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Title: Global Chemical Composition of Ambient Fine Particulate Matter for Exposure Assessment

Abstract

Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM 2.5 chemical composition at 0.1° × 0.1° spatial resolution for 2004–2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM 2.5 composition data set with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust, and sea salt. We found that global population-weighted PM 2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 μg/m 3), secondary inorganic aerosol (11.1 ± 5.0 μg/m 3), and mineral dust (11.1 ± 7.9 μg/m 3). Secondary inorganic PM 2.5 concentrations exceeded 30 μg/m 3 over East China. Sensitivity simulations suggested that population-weighted ambient PM 2.5 from biofuel burning (11 μg/m 3) could be almost as large as from fossil fuel combustion sources (17 μg/m 3). In conclusion, these estimates offer information about global population exposure to the chemical components and sources of PM 2.5.

Authors:
 [1];  [2];  [1];  [1];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [9];  [10];  [11]
  1. Dalhousie Univ., Halifax, NS (Canada). Dept. of Physics and Atmospheric Science
  2. Dalhousie Univ., Halifax, NS (Canada). Dept. of Physics and Atmospheric Science; Harvard-Smithsonian Center for Astrophysics, Cambridge, MA (United States)
  3. Tsinghua Univ., Beijing (China). Ministry of Education Key Lab. for Earth System Modeling
  4. Univ. of California, Los Angeles, CA (United States). Dept. of Atmospheric and Oceanic Sciences
  5. Peking Univ., Beijing (China). Dept. of Atmospheric and Oceanic Sciences, School of Physics
  6. Duke Univ., Durham, NC (United States). Nicholas School of the Environment and Earth Sciences
  7. Tsinghua Univ., Beijing (China). State Key Joint Lab. of Environment Simulation and Pollution Control
  8. Tsinghua Univ., Beijing (China). Center for Earth System Science
  9. Argonne National Lab. (ANL), Argonne, IL (United States). Decision and Information Sciences Division
  10. Agriculture and Agri-Food Canada, Agassiz, BC (Canada)
  11. Environment Canada, Gatineau, QC (Canada)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE; Natural Sciences and Engineering Research Council of Canada (NSERC); U.S. National Inst. of Health (NIH)
OSTI Identifier:
1261137
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Environmental Science and Technology
Additional Journal Information:
Journal Volume: 48; Journal Issue: 22; Journal ID: ISSN 0013-936X
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Philip, Sajeev, Martin, Randall V., van Donkelaar, Aaron, Lo, Jason Wai-Ho, Wang, Yuxuan, Chen, Dan, Zhang, Lin, Kasibhatla, Prasad S., Wang, Siwen, Zhang, Qiang, Lu, Zifeng, Streets, David G., Bittman, Shabtai, and Macdonald, Douglas J. Global Chemical Composition of Ambient Fine Particulate Matter for Exposure Assessment. United States: N. p., 2014. Web. doi:10.1021/es502965b.
Philip, Sajeev, Martin, Randall V., van Donkelaar, Aaron, Lo, Jason Wai-Ho, Wang, Yuxuan, Chen, Dan, Zhang, Lin, Kasibhatla, Prasad S., Wang, Siwen, Zhang, Qiang, Lu, Zifeng, Streets, David G., Bittman, Shabtai, & Macdonald, Douglas J. Global Chemical Composition of Ambient Fine Particulate Matter for Exposure Assessment. United States. doi:10.1021/es502965b.
Philip, Sajeev, Martin, Randall V., van Donkelaar, Aaron, Lo, Jason Wai-Ho, Wang, Yuxuan, Chen, Dan, Zhang, Lin, Kasibhatla, Prasad S., Wang, Siwen, Zhang, Qiang, Lu, Zifeng, Streets, David G., Bittman, Shabtai, and Macdonald, Douglas J. Fri . "Global Chemical Composition of Ambient Fine Particulate Matter for Exposure Assessment". United States. doi:10.1021/es502965b. https://www.osti.gov/servlets/purl/1261137.
@article{osti_1261137,
title = {Global Chemical Composition of Ambient Fine Particulate Matter for Exposure Assessment},
author = {Philip, Sajeev and Martin, Randall V. and van Donkelaar, Aaron and Lo, Jason Wai-Ho and Wang, Yuxuan and Chen, Dan and Zhang, Lin and Kasibhatla, Prasad S. and Wang, Siwen and Zhang, Qiang and Lu, Zifeng and Streets, David G. and Bittman, Shabtai and Macdonald, Douglas J.},
abstractNote = {Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM2.5 chemical composition at 0.1° × 0.1° spatial resolution for 2004–2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition data set with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust, and sea salt. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 μg/m3), secondary inorganic aerosol (11.1 ± 5.0 μg/m3), and mineral dust (11.1 ± 7.9 μg/m3). Secondary inorganic PM2.5 concentrations exceeded 30 μg/m3 over East China. Sensitivity simulations suggested that population-weighted ambient PM2.5 from biofuel burning (11 μg/m3) could be almost as large as from fossil fuel combustion sources (17 μg/m3). In conclusion, these estimates offer information about global population exposure to the chemical components and sources of PM2.5.},
doi = {10.1021/es502965b},
journal = {Environmental Science and Technology},
issn = {0013-936X},
number = 22,
volume = 48,
place = {United States},
year = {2014},
month = {10}
}

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