AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-4775-2018Separation of the optical and mass features of particle components in different aerosol mixtures by using
POLIPHON retrievals in synergy with continuous polarized Micro-Pulse Lidar (P-MPL) measurementsVertical separation of the particle optical and mass features in aerosol mixturesCórdoba-JaboneroCarmencordobajc@inta.eshttps://orcid.org/0000-0003-4859-471XSicardMichaëlhttps://orcid.org/0000-0001-8287-9693AnsmannAlbertdel ÁguilaAnaBaarsHolgerhttps://orcid.org/0000-0002-2316-8960Instituto Nacional de Técnica Aeroespacial (INTA), Atmospheric Research and Instrumentation Branch, Torrejón de Ardoz, Madrid, SpainCommSensLab, Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, SpainCiències i Tecnologies de l'Espai – Centre de Recerca de l'Aeronàutica i de l'Espai/Institut d'Estudis Espacials de Catalunya (CTE-CRAE/IEEC), Universitat Politècnica de Catalunya, Barcelona, SpainLeibniz Institute for Tropospheric Research (TROPOS), Leipzig, GermanyCarmen Córdoba-Jabonero (cordobajc@inta.es)15August20181184775479516January201826February201812June201816July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/4775/2018/amt-11-4775-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/4775/2018/amt-11-4775-2018.pdf
The application of the POLIPHON (POlarization-LIdar PHOtometer Networking)
method is presented for the first time in synergy with continuous
24/7 polarized Micro-Pulse Lidar (P-MPL)
measurements to derive the vertical separation of two or three particle
components in different aerosol mixtures, and the retrieval of their
particular optical properties. The procedure
of extinction-to-mass conversion, together with an analysis of the mass extinction efficiency (MEE) parameter, is described, and the relative mass
contribution of each aerosol component is also derived in a further step. The
general POLIPHON algorithm is based on the specific particle linear
depolarization ratio given for different types of aerosols and can be run in
either 1-step (POL-1) or 2 steps (POL-2) versions with dependence on either the
2- or 3-component separation. In order to illustrate this procedure, aerosol
mixing cases observed over Barcelona (NE Spain) are selected: a dust event
on 5 July 2016, smoke plumes detected on 23 May 2016 and a
pollination episode observed on 23 March 2016. In particular, the 3-component
separation is just applied for the dust case: a combined POL-1 with POL-2
procedure (POL-1/2) is used, and additionally the fine-dust contribution to
the total fine mode (fine dust plus non-dust aerosols) is estimated. The
high dust impact before 12:00 UTC yields a mean mass loading of
0.6±0.1 g m-2 due to the prevalence of Saharan coarse-dust
particles. After that time, the mean mass loading is reduced by two-thirds,
showing a rather weak dust incidence. In the smoke case, the arrival of fine
biomass-burning particles is detected at altitudes as high as 7 km.
The smoke particles, probably mixed with less depolarizing non-smoke
aerosols, are observed in air masses, having their origin from either North
American fires or the Arctic area, as reported by HYSPLIT back-trajectory
analysis. The particle linear depolarization ratio for smoke shows values in
the 0.10–0.15 range and even higher at given times, and the daily mean smoke
mass loading is 0.017±0.008 g m-2, around 3 % of that found
for the dust event. Pollen particles are detected up to 1.5 km in height from
10:00 UTC during an intense pollination event with a particle linear
depolarization ratio ranging between 0.10 and 0.15. The maximal mass loading
of Platanus pollen particles is 0.011±0.003 g m-2,
representing around 2 % of the dust loading during the higher dust
incidence. Regarding the MEE derived for each aerosol component, their values
are in agreement with others referenced in the literature for the specific
aerosol types examined in this work: 0.5±0.1 and
1.7±0.2 m2 g-1 are found for coarse and fine dust particles, 4.5±1.4 m2 g-1 is derived for smoke and
2.4±0.5 m2 g-1 for non-smoke aerosols with Arctic origin, and a MEE of 2.4±0.8 m2 g-1 is obtained
for pollen particles, though it can reach higher or lower values depending on
predominantly smaller or larger pollen grain sizes. Results reveal the high
potential of the P-MPL system, a simple polarization-sensitive elastic
backscatter lidar working in a 24/7 operation mode, to retrieve the
relative optical and mass contributions of each aerosol component throughout
the day, reflecting the daily variability of their properties. In fact, this
procedure can be simply implemented in other P-MPLs that also operate within the
worldwide Micro-Pulse Lidar Network (MPLNET), thus extending the aerosol
discrimination at a global scale. Moreover, the method has the advantage of also
being relatively easily applicable to space-borne lidars with an equivalent
configuration such as the ongoing Cloud-Aerosol LIdar with Orthogonal
Polarization (CALIOP) on board NASA CALIPSO (Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observation) and the forthcoming Atmospheric
Lidar (ATLID) on board the ESA EarthCARE mission.
Introduction
It is widely known that atmospheric aerosols contribute to climate change
due to their effects (direct and indirect) on the Earth's energy budget.
Different types of aerosols present different radiative properties and thus
contribute in different ways to climate change (Boucher et al., 2013; Myhre
et al., 2013). As far as estimates of aerosol direct radiative forcing are
concerned, knowledge of the aerosol types under study is thus critical.
The aerosol direct radiative properties involved in radiative transfer
calculations are the particle extinction (scattering and absorption)
coefficient, single-scattering albedo (the ratio of scattering to
extinction), the asymmetry factor as defined as the intensity-weighted average
cosine of the scattering angle and their vertical distribution. Referring
to the important factors in constraining the radiative effect of aerosols,
Boucher et al. (2013) stated, “Particularly important are the single-scattering albedo (especially over land or above clouds) and the AOD”. The
AOD is aerosol optical depth, i.e. the column-integrated aerosol extinction. These
two parameters can be estimated by or recalculated from the output of
lidar stand-alone algorithms such as Müller et al. (1999), Veselovskii
et al. (2002) or Böckmann et al. (2005) which employ state-of-the-art
elastic-Raman lidar measurements at several wavelengths. Such advanced
measurements are scarce, however, compared with the large database of
elastic lidar measurements worldwide.
For this reason, synergetic algorithms recently combine data from
multi-wavelength elastic lidar and passive instrumentation to retrieve the
extinction or both the extinction and the single-scattering albedo at
several wavelengths and discriminate between fine and coarse mode. These
algorithms are the Lidar-Radiometer Inversion Code (LIRIC; Chaikovsky et al.,
2016) and the Generalized Aerosol Retrieval from Radiometer and Lidar
Combined data (GARRLiC; Lopatin et al., 2013). GARRLiC is embedded in a more
generalized algorithm called the Generalized Retrieval of Atmosphere and
Surface Properties inversion code (GRASP; Dubovik et al., 2014). The drawback
of these algorithms is that they apply to at least 3-wavelength elastic
systems, while a majority of single- and dual-wavelength elastic systems are
operating worldwide. For less sophisticated systems, the primary way of
discriminating between aerosol types is to have a polarization-sensitive
channel, wherein the discrimination is based on the comparison of the particle
depolarization ratio measured with two reference particle depolarization ratio values.
Aerosol discrimination using particle depolarization was first formulated by
Chen et al. (2001) and then used by Shimizu et al. (2004) for the
observation of Asian dust in China and Japan with one elastic and one
depolarization sensitive channel. Since 2009, the method has been used in an
increasing number of studies to discriminate between dust and smoke (Tesche
et al., 2011), ash and fine-mode particles (Ansmann et al., 2011;
2012; Sicard et al., 2012), and pollen and background particles (Noh et al.,
2013; Sicard et al., 2016a). Very recently, this method, known as the
POlarization-LIdar PHOtometer Networking method (POLIPHON), has been refined by
Mamouri and Ansmann (2014) to retrieve up to three aerosol components, such
as fine and coarse dust and non-dust particles. POLIPHON is also the basis
of the retrieval of ice nuclei number concentration in desert dust layers
(Mamouri and Ansmann, 2015) and cloud condensation nucleus number
concentration (Mamouri and Ansmann, 2016). In addition, a similar method is
used for separating aerosol mixtures in HSRL systems (Burton et al., 2012, 2014).
In addition to their effects on climate, atmospheric aerosols are known
to have a significant impact on human health when they are inhaled. For
example, exposure to anthropogenic particles (pollution) is clearly
identified as a public health hazard causing acute and chronic effects to
the respiratory and cardiovascular systems (Dockery et al., 1993; Künzli
et al., 2000; WHO, 2003). Airborne pollen grains produced by wind-pollinated
plants are responsible for allergenic reactions when inhaled by humans
(Cecchi, 2013). More recently, Martiny and Chiapello (2013) highlighted the
role of desert dust on meningitis epidemics. Toxicological studies are
currently aiming to identify which particle characteristics are responsible
for which adverse health effects (e.g. particle number, mass, size,
surface, chemical composition). Among these properties, the aerosol that lidars
can probably estimate the best is mass concentration, when the aerosol type
has been previously identified, and thus the relation between aerosol
backscatter and extinction can be accurately related to specific aerosol
physical properties. However, mass concentration retrievals from lidar data
are not common and there is very little information available on the vertical
distribution of aerosol number and mass concentrations, although a number of
field experiments involving research and commercial aircraft have measured
aerosol concentrations (Heintzenberg et al., 2011).
Mass concentration profiles can be estimated by multiplying the
lidar-derived extinction coefficient by the mass extinction efficiency,
sometimes also called the specific extinction cross section, when the latter
is known or can be assumed. This conversion is often used to convert
lidar-derived optical properties into mass concentration to test and
evaluate transport models (Pérez et al., 2006; Sicard et al., 2015).
Lately, POLIPHON is also used to extract the
fractions of the high, moderate or low depolarizing particles from the total extinction, which can then be
converted separately into mass concentration (Mamouri and Ansmann, 2014,
2017). The method has been used for the estimation of the profile of mass
concentration of dust (Ansmann et al., 2011, 2012), volcanic ash (Ansmann et
al., 2012; Sicard et al., 2012) and pollen (Sicard et al., 2016b). It is
worth mentioning that another field that would greatly benefit from the
knowledge of the aerosol mass concentration profile is the air traffic, as
large particles can damage aircraft engines. By way of example, we recall
the impact of the ash-loaded eruption plume from the Icelandic
Eyjafjallajökull volcano on European air traffic in 2010 (Pappalardo et al., 2013).
The aim of this paper is to show the potential of simple lidar systems, with
one elastic and one depolarization sensitive channel, to discriminate
between several aerosol types and retrieve the
profiles of their optical properties and mass concentrations for each aerosol component. The instrument
used is the polarized version of the Micro-Pulse Lidar (P-MPL), the standard
system within NASA MPLNET (Micro Pulse Lidar Network; MPLNET, 2016), situated at the Universitat Politècnica de
Catalunya (UPC) at Barcelona (BCN) in north-eastern Spain. The P-MPL is an
elastic and monochromatic low-energy system which also includes a
depolarization-sensitive channel, operating in an automatic and continuous
24/7 mode. The algorithm used to optically discriminate components in
aerosol mixtures is the POLIPHON method, both 1-step and 2-step versions, in
order to assess the vertical separation of a maximum of three aerosol
components. The synergetic use of P-MPL/POLIPHON is tested with aerosol
mixtures containing specific climate-relevant aerosols, namely desert dust,
fire smoke and pollen. This is the first time that POLIPHON, which well
established for sophisticated powerful European Aerosol Research Lidar
NETwork (EARLINET) lidars, is applied to
worldwide (MPLNET) and continuous simple elastic P-MPL measurements.
Moreover, the method has the advantage of also being relatively easily applicable
to space-borne lidars with an equivalent configuration such as the
ongoing Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board
NASA CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observations) which has two elastic and one depolarization-sensitive
channel, and the forthcoming Atmospheric Lidar (ATLID) on board EarthCARE
(future ESA mission to be launched in 2019), which will have a high spectral
resolution receiver and a depolarization channel.
The paper is organized as follows: Sect. 1 presents the
introductory framework. The methodology is introduced in Sect. 2,
which breaks down into the description of the measurement station and of the
selected aerosol cases (Sect. 2.1) as well as the lidar system
used in this paper (Sect. 2.2), an extended overview of the
POLIPHON method (Sect. 2.3) and a detailed extinction-to-mass
conversion procedure (Sect. 2.4). Sect. 3 shows the
results and their discussion for each case (dust, smoke and pollen).
Finally, a summary of the work and the main conclusions are presented in
Sect. 4. In addition, a list of acronyms (symbols) identifying
the parameters and variables used in the work is shown in Appendix A.
MethodologyMeasurement station and selected aerosol case studies
Barcelona (BCN) station is an urban site located on the north-eastern Iberian
Peninsula (41.4∘ N, 2.1∘ E, 115 m a.s.l.),
along the coast of the Mediterranean Sea, on the north campus of UPC at the
centre of Barcelona. The typical background aerosol is a mixture of pollution particles with a minor contribution of marine aerosols, but this is only predominant
under particular clean conditions. Other aerosol types, such as desert dust,
fire smoke and pollen are also frequently found (Sicard et al., 2011).
BCN is a well-established EARLINET station, and a relatively new MPLNET
site, where a polarized Micro-Pulse Lidar (P-MPL) has been in routine operation
since 2014. BCN is also a NASA AERONET (AErosol RObotic NETwork,
AERONET, 2017) site, measuring AOD and the column-integrated
aerosol optical properties during the daytime (Holben et al., 1999).
Relative uncertainties for the P-MPL-derived particle optical properties
(at 532 nm wavelength) and mass concentrations. (n) and (d) stand for night-time
and daytime P-MPL measurements.
ParameterSymbol*Relative uncertainty (%)ReferencesParticle backscatter coefficient (km-1 sr-1)βp5–20 (n), 10–30 (d)Rocadenbosch et al. (2012)Particle extinction coefficient (km-1)σp10–30 (n), 15–40 (d)Derived from the errors in βp and LRLidar ratio (sr)LR5–10Derived from KF algorithmParticle linear depolarization ratioδp10–60Rodríguez-Gómez et al. (2017)Volume linear depolarization ratioδV10–50Derived from the errors in both P|| and P⊥Total mass concentration (g m-3)TMC10–40Derived from the error in AOD (=∑zσp(z)), mainly
* As denoted in the text.
In this work, three case studies on different aerosol mixtures (dust, fire
smoke and pollen, all mixed with local background aerosols) observed over
BCN are examined in order to introduce the combined application of POLIPHON
in synergy with continuous P-MPL measurements for the separation of, in
particular, Saharan dust, fire smoke and pollen particles from other
aerosols mixed with them. Those selected dust, smoke and pollen cases
occurred on 5 July, 23 May and 23 March 2016, respectively. HYSPLIT
back-trajectory (Hybrid Single Particle Lagrangian Integrated Trajectory
model version 4 developed by the NOAA's Air Resources Laboratory (ARL);
Draxler and Hess, 1998; Stein et al., 2015;
Rolph et al., 2017) analysis is
used to confirm the presence of dust and smoke over BCN for each particular
case. HYSPLIT back trajectories are calculated for those days ending over BCN
at given altitudes and several times in relation with the results obtained
and discussed later in Sect. 3 for the dust and smoke cases.
The 5-day back-trajectory analysis indicates Saharan air masses arriving at
high altitudes (>2000 m a.g.l.) on 5 July 2016 only before 12:00 UTC,
North Atlantic air masses are simultaneously arriving at lower heights
(see Fig. 1a–c); during the time period after 12:00 UTC,
air masses at all altitudes are mostly coming from North Atlantic and
central Spain regions (see Fig. 1d–f) but not from
Saharan desert. On the other hand, smoke plumes detected on 23 May 2016 over
BCN seem to be arriving from North America fires using 10-day
back trajectories; depending on the altitude and time of the arrival, air
masses are coming from either Canada and USA areas carrying fine biomass-burning particles or Arctic region with larger aerosols in comparison with
those smoke particles (see Fig. 1g–l). The pollen case was
selected as the day with the highest peak of daily
pollen concentration in the period March–April. This peak occurred on 23 March 2016 and the most
abundant taxon was Platanus. Belmonte (2016) counted a near-surface concentration of
around 1700 grains of Platanus taxon per cubic metre in central Barcelona on
23 March 2016. This value is close to the daily values found in the pollination
event of March 2015 in Barcelona described by Sicard et al. (2016a) as
particularly strong in terms of pollen concentration. These results will be
discussed in detail together with those obtained for each aerosol case in Sect. 3.
Polarized Micro-Pulse Lidar (P-MPL) system
The polarized Micro-Pulse Lidar system (P-MPL v. 4B, Sigma Space Corp.)
acquires vertical aerosol profiles with a relatively high frequency
(2500 Hz) using a low-energy (∼7µJ) Nd:YLF laser at 532 nm.
The P-MPL acquisition settings follow the NASA MPLNET requirements of 30 s
integrating time and 15 m vertical resolution. Polarization capabilities
rely on the collection of two-channel measurements (i.e. the signal
measured in the relative “co-polar” and “cross-polar” channels of
the instrument, denoted by Pco(z) and Pcr(z) signals,
respectively; see Sigma Space Corp. Manual, 2012, for more details). By
adapting the methodology described in Flynn et al. (2007), the parallel and
perpendicular P-MPL range-corrected signals (RCS, also called
normalized relative backscatter signals, NRB), represented by
P||(z) and P⊥(z) can be
expressed in terms of those P-MPL co- and cross-channel signals, Pco(z)
and Pcr(z), as (hereafter, the dependence with height is
omitted for simplicity)
P||=Pco+Pcr,
and
P⊥=Pcr.
Then, the total RCS, P, can be expressed as
P=P||+P⊥=Pco+2Pcr.
Final-corrected P, P|| and P⊥ are
obtained using the procedure described in Campbell et al. (2002) and Welton
and Campbell (2002). The linear volume depolarization ratio, δV,
in a classical sense (Sassen, 1991), can be defined as
δV=P⊥P||.
Then, the linear volume depolarization ratio δV for a MPL system
(Flynn et al., 2007) can be easily expressed as
δV=PcrPco+Pcr.
In order to increase the signal-to-noise ratio (SNR), both P||
and P⊥ are hourly averaged signals in this work.
However, higher uncertainties are found for daytime measurements due to the
SNR decrease. Relative uncertainties estimated for the main parameters as
derived from P-MPL measurements are shown in Table 1 (references included).
HYSPLIT back trajectories ending at different altitudes over BCN
depending on the aerosol case (only for the dust and smoke cases): (a)–(f) for
dust (5 days back) on 5 July 2016 and (g)–(l) for smoke
(10 days back) on 23 May 2016. Selected times of the air mass arrivals are
related to those aerosol profiles that are examined in particular (as shown in Sect. 3;
in particular, see Figs. 4 and 6).
The particle linear depolarization ratio δp is calculated by
the procedure shown in Cairo et al. (1999) and expressed as
δp=R×δV×δmol+1-δmol×δV+1R×δmol+1-δV+1,
where R is the backscattering ratio (R=βm+βpβm),
βm and βp are the molecular and
particle backscatter coefficients, and δmol is
the molecular depolarization ratio. Optical filters of the P-MPL receiving
system present a spectral band lower than 0.2 nm (Sigma Space Corporation,
2012), producing a temperature-independent δmol of 0.00363
according to Behrendt and Nakamura (2002). The particle backscatter
coefficient βp is obtained by applying the Klett–Fernald (KF)
algorithm (Fernald, 1984; Klett, 1985) to P (=P||+P⊥)
profiles obtained from P-MPL measurements in synergy with
simultaneous sun-photometer measurements that provide ancillary data of the
aerosol optical depth (AOD). Hence, a vertically averaged lidar ratio (LR,
extinction-to-backscatter ratio, denoted by Sa) can also be estimated
by using this KF iterative approach in P-MPL measurements, since the LR
value varies in each iteration, reaching convergence once the relative
difference between the lidar-derived height-integrated particle extinction
profile τMPL (=∑zσp(z)=∑z[Sa×βp(z)]) and the
AERONET AOD is lower than a given convergence factor (see
Córdoba-Jabonero et al., 2014 for more details of this iterative
convergence method applied to specific MPL measurements). In this study, a
convergence factor of 1 % is applied (relative uncertainties found for
Sa are 5 %–10 %; see Table 1). AERONET V2 inversion level 1.5
data were used for all the aerosol cases due to the unavailability of the
almucantar-derived data from the V3 inversion at any level and those scarce data
from V2 at level 2.0. Hence, the threshold limitation of AOD > 0.4
does not apply. Both AOD and the Ångström exponent (AEx)
with other AERONET parameters used in this work were also hourly averaged in
order to coincide with the 1 h averaging applied to P-MPL measurements.
POLIPHON methodGeneral features
The POLIPHON (POlarization-LIdar PHOtometer Networking) method was developed
at the Leibniz Institute for Tropospheric Research (TROPOS, http://www.tropos.de)
for application in polarization-lidar measurements in order
to separate the optical properties (backscatter, extinction) of aerosol
mixtures into their components with clearly different particle
depolarization ratios. POLIPHON can run two ways: as a 1-step retrieval (POL-1
approach hereafter) or in 2 steps (POL-2 approach hereafter), retrieving the
separation of two or three aerosol components. A complete
description of the POLIPHON discrimination technique can be found in Mamouri
and Ansmann (2014). In particular, the POL-1 approach has been successfully
applied to separate dust from biomass-burning smoke particles (Tesche
et al., 2011; Ansmann et al., 2012) and volcanic ash aerosols from other
fine particles (Ansmann et al., 2012; Sicard et al., 2012). The POL-2
approach has been used for the partition of coarse and fine dust components and
their discrimination from other non-dust aerosols (marine and anthropogenic
pollution) (Mamouri and Ansmann, 2017).
In this work, as stated before, the separation of the optical properties of
dust, smoke and pollen particles from their mixtures with other aerosols is
performed by applying POLIPHON to P-MPL measurements. The POL-1 approach
(2-component separation) is used for the selected smoke and pollen cases on
23 May and 23 March 2016, respectively, over BCN, in order to
discriminate the smoke (SM) signature from other non-smoke (NS) aerosols,
and the pollen (PL) particles from other local background aerosols (BA). The
dust case observed on 5 July 2016 is examined to present the separation into
three components: dust coarse (Dc), dust fine (Df) and non-dust (ND)
aerosols. However, particularly for this case, instead of the POL-2 approach
only, a combined version of POLIPHON using both POL-1 and POL-2
approaches (namely POL-1/2) is applied (Mamouri and Ansmann, 2017). A more
detailed description of this POL-1/2 retrieval and its use in this work is
shown in Sect. 2.3.2.
In general, one of the constraints of POLIPHON is that it is based on the
appropriate selection of the linear depolarization ratio for each “pure”
(not mixed) type of specific aerosols. Table 2 shows the particular
δi values assumed for each specific (i) aerosol component.
In particular, in the dust case, i=1 denotes total dust (DD)
and 2 is for non-dust (ND) when using POL-1. i=1 is for dust coarse (Dc),
2 is for dust fine (Df), and 3 is for non-dust (ND) when using POL-2. In the smoke
case, i=1 stands for smoke (SM) and 2 for non-smoke (NS). In the pollen case, i=1 is for pollen (PL), and 2 is for local
background aerosols (BA), which are likely a mixture of small pollution
particles that are mostly present in the urban environment of Barcelona city. After separation of the different aerosol components, the
respective extinction coefficients are calculated by assuming LR values
typical for each aerosol type: 55 sr for dust (Dc and Df components)
(Mamouri and Ansmann, 2014), 70 sr for smoke plumes (Groß et al.,
2013) and 50 sr for pollen particles (Sicard et al., 2016a).
The backscatter fraction for each aerosol component is presented throughout the day, as expressed in terms of the relative ratio between the specific
height-integrated backscatter coefficient for each aerosol component,
βi‾, and the total (sum of all the components)
height-integrated particle backscatter coefficient, βp‾,
i.e. the βi‾βp‾ ratio (%), as
calculated from the continuous 24/7 P-MPL measurements.
Aerosol cases observed over BCN on selected days. AERONET data at
particular times of the event (as shown in Figs. 4, 6 and 8), including those
KF-retrieved LR values (Sa), and parameters used in the POLIPHON retrieval
algorithm, depending on the version applied. References for the assumed particle
linear depolarization ratio for specific components δi
(either i=1–3 or i=1, 2, depending on the case) are also included. Errors
are shown in parentheses.
AerosolTimeSaAERONET data POLIPHONLinear depolarization ratio for each aerosol componentbcase and date(UTC)(sr)AODAExretrievalaδ1δ2δ3ReferenceDust02:0050 (10)0.33 (0.01)0.5 (0.03)POL-10.31 (DD)0.05 (ND)–Tesche et al. (2011); Ansmann et al.5 July 201616:0029 (6)0.25 (0.01)1.70 (0.01)POL-20.39 (Dc)0.16 (Df)0.05 (ND)(2012); Mamouri and Ansmann (2014)Smoke06:0081 (16)0.14 (0.02)1.30 (0.24)POL-10.15 (SM)0.05 (BA)–Groß et al. (2013)23 May 201614:0045 (9)0.16 (0.01)0.72 (0.05)Pollen10:0098 (20)0.12 (0.01)0.75 (0.02)POL-10.40 (PL)0.05 (BA)–Sicard et al. (2016)23 March 201615:0039 (8)0.10 (0.01)1.74 (0.03)
a POL-1: separation of two components; POL-2: separation
of three components. b Particular δi values assumed for
each specific aerosol component (i), regarded as pure aerosols: Dc, Df
and ND stand for dust coarse, dust fine and non-dust particles;
SM and NS stand for smoke and non-smoke aerosols; and PL and BA
stand for pollen particles and local background aerosols.
POL1/2 approach applied to the dust case: combined POL-1 and POL-2 versions
In dust events, POL-1 is used to separate dust (DD) from non-dust (ND)
aerosols. In contrast, POL-2 is a 2-step approach used to first (step 1)
separate Dc particles from the total fine mode (Df + ND) (ND are assumed
to be only fine aerosols as composed mostly of small pollution particles,
since AODs are large enough to neglect the marine impact), and then
(step 2) that fine contribution is separated into Df and ND particles (see
more details in Mamouri and Ansmann, 2014). In the overall POL-2 procedure,
the depolarization ratio for the total fine (Df + ND) mixture (i.e. the
residual fine depolarization ratio), δDf+ND, must be either
assumed or known. In our case, δDf+ND can be estimated by a
combined algorithm that uses both POL-1 and POL-2 versions (POL-1/2), as
also reported by Mamouri and Ansmann (2017). In particular, the statement
that the backscatter coefficient profiles obtained from the POL-1 retrieval
for the DD (Dc + Df) component, βDD(z)POL-1, is
identical to the sum of the backscatter coefficient profiles for the dust
coarse (Dc) and dust fine (Df) retrieved independently by the POL-2 version
(i.e. βDc(z)POL-2 and
βDf(z)POL-2) must be fulfilled. That is,
βDD(z)POL-1=βDc(z)POL-2+βDf(z)POL-2.
For that purpose, first, βDD(z)POL-1
profiles are derived. Then, a set of both βDc(z)POL-2 and
βDf(z)POL-2 are
obtained for several δDf+ND values ranging between the specific
depolarization ratios of Df particles (δDf=0.16) and ND
aerosols (δND=0.05) (see Table 2). Those
δDf+ND are iteratively introduced with steps of 0.01 in the POL-2
approach point-to-point along the whole profile in order to obtain an
optimal δDf+ND(z) profile, which must satisfy that the two terms
of the equality in Eq. (7) are equal at each z point. For
instance, the minimal value obtained for the root square differences,
Δ, between both terms in Eq. (7) at a given z,
min{Δ(z)}=minβDD(z)POL-1-βDc(z)POL-2+βDf(z)POL-22,
is used as proxy in that iteration process. Hence, once those min{Δ} are
achieved for a given δDf+ND along the whole profile,
the optimal vertical δDf+ND(z) profile is determined. Moreover,
since δDf+ND(z) is defined in a good approximation as
δDf+ND(z)=δDf×γ(z)+δND×(1-γ(z)),
where γ(z) and (1-γ(z)) are the fraction
of each Df and ND component that contributed to the total fine (Df + ND) mode
mixture, this contribution of each aerosol fine component to the total fine
mode can also be estimated with height and γ(z) is thus determined.
Once the profile of δDf+ND (and γ) is optimally
determined, the total particle backscatter coefficient profiles β(z)
can be separated into all three components (βDc, βDf
and βND) for the dust case by applying the POL-2 (step 2)
retrieval (see Mamouri and Ansmann, 2014, for more details). Hence, their
relative contribution (i.e. the βi‾βp‾
ratio, %) can also be derived.
POL-1 versus POL-2 differences in particle backscatter coefficient
profiles for each component (total dust βDD and non-dust
βND from POL-1; coarse βDc and fine
βDf dust, being βDc+βDf=βDD,
and non-dust βND from POL-2) retrieved for the dust case on
5 July 2016 at 02:00 and 16:00 UTC by using (optimally derived)
(a) a δDf+ND(z) profile and
(b) a single columnar δDf+NDc value.
For comparison, a columnar δDf+NDc value is also calculated
using the same POLIPHON procedure as described before, but the minimum of
the root mean square differences, Δ̃, between both terms in Eq. (7),
minΔ̃=min∑zβDD(z)POL-1-βDc(z)POL-2+βDf(z)POL-22n,
is used instead as the proxy applied in the iterative retrieval (n stand
for the number of z points along the overall profile). For instance,
Fig. 2 shows the particle backscatter coefficients profiles as
obtained from either POL-1 (βDD and βND) or POL-1/2
(βDc and βDf, being βDc+βDf=βDD,
and βND) approaches twice (at 02:00 and 16:00 UTC) on
5 July 2016, using both the optimal δDf+ND(z) profile
(Fig. 2a) and the columnar δDf+NDc
(Fig. 2b). Discrepancies are observed in both the dust and non-dust components by
using a single columnar δDf+NDc value instead of the optimal
δDf+ND(z) profile. For comparison between Fig. 2a
and b, differences are clearly found in βND at 02:00 UTC,
picked at around 4.5 km in height, as derived from either POL-1 or POL-1/2, in
addition to those found for βDD in comparison with βDc
and βDf (particularly evident at 16:00 UTC, with
βDD≪βDf between 1 and 2 km in height) (see Fig. 2b).
These results highlight that the use of a height-resolved δDf+ND
improves the retrieval. Indeed, the use of a single columnar (no
height-resolved) δDf+NDc (and γc) in the retrieval
can be inadequate due to the plausible variability of the relative fraction
of Df particles to the total fine (Df + ND) mode with height. In particular,
this is corroborated by looking at the optimal height-averaged δDf+ND‾
values obtained at 02:00 and 16:00 UTC are 0.12±0.04 (γ‾=66%±32 %)
and 0.09±0.05 (γ‾=40%±38 %) in comparison with those columnar
δDf+NDc values found at 02:00 and
16:00 UTC of
0.14 (γc=82 %) and 0.06 (γc=9 %).
The conversion from extinction (σ, m-1) to mass concentration
(M, g m-3) is performed for each component (i) by means of the
mass extinction efficiency (MEE, or mass-specific extinction
coefficient) (k, m2 g-1) by using the relationship (Ansmann et
al., 2012; Córdoba-Jabonero et al., 2016) at each altitude zMi(z)=σi(z)ki.
The effective MEE (keff, m2 g-1), linking the total aerosol
extinction from all aerosol components (i.e. AOD) to the total mass
concentration (TMC), is given by
keff=AODTMC,
where TMC =∑iMi‾ represents the total mass loading in
g m-2, with M‾i the height-integrated mass concentration
for each component (i.e. Mi‾=∑zMi(z)Δz,
with Δz the height resolution). keff is a measure of the
predominant particle size; keff values lower and higher than
1.5 m2 g-1 are representative of large and small particles
respectively
as reported by the Optical Properties of Aerosols and Clouds
database (OPAC; Hess et al., 1998). The mass contribution or
fraction of each aerosol component is expressed by the relative ratio
between Mi‾ and TMC (i.e. Mi‾/TMC, in %).
Columnar MEE values can be obtained from AERONET data and the particle
density (Pd, g cm-3) assumed for each aerosol component examined in
this work by using the expression (Ansmann et al., 2012):
kc,f=τc,fPd×VCc,f=1Pd×cvc,f,
where kc,f designates the MEE for coarse and fine modes, as denoted by
subscripts c and f. Similarly, VCc,f (10-12 Mm)
and τc,f are the AERONET V2 L1.5 volume concentrations and
extinction values for the coarse and fine modes.
cvc,f (=VCc,fτc,f) are the corresponding
extinction-to-volume conversion factors.
Parameters involved in the extinction-to-mass conversion for each
aerosol case: the AERONET-reported and -derived mass conversion factors (cv),
the assumed particle densities (Pd) and the mass extinction efficiency (k)
values. For the dust case (3-component separation), i=1 (Dc), 2 (Df) and 3 (ND)
and for the smoke and pollen cases (2-component separation), i=1 (SM/PL)
and 2 (NS/BA). Errors are shown in parentheses.
* c and f denote the particle coarse and fine modes.
Our strategy is to obtain the actual cvc,f values, and then the
kc,f, using typical particle densities, from AERONET sun-sky photometer
observations carried out simultaneously with P-MPL observations, for as long as
the separated aerosol components can be identified as being composed of pure
coarse or fine particles. Table 3 shows the AERONET parameters
involved in the extinction-to-mass conversion (VCc,f, τc,f)
at selected times for each aerosol case together with those
typical particle densities Pd for each aerosol component. In particular,
Pd values assumed for each type of aerosols are 2.60 g cm-3 for dust
(Ansmann et al., 2012), 1.30 g cm-3 for smoke (Reid et al., 2005) and
0.92 g cm-3 for pollen (Platanus) particles (Jackson and Lyford, 1999; Zhang et al.,
2014). For the other components, the particle density is obtained from the
OPAC database (Hess et al., 1998). A particle density Pd = 1.8 g cm-3
is assumed for both the ND and BA components in the dust and
pollen cases, respectively, corresponding to background urban aerosols,
mostly composed of fine pollution particles. For the NS component in the
smoke case, a PdNS=2.0 g cm-3, as reported by OPAC for
Arctic aerosols, is assumed since the NS signature is found when air masses
come from the Arctic, as indicated by back-trajectory analysis (see
Sect. 2.1). However, the corresponding cv and k values must
be examined in more detail in the extinction-to-mass conversion procedure
for each aerosol case, as explained next.
Dust case
As stated before, the POL-1/2 retrieval is used to separate three components
for the dust case (i= Dc, Df and ND). Conversion factors are only
reported for coarse- and fine-mode particles using AERONET data
(Eq. 13). In this case, the coarse mode is completely composed of
Dc particles (the ND component is assumed to be fine aerosols only; see
Sect. 2.3). Hence, the MEE for Dc particles, kDc, is easily obtained from
kDc=τcPdDc×VCc=1PdDc×cvc,
with PdDc=2.6 g cm-3 for dust. However, MEE for Df
particles, kDf, and ND aerosols, kND, must be determined from the
MEE value obtained for the total fine (Df + ND) mode, kDf+ND, that is,
kDf+ND=τfPdDf+ND×VCf=1PdDf+ND×cvf,
where PdDf+ND represents a weighted value of the particle density for
the overall fine (Df + ND) mode. Once estimated δDf+ND, and
γ (see Eq. 9), PdDf+ND can be expressed as
PdDf+ND=PdDf×γ+PdND×(1-γ),
where PdDf and PdND are the particle densities assumed for
dust (2.6 g cm-3) and non-dust aerosols (1.8 g cm-3)
(Table 3). Hence, the height-integrated mass concentration for the
total fine (Df + ND) mode, MDf+ND‾, can be calculated from
MDf+ND‾=kDf+ND-1×τDf+ND=MDf‾+MND‾,
where kDf+ND is calculated from Eq. (15), and MDf‾ and
MND‾ are the mass concentrations for Df and ND
aerosols (note that these quantities are height-integrated variables, i.e.
mass loadings). In particular, MDf‾ can be determined by assuming
a representative conversion factor cv for Df particles, since
MDf‾=τDf×PdDf×cvDf.
Mamouri and Ansmann (2017) reported statistical AERONET-based
extinction-to-mass conversion factors for fine-dust particles cvDf
in the interval of 0.21–0.25 (±0.05)×10-12 Mm. In this work,
this set of values is introduced in the algorithm in order to obtain an
optimal cvDf value satisfying the following condition:
MDf‾<MDf+ND‾, being estimated MDf‾ from
Eq. (18). At the same time, MND‾ is also obtained, since
MND‾=MDf+ND‾-MDf‾.
Hence, kDf and kND (and cvND) are calculated applying,
similarly to Eqs. (13)–(15), the following expressions:
kDf=1PdDf×cvDf,kND=τNDMND‾,
and
cvND=1PdND×kND.
Otherwise, MDf‾=MDf+ND‾ (and then,
kDf=kDf+ND) and MND‾=0. Finally, the total mass concentration
TMC (i.e. mass loading, in g m-2) is obtained from
TMC=MDc‾+MDf+ND‾=MDc‾+MDf‾+MND‾.
Those AERONET parameters used in the extinction-to-mass conversion with the cv and k values obtained at particular times
(see Table 3) are in agreement with those reported by other authors for dust
(i.e. Mamouri and Ansmann, 2014, 2017). In addition, kND
values are derived between 2.52 and 2.92 m2 g-1, similarly to those
reported by OPAC for urban aerosols (2.87 m2 g-1) and as assumed for
the ND component in this work.
Smoke and pollen cases
For both cases, optical properties are separated into two aerosol components
by using the POL-1 approach. Hence, mass concentrations are derived directly
from Eqs. (11)–(13) of the general extinction-to-mass conversion
procedure using AERONET data, satisfying that each component is composed
mostly of either coarse- or fine-mode particles, as described in Sect. 2.4.1.
In particular, the smoke (SM) component is composed of fine biomass-burning
particles, and the coarse mode is associated with the non-smoke (NS) component
by assuming particles larger than smoke coming from the Arctic area. For
instance, a kSM=4.5±1.4 m2 g-1 is derived for
fine smoke particles at 06:00 UTC (see Table 3). This value is in
good agreement with that reported for Canadian forest fire smoke aerosols
(Ichoku and Kaufman, 2005; Reid et al., 2005). However, a rather lower MEE
value is obtained for the coarse-mode NS particles (kNS=2.4±0.5 m2 g-1)
at the same time. In the pollen case, PL particles are
predominantly large particles in comparison with the fine (and less
depolarizing) component corresponding to local background aerosols (BA),
which are assumed to be composed of small pollution particles of urban origin
(marine contribution is neglected, as stated in Sect. 2). For
instance, a kPL=2.3±0.1 m2 g-1 is obtained for
pollen particles at 15:00 UTC, when the pollination event is enhanced, as
described later in Sect. 3.3.
Table 3 shows the derived MEE values (k, m2 g-1) at
selected times by using the corresponding cv factors and the assumed
particle densities (Pd, g cm-3) for each component. Particular
similarities and discrepancies found from those assumptions will be
discussed in more detail in Sect. 3.
Height-integrated mass concentration (Mi‾, i.e. mass
loading, g m-2) for each component and the total mass concentration (TMC)
indicated for two times for each aerosol case. Errors are shown in parentheses.
Dust event on 5 July 2016. Evolution of the relative
contribution (a)βi‾βp‾ (%)
and (b)Mi‾/TMC (%) (the bar over the variable
are removed in the figure for clarity) for each aerosol component throughout the day:
Dc (red bars), Df (green bars) and ND (blue bars), which denote
dust coarse, dust fine and non-dust aerosols. In (a) (right axis)
AERONET hourly averaged AOD and AEx (white and black–white stars, respectively)
and KF-derived Sa (lidar ratio, sr; square symbols) values are reported;
in (b) (right axis) TMC (total mass loading, g m-2; open circles)
is also included. Black arrows on the time axis indicate selected times for
those vertical profiles shown in Fig. 4.
ResultsDust case
A dust event occurred over BCN on 5 July 2016, which was mostly intense
before 12:00 UTC as confirmed by AERONET data with moderate AOD and
AEx < 0.5 values together with HYSPLIT back-trajectory analysis
(Sect. 2.1). The separation into three components (Dc, Df and ND)
of dust mixtures using the synergy of hourly averaged P-MPL measurements
and POL-1/2 retrieval was performed throughout the day. Prior to using POL-1/2,
vertical profiles of the total particle backscatter coefficient (βp),
as derived from the KF algorithm (if the KF retrieval is
feasible, estimated LR values are discussed later), and the linear particle
depolarization ratio (δp) are obtained throughout the day. Then,
the corresponding vertical profiles of the backscatter coefficients for each
specific component (βi, i= Dc, Df, ND) were retrieved by
using POL-1/2 (Sect. 2.3.2). The three specific depolarization
ratios selected for each pure aerosol component (δi, i= Dc,
Df, ND), required for the POL-1/2 retrieval, are shown in Table 2.
As mentioned before, height-integrated values of all these backscatter
coefficient profiles (βp‾, and the three βi‾
for each component) are calculated over 24 h (if
the KF retrieval is feasible) to obtain the daily temporal evolution of the
optical contribution for each aerosol component in terms of their specific
relative ratio βi‾βp‾ (in %).
Regarding the height-integrated mass concentration (Mi‾, i= Dc,
Df, ND; Sect. 2.4), the daily evolution of the specific mass
contribution ratio (i.e. the relative ratio Mi‾TMC,
in %) is also calculated for each aerosol component (note that
height-integrated mass concentrations represent the mass loading, expressed
in g m-2). For simplicity, the same notation is used for mass
concentration and mass loading.
Figure 3 shows the daily evolution of the specific (a) optical and
(b) mass relative contributions for each aerosol component throughout the day. A
high loading of large particles with peaks of 78 % for βDc and
98 % for MDc was obtained in the time interval before 12:00 UTC.
These peaks drop to minimums of 9 % and 43 % after that
time. Here, the optical contribution of the total dust (Dc+Df) varies
between 17 % and 46 %, while the mass contribution ratio varies between
56 % and 98 %. In terms of mean TMC (dust loading), values of 0.6±0.1
and 0.2±0.1 g m-2 are estimated at those
time intervals before and after 12:00 UTC. The last one represents a TMC
of 34 % with respect to that found in the previous period of the day.
Specific Mi‾ and TMC at given times are shown in Table 4.
Therefore, two differentiated dust scenarios with an intense and weak
dust impact are clearly observed throughout the day.
These results are related to the mean MEE values found for dust particles,
kDc=0.5±0.1 m2 g-1 and kDf=1.7±0.2 m2 g-1,
as obtained for Dc and Df particles.
These quantities are within and close to the range of values representative
of coarse- and fine-dominated dust particles as reported by
the OPAC database (Hess et al., 1998): 0.16–0.97 m2 g-1
(dust coarse) and 2.3–3.1 m2 g-1 (dust fine). Higher MEE
values are obtained for the ND component (kND=3.1±1.3 m2 g-1,
in daily average), indicating much smaller particles, and are
close to the value of 2.87 m2 g-1 reported by OPAC (Hess et al.,
1998) for urban aerosols (note that fine polluted aerosols with urban origin
were assumed for the ND component). For comparison, the corresponding mean
conversion factors cv obtained for Dc and Df particles are
cvDc=0.8±0.3×10-12 Mm and
cvDf=0.24±0.02×10-12 Mm, which are in good
agreement with other reported values (i.e. Mamouri and Ansmann, 2017).
Dust event on 5 July 2016. Vertical profiles of the particle
backscatter coefficients (total and for each specific component; left panels),
the linear depolarization ratios (volume δV and particle
δp; centre panels) and the estimated depolarization ratio for
the fine (Df + ND) mode (δDf+ND; right panels)
at two time intervals, illustrating the different aerosol scenarios observed throughout the day:
(a) at 02:00 UTC (high dust incidence) and (b) at 16:00 UTC
(low dust incidence). Specific depolarization ratios selected for each pure
aerosol component are also shown by vertical dashed lines (see legend) in the
centre panels.
AERONET AOD and AEx values provided throughout the day also
confirm these results (night-time data are
assumed equal to the first and last daytime values in each case; see Fig. 3a). In particular, AEx is close to 0.5
(coarse particle predominance) and higher than 1.5 (fine particle
prevalence) before and after 12:00 UTC. Regarding LR values
as derived from the KF algorithm (Fig. 3a, right axis), a daily
mean Sa=42±15 sr is obtained. No significant differences
are found between LR values obtained for those intense and weak dust
periods of the day and only a certain variability is observed throughout the day
as modulated by the dust loading, as expected.
In order to illustrate the vertical distribution of dust particles,
Fig. 4 shows an example in terms of the profiles of both the
particle backscatter coefficients (total βp, and
βDc, βDf and βND, left panels) and the linear
depolarization ratios (volume δV and particle δp,
right panels) of both aerosol scenarios: (1) when the dust event
presents a high incidence, as occurred for instance at 02:00 UTC
(Fig. 4a); and (2) after the dust particles have been almost completely
removed (i.e. situation observed at 16:00 UTC; see Fig. 4b). These
scenarios are also indicated in Fig. 3 by black arrows. An
enhanced dust impact is observed in Fig. 4a (02:00 UTC) due to a
high quantity of Dc particles confined in a layer located between 2 and 5 km
in height (red line in Fig. 4a). Contrarily, Fig. 4b (16:00 UTC)
shows a rather weaker dust incidence from the ground up to 4 km,
mostly due to a low loading of both Dc and Df particles (red and green
lines in Fig. 4b). Indeed, according to HYSPLIT
back trajectories (Sect. 2.1), no Saharan origin of air masses is
observed after 12:00 UTC (see Fig. 1d and e).
AERONET AOD and AEx and KF-derived LR values for those different dust
scenarios are also included in Table 2. In particular, a
Sa=50±10 sr is retrieved at 02:00 UTC that is within the
typical LR range determined for dust. Meanwhile a lower value (Sa=29±6 sr)
is found at 16:00 UTC, when a rather weaker dust incidence
occurs. Moreover, δp shows values close to the linear
particle depolarization ratio for pure Dc particles (δDc=0.39)
for the first aerosol scenario (Fig. 4a, centre
panels) and values slightly lower than 0.16 (δDf for pure fine dust
particles) for the second one (Fig. 4b, centre panels). In
addition, the δDf+ND profiles for those times are also shown in
Fig. 4 (right panels) in order to examine the corresponding
variability of the Df contribution to the particle fine mode with height:
δDf+ND is greater than 0.10, indicating that the Df fraction
within the fine mode is larger than 45.5 % at altitudes higher than 1.5
and around 4.0 km for those two dust situations
(Fig. 4a and b), in correspondence with the
backscatter profiles; otherwise, the Df fraction is reduced (<40 %)
at lower heights. In these two particular cases (Fig. 4), the
derived MEE values are close to the typical ranges for Dc (kDc:
0.5–0.6) and Df (kDf: 1.5–2.0) aerosols (see Table 3).
Smoke case
Smoke plumes were observed over BCN station on 23 May 2016. The two
principal areas from which air masses arrive are North America and the
Arctic, as reported by HYSPLIT back-trajectory analysis
(see Fig. 1g–l panels). The smoke origin is likely
from forest fires in North America (as stated in Sect. 2.1).
Hence, the smoke case is examined as a mixture of two components:
fine biomass-burning particles (SM for smoke) from Canada and USA fires, and
another particle type larger than smoke coming from the Arctic region
(hereafter referred to as non-smoke aerosols, NS). Their vertical
separation is achieved using a POL-1 retrieval (2-component separation), as
described in Sect. 2.3 and 2.4. Both the particular
backscatter coefficients and mass concentrations are retrieved for each
component. In particular, the arrival of smoke plumes over BCN is mostly at
altitudes above the boundary layer (BL); hence, this case is focused only on
those tropospheric features above the BL, thus disregarding aerosols from
other plausible local background BL sources.
The same as Fig. 3 but for the smoke case on 23 May 2016:
SM (red bars) and NS (blue bars) denote smoke and non-smoke
components. Black arrows on the time axis indicate selected times for
vertical profiles shown in Fig. 6.
Like for the dust case, Fig. 5 shows the relative fractions of
each SM and NS component in terms of the backscatter coefficient and the
mass concentration throughout the day. Those k values, together with the cv factors at selected
times, are shown in Table 3 along with the assumed Pd values:
1.30 g m-3 for SM and 2.0 g m-3 for NS aerosols (see Sect. 2.4).
Since values of δp higher than 0.1 are found at given
altitudes throughout the day, a high-limit value of the particle linear
depolarization ratio for smoke, δSM of 0.15, is assumed. This
rather high δSM value is typical for smoke particles mixed with
dust (Tesche et al., 2011; Groß et al., 2013), as one would expect
δSM<0.10 for pure biomass-burning particles (Müller
et al., 2005; Groß et al., 2013). In addition, AERONET AEx varies
between 1.25 and 1.55 before 12:00 UTC (see Fig. 5a), indicating
rather moderate AEx values compared to higher fresh smoke values (∼2.00),
as also measured by Sicard et al. (2011) in Barcelona. Hence, the
value of δSM=0.15 reflects a mixing state of biomass-burning
particles but not necessarily with dust. For the other, less depolarizing,
NS component, a δNS=0.05 is applied. Those particle linear
depolarization ratio values assumed for SM and NS are shown in Table 2.
In general, smoke particles are detected almost throughout the whole day,
representing approximately 40 %–60 % of the total height-integrated aerosol
backscatter. However, a sharp βSM‾βp‾
decrease from those values to around 4 % is observed at 15:00 and 16:00 UTC,
which coincides with the 47 % decrease found for AEx (see
Fig. 5a). Since lower AEx values are usually associated with the
predominance of large particles and/or the decrease in the fine mode, these results
are in agreement with the observed reduction of fine biomass-burning
particles in the same time interval. At those same times, the TMC
reaches high values (0.26±0.06 g m-2, in average) with respect
to the daily mean TMC background of 0.05±0.03 g m-2. This is
likely due to the major contribution of larger NS aerosols; meanwhile fine
SM particles represent only a 3 %–7 % of TMC at the same times. In
particular, the daily mean MSM‾ is 0.017±0.008 g m-2,
representing 2.7 % of the mean TMC found for the dust case. Regarding
KF-derived LR values (see Fig. 5a, right axis), a daily mean
Sa=56±23 sr is obtained. That value is lower compared to
typical LR of 70 sr for smoke (i.e. Groß et al., 2013, and
references therein), which together with the large relative deviation (42 %)
indicates a high aerosol variability throughout the day as expected due to
the singular arrival of air masses in height and time, and hence the
particular vertical aerosol mixing found with the smoke particles.
The same as Fig. 4 but for the smoke event on 23 May 2016
at (a) 06:00 UTC, and (b) 14:00 UTC. Specific depolarization
ratios selected for each smoke aerosol component are also shown by vertical
dashed lines (see legend for details).
Regarding the vertical structure, Fig. 6 shows examples of two
different aerosol scenarios observed on 23 May 2016 (smoke case): (1) a well-defined smoke
layer is observed, for instance, between 6 and 7.5 km in height with a certain
mixing with NS aerosols at 06:00 UTC (see Fig. 6a, red line); and
(2) the smoke signature can be detected as highly mixed with NS aerosols along
the atmospheric profile (i.e. situation observed at 14:00 UTC; see
Fig. 6b). Both these scenarios are also indicated in Fig. 5 with
black arrows. Indeed, the mean Sa values of 70±19 and 35±9 sr
found before and after 12:00 UTC reflect that
the smoke signature detected during the first of those time periods of the
day presents a lower mixing with other aerosols than that observed later.
Additionally, on average, the mean height-integrated mass concentration for
smoke is also obtained in those two scenarios: MSM‾=0.014±0.002
and 0.022±0.009 g m-2 are found for those time intervals. Those values represent 2.2 % and
3.4 % of the TMC found for the intense dust period.
Figure 6a clearly shows a smoke layer between 6 and 7.5 km in height,
also mixed with a certain NS contribution, exhibiting δp values
of 0.15 and higher. In addition, a smaller SM layer of about 300 m thickness
is also found below, at around 5.2 km in height, with rather higher δp
than 0.15, and another layer is observed between 3 and 4 km in height
corresponding to the presence of NS aerosols with a δp slightly
higher than 0.05. The fraction of smoke particles is around 50 % of
total backscatter (see Fig. 5a) with a height-integrated mass
concentration for smoke MSM‾=0.012±0.002 g m-2, representing
2 % of the mean TMC during the intense dust event (see Table 4).
Later in the day at 14:00 UTC, both SM and NS particles are found along all
the profile, with δp values close to 0.15, mainly between 4.0 and 4.5 km in height. In
addition, a single NS layer is also clearly observed, peaking at 2.5 km in
height, with δp values decreasing to 0.05 (see Fig. 6b).
These results agree with the δp value selected for NS
aerosols (δNS=0.05; see Table 2). At this time, a
MSM‾=0.023±0.001 g m-2 is obtained, being 4 % of the
mean TMC for the intense dust episode. Particular LR values
for those times shown in Fig. 6 are also included in
Table 2: Sa=81±16 sr is retrieved at 06:00 UTC,
which is within the typical LR range determined for smoke, while a lower LR
(Sa=45±9 sr) is found at 14:00 UTC, as expected. Particular
MEE values derived for smoke particles, kSM=4.5±1.1 and
1.9±0.4 m2 g-1, are obtained at 06:00 and
14:00 UTC. These results would indicate that smoke plumes detected in the first
scenario are predominantly composed of relatively pure fine biomass-burning
particles, with similar MEE values to those reported for Canadian boreal
forest fire aged smoke particles (Ichoku and Kaufman, 2005; Reid et al.,
2005). However, those observed in the second one would represent a mixed
state of smoke particles with an enhanced coarse mode, thus decreasing their
MEE. All those values are shown in Tables 3 and 4.
These results are corroborated by a more detailed analysis of the
back trajectories ending over BCN on 23 May 2016 (selected heights and times
of their arrival are shown in Fig. 1). In particular, air masses
arriving at 06:00 UTC carry smoke particles from Canada and USA
fires at altitudes higher than around 4500 m a.s.l. (see Fig. 1h and i),
while Arctic air masses arrive at lower heights (see
Fig. 1g). Later on, a smoke signature observed at 14:00 UTC
is distributed from altitudes higher than around 3000 m a.s.l.
(Fig. 1k and l), and the NS layer identified at around 2500 m
height (see Fig. 6b) actually corresponds to air masses from
the Arctic (see Fig. 1j).
Pollen case
The pollination period (i.e. the enhanced formation/presence of pollen
particles) in Barcelona is from local sources predominately occurring in
March from more abundant species, such as the Pinus and Platanus trees (Sicard et
al., 2016a). In this case, a pollen episode occurred on 23 March 2016,
corresponding to a high pollination event observed over BCN (Belmonte,
2016). As for the smoke case, a POL-1 retrieval is used to separate pollen (PL)
particles from background (BA) aerosols. These BA are supposed to be
mostly composed of fine urban pollution particles, and their exact origin,
whether they are local or not, is not relevant since they do not depolarize
and cannot be mistaken for highly depolarizing pollen particles. This is
also the reason that HYSPLIT back trajectories were not calculated. Particle
linear depolarization ratios for pure PL, δPL=0.40, and
BA, δBA=0.05, aerosols are shown in Table 2, and k (and cv) values are shown in Table 3. The relative
fractions of each aerosol component in terms of the backscatter coefficient
and the mass concentration are also calculated throughout the day.
The same as Fig. 3 but for the pollen event that occurred on 23 March 2016:
PL (red bars) and BA (blue bars) denote pollen and local
background aerosol components. Black arrows on the time axis indicate selected
times for those vertical profiles shown in Fig. 8.
The same as Fig. 4 but for the pollen event on 23 March 2016
at (a) 10:00 UTC (no PL detection) and (b) 15:00 UTC
(enhanced PL occurrence). Specific depolarization ratios selected for each pure
aerosol component are also shown by vertical dashed lines (see legend for details).
Pollen signature is clearly observed from 10:00 UTC, as shown in
Fig. 7 by the increase in their relative fraction
βPL‾βp‾, with a maximum around 30 %
between 12:00 and 16:00 UTC. The coincident increase in AEx (see
Fig. 7a) is probably associated with the formation of local urban
aerosols, which are much smaller than pollen grains. This
hypothesis suggests that local urban aerosols dominate the columnar-averaged
optical properties. A mean value of Sa=55±17 sr is obtained
during the pollen occurrence, while Sa=71±17 sr is found
for the no pollen detection period. The Sa value for pollen is close
to that considered in other works (Sicard et al., 2016a). The fraction of
the height-integrated mass concentration for pollen MPL‾ with
respect to the TMC reaches a maximum of around 40 % at 15:00 UTC. In
addition, the TMC evolution is fairly constant with a daily averaged TMC
of 0.029±0.003 g m-2 and a mean of MPL‾=0.007±0.003 g m-2
(i.e. 25 % of TMC) in the 12:00–23:00 UTC
interval. For comparison, these TMC levels represent only 1.1 % of the
dust TMC during their higher dust incidence, as discussed in Sect. 3.1.
Regarding the MEE derived for pollen particles, a mean kPL=2.4±0.8 m2 g-1
is obtained. Sicard et al. (2016a) estimated a
kPL=3.2 m2 g-1 considering an effective radius size of
24 µm for the pollen grains registered during a pollination episode in
March 2015 (data not shown). Hence, the kPL value found in this work
may be in agreement with the estimated value if pollen particles
detected in our case are larger than those observed by Sicard et al. (2016a),
as MEE decreases as particle size increases.
In order to display the vertical distribution for this case, profiles of the
particle backscatter coefficients and both the volume and particle linear
depolarization ratios are shown in Fig. 8 (see legend inside).
For instance, the vertical distribution is shown at 10:00 UTC when no
pollen particles are significantly detected (Fig. 8a), with low
δp values close to 0.05 from the surface up to around 1 km and
slightly increasing from that altitude up. This is likely due to uplifted
particles. In comparison, the situation occurred later in the day (i.e.
that observed at 15:00 UTC, Fig. 8b), the amount of pollen is clearly
enhanced: δp increases, reaching higher values between 0.10
and 0.15, and pollen particles are mostly confined up to 1.5 km from
the surface. These two scenarios are also indicated in Fig. 7 with
black arrows. The corresponding mass loading for pollen MPL‾ at
this time is 0.011±0.003 g m-2 (see Table 4).
Conclusions
The synergetic use of the POLIPHON (POlarization-LIdar PHOtometer Networking) retrieval with the MPLNET (Micro-Pulse Lidar NETwork)/P-MPL
(polarized MPL) measurements is introduced for the first time in order to
separate dust (both coarse Dc, and fine Df, modes) and biomass-burning smoke (SM)
particles from their mixtures with other aerosols (namely, non-dust ND,
and non-smoke NS aerosols). In addition, a case study of pollen (PL) mixed
with local urban background aerosols (BA) is also examined. In all cases,
the particle linear depolarization ratio for each pure aerosol component
is a relevant constraint in POLIPHON retrievals. The separation of aerosol
mixtures into their particle components is performed for different
depolarizing particles. In particular, typical linear depolarization ratios
found in the literature are assumed for each pure aerosol component: 0.39,
0.16 and 0.05 for Dc, Df and ND; 0.15 and 0.05
for SM and NS; and 0.40 and 0.05 for PL and BA.
In this work, a reasonable performance is achieved by obtaining the relative
optical and mass contributions of each aerosol component throughout the day as
based on P-MPL continuous 24/7 observations carried out in Barcelona
(NE Spain). Three case studies observed on 5 July, 23 May and 23 March 2016 are
examined respectively for dust, smoke and pollen occurrences. In
particular, the POLIPHON 1-step version (POL-1: separation into two
components) is applied for the smoke and pollen cases. In order to
illustrate the 3-component separation for the dust case, a combined
algorithm using both the POLIPHON 1-step (POL-1) and 2-step (POL-2) versions
(namely POL-1/2) is described in more detail. In addition, both the vertical
and columnar particle depolarization ratios for the total fine (Df + ND)
mode, δDf+ND, and correspondingly both the vertical and columnar
fraction of Df particles to the total fine (Df + ND) mode, are also
estimated using the POL-1/2 retrieval (the a priori assumption of those
variables is thus avoided). Minimal differences in the particle backscatter
coefficient, β, for each dust and non-dust component are
obtained from either POL-1 or POL-1/2 approaches, as long as a vertical
depolarization ratio for the total fine (Df + ND) mode δDf+ND(z)
is regarded. Otherwise, the use of a single columnar that is not height resolved,
δDf+NDc,
is inadequate due to the plausible Df variability, with respect to the total
fine mode with height.
The extinction-to-mass conversion procedure is described in terms of the
mass extinction efficiency (MEE: k, m2 g-1), a parameter
associated with the size of the particles. The MEE is estimated for each
aerosol component by using the corresponding conversion factors as
calculated from AERONET data (volume concentrations and extinctions for the
coarse and fine modes), as reported simultaneously with P-MPL
measurements, and the particles densities assumed for each type of aerosol.
In addition, the effective MEE (keff, a measure of the predominant size of those aerosol mixtures) is also
retrieved for each aerosol event. Hence, height-integrated mass
concentrations (i.e. mass loadings, g m-2) are obtained throughout the day
for each component. In general, the daily evolution of their relative
optical and mass contributions, with respect to the height-integrated total
backscatter coefficient and total mass concentration (total mass loading)
for each aerosol case, is also derived. Due to the variation of the aerosol
situation observed for each case study throughout the day, different aerosol
scenarios can be present, and hence their vertical distributions are examined.
In the dust case on 5 July 2016, a Saharan dust intrusion arrives
at BCN during the first part of the day (before 12:00 UTC). Meanwhile a weak
dust incidence is observed later on, as also confirmed by AERONET data and a
HYSPLIT back-trajectory analysis. This is due to the predominance of large
particles (Dc component) during this intense dust period of the day. In
terms of mean dust mass loading, values of TMC =0.6±0.1 and
0.2±0.1 g m-2 are obtained at time intervals before
and after 12:00 UTC. This last value just represents a mass loading of
34 % with respect to that found before. In addition, mean MEE values of
kDc=0.5±0.1 m2 g-1 and kDc=1.7±0.2 m2 g-1
are obtained for Dc and Df particles.
These quantities are within and close to the range of values representative
of coarse- and fine-dominated dust particles. AERONET AOD and
AEx values reported throughout the day confirm these results. In particular, AEx
is close to 0.5 (predominance of coarse particles) and higher than 1.5 (fine
particles prevalence) before and after 12:00 UTC. A mean
KF-derived lidar ratio Sa=42±15 sr is obtained with no
significant differences for those two time periods of the day.
Regarding particular aerosol scenarios, a Sa=50±10 sr is
retrieved at 02:00 UTC (within the typical range of lidar ratios defined for
dust); meanwhile a lower value (Sa=29±6 sr) is found at
16:00 UTC when a rather weaker dust incidence occurs. Moreover, δp
shows values close to the particle linear depolarization ratio for pure Dc
particles (0.39) during the intense dust scenario, and lower than 0.16
(typical for pure fine dust particles) for the weak one, highlighting the
prevalence of ND aerosols. In addition, the particle depolarization ratio
for the total fine (Df + ND) mode is greater than 0.10; that is, the
relative Df fraction within the total fine mode is larger than 45.5 %, at
altitudes higher than 1.5 and around 4.0 km for those
two particular dust situations. The derived MEE values are typical for Dc
(kDc: 0.5–0.6) and Df (kDc: 1.5–2.0) aerosols in those two
particular cases.
For a smoke case, air masses arriving over Barcelona (BCN) on 23 May 2016
come from two areas, North America and the Arctic, as reported by HYSPLIT
back-trajectory analysis. Fine biomass-burning particles originated from
fires in Canada and the USA, which were likely mixed with other aerosols larger
than smoke from the Arctic region (non-smoke aerosols, NS).
In general, both SM and NS particles were found along all the profile;
δp values are higher than 0.10 and close to 0.15 when SM
particles were mostly detected. Fine smoke particles are observed during
almost all the day, representing approximately 40 %–60 % of the total
height-integrated aerosol backscatter coefficient. The mean mass loading for
smoke is MSM‾=0.017±0.008 g m-2, representing
2.7 % of the mean TMC found for the dust case. However, individual
decreases in the relative smoke fractions of both the backscatter
coefficient and mass concentration are also observed throughout the day,
also coinciding in time with AEx decreases (as associated with
a predominance of coarse particles or reduction of fine ones).
Regarding the vertical structure, two aerosol scenarios are observed throughout
the day: the smoke signature is detected at defined layers in the
morning, while a vertical SM distribution mixed with
a layered NS structure is observed later on. Mean LR values of Sa=70±19
and 35±9 sr are found before and after
12:00 UTC that day, showing a lower smoke mixing for the first time
interval. In addition, the mean mass loadings for smoke as obtained in those
two different scenarios are MSM‾=0.014±0.002 and
0.022±0.009 g m-2 (i.e. 2.2 % and 3.4 % of the TMC found for the intense dust period). This is
likely due to the singular arrival of air masses in height and time, and
hence the particular vertical aerosol mixing found together with the smoke
particles over BCN. Corresponding MEE values derived for smoke particles in
those two scenarios are kSM=4.5±1.1 and 1.9±0.4 m2 g-1
indicating that smoke plumes detected in the
first scenario are predominantly composed of pure fine biomass-burning particles, unlike the second one, which has a mixed state
of smoke particles with an enhanced coarse mode.
In the pollen case on 23 March 2016, the PL signature is clearly
observed from 10:00 UTC, when the relative fraction of the
height-integrated backscatter coefficient for pollen enhances, reaching a
maximum around 30 % between 12:00 and 16:00 UTC, and δp
increases with values between 0.10 and 0.15 from the surface up to around
1.5 km. A mean LR of Sa=55±17 sr is obtained during
the pollen occurrence period. This value is close to that considered by
other authors. The relative fraction of mass loading for pollen reaches a
maximum of around 40 % at 15:00 UTC and is
MPL‾=0.011±0.003 g m-2 (i.e. 1.7 % of
that for dust during their higher incidence). In addition, the mean MEE
derived for pollen particles is kPL=2.4±0.8 m2 g-1,
representing an intermediate value between those reported for Df
particles (kDf=1.7±0.2 m2 g-1) and for smaller
local background urban polluted aerosols (kBA=3.4±0.7 m2 g-1).
However, the kPL can reach higher or lower values
depending on prevalently smaller or larger pollen grain sizes.
In summary, the vertical separation of aerosol mixtures into their
components is achieved using the POLIPHON retrieval in synergy with
continuous 24/7 P-MPL measurements together with AERONET data. The
methodology, including the extinction-to-mass conversion procedure, is
described and applied to several aerosol mixture case studies. Therefore,
vertical optical and mass features are obtained on a daily basis for
different climate-relevant aerosols: dust, smoke and pollen particles. It
should be noted that the method can be relatively easily applicable to other
P-MPLs also within the worldwide NASA Micro-Pulse Lidar Network (MPLNET),
since all those systems present the same instrumental and operating
configuration. Hence, the aerosol discrimination can be extended on a global
scale. In addition, it can also be adapted to space-borne lidars with an
equivalent configuration (elastic with a depolarization-sensitive channel),
such as the ongoing CALIOP/CALIPSO and the forthcoming ATLID/EarthCARE
(future ESA mission to be launched in 2019).
Data sets and source codes underlying this work can be requested via email to the corresponding author.
The Barcelona P-MPL data are available upon request via email (msicard@tsc.upc.edu). AERONET data are downloaded from the AERONET web page (AERONET,
2017). Backward trajectories analysis has been supported by air mass transport computation with the NOAA (National Oceanic and Atmospheric Administration)
HYSPLIT (HYbrid
Single-Particle Lagrangian Integrated Trajectory) model (HYSPLIT, 2017) using GDAS meteorological data (Stein et al., 2015; Rolph et al., 2017).
List of acronyms.
Symbola,bParameterUnitsPco, PcrossP-MPL signal channels: co-polar and cross-polara.u.P, P||, P⊥P-MPL range-corrected signals: total, parallel, perpendicular signals (P=P||+P⊥=Pco+2Pcross)a.u.βpTotal particle backscatter coefficientkm-1 sr-1βiBackscatter coefficient for a specific particle component (i)km-1 sr-1βp‾Height-integrated total particle backscatter coefficientsr-1βi‾Height-integrated backscatter coefficient for a specific particle component (i)sr-1βmolMolecular backscatter coefficientkm-1 sr-1ΔRoot square differences (see Eq. 8)km-1 sr-1Δ̃Root mean square differences (see Eq. 10)sr-1δVLinear volume depolarization ratio–δpLinear particle depolarization ratio–δiLinear particle depolarization ratio for a specific particle component (i)–δmolMolecular depolarization ratio–δDf+NDTotal fine (Df + ND) depolarization ratio (residual depolarization ratio)–δDf+NDcColumnar total fine (Df + ND) depolarization ratio–RBackscattering ratio (=βmol+βpβmol)–SaLidar ratio (LR) (KF derived)srσpTotal particle extinction coefficientkm-1σiExtinction coefficient for a specific particle component (i)km-1AODAerosol optical depth (total particle extinction, AERONET data)–AExÅngström exponent (AERONET data)–keffEffective mass extinction efficiency (MEE)m2 g-1kiMass extinction efficiency for a specific particle component (i)m2 g-1cvxExtinction-to-volume conversion factor for a specific particle size mode10-12 MmVCxVolume concentration for a specific particle size mode (AERONET data)10-12 MmτxExtinction for a specific particle size mode (AERONET data)–TMCTotal mass concentrationg m-3MiMass concentration for a specific particle component (i)g m-3TMC‾Total mass loading (height-integrated TMC, over-bar is removed for simplicity)g m-2Mi‾Mass loading (height-integrated Mi) for a specific particle component (i)g m-2
ai denotes the aerosol component: dust coarse (Dc), dust
fine (Df), non-dust (ND), smoke (SM), non-smoke (NS), pollen (PL), background
aerosols (BA); bx denotes the particle size mode: coarse (c), fine (f).
The authors declare that they have no conflict of
interest.
Acknowledgements
This work is supported by the Spanish Ministerio de Economía y
Competitividad (MINECO) under grant CGL2014-55230-R (AVATAR project) and the
ACTRIS-2 (Aerosols, Clouds, and Trace Gases Research Infrastructure Network)
Research Infrastructure Project funded by the European Union's Horizon 2020
research and innovation programme (grant agreement no. 654109). Lidar
measurements in Barcelona were also supported by the Spanish MINECO (project
TEC2015-63832-P) and EFRD (European Fund for Regional Development); by the
Department of Economy and Knowledge of the Catalan autonomous government
(grant 2014 SGR 583); and the Unidad de Excelencia Maria de Maeztu
(project MDM-2016-0600) financed by the Spanish Agencia Estatal de Investigación.
The MPLNET project is funded by the NASA Radiation Sciences Program and
Earth Observing System. The authors gratefully acknowledge the NOAA Air
Resources Laboratory (ARL) for the provision of the HYSPLIT transport and
dispersion model and/or READY website (http://www.ready.noaa.gov, November 2017) used in
this publication. Carmen Córdoba-Jabonero thanks the Ministerio de Educación, Cultura y
Deporte (MECD) support under grant PRX15/00375 for the 3-month research stay
at TROPOS (Germany); and Ana del Águila thanks the MINECO support (Programa de
Ayudas a la Promoción del Empleo Joven e Implantación de la
Garantía Juvenil en i + D + i) under grant PEJ-2014-A-52129.
Edited by: Andrew Sayer
Reviewed by: four anonymous referees
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