Optimal estimation for global ground‐level fine particulate matter concentrations

Aaron van Donkelaar; Randall V. Martin; Robert J. D. Spurr; Easan Drury; Lorraine A. Remer; Robert C. Levy; Jun Wang

doi:10.1002/jgrd.50479

journal article Jun 10, 2013

Optimal estimation for global ground‐level fine particulate matter concentrations

Aaron van Donkelaar

Randall V. Martin

Robert J. D. Spurr Easan Drury Lorraine A. Remer Robert C. Levy Jun Wang

Journal of Geophysical Research: Atmospheres Vol. 118 No. 11 pp. 5621-5636 · American Geophysical Union (AGU)

View at Publisher Save 10.1002/jgrd.50479

Abstract

We develop an optimal estimation (OE) algorithm based on top‐of‐atmosphere reflectances observed by the MODIS satellite instrument to retrieve near‐surface fine particulate matter (PM2.5). The GEOS‐Chem chemical transport model is used to provide prior information for the Aerosol Optical Depth (AOD) retrieval and to relate total column AOD to PM2.5. We adjust the shape of the GEOS‐Chem relative vertical extinction profiles by comparison with lidar retrievals from the CALIOP satellite instrument. Surface reflectance relationships used in the OE algorithm are indexed by land type. Error quantities needed for this OE algorithm are inferred by comparison with AOD observations taken by a worldwide network of sun photometers (AERONET) and extended globally based upon aerosol speciation and cross correlation for simulated values, and upon land type for observational values. Significant agreement in PM2.5 is found over North America for 2005 (slope = 0.89; r = 0.82; 1‐σ error = 1 µg/m3 + 27%), with improved coverage and correlation relative to previous work for the same region and time period, although certain subregions, such as the San Joaquin Valley of California are better represented by previous estimates. Independently derived error estimates of the OE PM2.5 values at in situ locations over North America (of ±(2.5 µg/m3 + 31%) and Europe of ±(3.5 µg/m3 + 30%) are corroborated by comparison with in situ observations, although globally (error estimates of ±(3.0 µg/m3 + 35%), may be underestimated. Global population‐weighted PM2.5 at 50% relative humidity is estimated as 27.8 µg/m3 at 0.1° × 0.1° resolution.

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References

Details

Published: Jun 10, 2013
Vol/Issue: 118(11)
Pages: 5621-5636
License: View

Authors

A

Aaron van Donkelaar

Department of Physics and Atmospheric Science Dalhousie University Halifax Nova Scotia Canada

R

Randall V. Martin

Department of Physics and Atmospheric Science Dalhousie University Halifax Nova Scotia Canada; Harvard‐Smithsonian Center for Astrophysics Cambridge Massachusetts USA

R

Robert J. D. Spurr

RT Solutions Inc. Cambridge Massachusetts USA

E

Easan Drury

National Renewable Energy Laboratory Golden Colorado USA

L

Lorraine A. Remer

JCET University of Maryland Baltimore Maryland USA

R

Robert C. Levy

Science Systems and Applications Inc. Lanham Greenbelt Maryland USA; Earth Science Division NASA Goddard Space Flight Center Greenbelt Maryland USA

J

Jun Wang

Department of Earth and Atmospheric Sciences University of Nebraska‐Lincoln Lincoln Nebraska USA

Cite This Article

Aaron van Donkelaar, Randall V. Martin, Robert J. D. Spurr, et al. (2013). Optimal estimation for global ground‐level fine particulate matter concentrations. Journal of Geophysical Research: Atmospheres, 118(11), 5621-5636. https://doi.org/10.1002/jgrd.50479

Optimal estimation for global ground‐level fine particulate matter concentrations

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