Environmental Science: Water Research & Technology

Wastewater contains a complex mixture of nucleic acids derived from human cells, microorganisms, diets, and agricultural or industrial inputs. Metatranscriptomics has the potential to broaden the understanding of wastewater viromes, but its utility is constrained by high-abundance RNA molecules that prevent the detection of lower-abundance signals, including human pathogens. Two major sources of this interfering background signal are ribosomal RNAs (rRNAs) from various organisms, and plant-infecting tobamoviruses from human diets, which are often the most abundant RNA viruses in wastewater. These rRNA molecules and RNA genomes can account for upwards of 95% of total RNA sequences, thereby obscuring the detection of lower-abundance viruses of public health importance. This study evaluated whether combined depletion of ribosomal RNAs and genomic RNA from abundant plant tobamoviruses can improve the recovery of underrepresented viral sequences from wastewater. RNA extracts from a large municipal wastewater treatment plant in Calgary, Canada, were processed with or without probe-based depletion of rRNAs and eight abundant tobamoviruses. Metatranscriptomes revealed that depletion reduced rRNA reads by 90%. Reads assigned to tobamoviruses, which accounted for up to 25% of viral reads in untreated samples, were reduced to <2% of viral reads after depletion. This decrease in undesired RNA signal led to an order of magnitude increase in viral read signals and improved assembled viral genome quality. Furthermore, the number of reads assigned to human and animal pathogens increased 42-fold after depletion, enabling the detection of important pathogens like influenza, RSV, and hepatitis A, which were undecteable in untreated samples. Together, these findings demonstrate that targeted depletion of abundant background RNAs improves the detection of low abundance viruses, and enhances the utility of metatranscriptomic approaches for wastewater viral surveillance.

Bench-scale microbial fuel cells (MFCs) treating mining wastewater frequently exhibit operational variability and uncharacterized degradation that obscure true biocatalytic performance. To decouple genuine biological treatment effects from mechanical failures, this paper presents an integrated diagnostic framework validated on two bench-scale systems treating heavy-metal-rich gold mine tailings. The first system evaluates Micractinium inermum algal bio-augmentation (System 1), while the second compares Psychrobacter alimentarius- and Trichococcus patagoniensis-dominated anodic consortia (System 2). To overcome single-reactor constraints, the framework integrates paired time-series statistical modeling, an adaptive percentile-floor change-point detector, equivalent-circuit modeling, and baseline-corrected spectroscopy (XRD/FTIR). Applying the framework to these systems uncovers previously masked dynamics: statistical analysis demonstrates that algal biocatalysis provides no voltage advantage under stable operation (+0.17%) but increases output by 27.54% under diurnal perturbation, while periodicity analysis links these diurnal shifts to the chamber photoperiod. Furthermore, heavy-metal remediation (up to 97.7%) is governed by system-level physicochemical mechanisms rather than algal-specific processes. The change-point detector successfully isolates distinct failure modes, distinguishing a recoverable excursion from terminal structural collapse. Finally, equivalent-circuit modeling reveals that the superior power density of Trichococcus consortia is driven by combined improvements in internal resistance and open-circuit voltage. Ultimately, pairing statistical controls with automated fault detection resolves operational ambiguity, offering a scalable baseline for health monitoring in bio-electrochemical wastewater treatment.

Polycyclic aromatic hydrocarbons (PAHs) are hazardous organic contaminants for which microbial bioaugmentation is a promising remediation strategy, but poor persistence of introduced microorganisms can limit efficacy. Encapsulation may improve persistence, yet the influence of capsule design, microbial species, and environmental conditions on performance remains poorly understood. We evaluated alginate encapsulation of the PAH-degrading bacteria Pseudomonas putida and Novosphingobium aromaticivorans across nutrient conditions and capsule formulations. Encapsulation effects varied by species and medium, influencing growth rate, maximum cell density, overall growth, and lag time; notably, encapsulation shortened lag time of N. aromaticivorans in sRB15 medium (36.9 h to 3.9-5.3 h). Enumeration methods also affected apparent cell recovery. After 8 weeks, encapsulation had no significant effect on P. putida but resulted in increased concentrations of N. aromaticivorans relative to planktonic cultures (1.22 x 10; vs. 2.05 x 10; CFU/mL). Capsule composition further influenced cell retention: increasing alginate approximately doubled capsule-associated cell concentrations, while chitosan coatings reduced cell concentrations within capsules without affecting external concentrations. These findings demonstrate that the benefits of encapsulation are species- and environment-dependent and that capsule formulation can be tuned to influence bacterial persistence and release, informing the design of encapsulated inoculants for bioaugmentation applications.

Hospital-based wastewater surveillance may complement community and clinical surveillance data in important ways, and may be useful in jurisdictions without community-based wastewater surveillance. From May 2024-April 2026, we analyzed weekly samples (n=190) from three hospitals in New York City using digital PCR to evaluate the sensitivity, specificity, and positive predictive value (PPV) of wastewater viral detection against facility SARS-CoV-2 and influenza A/B inpatient caseloads. Sensitivity was 38-42% for SARS-CoV-2 and 36-49% for influenza A/B, while specificity exceeded 72% for all pathogens. During respiratory seasons, sensitivity reached 81% for SARS-CoV-2 and 81% for influenza A; both had 100% sensitivity during peak case weeks. Notably, off-peak influenza detections occurred in hospital wastewater at all three hospitals in summer 2024 without corresponding hospital case detection, suggesting the presence of undiagnosed cases. These findings underscore the potential utility of hospital-based wastewater monitoring for tracking respiratory virus activity.

The 2020 SARS-CoV-2 pandemic renewed global interest in wastewater-based epidemiology (WBE) as a tool for monitoring public health. Molecular analyses of wastewater are often limited by the small volumes of wastewater that can be processed, due to column clogging, handling constraints, and processing time. Additionally, inhibitors in the complex wastewater matrix reduce the sensitivity of downstream assays such as RT-PCR and sequencing. To address these limitations, we developed a novel column by incorporating a hydrophobic pre-filtration layer and sequential glass fiber filters. This enhanced column design, PureBioX Xpurify Column, enables processing of 1.58 times more wastewater (a 58% increase in throughput) while reducing RT-PCR inhibitors and maintaining compatibility with existing workflows. Despite a modest reduction in nucleic acid yield, the modified column consistently improved viral RNA detection sensitivity, including for SARS-CoV-2. This accessible, scalable upgrade strengthens the utility of direct capture methods in WBE-based public health surveillance.

Electrochemical advanced oxidation processes (EAOPs) are increasingly investigated for tertiary wastewater treatment, yet their short-term effects on real wastewater effluent remain poorly constrained. This study investigated real wastewater treatment plant (WWTP) effluent following short-term boron-doped diamond (BDD) electrochemical treatment using an interdigitated double-diamond electrode (iDDE), combined with hydrochemical, stable-isotope and shotgun metagenomic analyses. After 10 min at 7.0 V and 1.02 A, physicochemical parameters and bulk carbon pools remained largely unchanged. In contrast, d18O-DO decreased by 31.3 +/-0.28 permille alongside a small increase in dissolved oxygen (0.16 +/-0.03 mmol/L), indicating substantial oxygen consistent with electrochemical oxygen generation. Shotgun metagenomics revealed pronounced restructuring of the recovered microbial DNA pool, including increases in Comamonadaceae (~12.3% vs. ~25.1%) and Zoogloeaceae (~3.3% vs. ~8.4%), whereas the oxidative-stress-defence gene repertoire showed no coherent response. These taxonomic changes cannot be interpreted as selective survival, as metagenomics do not resolve viability or intra-/extracellular DNA contribution. Overall, short-term iDDE treatment affected the microbial component of real WWTP effluent despite comparatively limited changes in bulk chemistry, highlighting that EAOP effects extend beyond conventional disinfection.

Wastewater sequencing using probe-capture enrichment of public health-relevant viruses is a powerful, non-intrusive approach to monitor infectious diseases, but performance depends on sample type and processing approach. We compared two processing pipelines from wastewater influent samples collected at three timepoints across three wastewater treatment facilities. Each sample was split for parallel processing, with viruses concentrated (1) directly from whole influent using nano-sized affinity particles and (2) by settling and dewatering the solids fraction. Combined DNA/RNA extracts were prepared into libraries using a pre-commercial Illumina protocol, then probe-capture enrichment was performed with Illumina Viral Surveillance Panel (VSP) v1 (66 targets) and v2 (242 targets). Sequencing depth was increased for VSP v2 to accommodate the expanded target set. On average, 160 +/- 146 viral species were detected per sample, with wastewater fraction (sample type) explaining 19% of the variance in viral abundances. Solids contained a higher abundance of DNA viruses compared to RNA viruses, while influent exhibited the inverse. Metagenomic analysis of the VSP v2 sequencing data detected more viral species in influent (median 358) than in solids (median 184), but probe-capture enrichment produced more comparable target-virus readouts across fractions, supporting the use of either fraction for wastewater-based epidemiology.

Antimicrobial resistance (AMR) is a major threat to modern medicine and an emerging environmental pollutant. Wastewater-based monitoring offers a scalable alternative to traditional surveillance by enabling population-level monitoring across One Health compartments. However, it remains unclear how well bacterial variants detected in wastewater reflect those causing clinical infections. Here, we assessed the prevalence of carbapenem-resistant Escherichia coli (CREC) in wastewater from Uppsala, Sweden, and compared wastewater isolates with clinical isolates from the same region and period. We observed partial overlap between wastewater and clinical CREC populations, but several common clinical variants were not detected in wastewater and vice versa. CREC prevalence was an order of magnitude higher in wastewater receiving hospital effluent, yet this did not improve concordance with clinical isolates, suggesting that hospital wastewater may artificially inflate CREC prevalence estimates. These findings indicate that wastewater CREC prevalence does not necessarily reflect the regional clinical burden. Furthermore, metagenomic sequencing of wastewater-derived E. coli populations rarely detected CREC resistance genes, suggesting that CREC abundance is below the detection limit of this approach and that targeted enrichment is required for reliable monitoring. Together, our results show that while wastewater-based monitoring can complement AMR surveillance, it should be interpreted cautiously when used as an indicator of clinical risk.

Wastewater-based epidemiology (WBE) has become a cornerstone of public health surveillance, yet routine regulatory monitoring remains largely focused on respiratory viruses and selecting antimicrobial resistant bacteria. Emerging foodborne parasites, such as Cyclospora cayetanensis, remain excluded from standard environmental surveillance panels despite increasing domestic outbreaks. This study presents a laboratory-based pilot protocol evaluating the feasibility of extracting and quantifying C. cayetanensis from urban wastewater across the Chicago metropolitan area. Using sample concentration via centrifugation and mechanical bead-beating, automated magnetic bead nucleic acid extraction, and digital PCR (dPCR), we demonstrate high-sensitivity detection in municipal influents and sewer nodes. These findings establish a proof-of-concept laboratory methodology to support the expansion of regulatory public health surveillance frameworks to include non-routine protozoan pathogens.

Wastewater is a key reservoir and transmission route for antibiotic resistance genes (ARGs), enabling their spread from influent to effluent and into receiving environments. However, how combined selective pressures (antibiotics, biocides, heavy metals, pharmaceuticals) influence resistant bacteria and ARG persistence over space and time remains poorly understood. Likewise, the role of the wastewater microbiome in ARG dynamics is still unclear, as few studies integrate microbiome shifts with chemical and environmental drivers. Here, we investigated how microbiome dynamics, chemical exposures, and environmental conditions shape clinically relevant ARG dynamics from sewage to receiving environments in Guadeloupe, French Caribbean. We analysed data collected from three wastewater continuums, (hospital-based, domestic, touristic) over four campaigns (September 2021-February 2023). We characterised ARG and microbiome composition spatiotemporal patterns and used a mixed-effect model to investigate ARG associations with potential drivers, including exposome factors, microbiome dissimilarity and environmental factors. Several ARGs were negatively associated with microbiome dissimilarity (Bray-Curtis distances) (aac(6)-Ib, aph(3)-III, blaSHV, blaTEM, intI1, qnrS, sul1 and tetM). Negative associations were also observed between upstream-downstream differences in anti-inflammatory drug concentrations and the abundance of aac(6)-Ib, aph(3)-III, blaCTX-M, ermB, intI1, and tetM. In contrast, ARG relative abundance was positively associated with upstream-downstream differences in antibiotic concentrations, suggesting selection along the continuum. These findings indicate that ARG dissemination along wastewater-to-coastal pathways is shaped by opposing processes, with microbiome turnover potentially limiting ARG persistence while chemical gradients promote specific gene enrichment. The outcome is ARG-specific, with implications for antimicrobial resistance risks associated with recreational waters, seafood consumption, and coastal ecosystem interactions.

Antimicrobial resistance poses a global health threat, yet the fate of antimicrobial resistance genes (ARGs) and microbial communities from wastewaters in receiving rivers is rarely quantified. This study investigated the degradation kinetics of ARGs in sewage-polluted river water mesocosms and under sulfamethoxazole and copper stress, separately and in combination. Seven clinically relevant ARGs (blaCTX-M, blaNDM, qnrS, sul2, tetW, ermF, and aph(3'')-Ib), a mobile genetic element marker (intI1) and bacterial marker genes (uidA, 16S rDNA) were quantified by qPCR. Bacterial community changes were monitored using 16S rDNA amplicon sequencing. All target genes decayed with approximately first-order kinetics in all conditions, with qnrS (half-life: 8.0 h) and blaCTX-M (8.4 h) declining most rapidly and intI1 (32.5 h) and sul2 (46.5 h) declining most slowly. Ordination showed that ARG composition shifted primarily over time rather than by antibiotic/metal treatment. Bacterial communities shifted from initially Campylobacterota dominated ([&ge;] 90%) to Pseudomonadota, Bacillota, and Actinomycetota dominated communities over 168 h. Quantitative microbiome profiling showed a 95% reduction in ASV richness. PICRUSt2 analysis suggested progressive enrichment of aerobic and several metabolic pathways, as the mesocosms transitioned from highly polluted anoxic conditions to oxygenated conditions. Shifts in microbial community composition were coupled with changes in the resistome as identified by Procrustes analyses (r = 0.841, p = 0.001) and other analyses. Together, results show that river self-purification can rapidly reduce ARG loads, at least in warmer climates. This study provides approximate kinetic parameters for mathematical models to predict the fate of ARGs in rivers.

The sewer microbiome is known to be the main source of process-critical bacteria involved in nitrogen and phosphorus removal in wastewater treatment plants (WWTPs) and is thus crucial for the assembly of activated sludge (AS) microbial communities. However, the extent of these bacterial loads arriving at WWTPs currently lacks scientific attention. We provide a quantitative description of the biomass loads from sewer systems to WWTPs by coupling microbial community data by 16S rRNA amplicon sequencing from the Aalborg municipality sewer catchment with mass balances and biomass production across gravity and pressure sewer systems. Accounting for microbial transformations in sewer wastewater, biofilm and sediments under steady state dry weather flow conditions, gravity sewers were found to produce higher biomass loads than pressure systems, with loads of 1.57 and 0.27 kgCOD m-3 d-1 respectively. A key feature of our sewer model is its ability to estimate biomass loads from specific taxa, such as polyphosphate accumulating organisms (PAOs). Using Aalborg West WWTP as a case study, model estimations correlated well with figures calculated based on influent wastewater data monitoring. The model-estimated loads for the PAOs Azonexus and Ca. Accumulibacter were 2.10 kg day-1 and 1.80 kg day-1, compared to the influent wastewater monitoring-based loads of 1.77 kg day-1 and 1.20 kg day-1, respectively. While providing a framework for further research on sewer microbial dynamics, this study offers a tool for gaining insights into microbial immigration, generating valuable information for improving microbial communities management in WWTPs, optimizing nutrient removal, and enhancing WWTP design.

Antibiotic resistance genes (ARGs) travel from cities into rivers and coastal waters through wastewater treatment plants. Monitoring them normally requires metagenomic sequencing, which is sufficiently costly that most utilities can sample only occasionally. Weather and location data are freely available on a daily basis for virtually any location worldwide, making them attractive predictors for identifying where limited sequencing resources should be prioritized. However, whether such models generalize to previously unseen cities remains largely untested, as most published studies train and evaluate models within the same catchments, thereby assessing interpolation rather than geographic transferability. To address this gap, we paired 235 wastewater metagenomes collected from five European cities with 23 abiotic predictors spanning geospatial, meteorological, hydrological, radiative, and temporal domains. Model performance was evaluated using leave-one-group-out (LOGO) cross-validation, in which all samples from one city were withheld for testing while the remaining cities were used for training. CatBoost achieved a median LOGO ROC-AUC of 0.929 and a median F1-score of 0.750. Using freely available environmental reanalysis predictors (meteorological, hydrological, radiative, geospatial, and temporal) - without any metagenomic sequencing - CatBoost achieved a mean ROC-AUC of 0.722, recovering 78% of the predictive performance of the full omics-integrated model. Removing latitude and longitude reduced ROC-AUC by only 0.003, whereas replacing random cross-validation with city-wise validation reduced ROC-AUC by 0.052. Predictive performance varied across ARG classes, ranging from a ROC-AUC of 0.981 for {beta}-lactam resistance genes to 0.762 for glycopeptide resistance genes. These findings demonstrate that freely available environmental reanalysis predictors - spanning meteorological, hydrological, radiative, geospatial, and temporal domains - recover 78% of the predictive signal for antibiotic resistance gene occurrence in urban wastewater, allowing scarce sequencing capacity to be directed to the catchments where it changes a decision.

Wastewater surveillance (WS) offers a non-invasive means of tracking population-level circulation of infectious agents, including viruses linked to cancer. This study provides the first Australian assessment of oncogenic viruses in municipal wastewater by screening 76 influent samples collected over four months from six wastewater treatment plants in Southeast Queensland, Australia. Ten gene targets representing seven oncogenic viruses including Epstein-Barr virus (EBV), hepatitis B virus (HBV), hepatitis C virus (HCV), human herpesvirus 8 (HHV-8), human papillomavirus 16 and 18 (HPV-16 and -18), human T-lymphotropic virus type 1 (HTLV-1), and Merkel cell polyomavirus (MCPyV) were analysed using PCR-based methods. All viruses were detected in wastewater at least once, though with substantial variation in frequency. MCPyV was the most frequently detected virus, appearing in 97.3% of samples with concentrations ranging from 3.09-3.85 log10 gene copies (GC)/50 mL, indicating widespread population exposure. HBV (26.3%) and EBV (15.8%) were detected intermittently across multiple catchments, while HPV-16/18, HHV-8, HTLV-1, and HCV were detected at the lowest frequencies (<8%). This study reports the first baseline dataset for oncogenic viruses in Australian wastewater. More broadly, positive detection of all targeted oncogenic viruses including those associated with low prevalence infections in wastewater demonstrates the potential of WS to complement existing cancer surveillance systems in tracking community-level circulation of these infectious agents.

Over the past 20 years, the Integrated Constructed Wetland (ICW) concept has been applied in Ireland for wastewater treatment, offering a nature-based solution for reducing pollutants and antimicrobial resistance genes (ARGs) in effluents and receiving environments. However, wastewater microbial communities remain inadequately characterized and their dynamics across treatment largely unexplored. Here, we present a culture-independent investigation of four Irish ICWs, aiming to advance our understanding of microbial dynamics in wastewater treatment. Shotgun metagenomic sequencing was conducted on influent and effluent samples collected in biological triplicates from four ICWs treating agricultural, companion animal, industrial, and municipal wastewater, alongside eight positive and ten negative controls for filtering and sequencing. End-to-end metagenomic analysis was performed with SqueezeMeta. Taxonomic placement of high-quality metagenome-assembled-genomes (HQ-MAGs) was confirmed with GTDB-Tk. Decontamination and statistical analyses were conducted in R. Coassembled contigs were screened for ARGs, virulence- and plasmid-associated sequences. Results revealed statistically significant shifts in taxonomic and functional profiles across treatment. Effluent populations exhibited generally higher richness than corresponding influents, yet were more similar to one another across locations. Multi-log-scale increases were observed in the relative abundance of environmental genera (Legionella, Methylotenera, Thiothrix), alongside reductions in common faecal/human indicators (Bacteroides, Lactococcus, Prevotella), and up to 80% ARGs removal. Collectively, our findings provide insights into ICW efficacy, showing that they can drive marked shifts in wastewater microbiome and ARG reduction. Our culture-independent approach retrieved 34 HQ-MAGs, including 19 potentially novel taxa, uncovering previously uncharacterized microbial diversity that is potentially unique to the Irish environment and warrants further investigation.

Wastewater-based epidemiology (WBE) has emerged as a powerful population-level surveillance tool, but its coverage is structurally concentrated in in-network urban areas, potentially leaving rural populations underrepresented. Routine human movement between sewered (in-network) and unsewered (off-network) areas may, however, cause wastewater treatment plant (WWTP) measurements to reflect infectious disease dynamics beyond sewer boundaries. We evaluated this hypothesis using daily clinical COVID-19 testing data (January 2021-April 2022) across New York State excluding New York City (NYC). We disaggregated weekly cases and tests into in-network (WWTP catchment area) and off-network (outside WWTP catchment area) components applied to two geographic frameworks: administrative counties (N = 53 mixed-coverage) and mobility-defined communities identified through Walktrap community detection applied to census tract-level movement networks (N = 32 mixed-coverage). In/off-network COVID-19 trends were strongly correlated under both frameworks. County-level statewide aggregate correlations were high (incidence r = 0.994, positivity r = 0.996), as were individual county correlations (median r = 0.909 and 0.932, respectively). Mobility-defined community-level statewide correlations were similarly strong (r = 0.990 and 0.992), with comparable unit-level medians (r = 0.877 and 0.894). The mobility-defined community framework provided better population balance between in-network and off-network strata (87.5% vs. 69.8% in balanced range) and a higher floor on representativeness (minimum r = 0.440 vs. 0.177). Population size was the dominant predictor of in-network/off-network alignment at both scales; wastewater infrastructure density and off-network signal variability provided additional explanatory power at the mobility-defined community level. WWTPs broadly represent COVID-19 dynamics in surrounding off-network populations, supporting their use as sentinel surveillance sites. Representativeness weakens in smaller, more rural communities, and mobility-defined communities provide a complementary framework for identifying where this occurs.

Coastal waters often experience fecal contamination from human (e.g., septic systems, wastewater treatment catchments and outfalls) and non-human (e.g., wildlife, domesticated pets, and livestock) origins, posing human health risks through contaminated fisheries and recreational waters. Quantitative PCR (qPCR) methods can identify and quantify fecal origins for their management by targeting Bacteroides, a dominant group of gastrointestinal bacteria with high host specificity. This study identified major contributors and drivers of fecal contamination to Mobile Bay, Alabama, an urbanized estuary in the southern United States. We utilized a multi-species qPCR source tracking approach targeting total Bacteroides and human-, pig-, dog-, cow-, and fowl-associated Bacteroides. Water samples and environmental parameters were collected over one year from 13 sites that represent a range of potential fecal inputs common to coastal waters. Human-associated feces were a consistent major contributor to the system (positive in 82% of samples), with localized inputs from pig (wild or domestic) and dog. Freshwater discharge and nitrogen constituents were good predictors of Bacteroides concentrations in this system, while areas with impervious surfaces and developed land cover may be contributing elevated fecal indicator levels. The multi-species qPCR source tracking approach described in this study can be implemented in other watersheds to support the development of better detection, identification, and fecal management strategies for community health of coastal areas.

Trace elements, such as iron, nickel, and cobalt are known to regulate methanogenic activity in anaerobic digestors used for waste valorisation, but the potential role of rare earth elements remains poorly understood. This study investigated the effects of lanthanum (La) supplementation on biogas production, methane generation, volatile fatty acid (VFA) formation, and carbohydrate utilisation in anaerobic digestion (AD). Biomethane potential (BMP) assays conducted under mesophilic conditions (37C) using methanogenic sludge granules, and glucose as substrate, were supplemented with 0.1, 1, 10, and 100 mg/L lanthanum chloride (LaCl3). Biogas production and composition was monitored over a 96-h incubation, while sacrificial, batch bioreactors were used to evaluate temporal VFA and carbohydrate profiles. La supplementation significantly enhanced biogas and methane production in a concentration-dependent manner. The highest cumulative biogas yield (478.9 mL, corresponding to 179.5 mL biogas/g COD) and methane production (285.7 mL, corresponding to 107.1 mL CH4/g COD) were observed with 100 mg/L LaCl3, corresponding to increases of 88.7% and 186%, respectively, compared with La-free controls. CO2 production also increased with La concentration, whereas hydrogen production remained comparatively low. Acetic and butyric acids represented the dominant fermentation products (80-88% of total VFAs), but profiles of accumulated VFA in the bioreactors diversified with La addition, including showing caproate production, indicating changed biodegradation dynamics in the methanogenic microbiome. These findings demonstrate that lanthanum can stimulate anaerobic digestion performance and methane generation, highlighting the potential as a novel trace element additive to enhance biogas production. Research is now required to elucidate the underlying microbial and biochemical mechanisms, and establish optimal dosing strategies for large-scale applications.

Since the COVID-19 pandemic, wastewater-based surveillance (WBS) has emerged as a key approach to assess community-level health and the evolution of pathogens. To date, most established WBS systems focus on polymerase chain reaction (PCR) based detection and targeted sequencing of known pathogens because these approaches are well-accepted and include amplification of pathogen target sequences of interest thereby enabling lower limits of detection. Metagenomic next-generation sequencing (mNGS) is a promising approach to enable pathogen detection and surveillance beyond predefined pathogen lists, but its regular application to WBS has not been yet widely adopted because many key performance characteristics are not well-understood, including limit of detection (LOD) and false positive/negative rates. This paper describes a computational analysis to estimate the operational LOD of various sequencing approaches using a simplified model of a local wastewater (WW) system involving a military base. This paper also presents findings from two types of experiments: 1) laboratory-spiked, those for which Atlantibacter subterraneus (Asub) is introduced into real-world WW samples in a laboratory setting, and 2) system-spiked, those for which Asub is introduced at a source location of a real-world WW system. Findings indicate that mNGS detection performance varies with sequencing method and the data analysis process. In addition, findings indicate that site-specific method characterization should be used when implementing mNGS for WBS because sites can have different WW system configurations, background organisms and sequencing inhibitors.

Background: Autonomous ultraviolet-C (UV-C) disinfection systems are increasingly used to supplement manual environmental cleaning, yet evidence-based guidance defining pathogen-specific UV-C dose requirements across representative surfaces remains limited. Aim: To characterize operational UV-C dose requirements for clinically relevant pathogens across diverse high-touch and healthcare surfaces and determine how experimentally derived microbial inactivation can inform operational exposure parameters. Methods: SARS-CoV-2, adenovirus, Pseudomonas aeruginosa, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, Candida auris, and Clostridioides difficile spores were exposed to defined UV-C doses on representative high-touch materials or stainless steel under standardized conditions, including a 10% fetal bovine serum organic soil challenge. Microbial inactivation was quantified by viable recovery. Dose-response analysis and operational modelling were used where supported by the experimental data. Findings: UV-C exposure significantly reduced viable recovery of all pathogens, with substantial differences in the exposure conditions associated with microbial inactivation. SARS-CoV-2 exhibited substantial inactivation at doses as low as 2.6 mJ/cm2, whereas the highest evaluated doses were 1,800 mJ/cm2 for C. difficile spores and 3600 mJ/cm2 for C. auris. For C. auris, multi-dose data estimated that approximately 1,410 mJ/cm2 was associated with a 2-log10 reference reduction, enabling distance-dependent exposure-time predictions. Conclusion: Experimentally quantified UV-C exposures produced substantial microbial inactivation across diverse pathogen classes and surfaces. Integrating delivered dose with microbial reduction provides a quantitative framework for translating laboratory efficacy into operational parameters for autonomous UV-C disinfection.