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Henry's Law Constants

www.henrys-law.org

Rolf Sander

Atmospheric Chemistry Division

Max-Planck Institute for Chemistry
Mainz, Germany

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Henry's Law Constants

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When referring to the compilation of Henry's Law Constants, please cite this publication:

R. Sander: Compilation of Henry's law constants (version 5.0.0) for water as solvent, Atmos. Chem. Phys., 23, 10901-12440 (2023), doi:10.5194/acp-23-10901-2023

The publication from 2023 replaces that from 2015, which is now obsolete. Please do not cite the old paper anymore.

Henry's Law Constants → Organic species with chlorine (Cl) → Chlorocarbons (C, H, Cl) → 1,2-dichlorobenzene

FORMULA:C6H4Cl2
TRIVIAL NAME: o-dichlorobenzene
CAS RN:95-50-1
STRUCTURE
(FROM NIST):
InChIKey:RFFLAFLAYFXFSW-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
5.6×10−3 3700 Schwardt et al. (2021) L 1)
5.9×10−3 5200 Brockbank (2013) L 1) 712)
6.8×10−3 5300 Fogg and Sangster (2003) L 713)
5.4×10−3 5900 Staudinger and Roberts (2001) L
5.4×10−3 5900 Staudinger and Roberts (1996) L
5.3×10−3 Mackay and Shiu (1981) L
8.0×10−3 4200 Hiatt (2013) M
6.3×10−3 Li et al. (2008) M
4.7×10−3 Ryu and Park (1999) M
5.1×10−3 Shiu and Mackay (1997) M
7.2×10−3 Hovorka and Dohnal (1997) M 12)
6.2×10−3 5000 Kondoh and Nakajima (1997) M
4.9×10−3 4400 Park et al. (1997) M
4.8×10−3 Li and Carr (1993) M
3.5×10−3 Yu (1992) M 12)
4.9×10−3 5100 Bissonette et al. (1990) M
5.3×10−3 1400 Ashworth et al. (1988) M 42) 279)
8.2×10−3 Oliver (1985) M
5.9×10−3 6700 Gossett et al. (1985) M
5.2×10−3 Mackay and Shiu (1981) M
5.1×10−3 Warner et al. (1980) M
3.5×10−3 Sato and Nakajima (1979b) M 14)
5.6×10−3 Mackay et al. (2006b) V
4.1×10−3 Shiu and Mackay (1997) V
8.6×10−3 Park et al. (1997) V
8.3×10−3 Lide and Frederikse (1995) V
4.1×10−3 Mackay et al. (1992a) V
6.0×10−3 Hwang et al. (1992) V
4.1×10−3 Bobra et al. (1985) V
4.9×10−3 Warner et al. (1980) V
4.0×10−3 Hine and Mookerjee (1975) V
3.5×10−3 Yaws (2003) X 238)
5.2×10−3 2800 Goldstein (1982) X 299)
5.2×10−3 Schüürmann (2000) C 21)
2.7×10−3 Ryan et al. (1988) C
5.1×10−3 Shen (1982) C
7.4×10−3 Keshavarz et al. (2022) Q
1.5×10−2 Duchowicz et al. (2020) Q 185)
4.0×10−3 Li et al. (2014) Q 242)
4.7×10−2 Gharagheizi et al. (2012) Q
6.2×10−3 Raventos-Duran et al. (2010) Q 243) 244)
6.2×10−3 Raventos-Duran et al. (2010) Q 245)
3.1×10−3 Raventos-Duran et al. (2010) Q 246)
3.0×10−3 Gharagheizi et al. (2010) Q 247)
8.2×10−3 Hilal et al. (2008) Q
4.5×10−3 Modarresi et al. (2007) Q 68)
4400 Kühne et al. (2005) Q
5.6×10−3 Yaffe et al. (2003) Q 249) 250)
7.1×10−3 Delgado and Alderete (2002) Q
8.0×10−3 Yao et al. (2002) Q 230)
4.7×10−3 English and Carroll (2001) Q 231) 232)
3.3×10−3 Katritzky et al. (1998) Q
2.3×10−3 Myrdal and Yalkowsky (1994) Q
8.4×10−3 Nirmalakhandan and Speece (1988) Q
5.1×10−3 Duchowicz et al. (2020) ? 21) 186)
4800 Kühne et al. (2005) ?
3.5×10−3 Yaws (1999) ? 21)
3.6×10−3 Abraham and Weathersby (1994) ? 21)
3.3×10−3 Yaws and Yang (1992) ? 21)
5.1×10−3 Abraham et al. (1990) ?
6.2×10−3 Chiou et al. (1980) ? 80)

Data

The first column contains Henry's law solubility constant Hscp at the reference temperature of 298.15 K.
The second column contains the temperature dependence d ln Hs cp / d (1/T), also at the reference temperature.

References

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  • Keshavarz, M. H., Rezaei, M., & Hosseini, S. H.: A simple approach for prediction of Henry’s law constant of pesticides, solvents, aromatic hydrocarbons, and persistent pollutants without using complex computer codes and descriptors, Process Saf. Environ. Prot., 162, 867–877, doi:10.1016/J.PSEP.2022.04.045 (2022).
  • Kondoh, H. & Nakajima, T.: Optimization of headspace cryofocus gas chromatography/mass spectrometry for the analysis of 54 volatile organic compounds, and the measurement of their Henry’s constants, J. Environ. Chem., 7, 81–89, doi:10.5985/JEC.7.81 (1997).
  • Kühne, R., Ebert, R.-U., & Schüürmann, G.: Prediction of the temperature dependency of Henry’s law constant from chemical structure, Environ. Sci. Technol., 39, 6705–6711, doi:10.1021/ES050527H (2005).
  • Li, J. & Carr, P. W.: Measurement of water-hexadecane partition coefficients by headspace gas chromatography and calculation of limiting activity coefficients in water, Anal. Chem., 65, 1443–1450, doi:10.1021/AC00058A023 (1993).
  • Lide, D. R. & Frederikse, H. P. R.: CRC Handbook of Chemistry and Physics, 76th Edition, CRC Press, Inc., Boca Raton, FL, ISBN 0849304768 (1995).
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  • Li, H., Wang, X., Yi, T., Xu, Z., & Liu, X.: Prediction of Henry’s law constants for organic compounds using multilayer feedforward neural networks based on linear salvation energy relationship, J. Chem. Pharm. Res., 6, 1557–1564 (2014).
  • Mackay, D. & Shiu, W. Y.: A critical review of Henry’s law constants for chemicals of environmental interest, J. Phys. Chem. Ref. Data, 10, 1175–1199, doi:10.1063/1.555654 (1981).
  • Mackay, D., Shiu, W. Y., & Ma, K. C.: Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals, vol. I of Monoaromatic Hydrocarbons, Chlorobenzenes, and PCBs, Lewis Publishers, Boca Raton, ISBN 0873715136 (1992a).
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  • Modarresi, H., Modarress, H., & Dearden, J. C.: QSPR model of Henry’s law constant for a diverse set of organic chemicals based on genetic algorithm-radial basis function network approach, Chemosphere, 66, 2067–2076, doi:10.1016/J.CHEMOSPHERE.2006.09.049 (2007).
  • Myrdal, P. & Yalkowsky, S. H.: A simple scheme for calculating aqueous solubility, vapor pressure and Henry’s law constant: application to the chlorobenzenes, SAR QSAR Environ. Res., 2, 17–28, doi:10.1080/10629369408028837 (1994).
  • Nirmalakhandan, N. N. & Speece, R. E.: QSAR model for predicting Henry’s constant, Environ. Sci. Technol., 22, 1349–1357, doi:10.1021/ES00176A016 (1988).
  • Oliver, B. G.: Desorption of chlorinated hydrocarbons from spiked and anthropogenically contaminated sediments, Chemosphere, 14, 1087–1106, doi:10.1016/0045-6535(85)90029-3 (1985).
  • Park, S.-J., Han, S.-D., & Ryu, S.-A.: Measurement of air/water partition coefficient (Henry’s law constant) by using EPICS method and their relationship with vapor pressure and water solubility, J. Korean Inst. Chem. Eng., 35, 915–920 (1997).
  • Raventos-Duran, T., Camredon, M., Valorso, R., Mouchel-Vallon, C., & Aumont, B.: Structure-activity relationships to estimate the effective Henry’s law constants of organics of atmospheric interest, Atmos. Chem. Phys., 10, 7643–7654, doi:10.5194/ACP-10-7643-2010 (2010).
  • Ryan, J. A., Bell, R. M., Davidson, J. M., & O’Connor, G. A.: Plant uptake of non-ionic organic chemicals from soils, Chemosphere, 17, 2299–2323, doi:10.1016/0045-6535(88)90142-7 (1988).
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Type

Table entries are sorted according to reliability of the data, listing the most reliable type first: L) literature review, M) measured, V) VP/AS = vapor pressure/aqueous solubility, R) recalculation, T) thermodynamical calculation, X) original paper not available, C) citation, Q) QSPR, E) estimate, ?) unknown, W) wrong. See Section 3.1 of Sander (2023) for further details.

Notes

1) A detailed temperature dependence with more than one parameter is available in the original publication. Here, only the temperature dependence at 298.15 K according to the van 't Hoff equation is presented.
12) Value at T = 293 K.
14) Value at T = 310 K.
21) Several references are given in the list of Henry's law constants but not assigned to specific species.
42) Fitting the temperature dependence dlnH/d(1/T) produced a very low correlation coefficient (r2 < 0.5). The data should be treated with caution.
68) Modarresi et al. (2007) use different descriptors for their calculations. They conclude that a genetic algorithm/radial basis function network (GA/RBFN) is the best QSPR model. Only these results are shown here.
80) Value at T = 297 K.
185) Value from the validation set for checking whether the model is satisfactory for compounds that are absent from the training set.
186) Experimental value, extracted from HENRYWIN.
230) Yao et al. (2002) compared two QSPR methods and found that radial basis function networks (RBFNs) are better than multiple linear regression. In their paper, they provide neither a definition nor the unit of their Henry's law constants. Comparing the values with those that they cite from Yaws (1999), it is assumed that they use the variant Hvpx and the unit atm.
231) English and Carroll (2001) provide several calculations. Here, the preferred value with explicit inclusion of hydrogen bonding parameters from a neural network is shown.
232) Value from the training dataset.
238) Value given here as quoted by Gharagheizi et al. (2010).
242) Temperature is not specified.
243) Value from the training dataset.
244) Calculated using the GROMHE model.
245) Calculated using the SPARC approach.
246) Calculated using the HENRYWIN method.
247) Calculated using a combination of a group contribution method and neural networks.
249) Yaffe et al. (2003) present QSPR results calculated with the fuzzy ARTMAP (FAM) and with the back-propagation (BK-Pr) method. They conclude that FAM is better. Only the FAM results are shown here.
250) Value from the training set.
279) Data are taken from the report by Howe et al. (1987).
299) Value given here as quoted by Staudinger and Roberts (1996).
712) Values at 298 K in Tables C2 and C5 of Brockbank (2013) are inconsistent, with 8 % difference.
713) Erratum for page 344 of Fogg and Sangster (2003): their reference [89] does not contain 1,2-dichlorobenzene.

The numbers of the notes are the same as in Sander (2023). References cited in the notes can be found here.

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