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DOI	10.1007/s10653-024-01885-9
	Spatiotemporal variations, sources, pollution status and health risk assessment of dissolved trace elements in a major Arabian Sea draining river: insights from multivariate statistical and machine learning approaches
	Singh, Shailja; Das, Anirban; Sharma, Paawan; Sudheer, A. K.; Gaddam, Mahesh; Ranjan, Rajnee
通讯作者	Das, A
来源期刊	ENVIRONMENTAL GEOCHEMISTRY AND HEALTH
ISSN	0269-4042
EISSN	1573-2983
出版年	2024
卷号	46 期号:4
英文摘要	River Mahi drains through semi-arid regions (Western India) and is a major Arabian Sea draining river. As the principal surface water source, its water quality is important to the regional population. Therefore, the river water was sampled extensively (n = 64, 16 locations, 4 seasons and 2 years) and analyzed for 11 trace elements (TEs; Sr, V, Cu, Ni, Zn, Cd, Ba, Cr, Mn, Fe, and Co). Machine learning (ML) and multivariate statistical analysis (MVSA) were applied to investigate their possible sources, spatial-temporal-annual variations, evaluate multiple water quality parameters [heavy metal pollution index (HPI), heavy metal evaluation index (HEI)], and health indices [hazard quotient (HQ), and hazard index (THI)] associated with TEs. TE levels were higher than their corresponding world average values in 100% (Sr, V and Zn), 78%(Cu), 41%(Ni), 27%(Cr), 9%(Cd), 8%(Ba), 8%(Co), 6%(Fe), and 0%(Mn), of the samples. Three principal components (PCs) accounted for 74.5% of the TE variance: PC-1 (Fe, Co, Mn and Cu) and PC-2 (Sr and Ba) are contributed from geogenic sources, while PC-3 (Cr, Ni and Zn) are derived from geogenic and anthropogenic sources. HPI, HEI, HQ and THI all indicate that water quality is good for domestic purposes and poses little hazard. ML identified Random forest as the most suitable model for predicting HEI class (accuracy: 92%, recall: 92% and precision: 94%). Even with a limited dataset, the study underscores the potential application of ML to predictive classification modeling.
英文关键词	Mahi river Trace elements Multivariate statistical analysis Pollution indices Health risk assessment Machine learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001184396400008
WOS关键词	WATER-QUALITY ASSESSMENT ; HEAVY-METALS ; DECCAN TRAPS ; VINDHYAN SUPERGROUP ; DRINKING-WATER ; MAHI RIVER ; GEOCHEMISTRY ; SURFACE ; INDIA ; CONTAMINANTS
WOS类目	Engineering, Environmental ; Environmental Sciences ; Public, Environmental & Occupational Health ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Public, Environmental & Occupational Health ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403571
推荐引用方式 GB/T 7714	Singh, Shailja,Das, Anirban,Sharma, Paawan,et al. Spatiotemporal variations, sources, pollution status and health risk assessment of dissolved trace elements in a major Arabian Sea draining river: insights from multivariate statistical and machine learning approaches[J],2024,46(4).
APA	Singh, Shailja,Das, Anirban,Sharma, Paawan,Sudheer, A. K.,Gaddam, Mahesh,&Ranjan, Rajnee.(2024).Spatiotemporal variations, sources, pollution status and health risk assessment of dissolved trace elements in a major Arabian Sea draining river: insights from multivariate statistical and machine learning approaches.ENVIRONMENTAL GEOCHEMISTRY AND HEALTH,46(4).
MLA	Singh, Shailja,et al."Spatiotemporal variations, sources, pollution status and health risk assessment of dissolved trace elements in a major Arabian Sea draining river: insights from multivariate statistical and machine learning approaches".ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 46.4(2024).