The frequent detection and occurrence of micropollutants (MPs) in aquatic ecosystems has raised public health concerns worldwide. In this study, the behavior of 50 MPs was investigated in three different domestic wastewater treatment plants (WWTPs). Furthermore, the Kruskal-Wallis test was used to assess the geographical and seasonal variation of MPs in the WWTPs. The results showed that the concentrations of 43 MPs ranged from less than 0.1 to 237.6 μg L-1, while other seven MPs including 17-ethynylestradiol, 17-estradiol, sulfathiazole, sulfamethazine, clofibric acid, simvastatin, and lovastatin were not detected in all WWTPs. Among the detected MPs, the pharmaceuticals such as metformin, acetaminophen, naproxen, and caffeine were prominent with maximum concentrations of 133.4, 237.6, 71.5, and 107.7 μg L-1, respectively. Most perfluorinated compounds and nitrosamines were found at trace levels of 1.2 to 55.3 ng L-1, while the concentration of corrosion inhibitors, preservatives (parabens), and endocrine disruptors ranged from less than 0.1 to 4310.8 ng L-1. Regardless of the type of biological treatment process such as MLE, A2O, and MBR, the majority of pharmaceuticals (except lincomycin, diclofenac, iopromide, and carbamazepine), parabens (except Methyl paraben), and endocrine disruptors were removed by more than 80%. However, the removal efficiencies of certain MPs such as atrazine, DEET, perfluorinated compounds (except PFHxA), nitrosamines, and corrosion inhibitors were relatively low or their concentration even increased after treatment. The results of statistical analysis reveal that there is no significant geographical difference in the removal efficacy of MPs, but there are temporal seasonal variations in all WWTPs.

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.