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Zhang X, Long T, Deng S, Chen Q, Chen S, Luo M, Yu R, Zhu X. Machine Learning Modeling Based on Microbial Community for Prediction of Natural Attenuation in Groundwater. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21212-21223. [PMID: 38064381 DOI: 10.1021/acs.est.3c05667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Natural attenuation is widely adopted as a remediation strategy, and the attenuation potential is crucial to evaluate whether remediation goals can be achieved within the specified time. In this work, long-term monitoring of indigenous microbial communities as well as benzene, toluene, ethylbenzene, and xylene (BTEX) and chlorinated aliphatic hydrocarbons (CAHs) in groundwater was conducted at a historic pesticide manufacturing site. A machine learning approach for natural attenuation prediction was developed with random forest classification (RFC) followed by either random forest regression (RFR) or artificial neural networks (ANNs), utilizing microbiological information and contaminant attenuation rates for model training and cross-validation. Results showed that the RFC could accurately predict the feasibility of natural attenuation for both BTEX and CAHs, and it could successfully identify the key genera. The RFR model was sufficient for the BTEX natural attenuation rate prediction but unreliable for CAHs. The ANN model showed better performance in the prediction of the attenuation rates for both BTEX and CAHs. Based on the assessments, a composite modeling method of RFC and ANN was proposed, which could reduce the mean absolute percentage errors. This study reveals that the combined machine learning approach under the synergistic use of field microbial data has promising potential for predicting natural attenuation.
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Affiliation(s)
- Xiaodong Zhang
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
- Department of Environmental Science and Engineering, School of Energy and Environment, Southeast University, Nanjing 210096, Jiangsu, China
| | - Tao Long
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
| | - Shaopo Deng
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
| | - Qiang Chen
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
| | - Sheng Chen
- Geo-engineering Investigation Institute of Jiangsu Province, Nanjing 210041, Jiangsu, China
| | - Moye Luo
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
- Department of Environmental Science and Engineering, School of Energy and Environment, Southeast University, Nanjing 210096, Jiangsu, China
| | - Ran Yu
- Department of Environmental Science and Engineering, School of Energy and Environment, Southeast University, Nanjing 210096, Jiangsu, China
| | - Xin Zhu
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
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Garner RE, Kraemer SA, Onana VE, Fradette M, Varin MP, Huot Y, Walsh DA. A genome catalogue of lake bacterial diversity and its drivers at continental scale. Nat Microbiol 2023; 8:1920-1934. [PMID: 37524802 DOI: 10.1038/s41564-023-01435-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 06/20/2023] [Indexed: 08/02/2023]
Abstract
Lakes are heterogeneous ecosystems inhabited by a rich microbiome whose genomic diversity is poorly defined. We present a continental-scale study of metagenomes representing 6.5 million km2 of the most lake-rich landscape on Earth. Analysis of 308 Canadian lakes resulted in a metagenome-assembled genome (MAG) catalogue of 1,008 mostly novel bacterial genomospecies. Lake trophic state was a leading driver of taxonomic and functional diversity among MAG assemblages, reflecting the responses of communities profiled by 16S rRNA amplicons and gene-centric metagenomics. Coupling the MAG catalogue with watershed geomatics revealed terrestrial influences of soils and land use on assemblages. Agriculture and human population density were drivers of turnover, indicating detectable anthropogenic imprints on lake bacteria at the continental scale. The sensitivity of bacterial assemblages to human impact reinforces lakes as sentinels of environmental change. Overall, the LakePulse MAG catalogue greatly expands the freshwater genomic landscape, advancing an integrative view of diversity across Earth's microbiomes.
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Affiliation(s)
- Rebecca E Garner
- Department of Biology, Concordia University, Montreal, Quebec, Canada
- Groupe de recherche interuniversitaire en limnologie, Montreal, Quebec, Canada
| | | | - Vera E Onana
- Department of Biology, Concordia University, Montreal, Quebec, Canada
- Groupe de recherche interuniversitaire en limnologie, Montreal, Quebec, Canada
| | - Maxime Fradette
- Département de géomatique appliquée, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Marie-Pierre Varin
- Département de géomatique appliquée, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Yannick Huot
- Groupe de recherche interuniversitaire en limnologie, Montreal, Quebec, Canada
- Département de géomatique appliquée, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - David A Walsh
- Department of Biology, Concordia University, Montreal, Quebec, Canada.
- Groupe de recherche interuniversitaire en limnologie, Montreal, Quebec, Canada.
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Hallin S. Environmental microbiology going computational-Predictive ecology and unpredicted discoveries. Environ Microbiol 2023; 25:111-114. [PMID: 36181387 PMCID: PMC10092848 DOI: 10.1111/1462-2920.16232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 01/21/2023]
Affiliation(s)
- Sara Hallin
- Swedish University of Agricultural Sciences, Department of Forest Mycology and Plant Pathology, Uppsala, Sweden
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