Environmental Science & Technology Letters

Respiratory infections caused by bacterial pathogens like Mycobacterium tuberculosis and Bordetella pertussis have increased since the COVID 19 pandemic, yet clinical surveillance of both suffers from underreporting and delayed diagnoses. Wastewater monitoring is a valuable public health surveillance tool that can help fill gaps in clinical data yet has rarely been applied to respiratory bacterial pathogens despite evidence of bacterial shedding via excretion types that enter wastewater. In this study, we investigated the possibility for wastewater monitoring of two bacterial respiratory diseases, tuberculosis and pertussis, using two case studies of wastewater monitoring for M. tuberculosis and B. pertussis. We retrospectively measured concentrations of these pathogens in wastewater samples collected longitudinally from communities with and without known outbreaks of these diseases. We designed and validated a novel B. pertussis specific assay for the NAD(P) gene; B. pertussis nucleic acids were detected sporadically in wastewater during an identified outbreak. We used a highly specific, established assay for M. tuberculosis nucleic acids, and found low concentrations of the marker in wastewater that were lag-correlated with clinical incidence rates 5 weeks later. Findings support the potential of wastewater monitoring for M. tuberculosis and B. pertussis to enable identification of communities with outbreaks of tuberculosis and pertussis and provide early warning for tuberculosis.

Benzalkonium chloride (BAC) is widely used as a disinfectant in cleaning products and is frequently detected in indoor dust. In this study, we assessed dust samples, along with information on cleaning product use, from 24 pregnant participants. Dust samples were analyzed for BAC concentration and microbial tolerance. Different chain lengths of BAC (C12, C14, and C16) were quantified using LC-MS/MS, and bacterial isolates were tested for BAC tolerance using minimum inhibitory concentration (MIC) assays. BAC was ubiquitously detected, with C12 and C14 being dominant. Higher BAC concentrations were associated with reported disinfectant use and increased microbial tolerance. These findings suggest that indoor antimicrobial use may promote microbial resistance, highlighting potential exposure risks in indoor environments and the need for further investigation into health and ecological impacts.

Understanding how airborne particulates disrupt the human alveolar barrier requires in vitro systems that accurately replicate its composition and function. We present a biodegradable lung alveoli-on-a-chip that reproduces the architecture and physiology of the human air-blood interface using a porous poly(lactic-co-glycolic acid) (PLGA) membrane positioned between epithelium and endothelium under air-liquid interface (ALI) culture. The membrane, fabricated by porogen-assisted nonsolvent-induced phase separation, exhibited >50 % porosity, [~]2 {micro}m thickness, and mechanical compliance over 100-fold higher than conventional Transwell inserts, closely resembling the native interstitium. During co-culture, gradual PLGA degradation was compensated by cell-secreted extracellular-matrix (ECM) proteins such as collagen IV and laminin, forming a self-remodeling barrier that maintained integrity for at least 11 days. The platform supported stable epithelial-endothelial co-culture, high transepithelial electrical resistance, and physiologically relevant permeability. To demonstrate its utility, the chip was used to assess pulmonary toxicity of four types of waste-combustion-derived particulates, including rubber, plastic bags, plastic bottles, and textile fibers, delivered apically under ALI conditions. All combustion products reduced cell viability, increased hydrogen-peroxide release, and elevated {gamma}-H2AX expression, indicating oxidative and genotoxic stress, while disrupting barrier permeability. Rubber combustion particles elicited the most severe toxicity, causing the greatest loss of viability, accumulation of reactive oxygen species, and formation of DNA double-strand breaks. Together, these results establish a biodegradable, ECM-remodeling lung alveoli-on-a-chip as a physiologically relevant platform for investigating source-specific particulate toxicity and alveolar-barrier pathophysiology. By bridging environmental exposure models with human-relevant lung biology, this system provides a quantitative and translatable tool for evaluating respiratory risks and therapeutic interventions.

Traditionally, studies have explored the impacts of individual water chemistry parameters on the persistence of Mycobacterium spp. and Legionella spp. in isolation with the underlying assumption that these associations are likely monotonic in nature. Yet chemical and microbiological changes are complex, and associations are likely highly combinatorial. In this study, we use interpretable machine learning models to disentangle the integrative and nonlinear associations between water chemistry and occurrence/abundance of Mycobacterium spp. and Legionella spp. Seasonal data from source water, point-of-entry and distribution systems of eight full-scale drinking water systems demonstrated that shifts in overall water chemistry were associated with the changes in microbial abundance during treatment and distribution. Machine learning models indicated moderate predictive ability of integrated water chemistry towards Legionella spp. abundance and towards the occurrence of both Legionella spp. and Mycobacterium spp., whereas predictive performance for Mycobacterium spp. abundance was limited. The association between nitrate and Legionella spp. abundance was disinfectant regimes dependent, while dissolved organic carbon exhibited a concentration dependent response type (i.e., positive and negative association). In chloraminated systems, Legionella spp. abundance was positively associated with ammonia and nitrate, highlighting the critical role of nitrification. Here, it appears that pH likely influences the initial colonization of Legionella spp. while ammonia governs its abundance in drinking water. Overall, this study demonstrates that integrated water chemistry and parameter-specific nonlinear effects collectively explain persistence of Mycobacterium spp. and Legionella spp. in drinking water systems.

Background Ambient air pollution is a leading global health risk and disproportionately affects populations of Low- and Middle-Income Countries (LMICs). In 2021, WHO revised its Air Quality Guidelines (AQG), lowering recommended annual limits for Particulate Matter 2.5 (PM2.5) and Nitrogen Dioxide (NO2). We estimated the potential health and economic impacts of achieving WHO Interim Target 3 (IT3) and AQG concentrations across LMICs. Methods We conducted a health impact assessment across 136 LMICs to quantify one-year changes in all-cause and cause-specific mortality (chronic obstructive pulmonary disease [COPD], ischaemic heart disease [IHD], and stroke) and disease incidence (COPD, dementia, IHD, and stroke) under WHO IT3 and AQG counterfactual scenarios for PM2.5 and NO2. Concentration-response functions were applied at 1km x 1km resolution. Economic welfare impacts of mortality risk reductions were estimated using country-adjusted values of a statistical life (VSL, Int$ PPP-adjusted 2021). Direct medical and productivity-related costs associated with incident cases were estimated using a cost-of-illness (COI) framework. Uncertainty intervals (UI) reflect uncertainty in concentration-response functions. Results Attainment of WHO IT3 and AQG concentrations for PM2.5 was associated with an estimated 16.04% reduction (6.58million, UI: 6.10-7.07million) and 22.97% reduction (9.43million, UI: 8.75-10.11million) in annual deaths, respectively. Corresponding VSL-based estimates of deaths averted were Int$5.5 trillion (7.0% of aggregate LMIC GDP) and Int$8.4 trillion (10.6% of GDP), respectively. For NO2, IT3 and AQG scenarios were associated with estimated reductions of approximately 1.06% (approximately 435,000 deaths, UI: 388,000-483,000) and 2.79% (435,000 deaths; UI: 388,000-483,000), yielding gains of Int$0.6 trillion (0.7% of GDP) and Int$1.5 trillion (1.9% of GDP). Disease-specific mortality reductions were most prominent for IHD and stroke in Asia and Africa. Under the PM2.5 AQG scenario, an estimated 2.82million (1.67-2.97) COPD, 1.10million (0.83-1.37) dementia, 7.3million (6.41-8.19) IHD, and 2.3million (2.19-2.41) stroke cases could be delayed or averted in one year. Associated reductions in direct medical and productivity-related costs were greatest for IHD, COPD, and stroke. NO2-related morbidity reductions were smaller across all outcomes. All estimates represent one-year changes in risk relative to counterfactual exposure and may reflect delayed rather than permanently avoided events. Discussion Achieving both WHO IT3 and AQG values in LMICs could yield substantial reductions in premature mortality and disease incidence, particularly for cardiovascular and respiratory conditions, alongside large, monetised welfare gains from reduced mortality risk. These findings underscore the considerable societal value of air quality improvements and support accelerated action toward meeting WHO guideline levels in regions bearing the highest pollution burden.

Wastewater sequencing is an increasingly valuable tool in tracking the spread of infectious disease agents across space and time in areas of dense human settlement. Among pathogens that can be readily detected by this approach is influenza A, which follows predictable patterns of prevalence through the winter months in North America. Here, we leverage routine surveillance of a municipal wastewater treatment plant in Northern California to describe an atypical, off-season spike in influenza A concentrations that rivals that of the winter respiratory virus season. Drawing upon metagenomic data generated through hybrid-capture sequencing, we assemble and subsequently characterize fragments of divergent influenza genomes that appear to derive predominantly from the avian H16 clade. These strains exhibit close evolutionary relationships to influenza isolated from migratory shorebirds, hinting at potential host species and mechanisms of geographic spread. Analysis of read abundances suggest that these avian strains dominate the pool of influenza circulating during the summer months, when typical human-infecting strains are essentially absent. Together, our results expand the value of wastewater sequencing to encompass sensitive tracking of outbreaks within animals in interface regions where human settlement abuts wildlands, increasing overall pandemic preparedness.

A compact, in-house developed ultraviolet germicidal irradiation (UVGI) system adaptable to static, mobile, or robotic platforms was developed for the effective sterilization of bacteria and fungi using a wireless mode of operation. Under controlled laboratory conditions, its efficacy was evaluated against three representative biofilm-forming pathogens: Bacillus subtilis (Gram-positive, spore-forming, motile bacterium), Escherichia coli K12 (Gram-negative, non-spore-forming, non-motile bacterium), and Candida albicans M-207 (multi-drug-resistant, clinical yeast isolate). Microbial viability following UVGI exposure was assessed using colony-forming unit (CFU) and MTT assays, and morphological alterations were characterized by scanning electron microscopy (SEM). Cultures were exposed to UV-C radiation at distances of 1-5 m for 15-90 min. CFU assay demonstrated 100% kill of all tested organisms at 1 m and 15 min, corresponding to doses of 600.3, 576 & 697.5 mJ/cm{superscript 2}. Although MTT assays indicated residual metabolic activity under the same conditions, CFU results confirmed that surviving cells were unable to proliferate, highlighting the robustness of UV treatment for long-term inactivation. SEM confirmed distinct morphological alterations such as complete destruction of extracellular matrix & reduction in number of cells indicating cell death with increase in UV dose as compared to controls. A dose & time-dependent inactivation of biofilm-forming bacteria & fungi was observed on exposure to UVGI. Therefore, this pilot study validates the effectiveness of the newly developed UVGI surface sterilizer against biofilm-forming bacterial and fungal pathogens. Overall, the system demonstrates proof-of-concept efficacy under laboratory conditions and holds strong potential for future development and validation in hospitals and other contaminated public spaces. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/715580v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@150cefcorg.highwire.dtl.DTLVardef@450831org.highwire.dtl.DTLVardef@1cfd6borg.highwire.dtl.DTLVardef@1419ba8_HPS_FORMAT_FIGEXP M_FIG C_FIG IMPORTANCEMicroorganisms that form biofilms on surfaces are difficult to eliminate and contribute to the spread of infections in healthcare and indoor environments. There is a need for practical, easy-to-use disinfection technologies that can effectively reduce such contamination. In this study, we developed a compact, in-house, wireless UV-C disinfection system designed for flexible operation across different surface types. The system was evaluated under controlled laboratory conditions using representative biofilm-forming bacterial and fungal pathogens. Our findings show that the system can effectively reduce microbial contamination, demonstrating proof-of-concept efficacy. This work highlights the potential of accessible, non-chemical UV-based technologies and supports their further validation for applications in real-world disinfection settings.

Microplastic (MP) pollution, which is present in the ecosystem in vast quantities, adversely affects human health and the environment, making it imperative to develop methods for its mitigation. The challenge of detecting or capturing MPs could potentially be addressed using plastic-binding peptides (PBPs). The ideal PBP for MP remediation would not only bind strongly to plastic, but also have other properties such as high solubility in water or great binding specificity to a certain plastic. However, the scarcity or absence of known PBPs for common plastics along with the lack of methods that can discover PBPs with all of the desired properties precludes the development of peptide-based MP remediation strategies. In this study, we discovered short linear PBPs with high predicted water solubility and binding specificity by employing an in-silico discovery pipeline that combines deep learning and biophysical modeling. First, a long short-term memory (LSTM) network was trained on biophysical modeling data to predict peptide affinity to plastic. High affinity peptides were generated by pairing the trained LSTM with a Monte Carlo tree search (MCTS) algorithm. Molecular dynamics (MD) simulations showed that the PBPs discovered for polyethylene, the most common plastic, had 15% lower binding free energy than PBPs obtained using biophysical modeling alone. PBPs with both high affinity and high predicted solubility in water were found by including the CamSol solubility score in the MCTS peptide scoring function, increasing the average solubility score from 0.2 to 0.9, while only minimally decreasing affinity for polyethylene. The framework also discovered peptides with high binding specificity between polystyrene and polyethylene, two major constituents of MP pollution, using a competitive MCTS approach that optimized the difference in affinity between the two plastics. MD simulations showed that competitive MCTS increased the binding specificity of PBPs for polystyrene and identified peptides with relatively great preference for either of the two plastics. The framework can readily be applied to design PBPs for other types of plastic. Overall, the high-affinity PBPs with desirable properties discovered by marrying artificial intelligence and biophysics can be valuable for remediating MP pollution and protecting the health of humans and the environment.

Wastewater surveillance is increasingly used for antimicrobial resistance (AMR) monitoring in urban environments, but low-resource settings often lack a piped sewerage system. Instead, coprophagous flies--flies that ingest feces--may serve as composite samplers for monitoring fecal wastes present in terrestrial environments. We evaluated whether the class 1 integron-integrase gene intI1 was associated with genetic markers of AMR and fecal source tracking markers (FST) in coprophagous flies collected from latrine entrances and food preparation areas in low-income urban Maputo, Mozambique. We quantified intI1, an enteric 16S rRNA target (for normalization), three FST markers, and 30 ARG targets using qPCR. We normalized concentrations of intI1 and each target to enteric 16S rRNA. We fit linear mixed models with a random intercept for housing compound to estimate within-fly associations between log10 relative abundance of intI1 and log10 relative abundance of each target with and without adjustment for fly taxonomic group, capture location, and standardized fly mass. We also modeled per-fly unique ARG count (i.e., number of ARG targets detected) using Poisson regression. Of 188 flies assayed, 176 passed internal controls; intI1 and enteric 16S rRNA were detected in 95% and 96% of flies, respectively. Higher relative abundance of intI1 was positively associated with ARG and FST targets, with the strongest associations observed for sulfonamide-(sul1: {beta} = 0.87; 95% CI: 0.81, 0.94; sul2: {beta} = 0.81; 95% CI: 0.73, 0.89), tetracycline- (tetA: {beta} = 0.78; 95% CI: 0.70, 0.85; tetB: {beta} = 0.69; 95% CI: 0.60, 0.79), and trimethoprim-related (dfrA17: {beta} = 0.78; 95% CI: 0.70, 0.86) genes. Associations with FST markers were weaker (i.e., human mtDNA: {beta} = 0.46; 95% CI: 0.37, 0.55; human-associated Bacteroides: {beta} = 0.34; 95% CI: 0.25, 0.43). Higher relative abundance of intI1 was also associated with a greater number of ARGs detected: each 10-fold increase in intI1 was associated with an 8% higher expected unique ARG count (aRR=1.08, 95% CI: 1.04-1.12). These findings support the need for further research across different settings exploring intI1 carried by coprophagous flies as a potential standardized screening target for AMR surveillance in unsewered terrestrial environments.

Mobile laboratories (MLs), whether vehicle mounted or portable, provide a versatile platform for on-site wastewater and environmental surveillance (WES) of pathogens, particularly in remote locations with limited laboratory infrastructure. However, molecular workflows intended for ML deployment require careful optimization to account for locally available equipment, consumables, infrastructure, workforce capacity, and operational constraints. In this study, we optimized an integrated ML workflow combining Oxford Nanopore Technologies (ONT) for shotgun metagenomics, multiplex metabarcoding for community level microbial analysis, and Biomeme based qPCR for targeted pathogen analysis. To further explore the potential of metagenomics for resistome assessment, we evaluated two whole metagenome enrichment approaches for their ability to improve detection of antimicrobial resistance genes. We introduce and validate a novel ONT based strategy for multiplexed sequencing small subunit (SSU) rRNA amplicon sequencing, enabling simultaneous profiling of bacteria, archaea, and microeukaryotes in complex microbial communities with multiplex metabarcoding. Sample pretreatment and nucleic acid (NA) extraction in this ML workflow were optimized using a combination chemical mechanical lysis approach followed by magnetic bead based NA purification. Workflow performance was verified using a mock community (ZymoBIOMICS Microbial Community Standard, Zymo Research, USA) and wastewater samples spiked with inactivated Mpox virus (MPXV), demonstrating accurate taxonomic representation and sensitive MPXV detection. Comparison with a commercial ZymoBIO bead beating kit for sediment sample showed ML NA extraction performed comparably. The time efficient multiplex metabarcoding workflow enabled simultaneous profiling of bacterial, archaeal, and eukaryotic diversity and produced results more concordant with qPCR based pathogen detection than the REPLIg Cell Whole Genome Amplification (WGA) & Whole Transcriptome amplification (WTA). The protocol for Mpox virus genome characterization was successfully validated for whole genome sequencing (WES) based detection and incorporated into the standard ML workflow. Across both high and low biomass environmental matrices, the Multiple Displacement Amplification (MDA) based metagenomic workflow, combined with the ML NA extraction procedure, reliably reproduced the expected composition of the Microbial Community Standard. Collectively, the integration of ONT technology with MDA metagenomics and mobile qPCR workflows provides an effective One Health approach for pathogen surveillance and outbreak response across heterogeneous environmental settings, which was later further enhanced by an offline bioinformatic and visualization pipeline enabling near real time detection of pathogens and AMR thus early risk assessment.

Silver (Ag+) ions are known to be toxic to bacteria, cells, organisms and living systems; yet its impacts on the locomotion of surface-crawling organisms remain poorly quantified. Here we investigated the short-term (0-6 hours) effects of Ag+ ions on the locomotion of Drosophila melanogaster larvae on flat agarose surfaces containing Ag+ ions at different concentrations (0, 1, 10, and 100 mM). By quantifying their locomotion, we found that Drosophila larvae showed shorter accumulated distances and reduced crawling speed. Additionally, we quantified the go/stop dynamics and peristalsis of the larvae and observed that Ag+ ions disrupted the normal, rhythmic, peristaltic contraction of the larvae and "trapped" them in the stop phase. Such toxic effects were dependent on Ag+ concentration and exposure duration.

The accumulation of microorganisms and macroorganisms on aquatic surfaces poses economic and ecological challenges, particularly in maritime transport. Traditional antifouling methods, such as biocidal coatings containing toxic compounds like tributyltin (TBT) and copper, are effective but harmful to the environment. This study investigates eco-friendly antifouling alternatives, focusing on nature-inspired compounds (NIAFs) GBA 26 (GBA) and DPC345DHC (DH345), derived from polyphenols and flavonoids, respectively. The ecotoxicity of these compounds was evaluated using standardized assays with various species, including embryos of Danio rerio (zebrafish) (OECD TG 236), the algae Raphidocelis subcapitata (OECD TG 201), and the bacteria Vibrio fischeri (ISO 11348-2), along with nuclear receptor transactivation assays in Mytilus galloprovincialis (Mediterranean mussel). Gallic acid derivative GBA and 24h-transformation products showed low toxicity in zebrafish embryos, while dihydrochalcone DH345 inflicted developmental toxicity in zebrafish at 1 mg/L and above. Comparatively, tralopyril, a commercial biocide, exhibited significant toxicity at lower concentrations. Transcriptomic analysis of zebrafish embryos treated with GBA revealed selective gene modulation related to stress response, ion transport, and protein synthesis. Both, GBA and DH345, were shown to inhibit algae growth at 0.1 mg/L. Vibrio fischeri assay showed no toxic effects for any of the tested compounds. Nuclear receptor transactivation assays conducted with GBA revealed no activation of PPAR or PXR receptors. These findings suggest GBA and DH345 as potential eco-friendly antifouling agents with lower environmental risks than established antifoulants such as tralopyril. However, further research is needed to evaluate their potential long-term ecological impacts, particularly chronic toxicity across various organisms. This study advances the pursuit of sustainable antifouling solutions that prioritize environmental protection.

Per- and polyfluoroalkyl substances (PFAS) are highly resistant to enzymatic C-F bond cleavage, and hydrolytic defluorination of long-chain PFAS has rarely been demonstrated. Here, we report selective hydrolytic defluorination of branched perfluorooctanoic acid (PFOA) isomers by a haloacid dehalogenase (4A) from Delftia acidovorans strain D4B. A fluoride-specific riboswitch biosensor was used for initial substrate screening, followed by scaled-up assays in which fluoride release was quantified using a fluoride ion-selective electrode. Defluorination products were subsequently identified by liquid chromatography-mass spectrometry (LC-MS). Although purified 4A (10 M) readily catalyzed hydrolytic defluorination of fluoroacetic acid, incubation of PFOA (0.5 mM) with purified 4A resulted in a statistically significant increase in fluoride release at elevated enzyme loading (500 M). High-resolution LC-MS/MS analysis revealed that defluorination products originated from minor branched PFOA isomers rather than linear PFOA. Molecular docking analyses supported catalytically plausible binding geometries for branched PFOA isomers, positioning the substrate -carbon within [~]4 [A] of the catalytic aspartate residue. These findings demonstrate previously unrecognized hydrolytic reactivity of a haloacid dehalogenase toward branched PFAS isomers and expand the known catalytic scope of the haloacid dehalogenase family. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/719434v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1c12fb1org.highwire.dtl.DTLVardef@224ae3org.highwire.dtl.DTLVardef@16293b7org.highwire.dtl.DTLVardef@d014b7_HPS_FORMAT_FIGEXP M_FIG C_FIG SYNOPSISEnzymatic defluorination of PFAS is rarely observed in environmental systems. This study identifies hydrolytic defluorination of branched PFOA isomers, improving understanding of PFAS defluorination at the enzyme level.

Understanding how airborne particulates disrupt the alveolar barrier requires in vitro systems that recapitulate both the structure and transport properties of the lung air-blood interface. Here, we report a biodegradable lung alveoli-on-a-chip enabled by porous poly(lactic-co-glycolic acid)/polycaprolactone (PLGA/PCL) membranes with an interconnected porous architecture generated via porogen-assisted phase separation process. The membrane exhibits tunable degradation behavior, allowing progressive increases in surface porosity ([~]40%) and reduction in thickness ([~]3 {micro}m) during culture, while PCL maintains mechanical integrity under dynamic conditions. These degradation-driven structural changes regulate membrane transport properties, leading to enhanced permeability and supporting the formation of a functional epithelial-endothelial barrier under air-liquid interface (ALI) culture with breathing-mimetic cycling strain. Primary human alveolar epithelial and microvascular endothelial cells formed confluent, junctional monolayers on opposing membrane surfaces, exhibiting stable barrier function and high viability throughout the culture period. As a functional application, the platform was used to assess diesel particulate matter (DPM)-induced alveolar injury. Apical exposure to DPM induced dose-dependent cytotoxicity, increased barrier permeability, elevated reactive oxygen species, and DNA damage in both epithelial and endothelial layers, demonstrating trans-barrier propagation of particulate-induced injury. Pharmacological modulation with roflumilast-N-oxide (RNO), a phosphodiesterase-4 (PDE4) inhibitor, selectively attenuated oxidative stress and inflammatory responses, with limited effects on barrier integrity. Together, this work establishes degradable PLGA/PCL membranes as tunable interface materials for lung-on-a-chip systems, where structural evolution during degradation directly governs transport and barrier function. The resulting platform provides a physiologically relevant approach for studying particulate toxicity and therapeutic modulation at the alveolar interface.

BackgroundBuilt environment microbiome studies have identified numerous factors that shape indoor microbiomes, yet the reproducibility of these findings across buildings, timepoints, and research groups remains unclear. Differences in sequencing protocols, sampling design, and environments pose major challenges for cross-study comparisons, particularly in low-biomass environments where technical variation can obscure biological signal. To address this gap, we constructed a simple ontology which groups samples into one of three categories: hand, hand-associated surfaces, and floor then applied it to four publicly available 16S rRNA gene datasets: a hospital, university dormitory, Air Force dormitory, and private residential houses. ResultsWe identified strong and reproducible separation between floors and surfaces with frequent human contact. We found that floors consistently harbored soil-associated taxa, including KD4-96, 67-14, Skermanella, and Sphingobacterium, whereas hands and hand-associated surfaces were enriched with skin-associated genera, such as Lawsonella and Cutibacterium. Within studies, these results were generally consistent across timepoints. Across studies, mixed-model PERMANOVA analysis revealed significant clustering by sample type, with modest effects of study, suggesting that biological signal outweighed differences in laboratory or sequencing methods. Leave-one-study-out random forest models achieved high AUCs for hand vs. floor comparisons (0.865 to 0.921), moderate AUCs for hand-associated vs. floor comparisons, and weaker performance for hand vs. hand-associated comparisons. Application of the batch-correction method DEBIAS-M did not improve effect sizes or classification performance, indicating that reproducible structure was already discernible without batch adjustment. ConclusionsDespite substantial temporal and environmental heterogeneity among studies, we found that the built environment microbiome has a reproducible bacterial signal. There was consistent enrichment of soil-derived taxa on floors and human-associated taxa on hands and hand-associated surfaces suggesting a stable microbiome despite differences in building type, occupancy, and methodology. These findings establish an important foundation for future studies, suggesting cross-study comparability, the accuracy of ecological inference, and the ability to support the development of predictive applications in indoor microbiome research.

Wastewater and environmental monitoring (WEM) was a critical public health surveillance tool for SARS-CoV-2 surveillance during the COVID-19 Pandemic. However, substantial methodological heterogeneity across laboratories continues to challenge the interpretation and thus compromise the actionability of resulting WEM measurements. This study quantifies interlaboratory concordance in SARS-CoV-2 WEM measurements using influent wastewater samples collected between September 2021 and January 2024 at a single wastewater treatment facility within the Ontario Wastewater Surveillance Initiative, analyzed independently by 12 laboratories using their routine methods. In the absence of a known true viral concentration, interlaboratory WEM measurements were evaluated against a facility-specific longitudinal benchmark derived from routine surveillance at the source facility and correlated to clinical surveillance metrics. Concordance was assessed across four WEM measurement units commonly used in practice: SARS-CoV-2 copies/mL, SARS-CoV-2 copies/copies of PMMoV, and their standardized counterpart wastewater viral activity level (WVAL) units of WVAL-standardized SARS-CoV-2 copies/mL and WVAL-standardized SARS-CoV-2 copies/copies of PMMoV. Measurements in each unit were analyzed using complementary analytical frameworks, including categorical concordance metrics, principal component analysis, and linear mixed-effects modelling. Across the study period, interlaboratory measurements consistently captured benchmark temporal dynamics, including major peaks and periods of low activity, but showed substantial variation in magnitude and public-health interpretation across laboratory methods. Concordance was strongest during epidemiological extremes and deteriorated during transitional periods, increasing the risk of misclassification with potentially implications for public health decision-making. To explore potential laboratory methodological drivers of agreement, associations between the benchmark concordance and the laboratory-specific concentration, extraction, and RT-qPCR analytical steps were assessed using Fishers exact tests, alongside extracted-mass threshold analyses. No single methodological factor showed a statistically significant association with benchmark concordance in this study; however, several parameters, including RNA template volume, total RT-qPCR reaction volume, and extracted mass of analyzed settled solids, may warrant further investigation in future studies.

Microbial degradation of suspended and sinking organic carbon regulates long-term oceanic carbon storage by controlling the efficiency of the biological pump. Yet microbial controls on carbon export and remineralization remain poorly constrained, limiting predictions of how ocean carbon cycling will respond to climate change. Here, we combined in situ sampling with ship-based incubations to quantify prokaryote-driven removal rates of suspended and sinking total organic carbon (TOC). Samples were collected below the mixed layer during three stages of a spring Phaeocystis pouchetii bloom in the Labrador Sea. Phaeocystis blooms can dominate regional phytoplankton biomass and are expected to increase under future climate. Removal rates were used as a proxy for carbon lability and combined with 16S rRNA metabarcoding and carbon composition analyses to link microbial community structure with substrate characteristics. Removal rates of sinking particles (0.02-0.06 d-1) were an order of magnitude higher than those of suspended TOC (0.002 d-1) during bloom-decline and non-bloom. In contrast, during late-bloom, suspended carbon exhibited rates of 0.01 d-1, comparable to sinking particles, and was enriched in exopolymer-rich colonies. Prokaryotic community composition varied primarily among bloom stages rather than carbon fractions, indicating that bloom stage-- and thus particle origin and composition--was the dominant control on bacterial degradation and assembly. Bacterial diversity peaked where carbon was refractory and originated from mixed phytoplankton. Together, these results demonstrate that suspended Phaeocystis-derived carbon can be rapidly remineralized when blooms produce exopolymer-rich colonies and highlight bloom stage as key regulator of microbial carbon processing and biological pump efficiency.

Linking bacteriophages (phages) to their hosts remains a fundamental challenge to understanding microbial ecology, viral evolution, and horizontal gene transfer. Although phages are the most abundant biological entities on Earth, the majority of them remain uncharacterized due to the lack of efficient host-linking approaches. Traditional methods, such as plaque assays, have significant limitations as they depend on visible lysis and therefore fail to detect phages that do not form plaques. Conversely, shotgun metagenomics can recover viral genomes directly from environmental samples; however, it cannot directly link phages to their bacterial hosts. In this study, we addressed this limitation by tackling the critical question of "who infects whom?" through the development of a novel, culture-independent approach that utilises an anucleate bacterial minicells-based platform to enrich for phages capable of infecting a target bacterial host. To validate our approach, purified Escherichia coli minicells were exposed to a concentrated viral fraction derived from sewage samples. Genomic DNA from phages that successfully infected and interacted with the E. coli minicells was isolated, amplified, and sequenced. Metagenomic analysis revealed a distinct E. coli-specific virome, including several putatively novel phage species and genera. This platform effectively bridges the gap between culture-dependent and metagenomic methods, providing a scalable, host-targeted tool for identifying phage-host pairs. Our approach also opens new opportunities for studying phage-host interaction networks in complex microbial ecosystems and enhances our ability to investigate viral diversity, host specificity, and the ecological roles of phages in natural environments.

Military aviation training noise remains understudied despite its widespread impacts across urban, rural, and wilderness areas. The predominance of low-frequency noise and repetitive training can create pervasive noise pollution, yet past research often fails to capture the full range of health and quality-of-life effects. This study analyzed two complaint datasets related to Whidbey Island Naval Air Station noise: U.S. Navy records (2017-2020) and Quiet Skies Over San Juan County data (2021-2023). We analyzed and mapped sentiment intensity from noise complaints relative to modeled annual noise exposure, developed a typology to classify impacts, and modeled the environmental and operational factors influencing complaints. Findings revealed widespread negative sentiment and anger, often beyond the bounds of estimated noise contours, suggesting that annual cumulative noise models inadequately estimate community impacts. Complaints consistently highlighted sleep disturbance, hearing and health concerns, and compromised home environments due to shaking, vibration, and disruption of daily life. Residents also reported significant social, recreational, and work disruptions, along with feelings of fear, helplessness, and concern for children's well-being. The number of complaints were strongly associated with training schedules, with late-night sessions being the strongest predictor. A delayed response pattern suggests residents reach a frustration threshold before filing complaints. Overall, our findings demonstrate persistent negative sentiment and diverse impacts from military aviation noise. Results highlight the need for improved noise metrics, modeling and operational adjustments to mitigate the most disruptive effects.

Wastewater surveillance (WWS) is established as a vital tool for monitoring polio and SARS-CoV-2 with potential to improve surveillance for many other infectious diseases. This study evaluated the feasibility of detecting measles virus (MeV) RNA in wastewater as part of a national WS preparedness trial in Brisbane, Australia, from March to June 2025. Composite and passive sampling methods were employed in parallel at three wastewater treatment plants serving populations between 230,000 and 584,000. Nucleic acids were extracted and analyzed using RT-qPCR targeting MeV N and M genes to distinguish wild-type and vaccine strains. MeV RNA were detected in both 24-hour composite and passive samples on May 26 to 27, 2025 from the largest catchment of 584,000 which also included an international airport. No measles cases were reported in this city or region within 4 weeks of the WS detections. These were confirmed as vaccine-derived measles virus (MeVV) strain via specific RT-qPCR assay. Extraction recoveries varied (11.5% to 70.5%), with passive sampling showing higher efficiency. This is the first report of use of passive samples for detection of MeV. These findings are consistent with other studies reporting WWS results of both MeVV genotype A and wild type genotype B and/or D. It demonstrates the potential for sensitive MeV WWS with rapid differentiation of MeVV from wild type MeV shedding, including in airport transport hubs and with different sample types. Use of WWS could strengthen measles surveillance by enabling rapid detection of MeV RNA and supporting outbreak preparedness and response. This requires optimised methods which are specific to or differentiate wild-type MeV from MeVV. Furthermore, the successful detection of MeV using passive sampling in this study highlights its potential for deployment in diverse global contexts which may include non-sewered settings.