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ORIGINAL ARTICLE
Spatial distribution of metals in ground/surface waters in theChandrapur district (Central India) and their plausible sources
D. R. Satapathy Γ P. R. Salve Γ Y. B. Katpatal
Received: 8 June 2007 / Accepted: 1 February 2008 / Published online: 26 February 2008
οΏ½ Springer-Verlag 2008
Abstract This study addresses a framework to evaluate
and map environmental hazard with reference to spatial
distribution of major and trace metal contamination and its
relationship with lithology in Chandrapur district of
Maharashtra, India using geospatial, statistical and GIS
tools. In all, 208 ground water and 35 surface water sam-
ples were collected using global positioning system (GPS)
synoptically with satellite imagery IRS P6 LISS III and
were analyzed in ICP-AES. Analytical results reflect the
presence of major and trace metals in ground water in
terms of % as Fe (48%), Mn (12%), Zn (9%), Al (8%), Pb
(7%), Cu (6%), Ni (4%), Cd (3%) and Cr (3%) of the total
average concentration. The contamination is attributed to
weathering of rocks and also to mining activities. Simi-
larly, surface water contribution of major and trace metals
was found as Al (47.8%), Fe (42.8%), Mn (5.5%), Zn
(2.3%), Pb (0.56%), Ni (0.42%), Cu (0.16%), Cr (0.16%)
and Cd (0.10%) of the total average concentration.
Ordinary kriging interpolation method was adopted to
assess the spatial distribution of different major and trace
metals in groundwater samples with their best model fit
variogram Classical statistical method like principal com-
ponent analysis (PCA) was carried out in order to establish
correlation between spatial pattern of metal contamination
and geology of the area in GIS environment. Various sur-
face and subsurface aspects like landuse/land cover,
structural features, hydrogeology, topography etc were also
considered to ascertain their impact to supplement the
inference of the study.
Keywords Mine environment οΏ½Major and trace metal contamination οΏ½Geostatistics οΏ½ GIS οΏ½ PCA οΏ½ Geology
Introduction
Water is an important facet to human life and hence water
quality assessment is inevitable for monitoring the envi-
ronmental quality. The natural quality of groundwater is
controlled by the geochemistry of the lithosphere, the solid
portion of the earth, and the hydrochemistry of the
hydrosphere, the aqueous portion of the earth. In igneous
rocks, the restricted opportunity for reactions to take place
is accentuated by the fact that groundwater storage and
flow is predominantly in fissures, giving short residence
times and low contact surface area. Groundwater in igne-
ous rocks is, therefore, often exceptionally lightly
mineralized, although characterized by high silica contents
(Hem 1989). Pure siliceous sands or sandstones without
soluble cement also contain ground water with very low
total dissolved solids (Mathess 1982). Sulphate may also be
produced by the oxidation of metallic sulphides, which are
present in small amounts in many rock types. The presence
of soluble cement may produce increased concentrations of
D. R. Satapathy (&)
Environmental Systems Design and Modelling Division,
National Environmental Engineering Research Institute,
Nehru Marg, Nagpur 440 020, India
e-mail: [email protected]
P. R. Salve
Environmental Impact and Risk Assessment Division,
National Environmental Engineering Research Institute,
Nehru Marg, Nagpur 440 020, India
e-mail: [email protected] ; [email protected]
Y. B. Katpatal
Department of Civil Engineering,
Visvesvaraya National Institute of Technology (VNIT),
Nagpur, India
e-mail: [email protected]
123
Environ Geol (2009) 56:1323β1352
DOI 10.1007/s00254-008-1230-3
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the major ions. Groundwater in carbonate rocks has pH
values above 7, and mineral contents usually dominated by
bicarbonate and calcium. Naturally, ground water contains
mineral ions and these ions slowly derived from soil par-
ticles, sediments, and rocks as the water travels along
mineral surfaces in the pores or fractures of the unsaturated
zone and the aquifer and may have originated in the pre-
cipitation water or river water that recharges the aquifer
(Cook et al. 1991). It is divided into three groups: major
constituents, minor constituents, and trace elements. The
naturally occurring dissolved solids are inorganic constit-
uents; minerals, nutrients, and trace elements, including
trace metals. In most cases, trace elements occur in such
low concentrations that they are not a threat to human
health. In fact, many of the trace elements are considered
essential for the human metabolism.
Mineral exploration and exploitation, although indis-
pensable for development of any country, release major
and trace elements, especially the so-called heavy metals,
which contaminate our natural environment due to their
toxicity, persistence and bioaccumulation problems.
Increased anthropogenic inputs of major and trace metals
in terrestrial environments have also caused considerable
concern relative to their impact on groundwater contami-
nation. Although most major and trace metals are generally
considered to be relatively immobile in the short term, their
mobility under certain chemical conditions may exceed
ordinary rates and pose a significant threat to groundwater
quality (Scokart et al. 1983). Coal mining is one of the core
industries in India, which plays a positive role in the eco-
nomic development of the country. Mining activity releases
the major and trace elements, especially the so-called
heavy metals, which pollute our natural environment due to
their toxicity, persistence and bioaccumulation problems.
Their occurrence in water resource and biota indicate the
presence of natural or anthropogenic sources (Rivail Da
Silva et al. 1996; Fang and Hong 1999; Klavins et al. 2000;
Tam and Wong 2000). High concentrations of major and
trace metals can also be recorded in the ground water near
contaminated sources posing serious health threats. Some
trace constituents that are associated with industrial pol-
lution may also occur in completely pristine ground water
at concentrations that are high enough to make that water
unsuitable for drinking purposes. A range of groundwater
contamination problems can be associated with mining
activities. The nature of the contamination depends on the
materials being extracted and the post-extraction process-
ing. Coal, salt, potash, phosphate and uranium mines are
major polluters (Todd 1980). Metalliferous mineral
extraction is also important, but stone, sand and gravel
quarries, although more numerous and widespread, are
much less important chemically. Both surface and under-
ground mines usually extend below the water table and
often major dewatering facilities are required to allow
mining to proceed. The water pumped, either directly from
the mine or from specially constructed boreholes, may be
highly mineralised and its usual characteristics include low
pH (down to pH 3) and high levels of iron, aluminium and
sulphate. The contamination of groundwater can also result
from the leaching of mine tailings and from settling ponds
and can, therefore, be associated with both present and past
mining activity.
In India, coal is the most important economic mineral
and is primary source of energy, thus its demand is
increasing day by day. Coal mining is one of the core
industries, which plays a positive role in the economic
development of the country. A huge volume of coal waste
is produced with considerable amount of residual pyrite
content. These kinds of wastes, which are usually disposed
off in the environment without specific treatment severely,
contaminate surface/ground water and endanger the eco-
systems of the mining area (Karathanasis et al. 1990; Haigh
1993; Hassett 1994; Poulin et al. 1996; Xue et al. 2006).
Several studies have shown that most of contamination in
coalmines can be released into the surrounding environ-
ment by leaching, and more attention should be paid to this
kind of contamination (Filcheva and Noustorova 2000;
Haigh 1995; Krothe et al. 1980; Li 1988).
Formation of acid drainage due to coal mining and its
consequences involve the movement of various chemical
elements through the environment as a result of physical,
chemical and biological processes that play important roles
in the production, release, mobility and attenuation of con-
taminants in mine waters. Physical aspects include
topography, climate, geology, hydrology and the effects of
mining and mineral processing (Lopez-Pamo et al. 1999).
Mining accentuates and accelerates natural processes. The
development of underground workings, open pits, ore piles,
mill tailings, and spoil heaps and the extractive processing of
ores enhance the likelihood of releasing chemical elements
to the surrounding area in large amounts and at increased
rates relative to unmined areas (Salomons 1995; Yan and
Bradshow 1995; Szucs et al. 2000). Groundwater from coal-
bearing sediments also has the potential to become acidified
with enhanced metal dissolving properties. Groundwater is
extracted from aquifers within the basins throughout the
district and forms the basic source of water supply for potable
and irrigation purposes. Most groundwater is of moderate
quality and is readily replaced by rain and river recharge.
Groundwater resources are being extracted to an alarming
rate (Department of Geology 2005).
Spotting the possible contamination sources and under-
standing its pattern is important. Some of the contaminating
sources are natural such as minerals present in the rocks.
Other sources may come from activity like mining that can
release large amount of major and trace metals into the
1324 Environ Geol (2009) 56:1323β1352
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ground water resources. At high levels, these metals pose a
health risk. The most serious environmental problems for
humans on the earth are the continuous or sporadic lack of a
contamination and disease free water supply for drinking
purpose. The problems associated with water contamination
are extremely serious. However, contamination is more
likely to result from chronic processes that discharge pol-
lutants directly into rivers. Groundwater unlike river water,
have long residence times varying from hundred and
thousands of years. Therefore, natural removal of pollutants
from groundwater is a very slow process, and correction is
very costly and difficult. Many regions of ground and sur-
face water within the study area are now contaminated with
major and trace metals that have an adverse effect on health.
Safe water for all can only be assured when access, sus-
tainability, and equity can be guaranteed. There has to be an
effort to sustain it, and there has to be a fair and equal
distribution of water to all segments of the society (Keller
1995). Geostatistics has proved to be a useful tool for the
study of spatial uncertainty and hazard assessment (Go-
ovaerts 1999, 2001). The geostatistical technique like
kriging in GIS environment was applied to evaluate the
quality of groundwater. The spatial dispersion of residual
contamination of major and trace metals viz. Fe, Al, Cd, Cr,
Cu, Mn, Ni, Pb and Zn in ground and surface water is
estimated and its spatial distribution is ascertained for
Chandrapur district of Maharashtra in India in order to map
environmental hazards within the region.
The purpose of this study is to develop an understanding
of regional groundwater and surface water quality in the
Chandrapur district in Central India where coal mining is
an important industry. The study strives to acquire
knowledge of the quality of ground water as well as surface
water with respect to its metal content. This study specif-
ically aims at predicting the spatial distribution of major
and trace metal contamination in the region using geosta-
tistical methods and application of statistical method to
correlate the geology of the area with the mine contami-
nation with respect to metals.
The study area
Chandrapur district is located in the eastern edge of
Maharashtra in βVidarbhaβ region lying between 19οΏ½250 N
to 20οΏ½450 N and 78οΏ½500 E to 80οΏ½100 E and covers an area of
11,364 km2 (Fig. 1). This district is considered to be one of
the most important mining deposits of Maharashtra, India.
70Β°0'0"E
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LegendMAHARASHTRA
INDIA
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LegendChandrapur
MAHARASHTRA
Fig. 1 Study area depicting the different tehsils of Chandrapur district of Maharashtra, India
Environ Geol (2009) 56:1323β1352 1325
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Intense mining activities along with natural processes like
rock alteration attributes to high major and trace metal
concentration in the ground water and stream water.
Chandrapur district of Maharashtra state, located in the
central part of India, is well known for its sprawling coal
mines, thermal power plant, paper mills, cement plant, etc.
There are several mines in the Chandrapur District viz.
coal, limestone, fluorite, chromium, fireclay, iron, copper,
etc. The mineral based industrial development and rapid
urbanization has resulted in environmental contamination
and degradation and its effects are now being felt. The
mining activities disturb the ground water balance. It is
also the fact that it increases ground water contamination as
mine water has lower pH values in the ranges of 2β4, which
dissolves metals from the surrounding geological forma-
tions. Wastewater released from mine pit contains
suspended solids, low pH, sulphates, major and trace
metals, etc. It is also a fact that the entire quantity of the
mine water is not discharged into the environment since
this is used for suppression of the dust through sprinkling
and to water the plantations in the abandoned mine areas.
Geology, mineralization and mineral development
Geology
Geologically, Chandrapur district forms a part of Gondw-
ana sedimentary basin. The Gondwana sedimentation took
place in Wardha valley where Gondwana sediments have
overlay the Archean rocks. Litologically Chandrapur dis-
trict presents a variety of statigraphic units right from
Archean to recent alluvium and laterites (Fig. 2).
The Archaean rocks comprise gneisses, quartzites, ban-
ded haematite quartzites (BHQ), schists with basic intrusives
like pyroxinites, amphibolites, etc. The rocks are intruded by
several dykes, trending NEβSW, are exposed in the eastern
part of Chandrapur district. Iron ore series and Sakoli series
are equivalent in age. Iron ore series constitutes the important
iron deposits of Chandrapur district. The rocks are quartzite,
BHQ, quartzite schist, phyllites, etc. The Dharwars have
been intruded on a very large scale and comprise of granites,
granitoids and gneisses. The Vindhyans are represented
mainly by flaggy and massive limestones, shaleβs and
sandstones. The lenticular patches of breccia with angular
fragments cemented by calcareous matrix are found at sev-
eral places in limestones. The limestones are dolomitic at
places. Sandstones and quartzities are hard copact and forms
ridges. Lower Gondwana includes hard quartzite, sand-
stones, grits, and conglomerates. The sandstones are fine-
grained whitish colored and calcareous in nature. The shales
are of red colour and are found in small patches in the
southeastern part of Chandrapur district. The Deccan trap
lava formation covers small part of the district. The
amygdaloidal softer variant varieties usually show calcite
filling. In the district, Alluvium is mostly of fluviatile origin
and comprises sand, silt and clays. It is generally found along
the banks of nallas and rivers. Its thickness varies from 8 to
35 m as observed along the Wardha river, the Erai and the
Wainganga river courses. It also contains gravel along with
sand, silt and clays at places.
Structures
Gondwana formations are structurally highly distributed on
a large scale. On a regional scale Gondwana formations
can be classified as broad anticlinal structure plugging
towards NNW. Chanda-Wardha Gondwana basin occuring
in the district is essentially a broad graben structure.
Structurally, Wardha valley basin represents by a number
of faults evident in few coalmines around Chandrapur. The
structural analysis of the Wardha valley shows that
Gondwana formations are aligned in three sub-parallel
troughs. The entire area between Durgapur and Lalpeth
Colliery lies between two major faults with regional trend
of NWβSE The same NWβSE trending fault system are
dominant between Isapur block and Durgapur in the north.
The structural features noticed in this rock are fractures,
joints and weathering. The thickness of weathered zones
normally ranges between 3 and 5 m in gneisses and Deccan
trap while it is comparatively less in Vindhyans.
Ore mineralization
The district is gifted with deposits of various minerals like
coal, iron, limestone, clay, copper, chromium, etc. Thermal
power plant, many coal mines, cement and paper factories,
huge lime stone deposits, iron, and chromite mines are the
sources of wealth for the district. Natural deposits of the
high-grade iron ore in Sindewahi taluka are estimated to be
2,200,000 tonnes; limestone in Rajura and Korpana talukas
(547,000,000 tonnes). Coal in Chandrapur taluka alone is
estimated to be 1,227,000,000 tonnes. Availability of huge
coal deposits has led to increased coal mining activity and
the power plant. Availability of limestone has prompted
cement industries particularly in Rajura Tehsil. Paper mills
are established because of availability of wood/bamboo
located on banks of river or nallas. Decadal growth rate of
the district is about 25%. Chandrapur taluka has experi-
enced 47% growth rate and is closely followed by the
Rajura taluka. This growth is mainly due to the abundance
of the minerals and industries developed in the district.
Mining exploitation
Opencast mining has more prominent environmental impact
than underground mining. With improved technology,
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opencast coal mining is being used extensively because of its
cost effectiveness and productivity though it results in large
scale land disturbance. Although underground mining has
considerably less impact than opencast mining, it causes
enough damage through subsidence, which inflicts severe
damages to engineering structures such as highways, build-
ings, bridges and drainage besides interfering with ground
water regime.
Mining and its associated activities, especially large and
deep open cast mines, not only use large quantities of water
but also disturb the hydrological regime of the district and
often affects the water quality. The water seeping from
aquifers into the mine and collected in the mine sump is
partly used up in the mine and the excess amount is dis-
charged into the surface drainage system, which
contaminates groundwater through weak zones. Average
production per mine per day is 1,790 tonnes and total
wastewater due to coal mining activity is estimated to be
around 186,886 m3/day. Hence, there is a likelihood of the
area in the immediate vicinity of the mine to be deprived of
the ground water approximately 5,340 m3 per day (MPCB
2006). Thus, focus of this study is to assess the ground
water quality in relation to major and trace metal around
sprawling coal mining areas within Chandrapur region.
Fig. 2 Geology of the
Chandrapur district,
Maharashtra, India
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Physiography and drainage
Chandrapur is located on the eastern edge of Maharashtra
state in βVidarbhaβ region. The district is surrounded by
Nagpur, Bhandara and Wardha on the northern side, Ya-
vatmal on the western side, Gadchiroli on the eastern side
and Adilabad district of Andhra Pradesh on the southern side.
The district has been divided into nine geomorphic units as,
regions of low level plateau, plain, low level structural pla-
teau on Gondwana rocks, structural plain on Gondwana
rocks, structural plain on Proterozoic rocks, structural hills
and valley, pediment/pediplain, laterite cover and flood plain
(including in-filled rivers). The elevation of various plateaus
in Chandrapur district ranges in between 350 and 550 m
from MSL, high level in southwestern and low level in
southeastern region (Fig. 3a). The structural ridges, struc-
tural hills, highly and moderately dissected plateau over the
Deccan traps are grouped into geomorphological units of
structural origin. The pediments/pediplains and denuda-
tional hills are grouped into geomorphological units of
denudational origin, whereas, the younger and older allu-
vium forms the units of fluvial origin.
Physiographically, the district is situated within the
Wainganga and Wardha river basins, which are the tribu-
taries of Godavari river, respectively flowing on the eastern
and western boundaries of the Wardha district. Wainganga
and Penganga are the important rivers in Chandrapur
district. The Wardha river flows into the district from the
western boundary and then flows along the boundaries of
Warora, Chandrapur, Korapna, Rajura, Ballarpur and
Gondpipri talukas. Penganga and Irai rivers are tributaries
of Wardha River. The Wainganga flows along the eastern
boundary of the district. This river flows from north to
south. The confluence of the Wardha and the Wainganga
River is near Shivani. Andhari and Mul (Uma) are two
other rivers in the district which flow in NNWβESE
direction. The drainage seems to have a lithological con-
trol. The dominant drainage pattern found in the area is
dendritic drainage where a number of tributaries and nallas
joint the main river in a dendritic manner. Trellies drainage
pattern is also present in the area. Locally, a control of
stream segments by fractures and faults and lineaments is
clearly recognizable.
In the flat areas, the drainage pattern tends to be den-
dritic, with a predisposition to parallel. A strong control by
linear features is also observed. Areas with absence of
drainage have been observed as a rather common feature in
some parts of the study area. Geological maps have con-
firmed the calcareous nature of bedrock in those zones,
where, due to karst phenomena, underground water circu-
lation is much more developed than surface runoff. In
southwestern part of the district characterized by folded
structure with a variable trend, greatly influence drainage
pattern and thus this area represents trellis like drainage.
Fig. 3 a Digital elevation model (DEM) of Chandrapur district, Maharashtra, India; b sampling locations of groundwater and surface water upon
stream network of the Chandrapur district, Maharashtra, India
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The drainage system developed in an area depends on
the slope, the nature and altitude of bedrock and soil type.
The regional and local drainage pattern may be visible on
remote sensing imagery, in turn indicates the type of
lithology and structure of a given area and can be of great
importance for ground water resources evaluation. Drain-
age is studied with respect to its pattern type and its texture
(or density of dissection) (Way 1973). Whilst the first
parameter is associated with the nature and structure of the
substratum, the second is related to rock/soil permeability
(and thus also to rock type). In fact, low permeability of
rocks results in less infiltration and increased surface run-
off. This gives origin to a well developed and fine drainage
system. On the other hand, in karst regions, where the
underground circulation of water is much more developed
than the surficial one, drainage is less developed or missing
altogether. The streams were digitized as vector layers,
using both the IRS P6 LISS III images (mainly false colour
composite 321) and the topographic maps as references at a
scale of 1:50,000.
Hydrogeology
The coal-belt in Chandrapur region comprises of semi-
consolidated stratified, sedimentary rocks of Gondwana
group. Amongst these formations, Kamthis and Barakars
contain mostly sandstone whereas Moturs and Talchirs are
predominantly clay/shale with thin sandstone bands. Nor-
mally, the sandstone horizons serve as aquifer material and
impervious clay/shale beds act as aquicludes. The Kamthi
and Barakar formations possess moderate porosity (pri-
mary). However, the secondary porosity plays significant
role in ground water occurrence and movement. Ground
water exists under both confined/semiconfined and
unconfined conditions. The unconfined aquifer is mostly in
soil/detrital mantle whereas the confined/semiconfined
aquifer lies at greater depths in Kamthi, Motur and Barakar
formations. The unconfined aquifer generally extends up to
a depth of 20 m below ground level and is tapped by dug-
wells. Depth to water table in this aquifer ranges form 1.0
to 19.0 m below ground level (BGL) in pre-monsoon
season. The elevation of water table varies from 230 m
(north western part) to 160 m (south eastern sector) above
MSL. Chandrapur district shows declining trend of ground
water levels (more than 20 cm per year) pre-Monsoon
(1995β2004) (MPCB 2006). The ground water flow is
towards the river Wardha and its tributaries thereby con-
firming the effluent nature of the river. The peripheral area
of the coal-belt is the recharge zone where as the discharge
belt is in the area of hydraulic lows and natural drains. The
recharge in this coalfield is computed to be around 10% of
normal rainfall. The potentiality of the unconfined aquifer
is poor to moderate. The hydraulic conductivity generally
varies from 2.0 to 6.0 m/day. The semi-confined nature of
the deep aquifer is established in major parts of the coal-
belt in view of the leakage form the overlying formation/
aquifer with or without impervious clay bed in between the
two aquifers. But, wherever huge pile of Moturs is
encountered in the dip side, the aquifer is expected to be in
confined conditions. The aquifer in Lower Barakars is also
expected to be under confined condition in the dip side due
to overlying composite coal seam. The semi-confined
aquifer in Kamthis possesses better ground water potential
in comparison to Upper Barakars. The highest potential is
observed in Kamthis in the eastern limb of the Chandrapur
region coal-belt around Lohara, Durgapur and Bhatadi
villages where the hydraulic conductivity ranges from 18 to
32 m/day. The ground water potential in general is poor to
very poor in Lower Barkars with some exceptions where,
the Kamthis directly overlap and recharge the Lower
Barakars. The Moturs have little/no hydrogeological sig-
nificance as potential aquifer except acting as a separating
medium between Kamthis and Barakars.
Water resources
Wardha, Wainganga and Penganga are the important rivers
in Chandrapur district. The total replenishable ground water
resource is of the order of 3.782 9 1010 m3/year provision
for domestic, industrial and other uses 1.24 9 1010 m3/year.
Available ground water resources for irrigation is 2.547 9
1010 m3/year and the net draft is 3.8 9 1010 m3/year.
Study design
Remote sensing of land use and land cover
Remote sensing has proved to be very useful for surveying
natural resources and monitoring the environment espe-
cially when fast and repeated observations are required.
The most obvious tool to correlate the spatial extent of
contamination with other resource feature both concisely
and rapidly is satellite data IRS P6 LISS III dated 15
January 2006 and was employed to discriminate various
land use/land cover classes.
Water sampling protocol
Sampling sites were selected such that each tehsil covering
mine sites were covered and the major land use types
of high-density residential, low-density residential, and
industrial/transportation were represented. Groundwater
samples were collected in the upper natural slopes and the
lower highly urbanized places in the area while surface
Environ Geol (2009) 56:1323β1352 1329
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water samples in vicinity of mines during summer season,
May 2006 (Fig. 3b). The land use/land cover map gener-
ated from satellite imagery viz. IRS P6 LISS III (Fig. 4)
was considered along with the coal mining areas in
selecting the sample locations. The contamination increa-
ses due to low dilution and this tends to the accumulation
of the metals (BAMAS 2005) and also, there is an
enhancement of mining activity in the study area for this
period. In all, 208 groundwater samples for metal analysis
were collected from sources viz. hand pumps, bore well,
dug well and 35 surface water samples from sources viz.
rivers, ponds and canals using GPS, Garmin eTrex Legend
in the vicinity of the study area. Surface and ground water
samples were collected in polyethylene bottles washed
with detergent, then with deionised water and further it is
rinsed with 2.0 M nitric acid (Merck), again with deionised
water. In order to prevent or minimize any physical
changes and chemical, biochemical reactions, which may
take place in the sample, will change the intrinsic quality of
the samples, it was necessary to preserve the sample before
shipping; the samples were thus acidified with HNO3 and
put in an ice bath and kept at 20οΏ½C until analysis in the
laboratory. Preservation of samples with nitric acid holds
the time up to 6 months. This minimizes cations metal
precipitation and adsorption onto the sample container wall
(APHA 1992). However, the samples were digested with
high purity nitric acid within a week of sample collection.
Laboratory analytical procedures
Inductively coupled plasma atomic emission spectrometry,
ICP-AES, was used to analyze Al, Cd, Cr, Cu, Fe, Pb, Ni
and Zn. A 100 ml representative aliquot of the well-mixed
sample was placed into an acid washed glass beaker to
which 3 ml of concentrated nitric acid (HNO3, Merck) was
added, and the beaker was covered with a watch glass. The
sample was heated on a hotplate and evaporated to
approximately 5 ml at 95οΏ½C without boiling. Sample
digestion with nitric acid allows total extraction of metals
from the samples. As far as groundwater samples are
concerned, it contains very less quantity of metal concen-
tration. In order to understand the cause and effect, total
extraction of metals was required since the study area is
composed of different sprawling mines as well as different
lithic units. The beaker was then allowed to cool, after
which, 3 ml of concentrated nitric acid (HNO3) was added
and the beaker was covered with a watch glass. The solu-
tion was again heated at 95οΏ½C and refluxed. Additional acid
Fig. 4 False color composite and landuse/landcover map of the study area
1330 Environ Geol (2009) 56:1323β1352
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was added and the reflux repeated until the appearance of
the digested solution becomes clear. The digested solution
was evaporated to 3 ml at 95οΏ½C, without boiling. After
cooling, a small quantity of 1:1 nitric acid was added to the
digested sample and covered with a watch glass to reflux
for an additional 15 min to dissolve any precipitate or
residue resulting from evaporation. After cooling, the
digested sample was transferred to a 25 ml volumetric
flask, diluted to 25 ml with Milli-Q deionised water (ana-
lytical grade), and the sample solutions were analyzed for
major and trace metals in the ICP-AES at specific wave-
length to measure different elements (Table 1). The
detection limit is defined as the lowest analytical signal to
be distinguished qualitatively at a specified confidence
level from the background signal (Kackstaetter and Hein-
richs 1997). In the ICP-AES analysis, the detection limits
of the measured elements in the study were defined as the
concentration value which corresponds to an absorbance
value, numerically equal to three times the standard devi-
ation of ten replicate blank measurements. Analytical
reagent blanks were prepared for each batch of the diges-
tion set and then analyzed for the same elements as the
samples. Analytical precision was in good agreement,
generally better than 5% RSD. The unit of measurement is
reported as lg/l unless otherwise stated. The analysis of
variance (ANOVA) method was used to estimate the
measurement uncertainty across the whole site, and for
different sampling locations. An important objective was to
check that the measurement variance originates in the
processes of measurement (sampling and analysis) to
indicate that variations in the analytical data are not caused
by field sampling and/or analytical errors but by real (e.g.,
geogenic or anthropogenic) spatial variations. The within
sample mean square can be used to give an estimate of the
analytical measurement variance, whereas the between-
sample mean square gives an estimate the sampling vari-
ance (Scaccia and Passerini 2001). In order to have clear
depiction of the natural geochemical variance, the com-
bined sampling and analytical variances for the data should
comprise not more than, say, 20% of the total variance. In
general, the analytical variance should comprise not more
than 4% of the total variance (Ramsey et al. 1992). The
data used for analysis was checked for normal distribution
and redundant data has been reduced. The differences in
metal determinations of replicate water samples, ten for
groundwater and five for surface water (within and between
batches) had been performed using SPSS software and
determined statistically (Table 2). The F statistic was used
to test the null hypothesis and the calculated value of F
(ratio of the between-sample to the within-sample vari-
ances) does not exceed the critical value for Fe, Al, Cd and
Ni at desired level (a \ 0.05) for groundwater where as in
case of surface water null hypothesis is true for all metals.
The ANOVA shows there are not significant differences
between analysts. To reiterate the interpretation of
ANOVA results, a calculated Fvalue that is greater than
Fcritical for a stated level of confidence (typically 95%)
means that the difference being tested is statistically sig-
nificant at that level for rest of the metals. In this
application, the measurement variance contributed 4.11%
of the total variance, which is well within 20% target
(Ramsey et al. 1992).
Geospatial data analysis
In this study, major and trace metal concentration in water
database was converted into ArcInfo coverage format and
merged with spatial database in GIS. Attributes include
coordinates of the sample, location of the sample and
results of the chemical analysis for a number of major and
trace metals. Some of the sample points were taken into
consideration beyond the district boundary. The clip
function was used in order to extract newly created inter-
polated layers. The output themes are in the same
coordinate system, i.e. UTM 44 N WGS 84 ellipsoid as the
input layers.
Maps provide helpful visual displays of the spatial
variability in the field and can be used for the summari-
zation and representation of spatial data for environmental
modeling (Goodchild et al. 1993). The maps and figures
show the locations of observation and measured values of
parameters, such as that of major and trace metal concen-
tration. The associated database contains information on
sampling and analytical methods including quality control
data. Information on rock type, hydrogeology, topography
and vegetation in this region is also included in the map.
The similar phenomenon may be observed in the case of
limestone, iron and chromites mining. Due to the non-
biodegradability of major and trace metals and their long
biological half-lives, their accumulation in the food chain
Table 1 Elements, spectral lines and instrument detection limit
measured by ICP-AES
Element Wavelength
(nm)
Instrument detection
limit (lg/l)
Al 308.215 45
Cd 228.802 2
Cr 283.563 5
Cu 324.754 7
Fe 259.940 25
Mn 257.610 1
Ni 231.604 5
Pb 220.253 10
Zn 213.856 10
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Table 2 One-way ANOVA of Replicate analysis of metal concentration in ground/ surface water
Major/trace metal Sources of variation Sum of squares df Mean square F
Groundwater
Fe Between groups 327,158.702 9 36,350.967 1.442
Within groups 48,617,446.337 1,929 25,203.445
Total 48,944,605.039 1,938
Mn Between groups 183,793.835 9 20,421.537 9.241
Within groups 4,285,113.399 1,939 2,209.960
Total 4,468,907.234 1,948
Pb Between groups 22,686.225 9 2,520.692 3.548
Within groups 1,413,022.795 1,989 710.419
Total 1,435,709.020 1,998
Zn Between groups 84,129.214 9 9,347.690 5.792
Within groups 3,274,418.223 2,029 1,613.809
Total 3,358,547.437 2,038
Cu Between groups 25,735.854 9 2,859.539 4.808
Within groups 1,141,334.379 1,919 594.755
Total 1,167,070.233 1,928
Cr Between groups 8,517.497 9 946.389 3.797
Within groups 423,423.646 1,699 249.219
Total 431,941.143 1,708
Al Between groups 7,092.466 9 788.052 0.603
Within groups 2,649,985.430 2,029 1,306.055
Total 2,657,077.896 2,038
Cd Between groups 1,947.451 9 216.383 0.295
Within groups 1,270,105.948 1,730 734.165
Total 1,272,053.399 1,739
Ni Between groups 13,245.650 9 1,471.739 2.976
Within groups 889,662.355 1,799 494.532
Total 902,908.004 1,808
Surface water
Fe Between groups 1,054,120.0 4 263,530.0 0.045
Within groups 985,651,966 170 5,797,952.7
Total 986,706,086 174
Mn Between groups 2,099.429 4 524.857 0.004
Within groups 21,546,909 170 126,746.521
Total 21,549,008 174
Pb Between groups 1,802.822 4 450.706 0.556
Within groups 68,946.778 85 811.139
Total 70,749.60 89
Zn Between groups 17,557.697 4 4,389.424 0.408
Within groups 1,516,987.9 140 10,835.628
Total 1,534,545.6 144
Cu Between groups 915.813 4 228.953 0.429
Within groups 37,385.333 70 534.076
Total 38,301.147 74
Cr Between groups 2,482.518 4 620.629 3.81
Within groups 13,033.176 80 162.91
Total 15,515.694 84
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will have a significant effect on human health in the long
term (Alloway 1990; Kabata-Pendias and Pendias 1992;
Lee et al. 2006).
Spatial interpolation of major and trace metals
in groundwater
Environmental variables vary in ways too complex to
represent them with simple deterministic functions (Buys
et al. 1992). In addition, it is somewhat difficult to measure
these variables at all positions of interest. Geostatistical
methods were developed to create mathematical models of
spatial correlation structures with a variogram as the
quantitative measure of spatial correlation (Matheron 1970;
Journel and Huijbregts 1978; Isaaks and Srivastava 1989;
Goovaerts 1997; Webster and Oliver 2001). The geosta-
tistical methodology presented in this study calculates
spatial patterns of contamination (variograms and spatial
covariances) with experimental data of contaminant major
and trace metals by using ordinary kriging.
The variogram is commonly used in geostatistics and the
interpolation technique, known as kriging, provides the
best, unbiased, linear estimate of a regionalized variable in
an unsampled location, where best is defined in a least-
squares sense (ESRI 1995). Among several estimation
methods, kriging is the most popular because it is a col-
lection of generalized linear regression techniques for
minimizing and estimating variance defined from a prior
model for a covarianceQ (Olea 2001). Kriging is not just
used to estimate values at unsampled areas; it is also used
to build probabilistic models of uncertainty about the
unknown, but estimated predicted values (Deutsch and
Journel 1998). The emphasis is laid on local accuracy, i.e.
closeness of the estimate to the actual, but unknown, value
without any regard for the global statistical properties of
the estimates. The krigging estimation variances are inde-
pendent of the value being estimated and are related only to
the spatial arrangement of the sample data and to the model
variogram. The kriging method can be viewed as one of
weighted linear interpolation, which takes the existing
spatial correlation between stations and does so through the
semi-variogram and tool such as GIS, especially those
which have a raster data model, is extremely useful for
preparing major and trace metal such as Fe, Al, Cd, Cr, Cu,
Mn, Ni, Pb and Zn contaminated map of Chandrapur dis-
trict. The result of the selected element is shown in
different figures.
Though substitution of values for non-detect results has
no theoretical basis of support, substitution also does not
interfere with the median value or other percentile ranks
provided that the amount of censored data is less than 25%
of the dataset. These statistical entities provide a much
better estimation of the central tendencies of nonparametric
data (Helsel 1990; Helsel and Hirsch 1992). Fortunately,
the database considered for analysis in the current study
contains nearly equal to 10% of censored data. In most
cases, detection limits are used as the substituted concen-
tration or activity values for samples below detection
limits. However, other statistically defined values can also
be used (Helsel 1990). This correction does not have a
strong effect on the sample mean (Helsel and Cohn 1988).
Thus in order to statistically account for analytical results
that are below the detection limits, detection limit of the
concerned metal was assigned to all results that were below
the method detection limit. Values measured as non-
detectable are considered indicating uncontaminated,
which represent naturally occurring background values. In
geostatistical analysis, since assuming zero for non-
detectable samples make erroneous in spatial distribution
mapping, detection limit of each metal was considered for
censored samples. This assumption will have no impact in
contamination point of view. In addition, since there is no
simple technique to determine which method of ordinary
kriging is applicable to metal concentration, an attempt was
made to undertake a selection of the best model for a
variety of cell sizes. In this analysis, a cell size of 25 to
1,200 m was used in a kriging estimation using all models
(i.e. linear, spherical, gaussian, circular and exponential).
Table 2 continued
Major/trace metal Sources of variation Sum of squares df Mean square F
Al Between groups 1,100,280.6 4 275,070.143 0.036
Within groups 1.308E+09 170 7,691,338.4
Total 1.309E+09 174
Cd Between groups 544.112 4 136.028 1.084
Within groups 15,060.4 120 125.503
Total 15,604.512 124
Ni Between groups 69.382 4 170,345 0.009
Within groups 95,650.0 50 1,913.0
Total 95719.382 54
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The semivariograms produced moderate results as it is
affected by the anthropogenic factors, the sudden high
concentration in a region, the method of sampling, the
close proximity to a point contamination sources, etc.
(ESRI 1998).
In order to assess the quality of an output surface by
comparing the predicted values for specified locations with
those measured in the field, validation has been performed. It
is often not possible to go back to the study area to collect an
independent validation dataset. The solution left over is to
divide the original dataset into two parts as training and test
sets. Kriging is unique in terms of producing prediction
standard error map unlike other interpolation methods. With
the prediction standard error map, one can clearly commu-
nicate the uncertainty in the predicted metal content in
groundwater and surface water map. The performance of the
kriging interpolation method is calculated using the esti-
mation variance generated during the model fit of the
semivariogram, producing a standard error map. Standard
Error maps are produced from the standard errors of inter-
polated values, as quantified by the minimized (root)
prediction mean squared error that makes kriging optimal.
These maps were generated using the kriging standard error
map function within the ArcGIS Geospatial Analyst exten-
sion (Waller and Gotway 2004).
Spatial principal component analysis of major and trace
metals in groundwater
In order to show the interrelationship between data and
variables there is a need to integrate statistical analysis into
the GIS. Principal Component Analysis is a robust statistical
technique to reduce data and develop composite variables.
PCA or factor analysis is a statistical technique to analysis
individual measurements that are inter-correlated. However,
it is not linked spatially. The data can be input to GIS and
display the spatial tendencies. Principal component analysis
is common multivariate statistical method and is widely used
to identify pollution sources, to apportion natural- versus
human-caused contribution, and to describe spatial distri-
bution of pollutants (Atteia et al. 1994; Tao 1995; Carlon
et al. 2001; Facchinelli et al. 2001). PCA converts the vari-
ables (analytic concentrations) into factors or principal
components. The first factor explains the most variance, the
second factor the next most and so on. It follows that the
dimensionality of the original data space can be reduced to a
few factors, commonly two or three, retaining most of the
overall variance. Furthermore, the factors can be rotated, in a
way that each factor explains a different subset of correlated
variables (i.e. analyzes). The PCA also calculates a factor
score for each sample and can be plotted. Samples with
similar analytic compositions (i.e. scores) are aggregated
closer than those with more dissimilar compositions. The
similarities among samples can then be used to elucidate
potential sources (Einax et al. 1997). A Varimax rotation is
employed to aid in interpretability of the low-variance
principal components. This makes closer to finding unique
metal concentration related to outcome but reintroduces
correlation, requiring analysis of the overlap of information
contained in such modes. Varimax rotation perturbs the ei-
gen vectors so as to maximize the variance within each
vector. As a result in each vector the number of variables
with intermediate loadings is decreased and the number with
either very large (absolute magnitude) or very small loadings
is increased.
The analytical results are complied into a statistical
database inserted into a GIS system (ArcGIS) for further
analysis. The data was then imported in to SPSS and PCA
was performed. It yielded three principle components and
the relationship to each component by sampling point. The
PCA analysis was then joined to the GIS by back trans-
forming the results of PCA (scores) into ArcGIS and an
additional spatial interpolation is performed using spatial
analysis to produce output maps. The major and trace
metals showing close association were identified in three
components to correlate its sources. The analysis is done by
extracting the eigenvalues and eigenvectors of the corre-
lation matrix and discarding redundant values as stated in
% values. PCA results for each data set are given with the
identified principal components and their explained vari-
ances, which together with their distribution over the
samples (scores) help to determine the possible origin or
causes of contamination in the study area. This approach is
the most universally used one in earth sciences when factor
analysis through PCA is applied (Hwang et al. 2001).
Analysis of surface water geochemical data
Surface water quality of major/trace metal assessment
involves the representation of spatial existence of surface
water viz. drainage, river, tributaries and canals since
cross-contamination with groundwater is a major concern.
The GIS based analysis was performed to represent major/
trace metal concentrations of the surface water bodies.
Sampling locations were monitored with the help of GPS.
The major/trace metal concentration of samples are con-
verted to dbf file that includes attributes for over nine
major/trace metal parameters, site sampling IDβs, names,
and geographic locations. The XβY analysis was used to
import sample locations as a layer onto a drainage map.
Since, it was required to map a single variable, the most
popular method of choosing class limits was to look for
natural breaks in the frequency distribution. The objective
was to use low points on the frequency distribution as class
limits. This minimizes the potential for having observations
with similar values grouped into different classes. Use of
1334 Environ Geol (2009) 56:1323β1352
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natural breaks in the frequency distribution is effective for
single variable map. The surface water geochemical data of
major/trace metals were grouped into five categories using
the ArcGIS natural break criterion (Campbell 1998; Ogu-
chia et al. 2000). The values in different groups has been
represented through graduated symbol map using pro-
gression values of major/trace metals with different
diameters and colours represented in maps. These maps can
provide valuable visual tools for demonstrating observa-
tions to various end users.
Correlation of water geochemical data with geology
A correlation-based study was performed to compare the
lithological characteristics of different rock units with water
geochemical data in Chandrapur district. The number of
samples considered for surface and groundwater samples
were 208 and 35, respectively, distributed randomly through
out the area. There was adequate information reported
elsewhere about the litho-geochemical data in different rock
types to compare with water geochemical data. A mapping
Fig. 5 Superimposition of
water sampling points over
specific lithology
Environ Geol (2009) 56:1323β1352 1335
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strategy designed specifically to compare lithogeochemical
data of different lithic units and their equivalents with water
chemistry. This has been accompanied by superimposing the
sampling points upon a specific lithology and estimating its
average value of each metal concentration (Fig. 5). The data
has correlated with the mean value of the geochemical data in
different lithic units. In all, seven lithic units were considered
to compare the relationship on the basis of chemical simi-
larity of rocks. The Pearson correlation coefficients are
obtained between surface/groundwater metal concentration
and different geological units.
Results and discussion
Major/trace metals in groundwater
Proportions of major/trace metals in groundwater
The major and trace metals characterization in ground water
with maximum, minimum and mean values of the parame-
ters are presented in Annexure I. The average concentration
of each metal is measured by calculating the arithmetic mean
concentration of each metal at all locations. Similarly, the
contribution of each metal is measured by calculating
the percentage of the average concentration of each metal to
the total average concentration of all metals in the sample.
The % contribution of each metal in groundwater samples
thus computed is observed as Fe (48%), Mn (12%), Zn (9%),
Al (8%), Pb (7%), Cu (6%), Ni (4%), Cd (3%) and Cr (3%) of
the total average concentration and is represented in the pie
diagram (Fig. 6). Metal contamination may be due to the
presence of different mines and with weathering of rocks or
geological formation (Robins 2002).
Univariate spatial distribution of major/trace metals
in groundwater
The resulting map of kriging interpolation of major and
trace metals in Chandrapur region indicates that the high
Al concentration spotted in Ballarpur and Rajura tehsil,
which may be due to existence of sedimentary and
igneous rock in the region (Fig. 7a). The high Cd con-
centration lies at Bhadravati and Warora tehsil may be
due to shales and sandstone like sedimentary rocks and
also may be attributed to coal mining activity (Fig. 7b).
The high Cr concentration was reported in Bhadravati,
Warora, Ballarpur, Rajura and Gondpipri tehsils attributed
to coal ash and igneous rocks (Fig. 7c). The high Cu
concentration was observed at Chimur and Sindewahi
tehsils due to presence of copper mines in the region
(Fig. 7d). The high Fe concentration occurs at Chimur,
Warora and Sindewahi tehsil, which may be due to oxi-
dation of pyrite within coal measures and associated strata
and removal of oxidation product by inflowing ground
water (Frost 1979) (Fig. 7e). The high Mn observed at
Chimur and Sindewahi tehsil may be derived from soil
and sediments, as the metamorphic sedimentary rock, also
contribute contamination of metals (Todd 1980) (Fig. 7f).
The high Ni concentration is observed in Warora, Bha-
dravati, Pombhurna and Rajura tehsil and basaltic rocks,
shales are the possible sources in the area (Fig. 7g). The
high Pb concentration is observed in Bhadravati, Chan-
drapur and Rajura tehsil, the trace metals may be due to
shale and igneous rocks. Galena (PbS) was abundant in
carboniferous rock (Fig. 7h). The high Zn concentration
occurs at Warora, Bhadravati, Sindewahi, Pombhurna,
Gondpipri and Rajura tehsil indicates the presence of zinc
blende (ZnS), basaltic, shales and carbonates as sedi-
mentary rocks are the possible sources in the region
(Fig. 7i). The amount of precipitation and the common
occurrence of poorly drained clayey soils also limit the
movement of dissolved major and trace elements from
coalmine area. Despite of these, coalmines are source of
dissolved metals like Mn, Fe, Cd, Cr, Pb, and Cu. These
mobile parameters of the metals may adversely affect the
quality of ground water and may contribute to the degree
of contamination of local groundwater (Xue et al. 2006).
Prediction standard error maps depict the errors between
the predicted points and the measured points. The predic-
tion maps for each metal are also validated by estimating
standard error maps using kriging operation. In the non-
attainment and near-non-attainment areas, the size of error
was found to be 33β35 (Al), 24β30 (Cd), 13β15.7 (Cr), 18β
32 (Cu), 133β170 (Fe), 41β52 (Mn), 23β25 (Ni), 24β29.5
% of Metal
Al8%
Cd3%
Cr3%
Cu6%
Fe48%
Mn12%
Ni4%
Pb7%
Zn9%
Al
Cd
Cr
Cu
Fe
Mn
Ni
Pb
Zn
Fig. 6 Percentage contribution of each metal in groundwater samples
1336 Environ Geol (2009) 56:1323β1352
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(Pb), and 33β44 (Zn) in lg/l. In general, the error intro-
duced is small compared with the large range of observed
metal concentrations (Fig. 8aβi), however, it is observed
that the prediction is usually better around those areas with
a great amount of points, but worse in the eastern most
edge and in central part of the district where the points are
scarcely. Areas with low prediction error, in yellowish
coloured area occupy a relatively large part of the district
for prediction of all metal concentration. High prediction
errors in brown coloured area are less and decision about
metal contamination should be done with utmost care.
Kriging techniques assume the source data have regional-
ized errors of estimation and generalize the data to
minimize estimation variance. Kriging tends to eliminate
local anomalies from the interpolation grid to portray a
general regional pattern. The error maps produced main-
tains mere pattern without spots, in which surface changes
gradually except in the case of Cu standard error map with
spotty pattern. This may be due to the points having much
higher or lower metal concentration than those points
around them, thus the predicting values are quite different
from them which stabilises locally. The closure of the
pattern indicates that the source is inside and there is a
steep gradient depicting especially in north-eastern region
attributing to copper mines.
The implication for different kriging error maps is that if
it is needed to know the contamination of metal in
groundwater, it would have to be considered the error range
while estimating the level of contamination at non-attain-
ment areas. Since regional contamination estimates are
generally based on varying sample sizes and consequently
turn up spatially varying standard errors, the contamination
estimates are independent of the data values of source rock
types locally. By borrowing-strength-from-the-ensemble,
Fig. 7 Ground water heavy
metals interpolation maps using
ordinary kriging superimposed
with lithologic units (Fig. 2) for
a Al; b Cd; c Cr; d Cu; e Fe; fMn; g Ni; h Pb and i Zn
Environ Geol (2009) 56:1323β1352 1337
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the impact of outliers is reduced and standard errors are
stabilized over space. This cannot achieve variance
homogeneity entirely nor could the tendency be shown
analytically. Thus, the error map is useful for predictive
performance of the kriging but less useful for analytical
inferences.
Fig. 7 continued
1338 Environ Geol (2009) 56:1323β1352
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Multivariate spatial distributions of major/trace metals
in groundwater
Three principal components were extracted accounting
over 80% of the total variance. The components were
ranked by their eigenvalues. The first and second principal
components are spatially interpolated over the shaded relief
image of the digital elevation model for the area that out-
lines metals concentrations variation in the map-area. In
PC1, the major and trace metals Cd, Cr, Fe, Mn, Ni, Pb,
and Zn are closely associated which explained over 46% of
the total variance (Table 3). The rotation of the matrix also
supports these results at places of Warora, Bhadravati,
Chandrapur, Ballarpur, Rajura, Bramhapuri, and Chimur
tehsil (Fig. 9a). This may indicate the influence of geo-
chemical processes and hydrodynamic behaviour. Iron and
manganese oxyhydroxide precipitation and its scavenging
effects on metals are the main controlling process that
influences the metal contents in groundwater (Schurch
et al. 2004). More specifically this may be due to mine-
induced activities also. In PC2, trace metals CuβNiβZnβCd
shows close association over 22% of total variance. The
Fig. 8 Ordinary kriging
prediction standard error maps
for a Al; b Cd; c Cr; d Cu; e Fe;
f Mn; g Ni; h Pb and i Zn
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Fig. 8 continued
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significant contribution indicates that corresponding trace
metals have the similar source of origin at different places
of Rajura, Chandrapur, Warora, Mul, Sindewahi, Chimur,
and Nagbhid tehsils (Fig. 9b). These trace metals are toxic
and are frequently found in sites contaminated by mining
activities. This can be considered as pollution sources,
which may have originated from the parental material like
igneous and sedimentary rocks viz. basalt and sandstone. In
PC3, Al shows 13 % of total variance indicating silica rich
composition in ground water derived from its parent
material like sandstone, and conglomerate pebbles.
Major/trace metals in surface water
The major and trace metals contamination in surface water
was influenced by physical (dispersion and filtration) and
geochemical processes. Anthropogenic sources include
availability of sprawling mines in the district in addition to
agricultural activities are the likely contamination sources
in the study area. The major and trace metals character-
ization in surface water is presented in Annexure II. The
percentage contribution of each metal observed as Al
(47.8%), Fe (42.8%), Mn (5.5%), Zn (2.3%), Pb (0.56%),
Ni (0.42%), Cu (0.24%), Cr (0.16%), and Cd (0.10%). The
high Al concentration occurs in NNW, western and NW
region may be due to sewage, sediment tapping and
mobilization effects (Fig. 10a). The high Cd concentration
was observed in NW and southern region and usually
coalmining activity is the possible source of contamination
(Fig. 10b). The high Fe concentration also reported in
NNW, NW and northern region might be due to discharge
of mine drainage into local river/stream (Fig. 10c). The
high Cr concentration lies at northern, NW and SW region
(Fig. 10d). The high Cu concentration occurred in NE and
NW region due to discharge of industrial effluents into
river/stream (Fig. 10e). The high Mn concentration occurs
in northern region (Fig. 10f) whereas high Ni concentration
is observed in northern, NW and SW region which may be
due to sewage effluent discharged into river/stream
Table 3 Varimax rotated factor loading and possible sources in
ground water
Variables Factor1 Factor2 Factor3
Al 0.124 0.178 0.955
Cd 0.735 0.413 0.005
Cr 0.665 0.458 0.274
Cu 0.104 0.895 0.296
Fe 0.882 0.007 0.155
Mn 0.920 0.005 0.007
Ni 0.695 0.606 -0.004
Pb 0.773 0.278 0.238
Zn 0.656 0.525 0.001
Eigenvalue 5.3 1.1 0.75
% of variance 46 22 13
Fig. 9 a Map of the first principal component over shaded relief image of the area highlighting the Cd, Cr, Fe, Mn, Pb and Zn enrichment;
b Map of the second principal component over shaded relief image of the area highlighting the Cu, Ni, Zn and Cd enrichment
Environ Geol (2009) 56:1323β1352 1341
123
Page 20
(Fig. 10g). The high Zn concentration is observed in
western region (Fig. 10h) whereas high Pb concentration
occurs in NNW, northern, central and southern region
attributing to discharge from coal mining activities into
river/streams (Fig. 10i).
Correlation of groundwater/surface water geochemistry
with geology
It is well known that naturally occurring groundwater with
neutral pH-7 is common throughout the basement partic-
ularly where calcite is absent or nearly so. Granite is a
typical host for acidic ground water. Measured values of
pH have a range from 5.4 upwards, though many basement
sources are alkaline. Most of the times alkaline nature
from groundwater contribution was not sufficient to reduce
the effect and most of the rivers receive groundwater base
flow (Cook et al. 1991; Robins 2002). In such cases,
groundwater is not potable due to contamination because of
mining activity or due to rock sources phenomena. An
attempt has been made to correlate the metal content in
different types of rocks reported elsewhere with the metal
contents found in ground/surface waters. The compositions
of metals in various types of rocks and ground/surface
waters are reported (Hem 1989; Fleisher et al. 1976)
(Table 4). This is done by superimposing the sampling
points over a specific lithology and calculating the mean
value of each metal. The average value of metal content
range in different rocks has been regressed with the mean
value of metal content in ground/surface waters (Table 5).
Pearsonβs correlation coefficients were evaluated as an
index of dependency among metals in water and rock types
at P B 0.05. The correlation between metals in water and
different types of rocks were observed to be weak (r \ 0.5)
with respect to groundwater indicating no cause effect
relationship which reflect the natural background values
Fig. 10 Representations of
heavy metal concentration in
surface water overlaid upon
shaded relief with lithologic
units (Fig. 2) for a Al; b Cd; cFe; d Cr; e Cu; f Mn; g Ni; h Zn
and i Pb
1342 Environ Geol (2009) 56:1323β1352
123
Page 21
Fig. 10 continued
Environ Geol (2009) 56:1323β1352 1343
123
Page 22
(Liaghati et al. 2003). However, a significant correlation
was observed for Cd (r = 0.82) and Zn (r = 0.60) at P
B 0.05 in surface water indicating inputs from base flow of
bedrocks of sedimentary and igneous origin.
Conclusion
The objective of this study was to assess the metal con-
tamination in ground and surface water in the vicinity of
mines of Chandrapur district and correlating its possible
sources. It is observed that mapping of metal concentration
in ground and surface water can be useful for depicting
levels of contamination by thresholds using GIS. On the
contrary the results from geostatistical analysis are
dependent upon the best-fit variogram. A correlation
between lithology and metal contents of water quality
using regression technique was found to be useful in order
to locate sources. The correlation between average metals
concentration and lithologic units was observed to be weak
in case of groundwater. However, the relationship also
indicates the source of aluminium in groundwater, which is
mainly influenced by natural phenomena. A strong corre-
lation of cadmium and zinc in surface water can indicate
origin from base flows of sedimentary and igneous bed-
rock. Since major portion of the area is flat terrain except
southwestern portion of the district, water is considered to
be stagnant at lower altitudes leading to higher concen-
tration of metals with the exception of river water. Based
on the finding of water quality parameters, spatial analysis,
geostatistical analysis, spatial PCA and correlation coeffi-
cient analysis, the hot spots are encountered at places of
Warora, Chandrapur, Rajura, and Chimur tehsil and is
characterized by high inputs from natural bedrock, mining
activities, urbanization. The CuβNiβZnβCd association can
indicate the origin from igneous and sedimentary rock-
types. This can also lead to sources from coal and copper
mines. Major and trace metals in subsurface environments
come from natural and anthropogenic sources. The
weathering of minerals is one of the major natural sources.
Anthropogenic sources include mining activity, fertilizer
application, and industrial effluent. The results obtained in
this study could possibly serve as a protocol in under-
standing the metal contamination in ground/surface waters
in the light of multivariate statistical analysis and GIS
environment.
Acknowledgments I wish to thank Dr. Sukumar Devotta, Director,
NEERI for his constant guidance and encouragement in thrust areas
of work. I am indebted to my teacher for his constant encourage-
ment and support that has been provided while carrying out the
work. Results shown in this paper are derived from Ph.D. work
performed at Visvesvaraya National Institute of Technology (VNIT),
Nagpur.
Ta
ble
4C
om
po
siti
on
of
maj
or
and
trac
em
etal
sin
var
iou
sty
pes
of
geo
log
ical
rock
s,in
gro
un
dw
ater
and
surf
ace
wat
er
Lit
ho
log
icu
nit
sM
etal
con
ten
tsin
lith
olo
gic
un
its
(av
erag
eco
nce
ntr
atio
nin
pp
m)
Met
alco
nte
nts
ing
rou
nd
wat
er(m
ean
con
cen
tra-
tio
nin
lg
/l)
Met
alco
nte
nts
insu
rfac
ew
ater
(mea
nco
nce
ntr
atio
n
inl
g/l
)
Cd
Cr
Cu
Pb
Zn
Al
Fe
Mn
Ni
Cd
Cr
Cu
Pb
Zn
Al
Fe
Mn
Ni
Cd
Cr
Cu
Pb
Zn
Al
Fe
Mn
Ni
Ult
ram
afic
ign
eou
sa,b
0.0
51
,80
01
51
40
79
,50
04
2,2
00
93
79
47
18
38
38
51
42
25
16
62
2
Bas
alti
cig
neo
usa
,b0
.22
20
90
61
10
79
,50
04
2,2
00
93
79
41
31
52
62
44
62
61
96
46
24
Gra
nit
icig
neo
usa
,b0
.15
20
15
18
40
79
,50
04
2,2
00
93
79
47
82
32
33
32
62
11
50
15
34
18
88
51
,96
02
,04
62
55
13
Sh
ales
and
clay
a,b
1.4
12
05
02
09
08
0,0
00
38
,80
05
75
29
23
20
30
44
61
55
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22
11
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11
73
51
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3,1
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2,4
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25
02
8
Bla
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hal
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C)c
1.0
10
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80
,00
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21
73
73
33
75
32
07
52
15
34
33
19
81
,02
18
75
15
46
Lim
esto
nes
a,b
,d0
.05
10
49
20
8,9
70
8,1
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84
21
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San
dst
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esa,b
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53
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35
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Geo
log
ical
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rvey
(19
69
)a
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rek
ian
and
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epo
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(19
61)
bP
ark
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96
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and
To
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lot
(19
70)
dW
edep
oh
l(1
97
0)
1344 Environ Geol (2009) 56:1323β1352
123
Page 23
Appendix
Tables 6, 7
Table 5 Pearson correlation coefficient for various metal composition in lithology versus ground/surface water
Correlation coefficient (r) Cd Cr Cu Pb Zn Al Fe Mn Ni
Groundwater 0.197 0.197 0.133 -0.147 -0.222 0.412 -0.358 -0.613 -0.061
Surface water 0.823 0.028 -0.132 0.177 0.609 -0.318 -0.356 -0.454 -0.105
Table 6 Trace metals (lg/l) in groundwater
Location Latitude (N) Longitude (E) Al Cd Cr Cu Fe Mn Ni Pb Zn
Visapur 19οΏ½53β 14ββ 79οΏ½20β 0ββ 120 11 15 25 220 40 6 60 42
Kem Rith 19οΏ½50β 45ββ 79οΏ½24β 26ββ 140 6 21 32 320 60 8 80 64
Kothari 19οΏ½47β 7ββ 79οΏ½29β 8ββ 210 8 23 64 120 60 9 40 82
Haran Payali 19οΏ½49β 8ββ 79οΏ½31β 28ββ 50 7 24 21 130 80 8 60 88
Katwali 19οΏ½45β 51ββ 79οΏ½30β 58ββ 80 6 21 58 140 90 6 50 100
Ballarpur 19οΏ½50β 11ββ 79οΏ½21β 7ββ 170 14 29 62 410 40 15 84 104
Hingnala 19οΏ½55β 3ββ 79οΏ½15β 56ββ 20 9 8 11 320 60 6 30 12
Hadasti 19οΏ½52β 53ββ 79οΏ½17β 30ββ 40 6 9 15 120 40 ND 40 20
Asegaon 19οΏ½55β 22ββ 79οΏ½27β 57ββ 40 5 10 12 220 50 9 50 25
Kinhi 19οΏ½50β 43ββ 79οΏ½28β 1ββ 30 7 8 14 150 90 10 50 38
Shivajinagar 20οΏ½7β 23ββ 79οΏ½1β 44ββ 21 41 32 32 380 80 40 89 102
Kondha 20οΏ½9β 7ββ 79οΏ½3β 57ββ 23 82 21 25 440 110 40 88 144
Telawasa 20οΏ½2β 59ββ 79οΏ½4β 46ββ 19 43 35 26 480 140 50 74 138
Kesurli 20οΏ½7β 48ββ 79οΏ½5β 57ββ 35 62 25 35 510 150 50 76 163
Belora 20οΏ½10β 12ββ 79οΏ½5β 13ββ 38 91 36 32 580 110 60 64 182
Chicholi 20οΏ½9β 27ββ 79οΏ½14β 34ββ 14 8 25 28 110 40 ND 20 85
Waigaon Tukum 20οΏ½15β 1ββ 79οΏ½15β 16ββ 160 9 26 54 350 30 16 30 45
Nandara 20οΏ½14β 27ββ 79οΏ½7β 9ββ 12 6 8 61 140 40 ND 40 24
Kacharala 20οΏ½4β 43ββ 79οΏ½13β 42ββ 16 7 9 11 150 30 ND 40 31
Moharli 20οΏ½10β 21ββ 79οΏ½20β 39ββ 19 8 9 12 180 30 8 40 9
Sonegaon Tukum 20οΏ½20β 28ββ 79οΏ½14β 11ββ 24 ND 8 8 140 30 9 60 7
Khokari 20οΏ½14β 14ββ 79οΏ½11β 30ββ 9 ND 7 9 160 40 8 20 17
Dhamani 20οΏ½15β 48ββ 79οΏ½8β 51ββ 8 ND 8 9 220 40 ND 30 18
Dongargaon Kharda 20οΏ½13β 2ββ 79οΏ½5β 11ββ 24 ND 9 8 140 40 ND 30 8
Kolari 20οΏ½43β 36ββ 79οΏ½46β 25ββ 25 ND ND 6 150 40 ND 30 9
Nilaj 20οΏ½30β 34ββ 79οΏ½56β 42ββ 9 ND ND 6 160 60 9 20 16
Pimpalgaon 20οΏ½40β 12ββ 79οΏ½54β 16ββ 14 ND ND 6 180 50 8 30 20
Mangli 20οΏ½31β 25ββ 79οΏ½54β 36ββ 24 ND 8 6 220 60 ND 20 25
Lohar Dongri 20οΏ½23β 58ββ 79οΏ½44β 43ββ 14 12 24 6 890 280 70 80 148
Chorti 20οΏ½32β 58ββ 79οΏ½47β 39ββ 11 ND ND 7 330 40 ND 30 23
Jugnala 20οΏ½31β 24ββ 79οΏ½52β 43ββ 21 ND ND 7 120 80 8 20 20
Khambada rith 20οΏ½28β 57ββ 79οΏ½48β 34ββ 16 ND ND 11 140 90 9 40 9
Bramhapuri 20οΏ½36β 34ββ 79οΏ½51β 50ββ 11 ND 12 12 130 80 7 50 15
Lakhapur 20οΏ½37β 50ββ 79οΏ½46β 44ββ 12 ND ND 13 140 40 6 60 18
Torgaon 20οΏ½39β 52ββ 79οΏ½46β 39ββ 9 ND ND 11 110 60 8 20 12
Durgapur 20οΏ½1β 44ββ 79οΏ½18β 59ββ 35 110 65 35 320 110 50 90 104
Vadholi 20οΏ½6β 11ββ 79οΏ½16β 57ββ 39 40 41 55 340 80 40 90 129
Morwa 20οΏ½0β 53ββ 79οΏ½13β 2ββ 42 60 31 56 380 120 60 80 138
Environ Geol (2009) 56:1323β1352 1345
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Page 24
Table 6 continued
Location Latitude (N) Longitude (E) Al Cd Cr Cu Fe Mn Ni Pb Zn
Sonegaon 19οΏ½58β 15ββ 79οΏ½10β 20ββ 48 60 35 61 510 140 60 60 82
Ghugus 19οΏ½56β 12ββ 79οΏ½5β 49ββ 52 140 24 62 420 120 60 70 65
Pipri 19οΏ½54β 30ββ 79οΏ½12β 43ββ 47 80 41 42 580 110 50 80 49
Chichala 19οΏ½58β 24ββ 79οΏ½14β 25ββ 35 70 32 58 420 90 40 80 38
Agadzari 20οΏ½6β 30ββ 79οΏ½19β 49ββ 144 50 31 36 480 110 50 84 34
Chichpalli 19οΏ½59β 10ββ 79οΏ½28β 12ββ 45 ND 8 21 110 60 ND 40 14
Niljai 19οΏ½59β 60ββ 79οΏ½34β 19ββ 158 ND 21 66 140 50 16 80 18
Neri 20οΏ½0β 13ββ 79οΏ½17β 2ββ 35 ND 9 24 120 60 ND 20 19
Ajgaon 20οΏ½40β 22ββ 79οΏ½33β 58ββ 48 60 35 45 380 110 70 90 30
Pethbhansuli 20οΏ½32β 32ββ 79οΏ½15β 57ββ 36 70 51 51 490 150 50 90 51
Majara Begde 20οΏ½31β 32ββ 79οΏ½16β 12ββ 47 90 22 48 510 140 40 95 49
Khursapur 20οΏ½28β 37ββ 79οΏ½12β 52ββ 11 8 ND 23 110 60 9 20 24
Mahalgaon 20οΏ½34β 29ββ 79οΏ½21β 34ββ 16 ND ND 35 140 80 8 20 33
Bhisi 20οΏ½38β 39ββ 79οΏ½24β 40ββ 61 24 91 52 810 210 50 80 114
Jambhul vihira 20οΏ½36β 47ββ 79οΏ½20β 19ββ 21 ND ND 11 340 120 ND 20 12
Chicholi 20οΏ½40β 29ββ 79οΏ½22β 27ββ 25 ND ND 23 210 40 9 30 14
Khokarla 20οΏ½40β 33ββ 79οΏ½29β 37ββ 23 ND ND 11 250 60 8 30 24
Pendhari 20οΏ½40β 45ββ 79οΏ½33β 7ββ 41 ND 21 11 210 90 8 30 29
Hirapur 20οΏ½37β 34ββ 79οΏ½31β 53ββ 9 ND ND 23 140 80 8 ND 38
Doma 20οΏ½34β 27ββ 79οΏ½30β 46ββ 8 ND ND 11 190 40 7 9 11
Pimpalgaon 20οΏ½32β 44ββ 79οΏ½29β 22ββ 39 23 35 25 780 240 42 60 97
Bothali 20οΏ½28β 46ββ 79οΏ½31β 29ββ 31 14 28 95 220 50 39 20 44
Motegaon 20οΏ½25β 39ββ 79οΏ½31β 5ββ 32 18 21 98 140 60 42 8 36
Nandara 20οΏ½26β 31ββ 79οΏ½24β 15ββ 14 ND 9 51 180 40 8 8 33
Satara 20οΏ½24β 10ββ 79οΏ½26β 45ββ 11 8 8 11 110 30 9 9 9
Wagheda 20οΏ½25β 3ββ 79οΏ½21β 35ββ 12 ND 7 12 120 40 ND 11 11
Piparda 20οΏ½20β 52ββ 79οΏ½28β 40ββ 16 ND 6 15 160 40 ND 21 6
Tohogaon 19οΏ½40β 4ββ 79οΏ½29β 41ββ 65 60 35 52 380 110 50 80 40
Lathi 19οΏ½32β 56ββ 79οΏ½30β 34ββ 32 40 52 42 420 120 40 90 114
Dhaba 19οΏ½36β 51ββ 79οΏ½38β 30ββ 81 8 12 32 140 30 12 12 43
Dubar Peth Chak 19οΏ½39β 44ββ 79οΏ½38β 46ββ 92 6 15 44 180 30 8 21 31
Gojoli Makta 19οΏ½39β 27ββ 79οΏ½40β 50ββ 11 7 21 49 210 30 11 11 25
Chak Pellur 19οΏ½45β 38ββ 79οΏ½39β 22ββ 8 8 8 12 150 40 9 32 11
Ralapeth 19οΏ½39β 39ββ 79οΏ½46β 23ββ 9 ND 6 15 220 40 6 11 9
Nandgaon 19οΏ½44β 3ββ 79οΏ½45β 10ββ 11 ND 8 17 330 40 9 14 17
Kharal Peth 19οΏ½44β 57ββ 79οΏ½42β 44ββ 12 ND 9 11 280 40 10 15 28
Gond Pipari 19οΏ½42β 56ββ 79οΏ½41β 22ββ 14 8 80 12 310 30 ND 11 24
Panora 19οΏ½37β 59ββ 79οΏ½46β 42ββ 36 15 24 11 280 30 42 12 27
Darur 19οΏ½35β 30ββ 79οΏ½42β 14ββ 47 8 6 12 140 30 11 16 38
Parsoda 19οΏ½45β 10ββ 78οΏ½51β 25ββ 51 7 11 35 210 30 9 21 25
Shivapur 19οΏ½41β 58ββ 78οΏ½52β 6ββ 62 8 19 45 260 30 8 11 23
Rupapeth 19οΏ½42β 27ββ 78οΏ½54β 5ββ 75 ND 21 65 250 40 9 50 20
Akola 19οΏ½44β 35ββ 78οΏ½56β 48ββ 111 ND 22 51 240 40 7 23 16
Tulsi 19οΏ½46β 57ββ 78οΏ½56β 49ββ 122 ND 14 52 140 40 7 35 19
Sawalhira 19οΏ½41β 30ββ 78οΏ½58β 11ββ 114 8 24 48 180 40 9 55 5
Yergavhan 19οΏ½41β 3ββ 79οΏ½1β 2ββ 52 8 22 36 190 30 11 32 9
Dhamangaon 19οΏ½45β 6ββ 79οΏ½8β 12ββ 184 7 29 34 210 20 12 11 7
Awalpur 19οΏ½47β 17ββ 79οΏ½7β 44ββ 214 ND 28 52 220 40 11 22 17
1346 Environ Geol (2009) 56:1323β1352
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Page 25
Table 6 continued
Location Latitude (N) Longitude (E) Al Cd Cr Cu Fe Mn Ni Pb Zn
Kawathala 19οΏ½50β 32ββ 79οΏ½10β 43ββ 56 ND 14 56 140 30 8 11 25
Sangoda 19οΏ½49β 49ββ 79οΏ½5β 43ββ 42 8 15 51 120 40 8 23 30
Goyegaon 19οΏ½53β 29ββ 79οΏ½10β 52ββ 62 8 11 24 130 30 9 24 60
Wansadi 19οΏ½44β 44ββ 79οΏ½2β 60ββ 14 ND 8 42 140 40 7 14 42
Palezari 19οΏ½46β 36ββ 79οΏ½6β 47ββ 16 ND 9 21 180 30 ND 13 25
Karwahi 19οΏ½50β 23ββ 79οΏ½6β 24ββ 17 ND 8 23 150 40 6 12 29
Kusal 19οΏ½43β 56ββ 79οΏ½1β 24ββ 25 6 6 11 210 40 8 14 28
Dhonda Arjuni 19οΏ½34β 54ββ 79οΏ½5β 33ββ 26 ND 7 12 320 40 9 15 40
Kumbhezari 19οΏ½33β 34ββ 79οΏ½3β 7ββ 23 ND 7 14 110 50 10 15 16
Chiroli 20οΏ½0β 7ββ 79οΏ½36β 33ββ 90 11 12 12 310 110 21 61 31
Naleshwar 19οΏ½57β 43ββ 79οΏ½38β 8ββ 110 8 14 14 340 120 14 52 25
Kawadpeth Mal 20οΏ½0β 51ββ 79οΏ½38β 58ββ 50 9 18 12 330 110 12 62 51
Dabgaon Tukum 19οΏ½56β 42ββ 79οΏ½35β 45ββ 30 8 21 13 340 110 15 66 36
Kelzar 19οΏ½58β 53ββ 79οΏ½33β 54ββ 12 6 6 11 140 30 9 21 24
Agdi 20οΏ½1β 51ββ 79οΏ½36β 2ββ 14 8 8 24 210 40 8 23 9
Mul 20οΏ½4β 30ββ 79οΏ½40β 53ββ 23 ND 8 23 250 30 9 14 40
Karwan 20οΏ½5β 24ββ 79οΏ½37β 46ββ 25 ND ND 31 320 50 ND 21 22
Morwahi 20οΏ½6β 34ββ 79οΏ½40β 35ββ 21 ND ND 42 160 30 ND 45 24
Padzari Chak 20οΏ½8β 40ββ 79οΏ½38β 36ββ 11 ND ND 14 210 30 ND 9 27
Ratnapur 20οΏ½10β 28ββ 79οΏ½39β 27ββ 14 ND ND 15 110 30 9 11 33
Rajoli 20οΏ½11β 28ββ 79οΏ½41β 44ββ 15 8 ND 32 120 30 8 13 20
Chak Kanhalgaon 20οΏ½8β 34ββ 79οΏ½43β 16ββ 24 7 ND 21 140 30 10 15 20
Takadi 20οΏ½5β 41ββ 79οΏ½44β 4ββ 23 ND 9 11 150 40 11 17 24
Borchandli 20οΏ½2β 23ββ 79οΏ½43β 9ββ 25 ND 10 12 150 40 13 21 21
Yejgaon 19οΏ½58β 57ββ 79οΏ½42β 15ββ 32 ND 11 16 170 40 9 14 31
Nandgaon 19οΏ½53β 53ββ 79οΏ½45β 7ββ 28 7 9 17 160 40 8 13 30
Chak Ghosari 19οΏ½55β 32ββ 79οΏ½39β 36ββ 47 ND 7 19 140 50 12 19 20
Govindpur 20οΏ½27β 49ββ 79οΏ½37β 38ββ 47 14 14 61 350 120 52 32 65
Nagbhid 20οΏ½34β 56ββ 79οΏ½41β 8ββ 24 8 8 42 140 30 12 9 20
Kodepar 20οΏ½32β 11ββ 79οΏ½42β 10ββ 21 7 6 21 120 20 21 11 27
Girgaon 20οΏ½23β 14ββ 79οΏ½36β 32ββ 11 ND ND 21 110 20 8 14 29
Chikhalgaon 20οΏ½22β 30ββ 79οΏ½39β 29ββ 12 ND ND 11 160 30 9 16 25
Wadhona 20οΏ½25β 3ββ 79οΏ½41β 5ββ 14 ND ND 23 180 20 12 19 28
Talodhi 20οΏ½26β 25ββ 79οΏ½40β 23ββ 11 ND 7 15 140 20 11 11 27
Akapur 20οΏ½25β 31ββ 79οΏ½43β 11ββ 9 ND 6 13 220 20 8 9 26
Sulezari 20οΏ½35β 25ββ 79οΏ½42β 11ββ 8 ND ND 12 310 20 9 9 22
Bamhani 20οΏ½37β 37ββ 79οΏ½40β 18ββ 18 ND ND 11 150 20 7 9 22
Dhorpa 20οΏ½39β 40ββ 79οΏ½45β 35ββ 19 ND 8 14 140 20 9 8 22
Pombhurna 19οΏ½52β 46ββ 79οΏ½38β 8ββ 52 8 21 51 310 110 21 51 69
Chak Hattibodi 19οΏ½53β 55ββ 79οΏ½36β 29ββ 41 7 28 42 340 120 23 62 120
Dongar Haldi mal 19οΏ½54β 48ββ 79οΏ½34β 46ββ 62 11 11 69 380 110 11 54 103
Jam Tukum 19οΏ½55β 7ββ 79οΏ½38β 6ββ 41 12 16 44 420 120 34 52 109
Dighori 19οΏ½52β 24ββ 79οΏ½42β 46ββ 59 7 21 32 310 140 42 68 108
Futana Mokasa 19οΏ½54β 30ββ 79οΏ½42β 26ββ 45 8 24 47 220 110 41 49 100
Thane wasana mal 19οΏ½50β 38ββ 79οΏ½44β 11ββ 44 12 17 81 140 30 52 52 84
Jungaon 19οΏ½53β 44ββ 79οΏ½47β 54ββ 39 16 22 99 150 30 59 56 87
Ashta 19οΏ½51β 5ββ 79οΏ½39β 52ββ 35 14 23 95 150 30 62 42 92
Kemara 19οΏ½49β 13ββ 79οΏ½37β 7ββ 21 10 25 121 110 30 71 21 84
Environ Geol (2009) 56:1323β1352 1347
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Page 26
Table 6 continued
Location Latitude (N) Longitude (E) Al Cd Cr Cu Fe Mn Ni Pb Zn
Bhimani 19οΏ½49β 3ββ 79οΏ½42β 13ββ 36 9 26 135 120 30 48 23 98
Sasti 19οΏ½49β 20ββ 79οΏ½20β 1ββ 55 50 51 41 510 120 80 61 71
Gowari 19οΏ½48β 14ββ 79οΏ½17β 16ββ 36 60 62 56 580 140 90 72 101
Sindi 19οΏ½39β 39ββ 79οΏ½27β 26ββ 44 50 42 51 440 130 80 76 109
Dhanora 19οΏ½37β 56ββ 79οΏ½28β 40ββ 52 60 32 62 310 140 90 56 124
Arvi 19οΏ½45β 32ββ 79οΏ½19β 43ββ 62 80 51 59 350 150 50 86 84
Antargaon 19οΏ½30β 56ββ 79οΏ½28β 27ββ 51 90 14 52 410 110 80 89 49
Ruyad 19οΏ½43β 35ββ 79οΏ½18β 40ββ 51 8 12 42 140 50 12 22 51
Hardona Bk 19οΏ½44β 27ββ 79οΏ½13β 37ββ 63 9 14 41 180 40 25 56 49
Kusumbi 19οΏ½38β 38ββ 79οΏ½8β 41ββ 111 7 14 48 190 40 23 11 46
Bhendvi 19οΏ½40β 49ββ 79οΏ½13β 15ββ 42 8 11 24 200 50 31 23 50
pellora 19οΏ½51β 46ββ 79οΏ½14β 15ββ 36 7 15 15 220 50 24 24 46
Manoli Bk 19οΏ½51β 10ββ 79οΏ½18β 38ββ 21 9 11 14 210 50 21 11 48
Chanakha 19οΏ½46β 18ββ 79οΏ½26β 53ββ 41 8 13 15 110 50 11 13 59
Marda 19οΏ½53β 40ββ 79οΏ½14β 41ββ 21 7 8 12 140 30 9 16 50
Kalamana 19οΏ½46β 9ββ 79οΏ½14β 36ββ 14 ND 9 11 90 30 8 9 25
Chikhali Bk 19οΏ½38β 26ββ 79οΏ½12β 40ββ 15 ND 10 9 80 40 9 11 31
Bhokasapur 19οΏ½31β 28ββ 79οΏ½13β 21ββ 17 ND ND 8 140 40 12 12 30
Rahapalli kh 19οΏ½33β 31ββ 79οΏ½8β 6ββ 21 8 ND 9 120 40 13 45 26
Siddheshwar 19οΏ½38β 53ββ 79οΏ½19β 18ββ 11 7 7 6 150 30 15 21 25
Kostala 19οΏ½34β 19ββ 79οΏ½23β 27ββ 16 ND ND 7 180 30 18 11 27
Shirsi 19οΏ½40β 54ββ 79οΏ½23β 5ββ 17 ND 9 12 110 30 8 63 32
Bamanwada 19οΏ½45β 3ββ 79οΏ½22β 30ββ 24 ND ND 14 130 30 9 45 29
Keroda 20οΏ½4β 54ββ 79οΏ½52β 58ββ 12 ND ND ND 110 30 8 9 12
Kapsi 20οΏ½4β 28ββ 79οΏ½57β 3ββ 14 ND ND ND 150 30 9 11 13
Umri 19οΏ½59β 44ββ 79οΏ½55β 27ββ 16 ND ND ND 160 30 7 8 15
Sindola 20οΏ½3β 37ββ 79οΏ½49β 24ββ 17 ND ND ND 90 40 7 8 12
Sadagad 20οΏ½6β 20ββ 79οΏ½45β 26ββ 21 ND ND ND 80 40 ND 8 6
Chinchbodi 20οΏ½8β 3ββ 79οΏ½53β 27ββ 14 ND ND ND 140 40 ND 9 10
Mundala 20οΏ½10β 18ββ 79οΏ½48β 57ββ 20 ND ND ND 150 30 ND 10 16
Antargaon 20οΏ½12β 15ββ 79οΏ½54β 38ββ 13 ND ND ND 120 30 9 11 15
Usarpar chak 20οΏ½16β 3ββ 79οΏ½50β 45ββ 17 ND ND ND 220 30 8 12 18
Bhanapur 20οΏ½14β 6ββ 79οΏ½50β 50ββ 18 ND ND ND 290 30 ND 8 24
Bhansi 20οΏ½2β 47ββ 79οΏ½58β 17ββ 16 ND ND ND 210 30 ND 9 25
Ratnapur 20οΏ½20β 43ββ 79οΏ½32β 33ββ 88 21 21 56 910 150 52 23 91
Mangli Chak 20οΏ½14β 2ββ 79οΏ½47β 30ββ 89 16 26 61 940 150 49 32 112
Sirkada 20οΏ½19β 35ββ 79οΏ½30β 47ββ 55 12 32 89 320 40 32 56 92
Chikmara 20οΏ½13β 28ββ 79οΏ½46β 47ββ 47 14 21 114 350 40 36 52 95
Pendhari 20οΏ½23β 52ββ 79οΏ½32β 38ββ 22 8 ND 62 120 40 12 21 64
Delanwadi 20οΏ½21β 22ββ 79οΏ½37β 45ββ 13 ND ND 11 140 30 8 14 51
Ladbori 20οΏ½18β 46ββ 79οΏ½37β 43ββ 14 ND ND 14 150 40 9 12 48
Umarwahi 20οΏ½18β 19ββ 79οΏ½35β 1ββ 12 ND ND 17 120 40 14 13 28
Jamsala 20οΏ½15β 31ββ 79οΏ½36β 14ββ 16 8 9 16 150 40 13 15 27
Kukadheti 20οΏ½13β 26ββ 79οΏ½36β 39ββ 24 9 8 18 220 50 14 16 30
Knihi 20οΏ½15β 20ββ 79οΏ½39β 11ββ 22 ND ND 8 230 30 21 42 20
Saradpar 20οΏ½13β 34ββ 79οΏ½39β 33ββ 13 ND ND 9 240 30 9 9 27
Dhanora 20οΏ½16β 0ββ 79οΏ½43β 14ββ 16 9 ND 12 210 50 8 11 25
Powanpar 20οΏ½17β 14ββ 79οΏ½48β 4ββ 17 ND ND 14 180 30 9 9 25
1348 Environ Geol (2009) 56:1323β1352
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Table 6 continued
Location Latitude (N) Longitude (E) Al Cd Cr Cu Fe Mn Ni Pb Zn
kanhalgaon 20οΏ½17β 26ββ 79οΏ½49β 48ββ 18 7 ND 8 190 40 7 14 21
Dongargaon 20οΏ½19β 4ββ 78οΏ½57β 52ββ 51 20 36 48 420 120 21 90 108
Mowada 20οΏ½20β 28ββ 79οΏ½2β 17ββ 62 80 32 42 480 110 91 92 120
Surla 20οΏ½14β 50ββ 79οΏ½2β 29ββ 69 60 41 32 510 130 62 95 129
Nimsada 20οΏ½16β 52ββ 78οΏ½59β 10ββ 71 50 58 51 590 140 81 90 106
Ekarjuma 20οΏ½12β 22ββ 79οΏ½0β 43ββ 45 110 69 52 580 150 63 82 108
Kohapara 20οΏ½15β 23ββ 78οΏ½49β 9ββ 32 8 21 21 140 20 22 23 43
Amadi 20οΏ½15β 21ββ 78οΏ½52β 6ββ 21 7 24 35 160 30 23 42 65
Soit 20οΏ½16β 57ββ 78οΏ½49β 3ββ 48 8 18 35 190 30 11 12 71
Barvha 20οΏ½25β 50ββ 78οΏ½57β 32ββ 38 11 13 32 110 30 35 11 80
Marda 20οΏ½14β 8ββ 78οΏ½56β 25ββ 21 ND 9 9 120 40 9 9 40
Nandara 20οΏ½17β 23ββ 78οΏ½56β 19ββ 42 ND 8 14 110 40 6 8 25
Kharwad 20οΏ½20β 43ββ 78οΏ½53β 14ββ 21 8 ND 24 150 40 8 22 28
Wanneri 20οΏ½18β 55ββ 78οΏ½50β 56ββ 14 9 ND 35 140 30 9 11 25
Keli 20οΏ½23β 14ββ 78οΏ½49β 23ββ 11 8 9 24 110 30 12 9 29
Nagari 20οΏ½24β 59ββ 78οΏ½51β 41ββ 12 7 8 14 130 30 11 14 24
Pimpalgaon Singara 20οΏ½24β 24ββ 78οΏ½58β 49ββ 18 ND 9 13 150 30 8 17 11
Hirapur 20οΏ½26β11ββ 79οΏ½1β 51ββ 11 ND 6 21 340 30 9 21 10
Mokhala 20οΏ½27β 9ββ 79οΏ½6β 21ββ 19 8 7 15 150 30 8 32 51
Gunjala 20οΏ½23β 48ββ 79οΏ½8β 11ββ 42 8 9 16 160 30 7 21 70
Sawari 20οΏ½27β 21ββ 79οΏ½12β 14ββ 24 ND 11 17 180 30 7 15 73
Arjuni 20οΏ½21β 34ββ 79οΏ½13β 44ββ 29 ND 12 21 110 30 8 9 43
Min 8 ND ND ND 55 20 ND ND 5
Max 214 140 91 135 940 280 91 95 182
Avg 38 13 14 29 236 59 19 34 45
SD 36 24 16 24 156 43 21 26 36
ND not detected
Table 7 Trace metals (lg/l) in surface water
Location Latitude (N) Longitude (E) Al Cd Cr Cu Fe Mn Ni Pb Zn
SWS1 20οΏ½ 02β 01ββ 79οΏ½ 17β 55ββ 5030 2 17 ND 3260 220 22 22 121
SWS2 19οΏ½ 48β 53ββ 79οΏ½ 22β 35ββ 1520 21 12 ND 380 90 24 41 41
SWS3 19οΏ½ 48β 24ββ 79οΏ½ 20β 23ββ 30 7 ND ND 540 110 23 93 32
SWS4 19οΏ½ 49β 25ββ 79οΏ½ 18β 09ββ 1,670 11 ND ND 1,260 110 ND ND 95
SWS5 19οΏ½ 49β 06ββ 79οΏ½ 16β 30ββ 1,860 ND 9 8 1,770 120 110 23 63
SWS6 19οΏ½ 49β 28ββ 79οΏ½ 19β 45ββ 1,050 ND ND ND 630 40 32 26 81
SWS7 19οΏ½ 50β 00ββ 79οΏ½ 19β 58ββ 1,640 2 6 ND 1,420 170 ND 21 46
SWS8 19οΏ½ 57β 15ββ 79οΏ½ 19β 04ββ 690 4 10 9 580 280 ND 42 132
SWS9 20οΏ½ 04β 00ββ 79οΏ½ 18β 00ββ 110 ND ND ND 20 20 ND ND 72
SWS10 20οΏ½ 02β 59ββ 79οΏ½ 17β 27ββ 480 2 ND ND 630 80 ND ND 184
SWS11 20οΏ½ 01β 26ββ 79οΏ½ 17β 29ββ 750 2 ND 11 690 50 ND ND 122
SWS12 20οΏ½ 00β 09ββ 79οΏ½ 16β 08ββ 1,250 3 ND 8 710 170 35 23 175
SWS13 19οΏ½ 59β 27ββ 79οΏ½ 15β 49ββ 1,770 ND 9 52 1,380 190 ND 31 146
SWS14 19οΏ½ 57β 23ββ 79οΏ½ 05β 52ββ 750 2 6 ND 650 40 ND 35 46
SWS15 19οΏ½ 57β 38ββ 79οΏ½ 06β 18ββ 2,030 ND ND ND 2,050 90 21 36 61
SWS16 20οΏ½ 01β 24ββ 79οΏ½ 09β 29ββ 7,130 2 54 10 6,490 290 36 42 110
Environ Geol (2009) 56:1323β1352 1349
123
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Location Latitude (N) Longitude (E) Al Cd Cr Cu Fe Mn Ni Pb Zn
SWS17 20οΏ½ 08β 48ββ 79οΏ½ 01β 33ββ 3,830 2 16 34 2,010 360 91 52 32
SWS18 20οΏ½ 11β 53ββ 79οΏ½ 01β 57ββ 7,680 2 13 13 6,440 710 ND ND 461
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Min 30 ND ND ND 20 20 ND ND ND
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