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Multivariate Statistics and Contamination Factor to Identify Trace Elements Pollution in Soil Around Gerga City, Egypt Ibrahim Said, Salman A

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Multivariate Statistics and Contamination Factor to Identify Trace Elements Pollution in Soil Around Gerga City, Egypt Ibrahim Said, Salman A Said et al. Bulletin of the National Research Centre (2019) 43:43 Bulletin of the National https://doi.org/10.1186/s42269-019-0081-2 Research Centre RESEARCH Open Access Multivariate statistics and contamination factor to identify trace elements pollution in soil around Gerga City, Egypt Ibrahim Said, Salman A. Salman and Ahmed A. Elnazer* Abstract Background: Gerga district contains different activities, urban, agriculture, and industry, which can impact adversely on the soil quality. Sixteen samples of the agricultural soil (0–30-cm depth) were collected to investigate the pollution of soil with trace elements (Co, Ni, Pb, and Mn). The statistical techniques were applied to discriminate the sources of these elements. Results: The studied soil ranged from uncontaminated to moderately contaminated with the studied trace elements based on the contamination factor index. The statistical analyses indicated the anthropogenic source of Co, Ni, and Pb as well as the natural source of Mn. Conclusions: The statistical analyses assisted in the discrimination of natural and anthropogenic sources of trace elements in the investigated soil samples. Mn is mainly of natural origin, affected by pedogenic factor, whereas traffic emissions and phosphate fertilizer, as well as domestic activities, are relevant sources of Co, Ni, and Pb elements in the studied soil. Consequently, the recommendation is periodic environmental monitoring and minimizing the fertilization rate. Keywords: Trace elements, Soil contamination, Multivariate statistics, Gerga, Upper Egypt Introduction Unfortunately, many researchers recorded soil pollution Trace element contamination has received considerable at- with trace elements in many parts of Egypt: Aswan (Darwish tention due to their negative impact on the human health and Pöllmann 2015), Assiut (Asmoay 2017), Helwan (Said and environment (Adriano 2001). The natural and an- 2015), Kafr El-Sheikh (Naggar et al. 2014), Sohag (Salman thropogenic inputs enrich the soil with trace elements. Pol- 2013), and (Salman et al. 2017). Salman et al. 2017 pointed lution occurs when an element quantity excess its out the accumulation of trace elements in the food chain background concentrations (Kabata-Pendias 2010). The ele- (Egyptian clover) in Sohag. The presence of such metals in ments that come from anthropogenic sources are generally the food chain can cause an adverse impact on human be- more bioavailable than pedogenic and lithogenic ones ings (Karim et al. 2015). (Kabata-Pendias 1993 and Kobierski and Many researchers used statistical analyses as powerful Dabkowska-Naskret 2012). Pedogenic metals are of litho- tools in geo-environmental studies (Zhang et al. 2009; genic and anthropogenic origin, but their distribution in soil Sundaray et al. 2011; Ming-Kai et al. 2013; Kelepertzis profiles changes due to mineral transformation and other 2014; Simu et al. 2016; and Guo et al. 2017). Statistical pedogenic processes (Kobierski and Dabkowska-Naskret analysis is a useful tool for assessing the possible sources 2012). Understanding the pollutant’s sources and distribu- of pollutants because it allows for consideration of tionsisamongthemostcriticalconcerns for environmental cause-and-effect relationships, highlighting exceedances. management and decision-making (Sun et al. 2013). Contamination indices also help in the understanding of ecological status. Contamination factor (CF) is employed to evaluate the level of soil contamination and to infer * Correspondence: [email protected] Geological Sciences Department, National Research Centre, POB: 12622, 33 El anthropogenic inputs from the natural one. In the Bohouth St. (former El Tahrir St.) - Dokki, Giza, Egypt present study, we applied both statistical analysis and © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Said et al. Bulletin of the National Research Centre (2019) 43:43 Page 2 of 6 contamination factor in an attempt to distinguish an- cultivated lands of the study area. The traffic emissions thropic sources from the natural one and to evaluate the besides application of fertilizers lead to the accumulation level of soil contamination. of trace elements which threaten agricultural soils and in turn the human health. Materials and methods Sixteen agricultural soil samples (0–30-cm depth) were Gerga district is one of the most important agricultural collected randomly as accessible (Fig. 1). ArcGIS10.2 areas in Sohag governorate, Egypt. It contains one of the (Desktop 2014) was used to prepare the sample map. biggest sugar factories in Egypt. This industry was re- Total trace elements were determined by digestion with ported as a pollution source of soil with different chemi- 3 HCl:1 HNO3 mixture and analyzed using the atomic cals (Zaki et al. 2015). It extends between longitudes 31° absorption spectrophotometer (Buck Scientific 205AA). 46′–31° 55′ E and latitudes 26° 12′–26° 22′ N (Fig. 1). The pH in soil was measured in 1:1 soil to water ratio Geologically, it consists of Quaternary deposits and by using the HANNA (HI93300) combined electrode. floodplain sediments of the River Nile (Said 1990). Due Calcium carbonate percentage (CaCO3%) and phosphor- to soil degradation since the construction of the High ous (P) were estimated by the titrimetric and colorimet- Dam in 1968, more fertilizer has been applied to restore ric methods, respectively. Soil organic matter percentage soil fertility. P and N fertilizers are the major used agro- (SOM %) was determined according to the modified chemicals in the Gerga area. Roadways are crossing the Walkley and Black method (USDA 2004). Fig. 1 Map of the study area showing soil sampling sites Said et al. Bulletin of the National Research Centre (2019) 43:43 Page 3 of 6 Contamination level was assessed using the contamin- adequacy (KMO MSA) for the set of variables included ation factor (CF) recognized in (Hakanson 1980) based in the analysis was 0.712. It exceeds the minimum re- on the following equation: quirement of 0.50 for overall MSA, with Bartlett’s test of sphericity (0.00), be less than the level of significance CF ¼ Cs=Cb (Tabachnick and Fidell 2007). PCA was developed based on the Ward method using squared Euclidean distances where Cs is the concentration of metal in the study (z-transformation) as a measure of similarity between samples and Cb is the baseline concentration. Baseline samples based on their element content (Co, Ni, Pb, Mn, concentrations as reported by (Turekian and Wedepohl and P). The clustering results were provided in a hier- 1961) were used as Cb during this study (Mn = 850 ppm, archical cluster (dendrogram). Co = 19 ppm, Ni = 68 ppm, and Pb = 20 ppm). Hakanson (1980) classified the contamination factor as follows: CF < 1 low, 1 to < 3 moderate, 3 to < 6 considerable, and > 6 Results high contamination. Table 1 illustrates the statistical summary of the analyt- Although the number of samples is relatively low, clus- ical data. The measured sand, silt, clay, pH, CaCO3, OM, ter analysis (CA) and principal component analysis P, Co, Ni, Pb, and Mn values were 68.8%, 12.4%, 18.9%, (PCA) were conducted to take into account the compli- 8.5, 2.8%, 2.1%, 0.3%, 12.6 ppm, 46.8 ppm, 11.9 ppm, and cated environmental situation of the study area. Several 990.6 ppm, respectively. P, Co, Ni, and Pb showed sources of contamination and several processes are influ- marked flocculation (C.V = 92.7%, 82.9%, 52.8%, and encing the occurrence of trace elements in the investi- 92.2% respectively), whereas Mn exhibited a uniform gated soil. The minimum sample size recommended for distribution pattern (C.V = 15.4%). The calculated CF for conducting principle component is debatable in the lit- Co, Ni, Pb, and Mn were around 0.7, 0.7, 0.6, and 1.2, erature. Generally, as the sample size increases, sampling respectively (Table 2). Generally, the studied samples error is reduced. Survey of literature relating to the were low contaminated with Co, Ni, and Pb and moder- minimum sample size used in principle component ately contaminated with Mn according to CF. studies exhibited wide range of variation from 2 or less Since the studied soil was found to be relatively to 20 times the number of variables (Lingard and Row- enriched in some trace elements compared to those re- linson 2006). However, it is fair to say that no absolute ported in Turekian and Wedepohl )1961(, PCA was per- rules can exist (Lingard and Rowlinson 2006). MacCal- formed to identify natural and anthropogenic sources. lum et al. (1999) report that when data are strong the Two principal components were extracted from the in- impact of sample size is greatly reduced. Strong data is vestigated soil data, explaining 75.94% of the variance data in which item commonalities that are consistently (Table 3). The first component (PC1) explains 48.74% high factors exhibit high loadings (≥ 0.8) on a substantial and accounts for the majority of the variance in the number of items (at least three or four) and the number dataset and includes the elements (Co, Ni, Pb, and P) of of factors is small. According to Guadagnoli and Velicer high variation coefficients. The second component (1988), if components possess four or more variables (PC2) is responsible for 27.16% of the total variance and with loadings above 0.60, the pattern may be interpreted shows significant positive loadings for Mn, carbonate, whatever the sample size used. In a word, with high and pH. loadings, any sample size is okay. The present data pos- sess seven variables with loadings of 0.712. The statis- Table 1 Descriptive statistical analysis of the studied soil data tical analysis was performed using SPSS 16.0 software.
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