In response to the finding of chironomid larvae in tap water, South Korea’s water treatment system has stepped up water quality monitoring. However, due to the challenging nature of larval behavior, effective elimination remains difficult despite the use of various purification techniques such as coagulation, sedimentation, filtration, and disinfection. Based on it, the aim of this study is to evaluate the effectiveness of coagulation and chlorination in inactivating chironomid larvae and investigate their behavior. The coagulation experiment tested how the behavior of the larvae changed in water with a high turbidity level of ±100 mg/L and 2 mg/L Al³⁺ as a coagulant, compared to water with a lower turbidity level of ±30 mg/L and 1 mg/L of Al³⁺ as a coagulant. The larvae were exposed to various doses of chlorine (0.5–20 mg/L as Cl) in 500 mL beaker glasses. The behavioral activity of the larvae was observed at different time points for 5 days. It was found that chironomid larvae exhibit different responses to exposure to coagulant and chlorine, with coagulation causing the formation of flocs that cover the larval body as a protective measure. Conversely, exposure to chlorine causes a decrease in activity and growth, leading to the death of the larvae and subsequent melting. The results showed that the first instar larvae dead after 48 h of exposure to coagulation treatment, while the first instar larvae exposed to chlorination experienced mortality after a mere 5 min of exposure to 10–20 mg/L as Cl. The larvae can still grow and transform into pupae and adults, especially during the third and fourth instars, even after exposure to coagulant and chlorine with low dosage. These findings suggest that the floc generated during coagulation must be thoroughly cleaned, as it may contain larvae that can persist and develop further. Furthermore, the presence of larvae during the chlorination process highlights the need for alternative, more effective oxidants to be utilized in place of the conventional chlorine treatment.

The anaerobic digestion of sewage sludge in South Korean wastewater treatment plants is affected by seasonal factors and other influences, resulting in lower digestion efficiency and gas production, which cannot reach optimal yields. The aim of this study was to improve the digestion efficiency and gas production of sludge anaerobic digestion in a wastewater treatment plant (WWTP) by using data mining techniques to adjust operational parameters. Through experimental data obtained from the WWTP in Daegu City, South Korea, an artificial neural network (ANN) technology was used to adjust the range of the organic loading rate (OLR) and hydraulic retention rate (HRT) to improve the efficiency and methane gas production from anaerobic sludge digestion. Data sources were normalized, and data analysis including Pearson correlation analysis, multiple regression analysis and an artificial neural network for optimal results. The results of the study showed a predicted 0.5% increase in digestion efficiency and a 1.3% increase in gas production at organic loads of 1.26–1.46 kg/m³ day and an HRT of 26–30 days. This shows that the ANN model that we established is feasible and can be used to improve the efficiency and gas production of sludge anaerobic digestion.