Rapid assessment of regional soil arsenic pollution risk via diffuse reflectance spectroscopy

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• 作者：Somsubhra Chakraborty^a ; David C. Weindorf^b ; ^{david.weindorf@ttu.edu} ; Shovik Deb^c ; Bin Li^d ; Sathi Paul^e ; Ashok Choudhury^c ; Deb Prasad Ray^f
• 关键词：DRS ; diffuse reflectance spectroscopy ; RPD ; residual prediction deviation ; VisNIR ; visible near infrared ; PLS ; partial least squares ; PCR ; principle component regression ; RF ; random forest ; MSW ; municipal solid waste ; LOI ; loss on ignition ; LASSO ; least absolute shrinkage and selection operator ; ENET ; elastic net ; RMSE ; root mean squared error ; RPIQ ; ratio of performance to inter-quartile distance ; 1D ; first derivative ; 2D ; second derivative ; OSC ; orthogonal signal correction
• 刊名：Geoderma
• 出版年：2017
• 出版时间：1 March 2017
• 年：2017
• 卷：289
• 期：Complete
• 页码：72-81
• 全文大小：1349 K
• 卷排序：289

文摘

Soil contamination with As is a serious environmental concern. We used DRS to predict soil As content of 200 soils collected from a landfill site. ENET model using VisNIR spectra produced best As prediction. DRS prediction followed by kriging produced spatial variability map of soil As. DRS prediction with indicator kriging identified As pollution hotspots.