SPATIAL ANALYSIS OF HEAVY METALS IN SOIL, PLANT AND GROUNDWATER IN NAGAON DISTRICT OF ASSAM USING GIS TECHNIQUE
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Date
2022
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Abstract
Geospatial as well as geostatistical approaches using GIS tool to assess and characterize heavy
metals namely Cd, Cr, Cu, Ni, Zn, Pb, Fe, As and Mn in soil, crop, and groundwater, their degree of
pollution level, and phytoextraction behaviours of key metals were carried-out in Nagaon district of
Assam during 2018 to 2021. To appraise the potential heavy metal sites, a random systematic method
was used for sampling strategy by dividing the study area into a grid of 5 km× 5 km and collecting 160
composite soil samples from 0-15 cm depth for the total content of heavy metals using Atomic
Absorption Spectrophotometer (Model: iCE 3500, Thermofisher). Geospatial analyses from different
thematic maps of heavy metals revealed significant vulnerable points of elevated concentrations of Cd (>
0.31 mg/kg ), Pb (> 24.45 mg/kg ) and Ni (> 0.05 mg/kg ) in soils and Cd (>0.01 mg/L), Cr (>0.05
mg/L), Cu (>1.3 mg/L ), Fe (> 0.3 mg/L ), As (> 20 mg/L ) and Mn (> 0.1 mg/L ) in groundwater which
is presumed to be due to anthropogenic factors. Geospatial interpolation pedagogies like Inverse Distance
Weighted, Global Polynomial Index, Local Polynomial Index, Kriging, Kernel Smoothing and Diffusion
Kernel were tested to estimate the metal concentrations at unsampled locations for assessment of their
performance by comparing the Root Mean Square Error (RMSE) for cross-validation and all models
provided more or less high prediction accuracy to mean value of the metals. Specific to the Kriging
model, it was found to be best fitted with the lowest RMSE in all the metals except Mn and Ni in the soil
where IDW and local Polynomial index was found to give the lowest RMSE. Other geospatial models
that interpreted better groundwater heavy metals content with lowest RMSE were Inverse Distance
Weighted Interpolation for Ni and Pb, Local Polynomial Index for Mn, Global Polynomial Index for Fe.
The three-dimensional trend over the distribution of metals throughout the district best fitted the secondorder
polynomial for Cd, Cu, Ni, Zn, Pb, Fe, As, and Mn in soils while both first and second-order
polynomials according to XZ and YZ dimensions fitted well for Cd, Ni, Mn, Pb, Ni, and Cu in
groundwater. Significant numbers of pairs of heavy metals to a certain extent were found to be spatially
autocorrelated and all the pairs away from X-axis towards the extreme right corner and far above the axis
reflected less influence of local characteristics of the heavy metal. Spatial autocorrelations were detected
for 9 heavy metals and the autocorrelation distances were; Cd 60; Cr 60; Cu 60; Ni 55; Zn 57; Cu 55; Pb
65; Fe 68; As 62 and Mn 65 km for soil and Cd 60 Cr 57; Ni 65; Zn 57; Pb 60; Fe 65; As 57and Mn 60
km for groundwater. Co-variance cloud with search direction from North to South revealed the existence
of spatial autocorrelation revealing a wider spatial shift of correlation towards the southern direction. The
Pollution Indices (Single Pollution Index, Geo-accumulation Index, Ecological Risk Factor) showed the
highest threat to the soil from Pb, Cd, and Ni respectively. Overall Multi Pollution Indices (Pollution
Load Index, Average Single Pollution Index, Enrichment Factor, and Nemerow Pollution Index)
encompassing all the metals showed that although there was considerable pollution in the soil, the soil
was under the critical limit but towards the higher side. The Bio-concentration, Bio-accumulation, and
Translocation Factor as was revealed from the pot culture experiment taking toria (variety TS-67) as test
crop was found below 1 for all the graded levels of Pb envisaging the crop inefficient to hyperaccumulate,
phyto-stabilize and phyto-extract Pb from the soil. A higher value of Bio-concentration
Factor (>1) for Cd and Ni, revealed the crop efficiently hyper-accumulates Cd and Ni. BAF (>1) for Cd
levels at 0.5 ppm, 1.0 ppm, and 1.5 ppm and Ni levels baring 60 ppm indicated the crop to be able to
phytostabilises both Cd and Ni at lower concentrations. The Translocation Factor (< 1) for Cd and Ni
was indicative of the inefficiency of toria to phytoextract Cd and Ni in its aerial parts. The study helped
to find out the hotspots for certain heavy metals in the district which would certainly help in further
decision making and take viable removal measures as well as suitable cropping systems. GIS maps
validated through geostatistical approaches help in contributing contamination characteristics, degree of
pollution of heavy metals in soils as well as groundwater based on which desired phytoremediation
planning may be adopted.