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Candida auris transmission is steadily increasing across the United States. We report culture-based detection of C. auris in wastewater and the epidemiologic link between isolated strains and southern Nevada, USA, hospitals within the sampled sewershed. Our results illustrate the potential of wastewater surveillance for containing C. auris.

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has garnered extensive public attention during the coronavirus disease pandemic as a proposed complement to existing disease surveillance systems. Over the past year, methods for detection and quantification of SARS-CoV-2 viral RNA in untreated sewage have advanced, and concentrations in wastewater have been shown to correlate with trends in reported cases. Despite the promise of wastewater surveillance, for these measurements to translate into useful public health tools, bridging the communication and knowledge gaps between researchers and public health responders is needed. We describe the key uses, barriers, and applicability of SARS-CoV-2 wastewater surveillance for supporting public health decisions and actions, including establishing ethics consideration for monitoring. Although wastewater surveillance to assess community infections is not a new idea, the coronavirus disease pandemic might be the initiating event to make this emerging public health tool a sustainable nationwide surveillance system, provided that these barriers are addressed.

Candida auris is considered a nosocomial pathogen of high concern and is currently spreading across the United States. Infection control measures for C. auris focus mainly on healthcare facilities, yet transmission levels may already be significant in the community before outbreaks are detected in healthcare settings. Wastewater-based epidemiology (culture, quantitative PCR, and whole-genome sequencing) can potentially gauge pathogen transmission in the general population and lead to early detection of C. auris before it is detected in clinical cases. To learn more about the sensitivity and limitations of wastewater-based surveillance, we used wastewater-based methods to detect C. auris in a southern Utah jurisdiction with no known clinical cases before and after the documented transfer of colonized patients from bordering Nevada. Our study illustrates the potential of wastewater-based surveillance for being sufficiently sensitive to detect C. auris transmission during the early stages of introduction into a community.

Genome sequencing from wastewater enables accurate and cost-effective identification of SARS-CoV-2 variants. However, existing computational pipelines have limitations in detecting emerging variants not yet characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns to identify SARS-CoV-2 variants. To build our model, we sequence 3659 wastewater samples collected over two years from urban and rural locations in Southern Nevada. We then develop a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources. These data-driven time-evolving and co-varying sources are compared to 8810 SARS-CoV-2 clinical genomes from Nevadans. Our method accurately detects the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach also reveals the spatial and temporal dynamics of variants in both urban and rural regions; achieves earlier detection of most variants compared to other computational tools; and uncovers unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and supports accurate early detection of SARS-CoV-2 variants. This feature offers a unique opportunity to detect emerging variants and pathogens, even in the absence of clinical testing. Wastewater surveillance can help in pandemic or outbreak response. Here, the authors report an unsupervised learning approach to detect emerging SARS-CoV-2 variants from rural and urban wastewater showing it achieves earlier detection than existing methods and detects new variants without clinical testing data.

Solids retention time (SRT) is one of the most important factors in designing and operating activated sludge systems for biological wastewater treatment. Longer SRTs have been shown to alter the structure and function of microbial communities, thereby leading to improved treatment efficacy with respect to bulk and trace organics, nutrient removal, and membrane fouling. Research has also shown that longer SRTs and/or higher influent antibiotic concentrations may lead to increased prevalence of antibiotic resistance. However, it is unclear whether elevated, yet subclinical, concentrations of antibiotics also impact the overall microbial community. The purpose of this study was to characterize changes in microbial community structure in a laboratory-scale activated sludge system as a function of SRT (2–20 days) and influent concentrations (1×–100× ambient) of ampicillin, sulfamethoxazole, tetracycline, trimethoprim, and vancomycin. Changes in microbial community structure were evaluated based on 16S rRNA gene sequencing, and microbial community function was evaluated based on changes in effluent water quality, including attenuation of bulk and trace organics. The results confirmed that longer SRTs—but not antibiotic loadings—had a significant impact on microbial community structure and effluent water quality. Therefore, moderate spikes in influent antibiotic concentrations are not expected to adversely impact biological wastewater treatment.

This study examined the effect of the amino acid composition of protein capsids on virus inactivation using ultraviolet (UV) irradiation and titanium dioxide photocatalysis, and physical removal via enhanced coagulation using ferric chloride. Although genomic damage is likely more extensive than protein damage for viruses treated using UV, proteins are still substantially degraded. All amino acids demonstrated significant correlations with UV susceptibility. The hydroxyl radicals produced during photocatalysis are considered nonspecific, but they likely cause greater overall damage to virus capsid proteins relative to the genome. Oxidizing chemicals, including hydroxyl radicals, preferentially degrade amino acids over nucleotides, and the amino acid tyrosine appears to strongly influence virus inactivation. Capsid composition did not correlate strongly to virus removal during physicochemical treatment, nor did virus size. Isoelectric point may play a role in virus removal, but additional factors are likely to contribute.

Potable reuse is becoming an increasingly common strategy for bolstering water resource portfolios in water-scarce regions. Each application poses unique challenges, whether related to treatment goals, regulatory requirements, or political and public acceptance, and these issues have a significant impact on the final treatment train selection. This review describes the various potable reuse frameworks and illustrates the importance of environmental buffers as a treatment barrier and as a distinction between ‘indirect’ and ‘direct’ potable reuse applications. This review also highlights more than 20 potable reuse treatment trains currently in operation or under construction throughout the world. The unit processes in each train are identified and a brief summary of their advantages and limitations in relation to alternative processes is included.

[...]treated effluent monitoring has limited (if any) value for assessing and responding to public health conditions in the upstream community. WBS data are not useful for monitoring permit compliance, evaluating wastewater treatment processes, or ensuring receiving water protection. [...]this regulatory framework is inappropriate for WBS. [...]WBS programs rely on input from both public health departments and WRRFs to produce the most valuable data for local public health interventions at the most suitable locations. Since the early days of the COVID-19 pandemic, many WRRFs have willingly volunteered to collaborate with health departments to create successful WBS programs by contributing samples and other resources. [...]NPDES permit-based monitoring and reporting requirements typically apply either to the WRRF influent (which provides data at larger regional scales than upstream sampling) or to treated effluent (which is not useful for WBS due to degradation of markers throughout the treatment processes). [...]permit-driven monitoring would either not provide the data granularity needed for public health interventions or would generate confounded data due to the effects of wastewater treatment on opioids. Many small WRRFs have limited resources and are often located in rural areas, where opioid abuse has been especially pronounced [25]. [...]updating all WRRFs to be equipped for on-site opioid analysis is impractical.

Common to all National Pollutant Discharge Elimination System (NPDES) permits in the United States is a limit on biochemical oxygen demand (BOD). Chemical oxygen demand (COD), total organic carbon (TOC), and fluorescence spectroscopy are also capable of quantifying organic content, although the mechanisms of quantification and the organic fractions targeted differ for each test. This study explores correlations between BOD₅ and these alternate test procedures using facility influent, primary effluent, and facility effluent samples from a full-scale water resource recovery facility. Relative reductions of the water quality parameters proved to be strong indicators of their suitability as surrogates for BOD₅. Suitable correlations were generally limited to the combined datasets for the three sampling locations or the facility effluent alone. COD exhibited relatively strong linear correlations with BOD₅ when considering the three sample points (r = 0.985) and the facility effluent alone (r = 0.914), while TOC exhibited a suitable linear correlation with BOD₅ in the facility effluent (r = 0.902). Exponential regressions proved to be useful for estimating BOD₅ based on TOC or fluorescence (r > 0.95).

Temporal variability and methodological differences in data normalization, among other factors, complicate effective trend analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) wastewater surveillance data and its alignment with coronavirus disease 2019 (COVID-19) clinical outcomes. As there is no consensus approach for these analyses yet, this study explored the use of piecewise linear trend analysis (joinpoint regression) to identify significant trends and trend turning points in SARS-CoV-2 RNA wastewater concentrations (normalized and non-normalized) and corresponding COVID-19 case rates in the greater Las Vegas metropolitan area (Nevada, USA) from mid-2020 to April 2023. The analysis period was stratified into three distinct phases based on temporal changes in testing protocols, vaccination availability, SARS-CoV-2 variant prevalence, and public health interventions. While other statistical methodologies may require fewer parameter specifications, joinpoint regression provided an interpretable framework for characterization and comparison of trends and trend turning points, revealing sewershed-specific variations in trend magnitude and timing that also aligned with known variant-driven waves. Week-level trend agreement corroborated previous findings demonstrating a close relationship between SARS-CoV-2 wastewater surveillance data and COVID-19 outcomes. These findings guide future applications of advanced statistical methodologies and support the continued integration of wastewater-based epidemiology as a complementary approach to traditional COVID-19 surveillance systems.