Using Chemical Transport Model Predictions To Improve
Exposure Assessment of PM2.5 Constituents
Posted on 2019-08-05 - 11:34
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.
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Hu, Jianlin; Ostro, Bart; Zhang, Hongliang; Ying, Qi; Kleeman, Michael J. (2019). Using Chemical Transport Model Predictions To Improve
Exposure Assessment of PM2.5 Constituents. ACS Publications. Collection. https://doi.org/10.1021/acs.estlett.9b00396
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AUTHORS (5)
JH
Jianlin Hu
BO
Bart Ostro
HZ
Hongliang Zhang
QY
Qi Ying
MK
Michael J. Kleeman