Introduction
Airborne geological dust sourced from topsoil and surface rocks critically
contribute to the total mass, composition, microphysical, and optical
properties of the atmospheric aerosol in continental regions, and largely
impacts Earth's different compartments by transport and deposition
(Scheuvens and Kandler, 2014). Crustal particles commonly constitute the
major mass fraction of the resuspended lithogenic materials and significantly influence both the PM mass concentration at the ground (Perrino et al.,
2009; Viana et al., 2014) and the mineralogical composition. The latter
varies mostly depending both on the rock types outcropping in the source
region (Dürr et al., 2005; Journet et al., 2014) and, consequently, on
the crystallization, sedimentation, and weathering processes tuning the
particle size and shape (Claquin et al., 1999). This has been observed for
mineral dust of African desert regions (Caquineau et al., 2002; Evans et
al., 2004; Stuut et al., 2009; Scheuvens et al., 2013; Formenti et al.,
2014) and of arid areas in other regions (Kim et al., 2006; Jeong, 2008;
Moreno et al., 2009; Agnihotri et al., 2015; Rashki et al., 2013).
Both microphysical (size distribution and complex refractive index) and optical
properties of airborne lithogenic dust vary as a consequence of the
mineralogical composition (Sokolik and Toon, 1999; Reid et al., 2003;
Hansell Jr. et al., 2011; Wagner et al., 2012; Di Biagio et al., 2014; Mahowald
et al., 2014; Smith and Grainger, 2014). When at a certain site intrusions
of lithogenic dust at the ground occur, like desert dust, the overall properties
of the PM may be altered, compared to periods when this contribution is
negligible (Meloni et al., 2006; Choobari et al., 2014). This also affects
the impact of airborne aerosol on the energy balance of the Earth. Airborne lithogenic dust plays a role both in the direct mechanisms
(light scattering and absorption) and in the indirect mechanisms
(cloud-aerosol interactions) which tune the Earth's radiative budget
(Sokolik et al., 2001; Choobari et al., 2014). While indirect effects depend
on the heterogeneous chemistry occurring at particles surface (Levin et al.,
1996; Buseck and Pósfai, 1999; Sokolik et al., 2001; Krueger at al.,
2004; Kandler et al., 2007), the light scattering and absorption are mostly
controlled by the mineralogical composition, shape features and
microphysical properties of geological particles (D'Almeida, 1987;
Kalashnikova and Sokolik, 2002, 2004; Kokhanovsky, 2008; Hansell Jr. et al.,
2011).
Most studies facing this issue relate to desert dust from Sahara and Sahel
regions (Kandler et al., 2007, 2009; Müller et al., 2009; Papayannis
et al., 2012; Wagner et al., 2012; Di Biagio et al., 2014). Nevertheless,
knowledge gaps still exist on this issue (Rodríguez et al., 2012), due
to the site-related large variability of dust mineralogical features
(elemental and mineralogical composition, crystalline structure, shape,
microphysical, and optical properties).
Also, only few of the published studies characterize the resuspended
geological dust of non-African regions (Falkovich et al., 2001; Peng and
Effler, 2007; Rocha-Lima et al., 2014). Large areas of Italy, especially
those closer to the Mediterranean Basin, are affected by dryness, heavy
anthropic impact (urbanization, farming, quarry activities, etc.), erosion,
and poor vegetation cover, leading to increased desertification risk. The
Atlas of desertification in Italy (Costantini et al., 2007, 2009) reports, for
instance, that the yearly average of dry soil days in the region of Latium
ranges 64/110. In Fig. S1 in the Supplement, this
is shown for the area of study of this work; the highest number of dry soil
days (86/110) is found in the northern zone of the study area
(Geoportale Nazionale, 2011).
Latium is also characterized by a large surface where poorly developed soils
and debris deposits are present, which are easily affected by massive
erosion.
Consequently, the resuspension of mineral dust from local lithological
domains contributes notably to the ambient PM10 of the Rome area. This is
shown in Figs. S2 and S3 for the Villa Ada site (Rome, urban background)
and the Montelibretti EMEP site (Rome outskirts, rural background),
respectively. Considering the 2005–2011 period, among days which show a
dominant (over 50 % of total PM10 mass) crustal contribution to the
ambient PM10 composition at these sites, desert dust intrusions
at the ground (DD days) account for 60 % at Montelibretti and 30 % at Villa
Ada, while the remaining days are reasonably affected by local crustal
contributions, given the background character of the considered sites
(LD days). Interestingly, among the above-described days, the mass
concentration of the crustal matter on LD days is in many cases comparable
with that observed on DD days. Within this picture, main goals of this work
were the following: to study the relationships between the local outcropped rocks (or
topsoil) and the dust particles in the PM10 sourced from these rocks,
and to gain knowledge on the microphysical and optical properties of the
mineral PM10 at geological dust source, and on the downward radiative
flux at BOA (bottom of atmosphere) related to an atmosphere where the only
aerosol component is the PM10 dust, in order to define the radiative
effects which are due to the local mineral dust. In a previous study,
we determined elemental source profiles of the PM10 fraction of local
mineral dust (Pietrodangelo et al., 2013). In this work, the PM10
fraction of the same samples was characterized with respect to the above
goals. To investigate relationships among these different aspects, a
multi-faceted analysis was performed, on the basis of the following
approaches: chamber resuspension of raw materials and PM10 sampling, to
simulate field sampling at dust source, scanning electron microscopy/X-ray
energy-dispersive microanalysis (SEM XEDS) of individual mineral particles,
X-ray diffraction (XRD) analysis of bulk dust samples, number and volume
size distribution (SD) building from microanalysis data of mineral particles
and fitting to log-normal curve, and radiative transfer modelling (RTM) to
retrieve optical properties and radiative effects. Results from experimental
and modelling analysis are discussed for their consistency with both the
lithological nature of major local dust sources and the microphysical
properties of the mineral dust samples.
Approach and methodology
Study area, dust collection and sample treatment
Mineral dust was collected from topsoil and debris of rural areas
surrounding the city of Rome within a perimeter of 50 km radius. On the
basis of criteria established after geological analysis of the Latium
region, the following geodynamics domains were considered: the volcanic
complexes, the marine (limestones, marlstones and sandstones) deposits, the
siliciclastic series (mainly flysch) and the quaternary deposits (mainly
travertines).
Sampling areas of about 4 km2 were selected within each local
geodynamics domain; a number of dust collection points was identified,
within each area, to obtain sub-samples of raw material, from which the
final samples were obtained. The number of sampling areas varies within each
domain, depending on the geographical extension and the geological
complexity of the domain. Furthermore, paved road dust was collected by
brushing the surface of different roads within the volcanics and the
travertine domains. PM10 dust was laboratory-resuspended from the bulk
rocks samples, and from road dust, by a resuspension chamber, and collected
by low-volume sampling on polycarbonate membranes for SEM XEDS
microanalysis. It is worth noting that, among laboratory methods of dust
generation or resuspension from bulk materials, fluidization by mechanical
ventilation in a resuspension chamber is widely acknowledged, either for not
affecting both the complete resuspension potential of the source material
and the original size distribution of the resuspended particles in the
material itself, and for simulating the resuspension of dust previously
deposited at a site (Gill et al., 2006, and references therein). By this
approach, good approximation of the field sampling at a dust source can be
achieved, making it suitable for studies on the mineralogical and
microphysical characterization of mineral dust (Gill et al., 2006, and
references therein; Feng et al., 2011; Aimar et al., 2012; Dobrzhinsky et
al., 2012).
The whole geological siting criteria, dust sampling strategy, laboratory
treatment details and elemental profiles of the resuspended dust types, are
fully described in Pietrodangelo et al. (2013). In that paper we discussed
how, under the perspective of mineralogical composition, the volcanics
(silicate-dominated rocks) and the travertine (calcite-dominated rocks) can
be considered as reference compositional end members of the overall
outcropping lithotypes in the Latium region (Cosentino et al., 2009), while
the sedimentary domains (marine deposits and siliciclastic series) represent
intermediate compositional terms. Therefore, for the scopes of this work the
complete procedure of dust characterization (elemental and mineralogical
composition, size distribution, optical properties and radiative downward
flux) described in the following sections was applied only to the volcanics
and travertine dust.
Individual particle microanalysis
An environmental scanning electron microscope Philips XL30 ESEM (FEI
Company, tungsten filament) equipped with an energy dispersive spectrometer
for x-ray microanalysis (EDAX/AMETEK Inc., USA) was used for individual
particle characterization of the PM10 dust. Instrumental calibration of
the magnification and of the XEDS spectrometer gain are routinely performed
on the basis of the US EPA Guidelines for SEM EDX microanalysis of
particulate matter samples (Willis et al., 2002). A small portion of sample
(about 8 % of total filter area) was cut in the centre of polycarbonate
membranes, fixed to aluminium stubs by self-adhesive carbon discs (TAAB, 12 mm diam.)
and coated with an ultra-thin carbon layer by a vacuum evaporator
(108 Carbon A, Cressington, Scientific Instruments Ltd., UK).
SEM XEDS acquisitions were performed under high vacuum (10-6 hPa) at 20 keV
accelerating voltage, allowing the K-line excitation of elements with
atomic number Z≤27 (Co, Kα 6.923 keV). Micrographs were
acquired by secondary electron detector (SED) at magnification, working
distance (WD), tilt angle, and spot size conditions properly adjusted on a
case-sensitive scale to optimize image resolution. The microanalysis was
performed at WD 10 mm (take-off angle 35∘ relative to the specimen
plane) on field areas of 1290–5200 µm2 (magnitude × 6000–3000) spread on the overall specimen surface; between 700 and 1000
particles were analysed per sample.
The Particle/phase analysis v.3.3 package (EDAX Inc., 2000) was used for the
automated individual particle microanalysis; threshold of the digitalized
object area to be analyzed was set at 80 %. Since a great number of
individual particles was analyzed, short live times (20–30 s) were imposed
to XEDS spectra acquisition. Each field of microanalysis was manually
selected prior to launching the automated scanning of particles. This choice
allows a field-specific tuning of the grey scale, in order to minimize
brightness artifacts in the automated identification of particles. Amplification time
and spot size were adjusted to ensure dead time around 30 % and total
counts rate above 500 cps. In addition to automated microanalysis, manual
acquisitions were carried out, both on field areas and on individual
particles, by using the EDAX control v. 3.3 package (EDAX Inc., 2000). About
20 to 30 field areas were selected from the different dust samples to
perform manual acquisitions. These have been run in triplicate on each field
(live time 10–20 s), to assess the repeatability of the microanalysis.
Further, XEDS spectra acquired from areas included in these fields were
quantified by the conventional standard-based quantification procedure of
bulk materials, to assess consistency with results previously obtained by
ED-XRF analysis (Pietrodangelo et al., 2013).
Manual microanalysis of 15 to 30 individual particles per-sample was also
performed, and high resolution micrographs of these particles were stored (as in Fig. S4).
Magnification above 6000× and longer live times (30–60 s) were employed,
so that resulting XEDS spectra have total counts rate ranging 5000–10 000 cps. These data were used both to assess the accuracy of microanalysis with
respect to different mineral particles (Table 1), and to perform the
quantification of individual-particle XEDS spectra by an internal standard
approach, as further discussed in Sects. 2.3 and 3.1.
Quality assessment of SEM XEDS microanalysis.
Dust
Mineral particle
K
Na
Ca
Mg
Fe
Mn
Al
Si
Ti
Repeatability (% rsda)
Volcanics
10
28
14
19
12
39
6
6
29
Road dust
35
43
33
23
33
34
36
31
54
Siliciclastics
15
24
10
13
17
31
11
8
56
Travertine
17
25
2
25
21
31
14
6
54
Consistency with XRF (% Δb ± prop. error)
Volcanics
2.4 ± 0.1
55.4 ± 0.1
22.7 ± 0.3
–
32.2 ± 0.1
500 ± 10
15.8 ± 0.2
10.8 ± 0.1
41.4 ± 0.5
Road dust
37.5 ± 0.3
> 100
28.2 ± 0.1
> 100
37.7 ± 0.7
> 100
22.3 ± 0.2
37.4 ± 0.2
87.6 ± 1.4
Siliciclastics
22.2 ± 0.1
78.7 ± 2.1
39.2 ± 0.7
52.4 ± 0.3
10.8 ± 0.2
470 ± 10
2.8 ± 0.1
50.7 ± 0.2
7.8 ± 0.5
Accuracy (g cm-3)
Quartz
–
–
–
–
–
–
–
3.4 ± 0.02
–
Kaersutite
0.07 ± 0.02
0.04 ± 0.03
0.24 ± 0.02
0.23 ± 0.03
0.67 ± 0.01
0.02 ± 0.01
0.49 ± 0.02
1.12 ± 0.02
0.11 ± 0.02
Calcite
–
–
4.1 ± 0.02
–
–
–
–
–
–
Accuracy (% Δc ± prop. error)
Quartz
–
–
–
–
–
–
–
28 ± 0.4
–
Kaersutite
37.3 ± 0.6
94.1 ± 18.3
47.4 ± 1.5
38.6 ± 0.3
28.6 ± 0.2
61.5 ± 1.8
28 ± 0.3
6.4 ± 0.3
40 ± 2
Calcite
–
–
52 ± 1
–
–
–
–
–
–
a % relative standard deviation, b % Δ: absolute percent difference between elemental
composition determined by SEM XEDS and elemental composition of same dust type determined by ED-XRF (Pietrodangelo et al., 2013). Prop. err.:
propagated error. c % Δ: absolute percent difference between the element %wt
in the mineral particle and the element wt % in the bulk standard mineral
(EDAX Inc.). Prop. err.: propagated error.
Quantification of individual particle XEDS spectra and procedure of particle
allocation to mineral classes
A large data set of XEDS spectra and size (Feret diameters, area, aspect
ratio, roundness) of individual dust particles was stored. To allocate dust
particles into main mineral classes of our dust samples, an ad hoc procedure
has been adopted.
First, the bulk mineralogical composition of dust samples was determined by
x-ray diffraction (XRD), to identify major minerals in the dust samples.
Then, XEDS spectra of individual particles were semi-quantified and matched
to spectra and to elemental composition of reference pure minerals expected
after XRD analysis. Results of matching were used to allocate individual
particles into main mineral classes. Details are described below and in
Sect. S1 of the Supplement.
The mineralogical characterization of dust samples has been carried out on
the 50 µm sieved dust fraction, by an automatic diffractometer
Scintag X1, equipped with a Si(Li) detector using a Cu Kα target,
under the following conditions: Ni-filtered radiation, step-scan modality
(2∘ step = 0.02∘), acquisition time of 10 s,
operating at 45 kV and 40 mA. Quantification of minerals has been obtained
according to procedures defined by Moore and Reynolds (1997).
A random orientation of particles was obtained by pressing 0.5 g of the 50 µm sieved materials with 5 atm for 10 s. Quantitative determinations
were obtained by using appropriated standards and elaborating spectra as
indicated in Giampaolo and Lo Mastro (2000). From XRD results and on the
basis of previous geological analysis of the area, mineral species to which
individual dust particles have to be allocated were identified.
Allocation of individual dust particles analyzed by SEM XEDS to mineral
classes can be carried out by matching XEDS spectra of particles to those of
pure minerals.
However, XEDS spectra of some minerals can be not available; in this case,
allocation can be performed by matching the quantified elemental composition
of particles with that of pure minerals. Therefore, prior to this step,
particle elemental composition has to be quantified.
When quantification of individual particle XEDS spectra is concerned, the
use of conventional methods for bulk and thin polished materials (Castaing,
1951) imposes some critical limitations, and proper adjustments and
assumptions for the theoretical treatment of X-ray generation and losses in
the particulate matrix are needed (Armstrong and Buseck, 1975; Van Dyck et
al., 1984; Choël et al., 2005, 2007).
In addition to bulk matrix effects, the particle size and shape play a major
role in the mass, absorption and fluorescence effects of particulate
matrices (Fletcher et al., 2011).
In this study, the mass effect (induced by particle thickness lower than the
spot size of primary electron beam) was considered negligible. Dust
particles selected for quantification, indeed, show an equivalent projected
area diameter (assumed as particle thickness according to Kandler et al.,
2007) above 2 µm, that is far larger than the spot size used (0.3/0.4 µm average). However, energy losses due to particle
absorption and fluorescence effects cannot be neglected. Among methods
described in literature to quantify environmental particles by XEDS
microanalysis (Fletcher et al., 2011), the particle standard approach was
adopted in this work. In particular, an internal standard was used; by this
choice, particle matrix effects are included in the quantification process,
and the conventional standard-based quantification method still can be used
(Castaing's first approximation corrected for bulk matrix effects by the ZAF
algorithm). Full details of this approach are described in Sect. S1 of
the Supplement.
Allocated mineral particles of the volcanics and travertine dusts were then
used to investigate the microphysical, optical and radiative properties of
the PM10 lithogenic dust of Rome.
Size distribution
In this work, the assumption of particle sphericity has been adopted, due to
the requirements of the 6SV (Second Simulation of a Satellite
Signal in the Solar Spectrum – Vector) code for radiative transfer modelling.
Therefore, physical size of particles was assumed as the diameter of the
equivalent spherical cross sectional area (ESD) (Reid et al., 2003; Kandler
et al., 2007; Choël et al., 2007) measured by SEM. Then, mineral density was
assigned to allocated particles; volume, mass, and aerodynamic diameter were
consequently calculated (Kulkarni et al., 2011). On this basis, the volume
size distributions of most representative mineral species observed in this
study (kaolinite, quartz, feldspar, and calcite) have been built.
Probability density function
The probability density functions (PDFs) of the volcanics and travertine
PM10 dust were estimated by fitting the frequency distribution of
particle size to log-normal curve.
Frequency distributions of volcanics and travertine were built on the basis
of the 13 size bins of the GRIMM 1.108 optical particle counter (OPC). The
fitting procedure was developed using the R-project programming environment
(R Core Team, 2013), and the routine was implemented by a nonlinear
regression model based on a weighted-least-squares function (Ritz and
Streibig, 2008). The procedure attended to minimise the deviation between
observed distribution and log-normal model. This is expressed by the
following equation (Davies, 1974):
dN(r)dlogr=N2πlogσexp-12logr-logrmlogσ2,
where N is the number of particles, rm is the mean radius of
particles, and σ is the standard deviation of r. The uncertainty of
each bin was estimated associating a Poisson error to the bin weight (Liley,
1992), i.e. calculating the square root of the total counts of particles
observed in each size range. Quality assurance of the fitted models was
evaluated considering the “chi squared” index (χ2) in order to
estimate the level of acceptance (Wilks, 2006).
This index is proportional to the sum of squares of the difference between
each data point and the corresponding computed value. The level of
acceptance was defined using the χ2 distribution tables.
Radiative transfer modelling
An atmospheric radiative transfer code was employed, to retrieve the optical
and radiative dust properties. The 6SV (Vermote et al., 2006; Kotchenova et
al., 2008) is the new-generation of open-source atmospheric radiative
transfer model 6S (Second Simulation of a Satellite Signal in the Solar
Spectrum), (Vermote et al., 1997). This code is able to retrieve optical
properties of the aerosol and to model the atmospheric radiative field by
using the aerosol microphysical properties, under the hypothesis of
spherical and dry particles. Microphysical properties of aerosol required
for the modelling are the size distribution and refractive index.
Size distributions targeted to the type of aerosol can be introduced as
input in the 6SV code. To this aim, frequency distributions of particle size
of the volcanics and travertine PM10 were processed for curve-fitting,
as described in Sect. 2.4.1. Log-normal curve parameters r and σ of
the two dust types were thus used as inputs in the 6SV code.
The real and imaginary parts of the refractive index (r.i.) were assumed
from literature.
This choice was driven by the fact that the 6SV code requires as input the
spectral trend of the real and imaginary parts of the r.i., and these
measurements were not available from our laboratory. Therefore, the
refractive index of the “water-insoluble” aerosol component reported in
Kokhanovsky (2008) was associated with the volcanics dust of the Rome area. This
component is indeed defined as mainly dust, rich in water-insoluble minerals
e.g. silicates, and is reported in literature in the spectral domain
considered by the 6SV code. In the case of travertine dust, the calcite
refractive index data reported by Sokolik and Toon (1999) and Gosh (1999) were used.
The 6SV code retrieves aerosol optical properties by the Mie theory and then
simulates the radiative modelling by solving the radiative
transfer equation (RTE) in the solar spectral domain. By this way, the
propagation of solar radiation in the Earth–atmosphere coupled system can be
completely described.
Runs of 6SV code were performed on a setting of parameters related to the
site-specific meteorological and atmospheric conditions, and to the aerosol
loading and microphysical properties. Concerning meteorological parameters,
the profiles of temperature, pressure and humidity were assumed by the 1976 U.S. Standard
Atmosphere included in the 6SV code.
Atmospheric conditions were established in order to model the radiative
field under daily maximum Sun elevation in the Rome area; a spring day, 12 May, at midday
was thus selected. Columnar contents of water vapour and of ozone were fixed
to 1.32 cm and 0.283 Db, respectively. To describe the aerosol loading, the
aerosol optical thickness (AOT) at 550 nm, τ550 (Vermote at al., 1997;
Kaufmann et al., 1997; Bassani et al., 2010, 2012) is commonly
considered. The atmospheric profile of the aerosol is assumed to be
exponential with a scale height of 2 km (Vermote at al., 1997). In this
study, however, a high value of aerosol optical thickness, τ550=0.7, was chosen, in order to allow describing a scenario where the local
geological dust loading has a major role when the radiative field in the
Earth–atmosphere coupled system is simulated.
Among optical properties, the single-scattering albedo (SSA) and the
asymmetry parameter (g) were chosen, as they are crucial to perform analysis of the
aerosol contribution on the radiative field (Dubovik et al., 2002; Kassianov
et al., 2007).
Concerning the simulation of the radiative quantities, the downward
irradiance was modelled, to the aim of performing a preliminary
investigation on the radiative impact of the different dust types in the
Earth–atmosphere coupled system. The volcanics and travertine PM10
local dust are expected to show significantly distinct microphysical
properties, due to their compositional differences. Radiative modelling has
been performed thus on the assumption of an atmosphere where the only
aerosol component is volcanics or travertine dust, separately.
In order to evaluate the direct radiative effect at the surface of the two
local dust components, the radiative forcing efficiency (RFE) at BOA has
been considered. In a recent modelling study, Gómez-Amo et al. (2011)
derives the RFE by using a radiative transfer code.
In this study, the RFE has been computed for each component by the
difference between the BOA flux simulated by 6SV code in case of atmosphere
with and without the dust component in the 250–4000 nm spectral domain and
normalized with respect to the AOT at 550 nm (Garcìa et al., 2008). The
comparison between the RFE of volcanics and travertine allows for the analyzing of the
dependence of surface forcing from aerosol types (microphysical properties)
and SSA independently from the aerosol loading (di Sarra et al., 2008, 2013;
Di Biagio et al., 2009, 2010). Results are shown in Sect. 3.5.2 and 3.5.3.
Results and discussion
Results of individual-particle XEDS spectra quantification and
classification into mineral species are reported in Sect. 3.1–3.3. In
particular, the discussion concerns the reliability of microanalysis and of
high-count spectra quantification (Sect. 3.1), a principal component
analysis (PCA) of particles elemental composition (Sect. 3.2), the
allocation into mineral classes and the reliability of quantification by the
internal standard approach used in this work (Sect. 3.3). In Sect. 3.4,
volume size distributions are discussed, and differences between calcite
from a lithogenic source (travertine dust) and from an anthropogenic
material (paved road dust) are also evidenced. Finally, in Sect. 3.5 the
microphysical and optical properties, and the downward component of
radiative flux at BOA related to an atmosphere where
the only component is the PM10 mineral dust (volcanics or travertine,
alternatively) are discussed, with respect to the features of the Rome area.
Reliability of XEDS microanalysis and quantification
In Table 1 (upper part) the repeatability of XEDS microanalysis and
consistency with the ED-XRF analysis are reported. Repeatability was
evaluated by triple field acquisitions (number of fields: 20/30)
from each PM10 dust sample. Large fluctuations around mean (%
relative standard deviation) are observed for light (Na and Mg principally)
and trace (Mn and Ti) elements. Consistency with previously obtained
elemental profiles of the PM10 fraction (Pietrodangelo et al., 2013) by
ED-XRF, was assessed by matching to the latter the percent weight element
composition of micro-areas of the dust samples obtained by XEDS field
microanalysis. Results indicate that the microanalysis is less reliable for
Na, Mn as well as for Si and Mg in the siliciclastics sample, while in all other
cases it shows a good agreement with ED-XRF bulk analysis.
Quantification results of manually acquired XEDS individual particle spectra
are also reported in Table 1 (lower part), for kaersutite, quartz, and
calcite. Quartz and calcite represent the compositional end-members of
mineral species observed in the dust samples of this work. Kaersutite
particles were frequently observed during manual acquisitions. As this
silicate mineral include in its composition non-negligible presence (above 4 wt %)
of the principal crustal elements (Al, Na, Mg, K, Ca, and Fe), it has
been assumed as reference term for the microanalysis of silicate particles.
XEDS spectra of kaersutite, calcite, and quartz particles are shown in Figs. S3, S4, and S5, respectively.
The element composition is reported in terms of element mass in the electron
interaction volume at 20 KeV; the latter was estimated for quartz,
kaersutite and calcite according to Potts (1987). Uncertainties of
quantification are large for K, Na, Mn, and Ti, as expected due to the poor
sensitivity of XEDS microanalysis to light and/or trace elements, whereas
they range 1/10 % relative error for other elements. Element
uncertainties reported in Table 1 were estimated following the approach by
Ziebold (1967), after assigning the proper peak-to-background ratio to each
element in each mineral particle. The compositional differences of
individual particles of quartz, calcite, and kaersutite, with respect to
related bulk mineral standards, are also reported in Table 1 (last rows) in
terms of absolute percent differences between the element wt % in the
standard mineral and that in the mineral particle.
As expected from the uncertainties, major compositional differences with
respect to mineral standards are observed in Na, Mn, and Ti quantification in
kaersutite; also the quantification of Ca differs largely from the mineral
standard, both in kaersutite and in calcite particles.
Elemental composition of individual dust particles
Particles included in the data set are individually codified with respect to
the related dust source, so that they are traceable in the statistical
processing of data. Comparing information extracted from a multivariate
statistical analysis of this data set, on the dust type to which each
particle is ascribed, to same information certainly known from particle
coding in the same data set, allows the evaluation of soundness of the
elemental composition data and, consequently, of the quantification approach
applied to particle XEDS spectra.
PCA of elemental ratios calculated on individual dust
particles composition: score plots of factors (F1, F2, F3) with eigenvalue
higher than unity. V: volcanics; S: siliciclastics; RD: road dust; T:
travertine.
Ca and Si abundances of particles with highest PCA score in F1
(a), F2 (b), or F3 (c), plotted vs. the particle equivalent spherical
volume.
To reach this goal, a principal component analysis (PCA) of the elemental ratios
commonly used to discriminate among different mineral classes (Al/Ca, Fe/Ca,
K/Ca, Mg/Ca, Ti/Ca, Si/Al, Si/Fe, Ti/Fe, and Ti/Mn) was performed. The XLSTAT
7.5 statistical package (Addinsoft) was used, with Varimax rotation and
extraction of the latent factors; results are shown in Figs. 1 and 2. Three
latent factors with eigenvalue higher than unity explain 76 % of the total
variance of particle composition. The element/Ca ratios mainly contribute to
the first factor (F1, eigenvalue 4.5), the Si/Al and Si/Fe ratios contribute
to the second factor (F2, eigenvalue 1.8), while the Ti ratios are
represented by the third factor (F3, eigenvalue 1.3). In Fig. 1, particle
scores are reported in the F2 vs. F1 and in the F3 vs. F2 plots.
Particles described by the F1 are ascribed to volcanics and, in a small fraction, to siliciclastics dust. The latter are indeed described mainly by
the F2. Finally, road dust, and travertine particles are grouped by the F3.
Marlstone particles were not included in the PCA, and in the subsequent parts
of the study, due to the smaller number of available data with respect to
the other samples. To assess the soundness of the PCA solution, the relative
Si and Ca abundance and the equivalent spherical volume (ESV) have been
examined, within clusters identified by the F1, F2, and F3. In Fig. 2, the Si
and Ca abundances of particles with factor score higher than unity on each
of the three latent factors have been plotted with respect to the ESV.
Since the average mass fractions of Si and Ca, in SiO2 and CaCO3
respectively, are 0.47 and 0.4, the threshold of 0.4 can be used to
discriminate qualitatively either between silica and silicates (for which Si
abundance is expected roughly below 0.4), and similarly between calcite and
other Ca-bearing particles. Median Si abundance of both volcanics and
siliciclastics particles in F1 is 0.37/0.39 (Fig. 2a); also Ca
abundance is similar in both particle groups (below 0.1). Median values of
particle ESV are 3 µm3 (volcanics) and 3.5 µm3
(siliciclastic), although a very large variability was measured. Upon the
above considerations on the Si mass fraction in SiO2, a silicate nature
of these particles might be supposed. Particles grouped by the F2 (Fig. 2b)
are mainly siliciclastic and only a minor fraction is ascribed to volcanics.
All of these particles share both the Si and Ca abundances (0.6 and 0.1,
respectively), and the median ESV (3/3.5 µm3), the
latter being comparable with the ESV of F1 particles. Si abundances far
above 0.4 suggest that these are silica particles. It should be also noted
that, as particles in F1 and F2 show similar ESV but different Si abundance,
differences in the particle density can be supposed between these two
groups. Finally, particles with the highest score in F3 (Fig. 2c) are mainly
ascribed to road dust and travertine samples and show Ca abundances around
0.4, which can be related to CaCO3.
The PCA solution found on the XEDS data set of particle elemental
composition is thus coherent with the real mineralogical nature (silicate or
calcite) of particles, indicating that the sample-targeted internal standard
approach applied to the quantification of particle composition provided
reliable results.
Mineralogy of samples and allocation of individual particles
The mineralogical composition quantified by XRD analysis is reported in
Table 2.
Average mineralogical composition (wt %) of dust samples by XRD
analysis.
Volcanic
Siliciclastic
Marlstones
Road dust
Road dust
Travertine∗
rocks
rocks
(Volcanics)
(Travertine)
Phyllosilicates
57
52
26
7
–
–
Tectosilicates
18
6
0.7
8.7
6
–
Inosilicates
26
–
1.5
22.7
10.2
–
Quartz
4
11
4
1.3
3
–
Calcite
–
31
68
60.3
81
> 90
∗ After Pentecost (2005)
The main differences include the increasing amount of calcite (volcanics < siliciclastic rocks < marlstones < travertine),
the absence of inosilicates in siliciclastic rocks and travertine, the
negligible amount of phyllosilicates in travertine and the considerable
presence of quartz in siliciclastic rocks. All of these features are
consistent with the geological processes involved in the formation of each
rock type. Calcite is a geochemical marker of the sedimentary environment
where rocks are formed, and it is associated with the chemical precipitation of
calcium carbonate. As a consequence of that, while its presence in volcanic
rocks is negligible, in the marine deposits (marlstones) it is dominant and
in the siliciclastic series it represents the second most abundant mineral
component after phyllosilicates.
Moreover, it is the almost exclusive component of travertine, generated by
the precipitation of CaCO3 near the hot hydrothermal springs of the
Tivoli basin (Pentecost, 2005; Faccenna et al., 2010). The mineralogical
composition of the silicate component in marlstones and siliciclastics dust
is strictly related to the originating materials. Rock-forming processes
(erosion, fluvial and marine transport, sedimentation) support, in this
case, the presence in the PM10 fraction, as detected by XRD in the bulk
dust sample, of stable silicates (plagioclase and quartz), the reduced
presence of inosilicates and the presence of alteration by-products, such as
phyllosilicates. Different processes must be considered in volcanic rocks,
which explain the mineralogical composition of silicates observed in the
PM10 resuspended from this geological material; specifically,
crystallization is the main responsible process, in this case.
Thus, the presence of most minerals observed in the PM10 from volcanic
rocks is coherent with the magmatological framework of central Italy.
Differently from the above considerations, however, the association
kaolinite – quartz, observed by SEM XEDS microanalysis in this PM10
dust type, has to be ascribed to rock alteration (weathering). In this case,
quartz is thus the product (with kaolinite) of the hydrolysis reaction of
feldspars (Jackson et al., 2010), and not a crystallization-derived phase.
Considering the results of the allocation procedure (Sect. 2.3), mineral
particles in the PM10 dust samples were mainly classified as follows:
phyllosilicates (kaolinite, smectite, and micas), representing more than half
of the silicate (non-quartz) fraction of the totality of samples,
tectosilicates (feldspar, chabazite, leucite, and plagioclase) and
inosilicates (clinopyroxene and amphibole), which contribute comparably to
the rest of silicate fraction, quartz, and calcite. About 65 % (percent
abundance) of total phyllosilicates are found in the volcanics, being mainly
kaolinite (observed in the volcanics only) and 60 % of total observed
smectite. Micas are also frequently observed, mainly in the siliciclastics
sample.
Concerning tectosilicates, the overall contribution apportioned to the
PM10 of each geological domain and of road dust has been found to be
47 % in the volcanics, 20 % in the siliciclastic rocks, 33 % in the
road dust, while it appears negligible in the marlstones. Inosilicates were
observed in similar amounts, and solely in the volcanics and in the road
dust. About half of quartz particles identified in the totality of PM10
dust samples is allocated to siliciclastic rocks, while volcanics, marlstone,
and road dust contribute equally to the remaining fraction. Finally, within
the non-travertine sedimentary rocks of this study, the marlstones provide
the most important contribution of calcite particles in the PM10
fraction (ca. 35 %), while the contribution of siliciclastic rocks is
around 8 %. Similar contributions of calcite particles are also allocated to
road dust. These results, obtained by the allocation procedure, are in good
agreement with the mineralogical composition by XRD (Table 2).
To assess the reliability of allocation, a mass closure approach was used.
The particulate mass fraction of each mineral group in the PM10 (sum of
particles mass within a mineral group, per dust sample) was estimated from
results of the allocation after SEM XEDS microanalysis. Afterwards, the
PM10 weight percent composition of the total silicate (including
quartz) content and of the calcite content were calculated; these quantities
were then compared to the corresponding quantity obtained by XRD analysis of
the 50 µm sieved fraction of each dust sample, as reported in Fig. 3.
Total silicate (including quartz) and calcite amounts (wt %) of
dust samples, obtained by X-ray diffraction bulk analysis and SEM XEDS
particle microanalysis.
Although the travertine was not analyzed by XRD, since it can be considered
a pure calcite term (Pentecost, 2005), results of the SEM XEDS microanalysis
of the travertine dust have been reported for this sample too. In all dust
samples (excluding the case of travertine), a good comparability with
analytical results of mineralogical composition by XRD are observed for mass
estimates obtained from the allocation of individual particles. Besides
indicating that the allocation procedure produced reliable results, this
also suggests that the silicate and calcite contents of the PM10 and
of the 50 µm sieved fractions of dust are likely similar, as yet
reported in literature (Rashki et al., 2013).
Connections between geochemical processes of rock sources and the PM10
fraction of minerals
The size distribution of resuspended geological materials is influenced by
two important contributions: the physical properties of particles (e.g. size
and density), which affect the dust resuspension and transport; and the
geological features of the rock source, which determine the particle
mineralogical identity. In this view, size distribution of the PM10
fraction has been discussed either for individual mineral species (quartz,
kaolinite, feldspars, and calcite), and for the overall local lithogenic
dusts (volcanics, siliciclastic rocks and travertine); in the latter case
the totality of mineral particles identified in each dust type was
considered.
In Sect. 3.4.1, size distributions of individual mineral species have been
investigated with respect to the clay fraction according to Claquin et al. (1999), Nickovic et al. (2012),
and Journet et al. (2014), while in Sect. 3.4.2 volume distributions (Formenti et al., 2014) of mineral species and of
lithogenic dusts are discussed.
Clay fraction of minerals
In this part of the study the classified mineral particles were treated with
respect to the geochemical processes which they can be related to, with the
aim of relating the size distribution of each mineral species to the
geochemical processes acting on the rocks to generate the PM10 fraction
of that mineral species. Therefore particles are here named as follows:
phyllosilicates, including clay-minerals (kaolinite, illite, smectite and
chlorite groups), and representing thus the contribution of weathering and
pedogenesis to the resuspension from outcropping rocks; “other silicates”,
including phases such as plagioclase, K-feldspar, pyroxene, and quartz, which
can be considered to be crystallization products in volcanics rocks, or debris
phases in sedimentary rocks; calcite, differentiated by lithogenic and road
dust particles. The approach of Claquin et al. (1999) was adopted in
choosing mineral species for which the mass percentage in the clay fraction
(particle size < 2 µm) was calculated, on the basis of the
particle ESD. Mass percentages of mineral in the clay fraction of PM10
dust samples of this study were compared with those obtained by Journet et
al. (2014) for the modelled global yearly average composition of airborne
minerals. With respect to the latter (abbr. gyac), the mineralogical composition
of the Rome local geological PM10 shows the following similarities, or
discrepancies: (1) the amount of quartz in the clay fraction of the
siliciclastic PM10 (20 %) and of the volcanics PM10 (8 %) is
significantly higher compared to the gyac (4.9 %); (2) feldspars in the clay
fraction of both the volcanics (4 %) and siliciclastic PM10 (2.5 %)
are comparable to feldspars in the gyac (3.6 %); (3) kaolinite dominates the
clay fraction of the volcanics PM10 (63 %) and it is negligible in
the other PM10 dust types (ca. 2.5 %), while in the gyac it represents
24.1 % of total mass; (4) smectite in the Rome local geological PM10
ranges 3 to 10 %, that is lower compared to gyac (15.3 %).
With respect to the mineralogical profiles of PM10 dust from sources
located in North Africa (N.A.) and Saudi Arabia (S.A.) (Ganor et al., 2009),
the dust samples of this study show the following differences: (1) large
variability in terms of calcite content (up to 90 % in travertine),
compared to PM10 from N.A. and S.A. (20–30 %); (2) large variability
in terms of tectosilicates (up to 20 % in volcanics) and clay minerals (up
to 57 % in volcanics) compared to PM10 from N.A. and S.A. (1–3 and 30–40 %, respectively); amount of quartz comparable to that in PM10
from N.A. and S.A. (2–4 %) in the case of the siliciclastic PM10, but
significatively different in travertine (undetectable) and in volcanics
(10 %). Moreover, the presence of inosilicates is not reported for the
PM10 from N.A. and S.A., while the latter show the presence of gypsum,
not observed in the PM10 dust samples of this study.
Considering the distribution of particles in the clay and non-clay (ESD > 2 µm) fractions of the mineral PM10 of the Rome area,
main differences are observed between the volcanics and the travertine
types. In the volcanics PM10 the weathering by-products (quartz,
kaolinite and smectite) are comparably distributed in the two size
fractions, indicating that weathering processes produce either small
grain-sized crystals, and altered phases which grow on the surface of large
crystals, resulting in larger particles. The crystallization phases produced
in the volcanics PM10 (feldspars and pyroxene) are instead enriched in
the non-clay size, as implied in the crystallization process.
Source-related differences between natural calcite from travertine and
calcite from road dust were also evidenced. The clay/non-clay
distributions of calcite in the PM10 of either the travertine dust and
the road dust travertine-related, differ significantly from the clay/non-clay calcite distribution in the PM10 of volcanics-related road dust. While in the first case the calcite is comparably
distributed in the clay/non-clay size, the mass percentage of this mineral
in the road dust volcanics-related is higher in the clay size (80 %) than
in the coarser size (60 %). Since the presence of calcite in the volcanics
PM10 is negligible, calcite content in the PM10 road dust
collected in the volcanics can only be ascribed to the asphalt contribution.
It is thus reasonable that this anthropogenic source enriches the size
fraction below 2 µm (ESD) of calcite, more than the coarser one. This
effect is less evident, instead, in the road dust travertine-related, where
the lithological influence of travertine rocks assumes a major role in the
clay/non-clay distribution of calcite.
Volume size distribution of the PM10 fraction:
minerals and lithogenic dust types
Normalized volume size distributions of most abundant silicates
(a), calcite (b), and local dust types (c) in the PM10 fraction.
Calcite is differentiated by natural (travertine) or anthropic (road dust)
origin.
The volume size distributions of quartz, feldspars, kaolinite and calcite
are reported in Fig. 4 vs. the aerodynamic diameter (a.d.); in this
figure, particles have been grouped with respect to belonging to a given
mineral species, without differentiating by geological domain. Figure 4a shows
the distributions of kaolinite, quartz, and feldspars, while in Fig. 4b
distributions of calcite in the two different road dust types and in
travertine are shown.
Volume distributions of the considered silicates are unimodal, with
overlapping maxima around 5 µm. Main differences are in the peak
width: weathering minerals, such as kaolinite, show a broader curve,
compared to minerals from crystallization phases, e.g. feldspars. This is
coherent with the above-described action of the weathering, of generating
particles either in the clay fraction, and in the coarser size, which
contributes to broaden the size range.
Quartz shows an intermediate behaviour, due to different processes acting on
quartz formation: weathering in the volcanics, crystallization in the
siliciclastics.
The different nature of geochemical processes affects also volume
distributions of the overall lithogenic dust types (Fig. 4c). Particularly,
volcanics and siliciclastics dusts show broader distribution than
travertine, due to the dominance of weathering, in the formation of
lithogenic PM10 from volcanic and siliciclastic rocks, with respect to
the importance of crystallization in the travertine domain.
More defined differences are highlighted among the volume distributions of
calcite from lithogenic or anthropic source: in PM10 from travertine,
the volume distribution of calcite shows a very narrowed shape, with maximum
at 5 µm. Conversely, calcite of both road dust types shows broader
distributions, extended to finer sizes, especially in the case of the
volcanics-related road dust. In the latter, the curve is bimodal with maxima
at 3.8 and 1.8 µm, while in the travertine-related road dust it is
unimodal, with maximum at 5.3 µm similarly to calcite in travertine.
The lithogenic or anthropic nature of processes tuning calcite size also
influence the height of volume distributions of calcite.
In the first case, calcite particles mainly originate from crystals formed
in the precipitation of calcium carbonate, as explained in Sect. 3.3; the
variability of particle size is thus limited by chemical – physical
conditions which rule travertine formation. In the second case, the variety
of mechanical solicitations affecting the surface of paved roads, e.g.
abrasion by vehicle riding, is described by a wider particle size range.
Discrepancies observed within volume distribution curves of Fig. 4 suggest
also that individual particle densities may differ within the same silicate
species or within calcite from different dust sources. It is acknowledged
that the density of mineral particles may range significantly due to the
petrological conditions (chemistry, kinetics, and thermodynamics involved in
the crystallization process) associated with the different crystallization
phases, by which mineral particles are formed.
In addition, some general considerations can be given on the particle
density, by taking into account the distribution of particle mass percentage
(discussed in Sect. 3.4.1) and ESD, with respect to the below/above 2 µm
size threshold (coherently with the clay/non-clay distribution).
Decreasing particle density should be expected from first to last of the
following cases: (1) both mass percentage and ESD of particles mainly
distributed below 2 µm; (2) mass percentage mainly observed below the
2 µm size and ESD comparably distributed with respect to this
threshold; (3) both mass percentage and ESD mainly distributed in the size
fraction above 2 µm. In Fig. 4, first case can be related to the
calcite of road dust volcanics-related (80 % of particles showing ESD < 2 µm), while second case applies to quartz and kaolinite,
and last case to feldspars and travertine calcite (60 and 80 %,
respectively, of particles showing ESD > 2 µm).
Height differences among volume distributions of the dust types can be thus
explained in connection with the different presence of a given mineral
species in a dust type. In particular, while the content of kaolinite is
higher in the volcanics than in siliciclastic dust, feldspars, and quartz are
more abundant in the latter. It is thus possible that these minerals play
contrasting roles in defining the average particle density of siliciclastic
dust, and consequently its volume distribution.
Microphysical, optical and radiative properties of the volcanics and
travertine PM10 dust in the Rome area
Microphysical properties
Probability density function fitted to log-normal distribution of
the volcanics and travertine PM10 dust. Error bars represent
uncertainties of bin weight.
In Fig. 5 results of the fitting procedure to log-normal curve, applied to
volcanics and travertine size distribution, are shown.
The curves are reported with respect to the particle physical radius, as
required by the 6SV radiative transfer code. The computed “chi squared”
(χ2) of fitting are, respectively, 0.34 for volcanics and 0.69 for
travertine. Considering 12 degrees of freedom corresponding to the 13
size bins of the optical particle counter, both fitting are below the level
of significance of 99.5 %. It is thus possible to refuse the null
hypothesis that these curves cannot be fitted to a log-normal function. The
following rm and σ values of volcanics and travertine size
distributions fitted to log-normal function are used thus, as input
parameters of 6SV code (Eq. 1):
rmvolc=1.64±0.29µm,σvolc=1.85±0.23µm;rmtrav=1.39±0.72µm,σtrav=2.34±0.46µm.
Results of fitting are in line with findings discussed by Mahowald et al. (2014).
Real (a) and imaginary (b) part of refractive index of the
volcanics and travertine PM10 dust.
The other microphysical property required for 6SV runs is the refractive
index. In Fig. 6 the real (n) and imaginary (k) part of the refractive index
have been interpolated at the 20 wavelenghts of the 6SV code (350, 400, 412, 443,
470, 488, 515, 550, 590, 633, 670, 694, 760, 860, 1240, 1536, 1650, 1950,
2250, 3750 nm), following the spectral data of water-insoluble (Kokhanovsky,
2008; WCP-112, 1986) and calcite-rich dust (Ghosh, 1999) refractive index, respectively, related to volcanics and travertine. While the spectral trend
of volcanics refractive index follows the commonly adopted trend used in the
radiative transfer modelling (RTM) of the dust component (Kokhanovsky,
2008), the travertine dust, being mainly composed of calcite, is a
non-absorbing aerosol in the spectral range considered in this study, as in
this range the imaginary part of calcite refractive index is close to zero
(Sokolik and Toon, 1999; Gosh, 1999).
Optical properties
Single scattering albedo (a) and asymmetry parameter (b) of the
volcanics and travertine PM10 dust.
Optical properties of the volcanics and travertine contribution to Rome's
local mineral dust have been modelled in the 20 wavelengths of the 6SV
code. In Fig. 7 the single-scattering albedo (SSA) and the asymmetry
parameter (g) are shown, which are critical to analyze the aerosol-induced
at-ground radiative flux (Kassianov et al., 2007). The lower SSA of
volcanics, with respect to travertine, attests that the volcanics dust
absorbs the solar radiation in the visible (VIS) spectral domain, as
commonly expected for mineral dust. Conversely, the SSA of travertine
reflects the non-absorbing behaviour of this dust type.
In Fig. 7b the spectral dependence of the asymmetry parameter (g) is shown
for the volcanics and the travertine. As g is higher in the volcanics, in
this dust type particles show higher forward scattering than in the
travertine, mainly in the near-infrared (NIR) spectral domain. These
findings suggest that the local geological dust of the Rome area affects
both the VIS and NIR spectral domains; consequently an influence on the
radiative field is expected as well.
Downward radiative flux at BOA
The radiative modelling has been focused on the downward component of the
radiative impact at BOA due to the volcanics and travertine dust in the Rome
area. This part of the study represents a preliminary investigation of the
direct radiative effect of the local dust component on the solar radiation
at the ground. In Fig. 8a the influence of both local dust types to the downward
BOA solar irradiance (I) in the VIS and NIR spectral domain is shown.
BOA downward solar irradiance (a) of an atmosphere composed by
only volcanics, or travertine, PM10 dust, and volcanics to travertine
irradiance ratio (b).
In order to evaluate the spectral dependence of the irradiance on the
mineralogical composition of dust, the volcanics / travertine ratio is
reported in Fig. 8b. In the VIS domain, the irradiance seems not to be
affected by the mineralogical composition, as the BOA downward irradiance
trends of the two dust types almost overlap. However, in the NIR a sharp
discrimination between the radiative impact of the volcanics and that of the
travertine dust is revealed. Finally, the BOA downward flux obtained by
integrating the downward solar irradiance over the solar spectral domain
(250–4000 nm) is reported in Fig. 9.
Diffuse, direct, and total BOA downward radiative flux (W m-2)
over the 250–4000 nm spectral domain, simulated by the 6SV code.
Direct component calculated in presence of volcanics-only and in presence of
travertine-only dusts show negligible differences, while the diffuse
component depends strongly on the mineral composition. The scattered
radiation of an atmosphere with travertine dust only shows a higher diffuse
component than in the case when volcanics only is assumed.
As a matter of fact, in the Rome area the total BOA downward flux is greater
for an atmosphere where the only aerosol component is the travertine dust
with respect to the sole presence of volcanics dust.
The evaluation of the radiative budget at surface of the local mineral dust
in the Rome area has been performed computing the radiative forcing efficiency
(RFE). The RFE is calculated by simulating the total BOA downward flux with
the local dust component in three conditions of the AOT at 550 nm (0.2, 0.5,
0.7), to estimate the uncertainty on the simulated RFE. The results
highlight the stronger cooling effect at the surface in case of volcanics
(-293±17 W m-2) with respect to travertine (-139±7 W m-2), with uncertainties lower than 5 %.
The aerosol radiative behaviour follow the general trend explained in
Gómez-Amo et al. (2011), that is aerosols with high SSA (low absorption,
travertine in case) produce a decrease in the absolute value of RFE, with
respect to aerosols characterized by high absorption, like the volcanics.
These results need to be confirmed by a more in-depth analysis on the
influence of the local geological dust resuspended from topsoil on the Earth–atmosphere radiative balance, in the Rome area.
Conclusions
In this work, a knowledge gap was faced concerning how, and to which
extent, the local mineral dust resuspended from rocks outcropped in a
site/area may contribute to the PM10 fraction, to the direct
interaction (light scattering and absorption) of the aerosol with solar
radiation, and to the radiative flux at BOA, within
the same source area of dust. To reach this goal, a methodology was developed that is suitable for general application; nevertheless, results reported
here are intrinsically narrowed to the features of the Rome area. Investigation
was carried following three paths: site-specific analysis of the geochemical
and mineralogical environment, individual-particle microanalysis aimed at determining the mineralogical and microphysical properties
of dust, and modelling of the dust radiative effects with respect to optical
features.
Main results concern relationships found between the following: (1) geochemical processes
acting on the source rocks and mineral species associated with particles in
the laboratory-resuspended PM10 fraction of different local dust types;
(2) mineralogical composition of the PM10 dust and variability of dust
microphysical properties (refractive index and size distribution); (3) dust-specific optical properties (single-scattering albedo and asymmetry
parameter) of the PM10 fraction, and total downward flux at BOA
in the visible and near infrared (VIS and NIR) spectral domains.
First issue was discussed on all major outcropped domains in the Rome area
(volcanic rocks, siliciclastic rocks, limestones, marlstones, and
travertine), and on the distinction between calcite from lithogenic source
and calcite from paved road dust, while the second and third issues focused on
the compositional end member of local dust types (volcanics and travertine).
With the exception of pure calcite (associated with PM10 from the
travertine domain (Tivoli basin), and from road dust), PM10 dust types
of the studied area show silicate-prevalent or calcite-prevalent
compositions, depending on the outcropped source rocks: volcanics or
siliciclastics in the first case, marlstones or limestones in the second
case.
Rock weathering processes tune the size and mineral identity of PM10
particles in the silicate-prevalent dust types, more than other processes
(e.g. debris formation, crystallization).
On the other side, chemical precipitation of CaCO3 influences mainly
the particle composition of calcite-prevalent dust types. These differences
reflect in the volume distributions, either of individual mineral species
(kaolinite, quartz, feldspars, calcite), or of dust types.
Weathering processes can be related to larger size variability observed for
some mineral species (e.g. kaolinite and quartz), with respect to feldspars
and to lithogenic calcite.
In the lithogenic PM10 of the Rome area, these minerals are instead mainly
associated with crystallization or to CaCO3 precipitation, occurring
under defined chemical, kinetic, and thermodynamic conditions, which limit
particle size and result in narrow volume distribution. Differences observed
between calcite from lithogenic source and calcite from road dust suggest a
major role of the variability of mechanical solicitations from vehicular
riding on the particle size of road dust calcite. Volume distribution of the
latter interestingly shows bimodal shape, broader width and larger
contribution to fine fraction, differing significantly from lithogenic
calcite and from other investigated mineral species.
These findings indicate that the microphysical properties of different
crustal components (e.g. road dust, dust from building activities,
transported mineral dust, etc.) may differ consistently with source type;
optical properties are reasonably expected to differ consequently.
Spectral trends of the complex refractive index, assumed from literature and
related to volcanics and travertine PM10, show that in the VIS and NIR
domains travertine PM10 is a non-absorbing dust, opposite to volcanics
PM10. We showed that these differences influence the diffuse component
of BOA downward flux, which is higher in the simulated case of an atmosphere
with travertine-only aerosol, coherently with the non-absorbing behaviour of
this dust type. Finally, it is important to underline that the above results
could be assessed only by considering the entire solar spectral domain,
instead of limiting the investigation to the VIS region. The radiative
effects of the two components in the 350–3750 nm spectral domain have been
evaluated by the RFE; results show higher efficiency of volcanics (-293±17 W m-2) in surface cooling effect, with respect to travertine
(-139±7 W m-2), as expected for aerosol with SSA smaller than 1
(Di Biagio et al., 2009, 2010), i.e. the volcanics dust in this case.
Further research on these issues is needed, thus, as it may aid improving
knowledge on the local effects of the presence of different crustal (natural
or anthropic) components of aerosol at a specific site/area, in terms of
aerosol interaction with solar radiation and radiative effects at BOA.