diff --git a/cara/apps/calculator/model_generator.py b/cara/apps/calculator/model_generator.py
index a6a41fab..06c29229 100644
--- a/cara/apps/calculator/model_generator.py
+++ b/cara/apps/calculator/model_generator.py
@@ -11,7 +11,7 @@ from cara import data
 import cara.data.weather
 import cara.monte_carlo as mc
 from .. import calculator
-from cara.monte_carlo.data import activity_distributions, virus_distributions
+from cara.monte_carlo.data import activity_distributions, virus_distributions, mask_distributions
 
 
 LOG = logging.getLogger(__name__)
@@ -359,7 +359,10 @@ class FormData:
     def mask(self) -> models.Mask:
         # Initializes the mask type if mask wearing is "continuous", otherwise instantiates the mask attribute as
         # the "No mask"-mask
-        mask = models.Mask.types[self.mask_type if self.mask_wearing_option == "mask_on" else 'No mask']
+        if self.mask_wearing_option == 'mask_on':
+            mask = mask_distributions[self.mask_type]
+        else:
+            mask = models.Mask.types['No mask']
         return mask
 
     def infected_population(self) -> mc.InfectedPopulation:
diff --git a/cara/apps/templates/common_text.md.j2 b/cara/apps/templates/common_text.md.j2
index 63512dee..ac8829d7 100644
--- a/cara/apps/templates/common_text.md.j2
+++ b/cara/apps/templates/common_text.md.j2
@@ -71,3 +71,6 @@ We wish to thank CERN’s HSE Unit, Beams Department, Experimental Physics Depar
 [54] Leung, N.H.L et al. Respiratory virus shedding in exhaled breath and efficacy of face masks. Nat Med (2020). 10.1038/s41591-020-0843-2.<br>
 [55] Asadi, S., Cappa, C.D., Barreda, S. et al. Efficacy of masks and face coverings in controlling outward aerosol particle emission from expiratory activities. Sci Rep 10, 15665 (2020). https://doi.org/10.1038/s41598-020-72798-7.<br>
 [56] Endo A, Abbott S et al. Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China [version 3; peer review: 2 approved]. Wellcome Open Res 2020, 5:67. doi:10.12688/wellcomeopenres.15842.3.<br>
+[57] Jin Pan, Charbel Harb, Weinan Leng & Linsey C. Marr (2021) Inward and outward effectiveness of cloth masks, a surgical mask, and a face shield, Aerosol Science and Technology, 55:6, 718-733, doi: 10.1080/02786826.2021.1890687.<br>
+[58] C. Makison Booth, M. Clayton, B. Crook, J.M. Gawn, Effectiveness of surgical masks against influenza bioaerosols, Journal of Hospital Infection, Volume 84, Issue 1, 2013, Pages 22-26, https://doi.org/10.1016/j.jhin.2013.02.007.<br>
+[59] Riediker, M., Monn, C. (2021). Simulation of SARS-CoV-2 Aerosol Emissions in the Infected Population and Resulting Airborne Exposures in Different Indoor Scenarios. Aerosol Air Qual. Res. 21, 200531. https://doi.org/10.4209/aaqr.2020.08.0531.<br>
\ No newline at end of file
diff --git a/cara/monte_carlo/data.py b/cara/monte_carlo/data.py
index 613b830c..4415efbb 100644
--- a/cara/monte_carlo/data.py
+++ b/cara/monte_carlo/data.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 import cara.monte_carlo as mc
-from cara.monte_carlo.sampleable import Normal,LogNormal,LogCustomKernel
+from cara.monte_carlo.sampleable import Normal,LogNormal,LogCustomKernel, Uniform
 
 
 # From CERN-OPEN-2021-04 and refererences therein
@@ -58,3 +58,13 @@ virus_distributions = {
                 infectious_dose=60/1.6,
                 ),
 }
+
+
+# From:
+# https://doi.org/10.1080/02786826.2021.1890687
+# https://doi.org/10.1016/j.jhin.2013.02.007
+# https://doi.org/10.4209/aaqr.2020.08.0531
+mask_distributions = {
+    'Type I': mc.Mask(Uniform(0.25, 0.80)),
+    'FFP2': mc.Mask(Uniform(0.83, 0.91)),
+}
\ No newline at end of file
diff --git a/cara/monte_carlo/sampleable.py b/cara/monte_carlo/sampleable.py
index 4333c93f..27907e49 100644
--- a/cara/monte_carlo/sampleable.py
+++ b/cara/monte_carlo/sampleable.py
@@ -28,6 +28,18 @@ class Normal(SampleableDistribution):
         return np.random.normal(self.mean, self.standard_deviation, size=size)
 
 
+class Uniform(SampleableDistribution):
+    """
+    Defines a continuous uniform distribution
+    """
+    def __init__(self, low: float, high: float):
+        self.low = low
+        self.high = high
+
+    def generate_samples(self, size: int) -> float_array_size_n:
+        return np.random.uniform(self.low, self.high, size=size)
+
+
 class LogNormal(SampleableDistribution):
     """
     Defines a lognormal distribution (i.e. Gaussian distribution vs. the