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ICMES2018 Spatial Distribution and Mapping of Heavy Metals in Agricultural Soils of the region (Gharb, )

N. El Khodrania,d*, S. Omraniab, A. Zouahric, A. Douaikc , H. Iaaichc, A. Yahyaouia, M. Fekhaouid aLaboratory of Zoology and General Biology, Faculty of Sciences, Mohammed V University, , Morocco. bLaboratory CERNE2D, Faculty of Sciences, Mohammed V University, Rabat, Morocco. cResearch Unit on Environment and Conservation of Natural Resources, Regional Center of Rabat, National Institute of Agricultural Research (INRA),Rabat, Morocco. dScientific Institute, Mohammed V University, Rabat,Morocco.

Abstract

Our study has the purpose of mapping the vulnerability of the soils of the Sfafaa region by the contamination of heavy metals through their monitoring. A campaign of soil sampling and analyzes in the laboratory has been realised. The approach followed is the spatial analysis using Geographic Information System (GIS). The data have been spatialized through the GIS in order to establish the relationship between the levels of contamination observed for each species and their spatial distribution. The total concentrations of heavy metals were assessed for samples from seventeen sites representative of the agricultural lands, selected along the Beht River, over a period from March to June of 2013 and 2014. Concentrations of eight elements were determined: Mn, Cd, Cr, Cu, Ni, Pb, Zn and Fe. The results show that Ni, Cr, and Cd present high concentrations in plots irrigated by groundwater. Cr concentration is the most important (319.7 ppm), exceeding the standard for a normal soil (100 ppm) while the average content of Zn is 89.8 ppm, below the standards for a normal soil (200 ppm). In general, the results showed a metal contamination that exceeds the standards for Ni (62.7 mg/kg in average and values ranging from 21.6 to 102.4 mg/kg), Cd (1.9 mg/kg in average and values ranging from 1.3 to 3.2 mg/kg), and Cr (274.8 mg/kg in average and values ranging from 200.7 to 327.4 mg/kg), due to the overuse of fertilizers. These results confirm the impact of the agriculture intensification on the quality of the soils in the Gharb region. © 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the International Conference on Materials and Environmental Science, ICMES 2018.

Keywords: Agricultural pollution, Heavy metals, PCA, Soil quality, Sfafaa, Sidi Slimane.

* Corresponding author. Tel.: +212663008685. E-mail address: [email protected]

2214-7853 © 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the International Conference on Materials and Environmental Science, ICMES 2018. El Khodrani et al / Materials Today: Proceedings 13 (2019) 832–840 833

1. Introduction

In the arid and semi-arid , where precipitations are scarce and irregular, there is a yearly growing water deficit. Consequently, irrigation becomes necessary to ensure agricultural production up to meet the food needs of the growing population. The agricultural development in these regions thus depends on irrigation using groundwater [1].These ground waters are rich in organic matter and nutrients [2]. However, they contain high rates of unneeded chemical elements, particularly heavy metals [3-5], which are source of risk for farmers, soils, plants, consumers, and the and the environment. The heavy metals are naturally present in the soil. Some heavy metals are essential and beneficial to living organisms such as manganese (Mn), zinc (Zn), boron (B), and copper (Cu) for which concentrations in the soil and cattle feed must be maintained at a certain level to allow normal growth, development, and reproduction. However, if levels are too high, toxicity mechanisms can be developed [6].Some heavy metals such as copper (Cu) and zinc (Zn) become toxic for plants at lesser concentrations than for humans [7-10]. Plants are also an obstacle that mitigates the potential risks for health [11, 12]. During the last decades, the problems associated with the increase in the levels of heavy metals and their constant presence in the environment interested researchers [13, 14]. Due to their persistence, toxicity and non-biodegradable nature, heavy metals are regarded as a serious concern in relation to human health [15, 16]. Considering the rising level of heavy metals in soils spatial studies are conducted to assess heavy metal contents in soils throughout the world. Spatial distribution of heavy metals in agricultural soils is correlated with their natural sources and anthropogenic inputs [17]. A combination of multivariate statistics (such as Principal component analysis) and spatial analysis using mapping techniques is an important tool for identifying pollution characteristics of heavy metals in soils and distinguishing their natural sources and anthropogenic inputs [18]. The objective of this study is to map the vulnerability of the soils of the Sfafaa region to contamination of heavy metals through their monitoring.

2. Material and Methods

2.1. Study area The study area is a part of the Sidi Slimane Province ( Rabat-Sale- region).The geographic coordinates are 34°15′0'' N for latitude and 6° 9′36'' W for longitude. It is limited to the North by the Province of (Rabat-Sale-Kenitra region), in the South-East by the rural commune of (Province of Sidi Slimane), and to the West by the rural commune of Kecybia (Province of Sidi Slimane). The rural commune of Sfafaa extends over approximately 197km2. With fertile soils, a temperate and humid climate and abundant water resources, the Sfafaa region is an important agricultural zone. It is a natural collector of surface waters. Its flat morphology (a plain of altitude lower than 12 m) does not favor the evacuation of the river flood waters to the sea. There are two irrigated sub-zones: Those irrigated by the Beht River water and those by the Sebou River water [19]. The meteorological station of Sidi Slimane [19] records minimum precipitation compared to the other stations in the Rabat-Sale-Kenitra region, because of the altitude and continentality effects. The most rainy months are November, December, and January and the driest are June, July, and August. July and August are the hottest months while the coolest are December, January, and February. The dry period lasts from May to September.

2.2. Methodology Soil sampling was done during the period of March to June during two years (2013 and 2014). The sampling is based on a grid developed using a topographic map of the study area. The samples were taken from 6 different zones: A, B, C, D, E, and F (Fig. 1). The GPS coordinates of each sample taken have been recorded for the development of the maps. The system of cartographic projection used is the conical Lambert consistent Zone 1 of Morocco. Soil samples are of 300 to 500g, collected from the 0 to 20cm horizon. They were dried in the open air for a week then grinded and sieved to 2mm and 0.2mm before undertaking chemical analysis at the National Institute of Agronomic Research laboratories in Rabat. The analysis of heavy metals (Cd, Cu, Cr, Mn, Pb, Ni, Fe and Zn) contents were done using an Atomic Absorption Spectrophotometer (AAS) [20].The dried sample is extracted with a hydrochloric/nitric acid mixture by standing for 16h at room temperature, followed by boiling under reflux for 2h.The extract is then clarified and made up to volume with nitric acid. The trace metal content of the extract can be determined in accordance with [21]. 834 El Khodrani et al / Materials Today: Proceedings 13 (2019) 832–840

Fig. 1. Study area and prospected soils and wells.

2.3. Statistical analysis First of all, a matrix of Pearson correlation coefficients between any two variables was computed and the coefficients were tested for their statistical significance. We then used Principal Component Analysis [22], a multivariate analysis technique that provides an excellent means for gaining useful information from data sets with many variables [23]. In particular, PCA can aid in the compression and classifcation of data. The purpose is to reduce the dimensionality of a data set by finding a new set of variables, smaller than the original set of variables, which nonetheless retains most of the sample’s variance. Success relies on the presence of correlations among at least some of the original variables; otherwise the number of new variables will be almost the same as the number of original variables. The new variables, called principal components, are uncorrelated, and are ordered by the fraction of the total variance each retains. The PCA was performed in this study using SPSS 20. Based on the results of analyzes and on the basis of the GPS coordinates, the maps of spatial variability of different elements were developed using the Inverse Distance Weighting (IDW) spatial interpolation approach . For this, we used the ArcGIS © version 10 software geographic information system (GIS) and its extension Geostatistical Analyst. The IDW method allows giving a value to a space not known from points to known values, and this on the basis of reverse weight to the distances [24].

3. Results and discussion

3.1. Heavy metals analysis The results of the measurement of the different heavy metals in the soils are presented in Table 1. The concentration and average for the heavy metals determined in the soil are given in Table 1. The percentage of exceeding has been calculated for all of the heavy metals as the ratio between the number of samples which exceed the limits to the total number of samples. a. Cadmium The levels of soil cadmium content show that the F zone, which contains the sites S15, S16,and S17, had Cd concentrations ranging between 2 and 3.19 mg/kg, with an average of 2.47mg/kg (Table 1) .It is followed by the E zone (sites S12,S13 ,and S14) with concentrations ranging between 2.06 and 2.42 mg/kg, and an average of 2.28mg/kg. The concentrations of Cd in the E and F zones exceed the standards of the European Union (2mg/kg) [26, 27]. The Cd contamination of the soils of E and F zones could be anthropogenic through the agricultural activities [28]. The spatial distribution of Cd concentrations (Fig. 2) shows that the highest concentrations are located in the north-western part of the Sfafaa region (F and E zones). El Khodrani et al / Materials Today: Proceedings 13 (2019) 832–840 835

b. Copper The distribution map of soil copper shows that the D zone , containing the sites S5 and S11, has levels of ranging between 19.6 and 92 mg/kg (Table 1), with a mean of 55.80 mg/kg; however, these levels did not exceed the standards of the European Union (100 mg/kg). The spatial distribution (Fig. 3) shows that the Cu concentration in the entire region is not high. Nevertheless, the maximum values were found in the North-Western part and to the South of the region (D and F zones).

c. Chromium The distribution map of soil chromium concentrations shows that the C zone, which contains the sites S9 and S10, had levels ranging from 212.67 to 327.37 mg/kg, and an average of 319.7 mg/kg (Table 1).

Table 1. Range, average (mg/kg) and the percentage of sites which exceed the limits of international standards for the Heavy Metals of agricultural soils, Sfafaa Region , Morocco. Zones Standard Na =17 (mg/l) A B C D E F Range 1.33-1.77 1.40-1.60 1.79-1.80 1.43-1.53 2.06-2.42 2.00-3.19 1.4 Mean 1.64 1.5 1.8 1.48 2.28 2.47 Cd % Excessb 75 50 100 100 100 100 Range 225.77- 324.19 200.74-260 312.00-327.37 252.67-307.65 223.11-264.58 256.22-303.65 64 Mean 269.92 230.37 319.7 280.16 241.52 283.41 Cr % Excessb 100 100 100 100 100 100

Range 19.50-61.25 22-24 23.6-25.0 19.60-92.00 11.52-23.40 12.60-106.40 100 Mean 40.03 23 24.3 55.80 19.38 49.67 Cu % Excessb 0 0 0 0 0 0

Range 11600-20800 24000-26000 25500-26000 11400-17000 22200-24000 23200-30500 - Fe Mean 18300 25000 25700 14200 23100 27000 % ------Excessb Range 40.30-106.40 20-22 30.00-33.34 28-160 28.80-51.60 18.00-212.80 - Mean 73.42 21 31.67 94 42.40 116.94 Mn % Excessb ------Range 42.52-95.58 25.00-35.46 80.00-84.16 48.67-65.26 56.58-76.36 21.62-102.43 50 Mean 58.76 30.23 82.08 56.96 64.08 63.09 Ni % Excessb 50 0 100 50 100 66.67

Range 23.92-34.62 30.00-36.40 24.20-35.12 17.65-31.67 36.28-38.65 36.26-43.24 70 Mean 30.66 33.20 29.66 24.66 37.5 40.03 Pb % Excessb 0 0 0 0 0 0

Range 33.22-56.62 35.00-36.20 59.64-60.00 39.95-43.33 76.63-83.00 73.18-106.43 200 Mean 45.72 35.60 59.82 41.64 79.84 89.81 Zn % Excessb 0 0 0 0 0 0

aN: total number of samples in the six zones. % excessb: percentage of sites that exceed the standards [25]. 836 El Khodrani et al / Materials Today: Proceedings 13 (2019) 832–840

It is followed by the F zone for which levels vary between 256.22 and 303.56 mg/kg and an average of 283.41 mg/kg. The sites from both zones exceed the standards of the European Union (100mg/kg).The contamination of these soils by the Cr is anthropogenic through the industrial activities. The distribution map (Fig. 4) shows a high level of Cr in the entire region. The maximal values were found in the South West, North West, and South parts of the region (C, D, and F zones). Indeed, 100% of sampling sites exceed the limit values (100 mg/kg) advocated for soil.

Fig. 2. Distribution map of soil cadmium concentrations (mg/kg).

Fig. 3. Distribution map of soil copper concentrations (mg/kg).

Fig. 4. Distribution map of soil chromium concentrations (mg/kg). d. Nickel The distribution map of soil nickel levels shows that the C zone, had contents that range between 80 and 84.16 mg/kg, with a mean of 82.08 mg/kg .It is followed by the E zone with concentrations ranging from 56.58 to El Khodrani et al / Materials Today: Proceedings 13 (2019) 832–840 837

76.36mg/kg, and a mean of 64.08 mg/kg, and the F zone with contents ranging from 21.62 mg/kg to 102,43mg/kg, and an average of 63.09 mg/kg (Table 1). These 3 zones exceed the standards of the European Union (50 mg/kg). The contamination of these soils by the Ni is anthropogenic through the agricultural activities [28]. As shown in the distribution map (Fig. 5), the concentrations of Ni are much higher in the South West, South East and North West parts of the region (C,E ,and F zones). There is ,in fact, 61.2% of sampling sites, that exceed the limit values (50 mg/kg).

Fig. 5. Distribution map of soil nickel concentrations (mg/kg). e. Iron The distribution map of soil iron contents shows that the F zone had levels that vary between 23183.87 and 30497.53 mg/kg, with an average of 27000 mg/kg. It is followed by the C zone with levels ranging between 25500 and 26043 mg/kg, and an average is 25700 mg/kg (Table 1). These zones exceed the limit values of reference (11000 mg/kg) of the Canadian Council for the protection of the Environment [25]. The iron is very abundant in the earth's crust, and is part of the constituents of the soil in the form of oxides. Moreover, it is difficult to estimate the background values in natural environments. In addition to this form of oxide (iron), all of these oxides are part of the essential factors affecting the mobility of metal and organic contaminants in the soil [29]. The evolution of the spatial distribution of the levels of iron (Fig. 6), is changing from North West to South West.

Fig. 6. Distribution map of soil iron concentrations (mg/kg).

3.2. Principal component analysis The first principal component (PC1) contains 56.3% of the total information contained in all of the variables and the 2nd component (PC2) contains 21.2% of this information (Table 2). These first two components contains, alone, more than three-quarters (77.5%) of the information. 838 El Khodrani et al / Materials Today: Proceedings 13 (2019) 832–840

Table 2. Eigen values and inertia corresponding to the first 3 axes.

Component Eigen values Variability (%) (%) Cumulated 1 3.940 56.3 56.3 2 1.486 21.2 77.5 3 0.855 12.2 89.7

The results in Table 3 and figure 7 allowed a first typology of the different variables according to their affinities and their groupings on the first two principal components from their contribution.

A GII

GIII GI

C B Eigen values 4

3

2

1

0 PC1 (56,3%) PC2 (21,2%) PC3 (12,2%)

Fig. 7. Graphical approach of the PCA to the variables and the studied sites. A: graphical representation of the analysis in PCA countries and in the soil. Factorial card of the variables. B: axis of inertia, C: factorial card of the studied sites. The first principal component (PC1) is strongly and positively correlated with cadmium (r=0.932), lead (r=0.909), electrical conductivity of soil (r=0.805), and clay (r=0.706); whereas it is strongly but negatively correlated to sand (r=-0.918).It defines a gradient of contamination defines by the soil nature of the stations. This component therefore opposed the samples of sandy nature to those with, high values of clay, cadmium, lead, and electrical conductivity.

El Khodrani et al / Materials Today: Proceedings 13 (2019) 832–840 839

Table 3. Correlations (r) of the original variables with the principal components.

Variables PC1 PC2

Cd 0.923 0.228 Pb 0.909 -0.200 Ni 0.372 0.718 Cr -0.329 0.848 ECs 0.805 0.040 Clay 0.706 -0.429 Sand -0.918 -0.001

The second principal component (PC2) is strongly and positively correlated to chromium (r =0.848) and nickel (r=0.718). It indicates the samples with high concentrations of those two heavy metals. This analysis has allowed the structuring of three different groups (GI, GII, and GIII) by projection on the PC1- PC2 plan (Fig. 7A):

 Group I defined by the PC1 axis (56.3% of the variance) characterized by high levels of Cd, Pb, ECs and clay.  Group II defined by the PC2 axis (21.2% of the variance) characterized by high values of Cr and Ni.  Group III present on the negative side of the PC1 axis characterized by the sandy nature. The interpretation of result in the PC1XPC2 plan of the soils of the studied sites, allow to find various and localized process, which can be anthropogenic due to agricultural activities. The sites S13, S14 and S16 (E and F zones) are characterized by high levels of lead, cadmium, clay, and EC and low levels of sand while the sites S9 and S10 (C zone) are characterized by high concentrations of nickel and chromium.

The spatial typology defined by this approach has helped to identify a metal contamination related to the nature of soils of sites of this study. In fact, the clayey soils retain strongly metals due to several factors such as the large surface area, the sorption, co-precipitation, the complex formation, etc [30]; in contrast, the sandy soils don’t keep the heavy metals.

4. Conclusion

The analysis of eight elements (cadmium, chromium, copper, iron, manganese, nickel, lead, and zinc) from soils of the region of Sfafaa has shown significant spatial distributions the six zones. According to total values of the contamination factor allows, the order of classification of the different stations in term of contamination, is the following: C> F > E > A > D > B. Regarding the importance of the heavy metals for the whole study area, they are classified in the following order: Cr>Cd>Ni>Zn>Cu>Pb. The manganese and iron were not taken into account, given that there are no guide values. The study of the heavy metals and in comparison with the standards of the Canadian Council for the Protection of the Environment (CCME), helped to identify an exceeding of the standard for chromium (100% of sites), cadmium (87.5% of sites), and nickel (61.1% of sites).The spatial typology of this contamination has been identified by the use of a multivariate statistical method (PCA). These results suggest that the origin of the contamination by these elements, are predominantly anthropogenic, due to agricultural activities (for Cd and Ni), and industrial activities (for Cr). The heavy metals kept by soil, can be a source of pollution of the environment, and pose ecotoxicological problem in the long term, but may also pose a problem for human health in the region of Sfafaa. In addition, if the main source is the use of chemical fertilizers, their accumulation establish a health risk for the population via the consumption of well waters without prior treatment but also there could be risks for remobilization of these metals, which may be available for the food plants (another source of contamination in more of the water).

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Acknowledgments-

We want to thank all the people who have helped us in the field and in the laboratory throughout this work.

References

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