Soil-water partition regression coefficient

FIGURE 2 Graphical representation of several quantitative models describing correlations between observed soil sorption coefficients ( om) 1-octanol/water partition coefficients (Kqw)- The corresponding regression equations and their sources are the following ... 

Some basic statistical requirements were also overlooked when quantitative models based on coefficients were developed. The most important assumption underlying the linear regression model is that the dependent variable contains all the errors in each data pair (Maes 1984 Irvin and Quickenden 1983). The range for 1-octanol/water partition coefficients obtained for each molecule clearly demonstrates that this basic assumption is not met. It has been demonstrated (York 1966) that if this basic assumption is violated, the fitted slopes can deviate by as much as 40% from the correct value. Thus, the validity and applicability of published quantitative models describing the relationship between the Kq coefficients and soil sorption coefficients are highly questionable. 

All that which has been said about empirical models based on the 1-octanol/water partition coefficients is also true for models based on the water solubilities (a) the measured water solubility data are of low precision (see TABLE 4), (b) there exists a wide variety of quantitative linear models correlating soil sorption coefficients and water solubility (Wijayaratne and Means 1984 Chiou et al. 1983 Banwart et al. 1982 Means et al. 1982 Briggs 1981 Kenaga and Goring 1980 Karickhoff etal. 1979), and (c) the statistical requirements for linear regression models are violated by the large errors in the experimental water solubility data. That some quantitative models (Wijayaratne and Means 1984 Banwart et al. 1982 Means et al. 1982) use non-molar units ( ig mL ) for water solubility data, further impedes their use. 

---