KONA Powder and Particle Journal No. 41 (2024) 42–57/https://doi.org/10.14356/kona.2024008

Review Paper

Measurement of Fugitive Particulate Matter Emission:
Current State and Trends †Tianyi Cai and Wu Zhou *Institute of Particle and Two-phase Flow Measurement, Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering,
University of Shanghai for Science and Technology, China

Fugitive particulate matter (FPM) refers to a mixture of solid particles
and liquid droplets that are released into the air without passing throughconfined flow equipment. These emissions of FPM can originate fromnatural processes and anthropogenic activities. FPM emissions are an
important source of PM2.5. Precisely measuring the size, concentration,and other properties of such particulate matter is crucial for effectively
controlling emission sources and improving air quality. However,compared with particulate matter emission from stationary sources, it isdifficult to monitor the FPM effectively owing to its dispersive andirregular emissions. Traditional measuring methods for FPM are basedon sampling, which is a point monitoring approach and can be timeconsuming. In recent years, several new techniques based on opticalprinciples, image-based processes and low-cost sensors have beendeveloped and applied for FPM measurement, with the advantages ofspatial and time resolutions. The current state and future development
of FPM measurements are reviewed in this paper.
Keywords: fugitive particulate matter, air quality, light scattering, image-based measurement, low-cost sensor, real-time

1. Introductions

Particulate matter (PM) has drawn massive attention
owing to its great impact on human health and wealth(Amoushahi et al., 2022; Chen et al., 2019; Hassan et al.,2022), environment, and global climate (Hassan et al.,2020; Mutlu, 2020; Yang et al., 2021). Fugitive particulatematter (FPM), which refers to a mixture of solid particles
or liquid droplets that are not discharged into the atmosphere through confined flow equipment (e.g., stack, chimney, pipe, vent, or duct), contributes a great portion to thetotal outdoor PM emission. Unlike particulate matter emitted from definable and stationary point sources, FPM isgenerally discharged from everywhere, such as anthropogenic land use, urban traffic, industrial activities, winderosion, vegetation growth, and other natural processes.
The emission intensity of FPM is different from site to site,
and it often fluctuates temporally and spatially. Accordingly, it is challenging to develop the time- and spaceresolved measurement for FPM emission but beneficial tounderstand its generation, track its migration, evaluate its
effects and damage on human beings and the environment,
†

*

42

Received 25 July 2022; Accepted 19 October 2022
J-STAGE Advance published online 17 June 2023
Corresponding author: Wu Zhou;
Add: 516 Jungong Rd., Shanghai 200093, China
E-mail: zhouwu@usst.edu.cn
TEL: +86-021-5527-7764 FAX: +86-021-5527-6854

and consequently figure out reliable mitigation measures toreduce FPM.
The objective of this review is to provide an overview of
the current state and trends in measuring fugitive particulate matter. We will first introduce the main sources and key
properties of FPM, analyze its effects and the current measurement standards on it. Then, we will provide a critical
overview of the conventional methods and discuss the new
trends for FPM measurement. Finally, we will propose abrief outlook on the future of FPM measurement.

1.1 Sources of FPMs

Typically, sources of FPMs can be separated into two
categories: natural processes and anthropogenic activities.
Common natural sources include soil & desert dust (Hassan
et al., 2016; Vos et al., 2021), wildfire smoke (Sedlacek etal., 2018), volcanic ash (Sasaki et al., 2021), sea sprayaerosol (February et al., 2021), and pollen & fungi grains(de Weger et al., 2020). The geomorphologic and geologic
conditions mainly contribute to the compositions and distribution of the natural FPMs. For instance, arid regions areplagued by wind-blown soil, and coastal areas are familiar
with sea salt-based or biogenic-enriched aerosols. Comparing with natural processes, human activities create more
FPMs, mainly derived from traffic & transportation (Kuhns
et al., 2010; Pervez et al., 2018; Wang H. et al., 2023),
heavy constructions (Noh et al., 2018), agriculture &Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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Review Paper

Table 1 Fugitive particulate matter sources and activities.

Table 2 Description of common particle size bases.

SourceName

Description

TSP

Total Suspended Particulate, as measured by thestandard high-volume air sampler, has a relatively
coarse size range. It was the basis for the previous
primary National Ambient Air Quality Standards of
the U.S. (NAAQS) for PM and is still the basis of
the secondary standard.

SP

Suspended Particulate, which is often used as a surrogate for TSP, is defined as PM with an aerodynamic diameter no greater than 30 μm.

IPInhalable Particulate, which is defined as PM with
an aerodynamic diameter no greater than 15 μm, canget into the human body by breath.

PM10

PM with an aerodynamic diameter no greater than
10 μm. It is the current primary NAAQS for particulate matter, representing the particle size range ofthe greatest regulatory interest. It is also known as
Thoracic particles since it may reach the upper partof the airways and lungs.

PM10–2.5

PM with diameters generally larger than 2.5 μm andsmaller than or equal to 10 μm. It is also known as
Coarse particles and is primarily generated frommechanical operations.

PM2.5

PM with an aerodynamic diameter no greater than
2.5 μm. It is also known as alveolar particles, indicating that it can penetrate more deeply into thelungs and may reach the alveoli. It is called Fine
Particulate (FP) as well.

UFP

Ultra Fine Particulate, which is defined as PM with
an aerodynamic diameter no greater than 0.1 μm,
can penetrate tissues and enter the bloodstream.

Activity

From natural processes
soil & desert dust

windy weather / sandstorm

wildfire smoke

forest fire / prairie fire / hill fire

volcanic ash

volcanic eruption

sea spray aerosol

air bubbles bursting at the air-sea
interfacepollen & fungi grainspollination / spore dispersal

From anthropogenic activities
traffic & transportation

roads / automotive exhaust / loading
and offloading

heavy constructionsindustrial & commercial building /road, bridge and other public constructions

agriculture &livestock operations

straw burning / grain harvest / storage
and distribution / pasture mowing /animal feedmining & quarrying

blasting / crushing / conveying andstorage / heavy equipment operations

metal processing

sintering / blasting / casting / plating

residential combustion cook / heating / cigarettes / incense

livestock operations (Gladding et al., 2020; Liu Y. et al.,2022), mining & quarries (Chang et al., 2010; Froyd et al.,2019), the metal industry (Zhang et al., 2022), and residential combustion (Nugraha et al., 2021). The main sources
and activities that contribute to the FPM emissions arelisted in Table 1.

1.2 Main properties of FPMs
1.2.1 Critical dimension and size distribution

Particle size has a great impact on the suspension, transport and distribution of FPM in the atmosphere. The residence time of submicron particles is on the order of
102–103 h, assuming the absence of precipitation. Particles
with diameters between 1 μm and 10 μm can exist in the airfor several days (Esmen and Corn, 1971). The typical residence time for 10 nm particles is about 15 minutes owing
to their primarily coagulation with larger particles (Kim etal., 2021). The potential drift distance of particles is also
related to particle size as well as the initial injection heightof the particle and the intensity of atmospheric turbulence.
Fugitive dust larger than about 100 μm is likely to settle out
near the source under a typical wind speed of 16 km/h.
Particles in the range of 30 μm to 100 μm are likely to undergo impeded settling without extra wind uplift. Smaller
particles with much slower gravitational settling velocities
are much easier to travel following atmospheric turbulence.
Particles propelled higher from the source also travel far-ther. For instance, volcanic ash can have an intercontinental
diffusion (Ansmann et al., 2011).
Particle size also has a major influence on the transportof particulate in the human respiratory system since thedeposition efficiency of particles in the respiratory tract
depends upon their size. Large particles can be hindered by
the nasal hair (rhinothrix), medium particles can reach theupper respiratory tract, and small particles can deposit on
alveoli or even invade the blood-circulatory system.
PM10 (particulate matter with a diameter of 10 μm or lessin size) and PM2.5 (particulate matter with a diameter of
2.5 μm or less in size) are currently common size bases forparticulate matter. However, some other particle size bases,
listed in Table 2, are used in research as well.
Different sources contribute to various size distributionsof FPM. It is generally a narrow size distribution and smallmean size for biomass-burning smoke, which is dominatedby submicron particles (Wang F. et al., 2019). However, thesize of smoke particles varies along different combustion
phases (Ordou and Agranovski, 2019). Particles generated
by mechanical force are normally widely distributed in size

43Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57(Zhang et al., 2022). Sea spray aerosols can range from a
few tens of nanometers to tens of micrometers (February etal., 2021). Moreover, the size distribution of some FPMs
may show bimodal or multimodal patterns (Chang et al.,2010; Chen et al., 2019).

1.2.2 Mass concentration, emission rate and emission
factors

PM10 and PM2.5 mass concentrations (γm) are the mostcommonly used criteria to evaluate air quality. The criteria
can further be separated into 24-hour average values andannual mean values. Most countries and many internationalorganizations have issued their standards or recommend
thresholds for PM10 and PM2.5 mass concentrations, as
listed in Table 3. However, no current standards are set up
for FPM.
Before determining the mass emission rate (ṁ), the volume flow rate of the flue (V ) should be measured. Then, themass emission rate can be calculated by multiplying themass concentration of the measured pollutant by its volume
flow rate, as shown in Eqn.
(11)(1):(1)m  γmVThe mass emission factor (Ḟ) is a representative value
indicating the emission intensity of a pollutant generated(1)
from a specific activity. It can be calculated following
Eqn. (2):

m
F 
a

(2)

where a represents the activity rate.
The mass emission factor is usually expressed as the(2) of the pollutant divided by the unit weight (g kg–1),
weightvolume (g m–3), distance (g km–1), or duration of the activity that emits the pollutant (g h–1).

1.2.3 Chemical components

Although the composition of FPM highly depends on its
source, it can be roughly classified into five categories:
minerals, metallic particles, soot, inorganic salts and bio-

genic substances. Wind-blown dust from dried land ismainly dominated by minerals, especially quartz. Volcanic
ash contains more metallic minerals, such as Al2O3, Fe2O3
and MgO. Construction, mining and quarrying, which utilize stones and ores, contribute to the mineral dust emission. Metal processing generates both metal-rich andmineral particles. Hence, mineral dust is the major PM
component at a global scale (Minguillón et al., 2013). Sea
spray aerosols are little droplets containing inorganic salts(e.g. NaCl) or biogenic substances. Animal- and vegetationrelated particles belong to biogenic ones. Soot is mainly
generated from biomass or fuel combustion, including
wildfire, residential combustion, and working engines.
Non-exhausted PM from traffic, originating from tire wear,
brake use and road dust resuspension, mainly consists ofmineral particles. Since the reduction of exhaust emissions
is performed worldwide, non-exhausted PM emissions will
dominate traffic-related emissions in the near future.

1.2.4 Shape and morphology

Mineral dust typically has complex irregular shapes derived from its intrinsic crystal orientation and the force
loading from crushing and abrasion (Kalashnikova and
Sokolik, 2004; Nousiainen, 2009). Metallic particles,
which are discharged from high-temperature processes likeburning and metal cutting, are usually spherical (Sanderson
et al., 2014). Soot is always amorphous and forms chainlike aggregates during its aging period (Adachi et al.,2010). Pollen and other biogenic particulates are often
nonspherical (Amina et al., 2020), although it is commonly
assumed that pollen grains are spherical (Theilade et al.,1993). Inorganic salts, including NaCl, sulfates and nitrates, are basically hydrophilic. Therefore, inorganic
aerosols contain moisture at relatively high humidity levels, resulting in a spherical shape. Otherwise, the shape
may be irregular (Ueda, 2021).

1.2.5 Optical properties

Complex refractive index (m) is the fundamental parameter to describe FPM, expressed in Eqn. (3).
m  n  iηTable 3 Criteria for PM10 and PM2.5.

PM2.5

PM10
Criteria

Annual(μg m–3)

Daily(μg m–3)

Annual(μg m–3)

Daily(μg m–3)

WHO

15

45

5

15

USA

—

150

12

35

China

70

150

35

75

EU

40

50

25

—

Japan

—

—

15

35

Korea

50

100

15

35

44

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where the real part, n is the refractive index indicating thedegree of light dispersion through the media, and the imag(3)inary part, ƞ is the absorption coefficient, donating the lightattenuation when it propagates through the media. If a particle can absorb light, its ƞ is positive.
Among all the common FPMs, black carbon (BC) is themost light-absorbing component with an imaginary part
ranging from 0.4 to 0.8 (Kelesidis and Pratsinis, 2019).
Organic carbon (OC), also known as brown carbon (BrC),
absorbs light as well (Zhang et al., 2017). Mineral andmetallic particles both slightly absorb light (Ebert et al.,
2002; Kandler et al., 2007).

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The extinction coefficient (bext) of FPM includes thescattering coefficient and absorption coefficient (as detailedin Section 2.3), and it can be converted to visibility (Vis) byadapting the Koschmieder equation (Cao et al., 2012; ten
Brink et al., 2018; Xiao et al., 2014):
Vis 

k
bext

(4)

where k is a constant assumed to be 3.912.
Single scatter albedo (SSA) is defined as the ratio of(4)particle-scattering
efficiency to the total extinction efficiency. It
is a key parameter in radiative transport models for estimating
aerosol direct radiative forcing (Jeong et al., 2020).

1.3 Effects and damage from FPMs
1.3.1 Human health

Short-term and long-term exposure to particles increases
the risk of developing cardiovascular and respiratory diseases, as well as lung cancer (Brook et al., 2010). Outdoor
PM2.5 is the fifth-largest risk factor for premature mortality
worldwide (Feigin et al., 2016). Infants, the elderly, andoutdoor workers are the most likely to be affected. The
health effects of FPMs depend considerably on their size.
Coarse PM reaches the upper part of the airways and lungs,
while fine PM can penetrate more deeply into the lungs andmay reach the alveoli. UFP can even directly translocate
into the bloodstream and cells (Martinelli et al., 2013).
Chemical composition issues in the health effects as well.
Exposure to metal or metal-containing dust increases therisk of cardiovascular dysfunction (Ain and Qamar., 2021).
Black carbon, as a universal carrier of various toxic constituents, has a strong association with premature mortality(Wang Y. et al., 2021). Inhalation of sand and concrete dust
can lead to silicosis, an irreparable form of lung damage(Hoy and Chambers, 2020). Additionally, inhalation ofpollen and other biogenic aerosols can induce allergy andrespiratory problems (D’Amato et al., 2020).

1.3.2 Wealth and safely

FPMs impair visibility. Due to the prominent ability oflight extinction, fine PM contributes more than gas pollutants to visibility degradation (Yu et al., 2016). On roads,
visibility reduction increases the risk of accidents and escalates the costs associated with vehicle cleaning and equipment maintenance. FPM emissions also result in thecancellations of flights and avoidance of outdoor activities(Guo et al., 2021). For example, sudden volcanic ash discharges directly affect aircraft engines, causing them tostall and posing a threat to human and cargo safety (Prata,
2008). Heavy wind erosion of soils and dust storms diminish land values. Excessive emissions can also damage
vegetation and reduce crop production. Burning smoke andflammable fine powders can cause fire or blast damage if
ignited.

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1.3.3 Environment and global climate

Various FPMs contribute differently to climate forcing
owing to their distinct optical properties. For instance,
black carbon aerosols strongly absorb solar radiation, leading to a warming effect (Bond et al., 2013). In contrast,
mineral dust cools the atmosphere due to its ability to increase radiation reflection into space (Choobari et al.,2014). Besides, FPMs also have an indirect effect on the
climate by modifying the properties of clouds given thatparticles act as cloud condensation nuclei (Williamson etal., 2019). For instance, soot is an efficient ice-nucleating
particle that influences cirrus formation at the tropopause.
Although the relatively short residence time and highlyvariable spatial distribution in the atmosphere further complicate the estimation of net effects of FPMs on the global
climate, scientists are trying to include FPM emissions into
global atmospheric models (Philip et al., 2017).

1.4 Current standards on FPM emissions

The most widely used method for PM measurements isbased on sampling, and mass concentration, emission rateand emission factors are the highly concerned parameters.
The International Organization for Standardization (ISO)has released the measurement standards for PM in the atmosphere and from the stationary source (ISO 11771-2010/ ISO 10155-1995 / ISO 9096-2017), and many countries
have all set domestic standards. Since there are no specificstandards for FPM, the above-mentioned ones are used for
FPM measurements. In addition, some monitoring methods
are recommended for FPM emissions by some institutions.
The US Environmental Protection Agency (EPA) Method
22 “Visual Determination of Fugitive Emissions” was proposed. Through this method, viewers position themselvesto have an unobstructed view of potential emissions. If
visible emissions are observed, the viewer measures theduration of time that the visible emissions were present
during the observation period. Method 22 is often used in
the regulation of fugitive emissions from toxic materials
that escape during material transfer (i.e. asbestos removal),
escape from buildings housing material processing or handling equipment (i.e. asbestos presence in building demolitions), and/or are not captured by an exhaust hood or are
emitted directly from process equipment. Method 9, known
as the “Visible Determination of Opacity of Emissions
from Stationary Sources,” involves the recording of readings as a percentage of opacity in terms of black and whiteparticulate plumes or dense clouds of matter. While
Method 9 is used extensively to sight and control visible
emissions from stationary and non-stationary industrial
processes, it is also used to measure visible emissions fromunconfined places such as roads and bulk material storage
areas.

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2. Conventional methods for FPM measurement

As mentioned in Section 1.4, sampling methods universally accepted for PM measurements are equally applicable
to FPM measurements. However, due to the temporary andunsteady-in-position of FPM emissions, it is essential to
implement well-performed sampling strategies and consider the use of multiple measurements to gather comprehensive information about FPM emissions.

2.1 Sampling strategies and conditions
2.1.1 Upwind-downwind method

Upwind-downwind sampling requires a high density ofvertical and/or horizontal measurements to determine thePM emission rate. Measurements of FPM emission rates
are usually performed using this method, which has been
described in detail in the previous study (Cowherd et al.,1974). For instance, particulate emission rates from gravel
processing sites and traffic-busy roads were calculated on
the basis of a mass balance approach using the upwind/
downwind technique, respectively (Chang et al., 2010;
Etyemezian et al., 2004).

2.1.2 Wind tunnel method

The wind tunnel method is suitable for measuring fugitive dust emissions from dried land. To conduct thismethod, a wind tunnel with a working section of several
tens of meters in length, several meters in width, and heightis required. A blower works at the tunnel entrance and PM
monitors sample the wind-blown dust along the tunnel toevaluate the dust emission properties. This method has
been successfully applied to measure fugitive emissions
from soil surfaces (Fitz et al., 2021; Gao et al., 2020; Roneyand White, 2006).

2.1.3 Isokinetic sampling method

The isokinetic sampling method, which means the sampling velocity is the same as the FPM/airflow, is employed
to prevent incorrect lower or higher particle concentrations
owing to anisokinetic effects from particle bypassing or
swarming, respectively.

2.1.4 Size-selective inlet and cascade impactor

A size-selective inlet removes larger particulates and allows the particulate smaller than the designed size to pass.
By applying a size-selective inlet, PM10, PM2.5 or UFP canbe separated from the pollutant flow (Su et al., 2020). Cascade impactors operate on the principle of curvilinear motion of particles in the aerosol stream, and as a result,particles of various sizes are divided and collected on different plates. In this way, the size distribution of the sampling FPM is obtained (Manojkumar and Srimuruganandam,2022).

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2.2 Filter-based techniques
2.2.1Direct gravimetric analysis

In direct gravimetric analysis, a filter is weighted by anelectronic microbalance before and after sampling underconstant temperature and humidity conditions. The masschange is used to calculate the degree of PM contamination(Lei et al., 2020). The collected filter can be used for offlinechemical component analysis (Guo et al., 2021).
However, despite providing accurate and reliable data,
the direct gravimetric method is time-consuming and inapplicable to FPM measurement.

2.2.2 Tapered element oscillating microbalances(TEOM)

Unlike the direct gravimetric method, the TEOM method
uses a hollow glass tube as a microbalance for real-time
mass loading measurement. Particles continuously depositon the glass tube, increasing its inertia and consequently
tuning its oscillation frequency. This frequency change isconverted to an electrical signal, which is amplified for
detection.
The TEOM method offers the advantage of high time
resolution (2 seconds) (Sullivan et al., 2017), but it should
be noted that this method is sensitive to temperature andmoisture. Therefore, the incoming sample flow is heatedand kept constant. Depending on these measuring conditions, it is difficult to precisely measure the semi-volatile
particles. A filter dynamic measurement system (FDMS) is
specially designed to overcome this drawback. TEOM instruments equipped with FDMS are operated alternately
between a measuring cycle and a reference cycle. The massloss of the glass tube in the reference cycle is used to correct the output in the measuring cycle, thereby increasing
the accuracy of the TEOM results (Grover, 2005).

2.2.3 Beta attenuation monitoring (BAM)

The main principle of BAM is based on Lambert–Beer’slaw. When airflow passes through the filter tape, particles
are left on it. The beta radiation is attenuated by the attached particles before reaching the detector on the otherside of the filter tape. The attenuation is an exponentialfunction of the deposit amount and it is roughly not affectedby particle composition or other properties. Hence, themass concentration of particles can be examined. However,
the moisture in the air may cause an error, so a heater isnecessarily equipped at the sample inlet. Typically, theBAM method can be used for continuous online measurement, providing the mass concentration data hourly(Mueller et al., 2013). Moreover, offline chemical analysis
can carry out on the used filter tape (Raja et al., 2017).

2.2.4 Offline characterization

Filter-based offline characterization has low time resolutions and fails to reflect the dynamic nature of the fugitive

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Sideward 90°

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Sideward 90°

Forward 0°

Sideward 90°

Forward 0°

×1

α = 0.5m = 1.33

α = 1.0m = 1.33

α = 2.0m = 1.33

Sideward 90°

Sideward 90°

Sideward 90°

× 10
× 10

Forward 0°

×1

× 1 Forward 0°

Forward 0°

α = 4.0m = 1.33

× 10
× 1 Forward 0°

α = 5.0m = 1.33

α = 6.0m = 1.33

Fig. 1 Diagram of scattered light intensities of different size. α = πD/λ, is a dimensionless particle size, where D is the mean particle diameter, m isparticle refractive index.

pollution sources, nevertheless, it gives more information
about the particulate.
Electron microscopy (EM) techniques are valuable todetect and quantify the shape and size of particles(Gelencsér et al., 2011; Omidvarborna et al., 2018).
Ion chromatography (IC) is performed to examine theconcentrations of the water-soluble ions in the particulate(Guo et al., 2021; Hu et al., 2019; Tian et al., 2020; Xiao etal., 2014; Zou et al., 2018).
Elements and trace metals, including Ca, Sc, Ti, V, Fe,
Co, Zn, and Mo, were analyzed by X-ray fluorescence(XRF) spectrometry (Gao et al., 2015; Tian et al., 2020;
Xiao et al., 2014) or Inductively Coupled Plasma-Mass
Spectrometry (ICP-MS) (Hu et al., 2019; Philip et al.,2017).
The concentrations of OC and element carbon (EC) are
analyzed by a thermal/optical carbon analyzer (Guo et al.,2021; Hu et al., 2019; Lei et al., 2020; Tian et al., 2020;
Xiao et al., 2014; Zou et al., 2018).
In general, filter-based techniques are the most technically mature. However, the sampling conditions should betightly controlled to reduce the effect of sampling conditions on accuracy (Le and Tsai, 2021).

2.3 Optical-based techniques
2.3.1 Basics of light scattering, absorption andextinction

The scattering pattern of a particle refers to its scattering
efficiency as a function of the angle. It depends on the particle size, shape, refractive index, and the wavelength (λ) of
the excitation light. The distribution of the scattering light
as a function of a dimensionless particle size parameter, α,
assuming the refractive index equals 1.33, is demonstrated
in Fig. 1.
It is shown that the smaller the particle is (compared to

dl

I0

I

Light pathL
Fig. 2

Diagram of light extinction.

the excitation wavelength), the more spherical the scattering pattern is. For large particles (e.g. α ≥ 4.0 in Fig. 1),
most of the scattered intensity deviates with smaller angles,
and side lobes appear as the diameter increases. Besides,
the polarization of scattering light is also influenced by theparticle properties, which are not shown in Fig. 1. Therefore, the particle size, shape, component, and concentration
can be predicted using the information from the scattering
signature theoretically. Mie theory (Hergert and Wriedt,2012) is the most common description of light scattering.
Despite its simple hypothesis that a uniform sphere is illuminated by a monochromatic plane wave, this theory has
proved to be quite robust and applicable in particle size andconcentration prediction.
When light passes through a cloud of particles, its intensity (I0) is attenuated because of the scattering and absorption effects of the particles, as seen in Fig. 2.
Assuming the particles disperse uniformly in the illuminated area, and then the transmitted light intensity (I) canbe described with Eqn. (5).

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I  I 0 e bext L(5)where L is the path length of the light beam through theparticles.
(5)
The extinction coefficient (bext) can be further divided
into two parts, the scattering coefficient (bsca) and the absorption coefficient (babs).
bext, as a function of the particle number, diameter D,
wavelength of the incident light λ, and the complex refractive index m, can be computed by Mie theory.

2.3.2 NephelometerA nephelometer is the most commonly used opticalequipment for PM measurement. The pollutant air is sampled into the measuring chamber of the nephelometer, illuminated by a light beam. The scattering light is detected bya light detector set to one side of the light source. Despite
the intensity is also related to the refractive index, shape,and size distribution of the measured particles, nephelometers are calibrated to a known particulate, then use anenvironmental-related factor to compensate for the error
accordingly. After calibrating to a known particulate, thePM mass concentration can be evaluated from the scattered
light intensity.
The DustTrakTM (TSI Co., Ltd.) is a classicalnephelometer-type tool to determine the mass concentration in real time. The DustTrak aerosol monitors (model
8520, TSI Co., Ltd.) were run in the kitchens and livingrooms of the selected sites to measure fine particulate matter (Sidra et al., 2015).
Portable In Situ Wind Erosion Laboratory (PI-SWERL)
is a portable wind tunnel surrogate, equipped with
DustTrak (Etyemezian et al., 2007). It is contained in an
open-bottomed, cylindrical chamber operated by adirect-current motor and set parallel to the soil surface. By
adjusting the rotation speed, the desired high winds withknown wind shear are created and the soil and dust particles are lifted. With the application of PI-SWERL, the behavior of FPM emissions from unpaved roads, farmlands,
and deserts were studied without the deployment of flux
tower systems (Kuhns et al., 2010; Sweeney et al., 2021;
Vos et al., 2021).
Thanks to the calibration method, the measurement accuracy depends on the assumption that the properties of
FPM are constant, except for the concentration. However,FPM emissions are complicated and diverse. Single- angle
scattering measurement can result in unreliable results. To
solve this problem, SHARP-5030i (Thermo Scientific Co.,
Ltd.) measures PM2.5 mass concentrations based on theprinciples of particle light scattering (nephelometer) andbeta attenuation (Su et al., 2020). The beta attenuation data
can be used to calibrate the nephelometer.

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2.3.3 Particulate counter

Optical particle counter (OPC) and condensed particle
counter (CPC) are both used to measure particle number
concentration and size distribution. OPC is based onsideward light scattering. The aerosols pass through thelight-illuminated measuring spot particle by particle andthe induced light scattering signal is detected by the lightdetector simultaneously. The particle size is a function of
the scattering light intensity and the particle number iscounted during the measuring period. Consequently, theparticle number size concentration is achieved. CPC introduces condensation equipment before OPC, inducing theparticle size growth by absorbing liquid, and therefore it
can measure smaller aerosols with diameters of a few nanometers.

2.4 Electrical methods
2.4.1 Electrical low-pressure impactor (ELPI)The ELPI (Dekati Ltd.) operates at 30 LPM and ambientparticles are charged when the air sample passes through aunipolar charger. The charged particles are size-classified
by aerodynamic diameter following the same principle as
the cascade impactor mentioned in Section 2.1. The electrical currents arising from particle deposition are measured
simultaneously from 12 stages with a multichannel electrometer in real time. Therefore, the ELPI is capable ofmeasuring the particle number concentration over a wide
size range (7 nm–10 μm) with 1–2 s resolution (Holmén etal., 2007).

2.4.2 Differential mobility analyzer (DMA)

The intake particles are electrically charged first andseparated by deflection since the particles with different
sizes have different electrical mobility. If it is combinedwith the CPC technique, known as scanning mobility particle sizer (SMPS), the lower limit of detection (several
nanometers) is reached. Fast mobility particle sizer (FMPS)
is another derived technique from DMA. It combines DMA
with an electrometer to improve the time resolution to one
second.

2.5 Real-time chemical characterization

The mass spectrometry techniques are quickly developed for the real-time chemical characterizations of ambient particulates, including aerosol mass spectrometer(AMS), high-resolution time-of-flight AMS, aerosol chemical speciation monitor, single-particle mass spectrometers,
and aerosol time-of-flight mass spectrometer. The comparison between these techniques is well illustrated in Ref.
(Wang F. et al., 2021).
However, continuous measurements at high time resolution remain impractical, and like most offline methods,
volatile or reactive aerosol species can change before
analysis.

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Review Paper

Conventional methods for FPM measurement all belong
to point measurement techniques since they require a sampling process, which means that the space resolutions ofthese techniques are poor. Rigorous sampling strategies
and multi-measuring equipment should be arranged to reveal the spatial distribution and movement of FPM. However, it is impractical to perform this arrangement
considering the equipment is cumbersome and expensive.
To achieve the high space- and time-resolved investigationof FPM, some cheap, portable, and remote sensing techniques are being developed.

3.1 Open-path optical techniques
3.1.1 Light detection and ranging (Lidar)

Lidar is an active light scattering detection method,
based on the analysis of the radiation backscattered from atarget. This technique is often used in climate research tomeasure altitude-dependent aerosol extinction. For instance, the distribution and movement of volcanic ash wereobserved with the ground-based lidar systems of theEuropean Aerosol Research Lidar Network (EARLINET)(Ansmann et al., 2011). The strong and long-lasting stratospheric aerosol perturbation was also monitored all over
Europe with EARLINET (Baars et al., 2019).
Lidar remote sensing has been introduced to measure theFPM near the ground. A scanning lidar system was applied
to measure FPM emissions from different industrial
sources simultaneously, determining the potential of lidar
for tracking and differentiating plumes from various sites(Pal et al., 1998). The reliable detection of early fire wasaccomplished using lidars operating at 355 nm, 532 nm,
1064 nm, and 1540 nm, and the maximum detection distance relied on the wavelength (Lavrov et al., 2003). A
three-wavelength scanning lidar system was built to simultaneously monitor FPM emissions from agricultural operations and road traffic. The mass concentration and size
distribution were inversed assuming a bimodal lognormal
size distribution and calibrated by standard facilities, including optical particle counter, PM10 and PM2.5 samplers,
multistage impactors, and an AMS (Zavyalov et al., 2006).
Traditional lidars are monostatic, requiring expensive
electronics to measure the time of flight of the backscattered light and its corresponding distance. The overlap effects between the backscattered signal and receiver field ofview near ground levels may impact the detection reliability for the nearby pollution. To avoid these disadvantages,
a bistatic lidar, named CLidar, was established. In the
CLidar, a laser source and a camera fitted with a wide-angle
lens were separated several hundred meters away fromeach other. The laser transmits light vertically into the atmosphere, and the scattering light was imaged by the camera, as shown in Fig. 3. Owing to this setup, the altituderesolution is excellent near ground level, providing an effi-

Laser beam

3. New trends for FPM measurement

Laser

θ

z

PM2.5 MonitorDdθ CCD camera

Acquisition and control computer
Fig. 3 Schematic of CLidar system. Reproduced from Ref. (Tao et al.,2016) with permission from Copernicus Gesellschaft mbH.

cient detector of pollution at low altitudes. The aerosol extinction coefficient along the altitude was inverted from theintensities of pixels. A grilling activity was successfully
monitored since the aerosol extinction increased from0.03 km–1 with no grilling to 0.085 km–1 (Kabir A. et al.,2018).
Moreover, the CCD-based scattering Lidar system (including sideward scattering and backward scattering) isverified to be useful to measure particle mass concentrationand size distribution of cooking fumes and open smoldering as well (Wang W. et al., 2019; Zhou W. et al., 2020).

3.1.2 Hybrid optical remote sensing (h-ORS)

The h-ORS was mainly developed by the University of
Illinois to measure FPM emissions from military-related
activities, including back blasts from firing artillery, open
burning and detonation, tracked and wheeled vehicle operation, and helicopter landing (Du et al., 2011; Varma et al.,2008; Yuen et al., 2013, 2015, 2017). It combines micropulse Lidar (MPL), open-path Fourier Transform Infrared
spectroscopy/Laser transmissometer (OP-FTIR/OP-LT),
and/or filter-based sampling techniques (DustTrak orTEOM). The measurement system is schematically demonstrated in Fig. 4.
2-D PM mass concentration profile and mass emission
factors can be calculated using the h-ORS system. First, the2-D MPL data obtained using scanning mode were converted to the 2-D light extinction profiles with the LIDAR
inversion algorithm. Second, the 2-D PM mass concentration profiles were determined from the 2-D light extinction
profiles by multiplying a factor. This factor refers to themass concentration at the reference point (as the blue point
shown in Fig. 4) divided by the local light extinction obtained in the first step. The mass concentration at the reference point can be derived from the light extinction
calculation performed by OP-FTIR/OP-LT or the result
from DustTrak and TEOM, respectively. Last, the massemission factors can be easily calculated if the wind speed
and direction are recorded.

49

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57

Review Paper

Bell 210 HelicopterWind

Withdraw

Approach

100 ⁓ 150 m

3m

300 m

Measurement surface

MPL paths

Dust plume
130 m

LIDAR

Reflective
targetScanning
MPL

Note: map not to scale

OP-FTIR &OP-LT

OP-FTIR and
OP-LT paths

Retroreflector

Fig. 4 Schematic of h-ORS system. The blue point represents the site for DustTraks or TEOM equipment. Reproduced from Ref. (Du et al., 2011) withpermission from the American Chemical Society.

Contrastingbackground

Retroreflector

Wind direction

Fugitive dust plume

Digital
CameraLaser
Transmissometer

Sun
Fig. 5 Schematic of the camera-based light extinction system. Reproduced from Ref. (Du et al., 2013) with permission from Elsevier Ltd.

3.2 Image-based optical techniques
3.2.1 Camera-based detection

Biomass burning, especially wildfire, should be monitored and detected as soon as possible to avoid the expansion of combustion. As mentioned in Section 3.1, Lidar isuseful to detect fire smoke in its early stage. It is demonstrated here that video monitoring is also effective for thissmoke early sensing (Jakovčević et al., 2013). The camerarecorded video was segregated into a photographic sequence, then region segmentation and categorization,
motion detection, and chromatic and shape analysis were
performed on these photos to finally discriminate firesmoke from fog, clouds, and other stuff.
Besides, the road dust from busy traffic can be monitored
by measuring the opacity of fugitive plume emissions. A
camera-based light extinction method was described forquantifying the opacities of FPM (Du et al., 2013). Thismethod was validated by performing simultaneous measurements using a co-located laser transmissometer, as
shown in Fig. 5. Moreover, the values of the R, G, and B

50

Fig. 6 The prototype of picture-based filter system. Reproduced from
Ref. (Carretero-Pena et al., 2019) with permission from MDPI.

channels, and the grey value were all consistent with theopacities from the OP-LT measurement.

3.2.2 Photographic filter analysis

The direct gravimetric analysis of the filter is timeconsuming and strict to the operating procedure. Picturebased filter analysis takes pictures of the filter before andafter use and evaluates the mass of particles deposited on
the filter depending on the difference of pictures accordingly. A smartphone is both easy to be attained and easy tooperate and therefore it is a good option to perform theanalysis. A prototype of such a system was proposed recently (Carretero-Pena et al., 2019), as displayed in Fig. 6.
A testing box was designed to provide stable lighting

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57

Review Paper

MSS

Drone

EZ-0048

K30

B5W-LD0101

PPD42NS

TM (LCS)PMSA0031

SLR-429M-RS2

Bluetooth

RASP-TSL7

Raspberry Pi 3
Model BOWL-LPB10010

CM-15-6

Cloud

DS1307OWL-LPB10010

BME280

TSL2591

RVS

Internet
Fig. 7

RASP-TSL7

SI1145

KP-9250

Laptop PC

RM (LCS)
LoRa(LPWA)

Schematic of the integrated system for in-situ measurements. Reproduced from Ref. (Madokoro et al., 2021) with permission from MDPI.

conditions, in which a stable LED strip was equipped. The
saturation parameter of the picture was proved to be able toestimate the mass concentration of dust deposited on thefilter.

3.3 Low-cost, compact, mobile and connectablesensor system

Low-cost PM measurement techniques most commonly
rely on the light-scattering-based (nephelometer or OPC)
principle, which is available for below 50 USD (Kuula etal., 2019; Yi et al., 2018). PMS7003 (Plantower Co., Ltd.)and SDS011 (Nova Fitness Co., Ltd.) sensors, based on
OPC and nephelometry, respectively, were confirmed precision and were linearly correlated with TEOM data(Kaźmierczak et al., 2018). However, these two sensors
both had a bias concerning PM2.5 concentrations obtained
from TEOM, indicating the importance of calibration of
low-cost devices before use. PMS7003 was capable ofmeasuring PM10 and PM2.5 with a measurement range from0 to 500 μg m–3, signal response of less than 1 s, and resolution of 1–2.5 μm and 2.5–10 μm, respectively (CamarilloEscobedo et al., 2022). SDS011 was successfully applied inresidential biomass combustion monitoring (Nugraha et al.,2021). The data obtained from SDS011 sensors were reliable, but it may cause errors since high RH (over 80 %)negatively affected the sensor response (Liu et al., 2019).
Usually, there is a trade-off between the accuracy of measurement and the cost of the sensor, which are both vital to
the wide and proper use of low-cost sensors (Castell et al.,2017).

3.3.1 Drone-based measurement and sensors
networks

Mobile measurements used to be carried out on airplanes(Froyd et al., 2019; Hobbs et al., 2003; Taubman, 2004;
Zhang et al., 2017). The drone is small and exquisite. It canquickly fly to the place where FPM emission happens, fa-

cilitating the monitoring of FPM. The fugitive dust at thequarry sites was measured using a Sniffer4D moduleequipped drone, providing measurements at 1 s intervals
through a light scattering method (Kim et al., 2021). The
volcanic ash was detected remotely by drone-based sensors(Sasaki et al., 2021). Small, lightweight, and cheap sensors
were integrated for multiple measurements of atmosphericphenomena and related environmental information. For insitu local area measurements, a long-range wireless communication module with real-time monitoring and visualizing software applications was integrated as established inFig. 7 (Madokoro et al., 2021).

3.4 Real-time laser-induced breakdown
spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has been
proven to perform elemental microanalysis of PM on filterswith high sensitivity (ppm-level) (Marina-Montes et al.,2021). The technique indicates the possibility of automatedchemical analysis collaborating with conventional filterbased measurements in quasi-real time.
Moreover, online LIBS in open space detection has also
been demonstrated in several field measurements, showing
great potential for rapid monitoring and control of particulate emissions generated in public areas, such as toxic dust
in a hostile environment of the steel industry (Girón et al.,2018), cigarette smoke in houses (Zhang et al., 2020), andincense smoke near around temples (Qu et al., 2021).
LIBS is a useful and promising tool to determine the elemental composition of FPM in situ. However, this technique is not sufficient for obtaining information on specificparticulate sizes because of the breakdown of all particulates.

4. Summary and outlook

FPM has contributed a great portion to the total outdoor
PM emissions, and it will account for more than PM

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Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57

emissions from stationary point sources, given that serious
regulations and effective control measures are applied to
the latter. However, the FPM emissions are irregular,
highly dispersive, and intensity-fluctuated. The precise
measurement of FPM is challenging but worthy, yet there iscurrently no uniform regulation or standard measurement
procedure in place. In this work, we first introduced thesources of FPMs both natural and artificial, including soil
& desert dust, wildfire smoke, volcanic ash, sea spray aerosols, pollen and fungi grains, traffic and transportation activities, heavy constructions, agriculture and livestock
operations, mining and quarrying, metal processing, andresidential combustion. Although the sources are diverse,
the FPM can be sorted into five main categories: minerals,
metallic particles, soot, inorganic salts, and biogenic substances. Each category possesses distinctive properties,
resulting in varying effects on human health and the environment. Next, we provided an overview of conventional
PM measurement methods. Sampling is necessary for thesemethods, with the upwind–downwind technique widelyadopted. Filter-based methods are accurate, but the time
resolution is generally poor (except for TEOM). Additionally, the filter can be used for offline chemical analysis. We
also discussed two conventional optical methods, namely
the nephelometer and particle counter, and demonstrated
the basics of light scattering. Furthermore, we briefly explained electrical methods and real-time chemical characterization techniques. Conventional PM measurement
belongs to point measurement which can provide scarce
information about the spatial distribution of FPM. In response, open-path and image-based technologies have beendeveloped. Lidar technology, for example, is an easyperformed method to detect FPM. The light extinction coefficient can be derived from the Lidar data to evaluate thedegree of pollution. A camera-based bistatic Lidar has been
proposed to avoid expensive time-of-flight electronics inmonostatic Lidar. Additionally, several hybrid remote sensing strategies combing Lidar with sampling gravimetricanalysis have been illustrated. Image-based optical methods have also been used to detect FPM, especially firesmoke. Low-cost sensors show great promise for widespread FPM monitoring because they are light and small,and can be equipped on drones for dynamic measurement.
Moreover, real-time LIBS holds great potential for in-situ
chemical analysis.
By far, the mass concentration is the most concernedparameter in FPM measurement. Other factors such as size
distribution, shape, and complex refractive index are lessmentioned. Light-scattering-based methods have the potential to reveal more information about particles by performing multi-angle, multi-wavelength, and polarization/
depolarization measurements. However, these measurement systems are much more complicated, and may not be
feasible for field measurements. The image-based optical

52

Review Paper

methods are promising for particle measurement since thespatial and temporal light signals (often scattering light)
can be recorded simultaneously. Besides, machinelearning/deep learning algorithms can be applied to decouple the relationship between light patterns and particle
clouds. It is feasible to build an FPM monitoring network
using low-cost, compact, mobile, and connectable sensors.
Individuals equipped with portable sensors can contributeto the data collection process, and drone-based sensor systems can effectively monitor dangerous or hard-to-reachedplaces, enhancing the overall effectiveness of the monitoring network.

Acknowledgments

Financial support from the National Key Research andDevelopment Project of China (2017YFC0211500) and theShanghai Sailing Program (22YF1429600) is gratefully
acknowledged.

Nomenclature
AMS

Aerosol mass spectrometer

BAM

Beta attenuation monitoringBC

Black carbon

BrC

Brown carbon

CPC

Condensed particle counter

DMA

Differential mobility analyzer

EARLINET

European Aerosol Research Lidar Network

EC

Element carbon

ELPI

Electrical low-pressure impactor

EM

Electron microscopy

EPA

U.S. Environmental Protection Agency

FDMS

Filter dynamic measurement system

FMPS

Fast mobility particle sizer

FP

Fine particulate

FPM

Fugitive particulate matterh-ORS

Hybrid optical remote sensingIC

Ion chromatography

ICP-MS

Inductively coupled plasma-mass spectrometry

IP

Inhalable particulate

ISO

International Organization for Standardization

LIBS

Laser-induced breakdown spectroscopy

Lidar

Light detection and ranging

LPM

Liter per minute

MPL

Micro pulse Lidar

NAAQS

National Ambient Air Quality Standards of the
U.S.

OC

Organic carbon

OPC

Optical particle counter

OP-FTIR

Open-path Fourier transform infrared spectroscopy

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57
OP-LT

Open-path laser transmissometer

PI-SWERL

Portable in-situ wind erosion laboratory

PM

Particulate matter

PM2.5

Particulate matter with a diameter of 2.5 μm orless in size

PM10–2.5

PM with diameters generally larger than 2.5 μm
and smaller than or equal to 10 μm

PM10Particulate matter with a diameter of 10 μm or lessin size

SMPS

Scanning mobility particle sizer

SP

Suspended particulate

SSA

Single scatter albedo

TEOM

Tapered element oscillating microbalances

TSP

Total suspended particulate

UFP

Ultra fine particulate

WHO

World Health Organization

XRF

X-ray fluorescenceapolluting activity rates

bext

extinction coefficient (Mm–1)

bsca

scattering coefficient (Mm–1)

babs

absorption coefficient (Mm–1)

D

particle diameter (μm)Ḟ

mass emission factorI

transmitted light intensity (J)I0initial light intensity (J)kKoschmieder constant (-)

L

path length of the light (m)ṁmass emission rate (μg h–1)mcomplex refractive index (-)

nrefractive index (-)

Vvolume flow rate of flue (m3 h–1)γmmass concentration (μg m–3)

Vis

visibility (km)(21)α

dimensionless particle size parameter (-)λ

wavelength of extinction light (nm)ƞabsorption coefficient (-)

References

Adachi K., Chung S.H., Buseck P.R., Shapes of soot aerosol particles andimplications for their effects on climate, Journal of GeophysicalResearch, 115 (2010) D15206. DOI: 10.1029/2009jd012868
Ain N.U., Qamar S.U.R., Particulate matter-induced cardiovascular dysfunction: a mechanistic insight, Cardiovascular Toxicology, 21(2021) 505–516. DOI: 10.1007/s12012-021-09652-3
Amina H., Ahmad M., Bhatti G.R., Zafar M., Sultana S., Butt M.A.,
Bahadur S., Haq I.-U., Ghufran M.A., Lubna, Ahmad S., Ashfaq S.,
Microscopic investigation of pollen morphology of Brassicaceae
from Central Punjab-Pakistan, Microscopy Research and Technique,83 (2020) 446–454. DOI: 10.1002/jemt.23432
Amoushahi S., Bayat R., Sanaei A., Szyszkowicz M., Faridi S.,
Hassanvand M.S., Health and economic impacts of ambient fine

Review Paper

particulate matter in Isfahan, Iran, Urban Climate, 41 (2022) 101048.
DOI: 10.1016/j.uclim.2021.101048
Ansmann A., Tesche M., Seifert P., Groß S., Freudenthaler V., Apituley A.,
Wilson K.M., Serikov I., Linné H., Heinold B., Hiebsch A., SchnellF., Schmidt J., Mattis I., Wandinger U., et al., Ash and fine-mode
particle mass profiles from EARLINET-AERONET observations
over central Europe after the eruptions of the Eyjafjallajökull volcano in 2010, Journal of Geophysical Research, 116 (2011) D00U02.
DOI: 10.1029/2010jd015567
Baars H., Ansmann A., Ohneiser K., Haarig M., Engelmann R., AlthausenD., Hanssen I., Gausa M., Pietruczuk A., Szkop A., Stachlewska I.S.,
Wang D., Reichardt J., Skupin A., Mattis I., et al., The unprecedented
2017–2018 stratospheric smoke event: decay phase and aerosol
properties observed with the EARLINET, Atmospheric Chemistry
and Physics, 19 (2019) 15183–15198. DOI: 10.5194/acp-19-151832019
Bond T.C., Doherty S.J., Fahey D.W., Forster P.M., Berntsen T.,
DeAngelo B.J., Flanner M.G., Ghan S., Kärcher B., Koch D., Kinne
S., Kondo Y., Quinn P.K., Sarofim M.C., Schultz M.G., et al., Bounding the role of black carbon in the climate system: A scientificassessment, Journal of Geophysical Research: Atmospheres, 118(2013) 5380–5552. DOI: 10.1002/jgrd.50171
Brook R.D., Rajagopalan S., Pope C.A., Brook J.R., Bhatnagar A., DiezRoux A.V., Holguin F., Hong Y., Luepker R.V., Mittleman M.A.,
Peters A., Siscovick D., Smith S.C., Whitsel L., Kaufman J.D., et al.,Particulate matter air pollution and cardiovascular disease, Circulation, 121 (2010) 2331–2378. DOI: 10.1161/CIR.0b013e3181dbece1
Camarillo-Escobedo R., Flores J.L., Marin-Montoya P., García-Torales
G., Camarillo-Escobedo J.M., Smart multi-sensor system for remote
air quality monitoring using unmanned aerial vehicle and LoRaWAN,
Sensors, 22 (2022) 1706. DOI: 10.3390/s22051706
Cao J.-j., Wang Q.-y., Chow J.C., Watson J.G., Tie X.-x., Shen Z.-x., Wang
P., An Z.-s., Impacts of aerosol compositions on visibility impairment in Xi’an, China, Atmospheric Environment, 59 (2012) 559–
566. DOI: 10.1016/j.atmosenv.2012.05.036
Carretero-Pena S., Calvo Blazquez L., Pinilla-Gil E., Estimation of PM10levels and sources in air quality networks by digital analysis ofsmartphone camera images taken from samples deposited on filters,
Sensors, 19 (2019) 4791. DOI: 10.3390/s19214791
Castell N., Dauge F.R., Schneider P., Vogt M., Lerner U., Fishbain B.,
Broday D., Bartonova A., Can commercial low-cost sensor platforms
contribute to air quality monitoring and exposure estimates?, Environment International, 99 (2017) 293–302. DOI: 10.1016/j.envint.
2016.12.007
Chang C.T., Chang Y.M., Lin W.Y., Wu M.C., Fugitive dust emission
source profiles and assessment of selected control strategies for particulate matter at gravel processing sites in Taiwan, Journal of the Air& Waste Management Association, 60 (2010) 1262–1268. DOI:
10.3155/1047-3289.60.10.1262
Chen S., Zhang X., Lin J., Huang J., Zhao D., Yuan T., Huang K., Luo Y.,
Jia Z., Zang Z., Qiu Y.A., Xie L., Fugitive road dust PM2.5 emissions
and their potential health impacts, Environmental Science & Technology, 53 (2019) 8455–8465. DOI: 10.1021/acs.est.9b00666
Choobari O.A., Zawar-Reza P., Sturman A., The global distribution ofmineral dust and its impacts on the climate system: a review,
Atmospheric Research, 138 (2014) 152–165. DOI: 10.1016/
j.atmosres.2013.11.007
Cowherd C.Jr., Axetell K. Jr., Guenther C.M., Jutze G.A., Development ofemission factors for fugitive dust sources, U.S. Environmental Protection Agency, NC, 1974.
Minguillón M.C., Viana M., Querol X., Particulate Matter: Environmental
Monitoring and Mitigation, Future Science Book Series, Future
Science Ltd, 2013, ISBN: 9781909453135. DOI: 10.4155/
9781909453135
D’Amato G., Chong-Neto H.J., Monge Ortega O.P., Vitale C., Ansotegui53

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57
I., Rosario N., Haahtela T., Galan C., Pawankar R., Murrieta-Aguttes
M., Cecchi L., Bergmann C., Ridolo E., Ramon G., Gonzalez DiazS., et al., The effects of climate change on respiratory allergy andasthma induced by pollen and mold allergens, Allergy, 75 (2020)
2219–2228. DOI: 10.1111/all.14476
de Weger L.A., Molster F., de Raat K., den Haan J., Romein J., van
Leeuwen W., de Groot H., Mostert M., Hiemstra P.S., A new portablesampler to monitor pollen at street level in the environment ofpatients, Science of The Total Environment, 741 (2020) 140404.
DOI: 10.1016/j.scitotenv.2020.140404
Du K., Rood M.J., Welton E.J., Varma R.M., Hashmonay R.A., Kim B.J.,
Kemme M.R., Optical remote sensing to quantify fugitive particulatemass emissions from stationary short-term and mobile continuous
sources: part i. method and examples, Environmental Science &Technology, 45 (2011) 658–665. DOI: 10.1021/es101904q
Du K., Shi P., Rood M.J., Wang K., Wang Y., Varma R.M., Digital Optical
Method to quantify the visual opacity of fugitive plumes, Atmospheric Environment, 77 (2013) 983–989. DOI: 10.1016/j.
atmosenv.2013.06.017
Ebert M., Weinbruch S., Rausch A., Gorzawski G., Helas G., Hoffmann P.,
Wex H., Complex refractive index of aerosols during LACE
98#x2010; as derived from the analysis of individual particles,
Journal of Geophysical Research: Atmospheres, 107 (2002) 8121.
DOI: 10.1029/2000JD000195
Esmen N.A., Corn M., Residence time of particles in urban air, Atmospheric Environment (1967), 5 (1971) 571–578. DOI: 10.1016/00046981(71)90113-2
Etyemezian V., Ahonen S., Nikolic D., Gillies J., Kuhns H., Gillette D.,
Veranth J., Deposition and removal of fugitive dust in the arid southwestern United States: measurements and model results, Journal of
the Air & Waste Management Association, 54 (2004) 1099–1111.
DOI: 10.1080/10473289.2004.10470977
Etyemezian V., Nikolich G., Ahonen S., Pitchford M., Sweeney M.,
Purcell R., Gillies J., Kuhns H., The portable in situ wind erosion
laboratory (PI-SWERL): a new method to measure PM10 windblown dust properties and potential for emissions, Atmospheric
Environment, 41 (2007) 3789–3796. DOI: 10.1016/j.atmosenv.
2007.01.018
February F.J., Piazzola J., Altieri K.E., Van Eijk A.M.J., Contribution ofsea spray to aerosol size distributions measured in a South African
coastal zone, Atmospheric Research, 262 (2021) 105790. DOI:
10.1016/j.atmosres.2021.105790
Feigin V.L., Roth G.A., Naghavi M., Parmar P., Krishnamurthi R., ChughS., Mensah G.A., Norrving B., Shiue I., Ng M., Estep K., Cercy K.,
Murray C.J.L., Forouzanfar M.H., Global burden of stroke and risk
factors in 188 countries, during 1990–2013: a systematic analysis for
the Global Burden of Disease Study 2013, The Lancet Neurology, 15(2016) 913–924. DOI: 10.1016/s1474-4422(16)30073-4
Fitz D., Pankratz D., Pederson S., Bristow J., A semi-portable enclosure tomeasure emission rates from small fugitive sources: leaf blower dust
example, Aerosol and Air Quality Research, 21 (2021) 200566. DOI:
10.4209/aaqr.200566
Froyd K.D., Murphy D.M., Brock C.A., Campuzano-Jost P., Dibb J.E.,
Jimenez J.L., Kupc A., Middlebrook A.M., Schill G.P., Thornhill
K.L., Williamson C.J., Wilson J.C., Ziemba L.D., A new method toquantify mineral dust and other aerosol species from aircraft
platforms using single-particle mass spectrometry, AtmosphericMeasurement Techniques, 12 (2019) 6209–6239. DOI: 10.5194/
amt-12-6209-2019
Gao S., Wang Y., Shan M., Teng Y., Hong N., Sun Y., Mao J., Ma Z., Xiao
J., Azzi M., Chen L., Zhang H., Wind-tunnel and modelled PM10
emissions and dust concentrations from agriculture soils in Tianjin,northern China, Aeolian Research, 42 (2020) 100562. DOI: 10.1016/
j.aeolia.2019.100562
Gao Y., Lai S., Lee S.C., Yau P.S., Huang Y., Cheng Y., Wang T., Xu Z.,

54

Review Paper

Yuan C., Zhang Y., Optical properties of size-resolved particles at a
Hong Kong urban site during winter, Atmospheric Research, 155(2015) 1–12. DOI: 10.1016/j.atmosres.2014.10.020
Gelencsér A., Kováts N., Turóczi B., Rostási Á., Hoffer A., Imre K.,Nyirő-Kósa I., Csákberényi-Malasics D., Tóth Á., Czitrovszky A.,
Nagy A., Nagy S., Ács A., Kovács A., Ferincz Á., et al., The red mud
accident in Ajka (Hungary): characterization and potential healtheffects of fugitive dust, Environmental Science & Technology, 45(2011) 1608–1615. DOI: 10.1021/es104005r
Girón D., Delgado T., Ruiz J., Cabalín L.M., Laserna J.J., In-situ monitoring and characterization of airborne solid particles in the hostileenvironment of a steel industry using stand-off LIBS, Measurement,
115 (2018) 1–10. DOI: 10.1016/j.measurement.2017.09.046
Gladding T.L., Rolph C.A., Gwyther C.L., Kinnersley R., Walsh K., Tyrrel
S., Concentration and composition of bioaerosol emissions from
intensive farms: pig and poultry livestock, Journal of Environmental
Management, 272 (2020) 111052. DOI: 10.1016/j.jenvman.2020.
111052Grover B.D., Measurement of total PM2.5 mass (nonvolatile plus semivolatile) with the Filter Dynamic Measurement System tapered element
oscillating microbalance monitor, Journal of Geophysical Research,
110 (2005) D07S03. DOI: 10.1029/2004jd004995
Guo L., Cui Y., He Q., Gao W., Pei K., Zhu L., Li H., Wang X., Contributions of aerosol chemical composition and sources to light extinction
during haze and non-haze days in Taiyuan, China, Atmospheric
Pollution Research, 12 (2021) 101140. DOI: 10.1016/j.apr.2021.
101140Hassan H., Kumar P., Kakosimos K.E., The impact of local fugitive particulate matter and emission inventories on air quality and health indry and arid areas, Science of The Total Environment, 824 (2022)
153799. DOI: 10.1016/j.scitotenv.2022.153799
Hassan H., Saraga D., Kumar P., Kakosimos K.E., Vehicle-induced fugitive particulate matter emissions in a city of arid desert climate,
Atmospheric Environment, 229 (2020) 117450. DOI: 10.1016/j.
atmosenv.2020.117450
Hassan H.A., Kumar P., Kakosimos K.E., Flux estimation of fugitive
particulate matter emissions from loose Calcisols at construction
sites, Atmospheric Environment, 141 (2016) 96–105. DOI: 10.1016/
j.atmosenv.2016.06.054
Hergert W., Wriedt T., The Mie Theory: Basics and Applications,
Springer, 2012, ISBN: 978-3-642-43614-7. DOI: 10.1007/978-3642-28738-1
Hobbs P.V., Sinha P., Yokelson R.J., Christian T.J., Blake D.R., Gao S.,
Kirchstetter T.W., Novakov T., Pilewskie P., Evolution of gases andparticles from a savanna fire in South Africa, Journal of GeophysicalResearch: Atmospheres, 108 (2003) 8485. DOI: 10.1029/
2002JD002352
Holmén B., Miller D., Hiscox A., Yang W., Wang J., Sammis T., Bottoms
R., Near-source particulate emissions and plume dynamics fromagricultural field operations, Journal of Atmospheric Chemistry, 59(2007) 117–134. DOI: 10.1007/s10874-007-9086-6
Hoy R.F., Chambers D.C., Silica-related diseases in the modern world,Allergy, 75 (2020) 2805–2817. DOI: 10.1111/all.14202
Hu J., Wang H., Zhang J., Zhang M., Zhang H., Wang S., Chai F., PM2.5
pollution in Xingtai, China: chemical characteristics, source apportionment, and emission control measures, Atmosphere, 10 (2019)121. DOI: 10.3390/atmos10030121
Jakovčević T., Stipaničev D., Krstinić D., Visual spatial-context based
wildfire smoke sensor, Machine Vision and Applications, 24 (2013)
707–719. DOI: 10.1007/s00138-012-0481-x
Jeong J.I., Jo D.S., Park R.J., Lee H.-M., Curci G., Kim S.-W., Parametricanalysis for global single scattering albedo calculations, Atmospheric
Environment, 234 (2020) 117616. DOI: 10.1016/j.atmosenv.2020.
117616
Kabir A.S., Sharma N.C., Barnes J.E., Butt J., Bridgewater M., Using a

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57
bistatic camera lidar to profile aerosols influenced by a local sourceof pollution, SPIE, 10636 (2018) 126–132. DOI: 10.1117/12.2303544
Kalashnikova O.V., Sokolik I.N., Modeling the radiative properties ofnonspherical soil-derived mineral aerosols, Journal of Quantitative
Spectroscopy and Radiative Transfer, 87 (2004) 137–166. DOI:
10.1016/j.jqsrt.2003.12.026
Kandler K., Benker N., Bundke U., Cuevas E., Ebert M., Knippertz P.,
Rodríguez S., Schütz L., Weinbruch S., Chemical composition andcomplex refractive index of Saharan Mineral Dust at Izaña, Tenerife(Spain) derived by electron microscopy, Atmospheric Environment,
41 (2007) 8058–8074. DOI: 10.1016/j.atmosenv.2007.06.047
Kaźmierczak B., Badura M., Batog P., Drzeniecka-Osiadacz A., Modzel
P., Kutyłowska M., Piekarska K., Jadwiszczak P., Optical particulatematter sensors in PM2.5 measurements in atmospheric air, E3S Web
of Conferences, 44 (2018) 00006. DOI: 10.1051/e3sconf/
20184400006
Kelesidis G.A., Pratsinis S.E., Soot light absorption and refractive index
during agglomeration and surface growth, Proceedings of the Combustion Institute, 37 (2019) 1177–1184. DOI: 10.1016/j.proci.
2018.08.025
Kim M.K., Jang Y., Heo J., Park D., A UAV-based air quality evaluation
method for determining fugitive emissions from a quarry during therailroad life cycle, Sensors, 21 (2021) 3206. DOI: 10.3390/
s21093206
Kuhns H., Gillies J., Etyemezian V., Nikolich G., King J., Zhu D.,
Uppapalli S., Engelbrecht J., Kohl S., Effect of soil type and momentum on unpaved road particulate matter emissions from wheeled andtracked vehicles, Aerosol Science and Technology, 44 (2010) 187–
196. DOI: 10.1080/02786820903516844
Kuula J., Kuuluvainen H., Rönkkö T., Niemi J.V., Saukko E., Portin H.,
Aurela M., Saarikoski S., Rostedt A., Hillamo R., Timonen H.,
Applicability of optical and diffusion charging-based particulatematter sensors to urban air quality measurements, Aerosol and Air
Quality Research, 19 (2019) 1024–1039. DOI: 10.4209/
aaqr.2018.04.0143
Lavrov A., Utkin A.B., Vilar R., Fernandes A., Application of lidar inultraviolet, visible and infrared ranges for early forest fire detection,
Applied Physics B: Lasers and Optics, 76 (2003) 87–95. DOI:
10.1007/s00340-002-1053-y
Le T.-C., Tsai C.-J., Inertial impaction technique for the classification ofparticulate matters and nanoparticles: a review, KONA Powder andParticle Journal, 38 (2021) 42–63. DOI: 10.14356/kona.2021004
Lei Y., Shen Z., Tang Z., Zhang Q., Sun J., Ma Y., Wu X., Qin Y., Xu H.,
Zhang R., Aerosols chemical composition, light extinction, andsource apportionment near a desert margin city, Yulin, China, PeerJ,
8 (2020) e8447. DOI: 10.7717/peerj.8447
Liu H.-Y., Schneider P., Haugen R., Vogt M., Performance assessment ofa low-cost PM2.5 sensor for a near four-month period in Oslo,
Norway, Atmosphere, 10 (2019) 41. DOI: 10.3390/atmos10020041
Liu Y., Shao L., Wang W., Chen J., Zhang H., Yang Y., Hu B., Study on
fugitive dust control technologies of agricultural harvesting machinery, Agriculture, 12 (2022) 1038. DOI: 10.3390/agriculture12071038
Madokoro H., Kiguchi O., Nagayoshi T., Chiba T., Inoue M., Chiyonobu
S., Nix S., Woo H., Sato K., Development of drone-mounted multiple
sensing system with advanced mobility for in situ atmospheric measurement: a case study focusing on PM2.5 local distribution, Sensors,
21 (2021) 4881. DOI: 10.3390/s21144881
Manojkumar N., Srimuruganandam B., Size-segregated particulate mattercharacteristics in indoor and outdoor environments of urban traffic
and residential sites, Urban Climate, 44 (2022) 101232. DOI:
10.1016/j.uclim.2022.101232
Marina-Montes C., Motto-Ros V., Pérez-Arribas L.V., Anzano J., MillánMartínez M., Cáceres J.O., Aerosol analysis by micro laser-induced
breakdown spectroscopy: a new protocol for particulate mattercharacterization in filters, Analytica Chimica Acta, 1181 (2021)

Review Paper

338947. DOI: 10.1016/j.aca.2021.338947
Martinelli N., Olivieri O., Girelli D., Air particulate matter and cardiovascular disease: a narrative review, European Journal of Internal
Medicine, 24 (2013) 295–302. DOI: 10.1016/j.ejim.2013.04.001
Mueller S.F., Mallard J.W., Mao Q., Shaw S.L., Fugitive particulate
emission factors for dry fly ash disposal, Journal of the Air & Waste
Management Association, 63 (2013) 806818. DOI: 10.1080/
10962247.2013.795201
Mutlu A., Air quality impact of particulate matter (PM10) releases from anindustrial source, Environmental Monitoring and Assessment, 192(2020) 547. DOI: 10.1007/s10661-020-08508-7
Noh H.-j., Lee S.-k., Yu J.-h., Identifying effective fugitive dust control
measures for construction projects in Korea, Sustainability, 10(2018) 1206. DOI: 10.3390/su10041206
Nousiainen T., Optical modeling of mineral dust particles: a review,
Journal of Quantitative Spectroscopy and Radiative Transfer, 110(2009) 1261–1279. DOI: 10.1016/j.jqsrt.2009.03.002
Nugraha M.G., Saptoadi H., Hidayat M., Andersson B., Andersson R.,
Particulate matter reduction in residual biomass combustion,
Energies, 14 (2021) 3341. DOI: 10.3390/en14113341
Omidvarborna H., Baawain M., Al-Mamun A., Al-Muhtaseb A.H., Dispersion and deposition estimation of fugitive iron particles from aniron industry on nearby communities via AERMOD, Environmental
Monitoring and Assessment, 190 (2018) 655. DOI: 10.1007/s10661018-7009-4
Ordou N., Agranovski I.E., Contribution of fine particles to air emission at
different phases of biomass burning, Atmosphere, 10 (2019) 278.
DOI: 10.3390/atmos10050278
Pal S.R., Hlaing D., Carswell A.I., Roy G., Bastille C., Scanning LIDAR
application for pollutant sources in an industrial complex, SPIE,
3504 (1998) 76–86. DOI: 10.1117/12.319574
Pervez S., Bano S., Watson J.G., Chow J.C., Matawle J.L., Shrivastava A.,
Tiwari S., Pervez Y.F., Source profiles for PM10-2.5 resuspended dust
and vehicle exhaust emissions in central India, Aerosol and Air
Quality Research, 18 (2018) 1660–1672. DOI: 10.4209/aaqr.2017.
08.0259
Philip S., Martin R.V., Snider G., Weagle C.L., van Donkelaar A., Brauer
M., Henze D.K., Klimont Z., Venkataraman C., Guttikunda S.K.,
Zhang Q., Anthropogenic fugitive, combustion and industrial dust isa significant, underrepresented fine particulate matter source inglobal atmospheric models, Environmental Research Letters, 12(2017) 044018. DOI: 10.1088/1748-9326/aa65a4
Prata A.J., Satellite detection of hazardous volcanic clouds and the risk toglobal air traffic, Natural Hazards, 51 (2008) 303–324. DOI:
10.1007/s11069-008-9273-z
Qu Y., Ji H., Oudray F., Yan Y., Liu Y., Online composition detection andcluster analysis of Tibetan incense, Optik, 241 (2021) 166999. DOI:
10.1016/j.ijleo.2021.166999
Raja S., Chandrasekaran S.R., Lin L., Xia X., Hopke P.K., Valsaraj K.T.,
Analysis of beta attenuation monitor filter rolls for particulate matter
speciation, Aerosol and Air Quality Research, 17 (2017) 14–23. DOI:
10.4209/aaqr.2016.03.0122
Roney J.A., White B.R., Estimating fugitive dust emission rates using anenvironmental boundary layer wind tunnel, Atmospheric Environment, 40 (2006) 7668–7685. DOI: 10.1016/j.atmosenv.2006.08.015
Sanderson P., Delgado-Saborit J.M., Harrison R.M., A review of chemicaland physical characterisation of atmospheric metallic nanoparticles,
Atmospheric Environment, 94 (2014) 353–365. DOI: 10.1016/j.
atmosenv.2014.05.023
Sasaki K., Inoue M., Shimura T., Iguchi M., In Situ, rotor-based drone
measurement of wind vector and aerosol concentration in volcanic
areas, Atmosphere, 12 (2021) 376. DOI: 10.3390/atmos12030376
Sedlacek Iii A.J., Buseck P.R., Adachi K., Onasch T.B., Springston S.R.,
Kleinman L., Formation and evolution of tar balls from northwestern
US wildfires, Atmospheric Chemistry and Physics, 18 (2018)55

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57
11289–11301. DOI: 10.5194/acp-18-11289-2018
Sidra S., Ali Z., Sultan S., Ahmed S., Colbeck I., Nasir Z.A., Assessmentof airborne microflora in the indoor micro-environments of residential houses of Lahore, Pakistan, Aerosol and Air Quality Research, 15(2015) 2385–2396. DOI: 10.4209/aaqr.2014.12.0338
Su C.P., Peng X., Huang X.F., Zeng L.W., Cao L.M., Tang M.X., Chen Y.,
Zhu B., Wang Y., He L.Y., Development and application of a massclosure PM2.5 composition online monitoring system, AtmosphericMeasurement Techniques, 13 (2020) 5407–5422. DOI: 10.5194/
amt-13-5407-2020
Sullivan B., Allawatt G., Emery A., Means P., Kramlich J., Posner J.,
Time-resolved particulate emissions monitoring of cookstove biomass combustion using a tapered element oscillating microbalance,
Combustion Science and Technology, 189 (2017) 923–936. DOI:
10.1080/00102202.2016.1253564
Sweeney M.R., Forman S.L., McDonald E.V., Contemporary and future
dust sources and emission fluxes from gypsum- and quartzdominated eolian systems, New Mexico and Texas, USA, Geology,
50 (2021) 356–360. DOI: 10.1130/g49488.1
Tao Z., Wang Z., Yang S., Shan H., Ma X., Zhang H., Zhao S., Liu D., Xie
C., Wang Y., Profiling the PM2.5 mass concentration vertical distribution in the boundary layer, Atmospheric Measurement Techniques, 9(2016) 1369–1376. DOI: 10.5194/amt-9-1369-2016
Taubman B.F., Smoke over haze: aircraft observations of chemical andoptical properties and the effects on heating rates and stability, Journal of Geophysical Research, 109 (2004) D02206. DOI: 10.1029/
2003jd003898
ten Brink H., Henzing B., Otjes R., Weijers E., Visibility in the Netherlands during New Year’s fireworks: the role of soot and salty aerosol
products, Atmospheric Environment, 173 (2018) 289–294. DOI:
10.1016/j.atmosenv.2017.11.020
Theilade I., Mærsk-Møller M.L., Theilade J., Larsen K., Pollen morphology and structure of Zingiber (Zingiberaceae), Grana, 32 (1993)
338–342. DOI: 10.1080/00173139309428961
Tian J., Wang Q., Han Y., Ye J., Wang P., Pongpiachan S., Ni H., Zhou Y.,
Wang M., Zhao Y., Cao J., Contributions of aerosol composition andsources to particulate optical properties in a southern coastal city of
China, Atmospheric Research, 235 (2020) 104744. DOI: 10.1016/j.
atmosres.2019.104744
Ueda S., Morphological change of solid ammonium sulfate particles
below the deliquescence relative humidity: experimental reproduction of atmospheric sulfate particle shapes, Aerosol Science andTechnology, 55 (2021) 423–437. DOI: 10.1080/02786826.
2020.1864277
Varma R.M., Hashmonay R.A., Du K., Rood M.J., Kim B.J., Kemme
M.R., A novel method to quantify fugitive dust emissions using
optical remote sensing, in: Kim Y.J., Platt U. (Eds.), Advanced
Environmental Monitoring, Springer Netherlands, Dordrecht, 2008,
pp.143–154, ISBN: 978-1-4020-6364-0. DOI: 10.1007/978-1-40206364-0_11
Vos H.C., Fister W., von Holdt J.R., Eckardt F.D., Palmer A.R., Kuhn N.J.,
Assessing the PM10 emission potential of sandy, dryland soils inSouth Africa using the PI-SWERL, Aeolian Research, 53 (2021)
100747. DOI: 10.1016/j.aeolia.2021.100747
Wang F., Yu H., Wang Z., Liang W., Shi G., Gao J., Li M., Feng Y., Review
of online source apportionment research based on observation forambient particulate matter, Science of The Total Environment, 762(2021) 144095. DOI: 10.1016/j.scitotenv.2020.144095
Wang F., Zhang Q., Xu X., Zhao W., Zhang Y., Zhang W., Thermo-opticaland particle number size distribution characteristics of smolderingsmoke from biomass burning, Applied Sciences, 9 (2019) 5259.
DOI: 10.3390/app9235259
Wang H., Han L., Li T., Qu S., Zhao Y., Fan S., Chen T., Cui H., Liu J.,
Temporal-spatial distributions of road silt loadings and fugitive roaddust emissions in Beijing from 2019 to 2020, Journal of Environmen-

56

Review Paper

tal Sciences, 132 (2023) 56–70. DOI: 10.1016/j.jes.2022.07.007
Wang W., Zhou W., Cai X., Qin J., Huang Y., A.. S., Remote measurement
method for mass concentration of fugitive-dust-emission particulate
based on backward light scattering (in Chinese), Acta Optica Sinica,
39 (2019) 1201001. DOI: 10.3788/aos201939.1201001
Wang Y., Li X., Shi Z., Huang L., Li J., Zhang H., Ying Q., Wang M., Ding
D., Zhang X., Hu J., Premature mortality associated with exposure to
outdoor black carbon and its source contributions in China,
Resources, Conservation and Recycling, 170 (2021) 105620. DOI:
10.1016/j.resconrec.2021.105620
Williamson C.J., Kupc A., Axisa D., Bilsback K.R., Bui T., CampuzanoJost P., Dollner M., Froyd K.D., Hodshire A.L., Jimenez J.L., Kodros
J.K., Luo G., Murphy D.M., Nault B.A., Ray E.A., et al., A large
source of cloud condensation nuclei from new particle formation in
the tropics, Nature, 574 (2019) 399–403. DOI: 10.1038/s41586-0191638-9
Xiao S., Wang Q.Y., Cao J.J., Huang R.J., Chen W.D., Han Y.M., Xu
H.M., Liu S.X., Zhou Y.Q., Wang P., Zhang J.Q., Zhan C.L., Longterm trends in visibility and impacts of aerosol composition on visibility impairment in Baoji, China, Atmospheric Research, 149 (2014)
88–95. DOI: 10.1016/j.atmosres.2014.06.006
Yang D., Zhang H., Wang Z., Zhao S., Li J., Changes in anthropogenic
particulate matters and resulting global climate effects since theIndustrial Revolution, International Journal of Climatology, 42(2021) 315–330. DOI: 10.1002/joc.7245
Yi E.E.P.N., Nway N.C., Aung W.Y., Thant Z., Wai T.H., Hlaing K.K.,
Maung C., Yagishita M., Ishigaki Y., Win-Shwe T.-T., Nakajima D.,
Mar O., Preliminary monitoring of concentration of particulate matter (PM2.5) in seven townships of Yangon City, Myanmar, Environmental Health and Preventive Medicine, 23 (2018) 53. DOI:
10.1186/s12199-018-0741-0
Yu X., Ma J., An J., Yuan L., Zhu B., Liu D., Wang J., Yang Y., Cui H.,
Impacts of meteorological condition and aerosol chemical compositions on visibility impairment in Nanjing, China, Journal of Cleaner
Production, 131 (2016) 112–120. DOI: 10.1016/j.jclepro.2016.
05.067
Yuen W., Du K., Koloutsou-Vakakis S., Rood M.J., Kim B.J., Kemme
M.R., Hashmonay R.A., Meister C., Fugitive particulate matteremissions to the atmosphere from tracked and wheeled vehicles in adesert region by hybrid-optical remote sensing, Aerosol and Air
Quality Research, 15 (2015) 1613–1626. DOI: 10.4209/aaqr.
2014.12.0310
Yuen W., Johnsen D.L., Koloutsou-Vakakis S., Rood M.J., Kim B.J.,
Kemme M.R., Open burning and open detonation PM10 mass emission factor measurements with optical remote sensing, Journal of the
Air & Waste Management Association, 64 (2013) 227–234. DOI:
10.1080/10962247.2013.851045
Yuen W., Ma Q., Koloutsou-Vakakis S., Du K., Rood M.J., Lidar equation
inversion methods and uncertainties in measuring fugitive particulatematter emission factors, Applied Optics, 56 (2017) 7691–7701. DOI:
10.1364/AO.56.007691
Zavyalov V.V., Marchant C., Bingham G.E., Wilkerson T.D., Swasey J.,
Rogers C., Ahlstrom D., Timothy P., Retrieval of physical propertiesof particulate emission from animal feeding operations using
three-wavelength elastic lidar measurements, SPIE, 2006. DOI:
10.1117/12.680967
Zhang H., Sun W., Li W., Wang Y., Physical and chemical characterizationof fugitive particulate matter emissions of the iron and steel industry,
Atmospheric Pollution Research, 13 (2022) 101272. DOI: 10.1016/j.
apr.2021.101272
Zhang Q., Liu Y., Yin W., Yan Y., Li L., Xing G., The in situ detection ofsmoking in public area by laser-induced breakdown spectroscopy,
Chemosphere, 242 (2020) 125184. DOI: 10.1016/j.chemosphere.
2019.125184
Zhang Y., Forrister H., Liu J., Dibb J., Anderson B., Schwarz J.P., Perring

Tianyi Cai et al. / KONA Powder and Particle Journal No. 41 (2024) 42–57
A.E., Jimenez J.L., Campuzano-Jost P., Wang Y., Nenes A., Weber
R.J., Top-of-atmosphere radiative forcing affected by brown carbon
in the upper troposphere, Nature Geoscience, 10 (2017) 486–489.
DOI: 10.1038/ngeo2960
Zhou W., Mei C., Qin J., Cai X., Wang T., He W., Shi A., A side-scattering
imaging method for the in-line monitoring of particulate matteremissions from cooking fumes, Measurement Science and Technol-

Review Paperogy, 32 (2020) 034006. DOI: 10.1088/1361-6501/abbd58
Zou J., Liu Z., Hu B., Huang X., Wen T., Ji D., Liu J., Yang Y., Yao Q.,
Wang Y., Aerosol chemical compositions in the North China Plainand the impact on the visibility in Beijing and Tianjin, AtmosphericResearch, 201 (2018) 235–246. DOI: 10.1016/j.atmosres.2017.
09.014

Authors’ Short Biographies
Tianyi Cai
Tianyi Cai received his bachelor’s degree and a Ph.D.in Engineering from Southeast University in2014 and 2020, respectively. He is now a lecturer in the School of Energy and Power Engineering at
the University of Shanghai for Science and Technology. His research interests include particles andtwo-phase flow measurement, heterogeneous reaction flow measurement. He also researches ontheoretical modeling of the heterogeneous reactions related to various carbon capture and utilization
processes.

Wu ZhouProf. Wu Zhou received her degree from Nanjing University of Science and Technology in 2006
and a Ph.D.in Engineering from Southeast University in 2011.She is now an professor in the Schoolof Energy and Power Engineering at the University of Shanghai for Science and Technology. Her
research interests include particles and two-phase flow measurement, combustion measurement,
mainly by image-based methods. She is also interested in mathematical modelling of the above processes. She is a member of Youth Council of the Chinese Society of Particuology.

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