干旱区生态环境知识资源中心(Arid): A new method to consider spatial risk assessment of cross-correlated heavy metals using geo-statistical simulation

Arid

DOI	10.22044/jme.2017.892
	A new method to consider spatial risk assessment of cross-correlated heavy metals using geo-statistical simulation
	Sakizadeh, M.; Sattari, M. T.; Ghorbani, H.
通讯作者	Sakizadeh, M
来源期刊	JOURNAL OF MINING AND ENVIRONMENT
ISSN	2251-8592
EISSN	2251-8606
出版年	2017
卷号	8 期号:3 页码:373-391
英文摘要	The soil samples were collected from 170 sampling stations in an arid area in Shahrood and Damghan, characterized by prevalence of mining activity. The levels of Co, Pb, Ni, Cs, Cu, Mn, Sr, V, Zn, Cr, and Tl were recorded in each sampling location. A new method known as min/max autocorrelation factor (MAF) was applied for the first time in the environmental research works to de-correlate these elements before their geo-statistical simulation. The high cross-correlation among some elements, while poor spatial correlation among the others, could have made spectral decomposition of MAFs unstable, resulting in some negative eigenvalues, so it was decided to reduce the dimensionality of the original variables by Principal Component Analysis (PCA). The resultant 6 heavy metals (Cr, Mn, Cu, V, Ni, and Co) were converted to their respective MAFs followed by their geo-statistical simulation using Sequential Gaussian Simulation (SGS) independently. Examination of the cross-variograms of MAFs indicated that the resultant factors had been rigorously de-correlated, especially at zero lag and around. lag distance. Several validation checks including reproduction of variograms in data and normal score space, close matching between distribution of MAFs versus simulated realizations, and reproduction of descriptive statistics and data histograms all confirmed that the data values had been honored by this applied method. The results obtained indicated that this method could reproduce the data values as well as the spatial continuity of heavy metals (e.g. semivariograms) successfully. In addition, this technique is simpler and more computationally efficient than its equivalent sequential Gaussian co-simulation as fitting a linear model of co-regionalization (LMC) is not required in the data-driven MAF method.
英文关键词	Decorrelation Geo-Statistical Simulation Min/Max Autocorrelation Factor
类型	Article
语种	英语
收录类别	ESCI
WOS记录号	WOS:000419393000004
WOS关键词	MIN/MAX AUTOCORRELATION FACTORS ; JOINT CONDITIONAL SIMULATION ; GEOSTATISTICAL ANALYSES ; AGRICULTURAL SOILS ; PYRITE OXIDATION ; GEOCHEMICAL DATA ; MULTIVARIATE ; CHINA ; IRAN ; CLASSIFICATION
WOS类目	Mining & Mineral Processing
WOS研究方向	Mining & Mineral Processing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/332468
作者单位	[Sakizadeh, M.] Shahid Rajaee Teacher Training Univ, Fac Sci, Dept Environm Sci, Tehran, Iran; [Sattari, M. T.] Univ Tabriz, Agr Fac, Dept Water Engn, Tabriz, Iran; [Ghorbani, H.] Shahrood Univ Technol, Sch Agr Engn, Shahrood, Iran
推荐引用方式 GB/T 7714	Sakizadeh, M.,Sattari, M. T.,Ghorbani, H.. A new method to consider spatial risk assessment of cross-correlated heavy metals using geo-statistical simulation[J],2017,8(3):373-391.
APA	Sakizadeh, M.,Sattari, M. T.,&Ghorbani, H..(2017).A new method to consider spatial risk assessment of cross-correlated heavy metals using geo-statistical simulation.JOURNAL OF MINING AND ENVIRONMENT,8(3),373-391.
MLA	Sakizadeh, M.,et al."A new method to consider spatial risk assessment of cross-correlated heavy metals using geo-statistical simulation".JOURNAL OF MINING AND ENVIRONMENT 8.3(2017):373-391.