Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America

Aaron Van Donkelaar; Randall V. Martin; Adam N. Pasch; James J. Szykman; Lin Zhang; Yuxuan X. Wang; Dan Chen

doi:10.1021/es3025319

Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America

Aaron Van Donkelaar
, Randall V. Martin
, Adam N. Pasch
, James J. Szykman
, Lin Zhang
, Yuxuan X. Wang
, Dan Chen

Department of Energy, Environmental & Chemical Engineering

Research output: Contribution to journal › Article › peer-review

62 Scopus citations

Abstract

We improve the accuracy of daily ground-level fine particulate matter concentrations (PM_2.5) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM_2.5. This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM_2.5, and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m³ + 67%) to ± (1 μg/m³ + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM_2.5 estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m ³ + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM_2.5 as compared to day-specific relationships.

Original language	English
Pages (from-to)	11971-11978
Number of pages	8
Journal	Environmental Science and Technology
Volume	46
Issue number	21
DOIs	https://doi.org/10.1021/es3025319
State	Published - Nov 6 2012

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10.1021/es3025319

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@article{cec26a3bf06a4503bbf30acea695b9ab,

title = "Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America",

abstract = "We improve the accuracy of daily ground-level fine particulate matter concentrations (PM2.5) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM2.5. This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM2.5, and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m3 + 67\%) to ± (1 μg/m3 + 54\%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM2.5 estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m 3 + 42\%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM2.5 as compared to day-specific relationships.",

author = "\{Van Donkelaar\}, Aaron and Martin, \{Randall V.\} and Pasch, \{Adam N.\} and Szykman, \{James J.\} and Lin Zhang and Wang, \{Yuxuan X.\} and Dan Chen",

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doi = "10.1021/es3025319",

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T1 - Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America

AU - Van Donkelaar, Aaron

AU - Martin, Randall V.

AU - Pasch, Adam N.

AU - Szykman, James J.

AU - Zhang, Lin

AU - Wang, Yuxuan X.

AU - Chen, Dan

PY - 2012/11/6

Y1 - 2012/11/6

N2 - We improve the accuracy of daily ground-level fine particulate matter concentrations (PM2.5) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM2.5. This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM2.5, and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m3 + 67%) to ± (1 μg/m3 + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM2.5 estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m 3 + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM2.5 as compared to day-specific relationships.

AB - We improve the accuracy of daily ground-level fine particulate matter concentrations (PM2.5) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM2.5. This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM2.5, and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m3 + 67%) to ± (1 μg/m3 + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM2.5 estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m 3 + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM2.5 as compared to day-specific relationships.

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DO - 10.1021/es3025319

M3 - Article

C2 - 23013040

AN - SCOPUS:84868567201

SN - 0013-936X

VL - 46

SP - 11971

EP - 11978

JO - Environmental Science and Technology

JF - Environmental Science and Technology

IS - 21

ER -

Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America

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