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Modeling Soil Sulfate Sorption Characteristics
Author(s) -
Comfort S.D.,
Dick R.P.,
Baham J.
Publication year - 1992
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq1992.00472425002100030021x
Subject(s) - sorption , soil water , langmuir , soil science , environmental chemistry , cation exchange capacity , chemistry , nonlinear regression , environmental science , langmuir adsorption model , sulfate , soil test , adsorption , regression analysis , mathematics , statistics , organic chemistry
Knowledge of soil SO 4 adsorption characteristics is required for predicting the fate of atmospheric S depositions on terrestrial ecosystems. This study developed two models for predicting the SO 4 sorption characteristics of field moist soils from routinely measured soil physical and chemical properties. Sixty‐two soil samples from the U.S. Northeastern (NE) and Southern Blue Ridge Province (SBRP) regions were used in model development. The first modeling approach used stepwise multiple regression to identify which combinations of soil properties best predicted each Langmuir constant. The Langmuir constant regression equations were then substituted back into the Langmuir model and used to predict SO 4 sorption for each equilibrium SO 4 concentration. This approach described SO 4 sorption reasonably well for both regions ( R 2 of 0.57–0.89). In the second approach, a better fit of SO 4 sorption was achieved ( R 2 of 0.81–0.92) using fewer soil properties by expressing the Langmuir constants as linear functions of selected soil properties and directly estimating the soil property coefficients by nonlinear regression. Soil properties used in the second model included: dithionite‐citrate‐extractable Al (Al d ), total organic C (TOC), and extractable SO 4 for the NE region; and Al d , TOC, clay content, pH, and CEC for the SBRP region. These results indicated that regional soil SO 4 sorption characteristics could be predicted for field moist soils with the knowledge of only a few routinely measured soil properties.