ABSTRACT
The outbreak of the coronavirus disease 2019 (COVID-19) raises questions about the effective inactivation of its causative agent, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in medical wastewater by disinfectants. For this reason, our study of wastewater from a selected hospital evaluated several different advanced oxidation methods (Fenton reaction and Fenton-like reaction and ferrate (VI)) capable of effectively removing SARS-CoV-2 RNA. The obtained results of all investigated oxidation processes, such as ferrates, Fenton reaction and its modifications achieved above 90% efficiency in degradation of SARS-CoV-2 RNA in model water. The efficiency of degradation of real SARS-CoV-2 from hospital wastewater declines in following order ferrate (VI) > Fenton reaction > Fenton-like reaction. Similarly, the decrease of chemical oxygen demand compared to effluent was observed. Therefore, all of these methods can be used as a replacement of chlorination at the wastewater effluent, which appeared to be insufficient in SARS-CoV-2 removal (60%), whereas using of ferrates showed efficiency of up to 99%.
ABSTRACT
Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerges to scientific research and monitoring of wastewaters to predict the spread of the virus in the community. Our study investigated the COVID-19 disease in Bratislava, based on wastewater monitoring from September 2020 until March 2021. Samples were analyzed from two wastewater treatment plants of the city with reaching 0.6 million monitored inhabitants. Obtained results from the wastewater analysis suggest significant statistical dependence. High correlations between the number of viral particles in wastewater and the number of reported positive nasopharyngeal RT-qPCR tests of infected individuals with a time lag of 2 weeks/12 days (R2 = 83.78%/R2 = 52.65%) as well as with a reported number of death cases with a time lag of 4 weeks/27 days (R2 = 83.21%/R2 = 61.89%) was observed. The obtained results and subsequent mathematical modeling will serve in the future as an early warning system for the occurrence of a local site of infection and, at the same time, predict the load on the health system up to two weeks in advance.