4.6 Article

Proton binding to soil humic and fulvic acids: Experiments and NICA-Donnan modeling

Journal

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.colsurfa.2013.08.010

Keywords

Soil humic acids; Soil fulvic acids; Proton binding; NICA-Donnan model; Generic model parameters; 'Carboxylic' group content

Funding

  1. Natural Science Foundation of China [40971144, 41201231]
  2. One Hundred Elitist Program of the Chinese Academy of Sciences
  3. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau
  4. Institute of Soil and Water Conservation of the Chinese Academy of Science

Ask authors/readers for more resources

Proton binding to one soil fulvic acid (JGFA), two soil humic acids (JGHA, JLHA) and a lignite-based humic acid (PAHA) was investigated. The results were fitted to NICA-Donnan model and compared directly with the predictions using the generic parameters. NICA-Donnan model can describe proton binding satisfactorily when parameter fitting is allowed for humic substance (HS). However, predictions based on the generic parameter sets deviate for soil samples in a non-systematic way from the measured results. Replacing O-max,O-H1 in generic parameter sets with material-specific values improves the predictions for soil HA significantly. For JGFA, the agreement between the model prediction and data is still not satisfactory after substitution. This is due to a very different pattern of site distribution of JGFA from that of generic FA. For two other soil FAs (FH-14, FH-22 of Milne's database) the generic predictions can be improved significantly with material-specific Q(max,H1). Adjusting also Q(max,H2) to HS material-specific value improves the prediction only slightly further. In practice, Q(max,H1) and Q(max,H2) of HS can be obtained in a relatively simple way by performing one acid-base titration at a given ionic strength and applying the procedure of Lenoir et al. to fit data to NICA equation. Introduction of thus obtained Q(max,H1) and Q(max,H2) into generic parameter sets improves the generic predictions significantly. The functional group contents as obtained by SG-method are not adequate for this purpose. (C) 2013 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available