Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA

Inorganic contaminants, including potentially toxic metals (PTMs), originating from un-reclaimed abandoned mine areas may accumulate in soils and present significant distress to environmental and public health. The ability to generate realistic spatial distribution models of such contamination is im...

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Autores principales:	Melissa Magno, Ingrid Luffman, Arpita Nandi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	geostatistics potentially toxic metals (PTMs) soils mining interpolation Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/655576c607a745fd999b7b81824cb013
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spelling	oai:doaj.org-article:655576c607a745fd999b7b81824cb0132021-11-25T17:42:39ZEvaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA10.3390/geosciences111104342076-3263https://doaj.org/article/655576c607a745fd999b7b81824cb0132021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3263/11/11/434https://doaj.org/toc/2076-3263Inorganic contaminants, including potentially toxic metals (PTMs), originating from un-reclaimed abandoned mine areas may accumulate in soils and present significant distress to environmental and public health. The ability to generate realistic spatial distribution models of such contamination is important for risk assessment and remedial planning of sites where this has occurred. This study evaluated the prediction accuracy of optimized ordinary kriging compared to spatial regression-informed cokriging for PTMs (Zn, Mn, Cu, Pb, and Cd) in soils near abandoned mines in Bumpus Cove, Tennessee, USA. Cokriging variables and neighborhood sizes were systematically selected from prior statistical analyses based on the association with PTM transport and soil physico-chemical properties (soil texture, moisture content, bulk density, pH, cation exchange capacity (CEC), and total organic carbon (TOC)). A log transform was applied to fit the frequency histograms to a normal distribution. Superior models were chosen based on six diagnostics (ME, RMS, MES, RMSS, ASE, and ASE-RMS), which produced mixed results. Cokriging models were preferred for Mn, Zn, Cu, and Cd, whereas ordinary kriging yielded better model results for Pb. This study determined that the preliminary process of developing spatial regression models, thus enabling the selection of contributing soil properties, can improve the interpolation accuracy of PTMs in abandoned mine sites.Melissa MagnoIngrid LuffmanArpita NandiMDPI AGarticlegeostatisticspotentially toxic metals (PTMs)soilsmininginterpolationGeologyQE1-996.5ENGeosciences, Vol 11, Iss 434, p 434 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	geostatistics potentially toxic metals (PTMs) soils mining interpolation Geology QE1-996.5
spellingShingle	geostatistics potentially toxic metals (PTMs) soils mining interpolation Geology QE1-996.5 Melissa Magno Ingrid Luffman Arpita Nandi Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA
description	Inorganic contaminants, including potentially toxic metals (PTMs), originating from un-reclaimed abandoned mine areas may accumulate in soils and present significant distress to environmental and public health. The ability to generate realistic spatial distribution models of such contamination is important for risk assessment and remedial planning of sites where this has occurred. This study evaluated the prediction accuracy of optimized ordinary kriging compared to spatial regression-informed cokriging for PTMs (Zn, Mn, Cu, Pb, and Cd) in soils near abandoned mines in Bumpus Cove, Tennessee, USA. Cokriging variables and neighborhood sizes were systematically selected from prior statistical analyses based on the association with PTM transport and soil physico-chemical properties (soil texture, moisture content, bulk density, pH, cation exchange capacity (CEC), and total organic carbon (TOC)). A log transform was applied to fit the frequency histograms to a normal distribution. Superior models were chosen based on six diagnostics (ME, RMS, MES, RMSS, ASE, and ASE-RMS), which produced mixed results. Cokriging models were preferred for Mn, Zn, Cu, and Cd, whereas ordinary kriging yielded better model results for Pb. This study determined that the preliminary process of developing spatial regression models, thus enabling the selection of contributing soil properties, can improve the interpolation accuracy of PTMs in abandoned mine sites.
format	article
author	Melissa Magno Ingrid Luffman Arpita Nandi
author_facet	Melissa Magno Ingrid Luffman Arpita Nandi
author_sort	Melissa Magno
title	Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA
title_short	Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA
title_full	Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA
title_fullStr	Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA
title_full_unstemmed	Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA
title_sort	evaluating spatial regression-informed cokriging of metals in soils near abandoned mines in bumpus cove, tennessee, usa
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/655576c607a745fd999b7b81824cb013
work_keys_str_mv	AT melissamagno evaluatingspatialregressioninformedcokrigingofmetalsinsoilsnearabandonedminesinbumpuscovetennesseeusa AT ingridluffman evaluatingspatialregressioninformedcokrigingofmetalsinsoilsnearabandonedminesinbumpuscovetennesseeusa AT arpitanandi evaluatingspatialregressioninformedcokrigingofmetalsinsoilsnearabandonedminesinbumpuscovetennesseeusa
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Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA

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