Breathing Zone Exposure Assessment - Aerosols: Measurement, Dosimetry, and Health Effects - page 43

(2001) describes many of the commonly used aerosol samplers for both ixed-location and personal
exposures. Rodes and Wiener (2001) also summarize personal aerosol monitors, including two min-
iature nephelometers.
Personal-level BZ exposures can now be considered on both acute (real-time sensing) and
chronic (integrated ilter collection) bases for sized aerosol. Advances in the miniaturization
of nephelometric particle sensors now allow real-time exposure data at the personal level (e.g.,
Delino et al., 2008; Adams et al., 2009). These BZ systems are currently fairly burdensome,
weighing at least several kilograms with batteries, but may become much less burdensome if the
research program described by Schmidt (2006) is successful. Without such burden level (weight,
size, and noise level) reductions, the likelihood of widespread use of these approaches in residen-
tial settings is limited.

2.4.3 e stiMation oF P otential e xPosures
McNabola et al. (2008) suggest that estimated potential doses of ine particles from BZ exposures
to commuters is likely to present a different picture than that built solely upon concentration data.
It is certainly reasonable that concentration adjustments for the levels of toxicants reaching into
the respiratory system should provide stronger associations with biological uptake and any resul-
tant adverse health outcomes. While McNabola et al. used models to estimate the potential doses,
technologies are now available that should allow such estimates to be made by the monitoring
system. Personal exposure characterizations provide data in concentration units (e.g., for sized par-
ticles, μg/m 3 ) over the integration period of interest. Personal, real-time monitoring provides either
instantaneous concentrations or data integrated over short times (minutes), but still in concentration
units. The tiny size of current technology accelerometer chips have now allowed them to be worn
as personal-level sensors for prediction of metabolic functions (e.g., Rosenberger et al., 2008). This
provides the potential for collecting estimates of ventilation (breathing) rate that can be combined
with the exposure data to estimate the potential dose reaching target respiratory system locations.
This would be accomplished by irst relating the accelerometric response to estimated ventilation
rate in a hopefully linear regression manner (see Equation 2.1) and then computing the potential
dose from the measured concentrations (see Equation 2.2) by multiplying the integrated exposure
concentrations over time t, by the estimated ventilation rate during the same time interval, and then
dividing this quantity by the weight of the person being monitored.

VR m AC b
x

= × + (2.1)

y

where
VR is the ventilation rate
m x is the regression slope
b y is the regression intercept

The estimated ventilation rate can then be multiplied by the concentration and normalized by the
body weight:

VR
BW

D C = × (2.2)

where D [mass/time/body weight] = {C [aerosol mass/volume × VR [volume/time]}/BW [body mass]
Equation 2.2 can alternatively be stated as follows: dose [μg/min/kg] = {concentration [μg/m 3 ] ×
ventilation rate [l/min]}/body weight [kg]. At rest, the dose can be computed, utilizing a ixed