Predicting Primary PM2.5 and PM0.1 Trace Composition for Epidemiological Studies in California

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. The UCD\\_P model has\n the potential to improve exposure estimates in epidemiology studies of\n PM trace chemical components and health.}},\nPublisher = {{AMER CHEMICAL SOC}},\nAddress = {{1155 16TH ST, NW, WASHINGTON, DC 20036 USA}},\nType = {{Article}},\nLanguage = {{English}},\nAffiliation = {{Kleeman, MJ (Corresponding Author), Univ Calif Davis, Dept Civil \\& Environm Engn, One Shields Ave, Davis, CA 95616 USA.\n Hu, Jianlin; Zhang, Hongliang; Kleeman, Michael J., Univ Calif Davis, Dept Civil \\& Environm Engn, Davis, CA 95616 USA.\n Chen, Shu-Hua, Univ Calif Davis, Dept Land Air \\& Water Resources, Davis, CA 95616 USA.\n Wiedinmyer, Christine, Natl Ctr Atmospher Res, Div Atmospher Chem, Boulder, CO 80307 USA.\n Vandenberghe, Francois, Natl Ctr Atmospher Res, Res Applicat Lab, Boulder, CO 80307 USA.\n Ying, Qi, Texas A\\&M Univ, Zachry Dept Civil Engn, College Stn, TX USA.}},\nDOI = {{10.1021/es404809j}},\nISSN = {{0013-936X}},\nEISSN = {{1520-5851}},\nKeywords-Plus = {{PARTICULATE AIR-POLLUTION; RESOLVED SOURCE APPORTIONMENT; LOW-LEVEL\n WINDS; LOS-ANGELES; COMPOSITION DISTRIBUTIONS; ULTRAFINE PARTICLES; SIZE\n DISTRIBUTION; MATTER EMISSIONS; QUALITY MODEL; WRF MODEL}},\nResearch-Areas = {{Engineering; Environmental Sciences \\& Ecology}},\nWeb-of-Science-Categories = {{Engineering, Environmental; Environmental Sciences}},\nAuthor-Email = {{mjkleeman@ucdavis.edu}},\nResearcherID-Numbers = {{Zhang, Hongliang/C-2499-2012\n Hu, Jianlin/C-2023-2014\n Hu, Jianlin/D-7663-2018\n }},\nORCID-Numbers = {{Zhang, Hongliang/0000-0002-1797-2311\n Hu, Jianlin/0000-0001-7709-439X\n Hu, Jianlin/0000-0001-7709-439X\n Chen, Shu-Hua/0000-0001-5929-1074\n Ying, Qi/0000-0002-4560-433X\n Wiedinmyer, Christine/0000-0001-9738-6592}},\nFunding-Acknowledgement = {{United States Environmental Protection AgencyUnited States Environmental\n Protection Agency {[}83386401]; United States Environmental Protection\n AgencyUnited States Environmental Protection Agency}},\nFunding-Text = {{This study was funded by the United States Environmental Protection\n Agency under Grant No. 83386401. Although the research described in the\n article has been funded by the United States Environmental Protection\n Agency it has not been subject to the Agency's required peer and policy\n review and therefore does not necessarily reflect the reviews of the\n Agency and no official endorsement should be inferred.}},\nNumber-of-Cited-References = {{71}},\nTimes-Cited = {{34}},\nUsage-Count-Last-180-days = {{2}},\nUsage-Count-Since-2013 = {{93}},\nJournal-ISO = {{Environ. Sci. Technol.}},\nDoc-Delivery-Number = {{AG9DY}},\nUnique-ID = {{WOS:000335720100041}},\nDA = {{2021-12-02}},\n}\n\n","author_short":["Hu, J.","Zhang, H.","Chen, S.","Wiedinmyer, C.","Vandenberghe, F.","Ying, Q.","Kleeman, M. 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