Spectral, spatial, and temporal sensitivity of correlating MODIS ...
Transcript of Spectral, spatial, and temporal sensitivity of correlating MODIS ...
Spectral, spatial, and temporal sensitivity ofcorrelating MODIS aerosol optical depth withground-based fine particulate matter (PM2.5)
across southern Ontario
Jie Tian and Dongmei Chen
Abstract. This paper evaluates the sensitivity of the aerosol optical depth (AOD) measurements derived from the Moderate
Resolution Imaging Spectroradiometer (MODIS) at wavelengths of 0.47, 0.55, and 0.66 mm from both the Terra and Aqua
satellites for the estimation of ground-level concentrations of fine particulate matter (PM2.5) in southern Ontario, Canada.
The correlation between MODIS AOD and ground-based PM2.5 measurements is compared at different seasons and
spatial aggregation levels. The results showed that the MODIS AOD data acquired in early afternoon (from Aqua)
seem to have slightly higher agreement with the ground-based measurement of PM2.5 than the late morning Terra data.
Moreover, MODIS AOD at 0.47 mm from both satellites had the highest correlation with ground-level PM2.5. More
detailed examination suggested that the correlation was stronger in the spring and summer and weaker in the fall and
winter. Aqua MODIS AOD appeared to have a better correlation with the average ground-based PM2.5 concentration over
1–3 h than with daily PM2.5. MODIS AOD values aggregated over 3 6 3 pixel groups correlate slightly better with PM2.5
than with the original single centre pixel values.
Resume. Dans cet article, on evalue la sensibilite des mesures de l’epaisseur optique des aerosols (AOD) derivees du capteur
MODIS (« Moderate Resolution Imaging Spectroradiometer ») dans les longueurs d’onde de 0,47 mm, 0,55 mm et 0,66 mm
des satellites Terra et Aqua pour l’estimation de la concentration au niveau du sol des particules fines (P2,5) dans le sud de
l’Ontario, au Canada. Les correlations entre l’AOD de MODIS et les mesures au sol de P2,5 sont comparees a differentes
saisons et pour differents niveaux d’agregation spatiale. Les resultats ont demontre que les donnees d’AOD de MODIS
acquises tot en apres-midi (a partir d’Aqua) semblent afficher une correlation legerement plus grande avec la mesure au sol
de P2,5 que les donnees acquises en fin d’avant-midi par Terra. En outre, l’AOD de MODIS a 0,47 mm, dans le cas des deux
satellites, etait meilleure dans la correlation globale par rapport aux mesures de P2,5 au niveau du sol. Un examen plus
detaille a suggere que la correlation etait plus forte au printemps et en ete et plus faible a l’automne et en hiver. L’AOD de
MODIS acquise par Aqua semblait avoir une meilleure correlation avec la concentration moyenne de P2,5 au sol sur 1–
3 heures que la mesure journaliere de P2,5. Les valeurs d’AOD de MODIS agregees par rapport a des groupes de pixels de
3 6 3 donnent une correlation legerement superieure avec les valeurs de P2,5 par comparaison avec les valeurs originales des
pixels a centre unique.
[Traduit par la Redaction]
Introduction
Particulate matter (PM) is one of the major pollutants
affecting regional air quality and is commonly recognized
as a complex mixture of microscopic solid or liquid particles
suspended in the air. PM is usually characterized according
to an aerodynamic size because of the different health effects
that develop from exposure to PM of various diameters. In
particular, fine PM (PM2.5) often refers to particles that are
2.5 mm or less in diameter. PM2.5 originates from a variety of
sources ranging from natural production (e.g., volcanic
eruptions and forest fires) to anthropogenic pollution (e.g.,
emissions from industrial facilities and combustion from
automobiles) (Arya, 1998). PM2.5 usually remains suspended
in the air for relatively long periods of time as compared with
coarser PM and can penetrate deep into the respiratory sys-
tem and cause disease and even premature death (Villeneuve
et al., 2002). Children, the elderly, and those with asthma,
respiratory problems, or cardiovascular or lung disease are
found to be most susceptible to PM2.5 (Pope et al., 1999;
Green and Armstrong, 2003; Kappos et al., 2004; Neuberger
et al., 2004).
A common practice in many industrialized countries to
monitor ambient air quality regionally is to establish a net-
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Received 2 November 2009. Accepted 18 May 2009. Published on the Web at http://pubservices.nrc-cnrc.ca/cjrs on XXXX XXXX XXXX.
J. Tian and D. Chen.1 Department of Geography, Queen’s University, Kingston, ON K7L 3N6, Canada.
1Corresponding author (e-mail: [email protected]).
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work of monitoring stations, equipped by receptors, across a
region of interest. However, although having a high tem-
poral sampling frequency, these stations usually take point
measurements and do not provide adequate coverage to ful-
fill the needs of mapping regional air quality. The spatial
interpolation of such station data therefore possesses limited
value to understanding the spatial–temporal dynamics of a
pollutant (Kumar et al., 2007).
Remote sensing provides an alternative data resource that
is important in studying the air quality of a relatively large
area. At the present time, a number of orbiting satellites
routinely measure the spectral radiation received at the top
of the atmosphere and provide data regarding atmosphere
composition. The Moderate Resolution Imaging Spectrora-
diometer (MODIS) is one of the key sensors onboard the
Terra and Aqua satellite platforms and collects data in 36
channels from a near-Earth Sun-synchronous polar orbit
with a revisit frequency of 1–2 days depending on the loca-
tion on the globe (Barnes et al., 1998). Aerosol optical depth
(AOD) is a parameter that measures the integral of atmo-
spheric aerosol extinction from the Earth’s surface to the top
of the atmosphere (Gupta et al., 2006). The MODIS AOD
data are derived at three wavelengths, namely 0.47, 0.55, and
0.66 mm (Ichoku et al., 2004).
Efforts have been made to correlate MODIS AOD and
ground-level PM2.5 concentration at the continental scale
(Donkelaar et al., 2006), the regional scale (Hutchison,
2003; Engel-Cox et al., 2004; Tian and Chen, 2007), and even
the local scale, focusing on a group of individual cities (Chu
et al., 2003; Li et al., 2009). Although MODIS AOD has
been commonly reported to have tremendous potential in
estimating PM2.5, unstable results have been observed in
terms of the strength of the correlation and its spatial–tem-
poral variation. For example, Wang and Christopher (2003)
reported relatively strong correlations (r 5 0.7) for their
study area of Jefferson County, Alabama. Al-Saadi et al.
(2005) and Engel-Cox et al. (2004), however, found weaker
correlations (r , 0.4) in the western US, where the higher
surface reflectance of the more arid surfaces was suspected to
reduce contrast. Another reason for the weaker correlation is
that the AOD retrieval model used for that region assumes
more dust and smoke than industrial aerosols. Furthermore,
air pollution mechanisms can vary spatially, and hence so
also can the strength of the AOD–PM2.5 correlation (Kumar
et al., 2007). Several research projects have been dedicated to
a search for the optimal scale to achieve the best correlation
(Li et al., 2009). However, there have been few studies focus-
ing on the AOD–PM2.5 correlation or its variation at the
regional scale in Canada. Tian and Chen (2007) attempted
to correlate MODIS AOD and PM2.5 in Ontario based on a
relatively small volume of data only from the Terra satellite
and found that there was a positive correlation between
them; the strengths of the correlation appeared to be at dif-
ferent levels in the summer and winter. However, Tian and
Chen did not provide a thorough or systematic analysis of
the correlation variation in terms of spectral, spatial, and
temporal sensitivity and did not compare AOD–PM2.5 cor-
relations from the two satellite platforms, namely Terra and
Aqua (acquiring data late morning and early afternoon local
time, respectively).
This paper presents the results of an inclusive and detailed
study to systematically evaluate the correlation of MODIS
AOD and PM2.5 in southern Ontario, Canada. The MODIS
algorithm for AOD retrieval is first reviewed to highlight the
evolution history of the MODIS AOD data. The MODIS
AOD measurements derived at wavelengths of 0.47, 0.55,
and 0.66 mm are compared in terms of their correlation
strength with PM2.5. The original AOD data are spatially
averaged over 3 6 3 pixel groups, and the hourly PM2.5 data
are temporally aggregated to represent longer time periods.
Detailed cross-comparison is then made to illustrate the
sensitivity of the AOD–PM2.5 correlation to a series of
changes in data resolution. The analyses are performed in
parallel for the AOD data from both Terra and Aqua so that
a sound discussion can be carried out to address possible
diurnal differences. Lastly, the distributional pattern of the
yearly PM2.5 average is produced by interpolating the mon-
itoring station data and is then compared with that of yearly
Terra MODIS AOD.
Review of MODIS algorithm for aerosoloptical depth retrieval
The MODIS algorithm for AOD retrieval consists of two
independent algorithms, one for deriving aerosols over land
and the other for deriving aerosols over oceans. Principally,
the land algorithm is applied only to a subset of those
MODIS pixels that have no ocean or inland water within
the field of view. The AOD retrieval analyses only include
those pixels that are not contaminated by cloud. The core
algorithm uses the atmospheric reflectance ratios between
0.47 and 2.12 mm and between 0.66 and 2.12 mm to retrieve
the AOD information (Remer et al., 2006). The AOD
retrieval is conducted based on a radiative transfer model
and uses Mie theory for fine aerosol models. The algorithms
of various versions differ in their logic of determining which
500 m pixels in a 20 6 20 pixel group should be used to
generate AOD values.
MODIS AOD data are organized by collections, where
groups of data were processed by similar, but not necessarily
the same, versions of the algorithm. The first globally vali-
dated data were produced using the collection 3 algorithms.
Collections 4 and 5 algorithms have improved in the detec-
tion of cloudy pixels. The current collection 5 algorithm over
land (C005-L) is regarded as a complete overhaul to the
collection 4 algorithm over land (C004-L) (Levy et al.,
2007). Whereas C004-L essentially retrieves aerosol prop-
erties independently in two visible channels (0.47 and
0.66 mm), C005-L performs the same operation in three
channels simultaneously (the two visible channels and the
2.12 mm channel). The C005-L algorithm is also superior in
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that it assumes the 2.12 mm channel contains information
about not only the surface but also the coarse mode aerosol.
There are additional differences in surface reflectance
assumptions and the aerosol look-up tables. A more detailed
description of the algorithms can be found in Kaufman et al.
(1997) and Remer et al. (2006).
Study area and ground-based data
This paper focuses on southern Ontario, which is the most
populous region in Canada. Southern Ontario is mainly
within the Great Lakes – St. Lawrence Lowlands physio-
graphic region (Bone, 2005). A continental climate affects
this temperate mid-latitude region. The climate is highly
modified by the influence of the Great Lakes: the addition
of moisture from the lakes increases precipitation amounts.
The latest studies indicate that areas of southern Ontario
often experience the highest levels of PM2.5 in eastern
Canada. Ontario is burdened with Can$9.6 billion in health
and environmental damage each year due to the impact of
ground-level fine PM and ozone (Yap et al., 2005), which are
generally attributed to the formation of smog.
The Ontario Ministry of the Environment (OME) oper-
ates and maintains the air quality monitoring network in
Ontario, which monitors concentrations of particulate and
gaseous air pollutants at the near-ground level (typically 3–
8 m above the ground). More specifically, the ThermoScien-
tific tapered element oscillating microbalance (TEOM) tech-
nology is used to measure ground-level PM2.5 concentrations.
Each station is equipped with a filter attached to a hollow,
tapered, and oscillating glass rod. The accumulation of mass
is measured over time from the change in the oscillation fre-
quency. The PM2.5 readings are provided on an hourly basis
in the standard units of micrograms per cubic metre (mg/m3),
with a precision of about ¡1.5 mg/m3. The PM2.5 data for this
study were obtained from the OME electronic archive of
historical air quality data. In 2004, PM2.5 was routinely mea-
sured by 38 monitoring stations mainly distributed across the
southern part of the province. Figure 1 shows the locations of
the monitoring stations.
Satellite data
MODIS aerosol images were collected for southern
Ontario to cover the entire calendar year of 2004. Collection
5 aerosol images were chosen because they are produced
based on the most advanced version of the retrieval algo-
rithm. Preliminary validation of the algorithm used for the
AOD retrieval in this collection showed much improved
results, where the MODIS – Aerosol Robotic Network (at
0.55 mm) regression has an equation y 5 1.01x + 0.03, with a
correlation coefficient of 0.90 (Levy et al., 2007).
In total, over 800 AOD images were obtained from both
Terra and Aqua. A MODIS aerosol image covers a 5 min
time interval and has typical dimensions of 203 cells (along
swath) 6 135 cells (across swath). The typical cell size, or
spatial resolution, is 10 km 6 10 km (at nadir). For southern
Ontario, there is usually more than one image, taken at dif-
ferent local times, available for each day.
The MODIS aerosol data are stored and provided in a
hierarchical data format (HDF) (Masuoka et al., 1998),
which is a multi-object file format for sharing scientific data
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Figure 1. Locations (solid triangles) of the air quality monitoring stations across Ontario.
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in multiplatform distributed environments. Each MODIS
aerosol product file stores 53 scientific parameters. Speci-
fically, MODIS AOD includes three channels, with each
containing the AOD values for one wavelength (0.47, 0.55,
or 0.66 mm). A sample image of Terra MODIS AOD (at
0.47 mm) is shown in Figure 2. A valid AOD value is dimen-
sionless and has a valid range from 0 to 5, with values over
unity generally being classified as heavy haze (Engel-Cox et
al., 2004). MATLAB programs were written to read the
MODIS HDF files systematically and extract the AOD
channels for land, which contain the data needed for this
study.
Analysis methods
The same analyses were performed in parallel with the
collected MODIS AOD images from Aqua and Terra for
southern Ontario. Initial attempts focused on correlating
the original MODIS AOD values (single pixels) at the three
wavelengths of 0.47, 0.55, and 0.66 mm, with the hourly read-
ings of ground-level PM2.5 concentration from the ground
stations. The MODIS AOD values and the PM2.5 readings
were associated based on their spatial and temporal prox-
imity to perform a series of intercomparative and quantita-
tive analyses. Each monitoring station was spatially
colocated to its nearest MODIS pixel. MATLAB programs
were developed to search for the nearest pixel from each indi-
vidual MODIS AOD image for every ground station. If the
MODIS pixel found nearest, or more normally, covering the
station had a valid AOD value, this value was associated
with the station. The AOD value was then paired with the
coincident PM2.5 reading that was measured by the asso-
ciated station and within the same hour as the MODIS
satellite overpass. Pearson’s r was calculated to quantify
the strength of the correlation between the hourly PM2.5
and the corresponding MODIS AOD values at the three
wavelengths.
It was borne in mind that PM2.5 and MODIS AOD mea-
sure different atmospheric loadings of aerosols. The former
represents the fine particulate matter concentration at a
point station near the ground, whereas the latter quantifies
the total columnar aerosol loading over a typical area of
10 km 6 10 km. In addition, there is a considerable discrep-
ancy in the temporal characteristics between the two sets of
data. The original PM2.5 data characterize hourly concentra-
tion averages, whereas MODIS AOD is retrieved based on
nearly instant observations. The original MODIS AOD
images were therefore further processed to assign each single
pixel with the average of the valid AOD values within its 3 63 pixel neighborhood. On the other hand, the hourly PM2.5
data were temporally aggregated up to multihour averages
and the daily average as well. A series of correlation analyses
was then performed to test the sensitivity of the AOD–PM2.5
correlation to these data resolution changes.
The overall analyses tend to even off the correlation var-
iations across the region and over time. More detailed exam-
ination was performed to focus on the individual station
sites. It is understandable that AOD and PM2.5 may be
strongly correlated in some geographical areas but weakly
correlated in others due to differences in meteorological
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Figure 2. Sample image of MODIS AOD at 0.47 mm covering southern Ontario (acquired on
23 September 2004 and processed by the version 5 algorithm).
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condition (e.g., humidity or atmospheric layering) and (or)
land use – land cover characteristics (e.g., surface reflectiv-
ity). The spatial variation of aerosol type may also account
for the spatial variation of AOD–PM2.5 correlation. More-
over, the AOD–PM2.5 correlation for various months was
examined to elucidate seasonal patterns. To provide some
idea of the data availability for the cities in southern Ontario,
the frequency (or availability) of the valid MODIS AOD
pixel values representing the stations within each month
was summarized.
Spatial interpolation is the most common approach used
to estimate values for unmeasured locations based on some
known point measurements. The yearly mean of PM2.5 was
calculated for each monitoring station and was spatially
interpolated to produce a concentration surface. The inverse
distance weighting (IDW) method was adopted in the inter-
polation process for the sake of simplicity and ease of com-
parison. A yearly AOD average map was produced by
assigning the average values from the Terra MODIS AOD
pixels. Then the interpolated PM2.5 surface map was com-
pared with the yearly AOD average map.
Results and discussion
Ground-level PM2.5 concentrations are classified into five
categories according to the US Environmental Protection
Agency Air Quality Index. These categories were used as a
reference in this study. The air quality category is considered
good if daily PM2.5 concentrations are between 0 and 15.4 mg/
m3, moderate if daily PM2.5 concentrations are between 15.5
and 40.4 mg/m3, unhealthy for sensitive groups (e.g., children
and seniors) if daily PM2.5 concentrations are between 40.5
and 65.4 mg/m3, unhealthy if daily PM2.5 concentrations are
between 65.5 and 150.4 mg/m3, and very unhealthy if daily
PM2.5 concentrations are between 150.5 and 250.4 mg/m3
(USEPA, 2003). Figure 3 uses two cities (Toronto and
Kingston) as an example to illustrate how the daily PM2.5
average, measured by the monitoring stations, changes
throughout the year 2004. As can be seen from Figure 3,
spring and summer exhibit relatively higher levels of PM2.5
and are likely to be more harmful to certain patients, infants,
or seniors compared to fall and winter. It is worth mention-
ing that PM2.5 is concentrated at levels higher than the daily
average during certain hours (e.g., rush hour).
The overall correlation analysis shows good agreement
between the MODIS AOD and the ground-based PM2.5 con-
centration measurements (see Figure 4). Larger AOD values
usually correspond to higher PM2.5 values. More detailed
information about the correlations is provided in Table 1,
which shows that all the correlations are statistically signifi-
cant because the probability values associated with the null
hypothesis are extremely small (,0.001). Table 1 shows how
MODIS AODs are correlated with hourly PM2.5, with coef-
ficients ranging from 0.51 to 0.65. In comparison, hourly
PM2.5 is better correlated with Aqua AODs (r 5 0.649,
0.614, and 0.566 at the three wavelengths) than with Terra
AODs (r 5 0.600, 0.561, and 0.509 at the three wavelengths)
by a slight margin. One possible reason might be that Aqua
MODIS images are taken in the early afternoon when the
temperature becomes relatively higher in the diurnal cycle
and the planetary boundary layer (PBL) is better mixed
due to the stronger convection. The aerosol vertical profile
therefore tends to be even smoother (Raj et al., 2008), and
measurements of columnar aerosol loading (e.g., AOD)
taken at that time are perhaps more representative of the
corresponding PM2.5.
In southern Ontario, MODIS AOD values at 0.47 mm
(mean AOD 5 0.289) are larger than those at 0.55 and
0.66 mm (mean AOD 5 0.236 and 0.197, respectively). A
clear trend is recognized in that the AOD at shorter wave-
lengths has a stronger correlation with PM2.5. The trend
appears to be rather consistent no matter how the PM2.5 data
were scaled up in time. This may be explained by the fact that
the smaller wavelength is more sensitive to smaller particles
and less sensitive to coarse mode. In other words, southern
Ontario is dominated by smaller aerosol particles, and the
MODIS AOD data at 0.47 mm (compared with those at 0.55
and 0.66 mm) are therefore relatively more representative of
the PM2.5.
The 3 6 3 AOD averages at the three wavelengths are also
found to correlate with PM2.5 in a positive manner (see
Figure 5). Overall, PM2.5 is slightly better correlated with
the 3 6 3 AOD averages (r 5 0.53–0.68) than with the single,
centre pixel AOD data (r 5 0.50–0.65). Some of the differ-
ences in correlation strength are significant. For example,
when the Aqua MODIS AOD data are aggregated over
3 6 3 pixel groups, the correlation with hourly PM2.5
increases from 0.649 (n 5 1779) to 0.682 (n 5 3305). Inter-
comparison between the 3 6 3 AOD averages at different
wavelengths shows that PM2.5 correlates best with the 3 6 3
AOD average at 0.47 mm (see Table 2). This somewhat dis-
agrees with earlier published studies (Hutchison et al., 2005),
where weaker correlations were found between PM2.5 and
the AOD average within a 3 6 3 pixel group. Although the
reasons for this disagreement are not yet clear, it may be
because of differences in the land surface characteristics
and changes in the current AOD retrieval algorithm over
the previous algorithms. The spatial averaging over 3 63 pixel groups produces many more AOD values than pro-
duced by the original single centre pixels. This processing
may compensate, to some degree, for scatter in the MODIS
AOD data due to, for example, cloud contamination.
Figure 6 exhibits the correlations between MODIS AOD
at 0.47 mm (chosen as an example) and the PM2.5 data at
various temporal resolutions (averaged over different
lengths of time to a maximum of 24 h). The degree of cor-
relation for Terra MODIS AOD does not seem to be very
sensitive to the temporal resolution of the PM2.5 data. In
comparison, the degree of correlation for Aqua MODIS
AOD appears to decline consistently as the PM2.5 data are
aggregated over increasingly longer intervals of time. Aqua
MODIS AOD is most representative of the coincident
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hourly or 3 h PM2.5 than the daily average. Overall, the
strongest correlation (0.685) is between the 3 6 3 AOD
average data from Aqua and the 3 h PM2.5 (see Table 2).
There may be an optimal match between the temporal reso-
lution of the point PM2.5 data and the spatial resolution of
the areal AOD data for an efficient correlation. Analysis
covering a larger spatial extent may allow spatial aggrega-
tions over larger windows and provide further information
related to the optimal spatial resolution.
The availability of MODIS AOD data and their correla-
tion with PM2.5 vary greatly with the seasons (see Figure 7).
For the monitoring station locations, there are few valid
MODIS AOD data available for January, February, March,
and December due to the extensive snow cover (having high
reflectance unfavorable to MODIS AOD retrieval). MODIS
therefore has limited value for PM2.5 estimation for southern
Ontario during winter. There are considerably more valid
AOD data for the study area during the rest of the year.
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Figure 3. Variation of the daily ground-level PM2.5 concentration measured by the monitor-
ing stations at Kingston (a) and Toronto (b), Ontario, for 2004.
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The dramatic increase in MODIS AOD data frequency in
April is perhaps because the snow has begun to melt and the
algorithm for MODIS AOD retrieval becomes applicable to
more areas. Somewhat fewer AOD data are available for
August compared with any other summer month, possibly
because the MODIS images acquired during that period are
contaminated by more clouds. Cloud cells need to be
screened out during the AOD retrieval, and too many clouds
will cause failure of retrieving a statistically valid value for
some areas. The significantly greater number of valid AOD
data in the early fall is attributed to the frequent presence of
ideal weather (e.g., clear sky) and land cover conditions (e.g.,
no snow) for MODIS AOD retrieval.
As can be seen in Figures 7a and 7b, the correlations
between MODIS AOD and PM2.5 change significantly from
month to month. May, July, August, and September show
the highest correlation coefficients of 0.70–0.85, and June has
weaker correlations of 0.50–0.60 compared with those of the
other summer months. The period of May–September can be
regarded as optimal for PM2.5 estimation using MODIS
AOD. Despite seasonal changes, MODIS AOD at a shorter
wavelength normally has a stronger correlation with PM2.5.
The estimated distribution of yearly ground-level PM2.5
concentration average is depicted in Figure 8a. Since the
interpolation was performed based only on the monitoring
stations currently available in southern Ontario, the result
appears to be rather general and does not provide much
detailed information on the change of PM2.5 over relatively
short distances. Also, the estimated distribution reflects the
instrumental character of the interpolation method and is
heavily dependent on the number and structure of the mon-
itoring stations. In contrast, the distribution of yearly Terra
MODIS AOD at 0.47 mm offers a much more detailed pic-
ture of aerosol loading across the region (Figure 8b).
Although MODIS AOD is retrieved based on instant obser-
vation and is not a direct proxy of daily PM2.5, a good agree-
ment (Pearson’s r of around 0.60) has been found between
these two variables (see Figure 6). The distribution seems to
have a pattern similar to that of the densely populated and
(or) industrialized areas in the region. Overall, the land
boundary areas are generally loaded with more aerosols than
the inland areas. The Greater Toronto Area, the most popu-
lated region in the province, is shaded in red in Figure 8b,
implying a heavily loaded subregion that is not suggested by
Figure 8a. Relatively heavy aerosol loadings are also found
over the Greater Windsor Area, the southern part of the
Golden Horseshoe, and the region between Grand Bend
and Tiverton. Due to the strong correlation between
MODIS AOD and PM2.5, a higher value of yearly MODIS
AOD can be interpreted as an indicator of potentially higher
concentrations of PM2.5 near the ground. This information
would provide a better regional PM2.5 distribution than
PM2.5 values directly interpolated from monitoring stations.
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Figure 4. Scatterplots of hourly ground-level PM2.5 concentration versus MODIS AOD at 0.47 mm from Terra (a) and Aqua (b).
Table 1. Overall correlations between MODIS single-pixel AODs
and hourly, 3 h, and daily ground-level PM2.5 concentrations.
PM2.5
average
AOD
wavelength (mm)
Terra Aqua
r n R n
Hourly 0.47 0.600 2104 0.649 1779
0.55 0.561 2105 0.614 1780
0.66 0.509 2106 0.566 1778
Three hour 0.47 0.608 2126 0.651 1794
0.55 0.566 2127 0.617 1795
0.66 0.513 2128 0.570 1793
Daily 0.47 0.593 2136 0.597 1804
0.55 0.553 2137 0.564 1805
0.66 0.501 2138 0.518 1803
Note: All r values (correlation coefficient) are significant at p , 0.001.n, number of valid pairs.
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Conclusions
This paper evaluates the spatial and temporal sensitivity
of the aerosol optical depth (AOD) measurements derived
from the Moderate Resolution Imaging Spectroradiometer
(MODIS) at wavelengths of 0.47, 0.55, and 0.66 mm from
both the Terra and Aqua satellites for the estimation of
ground-level concentrations of fine particulate matter
(PM2.5) in southern Ontario, Canada. As demonstrated in
previous research (Tian and Chen, 2007), overall, MODIS
AOD agrees well with ground-level PM2.5 concentrations.
However, PM2.5 is best correlated with the MODIS AOD
at a wavelength of 0.47 mm compared with at 0.55 or 0.66 mm.
The MODIS AOD acquired in the early afternoon (from
Aqua) seems able to better represent the condition of
PM2.5 than the late morning acquisition from Terra. The
correlation between Terra MODIS AOD and PM2.5 is not
very sensitive to the temporal resolution changes of the
PM2.5 data, but the Aqua MODIS AOD seems to be more
representative of the coincident PM2.5 averaged over 1–3 h
than the daily average. The mean AOD over 3 6 3 pixel
groups tends to have a relatively stronger correlation with
PM2.5 than the original single centre pixel AOD. In practice,
MODIS AOD images often appear to be patchy and can
provide little help in PM2.5 estimation in winter because of
the very limited number of valid data. Moreover, the
MODIS AOD–PM2.5 correlation is significantly stronger
in summer months than in winter months.
The correlation between MODIS AOD and PM2.5 also
varies substantially across space and through time. It is
unclear why the correlation between MODIS AOD and
PM2.5 varies at different spatial and temporal scales. The
potential impact factors would include meteorological con-
ditions, land use, cloud contamination, and station location.
m10-033.3d 27/8/10 19:13:01
Proof/Epreuve
Figure 5. Scatterplots of hourly ground-level PM2.5 concentration versus MODIS 3 6 3 AOD at 0.47 mm from Terra (a) and Aqua (b).
Table 2. Overall correlations between MODIS AOD over 3 63 pixel groups and ground-level PM2.5 concentrations.
PM2.5
average
AOD
wavelength (mm)
Terra Aqua
r n r n
Hourly 0.47 0.614 3765 0.682 3305
0.55 0.578 3766 0.649 3305
0.66 0.530 3765 0.604 3295
Three hour 0.47 0.630 3806 0.685 3332
0.55 0.593 3807 0.653 3332
0.66 0.543 3806 0.607 3322
Daily 0.47 0.616 3822 0.626 3352
0.55 0.580 3823 0.594 3352
0.66 0.531 3822 0.552 3342
Note: All r values are significant at p , 0.001.
Figure 6. Correlation strengths of the MODIS AOD data at
0.47 mm and the ground-level (GL) PM2.5 concentration averages
over different lengths of time (up to 24 h).
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Proof/Epreuve
Figure 7. Variation of the correlation between Terra MODIS AOD (a) and Aqua MODIS AOD (b) at the three wavelengths of 0.47, 0.55,
and 0.66 mm with hourly ground-level PM2.5 concentration, and the MODIS AOD data frequency (c) over various months in 2004.
Figure 8. Comparison of the yearly ground-level PM2.5 concentration average map (a) interpolated using the inverse distance weighting
(IDW) method and the yearly Terra MODIS AOD at 0.47 mm (dimensionless) map (b) interpolated from the grid points.
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The underlying mechanism should be addressed in future
research to obtain a better understanding of the relationship
between AOD and PM2.5. The impact of particle size on the
MODIS AOD and PM correlation is also worth exploring in
the future. Locations with heavy aerosol loadings suggestedby MODIS AOD data are likely to have higher ground-level
PM2.5 concentrations and therefore deserve more attention
from the environment protection agencies.
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