Latent adaptive filters improve convergence by performing adaptation in a low-dimensional latent space that serves as the decoder input, learned from acoustic impulse responses using an autoencoder. This latent-space adaptation can improve system identification and tracking performance relative to conventional adaptive filtering methods. In this work, we investigate two strategies for blending latent adaptive filters to improve spatial robustness and training efficiency. The first approach blends multiple latent adaptive filters with different spatial constraints, thereby incorporating more spatial information into an existing latent adaptive filtering framework. The second approach blends latent adaptive filters with traditional adaptive filters, allowing effective adaptation while reducing the number of samples required to train the decoder. Simulation results indicate that these blending strategies improve robustness to spatial variation and reduce decoder training requirements without sacrificing adaptation performance.

Adapting pretrained deep learning models to new and unknown environments remains a major challenge in underwater acoustic localization. We show that although the performance of pretrained models suffers from mismatch between the training and test data, they generally exhibit a higher uncertainty in environments where there is more mismatch. In addition, in the presence of environmental mismatch, spurious peaks can appear in the output of classification-based localization approaches, which inspires us to define and use a method to quantify the “implied uncertainty” based on the number of model output peaks. Leveraging this notion of implied uncertainty, we partition the test samples into sets with more certain and less certain samples, and implement a method to adapt the model to new environments by using the certain samples to improve the labeling for uncertain samples. Thus, using this efficient method for model uncertainty quantification, we showcase an innovative approach to adapt a pretrained model to unseen underwater environments at test time. This eliminates the need for labeled data from the target environment or the original training data. This adaptation is enhanced by integrating an independent estimate based on the received signal energy. We validate the approach extensively using real experimental data, as well as synthetic data consisting of model-generated signals with real ocean noise. The results demonstrate significant improvements in model prediction accuracy, underscoring the potential of the method to enhance underwater acoustic localization in diverse, noisy, and unknown environments.

Statistical models of distributed acoustic sensing (DAS) signals commonly follow Hartog’s Rayleigh backscattering framework, in which scatterer locations and scattering amplitudes are treated as independently and identically distributed and resampled between realizations. While this assumption is often justified by the stochastic nature of polarization and microscopic scattering, it neglects the possibility that effective scatterer motion is driven by physically causal mechanical dynamics. In this work, we hypothesize that a measurable component of DAS background noise statistics arises from mechanically induced, spatially correlated perturbations of scatterer locations. To investigate this, we couple Hartog’s discrete scatterer model with an elastic lattice model (ELM) that governs the spatio-temporal evolution of strain and displacement along the fiber. Material points are defined along the sensing cable and evolve according to elastic dynamics; scatterer positions are then advected via linear interpolation of the local displacement field rather than resampled independently. Using this hybrid ELM–scatterer framework, we simulate the resulting complex backscattered electric field and analyze its statistical structure. Power spectral density, spatial covariance, and related statistics are compared against experimental DAS recordings acquired under nominally quiescent conditions. These findings suggest a physics-informed pathway for modeling and interpreting DAS noise beyond purely stochastically i.i.d. assumptions.

Antibiotic resistance genes (ARGs) are important contaminants in water systems, and their detection depends strongly on methodological sensitivity. This study compared three molecular platforms, high-throughput quantitative polymerase chain reaction (HT-qPCR), hydrolysis probe-based qPCR, and droplet digital PCR (ddPCR), for detecting ARGs in wastewater, river water, and seawater in Thailand. HT-qPCR enabled broad resistome profiling, detecting 325-336 ARGs out of 373 targets (87.1-90.1%), with aminoglycoside, beta-lactam, macrolide-lincosamide-streptogramin B, sulfonamide, mobile genetic element, and integron genes most prevalent. qPCR quantified selected ARGs using standard curves constructed from plasmid standards whose absolute copy numbers were calibrated by ddPCR, yielding high efficiency and strong linearity. ddPCR detected the same target genes as qPCR with comparable concentration estimates and additionally identified blaIND, which was not observed by HT-qPCR or qPCR. Quantifications from qPCR and ddPCR showed high rank-based concordance across matrices. Combining HT-qPCR with qPCR improved coverage by about 1% relative to HT-qPCR alone, while HT-qPCR with ddPCR offered nearly identical values. In conclusion, HT-qPCR was most effective for comprehensive profiling, qPCR ensured reliable quantification, and ddPCR enabled detection of rare targets, supporting a tiered and cost-effective framework for smart ARG monitoring in tropical aquatic environments.

The development of communication systems critically depends on realistic channel models, yet there are no widely accepted standards in the underwater acoustic communications community. The situation is in stark contrast to terrestrial radio communications, where channel models have been standardized and are widely available. To address this gap, we present an open-access library of underwater acoustic channels derived from field experiments conducted across geographically distinct locations and varying propagation conditions, including shallow and deep water, short and long range, and fixed and mobile platforms. Each channel is described by a time-varying impulse response extracted from at-sea recordings using an adaptive algorithm that separately identifies the multipath structure and the Doppler-induced phase and delay drift. Each channel is also accompanied by a site-specific ambient noise model, which captures the statistics of colored Gaussian noise and impulsive noise. Spatial diversity reception across an array of hydrophones is supported for most channels, while time diversity is included for single-hydrophone scenarios. The library is accompanied by a simple replay interface through which a user supplies an arbitrary transmit signal, passes it through a chosen channel, adds noise at a desired signal-to-noise ratio (SNR), and obtains the received signal. The models are validated by comparing the output SNR of a baseline receiver operating on replayed signals with its performance on the original at-sea recordings, demonstrating close agreement across all channels. The library, including all channel impulse responses, noise model parameters, and replay software, is freely available for download as an open-source package.

We present a Bayesian universal beamforming framework for adaptive array processing in dynamic underwater acoustic environments with unknown and time-varying propagation geometry. Motivated by ideas from universal prediction and estimation, the proposed approach discretizes the angular domain into a finite set of steering hypotheses and recursively computes posterior probabilities over competing spatial models using observation-dependent likelihood functions. For Gaussian observation models, the posterior update reduces to an exponential-weights recursion driven by hypothesis-dependent beamformer evidence metrics. The resulting framework performs soft spatial inference and adaptive beamforming by continuously redistributing posterior probability across competing steering hypotheses while forming posterior-weighted combinations of branch outputs. The formulation naturally connects to classical adaptive beamformers including matched filtering and minimum mean-square error (MMSE) beamforming. In addition, the framework is extended toward broadband underwater acoustic communication receivers through frequency-domain beamformer synthesis and adaptive equalization. Posterior probabilities are updated according to branch-specific equalization errors, enabling joint spatial-temporal adaptation under multipath propagation, Doppler-induced distortions, and time-varying channel conditions. Experimental results using MACE data demonstrate reliable communication performance with low overhead, low data detection mean-squared error, and zero observed bit errors.

Traditional volumetric noise control typically relies on multipoint error minimization to suppress sound energy across a region, but offers limited flexibility in shaping spatial responses. This paper introduces a time domain formulation for linearly constrained minimum variance active noise control (LCMV ANC) for spatial control filter design. We demonstrate how the LCMV ANC optimization framework allows system designers to prioritize noise reduction at specific spatial locations through strategically defined linear constraints, providing a more flexible alternative to uniformly weighted multi point error minimization. An adaptive algorithm based of filtered X least mean squares (FxLMS) is derived for online adaptation of filter coefficients. Simulation and experimental results validate the proposed method's noise reduction and constraint adherence, demonstrating effective, spatially selective and broadband noise control compared to multipoint volumetric noise control.

In this paper, we study the underwater acoustic localization in the presence of environmental mismatch. Especially, we exploit a pre-trained neural network for the acoustic wave propagation in a gradient-based optimization framework to estimate the source location. To alleviate the effect of mismatch between the training data and the test data, we simultaneously optimize over the network weights at the inference time, and provide conditions under which this method is effective. Moreover, we introduce a physics-inspired modularity in the forward model that enables us to learn the path lengths of the multipath structure in an end-to-end training manner without access to the specific path labels. We investigate the validity of the assumptions in a simple yet illustrative environment model.

Distributed acoustic sensing (DAS) enables dense acoustic measurements using existing optical fiber networks, making it attractive for large-scale sensing applications. Previous DAS-based Doppler tracking methods have primarily focused on extracting source velocity from the direct acoustic arrival. However, in multipath environments, the direct application of such a method makes motion tracking even more challenging, limiting its robustness without further modifications. The proposed method recursively utilizes the motion prediction to guide beamforming, thereby isolating the direct arrival for Doppler-based velocity estimation. A steering vector that accounts for the directional strain sensitivity of the fiber is used within an adaptive beamformer to extract the direct-path signal and reject the higher-order arrivals. Applying the Doppler-based velocity-position estimation further innovates the source state in a bootstrapping-like fashion. By iteratively refining the state estimate, the approach enables robust tracking of source motion despite the presence of multipath interference. The framework will be evaluated in simulation using the Bellhop acoustic propagation model across various DAS array geometries, and its performance will be compared with that of the existing Doppler-based tracking method that assumes a sole direct arrival.

Beamformers often trade off white noise gain against the ability to suppress interferers. With distributed microphone arrays, this trade-off becomes crucial as different arrays capture vastly different magnitude and phase differences for each source. We propose the use of multiple random projections as a first-stage preprocessing scheme in a data-driven approach to dimensionality reduction and beamforming. We show that a mixture beamformer derived from the use of multiple such random projections can effectively outperform the minimum variance distortionless response (MVDR) beamformer in terms of signal-to-noise ratio (SNR) and signal-to-interferer-and-noise ratio (SINR) gain. Moreover, our method introduces computational complexity as a trade-off in the design of adaptive beamformers, alongside noise gain and interferer suppression. This added degree of freedom allows the algorithm to better exploit the inherent structure of the received signal and achieve better real-time performance while requiring fewer computations. Finally, we derive upper and lower bounds for the output power of the compressed beamformer when compared to the full complexity MVDR beamformer.

We present a method for adaptive acoustic communication channel estimation. This method utilizes the data detection mean-squared error (MSE) to simultaneously estimate the channel and track the Doppler-induced delay drift and phase offset by employing a delay-locked loop (DLL) coupled with a phase-locked loop (PLL). Through simulations, we compare the proposed method with conventional approaches, specifically the least mean squares (LMS) and recursive least squares (RLS) algorithms, demonstrating excellent results in terms of channel estimation normalized MSE. Using experimental data, we evaluate the proposed method in mobile scenarios, showcasing the effective tracking operation of the Doppler-induced delay drifts.

This work introduces a robotic dummy head that fuses the acoustic realism of conventional audiological mannequins with the mobility of robots. The proposed device is capable of moving, talking, and listening as people do, and can be used to automate spatially-stationary audio experiments, thus accelerating the pace of audio research. Critically, the device may also be used as a moving sound source in dynamic experiments, due to its quiet motor. This feature differentiates our work from previous robotic acoustic research platforms. Validation that the robot enables high quality audio data collection is provided through various experiments and acoustic measurements. These experiments also demonstrate how the robot might be used to study adaptive binaural beamforming. Design files are provided as open-source to stimulate novel audio research.

Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution to the fragile echo labeling problem to be effective. In previous publications, we proposed a general deep learning-based method for two-dimensional environment estimation which outperformed the state-of-the-art, both in simulation and in real-life experimental settings. A limitation of this method was that some prior information had to be provided by the user on the number and locations of the reflective boundaries, and that its neural networks had to be re-trained accordingly for different environments. Utilizing more advanced neural network and time delay estimation techniques, the proposed improved method no longer requires prior knowledge the number of boundaries or their locations, and is able to estimate two-dimensional environments with one or two boundaries. Future work will extend the proposed method to more boundaries and larger-scale environments.

The stimulus reconstruction-based auditory attention decoding (AAD) model reconstructs the attended auditory envelope from an electroencephalogram (EEG) recording with a finite impulse response (FIR) filter of an arbitrary order. Nonetheless, by nature, the filter coefficients, as well as the source of interest, are free to independently vary over time. To model such a phenomenon, the existing AAD works utilize piece-wise constant linear predictor of fixed observation length to track the switching source of interest as well as the dynamic linear predictive coefficients. However, such a method requires an appropriate selection of the fixed partitioning size, a priori. A suboptimal selection can exacerbate the overall performance, especially given sequences where the underlying structure does not conform to a constant partition size. Our proposed algorithm, the switching source online segmented recursive least squares (SS-OSRLS), based on the recursive Bellman equation identifies a set of partition points and the corresponding piece-wise constant linear predictors for each detected sub-segments, as well as the detected speaker of interest, all in real-time. We aim to demonstrate the real-time performance of our algorithm using the real EEG/audio dataset (i) without and (ii) with the pretrained decoder weight.

Filtered-X LMS (FxLMS) is commonly used for active noise control (ANC), wherein the soundfield is minimized at a desired location. Given prior knowledge of the spatial region of the noise or control sources, we could improve FxLMS by adapting along the low-dimensional manifold of possible adaptive filter weights. We train an auto-encoder on the filter coefficients of the steady-state adaptive filter for each primary source location sampled from a given spatial region and constrain the weights of the adaptive filter to be the output of the decoder for a given state of latent variables. Then, we perform updates in the latent space and use the decoder to generate the cancellation filter. We evaluate how various neural network constraints and normalization techniques impact the convergence speed and steady-state mean squared error. Under certain conditions, our Latent FxLMS model converges in fewer steps with comparable steady-state error to the standard FxLMS.

Importance: Normothermic machine perfusion (NMP) has been shown to reduce peritransplant complications. Despite increasing NMP use in liver transplant (LT), there is a scarcity of real-world clinical experience data. Objective: To compare LT outcomes between donation after brain death (DBD) and donation after circulatory death (DCD) allografts preserved with NMP or static cold storage (SCS). Design, setting, and participants: This single-center, retrospective observational cohort study included all consecutive adult LTs performed between January 2019 and December 2023 at the Mayo Clinic in Arizona. Data analysis was performed between February 2024 and June 2024. Outcomes of DBD-SCS, DBD-NMP, DCD-SCS, and DCD-NMP transplants were compared. Exposure: DBD and DCD livers preserved on NMP or SCS. Main outcomes and measures: The primary outcomes were early allograft dysfunction (EAD), intraoperative transfusion, and post-LT hospital resource use, including length of stay (LOS) and readmissions. Secondary outcomes included acute kidney injury (AKI) and 1-year graft and patient survival. Results: A total of 1086 LTs were included in the following 4 groups: DBD-SCS (n = 480), DBD-NMP (n = 63), DCD-SCS (n = 264), and DCD-NMP (n = 279). Among LT recipients, median (IQR) age was 60.0 years (52.0-66.0); 399 LT recipients (36.7%) were female. DCD-NMP had the lowest EAD rate (17.5%), followed by DCD-SCS (50.0%), DBD-NMP (36.8%), and DBD-SCS (27.3%) (P < .001). DCD-NMP had the lowest intraoperative transfusion requirement compared to all other groups. Hospital and intensive care unit (ICU) LOS were shortest in DCD-NMP (median [IQR] hospital LOS, 5.0 days [4.0-7.0]; P = .01; median [IQR] ICU LOS, 1.5 days [1.2-3.1]; P = .01). One-year cumulative readmission probability was 86% lower for DCD-NMP vs DCD-SCS (95% CI, 0.09-0.22; P < .001) and 53% lower for DBD-NMP vs DBD-SCS (95% CI, 0.26-0.87; P < .001). AKI events were lower in DCD-NMP (31.1%) vs DCD-SCS (47.4%) (P = .001). Compared to SCS, the NMP group had a 78% overall reduction in graft failure (hazard ratio [HR], 0.22; 95% CI, 0.10-0.49; P < .001). For those receiving DCD allografts, the risk reduction was even more pronounced, with an 87% decrease in graft failure (HR, 0.13; 95% CI, 0.05-0.33; P < .001). NMP was significantly protective from patient mortality vs SCS (HR, 0.31; 95% CI, 0.12-0.80; P = .02). Conclusions and relevance: In this observational high-volume cohort study, NMP significantly improved LT clinical outcomes and reduced hospital resource use, especially in DCD allografts. NMP may enhance access to LT by addressing the challenges historically linked with DCD liver use.

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Learn More CiteCitationCitation and abstractCitation and referencesMore citation options ShareShare onFacebookX (Twitter)WeChatLinkedInRedditEmailJump toExpandCollapse ViewpointJanuary 28, 2025Strengthening Policy Relevance of Wastewater-Based Surveillance for Antimicrobial ResistanceClick to copy article linkArticle link copied!Sheena Conforti*Sheena ConfortiEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf 8600, Switzerland*[email protected]More by Sheena ConfortiView Biographyhttps://orcid.org/0000-0002-0173-6170Amy PrudenAmy PrudenCivil and Environmental Engineering, Virginia Polytechnic Institute and State University, 418 Durham Hall, Blacksburg, Virginia 24061, United StatesMore by Amy Prudenhttps://orcid.org/0000-0002-3191-6244Nicole AcostaNicole AcostaUniversity of Calgary, Cumming School of Medicine, Calgary, AB T2N 1N4, CanadaMore by Nicole AcostaChristopher AndersonChristopher AndersonWest Virginia University, Morgantown, West Virginia 26506-6201, United StatesMore by Christopher AndersonHelmut BuergmannHelmut BuergmannEawag, Swiss Federal Institute of Aquatic Science and Technology, Kastenienbaum 6047, SwitzerlandMore by Helmut Buergmannhttps://orcid.org/0000-0002-5651-5906Juliana Calabria De AraujoJuliana Calabria De AraujoFederal University of Minas Gerais, Belo Horizonte, MG 31270-901, BrazilMore by Juliana Calabria De AraujoJudith R. CristobalJudith R. CristobalDepartment of Chemistry, University at Buffalo - The State University of New York, 633 Natural Science Complex, Buffalo, New York 14260, United StatesMore by Judith R. CristobalBarbara DrigoBarbara DrigoUniversity of South Australia, Adelaide, SA 5001, AustraliaMore by Barbara DrigoClaire EllisonClaire EllisonQueen's University, Beaty Water Research Center, Kingston, ON K7L 3N6, CanadaMore by Claire EllisonZanah FrancisZanah FrancisU.S. Department of Health and Human Services, Washington, D.C. 20201-0004, United StatesMore by Zanah FrancisDominic FrigonDominic FrigonMcGill University, Civil Engineering and Applied Mechanics, 817 Sherbrooke Street West, Montreal, QC H3A 0C3, CanadaMore by Dominic Frigonhttps://orcid.org/0000-0003-1587-8943Markus GaenzleMarkus GaenzleUniversity of Alberta, Edmonton, AB T6G 2R3, CanadaMore by Markus GaenzleJulia VierheiligJulia VierheiligTU Wien, Institute of Water Quality and Resource Management, ICC Water & Health, 1040 Wien, AustriaMore by Julia VierheiligTimothy R. JulianTimothy R. JulianEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf 8600, SwitzerlandMore by Timothy R. Julianhttps://orcid.org/0000-0003-1000-0306Uli KlümperUli KlümperInstitute for Hydrobiology, TU Dresden, Dresden 01062, GermanyMore by Uli Klümperhttps://orcid.org/0000-0002-4169-6548Liping MaLiping MaEast China Normal University, Dongchuan Road 500, Shanghai 200241, ChinaMore by Liping Mahttps://orcid.org/0000-0002-1646-6767Chand MangatChand MangatPublic Health Agency of Canada, Wastewater Surveillance Unit, National Microbiology Laboratory, Winnipeg, MB R3E 3R2, CanadaMore by Chand MangatMaya NadimpalliMaya NadimpalliGangarosa Department of Environmental Health, Emory University, Atlanta, Georgia 30322, United StatesMore by Maya Nadimpallihttps://orcid.org/0000-0002-6526-116XManami NakashitaManami NakashitaNational Institute of Infectious Diseases, Shinjuku-ku, Tokyo 162-8640, JapanMore by Manami NakashitaGilbert OsenaGilbert OsenaUniversity of Gothenburg, Goteborg, Västra Götaland 405 30, SwedenMore by Gilbert OsenaSasikaladevi RathinaveluSasikaladevi RathinaveluEawag, Swiss Federal Institute of Aquatic Science and Technology, Kastenienbaum 6047, SwitzerlandMore by Sasikaladevi RathinaveluRichard Reid-SmithRichard Reid-SmithPublic Health Agency of Canada Foodborne, Waterborne and Zoonotic Infections Division, Guelph, ON N1G 5B2, CanadaMore by Richard Reid-SmithMichael SaldanaMichael SaldanaSonny Astani Civil and Environmental Engineering, University of Southern California, 920 Downey Way, BHE 201, Los Angeles, California 90089-0001, United StatesMore by Michael SaldanaHeike SchmittHeike SchmittNational Institute for Public Health and the Environment, Bilthoven 3720 BA, The NetherlandsDelft University of Technology, Delft, Zuid-Holland 2600 AA, NetherlandsMore by Heike SchmittShuxian LiShuxian LiDepartment of Civil Engineering, The University of Hong Kong, Hong Kong 999077, ChinaMore by Shuxian LiAndrew C. SingerAndrew C. SingerCentre for Ecology & Hydrology, Mansfield Road, Oxford OX1 3SR, United KingdomMore by Andrew C. Singerhttps://orcid.org/0000-0003-4705-6063Tam T. TranTam T. TranNORCE Norwegian Research Centre AS, Tromso, Troms og Finnmark 9019, NorwayMore by Tam T. TranKadir YanacKadir YanacUniversity of Manitoba, Department of Civil Engineering, Winnipeg, MB CR3T 5V6, CanadaMore by Kadir YanacGustavo YbazetaGustavo YbazetaHealth Sciences North Research Institute, Sudbury, ON P3E 2H2, CanadaMore by Gustavo YbazetaMonika HarniszMonika HarniszUniversity of Warmia and Mazury in Olsztyn, Department of Water Protection Engineering and Environmental Microbiology, Prawochenskiego 1, Olsztyn 10-790, PolandMore by Monika HarniszOpen PDFEnvironmental Science & TechnologyCite this: Environ. Sci. Technol. 2025, XXXX, XXX, XXX-XXXClick to copy citationCitation copied!https://pubs.acs.org/doi/10.1021/acs.est.4c09663https://doi.org/10.1021/acs.est.4c09663Published January 28, 2025 Publication History Received 11 September 2024Published online 28 January 2025article-commentary© 2025 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0 . License Summary*You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. This publication is licensed underCC-BY 4.0 . License Summary*You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator.View full license *DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. License Summary*You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. View full license *DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. License Summary*You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. View full license *DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. ACS Publications© 2025 The Authors. Published by American Chemical SocietySubjectswhat are subjectsArticle subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article.Antibiotic resistanceAntimicrobial agentsImmunologyPeptides and proteinsWastewaterAntimicrobial resistance (AMR) is among the top 10 public health threats, with nearly 5 million deaths in 2019 linked to AMR-related bacterial infections. (1) A One Health approach is needed to combat AMR.Healthcare-based surveillance (HBS) of AMR provides incomplete information about the scope of the AMR threat. HBS screens only patients seeking medical attention, lacking community-level representativeness, and suffers from underreporting. (2) Consequently, researchers are turning to wastewater-based surveillance (WBS) to complement HBS. (3) WBS can provide information about AMR circulating within communities and hospitals, offering a comprehensive understanding of AMR prevalence. However, the surveillance targets and data obtained from WBS are distinct from those derived from HBS, creating uncertainty regarding their utility to the public health sector and ability to yield policy relevant information. In May 2024, participants in a workshop during the 7th Environmental Dimension of Antimicrobial Resistance (EDAR7) conference (Montréal, Canada) sought to answer four questions aimed at advancing the policy relevance of AMR data generated by WBS.What Public Health Relevant Indicators Are Currently Used to Drive Antimicrobial Stewardship Policy?Click to copy section linkSection link copied!There is a pressing need to integrate available information across One Health sectors (human health, agriculture, and environment) to inform policy and practice aimed at mitigating AMR (Figure 1). HBS aims to guide antibiotic prescriptions by generating antibiograms and provides data on AMR prevalence and trends by prescreening inpatients for carriage, assessing resistance of pathogens responsible for infections, and tracking in- and out-patient antibiotic prescription patterns. Tracking trends of multidrug-resistant (MDR) organisms in healthcare facilities helps to identify units experiencing high rates of nosocomial infections and informs the selection of appropriate treatment options. Well-established surveillance programs can result in public health reports used to define strategies to regulate antibiotic stewardship and to monitor and evaluate interventions.Figure 1Figure 1. Integration of data across One Health sectors to inform AMR policy and the potential role of WBS. Clinical settings, environmental studies, and agricultural surveillance can provide indicators used for antimicrobial stewardship and antimicrobial resistance (AMR) management. Indicators include prevalence of AMR and prescription patterns in clinical settings, geographic distribution and emerging threats in the environment to identify horizontal gene transfer (HGT) hot spots, and antibiotic/biocide residues and zoonotic pathways in agriculture. Wastewater-based surveillance (WBS) programs can be designed to integrate within and across all sectors.High Resolution ImageDownload MS PowerPoint SlideSurveillance in agriculture tends to be more focused on antibiotic use than tracking resistant infections, although some monitoring programs track resistant isolates in meat, produce, and other food products. Measuring antibiotic consumption in animals and crops can help identify hot spots for selective pressure and potential for AMR to spread. Monitoring residues in food of animal origin and biocides in vegetables can also contribute. Surveillance of AMR in livestock, wild animals, and the food chain can inform transmission pathways between animals and humans, providing insights into interventions to curb foodborne and zoonotic spread. Agricultural surveillance supports the establishment and enforcement of antibiotic stewardship in animals and plants, including antibiotic use regulations.There is increasing recognition of the environmental dimension of One Health approaches to inform AMR policy, but environmental surveillance programs, including WBS, are still limited. Environmental indicators of AMR, such as the presence and concentration of antibiotic resistance genes (ARGs) and resistant organisms in wastewater effluents, rivers, lakes, air, and soils, are primarily derived from spatial and longitudinal studies. These studies identify hot spots of resistance, guide environmental regulations like wastewater management policies, and inform agricultural practices to reduce antibiotic runoff. Environmental surveillance is also uniquely positioned for identifying emerging threats, including new ARGs, mobile genetic elements (MGEs), biocidal resistance genes, and resistant organisms. Monitoring targeted sources, including human, industrial, and agricultural wastewaters, has identified hot spots of resistance and selective pressure. The study of MGEs, in particular, offers insights into the mobility of ARGs and the potential acquisition of new resistance mechanisms in pathogens.What Public Health Relevant Targets and Data Can Be Derived from Wastewater through Monitoring Programs?Click to copy section linkSection link copied!WBS can include monitoring of antimicrobials, resistant organisms, ARGs, and MGEs in human, industrial, or agricultural/food production wastewaters, with monitoring locations selected to integrate across specific sources of interest. However, there is a need to better strategize and coordinate WBS of AMR in a manner that focuses on targets and data that are very likely to provide actionable information. One strategy could be prioritizing low-prevalence resistant bacteria of high clinical relevance, such as carbapenemase-producing Enterobacterales, vancomycin-resistant Enterococcus spp., or other bacteria of the World Health Organization Bacterial Priority Pathogen List. (4) An increase in the level of resistant organisms in wastewater can indicate rising community-level carriage, potential outbreak risks, or intervention failures. WBS may also help determine if outbreaks have ended or if asymptomatic cases persist in the community. However, it is important to be aware of population-scale detection limits and to determine the necessary temporal resolution (e.g., weekly monitoring) to achieve the monitoring goal. In contrast, monitoring pathogens that are already widespread does not necessarily add significant value to inform public health actions.Metagenomic approaches, i.e., sequencing of DNA across microbial populations encountered in wastewater, can offer a comprehensive view of ARGs and MGEs circulating within the corresponding population. Because metagenomics is a nontargeted approach, this perspective could identify emerging ARGs or provide an early warning regarding acquisition of ARGs by pathogens of concern in a community. For example, early detection of the mcr-1 gene conferring resistance to colistin through metagenomics led to the implementation of stricter colistin stewardship and monitoring in high-risk areas, such as units with high rates of MDR. (5)WBS can also target antimicrobials, thus filling knowledge gaps regarding the patterns and prevalence of the use of antimicrobials and other pharmaceuticals. Efforts are needed to improve reporting of antimicrobial use data. Where data are available, they tend to be highly aggregated and costly and with low spatial and temporal resolution. However, antibiotic testing does require sophisticated instrumentation and expertise and works best for antibiotics, such as macrolides and fluoroquinolones, that persist longer in wastewater environments. Fast-degrading antibiotics such as β-lactams might still be detected in the outflow from hospitals with short retention times.A general advantage of WBS is the ability to capture longitudinal and spatial trends across populations and sources of interest. Notably, different sanitation infrastructures and spatial scales of WBS provide distinct opportunities for measurement and interpretation. For example, in hospital wastewater, the indicators reflect carriage of resistant organisms or antibiotic usage within a specific facility. In municipal wastewater, the indicators reflect trends of resistance or antibiotic consumption within the community. Importantly, most of the world is served by nonsewered sanitation, particularly in low- and middle-income countries; surveillance in these settings might focus on tracking emergence and estimating prevalence in specific community settings (e.g., schools, universities, and hospitals). WBS can help to fill critical knowledge gaps in HBS, particularly in countries lacking comprehensive diagnostic capabilities.What Information, Resources, and Contextualization Are Needed to Align Public Health Indicators Derived from Wastewater with Other Public Health Indicators to Better Inform Our Epidemiological Understanding of AMR?Click to copy section linkSection link copied!A key consensus of the workshop was the need to integrate WBS data with HBS to better inform public health strategies.Information needed includes data on AMR prevalence from clinics, meaning the pathogens encountered in the population and corresponding rates of resistance to specific antibiotics obtained through HBS. Such monitoring can reveal clinically relevant targets for WBS and allow the establishment of standard methodologies for consistent data collection and interpretation. Whole genome sequencing of human and animal clinical strains can provide information needed to calibrate WBS data and track persistent pathogens and ARGs of concern in wastewater, potentially indicating ongoing transmission. Information about antibiotic usage in humans, animals, and plants, prescription practices, rates of antibiotic degradation in wastewater, flow data, and transport in sewage systems will help better align WBS and healthcare sector AMR indicators.Resources necessary for advancing WBS of AMR include institutional, financial, and human capital investments. These can support the design, implementation, and continuity of a monitoring plan to yield comprehensive and longitudinal data collection needed to infer AMR dynamics within the community. Initial costs for setting up laboratories, building infrastructure, and establishing workflows among stakeholders such as those who operate wastewater facilities and other monitoring locations are necessary to centralize analyses and build capacity. The investments made in infrastructure and organization for COVID-19 surveillance, and increasingly other pathogens, provide an opportunity to leverage existing resources for AMR monitoring. Establishing publicly accessible databases to collect, visualize, and analyze data from both wastewater and clinical surveillance will enhance collaboration among clinicians, policy makers, researchers, and other stakeholders.WBS indicators for AMR should be contextualized with respect to clinical and agricultural/food sector surveillance through strong collaborations among researchers, clinicians, and communities. While WBS alone may not always generate information about specific targets of interest, it can identify broader trends and emerging hot spots and inform public health strategies like early warnings and antimicrobial stewardship efforts. Notably, transitioning from WBS to wastewater-based epidemiology for AMR poses significant challenges, for example, in predicting the prevalence of AMR within the population. Complications include the dynamics and complexity of pathogen shedding rates and antibiotic resistance mechanisms, and the growth, fate, and transport processes in sewer networks. One key issue is the potential proliferation of resistant organisms within the sewer network, both in the wastewater and in biofilm, which can decouple wastewater-based quantitative estimates from inferences about AMR epidemiology. Indicators from WBS could be developed to help inform progress toward the Sustainable Development Goals or otherwise provide insight into key socioeconomic factors driving overall trends. Geographical and mobility patterns within sewersheds, and connections between industries and hospitals, should be considered to calibrate wastewater indicators and discern community-sourced data from other origins. Research on the fate of resistant bacteria in wastewater systems, along with cohort studies on resistant bacteria in human carriers, may help improve our understanding and interpretation of WBS-derived data.How Can the Information Derived from WBS of AMR Contribute to the Formulation of Effective Public Health Policies or Interventions?Click to copy section linkSection link copied!WBS offers population-integrated data at comparatively low cost and effort relative to monitoring individuals within a population. It provides broader views on population prevalence, independent of screening effort, participation rates, and the likelihood of reporting to health services. Additionally, it enables a comprehensive overview of the microbial genomes circulating in the environment and provides space- and time-resolved data that can be scaled to various needs. As critiqued in the recent 2024 NASEM report, we acknowledge the limitations of WBS for ARG-focused monitoring at the community level, which can be complicated by ARGs from non-human sources and the amplification of ARGs between the human source and the wastewater treatment plant. (6) However, it is important to recognize that WBS of AMR could provide much broader value beyond serving as an early warning system, especially in terms of evaluating long-term trends and effects of policy interventions on shaping these trends. We highlight alternative use cases that are of particular value for aligning WBS data with actionable public health objectives and HBS, such as detection of the emergence of novel resistance genes, or use of culture- and molecular-based methods to track long-term changes in community prevalence rates. (7)Integrating WBS data with existing surveillance methods (Figure 1) is a promising approach to enhance AMR understanding by correlating wastewater findings with clinical data, making policies actionable. WBS data can expand and provide greater resolution to traditional clinical antibiograms while also filling diagnostic gaps and better optimizing the selection of antibiotic treatments in regions with limited spatial and longitudinal AMR data.Public access and education, e.g., via media outlets, can increase AMR awareness, thereby enhancing public support and compliance with AMR policies. To inform effective public health interventions from WBS, it is necessary to have clear objectives and collaborate closely with stakeholders across One Health sectors, which can facilitate the implementation of targeted and efficient measures aimed at limiting the evolution and transmission of antibiotic-resistant pathogens.Author InformationClick to copy section linkSection link copied!Corresponding AuthorSheena Conforti - Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf 8600, Switzerland; https://orcid.org/0000-0002-0173-6170; Email: [email protected]AuthorsAmy Pruden - Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, 418 Durham Hall, Blacksburg, Virginia 24061, United States; https://orcid.org/0000-0002-3191-6244Nicole Acosta - University of Calgary, Cumming School of Medicine, Calgary, AB T2N 1N4, CanadaChristopher Anderson - West Virginia University, Morgantown, West Virginia 26506-6201, United StatesHelmut Buergmann - Eawag, Swiss Federal Institute of Aquatic Science and Technology, Kastenienbaum 6047, Switzerland; https://orcid.org/0000-0002-5651-5906Juliana Calabria De Araujo - Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, BrazilJudith R. Cristobal - Department of Chemistry, University at Buffalo - The State University of New York, 633 Natural Science Complex, Buffalo, New York 14260, United StatesBarbara Drigo - University of South Australia, Adelaide, SA 5001, AustraliaClaire Ellison - Queen's University, Beaty Water Research Center, Kingston, ON K7L 3N6, CanadaZanah Francis - U.S. Department of Health and Human Services, Washington, D.C. 20201-0004, United StatesDominic Frigon - McGill University, Civil Engineering and Applied Mechanics, 817 Sherbrooke Street West, Montreal, QC H3A 0C3, Canada; https://orcid.org/0000-0003-1587-8943Markus Gaenzle - University of Alberta, Edmonton, AB T6G 2R3, CanadaJulia Vierheilig - TU Wien, Institute of Water Quality and Resource Management, ICC Water & Health, 1040 Wien, AustriaTimothy R. Julian - Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf 8600, Switzerland; https://orcid.org/0000-0003-1000-0306Uli Klümper - Institute for Hydrobiology, TU Dresden, Dresden 01062, Germany; https://orcid.org/0000-0002-4169-6548Liping Ma - East China Normal University, Dongchuan Road 500, Shanghai 200241, China; https://orcid.org/0000-0002-1646-6767Chand Mangat - Public Health Agency of Canada, Wastewater Surveillance Unit, National Microbiology Laboratory, Winnipeg, MB R3E 3R2, CanadaMaya Nadimpalli - Gangarosa Department of Environmental Health, Emory University, Atlanta, Georgia 30322, United States; https://orcid.org/0000-0002-6526-116XManami Nakashita - National Institute of Infectious Diseases, Shinjuku-ku, Tokyo 162-8640, JapanGilbert Osena - University of Gothenburg, Goteborg, Västra Götaland 405 30, SwedenSasikaladevi Rathinavelu - Eawag, Swiss Federal Institute of Aquatic Science and Technology, Kastenienbaum 6047, SwitzerlandRichard Reid-Smith - Public Health Agency of Canada Foodborne, Waterborne and Zoonotic Infections Division, Guelph, ON N1G 5B2, CanadaMichael Saldana - Sonny Astani Civil and Environmental Engineering, University of Southern California, 920 Downey Way, BHE 201, Los Angeles, California 90089-0001, United StatesHeike Schmitt - National Institute for Public Health and the Environment, Bilthoven 3720 BA, The Netherlands; Delft University of Technology, Delft, Zuid-Holland 2600 AA, NetherlandsShuxian Li - Department of Civil Engineering, The University of Hong Kong, Hong Kong 999077, ChinaAndrew C. Singer - Centre for Ecology & Hydrology, Mansfield Road, Oxford OX1 3SR, United Kingdom; https://orcid.org/0000-0003-4705-6063Tam T. Tran - NORCE Norwegian Research Centre AS, Tromso, Troms og Finnmark 9019, NorwayKadir Yanac - University of Manitoba, Department of Civil Engineering, Winnipeg, MB CR3T 5V6, CanadaGustavo Ybazeta - Health Sciences North Research Institute, Sudbury, ON P3E 2H2, CanadaMonika Harnisz - University of Warmia and Mazury in Olsztyn, Department of Water Protection Engineering and Environmental Microbiology, Prawochenskiego 1, Olsztyn 10-790, PolandAuthor ContributionsS.C. and A.P. contributed equally to this work.NotesThe authors declare no competing financial interest.BiographyClick to copy section linkSection link copied!Sheena ConfortiHigh Resolution ImageDownload MS PowerPoint SlideSheena Conforti is a postdoctoral fellow in the Pathogens and Human Health group at the Department of Environmental Microbiology, Eawag, Switzerland. She earned her Ph.D. in 2024 from the Department of Biosystems Science and Engineering, at ETH Zurich. Her research focuses on antimicrobial resistance surveillance through wastewater and environmental monitoring, combining culture-based methods and whole-genome sequencing to study resistant bacteria and transmission dynamics. Her work supports the One Health framework and aims to inform public health strategies by integrating data from human, animal, and environmental sources.AcknowledgmentsClick to copy section linkSection link copied!The authors thank the 7th Conference on Environmental Dimension of Antimicrobial Resistance for the financial support of this publication. The authors acknowledge the Environmental Dimension of Antimicrobial Resistance (EDAR7) conference, held in May 2024 in Montréal, Canada, for hosting the workshop that contributed to the development of the manuscript. The authors thank all of the workshop participants and Molly Cantrell for their valuable input during the workshop. The authors also thank the Swiss National Science Foundation (Grant 192763) for funding S.C.ReferencesClick to copy section linkSection link copied! This article references 7 other publications. 1Antimicrobial Resistance Collaborators Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022, 399 (10325), 629– 655, DOI: 10.1016/S0140-6736(21)02724-0 Google ScholarThere is no corresponding record for this reference.2 Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report 2022. World Health Organization, 2022. https://www.who.int/publications/i/item/9789240062702.Google ScholarThere is no corresponding record for this reference.3Chau, K. K.; Barker, L.; Budgell, E. P.; Vihta, K. D.; Sims, N.; Kasprzyk-Hordern, B.; Harriss, E.; Crook, D. W.; Read, D. S.; Walker, A. S.; Stoesser, N. Systematic review of wastewater surveillance of antimicrobial resistance in human populations. Environ. Int. 2022, 162, 107171, DOI: 10.1016/j.envint.2022.107171 Google ScholarThere is no corresponding record for this reference.4 WHO Bacterial Priority Pathogens List 2024: bacterial pathogens of public health importance to guide research, development and strategies to prevent and control antimicrobial resistance. World Health Organization, 2024. https://www.who.int/publications/i/item/9789240093461.Google ScholarThere is no corresponding record for this reference.5von Wintersdorff, C. J. H.; Wolffs, P. F. G.; van Niekerk, J. M.; Beuken, E.; van Alphen, L. B.; Stobberingh, E. E.; Oude Lashof, A. M. L.; Hoebe, C. J. P. A.; Savelkoul, P. H. M.; Penders, J. Detection of the plasmid-mediated colistin-resistance gene mcr-1 in faecal metagenomes of Dutch travellers. J. Antimicrob. Chemother. 2016, 71 (12), 3416– 3419, DOI: 10.1093/jac/dkw328 Google ScholarThere is no corresponding record for this reference.6National Academies of Sciences, Engineering, and Medicine. Increasing the Utility of Wastewater-based Disease Surveillance for Public Health Action: A Phase 2 Report. The National Academies Press: Washington, DC, 2024. DOI: 10.17226/27516 Google ScholarThere is no corresponding record for this reference.7Conforti, S.; Holschneider, A.; Sylvestre, É; Julian, T. R. Monitoring ESBL-Escherichia coli in Swiss wastewater between November 2021 and November 2022: insights into population carriage. mSphere. 2024, 9 (5), e0076023 DOI: 10.1128/msphere.00760-23 Google ScholarThere is no corresponding record for this reference.Cited By Click to copy section linkSection link copied!This article has not yet been cited by other publications.Download PDFFiguresReferences Get e-AlertsGet e-AlertsEnvironmental Science & TechnologyCite this: Environ. Sci. Technol. 2025, XXXX, XXX, XXX-XXXClick to copy citationCitation copied!https://doi.org/10.1021/acs.est.4c09663Published January 28, 2025 Publication History Received 11 September 2024Published online 28 January 2025© 2025 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0 . License Summary*You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. Article Views-Altmetric-Citations-Learn about these metrics closeArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.Recommended Articles FiguresReferencesAbstractHigh Resolution ImageDownload MS PowerPoint SlideFigure 1Figure 1. Integration of data across One Health sectors to inform AMR policy and the potential role of WBS. Clinical settings, environmental studies, and agricultural surveillance can provide indicators used for antimicrobial stewardship and antimicrobial resistance (AMR) management. Indicators include prevalence of AMR and prescription patterns in clinical settings, geographic distribution and emerging threats in the environment to identify horizontal gene transfer (HGT) hot spots, and antibiotic/biocide residues and zoonotic pathways in agriculture. Wastewater-based surveillance (WBS) programs can be designed to integrate within and across all sectors.High Resolution ImageDownload MS PowerPoint SlideSheena ConfortiHigh Resolution ImageDownload MS PowerPoint SlideSheena Conforti is a postdoctoral fellow in the Pathogens and Human Health group at the Department of Environmental Microbiology, Eawag, Switzerland. She earned her Ph.D. in 2024 from the Department of Biosystems Science and Engineering, at ETH Zurich. Her research focuses on antimicrobial resistance surveillance through wastewater and environmental monitoring, combining culture-based methods and whole-genome sequencing to study resistant bacteria and transmission dynamics. Her work supports the One Health framework and aims to inform public health strategies by integrating data from human, animal, and environmental sources.References This article references 7 other publications. 1Antimicrobial Resistance Collaborators Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022, 399 (10325), 629– 655, DOI: 10.1016/S0140-6736(21)02724-0 There is no corresponding record for this reference.2 Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report 2022. World Health Organization, 2022. https://www.who.int/publications/i/item/9789240062702.There is no corresponding record for this reference.3Chau, K. K.; Barker, L.; Budgell, E. P.; Vihta, K. D.; Sims, N.; Kasprzyk-Hordern, B.; Harriss, E.; Crook, D. W.; Read, D. S.; Walker, A. S.; Stoesser, N. Systematic review of wastewater surveillance of antimicrobial resistance in human populations. Environ. Int. 2022, 162, 107171, DOI: 10.1016/j.envint.2022.107171 There is no corresponding record for this reference.4 WHO Bacterial Priority Pathogens List 2024: bacterial pathogens of public health importance to guide research, development and strategies to prevent and control antimicrobial resistance. World Health Organization, 2024. https://www.who.int/publications/i/item/9789240093461.There is no corresponding record for this reference.5von Wintersdorff, C. J. H.; Wolffs, P. F. G.; van Niekerk, J. M.; Beuken, E.; van Alphen, L. B.; Stobberingh, E. E.; Oude Lashof, A. M. L.; Hoebe, C. J. P. A.; Savelkoul, P. H. M.; Penders, J. Detection of the plasmid-mediated colistin-resistance gene mcr-1 in faecal metagenomes of Dutch travellers. J. Antimicrob. Chemother. 2016, 71 (12), 3416– 3419, DOI: 10.1093/jac/dkw328 There is no corresponding record for this reference.6National Academies of Sciences, Engineering, and Medicine. Increasing the Utility of Wastewater-based Disease Surveillance for Public Health Action: A Phase 2 Report. The National Academies Press: Washington, DC, 2024. DOI: 10.17226/27516 There is no corresponding record for this reference.7Conforti, S.; Holschneider, A.; Sylvestre, É; Julian, T. R. Monitoring ESBL-Escherichia coli in Swiss wastewater between November 2021 and November 2022: insights into population carriage. mSphere. 2024, 9 (5), e0076023 DOI: 10.1128/msphere.00760-23 There is no corresponding record for this reference.

With 40 million annual passenger flights, airports are key hubs for microbial communities from diverse geographic origins to converge, mix, and distribute. Wastewater derived from airports and aircraft represent both a potential route for the global dispersion of antimicrobial resistant (AMR) organisms and an under-utilised resource for strengthening global AMR surveillance. This study investigates the abundance and diversity of antimicrobial resistance genes (ARGs) in wastewater samples collected from airport terminals (n = 132), aircraft (n = 25), and a connected wastewater treatment plant (n = 11) at three international airports in the UK (London Heathrow, Edinburgh and Bristol). A total of 76 ARGs were quantified using high throughput qPCR (HT-qPCR) while a subset of samples (n = 30) was further analysed by metagenomic sequencing. Our findings reveal that aircraft wastewater resistomes were compositionally distinct from those observed at airport terminals, despite their similar diversity. Notably, flights originating from Asia and Africa carried a higher number of unique ARGs compared to those from Europe and North America. However, clustering of the ARG profile displayed no overall association with geography. Edinburgh terminal and pumping station wastewater had compositionally comparable resistomes to that of the connected urban wastewater treatment plant, though further research is needed to determine the relative contributions of the local population and international travellers. This study provides the first comprehensive investigation of AMR in wastewater from both aircraft and terminals across multiple international airports. Our results highlight aircraft wastewater as a potential route for cross-border AMR transmission and a valuable tool for global AMR surveillance. However, the findings also underscore the limitations and need for standardised approaches for AMR monitoring in airport environments, to effectively mitigate the global spread of AMR and enhance public health surveillance strategies.

In real-time spatial audio algorithms, adaptive filters are employed to learn and track spatial and acoustic filters. When the space of possible filter weights is close to a low-dimensional manifold, we can improve the convergence rate of adaptive filters by adapting along this manifold. This can be achieved using latent adaptive filters, which constrain the weights to remain within the range space of an auto-encoder's decoder by updating in its latent space. Although previous studies have explored various latent adaptive filters for acoustic system identification, a comparison of the convergence rates among different latent adaptive filter structures has not yet been conducted. Additionally, it is well-established that traditional adaptive filters often struggle to track changes in acoustic impulse responses caused by the continuous movement of the source and receiver. However, the tracking performance of latent adaptive filters has not been investigated. In this study, we empirically evaluate the performance of acoustic impulse response identification and tracking across different variants of latent adaptive filters.