Predicting Primary PM2.5 and PM0.1 Trace Composition for Epidemiological Studies in California. Hu, J., Zhang, H., Chen, S., Wiedinmyer, C., Vandenberghe, F., Ying, Q., & Kleeman, M. J. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 48(9):4971-4979, AMER CHEMICAL SOC, 1155 16TH ST, NW, WASHINGTON, DC 20036 USA, MAY 6, 2014. doi abstract bibtex 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 similar to 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 (I 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 PM0.1 that 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.
@article{ WOS:000335720100041,
Author = {Hu, Jianlin and Zhang, Hongliang and Chen, Shu-Hua and Wiedinmyer,
Christine and Vandenberghe, Francois and Ying, Qi and Kleeman, Michael
J.},
Title = {{Predicting Primary PM2.5 and PM0.1 Trace Composition for Epidemiological
Studies in California}},
Journal = {{ENVIRONMENTAL SCIENCE \& TECHNOLOGY}},
Year = {{2014}},
Volume = {{48}},
Number = {{9}},
Pages = {{4971-4979}},
Month = {{MAY 6}},
Abstract = {{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 similar to
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 (I 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 PM0.1
that 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.}},
Publisher = {{AMER CHEMICAL SOC}},
Address = {{1155 16TH ST, NW, WASHINGTON, DC 20036 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Kleeman, MJ (Corresponding Author), Univ Calif Davis, Dept Civil \& Environm Engn, One Shields Ave, Davis, CA 95616 USA.
Hu, Jianlin; Zhang, Hongliang; Kleeman, Michael J., Univ Calif Davis, Dept Civil \& Environm Engn, Davis, CA 95616 USA.
Chen, Shu-Hua, Univ Calif Davis, Dept Land Air \& Water Resources, Davis, CA 95616 USA.
Wiedinmyer, Christine, Natl Ctr Atmospher Res, Div Atmospher Chem, Boulder, CO 80307 USA.
Vandenberghe, Francois, Natl Ctr Atmospher Res, Res Applicat Lab, Boulder, CO 80307 USA.
Ying, Qi, Texas A\&M Univ, Zachry Dept Civil Engn, College Stn, TX USA.}},
DOI = {{10.1021/es404809j}},
ISSN = {{0013-936X}},
EISSN = {{1520-5851}},
Keywords-Plus = {{PARTICULATE AIR-POLLUTION; RESOLVED SOURCE APPORTIONMENT; LOW-LEVEL
WINDS; LOS-ANGELES; COMPOSITION DISTRIBUTIONS; ULTRAFINE PARTICLES; SIZE
DISTRIBUTION; MATTER EMISSIONS; QUALITY MODEL; WRF MODEL}},
Research-Areas = {{Engineering; Environmental Sciences \& Ecology}},
Web-of-Science-Categories = {{Engineering, Environmental; Environmental Sciences}},
Author-Email = {{mjkleeman@ucdavis.edu}},
ResearcherID-Numbers = {{Zhang, Hongliang/C-2499-2012
Hu, Jianlin/C-2023-2014
Hu, Jianlin/D-7663-2018
}},
ORCID-Numbers = {{Zhang, Hongliang/0000-0002-1797-2311
Hu, Jianlin/0000-0001-7709-439X
Hu, Jianlin/0000-0001-7709-439X
Chen, Shu-Hua/0000-0001-5929-1074
Ying, Qi/0000-0002-4560-433X
Wiedinmyer, Christine/0000-0001-9738-6592}},
Funding-Acknowledgement = {{United States Environmental Protection AgencyUnited States Environmental
Protection Agency {[}83386401]; United States Environmental Protection
AgencyUnited States Environmental Protection Agency}},
Funding-Text = {{This study was funded by the United States Environmental Protection
Agency under Grant No. 83386401. Although the research described in the
article has been funded by the United States Environmental Protection
Agency it has not been subject to the Agency's required peer and policy
review and therefore does not necessarily reflect the reviews of the
Agency and no official endorsement should be inferred.}},
Number-of-Cited-References = {{71}},
Times-Cited = {{34}},
Usage-Count-Last-180-days = {{2}},
Usage-Count-Since-2013 = {{93}},
Journal-ISO = {{Environ. Sci. Technol.}},
Doc-Delivery-Number = {{AG9DY}},
Unique-ID = {{WOS:000335720100041}},
DA = {{2021-12-02}},
}
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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 (I 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 PM0.1 that differed from traditional exposure estimates based on information at monitoring locations in California Metropolitan Statistical Areas, with a maximum divergence of 58% at Bakersfield. 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A comprehensive\n comparison between monthly average model results and available\n measurements yielded Pearson correlation coefficients (R) >= 0.8 at >= 5\n sites (out of a total of eight) for elemental carbon (EC) and nine trace\n elements: potassium, chromium, zinc, iron, titanium, arsenic, calcium,\n manganese, and strontium in the PM2.5 size fraction. Longer averaging\n time increased the overall R for PM2.5 EC from 0.89 (I day) to 0.94 (1\n month), and increased the number of species with strong correlations at\n individual sites. Predicted PM0.1 mass and PM0.1 EC exhibited excellent\n agreement with measurements (R = 0.92 and 0.94, respectively). The\n additional temporal and spatial information in the UCD\\_P model\n predictions produced population exposure estimates for PM2.5 and PM0.1\n that differed from traditional exposure estimates based on information\n at monitoring locations in California Metropolitan Statistical Areas,\n with a maximum divergence of 58\\% at Bakersfield. 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