Environmental Science & Technology Letters

Digital PCR (dPCR) is increasingly used for SARS-CoV-2 wastewater surveillance due to its precision, absolute quantification, and reduced sensitivity to inhibition compared to quantitative PCR. Although the Bio-Rad ddPCR and QIAGEN QIAcuity dPCR platforms are widely adopted, their performance has not been directly compared for wastewater applications. We conducted a blinded comparison of these platforms using 95 archived wastewater influent samples from North Carolina collected in 2021-2022, spanning three orders of magnitude in SARS-CoV-2 concentration (1x103 to 5x105 copies L-1). Samples were stratified into low, medium, and high concentration bins and analyzed in triplicate for N1 and N2 gene targets and a bovine coronavirus processing control. Both platforms demonstrated statistically equivalent quantification across all targets, with mean differences [≤]0.12 log copies L-1 (R2 > 0.93). Coefficients of variation were similar (3.96 - 7.61%), with no significant differences across concentration bins except for N2 in the low bin (difference: 0.87 percentage points). Measurement variability correlated strongly with wastewater treatment plant site (R2 = 0.89) rather than platform, indicating that sample matrix characteristics drive precision more than analytical platform. Process limits of detection ranged from 2,160-2,680 copies L-1 for Bio-Rad and 5,650-9,700 copies L-1 for QIAcuity for N1 and N2, respectively. The Bio-Rad platform processed samples 32% faster (305 vs. 435 minutes per 96 wells), while QIAcuity offered 29% lower consumables cost ($4.68 vs. $6.11 per well). These findings support the interchangeable use of both platforms for wastewater surveillance, with platform selection based on laboratory-specific operational needs. ImportanceAs wastewater-based epidemiology transitions from emergency response to sustained public health infrastructure, standardized molecular methods are essential for reliable data integration across surveillance networks. This study provides the first blinded comparison of two digital PCR platforms widely deployed for wastewater pathogen surveillance in the United States. We demonstrate quantitative equivalence between Bio-Rad ddPCR and QIAGEN QIAcuity platforms across three orders of magnitude in viral concentration, establishing that data from both platforms can be interpreted interchangeably for public health decision-making. This platform equivalence is critical as national surveillance systems aggregate data from diverse laboratories and as monitoring expands beyond SARS-CoV-2 to encompass additional respiratory viruses, antimicrobial resistance genes, and emerging pathogens. Our findings provide a methodological foundation for multi-platform surveillance networks and demonstrate that measurement variability is driven primarily by sample matrix characteristics rather than analytical platform choice.

The Province of Ontario (Canada) experienced a generational scale outbreak of measles in 2025. We applied wastewater surveillance concurrently with clinical-based surveillance to track measles incidence in southwestern Ontario adjacent to the United States. Measles virus (MeV) signal in wastewater was positively associated with clinical cases but did not provide early alert of changes in measles incidence when resolved by epidemiological week. Assessment of virus partitioning showed MeV RNA was broadly distributed in the liquid phase but is most concentrated in the solids. An assay was adapted for differentiation of vaccine and wildtype MeV and used to detect vaccine genotype measles following an inoculation campaign targeting underserved groups in the region. MeV shedding in wastewater was estimated through repeated sampling of sewer laterals serving a hospital treating confirmed measles infections. This measles outbreak serves as a case study highlighting the application of wastewater surveillance for measles while supporting method development in real-time.

We utilized traditional aerosol sampling to collect PM1 samples, and further apply redundancy analysis (RDA) to investigate the association of environmental factors (including PM1 chemical composition, oxidative potential, meteorological factors and gaseous pollutants) and airborne bacterial community. Our findings revealed that Bacteroidota positively correlated with Sn and Mn, Firmicutes with local primary pollutants, and Proteobacteria with transportation-related pollutants. Variance partitioning analysis (VPA) showed that PM1 chemical composition, meteorological factors and gaseous pollutants collectively explained up to 43.7% of community variance, and synergistic effects may exist among the three factors. In contrast, oxidative potential had minimal influence. Additionally, to investigate airborne viral presence, we employed a novel bioaerosol sampler targeting SARS-CoV-2 in hospital and campus settings. Viral loads were highest in negative pressure isolation rooms, followed by general hospital and campus areas. Also, the detection rates follow the same pattern, which is 87.5%, 58.3%, and 25.0%, respectively. Notably, detection rates near isolation wards increased during patient admissions, implying possible biocontamination despite containment measures. Peak human traffic flow emerged as a significant factor influencing viral detection. These results highlight how environmental factors shaping airborne microbial communities.

Wastewater monitoring enables non-invasive, population-scale tracking of community infections independent of healthcare-seeking behavior and clinical diagnosis. Metagenomic sequencing extends this capability by enabling broad, pathogen-agnostic detection, genomic characterization, and identification of novel or unexpected threats. Here, we present data from CASPER (the Coalition for Agnostic Sequencing of Pathogens from Environmental Reservoirs), a U.S.-based wastewater metagenomic sequencing network designed for deep, untargeted pathogen monitoring at national scale. This release includes 1,206 samples collected between December 2023 and December 2025 from 27 sites across nine states, covering 13 million people. Deep sequencing (~1 billion read pairs per sample) generated 1.2 trillion read pairs (347 terabases), enabling detection of even rare taxa, with CASPER representing 66% of all untargeted wastewater sequencing data currently available on the NCBI Sequence Read Archive. Virus abundance trends correlate with nationwide wastewater PCR and clinical data for SARS-CoV-2, influenza A, and respiratory syncytial virus, while the pathogen-agnostic approach captures emerging threats, including avian influenza H5N1 during initial dairy cattle outbreaks, West Nile virus, and measles, among hundreds of viral taxa. As the largest publicly available untargeted wastewater sequencing dataset to date, CASPER provides a shared and growing resource for pathogen surveillance and microbial ecology.

IntroductionWastewater-based epidemiology (WBE) was implemented at the 2024 Republican and Democratic National Conventions (RNC and DNC, respectively)--two prominent large-scale events, each with estimated attendances of >50,000 persons. In preparation for event monitoring, the Wisconsin and Chicago WBE programs (associated with the RNC and DNC public health response, respectively) developed site-specific monitoring strategies and response plans, prioritized additional pathogens for event surveillance, and further optimized laboratory workflows to ensure rapid daily data reporting to public health. The Chicago program expanded the sewer sampling network to include new locations closer to event venues than previously available. Sampling was also conducted before the events, to establish baselines for endemic pathogens, as well as after each event to monitor for residual community transmission. MethodsSurveillance was expanded from the four respiratory pathogens regularly assessed by both WBE programs (SARS-CoV-2, influenza A, influenza B, respiratory syncytial virus) to include 3 gastrointestinal pathogens (norovirus, Salmonella enterica, Shiga toxin-producing E. coli). The Wisconsin program also conducted monitoring for the measles, mumps, rubella, and hepatitis A viruses. Wastewater sampling for the RNC was conducted at the community water reclamation facility level, while at the DNC samples were collected from manholes located downstream of the event venues. For both events, WBE data were summarized and contextualized alongside traditional public health surveillance data in daily situation reports. ResultsBetween the RNC and DNC response, a total of 112 wastewater samples were collected and assayed to provide concentration data on as many as 11 distinct pathogens of interest. Concentration results for the suite of pathogens were available within 12 to 36 hours of sample collection. In each instance when wastewater concentrations exceeded pre-established thresholds for action and flagged as an alert, other sources of contemporaneous public health surveillance information (e.g., clinical data) did not corroborate the WBE findings. ConclusionExisting WBE infrastructure in two U.S. cities was readily adapted for public health surveillance at two high-profile, large-scale events. Assays for additional event-relevant pathogens were quickly incorporated into routine laboratory workflows and data from wastewater samples were generated and reported with rapid turnaround-time. In considering the unique benefits of wastewater data, WBE results were a valuable supplement to other public health surveillance data in monitoring potential public health threats during these two large-scale events.

Mexico has experienced recurrent viral epidemics of substantial intensity, including hyperendemic dengue, COVID-19, and recent reports of avian influenza A (H5N1) infections in birds, which pose an ongoing risk of zoonotic transmission. Mexico was also the location for the earliest detection of the pdmH1N1 virus during the 2009 influenza A pandemic. Under a One Health framework, markets represent a unique opportunity for low-cost virus monitoring at the human-animal interface. Under the hypothesis that these represent sentinel sites for an early virus detection, we implemented a pilot surveillance program at the central market of Merida city, Yucatan, Mexico, considered a regional hotspot for multiple and recent viral outbreaks. Longitudinal sampling was carried out over 11 months at 1-to-6-week intervals from April 2022 to February 2023. We used multi-type surveillance in mosquitoes, live poultry, and wastewater. All samples were screened using RT-qPCR. Positive samples for DENV, SARS-CoV-2 and avian influenza A were further sequenced and analysed under a phylogenetic and epidemiological approach. Through our entomological surveillance, we report the earliest detection of DENV-3 III-B3.2 (genotype III American II lineage, considered a major public health concern in Latin America) in Mexico, overlapping with the resurgence of DENV-3 as the predominant serotype driving the 2023 national epidemic, which showed an increased severity. Through wastewater surveillance, we consistently detect SARS-CoV-2 RNA in wastewater samples, coinciding with the two infection waves officially recorded at a city and state level. Finally, cloacal swabs taken from two juvenile birds at the market suggest that avian influenza A viruses circulated in live poultry sold at the market. These findings show that our market-based surveillance framework is effective for an early detection and monitoring of pathogenic viruses in urban settings, and could complement official epidemiological surveillance in low- and middle-income countries to strengthen early-outbreak warning systems.

BackgroundEnvironment-wide association studies (ExWAS) offer a systematic approach to identifying chemical biomarker-health outcome associations, yet few have applied rigorous multi-stage validation. MethodsWe screened 92 chemical biomarkers against 48 health outcomes in NHANES 2017-2018 (2,796 tests across four screening rounds; not all chemicals were crossed with all outcomes). Associations passing an initial FDR screen were subjected to cross-cycle validation in NHANES 2015-2016--the primary inferential safeguard given the adaptive screening design--followed by dose-response analysis and multiple sensitivity specifications. Survey-weighted regression models adjusted for age, sex, race/ethnicity, poverty-income ratio, BMI, and smoking. ResultsOf 26 associations passing FDR correction, 21 were testable in cross-cycle validation; of these, 15 (71%) replicated with concordant direction and p < 0.05 in a temporally independent NHANES 2015-2016 sample. Of these 15, 14 remained robust after analyte-specific sensitivity checks; urinary creatinine adjustment identified one association (iodine-BMI) as a dilution artifact. Two novel findings emerged: dimethylarsonic acid with uric acid ({beta} = 0.20 mg/dL per log-unit DMA, 95% CI: 0.15-0.26) and urinary perchlorate with BUN ({beta} = 1.21 mg/dL per log-unit perchlorate, 95% CI: 0.97-1.45); a third high-novelty association (methylmercury-waist circumference) is likely explained by fish consumption patterns. ConclusionsMulti-stage ExWAS with cross-cycle validation identified 14 robust chemical-health associations. Two novel findings--DMA-uric acid and perchlorate-BUN--survived all sensitivity checks and warrant prospective investigation.

Wastewater based epidemiology offers a valuable population level signal for monitoring respiratory virus activity, but its routine use in public health practice requires alerting methods that are transparent, interpretable, and comparable across locations. In this study, we propose a simple early warning framework that transforms wastewater viral RNA measurements into actionable alerts using a standardized statistical process control approach. The method relies on variance stabilization, site specific baseline normalization, and an exponentially weighted moving average to identify sustained increases in viral activity. To support operational relevance, wastewater derived alerts are benchmarked against established laboratory surveillance systems using a harmonized onset definition. The proposed framework emphasizes clarity, auditability and adaptability rather than complex forecasting, enabling straightforward interpretation by public health practitioners. Our results demonstrate that wastewater signals can provide timely situational awareness for respiratory virus circulation and support their use as a complementary tool for public health surveillance and preparedness.

The co-occurrence of per- and polyfluoroalkyl substances (PFAS) and volatile organic compounds (VOCs) in industrial environments poses complex toxicological risks that standard additive models fail to capture. This study elucidates a novel "metabolic blockade" mechanism wherein PFAS competitively inhibits the renal excretion of VOC metabolites, thereby amplifying neurotoxic burdens. Utilizing a Double Machine Learning (DML) framework on data from National Health and Nutrition Examination Survey (2005-2020), we analyzed a final intersectional cohort of 1,975 participants. We identified a robust inhibition of VOC metabolite clearance by serum PFAS. Specifically, PFNA significantly suppressed the excretion of the benzene metabolite URXPMA (Causal {beta}TMLE = -0.219, p < 0.001), with efficacy dependent on perfluorinated chain length. Molecular docking simulations revealed the biophysical basis of this antagonism: long-chain PFNA exhibited superior binding affinity to the Organic Anion Transporter 1 (OAT1) ({Delta}G = -6.333 kcal/mol) compared to native VOC metabolites ({Delta}G = -4.957 kcal/mol), confirming high-affinity competitive inhibition at the renal interface. In a neurocognitive sub-cohort (N = 1,200), this interference translated into functional synergism; high-PFNA exposure magnified VOC-associated cognitive impairment by 1.5-fold and significantly exacerbated the negative association between VOC burden and processing speed ({beta}int = -0.263, p = 0.004). These findings define PFAS as a "metabolic amplifier" of co-contaminant toxicity, necessitating a paradigm shift toward mixture-based hazardous material regulations that account for transporter-level interactions.

Wastewater surveillance has been widely adopted since the COVID-19 pandemic, but non-sewered or onsite sanitation is a common form of sanitation in cities of low- and middle-income countries. Environmental surveillance in these settings requires expanding analyses beyond wastewater. We collected 81 soil samples adjacent to public waste bins inside the sewered and non-sewered areas of Maputo and a 150-meter-wide buffer zone between the two areas, as well as from subsistence farms near the wastewater treatment plant for comparison. We cultured Escherichia coli (E. coli) using the IDEXX Quanti-Tray/2000 system and determined the prevalence of 29 unique enteric pathogens via RT-qPCR on TaqMan array cards. E. coli concentrations were significantly higher (p<.001) in soils adjacent to public waste bins (mean = 5.05x105 per gram) compared to soils from farms (mean = 8.70x101 per gram). The mean number of unique pathogens was higher in soils from the non-sewered area (mean = 7.9, n=32) and the 150-meter buffer area (mean = 10.5, n=10) compared to the sewered area (mean = 4.6, n=20) and soils from farms (mean=3.8, n=19). Findings demonstrate that the presence of enteric pathogens in soils adjacent to public waste bins were associated with neighborhood sanitation infrastructure and may be a useful matrix for surveillance. In high-burden settings with poor sanitation, direct examination of soils and other environmental matrices are potentially scalable means of environmental pathogen surveillance to consider beyond conventional sampling matrices.

We present findings from the first known pilot study of transatlantic airplane wastewater monitoring, conducted over six months at two connected international airports in the United States and the United Kingdom. This study demonstrates the feasibility of implementing bilateral wastewater-based pathogen surveillance at international travel hubs. We outline the operational and analytical methodologies employed, highlight key challenges encountered in transnational coordination, and provide recommendations for the design and implementation of future surveillance programs at points of entry.

Hospital-acquired infections are a known and growing problem worldwide. Far-UVC is a novel disinfection method that inactivates bacteria with limited penetration into human skin or eyes. A clustered, unmatched, randomized control trial (RCT) will be implemented in two Bolivian hospitals. The intervention arm will receive functioning Far-UVC lamps, whereas the control arm will receive identical lamps that do not emit UV light (shams). Based on baseline data, 40 lamp fixtures will be installed above hospital sinks, 10 per arm per hospital. Environmental samples (air and surface swabs) will be collected and analyzed via culture and sequencing. Simultaneously, air chemical monitoring data will be collected.

Wastewater-based epidemiology (WBE) is an effective surveillance approach for monitoring viruses of public health relevance at the community level, complementing clinical surveillance systems. Molecular methods such as PCR/qPCR are widely used for targeted detection, while next-generation sequencing (NGS) with targeted enrichment panels has emerged as a complementary strategy for broader viral detection and genomic characterization. This study comparatively evaluated conventional PCR/qPCR and a targeted enrichment whole-genome sequencing Viral Surveillance Panel (VSP, Illumina) for virus detection in wastewater. Fifty-six wastewater samples collected between 2017 and 2023 from a wastewater treatment plant in Cordoba, Argentina, were concentrated by polyethylene glycol precipitation and pooled by season and year, reaching a total of 14 pools. Each pool was analyzed in parallel by PCR/qPCR for eight human viruses of public health importance and by the VSP, targeting 66 viral species, sequenced on a NovaSeq 6000 platform, and analyzed with the DRAGEN pipeline. Detection frequencies for each virus using PCR/qPCR and VSP were: RoV A 100%/14.3%; NoV 100%/14.3%; AiV 50%/42.9%; SARS-CoV-2 14.3%/0%; HAV 42.9%/0%; HEV 14.3%/0%; JCPyV 35.7%/85.7%; BKPyV 28.6%/71.4%, respectively. In addition, VSP detected the genomes of Astrovirus (71.4%), Salivirus (21.4%), Coxsackie A (14.3%), Rotavirus C (14.3%), and Merkel Cell virus (7.1%), and enable the recovery of 16 near complete genomes (coverage > 92.5%) of AiV, JCPyV, BKPyV, Salivirus and Astrovirus. PCR/qPCR and targeted enrichment NGS provide complementary information wastewater viral surveillance. Their combined application improves virus detection and genomic characterization, reinforcing the value of integrated approaches in environmental virology and public health monitoring.

Indigenous, mountain communities residing upstream of Bhumibol Dam, Thailand, rely on vulnerable natural water sources for their water supply, yet remain unaware of the associated health risks. This study assessed the water quality, usage patterns and contamination pathways across six villages upstream of Bhumibol Dam to shed light on the obstacles to sustainable water security . Samples from 38 water sources of drinking and/or non-drinking water, soil, and the edible parts of crops were subjected to analyses of physical, chemical (NO3-N, pH), and qualitative pesticide-related variables, alongside a 6-month assessment of a community water filter system. Principal component analysis identified a "at-risk group" of preferred drinking water sources all exhibiting high NO3-N, highly alkaline pH, and substantial pesticide contamination, which was found to likely be caused by agricultural run-off. This was reinforced by the detection of pesticide residues in all soil samples and, critically, in the below-ground edible parts of crops (taro, lemongrass, arrowroot), confirming dietary exposure in the local communities. Further compounding the risks posed by the unsafe water supply, the community water filter was found to be ineffective throughout the 6-month analysis with there being no significant difference in water quality between before and after filtration. The residents paradoxical preference for high-risk, still water (from sand-filtered puddles) for drinking, rather than water from flowing sources, which they used only for cooking and cleaning. These findings reveal a severe, compounded public health threat of chronic exposure to minerals linked to urolithiasis and agrochemicals, highlighting the urgent need for quantitative risk assessment and the implementation of resilient, decentralized water treatment solutions in these mountain communities.

Antimicrobial resistance (AMR) is a growing global public health threat projected to cause up to 10 million deaths annually by 2050 if no immediate action is taken. While misuse and overuse of antibiotics are the main drivers of increasing AMR, the eco-evolutionary dynamics of AMR in the environment - particularly across the urban-rural continuum - remain poorly understood. Using shotgun sequencing, we investigated urban, farm, and rural water sources in the Berlin-Brandenburg region to explore the distinctness or overlap of their antibiotic resistance gene (ARG) profiles and the potential impact of wastewater treatment plants (WWTP). ARGs were identified using multiple databases and five bioinformatic tools, combining sequence-based alignment and deep learning approaches. This multi-tool approach allowed for the detection of up to 18 AMR classes--more than any single tool alone. The multi-tool screening approach for ARGs, combined with the ABRicate algorithm, was superior to all single ARG tools and databases, detecting more AMR classes, allowing for biocide and metal resistance detection, while less sensitive for detection of aminocoumarin resistance genes. ARG diversity was higher in urban lake sediments, urban waters, and wastewater compared to rural lake sediments and water. Among all environments, urban lake water showed the highest overall ARG abundance, second only to wastewater, and this pattern held across all AMR classes, except for aminoglycoside resistance, which was most prevalent in rural lake sediments. The WWTP was unable to remove the circulating pool of ARGs, despite a decrease in unique ARGs in the outflow.

Wastewater-based surveillance (WBS) is widely used to monitor respiratory viruses, yet uncertainties remain regarding how viral RNA concentrations in wastewater reflect infection dynamics. Specifically, diurnal variation in shedding and RNA losses during in-sewer transport can impact measured signals. We conducted a field study in a 5-km trunk sewer (travel time of one hour). Wastewater was sampled at the sewer inlet and outlet using autosamplers collecting time-proportional one-hour composite samples over 24 hours. The one-hour composite samples were analyzed for assessing intra-daily fluctuations, and 24-hour composites for signal change. Biofilms from the sewer-pipe walls were collected at three locations. Nucleic acids were extracted, and SARS-CoV-2, Influenza A/B, and Respiratory Syncytial Virus (RSV) RNA were quantified using a multiplex digital PCR assay. All viruses showed pronounced diurnal variation, with consistent morning load peaks. Viral RNA in the bulk liquid decreased during in-sewer transport, with modelled changes ranging from 15% to 72% across pathogens. Biofilms served as minor reservoirs of viral RNA; for SARS-CoV-2, sequencing revealed similarity between biofilm and bulk liquid RNA. Our study provides a full-scale assessment of in-sewer transport effects on viral RNA and highlights the need to account for complex in-sewer dynamics when interpreting WBS data.

Antibiotic resistance genes (ARGs) circulating among clinically relevant bacteria pose serious challenges to public health. Given the ancient and environmental bacterial origins of ARGs, a better understanding of the carriers of ARGs beyond the clinically most relevant species is urgently needed for more farsighted resistance monitoring and intervention measures. While the risks of emerging ARGs from environmental sources have been recognized, the identification bottlenecks stem from the limitations of shotgun metagenomic sequencing and bioinformatic methods. Here, we used long-read metagenomic sequencing and bacteria-specific methylation profiles to re-establish the links between established (well-described) or latent (absent in databases) ARGs and their bacterial and genetic contexts in wastewater. The base modification data produced by PacBio SMRT sequencing was analyzed by an in-house pipeline utilizing position weight matrices and UMAP visualizations. The approach was validated by a synthetic community with known bacterial composition. Our analysis revealed several previously unreported ARGs and their hosts with varying risk levels defined by their potential as emerging public health threats. For instance, Arcobacter, as one of the prevalent taxa in influent wastewater, was shown to carry a latent beta-lactamase gene with high predicted mobility potential. Of the other emerging beta-lactamases, we provided a real-life example of ongoing pdif module-mediated genetic reshuffling of the blaMCA gene occurring at least within Acinetobacter hosts in our samples. Additionally, we identified Simplicispira, Phycisphaerae, and environmental groups of the Bacteroidales order as the carriers of established, clinically important ARGs. These findings support the intermediate host roles of strictly environmental bacteria for the further dissemination of mobilized ARGs, highlighting the importance of exploring the uncultivated, or non-pathogenic, carriers of ARGs for the early detection of newly arising ARGs and mobility mechanisms.

Millions of outdoor workers cannot avoid wildfire smoke, likely leading to inequalities in exposure and health risk. We characterized work-related exposure to wildfire PM2.5 for 3,108 contiguous US counties during 2006-2019. Despite experiencing less ambient exposure to wildfire PM2.5, counties with higher portions of non-Hispanic Black and Hispanic Americans experienced higher work-related exposure. We also find suggestive evidence that the effect of ambient smoke fine particulate matter (PM2.5) concentrations on all-cause mortality may differ by workplace exposure. These findings suggest that workplace exposures should be considered in wildfire smoke adaptation measures.

Emerging infectious diseases often circulate undetected until they cause clinical illness, creating serious risks for public health and biosecurity. The U.S. blood supply, with millions of routinely collected and quality-controlled donations, offers an untapped national resource for proactive pathogen surveillance. We propose integrating metagenomic sequencing (MGS) into existing blood and plasma collection workflows to detect novel or unexpected viruses in deidentified residual samples. This approach complements environmental sampling from wastewater and air by enabling detection of blood-borne and vector-borne pathogens that cause asymptomatic viremia, threats that may be poorly captured by other systems. Our modeling analysis suggests that this system is highly cost-effective: an estimated annual investment of approximately $5.8 million could detect a novel HIV-like pathogen before it infects 0.01% of the population. Building on existing blood donor testing infrastructure, privacy frameworks, and an analogous national serosurveillance network established during COVID-19, MGS blood monitoring represents a deployment-ready, cost-effective addition to national biosecurity infrastructure, transforming the blood supply from a safety measure into an early warning system for emerging viral threats.

Antimicrobial resistance (AMR) is a growing problem, with annual deaths set to pass 10 million by 2050 if current trends continue. Wastewater surveillance has been proposed as a strategy to understand population-level resistance, and water reclamation facilities (WRFs) have been identified as a control point for environmental dissemination of resistant bacteria. Understanding dynamics of AMR across WRFs requires advanced molecular tools that elucidate host bacteria, especially for mobile resistance carried on plasmids. To that end, influent, activated sludge, and effluent were collected from three WRFs in North Carolina, Texas, and California during three weeks of Spring 2024. Samples were analyzed using Hi-C proximity ligation sequencing to identify the AMR host range for chromosomal and plasmid-based resistance. A total of 1,868 hits for 244 unique resistance genes were observed, with seven resistance genes identified in all samples. Resistance genes were more likely to be carried on a microbial plasmid in influent, but more likely to be in a chromosome in activated sludge. Seventeen total microbial hosts for resistance genes were identified in effluent, suggesting WRF effluents may be sources of resistant bacteria to receiving surface waters. A high proportion of all identified host relationships were confined to just four bacterial families. Hi-C contact mapping is a critical tool to more fully describe the AMR host range in complex matrices, particularly for plasmid-based resistance genes. ImportanceAntimicrobial resistance (AMR) threatens modern medicine. Water reclamation facilities receive a complex mixture of antibiotics and rely on active microbial communities for treatment, thereby acting as critical systems to prevent environmental spread of resistance. However, AMR dynamics are difficult to discern in complex wastewater environments due to antibiotic resistance genes (ARGs) being frequently carried on mobile pieces of DNA that are difficult to link to specific bacteria using conventional shotgun sequencing. Novel proximity ligation sample preparation techniques like Hi-C physically link co-located sequences of DNA before shotgun sequencing. This allows sequencing to elucidate the bacterial hosts for both stable and mobile ARGs. In the current study, Hi-C sequencing was carried out on influent, activated sludge, and effluent collected from water reclamation facilities in California, Texas, and North Carolina to assess the resistome host range across treatment. 5 Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/26346186v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1e4620eorg.highwire.dtl.DTLVardef@e1c3a7org.highwire.dtl.DTLVardef@1f40964org.highwire.dtl.DTLVardef@94b886_HPS_FORMAT_FIGEXP M_FIG C_FIG