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. O H H O H O C O H O H O C H O H 3 3 O H O C H O C H O C H 3 3 3 H O C 3 l G u a i a c o M e t h y l g u a i a c o l E t h y l g u a i a c o l P r o p y l g u a i a c o l l E u g e n o l c i s - I s o e u g e n o O H O H O H H C O O C H O H H C O O C H H C O O C H 3 3 O H 3 3 3 3 O H H C O O C H H C O O C H 3 3 O C H 3 3 3 S y r i n g o l l E t h y l s y r i n g o O M e t h y l s y r i n g o l A l l y l s y r i n g o l P r o p y l s y r i n g o l O H V a n i l l i n C H O 3 O H O H H C O O C H 3 3 O H H C O C 3 3 O A c e t o v a n i l l o n e O O H A c e t o s y r i n g o n e O C H O H 3 H O O C H 3 H C O O C H S i n a p y l a l d e h y d e 3 3 O H H C O O C H 3 3 C o n i f e r y l a l d e h y d e n G u a i a c y l a c e t o e P r o p y l s y r i n g o n e d S y r i n g a l d e h e y 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. PM2.5 (mg/m3) 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 (n=11) 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 >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

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. 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 Acknowledgements