The 20-kHz ultrasonic irradiation was applied to investigate bacterial inactivation and antibiotic susceptibility changes over time. Applied intensities of ultrasound power were varied at 27.7 W and 39.1 W by changing the amplitude 20 to 40 to three bacteria species (Escherichia coli, Enterococcus faecalis, and Staphylococcus aureus). By 15-min irradiation, E. coli, a gram-negative bacterium, showed 1.2- to 1.6-log removals, while the gram-positive bacteria, Enterococcus faecalis and Staphylococcus aureus, showed below 0.5-log removal efficiencies. Antibiotic susceptibility of penicillin-family showed a dramatic increase at E. coli, but for other antibiotic families showed no significant changes in susceptibility. Gram-positive bacteria showed no significant differences in their antibiotic susceptibilities after ultrasound irradiation. Bacterial re-survival and antibiotic susceptibility changes were measured by incubating the ultrasound-irradiated samples. After 24-hour incubation, it was found that all of three bacteria were repropagated to the 2- to 3-log greater than the initial points, and antibiotic inhibition zones were reduced compared to ones of the initial points, meaning that antibiotic resistances were also recovered. Pearson correlations between bacterial inactivation and antibiotic susceptibility showed negative relation for gram-negative bacteria, E. coli., and no significant relations between bacterial re-survival and its inhibition zone. As a preliminary study, further researches are necessary to find practical and effective conditions to achieve bacteria inactivation.

Objectives of this study were to identify the hotspot for displacement of the on-line water quality sensors, in order to detect illicit discharge of untreated wastewater. A total of twenty-six water quality parameters were measured in sewer networks of the industrial complex located in Daejeon city as a test-bed site of this study. For the water qualities measured on a daily basis by 2-hour interval, the self-organizing maps(SOMs), one of the artificial neural networks(ANNs), were applied to classify the catchments to the clusters in accordance with patterns of water qualities discharged, and to determine the hotspot for priority sensor allocation in the study. The results revealed that the catchments were classified into four clusters in terms of extent of water qualities, in which the grouping were validated by the Euclidean distance and Davies-Bouldin index. Of the on-line sensors, total organic carbon(TOC) sensor, selected to be suitable for organic pollutants monitoring, would be effective to be allocated in D and a part of E catchments. Pb sensor, of heavy metals, would be suitable to be displaced in A and a part of B catchments.