ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-11331-2017Effect of sea breeze circulation on aerosol mixing state and radiative
properties in a desert settingDerimianYevgenyyevgeny.derimian@univ-lille1.frChoëlMarieRudichYinonhttps://orcid.org/0000-0003-3149-0201DeboudtKarinehttps://orcid.org/0000-0003-0104-6281DubovikOleghttps://orcid.org/0000-0003-3482-6460LaskinAlexanderhttps://orcid.org/0000-0002-7836-8417LegrandMichelDamiriBahaiddinKorenIlanhttps://orcid.org/0000-0001-6759-6265UngaFlorinMoreauMyriamhttps://orcid.org/0000-0003-4720-4994AndreaeMeinrat O.https://orcid.org/0000-0003-1968-7925KarnieliArnonLaboratoire d'Optique Atmosphérique, UMR8518 CNRS, Universiteé de
Lille 1, Villeneuve d'Ascq, 59655, FranceLaboratoire de Spectrochimie Infrarouge et Raman, Université de
Lille 1, Villeneuve d'Ascq, 59655, FranceDepartment of Earth and Planetary Sciences, Weizmann Institute of
Science, Rehovot 76100, IsraelLaboratoire de Physico-Chimie de l'Atmosphère, Université du
Littoral Côte d'Opale, Dunkirk, 59140, FranceDepartment of Chemistry, Purdue University, West Lafayette, IN
47907-2084, USACimel Electronique, Paris, 75011, FranceBiogeochemistry Department, Max Planck Institute for Chemistry, P.O.
Box 3060, 55020 Mainz, GermanyDepartment of Geology, King Saud University, P.O. Box 2455, 11451
Riyadh, Saudi ArabiaRemote Sensing Laboratory, Jacob Blaustein Institutes for Desert
Research, Ben-Gurion University of the Negev, Sede Boker 84990, IsraelYevgeny Derimian (yevgeny.derimian@univ-lille1.fr)25September2017171811331113531December20168February201721June201710July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/11331/2017/acp-17-11331-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/11331/2017/acp-17-11331-2017.pdf
Chemical composition, microphysical, and optical properties of
atmospheric aerosol deep inland in the Negev Desert of Israel are found to be
influenced by daily occurrences of sea breeze flow from the Mediterranean
Sea. Abrupt increases in aerosol volume concentration and shifts of size
distributions towards larger sizes, which are associated with increase in
wind speed and atmospheric water content, were systematically recorded during
the summertime at a distance of at least 80 km from the coast. Chemical
imaging of aerosol samples showed an increased contribution of highly
hygroscopic particles during the intrusion of the sea breeze. Besides a
significant fraction of marine aerosols, the amount of internally mixed
marine and mineral dust particles was also increased during the sea breeze
period. The number fraction of marine and internally mixed particles during
the sea breeze reached up to 88 % in the PM1–2.5 and up to
62 % in the PM2.5–10 size range. Additionally, numerous
particles with residuals of liquid coating were observed by SEM/EDX analysis.
Ca-rich dust particles that had reacted with anthropogenic nitrates were
evidenced by Raman microspectroscopy. The resulting hygroscopic particles can
deliquesce at very low relative humidity. Our observations suggest that
aerosol hygroscopic growth in the Negev Desert is induced by the daily sea
breeze arrival. The varying aerosol microphysical and optical characteristics
perturb the solar and thermal infrared radiations. The changes in aerosol
properties induced by the sea breeze, relative to the background situation,
doubled the shortwave radiative cooling at the surface (from -10 to
-20.5 W m-2) and increased by almost 3 times the warming of the
atmosphere (from 5 to 14 W m-2), as evaluated for a case study. Given
the important value of observed liquid coating of particles, we also examined
the possible influence of the particle homogeneity assumption on the
retrieval of aerosol microphysical characteristics. The tests suggest that
sensitivity to the coating appears if backward scattering and polarimetric
measurements are available for the inversion algorithm. This may have an
important implication for retrievals of aerosol microphysical properties in
remote sensing applications.
Introduction
Chemical composition and mixing state of atmospheric particles evolve during
their transport in the atmosphere leading to changes in the aerosol optical
properties and radiative effect. For instance, airborne mineral dust
particles, which are often modeled as hydrophobic particles since they are
originally composed of non-soluble chemical species, can be transformed into
complex heterogeneous mixtures of non-reactive and reactive compounds
(Dentener et al., 1996; Krueger et al., 2003, 2004; Falkovich et al., 2004;
Laskin et al., 2005b). The appearance of secondary organics on the dust
surface (Falkovich et al., 2004) and heterogeneous reactions between
pollutants and components of dust can produce a deliquescent layer that
favors water uptake by mineral dust (Usher et al., 2003; Laskin et al.,
2005a). Also, laboratory experiments have shown that water adsorption can
occur even on non-reactive surfaces of dust particles (Navea et al., 2010).
Numerous field observations provide evidence of the presence of water-soluble
inorganic ions such as sulfates and nitrates as dust coating material (Levin
et al., 1996, 2005). Therefore, airborne mineral dust can be treated as a
potential surface for heterogeneous chemistry (Usher et al., 2003), which can
significantly alter its hygroscopic, morphological, and optical properties
during atmospheric lifetime. In the case of ambient aerosols, when the
changes occur in time and space, identification and evaluation of the
physicochemical transformations and their influence on radiative properties
can be particularly complex. In the current study, we show that a rather
regular sea breeze phenomenon can be a test case for exploring how the mixing
state of airborne particles changes under conditions of mixed air mass and
varying relative humidity. During sea breeze intrusions inland, marine
particles can mix with local pollutants in urban/industrial areas or with
aeolian dust in arid regions and heterogeneous reactions can take place. The
interactions can produce more complex atmospheric particles, with
microphysical and optical properties that may be difficult to model. In our
study, we focus on the variability in aerosol optical and physicochemical
properties during sea breeze intrusions into the Negev Desert of Israel.
The Negev Desert is known to be generally influenced by airborne mineral
dust, urban/industrial pollution, and marine aerosols (Maenhaut et al., 1997;
Andreae et al., 2002; Sobanska et al., 2003; Karnieli et al., 2009). However,
the influence of the daily sea breeze on the aerosol properties in the Negev
Desert has not been studied so far. In this study, we explore the influence of
penetrating marine air masses on the mixing state and hygroscopic properties
of aerosol particles observed at Sede Boker, a desert site located 80 km
away from the Mediterranean coast (the site is sometimes also referred to as
Sde Boker). The dust at this site originates from either local or remote dust
sources. The pollutants originate in the central and more polluted areas of
Israel or are transported from eastern Europe (Andreae et al., 2002; Karnieli
et al., 2009; Maenhaut et al., 2014). While windblown desert dust affects the
Negev Desert all year long with concentration peaking in spring and autumn, a
maximum of the anthropogenic aerosols appears in the summertime (Derimian et
al., 2006). Long-term observations at the site provide an extensive dataset
of aerosol characteristics and origins. The regular intrusion of the sea
breeze is now employed to elucidate of how the evolution of humid conditions
accompanied with the intrusion of aged sea-salt and pollution aerosols
modifies the mixing state of mineral dust and how this affects the aerosol
radiative effect. To address this goal, we employ a multidisciplinary
approach by utilizing a combination of comprehensive remote sensing
observations coupled with in situ aerosol measurements and
offline chemical imaging of atmospheric particles collected at the
site. The comprehensive observations were conducted during summer of 2012.
Chemical imaging of particles collected before and during sea breeze was
carried out by offline scanning electron microscopy, X-ray microanalysis, and
Raman microspectroscopy. Finally, effects of internally mixed particles on
their optical properties and consequent implications for remote sensing
algorithms are also discussed.
Mean diurnal variability in (a) wind direction and speed
and (c) relative humidity and air temperature calculated from 3
months (June, July, August 2012); error bars correspond to ± 1
standard deviation. Panels (b, d) show the diurnal cycle of the same
variables for 16 August 2012.
Measurement site and meteorological conditions
The Sede Boker site is located in the Negev Desert, in the southern part of
Israel (30∘51′ N, 34∘47′ E), and is remote from big
cities and industrial areas. It is about 80 km inland from the Mediterranean
Sea coast and 470 m above sea level. As a remote desert site for atmospheric
aerosol observations, the Sede Boker site was established in 1995 as part of
the AERONET network of sun/sky photometers and the ARACHNE program (e.g.,
Ichoku et al., 1999; Formenti et al., 2001; Andreae et al., 2002).
The main aerosol types that affect the site are local and long-range-transported mineral dust, transported pollutants, and marine particles. The
air masses in the summer period originate from the northwest, bringing
anthropogenic aerosols from densely populated areas of central Israel and
from eastern Europe (Andreae et al., 2002). The transport of anthropogenic
emissions is attributed to a persistent large-scale synoptic condition that
is characterized by a semi-permanent low-pressure trough extending from the
Persian Gulf to the Negev, while the diurnal variations of mixed layer depth
in this time are driven by surface heat fluxes and by the daily sea breeze
cycle (Dayan and Rodnizki, 1999). During the observation period in summer
2012, measurements from a local meteorological station showed generally
northwesterly wind direction with a regular sea breeze presence in afternoon.
Figure 1a shows that after about 14:00 UTC (local time is UTC + 3)
the mean wind direction is relatively constant and the mean wind speed
increases up to 8 m s-1. Despite that the average air temperature and
relative humidity near the ground (Fig. 1c) show a smooth behavior, abrupt
changes can be clearly distinguished in observations for any single day when
the sea breeze occurs. An example of a clear manifestation of the sea breeze
arrival at 14:00 UTC is presented for 16 August (Fig. 1b, d), i.e., drop of
temperature, quick rise of relative humidity, quick increase in wind speed
accompanied by stabilized wind direction from northwest. Here, 16 August is the
case study day when aerosol sampling before and during the sea breeze was
analyzed. This specific day is selected for a comprehensive analysis and
in-depth understanding of aerosol properties in the Negev Desert during the
sea breeze phenomenon because the sampling conditions and the selected timing
(i.e., start time, duration) were the most favorable on this day for
discussing the variability in aerosol microphysical and chemical
characteristics with respect to the variability in the optical measurements.
We would also like to mention that the similar aerosol samplings (with and
without sea breeze) were conducted on other days during the observation
period and the compositional characteristics of particles were quite similar.
(a, b) 24 h and (c) 3-day backward trajectories
ending at 13:00 and 14:30 UTC for altitudes above ground level (a.g.l.) of
10 m (in red), 500 m (in blue), and 1000 m (in green) at the Sede Boker
site; the corresponding relative humidity along the trajectories is also
presented.
In addition to the local meteorological measurements, 24 h and 3-day
backward trajectories are obtained using the HYSPLIT model for 16 August. The
3-day trajectories show a general northwest air mass origin (Fig. 2c) that
is typical for the summer season and the 24 h trajectories show a change in
direction of the near-ground air masses (red line for 10 m altitude) when
the sea breeze reaches the site (Fig. 2a, b). Before reaching the measurement
site, the air masses remain most of the time over the Mediterranean Sea
(Fig. 2c), and then they penetrate inland over the densely populated Gaza Strip and progress over the desert area for several hours. The backward
trajectories presented in Fig. 2 correspond to the starts of the samplings
before and during the sea breeze, i.e., 13:00 and 14:30 UTC, respectively.
The model also shows that the air masses and therefore the transported
aerosol particles at altitudes of 10 and 500 m are exposed to 60 to 80 %
RH levels several hours before sampling (see bottom panels in Fig. 2). The
model also points out some increase in RH (from 32 to 36 %) at 10 m
above ground level (a.g.l.) when the sea breeze starts. However, the RH
values provided by HYSPLIT are somewhat different from the corresponding
values measured by the local meteorological station (indicating increases
from 26 to 43 %); nevertheless, the increasing tendency is consistent.
The sun/sky photometric measurements at the Sede Boker site are performed as
part of the global Aerosol Robotic Network (AERONET) (Holben et al., 1998).
The measurements are conducted with a photometer manufactured by CIMEL
Electronique, Paris, France. The automatic direct sun photometric
measurements are normally conducted every 15 min and provide spectral
aerosol optical thickness at 340, 380, 440, 500, 675, 870, and 1020 nm
nominal wavelengths. The 940 nm channel is used to retrieve the atmospheric
water vapor content. The angular distribution of sky radiance is measured at
440, 670, 870, and 1020 nm. The measured spectral sun and sky radiances are
used for retrieval of aerosol optical parameters at four wavelengths by the
AERONET inversion code (Dubovik and King, 2000; Dubovik et al., 2006) that
employs models of homogeneous spheres and randomly oriented spheroids. It
should be mentioned that the aerosol optical thickness (AOT) during the analyzed period is not always
high enough to fulfill requirements of the retrieval accuracy (Dubovik et
al., 2000, 2002). In particular, accuracy of the complex refractive index can
be limited and variability can be important. Therefore, the parameters
designed to control the quality of the retrievals were additionally analyzed
and only stable retrievals were used. The spectral aerosol optical thickness
measurements are also used for calculating the Ångström exponent
(Å) that is an indicator of aerosol size. For instance, between the
wavelength of 440 and 870 nm, Å is calculated as
Å=-lnτ870τ440lnλ870λ440,
where τ is the AOT and λ is the
wavelength. An Ångström exponent below 0.5 generally indicates an
important contribution of coarse mode aerosols, the range between 0.5 and 1.0
corresponds to a bimodal size distribution, and a value above 1.0 indicates a
dominant fine-mode aerosol (Eck et al., 1999, 2010).
Thermal infrared radiometer
The multichannel thermal infrared (TIR) radiometer is designed to measure
thermal radiation emitted by the atmosphere and surface system. The
instrument has been developed in collaboration between the Laboratory of
Atmospheric Optics (LOA) of University of Lille (Legrand et al., 2000;
Brogniez et al., 2003) and the manufacturer CIMEL Electronique. This is the
same manufacturer as that of the AERONET photometers, and therefore both
instruments have convenient similarities in protocol of functionality that
facilitates operations in the field. The TIR radiometer provides radiances
and brightness temperatures of a target viewed with a 10∘ full field
of view. The instrument employed at the Sede Boker site was operating at
three 1 µm narrowband spectral channels, centered at 8.6, 10.8, and
12.0 µm, and at an extra broadband channel covering the spectrum
from 8 to 14 µm. The instrument operates in a sky-scanning mode and
in this study the analyzed values are the sky brightness temperatures from a
vertical upward-looking position. The radiometer is equipped with a humidity
sensor in order to shut down automatically in case of precipitation or dew to
prevent water deposition on the detector. In addition, the system can shut
down the instrument when relative humidity is about 80 %, which limits
the number of observations, mainly during nighttime. The instrument was set
up at the site by LOA for a 6-month experimental period and with the
purpose of complementary and intensive observations.
Lidar
The ground-based lidar observations at the Sede Boker site are conducted as
part of the NASA Micro-Pulse Lidar Network (MPLNET) (Welton et al., 2001),
wherein sites are generally co-located with the AERONET sites. The MPLNET is
a federated network of micropulse lidar systems (Spinhirne et al., 1995, 2002) that uses standardized calibrations, operational
protocols and processing. The network is supported by the NASA Earth Observing
System program (Wielicki et al., 1995). Data products at three levels of
processing provide real-time normalized relative backscatter, aerosol and
cloud heights, and optical property retrievals (Campbell et al., 2002; Welton
and Campbell, 2002), http://kimura.gsfc.nasa.gov/data. In our study, we
employ only the vertical distribution of lidar backscatter signal at 532 nm
for the purpose of illustration of vertical and temporal variability in the
aerosol loading.
Broadband solar flux
The Solar Radiation Network (SolRad-Net,
http://solrad-net.gsfc.nasa.gov) is associated with the AERONET network
of federated ground-based sensors that provides high-frequency measurements
of solar flux in quasi-real time. Similar to MPLNET, the sites are co-located
with AERONET, and standardized calibrations and operational protocols are
applied to the measurements. In general, SolRad-Net provides measurements
from several flux instruments including filtered and unfiltered pyranometers,
photosynthetically active radiation (PAR) and ultraviolet (UV-A and UV-B). In
this study, we use the broadband shortwave solar spectrum
(0.3–2.8 µm) irradiance, measured by a Kipp and Zonen CM-21
pyranometer. The data correspond to quality level 1.5, which means that
the data have been cloud-screened and cleared of any operational problems.
The instantaneous irradiance analyzed at the Sede Boker site is recorded at
10 min intervals.
Backward trajectories
The air mass backward trajectories are obtained using the 3-D HYSPLIT (HYbrid
Single-Particle Lagrangian Integrated Trajectory) model of the US National
Oceanic and Atmospheric Administration (NOAA) (Draxler and Hess, 1998). The
runs for backward trajectories are performed using the global data
assimilation system. This is performed for altitudes above ground level of
1000, 500, and 10 m, as an indicator of origin of air masses near the
surface. The relative humidity at the corresponding altitudes and time is
also available along the backward trajectories from HYSPLIT.
In situ measurement and samplingIntegrating nephelometer
Near ground, the light scattering extinction coefficient at 545 nm is
measured with a 2 min temporal resolution using a single-wavelength
integrating nephelometer (M903, Radiance Research, Seattle, WA, USA). The
inlet is located outdoors on a roof at ∼ 10 m above ground and faces
downward. The instrument itself is situated indoors and air is supplied
through plastic tubing of up to 3 m length, with a 2.2 cm internal
diameter. The instrument was set up in November 1999 and has been regularly
calibrated in the field until November 2003. The variability in the
calibration coefficients during this time was within 6 %. A different
strategy was applied afterwards when a series of reference tests with
particle-free air and CO2 as a calibration gas were periodically
conducted; the procedure enables the variability in the calibration
coefficients to be traced and correction to the measured values to be applied. In this study we
do not intend to evaluate the long-term temporal trend; the observations are
used only for a confirmation of the response of the near-ground aerosol
optical properties to the sea breeze arrival. Thus, the abovementioned
corrections are not needed in the analysis of diurnal variability in the
scattering coefficient on a specific day. Relative humidity (RH) in the
scattering volume of the instrument is also of importance since a nonlinear
increase in the scattering coefficient is possible when RH is above 80 %
(Andreae et al., 2002). Thus, early morning and late evening data, when the
RH is elevated, should be interpreted as a high limit. Nevertheless, the
behavior of the measured scattering coefficient observed in this study is
generally consistent with other independent measurements at the site.
Aerosol sampling
Aerosol samples were collected on the rooftop terrace of a three-story
building, adjacent to the nephelometer inlet. Ambient particles were sampled
before and during sea breeze flow using a three-stage cascade impactor
(PM10, Dekati Ltd.) at a flow rate of 10 L min-1. The nominal
cut-off sizes (i.e., aerodynamic diameters at 50 % of collection
efficiency for a particle density of 0.93 g cm-3; Marjamaki et al.,
2000) of the impactor stages were 10, 2.5, and 1 µm, respectively.
Note that because the size-segregated sampling by a cascade impactor is based
on an aerodynamic cut-off diameter at 50 % of collection efficiency and
depends on the particle density, particles smaller or bigger than the cut-off
diameter can also be present on the collection stage. Sampling durations
ranged from 15 min to an hour, depending on the ambient aerosol load.
Particles were deposited simultaneously onto 200-mesh copper TEM grids with
carbon type-B supporting films (Ted Pellar, Inc.) and
Nuclepore™ polycarbonate membranes for
SEM/EDX particle microanalysis. Additionally, particles were collected on
glass slides for Raman microspectroscopy. Samples were sealed in aluminum
foil bags and stored at 4 ∘C pending analysis. Among several samples
collected during the campaign, samples from 16 August 2012 were selected as
representative of the described phenomenon and are presented here in detail.
The sampling time and duration were most successful for representing
conditions the before and during the sea breeze; the phenomenon itself was
also well pronounced and measured by all other instruments. The sampling
conditions for this day are reported in Table 1 and discussed in detail in
Sect. 2.
Sampling conditions: values of relative humidity, air temperature,
and wind speed and direction are mean values during the sampling time.
Date:Sample 1 (S1):Sample 2 (S2):16 Aug 2012before seaduring seabreezebreezeStart time13:00 UTC14:30 UTCDuration60 min15 minRH (%)28.552.0Air temperature (∘C)3230Wind speed (m s-1)4.07.3Wind direction (∘)306 (NW)308 (NW)
Particle classification (colored text) and identification
(underlined text) based on normalized atomic percentages for elements with Z>10.
Chemical characterization at the particle scale
Offline laboratory chemical imaging of the sampled atmospheric particles was
carried out using SEM/EDX and Raman microspectroscopy.
Scanning electron microscopy with energy-dispersive X-ray
spectrometry (SEM/EDX)
Single-particle analysis by SEM/EDX was performed with a FEI Quanta 200 SEM
equipped with an ultrathin-window energy-dispersive X-ray detector enabling
the analysis of elements with atomic number higher than boron (Z≥5).
However, for samples collected on polycarbonate membranes, elements lighter
than sodium (Z<11) were not quantified because of high absorption within
the samples due to carbon coating and substrate material. Automated particle
analysis was run using the commercially available Link ISIS Series 300
Microanalysis system (Oxford Instruments®).
The procedure of automatic particle recognition and analysis is described
elsewhere (Choël et al., 2005). X-ray spectra were acquired with a
counting time of 30 s, with an accelerating voltage of 20 kV and a probe
current adjusted to 200 pA. The identification of individual particles is
based on their elemental composition obtained from SEM/EDX data; the
procedure and the criteria were described in a previous study (Deboudt et
al., 2010). Elemental composition of particles is reported in this work as
normalized atomic percent. Figure 3 shows the particle-classification chart
used in the case of Negev particles. To elucidate the mixing state of
particles, the analyzed particles were sorted into four different groups:
Dust, Marine, Mixed Dust/Marine, and Other. Particles sorted into the
“Dust” particle type were composed of silicate (Si-rich), aluminosilicate
(Al- and Si-rich), calcite (Ca-rich), dolomite (Ca- and Mg-rich), gypsum (Ca-
and S-rich), and Ti-rich particles. The “Marine” particle type comprises
fresh (Na- and Cl-rich) and aged (Cl-depleted) sea salts. Particles that
contain sea salts internally mixed with crustal elements were assigned to the
“Mixed Dust/Marine” particle type. Particles not assigned to the previous
particle types were sorted into the “Other” particle type, comprising
notably Mg-, S-, K-, and KCl-rich particles. Complementary manual examination
of particles was performed using a HORIBA S-4700 field emission scanning
electron microscope (FE-SEM).
Raman microspectroscopy
Raman spectra were recorded for atmospheric particles in the coarse fraction
(i.e., PM2.5–10). The glass plates with impacted particles were
directly mounted on the microscope stage of a LabRAM HR confocal Raman
microscope (Horiba Scientific) equipped with an Olympus 100 ×
objective with a numerical aperture of 0.90. Raman scattering was excited at
632.8 nm using a He–Ne laser. The laser spot size focused on the sample was
0.9 µm. To avoid laser damage to the sample, a neutral density
filter with an optical density value of 0.6 was used. Raman measurements were
carried out at ambient conditions (∼ 60 % RH and 295 K). Raman
spectral mapping provides the spatial distribution of the various molecular
species within heterogeneous samples. The acquisition of computer-controlled
Raman maps consisted in recording spectra in a point-by-point XY scanning
mode with a 1 µm step and 10 s of integration time. According to
the diffraction grating of 300 grooves per millimeter used in this work, Raman
spectra were acquired in the range 170–2440 cm-1 with a spectral
resolution of about 4 cm-1. The data processing of Raman maps was the
following. The baseline was estimated individually for each spectrum using
asymmetric least squares (ALS) proposed by Eilers (2003) and Eilers and
Boelens (2005). The order of differences d was set to 3 (classical value),
whereas the trade-off parameter (λ) and the asymmetry parameter (p)
were optimized for each map by visual inspection of the estimated baseline
and the spectra after correction. Best results were obtained for a p value
of 0.01 and 108 for λ. The color map was obtained from the baseline
corrected data using the net Raman intensity signal at a specific wavelength
over all point spectra. The differences in the center positions of
characteristic Raman bands were selected to minimize overlap of the
characteristic Raman peaks of the several compounds present in the aerosol
samples. For the color map, intensity close to zero corresponds to black and
the maximum signal intensity to bright color.
Remote sensing observations
Similarly to the meteorological parameters described in Sect. 2, recurrent
abrupt changes in atmospheric aerosol optical characteristics can be observed
nearly every day during the summertime, which is true not only for 2012 but
for all preceding and subsequent years. An example of several consecutive
days of atmospheric remote sensing measurements during August 2012 is
presented in Fig. 4. The figure shows daily variability in AOT at 440 nm,
Ångström exponent between 870 and 440 nm and total column water
vapor derived from AERONET photometric measurements, and sky brightness
temperature from the narrowband channels of the thermal infrared radiometer.
In conjunction with the meteorological parameters, an analysis of the data in
Fig. 4 suggests that the abrupt changes in the remote sensing measurements
coincide with sharp changes in the air mass as the sea breeze arrives. Note
that in this example of 9 days, the sea breeze occurs and influences the
optical measurements on 8 days. The AOT increases and the
Ångström exponent decreases significantly when the sea breeze arrives
(Fig. 4a). A decrease in the Ångström exponent indicates an increased
contribution of large aerosol particles. Attention can also be drawn to the
recurrent increase in the sky brightness temperature and change in its
spectral dependence, as measured by the thermal infrared radiometer from the
ground. The measurements therefore show that the spectral radiative properties in the thermal
infrared change significantly during the penetration of the sea breeze
(Fig. 4c), similar to the solar spectra (Fig. 4a). While the variability
in AOT and the Ångström exponent in the solar spectrum is due to the
change in aerosol particles properties only, several processes can cause
variability in the sky thermal infrared emission. Atmospheric water vapor can
absorb solar and thermal radiation as well as emit thermal radiation; water
droplets and aerosol particles can absorb and scatter solar and thermal
radiation as well as emit thermal radiation. Thus, in general, the sky brightness
temperature can increase either due to higher atmospheric water vapor content
or due to appearance of large mineral dust particles or water droplets.
Aerosol particles and droplets have primary radiative effects in the
10.8 µm channel of the radiometer, at the center of the
10 µm window, where the atmospheric gaseous transmittance is higher
(and the sky brightness temperature is minimum). Emission of the thermal
radiation by water vapor is stronger in the channels centered at 8.6 and
12 µm, located in the outer sides of the window. Now, the spectral
dependence (represented by the ratio of brightness temperatures) between the
channel at 10.8 µm and the channel at 8.6 µm, which is
more affected by water vapor, indicates a stronger increase in the sky
thermal emission at 10.8 µm relative to 8.6 µm during
periods with sea breeze (Fig. 4c). Therefore, an increase in the brightness
temperature ratio (10.8 to 8.6 µm) suggests the appearance of not
only water in the gas phase but also of large particles or water droplets.
Note also that the ratio of brightness temperatures in Fig. 4c is approaching
the value of one at the time of sea breeze arrival, that is, the spectral
dependence of brightness temperature is approaching to neutral, which is a
typical characteristic of clouds. The brightness temperature could also
increase due to the arrival of a warmer air mass. However, as the
meteorological data show, the arrival of the sea breeze is, however, associated with cooler air, while the water vapor content and amount of aerosol
increase (Fig. 4a, b).
Time series of (a) AERONET observations of AOT at 440 nm
(before application of the cloud screening algorithm, level 1.0; after
the cloud screening, level 2.0) and Ångström exponent between
870 and 440 nm, (b) AERONET-derived total column water vapor
and (c) sky brightness temperature as measured by the thermal
infrared radiometer at three spectral channels and ratio of brightness
temperatures at 10.8 to 8.6 µm. Arrows indicate the signal peaks
corresponding to the sea breeze arrival, which occurred on 8 of the 9
days presented.
A more quantitative interpretation of the TIR signal requires accurate
radiative transfer computations that also require information about vertical
profiles of the aerosol extinction, concentrations of gas phase species and
temperature, which are not available for the site of interest. The same
information is needed for evaluation of the aerosol radiative forcing in TIR.
However, the presented TIR radiometer measurements and the diurnal behavior
of the sky brightness temperature are already informative. The measured sky brightness temperature shows that
increase in the amount of water vapor and large size aerosol is likely to
increase the TIR radiative warming at the surface that generally counteract
the aerosol cooling effect in the solar spectrum. The TIR measurements are
also in line with the photometric observations in the solar spectrum by
AERONET. That is, an abrupt increase in the AOT is observed when the sea
breeze arrives. It is also interesting to note that the increase in the AOT
is often screened as a cloud because normally the aerosol properties do not
change as fast in time and the screening algorithm could fail. An example of
the unscreened AERONET data (level 1.0) and of these data after the
cloud-screening algorithm has been applied (level 2.0) is presented in
Fig. 4a.
In order to understand and describe in detail the phenomenon, we focus on a
specific, but typical, day (16 August) when a sampling of aerosols was
conducted and analyzed in conjunction with remote sensing observations before
and during the sea breeze. Figure 5 shows that at around 14:00 UTC
(17:00 local time), the AOT, total column water vapor, scattering coefficient
at the ground level, and sky brightness temperature have a sharp increase,
while the Ångström exponent decreases. The lidar backscatter signal
also increases in altitudes up to 1.8 km. The phenomenon reaches a maximum
at the sea breeze front and then decays gradually. The photometer acquisition
is stopped after 16:00 UTC because of the low sun, but the lidar, the
thermal infrared radiometer, and the nephelometer measurement remain
available. From the temporal variability in the signal of these three
instruments one can estimate that the effect of the sea breeze lasted until
about 17:00 UTC, i.e., for about 3 h. It can also be noted that some
gradual increase in water vapor and brightness temperature starts already
about 2 h before the front of the sea breeze arrives. The increase in
the water vapor amount towards noontime is a usual process related to
increasing temperature and evapotranspiration that influences the thermal
infrared signal. It is noteworthy that the increase in water vapor is also
correlated with a gradual decrease in the Ångström exponent, i.e.,
increase in the aerosol size, which can also be responsible for a gradual
increase in the sky brightness temperature before the abrupt change occurs.
In addition to the column-integrated remote sensing measurements, the
scattering coefficient, which is measured by nephelometer near the ground,
shows that the abrupt change in aerosol characteristics occurs also at the
surface level (Fig. 5c). This fact supports representativeness of the particle
sampling described in Sect. 3.3.2. The diurnal variability in scattering
coefficient is also correlated with the ambient RH (Fig. 1d). This is despite
the fact that the nephelometer generally dries the aerosols inside the measurement volume
and the RH inside the instrument is much more stable than the ambient RH. The
variability in the scattering coefficient can be due to change in either
aerosol concentration or microphysical characteristics, like size, but it is
difficult to draw a conclusion based on only a single wavelength measurement.
In summary, all the abovementioned observations of the aerosol optical
properties in the solar spectrum and radiation in the thermal infrared
wavelength region manifest a coherent abrupt response associated with the sea
breeze arrival. An abrupt response in the aerosol optical characteristics can
be due to not only a higher aerosol concentration but also to a change in the aerosol
microphysical characteristics and influence of the increasing atmospheric
water content.
Diurnal variability on 16 August 2012 of (a) vertical
distribution of lidar backscatter signal at 532 nm (it starts from about
700 m since observations are generally omitted in the first lower hundreds
meters); (b) AOT at 440 nm (level 2.0 – after the cloud screening
and quality assurance), Ångström exponent between 870 and 440 nm,
and total column water vapor; (c) scattering coefficient at 545 nm;
and (d) sky brightness temperature measured by the thermal infrared
radiometer in channels centered at 8.6, 10.8, and 12.0 µm and ratio of
brightness temperatures at 10.8 to 8.6 µm.
The left column shows aerosol volume size distributions and the
right column shows these size distributions normalized to total volumes. The
data are sorted by water vapor (WV) concentration. Panels (a, b) are for
16 August 2012; (c, d) are the averaged size distributions for the
sea breeze days of summer months (JJA), 2012; and (e, f) are for the
dust period (March–April) of 2012. Error bars in (a, b) correspond
to the standard error, and n is the number of data points used to compute the
averages; the error bars in (c–f) overlap, and for
clarity of the figure, are not shown.
In order to examine whether a change in the aerosol microphysical parameters is
taking place during the sea breeze, we use the remote sensing observations of
the aerosol volume size distribution and the complex refractive index as
retrieved by the AERONET algorithm. On 16 August, the average volume size
distribution during the sea breeze is significantly different from the size
distribution before the sea breeze. It shows an increase in the volume
concentration and a size shift towards large sizes (Fig. 6a, b). The aerosol
volume concentration is defined as a product of particle number
concentration and particle volume. Thus, both the number concentration and
the particle size may contribute. Indeed, a stronger wind speed during the
sea breeze can lift aerosols along the path of transport and increase the
aerosol number concentration. However, as Fig. 6b shows, the particle radii
are also increasing. The volume size distribution in Fig. 6b is normalized to
the total volume concentration in order to enable a better comparison of the
distribution shapes, which emphasizes the shift of the radii. It can be noted
that the average water vapor concentration is also increasing from 1.4 to
2.2 g cm-2. Figure 6c and d present average size distributions
obtained for 51 days when the AERONET inversions are available and the sea
breeze is clearly observed in the meteorological data during summer 2012.
Note that the sea breeze days occur almost 60 % of the time in this
case. Variability in the water vapor concentration is generally important on
such days. The averages are calculated for three different ranges of the
water vapor concentration. Figure 6c and d show that a shift in size distribution,
similar to on 16 August, occurs also in the 3 months of data of summer
2012. It also appears that the particles of the fine mode are affected more
strongly than those of the coarse mode. We thus do a similar analysis for
March and April of the same year (24 days are analyzed) when the aerosol
regime in the Negev Desert is very different and is governed mainly by
African dust transport. The average water vapor during this dry air mass
transport does not exceed 2 g cm-2 and no shift is observed either in
the fine or in the coarse modes of the size distributions (Fig. 6e, f). Also,
the maximum radius of the coarse mode during the spring is about
2 µm in contrast to 2.5–3 µm during the summer.
The left column shows the real part and the right column shows the
imaginary part of averaged values of the complex refractive index as
retrieved by AERONET. The data are sorted by water vapor (WV) concentration.
Panels (a, b) are for 16 August 2012; (c, d) are for sea breeze
days of summer months (JJA), 2012; and (e, f) are for the dust period
(March–April) of 2012. Error bars correspond to the standard errors, n is
the number of data points used to compute the averages.
The real and imaginary parts of the complex refractive index and their
spectral dependences are related to the aerosol particles' chemical
composition. As the real part of the refractive index of liquid water in the
visible spectrum is 1.33, it is expected that the real part of the refractive
index of water-containing aerosols will decrease and approach the value of
water. Figure 7 shows that this is the case for observations during the sea
breeze (Fig. 7a) and for sea breeze days associated with increased water
vapor concentration (Fig. 7c). The mean real refractive index is also
somewhat lower at increasing water vapor concentration in the dust case of
spring 2012 (Fig. 7e); however, there is no significant change in the
corresponding size distributions (Fig. 6f). The imaginary part of the complex
refractive index of pure water in the visible spectrum is practically zero;
the marine aerosol, for example, is known to be non-absorbing (Dubovik et
al., 2002). It is therefore expected that the imaginary part will also
decrease with increasing water content in the aerosol. The observations show,
however, that the imaginary part for all analyzed cases slightly increases
(Fig. 7b, d, f). Indeed, since the sea breeze air masses can bring pollution
aerosols, it is suggested that the reason for the increase in the imaginary
part may be the presence of absorbing carbonaceous particles. The imaginary
part also increases for the dust case, where we do not expect a carbonaceous
aerosol contribution. At the same time, the standard errors are overlapping
the means indicating that the differences for the imaginary part are not
significant. It has to be mentioned that the sensitivity of the AERONET
measurements to the complex refractive index is rather limited to bear a
solid conclusion; sensitivity to the changes in aerosol size distribution,
however, is quite high (Dubovik et al., 2000) since the sun photometer primarily
measures the forward-scattered radiation, which strongly depends on
the particle size.
The aerosol single-scattering albedo (SSA), which is defined as the ratio of
the scattering coefficient to the total extinction coefficient and represents
the scattering effectiveness in total extinction, is one of the key
parameters determining the aerosol radiative effect. The change in the
spectral SSA (at wavelengths 440/670/870/1020 nm) on 16 August is from
0.968/0.962/0.961/0.963 before the sea breeze to 0.955/0.953/0.955/0.957
during the sea breeze, respectively. As the imaginary part of refractive
index is higher, the SSA becomes generally lower, indicating a stronger
contribution of aerosol absorption. However, the SSA also depends on the
aerosol size, or more exactly on the size parameter, which is defined as the
ratio of the particle size to the wavelength of light. Because both the size
distribution and the complex refractive index change during the sea breeze,
it is interesting to evaluate their specific contribution to the changes in
the SSA. To address this question, we calculate the SSA assuming that only
the size distribution is changing, while the refractive index is the same and
vice versa. The difference in the SSA of the before-sea-breeze aerosol model
minus the SSA of the modified aerosol model is -0.002/-0.001/0.001/0.003
for the size change and 0.015/0.009/0.003/0.003 for the refractive index
change. The calculated differences show that the scattering effectiveness
increases at the shorter and decreases at the longer wavelengths due to the
size change, and decreases at all the wavelengths due to the compositional
change. The calculations show that there is a partial compensation of the
decrease in SSA at the shorter wavelengths because of the size shift.
Individual particle analysisElemental analysis of particles by SEM/EDX
Additional insights into the microphysical properties and mixing state of
ambient particles are provided by computer-controlled SEM/EDX (CCSEM/EDX)
analyses of aerosol sampled before and during the sea breeze on
16 August 2012. A total of 2077 particles were analyzed. Each particle was
assigned to one of the particle types, defined in Sect. 3.4.1: Marine, Dust,
Mixed Dust/Marine, and Other. The pie charts in Fig. 8 present particle-type
fractions in size range of PM1–2.5 and PM2.5–10
collected before and during the sea breeze event. Considering all analyzed
particles for this sampling day (n=2077), the most abundant elements
(excluding C, N, and O) were identified and the normalized average
composition was calculated as
Na1Mg0.08Al0.11Si0.31S0.07Cl0.04K0.03Ca0.28Fe0.04.
Overall, the “Marine” particle type represented 48.4 % of all analyzed
particles. Its average composition is
Na1Mg0.04S0.03Cl0.04, indicative of nearly complete
processing of sea-salt particles by HNO3 and formation of NaNO3 as
a reaction product. The “Dust” particle type accounted for 34.3 % of
all analyzed particles. Its average composition is
Mg0.05Al0.36Si1Ca0.82Fe0.11, suggestive of
aluminosilicates and calcium carbonates. The “Mixed Dust/Marine” type
contributed 14.6 % of all analyzed particles with an average composition
of
Na1Mg0.11Al0.08Si0.21S0.1Cl0.04K0.05Ca0.36Fe0.04,
which is typical of internal mixtures of dust and processed sea salts. With
an average composition of Mg0.65S0.65Cl0.3K1, the
“Other” particle group represented 2.7 % of all analyzed particles and
comprised Mg-, S-, and K-rich particles.
Percentage of particles number for each aerosol type obtained from
CCSEM/EDX analysis of particles collected before the sea breeze (a)
and during the sea breeze (b). PM1–2.5 and
PM2.5–10 denote the particle size intervals that correspond to
the aerodynamic cut-off diameters (1 and 2.5 µm, respectively) of
the cascade impactor stages. Particles are sorted into the following four
main types: Marine, Dust, Mixed Dust/Marine, and Other. The total number (n)
of the analyzed particles is also indicated in each panel of the figure.
Before the sea breeze, dust was the predominant particle type in the coarse
fraction (56 %) and was the second largest category in the fine fraction
(35 %). An increase in both marine and mixed dust/marine particle types
was clearly observed during the sea breeze. Therefore, the transport of
marine particles during the sea breeze was confirmed by an increase from 38
to 44 % in the coarse fraction and from 54 to 61 % in the fine
fraction. Similarly, the internal mixing of dust with marine particles
increased from 5 to 18 % in the coarse fraction and from 9 to 27 % in
the fine fraction. These results are consistent with those obtained by
Sobanska et al. (2003) at the same sampling site on a specific day in the
summer period (sampling duration includes before/during/after sea breeze): a
high proportion of sea salt (35 % in the coarse size fraction
PM2–10 and 12 % in the fine fraction PM2) and mixed sea
salt/mineral dust (∼ 15 % in the fine fraction PM2)
representative of a marine source contribution. In addition, they reported a
high proportion of aluminosilicates (∼ 30 %) and CaCO3
(∼ 17 %) in approximately the same proportion in fine and coarse
fractions. It is noteworthy that during the sea breeze a new type of
particles was detected, accounting for 2 and 7 % of the analyzed
particles in the fine and coarse fractions, respectively. These particles
sorted into the “Other” particle type were smaller than 1 µm in
diameter and composed of potassium salts. Submicrometer-sized K-rich and
KCl-rich particles can originate from biomass and waste burning emissions (Li
et al., 2003), which however are not typical as known aerosol sources in the
Negev Desert. However, given that the air masses passed over the densely
populated coastal area (see trajectories over Gaza area in Fig. 2), an
anthropogenic source of these K-rich particles, e.g., from waste burning
fires and cooking, is plausible. Moreover, the K-rich particles in the fine
fraction have been already reported for the Sede Boker site previously
(Formenti et al., 2001).
Number size distributions of particles analyzed by CCSEM/EDX –
(a) before the sea breeze, (b) during the sea breeze.
(c) Size distributions normalized to the total number of particles
analyzed for cases before and during the sea breeze.
Number size distribution of particles by SEM/EDX
Figure 9 shows the particle number size distributions derived from analysis
of images acquired by CCSEM/EDX. The presented radius is the one of an
equivalent circle area of the 2D-projected particle on SEM images, which
represents the geometric properties. Note also that the maximal nominal
cut-off aerodynamic diameter of the analyzed stage of the impactor is
10 µm. Therefore, it should be realized that the size distributions
in Fig. 9 and those retrieved from remote sensing in Fig. 6 are directly
incomparable, for example, see discussions in Reid et al. (2003). In
addition, it should be realized that the size distributions of the particle
types in Fig. 9 are not directly comparable to the percentage of the particle
types per size fraction in Fig. 8. This is because the particle type
percentages in Fig. 8 are for the size fractions of a cascade impactor, which
are defined by aerodynamic cut-off diameters, while Fig. 9 presents the
geometric radius derived from equivalent circle area of particles observed by
SEM. However, the size distributions per particle type in Fig. 9 and their
relative variability can be informative. Figure 9 shows the total number size
distributions and the number size distributions for the four aerosol types
separately before and during the sea breeze. The total size distribution
before the arrival of the sea breeze is mainly defined by dust and marine
particles with concentration maxima at radii of about 0.25 and
0.75 µm, respectively (Fig. 9a). During the sea breeze, the total
number size distribution is split into two modes with maxima centered at
radii of about 0.4 and 1.75 µm (Fig. 9b). This is mainly due to
contributions from marine particles. The mean size of coarse marine particles
observed during the sea breeze is shifted towards larger sizes, i.e., an
increase in radius from about 0.75 to 1.75 µm. Note that although
the SEM observations are performed under high vacuum and water-solvated ions
dehydrate in the SEM chamber, the initially hydrated particles generally
appear as larger in the SEM images because of the wettability of the
substrate and residues surrounding the particle core as shown in Fig. 10.
Therefore, the geometric size of the hydrated particles given by SEM is
generally larger than that of dehydrated before sampling. This leads to the
conclusion that the shift towards larger sizes of the marine particles during
the sea breeze can be due to hygroscopic growth. The number size
distributions of dust and mixed dust/marine particle types are also changing
during the sea breeze, with both size distributions broadening. Figure 9c
shows the same total number size distributions as in Fig. 9a and b, but
normalized to the total number of particles. This presentation facilitates a
proper comparison of the distribution shapes and clearly illustrates the
shift toward larger particle sizes during the sea breeze. The actual size
shift may be even stronger because the SEM analysis provides partially dried
aerosol size distributions. Thus, the size underestimation can be due to low
thickness of the residues at the border of dehydrated particles. The fact
that the sea breeze has an influence on the aerosol size distribution is
evident, but the exact explanation can be complex. For instance, a stronger
contribution of coarse dust particles can also appear due to local aeolian
resuspension of dust caused by the increased surface wind speed. It should
also be mentioned that the size-selective dry deposition (Seinfeld and
Pandis, 1998) can also take place during the aerosol transport and can have
an influence on the size distribution because the wind speed and atmospheric
residence time of particles are different before and during the sea breeze.
Nevertheless, the observed size shift is in line with the results obtained by
remote sensing. In addition, given the relative proportions of particle
types, the fraction of hygroscopic particles can be estimated by the
cumulative fractions of marine and mixed dust/marine particle types. This
cumulative fraction significantly increases from 63 to 88 % in the fine
fraction and from 43 to 62 % in the coarse fraction (Fig. 8), which
supports the shift toward larger sizes during the sea breeze. Furthermore,
the percentage of hygroscopic particles is largely underestimated by an
addition of “Marine” and “Mixed Dust/Marine” particle counts, if the
fraction of hygroscopic dust was not taken into account. Previous studies
reported that mineral dust in the Negev Desert predominantly consists of
aluminosilicates and also calcium carbonates (Maenhaut et al., 1999). The
solid calcium carbonate-containing particles can undergo heterogeneous
reaction with gaseous nitric acid to form highly hygroscopic calcium nitrate
particles. In fact, transformation of non-hygroscopic mineral dust into
water-soluble dust has been previously observed in aerosol samples collected in the
Negev Desert (Laskin et al., 2005a). In this study, we now subclassify all
particles sorted in the “Dust” particle type into five categories:
aluminosilicates AlSi, Ca-rich, mixed AlSi/Ca-rich, gypsum, and other AlSi.
With an average composition of Mg0.03Al0.38Si1Fe0.09, the
predominant subtype was AlSi, accounting for 43.5 % of dust particles,
followed by mixed AlSi/Ca-rich particles, representing 34.1 % with an
average composition of Mg0.05Al0.16Si0.53Ca1Fe0.08.
The Ca-rich particles represented 17.4 % of dust particles with an
average composition of Mg0.03Ca1 typical of calcium
carbonate-containing minerals (calcite and dolomite). With a frequency of 2.8
and 2.2 % respectively, gypsum particles (S0.75Ca1) and “other
AlSi”
(Al0.06Si0.19P0.23S0.09Ca0.78Ti1Fe0.4)
were minor subtypes of dust particles, the latter comprising calcium
phosphates and TiOx-rich aluminosilicates. To sum up, among the particles
sorted in the “Dust” particle type, Ca-rich and mixed AlSi/Ca-rich
particles accounted for 51.5 % and could certainly be considered as
hygroscopic dust. Further manual examination of the particles was performed
to elucidate the nature of hygroscopic particles.
Secondary electron images of (a) particles of the coarse
fraction, (b) particles of the fine fraction,
(c–h) individual particles typical of (c) fresh marine,
(d) aged marine, (e) unreacted dust (silicate),
(f) internally mixed dust/marine, (g) unreacted dust
(calcite), and (h) aged Ca-rich dust (calcite partly converted to
calcium nitrate). Arrows mark the presence of halos. Dotted circles depict
the boundaries of halos.
Core–shell particle morphologies observed by SEM/EDX
Analysis of the SEM/EDX observations also showed a large number of particles
surrounded by halos (see particles marked by an arrow in Fig. 10a, b).
Volatile components and water are lost due to the high-vacuum operating
conditions in the SEM chamber and/or during metal coating. As a result, the
dry residual compounds form halos around solid cores. This gives direct
evidence that the halos consist of residues of a hygroscopic surface layer
after dehydration. In our sample, the halos were found on aged deliquescent
marine (Fig. 10d), internally mixed dust/marine (Fig. 10f) and dust
particles. The presence of halos surrounding some dust particles confirms
that the surface of the dust can be covered by potentially hydrophilic
layers. The size of such coated dust particles may vary by hygroscopic growth
during sea breeze events.
Figure 11 shows elemental maps and EDX spectra of an individual dust
(AlSi/Ca-rich) particle with a halo. Calcium is relatively more abundant in
the halo than in the core, pointing to a probable presence of liquid nitrate
coating of dust in the form of calcium nitrate. As particles were collected
on polycarbonate membranes, the detection of nitrogen is hampered. To
confirm the presence of water-solvated nitrate coatings on some dust
particles, complementary analysis was carried out using Raman
micro-spectrometry.
Secondary electron image, elemental energy dispersive X-ray (EDX)
mappings (Al, Si, and Ca), and EDX spectra of an individual internally mixed
calcium nitrate/aluminosilicate particle. Scale marker bars correspond to
800 nm. Arrows mark some visible boundaries of a halo.
Optical images (a, d) of coarse particles collected before (a–c) and
during (d–f) sea breeze and corresponding Raman molecular
mappings (b, c, e, f). Raman maps are
colored according to the band intensity at 1050 cm-1 (single component
maps on the middle panel), and 1068, 1086, 1017, and 993 cm-1 (multi-component overlay maps on the right panel), respectively. White:
water-solvated nitrate ion; green: solid sodium nitrate (nitratine); yellow:
calcite; cyan: calcium sulfate anhydrite; pink: solid sodium sulfate
(thenardite). The meaning of the colored arrows is described in the text.
Raman maps of particles
Complementary to elemental analysis by SEM/EDX, Raman microspectroscopy
distinguishes between solid, deliquescent, and solid inorganic nitrate
compounds based on the nitrate band shift. In the liquid state, however, the
characteristic nitrate band is identical to those of sodium and calcium
nitrate. An example of the Raman molecular mappings is presented in Fig. 12.
The spectral map of the 1086 cm-1 peak, attributed to the CO32-
stretching vibration, illustrates the distribution of calcium carbonate
(calcite) within the particles and is shown in yellow. The spectral map of
the 1050 cm-1 peak attributed to the liquid NO3- stretching
vibration provides the spatial distribution of water-solvated nitrate and is
shown in white. The spectral map of the 993 cm-1 peak assigned to
sodium sulfate (thenardite) is reported in pink. The spectral map of the
1017 cm-1 peak characteristic of calcium sulfate anhydrite
(recrystallized sea-salt droplets) is depicted in cyan. Finally, the spectral
map obtained at 1068 cm-1, indicative of solid sodium nitrate
(nitratine), is shown in green. The observed particles consist mainly of
NaNO3 solid cores agglomerated with some amount of Na2SO4
(thenardite) surrounded by a liquid droplet containing NO3- ions.
These particles, marked with white arrows in Fig. 12, were classified as
sea salts when observed by SEM/EDX. Numerous particles are also formed as a
mixture of solid NaNO3, CaSO4⚫ 2H2O and liquid nitrate ion (an
example marked by a red arrow in Fig. 12). They were classified as mixed
dust/sea-salt particles when analyzed by SEM/EDX. Generally, sodium nitrate
particles partially recrystallize during analysis due to local heating under
the laser beam. The remaining particles probably contain Raman inactive NaCl
and undetected species. Furthermore, Raman analysis is conducted at
∼ 60 % RH. It is remarkable that some particles remain with a
droplet shape (marked by green arrows in Fig. 12). This points to a probable
presence of calcium nitrate with very low deliquescence RH in the range of
10–18 % (Laskin et al., 2005a; Tang et al., 2016). Thus, these Ca-rich
particles may have been collected as droplets. They were classified as dust
particles when examined by SEM/EDX.
The impact of the sea breeze on the aerosol radiative effect
In this section, we evaluate the impact of the sea breeze on the broadband
solar radiation, through perturbation of the aerosol properties. The diurnal
variability in the solar radiative flux at the Earth's surface generally
follows a monotonic and smooth curve as a function of time or solar zenith
angle, if the sky is clear and the atmospheric conditions are stable.
Perturbations of the solar flux can appear due to the presence of clouds or
to changes in aerosol characteristics. In Fig. 13a, we present the solar flux
at the surface as a function of time, which is measured by the pyranometer of
SolRad-Net for the afternoon of 16 August. An irregular drop in the solar
flux occurs at 14:00 UTC, which is the time of arrival of the sea breeze
front. The discontinuity in the slope implies a loss of solar energy received
at the surface presumably due to changes in the aerosol properties or
atmospheric water vapor content. To evaluate the sea-breeze-induced radiative
effect, we calculated the solar fluxes and the net instantaneous direct
aerosol radiative effect using a computational tool described in Derimian et
al. (2016). Note that the calculated solar flux is for the wavelength range
of 0.2–4.0 µm, while the measured is for 0.3–2.8 µm,
which implies about 3 % bias due to the cut-off of the spectral range
(the accuracy of the measurements themselves is about 3–5 % as well).
Nevertheless, this discrepancy in the spectral ranges does not affect
analysis of the relative perturbation of the solar flux when evaluated using
the measurements or the calculations separately. The calculations of the
solar flux employ the aerosol models retrieved by AERONET and the parameters
of the gaseous concentrations and surface reflectance at the site for
16 August that are adopted from the database of the AERONET operational code.
The results of the simulated solar flux are superimposed on the results of
the measurements in Fig. 13b and are presented as a function of the
corresponding solar zenith angles. The fluxes that are calculated for the
aerosol characteristics retrieved just before (red line) and during (blue
line) the sea breeze are in good agreement with the measurements and the
magnitude of the drop in the measured flux. Thus, the difference between the
red and the blue lines, for the same solar zenith angle, corresponds to the
loss of solar energy reaching the surface due to the sea breeze. For example,
at a solar zenith angle of 60∘, which corresponds to the time of the
sea breeze front, the reduction of the solar flux is about 23 W m-2.
This amounts to 4.6 % reduction of the total solar flux that would reach
the surface without the sea breeze effect. It should be realized, however,
that the reduction in the solar flux is not only due to the change in aerosol
properties but also due to the increase in the water vapor content. In order
to estimate the role of each component, additional calculations were
conducted assuming that only the increase in the water vapor takes place, and
then assuming that only the aerosol properties change. The results show that
the increase in the water vapor (from 1.62 to 2.13 g cm-2) is
responsible for a loss of 7.5 W m-2 in the solar flux reaching the
surface, while the change in the aerosol properties is responsible for
15.5 W m-2 of the total 23 W m-2 difference, which amounts to
1.5 and 3.1 %, respectively. We now evaluate the aerosol instantaneous
net direct radiative effect, which is defined as the difference between
downwelling and upwelling fluxes at a given atmospheric layer in aerosol-free
and aerosol-laden conditions. The instantaneous radiative effect refers to a
value at a particular solar zenith angle. The radiative effect is formulated
so that a negative sign signifies a radiative cooling. Thus, a negative value
at the bottom of atmosphere signifies a radiative cooling at the surface. At
the top of the atmosphere, a negative value signifies additionally reflected
radiation due to aerosol presence and therefore a radiative cooling of the
whole surface–atmosphere system. More details about the calculation tool and
approach used can be found in Derimian et al. (2016). Figure 13c and d present
the calculated instantaneous net aerosol radiative effect at the bottom and
the top of the atmosphere before and during sea breeze. For example, before the
sea breeze the background aerosol produces a radiative effect of up to about
-10 W m-2 at the ground and -5 W m-2 at the top of the
atmosphere. Then, the negative aerosol radiative effect increases during the
sea breeze up to -20.5 W m-2 at the ground and -6.6 W m-2
at the top of the atmosphere. We can therefore estimate a doubling of the aerosol
radiative cooling effect at the surface and an increase by almost one-third
at the top of the atmosphere due to the sea breeze effect on this specific day.
The difference between the net top and net bottom radiative effects is the
atmospheric radiative effect. It represents the part of the energy that is
trapped in the atmosphere due to the aerosol presence. The atmospheric
radiative effect is always positive and represents the radiative warming of
the atmospheric layer. The atmospheric radiative effect increases during the
sea breeze by almost 3 times, that is, from about 5 W m-2 before
the sea breeze to about 14 W m-2 during the sea breeze. We therefore
can conclude that the sea-breeze-induced changes in the aerosol
characteristics can lead to an important relative change in the background
aerosol radiative effect.
(a) Downwelling solar flux measured at the surface by the
pyranometer of SolRad-Net in the afternoon of 16 August.
(b) Measured solar flux and results of the flux simulations based on
the AERONET retrievals of aerosol characteristics just before and during the
sea breeze. (c, d) Net aerosol radiative effect calculated before
and during the sea breeze at the bottom and the top of the atmosphere. The
measured flux is presented in panel (a) as a function of time and in
panels (b–d) as a function of the corresponding solar zenith angles
(SZA).
Aerosol core–shell structure and implication for remote sensing
As follows from the individual particle analysis presented in Sect. 5,
coatings of particles by a liquid layer are quite probable even in locations
believed to be dominated by hydrophobic aerosols. At the same time, only a
homogeneous particle model is used in remote sensing algorithms. Generally,
the reason for this is a lack of sensitivity of the remote sensing
measurements to detailed aerosol microphysical characteristics. In this
section, we attempt to verify the possible impact of the core–shell structure
on the aerosol microphysical parameters retrieved using the homogeneous
particle assumption in the AERONET operational algorithm. We also discuss the
implications for other types of remote sensing measurements, motivated by the
possibility that particles with complex microphysics can provide optical
characteristics that are hard to reproduce using a homogeneous particle
model. Indeed, it is also possible that the aerosol microphysical
characteristics retrieved using the homogeneity assumption will be an
equivalent that satisfies the radiative properties of more complex
microphysics. The question that we therefore examine is, how can the
core–shell structure affect the retrieved aerosol spectral complex refractive
index, volume size distribution, and fractions of spherical-nonspherical
aerosols if a homogeneous particle model is assumed in the retrievals? It
should be mentioned here that, with respect to the AERONET retrievals,
Dubovik et al. (2000) already provided a test of the effect of internal
(core–shell) mixture on the retrieved aerosol microphysical parameters using
a simple model of black carbon core and water-soluble substance shell. Tests
were also performed for the possible effects of external mixture and the
assumption of aerosol sphericity as part of the accuracy assessment of
aerosol optical properties retrievals from AERONET. It is noteworthy that,
because the aerosol sphericity assumption was found to cause artifacts, the
randomly oriented spheroids model was introduced in the retrieval algorithms
(Dubovik et al., 2006). However, the tests of Dubovik et al. (2000) for the
influence of external and internal (core–shell structure) aerosol mixture on
the retrievals did not show anomalies in the retrieved size distribution,
while the retrieved real and imaginary parts of the complex refractive index
yielded equivalent values that were generally in between the refractive
indexes of the components constituting the mixture.
In the current study, we first conduct the forward calculation of the
directional aerosol optical properties of homogeneous and core–shell
particles reflecting our observations in the Negev Desert. They are then
inverted using the same inversion scheme as AERONET in order to verify the
applicability of the conclusions in Dubovik et al. (2000) to our case study.
We also analyze a case where the phase function across the full angular range
is available for the retrievals. Note that the calculations presented here
are performed in a single-scattering approximation and not for radiances as
they would be observed by a sun/sky photometer and as presented in Dubovik
et al. (2000). The reasoning is that if the differences are not significant
in a single-scattering case, then they will be diminished even more in the
case of multiple scattering under real atmospheric conditions.
(a) Spectral aerosol optical thickness (AOT),
(b) phase function at 440 nm, and (c) degree of linear
polarization at 440 nm calculated under the assumption of spherical
homogeneous and coated particles, where the coating thickness is 10 and
40 % of the total particle radius. The calculated characteristics are for
an aerosol size distribution observed during the sea breeze and complex
refractive index of core and shell as described in the text; the refractive
index for the homogeneous case is equal to that of the core value. The
presented AOTs are normalized to the maximum value.
Three simplified scenarios are considered: first, the particles are
homogeneous spheres, and second and third, a liquid water layer coats the
particles with a thickness that corresponds to 10 and 40 % of the total
particle radius, respectively. This percentage is assumed because, at a
thickness of about 10 %, the differences in optical characteristics become
notable and for about 40–50 % the residual of the fit in the inversion
procedure reaches a maximum. This indicates the largest discrepancy between
the core–shell model and the particle homogeneity assumption as used in the
inversion. The percentage used here can be putted in the context of real observations by converting to widely used geometric hygroscopic growth factor, which is the ratio between humidified and dry particle diameter. Thus, 10 % corresponds to a growth factor of 1.11,
which can be defined as a low to moderate value, and 40 % corresponds to
1.67, which is near the upper limit of values in the review by Swietlicki et
al. (2008), for instance. It is noteworthy that our tests show important
differences in optical characteristics and increased residuals of fit also
for 30 and 20 % shell thickness. In fact, the effect of the coating also
depends on the shape of the particle size distribution and the contrast in
refractive indexes of core and shell; therefore, the subject merits some more
detailed studies. In the forward calculations of the presented work, the complex refractive index of the core is assumed to be 1.47 + 0.003i, which is based on the values obtained for the aerosol model before the sea breeze; it is also assumed to be spectrally independent for simplicity. The
assumed complex refractive index of the shell is 1.33 + 0.0i, the value
for pure water, and is also assumed to be spectrally independent. The
refractive index used in the case of homogeneous particles is the same as
that of the core. It is important to note that the total particle radius is
kept constant in all three scenarios in order to rule out the effects of
changing aerosol size distribution. In the case of the coated particles, the
size of the core is therefore decreased proportionally.
Figure 14a and b present the forward-calculated spectral aerosol optical
thickness and the directional distribution of scattered light intensity
(P11(θ)⋅AOTscat) that are further used for the
inversion, employing a conventional homogeneous particle model; panel (c)
illustrates the directional distribution of the degree of linear polarization
(-P12(θ)/P11(θ)) of the scattered light, with
AOTscat being the aerosol optical thickness for scattering
and P11(θ) and P12(θ) being elements of the scattering matrix,
where P11(θ) fulfills the normalization condition of
12∫0πP11(θ)⋅sinθdθ=1.
Note that P11θ is calculated for scattering angles from
0 to 180∘ with a resolution of 1∘, which represents an ideal
possible scenario of measurements. The wavelengths employed for the spectral
AOT and P11(θ)⋅AOTscat are 440, 670, 870, and
1020 nm, which are the operational wavelengths of the AERONET retrievals.
(a) Aerosol size distributions, (b) real, and
(c) imaginary part of the complex refractive indexes as assumed in
the forward calculations of the aerosol optical characteristics (labeled
“True”, “Core”, “Shell”) and as a result of the inversion of the
optical characteristics of homogeneous and core–shell particles presented in
Fig. 14. The inserted table summarizes the retrieved percentage of spherical
particles and the residual error between the assumed and the fitted aerosol
optical characteristics. The results are obtained for the case when the
scattering angle of the phase function ranges from 0 to 180∘.
The spectral AOT presented in Fig. 14a is normalized to the maximum value in
order to show the variability in spectral dependence due to the coating. A
change in the spectral AOT and the angular dependence of P11(θ)⋅AOTscat (Fig. 14a and b) is perceptible for the case of
10 % and is significant for the case of 40 % shell thickness. In
order to evaluate the impact on the retrieved microphysical parameters, we
now invert simultaneously the spectral AOT and P11(θ)⋅AOTscat.
Same as Fig. 15 but for the range of scattering angles from 0 to
120∘.
For the case when the forward calculations are conducted using the
homogeneous aerosol model, the inversion procedure reproduces very well the
assumed aerosol size distribution, the real and imaginary part of the complex
refractive index, and the fraction of spherical particles (Fig. 15,
homogeneous case). The homogeneous scenario is an initial and necessary test,
which illustrates first of all the consistency of the calculations and
secondly that accurate characteristics can be retrieved with spectral AOT
and full angular range phase function. In the case of forward calculations
conducted for a 10 % coating thickness, the retrieved real refractive
index is greater than that of the core, a discrepancy appears in the
sphericity fraction, and the residual of the fit increases (see inserted
table in Fig. 15). The most important disagreement appears in the case of
40 % coating thickness. The retrieved refractive indexes significantly
exceed those of the core, and a quite different size distribution is required
in order to fit the spectral AOT and the phase function using a homogeneous
particle model. It is expected that, in the case of mixed aerosol, the values
of the retrieved refractive index will be in between the refractive indexes
of the two components. The facts that the retrieved values are greater and
that the size distribution is modified suggest that the inversion algorithm
attempts to compensate for the specific particle morphology by an exceptional
aerosol model. Note that the residual error of the fit is as high as of
14 %, in contrast to 1 or 1.9 % in the two previous scenarios,
indicating the difficulty to obtain an accurate reproduction of the spectral
AOT and the full angular range phase function. It follows that, at least for
the case considered here, the core–shell particle structure can have
characteristics that are difficult to reproduce by an equivalent homogeneous
aerosol model. As can be seen from Fig. 14b, the main differences in the
phase function of a core–shell aerosol relative to a homogeneous one are in
the backward scattering angles. However, ground-based photometers cannot
observe backward-scattered light and measure the radiation scattered mostly
in the forward direction – up to about 120∘ of the scattering angle,
depending on the sun elevation angle during almucantar measurements. In order
to mimic the AERONET angular observations range, we conduct calculations for
the phase function in the angular range from 0 to 120∘. By limiting
the angular range we clearly lose sensitivity and, in the case of homogeneous
particles, the real and imaginary refractive indexes are now not as well
retrieved as with the full range of the scattering angles (red line in
Fig. 16b, c versus in Fig. 15b, c). However, the values are still comparable
to the originally assumed values – i.e., those of the core. The scenario of
10 % coating thickness provides results very similar to the homogeneous
case. A difference, however, appears for the scenario of 40 %, when the
real refractive index is clearly in between the refractive indexes of the
dust core and water shell components. It is noteworthy that the result is
consistent with the tendency observed at the Sede Boker site, i.e., the real
refractive index decreases as the water vapor concentration increases. Also
notable is the appearance of a similar spectral dependence of the real refractive
index as in the case of high water vapor content, i.e., lower values at
shorter wavelengths. This can be due to a stronger sensitivity of the
radiation at shorter wavelengths to the shell material on the surface of the
particle, whereas radiation at longer wavelengths is more influenced by the
internal part of the particle. The imaginary refractive index becomes greater
than the one of core, a tendency that is also visible in the observations.
The residual of the fit is quite high, which means that a physical
interpretation of the retrieved microphysical parameters should be advanced
with caution. In addition, retrievals with high residuals are generally
screened in final products and therefore cases where the aerosol morphology
differs drastically from the morphology assumed in the retrieval algorithms
may be omitted. However, the obtained high residuals show that the
measurements have sensitivity to the core–shell structure. That is, it
appears that a homogeneous particles model is not able to reproduce
accurately the characteristics of the core–shell structure. Similar
conclusions can also be drawn for the case of the inversion of the phase
function over the full angular range. Additionally, Fig. 14c presents a not
yet discussed variability in the degree of linear polarization. This
deviation of the degree of linear polarization from the homogeneous particle
scenario is even stronger than that of the phase function. For instance,
there is even a sign reversal in the peak at a scattering angle of about
170∘ in the case of 40 % coating thickness. The results of our tests
(not shown here as graphs) of a simultaneous inversion of spectral AOT,
P11(θ)⋅AOTscat, and -P12(θ)/P11(θ)
show similar tendencies of increasing the residual error of the fit and
aberrant refractive index and size distribution as the thickness of the
coating increases. Thus, an even stronger sensitivity to the core–shell
structure is expected if polarization is measured.
It can be concluded that, in some measurement configurations, an equivalent
homogeneous particle model can indeed represent the optical characteristics
of a liquid coating even under the assumption of a single-scattering
approximation. This conclusion is in general agreement with the results of
Dubovik et al. (2000); however, we can also conclude that including backward
scattering angles and polarimetric measurements present more sensitivity to
the core–shell structure. This is because the main differences, due to the
aerosol core–shell structure, are observed in the angular and polarimetric
characteristics of the light scattered in the backward angles. Thus, since
the backward scattering is a primary signal measured by satellites and lidar,
important implications for these types of measurements are possible. For
example, the aerosol core–shell structure will affect the lidar ratio and a
parameterized core–shell aerosol model can be considered in satellites
retrievals.
Conclusions
The influence of the sea breeze on the atmospheric aerosol chemical
composition, microphysical, optical, and radiative characteristics in the
Negev Desert of Israel during summertime is shown for the first time and
discussed in detail. We employed extensive remote sensing observations, in
situ aerosol sampling and laboratory physicochemical characterization of the
particles. We found that, at an arid location inland at a distance of at least
80 km from the Mediterranean seashore, marine aerosol particles and air
masses are influencing daily the desert aerosol composition. While the entire
phenomenon lasts for about 3 h, an abrupt increase and a peak in
aerosol size, volume concentration, and optical thickness, as derived by
AERONET observations in the solar spectrum, occur with the arrival of the sea
breeze front. Simultaneously, the sky brightness temperature, derived by
radiometric measurements in the thermal infrared spectrum, also increases and
shows a weaker spectral dependence, which indicates a contribution of large
aerosol particles and/or water droplets (the former behaves like a black body
in TIR). The effect of the sea breeze front on the atmospheric radiative
characteristics was most obvious in the measurements by the thermal infrared
radiometer. This illustrates the great potential of the simultaneous and
complementary observations of solar and thermal infrared radiances for
aerosol studies.
We found that the fraction of hygroscopic marine and internally mixed
dust/marine aerosol particles increases significantly during the sea breeze;
however, similar particles are present as a background in the Negev Desert
also before the sea breeze arrival. The increase in atmospheric water vapor
content and relative humidity is associated with the sea breeze arrival; thus, the aerosol size distributions show a shift towards larger particles. We
suggest that hygroscopic growth can explain the observed shift in the aerosol
size distribution. This hypothesis is supported by SEM/EDX analyses, which
show that a large number of particles are surrounded by liquid residuals.
Despite the desert location of the site, we also found that a large fraction
of the sampled particles is composed of highly hygroscopic material. Although
particles of all sizes grew, the size shift of the fine mode was stronger
(see Fig. 6). This can be linked to the size-dependent aerosol composition,
which shows higher fractions of hygroscopic particles in PM1, consisting
of marine and internal dust/marine mixtures. We also observed that many dust
particles had a liquid coating in the form of water-solvated nitrate. These
nitrates have an anthropogenic origin and their internal mixture with dust,
namely Ca-rich particles, makes dust highly hygroscopic; the point of
deliquescence of such particles can be at RH values as low as about 10 to
20 %. Thus, even in a dry desert environment, such dust particles can
contain a substantial amount of liquid water, as was also observed in our
samples. These results are also in line with the decreasing values of the
AERONET-retrieved real refractive index, indicating a presence of water in the
aerosol composition. The observed liquid coating of particles can have more
general implications for the modeling of the aerosol scattering and
absorption properties. This is because all present-day remote sensing
algorithms for the retrieval of aerosol microphysical properties assume
homogeneous particles. Indeed, there are practical reasons for this
assumption related to issues of measurement sensitivity, as discussed in
Sect. 7. Based on the numerical simulations presented here we suggest that a
sensitivity of remote sensing to the core–shell structure exists in those
observational configurations where information about the phase function in an
extended angular range and polarimetric measurements are used, and that
scattering in the backward directions is particularly important.
In addition to the individual particle microphysics, the sea breeze also
perturbs the radiative budget. On a specific day, 16 August 2012, we
estimated 4.6 % reduction in the broadband solar radiation reaching the
surface due to the sea breeze, where 1.5 % is due to increase in the
water vapor content and 3.1 % is due to the change in aerosol
concentration and microphysics. The background net aerosol radiative cooling
was doubled at the surface and increased by about one-third at the top of the
atmosphere. The atmospheric radiative warming, which is the difference
between the net top and the net bottom radiative effects, increased by about
a factor of 3. The cooling of the surface and the warming of the
atmospheric layer can change the gradient of the atmospheric temperature
profile, which may imply a feedback on the sea breeze dynamic; this subject
merits a separate dedicated study.
This study illustrates the complexity of the aerosol microphysics when
marine, desert, and pollution air masses interact. An understanding and
proper modeling of aerosol optical properties in coastal areas should be of
high importance because densely populated and industrial centers are
frequently located on the seashores. The sea breeze occurs in many locations
around the world and this systematic phenomenon can be used as a natural
laboratory to study and evaluate the impact of the aerosol mixing state and
hygroscopicity on aerosol optical properties and radiation.
Data from the ground-based
meteorological station, the integrating nephelometer measurements, the
laboratory analysis of sampled aerosol, and results of the numerical
simulations are available upon request to the corresponding author
(yevgeny.derimian@univ-lille1.fr). The air mass backward trajectory,
AERONET, MPLNET, and SolRad-Net data are available from
http://ready.arl.noaa.gov/HYSPLIT_traj.php,
https://aeronet.gsfc.nasa.gov/index.html,
https://mplnet.gsfc.nasa.gov, and http://solrad-net.gsfc.nasa.gov,
respectively. Quick looks of the thermal infrared radiometer data are visible
at http://www-loa.univ-lille1.fr/index.php/observation/sites.html and
the data are available upon request from the corresponding
author.
The authors declare that they have no conflict of
interest.
Acknowledgements
The CaPPA project (Chemical and Physical Properties of the Atmosphere) is
funded by the French National Research Agency (ANR) through the PIA
(Programme d'Investissement d'Avenir) under contract “ANR-11-LABX-0005-01”
and by the Regional Council “Nord Pas de Calais – Picardie” and the
European Funds for Regional Economic Development (FEDER). The SEM facility in
Lille (France) is supported by the Conseil Regional du Nord-Pas de Calais
and the European Regional Development Fund (ERDF). Alexander Laskin acknowledges support
from the W.R. Wiley Environmental Molecular Sciences Laboratory (EMSL), a
national scientific user facility located at PNNL, and sponsored by the
Office of Biological and Environmental Research of the US DOE. PNNL is
operated for the US DOE by the Battelle Memorial Institute under contract no.
DEAC06-76RL0 1830. The MPLNET project is funded by the NASA Radiation
Sciences Program and Earth Observing System. We thank the MPLNET PIs
Ellsworth Judd Welton and Sebastian A. Stewart for their effort in
establishing and maintaining the Sede Boker site. The authors gratefully
acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the
HYSPLIT transport and dispersion model and the READY website
(http://ready.arl.noaa.gov) used in this publication. Special thanks to
Alexander Goldberg from the Jacob Blaustein Institute for Desert Research,
Ben Gurion University, Sede Boker Campus, for highly valuable technical help,
which enabled the proper functioning of the instrumentation at the site. We
also thank Abraham Zangvil and David Klepach from the same institution for
providing the meteorological data. We finally thank the anonymous reviewers
for their thoughtful reading and for suggesting additional
conclusions. Edited by: Nikolaos
Mihalopoulos Reviewed by: three anonymous referees
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