Machine learning predicts ecological risks of nanoparticles to soil microbial communities
Xu, Nuohan (Zhejiang University of Technology. College of Environment)
Kang, Jian (Zhejiang University of Technology. College of Environment)
Ye, Yangqing (Zhejiang University of Technology. College of Mechanical Engineering)
Zhang, Qi (Zhejiang University of Technology. College of Environment)
Ke, Mingjing (Zhejiang University of Technology. College of Environment)
Wang, Yufei (Huazhong University of Science and Technology. Tongji Medical College)
Zhang, Zhenyan (Zhejiang University of Technology. College of Environment)
Lu, Tao (Zhejiang University of Technology. College of Environment)
Peijnenburg, W. J. G. M. (Leiden University. Institute of Environmental Sciences)
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Bao, Guanjun (Zhejiang University of Technology. College of Mechanical Engineering)
Qian, Haifeng (Zhejiang University of Technology. College of Environment)

Date:	2022
Abstract:	With the rapid development of nanotechnology in agriculture, there is increasing urgency to assess the impacts of nanoparticles (NPs) on the soil environment. This study merged raw high-throughput sequencing (HTS) data sets generated from 365 soil samples to reveal the potential ecological effects of NPs on soil microbial community by means of metadata analysis and machine learning methods. Metadata analysis showed that treatment with nanoparticles did not have a significant impact on the alpha diversity of the microbial community, but significantly altered the beta diversity. Unfortunately, the abundance of several beneficial bacteria, such as Dyella, Methylophilus, Streptomyces, which promote the growth of plants, and improve pathogenic resistance, was reduced under the addition of synthetic nanoparticles. Furthermore, metadata demonstrated that nanoparticles treatment weakened the biosynthesis ability of cofactors, carriers, and vitamins, and enhanced the degradation ability of aromatic compounds, amino acids, etc. This is unfavorable for the performance of soil functions. Besides the soil heterogeneity, machine learning uncovered that a) the exposure time of nanoparticles was the most important factor to reshape the soil microbial community, and b) long-term exposure decreased the diversity of microbial community and the abundance of beneficial bacteria. This study is the first to use a machine learning model and metadata analysis to investigate the relationship between the properties of nanoparticles and the hazards to the soil microbial community from a macro perspective. This guides the rational use of nanoparticles for which the impacts on soil microbiota are minimized.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Subject:	Machine learning ; Soil ecosystems ; Microbiota ; Nanoparticles ; Metadata analysis ; Ecotoxicity
Published in:	Environmental pollution, Vol. 307 (August 2022) , art. 119528, ISSN 1873-6424

DOI: 10.1016/j.envpol.2022.119528

39 p, 1.3 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Experimental sciences > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Articles > Research articles
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