ACS ES&T Water

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.

Mass gatherings pose a concern for public health because they are associated with dense crowds, increased social interaction, and travel, all of which can facilitate the rapid transmission of infectious diseases. Wastewater and environmental surveillance (WES) were used for pathogen monitoring during the 2024 NFL Annual Player Selection Meeting (the Draft) in Detroit, MI, an event that drew an estimated 775,000 attendees. Wastewater and environmental samples were queried for respiratory viruses and clinically relevant antimicrobial resistance genes (ARG). WES did not detect an increase in the concentration of monitored respiratory viruses (SARS-CoV-2, IAV, IBV, and RSV) associated with the 2024 NFL Draft. In contrast, WES detected a transient increase in carbapenemase targets in wastewater, primarily driven by a fourfold increase in blaOXA-48. Resistome structure in wastewater was dominated by sampling site characteristics rather than changes associated with the event. The Draft weekend coincided with rainfall-driven combined sewer overflow (CSO), potentially allowing the dissemination of ARG to the environment. In surface waters receiving wastewater effluent, an increase in detection frequency and normalized concentrations for multiple ARG were observed following the Draft. WES provided an overview of pathogen prevalence before, during, and after a large-scale gathering, showing how it can warn of emerging health risks in near real time.

Mass gatherings pose significant public health risks by facilitating the spread of infectious diseases. While wastewater-based surveillance (WBS) has been widely used to monitor pathogens in high-income settings, its use as a practical, multi-pathogen surveillance tool during mass gatherings in low- and middle-income countries remains limited. This study aimed to assess the operational feasibility, epidemiological significance, and public health utility of multi-pathogen WBS during the African Nations Championship (CHAN) football tournament in Uganda. Wastewater surveillance was conducted at Mandela National Stadium during eight match days in August 2025. Moore swabs were deployed at 38 manholes receiving wastewater from different toilet facilities across the stadium to capture representative wastewater samples. Samples were processed using Nanotrap(R) microbiome virus particles to concentrate pathogens, followed by nucleic acid extraction. Samples were analyzed for multiple enteric and respiratory pathogens, including Mpox, using quantitative PCR (qPCR). Descriptive analyses were performed to characterize pathogen detection patterns, positivity rates, and temporal distribution across surveillance sites. A total of 304 wastewater samples were collected and analyzed, of which 259 (85.2%) tested positive for at least one pathogen. Multiple pathogens were consistently detected across sampling days, with enteric pathogens predominating, particularly Shigella spp. (53.6%), Rotavirus A (35.9%) and Enterovirus (32.2%). The mpox virus was also detected in a notable proportion of samples (28.6%) across several sampling days. Respiratory pathogens, including SARS-CoV-2 (11.8%) and Influenza B (8.2%), were identified intermittently at lower frequencies. Pathogen diversity varied over time, with up to eight pathogens detected on a single day, and co-detection of multiple pathogens observed in the majority of positive samples. Cq value distributions further demonstrated variability in detected signal patterns across pathogens. Surveillance findings informed real-time public health interventions, including sanitation reinforcement, intensified hygiene promotion, environmental disinfection, and targeted risk communication, strengthened syndromic surveillance with on-site triage, and targeted environmental health assessments of food handling and wastewater infrastructure. These findings demonstrate the operational feasibility and public health utility of integrating multi-pathogen wastewater-based surveillance into mass-gathering preparedness and response frameworks in low-resource settings. By capturing diverse pathogen signals and informing targeted interventions during the CHAN football tournament, WBS can provide actionable population-level insights that can support outbreak preparedness and response. Scaling WBS within national preparedness systems could strengthen epidemic intelligence, enhance early warning capacity, and support data-driven public health decision-making during future mass gatherings and emerging infectious disease threats.

To understand the utility of healthcare facility-level wastewater surveillance (WWS) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is important to correlate wastewater SARS-CoV-2 RNA detection with the number of clinical infections. WWS for SARS-CoV-2 was performed at three skilled nursing facilities (SNFs) over 25 weeks. Electronegative membrane filtration (enMF) and Nanotrap(R) Magnetic Virus Particles (NP) virus concentration methods were compared. Extracts were tested by droplet digital polymerase chain reaction. Spearman's correlations ({rho}) between wastewater virus RNA concentrations and infection counts were calculated. From split wastewater samples, enMF recovered higher SARS-CoV-2 RNA concentrations than NP. Combining data from all facilities, the median concentrations were 53.0 versus 38.6 gc/100 mL for enMF and NP, respectively (p=0.001). Using enMF, correlations were moderate to strong at SNF A ({rho} ranged 0.67 to 0.86, all p-values <0.001). Weak to moderate correlations can be explained by the sampled manhole not representing the entire facility (SNF B, {rho} ranged 0.47 to 0.72, p-values ranged <0.001 to 0.12) and longitudinal data gaps from summer heat and equipment maintenance (SNF C, {rho} ranged 0.14 to 0.59, p-values ranged 0.52 to <0.01). WWS can be a valuable tool for tracking dynamics of SARS-CoV-2 infections in healthcare facilities.

Groundwater contamination presents challenges across world, yet remediation solutions in variably oxidized regions are limited and many co-interactions between contaminant metals and microbial reactions occur. Here we present a genomic and metabolic study into the biogeochemistry of a uranium-contaminated surficial aquifer site in Riverton, WY. We identified unique communities that varied based on geochemistry, geography, and compartment, matching microbial subsurface studies. Cross-site metabolism tests showed communities had functional capabilities of nitrogen respiration, manganese reduction, iron reduction, and sulfide oxidization. No sites showed evidence of microbial U-bioreduction nor ammonium oxidation. Only former tailings area groundwater and ditch surface water sites nearest a retention pond, and a downgradient oxbow lake exhibited sulfate reduction metabolisms. This was contrary to our hypothesis of near-river downgradient groundwater sites having U and S reduction capability. Most communities which showed S reduction capacity exhibited Fe oxidation capacity. Modeling demonstrated U as calcium uranyl carbonates. Based on our metabolism tests and known mineral and microbial metabolism reduction potentials, this suggests U reduction could only be achieved via abiotic reaction with biogenic sulfide. Of eleven sites tested, it is possible in four. This has impact on future site-specific remediation plans and understanding of microbial reactions in variably reduced zones. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/729369v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@1926923org.highwire.dtl.DTLVardef@13486f9org.highwire.dtl.DTLVardef@1895a91org.highwire.dtl.DTLVardef@990213_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical Abstract TextWe performed microbial membership and metabolism measurements across a uranium-contaminated sites surface and ground waters, then performed analyses relating these metrics to geochemistry at the site. Findings showed variations in the membership, yet mainly similar functional capabilities. Metabolic differences were explained in relationship to uranium cycling and remediation implications.

Withdrawal statementThe authors have withdrawn this manuscript as appropriate permission from the sample providers was not obtained prior to submission. This oversight was unintentional, and we sincerely apologise for the error. We are currently in the process of contacting the relevant sample providers to seek the necessary permissions and will address this matter accordingly. In the meantime, the authors request that this work not be cited as a reference. For any questions or further information, please contact the corresponding author.

Sewer biofilms represent dynamic interfaces for exchange of bacteria and antibiotic resistance genes between biofilms and the overlying wastewater. Using inline, biofilm reactors, the movement of bacteria and 16S rRNA and carbapenemase genes (blaKPC, blaVIM, blaNDM, blaOXA-48-like, and blaIMP) between wastewater and sewer biofilms was investigated. Established, complex biofilms without these {beta}-lactamase (bla) genes, absorbed resistant bacteria within two minutes of exposure to high concentrations of resistant cultures in lab settings. Carbapenem-resistant organisms from these high-concentration source biofilms transferred to downstream biofilms over 60 minutes of representative sewer shear flows. Mass balances of bacteria and genes in biofilms versus wastewater under representative shear flow showed that biofilms exposed to resistant cultures contributed more to the wastewater than to the downstream biofilms. In field studies, established, complex biofilms without target carbapenem-resistant bacteria and genes from wastewater within hours and then stabilized between 2 to 15 days, not varying by more than 0.5 MPN/cm2 or 0.5 log gene copies (GC)/cm2. In contrast, metagenomic profiles of the bacterial community species continued to change up to 21 days. Established biofilms with resistant bacteria and genes exposed to tertiary-treated wastewater without target carbapenemase genes or meropenem antibiotics did not lose resistant genes or bacteria over nine days of exposure (i.e., < 1 log GC/cm2 reduction). Results show that sewer biofilms contribute to the resistance-gene signal found in sewer wastewater by absorbing and releasing bacteria and genes. Consideration of sewer biofilm dynamics is essential for more accurately interpreting wastewater bacterial concentrations in wastewater-based epidemiology studies. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/726639v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@19f6ce0org.highwire.dtl.DTLVardef@1a507c8org.highwire.dtl.DTLVardef@1a2013dorg.highwire.dtl.DTLVardef@ff8613_HPS_FORMAT_FIGEXP M_FIG C_FIG

Wastewater genomic surveillance provides an opportunity to detect human and animal influenza A virus (IAV). We aimed to implement an IAV genomic surveillance framework agnostic to subtype, which enables recovery of IAV from multiple hosts and estimation of proportions across subtypes. We conducted IAV genomic surveillance in wastewater during the 2024-2025 flu season at multiple sites in California and compared these data with available human clinical IAV sequences and test positivity. We applied a custom whole-genome, multi-host IAV probe enrichment panel and adapted our custom expectation-maximization (EM) algorithm to deconvolute IAV mixtures in wastewater and infer subtype relative abundances. Absolute IAV concentrations were quantified using RT-PCR-based assays. H5N1 wastewater and clinical sequences were further characterized by constructing a whole-genome maximum-likelihood phylogenetic tree. Finally, we performed variant analysis to examine amino acid substitutions detected in wastewater. Our IAV probe enrichment method and EM algorithm successfully enriched all eight segments of three circulating IAV subtypes and accurately estimated subclade relative abundances for mixed IAV samples. Seasonal human H1N1pdm09 and H3N2 were detected throughout the study period from both wastewater and clinical sequencing data, with H1N1 subclades 6B.1A.5a.2a.1 and 6B.1A.5a.2a co-circulating, and H3N2 dominated by subclade 3C.2a1b.2a.2a.3a.1. Wastewater surveillance consistently detected H5N1 clade 2.3.4.4b across three monitored wastewater sites, while clinical H5N1 detections, from anywhere in CA, were sporadic and rare. Whole-genome phylogenetic analysis revealed that wastewater H5N1 sequences clustered with reference sequences associated with dairy cow and avian infections, while all human clinical H5N1 sequences clustered exclusively with reference sequences associated with dairy cow infections. Amino acid substitutions were identified across viral segments, and no mutations associated with mammalian adaptation were observed from wastewater samples.

Oil sands process-affected water (OSPW) contains complex mixtures of naphthenic acids (NA) that are the central targets for water treatment and reclamation. Here, we compared three whole-cell bacterial NA biosensors with Orbitrap mass spectrometry (MS) for quantifying NA remediation in greenhouse mesocosms and a pilot-scale constructed wetland. Solid-phase extracts from both systems were analyzed in parallel by Orbitrap MS and biosensor assays, enabling direct comparison of biosensor-derived NA estimates with MS-derived naphthenic acid fraction compounds (NAFC) concentrations. Across both treatment systems, biosensor outputs broadly tracked declines in NAFC measured by Orbitrap MS, and positive linear relationships were observed between methods. Biosensor 2 (3680-lux) and biosensor 3 (atuA-lux) showed, early rapid decreases in NA, whereas biosensor 1 (marR-lux) frequently remained elevated later in treatment. These differences are consistent with the distinct chemical response profiles of the biosensor panel and suggest that biosensor outputs reflect shifts in NA mixture composition as remediation proceeds. This interpretation is supported by the published Orbitrap analysis of the constructed wetland, which showed decreasing O2-NAFCs and increasing O3/O4-containing species, consistent with oxidative degradation. Together, these results support the use of NA-responsive biosensors as rapid and scalable complementary tools for tracking remediation trends, while Orbitrap MS remains the reference method for molecular-level characterization.

Influenza A viruses (IAV) remain a persistent One Health threat, and whole-genome sequencing from wastewater offers a promising surveillance tool. However, IAV is at low abundance in wastewater, making it difficult to sequence. We benchmarked four targeted enrichment methods suited for whole-genome sequencing including custom and off-the-shelf amplicon and probe-based methods. Our custom HA tiled-amplicon panel was sensitive, fast, and cost-effective, making it suitable for monitoring low-abundance seasonal variants of known subtypes. However, its reliance on conserved and intact primer-binding sites limited primer design to fewer subtypes. A previously published universal amplicon method targeted all IAV subtypes, but it performed poorly in wastewater due to its reliance on intact genome segments. Probe-capture methods were resilient to RNA degradation and mismatches, potentially enabling broader surveillance and detection of emerging strains. However, probes were costly, labor-intensive, and less sensitive than tiled-amplicon. When testing compatibility of sequencing methods with upstream virus concentration and extraction methods, ultrafiltration-based virus concentration outperformed large-volume direct extraction with all four sequencing methods. This set of benchmarking comparisons and custom panels provides needed information for the translation of IAV genomic sequencing into a routine component of wastewater surveillance.

O_LIHeavy metals are pervasive environmental contaminants that can impair the health of organisms globally. As the largest anthropogenic source of the potent neurotoxin mercury (Hg), gold mining has amplified these threats throughout the tropics. Consequently, there is a mounting need to monitor Hg contamination of the richest biological communities on Earth. Venous whole blood provides a reliable, nonlethal measurement of recent dietary and site-derived contamination, but collecting and cold-storing samples can be impractical in field conditions. C_LIO_LITo overcome these challenges, we developed and evaluated a method to assay Hg exposure in vascular organisms by measuring the volume of dried blood spots (DBS) in the field, which can be stored at ambient temperatures until analysis. We explored the methods precision and accuracy in estimating whole blood Hg concentrations by collecting paired whole blood and DBS aliquots from birds (n = 527 individuals, 140 species) along a trophic gradient (i.e., granivores to piscivores) in Belize and Peru. C_LIO_LIUsing a Bayesian linear mixed-effects model, we found a highly precise and unbiased relationship between DBS and whole blood total Hg concentrations that was centered at perfect unity (R2 = 0.99; {beta} = 1.00 {+/-} 0.03; 95% CrI: 0.95-1.05). Agreement between individual paired aliquots was more variable, in which approximately 12% of DBS containing at least 1 ng THg differed from whole blood by more than {+/-}20%. However, DBS accuracy increased at higher THg concentrations, suggesting that disagreement at low concentrations is an expected consequence of higher measurement error near the analytical limit of detection of our instruments. C_LIO_LICompared to whole-blood collection and analysis workflows, DBS offer substantial logistical advantages by eliminating cold-chain dependence and reducing transport burden, laboratory handling time, and overall operational costs. Consequently, volume-measured DBS provide a practical and highly reliable alternative for monitoring Hg contamination in both humans and wildlife, particularly for ecological and population-level applications in remote and resource-limited environments. C_LI

Acid mine drainage is a global pollution problem characterized by low pH and high concentrations of metals. Active remediation is often cost-prohibitive, but Fe(II) oxidizing microbes may be used for passive bioremediation. To leverage these species, we must understand the factors that control their distribution. Here, we examine the environmental and ecological factors that control these species with the aim of determining if microbial seeding is a viable remediation strategy. Although stochastic processes appear to control the distribution of majority of taxa inhabiting AMD ecosystems, the distribution of Fe(II) oxidizers is driven by environmental filtering and competition. The abundance of all the major Fe(II) oxidizing genera have significant relationships with pH, with pH explaining 10 - 38% of the variation in their abundance. The genera appear to have pH preferences with Acidithiobacillus and Leptospirillum preferring environments below pH 3, Gallionella, Sideroxydans, and Ferritrophicum preferring environments above pH 3.5, and Ferrovum preferring intermediate pH environments. Once the effect of pH is removed, genera that share pH preferences are negatively correlated, indicating that they are likely competing for the Fe(II) oxidizing niche in their preferred environments. Communities are also shaped by dispersal limitation, which suggests that microbial seeding is possible in these environments. Future seeding attempts should consider species interactions and ecology more generally to inform their efforts.

Ballast water (BW) is an important vector for the global translocation of bacterial taxa, including pathogens. Legal frameworks establishing limits on the discharge of live organisms into recipient environments have been designed to reduce risks of microbial invasions, and understanding the impacts of BW management on bacterial communities is critical for assessing the effectiveness of these practices. Here we evaluate changes in bacterial communities associated with both BW treatment (BWT) and a combined management approach of BWT plus BW exchange (BWT+E). Samples were collected on two experimental voyages designed specifically to compare BW-associated biota before and after management. Microbial community structure and inferred function were assessed based on high throughput sequencing of 16S rRNA amplicons, and bacterial indicator taxa E. coli and enterococci were analyzed using targeted qPCR. As expected, both BWT alone and BWT+E dramatically changed bacterial communities, with the latter resulting in the largest overall decreases in bacterial diversity. Increases in Gammaproteobacteria, especially in the genus Pseudomonas, were particularly notable, with concomitant decreases in Alphaproteobacteria and Bacteroidia. Shifts in predicted bacterial function associated with BWT were similar for both voyages, despite significant differences in community structure, and may represent selection for r-strategists capable of active regrowth after BWT. qPCR estimates of indicator taxa were similar to those obtained through standard culture methods but may offer increased sensitivity for detecting changes associated with management. Our results indicate that BW management is effective at reducing bacterial communities but suggest that further research is needed to understand risks associated with taxa that may survive BWT.

BackgroundRising global temperatures and eutrophication are increasing the intensity and frequency of cyanobacterial harmful algal blooms that release toxins including microcystin-LR (MC-LR). MC-LR inhibits protein phosphatases in the human liver and brain, but its accumulation in the placenta is unclear. Placental transporter expression varies across pregnancy and is influenced by physiological cues, such as low oxygen concentrations which activate HIF1A, and trophoblast cell fusion forming syncytiotrophoblasts that engage CREB-driven transcription. This study examined whether MC-LR accumulates in placental cells, which transporters mediate uptake, and how these transporters are regulated by HIF1A and CREB. MethodsIntracellular accumulation of MC-LR (0.1-10 {micro}M, 3 hour) was measured in human cytotrophoblasts (JAR, BeWo) and extravillous trophoblasts (HTR-8/SVneo) by western blotting for MC-LR-adducted proteins. Organic anion transporting polypeptide (OATP) involvement was tested using cyclosporin A (10 {micro}M), an OATP inhibitor, before exposure to the OATP substrate or MC-LR. Cells were also cultured under 3%, 8%, or 20% O2 to induce hypoxic responses or treated with forskolin (a potent intracellular cAMP inducer) to stimulate cell fusion before MC-LR exposure. ResultsMC-LR accumulated in all three placenta cell lines in a concentration-dependent manner. Cyclosporin A reduced MC-LR uptake by 57% in JAR cells, confirming OATP-mediated transport. Low O2 increased OATP4A1 expression and function but reduced protein phosphatase expression, decreasing MC-LR-bound proteins by 52-72%. Forskolin increased OATP4A1 expression and enhanced MC-LR uptake >2.5-fold. ConclusionMC-LR enters placental trophoblasts via active OATP transport, likely OATP4A1, and uptake increases under hypoxia and trophoblast fusion.

Background: Beaches are popular summertime destinations in Canada. However, they can be affected by specific fecal pollution sources, increasing the risk of recreational water illness. Objectives: This study was conducted to determine the risks of acute gastrointestinal illness (AGI) among Canadian beachgoers and to evaluate the influence of different fecal indicator bacteria (FIB) and other water quality measures on assessing these risks. Methods: In a prospective cohort design, beachgoers were recruited at sites across Canada from 2023 to 2025. Sociodemographic characteristics and exposures were determined through an on-site survey, with a 7-day follow-up survey to determine risks of AGI. Bayesian mixed-effects logistic regression models were fitted to evaluate the effects of an ordinal water contact variable (no contact, minimal contact, body immersion, and swallowed water) on the incident risk of AGI, with an interaction included for water quality indicators. The levels of six FIB and water quality measures were assessed: Escherichia coli, enterococci DNA, three microbial source tracking DNA markers (human HF183/BacR287, human mitochondria, seagull Gull4), and turbidity. Results: A total of 4085 participants were recruited, with 67.6% completing the follow-up survey. The overall incident risk of AGI was 2.6%. Both swallowing water and body immersion increased AGI risks compared to no water contact: median of 20 excess cases (95% Credible Interval [CrI]: 4, 64) and 5 excess cases (95% CrI: 1, 19) of AGI predicted per 1000 beachgoers, respectively. Escherichia coli and seagull DNA marker levels were associated with AGI among those who had water contact, particularly among those who reported swallowing water. Discussion: While the overall burden of AGI due to beach water contact in Canada was low, increased risks are associated with E. coli levels particularly among those who swallow water. This could be related to fecal contamination from seagulls. However, there is substantial uncertainty in the predicted effect sizes.

Wastewater-based surveillance (WBS) is increasingly used to monitor infectious disease dynamics, yet most evaluations focus on correlation or forecasting - neither of which directly assesses whether wastewater signals can identify the epidemiological events most relevant to public health decision-making. We argue that outbreak onset and epidemic peak detection are the operationally critical use cases of WBS, requiring a fundamentally different evaluation framework. We introduce a classification-based framework that treats WBS as an event-detection problem, defining outbreaks and peaks as discrete events, establishing detection intervals to account for timing uncertainty, and incorporating censoring and data completeness criteria for valid comparisons against imperfect clinical reference outcomes. Within this framework, we apply a Bayesian exponential growth model for outbreak detection - benchmarked against a standard reproductive number (Rt)-based method - and a rule-based algorithm for peak detection, evaluating performance via sensitivity and positive predictive value (PPV). Applied to county-level SARS-CoV-2 wastewater data from 281 U.S. counties (Biobot, 2021-2024), the exponential growth approach substantially outperforms the Rt-based baseline: sensitivity 0.82 and PPV 0.64 versus sensitivity 0.58 and PPV 0.19 for the best-performing Rt variant. Peak detection achieves sensitivity 0.84 and PPV 0.70 at the county level. Both peak and outbreak detection achieve strong and consistent performance against hospitalizations and deaths at the state level. Spatial aggregation yields a statistically significant improvement in peak detection PPV against a curated reference standard ($p < 0.001$), while outbreak detection improvements under aggregation are directionally consistent but not statistically significant. Wastewater leads case-defined outbreaks by 4-6 days but minimally leads epidemic peaks, consistent with wastewater approximating prevalence rather than incidence. These findings demonstrate that wastewater signals can reliably detect outbreak onset and epidemic peaks across spatial scales and clinical outcomes, and that the choice of detection method matters substantially in practice. The classification framework developed here provides a reusable and principled tool for evaluating any surveillance signal as an event-detection system, with direct relevance to how WBS is actually used in public health decision-making.

Enteroviruses (EVs) are ubiquitous contaminants of surface waters, where they can remain infectious for long periods of time. Most methods used for EV monitoring are unable to distinguish between infectious and non-infectious particles or between EV types. Because different types exhibit both distinct environmental persistence and health implications, there is a need for type-resolved infectivity measurements. Here we developed Integrated Cell Culture-Nanopore Sequencing (ICC-NanoporeSeq), a method combining short-term cell culture amplification with Nanopore sequencing of the VP1 gene. The ICC approach was adapted from a previously described ICC-RTqPCR protocol, while the NanoporeSeq workflow was derived from a clinical EV typing protocol and optimized for environmentally circulating EV types. Using samples containing known concentrations of ten EV types, the NanoporeSeq method accurately and reproducibly recovered the original proportions of all EV types after correction of biases. Furthermore, type-specific calibration curves generated with ICC-NanoporeSeq enabled quantification of the infectious concentrations of six EV types, allowing a simultaneous and type-resolved assessment of infectivity in mixed samples. Overall, ICC-NanoporeSeq provides a scalable approach for the parallel analysis of multiple EV types. Compared with the predecessor ICC-RTqPCR method, it eliminates the need for multiple type-specific PCR primers and can therefore be readily expanded to include additional EV types. IMPORTANCECurrent methods used to detect EVs in environmental samples generally measure viral genome copies without determining whether viruses remain infectious, limiting their use in public health risk assessment or water quality monitoring. At the same time, available infectivity assays are often labor-intensive and cannot distinguish between different EV types. Here, we developed ICC-NanoporeSeq, a method combining cell culture and Nanopore sequencing to simultaneously quantify the infectious concentrations of multiple EV types in samples containing mixed EV populations. The method provides an efficient and scalable approach for studying EVs in complex environmental matrices. ICC-NanoporeSeq has potential applications in wastewater-based epidemiology, environmental surveillance, and disinfection studies, where understanding the persistence of different EV types simultaneously is crucial.

BackgroundCase-based infectious disease surveillance is subject to ascertainment bias when testing intensity varies across time and population subgroups. We previously developed a regression-based test adjustment methodology using Standardized Testing Ratios (STRs) to correct for differential testing patterns in COVID-19 surveillance data. Wastewater-based surveillance (WWS) measures viral burden in the community independently of diagnostic testing behavior, making it a valuable external validation tool for test-adjusted case estimates. MethodsWe analyzed 111 weeks of paired wastewater and case surveillance data from Ontario, Canada (July 19, 2020 to August 28, 2022). Wastewater SARS-CoV-2 signals from 107 sewersheds across 34 public health units were normalized within sewersheds and aggregated using population-weighted averages. We compared wastewater correlations with crude reported and test-adjusted case counts using Spearman rank correlations, linear regression, and negative binomial distributed lag nonlinear models (DLNM), stratified by epidemic period. ResultsTest-adjusted cases correlated substantially more strongly with wastewater signals than crude reported cases overall (Spearman {rho} = 0.849 vs. 0.679; linear R{superscript 2} = 0.609 vs. 0.191). The advantage of test adjustment was greatest during the Omicron wave, when population-level diagnostic testing contracted sharply following PCR eligibility restrictions ({rho} = 0.924 vs. 0.604; R{superscript 2} = 0.815 vs. 0.470). DLNM incorporating the wastewater signal explained substantially more variance in test-adjusted than crude reported cases (McFadden pseudo-R{superscript 2} 0.898 vs. 0.776), despite similar lag-response structure for both outcomes. ConclusionsWastewater surveillance provides compelling independent validation of a previously described test adjustment methodology for COVID-19 case surveillance. The agreement between wastewater signals and test-adjusted cases was strongest precisely when testing scarcity was most severe, supporting the use of test adjustment to recover accurate infection dynamics from case surveillance data during periods of changing testing access and policy.

The 18S rRNA gene has emerged as the primary molecular marker for amplicon-based characterization of microalgal communities, including in wastewater treatment systems, yet trade-offs between short- and long-read approaches remain poorly defined. We systematically compared V8-V9 short-read sequencing (Illumina MiSeq), full-length long-read sequencing with ss5ss3 primers (PacBio Sequel II), and computationally extracted V8-V9 regions from long-read data. Both in silico and in vitro analyses confirmed V8-V9 captured broader taxonomic coverage than ss5ss3, though partial reference sequences and taxonomic mis-annotations biased in silico assessments. Long-reads taxonomic advantage was database-dependent, constrained by SILVA databases genus-level curation but fully realized when paired with the species-level-curated and eukaryotes-focused PR{superscript 2} (Protist Ribosomal Reference) database. Long-read sequencing uniquely identified amplicon sequence variants (ASVs) assigned to key phosphorus assimilators (Scenedesmus obliquus, Desmodesmus sp., and Acutodesmus sp.) at species level during successful phosphorus removal in a full-scale microalgal cultivation system, while V8-V9 short-read sequencing revealed ASVs assigned to algal-predatory (Leptophryidae) and bacterivorous (Choanoflagellata and Rhogostoma-lineage) protists when performance declined, suggesting grazing pressure on the phosphorus-removing community. Although both approaches performed comparably for operational monitoring, these complementary strengths support short-read sequencing for routine community profiling and long-read sequencing for detailed functional investigations of Chlorophyta.

Numerous studies have shown that the abundance of antibiotic-resistant bacteria (ARBs) or antibiotic-resistance genes (ARGs) in soil increases after irrigation with wastewater. However, it is unclear whether this increase is due to the selection effects of pharmaceutical residues in the irrigation water or the continuous introduction of ARBs and ARGs with the wastewater. Further, it is unclear how the binding of antibiotics to natural colloids (1-1000 nm) affects their biological effects compared to truly dissolved substances (< 1 nm). We conducted competition experiments with resistant and susceptible Acinetobacter baylyi BD413 strains in wastewater, as well as in colloidal and truly dissolved extracts of soils irrigated with wastewater. Although the concentrations of our six target antibiotics were far below the measured minimum selective concentrations of the tested strains, we demonstrate that the resistant strain was favored in the wastewater and the colloidal extracts. In contrast, the truly dissolved fractions exhibited weaker and more variable selective effects. A non-targeted analysis revealed the presence of 82 additional substances in our extracts, including further antibiotics, pesticides, and different non-antibiotic drugs that may influence the selection of our resistant A. baylyi BD413 strain. Our findings suggest that antibiotic resistance is selected for in wastewater and wastewater-irrigated soils. This cannot be explained by antibiotic concentrations alone, but may also arise from the effects of complex mixtures of co-occurring contaminants, particularly those associated with colloidal particles.