diff --git a/caimira/docs/full_diameter_dependence.rst b/caimira/docs/full_diameter_dependence.rst
index 90b10d96..9abb73c6 100644
--- a/caimira/docs/full_diameter_dependence.rst
+++ b/caimira/docs/full_diameter_dependence.rst
@@ -78,8 +78,8 @@ The estimate of the concentration of virus-laden particles in a given room is ba
 * **Box 2** - short-range exposure: also known as the *exhaled jet* concentration in close-proximity, corresponds to the exposure of airborne virions where the susceptible (exposed) host is distanced between 0.5 and 2 m from an infected host, considering the result of a two-stage exhaled jet model.
 
 Note that most of the methods used to calculate the concentration are defined in the superclass :meth:`caimira.models._ConcentrationModelBase`, while the specific methods for the long-range virus concentration are part of the subclass :meth:`caimira.models.ConcentrationModel`.
-The specific removal rate, atmospheric concentration and normalization factors will depend on what concentration is being calculated (e.g. viral concentration or CO2 concentration) and are respectively defined in :meth:`caimira.models._ConcentrationModelBase.removal_rate`, 
-:meth:`caimira.models._ConcentrationModelBase.atmosphere_concentration` and :meth:`caimira.models._ConcentrationModelBase.normalization_factor`. 
+The specific removal rate, background concentration and normalization factors will depend on what concentration is being calculated (e.g. viral concentration or CO\ :sub:`2` concentration) and are respectively defined in :meth:`caimira.models._ConcentrationModelBase.removal_rate`, 
+:meth:`caimira.models._ConcentrationModelBase.background_concentration` and :meth:`caimira.models._ConcentrationModelBase.normalization_factor`. 
 
 Long-range approach
 *******************
@@ -317,8 +317,8 @@ The estimate of the concentration of CO\ :sub:`2` in a given room to indicate th
 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`).
 
-Since the CO\ :sub:`2` concentration differs from the virus concentration, the specific removal rate, atmospheric concentration and normalization factors are respectively defined in :meth:`caimira.models.CO2ConcentrationModel.removal_rate`, 
-:meth:`caimira.models.CO2ConcentrationModel.atmosphere_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. 
+Since the CO\ :sub:`2` concentration differs from the virus concentration, the specific removal rate, CO\ :sub:`2` atmospheric concentration and normalization factors are respectively defined in :meth:`caimira.models.CO2ConcentrationModel.removal_rate`, 
+:meth:`caimira.models.CO2ConcentrationModel.background_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. 
 
 .. _caimira-uml-diagram:
 
diff --git a/caimira/models.py b/caimira/models.py
index b6875d53..e869b7a3 100644
--- a/caimira/models.py
+++ b/caimira/models.py
@@ -957,6 +957,10 @@ class Cases:
 
 @dataclass(frozen=True)
 class _ConcentrationModelBase:
+    """
+    A generic superclass that contains the methods to calculate the
+    concentration (e.g. viral concentration or CO2 concentration).
+    """
     room: Room
     ventilation: _VentilationBase
 
@@ -974,9 +978,9 @@ class _ConcentrationModelBase:
         """
         raise NotImplementedError("Subclass must implement")
 
-    def atmosphere_concentration(self) -> _VectorisedFloat:
+    def background_concentration(self) -> _VectorisedFloat:
         """
-        Background concentration in the atmosphere
+        Background concentration in the scenario
         (in the same unit as the concentration)
         """
         return 0.
@@ -1001,11 +1005,11 @@ class _ConcentrationModelBase:
         dependence has been solved for.
         """
         if not self.population.person_present(time):
-            return self.atmosphere_concentration()/self.normalization_factor()
+            return self.background_concentration()/self.normalization_factor()
         V = self.room.volume
         RR = self.removal_rate(time)
 
-        return (1. / (RR * V) + self.atmosphere_concentration()/
+        return (1. / (RR * V) + self.background_concentration()/
                 self.normalization_factor())
 
     @method_cache
@@ -1077,7 +1081,7 @@ class _ConcentrationModelBase:
         # The model always starts at t=0, but we avoid running concentration calculations
         # before the first presence as an optimisation.
         if time <= self._first_presence_time():
-            return self.atmosphere_concentration()/self.normalization_factor()
+            return self.background_concentration()/self.normalization_factor()
         next_state_change_time = self._next_state_change(time)
         RR = self.removal_rate(next_state_change_time)
         conc_limit = self._normed_concentration_limit(next_state_change_time)
@@ -1107,7 +1111,7 @@ class _ConcentrationModelBase:
         normalized by normalization_factor.
         """
         if stop <= self._first_presence_time():
-            return (stop - start)*self.atmosphere_concentration()/self.normalization_factor()
+            return (stop - start)*self.background_concentration()/self.normalization_factor()
         state_change_times = self.state_change_times()
         req_start, req_stop = start, stop
         total_normed_concentration = 0.
@@ -1202,7 +1206,7 @@ class CO2ConcentrationModel(_ConcentrationModelBase):
     def removal_rate(self, time: float) -> _VectorisedFloat:
         return self.ventilation.air_exchange(self.room, time)
 
-    def atmosphere_concentration(self) -> _VectorisedFloat:
+    def background_concentration(self) -> _VectorisedFloat:
         """
         Background CO2 concentration in the atmosphere (in ppm)
         """
diff --git a/caimira/tests/models/test_concentration_model.py b/caimira/tests/models/test_concentration_model.py
index 0a22bca2..7f3d5052 100644
--- a/caimira/tests/models/test_concentration_model.py
+++ b/caimira/tests/models/test_concentration_model.py
@@ -10,7 +10,7 @@ from caimira import models
 @dataclass(frozen=True)
 class KnownConcentrationModelBase(models._ConcentrationModelBase):
     """
-    A _ConcentrationModelBase where all the class methods are
+    A _ConcentrationModelBase class where all the class methods are
     redefined with a value taken from new parameters. Useful for testing.
 
     """
@@ -18,7 +18,7 @@ class KnownConcentrationModelBase(models._ConcentrationModelBase):
 
     known_removal_rate: float
 
-    known_atmosphere_concentration: float
+    known_background_concentration: float
 
     known_normalization_factor: float
 
@@ -29,8 +29,8 @@ class KnownConcentrationModelBase(models._ConcentrationModelBase):
     def removal_rate(self, time: float) -> float:
         return self.known_removal_rate
 
-    def atmosphere_concentration(self) -> float:
-        return self.known_atmosphere_concentration
+    def background_concentration(self) -> float:
+        return self.known_background_concentration
 
     def normalization_factor(self) -> float:
         return self.known_normalization_factor
@@ -180,7 +180,7 @@ def test_integrated_concentration(simple_conc_model):
 
 
 @pytest.mark.parametrize([
-    "known_atmosphere_concentration", 
+    "known_background_concentration", 
     "expected_normed_integrated_concentration"], 
     [
         [0.0, 0.00018533333708996207],
@@ -190,10 +190,10 @@ def test_integrated_concentration(simple_conc_model):
         [1000., 200.0001853407918],
     ]
 )
-def test_normed_integrated_concentration_with_atmosphere_concentration(
+def test_normed_integrated_concentration_with_background_concentration(
     simple_conc_model: models.ConcentrationModel,
     dummy_population: models.Population,
-    known_atmosphere_concentration: float, 
+    known_background_concentration: float, 
     expected_normed_integrated_concentration: float):
 
     known_conc_model = KnownConcentrationModelBase(
@@ -201,14 +201,14 @@ def test_normed_integrated_concentration_with_atmosphere_concentration(
         ventilation = simple_conc_model.ventilation, 
         known_population = dummy_population,
         known_removal_rate = 100.,
-        known_atmosphere_concentration = known_atmosphere_concentration,
+        known_background_concentration = known_background_concentration,
         known_normalization_factor = 10.)    
     npt.assert_almost_equal(known_conc_model.normed_integrated_concentration(0, 2), expected_normed_integrated_concentration)
 
 
 @pytest.mark.parametrize([
     "known_removal_rate",
-    "known_atmosphere_concentration", 
+    "known_background_concentration", 
     "known_normalization_factor",
     "expected_normed_integrated_concentration"], 
     [
@@ -223,7 +223,7 @@ def test_normed_integrated_concentration_vectorisation(
     simple_conc_model: models.ConcentrationModel,
     dummy_population: models.Population,
     known_removal_rate: float,
-    known_atmosphere_concentration: float, 
+    known_background_concentration: float, 
     known_normalization_factor: float,
     expected_normed_integrated_concentration: float):
 
@@ -232,7 +232,7 @@ def test_normed_integrated_concentration_vectorisation(
         ventilation = simple_conc_model.ventilation, 
         known_population = dummy_population,
         known_removal_rate = known_removal_rate,
-        known_atmosphere_concentration = known_atmosphere_concentration,
+        known_background_concentration = known_background_concentration,
         known_normalization_factor = known_normalization_factor)
     
     integrated_concentration = known_conc_model.normed_integrated_concentration(0, 2)