assessment of elemental distribution and heavy metals contamination in phosphate deposits: potential...
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ORIGINAL PAPER
Assessment of elemental distribution and heavy metalscontamination in phosphate deposits: potential health riskassessment of finer-grained size fraction
Mohammad Al-Hwaiti • Mustafa Al Kuisi •
Ghazi Saffarini • Khitam Alzughoul
Received: 20 June 2013 / Accepted: 21 November 2013
� Springer Science+Business Media Dordrecht 2013
Abstract The concentrations and chemical distribu-
tions of heavy metals (Cd, Cr, Ni, Zn, U, and V) in the
Al-Jiza phosphate ores were investigated. Typically,
the mean concentration values of Cd, Cr, Ni, U, and Zn
are 15 ± 8, 109 ± 21, 34 ± 6, 211 ± 55, 142 ± 55,
and 161 ± 57 mg kg-1, respectively. On the other
hand, the encountered average concentration values of
Cd, Cr, Ni, Zn, U, and V in the phosphate dust particles
(\0.053) were found to be 22 ± 5, 179 ± 5, 67 ± 11,
441 ± 14, 225 ± 58, and 311 ± 9 mg kg-1, respec-
tively. The contamination factors of U and Cr are
greater than 1, indicating that these heavy metals could
be potentially hazardous, if released to the environ-
ment. Multivariate statistical analysis allowed the
identification of three main factors controlling the
distribution of these heavy metals and the other
chemical constituents. The extracted factors are as
follows: francolite mineral factor, clay minerals factor,
and diagenesis factor. Health risk assessments of non-
cancerous effects in finer-grained size fraction that
might be caused by contamination with the heavy
elements have been calculated for both children and
adults. The risk assessments in case of children for non-
cancerous effects showed that U has values greater
than the safe level of hazard index (HI = 1). In case of
adults, the value of risk for U is also higher as compared
to those of Cd, Ni, Cr, and Zn where it lies within the
safe range of hazard index (HI \ 1). Child health risk
assessment indicates that children are more vulnerable
to contaminants from phosphate mining than adults.
Keywords Heavy metals � Phosphate �Finer-grained size fraction � Daily oral intake �Ingestion rate � Inhalation rate � Health risk �Jordan
Introduction
Phosphate rocks constitute the main raw materials
used in the manufacturing of phosphate fertilizers and
some phosphorus-based chemicals. As a matter of fact,
phosphorus is an essential element for plants growth
(Slansky 1986). The heavy metals that might be
present in the phosphate rocks are U, Cd, As, Cr, Pb,
Ni, Zn, and V. Their toxicity and ability to accumulate
in air, soils, plants, and animals are also well known
(Kramer and Allen 1988).
M. Al-Hwaiti (&)
Environmental Engineering Department,
Faculty of Engineering, Al-Hussein Bin Talal University,
P.O. Box (20), Ma’an, Jordan
e-mail: [email protected]; [email protected]
M. Al Kuisi � G. Saffarini
Department of Applied and Environmental Geology,
Faculty of Science, The University of Jordan,
P.O. Box 13437, Amman 11942, Jordan
K. Alzughoul
Department of Earth Sciences and Environment,
Faculty of Natural Resources and Environment,
The Hashemite University, Zarqa 13115, Jordan
123
Environ Geochem Health
DOI 10.1007/s10653-013-9587-y
Through mining activities, atmosphere is the first
environmental element to be polluted. The heavy
metals get transported to the environment as an
integral part of the suspended sediments. The expected
effect of these substances may be illustrated by risk
assessment. The heavy metals that enter the environ-
ment are likely to end up in the food chain. The
harmful health effects of heavy metals accumulation
with time in the human body are numerous (Hutton
1983). The most affected are children and elderly
people. They cause a number of serious diseases.
Hence, their enrichment in the environment is of great
concern because of their toxic nature and threats to
human health (Abbasi and Tufail 2013). Toxicity may
also be due to waste from industries, fertilizers,
herbicides, insecticides, and other human activities
(Zhang et al. 2011). Hence, the users of phosphate
rocks in fertilizer production must ensure that the
presence of heavy metals in the used phosphates is
well below the permissible limits, as these metals
could be toxic for plants or could contaminate
groundwaters after being released (Richards et al.
1998; McLaren et al. 2004).
Heavy metals have different routes of exposure to
human health. Three major exposure pathways can be
brought as examples: (1) direct ingestion of soil
substrate particles; (2) dermal absorption of heavy
metals in particles adhered to exposed skin; and (3)
inhalation of suspended particles through mouth and
nose are used to estimate potential health risk (Lai
et al. 2010). The suspended dust particles size plays an
important role through inhalation. The particles that
ultimately take part in the inhalation process disperse
according to their sizes and densities (Abbasi et al.
2012). In Jordan, very few studies on health risk
assessment pertaining to these elements have been
carried out so far (Batayneh 2012). The phosphate
dust, which is an important aspect of this study, has the
greatest probability of interaction with the human
beings. Health risks due to the metal pollution can be
more harmful to children than adults because of their
low tolerance toward the pollutants and because of
their hand-to-mouth activities (Zheng et al. 2010; Faiz
et al. 2012).
In Jordan, the problem is this: we know that there is
no totally harmless level of heavy metals and that
exposure is cumulative. We do not really understand
the relationship between phosphate strip mining and
the release of these materials into the environment.
Because of that, we do not know how that exposure
might affect the plants, animals, and people who make
the vicinities of phosphate mines their home. It is just
one more phosphate mining risk.
The investigation of the heavy metal contamination
and the health risk assessment of phosphate dust have
not been previously assessed in the study area.
Accordingly, such an investigation is required if the
potential contamination from phosphate dust is to be
understood quantitatively and qualitatively. The main
objectives were to (a) determine heavy metal concen-
trations in both phosphatic beds, (b) identify the
factors governing the chemical variability of the
studied phosphates using multivariate statistical tech-
niques, e.g., principal components analysis (PCA),
varimax rotated factor analysis (VRFA) and hierar-
chical cluster analysis (HCA), (c) and carry out the
health risk assessment in the finer-grained size fraction
for the two exposure pathways (ingestion and inhala-
tion). The calculated daily intake doses (DD) are used
in finding the hazard quotient (HQ) for each exposure
path, and then, the total risk given by hazard index (HI)
was calculated by adding the two hazard quotients for
the exposure pathways.
Geological setting
The Jordan phosphate deposits are part of the Upper
Cretaceous-Eocene Tethys Phosphorite belt, which
extends from the Middle East to North Africa. They
occur in several horizons and are exposed in a broad
belt that extends from the NW corner of Jordan to its
SE one (Fig. 1).
The economic phosphorites in Jordan are geo-
graphically found in the Ruseifa area in the North, in
Al-Hasa and Al-Abied in central Jordan, and in
Eshidiya area in the SE. The Eshidiya phosphate
deposits are the largest of all. The phosphates
produced from these occurrences are mainly exported
and partly consumed in fertilizer production.
Generally, the Al-Jiza phosphorite represents the
lower most part of Al-Hasa Phosphorite Formation
(AHP), to which Al-Jiza phosphates belong. It consists
of alternating beds of chalk, chalky limestone, phos-
phate, phosphatic limestone, phosphatic chert, marl,
chert, and micritic limestone. The economic phos-
phate beds are soft or slightly cemented with calcite. It
consists of sand-sized phosphate particles: pellets,
Environ Geochem Health
123
intraclasts, bone and teeth fragments, and marine
reptiles debris. Francolite is the main mineral phase,
and cellophane is present in bones and scales. The
gangue materials associated with phosphate particles
are mainly marl and clay, calcite cement and silica
cement, while detrital quartz is almost absent
(Al-Hunjul 1995). Al-Jiza phosphorite consists of
two phosphatic beds I and II. Bed I consists of soft
phosphate, with thickness of about 1 m, separated
from bed II by interwaste (IW) mainly composed of
sedimentary rocks including marl, clay, and chert,
with thickness of about 1.3 m. Phosphatic bed II
consists of soft phosphate, with thickness of about
0.85 m.
Materials and methods
Sample collection and preparation
One hundred and forty samples, mainly core ones,
were collected from the two phosphate beds (I and II)
for geochemical studies (Fig. 1). The samples were
selected from seventy-one boreholes. The seventy-one
boreholes were drilled on a regular 100 9 100 m grid.
The selected samples were then air dried, homoge-
nized, and stored in cloth bags.
Size fractionation
For size distribution purposes, particle size fraction
separation was carried out on the phosphate bed I and
bed II samples. From each bed, two composite
samples of about 8–10 kg were selected and dry
screened to separate (\12.7 mm) from ([12.7 mm)
fractions, and then stored in cloth bags before
chemical analysis. Approximately 500 g of each
composite sample (\12.7 mm) was passed through a
series of stainless-steel sieves to produce the following
three size fractions: coarse (100 mesh [0.15 mm]),
medium (100–270 mesh [0.15–0.053 mm]), and fine
(\270 mesh [\0.053 mm]). Approximately 50 g of
finer-grained size fraction (\0.053 mm) was stored in
cloth bags before heavy metals chemical analysis.
Chemical analysis
X-ray fluorescence (XRF) analysis
Major, minor, and trace elements in phosphate beds I
and II were determined by X-ray fluorescence (XRF)
technique. For that purpose, fused pellets were
produced as follows: approximately 0.8 g from each
sample and 7.2 g of Li2B4O7 were put in an Au/Pt
crucible and fused using a flexor machine Leco 2000
Fig. 1 Geological map of the study area showing borehole locations
Environ Geochem Health
123
for 3–4 min at 1,200 �C. The melts were poured in a
dish and left to cool to form a glass disk. The
advantage of the fused pellet is that there is a low
matrix or textural effects because the glass disks are
more homogeneous. According to Levinson (1980),
higher abundance of all elements can be accurately
measured by XRF. The accuracy and precision of the
elements concentrations were calibrated using inter-
national geochemical standards.
Atomic absorption spectrometer (AAS) analysis
Total digestions were performed on 20 samples of
finer-grained size fraction (\0.053 mm). Twenty
milligrams from each sample was put in Teflon
beakers, with 3 ml HCl, 2 ml HNO3, 1 ml HClO4,
and 2 ml HF added to each beaker. Samples were dried
overnight on a hotplate. About 1 ml HClO4 was then
added and allowed to dry. The dried samples were
removed from hotplate and cooled, and 1 ml Aqua
Regia was then added. A pre-set volume of 1 % HNO3
was added to each beaker. The solutions were then
analyzed for Cd, Cr, Ni, U, and Zn by atomic
absorption spectrometry (AAS). Quality assurance
and control were assessed using duplicates and blanks
method.
Multivariate statistical analysis
Principal component analysis (PCA), varimax rotated
factor analysis (VRFA), hierarchical cluster analysis
(HCA), and interelemental correlations were con-
ducted using SPSS software (version 16) and STATS-
TICA (version 5).
Results and discussions
The elemental concentrations descriptive statistics is
listed in Table 1. The treated elements include the
major elements P2O5, SiO2, CaO, Cl, Al2O3, Fe2O3,
MgO, Na2O, K2O, and LOI. The analyzed trace
elements are Sr, V, Zn, U, Cr, Y, Ti, Ni, and Cd. The
mean concentration values and their corresponding
confidence intervals for P2O5, SiO2, CaO, Cl, Al2O3,
Fe2O3, MgO, Na2O, and K2O and LOI are 28.98 ±
3.55, 5.99 ± 3.36, 49.50 ± 3.31, 0.10 ± 0.05, 0.36 ±
0.14, 0.15 ± 0.06, 0.25 ± 0.04, 0.44 ± 0.05, 0.04 ±
0.01, and 9.24 ± 4.05 mg kg-1, respectively. Contour
maps showing spatial distribution patterns of trical-
cium phosphate (TCP = P2O5 9 2.184) in both beds
are shown in Fig. 2. The maps were constructed using
Minex software (version 4) available at the Jordanian
Table 1 Descriptive statistical analysis of major oxides and trace elements concentrations in Al-Jiza phosphate samples from the
two phosphatic beds I and II
P2O5 SiO2 LOI CaO Cl Al2O3 Fe2O3 MgO Na2O K2O
Major oxides (%)
Minimum 17.57 1.79 3.73 38.34 0.04 0.18 0.04 0.17 0.35 0.03
Maximum 34.20 16.43 23.58 55.04 0.26 0.76 0.30 0.36 0.57 0.07
Mean 28.98 5.99 9.24 49.50 0.10 0.36 0.15 0.25 0.44 0.04
Standard deviation 3.55 3.36 4.05 3.31 0.05 0.14 0.06 0.04 0.05 0.01
Standard error 0.42 0.40 0.48 0.39 0.01 0.02 0.01 0.01 0.01 0.00
Kurtosis 1.46 1.43 2.91 2.09 0.08 1.07 -0.43 0.04 0.43 1.27
Skewness -1.15 1.24 1.58 -1.29 0.98 1.07 0.31 0.32 0.48 0.90
Sr Ti Zn Cr V U Y Zr Ni Cd
Trace elements (mg kg-1)
Minimum 770 40 127 64 48 50 63 27 25 5
Maximum 1,360 235 287 130 239 204 102 58 42 31
Mean 1,085 118 211 109 161 142 79 41 34 15
Standard deviation 168.16 75.54 55.10 20.51 57.33 55.30 11.32 8.13 6.24 8.29
Standard error 19.96 8.97 6.54 2.43 6.80 6.56 1.34 0.96 0.74 0.98
Kurtosis -0.42 -1.62 -1.36 0.08 -0.56 -1.13 -0.10 0.38 -1.52 -0.69
Skewness -0.20 0.43 -0.09 -1.02 -0.45 -0.62 0.52 0.44 -0.11 0.68
Environ Geochem Health
123
Phosphates Mines Company. In bed I, no spatial
distribution trends were noticed; however, in bed II,
TCP values increase in NE and SW directions away
from the center. This indicates that the studied
elements in the phosphates of the study area are
variably dispersed in the two phosphatic beds. The
interpolation technique used in constructing the maps
is the inverse distance weighing technique.
The mean concentration values and their corre-
sponding confidence intervals for Cd, Cr, Ni, U, and Zn
are 15 ± 8, 109 ± 21, 34 ± 6211 ± 55, 142 ± 55,
and 161 ± 57 mg kg-1, respectively. A comparison
of Al-Jiza phosphate mean heavy metals concentra-
tions with other phosphate deposits from Jordan is
shown in Table 2. Generally speaking, the heavy
metals abundances in Al-Jiza phosphates are higher
than their abundances in shale with the exception of Ni.
Cadmium, Ni, U, V, and Zn concentrations exhibit also
higher abundances when compared with both south
and central Jordan phosphates. In particular, Al-Jiza
phosphates exhibit high U, Zn, and V abundances when
compared with worldwide phosphates.
Distribution in the different size fractions
The heavy metals distributions in the different size
fractions in the phosphatic beds I and II are shown in
Table 3. For mining purposes, the coarse fraction
([12.7 mm) is being rejected and considered as waste,
and the finer fraction (\12.7 mm) is accepted as feed.
The feed requires further treatment to achieve the final
product. From Table 3, it can be concluded that the
heavy metals concentrations are present in both the finer
and coarser fractions with almost similar amounts.
For example, in beds I and II, a comparison of Cd
values in the head sample shows that Cd contents are 15
and 17 mg kg-1, respectively. Meanwhile, Cd distri-
bution in the beds I and II using the finer fraction
(\12.7 mm) and the coarser fraction ([12.7 mm) gave
14 and 16 mg kg-1 and 16 and 17 mg kg-1, respec-
tively. Accordingly, the distributed Cd amounts repre-
sent approximately 93 and 94 % and 94 and 100 % of
the original Cd present in the head sample, respectively.
The difference between Cd, Ni, Cr, U, Zn, and V
contents in the head sample and in the coarser and finer
Fig. 2 Inverse distance interpolated spatial distribution pat-
terns of tricalcium phosphate (P2O5 9 2.184) in the phosphatic
beds I and II
Table 2 Heavy metals concentration means (mg kg-1) in Al-Jiza phosphate rock, in phosphate deposits from Jordan and World
phosphorite and in shale
Cd Cr Ni U Zn V References
This study 15 108 34 142 160 212
Shale 0.3 90 68 3.7 95 130 Turekian and Wedepohl (1961)
World phosphorite 18 125 53 120 195 100 Kolodny (1981)
NW Jordan nd 102 128 nd 265 nd Al-Agha (1985)
Central Jordan 5.5 145 9 66 155 87 Abed et al. (2008)
South Jordan 5 57 20 42 61 87 Al-Hwaiti (2000)
nd not detected
Environ Geochem Health
123
size fractions is released to the environment. Accord-
ingly, it can be concluded that the heavy metals
distribution in Al-Jiza phosphate rocks is not dependent
on their physical bonding forms. Therefore, the finer-
grained size fraction (\12.7 mm) was passed through a
series of sieves to produce the following fractions:
coarse (0.15 mm), medium (0.15–0.053 mm), and fine
(\0.053 mm) (Table 4). The emphasis was then made
on the finer size fraction (\0.053 mm). This was done to
examine the heavy elements distribution in the different
size fractions, whether the physical segregation might
affect the heavy metals distribution or not, and to
estimate the potential health risk values for the two
exposure pathways (ingestion and inhalation) in the
finer-grained size fraction.
Contamination factor (CF)
The contamination factor permits to classify the
chemical elements in sample materials with regard
to their normal abundances (Altschuler 1980;
Kauwenbergh 1997). Contamination and depletion
factors usually relate the elemental composition of
phosphate with average shale, average phosphorite
worldwide, or with a referential element content. The
contamination factor is defined as the average con-
centration of the element in the phosphate rock group,
divided by its concentration in the material to be
compared with. For the calculation of contamination
factors (CF), the following equations were used:
CFðP=SÞ ¼ N Al-Jiza phosphoritesð Þ=N Shaleð Þ
CFðP=WÞ ¼N Al-Jiza phosphoritesð Þ=N World Phosphoritesð Þ
CF P=Rð Þ ¼ element content in phosphate=
referential element content or
permissible limit
In order to obtain a more accurate result, we use a
reference element in the calculation of contamination
Table 3 Physical and chemical analysis of heavy metals concentrations (mg kg-1) in different particle size fractions: course
([12.7 mm) and fine (\12.7 mm) from Al-Jiza phosphate rock deposits
Particle size fractions Wt % Cd Ni Cr U Ti V Y Zn
South area (N = 10)
Bed I
Head sample 100 15 36 177 190 62 181 86 230
[12.7 mm 53 14 34 176 189 57 180 82 227
\12.7 mm 47 16 41 178 182 68 183 93 234
\0.053 mm 9.43 23 64 197 255 125 233 95 269
Bed II
Head sample 100 17 46 158 242 49 301 76 417
[12.7 mm 70 16 45 157 240 47 298 75 412
\12.7 mm 30 18 47 160 243 51 302 79 422
\0.053 mm 8.83 28 55 180 292 63 321 105 453
West area (N = 10)
Bed I
Head sample 100 13 42 139 153 140 173 65 371
[12.7 mm 78 14 40 138 151 137 170 63 370
\12.7 mm 22 15 44 143 155 141 178 67 376
\0.053 mm 6.36 18 72 174 190 183 306 80 425
Bed II
Head sample 100 14 45 161 151 168 195 76 407
[2.7 mm 86 13 44 165 149 166 190 73 401
\12.7 mm 14 15 45 172 253 171 203 80 414
\0.053 mm 4.53 20 75 184 194 189 306 80 445
Environ Geochem Health
123
factor in the sense of Tersic et al. (2009) and Hakanson
(1980). The following terminologies are used to
describe the contamination factor: CF \ 1, low con-
tamination factor; C1 CF B 3, moderate contamina-
tion factors; [3 CF B 6, considerable contamination
factors; and CF [ 6, very high contamination factor.
After the examination of the spatial distributions of the
analyzed elements, it was decided to use Y and Ti as
referential elements, as they represent good example
of the natural uncontaminated components of the
elemental distributions in the studied area. A contam-
ination factor for mean heavy metals concentration in
the studied phosphates with respect to shale is shown
in Fig. 3. The results indicate that Cd, U, V, and Ni
analyzed in this study exhibit moderate to consider-
able CF’s, the estimated CF’s mounted to levels of 6.0,
4.7, 2.85, and 1.16, respectively. Despite the fact that
Cd and U exhibit elevated EF factors above 4, their
concentrations are still below the limiting concentra-
tions adopted by the fertilizers importing countries.
The mean contamination factors for heavy metal
concentration in the studied phosphorites with regard
to the referential element Ti are shown in Fig. 4. The
results indicate that U, Cr, Zn, and V, analyzed in the
study, show CF’s greater than 1, indicating that these
heavy metals can be potentially hazardous and may be
released to the environment. Ni and Cd, on the other
hand, exhibited contamination factors less than 1
suggesting thus no threat to human health by consum-
ing crops produced from Al-Jiza phosphate fertilizer.
Correlation coefficient analysis
Correlation coefficients have been widely used in
determining the interrelationships between elements
in sediments and have proven to be effective (Liu et al.
2003; Yalcin and Ilhan 2008; Zhang et al. 2011).
Furthermore, the degree of correlation between trace
elements and other major constituents is often used to
indicate the origin of trace elements (Windom et al.
1989; Han et al. 2006). The correlation coefficients
between the analyzed element concentrations are
summarized in Table 5. The results indicate that P,
Table 4 Dry physical analysis of finer-grained size fraction (\12.7 mm)
Sieve no. South area West area
(#) mm Bed I Bed II Bed I Bed II
?4 4.75 2.53 3.28 14.32 18.29
?20 0.85 6.36 9.44 20.43 20.48
?40 0.425 21.04 27.26 20.74 17.48
?100 0.15 42.18 34.73 21.48 20.78
?200 0.075 13.44 12.52 11.98 13.46
?270 0.053 5.02 4.39 4.69 4.98
-270 -0.053 9.43 8.38 6.36 4.53
Total mass = 500 g 100.00 100.00 100.00 100.00
0
1
2
3
4
5
6
Cd
CaO
P2O
5
Sr U V Ni Y
Na2
O Zr
Zn
Mg
O
SiO
2 Cr Ti
Al2
O3
Fe2
O3
K2O
Co
nta
min
atio
n F
acto
r
Fig. 3 Average enrichment factors of measured elements in Al-
Jiza phosphorites with regard to averages of elements in shale
(values are normalized on Y)
0
1
2
3
4
5
6
7
U
P2O
5 Cr
Zn V Ti
CaO
Al2
O3 Ni
Zr
Cd
Sr
SiO
2
K2O
Mg
O Y
Fe2
O3
Co
nta
min
atio
n F
acto
r
Fig. 4 Average enrichment factors of measured elements in Al-
Jiza phosphorites with regard to world averages of elements in
phosphorites (values are normalized on Ti)
Environ Geochem Health
123
U, Cr, and V are significantly positively correlated.
This suggests their association with the francolite
structure, while Cd and Ti are significantly negatively
correlated with P2O5, indicating thus a common
substitution in the francolite structure. The significant
positive correlations between Na, K, and Ti suggest
their feldspar origin. On the other hand, the significant
positive correlations existing between Al, Fe, Mg, Ti,
and Zr signify a common source such as clay mineral
phase. The insignificant correlations between Ca and
other elements except for Sr suggest its carbonate
origin. It is not surprising that Na, Ti, Cr, and V had a
close relationship since they have similar geochemical
characteristics. Particularly, the displayed significant
positive correlation between Na, K, Ti and Zr is a
result of their incorporation in the silt-size detrital
zircon and rutile structures.
Principal components analysis
This statistical technique is a factor extraction method
used to form uncorrelated linear combinations of the
observed variables. The first component has maximum
variance. Successive components explain progressively
smaller portions of the variance and are all uncorrelated
with each other. Principal components analysis is used
to obtain the initial factor solution. To reduce the high
dimensionality of the variable space, a PCA was applied
to the available dataset including U, Cd, Cr, Ni, Zn, V,
Ti, Zr, Sr, and Y.
Three principal components were extracted from
the available dataset. They explained a total variance
of approximately 78.94 % (Table 6). Based on the
loading distribution of the elemental variables, P2O5,
CaO, U, Cd, Ti, and LOI in PCA, one can confirm their
relationship with the francolite mineral phase (PC1).
The loading distribution of the elemental variables,
Al2O3, Fe2O3, MgO, K2O, Ti, Zr, Cr, and V, constitute
a firm relationship with the clay mineral phase (PC2),
while the third phase is composed of CaO, Sr, Zn,
and Y, suggesting close relationship with the calcium
carbonate phase (PC3).
Varimax rotated factor analysis
Factor analysis attempts to identify variables, or
factors, that explain the pattern of correlations within
a set of observed variables (Reeves and Saadi 1971). It
Table 5 Correlation coefficients matrix for phosphate samples from Al-Jiza phosphate rock (N = 71)
P2O5 SiO2 CaO Al2O3 Fe2O3 MgO Na2O K2O Sr Ti Zn Cr V U Y Zr Ni Cd
P2O5
SiO2
? - - CaO
– Al2O3
?? Fe2O3
?? ?? MgO
Na2O
– ?? ?? ?? K2O
?? – – ?? Sr
11 11 11 11 ?? Ti
Zn
11 1 ?? Cr
1 11 11 ?? V
11 11 U
– ?? Y
11 11 11 1 11 ? Zr
11 – – Ni
– – 1 1 – – – Cd
Symbols ? or - indicate above 95 % significance level (r95 = 0.55); ?? or - - indicate above 99 % significance level
(r99 = 0.65)
Environ Geochem Health
123
is often used in data reduction to identify a small
number of factors that explain most of the variance
that is observed in a much larger number of manifest
variables. In this study, the varimax rotation factor
method was used to minimize the number of variables
that have high loadings on each factor. This method
simplifies the interpretation of the factors. Factor
extraction was done with a minimum acceptable
eigenvalue as 1 (Kaiser 1958; Harman 1960). The
eigenvalues and the cumulative percentages of vari-
ance associated with each factor were computed.
From varimax rotated factors, the first three factor
components (F1, F2, and F3) (E) [ 1 were selected
(Table 6) as they accounted for more than 75 % of the
total variance (Table 6). The remaining components
were considered less significant. Based on the com-
ponent loading after the varimax rotation (Table 6),
factor 1 accounts for *31 % of the total variance. The
high positive loadings of P2O5, CaO, Na2O, Cd, Sr, Cr,
V, and U are clearly due to the association of these
elements with carbonate flour apatite (they replace Ca
in the Francolite lattice). The mineralogical results
show that francolite is the major component in Al-Jiza
phosphate rock. This factor reflects the substitution of
PO43- by CO3
2-, in which P2O5 in the studied samples
is positively correlated with CaO (positive loading) to
the francolite. The clay mineral phase of Al-Jiza
phosphate rock is well explained by factor 2, which
explains 27 % of the total variance (Table 6). The
positive loadings for Al2O3, Fe2O3, MgO, K2O, Ti,
and Zr, reflect depositional associations related to clay
mineral phases. The interelemental relationships
(Table 6) explain this association of elements in
which Fe and Mg display a strong affinity to be fixed
in alumino-silicates (kaolinite and illite) admixed with
quartz. The loading of some trace elements such as Ti
and Zr on this factor may result from silt-size detrital
zircon and rutile.
Factor 3 explains *18 % of the total variance
(Table 6). This factor includes positive loadings of
Table 6 Loading of the components obtained from principal component analysis and varimax rotated factors (N = 71)
Variable Principal components Varimax rotated factors Communality
PC1 PC2 PC3 PC4 F1 F2 F3 F4
Al2O3 0.7 0.7 0.9 0.99
SiO2 -0.6 0.9 0.93
Fe2O3 0.8 0.9 0.99
MgO 0.6 0.8 0.9 0.98
CaO 0.6 0.6 0.7 -0.9 0.99
P2O5 -0.9 0.9 0.97
Na2O 0.8 0.7 0.87
K2O 0.8 0.6 0.9 0.98
Cl 0. 7 0.97
Cd 0.8 -0.6 0.6 0.92
Ni 0.5 0.9 0.95
Cr 0.5 0.9 0.96
Sr -0.8 0.9 0.98
Ti 0.7 0.7 0.9 0.96
U -0.9 0.7 0.5 0.90
V 0.8 0.9 0.97
Y 0.6 -0.5 0.8 0.90
Zn 0.7 -0.9 0.94
Zr 0.8 0.8 0.99
% of variance 36.2 28.3 14.4 9.9 30.9 27.3 17.6 10.1
% Cumulative 36.2 64.5 78.9 88.9 30.9 58.2 75.8 85.9
Loadings less than 0.5 were omitted; varimax rotation method: with Kaiser normalization
Environ Geochem Health
123
SiO2, Ni, and U and negative loadings of CaO and Cd.
This is most likely due to the diagenesis processes,
which prevailed during the formation of the studied
phosphorites. Mineralogical results show that quartz
and calcite are a minor component in Al-Jiza phos-
phate rock. The positive loadings of SiO2, Ni, and U in
this factor can be interpreted as due to the presence of
these elements in siliceous mineral phases, while
negative loadings of CaO and Cd in this factor can be
explained in terms of the diagentically precipitated
carbonate, i.e., calcite. This factor is in full agreement
with the data from Abed and Fakhouri (1996) and
Sadaqah (2001), who showed that carbonate may be
diagentically precipitated as calcite and silica as
chalcedony.
Hierarchical cluster analysis
The hierarchical tree clustering method is used to
produce a graphical representation of individual
groups using dendrograms. To perform CA, an
agglomerative hierarchical clustering was adopted
using a combination of the Ward’s linkage method
(Ward 1963) and squared Euclidean distances as a
measure of similarity. In order to verify the presence
of elemental groupings revealed by factor analysis,
R-mode cluster analysis was also applied to the
phosphate rock. The resulting dendrogram is pre-
sented in Fig. 5. The obtained results classified into
three main groups. Group one is similar to that
embraced in PCA 1 (francolite mineral) for the same
bed and embraces P, Ca, Cr, Cd, Ni, Sr, V, U, Y, and
Zn. The second group includes Al, Fe, Si, Mg, Na, K,
Ti, and Zr. It corresponds to the group of elements
embraced in PCA 2 (clay minerals factor). The third
group includes Ca, Sr, Zn, and Y, which is similar to
that adopted in PCA 3. This finding is in agreement
with the result of principal component analysis. This
may indicate also that the controlling factors of the
heavy metals distribution in sediments are of different
origins.
Potential health risk assessment
The potential risk assessment process consists of four
basic steps: (i) collection of data relevant to human
health, especially heavy metal concentrations in the
studied medium, (ii) estimation of the magnitude of
potential human exposures, (iii) toxicity assessment,
and (iv) characterization of risk (Wcislo 2006; Grzetic
and Ghariani 2008; Ogunkunle et al. 2013). Three
transmission media can be adopted for calculating the
risk assessment of heavy metals, namely soil, ground-
water, and air (Lai et al. 2010). In this study, risk
assessment was based on the exposure pathway of soil
medium by oral intake as determined by USEPA (1989)
and HESP model (Veerkamp and ten Berge 1999).
In order to assess the potential health risks caused
by fine-grained phosphatic clay (dust) for both chil-
dren and the adults, the methods of US Environmental
Protection Agency (USEPA 1996) have been applied
for the two exposure pathways of ingestion and
inhalation contact with the fine-grained (dust) parti-
cles of the phosphate under study. The dose estimates
exposure pathways were calculated for each element
as daily doses using the following equations (USEPA
1996; USEPA 2002):
Ingestion dose:
DDing mg kg�1� �
¼ C� IRing � EF� ED
BW� AT
Inhalation dose:
DDinh mg kg�1� �
¼ C� IRinh � EF� ED
BW� AT
where C is the concentration (mg kg-1) of the heavy
element in fine-grained dust sample, IRing is the
ingestion rate: 200 and 100 mg day-1 for children and
adults, respectively (USEPA 2001), IRinh is the
inhalation rate: 7.6 and 20 m3 day-1 for children and
adults, respectively (Van den Berg 1995), EF is the
exposure frequency: 365 days year-1 (USEPA 1997),
ED is the exposure duration: 6 and 30 years for non-
Fig. 5 Hierarchical clustering results (dendrogram) of the
measured elements (Ward’s method) using Euclidean distance
as a measure of similarity
Environ Geochem Health
123
cancerous effects in children and adults, respectively
(USEPA 2001), AT is the averaging time (days) for
non-cancerous, and
AT ¼ EF� ED:
The average doses calculated for each element and
for each exposure route per day (ADD) are then divided
by the reference dose (RfD) to get the hazard quotient
(HQ) that will be summed up for the two exposure
pathways to get the overall non-cancer risk (HI). The
health risk was calculated using the following relation
(Khairy et al. 2010; Abbasi and Tufail 2013):
Hazardquotient HQ ¼ ADD
RfD
Unlike a carcinogen, the toxicity is important only
during the time of exposure, which may be 1 day, a
few days, or years. The HQ has been defined so that if
it is less than 1.0, and there should be no significant
risk or systemic toxicity. Ratios above 1.0 could
represent a potential risk (Ogunkunle et al. 2013). A
HQ above 6 is a high risk factor (Hakanson, 1980) and
is considered to impose high potential risk according
to Ogunkunle et al. 2013. When exposure involves
more than one element, the sum of the individual
hazard quotients for each element is used as a measure
of the potential for harm. This sum is called the hazard
index (HI):
HI ¼X
HQ
The greater is the value of HI above 1, the greater is
the level of concern, since the accepted standard is 1.0
below which there will be no significant health hazard
(Grzetic and Ghariani 2008; Lai et al. 2010). The
probability of experiencing long-term health hazard
effects increases with the increasing HI value (Wang
et al. 2012), and according to Lemly (1996), HI
ranging between 1.1 and 10 refers to moderate hazard
while an HI value greater than 10 refers to high hazard.
The concentration values of the heavy elements in
the finer-grained size fraction (\0.053 mm) are given
in Table 7. The metals detected were found to be of
values twofolds higher than those of the phosphate ore
(Table 1). However, the finer-grained size fraction in
phosphate ore is released to the environment by
different means, including mining process, wind flow,
and the traffic movements.
The overall ingestion and inhalation hazard quo-
tient (HI) calculated values for children and adults are
listed in Table 8. This table shows, as well, the
elemental hazard indices magnitudes in an increasing
Table 7 Heavy metal concentrations in the finer-grained size fraction (\0.053 mm) from the studied phosphate
Statistical value South West
Cd Ni Cr U Zn Cd Ni Cr U Zn
Minimum 23 55 180 255 369 18 72 174 190 425
Maximum 28 64 197 292 453 20 75 184 194 445
Mean 26 60 189 274 411 19 73.5 179 192 435
SD 4 6 12 26 59 1 2 7 3 14
Table 8 Hazard indices (HI) averages of non-cancerous elements calculated for the elements Cd, Ni, Cr, U, and Zn
Element Ingestion Inhalation
HIing HIing HIinh HIinh
Adults Children Adults Children
Cd 0.064 0.297 0.0229 0.0028
Ni 0.009 0.041 0.0034 0.0003
Cr 0.175 0.817 0.0631 0.0051
U 0.443 2.069 0.0795 0.0065
Zn 0.008 0.038 0.0800 0.0001
HI magnitude in
increasing order
Zn \ Ni \ Cd \ Cr \ U Zn \ Ni \ Cd \ Cr \ U Ni \ Cd \ Cr \ U\Zn Zn \ Ni \ Cd \ Cr \ U
Environ Geochem Health
123
order. A careful examination of this table indicates
that children are more likely to be affected by the
element U as its ingestion HI is greater than 1
(Table 8). Other elements exhibit similar health risk
values increasing orders except that of adults inhala-
tion HI.
Conclusions
This study presents a dataset of elemental composi-
tions in the Al-Jiza phosphate deposits. The statistical
treatment of the data showed that the mean concen-
tration values and their corresponding confidence
limits for the heavy metals Cd, Cr, Ni, Zn, U, and V are
15 ± 8, 109 ± 21, 34 ± 6,211 ± 55, 142 ± 55, and
161 ± 57 mg kg-1, respectively.
Multivariate statistical analysis of the dataset and
correlation analysis suggested that U, Cd, Cr, and V are
commonly incorporated in the francolite structural
lattice and only few elements, especially Zn and Ni,
exhibit association in the diagentically precipitated
minerals forming calcite and silica cements, respec-
tively. These entire heavy metals exhibit relatively
depleted values when compared with their correspond-
ing background values. Other applied techniques such
as principal component analysis, varimax rotated
factor analysis, and hierarchical cluster analysis) used
in data treatment highlight three phenomena: the
compositional controls of francolite minerals, clay
minerals, and diagenesis controls during the formation
of the studied phosphorites.
This study has shown that the impacts of the finer-
grained size fraction in the study area cannot be
neglected. The risk assessment for oral exposure of
inhabitants in the area indicated that the non-cancer-
ous risk tends to become significant for children and
adults with exposure duration of 6 and 30 years,
respectively, mainly for U exposure since the calcu-
lated indices exceeded the acceptable limits of non-
cancerous hazard quotient. The cumulative hazard
quotient index (HI) of the study area indicated a
serious potential health hazard that might be posed by
U. On the other hand, the health risk assessment of the
heavy element pollutants Cd, Ni, Cr, and Zn showed
lower levels of hazard quotient for non-cancerous
effects. To summarize, the children are at a greater
health risks than the adults in the vicinity of Al-Jiza
phosphorites.
Acknowledgments The authors would like to thank the Jordan
Phosphate Mines Company (JPMC) for financial support and
access to the exploration boreholes. The authors thank also Al-
Rawashdeh, I., Al-Majali, T., Al-Zgool, H., Al-Mohtseeb, M., Al-
Samadi, M., and Qatami H. from the Jordan Phosphate Mines
Company for their contribution in the field and laboratory work.
Thanks are also due to the anonymous reviewers for their useful
and valuable comments and suggestions.
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