:py:mod:`gnome.utilities.weathering.lee_huibers`
================================================

.. py:module:: gnome.utilities.weathering.lee_huibers


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   gnome.utilities.weathering.lee_huibers.Toluene
   gnome.utilities.weathering.lee_huibers.LeeHuibers




.. py:class:: Toluene


   Bases: :py:obj:`object`

   The measured values of the known aromatic, toluene

   .. py:attribute:: mol_wt
      :value: 92.1

      

   .. py:attribute:: density
      :value: 866.0

      

   .. py:attribute:: k_ow
      :value: 1000.0

      


.. py:class:: LeeHuibers


   Bases: :py:obj:`object`

   The combination of correlations by Huibers and Katritzky (1998)
   and Lee et al (1992) to estimate the correlation between
   a specific aromatic hydrocarbon's density and molecular weight
   with its partition coefficient.

       rho_arom = density of aromatic

       mol_wt = molecular weight

       s = solubility

       s = Huibers(rho_arom, mol_wt)

       k_ow = Lee(s)

            = Lee(Huibers(rho_arom, mol_wt))

   We calibrate an empiric coefficient A with the measured values of
   Toluene.

   .. py:attribute:: A

      

   .. py:method:: partition_coeff(mol_wt, density)
      :classmethod:

      :param mol_wt: Molecular weight in kg/kmole
      :param density: Density in kg/m^3