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. Author manuscript; available in PMC: 2020 May 18.
Published in final edited form as: Chemosphere. 2019 May 9;230:308–315. doi: 10.1016/j.chemosphere.2019.05.034

Novel calculator for estimation of Freundlich partitioning coefficient

William P Eckel 1
PMCID: PMC7232648  NIHMSID: NIHMS1586029  PMID: 31108442

Abstract

An estimation method for the Freundlich organic carbon normalized partitioning coefficient (Kfoc) is presented. It is based on regressions developed from batch equilibrium experiments submitted to U.S. EPA for pesticide registration. The regressions represent 18 specific agricultural soils from the U.S. and Europe, and are based on 41 pesticide active ingredients or metabolites. The predictive variable used is the subcooled liquid solubility (Sscl), whose calculation requires the compound’s solubility, and if a solid (mp > 25 °C), its melting point. Sscl represents the compound’s solubility as if it were a liquid at room temperature and does not assume that the compound is neutral. The estimation method was tested on 333 pesticides, and estimated means matched published Kfoc values to within 10× for 83%, 3× for 65%, and 2× for 43%. The estimation method (Kfoc Estimator v0.1) was further tested against data for 94 herbicides, 55 Superfund Priority Pollutants and 22 polychlorinated biphenyls. The estimated mean and median Kfoc values were highly correlated (R2 > 0.63 to 0.87) with the measured values. Matching of reported to estimated values was generally good. The herbicides were 51%–54% within 2×, 74%–77% within 3×, and 93%–94% within 10×. The Priority Pollutants were 22%–29% within 2×, 38%–55% within 3×, and 73%–75% within 10×. The estimates for the PCBs were low, but the maximum estimated Kfoc was consistently within 10 × of the reported Koc. This may be due to the PCB data representing a single soil, rather than the central-tendency estimates produced by Kfoc Estimator v0.1.

Keywords: Freundlich partition coefficient, Pesticides, Estimation, Agriculture, Soil, Kf, Kfoc, Subcooled liquid solubility, Exponent

1. Introduction

A coefficient to describe soil-water partitioning is essential to computer modeling of environmental pollutants, including pesticides and industrial chemicals. The organic carbon normalized partitioning coefficient (Koc) and its Freundlich equivalent (Kfoc) are in common usage for this purpose. In the U.S., these parameters are measured in batch equilibrium studies according to harmonized guideline 835.1230 (U.S. EPA, 2008).

The U.S. EPA Office of Pesticides Programs (OPP) receives dozens of batch equilibrium studies each year, as routine FIFRA (Federal Insecticide, Fungicide and Rodenticide Act) data requirements, for both new pesticides and those under periodic review. Because the soils used in these tests are required to be representative of the area of intended use, particular agricultural soils are used repeatedly in studies submitted to OPP. The pesticide active ingredients that are tested range from neutral organics, to compounds that are ionic, either positively or negatively charged at pH7.

A comprehensive review of the literature for Kfoc and Koc prediction methods is beyond the scope of this article. Estimation methods for Koc or Kfoc have been reviewed by Brusseau (1993), Sabljić et al. (1995), Doucette (2003), Shao et al. (2014), and most recently by Vitale and DiGuardo (2019). Many QSARs (Quantitative Structure-Activity Relationships) for Koc or Kfoc use the octanolwater partitioning coefficient (Kow), Molecular Connectivity Index (MCI) or solubility as the independent variable. In practice, OPP uses the KOCWIN v2.00 program in EPISuite (US EPA, 2012), which gives two estimates, one based on Kow and the other on MCI. The estimates provided do not quantitatively account for ionization of the target compound and are not explicitly related to agricultural soils.

In this paper, Kfoc is regressed against the Sscl, which is essentially an estimate of the solubility of a compound as if it were a liquid at room temperature. The use of Sscl (Liu et al., 2013) as the independent variable is intended to make the QSAR equally applicable to neutral and ionic compounds, and to compounds that are solids or liquids at 25 °C.

This paper is an attempt to use measurements of Kfoc on eighteen agricultural soils to develop a general-purpose QSAR for prediction of both Kfoc and the associated exponent, 1/n. The significance of this work is in the use of Kfoc measurements from commonly-tested agricultural soils, based on a common EPA testing guideline, to give central-tendency estimates of Kfoc for an unmeasured compound from a greater number of soils than otherwise would be submitted. The estimation method is intended to be robust and to work equally well for solids and liquids, as well as neutral and anionic compounds.

2. Materials and methods

Batch equilibrium studies in OPP’s electronic data holdings were searched to find studies that had used the same soils, for at least three different compounds (active ingredients or metabolites thereof). A total of 18 soils with studies for at least three, and as many as ten compounds, were identified (Table 1). Eleven were U.S. soils, and seven were from Europe. Soils were identified by name, for example, the Don Uglem clay loam from North Dakota. The soil identity in different batch equilibrium studies was confirmed by comparing percent sand, silt, and clay content, and well as fraction organic carbon.

Table 1.

Properties of soils and chemicals used in regressions.

Soil Soil properties Chemical Properties


Avg f-oc %sand %silt %clay No. Of chemicals solid sol. (mg/L) mp range (K)

CA Fresno Atwater loamy sand 0.0036 82 10 8 3 0.492 to 429 373 to 456
FRG Laacher Hof Axxa sandy loam 0.0173 72.4 22.6 5 5 0.029 to 436 378 to 490
FRG Hoefchen am Hohenseh silt 0.021 8.5 81.3 10.2 5 0.029 to 42,000 378 to 503
NC Pikeville loamy sand 0.014 77 20 3 4 10.9 to 71,000 384 to 474
ND Ostlie East sandy clay loam 0.032 52 24 24 4 0.087 to 2100 337 to 453
NJ Penn silt loam 0.014 25 52 23 3 14 to 2100 377 to 463
WA Ephrata loamy sand 0.0042 75.7 20.5 3.8 3 0.03 to 42,000 490 to 503
SWTZ Gartenacker loam/silt loam 0.021 45 43 12 3 0.98 to 119,000 337 to 369
ND Roger Myron sandy loam 0.012 77 10 13 5 0.087 to 37,100 298 to 453
ND Mutchler sandy (clay) loam 0.018 59 20 21 4 1 to 37,100 359 to 453
ND Horse Camp Bridge silt loam 0.043 21 54 25 5 436 to 69,100 365 to 489
FRG Speyer/LUFA2.2 loamy sand 0.022 82 13.6 4.4 10 0.07 to 8.86E+5 369 to 510
FRG LUFA/Speyer2.1 sand 0.0072 89 7 4 6 0.08 to 400 369 to 510
SWTZ Vetroz silt loam 0.039 18.2 58.5 23.3 4 0.02 to 2.5 332 to 442
SWTZ Les Evouettes silt loam 0.014 31.9 61.1 7 3 0.046 to 2.5 332 to 442
MS Bosket sandy loam 0.0038 49 44 7 3 0.07 to 25,700 375 to 447
SWTZ Illarsaz humicsoil 0.197 humic 3 0.02 to 4 332 to 442
ND Don Uglem clay loam 0.039 39 30 31 5 0.087 to 2100 298 to 453

Table 1 also summarizes the number of compounds tested for each of the soils, along with the range of solubility (of the solid), and the range of melting points (Kelvins) (Lewis et al., 2016). At least 3, and as many as 10 compounds were included in the regressions for each soil. For each regression, estimates of Kfoc were compared to the measured values to ensure that the estimates were reasonable (data not shown). Overall, 41 different active ingredients or degradates were included in the 18 regressions.

In order to calculate the Sscl, only compounds with reported solubility and melting point were retained for the regressions. Sscl was calculated by the method presented in Liu et al. (2013) using the assumption of 56.5 kJ/mol/K for the entropy of fusion. The uncertainty introduced by the use of Sscl is in its estimation for solid compounds. The further a compound’s melting point is from 25 °C, the greater the possible extrapolation in Sscl from the solubility from of the solid-state compound. The assumption for the entropy of fusion may over- or under-estimate the actual value, so the error is believed to largely cancel out.

Reported Kf values, not normalized for the fraction of organic carbon in the soil, were regressed versus calculated Sscl values and plotted on a log-log scale. A power regression was run for each soil, because of the very wide range of Sscl (8 orders of magnitude in Fig. 1) and Kf (over 3 orders of magnitude) values, so that a straight line was obtained on the log-log plot. Power regressions were used for all soils to be consistent. The resulting slopes, exponents and R2 values were recorded for each soil.

Fig. 1.

Fig. 1.

Kf as a function of subcooled liquid solibility for Speyer 2.2 loamy sand (R2=0.75).

The regression models for the 18 soils, in the form of the Kfoc Estimator v0.1 Excel spreadsheet, were used to estimate Kfoc for 333 pesticides from the Footprint database (Lewis et al., 2016), 94 herbicides (Ahrens, 1994), 55 Superfund Priority Pollutants (US EA, 1996) and 22 Polychlorinated biphenyls (Hansen et al., 1999).

3. Results

Fig. 1 presents the plot for the Speyer 2.2 loamy sand soil from Germany, which had the most data. Note that the relationship between Kf and Sscl spans 8 orders of magnitude of Sscl. Table 2 presents the slope and exponent of the power function describing the log-log plot of Kf versus sub-cooled liquid solubility for each soil. These are the regression parameters used in the Kfoc Estimator v0.1 model to produce the 18 individual Kfoc estimates.

Table 2.

Regression Parameters for Kf vs. Sscl and 1/n vs. Melting Point.

Soil Kf vs. Sscl (power regression) 1/n vs. Melting Point (linear regression)


intercept exponent R2 slope intercept

CA Fresno Atwater loamy sand 2.3734 −0.421 0.87 0.0007 0.5481
FRG Laacher Hof Axxa sandy loam 32.53 −0.336 0.81 0.0009 0.5041
FRG Hoefchen am Hohenseh silt 21.079 −0.333 0.79 0.0005 0.6845
NC Pikeville loamy sand 18.507 −0.167 0.75 −0.0005 1.1234
ND Ostlie East sandy clay loam 111.43 −0.379 0.79 −0.0005 1.0405
NJ Penn silt loam 89.923 −0.502 0.97 0.0007 0.5885
WA Ephrata loamy sand 18.318 −0.381 0.78 −0.0081 5.0037
SWTZ Gartenacker loam/silt loam 80.412 −0.477 0.99 −0.0015 1.4041
ND Roger Myron sandy loam 51.163 −0.312 0.70 −0.0007 1.1279
ND Mutchler sandy (clay) loam 91.721 −0.367 0.92 0.0005 0.6751
ND Horse Camp Bridge silt loam 4.9906 −0.124 0.03 −0.0002 0.9694
FRG Speyer/LUFA2.2 loamy sand 78.719 −0.356 0.75 −0.0027 2.0066
FRG LUFA/Speyer2.1 sand 26.7 −0.287 0.72 −0.0018 1.6124
SWTZ Vetroz silt loam 502.43 −0.877 0.98 0.0006 0.719
SWTZ Les Evouettes silt loam Switz 128.02 −1.092 0.98 0.0012 0.4282
MS Bosket sandy loam 80.248 −0.378 0.93 −0.0001 0.9898
SWTZ lllarsaz humic soil 19471947.8 −0.757 0.97 0.0004 0.82
ND clay loam Don Uglem 250.43 −0.372 0.71 −0.0006 1.0761

Equations for the Freundlich exponent 1/n were estimated by regressing, within the data set for each of the 18 soils, the reported 1/n value versus the melting point of each compound. These results are also reported in Table 2.

In general, the regression of 1/n versus melting point gave an intercept less than one with a shallow positive slope, or an intercept greater than one with a shallow negative slope, so that a value of 1/n = 1 was reached at some melting point. With a few exceptions, the intercepts were close to one (0.5–2). Larger intercepts (Ephrata loamy sand) had correspondingly steeper slopes. Only two soils (Horse Camp Bridge and Bosket sandy loam) had intercepts less than one and a negative slope, but in both cases the slope was very close to one and the R2 value was low (0.002–0.0561), indicating that 1/n was close to one in all cases. Overall, these results indicate that 1/n is not a strong function of melting point, and that use of a value of 1/n = 1 would not be unreasonable.

A calculator (Kfoc Estimator v. 0.1) was constructed from the results in Tables 1 and 2 to estimate Kfoc and 1/n from the melting point and solubility of 333 pesticide active ingredients found in the Footprint database (Lewis et al., 2016). Each of the 333 pesticides had reported Kfoc, melting point, and solubility data, and were generally organic chemicals. No attempt was made to segregate the 333 pesticides by structural class. Values of pKa were recorded to test whether Kfoc predictions were affected by chemicals being ionized rather than neutral. The calculator estimates Kfoc for each of the 18 soils, then reports the median, average, standard deviation, minimum and maximum values of Kfoc, and calculates 1/n for each soil based on the melting point regression.

Fig. 2 shows a plot of the predicted versus reported average Kfoc values for the 333 pesticides. The 1:1 line is indicated, showing general good agreement, with some scatter as expected since many different structural classes, not all expected to partition to organic carbon, are included. Predictions were within a factor of 10 for 83%, within a factor of 3 for 65%, and within a factor of 2 for 43% of the 333 pesticides. The worst predictions were obtained for quaternary ammonium compounds (e.g., paraquat) since these cationic compounds are not expected to partition to organic carbon.

Fig. 2.

Fig. 2.

Footprint Avg kfoc vs predict Avg kfoc N=333.

To test whether the Kfoc of ionic compounds was predicted well, the ratio of the predicted and reported Kfoc values was plotted versus the reported pKa for each compound (data not shown). The ratio of predicted Kfoc to reported Kfoc was 1 or less for 99 compounds, and >1 for 74 compounds; 65 were within 2×. The lack of an apparent trend in the data suggests that pKa had little influence on the quality of the prediction.

The Kfoc Estimator v. 0.1 program was further tested against published data for Koc for 95 diverse herbicides (WSSA, 1994), 56 Superfund Priority Pollutants (U.S. EPA, 1996), and 22 PCBs (Hansen et al., 1999) measured in one soil. For the herbicides (Fig. 3), the estimated Kfoc values were well-correlated (R2 = 0.7685 vs. median estimates, R2 = 0.6347 vs. mean estimates).

Fig. 3.

Fig. 3.

kfoc estimated vs WSS Handbook (Ahrens,1994) koc.

Quaternary ammonium compounds and arsenic-containing compounds were not well-predicted, presumably because their sorption behavior is not correlated to organic carbon content. For the Priority Pollutants (Fig. 4), measured values were predicted with R2 values of 0.8737 vs. median predicted and 0.8664 vs. mean predicted. Finally, the Koc values for the PCBs (Fig. 5) were estimated with R2 values of 0.8316 vs. median estimated and 0.8303 vs median estimated.

Fig. 4.

Fig. 4.

Superfund priority poputant Geometrie mean koc vs Estimate mean vs median koc.

Fig. 5.

Fig. 5.

PCB congener logkoc (Hansen, et al. 1999) vs Estimate mean and median lofkoc.

As can be seen in Figs. 3 to 5, the accuracy of the mean and median estimates differs for the three data sets. In Fig. 3, the mean estimates have a slope near 1, while the median estimates have a slope near 0.5. In Fig. 4 for Superfund Priority Pollutants, the regression lines are reasonably near the 1:1 values, with estimates underpredicting to some degree as the values approach 100,000. In Fig. 5, for PCBs, while the estimates are highly correlated with the measured values, there is considerable underestimation. This is probably because the measured values are from a single soil, and so do not represent an ‘average’ soil in the way that the Kfoc Estimator v0.1 attempts to do. Literature and estimated values for Figs. 35 are in the Appendix.

4. Discussion

The calculated Sscl was well-correlated with the reported Kf values, even over 8 orders of magnitude of Sscl for the Speyer 2.2 soil (see Fig. 1). R2 values for the 18 soils ranged from 0.70 to 0.99, with one exception, indicating that Sscl is a useful independent variable for prediction of Kf.

The correlation between mean and median predicted Kfoc values from the Kfoc Estimator v0.1 is shown in Figs. 2 to 5 for different data sets. The data in Fig. 2 (333 pesticide active ingredients) show a fair correlation between the mean predicted Kfoc and the reported values (R2 = 0.39). This data set includes compounds such as quaternary ammonium cations, whose very strong partitioning behavior does not reflect their very high solubility. These compounds’ Kfoc values are predicted poorly by Kfoc Estimator v0.1, which contributes to the relatively low R2. Even so, predictions were within a factor of 10 for 83%, within a factor of 3 for 65%, and within a factor of 2 for 43% of the 333 pesticides.

Fig. 3 shows the results for a set of 94 herbicides. The correlations between reported Koc estimated mean Kfoc (R2 = 0.63) and estimated median Kfoc (R2 = 0.77) are good. The estimated means are within a factor of 2 for 51% of the 94 herbicides, within a factor of 3 for 77% and within a factor of 10 for 94%. The estimated median Kfoc values are within 2× for 54%, 3× for 74% and 10× for 93% of the 94 herbicides. This indicates generally good agreement between reported and estimated values.

Fig. 4 shows the correlation between reported geometric mean Koc for 55 Superfund Priority Pollutants, and the mean and median estimated Kfoc values from Kfoc Estimator v0.1. Correlations are good (mean, R2 = 0.86; median, R2 = 0.87). The estimated mean Kfoc values are with 2× of reported Koc values for 22%, within 3× for 38% and within 10× for 75% of the 55 compounds. The estimated median Kfocs are within 2× for 29%, within 3× for 55% and within 10× for 73% of the 55 compounds. Agreement is not as good as for the 94 herbicides. This may be due to the structural diversity of the 55 Priority Pollutants, and due to the structural differences from pesticides in general.

Fig. 5 shows the correlation between reported logKoc values for 22 PCB congeners tested on a single soil, and the mean and median estimated Kfoc values. Correlations are good (R2 = 0.83 for both mean and median). However, the values estimated by Kfoc Estimator v0.1 are significantly lower than the reported values. The mean estimates run from 2 to 14% of reported Koc, and the median values run from 0.65 to 10.4% of reported Koc. Seven estimated means and only one estimated median are within 10× of reported Koc. Estimated maximum Kfoc values were within a factor of 10 of the reported Koc. The low estimates for this data set are attributed to the fact that the reported values are for a single soil, while Kfoc Estimator v0.1 gives the central tendency for estimates from 18 soils. These central tendency values are not intended to reflect any one soil.

5. Conclusions

A calculator (Kfoc Estimator v. 0.1) for the Freundlich Organic Carbon-normalized partition coefficient (Kfoc) and its associated exponent (1/n) has been constructed from studies submitted to the U.S. EPA pesticides program. The calculator is based on regressions for 41 active ingredients and metabolites in 18 specific agricultural soils. It produces 18 separate estimates, so soil-to-soil variability may be assessed; mean and median results represent central tendencies across the 18 soils. The use of the subcooled liquid solubility (Sscl) as the independent variable makes the calculator applicable to both neutral and anionic organic compounds. Predictions of Kfoc for 333 active ingredients were within 10× for 83%, 3× for 65%, and 2× for 43%. Predictions are well-correlated with published values for diverse sets of compounds, including herbicides, Priority Pollutants and polychlorinated biphenyls. Testing shows that the Kfoc Estimator is not applicable to quaternary ammonium or organometallic compounds. A copy of Kfoc Estimator v. 0.1 is available from the author.

HIGHLIGHTS.

  • Freundlich soil-water partition coefficient (Kfoc) is predicted from subcooled liquid solubility.

  • Predictions are based on measured values from 41 pesticides in 18 agricultural soils, giving robust central-tendency estimates.

  • Mean and Median Kfoc estimates give estimates within a factor of 10 of literature values for a majority of compounds from 3 of 4 data sets.

Acknowledgments

Copyright

This is a work of the U.S. Government and is not subject to copyright protection in the U.S. It has been subjected to review by the Office of Pesticide Programs and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsemenst or recommendation for use.

Appendix. Measured and Estimated Koc/Kfoc Values from Hansen (1999), U.S. EPA (1996) and Ahrens (1994). Values estimated by Kfoc Estimator v. 0.1 marked as “this work.”

CAS No name Water solubility mg/L mp,°C Geomean Koc This Work

Ea Mean Kfoc Est Median Kfoc

50-32-5 Benzo(a)pyrene 162E-03 179 968,774 37,561 11,909
72-43-5 Methoxychlor 4,50E-02 82.5 80,000 16,551 8039
50-29-3 DDT 2,50E-02 108.5 677,934 16,493 8025
56-55-3 Benz(a)anthracene 9.40E-03 155 357,537 15,591 7798
117-81-7 Bis(2-ethylhexyl)phthalate 3.40E-01 −50 111,123 10,367 6188
57-74-9 Chlordane 5.60E-02 105 51,310 10,233 6137
72-54-8 DDD 9.00E-02 109.5 45,800 7225 4885
76-44-8 Heptachlor 1.80E-01 95.5 9,528 5844 4129
309-00-2 Aldrin 1.80E-01 104.3 48,685 5237 3902
206-44-0 Fluoranthene 2.06E-01 107 49,096 4716 3654
8001-35-2 Toxaphene 7.40E-01 65 95,816 3394 3284
129-00-0 Pyrene 1.35E-01 156 67,992 3316 2896
85-68-7 Butyl benzyl phthalate 2.69E+00 −35 13,746 3308 2892
120-12-7 Anthracene 4.34E-02 218 23,493 2900 2440
60-57-1 Dieldrin 1.95E-01 175.5 25,546 2267 2043
86-73-7 Fluorene 1.98E+00 116 7,707 1494 1225
83-32-9 Acenaphthene 4.24E+00 95 4,898 1329 1045
72-20-8 Endrin 2.50E-01 228 10,811 1224 934
115-29-7 Endosulfan 5.10E-01 209 2,040 1093 825
319-84-6 a-HCH (a-BHC) 2.00E+00 158 1,762 1008 779
58-89-9 g -HCH (Lindane) 6.80E+00 112.5 1,352 937 739
91-20-3 Naphthalene 3.10E+01 80.2 1,191 693 596
106-46-7 1,4-Dichlorobenzene 7.38E+01 53.1 616 631 536
95-50-1 1,2-Dichlorobenzene 1.56E+02 −17 379 606 511
108-38-3 m-Xylene 1.61E+02 −47.4 196 599 504
319-85-7 b-HCH (b-BHC) 2.40E-01 312 2,139 591 496
100-41-4 Ethylbenzene 1.69E-02 −95 204 588 494
95-47-6 o-Xylene 1.78E+02 −25 241 577 482
106-42-3 p-Xylene 1.85E+02 13 311 569 474
127-18-4 Tetrachloroethylene 2.00E+02 −22 265 552 458
120-82-1 1,2,4-Trichlorobenzene 3.00E+02 17 1,659 476 384
100-42-5 Styrene 3.10E+02 −30 912 470 378
108-90-7 Chlorobenzene 4.72E+02 −45 224 403 315
108-88-3 Toluene 5.26E+02 −94 140 388 300
118-74-1 Hexachlorobenzene 6.20E+00 228 80,000 362 277
56-23-5 Carbon tetrachloride 7.93E-r02 −23 152 335 251
84-66-2 Diethylphthalate 1.08E+03 −40.5 82 300 220
79-01-6 Trichloroethylene 1.10E+03 −87 94 298 218
71-55-6 1,1,1-Trichloroethane 1.33E+03 −35 135 278 201
71-43-2 Benzene 1.75E+03 5 62 253 179
98-95-3 Nitrobenzene 2.09E+03 6 119 238 168
75-35-4 1,1 -Dichloroethylene 2.25E+03 −122 65 232 164
78-87-5 1,2-Dichloropropane 2.80E+03 −100.3 47 215 151
79-34-5 1,1,2,2-Tetrachloroethane 2.97E+03 −36 79 210 148
75-25-2 Bromoform 3.10E+03 7.5 126 207 146
79-00-5 1,1,2-Trichloroethane 4.42E-i-03 −37 75 183 128
75-34-3 1,1-Dichloroethane 5.06E-f03 −97 53 175 122
156-60-5 trans-1,2-Dichloroethylene 6.30E+03 −50 38 162 113
67-66-3 Chloroform 7.92E-f-03 −63 53 150 104
107-06-2 1,2-Dichloroethane 8.52E-r03 −35 38 147 101
75-09-2 Methylene chloride 1.30E+04 −95.1 10 127 87
74-83-9 Methyl bromide 1.52E+04 −94 9 121 82
111-44-4 Bis(2-chloroethyl)ether 1.72E+04 −50 76 116 79
PCB# Log Koc This Work Sol mg/L mp °C This Work


Log est mean Log est median Est mean Kfoc Est median Kfoc

183 6.32 5.06 4.29 4.90E-03 83 114,096 19,631
138 6.2 4.84 4.20 7.29E-03 80.5 68,990 15,718
180 6.37 4.81 4.18 3.85E-03 112 64,527 15,248
153 6.19 4.58 4.08 9.14E-03 103 37,945 11,920
151 6.02 4.47 4.03 1.36E-02 100 29,375 10,611
101 5.78 4.43 4.01 2.63E-02 77 26,612 10,133
118 5.99 4.41 4.00 1.34E-02 109 25,474 9927
87 5.82 4.37 3.98 2.84E-02 81 23,449 9543
44 5.1 4.22 3.91 1.00E-01 47 16,677 8070
49 5.09 4.16 3.87 7.84E-02 67 14,398 7484
70 5.26 4.13 3.86 3.62E-02 106 13,372 7200
95 5.62 4.09 3.84 5.41 E-02 94 12,250 6866
28 4.98 4.05 3.82 1.43E-01 57 11,303 6533
31 4.96 3.99 3.78 1.43E-01 67 9770 5959
66 5.38 3.98 3.77 3.68E-02 128 9568 5880
18 4.79 3.93 3.74 2.99E-01 44 8576 5476
52 5.1 3.93 3.74 1.13E-01 87 8537 5460
60 5.38 3.88 3.71 3.89E-02 142 7634 5070
40 5.14 3.82 3.66 8.07E-02 121 6603 4594
8 4.76 3.79 3.64 5.38E-01 43 6187 4392
15 4.79 3.74 3.60 6.00E-02 149 5467 4024
2 4.58 3.73 3.60 9.00E-01 32 5340 3956
compound water solubility mg/L mp, °C Koc This Work

mean est Kfoc median est Kfoc

fluridone 12 154–155 1000 526 433
oxyfluorfen 0.1 76–80 5483 10,444 6217
lactofcn 0.1 419–455 10000 17,170 8190
fluazifop Butyl Ester 1.1 10 5700 5222 3894
fenoxaprop Ethyl Ester 0.5 89–91 9490 3646 3089
benefin 0.1 65–66.5 9000 12,631 6985
trifluralin 0.3 46–47 7000 8327 5371
ethalfluralin 0.3 57–59 4000 7088 4822
isopropalin 0.08 no data 10,000 28,040 10,384
prodiamine 0.013 122.5–124 13,000 20,643 8973
cinmethylin 6.3 no data 300 855 692
pendimethalin 0.275 47–57 17,200 8083 5267
oxadiazon 0.7 87 3200 3196 2804
sethoxydim pH7 4390 no data 100 184 129
acifluorfen 120 142–160 113 253 180
bifenox 0.398 84–86 10,000 4337 3466
tridiphane 1.8 428 5600 3296 2884
napropamide 73 748–755 700 525 432
norflurazon 28 177 700 320 238
bensulide 25 344 1000 1132 846
desmedipham 7 120 1500 865 698
phenmedipham 10 143–144 2400 618 523
imazaquin 60 219–224 20 171 120
butylate 44 no data 400 984 766
clomazone 1100 25 300 297 218
thiobencarb 30 3.3 1700 1146 857
fomesafen Na 600,000 220–221 60 10 5
haloxyfop acid 433 107–108 60 487 394
isoxaben 1 176–179 380 1117 838
dithiopyr 1.38 65 1638 2936 2483
triclopyr BE 23 148–150 780 432 342
triallate 4 29–30 2400 2619 2268
naptalam 200 185 20 152 105
oryzalin 2.6 141–142 600 1058 806
prometryn 33 118–120 400 486 394
diallate 14 25–30 500 1569 1308
DCPA 0.5 156 5000 1818 1648
metolachlor 488 −40 200 398 310
ametryn 200 84–85 300 338 254
triasulfuron pH7 815 186 121 94 63
pronamide 15 155–156 800 480 389
linuron 75 93–94 400 447 357
siduron 18 133–138 420 527 434
chlorsulfuron 31,800 174–178 40 32 20
alachlor 200 39.5–41.5 124 486 394
pebulate 60 <25 430 871 702
vernolate 108 no data 260 695 597
fluometuron 110 163–164.5 100 219 155
flumetsulam acid 5600 253 700 32 20
atrazine 33 175–177 100 307 226
propachlor 580 77 112 245 174
ethofumesate 110 70–72 340 467 376
molinate 970 <25 190 311 230
propanil 500 85–89 149 239 169
EPTC 370 no data 200 440 350
simazine 6.2 225–227 130 368 282
prometon 720 91–92 150 204 143
cyanazine 160 1673–169 190 187 131
hexazinone 33X100 115–117 54 48 30
cycloate 85 no data 650 762 637
diuron 42 158–159 48CJ 323 241
2,4DB acid 46 120–121 440 427 337
MCPB Na salt 200,000 100–101 20 31 19
triflusulfuron pH7 110 150–154 80 241 171
dazomet 2000 100–106 10 131 90
diethatyl 105 49–50 1400 572 478
mefluidide 180 183–185 200 160 111
bromacil 815 158–159 32 116 79
methazole 42 123–124 3000 1029 791
metribuzin 1100 1255–1263 60 134 92
terbacil 710 175–177 55 106 72
mecoprop acid 620 94–95 20 209 148
tebuthiuron 2500 161.5–164 80 77 51
picloram 430 no data 90 416 327
sodium chlorate 1,000,000 248 10 8 3
2,4-D acid 900 135–138 20 133 91
MCPA acid 825 118–119 110 157 1(19
primisulfuron pH7 243 203 50 124 85
dicamba acid 4500 114–116 2 91 61
asulam 534,000 135–137 40 18 10
sulfometuron pH7 300 203–205 78 114 78
metsulfuron 2790 158 35 78 51
tribenuron pH7 2040 141 46 98 65
clopyralid 1000 151–152 33 115 78
endothall 100 144 120 266 190
thifensulfuron pH7 2240 186 45 68 44
bentazon 500 137–139 34 160 111
chlorimuron 1200 185–187 110 83 55
glufosinate 1,370000 215 100 9 4
nicosulfuron pH7 12,200 172–173 30 44 27
maleic hydrazide acid 4500 300 250 25 15
amitrole 280,000 159 100 19 11
acrolein 237.628 −87 05 49 31
fosamine 1,790000 175 150 10 5

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