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.

Guaiaco l OH OC H 3 OH OCH 3 OH OCH 3 OH OC H 3 O H OCH 3 OH OCH 3 Methylguaiacol EthylguaiacolPropylguaiacol Eugenol cis-Isoeugeno l OH OCH 3 H 3 CO OH OCH 3 H 3 CO OH OCH 3 H 3 CO OH OCH 3 H 3 CO OH OCH 3 H 3 CO OH OCH 3 O Syringol Methylsyringol Ethylsyringo l Propylsyringol Allylsyringol Vanillin OH O CH 3 O OH OCH 3 O H 3 CO OH OCH 3 O H 3 CO OH OCH 3 O OH OC H 3 H 3 C O O OH OCH 3 O Acetovanillone Syringaldeh y d e Acetosyringone Coniferylaldehyde Sinapylaldehyde Guaiacylaceto n e O H OCH 3 O H 3 CO Propylsyringone Levoglucosan Selected markers for biomass combustion Relative 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 (t 1/2 ~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 PM 2.5, 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 PM 2.5 (  g/m 3 ) 2 hr TWA values

Excretion rates for syringol and guaiacol 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. T 1/2 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/m 3

III. Wildland firefighter study

Study data 20 shifts worked by 13 firefighters –Part of dataset collected by UGA, CDC –Chosen to cover range of PM 2.5 exposures Personal TWA levels of CO, PM 2.5, LG –CO measured via datalogging monitor –PM 2.5, LG measured from single filter –Qxr re: smoked/grilled foods, smoking Pre- /post-shift urinary measures

PM 2.5, CO, and LG correlations Full-shift exposure data only (n=11) Pearson correlations for LG and CO; Spearman for PM Spearman rho =0.002 p = 0.99 Pearson r =0.077 p = Spearman rho p = 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

Conclusions: exposure measurements LG and PM 2.5 significantly correlated LG and CO significantly correlated PM 2.5 and CO not correlated –Literature generally shows strong correlation between PM 2.5 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 PM 2.5 –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 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