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Discussion papers
https://doi.org/10.5194/acp-2018-962
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-962
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Oct 2018

Research article | 04 Oct 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite observed aerosol optical depth

Xiaomeng Jin1, Arlene M. Fiore1, Gabriele Curci2,3, Alexei Lyapustin4, Kevin Civerolo5, Michael Ku5, Aaron van Donkelaar6, and Randall V. Martin6,7 Xiaomeng Jin et al.
  • 1Department of Earth and Environmental Sciences of Lamont-Doherty Earth Observatory and Columbia University, Palisades, NY, USA
  • 2Department of Physical and Chemical Sciences, University of L'Aquila, L'Aquila, Italy
  • 3CETEMPS, University of L'Aquila, L'Aquila, Italy
  • 4NASA Goddard Space Flight Center, MD, USA
  • 5New York State Department of Environmental Conservation, Albany, NY, USA
  • 6Department of Physics and Atmospheric Science, Dalhousie University, NS, Canada
  • 7Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA

Abstract. Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1km2 resolution for 2011 over the Northeast USA by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12km2 horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly, to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5/AOD can explain more than 70% of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5/AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5/AOD lead to an error of 11μg/m3 in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8μg/m3. Using multi-platform ground, airborne and radiosonde measurements, we show that uncertainties of modeled PM2.5/AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model uncertainties of relative humidity and aerosol vertical profile shape contribute some systematic biases. The parameterization of aerosol optical properties, which determines the mass-extinction efficiency, also contributes to random uncertainty, with the size distribution the largest source of uncertainty, and hygroscopicity of inorganic salt the second. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.

Xiaomeng Jin et al.
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Status: open (until 29 Nov 2018)
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Short summary
We use a forward geophysical approach to derive surface PM2.5 from satellite AOD over the Northeast USA by applying relationships between surface PM2.5 and column AOD from a regional air quality model (CMAQ). We use multi-platform surface, aircraft and radiosonde measurements to quantify different sources of uncertainties. We highlight model representation of aerosol vertical distribution and speciation as major sources of uncertainties for satellite-derived PM2.5.
We use a forward geophysical approach to derive surface PM2.5 from satellite AOD over the...
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