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Removal of pharmaceuticals in source-separated urine is an important step toward gaining acceptance of urine-derived fertilizers among important stakeholders such as consumers, farmers, and regulatory agencies. Advanced oxidation processes (AOPs) have been studied for the removal of pharmaceuticals in various complex matrices, including treated wastewaters. A complexity associated with AOP methods that rely primarily on hydroxyl radicals as the oxidizing agents is that they readily lose effectiveness in the presence of scavengers. Here, we investigated the potential for capturing the synergistic effects of producing multiple oxidative chemical species simultaneously in a plasma reactor to oxidize six pharmaceuticals (acetaminophen, atenolol, 17α-ethynyl estradiol, ibuprofen, naproxen, and sulfamethoxazole) in source-separated urine being processed into a fertilizer. The results show that the plasma reactor produced hydroxyl radicals as the primary oxidizing agent and the effects of other oxidizing species were minimal. Plasma experienced scavenging in both fresh and hydrolyzed urine; furthermore, it oxidized pharmaceuticals at similar rates across both matrices. Additionally, the negative impacts of electrical discharge formation stemming from increased solution conductivity appeared to plateau. The energy required per order of magnitude of pharmaceutical transformed was up to 2 orders of magnitude higher for plasma than for a traditional UV/H 2 O 2 reactor and depended upon the matrix. Despite scavenging and energy concerns, plasma can oxidize pharmaceuticals in fresh and hydrolyzed urine and is worthy of further development for on-site or building-scale applications where the value of convenience, simplicity, and performance offsets energy efficiency concerns. 

UV254 disinfection strategies are commonly applied to inactivate pathogenic viruses in water, food, air, and on surfaces. There is a need for methods that rapidly predict the kinetics of virus inactivation by UV254, particularly for emerging and difficult-to-culture viruses. We conducted a systematic literature review of inactivation rate constants for a wide range of viruses. Using these data and virus characteristics, we developed and evaluated linear and nonlinear models for predicting inactivation rate constants. Multiple linear regressions performed best for predicting the inactivation kinetics of (+) ssRNA and dsDNA viruses, with cross-validated root mean squared relative prediction errors similar to those associated with experimental rate constants. We tested the models by predicting and measuring inactivation rate constants of a (+) ssRNA mouse coronavirus and a dsDNA marine bacteriophage; the predicted rate constants were within 7% and 71% of the experimental rate constants, respectively, indicating that the prediction was more accurate for the (+) ssRNA virus than the dsDNA virus. Finally, we applied our models to predict the UV254 rate constants of several viruses for which high-quality UV254 inactivation data are not available. Our models will be valuable for predicting inactivation kinetics of emerging or difficult-to-culture viruses. 

Wastewater-based epidemiology has gained attention throughout the world for detection of SARS-CoV-2 RNA in wastewater to supplement clinical testing. Raw wastewater consists of small particles, or solids, suspended in liquid. Methods have been developed to measure SARS-CoV-2 RNA in the liquid and the solid fraction of wastewater, with some studies reporting higher concentrations in the solid fraction. To investigate this relationship further, six laboratories collaborated to conduct a study across five publicly owned treatment works (POTWs) where both primary settled solids obtained from primary clarifiers and raw wastewater influent samples were collected and quantified for SARS-CoV-2 RNA. Settled solids and influent samples were processed by participating laboratories using their respective methods and retrospectively paired based on date of collection. SARS-CoV-2 RNA concentrations, on a mass equivalent basis, were higher in settled solids than in influent by approximately three orders of magnitude. Concentrations in matched settled solids and influent were positively and significantly correlated at all five POTWs. RNA concentrations in both settled solids and influent were correlated to COVID-19 incidence rates in the sewersheds and thus representative of disease occurrence; the settled solids methods appeared to produce a comparable relationship between SARS-CoV-2 RNA concentration measurements and incidence rates across all POTWs. Settled solids and influent methods showed comparable sensitivity, N gene detection frequency, and calculated empirical incidence rate lower limits. Analysis of settled solids for SARS-CoV-2 RNA has the advantage of using less sample volume to achieve similar sensitivity to influent methods.