More than 70% of the human population lives within five kilometres of a natural water feature. These aquatic ecosystems are heavily used for resource provision and recreation, and represent the interface between human populations and aquatic microbiomes, which can sometimes negatively impact human health. Diverse species of endemic aquatic microorganisms, including toxic microalgae and pathogenic bacteria, can be harmful to humans. Aquatic ecosystems are also subject to intrusions of allochthonous pathogenic microorganisms through pollution and runoff. Notably, environmental processes that amplify the abundance and impact of harmful aquatic microorganisms are occurring with increasing frequency owing to climate change. For instance, increases in water temperature stimulate outbreaks of pathogenic and toxic species, whereas more intense precipitation events escalate microbial contamination from stormwater discharge. In this Perspective we discuss the influence of aquatic microbiomes on the health and economies of human populations and examine how climate change is increasing these impacts.

Anthropogenic particles (AP), which include microplastics and other synthetic, semisynthetic, and anthropogenically modified materials, are pollutants of concern in aquatic ecosystems worldwide. Rivers are important conduits and retention sites for AP, and time series data on the movement of these particles in lotic ecosystems are needed to assess the role of rivers in the global AP cycle. Much research assessing AP pollution extrapolates stream loads based on single time point measurements, but lotic ecosystems are highly variable over time (e.g., seasonality and storm events). The accuracy of models describing AP dynamics in rivers is constrained by the limited studies that examine how frequent changes in discharge drive particle retention and transport. This study addressed this knowledge gap by using automated, high-resolution sampling to track AP concentrations and fluxes during multiple storm events in an urban river (Milwaukee River) and comparing these measurements to commonly monitored water quality metrics. AP concentrations and fluxes varied significantly across four storm events, highlighting the temporal variability of AP dynamics. When data from the sampling periods were pooled, there were increases in particle concentration and flux during the early phases of the storms, suggesting that floods may flush AP into the river and/or resuspend particles from the benthic zone. AP flux was closely linked to river discharge, suggesting large loads of AP are delivered downstream during storms. Unexpectedly, AP concentrations were not correlated with other simultaneously measured water quality metrics, including total suspended solids, fecal coliforms, chloride, nitrate, and sulfate, indicating that these metrics cannot be used to estimate AP. These data will contribute to more accurate models of particle dynamics in rivers and global plastic export to oceans.Practitioner Points Anthropogenic particle (AP) concentrations and fluxes in an urban river varied across four storm events. AP concentrations and fluxes were the highest during the early phases of the storms. Storms increased AP transport downstream compared with baseflow. AP concentrations did not correlate with other water quality metrics during storms. This study used automated samplers to collect anthropogenic particles (AP) from the Milwaukee River during four storm events. AP concentrations and fluxes varied with storm phase, and storms increased AP transport downstream compared with baseflow. AP concentrations did not correlate with other water quality metrics during storms. image

Plasmids are extrachromosomal genetic elements that often encode fitness-enhancing features. However, many bacteria carry "cryptic" plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes and is 14 times as numerous as crAssphage, currently established as the most abundant extrachromosomal genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales, and although it does not appear to impact bacterial host fitness in vivo, it can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an alternative approach to track human colonic inflammatory states.

Infrastructure renewal and public health efforts require prediction of climate change effects on the occurrence of pathogens in the Great Lakes' urban coastal waters. This paper presents an investigation that addressed the climate change effects on transport and the fate of bacteria in Milwaukee's urban coastal area. This investigation was part of a study on climate change risks and impacts that included downscaling of climate change data for meteorological stations around Lake Michigan, and implementation of a hydrologic model that predicts tributary flows and bacteria loads. A method to select scenarios appropriate to link watershed and lake transport processes is presented. For the watershed, the sensitivity of bacterial loads with respect to changes in spring-season precipitation and air temperature is critical, while for lake transport, the most important driver is the wind field. Watershed and lake processes are linked by using spring-season watershed loading in the simulation of coastal transport. Scenarios for hydrodynamic modeling were developed by selecting climate projections that yielded high-and-low percentile projected spring-season wind speed. The patterns of bacteria transport showed significant changes under climate change conditions, and the changes in fecal coliform concentration at critical locations were explained by changes in current vector fields. HIGHLIGHTS Climate change impacts bacteria transport in the coastal region by developing a numerical model. Downscaling of climate change data for Lake Michigan. Hydrologic model that predicts tributary flows and bacteria loads under climate change scenarios. Sensitivity analysis of bacterial loads from tributaries due to climate changes. The patterns of bacteria transport due to changes under climate change.

Human population pressures and activities pose unprecedented challenges to water resources in urban environments. However, standard methods of assessing microbial water quality have relied on the same cultured organisms for decades. We show that there is a conserved microbial assemblage in untreated sewage that can be exploited to improve global sewage surveillance. Among harbour and coastal water samples from 18 cities across 5 continents (n = 442), nearly half had evidence of sewage contamination using two human faecal bacteria as molecular indicators. In contrast, conventional measures using cultured Escherichiacoli or enterococci only exceeded water quality limits in similar to 18% of samples, with less than half of these demonstrating sewage indicators. Contaminated locations also displayed a signature characteristic of microorganisms mainly derived from sewer infrastructure. Given the human health risk, loss of ecosystem services and economic costs associated with contaminated coastal waters, molecular approaches could provide more reliable information on sewage contamination of urban waterways.

Human sewage contaminates waterways, delivering excess nutrients, pathogens, chemicals, and other toxic contaminants. Contaminants and various sewage indicators are measured to monitor and assess water quality, but these analytes vary in their representation of sewage contamination and the inferences about water quality they support. We measured the occurrence and concentration of multiple microbiological (n = 21) and chemical (n = 106) markers at two urban stream locations in Milwaukee, Wisconsin, USA over two years. Five-day composite water samples (n = 98) were collected biweekly, and sewage influent samples (n = 25) were collected monthly at a Milwaukee, WI water reclamation facility. We found the vast majority of markers were not sensitive enough to detect sewage contamination. To compare analytes for monitoring applications, five consistently detected human sewage indicators were used to evaluate temporal patterns of sewage contamination, including microbiological (pepper mild mottle virus, human Bacteroides, human Lachnospiraceae) and chemical (acetaminophen, metformin) markers. The proportion of human sewage in each stream was estimated using the mean influent concentration from the water reclamation facility and the mean concentration of all stream samples for each sewage indicator marker. Estimates of instream sewage pollution varied by marker, differing by up to two orders of magnitude, but four of the five sewage markers characterized Underwood Creek (mean proportions of human sewage ranged 0.0025 % - 0.075 %) as less polluted than Menomonee River (proportions ranged 0.013 % - 0.14 %) by an order of magnitude more. Chemical markers correlated with each other and yielded higher estimates of sewage pollution than microbial markers, which exhibited greater temporal variability. Transport, attenuation, and degradation processes can influence chemical and microbial markers differently and cause variation in human sewage estimates. Given the range of potential human and ecological health effects of human sewage contamination, robust characterization of sewage contamination that uses multiple lines of evidence supports monitoring and research applications.

Wastewater monitoring has provided health officials with early warnings for new COVID-19 outbreaks, but to date, no approach has been validated to distinguish signal (sustained surges) from noise (background variability) in wastewater data to alert officials to the need for heightened public health response. We analyzed 62 wk of data from 19 sites participating in the North Carolina Wastewater Monitoring Network to characterize wastewater metrics around the Delta and Omicron surges. We found that wastewater data identified outbreaks 4 to 5 d before case data (reported on the earlier of the symptom start date or test collection date), on average. At most sites, correlations between wastewater and case data were similar regardless of how wastewater concentrations were normalized and whether calculated with county-level or sewershed-level cases, suggesting that officials may not need to geospatially align case data with sewershed boundaries to gain insights into disease transmission. Although wastewater trend lines captured clear differences in the Delta versus Omicron surge trajectories, no single wastewater metric (detectability, percent change, or flow-population normalized viral concentrations) reliably signaled when these surges started. After iteratively examining different combinations of these three metrics, we developed the Covid-SURGE (Signaling Unprecedented Rises in Groupwide Exposure) algorithm, which identifies unprecedented signals in the wastewater data. With a true positive rate of 82%, a false positive rate of 7%, and strong performance during both surges and in small and large sites, our algorithm provides public health officials with an automated way to flag community-level COVID-19 surges in real time.

Plasmids are extrachromosomal genetic elements that often encode fitness enhancing features. However, many bacteria carry ‘cryptic’ plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes, and is 14 times as numerous as crAssphage, currently established as the most abundant genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one’s mother. pBI143 can transfer between Bacteroidales and although it does not appear to impact bacterial host fitness in vivo, can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an inexpensive alternative for detecting human colonic inflammatory states.

Human sewage contamination of waterways is a major issue in the United States and throughout the world. Models were developed for estimation of two human-associated fecal-indicator and three general fecal-indicator bacteria (HIB and FIB) using in situ optical field-sensor data for estimating concentrations and loads of HIB and FIB and the extent of sewage contamination in the Menomonee River in Milwaukee, Wisconsin. Three commercially available optical sensor platforms were installed into an unfiltered custom-designed flow-through system along with a refrigerated automatic sampler at the Menomonee River sampling location. Ten-minute optical sensor measurements were made from November 2017 to December 2018 along with the collection of 153 flow-weighted discrete water samples (samples) for HIB, FIB, dissolved organic carbon (DOC), and optical properties of water. Of those 153 samples, 119 samples were from event-runoff periods, and 34 were collected during low-flow periods. Of the 119 event-runoff samples, 43 samples were from event-runoff combined sewer overflow (CSO) influenced periods (event-CSO periods). Models included optical sensor measurements as explanatory variables with a seasonal variable as an interaction term. In some cases, separate models for event-CSO periods and non CSO-periods generally improved model performance, as compared to using all the data combined for estimates of FIB and HIB. Therefore, the CSO and non-CSO models were used in final estimations for CSO and non-CSO time periods, respectively. Estimated continuous concentrations for all bacteria markers varied over six orders of magnitude during the study period. The greatest concentrations, loads, and proportion of sewage contamination occurred during event-runoff and event-CSO periods. Comparison to water quality standards and microbial risk assessment benchmarks indicated that estimated bacteria levels exceeded recreational water quality criteria between 34 and 96% of the entire monitoring period, highlighting the benefits of high-frequency monitoring compared to traditional grab sample collection. The application of optical sensors for estimation of HIB and FIB markers provided a thorough assessment of bacterial presence and human health risk in the Menomonee River.

Microbes have inhabited the oceans and soils for millions of years and are uniquely adapted to their habitat. In contrast, sewer infrastructure in modern cities dates back only similar to 150 years. Sewer pipes transport human waste and provide a view into public health, but the resident organisms that likely modulate these features are relatively unexplored. Here, we show that the bacterial assemblages sequenced from untreated wastewater in 71 U.S. cities were highly coherent at a fine sequence level, suggesting that urban infrastructure separated by great spatial distances can give rise to strikingly similar communities. Within the overall microbial community structure, temperature had a discernible impact on the distribution patterns of closely related amplicon sequence variants, resulting in warm and cold ecotypes. Two bacterial genera were dominant in most cities regardless of their size or geographic location; on average, Arcobacter accounted for 11% and Acinetobacter 10% of the entire community. Metagenomic analysis of six cities revealed these highly abundant resident organisms carry clinically important antibiotic resistant genes bla(CTX-M), bla(OXA), and bla(TEM). In contrast, human fecal bacteria account for only similar to 13% of the community; therefore, antibiotic resistance gene inputs from human sources to the sewer system could be comparatively small, which will impact measurement capabilities when monitoring human populations using wastewater. With growing awareness of the metabolic potential of microbes within these vast networks of pipes and the ability to examine the health of human populations, it is timely to increase our understanding of the ecology of these systems. IMPORTANCE Sewer infrastructure is a relatively new habitat comprised of thousands of kilometers of pipes beneath cities. These wastewater conveyance systems contain large reservoirs of microbial biomass with a wide range of metabolic potential and are significant reservoirs of antibiotic resistant organisms; however, we lack an adequate understanding of the ecology or activity of these communities beyond wastewater treatment plants. The striking coherence of the sewer microbiome across the United States demonstrates that the sewer environment is highly selective for a particular microbial community composition. Therefore, results from more in-depth studies or proven engineering controls in one system could be extrapolated more broadly. Understanding the complex ecology of sewer infrastructure is critical for not only improving our ability to treat human waste and increasing the sustainability of our cities but also to create scalable and effective sewage microbial observatories, which are inevitable investments of the future to monitor health in human populations.

Wastewater-based epidemiology has gained attention throughout the world for detection of SARS-CoV-2 RNA in wastewater to supplement clinical testing. Raw wastewater consists of small particles, or solids, suspended in liquid. Methods have been developed to measure SARS-CoV-2 RNA in the liquid and the solid fraction of wastewater, with some studies reporting higher concentrations in the solid fraction. To investigate this relationship further, six laboratories collaborated to conduct a study across five publicly owned treatment works (POTWs) where both primary settled solids obtained from primary clarifiers and raw wastewater influent samples were collected and quantified for SARS-CoV-2 RNA. Settled solids and influent samples were processed by participating laboratories using their respective methods and retrospectively paired based on date of collection. SARS-CoV-2 RNA concentrations, on a mass equivalent basis, were higher in settled solids than in influent by approximately three orders of magnitude. Concentrations in matched settled solids and influent were positively and significantly correlated at all five POTWs. RNA concentrations in both settled solids and influent were correlated to COVID-19 incidence rates in the sewersheds and thus representative of disease occurrence; the settled solids methods appeared to produce a comparable relationship between SARS-CoV-2 RNA concentration measurements and incidence rates across all POTWs. Settled solids and influent methods showed comparable sensitivity, N gene detection frequency, and calculated empirical incidence rate lower limits. Analysis of settled solids for SARS-CoV-2 RNA has the advantage of using less sample volume to achieve similar sensitivity to influent methods.

Wastewater surveillance for SARS-CoV-2 is being used worldwide to understand COVID-19 infection trends in a community. We found the emergence and rapid timeline for dominance of the Omicron variant was accurately reflected in wastewater when measured with droplet digital (dd)PCR. We were able to distinguish Omicron from the circulating Delta variant because Omicron has a mutation in the N1 probe binding region that diminished the fluorescent signal within individual droplets. The ddPCR platform may be advantageous for wastewater surveillance since analysis of the data can segregate fluorescent signals from different individual templates. In contrast, platforms such as qPCR that rely solely on the intensity of fluorescence for quantification would not distinguish a subset of variants with mutations affecting the reaction and could underestimate SARS-CoV-2 concentrations. The proportion of Omicron in wastewater was tightly correlated to clinical cases in five cities and provided a higher resolution timeline of appearance and dominance (>75%) than sequenced clinical samples, which were limited in less populated areas. Taken together, this work demonstrates wastewater is a reliable metric for tracking SARS-CoV-2 at a population level.

There are few biological indicators for freshwater systems subjected to high chloride levels. Freshwater systems receive many forms of chloride such as road salts (e.g., NaCl, CaCl2, MgCl2), fertilizers (e.g., KCl), and year-round water softener pollution. The goal our study was to investigate Halomonadaceae populations as prospective biological indicators of chloride-impacted freshwaters. The bacterial family Halomonadaceae are halophiles that generally require the presence of salt to survive, which make them an attractive candidate in determining chloride impaired areas. Field sediment surveys assessed how salt tolerant and halophilic bacteria abundance corresponded to chloride and conductivity measurements. Colony forming unit (CFU) counts on modified M9 6% NaCl plates (w/v) at urbanized sites compared to the rural sites had highest counts during winter and spring when chloride concentrations were also highest. Select isolates identified as Halomonadaceae through 16S rRNA sequencing were kept as active cultures to determine the NaCl concentration and temperature preference that resulted in the isolates optimal growth. Isolates tested under 5 °C (cold) grew optimally in 2 % NaCl (w/v), whereas under 18 °C (warm), isolates showed optimal growth at 6 % NaCl. The majority of isolates had maximum growth in the warmer temperature, however, select isolates grew better in the cold temperature. Culture-independent methods were used and identified Halomonadaceae were widespread and permeant members of the microbial community in a Lake Michigan drainage basin. Quantitative polymerase chain reaction (qPCR) targeting Halomonadaceae genera demonstrated that abundance varied by site, but overall were present throughout the year. However, community sequencing revealed there were a large relative proportion of specific Halomonadaceae populations present in winter versus summer. Methods targeting salt tolerant bacteria and specific members of Halomonadaceae appears to be a promising approach to assess chloride-impacted areas to better understand the long-term ecological impacts as we continue to salinize freshwater resources.

Anthropogenic waste streams can be major sources of antibiotic resistant microbes within the environment, creating a potential risk to public health. We examined patterns in the occurrence of a suite of antibiotic resistance genes (ARGs) and their links to enteric bacteria at a popular swimming beach in Australia that experiences intermittent contamination by sewage, with potential points of input including stormwater drains and a coastal lagoon. Samples were collected throughout a significant rainfall event (40.8 mm over 3 days) and analysed using both qPCR and 16S rRNA amplicon sequencing. Before the rainfall event, low levels of faecal indicator bacteria and a microbial source tracking human faeces (sewage) marker (Lachno3) were observed. These levels increased over 10x following rainfall. Within lagoon, drain and seawater samples, levels of the ARGs sulI, dfrA1 and qnrS increased by between 1 and 2 orders of magnitude after 20.4 mm of rain, while levels of tetA increased by an order of magnitude after a total of 40.8 mm. After 40.8 mm of rain sulI, tetA and qnrS could be detected 300 m offshore with levels remaining high five days after the rain event. Highest levels of sewage markers and ARGs were observed adjacent to the lagoon (when opened) and in-front of the stormwater drains, pinpointing these as the points of ARG input. Significant positive correlations were observed between all ARGs, and a suite of Amplicon Sequence Variants that were identified as stormwater drain indicator taxa using 16S rRNA amplicon sequencing data. Of note, some stormwater drain indicator taxa, which exhibited correlations to ARG abundance, included the human pathogens Arcobacter butzleri and Bacteroides fragilis. Given that previous research has linked high levels of ARGs in recreationally used environments to antimicrobial resistant pathogen infections, the observed patterns indicate a potentially elevated human health risk at a popular swimming beach following significant rainfall events.

Water quality impairments at urban beaches can be challenging to manage due to a wide range of possible pollutant sources, including both regional and local sources. In this study, we used high-resolution time series data as the input into a 3D hydrodynamic model to predict Escherichia coli (E. coli) and human Bacteroides marker concentrations at nearby beaches. Model results predicted that both organisms reach beaches following select wet weather events and during combined sewer overflows (CSOs), but in 50% of these events, the E. coli model predictions were <235 CFU/100 mL when human sources were present. Local pollutant sources were also evaluated during a variety of conditions using fecal source markers, including human, gull, and dog bacterial markers. Gull waste was frequently detected at beaches. In contrast, human markers were rarely detected, but when present, detection generally coincided with CSOs and low levels of E. coli, suggesting dilute regional contamination. Together, the results suggest that regional fecal pollution containing human sources does not coincide with elevated E. coli levels, and localized contamination likely drives E. coli levels at beaches. This work highlights the need to consider risk-based metrics rather than relying on a single easily measured indicator at beaches.

Escherichia coli is commonly used as an indicator of recent fecal pollution in recreational water despite its known ability to survive in secondary environments, such as beach sand. These long-term survivors from sand reservoirs can be introduced into the water column through wave action or runoff during precipitation events, thereby impacting the perception of local water quality.

Wastewater surveillance for SARS-CoV-2 uses droplet digital (dd)PCR for absolute quantification and high sensitivity. We found ddPCR can also distinguish and accurately quantify Delta and Omicron variants due to a mutation in the probe binding site.

Wastewater surveillance for SARS-CoV-2 is becoming a widespread public health metric, but little is known about pre-analytical influences on these measurements. We examined SARS-CoV-2 loads from two sewer service areas with different travel times that were within the same metropolitan area. Throughout the one-year study, case rates were nearly identical between the two service areas allowing us to compare differences in empirical concentrations relative to conveyance system characteristics and wastewater treatment plant parameters. We found time did not have a significant effect on degradation of SARS-CoV-2 when using average transit times (22 vs. 7.5 h) (p = 0.08), or under low flow conditions when transit times are greater (p = 0.14). Flow increased rather than decreased SARS-CoV-2 case-adjusted concentrations, but this increase was only significant in one service area. Warmer temperatures (16.8–19.8 °C) compared with colder (8.4–12.3 °C) reduced SARS-CoV-2 case-adjusted loads by ~50% in both plants (p < 0.05). Decreased concentrations in warmer temperatures may be an important factor to consider when comparing seasonal dynamics. Oxygen demand and suspended solids had no significant effect on SARS-CoV-2 case-adjusted loads overall. Understanding wastewater conveyance system influences prior to sample collection will improve comparisons of regional or national data for SARS-CoV-2 community infections.

Background: In just over 2 years, tracking the COVID-19 pandemic through wastewater surveillance advanced from early reports of successful SARS-CoV-2 RNA detection in untreated wastewater to implementation of programs in at least 60 countries. Early wastewater monitoring efforts primarily originated in research laboratories and are now transitioning into more formal surveillance programs run in commercial and public health laboratories. A major challenge in this progression has been to simultaneously optimize methods and build scientific consensus while implementing surveillance programs, particularly during the rapidly changing landscape of the pandemic. Translating wastewater surveillance results for effective use by public health agencies also remains a key objective for the field. Objectives: We examined the evolution of wastewater surveillance to identify model collaborations and effective partnerships that have created rapid and sustained success. We propose needed areas of research and key roles academic researchers can play in the framework of wastewater surveillance to aid in the transition from early monitoring efforts to more formalized programs within the public health system. Discussion: Although wastewater surveillance has rapidly developed as a useful public health tool for tracking COVID-19, there remain technical challenges and open scientific questions that academic researchers are equipped to address. This includes validating methodology and backfilling important knowledge gaps, such as fate and transport of surveillance targets and epidemiological links to wastewater concentrations. Our experience in initiating and implementing wastewater surveillance programs in the United States has allowed us to reflect on key barriers and draw useful lessons on how to promote synergy between different areas of expertise. As wastewater surveillance programs are formalized, the working relationships developed between academic researchers, commercial and public health laboratories, and data users should promote knowledge co-development. We believe active involvement of academic researchers will contribute to building robust surveillance programs that will ultimately provide new insights into population health. https://doi.org/10.1289/EHP11519