spectroscopic characterization of dust-fall samples collected from

Spectroscopic Characterization of Dust-Fall Samples Collectedfrom Greater Cairo, Egypt

Abdallah A. Shaltout1,2 • Mousa A. Allam1,2• Nasser Y. Mostafa2,3 •

Zein K. Heiba2,4

Received: 28 August 2015 / Accepted: 13 December 2015 / Published online: 28 December 2015

� Springer Science+Business Media New York 2015

Abstract This work aimed to characterize dust-fall

samples collected from street’s trees in Greater Cairo (GC),

Egypt, and its surroundings by different spectroscopic

techniques, namely; X-ray diffraction (XRD), attenuated

total-reflection Fourier transform infrared (ATR-FTIR),

particle-size analyzer, and scanning electron microscopy

(SEM) combined with energy dispersive X-ray measure-

ments. Samples were collected from 19 different locations

inside and outside of GC. Quantitative phase analysis of the

dust-fall samples was performed using the Rietveld

method. Results showed that the most frequently observed

phases in the dust-fall samples were calcite (CaCO3),

dolomite (CaMg(CO3)2), gypsum (CaSO4�2H2O), and

quartz (SiO2) with average concentrations of 39 ± 16,

8 ± 7, 22 ± 13, and 33 ± 14 wt%, respectively. The

occurrence of these constituents referred to a combination

of different anthropogenic and natural sources. The ATR-

FTIR results are in good agreements with XRD data of the

different observed phases. Based on the SEM and particle-

size measurements, quantitative determination of the par-

ticle-size distribution was described. It was found that not

only the large-sized particles are deposited but also the

small-sized ones (PM10 and PM2.5). In addition, the particle

size of the collected dust-fall samples varied from 0.1 to

200 lm with an average particle size of 17.36 lm; how-

ever, the particle size ranged from 2.5 to 40 lm predomi-

nated in all of the dust-fall samples.

Dust-fall pollution represents one of the major concerns in

Egypt because it significantly contributes to environmental

damage and health problems (Boman et al. 2012; Castilho

et al. 2012; Shaltout et al. 2012a, b, 2013). It represents air

particulate matter containing a mixture of organic and

inorganic pollutants. After temporary suspension in air,

dust fall has the capability to settle down (Espinosa et al.

2001). Generally, dust fall originates from natural and

anthropogenic sources such as soils, surrounding desert

environment, industrial wastes, traffic congestion, incom-

plete fuel combustion in domestic heating, industrial

plants, and vehicular exhausts (Warner 1976; Godish

2005). Soil dust represents major constituents in the dust-

fall samples. Physical, chemical, and mineralogical cau-

terisations of the dust-fall samples are essential to under-

standing the influence of dust fall in the environment and in

human health. Dust fall has a wide range of particle-size

distribution ranging from 0.001 to 300 lm (Warner 1976).

It includes both ‘‘inhalable coarse particles’’ with diameters

ranging from 2.5 lm to 10 lm (PM10) and ‘‘fine particles’’

with diameters B2.5 lm, PM2.5 (Jaradat et al. 2004; Al-

Rajhi et al. 1996; Rodrıguez-Navarro and Sebastian 1996;

Rodriguez-Flores and Rodriguez-Castellon 1982; El Shazly

1990). These particles show many shapes and can be made

up of hundreds of different chemicals. Some particles,

known as primary particles, are emitted directly from a

source such as construction sites, unpaved roads, fields,

smokestacks, or fires. Others are formed in complicated

& Abdallah A. Shaltout

[email protected]

1 Spectroscopy Department, Physics Division, National

Research Center, El Behooth Street, 12622 Dokki, Cairo,

Egypt

2 Faculty of Science, Taif University, P. O. Box 888,

21974 Taif, Saudi Arabia

3 Chemistry Department, Faculty of Science, Suez Canal

University, 41522 Ismailia, Egypt

4 Physics Department, Faculty of Science, Ain Shams

University, Cairo, Egypt

123

Arch Environ Contam Toxicol (2016) 70:544–555

DOI 10.1007/s00244-015-0256-2

http://crossmark.crossref.org/dialog/?doi=10.1007/s00244-015-0256-2&domain=pdf

http://crossmark.crossref.org/dialog/?doi=10.1007/s00244-015-0256-2&domain=pdf

reactions in the atmosphere and comprise chemicals such

as sulfur dioxide and nitrogen oxides, which are emitted

from power plants, industries, and automobiles. Recently

an extensive research on atmospheric particles in urban

environments was performed (Jaradat et al. 2004; Al-Rajhi

et al. 1996; Rodrıguez-Navarro and Sebastian 1996;

Rodriguez-Flores and Rodriguez-Castellon 1982; El Shazly

1990). The inorganic phases, as well as the elemental

compositions of the dust fall are not constant, and

remarkable variations could be recognized. In Greater

Cairo (GC), there are increased air pollutants due to

automobile traffic as well as the use of agriculture land for

constructing buildings. Moreover, because there is no

control of exhaust gases, black smoke from incomplete

combustion fuel are emitted all over the city (Mahmoud

et al. 2008). In addition to motor vehicles, industrial

enterprises situated within the city, thermal power station,

stone crusher plants, and brick kilns contribute substan-

tially to the atmospheric pollution. Furthermore, dry

weather and roadside burning of rubbish are also causes of

air dust, which may lead to the production of noxious

smokes. Poisonous phosgene (carbonyl chloride) and

hydrogen chloride gases resulting from burning of PVC

plastics are added to the existing pollution. Few previous

studies were found in the literature about the characteri-

zation of the dust-fall samples by X-ray diffraction (XRD)

as well as identification of their crystalline forms (Esteve

et al. 1997; O’Connor and Jaklevic 1981; Dıaz-Hernandez

et al. 2011; Shen et al. 2009; Bhattacharjee et al. 2010;

Menendez et al. 2007; Schulze and Bertsch 1995). The

XRD patterns would refer to the chemical reactions taking

place and the origin of chemical species present in the

atmosphere emitted or formed through atmospheric reac-

tions. Characterization of air particulates and inorganic

constituents of dust-fall samples collected from GC can

generate a highly specific information source of great

importance to environmental issues because some of them

causes respiratory and carcinogenic diseases (Green and

Armstrong 2003).

Therefore, the present study refers to a dust-fall problem

of great national and international importance, which is

represented by air-quality control. It aimed to characterize

and evaluate inorganic air pollutants in dust-fall samples

collected from GC. The GC location was chosen because it

is one of the most highly populated mega-cities in the

world. Qualitative and quantitative characterization of the

chemical composition of dust-fall samples were performed

using some cooperative techniques, namely, attenuated

total reflection Fourier transform infrared (ATR-FTIR) and

XRD. In addition, the particle-size shape and distribution

were investigated using scanning electron microscopy

(SEM) and a particle-size analyzer, respectively.

Experimental Setup

Sampling

During July 2010, the samples were collected from 17

different locations in GC as well as 2 other locations out-

side GC representing the surrounding provinces (Shaltout

et al. 2012a, b, 2013). The collected samples represent dust

fall from ambient air deposited on leaves of the street’s

trees (i.e., leaf dust). Using clean sweeping tools, dust fall

was gently brushed out and collected from the evergreen

upper leaf surfaces of Fargesia nitida trees at the central

and peripheral branches 2 m above the ground. For each

sample, tens of leaves at the same height were used. The

selection of F. nitida trees is based on their widespread

presence in Cairo’s streets and the possibility to collect a

few grams of dust samples from their upper leaf surfaces

which is enough for different analytical techniques used in

this work. In addition, the dust deposited on leaves repre-

sents the normal distribution of dust from the ambient air.

Furthermore, the leaves of the F. nitida trees are not waxy,

and it is easy to collect all of the particulate matter from

their upper surfaces. Each sample was air dried, manually

homogenized, reserved in auto-sealable polyethylene bags,

and stored in a dessicator until analysis and measurements.

Figure 1 and Table 1 depict the details of these locations

and the samples’ codes.

The sampling sites were selected from areas having

different pollution levels originating from the traffic den-

sities, human population, economic units that produce

gaseous pollutants, and different industrial activities. A

group of samples was collected from urban–industrial

locations near different cement and iron plants as well as

other industrial activities and were named AP03, AP07,

AP08, AP09, and AP19. Another group of samples was

collected from the most famous squares around the city,

which always have heavy traffic (named AP05, AP10,

AP11, and AP17). The rest of samples were collected from

urban–residential areas (named AP01, AP02, AP04, AP06,

and AP18). In addition, two samples were also collected

outside GC. The first one, AP15, is collected from a

location close to the agricultural Delta region northern GC,

70 km away from GC, and far away from the industrial

sources of pollution. The second site is an urban–industrial

location at Al Sadat city (AP16), which has had a

remarkable increase in population and is located 120 km

away from GC.

XRD

The main purpose of XRD measurements was to achieve

information about the phase composition of dust-fall

Arch Environ Contam Toxicol (2016) 70:544–555 545

123

samples. Previously, there was a remarkable difficulty in

obtaining quantitative results for a small amount of the

dust-fall samples. However, this problem can be overcome

using a recent-model XRD diffractometer or synchrotron

radiation–XRD (Schulze and Bertsch 1995). In the present

study, the XRD measurements were performed using

Bruker X-ray diffractometer Model D8 FOCUS with a h–hgoniometer, equipped with a Cu-tube with excitation con-

ditions of 40 mA and 40 kV, and a scintillation counter. To

obtain a sufficient number of experimental data points for

peak-fitting and an acceptable number of counts, a step size

of 0.01� with a step time of 10 s/step were used. The

diffractograms were collected in the range of 5� to 80� (2h)

with an aperture slit of 2 mm, an antiscatter slit of 6 mm,

and a receiving slit of 0.2 mm. The diffractograms were

analyzed by employing the DIFFRAC plus program and

the ICDD database to identify the phases (chemical com-

pounds) present in each sample. For quantitative phase

analysis, Rietveld method was applied for all dust-fall

samples using MAUD software (http://www.ing.unitn.it/

*maud/, accessed 13 December 2012).

SEM

To characterize shapes and sizes of the dust-fall samples,

SEM was used. The dust-fall samples were analyzed using

SEM (JED-2300, Jeol, Japan) interfaced with an energy

dispersive X-ray analysis system (EDX) and Si(Li) detec-

tor. The SEM produces very high-resolution images of the

dust fall-sample surface showing details \1 nm in size.

Fine and coarse fractions were identified using magnifica-

tion ranging from 500 to 3000 times. Due to the very

Fig. 1 Locations of dust-fall sampling inside GC. Map was taken from Google Earth

Table 1 Locations of deposited dust-fall samples

Sample ID Location Location type

AP01 15 May City, Outostrad Road Urban–residential

AP02 15 May City, Garden Urban–residential

AP03 Al Maasara-Outostrad Road Urban–residential

AP04 Saker Kouriesh Urban–residential

AP05 Al Abassea Square Urban

AP06 Masr El Gededa Urban–residential

AP07 Helwan Urban–residential

AP08 Helwan Metro Station Urban–residential

AP09 Torra El Balad Metro Station Urban–residential

AP10 Ramsis Square Urban

AP11 Al Tahrir Square Urban

AP12 Al Ataba Square Urban

AP13 Al Dokki Square Urban

AP14 Sphenkis Square Urban

AP15 Astobary Villagea Rural

AP16 Al Sadat Citya Urban–industrial

AP17 Giza Square Urban

AP18 Harram Street Urban–residential

AP19 Shobra El-Khema Industrial

a Samples collected from outside GC

546 Arch Environ Contam Toxicol (2016) 70:544–555

123

http://www.ing.unitn.it/%7emaud/


narrow electron beam, SEM micrographs have a large

depth of field yielding a characteristic three-dimensional

appearance that is useful for understanding the surface

structure of the dust-fall sample. EDX was performed at

accelerating voltage of 25 kV, probe current of 1 nA, and

real measuring time of 60 s. The low accelerating voltage

for low atomic number X-ray detection that was chosen to

decrease the background originated from the continuous

X-rays and an increase in the signal from the elements

present in the dust-fall samples. Simple ratio corrections

for background shape and overlap were also performed

using software.

Particle-Size Analysis

Particle-size measurements employ the phenomenon of scat-

tered light from multiple laser beams by a stream of particles.

The amount and direction of light scattered by the particles are

measured and analyzed by an optical detector array. Particle-

size distribution of the dust-fall samples was investigated

using a Microtrac particle-size analyzer operated with FLEX

software (Microtrac S3500, USA). In addition, the Microtrac

particle-size analyzer is configured for the broadest range of

particle-size measurements with high measurement accuracy.

Wet dust-fall samples were inserted as suspensions with

double-distilled water. The range of particle-size measure-

ments was set from 0.01 to 2800 lm. A representative small

amount (approximately 5 mg) of the dust-fall sample was

used; however, a larger amount may be required if the particle-

size distribution is broad enough to provide statistically valid

measurements. Ultrasonic waves were used for making the

suspension more homogenous. A recirculator was used to

disperse the material sample uniformly in a fluid and deliver it

to the analyzer. The recirculator consists of a reservoir where

the sample is introduced, a fluid pump, a valve to drain the

system, and the necessary tubing and connections. After each

injection, the sampling system was auto cleaned for 5 min

using ultrasonic waves to avoid any interference with the

previous measurements.

ATR-FTIR

To identify the constituents of the collected dust-fall

samples, FTIR measurements were performed using an

ATR-FTIR spectrometer (Bruker, Germany). The dust-fall

samples were placed undiluted on the ATR-FTIR setup.

The spectrum for each sample was collected within a few

seconds without requiring complex preparations. The FTIR

spectra were obtained in the spectral range from 4000 to

400 cm-1. Spectral acquisition, data processing, and cal-

culations were performed using OPUS 7.0. IR software

(Bruker, Germany).

Results and Discussion

SEM

All of the dust-fall samples were characterized using SEM

combined with EDX detection. Complete information about

the shape and elemental composition of collected particles

were obtained. Figure 2 shows the scanning electron micro-

graphs for two selected dust-fall samples inside and outside of

the GC area (AP02 and AP15) at different magnifications,

namely; 91000, 91200, 92000, and 92500. Fine and coarse

particles were identified. The coarse airborne particles have

diameters [2.5–10 lm, whereas the fine particles have

diameters B2.5 lm. In addition, some dust-fall samples

exhibited a greater loading of coarse-mode particles with a

more even distribution across the particle sizes collected from

the different locations. It was found that diameters of the dust-

fall particles varied from approximately 0.3–250 lm.

In the case of coarse particles, spherical and semispherical

shapes were predominant in most of the dust-fall samples. This

could be related to the compounds of the main elements in the

earth’s crust such as silicon, carbon, calcium, and iron. The

majority of the coarse particles in the dust-fall samples col-

lected from rural, urban, urban–industrial, and industrial sites

originate from natural sources. Due to different natural sources

(desert dust storm, soil erosion, etc.), fine dust-fall particles also

existed in the dust-fall samples. Different shapes of fine parti-

cles—such as angle shapes with sharp edges, spherical shapes,

and semispherical shapes—were noted. It was found that all of

the samples from all locations had comparable shapes. How-

ever, shapes of the dust-fall particles collected from the urban,

urban–industrial and rural locations were different based on the

total activities occurring in these locations. Based on the EDX

measurements, a standardless semiquantitative analysis was

performed using ZAF method (Van Borm and Adams 1991).

Using the area under the peak of each identified elements and

the calculated sensitivity factor, the weight or atomic percent of

each element can be quantified with this method. Figure 3

shows the EDX spectrum for the AP02 dust-fall sample. The

main detected elements in the collected dust-fall samples are C,

O, Na, Mg, Al, Si, S, Cl, K, Ca, and Fe at the energy range of

0–10 kV. The concentrations of the elements in weight percent

are up to 1, 20, 6.44, 10.78, 2.64, 4.70, 18.37, and 4.07 mass %

for K, C, Al, Si, S, Cl, Ca, and Fe, respectively. Remarkable

high concentrations of Ca, Si, and Fe were also recognized in all

dust-fall samples, and these elements mainly originate from

natural sources. The high carbon concentrations may be

attributed to anthropogenic sources such as incomplete fuel

combustion in domestic heating, power plants, and vehicular

exhausts. The obtained semiquantitative information is enough

to identify the different composition given by XRD data.


123

Fig. 2 Scanning electron micrographs of dust-fall samples (AP02 and P15) at different magnifications

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

keV

0

400

800

1200

1600

2000

2400

2800

3200

3600

4000

stnuoC

CKa

OKa

NaK

aMgK

aAlK

aSiKa

SKa

SKb

ClK

aClK

b

KKaKKb

CaK

aCaK

b

FeLl

FeLa

FeKesc

FeKa

FeKb

Fig. 3 EDX for the dust fall

sample AP02


123

Accurate quantitative analysis results for these samples have

been described elsewhere (Shaltout et al. 2012a, b, 2013).

Particle-Size Measurements

Figure 4 depicts a representative histogram of the particle-

size distribution for the most collected dust-fall samples

versus each of (Fig. 4a) volume frequencies in percent and

(Fig. 4b) cumulative volumes in percent. It was found that

90 % of the particle sizes of the dust-fall samples are

\50 lm. These findings are relatively in agreement with

those of previous studies (Dong et al. 1983; Iijima et al.

2007; Igathinathane et al. 2009) despite the fact that the

different pollution sources (natural and anthropogenic)

may vary from one location to another.

However, meteorological factors (temperature, wind

speed, and relative humidity) seem to be relatively similar

for all sampling locations. There were no remarkable dif-

ferences between the particle-size distributions of the dust-

fall samples as shown in Fig. 4 with the exception of samples

AP05 and AP14 where the percentages of the particle size

were 14.9 and 9.3 lm respectively at particle size greater

than 50 lm. Table 2 lists the quantitative distribution of the

existed particle sizes. According to the obtained results, the

particle-size distribution of the collected dust-fall samples

ranged from 0.1 to 250 lm with an average particle size of

0

1

2

3

4

5

6

7

8

9

10

11

12

2000

995.5

497.8

248.9

124.4

62.22

31.11

15.557.7

83.8

91.9

440.9

720.4

86

0.243

0

0.122

0

0.061

0

0.030

0

0.015

20

Size, µm

Volu

me

Freq

uenc

ies,

%

AP02AP03AP06AP07AP08AP09AP10AP11AP12AP13AP14AP16AP17AP18AP19

A

0

10

20

30

40

50

60

70

80

90

100

2000

995.5

497.8

248.9

124.4

62.22

31.11

15.557.7

83.8

91.9

440.9

720.4

86

0.243

0

0.122

0

0.061

0

0.030

0

0.015

20

Size, µm

Cum

ulat

ive

Volu

mes

, %

AP02AP03AP06AP07AP08AP09AP10AP11AP12AP13AP14AP16AP17AP18AP19

B

Fig. 4 Particle-size distribution

of the collected dust-fall

samples versus volume

frequencies (a) and cumulative

volumes (b)


123

17.36 lm. The majority of the particle sizes ranged from 5 to

40 lm, although a giant particle with diameter[200 lm also

was noted. Although the small-sized air particulate matters

spend a relatively long time in the atmosphere, they were

found with a remarkable percent in the present dust-fall

samples. Based on the obtained particle-size distribution, not

only large-sized air particulate matters were settled in the

dust-fall samples, but small particles with aerodynamic

diameters\10 and[2.5 lm (PM10)—as well as air partic-

ulate matters with aerodynamic diameters B2.5 lm

(PM2.5)—were also present. The average percentage of the

particulate matter with aerodynamic diameter B2.5 lm was

10.10 ± 2.65 %, whereas the percentage of coarse particu-

late matters with aerodynamic diameter ranging from 10 to

2.5 lm was 23.21 ± 4.25 %. In contrast, the particulate

matter with aerodynamic diameter ranging from 10 to 50 lm

was predominant and represented 61.55 ± 5.63 % as listed

in Table 2. The smallest ratio represents the particulate

matter with aerodynamic diameter[50 lm at a percent of

5.15 ± 3.22 %. This particle-size distribution reflects the

different origins of these dust-fall samples such as natural

erosion of the Earth’s surface and deposition coming from

different anthropogenic sources. Reproducibility of the

particle-size measurements appears to be very good for a

wide range of particle size. Based on the calculation of the

correlation coefficient (Shaltout et al. 2011a, b, 2012a, b),

there are strong positive correlations between particle-size

distributions of samples collected from all different loca-

tions. The correlation coefficient is ranging from 0.88 to 1.

XRD Measurements

Knowledge of the chemical forms (phases) present in the

dust-fall samples is essential to understand the environ-

mental impact of pollution. Figure 5 illustrates the XRD

patterns obtained from representative dust-fall samples

(AP01, AP03, AP11, and AP15). These diffraction patterns

were analyzed for the phases (chemical compounds) pre-

sent in each sample by applying the search-match program

(X’pert High score plus). The obtained quantitative ele-

mental analysis by EDX is an important prerequisite for

facilitating the phase-matching process. The obtained

phases present in the collected samples are listed in

Table 3. After knowing the chemical compounds (phases)

present in each sample, the percentage of each phase was

determined using quantitative phase analysis.

Because of the nonavailability of standards, it is

mandatory to use standardless methods. A standardless

quantitative phase analysis procedure based on the Rietveld

method for structure refinement was used applying MAUD

software. This program performs a nonlinear least-squares

fitting between measured and calculated diffraction pat-

terns through refining the structural parameters as well as

the percentage of each phase. A mathematical diffraction

pattern of the sample is calculated as the sum of diffraction

patterns of the phases present using their structural data.

The expected errors in the standardless analysis algorithm

for the quantitative phase analysis may originate from

possible errors associated with preferred orientations, the

Table 2 Quantitative

distribution of particle sizes

existing in dust-fall samples

Sample PM2.5 (%) PM2.5–0 (%) PM10–50 (%) PM[50 (%)

AP01 11.3 22.5 61.8 4.4

AP02 12.9 23.4 60.4 3.3

AP03 13.7 34.9 48.9 2.5

AP04 12.1 22.6 59.4 5.9

AP05 8.7 24.1 52.3 14.9

AP06 11.7 26.7 58.7 2.9

AP07 10.2 24.2 63.8 1.8

AP08 13.2 27.1 58.0 1.7

AP09 10.1 24.0 62.5 3.4

AP10 8.9 22.3 64.4 4.4

AP11 10.4 24.3 62.7 2.6

AP12 11.1 20.6 65.3 3.0

AP13 6.8 21.5 67.4 4.3

AP14 2.7 12.9 75.1 9.3

AP15 9.3 20.8 63.0 6.9

AP16 12.2 25.4 57.6 4.8

AP17 6.5 18.8 66.2 8.5

AP18 9.9 20.3 63.3 6.5

AP19 10.1 24.5 58.6 6.8

Average 10.10 ± 2.65 23.21 ± 4.25 61.55 ± 5.63 5.15 ± 3.22


123

tabulated mass absorption coefficients, particle-size distri-

bution, and the variability of the chemical compositions of

certain compounds. Figure 6 illustrates the fitted diffrac-

tion pattern obtained from Rietveld analysis for sample

AP11. Accurate quantitative phase analysis is obtained for

each sample. Table 3 lists the calculated percentages of the

phases present in each dust fall sample. As can be seen

from this table, calcite (CaCO3), dolomite (CaMg(CO3)2),

gypsum (CaSO4�2H2O), and quartz (SiO2) represent the

major components in the studied dust-fall samples. It

should be mentioned that these minerals usually dominate

in both clay and soil constituents. However, greater levels

of calcite wre encountered.

Quantitative analysis showed that calcite, dolomite,

gypsum, and quartz minerals accounted for approximately

3–76, 3–28, 0–59, and 12.8–60 % of the samples, respec-

tively. Furthermore, three other phases, namely, cristo-

balite, magnetite, and anorhtite, were found in some

samples. There were remarkable differences in the com-

position of dust-fall samples due to the nature of different

sampling locations. The obtained constituents from XRD

measurements refereed to a combination of different

sources (natural and anthropogenic). Due to the lack of rain

fall and the soil erosion in GC and the surrounding areas,

there is a strong relationship between the atmospheric

conditions and the composition of dust-fall samples. Fur-

thermore, there has been a remarkable increase in the

population of and industrialization in the GC area. In the

last few years, cement production has increased, which has

made remarkable contributions to the composition of dust-

fall samples, especially in areas near the cement plants. In

addition, new lead smelters and phosphorous fertilizer

plants were installed in some areas in GC (Ali et al. 2011).

In addition, iron plants make also a remarkable contribu-

tion to dust-fall samples (El Ghandour 1982).

It is well known that calcite plays an important role in

neutralizing acid aerosols. It is produced in dust-fall sam-

ples due to the combination of different anthropogenic

sources such as construction materials and other human

activities including vehicular operation. Unfortunately, the

main as well as small roads in the different districts of GC

make up a remarkably poor infrastructure. In addition, the

traffic flows are chaotic with motor vehicles, trucks, buses,

mini-buses, and pedestrians all sharing the same space. In

addition, the long-term accumulation of dust originating

from the desert, which contains high concentrations of

carbonates, may lead to the increase of calcite in the dust-

fall samples. In contrast, the average concentration of

gypsum is approximately 21 %. Gypsum crystals are

mostly composed of automorphic lm-sized microlite.

Therefore, one would expect that the sulfate mineral in the

dust-fall samples originated from the soil surface, fossil-

fuel combustion, and biomass burning.

Furthermore, crystalline silica, including quartz and

cristobalite (SiO2), is more abundant in coarse particulate

matter, and this can be attributed to natural geological

process. The content of quartz in the present dust-fall

samples ranged from 0 to 60 %. It is well known that

crystalline silica has a long-term hazardous effect on

human health such as carcinogenic risks, silicosis, fibrosis,

bronchitis, asthma, and skin irritation.

The obtained results suggest that the dust-fall samples

originated mainly from natural processes such as physical

and chemical weathering, which could vary from one site

to another. Depending on seasonable variation, a dust cloud

is created over GC that carries soil and desert dust from

nearby locations. This is clear from different samples such

as AP19, AP09, AP17, AP15, and AP16, which are close to

agricultural and desert areas. In the case of the samples

collected from the famous squares of GC (AP10 through

AP14 and AP17), with the exception of Al-Abassea Square

(Ap05), the ratios between calcite and quartz range from

0.68 to 1.38. This points more or less to the same natural

sources as the main source and anthropogenic sources (i.e.,

Fig. 5 XRD patterns of some representative dust-fall samples.

G = gypsum; Q = quartz; C = calcite; A = anorhtite; D = dolo-

mite; F = magnetite


123

different density of vehicular movements, industrial and

power plants, etc.) as a secondary source. Looking at the

urban–residential areas (samples AP01, AP02, AP04,

AP06, and AP18), the percentage of the calcite phase

seems to be constant (43–47.7 %), whereas the percentage

of quartz varies from 12.8 to 34 %. However, the ratios

between calcite and quartz vary from 1.59 to 3.72. This is

an indication that all of the squares in GC have the same

Table 3 Quantitative phase

analysis of dust-fall samples

using XRD

Sample Phase percentage wt%

Calcite Dolomite Gypsum Quartz Cristobalite Magnetite Anorhtite

AP01 47.7 3.5 36.0 12.8 0.0 0.0 0.0

AP02 45.0 12.0 30.0 13.0 0.0 0.0 0.0

AP03 3.0 0.0 59.0 38.0 0.0 0.0 0.0

AP04 47.0 3.0 28.0 22.0 0.0 0.0 0.0

AP05 76.2 3.6 5.6 14.6 0.0 0.0 0.0

AP06 43.0 5.0 25.0 27.0 0.0 0.0 0.0

AP07 50.0 14.0 7.0 29.0 0.0 0.0 0.0

AP08 57.0 0.0 27.0 16.0 0.0 0.0 0.0

AP09 32.0 0.0 18.0 46.0 4.0 0.0 0.0

AP10 30.3 5.7 16.0 44.4 0.0 3.6 0.0

AP11 40.0 28.0 3.0 29.0 0.0 0.0 0.0

AP12 45.0 4.0 26.0 25.0 0.0 0.0 0.0

AP13 36.0 0.0 15.0 49.0 0.0 0.0 0.0

AP14 43.0 0.0 20.0 37.0 0.0 0.0 0.0

AP15 19.0 7.0 0.0 39.0 0.0 9.0 26.0

AP16 24.0 0.0 32.0 44.0 0.0 0.0 0.0

AP17 40.0 5.0 9.0 46.0 0.0 0.0 0.0

AP18 46.0 .0 16.0 34.0 0.0 0.0 0.0

AP19 17.0 0.0 23.0 60.0 0.0 0.0 0.0

Mean 39 ± 16 8 ± 7 22 ± 13 33 ± 14

Fig. 6 Fitting of the diffraction pattern based on Rietveld analysis (sample AP11)


123

dust sources with some variations. Furthermore, the per-

centage of the dolomite phase seems to be very low in these

squares: It varies from 3 to 12 %. In the case of gypsum,

the phase percentages seem to be constant with the

exception of the Harram Street sample (AP18). Indeed, the

anthropogenic processes—such as cement additives, glass

fiber, and ceramics—also indicate the presence of crys-

talline silica in the dust-fall samples. The cristobalite phase

appeared in a sample from an urban–industrial location

(Torra El-Balad, AP09) as a minor (4 %) contributor. It is a

crystalline form of quartz (SiO2) and can be found natu-

rally occurring in volcanic rock, or it can be synthetically

produced by heating amorphous materials. In addition,

cristobalite is used in the manufacture of insulation, con-

struction-related activities, glass filters, and refractory

materials. From an industrial point of view, cristobalite can

be formed at temperatures [1470 �C; however, a disor-

dered form of cristobalite may often occur at many low

temperatures (National Institute for Occupational Safety

and Health: Criteria for a Recommended Standard Occu-

pational Exposure to Crystalline Silica [National Institute

for Occupational Safety and Health 1974]). Occupational

exposure to cristobalite can occur during the manufacture

of stone, clay, glass, and other ceramic products. Other

sources of exposure to cristobalite can occur in diatoma-

ceous earth operations or in high-temperature operations

such as foundries, cements, and ceramics industries. It

should be mentioned that the location of sample AP09 is

closed to a group of cement industries that explains the

presence and expected source of cristobalite. More focused

study of the different forms of crystalline silica (quartz and

cristobalite) in dust-fall samples could be investigated in

our future work.

ATR-FTIR Measurements

As a cooperative and confirmative tool to XRD, FTIR

analysis of the dust-fall samples was performed to provide

more information about the chemical structure. Therefore,

the chemical structure of the different salts found by XRD

data were confirmed by FTIR measurements. Figure 7

shows the IR spectra of selected samples (AP01, AP03,

AP11, and AP15). There are slight but distinct differences

among the dust-fall samples. The spikes from water vapor

present in the spectra may refer to the different grain size

of the dust-fall samples. However, the presence of these

Fig. 7 Reflection mode of the IR spectra of selected dust-fall samples

Table 4 Peak-height ratios of the main characteristic absorption

bands of different salts found in dust samples compared with a ref-

erence band at 2919 cm-1

Sample R920/2919

SiO2

R870/2919

CO3

R668/2919

SO4

AP01 5.3 13.3 9.3

AP02 8.0 18.0 11.0

AP03 5.0 15.5 7.0

AP04 3.2 10.0 5.6

AP05 0.9 2.9 1.1

AP06 1.8 3.6 1.8

AP07 1.8 3.0 3.0

AP08 5.0 17.0 6.0

AP09 4.0 8.0 5.5

AP10 1.4 3.8 2.0

AP11 1.5 3.2 1.7

AP12 3.0 11.3 4.7

AP13 1.2 3.3 1.7

AP14 1.7 3.3 1.3

AP15 2.6 1.1 2.0

AP16 4.5 7.5 7.0

AP17 1.3 3.0 1.3

AP18 1.3 4.0 1.3

AP19 2.0 4.0 0.8

Table 5 Percent concentrations of inorganic salts obtained by both

ATR-FTIR and XRD for selected dust-fall samples

Sample CO3 (calcite) SO4 (gypsum) SiO2 (silicate)

FTIR XRD FTIR XRD FTIR XRD

AP01 47.6 47.7 33.3 36 19 12.7

AP02 48.6 45 29.7 30 21.6 13

AP03 56.3 57.4 25.9 59 18.5 38

AP04 53.2 47 29.7 28 17 22

AP06 50 43 25 25 25 27

AP08 60.7 57 21.4 27 17.9 29

AP14 52 43 20.6 20 27 49

AP15 19.3 19 35 0 45.6 37


123

spikes did not influence the clarity of the calculated

absorption bands.

Careful investigation of the spectra of all samples showed

that they exhibit the main characteristic bands of three salt

ions, namely, carbonate CO32- at 1450, 1400, and 872 cm-1

(Othmer and Kirk 1982; Fowler et al. 1966; Silva et al. 2002;

Moharram et al. 1987); sulfate SO42- at 668 cm-1 (Mo-

harram et al. 1987); and silicate SiO2 at 920 cm-1 (Shaltout

et al. 2011a, b; Bensted 1976; Lane 2007; Moore and White

1971). It is well known that SO42- and SiO2 ions exhibit

bands at approximately 1000–1130 cm-1; thus, one can

conclude that these ions contribute to the broad band that

appeared at 1110 cm-1 in the IR spectra of the studied

samples. The spectral features, together with the obtained

XRD data for these samples, showed that these salts are

mainly calcium salts (calcite, dolomite, gypsum, and sili-

cates). In contrast, the spectral features of traces of organic

matter were found in the spectral region from 3000 to

2800 cm-1 for all dust-fall samples. The bands at 2919 and

2842 cm-1 are attributed to CH asymmetric and symmetric

stretching vibrations of such organic matter (Zhao et al.

2011). Because these peaks cannot be exhibited by any of the

three inorganic ion salts, they can be used as a reference peak

in a semiquantitative FTIR study for the relative concen-

trations of such salts in the studied samples. In this study, the

peak heights and peak-height ratios of the spectral data for all

samples were calculated and compared with those of the

peak at 2919 cm-1 as a reference peak specific for each

sample (Table 4). As shown from this table, sample AP02

appears to contain the highest concentration ratios of all salts.

In contrast, samples AP05 through AP07, AP10, AP11,

AP13 through AP15, and AP17 through AP19 seem to have

considerably lower concentration ratios from those salts.

These results were found to be good agreement with XRD

results. Table 4 lists these values (in bold) compared with the

highest ones found for sample AP02. A very important result

can be noted from the IR spectra of these samples: Not all IR

spectra of the dust-fall samples exhibit the characteristic

absorption bands of cyanide or thiocyanide ions, which

usually appear for related samples in the spectral range

2200–2000 cm-1 (Altenkirch et al. 1979; Alder et al. 1986).

The XRD and the ATR-FTIR results are in good accordance

regarding either the existence or percentage of each salt,

especially the predominance of carbonate (CO3). Table 5

lists a comparable agreement between ATR-FTIR and XRD

results for the selected dust-fall samples.

Conclusion

The present work represents an early initiative for the

characterization and quantitative phase analysis of dust fall

collected from the streets’ trees using different

spectroscopic techniques, namely; XRD, ATR-FTIR, par-

ticle-size distribution, and SEM. Nineteen samples were

collected from different locations inside and outside GC

covering different districts. According to the measurements

of SEM and the particle-size analyzer, the particle-size

distribution of the present samples varied from 0.1 to

250 lm, and the average particle size was 17.36 lm. The

average percentages of the particulate matter for PM2.5,

PM10, PM50–10, and PM[50 are 10.10 ± 2.65,

23.21 ± 4.25, 61.55 ± 5.63, and 5.15 ± 3.22 %, respec-

tively. The particle-size distribution represents an indica-

tion of the different natural and anthropogenic sources of

the collected dust-fall samples. Based on XRD measure-

ments, the major salts found in the collected dust-fall

samples were calcite (CaCO3), dolomite (CaMg(CO3)2),

gypsum (CaSO4�2H2O), and quartz (SiO2), and their

quantitative results are 3–6, 3–28, 0–59, and 12.8–60 %,

respectively. Based on ATR-FTIR measurements, there is a

good agreement with XRD results. The ATR-FTIR results

confirm that there are no characteristic absorption bands of

cyanide or thiocyanide ions.

References

Alder JF, Bentley AE, Drew PKP (1986) Determination of hydrogen

cyanide in air using mass amplification by heavy ligand

replacement on a coated quartz piezoelectric crystal. Anal Chim

Acta 182:123–131

Ali AAS, El Taieb NM, Hassan AMA, Ibrahim YH, Abd El Wahab

SG (2011) Heavy metals enrichment in deposited particulate

matter at Abu Zaabal Industrial Area—Egypt. J Am Sci

7(8):347–352

Al-Rajhi MA, Al-Shayeb SM, Seaward MRD, Edwards HGM (1996)

Particle size effect for metal pollution analysis of atmospheri-

cally deposited dust. Atmos Environ 30(1):145–153

Altenkirch RA, Peck RE, Chen SL (1979) The appearance of nitric

oxide and cyanide in one-dimensional coal dust/oxidizer flames.

Combust Sci Technol 20(1–2):49–58

Bensted J (1976) Characterization of sulphate in the IR spectra of

minerals. Naturwissenchaften 63:193

Bhattacharjee A, Mandal H, Roy M, Kusz J, Zubko M, Gutlich P

(2010) Microstructural and magnetic characterization of dusts

from a stone crushing industry in Birbhum, India. J Magn Magn

Mater 322:3724–3727

Boman J, Shaltout AA, Abozied AM, Hassan SK (2012) On the

elemental composition of PM2.5 in central Cairo, Egypt. Xray

Spectrom 42:276–283

Castilho INB, Welz B, Vale MGR, de Andrade JB, Smichowski P,

Shaltout AA et al (2012) Comparison of three different sample

preparation procedures for the determination of traffic-related

elements in airborne particulate matter collected on glass fiber

filters. Talanta 88:689–695

Dıaz-Hernandez JL, Martın-Ramos JD, Lopez-Galindo A (2011)

Quantitative analysis of mineral phases in atmospheric dust

deposited in the south-eastern Iberian Peninsula. Atmos Environ

45:3015–3024

Dong A, Simsiman GV, Chesters G (1983) Particles-size distribution

and phosphorus levels in soil, sediment, and urban dust and dirt


123

samples from the Menomonee River watershed, Wisconsin,

USA. Water Res 17:569–577

El Ghandour MFM (1982) Studies on air pollution from construction

plants in the Helwan industrial area. III. Alkali, earth alkali and

heavy metal constituents of dust-fall. Environ Pollut B Chem

Phys 4(4):303–313

El Shazly SM (1990) Study on particle size distributions of the fallout

dust from the atmosphere of Qena, Egypt. Atmos Environ B

Urban Atmos 24(3):437–441

Espinosa AJF, Rodriguez MT, DelaRoza FJB, Sanchez JCJ (2001)

Size distribution of metals in urban aerosols in Seville (Spain).

Atmos Environ 35(14):2595–2601

Esteve V, Rius J, Ochando LE, Amigo JM (1997) Quantitative X-ray

diffraction phase analysis of coarse airborne particulate collected

by cascade impactor sampling. Atmos Environ 31:3963–3967

Fowler BO, Moreno EC, Brown WE (1966) Infrared spectra of

hydroxyapatite octacalcium phosphate and pyrolysed octacal-

cium phosphate. Arch Oral Biol 11:477–492

Godish T (2005) Air quality, 4th edn. Lewis, Boca Raton

Green LC, Armstrong SR (2003) Particulate matter in ambient air and

mortality: toxicologic perspectives. Regul Toxicol Pharmacol

38(3):326–335

Igathinathane C, Melin S, Sokhansanj S, Bi X, Lim CJ, Pordesimo LO

et al (2009) Machine vision based particle size and size

distribution determination of airborne dust particles of wood

and bark pellets. Powder Technol 196(2):202–212

Iijima A, Sato K, Yano K, Tago H, Kato M, Kimura H et al (2007)

Particle size and composition distribution analysis of automotive

brake abrasion dusts for the evaluation of antimony sources of

airborne particulate matter. Atmos Environ 41(23):4908–4919

Jaradat QM, Momani KA, Jbarah AQ, Massadeh A (2004) Inorganic

analysis of dust fall and office dust in an industrial area of

Jordan. Environ Res 96(2):139–144

Lane MD (2007) Mid-infrared emission spectroscopy of sulfate and

sulfate-bearing minerals. Am Mineral 92:1–18

Mahmoud KF, Alfaro SC, Favez O, Abdel Wahab MM, Sciare J

(2008) Origin of black carbon concentration peaks in Cairo

(Egypt). Atmos Res 89(1–2):161–169

MAUD (2015) Materials analysis using diffraction. http://www.ing.

unitn.it/*maud/. Accessed 13 Dec 2012

Menendez I, Dıaz-Hernandez JL, Mangas J, Alonso I, Sanchez-Soto

PJ (2007) Airborne dust accumulation and soil development in

the North-East sector of Gran Canaria (Canary Islands, Spain).

J Arid Environ 71:57–81

Moharram MA, Abdel Hakeem N, Easa FM (1987) Infrared study of

the decarbonation of carbonate bearing apatite. TIZ-Fachberichte

111:42–46

Moore RK, White WB (1971) Vibrational spectra of the common

silicates: I. The garnets. Am Mineral 56:54–71

National Institute for Occupational Safety and Health (1974) Criteria

for a recommended standard occupational exposure to crystalline

silica. DHEW/NIOSH publication no. 75-120). NIOSH,

Cincinnat

O’Connor BH, Jaklevic JM (1981) Characterization of ambient

aerosol particulate samples from the St. Louis area by X-ray

powder diffractometry. Atmos Environ 15(9):1681–1690

Othmer DF, Kirk R (1982) Kirk-Othmer encyclopedia of chemical

technology, vol 20, 3rd edn. Wiley, New York, pp 756–757

Rodriguez-Flores M, Rodriguez-Castellon E (1982) Lead and

cadmium levels in soil and plants near highways and their

correlation with traffic density. Environ Pollut 4B(4):281–290

Rodrıguez-Navarro C, Sebastian C (1996) Role of particulate matter

from vehicle exhausts on porous building stone (limestone)

sulfation. Sci Total Environ 187(2):79–91

Schulze DG, Bertsch PM (1995) Synchrotron X-ray techniques in

soil, plant, and environmental research. Adv Agron 55:1–66

Shaltout AA, Welz B, Ibrahim M (2011a) Influence of the grain size

on the quality of standardless WDXRF analysis of river Nile

sediments. Microchem J 99:356–363

Shaltout AA, Allam MA, Moharram MA (2011b) FTIR Spectro-

scopic, thermal and XRD characterization of hydroxyapatite

from new natural sources. Spectrochem Acta A 83:56–60

Shaltout AA, Khoder MI, El-Abssawy AA, Hassan SK, Borges DLG

(2012a) Determination of rare earth elements in dust deposited

on tree leaves from Greater Cairo using inductively coupled

plasma mass spectrometry. Environ Pollut 178:197–201

Shaltout AA, Gomma MM, Ali-Bik MW (2012b) Utilization of

standardless analysis algorithms using WDXRF and XRD for

Egyptian iron ore identification. X-ray Spectrom 41(6):355–362

Shaltout AA, Welz B, Castilho INB (2013) Determinations of Sb and

Mo in Cairo’s dust using high-resolution continuum source

graphite furnace atomic absorption spectrometry and direct solid

sample analysis. Atmos Environ 81:18–24

Shen Z, Caquineau S, Cao J, Zhang X, Han Y, Gaudichet A, Gomes L

(2009) Mineralogical characteristics of soil dust from source

regions in northern China. Particuology 7:507–512

Silva CC, Thomazini D, Pinheiro AG Jr, Lanciotti F, Sasaki JM, Geos

JC et al (2002) Optical properties of hydroxyapatite obtained by

mechanical alloying. J Phys Chem Solids 63:1745–1757

Van Borm WA, Adams FC (1991) A standardless ZAF correction for

semi-quantitative electron probe microanalysis of microscopical

particles. X-ray Spectrom 20(2):51–62

Warner PO (1976) Analysis of air pollution. Wiley, New York

Zhao J, Peng P, Song J, Ma S, Sheng G, Fu J (2011) Characterization

of macromolecular organic matter in atmospheric dust from

Guangzhou, China. Atmos Environ 45:5612–5620


123



spectroscopic characterization of dust-fall samples collected from

Documents