Multivariate Analysis of Soil Heavy
Metals Pollution Along Irbid – Zarqa
Highway
Sana’a Odat
sanaa.owdat@yu.edu.jo
Department of Earth and Environmental Sciences, Faculty of Science, Yarmouk University.
Abstract: Problem statement: In this study, selected statistical methods (Correlation analysis, Principal component analysis and Multivariate analysis) were used to determine the heavy metal accumulation and its controlling factor and to identify the origin of these metals in soil samples collected from sediment of Irbid, Jordan. Approach: Twenty one soil samples were collected and analyzed in the laboratory for some heavy metals by atomic absorption Spectrophotometric method and multivariate statistical techniques. Results: The overall decreasing metal concentration order was: Fe > K > Mg > Mn >Na > Cu >Pb > Zn . Significantly positive correlation was only found between Cu, Mn and Zn in one hand and between PH and NO3 in the other hand. Factor analysis shows that sediment quality data consists of four major components accounting for 74.982% of cumulative variance of the contamination: Conclusion: This study concluded that the concentrations of all metals measured in Irbid can be considered to present a low level of contamination and that multivariate statistical analysis is a useful tool in understanding contaminants relationships.
Keywords: Environment; Soil Pollution; Correlation analysis, Principal component analysis and Multivariate analysis, Heavy metals, Irbid.
1. Introduction:
The Knowledge of the heavy metal accumulation in soil, the origin of these metals and their possible interactions with soil properties are priority objectives in many environmental monitoring. Statistical analysis procedures, as powerful tools, can provide such knowledge and assist the interpretation of environmental data (Tuncer and Balkas, 1993), (Sena and Poppi, 2002) ,(Einax and Soldt, 1999).
In recent times, the statistical methods (univariate or multivariate) have been applied widely to investigate heavy metal concentration, accumulation and distribution in soils. This is documented by a large number of reported studies which apply statistical methods to heavy metal accumulation in soils, e.g. Modak and others (1992), Arakel and Hangjun (1992), Ratha and others (1993), Chakrapani and Subramanian (1993), Cambier (1994) studied the behavior and distribution of heavy metals in soils using multivariate statistical methods ( Salman and Abu Rukah, 1999).
Methods of multivariate analysis have been widely used to identify pollution sources and to apportion natural vs. anthropogenic contribution (Facchinelli et al., 2001; Slavkovic´ et al., 2004; Mico et al., 2006; Luo et al., 2007).
The present study was carried out as a preliminary survey on soil contamination .The aims of this study were: (i) to determine concentrations of seven heavy metals (Cd, Cr, Cu, Mn, Ni, Pb and Zn) in soils of investigated area as a basis for future geochemical surveys; (ii) to reveal their relationships with both physicochemical characteristics of the soil (iii) to analyze their mutual relationships and (iv) to highlight their lithogenic or anthropogenic origin by both Principal component analysis and cluster analysis (CA).
2. Methodology
2.1. Study area:
The study area is located in the northern part of Jordan ,in the northeast part of province ,Irbid limited to between 35º 75´ – 36 º10 ´ N, and between 32º 25´ – 32 º45 ´E (Fig. 1).
Fig. 1: Location map of the study area.
2.2 Sample collection
21 soil samples were collected during January 2011 from different depth with an interval of 10 -50 cm. 1 kg of each soil sample were collected using a stainless steel spade and a plastic scoop, All Samples collected were stored in sealed polythene bags and transported to the laboratory for pre-treatment and analyses.
2.3 Laboratory analysis
The soil samples were air-dried, mechanically ground using a stainless steel roller and sieved to obtain <2
mm fraction. A 20-30 g sub sample was drawn from the bulk soil (<2 mm fraction) and reground to obtain <200 μm fraction using a mortar and pestle. This fine material was used to determine total metal content in soil.
The <2 mm fraction was used to determine pH (1: 5 soil water extract and particle size analysis using methods
outlined by Rayment and Higginson (1992). Soil samples were digested in a mixture of concentrated nitric acid (HNO3), concentrated Hydrochloric acid (HCl) and 27.5% hydrogen peroxide (H2O2) according to the USEPA
Method 3050B for the analysis of heavy metals (USEPA, 1996). A reagent blank was run for each set of six samples. The extracts were analyzed by atomic absorption spectrophotometer (Perkin Elmer, Model No. 2380). 2.4 Statistical analysis
Correlation analysis: In order to quantitively analyze and confirm the relationship among soil properties (pH, moisture content, EC, NO3 and PO4) and heavy metal content, a Pearson’s correlation analysis was applied to
Factor Analysis (FA) was adopted to assist the interpretation of elemental data. This powerful method allows identifying the different groups of metals greatly variable. An important feature of the scenery is that correlates and thus can be considered as having a similar behavior and common origin (Tahri et al., 2005). It should be
noted that parametric statistical tests require the data to be normally distributed. Therefore, it was checked if the data came from a population with normal distribution by applying Shapiro-Wilk’s test (significance level=0.05). The non-normal data were transferred logarithmically to ensure normal distribution. All the statistical analysis were performed using SPSS for Windows (release Ver.15, Inc, Chicago, IL) (Qishlaqi and Moore ,2010) Multivariate analysis (MA) was performed using SPSS 15 software for Windows (SPSS 15, 2010). Agglomeration schedule of CA based on Pearson correlation as an amalgamation rule and the squared Euclidean distance as a measure of the proximity between samples are shown in Table 4. The results obtained by CA are presented by dendrogram where the distance axis represents the degree of association between groups of variables, i.e. the lower the value on the axis, the more significant the association (Fig. 2). As can be seen Mg, K, Cu, Mn, Na, Pb and Zn are grouped into one branch, while Cd is isolated.
Results and discussion:
A close look at the Table 1 shows that high mean metal concentrations were found for all the metals. Average Fe concentration was 38996.14ppm followed by K, Mg, Mn, Na, Cu, Pb and Zn at 1353.33, 1157.25, 684.16, 671.47, 330.79, 145.56 and 116.16 ppm, respectively. The variability in range of all the metal distributions as compared with their means respectively is an indication of a pollution of the sediment with that metal ion. The decreasing trend of average metal levels was as follows:
Fe > K > Mg > Mn >Na > Cu >Pb > Zn
The mean and median were used as estimates of central tendency. Standard errors of the mean were all small. The distribution of original data for Fe, Zn and Cu are positively skewed while K, Mg, Mn, Na and Pb were negatively skewed. Despite this skewness, the mean and median values are quite similar for Cu with medians having smaller values than means. This indicates that measures of central tendency are not dominated by outliers in the distribution. The effect of extreme outliers is greater for the distribution of K, Mg, Mn, Na, Cu, Pb and Zn values. In each case, test for normality were conducted using the test based on analysis of the combined effects of skewness and kurtosis. The substantial difference in the symmetric parameters in the case of Zn, Cu and Cd indicated a non-normal distribution, thus supporting a possibility of random infiltration of the metals from some anthropogenic sources. Large standard deviations in the case of Fe, K, Mg, Mn, Na, Cu, Pb and Zn levels revealed their randomly fluctuating concentration levels in the sediment.
They are important factors controlling the mobility, availability and distribution of heavy metals. These are PH ,Moisture content, EC, PO4 and NO3. This fact supported by using Pearson's correlation coefficients
analysis that can be used to measure the degree of correlation between the heavy metal concentration and physico chemical parameters in soil samples collected from Irbid area. The correlation coefficient are shown in ( Table 2) . Highly correlation exists between Cu, Mn and Zn in one hand and between PH and NO3 in the other hand. pH is considered as the most important mechanisms regulating the behavior of heavy metal . Basic sols of arid and Semi-arid regions are considered as an excellent sink for pb (Adriano, 1986). According to Alloway (1990. These high correlations support the idea that anthropogenic activities such as traffic movement are the main source of heavy metals in soils.
In addition to correlation analysis, Factor Analysis (FA) of the studied road deposited sediment samples was performed in order to get an overall impression about assembling the samples in a multidimensional space defined by the chosen metals. The FA has emerged as a useful tool for better understanding of the relationships among the variables (e.g., metal concentrations in the present study) and for revealing groups (or clusters) that are mutually correlated within a data body. This procedure reduces overall dimensionality of the linearly correlated data by using a smaller number of new independent variables, called Varifactor (VF), each of which is a linear combination of originally correlated variables. The rotated Principal Component Loadings (Factors) are given in Table 3.
Four Factor Components (Eigen values>1) emerged accounting for 74.982% of cumulative variance. The first Factor loading with 33.696% variance showed higher loadings for K, Na, Fe Pb and Cu and moderate loading for Mn. This factor represents pollution caused by the behavior of the metals within the group. This factor represents pollution caused by emissions from traffic (Banerjee,2003).High levels of Pb in soil samples have been recognized for a long time to be linked mainly to traffic activities due to this utilization of leaded gasoline (Yongming Pelxuan,Junji & Posmentier,2006). The second Factor loading with 16.756% of total variance had higher loadings for Zn, PH and Mn. These could be conceived to mainly originate from domestic waste discharged in some of the areas and decomposition of vehicle and machine scraps apart from their natural occurrence. These might be due to automobiles and paints that are very important sources of Pb contamination in urban environments (Baptista Neto et al., 2000). The sediment physico-chemical properties such as pH could
Factor had higher loadings for moisture content, NO3, PO4 and Mg at 13.099% of total variance, while the
fourth Factor loading at 11.441% of total variance showed higher loadings for EC and moisture content. This factor represents the physico chemical source of the a variability and this factor has no significance.
Agglomeration schedule of CA based on Pearson correlation as an amalgamation rule and the squared Euclidean distance as a measure of the proximity between samples are shown in Table 4. The results obtained by CA are presented by dendrogram where the distance axis represents the degree of association betweengroups of variables, i.e. the lower the value on the axis, the more significant the association (Fig. 2). As can be seen Cr, Cu, Mn, Ni, Pb and Zn are grouped into one branch, while Cd is isolated.
The application of CA showed the attribution of the metals in two factors: the first one contained elements already interpreted as lithogenic and the second cluster discriminated the anthropogenic Cd. Results obtained by applying this multivariate method are consistent with those obtained by correlation analysis of heavy metal contents with soil physicochemical characteristics.
Table 1: Basic statistical parameters for the distribution of heavy metals in the investigated soil samples.
Variables Min Max Mean SD Median Skewness Kurtosis
PH 7.2 8.2 7.75 0.211 7.80 -0.35 1.50
M.C % 7.6 30.8 18.97 6.22 18.30 0.86 -0.51
EC µs/cm 174 1908 334.14 369.76 243 4.23 18.68
PO4% 5.78 34.86 12.96 9.05 1.85 1.85 2.18
NO3% 3.34 4.72 3.60 0.28 3.58 3.43 14.22
Cu (ppm) 7.51 50.21 27.72 8.84 27.10 0.13 1.49
Fe (ppm) 9567.95 25781.46 21711.18 4613.62 23379.58 -2.15 4.59
Cr (ppm) 23.85 124.23 67.40 23.23 64.66 0.42 0.70
Cd (ppm) 7.51 17.22 11.61 2.72 11.16 0.33 -1.07
Mn (ppm) 136.65 1115.28 709.07 320.87 746.13 -0.50 -0.93
Co (ppm) 11.16 1544.24 506.02 472.80 353.87 1.15 0.46
Pb (ppm) 18.51 79.99 47.95 16.96 51.22 0.12 -0.69
Zn (ppm) 113.30 325.22 193.33 46.80 193.67 1.08 2.21
Table 2: Pearson correlation coefficient matrix for heavy metals in the investigated soil samples.
Cu Fe K Mg Mn Na Pb Zn PH M. C PO4 NO3 EC
Cu
1.00 Fe
-0.52** 1.00 K
-0.53** 0.90** 1.00 Mg
-0.09 0.21 0.14 1.00 Mn
-.26 0.57** 0.54** 0.42* 1.00 Na
-0.48* 0.85** 0.88** 0.16 0.52** 1.00 Pb
0.80**
-0.58** -0.65** -0.18 -0.36 -0.65** 1.00
Zn
0.76** -0.18 -0.21 0.07 0.02 -0.24 0.42* 1.00 PH
-0.42* -0.09 -0.13 -0.04 -0.11 0.01 -0.12
-0.59** 1.00 EC
0.23 -0.15 -0.11 -0.23 0.30 -0.14 0.06 0.16
-0.05 1.00 PO4
-0.24 -0.01 0.18 -0.33 -0.24 0.11 -0.23 -0.04 0.03 0.112 1.00 NO3
0.01 0.03 -0.02 0.20 0.34* 0.19 -0.09 -0.02 0.17 -0.16 -0.21 1.00 M. C
-0.133 0.008 0.153 -0.67 -0.67 -0.20 -0.11 0.01 -0.18
0.37 0.112 -0.16 1.00
* Correlation is significant at the 0.05 level (1-tailed). **Correlation is significant at the 0.01 level (1-tailed).
Table 3.Varimax normalized rotated principal component loadings of selected metals and sediment properties.
Parameters Factor 1 Factor 2 Factor 3 Factor 4
M.C 0.066 0.211 0.597 0.557
EC -0.156 0.350 0.314 0.682
PH 0.140 -0.659 -0.397 0.396
PO4 0.120 -0.410 0.579 -0.230
NO3 0.153 0.217 -0.594 0.276
Cu -0.791 0.488 -0.034 -0.194
Fe 0.871 0.238 0.066 -0.216
K 0.888 0.198 0.249 -0.184
Mg 0.269 0.376 -0.533 -0.054
Mn 0.602 0.614 -0.103 0.361
Na 0.878 0.141 -0.041 -0.254
Pb -0.834 0.148 -0.115 -0.081
Zn -0.467 0.684 0.157 -0.322
Eigen value 4.379 2.178 1.703 1.487
Total Variance % 33.686 16.756 13.099 11.441
Table 4: Agglomeration schedule of the cluster analysis based on the correlation coefficients.
stage Cluster combine
Coefficients Stage Cluster First Appears
Next stage
Cluster 1 Cluster 2 Cluster 1 Cluster 2
1 2 3 0.900 0 0 2
2 2 6 0.865 1 0 5
3 1 7 0.798 0 0 4
4 1 8 0.586 3 0 6
5 2 5 0.545 2 0 6
6 1 2 0.390 4 5 7
7 1 4 0.181 6 0 0
Rescaled Distance Cluster Combine
C A S E 0 5 10 15 20 25
Label Num +---+---+---+---+---+ Fe 2
K 3
Na 6
Mn 5
Cu 1
Pb 7
Zn 8
Mg 4
Fig. 2. Dendrogram derived from the hierarchial cluster analysis of heavy metals content in analyzed soils.
CONCLUSION
In this study, correlation and factor analyses were used for determining the environmental quality of sediments in terms of heavy metal accumulation and some soil properties. Correlation analysis shows a strong relationship between PH, EC, PO4, NO3 and moisture content on heavy metal accumulation. Principal component analysis summarizes (reduces) the dataset into four major components representing the different sources of the elements. This study generally concludes that the statistical methods can be a strong tool for monitoring of current environmental quality of sediments in terms of heavy metal accumulation and for predicating the future soil contamination.
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