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Estimating Ground-Level PM
Estimating Ground-Level PM
2.5
in the
Eastern United States Using
Satellite Remote Sensing
Y A N G L I U , *
,
J E R E M Y A . S A R N A T , V A S U K I L A R U ,
§
D A N I E L J . J A C O B ,
|
A N D
P E T R O S K O U T R A K I S Division of Engineering and Applied Sciences (DEAS) and
Department of Earth and Planetary Sciences,
Harvard University, Cambridge, Massachussetts 02138,
Department of Environmental Health, Harvard School of
Public Health, Boston, Massachussetts 02215, and National
Exposure Research Laboratory, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina 27709
An empirical model based on the regression between
daily PM
2.5
(particles with aerodynamic diameters of less
than 2.5 µm) concentrations and aerosol optical thickness
(AOT) measurements from the multiangle imaging
spectroradiometer (MISR) was developed and tested
using data from the eastern United States during the period
of 2001. Overall, the empirical model explained 48% of
the variability in PM
2.5
concentrations. The root-mean-
square error of the model was 6.2 µg/m
3
with a corresponding
average PM
2.5
concentration of 13.8 µg/m
3
. When PM
2.5
concentrations greater than 40 µg/m
3
were removed, model
results were shown to be unbiased estimators of
observations. Several factors, such as planetary boundary
layer height, relative humidity, season, and other
geographical attributes of monitoring sites, were found to
influence the association between PM
2.5
and AOT. The
findings of this study illustrate the strong potential of satellite
remote sensing in regional ambient air quality monitoring
as an extension to ground networks. With the continual
advancement of remote sensing technology and global data
assimilation systems, AOT measurements derived from
satellite remote sensors may provide a cost-effective approach
as a supplemental source of information for determining
ground-level particle concentrations.
Introduction
Epidemiological studies around the world have found strong
and consistent correlations between adverse health effects
and levels of fine particles (PM
2.5
, particles with aerodynamic
diameters of less than 2.5 µm) measured at central monitoring
stations, which serve as a major surrogate to actual population
exposure level (1-3). In addition, health effects associated
with particle exposure have shown no apparent threshold at
lower concentrations (4). To date, assessments of chronic
population exposures over a large geographical region have
been limited since they generally require long-term moni-
toring data from a comprehensive network such as the United
States Environmental Protection Agencys (EPA) compliance
network. Operating and maintaining such networks are very
costly, especially for many developing countries. Where
monitoring networks do not exist, air quality models can be
used to estimate PM
2.5
concentrations. However, daily PM
2.5
concentrations predicted by these models may be biased for
various reasons such as a lack of background information of
certain particle species and simplified model assumptions.
Another major hurdle with air quality modeling is that they
rely heavily on detailed emission inventories which are often
difficult to accurately estimate and maintain.
Polar orbiting satellites can provide information on aerosol
optical properties for almost complete global coverage at a
moderate spatial resolution over multiple years, which have
emerged as another potential method of estimating ground-
level air quality. In December 1999, the National Aeronautics
and Space Administration (NASA) launched its Terra Earth
Observing Satellite (5). The multiangle imaging spectrora-
diometer (MISR) aboard Terra employs nine cameras pointed
at different fixed angles to simultaneously observe reflected
and scattered sunlight in four wavelength bands. This unique
design enables MISR to retrieve columnar aerosol optical
thickness (AOT) at 17.6 km resolution over ocean and most
land surfaces (6-8). In a previous study, we showed that
MISR AOT values agreed well with ground-level standard
AOT measurements from the Aerosol Robotic Network
(AERONET) (9). It has also been shown that MISR AOT
measurements are sensitive to particles with diameters
ranging from 0.05 to 2.0 µm (10), which roughly corresponds
to the definition of PM
2.5
.
Earlier studies have shown that earth-observing satellites
can detect and track the transport of particles as well as
severe pollution episodes on a regional scale (11-14). In this
analysis, we examine the relationship between ground-level
PM
2.5
measurements and MISR AOT measurements in the
eastern United States using a generalized linear regression
model. To account for the variation in particle vertical profiles,
composition, and optical properties, planetary boundary layer
height and relative humidity data from the Goddard Earth
Observing System (GEOS-3) have been included in the model
(15, 16). Model validation using an independent dataset and
a graphical display of the model results are also presented.
The objective of this study is to explore the efficacy and
accuracy of satellite remote sensing data as a cost-effective
approach for predicting ground-level PM
2.5
, thus providing
an independent and supplemental data source to in situ
monitoring and computational modeling.
Method
(a) Data Collection and Processing. (1) PM
2.5
Measurement
Collection and Processing. A total of 2505 gravimetrically
based daily average PM
2.5
measurements were collected from
346 sites within the EPAs compliance network in the eastern
United States from the year 2001 (Figure 1). The study area
was, subsequently, divided into three subregions to examine
geographic variability among the observed results. The New
England region included Maine, New Hampshire, Massa-
chusetts, Connecticut, Vermont, and Rhode Island. The mid-
Atlantic region included New York, New Jersey, Pennsyl-
vania, Delaware, District of Columbia, Maryland, West
Virginia, and Virginia. The south Atlantic region included
North Carolina, South Carolina, Georgia, and Florida. In
addition to PM
2.5
mass concentrations (reported in µg/m
3
),
site geographic location (latitude and longitude), land use,
* Corresponding author phone: (703) 516-2366; e-mail: yliu@
environcorp.com. Present address: ENVIRON International Corp.,
4350 N. Fairfax Dr., Suite 300, Arlington, VA 22203. DEAS, Harvard University. Harvard School of Public Health.
§
U.S. Environmental Protection Agency.
|
Department of Earth and Planetary Sciences, Harvard Uni-
versity.
Environ. Sci. Technol.
2005,
39,
3269-3278
10.1021/es049352m CCC: $30.25
©
2005 American Chemical Society
VOL. 39, NO. 9, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
3269
Published on Web 03/10/2005 and other site attribute information were also obtained. The
sites were also classified according to their distance to the
coast (i.e., e100 km away or >100 km away), since a previous
study indicated that mixing height growth is heavily influ-
enced by the land-ocean interaction of the atmosphere in
areas located within 100 km from the coast (17).
(2) MISR Level 2 Aerosol Data Collection and Processing.
For the current analysis, MISR AOT data covering the east
coast was downloaded from the Atmospheric Sciences Data
Center at NASA Langley Research Center (http://edg.larc.
nasa.gov/). The mean and standard deviation of the AOT
measurements from each 3
× 3 MISR region (a 17.6 × 17.6
km
2
MISR pixel is called an MISR region) centered at a given
EPA site were calculated and matched with the PM
2.5
measurement taken at that site on the same day. Studies
have shown a relatively high degree of spatial homogeneity
of PM
2.5
concentrations over a 24 h period (18). Extreme
variability of AOT values within the 3
× 3 MISR regions may
indicate that cloud screening prior to AOT retrieval in some
of the MISR regions was insufficient or that the MISR retrieval
algorithm could not identify the particle composition in
certain regions. To limit the impact of erroneous spatial
variation in AOT measurements, we require each 3
× 3 region
to have at least three AOT measurements. In addition, the
upper limit of the coefficient of variation (standard deviation
divided by mean AOT) calculated from those valid 3
× 3
regions was set to be less than 0.5 to further reduce the
likelihood of data contamination.
(3) GEOS-3 Assimilated Meteorological Field Processing.
The GEOS-3 meteorological fields were given at 1
°
latitud