Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches
Abstract Effluent from wastewater treatment plants is considered an important source of micropollutants (MPs) in aquatic environments. However, monitoring MPs in effluents is often inefficient owing to the variety in their types. Thus, this study derived marker constituents to estimate the behavior...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-10-01
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| Series: | npj Clean Water |
| Online Access: | https://doi.org/10.1038/s41545-023-00282-6 |
| _version_ | 1797647421610655744 |
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| author | Seung Ji Lim Jangwon Seo Mingizem Gashaw Seid Jiho Lee Wondesen Workneh Ejerssa Doo-Hee Lee Eunhoo Jeong Sung Ho Chae Yunho Lee Moon Son Seok Won Hong |
| author_facet | Seung Ji Lim Jangwon Seo Mingizem Gashaw Seid Jiho Lee Wondesen Workneh Ejerssa Doo-Hee Lee Eunhoo Jeong Sung Ho Chae Yunho Lee Moon Son Seok Won Hong |
| author_sort | Seung Ji Lim |
| collection | DOAJ |
| description | Abstract Effluent from wastewater treatment plants is considered an important source of micropollutants (MPs) in aquatic environments. However, monitoring MPs in effluents is often inefficient owing to the variety in their types. Thus, this study derived marker constituents to estimate the behavior of MPs in each cluster using the self-organizing map (SOM), a machine learning-based clustering analysis method. In SOM analysis, the physicochemical properties, functional groups, and the initial biotransformation rules of 29 out 42 MPs were used to ultimately estimate the degradation rate constants of 13 MPs. Consequently, when the physicochemical properties and functional groups were considered, SOM analysis showed outstanding performance to label MPs with an accuracy value of 0.75 for each aerobic and anoxic condition. Based on the clustering results, 11 MPs were determined to be marker constituents under each aerobic and anoxic condition. Moreover, an estimation method for the rate constants of unlabeled MPs was successfully developed using the identified markers with the random forest classifier. The proposed algorithm could estimate both sorption and biotransformation of MPs regardless of dominant removal mechanisms, whether the MPs were removed by sorption or biotransformation. An accuracy of 0.77 was calculated for estimating rate constants under both aerobic and anoxic conditions, which is remarkably higher than those reported previously. The proposed procedure could be extended further to efficiently monitor MPs in effluents. |
| first_indexed | 2024-03-11T15:17:06Z |
| format | Article |
| id | doaj.art-acfef6b861d54910afd38c4ad80de4d9 |
| institution | Directory Open Access Journal |
| issn | 2059-7037 |
| language | English |
| last_indexed | 2024-03-11T15:17:06Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Clean Water |
| spelling | doaj.art-acfef6b861d54910afd38c4ad80de4d92023-10-29T12:13:00ZengNature Portfolionpj Clean Water2059-70372023-10-016111010.1038/s41545-023-00282-6Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approachesSeung Ji Lim0Jangwon Seo1Mingizem Gashaw Seid2Jiho Lee3Wondesen Workneh Ejerssa4Doo-Hee Lee5Eunhoo Jeong6Sung Ho Chae7Yunho Lee8Moon Son9Seok Won Hong10Center for Water Cycle Research, Korea Institute of Science and Technology (KIST)Center for Water Cycle Research, Korea Institute of Science and Technology (KIST)Center for Water Cycle Research, Korea Institute of Science and Technology (KIST)Center for Water Cycle Research, Korea Institute of Science and Technology (KIST)Center for Water Cycle Research, Korea Institute of Science and Technology (KIST)Mass Spectrometer Laboratory, National Instrumentation Center for Environmental ManagementCenter for Water Cycle Research, Korea Institute of Science and Technology (KIST)Center for Water Cycle Research, Korea Institute of Science and Technology (KIST)School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST)Center for Water Cycle Research, Korea Institute of Science and Technology (KIST)Center for Water Cycle Research, Korea Institute of Science and Technology (KIST)Abstract Effluent from wastewater treatment plants is considered an important source of micropollutants (MPs) in aquatic environments. However, monitoring MPs in effluents is often inefficient owing to the variety in their types. Thus, this study derived marker constituents to estimate the behavior of MPs in each cluster using the self-organizing map (SOM), a machine learning-based clustering analysis method. In SOM analysis, the physicochemical properties, functional groups, and the initial biotransformation rules of 29 out 42 MPs were used to ultimately estimate the degradation rate constants of 13 MPs. Consequently, when the physicochemical properties and functional groups were considered, SOM analysis showed outstanding performance to label MPs with an accuracy value of 0.75 for each aerobic and anoxic condition. Based on the clustering results, 11 MPs were determined to be marker constituents under each aerobic and anoxic condition. Moreover, an estimation method for the rate constants of unlabeled MPs was successfully developed using the identified markers with the random forest classifier. The proposed algorithm could estimate both sorption and biotransformation of MPs regardless of dominant removal mechanisms, whether the MPs were removed by sorption or biotransformation. An accuracy of 0.77 was calculated for estimating rate constants under both aerobic and anoxic conditions, which is remarkably higher than those reported previously. The proposed procedure could be extended further to efficiently monitor MPs in effluents.https://doi.org/10.1038/s41545-023-00282-6 |
| spellingShingle | Seung Ji Lim Jangwon Seo Mingizem Gashaw Seid Jiho Lee Wondesen Workneh Ejerssa Doo-Hee Lee Eunhoo Jeong Sung Ho Chae Yunho Lee Moon Son Seok Won Hong Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches npj Clean Water |
| title | Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches |
| title_full | Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches |
| title_fullStr | Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches |
| title_full_unstemmed | Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches |
| title_short | Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches |
| title_sort | clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches |
| url | https://doi.org/10.1038/s41545-023-00282-6 |
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