ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-1003-2016The impacts of aerosol loading, composition, and water uptake on aerosol
extinction variability in the Baltimore–Washington, D.C. regionBeyersdorfA. J.andreas.j.beyersdorf@nasa.govhttps://orcid.org/0000-0002-4496-2557ZiembaL. D.ChenG.CorrC. A.CrawfordJ. H.DiskinG. S.MooreR. H.https://orcid.org/0000-0003-2911-4469ThornhillK. L.WinsteadE. L.AndersonB. E.NASA Langley Research Center, Hampton,
Virginia, USAOak Ridge Associated Universities, Oak Ridge,
Tennessee, USAScience Systems and Applications, Inc., Hampton,
Virginia, USAA. J. Beyersdorf (andreas.j.beyersdorf@nasa.gov)28January2016162100310155August201528August201516November201521December2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/1003/2016/acp-16-1003-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/1003/2016/acp-16-1003-2016.pdf
In order to utilize satellite-based aerosol measurements for the
determination of air quality, the relationship between aerosol optical
properties (wavelength-dependent, column-integrated extinction measured by
satellites) and mass measurements of aerosol loading (PM2.5 used for air
quality monitoring) must be understood. This connection varies with many
factors including those specific to the aerosol type – such as composition,
size, and hygroscopicity – and to the surrounding atmosphere, such as
temperature, relative humidity (RH), and altitude, all of which can vary
spatially and temporally. During the DISCOVER-AQ (Deriving Information on
Surface conditions from Column and Vertically Resolved Observations Relevant
to Air Quality) project, extensive in situ atmospheric profiling in the
Baltimore, MD–Washington, D.C. region was performed during 14
flights in July 2011. Identical flight plans and profile locations throughout
the project provide meaningful statistics for determining the variability in
and correlations between aerosol loading, composition, optical properties, and
meteorological conditions.
Measured water-soluble aerosol mass was composed primarily of ammonium
sulfate (campaign average of 32 %) and organics (57 %). A distinct
difference in composition was observed, with high-loading days having a
proportionally larger percentage of sulfate due to transport from the Ohio
River Valley. This composition shift caused a change in the aerosol
water-uptake potential (hygroscopicity) such that higher relative
contributions of inorganics increased the bulk aerosol hygroscopicity. These
days also tended to have higher relative humidity, causing an increase in the
water content of the aerosol. Conversely, low-aerosol-loading days had lower
sulfate and higher black carbon contributions, causing lower single-scattering
albedos (SSAs). The average black carbon concentrations were
240 ng m-3 in the lowest 1 km, decreasing to 35 ng m-3 in the
free troposphere (above 3 km).
Routine airborne sampling over six locations was used to evaluate the
relative contributions of aerosol loading, composition, and relative humidity
(the amount of water available for uptake onto aerosols) to variability in
mixed-layer aerosol extinction. Aerosol loading (dry extinction) was found to
be the predominant source, accounting for 88 % on average of the measured
spatial variability in ambient extinction, with lesser contributions from
variability in relative humidity (10 %) and aerosol composition
(1.3 %). On average, changes in aerosol loading also caused 82 % of
the diurnal variability in ambient aerosol extinction. However on days with
relative humidity above 60 %, variability in RH was found to cause up to
62 % of the spatial variability and 95 % of the diurnal variability
in ambient extinction.
This work shows that extinction is driven to first order by aerosol mass
loadings; however, humidity-driven hydration effects play an important
secondary role. This motivates combined satellite–modeling assimilation
products that are able to capture these components of the aerosol optical depth (AOD)–PM2.5
link. Conversely, aerosol hygroscopicity and SSA play a minor role in driving
variations both spatially and throughout the day in aerosol extinction and
therefore AOD. However, changes in aerosol hygroscopicity from day to day
were large and could cause a bias of up to 27 % if not accounted for.
Thus it appears that a single daily measurement of aerosol hygroscopicity can
be used for AOD-to-PM2.5 conversions over the study region (on the order
of 1400 km2). This is complimentary to the results of Chu et
al. (2015), who determined that the aerosol vertical distribution from “a single lidar is
feasible to cover the range of 100 km” in the same region.
Introduction
Aerosols are detrimental to human health and are regulated as a criteria
pollutant by the United States Environmental Protection Agency (EPA, 2016)
and international agencies (Vahlsing and Smith, 2012), with compliance based
on measurements at ground sites. However, satellites allow for the
measurement of atmospheric conditions with a larger spatial coverage than
possible with a ground-based network of instruments and thus have the
potential to be useful tools in diagnosing ground-level air quality,
particularly of aerosols (Al-Saadi et al., 2005). Additionally, satellites
have the advantage of detecting regional air quality events in areas without
historical air quality problems which thus have no or limited ground-based
sensor stations.
In order to relate satellite aerosol measurements to surface air quality, the
connection between aerosol optical depth (AOD) measured by satellites and
ground-level fine-mode aerosol mass (PM2.5) must be known. The
relationship between AOD and PM2.5 has been widely studied (Hoff and
Christopher, 2009, and the references therein; Crumeyrolle et al., 2014, for
the current region), and ground-level PM2.5 has been estimated based on
AOD measurements both empirically (Liu et al., 2005) and through the use of
global models. Van Donkelaar et al. (2006) found that the relative vertical
extinction profile is the most important factor in the AOD-to-PM2.5
relationship. Thus this relationship is weakest in regions where the vertical
distribution cannot be reasonably modeled and is best in regions with fairly
uniform aerosol type and vertical distribution (well-mixed boundary layer
with minimal free-tropospheric aerosol) such as the northeast USA (Engel-Cox
et al., 2004). Based on lidar measurements in the Baltimore, MD–Washington, D.C.
region, Chu et al. (2015) suggested that a single lidar
could provide adequate information on the vertical distribution to allow for
retrievals of PM2.5 from AOD measurements made within 100 km of the
lidar. However, the AOD–PM2.5 relationship is dependent not only on the
aerosol vertical distribution but also on variability in aerosol composition and
relative humidity (RH), both of which can be large in urban areas due to the
densely located nature of local and regional sources. This work is an
analysis of spatial and temporal variability in aerosol loading, composition,
and RH in the Baltimore, MD–Washington, D.C. region and their effect on
variability in aerosol extinction.
DISCOVER-AQ (Deriving Information on Surface conditions from Column and
Vertically Resolved Observations Relevant to Air Quality) was a multi-city
NASA project designed to better elucidate the connection between satellite
measurements and air quality by studying the variability in gas-phase and
particulate pollutants in urban environments. The first campaign was
performed in the Baltimore–Washington region in July 2011 and combined
remote-sensing instruments on the NASA Langley UC-12 flying at 9 km, ground-based
observations at multiple sites throughout the region, and in situ airborne
measurements from the NASA Wallops P-3B for the detailed analysis of
atmospheric composition in the Baltimore–Washington urban airshed. The P-3B
flight plans (Fig. 1) were consistent among the 14 flights over 29 days to
provide meaningful statistics (Table 1).
Flight path for flight 1. Portions below 1 km are shown in red, and
those above in black. Flights originated at NASA Wallops Flight Facility
(southeast of the area shown) to ground sites 1 through 6 in order, with a
spiral performed at each site. The circuit was typically flown three times per
flight before returning to Wallops. Water is denoted as blue, with the
Chesapeake Bay at the center and the Delaware Bay on the right edge.
DISCOVER-AQ flight dates including complete circuits over all six
sites flown.
DISCOVER-AQ provides a valuable data set to determine the variability in
aerosol extinction throughout the region. However, it is important to note
that changes in aerosol extinction are not necessarily solely due to an
increase or decrease in aerosol loadings but can also be indicative of
variability in relative humidity and aerosol composition. Thus these data
will be used to examine
the influence that aerosol loading, composition, and relative humidity
have on variability in aerosol extinction in the Baltimore–Washington region;
the spatial and temporal resolution requirements of these parameters
necessary to reproduce the variability in aerosol extinction.
These questions are relevant to scientists and policy makers seeking to
assess the ability of satellite AOD retrievals to diagnose ground-level air
quality.
Experimental design
The NASA P-3B was equipped with a variety of in situ aerosol and gas-phase
measurements. The current analysis uses a subset of these measurements
including aerosol scattering, absorption, size distribution, and composition.
Air was sampled with an isokinetic inlet which efficiently collects and
transmits particles with a diameter smaller than 4 µm (McNaughton
et al., 2007). Scattering coefficients at 450, 550, and 700 nm were measured
with an integrating nephelometer (TSI, Inc. model 3563) and corrected for
truncation errors according to Anderson and Ogren (1998), while absorption
coefficients at 470, 532, and 660 nm were measured with a particle soot
absorption photometer (PSAP, Radiance Research) and corrected for filter
scattering according to Virkkula (2010). In order to calculate extinction,
the measured Ångström exponent was used to adjust the scattering at 550 to
532 nm (Ziemba et al., 2013).
During sampling, the RH of the air is modified due to the temperature
gradient between the outside and inside of the plane. This causes a change in
the scattering coefficient due to the generally hygroscopic nature of
aerosol. To provide a stable scattering signal, the sample is initially dried
to approximately 20 % RH utilizing a nafion drier and then sampled with
tandem nephelometers (with and without humidification) to find the dry
(σscat,dry at a RHdry of approximately 20 %) and
humidified scattering coefficients (σscat,wet at a
RHwet of approximately 80 %). These scattering measurements are
related via a single-parameter monotonic growth curve (Gasso et al., 2000):
σscat,wet=σscat,dry⋅100-RHwet100-RHdry-γ,
where γ is an experimentally determined variable of the
hygroscopicity, with water uptake increasing with increasing γ.
σscat,dry was corrected to 20 % RH based on Eq. (1) to
account for any variability in RHdry. Once γ is determined,
the scattering at ambient RH (σscat,amb, RHamb) is
found from
σscat,amb=σscat,dry⋅100-RHamb80-γ.
Ambient RH was calculated based on measurements of water vapor concentration
by an open-path diode laser hygrometer (Diskin et al., 2002), static
temperature, and pressure. Aerosol extinction at ambient RH (σext,amb) can then be found by summing σscat,amb and
absorption (σabs):
σext,amb=σscat,dry⋅100-RHamb80-γ+σabs.
The dependence of aerosol absorption on RH is highly uncertain (Redemann et
al., 2001; Mikhailov et al., 2006; Brem et al., 2012) and is therefore not
incorporated but likely manifests as only a small uncertainty in total
extinction due to the fact that absorption was only a minor component of
extinction (4 % on average).
Ziemba et al. (2013) showed a good correlation (R2 of 0.88 based on
comparison of 668 data points) between extinction measurements from the P-3B
and coincident measurements performed by a high-spectral-resolution lidar
(HSRL) on the UC-12. Recent work (Brock et al., 2015a; Wagner et al., 2015)
has suggested an additional model for aerosol hygroscopicity known as the
kappa (κext) parameterization. However, these two models (based on
γ and κext) are fairly consistent (scattering within 5 %) at
RHs below 85 %, a range which comprised 96 % of the data measured by
the P-3B. In addition, the good agreement between HSRL and in situ data
(utilizing the γ correction scheme) suggests this is a valid model for
the aerosol measured in Baltimore during DISCOVER-AQ (Ziemba et al., 2013).
Time series of extinction (at ambient RH and 532 nm) and altitude
(gray dashed line) for flight 9 (upper panel). Extinction measurements during
each circuit are highlighted by differing background color. Each circuit is
then plotted in the bottom panel to show the changes in aerosol between the
circuits. Profile locations correspond to those shown in Fig. 1.
Single-scattering albedo (SSA) describes the relationship between aerosol
scattering and extinction:
SSA=σscat,dryσext,dry=σscat,dryσscat,dry+σabs.
SSA can vary with RH (as scattering increases) but is here defined as the SSA
under dry conditions (20 % RH). Thus Eq. (3) can be rewritten as
σext,amb=σext,dry⋅1+SSA⋅100-RHamb80-γ-1.
Black carbon (BC) mass was measured with a Single Particle Soot Photometer
(SP2, Droplet Measurement Technologies), while a pair of Particle-Into-Liquid
Samplers (PILS, Brechtel Manufacturing, Inc.; Weber et al., 2001) were used
to measure water-soluble organic and inorganic species. The PILS captures
particles in the sampled air flow into a liquid flow of deionized water.
Denuders prior to the PILS removed gas-phase organic compounds (parallel-plate
carbon filter denuders, Sunset Laboratory, Inc.) and inorganic acids
and bases (annular denuders coated with sodium carbonate and phosphoric acid,
URG Corporation). Laboratory testing prior to the campaign showed the use of
denuders resulted in a size cut of approximately 2 microns for the PILS
systems.
The first PILS was coupled to a total organic carbon (TOC) analyzer (Sievers
Model 800) to give the mass of water-soluble organic carbon (WSOC) at a 10 s
time resolution. The TOC analyzer reports the organic carbon mass in
µgC m-3 and not the total organic mass (which includes mass
due to bonded hydrogen and oxygen atoms). Thus, to determine total
water-soluble organic matter (WSOM), a multiplier ranging from 1.6 for urban
to 2.1 for non-urban aerosols must be applied (Turpin and Lim, 2001). For the
present work, a value of 1.8 is used based on Hand and Malm (2007). However,
it should be noted that this does not include mass from any water-insoluble
organic compounds.
Vertical profiles of aerosol extinction (at ambient RH and 532 nm)
for flight 9 segregated by circuit and profile site. Horizontal lines
represent the boundary layer (solid line) and buffer layer (dashed line)
heights during each circuit at site 2 based on airborne measurements of the
potential temperature profile.
The liquid flow from the second PILS was collected in vials at a resolution
of 3.25 or 5 min for offline ion chromatographic (IC) analysis of chloride,
nitrate, nitrite, sulfate, sodium, ammonium, potassium, magnesium, and
calcium mass concentrations. The IC (Dionex ICS-3000 with an auto-sampler)
utilized a CS12A column for cation analysis and an AS11 column for anion
analysis with run times of 15 and 20 min, respectively. Standards were run
periodically for calibration and to ensure system stability. Dilution was
measured in the PILS through the addition of lithium bromide to its water
supply. Complete inorganic composition data are not available from the first
three flights due to contamination from the sample vials; alternate vials
were used for the remainder of the campaign. Aerosol size distributions were
measured with an Ultra-High Sensitivity Aerosol Spectrometer (UHSAS, Droplet
Measurement Technologies) calibrated with ammonium sulfate. All data are
publicly available from the NASA Langley Atmospheric Science Data Center
(ASDC, 2015).
As the PILS is unable to measure insoluble aerosol, the measured aerosol mass
is a lower limit for the actual mass. The PILS mass can be compared to the
volume measured by the UHSAS utilizing a density determined based on the
measured mass of organics (1.2 g cm-3; Turpin and Lim, 2001) and
ammonium sulfate (1.77 g cm-3). Based on this analysis, the PILS
measured approximately 82 % of the aerosol mass, with the other 18 %
assumed to be insoluble organic compounds. Higher insoluble organic masses
are estimated for higher loadings days, with insoluble loadings near zero for
low-loading days. However, this analysis has a large uncertainty due to a
difference in size range measured by the two instruments and volatilization
of aerosol at the PILS tip. Measurements by Sorooshian et al. (2006) show
that slightly more than 10 % of the ammonium is lost in the PILS with a
tip temperature of approximately 100 ∘C. Good closure (slope of
0.98) between cations and anions (equivalence) suggests that any loss
mechanisms are equivalent for all species. Thus, while this analysis gives an
approximation of possible insoluble mass, this estimation is not included in
future analysis due to the high uncertainty.
Results – mission overview
Each DISCOVER-AQ Maryland flight can be broken into two to three repetitive
circuits which encompassed spirals from 0.3 to 4.5 km centered over six
primary ground sites (labelled as sites 1–6 in Fig. 1). When time permitted,
additional spirals were performed at select sites at the end of the flight,
resulting in two to four spirals over each site per flight. A time series of
aerosol extinction during flight 9 highlights an altitude dependence of
aerosol scattering, with values oscillating between near zero in the free
troposphere and greater than 200 Mm-1 in the mixing layer (Fig. 2).
The repetitive flight plan allows for the analysis of differences in aerosol
properties and their vertical distributions at each site as source profiles
and boundary layer dynamics changed during the day, as seen for flight 9 in
Fig. 3. During the first circuit (11:00–13:30 local time), a mixed layer up
to 1.5 km is seen, capped by a residual layer between 1.5 and 2.5 km.
Surface heating causes the two layers to merge by the time the second circuit
was performed (13:30–15:30), with fairly constant extinction to 1.5 km and a
gradual decrease to near-zero extinction by 2.5 km. Circuit 3 (15:30–17:30)
had constant extinction below 1.5 km but little indication of a residual
layer. In addition, the profiles among the sites become more homogeneous as
the day progresses (Fig. 3). In general, the mixing layer was consistently
greater than 1 km throughout the flights; therefore, data below 1 km are
used as a measure of mixing layer aerosol properties.
Average AOD (at ambient RH) along with boundary layer (below 1 km)
extinction, aerosol mass, effective radius, and composition for each of the
14 flights. Aerosol mass and composition data are not available for the
first three flights. Flights with predominantly westerly transport from the
Ohio River Valley are indicated by stars at the top of the plots.
Aerosol mass loadings varied by a factor of 6 (Fig. 4) between the flights,
with average aerosol mass in the lowest 1 km ranging from 3.8 to
26 µg m-3. Aerosol optical measurements varied by an even
greater amount, with ambient aerosol extinction in the lowest 1 km ranging
from 20 to 290 Mm-1 and AODs (calculated from the integration of the
extinction profile) ranging from 0.05 to 0.57. In situ AOD measurements
showed good agreement (within 0.04) with ground-based radiometer measurements
made by the Aerosol Robotic Network (AERONET; Holben et al., 1998) in the region (L. Ziemba, personal communication, 2015). The fact that the highest extinction below 1 km
(flight 9) and AODs (flight 14) were not measured during the same flights
highlights the potential disconnect between AOD and surface layer aerosol
loading. Flight 14 had a deeper aerosol layer and more aerosol in an elevated
layer than flight 9 (Fig. 5); thus flight 14 had a higher AOD despite having
less near-surface extinction than flight 9. Other surface-independent factors
influencing AOD may include aerosol cloud processing. Indeed, Eck et
al. (2014) observed large increases in AOD (average of 25 %) in the
vicinity of non-precipitating cumulus clouds. Consistent with these findings,
in situ measurements showed increases in aerosol scattering, volume, and mass
in spirals measured before and after cloud formation. These included a
doubling of water-soluble organics and 50 % increase in sulfate.
Average vertical profiles of aerosol extinction (at ambient RH and
532 nm) for all flights, with flights 9 and 14 highlighted (left panel).
These profiles can then be normalized to the total aerosol loading (AOD) to
get the normalized vertical profile (right panel, arbitrary units).
In general, the fraction of aerosol measured was primarily a mixture of WSOM
(campaign average of 57 % by mass, Fig. 4), sulfate (23 %), and
ammonium (10 %), with minor contributions from nitrate (2.1%), BC
(2.2 %), chloride (2.0 %), and sodium (1.3 %). The molar ratio of
ammonium to sulfate was 1.92, showing that sulfate is almost completely
neutralized as ammonium sulfate, (NH4)2SO4, with minimal
bisulfate, (NH4)HSO4. Further, this ratio is higher (above 2) if
PILS volatilization of ammonium (12 % loss of mass; Sorooshian et al.,
2006) and sulfate (1 % loss) is considered. Composition varied between
flights with polluted days (as noted in Fig. 4) exhibiting a higher fraction
of ammonium and sulfate. Back-trajectory analysis with the Hybrid
Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT; Draxler and Hess,
1998; Draxler and Rolph, 2015) suggested these high-aerosol-loading days were
related to long-range transport from the Ohio River Valley (Fig. 6), which has
enhanced sulfur dioxide emissions due to a high density of coal-fired power
plants in the region (Hand et al., 2012). These days were generally
associated with low-pressure systems to the northwest of the study region.
Conversely, low-loading days tended to have northerly flow due to high-pressure systems to the west.
The flights with transport from the west and higher aerosol loadings (starred
in Fig. 4) were found to have relatively more sulfate (28 % of mass
compared to 15 % for clean days) and ammonium (polluted, 11 %; clean,
7.5 %) and less organics (polluted, 52 %; clean, 65 %). Less
polluted days had higher percentages of nitrate (polluted, 1.1 %; clean,
3.9 %) and BC (polluted, 2.0 %; clean, 2.7 %). The higher BC mass
percentage also leads to higher absorption relative to scattering and
therefore lower SSA on these less polluted days (polluted, 0.98; clean, 0.93;
Fig. 7). However, on an absolute basis the polluted days had higher BC and
absorption than on the clean days. Average BC concentrations for the entire
month were 240 ng m-3 in the lowest 1 km, decreasing to
35 ng m-3 in the free troposphere (above 3 km).
72 h back trajectories based on HYSPLIT for the first circuit of
each flight at site 5 at an altitude of 1 km colored by the average AOD
measured during that flight.
Average profiles for extinction (at ambient RH and 532 nm), γ, SSA, and composition for all flights (black line), days with predominantly
westerly transport from the Ohio River Valley (red line), and days with
northerly transport (blue line).
The polluted flight days also had higher γ values (Fig. 7, Eq. 5).
This water uptake is largely dependent on aerosol composition, with soluble
organics having lower hygroscopicity than inorganic compounds. This can be
seen as an inverse relationship with γ=0.60-0.0042× organic mass fraction (Fig. 8). These values are
intermediate between measurements made in other urban areas (Asia and USA –
Quinn et al., 2005; Texas – Massoli et al., 2009) and in the remote atmosphere
(the Indian Ocean – Quinn et al., 2005). Differences are likely due to
differences in the measurement of organics; the current study uses PILS to
measure only water-soluble organics, while the other studies use aerosol mass
spectrometry or thermo-optical methods which are sensitive to all organic
species. In addition to an elevated γ, high-loading days were
typically more humid (64 ± 7 % compared to 49 ± 7 %).
These higher humidities and γ values resulted in a higher water
content of the aerosols as evident from ambient extinctions that were
25 % higher than dry values on high-loading days compared to the 12 %
observed on low-loading days. The highest daily-averaged water content of
aerosol extinction was 40 % measured during flight 8.
Relationship between γ (at 532 nm) and organic mass
fraction for the present study (data below 1 km), Texas (Massoli et al.,
2009), the western Pacific, the northeast USA, and the Indian Ocean (Quinn et
al., 2005). The organic mass fraction is found by dividing the WSOM by the
total mass measured by the PILS and SP2. Other studies used organic mass
measured by aerosol mass spectrometer or thermo-optical methods. The ratio of
scattering at 80 % RH to 20 % [f(RH)] is shown on the right
y axis (note the irregular spacing).
Aerosol mass is the primary measurement of aerosol loading and the basis on
which ground air quality is regulated. Boundary layer dry extinction, ambient
extinction, and AOD are additional measures of aerosol loading
but incorporate an increasing amount of confounding factors. For instance,
dry extinction is dependent on the aerosol mass loading in addition to
aerosol size and composition. Ambient extinction is dependent on these same
factors plus the aerosol hygroscopicity and RH. Finally, AOD is also
dependent on the vertical distribution of aerosols and RH. Aerosol mass
loading, dry extinction (not shown), ambient extinction, and AOD follow
similar trends (Fig. 4), suggesting that aerosol mass loadings are the primary
factor controlling day-to-day variability in aerosol optical properties.
However, aerosol mass measurements via PILS do not account for insoluble
aerosol. Dry mass extinction efficiencies calculated from extinction and mass
measurements were variable, ranging between 3.2 and 8.3 m2 g-1.
The highest mass extinction efficiencies (measured on high-loading days)
likely are indicative of the presence of insoluble organic material.
Therefore, because of the variable quantity of insoluble mass and the low
time resolution of the PILS measurements, future analysis will use the dry
extinction as a proxy for aerosol loadings.
Average normalized 532 nm dry extinction (left panel), RH (center),
and 532 nm ambient extinction (right) for all of the circuits (data are
normalized to the average value for that circuit). The site with the maximum
value is labelled.
Results – regional variability
Aerosol extinction varied not only on a temporal basis (Fig. 4) but also
spatially. Because there is such a large difference in aerosol loadings,
optical properties (related to composition), and RH between flights, using
campaign averages would distort the spatial trends. Therefore, each circuit
consisting of spirals over six ground sites is treated as a separate
“snapshot” of the region, and the properties measured over each site are
normalized to the circuit average to study the spatial variability. Data
below 1 km pressure altitude were used from 34 circuits for which spirals
were performed over all six sites (absorption measurements were not available
for one additional circuit, and therefore it was not included in this
analysis).
In order to get a general overview of aerosol variability in the regional,
the average normalized dry and ambient extinctions along with RH for all of
the circuits are shown in Fig. 9. The data are first normalized to the average
for the circuit, and then the normalized values are averaged. The highest dry
aerosol extinction was nearest downtown Baltimore, with site 5 extinction
5.6 % larger than the average. However, the average ambient extinction
measured was highest at the north end of the region where site 3 is 5.5 %
larger. This is consistent with the observed latitudinal gradient in RH. This
shows that meteorological conditions (RH) can alter spatial trends in ambient
extinction. Theoretically, it is possible for the entire region to have the
same aerosol loading but differing extinction due to variability in
composition and RH. Conversely, it is possible that the entire region could
have a gradient in aerosol loading yet the composition and RH vary in such a
way that extinction is constant throughout the region.
However, in order to study aerosol variability, it is important to analyze
each circuit individually (and not as a campaign average as done in Fig. 9).
Equation (5) shows the dependence of aerosol ambient extinction on aerosol loading
(σext,dry), composition (SSA and γ), and RH, and it can be
used as a simple model to determine the factors controlling aerosol ambient
extinction. From this, an assessment of the accuracy needed for each of these
parameters to relate aerosol extinction (which can be derived from satellite
measurements) to aerosol loading can be performed. In order to determine the
relative importance of aerosol loading, composition, and RH on extinction, the
partial derivatives of Eq. (5) can be determined:
∂σext,amb∂σext,dry=1+SSA⋅100-RHamb80-γ-1,∂σext,amb∂SSA=σext,dry⋅100-RHamb80-γ-1,∂σext,amb∂RH=σext,dry⋅SSA⋅γ80100-RHamb80-γ-1,∂σext,amb∂γ=-σext,dry⋅SSA⋅100-RHamb80-γ⋅ln100-RHamb80.
As expected, ambient extinction is linear with dry extinction (the partial
derivative does not contain σext,dry). The positive linear
dependence on SSA shows that, if all other variables are held constant, as SSA
increases scattering becomes a larger fraction of extinction and at any RH
above 20 % will cause an increase in extinction due to water uptake. The
dependence on RH and γ are both nonlinear, and thus their effects are
most important when the RH is high or the aerosol is very hygroscopic.
Average 532 nm ambient extinction, dry extinction, and RH below
1 km during spirals over the six sites during flights 1 and 14.
Relative contribution of dry extinction and RH to the spatial
variability in ambient extinction as a function of RH (left) and to the
diurnal variability (right). Diamonds represent the average relative
contributions for 10 % RH increments.
Equations (6) through (9) can be combined to give the total differential for
σext,amb:
dσext,amb=∂σext,amb∂σext,dry⋅dσext,dry+∂σext,amb∂SSA⋅dSSA+∂σext,amb∂RH⋅dRH+∂σext,amb∂γ⋅dγ.
Assuming that the four variables are independent,
sσext,amb=∂σext,amb∂σext,dry⋅sσext,dry2+∂σext,amb∂SSA⋅sSSA2+∂σext,amb∂RH⋅sRH2+∂σext,amb∂γ⋅sγ21/2,
where s(x) is the standard deviation in x, which is used as a measure of
the variability in measurements made at the six sites during one circuit.
Each term signifies the explained variance due to each of the four
properties. Thus the relative contribution (RC) of dry aerosol scattering to
the variability in ambient extinction in the region can then be found by
RCσext,dry=∂σext,amb∂σext,dry⋅sσext,dry2sσext,amb2.
Using this method, the RC for each of the four variables can be determined
for each circuit.
Trends in 532 nm ambient extinction, dry extinction, and RH below
1 km during spirals at site 4 during flights 1 and 14.
In order to determine the relative contribution of each factor to the
variability in ambient aerosol extinction, each circuit was analyzed
separately. Shown in Fig. 10 are two extreme cases. During flight 1, ambient
relative humidity was low (37 ± 4 %), resulting in little water
uptake (the shaded portion on the upper panel). Thus variability in dry
extinction (aerosol loading) is the major contributor (RC(σext,dry)=99 %) to variability in ambient extinction. The
second case during flight 14 shows a period of high RH (64 ± 8 %).
Water uptake was substantial and greatest at site 3, where the RH is the
highest. In this case, the variability in aerosol extinction is
dependent not only on variability in dry extinction (41 %) but also on relative
humidity (57 %).
On average, aerosol loading (dry extinction) accounted for 88 % of the
spatial variability in extinction, with 27 of the 34 complete circuits having
RC(σext,dry) above 80 % (Fig. 11). Variability in RH only
accounted for 10 % of the ambient extinction variability on average, with
only five circuits having RC(RH) greater than 20 %. Four of these cases
where RH had a large effect on ambient extinction variability corresponded to
days with high RH (above 60 %). This is due to the nonlinearity of
extinction with respect to RH (Eq. 8). Thus at low relative humidities,
changes in RH minimally impact ambient extinction. Conversely, when RHs are
high, small changes can produce large variations in ambient extinction.
Changes in γ and SSA were smaller contributors to ambient extinction
variability (1.3 % and less than 0.1 % on average, respectively).
Results – diurnal variability
A similar analysis can be performed to examine the diurnal variability of
aerosol extinction. For this analysis, each variable was averaged for each of
the six sites during each flight. This produced data at each spiral site
approximately every 2 h during each flight period (three to four values per site
per day); the comparison between these values was then used to determine the
diurnal variability in each parameter over the course of each flight. Sites
with only two spirals during a flight were not included in this analysis.
Figure 12 shows data at site 4 from the same flights used for the regional
variability analysis. For flight 1, little water uptake occurred during the
flight period, so more than 99% of the diurnal change in ambient extinction
is due to changes in aerosol loading. In contrast, during flight 14,
extinction variability is dependent on changes both in aerosol loading and RH
(51 and 49 %, respectively). From the first to second circuit, ambient
extinction dropped as a result of an RH change from 70 to 59 %. After
16:00 local time, the RH continued to drop but ambient extinction increased
due to an increase in dry aerosol extinction. Thus in this case, knowledge of
the aerosol loading and RH trends are needed to interpret the aerosol
extinction diurnal trends. On average, diurnal extinction variability was
dominated by changing aerosol loading (82 %) with smaller contributions
from changes in RH, γ, and SSA (16, 1.6, and less than 0.1 %,
respectively). However, RC(RH) values greater than 90 % were measured
during flight 9 (highest orange markers on the right panel of Fig. 11), a day
with high RH and highly variable RH.
Average γ for each flight (top) along with estimated
ambient extinction and percent bias if the flight-average (left) and
campaign-average (right) γ are used.
Discussion
The conversion of extinction at ambient RH to extinction at a reduced
(“dry”) RH is important in relating remote-sensing measurements of ambient
extinction to dry aerosol mass. Though the analysis above shows that
variability in γ and SSA are only minor contributors to ambient
extinction variability, converting between ambient and dry extinction
requires knowledge of both parameters, as evidenced by Eq. (3). However, both
γ and SSA are not routinely measured at air quality monitoring sites.
So the question could be asked, “at what frequency (both spatially and
temporally) do γ and SSA need to be known to determine the proper RH
conversion?” This can be examined by analyzing the DISCOVER-AQ Maryland data
recorded below 1 km and determining how using more averaged data yields
differing ambient aerosol extinctions.
As a result of changes in composition seen in Fig. 4, γ varied
between 0.14 (flight 1) and 0.47 (flight 8), with an average of 0.32
(Fig. 13). Comparing the ambient extinction calculated during each spiral
with the extinction calculated using the daily-average γ resulted in
a bias of ±1.6 % in ambient extinction, with no clear trend with
respect to aerosol extinction. Using the monthly average for the entire
region causes a bias of ±6.8 % (Table 2) with deviations of up to
27 % at high aerosol extinction because γ tended to be higher on
high-aerosol-loading days (Fig. 8). We conclude that spatial γ
differences in the Baltimore region are not large enough to cause significant
biases in deriving dry extinction from ambient values. However, day-to-day
variability in γ can cause large discrepancies. Thus it appears that
a single daily measurement of γ (or one based on compositional
measurements, Fig. 8) is able to be used for AOD-to-PM2.5 correlations
over the study region (on the order of 1400 km2) within an uncertainty
of 2 %.
Average SSA for each flight (top) along with estimated ambient
extinction and percent bias if the flight-average (left) and
campaign-average (right) SSA are used.
Percent bias in ambient extinction based on daily and monthly
averaging of contribution variables.
VariablePercent bias based on averaging DailyMonthlyDry extinction22111RH6.210.7γ1.66.8SSA0.210.49
A similar analysis can be performed to evaluate the importance of SSA in
retrieving dry extinction from ambient extinction (Fig. 14 and Table 2). SSA
varied from 0.91 to 0.99 during the mission with higher SSA measured on
high-aerosol-loading days due to the increased loading of sulfate and other
secondary aerosols which are typically more scattering than primary aerosols.
Comparing the ambient extinction calculated during each spiral with the
extinction calculated using the daily-average SSA resulted in a bias of
±0.2 % in ambient extinction, showing that regional variability in SSA
was not high enough to make a significant difference. Using the monthly
average for the entire region produces biases of ±0.5 % with
deviations of up to 1.0 % at high aerosol extinction.
Doing the same analysis for dry aerosol extinction or RH shows markedly
different results (Figs. 15 and 16, Table 2). The use of a daily-average dry
extinction causes a bias of ±22 %, showing that regional variation in
aerosol loading must be accounted for. Utilizing a monthly average extinction
causes discrepancies of ±111 % due to the large day-to-day
variability in aerosol loading. Biases based on limited knowledge of RH were
smaller, with ±6.2 % for daily and 11 % for monthly RH. Thus,
Table 2 gives a hierarchy of factors for variability in extinction
measurements: loading > RH >γ> SSA.
Average dry extinction for each flight (top) along with estimated
ambient extinction and percent bias if the flight-average (left) and
campaign-average (right) dry extinction are used.
An analysis of the effects of aerosol and meteorological parameters on AOD in
the southeastern USA based on 37 airborne profiles (Brock et al., 2015b)
shows similar trends in the significance of factors, with aerosol mass being the most
important. Relative humidity had a nonlinear significance on AOD, with the
greatest significance for extremely humid conditions (the 90th percentile RH
profiles). Varying aerosol size parameters and the vertical distribution of
the aerosols resulted in moderate AOD changes, while AODs were largely
insensitive to refractive index in a fashion similar to the present findings
of SSA as a minor contributor to extinction variability.
Conclusions
Measurements made in the Baltimore–Washington, D.C. region during DISCOVER-AQ
in July 2011 can be generalized as follows: on days influenced by transport
from the Ohio River Valley, aerosol loadings were higher (aerosol mass
concentrations of 18.7 ± 4.4 µg m-3 and AODs of
0.43 ± 0.12) and the aerosol were more hygroscopic (γ of
0.36 ± 0.07) because of a larger percentage of ammonium and sulfate
(38 % of water-soluble mass) in comparison to days impacted by northerly
transport (aerosol masses of 5.4 ± 1.3 µg m-3, AODs of
0.08 ± 0.03, γ of 0.26 ± 0.09, 20 % ammonium and
sulfate). In both cases, the regional and diurnal variability in aerosol
extinction are controlled primarily by changes in aerosol loadings. However,
on days associated with westerly transport (which also were more humid)
variability in RH also contributed significantly to the regional (14 %)
and diurnal (22 %) variability in extinction. Thus changes in AOD cannot
directly be seen as changes in PM2.5 but must take into account spatial
and temporal variability in RH.
Average RH for each flight (top) along with estimated ambient
extinction and percent bias if the flight-average (left) and
campaign-average (right) RH are used.
Variability in aerosol composition (as indicated by γ and SSA) was
found to have a very small contribution to variability in aerosol extinction
both diurnally and regionally. However, day-to-day changes in γ were
large enough that utilization of a monthly average would result in a bias of
±6.8 % in aerosol extinction with biases up to 27 % for
high-aerosol-loading days. Thus, daily measurement of γ (or a value
derived from compositional measurements) at one location is needed to provide
information for the entire study region. This is similar to the results of
Chu et al. (2015) that the aerosol vertical distribution from “a single
lidar is feasible to cover the range of 100 km” in the same region.
However, this may not apply for regions outside of the US northeast which
have lower AOD-to-PM2.5 correlation because of more variable aerosol
composition and vertical distributions (Engel-Cox et al., 2004).
Acknowledgements
This research was funded by NASA's Earth Venture-1 Program through the Earth
System Science Pathfinder (ESSP) Program Office. We thank the DISCOVER-AQ
Science Team, especially the pilots and flight crews of NASA's P-3B. Boundary
layer heights based on airborne measurements of the potential temperature
profile were provided by Don Lenschow of the University Corporation for
Atmospheric Research (UCAR). Thanks also to Joshua DiGangi and Michael Shook
(both of NASA Langley) for valuable discussions during manuscript
preparation. Edited by: P. DeCarlo
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