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

• Rationale for methoxyphenols as a biomarker of woodsmoke exposure

• Biomonitoring of woodsmoke exposure– Managed exposure study– Wildland firefighter exposure study

• Conclusions and Future prospects

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.

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

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)

I. ‘Campfire’ exposures

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

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

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

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

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

III. Wildland firefighter study

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

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

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

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)

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

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

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

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

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