Transcript EHS500.6.24.04.ExposureAssmt.m3

Body or Tissue Burden
Some Toxicants With Very Slow Elimination
Half-Life Estimates
Person-Specific Factors Affecting Uptake,
Distribution, Metabolism, Elimination
Physical
stress
exercise
uptake increases
blood chemistry increases/decreases
excretion rate increases
(e.g. Cr: 5 x increase with exercise)
Diet
high fat
increase in blood solubility for organic
solvents
Health
status
disease
 liver function
 kidney function
 lung function
Smoking
source of
increase in Pb, Ni, Cr, Cd
increase in CO, CN-
Alcohol
----
affects metabolism of other solvents by
competing for metabolic enzymes
Dose associated with exposure to
biological agents
• Exposures are usually of the ‘oneshot’ (acute) variety (as opposed to
‘chronic’): for example:– ingesting infected food or water
– inhaling organisms suddenly present in
ambient or workplace air
– bite from a malaria-infected mosquito
Current hot topics
• blood-borne pathogens
• drug-resistant TB
• ‘bioterrorism’
‘Yardstick’ for dose of
infectious agents
• Infectious dose (ID)
– number of microorganisms (in
the exposure) needed to
initiate infection in the
exposed subject.
Factors To Consider
• Viability and virulence of
biological agent?
• Host susceptibility?
• Aerosolization?
Example: Acute Exposure To A
Biological Agent (e.g., by
inhalation of M.Tuberculosis)
(proximity to infected person, person coughs,
bacteria released into the air  inhaled by subject)

EXPOSURE TO RISK OF TUBERCULOSIS
INSTANTANTEOUS DOSE
RAPID BIOLOGICAL RESPONSE (e.g., cellular changes)

DISEASE
T
I
M
E
Some Typical Numbers
Disease
Route
ID
Anthrax
Inhalation
1,300
Q-fever
Inhalation
10
TB
Inhalation
10
Scenario for estimating risk of tb
infection (rough!)
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TB-infected person on aircraft
Cabin volume, V m3
Ventilation air changes per hour, N hr-1
Person coughs X times per hour
Release n organisms per cough
Breathing rates of each passenger, R L/min
Infectious dose, I organisms
• Can we estimate risk of infection?
Back of the envelope . .
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Total air volume = NV m3/hr
Total organisms released = nX #/hr
Average concentration = nX/NV #/m3
Breathing rate = R x 10-3 x 60 m3/hr
Dose rate = (nX/NV)(Rx10-3x60) #/hr
Total dose = (nX/NV)(Rx10-3x60).t
organisms
• Probability of infection for each passenger
=
nXRt
PIpersonal ≡
NVI
0.06
Rough estimate . . .
N = 10 hr -1, V = 1000 m3, X = 1 hr -1,
n = 100 #/cough, R = 7 L/min, t = 7 hr, I
= 10 #
• For each individual on the plane, risk
of infection is
PIpersonal = 3 x 10-3
• Risk that someone of the plane will be
infected (200 passengers) is
PIanyone = 0.6
!!!!!
Some Common Options for Environmental Exposure
Assessment
• Direct Measurement or Observation of
Individuals or their Environment
– Measurements outside front door or in home
(Residential Radon)
– Personal dosimetry (Personal monitors for ELF-EMF
worn for 24 hours)
– Proximity to toxic waste dump, industrial source,
power lines
• Biomonitoring
– Measurement of organochlorines in blood or tissue
Use of Routinely Collected Environmental
Monitoring Data
• Assigning values to study participants
or populations
– Mean levels for city applied to all
residents
– Mean levels from closest monitoring
station
– Mean value based on spatial modeling
• Monitoring equipment and analysis
• Years of availability
• Averaging time
Advantages of Geographic Information Systems
(GIS)
• A universal coding scheme for
geographic based information
• A means to integrate data from
multiple sources
• More precise mapping and imaging
Using GIS for Wetlands Vulnerability
Exposure to Air Pollution in Stockholm*
• Postal questionnaire: all residences for 1+
years since 1955 (follow-up 1990-1995)
– 10,800 addresses geocoded
• Dispersion modeling
– traffic (traffic on roads with >1000 vehicles/24 hrs,
estimated to be 90% of total traffic emissions)
– 500 point sources (emissions from industries, power
plants, and ferries)
– Areas sources related to population density &
commercial uses
* Bellander et al. Using Geographic Information systems to Assess
Individual Historical Exposure to Air Pollution from Traffic and House
Heating in Stockholm. Env Health Persp 2002;109:633-639.
Air Pollution Modeling
Gaussian Plume Model: Plume profiles under steady-state conditions
may appear to have Gaussian distribution when averaged over time and
space.
Basic formulation
asumes:
 steady state, averaged
concentrations (1 hour)
constant winds
 vertical and crosswind
distributions are known &
Gaussian
 negligible mass
diffusion in x direction
 conservative pollutants
(no transformation)
 no deposition and
gravitational settling
Determinants of Exposure Modeling: Indoor Air
Pollution*
• 60 homes, 24 hour samples for smoke, SO2, at
least 7 successive days/home
• 800 paired indoor/outdoor
• Multiple regression, dependent: indoor levels
• Determinants of exposure:
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Year of construction
Type of heating
Smoking habits
Outdoor levels
* Biersteker et al. Indoor Air Pollution in Rotterdam Homes.
Int J Air Water Poll 1965;9:343-350.