Stefan Daniel Maramis, Rika Ernawati and Waterman Sulistyana Bargawa

How to cite:

Stefan Daniel Maramis, Rika Ernawati and Waterman Sulistyana

Bargawa. (2021). Distribution analysis of heavy metal contaminants in soil

with geostatistic methods; paper review. Journal Eduvest. 1(7): 620-628

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Eduvest – Journal of Universal Studies

Volume 1 Number 7, July 2021

p- ISSN 2775-3735 e-ISSN 2775-3727

DISTRIBUTION ANALYSIS OF HEAVY METAL CONTAMINANTS

IN SOIL WITH GEOSTATISTIC METHODS; PAPER REVIEW

Stefan Daniel Maramis, Rika Ernawati and Waterman Sulistyana Bargawa

UPN Veteran Yogyakarta

E-mail: [email protected]

ARTICLE INFO ABSTRACT

Received:

June, 29

2021

Revised:

July, 9

2021

Approved:

July, 15

2021

Heavy metal contaminants in the soil will have a direct effect on

human life. The spatial distribution of naturally occurring heavy

metals is highly heterogeneous and significantly increased

concentrations may be present in the soil at certain locations.

Heavy metals in areas of high concentration can be distributed to

other areas by surface runoff, groundwater flow, weathering and

atmospheric cycles (eg wind, sea salt spray, volcanic eruptions,

deposition by rivers). More and more people are now using a

combination of geographic information science (GIS) with

geostatistical statistical analysis techniques to examine the

spatial distribution of heavy metals in soils on a regional scale.

The most widely used geostatistical methods are the Inverse

Distance Weighted, Kriging, and Spatial Autocorrelation

methods as well as other methods. This review paper will explain

clearly the source of the presence of heavy metals in soil,

geostatistical methods that are often used, as well as case studies

on the use of geostatistics for the distribution of heavy metals.

The use of geostatistical models allows us to accurately assess

the relationship between the spatial distribution of heavy metals

and other parameters in a map.

KEYWORDS

Contaminants, Heavy Metals, Soil, Geostatistics

This work is licensed under a Creative Commons

Attribution-ShareAlike 4.0 International

INTRODUCTION

Heavy metal contaminants in the soil will be a very serious problem because it takes

a long time to repair and restore soil conditions to normal (Handayanto, Nuraini,

Muddarisna, Syam, & Fiqri, 2017). Examining these uncertainties is essential for designing

and implementing risk mitigation strategies, and only focusing on reducing soil

 
Stefan Daniel Maramis, Rika Ernawati and Waterman Sulistyana Bargawa  
 
Distribution analysis of heavy metal contaminants in soil with geostatistic 
methods; paper review    621 
concentrations when deemed necessary. Statistical analysis has been used across various 
disciplinary boundaries to address soil contamination problems, including geoscience, soil 
science,  atmospheric  studies,  environmental  engineering,  chemometrics  (Gholizadeh, 
Saberioon, Ben-Dor, & Borůvka, 2018). 
Heavy metal contamination in soil has become a serious problem globally (Han et 
al.,  2020).  A  number  of  hazardous  heavy  metals  can  enter  the  human  body  from 
contaminated  soil  through  exposure  routes  such  as  direct  or  indirect  consumption, 
inhalation and skin contact which will potentially result in human health effects (Changfeng 
Li et al., 2019). Heavy metals can also show ecotoxicity which causes hampered ecological 
health in addition to bioaccumulation in the food chain (Shahid et al., 2020). 
Judging from the dangers posed by heavy metals if they accumulate in soil and 
sediment, in this case an analysis of the level of heavy metal pollution on soil and sediment 
quality must be carried out, namely an analysis of soil and sediment quality based on heavy 
metal content data using several indicators, which can be grouped into a single index (Niu 
et al., 2020). Single index is an indicator used to calculate contamination of a single metal 
(only  one  heavy  metal)  by  calculation  (contamination  factor)  or  contamination  factor 
(Werdianti,  2018).  The  heavy  metals  that  have  been  studied  most  intensively  in  the 
publications reviewed include Pb, Zn, Cu, Ni, Cr, and Cd, listed in descending order of 
frequency. 
To  solve this problem, the fate and transport of heavy metals in  soil,  as well as 
remediation of contaminated soil, have been studied intensively. It is also very important 
to be able to  strongly distinguish  the spatial distribution of heavy metals  in  soils on  a 
regional scale, to enable a sound human and ecological risk assessment, and to implement 
efficient pollution mitigation measures where necessary. Techniques such as geostatistics 
have an important role in this task. Several specific challenges exist in overcoming heavy 
metal contamination of soil (Shi et al., 2018): i) heavy metals cannot be degraded and will 
often naturally accumulate in the soil ii) they cause a wide range of health effects, and 
health risks are complicated by oxidation states and associated differences in bioavailability 
(Rahman  &  Singh,  2019);  iii)  there  are  many  widespread  sources  of  heavy  metal 
contamination.  Understanding  heavy  metal  concentrations  on  a  regional  scale  is  very 
relevant  for  policy  makers.  Regional  soil  studies  help  guide  action  in  combating  the 
pollutant link – managing risk rather than molecules. It is important to understand all the 
uncertainties regarding contaminant concentration, shape, spatial distribution and temporal 
changes. 
In recent years, more and more studies have used integrated geographic formation 
systems (GIS) and multivariate analysis for regional soil quality assessments. This is partly 
due to the use of specialized software that can handle the large spatial data sets presented 
in GIS. However, many statistical techniques fail to recognize the role of spatial correlation. 
GIS  and  GIS-based  geostatistics  have  proven  to  be  powerful  tools  in  studying  soil 
contamination and very useful tools for understanding background levels of heavy metals 
in soils (Hou, O’Connor, Nathanail, Tian, & Ma, 2017). This paper review aims to clearly 
dissect the source of the presence of heavy metals in soil, geostatistical methods that are 
often used to determine the distribution of heavy metal contaminants, as well as case studies 
of the use of geostatistics for the distribution of heavy metals in other countries. 
 
RESEARCH METHODS 
 
The method used in this research is literature study. Activities to collect information 
relevant to the topic or problem that is the object of research. This information can be 
obtained from books, journals, proceedings as well as writings related to the research from 

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622 http://eduvest.greenvest.co.id

the literature review so as to make writings on the Analysis of Distribution of Heavy Metal

Contaminants in Soil Using Geostatistical Methods; Paper Review.

RESULTS AND DISCUSSION

1. Source of Heavy Metal Pollution in Soil

Heavy metals naturally exist in the earth's crust and surface soil (Esmaeilzadeh et

al., 2019). The spatial distribution of naturally occurring heavy metals is highly

heterogeneous and significantly increased concentrations may be present in the soil at

certain locations. Heavy metals in areas of high concentration can be distributed to other

areas by surface runoff, groundwater flow, weathering and atmospheric cycles (e.g. wind,

sea salt spray, volcanic eruptions, deposition by rivers. Typical anthropogenic sources of

heavy metal contamination in urban soils include exhaust vehicles, sewage, sewage,

industrial emissions.Increased concentrations of heavy metals in rural soils usually come

from impurities in agrochemicals such as application of pesticides and fertilizers, irrigation

with contaminated water, surface runoff from local industrial facilities, extraction of

mineral ores, and subsequent disposal of waste , road dust, sewage sludge, sewage and

livestock manure, and atmospheric deposition. Soil heavy metal pollution is usually studied

on a regional scale or on a site specific basis. On a regional scale (usually ranging from

about 10 km2 to 10,000 km2), investigations are carried out to set geok background level

chemistry, source tracking, and public health protection (Chen, Zhou, Gao, & Hu, 2015).

Many regional soil quality studies have been carried out, but only in the last two decades

have researchers applied a GIS-based approach to geochemical interpretation of soil data.

At site-specific scales (typically ranging from 0.01 km2 to 10 km2), investigators typically

aim to determine the spatial level, concentration, and fate and transport of contamination

to assess risks to human health and ecological systems and to identify remediation

alternatives (Wu et al., 2015). Differences between regional and site-specific assessments

were also found in the depth, method, and density of sampling.

2. GIS and Geostatistical Methods

GIS was originally developed as a tool for storage, retrieval and display of

geographic information, and was later enhanced for spatial analysis (Fotheringham &

Rogerson, 2013). It has been widely used in soil-related research fields, such as precision

agriculture, engineering geology, soil erosion, and land degradation. Various spatial

interpolators were used, including the Inverse Distance Weighted (IDW) Method.

Geostatistical methods are used in GIS to estimate unknown soil properties between known

sampling locations. Two of the most commonly used methods are kriging and conditional

simulation. Both methods calculate the value of land properties based on the weighted

values assigned to the sample values at the nearest location. The following subsections

provide a brief description of the basics of spatial autocorrelation and the most commonly

used spatial interpolators.

- Kriging

The Kriging geostatistical technique was the most widely used interpolation

approach among the studies reviewed, with 20 of 29 studies explicitly indicating that they

used kriging. Kriging is derived from Regional Variable Theory and was first introduced

to the GIS field in the 1990s. Unlike IDW and some other interpolation methods which

treat soil properties at unsampled locations as a specific mathematical function of

continuous spatial variables, the kriging method is based on a stochastic spatial variation

model.

Stefan Daniel Maramis, Rika Ernawati and Waterman Sulistyana Bargawa

Distribution analysis of heavy metal contaminants in soil with geostatistic

methods; paper review 623

The underlying assumption is that soil properties behave as intrinsically stationary

regionalized random variables. Therefore, the kriging method can be used to estimate

confidence intervals for derived values at unsampled locations. The general equation for

kriging is explained as follows:

……. I

where z(B) is the estimate over the ground area and li is the weight, which amounts

to one to ensure that there is no bias and, subject to this, is chosen to minimize the variance

of the estimate. The accuracy of kriging is affected by the variability and spatial structure

of the data, and the choice of variogram modeling parameters including the variogram

shape, range, threshold, and nugget value and search radius, and a number of other

measurements used in the calculations show that lognormal ordinary kriging can improve

estimation precision compared to ordinary kriging. This is particularly relevant for heavy

earth metals because the data will often display a log-normal distribution function. The

Kriging method invented by the Directorate General of Daniel Gerhardus Krige which was

inaugurated in 1960 by an engineer from France, Georges Matheron, is a geostatistical

method used to estimate the value of a point or block as a linear combination of sample

values located around the point to be estimated. . The kriging weight is obtained from the

minimum variance estimation result by expanding the use of the semivariogram. The

kriging estimator is an unbiased estimator and the sum of all weights is one. This weight is

used to estimate the value of thickness, height, grade or other variables (Bargawa, Nugroho,

Hariyanto, Lusantono, & Bramida, 2020). In its development, many kriging methods have

been used to solve various cases in geostatistical data, for example, there is a sampled

mineral content that does not have a certain trend. The kriging method that is suitable for

solving this case is ordinary kriging because this method can be used when the population

mean is unknown (Bargawa, 2020).

- IDW Interpolation

The Inverse Distance Weighted (IDW) method has been used in several regional soil

quality survey studies that integrate GIS with multivariate statistical analysis (Dongqing

Li, Huang, Guo, & Guo, 2015). This method is one of the most frequently used spatial

interpolation methods because of its fast implementation, ease of use, and straightforward

interpretation. The general equation for IDW is described by the following equation:

……II

where zx,y is the point to be estimated, zi represents the control value for the i-th

sample point, dx,y,i is the distance between zx,y, and zi, and b is the user-defined exponent.

This weighing strategy assigns more weight to spatially close points than distant points

based on the reciprocal of the distance to a power, which conforms to logical intuition. The

accuracy of the IDW can be increased by wisely choosing the optimal number of

surrounding points (n) and exponent value (b) to produce optimal agreement between the

measured and estimated data. Its biggest drawback is that it is not based on a specific spatial

correlation model for the parameters studied, while, as discussed above, spatial

autocorrelation often exists and can be used to provide better interpolations.

- Spatial Autocorrelation

Spatial autocorrelation refers to the lack of independence between pairs of

observations at a given distance in space, i.e. the similarity between samples for a particular

attribute variable as a function of spatial distance. In the early days of GIS research, spatial

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autocorrelation was treated as a problem requiring correction rather than an inherent

property of spatial data. However, researchers have found that spatial autocorrelation is

ubiquitous, occurring on spatial scales from micrometers to hundreds of kilometers, for

reasons ranging from external environmental factors to intrinsic dispersion mechanisms.

According to Tobler's First Law of Geography, things that are near are more closely related

than things that are far away (Waters, 2017). To obtain values for a given attribute variable

in the area between the observed samples, spatial autocorrelation needs to be taken into

account.

- Other Methods

Various other geostatistical methods have been used for spatial prediction of soil

properties. Keskin & Grunwald found an inverse relationship between the accuracy of the

Kriging Regression model and variations in soil properties in the original dataset. A new

modified RK method is proposed for further investigation to predict soil properties and

classes (Keskin & Grunwald, 2018). Kim et al., used co-kriging with the aim of reducing

the economic costs of heavy metal sampling (Kim et al., 2019). They achieved this by

measuring the soil cation exchange capacity (CEC) as a covariate, which is easier and

cheaper to measure than the Cu, Zn, and Cr determinants of concern. It should be noted

that the simple overlay method was used in most of the studies reviewed, although it is not

considered a geostatistical method alone.

3. Geostatistical Use Case Study

The following cases are the use of Geostatistical Methods in an area to determine the

level of distribution of heavy metals

The Use of Geostatistical Models to Determine the Distribution of Mercury in Soil

in Former Mining Areas: Mount Karczówka., Mount Miedzianka., and Rudki (Central-

South Poland)

The study, conducted by (Dołęgowska & Michalik, 2019), evaluated metal

concentrations in the post-mining soil of Mount Karczówka, Mount Miedzianka, and Rudki

for the assessment of pollution levels and to make further decisions on the actions to be

taken. The heterogeneous and special character of this area, makes this assessment focus

on the anthropogenic and geogenic sources of mercury that have been identified in the three

post-mining areas using an integrated map of the spatial distribution of mercury, calculated

geochemical factors (BG, LEF), and the results of cluster analysis in the soil due to

exposure mining impact. The use of combined geostatistical models confirms a direct

relationship between mercury content and ex-mining operations. We document that

although mining activity ceased in the mid-twentieth century and even in the case of the

Rudkin where reclamation work has been carried out, this correlation is still visible. The

highest mean mercury concentration was recorded in soil samples from Miedzianka Mt.

(0.501 mg kg−1). Very high enrichment in this metal (20 LEF < 40) was also reported at

one location from this area as a result of the occurrence of Hg-rich copper sulphide. Due to

the lack of mercury minerals in the soil of Karczówka Mt. and Rudki, the burning of fossil

fuels and other emitters (housing and local roads) are classified as the main sources of this

element. The correlation between mercury content and history of mining operations can be

explained by the presence of clay minerals and Fe/Mn oxides and hydroxides which are

scavengers of atmospheric mercury. The results of multivariate analyzes performed for

mercury (FA) and non-mercuric (CA) biased data sets emphasize the association between

the presence of other trace metals and mercury. The use of a unified geostatistical model

which is a combination of multivariate statistics and geostatistical parameters presented by

GIS allows us to accurately assess the relationship between the spatial distribution of

mercury and other parameters in small-scale maps.

Stefan Daniel Maramis, Rika Ernawati and Waterman Sulistyana Bargawa

Distribution analysis of heavy metal contaminants in soil with geostatistic

methods; paper review 625

Figure 1. Map of spatial distribution of mercury integrated with geochemical factors

a. Mt. Miedzianka, b. Mt. Karczówka, c. Rudki

Geostatistical Modeling and Characteristics of Contaminants and Precious Metals

from Cu–Au Tailings Dam Abandoned In Taltal (Chile)

Research conducted by (Tripodi, Rueda, Céspedes, Vega, & Gómez, 2019) in the

city of Taltal, located on the northern coast of Chile, with central coordinates S 25° 23 '52”

and W 70° 28′ 35 merupakan, is an important area for small-scale mining. Depending on

the price of the metal, the population shifts between mining and fishing. From their

research, it was found that the main copper minerals detected were chrysocolla, atacamite,

tenorite, and chalcopyrite. The gangue mineralogy is dominated by the presence of quartz,

feldspar, magnetite, and clay. The particle sizes for S1 and S2 mainly correspond to the

clay and silt categories (P80 66.4 m S1 and 46.8 m S2). The degree of release revealed the

presence of degrees of occlusion for the different minerals despite the fine particle size.

The presence of copper in both tailings composites was mainly associated with oxide and

oxidized type minerals (70% for S1 and 41% for S2). Based on the type of mineral, the

main Cu content in the S1 composite sample was atacamite (31.46%) and chalcopyrite

(29.66%). For S2 the main copper mineral is chalcopyrite (46.65%). Arsenic, mercury and

copper are the detected elements with the greatest potential for contamination. More than

90% of the samples exceed Finnish standards. Tailings can have economic added value

because they contain significant copper (0.27% S1, 0.48% S2 average) and gold (0.26 ppm

S1, 0.53 ppm S2 average).

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Figure 2. Element distribution according to geostatistical modeling for: A. Copper,

B. Mercury, C. Zinc, D. Lead, E. Gold and F. arsenic. The left box shows the concentration

range in ppm. The result of S1 is the one from the left side, the right side depicts the graph

for tailings dam S2.

In addition, the early presence of different rare earth piles reported in a timely

manner should be considered. The line of open interest as a result of this research in terms

of contamination and reprocessing, indicates the need for further research aimed at

providing additional details in the real contamination problems that can be attributed to the

detected metal and/or evaluation of the beneficiation of metallurgical options for value-

added metals. The coefficient of variance and the geostatistical model show the differences

in the level of dispersion found. In general, moderate or high levels of variability,

dispersion and heterogeneity were found for the two tailings. In particular, the variability

increased from the low, intermediate values of Zn for As, Cu, Ag, Pb to the high values

identified for Hg. The block model allows estimates of total material quantities of 8400

tonnes for S2 and 39794 tonnes for S1. The low concentration and distribution of the

contents requires that in order to find out if there are additional benefits, tailings deposits

must be considered as a whole.

CONCLUSION

Increased concentrations of heavy metals in soil usually come from impurities in

agrochemicals such as application of pesticides and fertilizers, irrigation with contaminated

water, surface runoff from local industrial facilities, extraction of mineral ores, and disposal

of sewage, road dust, sewage sludge, sewage and sewage livestock, and atmospheric

deposition. Soil heavy metal pollution is usually studied in scale areas or based on specific

locations. To determine the distribution of heavy metals in an area, geostatistical methods

can be used, the most widely used geostatistical methods are the Inverse Distance

Weighted, Kriging, and Spatial Autocorrelation methods and other methods. The use of

geostatistical models allows us to accurately assess the relationship between the spatial

distribution of heavy metals in soil and other parameters in a map. It is hoped that the next

literature review will discuss in more detail about each of the renewable geostatistical

models.

 
Stefan Daniel Maramis, Rika Ernawati and Waterman Sulistyana Bargawa  
 
Distribution analysis of heavy metal contaminants in soil with geostatistic 
methods; paper review    627 
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