Root–shoot allocation of biomass is an underrepresented criterion that could be used for tree selection in phytoremediation. We evaluated how root–shoot allocations relate to biomass production and overall health of poplar and willow clones grown in landfill soil treatments. Fifteen poplar clones and nine willows were grown in a greenhouse for 65 days in soils from five Wisconsin landfills and one greenhouse control. We tested for treatment, clone, and interaction differences in root–shoot ratio (RSR), health, and growth index, along with relationships between RSR with diameter, health, height, total biomass, and growth index. Treatments, clones, and their interactions were not significantly different for poplar RSR, but willow clones differed (p = 0.0049). Health significantly varied among willow clones (p < 0.0001) and among the clone × treatment interaction for poplars (p = 0.0196). Analysis of means showed that willow clones ‘Allegany’ and ‘S365’ exhibited 28% and 21% significantly greater health scores than the overall mean, respectively. Root–shoot ratio was not significantly correlated with health in either genus but was positively correlated with growth index for poplars, which was corroborated via regression analyses. Selecting clones based on a combination of biomass allocation, health, and growth indices may be useful for using phyto-recurrent selection to satisfy site-specific ecosystem services objectives.

Recent SARS-CoV-2 wastewater-based epidemiology (WBE) surveillance have documented a positive correlation between the number of COVID-19 patients in a sewershed and the level of viral genetic material in the wastewater. Efforts have been made to use the wastewater SARS-CoV-2 viral load to predict the infected population within each sewershed using a multivariable regression approach. However, reported clear and sustained variability in SARS-CoV-2 viral load among treatment facilities receiving industrial wastewater have made clinical prediction challenging. Several classes of molecules released by regional industries and manufacturing facilities, particularly the food processing industry, can significantly suppress the SARS-CoV-2 signals in wastewater by breaking down the lipid-bilayer of the membranes. Therefore, a systematic ranking process in conjugation with metabolomic analysis was developed to identify the wastewater treatment facilities exhibiting SARS-CoV-2 suppression and identify and quantify the chemicals suppressing the SARS-COV-2 signals. By ranking the viral load per diagnosed case among the sewersheds, we successfully identified the wastewater treatment facilities in Missouri, USA that exhibit SARS-CoV-2 suppression (significantly lower than 5 × 10¹¹ gene copies/reported case) and determined their suppression rates. Through both untargeted global chemical profiling and targeted analysis of wastewater samples, 40 compounds were identified as candidates of SARS-CoV-2 signal suppressors. Among these compounds, 14 had higher concentrations in wastewater treatment facilities that exhibited SARS-CoV-2 signal suppression compared to the unsuppressed control facilities. Stepwise regression analyses indicated that 4-nonylphenol, palmitelaidic acid, sodium oleate, and polyethylene glycol dioleate are positively correlated with SARS-CoV-2 signal suppression rates. Suppression activities were further confirmed by incubation studies, and the suppression kinetics for each bioactive compound were determined. According to the results of these experiments, bioactive molecules in wastewater can significantly reduce the stability of SARS-CoV-2 genetic marker signals. Based on the concentrations of these chemical suppressors, a correction factor could be developed to achieve more reliable and unbiased surveillance results for wastewater treatment facilities that receive wastewater from similar industries.