Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning
Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts on river ecosystems based on high-through dataset...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412024008274 |
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| author | Zhangmu Jing Yi Zhang Xiaoling Liu Qingqian Li Yanji Hao Yeqing Li Hongjie Gao |
| author_facet | Zhangmu Jing Yi Zhang Xiaoling Liu Qingqian Li Yanji Hao Yeqing Li Hongjie Gao |
| author_sort | Zhangmu Jing |
| collection | DOAJ |
| description | Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts on river ecosystems based on high-through datasets. This study employed an ML framework and 16S rRNA sequencing data to reveal microbial dynamics and trace human activities across China. The results revealed that the microbial assembly was mainly dominated by deterministic factors (environmental factors and interactions between species), and the metacommunity partition was significantly associated with human activities in both water and sediment (Chi-square testw P = 1.93 × 10-22; Chi-square tests P = 6.00 × 10-6). Human activities increased the vulnerability of interspecific occurrence networks and the influence of environmental factors on the OTUs similarity and phylogenetic distance. Combined of microbiological indices (MBIs), microbial relative abundance (MRA), and environmental and geographical indices (EGIs), the source classifier machine learning (SCML) algorithm was used to categorize five human activities (i.e., low human-impact, agricultural inputs, domestic inputs, industrial inputs, and dam construction). The SCML optimal configuration is (MBIs + MRA + EGIs) exhibited strong performance with TestW R2 of 0.882 and TestS R2 of 0.924. This study provides valuable insights for improving ecosystem management, supporting sustainable water resource management and advancing pollution mitigation efforts. |
| format | Article |
| id | doaj-art-add67d3f65084b6f89fa12b5407a2e0f |
| institution | Kabale University |
| issn | 0160-4120 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj-art-add67d3f65084b6f89fa12b5407a2e0f2025-01-24T04:44:11ZengElsevierEnvironment International0160-41202025-01-01195109240Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learningZhangmu Jing0Yi Zhang1Xiaoling Liu2Qingqian Li3Yanji Hao4Yeqing Li5Hongjie Gao6State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Environmental Protection Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; School of Civil and Environmental Engineering, Nanyang Technological University, 639798, SingaporeState Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Environmental Protection Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environmental Science, Beijing 100012, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Environmental Protection Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environmental Science, Beijing 100012, ChinaState Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing, 102249, ChinaState Key Laboratory of Heavy Oil Processing, Beijing Key Laboratory of Biogas Upgrading Utilization, College of New Energy and Materials, China University of Petroleum Beijing (CUPB), Beijing, 102249, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Environmental Protection Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Corresponding author.Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts on river ecosystems based on high-through datasets. This study employed an ML framework and 16S rRNA sequencing data to reveal microbial dynamics and trace human activities across China. The results revealed that the microbial assembly was mainly dominated by deterministic factors (environmental factors and interactions between species), and the metacommunity partition was significantly associated with human activities in both water and sediment (Chi-square testw P = 1.93 × 10-22; Chi-square tests P = 6.00 × 10-6). Human activities increased the vulnerability of interspecific occurrence networks and the influence of environmental factors on the OTUs similarity and phylogenetic distance. Combined of microbiological indices (MBIs), microbial relative abundance (MRA), and environmental and geographical indices (EGIs), the source classifier machine learning (SCML) algorithm was used to categorize five human activities (i.e., low human-impact, agricultural inputs, domestic inputs, industrial inputs, and dam construction). The SCML optimal configuration is (MBIs + MRA + EGIs) exhibited strong performance with TestW R2 of 0.882 and TestS R2 of 0.924. This study provides valuable insights for improving ecosystem management, supporting sustainable water resource management and advancing pollution mitigation efforts.http://www.sciencedirect.com/science/article/pii/S0160412024008274Source classifier machine learningMicrobial communities16S rRNA sequencing dataHuman activitiesPollution source tracing |
| spellingShingle | Zhangmu Jing Yi Zhang Xiaoling Liu Qingqian Li Yanji Hao Yeqing Li Hongjie Gao Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning Environment International Source classifier machine learning Microbial communities 16S rRNA sequencing data Human activities Pollution source tracing |
| title | Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning |
| title_full | Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning |
| title_fullStr | Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning |
| title_full_unstemmed | Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning |
| title_short | Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning |
| title_sort | identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning |
| topic | Source classifier machine learning Microbial communities 16S rRNA sequencing data Human activities Pollution source tracing |
| url | http://www.sciencedirect.com/science/article/pii/S0160412024008274 |
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