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Submitted to: Soil Science Society of America Annual Meeting
Publication Type: Abstract Only Publication Acceptance Date: 6/10/2016 Publication Date: 11/9/2016 Citation: Bolster, C.H. 2016. Using proper regression methods for fitting the Langmuir model to sorption data. Soil Science Society of America Annual Meeting. 1. Interpretive Summary: Technical Abstract: The Langmuir model, originally developed for the study of gas sorption to surfaces, is one of the most commonly used models for fitting phosphorus sorption data. There are good theoretical reasons, however, against applying this model to describe P sorption to soils. Nevertheless, the Langmuir model is still commonly used for describing P sorption to soils; in part because several linearized versions of the Langmuir model exist and thus sorption parameters can be easily obtained through linear regression. Furthermore, fitting the Langmuir model provides an estimate of the maximum sorption capacity of the soil, a parameter which has utility in P management planning. However, the sorption parameters are only valid if the model is a reasonably accurate representation of the physical mechanisms governing P sorption to soils. In this study, P sorption data collected on a variety of soils were fit with the Langmuir, Freundlich, and Temkin models using weighted least-squares regression. By weighting the data by the inverse of their measurement variance, the model fits can be statistically evaluated with the chi-square statistic. Results show that only the Freundlich model yielded statistically significant chi-square values. These results provide convincing statistical evidence that the Langmuir model is not the most appropriate model for describing P sorption to soils. |
