diff --git a/cara/apps/calculator/model_generator.py b/cara/apps/calculator/model_generator.py
index dfd5c581..0d1ea2b2 100644
--- a/cara/apps/calculator/model_generator.py
+++ b/cara/apps/calculator/model_generator.py
@@ -24,7 +24,7 @@ minutes_since_midnight = typing.NewType('minutes_since_midnight', int)
 # Used to declare when an attribute of a class must have a value provided, and
 # there should be no default value used.
 _NO_DEFAULT = object()
-_DEFAULT_MC_SAMPLE_SIZE = 50000
+_DEFAULT_MC_SAMPLE_SIZE = 250000
 
 
 @dataclasses.dataclass
diff --git a/cara/models.py b/cara/models.py
index b91e2407..7b5d034d 100644
--- a/cara/models.py
+++ b/cara/models.py
@@ -580,7 +580,7 @@ class MultipleExpiration(_ExpirationBase):
     Group together different modes of expiration, that represent
     each the main expiration mode for a certain fraction of time (given by
     the weights).
-    Obsolet class that can only be used with single diameters (it cannot
+    This class can only be used with single diameters (it cannot
     be used with diameter distributions).
     """
     expirations: typing.Tuple[_ExpirationBase, ...]
@@ -900,6 +900,13 @@ class ConcentrationModel:
 
 @dataclass(frozen=True)
 class ExposureModel:
+    """
+    Represents the exposure to a concentration of virions in the air.
+    NOTE: the infection probability formula assumes that if the diameter
+    is an array, then none of the ventilation parameters, room volume or virus
+    decay constant, are arrays as well.
+    TODO: implement a check this is the case, in __post_init__
+    """
     #: The virus concentration model which this exposure model should consider.
     concentration_model: ConcentrationModel
 
@@ -932,7 +939,12 @@ class ExposureModel:
         corresponds to an integration on diameters).
         """
         emission_rate = self.concentration_model.infected.emission_rate_when_present()
-        if np.isscalar(self.concentration_model.infected.expiration.diameter):
+        if (not isinstance(self.concentration_model.infected,InfectedPopulation)
+            or not isinstance(self.concentration_model.infected.expiration,Expiration)
+            or np.isscalar(self.concentration_model.infected.expiration.diameter)
+            ):
+            # in all these cases, there is no distribution of
+            # diameters that need to be integrated over
             return self._normed_exposure() * emission_rate
         else:
             # the mean of the diameter-dependent exposure (including
diff --git a/cara/tests/apps/calculator/test_model_generator.py b/cara/tests/apps/calculator/test_model_generator.py
index 2c61c63e..a01cd3f1 100644
--- a/cara/tests/apps/calculator/test_model_generator.py
+++ b/cara/tests/apps/calculator/test_model_generator.py
@@ -12,10 +12,9 @@ from cara import models
 from cara.monte_carlo.data import expiration_distributions
 
 # TODO: seed better the random number generators
-# this is only for test_blend_expiration
 np.random.seed(2000)
-SAMPLE_SIZE = 500000
-TOLERANCE = 0.01
+SAMPLE_SIZE = 250000
+TOLERANCE = 0.02
 
 def test_model_from_dict(baseline_form_data):
     form = model_generator.FormData.from_dict(baseline_form_data)
diff --git a/cara/tests/test_monte_carlo_full_models.py b/cara/tests/test_monte_carlo_full_models.py
index fbf9f105..b13ea0c7 100644
--- a/cara/tests/test_monte_carlo_full_models.py
+++ b/cara/tests/test_monte_carlo_full_models.py
@@ -5,10 +5,12 @@ import pytest
 import cara.monte_carlo as mc
 from cara import models,data
 from cara.monte_carlo.data import activity_distributions, virus_distributions
+from cara.monte_carlo.data import expiration_distribution, expiration_distributions
+from cara.apps.calculator.model_generator import build_expiration
 
 # TODO: seed better the random number generators
 np.random.seed(2000)
-SAMPLE_SIZE = 50000
+SAMPLE_SIZE = 250000
 TOLERANCE = 0.05
 
 # references values for infection_probability and expected new cases
@@ -42,10 +44,7 @@ def shared_office_mc():
             presence=mc.SpecificInterval(((0., 2.), (2.1, 4.), (5., 7.), (7.1, 9.))),
             mask=models.Mask(η_inhale=0.3),
             activity=activity_distributions['Seated'],
-            expiration=models.MultipleExpiration(
-                    expirations=(models.Expiration.types['Talking'],
-                                 models.Expiration.types['Breathing']),
-                    weights=(0.3, 0.7)),
+            expiration=build_expiration({'Talking': 0.3, 'Breathing': 0.7}),
         ),
     )
     return mc.ExposureModel(
@@ -86,7 +85,7 @@ def classroom_mc():
             presence=mc.SpecificInterval(((0., 2.), (2.5, 4.), (5., 7.), (7.5, 9.))),
             mask=models.Mask.types['No mask'],
             activity=activity_distributions['Light activity'],
-            expiration=models.Expiration.types['Talking'],
+            expiration=expiration_distributions['Talking'],
         ),
     )
     return mc.ExposureModel(
@@ -117,7 +116,7 @@ def ski_cabin_mc():
             presence=mc.SpecificInterval(((0., 1/3),)),
             mask=models.Mask(η_inhale=0.3),
             activity=activity_distributions['Moderate activity'],
-            expiration=models.Expiration.types['Talking'],
+            expiration=expiration_distributions['Talking'],
         ),
     )
     return mc.ExposureModel(
@@ -150,7 +149,7 @@ def gym_mc():
             presence=mc.SpecificInterval(((0., 1.),)),
             mask=models.Mask.types["No mask"],
             activity=activity_distributions['Heavy exercise'],
-            expiration=models.Expiration.types['Breathing'],
+            expiration=expiration_distributions['Breathing'],
         ),
     )
     return mc.ExposureModel(
@@ -182,10 +181,7 @@ def waiting_room_mc():
             presence=mc.SpecificInterval(((0., 2.),)),
             mask=models.Mask.types["No mask"],
             activity=activity_distributions['Seated'],
-            expiration=models.MultipleExpiration(
-                    expirations=(models.Expiration.types['Talking'],
-                                 models.Expiration.types['Breathing']),
-                    weights=(0.3, 0.7)),
+            expiration=build_expiration({'Talking': 0.3, 'Breathing': 0.7})
         ),
     )
     return mc.ExposureModel(
@@ -218,11 +214,7 @@ def skagit_chorale_mc():
             presence=mc.SpecificInterval(((0., 2.5),)),
             mask=models.Mask.types["No mask"],
             activity=activity_distributions['Light activity'],
-            expiration=models.Expiration(10.0761),
-            # The aerosol diameter given (10.0761 microns) is an equivalent
-            # diameter, chosen in such a way that the aerosol volume is
-            # the same as the total aerosol volume given by the full BLO model
-            # with (5, 5, 5) for the B/L/O weights.
+            expiration=expiration_distribution((5., 5., 5.)),
         ),
     )
     return mc.ExposureModel(
@@ -295,10 +287,7 @@ def test_small_shared_office_Geneva(mask_type, month, expected_pi,
             presence=mc.SpecificInterval(((9., 10+2/3), (10+5/6, 12.5), (13.5, 15+2/3), (15+5/6, 18.))),
             mask=models.Mask.types[mask_type],
             activity=activity_distributions['Seated'],
-            expiration=models.MultipleExpiration(
-                    expirations=(models.Expiration.types['Talking'],
-                                 models.Expiration.types['Breathing']),
-                    weights=(0.33, 0.67)),
+            expiration=build_expiration({'Talking': 0.33, 'Breathing': 0.67}),
         ),
     )
     exposure_mc = mc.ExposureModel(