Statistical equivalence of prediction models of the soil sorption coefficient obtained using different log P algorithms.

The soil sorption coefficient normalized to the organic carbon content (K) is a physicochemical parameter used in environmental risk assessments and in determining the final fate of chemicals released into the environment. Several models for predicting this parameter have been proposed based on the relationship between log K and log P. The difficulty and cost of obtaining experimental log P values led to the development of algorithms to calculate these values, some of which are free to use.

An alternative approach for the use of water solubility of nonionic pesticides in the modeling of the soil sorption coefficients.

The collection of data to study the damage caused by pesticides to the environment and its ecosystems is slowly acquired and costly. Large incentives have been established to encourage research projects aimed at building mathematical models for predicting physical, chemical or biological properties of environmental interest. The organic carbon normalized soil sorption coefficient (K(oc)) is an important physicochemical property used in environmental risk assessments for compounds released into the environment.

The effect of different log P algorithms on the modeling of the soil sorption coefficient of nonionic pesticides.

Collecting data on the effects of pesticides on the environment is a slow and costly process. Therefore, significant efforts have been focused on the development of models that predict physical, chemical or biological properties of environmental interest. The soil sorption coefficient normalized to the organic carbon content (Koc) is a key parameter that is used in environmental risk assessments.