assessment of elemental distribution and heavy metals contamination in phosphate deposits: potential...

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ORIGINAL PAPER Assessment of elemental distribution and heavy metals contamination in phosphate deposits: potential health risk assessment of finer-grained size fraction Mohammad Al-Hwaiti Mustafa Al Kuisi Ghazi Saffarini Khitam Alzughoul Received: 20 June 2013 / Accepted: 21 November 2013 Ó 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

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Page 1: Assessment of elemental distribution and heavy metals contamination in phosphate deposits: potential health risk assessment of finer-grained size fraction

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

Page 2: Assessment of elemental distribution and heavy metals contamination in phosphate deposits: potential health risk assessment of finer-grained size fraction

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

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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

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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

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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

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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

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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

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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)

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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

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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

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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

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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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