diff --git a/caimira/docs/full_diameter_dependence.rst b/caimira/docs/full_diameter_dependence.rst
index 626f21c2..1e684ebb 100644
--- a/caimira/docs/full_diameter_dependence.rst
+++ b/caimira/docs/full_diameter_dependence.rst
@@ -314,8 +314,8 @@ CO\ :sub:`2` Concentration
 The estimate of the concentration of CO\ :sub:`2` in a given room to indicate the air quality is given by the same approach as for the long-range virus concentration, 
 :math:`C_{\mathrm{LR}}(t, D)`, where :math:`C_0(D)` is considered to be the background (outdoor) CO\ :sub:`2` concentration (:meth:`caimira.models.CO2ConcentrationModel.CO2_atmosphere_concentration`).
 
-Note that in order to calculate the CO\ :sub:`2` concentration one should use the concentration method defined in the superclass - :meth:`caimira.models._ConcentrationModelBase.concentration` - for a dedicated :class:`caimira.models.CO2ConcentrationModel` scenario.
-A fraction of 4.2% of the exhalation rate of the defined activity was considered as the  supplied to the room (:meth:`caimira.models.CO2ConcentrationModel.CO2_fraction_exhaled`).
+In order to compute the CO\ :sub:`2` concentration one should then simply use the :meth:`caimira.models.CO2ConcentrationModel.concentration` method.
+A fraction of 4.2% of the exhalation rate of the defined activity was considered as supplied to the room (:meth:`caimira.models.CO2ConcentrationModel.CO2_fraction_exhaled`).
 
 Note still that nothing depends on the aerosol diameter :math:`D` in this case (no particles are involved) - hence in this class all parameters are constant w.r.t :math:`D`.
 
diff --git a/caimira/tests/models/test_concentration_model.py b/caimira/tests/models/test_concentration_model.py
index 17228588..568e6757 100644
--- a/caimira/tests/models/test_concentration_model.py
+++ b/caimira/tests/models/test_concentration_model.py
@@ -179,6 +179,8 @@ def test_integrated_concentration(simple_conc_model):
     npt.assert_almost_equal(c1, c2 + c3, decimal=15)
 
 
+# The expected numbers were obtained via the quad integration of the 
+# normed_integrated_concentration method with 0 (start) and 2 (stop) as limits.
 @pytest.mark.parametrize([
     "known_min_background_concentration", 
     "expected_normed_integrated_concentration"], 
@@ -206,6 +208,8 @@ def test_normed_integrated_concentration_with_background_concentration(
     npt.assert_almost_equal(known_conc_model.normed_integrated_concentration(0, 2), expected_normed_integrated_concentration)
 
 
+# The expected numbers were obtained via the quad integration of the 
+# normed_integrated_concentration method with 0 (start) and 2 (stop) as limits.
 @pytest.mark.parametrize([
     "known_removal_rate",
     "known_min_background_concentration",