Introduction
Atmospheric aerosols are known to play an important role in not only air
quality but also climate change (Kaufman et al., 2002; WHO, 2005; IPCC,
2013). In terms of air quality, surface-level aerosol concentrations have
been found to be strongly associated with impaired visibility (Baumer et al.,
2008) and adverse effects on human health, such as respiratory and
cardiovascular diseases (Pope et al., 2002; Kappos et al., 2004; Brook et
al., 2010; Brauer et al., 2012). Therefore, several ground-based aerosol
monitoring networks, such as the Interagency Monitoring of Protected Visual
Environments (IMPROVE; http://vista.cira.colostate.edu/improve/) and
the EPA's State and Local Air Monitoring Stations (SLAMS;
http://www.epa.gov/ttn/amtic/slams.html), have been installed and
operated to further understand the spatial and temporal variability of the
chemical and physical characteristics of aerosols (Wang and Christopher,
2003).
However, due to the spatial limitations of in situ measurements, the
coordination of dense networks of multiple sites is required to monitor
spatial variations in surface air quality in certain areas. To overcome the
spatial limitations of such in situ measurements, additional efforts have
been made to estimate surface air quality from satellite measurements. Table
1 summarizes the implementation of several different approaches that have
been used to derive surface particulate matter (PM) concentrations using
aerosol optical depth (AOD) measurements obtained from sun photometer and
satellite instruments. An empirical linear model using only AOD as a
predictor for PM estimation showed correlation coefficients between measured
and predicted PM2.5 of 0.2–0.75 (Chu et al., 2003; Wang and
Christopher, 2003; Engel-Cox et al., 2004; Gupta and Christopher, 2008;
Schaap et al., 2009). When the additional effects of boundary layer height
(BLH) and relative humidity (RH) were incorporated into the empirical linear
model (Engel-Cox et al., 2006; Koelemeijer et al., 2006; Emili et al., 2010;
Wang et al., 2010), correlations between measured and predicted PM2.5
were further improved when compared with correlations obtained from linear
models using only AOD. A multiple linear regression model between measured
and predicted PM2.5 concentrations in urban areas yielded a correlation
of 0.71 (Liu et al., 2007). Spatial distributions of PM2.5 can also be
estimated by applying the ratio of AOD to PM2.5, as calculated from
chemical transport models (CTMs), such as the Goddard Earth Observing
System-Chemistry (GEOS-CHEM) transport model and the Community Multiscale Air
Quality (CMAQ) model (Liu et al., 2004; Choi et al., 2009; van Donkelaar et
al., 2010). These previous studies have demonstrated the strong possibility
of deriving surface PM concentrations from AOD data.
However, if we are to further improve and validate PM estimates, additional
physical parameters should be considered as inputs into the empirical models,
so as to obtain accurate estimates of PM concentrations from AOD data.
Additionally, the effects of various environmental characteristics on the
relationship between PM and AOD, as well as spatial and temporal variations
in this relationship, need to be investigated, especially in complex urban
regions which include aerosol particles generated from various industrial and
residential sources. Despite the need to monitor the rapidly changing PM
concentrations in megacities with large populations and many sources of
pollution, only a small number of studies have been conducted, especially in
Asia, and the numbers of ground-based PM monitoring stations in these studies
have been limited (Kumar et al., 2007; Guo et al., 2009). In addition to
limitations based on sample size, obtaining accurate estimates of PM from AOD
data has proved difficult in Asia on account of the complexity of the aerosol
compositions derived from both natural and anthropogenic sources,
particularly during the spring (Kim et al., 2007; Song et al., 2009; Lee et
al., 2010).
In an effort to address these problems, the present study uses aerosol
measurements collected during the Distributed Regional Aerosol Gridded
Observation Network (DRAGON)-Asia 2012 campaign, which is just one of the
DRAGON campaigns that has been conducted globally
(http://aeronet.gsfc.nasa.gov/new_web/DRAGON-Asia_2012_Japan_South_Korea.html).
The intensive DRAGON campaigns have provided valuable data sets, with
well-coordinated measurements made in areas where aerosol concentrations are
highly variable in space and time, and dependent on sources and other
factors. The DRAGON campaigns have been conducted in urban and industrial
areas, including Washington D.C., the San Joaquin Valley of California, and
the Houston metropolitan region of Texas. By using the campaign data sets obtained
from the dense coverage of both column and surface-level aerosol
measurements, assessments of surface-level PM concentrations based on remote
sensing observations can be substantially improved, especially in spring.
The purpose of this study is to investigate the relationship between AOD and
PM concentrations in Seoul, one of the largest megacities in northeast Asia,
using the DRAGON-Asia campaign data set. The detailed objectives of this study
are (1) to estimate PM10 concentrations using AOD data from both
ground- and satellite-based measurements in a megacity, with additional
consideration of the various parameters within the empirical models, and to
thereby evaluate derived PM concentrations; (2) to identify the spatial
variability of the empirical model performance at different types of
measurement site; and (3) to investigate the seasonal variability of the
performance of each model. Based on this study, it is expected that PM10
estimations using ground-based and satellite-derived AOD data will become an
effective approach to monitoring air quality over large spatial domains,
especially in complex urban areas.
Measurements during the DRAGON-Asia campaign
The study area, Seoul, is a megacity located in a downwind region of
northeast Asia, in which air quality is often affected by both pollutants
transported over long distances from continental interior and locally
generated aerosol. The present study used the column aerosol optical
properties measured at 10 Aerosol Robotic Network (AERONET) sites in Seoul,
as well as those obtained by a dense mesoscale network of ground-based
instruments during the DRAGON-Asia 2012 campaign, which was conducted over
the 3-month period March–May 2012 (Fig. 1). The hourly-averaged
PM10 concentrations were also measured at 10 sites operated by a
national air quality monitoring network during the campaign (http://www.airkorea.or.kr).
The spatial distribution of AERONET stations, PM10 monitoring
sites, and weather observation sites across the Seoul metropolitan area.
Orange and yellow boxes indicate the locations of AERONET level 2.0 and 1.5
sites, respectively. The colored circles denote the locations of PM10
monitoring sites. Red, green, and blue sites represent near-source (NS),
typical urban (TU), and residential area (RA) site types, respectively.
Column AOD and surface PM measurements
The AERONET sun photometers (http://aeronet.gsfc.nasa.gov/index.html),
which provide aerosol optical and microphysical properties based on direct
sun and diffuse sky measurements (Holben et al., 1998), have been widely used
as references for measurements from different satellite platforms. The AOD
and the Ångström exponent (AE) can be retrieved from direct sun measurements
in several spectral bands, usually between 340 and 1020 nm (Holben et al.,
1998). Diffuse sky measurements, which are performed at a minimum of four
wavelengths (440, 670, 870, and 1020 nm), use an inversion method to provide
detailed aerosol properties, such as the size distribution, phase function,
single scattering albedo, refractive index, etc. (Holben et al., 1998;
Dubovik and King, 2000). The AOD at 550 nm was obtained from AERONET level
2.0 direct sun measurements (cloud-screened and quality-assured) at seven
sites, and level 1.5 products (cloud-screened) at three sites. In addition to
the AERONET AOD, the effective radius for the total (fine and coarse modes)
size distribution obtained from the inversion product was also used to
represent the aerosol size information in the empirical regression models
(Dubovik and King, 2000). Although cloud-screened AERONET data were used,
additional cloud screening was performed for further quality control using
the cloud amount data provided by the Korea Meteorological Administration
(KMA; http://www.kma.go.kr) and the attenuated backscattering signal
measured from the two-wavelength Mie lidar located at Seoul National
University (SNU). A cloud-free sky condition was defined as a cloud amount of
less than 20 % (cf. Ogunjobi et al., 2004) and no detections of strong
scattering peaks of lidar measurements due to clouds. AOD measurements from
the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Terra
and Aqua satellites were also used to formulate the empirical regression
models (Remer et al., 2005; Levy et al., 2007). To identify the optimal grid
size for the MODIS AOD in this mesoscale spatial domain, the collocation
criteria of the MODIS 550 nm AOD products collected at spatial resolutions
of 10 and 30 km were tested and compared with averaged AERONET 550 nm AOD
measurements within ±30 min of the satellite overpassing time. As
shown in Fig. 2, the MODIS and AERONET AOD data are highly correlated,
showing a correlation coefficient (R) greater than 0.85 at most AERONET
sites during the DRAGON-Asia campaign. At all AERONET sites except the
DRAGON_NIER station, higher correlations between AERONET and MODIS data were
found for MODIS resolutions of 10 km than for those with MODIS resolutions
of 30 km.
The distribution of correlation coefficients between AERONET AOD
and MODIS AOD at 0.55 µm with respect to different spatial
resolutions. The inner (outer) circle indicates the correlation between
AERONET AOD and MODIS AOD with a resolution of 10 km (30 km).
The MODIS AOD data at a 10 km resolution at nadir
(“Optical_Depth_Land_And_Ocean”) obtained from MODIS Collection 5
aerosol products were also screened out when the MODIS cloud fraction over
land (“Cloud_Fraction_Land”) was higher than 0.5 or the
cloud amount from the KMA was higher than 20 %. Table 2 shows a
statistical summary of the AERONET and MODIS AOD data that were available at
the measurement sites for the entire campaign period before and after
additional cloud screening. The maximum AERONET AOD was reduced by 1.57 (from
2.99 to 1.42) after additional cloud screening, while that of MODIS did not
change. The mean and median AERONET AOD values were also reduced after cloud
screening, and those of MODIS changed slightly. Also, the number of data sets
after cloud screening was reduced by approximately 38.0 %, and the
reduction in the available MODIS data after cloud screening was 13.7 %.
Time plot of attenuated backscatter coefficients observed from the
two-wavelength Mie lidar at Seoul National University, and boundary layer
height (marked by black squares) retrieved by the automated wavelet
covariance transform (WCT) method of Brooks (2003).
Hourly-averaged PM10 concentrations, measured routinely at 10 national air
quality monitoring sites, were used during the DRAGON-Asia campaign. The
PM10 concentrations were measured by a beta (β)-ray absorption method
using a PM10 Beta Gauge (model PM10B.G, W&A Inc.), which operates on the
premise that the absorption of beta rays increases in proportion to the
number of particles collected in the filter (Hauck et al., 2004).
Statistical summary of AOD and cloud-screened AOD
(AODcl) observed by AERONET and MODIS during the DRAGON-Asia
campaign period.
AERONET
MODIS
AOD
AODcl
AOD
AODcl
Mean ± SD
0.51 ± 0.34
0.42 ± 0.26
0.74 ± 0.38
0.73 ± 0.37
Min
0.09
0.09
0.03
0.03
Median
0.43
0.35
0.72
0.68
Max
2.99
1.42
1.94
1.94
N
3406
2112
292
252
To investigate the relationship between columnar AOD and surface-level
PM10, the measurements must be collocated both spatially and temporally.
The AERONET AOD data obtained from the station nearest to the PM monitoring
site (within a maximum distance of approximately 4.5 km) were used. On the
other hand, the MODIS AOD data, which were measured at different spatial grid
resolutions, were extracted within a maximum distance of 0.2∘ of the
PM10 measurement sites. The AERONET and MODIS AOD were both temporally
collocated within ±30 min of the hourly PM10 measurement time.
Meteorological measurements
Meteorological data were used to investigate the relationship between AOD and
PM10 concentrations. The attenuated backscatter coefficient at 532 nm,
measured by the two-wavelength Mie lidar located at Seoul National University
(http://www-lidar.nies.go.jp/Seoul/), was used to calculate the hourly
BLH using the automated wavelet covariance transform (WCT) method (Brooks,
2003). The WCT method was applied to backscattered lidar signals at heights
above 300 m from the surface to avoid the problem of uncertainty in lidar
overlap (Campbell et al., 2002). Figure 3 shows an example of temporal
variation in the BLH obtained by application of the WCT method.
In addition to the BLH, other meteorological data such as temperature,
relative humidity, cloud amount, and wind speed and direction were obtained
from hourly measurements at a KMA weather observation station in Seoul
(37.57∘ N, 126.97∘ E). All meteorological data within
± 30 min of the PM10 observation time were used for this
investigation.
The empirical linear models used for PM10 estimations in this
study.
Model
Model description
Application
M1
PM10 = aAOD + b
AERONET, MODIS
M2
PM10 = aAODBLH+b
AERONET, MODIS
M3
PM10 = aAOD×ReffBLH+b
AERONET
M4
PM10 = aAODBLH×fRH+b
AERONET, MODIS
M5
PM10 = aAOD×ReffBLH×fRH+b
AERONET
M6
Sect. 3.2, Eq. (5) (multiple linear regression model)
AERONET
Methodology
Relationship between column AOD and surface PM concentration
The AOD is the integration of the radiative extinction due to aerosols from
the surface up to the top of the atmosphere (TOA) at a given wavelength. The
AOD can be defined as (Koelemeijer et al., 2006)
AOD=π∫0H∫0∞Qext,amb(m,r,λ)namb(r,z)r2drdz=πf(RH)∫0H∫0∞Qext(m,r,λ)n(r,z)r2drdz,
where Qext,amb(m,r,λ) is the unitless
extinction efficiency influenced by the refractive index (m), particle
radius (r), and wavelength (λ) under ambient conditions;
Qext(m,r,λ) the extinction efficiency under dry
conditions; namb(r,z) the size distribution under ambient
conditions representing the number of aerosols at corresponding height (z)
with a radius (r); n(r,z) the size distribution under dry conditions; and
H the top height for the integration.
Scatterplots of the various parameters – including (a) AOD, (b) BLH,
(c) effective radius, and (d) RH – against the dependent variable of PM10
concentration. The regression line is shown as a blue dashed line.
The PM10 concentration, which is the mass concentration of surface-level
aerosols with diameters less than 10 µm in dry conditions, is given by
PM10=43πρ∫05r3n(r)dr,
where ρ is the particle mass density and r is the dry aerosol radius.
With the assumption of a homogeneous aerosol distribution within the BLH, the
integration from the surface up to the TOA (H) can be simplified by
multiplying by the BLH. Also, the ambient environmental condition can be
converted into the dry condition by using the particle hygroscopic growth
factor, f(RH). By combining Eqs. (1) and (2), the PM10 concentration
can be expressed as
PM10=AODBLH⋅fRH4ρReff3Qext,
where the effective radius Reff and the average of the extinction
efficiency over the size distribution < Qext > are defined as
Reff=∫r3n(r)dr∫r2n(r)dr,Qext=∫r2Qext(r)n(r)dr∫r2n(r)dr.
In order to extend this analysis to the PM2.5, the upper size limit in the integral
in Eq. (2) needs to be corrected and fine-mode fraction (FMF) to be
additionally considered in Eq. (3). However, since available PM2.5
measurements were quite limited in this area and time, we focused only on
PM10 in this present study. In Eq. (3), various physical parameters are
involved in the relationship between AOD and PM10. The PM10
concentration is proportional to AOD, Reff, and particle mass
density ρ; on the other hand, PM10 is inversely proportional to
BLH, f(RH), and <Qext>. To gain insight
into the relationship between PM10 and major predictors, all PM10
concentration was plotted against AOD, BLH, RH, and Reff, which
were used in this study for development and validation of the PM10
estimation as shown in Fig. 4. The correlation coefficient (R) between
PM10 and AOD was 0.5, and that of Reff was 0.32. As expected,
BLH showed negative correlation with PM10 (-0.36). However, RH did not
show any significant relationship with PM10. Among these parameters, BLH
and f(RH) have been used as parameters in empirical models to estimate PM
concentrations using AOD data, as described in Table 1. On the other hand,
parameters such as ρ, Reff, and <Qext> have been rarely included in empirical models.
In the present study, the effective radius of the aerosol size distribution
was included for the first time as an additional parameter in the empirical
models. The empirical models, and the parameters considered in those models,
are described in detail in Sect. 3.2.
Description of empirical linear models for PM10 estimation
Table 3 presents a summary of the various models used in this study. Models
M1 to M5 are empirical models based on the relationship between AOD and PM
concentration, as described in Sect. 3.1, whereas M6 represents a multiple
linear regression model. Among the empirical models, M1, M2, and M4 have been
used in previous studies (e.g., Chu et al., 2003; Wang and Christopher, 2003;
Engel-Cox et al., 2004, 2006; Koelemeijer et al., 2006; Gupta and
Christopher, 2008; Schaap et al., 2009; Emili et al., 2010; Wang et al.,
2010). Model M1 includes only AOD as a predictor of the PM10
concentration, while M2 additionally includes BLH to consider the aerosol
vertical extension. The vertical correction on AOD is represented in M2 by
dividing AOD by BLH, with the assumption that aerosols within the boundary
layer are homogeneously mixed. Model M4 corrects for RH by using an aerosol
hygroscopic growth factor term f(RH) which represents the effects of aerosol
hygroscopic growth caused by variations in relative humidity, in addition to
the parameters in M2. In this study, f(RH) based on experimental data
obtained near the Beijing megacity during the spring was employed (Pan et
al., 2009), which is appropriate to this study with respect to both temporal
and spatial conditions. Models M3 and M5, which also included the parameters
used in M1, M2, and M4, were the first empirical models to include the
effective radius of the aerosol size distribution as a size correction
factor. Model M3 includes the aerosol effective radius in addition to the
parameters in M2 to account for the size of aerosol particles. Model M5
reflects all parameters, including AOD, f(RH), BLH, and the effective radius,
as shown in Table 3. The effective radius of the aerosol size distribution
for the total mode, which was used in M3 and M5, was obtained from AERONET
inversion products (Dubovik and King, 2000; Dubovik et al., 2000). This
Reff is one of the main features derived by the particle volume
size distribution retrieved by the AERONET inversion algorithm, which was
demonstrated to be adequate in practically all situations, especially for
the intermediate particle size range (0.1 ≤ r ≤ 7 µm) with
10–35 % of retrieval errors, as reported by Dubovik et al. (2002).
In addition to the simple empirical models (M1–M5) derived from the
relationship between AOD and PM (Eq. 3), a multiple linear regression (MLR)
model was used as a statistical approach to determine PM10
concentrations as a function of eight different parameters associated with PM
estimation:
PM10=expβ0×AODβAODBLHβBLHAEβAE×expβlocLocation+βWS(WS)+βWDWD+βRHRH+βTempTemp.
This MLR model of Eq. (4) can be log-transformed into a simpler form of
linear regression as shown in Eq. (5).
lnPM10=β0+βAODlnAOD+βBLHlnBLH+βAElnAE+βlocLocation+βWSWS+βWDWD+βRHRH+βTempTemp
The dependent variable in Eq. (5) is the logarithm of the hourly
PM10 concentration measured at the PM monitoring sites. The independent
variables include aerosol optical properties such as AOD and AE; various
meteorological measurements such as BLH, temperature (Temp), and wind speed
(WS); and two categorical variables: type of measurement site (Location) and
wind direction (WD). The Reff inversion product is from diffuse
sky radiance measurement which has strict stability criteria. Thus, the
number of data (N = 713) is quite lower than products from direct sun
measurements including AE (N = 2112), which also implies the aerosol size
information. For that reason, AE was used as a variable in the MLR model
instead of Reff to secure a sufficient number of data samples (Dubovik
et al., 2000; Schuster et al., 2006). Measurement sites were categorized into
three types: near source (NS), typical urban (TU), and residential area (RA),
as shown in Fig. 1. The NS sites were those located within 500 m of sources;
sources in this case included traffic-congested roads and industrial
complexes. The TU sites were located more than 500 m from sources, in either
commercial or residential areas. The RA sites were located more than 500 m
from sources and in residential areas only. Wind directions were classified
as east, south, west, or north. Regression coefficients (β) were
determined for each of the independent variables. This MLR analysis was
conducted using the AERONET data set only, because this was sufficient to yield
credible results.
Estimated regression coefficients for the multiple linear regression
model (M6) (described in Sect. 3.2, Eq. 5) using AERONET data
(N = 1058).
Model parameter
Estimate
Standard error
P value
Intercept
4.363
0.080
< 0.0001
ln(AOD)
0.527
0.022
< 0.0001
ln(BLH)
-0.280
0.028
< 0.0001
ln(AE)
0.066
0.033
0.047
Location type
Near source
0.233
0.032
< 0.0001
Urban
0.013
0.032
0.684
Suburban
0.000
–
–
Wind speed
0.015
0.008
0.052
Wind direction
From the north
0.205
0.054
< 0.0001
From the south
0.164
0.045
< 0.0001
From the west
0.307
0.036
< 0.0001
From the east
0.000
–
–
RH
-0.610
0.116
< 0.0001
Temperature
-0.010
0.002
< 0.0001
Correlation coefficient (R), root mean square error (RMSE), mean
normalized bias (MNB), and mean fractionalized bias (MFB) between measured
PM10 concentrations and those estimated by the different empirical
linear models, using AERONET and MODIS data, during the DRAGON-Asia campaign
period in Seoul. Numbers in parentheses represent results corresponding to
the same number of data points as used in M3 and M5, when effective radius of
aerosol data were available.
Model
M1
M1cl
M2
M3
M4
M5
M6
AERONET
R
0.40
0.54
0.62 (0.46)
0.55
0.63 (0.47)
0.58
0.68
R2
0.16
0.29
0.39 (0.21)
0.30
0.40 (0.23)
0.34
0.47
N
1712
1054
1054 (373)
373
1054 (373)
373
1054
RMSE (µg m-3)a
28.62
23.79
22.11 (23.27)
22.01
22.11 (22.98)
21.32
21.05
MNB ( %)b
27.70
21.75
21.27 (25.39)
22.11
21.27 (24.54)
20.66
5.65
MFB ( %)c
10.96
9.20
8.97 (10.43)
9.08
8.97 (10.17)
8.50
-0.83
MODIS
R
0.46
0.50
0.72
–
0.71
–
–
R2
0.21
0.25
0.51
–
0.51
N
291
252
252
–
252
–
–
RMSE (µg m-3)
28.49
28.55
23.02
–
23.19
–
–
MNB (%)
22.09
21.83
14.80
–
14.92
–
–
MFB (%)
9.53
9.64
6.40
–
6.53
–
–
a RMSE (root mean square error) = 1N∑i=1Nmi-oi2;
b MNB (mean normalized bias) = 1N∑i=1Nmi-oioi × 100 %;
c MFB (mean fractionalized bias) = 1N∑i=1Nmi-oimi+oi2×100 %;
mi and oi indicate estimated PM10 using models and observed PM10
concentrations, respectively. N is the number of data points.
The distribution (5, 10, 25 %, median, 75,
90, and 95 %) of AOD, boundary layer height (BLH), relative
humidity (RH), effective radius, and PM10 concentrations in the modeling
and validation groups derived from AERONET data sets collected during the
DRAGON-Asia campaign in Seoul. The red dashed line in the plot denotes the
mean value.
For an unbiased assessment of model performance, the entire AERONET data set
was randomly divided into two groups, a modeling group (N = 1058 for M1,
M2, M4, and M6, and N = 369 for M3 and M5) that was used to develop the
empirical models, and a validation group (N = 1054 for M1, M2, M4, and M6,
and N = 373 for M3 and M5) that was used to validate these models. To
minimize the effects of temporal autocorrelation, data were selected such
that the time interval between validation and modeling data was at least 24
h. Summary statistics for the variables involved in the modeling and
validation data sets are shown in Fig. 5. All empirical models for hourly
PM10 estimates based on the AERONET data sets were fitted using the
modeling data set to estimate the model coefficients. Estimated regression
coefficients (β), standard errors, and p values of parameters used
in M6 (Eq. 5) are summarized in Table 4. As shown in Table 4, most
parameters used in M6 were found to be highly significant (p < 0.0001)
predictors of the PM10 concentration. The positive sign of the
coefficient for AOD (0.527 ± 0.022) shows a direct correspondence
between AOD and surface PM10, given that other conditions remained
constant. On the other hand, the estimated power of the BLH relationship was
negative (-0.280 ± 0.028), which indicates an inverse relationship
between BLH and the PM10 concentration. The reason for this inverse
relationship is that a lower BLH confines aerosols to a thinner atmospheric
layer, resulting in higher surface PM10 concentrations. A negative
coefficient was also obtained for RH (-0.610 ± 0.116), showing that
higher RH conditions result in lower PM10 concentrations (given constant
AOD values) – i.e., the effect of aerosol hygroscopic growth is reflected in
the MLR model (M6).
In this analysis, MODIS data sets collected over Seoul during the DRAGON-Asia
campaign were not divided into two groups (for model development and
validation) due to the relatively small size of the MODIS data set obtained
during the campaign (N = 252 for M2 and M4, as compared with N = 1054
for M2 and M4 for the AERONET data set).
Results and discussion
Evaluation of estimated PM10 using various empirical linear models
The hourly PM10 concentrations estimated by the various empirical models
were evaluated by comparing them with measured hourly surface-level
PM10. Table 5 shows a summary of the correlations and statistics between
the measured and estimated PM10 concentrations using the various model
types, obtained using the AERONET and MODIS data sets. The simplest model
(M1), with only AOD as a predictor, yields the lowest correlation of 0.40
(0.46) using the AERONET (MODIS) data set for the PM10 estimation. The
correlation obtained using the cloud-screened AOD data (M1cl) is
higher than that obtained using the raw AOD data (M1), which implies that
cloud screening contributes to an increase in the correlation between
measured PM and AOD by removing overestimated AOD measurements resulting from
cloud contamination (e.g. Schaap et al., 2009).
Spatial variations of the correlation coefficient (R), root mean
square error (RMSE), mean normalized bias (MNB), and mean fractionalized bias
(MFB) between measured PM10 concentrations and those estimated from the
different empirical linear models for the three different site categories,
using the data collected by AERONET and MODIS, during the DRAGON-Asia
campaign period in Seoul.
Performance of empirical models used to estimate hourly PM10
using AERONET data sets with respect to model and measurement site types
Model
M1cl
M2
M3
M4
M5
M6
NS
R
0.49
0.57 (0.49)
0.59
0.57 (0.49)
0.61
0.61
R2
0.24
0.32 (0.24)
0.35
0.33 (0.24)
0.38
0.37
N
807
807 (237)
237
807 (237)
237
190
RMSE (µg m-3)
26.29
24.79 (26.90)
24.79
24.67 (26.79)
24.28
22.85
MNB (%)
21.21
20.54 (27.32)
24.57
20.38 (26.95)
23.48
7.12
MFB (%)
9.03
8.56 (15.21)
13.05
8.65 (15.05)
12.35
-0.37
TU
R
0.51
0.60 (0.43)
0.61
0.61 (0.45)
0.64
0.72
R2
0.26
0.36 (0.18)
0.37
0.37 (0.20)
0.42
0.51
N
891
891 (367)
367
891 (367)
367
190
RMSE (µg m-3)
22.61
21.11 (21.77)
19.06
20.94 (21.50)
18.43
17.69
MNB ( %)
24.27
24.25 (23.91)
20.26
23.47 (22.67)
18.46
4.16
MFB ( %)
10.09
9.84 (6.14)
5.39
9.88 (5.78)
4.86
-1.85
RA
R
0.63
0.73 (0.63)
0.69
0.73 (0.64)
0.70
0.76
R2
0.40
0.53 (0.40)
0.47
0.54 (0.42)
0.50
0.59
N
414
414 (109)
109
414 (109)
109
92
RMSE (µg m-3)
19.67
17.35 (18.02)
16.92
17.26 (17.81)
16.53
17.09
MNB (%)
17.31
16.08 (25.85)
22.60
15.32 (25.19)
21.52
5.99
MFB (%)
8.15
7.26 (13.61)
12.08
6.94 (13.41)
11.62
0.47
Performance of empirical models to estimate hourly PM10
using MODIS data sets with respect to model and measurement site types
Model
M1cl
M2
M3
M4
M5
M6
NS
R
0.37
0.68
–
0.68
–
–
R2
0.14
0.46
–
0.46
–
–
N
105
105
–
105
–
–
RMSE (µg m-3)
32.09
27.34
–
27.32
–
–
MNBE (%)
24.93
16.10
–
15.98
–
–
MFB (%)
10.80
6.99
–
7.04
–
–
TU
R
0.42
0.72
–
0.71
–
–
R2
0.18
0.52
–
0.50
–
–
N
95
95
–
95
–
–
RMSE (µg m-3)
26.41
20.08
–
20.45
–
–
MNBE (%)
21.23
15.23
–
15.58
–
–
MFB (%)
9.42
6.55
–
6.78
–
–
RA
R
0.50
0.76
–
0.74
–
–
R2
0.25
0.57
–
0.55
–
–
N
52
52
–
52
–
–
RMSE (µg m-3)
23.66
17.93
–
18.33
–
–
MNBE (%)
17.36
11.40
–
11.57
–
–
MFB (%)
7.96
4.93
–
5.04
–
–
Model M2, in which BLH is an added parameter, shows a correlation coefficient
of 0.62 (0.72) and a root mean square error (RMSE) of 22.11 (23.02) µg m-3
between measured and estimated PM10 using the AERONET (MODIS)
AOD data; in this case, the estimate using MODIS AOD data as an input is a
better predictor than the estimate obtained using the AERONET data (Table 5).
This higher performance of the MODIS AOD and model M2 can be attributed to a
MODIS overpass time near midday, when aerosols are generally well mixed in
the boundary layer, as compared with the situation in the early morning or
late afternoon. These improved correlation coefficients imply that a vertical
correction on AOD using the BLH value improves PM10 estimates. The
correlations between measured and estimated PM10 using M2 with MODIS
data are slightly higher than those obtained in the previous work of Emili et
al. (2010), which was based on a combination of Spinning Enhanced Visible and
Infrared Imager (SEVIRI) and MODIS AOD data to estimate hourly PM10
concentrations over the European Alpine regions. The differences between the
results of Emili et al. (2010) and those obtained here could be associated
with uncertainties in surface reflectance in Alpine regions that resulted in
relatively larger errors in the Alpine AOD data as compared with those
obtained in Seoul.
Aerosol effective radius data obtained from AERONET measurements was used as
a parameter in model M3 to estimate PM10. The effective radius of
aerosol, as derived from sky radiances obtained from solar almucantar
measurements, is available only when the solar zenith angle (SZA) is larger
than 50∘ (except for near local noon), which avoids polarization
effects (Holben et al., 1998; Dubovik and King, 2000). Consequently, in
contrast to the AOD data, the effective radius data are available only in a
limited time window. The limited number of effective radius measurements was
used as an input to M3 to estimate PM10; we used 35.4 %
(N = 373) of the total number of AERONET validation data sets
(N = 1054) in which data were simultaneously available for both AOD and
the effective radius. Model M3 was not implemented using MODIS AOD due to a
lack of effective radius information in the MODIS data sets over land areas.
As shown in Table 5, the correlation between measured PM10 and those
estimated from M3 is higher than that obtained using M2 with the same number
of data sets (N = 373; RM3,AERO = 0.55,
RM2,AERO = 0.46). Although the results are subject to
further validation, aerosol size corrections using the effective radius (M3),
in general, lead to better estimates of PM10 concentrations than do
those without (M2), at least during the time frame of the intensive campaign
period.
Model M4, which incorporates the aerosol hygroscopic growth factor (f(RH)) in
PM10 estimation, yields a correlation coefficient of 0.63 (0.71) for the
AERONET (MODIS) data set (Table 5). These correlations are similar to those
obtained using M2, in which the RH correction is absent. The results suggest
that RH levels do not significantly influence BLH-corrected estimates at our
measurement sites during the campaign period, during which average daytime RH
values were 30.5 ± 11.0 %; at these RH levels, it appears
that aerosols are largely unaffected by hygroscopic growth.
The PM10 estimates derived from M5 – which considers BLH, f(RH), and the
effective radius – were also evaluated by comparisons with PM10
concentrations measured at the surface. As discussed previously, the number
of samples for the effective radius used in M3 (N = 373) was also used in
M5, and M5 was also evaluated using the AERONET data. Table 5 shows that the
correlation coefficient obtained using model M5 was 0.58, while those from M3
and M4 were 0.55 and 0.47, respectively, using the same number of data sets
(N = 373). The correlation between measured and estimated PM10
concentrations obtained from M5 is higher than that obtained from M4, on
account of the addition of aerosol size information. However, this
correlation obtained from M5 is slightly improved relative to that obtained
from M3, as the effect of the RH correction is considered to be negligible
for PM10 estimations using data collected during the DRAGON-Asia
campaign when average RH values were low.
The PM10 concentrations were also estimated from the MLR model (M6). As
discussed in Sect. 3.2, 1054 AERONET data sets were used for the validation of
PM10 estimated using M6. The correlation coefficient between the
measured PM10 and those estimated from M6 is 0.68. This correlation
coefficient is the highest among those obtained by any of the empirical
models in this study, and it shows that various meteorological parameters – such
as RH, temperature, wind speed, and wind direction – contribute to a
substantial increase in the accuracy of PM10 estimates.
The BLH and the effective radius of aerosols are the dominant predictors of
PM10 in the empirical models, while the effect of RH on PM10
estimation during the campaign period is negligible. However, the
contribution of the RH correction may vary seasonally, which is further
discussed in Sect. 4.3. In terms of the errors in the estimated PM10
concentrations, the RMSE of PM10 estimated using M6 (M2) with the input
of the AERONET (MODIS) data set is 21.05 (23.02) µg m-3, which
is the lowest among those calculated with the empirical models (Table 5). The
RMSE values between measured PM10 concentrations and those estimated
using M1cl (N = 1054), M2 (N = 1054), and M4
(N = 1054), based on the same number of AERONET data sets as inputs, are
23.79, 22.11, and 22.11 µg m-3, respectively, showing that
the models tend to improve (i.e., the errors tend to decrease) when using the
BLH as a predictor in the empirical models. This improvement in the models
was also found when a vertical correction is applied to the MODIS data. The
RMSE values of PM10 estimated using M1cl (N = 252), M2
(N = 252), and M4 (N = 252), and based on inputs of MODIS data,
were 28.55, 23.02, and 23.19 µg m-3, respectively. The RMSE
values of PM10 estimated using M2 (N = 373), M3 (N = 373),
and M5 (N = 373), and based on the same number of AERONET data sets,
were 23.27, 22.01, and 21.32 µg m-3, respectively, when a
size correction using the aerosol effective radius and an RH correction using
the particle hygroscopic growth factor were incorporated into the models. To
evaluate the empirical model performance for PM10 estimation, the mean
normalized bias (MNB) and the mean fractionalized bias (MFB) were also
calculated (these statistical parameters are described in the footnote of
Table 5). The tendencies of both the MNB and MFB are similar to those of the
RMSE, except for M6. All MFB values (except for M6) are positive, which
indicates that the PM10 concentrations derived from the models are
generally overestimated when compared with measured PM10 values. The MFB
of M6 was -0.83 %, which shows that M6 tends to underestimate the
PM10 concentration, especially at high concentrations on account of the
log transformation of the data.
Seasonal variations of the correlation coefficient (R) and root mean
square error (RMSE) between measured PM10 concentrations and those
estimated by the different empirical linear models using AERONET data sets,
collected at Yonsei University for 17 months.
Performance of empirical models to estimate hourly PM10
using AERONET data sets with respect to model and season
Model
M1cl
M2
M3
M4
M5
Spring
R
0.39
0.47 (0.45)
0.48
0.48 (0.46)
0.54
R2
0.15
0.22 (0.20)
0.24
0.23 (0.21)
0.29
N
465
465 (142)
142
465 (142)
142
RMSE (µg m-3)
41.17
39.60 (29.39)
28.77
39.37 (29.16)
27.76
Summer
R
0.70
0.67 (0.64)
0.64
0.71 (0.67)
0.66
R2
0.50
0.46 (0.41)
0.41
0.50 (0.45)
0.43
N
85
85 (21)
21
85 (21)
21
RMSE (µg m-3)
13.85
14.39 (13.75)
13.60
13.81 (13.13)
10.39
Autumn
R
0.60
0.64 (0.52)
0.54
0.66 (0.57)
0.58
R2
0.36
0.42 (0.28)
0.29
0.41 (0.33)
0.34
N
212
212 (99)
99
212 (99)
99
RMSE (µg m-3)
13.41
12.89 (14.86)
14.71
12.66 (14.32)
14.17
Winter
R
0.63
0.70 (0.70)
0.81
0.70 (0.70)
0.81
R2
0.40
0.49 (0.49)
0.65
0.49 (0.49)
0.66
N
284
284 (116)
116
284 (116)
116
RMSE (µg m-3)
19.81
18.17 (21.16)
17.44
18.10 (21.01)
17.29
Spatial characteristics of correlations between measured and estimated PM10
Large variations in the mean and standard deviation of the measured PM10
concentrations were observed, with the size of the deviations dependent on
the measurement site type. As described in Sect. 3.2, the site types include
NS, TU, and RA site types in
Seoul, as identified during the DRAGON-Asia campaign. The means (standard
deviations) of the hourly measured PM10 concentration in Seoul during
the campaign period were 62.21 (± 33.78), 53.42 (± 28.40), and
52.19 (± 26.15) µg m-3 at the NS, TU, and RA site types,
respectively. The highest mean and standard deviation of the PM10
concentrations were found at the NS site type, while the lowest were found at
the RA site type.
To identify spatial variability within the performance of the empirical
models, the correlations between measured and estimated PM10
concentrations were further investigated with respect to the classification
of site types (NS, TU, and RA). Table 6 shows the correlations between
measured and estimated PM10, with inputs of AERONET and MODIS data, and
as dependent on the measurement site type. As shown in Table 6, correlation
coefficients for the RA site show good model performances (0.69–0.76) using
M3, M5, and M6; however, model performances for the NS and TU site types fall
within the ranges 0.59–0.61 and 0.61–0.72, respectively. Correlation
coefficients for the RA site type are in the range 0.63–0.73 for models
M1cl, M2, and M4, whereas those for the NS and TU site types are
0.49–0.57 and 0.51–0.61, respectively. The RMSE values are 16.53–19.67,
17.69–22.61, and 22.85–26.79 µg m-3 for the RA, TU, and NS
site types, respectively, showing that errors in PM10 estimates at NS
site types are higher than those at TU and RA site types. Thus, the highest
correlation in each empirical model was obtained for the RA sites, while the
lowest was found at the NS sites (Table 6). The NS site type shows large
spatial and temporal variability in surface PM10 concentrations due to
large anthropogenic aerosol emissions, which presents difficulties in the
development of empirical models for estimating PM10 concentrations. The
results obtained using the AERONET data (Table 6) also demonstrate that
hourly PM10 estimations depend largely on both the empirical model used
for the estimation and the site type in megacity areas, where the spatial and
temporal variability of aerosol concentrations is large.
As discussed in Sect. 4.1, the spatial dependency of empirical model
performance using the MODIS data inputs was investigated only for models
M1cl, M2, and M4, due to a lack of effective radius information
in the MODIS data. The spatial dependency of the empirical model performance
using the MODIS data is large. Table 6 shows the highest correlations for the
RA site type, while the lowest are for the NS sites. The correlation
coefficients for the RA sites were between 0.50 and 0.76 for models
M1cl, M2, and M4, whereas those for the TU and NS sites were
0.42–0.72 and 0.37–0.68, respectively. The highest correlation between
measured and estimated PM10, obtained using M2 with MODIS data, was
comparable with those obtained using M5 and M6 with AERONET data. This high
performance of the empirical models using the MODIS data can be explained by
the overpass time of the MODIS data, which was around midday, when aerosols
are generally well-mixed within the boundary layer compared with other times
of the day (Schaap et al., 2009).
Distribution of hourly surface PM10 concentrations estimated
by model M2 using the MODIS data sets for (a) 04:00 UTC on 10 May 2012 and
(b) 02:00 UTC on 21 May 2012. Circles indicate the observed PM10 concentrations at
PM monitoring sites.
The inverse distance weighting (IDW) interpolation method was applied to
estimate the PM10 concentrations using M2 with MODIS AOD data over
various campaign sites, at a spatial resolution that was finer than that
established for the original MODIS data (10 km). The IDW method was used to
estimate PM10 concentrations in Alpine regions with simple PM source
distributions (Emili et al., 2010). In this study, model M2 with MODIS AOD
and lidar BLH data was used to estimate PM10 concentrations at a
resolution of 0.02∘ (ca. 2 km) over the Seoul area. Slopes and
intercepts of M2 were spatially interpolated to a resolution of
0.02∘ using the IDW method, as calculated from values at the four
closest pixels. Figure 6 shows PM10 estimates at the 2 km resolution
based on the IDW method, where the colored circles represent PM10
concentrations measured at PM monitoring sites. The spatial distribution of
the estimated and measured PM10 concentrations is generally in good
agreement. However, a discontinuity is observed in Fig. 6, which could be due
to a problem associated with AOD input at a low resolution and its
interpolation based on inhomogeneous sampling of a small number of data
points. In order to understand smaller-scale features of the air quality,
higher spatial resolution AOD products such as a MODIS 3 km product are under
development. Although this high-resolution product has been expected to
explain aerosol gradients in detail at a small scale, the 3 km product showed
poor performances compared to the 10 km product due to improper
characterization of the urban surfaces (Levy et al., 2013; Munchak et al.,
2013). This bias in surface reflectance of MODIS algorithm indeed resulted in
misfit between column AOD and surface PM concentration, as discussed in
Escribano et al. (2014). Thus, estimated spatial characteristics of surface
PM concentrations are reliable when aerosol products are satisfied with both
higher quality and finer resolution.
Seasonal characteristics of correlations between measured and estimated PM10
The empirical models proposed in Sect. 3.2 were applied to data collected for
an extended time period (beyond that of the DRAGON-Asia campaign) at a Yonsei
University (YU) site to investigate the seasonal characteristics of the
various empirical model performances for PM10 estimation. Seasonal
effects were studied during all four seasons (spring, summer, autumn, and
winter), defined here as the periods March–May, June–August,
September–November, and December–February, respectively. The AERONET level
2.0 data were used from March 2011 to July 2012 at the YU site, as YU is the
only site in Seoul where AERONET level 2.0 data are available for the period
that covers all four seasons, and which also includes the DRAGON-Asia
campaign period. All models except for MLR model M6 were used to identify the
seasonal dependency of model performance; M6 was not used because the number
of data sets was insufficient to determine the regression coefficients for the
four different seasons.
Table 7 summarizes the seasonal variations in the correlations between
measured and estimated PM10 using the various empirical models and the
AERONET data. The overall statistics (including correlation coefficients) of
the models using the AERONET data were found to be poorest in spring, when
compared with those of other seasons. Correlation coefficients using all
empirical models and the AERONET data (Table 7) were in the range 0.39–0.54
for spring, whereas those for summer, autumn, and winter were 0.64–0.71,
0.52–0.66, and 0.63–0.81, respectively. The poor performance in spring can
be attributed to frequent occurrences of Asian dust events as well as
persistent anthropogenic influences at YU which is located in a continental
downwind region. The Asian dust events in spring generate inhomogeneous
aerosol vertical distributions due to elevated aerosol layers above the BLH,
which are not taken into account by using BLH in the empirical models
(Murayama et al., 2001; S. W. Kim et al., 2007). Therefore, the performance
using M2 in spring (R = 0.47) is still much poorer than performances in
other seasons (R ≥ 0.64), which could be attributed to the presence
of multiple aerosol layers and mixtures of different types of aerosols in
spring.
The highest correlations of estimated and measured PM10 concentrations
occur in winter using M3 and M5 (R = 0.81), which both consider the BLH
and the effective radius of aerosol. In winter, a lower aerosol mixing height
and homogeneous microphysical and optical properties within the BLH are
thought to result in BLH and the effective radius being the dominant
predictors in the empirical models for PM10 estimation. A lower aerosol
mixing height is often induced by a temperature inversion in winter, when
homogeneous aerosol properties are likely to be present within the boundary
layer due to the reduced influence of the long-range transport of aerosols
above the BLH. The correlation using M4, which considers BLH and RH in summer
(N = 85) and autumn (N = 212), when RH is relatively higher than
in the other two seasons, yields correlations of 0.71 and 0.66, respectively.
In summer, the RH correction improves the PM10 estimation, yielding a
correlation of 0.71 using M4 compared with a correlation of 0.67 using M2. In
autumn, incorporation of the RH correction into the models (M4) yields a
slightly improved correlation compared with models that exclude the RH
correction (M2); i.e., 0.66 and 0.64, respectively. The correlation also
increases from 0.64 (M3) to 0.66 (M5), and from 0.54 (M3) to 0.58 (M5), in
summer and autumn, respectively, when including the RH correction, which
shows that the RH correction is effective in conditions of higher RH.
Conclusions
Concentrations of PM10 were estimated in Seoul, Korea, during the
DRAGON-Asia campaign period by considering the effective radius of the
aerosol size distribution, together with BLH, RH, and AOD, within empirical
models that used AERONET data obtained at multiple sites for the first time.
The performances of various empirical models were also evaluated for hourly
PM10 estimations using AERONET and MODIS data sets. The improved
performances were found when the vertical correction on AOD using the BLH was
applied in both AERONET and MODIS data sets (M2) compared to the simplest
model (M1). These empirical model performances were further enhanced by
additionally including the effective radius for size correction (M3, M5).
However, not meaningful improvements were found when RH was considered
additionally (M4). Among different empirical models based on the physical
relationship between AOD and PM concentration (M1–M5), model M5, which
follows the nearest form of that relationship with the largest number of
parameters, showed the best performance. In general, BLH and the effective
radius were found to be the key parameters when estimating PM10 using
the empirical models, while RH did not show any significant effect on
PM10 estimation using the multiple data sets collected during the spring
campaign period, when RH is relatively lower than summer and autumn.
The spatial variability of empirical model performance was also investigated
for three different site types, which were categorized according to the
distance between sources and instruments. The highest correlation for each
empirical model using both AERONET and MODIS data occurred for the RA sites,
while the lowest was for the NS sites, where the spatiotemporal variability
of aerosols is high. The selection of site types either dominates or is
comparable with the specific empirical model selected for estimating
PM10 concentrations in Seoul, as results are significantly affected by
the spatial variability of aerosols. The performances of the models for
estimating PM10 were also good at midday when aerosols are well mixed
within the boundary layer, which suggests a dependence of PM10
estimation on the measurement time.
Seasonal variations in the performances of the empirical models for PM10
estimation were detected. The highest correlation was found using M3 and M5
in winter (R = 0.81), when both BLH and the effective radius of the
aerosol are considered; the high correlations can be attributed to a lower
aerosol mixing height and homogeneity in the optical and microphysical
properties of aerosols within the BLH. The poorest performance was found in
spring, when the impact of Asian dust events on both inhomogeneous vertical
structure of particle number and aerosol composition at the measurement sites
is common, and leads to variable effects.
As discussed in this study, the spatial distribution of surface-level PM10
concentrations can be estimated using empirical models. The use of satellite
measurements in these various empirical models has the advantage of both
simplicity and wide spatial coverage over megacity areas. However, the
predictability of PM10 distributions using empirical models should be
improved. For better estimating surface PM concentrations by satellite
remote sensing, especially in urban areas where diverse aerosol sources are
distributed, aerosol products with a higher quality and a finer resolution
are required. Additionally, accurate and detailed information about aerosol
vertical distribution, size distribution, and composition will contribute to
improve empirical models. Also, to enhance the accuracy of PM10 estimations
in other seasons, further work is required to investigate seasonal effects
on the spatial variability of PM10 estimations in Seoul. In addition to the
evaluation of multiple empirical models in the megacity area, a CTM
should also be performed and validated for PM10
estimations.