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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 Please do not cite the old paper anymore.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) → 2,3-dichloropropene

FORMULA:C3H4Cl2
CAS RN:78-88-6
STRUCTURE
(FROM NIST):
InChIKey:FALCMQXTWHPRIH-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
2.8×10−3 Mackay and Shiu (1981) L
3.5×10−3 Albanese et al. (1987) M
2.4×10−3 Duchowicz et al. (2020) V 187)
2.7×10−3 Dilling (1977) V
2.4×10−3 Yaws (2003) X 238)
4.0×10−3 Duchowicz et al. (2020) Q
4.6×10−3 Gharagheizi et al. (2012) Q
3.9×10−3 Raventos-Duran et al. (2010) Q 243) 244)
6.2×10−3 Raventos-Duran et al. (2010) Q 245)
3.9×10−4 Raventos-Duran et al. (2010) Q 246)
2.4×10−3 Gharagheizi et al. (2010) Q 247)
4.8×10−3 Hilal et al. (2008) Q
3.8×10−3 Modarresi et al. (2007) Q 68)
2.9×10−3 Yaffe et al. (2003) Q 249) 250)
1.8×10−3 Yao et al. (2002) Q 230) 268)
2.0×10−3 Katritzky et al. (1998) Q
2.4×10−3 Yaws (1999) ? 21)

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

  • Albanese, V., Milano, J. C., & Vernet, J. L.: Etude de l’evaporation de quelques hydrocarbures halogenenes de faible masse moleculaire dissous a l’etat de traces dans l’eau, Environ. Technol. Lett., 8, 657–668, doi:10.1080/09593338709384529 (1987).
  • Dilling, W. L.: Interphase transfer processes. II. Evaporation rates of chloro methanes, ethanes, ethylenes, propanes, and propylenes from dilute aqueous solutions. Comparisons with theoretical predictions, Environ. Sci. Technol., 11, 405–409, doi:10.1021/ES60127A009 (1977).
  • Duchowicz, P. R., Aranda, J. F., Bacelo, D. E., & Fioressi, S. E.: QSPR study of the Henry’s law constant for heterogeneous compounds, Chem. Eng. Res. Des., 154, 115–121, doi:10.1016/J.CHERD.2019.12.009 (2020).
  • Gharagheizi, F., Abbasi, R., & Tirandazi, B.: Prediction of Henry’s law constant of organic compounds in water from a new group-contribution-based model, Ind. Eng. Chem. Res., 49, 10 149–10 152, doi:10.1021/IE101532E (2010).
  • Gharagheizi, F., Eslamimanesh, A., Mohammadi, A. H., & Richon, D.: Empirical method for estimation of Henry’s law constant of non-electrolyte organic compounds in water, J. Chem. Thermodyn., 47, 295–299, doi:10.1016/J.JCT.2011.11.015 (2012).
  • Hilal, S. H., Ayyampalayam, S. N., & Carreira, L. A.: Air-liquid partition coefficient for a diverse set of organic compounds: Henry’s law constant in water and hexadecane, Environ. Sci. Technol., 42, 9231–9236, doi:10.1021/ES8005783 (2008).
  • Katritzky, A. R., Wang, Y., Sild, S., Tamm, T., & Karelson, M.: QSPR studies on vapor pressure, aqueous solubility, and the prediction of water-air partition coefficients, J. Chem. Inf. Comput. Sci., 38, 720–725, doi:10.1021/CI980022T (1998).
  • 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).
  • 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).
  • 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).
  • Yaffe, D., Cohen, Y., Espinosa, G., Arenas, A., & Giralt, F.: A fuzzy ARTMAP-based quantitative structure-property relationship (QSPR) for the Henry’s law constant of organic compounds, J. Chem. Inf. Comput. Sci., 43, 85–112, doi:10.1021/CI025561J (2003).
  • Yao, X., aand X. Zhang, M. L., Hu, Z., & Fan, B.: Radial basis function network-based quantitative structure-property relationship for the prediction of Henry’s law constant, Anal. Chim. Acta, 462, 101–117, doi:10.1016/S0003-2670(02)00273-8 (2002).
  • Yaws, C. L.: Chemical Properties Handbook, McGraw-Hill, Inc., ISBN 0070734011 (1999).
  • Yaws, C. L.: Yaws’ Handbook of Thermodynamic and Physical Properties of Chemical Compounds, Knovel: Norwich, NY, USA, ISBN 1591244447 (2003).

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

21) Several references are given in the list of Henry's law constants but not assigned to specific species.
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.
187) Estimation based on the quotient between vapor pressure and water solubility, 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.
238) Value given here as quoted by Gharagheizi et al. (2010).
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.
268) Value from the test set.

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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