Wastewater-based epidemiology (WBE) is utilized globally as a tool for quantifying the amount of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within communities, yet the efficacy of community-level wastewater monitoring has yet to be directly compared to random Coronavirus Disease of 2019 (COVID-19) clinical testing; the best-supported method of virus surveillance within a single population. This study evaluated the relationship between SARS-CoV-2 RNA in raw wastewater and random COVID-19 clinical testing on a large university campus in the Southwestern United States during the Fall 2020 semester. Daily composites of wastewater (24-hour samples) were collected three times per week at two campus locations from 16 August 2020 to 1 January 2021 ( n = 95) and analyzed by reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) targeting the SARS-CoV-2 E gene. Campus populations were estimated using campus resident information and anonymized, unique user Wi-Fi connections. Resultant trends of SARS-CoV-2 RNA levels in wastewater were consistent with local and nationwide pandemic trends showing peaks in infections at the start of the Fall semester in mid-August 2020 and mid-to-late December 2020. A strong positive correlation ( r = 0.71 ( p < 0.01); n = 15) was identified between random COVID-19 clinical testing and WBE surveillance methods, suggesting that wastewater surveillance has a predictive power similar to that of random clinical testing. Additionally, a comparative cost analysis between wastewater and clinical methods conducted here show that WBE was more cost effective, providing data at 1.7% of the total cost of clinical testing ($6042 versus $338,000, respectively). We conclude that wastewater monitoring of SARS-CoV-2 performed in tandem with random clinical testing can strengthen campus health surveillance, and its economic advantages are maximized when performed routinely as a primary surveillance method, with random clinical testing reserved for an active outbreak situation.

Dynamic vapor microextraction (DVME) is a new method that enables rapid vapor pressure measurements on large molecules with state-of-the-art measurement uncertainty for vapor pressures near 1 Pa. Four key features of DVME that allow for the rapid collection of vapor samples under thermodynamic conditions are (1) the use of a miniature vaporequilibration vessel (the "saturator") to minimize the temperature gradients and internal volume, (2) the use of a capillary vapor trap to minimize the internal volume, (3) the use of helium carrier gas to minimize nonideal mixture behavior, and (4) the direct measurement of pressure inside the saturator to accurately account for overpressure caused by viscous flow. The performance of DVME was validated with vapor pressure measurements of n-eicosane (C 20 H 42 ) at temperatures from 344 to 374 K. A thorough uncertainty analysis indicated a relative standard uncertainty of 2.03−2.82% for measurements in this temperature range. The measurements were compared to a reference correlation for the vapor pressures of n-alkanes; the deviation of the measurements from the correlation was ≤2.85%. The enthalpy of vaporization of n-eicosane at 359.0 K was calculated to be Δ vap H = 91.27 ± 0.28 kJ/mol compared to Δ vap H(corr) = 91.44 kJ/mol for the reference correlation. Total measurement periods as short as 15 min (3 min of thermal equilibration plus 12 min of carrier gas flow) were shown to be sufficient for high-quality vapor pressure measurements at 364 K.

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The COVID-19 pandemic prompted a global integration of wastewater-based epidemiology (WBE) into public health surveillance. Among early pre-COVID practitioners was Greater Tempe (population ~200,000), Arizona, where high-frequency, high-resolution monitoring of opioids began in 2018, leading to unrestricted online data release. Leveraging an existing, neighborhood-level monitoring network, wastewater from eleven contiguous catchment areas was analyzed by RT-qPCR for the SARS-CoV-2 E gene from April 2020 to March 2021 (n=1,556). Wastewater data identified an infection hotspot in a predominantly Hispanic and Native American community, triggering targeted interventions. During the first SARS-CoV-2 wave (June 2020), spikes in virus levels preceded an increase in clinical cases by 8.5+/-2.1 days, providing an early-warning capability that later transitioned into a lagging indicator (-2.0+/-1.4 days) during the December/January 2020-21 wave of clinical cases. Globally representing the first demonstration of immediate, unrestricted WBE data sharing and featuring long-term, innovative, high-frequency, high-resolution sub-catchment monitoring, this successful case study encourages further applications of WBE to inform public health interventions.