Is the computed speciation of copper in a wide range of Chinese soils reliable?

Free Cu species in soils is a key issue to its bioavailability. However, predictive models for Cu speciation across a wide range of soils were still unavailable. In this study, Cu speciation in 34 contaminated soil samples were investigated via analytical technique and predictive models. The results...

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Main Authors: Bo Li, Yibing Ma, Junxing Yang
Format: Article
Language:English
Published: Taylor & Francis Group 2017-01-01
Series:Chemical Speciation & Bioavailability
Subjects:
Online Access:http://dx.doi.org/10.1080/09542299.2017.1404437
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spelling doaj-93f45d4f4d1d4157a965e06cf361e4e22020-11-25T01:34:25ZengTaylor & Francis GroupChemical Speciation & Bioavailability0954-22992047-65232017-01-0129120521510.1080/09542299.2017.14044371404437Is the computed speciation of copper in a wide range of Chinese soils reliable?Bo Li0Yibing Ma1Junxing Yang2Liaoning Academy of Agricultural SciencesChinese Academy of Agricultural SciencesChinese Academy of SciencesFree Cu species in soils is a key issue to its bioavailability. However, predictive models for Cu speciation across a wide range of soils were still unavailable. In this study, Cu speciation in 34 contaminated soil samples were investigated via analytical technique and predictive models. The results showed that most of free Cu2+ was underestimated when using default log KCuFA and 65% active fulvic acid as inputs in models of WHAM VI and NICA-Donnan. The best prediction was found when using either adjusted active fulvic acid from 10% to 125% for WHAM VI or from 15% to 65% for NICA-Donnan model with the RMSE < 0.32 and r2 > 0.96. In contrast, NICA-Donnan demonstrated a slightly stronger binding for Cu than WHAM VI due to extra 26% of samples was underestimated. This work presents a comprehensive database of Cu speciation and an effective attempt of free Cu2+ prediction in a wide range of Chinese soils.http://dx.doi.org/10.1080/09542299.2017.1404437Copper activitysoil pore waterdistributionmodel prediction
collection DOAJ
language English
format Article
sources DOAJ
author Bo Li
Yibing Ma
Junxing Yang
spellingShingle Bo Li
Yibing Ma
Junxing Yang
Is the computed speciation of copper in a wide range of Chinese soils reliable?
Chemical Speciation & Bioavailability
Copper activity
soil pore water
distribution
model prediction
author_facet Bo Li
Yibing Ma
Junxing Yang
author_sort Bo Li
title Is the computed speciation of copper in a wide range of Chinese soils reliable?
title_short Is the computed speciation of copper in a wide range of Chinese soils reliable?
title_full Is the computed speciation of copper in a wide range of Chinese soils reliable?
title_fullStr Is the computed speciation of copper in a wide range of Chinese soils reliable?
title_full_unstemmed Is the computed speciation of copper in a wide range of Chinese soils reliable?
title_sort is the computed speciation of copper in a wide range of chinese soils reliable?
publisher Taylor & Francis Group
series Chemical Speciation & Bioavailability
issn 0954-2299
2047-6523
publishDate 2017-01-01
description Free Cu species in soils is a key issue to its bioavailability. However, predictive models for Cu speciation across a wide range of soils were still unavailable. In this study, Cu speciation in 34 contaminated soil samples were investigated via analytical technique and predictive models. The results showed that most of free Cu2+ was underestimated when using default log KCuFA and 65% active fulvic acid as inputs in models of WHAM VI and NICA-Donnan. The best prediction was found when using either adjusted active fulvic acid from 10% to 125% for WHAM VI or from 15% to 65% for NICA-Donnan model with the RMSE < 0.32 and r2 > 0.96. In contrast, NICA-Donnan demonstrated a slightly stronger binding for Cu than WHAM VI due to extra 26% of samples was underestimated. This work presents a comprehensive database of Cu speciation and an effective attempt of free Cu2+ prediction in a wide range of Chinese soils.
topic Copper activity
soil pore water
distribution
model prediction
url http://dx.doi.org/10.1080/09542299.2017.1404437
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