Repeated hydrogen peroxide dosing briefly reduces cyanobacterial blooms and microcystin while increasing fecal bacteria indicators in a eutrophic pond

This study explored the effects of H_(2)O_(2)on Cyanobacteria and non-target microbes using fluorometry,microscopy,flow cytometry,and high throughput DNA sequencing of the 16S rRNA gene during a series of mesocosm and whole-ecosystem experiments in a eutrophic pond in NY,USA.The addition of H_(2)O_(2)(8 mg/L)significantly reduced Cyanobacteria concentrations during a majority of experiments(66%;6 of 9)and significantly increased eukaryotic green and unicellular brown algae in 78%and 45%of experiments,respectively.While heterotrophic bacteria declined significantly following H_(2)O_(2)addition in all experiments,bacteria indicative of potential fecal contamination(Escherichia coli,Enterococcus,fecal coliform bacteria)consistently and significantly increased in response to H_(2)O_(2),evidencing a form of‘pollution swapping’.H_(2)O_(2)more effectively reduced Cyanobacteria in enclosed mesocosms compared to whole-ecosystem applications.Ten whole-pond H_(2)O_(2)applications over a twoyear period temporarily reduced cyanobacterial levels but never reduced concentrations below bloom thresholds and populations always rebounded in two weeks or less.The bacterial phyla of Cyanobacteria,Actinobacteria,and Planctomycetes were the most negatively impacted by H_(2)O_(2).Microcystis was always reduced by H_(2)O_(2),as was the toxin microcystin,but Microcystis remained dominant even after repeated H_(2)O_(2)treatments.Although H_(2)O_(2)favored the growth of eukaryotic algae over potentially harmful Cyanobacteria,the inability of H_(2)O_(2)to end cyanobacterial blooms in this eutrophic waterbody suggests it is a non-ideal mitigation approach in high biomass ecosystems and should be used judiciously due to potential negative impacts on non-target organisms and promotion of bacteria indicative of fecal contamination.

Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity:A prediction study

Background:The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems.The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19.We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data.Methods:Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties,we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29,2020 to June 30,2022.We included covariates such as COVID-19 vaccine coverage in the county,comorbidities,demographic variables,and holiday gatherings.Findings:Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission.Models that included wastewater had higher predictive power than models that included clinical cases only,increasing the accuracy of the model by 15%.Predicted hospital admissions correlated highly with observed admissions(r¼0.77)with an average difference of 0.013 hospitalizations per 100,000(95%CI¼[0.002,0.025])Interpretation:Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone.The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.