ACS ES&T Water

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/22279459v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@debf50org.highwire.dtl.DTLVardef@1e21da2org.highwire.dtl.DTLVardef@78708org.highwire.dtl.DTLVardef@3239ee_HPS_FORMAT_FIGEXP M_FIG C_FIG The primary objective of this study was to identify a universal wastewater biomarker for population normalization for SARS-CoV-2 wastewater-based epidemiology (WBE). A total of 2,624 wastewater samples (41 weeks) were collected weekly during May 2021-April 2022 from 64 wastewater facilities across Missouri, U.S. Three wastewater biomarkers, caffeine and its metabolite, paraxanthine, and pepper mild mottle virus (PMMoV), were compared for the population normalization effectiveness for wastewater SARS-CoV-2 surveillance. Paraxanthine had the lowest temporal variation and strongest relationship between population compared to caffeine and PMMoV. This result was confirmed by data from ten different Wisconsins WWTPs with gradients in population sizes, indicating paraxanthine is a promising biomarker of the real-time population across a large geographical region. The estimated real-time population was directly compared against the population patterns with human movement mobility data. Of the three biomarkers, population normalization by paraxanthine significantly strengthened the relationship between wastewater SARS-CoV-2 viral load and COVID-19 incidence rate the most (40 out of 61 sewersheds). Caffeine could be a promising population biomarker for regions where no significant exogenous caffeine sources (e.g., discharges from food industries) exist. In contrast, PMMoV showed the highest variability over time, and therefore reduced the strength of the relationship between sewage SARS-CoV-2 viral load and the COVID-19 incidence rate, as compared to wastewater data without population normalization and the population normalized by either recent Census population or the population estimated based on the number of residential connections and average household size for that municipality from the Census. Overall, the findings of this long-term surveillance study concluded that the paraxanthine has the best performance as a biomarker for population normalization for SARS-CoV-2 wastewater-based epidemiology.

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.

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.

Concurrent biological phosphorus (P) recovery and nitrogen (N) removal in treating high-strength wastewater (such as anaerobic digestate) has been considered incompatible due to presumed conflicts in the conflicting optimum conditions required by phosphorous accumulating organisms (PAO) and nitrifiers. However, this study achieved a stable nitrite accumulation while still maintained PAO activities in one sequencing batch reactor for treating the manure digestate under two aeration schemes (continuous versus intermittent aeration). Nitrite accumulated up to 80.5 {+/-} 21.1 mg-N/L under continuous aeration (6 h) mode. Switching to intermittent aeration (equivalent to 3 h) halved nitrite accumulation but increased total nitrogen removal efficiency from 53.5 {+/-} 12.2% to 84.7 {+/-} 9.4%. Mass balance analysis indicates that nearly all ammonia was removed as N2O. Both Enhanced Biological Phosphorus Removal (EBPR) activity assessment and phenotypic trait detection via single cell Raman spectrum (SCRS) confirmed the existence of yet to be identified PAOs that are resistant to high nitrite inhibition in our system. Visual Minteq calculation indicates that high concentrations of Ca in manure digestate may form precipitates and influence the bioavailability of P forms. Therefore, both biotic and abiotic pathways lead to a total P removal rate around 61.0 {+/-} 6.8%. This study highlights new opportunities to combine short-cut nitrogen removal via partial nitrification, nitrous oxide (N2O) collection, and EBPR in commercial farm-collected digested manure wastewater. Higher N and P removal efficiency could potentially be achieved by tuning aeration schemes in combination with down-stream anammox process. SynopsisConcurrent partial nitrification, N2O accumulation, and EBPR activity were found, leading to the exploration of novel nitrite-resistant PAOs, simultaneously N/P recovery, and waste-energy conversion in treating high strength wastewater.

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.

Opportunistic pathogens (OPs) within the Legionella and Mycobacterium can persist and sometimes proliferate in drinking water systems and pose a risk to public health. Most prior research has focused on isolated system components of the drinking water treatment and distribution system and has rarely examined spatiotemporal dynamics across the entire source water, treatment process, and distribution system continuum. This study addresses this critical knowledge gap by quantitative profiling of microbial communities with full length 16S rRNA gene sequencing and flow cytometry, and associated water chemistry parameters, including disinfection byproducts (DBPs), across five full-scale utilities. These utilities reflect varying source water types, geographic locations, treatment regimes, and climate zones. Microbial communities, including Legionella and Mycobacterium populations, in distribution system were shaped by source water type and exhibited significant community divergence across utilities. Within the same genus, strain-level analyses revealed highly distinct Legionella and Mycobacterium sequence variants unique to each utility. Interestingly, a substantial proportion of Legionella and Mycobacterium amplicon sequence variants were both utility specific and often specific to locations within the distribution system, indicating strong geographic structuring both across and within drinking water systems. Understanding the mechanistic underpinnings of this geographic structuring is critical to develop robust strategies for managing and monitoring Legionella and Mycobacterium populations in drinking water systems.

The use of wastewater-based epidemiology has increased in recent years due to the publication of COVID-19 online trackers and the focus of the media on the pandemic. Yet the analysis of viromes in wastewater has been widely applied for several decades in conjunction with traditional chemical analysis approaches. However, even though real time quantitative polymerase chain reaction (RT-qPCR) based molecular detection methods are now mainstream in large and small labs alike, wastewater sampling and nucleic acid extraction procedures are not yet standardized or optimized to enable routine and robust analysis and results interpretation. Here, we employ a flotation-based nucleic acid extraction method using microbubbles that allows for simple direct collection and lysis of total wastewater samples without the requirement for pasteurization or filtration of solid components prior to analysis. An additional advantage discovered during testing was reduced sample input needs while maintaining sensitivity compared to precipitation and ultrafiltration-based methods. Microbubbles designed to bind nucleic acids enable convenient workflows, fast extraction, and concentration and purification of RNA and DNA that is compatible with downstream genomic analyses. SUMMARYMicrobubble-based capture of nucleic acids from raw (unpasteurized) and unfiltered (containing solids) wastewater with subsequent elution offers several advantages over existing methods. Using microbubbles, the required sample input and protocol duration are reduced while sensitivity of downstream genomic analysis is increased.

Wastewater monitoring for SARS-CoV-2 has been suggested as an epidemiological indicator of community infection dynamics and disease prevalence. We report wastewater viral RNA levels of SARS-CoV-2 in a major metropolis serving over 3.6 million people geographically spread over 39 distinct sampling sites. Viral RNA levels were followed weekly for 22 weeks, both before, during, and after a major surge in cases, and simultaneously by two independent laboratories. We found SARS-CoV-2 RNA wastewater levels were a strong predictive indicator of trends in the nasal positivity rate two-weeks in advance. Furthermore, wastewater viral RNA loads demonstrated robust tracking of positivity rate for populations served by individual treatment plants, findings which were used in real-time to make public health interventions, including deployment of testing and education strike teams.

Coagulation, flocculation, and sedimentation (CFS) is widely applied as a combined unit process in the treatment of drinking water, wastewater, and recycled water; however, virus reduction through CFS has not been sufficiently characterized to assign pathogen log reduction value (LRV) credits. This study collected data through a systematic review that yielded over 1000 LRVs from 43 manuscripts covering 46 viruses to characterize virus reduction through CFS. The results demonstrate that CFS is effective at reducing viruses, with 68% of virus LRVs greater than 1. A mixed-effects model was used to identify potential mechanisms of virus reduction with ferric and aluminum coagulants, as well as factors associated with variability in performance. Key insights from the model show that virus reduction is: (1) improved at lower pH, similar to natural organic matter (NOM) reduction, (2) lower in secondary effluent than surface water for drinking water treatment, (3) virus-dependent, and (4) dependent on virus enumeration methods, with lower LRVs observed for molecular techniques. These findings demonstrate the potential for CFS to provide consistent and explainable virus reduction, potentially establishing a foundation for regulatory crediting in potable reuse applications. Future crediting frameworks will need to account for the factors impacting performance to accurately quantify and assign credit for virus reduction.

Wastewater surveillance of vaccine-preventable diseases may provide early warning of outbreaks and identify areas to target for immunization. To advance wastewater monitoring of measles, mumps, and rubella viruses, we developed and validated a multiplexed RT-ddPCR assay for the detection of their RNA. Because the measles-mumps-rubella (MMR) vaccine is an attenuated live virus vaccine, we also developed an assay that distinguishes between wild-type and vaccine strains of measles in wastewater and validated it using a wastewater sample collected from a facility with an active measles outbreak. We also evaluated the partitioning behavior the viruses in between the liquid and solid fractions of influent wastewater. We found that assaying the liquid fraction of the wastewater resulted in more sensitive detection of the viruses despite the fact that the viral RNA was enriched in the solid fraction due to the low solids content of the influent wastewater. Finally, we investigated the stability of measles, mumps, and rubella RNA in wastewater samples spiked with viruses over 28 days at two different concentrations and two temperatures (4{degrees}C and room temperature) and observed limited viral decay. Our study supports the feasibility of wastewater monitoring for measles, mumps, and rubella viruses for population-level surveillance.

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.

The COVID-19 pandemic has exacerbated the disparities in healthcare delivery in the US. Many communities had, and continue to have, limited access to COVID-19 testing, making it difficult to track the spread and impact of COVID-19 in early days of the outbreak. To address this issue we monitored severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA at the population-level using municipal wastewater influent from 19 cities across the state of Minnesota during the COVID-19 outbreak in Summer 2020. Viral RNA was detected in wastewater continually for 20-weeks for cities ranging in populations from 500 to >1, 000, 000. Using a novel indexing method, we were able to compare the relative levels of SARS-CoV-2 RNA for each city during this sampling period. Our data showed that viral RNA trends appeared to precede clinically confirmed cases across the state by several days. Lag analysis of statewide trends confirmed that wastewater SARS-CoV-2 RNA levels preceded new clinical cases by 15-17 days. At the regional level, new clinical cases lagged behind wastewater viral RNA anywhere from 4-20 days. Our data illustrates the advantages of monitoring at the population-level to detect outbreaks. Additionally, by tracking infections with this unbiased approach, resources can be directed to the most impacted communities before the need outpaces the capacity of local healthcare systems.

We used wastewater monitoring data to evaluate the impact of public health policies and interventions on the spread of COVID-19 among a university population. We first evaluated the correlation between incident, reported COVID-19 cases and wastewater SARS-CoV-2 RNA concentrations and observed changes to the correlation over time. Using a difference-in-differences approach, we evaluated the association between university COVID-19 policy changes and levels of SARS-CoV-2 RNA concentrations in wastewater. Policy changes associated with a significant change in campus wastewater SARS-CoV-2 RNA concentrations included changes to face covering recommendations, indoor gathering bans, and routine surveillance testing requirements and availability. We did not observe changes in SARS-CoV-2 RNA concentrations associated with other policy changes. The work presented herein demonstrates how longitudinal wastewater monitoring of viruses may be used for causal inference such as policy impact evaluation, especially at small geographic scales.

The worlds first full-scale, 280 nm UV LED reactor for wastewater disinfection was tested at flows of 545 and 817 m3 day-1. The system achieved a >3 average log reduction of total coliform at 545 m3 day-1 and the 817 m3 day-1 flow rate achieved over a >2.5 average log reduction for all operational conditions. The delivered fluence of the full-scale system ranged from 28-148 mJ cm-2 and aligns with a UV auditing study that was conducted prior to the installation of the wastewater reactor. These results benchmark the performance that can be achieved by UV LED disinfection and further connect bench-scale disinfection results with full-scale performance. The approach established in this manuscript provides a novel tool for utilities when considering emerging UV disinfection technologies. In summary, this study establishes that UV LEDs are an effective wastewater disinfectant at-scale and are comparable to conventional low-pressure UV systems. This is the first instance where the efficacy of UV LEDs for municipal wastewater disinfection has been demonstrated using a large-scale installation at a functioning wastewater facility. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/24308830v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@156a9eeorg.highwire.dtl.DTLVardef@ae836aorg.highwire.dtl.DTLVardef@135c44corg.highwire.dtl.DTLVardef@ea73d8_HPS_FORMAT_FIGEXP M_FIG C_FIG

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

The copper biotic ligand model (BLM) has been used for environmental risk assessment by taking into account the bioavailability of copper in freshwater. However, the BLM-based environmental risk of copper has been assessed only in Europe and North America, with monitoring datasets containing all of the BLM input variables. For other areas, it is necessary to apply surrogate tools with reduced data requirements to estimate the BLM-based predicted no-effect concentration (PNEC) from commonly available monitoring datasets. To develop an optimized PNEC estimation model based on an available monitoring dataset, an initial model that considers all BLM variables, a second model that requires variables excluding alkalinity, and a third model using electrical conductivity as a surrogate of the major cations and alkalinity have been proposed. Furthermore, deep neural network (DNN) models have been used to predict the nonlinear relationships between the PNEC (outcome variable) and the required input variables (explanatory variables). The predictive capacity of DNN models in this study was compared with the results of other existing PNEC estimation tools using a look-up table and multiple linear and multivariate polynomial regression methods. Three DNN models, using different input variables, provided better predictions of the copper PNECs compared with the existing tools for four test datasets, i.e., Korean, United States, Swedish, and Belgian freshwaters. The adjusted r2 values in all DNN models were higher than 0.95 in the test datasets, except for the Swedish dataset (adjusted r2 > 0.87). Consequently, the most applicable model among the three DNN models could be selected according to the data availability in the collected monitoring database. Because the most simplified DNN model required only three water quality variables (pH, dissolved organic carbon, and electrical conductivity) as input variables, it is expected that the copper BLM-based risk assessment can be applied to monitoring datasets worldwide.

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.

The COVID-19 pandemic prompted a global integration of wastewater-based epidemiology (WBE) into public health surveillance. Among early pre-COVID practitioners was Greater Tempe (population ~200,000), Arizona, where high-frequency, high-resolution monitoring of opioids began in 2018, leading to unrestricted online data release. Leveraging an existing, neighborhood-level monitoring network, wastewater from eleven contiguous catchment areas was analyzed by RT-qPCR for the SARS-CoV-2 E gene from April 2020 to March 2021 (n=1,556). Wastewater data identified an infection hotspot in a predominantly Hispanic and Native American community, triggering targeted interventions. During the first SARS-CoV-2 wave (June 2020), spikes in virus levels preceded an increase in clinical cases by 8.5{+/-}2.1 days, providing an early-warning capability that later transitioned into a lagging indicator (-2.0{+/-}1.4 days) during the December/January 2020-21 wave of clinical cases. Globally representing the first demonstration of immediate, unrestricted WBE data sharing and featuring long-term, innovative, high-frequency, high-resolution sub-catchment monitoring, this successful case study encourages further applications of WBE to inform public health interventions.

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.

While real-time monitoring of physicochemical parameters has widely been incorporated into drinking water treatment systems, real-time microbial monitoring has lagged behind, resulting in the use of surrogate parameters (disinfectant residual, applied dose, concentration x time [CT]) to assess disinfection system performance. Near real-time flow cytometry (NRT-FCM) allows for automated quantification of total and intact microbial cells but has not been widely implemented in full-scale systems. This study sought to investigate the feasibility of NRT-FCM for full-scale drinking water ozone disinfection system performance monitoring. A water treatment plant with high lime solids turbidity in the ozone contactor influent was selected to evaluate the NRT-FCM in challenging conditions. Total and intact cell counts were monitored for 40 days and compared to surrogate parameters (ozone residual, ozone dose, and CT) and grab sample assay results for cellular adenosine triphosphate (cATP), heterotrophic plate counts (HPC), impedance flow cytometry, and 16S rRNA gene sequencing. NRT-FCM provided insight into the dynamics of the full-scale ozone system, including offering early warning of increased contactor effluent cell concentrations, which was not observed using surrogate measures. A strong correlation between log intact cell removal and CT was also not observed (Kendalls tau= -0.09, p=0.04). Positive correlations were observed between intact cell counts and cATP levels (Kendalls tau=0.40, p<0.01), HPC (Kendalls tau=0.20, p<0.01), and impedance flow cytometry results (Kendalls tau=0.30, p<0.01). However, 16S rRNA gene sequencing results showed that passage through the ozone contactor significantly changed the microbial community (p<0.05), supporting the hypothesis that regrowth was occurring in the later chambers of the contactor. This study demonstrates the utility of direct, near real-time microbial analysis for monitoring full-scale disinfection systems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/23300640v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@13f5e68org.highwire.dtl.DTLVardef@14f33d3org.highwire.dtl.DTLVardef@d3821dorg.highwire.dtl.DTLVardef@373af_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LINear real-time flow cytometry (NRT-FCM) was effective for ozone system monitoring. C_LIO_LIIntact microbial cell counts were consistent with cellular ATP and HPC results. C_LIO_LINRT-FCM provided early detection of increased effluent cell concentrations. C_LIO_LIThe ozone contactor influent and effluent microbial communities were distinct. C_LI