Environ Sci Pollut Res DOI 10.1007/s11356-017-9353-2
RESEARCH ARTICLE
Distribution of heavy metals around the Barakah nuclear power plant in the United Arab Emirates Mouza Rashid Al Rashdi 1 & Sulaiman Alaabed 1 & Mohamed El Tokhi 1 & Fares M. Howari 2 & Walid El Mowafi 3 & Alya A. Arabi 2
Received: 26 December 2016 / Accepted: 23 May 2017 # Springer-Verlag Berlin Heidelberg 2017
Abstract Inductively coupled plasma emission spectroscopy was used to measure the concentrations of heavy metals in 58 samples collected from the Barakah nuclear power plant (BNPP) area, UAE. The grain size distribution was symmetric, but the samples ranged from fine to coarse sand. The inverse relationship between grain size and heavy metal contaminations was validated. The pre-operational average heavy metal contaminations around the BNPP were 0.03, 0.40, 1.2, 2.05, 1.66, 1.6, 5.9, 7.3, 7, 8.8, 60, and 2521 ppm for Cd, Mo, Co, Cu, Pb, As, Zn, Ni, V, Cr, Mn, and Fe, respectively. The spatial distribution was more compact in the south compared to the north, with less severe contaminations in the east and west. The negative geoaccumulation indices suggest an uncontaminated area, and the BNPP has minor enrichments. All concentrations were significantly below the safe limits set by the Dutch guidelines. The levels of heavy metals reported in the UAE were lower than levels reported in countries around the world.
Keywords Heavy metal contamination . Pollution . Barakah nuclear power plant . Geoaccumulation index . Pollution load index . Enrichment factor Responsible editor: Philippe Garrigues * Alya A. Arabi
[email protected] 1
Geology Department, College of Science, United Arab Emirates University, PO Box 17551, Al Ain, United Arab Emirates
2
College of Natural and Health Sciences, Zayed University, PO Box 144534, Abu Dhabi, United Arab Emirates
3
Federal Authority of Nuclear Regulation, PO Box 1122021, Abu Dhabi, United Arab Emirates
Introduction Anthropogenic activities such as construction, mining, transportation, power plants, sewage treatment plants, industrial activities, urban waste, and agricultural runoff have significantly affected the distribution and the level of contamination of heavy m etals in marine, soil, and sediments. Bioaccumulation of heavy metals in flora, phauna, and human tissues causes severe health consequences such as damages in the central and peripheral nervous systems, liver, kidneys, or even death in severe cases as highlighted in Jarup (2003), Singh et al. (2011), Jaishankar et al. (2014), and references therein. Some heavy metals such as copper lead and zinc are associated with a high risk as they tend to accumulate in aquatic organism consumed by humans (Luoma 1990). It is thus vital to monitor the levels of heavy metals in order to limit their distribution and thus minimize health risks. The coastal region of the United Arab Emirates (UAE) consists of the Arabian Gulf Coastal and the Eastern Coast regions. Many studied covered the distribution of heavy metals in the Arabian Gulf (Abaychi and Douabul 1986; AlArfaj and Alam 1993; Fowler et al. 1993; Juma 1995; AlAbdali et al. 1996; El Tokhi et al. 2015; Freije 2015; Al Rashdi et al. 2015; El Tokhi et al. 2016). The major findings of these studies done in the Arabian Gulf are listed here in chronological order. Abaychi and Douabul investigated the trace element geochemical associations in the Arabian Gulf. They determined the geochemical fraction of Cd, Cr, Cu, Fe, Mn, Ni, V, and Zn in sediments from the northwestern part of the Arabian Gulf. They found that in the non-lithogenous fraction, the easily or freely leachable and exchangeable fraction is not geochemically significant, while the carbonates and Fe-Mn oxides and hydroxide fractions appear in the most dominant phases (Abaychi and Douabul 1986). Fowler et al. studied in 1993 the distribution of petroleum hydrocarbons,
Environ Sci Pollut Res
trace metals, and biota in the Arabian Gulf sediments, near the shore, before and after the 1991 Gulf War (Fowler et al. 1993). They concluded that the highest concentrations were found along the northern coast of Saudi Arabia as evident from the elevated concentrations of hydrocarbon compounds in the subtidal sediments. Al-Arfaj and Alam studied the chemical characterization of sediments from the Arabian Gulf after the 1991 oil spill (Al-Arfaj and Alam 1993). Juma studied in 1995 the heavy metal and mineral concentrations in the sediments of the eastern coast of the UAE (Juma 1995). Al-Abdali et al. found chronic contamination of iron, vanadium, copper, nickel, and lead in the northern, central, and eastern areas of the Arabian Gulf (Al-Abdali et al. 1996). They also found that the contamination of trace metals in the western area, known for its pollution by the Kuwait oil slick, does not exceed the permissible natural background levels. A recent study by El Tokhi et al. on the distribution of heavy metals in bottom sediments of the Arabian Gulf near the UAE coast (Dubai, Sharjah, Ajman, and Ras Al-Khaimah) indicated that the concentration of Cu, Zn, Pb, Fe, Mn, Ni, Cd, and V does not exceed the safe limits suggesting no pollution around the studied area (El Tokhi et al. 2015). Another recent study by Al Rashdi et al. investigated the concentrations of heavy metals along the coastal area of Abu Dhabi. It was found that the contamination of heavy metals including antimony, arsenic, barium, cadmium, cobalt, copper, mercury, lead, molybdenum, nickel, and zinc has increased in the coastal area of Abu Dhabi from 2004 to 2014 (De Mora et al. 2004; Al Rashdi et al. 2015). Nonetheless, the area remains still unpolluted (pollution load index is 0.3) with some contamination of arsenic and minor levels of nickel that do not exceed the safety limits set by the Dutch guidelines (Al Rashdi et al. 2015). Heavy metal concentrations in the UAE are generally within the natural background levels (El Tokhi et al. 2015; Al Rashdi et al. 2015). However, elevated levels of heavy metals may be associated with anthropogenic activities such as oil refiners, desalination plants, and power plants. It is thus important to establish a geochemical baseline for areas that are subject to serious contaminations over the years. The aim of this study is to examine the heavy metal concentrations of the first nuclear power plant in the UAE, the Barakah nuclear power plant (BNPP), to establish a baseline before its operation between years 2017 and 2020. A grain size analysis was also completed to examine the correlation between the levels of heavy metal contamination and the size of the grain.
limestone) related to the Miocene period (Alsharhan and Kendall 2003). The study area, Barakah, is located to the west of Abu Dhabi, the capital of the UAE. Holocene carbonates and evaporate complex dominate the northern coast of the UAE, while terrigenous sediments with a range of mountains cover the eastern coast (Basaham and El-Sayed 1998; AlRashdi 2004). The Barakah is a flat area at the sea level with elevations estimated to be 3 or 4 m. The coastal area of the site consists of unconsolidated bioclastic, oolitic carbonate sands, haliferous and interspersed dunes, and beach ridges next to the shoreline. The Inland area is dominated by calcareous and gypsiferous silt and sand (Alsharhan and Kendall 2003). The Jebel Dhannah region, also included in this study, is located to the west of Abu Dhabi city, and it is about 45 km away from the BNPP. Whybrow et al. studied the geological setting of Jebel Dhannah including the upper dam formation to the west of Abu Dhabi (Whybrow et al. 1999). They described the formation of the lower Shuweihat and the upper Baynunah. The lower Shuweihat is mainly composed of sedimentary rocks with pink to red cross-bedded layers of quartz sands from salt flats, fluvial and aeolian origins (Bristow 1999). The upper Baynunah is composed mainly of sandstones and mudstones from fluvial settings with fossil accumulation at various levels (Whybrow et al. 1999). The findings of Whybrow et al. suggest the presence of a (currently disappearing) large river system in the Baynunah area as evident by the abundance of reptiles and fish remains (Whybrow et al. 1999). Figure 1 is a schematic illustration of the geology of the area covered in this study. This diagram is based on the geological map created by Macklin et al. (2011). Strait of Hormuz travels by counter-clockwise density currents around the basin of the Arabian Gulf (Sheppard et al. 1992). The coast of the UAE is relatively shallow with a dense water at a depth that does not exceed 20 m (Kampf and Sadrinasab 2006). Semidiurnal and diurnal are the types of tides in the Arabian Gulf (Michael Reynolds 1993). The western coast of
Geology of the study area The United Arab Emirates is located in the eastern part of the Arabian Peninsula. Being at the south coast of the Strait of Hormuz, the UAE’s location is strategic as a vital transit point for world crude oil. The western area of the Abu Dhabi emirate contains terrestrial sediments (marls, sandstone, and
Fig. 1 A simple geological map of the study area
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Abu Dhabi is dominated by diurnal tides, while the eastern coast is dominated by semidiurnal tides (Sheppard et al. 1992). The UAE coastlines is mainly affected by north winds associated with surface currents and waves (Alsharhan and Kendall 2003).
Methodology A total of 58 sediment and soil samples were collected in November 2014 (soil and shore samples) and May 2015 (bottom sediments). Going from west to east, the samples were collected from the Sila, Barakah, and Jebel Dhannah areas. The collected samples were classified in three categories (as listed in Table 1): sediments along the shoreline in the Barakah area BB1–B16^ referred to as Bshore^ samples, soil from sand dunes at 200 m inland in the Barakah area BS1–S24^ referred to as Bsoil^ samples, and marine sediments at a distance of 500 to 2000 m from the shore and at a depth of 4 to 7 m BM1– M18^ referred to as marine Bbottom^ samples. The bottom samples were collected from all three areas: Sila, Barakah, and Jebel Dhannah. Figure 2 is a map showing the location of the 58 samples collected for this study. Using a 25 × 25 × 5 cm stainless steel box, 16 shore samples were collected along the shoreline, near the Barakah station (B1–B16), and 24 soil samples (S1– S24) were collected from the south area of Barakah. The 18 marine bottom samples (M1–M18) were collected by grab sampler. The inductively coupled plasma emission spectroscopy (ICP-ES) analysis, for the determination of heavy metal content, was conducted in the certified Bureau Veritas Minerals Laboratories (BVML) in Turkey, Ankara. Samples were digested over 1 h using Aqua Regia solution of concentrated HCl, HNO3, and DI H2O (1:1:1). HCl was used to dilute the samples. The grain size analysis was performed for the 58 samples using ASTM Sieves. In order to study the relation between heavy metal concentrations and the grain size of the marine sediments, each of the 18 bottom samples was split in three subsamples (coarse, medium, and fine) according to the grain size (>0.5, >0.25, and >0.125 mm, respectively). Heavy metal analysis was conducted at BVML for all three fractions of each of the 18 sample. Arcmap 10 was used to plot spatial distribution maps by interpolation method (kernel smoothing). Graphpad PRISM 7.0b was used, with a threshold value of Q = 1% to exclude outliers. All statistics were reported after the exclusion of the outliers. From the marine samples, 44 of the 648 readings were outliers, i.e., 7%. From the soil readings, only 1 out of 288 readings was an outlier, i.e., 0.3%; and from the shore samples, 3 out of 192 readings were outliers, i.e., 2%.
Results and discussion Grain size analysis The mean size values in the shore, soil, and bottom samples are on average 0.34, 0.72, and 0.62 mm, respectively. According to Udden (1914), the ranges of the sizes for very coarse, coarse, medium, fine, and very fine sand grain sizes are 1–2, 0.5–1, 0.25–0.5, 125–250, and 62.5– 125 μm. Although the average of both shore and soil lies in medium sand, Fig. 3 shows that the mean size of shore samples lies in medium sand, while most of the soil samples consist of coarse sand. The dominance of coarse sand in soil samples suggests a higher energy in the depositional environment, which is mainly controlled by wind. Shoreline turbulence prevents small particles from settling and transports them towards the sea (Yuan et al. 2008). The bottom samples are dominated by coarse and medium sand with a mean size average value of 0.62 mm (coarse sand). The highest mean size reading appears in sample M4 of Sila area, which was associated, during the sampling process, with the presence of very coarse shell fragments and coarse sediments. Figure 4 shows that most of the grains range in size from 0.13 to 1.00 mm; i.e., the samples consist of fine to coarse sand. This result is also reproduced in Fig. 3. Compared to the rest of the samples, the soil samples, S1–S24, contained more grains with size greater than 2 mm, but they also contained more grains with size 0.06 mm or less, which means that the standard deviation is relatively high as discussed below. Table 2 summarizes the statistical values of the grain size analysis of the 58 samples considered in this study. The standard deviations for the shore and bottom samples are roughly 30% of the average grain size, suggesting that the grain sizes are relatively within a narrow range, but they are not well sorted. For the soil samples, the range of the grain sizes is wide, as also shown from the standard deviation which is almost 50% of the average grain size, suggesting a scattered sorting of the grains. The large standard deviation for the soil samples is expected given the distribution of the grain size shown in Fig. 4. The sorting of the grain size depends on several factors such as the extent of weathering, the distance of transportation, and the energy variation of the depositing agents. The skewness values in all cases are positive, meaning that the data is weighted to the right. The skewness values are almost unity or less than 1 in all cases suggesting that the data is not skewed. The kurtosis values for the shore and bottom samples suggest that the peak of the data distribution is rather flat. The data distribution in the soil is light tailed.
Environ Sci Pollut Res Table 1
List of samples collected with their label, coordinates, and location
Shore samples (sediments in Barakah)
Label
Coordinates
Label
Coordinates
B1
N 23 57 33.2 E 52 08 54.2
S1
N 23 56 22.1 E 52 08 54.0
B2
N 23 57 38.9 E 52 10 10.2
S2
N 23 56 35.6 E 52 10 13.2
B3
N 23 57 41.2 E 52 11 19.1
S3
N 23 56 51.2 E 52 11 10.6
B4
N 23 57 43.7 E 52 11 46.1 N 23 58 50.5 E 52 16 0.5
S4
N 23 56 36.8 E 52 11 51.5 N 23 56 51.5 E 52 12 03.4
B5 B6 B7 B8
N 23 58 55.2 E 52 16 27.8 N 23 59 05.7 E 52 17 03.6 N 23 59 35.7 E 52 18 17.5
Soil samples (soil in Barakah)
S5 S6 S7 S8
N 23 57 06.2 E 52 13 55.0 N 23 57 33.8 E 52 14 26.9 N 23 57 55.6 E 52 15 10.6
Bottom samples (marine sediments M1–M5 in Sila, M6–M10 in Barakah, and M11–M18 in Jebel Dhannah)
Label
Coordinates
M1
N 24 04 12.7 E 51 47 37.2 N 24 00 41.6 E 51 53 22.2
M2 M3
N 24 03 06.5 E 51 56 37.8
M4
N 24 02 46.8 E 52 01 08.0 N 24 01 47.5 E 52 04 42.4
M5 M6 M7 M8
N 23 58 36.9 E 52 09 22.1 N 23 58 12.3 E 52 11 22.3 N 23 58 19.9 E 52 12 32.5
B9
N 24 00 01.4 E 52 19 13.7
S9
N 23 57 57.2 E 52 15 33.0
M9
N 23 59 09.5 E 52 15 40.5
B10
N 24 00 48.9 E 52 19 52.8 N 24 01 23.6 E 52 20 54.1
S10
N 23 57 11.9 E 52 15 17.7 N 23 58 01.5 E 52 16 23.7
M10
N 24 01 06.4 E 52 18 23.8 N 24 02 59.3 E 52 20 37.8
B11 B12 B13 B14 B15 B16
N 24 02 04.1 E 52 22 02.3 N 24 02 20.4 E 52 22 34.5 N 24 02 41.5 E 52 23 33.3 N 24 02 54.2 E 52 24 29.4 N 24 03 16.3 E 52 25 24.6
S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24
Correlation between the grain size and the heavy metal contamination in the bottom samples Figure 5 shows the relationship between the average concentrations of heavy metal and the grain size of the
N 23 58 19.0 E 52 16 39.5 N 23 58 32.2 E 52 17 45.1 N 23 58 43.6 E 52 17 46.3 N 23 59 15.3 E 52 18 45.0 N 23 59 25.5 E 52 19 25.5 N 23 59 45.8 E 52 19 41.3 N 23 59 08.5 E 52 12 24.6
M11 M12 M13 M14 M15 M16 M17 M18
N 24 03 38.0 E 52 23 35.9 N 24 06 20.1 E 52 25 44.9 N 24 08 41.2 E 52 27 34.2 N 24 08 00.9 E 52 30 55.9 N 24 09 25.9 E 52 32 55.6 N 24 10 49.2 E 52 33 46.9 N 24 12 21.7 E 52 34 33.9
N 24 00 21.0 E 52 20 39.7 N 24 00 55.5 E 52 21 40.4 N 24 01 11.0 E 52 22 46.8 N 24 01 19.5 E 52 23 58.1 N 24 01 37.4 E 52 25 06.4 N 24 01 58.1 E 52 26 12.3
bottom samples. Samples were classified in three size categories: coarse (>0.5 mm), medium (0.25–0.5 mm), and fine (0.125–0.25 mm). The results confirm the inverse relationship between the grain size and levels of contamination of heavy metals. As the grain size gets finer, the
Environ Sci Pollut Res
Fig. 2 Map showing the location of the 58 sampling sites around the BNPP
specific surface area increases causing an increase in the heavy metal contamination. The only exception was that of Cu, where fine particles (on average) were less contaminated than the medium particles. Table 3 summarizes the value of all heavy metal contaminations in the three categories of grain sizes for the bottom samples. The values in red are outliers. It is clear from this table that the inverse relationship between the contamination for each site and the grain size has some exceptions, especially for Cd and Ni, where coarse grains contained higher concentrations of heavy metals than fine grains. The formation of agglomerates from contaminated fine grains could be the reason for these exceptions. The agglomeration of the small particles could happen either in the presence of organic matter or by sea salts from the marine sediments (Parizanganeh 2008). Chakraborty et al. also showed that a higher contamination of heavy metals in the coarse grains is also related to the quality and quantity of organic matter and the distribution of different mineral phases (Chakraborty et al. 2009).
Concentrations of heavy metals in shore, soil, and bottom samples Figure 6 depicts the average distributions of all heavy metals in the shore, soil, and bottom samples. Both Fe and Mn are present in the highest concentrations, while Cd has the lowest concentrations in all areas. The shore samples had the lowest level of heavy metal contaminations despite having the smallest average grain size (0.34 mm) (see Table 2) compared to the soil (0.72 mm) and bottom (0.62 mm) samples. Despite having the largest grain size, the soil was the most contaminated sample. The correlation between grain size and levels of contamination is not clear in this case because the samples vary from soil samples to shore or marine sediments. The shore and bottom sediments are subject to the convection cycle of water along with possible tidal activities and turbidities, which can wash away heavy metals (Scoullos et al. 2014). The most contaminated samples were those of the soil, i.e., 200 m away from the water. The soil samples were more contaminated than the bottom sediments by almost a factor of 4 for Cu,
Environ Sci Pollut Res 1.60
Sila
Barakah
Jebel Dhannah
1.40
Shore Soil Bottom
1.20
Grain Size (mm)
Fig. 3 Mean size distribution for all 58 samples in mm. All the samples, grouped in shore (orange line), soil (dashed green line), and bottom (blue line), are shown in this plot. The locations are listed from left to right in chronological order as shown in Table 1. The vertical lines separate the samples according to the area they were taken from. The horizontal line shows the threshold for fine, medium, and coarse grains according to Udden classification
Very coarse sand
1.00 0.80
Coarse sand 0.60 0.40
Medium sand 0.20
Fine sand 0.00
Site
Zn, Ni, Co, Mn, Fe, and Mo; by a factor of 3 for V and Cr; and by a factor of 2 for Pb and Cd, and the ratio was close to unity for As. The level of contaminations of the bottom compared to the shore samples is almost equal or higher by a factor of maximum 1.6 with two exceptions, Ni and Mo, where the levels are doubled. The average heavy metal concentrations in the shore sediments covered in this study ranked from highest to lowest as f o l l o w s : Cd < Mo < Co < Cu < Pb < As < Ni < Zn < V < Cr < Mn < Fe. For the soil samples, the ranking of the level of contaminat i o n s o f m e t a l s i s a s f o l l o w s : Cd < Mo < As < Pb < Co < Cu < Zn < V < Ni < Cr < Mn < Fe. For the bottom marine sediments, the ranking of the level of
contaminations of metals is as follows: Cd < Mo < Co < Cu < Pb < As < Zn < Ni < V < Cr < Mn < Fe. These rankings clearly show that the shore, soil, and bottom samples are contaminated mostly by iron followed by manganese (as shown in Fig. 6), then chromium. Among the metals considered in this study, these samples are the least contaminated with cadmium and molybdenum (again as shown in Fig. 6). The level of contamination of zinc, nickel, vanadium, and chromium (in different orders for the shore, soil, and bottom samples) is in the middle range, yet closer to the high end of contaminations, and that of lead, copper, cobalt, and arsenic (in different orders for the shore, soil, and bottom samples) is in the middle range, yet closer to the low end of contaminations (Fig. 5).
100%
90%
80%
70%
<0.062 mm
% by Weitght
0.06 mm 60%
0.13 mm 0.25 mm
50%
0.5 mm 1 mm
40%
2 mm
4 mm
30%
20%
10%
B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14 M15 M16 M17 M18
0%
Site
Fig. 4 Stacked column, for all the 58 sites, showing the percent by weight of the grains with different sizes
Environ Sci Pollut Res Table 2 Statistical analysis (all values in mm) for the grain size for all 58 samples classified in the shore, soil, and bottom areas
Average grain size (mm)
Standard deviation
Minimum
Maximum
Skewness
Shore
0.34
0.11
0.19
0.59
0.89
0.24
Soil
0.72
0.33
0.23
1.37
0.21
−0.95
Bottom
0.62
0.19
0.39
1.04
1.01
0.29
Compared to other soil samples, the soil sample S14, which was mainly a salt flat, was exceptionally more contaminated especially with Cu (8.11 ppm), Zn (22.5 ppm), Co (4.4 ppm), Fe (10,300 ppm), V (19 ppm), and Cr (58.10 ppm) (as shown in Table 4). Compared to other shore samples (Table 4), the shore sample B4, which contained many shell fragments, showed slightly higher metal concentrations especially for the metals Cu (1.2 ppm), Pb (2.42 ppm), Zn (3.9), Ni (4.0 ppm), Co (0.8 ppm), Mn (42 ppm), Fe (1300 ppm), V (4 ppm), and Cr (5.7 ppm). The bottom sample M17 was also exceptionally more contaminated with Cu (4.57 ppm), Zn (25 ppm), Ni (19.37 ppm), Co (2.43 ppm), Mn (123.33 ppm), Fe (5300 ppm), Cd (0.08 ppm), V (13.67 ppm), and Cr (18.53 ppm) compared to other bottom samples (see Table 4). The M17 site is 2 km north of the Shuweihat power company, and it is adjacent to a harbor that is 760 m north this site. The maps in Fig. 7 provide a comprehensive illustration of the spatial distribution of all heavy metals over the studied area. These maps clearly show how soil samples (from areas coded with blue) are more contaminated than the shore and bottom sediments. The shore sediments had lower levels of contamination likely due to the tidal fluctuation and wave 2000 1500 1000
Coarse Medium Fine
55 45
Concentration (ppm)
35 25 12 10 8 6 4 2 0
Cu Pb Zn Ni Co Mn Fe Cd V Cr As Mo
Fig. 5 Average concentrations (in ppm) of various heavy metals for each of the course, medium, and fine grains of the bottom samples
Kurtosis
currents (Caetano et al. 1997). The relatively higher concentration of heavy metals in the soil samples could be because of the erosion. As mentioned earlier in the introduction, the studied area is dominated sandstones and limestones from the Miocene age (Alsharhan and Kendall 2003). The southern part of the study area is occupied by sand dunes that are thought to originate from the extensive erosion of the Miocene rocks. Another explanation for the high concentrations of heavy metals in the soil samples is the active and differential enrichment in metals through the formation of carbonate and sulfide by the stromatolite-forming cyanobacteria (Renfro 1974). The distributions of Cr, Ni, and Mn in shore, soil, and bottom samples are in very similar dispositions, as shown in Fig. 7. The concentrations decrease significantly from the south to the north, with relatively mild contaminations in the east while the west remains virtually intact. V, Fe, Co, Mo, Zn, and Cu exhibited similar distribution patterns; the maximum concentrations were found in the south central zone with relatively elevated concentrations (especially for Cu) to the east (Jebel AlDhannah) and west (Sila). Overall, for the Cr, Ni, Mn, V, Fe, Co, Mo, Zn, and Cu, the general trend of the concentration distribution is maximal in the center of the southern Barakah area and minimal in the shore and bottom sediments in the northern area. The spatial distribution maps of Pb, Cd, and As (as shown in Fig. 7) are unique compared to the distributions of the rest of the heavy metals considered in this study. The lead was mainly concentrated around the western Arabian Gulf near the Sila area; cadmium was spread intermittently in the eastern part (Jebel AlDhannah) and the central southern part (Barakah), with exceptionally high concentrations in the western part (Sila), and arsenic was spread across the entire area, with particularly higher concentrations in the western part (Sila). The toxicity generated by the elevated concentrations of lead in the western Arabian Gulf sediments may lead to extinctions of endangered marine species, thus causing a change in the structure of the marine biota (Moriarty 1975; Bowen 1979). Despite its unique distribution, cadmium (as shown in Fig. 7) is found in the lowest concentrations among other metals considered in this study, with the highest value, 0.15 ppm, being in M4 in the Arabian Gulf. Al Abdali et al. concluded that Cd is a natural constituent of the gulf marine environment, and not an element derived from pollutant sources (Al-Abdali et al. 1996).
Environ Sci Pollut Res Table 3
Heavy metal concentrations (in ppm) in bottom sediment samples
Sample
Cu
Pb
Zn
Ni
Co
Mn
Fe
Cd
V
Cr
As
Mo
M1 C
3.92
1.93
5.5
7.1
1
34
2100
<0.01
7
6.7
2.8
0.83
M2 C
0.47
0.51
1.8
0.8
0.3
12
500
<0.01
2
1.6
1.4
0.12
M3 C M4 C
1.14 0.47
0.85 0.64
1.5 2.6
1.1 <0.1
0.4 0.2
21 27
1800 400
<0.01 0.15
<2 <2
3.3 0.9
1.2 1.8
0.18 0.13
M5 C M6 C
0.58 0.61
0.73 0.45
0.8 0.8
1.8 2.1
0.5 0.4
16 14
600 500
<0.01 <0.01
2 4
2 2.4
1.7 1.7
0.22 0.11
M7 C
2.61
16.11
5.9
2.2
0.5
23
1500
0.03
3
3.8
2.1
0.3
M8 C M9 C
2.98 0.94
12.19 0.84
5.6 2.7
3.8 3.9
0.6 0.4
33 26
1700 1000
0.01 0.03
5 4
4.9 4.4
2.1 1.4
0.71 0.19
M10 C M11 C
0.35 0.36
0.9 0.56
2.6 1.2
1 1.5
<0.1 <0.1
13 13
300 200
0.04 0.04
<2 <2
1.5 1.6
1.1 0.9
0.13 0.09
M12 C
0.35
0.98
2.2
1
<0.1
13
300
0.02
<2
1.6
1
0.15
M13 C M14 C
0.38 0.97
0.63 1.38
2.1 2.6
0.9 2.5
0.1 0.4
19 25
300 1000
0.05 0.03
<2 2
1.8 4.3
1.1 1.6
0.13 0.27
M15 C M16 C
0.64 0.93
0.75 1.36
1.7 3.5
2 2.1
0.2 0.2
23 32
600 900
0.03 0.04
3 6
2.7 3.6
1.6 2.5
0.17 0.32
M17 C
5.44
2.09
30.8
29.3
2.8
147
6100
0.11
17
21.5
1.4
0.49
M18 C Average Standard deviation M1 M
6.66 0.63 0.28 5.04
1.75 1.02 0.52 5.58
11.4 2.7 1.6 7.1
12.7 2.1 1.7 9
1 0.4 0.3 1.3
59 24 12 41
2100 929 662 2800
0.07 0.03 0.02 0.04
6 3 2 9
7.7 3.2 1.9 9.3
2.4 1.7 0.5 4.3
1.12 0.2 0.11 1.14
M2 M M3 M M4 M M5 M M6 M
1.33 2.35 1.41 0.76 3.97
5.87 11.29 6.62 2.15 2.18
3.1 6.7 2.9 2.1 5.8
2.7 3.2 2.4 2.8 3.6
0.4 0.4 0.4 0.4 0.6
16 23 21 23 25
700 2100 1000 900 1600
<0.01 <0.01 0.01 <0.01 0.01
3 <2 2 3 5
2.5 3.7 3.3 3.9 5.9
2.1 1.4 1.4 1.7 2
0.18 0.38 0.28 0.2 0.3
M7 M M8 M M9 M M10 M M11 M
1.69 2.15 1.2 0.48 0.51
1.94 1.96 0.96 0.57 0.56
2.6 3.6 3.8 1.2 0.8
2.7 6.4 4 0.7 1
0.5 0.8 0.5 <0.1 0.2
25 42 35 14 11
1500 2000 1300 400 200
<0.01 0.02 0.03 0.04 0.04
4 8 4 <2 <2
4.8 7 5.7 2 1.7
1.7 2.5 1.4 1.2 1.2
0.24 0.77 0.21 0.13 0.1
M12 M M13 M M14 M M15 M M16 M
0.68 0.71 1.32 0.79 0.9
0.8 0.89 1.18 1.16 1.11
2.1 1.8 3.3 2.2 2.4
0.5 2.2 4.4 2 2.6
<0.1 0.2 0.6 0.2 0.3
14 28 35 24 24
200 300 1400 700 800
0.04 0.05 0.03 0.03 0.03
<2 3 4 4 4
1.8 2.7 6.9 3.2 4
1.6 1.5 1.5 1.7 2.7
0.12 0.13 0.3 0.17 0.25
M17 M M18 M Average Standard deviation M1 F M2 F M3 F M4 F M5 F M6 F M7 F M8 F M9 F
4.42 4.06 1.88 1.48 5.75 1.86 1.5 1.38 1.5 1.99 1.38 2.64 1.81
2.36 1.4 1.37 0.63 2.63 1.73 2.2 2.75 1.88 3.49 1.75 1.82 1.17
23.5 11.7 3.2 1.8 8.5 3.4 2.6 2.3 3.6 4 3.2 5.2 4.5
15.7 9.7 3.5 2.6 11.5 11.3 7.1 11.1 5.9 18.5 5.5 10.3 9.6
2.4 1.2 0.5 0.3 1.7 0.8 0.7 0.6 1 0.8 0.9 1.5 1.1
111 72 25 9 63 60 37 38 54 42 43 79 73
5000 2900 1433 1210 3400 2200 2100 1600 2000 2500 1800 2900 2400
0.08 0.06 0.03 0.02 0.04 <0.01 <0.01 0.04 0.02 <0.01 0.04 0.02 0.03
12 6 4 2 12 8 5 5 7 6 7 11 8
17.3 9.5 4.6 2.5 12.4 13.1 7.7 14.3 9.3 32.5 9.1 10.6 11
1.6 2.1 1.7 0.4 4.5 3.4 1.8 2.1 2.3 2.5 2.7 3.2 1.7
0.4 0.72 0.23 0.09 1.07 0.32 0.28 0.32 0.25 0.41 0.27 0.8 0.31
Environ Sci Pollut Res Table 3 (continued) Sample
Cu
Pb
Zn
Ni
Co
M10 F M11 F
1.21 0.96
1.86 4.29
4.5 2.3
2 0.8
M12 F
0.6
0.85
1.9
0.7
0.2
M13 F M14 F
0.6 1.75
0.89 1.27
1.4 5
1 6.7
0.2 1.1
M15 F
0.95
1.25
3
1.5
0.3
M16 F M17 F
1.34 3.84
1.52 1.8
4.7 21.5
4.6 13.1
0.4 2.1
M18 F Average
4.12 1.73
1.56 1.93
15.1 3.8
9.6 7.3
Standard deviation
0.99
0.89
1.7
5
Table 5 shows the average concentrations of some heavy metals in this study, BNPP, compared with previous studies done by Al Rashdi et al., on shore sediments in Abu Dhabi (Al Rashdi et al. 2015), and by Al Abdali et al. (1996) and El Tokhi et al. (2016) on bottom sediments of the Arabian Gulf. The results of the study by Al Rashdi et al. for the shore sediments were higher than those in this study, which proves that there is a wide variation in the distributions of heavy metals in Abu Dhabi depending on the specific areas
6000
Shore 4000 2000
Bottom Soil
50
30 16
Fe
Cd
V
Cr
As
Mo
900 500
0.03 0.03
3 3
4.1 2.8
1.8 1.1
0.18 0.16
15
400
0.03
<2
2.4
2.8
0.13
23 79
500 2500
0.02 0.03
3 8
2.8 11
1.1 2.5
0.14 0.32
32
1200
0.02
5
4.4
1.8
0.19
36 112
1400 4800
0.03 0.06
4 12
7 16.8
2.3 1.3
0.22 0.34
1.4 0.9
92 51
3400 2028
0.06 0.03
8 7
11.5 10.2
1.9 2.3
0.89 0.26
0.6
27
1156
0.01
3
7
0.9
0.08
considered. In this case, the Barakah area is less contaminated than Abu Dhabi (the capital) coastal line. The results by Al Rashdi et al., however, are comparable, for all metals, with the results founds for the soil samples in this study. This observation is not trivial and needs further investigations to assess the reasons (if any) for this similarity. For the bottom sediments, compared to the results reported by El Tokhi et al., the values in this study are less than half for all metals except for lead which is present in roughly equal concentrations, 1.91 and 1.4 ppm, respectively. Results of Al Abdali et al. were significantly higher for all metals compared to those reported in this study and by El Tokhi et al. The significant drop in Pb from 15 to 30 ppm in 1996 (according to Al Abdal et al.) to 1.9 ppm in 2015 (according to El Tokhi et al. and to this study) could be attributed to the banned use of leaded gasoline in the UAE since January 2003. Comparison with international studies
40
Concentration (PPM)
0.4 <0.1
Mn
30 20 8
6
4
2
0 Cu Pb Zn Ni Co Mn Fe Cd V Cr As Mo
Fig. 6 Average concentrations (in ppm) of the heavy metals and their standard deviations in the shore, soil, and bottom samples
Table 6 shows contaminations of heavy metals in many countries across different continents. India, Greece, and Scotland were contaminated with Cd more than other countries listed in this table. The amount of copper varied significantly from one place to another with the UAE being the least polluted and Scotland followed by India being the most polluted with Cu. India and Aegean had exceptionally high levels of Pb. Arsenic levels were exceptionally high in China. Zinc levels in India were at least one order of magnitude higher than the highest levels reported in Greece. Iran is particularly contaminated with nickel. Chromium levels exceeded 240 ppm in India and Aegean. China had at least one order of magnitude more Mn compared to the level reported in Kenya which is the second highest. India, New Zealand, and Kenya had the highest levels of Fe. The levels of contaminations of all heavy metals in the UAE were the lowest among all countries listed in Table 6. Overall, among the
Environ Sci Pollut Res Table 4
Heavy metal concentrations (in ppm) for the shore and soil sediment samples
Sample
Cu
Pb
Zn
Ni
Co
Mn
Fe
Cd
V
Cr
As
Mo
B01
0.65
1.07
2.5
2.4
0.2
23
900
0.03
3
3.3
1.9
0.11
B02
0.79
1.9
3.9
1.8
0.5
20
700
0.04
<2
2.9
1.4
0.05
B03 B04
0.77 1.21
1.16 2.42
3.4 3.9
5 4
0.6 0.8
28 42
900 1300
0.02 0.03
2 4
4 5.7
0.7 1.4
0.09 0.12
B05 B06
0.88 0.97
1.1 1.21
3 3.7
3 2.4
0.4 0.4
37 32
1200 1100
0.04 0.02
5 4
4.2 4.3
1.6 1.4
0.11 0.25
B07
0.75
1.48
3.2
1.6
0.3
22
800
0.02
3
3.2
1.3
0.21
B08 B09
0.5 0.9
0.89 1.56
1.5 2.5
0.9 2.1
0.3 0.4
21 29
700 1900
0.03 0.01
<2 3
3.1 4.6
1.8 1.2
0.08 0.17
B10 B11
0.58 0.49
0.94 0.93
1.9 1.9
1.8 1.6
0.3 0.1
20 11
900 500
0.02 0.02
2 <2
3.5 2.4
1.5 1.4
0.07 0.07
B12
0.52
0.84
2
1.5
0.2
19
600
0.02
3
3.1
1
0.06
B13 B14
0.67 0.69
0.85 0.93
2 3.5
1.3 1.5
0.6 0.5
18 21
600 700
0.02 0.02
3 3
2.6 3.2
1.5 1.2
0.06 0.08
B15 B16
1.15 1.03
0.88 1.33
2.7 2.9
3.1 1.9
0.7 0.7
39 35
1500 1300
0.03 0.03
4 3
5 4.6
1.6 1.5
0.12 0.12
Average
0.78
1.14
2.8
2.1
0.4
26
975
0.03
3
3.7
1.4
0.1
Standard deviation S01 S02 S03
0.22 1.62 2.06 2.09
0.31 1.29 1.97 2.77
0.8 5 6.8 5.9
0.8 5.3 8 7.1
0.2 1.1 1.3 1.1
9 49 72 68
382 1700 2600 2500
0.01 0.02 0.03 0.03
1 5 6 6
0.9 5.9 8.3 8.2
0.3 1.4 2.6 2.9
0.04 0.25 0.19 0.18
S04 S05 S06 S07 S08
3.43 3.21 5.86 3.32 1.58
3.08 2.31 4.04 2.23 1.07
13.4 7 21 9.4 7.9
45.1 10.1 19.2 9.4 5.4
3.3 1.7 3.8 1.9 0.8
92 88 213 70 51
3700 3100 6600 4300 1800
0.04 0.03 0.09 0.02 0.05
7 8 19 11 5
22.9 9.9 21.3 15.5 6.3
1.8 2.5 2 0.9 1.6
1.17 0.24 1.21 0.23 0.31
S09 S10 S11 S12 S13
4.82 4.9 3.92 4.56 4.89
2.61 2.45 3.65 2.46 2.48
13.9 11.4 10.4 12.5 14.7
20.7 19.9 9.2 21.6 18.1
3.3 3.3 2.5 2.5 3
170 167 162 118 133
6900 7000 5400 5400 6700
0.08 0.07 0.06 0.04 0.03
16 15 13 13 17
22.4 23.5 13.1 19.2 28.7
1.3 0.9 1.2 1.4 1.4
0.51 0.62 1.75 0.55 0.76
S14 S15 S16 S17 S18
8.11 5.91 5.26 5.24 4.5
2.66 3.03 2.79 2.59 2.23
22.5 16.1 12.2 11.9 13.2
18.7 24 23.2 16.9 15.6
4.4 4.2 3.3 3 3.2
122 231 165 128 169
10,300 8500 6000 6300 6000
0.04 0.07 0.07 0.04 0.08
19 23 15 16 16
58.1 29.3 21 19.8 19.5
1.1 2 1.8 1.2 0.8
0.53 1.97 1.26 0.63 2.18
S19 S20 S21 S22 S23 S24 Average Standard deviation
4.75 6.28 3.19 4.37 2.7 3.83 4.18 1.58
2.97 2.34 2.34 1.75 1.45 1.78 2.43 0.69
12.9 15.9 8.6 8.6 7 8.9 11.5 4.5
14.1 21.9 8.6 12.6 5.9 9.7 15.4 8.8
3.1 3.7 2.1 2.3 1.3 2.3 2.6 1
151 166 77 127 64 122 124 51
6400 6500 4900 5400 3300 4700 5250 2108
0.05 0.05 0.03 0.04 0.04 0.05 0.05 0.02
14 22 9 11 6 12 13 5
18.2 38.3 12.4 13 9.5 13.8 17.4 8.2
1.2 2.9 0.8 1.5 1.3 1.3 1.6 0.6
0.53 1.75 0.49 1.67 1.02 1.25 0.89 0.62
The values in bold are outliers that were excluded from the statistical analysis
values reported in this table, India is the most contaminated country; this is because of anthropogenic activities
such as industrial wastewater, coal-fueled iron and steel industries, and municipal sewage (Raj and Jayaprakash
Environ Sci Pollut Res
Fig. 7 Spatial distribution maps showing the heavy metal distributions across all 58 sampling sites which are represented by dots. The blue line represents the coastline, and the star is the location of the BNPP. The
color codes from yellow to green to blue correspond to concentrations from low to medium to high, respectively
Environ Sci Pollut Res Table 5
Heavy metal contaminations in BNPP (for shore, soil, and bottom samples) in comparison with other studies in the UAE Cd
Mo
Co
Cu
Pb
Shore (BNPP)
0.03
0.1
0.4
0.78
1.14
1.4
2.8
2.1
3
3.7
26
975
Bottom (BNPP)
0.03
0.23
0.5
1.17
1.4
1.8
3.2
4.3
4
5.3
30
1339
Soil (BNPP) Al-Abdali et al. (1996)
0.05 1.2–2
0.89 –
2.6 –
4.18 15–30
2.43 15–30
1.6 –
11.5 30–60
15.4 70–80
13 20–30
17.4 –
124 300–600
5250 10,000–20,000
Al Rashdi et al. (2015) El Tokhi et al. (2016)
0.1 0.08
0.5 –
4.1 1.28
3.8 –
1.9 1.91
2.8 –
8.2 11.94
25.3 10.55
– 11.43
– 17.53
– 92.26
– 2800
2007). Among the countries reported in this table, the UAE was the least polluted with all metals despite the rapid growth of anthropogenic activity in the area. Table 7 shows a comparison of heavy metal concentrations (in ppm) in soil samples from the BNPP to those reported in other countries across the world. Values of heavy metals in South Africa were higher than the allowed maximum limits set for most countries, which is indicative of pollution at the vicinity of three coal-fired power stations (Okedeyi et al. 2013). The heavy metal values recorded near an industrial area in Romania decrease with increasing the distance from the focal point of the industry (Velea et al. 2008). This reflects the impact of the industrial activities on the accumulation of heavy metals in the surrounding area. Levels of some heavy metals reported from surface soils of e-waste recycling areas in India exceeded the safe limits suggested by the US Environmental Protection Agency (Pradhan and Kumar 2014) exposing human health to serious hazard. The exceptionally high values of Cd (36.8 ppm) in Pakistan emerge from effluents of pharmaceutical industries (Malik et al. 2009). The concentrations of Pb, Cr, Zn, Cd, and Hg are 62.17, 83.93, 146.94, 4.98, and 1.81 ppm for soil samples in Jordan around a cement factory in Fuheis (Banat et al. 2005). The concentration of heavy metals (in ppm) in BNPP coastal region is shown in Table 8 along with other values reported in other coastal regions of the world. There is a minor enrichment of As, Cd, Pb, and Zn in coastal sediments of a stream passing through the area of Izmit Bay (Turkey) which is highly industrialized (Pekey 2006). The coastal sediments of the Bay of Bohai Sea in China are rather unpolluted indicating a limited influence of the anthropogenic activities (Gao and Chen 2012). The levels of Cd, Cu, Pb, Ni, and Fe in the coastal sediments from the Red Sea coast of Hodeida in Yemen were roughly twice as much in the site surrounded by industrial and domestic water wastes (polluted site) compared to an unpolluted site in the same area (Saleh and Marie 2014). Along the Jordan River, the levels of Pb, Zn, and Cd were 14–126, 110–305, and 5.3 to 14 ppm, respectively (Banat and Howari 2003). It is worth noting that the comparison in all the tables above is not necessarily accurate because of the variations in the location of the sites, the levels of anthropogenic activities around the area, the geological
As
Zn
Ni
V
Cr
Mn
Fe
conditions, and the differences in the analytical techniques used for the measurements of the concentrations of heavy metals. To evaluate the anthropogenic influences of heavy metals in the Barakah area, the enrichment factors were calculated using EF = Mx × Feb / Mb × Fex, where Mx is the average concentration of the metal in the study area, Mb is the concentration of the metal in the background, shale average (Turekian and Wedepohl 1961), Fex is the average concentration of iron in the samples, and Feb is the iron concentration in the background. Enrichment factors EF < 1, EF = 1–3, 3–5, 5– 10, 10–25, 25–50, and EF > 50 indicate no enrichment (I), minor enrichment (II), moderate enrichment (III), moderate severe enrichment (IV), severe enrichment (V), very severe enrichment (VI), and extremely severe enrichment (VII), respectively. As shown in Table 9, the shore samples were enriched the most among other samples. The enrichment was in As, followed by Cd, then the rest of the metals, with no enrichment exhibited for Cu. The bottom samples were marginally more enriched compared to the soil samples. Overall, the study area had minor enrichment in all metals, but no enrichment in Cu or V. For all samples, Co, Zn, and Mn exhibited minor enrichment, but the values are at the lower end of the range. The pollution load index was calculated using PLI = (EF1 × EF2 × EF3. . . × EFn)1/n. If PLI < 1, the place is not polluted; if PLI > 1, the area is polluted (Tomlinson et al. 1980). In this case, PLI was greater than unity in each of the soil, shore, and bottom samples. The overall PLI for the area studied was 1.5, i.e., slightly above unity, which means that the study area was barely polluted. While the area was not enriched with some heavy metals, it had minor to moderate enrichment with some other metals. Thus, the conclusions reached in terms of the enrichment (which is a measure for individual metals) are aligned with those reached from the PLI (which is a measure of the amount of not individual metals but all metals considered in the study, as shown in the equation of PLI above). On another note, it is possible to evaluate the level of contamination and pollution using the contamination factors and the CF-based PLI instead of the EF-based PLI. The contamination factor is evaluated using CF = Mx / Mb, where Mx is the average concentration of the metal in the study area
India
Iran Kenya New Zealand
Aegean Greece
Spain Scotland
Aydin-Onen et al. (2015) Belias et al. (2003)
Mendiguchia et al. (2006) Dean et al. (2007)
Yohannes et al. (2013) Mohan et al. (2012)
Anithamary et al. (2012)
Ethiopia India
Raj and Jayaprakash (2007) Xu et al. (2015) Cheng et al. (2015) Zvab Rozic et al. (2012)
Keshavarzi et al. (2015) Otachi et al. (2014) Abrahim and Parker (2007)
UAE
India China China Croatia
This study (bottom sediments)
Location
3.5
0.037–0.38 2.0–3.48
0.24 0.34 0.28
0.21 9.5
4.6–7.5 0.1 0.11 0.07–2
0.03
Cd
<1.25
0.2–1.4
0.23
Mo
5.49
0.2–2.5
5.8–11.8
0.5
Co
2.1–27.5 805
2.1–50.1 31.7–66.9
20.45 11.5 34.5
8.69 166.1
385–657 31.1 39.3 1.05–6.6
1.17
Cu
42.2–151.8 30.5–41.1 6.1–20.0
8.09 12.5 73.3
7.02
15.7 221.4
24.9–40 27.9 41.1 3.3–12.3
1.4
Pb
4.25
4.02
2.3–8
11.2
1.8
As
4.1–101.5 214–679 25.5–91.0 921
48.89 138 207.2
51.4
93.8 1249.4
71.3–201 102.3 72.4 4.0–33.0
3.2
Zn
Heavy metal concentrations (in ppm) in marine sediments of BNPP in comparison with other international studies
References
Table 6
73.66
20.2
2.8–15.6
19.8–53.4
4.3
Ni
5.3–19.3
4
V
7.8–272.9
48.79
8.27
148.6–243.2 83.3 53.6 20–40
5.3
Cr
633
32.1
1633.5
284–460
30
Mn
24,800 25,100
2744
17,000–37,000
1339
Fe
Environ Sci Pollut Res
Environ Sci Pollut Res Table 7
Heavy metal concentrations (in ppm) in soil samples from BNPP in comparison with other international studies
References
Location
This study (soil)
UAE
Cd
Mo
0.05 0.89
Pradhan and Kumar (2014) India
1.29
Velea et al. (2008) Zhou et al. (2013)
Romania China
7 0.17
Bai et al. (2014) Hu et al. (2013)
China China
0.42
Cheng (2003)
China
0.1
Malik et al. (2009) Milenkovic et al. (2015)
Pakistan Serbia
Okedeyi et al. (2013)
South Africa
Co
Cu
2.6
Pb 4.18
12.4 4291.6 350
750 25
0.06
35.5 22.6
Zn
V
Cr
11.5
15.4
776.8
126.4
115.5
35
79
28.59 26
53.12 67.2
1300 74
12
85.3
22.6
26
74.2
26.85 28.2
121.4 47.1
94.2 127.6
16.1
52.05
86.49
13
Mn
1.6
20.76 51.4
56.15
Ni
17.08
33.85
and Mb is the concentration of the metal in the background, shale average (Turekian and Wedepohl 1961). The corresponding PLI index will thus be evaluated using PLI = (CF1 × CF2 × CF3. . . × CFn)1/n. The evaluated CF ranged for all sites and all metals from 0.01 to 0.14 with a CF-based PLI of 0.03. The CFs indicate an area with low contamination for all metals because all CFs are less than 1 (Hakanson 1980), and the CF-based PLI (0.03 < 1) indicates an unpolluted area. Thus, once again, the CFs and CF-based PLI lead to a coherent conclusion. It is also worth noting that the average concentrations of all heavy metals are an order of magnitude less than the safe limits set by the Dutch guidelines (Lijzen et al. 2001), i.e., the guidelines accepted and referred to in Abu Dhabi. Thus, even though the area is polluted, it is still safe. The geoaccumulation index is calculated using Igeo = log2 [Cn / (1.5Bn)], where Cn is the measured concentration of element n in a sample and Bn is the average for shale for the element n by Turekian and Wedepohl (1961). Classifications of geoaccumulation indices are class 0 uncontaminated, class 1 uncontaminated to moderately contaminated, class 2 moderately contaminated, Table 8
2.43 2645.3
8.6 36.8
As
85.46 120.1
17.4
155 109.3
31.79
63.27
Fe
124
5250 4129.8
371
5092 17,991.6
1090.4 215.2
1835.7
class 3 moderately to strongly contaminated, class 4 strongly contaminated, class 5 strongly to extremely contaminated, and class 6 extremely contaminated, for Igeo < 0, 0–1, 1–2, 2–3, 3–4, 4–5, and >5 (Muller 1979). For the entire study area, and for shore, soil, and bottom samples separately, the geoaccumulation index was negative indicating that the area is classified as uncontaminated. The negative geoaccumulation index indicates that there has not been accumulation of heavy metals over the time.
Conclusions In conclusion, ICP-ES was used to determine the baseline values of heavy metal concentrations in the Barakah area. The focus was on sites around the Barakah nuclear power plant which will start operating in 2017. The heavy metals considered in this study were Cd, Mo, Co, Cu, Pb, As, Zn, Ni, V, Cr, Mn, and Fe. Fifty-eight samples were collected across three areas: Sila, Barakah, and Jebel Dhannah. According to the grain size analysis, and Udden
Heavy metal concentrations (in ppm) in shore samples from BNPP in comparison with other international studies
References
Location
Cd
Mo Co Cu
Pb
As Zn
Ni
V Cr
This study (shore) Udechukwu et al. (2014) Ali et al. (2014) Satpathy et al. (2011) Saleh and Marie (2014) Saleh and Marie (2014)
UAE Malaysia
0.03 0.94
0.1 0.4 0.78 46.89
1.14 78.8
1.4 2.8 1023
2.1 35.54
3
Pakistan India Yemen (polluted site) Yemen (unpolluted site) USA Turkey China
0.4 BDL–9 5.5 2.8
Lu et al. (2005) Pekey (2006) Gao and Chen (2012)
BDL below detection limit
0.14 4.9 0.22
1.1 64.2 0.9–112.2 76 39.1 33 67.6 38.5
Mn Fe 3.7 26
45 BDL–69.2 6 4.4
68 34 0.7–91.7 BDL–14.3 42.2 8.6
171 42.1
19 102 34.7
60 930 131.1
19 74.3 101.4
33 40.7
975 71,400
200–14,800 199.8 100.5
Environ Sci Pollut Res Table 9 The safe limits of heavy metal contaminations set by the Dutch guidelines, with the average shale background values and the enrichments factors, geoaccumulation indices for the shore, soil, bottom areas as well as the average for all samples together
Safe limits set by the Dutch guidelines (ppm) (Lijzen et al. 2001) Average shale by Turekian and Wedepo (1961) Enrichment factor (shore) Enrichment factor (bottom)
Cd (ppm)
Mo (ppm)
Co (ppm)
Cu (ppm)
Pb (ppm)
As (ppm)
Zn (ppm)
Ni (ppm)
V (ppm)
Cr (ppm)
Mn (ppm)
Fe (ppm)
13
200
240
190
530
85
720
210
–
220
–
–
0.3
2.6
19
45
20
13
95
68
130
90
85
47200
4.03 3.43
1.89 3.08
1.11 0.97
0.84 0.92
2.75 2.47
5.21 4.97
1.42 1.2
1.47 2.22
1.12 1.17
2.01 2.08
1.48 1.24
1 1
Enrichment factor (soil)
1.44
3.06
1.23
0.84
1.09
1.09
1.09
2.04
0.88
1.74
1.31
1
Enrichment factor (all) Classification according to EF (shore) Classification according to EF (bottom) Classification according to EF (soil) Classification according to EF (all) Geoaccumulation index (shore)
2.12 III
2.91 II
1.17 II
0.85 I
1.55 II
2.31 IV
1.15 II
2 II
0.96 II
1.83 II
1.32 II
1 –
III
III
I
I
II
III
II
II
II
II
II
–
II
III
II
I
II
II
II
II
I
II
II
–
II
II
II
I
II
II
II
II
I
II
II
–
−4.2
−5.3
−6
−6.4
−4.7
−3.8
−5.7
−5.6
−6
−5.2
Geoaccumulation index (bottom) −3.9 Geoaccumulation index (soil) −3.2 Geoaccumulation index (all) −3.7
−4.1 −2.1 −3.3
−5.8 −3.5 −4.6
−5.8 −4 −5
−4.4 −3.6 −4.2
−3.41 −3.63 −3.6
−5.5 −3.6 −4.6
−4.6 −2.7 −3.8
−5.5 −3.9 −4.9
−4.7 −3 −3.9
−5.6 −5.4 −3.4 −4.4
−6.2 −5.7 −3.8 −4.8
EF enrichments factors, I-geo geoaccumulation indices
classifications, most of the samples were mainly composed of medium to coarse sand particles. The grain size distribution for all samples was symmetric with very slight skewness weighted to the right. The inverse relationship between the grain size and the contamination of heavy metals was observed for all metals. On average, soil samples were more contaminated than the bottom samples, which, in turn, were more contaminated than the shore samples. Overall, iron and manganese were present in the highest concentrations, while cadmium was present in the lowest concentrations. The concentrations reported in this study were less than those reported in three other studies for three other sites at the Arabian Gulf in the UAE. The special distributions for the Cr, Ni, Mn, V, Fe, Co, Mo, Zn, and Cu were maximal only in the center of the southern Barakah area. For V, Fe, Co, Mo, Zn, and Cu, there is slight contamination in the east and west sides. The spatial distribution maps of Pb, Cd, and As show a rather even distribution across the area. The enrichment factor for all sites considered in this study shows that the area has no enrichment to minor enrichment for all metals. The geoaccumulation indices for all sites and metals were negative indicating an uncontaminated area. The levels of heavy metals were orders of magnitudes smaller than the safe limits set by the
Dutch guidelines. The levels of heavy metals in the shore sediments, marine samples, and soil samples were the lowest in the UAE compared to all other international countries cited in this study. Thus, Barakah is an unpolluted area.
Acknowledgements The authors are grateful to the College of Graduate Studies at the United Arab Emirates University (UAEU) for covering all the costs associated with this research project.
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