Nonylphenol (NP) as an intermediate from anaerobic degradation of widely used nonionic surfactants occurs widespread in the environment. Partition behavior of this toxic and endocrine-disrupting chemical between soil and water was not examined until yet. The objective of this investigation was to quantify sorption and desorption behavior of 4-nonyl[14C]phenol in a set of 51 soils using the batch equilibrium approach. Kinetic studies indicated apparent equilibrium within 20 h. Sorption was influenced by sorbate structure as could be shown with branched 4-nonyl[14C]phenol and the linear 4-n-NP, respectively. Linear 4-n-NP behaves differently from the branched isomers of 4-NP. Sorption of 4-nonyl[14C]phenol tested with five different initial concentrations resulted in linearly fitted isotherms that provided calculation of sorption partition coefficients (KP). Desorption partition coefficients (KP-des) revealed hysteresis independent of soil properties but decreasing with decreasing initial NP concentrations. KP values were correlated with organic carbon content of the soils yielding a log KOC of 3.97.

Partition coefficients K P of nonylphenol (NP) in soil were determined for 193 soil samples which differed widely in content of soil organic carbon (SOC), hydrogen activity, clay content, and in the content of dissolved organic carbon (DOC). By means of multiple linear regression analysis (MLR), pedotransfer functions were derived to predict partition coefficients from soil data. SOC and pH affected the sorption, though the latter was in a range significantly below the pK a of NP. Quality of soil organic matter presumably plays an important but yet not quantified role in sorption of NP. For soil samples with SOC values less than 3 g kg −1 , model prediction became uncertain with this linear approach. We suggest that using only SOC and pH data results in good prediction of NP sorption in soils with SOC higher than 3 g kg −1 . Considering the varying validity of the linear model for different ranges of the most sensitive parameter SOC, a more flexible, nonlinear approach was tested. The application of an artificial neuronal network (ANN) to predict sorption of NP in soils showed a sigmoidal relation between K P and SOC. The nonlinear ANN approach provided good results compared to the MLR approach and represents an alternative tool for prediction of NP partitioning coefficients.