Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 635, Issue -, Pages 859-866Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.scitotenv.2018.04.191
Keywords
Multi-surface model; Suspension density; Pore water; Field condition
Categories
Funding
- National Natural Science Foundation of China [21577062]
- Key Research and Development Program of Jiangsu Province [BE2015708]
- special fund for Agriculture Land Pollution Control Project of Jiangsu Province [2017-123]
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Metal partitioning in soils is a key process controlling metal bioavailability and mobility and is greatly influenced by the solid/liquid ratio. However, metal partitioning is difficult to describe either by a simple partition coefficient or by isotherm adsorption equations. This study investigated the solubility of Ni(II) and Cd(II) in 19 soils as a function of three extraction reagents (water, 0.01 M NaNO3, and 0.01 M CaCl2), five solid/liquid ratios (5-400 g/L) and field condition extracted by Rhizon samplers. Thermodynamically based multi-surface models (MSMs) that included generic parameters were used to describe metal partitioning under the studied conditions. The results showed that Ni/Cd solubility depended on the soil type, extraction reagent, and solid/liquid ratio. Soil major background cations (especially Ca2+, Mg2+, Fe3+ and Al3+) had a significant effect on the model's prediction ability. The MSM was able to predict the extractable metal in 0.01 M CaCl2 in various soils at different solid/liquid ratios when soil background cations were included in the calculation; without the background cations, the model was able to predict metal partitioning only at solid/liquid ratios of < 100 g/L. In addition, the model failed to predict water-extracted and 0.01 M-NaNO3-extracted Ni/Cd if background cations were not included, but could reasonably do so if they were included. More importantly, after the background cations were included, MSMs relatively well predicted the Ni/Cd content in soil pore water under 80% field capacity conditions with water as the solution matrix. (C) 2018 Elsevier B.V. All rights reserved.
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