In this study, the photocatalytic treatment of an organic wastewater with/without phenolic compounds by means of ultraviolet irradiation, titanium dioxide and hydrogen peroxide was examined in an annular photoreactor. Specifically, the effect of initial total carbon concentration, catalyst loading and H₂O₂ amount on the removal of total carbon was first examined in the case of a synthetic organic wastewater. The influence of partial carbon substitution by phenol, 2-chlorophenol, 2,4-discholophenol, trichlorophenol, and 4-nitrophenol on total carbon removal and target compounds’ conversion was studied keeping constant the initial organic carbon load. It was shown that the process applied was effective in treating the wastewater for initial total carbon 32 mg L^-1, 0.5 g L^-1 TiO₂, and 66.6 mg L^-1 H₂O₂. Applying UV/TiO₂ and UV/H₂O₂, 58% and 53% total carbon removals were achieved, respectively, but combining TiO₂ and H₂O₂ did not result in a better performance in the case of the synthetic wastewater without any phenolic compounds. In contrast, when a phenolic compound was added, the addition of H₂O₂ was beneficial, eliminating the differences observed from one phenolic compound to another. The total carbon removals observed were lower than the corresponding final conversions of the target phenolic compounds. Finally, the electric energy per order values were calculated and found to range in 52–248 kWh/m³/order, being dependent from the process applied and the phenolic compound present in the wastewater.

There is a worldwide effort of the research community to explore the medical, economic and sociologic impact of the COVID-19 pandemic. Many different disciplines try to find solutions and drive strategies to a great variety of different very crucial problems. The present study presents a novel analysis which results to clustering countries with respect to active cases, active cases per population and active cases per population and per area based on Johns Hopkins epidemiological data. The presented cluster results could be useful to a variety of different policy makers, such as physicians and managers of the health sector, economy/finance experts, politicians and even to sociologists. In addition, our work suggests a new specially designed clustering algorithm adapted to the request for comparison of the various COVID time-series of different countries.