materials), as no standardized local reference exists.

## 4. Conclusion

This study aimed to investigate the variability of key drivers influencing metal-sediment interactions in two semi-arid mangroves, under

the assumption that both tidal gradients and land use affect these interactions through three main aspects: (i) whether classical metal retention mechanisms are maintained, (ii) implications for metal mobility, and (iii) the spatial distribution of metal stocks.

Comparative analysis of the Jaguaribe and Pacoti estuaries revealed distinct metal-sediments associations. In Jaguaribe, elevated inputs of metals and nitrogen-rich OM, mainly from shrimp farming and agricultural effluents, favored the interaction of Zn, Hg, and Co with OM throughout the entire mangrove. Specifically, Zn and Hg were preferentially associated with nitrogen-rich OM, while Co exhibited less selective interactions. This pattern deviates from the classical model of metal retention in mangroves, which posits dominant binding to sulfide phases under reducing conditions.

In contrast, the more preserved Pacoti mangrove showed lower concentrations of Zn and Co, but Hg levels were high enough to indicate preferential binding to sulfur compounds, aligning with conventional metal-sulfur interactions. Notably, both sites exhibited peaks in Hg and Zn around 30–50 cm depth, although driven by different mechanisms: sulfur-associated retention in Pacoti and nitrogen-associated retention in Jaguaribe.

These differences have important implications for metal mobility. Metals accumulated at intermediate depths (~30 cm) are more vulnerable to remobilization via bioturbation or tidal action. In Pacoti, this risk is negligible due to low overall metal concentrations. In Jaguaribe, elevated metal levels, in shrimp core, are found in deeper layers (>40 cm) but they remain potentially mobile due to their association with nitrogenous OM, which is more labile than sulfur-bound forms such as pyrite. This nitrogen-metal interaction suggests a greater risk of remobilization and transport across environmental compartments.

Regarding tidal influence, no significant variations in metal-sediment interactions were observed along the upstream-downstream or river-inland axes in either mangrove. However, in Jaguaribe, metal concentrations varied along the upstream-downstream gradient, driven mainly by granulometry (coarser sediments upstream) and proximity to pollution sources. These findings underscore that land use exerts a stronger influence than tidal position on metal behavior in sediments.

Both mangroves exhibited low site density and K constants for Cu and OM. Zn showed limited sediment retention in Jaguaribe relative to its high concentration in the river, likely due to the poor complexation capacity of OM, primarily in the context of substantial external inputs. In contrast, Hg levels in suspended and deposited sediments were comparable, suggesting stronger retention. Jaguaribe and Pacoti show similar excess inventories of Hg and Cr, but Co and Zn are higher in Jaguaribe. Within Jaguaribe, Cr accumulates upstream, while Zn and Co are enriched downstream, likely due to greater inputs and fine particle accumulation near shrimp farms. This downstream accumulation may pose environmental risks due to coastal erosion and potential remobilization. Pacoti, by contrast, exhibited more homogeneous metal stocks along the tidal gradient. These patterns highlight that the mangrove's effectiveness as a metal sink depends on (i) the metal species (ii) the nature and origin of OM, shaped by land use (iii) granulometry, related to natural conditions (proximity of dunes) or land use (damming upstream). Granulometry especially played a critical role in metal retention by influencing surface area available for complexation and redox conditions. Except for chromium—whose behavior warrants further investigation—metals were not retained in the sandy sediment cores of Jaguaribe, challenging the common assumption that mangroves act as metal sinks solely due to fine particle accumulation (Harbison, 1986). In Ceará, where damming and coastal erosion increase sandy sediment inputs into semi-arid mangroves, such granulometric shifts reduce the system's capacity to retain metals. This study also highlights the need to reconsider the perception of nitrogen-rich effluents from shrimp farms as harmless. Elevated nitrogen inputs alter mangrove biogeochemistry and promote metal remobilization. We therefore recommend implementing wastewater treatment prior to discharge and strengthening environmental monitoring programs. In particular, the release of metals
such as mercury in impacted mangroves should be carefully tracked, given their potential for bioamplification in aquatic species consumed by local populations. Although no elevated mercury concentrations have yet been detected in aquatic organisms from the Jaguaribe mangrove (Costa and Lacerda, 2014), continued monitoring is essential to safeguard both ecosystem integrity and human health.

### CRediT authorship contribution statement

**Hélène Plihon:** Writing – original draft, Methodology, Investigation, Formal analysis, Data curation. **Rozane Marins:** Writing – review & editing, Supervision, Investigation, Conceptualization. **Stéphane Mounier:** Writing – review & editing, Supervision, Funding acquisition, Conceptualization.

### Consent to participate

Not applicable.

### Consent to publish

Not applicable.

### Ethical approval

Not applicable.

### Fundings & acknowledgment

We would like to express our gratitude to the Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE) for providing the size measurement analyses. We also extend our thanks to the Coordenação de Aperfeiçoamento do Pessoal de Nível Superior (CAPES) and the Comité Français d'Évaluation de la Coopération Universitaire et Scientifique avec le Brésil (COFECUB) that funded the project Te 980/20, "Biogeochemistry of metals in estuaries under dry conditions in Brazil and France related to global climate change". Finally, we acknowledge the Doctoral School ED 548 for the mobility grant, which supported the research stay, and the École Normal Supérieure de Lyon for the doctoral grant.

### Declaration of competing interest

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

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecss.2025.109564.

### Data availability

Data will be made available on request.

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