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The prediction of cadmium, copper, l...

Upson, Geoffrey L.

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The prediction of cadmium, copper, lead, and zinc partitioning in contaminated soils.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The prediction of cadmium, copper, lead, and zinc partitioning in contaminated soils./
Author:	Upson, Geoffrey L.
Description:	262 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 1822.
Contained By:	Dissertation Abstracts International66-04B.
Subject:	Agriculture, Soil Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3173096
ISBN:	9780542099656

The prediction of cadmium, copper, lead, and zinc partitioning in contaminated soils.
Upson, Geoffrey L.

The prediction of cadmium, copper, lead, and zinc partitioning in contaminated soils. - 262 p.

Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 1822.

Thesis (Ph.D.)--Colorado State University, 2005.

Determination of metal partitioning in contaminated soils can provide critical data in support of environmental risk assessments. This research focused on the development of a general modeling approach that can predict metal partitioning in a variety of soils.

ISBN: 9780542099656Subjects--Topical Terms:

1017824
Agriculture, Soil Science.

The prediction of cadmium, copper, lead, and zinc partitioning in contaminated soils.
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100 1 $a Upson, Geoffrey L. $3 1913143
245 1 4 $a The prediction of cadmium, copper, lead, and zinc partitioning in contaminated soils.
300 $a 262 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 1822.
500 $a Adviser: Greg Butters.
502 $a Thesis (Ph.D.)--Colorado State University, 2005.
520 $a Determination of metal partitioning in contaminated soils can provide critical data in support of environmental risk assessments. This research focused on the development of a general modeling approach that can predict metal partitioning in a variety of soils.
520 $a A competitive modeling approach (CMA) and a non-competitive modeling approach (NCMA) were developed to predict the partitioning of cadmium, copper, lead, and zinc in contaminated soils near Leadville, CO. The modeling approaches consisted of surrogate soils comprised of five specimen materials; kaolinite, illite, montmorillonite, iron oxide (FeOOH) and soil organic matter (SOM). Surrogate soil compositions were adjusted to approximate natural soils by applying a unique set of clay, FeOOH, and SOM weighting factors. The weighting factors were calculated from XRD and total aluminum, iron, and soil organic carbon (SOC) data. The Vanselow selectivity coefficient and four surface complexation models (Constant Capacitance Model, Generalized Two-Layer Model, Stockholm Humic Model, and a non-electrostatic surface complexation model) were applied to the surrogate soils to describe the sorption of the four metals to individual specimen materials.
520 $a Predicted concentrations of exchangeable, sorbed, and complexed metals were compared to experimental metals data generated from the selective extraction of four contaminated soils. The NCMA and CMA were tested across a range of pHs, soil textures, SOC levels, concentrations of soluble cadmium, copper, lead, and zinc, and total aluminum and iron.
520 $a Both modeling approaches were successful in estimating the experimental data within a range of one order of magnitude. Qualitatively, the CMA was a better predictor of sorbed and complexed metals data, while the NCMA was a slightly better predictor of exchangeable metals data. Careful evaluation of the data used to calculate weighting factors is recommended since errors in weighting factor values can cause significant changes in predicted metal partitioning.
520 $a Compared to the high concentrations of total metals reported in these soils, low concentrations of soluble, exchangeable, sorbed, and complexed metals were extracted by the selective extraction process or predicted by the NCMA and CMA. The results suggest that environmental assessments based primarily on total metals data may not describe accurately the potential a contaminated site poses to the environment.
590 $a School code: 0053.
650 4 $a Agriculture, Soil Science. $3 1017824
650 4 $a Environmental Sciences. $3 676987
690 $a 0481
690 $a 0768
710 2 0 $a Colorado State University. $3 675646
773 0 $t Dissertation Abstracts International $g 66-04B.
790 1 0 $a Butters, Greg, $e advisor
790 $a 0053
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3173096