Predicting micropollutant fate during wastewater treatment using refined classical kinetic model based on quantitative monitoring in multi-metropolitan regions of South Korea

Sung Ho Chae; Seung Ji Lim; Mingizem Gashaw Seid; Wondesen Workneh Ejerssa; Aseom Son; Heejong Son; Sangki Choi; Woongbae Lee; Yunho Lee; Seok Won Hong

doi:10.1016/j.watres.2023.120627

Predicting micropollutant fate during wastewater treatment using refined classical kinetic model based on quantitative monitoring in multi-metropolitan regions of South Korea

Water Res. 2023 Oct 15:245:120627. doi: 10.1016/j.watres.2023.120627. Epub 2023 Sep 11.

Authors

Affiliations

¹ Center for Water Cycle Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.
² Center for Water Cycle Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Energy and Environment Technology, KIST-School, University of Science and Technology, Seoul 02792, Republic of Korea.
³ Water Quality Institute, Busan Water Authority, Gimhae-si, Gyeongsangnam-do 50804, Republic of Korea.
⁴ Water Quality Institute, Busan Water Authority, Gimhae-si, Gyeongsangnam-do 50804, Republic of Korea; School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
⁵ School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
⁶ Center for Water Cycle Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Energy and Environment Technology, KIST-School, University of Science and Technology, Seoul 02792, Republic of Korea. Electronic address: swhong@kist.re.kr.

PMID: 37717334
DOI: 10.1016/j.watres.2023.120627

Abstract

This study aimed to implement an extensive prediction model for the fate of micropollutants (MPs) in wastewater treatment plants (WWTPs). Five WWTPs equipped with seven different biological treatment processes were monitored from 2020 to 2022 with three to four sampling events in each year, and 27 datasets for 20 MPs were collected. Among these datasets, 12 were used to investigate the behavior and fate of MPs in WWTPs in South Korea. Metformin, acetaminophen, caffeine, naproxen, and ibuprofen were the MPs with the highest influent concentrations (ranging from 3,933.3-187,637.0 ng L^-1) at all WWTPs. More than 90% of MPs were removed by biological treatment processes in all WWTPs. The Kruskal-Wallis test verified that their efficacy did not differ statistically (p-value > 0.05). Meanwhile, to refine the performance of the prediction model, this study optimized the biodegradation rate constants (k_bio) of each MP according to the variation of seasonal water temperature. As a result, compared to the original prediction model, the mean difference between the actual data and predicted results (MEAN) decreased by 6.77%, while the Nash-Sutcliffe efficiency (NSE) increased by 0.226. The final MEAN and NSE for the refined prediction model were calculated to be 5.09% and 0.964, respectively. The prediction model made accurate predictions, even for MPs exhibiting behaviors different from other cases, such as estriol and atrazine. Consequently, the optimization strategy proposed in this study was determined to be effective because the overall removal efficiencies of MPs were successfully predicted even with limited reference datasets.

Keywords: Biodegradation rate constant; Biological treatment; Classical kinetic model; Emerging contaminants; Parameter optimization.