# Antimicrobial resistance genes (ARGs) in sea surface aerosols over the Atlantic Ocean

Florent Rossi, Caroline Duchaine, Eloïse Le Bras, Cindy Dumais, Nathalie Turgeon, Marc Veillette, Jean-Luc Baray, Pierre Amato

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Florent Rossi, Caroline Duchaine, Eloïse Le Bras, Cindy Dumais, Nathalie Turgeon, et al. Antimicrobial resistance genes (ARGs) in sea surface aerosols over the Atlantic Ocean. Science of the Total Environment, 2025, 1003, pp.180726. 10.1016/j.scitotenv.2025.180726. hal-05344245

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# Antimicrobial resistance genes (ARGs) in sea surface aerosols over the Atlantic Ocean

Florent Rossi<sup>a,f,*</sup>, Caroline Duchaine<sup>a,f</sup>, Eloïse Le bras<sup>b</sup>, Cindy Dumais<sup>a,f</sup>, Nathalie Turgeon<sup>a</sup>, Marc Veillette<sup>a</sup>, Jean-Luc Baray<sup>c,d</sup>, Pierre Amato<sup>e</sup>

## HIGHLIGHTS

* Antimicrobial resistance genes (ARGs) are detected in the air oversea from coast to coast.

* Offshore prevalence of quinolone ARGs suggests the ocean is a major resistance reservoir.

* Marked heterogeneity in ARGs composition reflects multiple source origins.

* Continental signatures persist offshore, supporting long distance transport of ARGs.

## GRAPHICAL ABSTRACT

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

## ARTICLE INFO

**Keywords:**

Bioaerosols

Atmospheric antimicrobial resistance

Atlantic ocean

ARGs

## ABSTRACT

The large-scale abundance and distribution of antibiotic resistance genes (ARGs) within the atmosphere remains poorly documented, particularly over oceans. This study explores bacterial loads, diversity, and associated antimicrobial resistance genes in aerosols over the North Atlantic Ocean.

Aerosol samples were collected from a ship during a cruise from Brest (France) to Woods Hole (USA) for 24-h periods using a mast-mounted system, with additional one-hour spot samples taken daily and nightly using high-flow rates samplers. The airborne concentrations of bacteria along with 21 ARG subtypes as indicators of key resistance families were monitored using qPCR. These were related to the bacterial diversity obtained from same samples through ribosomal gene amplicon sequencing, and to the geographical origin of the air masses estimated using atmospheric dynamics models.

Total ARG concentrations ranged from background concentrations of a few copies to >10⁵ copies/m³ of air. Near coasts, macrolide and tetracycline resistance genes were dominant (up to 93 % and 38 % of the total ARG monitored here, respectively). While sulfonamide resistance genes were also detected further offshore, those

<footnote><sup>a</sup> Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Quebec City, Quebec, Canada</footnote>

<footnote><sup>b</sup> Blue Observer, 165 quai Eric Tabarly, 29200, Brest, France</footnote>

<footnote><sup>c</sup> Université Clermont Auvergne, CNRS, Observatoire de Physique du Globe de Clermont-Ferrand, UAR 833, F-63000, Clermont-Ferrand, France</footnote>

<footnote><sup>d</sup> Université Clermont Auvergne, CNRS, Laboratoire de Météorologie Physique, UMR 6016, F-63000, Clermont-Ferrand, France</footnote>

<footnote><sup>e</sup> Université Clermont Auvergne, CNRS, Laboratoire Microorganismes: Génome et Environnement, UMR 6023, F-63000, Clermont-Ferrand, France</footnote>

<footnote><sup>f</sup> Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Quebec City, Quebec, Canada</footnote>

<footnote>* Corresponding author at: Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada. E-mail address: florent.rossi.1@ulaval.ca (F. Rossi).</footnote>
related to transposases and β-lactamases were detected only sporadically. The multiple observed correlations between the aforementioned gene concentrations in the air and potential soil-derived microorganisms may be indicative of continental inputs. Conversely, the prevalence of quinolone resistance (*qepA*) in the air over the open ocean points toward a contribution from marine surfaces, supported by associations between several ARGs and marine microorganisms including cyanobacteria. These may thus act as environmental reservoirs of ARGs, and sources for further environmental spread notably by air means.

# 1. Introduction

The atmospheric environment carries a diverse mosaic of microorganisms of various geographical origins, transported across seas and continents. Depending on the location, the airborne microbial diversity is therefore composed of a mix of terrestrial and marine microorganisms. Their abundance and proportion can vary greatly in time and space (seasons, day/night cycles land-use) (DeMott et al., 2016; Tignat-Perrier et al., 2020). Over oceans and seas, the airborne microbial diversity and abundance remain poorly described. Recent studies have documented bacterial abundances ranging from $5 \times 10^2$ to $8 \times 10^4$ cells·m$^{-3}$ of air, dominated by Alpha-, Beta and Gamma- Pseudomonadota and Bacteroidetes (Archer et al., 2020; Lang-Yona et al., 2022; Mayol et al., 2017). The patterns of microbial assemblages within the marine planetary boundary layer suggest that the majority of these inputs (33–68 %) are emitted from the surrounding water surfaces. The contribution of terrestrial microbes can be important as well, although declining exponentially with the distance from lands (Mayol et al., 2017). Conversely, the influence of marine sources on terrestrial microbiome is expected to be high. Marine surfaces roughly cover more than 70 % of the Earth's surface, also providing particles and water vapor that are necessary components for the formation of clouds. Regarding clouds, more than two-thirds of the air masses (out of 208) collected at the puy de Dôme meteorological observatory (1465 m a.s.l., Central France, ~400 km East from the Atlantic Ocean) retained clear marine chemical signatures (Renard et al., 2020), with influence on the microbial content (Amato et al., 2007).

Alongside microorganisms, antimicrobial resistance genes (ARGs) are susceptible to reach the atmosphere. They have been reported in aerosols collected in various human facilities (pig barns, chicken coops, stables, hospitals), both indoor and outdoor (Kormos et al., 2022; Shawver et al., 2021). Recently, these were also quantified in clouds and aerosols collected at high altitude (1465 m a.s.l.), indicating the potential of ARGs to transit over continental scales (Rossi et al., 2024; Rossi et al., 2023). As for microorganisms, the geographical origin of the air masses was linked with variations in the diversity and abundance of airborne ARGs, suggesting multiple and distinct sources. Very distinct patterns were found between clouds influenced by marine surfaces and those influenced by continental surfaces (Rossi et al., 2023). Resistance to quinolones, in particular the plasmidic gene *qepA* encoding efflux pumps was widespread in clouds of marine origin, whereas resistance to compounds usually used in food-producing animals (tetracycline, macrolide and β-lactamase), were more abundant in clouds of continental origin. Efflux pump mechanisms are very common in the environment and serve a large variety of purposes, most of which consist in evacuating toxic compounds from the cell (Sharma et al., 2023). In seas and oceans (Cuadrat et al., 2020), including coastal areas, these were found to contribute by far the most abundant subtype of antimicrobial resistances, suggesting that marine surfaces could be an important source of ARGs to be disseminated at large scale by air means.

To date, the emissions of ARGs from seas and oceans remain poorly studied. A major challenge lies in distinguishing the contributions of continental sources within the pool of airborne biological material, as most studies have collected marine aerosols from coastal sampling sites. A recent comparison between coastal aerosols collected in Qingdao, China, and marine aerosols sampled aboard a sailing cruise over the Yellow and Bohai Seas reported comparable absolute concentrations of

ARGs ($10^4–10^5$ copies/m$^3$ of air) and no significant differences in richness between the two environments (Jia et al., 2025). However, marked differences emerged in their relative profiles: coastal aerosols appeared enriched in tetracycline resistance genes, consistent with terrestrial and agricultural influences, whereas marine aerosols exhibited proportionally higher contributions of sulfonamide and, to a lesser extent, β-lactam resistance genes. These patterns partially align with ARGs previously reported in seawater, although site- and season-specific effects can deeply affect microbial content and related resitome (Cáliz et al., 2022; Bonanno Ferraro et al., 2024). Importantly, the coastal samples in that study were collected using 24-hour integrative approaches, which may not fully capture short-term shifts in air mass composition. The rapid dynamics of air masses results in great spatial and temporal variability in the airborne microbial composition, making it difficult to identify specific sources. Addressing this limitation requires sampling close to targeted potential sources of emission, employing varied timeframes to capture temporal variability, while analyzing air mass movements at the meso-scale level. In this study, a 22-day transatlantic sampling campaign was performed from Brest, France, to Woods Hole on the East coast of the United States, in December 2021. Aerosols were collected on a daily basis using both integrative (24-h) and spot (1-h, day/night) sampling methods to capture the variability of air masses reaching the boat. For each event, total bacteria and a panel of 21 different subtypes of ARGs were quantified by qPCR, and interpreted in regards to bacterial diversity (determined using 16S rRNA sequencing) and the geographical origin of the corresponding air masses. Overall, this study provides novel insights into the atmospheric circulation of antimicrobial resistances in the global environment and open ocean and emphasizes the role of marine surface microbiota as potential actors in the aerial spread of antimicrobial resistances at a large scale.

# 2. Materials and methods

## 2.1. Sampling set-up

Sampling was carried out during a campaign crossing the Atlantic Ocean from Brest (France, 48°19'46.9"N 4°27'42.8"W) to Woods Hole (USA, 41°31'28.0"N 70°40'39.4"W) (roughly 7060 km) aboard the sailing vessel "Iris". The campaign spanned 22 days, from November, 14th, to December, 6th, 2021. Detailed itinerary and GPS coordinates of each sampling point are presented in Fig. 1 and Supplementary Tables 2&3. The vessel measures 25.70 m in length and 5.40 m in width accommodating a crew member capacity of 7 persons, including a dedicated scientific coordinator. Onboard equipment comprises a microbiology laboratory equipped with a laminar flow hood for sampler and sample preparation and handling, and a UV light for material sterilization.

Aerosol sampling was conducted using two complementary strategies: short-term high-flow spot sampling (1 h, during the day and night) to capture fine-scale variations in airborne ARGs potentially linked to rapid air mass changes, and continuous 24-hour integrative sampling to provide a broader, time-averaged perspective on overall ARG emissions. For spot sampling, one SASS 3100 (Research International, Inc. Monroe, USA) running on batteries was deployed on the boat's intermediate spreader (11.80 m above the deck) every day and night for 1 h. This collected aerosols on an electret dry filter (Research International, Inc. Monroe, USA) at an air flow rate of 300 L/min (n = 28 samples in total; 14 day and 14 night); each individual filter therefore contained aerosols
from 18 m³ of air. In parallel, for 24 h-integrative sampling, three individual sampling lines were mounted facing downward on the boat's lowest spreader (7.40 m above the deck), to collect aerosols at an airflow of ~7 L/min (n = 45). This vertical configuration was chosen to minimize human interference while accounting for practical constraints related to tubing length and onboard maneuverability. Additional detail on samplers positioning is provided in Figs. 6&7SI. Each individual filter thus contained aerosols from 10 m³ of air. Each line consisted of a 47 mm stainless-steel filter-holder (Millipore, ref. C806) holding a pre-calcined (450 °C for 4–5 h) 47 mm quartz fiber filter (Millipore, ref. AQFA04700) and connected to an individual 12 V vacuum pump (Charles Austen ref. LD-DBM30B) and an online 15 L/min bead rota-meter (Omega) to monitor the air flow rate. The filter holders were decontaminated between each 24-h sampling series by exposure to UV light for 15 min within the laminar flow hood onboard. Controls consisted of field blanks (filter placed in the filter holder for a few minutes, without pumping) and transportation blanks (unexposed filters carried to the field), conducted every 2 days for the SASS3100 (spot sampling), and daily for the filter-holders (24-h integrative sampling), between each sampling series. After sampling, filters were recovered using sterile clamps and handled within the laminar flow hood previously exposed to UV light. Filters were stored individually in sterile air-tight 50 mm diameter Petri dishes placed within individual sterile plastic bags, in plastic boxes containing dry packs, at room temperature. Outdoor temperatures and wind speed were not systematically recorded on a daily basis; however, temperatures were always above 0 °C, and no sampling was conducted during rainfall events or when wind speed exceeded 60 km/h. In addition, no water samples could be collected due to logistical constraints.

## 2.2. DNA extraction

Because quartz-fiber filters and electret dry filters differ in their composition, the very first steps of the DNA extractions were adjusted to fit these differences.

Electret dry filters from the SASS 3100 were first extracted using the electret dry filter particle extractor (Research International, Inc.) with 7 mL of the corresponding elution buffer. Collected volumes were then estimated by weighting (assuming a density of 1, average of 6.3 ± 0.2 mL) and concentrated by four successive centrifugations (21,000 g for

10 min) in 1.7 mL tubes to process the entire volume. The eluates were then stored at -20 °C before further processing. DNA extraction were performed afterward using DNeasy PowerWater kit (Qiagen, Germantown, USA) following manufacturer's instructions, with a final elution volume of 50 µL.

For quartz-fiber filters (integrative 24-h sampling), triplicate samples were pooled and DNA was extracted using the DNeasy PowerWater kit (Qiagen, Germantown, USA) using the protocol adapted for quartz filters (Dommergue et al., 2019), with a final elution volume of 50 µL. All DNA extracts were stored at -20 °C before any downstream application.

## 2.3. Detection of total bacteria and ARGs

Total bacterial concentration in aerosol samples was estimated by qPCR using the EUBf and EUBr universal primers targeting the V2-V3 region of the 16S rRNA gene. The qPCR reactions were performed in a 20 µL final volume containing 2 µL of template DNA (diluted to 1/10), 10 µL of IQ Green supermix (BioRad), 7.88 µL of RNase-free H₂O, 0.25 L of a mix containing both primers (50 µM each) and 0.13 µL of taqman probe (10 µM). Cycling conditions were set at 94 °C for 3 min as initial denaturation step, followed by 40 cycles of 95 °C for 20 s and 62 °C for 30 s. Total bacterial genomes concentration in the air was estimated by dividing the 16S rRNA genes values by the estimated average number of ribosome copies per bacteria of 4.3 (Klappenbach, 2001).

Additionally, a panel of 33 ARGs conferring resistance to the main families of antibiotics, including one mobile genetic element (MGE) were monitored by qPCR. The panel was selected from the array described by Stedtfeld et al. (2018) and validated for air samples to ensure comparability across collaborators within the “Discovery Frontier” project (George et al., 2022). The entire panel was first tested on the most concentrated samples based on 16S rRNA gene concentrations, and afterward, 20 different subtypes and one MGE were retained and assessed in all air samples. The final panel comprised resistance determinants to quinolones (qepA, qnrB), β-lactams (blaOXA, blaTEM, blaVIM), macrolides (erm35, ermX, ermB), tetracycline (tetL, tetM, tetO, tetQ, tetW, tetX), sulfonamides (sul1, sul2), aminoglycosides (aac(3')-iid, aac(6')-Ib), vancomycins (vanA, vanB) together with one MGE (tmpA). The qPCR reactions were performed in a 20 µL final volume containing 2 µL of template DNA (diluted to 1/10), 10 µL of IQ Green supermix (BioRad), 7.88 µL of RNase-free H₂O and 0.12 µL of a mix containing

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Twenty-two days sampling itinerary performed during the crossing section of the Atlantic Ocean from Brest (France) to Woods Hole (USA). Integrative sampling (24-hour periods) are represented in red, and spot sampling (1-hour periods) in white (day) and dark (night). Purple dots indicates the blanks performed with the SASS samplers.</figcaption></figure>
both primers (50 µM each). Cycling conditions were identical between the different subtypes of ARGs targeted and were set at 95 °C for 3 min as initial denaturation step, followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s. More details about the targeted ARGs sequences and the corresponding sets of primers are described in literature (Nijhuis et al., 2016; Stedtfeld et al., 2018). All qPCR reactions were performed in technical duplicates and field/control blanks and no-template controls (i.e. ultrapure water) were processed under the same conditions. Ct values above 37 were always considered below the detection limit. Ct values were then converted into starting concentrations of the targeted genes (expressed in copies/µL) by comparison to a 10-fold dilution series of a 10⁶ copies/µL control plasmid (pUCIDT-AMP) containing the target sequence (one different construction for each target gene, including the 16S rRNA gene). The concentration values obtained in controls were systematically deduced from those in the samples, and final concentrations in the air (in copies/m³ of air) were obtained by direct normalization by the volume of air sampled.

## 2.4. MiSeq Illumina amplicon sequencing and bioinformatics pipeline

Amplicon libraries were created by targeting the 16S rRNA hyper variable regions V3–V4 using a two-step dual-indexed PCR approach coupled with the BactV3-V4-F and BactV3-V4-R primer set (341F 5’-CCTACGGGNGGCWGCAG-3’ and 805R – 5’GACTACHVGGGTATC TAATCC3’) as in Mbareche et al. (2019) (Mbareche et al., 2019). Briefly, the gene-specific sequence was first fused to the Illumina® TruSeq sequencing primers and PCR was carried out in a total volume of 25 µL containing 1 × Q5 buffer (NEB, Ipswish, MA, USA), 0.25 µM of each primer, 200 µM of each of the dNTPs, 1 U of Q5 High-Fidelity DNA polymerase (NEB) and 1 µL of template DNA. The PCR program involved an initial denaturation at 98 °C for 30 s followed by 35 cycles of denaturation at 98 °C for 10 s, annealing at 55 °C for 10 s, extension at 72 °C for 30 s and a final extension at 72 °C for 2 min. PCR reaction products were purified using the Axygen PCR cleanup kit (Axygen®, Waltham, MA, USA) and their quality was checked on a 1 % agarose gel. A second PCR was run from a 50 to 100-fold dilution of the purified PCR products to add barcodes (dual-indexed) and for missing sequences required for Illumina sequencing using the same conditions as described above with 10 cycles. PCR reactions were purified again in the same way as above, checked for quality on a DNA7500 Bioanalyzer chip (Agilent®, Santa Clara, CA, USA) and then quantified spectrophotometrically with the Nanodrop® 1000 (Thermo Fisher Scientific, Waltham, MA, USA). Barcoded amplicons were pooled in equimolar concentrations for sequencing on the Illumina® MiSeq machine. The library was further sequenced on Illumina MiSeq platform using 2 × 250 paired-end (PE) chemistry which generated 0.5 million reads per sample. Library preparation and samples sequencing were conducted at the Plateforme d'analyses génomiques (IBIS, Université Laval, Québec, QC, Canada). Only samples with sufficient estimated bacterial concentration, based on 16S rRNA gene concentration obtained by qPCR, were send for amplicon sequencing (seven 24 h-integrative samples and 24 1 h-spot samples). Field blanks from the 1-h spot sampling were pooled, whereas those from the integrative sampling were processed individually. All blanks underwent the same processing as the samples.

The bioinformatics pipeline was carried out using a Quantitative Insights Into Microbial Ecology 2 (QIIME2) (v2023.2) (Bolyen et al., 2019). Demultiplexed forward and reverse sequences were trimmed to remove PCR primers, denoised and merged with a 21 bp overlap all at once using the "qiime dada2 denoise-paired" function (Callahan et al., 2016). Feature tables were then constructed using the "qiime feature-table summarize" function to obtain Amplicon Sequence Variants (ASVs) that were further assigned a taxonomic classification using the "qiime feature-classifier classify-sklearn" function on a pre-trained naive-bayes classifiers obtained from the non-redundant small subunit SILVA 138 reference database (SSUNR99) build by a dereplication of the full SSU/LSU reference database using a 99 % identity criterion. All ASVs

assigned to mitochondrial and chloroplast sequences were removed from the data set using the "qiime taxa filter-table" and imported into the R environment for further analyses. Samples displaying a number of reads <2500 after denoising and chimera's removal were discarded. Raw sequence data and associated metadata are archived under the ENA project accession number PRJEB96326.

## 2.5. Characterization of 72-hour backward trajectories of air masses

Backward trajectories of the air masses reaching the boat during its transit across the Atlantic Ocean were computed using the CAT three-dimensional (3D) kinematic trajectory (Baray et al., 2020). The trajectory model integrates dynamic fields derived from the ECMWF ERA-5 (European Centre for Medium-Range Weather Forecasts) meteorological data archive (https://www.ecmwf.int/en/forecasts/datasets/reana lysis-datasets/era5). This integration enables the estimation of both the geographical origins and the altitudinal history profiles of the air masses, inclusive of their relative position to the atmospheric boundary layer (Renard et al., 2020). Backward trajectories were reconstructed for a duration of 72 h (i.e., the estimated average residence time of bacteria in the air) (Burrows et al., 2009) prior to each onboard aerosol sampling event with a spatial resolution of wind fields at 0.5° for both latitude and longitude. Each trajectory set comprised 45 individual paths, arranged in a 3 × 3 grid horizontally around the ship (with a 0.1° deviation) and at 5 different altitudes near the sea level, spanning from 980 to 1000 hPa at 5 hPa intervals. These trajectories were calculated during the aerosol measurement periods with a temporal resolution of 15 min. The reported values correspond to the average percentages of time spent by air masses above and within the atmospheric boundary layer for continental and marine surfaces, respectively for both hourly day/night spot sampling and 24-hour integrative sampling. In the latter, we averaged two sets of 24-hour backward trajectories (one from the start and one from the end of each sampling interval). In total, 32 backward trajectory plots were obtained for spot samples (13 days, 14 nights, and 5 blank samples) and 30 for integrative samples (15 at the start and 15 at the end of each sampling interval). All plots and related parameters are provided in the Supplementary Information (Table 2SI and Fig. 5SI).

## 2.6. Statistical analysis

The diversity of airborne bacteria, expressed as amplicon sequence variants (ASVs) and antimicrobial resistance genes were assessed from three diversity indices: total richness, Shannon-Weiner index, and evenness. All alpha and beta diversity analyses, including diversity indices and non-metric multidimensional scaling (NMDS) and Venn diagram, were conducted on datasets rarefied to 2500 reads to ensure comparability across samples. Statistical differences between sampling periods for each of the calculated diversity indices and for the total concentrations of ARGs were assessed through Kruskal-Wallis non-parametric test followed by multiple pairwise comparisons based on Tukey contrast and using the "nparcomp" library (Konietschke et al., 2015) (http://www.jstatsoft.org/v64/i09/). Differences in ASVs community structures and ARGs patterns between sampling periods (i.e. 1 h-spot samples day/night and 24 h-integrative samples) were assessed by non-metric multidimensional scaling (NMDS) using the Bray-Curtis dissimilarity index using the "vegan" library (vegan: Community Ecol- ogy Package. R package version 2.6-4, n.d.). The number of shared ASVs between sampling periods was highlighted by Venn diagram using the "VennDiagram" library (Chen and Boutros, 2011). The relationships between the 20 different subtypes of ARGs and the MGE retained in aerosol samples, and bacterial ASVs affiliated to the genera were assessed by weighted bipartite network analysis based on Spearman rank correlations between ARG concentrations and bacterial genera. The two separate networks, the first one for spot samples, the other for integrative samples, were constructed in a similar manner, where ARGs and bacterial genera are represented as nodes and correlations
(Spearman "ρ" coefficient) as edge thickness. Variables presenting >10 % of null values were discarded and positive and significant correlations only were retained (P < 0.05). The importance of each node (ARGs or bacterial genera) in the networks was estimated by degree centrality measurements, that is, the number of connected edges to each node. Network analyses were first conducted using the "igraph" library (Csardi and Nepusz, 2006) (https://igraph.org) and transferred to Cytoscape 3 for network representation using the "createNetworkFromGraph" function. All statistical analyses were conducted in the R environment (R Core Team, 2013) (https://www.R-project.org/).

## 3. Results

### 3.1. Airborne concentration of total bacteria and ARGs in sea surface aerosols

From 1-hour spot samples, the observed median bacterial genome concentrations were similar between day and night samples, with values estimated to $8.4 \times 10^2$ and $9.3 \times 10^2$ per m$^3$ of air, respectively. However, large variations were observed between samples, particularly during the night, with min:max ratios of ~1:30 and 1:1000, respectively (Fig. 2A & Tables 1 & 4SI). Airborne bacterial concentrations along the Atlantic Ocean crossing did not differ from coast to coast, with no statistical difference between sampling periods (Kruskal-Wallis, P = 0.21).

The bacterial genome concentrations derived from integrative samples were slightly higher than those obtained from spot samples (median of $3.1 \times 10^3$ genomes/m$^3$ of air), with large variations between samples as well. Noteworthy, four samples fell below the detection limits (20201116/17, 20201117/18, 20201122/23, 20201128/29, 20201129), and were excluded from the graphic (Fig. A). In contrast, a pronounced peak in airborne concentrations was observed approximately 2000 km from the departure, near the Acores islands of São Miguel and Santa Maria, in both integrative and night spot samples, potentially indicating a direct influence of nearby lands. Here, values reached up to $9.2 \times 10^4$ and $1.6 \times 10^5$ genomes/m$^3$ of air in night spot samples 6 and 7 respectively (20201120_SASS_N and 20201121_SASS_N) and $6.5 \times 10^6$ genomes/m$^3$ of air in 24 h-integrative sample day 8 (20201121/22, 20201122).

When assessing the resistome of oversea aerosols, 19 different subtypes of ARGs (out of 20 from the reduced panel), together with one MGE, could be detected at least once in both spot and integrative samples (Table 3SI). The resulting estimated median concentrations of total ARGs (i.e. the sum of individual ARG concentrations) were similar between spot and integrative samples (Kruskal-Wallis, P = 0.17). The estimated values were $4.5 \times 10^2$ ARGs/m$^3$ of air for the 24 h integrative samples (mean ± Standard Error of the Mean (SEM) of $2.7 \times 10^4$ ± 1.9 × $10^4$ ARGs/m$^3$ of air) and $9.7 \times 10^1$ and $4.9 \times 10^2$ for the day and night spot samples, respectively (mean ± SEM of $2.8 \times 10^3$ ± $2.4 \times 10^3$ and $2.5 \times 10^3$ ± $1.2 \times 10^3$ ARGs/m$^3$ of air, respectively) (Fig. 2A & Tables 1 & 4SI). Additionally, total ARG concentrations correlated positively with total bacterial concentrations, although only significant in 24 h-integrative samples (Spearman $ρ$ = 0.57, P < 0.001; Fig. 2SI), supporting a link between microbial biomass and resistome abundance in oversea aerosols.

Breaking down further the specific contribution of the ARGs monitored, distinct variations in the proportion of the main families of antibiotic resistances were observed between integrative and spot samples, although their respective ARG patterns did not differ significantly (Figs. 2B and 3A). In the 24 h-integrative samples, the most prevalent observed subtypes of ARGs were associated with quinolone, (qepA, qnrB), vancomycin (vanA, vanB) and aminoglycoside (aac(3)-II) classes of antibiotic resistance. Specifically, quinolone resistance represented ~85 % on average of the total concentration of ARGs in the 24 h integrative samples, predominantly due to qepA (>99 % of quinolone resistance genes). Such a contribution decreased near the coasts: the proportions of tetracycline and sulfonamide resistance were higher at

departure near the French coast (up to 38 % and 27 % of the total ARG concentration, respectively), while the contribution of macrolide resistance increased (up to 97 %) upon arrival, near the US coast. Accordingly, the time spent by air masses within the continental boundary layer within 72 h before reaching the boat was positively correlated with the abundance of tetracycline resistance genes (Spearman's $ρ$ = 0.57, P < 0.05, Fig. 2 SI), suggesting a clear terrestrial imprint consistent with agricultural or soil-related sources. Conversely, aminoglycoside resistance genes positively correlated with the time spent within the marine boundary layer ($ρ$ = 0.65, P < 0.01), indicating a potential marine source signature, and possibly reflecting contributions from surface ocean emissions.

In day/night spot samples, the contribution of the different classes of antibiotic resistance genes appeared more variable than in integrative samples, but their patterns did neither significantly differ from each other nor from those observed in the integrative samples (Figs. 2C and 3A). Quinolone resistance remained dominant overall, indicating a possible link with marine emissions, although more constrained to specific sampling intervals in the middle of the Atlantic Ocean. Overall, quinolone resistance contributed to an average ± SEM 35 ± 9 % and 46 ± 10 % of the total ARG concentration during the day and during the night, respectively. Meanwhile, the abundance of macrolide and tetracycline resistances increased all along the crossing, averaging 21–25 % of total ARGs, and it tended to be more prevalent near the coasts. Additional low contributions from various classes of antibiotic resistances were also observed, including β-lactam and sulfonamide, as well as mobile genetic elements (i.e. transposase). Only the abundance of sulfonamide resistance genes could be specifically linked with air mass backward trajectory, i.e., this positively correlated with the time spent by air masses below the marine boundary layer before reaching the boat ($ρ$ = 0.41, P < 0.05).

### 3.2. Airborne bacterial diversity in sea surface aerosols

Overall, the patterns of bacterial ASVs diversity differed significantly between spot and integrative samples (permanova, P < 0.001, Fig. 3A & B). A total of 157 distinct ASVs were detected across the five 24-h integrative samples retained after denoising and quality filtering (Fig. 3B), with only 12 ASVs shared with night samples, 10 with day samples and 40 with both. Individual samples exhibited a median richness of 37 ASVs (min – max of 26–66), indicating a substantial inter-sample variability in terms of community composition (Table 1). The corresponding median Shannon's diversity index was 2.17, highlighting a relatively low richness and uneven communities, dominated by a few highly abundant taxa. Overall, most sequences were affiliated with the phylum Pseudomonadota, which accounted for 93 % of total reads on average. Dominant genera included *Stenotrophomonas* (33.7 %), *Pseudomonas* (27.8 %), *Herbaspirillum* (12.5 %), *Sphingomonas* (8.4 %), *Brevundimonas* (1.9 %), *Massilia* (0.4 %), *Sphingobium* (0.3 %), *Rhodopseudomonas* (0.1 %), and *Delftia* (0.1 %), as well as unclassified taxa at the genus level, belonging to *Burkholderiales* (0.3 %) and *Xanthobacteraceae* (0.2 %) (Fig. 4A). Such a taxonomic profile is consistent with a strong imprint of environmental or soil-associated bacteria. Additionally, sequences from the phylum Bacillota were also detected (0–13 %, ~5 ± 2.4 % on average), including the genera *Staphylococcus* (2.1 %) and *Streptococcus* (1.1 %). Other detected taxa included members of the phylum Actinomycetota (0–3.6 %, ~1.6 ± 0.7 %), such as *Curtobacterium* (0.5 %) and *Cupriavidus* (0.3 %), and of the phylum Bacteroidota (0–0.8 %, ~0.2 ± 0.1 %), such as *Pedobacter* (0.2 %).

In contrast, spot samples exhibited much higher bacterial ASV diversity: there were 367 and 645 distinct ASVs across day and night samples respectively, although community compositions did not statistically differ between the two time periods (Fig. 3A & B). Nevertheless, there were only 98 distinct ASVs shared between day and night spot samples (Fig. 3B). The median richness consisted of 50 different bacterial ASVs in day spot samples (min – max of 31–88) and 73 (min – max of
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Evolution of total bacterial (solid lines) and total ARGs (filled areas) concentrations in aerosols sampled over the Atlantic Ocean for integrative (red) and day/night (blue/black) spot sampling (A). Evolution of the different families of antibiotic resistance for integrative sampling (B), and day/night spot sampling (C). The X scale is expressed as the straight distance from departure (km).</figcaption></figure>
<caption>**Table 1**</caption>

Median (min – max) values for the calculated diversity indices for bacterial ASVs rarefied to 2500 reads, and antimicrobial resistance genes (ARGs), including total concentrations of airborne bacteria and ARGs. Statistically significant differences between the sampling strategies are shown in bold (Kruskal-Wallis, *P* < 0.05).

<table><thead><tr><th></th><th></th><th>1 h-spot samples day</th><th>1 h-spot samples night</th><th>24 h-integrative samples</th><th>P value</th></tr></thead><tbody><tr><td rowspan="6">ARGs diversity indices</td><td>Total bacteria (genomes m<sup>-3</sup> of air)</td><td>8.4 × 10<sup>2</sup> (6.8 × 10<sup>1</sup>–1.7 × 10<sup>3</sup>)</td><td>9.3 × 10<sup>2</sup> (1.8 × 10<sup>2</sup>–1.6 × 10<sup>5</sup>)</td><td>3.1 × 10<sup>3</sup> (0–6.5 × 10<sup>6</sup>)</td><td><strong>0.2061</strong></td></tr><tr><td>Total ARGs (copies m<sup>-3</sup> of air)</td><td>9.8 × 10<sup>1</sup> (7.9–3.4 × 10<sup>4</sup>)</td><td>5.0 × 10<sup>2</sup> (3.3 × 10<sup>1</sup>–1.5 × 10<sup>4</sup>)</td><td>4.6 × 10<sup>2</sup> (2.93–2.8 × 10<sup>5</sup>)</td><td><strong>0.1657</strong></td></tr><tr><td>Number of reads</td><td>5.3 × 10<sup>4</sup> (3.9 × 10<sup>4</sup>–6.2 × 10<sup>4</sup>)</td><td>4.5 × 10<sup>4</sup> (1.5 × 10<sup>4</sup>–6.2 × 10<sup>4</sup>)</td><td>3.0 × 10<sup>4</sup> (2.6 × 10<sup>3</sup>–6.6 × 10<sup>4</sup>)</td><td><strong>0.3231</strong></td></tr><tr><td>Richness</td><td>6 (4–12)</td><td>8.5 (6–14)</td><td>7 (2–13)</td><td><strong>0.0541</strong></td></tr><tr><td>Shannon's H</td><td>1.01 (0.04–1.56)</td><td>0.67 (0.08–1.92)</td><td>0.24 (0.03–1.8)</td><td><strong>0.0906</strong></td></tr><tr><td>Evenness</td><td>0.27 (0.01–0.44)</td><td>0.16 (0.01–0.39)</td><td>0.05 (0.01–0.6)</td><td><strong>0.1741</strong></td></tr><tr><td rowspan="2">ASVs diversity indices</td><td>Richness</td><td>50 (31–88)</td><td>73 (38–273)</td><td>37 (26–66)</td><td><strong>0.0599</strong></td></tr><tr><td>Shannon's H</td><td>3.32 (2.72–3.83)</td><td>3.52 (2.83–4.49)</td><td>2.17 (1.54–2.64)</td><td><strong>0.0024</strong></td></tr><tr><td></td><td>Evenness</td><td>0.24 (0.21–0.26)</td><td>0.22 (0.17–0.26)</td><td>0.2 (0.16–0.25)</td><td><strong>0.3068</strong></td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Diversity of bacterial ASVs (phylum) observed in Atlantic Ocean aerosols for integrative sampling (A) and spot sampling (B). Only samples whose total reads number exceeds 2500 are shown. Non-metric multidimensional scaling (NMDS) performed on bacterial ASVs diversity and calculated using the Bray-Curtis dissimilarity index rarefied to 2500 (C). Associated Venn diagram representing the number of shared ASVs between integrative and day/night spot sampling (D).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 4. Correlation network showing the association between the 22 different ARGs subtypes and the bacterial ASVs observed in integrative sampling. Only positive and significant correlations were retained (Spearman, *P* < 0.05).</figcaption></figure>
38–273) for the night samples, again highlighting substantial variability among air samples collected during the same timeframe. The significantly higher Shannon diversity indices observed in day (3.32) and night (3.52) spot samples compared to integrative ones suggest both greater diversity and a more balanced community structure, despite the lack of significant differences in evenness indices (Table 1). The most abundant sequences were affiliated with the phylum Bacillota, representing 5–75 % of total reads (~51 ± 3.5 % of the total reads on average). Dominant genera included *Staphylococcus* (25.9 %), *Streptococcus* (7.3 %) and *Paenibacillus* (7.1 %) (Fig. 4B), indicating a prominence of commensal or potentially pathogenic genera in spot samples. These were followed by members of the phylum Pseudomonadota (9–48 %, ~28 ± 2.4 % on average), including *Comamonadaceae* (4.7 %), *Enterobacteriaceae* (3.2 %), *Neisseria* (1.7 %). *Sphingomonas* (1.5 %), *Pseudomonas* (0.6 %) and *Stenotrophomonas* (0.7 %). Taxa from the phylum Actinomycetota were also detected (1.4–40.7 %, ~12.7 ± 2.1 % on average) including *Kocuria* (2.4 %), *Corynebacterium* (1.6 %) and *Rothia* (1.5 %), and Bacteroidota (0–14 %, ~4.7 ± 0.8 % on average) such as *Sediminibacterium* (1.3 %) and *Carnobacterium* (0.5 %). Overall, the broader diversity and distinct taxonomic profiles captured by spot samples suggest they are more sensitive to transient or localized microbial inputs, whereas integrative samples emphasize persistent background communities.

### 3.3. Relationship between bacteria taxonomy and ARGs

The integrative sample network encompassed a relatively small set of bacterial genera, reflecting a limited number of positive correlations between taxonomy and ARG composition. Of the 28 genera retained, 15 (53.5 %) were affiliated with Pseudomonadota, 7 (25 %) with Actinomycetota, 5 (17.9 %) with Bacillota and one with Bacteroidota (0.3 %) (Fig. 5). Together, these taxa accounted for ~92 % of the bacterial abundance observed in diversity analyses, underscoring their strong representativeness and pointing to a stable background resistome. The network was characterized by a central core around 6 different subtypes of ARGs related to quinolone (*qnrB*, *qepA*, Degree Centralities (DCs) = 6 and 7 respectively), aminoglycoside (*aac(3)-II*, DC = 5), β-lactams (*blaTEM*, DC = 5), vancomycin (*vanB*, DC = 4) and macrolide (*ermX*, DC = 3). These were correlated with 7 different bacterial genera whose average DC was 5.42 ± 0.42 (mean ± SEM) and included few of the most abundant genera, such as *Pseudomonas* (DC = 5) and *Herbaspirillum* (DC = 5). These also included *Pedobacter* (DC = 7), *Rhizobium* (DC = 5), *Rhodopseudomonas* (DC = 5) and some unclassified *Sphingomonadaceae* (DC = 7). Pseudomonadota at large were by far the phylum displaying the higher number of positive correlations with the different subtypes of ARGs (DC of 11). *Stenotrophomonas*, the most abundant genus from Pseudomonadota in integrative samples (33.7 % of ASVs) was only significantly correlated with the *erm35* resistance gene. *Pseudomonas*, the second most abundant one (27.8 % of ASVs) was correlated with *qnrB*, *qepA*, *erm35*, *aac(3)-II* and *tetW*, while *Sphingomonas* only correlated with the *tetQ* resistance gene. Additionally, 10 different genera (~35 %) could be associated to the human microbiota, but their total relative abundance was low (3.36 % of total ASVs): *Staphylococcus*, *Corynebacterium*, *Actinomyces*, *Finegoldia*, *Gemella*, *Lawsonella*, *Neisseria*, *Haemophilus*, *Rothia* and *Escherichia-Shigella*, correlated with *erm35*, *tetW* and *tetQ*.

By contrast, the spot-sample network encompassed a broader range of genera, but these together represented only 18.9 % of the ASVs detected in diversity analyses, highlighting a wider yet less representative set of associations (Fig. 6). Of the 43 different bacterial genera retained, 15 (34.9 %) were affiliated with Pseudomonadota, 6 (13.9 %) with Actinobacteriota, 13 (30.2 %) with Fimicutes and 5 (11.6 %) with Bacteroidota. Notably, the most abundant genera observed in diversity analyses for day/night spot sampling from (*Staphylococcus*, *Streptococcus*, and *Paenibacillus*) displayed no significant correlation with any of the ARGs monitored, indicating associations mostly driven by environmental genera rather than the dominant potential commensal taxa.

Additionally, the network appeared less connected than for integrative samples. Quinolone resistances were connected to 14 different bacteria genera, most of which affiliated with Pseudomonadota (*qnrB*, *qepA*, DCs = 9 and 5 respectively), while tetracycline resistances were correlated with 25 different genera, including many Bacillota and some Actinobacteriota and Pseudomonadota (*tetM*, *tetW*, *tetQ*, *tetX*, *tetL* and *tetO*, DCs = 9, 6, 5, 5, 4 and 2, respectively). Macrolide resistance correlated to 7 genera (*ermB*, *ermX*, DCs = 7 and 1 respectively), and to a lesser extent to β-lactams (*blaTEM*, DC = 3) and sulfonamide resistances (*sul1*, DC = 3). Approximately one third of the bacteria genera were linked to the human microbiota, contributing ~4.8 % of the total ASV count. This included *Finegoldia* and *Abiotrophia* (correlated with *tetX*), *Clostridium_sensu_stricto_1*, *Granulicatella* and *Lactobacillus* (correlated with *tetW*), *Enterococcus* (correlated with *BlaTEM*), *Cutibacterium* and *Prevotella* (correlated with *tetM*) and *Turicibacter* (correlated with *tetW*, *tetW* and *qepA*). Besides, few low abundance genera could be potentially associated with marine environments: *Alliterella* (correlated with *ermB*, *ermX* and *qepA*), *Marinococcus* (correlated with *ermB*), and *Sediminibacterium* (correlated with *sul1*).

## 4. Discussion

### 4.1. Aerosols collection at sea: when sampling method matters

We deployed two distinct aerosol sampling strategies in parallel: a continuous, low-flow setup (~7 L/min) using quartz filters positioned 7.8 m above the ship's deck to collect 24-h integrative samples, and high-flow SASS samplers (300 L/min) using electret dry filters to collect 1-h spot samples at 11.8 m above the deck. Despite comparable total air volumes sampled (30 m³ and 18 m³, respectively), the two approaches yielded substantial differences in observed bacterial diversity. Notably, spot samples consistently exhibited higher bacterial richness, regardless of the sampling timeframe. These samples were characterized by a higher prevalence of *Staphylococcus*, *Streptococcus*, *Corynebacterium*, *Cutibacterium*, and genera affiliated with *Enterobacteriaceae*, and shared only 40 ASVs with the 24-hour integrative samples. These discrepancies may result from multiple factors: (i) differences in flow rate and sampler inlet design, which can affect the capture efficiency across aerosol particle size classes; (ii) filter material, potentially impacting DNA extraction efficiency; and (iii) sampling height, which may influence exposure to surface ocean emissions versus open air. Notably, for four integrative samples (November 17, 18, 23, and 29), the total bacterial concentration estimated by qPCR (based on the 16S rRNA gene) was below the detection limit. This is most likely due to the presence of sea salts and other PCR inhibitors. Although extracted DNAs were systematically diluted prior to analysis, the overall low airborne biomass limited the extent to which dilution could be applied, likely explaining the absence of quantifiable values. This limitation was not observed in the spot samples. This suggests that, in marine environments, there may be a lower threshold of airborne bacterial concentration below which integrative sampling becomes ineffective. In addition, the dynamic and heterogeneous nature of air masses, both spatially (Péguilhan et al., 2023) and temporally, adds further complexity to microbial aerosol composition. While integrative samplers may appear less sensitive at first glance, they provide a more representative and time-averaged overview of air mass diversity. Specifically, bacterial assemblages captured by integrative sampling were dominated at the phylum level by Proteobacteria, which is also the dominant phylum in marine surface waters. In contrast, spot samplers may be more prone to detecting commensal or potentially pathogenic microflora from wildlife or onboard contamination, due to their short sampling window and higher sensitivity to local fluctuations. Overall, our results emphasize the critical importance of sampling strategy — including device type, duration, and airflow — in shaping the observed structure of airborne bacterial communities and associated resistomes at sea. The dual approach employed here, despite its limitations, allowed us to better capture the
<figure><img src="image_9.png" /><figcaption>Fig. 5. Correlation network showing the association between the 22 different ARGs subtypes and the bacterial ASVs observed in spot sampling. Only positive and significant correlations were retained (Spearman, *P* < 0.05).</figcaption></figure>
variability in airborne microbial communities linked to diverse air masses encountered throughout the oceanic transect and thus served as a key element in interpreting the dynamics of antimicrobial resistance dissemination over marine environments.

## 4.2. Open sea aerosols harbor antimicrobial resistance genes from multiple sources

In this study, we report bacterial loads, taxonomic diversity, and key associated resistance genes in aerosols collected above the Atlantic Ocean, with the aim of mapping the distribution of the main families of airborne ARGs along a transect from France to the USA. Based on a panel of 20 different subtypes of ARGs and one MGE, we could estimate that open sea aerosols harbor concentrations of ARGs that average $1.1 \times 10^4 \pm 6.8 \times 10^3$ copies/m$^3$ of air (mean ± SEM). This is comparable to the values reported in aerosols and clouds at a mid-altitude mountain site (Rossi et al., 2024; Rossi et al., 2023), although two order of magnitude lower than those reported in marine aerosol from the Yellow and Bohai seas (Jia et al., 2025). Not all these ARGs are necessarily attributable to surface water emissions. The Atlantic Ocean is influenced by the Azores High anticyclone, drawing winds from the American and European continents. This phenomenon causes the accumulation of airborne particles and gases, both from local sources and those transported over long distances, by causing the subsidence of air masses from the upper troposphere and subsequently trapping them in anticyclonic areas (Thishan Dharshana et al., 2010; Wei et al., 2011). Punctual intrusions of Saharan dust over the Atlantic Ocean are also a documented feature (Cruz et al., 2021). Dust transport events are most coarse, frequent and intense during summer, but occur predominantly within the first 3 km during winter (Van Der Does et al., 2021), which may further shape atmospheric microbiota. The 72-h backward trajectories provide further evidences that terrestrial surfaces strongly influenced the air masses reaching the sailing ship along the crossing, especially near the French and U.S. coasts (Mayol et al., 2017). Notably, there was a peak observed between November 21st and 23rd (20201120_SASS_N, 20201121_SASS_N and 20201121/22 PF) that can be putatively imputed to the close proximity of the nearby lands, including southern Europe and the Acores islands of São Miguel and Santa Maria.

Terrestrial influence on aerosols taxonomic composition is particularly pronounced over the Atlantic Ocean compared to other marine regions such as the Pacific Ocean (Lang-Yona et al., 2022). Overall, the observed bacterial concentration remained relatively stable from coast to coast, ranging from $10^2$ to $10^3$ bacteria/m$^3$ of air, which fits the previously reported offshore values (Mayol et al., 2017). More generally, oversea air samples are enriched in Actinomycetota and Bacillota compared to underlying seawater samples (Lang-Yona et al., 2022). Such a pattern supports a more distant terrestrial or anthropogenic sources origins rather than local contamination from crew members or local birds, although such sources cannot be entirely excluded. Also, while taxon-specific aerosolization from the ocean surface remains a possibility (Michaud et al., 2018), it alone is unlikely to explain the high airborne abundance of these genera. Moreover, only limited correlations were observed between these and ARG concentrations in our dataset, suggesting a minor contribution on the observed aerial antimicrobial resistance to the observed antimicrobial resistance.

## 4.3. Continents are major sources of airborne ARGs travelling oversea and over long distances

Over the French coast at the departure, the air masses originated from northeast (i.e. from continent, English Channel, northern Europe). Later on November 20th, these then shifted southward from the Spanish peninsula. Another regime change was observed starting from December 1st, characterized by air masses originating from the western American continent, although primarily transiting within the free troposphere. In both cases, this coincided with an increased contribution

of macrolide resistance (*erm35*), and to a lesser extent tetracycline resistance (*tetM*, *tetW*) and sulfonamide (*sul1*), with no impact on total ARGs' concentrations in both integrative and spot sampling. Atmospheric aerosols mirror the characteristics of their underlying surfaces: this may underline a stronger contribution of terrestrial surfaces. Indeed, while multidrug efflux mechanisms also dominate soil resistome (typically over 50 % of total abundance), quinolone resistance, particularly that mediated by *qepA*, remains negligible in comparison (<1 % of total ARGs abundance). Instead, β-lactam, macrolide - lincosamide - streptogramin (MLS) and tetracycline resistances appear more prevalent, both in terms of subtypes richness and abundance (*Delgado-Baquerizo et al., 2022*; *Qian et al., 2021*). Additionally, the potential contribution of agricultural activities to atmospheric ARGs was previously reported. In free-tropospheric clouds with clear continental influence, a good agreement was observed between the relative abundances of ARGs and the proportions of antibiotic classes used in the countries beneath for veterinary purposes (Rossi et al., 2023). Here, the observed *erm35* gene, encoding a 23S ribosomal RNA methyltransferase and conferring resistance to MLS antibiotics, is widely used in both human medicine and animal husbandry for treating various Gram-positive infections (Roberts, 2008). Similarly, *tetM* and *tetW* encode ribosomal protection proteins, while *sul1* is associated with a mutation on the dihydropteroate synthase of Gram-negative bacteria. Both antibiotic classes have a long history of intensive use in food-producing animals, which likely explains their recurrent detection in continental-influenced air masses. As a comparison point, the population-weighted mean consumption of macrolide, sulfonamide, and tetracycline antibiotics in food-producing animals approximates 7.4 %, 9 % and 30.5 % respectively in Europe (European Food Safety Authority et al., 2021). In the U. S.A., they represent 9 %, 5 % and 65 %, respectively, of the total sales of antibiotics considered medically important for food-producing (Food, U. S., and Administration, D, 2023).

Additionally, these ARGs were also detected further offshore, albeit more sporadically, illustrating the potential for intercontinental dissemination of antimicrobial resistance *via* atmospheric transport, following a gradient from coastal zones to the open ocean, and even reaching remote marine environments. However, it is important to keep in mind that antimicrobial resistance may originate from multiple sources at once. For instance, the positive correlation observed between sulfonamide resistance and the time spent by air masses over the Atlantic Ocean suggests a complex interplay of emission processes. This highlights the difficulty of accurately linking airborne observations to specific sources, especially in regions influenced by long-range atmospheric transport.

One to two-thirds of the bacterial richness connected with ARGs in integrative samples are potential soil inhabitants; some were positively correlated with the aforementioned resistance genes. *Stenotrophomonas* are highly versatile bacteria ubiquitously spread in the environment and often reported as multidrug resistant (Ryan et al., 2009). Some species isolated from coastal water were found to carry the *BlaKPC* resistance gene (*Ghosh et al., 2020*; *Mançano et al., 2020*; *Wang et al., 2018*). *Stenotrophomonas maltophili*, the most widespread species usually associated with plants (mutualism), has been considered as an emerging human pathogen displaying a large array of antibiotic resistances (Wang et al., 2018). Here, *Stenotrophomonas* appeared as probable important contributor of macrolide resistance in oversea aerosols. There were also several genera with a clear soils and/or plants origin that were associated with different subtypes of ARGs. This is notably the case of *Pedobacter*, *Rhizobium*, *Herbaspirillum*, *Sphingomonas*, and *Skermenlella* among others, potentially carrying multiple resistance genes and further highlighting the strong influence of terrestrial emissions on antimicrobial resistances in the air offshore.
## 4.4. Deciphering the specific contribution of marine surfaces to atmospheric antimicrobial resistance

Due to instrument failure and logistical constraints, seawater could not be collected or stored, making direct comparison with potential marine sources impossible. In this context, backward air mass trajectories provide valuable insights into the possible contribution of the ocean surface. Notably, there was a significant change in the dynamic regime around November 21st, with air masses originating purely from the ocean (mid-Atlantic, from West), occasionally from tropics (south/southeast). This coincided with an increase prevalence of quinolone efflux pumps mechanisms, particularly for integrative sampling, as previously reported within the resistome of oceanic water and clouds with clear marine signatures (Rossi et al., 2023; Cuadrat et al., 2020). Specifically, *qeqA*, encoding a plasmidic efflux mechanism from the major facilitator superfamily and conferring resistance to fluoroquinolones, showed positive associations with multiple genera within the Pseudomonadota, regardless of the sampling interval. They were shown to be the most abundant genera in the Atlantic Ocean (Lang-Yona et al., 2022). This includes *Pseudomonas* and *Rhodopseudomonas* species, among others, which are frequently reported in marine ecosystems. Such converging lines of evidence point toward a predominant contribution of marine emissions to airborne quinolone resistance, although a background terrestrial influence likely persists. Notably, potential soil inhabitants such as *Herbaspirillum* and *Rhizobium* were also correlated with *qepA*, while Pseudomonas correlated with the aforementioned *erm35* resistance gene, underlining its ubiquity across terrestrial ecosystems.

The presence of several other ARGs in the air can putatively be attributed to surface water as well. Notably, the presence of aminoglycoside antibiotics (streptomycin, azithromycin and clarithromycin) and associated resistance genes have already been reported in seawater (Felis et al., 2020; Hernández et al., 2019; Lu et al., 2024). Resistance to aminoglycoside, specifically the *aac(3)-Ilb* gene encoding enzymes, responsible for the inactivation of aminoglycosides through 3-amino acetylation, correlated with the time spent by the air masses within the marine boundary layer in 24 h-integrative samples. This gene was also associated with multiple potential seawater inhabitants, including *Pseudomonas* and members of the *Sphingomonadaceae*. However, aminoglycoside gene's detection remained infrequent and was exclusive to integrative samples. This discrepancy suggests that marine ARG emissions into the atmosphere may be sporadic, possibly taxon-specific (Michaud et al., 2018) and occur only under certain environmental conditions. The integrative sampling approach likely enhances the probability of capturing such transient aerosolization events, which may be missed by spot sampling, despite comparable air sampled volumes.

Our study also highlights strong correlations between *Aliterella* and both *ermX* and *ermB*. Macrolide resistance genes have been reported in many other cyanobacteria species, including the bloom-forming *C. issatschenkoi* (Timms et al., 2023), which can therefore act as environmental reservoirs and sources of such resistance. More generally, a growing number of scientific reports tend to emphasize a potentially significant contribution of cyanobacteria to the pool of environmental ARGs and their transfer between microbial species (Timms et al., 2023; Xin et al., 2024). An increasing number of ARGs were attributable to cyanobacteria during bloom episodes in lakes, promoting their conjugative transfer between species (Wang et al., 2020; Wu et al., 2024; Zhang et al., 2020).

## 5. Conclusion

Our study provides new insights into the atmospheric circulation of antimicrobial resistances in the global environment and open ocean. Overall, the resistome of oversea aerosols appears composed of a mix of antimicrobial resistance elements whose abundances vary with distance from land. Agricultural imprints, including tetracycline, sulfonamide,

and macrolide resistance genes, were overall more prevalent near the coasts. However, their sporadic detection in open-sea samples, together with their found association with potential soil inhabitants, attests to their potential for transcontinental transport. The airborne antimicrobial resistance and associated bacterial diversity shifted with distance from land, indicating the specific contribution of Atlantic Ocean emissions. The *qepA* gene, which confers resistance to quinolone antibiotics, was notably widespread in the open sea. Other ARGs, including those for macrolide and aminoglycoside resistance, could also be correlated with surface water influence. Overall, our study illustrates the widespread presence of ARGs in the environment far from human activities, and highlights the role of marine surfaces' microbiota as potential actors in the aerial spread of antimicrobial resistances at large scale.

## Authors' contribution

Pierre Amato and Caroline Duchaine secured funding, designed the original study, and provided the onboard scientific equipment for the BlueObserver Oceanic Platform. Nathalie Turgeon and Marc Veillette lent their expertise to the onboard sampling of bioaerosols and their subsequent processing. Eloïse Le bras managed the onboard sample collection, processing, storage, and shipment to Laval University. Cindy Dumais was responsible for performing the DNA extractions. Jean-Luc Baray calculated the backward trajectories of the air masses and did the analysis of atmospheric dynamics. Florent Rossi conducted the monitoring of total bacteria and ARGs by qPCR, prepared samples for 16S amplicon sequencing, handled the data treatment and analysis, and authored the initial draft of the manuscript. All authors made significant contributions to the manuscript review.

## CRediT authorship contribution statement

**Florent Rossi:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Caroline Duchaine:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.
**Eloïse Le bras:** Investigation.
**Cindy Dumais:** Writing – review & editing, Validation, Investigation, Data curation.
**Nathalie Turgeon:** Writing – review & editing, Supervision, Resources, Project administration, Methodology.
**Marc Veillette:** Writing – review & editing, Supervision, Resources, Project administration, Methodology.
**Jean-Luc Baray:** Writing – review & editing, Visualization, Validation, Software, Methodology, Formal analysis, Data curation.
**Pierre Amato:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Funding

This study was founded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Frontiers opportunity (grant n° 539024-2019) and the French National Research Agency (ANR), MOBIDIC project (grant n° ANR-17-MPGA-0013). Caroline Duchaine holds the Tier-1 Canada Research Chair on Bioaerosols.

## Declaration of competing interest

The authors declare no conflict of interest.

## Acknowledgements

We extend our sincere thanks to Eric Defert, Skipper of the sailing ship "IRIS" and his entire crew for their successful completion of the Atlantic Ocean crossing. We are also grateful to all members of the Blue Observer Oceanic Platform for their meticulous preparation of the
sampling campaign. Special thanks are due to Muriel Joly for her help in laboratory work and preparation of the campaign. We also thank Brian Boyle from the IBIS genomic analysis platform for library preparation and samples sequencing.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.180726.

## Data availability

Data will be made available on request.

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