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.

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.

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) 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.

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.

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.

Antimicrobial resistance (AR) is a serious global problem due to the overuse of antimicrobials in human, animal, and agriculture sectors. There is intense research to control the dissemination of AR, but little is known regarding the environmental drivers influencing its spread. Although AR genes (ARGs) are detected in many different environments, the risk associated with the spread of these genes to microbial pathogens is unknown. Recreational microbial exposure risks are likely to be greater in water bodies receiving discharge from human and animal waste in comparison to less disturbed aquatic environments. Given this scenario, research practitioners are encouraged to consider an ecological context to assess the effect of environmental ARGs on public health. Here, we use a stratified, probabilistic survey of nearly 2000 sites to determine national patterns of the anthropogenic indicator class I integron Integrase gene (intI1) and several ARGs in 1.2 million kilometers of United States (US) rivers and streams. Gene concentrations were greater in eastern than in western regions and in rivers and streams in poor condition. These first of their kind findings on the national distribution of intI1 and ARGs provide new information to aid risk assessment and implement mitigation strategies to protect public health.

The COVID-19 pandemic highlighted a wide range of public health system challenges for infectious disease surveillance. The discovery that the SARS-CoV-2 virus was shed in feces and can be characterized using PCR-based testing of sewage samples offers new possibilities and challenges for wastewater surveillance (WWS). However, WWS standardization of practices is needed to provide actionable data for a public health response. A workshop was convened consisting of academic, federal government, and industry stakeholders. The objective was to review WWS sampling protocols, testing methods, analyses, and data interpretation approaches for WWS employed nationally and identify opportunities for standardizing practices, including the development of documentary standards or reference materials in the case of SARS-CoV-2 surveillance. Other WWS potential future threats to public health were also discussed. Several aspects of WWS were considered and each offers the opportunity for standards development. These areas included sampling strategies, analytical methods, and data reporting practices. Each of these areas converged on a common theme, the challenge of results comparability across facilities and jurisdictions. For sampling, the consensus solution was the development of documentary standards to guide appropriate sampling practices. In contrast, the predominant opportunity for analytical methods was reference material development, such as PCR-based standards and surrogate recovery controls. For data reporting practices, the need for establishing the minimal required metadata, a metadata vocabulary, and standardizing data units of measure including measurement threshold definitions was discussed. Beyond SARS-CoV-2 testing, there was general agreement that the WWS platform will continue to be a valuable tool for a wide range of public health threats and that future cross-sector engagements are needed to guide an enduring WWS capability.

Background Across taxa, animals with depleted intestinal microbiomes show disrupted behavioral phenotypes. Axenic (i.e., microbe-free) mice, zebrafish, and fruit flies exhibit increased locomotor behavior, or hyperactivity. The mechanism through which bacteria interact with host cells to trigger normal neurobehavioral development in larval zebrafish is not well understood. Here, we monoassociated zebrafish with either one of six different zebrafish-associated bacteria, mixtures of these host-associates, or with an environmental bacterial isolate. Results As predicted, the axenic cohort was hyperactive. Monoassociation with three different host-associated bacterial species, as well as with the mixtures, resulted in control-like locomotor behavior. Monoassociation with one host-associate and the environmental isolate resulted in the hyperactive phenotype characteristic of axenic larvae, while monoassociation with two other host-associated bacteria partially blocked this phenotype. Furthermore, we found an inverse relationship between the total concentration of bacteria per larvae and locomotor behavior. Lastly, in the axenic and associated cohorts, but not in the larvae with complex communities, we detected unexpected bacteria, some of which may be present as facultative predators. Conclusions These data support a growing body of evidence that individual species of bacteria can have different effects on host behavior, potentially related to their success at intestinal colonization. Specific to the zebrafish model, our results suggest that differences in the composition of microbes in fish facilities could affect the results of behavioral assays within pharmacological and toxicological studies.

Antimicrobial resistance (AMR) in the environment is a growing global health concern, especially the dissemination of AMR into surface waters due to human and agricultural inputs. Within recent years, research has focused on trying to understand the impact of AMR in surface waters on human, agricultural and ecological health (One Health). While surface water quality assessments and surveillance of AMR have historically utilized culture-based methods, culturing bacteria has limitations due to difficulty in isolating environmental bacteria and the need for a priori information about the bacteria for selective isolation. The use of molecular techniques to analyze AMR at the genetic level has helped to overcome the difficulties with culture-based techniques since they do not require advance knowledge of the bacterial population and can analyze uncultivable environmental bacteria. The aim of this review is to provide an overview of common contemporary molecular methods available for analyzing AMR in surface waters, which include high throughput real-time polymerase chain reaction (HTqPCR), metagenomics, and whole genome sequencing. This review will also feature how these methods may provide information on human and animal health risks. HT-qPCR works at the nanoliter scale, requires only a small amount of DNA, and can analyze numerous gene targets simultaneously, but may lack in analytical sensitivity and the ability to optimize individual assays compared to conventional qPCR. Metagenomics offers more detailed genomic information and taxonomic resolution than PCR by sequencing all the microbial genomes within a sample. Its open format allows for the discovery of new antibiotic resistance genes; however, the quantity of DNA necessary for this technique can be a limiting factor for surface water samples that typically have low numbers of bacteria per sample volume. Whole genome sequencing provides the complete genomic profile of a single environmental isolate and can identify all genetic elements that may confer AMR. However, a main disadvantage of this technique is that it only provides information about one bacterial isolate and is challenging to utilize for community analysis. While these contemporary techniques can quickly provide a vast array of information about AMR in surface waters, one technique does not fully characterize AMR nor its potential risks to human, animal, or ecological health. Rather, a combination of techniques (including both molecular- and culture-based) are necessary to fully understand AMR in surface waters from a One Health perspective.