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Mohammadi A, Nemati Mansour S, Faraji M, Abdolahnejad A, Toolabi A, Miri M. A Protocol for Pollution Index, Source Identification, and Spatial Analysis of Heavy Metals in Top Soil. J Environ Health Sustain Dev. 2020; 5 (4) :1155-1161
URL: http://jehsd.ssu.ac.ir/article-1-285-en.html
Department of Public Health, Environmental Health Engineering, Maragheh University of Medical Sciences, Maragheh, Iran.
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A Protocol for Pollution Index, Source Identification, and Spatial Analysis of Heavy Metals in Top Soil
Amir Mohammadi 1*, Sepideh Nemati Mansour 2,3, Maryam Faraji 4,5, Ali Abdolahnejad 1, Ali Toolabi 6, Mohammad Miri 7
1 Department of Public Health, Environmental Health Engineering, Maragheh University of Medical Sciences, Maragheh, Iran.
2 Department of Environmental Health Engineering, School of Public Health, Tabriz University of Medical Sciences, Tabriz, Iran.
3 School of Health, Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
4 Environmental Health Engineering Research Center, Kerman University of Medical Sciences, Kerman, Iran
5Department of Environmental Health, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran.
6Department of Environmental Health Engineering, School of Public Health, Bam University of Medical Sciences, Bam, Iran.
7Non-communicable Disease Research Center, Department of Environmental Health Engineering, Sabzevar University of Medical Sciences, Sabzevar, Iran.
ORIGINAL ARTICLE   Introduction: This study aimed to assess a good protocol for the contamination indexes, concentration, spatial analysis, and source identification of toxic metals in top soils.
Materials and Methods: In the first step, samples were taken from top soil (30 cm) and the metals were extracted and detected with ICP-AES. In the second step, Enrichment Factor, Geoaccumulation Index, and Contamination Factor of metals were calculated to determine soil contamination degree. Furthermore, the principal component analysis and correlation between metals were conducted for source identification.
Results: Spatial analysis, as an important section of the present protocol, was performed using Arc GIS, kriging, and Moran's I models. As results of Moran's I model showed, distribution pattern for Fe, As, Cd, Cu, Ni, Pb, and Zn were random (z-scores ranged from -1.17 to 1.09), indicatingthat these elements could be emitted from different potential sources. In Moran's model, spatial autocorrelation of each pollutant could be measured based on its value and location.
Conclusion: The finding of this protocol can be used for extraction of contamination indexes, concentration, spatial analysis, and source identification of toxic metals in top soils.
Article History:
Received: 22 September 2020
Accepted: 20 November 2020
*Corresponding Author:
Amir Mohammadi
+98 9177094695
Environmental Pollution,
Metals, Heavy,
Spatial Analysis,
Moran's model.
Citation: Mohammadi A, Nemati S, Faraji M, et al. A Protocol for Pollution Index, Source Identification, and Spatial Analysis of Heavy Metals in Top Soil. J Environ Health Sustain Dev. 2020; 5(4): 1155-61.
The soil is part of environment in earth, that is largely  exposed to heavy metal aggregation 1. Soil pollution with heavy metals (HM) possibly results in contamination of rural products and causes other unhealthy impacts The soil pollution by HM is associated to nature and texture of soil, content of clay fraction, and its physicochemical quality 2. In the cities and villages residential regions, HM in soils can penetrate the human tissues through direct skin contact, ingestion, and breath. Consequently, they may  gather in various body tissues including the kidneys, bones, and liver 3. The main toxic heavy metal included Pb, As, Hg, Cd, Cu, and Cr. The Cu had a harmful effect on the liver and kidney role, lung cancer, and chief nervous system. Long-term contact of lead can harm the nervous system creating symptoms, for example insomnia, memory loss, and headache .The Cr affect gastrointestinal disorders and sometime may lead to death. The high dose contact to Arsenic can be damaging to the skin as well as the respiratory and cardiovascular systems .The Cd accumulation in human tissues can harm the nervous system and resulted to renal 3-6. In several studies soil pollution was reported by high values of heavy metals; for example, in northern Colombia high values of Pb, Cd and Hg were reported 7. In Tabriz, a industrial City of Iran, contamination with Cd, Cu, Cr, and Pb were mentioned 8. In some cities of China, high levels of Cr, , Pb, As, Hg, and Cd 9 were found. In Zwierzyniec of Poland, contamination with lead and copper was indiacted.1 In Van region in Eastern Turkey, contamination with Cd, As, Pb, Cr, and Hg was represented 10. In Gebze town of Kocaeli City in Turkey, contamination with Cd, Pb, Cu, and Co was reported11. This study aimed to found a protocol for the contamination indexes, concentration, spatial analysis, and source identification of the toxic metals in top soils.

Materials and Methods
Study area and sampling points
The study was conducted around Lake Urmia in North-west of Iran. The region of study has about 4000 km2 area12. Also the control station selected in prevented land in south of Urmia city. To select the study location, GIS was used and 32 zones were selected. Furthermore, one control point was selected from unpolluted soil in Urmia (Figure 1). The methods applied to select the study area and sampling points were performed and explained in previous studies 13, 14.

Figure 1: Geographic location of present protocol study (“S: sampling point)”
Soil sampling and chemical analysis
To conduct the study, 97 topsoil (0-30 cm) samples were collected in triplicate. Sampling was done in non-rainy days.  During the sampling period, the average temperature was 16 °C. The dry weight of each sample was about 1 kg, which was collected from three subsamples in approximately 5 meters within each sampling zone to prepare the composite 14.  Samples were packed into bags of polyethylene and moved to the soil lab. Soil samples were dried for 24 hours at 60 °C before analysis; later, they were cleaned and selected using a sieve with size100 mesh (0.15 mm) 7, 14 Extraction of HM was done using the USEPA 1996 Technique 3050B. First 0.5 gram of each prepared soil sample was warmed (2 hour at 50 ºC) and poured into the solution with 70 % HNO3 (9 ml) and HCL (3 ml), and 40 % HF (1 mL). This mixture was mineralized to digested using a Teflon high-pressure digestion vial (Applied Plastics Technology, Inc., Bristol, USA) in 170 °C for 4 h. In next step, digested solution were cooled and then were added H2O2 (2 mL - 30%) and concentrated HNO3 (3 mL) into it 14. This extraction liquid using Milli-Q water was weakened to 50 mL, and finally, by a micro-porous membrane with the pore size of (0.45 μm) filtered into bottles (PET). Also, a blank digest solution used for quality control.
In last section, HM of soil sample were detected using US EPA Method 200.7 by inductively coupled plasma-atomic emission spectrometry (ICP-AES, Model: Arcous, German),
Indexes of soil pollution
Enrichment factor (EFs)
EF was used to determine the probable pollutant origin, the impacts of unnaturally actions on the grade of soil pollution and health risk. For each metal, EF was calculated separately by formula. (2) based on the formula reccomended by Taylor 15.

Where, the nth metal concentration (mg/kg) is Cn and Iron concentration is CFe (reference metal used for standardization in our study, mg/ kg) in the soil of experienced environment and the reference environment.
In this Eq, normaliization of a HM was used against a reference HM to recognize accumulation of HM in soil. The greatest conventional reference HM were applied in previous studies comprising Al and Fe 10, 16.  Gradation of metal pollution may be categorized  into 7 groups as the rate of EF in soil: no enrichment (EF < 1); minor enrichment (1 ≤ EF < 3); moderate enrichment (3 ≤  EF < 5); moderately severe enrichment (5 ≤ EF < 10); severe enrichment (10 ≤ EF < 25); very severe enrichment (25 ≤  EF < 50); extremely severe Enrichment (EF > 50) 7.

Index of geoaccumulation (Igeo)
The geoaccumulation (Igeo) index estimates the levels of HM contamination in n cities and villages soils. The Igeo was founded the including  between the current and pre-industrial HM value 17. This index is measured by Eq. (3):

Here, Cn are the concentrations of HM evaluated in the soil samples of environment and Bn  is related soil sample of the control area. The coefficient 1.5 use to evaluate usual fluctuations in contents of a given substantial in the environment and to discriminate very slight manmade influences. The lgeo consists  of 7 groups  9: uncontaminated (Igeo ≤ 0); uncontaminated to moderately contaminated (0 <  lgeo ≤ 1); moderately contaminated (1 < Igeo ≤ 2); moderately to heavily contaminated (2 < lgeo ≤ 3); heavily contaminated (3 < Igeo ≤ 4); heavily to extremely contaminated (4 < Igeo ≤ 5), and extremely contaminated (5 ≤ Igeo).
Contamination degree (Cdeg)
The another factors to identify soil contamination in heavy metals are the contamination factor (CF) and contamination degree (CD)  18. The contamination factor is computed by Eq. (4) regarding value of each HM in the sampling soil point and background soil:
The CF is separated into 4 groupings 10: Low contamination (CF < 1); Moderate contamination (1 < CF ≤ 3); moderate to strong contamination (3 < CF ≤ 6) and very strong contamination (CF > 6).
Additionally, the Cdeg is extracted using Eq (5) that considers the sigma of CF for heavy metal pollutants:

The Cdeg is divided into 4 cataloging 10: Low degree of contamination (Cdeg < 8); Moderate degree of contamination (8 < Cdeg ≤ 16); moderate to a strong degree of contamination (16 < Cdeg ≤ 32); and a very strong degree of contamination (Cdeg > 32).
Spatial distribution of metal elements and Cdeg
Cdeg maps were drawn using ArcGIS 10.1 to show the spatial distribution in assessing contamination values of the heavy metals in around Urmia Lake. To create raster layers in independent form for Cdeg, the Kriging interpolation technique was applied. Afterwards, the raster computer function was recycled to overlay each layer to create distribution map pollutants averages (Figure 2).

Figure 2: Spatial distribution map of soil samples' contamination with heavy metals

The regions with high and low contaminated of HM were highlighted by rane of high and low color . Based on the findings, red region, showing heavy metal concentrations was wider than the green region with low soil contamination.
Spatial autocorrelation
Global Moran's Index (Moran's I) was applied to evaluated the local variation distribution pattern (which can be as a disperse, random or clustered) of metal elements in soil samples. In this model, spatial autocorrelation of each pollutant can be measured based on its value and location. In Moran's I, the null hypothesis showed random distribution of  values across the study area 19, 20. In the spatial autocorrelation model, in addition to Morans I, z-score and P-value can be measured to evaluate significance of the mentioned Index 21. The Global Moran's I can be calculated as follows 20, 22, 23:

Where, (S0) and (n)  are the sum of all spatial weights and the total number of features, respectively; wij is the spatial weight between feature i and j; and zi represents the deviation of an attribute for feature i from its mean (xi – X) 23, 24:

Finally, the ZI-score for the statistic can be calculated as follows:
 Where, E[I] and V[I] are equal to -1/(n-1) and E[I2] – E[I]2, respectively 21.
In this model, the z-scores lower than -1.65 indicated dispersed emission pattern, z-scores from -1.65 to 1.65 represent random emission, and z-scores higher than 1.65 indicated clustered emission pattern 21, 25. According to the results of Moran's I model in figure 3, distribution patterns for Fe, As, Cd, Cu, Ni, Pb, and Zn were random (z-score ranged between -1.17 to 1.09), indicating that these elements could be emitted from different potential sources.

Figure 3: Spatial autocorrelation model (Moran's I) for measured metals around Urmia Lake
Descriptive statistics of the metal element concentrations
Table 1 lists the basic descriptive statistics of the metal elements' concentrations in sampled soils around Urmia Lake. The results indicated that the means of As, Cd, Cu, Ni, Pb, and, Zn content were 6, 0.4, 58, 81, 31, 129, and 390 mg/kg in sampling site top soils, respectively. Therefore, elements in the sampling sites were ranked based on their mean concentrations from highest to lowest: Zn > Ni > Cu > Pb > As >Cd.
Table 1: Statistical descriptive of metal elements in sampling soils (n: 96) around Urmia Lake (mg/kg)
Element Fe As Cd Cu Ni Pb Zn
Mean 3.1% 6 0.4 58 81 31 129
Std. deviation 1.8% 1.17 0.60 77.95 174.3 59.96 199.50
Median 2.7% 5 0.26 30 40 13 84
Maximum 10% 8.1 2.7 344 743 258 888
Minimum 1.9% 3.5 0.2 20 15 8 54
C.V 0.58 0.20 1.50 1.37 2.15 1.93 1.55
Skewness 3.48 0.310 3.74 3.32 3.74 3.70 3.76
Kurtosis 11.68 -0.51 12.95 10.81 12.97 12.75 13.08

Spatial distribution patterns of the contamination rates in the soils around Urmia Lake are illustrated in figure 2. As represented, mapping pollutant values is a good graphical method for assessing the probable enrichment sources and identifying the critical points with high pollution 26, 27.
Spatial analysis showed that distribution trends for Cd, Cu, Pb, Ni, and Zn gained similarity with non-identical values. This same pattern indicates that they can be emitted from the same sources.
Source identification
To identify possible sources of HM, the principal component analysis (PCA) and spearman's correlation tests were employed. The PCA is a method suggested by the US Environmental Protection Agency 28 to investigate the distribution of heavy metal contamination by source.
Based on the findings, two principal components were extracted with eigenvalues of higher than 1. These two principle components counted for 85.3% of the total variation among the variables.
The first PCA, factor 1, accounted for 70.8% of the total variance and consisted of Cd, Cu, Ni, Pb, and Zn as heavy metal elements. This factor represents that soil contamination may be caused by anthropogenic sources such as industries and traffic. The same results were also reported in several previous studies. In a study in urban soil of south India, based PCA analysis showed that Cu and Pb were attributed to traffic activities and Zn, Pb, and Cu had anthropogenic origin. In another study in villages of Reasi district, India, principal component analysis showed that Zn, Cu, Pb, and Cr were associated with vehicle emissions, traffic sources, and industrial sources.3-6
The findings of this protocol can be used for determining soil and dust soil contamination indexes, concentration, spatial analysis, and source identification of toxic metals in top soils.
This study was financially supported by Maragheh University of Medical Sciences (grant number of 97 DAY19-MOA 01). The code of research ethics certificate in my article is"IR.MARAGHEHPHC.REC.1398.001".
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence this paper.
It is an Open-Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt, and build upon this work for commercial use.

  1. Mazurek R, Kowalska J, Gąsiorek M, et al. Assessment of heavy metals contamination in surface layers of Roztocze National Park forest soils (SE Poland) by indices of pollution. Chemosphere. 2017;168:839-50.
  2. Nouri J, Khorasani N, Lorestani B, et al. Accumulation of heavy metals in soil and uptake by plant species with phytoremediation potential. Environ Earth Sci. 2009;59(2):315-23.
  3. Adimalla N, Chen J, Qian H. Spatial characteristics of heavy metal contamination and potential human health risk assessment of urban soils: A case study from an urban region of South India. Ecotoxicol Environ Saf. 2020;194:110406.
  4. Kaur M, Kumar A, Mehra R, et al. Quantitative assessment of exposure of heavy metals in groundwater and soil on human health in Reasi district, Jammu and Kashmir. Environ Geochem Health. 2020;42(1):77-94.
  5. Minkina T, Konstantinova E, Bauer T, et al. Environmental and human health risk assessment of potentially toxic elements in soils around the largest coal-fired power station in Southern Russia. Environ Geochem Health. 2020:1-16.
  6. Zhang K, Zheng X, Li H, et al. Human Health Risk Assessment and Early Warning of Heavy Metal Pollution in Soil of a Coal Chemical Plant in Northwest China. Soil Sediment Contam. 2020;29(5):481-502.
  7. Marrugo-Negrete J, Pinedo-Hernández J, Díez S. Assessment of heavy metal pollution, spatial distribution and origin in agricultural soils along the Sinú River Basin, Colombia. Environ Res. 2017;154:380-8.
  8. Taghipour H, Mosaferi M, Armanfar F, et al. Heavy metals pollution in the soils of suburban areas in big cities: a case study. Int J Environ Sci Technol. 2013;10(2):243-50.
  9. Wei B, Yang L. A review of heavy metal contaminations in urban soils, urban road dusts and agricultural soils from China. Microchem J. 2010;94(2):99-107.
  10. Yaylalı-Abanuz G. Heavy metal contamination of surface soil around Gebze industrial area, Turkey. Microchem J. 2011;99(1):82-92.
  11. Türkdoğan MK, Kilicel F, Kara K, et al. Heavy metals in soil, vegetables and fruits in the endemic upper gastrointestinal cancer region of Turkey. Environ Toxicol Pharmacol. 2003;13(3): 175-9.
  12. Mohammadi A, Faraji M, Conti GO, et al. Mortality and morbidity due to exposure to particulate matter related to drying Urmia Lake in the NW Iran. Eur J Intern Med. 2019;60:e14-e5.
  13. Shamsaddin H, Jafari A, Jalali V, et al. Spatial distribution of copper and other elements in the soils around the Sarcheshmeh copper smelter in southeastern Iran. Atmos Pollut Res. 2020;11(10): 1681-91.
  14. Mohammadi AA, Zarei A, Esmaeilzadeh M, et al. Assessment of heavy metal pollution and human health risks assessment in soils around an industrial zone in Neyshabur, Iran. Biol Trace Elem Res. 2020:195:343-52.
  15. Taylor S. Abundance of chemical elements in the continental crust: a new table. Geochim Cosmochim Acta. 1964;28(8):1273-85.
  16. Miri M, Allahabadi A, Ghaffari HR, et al. Ecological risk assessment of heavy metal (HM) pollution in the ambient air using a new bio-indicator. Environ Sci Pollut Res. 2016;23(14): 14210-20.
  17. Muller G. Index of geoaccumulation in sediments of the Rhine River. Geo Journal. 1969;2(3):108-18.
  18. Hakanson L. An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res. 1980;14(8):975-1001.
  19. Baller RD, Anselin L, Messner SF, et al. Structural covariates of US county homicide rates: Incorporating spatial effects. Criminology. 2001;39(3):561-88.
  20. Anselin L. The Moran scatterplot as an ESDA tool to assess local instability in spatial association: Regional Research Institute, West Virginia University Morgantown, WV; 1993.
  21. Miri M, Alahabadi A, Ehrampush MH, et al. Mortality and morbidity due to exposure to ambient particulate matter. Ecotoxicol Environ Saf. 2018;165:307-13.
  22. Kanaroglou PS, Adams MD, De Luca PF, et al. Estimation of sulfur dioxide air pollution concentrations with a spatial autoregressive model. Atmos Environ. 2013;79:421-7.
  23. Dong L, Liang H. Spatial analysis on China's regional air pollutants and CO 2 emissions: emission pattern and regional disparity. Atmos Environ. 2014;92:280-91.
  24. Dormann C, Mc Pherson J, Araújo M, et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography. 2007;30(5):609-28.
  25. Dai F, Zhou Q, Lv Z, et al. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecol Indic. 2014;45:184-94.
  26. Zhao L, Xu Y, Hou H, et al. Source identification and health risk assessment of metals in urban soils around the Tanggu chemical industrial district, Tianjin, China. Sci Total Environ. 2014;468:654-62.
  27. Sun Y, Zhou Q, Xie X, et al. Spatial, sources and risk assessment of heavy metal contamination of urban soils in typical regions of Shenyang, China. J Hazard Mater. 2010;174(1):455-62.
  28. Huang S, Tu J, Liu H, et al. Multivariate analysis of trace element concentrations in atmospheric deposition in the Yangtze River Delta, East China. Atmos Environ. 2009;43(36): 5781-90.

Type of Study: Original articles | Subject: Environmental Health, Sciences, and Engineering
Received: 2020/09/22 | Accepted: 2020/11/20 | Published: 2020/12/26

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