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The prediction of partitioning coefficients for chemicals causing environmental concern
The Science of the Total Environment 248 Ž2000. 1᎐10
The prediction of partitioning coefficients for chemicals causing environmental concern Wei ChuU , Kwai-Hing Chan Department of Ci¨ il and Structural Engineering, Research Centre for Urban En¨ ironmental Technology and Management, The Hong Kong Polytechnic Uni¨ ersity, Hunghom, Kowloon, Hong Kong Received 24 September 1999; accepted 17 November 1999
Abstract Various types of partition coefficients have been used to facilitate the prediction of the concentration of pollutants in different phases in the environment. Many thousands of chemicals may exist in our environment which makes prediction work difficult or impossible due to a deficiency of knowledge of those unfamiliar compounds. In this study, the correlation between an octanol᎐water partition coefficient Ž K ow ., water solubility Ž S . and a normalized soilrsediment partition coefficient Ž K oc . was investigated though the examination of 148 model chemicals. These model chemicals were classified into five major categories for easier adoption in future use. They are aliphatic compounds, aromatic compounds, pesticides, herbicides and polyaromatic hydrocarbons ŽPAH.. Linear models are developed to correlate these partition coefficients in each category. The prediction of unfamiliar chemicals in the same category becomes possible if the fundamental properties of these chemicals Žsuch as solubility. are previously known. 䊚 2000 Elsevier Science B.V. All rights reserved. Keywords: Partitioning coefficients; Remediation; Soil
1. Introduction Contamination of soils and sediments by organic pollutants is an environmental concern.
U
Corresponding author. E-mail address: [email protected] ŽW. Chu.
Sources of these contaminants include underground storage tanks, hazardous waste disposal sites, septic tanks, municipal landfills, and accidental spills. As a consequence, the remediation of soil or sediment which contains hazardous materials is often initiated by extraction of the contaminants from the soil to the aqueous phase, followed by chemical, physical, andror biological
0048-9697r00r$ - see front matter 䊚 2000 Elsevier Science B.V. All rights reserved. PII: S 0 0 4 8 - 9 6 9 7 Ž 9 9 . 0 0 4 7 2 - 6
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W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
treatment. However, the design and operation of these extraction processes is often limited by a lack of knowledge of the pollutants’ characteristics. For example, hydrophobic compounds do not easily partition to the liquid phase from the soil phase to the extent necessary to make subsequent treatment a high performance process; therefore, extended extraction or treatment is often required to clean up a contaminated site. In addition, each contaminated site may contain different types of pollutants that make the optimization of the cleaning process more difficult. In the soil remediation process two phases are mainly involved Žsoilrwater or soilrsolvent..
Parameters, such as the octanol᎐water partition coefficient Ž K ow . and water solubility Ž S . have been used to characterize the partition of a specific organic compound between the organic Žhydrophobic. and water Žhydrophobic. phases ŽBaughman and Perenich, 1988; Hawker and Connell, 1988.. In addition, the soilrsediment adsorption partition coefficient normalized to organic carbon Ž K oc . is extensively used to assess the fate of organic chemicals in hazardous waste sites. A better understanding of the concepts defining the relationship between K ow , K oc , and S should extend the usefulness of these parameters in interpreting the behavior of organic com-
Table 1 List of the selected aliphatic compounds and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
1,1,1-Trichloroethane wmethylchloroformx 1,1,2,2-Tetrachloroethane 1,1,2-Trichloroethane wvinyltrichloridex 1,1-Dichloroethane wethylidine chloridex 1,1-Dichloroethene wvinylidine chloridex 1,2-Dichloroethane wethylene dichloridex 1,2-Dichloroethene Ž cis . 1,2-Dichloroethene Ž trans. 1,3-Butadiene Acetonitrile wmethyl cyanidex Acrylonitrile w2-propenenitrilex Bromodichloromethane wdichlorobromomethx Chloroethane wethyl chloridex Chloroethene wvinyl chloridex Chloromethane wmethyl chloridex Dibromochloromethane Dichlorodifluoromethane wFreon12x Dichloromethane wmethylene chloridex Ethylene dibromide wEDBx Hexachlorobutadiene Hexachlorocyclopentadiene Hexachloroethane wperchloroethanex Iodomethane wmethyl iodidex Pentachloroethane wpentalinx Tetrachloroethene wPERCx Tetrachloromethane wcarbontetrachloride x Tribromomethane wbromoformx Trichloroethene wTCEx Trichlorofluoromethane wFreon 11x Trichloromethane wchloroformx
1.33Eq 02 1.68Eq 02 1.33E q 02 9.90Eq 01 9.70Eq 01 9.90Eq 01 9.70Eq 01 9.70Eq 01 5.41Eq 01 4.11Eq 01 5.31Eq 01 1.64Eq 02 6.45Eq 01 6.25Eq 01 5.10Eq 01 2.08Eq 02 1.21Eq 02 8.49Eq 01 1.88Eq 02 2.61Eq 02 2.73Eq 02 2.37Eq 02 1.42Eq 02 2.02Eq 02 1.67Eq 02 1.54Eq 02 2.53Eq 02 1.33Eq 02 1.37Eq 02 1.19Eq 02
1.12Ey 02 1.73Ey 02 3.37Ey 02 5.56Ey 02 2.32Ey 02 8.61Ey 02 3.61Ey 02 6.50Ey 02 1.36Ey 02 Infinity 1.50Eq 00 2.69Ey 02 8.90Ey 02 4.27E y 02 1.27Ey 01 1.92Ey 02 2.32Ey 03 2.35Ey 01 2.29Ey 02 5.74Ey 07 7.69Ey 06 2.11Ey 04 9.86Ey 02 1.83Ey 04 8.97Ey 04 4.92Ey 03 1.19Ey 02 8.29Ey 03 8.01Ey 03 6.87Ey 02
1.52Eq 02 1.18Eq 02 5.60Eq 01 3.00Eq 01 6.50Eq 01 1.40Eq 01 4.90Eq 01 5.90E q 01 1.20Eq 02 2.20Eq 00 8.50Ey 01 6.10Eq 01 1.70Eq 01 5.70Eq 01 3.50Eq 01 8.40Eq 01 5.80Eq 01 8.80Eq 00 4.40E q 01 2.90Eq 04 4.80Eq 03 2.00Eq 04 2.30Eq 01 1.90Eq 03 3.64Eq 02 4.39Eq 02 1.16Eq 02 1.26Eq 02 1.59Eq 02 4.70Eq 01
3.16Eq 02 2.45Eq 02 2.95Eq 02 6.17Eq 01 6.92Eq 01 3.02Eq 01 5.01Eq 00 3.02Eq 00 9.77Eq 01 4.57Ey 01 1.78Eq 00 7.59Eq 01 3.50Eq 01 2.40Eq 01 9.50Ey 01 1.23Eq 02 1.45Eq 02 2.00Eq 01 5.75Eq 01 6.02Eq 04 1.10Eq 05 3.98Eq 04 4.90Eq 01 7.76Eq 02 3.98E q 02 4.37Eq 02 2.51Eq 02 2.40Eq 02 3.39Eq 02 9.33Eq 01
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
pounds in the environment. For example, several attempts have been made to estimate the value of K oc from the chemical structure. A non-linear
3
model for estimating K oc , applicable to polar and non-polar organics based on an artificial neural network using the K ow and S, was developed by
Table 2 List of the selected aromatic compounds and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
1,2,3,4-Tetrachlorobenzene 1,2,3,5-Tetrachlorobenzene 1,2,3-Trichlorobenzene 1,2,4,5-Tetrachlorobenzene 1,2,4-Trichlorobenzene 1,2-Dichlorobenzene w o-dichlorobenzenex 1,3,5-Trichlorobenzene 1,3-Dichlorobenzene w m-dichlorobenzenex 1,3-Dinitrobenzene 1,4-Dichlorobenzene w p-dichlorobenzenex 2,3,4,6-Tetrachlorophenol 2,3-Dinitrotoluene 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol 2,4-Dichlorophenol 2,4-Dimethylphenol was-m-xylenolx 2,4-Dinitrophenol 2,4-Dinitrotoluene 2,5-Dinitrotoluene 2,6-Dinitrotoluene 2-Chlorophenol w o-chlorophenolx 3,4-Dinitrotoluene 4,6-Dinitro-o-cresol 4-Chloro-m-cresol wchlorocresolx Benzene Bromobenzene wphenyl bromidex Chlorobenzene Chlorotoluene wbenzyl chloridex Cresol ŽTechnical. wmethylphenolx Diethylstilbestrol wDESx Ethylbenzene wphenylethane x Hexachlorobenzene wperchlorobenzenex Hexachlorophene wdermadexx m-Chlorotoluene m-Xylene w1,3-dimethylbenzenex Nitrobenzene o-Chlorotoluene o-Xylene w1,2-dimethylbenzenex p-Chlorotoluene Pentachlorobenzene Pentachloronitrobenzene wquintozenex Pentachlorophenol Phenol p-Xylene w1,4-dimethylbenzenex Toluene wmethylbenzenex Xylene Žmixed.
2.16Eq 02 2.16Eq 02 1.81E q 02 2.16Eq 02 1.81Eq 02 1.47Eq 02 1.81Eq 02 1.47Eq 02 1.68Eq 02 1.47Eq 02 2.32Eq 02 1.82Eq 02 1.97Eq 02 1.97Eq 02 1.63Eq 02 1.22Eq 02 1.84Eq 02 1.82E q 02 1.82Eq 02 1.82Eq 02 1.29Eq 02 1.82Eq 02 1.98Eq 02 1.43Eq 02 7.81Eq 01 1.57Eq 02 1.13Eq 02 1.27Eq 02 1.08Eq 02 2.68Eq 02 6.28Eq 02 2.85Eq 02 4.07Eq 02 1.27Eq 02 1.06E q 02 1.23Eq 02 1.27Eq 02 1.06Eq 02 1.27Eq 02 2.50Eq 02 2.95E q 02 2.66Eq 02 9.41Eq 01 1.06Eq 02 9.21Eq 01 1.06Eq 02
1.62Ey 05 1.11Ey 05 6.61Ey 05 2.78Ey 05 1.65Ey 04 6.80Ey 04 3.20Ey 05 8.37Ey 04 2.80Ey 03 5.37Ey 04 3.02Ey 05 1.70Ey 02 6.03Ey 03 4.05Ey 03 2.82Ey 02 3.44Ey 02 3.04Ey 02 1.32Ey 03 7.25Ey 03 7.25Ey 03 2.26Ey 01 5.93Ey 03 1.46Ey 03 2.70Ey 02 2.24Ey 02 2.84Ey 03 4.14Ey 03 2.61Ey 02 2.87Ey 01 3.58Ey 08 2.42Ey 04 2.11Ey 08 9.83Ey 09 3.79Ey 04 1.22Ey 03 1.54Ey 02 5.69Ey 04 1.65Ey 03 3.48Ey 04 5.39Ey 07 2.41Ey 07 5.26Ey 05 9.88Ey 01 1.86Ey 03 5.81Ey 03 1.86Ey 03
1.80Eq 04 1.78Eq 04 7.40Eq 03 1.60Eq 03 9.20Eq 03 1.70Eq 03 6.20Eq 03 1.70Eq 03 1.50Eq 02 1.70Eq 03 9.80Eq 01 5.30Eq 01 8.90Eq 01 2.00Eq 03 3.80Eq 02 2.22Eq 02 1.66Eq 01 4.50Eq 01 8.40Eq 01 9.20Eq 01 4.00Eq 02 9.40Eq 01 2.40Eq 02 4.90Eq 02 8.30Eq 01 1.50Eq 02 3.30Eq 02 5.00Eq 01 5.00E q 02 2.80Eq 01 1.10Eq 03 3.90Eq 03 9.10Eq 04 1.20Eq 03 9.82Eq 02 3.60Eq 01 1.60Eq 03 8.30Eq 02 1.20Eq 03 1.30Eq 04 1.90Eq 04 5.30Eq 04 1.42Eq 01 8.70Eq 02 3.00Eq 02 2.40Eq 02
2.88Eq 04 2.88Eq 04 1.29Eq 04 4.68Eq 04 2.00Eq 04 3.98Eq 03 1.41Eq 04 3.98Eq 03 4.17Eq 01 3.98Eq 03 1.26Eq 04 1.95Eq 02 5.25Eq 03 7.41Eq 03 7.94Eq 02 2.63Eq 02 3.16Eq 01 1.00Eq 02 1.90Eq 02 1.00Eq 02 1.45Eq 02 1.95Eq 02 5.01Eq 02 9.80Eq 02 1.32Eq 02 9.00Eq 02 6.92Eq 02 4.27Eq 02 9.33Eq 01 2.88Eq 05 1.41Eq 03 1.70Eq 05 3.47Eq 07 1.90Eq 03 1.82Eq 03 7.08Eq 01 2.60Eq 03 8.91Eq 02 2.00Eq 03 1.55Eq 05 2.82Eq 05 1.00Eq 05 2.88Eq 01 1.41Eq 03 5.37Eq 02 1.83Eq 03
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W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
Table 3 List of the selected herbicides and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
2,4,5-Trichlorophenoxyacetic acid 2,4-Dichlorophenoxyacetic acid w2,4-Dx Alachlor Amitrole waminotriazolex Atrazine Cacodylic acid Chloramben Diallate Dichlobenil w2,6-dichlorobenzonitrilex Diuron Fenuron Fluometuron Linuron Monuron Paraquat Picloram Propazine Simazine Trifluralin
2.55Eq 02 2.21Eq 02 8.91Eq 01 8.41Eq 01 2.16Eq 02 1.38Eq 02 2.06Eq 02 2.70Eq 02 1.72Eq 02 2.33Eq 02 1.64Eq 02 2.32Eq 02 2.49Eq 02 1.99Eq 02 4.08Eq 02 2.41Eq 02 2.30Eq 02 2.02Eq 02 3.35Eq 02
9.32Ey 04 2.80Ey 03 2.72Ey 03 3.33Eq 00 1.53Ey 04 6.01Eq 00 3.40Ey 03 5.18Ey 05 1.05Ey 04 1.80Ey 04 2.34Ey 02 3.88Ey 04 3.01Ey 04 1.16Ey 03 2.45Eq 00 1.78Ey 03 3.74Ey 05 1.74Ey 05 1.79Ey 06
8.01Eq 01 1.96Eq 01 1.90E q 02 4.40Eq 00 1.63Eq 02 2.40Eq 00 2.10Eq 01 1.90Eq 03 2.24Eq 02 3.82Eq 02 4.22Eq 01 1.75Eq 02 8.63Eq 02 1.83Eq 02 1.55Eq 04 2.55Eq 01 1.53E q 02 1.38Eq 02 1.37Eq 04
4.00Eq 00 6.46Eq 02 4.34Eq 02 8.32Ey 03 2.12Eq 02 1.00Eq 00 1.30Eq 01 5.37Eq 00 7.87Eq 02 6.50Eq 02 1.00Eq 01 2.20Eq 01 1.54Eq 02 1.33Eq 02 1.00Eq 00 2.00Eq 00 7.85Eq 02 8.80Eq 01 2.20Eq 05
Table 4 List of the selected polyaromatic hydrocarbons and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
1,2,7,8-Dibenzopyrene 1-Napthylamine 2-Methylnapthalene 2-Napthylamine Acenaphthylene Acenapthene Anthracene Benzow axanthracene Benzow axpyrene Benzow b xfluoranthene Benzow ghi xperylene Benzow k xfluoranthene Chrysene Dibenzw a,hxanthracene Fluoranthene Indenow1,2,3-cd xpyrene Napthalene wnapthenex Phenanthrene Pyrene
3.02Eq 02 1.43Eq 02 1.42Eq 02 1.43E q 02 1.52Eq 02 1.54Eq 02 1.78Eq 02 2.28Eq 02 2.52Eq 02 2.78Eq 02 2.76Eq 02 2.78Eq 02 2.28Eq 02 2.78Eq 02 2.02Eq 02 2.76Eq 02 1.28Eq 02 1.78Eq 02 2.02Eq 02
3.34Ey 07 1.64Ey 02 1.79Ey 04 4.09Ey 03 2.58Ey 05 2.22Ey 05 2.52Ey 07 2.50Ey 08 4.76Ey 09 5.03Ey 08 2.54Ey 09 1.54Ey 08 7.89Ey 09 1.80Ey 09 1.02Ey 06 1.92Ey 09 2.47E y 04 5.61Ey 06 6.53Ey 07
1.20Eq 03 6.10Eq 01 8.50Eq 03 1.30Eq 02 2.50Eq 03 4.60Eq 03 1.40Eq 04 1.38Eq 06 5.50Eq 06 5.50Eq 05 1.60Eq 06 5.50Eq 05 2.00Eq 05 3.30Eq 06 3.80Eq 04 1.60Eq 06 1.30Eq 03 1.40Eq 04 3.80Eq 04
4.17Eq 06 1.17Eq 02 1.30Eq 04 1.17Eq 02 5.01Eq 03 1.00Eq 04 2.82Eq 04 3.98Eq 05 1.15Eq 06 1.15Eq 06 3.24Eq 06 1.15Eq 06 4.07Eq 05 6.31Eq 06 7.94Eq 04 3.16Eq 06 2.76Eq 03 2.88Eq 04 7.59Eq 04
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
Gao Ž1996.. Hou et al. Ž1991. used K ow and S to estimate the partition coefficients of dyes. Chiou and Schmedding Ž1982. verified that the liquid solute incompatibility in the octanol phase and the effect of dissolved octanol on water solubility increased systematically with decreasing S. Jafvert Ž1991. demonstrated the linear correlation of log K oc of 13 polyaromatic hydrocarbons ŽPAH. compounds with their log K ow by the method of least squares through the origin, in order to estimate the sorption of PAHs and other non-polar compounds to most soils and sediments.
5
In reality, hundreds or even thousands of chemicals may co-exist in the same place, which makes the prediction work of pollutants partitioning in various phases difficult or impossible ŽConnell et al., 1993.. In this study, the correlation between K ow , K oc , and S for 148 compounds causing environmental concern in five different categories of pollutants was investigated to make the partition estimation of certain chemicals in the environment feasible even when there is inadequate information regarding the target chemicals, or when a quick estimation is required for an
Table 5 List of the selected pesticides and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
1,2-Dibromo-3-chloropropane wDBCPx 1,2-Dichloropropane 1,3-Dichloropropene wtelonex 2,3,7,8-Tetrachlorodibenzo-p-dioxin ␣-Hexachlorocyclohexane Aldrin -Hexachlorocyclohexane Captan Carbaryl wsevinx Carbofuran ChIordane Chlorobenzilate Chlorpyrifos wdursbanx Cyclophosphamide DDD DDE DDT ␣-Hexachlorocyclohexane Diazinon DieIdrin Dinoseb Ethylene oxide ␥-Hexachlorocyclohexane Kepone Leptophos Malathion Methoxychlor Methyl parathion Mirex wdechloranex N, N-diphenylamine Parathion p-Chloroaniline w4-chlorobenzenaminex Toxaphene Trichlorfon wchlorofosx
2.36Eq 02 1.13Eq 02 1.11Eq 02 3.22Eq 02 2.91Eq 02 3.65Eq 02 2.91Eq 02 3.01Eq 02 2.01E q 02 2.21Eq 02 4.10Eq 02 3.25Eq 02 3.51Eq 02 2.61Eq 02 3.20Eq 02 3.55Eq 02 2.55Eq 02 2.91Eq 02 3.04Eq 02 3.81Eq 02 2.40Eq 02 4.41Eq 01 2.91Eq 02 4.91Eq 02 4.12Eq 02 3.30Eq 02 3.46Eq 02 2.63Eq 02 5.46Eq 02 1.69Eq 02 2.91Eq 02 1.28Eq 02 4.14Eq 02 2.57Eq 02
4.23E᎐03 2.39E᎐02 2.52E᎐02 6.21E᎐10 5.60E᎐06 4.93E᎐07 8.25E᎐07 1.66E᎐06 1.99E᎐04 1.88Ey 03 1.37Ey 06 6.73Ey 05 8.56Ey 07 5.02Eq 03 3.12Ey 07 1.13Ey 07 1.96Ey 08 1.08Ey 04 1.31Ey 04 5.12Ey 07 2.08Ey 04 2.27E q 01 2.68Ey 05 2.02Ey 08 5.82Ey 06 4.39Ey 04 8.68Ey 09 2.28Ey 04 1.10Ey 06 3.40Ey 04 8.24Ey 05 4.15Ey 02 1.21Ey 06 5.98Ey 01
9.80Eq 01 5.10Eq 01 4.80Eq 01 3.30Eq 06 3.80Eq 03 9.60Eq 04 3.80Eq 03 6.40Eq 03 2.30Eq 02 2.94Eq 01 1.40Eq 05 8.00Eq 02 1.36Eq 04 4.20E-02 7.70E q 05 4.40Eq 06 2.43Eq 05 6.60Eq 03 8.50Eq 01 1.70Eq 03 1.24Eq 02 2.20Eq 00 1.08Eq 03 5.50Eq 04 9.30Eq 03 1.80Eq 03 8.00Eq 04 5.10Eq 03 2.40E q 07 4.70Eq 02 1.07Eq 04 5.61Eq 02 9.64Eq 02 6.10Eq 00
1.95Eq 02 1.00Eq 02 1.00Eq 02 5.25Eq 06 7.94Eq 03 2.00Eq 05 7.94Eq 03 2.24Eq 02 2.29Eq 02 2.07Eq 02 2.09Eq 03 3.24Eq 04 6.60Eq 04 6.03Ey 04 1.58Eq 06 1.00Eq 07 1.55Eq 06 1.26Eq 04 1.05Eq 03 3.16Eq 03 1.98Eq 02 6.03Ey 01 7.94Eq 03 1.00Eq 02 2.02Eq 06 7.76Eq 02 4.75Eq 04 8.13Eq 01 7.80Eq 06 3.98Eq 03 6.45Eq 03 6.76Eq 01 2.00Eq 03 1.95Eq 02
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W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
incidental spill. Therefore, several linear models are proposed that may assist the prediction of chemical distribution in the environment or facilitate the design andror operation of soil remediation systems.
2. Methodology One hundred and forty-eight compounds causing environmental concern were selected as probes for data analysis and divided into five major categories including aliphatic compounds Ž30., aromatic compounds Ž46., herbicides Ž19., pesticides Ž34., and PAHs Ž19.. These 148 compounds with their properties of molecular weight, K ow , K oc , and S, are separately listed in Tables 1᎐5. These data were collected and reorganized from several sources including ‘Basics of Pumpand-Treat Ground-water Remediation Technology’ published by EPA report Ž1990. and the Handbook of Environmental Data on Organic Chemicals ŽVerschueren, 1983.. A simple log to log linear regression equation was used in this study to correlate the parameters wsee Eq. Ž1.x, since such correlation between partition coefficients is typically used in numerous studies ŽKarickhoff, 1984; Jafvert, 1990.: log K 1 s ␣ log K 2 q 
Ž1.
in which K 1 and K 2 are partition coefficients Ž K oc or K ow . or solubility Ž S ., and ␣ and  are data fitted coefficients.
3. Results and discussion According to Eq. Ž1., S, K ow , and K oc were correlated with each other by Eqs. Ž2. ᎐ Ž4.. The data in columns 3᎐5 shown in Tables 1᎐5 were used to fit to the equations by the ‘least squares’ method. The results of the intercept, slope, and R 2 for each category of chemicals are summarized in Table 6, in which the regression data of the aliphatic compounds are also illustrated as typical examples in Fig. 1a᎐c.
Log S s log aq b log K ow
Ž2.
Log K oc s log c q d log K ow
Ž3.
Log K oc s log e q f log S
Ž4.
Eq. Ž2. shows the correlation between water solubility, S, and the octanol᎐water partition coefficient, K ow Žrefer to Table 6.. The R 2 values for different categories of chemicals in this model range from 0.51 to 0.89. Unsurprisingly, lower R 2 values are observed for both herbicide Ž0.59. and pesticide Ž0.51. compounds, compared to those for other categories of chemicals. This is probably because herbicides and pesticides are all artificial products and are specially designed for field application. The chemicals in these two categories consist of broad ranges of functional groups for different control purposes, thus their physicochemical characteristics are of less consistency. Eq. Ž3., together with Table 6, illustrates the correlation between the equilibrium organic carbon normalized distribution coefficient, K oc , and K ow . Almost all the R 2 values for different categories of chemicals are adequate for prediction work, except in the category of herbicide, in which the R 2 value is to be too low Ž0.24. to give a confident estimation. Similarly, the relationship between K oc and S is shown in Table 6 by using Eq. Ž4.. The R 2 values for herbicides and aromatic chemicals are 0.21 and 0.39, respectively. As stated previously, it is not surprising that the former have a low R 2 , and it is interesting to explore the reasons behind the low R 2 for aromatic compounds. In theory, the benzene ring itself is relatively chemically inert, bearing more insoluble characteristics compared to that of aliphatic compounds. However, it is not difficult to alter its solubility by attaching more watersoluble functional groups such as ᎐OH and ᎐COOH to the ring and significantly increasing the water solubility of the whole molecule. The solubility of aromatic compounds is therefore controlled by the attached functional groups and not by the number of benzene rings in the molecular structure. On the other hand, K oc is normally used to examine the hydrophobicity of or-
log S s log aq b log Kow
Herbicides Pesticides Aliphatic PAH Aromatic All chemicals
log Koc s log c q d log Kow 2
Intercept Žlog a.
Slope Ž b.
R
y1.4209 y1.0526 0.3060 0.8348 1.1813 y0.0617
y0.8694 y0.9609 y1.0592 y1.4092 y1.3052 y1.1184
0.5991 0.5094 0.7867 0.8884 0.8151 0.7050
log Koc s log e q f log S 2
Intercept Žlog c .
Slope Ž d.
R
1.6764 0.6390 0.4735 0.2729 0.8704 0.7549
0.3071 0.7956 0.7273 0.8726 0.5742 0.6993
0.2383 0.6674 0.7981 0.7142 0.5807 0.6747
Intercept Žlog e .
Slope Žf.
R2
1.4446 0.7950 0.7939 0.6687 1.7531 1.0555
y0.2581 y0.6001 y0.6341 y0.6390 y0.3260 y0.5317
0.2123 0.6881 0.8651 0.8560 0.3913 0.6919
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
Table 6 Summary on the plots on log S against log K ow , log K oc against log K ow , and log K oc against log S
7
8
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
ganic compounds, and is known to be strongly related to the number of benzene rings in the molecule; therefore, a fluctuation in the correlation results. Other than the direct correlations between K oc , K ow , and S, it is also beneficial to investigate the possible connections between constants a, b, c, d, e and f in Eqs. Ž2. ᎐ Ž4. ŽFigs. 2 and 3.. An understanding of such connections could be useful when the partition information of the target compounds is unavailable. Mathematically, Eqs. Ž2. and Ž3. could be rearranged as: log K ow s
1 w log S y log a x b
Ž5.
log K ow s
1 w log K oc y log c x d
Ž6.
The left-hand sides of Eqs. Ž5. and Ž6. carry the same term, so the right-hand sides of both equations theoretically should be the same, therefore, after the rearrangement: log K oc s log c y log a d r b q
d log S b
Ž7.
Fig. 1. Ž Continued.
By referring to Eq. Ž4., the slope f in Eq. Ž4. is equivalent to that Ž drb. in Eq. Ž7., and so is the intercept, therefore: fs Fig. 1. Ža. Plot on log S against log K ow for aliphatic compounds; Žb. plot on log K oc against log K ow for aliphatic compounds; Žc. plot on log K oc against S for aliphatic compounds.
d b
log e s log
Ž8.
ž ac / dr b
Ž9.
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
Fig. 2. Plot on log a against b.
9
Fig. 3. Plot on log c against d.
The calculated value for f and e based on Eqs. Ž8. and Ž9. are compared to the regressive data that was originally resolved from the database. The results are summarized in Table 7, in which the calculated values and regressive values are similar within each category of chemicals. The differences between the calculated f Ži.e. drb. and the regressive f are to be found to be within 3᎐27%, indicating that the prediction of K oc , K ow , or S is feasible by using the relationship in Eq. Ž8.. As for calculated and regressive log e, the variations mostly range from 14 to 32%, except the pesticides, which show a difference of more than 100% with an inverted sign. Therefore, this approach wEq. Ž9.x is obviously not applicable to pesticide materials, but might work for all other categories of compounds. It is also noted that the
difference in e is slightly higher than that in f, judging from Eqs. Ž2. ᎐ Ž4., Ž8. and Ž9., and it is interesting to find that the slopes in Eqs. Ž2. ᎐ Ž4. Ži.e. b, d, f . correlate to each other directly without the involvement of any other parameter wsee Eq. Ž8.x. However, the intercepts in Eqs. Ž2. ᎐ Ž4. Ži.e. a, c, e . depend not only on each other but also on the slopes, which introduce higher errors.
4. Conclusions The direct measurement of K ow , K oc andror S of a particular chemical in a laboratory is sometime difficult and impractical. The use of simple linear equations to predict the K ow , K oc
Table 7 Summary on the plots on regression coefficients
Herbicides Pesticides Aliphatic PAH Aromatics All chemicals
a
b
c
d
log e
log Ž cradrb .
f
drb
0.038 0.089 2.023 6.836 15.181 0.868
y0.869 y0.961 y1.059 y1.409 y1.305 y1.118
47.468 4.355 2.975 1.875 7.420 5.687
0.307 0.796 0.727 0.873 0.574 0.699
1.445 0.795 0.794 0.669 1.753 1.056
1.174 y0.232 0.680 0.790 1.390 0.716
y0.258 y0.600 y0.634 y0.639 y0.326 y0.532
y0.353 y0.828 y0.687 y0.619 y0.440 y0.625
10
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
andror S of unfamiliar chemicals Ži.e. with limited known properties. in the environment is found to be feasible if the target compound can be characterized into a proper category of chemicals. According to our results, the classification of chemicals into aliphatic compounds, aromatic compounds, and PAH is quite successful in this respect. However, the use of herbicide and pesticide to define the group property of chemicals is likely to be unsuitable. This is because most of the herbicides and pesticides available in the market are specially designed for distinct field application, so the diversity of chemical characteristics of these compounds is generally too high to be predicted. Acknowledgements The work described in this paper was supported by a grant from the University Research Fund of the Hong Kong Polytechnic University. References Baughman GL, Perenich TA. Fate of dyes in aquatic systems: I. Solubility and partitioning of some hydrophobic dyes and related compounds. Environ Toxicol Chem 1988;7:183᎐199.
Chiou CT, Schmedding DW. Partitioning of organic compounds in octanol᎐water systems. Environ Sci Technol 1982;16:4᎐10. Connell DW, Braddock RD, Mani SV. Prediction of the partition coefficient of lipophilic compounds in the airmammal tissue system. Sci Total Environ 1993; Suppl:1383᎐1396. EPA report. Basics of Pump-and-Treat Ground-Water Remediation Technology. EPA-600r8-90r003, 1990. Gao C. Predicting soil sorption coefficients of organic chemicals using a neural network model. Environ Toxicol Chem 1996;15:1089᎐1096. Hawker DW, Connell DW. Octanol-water partition coefficients of polychlorinated biphenyl congeners. Environ Sci Technol 1988;22:382᎐387. Hou MH, Baughman GL, Perenich TA. Estimation of water solubility and octanolrwater partition coefficient of hydrophobic dyes. Part II: Reverse-phase high performance liquid chromatography. Dyes Pigm 1991;16:291᎐297. Jafvert CT. Distribution of hydrophobic ionogenic organic compounds between octanol and water: organic acids. Environ Sci Technol 1990;24:1795᎐1803. Jafvert CT. Sediment- and saturated-soil-associated reactions involving an anionic surfactant Ždodecylsulfate.. 2. Partition of PAH compounds among phases. Environ Sci Technol 1991;25:1039᎐1045. Karickhoff SW. Organic pollutant sorption in aquatic systems. J Hydraulic Engineering 1984;110:707᎐735. Verschueren K. Handbook of environmental data on organic chemicals. 2nd ed. Van Nostrand Reinhold Co., Inc, 1983.
The prediction of partitioning coefficients for chemicals causing environmental concern Wei ChuU , Kwai-Hing Chan Department of Ci¨ il and Structural Engineering, Research Centre for Urban En¨ ironmental Technology and Management, The Hong Kong Polytechnic Uni¨ ersity, Hunghom, Kowloon, Hong Kong Received 24 September 1999; accepted 17 November 1999
Abstract Various types of partition coefficients have been used to facilitate the prediction of the concentration of pollutants in different phases in the environment. Many thousands of chemicals may exist in our environment which makes prediction work difficult or impossible due to a deficiency of knowledge of those unfamiliar compounds. In this study, the correlation between an octanol᎐water partition coefficient Ž K ow ., water solubility Ž S . and a normalized soilrsediment partition coefficient Ž K oc . was investigated though the examination of 148 model chemicals. These model chemicals were classified into five major categories for easier adoption in future use. They are aliphatic compounds, aromatic compounds, pesticides, herbicides and polyaromatic hydrocarbons ŽPAH.. Linear models are developed to correlate these partition coefficients in each category. The prediction of unfamiliar chemicals in the same category becomes possible if the fundamental properties of these chemicals Žsuch as solubility. are previously known. 䊚 2000 Elsevier Science B.V. All rights reserved. Keywords: Partitioning coefficients; Remediation; Soil
1. Introduction Contamination of soils and sediments by organic pollutants is an environmental concern.
U
Corresponding author. E-mail address: [email protected] ŽW. Chu.
Sources of these contaminants include underground storage tanks, hazardous waste disposal sites, septic tanks, municipal landfills, and accidental spills. As a consequence, the remediation of soil or sediment which contains hazardous materials is often initiated by extraction of the contaminants from the soil to the aqueous phase, followed by chemical, physical, andror biological
0048-9697r00r$ - see front matter 䊚 2000 Elsevier Science B.V. All rights reserved. PII: S 0 0 4 8 - 9 6 9 7 Ž 9 9 . 0 0 4 7 2 - 6
2
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
treatment. However, the design and operation of these extraction processes is often limited by a lack of knowledge of the pollutants’ characteristics. For example, hydrophobic compounds do not easily partition to the liquid phase from the soil phase to the extent necessary to make subsequent treatment a high performance process; therefore, extended extraction or treatment is often required to clean up a contaminated site. In addition, each contaminated site may contain different types of pollutants that make the optimization of the cleaning process more difficult. In the soil remediation process two phases are mainly involved Žsoilrwater or soilrsolvent..
Parameters, such as the octanol᎐water partition coefficient Ž K ow . and water solubility Ž S . have been used to characterize the partition of a specific organic compound between the organic Žhydrophobic. and water Žhydrophobic. phases ŽBaughman and Perenich, 1988; Hawker and Connell, 1988.. In addition, the soilrsediment adsorption partition coefficient normalized to organic carbon Ž K oc . is extensively used to assess the fate of organic chemicals in hazardous waste sites. A better understanding of the concepts defining the relationship between K ow , K oc , and S should extend the usefulness of these parameters in interpreting the behavior of organic com-
Table 1 List of the selected aliphatic compounds and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
1,1,1-Trichloroethane wmethylchloroformx 1,1,2,2-Tetrachloroethane 1,1,2-Trichloroethane wvinyltrichloridex 1,1-Dichloroethane wethylidine chloridex 1,1-Dichloroethene wvinylidine chloridex 1,2-Dichloroethane wethylene dichloridex 1,2-Dichloroethene Ž cis . 1,2-Dichloroethene Ž trans. 1,3-Butadiene Acetonitrile wmethyl cyanidex Acrylonitrile w2-propenenitrilex Bromodichloromethane wdichlorobromomethx Chloroethane wethyl chloridex Chloroethene wvinyl chloridex Chloromethane wmethyl chloridex Dibromochloromethane Dichlorodifluoromethane wFreon12x Dichloromethane wmethylene chloridex Ethylene dibromide wEDBx Hexachlorobutadiene Hexachlorocyclopentadiene Hexachloroethane wperchloroethanex Iodomethane wmethyl iodidex Pentachloroethane wpentalinx Tetrachloroethene wPERCx Tetrachloromethane wcarbontetrachloride x Tribromomethane wbromoformx Trichloroethene wTCEx Trichlorofluoromethane wFreon 11x Trichloromethane wchloroformx
1.33Eq 02 1.68Eq 02 1.33E q 02 9.90Eq 01 9.70Eq 01 9.90Eq 01 9.70Eq 01 9.70Eq 01 5.41Eq 01 4.11Eq 01 5.31Eq 01 1.64Eq 02 6.45Eq 01 6.25Eq 01 5.10Eq 01 2.08Eq 02 1.21Eq 02 8.49Eq 01 1.88Eq 02 2.61Eq 02 2.73Eq 02 2.37Eq 02 1.42Eq 02 2.02Eq 02 1.67Eq 02 1.54Eq 02 2.53Eq 02 1.33Eq 02 1.37Eq 02 1.19Eq 02
1.12Ey 02 1.73Ey 02 3.37Ey 02 5.56Ey 02 2.32Ey 02 8.61Ey 02 3.61Ey 02 6.50Ey 02 1.36Ey 02 Infinity 1.50Eq 00 2.69Ey 02 8.90Ey 02 4.27E y 02 1.27Ey 01 1.92Ey 02 2.32Ey 03 2.35Ey 01 2.29Ey 02 5.74Ey 07 7.69Ey 06 2.11Ey 04 9.86Ey 02 1.83Ey 04 8.97Ey 04 4.92Ey 03 1.19Ey 02 8.29Ey 03 8.01Ey 03 6.87Ey 02
1.52Eq 02 1.18Eq 02 5.60Eq 01 3.00Eq 01 6.50Eq 01 1.40Eq 01 4.90Eq 01 5.90E q 01 1.20Eq 02 2.20Eq 00 8.50Ey 01 6.10Eq 01 1.70Eq 01 5.70Eq 01 3.50Eq 01 8.40Eq 01 5.80Eq 01 8.80Eq 00 4.40E q 01 2.90Eq 04 4.80Eq 03 2.00Eq 04 2.30Eq 01 1.90Eq 03 3.64Eq 02 4.39Eq 02 1.16Eq 02 1.26Eq 02 1.59Eq 02 4.70Eq 01
3.16Eq 02 2.45Eq 02 2.95Eq 02 6.17Eq 01 6.92Eq 01 3.02Eq 01 5.01Eq 00 3.02Eq 00 9.77Eq 01 4.57Ey 01 1.78Eq 00 7.59Eq 01 3.50Eq 01 2.40Eq 01 9.50Ey 01 1.23Eq 02 1.45Eq 02 2.00Eq 01 5.75Eq 01 6.02Eq 04 1.10Eq 05 3.98Eq 04 4.90Eq 01 7.76Eq 02 3.98E q 02 4.37Eq 02 2.51Eq 02 2.40Eq 02 3.39Eq 02 9.33Eq 01
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
pounds in the environment. For example, several attempts have been made to estimate the value of K oc from the chemical structure. A non-linear
3
model for estimating K oc , applicable to polar and non-polar organics based on an artificial neural network using the K ow and S, was developed by
Table 2 List of the selected aromatic compounds and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
1,2,3,4-Tetrachlorobenzene 1,2,3,5-Tetrachlorobenzene 1,2,3-Trichlorobenzene 1,2,4,5-Tetrachlorobenzene 1,2,4-Trichlorobenzene 1,2-Dichlorobenzene w o-dichlorobenzenex 1,3,5-Trichlorobenzene 1,3-Dichlorobenzene w m-dichlorobenzenex 1,3-Dinitrobenzene 1,4-Dichlorobenzene w p-dichlorobenzenex 2,3,4,6-Tetrachlorophenol 2,3-Dinitrotoluene 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol 2,4-Dichlorophenol 2,4-Dimethylphenol was-m-xylenolx 2,4-Dinitrophenol 2,4-Dinitrotoluene 2,5-Dinitrotoluene 2,6-Dinitrotoluene 2-Chlorophenol w o-chlorophenolx 3,4-Dinitrotoluene 4,6-Dinitro-o-cresol 4-Chloro-m-cresol wchlorocresolx Benzene Bromobenzene wphenyl bromidex Chlorobenzene Chlorotoluene wbenzyl chloridex Cresol ŽTechnical. wmethylphenolx Diethylstilbestrol wDESx Ethylbenzene wphenylethane x Hexachlorobenzene wperchlorobenzenex Hexachlorophene wdermadexx m-Chlorotoluene m-Xylene w1,3-dimethylbenzenex Nitrobenzene o-Chlorotoluene o-Xylene w1,2-dimethylbenzenex p-Chlorotoluene Pentachlorobenzene Pentachloronitrobenzene wquintozenex Pentachlorophenol Phenol p-Xylene w1,4-dimethylbenzenex Toluene wmethylbenzenex Xylene Žmixed.
2.16Eq 02 2.16Eq 02 1.81E q 02 2.16Eq 02 1.81Eq 02 1.47Eq 02 1.81Eq 02 1.47Eq 02 1.68Eq 02 1.47Eq 02 2.32Eq 02 1.82Eq 02 1.97Eq 02 1.97Eq 02 1.63Eq 02 1.22Eq 02 1.84Eq 02 1.82E q 02 1.82Eq 02 1.82Eq 02 1.29Eq 02 1.82Eq 02 1.98Eq 02 1.43Eq 02 7.81Eq 01 1.57Eq 02 1.13Eq 02 1.27Eq 02 1.08Eq 02 2.68Eq 02 6.28Eq 02 2.85Eq 02 4.07Eq 02 1.27Eq 02 1.06E q 02 1.23Eq 02 1.27Eq 02 1.06Eq 02 1.27Eq 02 2.50Eq 02 2.95E q 02 2.66Eq 02 9.41Eq 01 1.06Eq 02 9.21Eq 01 1.06Eq 02
1.62Ey 05 1.11Ey 05 6.61Ey 05 2.78Ey 05 1.65Ey 04 6.80Ey 04 3.20Ey 05 8.37Ey 04 2.80Ey 03 5.37Ey 04 3.02Ey 05 1.70Ey 02 6.03Ey 03 4.05Ey 03 2.82Ey 02 3.44Ey 02 3.04Ey 02 1.32Ey 03 7.25Ey 03 7.25Ey 03 2.26Ey 01 5.93Ey 03 1.46Ey 03 2.70Ey 02 2.24Ey 02 2.84Ey 03 4.14Ey 03 2.61Ey 02 2.87Ey 01 3.58Ey 08 2.42Ey 04 2.11Ey 08 9.83Ey 09 3.79Ey 04 1.22Ey 03 1.54Ey 02 5.69Ey 04 1.65Ey 03 3.48Ey 04 5.39Ey 07 2.41Ey 07 5.26Ey 05 9.88Ey 01 1.86Ey 03 5.81Ey 03 1.86Ey 03
1.80Eq 04 1.78Eq 04 7.40Eq 03 1.60Eq 03 9.20Eq 03 1.70Eq 03 6.20Eq 03 1.70Eq 03 1.50Eq 02 1.70Eq 03 9.80Eq 01 5.30Eq 01 8.90Eq 01 2.00Eq 03 3.80Eq 02 2.22Eq 02 1.66Eq 01 4.50Eq 01 8.40Eq 01 9.20Eq 01 4.00Eq 02 9.40Eq 01 2.40Eq 02 4.90Eq 02 8.30Eq 01 1.50Eq 02 3.30Eq 02 5.00Eq 01 5.00E q 02 2.80Eq 01 1.10Eq 03 3.90Eq 03 9.10Eq 04 1.20Eq 03 9.82Eq 02 3.60Eq 01 1.60Eq 03 8.30Eq 02 1.20Eq 03 1.30Eq 04 1.90Eq 04 5.30Eq 04 1.42Eq 01 8.70Eq 02 3.00Eq 02 2.40Eq 02
2.88Eq 04 2.88Eq 04 1.29Eq 04 4.68Eq 04 2.00Eq 04 3.98Eq 03 1.41Eq 04 3.98Eq 03 4.17Eq 01 3.98Eq 03 1.26Eq 04 1.95Eq 02 5.25Eq 03 7.41Eq 03 7.94Eq 02 2.63Eq 02 3.16Eq 01 1.00Eq 02 1.90Eq 02 1.00Eq 02 1.45Eq 02 1.95Eq 02 5.01Eq 02 9.80Eq 02 1.32Eq 02 9.00Eq 02 6.92Eq 02 4.27Eq 02 9.33Eq 01 2.88Eq 05 1.41Eq 03 1.70Eq 05 3.47Eq 07 1.90Eq 03 1.82Eq 03 7.08Eq 01 2.60Eq 03 8.91Eq 02 2.00Eq 03 1.55Eq 05 2.82Eq 05 1.00Eq 05 2.88Eq 01 1.41Eq 03 5.37Eq 02 1.83Eq 03
4
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
Table 3 List of the selected herbicides and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
2,4,5-Trichlorophenoxyacetic acid 2,4-Dichlorophenoxyacetic acid w2,4-Dx Alachlor Amitrole waminotriazolex Atrazine Cacodylic acid Chloramben Diallate Dichlobenil w2,6-dichlorobenzonitrilex Diuron Fenuron Fluometuron Linuron Monuron Paraquat Picloram Propazine Simazine Trifluralin
2.55Eq 02 2.21Eq 02 8.91Eq 01 8.41Eq 01 2.16Eq 02 1.38Eq 02 2.06Eq 02 2.70Eq 02 1.72Eq 02 2.33Eq 02 1.64Eq 02 2.32Eq 02 2.49Eq 02 1.99Eq 02 4.08Eq 02 2.41Eq 02 2.30Eq 02 2.02Eq 02 3.35Eq 02
9.32Ey 04 2.80Ey 03 2.72Ey 03 3.33Eq 00 1.53Ey 04 6.01Eq 00 3.40Ey 03 5.18Ey 05 1.05Ey 04 1.80Ey 04 2.34Ey 02 3.88Ey 04 3.01Ey 04 1.16Ey 03 2.45Eq 00 1.78Ey 03 3.74Ey 05 1.74Ey 05 1.79Ey 06
8.01Eq 01 1.96Eq 01 1.90E q 02 4.40Eq 00 1.63Eq 02 2.40Eq 00 2.10Eq 01 1.90Eq 03 2.24Eq 02 3.82Eq 02 4.22Eq 01 1.75Eq 02 8.63Eq 02 1.83Eq 02 1.55Eq 04 2.55Eq 01 1.53E q 02 1.38Eq 02 1.37Eq 04
4.00Eq 00 6.46Eq 02 4.34Eq 02 8.32Ey 03 2.12Eq 02 1.00Eq 00 1.30Eq 01 5.37Eq 00 7.87Eq 02 6.50Eq 02 1.00Eq 01 2.20Eq 01 1.54Eq 02 1.33Eq 02 1.00Eq 00 2.00Eq 00 7.85Eq 02 8.80Eq 01 2.20Eq 05
Table 4 List of the selected polyaromatic hydrocarbons and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
1,2,7,8-Dibenzopyrene 1-Napthylamine 2-Methylnapthalene 2-Napthylamine Acenaphthylene Acenapthene Anthracene Benzow axanthracene Benzow axpyrene Benzow b xfluoranthene Benzow ghi xperylene Benzow k xfluoranthene Chrysene Dibenzw a,hxanthracene Fluoranthene Indenow1,2,3-cd xpyrene Napthalene wnapthenex Phenanthrene Pyrene
3.02Eq 02 1.43Eq 02 1.42Eq 02 1.43E q 02 1.52Eq 02 1.54Eq 02 1.78Eq 02 2.28Eq 02 2.52Eq 02 2.78Eq 02 2.76Eq 02 2.78Eq 02 2.28Eq 02 2.78Eq 02 2.02Eq 02 2.76Eq 02 1.28Eq 02 1.78Eq 02 2.02Eq 02
3.34Ey 07 1.64Ey 02 1.79Ey 04 4.09Ey 03 2.58Ey 05 2.22Ey 05 2.52Ey 07 2.50Ey 08 4.76Ey 09 5.03Ey 08 2.54Ey 09 1.54Ey 08 7.89Ey 09 1.80Ey 09 1.02Ey 06 1.92Ey 09 2.47E y 04 5.61Ey 06 6.53Ey 07
1.20Eq 03 6.10Eq 01 8.50Eq 03 1.30Eq 02 2.50Eq 03 4.60Eq 03 1.40Eq 04 1.38Eq 06 5.50Eq 06 5.50Eq 05 1.60Eq 06 5.50Eq 05 2.00Eq 05 3.30Eq 06 3.80Eq 04 1.60Eq 06 1.30Eq 03 1.40Eq 04 3.80Eq 04
4.17Eq 06 1.17Eq 02 1.30Eq 04 1.17Eq 02 5.01Eq 03 1.00Eq 04 2.82Eq 04 3.98Eq 05 1.15Eq 06 1.15Eq 06 3.24Eq 06 1.15Eq 06 4.07Eq 05 6.31Eq 06 7.94Eq 04 3.16Eq 06 2.76Eq 03 2.88Eq 04 7.59Eq 04
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
Gao Ž1996.. Hou et al. Ž1991. used K ow and S to estimate the partition coefficients of dyes. Chiou and Schmedding Ž1982. verified that the liquid solute incompatibility in the octanol phase and the effect of dissolved octanol on water solubility increased systematically with decreasing S. Jafvert Ž1991. demonstrated the linear correlation of log K oc of 13 polyaromatic hydrocarbons ŽPAH. compounds with their log K ow by the method of least squares through the origin, in order to estimate the sorption of PAHs and other non-polar compounds to most soils and sediments.
5
In reality, hundreds or even thousands of chemicals may co-exist in the same place, which makes the prediction work of pollutants partitioning in various phases difficult or impossible ŽConnell et al., 1993.. In this study, the correlation between K ow , K oc , and S for 148 compounds causing environmental concern in five different categories of pollutants was investigated to make the partition estimation of certain chemicals in the environment feasible even when there is inadequate information regarding the target chemicals, or when a quick estimation is required for an
Table 5 List of the selected pesticides and their properties Chemical name
MW
Water solubility Žmolrl.
Koc Žmlrg.
Kow
1,2-Dibromo-3-chloropropane wDBCPx 1,2-Dichloropropane 1,3-Dichloropropene wtelonex 2,3,7,8-Tetrachlorodibenzo-p-dioxin ␣-Hexachlorocyclohexane Aldrin -Hexachlorocyclohexane Captan Carbaryl wsevinx Carbofuran ChIordane Chlorobenzilate Chlorpyrifos wdursbanx Cyclophosphamide DDD DDE DDT ␣-Hexachlorocyclohexane Diazinon DieIdrin Dinoseb Ethylene oxide ␥-Hexachlorocyclohexane Kepone Leptophos Malathion Methoxychlor Methyl parathion Mirex wdechloranex N, N-diphenylamine Parathion p-Chloroaniline w4-chlorobenzenaminex Toxaphene Trichlorfon wchlorofosx
2.36Eq 02 1.13Eq 02 1.11Eq 02 3.22Eq 02 2.91Eq 02 3.65Eq 02 2.91Eq 02 3.01Eq 02 2.01E q 02 2.21Eq 02 4.10Eq 02 3.25Eq 02 3.51Eq 02 2.61Eq 02 3.20Eq 02 3.55Eq 02 2.55Eq 02 2.91Eq 02 3.04Eq 02 3.81Eq 02 2.40Eq 02 4.41Eq 01 2.91Eq 02 4.91Eq 02 4.12Eq 02 3.30Eq 02 3.46Eq 02 2.63Eq 02 5.46Eq 02 1.69Eq 02 2.91Eq 02 1.28Eq 02 4.14Eq 02 2.57Eq 02
4.23E᎐03 2.39E᎐02 2.52E᎐02 6.21E᎐10 5.60E᎐06 4.93E᎐07 8.25E᎐07 1.66E᎐06 1.99E᎐04 1.88Ey 03 1.37Ey 06 6.73Ey 05 8.56Ey 07 5.02Eq 03 3.12Ey 07 1.13Ey 07 1.96Ey 08 1.08Ey 04 1.31Ey 04 5.12Ey 07 2.08Ey 04 2.27E q 01 2.68Ey 05 2.02Ey 08 5.82Ey 06 4.39Ey 04 8.68Ey 09 2.28Ey 04 1.10Ey 06 3.40Ey 04 8.24Ey 05 4.15Ey 02 1.21Ey 06 5.98Ey 01
9.80Eq 01 5.10Eq 01 4.80Eq 01 3.30Eq 06 3.80Eq 03 9.60Eq 04 3.80Eq 03 6.40Eq 03 2.30Eq 02 2.94Eq 01 1.40Eq 05 8.00Eq 02 1.36Eq 04 4.20E-02 7.70E q 05 4.40Eq 06 2.43Eq 05 6.60Eq 03 8.50Eq 01 1.70Eq 03 1.24Eq 02 2.20Eq 00 1.08Eq 03 5.50Eq 04 9.30Eq 03 1.80Eq 03 8.00Eq 04 5.10Eq 03 2.40E q 07 4.70Eq 02 1.07Eq 04 5.61Eq 02 9.64Eq 02 6.10Eq 00
1.95Eq 02 1.00Eq 02 1.00Eq 02 5.25Eq 06 7.94Eq 03 2.00Eq 05 7.94Eq 03 2.24Eq 02 2.29Eq 02 2.07Eq 02 2.09Eq 03 3.24Eq 04 6.60Eq 04 6.03Ey 04 1.58Eq 06 1.00Eq 07 1.55Eq 06 1.26Eq 04 1.05Eq 03 3.16Eq 03 1.98Eq 02 6.03Ey 01 7.94Eq 03 1.00Eq 02 2.02Eq 06 7.76Eq 02 4.75Eq 04 8.13Eq 01 7.80Eq 06 3.98Eq 03 6.45Eq 03 6.76Eq 01 2.00Eq 03 1.95Eq 02
6
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
incidental spill. Therefore, several linear models are proposed that may assist the prediction of chemical distribution in the environment or facilitate the design andror operation of soil remediation systems.
2. Methodology One hundred and forty-eight compounds causing environmental concern were selected as probes for data analysis and divided into five major categories including aliphatic compounds Ž30., aromatic compounds Ž46., herbicides Ž19., pesticides Ž34., and PAHs Ž19.. These 148 compounds with their properties of molecular weight, K ow , K oc , and S, are separately listed in Tables 1᎐5. These data were collected and reorganized from several sources including ‘Basics of Pumpand-Treat Ground-water Remediation Technology’ published by EPA report Ž1990. and the Handbook of Environmental Data on Organic Chemicals ŽVerschueren, 1983.. A simple log to log linear regression equation was used in this study to correlate the parameters wsee Eq. Ž1.x, since such correlation between partition coefficients is typically used in numerous studies ŽKarickhoff, 1984; Jafvert, 1990.: log K 1 s ␣ log K 2 q 
Ž1.
in which K 1 and K 2 are partition coefficients Ž K oc or K ow . or solubility Ž S ., and ␣ and  are data fitted coefficients.
3. Results and discussion According to Eq. Ž1., S, K ow , and K oc were correlated with each other by Eqs. Ž2. ᎐ Ž4.. The data in columns 3᎐5 shown in Tables 1᎐5 were used to fit to the equations by the ‘least squares’ method. The results of the intercept, slope, and R 2 for each category of chemicals are summarized in Table 6, in which the regression data of the aliphatic compounds are also illustrated as typical examples in Fig. 1a᎐c.
Log S s log aq b log K ow
Ž2.
Log K oc s log c q d log K ow
Ž3.
Log K oc s log e q f log S
Ž4.
Eq. Ž2. shows the correlation between water solubility, S, and the octanol᎐water partition coefficient, K ow Žrefer to Table 6.. The R 2 values for different categories of chemicals in this model range from 0.51 to 0.89. Unsurprisingly, lower R 2 values are observed for both herbicide Ž0.59. and pesticide Ž0.51. compounds, compared to those for other categories of chemicals. This is probably because herbicides and pesticides are all artificial products and are specially designed for field application. The chemicals in these two categories consist of broad ranges of functional groups for different control purposes, thus their physicochemical characteristics are of less consistency. Eq. Ž3., together with Table 6, illustrates the correlation between the equilibrium organic carbon normalized distribution coefficient, K oc , and K ow . Almost all the R 2 values for different categories of chemicals are adequate for prediction work, except in the category of herbicide, in which the R 2 value is to be too low Ž0.24. to give a confident estimation. Similarly, the relationship between K oc and S is shown in Table 6 by using Eq. Ž4.. The R 2 values for herbicides and aromatic chemicals are 0.21 and 0.39, respectively. As stated previously, it is not surprising that the former have a low R 2 , and it is interesting to explore the reasons behind the low R 2 for aromatic compounds. In theory, the benzene ring itself is relatively chemically inert, bearing more insoluble characteristics compared to that of aliphatic compounds. However, it is not difficult to alter its solubility by attaching more watersoluble functional groups such as ᎐OH and ᎐COOH to the ring and significantly increasing the water solubility of the whole molecule. The solubility of aromatic compounds is therefore controlled by the attached functional groups and not by the number of benzene rings in the molecular structure. On the other hand, K oc is normally used to examine the hydrophobicity of or-
log S s log aq b log Kow
Herbicides Pesticides Aliphatic PAH Aromatic All chemicals
log Koc s log c q d log Kow 2
Intercept Žlog a.
Slope Ž b.
R
y1.4209 y1.0526 0.3060 0.8348 1.1813 y0.0617
y0.8694 y0.9609 y1.0592 y1.4092 y1.3052 y1.1184
0.5991 0.5094 0.7867 0.8884 0.8151 0.7050
log Koc s log e q f log S 2
Intercept Žlog c .
Slope Ž d.
R
1.6764 0.6390 0.4735 0.2729 0.8704 0.7549
0.3071 0.7956 0.7273 0.8726 0.5742 0.6993
0.2383 0.6674 0.7981 0.7142 0.5807 0.6747
Intercept Žlog e .
Slope Žf.
R2
1.4446 0.7950 0.7939 0.6687 1.7531 1.0555
y0.2581 y0.6001 y0.6341 y0.6390 y0.3260 y0.5317
0.2123 0.6881 0.8651 0.8560 0.3913 0.6919
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
Table 6 Summary on the plots on log S against log K ow , log K oc against log K ow , and log K oc against log S
7
8
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
ganic compounds, and is known to be strongly related to the number of benzene rings in the molecule; therefore, a fluctuation in the correlation results. Other than the direct correlations between K oc , K ow , and S, it is also beneficial to investigate the possible connections between constants a, b, c, d, e and f in Eqs. Ž2. ᎐ Ž4. ŽFigs. 2 and 3.. An understanding of such connections could be useful when the partition information of the target compounds is unavailable. Mathematically, Eqs. Ž2. and Ž3. could be rearranged as: log K ow s
1 w log S y log a x b
Ž5.
log K ow s
1 w log K oc y log c x d
Ž6.
The left-hand sides of Eqs. Ž5. and Ž6. carry the same term, so the right-hand sides of both equations theoretically should be the same, therefore, after the rearrangement: log K oc s log c y log a d r b q
d log S b
Ž7.
Fig. 1. Ž Continued.
By referring to Eq. Ž4., the slope f in Eq. Ž4. is equivalent to that Ž drb. in Eq. Ž7., and so is the intercept, therefore: fs Fig. 1. Ža. Plot on log S against log K ow for aliphatic compounds; Žb. plot on log K oc against log K ow for aliphatic compounds; Žc. plot on log K oc against S for aliphatic compounds.
d b
log e s log
Ž8.
ž ac / dr b
Ž9.
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
Fig. 2. Plot on log a against b.
9
Fig. 3. Plot on log c against d.
The calculated value for f and e based on Eqs. Ž8. and Ž9. are compared to the regressive data that was originally resolved from the database. The results are summarized in Table 7, in which the calculated values and regressive values are similar within each category of chemicals. The differences between the calculated f Ži.e. drb. and the regressive f are to be found to be within 3᎐27%, indicating that the prediction of K oc , K ow , or S is feasible by using the relationship in Eq. Ž8.. As for calculated and regressive log e, the variations mostly range from 14 to 32%, except the pesticides, which show a difference of more than 100% with an inverted sign. Therefore, this approach wEq. Ž9.x is obviously not applicable to pesticide materials, but might work for all other categories of compounds. It is also noted that the
difference in e is slightly higher than that in f, judging from Eqs. Ž2. ᎐ Ž4., Ž8. and Ž9., and it is interesting to find that the slopes in Eqs. Ž2. ᎐ Ž4. Ži.e. b, d, f . correlate to each other directly without the involvement of any other parameter wsee Eq. Ž8.x. However, the intercepts in Eqs. Ž2. ᎐ Ž4. Ži.e. a, c, e . depend not only on each other but also on the slopes, which introduce higher errors.
4. Conclusions The direct measurement of K ow , K oc andror S of a particular chemical in a laboratory is sometime difficult and impractical. The use of simple linear equations to predict the K ow , K oc
Table 7 Summary on the plots on regression coefficients
Herbicides Pesticides Aliphatic PAH Aromatics All chemicals
a
b
c
d
log e
log Ž cradrb .
f
drb
0.038 0.089 2.023 6.836 15.181 0.868
y0.869 y0.961 y1.059 y1.409 y1.305 y1.118
47.468 4.355 2.975 1.875 7.420 5.687
0.307 0.796 0.727 0.873 0.574 0.699
1.445 0.795 0.794 0.669 1.753 1.056
1.174 y0.232 0.680 0.790 1.390 0.716
y0.258 y0.600 y0.634 y0.639 y0.326 y0.532
y0.353 y0.828 y0.687 y0.619 y0.440 y0.625
10
W. Chu, K.H. Chan r The Science of the Total En¨ ironment 248 (2000) 1᎐10
andror S of unfamiliar chemicals Ži.e. with limited known properties. in the environment is found to be feasible if the target compound can be characterized into a proper category of chemicals. According to our results, the classification of chemicals into aliphatic compounds, aromatic compounds, and PAH is quite successful in this respect. However, the use of herbicide and pesticide to define the group property of chemicals is likely to be unsuitable. This is because most of the herbicides and pesticides available in the market are specially designed for distinct field application, so the diversity of chemical characteristics of these compounds is generally too high to be predicted. Acknowledgements The work described in this paper was supported by a grant from the University Research Fund of the Hong Kong Polytechnic University. References Baughman GL, Perenich TA. Fate of dyes in aquatic systems: I. Solubility and partitioning of some hydrophobic dyes and related compounds. Environ Toxicol Chem 1988;7:183᎐199.
Chiou CT, Schmedding DW. Partitioning of organic compounds in octanol᎐water systems. Environ Sci Technol 1982;16:4᎐10. Connell DW, Braddock RD, Mani SV. Prediction of the partition coefficient of lipophilic compounds in the airmammal tissue system. Sci Total Environ 1993; Suppl:1383᎐1396. EPA report. Basics of Pump-and-Treat Ground-Water Remediation Technology. EPA-600r8-90r003, 1990. Gao C. Predicting soil sorption coefficients of organic chemicals using a neural network model. Environ Toxicol Chem 1996;15:1089᎐1096. Hawker DW, Connell DW. Octanol-water partition coefficients of polychlorinated biphenyl congeners. Environ Sci Technol 1988;22:382᎐387. Hou MH, Baughman GL, Perenich TA. Estimation of water solubility and octanolrwater partition coefficient of hydrophobic dyes. Part II: Reverse-phase high performance liquid chromatography. Dyes Pigm 1991;16:291᎐297. Jafvert CT. Distribution of hydrophobic ionogenic organic compounds between octanol and water: organic acids. Environ Sci Technol 1990;24:1795᎐1803. Jafvert CT. Sediment- and saturated-soil-associated reactions involving an anionic surfactant Ždodecylsulfate.. 2. Partition of PAH compounds among phases. Environ Sci Technol 1991;25:1039᎐1045. Karickhoff SW. Organic pollutant sorption in aquatic systems. J Hydraulic Engineering 1984;110:707᎐735. Verschueren K. Handbook of environmental data on organic chemicals. 2nd ed. Van Nostrand Reinhold Co., Inc, 1983.