Abstract 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.

Wastewater surveillance has proven to be a widely useful means for tracking the dynamics of COVID-19, particularly as the emphasis on clinical testing and reporting of case data continues to decline. Here we present wastewater monitoring data from a multi-year sampling campaign at 11 wastewater collection facilities in Ohio. We found strong correlations between flow-adjusted wastewater concentrations of the virus (as represented through quantification of N2 gene fragments) and reported cases and used sequencing to confirm the sequential arrival of several variants of concern (VOCs) between winter 2020 and spring 2022. We observed that the three main VOCs in our dataset, alpha, delta, and omicron, showed differing temporal dynamics like length of time from first detection to dominating the wastewater signal. We also found credible variation in the relationship between wastewater concentration and clinical cases during different periods within our time series (delineated based on the dominant VOC), indicating the possibility of differential fecal shedding by the three variants.

ABSTRACT Wild birds are a major source of Campylobacter jejuni, but there is limited information on its virulence, infectivity, and its human health risks associated with exposure to wild bird infested surface waters. This study focused on understanding the virulence of C. jejuni 63A isolated from California gull excreta. Whole-genome analyses revealed its multilocus sequence typing (ST2654) and high similarity of DNA sequences (97%–99%) with other pathogenic Campylobacter spp. strains, particularly those isolated from patients suffering from Guillain–Barre´ syndrome (GBS). Furthermore, analyses revealed that the lipooligosaccharide (LOS) biosynthesis gene cluster, capsular polysaccharide (CPS) gene cluster, and the genes responsible for producing cytolethal distending toxin (CDT) operons/genes (cdtABC) were all present and shared high sequence conservation with the same clinical isolates collected from GBS patients. The unique LOS genes (neuA1, neuB1, neuC1, and cstIII) to GBS patient isolates were identified and categorized 63A as group 1 LOS locus type. The presence of an intact CPS gene cluster and cdtABC genes in 63A along with their high sequence similarity to clinical isolates was strikingly different from many other wild-bird isolates. These findings suggest the high possibility of significant virulence associated with the new isolate. Additionally, 63A may not be limited to only California gull. Phylogenetic analysis of 63A with other hosts including waterfowl such as sandhill cranes shows that the 63A isolate is closely related to a diverse range of hosts acting as reservoirs and/or vectors. Lastly, dose–response analyses of this isolate estimated a median infectious dose of 7.16 × 107 CFU, further suggesting that the 63A isolate has zoonotic potential.IMPORTANCEC. jejuni is one of the most common foodborne zoonotic pathogens worldwide. This study focused on the virulence and infection of C. jejuni derived from California gull. We found that C. jejuni 63A shares high genetic similarity with other pathogenic C. jejuni strains. These strains are particularly associated with patients suffering from GBS. Whole-genome analyses revealed virulence factors like lipooligosaccharide, capsular polysaccharide, and cytolethal distending toxin operons/genes present in 63A. These genes showed high sequence conservation with clinical isolates collected from GBS patients. We also analyzed its presence in other avian feces, like those of sandhill cranes. Our chick infection experiment and dose–response analyses indicate that the 63A isolate has zoonotic potential and could infect humans. These findings highlight the importance of understanding the virulence and potential health risks associated with C. jejuni strains of wild birds.

Wastewater treatment plants (WWTPs) house diverse populations of microbial communities that are dynamic across treatment stages. Although viruses, especially non-pathogenic ones are relatively less studied compared to their bacterial counterparts, they are equally important players in WWTP microbiomes. In this study, we sampled influent, activated sludge, and effluent stages of a WWTP in southwest Ohio to sequence their viral population using multiple displacement amplification (MDA) and metagenomic approaches. We recovered 1003 viral genomes, the majority of which were ssDNA (single-stranded DNA) viruses that formed distinct clusters representing novel species and phylogenetic groups not closely related to known human gut or terrestrial viruses. Additionally, tracking viruses across treatment stages showed several persisted across multiple treatment stages. These results suggest that further studies are needed to understand the persistence of viral populations, particularly non-pathogenic ones, across different stages of WWTPs.

Cross-border flow of untreated sewage from Mexico into the USA via the Tijuana River is public health issue with negative consequences for coastal communities. Here we evaluate the potential application of fluorescence-based, submersible tryptophan-like (TRP) and humic-like (CDOM) fluorescence sensors for real-time tracking of wastewater pollution in an estuarine environment. Sonde fluorescence measurements were compared with benchtop fluorescence, fecal indicator bacteria (FIB) concentrations, and real-time specific conductivity measurements in the Tijuana River Estuary during dry and wet weather conditions, and with and without cross-border flow. TRP and CDOM fluorescence concentrations were low during times without cross-border flow and two-three orders of magnitude higher during storm events and after cross-border sewage flow events. Major deterioration in water quality, including hypoxic conditions, was observed after consistent, long-term cross-border sewage flow. Real-time TRP and CDOM fluorescence concentrations had a significant linear relationship with fecal indicator bacteria (FIB) concentrations during dry weather periods with cross-border flow (p < 0.001) but were poorly correlated during stormflow and during less polluted periods with no cross-border flow. TRP and CDOM fluorescence acquired on discrete samples using a benchtop fluorometer correlated significantly (p < 0.001) with FIB concentrations under all cross-border flow conditions. Based on relationships between benchtop TRP fluorescence and percent wastewater, the greatest amount of untreated wastewater in the estuary's surface layer during cross-border flow events was estimated at >80 % and occurred during neap tides, when concentrated, sewage-laden freshwater flowed over dense saline seawater due to stratification and lack of mixing in the estuary. These results are important because exposure to untreated sewage poses severe health risks for residents and visitors to adjacent coastal areas. While benchtop fluorescence was more effective for estimating the degree of wastewater pollution, submersible TRP and CDOM sensors provided a real-time alert of sewage contamination, which can be utilized in other sewage impacted estuarine environments.

Concentrations of viral gene targets in combined wastewater or greywater at four facilities. Three targets may be good candidates for evaluating whether onsite water treatments could reduce viral infection risk by 10−2 infections per person per year.

Antimicrobial resistance (AMR) is a world-wide public health threat that is projected to lead to 10 million annual deaths globally by 2050. The AMR public health issue has led to the development of action plans to combat AMR, including improved antimicrobial stewardship, development of new antimicrobials, and advanced monitoring. The National Antimicrobial Resistance Monitoring System (NARMS) led by the United States (U.S) Food and Drug Administration along with the U.S. Centers for Disease Control and U.S. Department of Agriculture has monitored antimicrobial resistant bacteria in retail meats, humans, and food animals since the mid 1990’s. NARMS is currently exploring an integrated One Health monitoring model recognizing that human, animal, plant, and environmental systems are linked to public health. Since 2020, the U.S. Environmental Protection Agency has led an interagency NARMS environmental working group (EWG) to implement a surface water AMR monitoring program (SWAM) at watershed and national scales. The NARMS EWG divided the development of the environmental monitoring effort into five areas: (i) defining objectives and questions, (ii) designing study/sampling design, (iii) selecting AMR indicators, (iv) establishing analytical methods, and (v) developing data management/analytics/metadata plans. For each of these areas, the consensus among the scientific community and literature was reviewed and carefully considered prior to the development of this environmental monitoring program. The data produced from the SWAM effort will help develop robust surface water monitoring programs with the goal of assessing public health risks associated with AMR pathogens in surface water (e.g., recreational water exposures), provide a comprehensive picture of how resistant strains are related spatially and temporally within a watershed, and help assess how anthropogenic drivers and intervention strategies impact the transmission of AMR within human, animal, and environmental systems.

Antimicrobial resistance (AMR) has long been framed as an epidemiological and public health concern. Its impacts on the environment are unclear. Yet, the basis for AMR is altered cell physiology. Just as this affects how microbes interact with antimicrobials, it can also affect how they interact with their own species, other species, and their non-living environment. Moreover, if the microbes are globally notorious for causing landscape-level environmental issues, then these effects could alter biodiversity and ecosystem function on a grand scale. To investigate these possibilities, we compiled peer-reviewed literature from the past 20 years regarding AMR in toxic freshwater cyanobacterial harmful algal blooms (HABs). We examined it for evidence of AMR affecting HAB frequency, severity, or persistence. Although no study within our scope was explicitly designed to address the question, multiple studies reported AMR-associated changes in HAB-forming cyanobacteria (and co-occurring microbes) that pertained directly to HAB timing, toxicity, and phase, as well as to the dynamics of HAB-afflicted aquatic food webs. These findings highlight the potential for AMR to have far-reaching environmental impacts (including the loss of biodiversity and ecosystem function) and bring into focus the importance of confronting complex interrelated issues such as AMR and HABs in concert, with interdisciplinary tools and perspectives.

Correction for ‘Evaluation of intra- and inter-lab variability in quantifying SARS-CoV-2 in a state-wide wastewater monitoring network’ by Angela Davis et al. , Environ. Sci.: Water Res. Technol. , 2023, https://doi.org/10.1039/D2EW00737A.

In December 2019, SARS-CoV-2, the virus that causes coronavirus disease 2019, was first reported and subsequently triggered a global pandemic. Wastewater monitoring, a strategy for quantifying viral gene concentrations from wastewater influents within a community, has served as an early warning and management tool for the spread of SARS-CoV-2 in a community. Ohio built a collaborative statewide wastewater monitoring network that is supported by eight labs (university, government, and commercial laboratories) with unique sample processing workflows. Consequently, we sought to characterize the variability in wastewater monitoring results for network labs. Across seven trials between October 2020 and November 2021, eight participating labs successfully quantified two SARS-CoV-2 RNA targets and human fecal indicator virus targets in wastewater sample aliquots with reproducible results, although recovery efficiencies of spiked surrogates ranged from 3 to 75%. When SARS-CoV-2 gene fragment concentrations were adjusted for recovery efficiency and flow, the proportion of variance between laboratories was minimized, serving as the best model to account for between-lab variance. Another adjustment factor (alone and in different combinations with the above factors) considered to account for sample and measurement variability includes fecal marker normalization. Genetic quantification variability can be attributed to many factors, including the methods, individual samples, and water quality parameters. In addition, statistically significant correlations were observed between SARS-CoV-2 RNA and COVID-19 case numbers, supporting the notion that wastewater surveillance continues to serve as an effective monitoring tool. This study serves as a real-time example of multi-laboratory collaboration for public health preparedness for infectious diseases.

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

In urban areas, exposure to greenspace has been found to be beneficial to human health. The biodiversity hypothesis proposed that exposure to diverse ambient microbes in greener areas may be one pathway leading to health benefits such as improved immune system functioning, reduced systemic inflammation, and ultimately reduced morbidity and mortality. Previous studies observed differences in ambient outdoor bacterial diversity between areas of high and low vegetated land cover but didn't focus on residential environments which are important to human health. This research examined the relationship between vegetated land and tree cover near residence and outdoor ambient air bacterial diversity and composition. We used a filter and pump system to collect ambient bacteria samples outside residences in the Raleigh-Durham-Chapel Hill metropolitan area and identified bacteria by 16S rRNA amplicon sequencing. Geospatial quantification of total vegetated land or tree cover was conducted within 500 m of each residence. Shannon's diversity index and weighted UniFrac distances were calculated to measure α (within-sample) and β (between-sample) diversity, respectively. Linear regression for α-diversity and permutational analysis of variance (PERMANOVA) for β-diversity were used to model relationships between vegetated land and tree cover and bacterial diversity. Data analysis included 73 ambient air samples collected near 69 residences. Analysis of β-diversity demonstrated differences in ambient air microbiome composition between areas of high and low vegetated land (p = 0.03) and tree cover (p = 0.07). These relationships remained consistent among quintiles of vegetated land (p = 0.03) and tree cover (p = 0.008) and continuous measures of vegetated land (p = 0.03) and tree cover (p = 0.03). Increased vegetated land and tree cover were also associated with increased ambient microbiome α-diversity (p = 0.06 and p = 0.03, respectively). To our knowledge, this is the first study to demonstrate associations between vegetated land and tree cover and the ambient air microbiome's diversity and composition in the residential ecosystem.

CONTEXT:Prior to the COVID-19 pandemic, wastewater influent monitoring for tracking disease burden in sewered communities was not performed in Ohio, and this field was only on the periphery of the state academic research community.PROGRAM:Because of the urgency of the pandemic and extensive state-level support for this new technology to detect levels of community infection to aid in public health response, the Ohio Water Resources Center established relationships and support of various stakeholders. This enabled Ohio to develop a statewide wastewater SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) monitoring network in 2 months starting in July 2020.IMPLEMENTATION:The current Ohio Coronavirus Wastewater Monitoring Network (OCWMN) monitors more than 70 unique locations twice per week, and publicly available data are updated weekly on the public dashboard.EVALUATION:This article describes the process and decisions that were made during network initiation, the network progression, and data applications, which can inform ongoing and future pandemic response and wastewater monitoring.DISCUSSION:Overall, the OCWMN established wastewater monitoring infrastructure and provided a useful tool for public health professionals responding to the pandemic.

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Antimicrobial resistance (AMR) is a global crisis threatening human, animal, and environmental health. The natural environment, specifically water resources, has been recognized as a reservoir and dissemination pathway for AMR; however, urban karst aquifer systems have been overlooked. This is a concern as these aquifer systems provide drinking water to about 10 % of the global population; yet, the urban influence on the resistome in these vulnerable aquifers is sparingly explored. This study used high-throughput qPCR to determine the occurrence and relative abundance of antimicrobial resistant genes (ARG) in a developing urban karst groundwater system in Bowling Green, KY. Ten sites throughout the city were sampled weekly and analyzed for 85 ARGs, as well as seven microbial source tracking (MST) genes for human and animal sources, providing a spatiotemporal understanding of the resistome in urban karst groundwater. To further understand ARGs in this environment, potential drivers (landuse, karst feature type, season, source of fecal pollution) were considered in relation to the resistome relative abundance. The MST markers highlighted a prominent human influence to the resistome in this karst setting. The concentration of targeted genes varied between the sample weeks, but all targeted ARGs were prevalent throughout the aquifer regardless of karst feature type or season, with high concentrations captured for sulfonamide (sul1), quaternary ammonium compound (qacE), and aminoglycoside (strB) antimicrobial classes. Higher prevalence and relative abundance were detected during the summer and fall seasons, as well as at the spring features. Linear discriminant analysis suggested that karst feature type had higher influence on ARGs in the aquifer compared to season and the source of fecal pollution had the least influence. These findings can contribute to the development of effective management and mitigation strategies for AMR.

A data-driven approach to characterizing the risk of cyanobacteria-based harmful algal blooms (cyanoHABs) was undertaken for the Ohio River. Twenty-five years of river discharge data were used to develop Bayesian regression models that are currently applicable to 20 sites spread-out along the entire 1579 km of the river’s length. Two site-level prediction models were developed based on the antecedent flow conditions of the two blooms that occurred on the river in 2015 and 2019: one predicts if the current year will have a bloom (the occurrence model), and another predicts bloom persistence (the persistence model). Predictors for both models were based on time-lagged average flow exceedances and a site’s characteristic residence time under low flow conditions. Model results are presented in terms of probabilities of occurrence or persistence with uncertainty. Although the occurrence of the 2019 bloom was well predicted with the modeling approach, the limited number of events constrained formal model validation. However, as a measure of performance, leave-one-out cross validation returned low misclassification rates, suggesting that future years with flow time series like the previous bloom years will be correctly predicted and characterized for persistence potential. The prediction probabilities are served in real time as a component of a risk characterization tool/web application. In addition to presenting the model’s results, the tool was designed with visualization options for studying water quality trends among eight river sites currently collecting data that could be associated with or indicative of bloom conditions. The tool is made accessible to river water quality professionals to support risk communication to stakeholders, as well as serving as a real-time water data monitoring utility.

Wastewater monitoring of SARS-CoV-2 presents a means of tracking COVID-19 community infection dynamics on a broader geographic scale. However, accounting for environmental and sample-processing losses may be necessary for wastewater measurements to readily inform our understanding of infection prevalence. Here, we present measurements of the SARS-CoV-2 N1 and N2 gene targets from weekly wastewater samples at three sites in Hamilton County, Ohio, during an increase and subsequent decline of COVID-19 infections. The concentration of N1 or N2 RNA in wastewater, measured over the course of six months, ranged from below the detection limit to over 104 gene copies/l, and correlated with case data at two wastewater treatment plants, but not at a sub-sewershed-level sampling site. We also evaluated the utility of a broader range of variables than has been reported consistently in previous work, in improving correlations of SARS-CoV-2 concentrations with case data. These include a spiked matrix recovery control (OC43), flow-normalization, and assessment of fecal loading using endogenous fecal markers (HF183, PMMoV, crAssphage). We found that adjusting for recovery, flow, and fecal indicators increased these correlations for samples from a larger sewershed (serving ~488,000 people) with greater industrial and stormwater inputs, but raw N1/N2 concentrations corresponded better with case data at a smaller, residential-oriented sewershed. Our results indicate that the optimal adjustment factors for correlating wastewater and clinical case data moving forward may not be generalizable to all sewersheds.

Roof runoff has the potential to serve as an important local water source in regions with growing populations and limited water supply. Given the scarcity of guidance regulating the use of roof runoff, a need exists to characterize the microbial quality of roof runoff. The objective of this 2-year research effort was to examine roof runoff microbial quality in four U.S. cities: Fort Collins, CO; Tucson, AZ; Baltimore, MD; and Miami, FL. Seven participants, i.e., homeowners and schools, were recruited in each city to collect roof runoff samples across 13 precipitation events. Sample collection was done as part of a citizen science approach. The presence and concentrations of indicator organisms and potentially human-infectious pathogens in roof runoff were determined using culture methods and digital droplet polymerase chain reaction (ddPCR), respectively. The analyzed pathogens included Salmonella spp., Campylobacter spp., Giardia duodenalis, and Cryptosporidium parvum. Several factors were evaluated to study their influence on the presence of potentially human-infectious pathogens including the physicochemical characteristics (total suspended solids, volatile suspended solids, total dissolved solids, chemical oxygen demand, and turbidity) of roof runoff, concentrations of indicator organisms, presence/absence of trees, storm properties (rainfall depth and antecedent dry period), percent of impervious cover surrounding each sampling location, seasonality, and geographical location. E. coli and enterococci were detected in 73.4% and 96.2% of the analyzed samples, respectively. Concentrations of both E. coli and enterococci ranged from <0 log10 to >3.38 log10 MPN/100 mL. Salmonella spp. invA, Campylobacter spp. ceuE, and G. duodenalis β - giardin gene targets were detected in 8.9%, 2.5%, and 5.1% of the analyzed samples, respectively. Campylobacter spp. mapA and C. parvum 18S rRNA gene targets were not detected in any of the analyzed samples. The detection of Salmonella spp. invA was influenced by the geographical location of the sampling site (Chi-square p-value < 0.001) as well as the number of antecedent dry days prior to a rain event (p-value = 0.002, negative correlation). The antecedent dry period was negatively correlated with the occurrence of Campylobacter spp. ceuE as well (p-value = 0.07). On the other hand, the presence of G. duodenalis β-giardin in roof runoff was positively correlated with rainfall depth (p-value = 0.05). While physicochemical parameters and impervious area were not found to be correlated with the presence/absence of potentially human-infectious pathogens, significant correlations were found between meteorological parameters and the presence/absence of potentially human-infectious pathogens. Additionally, a weak, yet significant positive correlation, was found only between the concentrations of E. coli and those of Giardia duodenalis β-giardin. This dataset represents the largest-scale study to date of enteric pathogens in U.S. roof runoff collections and will inform treatment targets for different non-potable end uses for roof runoff. However, the dataset is limited by the low percent detection of bacterial and protozoan pathogens, an issue that is likely to persist challenging the characterization of roof runoff microbial quality given sampling limitations related to the volume and number of samples.