# Persistent organic pollutants in food systems: A comparative study across four contrasting socio-ecosystems: Portugal, Senegal, French Guiana, and Guadeloupe

Nathalie El Deghel, Michael Rapinski, Priscilla Duboz, Richard Raymond, Damien Davy, Nuno Durães, André-Marie Dendievel, Eduardo Ferreira da Silva, Pascal Jean Lopez, Vincent Vaccher, et al.

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Nathalie El Deghel, Michael Rapinski, Priscilla Duboz, Richard Raymond, Damien Davy, et al. "Persistent organic pollutants in food systems: A comparative study across four contrasting socio-ecosystems: Portugal, Senegal, French Guiana, and Guadeloupe." Food Chemistry, 2025, 493, pp.145766. 10.1016/j.foodchem.2025.145766. hal-05197539

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# Persistent organic pollutants in food systems: A comparative study across four contrasting socio-ecosystems: Portugal, Senegal, French Guiana, and Guadeloupe

Nathalie El Deghel<sup>a, *</sup>, Michael Rapinski<sup>b</sup>, Priscilla Duboz<sup>c</sup>, Richard Raymond<sup>b</sup>, Damien Davy<sup>d</sup>, Nuno Durães<sup>e</sup>, André-Marie Dendievel<sup>a</sup>, Eduardo Ferreira da Silva<sup>c</sup>, Pascal Jean Lopez<sup>f</sup>, Vincent Vaccher<sup>g</sup>, Anaïs Vénisseau<sup>g</sup>, Julie Anne Nazare<sup>h</sup>, Anne-Laure Badin<sup>a</sup>, Jean-Philippe Bedell<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Human environment observatories (OHM)

Food systems

Persistent organic pollutants (POPs)

PCDD/Fs

PCBs

Toxic equivalents (TEQ)

Dietary exposure

## ABSTRACT

This study quantifies persistent organic pollutants (POPs) in food across four territories undergoing dietary transitions: French Guiana, Guadeloupe, Senegal, and Portugal. A total of 200 local and imported food samples from 12 categories were analyzed for polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs). Concentrations varied by food matrix, region, and origin. The highest PCDD levels were found in butter from Senegal (16.64 pg/g), and the highest PCDFs in leafy vegetables from Portugal (2.4 pg/g). Fish category showed the highest levels of dioxin-like PCBs (600.7 pg/g) and non-dioxin-like PCBs (3.6 ng/g). All values complied with EU Regulation 2023/915. To estimate dietary exposure, the maximum allowable daily intake (g of food/day) was calculated using Toxic Equivalents (TEQ), EFSA's tolerable weekly intake (TWI: 2 pg TEQ/kg bw/week), and compared to national food consumption data. In Portugal, exposure from fish exceeded the TWI, highlighting the influence of regional contamination sources and dietary habits.

## 1. Introduction

Addressing complex health risks requires a comprehensive approach, as emphasized by the *One Health* concept, which highlights the close and complex interdependence between human, animal, plant, and ecosystem health (Andrivon et al., 2022; Garcia et al., 2020; Leal Filho et al., 2022; Rizzo et al., 2021).

Food systems evolve across different territories, influenced by both external factors (e.g., climate change, globalization, sustainability) and internal factors (e.g., technological advancements in production and agricultural practices), as well as cultural factors, including social,

economic, and political dynamics (Bernstein, 2016; Chen & Antonelli, 2020; Mukahhal et al., 2022). These transformations can reshape dietary practices and influence exposure to chemical contaminants.

Among these contaminants, persistent organic pollutants (POPs), such as polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs) (Squadrone et al., 2015) are classified among the “Dirty Dozen” under the Stockholm Convention (UNEP, 2009). Due to their persistence (long half-life), toxicity, and lipophilic properties, POPs can be found in various matrices and accumulate in fat-rich foods such as fish, meat, milk, and dairy products, making these foods a significant route of

<footnote><sup>a</sup> Université Claude Bernard Lyon 1, LEHNA UMR 5023, CNRS, ENTPE, F-69518 Vaulx-en-Velin, France</footnote>

<footnote><sup>b</sup> UMR 7206 Eco-Anthropologie, MNHN/CNRS Université de Paris Cité, Paris 75116, France</footnote>

<footnote><sup>c</sup> Faculté de Médecine Nord, UMR 7268 ADES, CNRS / EFS / AMU, Marseille Cedex 15 13916, France</footnote>

<footnote><sup>d</sup> UAR 3456 LEEISA, CNRS, Université de Guyane, IFFREMER, Cayenne 97300, French Guiana</footnote>

<footnote><sup>e</sup> GEOBIOTEC & Departamento de Geociências, Universidade de Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal</footnote>

<footnote><sup>f</sup> UMR 8067 BOREA, MNHN/CNRS/Sorbonne Université/IRD/Université des Antilles, Paris 75005, France</footnote>

<footnote><sup>g</sup> Oniris, INRAE, LABERCA, 44300, Nantes, France</footnote>

<footnote><sup>h</sup> Centre de Recherche En Nutrition Humaine Rhône-Alpes, Laboratoire CarMeN, INSERM U.1060, INRAe U. 1397, Université Claude Bernard Lyon1, Hospices Civils de Lyon, Cens, Université, Pierre Bénite, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: nathalie.eldeghel@entpe.fr (N. El Deghel).</footnote>
exposure (Canlı et al., 2024; Yüce et al., 2025).

POPs are released through human activities, particularly in the in-
dustrial and agricultural sectors (Guo et al., 2019). Their potential
transport over long distances via water, air, sediments, and plants is
amplified by the grasshopper effect, a mechanism by which semi-
volatile pollutants undergo repeated cycles of volatilization and
condensation, driven by temperature gradients, allowing them to move
as they move through the atmosphere from warmer to colder regions,
thereby increasing their global impact (Canlı et al., 2022; Güzel et al.,
2022; Mandal et al., 2024; Mishra et al., 2022; UNEP, 2009; Vaccher
et al., 2018).

PCDD/Fs are unintentionally generated during incomplete combus-
tion, waste incineration, the production of certain pesticides and
chemicals, and activities such as metal extraction and automobile traffic,
particularly from diesel engines (Canlı et al., 2022; González & Domi-
ngo, 2021; Güzel et al., 2024). Among them, 17 PCDD/Fs are particu-
larly interesting due to their high toxicity and widespread presence in
the environment (EFSA et al., 2018).

In contrast, PCBs were industrially produced until the 1980s and
massively used as heat exchange fluids in electrical transformers and
capacitors, and additives in various materials (Perelló et al., 2012; Sirot
et al., 2012).

These compounds include two categories: dioxin-like PCBs (DL-PCBs), including coplanar PCBs, and mono-ortho-substituted PCBs, whose toxicological effects are similar to those of PCDD/Fs due to their affinity for the Aryl Hydrocarbon Receptor (AhR); and non-dioxin-like PCBs (NDL-PCBs), which act through distinct mechanisms and are associated with various effects on the liver, thyroid, immune system, reproduction, behavior, and potential carcinogenicity (EFSA et al., 2018; Harmouche-Karaki et al., 2019; WHO, 2016).

The Stockholm Convention recommends monitoring six NDL-PCBs,
known as indicator PCBs (PCB congeners 28, 52, 101, 138, 153, and
180), which account for 50 % of food contamination along with PCDD/
Fs, DL-PCBs, and other POPs (UNEP, 2009).

Since food is a major exposure pathway, it also plays a central role in
social and cultural dynamics reflecting the values and beliefs of society,
shaping individual and group identities (Frez-Muñoz et al., 2021;
Hubert, 1991). Therefore, from a public health perspective, regulatory
limits for dioxins and PCBs in foodstuffs have been established under the
European Commission Regulation 2023/915 to reduce health risks (EC,
2023).

However, exposure to POPs varies by territories, depending on fac-
tors such as local dietary habits, food (e.g., imported vs. locally pro-
duced), food processing methods, and environmental contamination
levels.

In this global context, the *Human-Environment Observatories (Observatoires Hommes-Milieux*, OHMs) provide valuable insights into food-related risks as part of a holistic approach to understanding behaviors and lifestyles. They support research initiatives in multiple territories worldwide through the Institute of Ecology and Environment (InEE) of the National Centre for Scientific Research (CNRS), France. These observatories examine human-impacted socio-ecosystems subjected to disturbances known as disrupting events. Such disruptive events can affect long-standing environmental, social, and ecological equilibria, potentially leading to socio-ecosystemic crises. Consequently, these territories become key research areas, allowing for comparisons of knowledge before and after such events (Chenorkian, 2012, 2020).

This study focuses on four territories: the Oyapock River Estuary (OHM Oyapock, French Guiana/Brazil), Guadeloupe-French West Indies (OHM LC: Littoral Caraïbes, Coastal Caribbean), the Sahelian region of Tessékéré (OHMi Tessékéré, Senegal), and Central Portugal (OHMi Estarreja). These territories were selected for their contrasting environmental contexts, levels of industrialization, and food systems, which range from traditional subsistence models to globalized supply chains.

They form a socio-environmental mosaic: tropical forests along
rivers (Oyapock) and coastal island systems (Guadeloupe), Sahelian

drylands (Tessékéré), or temperate industrial ecosystems (Estarreja).
Cultural and linguistic diversity further shapes distinct worldviews and
food practices. Each territory was influenced by different disrupting
events (Chenorkian, 2020): the construction of cross-border infrastruc-
ture in Oyapock, the intensification of maritime trade in Guadeloupe,
the Great Green Wall project in Tessékéré, and the pollution mitigation
efforts in Estarreja. These transformations have restructured food sys-
tems (self-production, local markets, and imports), which are considered
"healthy" or "harmful" food, alongside exposure to chemical contami-
nants. Consequently, these changes have reshaped the relationship be-
tween local populations and their surrounding environment (Rapinski
et al., 2023a).

A previous study by Rapinski et al. (2025) assessed the dietary and nutritional transition linked to globalization. It also highlighted chemical contamination in these territories through studies conducted on the typical diet, such as organic and inorganic contamination in food, water, and soil in Estarreja, mercury and lead in fish and products derived from cassava in Oyapock, and chlordecone in banana plantations in Guadeloupe (Rapinski et al., 2023a).

However, research on food contamination by POPs remains limited or nonexistent in these regions. A comprehensive sampling strategy was developed to address this gap, combining local dietary habits with the analysis of PCDD/Fs and PCBs in selected key foods. This approach was done using responses to the Dietary Diversity Questionnaire (DDQ) based on 24-h recall (Rapinski et al., 2023b). This survey, conducted following stabilized and established guidelines and methodology to calculate dietary diversity (Kennedy et al., 2011), integrates Monteiro's NOVA classification for the degree of food processing (Monteiro, Can-non, Lawrence, Louzada and Machado, 2019), while also considering the provenance of food (i.e., their mode or source of acquisition) and participants' perception of these food as "local", "healthy" and "tradi-tional" (Rapinski et al., 2024).

The primary objective of our study is to detect and quantify PCDD/Fs and PCBs in foods identified through a local dietary survey, providing contamination profiles relevant to real consumption habits. A major contribution lies in generating original data from four contrasting OHMs, including territories with limited or non-existent information on food contamination by POPs. This helps to fill critical gaps in the liter-ature and supports future health risk assessments.

The secondary objective is to assess dietary risk by calculating and comparing the Daily and Weekly Estimated Food Intake (g) for adults and children, considering the maximum recommended Tolerable Weekly Intake (TWI) of PCDD/Fs + DL-PCBs, with available national data.

## 2. Materials and methods

### 2.1. *Sampling*

#### 2.1.1. *Characteristics and origin of samples*

A multi-matrix approach was used to assess contamination levels through a non-targeted screening of the POPs food groups mentioned by individuals interviewed during the 24-h recall survey conducted in the four targeted OHMs by Rapinski et al., 2025.

The selection of samples in the field was based on two main criteria:

(i) The first key criterion was the origin of food products (local vs. imported). Foods representing local food systems, derived from hunting, fishing, gathering, farming, and cultivation, and consumed locally, were selected. These local products were compared to imported foods available in grocery stores and supermarkets, reflecting the globalized food systems.

(ii) The second criterion focused on the nutritional contribution of food matrices based on their macronutrient content (carbohydrates, proteins, and lipids) or micronutrient composition (vitamins, minerals, etc.).
The sample collection was carried out from May to November 2023 in the four studied territories (OHM). For OHMi Estarreja (Portugal), samples of local products were collected from agricultural fields near the industrial zone, the main source of contamination, where fruit and vegetables are still grown, and in family gardens. In addition, other local samples were collected from the weekly market of Estarreja, where local fruits, vegetables, meat, and other food products are sold. Some cereals, such as rice and corn, were obtained from local producers. All fish samples were collected post-mortem through local markets and fishermen. The research team did not participate in the capture or handling of live specimens. Finally, imported samples, like local products, were collected from supermarkets. In OHMi Tessékéré (Senegal), samples were collected from the weekly markets in the Ferlo region (Amali and Widou) and small shops between camps. These included staple foods regularly consumed and imported processed products, such as powdered milk, which is gradually replacing fresh milk due to declining production caused by drought (Ka et al., 2020). For OHM Oyapock (French Guiana-Brazil), samples were collected on both sides of the border from supermarkets (grocery stores, mini-markets), open markets, or directly from fishermen. These samples comprise local food as well as products introduced into the population's diet due to food trade facilitated by the opening of the Franco-Brazilian cross-border region through road access. Finally, for Guadeloupe (OHM-LC), local samples were collected from processing sites, such as cassava mills (where cassava-based products are made) or via direct fishing; all fish specimens were collected post-mortem. Imported products were purchased from supermarkets, small grocery stores, and open markets.

Thus, a total of 200 samples were collected and distributed as follows: 66 for Estarreja, 47 for Tessékéré, 39 for Oyapock, and 48 for Littoral-Caraïbes. The number of food samples collected per observatory varies due to the difference in food availability, logistical feasibility, and the prioritization of foods most commonly cited in the dietary survey at each site. The collected foods were classified into 12 categories: oleaginous fruits and seeds, oils and fats, vegetables, dark green leafy vegetables, sweets, fruits, legumes, cereals, milk and dairy products, fish and shellfish, meat (muscle) and animal proteins, and roots, tubers, and starchy foods (Supplementary Data, Table A1).

### 2.1.2. Grouping of samples

The samples (ss) from each category and each OHM were grouped according to their source (local or imported) to form composite samples (denoted as *n*) for analysis. For example, in the “meat (muscle) and animal proteins” category, if 10 ss were available, including 4 beef pieces (2 local, 2 imported) and 6 chicken pieces (3 local, 3 imported), the procedure involved grouping the subsamples by source. Thus, the beef samples were combined into two composite samples ($n = 2$: one for local beef and one for imported beef) by mixing equal amounts of each sample (ss). Similarly, the chicken samples were grouped into two composite samples ($n = 2$: one for local chicken and one for imported chicken). This method was applied to all food categories. The 200 collected samples were grouped into 71 composite samples for analysis.

## 2.2. Analytical protocol

### 2.2.1. Sample preparation for POPs analysis

A preliminary treatment was performed on all samples to use only the edible parts of the food matrices. Fruits and vegetables were peeled, and seeds and pits were removed. Bones, scales, fins, and entrails were removed from fish samples. For meat and chicken, the skin and bones were eliminated. After these separations, the edible parts were stored at -20 °C before freeze-drying. All samples were analyzed in their raw state, without any cooking procedure.

### 2.2.2. POPs analysis

The detection and quantification of 17 PCDD/Fs, 12 DL-PCBs, and 6 NDL-PCBs congeners were performed at LABERCA (LABoratoire d'Étude

des Résidus et Contaminants dans les Aliments; Unité de recherche de l'École Nationale Vétérinaire, Agroalimentaire et de l'Alimentation (Oniris), Nantes, France), using a validated and ISO 17025:2017 accredited method (ISO, 2017).

The sample preparation was conducted in a pressurized room to minimize the risk of contamination. First, the target analytes were extracted using an adapted process to the physical characteristics of the samples. Solid samples were freeze-dried and ground into fine particles to ensure a homogeneous and representative sample for subsequent analyses (Vaccher et al., 2018).

Before extraction, ¹³C-labeled internal standards, including 17 congeners ¹³C₁₂-PCDD/Fs and 18 congeners ¹³C₁₂-PCBs, were introduced to accurately quantify the target substances. The hydrophobic fraction was extracted using the “Pressurized Liquid Extraction (PLE)” method, using a Büchi SpeedExtractor E-914 (Rungis, France), operating under high pressure and temperature (P = 100 bars, T = 120 °C). A toluene/acetone mixture (70/30, v/v) was injected three times into the extraction cell, with 5-min cycles each, for 53 min. The solvents of the hydrophobic fraction in the recovery flasks were then dried using rotary evaporators (Büchi) and weighed to determine the amount of extracted fat. Finally, the extract was dissolved in 5 mL of hexane and 0.6 mL of ethyl acetate before purification.

The purification and separation of PCDD/Fs and PCBs were performed using an automated system (Autosym GO-4-HT; Miura, Japan) in four sequential chromatographic steps. At the end of this step, the target compounds were eluted into two fractions: the first containing PCDD/Fs and non-ortho PCBs, and the second containing mono- and di-ortho PCBs. The principle of this system and the detailed procedure used are described in the supplementary material (Paragraph S1).

After the purification step, ¹³C-labeled external standards were added to both extracted fractions to determine the recovery rates. The extracts were then injected into gas chromatography (7890 A; Agilent Technologies, Santa Clara, CA) coupled with high-resolution mass spectrometry (double-focusing magnetic sector mass spectrometer, JMS-700D and 800D; JEOL, Japan). The separation steps and temperature programs used for identifying and quantifying all congeners are provided in the supplementary material (Paragraph S2).

### 2.2.3. Accuracy assessment using certified reference materials (CRM)

To verify analytical accuracy, certified reference materials were used for both PCDD/Fs and DL-PCBs. For PCDD/Fs, the certified milk powder CRM 607 was analyzed. The ratio of the measured to the certified value was used to assess accuracy. The certified concentration was 2.43 ± 0.084 pg-TEQ/g, and the mean measured concentration was 2.33 ± 0.072 pg-TEQ/g.

For DL-PCBs, the fish-based CRM WMF-01 was analyzed. The certified concentration was 104.49 ± 35.182 pg-TEQ/g, and the mean measured concentration was 98.03 ± 2.69 pg-TEQ/g.

These results confirm the reliability of the method for both compound groups.

## 2.3. Expression of results

### 2.3.1. Concentration and toxicity values

The concentration results for the various PCDD/F, DL-PCBs, and NDL-PCBs congeners are expressed on wet weight basis (ww) for the different food categories in the four OHMs. The results for PCDD/Fs and DL-PCBs are expressed according to the Upper-Bound (UB) and Lower-Bound (LB) concepts. The UB concept uses the quantification limit to assess the contribution of each unquantified congener (below the limit of quantification: LOQ). The LB concept consists of assigning a zero score for the contribution of each unquantified congener (EC, 2017). Values expressed in UB will be used for subsequent analyses in this paper, following data review and recommendations from the World Health Organization (WHO) and the European Food Safety Authority (EFSA) regarding low levels of contaminants in food (Vaccher et al., 2020).
Values expressed in LB will be provided but not discussed in **Supplementary Data, Table A2**.

To estimate the toxicity of the target congeners in the different food categories, the Toxicity Equivalency Factor (TEF) concept from the WHO was used. This approach allows for assessing risks associated with exposure to the 17 PCDD/Fs and 12 DL-PCBs, which share similar toxicity mechanisms through their affinity for the AhR (*Aryl Hydrocarbon Receptor*) (Gao et al., 2023).

The TEF values are weighting coefficients that express the toxicity of each congener (individually) relative to the reference congener, 2,3,7,8-Tetrachloro-Dibenzo-p-dioxin (2,3,7,8-TCDD), known as the Seveso dioxin (Van den Berg et al., 2006). These factors allow for the calculation of Toxic Equivalents (TEQs), which are defined as the sum of the products of the concentrations of each congener multiplied by its corresponding TEF value, as established by WHO in 2005 (TEF WHO-2005) (Van den Berg et al., 2006) (Supplementary material; S3.1 and S3.2). The concentrations will be reported in pg/g of wet weight (ww) for Dioxins and DL-PCBs congeners and in pg TEQ/g of wet weight for TEQ values.

### 2.3.2. Estimation of the amount of food (g) containing the tolerable weekly intake (TWI) of dioxins and PCBs

Assessing health risks associated with food consumption is an important approach to evaluate the potential risk of human exposure to toxic substances, providing critical information to policymakers to protect consumer health (Barone et al., 2021). The EFSA has established a tolerable weekly intake (TWI) of 2 pg TEQ per kg of body weight per week for the sum of dioxins and dioxin-like PCBs (EFSA et al., 2018).

In the context of this study, data on the quantities of food consumed are not available for each studied territory. Therefore, the maximum daily and weekly amounts allowed for the consumption of the tested products were calculated following the TWI (Usydus et al., 2009). These quantity estimates can serve as a proxy to assess the potential exposure of local populations to pollutants while considering the limitations related to differences in dietary habits.

This calculation is based on the following formula, used to estimate the daily intake (Ai) of foods, expressed as:

$$TWI_{PCDD/F+PCB} (\text{pg TEQ/kg of body weight/week}) = \frac{(\text{Total TEQ} * \text{Ai})*7}{\text{BW}} \quad (1)$$

Isolating Ai, it comes:

$$A_i = \frac{\text{TWI (PCDD, F + PCB)} * \text{BW}}{(\text{Total TEQ} * 7)} \quad (2)$$

where: the tolerable weekly intake (TWI) for dioxins and dioxin-like PCBs is set at 2 pg TEQ per kilogram of body weight per week (pg TEQ/kg of body weight/week); the Total TEQ (pg TEQ/g) corresponds to the toxic equivalent of a specific food item i; the variable Ai (g/day) represents the maximum daily quantity of a specific food item i that can be consumed; the BW (Body Weight) refers to the average body weight of an adult, estimated at 70 kg. Finally, 7 refers to the number of days of the week.

## 3. Results and discussion

This study provides important data through the analysis of raw foods, offering a directly exploitable database. It does not claim to be a Total Diet Study (TDS), which would involve a larger number of samples prepared according to the cooking practices of the populations (Sirot et al., 2012; Vaccher et al., 2020). These data can later be adapted for more specific comparisons or cooking models. While the grouping of samples into categories may sometimes differ from the exact composition of other studies, it still allows for a general comparison of observed trends. Finally, the use of composite samples, while limiting the level of detail in the provided data, remains a relevant method for exploring

global dynamics and identifying promising avenues for future research (Perelló et al., 2012; Windal et al., 2010).

### 3.1. Quality of the analyses and measurement of POPs

The 17 PCDD/Fs, 12 DL-PCBs, and 6 NDL-PCBs were analyzed for the 71 samples. The samples grouped into categories for each observatory, along with the fat content and mass concentrations expressed in UB for each congener, are presented in Table A1 (Supplementary Data). The WHO-TEQ values for the PCDD/Fs, DL-PCBs, the sum of PCDD/Fs and DL-PCBs, and the sum of mass concentrations of NDL-PCBs, were calculated for each analyzed sample. The values expressed in LB are presented in Table A2 (Supplementary Data). The recovery rates of the standards added during the analyses ranged from 34.5 % to 153 % (Supplementary Data, Table A3).

According to the adopted method, the proportion of samples where quantification is possible varies for each congener. For example, for all the analyzed samples, the quantification rates for the congeners 2,3,7,8-TCDD, 1,2,3,4,7,8-HxCDD, 1,2,3,7,8,9-HxCDF, 1,2,3,4,7,8,9-HpCDF, and OCDF were 28.2 %, 38 %, 31 %, 11.3 %, and 33.8 %, respectively (Supplementary Data, Table A1). In contrast, some congeners, such as OCDD, 1,2,3,4,6,7,8-HpCDF, PCB 77, PCB 105, PCB 118, as well as the 6 NDL-PCBs, were consistently quantified in all analyzed samples, confirming their ubiquity in the tested foods. These results reflect varying proportions of left-censored data of each congener and confirm the high sensitivity of the analytical methods used (Vaccher et al., 2020).

Regarding animal-based food categories, the average detection percentages for the congeners were 81 % for milk and dairy products, 78 % for meat and meat products, and 76 % for fish. All congeners were detected in the butter sample analyzed (Supplementary Data, Table A4). The details of the quantification percentages for the 17 PCDD/Fs, and the 12 DL-PCBs, by categories of all foods from the 4 observatories, are presented in Fig. 1. As for the 6 NDL-PCBs, they were quantified in all categories, as previously mentioned.

### 3.2. Concentrations of POPs in the samples of the 4 observatories

The average levels of PCDD/Fs and PCBs, as well as the sum of NDL-PCBs, analyzed for each category in the respective observatory, are presented in Fig. 2. The analytical methods yielded low values (in order of magnitude and/or units), demonstrating high precision and data reliability. Therefore, the analyzed POP concentrations varied depending on the food categories and observatories. As expected for lipophilic pollutants, the analyzed POPs concentrations varied depending on the food matrix, particularly its fat content (EFSA et al., 2018), with a significant correlation observed between fat content and POPs concentrations ($p < 0.001$), as well as the environmental characteristics of each observatory.

For PCDDs, the highest concentrations were quantified in butter ($ss = 2, n = 1$) from OHMi-Tessékéré, locally produced, with values reaching 16.64 pg/g ww. In contrast, the lowest PCDD concentrations (0.01 pg/g ww) were observed in low-fat foods, in fruits ($ss = 13, n = 7$) and tubers ($ss = 6, n = 2$) at OHMi-Estarreja and fruits ($ss = 9, n = 3$) and vegetables ($ss = 5, n = 2$) analyzed at OHM-LC. Similarly, the lowest concentration of PCDFs was observed in the vegetables analyzed in OHM-LC (0.006 pg/g ww), while the highest value (2.4 pg/g ww) was found in the dark green leafy vegetable samples ($ss = 4, n = 1$) in OHMi-Estarreja, potentially due to direct atmospheric deposition, as reported in previous studies (Quéguiner et al., 2010).

Fish samples ($ss = 8, n = 2$) from OHMi-Estarreja and the butter from OHMi-Tessekéré presented the highest concentrations of coplanar PCBs (16.22 pg/g ww and 11.34 pg/g ww, respectively). However, the lowest levels were measured in fruits and tubers at OHMi-Estarreja (0.02 pg/g ww) and in fruits at OHM-LC (0.02 pg/g ww).

The highest concentrations of non-coplanar PCBs were recorded in fish from OHMi-Estarreja (584.52 pg/g ww), while the lowest were
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Percentage of quantification of PCDD/F and DL-PCB congeners in the different categories. [a] Cereals; [b] Fish and shellfish; [c] Fruits; [d] Legumes; [e] Meat (muscle) and animal proteins; [f] Roots, tubers, and starchy foods; [g] Vegetables; [h] Milk and dairy products; [i] Oils and fats; [j] Oleaginous fruits and seeds; [k] Sweets; [l] Dark green leafy vegetables (for details see Materials and Methods section). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

detected in tubers and legumes from the same OHM with respective values of 0.33 and 0.37 pg/g ww. Similarly, NDL-PCBs showed low values in tubers (0.002 ng/g ww), fruits, and legumes (0.004 ng/g ww) at OHMi-Estarreja, while the highest values were measured in fish from the same OHM, with a concentration of 3.57 ng/g ww.

These results suggest the influence of regional factors, agricultural practices, and local soil and water contamination on the distribution of persistent organic pollutants in various environments and food types. In Estarreja, high levels of POPs in fish can be associated with the historical discharge of industrial effluent into the Ria de Aveiro. However, it should be noted that the reported mean values reflect both local and imported fish, which may influence the final concentrations, a point that will be further discussed in the following sections. In Tessékéré, the high concentration of POPs in butter could be attributed to several factors: first, the high fat content of butter promotes the accumulation of lipophilic compounds like dioxins; second, in pastoral systems of Tessékéré, animals may be exposed to environmental contaminations thought soil, water, or contaminated feed may contribute to elevated levels in meat and milk, and the by-products (Amutova et al., 2021). To date, however, no study has assessed environmental levels of POPs in this region.

### 3.3. Toxicity evaluation by TEQs values calculation in the 4 observatories

The main route of exposure (> 90 % of POP intake) comes from the consumption of animal products, fish, and seafood (Bruce-Vanderpuije et al., 2019). For this reason, we conducted an inter-observatory comparison (comparison across observatories) of the results for animal-based food categories.

Using these concentrations and the WHO 2005-TEF (Van den Berg et al., 2006) for each congener, the average WHO-TEQ value (pg/g ww) for each food group was estimated in Table 1. The values obtained for all samples are below the maximum limits set for WHO-TEQs 2005, by European Commission Regulation 2023/915, which establishes maximum levels for PCDD/Fs, DL-PCBs, and NDL-PCBs in foods such as meat, fats, and liver from cattle, sheep, poultry, and pigs; milk and dairy products, eggs and egg-derived products, animal fats, fish (EC, 2023).

#### 3.3.1. OHMi Estarreja

TEQ values in this observatory ranged from 0.002 pg/g ww in fruits to 0.33 pg/g ww in fish for WHO-TEQ PCDD/F. For WHO-TEQ (2005) DL-PCB, concentrations varied from 0.0003 pg/g ww in fruits and tubers to 0.47 pg/g ww in fish. The lowest total TEQ was observed in fruits (0.0025 pg/g ww), and the highest was also found in the fish. Regarding
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Cartography of the mean concentrations of persistent organic pollutants (expressed in wet weight: ww) in food categories representative of each studied site, with n indicating the total analyzed samples, and subsamples (ss) referring to the number of samples collected in each category (shown in brackets) [a] Cereals; [b] Fish and shellfish; [c] Fruits; [d] Legumes; [e] Meat (muscle) and animal proteins; [f] Roots, tubers, and starchy foods; [g] Vegetables; [h] Milk and dairy products; [i] Oils and fats; [j] Oleaginous fruits and seeds; [k] Sweets; [l] Dark green leafy vegetables (for details see Material and Methods section). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

NDL-PCBs, concentrations ranged from 0.00194 ng/g ww in the roots and tubers to a maximum of 3.571 ng/g ww in fish, highlighting fish as the most contaminated category in this observatory.

In the meat category (ss = 4; n = 2), the total WHO-TEQ was 0.05 pg/ g ww. Local meat exhibited higher PCDD/F TEQ values (0.03 pg/g ww) than imported meat (0.019 pg/g ww), suggesting a potential impact of local environmental contamination. DL-PCB levels and total TEQ were similar between the sources. However, for the NDL-PCB, the local meat contributed more to the average concentration, reaching 0.215 ng/g, compared to 0.149 ng/g in the imported meat.

In the fish and seafood category (ss = 8; n = 2), the average WHO TEQ-PCDD/F-PCBs reached 0.8 pg/g ww, and NDL-PCBs were as high as 3.5711 ng/g ww.

When comparing fish origin, local fish had higher TEQ-PCDD/F-PCBs values (1.022 pg/g ww) than imported fish (0.59 pg/g ww), mainly driven by PCDD/F TEQ in local samples (0.56 pg/g ww).

These elevated levels of PCDD/Fs in both meat and fish are consistent with the concentrations of dioxin-like compounds determined in the sediments from the Ria de Aveiro, likely linked to historical discharges from a former chloro-alkali industry (Nunes et al., 2014). However, certain imported samples, like fish, may also contain significant levels of ND-PCB, indicating more widespread contamination. Even though the concentrations measured are generally low, the bioaccumulation of these pollutants in the environment raises toxicological concerns regarding potential health risks for humans in case of regular

consumption.

### 3.3.2. OHM Oyapock

In this observatory, the lowest values of TEQ PCDD/F and DL-PCBs were found in fruits (0.002 pg/g and 0.0003 pg/ ww, respectively), while the lowest NDL-PCB concentrations were found in the fish category (0.0037 ng/g ww). In contrast, milk presented the highest values for all contaminant groups.

For milk (ss = 6, n = 2), the average total WHO-TEQ values were 0.031 pg/g ww in liquid milk and 0.007 pg/g ww in powdered milk. This difference may likely reflect the lower fat content in powdered milk (2.33 %) compared to liquid milk (20.47 %). As both products were commercially processed, they may not accurately represent contamination in locally produced milk or traditional dairy practices.

In the meat category, only imported samples (chicken and meat) were analyzed, with the total WHO-TEQ and NDL-PCB values of 0.006 pg total TEQ/g ww, 0.005 ng/g ww, respectively.

In fish and seafood (ss = 6; n = 2), the average total WHO-TEQ value was 0.01 pg/g ww while the sum of NDL-PCBs averaged 0.0037 ng/g ww. When considering origin, imported fish from the Brazilian side (Imported Fish 2 – Hoplosternum littorale) exhibited a total TEQ value (0.018 pg/g ww), almost three times higher than that of the local fish (Fish 1 – Hoplerythrinus unitaeniatus, 0.005 pg/g ww). However, local fish (Fish 1) had higher NDL-PCBs levels (0.005 ng/g) compared to the imported ones (0.003 ng/g). These results align with findings from other
<caption>**Table 1**</caption>

PCDD/Fs-WHO-TEQ (2005), DL-PCBs-WHO-TEQ (2005), PCDD/Fs + DL-PCBs-WHO-TEQ (2005) and sum of NDL-PCBs mean concentrations for each OHM. Results are expressed on a wet weight basis.

<table><thead><tr><th rowspan="2">OHM</th><th rowspan="2">Category</th><th rowspan="2">N (ss)</th><th rowspan="2">Food core</th><th rowspan="2">Mean</th><th rowspan="2">Lipids (%)</th><th colspan="3">Mean Upperbound concentrations (pg/g ww)</th><th>Mean Upperbound concentrations (ng/g ww)</th></tr><tr><th>WHO-TEQ (2005) PCDD/F</th><th>WHO-TEQ (2005) DL-PCB</th><th>TOTAL-TEQ (2005) (PCDD/F + DL-PCB)</th><th>Sum of 6 NDL-PCB</th></tr></thead><tbody><tr><td rowspan="12">Oyapock</td><td rowspan="2">Cereals</td><td rowspan="2">2 (6)</td><td>Imported Rice</td><td></td><td>0.32</td><td>0.008</td><td>0.002</td><td>0.010</td><td>0.020</td></tr><tr><td>Local Rice</td><td></td><td>0.43</td><td>0.006</td><td>0.001</td><td>0.007</td><td>0.009</td></tr><tr><td rowspan="4">Roots, tubers, and starchy foods</td><td rowspan="4">3 (9)</td><td>Imported Couac 1</td><td>Mean</td><td>0.38</td><td>0.007</td><td>0.002</td><td>0.008</td><td>0.015</td></tr><tr><td>Imported Couac 2</td><td></td><td>0.11</td><td>0.010</td><td>0.001</td><td>0.011</td><td>0.007</td></tr><tr><td>Local Couac</td><td></td><td>0.11</td><td>0.006</td><td>0.001</td><td>0.007</td><td>0.006</td></tr><tr><td>Mean</td><td>0.34</td><td>0.005</td><td>0.002</td><td>0.007</td><td>0.005</td></tr><tr><td rowspan="2">Legumes</td><td rowspan="2">1 (3)</td><td>Imported Beans</td><td></td><td>0.19</td><td>0.007</td><td>0.001</td><td>0.008</td><td>0.006</td></tr><tr><td>Mean</td><td>1.73</td><td>0.005</td><td>0.001</td><td>0.006</td><td>0.006</td></tr><tr><td rowspan="2">Fruits</td><td rowspan="2">2 (3)</td><td>Imported Wassaï</td><td></td><td>5.44</td><td>0.002</td><td>0.0004</td><td>0.002</td><td>0.013</td></tr><tr><td>Local Wassaï</td><td></td><td>5.033</td><td>0.002</td><td>0.0003</td><td>0.002</td><td>0.012</td></tr><tr><td rowspan="2">Milk and dairy products</td><td rowspan="2">2 (6)</td><td>Imported Milk (Liquid)</td><td></td><td>20.47</td><td>0.019</td><td>0.011</td><td>0.031</td><td>0.022</td></tr><tr><td>Imported Milk (Powder)</td><td></td><td>2.33</td><td>0.003</td><td>0.005</td><td>0.007</td><td>0.023</td></tr><tr><td rowspan="2">Meat (muscle) and animal proteins</td><td rowspan="2">2 (6)</td><td>Imported Chicken</td><td>Mean</td><td>11.40</td><td>0.011</td><td>0.008</td><td>0.019</td><td>0.022</td></tr><tr><td>Imported Meat</td><td></td><td>3.45</td><td>0.007</td><td>0.001</td><td>0.008</td><td>0.007</td></tr><tr><td rowspan="2">Fish and shellfish</td><td rowspan="2">2 (6)</td><td>Imported Fish 1</td><td>Mean</td><td>2.59</td><td>0.006</td><td>0.001</td><td>0.006</td><td>0.005</td></tr><tr><td>Imported Fish 2</td><td></td><td>3.42</td><td>0.004</td><td>0.001</td><td>0.005</td><td>0.005</td></tr><tr><td rowspan="12">Littoral Caraïbes</td><td rowspan="2">Cereals</td><td rowspan="2">2 (3)</td><td>Imported Rice</td><td></td><td>3.17</td><td>0.010</td><td>0.002</td><td>0.011</td><td>0.004</td></tr><tr><td>Local Rice</td><td></td><td>0.44</td><td>0.005</td><td>0.001</td><td>0.006</td><td>0.007</td></tr><tr><td rowspan="2">Fish and shellfish</td><td rowspan="2">4 (12)</td><td>Imported Fish 1</td><td>Mean</td><td>0.32</td><td>0.006</td><td>0.001</td><td>0.007</td><td>0.003</td></tr><tr><td>Local Fish 1</td><td></td><td>0.79</td><td>0.006</td><td>0.018</td><td>0.024</td><td>0.124</td></tr><tr><td rowspan="2">Fruits</td><td rowspan="2">3 (9)</td><td>Imported Fish 2</td><td></td><td>0.67</td><td>0.002</td><td>0.001</td><td>0.003</td><td>0.002</td></tr><tr><td>Local Fish 2</td><td></td><td>0.54</td><td>0.002</td><td>0.001</td><td>0.003</td><td>0.008</td></tr><tr><td rowspan="2">Legumes</td><td rowspan="2">1 (2)</td><td>Local Mango</td><td>Mean</td><td>0.7308</td><td>0.004</td><td>0.007</td><td>0.010</td><td>0.037</td></tr><tr><td>Imported Oranges</td><td></td><td>0.67</td><td>0.002</td><td>0.0003</td><td>0.002</td><td>0.008</td></tr><tr><td rowspan="2">Meat (muscle) and animal proteins</td><td rowspan="2">2 (5)</td><td>Imported Chicken</td><td></td><td>0.00</td><td>0.001</td><td>0.0002</td><td>0.002</td><td>0.002</td></tr><tr><td>Imported Chicken</td><td></td><td>0.22</td><td>0.002</td><td>0.0003</td><td>0.002</td><td>0.004</td></tr><tr><td rowspan="2">Vegetables</td><td rowspan="2">2 (5)</td><td>Imported Beans</td><td>Mean</td><td>1.64</td><td>0.005</td><td>0.002</td><td>0.007</td><td>0.006</td></tr><tr><td>Imported Tomatoes</td><td></td><td>1.64</td><td>0.005</td><td>0.002</td><td>0.007</td><td>0.006</td></tr><tr><td rowspan="12">Tessekéré</td><td rowspan="2">Milk and dairy products</td><td rowspan="2">1 (4)</td><td>Local Chicken</td><td>Mean</td><td>2.00</td><td>0.004</td><td>0.003</td><td>0.008</td><td>0.014</td></tr><tr><td>Imported Chicken</td><td></td><td>1.75</td><td>0.005</td><td>0.001</td><td>0.005</td><td>0.005</td></tr><tr><td rowspan="4">Roots, tubers, and starchy foods</td><td rowspan="4">3 (12)</td><td>Local Yam</td><td>Mean</td><td>1.88</td><td>0.004</td><td>0.002</td><td>0.007</td><td>0.009</td></tr><tr><td>Imported Yam</td><td></td><td>0.11</td><td>0.001</td><td>0.0003</td><td>0.001</td><td>0.002</td></tr><tr><td>Local Cassava</td><td></td><td>0.21</td><td>0.003</td><td>0.0004</td><td>0.003</td><td>0.001</td></tr><tr><td>Mean</td><td>0.56</td><td>0.013</td><td>0.0022</td><td>0.015</td><td>0.053</td></tr><tr><td rowspan="2">Vegetables</td><td rowspan="2">2 (5)</td><td>Local Tomato</td><td></td><td>0.30</td><td>0.005</td><td>0.001</td><td>0.006</td><td>0.019</td></tr><tr><td>Imported Tomato</td><td></td><td>–</td><td>0.001</td><td>0.0002</td><td>0.001</td><td>0.003</td></tr><tr><td rowspan="2">Oils and fats</td><td rowspan="2">1 (2)</td><td>Local Butter (cow)</td><td>Mean</td><td>26.17</td><td>0.039</td><td>0.014</td><td>0.053</td><td>0.278</td></tr><tr><td>Local Butter (cow)</td><td></td><td>26.17</td><td>0.039</td><td>0.014</td><td>0.053</td><td>0.278</td></tr><tr><td rowspan="2">Legumes</td><td rowspan="2">1 (4)</td><td>Local Butter (cow)</td><td>Mean</td><td>99.62</td><td>0.637</td><td>0.197</td><td>0.834</td><td>0.518</td></tr><tr><td>Local Butter (cow)</td><td></td><td>99.62</td><td>0.637</td><td>0.197</td><td>0.834</td><td>0.518</td></tr><tr><td rowspan="2">Oleaginous fruits and seeds</td><td rowspan="2">1 (4)</td><td>Cowpea</td><td>Mean</td><td>2.33</td><td>0.006</td><td>0.001</td><td>0.007</td><td>0.016</td></tr><tr><td>Peanuts</td><td></td><td>2.33</td><td>0.006</td><td>0.001</td><td>0.007</td><td>0.016</td></tr><tr><td rowspan="2">Oleaginous fruits and seeds</td><td rowspan="2">1 (4)</td><td>Peanuts</td><td>Mean</td><td>49.62</td><td>0.022</td><td>0.007</td><td>0.029</td><td>0.189</td></tr><tr><td>Peanuts</td><td></td><td>49.62</td><td>0.022</td><td>0.007</td><td>0.029</td><td>0.189</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th rowspan="2">OHM</th><th rowspan="2">Category</th><th rowspan="2">N (ss)</th><th rowspan="2">Food core</th><th rowspan="2">Mean</th><th rowspan="2">Lipids (%)</th><th colspan="3">Mean Upperbound concentrations (pg/g ww)</th><th>Mean Upperbound concentrations (ng/g ww)</th></tr><tr><th>WHO-TEQ (2005) PCDD/F</th><th>WHO-TEQ (2005) DL-PCB</th><th>TOTAL-TEQ (2005) (PCDD/F + DL-PCB)</th><th>Sum of 6 NDL-PCB</th></tr></thead><tbody><tr><td rowspan="12">Estarreja</td><td rowspan="3">Cereals</td><td rowspan="3">3 (7)</td><td>Local Rice</td><td>0.44</td><td>0.012</td><td>0.002</td><td>0.014</td><td>0.012</td></tr><tr><td>Imported Rice</td><td>0.77</td><td>0.009</td><td>0.001</td><td>0.009</td><td>0.013</td></tr><tr><td>Millet</td><td>5.27</td><td>0.010</td><td>0.001</td><td>0.011</td><td>0.007</td></tr><tr><td rowspan="2">Fruits</td><td rowspan="2">1 (5)</td><td rowspan="2">Tamarind</td><td>Mean 2.16</td><td>0.010</td><td>0.001</td><td>0.011</td><td>0.011</td></tr><tr><td>4.52</td><td>0.016</td><td>0.003</td><td>0.019</td><td>0.019</td></tr><tr><td rowspan="3">Vegetables</td><td rowspan="3">2 (6)</td><td rowspan="3">Onions African eggplant</td><td>Mean 4.52</td><td>0.016</td><td>0.003</td><td>0.019</td><td>0.019</td></tr><tr><td>0.53</td><td>0.002</td><td>0.000</td><td>0.002</td><td>0.006</td></tr><tr><td>1.01</td><td>0.003</td><td>0.001</td><td>0.004</td><td>0.005</td></tr><tr><td rowspan="2">Dark green leafy vegetables</td><td rowspan="2">1 (3)</td><td rowspan="2">Bissap leaf</td><td>Mean 0.77</td><td>0.002</td><td>0.001</td><td>0.003</td><td>0.006</td></tr><tr><td>1.36</td><td>0.012</td><td>0.002</td><td>0.013</td><td>0.007</td></tr><tr><td rowspan="3">Meat (muscle) and animal proteins</td><td rowspan="3">3 (9)</td><td rowspan="3">Local Meat Type 1 Local Meat Type 2 Local Chicken</td><td>Mean 1.36</td><td>0.012</td><td>0.002</td><td>0.013</td><td>0.007</td></tr><tr><td>4.24</td><td>0.008</td><td>0.003</td><td>0.011</td><td>0.005</td></tr><tr><td>8.00</td><td>0.013</td><td>0.004</td><td>0.017</td><td>0.010</td></tr><tr><td rowspan="3">Sweets</td><td rowspan="3">2 (3)</td><td rowspan="3">Local Biscuits Imported Biscuits</td><td>Mean 1.90</td><td>0.060</td><td>0.009</td><td>0.069</td><td>0.036</td></tr><tr><td>4.71</td><td>0.027</td><td>0.005</td><td>0.032</td><td>0.017</td></tr><tr><td>3.77</td><td>0.007</td><td>0.004</td><td>0.011</td><td>0.011</td></tr><tr><td rowspan="12">Estarreja</td><td rowspan="5">Cereals</td><td rowspan="5">4 (11)</td><td rowspan="5">Local Rice Imported Rice Local Corn Type 1 Local Corn Type 2</td><td>Mean 4.03</td><td>0.008</td><td>0.003</td><td>0.011</td><td>0.010</td></tr><tr><td>1.99</td><td>0.004</td><td>0.001</td><td>0.005</td><td>0.012</td></tr><tr><td>0.90</td><td>0.003</td><td>0.000</td><td>0.003</td><td>0.006</td></tr><tr><td>3.14</td><td>0.005</td><td>0.001</td><td>0.006</td><td>0.028</td></tr><tr><td>2.97</td><td>0.003</td><td>0.001</td><td>0.004</td><td>0.053</td></tr><tr><td rowspan="2">Dark green leafy vegetables</td><td rowspan="2">1 (4)</td><td rowspan="2">Local Turnip</td><td>Mean 2.25</td><td>0.004</td><td>0.001</td><td>0.005</td><td>0.025</td></tr><tr><td>0.82</td><td>0.184</td><td>0.023</td><td>0.206</td><td>0.012</td></tr><tr><td rowspan="2">Fish and shellfish</td><td rowspan="2">2 (8)</td><td rowspan="2">Local fishes Imported Fishes</td><td>Mean 0.82</td><td>0.184</td><td>0.023</td><td>0.206</td><td>0.012</td></tr><tr><td>11.95</td><td>0.557</td><td>0.464</td><td>1.022</td><td>3.407</td></tr><tr><td rowspan="5">Fruits</td><td rowspan="5">7 (13)</td><td rowspan="5">Local Oranges Imported Oranges Local lemons Imported Lemons Local Tamarillo Local Kiwi Imported Kiwi</td><td>Mean 17.73</td><td>0.338</td><td>0.468</td><td>0.805</td><td>3.571</td></tr><tr><td>1.08</td><td>0.002</td><td>0.0002</td><td>0.002</td><td>0.006</td></tr><tr><td>0.56</td><td>0.002</td><td>0.0003</td><td>0.002</td><td>0.003</td></tr><tr><td>1.64</td><td>0.002</td><td>0.0001</td><td>0.002</td><td>0.003</td></tr><tr><td>1.75</td><td>0.001</td><td>0.0001</td><td>0.002</td><td>0.002</td></tr><tr><td rowspan="5">Legumes</td><td rowspan="5">2 (6)</td><td rowspan="5">Local Beans Imported Beans</td><td>Mean 1.34</td><td>0.003</td><td>0.0003</td><td>0.003</td><td>0.004</td></tr><tr><td>1.03</td><td>0.002</td><td>0.0002</td><td>0.002</td><td>0.004</td></tr><tr><td>1.71</td><td>0.004</td><td>0.001</td><td>0.005</td><td>0.003</td></tr><tr><td>1.30</td><td>0.002</td><td>0.0003</td><td>0.003</td><td>0.004</td></tr><tr><td>2.39</td><td>0.003</td><td>0.001</td><td>0.004</td><td>0.004</td></tr><tr><td rowspan="3">Meat (muscle) and animal proteins</td><td rowspan="3">2 (4)</td><td rowspan="3">Local Meat Imported Meat</td><td>Mean 2.49</td><td>0.003</td><td>0.001</td><td>0.003</td><td>0.004</td></tr><tr><td>19.39</td><td>0.030</td><td>0.023</td><td>0.053</td><td>0.215</td></tr><tr><td>29.15</td><td>0.019</td><td>0.029</td><td>0.048</td><td>0.149</td></tr><tr><td rowspan="3">Roots, tubers, and starchy foods</td><td rowspan="3">2 (6)</td><td rowspan="3">Local Potatoes Imported Potatoes</td><td>Mean 24.27</td><td>0.024</td><td>0.026</td><td>0.050</td><td>0.182</td></tr><tr><td>0.44</td><td>0.004</td><td>0.0003</td><td>0.004</td><td>0.002</td></tr><tr><td>0.10</td><td>0.002</td><td>0.0003</td><td>0.002</td><td>0.002</td></tr><tr><td rowspan="5">Vegetables</td><td rowspan="5">4 (14)</td><td rowspan="5">Local Cabbages Local Bell Pepper Imported Bell Pepper Local Zucchini</td><td>Mean 0.2675</td><td>0.003</td><td>0.0003</td><td>0.003</td><td>0.002</td></tr><tr><td>0.60</td><td>0.001</td><td>0.001</td><td>0.002</td><td>0.022</td></tr><tr><td>0.79</td><td>0.008</td><td>0.001</td><td>0.010</td><td>0.021</td></tr><tr><td>0.89</td><td>0.003</td><td>0.001</td><td>0.003</td><td>0.009</td></tr><tr><td>0.46</td><td>0.006</td><td>0.002</td><td>0.008</td><td>0.019</td></tr><tr><td></td><td></td><td></td><td>Mean 0.68</td><td>0.005</td><td>0.001</td><td>0.006</td><td>0.018</td></tr></tbody></table>

Brazilian aquatic systems, such as Guanabara Bay, where legacy pollution from industrial and urban activities has led to background contamination in fish species (Machado Torres & Krykhtine, 2023).

While most studies in Oyapock have focused on lead contamination and exposure, no environmental assessments have yet addressed POPs.

However, the presence of the pollutants in fish from the Brazilian side raises concerns about regional background pollution, linked to the broader aquatic ecosystems. These findings call for further investigations into local and transboundary contamination pathways in the cross-border region
### 3.3.3. OHM Littoral Caraïbes

The lowest total TEQ in this OHM was observed in vegetables, while the lowest NDL-PCBs concentrations (0.004 ng/g ww) were found in fruits. The fish category showed the highest values for total TEQ (0.01 pg/g ww) and the sum of 6 NDL-PCB (0.04 ng/g ww).

In meat, total TEQ and NDL-PCB values were obtained for this category in the OHM-LC (0.007 pg total TEQ/g ww; 0.0094 ng/g ww), with higher concentrations of DL-PCB and NDL-PCB in local chicken.

In fish, the concentrations measured in the different types of fish analyzed (ss = 12; n = 4) are 0.01 pg/g ww on average for total TEQs and 0.037 ng/g ww for NDL-PCBs. Among the species studied, the red snapper (*Lutjanus synagris* – Local Fish 1) has the highest values of total TEQ (0.024 pg WHO-TEQ total /g ww) and NDL-PCBs (0.124 ng/g) compared to the imported red snapper purchased from supermarkets (*Lutjanus* sp., *Lutjanus sanguineus*, *Lutjanus purpureus* – Imported Fish 1), which show concentrations of 0.011 pg WHO-TEQ total /g ww and 0.014 ng/g ww for NDL-PCBs. According to our literature review, the PCDD/F TEQ values measured in the Caribbean in *Lutjanus* species (0.004 and 0.006 pg/g ww) are lower than those observed in fish from the same family analyzed along the west coast of Peninsular Malaysia (*Lutjanus argentimaculatus*, 0.34 and 0.37 pg/g ww) (Nasir et al., 2011) and in China (*Lutjanus sanguineus*, 1.30 pg/g ww) (Zhang et al., 2007).

Moreover, the PCB TEQ values obtained in our study (0.007 and 0.018 pg WHO-TEQ PCBs) are lower than those measured in *Lutjanus griseus* from the northwestern Florida region (0.016 and 0.076 pg WHO-TEQ PCBs/ww) (Snyder & Karouna-Renier, 2009).

Regarding the parrotfish (Fish 2) in the Littoral Caraïbes, whether local (*Sparisoma viride* – Fish 2 Local) or imported (*Scarus ghobban* – Fish 2 Imported), the total TEQ values (0.003 pg/g ww) and NDL-PCBs (0.0024 and 0.0077 ng/g ww) are lower than those measured in the red snappers. These total TEQ values are also lower than those reported for *Sparisoma chrysopterum*, collected around Martinique island, also located in the Caribbean Sea (0.01–0.34 pg WHO-TEQ total /g ww) (Bertrand et al., 2009).

In Guadeloupe, there are no prior studies on PCDD/Fs or dioxin-like PCBs in food or the environment, despite known contamination by other organochlorines like chlordecone. This study offers the first insights into dietary exposure to these POPs in the region. Elevated levels in some local fish (e.g., *Lutjanus synagris*) highlight the need for targeted environmental monitoring to better assess public health risks.

### 3.3.4. OHMi Tessékéré

In OHMi Tessékéré, only commercial milk (ss = 4, n = 1) was available at the time of sampling due to drought conditions. These samples exhibited relatively high average values of WHO-TEQ-PCDD/F-PCBs (0.053 pg/g ww) and NDL-PCBs (0.277 ng/g ww) were observed. However, since no data were available on reconstitution practices (e.g., dilution ratio or water quality), which could significantly influence final pollutant concentrations. This underlines the need for further investigation into local milk preparation methods to better assess dietary exposure in this context. Ideally, such research should be conducted in the winter season in Tessékéré, where fresh milk is more available and traditionally used to prepare milk-based products.

For meat (ss = 9; n = 3), only locally raised meat was sampled. The average total WHO-TEQ value was 0.032 pg/g ww, which is comparable to values reported in the study by Vaccher et al., 2020 on total diet in Sub-Saharan Africa (0.03 pg/g ww). However, the average NDL-PCB values in our study (0.0171 ng/g ww) are almost three times lower than those found in the same study (0.044 ng/g ww).

In this observatory, findings underscore the importance of accounting for seasonal and preparation factors in exposure assessments and support the need for more context-specific monitoring in arid environments like Tessékéré.

### 3.3.5. Cross-OHM comparative synthesis

The comparative analysis of POPs contamination across the four

OHMs reveals marked contrasts. TEQ and NDL-PCB levels were the highest in Estarreja, particularly in local fish, reflecting known industrial legacy contamination. Littoral-Caraïbe also showed elevated values in locally caught fish. In contrast, Oyapock and Tessékéré presented lower concentrations overall, with few expectations, such as commercial powdered milk in Tessékéré.

Across all OHMs, local products generally showed higher contamination than imported ones, especially for meat and fish, highlighting the influence of local environmental conditions. However, some imported samples, particularly fish from Brazil, also contained notable POPs levels, suggesting diffuse or regional pollution beyond localized sources.

These findings emphasize the importance of combining environmental context with food origin in interpreting contamination patterns. They also highlight the need for expanded monitoring beyond Estarreja to better understand exposure risks in tropical and arid regions, where data on POPs remain scarce.

## 3.4. Preliminary analysis of food consumption and exposure to dioxins and PCBs

The data presented in Table 2 showed the maximum daily quantities of the tested products that a 70 kg adult person can consume while staying within the TWI value (2 pg Total TEQ /kg body weight/week) (using eq. 2: part 2.3.2 in Materials and Methods). Among the lowest recommended daily consumption amounts were emitted for butter in the OHMi Tessékéré (23.98 g/day) and fish in the OHMi Estarreja, with an average of 24.84 g/day (19.57 g/day for local fish and 33.96 g/day for imported fish).

Although participants did not specify the quantities or the origin of food consumed for each territory in the 24-h recall, national-level consumption data are available for Portugal (Lopes et al., 2018), Senegal (Anderson et al., 2010), and Guadeloupe (Anses, 2017). However, no data could be identified for the OHM Oyapock. These national datasets correspond to certain food categories or matrices that align with those analyzed in our study.

To contextualize our study, we compared the recommended daily consumption thresholds calculated from contaminant levels with national intake data. Among all the OHMs studied, Estarreja is the only case where the recommended fish intake is 24.84 g/day for adults, raising health concerns given that the Portuguese average is almost twice as high, at 44 g/day. This suggests a potential dietary exposure concern at this site. As such, a more detailed exposure estimation was carried out for Estarreja, assuming that local consumption patterns are comparable to national averages.

Conversely, for the other territories, the calculated daily consumption limits remained higher than the national average consumption, indicating a lower potential exposure and thus no additional risk assessment was considered necessary.

Focusing on Estarreja, and if we assume an average fish consumption by an adult similar to the national level, and based on our results for local fish (0.8 pg TEQ/g ww) (using eq. 1: part 2.3.2 in Materials and Methods), the estimated daily intake would be 35.2 pg WHO-TEQ/day. This value is slightly lower than that found in Catalonia (Spain), where the estimated intake for a standard adult male (70 kg) is 38.0 pg WHO-TEQ/day, but significantly higher than that estimated in Italy (2.65 pg WHO-TEQ/day) following fish consumption (Storelli et al., 2011).

This daily intake for a 70 kg adult can be used to make a first estimate of relative exposure and a health risk assessment. The average daily exposure from fish consumption would be 0.502 pg WHO-TEQ/kg body weight/day. This value is half that estimated in Greece (1.3 pg WHO-TEQ/kg body weight/day) (Costopoulou et al., 2016) but almost three times higher than that found in Taiwan (0.169 pg WHO-TEQ/kg body weight/day) (Chang et al., 2012).

The estimated average weekly exposure in our study, following the consumption of the tested fish, would be 3.52 pg WHO-TEQ/kg body weight/week, a value higher than those found in two studies conducted
<table><caption>**Table 2**
Daily and Weekly Estimated Food Intake (g) for a 70 kg Male Adult Containing the Maximum Recommended Tolerable Weekly Intake (TWI) of PCDD/F + PCB DL.</caption><thead><tr><th>OHM</th><th>Category</th><th>Food core</th><th>Mean</th><th>Estimated daily of food Ai (g/d)<sup>1</sup></th><th>Estimated Weekly intake containing the TWI (g/week)<sup>2</sup></th><th>Daily intake (National Level) (g/d)<sup>3</sup></th></tr></thead><tbody><tr><td rowspan="12">Oyapock</td><td rowspan="2">Cereals</td><td>Imported Rice</td><td></td><td>2000</td><td>14,000</td><td></td></tr><tr><td>Local Rice</td><td>Mean</td><td>2857.1</td><td>20,000</td><td></td></tr><tr><td rowspan="3">Roots, tubers, and starchy foods</td><td>Imported Couac 1</td><td></td><td>1818.2</td><td>12,727.3</td><td></td></tr><tr><td>Imported Couac 2</td><td></td><td>2857.1</td><td>20,000</td><td></td></tr><tr><td>Local Couac</td><td>Mean</td><td>2857.1</td><td>20,000</td><td></td></tr><tr><td rowspan="2">Legumes</td><td>Imported Beans</td><td></td><td>2500</td><td>17,500</td><td></td></tr><tr><td>Imported Wassaï</td><td>Mean</td><td>3333.3</td><td>23,333.3</td><td></td></tr><tr><td rowspan="2">Fruits</td><td>Local Wassaï</td><td></td><td>10,000</td><td>70,000</td><td></td></tr><tr><td>Imported Milk (Liquid)</td><td>Mean</td><td>10,000</td><td>70,000</td><td></td></tr><tr><td rowspan="2">Milk and dairy products</td><td>Imported Milk (Powder)</td><td></td><td>645.2</td><td>4516</td><td></td></tr><tr><td>Imported Chicken</td><td>Mean</td><td>2857.1</td><td>20,000</td><td></td></tr><tr><td rowspan="2">Meat (muscle) and animal proteins</td><td>Imported Meat</td><td></td><td>2500</td><td>7368.4</td><td></td></tr><tr><td>Local Fish 1</td><td>Mean</td><td>4000</td><td>17,500</td><td></td></tr><tr><td rowspan="2">Fish and shellfish</td><td>Imported Fish 1</td><td></td><td>4000</td><td>28,000</td><td></td></tr><tr><td>Local Fish 1</td><td>Mean</td><td>1111.1</td><td>7777.8</td><td></td></tr><tr><td rowspan="2">Cereals</td><td>Imported Rice</td><td></td><td>1818.2</td><td>12,727.3</td><td></td></tr><tr><td>Local Rice</td><td>Mean</td><td>2857.1</td><td>23,333.3</td><td></td></tr><tr><td rowspan="3">Fish and shellfish</td><td>Imported Fish 1</td><td></td><td>1818.2</td><td>12,727.3</td><td></td></tr><tr><td>Local Fish 1</td><td>Mean</td><td>833.3</td><td>5833.3</td><td></td></tr><tr><td>Imported Fish 2</td><td></td><td>6666.7</td><td>46,666.7</td><td></td></tr><tr><td rowspan="3">Fruits</td><td>Local Fish 2</td><td></td><td>6666.7</td><td>46,666.7</td><td></td></tr><tr><td>Local Mango</td><td>Mean</td><td>2000</td><td>14,000</td><td></td></tr><tr><td>Imported Oranges</td><td></td><td>6666.7</td><td>46,666.7</td><td></td></tr><tr><td rowspan="5">Littoral Caraïbes</td><td rowspan="2">Legumes</td><td>Imported Beans</td><td>Mean</td><td>10,000</td><td>70,000</td><td></td></tr><tr><td>Imported Chicken</td><td>Mean</td><td>2857.1</td><td>20,000</td><td></td></tr><tr><td rowspan="2">Meat (muscle) and animal proteins</td><td>Local Chicken</td><td></td><td>2500</td><td>17,500</td><td></td></tr><tr><td>Imported Chicken</td><td>Mean</td><td>4000</td><td>28,000</td><td></td></tr><tr><td rowspan="2">Roots, tubers, and starchy foods</td><td>Local Yam</td><td></td><td>20,000</td><td>140,000</td><td></td></tr><tr><td>Imported Yam</td><td>Mean</td><td>6666.7</td><td>46,666.7</td><td></td></tr><tr><td rowspan="2">Vegetables</td><td>Local Cassava</td><td></td><td>1333.3</td><td>9333.3</td><td></td></tr><tr><td>Local Tomato</td><td>Mean</td><td>20,000</td><td>140,000</td><td></td></tr><tr><td rowspan="2">Milk and dairy products</td><td>Imported Milk (powder)</td><td></td><td>20,000</td><td>140,000</td><td></td></tr><tr><td>Local Butter (cow)</td><td>Mean</td><td>24.0</td><td>167.8</td><td></td></tr><tr><td rowspan="2">Oleaginous fruits and seeds</td><td>Cowpea</td><td>Mean</td><td>2857.1</td><td>20,000</td><td></td></tr><tr><td>Peanuts</td><td>Mean</td><td>689.7</td><td>4827.6</td><td></td></tr><tr><td rowspan="2">Cereals</td><td>Local Rice</td><td></td><td>1428.6</td><td>10,000</td><td></td></tr><tr><td>Imported Rice</td><td>Mean</td><td>2222.2</td><td>15,555.6</td><td></td></tr><tr><td rowspan="5">Tessekéré</td><td rowspan="2">Fruits</td><td>Millet</td><td>Mean</td><td>1818.2</td><td>12,727.3</td><td></td></tr><tr><td>Tamarind</td><td>Mean</td><td>1818.2</td><td>12,727.3</td><td></td></tr><tr><td rowspan="2">Vegetables</td><td>Onions</td><td></td><td>10,000</td><td>70,000</td><td></td></tr><tr><td>African eggplant</td><td>Mean</td><td>5000</td><td>35,000</td><td></td></tr><tr><td rowspan="2">Dark green leafy vegetables</td><td>Bissap leaf</td><td>Mean</td><td>1538.5</td><td>10,769.2</td><td></td></tr><tr><td>Local Meat Type 1</td><td>Mean</td><td>1818.2</td><td>12,727.3</td><td></td></tr><tr><td rowspan="2">Meat (muscle) and animal proteins</td><td>Local Meat Type 2</td><td></td><td>1176.5</td><td>8235.3</td><td></td></tr><tr><td>Local Chicken</td><td>Mean</td><td>289.9</td><td>2029</td><td></td></tr><tr><td rowspan="2">Sweets</td><td>Local Biscuits</td><td></td><td>625</td><td>4375</td><td></td></tr><tr><td>Imported Biscuits</td><td>Mean</td><td>1818.2</td><td>12,727.3</td><td></td></tr><tr><td rowspan="3">Estarreja</td><td rowspan="2">Cereals</td><td>Local Rice</td><td></td><td>4000</td><td>28,000</td><td></td></tr><tr><td>Imported Rice</td><td>Mean</td><td>6666.7</td><td>46,666.7</td><td></td></tr><tr><td>Local Corn Type 1</td><td></td><td>3333.3</td><td>23,333.3</td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 2 (continued)**</caption><thead><tr><th>OHM</th><th>Category</th><th>Food core</th><th>Mean</th><th>Estimated daily of food Ai (g/d)<sup>1</sup></th><th>Estimated Weekly intake containing the TWI (g/week)<sup>2</sup></th><th>Daily intake (National Level) (g/d)<sup>3</sup></th></tr></thead><tbody><tr><td rowspan="3">Dark green leafy vegetables</td><td>Local Corn Type 2</td><td>Mean</td><td>5000</td><td>35,000</td><td></td><td rowspan="3">69<sup>c</sup></td></tr><tr><td>Local Turnip</td><td>Mean</td><td>4000</td><td>28,000</td><td></td></tr><tr><td>Local fishes</td><td>Mean</td><td>97.1</td><td>679.6</td><td></td></tr><tr><td rowspan="2">Fish and shellfish</td><td>Imported Fishes</td><td>Mean</td><td>34.0</td><td>137</td><td></td><td rowspan="2">44<sup>c</sup></td></tr><tr><td></td><td></td><td>24.8</td><td>237.7</td><td></td></tr><tr><td rowspan="6">Fruits</td><td>Local Oranges</td><td>Mean</td><td>10,000</td><td>70,000</td><td></td><td rowspan="6">129<sup>c</sup></td></tr><tr><td>Imported Oranges</td><td>Mean</td><td>10,000</td><td>70,000</td><td></td></tr><tr><td>Local lemons</td><td>Mean</td><td>10,000</td><td>70,000</td><td></td></tr><tr><td>Imported Lemons</td><td>Mean</td><td>10,000</td><td>70,000</td><td></td></tr><tr><td>Local Tamarillo</td><td>Mean</td><td>6666.7</td><td>46,666.7</td><td></td></tr><tr><td>Local Kiwi</td><td>Mean</td><td>10,000</td><td>70,000</td><td></td></tr><tr><td rowspan="2">Legumes</td><td>Imported Kiwi</td><td>Mean</td><td>4000</td><td>28,000</td><td></td><td rowspan="2">19<sup>c</sup></td></tr><tr><td></td><td></td><td>6666.7</td><td>46,666.7</td><td></td></tr><tr><td rowspan="2">Meat (muscle) and animal proteins</td><td>Local Beans</td><td>Mean</td><td>5000</td><td>35,000</td><td></td><td rowspan="2">85<sup>c</sup></td></tr><tr><td>Imported Beans</td><td>Mean</td><td>6666.7</td><td>46,666.7</td><td></td></tr><tr><td rowspan="2">Roots, tubers, and starchy foods</td><td>Local Meat</td><td>Mean</td><td>377.4</td><td>2641.5</td><td></td><td rowspan="2">129<sup>c</sup></td></tr><tr><td>Imported Meat</td><td>Mean</td><td>416.7</td><td>2916.7</td><td></td></tr><tr><td rowspan="2">Vegetables</td><td>Local Potatoes</td><td>Mean</td><td>400</td><td>2800</td><td></td><td rowspan="2">85<sup>c</sup></td></tr><tr><td>Imported Potatoes</td><td>Mean</td><td>5000</td><td>35,000</td><td></td></tr><tr><td rowspan="6">Vegetables</td><td>Local Cabbages</td><td>Mean</td><td>6666.7</td><td>46,666.7</td><td></td><td rowspan="6">153<sup>c</sup></td></tr><tr><td>Local Bell Pepper</td><td>Mean</td><td>10,000</td><td>70,000</td><td></td></tr><tr><td>Imported Bell Pepper</td><td>Mean</td><td>2000</td><td>14,000</td><td></td></tr><tr><td>Local Zucchini</td><td>Mean</td><td>6666.7</td><td>46,666.7</td><td></td></tr><tr><td></td><td></td><td>2500</td><td>17,500</td><td></td></tr><tr><td></td><td></td><td>3333.3</td><td>23,333.3</td><td></td></tr></tbody></table>

For OHM Oyapock, no data was found in the bibliography.

in Italy (0.31 pg and 1.31 pg WHO-TEQs/kg body weight/week) (Barone et al., 2021; Storelli et al., 2011). However, it remains lower than those found in Finland (9.87 WHO-TEQs/kg body weight/week) and other non-European countries (Storelli et al., 2011). This estimated value exceeds the tolerable weekly intake of 2 pg TEQ/kg body weight/week for the sum of dioxins and dioxin-like PCBs, highlighting a potential risk associated with consuming the tested fish.

To further illustrate the potential health risk, we extended our estimation to a more vulnerable group, young children. Based on a body weight of 15 kg, the tolerable weekly intake (TWI) for total TEQ is 30 pg WHO-TEQ/week. Applying the same concentration levels observed in fish from Estarreja (0.8 pg WHO-TEQ/g ww), the recommended maximum weekly intake would be only 37.5 g/week, or approximately 5.4 g/day. This very low threshold highlights the need for adapted dietary guidelines for this population.

It should be noted that the foods analyzed in this study were fresh and did not account for the effects of subsequent cooking and preservation practices. However, the complex relationships between food processing, high-temperature cooking methods (such as grilling, barbecuing, smoking, frying, or roasting), and human health have to be considered (Chiozzi et al., 2022; Vignesh et al., 2024). For instance, Vaccher et al., 2020 reported a three-fold increase in the average WHO-TEQ PCDD/F values in smoked fish. This emphasizes the importance of considering the type of material used for combustion during the drying or smoking of fish, as it may be an additional source of contamination, thus increasing the exposure risk to POPs. Nonetheless, certain practices may reduce the POPs content in food. Indeed, Bayen et al., 2005 showed that simple cooking (pan, microwave, boiling, oven) of a salmon fillet results in an average loss of 26 % of the initial POPs, mainly due to the

migration of lipophilic contaminants to the lipids exuded during cooking. Additionally, a further reduction of 9 % was observed when the fish skin was removed before consumption.

The findings emphasize the role of both food origin and preparation in influencing actual dietary exposure. While this study lacked region-specific consumption frequency by food source and traditional cooking practices, future risk assessments should incorporate these factors to more accurately estimate public health risks and to guide recommendations for reducing exposure to POPs.

## 4. Conclusion and perspectives

The present study is the first to quantify POPs in fresh foods representative of the typical diet of the four human environment observatories (OHM), located in Senegal (OHMi-Tessékéré), in Portugal (OHMi-Estarreja), in Guadeloupe (OHM-LC), and at the French Guiana / Brazil border (OHM-Oyapock).

These observatories represent highly contrasting socio-ecological contexts and food systems, which pose challenges for direct comparisons but allow for an integrative assessment of food contamination under global pressures.

Our findings provide novel and region-specific data on the occurrence of PCDD/Fs and PCBs in both local and imported foods, establishing an essential baseline for future monitoring risk assessment efforts. The observed contamination patterns reflect not only environmental background differences but also variations in food sourcing and dietary practices.

To better assess the health risks related to food exposure, further research is needed on actual consumption levels through Total Diet

<footnote n="1">¹ Calculated using eq. 2 (see M&M 2.3.2) and using TEQ values presented in Table 1.</footnote>

<footnote n="2">² Estimated Weekly intakes containing the TWI = Ai*7(g/week).</footnote>

<footnote n="3">³ The values presented are derived from studies conducted in various countries and found in research articles.</footnote>

<footnote>a : Average consumption (in g/day) of food products among adults in Guadeloupe (Anses, 2017).</footnote>

<footnote>b : Median daily intake (in g/day) of the most consumed foods by Senegalese men (Anderson et al., 2010).</footnote>

<footnote>c : Average consumption (in g/day) of certain food groups at the national level among adults in the Portuguese population (Lopes et al., 2018).</footnote>
Studies or Food Frequency Questionnaires (FFQ). A comprehensive understanding of exposure also requires a multidisciplinary approach that includes plate composition analysis, food preparation practices, and sociocultural determinants of diet. Biomonitoring studies (e.g., in blood, adipose tissue, or breast milk) would provide complementary data on internal exposure.

Finally, attention should also be given to other groups of emerging contaminants, such as per- and poly-fluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), microplastics, and trace elements (MTEs), which may co-occur in food and contribute to complex exposure mixtures. Untargeted screening approaches are therefore essential to assess cumulative risks and guide public health interventions.

### CRediT authorship contribution statement

**Nathalie El Deghel:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Michael Rapinski:** Writing – review & editing, Methodology. **Priscilla Duboz:** Writing – review & editing, Funding acquisition. **Richard Raymond:** Writing – review & editing, Funding acquisition. **Damien Davy:** Writing – review & editing, Funding acquisition. **Nuno Durães:** Writing – review & editing, Methodology. **André-Marie Dendievel:** Writing – review & editing, Formal analysis, Conceptualization. **Eduardo Ferreira da Silva:** Writing – review & editing, Funding acquisition. **Pascal Jean Lopez:** Writing – review & editing, Funding acquisition. **Vincent Vaccher:** Writing – review & editing. **Anaïs Vénisseau:** Writing – review & editing, Investigation. **Julie Anne Nazare:** Writing – review & editing. **Anne-Laure Badin:** Writing – review & editing, Conceptualization. **Jean-Philippe Bedell:** Writing – review & editing, Methodology, Funding acquisition, Formal analysis, Conceptualization.

### Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used ChatGPT in order to improve the readability and language of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

### Funding

This work was carried out within the framework of the OHMi-Téssékéré, OHMi-Estarreja, OHM-Oyapock, and OHM-Littoral Caraïbes, who all benefited from state funding managed by the National Research Agency under the LabEx DRIIHM "Investing in the Future Program" (reference # ANR-11-LABX-0010) as part of the Transverse Project SASI (Systèmes Alimentaires Sous Influence).

For OHM-Littoral Caraïbes, part of the funding comes from the PADIT project (Paysage alimentaire et développement d'une interface de transport. Les systèmes alimentaires en Guadeloupe, entre globalisation et territoire), funded by the Ittecop program and Ademe.

Nathalie El Deghel's thesis was funded by the research direction of ENTPE.

### Declaration of competing interest

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

### Acknowledgments

We sincerely thank the students who contributed to sampling in each

observatory. We especially acknowledge Marie Belin, Evy Legros, Flo-rine Pages, Daniela Calado, Rafael Rebelo, Denise Terroso, Heewon Kang, and Jean Lubszynski for their hard work and assistance in col-lecting samples.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodchem.2025.145766.

### Data availability

Data will be made available on request.

### References

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