Particle Size and Morphological Evaluation of Airborne Urban Dust
Particles by Scanning Electron Microscopy and Bidimensional
Empirical Mode Analysis
MARCELA MORVIDONE1, IVANA MASCI2, DIANA RUBIO1, MELISA KURTZ2,
DEBORAH TASAT2 AND ROSA PIOTRKOWSKI1
1Centro de Matemática Aplicada,
Instituto de Tecnologías Emergentes y Ciencias Aplicadas (ITECA) UNSAM-CONICET,
25 de Mayo y Francia, B1650 San Martin, Buenos Aires
ARGENTINA
2Laboratorio de Bio Toxicología Ambiental,
Instituto de Tecnologías Emergentes y Ciencias Aplicadas (ITECA) UNSAM-CONICET,
25 de Mayo y Francia, B1650 San Martin, Buenos Aires,
ARGENTINA
Abstract: - Airborne particles affect the health of the population. As particles decrease in size, they can
penetrate deeper into the respiratory system, reaching the terminal bronchioles and alveoli. Particles as small as
0.1 µm in diameter may translocate into the bloodstream, potentially impacting various organs. Additionally,
the smaller the particle size, the longer they remain suspended in the air, thereby increasing their deleterious
damages. The aim of this work is to study the size distribution of airborne particles emitted from anthropogenic
sources of air pollution, with a special emphasis on estimating the distribution of micro and nanoparticles
considered the most harmful to health. The Bidimensional Empirical Mode Decomposition (BEMD) algorithm
was used on micrographs of the particles obtained by Scanning Electron Microscopy (SEM). BEMD is a
current empirical computational tool applied to image analysis that allows extracting non-linear heterogeneous
oscillations of brightness. We studied ROFA (Residual Oil Fly Ash) from industrial sources and DEP (Diesel
Exhaust Particles) from vehicular emissions as airborne particles. After collecting the particles on filters,
micrographs were taken using SEM at different magnifications to which the BEMD algorithm was applied.
Particle size and asymmetry distributions were obtained for each mode, allowing the identification of the most
deleterious particles. The methodology employed herein is relatively simple and effective for inferring the
impact of airborne particulate matter on health and the environment.
Key-Words: - airborne particles, morphological characterization, Scanning Electron Microscopy (SEM),
Bidimensional Empirical Mode Decomposition (BEMD), Generalized Extreme Value
Distribution (GEV).
Received: March 26, 2024. Revised: August 27, 2024. Accepted: September 19, 2024. Published: October 21, 2024.
1 Introduction
Proper characterization of bidimensional structures
significantly impacts applications, leading to better
decisions in assessing pollution consequences.
Multiscale methods and algorithms, such as
Bidimensional Empirical Mode Decomposition
(BEMD), are continuously evolving. Their
optimization allows for simpler, more accurate, and
precise representation of information. Empirical
Mode Decomposition (EMD), introduced by
Huang et al. in 1989 [1] for one-dimensional
analysis, is effective for non-stationary and
nonlinear time series. In 2003, [2], extended EMD
to BEMD for image texture analysis, leading to
ongoing advancements in BEMD, [3]. The BEMD
algorithm is a computational tool that enables the
extraction of nonlinear, heterogeneous brightness
oscillations from an image. This study focuses on
analyzing multimodal images and their
decomposition using BEMD, applied to Scanning
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Electron Microscopy images of airborne urban dust
particles.
Air pollution is a mixture of gasses and
suspended Particulate Matter (PM) from both
anthropogenic sources [4] and natural events [5].
Urban environments are significantly impacted by
airborne dust particles, which pose risks to human
health, environmental quality, and urban
infrastructure. Epidemiological investigations reveal
a correlation between elevated concentrations of PM
from different sources and heightened rates of
morbidity and mortality associated with
cardiopulmonary diseases, as well as an increased
incidence of lung cancer, [6], [7], [8]. In 2009, the
World Health Organization [9] (WHO, 2009)
declared that air pollution was responsible for 8% of
lung cancer and 5% of deaths from cardiopulmonary
causes in the world. Furthermore, in 2013, the
International Agency for Research on Cancer
(IARC) classified MP as carcinogenic to humans,
[10].
In this context, several in vitro and in vivo
experimental studies have provided conclusive
evidence regarding the direct genotoxic effects of
airborne PM resulting from its physicochemical
characteristics, as well as the indirect genotoxicity
observed in response to PM-induced inflammation,
[11], [12]. Specifically, concerning the subjects
discussed in this article, various authors have
consistently demonstrated and continue to elucidate
the detrimental impacts of anthropogenic particulate
matter derived from urban and industrial sources,
such as diesel exhaust particulate matter (DEP) and
Residual Oil Fly Ash (ROFA), on human health,
[13], [14], [15], [16], [17], [18].
The atmosphere, whether in urban or remote
areas, contains significant concentrations of aerosol
particles sometimes as high as 107-108 cm-3. The
diameters of these particles span over four orders of
magnitude, from a few nanometers to around
100μm. To appreciate this wide size range, one just
needs to consider that the mass of a 10μm diameter
particle is equivalent to the mass of one billion 10-
nm particles. Combustion generated particles, such
as those from automobiles, power generation, and
woodburning, can be as small as a few nanometers
and as large as 1μm. Windblown dust, pollens, plant
fragments, and seasalt are generally larger than
1μm. Material produced in the atmosphere by
photochemical processes is found mainly in
particles smaller than 1μm. The size of these
particles affects both their lifetime in the
atmosphere and their physical and chemical
properties. It is therefore necessary to develop
methods to mathematically characterize particle size
distributions, [19].
The topic concerning the number size
distribution of particles in the range 1-1000 nm is
presently under active investigation in current
research, e.g. various advanced particle size
magnifiers were developed in the last ten decades,
[20], [21]. SEM and image processing have been
effectively employed in prior studies to assess the
morphology, particle size, and particle size
distribution of airborne hardwood sanding dust,
[22].
The aim of this work is to study the size
distribution of airborne particles emitted by
anthropogenic sources of pollution, with a special
emphasis on estimating the distribution of micro-
and nanoparticles, which are considered the most
harmful to health.
2 Problem Formulation
To achieve the precision needed for obtaining air
particle size distribution and morphological
characteristics, advanced techniques such as
Scanning Electron Microscopy (SEM) and
Bidimensional Empirical Mode Decomposition
(BEMD) are used in the initial stage. SEM allows
for high-resolution imaging and BEMD offers a
refined approach to decompose complex structures
into simpler components.
Secondly, another challenge lies in accurately
characterizing the size and morphology of airborne
urban dust particles to better understand their health
impacts. To this end, we need adequate
characterization for the location, size and
morphology of particles.
3 Materials and Methods
3.1 Particle Sources
Herein, we employed particles from two different
sources: Residual Oil Fly Ash (ROFA) and Diesel
Exhaust Particles (DEP).
Residual Oil Fly Ash (ROFA) collected from
the Mystic Power Plant, CT, USA, was
employed as a recognized surrogate ambient
particulate matter and was kindly provided by J.
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N
BIMFk , k=1,…,Nm
Initialize k=1
Is k=Nm+1?
Calculate all local maxima
in BIMFk
Update k=k +1
Calculate horizontal and
vertical nearest minima:
r
1,
r
2.
r
3.
r
4
Calculate particle size
d=
2
r
r
=max{
r
1,
r
2,
r
3,
r
4 }
Calculate diameters
dH=r2+r4
dV=r1+r3
Calculate particle
asymmetries
A=
|
dH-dV
|/max{
dH,dV
}
\
Y
End
Godleski (Harvard School of Public Health,
Boston, MA, USA).
Diesel exhaust particles (DEP) (SRM2975)
were purchased from the US National Institute
of Standards and Technology.
3.2 Scanning Electron Microscopy
Average particle size (APS), size distribution, and
morphology of both types of particulate matter,
ROFA and DEP, were studied using SEM (Quanta
250 FEISEM, Pantelimon, Romania) coupled to a
(ThermoFisher) energy-dispersive X-ray
spectroscopy (EDX) detector for chemical
composition analysis. For this purpose, the PM
particles were attached to a double-sided carbon
conductive tape, and loose powder was eliminated
using a N2 gun to avoid the release of particles
inside the microscope chamber when starting the
vacuum pump or ventilation; the samples were
analyzed after coating them with gold by direct
current sputtering. Images were obtained using a
high-efficiency in-lens detector to achieve clear
topographic images in high vacuum mode at an
acceleration voltage of 4kV-10kV.
3.3 Algorithm for Particle Characterization
Once the image (matrix) has been decomposed
following, [23] into Bidimensional Implicit Mode
Functions (BIMFs), the particles or structure
elements can be identified by locating the local
maximum values within each of these matrices.
Subsequently, for every detected particle, its size is
determined by assessing the local minimum values
along the horizontal (H) and vertical (V) direction
from each local maximum. This process yields four
values, denoted clockwise as
r
1,
r
2.
r
3
,
and
r
4. Note
that, since the particles are randomly oriented, these
values are of statistical nature. The particle diameter
(d) and asymmetry (A) are defined as:
d
=2 max{
r
1,
r
2,
r
3,
r
4} (1)
(2)
where
dH=r
2+
r
4,
dV=r
1+
r
3
(3)
The flowchart of the proposed algorithm that
determines and computes particle sizes and
asymmetries is presented in Figure 1.
Fig. 1: Flowchart of the proposed algorithm
N
Image Matrix I
I
Initialize r=I, m=1, h=r
Define envelopes Emax Emin
Compute mean envelope EM
Update h=h - EM
Update r = r - h
Y
Define
BIMFm = h
Update m= m+1
N
Number of
Modes=Nm
Y
Y
Mean(h)=0?
Start
Number of
extrema(r)<2?
2w2 (h)=0?
Calculate extrema of h
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After applying the algorithm, we proceed to
obtain the histogram of the particle sizes for each
mode.
Each histogram is fitted by a statistical
distribution that is selected according to qqplot
(quantile-quantile plot) that is a graphical method
for comparing two probability distributions by
plotting their quantiles against each other.
In the next section, the algorithm is applied to
two examples: ROFA and DEP images.
4 Results and Discussion
PM sizes, air particles are conventionally classified
as coarse (PM10), fine (PM2.5), and ultrafine
(PM0.1) particles, corresponding to their
aerodynamic diameters being lower than 10μm,
2.5μm, and 100nm, respectively. Lower diameters
imply higher deleterious impact on public health.
We show for ROFA and DEP one original SEM
image and significant BIMFs images obtained by
BEMD in Figure 2 and Figure 3.
Then we select the significant modes for each
case and obtain the histogram for particle sizes for
these modes. Significant modes are those where the
smallest details correspond to individual particles
rather than boundaries. In the higher modes,
associated with larger structures, enough particles
should be detected to obtain a representative
distribution. After evaluating various distribution
types for fitting the histogram for particle sizes,
Generalized Extreme Values (GEV) distribution
was selected for both examples, due to its superior
performance according to the qqplot. The fitting
parameters for GEV distribution are:
: Shape parameter (type of tail behavior),
: Scale parameter (spread or dispersion),
: Location parameter (central value).
Section 4.1 is concerned with ROFA particles.
Section 4.2 is concerned with DEP particles.
4.1 Example 1: Application to ROFA
By comparing the original ROFA image in Figure 2,
it can be observed that BIMF1 and BIMF2 primarily
correspond to particle boundaries, while BIMF3 and
BIMF4 display structures resembling particles of
varying sizes. In contrast, BIMF5 presents a more
diffuse image with fewer particles. For this reason,
only Modes 3 and 4 are considered significant.
Figure 4 shows a histogram of the particle size
distribution along with the fitted GEV distribution
for the ROFA significant modes. The estimated
GEV parameters are also included in Figure 4 while
the confidence intervals are in Table 1. As expected,
Mode 3 identified a significant number of the
smallest particles. This is highlighted by the location
parameter μ (GEV location parameter), which takes
a value close to 8μm. Moreover, particle sizes in
BIMF3 are more concentrated since the value of
spread parameter (GEV parameter
:) is smaller
than BIMF4 and has a shorter tail (related to the
GEV parameter
).
There is not a clear relationship between the
degree of asymmetry and the size of a particle.
However, the plots of asymmetry versus diameter
presented in Figure 5 suggest that the degree of
asymmetry of the particles tends to increase with
diameter in both Mode 3 and Mode 4.
4.2 Example 2: Application to DEP
In this example, Figure 3 suggests that modes
BIMF2, BIMF3 and BIMF4 show structures related
to particles having different sizes while BIMF5
shows a more diffuse image with a lower quantity of
particles. For this reason, in this case Modes 2, 3
and 4 are considered for analysis.
In Figure 6 and Figure 7 are plotted the results
obtained for particle sizes and asymmetries for DEP
for each significant mode.
In this case, GEV distribution showed a better
performance for fitting the histogram. The estimated
parameters for each Mode are included in Figure 6
while the confidence intervals are in Table 2.
Modes 2 and 3 primarily identify ultrafine
particles while Mode 4 captures fine particles. It is
confirmed that, as usual, both particle size and
spread increase with the mode number.
To analyze the degree of symmetry, we plotted
asymmetry versus diameter in Figure 7, as it was
done for ROFA (Figure 4). In DEP example, the
points representing asymmetry versus size for each
detected particle are more randomly dispersed.
These findings are important because, as it was
mentioned before, the smallest airborne particles
significantly impact human health due to their
ability to penetrate deep into the respiratory system
and enter the bloodstream. Once inside the body,
they can cause inflammation, oxidative stress, and
cellular damage. Prolonged exposure to ultrafine
particles has been linked to a range of adverse
health effects, including respiratory and
cardiovascular diseases, and even cancer.
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a)
b)
c)
d)
e)
f)
Fig. 2: a) Original ROFA SEM image. b) -f)
Successive BEMD Modes BIMF1–BIMF5
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a)
b)
c)
d)
e)
f)
Fig. 3: a) Original DEP SEM image. b)-f)
Successive significant BEMD Modes IMF1–IMF5
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Fig. 4: ROFA particles histogram and GEV fitting
distribution for the diameters identified in
significant Mode 3 (top) and Mode 4 (bottom)
Fig. 5: ROFA particles asymmetries vs. diameters of
the identified particles in significant Mode 3 (top)
and Mode 4 (bottom)
Table 1. Confidence Intervals for GEV fitting
parameters (ROFA)
Fig. 6: DEP particles histogram and GEV fitting
distribution for the diameters identified in
significant IMF: Mode 2 (top), Mode 3 (middle) and
Mode 4 (bottom)
Mode
:
3
[0.031, 0.097]
[6.145, 6.624]
[7.754, 8.398]
4
[0.0912 0.247]
[12.06, 14.27]
[13.04,15.875]
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Table 2. Confidence Intervals for GEV fitting
parameters (DEP)
Fig. 7: DEP particles asymmetries versus diameters
of the identified particles in Mode 2 (top), Mode 3
(middle) and Mode 4 (bottom)
5 Conclusion
The Bidimensional Empirical Mode Decomposition
(BEMD) allowed obtaining the multimodal
distribution of airborne dust particle size from
scanning electron micrographs. We proposed an
algorithm that begins with BEMD. Once the
component images are obtained, they are compared
with the original image to identify the significant
modes. In the applications presented in this work,
these modes are associated with particles, with the
consideration that enough particles are necessary for
statistical size distribution analysis. The algorithm
then identifies particles for each significant mode,
calculates their sizes, and evaluates their
asymmetry. This technique was applied to SEM
micrographs of ROFA and DEP particles, providing
valuable insights into the morphological features of
these airborne particles. The ROFA samples,
consisting mainly of microparticles, were
distinguished by their irregular shapes and varying
sizes, whereas the DEP samples comprised
relatively more symmetric nanoparticles, reflecting
their different sources and formation processes. For
each significant mode, histograms of particle size
distribution were appropriately fitted with
Generalized Extreme Value (GEV) distributions.
This fitting process provided a robust statistical
framework to describe the particle populations,
highlighting the prevalence of certain size ranges
and the variability within the samples.
While further analysis is needed to gather robust
information for decision-making, the methodology
employed herein is relatively simple and effective
for inferring the impact of the different sources of
airborne particulate matter on health and the
environment.
Declaration of Generative AI and AI-Assisted
Technologies in the Writing Process
During the preparation of this work the authors
used Grammarly for language editing. After using
this service, the authors reviewed and edited the
content as needed and take full responsibility for the
content of the publication.
Mode
:
2
[-0.085, 0.054]
[-0.10, -0.02]
[ -0.152, 0.06]
3
[0.074, 0.178]
[0.197, 0.216]
[ 0.42, 0.520]
4
[0.268, 0.273]
[0.591, 0.617]
[ 1.22, 1.36]
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors contributed equally to this research,
participating in all stages from problem formulation
to final findings, solutions and writing. M.
Morvidone, D. Rubio and R. Piotrkowski. were
engaged in the mathematical and computations
formulation and the algorithm development and
writing. I. Masci, M. Kurtz and D. Tasat in the
biological formulation, the SEM images and
writing.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This work is part of the Scientific and Technological
Research Project (PICT 2021-I-A-00598)
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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