Journal of Global Resources
Volume 4 (02)
July 2018
ISSN: 2395-3160 (Print), 2455-2445 (Online)

Page 156-165

20
GEO- SPATIAL APPLICATION TO ASSESS THE IMPACT OF LAND USE/LAND COVER ONHEAVY METAL CONTAMINATION IN THE SURFACE SOIL OF WESTERN ASSAM, INDIA
Ravi Prakash Singh1, Saumya Singh2, Neha Singh2 and Saumitra
1

National Remote Sensing Centre, Hyderabad, India
2
Jawaharlal Nehru University, New Delhi, India
Email: singhravi004@gmail.com

Abstract: Remote sensing techniques along with Geographic Information System (GIS) are an
important tool to assess the health, monitoring and management of soil resources by providing a
synoptic view of the area. The integrated study of geospatial technology and conventional lab
analysis of soil samples provide a holistic view of the existing health of the surface soil. Any typeof change in land use pattern directly or indirectly affects the natural resources of the area. To
assess the change in soil health in term of the chemical contamination of heavy metals insurface soil, a integrated study of geospatial technology and chemical analysis of soil sample
have been conducted in this study. A Land use Land cover (LULC) layer have been generated
through 23.5 meter resolution LISS III dataset and 28 soil samples have been collected fromdifferent land use features from the selected area and chemical analysis were performed. The
result obtained through this study indicated that chemical properties of soil are largely affectedby landuse pattern. Some of the soil samples are affected by Pb, Cr, and Zn contamination due
to vehicular pollution, agricultural fertilisers, agricultural pesticides and change in land use
pattern.
Key words: GIS, LULC, Heavy metal
Introduction
The characteristics of soil depend upon the parent material, regional climate, topography, biotic
potential, and the passage of time. The soil is the cumulative results of lithology, weatheringexperienced by the source area and subsequent changes during transportation and depositionof soil. From the past few decades, much attention has been drawn towards the heavy metal
contamination (Kanmani and Gandhimathi, 2013). The Heavy metals like iron, zinc, manganese,and copper are essential at low levels and are beneficial to plants, but if present above certain
level they may be toxic to them, while metals like cadmium, chromium, lead and arsenic are
toxic and may cause a great harm to plants, animals and humans through the food chain (Costa,
2000). Heavy metals in soil may be present in dissolved or exchangeable forms as structuralcomponents of the lattices or insolubly precipitated (Zalidis et al., 1999, Aydinalp and Marinova,
2003) but only dissolved and exchangeable forms of metals can be utilized by plants(Peijnenburg and Jager, 2003), if heavy metal present in inert form, are not harmful even though
they are present in high concentration, but they can become mobile as a result of change in landuse, agricultural input or climate change and it may affect the soil quality (Stigliani, 1993). Soil
may be subjected to number of pollutants and simultaneously they can also be a source ofpollution to surface and ground water because of anthropogenic activities such as mining or
industrial activities, chemical fertilizers, pesticides industrial effluents, sewage sludge andwastewater irrigation is a matter of concern in agricultural production (Ramadan and Alashkar,
2007; Kuo et al., 2006). The mobility and availability of heavy metals are controlled byadsorption and desorption characteristics of soil (Krishnamurti et al., 1999) which in turn arecontrolled by many factors like pH, organic matter content, cation exchange capacity, contents ofclay minerals and soil texture (Usman et al., 2008). The speciation and solubility of metal in thesoil is depending upon the soil pH (Zhao et al., 2010). Organic matter content affects the heavy
metal availability by retaining them in an exchangeable form. The dissolved organic matter in soil
can increase the mobility and uptake of heavy metals to plant roots (Du Laing et al., 2009). Soil156

textural composition controls the soil water availability and the activity of soil organisms byaffecting the pore size distribution and pore continuity (Hassink et al., 1993 a). Soil organic
matter comprises of partly disintegrated and decomposed plant and animal residues which helps
in maintaining the soil structure, retaining water and act as an utrient reserve and chemical
buffer (Ling at et al., 2005). The assessment of metal content in an area is of vital importanceand it can be assessed using geo-accumulation index, enrichment factor. This indicator reflects
the type and level of chemical contamination in the soil. The objectives of this study are toassess the impact of land use land cover on the surface soil physical and chemical properties.
Study area
The selected study area covers Goalpara and Bongaigaoun district of Assam (Fig.1). The district
Goalpara is situated on the Southern bank of river Brahmaputra and covers an area of 1,842 sq.
km. and is located between 25°53’ N & 90°07’ E and 26°15’N & 91°05’ E. The district
Bongaigaoun is situated on the northern bank of river Brahmaputra, adjacent to Goalpara district
covering an area about 1725 sq. km. and is located between 26°30’ N and 90°23’ E and 26°10’
N and 90° 52’ E. The geological formation in the area is the Archean group of rock comprising
Biotite Hornblend gneiss, granulites, and schists. The major part of the area is lying underyounger alluvial plain which consists of gravel, pebbles, sand, silt and clay while older alluvium iscompact and contains limonitic clay, unsorted boulders, pebbles, gravel and sand. (GSI report2009). The annual average rainfall of the state varies between 160 cm and 430 cm from place toplace. The average temperature varies from 4° C to 19° C during winter and 26° C to 37° C
during the summer. Rain makes its first appearance in the month of April with occasional andirregular light showers. Southwest tropical monsoon has dominant influence on the Assam from
April to October with occasional winter showers.
Figure 01 : Soil Sampling points

Materials and Method(a) Sampling and Chemical analysis
Soil samples (n= 28) were collected from different types of landuse / landcover, features using
hand auger from a depth ranging up to 20 cm and were stored in polypropylene bags inDecember 2014. All these soil samples, after being brought to the laboratory, were kept at
4°C.The moisture content of the soil was calculated in the lab. In an oven dried and pre weighed
petridish (W1), 10.01 g of fresh soil (W2) was added and dried in an oven at 120°C until a157

constant weight was attained (W3). The moisture content was then calculated by using below
formulaMoistureCo ntent (%) = [(W 2 − W 3) /(W 2 − W 1)] × 100

For measurement of the soil pH and electric conductivity, soil solution was prepared byadding 50 ml of water in 20 g of soil. The mixture was stirred for 10 min and after 30 min it wasagain stirred for 2 min. pH of the supernatant was measured immediately with the help of pH
probe. The mixture was then left for one hour to settle. Then, the conductivity of the supernatant
was measured with the help of conductivity meter. Preparation of the samples for textural
analysis involved the following steps:(1) Taking 50-100 gm of the uniformly powdered sample,
removing carbonates by adding 1N HCl with stirring and rim washing, followed by decanting the
HCl. (2) Removing organic matter by adding 6% to 30% H2O2, stirring and rim washing.(3)Adding distilled water and heating on the oven for 12 hrs at 40°C.(4) Removing Iron oxide byadding distilled water, aluminium foil and 15gm oxalic acid with stirrer and heating in an oven for
25-30 minutes followed by decanting excess clear water.(5) Drying and Weighing. The particle
size analysis of soil helps in the estimation of sand, silt and clay percentage in soil content.
Based on the proportions of different particle sizes, a soil textural category may be assigned tothe sample. The particles having diameter of (2.00-0.05mm) comes under sand, (0.05-0.002mm) under silt and (< 0.002 mm) under clay. X-ray diffraction study of soil was done for mineral
identification. Slides were subjected to XRD in which a monochromatic beam of X-ray is passed
through a mineral grain. The beam is scattered by the atoms which compose the minerals and at
specific angles of incidence, the scattered X-ray is in phase producing an intensified secondary
beam. The general relationship is expressed by the Bragg equationn λ = 2 d sin θ
Data was stored in the .xrdml, .dat and .udf format. The results were analyzed using X’
Pert PRO software. Soil samples were analyzed using Wavelength Dispersive X-Ray
fluorescence (WDXRF) (Pan Analytical) to identify their multi-element chemical composition. XRay Fluorescence (XRF) spectrometry is an analytical technique to do a qualitative andquantitative analysis of elemental composition by excitation of atoms and detection of theircharacteristic X-rays. The elemental range for WD-XRF goes from Beryllium to Uranium (Be to
U). For XRF analysis, in 2 gm of dried soil (size < 63µ) 0.2 gm of boric acid powder was mixed in
mortar and pestle. Then pellets were prepared by pressing the samples for one minute at 12
tons of pressure in a semi-autonomous press (In smart system). After making the pellets
samples were analyzed in WD-XRF.
LULC map generation
The landuse land cover (LULC) map of the area was prepared at GIS platform through visualinterpretation of satellite data (LISS-III) having a23.5meter spatial resolution. The features havebeen identified on the basis of the tone, shape, size pattern on the satellite data.
RESULTS AND DISCUSSION
pH and EC
The heavy metals equilibrium, solubility and their adsorption to colloids are affected by soil pH(Sommers and Lindsay, 1979; Brady, 2002). All the soil samples are acidic in nature (Table 1)
and range from 4.5 to 6.1. The acidity of soil may be due to its geographical locations which
receive the major part of the Indian monsoon from the month of April to end of September. The
rapid weathering and intense leaching under high rainfall condition augmented the developmentof soil acidity (Brady, 2002).The study area covered with healthy vegetation and open forest due
to rich vegetation, soils richer in organic matter which forms complexes with anon-nutrient cationsuch as Ca and Mg and facilitates the loss of these cations by leaching. Soil acidity creates the
agricultural problem leading to toxicity of iron and aluminium and also affects the microbialactivities in soil (Reza et al., 2012). The electrical conductivity of soil samples ranges between
500 to 3500 µs/cm which are under the permissible limit.

158

Soil Texture
Particle size alters the soil moisture and metal holding capacity of soil. The soil moisture in thearea is generally higher and its maximum value is 25.8% and a minimum value of 12.1 %.
(Table1). The higher soil moisture in the area is due to high rainfall and several small and large
rivers draining the area. Out of 28 soil samples, 32 % samples are loamy coarse sand, 46.4%
coarse sandy loam, 10.7% sandy loam, and sample (S23) was sandy loam, (S13) was silt loam,(S21) was loamy sand (Fig 2). As compared to loam, sand particles have the lower water
holding capacity and higher free drainage of water (Brady, 2002). The size of particles also
determines the chemistry of soil; the clay silt fraction (<63 µm) are mostly involved inphysicochemical sorption reaction in soil due to the high specific area (Hart, 1982; Maher et al.,
1999; Haque and Subramanian, 1982).The mineralogical composition of the sediment and soil
samples of the area is reflected by the geological and mineralogical formation of the study area.
The mineralogical study of soil samples of the area reveals that the samples are constituted ofquartz, calcite, haematite, fluorite minerals (Fig 3). Hematite acts as the source of Iron in thesamples while calcite and fluorite act as the source of calcium and fluoride in the samples
respectively.

Samples
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
S15
S16
S17
S18
S19
S20
S21
S22
S23
S24
S25
S26
S27
S28

Ph
5.4
5.1
6.1
5.3
5.0
4.6
5.1
4.6
4.6
4.5
4.7
5.8
5.2
4.9
5.5
4.9
5.2
5.5
4.9
5.6
5.3
5.2
4.9
5.1
5.3
5.6
5.2
5.1

Table 01: Physical Parameters of Soil Samples
EC (µs/cm)% Moisture
Soil Texture
1600
16.5
Loamy Course Sand
1200
13.1
Sand
700
17.9
Sandy Loam
1000
15.0
Course Sandy Loam600
18.0
Loamy Course Sand
3700
14.0
Loamy Course Sand400
13.5
Loamy Course Sand
2700
15.8
Course Sandy Loam
3500
13.6
Loamy Course Sand600
18.9
Course Sandy Loam500
16.7
Loamy Course Sand
1280
23.2
Course Sandy Loam
2500
12.1
Silt Loam
1500
18.2
Course Sandy Loam
1100
16.8
Course Sandy Loam700
15.2
Loamy Course Sand
2200
21.0
Course Sandy Loam
1300
19.0
Course Sandy Loam
900
22.0
Loamy Course Sand
3200
18.3
Sandy Loam
2700
22.2
Loamy Sand
3200
16.7
Loamy Course Sand
2700
18.8
Sandy Loam
1400
13.0
Course Sandy Loam
1800
24.8
Course Sandy Loam
1300
13.6
Course Sandy Loam
1200
14.9
Course Sandy Loam800
23.0
Course Sandy Loam

159

Figure 02: Soil Texture of the Study Area

Figure 03: XRD Spectra showing peaks of Minerals in Soil Sample (S 9)Heavy Metals and Land use/Land cover (LULC)
A comprehensive analysis of landuse/ land over and the influence of land use on the soil
behaviour are important for soil management. Land cover of an area is based on the
topography, climate and edaphic factors while landuse represents the anthropogenic activities
operating in the area. The LULC map of the study area has been classified in following 6
features (Fig 4) (a) Agricultural field (b) Forest area (c) River (d) Settlement (e) Tree-clad area (f)Wetlands-inland. The study area is mostly covered by agricultural field (38%) and tree clad area(25%). Along with Brahmaputra River, several annual and perennial rivers pass through study
area and they cover about 24% of the study area. The concentration of heavy metal in soil
samples are summerise in table-3. The geochemical index and enrichment factors have been
calculated to assess the level of heavy metal contamination.
160

Geo-accumulation Index
Heavy metal contamination in soil was evaluated by calculating geoaccumulation index (Muller,
1981). The index of soil in the study area was calculated on background of world average
concentration of these elements reported for shale (Turkian and Wedephol, 1961) and it wasdetermined by the following equation:
Igeo = log2Cn/1.5Bn
Where, Cn is the measured concentration of element n (mg/kg) in the sediment sample (<2 µm)
while Bn is the element content in average shale (Turkian and Wedephol, 1961). On this basis,
Muller (1981) has distinguished 6 classes of the geoaccumulation index (Table 2). The
geoaccumuation index of heavy metal in soil samples were measured as shown in graph Fig.5.
Igeo Class
0
1
2
3
4
5
6

Table 02: Geo-accumulation Index Classification
Igeo Value
Contamination level
Igeo< 0Uncontaminated
0<Igeo< 1
Uncontaminated/ moderately contaminated
1<Igeo< 2
Moderately contaminated
2<Igeo< 3
Moderately / strongly contaminated
3<Igeo< 4Strongly contaminated
4<Igeo< 5Strongly/extremely contaminated
5<IgeoExtremely contaminatedFigure 04: LULC map of the study area and soil samples

161

Table 03: Concentration of Metal (mg/ kg) in Soil Samples
Samples

Zn

Pb

Fe

As

Cu

Cr

Ni

S1
S2

64.5
106.5

20.8
24.1

37107.635854.4

10.9
9.0

48.0
52.9

109.2
77.2

49.8
33.4

S3

82.3

22.4

41215.2

6.8

49.5

80.9

35.6

S4

88.4

22.8

34879.7

9.0

46.0

77.2

31.5

S5
S6

105.9
116.7

30.1
33.6

36968.4
47202.5

9.4
8.2

55.1
57.3

84.6
91.6

36.0
38.8

S7

120.3

29.3

40449.4

8.6

57.6

77.2

36.4

S8

110.5

42.4

48803.8

6.7

61.9

93.1

47.6

S9
S10

110.8
123.8

32.7
29.6

42816.540936.7

7.0
8.5

59.0
54.3

105.2
83.7

61.1
35.9

S11

130.7

36.7

53189.9

6.2

56.1

91.0

41.5

S12

97.0

25.1

46088.6

6.6

51.9

88.2

39.8

S13
S14

78.5
99.9

21.3
24.6

38012.7
41006.3

7.8
9.4

49.0
56.8

82.9
87.8

32.1
45.9

S15

104.0

23.8

39544.3

9.1

52.8

83.9

38.8

S16

87.9

21.1

41145.6

8.1

46.9

81.1

42.7

S17
S18

97.6
113.4

20.1
29.9

37734.2
41632.9

10.0
9.0

52.1
59.9

84.3
80.0

34.6
33.1

S19

101.7

30.9

40170.9

6.8

56.3

86.0

49.0

S20

119.0

27.4

41632.9

8.7

46.3

86.7

40.3

S21
S22

112.8
78.7

28.8
36.8

41841.840936.7

9.6
8.9

48.6
58.1

83.2
79.9

39.0
41.1

S23

72.8

31.2

41145.6

10.6

56.0

80.9

36.5

S24

97.9

29.8

39613.9

10.0

53.2

85.7

37.9

S25
S26

91.9
108.5

30.2
29.0

41563.3
41006.3

8.0
8.5

50.2
53.1

84.6
93.3

35.7
43.2

S27

78.9

21.0

40588.6

9.9

51.2

84.2

37.3

S28

91.2

26.6

39474.7

9.1

45.9

91.1

39.9

Max
Min

130.7
64.5

42.4
20.0

53189.834879.7

10.9
6.1

61.9
45.8

109.1
77.1

61.1
31.5

Mean

100.7

28.4

41577.7

8.6

53.3

87.0

40.5

Stdv

16.5

5.5

3852.81

1.2

4.5

7.4

6.3

Figure 05: Geo-accumulation Index of Metals

Enrichment Factors
Enrichment factors of the metal in soil are used to assess the level of contamination andinfluence of human activities on sediment/soil. The Enrichment factor calculation is based upon
the standardisation of tested element against conservative element (low occurrence variability)
as reference element (Al, Fe, Ti) (Sutherland, 2000; Reimann and De Caritat, 2000). In thisstudy, Al has been used as a conservative element for geochemical normalisation by using
following formula:
162

EF =

Cn ( sample ) / Cref ( sample )Bn (background ) / Bref (background )The Contamination categories on the basis of the enrichment factor described in table (BuatMenard et al., 1979) in table 4 and the enrichment factors of metals summarised in table 5
Table 04: Contamination Categories
EF < 2
EF = 2-5
EF = 5- 20
EF = 20-40
EF > 40

Deficiency to minimal enrichment
Moderate enrichment
Significant enrichment
Very high enrichment
Extremely high enrichment

Table 05: Enrichment factors of the Metals
EF maxEF minEF mean

Zn
7.92
0.91
1.86

Pb
34.65
3.41
7.47

Fe
9.18
0.77
1.75

As
0.84
0.48
0.65

Cu
12.45
1.05
2.36

Cr
14.16
0.80
2.22

Ni
0.90
0.46
0.61

Zinc (Zn)
Availability of zinc in soil is essential for growth of plant but at the levated level, it has adverse
effect on flora. The concentration of zinc in samples varies between 64.5 to 130.7 mg/kg with amean concentration of 100.7 ± 16.5. Igeo value for Zn ranged from -0.12 to -1.14 (Fig 5.5) whichshows no soil contamination in the area. Although all the soil samples of the area haveenrichment value lower with a mean value of 0.91 but sample S1 have enrichment value 7.9
which shows the anthropogenic influence over the soil. This soil sample belongs to an
agricultural field near the settlement (Fig 5.4). The enrichment of Zn in the field may be due tothe excessive use of agrochemical pesticide in the field (Huai-yang et al., 2004).
Lead (Pb)
Lead species are mainly associated with clay minerals in soil samples and their average
concentration in samples of the study area was 28.4 ± 5.5 mg/kg. The maximum concentrationof lead in soil was 42.4 mg/kg while minimum concentration was 20.0 mg/kg. The acidic pH ofsoil enhances the solubility of Pb in water (Kumar, 2005). The geo accumulation index of lead ishigher among all the heavy metals of soil samples of the area and ranges between low
contaminated to moderately contaminated. The enrichment factor (EF) of the lead in all the soil
samples are in the range of moderate enrichment but three samples S1, S4, and S25 show
severe enrichment. All these three samples were taken from an agricultural field which is lying
near the highway from where several trucks pass so the probability of vehicular exhaust whichgets deposited through air fall may be considered for the lead enrichment in soil samples(Binggan Wei, 2010).
Iron (Fe)The concentration of iron in soil samples of the study area is higher in comparison to otherheavy metals and its mean concentration is 41577.7±3852 mg/kg, while the average
concentration of iron in an uncontaminated sediment and soil are 26000 mg/kg (Bown, 1979,
Ghrefat et al., 2010). The geoaccumulation index value (Igeo) and enrichment value are lower
for most of the samples (EF mean< 3) except for two soil samples S1 (EF > 9) and S4 (EF 3.04).
It shows that most of the samples are an uncontaminated and major source of iron is parent rockmaterials in the study area and there is no any anthropogenic enrichment of iron in the soil.
Magnetite, haematite and quartzite are reported to be associated with the gneiss in the study
area which may act as a natural source of iron (GSI reports, 2009). Sample S1 and S4 arecollected from the agricultural field which is near the settlement (Fig 5) and there was the activityof vehicle repairing and rod cutting so there may be chances of iron contamination of samples
through human activities.
Copper (Cu)
The organic and inorganic compound of copper is extensively used in agricultural pesticide. The
concentration of copper in soil ranges between 45.8 to 61.9 mg/kg with a mean value of163

53.5±4.5mg/kg. Most of the collected soils samples belong to an agricultural field. The meanvalue of Igeo for copper was -0.35 which shows no contamination of soil with respect to copper.
Although the mean value of enrichment factor (EF) for Cu is 2.36 but most of the soil samples
have EF > 2. The enrichment factor is higher for two samples S1 (EF= 12.4) and S4 (EF=4.2)
which means that except these two points, all the soil samples are less enriched and there is norole of human activities to alter copper concentration in soil samples of the area. In contrast toothers soil sample, S1 (Extremely contaminated) and S4 (Moderately contaminated) shows theenrichment of the most metals which may be due to anthropogenic activity.
Chromium (Cr)
Chromium is low mobile element and adsorption of their species varies according to soil acidity
and soil alkalinity. The Cr6+ is toxic for the biological system and the normal range of chromium
in soil is 100 mg/kg (Wedephol, 1995). In the study area, out of 28 soil samples, only two
samples (S9 & S10) have slightly higher concentration of chromium in soil (Table 5.2). Geo
accumulation index value of chromium is very low which shows that there is no contamination insoil. The enrichment of the chromium is also low for most of the samples except two samples S1and S4 which belong to the agricultural field but near the market area painting works was being
done in vehicle repair house which may be one of the reasons for chromium enrichment in thesesamples. Chromium is primarily used in tannery industry and pigment production (Govil et al.,2001).
Nickel (Ni)Nickel content in soil samples ranged from 31.5 to 61.1 mg/kg with a mean value of 40.5 ± 6.3
mg/kg. In soil, Ni is normally present in the organically bound form whose mobility andbioavailability under acidic and neutral environment gets increased (Amundsun et al., 1992). The
Igeo value for Ni ranges from maximum - 0.74 to a minimum -1.69 with a mean value of -1.39
and all the samples belong to uncontaminated category (Muller, 1981). The enrichment of Ni inall the soil samples is not significant and maximum enrichment was 0.91 which shows that there
is no contribution of anthropogenic activities in the concentration of nickel.
Arsenic (As)The mean arsenic content in soil was 8.6 ± 1.2 mg/kg with a maximum concentration of 10.9
mg/kg at location S10. The concentration of arsenic in the surface soil of all points is higher than
mean content in the soil of 2mg/kg (Chen et al., 2001). Mean geoaccumulation (Igeo) for arsenic
was – 1.20 and mean enrichment factor (EF) was 0.65 which shows that the arsenic content insoil sample of the area is not under influence of anthropogenic activity and the main source ofarsenic in the area is geogenic in nature (Zhang and Liu, 2002).
Conclusions
An integrated approach to geospatial technology and lab analysis of soil samples provides aholistic approach to identifying the probable source of surface soil contamination. The land use
pattern has significant impact on physical and chemical properties of surface soil. The
agricultural soil samples near to national highway contain higher enrichment of lead and soil
samples near to paint application industries having greater concentration of chromium. The
anthropogenic activities identified as an agent for thee nrichment of heavy metal in soil samples
which directly or indirectly affecting the flora and fauna of the area.
Acknowledgement
The lead author acknowledges Council of Scientific and Industrial Research (CSIR) for providing
fellowship during research work. The author also acknowledges to Jawaharlal Nehru University
for providing instrumentation facilities.
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