Analysis of the impact of the forest fir (1)
This article appeared in a journal published by Elsevier. The attached
copy is furnished to the author for internal non-commercial research
and education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
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In most cases authors are permitted to post their version of the
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encouraged to visit:
http://www.elsevier.com/copyright
Author's personal copy
Atmospheric Environment 43 (2009) 3310–3318
Contents lists available at ScienceDirect
Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv
Analysis of the impact of the forest fires in August 2007 on air quality
of Athens using multi-sensor aerosol remote sensing data,
meteorology and surface observations
Yang Liu c, *, Ralph A. Kahn a, Archontoula Chaloulakou b, Petros Koutrakis c
a
b
c
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
National Technical University of Athens, Department of Chemical Engineering, Heroon Polytechniou 9, GR-15780 Zografos, Athens, Greece
Department of Environmental and Health, Harvard University, School of Public Health, Boston, MA 02215, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 3 October 2008
Received in revised form
6 April 2009
Accepted 6 April 2009
Data from multiple satellite remote sensors are integrated with ground measurements and meteorological data to study the impact of Greek forest fires in August 2007 on the air quality in Athens. Two
pollution episodes were identified by ground PM10 measurements between August 23 and September 4.
In the first episode, Evia and Peloponnese fires contributed substantially to the air pollution levels in
Athens. In the second episode, transport of industrial pollution from Italy and Western Europe as well as
forest fires in Albania contributed substantially to the air pollution levels in Athens. Local air pollution
sources also contributed to the observed particle levels during these episodes. Satellite data provide
valuable insights into the spatial distribution of particle concentrations, thus they can be used identify
pollution sources. In spite of a few weaknesses in current satellite data products identified in this
analysis, combining satellite aerosol remote sensing data with trajectory models and ground measurements is a powerful tool to study intensive particle pollution events such as forest fires.
Ó 2009 Elsevier Ltd. All rights reserved.
Keywords:
MISR plume height
MODIS
AOD
OMI
Greek forest fires
HYSPLIT
1. Introduction
From late August to early September 2007, Greece suffered the
worst forest fires in the past 50 years. A total of 2700 square kilometers of forest, olive groves and farmland were destroyed by the
fires, and 84 people, including firefighters, lost their lives. In addition to the direct fire damage, these devastating fires produced
large quantities of gaseous air pollutants and particles (PM10 and
PM2.5, airborne particles smaller than 10 mm and 2.5 mm in size,
respectively) dispersed over the region. Athens, the capital of
Greece with a population of over four million inhabitants, was
affected by both the Peloponnese and Evia fires. In addition to
causing deterioration of air quality, fire smoke has adverse health
effects on exposed populations, such as increased respiratory
diseases, asthma, bronchitis, and eye irritation (Kunzli et al., 2006;
Naeher et al., 2007). Exposure severity depends upon wind direction, fire intensity, and precipitation, and forest fire emissions can
* Corresponding author. Present address: Department of Environmental and
Occupational Health, Emory University, Rollins School of Public Health, 1518 Clifton
Road NE, Atlanta, GA 30322, USA. Tel.: þ1 404 7272131; fax: þ1 404 7278744.
E-mail address: yang.liu@emory.edu (Y. Liu).
1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.atmosenv.2009.04.010
also be mixed with those from other air pollution sources. Therefore, it is often difficult to reliably assess the spatial and temporal
extent of population exposure to smoke emissions solely from
ground-based air quality measurements.
Beginning in 1999, the National Aeronautics and Space Administration (NASA) launched a series of Earth Observing System (EOS)
satellite sensors, including the Multiangle Imaging SpectroRadiometer (MISR) (Diner et al., 2002) and the Moderate Resolution
Imaging Spectroradiometer (MODIS) (Salomonson et al., 1989).
Both sensors can measure aerosol abundance and size over both
land and water with nearly global coverage at moderate spatial
resolutions. In addition, MISR is able to provide information on
aerosol type and plume top heights (Kahn et al., 2008). Particle
information retrieved by satellite sensors may be suitable for
monitoring the spatial and temporal trends of particle concentrations over large geographical areas. Dense aerosol plumes are easily
observable and visualized through satellite remote sensing, making
it possible to monitor their transport and transformations. The
extensive spatial coverage satellite imaging offers is of particular
benefit in areas with limited numbers of surface observations.
Aerosol information retrieved by MISR and MODIS can provide
a quantitative measure of aerosol event severity and potential air
quality impact. The Ozone Monitoring Instrument (OMI), which
Author's personal copy
Y. Liu et al. / Atmospheric Environment 43 (2009) 3310–3318
continues the Total Ozone Mapping Spectrometer (TOMS)
measurements, was launched in 2004 aboard the EOS Aura satellite
with daytime equator crossing at approximately 1:30 p.m. local
time (Schoeberl et al., 2006). The OMI instrument can distinguish
between aerosol types, such as smoke, dust, and sulfates, and
measures cloud pressure and coverage, which provide data to
derive tropospheric ozone. OMI, in conjunction with other Aura
instruments, provides global mapping of several key tropospheric
constituents including aerosols. Together with surface observations
and meteorological information, these satellite sensors provide
a better understanding of the spatial, temporal and chemical
characteristics of the aerosol than can any single remote or surfacebased observation. In this paper, we combine aerosol and meteorological measurements from multiple sources to characterize two
particle pollution episodes identified by ground measurements
between August 23 and September 3, 2007 in Athens. The objectives of this study are: (1) to assess the contribution of the Greek
forest fires to particle pollution levels during these two episodes in
Athens by combining satellite aerosol data, meteorological modeling,
and ground measurements; and (2) to evaluate the accuracy and
value of various satellite data products under these conditions.
2. Data and methods
The data used in this analysis consist of satellite images, satellite-retrieved aerosol optical depth and plume top heights, ground
observations of particulate matter concentrations, and air mass
trajectories calculated from assimilated meteorology. Each data
source is described briefly below.
2.1. Ground air quality monitoring data
The Athens Basin is surrounded by high altitude terrain
(over 1000 m asl) to the north, east, and southeast. Daily PM10
concentrations were collected at four sites (ARI, LYK, MAR, and
THR) in this region (Fig. 1). A detailed description of these sites is
given elsewhere (Grivas et al., 2008). Briefly, the background
3311
station of THR is located in a remote area to the north. LYK, ARI and
MAR measure suburban and urban air pollution levels. Calculations
based on data of 2001–2004 show that the three sites have similar
median daily PM10 concentrations, with MAR being slightly cleaner
than LYK and ARI (Grivas et al., 2008). Previous research indicates
that traffic emissions are the dominant air pollution source in
Athens (Chaloulakou et al., 2003; Grivas et al., 2008). Since MODIS
AOD represents the average particle concentration within the
10 10 km2 pixel cell, it is more appropriate to compare it
with regional average PM10 concentrations. Therefore, PM10
concentrations from the three traffic-affected sites were averaged,
to represent urban pollution levels over existing background
concentrations produced by local primary emissions.
2.2. Satellite datasets
The MODIS instruments, aboard both the EOS Terra and Aqua
satellites, cross the equator on the day side at approximately
10:30 a.m. and 1:30 p.m. local time, respectively (Remer et al.,
2005). MODIS red–green–blue true-color images at 250 m resolution were downloaded from the MODIS Rapid Response System
website (http://rapidfire.sci.gsfc.nasa.gov/subsets/). Active fire
locations at 1-km resolution, derived from MODIS 4 mm radiances,
are indicated as black polygons on the images (Giglio et al., 2003).
These images are a valuable resource for tracking fires, as they are
generated in near-real-time over Earth’s landmasses. A dimensionless indicator of particle abundance, AOD is defined as the
integral of aerosol extinction coefficients along the vertical atmospheric column from the ground to top of the atmosphere. When
particle composition, vertical profiles, and atmospheric humidity
are constant, AOD varies linearly with ground level particle mass
loading (Liu et al., 2005). The currently operating MODIS aerosol
retrieval algorithm uses its blue (440 nm), red (670 nm), and
thermal infrared (2.13 mm) wavelength bands to identify Dense
Dark Vegetation pixels, where it preferentially performs aerosol
retrievals over land. AOD and other aerosol information are calculated based on simplified assumptions about particle composition,
Fig. 1. Overview of Athens and surrounding areas. Four PM10 monitoring sites (black dots) are labeled.
Author's personal copy
3312
Y. Liu et al. / Atmospheric Environment 43 (2009) 3310–3318
and are reported at 10 km resolution at nadir. Since AOD values
depend on wavelength, MODIS reports AOD at 550 nm wavelength
(Remer et al., 2005). Although AOD values measured from the
ground may range from near zero in pristine environments up to
two or more in heavily polluted urban areas or dust storms (Jiang
et al., 2007), an upper bound of 3.0 is set in the MODIS AOD
retrieval process to minimize cloud contamination. Both Terra and
Aqua MODIS AOD retrievals over land are highly correlated with
ground truth (correlation coefficients w 0.9), and show little biases
(Remer et al., 2008). AOD values (MODIS parameter name: Optical_Depth_Land_And_Ocean) covering eastern Italy, Greece and
western Turkey were downloaded from the Goddard Space Flight
Center MODIS Level 1 and Atmosphere Archive and Distribution
System (http://ladsweb.nascom.nasa.gov) for this study.
OMI observes solar backscatter radiation at visible and ultraviolet (UV) wavelengths. OMI-retrieved UV Aerosol Index (AI) is
a measure of the degree to which backscattered UV radiation from
an atmosphere containing aerosols differs from that of a pure
molecular atmosphere. By definition, UV AI is positive (>0.2) for
absorbing aerosols, near zero (0.2) in the presence of clouds or
larger non-absorbing particles, and negative (
copy is furnished to the author for internal non-commercial research
and education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
article (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
http://www.elsevier.com/copyright
Author's personal copy
Atmospheric Environment 43 (2009) 3310–3318
Contents lists available at ScienceDirect
Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv
Analysis of the impact of the forest fires in August 2007 on air quality
of Athens using multi-sensor aerosol remote sensing data,
meteorology and surface observations
Yang Liu c, *, Ralph A. Kahn a, Archontoula Chaloulakou b, Petros Koutrakis c
a
b
c
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
National Technical University of Athens, Department of Chemical Engineering, Heroon Polytechniou 9, GR-15780 Zografos, Athens, Greece
Department of Environmental and Health, Harvard University, School of Public Health, Boston, MA 02215, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 3 October 2008
Received in revised form
6 April 2009
Accepted 6 April 2009
Data from multiple satellite remote sensors are integrated with ground measurements and meteorological data to study the impact of Greek forest fires in August 2007 on the air quality in Athens. Two
pollution episodes were identified by ground PM10 measurements between August 23 and September 4.
In the first episode, Evia and Peloponnese fires contributed substantially to the air pollution levels in
Athens. In the second episode, transport of industrial pollution from Italy and Western Europe as well as
forest fires in Albania contributed substantially to the air pollution levels in Athens. Local air pollution
sources also contributed to the observed particle levels during these episodes. Satellite data provide
valuable insights into the spatial distribution of particle concentrations, thus they can be used identify
pollution sources. In spite of a few weaknesses in current satellite data products identified in this
analysis, combining satellite aerosol remote sensing data with trajectory models and ground measurements is a powerful tool to study intensive particle pollution events such as forest fires.
Ó 2009 Elsevier Ltd. All rights reserved.
Keywords:
MISR plume height
MODIS
AOD
OMI
Greek forest fires
HYSPLIT
1. Introduction
From late August to early September 2007, Greece suffered the
worst forest fires in the past 50 years. A total of 2700 square kilometers of forest, olive groves and farmland were destroyed by the
fires, and 84 people, including firefighters, lost their lives. In addition to the direct fire damage, these devastating fires produced
large quantities of gaseous air pollutants and particles (PM10 and
PM2.5, airborne particles smaller than 10 mm and 2.5 mm in size,
respectively) dispersed over the region. Athens, the capital of
Greece with a population of over four million inhabitants, was
affected by both the Peloponnese and Evia fires. In addition to
causing deterioration of air quality, fire smoke has adverse health
effects on exposed populations, such as increased respiratory
diseases, asthma, bronchitis, and eye irritation (Kunzli et al., 2006;
Naeher et al., 2007). Exposure severity depends upon wind direction, fire intensity, and precipitation, and forest fire emissions can
* Corresponding author. Present address: Department of Environmental and
Occupational Health, Emory University, Rollins School of Public Health, 1518 Clifton
Road NE, Atlanta, GA 30322, USA. Tel.: þ1 404 7272131; fax: þ1 404 7278744.
E-mail address: yang.liu@emory.edu (Y. Liu).
1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.atmosenv.2009.04.010
also be mixed with those from other air pollution sources. Therefore, it is often difficult to reliably assess the spatial and temporal
extent of population exposure to smoke emissions solely from
ground-based air quality measurements.
Beginning in 1999, the National Aeronautics and Space Administration (NASA) launched a series of Earth Observing System (EOS)
satellite sensors, including the Multiangle Imaging SpectroRadiometer (MISR) (Diner et al., 2002) and the Moderate Resolution
Imaging Spectroradiometer (MODIS) (Salomonson et al., 1989).
Both sensors can measure aerosol abundance and size over both
land and water with nearly global coverage at moderate spatial
resolutions. In addition, MISR is able to provide information on
aerosol type and plume top heights (Kahn et al., 2008). Particle
information retrieved by satellite sensors may be suitable for
monitoring the spatial and temporal trends of particle concentrations over large geographical areas. Dense aerosol plumes are easily
observable and visualized through satellite remote sensing, making
it possible to monitor their transport and transformations. The
extensive spatial coverage satellite imaging offers is of particular
benefit in areas with limited numbers of surface observations.
Aerosol information retrieved by MISR and MODIS can provide
a quantitative measure of aerosol event severity and potential air
quality impact. The Ozone Monitoring Instrument (OMI), which
Author's personal copy
Y. Liu et al. / Atmospheric Environment 43 (2009) 3310–3318
continues the Total Ozone Mapping Spectrometer (TOMS)
measurements, was launched in 2004 aboard the EOS Aura satellite
with daytime equator crossing at approximately 1:30 p.m. local
time (Schoeberl et al., 2006). The OMI instrument can distinguish
between aerosol types, such as smoke, dust, and sulfates, and
measures cloud pressure and coverage, which provide data to
derive tropospheric ozone. OMI, in conjunction with other Aura
instruments, provides global mapping of several key tropospheric
constituents including aerosols. Together with surface observations
and meteorological information, these satellite sensors provide
a better understanding of the spatial, temporal and chemical
characteristics of the aerosol than can any single remote or surfacebased observation. In this paper, we combine aerosol and meteorological measurements from multiple sources to characterize two
particle pollution episodes identified by ground measurements
between August 23 and September 3, 2007 in Athens. The objectives of this study are: (1) to assess the contribution of the Greek
forest fires to particle pollution levels during these two episodes in
Athens by combining satellite aerosol data, meteorological modeling,
and ground measurements; and (2) to evaluate the accuracy and
value of various satellite data products under these conditions.
2. Data and methods
The data used in this analysis consist of satellite images, satellite-retrieved aerosol optical depth and plume top heights, ground
observations of particulate matter concentrations, and air mass
trajectories calculated from assimilated meteorology. Each data
source is described briefly below.
2.1. Ground air quality monitoring data
The Athens Basin is surrounded by high altitude terrain
(over 1000 m asl) to the north, east, and southeast. Daily PM10
concentrations were collected at four sites (ARI, LYK, MAR, and
THR) in this region (Fig. 1). A detailed description of these sites is
given elsewhere (Grivas et al., 2008). Briefly, the background
3311
station of THR is located in a remote area to the north. LYK, ARI and
MAR measure suburban and urban air pollution levels. Calculations
based on data of 2001–2004 show that the three sites have similar
median daily PM10 concentrations, with MAR being slightly cleaner
than LYK and ARI (Grivas et al., 2008). Previous research indicates
that traffic emissions are the dominant air pollution source in
Athens (Chaloulakou et al., 2003; Grivas et al., 2008). Since MODIS
AOD represents the average particle concentration within the
10 10 km2 pixel cell, it is more appropriate to compare it
with regional average PM10 concentrations. Therefore, PM10
concentrations from the three traffic-affected sites were averaged,
to represent urban pollution levels over existing background
concentrations produced by local primary emissions.
2.2. Satellite datasets
The MODIS instruments, aboard both the EOS Terra and Aqua
satellites, cross the equator on the day side at approximately
10:30 a.m. and 1:30 p.m. local time, respectively (Remer et al.,
2005). MODIS red–green–blue true-color images at 250 m resolution were downloaded from the MODIS Rapid Response System
website (http://rapidfire.sci.gsfc.nasa.gov/subsets/). Active fire
locations at 1-km resolution, derived from MODIS 4 mm radiances,
are indicated as black polygons on the images (Giglio et al., 2003).
These images are a valuable resource for tracking fires, as they are
generated in near-real-time over Earth’s landmasses. A dimensionless indicator of particle abundance, AOD is defined as the
integral of aerosol extinction coefficients along the vertical atmospheric column from the ground to top of the atmosphere. When
particle composition, vertical profiles, and atmospheric humidity
are constant, AOD varies linearly with ground level particle mass
loading (Liu et al., 2005). The currently operating MODIS aerosol
retrieval algorithm uses its blue (440 nm), red (670 nm), and
thermal infrared (2.13 mm) wavelength bands to identify Dense
Dark Vegetation pixels, where it preferentially performs aerosol
retrievals over land. AOD and other aerosol information are calculated based on simplified assumptions about particle composition,
Fig. 1. Overview of Athens and surrounding areas. Four PM10 monitoring sites (black dots) are labeled.
Author's personal copy
3312
Y. Liu et al. / Atmospheric Environment 43 (2009) 3310–3318
and are reported at 10 km resolution at nadir. Since AOD values
depend on wavelength, MODIS reports AOD at 550 nm wavelength
(Remer et al., 2005). Although AOD values measured from the
ground may range from near zero in pristine environments up to
two or more in heavily polluted urban areas or dust storms (Jiang
et al., 2007), an upper bound of 3.0 is set in the MODIS AOD
retrieval process to minimize cloud contamination. Both Terra and
Aqua MODIS AOD retrievals over land are highly correlated with
ground truth (correlation coefficients w 0.9), and show little biases
(Remer et al., 2008). AOD values (MODIS parameter name: Optical_Depth_Land_And_Ocean) covering eastern Italy, Greece and
western Turkey were downloaded from the Goddard Space Flight
Center MODIS Level 1 and Atmosphere Archive and Distribution
System (http://ladsweb.nascom.nasa.gov) for this study.
OMI observes solar backscatter radiation at visible and ultraviolet (UV) wavelengths. OMI-retrieved UV Aerosol Index (AI) is
a measure of the degree to which backscattered UV radiation from
an atmosphere containing aerosols differs from that of a pure
molecular atmosphere. By definition, UV AI is positive (>0.2) for
absorbing aerosols, near zero (0.2) in the presence of clouds or
larger non-absorbing particles, and negative (