Using Chemical Transport Model Predictions To Improve Exposure Assessment of PM2.5 Constituents Academic Article uri icon

abstract

  • 2019 American Chemical Society. Air pollution health-effect studies commonly use central monitor concentrations (CMCs) of airborne fine particulate matter (PM2.5) to represent population exposure near the monitoring sites. The spatial distribution of PM2.5 constituents is presumed to be the same and is well-represented by the CMC. Here we apply chemical transport models in California and show that the population-weighted concentrations (PWCs) of secondary PM2.5 constituents within the 12 km buffer zone are within 20% of the respective CMC values, but the PWCs of primary PM2.5 constituents differ from the CMC values by -40 to +60%. The appropriate CMC representative distance varies significantly in different cities due to the unique combination of population distribution, emissions patterns, and meteorology at each location. We conclude that exposure misclassification can be significant if the same representative distance is assumed for multiple CMC PM2.5 constituents across all sites in a single air pollution epidemiology study that has a large spatial and temporal range. This misclassification will increase the variance around the effect estimate and therefore reduce the likelihood of finding a statistically significant effect.

published proceedings

  • Environmental Science & Technology Letters

author list (cited authors)

  • Hu, J., Ostro, B., Zhang, H., Ying, Q. i., & Kleeman, M. J.

publication date

  • January 1, 2019 11:11 AM