The reproducibility and sensitivity of 36 methods for quantifying the genetic signal of SARS-CoV-2 in wastewater was evaluated in a nationwide interlaboratory assessment in the U.S.

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In response to COVID-19, the international water community rapidly developed methods to quantify the SARS-CoV-2 genetic signal in untreated wastewater. Wastewater surveillance using such methods has the potential to complement clinical testing in assessing community health. This interlaboratory assessment evaluated the reproducibility and sensitivity of 36 standard operating procedures (SOPs), divided into eight method groups based on sample concentration approach and whether solids were removed. Two raw wastewater samples were collected in August 2020, amended with a matrix spike (betacoronavirus OC43), and distributed to 32 laboratories across the U.S. Replicate samples analyzed in accordance with the project's quality assurance plan showed high reproducibility across the 36 SOPs: 80% of the recovery-corrected results fell within a band of +/- 1.15-log10 genome copies/L with higher reproducibility observed within a single SOP (standard deviation of 0.13-log10). The inclusion of a solids removal step and the selection of a concentration method did not show a clear, systematic impact on the recovery-corrected results. Other methodological variations (e.g., pasteurization, primer set selection, and use of RT-qPCR or RT-dPCR platforms) generally resulted in small differences compared to other sources of variability. These findings suggest that a variety of methods are capable of producing reproducible results, though the same SOP or laboratory should be selected to track SARS-CoV-2 trends at a given facility. The methods showed a 7-log10 range of recovery efficiency and limit of detection highlighting the importance of recovery correction and the need to consider method sensitivity when selecting methods for wastewater surveillance.

Traditionally, microbial risk assessors have used point estimates to evaluate the probability that an individual will become infected. We developed a quantitative approach that shifts the risk characterization perspective from point estimate to distributional estimate, and from individual to population. To this end, we first designed and implemented a dynamic model that tracks traditional epidemiological variables such as the number of susceptible, infected, diseased, and immune, and environmental variables such as pathogen density. Second, we used a simulation methodology that explicitly acknowledges the uncertainty and variability associated with the data. Specifically, the approach consists of assigning probability distributions to each parameter, sampling from these distributions for Monte Carlo simulations, and using a binary classification to assess the output of each simulation. A case study is presented that explores the uncertainties in assessing the risk of giardiasis when swimming in a recreational impoundment using reclaimed water. Using literature-based information to assign parameters ranges, our analysis demonstrated that the parameter describing the shedding of pathogens by infected swimmers was the factor that contributed most to the uncertainty in risk. The importance of other parameters was dependent on reducing the a priori range of this shedding parameter. By constraining the shedding parameter to its lower subrange, treatment efficiency was the parameter most important in predicting whether a simulation resulted in prevalences above or below non outbreak levels. Whereas parameters associated with human exposure were important when the shedding parameter was constrained to a higher subrange. This Monte Carlo simulation technique identified conditions in which outbreaks and/or nonoutbreaks are likely and identified the parameters that most contributed to the uncertainty associated with a risk prediction.

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Although adverse reproductive outcomes have been associated with arsenic exposure, the extent and severity of the effects of chronic inhalation of low levels of arsenic on reproduction are not known. We conducted a hospital-based case-control study of stillbirths in a central Texas community that included a facility with more than a 60-year history of producing primarily arsenic-based agricultural products. We collected data on 119 cases and 267 controls randomly selected from healthy live-births at the same hospital and matched for year of birth. We abstracted medical and demographic data for the period January 1, 1983, to December 31, 1993, from hospital records and estimated socioeconomic status by median income from the 1990 Population and Housing Census data. We estimated arsenic exposure levels from airborne emission estimates and an atmospheric dispersion model and linked the results to a geographical information system (GIS) database. Exposure was linked by GIS to residential address at time of delivery. A conditional logistic regression model was fitted including maternal age, race/ethnicity, parity, income group, exposure as a categorical variable, and exposure-race/ethnicity interaction. The prevalence odds ratio observed for Hispanics in the high-exposure group (>100 ng per m3 arsenic) was 8.4, with a 95% confidence interval of 1.4-50.1.

A scientific advisory panel was convened by the State of California to recommend monitoring for chemicals of emerging concern (CECs) in aquatic systems that receive discharge of municipal wastewater treatment plant (WWTP) effluent and stormwater runoff. The panel developed a risk-based screening framework that considered environmental sources and fate of CECs observed in receiving waters across the State. Using existing occurrence and risk threshold data in water, sediment, and biological tissue, the panel applied the framework to identify a priority list of CECs for initial monitoring in three representative receiving water scenarios. The initial screening list of 16 CECs identified by the panel included consumer and commercial chemicals, flame retardants, pesticides, pharmaceuticals and personal care products, and natural hormones. The panel designed an iterative, phased strategy with interpretive guidelines that direct and update management actions commensurate with potential risk identified using the risk-based framework and monitoring data. Because of the ever-changing nature of chemical use, technology, and management practices, the panel offered recommendations to improve CEC monitoring, including development of bioanalytical screening methods whose responses integrate exposure to complex mixtures and that can be linked to higher-order effects; development or refinement of models that predict the input, fate, and effects of future chemicals; and filling of key data gaps on CEC occurrence and toxicity. Finally, the panel stressed the need for adaptive management, allowing for future review of, and if warranted, modifications to the strategy to incorporate the latest science available to the water resources community.

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