Environmental Science: Water Research & Technology

This study focused on the development of machine-learning- (ML) based strategies for mitigating nitrous oxide (N2O) emissions from various wastewater treatment systems in the United States measured using a benchmark USEPA-endorsed protocol. Results revealed that in general, poor process performance correlated with higher N2O emissions. Specifically, local variables including zone-specific dissolved oxygen, ammonia, and nitrite concentrations and global variables including effluent nitrite and nitrate concentrations contributed positively towards N2O emissions from both aerobic and anoxic zones of the process bioreactors. The optimal operational conditions identified for minimizing N2O emissions included operation of aerobic and anoxic zones at DO < 4 mg O2 L-1 and < 1 mg O2 L-1, respectively, coupled with appropriate solids retention times (SRTs) that maximize process performance. Accordingly, our results strongly underscore the utility of ML models in combination with bioprocess fundamentals for predicting and mitigating N2O emissions, while concomitantly optimizing wastewater treatment operations.

Source-separated organics (SSO) are widely processed via anaerobic digestion to produce biogas, yet alternative conversion pathways could generate higher-value products. Here, we demonstrate long-term continuous production and recovery of medium-chain carboxylic acids (MCCAs) from SSO via microbial chain elongation using a bench-scale anaerobic bioreactor operated for 911 days. The reactor was fed with SSO samples collected from two full-scale municipal organics processing facilities in Toronto, Canada, capturing facility-specific and seasonal variability in SSO composition. MCCA production depended strongly on the availability of lactate as an electron donor, which varied with SSO preprocessing operations and outdoor collection temperatures. To mitigate product inhibition, an in-line extraction system using hollow-fiber polydimethylsiloxane (PDMS, also known as silicone) membranes was integrated with the anaerobic membrane bioreactor, providing a robust and solvent-free alternative to solvent-based extraction methods. Maximum MCCA yields reached 0.31 g MCCA/ g VSfeed, with notable octanoic acid production (up to 20% of total MCCA), and production rates up to 0.84 g L-1 d-1. Acidification of the alkaline extract produced a phase-separated MCCA-rich oil ([~]95% purity) without addition of downstream separation steps. Microbial community analysis of the reactor revealed enrichment of putative chain-elongating bacteria, including Eubacterium and Pseudoramibacter species, while shifts in SSO feedstock microbiomes influenced substrate availability and product spectra. These results demonstrate the feasibility of sustained MCCA production from municipal organic waste streams and highlight opportunities to integrate chain elongation with existing anaerobic digestion infrastructure.

Despite the wide adoption of wastewater surveillance, more research is needed to understand the fate and transport of viral genetic markers in wastewater. This information is essential for the interpretation of wastewater surveillance data and the development of mechanistic models that link wastewater measurements to the number of individuals shedding virus. In this study, we examined the solid-liquid partitioning behavior of four viruses in wastewater: SARS-CoV-2, respiratory syncytial virus (RSV), rhinovirus (RV), and F+ coliphage/MS2. We used two approaches to achieve this: we (1) conducted laboratory partitioning experiments using lab-grown viruses and (2) examined the distribution of endogenous viruses in wastewater. Partition experiments were conducted at 4{degrees}C and 22{degrees}C; wastewater samples were spiked with varying concentrations of each virus and stored for three hours to allow the system to equilibrate. Solids and liquids were separated via centrifugation and viral RNA concentrations were quantified using reverse-transcription-digital droplet PCR (RT-ddPCR). For the distribution experiment, wastewater samples were collected from six wastewater treatment plants and processed without spiking exogenous viruses; viral RNA concentrations were measured in wastewater solids and liquid. Overall, RNA concentrations were higher in solids than the liquid fraction of wastewater by approximately 3-4 orders of magnitude. Partition coefficients (KF) from laboratory experiments were determined using the Freundlich model and ranged from 2,000-270,000 ml{middle dot}g-1 across viruses and temperature conditions. Distribution coefficients (Kd) determined from endogenous wastewater viruses were consistent with results from laboratory experiments.Further research is needed to understand how virus and wastewater characteristics might influence the partition of viral genetic markers in wastewater. SynopsisWe examined the solid-liquid partitioning behavior of SARS-CoV-2, RSV, RV, and F+coliphage/MS2 RNA in wastewater influent. Overall, partition/distribution coefficients were similar across viruses and temperature conditions.

Worldwide efforts to depollute environments altered by human industrial activity have begun to produce an ever-increasing number of "clean" sites. "Clean" is defined by local regulatory processes and often responds to low compound concentrations or risk evaluations. Yet, these sites have been critically derailed from their historical biological activity by both the pollution event and the clean-up technology. This work explored the impact of contaminated (and remediated) sites on local microbial ecosystems. Different parcels of the same field site with the same relatively uniform microbial ecology were polluted and cleaned-up over the last 15 years. The statistical evaluation of the perturbation described changes to the local ecosystem that went back to the original baseline microbial composition although the pollution sources and the clean-up technologies affected the rate of return to the pre-disturbed condition. This rate reflected the intensity of the clean-up treatments. The role played by microbial communities on ecosystem maintenance and mitigation of pollution events lays the groundwork for predicting the microbial community responses to perturbations and the ability to reassert themselves. Predictions of ecosystem response to anthropogenic impacts could support decision-making on environmental management strategies for contaminated sites clean-up, depending on the ecosystem services desired to maintain or the risk posed to sensitive receptors.

The rapid emergence of novel antibiotic resistance genes (ARGs) decreases the effectiveness of empirical antibiotic treatment for pathogen infections. Environmental ARG surveillance is an early-warning approach that can better inform antibiotic usage. Quantitative polymerase chain reaction (qPCR) is widely used for ARG surveillance because qPCR is easy, fast, and highly sensitive. However, it can only identify DNA targeted by pre-selected primers. In contrast, metagenomic sequencing can identify ARGs agnostically. However, sequencing is more expensive, less sensitive, and requires lengthy analysis of complex data sets. Target-enrichment metagenomic sequencing (TEMS), a method we developed previously, can mitigate the disadvantage of low sensitivity of traditional metagenomic sequencing. In this study, we propose a hybrid ARG surveillance pipeline for wastewater that capitalizes on both TEMS and qPCR. It uses a large-scale target determination of ARGs by using TEMS, followed by fine-scale routine surveillance of ARGs using qPCR. To connect the two steps, we developed a primer design tool, MSEDAP, which automatically analyzes metagenomic sequencing data and designs qPCR primers for ARG surveillance. This new workflow was ground-truthed using wastewater samples. TEMS identified seventeen {beta}-lactamase gene targets of potential clinical importance. qPCR validated their presence and abundance using primers generated by MSEDAP. SynopsisA metagenomics-qPCR hybrid scheme can be used for environmental surveillance of antibiotic resistance genes (ARGs) to discover emerging ARGs originating from communities. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/665820v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@625e06org.highwire.dtl.DTLVardef@ae34aorg.highwire.dtl.DTLVardef@d03e7borg.highwire.dtl.DTLVardef@1b625a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Wastewater surveillance offers an objective, comprehensive, and cost-effective means of monitoring the prevalence and genomic heterogeneity of pathogens circulating in a community. Here, a novel two-step extraction procedure for the direct capture of SARS-CoV-2 RNA from raw wastewater is presented. Combined with reverse transcription-droplet digital polymerase chain reaction (RT-ddPCR) detection, the method provides a fast and sensitive method for measuring viral RNA concentrations in wastewater. The method was used to measure the concentration of SARS-CoV-2 RNA in daily samples of wastewater entering a major metropolitan wastewater treatment plant over the course of 32 months, from November 2020 through June 2023. In addition, targeted mutation assays were used with RT-ddPCR to characterize the evolving presence and prevalence of specific SARS-CoV-2 variant sub-lineages in the wastewater stream over time. The results demonstrate the utility of these methods to accurately measure the total load of SARS-CoV-2 RNA, and chronicle its evolving variant composition, in wastewater treatment plant influent, providing near-real-time characterization of COVID-19 disease prevalence and trends in the served community. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/24311866v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1789a8aorg.highwire.dtl.DTLVardef@e9ec37org.highwire.dtl.DTLVardef@1933133org.highwire.dtl.DTLVardef@831134_HPS_FORMAT_FIGEXP M_FIG C_FIG

Coffee processing wastewater (CPW), a byproduct of agro-industrial operations, contains high organic loads alongside recalcitrant and potentially inhibitory compounds such as caffeine and tannins. This study evaluated the performance of micro-aeration-enhanced anaerobic digestion (MA-AD) for the treatment and valorization of CPW to promote a more sustainable approach to coffee production. Oxygen was intermittently introduced via oxidation-reduction potential-controlled dosing, allowing for comparative assessment across anaerobic and micro-aerobic redox regimes. While both conventional anaerobic digestion (AD) and MA-AD achieved comparable reductions in total and volatile solids (>48% and >60%, respectively) and total and soluble chemical oxygen demand (>66% and >86%, respectively), MA-AD exhibited significantly higher total suspended solids concentrations and turbidity in later phases, likely due to gas sparging-induced floc disruption and particulate release. pH profiles indicated a shift toward increased acidification under MA-AD, without compromising process stability, with both reactors stabilizing between pH 6.8-7.1. Caffeine degradation was accelerated under MA-AD in the first dosing phase (>85% removal in 28 h), though long-term degradation efficiency converged with the control. Methane production was consistently lower in MA-AD (up to 43% reduction), attributed to the oxygen sensitivity of methanogens and possible substrate competition. These results underscore the importance of oxygen dose regulation, redox control, and microbial adaptation in optimizing MA-AD performance. The findings support MA-AD as a promising strategy for enhancing hydrolysis and partial removal of recalcitrant compounds in CPW. However, further refinement is required to sustain biogas quality and yield at scale.

BackgroundInfluenza A virus (IAV) is a major cause of morbidity and mortality globally, causing seasonal influenza in humans and infecting birds and some mammals. In 2024, IAV H5N1 highly pathogenic avian influenza (HPAI) in the United States moved into cattle. While the outbreak is currently of low risk to the public, there is an urgent need to monitor the disease and prevent spread. MethodsWe conducted a nationwide study evaluating the relationship between H5 hemagglutinin gene RNA concentrations in wastewater and reported outbreaks of IAV H5N1 in animals and humans. We utilized an H5-specific droplet digital RT-PCR test to quantify H5 RNA in wastewater in 40 states across the United States, and 1) examined the temporal association between outbreaks and wastewater detections and 2) utilized linear mixed models (LMM) to determine the relationship between measurements in wastewater and outbreak-related factors in the local area. ResultsWe find that there is a significant temporal association between wastewater H5 detections and the incidence of outbreaks in poultry and wild birds, but not in cattle or with human infections. However, outbreaks tended to occur at the same time across populations - wild bird detections were also associated with H5N1 in herds, poultry, and humans. Utilizing a LMM, we find that for individual sites, there is a relationship between H5 measurements in wastewater and both poultry outbreaks and the presence of dairy industry locally, but that there was either no relationship or a negative relationship with H5 measurements and either combined systems that accept storm water or those with detection of H5 in wild birds. ConclusionsThe study highlights how wastewater monitoring can supplement traditional surveillance, providing vital data that reflects public health threats. The findings underscore the potential of scaled wastewater surveillance as a proactive tool in monitoring and managing future outbreaks.

Wastewater surveillance is a powerful tool for monitoring the prevalence of infectious disease. Systems for wastewater monitoring were put in place throughout the world during the COVID-19 pandemic. These systems use viral RNA copies as the basis of estimates of COVID-19 cases in the sewershed area, thereby providing data critical for public health responses. However, the potential to measure other biomarkers in wastewater during outbreaks has not been fully explored. Here we report a novel approach for detecting specific human antibodies from wastewater. We measured the abundance of anti-SARS-CoV-2 spike IgG and IgA from fresh samples of community wastewater and from archived frozen samples dating from 2020-22. The assay described can be performed with readily available reagents, at a moderate per-sample cost. Our findings demonstrate the feasibility of noninvasive serological surveillance via wastewater, enabling a new approach to immunity-based monitoring of populations.

This study showcases the beneficial integration of non-sewered sanitation systems (NSSS) with sewered wastewater treatment plants (WWTPs). Treating increasing fractions of influent wastewater loads via six different types of NSSS offered correspondingly increasing savings in operating energy costs at five WWTPs, employing a broad range of typically employed treatment processes. Two NSSS that treat both greywater and blackwater (gb-HRT) and blackwater alone (b-HRT) yielded the highest savings in annual operating energy costs across most WWTPs. Distinctly, NSSS involving urine-separation with and without internal recirculation promoted energy-positive operations, by enhancing anaerobic digestion in selected WWTPs. At the highest NSSS coverage tested (treating 50% of the influent sewage), savings in annual sewered WWTPs operating energy costs ranged from $76k to $800k and increased further to the range $301k to $1.1M annually with process optimization. Therefore, integration of NSSS with sewered WWTPs can improve overall treatment efficiency, while facilitating resilient sanitation practices.

The sequencing of human virus genomes from wastewater samples is an efficient method for tracking viral transmission and evolution at the community level. However, this requires the recovery of viral nucleic acids of high quality. We developed a reusable tangential-flow filtration system to concentrate and purify viruses from wastewater for whole-genome sequencing. A pilot study was conducted with 94 wastewater samples from four local sewersheds, from which viral nucleic acids were extracted, and the whole genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was sequenced using the ARTIC V4.0 primers. Our method yielded a high probability (0.9) of recovering complete or near-complete SARS-CoV-2 genomes (>90% coverage at 10x depth) from wastewater when the COVID-19 incidence rate exceeded 33 cases per 100 000 people. The relative abundances of sequenced SARS-CoV-2 variants followed the trends observed from patient-derived samples. We also identified SARS-CoV-2 lineages in wastewater that were underrepresented or not present in the clinical whole-genome sequencing data. The developed tangential-flow filtration system can be easily adopted for the sequencing of other viruses in wastewater, particularly those at low concentrations. SYNOPSISThe tangential-flow filtration method extracts viral nucleic acids of high enough quality from wastewater for robust and successful whole-genome sequencing. GRAPHIC FOR TABLE OF CONTENTS (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/22279692v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@566377org.highwire.dtl.DTLVardef@19c3ba7org.highwire.dtl.DTLVardef@106c70org.highwire.dtl.DTLVardef@3f3f8f_HPS_FORMAT_FIGEXP M_FIG C_FIG

We used a sensitive, specific PCR assay to detect mpox clade Ib DNA in over 3000 wastewater samples collected prospectively across the United States. Mpox clade Ib DNA was detected in one sample from a location with a confirmed case; it was not detected in locations with no confirmed cases.

We assessed concentrations of parvovirus B19 DNA from two wastewater treatment plants in a county with a known outbreak in 2024. Wastewater viral concentrations correlated significantly with clinical cases, demonstrating wastewaters potential for tracking parvovirus B19 infections. Peaks in wastewater concentrations were aligned with the peak in hydrops fetalis diagnoses.

BackgroundWastewater represents a composite biological sample from the entire contributing population. People infected with monkeypox virus (MPXV)1 may excrete viral DNA into wastewater via multiple ways such as in feces, urine, skin lesions, and/or saliva. We describe results from rapid establishment of wastewater surveillance in selected regions in California within a month of the first reported case of monkeypox in the United States. MethodsPCR assays targeting genomic DNA from MPXV were deployed in an ongoing wastewater surveillance program in California. MPXV DNA concentrations were measured daily in settled solids samples from nine wastewater plants. Results over a four-week period were validated across different MPXV assays, compared using influent and solids samples, and correlated using non-parametric methods (Kendalls tau) with the number of monkeypox cases reported from each sewershed. ResultsMPXV DNA was detected at all nine sites between June 19 and August 1, 2022; 5 of 9 sites detected MPXV DNA prior to or within a day of the first case identified in the source sewershed. At the four sites with >10 positive detections, we observed a positive, statistically significant correlation (p <0.001) between MPXV DNA in wastewater solids and incidence rate of reported cases. ConclusionsOur findings suggest wastewater can be used to effectively detect the introduction of MPXV and monitor its circulation in the community to inform public health and clinical response. This flexible wastewater surveillance infrastructure may be rapidly leveraged to monitor other pathogens of public health importance that are shed into wastewater.

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

Wastewater-based epidemiology is an efficient method for monitoring the transmission of diverse pathogens in communities. Standard wastewater surveillance workflows typically involve wastewater concentration, nucleic acid extraction, and pathogen quantification. While various concentration methods are used, most comparisons of concentration methods have focused primarily on SARS-CoV-2, highlighting the need for further research to guide method selection for monitoring a suite of diverse pathogens. In this study, a head-to-head comparison of six different concentration methods was performed, including direct extraction (with and without bead beating), electronegative (HA) filtration, solids concentration, and magnetic bead-based concentration (using Nanotrap(R) particles; with and without bead beating). Methods were assessed for sensitivity, inhibitor removal, and recovery rates of fourteen microorganisms, including viruses, bacteria, and fungal pathogens. The cost of each method was also estimated. Results showed that the concentration method selection significantly impacts the sensitivity and economic costs of the wastewater monitoring workflow. Based on the results, a concentration approach that combines HA filtration and solids concentration is recommended to optimize detection across various pathogens. This study provides data-driven insights to enhance the reliability and cost-effectiveness of wastewater surveillance systems that can support public health responses for a broad range of diseases. SynopsisSix concentration methods were compared in terms of sensitivity and cost for the detection of 14 diverse pathogens in wastewater.

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related deaths in the United States. Individual screening is typically done with either a clinical stool-based test or direct clinical examination such as a colonoscopy. Given the low compliance with current screening recommendations and the high morbidity and mortality observed in areas with health disparities, we consider whether population-based testing using human RNA biomarkers in wastewater might effectively track the presence of CRC at the neighborhood level might be feasible. Wastewater samples were collected from four clusters in Louisville, KY: three representing cancer hotspots and one serving as a control neighborhood for feasibility data. Three wastewater replicates were obtained from each cluster. Human RNA biomarkers were isolated, quantified, and their RNA concentration levels were compared to clinical correlates. All replicates showed detectable levels of human cancer-associated RNA, including CDH1, which is a colorectal neoplasia-associated biomarker. Among CRC cluster sewershed samples, 8 of 9 replicate samples (89%) had a ratio of CDH1/GAPDH >=1 while the control sewershed sample showed ratio <1 for 2 of 3 samples. These preliminary data indicate that human RNA biomarkers can be detected in pooled community wastewater samples. While we have successfully identified the presence of these markers, further investigation with additional samples and closer alignment with documented case activity is necessary.

Antimicrobial resistance (AMR) is a major threat to global health and resistant bacterial populations have been observed to develop and spread in and around wastewater. However, in vitro studies on AMR development are typically conducted in ideal media conditions which can differ in composition and nutrient density from wastewater. In this study, we compare the growth and AMR development of E. coli in standard LB broth to a synthetic wastewater recipe and autoclaved wastewater samples from the Massachusetts Water Resources Authority (MWRA). We found that synthetic wastewater and real wastewater samples both supported less bacterial growth compared to LB. Additionally, bacteria grown in synthetic wastewater and real wastewater samples had differing susceptibility to antibiotic pressure from Doxycycline, Ciprofloxacin, and Streptomycin. However, AMR development over time during continuous passaging under subinhibitory antibiotic pressure was similar in fold change across all media types. Thus, we find that while LB can act as a proxy for wastewater for AMR studies in E. coli, synthetic wastewater is a more accurate predictor of both E.coli growth and antibiotic resistance development. Moreover, we also show that antibiotic resistance can develop in real wastewater samples and components within wastewater likely have synergistic and antagonistic interactions with antibiotics. ImportanceAntimicrobial resistance (AMR) ranks among the leading global threats to public health and development. In 2019, bacterial AMR was estimated to have directly caused 1.27 million deaths worldwide and contributed to 4.95 million deaths overall (Murray, C. J., et al., (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet, 399(10325), 629-655.). With estimations of AMR only getting worse, it is imperative that we understand the complex dimensionalities that drive the genesis of antimicrobial resistance to where it begins-the environment. The paper investigates bacterial growth and AMR in real wastewater samples and highlights the importance of using a media that closely mimics real wastewater in AMR studies, compared to standard lab media like LB broth. This is crucial for understanding how E. coli and other bacteria develop AMR in environments similar to actual wastewater, which can inform more effective strategies to combat AMR in natural and engineered settings.

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.

Wastewater monitoring for infectious disease targets is increasingly used to better understand circulation of diseases. The present study validated hydrolysis-probe digital droplet (reverse-transcriptase (RT))-PCR assays for important enteric viruses (rotavirus, adenovirus group F, norovirus GI and GII, and enteroviruses), outbreak or emerging viruses (hepatitis A and West Nile virus), and an emerging drug resistant fungal pathogen (Candida auris). We used the assays to retrospectively measure concentrations of the targets in wastewater solids. Viral and fungal nucleic-acid concentrations were measured in two wastewater solids samples per week at two wastewater treatment plants in the San Francisco Bay Area of California, USA for 26 months. We detected all targets in wastewater solids with the exception of West Nile virus. At both wastewater treatment plants, human adenovirus group F was detected at the highest concentrations, followed by norovirus GII, enteroviruses, norovirus GI, and rotavirus at the lowest concentrations. Hepatitis A and C. auris were detected less consistently than the aforementioned viruses. Enterovirus D68 was detected in a limited time frame during fall 2022 at both sites. The measurements reported herein, and in some cases their seasonal trends, are consistent with previous reports of these targets in wastewater. These measurements represent some of the first quantitative measurements of these infectious disease targets in the solid fraction of wastewater. This study lays a foundation for the use of wastewater solids for the detection of specific infectious disease targets in wastewater monitoring programs aimed to better understand the spread of these diseases.