Estimation of soil organic partition coefficients: from retention factors measured by soil column chromatography with water as eluent

The retention factors (k) of 104 hydrophobic organic chemicals (HOCs) were measured in soil column chromatography (SCC) over columns filled with three naturally occurring reference soils and eluted with Milli-Q water. A novel method for the estimation of soil organic partition coefficient (K-oc) was developed based on correlations with k in soil/water systems. Strong log K-oc versus log k correlations (r>0.96) were found. The estimated K-oc values were in accordance with the literature values with a maximum deviation of less than 0.4 log units. All estimated K-oc values from three soils were consistent with each other. The SCC approach is promising for fast screening of a large number of chemicals in their environmental applications. (C) 2002 Elsevier Science B.V. All rights reserved.

A column method for determination of soil organic partition coefficients of eight pesticides

A column method was developed to conveniently and reliably determine the soil organic partition coefficients (K-oc) of three insecticides (methiocarb, azinphos-methyl, fenthion), four fungicides (triadimenol, fuberidazole, tebuconazole, pencycuron), and one herbicide (atrazine), in which real soil acted as a stationary phase and the water solution of pesticide as an eluent. The processes of sorption equilibrium were directly shown through a breakthrough curve(BTC). The log K-oc values are 1.69, 1.95, 2.25, 2.55, 2.69, 2.67, 3.10, and 3.33 for atrazine, triadimenol, methiocarb, fuberidazole, azinphos-methyl, tebuconazole, fenthion and pencycuron, respectively.

Soil column chromatography for correlation between capacity factors and soil organic partition coefficients for eight pesticides

A soil column chromatographic method was developed to measure the capacity factors (k') of pesticides, in which soil acted as a stationary phase and methanol-water mixture as an eluent. The k' values of eight pesticides, including three insecticides (methiocarb, azinphos-methyl, fenthion), four fungicides (triadimenol, fuberidazole, tebuconazole, pencycuron), and one herbicide (atrazine), were found to be well fitted to a retention equation, ln k'=ln k(w)'-S-phi. Due to similar interactions of solutes with soil and solvent in both sorption determination and retention experiment, log k' has a good linear correlation with log K-oc for the eight pesticides from different classes, in contrast with poor correlation between log k' from C-18 column and log K-oc. So the method provides a tool for rapid estimation of K-oc from experimental k'. (C) 1999 Elsevier Science Ltd. All rights reserved.

Prediction of soil organic partition coefficients by a soil leaching column chromatographic method

The soil organic partition coefficient (K-oc) is one of the most important parameters to depict the transfer and fate of a chemical in the soil-water system. Predicting K-oc by using a chromatographic technique has been developing into a convenient and low-cost method. In this paper, a soil leaching column chromatograpy (SLCC) method employing the soil column packed with reference soil GSE 17201 (obtained from Bayer Landwirtschaftszentrum, Monheim, Germany) and methanol-water eluents was developed to predict the K-oc of hydrophobic organic chemicals (HOCs), over a log K-oc range of 4.8 orders of magnitude, from their capacity factors. The capacity factor with water as an eluent (k(w)') could be obtained by linearly extrapolating capacity factors in methanol-water eluents (k') with various volume fractions of methanol (phi). The important effects of solute activity coefficients in water on k(w)' and K-oc were illustrated. Hence, the correlation between log K-oc and log k(w)' (and log k') exists in the soil. The correlation coefficient (r) of the log K-oc vs. log k(w)' correlation for 58 apolar and polar compounds could reach 0.987, while the correlation coefficients of the log K-oc-log k' correlations were no less than 0.968, with phi ranging from 0 to 0.50. The smaller the phi, the higher the r. Therefore, it is recommended that the eluent of smaller phi, such as water, be used for accurately estimating K-oc. Correspondingly, the r value of the log K-oc-log k(w)' correlation on a reversed-phase Hypersil ODS (Thermo Hypersil, Kleinostheim, Germany) column was less than 0.940 for the same solutes. The SLCC method could provide a more reliable route to predict K-oc indirectly from a correlation with k(w)' than the reversed-phase liquid chromatographic (RPLC) one.

Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics

Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop

Extraction and Clustering of Motion Trajectories in Video

A system is described that tracks moving objects in a video dataset so as to extract a representation of the objects' 3D trajectories. The system then finds hierarchical clusters of similar trajectories in the video dataset. Objects' motion trajectories are extracted via an EKF formulation that provides each object's 3D trajectory up to a constant factor. To increase accuracy when occlusions occur, multiple tracking hypotheses are followed. For trajectory-based clustering and retrieval, a modified version of edit distance, called longest common subsequence (LCSS) is employed. Similarities are computed between projections of trajectories on coordinate axes. Trajectories are grouped based, using an agglomerative clustering algorithm. To check the validity of the approach, experiments using real data were performed.

Isoperimetric Graph Partitioning for Data Clustering and Image Segmentation

Spectral methods of graph partitioning have been shown to provide a powerful approach to the image segmentation problem. In this paper, we adopt a different approach, based on estimating the isoperimetric constant of an image graph. Our algorithm produces the high quality segmentations and data clustering of spectral methods, but with improved speed and stability.

Eliminating Redundant Training Data Using Unsupervised Clustering Techniques

Training data for supervised learning neural networks can be clustered such that the input/output pairs in each cluster are redundant. Redundant training data can adversely affect training time. In this paper we apply two clustering algorithms, ART2 -A and the Generalized Equality Classifier, to identify training data clusters and thus reduce the training data and training time. The approach is demonstrated for a high dimensional nonlinear continuous time mapping. The demonstration shows six-fold decrease in training time at little or no loss of accuracy in the handling of evaluation data.