TY - JOUR
T1 - Predicting primary PM2.5 and PM0.1 trace composition for epidemiological studies in California
AU - Hu, Jianlin
AU - Zhang, Hongliang
AU - Chen, Shu Hua
AU - Wiedinmyer, Christine
AU - Vandenberghe, Francois
AU - Ying, Qi
AU - Kleeman, Michael J.
PY - 2014/5/6
Y1 - 2014/5/6
N2 - The University of California-Davis-Primary (UCD-P) chemical transport model was developed and applied to compute the primary airborne particulate matter (PM) trace chemical concentrations from ∼900 sources in California through a simulation of atmospheric emissions, transport, dry deposition and wet deposition for a 7-year period (2000-2006) with results saved at daily time resolution. A comprehensive comparison between monthly average model results and available measurements yielded Pearson correlation coefficients (R) >0.8 at >5 sites (out of a total of eight) for elemental carbon (EC) and nine trace elements: potassium, chromium, zinc, iron, titanium, arsenic, calcium, manganese, and strontium in the PM2.5 size fraction. Longer averaging time increased the overall R for PM2.5 EC from 0.89 (1 day) to 0.94 (1 month), and increased the number of species with strong correlations at individual sites. Predicted PM0.1 mass and PM0.1 EC exhibited excellent agreement with measurements (R = 0.92 and 0.94, respectively). The additional temporal and spatial information in the UCD-P model predictions produced population exposure estimates for PM2.5 and PM0.1that differed from traditional exposure estimates based on information at monitoring locations in California Metropolitan Statistical Areas, with a maximum divergence of 58% at Bakersfield. The UCD-P model has the potential to improve exposure estimates in epidemiology studies of PM trace chemical components and health.
AB - The University of California-Davis-Primary (UCD-P) chemical transport model was developed and applied to compute the primary airborne particulate matter (PM) trace chemical concentrations from ∼900 sources in California through a simulation of atmospheric emissions, transport, dry deposition and wet deposition for a 7-year period (2000-2006) with results saved at daily time resolution. A comprehensive comparison between monthly average model results and available measurements yielded Pearson correlation coefficients (R) >0.8 at >5 sites (out of a total of eight) for elemental carbon (EC) and nine trace elements: potassium, chromium, zinc, iron, titanium, arsenic, calcium, manganese, and strontium in the PM2.5 size fraction. Longer averaging time increased the overall R for PM2.5 EC from 0.89 (1 day) to 0.94 (1 month), and increased the number of species with strong correlations at individual sites. Predicted PM0.1 mass and PM0.1 EC exhibited excellent agreement with measurements (R = 0.92 and 0.94, respectively). The additional temporal and spatial information in the UCD-P model predictions produced population exposure estimates for PM2.5 and PM0.1that differed from traditional exposure estimates based on information at monitoring locations in California Metropolitan Statistical Areas, with a maximum divergence of 58% at Bakersfield. The UCD-P model has the potential to improve exposure estimates in epidemiology studies of PM trace chemical components and health.
UR - https://www.scopus.com/pages/publications/84899835657
U2 - 10.1021/es404809j
DO - 10.1021/es404809j
M3 - Article
C2 - 24694302
AN - SCOPUS:84899835657
SN - 0013-936X
VL - 48
SP - 4971
EP - 4979
JO - Environmental Science and Technology
JF - Environmental Science and Technology
IS - 9
ER -