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Biological monitoring of exposure to woodsmoke

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Wildland firefighter exposure study. Conclusions and Future prospects. Exposure monitoring issues ... worked by 13 firefighters. Part of dataset collected ... – PowerPoint PPT presentation

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Title: Biological monitoring of exposure to woodsmoke


1
Biological monitoring of exposure to woodsmoke
  • Christopher Simpson, Ph.D.
  • Department of Environmental and Occupational
    Health Sciences
  • University of Washington, Seattle

For presentation at the Georgia Air Quality and
Climate Summit May 7, 2008
2
Outline
  • Rationale for methoxyphenols as a biomarker of
    woodsmoke exposure
  • Biomonitoring of woodsmoke exposure
  • Managed exposure study
  • Wildland firefighter exposure study
  • Conclusions and Future prospects

3
Exposure monitoring issues
  • Biomass smoke exhibits significant spatial and
    temporal variability
  • Central monitoring may be a poor surrogate for
    personal exposure
  • Traditional personal exposure monitoring (pumps
    and filters) may be too expensive, or impractical
    for some populations
  • A biomarker approach may provide a better measure
    of personal exposure than traditional monitors.

4
Selected markers for biomass combustion
Relative proportions of MPs, vary depending on
type of wood

Levoglucosan
O
O
O
O
O
5
Methoxyphenols as biomarkers of woodsmoke
  • Unique to woodsmoke
  • Derived from lignin pyrolysis
  • Abundant in woodsmoke
  • 2.5 relative to PM, 2500 mg/kg
  • Readily excreted in urine
  • minimal phase 1 metabolism for LMWT compounds
  • Rapid urinary elimination (t1/2 2-6 hr)

6
I. Campfire exposures
7
Study design
  • Nine healthy subjects
  • 2 hour managed exposure to mixed hardwood and
    softwood smoke
  • Personal monitoring of integrated PM2.5, LG, MPs
    (filter samples)
  • Real-time monitoring of PM and CO on one subject
  • Collect serial urine samples for 72 hours
    centered on exposure
  • Dietary restrictions imposed

8
I. Campfire exposures
2 hr TWA values
9
Excretion rates for syringol and guaiacol
syringol
guaiacol
10
Dose-response for methoxyphenol biomarker
Biomarker is sum of 12-hr average creatinine
adjusted urinary concentration for 5
methoxyphenols that showed maximum response to
woodsmoke exposure
11
Conclusions from managed exposure study
  • Urinary concentrations of multiple syringyls and
    guaiacols increased after acute (2hr) exposure to
    woodsmoke.
  • T1/2 for urinary excretion 2-6 hrs
  • Biomarker levels increased proportionately with
    exposure
  • exposure to LG explained 80 of variability in
    urinary biomarker
  • Threshold to detect exposure event 600 ?g/m3

12
III. Wildland firefighter study
13
Study data
  • 20 shifts worked by 13 firefighters
  • Part of dataset collected by UGA, CDC
  • Chosen to cover range of PM2.5 exposures
  • Personal TWA levels of CO, PM2.5, LG
  • CO measured via datalogging monitor
  • PM2.5, LG measured from single filter
  • Qxr re smoked/grilled foods, smoking
  • Pre- /post-shift urinary measures

14
PM2.5, CO, and LG correlations
Spearman rho 0.002 p 0.99
Pearson r 0.077 p 0.0006
Spearman rho -0.27 p 0.41
Full-shift exposure data only (n11)
Pearson correlations for LG and CO Spearman for
PM
15
Significant creatinine-adjusted urinary MP
correlations
  • Four guaiacol-type MPs
  • Guaiacol, methylguaiacol, ethylguaiacol and
    propylguaiacol (Pearson r gt0.6, plt0.01)
  • Three syringol-type MPs
  • Syringol, methylsyringol, and ethylsyringol
    (Pearson r gt0.6, plt0.01)
  • Levels for these MPs combined into summed
    guaiacol and syringol variables
  • For summed variables only, ND values assigned
    method LOD/2 and used

16
CO vs. change in creatinine-adjusted summed
guaiacols
17
Conclusions exposure measurements
  • LG and PM2.5 significantly correlated
  • LG and CO significantly correlated
  • PM2.5 and CO not correlated
  • Literature generally shows strong correlation
    between PM2.5 and CO for firefighters
  • Lack of correlation in our study possibly due to
    small sample size

18
Conclusions urinary MPs vs. exposures
  • Cross-shift urinary MPs
  • Significant changes in 14 of 22 urinary MPs
  • Exposures. vs. MPs
  • Individual and summed creatinine-adjusted
    guaiacols highly associated with CO levels
  • (softwoods predominant tree species in this
    forest)
  • Smaller association with LG none with PM2.5
  • In regression models, LG and CO exposures explain
    up to 80 the variance in urinary MP
    concentrations

19
Overall evaluation of urinary methoxyphenols as
biomarkers of woodsmoke exposure
  • Urinary MPs were associated with woodsmoke
    exposures in 3 studies where exposure to
    woodsmoke were high
  • They were not associated with low woodsmoke
    exposures in Seattle!
  • Dietary confounding and baseline variability
    limit application of this biomarker to high
    exposure situations
  • Questionnaires useful to identify confounding
  • In acute exposure situations calculate changes in
    biomarker levels to reduce importance of baseline
    variability

20
Woodsmoke exposure biomarkers next steps
  • Further research required to
  • Quantify the influence of fuel type and
    combustion conditions on biomarker response
  • Evaluate population heterogeneity in woodsmoke
    exposure-biomarker response relationship

21
Acknowledgements
  • UW researchers
  • David Kalman, PhD
  • Russell Dills, PhD
  • Michael Paulsen
  • Sally Liu, PhD
  • Jacqui Ahmad
  • Rick Neitzel
  • Meagan Yoshimoto
  • Elizabeth Grey
  • Bethany Katz
  • Collaborators
  • Kirk Smith, PhD (UCB)
  • Michael Clarke (UCB)
  • Luke Naeher, PhD (UGA)
  • Alison Stock (CDC)
  • Dana Barr (CDC)
  • Kevin Dunn (CDC)
  • USFS Savannah River Site
  • Funding
  • USEPA, NIOSH
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