The present study investigates and compares the ground and in-flight
performance of three miniaturized aerosol absorption sensors integrated
on board small-sized Unmanned Aerial Systems (UASs). These sensors were
evaluated during two contrasted field campaigns performed at an urban site,
impacted mainly by local traffic and domestic wood burning sources (Athens,
Greece), and at a remote regional background site, impacted by long-range
transported sources including dust (Cyprus Atmospheric Observatory, Agia
Marina Xyliatou, Cyprus).
The miniaturized sensors were first intercompared at the ground-level
against two commercially available instruments used as a reference. The
measured signal of the miniaturized sensors was converted into the
absorption coefficient and equivalent black carbon concentration (eBC). When
applicable, signal saturation corrections were applied, following the
suggestions of the manufacturers. The aerosol absorption sensors exhibited
similar behavior against the reference instruments during the two campaigns,
despite the diversity of the aerosol origin, chemical composition, sources,
and concentration levels. The deviation from the reference during both
campaigns concerning (eBC) mass was less than 8 %, while for the absorption
coefficient it was at least 15 %. This indicates that those sensors that
report black carbon mass are tuned and corrected to measure eBC more accurately than the absorption coefficient.
The overall potential use of miniature aerosol absorption sensors on board
small UASs is also illustrated. UAS-based absorption measurements were used
to investigate the vertical distribution of eBC over Athens up to 1 km above
sea level during January 2016, exceeding the top of the planetary boundary
layer (PBL). Our results reveal a heterogeneous boundary layer concentration
of absorbing aerosol within the PBL intensified in the early morning hours
due to the concurrent peak traffic emissions at ground-level and the fast
development of the boundary layer. After the full development of the PBL,
homogenous concentrations are observed from 100 m a.g.l. to the PBL top.
Introduction
Atmospheric aerosol particles scatter and absorb solar radiation, thus
directly affecting the radiative balance of the atmosphere
(Haywood and
Boucher, 2000). Their contribution to climate change is still associated
with large uncertainties when estimating their radiative forcing (RF)
(Bond
et al., 2013; IPCC, 2013). A major contributor to these uncertainties is the
RF induced by black carbon (BC), which exhibits a relative standard
deviation exceeding 40 % among different numerical climate models
(Myhre
et al., 2013). The BC direct RF has been estimated to be 0.71 Wm-2 with
an uncertainty range of 0.08 to 1.27 Wm-2 (Bond et al., 2013), while in
a more recent study it ranged from 0.14 to 1.19 Wm-2 (90 %
confidence interval) with an average value of 0.53 Wm-2
(Wang et al., 2016). Major factors
responsible for the wide range of the BC's RF estimates include the
inaccurately predicted BC emission rates, poorly understood interactions of
BC with clouds, and the inaccuracy in representing its vertical distribution
(Bond et al., 2013). In addition, BC has been identified to reduce the
albedo of snow surfaces (Hadley and Kirchstetter, 2012) and
to suppress the turbulence of the boundary layer
(Wilcox et al., 2016).
An array of techniques and instruments are employed worldwide with the aim
of increasing the spatial and temporal resolution of BC observations. The
instrumentation employed is based on different operating principles,
including offline or near-real-time methods for measuring elemental carbon
(EC), such as thermal–optical reflectance and transmittance (cf. Lack et al.,
2014 and references therein for more details) as well as online, real-time
methods. The latter are mainly based on the aerosol light-absorbing
properties of BC (cf.
Moosmüller et al., 2009; Petzold et al., 2013; Lack et al., 2014 and
references therein for more details).
Most of the aerosol absorption observations available in the literature are
conducted at ground level. Consequently, they lack critical information
regarding the vertical distribution of aerosol absorption a key parameter for
constraining atmospheric models and accurately assessing aerosol radiative forcing
effects (Samset et al., 2018). One way to fill this
gap is by conducting manned airborne aerial absorption measurements
(Seinfeld
et al., 2004; Subramanian et al., 2010; Freney et al., 2014; Kassianov et
al., 2018; Katich et al., 2018; Sedlacek et al., 2018). However, these are
costly and cover a limited period of observations. In the pioneering work of
Corrigan et al. (2008),
vertical absorption profiles over the Indian Ocean were measured using parts
from a standard (rack) size instrument on board a medium-scale (25–150 kg)
unmanned aerial system (UAS). Since then, the size and weight of absorption
monitors have been reduced, and the use of lightweight miniaturized sensors
on board small UASs or tethered balloons provides cost-effective
alternatives able to fill the measurement gap and to enhance the vertical
and temporal density of aerosol absorption observations. A UAS is defined as
small if its gross weight is less than 25 kg (US Federal Aviation
Administration, CFR 14, 2015). Vertical aerosol absorption observations using
small UASs or tethered balloons have already been conducted in different
regions such as the Indian Ocean
(Höpner
et al., 2016), India
(Bisht
et al., 2016), the Arctic
(Bates
et al., 2013; Ferrero et al., 2016), Italy
(Ferrero
et al., 2011, 2014), Poland
(Chilinski et al., 2016), and
China (Ran et al., 2016).
These measurements can be further used to obtain the vertically resolved
heating rate, including contributions from different sources and
carbonaceous aerosol fractions
(Ferrero
et al., 2014, 2018). The employment of UASs in some of the above-mentioned
campaigns proves to be a viable option to obtain information on aerosol
absorption vertical distribution. Even though small UASs are subject to
significant payload restrictions compared to manned aircrafts, they have a
distinct advantage over their manned counterparts in terms of relatively low
platform and operation cost, capability of performing autonomous flight
operations, and ability to fly closer to the ground with greater spatial
accuracy and collect spatially dense data (due to low-speed operation)
under reduced workload (Villa et al.,
2016). In addition, they have the advantage of better controllability in
comparison to balloons and zeppelins, since the latter are more delicate under
stronger winds (Inoue et al., 2000; Jensen et
al., 2007). In terms of instrumentation, ground-based aerosol absorption
instruments have been qualified in many intercomparison studies (e.g.,
Müller et al., 2011). On the contrary, their miniaturized counterparts'
behavior is still poorly demonstrated in-flight. The measurement quality
delivered by these sensors during flight is challenged by fast changes in
pressure, temperature, and humidity, which are difficult to assess from
concurrent ground-level measurements.
In this work, we focus on vertical distributions of aerosol absorption,
measured with miniature absorption sensors on board small- and medium-sized UASs
during two intensive field campaigns at contrasted locations in the eastern
Mediterranean: an urban site (Athens, Greece) and a remote regional
background site (Cyprus Atmospheric Observatory, CAO, Cyprus). The vertical
distribution of aerosols in the eastern Mediterranean is of particular
importance because it lies at the crossroads of diverse air masses
(Lelieveld et
al., 2002) carrying aerosol of different compositions, including mineral dust
from Africa and the Middle East, pollution from Europe and the nearby Middle
East, and marine aerosol
(Erel
et al., 2006; Gerasopoulos et al., 2007; Kalivitis et al., 2007). In
addition, aerosol absorption measurements, whether ground or aerial based,
are rather scarce in the region. The sites were selected to represent two
different and contrasting sources of ambient aerosol, with high concentration
levels of freshly emitted BC from traffic and/or biomass burning (domestic
heating) in Athens and low concentrations of aged regionally transported
aerosol, occasionally mixed with moderate levels of dust in Cyprus.
Aerosol vertical profiles were monitored using several types of fixed- and
rotary-wing UASs. In addition to the aerial observations, three miniature
attenuation monitors were also characterized against ground-based commercial
instruments. Secondly, these miniature sensors were compared and
characterized in-flight with different UASs and diverse absorbing aerosol
concentrations and types.
Summary of UASs used during the Athens and Cyprus campaigns. A UAS
is considered small if its gross weight is smaller than 25 kg and medium if
its gross weight ranges between 25 and 50 kg.
USRL fleet of UASTypeMTOWbPayloadaEndurance*CeilingaManufacturerMedium-sized35 kg12 kg4 h3 km a.s.l.ET-AirFixed wingSmall-sized5 kg3 kg1 h3 km a.s.l.SkywalkerFixed wingSmall-sized11 kg4 kg30 min1 km a.s.l.DJiRotary wing
a UAS characteristics as configured specifically for these studies (BACCHUS and ACTRIS campaigns). b Maximum takeoff weight.
InstrumentationUnmanned aerial system types
Three types of UAS have been used in this study: they differ with respect to
the payload, autonomy, wing type, and landing requirements. Their
specifications and capabilities, described below, are summarized in Table 1.
In addition and as mentioned before, UASs are characterized as small when
their gross weight is less than 25 kg and medium if their gross weight
ranges between 25 and 150 kg (US Federal Aviation Administration, CFR 14, 2015).
Despite having the ability to reach altitudes higher than 2 km above ground
level (a.g.l.), the UASs were limited to 1 and 2 km during the Athens and
Cyprus campaigns, respectively, due to restrictions posed by the civil
aviation authorities.
UAS “Cruiser”
The Cruiser is a medium-sized fixed-wing UAS (Table 1) with a payload
capacity up to 12 kg, which also includes the weight of the fuel to power the
engine and the battery used for the instrumentation. The Cruiser's payload
bay, available inside the UAS, measures 1.3 m ×0.23 m ×0.34 m (L×W×H). The UAS features a wingspan of 3.8 m. It has been configured
with an internal combustion two-stroke engine placed in a push configuration
enabling an altitude ceiling of 4 km and maximum takeoff weight of 35 kg.
Depending on payload and environmental conditions the Cruiser can reach a
flying endurance up to 8 h. During the flight, atmospheric sampling
occurs at a velocity of 28±5 m s-1, which is the typical cruising
air speed of this type of UAS. Under its current configuration the
environmental conditions to ensure safe operation are limited to winds up to
13 m s-1 and temperatures above the dew point in order to prevent icing
on the engine's carburetor. The Cruiser is equipped with an autopilot system
(Micropilot MP2128G2), which includes all the sensors and telecommunication
systems (e.g., GPS, barometric altimeter, accelerometer, air speed sensor,
electronic compass, modems, antennas) that allows autonomous flights with
real-time monitoring and control from the ground providing that
predetermined flight plans are set. At any time, the UAS operator is able to
modify the active flight plan in real time. In addition, the system is
capable of detecting faults and alter its flight plan accordingly (e.g.,
automatically return to home upon communication loss). The modular design of
the Cruiser facilitates switching instruments between scientific missions
provided that the total mass does not exceed the payload limit. To support
its multi-instrument capability, a central data acquisition system built
around the National Instruments controller, myRIO, with a variety of
interface possibilities and a graphical user interface (GUI), has been
developed. The graphical programming language Labview (from National
Instruments) has been utilized to develop the GUI with capabilities of
real-time visualization of the instrumentation data as well as controlling
and automation of the onboard instruments. All the instruments and avionics
sensitive to vibration have been mounted into the Cruiser fuselage using
special anti-vibration dampers in order to insulate them from the
high-frequency oscillations produced by the UAS engine. Vibration insulation
is essential in order to improve the flying reliability of the UAS as well
as to keep the quality of the scientific measurements to its higher
standards.
Due to the Cruiser's size, a flat (ideally paved) runway is required for
takeoff and landing. During the Cyprus campaign, the Cruiser was taking off
and landing on Cyprus Institute's private runway (see Fig. 1).
Aerial view of the Orounda runway in Cyprus.
UAS “Skywalker X8”
The Skywalker X8 is a small delta-wing type UAS with an electric motor
providing propulsion. Made from foam, it is a much smaller and lower-cost UAS compared to the Cruiser. Its wingspan is 2.10 m and its maximum
takeoff weight is about 5.5 kg. It can fly for approximately 1 h up to
3 km altitude with a payload of ca. 3 kg, which includes the battery (14.8V
Lithium Polymer, 9Ah) that powers the motor. This UAS is equipped with the
same avionics as the Cruiser, maintaining all of its advanced automation
characteristics. The Skywalker X8 can takeoff using a bungee launcher
catapult system and can land on its belly on any flat surface, thus
minimizing the requirements for a specialized aerodrome.
A modified version of the commercially available octocopter DJi S1000+ was
used during the Athens campaign to overcome strong constraints related to a
limited ground area for takeoff and landing and flying in the limited air
space. This platform has been optimized to reach an altitude up to 1 km
above sea level (a.s.l.) for a maximum takeoff weight of 11 kg and a
payload of 4 kg, including the motor battery (22 V Lithium Polymer, 22 Ah). In
order to ensure that sampling was not influenced by the turbulence created
by the octocopter's blades, the sampling inlet was extended by 1 m out of
the propeller downdraft. This distance ensured representative sampling while
ascending. However, during descent this length was not sufficient to avoid
the created vortex when a columnar path was followed. During the Athens
campaign, the landing site was near the edge of a cliff and inside an
archeological area where pedestrians could freely access (Fig. 2),
prohibiting deviation from a columnar flight path. As a result, the quality
of the descent flights was compromised at the expense of safety and thus
only ascending flights are used in this work.
Aerosol absorption instrumentationPrinciple of operation
The most widely used instruments for the determination of the aerosol
absorption coefficient are filter photometers. They sample ambient air
through a filter, where the sample is collected. The filter is illuminated
and the light transmitted through the filter is measured. Transmission of
the sample-laden filter is normalized to the transmission of the sample-free
filter (reference signal) and the attenuation is calculated based on Eq. (1).
ATNλ=100×lnIrefλIsampleλ,
where Iref(λ) and Isample(λ) are the reference and
sample light intensities at the detectors under the filter, respectively,
and ATN(λ) the attenuation at wavelength λ. The attenuation
rate dATN(λ)/dt (calculated from consecutive measurements)
determines the attenuation coefficient (batn(λ)) based on Eq. (2).
batn(λ)=A100QdATN(λ)dt,
where A is the sample spot area, Q the airflow rate, and dt the time period for
which the attenuation change is considered. It typically equals 1s for
all the miniaturized instruments examined in this study. The instrument
specific batn(λ) can be converted to absorption coefficient
babs(λ), when accounting for the multiple scattering effects
caused by the filter and/or by the sampled particles, together with the
filter loading effects that the latter are causing. Due to a lack of a
reference method for providing the aerosol absorption coefficient and
because every manufacturer is using different filter materials, several
empirical corrections have been proposed in the literature
(e.g.,
Weingartner et al., 2003; Virkkula et al., 2005; Collaud Coen et al., 2010;
Ogren, 2010; Drinovec et al., 2015). For instance, many studies reporting
absorption measurements calculate babs(λ) based on
Eq. (3) (Weingarter at al., 2003):
babs(λ)=batn(λ)C⋅R(ATN(λ)),
where C is the optical enhancement factor due to multiple scattering within
the filter medium and R(ATN(λ)) describes nonlinearities caused by the
particles loaded on the filter. Other absorption monitor manufacturers
use different approaches for deriving babs(λ), which can be
found in Sect. 2.2.2 and 2.2.3 for the instruments used in this study.
Summary of standardized properties of each attenuation monitor. The
term λ refers to the wavelength used in nm. NA – not available.
InstrumentManufacturerMassOpticalReferencenameattenuationenhancementcross sectionfactor (C)(m2 g-1)AE33Magee Scientific10730.48/λ1.57Drinovec et al. (2015)AE51Magee Scientific11000/λ2.05Ferrero et al. (2011)STAPBrechtelNA*NA*Ogren et al. (2010)MAAPThermo Scientific6.6 at 670 nmNAPetzold and Schönlinner (2004)Dual-wavelengthCustom-made11000/λ2.05NAprototype (DWP)from AE5111000/λ2.05NA
* Equation (7) is used instead.
The equivalent black carbon (eBC) mass concentration (expressed in µg m-3) can be calculated based on 880 nm wavelength
batn(λ) (Ramachandran and
Rajesh, 2007), using either Eq. (4) or (5),
4eBC=batn(880nm)σatn(880nm),5eBC=babs(880nm)MAC(880nm),
where σatn(λ) is the mass attenuation cross section and
MAC is the mass absorption cross section. Table 2 summarizes C and σatn(λ) factors used for each instrument in this study. Based on
these two parameters MAC can also be calculated by combining Eqs. (3), (4), and (5).
In this work, the term eBC was chosen instead of BC
(Petzold et al., 2013) to stress that BC mass
concentration is calculated from optical measurements.
Factor C is considered to be constant during each campaign as it is relevant
to the filter tape only, while R is unity for an unloaded filter and reduces
when particles are deposited onto the filter (Weingarter at al., 2003). The
filter strip of the miniaturized instruments evaluated in this study is
changed manually before every flight to keep the attenuation during a single
flight below a threshold value of ATN < 0.1 to 0.2, above which
loading correction is required (Weingartner et al., 2003; Ferrero et al.,
2011).
Ground-based (reference) instruments (AE33, MAAP)
To overcome the filter loading effect discussed previously,
Drinovec et al. (2015) developed the
“dual spot” aethalometer (Magee Scientific, model AE33), which uses two
sample spots where particles are deposited with different flow rates and one
“blank” spot as reference. The principle idea behind this approach is that
any artifact induced by the accumulation of the particles onto the filter
will have the same characteristics (i.e., both sample spots are probing the
same particles), but the magnitude of saturation on each spot will differ due
to the different amount of the sample on each respective spot. By combining
the results from both sample spots, the measurements are extrapolated to
zero loading and the compensated and corrected eBC mass and light absorption can be
obtained without using any assumptions on the physicochemical properties of
the measured particles.
Another approach for reducing the measuring biases in particle absorption
coefficient induced by the accumulation of particles collected on the filter
sample spot is employed by the Multiangle Absorption Photometer (MAAP)
instrument (Thermo Fisher Scientific), which applies corrections on the
measured absorption coefficient based on the sample-laden particles'
scattering at different angles (Petzold
and Schönlinner, 2004).
In this study, these two commercially available absorption monitors (Magee
Scientific – Model AE33; Thermo Scientific Fisher – multi-angle absorption
photometer, model 5012) were used as a ground-based reference for UAS-based
absorption measurements. Nominally MAAP measurements, which have been shown
to agree well against other methods
(Sheridan et al., 2005), were
used after being corrected based on Eq. (6)
(Müller
et al., 2011).
babs(637)=1.05MACBCMAAP⋅eBC,
where babs(637) is the absorption coefficient at 637 nm (expressed in
Mm-1), the specific mass absorption coefficient of black carbon
proposed by the MAAP manufacturer is equal to 6.6 m2 g-1 (Petzold and Schönlinner, 2004),
and eBC is the equivalent mass concentration of black carbon reported by the
instrument (in µg m-3). Equation (6) assumes that the MAAP operates
at a nominal wavelength of 637 nm, as measured by Müller et al. (2011), and
not at 670 nm, as proposed by the manufacturer.
The absorption coefficient at wavelengths other than 637 nm was
calculated based on the Ångström law (Eq. 7).
τ(λ)=τ(λ0)λλ0-α,
where τ(λ) and τ(λ0) are the calculated and
reference absorption parameters, respectively, and α is the
absorption Ångström exponent (AAE). The reported eBC measurements of
AE33 were used to calculate batn(λ) and babs(λ) based
on Eqs. (3) and (4) and using values of mass attenuation cross section and
optical enhancement factor reported in the literature (Table 2). In this
work, the absorption coefficient calculated by the AE33 will be scaled to
match measurements from MAAP. For the MAAP instrument, the reference
absorption (λ0) is at 637 nm, as suggested by Eq. (6). The
Ångström exponent was calculated by linear regression of the natural
logarithm of the seven wavelength absorption coefficients measured by AE33
(370, 470, 520, 590, 660, 880, and 950 nm) and used for extrapolating into
shorter and longer wavelengths of the absorption coefficients measured by
the MAAP. Loading correction was not applied to the AE33 measurements as it
incorporates a loading compensation measurement scheme (Drinovec et al.,
2015).
The AE33 was always operated at a 1 min time resolution; the MAAP operated
at a 30 min time resolution during the Athens campaign and at a higher (2 min) time resolution during the Cyprus campaign.
Characteristics of the miniature absorption instruments.
* Refers to the weight of the instrument alone. Dryer and sampling inlet used
are not accounted for.
Miniature absorption monitors (AE51, DWP, STAP)
Three miniaturized instruments having optimal specifications to fly on board
UASs were evaluated. They consist of (1) a single-wavelength commercially
available absorption monitor (Aethlabs, Model AE51), (2) a dual-wavelength
prototype (DWP) monitor based on the AE51 concept, and (3) a single-channel
tricolor absorption photometer (STAP; Brechtel Inc – Model 9406). These
three instruments will be referred to as AE51, DWP, and STAP, respectively,
in the following sections. Table 3 summarizes the characteristics of each
monitor.
The AE51 is the lightest instrument (280 g), which is a major asset for small
UAS observations. On the other hand, due to a relatively low air sampling
flow rate (0.1–0.2 L min-1 set by the user), it may lack sensitivity
for low concentrations of absorbing aerosols, which can be an issue when
investigating the low amounts of aerosols usually met aloft. The two other
instruments (DWP and STAP) have higher flow rates (2 and 1.3 L min-1, respectively), which may improve sensitivity for low
concentrations. These two instruments also have the potential to derive
additional information regarding absorbing material (other than black
carbon) using the aethalometer model reported by
Sandradewi et al. (2008). On the other hand, they are significantly heavier (660 g and 1.1 kg for STAP and DWP, respectively), which may represent a major constraint on
small UAS operations. The DWP has been constructed as a modification of the
AE51, by placing an additional light source emitting at 370 nm.
Additionally, the sampling flow rate has been increased to 2 L min-1,
by replacing the original AE51 pump with an external pump whose flow rate is
controlled by a critical orifice. The external pump resulted in additional
weight to DWP. In order to assess the possible impact of changes in relative
humidity on the attenuation measurements, a second DWP monitor was installed
in series behind the one which has been evaluated here. The hypothesis here
is that both DWP should be similarly affected by artifacts induced by water
absorption and desorption onto the filter strips. An underlying assumption is
that both monitors were operating under the same temperature. Under normal
(dry) conditions, the second DWP should always report zero concentrations.
The STAP, formerly named ABS (see
Bates et al., 2013) has
been manufactured following the design of the Particle Soot Absorption
Photometer (PSAP; Bond et al., 1999), except
that the detection electronics have been completely redesigned to
significantly improve signal-to-noise and provide a detection limit of
∼0.2 Mm-1. Light from three LED sources with wavelengths
centered at 445, 515, and 633 nm (Table 3) is alternatively transmitted
through glass windows with 50 Hz frequency. The diffused light, which is
transmitted through two filter-holding spots that typically carry glass
fiber filters, is continuously monitored by two photodetectors. One filter
spot is only loaded with the sample aerosol while the other remains
sample-free, acting as a reference. The highest measurement rate achieved is
1 Hz. The glass fiber filters minimize light from being transmitted in the
forward direction (forward scattering), thus reducing the bias due to
scattering by the collected aerosol, while they allow the sampled particles
to be embedded within the filter, integrating them in the optically
diffusive environment. A laminar flow element is used to measure the sample
volumetric flow rate in real time and an onboard software automatically
controls the small integrated vacuum pump to maintain a constant sample
volume flow independent of the unmanned aerial vehicle altitude. The sample flow is dried to
eliminate artifacts due to water uptake by the filters.
Calculated absorption from the three miniature instruments was derived directly
from the sample and reference signals, using Eqs. (1), (2), and (3) without taking
into account the computed eBC or batn(λ) reported by the
instruments. For AE51 and DWP, the difference between the calculated and
reported absorption values was 0.01 % or less. The batn(λ)
reported by STAP was initially processed with a 60 s moving average which
was deemed too long. To address that issue, a custom-made moving average was
applied to the raw (1 Hz time resolution) babs(λ) signal in order
to reduce the signal-to-noise ratio (more details in Sect. 4).
Furthermore, this custom moving average allowed a more accurate
determination of babs(370) and babs(880) based on Eq. (7) for STAP. The STAP
manufacturer suggests conversion from batn(λ) to
babs(λ) based on Eq. (8) (Ogren et al., 2010), which also accounts
for loading artifacts. This conversion has been applied explicitly on STAP
measurements instead of Eq. (3) (which has been applied to other miniature
absorption monitors).
babs(λ)=0.85batn(λ)1.22(1.0796I(t)Iwf+0.71),
where I(t) is the attenuation at a given time (t) and Iwf the measured
attenuation of a clean and new filter under particle-free air.
Sampling sites
Sampling was conducted at two contrasting locations in the eastern
Mediterranean basin: an urban site (Athens, Greece) for a weeklong intensive
period starting from 14 January 2016 and a background location in Cyprus for
a monthlong intensive campaign in April 2016.
The Athens campaign
In the framework of the European project ACTRIS 2 (Aerosols, Clouds, and
Trace Gases Research Infrastructure), three miniaturized absorption
instruments were tested and intercompared for a period of 1 week (14–21 January 2016), on board a multicopter over Athens, a city highly impacted by
strong UV-absorbing domestic heating biomass burning aerosols during winter
(Florou
et al., 2017; Fourtziou et al., 2017). Flights were conducted at Lofos
Nymphon (37∘58′19.68′′ N, 23∘43′5.32′′ E) situated at the
historical center of Athens, a metropolitan area of more than 4 000 000
inhabitants. Lofos Nymphon is a rock plateau inside a small forested area
(Fig. 2), at a 50 m elevation from its surroundings. Traffic roads, marked
with red lines in Fig. 2, are located west of the site, the closest of
which is 150 m away from the measurement site. In order to comply with air
space restrictions made by the Hellenic civil aviation authorities at Lofos
Nymphon, the multicopter, described in detail in Sect. 2.1.3, was selected
for its capacity to takeoff and land vertically.
A total of 26 flights were performed during periods without precipitation or
strong winds. Each flight lasted for 15min and reached as high as 1 km a.s.l. in altitude, a limit set by the Hellenic civil aviation authorities.
During this campaign, the flight plan has been elaborated as the following:
two early morning flights were performed at an interval of ca. 1 h
starting at sunrise (05:00 UTC) to investigate the stratification of the
atmosphere (boundary layer, low free troposphere). Two late afternoon
flights ending approximately at sunset (16:00 UTC) were performed to
investigate the vertical mixing of urban emissions in the atmospheric
column. On 19 January 2016, intensive (hourly) flights were performed to
investigate the impact of the diurnal development of the boundary layer on
the vertical distribution of absorbing aerosols. These flights are further
discussed in Sect. 7.
Due to payload restrictions (2 kg maximum for scientific instrumentation and
another 2 kg payload for the batteries, dryer, and inlet), not all the
miniature monitors could fly simultaneously on board the multicopter. The
monitors that could not fly were operated at the colocated National
Observatory monitoring station at Lofos Nymphon, together with two
commercially available instruments (AE33; MAAP). In addition, the absorption
monitor on board the multicopter was set to measure at ground level for 2–3 min before and after each flight for a direct comparison against
ground-based instruments.
The Cyprus campaign
In the framework of the European project BACCHUS (Impact of Biogenic versus
Anthropogenic emissions on Clouds and Climate; towards a Holistic
UnderStanding) a 1-month campaign (30 March–28 April 2016) was performed
at the Cyprus Atmospheric Observatory (CAO, 35∘2′17.97′′ N, 33∘3′28.50′′ E), a remote regional background site at the Agia
Marina Xyliatou in Cyprus.
Vertical profiles of aerosol absorption were performed above a dedicated UAS
airfield (35∘5′41.93′′ N, 33∘4′54.26′′ E) located approximately 7 km north of the CAO (Fig. 2). The airfield, shown in Fig. 1,
is associated with a 500 m radius (in the x–y plane) UAS airspace and an
additional 500 m radius buffer zone, yielding a total of 1 km radius flight
zone granted by the Cypriot civil aviation authorities and extending up to a
height of approximately 2.4 km a.g.l. (2.7 km a.s.l.).
In this work, only the absorption measurements will be examined
corresponding to a total of 17 flights performed with the Skywalker X8 and 6
flights with the Cruiser. The UAS flight strategy was designed to
characterize the boundary layer and free troposphere with respect to aerosol
absorption, number size distribution, and ice nuclei (IN) concentrations
(see Schrod et al., 2017). The typical
UAS flight period usually spanned from sunrise (05:00 UTC) to 09:00 UTC. Two
types of fixed-wing UASs were used during this campaign: two Skywalker UASs
(Model X8) and one Cruiser UAS (see Sect. 2.1). Skywalker X8 flights
typically lasted 30 min, while each Cruiser flight lasted between 1 and 1.5 h.
Vertical profiles were performed almost on a daily basis provided
meteorological conditions were favorable and engaged a team of eight persons
(two pilots, two ground control station operators, two electronic and mechanical
engineers, and two scientific staff for the operation of the miniaturized
instruments).
Ground-based absorption measurements were conducted in parallel at CAO using
two commercially available instruments (AE33 and MAAP; see Sect. 2.2.2).
CAO is located 6.74 km south and at a 200 m elevation from the airfield
(Fig. 2). Because of no significant local contamination sources in the
surrounding area
(Kleanthous
et al., 2014; Pikridas et al., 2018), it has been assumed that the
atmospheric composition at CAO and the UAS airfield were similar, allowing a
direct comparison between the ground and airborne measurements. During this
campaign, regional dust transport originating from Africa was identified on
two occasions: 9 and 20 April 2016
(Schrod et al., 2017).
During both campaigns lidar measurements at 532 nm from the EARLINET
PollyXT-NOA system, described by Engelmann et al. (2016), were used to detect the planetary boundary
layer (PBL) depth. During the Athens campaign,
measurements were colocated with the in situ measurements described in
Sect. 2.2.2. During the Cyprus campaign, the PollyXT measurements were
located 21 km east of the ground-based measurements. Nevertheless,
spatiotemporal homogeneity has been observed between the two sites for that
specific period
(Mamali
et al., 2018; Marinou et al., 2019). The PollyXT lidar quick looks from both
campaigns can be found online (http://polly.tropos.de, last access: 1 October 2019).
Data exploitation: improvement of the optimized noise-reduction averaging
(ONA) smoothing algorithm
The three miniature absorption monitors were set to sample at a rate of 1
Hz. However, all measurements were subjected to non-negligible instrumental
noise (defined as one single standard deviation of the absorption
coefficient) making the data exploitation for short time intervals
challenging. The use of a standard averaging method (average, rolling
average, least-squares fit) would require setting a fixed time step during
which all measurements will be averaged regardless of the signal-to-noise
ratio. This will reduce noise but may compromise the need for high time
(spatial) resolution required for UAS-based vertical profile measurements.
Instead, Hagler et al. (2011) proposed a method where the averaging step is
not defined by the time but is based on the measured attenuation. In that
method, named optimized noise-reduction averaging (ONA), dATN(λ)/dt should
only be positive or zero (but not negative, an assumption which is valid in
our case without any fresh volatile sample fraction). As a result, for a
predefined configuration (sample volume, sample spot area), the same
averaging attenuation step (ΔATN) will require more data points to
be averaged during periods with low atmospheric concentrations (i.e., lower
time resolution) compared to periods with high atmospheric concentrations.
Therefore, using ONA, the averaging time step is dynamically set to be
inversely proportional to the sampled concentration (see also Eq. 2),
resulting in a fixed signal-to-noise ratio. Since the method is based on
attenuation changes, it can only be applied to individual spots, where the
sample is accumulated, in a continuous monitor or an individual filter in
semicontinuous monitors such as the miniature absorption monitors
investigated in this work.
Examples of the use of the improved ONA algorithm for the three
attenuation monitors examined in this study. Raw data (black dots) are shown
against the traditional ONA algorithm (Hagler et al., 2011; blue), the
improved ONA using a rolling average and the ΔATN proposed in
Sect. 4 (red), and the improved ONA using the rolling average but with
increased ΔATN by a factor of 3 (over-smoothed green). The proposed
ΔATN used are 0.01, 0.03, and 0.03 for AE51, DWP, and STAP, respectively.
The algorithm proposed by Hagler et al. (2011) results in an integrated-like (fragmented) data structure that
lowers the vertical resolution of our UAS-based absorption measurements
significantly (blue dots in Fig. 3). To cope with this issue, an improvement
of the ONA algorithm is proposed here. A moving average is implemented
instead of the one applied in the ONA algorithm, resulting in a more
continuous-like data structure and improved vertical resolution (red dots in
Fig. 3). If more than one wavelength is monitored, then the improved ONA
algorithm can be applied to each of the wavelengths but based on the same
attenuation, in order to produce comparable averaging results. The same
strategy can be applied to external datasets for comparison purposes,
provided they are produced or conditioned to have the exact same time
resolution.
The flow diagram of the proposed improved ONA algorithm is presented in
Supplement Fig S1. A link to the actual code is also provided, via a
file-sharing portal, in the Supplement. The user supplies attenuation and
instrument response (e.g., eBC mass, babs, or an external measurement) as
time series along with the desired attenuation step (ΔATN). The
calculated time interval includes attenuation values in the range
[-0.5×ΔATN, +0.5×ΔATN] centered at
the selected data point. If the attenuation change of a data point is
greater by 0.5×ΔATN with respect to its neighbors, then
this data point will not be smoothed. The time interval is limited to
correspond to only one sample spot. The same averaging times can be then
applied to the remaining monitored wavelengths, if any. Discrepancies could
arise when abrupt concentration gradients are sampled, e.g., monitoring the
vertical profile of a polluted boundary layer followed by clean air masses.
In this case, the rate of attenuation change will decrease, since the air
mass contains less absorbing aerosol. If the concentration gradient between
the two layers is large enough, the algorithm may lead to a fictitious shift
of the boundary layer height because more data points from the clean air
mass than the polluted boundary layer will be accounted for in the average.
The discrepancy is solved if weights inversely proportional to the number of
data points are used for the average before (-0.5×ΔATN)
and after (+0.5×ΔATN) the sample point to be examined.
The improved ONA algorithm incorporates filters that cope with this problem.
Erroneous results may also arise from outliers in the time series,
especially if small ΔATN is applied or if the time series is over
smoothed. An example of over smoothing is shown in Fig. 3 (green line). For
all the reasons discussed above, it is advised to examine the result using
different ΔATN and against the raw input.
High ΔATN values will reduce noise but reduce the time (vertical)
resolution. A ΔATN equal to 0.01, 0.03, and 0.03 is suggested for AE51,
DWP, and STAP, respectively, and these values take into account the air face
velocity set for each instrument. Vertical profile case studies are
therefore discussed later in Sect. 7 with the above-proposed attenuation
steps. Note that Hagler et al. (2011) suggests a higher ΔATN, equal
to 0.05, for all monitors regardless of individual face velocity.
Quality assurance
Despite the fact that all the available methods have the scope for reporting the mass
concentration of BC, discrepancies between the different techniques or even
instruments that are based on the same operating principles have been
reported
(eg.,
Watson et al., 2005; Slowik et al., 2007; Müller et al., 2011). These
discrepancies are not only attributed to the different measurement
techniques and instruments used but also to the large variability of the
physicochemical properties of atmospheric or laboratory-generated
carbonaceous particles. For instance, the optical properties of carbonaceous
particles depend on their size and morphology
(Bond and Bergstrom,
2006; García Fernández et al., 2015), on their mixing state and/or
coating thickness with other atmospheric relevant species, including
sulfate, water, organic, or dust
(Lack
and Cappa, 2010; Shiraiwa et al., 2010; Lack et al., 2014; Liu et al., 2015;
Zhang et al., 2015, 2018) As a result, aerosol absorption measurements need
to be associated with a comprehensive understanding of the methods and
uncertainties associated with each instrument and how they have been
operating and operated in the field. Condensation or volatilization of water
on the filter spot of the miniature sensors can greatly affect absorption
measurements
(Hale
and Querry, 1972; Düsing et al., 2019). In order to minimize this
artifact, a custom-built (lightweight) silica-gel dryer was installed at the
inlet of each miniature sensor and regenerated before each flight. Each
sensor operated with its own respective inlet and dryer during both
campaigns and even when two sensors were airborne simultaneously in one UAS.
However, to reduce weight, no size-selective inlet was employed.
Ground-based sensors were similarly configured, at least when UAS flights
were ongoing.
In the following sections, the level of agreement, at the 95 % confidence
interval (CI) between standard (rack) size absorption monitors and miniature
absorption sensors will be evaluated using an adaptation of the standard
Student's t test (Welch, 1947) that accounts for samples with
unequal variances and unequal sample sizes. Because the test is valid only
for normal distributions the datasets to be compared are transformed (e.g.,
Box Cox transformation) and tested by an F test (Box, 1953)
to satisfy this assumption.
Aerosol absorption derived by AE33 and MAAP
During the Athens campaign AE33 and MAAP showed excellent correlation
(R2=0.98, N=381) with respect to the eBC mass concentration trend at a
30 min time resolution (Fig. 4). However, AE33 reported higher eBC by
20±11 % compared to MAAP, and higher absorption coefficient at 370,
637, and 880 nm of more than a factor of 2. Each of these differences is
statistically significant at the 95 % CI. During the Cyprus campaign, both
monitors also showed a very good correlation (R2=0.89, N=1434) at a
30 min time resolution. However, similar to the Athens campaign, AE33 showed
eBC mass concentration higher by 13±5 % compared to MAAP, and higher
absorption coefficient at 370, 637, and 880 nm by almost a factor of 2, which
was also significant at 95 % CI. It is noted that for both campaigns the
absorption coefficient has been derived from eBC for both instruments. The
large difference observed concerning the absorption coefficient is due to
the different generic MAC values applied to each instrument. As an example, the
MAC value employed by MAAP is equal to 6.6 m2 g-1 at 637 nm (Table 4),
while the MAC(637) calculated for AE33 is equal to 10.7 m2 g-1. For both
campaigns, the comparison of eBC and the absorption coefficient at 370 and 880 nm is shown in Fig. 4 and for the absorption coefficient at 637 nm at
Fig. S2.
Results from the comparison of the miniature sensors with
ground-based commercial instruments (AE33 and MAAP) shown in Figs. 5 and 6. NA – not available.
Drinovec et al. (2015) suggested that AE33 could overestimate eBC up to
approximately 7 % when compared to MAAP. Müller et al. (2011)
calculated the absorption coefficient at 637 nm of single spot aethalometers
measuring ambient air and showed that it can be up to 60±20 %
overestimated when compared to MAAP. Finally, MAAP has been reported to
underestimate eBC in polluted environments
(Hyvärinen
et al., 2013) when the measured eBC concentration exceeds 3 µg m-3.
Table 4 summarizes the results from both campaigns (illustrated in Fig. 4).
This comparison suggests that AE33 and MAAP exhibit a better match with
respect to eBC mass rather than with the absorption coefficient.
In the comparison presented above, MAAP was chosen as the reference
instrument because it has been shown to exhibit good agreement against
ambient absorption methods
(Sheridan et al., 2005) that do
not require correction schemes (e.g., photoacoustic spectrometers) and
because its unit-to-unit variability was reported to be small (approximately
5 %; Müller et al., 2011). However, MAAP monitors absorption at a
single wavelength and samples at lower temporal resolution than the one
desired for this study (30 min in the Athens campaign and 2 min in the
Cyprus campaign).
In the following sections, we investigate how measurements from miniature
attenuation monitors relate to the commercial ones discussed in this
section. AE33 is always utilized as a reference because of its high temporal
resolution (1 min). For this purpose, AE33 results are first scaled to match
those of MAAP, to approximate, at least on average, the suggested
“reference” values taking advantage of the excellent trend agreement
between these two instruments. The eBC by the AE33 was consequently decreased
by 20 % and 13 %. The difference in the scaling factor between the two
campaigns is attributed to instrument variability since two different pairs
(of AE33 and MAAP) were employed in each campaign and to the different
aerosol sampled, fresh vs aged during the Athens and Cyprus campaigns,
respectively. Consequently, babs(370) was decreased by a factor of 2.4
and 1.93, and babs(880) was decreased by a factor of 2.2 and 1.83 during
the Athens and Cyprus campaigns, respectively.
UAS-based absorption measurements
The loading correction term in Eq. (3) was neglected in our study, assuming a
value equal to unity when attenuation was low. It is noted that currently
most loading correction schemes are applied to continuous monitors that
change sample spots automatically. Attenuation of AE51 provided by the
instrument never exceeded 0.01 during the Athens campaign due to the
combination of low sampling flow rate and limited sampling times
(approximately 15 min) of each flight. During the Cyprus campaign, it
reached up to 0.02 because sampling time was higher (1–1.5 h) despite the
lower measured eBC concentrations. Because of its higher sampling flow rate,
the attenuation of DWP at 880 nm exceeded 0.15 five times in each of the
two campaigns. In order to examine whether measurements by DWP exceeding
attenuation of 0.1 were significantly affected by the filter loading effect,
a comparison with respect to babs(880) was conducted against both AE51 and
AE33. The comparison results, shown in the Supplement (Fig. S3), support the assumption of a loading correction (R) equal to unity
was valid during both campaigns (as already discussed by Weingartner et al.,
2003).
As discussed in Sect. 2.2.3, the DWP configuration consisted of two
monitors sampling in series, in order to assess the possible impact of
changes in relative humidity on the attenuation measurements. Under dry
conditions, the second DWP should always report zero concentrations; this
was the case during the Athens campaign with the exception of one flight
performed on the 15 January 2016 when the silica gel dryer was
removed. During this flight, the second DWP provided attenuation
measurements deviating from zero, as high as 30 M m-1 at 880 nm, suggesting that the first DWP measurements may also have been affected
by sampling bias during this particular flight (Fig. S4). Recently,
Düsing et al. (2019) evaluated the discrepancy due to RH gradients of
STAP to be 10.08 Mm-1 s-1 for every 1 % change in RH.
Comparison of miniature attenuation monitors against reference
instruments
Since most of the commercially available sensors provide BC readings
(instead of absorption like STAP), we have decided to extend our absorption
intercomparison to eBC. Despite BC being the most absorbing material in ambient
air, other components, such as brown carbon and dust, could also contribute
to absorption especially at shorter wavelengths (Andreae and
Gelencsér, 2006). In addition to eBC, aerosol absorption coefficients at
370 and 880 nm were also selected because two of the three miniaturized
sensors measured at least at one of those wavelengths (see Table 3).
Extrapolation based on the Ångström law (Eq. 7) was applied for STAP
that did not measure at these two specific wavelengths using the
445 and the 633 nm channels as a base to convert to 370 and 880 nm,
respectively.
Overview of the temporal and diurnal variability of ground-based eBC during
the Athens and Cyprus campaigns
During the Athens campaign, the average eBC concentration determined by AE33
was 1.5±2.1µg m-3, ranging from 0.3 to 15 µg m-3. The presence of BC from biomass burning (BCbb), was
identified and quantified throughout the campaign (Fig. S5), using the
Sandradewi et al. (2008) model, but never exceeded 20 % of the total eBC during daytime
(05:00–15:00 UTC). During the nighttime, BCbb concentration was always
elevated, reaching 40 %–60 % of the total eBC that typically remained below 2 µg m-3. On two occasions (14 January 16:00 UTC–15 January 05:00 UTC and 21 January 15:00 UTC–22 January 00:00 UTC) eBC exceeded 5 µg m-3 for several hours
dominated by BCbb. On average, BCbb was identified from 16:00 UTC
till 04:00 UTC of the following day and was more prominent during the
periods featuring a low boundary layer and the need for heating due to low
temperatures. Similar behavior attributed to biomass burning aerosol has
been reported previously in Athens
(Florou
et al., 2017; Fourtziou et al., 2017) and other major Greek cities
(Petrakakis et al., 2013; Pikridas et
al., 2013). BC related to fossil fuel also exhibited a distinct diurnal
pattern that included two maxima (Fig. S5). The first was observed at
approximately 06:00 UTC and was attributed to the rush hour traffic period
and the second in the late afternoon and evening (after 16:00 UTC) simultaneously
with the period when biomass-burning-related BC was observed. Increased
biomass burning, especially during nighttime for domestic heating purposes,
due to the economic crisis in Greece, has been reported for another major
Greek city (Saffari et al., 2013).
Comparison of AE33 against MAAP for eBC (a, c) and
babs(b, d) at 370 nm (red dots) and 880 nm (green dots) during the
Athens (a, b) and Cyprus (c, d) campaigns, respectively. Error
bars correspond to one standard error from the mean. Not visible error bars
suggest that the error estimate is smaller than the area covered by the
symbol. The 1:1 and regression lines are shown by a solid black and a dashed
line colored according to the instrument, respectively. Results are shown
in Table 4.
During the Cyprus campaign, eBC measured by AE33 did not exceed 2 µg m-3 and most of the time it was found below 0.8 µg m-3. The
highest hourly concentration (1.9 µg m-3) was observed on the 10 April 2016 (Fig. S6) when the site was influenced by air masses from
North Africa, and the lowest (< 0.1 µg m-3) on the 12 and 14 April 2016. During the Cyprus campaign, dust transport from the Saharan
desert was identified on three occasions (7–10, 15–17, and 21–27 April 2016)
based on combined information from (i) elevated coarse-mode particulate
matter concentrations measured by a tapered element oscillating microbalance
(Thermo model 1400a), (ii) aerosol spectral properties of the entire
atmospheric column measured by sun photometry, (iii) back-trajectory analysis,
and (iv) satellite pictures (MODIS aerosol optical depth product). The diurnal pattern of eBC
during the Cyprus campaign was relatively flat, as expected in a remote
background site, and characterized by an almost invariable concentration
approximately at 0.4 µg m-3 (campaign average equal to
0.39±0.24µg m-3).
Comparison of miniature monitors (STAP: green triangles; DWP: red
squares; AE51: blue circles) while on the ground against the corrected AE33
during the Athens campaign with respect to eBC mass (a), absorption
Ångström exponent (b), and the absorption coefficient at 880 nm (c) and
370 nm (d). Error bars correspond to one standard error from the mean with
respect to AE51. The standard error concerning DWP and STAP with respect
babs and eBC is smaller than the symbol in the graph for the vast majority
of the cases and is not presented for clarity. The 1:1 and regression lines
are shown by a solid black and a dashed line colored according to the
instrument, respectively. Results are shown in Table 4.
Ground-based intercomparison of aerosol absorption
During the Athens campaign, each miniature sensor not performing vertical
profiling was operating at ground level in parallel with AE33 and MAAP,
allowing a direct comparison. Additionally, the miniature sensors on board
the multicopter were measuring at ground level (2–3 min) before takeoff
and after landing. It is noted that the same setup (sampling lines,
diffusion dryer) was utilized whether the miniature samples were mounted in
the UAS platform or not. Based on the combination of these datasets
resampled to 1 min (the time resolution of AE33), DWP exhibited good
correlation, with respect to eBC against AE33 (R2=0.90, slope =0.93,
N=417) shown in Fig. 5a, while the AE51 produced slightly poorer
correlation (R2=0.76, slope =0.94, N=125) (see Table 4). One
possible explanation is the lower signal-to-noise ratio of AE51. Both
monitors measured eBC concentrations lower by 6 %–7 % compared to the reference
measurements. This difference is not statistically significant, at the
95 % CI, for either DWP or AE51. STAP does not report eBC mass concentration
and was excluded from this comparison for that purpose.
Comparison of miniature attenuation monitors (DWP: red squares;
AE51: blue circles) while airborne against the corrected AE33 during the
Cyprus campaign with respect to eBC mass (a), absorption Ångström
exponent (b), and the absorption coefficient at 880 nm (c) and 370 nm (d).
Miniature monitors sampled airborne. Error bars correspond to one standard
error from the mean with respect to AE51. The standard error concerning DWP
and STAP with respect babs and eBC is smaller than the symbol in the graph
for the vast majority of the cases and is not presented for clarity. The 1:1
and regression lines are shown by a solid black and a dashed line colored
according to the instrument, respectively. Results are shown in Table 4.
With respect to babs(λ) at 370 and 880 nm, both STAP and DWP
showed good correlation (at 370 nm : R2=0.89 and 0.87 and N=519 and
417 for STAP and DWP, respectively; at 880 nm: R2=0.88 and 0.9 and
N=519 and 417 for STAP and DWP, respectively) against AE33, while the
correlation with AE51 was slightly poorer (R2=0.76, N=125) at 880 nm (Fig. 5c).
However, DWP overestimated babs(880) by 29±20 % (significant at
95 % CI) compared to the corresponding reference measurements, even though
the eBC mass, calculated from the same wavelength, was underestimated by 7 %.
Similar to DWP, AE51 overestimated babs(880) by 30±12 % even though
eBC mass was underestimated by 6 %. This difference was statistically
significant at 95 % CI but only marginally (p value equal to 0.049). Both
DWP and AE51 share the same σatn and C values (Table 4). For
both instruments, a generic MAC(880) value equal to 6.1 m2 g-1 is applied
to convert eBC to babs, instead of 7.8 m2 g-1 used by AE33 at the
same wavelength. However, both miniature sensors underestimate with respect
to eBC but at the same time overestimate with respect to the absorption
coefficient, mainly due to the higher correction factor applied to the AE33
measurements concerning the latter (approximately a factor of 2) compared to
the former (≈20 %) to match those of MAAP as discussed in Sect. 5.1.
STAP was found to overestimate babs(880) by 6±8.5 % and
underestimate babs(370) by 7±7 %. Both differences were not
significant at 95 % CI. During a laboratory comparison Müller et al.
(2011) reported that a continuous single spot aethalometer (Magee Model AE31)
overestimated babs compared to MAAP by 37 %–60 % at 660 nm. The same
study also reported underestimation of the absorption coefficient at 650 and
585 nm against MAAP compared to the PSAP (the rack-mounted equivalent of
STAP) by 1 %–14 %. These laboratory comparison results are similar to those
reported in this study (AE51 overestimates and STAP underestimates the
absorption coefficient by a similar extent against the reference).
The miniature sensors intercompared during the Athens campaign exhibit
better agreement with respect to the parameter they report. Concerning AE51
and DWP, this parameter was eBC concentration, which was within 10 %, rather
than the absorption coefficient, suggesting that the absorption coefficient
should be preferentially calculated based on a single set of
wavelength-dependent MAC values (Eq. 5) instead if these are known or can be
calculated. On the other hand, STAP that does not report eBC, but babs
exhibited good agreement, within 10 %, against the reference on that
property. On average the calculated AAE of DWP and STAP is underestimated by
13 % and 12 %, respectively, against that of AE33.
During the Cyprus campaign, aerosol absorption was also monitored at the
ground by an AE33 and a MAAP located at CAO, approximately 7 km away and at
200 m higher elevation above the UAS airfield. Only DWP and AE51 were used on
UASs during this campaign. Assuming homogeneity between the two sites, a
direct comparison was conducted between ground and UAS measurements.
The comparison results, shown in Fig. 6, indicate that the correlation
between the ground measurements and UAS (AE51 and DWP) measurements led to
less satisfactory results compared to the Athens campaign (see also Table 4). The correlation between AE33 and DWP was still acceptable
(R2=0.71; N=91) with respect to eBC and the absorption coefficient at
370 and 880 nm at 1 min time resolution. But the correlation between AE33
and AE51 was found to be poor (R2=0.32, N=48) with respect to both eBC and
babs(880).
The atmospheric concentration of absorbing material (eBC measurements) was
found to be on average 4 times lower in Cyprus (mean of 0.39±0.24µg m-3) compared to Athens (mean of 1.5±2.1µg m-3).
Additionally, the range of atmospheric concentrations was also reduced by a
factor of 6 in Cyprus (maximum hourly averaged eBC was 1.9 µg m-3)
compared to Athens (maximum hourly averaged eBC was 12.2 µg m-3),
leading to less favorable conditions for direct instrument-by-instrument
comparisons due to the smaller range of comparison data. These conditions
had a direct impact on the uncertainty related to the measurement agreement
between the AE33 and the miniature monitors. During the Cyprus campaign, the
uncertainty was always greater than the respective uncertainty of the Athens campaign.
As an example during the Cyprus campaign, DWP underestimated eBC by 6±20 % and overestimated babs(880) by 20±26 % (both significant at
95 % CI), while during the Athens one the respective numbers were 7±15% and 29±20 % (Table 4). The effect was greater concerning
AE51, which overestimated eBC by 22±52 % and babs(880) by 55±66 %, while during the Athens campaign the respective numbers were
6±9 % and 30±12 % (Table 4). Due to the large uncertainty
exhibited by AE51, the null hypothesis that the population mean of the
reference instrument (AE33) and of AE51 are different was not met. Hence,
the reported differences are not significant at the 95 % CI. It is unclear
whether the absorbing properties of the sampled aerosol (fresh at Athens and
aged in Cyprus) had any effect on this comparison.
On-flight intercomparison of aerosol absorption
During flights, vibrations, as well as strong gradients of pressure,
temperature, and RH may affect the performance of the miniature sensors. In
order not to surpass the maximum takeoff weight of the multicopter used
during the Athens campaign, STAP and DWP did not fly simultaneously.
However, the lower weight of AE51 enabled on-flight cross-comparison with
DWP and STAP, respectively, during eight flights of the Athens campaign. The
correlation of AE51 airborne with both DWP and STAP was very good
(R2=0.65, N=493 and R2=0.87, N=1875, respectively)
provided that the sampled air was dried (Fig. 7) and the dataset post-processed with a noise-reducing algorithm as suggested in Sect. 4. Error
bars shown in Fig. 7 correspond to one standard error for 1 s time
resolution. In the case that the algorithm did not average a sampling point
with its neighbors, then, by default, the standard deviation and standard
error were zero, indicated by a lack of an error estimate in Fig. 7. Note
that if no smoothing is applied, the correlation deteriorates sharply
(R2=0.01) for either DWP or STAP. The ΔATN used for this
comparison were 0.01, 0.03, and 0.03 for AE51, DWP, and STAP, respectively,
as suggested in Sect. 4. STAP is shown to underestimate babs by 12 %
(significant at 95 % CI) compared to AE51 (Fig. 7), consistent with the
comparison against AE33 discussed in Sect. 6.2. The very good correlation
(comparison slope =0.87) between the two when airborne also suggests that
on average, no significant bias during the flights was present. The
difference between AE51 and DWP was 8 %, which was not significant at
95 % CI.
Comparison of AE51 against STAP (green triangles) and DWP (red
squares) during eight flights of the Athens campaign. The reported agreement
in the correlation suggests that no significant bias affected the monitors.
The correlation deteriorates (R2=0.01) if data are not processed with
the noise reduction algorithm (Sect. 4). Error bars correspond to one
standard error from the mean. Not visible error bars suggest that the
smoothing algorithm did not average to that sampling point with its neighbors,
resulting in a standard deviation and standard error equal to zero. The 1:1
and regression lines are shown by a solid black and a dashed blue line,
respectively.
Diurnal vertical profiles of black carbon above Athens: a case study
As part of the Athens campaign, intensive vertical absorption profiles were
performed with the objective to assess the influence of the diurnal
development of the planetary boundary layer (PBL) on the vertical dispersion
of ground-based black carbon emissions. UAS-based measurements were
conducted for that purpose on the 19 January at sunrise (05:38 UTC) and were
continued on an hourly basis till the PBL depth exceeded the maximum height
allowed to operate (1 km a.s.l.), approximately at 10:00 UTC. Two additional
flights were conducted later on that day; 1 h before and during sunset
(15:38 UTC). The reconstructed vertical distribution of eBC based on the six
ascending vertical profiles from 05:30 till 09:45 (UTC) is shown in Fig. 8,
complemented by ground measurements during the same day by AE33. The actual
vertical profiles for the entire day (N=8) are also shown in Fig. 9. We
present a very detailed study of vertical dispersion of ground-based black
carbon emissions dynamically assessed above a major city. Our results
suggest a nonhomogeneous boundary layer that evolved at a rate of 132 m h-1 during 19 January 2016 starting from an elevation of 265 m a.s.l.
before sunrise. Starting at 05:00 UTC eBC increased by a factor of 8 at 07:00 UTC. The emission's pattern and the Ångström exponent, calculated
based on AE33 measurements, which was equal to 1.1 when concentrations
maximized, suggest that this increase in eBC was due to local traffic emissions
(see also Fig. 8). After 10:00 UTC eBC remained relatively stable at 1.5 µg m-3 (≈5 Mm-1 at 880 nm).
Reconstruction of eBC mass vertical distribution (a) based on six flights between 05:30 and 09:30
(UTC), 19 January 2016 (Athens campaign). The lidar-determined vertical distributions are shown in Fig. 9. The
corresponding ground measurements are also shown in (b). The
concentration of BC from fossil fuel (ff) and biomass burning (bb) are shown
with grey and brown color, respectively. Dashed red lines indicate the start
of each of the six flights the reconstructed eBC profiles were based upon.
Vertical profiles (blue lines) of the eBC mass (a), measured during
19 January 2016 (Athens campaign), accompanied by the mixing height
(dashed red line) of the lower layer derived by Polly-XT measurements.
During the 13:49 UTC flight, mixing height was higher than the maximum altitude
of flight and it is not shown. The corresponding time–height display of the
1064 nm attenuated backscatter measured with Polly-XT is also shown (b).
Dashed white lines correspond to the start of each of the eight flights
performed during that day.
Above the PBL, which was determined by Polly-XT measurements
(Baars et al., 2008; dashed red
lines in Fig. 9), the measured concentration of eBC was always lower than the
respective one measured within by at least 20 %. The highest eBC
concentrations above the PBL were observed during sunrise and sunset (first
and last diurnal profile in Fig. 9) equal to 1.9 and 2.0 µg m-3,
respectively, which we interpret as the residual layer of the previous day
in the morning and the newly formed residual layer after sunset. The lowest
eBC concentration in this layer, equal to 0.3 µg m-3, was observed at
06:30 UTC but steadily increased to 0.4, 0.9, and 1.7 µg m-3during
07:38, 08:39, and 09:44 UTC, respectively. Due to flight restrictions, free
tropospheric measurements could not be monitored after 10:00 UTC. PBL was
also identified by vertical profiles of potential temperature, which are in
good agreement with those derived by Polly-XT.
Before sunrise, our results suggest the presence of a stable boundary layer
in contact with the ground that has been radiatively cooled; on top of the
boundary layer, there is a residual layer. As the sun rises, the stable
boundary layer's depth increases and simultaneously the residual layer is
mixed with the free troposphere. On the 19 January 2016, mixing took place
between 05:45 and 06:30 UTC. The concentration of eBC in the residual layer drops
to near zero because the trapped pollutants are now diluted in the free
troposphere.
However, the concentration of eBC above the boundary layer exhibited an
increasing trend suggesting either convection of pollutants from the PBL or
advection of regionally transported PM involving absorbing material that did
not intrude the PBL. During the period when absorbing material was directly
emitted from the ground and the boundary layer height increased (from
05:30–08:30 UTC), eBC dispersion inside the PBL was not homogeneous but was
gradually decreasing with increasing altitude. The effect is more evident
when emissions from the ground exhibited an increasing trend (approximately
from 06:30 to 07:40 UTC). Once ground emissions reached their minimum and the
PBL stabilized, the concentration inside the PBL became homogeneous (from
10:00 UTC till sunset). During sunset, stratification of a new stable
boundary layer was observed and on top of it a new residual layer was forming.
The vertical absorption distribution was reconstructed based on the
absorption profiles shown in Fig. 8 on 19 January 2016 between 05:34 and
09:36 (UTC) and also shown in Fig. 9 against calculated attenuated
backscatter at 1064 nm measured by a PollyXT.
Conclusions
Two field campaigns were conducted in Athens (Greece) and in CAO (Cyprus) in
order to (i) study the vertical distribution of aerosol absorption and (ii) to
evaluate the performance of three miniature absorption sensors in contrasted
atmospheric environments against ground-based reference instruments (MAAP
and AE33). Measurements were conducted on the ground and air using three
different models of UASs. Our results suggest that the absorption monitors
used in this work agree better at the parameter they report, which is eBC in
most cases, rather than the absorption coefficient. This discrepancy is
directly related to the generic MAC values suggested by the manufacturer of
each instrument. In case the absorption coefficient is not directly
reported, it should be preferentially calculated based on a single set of
wavelength-dependent MAC values specific to each site if these are known or
can be calculated, instead of the generic value provided by the manufacturer.
The influence of humidity on attenuation measurements was investigated
during the Athens campaign by placing two DWP in series, with the second
measuring filtered air from the exhaust of the first. Sample drying
minimized the influence of water adsorption/desorption on attenuation
measurements.
During January 2016, the miniature sensors sampled urban aerosols at the
center of Athens, Greece. On the ground, STAP and DWP followed the
observed variations in the absorption well (R2≈0.90) against an
AE33, while AE51's performance (R2=0.76) was poorer due to low
sampling flow rate. STAP was found to overestimate absorption coefficient at
880 nm by 10 %, while AE51 and DWP overestimate it by 40 % and 30 %,
respectively. However, with respect to eBC mass, the agreement was closer
(within 7 %). An in-flight intercomparison between the lightweight AE51 and
either the STAP or DWP was achieved during the Athens campaign. No
correlation between the AE51 and STAP or DWP could be achieved for
unconditioned high time resolution (1 Hz) measurements. An improvement of
the smoothing algorithm suggested by Hagler et al. (2011) was applied here
leading to improved correlations (R2 > 0.70)
between miniature sensors (AE51, DWP, and STAP). Based on four UAS flights,
DWP and AE51 correlated very well (comparison slope equal to 0.92) with
respect to the absorption coefficient at 880 nm (babs(880)), while STAP was
found to underestimate babs(880) by 12 %, which was consistent with the
intercomparison performed at ground level against the AE33.
The Cyprus campaign took place at the Cyprus Atmospheric Observatory, a
remote location 7 km away from the UAS runway, and two of the miniature
sensors (DWP and AE51) were evaluated in-flight against ground-based
reference instruments, taking advantage of the elevation difference between
the two sites. In comparison with the Athens campaign, the correlation of both
sensors (against reference instruments) deteriorated because of low
atmospheric aerosol concentrations (4 times lower) and reduced atmospheric
variability (6 times lower). While DWP showed relatively good correlation
(R2=0.71, N=91 data points) and the same level of
agreement as during the Athens campaign (6 % overestimate), the poor
performance of AE51 (R2=0.32, N=91) was attributed to a lack of
sensitivity of this sensor operating at a flow rate ca. 10 times lower
compared to DWP.
The overall potential use of a miniature aerosol absorption sensor on board
small UASs was illustrated with results of the campaign performed in Athens.
During this campaign, the diurnal variability of the vertical distribution
(0–1 km a.g.l.) of equivalent black carbon was investigated. It was found
that eBC concentrations are not homogeneous in the boundary layer when it
develops (PBL depth increases) and simultaneously absorbing material is
emitted at ground level by traffic. Vertical homogeneity of eBC is reached in
the afternoon when the boundary layer height is stabilized and emissions at
the ground are reduced.
Data availability
All data related to this publication are available upon request from the corresponding authors.
Nomenclature
AbbreviationDescriptionAAEAbsorption Ångström exponentACTRISAerosols, Clouds, and Trace Gases Research Infrastructurea.s.l.Above sea levelATNAttenuationbatnLight attenuation coefficientBACCHUSImpact of Biogenic versus Anthropogenic emissions on Clouds and Climate;towards a Holistic UnderStandingbabsLight absorption coefficientBCBlack carbonBCbbBC related to biomass burningCOptical enhancement factorCAOCyprus atmospheric observatoryCIConfidence intervalDWPDual-wavelength prototypeEARLINETEuropean Aerosol Research Lidar NetworkeBCEquivalent black carbonECElemental carbonGUIGraphical user interfaceMAAPMultiangle Absorption PhotometerMACMass absorption cross sectionMTOWMaximum takeoff weightONAOptimized noise-reduction averagingPBLPlanetary boundary layerPSAPParticle soot absorption photometerRFilter loading parameterSTAPSingle-channel tricolor absorption photometerUASsUnmanned aerial systemsαÅngström exponentλWavelengthσatnMass attenuation cross section
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-6425-2019-supplement.
Author contributions
The authors MV, NM, and JS were responsible for the conceptualization of the measurement studies, which were implemented by MP, IS, and SB along with the aerial research team that consists of GD, PA, PV, MA, and CK. EL and IS were responsible for the ground-based measurements during the Athens campaign. Data curation and investigation was performed by MP, except for the remote-sensing measurements that were performed by EM and VA. The first version of this work was written by MP and SB to undergo extensive editing by MV, NM, JS, LD, and GM. The smoothing algorithm concept and implementation were done by MP. LD and FB provided essential insights on the instrumentation used.
Competing interests
Luka Drinovec and Griša Močnik were, at the time of the measurement campaigns, employed by the manufacturer of most instruments used in the study. Fred Brechtel represents the manufacturer of STAP.
Acknowledgements
Mihalis Vrekoussis acknowledges support from the DFG-Research Center/Cluster of Excellence “The Ocean in the Earth System-MARUM”.
Financial support
The two field campaigns (Athens, Cyprus) are a contribution to the ACTRIS2 project that has received funding from the European Union’s Horizon 2020 research and innovation program (grant no. 654109). Financial support was also acquired by the EU FP7 project BACCHUS (grant no. 603445). MP acknowledges the financial support of the CURE-3AB project, which is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation (grant no. 0108). EM acknowledges the financial support of the Deutscher Akademischer Austauschdienst (grant no. 57370121). VA acknowledges the financial support of the European Research Council (grant no. 725698, D-TECT).
Review statement
This paper was edited by Pierre Herckes and reviewed by two anonymous referees.
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