干旱区生态环境知识资源中心(Arid): Prediction of microbial activity and abundance using interpretable machine learning models in the hyporheic zone of effluent-dominated receiving rivers

Arid

DOI	10.1016/j.jenvman.2024.120627
	Prediction of microbial activity and abundance using interpretable machine learning models in the hyporheic zone of effluent-dominated receiving rivers
	Wang, Longfei; Yin, Haojie; Li, Yi; Yang, Zhengjian; Wang, Yutao; Liu, Xianwei
通讯作者	Yang, ZJ
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN	0301-4797
EISSN	1095-8630
出版年	2024
卷号	357
英文摘要	Serving as a vital linkage between surface water and groundwater, the hyporheic zone (HZ) plays a fundamental role in improving water quality and maintaining ecological security. In arid or semi-arid areas, effluent discharge from wastewater treatment facilities could occupy a predominant proportion of the total base flow of receiving rivers. Nonetheless the relationship between microbial activity, abundance and environmental factors in the HZ of effluent-receiving rivers appear to be rarely addressed. In this study, a spatiotemporal field study was performed in two representative effluent-dominated receiving rivers in Xi'an, China. Land use data, physical and chemical water quality parameters of surface and subsurface water were used as predictive variables, while the microbial respiratory electron transport system activity (ETSA), the Chao1 and Shannon index of total microbial community, as well as the Chao1 and Shannon index of denitrifying bacteria community were used as response variables, while ETSA was used as response variables indicating ecological processes and Shannon and Chao1 were utilized as parameters indicating microbial diversity. Two machine learning models were utilized to provide evidence-based information on how environmental factors interact and drive microbial activity and abundance in the HZ at variable depths. The models with Chao1 and Shannon as response variables exhibited excellent predictive performances (R-2: 0.754-0.81 and 0.783-0.839). Dissolved organic nitrogen (DON) was the most important factor affecting the microbial functions, and an obvious threshold value of similar to 2 mg/L was observed. Credible predictions of models with Chao1 and Shannon index of denitrifying bacteria community as response variables were detected (R-2: 0.484-0.624 and 0.567-0.638), with soluble reactive phosphorus (SRP) being the key influencing factor. Fe (II) was favorable in predicting denitrifying bacteria community. The ESTA model highlighted the importance of total nitrogen in the ecological health monitoring in HZ. These findings provide novel insights in predicting microbial activity and abundance in highly-impacted areas such as the HZ of effluent-dominated receiving rivers.
英文关键词	Hyporheic zone Effluent-receiving river Machine learning Microbial activity Microbial abundance
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001216296600001
WOS关键词	DISSOLVED ORGANIC NITROGEN ; WASTE-WATER ; REMOVAL ; NITRATE ; RESPIRATION ; GROUNDWATER ; IMPACT ; IRON ; FLUX
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404465
推荐引用方式 GB/T 7714	Wang, Longfei,Yin, Haojie,Li, Yi,et al. Prediction of microbial activity and abundance using interpretable machine learning models in the hyporheic zone of effluent-dominated receiving rivers[J],2024,357.
APA	Wang, Longfei,Yin, Haojie,Li, Yi,Yang, Zhengjian,Wang, Yutao,&Liu, Xianwei.(2024).Prediction of microbial activity and abundance using interpretable machine learning models in the hyporheic zone of effluent-dominated receiving rivers.JOURNAL OF ENVIRONMENTAL MANAGEMENT,357.
MLA	Wang, Longfei,et al."Prediction of microbial activity and abundance using interpretable machine learning models in the hyporheic zone of effluent-dominated receiving rivers".JOURNAL OF ENVIRONMENTAL MANAGEMENT 357(2024).