# Wastewater-based AMR surveillance associated with tourism on a Caribbean island (Guadeloupe)

Mélanie Pimenta, Maria Alexa, Degrâce Batantou Mabandza, Sylvain Dulaurent, Bich-Tram Huynh, Margaux Gaschet, Lulla Opatowski, Sebastien Breurec, Marie-Cécile Ploy, Christophe Dagot

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## To cite this version:

Mélanie Pimenta, Maria Alexa, Degrâce Batantou Mabandza, Sylvain Dulaurent, Bich-Tram Huynh, et al.. Wastewater-based AMR surveillance associated with tourism on a Caribbean island (Guadeloupe). Journal of Global Antimicrobial Resistance, 2025, 43, pp.27-34. 10.1016/j.jgar.2025.03.010. hal-05012328

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# Wastewater-based AMR surveillance associated with tourism on a Caribbean island (Guadeloupe)

<figure><img src="image_4.png" /></figure>

Mélanie Pimenta<sup>a</sup>, Maria Alexa<sup>b,e</sup>, Degrâce Batantou Mabandza<sup>c</sup>, Sylvain Dulaurent<sup>d</sup>, Bich-Tram Huynh<sup>b,e</sup>, Margaux Gaschet<sup>a</sup>, Lulla Opatowski<sup>b,e</sup>, Sébastien Breurec<sup>c,f,g,h</sup>, Marie-Cécile Ploy<sup>a</sup>, Christophe Dagot<sup>a,*</sup>

## ARTICLE INFO

**Article history:**
Received 15 July 2024
Revised 23 February 2025
Accepted 22 March 2025
Available online 26 March 2025

Editor: Marisa Haenni

**Keywords:**
Antimicrobial resistance
Wastewater
Tourism
Guadeloupe

## ABSTRACT

**Objectives:** Antimicrobial resistance (AMR) is a major public health concern worldwide. International travel is a risk factor for acquiring antibiotic-resistant bacteria (ARB) and antibiotic-resistance genes (ARGs). Therefore, understanding the transmission of ARB and ARGs is instrumental in tackling AMR. This longitudinal study aimed to assess the benefit of wastewater monitoring in Guadeloupe to evaluate the role of tourism in the spread of AMR.

**Methods:** A wastewater-based surveillance (WBS) study was conducted to monitor AMR in Guadeloupe in 2022 during dry and wet seasons. We characterized the resistome, microbiome and exposome of water samples collected in wastewater treatment facilities of two cities with different levels of tourism activities, in the content of aircraft toilets, and the pumping station receiving effluents from hotels.

**Results:** The results show that the WBS approach facilitates the differentiation of various untreated effluents concerning exposome, microbiome, and resistome, offering insights into AMR dissemination. Additionally, the findings reveal that microbiome and exposome are comparable across sites and seasons, while resistome characterisation at specific locations may be pertinent for health surveillance. The microbiome of aircraft was predominantly composed of anaerobic bacteria from human intestinal microbiota, whereas the other locations exhibited a blend of human and environmental bacteria. Notably, individuals arriving by air have not introduced clinically significant resistance genes. Exposome compounds have been shown to influence the resistome's variance.

**Conclusions:** Clear differences were seen between the aircraft and the local sampling sites, indicating that the contribution of tourism to the observed resistance in Guadeloupe is not significant.

© 2025 The Author(s). Published by Elsevier Ltd on behalf of International Society for Antimicrobial Chemotherapy. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

## 1. Introduction

Antimicrobial resistance (AMR) is a global issue [1], and the prevalence of resistant strains varies according to geographical locations [2]. Various determinants, such as socio-economic factors and climate, have been described as drivers of AMR spread [2,3].

To curb AMR, we need to determine the routes and factors driving the emergence and spread of antibiotic-resistant bacteria (ARB).

Travels significantly contribute to AMR dissemination, particularly in the context of globalisation [4]. Tourists as well as refugees and asylum seekers are exposed to ARB endemic to the countries they travel to [2]. They can also introduce ARB and antibiotic-resistance genes (ARGs) to their destination country [5–9]. Tourists may also carry ARB and ARGs back to their home country. Specifically, it is well established that ARGs, such as ESBL-encoding bla<sub>CTX-M</sub> genes or carbapenemase-encoding genes, have spread

<footnote><sup>a</sup>INSERM, CHU Limoges, RESINFIT, U1092, University of Limoges, Limoges, France</footnote>

<footnote><sup>b</sup>Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), France</footnote>

<footnote><sup>c</sup>Transmission, Reservoir and Diversity of Pathogens Unit, Pasteur Institute of Guadeloupe, Pointe-à-Pitre, France</footnote>

<footnote><sup>d</sup>Department of Pharmacology, Toxicology and Pharmacovigilance, Limoges University Hospital, Limoges, France</footnote>

<footnote><sup>e</sup>Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM, Montigny-le-Bretonneux, France</footnote>

<footnote><sup>f</sup>Faculty of Medicine Hyacinthe Bastaraud, University of Antilles, Pointe-à-Pitre, France</footnote>

<footnote><sup>g</sup>Centre for Clinical Investigation 1424, INSERM, Pointe-à-Pitre/Les Abymes, France</footnote>

<footnote><sup>h</sup>Laboratory of clinical microbiology, University hospital Center of Guadeloupe, Pointe-à-Pitre/Les Abymes, France</footnote>

<footnote>* Corresponding author.
E-mail address: christophe.dagot@unilim.fr (C. Dagot).</footnote>
worldwide partly due to travellers [10–11]. In addition, it has been shown that resistant *E. coli* can persist in the gut of travellers for up to six months after their return [12,13]. Monitoring the diversity and prevalence of ARB and ARGs among travellers could bolster global surveillance efforts, particularly by examining transmission dynamics related to transport and airports as hotspots, and inform targeted interventions in high-risk areas, such as popular tourist destinations, where the influx of visitors may introduce novel resistance profiles [14].

AMR surveillance through the analysis of human waste is a rapid and cost-effective method to monitor the transmission of pathogens in a community [12]. Urban wastewater partly reflects the characteristics of the human gut microbiota, thereby affecting the resistome composition in these effluents [14]. Consequently, wastewater-based surveillance (WBS) is a pivotal approach, whereby biomarkers of interest are assessed by collecting wastewater samples, especially influents received at wastewater treatment facilities (WWTF), providing information on community health [15]. WBS was introduced years ago and was widely used during the COVID-19 pandemic to monitor the circulation of the SARS-CoV-2 virus in communities and it is now utilised to monitor bacteria and AMR [16–19]. The WBS approach represents a powerful tool for understanding the global spread of ARB and ARGs, particularly when applied to tourist communities, as it could facilitate the development of targeted sanitation policies and hygiene guidelines aimed at mitigating the risks associated with tourism.

In that regard, it has been suggested that the dynamics of AMR acquisition could be traced through intercontinental flights [12]. Heß et al. showed that the relative abundance of ARGs in aircraft effluent was higher than that in typical municipal effluents [13]. Nevertheless, most studies examining the impact of tourism have focused only on clinically relevant ARGs or *Enterobacteriaceae* species, overlooking the broader microbiome [5–8]. Given that environmental bacteria are a wide reservoir of ARGs [20], it is worth considering the entire microbiome to understand the dynamics of resistance spread associated with tourism activities.

To address this knowledge gap, we conducted a study over two seasons (dry and wet), sampling weekly for three weeks in each season, on the French Caribbean island of Guadeloupe, which receives approximately 500,000 tourists per year, most of whom arrive on international flights. To assess the impact of tourism on the spread of antimicrobial resistance, we designed a longitudinal study focusing on the microbiome, resistome and exposome. The objectives of this study are i) to track the dynamics of the spread of AMR in Guadeloupe, with international flights as a potential source of AMR, ii) to monitor the AMR dissemination through a continuum, that includes untreated water from the pumping station of the hotels in a tourist area, as well as untreated wastewater inflows from of the WWTF serving the hotels, compared to those from a WWTF in a less tourist-impacted area; and iii) to precisely quantify specific ARGs in both tourist and non-tourist areas. These findings could ultimately inform local and national public health authorities and guide community education efforts to reduce the spread of AMR on the island.

## 2. Materials and methods

### 2.1. Sampling campaigns

Guadeloupe, a French overseas territory located in the Caribbean, spans an area of 1628 km² with a population of 383 559 inhabitants. Characterized by a tropical climate, the region experiences two distinct seasons: a dry season from January to June and a wet one from July to December.

Two sampling campaigns were performed in 2022, in January during the dry season (campaign 1) and in July during the wet

season (campaign 2), once a week during 3 consecutive weeks for each season, on the same day, in the morning and at the same time of the day.

Samples were collected from four different sources. Samples representative of the tourist population were collected from toilets from aircrafts (AT) primarily arriving from mainland France, from untreated inflow wastewater from a WWTF and a pumping station collecting wastewater from hotels in Le Gosier, a city with a high level of tourist (HT) activities, (see details in Fig. 1). This seaside resort is one of the main tourist destinations and has the highest number of accommodations in Guadeloupe. In 2022, the hotel occupancy rate in Guadeloupe was 57% during the dry season and 56% in the wet season. Therefore, the Le Gosier influent wastewater and pumping station samples, in addition to AT, provide a good representation of the tourist population.

On the other hand, untreated inflow wastewater samples were collected from a WWTF of a rural city, Lamentin, with a low level of tourist (LT) activities. Wastewater from WWTFs were 24-hour composite samples, and wastewater at the pumping station of the hotels (PSH) were grab samples collected at the same location and during activity times. The volume collected for each sample was 3 L. Characteristics of the WWTFs are listed in Table S1.

Three grab samples of 50 mL each were collected from the content of the AT when the effluent was discharged from vacuum trucks. AT samples were collected from 5 aircraft over 3 consecutive weeks for each sampling campaign, totalling 15 AT samples per campaign. A vacuum system in the aircraft toilets routed waste through a piping network to a wastewater tank for feces storage. A “blue” product, comprising a quaternary ammonium germicide to prevent the development of micro-organisms and ethylene glycol as an antifreeze, was added to the toilets. Quaternary ammonium compounds may contribute to antibiotic resistance by selecting bacteria with genetic elements carrying both antibiotic and quaternary ammonium compound resistance genes. The tanks were emptied upon arrival at the airport, and the effluent was fed into the treatment system.

In total, 48 samples were collected (24 per campaign), including 3 samples of wastewater from PSH, 3 samples from HT, 3 samples from LT and 15 samples from AT per campaign (Fig. 1).

All the samples were transported to the laboratory at 4 °C, stored at 4 °C, and then processed on the same day as sampling.

### 2.2. DNA extraction

Wastewater samples were filtered in triplicate until the filter became saturated, using a filtration ramp (Sartorius) on sterile filters with a pore size of 0.45 µm (Sartorius). The filtration volume ranged from 27 to 86.8 mL, depending on the sample. DNA was extracted for each triplicate using the DNeasy PowerWater kit (Qiagen®) according to the manufacturer's instructions. The DNA concentration was measured using the Qubit™ dsDNA BR Assay Kits (Invitrogen™). Depending on the concentrations, DNA was diluted or concentrated with a SpeedVac Vacuum Concentrator (ThermoFisher) to a final concentration of 10 ng/µL for downstream analysis. DNA was stored at -20 °C until its use.

### 2.3. Fluidigm PCR

High-throughput qPCR was used to quantify the abundance of genes, including 80 genes conferring resistance to antibiotics, heavy metals and quaternary ammonium compounds (QACs), grouped into 16 classes. Additionally, 9 mobile genetic elements (MGEs) and integron integrase genes (intI1, intI2, and intI3) were also targeted, as described previously [21,22]. Universal primers were used to target the bacterial 16S rRNA gene. Primer design and validation were done previously [21,22]. The reference sequences
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Map of Guadeloupe depicting the sampled sites. The sampling campaign was conducted on aircraft arriving on the island and in a high tourism (HT) and a low tourism (LT) city of Guadeloupe. The content of the aircraft tank (AT) sampled came mainly from mainland France. The HT city of Le Gosier has the highest number of accommodations in Guadeloupe, with a hotel occupancy rate of approximately 57% in 2022. For this city, we sampled effluents from the pumping station (PSH), collecting effluents from hotels and effluents arriving at the WWTF to which PSH is connected. This facility also receives urban and clinical effluent from a private hospital treating patients with medical pathologies, particularly renal, cardiovascular and diabetic diseases. Lamentin is a low tourism (LT) city in a rural area where we sampled the effluents arriving at the city WWTF. This WWTF exclusively processes urban wastewater. Samples were collected once a week for three consecutive weeks during two sampling campaigns.</figcaption></figure>

on which primers have been developed are listed in Table S2. The 96.96 BioMark™ Dynamic Array for Real-Time PCR (Fluidigm Corporation) was used to perform real-time PCR as described previously [21,22]. The PCRs were done in triplicate. A sample of wastewater containing each targeted gene was used as a positive control, and sterile DNA-free water was used as a negative control.

The relative abundance of resistance genes was calculated relative to the abundance of the 16S rRNA encoding gene using the formula $(2^{\frac{[C_t-C_{ARG}]}{C_{16S rRNA}}}$, and the cumulative abundance of antibiotic resistance classes was calculated by summing the relative abundance of each gene belonging to the same antibiotic class.

## 2.4. 16S rRNA gene sequencing

Variable regions of the 16S rRNA encoding gene (V2, V3, V4, V6, V7, V8, V9) were amplified using the Ion 16S™ Metagenomics Kit (Thermo Fischer), according to manufacturer's instructions. Libraries were built using the Ion Plus Fragment Library Kit for AB Library Builder™ System (Ion Torrent™) according to the manufacturer's instructions. For aircraft toilets, libraries from samples collected the same week were pooled to improve the overall coverage of sequences. Quality controls and quantifications were carried out using an Agilent Bioanalyzer 2100. Ion S5 technology was used for sequencing after libraries were loaded onto Ion 530™ chips (Ion Torrent™). Sequencing data were analyzed using the Curated MicroSEQ® 16S Reference Library v2013.1 on Ion Reporter Software with default parameters. Reads from Ion Torrent sequencing were processed by discarding reads of <150 bp. The percentage identity values used to determine a genus ID were 97.0% and 99.0% for species ID. Results were based on the V3 region.

## 2.5. Exposome analysis

This study defines the exposome as pharmaceuticals, biocides, and heavy metals measured from the wastewater samples collected in PSH, HT and LT. Samples were stored at 4 °C until their use. Pharmaceuticals and biocides were measured by solid-phase extraction (SPE) and liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). In total, 19 molecules were analysed, including eight antibiotics (ciprofloxacin, ofloxacin, azithromycin, erythromycin, sulfamethoxazole, sulfaguanidine, sulfathiazole, trimethoprim), eight biocides (pyrimethamine, albendazole, flubendazole, thiabendazole, levamisole, pyrantel, tebuconazole, sulfoxaflor) and three non-steroidal anti-inflammatory drugs (NSAIDs) (ibuprofen, ketoprofen, diclofenac).

Samples were loaded in Oasis HLB 3cc (60 mg) cartridges (Waters) for SPE, and the final dry residues were reconstituted and injected into the LC-MS system. A 5500 QTRAP (Sciex) mass spectrometer was used with a multiple reaction monitoring (MRM) mode to perform the quantitative analysis of the molecules.

Inductively coupled plasma mass spectrometry (ICP-MS) was used to quantify heavy metals concentration (Zn, Cu, Ni, Pb, Cr, Gd, Hg, As and Cd) in the water samples using the ICP-MS NexIon 350D (Perkin Elmer). Scandium (Sc), Yttrium (Y), Indium (In) and Rhenium (Re) were used as internal standards.

## 2.6. Statistical analysis

The significance of the difference between the samples was tested using the non-parametric Mann-Whitney test, with a significance level of 95%, using R studio v2023.06.0 (vegan package). Principal Component Analysis (PCA) has been used to explore linear combinations among the different variables (resistome and microbiome) data using R studio v2023.06.0 and Shaman [23]. A variation partitioning analysis was conducted with R studio v2023.06.0 to determine which chemical group of the exposome accounted for the largest portion of the variation in the resistome data. Network visualization was carried out using Gephi 0.10.1 software [24]. Network analysis provides a representation of the genes of interest
shared across the different sites studied, namely the aircraft, the pumping station and the two WWTPs.

## 3. Results

### 3.1. Comparing relative abundances of the resistome across sites and campaigns

A significantly higher total relative abundance of the resistome was found in aircraft toilets (AT) compared to the pumping station of hotels (PSH) (P = 0.0001), high level of tourism (HT) (P = 6.4 × 10⁻⁵) and low level of tourism activities (LT) (P = 0.002), with a mean abundance of 0.14. No significant differences were seen between PSH and LT and between PSH and HT, whereas HT displayed the lowest relative abundance (Fig. 2).

The analysis of the proportional abundance of each targeted gene class revealed that wastewater from the PSH, HT and LT had a resistome composition distinct from that of AT (Fig. S1). AT exhibited a predominant presence of tetracycline resistance genes in their resistome, accounting for approximately 35% of the resistome. The PSH contained fewer tetracycline resistance genes than AT (more than 20%), but in higher proportions than in the two WWTFs, where they accounted for around 10% of each facility.

We then selected a list of resistance genes of interest, which were used as targets in the Fluidigm analysis and are known for being commonly found in environments exposed to anthropogenic activities (Fig. 3A) [25]. The comparative analysis showed a significantly higher abundance of *qnrS*, *bla*<sub>TEM-1</sub>, ESBL-encoding gene (*bla*<sub>SHV-12</sub>, *bla*<sub>CTX-M</sub>) and carbapenemase genes (*bla*<sub>KPC</sub>, *bla*<sub>OXA-48</sub>) in both HT and LT, in contrast with AT samples. Conversely, the tetracycline resistance gene *tetM* was found in higher abundance in AT samples (Fig. 3A).

Network analysis (Fig. 3B) showed that genes of high clinical interest, such as *bla*<sub>NDM</sub>, *bla*<sub>KPC</sub>, and *mcr-1*, were shared between the PSH and both WWTF influents (with low relative abundances, Fig. 3A) but were always absent from the AT. Interestingly, these genes were globally less abundant in the wet season, while *bla*<sub>NDM</sub> was absent. Most of the other resistance genes were shared across all four sites.

The PCA performed on the resistance genes revealed that the two first principal components explained 44% and 23.6% of the total differences between the resistomes of the study sites. The PCA analysis also showed that in both seasons, the resistome of AT

samples formed a distinct cluster from the other resistomes, while HT, LT, and PSH resistomes remained relatively close (Fig. 4).

### 3.2. Comparing microbiomes across sites and periods

Overall, the three weeks of sampling during each campaign showed only minor differences in the composition of the bacterial communities between touristic and non-touristic sites, with the most abundant genera at each site remaining consistent (Fig. S2). We did not see any seasonal effect that affected the bacterial proportions.

Despite the presence of disinfectants in AT, our analysis revealed DNA concentrations in AT that were similar to those of the other sites, allowing comparative analyses.

There was a clear difference in the microbiome composition between AT and the three other sites, probably linked to the presence of the disinfectant in AT (Fig. S2). The PSH microbiome composition was also different from that of HT and LT.

*Pseudomonadota*, *Bacillota* and *Bacteroidota* were the most abundant phyla in AT, PSH, HT and LT samples, collectively representing more than 90% of the total phyla. At the genus level, the PSH and the influent of WWTFs carried a mix of bacteria belonging to the human gut microbiota (*Bacteroides*, *Prevotella* and *Faecalibacterium*) [26,27] and environmental bacteria.

The predominant genus in AT was *Atopostipes*, a Gram-positive bacterium that is facultatively anaerobic and belongs to the *Bacillota* phylum (Fig. S2). It accounted for 21% of the genera found and was exclusively present in AT. Other abundant genera included *Bacteroides*, *Prevotella*, and *Faecalibacterium*, accounting for 13%, 6%, and 10% of the AT, respectively. These bacterial genera are part of the human gut microbiota. They are present in AT, and their abundance is consistent with those reported in the literature for human feces, confirming a link between AT composition and human gut microbiota [28].

The two principal components extracted from the PCA explained 74.8% and 8.6% of the variance observed between the microbiomes of AT, PSH, HT and LT samples, respectively. The PCA plot clearly distinguished AT samples from those of other sites. Interestingly, samples of LT and HT clustered together, while the PSH samples clustered closer to both WWTFs but remained distinct from them, confirming the differences observed in the analysis of the proportional abundance (Fig. 5).

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Total mean relative abundance of resistance genes, mobile genetic elements (MGEs) and integrons for the aircraft toilets (AT) (n = 6), the pumping station of the hotels (PSH) (n = 6), the high tourism (HT) (n = 6) and the low tourism (LT) samples collected during the dry and wet season. Significant differences are indicated by * with \*P ≤ 0.05, \*\*P ≤ 0.01, \*\*\*P ≤ 0.001.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Resistance genes over the different sampling points (A) Mean relative abundance of the genes of interest selected for the four studied sites in the samples collected for three weeks (w1 to w3) during dry and wet seasons. Red color represents the most abundant resistant genes and blue the least abundant, white color means that the gene is not detected. (B) Network representation of resistance genes shared by the sampled sites providing insights into genes of interest between the various sites studied, with aircraft toilets (AT) in red, the pumping station of the hotels (PSH) in purple, the high tourism (HT) in green and the low tourism (LT) in blue, for both sampling campaigns. Genes are coloured according to the class they belong to. The genes shared by the 4 sites studied are grouped together in the middle. AT = aircraft toilets, PSH = pumping station of the hotels, HT = high tourism, LT = low tourism, W1 = week 1, W2 = week 2 and W3 = week 3.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Results from the principal component analysis of the resistome. Colour dots represent in red the aircraft samples, in purple the samples of the pumping station of Le Gosier, in green the samples of the influent of the WWTP of Gosier and in blue the samples from the influent of Lamentin, for both campaigns.</figcaption></figure>

### 3.3. Exposome analysis

The dry season was characterized by a significantly higher exposure level to exposome compounds than the wet one, mainly consisting of non-steroidal anti-inflammatory drugs (NSAIDs). Mean concentrations of NSAIDs were 3533.42 ng/L for PSH, 4177.69 ng/L for HT and 5190.71 for LT in the dry season, while they were

490.59 ng/L for PSH, 411.43 ng/L for HT and 635 ng/L for LT in the wet season (Fig. S3). Azithromycin and erythromycin were the most abundant antibiotics detected in all the sites studied, with concentrations ranging from 1.8 ng/L to 368.4 ng/L and from undetected to 511.3 ng/L, respectively. Flubendazole, an intestinal anthelmintic, was the biocide present at the highest concentration in all samples.
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Principal component analysis of the microbiome of the different sites for the two campaigns. Dots represent in blue the aircraft samples, in yellow the samples of the pumping station of Le Gosier, in grey the samples of the WWTF of Le Gosier and in red the samples from the WWTP of Lamentin for the dry season (A) and the wet season (B).</figcaption></figure>

Heavy metals were also present at high concentrations, and zinc was the most abundant of the heavy metals selected (Fig. S3).

The concentrations measured at each sampling point are reported in Table S3.

## 4. Discussion

Antimicrobial resistance is a growing threat to global public health, exacerbated by globalisation and international tourism. In-

ternational flights play a crucial role in accelerating the spread of ARB and ARGs between geographically distant regions. This is a significant risk factor for introducing ARB and ARGs [4], as aircraft passengers can introduce new strains into the local ecosystems of places like Guadeloupe.

To assess the impact of passengers aboard transatlantic flights on the resistome of the local population and, consequently, on the spread of AMR, we investigated the impact of tourism on AMR by monitoring the resistome, microbiome, and exposome of wastew-
ater collected from aircraft toilets, the pumping station receiving effluents from hotels and WWTFs from two cities with different levels of tourism activity, in Guadeloupe during dry and wet seasons.

The data revealed differences in the relative abundances and composition of resistance genes across the samples, particularly between the AT site and the other locations. Moreover, the AT resistome formed a distinct cluster from the other resistomes, while HT, LT, and PSH resistomes remained relatively close (Fig. 5). These data suggest that travellers harbor a resistome that is distinct from that of their destination and do not exert a specific influence on the local resistome.

In line with the study by Petersen et al., which included multiple global regions, our results demonstrated that tetracycline resistance genes, particularly *tetM*, were the most prevalent in AT samples [29]. Furthermore, we identified the bacterial genus *Atopostipes* as the predominant genus in the AT samples. This genus has been recognised as a significant host for ARGs in sheep and cow manure [30]. Interestingly, *Atopostipes* has also been previously discovered in manure harbouring the *tetM* gene [31]. Moreover, it is known that tetracycline resistance genes can persist in the environment under harsh conditions [32]. Clinically relevant genes conferring resistance to carbapenems (*bla*<sub>NDM</sub>, *bla*<sub>KPC</sub>) or to colistin (*mcr*-*1*) were not found in AT samples but only at the other sampled sites, suggesting that these genes do not seem to be introduced by aircraft passengers. This is not surprising, as tourism data indicate that 70% of travellers arriving in Guadeloupe by air in 2022 came from mainland France (https://www.rivieraguadeloupe.com/IMG/pdf/note_flash_tourisme_periode_estivale_2023-2.pdf).

Surveillance data on antimicrobial resistance in French patients from mainland France suggest that the carbapenemase genes, such as *bla*<sub>NDM</sub> and *bla*<sub>KPC</sub>, were not prevalent in 2022, confirming our hypothesis about the low likelihood of tourists importing these genes [33]. On flights lasting over 8 h from Paris to Guadeloupe, passengers have several opportunities to use on-board restrooms. The design of the onboard waste storage tanks, with their specific characteristics, creates a unique environment for bacteria. However, studying aircraft waste alone may not provide a complete picture of the spread of AMR from travellers. Indeed, it has been demonstrated that during long-haul flights of more than 6 h, approximately only 36% of passengers are likely to defecate [34]. While this represents a significant proportion, it does not account for the majority of passengers, as each aircraft from Paris to Guadeloupe carries about 400 passengers. This highlights the importance of including wastewater analysis from hotels where tourists typically stay. Focusing on hotels is epidemiologically relevant, as the length of stay for tourists generally ranges from a few hours to several days. Establishing regular monitoring systems for wastewater in tourist zones could aid in detecting the presence of resistant bacteria and resistance genes. Targeted surveillance in tourist areas, concentrating on "hot spots," would enable proactive risk management by facilitating the early detection of resistant pathogens, hence allowing the implementation of locally targeted preventive measures. From a pragmatic perspective, enhancing the reliability of treatment plant operations and monitoring their discharges, particularly microbiological ones, would enhance the quality of coastal areas, which often serve as tourist destinations.

To explore the dynamics of antibiotic resistance in the samples, the proportions of MGE were compared. AT samples showed lower MGE levels (18% on average) than hotel wastewater and downstream WWTFs (25% and 26%, respectively), alongside lower bacterial diversity. In contrast, LT samples, reflecting local populations, exhibited a significantly (P < 0.05) higher MGE rate (32%). This difference could be related to the different composition of the microbiome between AT and the other sites.

Exposome analysis did not reveal a significant impact of tourist flows on the composition of the effluents of PSH, HT, and LT during the same monitoring campaign, irrespective of whether it was the dry or wet season (P > 0.05) (Fig. S3). Certain compounds, such as biocides and heavy metals, are known to adversely affect bacterial survival either by killing bacteria or exerting selective pressure that could promote the spread of AMR through co-selection mechanisms [35,36]. In this sense, a variation partitioning analysis was performed to assess the contribution of the measured compounds to the variance of the resistome at the sampled sites. The combined exposure to pharmaceuticals (including antibiotics and NSAIDs), biocides, and heavy metals appears to contribute more to the resistome's variability than pharmaceuticals alone (Fig. S4). Biocides emerged as the primary contributor to resistome variance, while pharmaceuticals exhibited the least contribution to the variance of the resistome. Previous studies have demonstrated that exposure to biocides increases the risk of resistance to a wide range of antimicrobials [37]. We observed that zinc had the highest concentration among the elements in the effluents; it has been identified as capable of selecting strains of *Pseudomonas aeruginosa* resistant to various metals and imipenem [38]. Disinfectants such as quaternary ammonium compounds may have a lethal effect on certain bacteria, altering the composition of microbial communities. This can lead to the under-representation of specific species, including resistant bacteria. Conversely, disinfectants could also promote the survival of resistant bacteria by selecting for MGEs carrying both ARG and quaternary ammonium compound *qac* resistance genes, potentially skewing the results of ARG relative abundance by increasing the proportion of resistant strains. For instance, a higher incidence of integrons carrying ARGs was observed in a quaternary ammonium compound-polluted environment [39].

## 5. Conclusion

Overall, there was no noticeable change in the Guadeloupe resistance, transatlantic supply of ARGs, or bacteria associated with travellers. This may be attributed to the spatial and temporal limitations of the study. Although our study included several sites, the geographical representation remains limited, highlighting the need for more extensive studies across other tourist destinations. Future research should also investigate how the length of stay of tourists influences the transmission of ARGs and their interactions with the local microbiomes. An analysis of wastewater from aircraft toilets after returning from Guadeloupe could also be of interest to determine whether travellers became colonised during their stay and carried these bacteria back to their home country.

Our results showed that while monitoring the resistome of air travellers' stools may be of interest during a health crisis, it may not adequately track the spread of AMR. Focal points such as tourist hotels can provide a more reliable and sustainable insight into the dynamics of AMR dissemination by travellers.

**Acknowledgements:** The authors thank Thibault Stalder for the critical reading of the manuscript.

**Funding:** This work was supported by the French Funding Agency for Research [ANR-20-AMBR-0001-01]. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Declaration of competing interests:** None declared.

**Ethical approval:** Not required.
## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jgar.2025.03.010.

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