Wastewater surveillance of SARS-CoV-2 RNA is increasingly being incorporated into public health efforts to respond to the COVID-19 pandemic. In order to obtain the maximum benefit from these efforts, approaches to wastewater monitoring need to be rapid, sensitive, and relatable to relevant epidemiological parameters. In this study, we present an ultracentrifugation-based method for the concentration of SARS-CoV-2 wastewater RNA and use crAssphage, a bacteriophage specific to the human gut, to help account for RNA loss during transit in the wastewater system and sample processing. With these methods, we were able to detect, and sometimes quantify, SARS-CoV-2 RNA from 20 mL wastewater samples within as little as 4.5 hours. Using known concentrations of bovine coronavirus RNA and deactivated SARS-CoV-2, we estimate recovery rates of approximately 7-12% of viral RNA using our method. Results from 24 sewersheds across Upstate New York during the spring and summer of 2020 suggested that stronger signals of SARS-CoV-2 RNA from wastewater may be indicative of greater COVID-19 incidence in the represented service area approximately one week in advance. SARS-CoV-2 wastewater RNA was quantifiable in some service areas with daily positives tests of less than 1 per 10,000 people or when weekly positive test rates within a sewershed were as low as 1.7%. crAssphage DNA concentrations were significantly lower during periods of high flow in almost all areas studied. After accounting for flow rate and population served, crAssphage levels per capita were estimated to be about 1.35 × 10 11 and 2.42 × 10 8 genome copies per day for DNA and RNA, respectively. A negative relationship between per capita crAssphage RNA and service area size was also observed likely reflecting degradation of RNA over long transit times. Our results reinforce the potential for wastewater surveillance to be used as a tool to supplement understanding of infectious disease transmission obtained by traditional testing and highlight the potential for crAssphage co-detection to improve interpretations of wastewater surveillance data.

Wastewater surveillance of SARS-CoV-2 has become an attractive tool for combating the spread of COVID-19 by assessing the presence or levels of the virus shed in a population. However, the methods to quantify viral RNA and to link those quantities to the level of infection within the community vary. In this study, we sought to identify and optimize scalable methods for recovery of viral nucleic acids from wastewater and attempted to use a constitutive member of the gut virome, human-specific crAssphage, to help account for unknown levels of SARS-CoV-2 decay and dilution in the wastewater infrastructure. Results suggest that ultracentrifugation of a small volume of wastewater through a 50% sucrose cushion followed by total nucleic acid extraction yielded quantifiable virus in an area with a modest number of COVID-19 cases. Further, the ratio of log10(SARS-CoV-2):log10(crAssphage) appears to be associated with the cumulative incidence of COVID-19 in the Syracuse, NY area. In areas where ultracentrifuges are available, these methods may be used to link SARS-CoV-2 quantities in wastewater to levels of transmission within communities with sewer service.

Several key studies have found that a small minority of producers, polluting at levels far exceeding group averages, generate the majority of overall exposure to industrial toxics. Frequently, such patterns go unnoticed and are understudied outside of the academic community. To our knowledge, no research to date has systematically described the scope and extent of extreme variations in industrially based exposure estimates and sought to link inequities in harm produced to inequities in exposure. In an analysis of all permitted industrial facilities across the United States, we show that there exists a class of hyper-polluters-the worst-of-the-worst-that disproportionately expose communities of color and low income populations to chemical releases. This study hopes to move beyond a traditional environmental justice research frame, bringing new computational methods and perspectives aimed at the empirical study of societal power dynamics. Our findings suggest the possibility that substantial environmental gains may be made through selective environmental enforcement, rather than sweeping initiatives.