Field observations show that individual aerosol particles are a complex mixture of a wide variety of species, reflecting different sources and physico-chemical transformations. The impacts of individual aerosol morphology and mixing characteristics on the Earth system are not yet fully understood. Here we present a sensitivity study on climate-relevant aerosols optical properties to various approximations. Based on aerosol samples collected in various geographical locations, we have observationally constrained size, morphology and mixing, and accordingly simulated, using the discrete dipole approximation model (DDSCAT), optical properties of three aerosols types: (1) bare black carbon (BC) aggregates, (2) bare mineral dust, and (3) an internal mixture of a BC aggregate laying on top of a mineral dust particle, also referred to as polluted dust.
DDSCAT predicts optical properties and their spectral dependence consistently
with observations for all the studied cases. Predicted values of mass
absorption, scattering and extinction coefficients (MAC, MSC, MEC) for bare
BC show a weak dependence on the BC aggregate size, while the asymmetry
parameter (
This paper demonstrates that observationally constrained DDSCAT simulations allow one to better understand the variability of the measured aerosol optical properties in ambient air and to define benchmark biases due to different approximations in aerosol parametrization.
Black carbon (BC), a distinct type of carbonaceous aerosol particle, is
produced by incomplete combustion of fossil and biomass fuels. BC is a strong
light absorber and therefore can contribute to atmospheric warming and
surface dimming. Estimates of direct BC radiative forcing (DRF) are highly
uncertain and range from 0.2 to 1.2 W m
In
Despite that, the core-shell configuration cannot always represent the
absorption variability in the laboratory and field observations
Several field campaigns have been showing the occurrence of internal mixing
of BC with dust aerosols in the accumulation mode
Internal mixing of mineral dust and BC aggregates has a strong impact on the
optical properties of originally externally mixed aerosol, on their radiative
forcing
An accurate parametrization of aerosol optical properties due to variability
in morphology and mixing with other aerosol compounds is crucial for a number
of disciplines involving not only radiative forcing analysis
For example, retrievals of aerosol (and ocean) properties require the assumption
of (1) the scattering phase function, (2) single scattering albedo (SSA),
(3) estimates of ozone absorption and molecular scattering, and (4) for
satellite applications, estimates of surface reflectance/albedo. Both orbital
and ground-based remote sensing techniques use a pre-selected library of
aerosol types in the analysis of radiometric data. The computations of
optical properties for the library often make use of spherical shape
assumptions. The assumptions of the mineral dust particles' shape may vary in the
retrieval algorithms. AERONET retrieval assumes mineral dust particles to be
spheroidal
The capability of the satellite and AERONET aerosol global network to provide
spatiotemporal distributions of both dust and BC at different spatial
scales relies on how well the aerosol library used in the retrieval “fits”
the aerosol mixture in the atmosphere; therefore, it is dependent on the
accuracy of the retrieval assumptions on dust and BC optical properties.
Therefore, non-sphericity and chemical anisotropy of the particles are
sources of potential inaccuracy and biases of data products. These
inaccuracies may affect the retrieval of aerosol characteristics, such as
refractive index, size, aerosol optical depth, aerosol absorption optical
depth, etc.
In this paper we present an “observationally constrained” sensitivity study
of the optical properties of BC aggregates internally mixed with mineral dust
aerosols in the UV–IR spectral range (computationally intensive). The
spectral range used in this study is of interest for applications in climate
modeling, remote sensing of aerosol and ocean properties, and visibility
forecast. Computations are performed using the discrete dipole approximation
(DDA – DDSCAT7.3)
We generate synthetic BC aggregates by aggregation of monomers in random walk
The reader should be aware that the morphological characterization of the
ambient aerosol is determined by processing 2-D electron microscope images on
aerosol particles laying on a substrate and assuming orientational anisotropy
over a statistically representative sample. Therefore, as a
Characteristics of the modeled aerosol particles. Dust particles have an aspect ratio for all three axes (AR) of 1.75 in all cases (oblate). The optical properties of dust, BC and mixtures were averaged over 1000 random orientations.
In this paper, we describe the BC aggregate morphology and chain-like
structure in terms of (1) fractal dimension, porosity
BC particles can be represented as fractals, where each particle is described
as an aggregate with monomers of the same size, approximately obeying the
following scaling law
The aspect ratio (AR) of the fractal aggregate, is defined as the ratio of
the major axis (
The roundness of a fractal aggregate, is defined as the ratio of the
projected area (
The chain-like structure has been characterized in terms of convexity,
porosity and fractal dimension (see Eq.
References of the wavelength-dependent refractive indices and
density values used for BC and mineral dust. The
The porosity (
Morphological descriptors of synthetic BC aggregates are calculated from projected images of 50 random particle orientations.
The morphology of suspended mineral dust might take various forms, as natural
dust is an aggregate of internally mixed minerals. Different field studies
show AR median values ranging between 1.4 and 1.9
We summarize the characteristics of the synthetic/modeled aerosol particles
in Table
We have numerically simulated the optical properties for an ensemble of bare
mineral dust, bare open-chain-like BC aggregates and internal mixtures of BC
and mineral dust (see Table
The following are the optical properties discussed in this study.
The mass absorption, scattering and extinction coefficients (MAC, MSC and
MEC): The aerosol absorption, extinction, and scattering Ångström exponent
(AAE, EAE, SAE) computed from the slope of the linear fit passing though MAC,
MSC and MEC curves (in log–log scale). The AAE and EAE are typically used as
indicators of aerosol type and size. The SSA is calculated as The asymmetry parameter is defined as SSA and
We have observed BC internally mixing with suspended mineral dust (BC particles
laying on top of dust particles) in various field campaigns. In
Fig.
The morphological characteristics of the BC and mineral dust particles are
summarized in Table
SEM images of internally mixed mineral dust and BC particles
observed during various field campaigns:
Representation of four BC aggregates (not in scale) with increasing
number of monomers (see Table
Synthesis of morphological descriptors for BC and mineral dust aerosol particles sampled in various field campaigns.
In order to ensure that the shape of the synthetic BC aggregates are
representative of ambient air samples, we processed the 2-D binary images of
the synthetic particles at 50 random orientations. For synthetic aggregates
presented in Fig.
The accuracy of
Comparison between the actual monomer (
Morphological characterization of synthetic BC particles.
The spectral dependence of mass extinction, absorption and scattering
coefficients (MEC, MAC, MSC) is presented in Fig.
It is well known that bare/uncoated fresh BC absorbs more radiation than it
scatters
The range of predicted MAC values at 550 nm is in agreement with field
measurements by
However, several studies
AAE, EAE and SAE values are wavelength dependent (see
Table
Summary of optical properties predicted by DDSCAT for bare BC
aggregates at 550 nm. AAE and EAE have been calculated in different
wavelength ranges: (a) 340–1000 nm, (b) 400–675 nm and (c) 340–1600 nm
(spectral range not shown in Fig.
In all the field campaigns presented here, we have found mineral dust
particles with jagged surfaces and irregular shape (see Fig.
The sensitivity of
Differences in percentage between extinction, absorption and scattering efficiency for spheroids vs. rectangular prisms.
We have modeled binary internal mixtures of BC aggregates and mineral dust,
as visualized in Fig.
Visual representation of polluted dust, as an internal mixture of BC and mineral dust. The shape of the particle is represented by an array of coordinates (small dots or spheres) to which is associated a dipole moment. Brown dots represent the dust particle dipoles, while grey, small spheres represent the dipoles of the BC aggregate. The cases BL2S1, BL2S2, BL2S3, and BL2S5 have BL2, respectively, on the surface of S1, S2, S3 and S5. Arrows show that sides of the rectangular prism can vary keeping the aspect ratio constant to a value of 1.75.
In Fig.
Bare mineral dust aerosols (see cases S1–S5 in
Fig.
The internally mixed particles (cases BL2S1–BL2S5, also referred to as
polluted dust) have higher MAC values for smaller particles (BL2 has the
highest MAC). As expected, DDSCAT predicts higher MAC values for polluted
dust than for unpolluted/bare dust, with an average MAC value of
0.26
Furthermore, MSC values of bare mineral dust aerosols have a strong variability
with size and wavelength. DDSCAT predicts an average MSC value at 550 nm of
2.1
Furthermore, representation of the state of aerosol mixing, whether internal
(such cases BL2S
The latter might be due to the combination of (1) small electromagnetic
interactions between the BC aggregate and the mineral dust particle, due to
the small size parameter; and (2) the small difference in size between BC and
mineral dust particles (with a mixture
SSA for different particle sizes in the accumulation mode:
The SSA spectral signatures of bare BC (BL2), an ensemble of mineral dust
(cases S1–S5), and internal mixtures of the two aerosol components
(BL2S1–BL2S5) are shown in Fig.
For wavelengths shorter than 500 nm, small polluted dust particles
(BL2S1 and BL2S2) show a stronger decrease in the SSA magnitude compared to
unpolluted dust particles (S1 and S2); perturbation of dust optical properties
of the same order of magnitude was also found in the Aerosol Characterization
Experiment (ACE) field campaign
In an attempt to synthesize the differences between the above-discussed
optical properties of bare BC and internal mixtures, we found that with the
increase in size of mineral dust, the absorption increases; however, also the
scattering of the internal mixture (cases BL2S1–BL2S5) increases, leading to
larger SSA values for internal mixtures compared to bare BC (case BL2) (not
shown here, as we provide MAC normalized by the total mass of the particle,
not just BC mass). The increase in the absorption, despite no embedding (no
“lens effect”; see also
DDSCAT predicts a wavelength-dependent asymmetry parameter
Summary of simulated optical properties for mineral dust and internal mixtures with BC aggregates. AAE and EAE have been calculated in two different wavelength ranges: (a) 340–1020 nm and (b) 400–675 nm.
Microscope images of ambient air aerosol samples collected in various
locations of the globe show the occurrence of internal mixtures of BC aggregates
and mineral dust aerosols
In this study, we carried out numerical simulations to investigate the sensitivity of climate-relevant aerosol optical properties to various approximations on aerosol size, shape and state of mixing and draw benchmark considerations for climate studies and remote sensing applications. Based on aerosol samples collected in Mexico, England, USA (California) and Portugal, we have observationally constrained morphology and mixing and modeled optical properties accordingly, of three different types of aerosols: (1) bare BC aggregates, (2) bare mineral dust, and (3) an internal mixture of BC and dust particles, also referred to as polluted dust.
Optical properties including MAC, MEC, MSC, AAE, EAE, SSA and
Key results for bare BC aggregates include (i) a weak MAC dependency on the
aggregate size, but stronger MAC dependency on the refractive index, in
agreement with
Key results for bare mineral dust aerosol include (i) a strong sensitivity
of dust optical properties to shape (DDSCAT predicts at 550 nm an average
difference between spheroids and prisms of about 20 % for MEC and MSC,
while of about 5 % for MAC); (ii) a consistency between DDSCAT-predicted and observed values of MAC, MSC and SSA reported by
Key results for polluted mineral dust, an internal mixture of BC and mineral
dust, include (i) a strong decrease in MAC values with the increase in dust
particle size (case BL2S1 presents largest values), while the opposite for
SSA values. (ii) A decrease in the SSA magnitude compared to bare dust for
smaller dust particle sizes (cases BL2S1 and BL2S2) in agreement with
With this study, we demonstrated the importance of (i) characterizing and defining microphysical properties, such as morphology/shape and mixing of different aerosol types collected in ambient air, (ii) estimating optical properties accordingly to observations, and (iii) defining eventual benchmark errors due to use of approximations in shape and mixing. More studies are needed to assess the abundance of polluted dust particles in the atmosphere. In fact, the occurrence of such configuration is currently highly uncertain and might strongly depend on source and transport regions. Accounting for changes in optical properties, induced by mixing as well as by the abundance of mixed particles, might be critical not only for calculating the relevance of such particles on regional radiative forcing but also to understand biases in remote sensing techniques and to explore the potential of such techniques in remotely detected mixed particle cases.
The work in this paper has been funded by the Research Initiation Program at the Naval Postgraduate school. Some of the work discussed in this paper was funded through the following grants: NASA (grant NNX13AN68H), NSF (grant AGS-1110059), DOE (grants DE-SC0006941 and DE-SC0010019). S. China and C. Mazzoleni would also like to acknowledge the contribution of several collaborators while collecting aerosol samples in several field campaigns and utilized here. Scarnato would like to acknowledge Denis Richard for providing the aggregation code, and Sanaz Vahidinia for building a first version of the internal mixing code. Edited by: M. C. Facchini