Does personalization in exposure assessment change ambient air pollution exposure-response relationships? A panel...

Area of Science:

• Environmental Health Sciences
• Epidemiology
• Toxicology

Background:

• Accurate assessment of personal exposure to ambient air pollution is critical for understanding its health effects, particularly on cardiopulmonary diseases.
• Traditional air quality monitors (AQM) provide ambient concentrations but do not capture individual behavioral variations influencing personal exposure.
• The Exposure Model for Individuals (EMI) offers a potential alternative for more personalized exposure assessment.

Purpose of the Study:

• To compare the exposure-response relationships between ambient air pollution (PM2.5 and O3) and cardiopulmonary biomarkers using both stationary AQM and EMI.
• To evaluate the consistency of these relationships across different exposure assessment methods.

Main Methods:

• A panel study involving 28 participants with 134 total visits, including spirometry and blood sampling.
• Calculation of PM2.5 and O3 concentrations using AQM and EMI for each visit and preceding 4 days (lag0-4).
• Application of a mixed-effects model to analyze associations between air pollution exposure and cardiopulmonary biomarkers.

Main Results:

• AQM and EMI showed strong correlation for PM2.5 (ρ=0.89) and moderate for O3 (ρ=0.46).
• Short-term PM2.5 exposure was consistently associated with increased oxLDL and altered vWF levels across both AQM and EMI methods, suggesting cardiovascular risk.
• O3 exposure showed less consistency between AQM and EMI, with varied associations with vWF, D-dimer, and tPA, highlighting challenges in O3 exposure modeling.

Conclusions:

• Exposure-response associations for short-term PM2.5 with oxLDL and coagulation markers are consistent between AQM and EMI, supporting their use in assessing cardiovascular disease risk.
• The variability in O3 exposure-response relationships between AQM and EMI underscores the complexities and challenges in accurately modeling and assessing O3 exposure and its health impacts.