biological monitoring of exposure to woodsmoke
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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. Outline. - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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.
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Guaiacol
OHOCH 3
OHOCH3
OHOCH3
OHOCH3
OHOCH3 OH
OCH3
Methylguaiacol Ethylguaiacol Propylguaiacol Eugenol cis-Isoeugenol
OHOCH3H3CO
OHOCH3H3CO
OHOCH3H3CO OH
OCH3H3CO
OHOCH3H3CO OH
OCH3
O
Syringol
MethylsyringolEthylsyringol
Propylsyringol Allylsyringol VanillinOHOCH3
O
OHOCH3
O
H3CO
OHOCH3
O
H3CO
OHOCH3
O
OHOCH3H3CO
OOH
OCH3
O
Acetovanillone
Syringaldehyde
Acetosyringone
Coniferylaldehyde
Sinapylaldehyde
Guaiacylacetone
OHOCH3
O
H3CO
Propylsyringone
OHO
OH
HO
O
Levoglucosan
Selected markers for biomass combustionRelative proportions of MPs, vary depending on type of wood
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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)
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I. ‘Campfire’ exposures
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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
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I. ‘Campfire’ exposures
0
500
1000
1500
2000
2500
3000
1 2 3 4 5 6 7 8 9
Subject #
PM
2.5 (
g/m
3 )
2 hr TWA values
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Excretion rates for syringol and guaiacol
0
0.2
0.4
0.6
0.8
1
1.2
-40 -20 0 20 40 60
hours post exposure
Exce
tion
rate
(µg/
min
) Nor
mal
ized
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
-40 -20 0 20 40 60
hours post exposure
Excre
tion R
ate (
µg/m
in) N
orma
lized
syringol guaiacol
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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
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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
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III. Wildland firefighter study
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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
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PM2.5, CO, and LG correlations
Full-shift exposure data only (n=11)
Pearson correlations for LG and CO; Spearman for PM
0
20
40
60
80
100
120
140
160
180
0 500 1000 1500 2000
PM2.5 concentration (ug/m3)
LG c
once
ntra
tion
(ug/
m3)
0
1
2
3
4
5
6
7
0 50 100 150 200
LG concentration (ug/m3)
CO
con
cent
ratio
n (p
pm)
0
1
2
3
4
5
6
7
0 500 1000 1500 2000
PM2.5 concentration (ug/m3)
CO
con
cent
ratio
n (p
pm)
Spearman rho =0.002p = 0.99
Pearson r =0.077p = 0.0006
Spearman rho -0.27p = 0.41
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Significant creatinine-adjusted urinary MP correlations
• Four guaiacol-type MPs– Guaiacol, methylguaiacol, ethylguaiacol and
propylguaiacol (Pearson r >0.6, p<0.01)
• Three syringol-type MPs– Syringol, methylsyringol, and ethylsyringol
(Pearson r >0.6, p<0.01)
• Levels for these MPs combined into summed guaiacol and syringol variables– For summed variables only, ND values assigned
method LOD/2 and used
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CO vs. change in creatinine-adjusted summed guaiacols
CO (ppm) vs Summed Guaiacols (g/mg creatinine)
y = 0.283x - 0.051p = 0.002r^2 = 0.63
-2
0
2
4
6
8
10
0 2 4 6 8 10 12
CO concentration (ppm)
Cro
ss-S
hif
t d
iffe
ren
ce in
su
mm
ed
gu
aiac
ol c
on
cen
trat
ion
(m
g/m
g
crea
tin
ine)
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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
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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
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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
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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
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Acknowledgements
UW researchersDavid Kalman, PhD
Russell Dills, PhD
Michael Paulsen
Sally Liu, PhD
Jacqui Ahmad
Rick Neitzel
Meagan Yoshimoto
Elizabeth Grey
Bethany Katz
CollaboratorsKirk Smith, PhD (UCB)
Michael Clarke (UCB)
Luke Naeher, PhD (UGA)
Alison Stock (CDC)
Dana Barr (CDC)
Kevin Dunn (CDC)
USFS Savannah River Site
FundingUSEPA, NIOSH