def speaking(T,RH):
NaCl_con = 80.0/1000; # 80.0 mmol/L converted to mol/L for saliva, from Kallapur et al.
sizeclass = [3,6,12,20,28,36,45,62.5,87.5,112.5,137.5,175,225,375,750] # in um
numcon = [4.59,66.21,22.23,11.33,7.87,4.32,3.37,4.57,3.44,4.52,4.31,4.52,3.85,3.45,1.11] # in cm^-3
#sizeclass = [12,20,28,36,45,62.5,87.5,112.5,137.5,175,225,375,750,1500] # in um
#numcon = [22.23,11.33,7.87,4.32,3.37,4.57,3.44,4.52,4.31,4.52,3.85,3.45,1.11,0.00] # in cm^-3
t_settle = []
sizepeak = 6
for binnum in range(15):
size = sizeclass[binnum]
#eqsize = 2*10**6 * settle.kohler(T, RH, size, NaCl_con)
count = numcon[binnum]
settime = settle.settling_time(T,RH,size,NaCl_con,1.5,model='sc')
#if eqsize > 10:
# settime = settle.settling_time(T,RH,size,NaCl_con,1.5,model='empirical_big')
#else:
# settime = settle.settling_time(T,RH,size,NaCl_con,1.5,model='empirical_small')
#print(eqsize,settime)
t_settle.append(settime)
t_peak = settle.settling_time(T,RH,sizepeak,NaCl_con,1.5,model='sc')
#eqsizepeak = 2*10**6 * settle.kohler(T, RH, sizepeak, NaCl_con)
#if eqsizepeak > 10:
#t_peak = settle.settling_time(T,RH,sizepeak,NaCl_con,1.5,model='empirical_big')
#else:
#t_peak = settle.settling_time(T,RH,sizepeak,NaCl_con,1.5,model='empirical_small')
return(sizeclass,numcon,t_settle,sizepeak,t_peak)
def riskfactor2(T, RH, mode):
mode_to_test = mode
risk_fac = 0
if mode_to_test == 'speaking':
[sizeclass, numcon, t_settle, sizepeak,
t_peak] = module_initsize.speaking(T, RH)
NaCl_con = 80.0 / 1000
# 80.0 mmol/L converted to mol/L for saliva, from Kallapur et al.
elif mode_to_test == 'coughing':
[sizeclass, numcon, t_settle, sizepeak,
t_peak] = module_initsize.coughing(T, RH)
NaCl_con = 91.0 / 1000
# Didn't find any data on dry cough droplet sodium level, here I assume it's close to mouth breathing?
elif mode_to_test == 'breathing':
[sizeclass, numcon, t_settle, sizepeak,
t_peak] = module_initsize.breathing(T, RH)
viralload = 10**8 # viral particles per mL saliva
for binnum in range(len(sizeclass)):
size = sizeclass[binnum]
count = numcon[binnum]
dropvol = 4 / 3 * np.pi * (
(size / 2)**3) * (10**(-12)) # droplet volume converted to mL
poi_lambda = viralload * dropvol # in viral particles per droplet
decay_rate = viability.kdecay(T, RH, 1.9)
if size > 10:
settling_time = settle.settling_time(T,
RH,
size,
NaCl_con,
1.5,
model='empirical_big')
else:
settling_time = settle.settling_time(T,
RH,
size,
NaCl_con,
1.5,
model='empirical_small')
viable_numcon_10min = count * poi_lambda * np.exp(-decay_rate *
(1 / 6) * 60)
risk_10min = viable_numcon_10min * (np.maximum(
1 - ((1 / 6) / settling_time), 0)**2)
risk_fac = risk_fac + risk_10min # sum up the risk factors for a total risk at this T and RH condition
return risk_fac
def coughing(T,RH):
NaCl_con = 91.0/1000; # Didn't find any data on dry cough droplet sodium level, here I assume it's close to mouth breathing?
sizeclass = [3,6,12,20,28,36,45,62.5,87.5,112.5,137.5,175,225,375,750,1500] # in um
numcon = [86,1187,444,144,54,50,41,43,30,36,34,93,53,44,30,0] # in cm^-3
t_settle = []
sizepeak = 6
for binnum in range(16):
size = sizeclass[binnum]
count = numcon[binnum]
if size > 10:
settling_time = settle.settling_time(T,RH,size,NaCl_con,1.5,model='empirical_big')
else:
settling_time = settle.settling_time(T,RH,size,NaCl_con,1.5,model='empirical_small')
t_settle.append(settling_time)
t_peak = settle.settling_time(T,RH,sizepeak,NaCl_con,1.5,model='empirical_big')
return(sizeclass,numcon,t_settle,sizepeak,t_peak)
import settle
# input T[C], RH[%], d0[um], cNaCl[mol/L], fall_height[m], model='sc'
# default model selection of stokes cunningham
# output settling time [hr]
t_stl = settle.settling_time(25, 90, 10, 0.08, 1.5, 'sc')
print(t_stl)
print(type(t_stl))
# model parameter takes other arguments
t_stl = settle.settling_time(25, 90, 10, 0.08, 1.5, 'epstein')
print(t_stl)
print(type(t_stl))
# get Knudsen number of air
for T in [273, 278, 283, 288, 293, 298, 303, 308, 313]:
print(settle.Kn_atm_air(T))
import settle
import viability
import weather
import matplotlib.pyplot as plt
# In[15]:
# Weather data for LA downtown in Febuary
filename = 'LADTweather.csv' # Input the .csv data file here
[tempC, RH] = weather.weatherdataprocess(filename)
t_settle = []
for day in range(len(tempC)):
daily_T = tempC[day]
daily_RH = RH[day]
t = settle.settling_time(daily_T, daily_RH, 10, 0.05, 1.5)
t_settle.append(t)
# In[16]:
plt.plot(t_settle)
plt.ylabel('Daily Droplet Settling Time in hr')
plt.show()
weather.weatherdatapplot(tempC, RH)
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