干旱区生态环境知识资源中心(Arid): Spatial variability of some heavy metals in arid harrats soils: Combining machine learning algorithms and synthetic indexes based-multitemporal Landsat 8/9 to establish background levels

Arid

DOI	10.1016/j.catena.2023.107579
	Spatial variability of some heavy metals in arid harrats soils: Combining machine learning algorithms and synthetic indexes based-multitemporal Landsat 8/9 to establish background levels
	Sulieman, Magboul M.; Kaya, Fuat; Keshavarzi, Ali; Hussein, Abdullahi M.; Al-Farraj, Abdullah S.; Brevik, Eric C.
通讯作者	Sulieman, MM
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2024
卷号	234
英文摘要	Characterizing the background content and source(s) of heavy metals (HMs) is important for the evaluation of potential pollution in soils. Samples were collected from the topsoil layers (0.3 m) of 19 soil pedons in the harrats arid region in Saudi Arabia, and the concentrations of Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were analyzed. Stepwise multiple linear regression (SMLR) and random forest (RF) were used to model and digitally map the studied HMs concentrations and establish their background levels using fifty-two environmental covariates (ECOVs) extracted from time series Landsat 8/9 OLI and digital elevation models. The values of the studied HMs ranged from 0.95 to 22.2 mg kg-1 (Co), 0.8 to 58.8 mg kg-1 (Cr), 1.65 to 34 mg kg-1 (Cu), 2,206 to 25,970 mg kg-1 (Fe), 33.5 to 788 mg kg -1 (Mn), 1.8 to 134.7 mg kg -1 (Ni), 2.25 to 12.5 mg kg -1 (Pb), and 2.1 to 55.9 mg kg -1 (Zn) with a coefficient of variation that ranged between 27.3 % (Pb) and 63.7 % (Ni). These are all very low compared to results from anthropogenically affected areas near the border of the harrats region, precisely mining (i.e., Mahad AD'Dahab) and industrial (i.e., Riyadh and Jubail) areas as well as to the common average ranges for these HMs in soil. The model's predictions and available observations were compared with statistics from 5-fold cross -validation with 3 repetitions. Findings revealed that mean R2 varied between 0.38 (Zn) and 0.54 (Cu) with normalized root mean square errors (NRMSEs) between 18.53 % (Zn) and 26.03 % (Cr) for SMLR, which better than RF that yielded mean R2 that ranged from 0.17 (Ni) to 0.40 (Cu) with NRMSEs between 19.15 % (Co) and 27.76 % (Mn). The studied HMs are considered background concentrations in the area, and therefore are important for any future environmental pollution/monitoring studies. Our study demonstrated the capacity of SMLR to use available ECOVs to predict HMs and generate background levels at a large scale in specific locations. Additionally, this study investigated the relationships between the factors affecting HMs accumulation in natural surface soil in arid regions. The methodology proposed was effective for describing HMs spatial variability and can be easily implemented in other ecosystems.
英文关键词	Digital soil mapping Environmental covariates Geogenic origin Harmful metals Multivariate statistical analysis
类型	Article
语种	英语
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:001106429600001
WOS关键词	POTENTIALLY TOXIC ELEMENTS ; SURFACE SOILS ; MAGNETIC-SUSCEPTIBILITY ; PEDOTRANSFER FUNCTIONS ; COMBINED GEOCHEMISTRY ; POLLUTION ASSESSMENT ; TRACE-ELEMENTS ; NEURAL-NETWORK ; HUMAN HEALTH ; SEDIMENTS
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403120
推荐引用方式 GB/T 7714	Sulieman, Magboul M.,Kaya, Fuat,Keshavarzi, Ali,et al. Spatial variability of some heavy metals in arid harrats soils: Combining machine learning algorithms and synthetic indexes based-multitemporal Landsat 8/9 to establish background levels[J],2024,234.
APA	Sulieman, Magboul M.,Kaya, Fuat,Keshavarzi, Ali,Hussein, Abdullahi M.,Al-Farraj, Abdullah S.,&Brevik, Eric C..(2024).Spatial variability of some heavy metals in arid harrats soils: Combining machine learning algorithms and synthetic indexes based-multitemporal Landsat 8/9 to establish background levels.CATENA,234.
MLA	Sulieman, Magboul M.,et al."Spatial variability of some heavy metals in arid harrats soils: Combining machine learning algorithms and synthetic indexes based-multitemporal Landsat 8/9 to establish background levels".CATENA 234(2024).