In this proof-of-concept paper, we apply a bulk-mass-modeling method using
observations from the NASA Cloud-Aerosol Lidar with Orthogonal Polarization
(CALIOP) instrument for retrieving particulate matter (PM) concentration over
the contiguous United States (CONUS) over a 2-year period (2008–2009).
Different from previous approaches that rely on empirical relationships
between aerosol optical depth (AOD) and PM2.5 (PM with particle
diameters less than 2.5 µm), for the first time, we derive PM2.5
concentrations, during both daytime and nighttime, from near-surface CALIOP
aerosol extinction retrievals using bulk mass extinction coefficients and
model-based hygroscopicity. Preliminary results from this 2-year study
conducted over the CONUS show a good agreement (r2∼0.48;
mean bias of -3.3µgm-3) between the averaged nighttime
CALIOP-derived PM2.5 and ground-based PM2.5 (with a lower r2
of ∼0.21 for daytime; mean bias of -0.4µgm-3),
suggesting that PM concentrations can be obtained from active-based
spaceborne observations with reasonable accuracy. Results from sensitivity
studies suggest that accurate aerosol typing is needed for applying CALIOP
measurements for PM2.5 studies. Lastly, the e-folding correlation
length for surface PM2.5 is found to be around 600 km for the entire
CONUS (∼300km for western CONUS and ∼700km
for eastern CONUS), indicating that CALIOP observations, although sparse in
spatial coverage, may still be applicable for PM2.5 studies.
Introduction
During the last decade, an extensive number of studies have researched the
feasibility of estimating PM2.5 (particulate matter with particle
diameters smaller than 2.5 µm) pollution with the use of
passive satellite-derived aerosol optical depth (AOD; e.g., Liu et
al., 2007; Hoff and Christopher, 2009; van Donkelaar et al., 2015).
Monitoring of PM concentration from space observations is needed, as
PM2.5 pollution is one of the known causes of respiratory-related
diseases as well as other health-related issues (e.g., Liu et al., 2005;
Hoff and Christopher, 2009; Silva et al., 2013). Yet, ground-based
PM2.5 measurements are often inconsistent or have limited
availability over much of the globe.
In some earlier studies, empirical relationships of PM2.5 concentrations and
AODs were developed and used for estimating PM2.5 concentrations from passive-sensor-retrieved AODs (e.g., Wang and
Christopher, 2003; Engel-Cox et al., 2004; Liu et al., 2005; Kumar et al.,
2007; Hoff and Christopher, 2009). One of the limitations of this approach
is that vertical distributions and the thermodynamic state of aerosol particles
vary with space and time. Especially for regions with elevated aerosol
plumes, deep boundary layer entrainment zones, or strong nighttime
inversions, column-integrated AODs are not a good approximation of surface
PM2.5 concentrations at specific points and times (e.g., Liu et al.,
2004; Toth et al., 2014; Reid et al., 2017). Indeed, Kaku et al. (2018)
recently showed that surface PM2.5 had longer spatial correlation
lengths than AOD, even in the “well behaved” southeastern United States
where previous studies showed good correlation between PM2.5 and AOD
(e.g., Wang and Christopher, 2003). To account for variability in aerosol
vertical distribution, several studies have attempted the use of chemical
transport models, or CTMs (e.g., van Donkelaar et al., 2015). Satellite data
assimilation of AOD has become commonplace, vastly improving AOD analyses
and short-term prediction (e.g., Zhang et al., 2014; Sessions et al., 2015).
Yet, PM2.5 simulations remain poor (e.g., Reid et al., 2016).
Uncertainties in such studies are unavoidable due to uncertainties in
CTM-based aerosol vertical distributions, and no nighttime AODs are
currently available from passive satellite retrievals.
It is arguable that from a climatological and long-term average perspective, the
use of AOD as a proxy for PM2.5 concentrations nevertheless has
certain qualitative skill (e.g., Toth et al., 2014; Reid et al., 2017) due
to the averaging process that suppresses sporadic aerosol events with highly
variable vertical distributions. Still, as illustrated in Fig. 1, in which
2-year (2008–2009) means of Moderate Resolution Imaging Spectroradiometer
(MODIS) AOD are plotted against PM2.5 concentrations throughout the
contiguous United States (CONUS), although a linear relationship is
plausibly shown, a low r2 value of 0.08 is found. To construct Fig. 1,
Aqua MODIS Collection 6 (C6) Optical_Depth_Land_And_Ocean data (0.55 µm),
restricted to “very good” retrievals as reported by the
Land_Ocean_Quality_Flag, are
first collocated with daily surface PM2.5 measurements in both space
and time (i.e., within 40 km in distance and the same day) and then
collocated daily pairs are averaged into 2-year means (for each PM2.5 site). Figure 1
may indicate that even from a long-term mean
perspective, aerosol vertical distributions are not uniform across the
CONUS, which is also confirmed by other studies (e.g., Toth et al., 2014).
AOD retrievals themselves, with known uncertainties due to cloud
contamination and assumptions in the retrieval process (e.g., Levy et al.,
2013), may also introduce uncertainties to that task.
For 2008–2009, scatter plot of mean PM2.5 concentration from ground-based U.S. EPA
stations and mean column AOD (550 nm) from collocated Collection 6 (C6) Aqua MODIS observations. The red line
represents the Deming regression fit.
On board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observations (CALIPSO) satellite, the Cloud-Aerosol Lidar with Orthogonal
Polarization (CALIOP) instrument provides observations of aerosol and cloud
vertical distributions during both day and night (Hunt et al., 2009; Winker et
al., 2010). Given that CALIOP provides aerosol extinction retrievals near
the ground, it is interesting and reasonable to raise the following question: can near-surface CALIPSO extinction be used as a better physical quantity than AOD
for estimating surface PM2.5 concentrations? This is because unlike
AOD, which is a column-integrated value, near-surface CALIPSO extinction is,
in theory, a more realistic representation of near-surface aerosol
properties. Yet, in comparing with passive sensors such as MODIS, which has
a swath width on the order of ∼2000km, CALIOP is a nadir-pointing instrument with a narrow swath of ∼70m and a repeat
cycle of 16 days (Winker et al., 2009). Thus, the spatial sampling of CALIOP
is sparse on a daily basis and temporal sampling or other conditional or
contextual biases are unavoidable if CALIOP observations are used to
estimate daily PM2.5 concentrations (Zhang and Reid, 2009; Colarco et
al., 2014). Also, there are known uncertainties in CALIPSO-retrieved
extinction values due to uncertainties in the retrieval process, such as the
lidar ratio (extinction-to-backscatter ratio), calibration, and the
“retrieval fill value” (RFV) issue (Young et al., 2013; Toth et al.,
2018).
Even with these known issues, especially the sampling bias, it is still
compelling to investigate if near-surface CALIOP extinction can be utilized
to retrieve surface PM2.5 concentrations with reasonable accuracy from
a long-term (i.e., 2-year) mean perspective. CALIOP data have been
successfully used in PM2.5 studies in the past but primarily for
assisting passive AOD and PM2.5 analyses using aerosol vertical
distribution as a constraint (e.g., Glantz et al., 2009; van Donkelaar et
al., 2010; Val Martin et al., 2013; Toth et al., 2014; Li et al., 2015; Gong
et al., 2017). However, the question of the efficacy of the
direct use of CALIOP retrievals remained. To demonstrate a concept, we developed a
bulk mass scattering scheme for inferring PM concentrations from near-surface aerosol extinction retrievals derived from CALIOP observations. The
bulk method used here is based upon the well-established relationship
between particle light scattering and PM2.5 aerosol mass
concentration (e.g., Charlson et al., 1968; Waggoner and Weiss, 1980; Liou,
2002; Chow et al., 2006), discussed further, with the relevant equations, in
Sect. 2.
In this study, using 2 years (2008–2009) of CALIOP and United States
(U.S.) Environmental Protection Agency (EPA) data over the CONUS, the
following questions are addressed:
Can CALIOP extinction be used effectively for estimating PM2.5 concentrations through a bulk mass scattering scheme from a 2-year mean
perspective for both daytime and nighttime?
Can CALIOP extinction be used as a better parameter than AOD for estimating
PM2.5 concentrations from a 2-year mean perspective?
What sampling biases can be expected in CALIOP estimates of
PM2.5?
How do uncertainties in bulk properties compare to overall CALIOP-retrieved
PM2.5 uncertainty?
Details of the methods and datasets used are described in Sect. 2. Section 3
shows the preliminary results using 2 years of EPA PM2.5 and CALIOP
data, including an uncertainty analysis. The conclusions of this paper are
provided in Sect. 4.
Data and methods
Since 1970, the U.S. EPA has monitored surface PM using a number of
Federal Reference/Equivalent Methods (FRMs/FEMs), which employ gravimetric,
tapered element oscillating microbalance (TEOM), and beta gauge instruments
(Federal Register, 1997; Greenstone, 2002). A total of 2 years (2008–2009) of daily
PM2.5 local conditions (EPA code = 88101) data were acquired from the
EPA Air Quality System for use in this investigation, consistent with our
previous PM2.5 study (Toth et al., 2014). These data represent
PM2.5 concentrations over a 24 h period and include two scenarios:
one sample is taken during the 24 h duration (i.e., filter-based
measurement) or an average is computed from hourly samples within this time
period (every hour may not have an available measurement, however).
Note that uncertainties have been reported for hourly PM measurements (Kiss
et al., 2017). Examples of some uncertainties in these PM2.5 measurements depend upon the instrument/method used: gravimetric (e.g.,
transport to the lab/human error and volatization of PM during the drying
process; Patashnick et al., 2001), TEOM (e.g., errors due to improper inlet
tube temperature; Eatough et al., 2003), and beta attenuation monitors
(e.g., changes in the sample flow rate due to variations in temperature and
moisture; Spagnolo, 1989). Also, it has been found that beta attenuation
monitors may be more accurate than TEOM, as TEOM can underestimate
PM2.5 at low temperatures (e.g., Chung et al., 2001). Still, as
suggested by Kiss et al. (2017), PM data collected over a longer period of
time are much less likely to be biased. Thus, we expect lower uncertainties
from data collected over 24 h than from daily data generated by averaging
hourly observations. Fully quantifying the differences from the two
different PM observing methods, however, is the subject for a future study.
CALIOP, flying aboard the CALIPSO platform within the A-Train satellite
constellation, is a dual-wavelength (0.532 and 1.064 µm) lidar that
has collected profiles of atmospheric aerosol particles and clouds since
summer 2006 (Winker et al., 2007). In this study, daytime and nighttime
extinction coefficients retrieved at 0.532 µm from the version 4.10
CALIOP Level 2 5 km aerosol profile (L2_05kmAPro) product
were used. Using parameters provided in the L2_05kmAPro
product, as well as the corresponding Level 2 5 km aerosol layer
(L2_05kmALay) product, a robust quality-assurance (QA)
procedure for the aerosol observations was implemented (Table 1). Further
information on the QA metrics and screening protocol are discussed in detail
in previous studies (Kittaka et al., 2011; Campbell et al., 2012; Toth et
al.,
2013, 2016). Once the QA procedure was applied, the aerosol profiles were
linearly re-gridded from 60 m vertical resolution (above mean sea
level,
a.m.s.l.) to 100 m segments (i.e., resampled to 100 m resolution) referenced
to the local surface (above ground level, a.g.l.; Toth et al., 2014, 2016).
The choice of 100 m was arbitrary, and the profiles were re-gridded in order
to obtain a dataset corrected for a.g.l., as opposed to the a.m.s.l.-referenced
profiles provided by the L2_05kmAPro product. Surface
elevation and relative humidity (RH) were taken from collocated model data
included in the CALIPSO L2_05kmAPro product (Vaughan et al., 2018; RH was taken from the Modern Era
Retrospective-Analysis for Research, or MERRA-2 reanalysis product). To
limit the effects of signal attenuation and increase the chances of
measuring aerosol presence near the surface, the atmospheric volume
description parameter within the L2_05kmAPro dataset is used
to cloud-screen each aerosol profile as in Toth et al. (2018).
The parameters, and corresponding values, used to assure the quality
of
the CALIOP aerosol extinction profile.
ParameterValuesIntegrated_Attenuated_Backscatter_532≤0.01sr-1Extinction_Coefficient_532≥0 and ≤1.25km-1Extinction_QC_532=0, 1, 2, 16, or 18CAD_Score≥-100 and ≤-20Extinction_Coefficient_Uncertainty_532≤10km-1Atmospheric_Volume_Description (Bits 1–3)=3Atmospheric_Volume_Description (Bits 10–12)≠0
In this study, near-surface PM mass concentration (Cm) is derived from
near-surface CALIOP extinction based on a bulk formulation as in Eq. (1)
(e.g., Liou, 2002; Chow et al., 2006):
β=Cmascatfrh+aabs×1000,
where β is CALIOP-derived near-surface extinction per kilometer,
Cm is the PM mass concentration in micrograms per cubic meter, ascat and
aabs are dry mass scattering and absorption efficiencies in
square meters per gram, and frh represents the light scattering
hygroscopicity. As a preliminary study, for the purpose of
demonstrating this concept, we assume the dominant aerosol type over the
CONUS is pollution aerosol (i.e., the most prevalent
near-surface aerosol type reported in the CALIOP products for the CONUS
during 2008–2009 is polluted continental) with ascat and aabs values
of 3.40 and 0.37 m2g-1 (Hess et al., 1998; Lynch et al., 2016),
respectively. These values are similar to those reported in Hand and Malm
(2007) and Kaku et al. (2018) but are interpolated to 0.532 µm from
values at 0.450 and 0.550 µm obtained from the Optical
Properties of Aerosols and Clouds (OPAC) model (Hess et al., 1998). Still,
both ascat and aabs have regional and species-related dependencies.
Also, only 2-year averages are used in this study, and we assume that
sporadic aerosol plumes are smoothed out in the averaging process and that
bulk aerosol properties are similar throughout the study region. We have
further explored the impact of aerosol types on PM2.5 retrievals in a
later section. Furthermore, to aid in focusing this study on fine-mode and anthropogenic aerosols, those aerosol extinction range bins classified
as dust by the CALIOP typing algorithm were excluded from the analysis.
Also, surface PM concentrations are dry mass measurements. To account for
the impact of humidity on ascat (it is assumed that aabs is not
affected by moisture; Nessler et al., 2005; Lynch et al., 2016), we
estimated the hygroscopic growth factor for pollution aerosol based on Hänel
(1976), as shown in Eq. (2):
frh=1-RH1-RHref-Γ,
where frh is the hygroscopic growth factor, RH is the relative
humidity, and RHref is the reference RH and is set to 30 % in this
study (Lynch et al., 2016). Γ is a unitless value (a fit parameter
describing the amount of hygroscopic increase in scattering) and is assumed
to be 0.63 (i.e., sulfate aerosol) in this study (Hänel, 1976; Chew et al.,
2016; Lynch et al., 2016).
Additionally, the CALIOP-derived PM density is for all particle sizes. To
convert from mass concentration of PM (Cm) to mass concentration of
PM2.5 (Cm2.5), which represents mass concentration for particle
diameters smaller than 2.5 µm, we adopted the PM2.5-to-PM10 (PM with diameters less than 10 µm) ratio (φ) of
0.6 as measured during the Studies of Emissions and Atmospheric Composition,
Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign over
the US (Kaku et al., 2018). Again, the ratio of PM2.5 to PM10
can also vary spatially; however we used a regional mean to demonstrate the
concept. Analyses in a later section using 2 years (2008–2009) of surface
PM2.5 to PM10 data suggest that 0.6 is a rather reasonable
number to use for the CONUS for the study period. Here we assume that mass
concentrations for particle diameters larger than 10 µm are
negligible over the CONUS. Thus, we can rewrite Eq. (1) as
Cm2.5=β×ϕascat×frh+aabs×1000,
where Cm2.5 is the CALIOP-derived PM2.5 concentration in
units of micrograms per cubic meter.
Lastly, we note that most of the results are shown in the form of scatter
plots with fits from a Deming regression (Deming, 1943). Due to uncertainties
in PM2.5 data, we show slopes computed from Deming regression
analyses instead of those from simple linear regression. Deming regression
in particular is appropriate here, as it accounts for errors in both the
independent and dependent variables (Deming, 1943) and has been used in
past PM2.5-related studies (e.g., Huang et al., 2014).
Results and discussionRegional analysis
Figure 2a shows the mean PM2.5 concentration using 2 years
(2008–2009) of daily surface PM2.5 data from the U.S. EPA
(PM2.5_EPA), not collocated with CALIOP observations. A
total of 1091 stations (some operational throughout the entire period;
others only partially) are included in the analysis, and observations from
those stations are further used in evaluating CALIOP-derived PM2.5 concentrations
(Cm2.5), as later shown in Fig. 3. PM2.5 concentrations of ∼10µgm-3 are found over the
eastern CONUS. In comparison, much lower PM2.5 concentrations of
∼5µgm-3 are exhibited for the interior CONUS,
over states including Montana, Wyoming, North Dakota, South Dakota, Utah,
Colorado, and Arizona. For the west coast of the CONUS, and especially over
California, higher PM2.5 concentrations are observed, with the
maximum 2-year mean near 20 µgm-3. Note that the spatial
distribution of surface PM2.5 concentrations over the CONUS as shown
in Fig. 2a is consistent with reported values from several studies (e.g.,
Hand et al., 2013; Van Donkelaar et al., 2015; Di et al., 2017).
For 2008–2009 over the CONUS, (a) mean PM2.5 concentration
(µgm-3) for those U.S. EPA stations with reported daily
measurements, and (c) 1∘× 1∘ average CALIOP-derived
PM2.5 concentrations for the 100–1000 m a.g.l. atmospheric layer, using
Eq. (3), for combined daytime and nighttime conditions. Also shown are
the pairwise PM2.5 concentrations from (b) EPA daily measurements and
(d) those derived from CALIOP (day and night combined), both averaged for
each EPA station for the 2008–2009 period. For all four plots, values
greater than 20 µgm-3 are colored red.
For 2008–2009 over the CONUS, 1∘× 1∘
average CALIOP extinction, relative to the number of cloud-free 5 km CALIOP
profiles in each 1∘× 1∘ bin, for the
100–1000 m a.g.l. atmospheric layer, for (a) daytime and (b) nighttime measurements. Also
shown are the corresponding CALIOP-derived PM2.5 concentrations,
using Eq. (3) for (c) daytime and (d) nighttime conditions. Values
greater than 0.2 km-1 and 20 µgm-3 for (a, b) and (c, d), respectively, are colored red. Scatter plots of mean PM2.5 concentration from ground-based U.S. EPA stations and those derived from
collocated near-surface CALIOP observations are shown in the bottom row,
using (e) daytime and (f) nighttime CALIOP data. The red lines represent the
Deming regression fits.
Figure 3a shows the 2-year averaged 1∘× 1∘
(latitude and longitude) gridded daytime CALIOP aerosol extinction over the
CONUS using CALIOP observations from 100 to 1000 m, referenced to the number of
cloud-free L2_05kmAPro profiles in each 1×1∘ bin.
The lowest 100 m of CALIOP extinction data are not used in the analysis due
to the potential of surface return contamination (e.g., Toth et al., 2014),
although this has been improved for the version 4 CALIOP products but may
still be present in some cases. Here the averaged extinction from
100–1000 m
is used to represent near-surface aerosol extinction. This selection of the
100–1000 m layer is somewhat arbitrary, even though it is estimated from the
mean CALIOP-based aerosol vertical distribution over the CONUS (Toth et al.,
2014). Thus, a sensitivity study is provided in a later section to
understand the impact of this aerosol layer selection on CALIOP-based
PM2.5 retrievals. As shown in Fig. 3a, a higher mean near-surface
CALIOP extinction of 0.1 km-1 is found for the eastern CONUS and over
California, while lower values of 0.025–0.05 km-1 are found for the
interior CONUS. Figure 3b shows a plot similar to Fig. 3a but using
nighttime CALIOP observations only. Although similar spatial patterns are
found during both day and night, the near-surface extinction values are
overall lower for nighttime than daytime, and nighttime data are less noisy
than daytime. These findings are not surprising, as daytime CALIOP
measurements are subject to contamination from background solar radiation
(e.g., Omar et al., 2013).
To investigate any diurnal biases in the data, Fig. 3c and d show the
derived PM2.5 concentration using daytime and nighttime CALIOP data,
respectively, based on the method described in Sect. 2. Both Fig. 3c
and d suggest a higher PM2.5 concentration of ∼10–12.5 µgm-3
over the eastern CONUS and a much lower PM2.5 concentration of ∼2.5–5 µgm-3 over the
interior CONUS. High PM2.5 values of 10–20 µgm-3 are also
found over the west coast of the CONUS, particularly over California. The
spatial distribution of PM2.5 concentrations, as derived using near-surface CALIOP data (Fig. 3c and d, as well as the combined daytime and
nighttime perspective shown in Fig. 2c), is remarkably similar to the
spatial distribution of PM2.5 values as estimated based on
ground-based observations (Fig. 2a). Still, day and night differences in
PM2.5 concentrations are also clearly visible, as higher PM2.5
values are found, in general, during daytime, based on CALIOP
observations. The high daytime PM2.5 values, as shown in Fig. 3c, may
represent stronger near-surface convection and more frequent anthropogenic
activities during daytime. However, they may also be partially contributed
by solar radiation contamination. Another possibility is that the daytime
mean extinction coefficients (from which the mean PM2.5 estimates are
derived) appear artificially larger than at night due to high daytime noise
limiting the ability of CALIOP to detect fainter aerosol layers during
daylight operations. Note that, for context, maps of the number of days and
CALIOP Level 2 5 km aerosol profiles used in the creation of Fig. 3a–d are
shown in Appendix Fig. A1.
Figure 3e shows the intercomparison between PM2.5_EPA
and PM2.5_CALIOP concentrations. Note that only CALIOP
and ground-based PM2.5 data pairs, which are within 100 km of each
other and have reported values for the same day (i.e., year, month, and
day), are used to generate Fig. 3e. Still, although only spatially and
temporally collocated data pairs are used, ground-based PM2.5 data
represent 24 h averages, while CALIOP-derived PM2.5 concentrations
are instantaneous values over the daytime CALIOP overpass. To reduce this
temporal bias, 2 years (2008–2009) of collocated CALIOP-derived and
measured PM2.5 concentrations are averaged and only the 2-year
averages are used in constructing Fig. 3e. Also, to minimize the
abovementioned temporal sampling bias, ground stations with fewer than 100
collocated pairs are discarded. This leaves a total of 276 stations for
constructing Fig. 3e.
As shown in Fig. 3e, an r2 value of 0.21 (with slope of 1.07) is found
between CALIOP-derived and measured surface PM2.5 concentrations,
with a corresponding mean bias of -0.40µgm-3 (PM2.5_CALIOP-PM2.5_EPA). In
comparison, Fig. 3f shows results similar to Fig. 3e, but for nighttime
CALIOP data. A much higher r2 value of 0.48 (with slope of 0.96) is
found between CALIOP-derived and measured PM2.5 values from 528
EPA stations, with a corresponding mean bias of -3.3µgm-3 (PM2.5_CALIOP-PM2.5_EPA). This
may be related to the diurnal variability of PM2.5 concentrations, as
the daily mean EPA measurement might be closer to the CALIOP morning retrieval
than to its afternoon counterpart. Still, data points are more scattered in Fig. 3e
in comparison with Fig. 3f, which again indicates that daytime CALIOP
data are noisier, possibly due to daytime solar contamination as well as
other factors such as biases in relative humidity. Details of these biases
are further explored in Sect. 3.2.
Scatter plot of mean PM2.5 concentration from ground-based
U.S. EPA stations and those derived from collocated near-surface CALIOP
observations using combined daytime and nighttime CALIOP data. The red line
represents the Deming regression fit.
To supplement this analysis, a pairwise PM2.5_EPA and
PM2.5_CALIOP (day and night CALIOP combined)
analysis is presented in the spatial plots of Fig. 2b and d. Here,
however, we lift the requirement of 100 collocated pairs to increase data
samples for better spatial representativeness. The spatial variability of
PM2.5 over the CONUS is consistent with the observed patterns of
non-collocated data (i.e., Fig. 2a and c), but with generally higher
values due to differences in sampling. Also, comparing Fig. 2b and d,
PM2.5_EPA spatial patterns match well with those of
PM2.5_CALIOP, yet with larger values for
PM2.5_EPA (consistent with the biases discussed above).
Lastly, a scatter plot of the pairwise analysis shown in Fig. 2b and d is
provided in Fig. 4. An r2 value of 0.40 is found between EPA and
CALIOP-derived PM2.5 concentrations from a combined daytime and
nighttime CALIOP perspective. Overall, Figs. 2, 3, and 4 indicate that near-surface CALIOP extinction data can be used to estimate surface PM2.5 concentrations with reasonable accuracy.
Uncertainty analysis
In this section, uncertainties in the CALIOP-derived 2-year averaged
PM2.5 concentrations are explored as functions of aerosol vertical
distribution, PM2.5-to-PM10 ratio, RH, aerosol type, and cloud
presence above. Spatial-sampling-related biases as well as prognostic errors
are also studied.
Prognostic errors in Cm2.5
As a first step for the uncertainty analysis, we estimated the prognostic
error of 2-year averaged PM2.5_CALIOP. Figure 5
shows the root-mean-square error (RMSE) of CALIOP-based PM2.5
concentrations against those from EPA stations as a function of
CALIOP-based PM2.5 for the 2008–2009 period over the CONUS. RMSEs
were computed for five equally sampled bins, determined from a cumulative
histogram analysis. Each point in Fig. 5, from left to right, represents the
RMSE and mean PM2.5 concentration derived from CALIOP for 0 %–20 %,
20 %–40 %, 40 %–60 %, 60 %–80 %, and 80 %–100 % cumulative frequencies. A
mean combined daytime and nighttime RMSE of ∼4µgm-3 is found, with a mean value slightly greater for nighttime
(∼ 4.3 µgm-3) than daytime (∼3.7µgm-3).
While most bins exhibit larger nighttime RMSEs, daytime
RMSEs are larger for the greatest mean CALIOP-derived PM2.5 concentrations.
Root-mean-square errors of CALIOP-derived PM2.5 against EPA
PM2.5 as a function of CALIOP-derived PM2.5, using both
daytime (in red) and nighttime (in blue) CALIOP observations. The five bins
are equally sampled based upon a cumulative histogram analysis, and each
point from left to right represents the RMSE and mean PM2.5
concentration derived from CALIOP for 0 %–20 %, 20 %–40 %, 40 %–60 %,
60 %–80 %, and 80 %–100 % cumulative frequencies.
Statistical summary of a sensitivity analysis varying the height of
the surface layer, including R2, slope from Deming regression, mean
bias (CALIOP-EPA) of PM2.5 in micrograms per cubic meter, and percent error
change in derived PM2.5, defined as ((mean new PM2.5-mean
original PM2.5)/mean original PM2.5)×100. The row in bold
represents the results shown in the remainder of the paper.
A sensitivity study was conducted for which PM2.5 was derived from
near-surface CALIOP aerosol extinction by varying the height of the surface
layer in increments of 100 m from the ground to 1000 m. Note that the
surface layer (0–100 m) is included for this sensitivity study only. The
statistical results of this analysis, for both daytime and nighttime
conditions, are shown in Table 2. Four statistical parameters were computed,
consisting of r2, slope from Deming regression, mean bias (CALIOP-EPA) of PM2.5, and percent error change in derived PM2.5,
defined as ((mean_new_PM2.5-mean_original_PM2.5)/mean_original_PM2.5)×100. For context, the bottom row of
Table 2 shows the results from the original analysis. In terms of r2
and slope, optimal values peak at different surface layer heights between
daytime and nighttime. For example, for daytime, the largest correlations
are found for the 0–600 and 0–700 m layers, while for nighttime these are
found for the 0–300 and 0–400 m layers. However, the 0–300 m layer
exhibits the lowest mean bias for the daytime analysis, and the
100–1000 m
layer exhibits the lowest mean bias for the nighttime analysis. Overall,
marginal changes are found for varying the height of the surface layer. Yet
the largest mean bias is found for the 0–100 m layer, indicating the need
for excluding the 0–100 m layer in the analysis.
RH sensitivity study
Profiles of RH were taken from the MERRA-2 reanalysis product, as these
collocated data are provided in the CALIPSO L2_05kmAPro
product. However, biases may exist in this RH dataset. Thus, we examined the
impact of varying the RH values by ±10 % on the CALIOP-derived
PM2.5 concentrations. For both daytime and nighttime analyses, no
significant differences in the r2 and slope values were found. However,
a +15 % change in the mean derived PM2.5 values was found by
decreasing the RH values by 10 %, while a -15 % change in the mean
derived PM2.5 values was found by increasing the RH values by 10 %.
PM2.5-to-PM10 ratio sensitivity study
Another source of uncertainty in this study is the PM2.5/PM10
ratio. Using surface-based PM2.5 and PM10 data from those EPA
stations over the CONUS for 2008–2009 with concurrent PM2.5 and
PM10 daily data available (i.e., 409 stations), we computed the mean
PM2.5/PM10 ratio and its corresponding standard deviation. The
mean ratio was 0.56 with a standard deviation of 0.32. It is interesting to
note that the mean PM2.5/PM10 ratio estimated from 2 years of
surface observations over the CONUS is close to 0.6 (the number used in this
study), as reported by Kaku et al. (2018). We also tested the sensitivity of
the derived PM2.5 concentrations as a function of
PM2.5/PM10 ratio for two scenarios: ±1 standard deviation
of the mean (Table 3). In general, a ±50 % to 60 % change is
found with the variation in the PM2.5/PM10 ratio at the range of
±1 standard deviation of the mean. As suggested from Table 3, the
lowest mean daytime bias is found for a ratio of 0.6, and for nighttime the
lowest mean bias occurs using a ratio of 0.88.
Statistical summary of a sensitivity analysis varying the
PM2.5-to-PM10 ratio used, including slope from Deming
regression, mean bias (CALIOP-EPA) of PM2.5 in micrograms per cubic meter,
and percent error change in derived PM2.5, defined as ((mean new
PM2.5-mean original PM2.5)/mean original PM2.5)×100. The
row in bold represents the results shown in the remainder of the paper.
As mentioned in the introduction section, a sampling bias, due to the very
small footprint size and ∼16-day repeat cycle of CALIOP, can
exist when using CALIOP observations for PM2.5 estimates (Zhang and
Reid, 2009). This sampling-induced bias is investigated from a 2-year mean
perspective by comparing histograms of PM2.5_EPA and
Cm2.5 concentrations as shown in Fig. 6. To generate Fig. 6, all
available daily EPA PM2.5 data are used to represent the “true” 2-year
mean spectrum of PM2.5 concentrations over the EPA sites. The aerosol
extinction data spatially collocated to the EPA sites (Sect. 3.1), but not
temporally collocated, are used for estimating the 2-year mean spectrum of
PM2.5 concentrations as derived from CALIOP observations. To be
consistent with the previous analysis, only cloud-free CALIOP profiles are
considered. The PM2.5_EPA concentrations peak at
∼10µgm-3 (standard deviation of ∼3µgm-3), and CALIOP-derived PM2.5 peaks at
∼9µgm-3 (daytime; standard deviation of
∼4µgm-3) and ∼7µgm-3
(nighttime; standard deviation of ∼2µgm-3). The distribution shifts towards smaller concentrations for
CALIOP, more so for nighttime than daytime (possibly due to CALIOP daytime
versus nighttime detection differences).
The 2-year (2008–2009) histograms of mean PM2.5 concentrations from the U.S. EPA (in black) and those derived from aerosol
extinction using nighttime (in blue) and daytime (in red) CALIOP data. The
U.S. EPA data shown are not collocated, while those derived using CALIOP are
spatially (but not temporally) collocated, with EPA station observations.
Still, Fig. 6 may reflect the diurnal difference in PM2.5
concentrations as well as the retrieval bias in Cm2.5 values. Thus,
we have re-performed the exercise shown in Fig. 6 using spatially and
temporally collocated PM2.5_EPA and Cm2.5 data as
shown in Fig. 7. To construct Fig. 7, PM2.5_EPA and
Cm2.5 data are collocated following the steps mentioned in Sect. 3.1,
with CALIOP and EPA PM2.5 representing 2-year mean values for each
EPA station. Again, only cloud-free CALIOP profiles are considered for this
analysis. As shown in Fig. 7a, the PM2.5_EPA
concentrations peak at ∼12µgm-3 (standard
deviation of ∼4µgm-3), and daytime Cm2.5
peaks at ∼10µgm-3 (standard deviation of
∼4µgm-3). In comparison, with the use of
collocated nighttime Cm2.5 and PM2.5_EPA data as
shown in Fig. 7b, the peak PM2.5_EPA value is about 5 µgm-3
higher than the peak Cm2.5 value (with similar
standard deviations as found in the analyses of Fig. 7a). Considering both
Figs. 6 and 7, it is likely that the temporal sampling bias seen in Fig. 6
is at least in part due to retrieval bias as well as the difference in
PM2.5 concentrations during daytime and nighttime.
The 2-year (2008–2009) histograms of mean PM2.5 concentrations from the U.S. EPA and those derived from spatially and
temporally collocated aerosol extinction using (a) daytime and (b) nighttime
CALIOP data.
CALIOP AOD analysis
Most past studies focused on the use of column AODs as proxies for surface
PM2.5 (e.g., Liu et al., 2005; Hoff and Christopher, 2009; van
Donkelaar et al., 2015). Therefore, it is interesting to investigate whether
near-surface CALIOP extinction values can be used as a better physical
quantity to estimate surface PM2.5 in comparing with column-integrated
CALIOP AOD. To achieve this goal, we have compared CALIOP column AOD and
PM2.5 from EPA stations, as shown in Fig. 8. Similar to the
scatter plots of Fig. 3, each point represents a 2-year mean for each EPA
site and was created from a dataset following the same spatial–temporal
collocation as described above. As shown in Fig. 8, r2 values of 0.04
and 0.13 are found using CALIOP daytime and nighttime AOD data,
respectively, similar to the MODIS-based analysis shown in Fig. 1. This is
expected, as elevated aerosol layers will negatively impact the relationship
between surface PM2.5 and column AOD. The derivation of surface
PM2.5 from near-surface CALIOP extinction, as demonstrated from this
study, however, provides a much better spatial matching between the
quantities being compared, with potential error terms that can be well
quantified and minimized in later studies.
For 2008–2009, scatter plots of mean PM2.5 concentration from ground-based U.S. EPA stations and mean column AOD from
collocated CALIOP observations, using (a) daytime and (b) nighttime CALIOP
data. The red lines represent the Deming regression fits.
For 2008–2009, scatter plots of mean PM2.5 concentration
from ground-based U.S. EPA stations and those derived from collocated
all-sky (including cloud-free and cloudy profiles) near-surface CALIOP
observations, using (a) daytime and (b) nighttime CALIOP data. The red lines
represent the Deming regression fits.
Cloud flag sensitivity study
For most of this paper, a strict cloud screening process is implemented,
during which no clouds are allowed in the entire CALIOP profile. However, in
contrast to passive sensor capabilities (e.g., MODIS), near-surface aerosol
extinction coefficients can be readily retrieved from CALIOP profiles even
when there are transparent cloud layers above. Therefore, we conducted an
additional analysis for which no cloud flag was set (i.e., all-sky
conditions). Results are shown in scatter plot form in Fig. 9, in a
manner similar to in Fig. 3e and f, with an additional 97 points for the daytime
analysis and 156 points for the nighttime analysis. Comparing the all-sky
results with those of Fig. 3e and f (cloud-free conditions), the r2
values are similar. This is also true in terms of mean bias, with similar
values of 0.70 µgm-3 found for daytime, and -2.68µgm-3
for nighttime, all-sky scenarios. This indicates that our method
performs reasonably well from an all-sky perspective. However, we note that
restricting the analysis to solely those cases that are cloudy (not shown),
the method does not perform as well. For example, the r2 value
decreases by 71 % for the daytime analysis compared to the cloud-free
results (Fig. 3e). The corresponding nighttime r2 value decreases by
90 %. This is expected, as any errors made in estimating the optical
depths of the overlying clouds will propagate (as biases) into the
extinction retrievals for the underlying aerosols.
Aerosol type analysis
Also, for this study, we assume that the primary aerosol type over the CONUS
is pollution (i.e., sulfate) aerosol, which is generally composed of smaller
(fine mode) particles that tend to exhibit mass extinction efficiencies
∼4m2g-1. However, even after implementing our
dust-free restriction, the study region can also be contaminated with
non-pollution aerosols, which can have a larger particle size and exhibit
lower mass extinction efficiencies (e.g., Hess et al., 1998; Malm and Hand,
2007; Lynch et al., 2016). The use of PM2.5 versus PM10 somewhat
mitigates this size dependency, but nevertheless coarse mode dust or sea
salt can dominate PM2.5 mass values (e.g., Atwood et al., 2013).
Thus, in this section, the impact of aerosol types on the derived PM2.5
concentrations was explored by varying the mass scattering and absorption
efficiencies and gamma values associated with each aerosol type. The three
aerosol types chosen for this sensitivity study were dust, sea salt, and
smoke, based upon Lynch et al. (2016). The mass scattering and absorption
values for dust and sea salt were interpolated to 0.532 µm from
values at 0.450 and 0.550 µm from OPAC (as was performed for the
sulfate case; Hess et al., 1998). For smoke, these values were interpolated
to 0.532 µm from values at 0.440 and 0.670 µm as
provided by Reid et al. (2005) for smoke cases over the US and Canada. The
gamma values were taken from Lynch et al. (2016) and the references within.
These values, as well as the results from this sensitivity study, are shown
in Table 4. If we assume all aerosols within the study region are smoke
aerosols, no major changes in the retrieved CALIOP PM2.5 values are
found. However, significant uncertainties on the order of ∼200 % are found if sea salt, or ∼800 % if dust, aerosol
mass scattering and absorption efficiencies and gamma values are used instead.
Clearly, this study suggests that accurate aerosol typing is necessary for
future applications of CALIOP observations for surface PM2.5 estimations.
Statistical summary of a sensitivity analysis varying the aerosol
type assumed in the derivation of PM2.5, including R2, slope from
Deming regression, mean bias (CALIOP-EPA) of PM2.5 in
micrograms per cubic meter, and percent error change in derived PM2.5, defined as
((mean new PM2.5-mean original PM2.5)/mean original
PM2.5)×100. The row in bold represents the results shown in the
remainder of the paper.
Analysis (day/night) Aerosol type R2DemingMean biasErrorascataabsΓslope(CALIOP-EPA; µgm-3)change (%)Smoke5.260.260.180.10/0.440.86/0.78-1.81/-4.26-11.53/-10.54Sea salt1.420.010.460.18/0.482.92/2.6422.42/12.93184.12/184.99Dust0.520.080.000.05/0.399.01/8.18102.04/70.82826.94/843.33Sulfate3.400.370.630.21/0.481.07/0.96-0.39/-3.34E-folding correlation length for PM2.5 concentrations over the CONUS
As a last study, we also estimated the spatial e-folding correlation length
for PM2.5 concentrations over the CONUS. This provides us an
estimation of the correlation between a CALIOP-derived and actual PM2.5
concentration for a given location as a function of distance between the
CALIOP observation and the given location. To accomplish this, all EPA
stations over the CONUS with at least 50 days of daily data available for
the 2008–2009 period were first determined. Next, the distances between each
pair of these EPA stations, and their corresponding correlation of daily
PM2.5 concentrations, were computed. Results are shown in Fig. 10 as
a scatter plot, with individual points in gray and the black curve
representing the exponential fit to the data. A decrease in PM2.5
correlation with distance between EPA stations is found, and the e-folding
length in correlation (e.g., correlation reduced to 1/e, or 0.37) is
∼600km (from an AOD standpoint, this value is 40–400 km, as
suggested by Anderson et al., 2003).
For 2008–2009 over the CONUS, scatter plot of distance (km)
between any two U.S. EPA stations and the corresponding spatial correlation
of PM2.5 concentration between each pair of stations. The black curve
represents the exponential fit to the data for the entire CONUS, and the red
and blue dashed lines represent 10 km bin averages for the western and
eastern CONUS, respectively.
Also included in Fig. 10 are results from a corresponding regional analysis,
with the red and blue lines showing bin averages (10 km) for the western and
eastern CONUS, respectively (regions partitioned by the -97∘
longitude line). The e-folding length is ∼300km for the
western CONUS and ∼700km for the eastern CONUS, indicating
a much shorter correlation length for pollution over the western CONUS,
possibly due to a more complex terrain such as mountains. Overall, these
PM2.5e-folding lengths suggest that CALIOP-derived PM2.5
concentrations could still have some representative skill within a few
hundred kilometers of a given location.
Conclusions
In this paper, we have demonstrated a new bulk-mass-modeling method for
retrieving surface particulate matter (PM) with particle diameters smaller
than 2.5 µm (PM2.5) using observations acquired by the
NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument
from 2008 to 2009. For the purposes of demonstrating this concept, only
regionally averaged parameters, such as mass scattering and absorption
coefficients, and PM2.5-to-PM10 (PM with particle diameters
smaller than 10 µm) conversion ratio, are used. Also, we assume the
dominant type of aerosols over the study region is pollution aerosols
(supported by the occurrence frequencies of aerosol types determined by the
CALIOP algorithms) and exclude aerosol extinction range bins classified as
dust from the analysis. Even with the highly averaged parameters, the
results from this paper are rather promising and demonstrate a potential for
monitoring PM pollution using active observations from lidars. Specifically,
the primary results of this study are as follows.
CALIOP-derived PM2.5 concentrations of ∼10–12.5 µgm-3
are found over the eastern contiguous United States
(CONUS), with lower values of ∼2.5–5 µgm-3 over
the central CONUS. PM2.5 values of ∼10–20 µgm-3
are found over the west coast of the CONUS, primarily California.
The spatial distribution of 2-year mean PM2.5 concentrations derived
from near-surface CALIOP aerosol data compares well to the spatial
distribution of in situ PM2.5 measurements collected at the ground-based
stations of the U.S. Environmental Protection Agency (EPA). The use of
nighttime CALIOP extinction to derive PM2.5 results in a higher
correlation (r2=0.48; mean bias =-3.3µgm-3) with
EPA PM2.5 than daytime CALIOP extinction data (r2=0.21; mean
bias =-0.40µgm-3).
Correlations between CALIOP aerosol optical depth (AOD) and EPA PM2.5 are much lower
(r2 values of 0.04 and 0.13 for daytime and nighttime
CALIOP AOD data, respectively) than those obtained from derived PM2.5 using near-surface CALIOP aerosol extinction. A similar correlation is
also found between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD
and EPA PM2.5 from 2-year (2008–2009) means. This suggests that
CALIOP extinction may be used as a better parameter for estimating
PM2.5 concentrations from a 2-year mean perspective. Also, the
algorithm proposed in this study is essentially a semi-physical-based
method, and thus the retrieval process can be improved, upon a careful study
of the physical parameters used in the process.
Spatial and temporal sampling biases, as well as a retrieval bias, are
found. Also, several sensitivity studies were conducted, including surface
layer height, cloud flag, PM2.5/PM10 ratio, relative humidity, and
aerosol type. The sensitivity studies highlight the need for accurate
aerosol typing for estimating PM2.5 concentrations using CALIOP
observations.
Using surface-based PM2.5 at EPA stations alone, the e-folding
correlation length for PM2.5 concentrations was found to be about 600 km
for the CONUS. A regional analysis yielded values of ∼300 and ∼700km for the western and eastern CONUS,
respectively. Thus, while limited in spatial sampling, measurements from
CALIOP may still be used for estimating PM2.5 concentrations over the
CONUS.
As noted earlier, CALIOP observations are still rather sparse, and concerns
related to reported CALIOP aerosol extinction values also exist, such as
solar and surface contamination and the retrieval fill value issue
(e.g., Toth et al., 2018). Yet, the future High Spectral Resolution Lidar
(HSRL) instrument on board the Earth Clouds, Aerosol, and Radiation Explorer
(EarthCARE) satellite (Illingworth et al., 2015), as well as forthcoming
space-based lidar missions in response to the 2017 Decadal Survey, offer
opportunities to further explore aerosol extinction-based PM
concentrations. Ultimately the results from this study show that the
combined use of several lidar instruments for monitoring regional and global
PM pollution is potentially achievable.
Data availability
The CALIPSO Level 2 5 km aerosol profile (Vaughan et al., 2018; 10.5067/CALIOP/CALIPSO/LID_L2_05kmAPro-Standard-V4-10;
last access: 26 September 2018) and aerosol layer
(Vaughan et al., 2018; 10.5067/CALIOP/CALIPSO/LID_L2_05kmALay-Standard-V4-10;
last access: 26 September 2018) products were obtained from NASA Langley Research Center
Atmospheric Science Data Center. MODIS data were obtained from NASA Goddard Space Flight Center
(http://ladsweb.nascom.nasa.gov; last access: 12 March 2019). The PM2.5 data were
obtained from the EPA AQS site (https://aqs.epa.gov/aqsweb/airdata/download_files.html;
last access: 12 March 2019).
For 2008–2009 over the CONUS, for each 1∘× 1∘ grid
box, the number of days and CALIOP Level 2 5 km aerosol profiles used in the
creation of the maps in Fig. 3 for (a, c) daytime and (b, d) nighttime
measurements. Values greater than 400 profiles for (c, d) are colored
red.
Author contributions
JZ and TDT designed the experiment, TDT performed data analysis,
and JSR and MAV made extensive suggestions and revised the study. All authors were involved in the writing of the
paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This research was funded with the support of the NASA Earth and Space
Science Fellowship program (NNX16A066H). Author Jianglong Zhang acknowledges the support
from NASA grant NNX17AG52G. Jeffrey S. Reid was supported by ONR 322. We thank the two anonymous
reviewers for their constructive and valuable comments in improving the paper.
Edited by: Vassilis Amiridis
Reviewed by: two anonymous referees
ReferencesAnderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and Holmén,
K.: Mesoscale
Variations of Tropospheric Aerosols, J. Atmos. Sci., 60, 119–136,
10.1175/1520-0469(2003)060<0119:MVOTA>2.0.CO;2, 2003.Atwood, S. A., Reid, J. S., Kreidenweis, S. M., Cliff, S. S., Zhao, Y.,
Lin, N.-H., Tsay, S.-C.,
Chu, Y.-C., and Westphal, D. L.: Size resolved measurements of springtime aerosol
particles over the northern South China Sea, Atmos. Environ., 78,
134–143, 10.1016/j.atmosenv.2012.11.024, 2013.Campbell, J. R., Tackett, J. L., Reid, J. S., Zhang, J., Curtis, C. A., Hyer, E. J.,
Sessions, W. R., Westphal, D. L., Prospero, J. M., Welton, E. J., Omar, A. H.,
Vaughan, M. A., and Winker, D. M.: Evaluating nighttime CALIOP 0.532 µm
aerosol optical depth and extinction coefficient retrievals, Atmos. Meas. Tech., 5, 2143–2160, 10.5194/amt-5-2143-2012, 2012.Charlson, R. J., Ahlquist, N. C., and Horvath, H.: On the generality of
correlation of atmospheric
aerosol mass concentration and light scatter, Atmos. Environ., 2,
455–464, 10.1016/0004-6981(68)90039-5, 1968.Chew, B. N., Campbell, J. R., Hyer, E. J., Salinas, S. V., Reid, J. S.,
Welton, E. J., Holben, B. N.,
and Liew, S. C.: Relationship between aerosol optical depth and particulate
matter over Singapore: Effects of aerosol vertical distributions, Aerosol
Air Qual. Res., 16, 2818–2830,
10.4209/aaqr.2015.07.0457, 2016.Chow, J. C., Watson, J. G., Park, K., Robinson, N. F., Lowenthal, D. H.,
Park, K., and Magliano,
K. A.: Comparison of particle light scattering and fine particulate matter
mass in central California, J. Air Waste Manage., 56, 398–410, 10.1080/10473289.2006.10464515,
2006.
Chung, A., Chang, D. P., Kleeman, M. J., Perry, K. D., Cahill, T. A., Dutcher, D., McDougall, E. M., and Stroud, K.:
Comparison of real-time instruments used to monitor airborne particulate
matter, J. Air Waste Manage., 51,
109–120, 2001.Colarco, P. R., Kahn, R. A., Remer, L. A., and Levy, R. C.: Impact of satellite
viewing-swath width on global and regional aerosol optical thickness statistics
and trends, Atmos. Meas. Tech., 7, 2313–2335, 10.5194/amt-7-2313-2014, 2014.
Deming, W. E.: Statistical Adjustment of Data, Wiley, New York, 1943.Di, Q., Wang, Y., Zanobetti, A., Wang, Y., Koutrakis, P., Choirat, C., Dominici, F., and Schwartz, J. D.: Air
pollution and mortality in the Medicare population, New Engl. J.
Med., 376, 2513–2522, 10.1056/NEJMoa1702747, 2017.
Eatough, D. J., Long, R. W., Modey, W. K., and Eatough, N. L.: Semi-volatile
secondary organic
aerosol in urban atmospheres: meeting a measurement challenge, Atmos. Environ., 37, 1277–1292, 2003.Engel-Cox, J. A., Holloman, C. H., Coutant, B. W., and Hoff, R. M.:
Qualitative and quantitative
evaluation of MODIS satellite sensor data for regional and urban scale air
quality, Atmos. Environ., 38, 2495–2509,
10.1016/j.atmosenv.2004.01.039, 2004.
Federal Register: National ambient air quality standards for particulate
matter, Final Rule
Federal Register/vol. 62, no. 138/18 July 1997/Final Rule, 40 CFR Part 50,
1997.Glantz, P., Kokhanovsky, A., von Hoyningen-Huene, W., and Johansson, C.:
Estimating
PM2.5 over southern Sweden using space-borne optical
measurements, Atmos. Environ., 43, 5838–5846,
10.1016/j.atmosenv.2009.05.017, 2009.Gong, W., Huang, Y., Zhang, T., Zhu, Z., Ji, Y., and Xiang, H.: Impact and
Suggestion of
Column-to-Surface Vertical Correction Scheme on the Relationship between
Satellite AOD and Ground-Level PM2.5 in China, Remote Sens., 9, 1038,
10.3390/rs9101038, 2017.Greenstone, M.: The impacts of environmental regulations on industrial
activity: Evidence from
the 1970 and 1977 clean air act amendments and the census of manufactures,
J. Polit. Econ., 110, 1175–1219,
10.1086/342808, 2002.Hand, J. L. and Malm, W. C.: Review of aerosol mass scattering efficiencies
from ground-based measurements since 1990, J. Geophys. Res.-Atmos., 112, D16203, 10.1029/2007JD008484,
2007.Hand, J. L., Schichtel, B. A., Malm, W. C., and Frank, N. H.: Spatial and
temporal trends in PM2.5
organic and elemental carbon across the United States, Adv.
Meteorol., 2013, 367674, 10.1155/2013/367674, 2013.Hänel, G.: The properties of atmospheric aerosol particles as functions
of the relative humidity at
thermodynamic equilibrium with the surrounding moist air, Adv.
Geophys., 19, 73–188, 10.1016/S0065-2687(08)60142-9, 1976.Hess, M., Koepke, P., and Schult, I.: Optical properties of aerosols and
clouds: The software
package OPAC, B. Am. Meteorol. Soc., 79,
831–844,
10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2,
1998.Hoff, R. M. and Christopher, S. A.: Remote sensing of particulate
pollution from
space: have we reached the promised land?, J. Air Waste Manage., 59.6, 645–675,
10.3155/1047-3289.59.6.645, 2009.Huang, X. H., Bian, Q., Ng, W. M., Louie, P. K., and Yu, J. Z:
Characterization of PM2.5 major
components and source investigation in suburban Hong Kong: a one year
monitoring study, Aerosol Air Qual. Res., 14, 237–250, 2014.Hunt, W. H., Winker, D. M., Vaughan, M. A., Powell, K. A., Lucker, P. L.,
and Weimer, C.:
CALIPSO lidar description and performance assessment, J. Atmos.
Ocean. Tech., 26, 1214–1228,
10.1175/2009JTECHA1223.1, 2009.Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N.,
Cole, J., Delanoë, J., Domenech, C., Donovan, D. P., Fukuda, S., Hirakata, M., Hogan, R. J.,
Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T. Y., Nishizawa, T.,
Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A.,
Wandinger, U., Wehr, T., and Van Zadelhoff, G.-J.: The EarthCARE satellite: The next step
forward in global
measurements of clouds, aerosols, precipitation, and radiation, B. Am. Meteorol. Soc., 96, 1311–1332,
10.1175/BAMS-D-12-00227.1, 2015.Kaku, K. C., Reid, J. S., Hand, J. L., Edgerton, E. S., Holben, B. N.,
Zhang, J., and Holz, R. E.:
Assessing the challenges of surface-level aerosol mass estimates from remote
sensing during the SEAC4RS and SEARCH campaigns: Baseline surface
observations and remote sensing in the southeastern United States, J. Geophys. Res.-Atmos., 123, 7530–7562,
10.1029/2017JD028074, 2018.Kiss, G., Imre, K., Molnár, Á., and Gelencsér, A.: Bias caused by water
adsorption in hourly PM measurements, Atmos. Meas. Tech., 10, 2477–2484, 10.5194/amt-10-2477-2017, 2017.Kittaka, C., Winker, D. M., Vaughan, M. A., Omar, A., and Remer, L. A.: Intercomparison of
column aerosol optical depths from CALIPSO and MODIS-Aqua, Atmos. Meas. Tech., 4, 131–141, 10.5194/amt-4-131-2011, 2011.Kumar, N., Chu, A., and Foster, A.: An empirical relationship between
PM2.5
and aerosol optical
depth in Delhi Metropolitan, Atmos. Environ., 41, 4492–4503,
10.1016/j.atmosenv.2007.01.046, 2007.Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F.,
and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, 10.5194/amt-6-2989-2013, 2013.Li, J., Carlson, B. E., and Lacis, A. A.: How well do satellite AOD
observations represent the
spatial and temporal variability of PM2.5 concentration for the United
States?, Atmos. Environ., 102, 260–273,
10.1016/j.atmosenv.2014.12.010, 2015.
Liou, K.-N.: An introduction to atmospheric radiation, Academic
Press, 84, 9, 2002.Liu, Y., Park, R. J., Jacob, D. J., Li, Q., Kilaru, V., and Sarnat, J. A.:
Mapping annual mean ground-
level PM2.5 concentrations using Multiangle Imaging Spectroradiometer
aerosol optical thickness over the contiguous United States, J.
Geophys. Res.-Atmos., 109, D22206,
10.1029/2004JD005025, 2004.Liu, Y., Sarnat, J. A., Kilaru, V., Jacob, D. J., and Koutrakis, P.:
Estimating ground-level PM2.5
in the eastern United States using satellite remote sensing, Environ.
Sci. Technol., 39, 3269–3278, 10.1021/es049352m, 2005.Liu, Y., Franklin, M., Kahn, R., and Koutrakis, P.: Using aerosol optical
thickness to predict
ground-level PM2.5 concentrations in the St. Louis area: A comparison
between MISR and MODIS, Remote Sens. Environ., 107, 33–44,
10.1016/j.rse.2006.05.022, 2007.Lynch, P., Reid, J. S., Westphal, D. L., Zhang, J., Hogan, T. F., Hyer, E. J., Curtis, C. A.,
Hegg, D. A., Shi, Y., Campbell, J. R., Rubin, J. I., Sessions, W. R., Turk, F. J., and Walker, A. L.:
An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate
sciences, Geosci. Model Dev., 9, 1489–1522, 10.5194/gmd-9-1489-2016, 2016.Malm, W. C. and Hand, J. L.: An examination of the physical and optical
properties of aerosols
collected in the IMPROVE program, Atmos. Environ., 41,
3407–3427, 10.1016/j.atmosenv.2006.12.012, 2007.
Nessler, R., Weingartner, E., and Baltensperger, U.: Effect of
humidity on aerosol light
absorption and its implications for extinction and the single scattering
albedo illustrated for a site in the lower free troposphere, J. Aerosol Sci., 36, 958–972, 2005.Omar, A. H., Winker, D. M., Tackett, J. L., Giles, D. M., Kar, J., Liu, Z.,
Vaughan, M. A., Powell,
K. A., and Trepte, C. R.: CALIOP and AERONET aerosol optical depth
comparisons: One size fits none, J. Geophys. Res.-Atmos., 118, 4748–4766,
10.1002/jgrd.50330, 2013.
Patashnick, H., Rupprecht, G., Ambs, J. L., and Meyer, M. B.: Development of
a reference
standard for particulate matter mass in ambient air, Aerosol Sci.
Technol., 34, 42–45, 2001.Reid, J. S., Eck, T. F., Christopher, S. A., Koppmann, R., Dubovik, O., Eleuterio, D. P.,
Holben, B. N., Reid, E. A., and Zhang, J.: A review of biomass burning emissions
part III: intensive optical properties of biomass burning particles, Atmos. Chem. Phys., 5, 827–849, 10.5194/acp-5-827-2005, 2005.
Reid, J. S., Kaku, K., Xian, P., Benedetti, A., Colarco, P. R., da Silva Jr., A. M., Holben, B. N., Rubin, J., Tanaka, T. Y., and Zhang, J.:
Skill of Operational Aerosol Forecast Models in
Predicting Aerosol Events and
Trends of the Eastern United States, A11B-001, AGU Fall meeting, San
Francisco, 12–16 December 2016.Reid, J. S., Kuehn, R. E., Holz, R. E., Eloranta, E. W., Kaku, K. C., Kuang, S.,
Newchurch, M. J., Thompson, A. M., Trepte, C. R., Zhang, J., Atwood, S. A.,
Hand, J. L., Holben, B. N., Minnis, P., and Posselt, D. J.: Ground-based High Spectral Resolution Lidar observation of aerosol
vertical distribution in the summertime Southeast United States, J.
Geophys. Res.-Atmos., 122, 2970–3004,
10.1002/2016JD025798, 2017.Sessions, W. R., Reid, J. S., Benedetti, A., Colarco, P. R., da Silva, A., Lu, S., Sekiyama, T.,
Tanaka, T. Y., Baldasano, J. M., Basart, S., Brooks, M. E., Eck, T. F., Iredell, M.,
Hansen, J. A., Jorba, O. C., Juang, H.-M. H., Lynch, P., Morcrette, J.-J., Moorthi, S.,
Mulcahy, J., Pradhan, Y., Razinger, M., Sampson, C. B., Wang, J., and Westphal, D. L.:
Development towards a global operational aerosol consensus: basic climatological
characteristics of the International Cooperative for Aerosol Prediction Multi-Model
Ensemble (ICAP-MME), Atmos. Chem. Phys., 15, 335–362, 10.5194/acp-15-335-2015, 2015.Silva, R. A., West, J. J., Zhang, Y., Anenberg, S. C., Lamarque, J. F.,
Shindell, D. T., Collins,
W. J., Dalsoren, S., Faluvegi, G., Folberth, G., and Horowitz, L. W.: Global
premature mortality due to anthropogenic outdoor air pollution and the
contribution of past climate change, Environ. Res. Lett., 8,
034005, 10.1088/1748-9326/8/3/034005, 2013.
Spagnolo, G. S.: Automatic instrument for aerosol samples using the
beta-particle attenuation,
J. Aerosol Sci., 20, 19–27, 1989.Toth, T. D., Zhang, J., Campbell, J. R., Reid, J. S., Shi, Y., Johnson, R.
S., Smirnov, A., Vaughan,
M. A., and Winker, D. M.: Investigating enhanced Aqua MODIS aerosol optical
depth retrievals over the mid-to-high latitude Southern Oceans through
intercomparison with co-located CALIOP, MAN, and AERONET data sets, J. Geophys. Res.-Atmos., 118, 4700–4714,
10.1002/jgrd.50311, 2013.Toth, T. D., Zhang, J., Campbell, J. R., Hyer, E. J., Reid, J. S., Shi, Y., and Westphal, D. L.:
Impact of data quality and surface-to-column representativeness on the PM2.5/satellite AOD
relationship for the contiguous United States, Atmos. Chem. Phys., 14, 6049–6062, 10.5194/acp-14-6049-2014, 2014.Toth, T. D., Zhang, J., Campbell, J. R., Reid, J. S., and Vaughan, M. A.:
Temporal variability of
aerosol optical thickness vertical distribution observed from CALIOP,
J. Geophys. Res.-Atmos., 121, 9117–9139,
10.1002/2015JD024668, 2016.Toth, T. D., Campbell, J. R., Reid, J. S., Tackett, J. L., Vaughan, M. A., Zhang, J.,
and Marquis, J. W.: Minimum aerosol layer detection sensitivities and their subsequent
impacts on aerosol optical thickness retrievals in CALIPSO level 2 data
products, Atmos. Meas. Tech., 11, 499–514, 10.5194/amt-11-499-2018, 2018.Val Martin, M., Heald, C. L., Ford, B., Prenni, A. J., and Wiedinmyer, C.:
A decadal satellite analysis of the origins and impacts of smoke in Colorado, Atmos. Chem. Phys., 13, 7429–7439, 10.5194/acp-13-7429-2013, 2013.Van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco,
C., and Villeneuve,
P. J.: Global Estimates of Ambient Fine Particulate Matter Concentrations
from Satellite Based Aerosol Optical Depth: Development and Application,
Environ. Health Persp., 118, 847–855,
10.1289/ehp.0901623, 2010.Van Donkelaar, A., Martin, R. V., Spurr, R. J., and Burnett, R. T.:
High-resolution satellite-derived
PM2.5 from optimal estimation and geographically weighted regression over
North America, Environ. Sci. Technol., 49,
10482–10491, 10.1021/acs.est.5b02076, 2015.Vaughan, M., Pitts, M., Trepte, C., Winker, D., Detweiler, P., Garnier, A., Getzewich, B.,
Hunt, W., Lambeth, J., Lee, K.-P., Lucker, P., Murray, T., Rodier, S., Tremas, T.,
Bazureau, A., and Pelon, J.: Cloud-Aerosol LIDAR Infrared Pathfinder Satellite
Observations (CALIPSO) data management system data products catalog, Release 4.20,
NASA Langley Research Center Document PC-SCI-503, available at: https://www-calipso.larc.nasa.gov/products/CALIPSO_DPC_Rev4x20.pdf,
last access: 26 September 2018 (data available at: 10.5067/CALIOP/CALIPSO/LID_L2_05kmAPro-Standard-V4-10 and https://doi.org10.5067/CALIOP/CALIPSO/ LID_L2_05kmALay-Standard-V4-10).
Waggoner, A. P. and Weiss, R. E.: Comparison of fine particle mass
concentration and light
scattering extinction in ambient aerosol, Atmos. Environ., 14, 623–626, 10.1016/0004-6981(80)90098-0, 1980.Wang, J. and Christopher, S. A.: Intercomparison between satellite-derived
aerosol optical
thickness and PM2.5 mass: implications for air quality studies, Geophys.
Res. Lett., 30, 21, 10.1029/2003GL018174, 2003.Winker, D. M., Hunt, W. H., and McGill, M. J.: Initial performance
assessment of CALIOP,
Geophys. Res. Lett., 34, L19803, 10.1029/2007GL030135,
2007.Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z.,
Hunt, W. H., and Young,
S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing
Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323,
10.1175/2009JTECHA1281.1, 2009.Winker, D. M., Pelon, J., Coakley Jr., J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R.,
Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L., LeTreut, H., McCormick, M. P.,
Mégie , G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO mission: A global 3D view of aerosols and
clouds, B. Am. Meteorol. Soc., 91, 1211–1230,
10.1175/2010BAMS3009.1, 2010.Young, S. A., Vaughan, M. A., Kuehn, R. E., and Winker, D. M.: The retrieval
of profiles of
particulate extinction from Cloud–Aerosol Lidar and Infrared Pathfinder
Satellite Observations (CALIPSO) data: Uncertainty and error sensitivity
analyses, J. Atmos. Ocean. Tech., 30, 395–428,
10.1175/2009JTECHA1281.1, 2013.Zhang, J. and Reid, J. S.: An analysis of clear sky and contextual biases
using an operational over
ocean MODIS aerosol product, Geophys. Res. Lett., 36, L15824,
10.1029/2009GL038723, 2009.Zhang, J., Campbell, J. R., Hyer, E. J., Reid, J. S., Westphal, D. L., and Johnson, R. S.:
Evaluating
the impact of multisensor data assimilation on a global aerosol particle
transport model, J. Geophys. Res.-Atmos., 119, 4674–4689, 10.1002/2013JD020975,
2014.