Risk assessment of agricultural soils surrounding an iron ore mine: A field study from Western Ghat of Goa, India

Daripa, Amrita; Chattaraj, S.; Malav, Lalchand; Ray, Prasenjit; Sharma, R. P.; Mohekar, D. S.; Ramamurthy, V. V.; Raghuvanshi, M. S.; Patil, N. G.

doi:10.1080/15320383.2022.2111403

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…For analysis of Zn, Cu, Cd, Pb, Cr and Ni working standards of 0.01, 0.05, 0.1, 0.25 mg kg -1 were used; whereas 0.25, 0.50, 1.00, 2.00 mg kg -1 working standards were used for analysis of Fe and Mn in ICP-AES. The recovery percentage was 99.5%" [24].…”

Section: Quality Controlmentioning

confidence: 93%

“…Subsequently at the low lands slopes flattens out and cultivated lands are mostly present in this zone; majority of which were turned into fallow lands as indicated in the land use land cover map due to long term mining deposits accumulation from uphill side that leads to poor soil drainage. The principal crops includes rice, coconut, areca nut, cashewnut, and vegetable crops" [24].…”

Section: Site Descriptionmentioning

confidence: 99%

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Land Use Based Distribution Pattern, Fractionation forms and Health Risk Assessment of Heavy Metals in Mining-affected Soils of Western Ghat of Goa, India

Daripa

Chattaraj²,

Malav³

et al. 2022

IJPSS

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Goa is a biodiversity hotspot and a major iron ore producer state in India. The study investigated heavy metal (HM) distribution pattern, fractionation forms under different land use types near an iron mining including health risks associated with different exposure pathways. Georeferenced 38 soil surface samples, 8 soil profile samples and 6 plant samples were collected from three land use types- agriculture cropland, fallow land and natural vegetation. Collected samples were analyzed for HM contamination in inductively coupled plasma atomic emission spectrophotometer (ICPAES). The mean total HM concentration in surface soil exceeded the background value. Weak acid soluble fraction % of metals posed no to medium risk values. Fallow land had higher metal content in surface soil. Principal component (PC) analysis showed Pb, Cd and Cr metals were more associated with mining activity. Vertical distribution pattern of HM accumulation in soil profiles showed a drastic reduction in HM concentration below plough layer in fallow land indicating anthropogenic sources of origin. Hazard index (HI) in the descending order were Cd> Cr >Pb. Non-carcinogenic HI value were considerably higher than safe level. Higher organic carbon (OC) and clay content in soil had a tendency to accumulate ample HM in unavailable form due to chelation effect. Soils had higher pool of unavailable residual HM as compared to available. Overall the land uses are in medium risk category and site specific management is requisite of the time to maintain the balance of this fragile ecosystem with anthropogenic activities in long run.

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Section: Quality Controlmentioning

confidence: 93%

Section: Site Descriptionmentioning

confidence: 99%

Land Use Based Distribution Pattern, Fractionation forms and Health Risk Assessment of Heavy Metals in Mining-affected Soils of Western Ghat of Goa, India

Daripa

Chattaraj²,

Malav³

et al. 2022

IJPSS

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“…Optimum number of clusters was decided based on coinciding minimal values of both fuzzy performance index (FPI) and normalized classification entropy (NCE) (Davatgar et al., 2012; Verma et al., 2018). FPI and NCE were calculated using the following equations (Daripa et al., 2022):

FPI goodbreak= 1 goodbreak- \frac{c}{c - 1} (][, 1 goodbreak- \frac{\sum_{i = 1}^{c} \sum_{k = 1}^{n} {(μ_{ik})}^{2}}{n})

NCE goodbreak= \frac{n}{n - c} (][, goodbreak- \frac{\sum_{k = 1}^{n} \sum_{i = 1}^{c} μ_{ik} \log_{a} ()(, μ_{ik})}{n})

where c = number of clusters, n = number of observations, μ ik = fuzzy membership and log a = natural logarithm. The means of clusters (MZs) were compared using one‐way analysis of variance (ANOVA) in R statistical software.…”

Section: Methodsmentioning

confidence: 99%

“…Optimum number of clusters was decided based on coinciding minimal values of both fuzzy performance index (FPI) and normalized classification entropy (NCE) (Davatgar et al, 2012;Verma et al, 2018). FPI and NCE were calculated using the following equations (Daripa et al, 2022):…”

Section: Statistics and Uncertainty Analysismentioning

confidence: 99%

“…Hence, delineation of homogenous carbon management zones (CMZs) with the help of DSM model output will not only boost site‐specific carbon management (SSCM) in a sustainable manner but also help to identify areas for improved ecosystem services viz., food production, soil health, degradation neutrality and climate resilience. Principal component analysis (PCA) and fuzzy clustering; particularly the fuzzy c ‐means algorithm are used for the demarcation of management zones (MZs) (Behera et al., 2021; Daripa et al., 2022; Li et al., 2020; Verma et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

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Delineation of high‐resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India

Reza,

Chattaraj,

Mukhopadhyay

et al. 2023

Soil Use and Management

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Delineation of carbon management zones (CMZs) by capturing geospatial distribution of soil organic carbon (SOC) stock down the profile is an effective strategy for precision agriculture and climate change mitigation. Satellite (Landsat OLI 8), terrain (SRTM 30 m DEM) and bioclimatic (WorldClim dataset) factors were used as covariables in this digital soil mapping approach. Depth harmonization using the quadratic spline method (equal‐area) was carried out prior to quantile regression forest (QRF) algorithm‐based modelling to estimate SOC stock at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). Soil depth and SOC stock for the whole soil profile were used for the delineation of CMZs using fuzzy k‐means clustering. The predicted SOC stock, varied from 14.68 to 42.35 Mg ha−1 in the top layer (0–5 cm depth), while 17.91 to 36.88, 14.15 to 34.70, 12.55 to 35.59, 10.30 to 28.52 and 7.26 to 20.16 Mg ha−1 in the depths of 5–15, 15–30, 30–60, 60–100 and 100–200 cm, respectively. The QRF algorithm performed well in predicting SOC stock with high R2, which ranged from .67 to .83 for all the soil depths. To delineate three CMZs, modified partitioning entropy and the fuzzy performance index were used. In CMZ2, there was a significant increase in SOC stock, followed by CMZ1 and CMZ3. This zone (CMZ2) was located in the central region of the study area and was mostly covered by dense forest and perennial plantations (rubber). The CMZs provided the necessary foundation for the development of site‐specific carbon management techniques that can enhance ecosystem service and meet climate change mitigation goals.

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Influence of Soil Organic Carbon, Water Holding Capacity, and Moisture Content on Heavy Metals in Rice Paddy Soils of Western Ghats of India

Hegade

Chethanakumara

Krishnamurthy

2023

Water Air Soil Pollut

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Analysis of soil samples collected from 16 rice paddy elds located in Western Ghats region was made to quantify the heavy metals concentration. Heavy metal analysis was done for Cu, Zn, Mn, Fe, Ni, Cr, Cd, and Pb using atomic absorption spectroscopy. High concentration of these heavy metals was found in rice paddy elds regularly being cultivated using agrochemicals. We compared this concentration with soils of rice paddy eld that was not under cultivation. Cu, Zn, Mn, Fe, Cr, Ni, Pb, and Cd showed a build up at an order of 1.2, 1.3, 2.3, 2.2, 1.8, 2.8, 1.8, and 8.5 times higher in the rice paddy elds cultivated with agrochemical applications. Heavy metals exhibited maximum association (PCA) with soil moisture content (Zn, Fe, Cr, Ni, and Cd), organic carbon content (Fe, Cr, Ni, and Cd) and water holding capacity (Cu, Pb, and Cd). Such associations were absent in the reference site. This indicates the build-up of heavy metals in rice paddy soils under the strong in uence of moisture content, water holding capacity, and organic carbon content of the soil.

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Risk assessment of agricultural soils surrounding an iron ore mine: A field study from Western Ghat of Goa, India

Cited by 8 publications

References 40 publications

Land Use Based Distribution Pattern, Fractionation forms and Health Risk Assessment of Heavy Metals in Mining-affected Soils of Western Ghat of Goa, India

Land Use Based Distribution Pattern, Fractionation forms and Health Risk Assessment of Heavy Metals in Mining-affected Soils of Western Ghat of Goa, India

Delineation of high‐resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India

Influence of Soil Organic Carbon, Water Holding Capacity, and Moisture Content on Heavy Metals in Rice Paddy Soils of Western Ghats of India

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