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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 Please do not cite the old paper anymore.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) → 1,3-dichlorobenzene

FORMULA:C6H4Cl2
TRIVIAL NAME: m-dichlorobenzene
CAS RN:541-73-1
STRUCTURE
(FROM NIST):
InChIKey:ZPQOPVIELGIULI-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
3.1×10−3 3700 Schwardt et al. (2021) L 1)
3.1×10−3 4400 Brockbank (2013) L
3.4×10−3 4300 Fogg and Sangster (2003) L
2.8×10−3 Mackay and Shiu (1981) L
5.2×10−3 4800 Hiatt (2013) M
2.9×10−3 Li et al. (2008) M
3.7×10−3 de Wolf and Lieder (1998) M 88)
4.7×10−3 Hovorka and Dohnal (1997) M 12)
3.8×10−3 4400 Kondoh and Nakajima (1997) M
3.4×10−3 Hoff et al. (1993) M
3.0×10−3 2600 Ashworth et al. (1988) M 33) 279)
5.5×10−3 Oliver (1985) M
3.8×10−3 Warner et al. (1980) M
2.1×10−3 Sato and Nakajima (1979b) M 14)
3.1×10−3 Mackay et al. (2006b) V
2.7×10−3 Shiu and Mackay (1997) V
5.6×10−3 Lide and Frederikse (1995) V
2.7×10−3 Mackay et al. (1992a) V
2.7×10−3 Bobra et al. (1985) V
3.3×10−3 Warner et al. (1980) V
2.1×10−3 Hine and Mookerjee (1975) V
2.9×10−3 Yaws (2003) X 238)
3.9×10−3 2400 Goldstein (1982) X 299)
3.7×10−3 Ryan et al. (1988) C
3.8×10−3 Shen (1982) C
7.4×10−3 Keshavarz et al. (2022) Q
9.2×10−3 Duchowicz et al. (2020) Q 300)
3.9×10−2 Gharagheizi et al. (2012) Q
6.2×10−3 Raventos-Duran et al. (2010) Q 244) 272)
4.9×10−3 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)
4.7×10−3 Hilal et al. (2008) Q
3.2×10−3 Modarresi et al. (2007) Q 68)
4100 Kühne et al. (2005) Q
3.8×10−3 Yaffe et al. (2003) Q 249) 250)
4.5×10−3 Delgado and Alderete (2002) Q
5.0×10−3 Yao et al. (2002) Q 230) 268)
3.7×10−3 English and Carroll (2001) Q 231) 275)
4.2×10−3 Katritzky et al. (1998) Q
2.3×10−3 Myrdal and Yalkowsky (1994) Q
8.4×10−3 Nirmalakhandan and Speece (1988) Q
3.8×10−3 Duchowicz et al. (2020) ? 21) 186)
4500 Kühne et al. (2005) ?
2.9×10−3 Yaws (1999) ? 21)
2.2×10−3 Abraham and Weathersby (1994) ? 21)
3.0×10−3 Yaws and Yang (1992) ? 21)
2.7×10−3 Abraham et al. (1990) ?

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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  • Brockbank, S. A.: Aqueous Henry’s law constants, infinite dilution activity coefficients, and water solubility: critically evaluated database, experimental analysis, and prediction methods, Ph.D. thesis, Brigham Young University, USA, URL https://scholarsarchive.byu.edu/etd/3691/ (2013).
  • Delgado, E. J. & Alderete, J.: On the calculation of Henry’s law constants of chlorinated benzenes in water from semiempirical quantum chemical methods, J. Chem. Inf. Comput. Sci., 42, 559–563, doi:10.1021/CI0101206 (2002).
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  • English, N. J. & Carroll, D. G.: Prediction of Henry’s law constants by a quantitative structure property relationship and neural networks, J. Chem. Inf. Comput. Sci., 41, 1150–1161, doi:10.1021/CI010361D (2001).
  • Fogg, P. & Sangster, J.: Chemicals in the Atmosphere: Solubility, Sources and Reactivity, John Wiley & Sons, Inc., ISBN 978-0-471-98651-5 (2003).
  • 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).
  • Goldstein, D. J.: Air and steam stripping of toxic pollutants, Appendix 3: Henry’s law constants, Tech. Rep. EPA-68-03-002, Industrial Environmental Research Laboratory, Cincinnati, OH, USA (1982).
  • Hiatt, M. H.: Determination of Henry’s law constants using internal standards with benchmark values, J. Chem. Eng. Data, 58, 902–908, doi:10.1021/JE3010535 (2013).
  • 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).
  • Hine, J. & Mookerjee, P. K.: The intrinsic hydrophilic character of organic compounds. Correlations in terms of structural contributions, J. Org. Chem., 40, 292–298, doi:10.1021/JO00891A006 (1975).
  • Hoff, J. T., Mackay, D., Gillham, R., & Shiu, W. Y.: Partitioning of organic chemicals at the air–water interface in environmental systems, Environ. Sci. Technol., 27, 2174–2180, doi:10.1021/ES00047A026 (1993).
  • Hovorka, Š. & Dohnal, V.: Determination of air–water partitioning of volatile halogenated hydrocarbons by the inert gas stripping method, J. Chem. Eng. Data, 42, 924–933, doi:10.1021/JE970046G (1997).
  • 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).
  • 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).
  • Lide, D. R. & Frederikse, H. P. R.: CRC Handbook of Chemistry and Physics, 76th Edition, CRC Press, Inc., Boca Raton, FL, ISBN 0849304768 (1995).
  • Li, J.-Q., Shen, C.-Y., Xu, G.-H., Wang, H.-M., Jiang, H.-H., Han, H.-Y., Chu, Y.-N., & Zheng, P.-C.: Dynamic measurements of Henry’s law constant of aromatic compounds using proton transfer reaction mass spectrometry, Acta Phys. Chim. Sin., 24, 705–708 (2008).
  • 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).
  • Mackay, D., Shiu, W. Y., Ma, K. C., & Lee, S. C.: Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals, vol. II of Halogenated Hydrocarbons, CRC/Taylor & Francis Group, doi:10.1201/9781420044393 (2006b).
  • 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).
  • 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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  • Schwardt, A., Dahmke, A., & Köber, R.: Henry’s law constants of volatile organic compounds between 0 and 95∘C – Data compilation and complementation in context of urban temperature increases of the subsurface, Chemosphere, 272, 129 858, doi:10.1016/J.CHEMOSPHERE.2021.129858 (2021).
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  • Warner, H. P., Cohen, J. M., & Ireland, J. C.: Determination of Henry’s law constants of selected priority pollutants, Tech. rep., U.S. EPA, Municipal Environmental Research Laboratory, Wastewater Research Division, Cincinnati, Ohio, 45268, USA (1980).
  • 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).
  • Yaws, C. L. & Yang, H.-C.: Henry’s law constant for compound in water, in: Thermodynamic and Physical Property Data, edited by Yaws, C. L., pp. 181–206, Gulf Publishing Company, Houston, TX, ISBN 0884150313 (1992).

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.
33) Fitting the temperature dependence dlnH/d(1/T) produced a low correlation coefficient (r2 < 0.9). 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.
88) Value at T = 295 K.
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.
238) Value given here as quoted by Gharagheizi et al. (2010).
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.
272) Value from the validation dataset.
275) Value from the test dataset.
279) Data are taken from the report by Howe et al. (1987).
299) Value given here as quoted by Staudinger and Roberts (1996).
300) Value from the test set for true external validation.

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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