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Though safe drinking water for all is a global public health goal, disparities in access persist worldwide. We present a critical review of primary‐data based environmental justice (EJ) studies on drinking water. We examine their findings in relation to the broader EJ and drinking water literatures. Using pre‐specified protocols to screen 2423 records, we identified 33 studies for inclusion. We organized our results using the following questions: (1) what sampling and data collection methods are used; (2) how is (un)just access to water defined and measured; (3) what forms of environmental injustice are discussed; (4) how are affected communities resisting or coping; and (5) what, if any, mechanisms of redress are advocated? We find that while many studies analyze the causes and persistence of environmental injustices, most primary‐data studies on drinking water are cross‐sectional in design. Many such studies are motivated by health impacts but few measure drinking water exposures or associated health outcomes. We find that, while distinct types of injustice exist, multiple types are either co‐produced or exacerbate one another. Recognitional injustice is emerging as an undergirding injustice upon which others (distributional or procedural) can take hold. Tensions remain regarding the role of the state; redress for inequitable water access is often presumed to be the state's responsibility, but many EJ scholars argue that the state itself perpetuates inequitable conditions. The accountability for redress under different forms of water governance remains an important area for future research.

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Human Water > Methods

 

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020–2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech’s main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident–rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.