Transect Approach for Assessing Major and Trace Elemental Contamination in Agricultural Soils of the Palamaner Division, Chittoor District, Andhra Pradesh, India

doi:10.21203/rs.3.rs-4861335/v1

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Transect Approach for Assessing Major and Trace Elemental Contamination in Agricultural Soils of the Palamaner Division, Chittoor District, Andhra Pradesh, India

https://doi.org/10.21203/rs.3.rs-4861335/v1

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Three soil transects were utilized in the granitic terrains of Palamaner mandal, Andhra Pradesh, India, to evaluate the pollution levels of primary and secondary metals (Si, Al, Fe, Ca, Mg, Na, K, Fe, Cu, Mn, P, and Zn) and determine the extent of soil pollution in agricultural landscapes. The soils along the transects ranged from slightly acidic to neutral, with significant variations with landscape position (F = 8.07). These soils are categorized as having low levels of organic carbon with moderate variability in A horizons (CV of 33.56%). The two-way ANOVA results indicated significant differences in cation exchange capacity (F = 4.34 for landscape; F = 4.65 for horizons) and organic carbon (F = 5.26 for landscape and 38.86 for horizons). Under semiarid conditions, the high SiO₂ content played a role in limiting the dissolution of quartz. This led to the remobilization of Fe₂O₃ in the argillic B horizons, indicating that Fe migrated along with clay. Two-way ANOVA showed a significant difference in the total zinc content between horizons and landscape positions. Interestingly, Si, Al, and Cu exhibited slight enrichment and negative geoaccumulation indices in all the soil profiles, suggesting that they were not polluted. The pollution indices formed three clusters, with significant differences in the contamination factors for SiO₂ and Cu between the clusters. A total of five principal components were identified, accounting for 76% of the overall variance. These components primarily originate from geogenic sources and fall within acceptable limits.

ANOVA

principal component analysis

cluster analysis

pollution indices

soil transects

Mysore Plateau

The global environmental issue of high concentrations of heavy metals, particularly in agricultural soils, poses a significant challenge to food production and security(Kowalskaet al.2018). The incorporation of heavy metals into the trophic chain can potentially impact the health of animals and humans(Al-Anbari et al. 2015; Mohamed et al. 2014; Pejman et al. 2015). The problem of heavy metal pollution is not limited to urban centers and farmlands near pollution sources, but it also extends to other areas. The increasing awareness of expanding industrialization and intensive agricultural soil use necessitates the evaluation and determination of the ecological risk associated with heavy metal content in the soil. To understand the development of industrialization and fertilizer use over the past several decades, it is crucial to analyze studies on the temporal trends of heavy metal contents in soils(Kowalska et al. 2016).

Soil contamination by heavy metals (HMs) is a significant concern in both industrialized and nonindustrialzed nations, as these pollutants are widely recognized as the most severe (Aelion 2004; Guan 2015). With the escalation of geological and human activities, as well as global economic development, soil pollution has become a prevalent issue worldwide. This predicament poses a significant threat to human society, as numerous HMs, including arsenic (As), copper (Cu), cadmium (Cd), lead (Pb), chromium (Cr), nickel (Ni), mercury (Hg), and zinc (Zn), accumulate and jeopardize both the environment and public health (Alengebawy et al. 2021). The accumulation of heavy metals and metalloids in approximately 5 million soil sites worldwide surpasses permissible limits, resulting in their contamination. Such high concentrations of heavy metals pose various risks to ecosystems and humans, causing harm to the food chain, compromising food quality, and hindering agricultural production (Zhao et al. 2022). The concentration of HMs in soil plays a pivotal role in soil fertility and nutrient status, thereby impacting the overall health of the soil. While certain metals, such as copper (Cu), zinc (Zn), and selenium (Se), are essential for the proper growth of plants and living organisms, excessive concentrations can prove toxic (Gowd et al. 2010). The natural sources of these HMs in the environment are sustained through various processes within their natural cycle, including rock weathering and volcanic activities. However, it is imperative to acknowledge that human activities have resulted in profound alterations in the fundamental characteristics of soil. These alterations encompass a wide array of factors, such as texture, cation exchange capacity (CEC), pH, and bulk density. Regrettably, this transformation has given rise to the deposition of hazardous materials (HMs) within the soil, thereby exacerbating pollution levels to unprecedented heights (Wang and Zhang 2018). Previously, soil elemental datasets have been utilized to assess metal contamination through the calculation of the geoaccumulation index, enrichment factor, contamination factor, pollution load index, ecological risk, and potential ecological risk index (Rajkumar et al., 2019; Hoque et al. 2022,). However, due to the limitations of these approaches, multivariate techniques such as principal component analysis and cluster analysis have been employed (Giri et al. 2017; Herojeet et al. 2016; Kowalska et al. 2018; Rajkumar et al. 2019).

The ecological assessment of metal contamination among agricultural soils is a crucial reference point for understanding the distribution patterns of metals in soil profiles along transects. The variation in soil geochemistry among different types of catena sequences has been attributed to significant factors such as lithology, climatic conditions, water regime, slope gradients, land use, and vegetation patterns (Rieuwerts et al. 2006; Samonova and Aseyeva 2006). Catena studies have proposed various methods for analyzing the distribution patterns of elements, identifying groups of elements involved in migration, and evaluating the factors driving geochemical changes along catenary sequences (Glazovskaya 2002; Thanachit et al. 2006a, b). The influence of topography and pedological characteristics on metal concentrations in soils has been established as a significant factor. Finer fractions of soil contain a wide range of metals that are more easily transported within the soil profile and downslope (Plyaskina and Ladonin 2005). However, metal contamination datasets collected from soil-landscape systems in the study region have not been explored. Limited information is available, for instance, regarding the distribution of metals across catenary systems. The topographic influence on the variation of metal contents and their distribution in agricultural soil profiles was hypothesized based on the literature. Are studies using soil transects able to capture these variations, affinities, and contamination levels? In the specific context of the Palamaner division of Chittoor district in India, which is part of the Mysore Plateau, intensive agricultural practices covering 7810.94 hectares or 51.5% of the area, and surface mining activities covering 112.85 square kilometers were carried out. The present research aims to: (1) ascertain elemental contamination with respect to topographic position using various indices; (2) determine the elemental makeup and profile distribution (Si, Al, Fe, P, K, Na, Ca, Mg, Cu, Mn, and Zn) in agricultural soils of granitic landscapes; and (3) identify the sources of the metals in the soils via multivariate statistical analysis. Therefore, this study represents a novel attempt to evaluate the impact of slope position and transect on metal contamination of the surface and subsurface soils of the agriculture-dominated granitic terrain of the Mysore Plateau, utilizing geochemical dataset.

2.1 Description of the study sites

The study area within the Palamaner division encompasses a total area of 1825 square kilometers. It is situated between the latitudes of 77° 54' and 79° 10' N, and the longitudes of 13° 41' and 12° 36' E (refer to Fig. 1.). This study area is a component of the Mysore Plateau and is characterized by an elevation range of 600 to 900 meters above mean sea level. The prevailing geological formation is the Peninsular Gneissic Complex (PGC, CGWB 2008), which is accompanied by various denudational landforms such as inselbergs, pediments, basins, domes, dykes, and sill ridges, which are intrusive structures (NRSA 2016).

The climate in this region is characterizedby a semiarid monsoon, climate, characterized by distinct summer (April to June), rainy (July to November), and winter (December to March) seasons. Analysis of meteorological data from the past 30 years (1991–2021) reveals that the area has experienced an average annual rainfall of 973.90 mm, with 94.94% of that total occurring between May and December (Fig. 3). A detailed examination of regional rainfall patterns indicated that November typically receives a mean rainfall of 199.47 ± 161.69 mm, with a coefficient of variation (CV) of 81.06%. In October, the mean rainfall is 184.27 ± 89.82 mm, with a CV of 48.74%. September exhibited irregular trends, with a mean rainfall of 116.98 ± 44.25 mm and a CV of 37.83%. On the other hand, February experienced a mean rainfall of 7.38 ± 16.48 mm, with a CV of 223.31%. Skewness coefficients greater than 1 are observed in November, January, and June, indicating a significant departure from the normal distribution. Overall, the results clearly demonstrate an irregular rainfall distributions, with a frequency of occurrence of 20 out of 30 years (66%). This erratic distribution occasionally leads to flash floods and droughts in the region. The annual precipitation concentration index (PCI) reveals a sequence of moderate to irregular distribution, particularly during the years 2014 to 2016. The mean PCI was calculated to be 17.25 ± 3.26, with a CV of 18.92%. Decadal variations in the monthly seasonality index indicate extreme seasonality (Kanellopoulou, 2002) from July to November, while June (2001 to 2010) exhibits marked seasonality. For the remaining months, the seasonality is characterized by a shorter drier season (Jayasree, 2023). The soil temperature regime in this region is classified as isohyperthermic, with austic moisture regime and a growing period lasting between 90 and 150 days (Sehgal and Mandal, 1994). Similar observations have been reported for this region, including an annual evaporation rate of 1,840 mm, with monthly variations ranging from 72 to 263 mm (Vittal et al. 2013).

2.2 Soil transect technique

This study focused on a rocky quartz granitic-gneiss outcrop spanning approximately 2 km in length and located in the granitic terrain of the Palamaner division. Three transects were established, each comprising three distinct landscape units: summits (upper slopes), middle slopes (back slopes), and lower slopes (foot slopes, Fig. 2). The aim of this study was to investigate the interactions between soil and landscapes, and to comprehend the genetic connections between surface processes and surface disturbances, with a particular focus on chemical weathering and erosion processes affecting elemental mobility. The transect technique, as outlined by Johonson (1961), was employed to achieve this objective. Six soil profiles were selected at random to create the soil transect technique.

Soil transect 1 was divided into three slopes: upper (> 750 m), middle (730–750 m), and lower (730 m) slopes. Field data revealed that upper slopes host soils with Kanalillu, P1 and Kummaragunta, P2; middle slopes with Chellargunta, P3 and Settipalle P4; and lower slopes with Gorreladoddi P5 and Chinnapuram P6.

Soil transect 2 spanned a length of 25 km and an elevation difference of 710 m on top slopes to 650 m on the lower slopes. It was located between Atukurallapalle on the northern aspect and Jidimakulapalle in the western aspect. The transect comprised upper slopes (Atukurallapalle (P7) and Samalagadda (P8) ;> 690 m), medium slopes (Gangavaram (P9) and Kongavaripalli (P10); 680 − 660 m), and lower slopes (Maredupalli (P11) and Jidimakulapalli (P12); 660 m).

Soil Transect 3 is divided into three slopes: the top (> 700 m), middle (600–700 m), and lower slopes. This transect is situated in the Mogili forest region (450–500 m). Within this transect, there are six soil profiles: Zararipalli (P13), Kilapatla (P14), Gandhinagar (P15), Mogili (P16), Gourishankarapuram (P17), and Mittur (P18).

2.3 Sample collection and processing

Each soil profile sample, weighing approximately 1 kg, was carefully packed and sealed in polyethylene bags prior to transportation to the laboratory. The samples were then subjected to air drying at room temperature and sieved through a 2 mm sieve to obtain the fine earth fraction for analysis of particle size and chemical properties such as pH, organic carbon, calcium carbonate content, and CEC. To ensure uniformity and reduce sample variability, subsampling of the dried soils was performed using the coning and quartering method until the desired weight was achieved. In the laboratory, a pulverizing mill equipped with a zirconia oxide grinding system was used to pulverize 10 g of each soil sample. The resulting pulverized soil was then sieved through a 100-mesh nylon sieve for total elemental analysis (Alloway, 2013).

2.4 Laboratory analysis

2.4.1 Particle size and chemical analysis

The international pipette method was used to conduct the particle size analysis, with the resulting fractions expressed as percentages of sand (2.00–0.05 mm), silt (0.05 − 0.002 mm), and clay (< 0.002 mm) in accordance with Piper's procedure (1966). The clod method was employed to determine the bulk density (Black and Hartge 1986). The pH was measured using a digital pH meter (Systronics µ pH system 361) in a 1: 2.5 soil-water ratio, while the electrical conductivity was determined using an Elico CM 180 conductivity meter (Jackson 1973). Organic carbon was estimated using Walkley and Black's wet oxidation method, and free calcium carbonate was determined by treatment with a known volume of standard HCl and back titrating with standard alkali using bromothymol blue as an indicator (Piper 1966). The cation exchange capacity of the soil samples was determined by saturating a known weight of the soil with 1 N sodium acetate (pH 8.2), followed by leaching with 95% ethanol to remove excess sodium acetate. The Adsorbed sodium was then displaced with 1 N neutral ammonium acetate (pH 7.0), and the remaining sodium concentration in the leachate was measured using a flame photometer (Systronics flame photometer 128). The CEC was calculated and expressed as cmol (p+) kg^-1 soil (Bower et al. 1952). The exchangeable cations, potassium and sodium were measured using a flame photometer (Systronics flame photometer 128), while calcium and magnesium were measured using the versenate method. The concentrations of the exchangeable cations Ca²⁺, Mg²⁺, K⁺ and Na⁺ were expressed in cmol (p+) kg^-1 soil (Chopra and Kanwar, 1991).

2.4.2 Elemental analysis

A soil sample weighing one gram was dried in an oven, passed through a 100-mesh sieve, and carefully measured and placed into a 150 ml Erlenmeyer flask for acid digestion. The acid digestion process involved the use of concentrated nitric acid, perchloric acid, and sulfuric acid at a ratio of 5:1:1, as described by Rajkumar et al. (2019). The resulting acid dissolved solutions were then subjected to analysis to determine the presence and concentration of various elements, including Si, Al, Fe, P, K, Na, Ca, Mg, Cu, Mn, and Zn. The analysis of these elements was conducted using atomic absorption spectroscopy, specifically utilizingVarian AA240FS instrument. The results obtained from this analysis were expressed as percentages for SiO₂, Al₂O₃, Fe₂O₃, P₂O₅, K₂O, Na₂O, CaO, and MgO. However, for Cu, Mn, and Zn, the results are expressed in milligrams per kilogram (mgkg^{- 1}). To ensure accurate measurements, a calibration process was carried out using various materials. This included the use of blank samples containing 10 mL of 65,145% HNO₃, elemental standard solutions with concentrations of 2, 5, and 10 ppm, and a standard reference material. The specific absorbance of each metal being measured was associated with wavelengths, acceptable limits, and detection limits. To improve the detection limits, replicate injections were employed. All detection limits were determined based on a 98 to 100% confidence level, using 3 standard deviations. The recovery rate for the metals ranged from 95.3 to 99.9%, depending on the specific metal being measured.

2.5 Environmental indicesfor assessing anthropogenic influence

Soil samples collected from three agricultural transects within the Palamaner division were subjected to analysis for major and trace elements using atomic absorption spectroscopy (AAS). The purpose of this analysis was to determine the concentrations of each element present in the soil samples, with a focus on environmental indices such as the enrichment factor (EF), Ige accumulation index, contamination factor (CF), degree of contamination (Cd),modified degree of contamination, Nomrerow comprehensive index (Ps) and pollution load index (PLI). To establish a contextual framework for our study, we also examined the elemental composition of the upper continental crust, as reported by Taylor and Mclennan in 1995.

2.5.1 Enrichment factor (EF)

The enrichment factor (EF) was determined for each metal present in the soil by employing Eq. (1): EF = (CX/CAl) s (CX/CAl) c (1), where CX and CAl represent the concentrations of elements X and Al in the soil (s) and earth's crust (c), respectively. The reference element used was Al, as suggested by Chester and Stoner (1973) and Vieira et al. (2004). The EF was calculated for both trace and major elements (X) in soil horizon samples, thereby normalizing the element concentrations.

2.5.2 I_geo accumulation index

The I_geo accumulation index (I_geo) was calculated using Eq. (2) proposed by Muller (Muller 1969). Eq. 2 is defined as follows:

I_geo = log₂(Cn│1.5Bn)

In this equation, Cn represents the metal content in soil sample n, while Bn represents the geochemical background value of the metal in the corresponding soil. The constant 1.5 is a correction factor that accounts for the lithogenic effects and natural fluctuations of metals in the environment (Kamani et al. 2017).

2.5.3 Contamination factor(Cf), degree of contamination (Cd) and modified degree of contamination(mCd)

The term "Cif" is defined as the ratio of the mean content of metal in soil (CiS) to the concentration of metal in the earth’s crust (Cin) (Loska et al. 2004). Cif = Cis / Cin Here, Cis represents the measured concentration of metal "i" in the soil sample, while Cin represents the geochemical background value of metal "i" (average shale in this study).

The degree of contamination in soil (Cd) was determined by summing the contamination factors for all trace elements (Cif).

mCd = ∑(Cif) / n The parameter mCd, known as Hakanson's modified degree of contamination index, calculates the overall contamination of metals at a specific location by summing all the pollution indices (Cif) (Hakanson 1980).

2.5.4 Pollution load index

The pollution load index is the geometric average of the impurity coefficient (CiF) and provides an assessment of the contribution of all metals in each area (Tomlinson et al. 1980; Jorfi et al. 2017). This parameter is useful for evaluating the level of environmental contamination and determining the need for monitoring or remediation activities to improve soil quality. PLI was calculated as follows: PLI = (CF1 × CF2 × CF3 ×···× CFn) 1/n

2.5.5 Nemerow comprehensive index

The purpose of this calculation is to assess the contamination levels of trace elements in the soil, enabling the evaluation of the overall extent of soil pollution. This evaluation encompasses the analysis of all heavy metals present in the soil. (Gong et al. 2008).It was calculated as follows:

Ps = \(\:\sqrt{\frac{\sqrt{{\left(\varvec{m}\varvec{C}\varvec{d}\right)}^{2}}+({\varvec{C}\varvec{i}\varvec{f}\varvec{m}\varvec{a}\varvec{x})}^{2}}{2}}\)

Where,Cifmax is the maximum value of a single pollution index of metals measured in the soil sample. The classification of all indices utilized in the study is presented in Table 1.

Table 1

Classification of enrichment ratio, Igeo, contamination factor (cf), pollution load index (PLI), and Nemerow index (Ps)
Enrichment factor	Enrichment intensity	Igeo	Pollution intensity	CF	CF intensity	mCd	Intensity	Pollution load index	Intensity	Nemerow index	Intensity
EF < 2	Minimal	Igeo < 0	Unpolluted	CF < 1	Low	mCd < 1.5	Unpolluted	P LI < 1	No pollution	Ps < 1	Uncontaminated
2 ≤ EF < 5	Moderate	0 ≤ Igeo < 1	Unpolluted to moderately polluted	1 ≤ CF < 3	Moderate	1.5 ≤ mCd < 2	Slightly polluted	1 < P LI < 2	Moderate pollution	1 ≤ Ps < 2	Slightly contaminated
5 ≤ EF < 20	Significant	1 ≤ Igeo < 2	Moderately polluted	3 ≤ CF < 6	High	2 ≤ mCd < 4	Moderately polluted	2 < P LI < 3	Heavy pollution	2 ≤ Ps < 3	Moderately contaminated
20 ≤ EF < 40	Very high	2 ≤ Igeo < 3	Moderately to strongly polluted	CF ≥ 6	Very high	4 ≤ mCd < 8	Consideraby polluted	3 < P LI	Extremely heavy pollution	3 ≤ Ps < 5	Strongly contaminated
EF ≥ 40	Dangerous	3 ≤ Igeo < 4	Strongly polluted			8 ≤ mCd < 16	Highly polluted			Ps ≥ 5	Seriously contaminated
		4 ≤ Igeo < 5	Strongly to very strongly polluted			16 ≤ mCd < 32	Strongly polluted
		Igeo ≥ 5	Very strongly polluted			mCd ≥ 32	Extremely polluted

2.6 Statistical analysis

Descriptive statistics, normality tests, and analysis of variance (ANOVA) were conducted to evaluate the level of variation in elemental composition across different landscape positions in the transect. In general, the coefficient of variation (CV %) = standard deviation/mean) is used to measure the degree of variation. A value of 0 ≤ CV ≤ 30%, can be considered weak variation; 30% < CV ≤ 100% can be considered medium variation; and CV > 100% can be called strong variation (Jiang et al.2020). The statistical analysis was performed using the following online free packages: Statistics Kingdom and Stat Blue. ANOVA was utilized to examine the variability of chemical variables, assuming a normal distribution, in relation to both transects and slope positions. Furthermore, cluster analysis was employed to assess the similarities between chemical indices used for contaminants. The hierarchical group linking cluster was created using the Euclidean distance and Ward method, which are recognized for generating significant similar clusters (Somerfield and Clarke 2013). Multivariate statistical modeling of the input data was carried out using hierarchical cluster analysis (HCA) and principal component analysis (PCA), as described by Massart and Kaufman (1983) and Vandeginste et al. (1997). The objective of hierarchical agglomerative cluster analysis is to classify data into groups of similarity (clusters) based on objects in n-dimensional space that are in close proximity to one another, while also separating stable clusters from other clusters. Typically, sampling sites are considered as objects for classification, with each site being determined by a set of variables such as chemical concentrations.

The cluster analyses were conducted using SPSS. PCA, or principal component analysis, is a technique employed to reduce the dimensionality of a variable space by identifying the directions of highest variance in the system. The new variables, referred to as principal components or latent factors, are linear combinations of the original variables and replace the previous coordinates in the factor space. Chemists are particularly interested in interpreting these new factors as they offer valuable insights into the underlying relationships within the dataset. The results of PCA are represented by two sets of information. The first set, known as factor scores, provides the new coordinates of the factor space and indicates the location of the objects within the dataset. The second set, called factor loadings, provides information on the relationships between the variables. Notably, only statistically significant loadings, typically those exceeding a threshold of 0.70, are considered important for the modeling procedure. To enhance the interpretability of the PCA solution, a commonly used rotation method called normalized varimax rotation is often applied. This rotation method, as highlighted by Hervé (2010), allows for a better explanation of the system by emphasizing the latent factors with a greater impact on the variation explanation and reducing the influence of principal components with lower impact. It is worth mentioning that the sequence of rotated factors may not necessarily be arranged in an order of decreasing percentage of variance explained, although the total variance explained remains the same before and after rotation. The use of rotation strategy, as established by Thurston (1974) and Cattell (1978), simplifies the structure of factors and facilitates their interpretation, making the results more reliable and easier to understand. Pearson's correlation coefficient (r) was used to determine linear associations, using the Statistical Package for Social Sciences. Correlation coefficients with p-values less than 0.05 and 0.01.Therule for interpretating the correlation results were clearly established as per Mukaka, 2012.

3.1 Particle size distribution,bulkdensity,pHand EC of the soil transects

The descriptive statistical analysis included the examination of particle size distribution, bulk density, pH, and electrical conductivity (EC) using data collected from three selected transects within the study area. The particle size distribution (PSD) revealed that the soil was predominantly composed of sand, followed by clay and silt. The mean sand content was 60.97% ± 17.84%, with a CV of 29.26%. However, in Transect-1, the mean sand content was slightly lower at 48.88% ± 22.03%. However, the mean silt content was 24.54% ± 14.3% in Transect-1, but decreased to 16.59% ± 12.35% in Transect-3. The mean clay content varied from 25 (T1) to 22.59% ± 7.93% (T3), with standard deviations of 9.06 (T1) and 8.13 (T2). The mean bulk density ranged from 1.43 Mg/m³ (T1) to 1.48 Mg/m³ (T3), with a low variation (CV < 7.39%). However, the sand, silt, and clay contents exhibited moderate variability (CV > 30%), except for the sand content in T3, which showed low variation. It is worth noting that the transect sites displayed a continuous increase in clay content from the upper slopes to the lower slopes, resulting in a moderate variability in particle size and higher silt/clay ratios (Shulka et al., 2004; Botros et al., 2009; Obi et al., 2011). The pH of the soil ranged from 6.34 (T2) to 6.61 (T1), indicating a slightly acidic nature. The variation in pH was relatively low, with coefficients of variation (CVs) ranging from 2.25% in T3 to 5.99% in T2. The average electrical conductivity (EC) suggests a low level of salt content, ranging from 0.13dSm^-1 in Ti to 0.2 dSm^-1 in T3. The degree of variability fell within a moderate range (Table 2).

Table 2

Transect wise descriptive statistical summary for particle size, BD, pH and EC of soils in Palamaner
Statistics	Particle size distribution (%.<2mm)				BD (Mg/m³)	pH	EC (dSm^-1)
Statistics	Sand (2 to 0.05mm)	Silt (0.002 to 0.05mm)		Clay (< 0.002mm)	BD (Mg/m³)	(1:2.5 soil water ratio)
Transect-1
Mean	48.88		24.54	25.00	1.43	6.61	0.13
SD	22.03		14.30	9.06	0.04	0.31	0.05
CV	45.07		58.30	36.23	5.74	4.66	38.09
Transect-2
Mean	51.90		22.73	25.37	1.45	6.34	0.20
SD	19.55		12.80	8.13	0.11	0.38	0.12
CV	37.67		56.30	32.03	7.39	5.99	59.87
Transect-3
Mean	60.97		16.59	22.59	1.48	6.39	0.13
SD	17.84		12.35	7.93	0.10	0.14	0.11
CV	29.26		74.47	35.10	6.73	2.25	81.26

3.2 Chemical characteristics of the soil transects

The study area exhibited moderate variability, as indicated by the coefficient of variation (CV) of soil organic carbon (SOC), calcium carbonate, cation exchange capacity (CEC), and exchangeable bases (Table 3). The mean SOC ranged from 0.26% (T3) to 0.29% (T1), with a CV greater than 60%, indicating high variability. The mean calcium carbonate content was 3.22% in T3 with moderate variability and 6.69% in T1 which exhibited low variability (CV of 15.82%). The mean CEC was 18.19 ± 9.34 cmol/kg in T3 and 22.82 ± 10.27cmol/kg in T1, with moderate variability across all transects. The exchangeable bases showed moderate variability, with Ca being the dominant cation followed by Mg, Na, and K. The mean Ca content ranged from 8.7 ± 5.11cmol/kg in T3 to 11.49 ± 4.92cmol/kg in T1, while Mg ranged from 4.42 ± 2.62cmol/kg in T2 to 5.15 ± 3.80cmol/kg in T1. The mean Na content was 0.50 ± 0.37cmol/kg in T3 and 0.46 ± 0.27cmol/kg in T1, while K ranged from 0.11 ± 0.07cmol/kg to 0.15 ± 0.06cmol/kg. Overall, the majority ofthe soil properties measured in the study area were similar in terms of the mean, indicating similar to the findings of Brejda et al. (2000) and Warrick (1986).

Table 3

Transectwise descriptive statistical summary for OC, CEC and Exchangeable bases
Statistics	OC	CaCO₃	CEC	Exchangeable Cations
	(%)		CEC	Ca²⁺	Mg²⁺	K⁺	Na⁺
	(%)		---------------(cmol/kg)-----------------
Transect-1
Mean	0.29	6.69	22.82	11.49	5.15	0.15	0.46
SD	0.19	1.06	10.27	4.92	3.80	0.06	0.27
CV	66.40	15.82	44.79	42.78	73.79	42.06	58.43
Transect-2
Mean	0.27	4.81	19.87	9.88	4.42	0.13	0.39
SD	0.21	1.74	8.93	4.67	2.62	0.06	0.29
CV	76.31	36.21	44.94	47.26	59.30	46.53	74.69
Transect-3
Mean	0.26	3.22	18.19	8.70	4.58	0.11	0.50
SD	0.19	1.73	9.34	5.11	3.19	0.07	0.31
CV	73.83	53.87	51.34	58.72	69.56	59.13	61.09

3.3 Horizon wisevariations in particle size and chemical properties

Table 4 presents a comprehensive overview of the selected properties in relation to the horizons of each transect under investigation. It is evident that the sand content is notably greater in the A horizon than in the B horizon, while the silt and clay contents exhibit greater concentrations in the B horizons across all transects. In terms of the sand content, the mean percentage in the A horizons is an impressive 72.22% in T1, 66.93% in T2, and 75.40% in T3. Conversely, the B horizons display a sand content of 39.06% in T1, 42.79% in T2, and 54.51% in T3. Notably, the coefficient of variation is low for the A horizons in all transects, but moderate for the B horizons. Moving on to the silt content, the mean percentage in the A horizons ranged from 13.26 ± 4.27% in T1 to 14.89 ± 4.56% in T2. In contrast, the B horizons exhibited a mean silt content of 19.23 ± 13.45% in T3 and 29.29 ± 14.43% in T1. Notably, the silt content demonstrated moderate variability across the transects. For the clay content, the Ap horizons had a mean of 14.39 ± 5.3% in T3 and 18.18 ± 5.3% in T2, showcasing low variability. On the other hand, the clay-enriched B horizons exhibited a mean of 26.47 ± 5.9% in T3, with low variability, and 29.76% in T1/T2, with moderate variability. The mean bulk density ranged from 1.38 ± 0.09 Mg/m³ in the A horizon of T1 to 1.50 ± 0.10 Mg/m³ in the B horizons of T3, demonstrating low variability across all transects. Similarly, the mean pH varies from 6.36 in both the A and B horizons to 6.76 in the A horizon of T1, with low variability.

Table 4

Horizonwise variations in particle size and chemical properties
Statistics	Sand	Silt	Clay	BD	pH	EC	OC	CaCO₃	CEC
	(%)			Mg/m³		dSm^-1	(%)		cmol/kg
Transect-1
A horizon
Mean	72.22	13.26	14.52	1.38	6.74	0.15	0.47	6.63	11.39
SD	6.11	4.27	4.52	0.09	0.25	0.06	0.16	0.83	4.10
CV	8.46	32.18	31.11	6.83	3.78	42.36	33.56	12.6	35.96
B horizon
Mean	39.06	29.29	29.41	1.45	6.56	0.13	0.21	6.71	29.12
SD	18.51	14.43	6.42	0.07	0.32	0.04	0.14	1.16	8.11
CV	47.40	49.26	21.83	4.63	4.87	35.14	68.69	17.26	27.84
Transect-2
A horizon
Mean	66.93	14.89	18.18	1.39	6.36	0.20	0.52	4.84	12.45
SD	6.08	4.56	5.37	0.07	0.36	0.10	0.16	0.83	4.24
CV	9.09	30.62	29.56	5.03	5.59	49.93	30.89	17.12	34.07
B horizon
Mean	42.79	27.45	29.76	1.47	6.37	0.18	0.17	4.71	24.21
SD	21.64	15.13	9.62	0.12	0.40	0.12	0.12	2.15	8.46
CV	50.57	55.14	32.32	7.99	6.34	63.61	67.19	45.68	34.94
Transect-3
A horizon
Mean	75.40	10.21	14.39	1.42	6.46	0.09	0.47	4.12	10.84
SD	5.75	2.43	5.30	0.08	0.17	0.08	0.15	1.77	5.68
CV	7.62	23.84	36.85	5.99	2.70	82.28	32.76	43.08	52.44
B horizon
Mean	54.51	19.23	26.47	1.50	6.36	0.15	0.16	2.79	21.69
SD	16.99	13.45	5.90	0.10	0.12	0.12	0.12	1.59	8.84
CV	31.17	69.92	22.29	6.32	1.90	77.16	72.93	56.96	40.74

The EC values in all transects exhibit a low mean, ranging from 0.09 dSm^-1 ± 0.08 in T3 of the A horizons to 0.2 ± 0.1dSm^-1 in the T2 A horizon. Despite this, there was a moderate level of variability observed across the transects. In contrast, the organic carbon content in the A horizons is notably high, with values ranging from 0.47 ± 0.16% in T1 to 0.52 ± 0.16% in T2. This high mean was accompanied by a moderate level of variability. Moving on to the B horizons, the mean OC showed moderate variability, ranging from 0.16 ± 0.12% in T3 to 0.21 ± 0.14% in T1. On the other hand, the CaCO₃ content displays slight variations between the horizons. However, the mean values of CaCO₃ vary significantly, ranging from 2.79 ± 1.59% in the B horizons of T3 to 6.71 ± 1.16% in the B horizons of T1. While the A and B horizons of T1 and T2 exhibit low variability, the CaCO₃ content in the B horizons of T2 and T3 shows a moderate level of variability. The cation exchange capacity (CEC) exhibits moderate variability, with significantly greater values observed in the B horizons than in the transects. In the A horizon, the average CEC ranged from 10.84 ± 5.68 cmol/kg in T3 to 12.45 ± 4.24 cmol/kg in T2. Conversely, in the B horizons, the CEC approximately doubled, with an average of 21.69 ± 8.84 cmol/kg in T3 and 29.12 ± 8.11 cmol/kg in T1.

3.4 Elemental composition

Upper slopes

Soil transect studies conducted in the granitic terrain of the study area have provided valuable insights into the elemental composition in relation to slope position. The results, presented in Table 5, reveal the dominant presence of SiO₂ in the upper slopes. The mean content of SiO₂ ranged from 69.01 ± 3.82% in T1 to 75.06 ± 6.00% in T2, with relatively low variability (CV of 5.54 to 14.48%). Overall, the mean SiO₂ content across the three transects was 71.62 ± 7.39%, with a coefficient of variation (CV) of 10.32%. The distribution of SiO2 is symmetric, with skewness values ranging from − 0.19 in TI to -0.54 in T2 and exhibits a mesokurtic pattern. Following SiO₂, Al₂O₃ emerges as the dominant element, with a mean content of 15.75 ± 8.41% in T3, compared to the overall transect mean of 15.61 ± 6.56%. The CV for Al₂O₃ is moderate, ranging from 43.14 to 53.42%, but relatively low in T1 (CV of 15.57%). The transect data for Al₂O₃ show a symmetrical distribution (skewness of 0.24), except for Transect-2, which exhibits extreme skewness (skewness of 1.24) and a platykurtic pattern. The mean Fe₂O₃ content in T-2 was 6.99 ± 0.61%, indicating a platykurtic and symmetrical distribution. The variability inFe₂O₃was low in all transects, except in T-1, where it is moderately variable. The P₂O₅ contents are generally low, with a mean of 0.1 ± 0.3% in T-1, gradual decrease to 0.05 ± 0.01% in T-2 and a further increase to 0.03 ± 0.01% in T-3. Overall, the mean P₂O₅ content across all transects was 0.06 ± 0.03%, indicating moderate variability. The K₂O content displays low variability across transects, but its mean varies from 0.41% in T3 to 0.37% in TI and T2. The distribution of K₂O in T2 is extremely skewed and platy Kurtic. The Na₂O content had an overall transect mean of 0.13 ± 0.04% with moderate variability in T1 and T2, but low variability in T3. The mean Na₂O content is 0.12% in T2/T3 and 0.14% for Ti. The distribution of Na₂O is slightly skewed in Ti but symmetrical in T2/T3, yet platy Kurtic across transects. The KS test values in the exponential form indicate a significant difference from normal distribution across transects, except for MgO in T2, Al₂O₃ in T2/T3, Fe₂O₃ in T1, Na₂O in T2, and Mn in T3, where no significant difference is

Table 5

Transectwise descriptive statistics for the elemental composition of soils on upper slopes
Statistics	Elemental composition (%) (mg/kg)
Statistics	SiO₂	Al₂O₃	Fe₂O₃	P₂O₅	K₂O	Na₂O	CaO	MgO	Cu	Mn	Zn
Transect-1
Mean	69.01	19.47	4.41	0.10	0.37	0.14	1.59	0.50	29.61	285.56	35.45
SD	3.82	3.03	1.60	0.03	0.06	0.05	0.50	0.14	8.40	46.85	4.50
CV	5.54	15.57	36.20	26.32	16.02	34.82	31.40	28.32	28.35	16.41	12.69
SK	-0.19	0.86	0.09	0.88	0.07	0.69	1.43	0.04	0.57	0.52	0.48
kurt	0.36	0.57	-0.63	0.56	-0.86	-0.48	1.77	-1.28	0.38	-0.88	-0.43
KS test	0.59	0.56	0.37	0.48	0.54	0.45	0.5	0.47	0.44	0.55	0.57
p-value	0.002	0.0043	0.12	0.021	0.0072	0.039	0.015	0.024	0.042	0.0049	0.0032
Transect-2
Mean	75.06	11.61	6.99	0.05	0.38	0.12	1.47	0.68	28.60	246.25	40.68
SD	6.00	5.01	0.61	0.01	0.10	0.04	0.28	0.22	8.07	77.78	6.30
CV	8.00	43.14	8.70	23.13	25.60	31.63	19.04	32.49	28.22	31.59	15.48
SK	-0.54	1.24	0.52	-0.42	1.51	0.23	0.68	-0.11	0.67	0.72	0.45
kurt	1.06	1.85	-0.59	0.34	2.36	0.18	0.09	-1.10	0.63	-0.23	0.10
KS test	0.57	0.39	0.59	0.44	0.54	0.41	0.53	0.39	0.46	0.45	0.55
p-value	0.0034	0.09	0.002	0.044	0.0069	0.071	0.0087	0.093	0.033	0.036	0.0057
Transect-3
Mean	70.79	15.75	6.80	0.03	0.41	0.12	2.06	0.71	29.78	205.87	29.31
SD	10.25	8.41	1.38	0.01	0.07	0.02	0.33	0.23	5.41	76.67	7.77
CV	14.48	53.42	20.30	29.43	17.45	17.20	16.18	31.90	18.18	37.24	26.52
SK	-0.28	0.51	-0.12	-0.27	0.08	-0.16	-0.50	0.31	0.62	0.26	-0.19
kurt	-1.65	-1.31	-0.96	-0.76	-1.34	-1.06	-0.33	-0.76	0.88	-1.69	-1.22
KS test	0.54	0.32	0.5	0.44	0.53	0.52	0.51	0.44	0.53	0.42	0.46
p-value	0.0062	0.25	0.014	0.043	0.0085	0.01	0.013	0.045	0.0081	0.059	0.029
Total
Mean	71.62	15.61	6.06	0.06	0.39	0.13	1.71	0.63	29.33	245.89	35.15
SD	7.39	6.56	1.71	0.03	0.08	0.04	0.45	0.22	7.14	73.79	7.71
CV	10.32	42.03	28.15	55.36	20.06	28.62	26.26	34.14	24.35	30.01	21.94
SK	-0.26	0.24	-0.74	1.13	0.79	0.67	0.52	0.41	0.52	-0.04	-0.26
kurt	-0.47	-0.87	0.07	0.81	0.37	0.55	-0.74	-0.63	0.17	-0.71	0.52
KS test	0.54	0.31	0.38	0.29	0.52	0.43	0.47	0.40	0.44	0.37	0.43
p-value	0.9e-7	0.0091	0.00049	0.015	6.7e-7	0.000053	0.00	0.00028	0.000031	0.00094	0.00007

Middle slopes

The descriptive statistics for transect data of elemental composition in middle slopes reveal that SiO2 is the dominant element, with a mean of 71.42 ± 7.88% (Table 6). The KS test values for SiO2 are found to be significant (D = 0.54). Furthermore, the mean SiO₂ content in midslopes is greater than 70% and exhibits low variability, with a coefficient of variation (CV) of less than 30%. The mean Al₂O₃ content across the transects was 15.61 ± 6.56%, ranging from 15.11 ± 5.75% in T2 to 16.51 ± 9.1% in T3. The variability in theAl₂O₃ content is moderate, with a CV of 55.12% in T3 and 33.19% in T1. On the other hand, the mean Fe₂O₃ content was 5.19% in T1, with a moderate variability (CV of 52.49%), and increased to 7.02% in T3 with low variability. The KS test results indicate no significance for both Al₂O₃ and Fe₂O₃ in Transect-1, but significance in the other transects. The mean P₂O₅ content showed moderate variability across the transects, with a mean of 0.07 ± 0.03%. In midslopes, the mean P₂O₅ content was 0.11 ± 0.03% in T1 and 0.049 ± 0.012% in T3, indicating low variability. However, at T2, the mean P₂O₅ content was moderately variable, with a value of 0.046 ± 0.014%. The mean K₂O, Na₂O, and MgO contents are less than 1% in all transects, with low to moderate variability. Conversely, the mean CaO content ranged from 1.4% in T3 to 1.72% in T1. Among the trace elements, Zn and Mn exhibited moderate variability across the transects, with mean values of 230.95 ± 33.9mg/kg in T2 and 274.56 ± 30.39mg/kg in T1. The KS test values are nonsignificant for Al₂O₃ in T2/T3, Fe₂O₃ in T1, Cu, and MgO in T3.

Table 6

Transectwise descriptive statistics for the elemental composition of soils on middle slopes
Statistics	Elemental composition (%)									mg/kg
Statistics	SiO₂	Al₂O₃	Fe₂O₃	P₂O₅		K₂O	Na₂O	CaO	MgO	Cu	Mn	Zn
Transect-1
Mean	72.29	15.28	5.19	0.11		0.43	0.13	1.72	0.58	26.26	274.56	26.78
SD	7.06	5.07	2.72	0.03		0.08	0.05	0.55	0.14	7.37	30.39	4.84
CV	9.77	33.19	52.49	25.96		18.93	36.83	32.29	23.71	28.05	11.07	18.08
SK	0.52	-0.44	0.13	1.32		-0.24	0.31	0.47	-0.12	0.43	-1.04	0.14
kurt	0.75	-0.80	-1.23	0.79		-1.43	-0.88	-1.56	-1.93	-1.00	1.26	-0.26
KS test	0.58	0.37	0.26	0.53		0.53	0.41	0.46	0.51	0.49	0.54	0.52
p-value	0.0058	0.17	0.56	0.015		0.015	0.1	0.049	0.022	0.029	0.011	0.018
Transect-2
Mean	71.47	15.11	6.5	0.046		0.35	0.14	1.53	0.59	31.26	230.95	31.26
SD	6.56	5.75	1.03	0.014		0.075	0.038	0.3	0.2	7.72	33.9	7.72
CV	9.18	38.05	15.85	30.43		21.4	27.1	19.61	33.90	24.7	14.68	24.70
SK	1.14	-0.95	0.08	0.51		0.11	0.99	0.76	0.99	0.14	0.084	0.14
kurt	1.96	0.62	-0.36	0.83		-1.46	0.68	-0.17	1.7	-0.42	-0.83	-0.42
KS test	0.59	0.39	0.52	0.43		0.51	0.5	0.55	0.43	0.46	0.54	0.46
p-value	0.0024	0.097	0.009	0.048		0.013	0.015	0.0054	0.054	0.031	0.0069	0.031
Transect-3
Mean	70.5	16.51	7.02	0.049	0.46		0.13	1.4	0.57	37.06	257.04	41.78
SD	10.58	9.1	0.94	0.012	0.1		0.032	0.31	0.21	12.24	65.69	10.45
CV	15.01	55.12	13.39	24.49	21.74		24.62	22.14	36.84	33.03	25.56	25.01
SK	0.3	-0.03	-0.3	-0.29	0.46		-1.09	-0.31	-0.55	-0.54	0.35	0.68
kurt	-1.06	-0.88	-0.91	-1.98	-1.14		2.29	-1.15	-0.46	-0.44	-1.45	-0.023
KS test	0.55	0.28	0.54	0.46	0.52		0.46	0.48	0.34	0.37	0.5	0.49
p-value	0.0098	0.45	0.011	0.047	0.016		0.05	0.033	0.25	0.17	0.025	0.031
Total
Mean	71.42	15.61	6.25	0.07	0.41		0.14	1.55	0.58	31.52	253.25	33.19
SD	7.88	6.56	1.84	0.03	0.09		0.04	0.40	0.18	9.94	47.47	9.91
CV	11.03	42.04	29.40	50.28	22.75		29.13	26.05	30.77	31.55	18.74	29.86
SK	0.33	-0.09	-1.01	1.26	0.34		0.28	0.86	0.18	0.34	0.22	0.99
kurt	-0.23	-0.09	1.02	1.44	-0.40		0.15	0.39	0.27	-0.59	-0.62	1.09
KS test	0.54	0.32	0.39	0.33	0.46		0.40	0.45	0.39	0.42	0.50	0.44
p-value	0.00	0.01	0.00	0.01	0.00		0.00	0.00	0.00	0.00	0.00	0.00

Lower slopes

The mean SiO₂ content across the transects was 69.6% with a standard deviation of 6.46%. The mean Al₂O₃ content is 17.68% with a standard deviation of 6.00%, while the mean Fe₂O₃content was 5.76% with a standard deviation of 1.68% (Table 7). In transect T1, the mean Al₂O₃content was 20.99%, whereas in transect T2 it is 12% and in transect T3 it is 18.54%, all with moderate variability. The mean P₂O₅, K₂O, Na₂O, and MgO contents are all less than 1% with moderate variability. The CaO content had a mean value of 1.71% with a standard deviation of 0.54%, but in transect T3 it had a recorded mean value of 2.09% with low variability. The overall mean Cu content across all transects was 36.19mg/kg with low variability. Among the transects, transect T2 had a mean Cu content of 39.8mg/kg with a standard deviation of 6.73%, while transect T1 had a mean Cu content of 31.71mg/kg with low variability. The mean Mn content was more than 200mg/kg with an overall mean value of 228.39mg/kg. In transect T3, the mean Zn content is 39.73mg/kg with a standard deviation of 5.10mg/kg, whereas in transect T2 it is 31.56mg/kg and in T1 it is 36.09mg/kg, all with varying standard deviations. The coefficient of variation for all the elements under study was categorized as low (CV < 30%). The KS test values indicate significant differences from the normal distribution for all elements under study, except for Al₂O₃ (all transect data), Fe₂O₃ (T-1 and all transect data), and P₂O₅ (T2 and T3).

Table 7

**Transectwise descriptive statistics for the elemental composition of soils on lower slopes**
Statistics	Elemental composition(%) (mg/kg)
Statistics	SiO₂	Al₂O₃	Fe₂O₃	P₂O₅	K₂O	Na₂O	CaO	MgO	Cu	Mn	Zn
Transect-1
Mean	66.75	20.99	5.14	0.09	0.36	0.12	1.38	0.52	31.71	235.71	36.09
SD	5.12	5.10	2.22	0.02	0.06	0.04	0.33	0.18	9.11	50.17	10.57
CV	7.68	24.28	43.06	24.56	18.22	38.29	23.88	34.59	28.72	21.28	29.30
SK	0.00	0.89	-0.56	1.07	-0.59	0.50	-0.41	-0.43	1.10	0.43	0.43
kurt	-0.97	-0.30	-0.88	0.26	-0.54	0.19	-0.68	-1.32	1.08	-0.65	0.14
KS test	0.59	0.51	0.30	0.52	0.49	0.36	0.45	0.37	0.50	0.51	0.44
p-value	0.00	0.01	0.27	0.01	0.01	0.12	0.03	0.10	0.01	0.01	0.03
Transect-2
Mean	75.08	12.68	6.24	0.05	0.40	0.11	1.75	0.50	39.8	239.4	31.56
SD	4.30	3.84	1.24	0.02	0.11	0.04	0.59	0.18	6.73	43.35	7.52
CV	5.73	30.28	19.87	35.93	27.3	34.1	33.72	36.17	16.8	18.10	23.83
SK	-1.08	0.46	0.30	-0.14	-0.32	-0.26	0.33	0.94	0.22	-0.03	0.32
kurt	1.78	-1.91	-0.47	-0.33	-1.39	-0.41	-1.80	0.91	-0.86	-0.97	0.13
KS test	0.53	0.49	0.51	0.37	0.45	0.38	0.47	0.43	0.53	0.53	0.47
p-value	0.015	0.03	0.02	0.18	0.05	0.15	0.038	0.07	0.01	0.015	0.038
Transect-3
Mean	67.69	18.54	6.04	0.04	0.39	0.12	2.09	0.85	38.0	208.1	39.73
SD	6.84	5.96	1.18	0.01	0.08	0.02	0.50	0.40	5.09	28.79	5.10
CV	10.10	32.17	19.59	35.34	21.3	17.7	24.07	47.03	13.3	13.83	12.84
SK	-0.93	1.21	0.45	0.82	0.26	-0.50	0.12	1.25	0.44	0.40	-0.03
kurt	0.66	1.93	-1.72	2.00	-0.35	-0.25	-1.56	0.86	-0.89	-0.40	-1.02
KS test	0.55	0.47	0.55	0.39	0.5	0.5	0.49	0.41	0.57	0.55	0.56
p-value	0.0091	0.042	0.009	0.13	0.02	0.02	0.027	0.09	0.07	0.009	0.0086
Total
Mean	69.60	17.68	5.76	0.06	0.38	0.12	1.71	0.61	36.19	228.39	35.82
SD	6.46	6.00	1.68	0.03	0.08	0.04	0.54	0.30	7.96	43.08	8.61
CV	9.28	33.96	29.27	47.82	22.34	30.65	31.89	48.95	21.99	18.86	24.03
SK	-0.44	0.62	-0.86	0.75	0.09	0.17	0.46	1.79	0.06	0.52	0.15
kurt	-0.34	0.16	0.82	0.27	-0.70	0.26	-0.70	4.43	-0.51	-0.50	0.01
KS test	0.54	0.38	0.39	0.32	0.47	0.39	0.4	0.33	0.46	0.52	0.43
p-value	2.40E-07	0.00079	0.00043	0.0088	1.3E-05	0.00062	0.00029	0.0057	2.4E-05	8.90E-07	8.7E-05

3.5 Enrichment ratio

The enrichment Factor (EF) is a useful tool for estimating the disparity between the presence of individual metals resulting from human activities and those of natural origin or derived from a combination of heavy metals. To calculate the EF, the element content is compared to that of the unpolluted Earth's crust, with aluminum (Al) commonly chosen as the reference element (Relić et al. 2019; Adimalla et al. 2020). It is essential to select a reference element that is not of anthropogenic origin within the study area.

The depth function for the EF in transect-2 (T2) exhibited irregular patterns across all the elements studied in dry environments. The enrichment ratio for SiO₂ and Cu in the soils ranged from 1 to 3, indicating slight enrichment in the profiles (Relic et al., 2019). An EF value less than 1.0 implies that the heavy metal in question originated solely from the Earth's crust or from natural weathering processes (Adimalla et al., 2020). The BA horizon in P7 (EF = 3.36) and the C1 horizon in P10 (EF of 5.51) demonstrated moderate enrichment of SiO₂, while the C horizon did.

In transect-1, there was a slight increase in the concentration of all the elements being investigated, with an enrichment factor (EF) value of less than 3 in the profiles exhibiting irregular depth trends. The average EF for the entire transect was 1 for SiO₂ and Cu, while for the remaining elements, it was less than 1. The degree of enrichment ranged from 21.05% for K2O to 37.39% for Zn with an EF of 7.52 indicating significant enrichment of Cu. The enrichment ratios for elements determined via transect wise, analysis revealed that SiO₂ exhibited a mild enrichment in T2 (Ⴟ = 1.60, with a maximum of 5.51 and a minimum of 0.65) and T3 (Ⴟ = 1.39, with a maximum of 2.08 and a minimum of 0.41). On the other hand, Cu had a mean ER of 1.99 in T2, followed by T1 (Ⴟ = 1.70) and T3 (Ⴟ = 1.50), indicating mild enrichment. In contrast, the mean ER for the other elements is less than 1, suggesting a natural origin from weathering processes. Furthermore, the percent enrichment factor for all the elements under study exceeds 55% in lower slopes but is relatively moderate in the middle and upper slopes, ranging from 19.55 for SiO₂ in T2 to 37.39 for Zn in T1. In terms of slope position, middle slopes exhibited a mean ER of 1.53 for SiO2 and 1.99 for Cu, with a high variability (CV of 96.09% for SiO₂ and 97.29% for Cu) compared to the upper slopes (Ⴟ = 1.35) and lower slopes (Ⴟ = 1.04), which displayed moderate variability. The mean ER for the other elements was also greater for the middle slopes than for the upper and lower slopes (Table 8).

Table 8

**Transectwise descriptive statistics for the enrichment ratio**
Statistics	Enrichment ratio (ER)
Statistics	SiO₂	Fe₂O₃	P₂O₅	K₂O	Na₂O	CaO	MgO	Cu	Mn	Zn
Transect − 1
Mean	0.95	0.45	0.44	0.1	0.03	0.33	0.21	1.04	0.44	0.4
Max	2.79	1.12	1.11	0.29	0.08	0.92	0.5	2.88	1.06	0.8
Min	0.47	0.06	0.19	0.05	0.01	0.1	0.07	0.46	0.18	0.17
EF(%)	20.59	36.44	27.51	21.05	25.03	28.37	32.09	24.13	28.77	37.39
Transect-2
Mean	1.6	0.92	0.32	0.16	0.05	0.52	0.38	2.45	0.63	0.73
Max	5.51	2.86	0.83	0.55	0.2	1.35	1.2	7.52	2.18	1.93
Min	0.65	0.44	0.12	0.06	0.02	0.21	0.11	1.2	0.25	0.31
EF(%)	19.55	19.9	28.86	19.93	16.74	27.11	24.53	19.75	19.41	25.61
Transect-3
Mean	1.39	0.82	0.25	0.16	0.04	0.52	0.38	1.71	0.44	0.62
Max	2.08	1.23	0.37	0.24	0.06	0.79	0.57	2.56	0.67	0.93
Min	0.41	0.24	0.05	0.04	0.01	0.14	0.09	0.58	0.11	0.14
EF(%)	58.52	58.35	61.3	59.38	55.76	59.66	60.34	56.8	60.25	60.85
Note: EF (%) = \(\:\left((\mathbf{m}\mathbf{e}\mathbf{a}\mathbf{n}-\mathbf{m}\mathbf{i}\mathbf{n}\mathbf{i}\mathbf{m}\mathbf{u}\mathbf{m})\|(\mathbf{m}\mathbf{a}\mathbf{x}\mathbf{i}\mathbf{m}\mathbf{u}\mathbf{m}-\mathbf{m}\mathbf{i}\mathbf{n}\mathbf{i}\mathbf{m}\mathbf{u}\mathbf{m}\right))\:\) X 100

3.6 Igeo index

The geoaccumulation index (I_geo) serves as a valuable tool for assessing both the natural variation in metal distribution and the impact of human activities on the environment. It plays a crucial role in distinguishing the effects of anthropogenic activities (Shafie et al. 2013). A statistical summary of the elemental oxides in the soil profiles was compiled and presented in a transect wise manner, providing data for the I_geo accumulation index (Table 9). The Igeo index applies a logarithm operation to the dataset, using a constant value of 1.5 to account for potential variations in background content (Bn) and the minor influence of anthropogenic activity (Muller 1981). In this study, a modified version of the I_geo index is employed, considering the background value as the concentration of trace elements in the Earth's crust (Taylor and McLennan 1995). The results of the I_geoaccumulation index indicate that most of these soils are uncontaminated for all the elements, except for Al and Cu, which show positive signs of contamination.

Table 9

Transectwise descriptive statistical summary for the Igeo accumulation index
Statistics	Igeo accumulation index
Statistics	SiO₂	Fe₂O₃	Al₂O₃	P₂O₅		K₂O	Na₂O	CaO	MgO	Cu	MnO	Zn
Transect-1
Mean	-0.52	-1.77	0.25	-1.64		-3.77	-5.61	-2.08	-2.70	0.18	-1.06	-1.14
Max	-0.21	-0.69	0.97	-0.93		-3.26	-4.73	-1.24	-2.16	1.04	-0.57	-0.34
Min	-0.73	-3.58	-1.11	-2.18		-4.41	-6.87	-2.94	-3.78	-0.54	-1.68	-1.87
SD	0.12	0.75	0.43	0.34		0.28	0.54	0.42	0.47	0.40	0.27	0.36
CV%			169.32							224.09
Transect-2
Mean	-0.20	-0.03	-0.86	-2.00	-3.11		-4.73	-1.28	-1.69	0.42	-1.44	-1.02
Max	-0.64	-2.67	-1.75	-3.77	-4.41		-6.87	-2.51	-3.56	-1.11	-2.83	-2.40
Min	-0.42	-0.91	-1.20	-2.74	-3.80		-5.59	-2.04	-2.56	-0.24	-2.17	-1.53
SD	0.11	0.63	0.23	0.45	0.35		0.49	0.35	0.51	0.40	0.31	0.33
CV%	-27.26	-68.92	-18.91	-16.46	-9.18		-8.69	-17.07	-20.13	-169.38	-14.50	-21.37
Transect-3
Mean	-0.20	0.43	0.43	-2.10	-3.04		-5.10	-1.16	-1.03	0.49	0.37	-0.82
Max	-0.86	-2.89	-2.89	-3.91	-4.24		-6.60	-2.77	-3.91	-1.14	-2.99	-2.55
Min	-0.52	-0.61	-0.61	-2.94	-3.63		-5.61	-1.81	-2.33	-0.16	-1.91	-1.59
SD	0.19	0.81	0.81	0.50	0.30		0.32	0.40	0.60	0.39	0.72	0.40
CV%	-36.98	-131.61	-131.61	-16.84	-8.21		-5.78	-22.27	-25.81	-247.70	-37.77	-25.37

The I_geo values for metals in the agricultural soils of the Palamaner division ranged from − 0.20 (T2/T3) to -0.52 (T1) for SiO₂, -0.03 (T2) to -1.71 (T1) for Fe₂O₃, and − 0.86 (T2) to 0.43 (T3) for Al₂O₃. On average, the I_geo values are less than 0for P₂O₅, K₂O, Na₂O, CaO, MgO, Mn, and Zn, indicating an unpolluted status, except for Al₂O₃ and Cu, which range from unpolluted to moderately polluted, with mean values of 0.25 (T1) for Al₂O₃ and 0.43 in T3. For Cu, the mean values are 0.18 in T1 and 0.42 (T2) to 0.49 in T3. The depth function of Al₂O₃ and Cu, as illustrated in Fig. 3, indicates that regardless of the slope position in transect-1, there is a positive correlation between Al and Cu with a gradual increase of Al in the B horizons of P1/P2 on upper slopes (0.54 to 0.75 in P1 and 0.07 to 0.41 in P2). On the middle slope of transect-1, there was a gradual decrease in theI_geo index for Cu from 0.61 to 0.29 in P3 and in lower slopes from 0.55 to 0.05 in P5, in contrast to that for Al which had an index ranging from 0.95 to -0.01 (P5). In T2 and T3, Cu had positive signs in only a few horizons, but all the element concentrations were negative irrespective of the slope position.

3.7 Pollution assessment

Atransect analysis of the contamination factors for 11 elements was conducted. The results indicate that SiO2 has a mean value of 1.05 for T1/T3 and 1.12 for T3, with a percent Cf value ranging from 36.93% in T1 to 47.74% in T2/T3 (Table 10). Moreover, the mean Al₂O₃concentration was 1.24 in T1 and 0.73 in T2, indicating moderate intensity of contamination. Cu also shows a similar trend, with a mean value of 1.18 in T1 to 1.39 in T3. The other elements had mean Cf values less than 1, indicating a low intensity of contamination. The mean Cf values for the elements generally follow the sequence of Al₂O₃ > Cu > SiO₂ > Fe₂O₃ = Mn > Zn > P₂O₅ > CaO > MgO > K₂O > Na₂O in T1; Cu > SiO₂ > Al₂O₃ > Fe₂O₃ > Zn > CaO > Mn > MgO > P₂O₅ > K₂O > Na₂O in T2; and Cu > Al₂O₃ > SiO₂ > Fe₂O₃ > Zn > CaO > MgO > Mn > P₂O₅ > K₂O > Na₂O in T3. According to Sadhu et al. (2012) and Likuku et al. (2013), the level of contamination in these soils is classified as moderate for SiO₂, Al₂O₃, and Cu, but low for other elements. Overall, the study provides valuable insights into the contamination levels of the soil and can aid in developing strategies to mitigate the impact of contamination on the environment.

Table 10

Contamination factor, degree of contamination, modified degree of contamination, pollution load index and Nemerow comprehensive index (Ps)
Statistics	Contamination factor											Cd	mcd	PLI	Ps
Statistics	SiO₂	Al₂O₃	Fe₂O₃	P₂O₅	K₂O	Na₂O	CaO	MgO	Cu	Mn	Zn	Cd	mcd	PLI	Ps
Transect-1
Mean	1.05	1.24	0.49	0.50	0.11	0.03	0.37	0.24	1.18	0.49	0.47	6.16	0.56	1.01	1.43
Maxi	1.29	1.95	0.93	0.79	0.16	0.06	0.64	0.34	2.05	0.67	0.79	7.74	0.70	1.03	1.47
Mini	0.91	0.46	0.13	0.33	0.07	0.01	0.20	0.11	0.69	0.31	0.27	5.34	0.49	1.00	1.42
cf(%)	36.93	52.03	45.76	36.07	48.15	45.53	39.40	58.07	35.71	48.52	37.15	34.11	34.11	31.60	31.21
Transect-2
Mean	1.12	0.87	0.56	0.13	0.14	0.03	0.41	0.23	1.70	0.34	0.47	6.02	0.52	1.01	1.47
Maxi	1.30	1.47	0.83	0.25	0.17	0.06	0.62	0.46	2.01	0.50	0.74	6.47	0.59	1.01	1.48
Mini	0.96	0.72	0.52	0.09	0.12	0.01	0.28	0.13	1.35	0.30	0.39	5.65	0.44	1.00	1.46
cf(%)	47.78	20.19	14.66	24.68	40.00	36.47	38.87	30.00	52.86	17.22	24.60	44.71	56.01	35.34	53.24
Transect-3
Mean	1.06	1.11	0.66	0.21	0.12	0.03	0.44	0.32	1.39	0.29	0.52	6.16	0.56	1.01	1.13
Maxi	1.31	2.03	0.90	0.35	0.18	0.04	0.67	0.74	2.11	0.46	0.85	7.29	0.66	1.01	1.19
Mini	0.83	0.20	0.48	0.10	0.08	0.02	0.22	0.10	0.68	0.15	0.26	5.18	0.47	1.00	1.09
cf(%)	47.74	49.98	44.79	42.40	42.86	56.36	49.59	35.09	49.63	46.25	43.81	46.39	46.39	47.48	46.40

In the study of soil transects, the pollution load index (PLI) was calculated to have a mean value of 1.01, with a minimum value of 1.00 and a maximum value of 1.03. Across all transects, the PLI fell into the category of no pollution. The mean values of mCd varied across transects, with T1 having a mean value of 0.56, a minimum value of 0.49, and a maximum value of 0.70. For T2, the mean mCd was 0.52, and for T3, it was 0.56, indicating no to very low degree of contamination. The degree of contamination was calculated as the sum of all pollution factors in each basin, and the mean for all transects was more than 6, indicating a moderate degree of pollution. The mean Cd was 6.16, with a range of 7.74 to 5.34 in T1, a mean of 6.02 with a range of 6.47 to 5.65 in T2, and a mean of 6.16 with a range of 7.29 to 5.18 in T3. The calculated Nemerow comprehensive index (Ps) yielded a mean of 1.43 for T1, 1.47 for T2, and 1.13, indicating slight contaminated. The percentage of contamination factor (Cf) varied from 31% for PLI to 48% for K₂O in T1, with similar trends observed for T2 and T3.

4.1 Soil transects

Numerous studies have emphasized the significance of soil-landscape research in comprehending the genetic associations between surface processes and disturbances (Comino et al. 2016). To assess the impact of future land use changes on soil utilization and management, it is imperative to comprehend soil formation, as well as the distribution of soil properties in relation to landscape features (Kosmas et al. 2000). These investigations have played a crucial role in enhancing our understanding of chemical weathering in relation to elemental contamination across agricultural landscapes, which is a topic of great interest (Courchensne 2006; Kaolack 2012). For the present study, three transects were specifically chosen in the Palamaner division on a rocky quartz granitic-gneiss outcrop. Six soil profiles were selected randomly to investigate the correlation between soil and landscape characteristics, which may be indicated by the recurring patterns observed among similar landforms (Fig. 2). These transects cover three distinct landscape units, namely the summits (upper slopes), middle slopes (back slopes), and lower slopes (foot slopes) which encompasses various soil and relief units, as well as environmental factors such as fauna, human activity, and vegetation cover as described below:

4.2 Soil morphology

Along transect-1, the upper slopes contain soils of different colors and textures. The soils have a dark grayish brown to strong brown moist matrix and a dry matrix of light brownish grey to strong brown. These two soils are deep. The Ap horizon has a sandy clay loam and sandy loam texture with a clear and smooth boundary. The B horizon has clay loam and silty clay loam textures in P1 (TypicHaplustepts) and sandy clay loam in P2(TypicHaplustalfs), with a diffuse wavy boundary. The middle slopes had soils with horizon sequences Ap-AC1-AC2 and Ap-Bt1-Bt2-Bt3-Bt4. The moist matrix had a dark brown color (7.5 YR 3/3, P3) and dark reddish-brown color (5 YR 3/4, P4), while the dry matrix had a brown color (7.5YR 5/3, P3-Typic Ustorthents) and reddish-brown color (5YR 4/3, P4-Typic Haplustalfs). There were textural variations in the soil. The depth of the soil ranged from shallow (P3) to deep (P4). P-3 had a texture ranging from sandy loam to sandy clay loam, while P4 had a texture ranging from sandy loam to clay loam. The lower slope soils displayed Ap-Bt1-Bt2-Bt3-Bt4 (P5-Typic Haplustalfs) and Ap-B/A-Bt1-Bt2 (P6-Typic Haplustalfs) horizon sequences. These soils aredark brown to reddish brown under moist conditions, and strong brown to reddish brown under dry conditions. These soils are deep to moderately deep. The surface texture is sandy loam, while the B horizon transitions from sand to clay to sandy clay loam.

Along transect-2, the upper slope soils contained Ap-BA-Bt1-Bt2(P7-Typic Rhodustalfs) and Ap-A/B-B/A-Bw1-Bss1 horizon sequences (P8-Typic Haplusterts). The wet matrix ranges from dark yellowish brown to weak red, while the dry matrix ranges from light yellowish brown to weak red. These soils are moderately deep to deep, according to the Soil Survey Manual (2017). The Ap horizon has a sandy clay loam texture with a clear and smooth boundary. On the middle slopes, the deep and sandy loam to sandy clay loam soils exhibit Ap-Bt1-C1-2Bt1 (P9) and Ap-Bt1-C1-Bt2-Bt3 (P10-Typic Haplustalfs) horizon sequences. The moist matrix is reddish brown and dark brown, while the dry matrix is reddish brown and light yellowish brown.The lower slope soils have Ap-AB-Bw and Ap-Bw1-Bw2-Bw3-Bw4 horizon sequences. The soils on lower slopes are deep and have a moist matrix that ranges from dark reddish brown to dark grey, and a dry matrix that varies from red to light grey in the B horizon. The texture of P11 (Typic Haplustepts)is sandy clay loam to sandy loam in and of that of P12(Vertic Haplustepts) is sandy clay loam.

The upper slopes of transect-3 (T3) exhibit deep soils composed of horizon sequences, namely Ap-A/B-Bt1-Bt2 (P13-Typic Haplustalfs) and Ap-Bt1-Bt2-2Bt1-2Bt2 (P14-Typic Haplustalfs). These soils possess a matrix that ranges from dark brown (7.5 YR 3/4, P13) to brown (7.5 YR 4/4, P14) in color. Additionally, the matrix displays a light brown hue (7.5 YR 6/4, P13 and 7.5 YR 6/4, P14). The texture of the Ap horizon is sandy loam, while the B horizons exhibit a sandy clay loam texture. On the middle slopes, the deep soils undergo abrupt textural changes. These soils are characterized by a dark reddish brown (5 YR 3/4), dark reddish brown (2.5 YR 3/3,), reddish brown (5 YR 4/4, P15-Typic Haplustalfs), and red (2.5 YR 4/6, P16-Typic Haplustalfs) matrix. The P15 within this region displays varying textures, ranging from sandy loam to sandy clay loam, and ultimately transitioning to clay (P16). Moving to the lower slope, the deep soils contain a moist matrix that ranges from very dark grey (7.5 YR 3/1, P17-Typic Haplustalfs) to dark brown (7.5 YR 3/3, P18). However, under dry conditions, the matrix changes to dark grey (7.5 YR 4/1, P17) to brown (7.5 YR 5/3, P18-Ultic Haplustalfs). The surface texture of these soils is composed of loamy sand and sandy loam. In the B horizons of P17 and P18, the texture transitions from loamy sand to sandy clay loam. The presence of red subsoils enriched with clay has been observed in the Vedavathi river basin in Karnataka. These soils exhibit cutanic skins on the ped surfaces of argillic horizons, resembling those found in granitic landscapes. Bhaskar et al. (2021) have classified these soils as subgroups of Alfisols and associated soils. Furthermore, transect-2 has identified the occurrence of red soils belonging to Vertisol and vertic subgroups, consistent with previous discoveries in the Purna valley of Central India (Raja et al. 2010).

4.3 Particle size distribution and bulkdensity

The Ap horizons of all pedons in transect-1 contained more sand, while the deeper horizons had higher levels of silt and clay due to external factors like soil pedogenesis caused by low rainfall and soil erosion. According to Zhao et al. (2011), silt and clay were less affected by soil erosion processes caused by rainfall due to their aggregate structures. The particle size class for P1, P2, P4, P5, and P6 was defined as fine loamy, while P3 was coarse loamy based on its clay content (Soil Survey Staff 2014). The lithological discontinuities in soil profiles were accomplished through the sand to silt ratio, as demonstrated by previous studies (Schaetzl 1998; Tsai and Chen 2000; Ibrahim et al. 2011). The abrupt changes observed in profiles P1, P4, and P5 are indicative of the presence of discontinuities, and consistent with field descriptions. In terms of the transect analysis, the sand, silt, and clay fractions exhibit erratic trends in relation to the landscape position. These variations may be attributed to the processes of eluviation in the horizons and illuviation of clay in the subsoils, as suggested by Ezeabasili et al. (2014). In transect-2, the sand content shows a gradual decrease from 62.82–36.81% in the Bt1 layer(P7). However, the silt and clay content exhibit a gradual increase from 13.46–29.72% and 23.72–33.47%, respectively. The sand fraction displays an abrupt increase from the Ap to A/B horizon, rising from 65.36–72.79%. Nevertheless, it then gradually decreases with depth, reaching 68.23% at P8. The sand content in P12 exhibited a decreasing trend, from 68.13% in Ap to 18.16% in Bw3 horizon. The weighted mean of sand content was calculated to be 28.83%. On the other hand, the silt and clay content displayed a gradual increase with minor variations in depth. The sand content of slopes with steep gradients is found to be higher compared to slopes with gentle gradients, as indicated by Rezaei and Gilkes (2005). This observation can be logically explained by the prevalence of accumulation processes in lower slopes, which typically have gradients ranging from 2–4%. In these areas, the sand content is likely to increase due to the deposition of sediments over time.

Across transects, two-way ANOVA of the particle size data suggested that there was no significant variation in the distribution of sand, silt, and clay between different horizons and landscape positions in Transect-1. Soil bulk density varied from 1.29 to 1.59 Mg m^-3. The mean bulk density of Transect-I soils was 1.43 ± 0.08 Mg m^-3 with a CV of 5.74%. In Ap horizons, the mean bulk density was 1.38 ± 0.09 Mg m^-3 with a CV of 6.83%, while in B horizons, it was 1.45 ± 0.07 Mg m^-3 with a CV of 4.63%. There was no discernible pattern in soil bulk density with depth. The difference in bulk density was due to the moisture content and type of clay minerals. Comparable findings were reported by Sharma and Kumar (2003) in the Himachal Pradesh's Maul Khad catchment and Chandragirimandal soils of Andhra Pradesh, respectively (Basavaraju et al. 2005). The ANOVA revealed a noteworthy difference in bulk density among horizons (F = 4.69; p = 0.02 level at 3 and 23 degree of freedom). However, there was no significant difference observed in bulk density with respect to landscape positions (F value of 0.09 and p value of 0.9). The regression equation indicated a strong positive correlation between bulk density and organic carbon and expressed as:Bulk density (Mg m^-3) = 1.51–0.268 (organic carbon %) and an R² of 0.34. The F value of 15.61 for 1 and 25 degrees of freedom further supported this relationship.

4.4 Chemical characteristics

The soils found in the area have a slightly acidic to neutral pH and a low concentration of electrolytes. The lower slopes of the region have soils that are slightly acidic. However,Bw3 and Bw4 horizons of P12 are neutral. These findings are consistent with the research conducted by Mekuria et al. (2007), which suggested that the buildup of organic matter can help minimize soil erosion and increase the presence of soluble base cations such as Ca²⁺ and Mg²⁺. Two-way ANOVA revealed that there was a notable variation in pH levels among various landscape positions in T1 (F = 8.07; p = < 0.006 level, Table 11). Conversely, there is no substantial distinction in pH levels between different horizons (F value of 0.86 and p value of 0.49). The significance of pH for T2 followed a similar pattern, with an F value of 5.27 and p value of 0.02. Karltun et al. (2013) ratings indicate that the SOC of transect-I soils is low, which can be attributed to the high summer temperature and low OC accumulation in the semiarid climate.

Table 11

Two way ANOVA results for selected soil properties
Source of variation	Transect-1		Transect-2		Transect-3
	F value	p value	F value	p value	F value	p value
BD(Mg/m³)
Landscape position	0.09	0.90	4.93	0.03	4.44	0.04
Horizons	4.69	0.02	2.41	0.12	2.63	0.10
Landscape position X horizons	2.28	0.11	0.33	0.91	0.31	0.92
pH
Landscape position	8.07	0.006	5.27	0.02	0.10	0.40
Horizons	0.86	0.49	0.53	0.67	2.70	0.09
Landscape position X horizons	0.48	0.81	0.21	0.97	1.17	0.38
OC(%)
Landscape position	1.08	0.37	3.99	0.47	5.26	0.02
Horizons	1.75	0.21	11.48	0.001	38.86	0.001
Landscape position X horizons	0.71	0.65	0.54	0.77	3.71	0.03
CEC(cmol/kg)
Landscape position	0.97	0.41	4.34	0.04	2.23	0.15
Horizons	2.79	0.09	4.65	0.02	5.58	0.01
Landscape position X horizons	0.13	0.99	0.85	0.55	0.91	0.51

The Ap horizon showed moderate variability with a mean of 0.47 ± 0.16% and a CV of 33.56%, while the B horizon exhibited high variability with a mean of 0.21 ± 0.14% and a CV of 68.69%. In transect-2, the organic carbon content decreased with depth, with a weighted mean of 0.10% in P10 to 0.3% in P11. Meanwhile, transect III has a mean organic carbon content of 0.26 ± 0.19% with high variability (CV of 73.83%). The A horizons in this transect have a mean of 0.47 ± 0.15% of OC with moderate variability (CV of 32.76%), while the B horizons have a mean of 0.16 ± 0.12% with high variability (CV of 72.93%). Overall, these soils are poor in organic carbon and exhibit decreasing trends with profile depth. It is evident from the two-way ANOVA results that there is a significant difference in organic carbon (OC) between landscape positions and horizons in T3. The F-value for landscape positions and horizons are 5.26 (p = 0.02) and 38.86 (p = 0.001), respectively. However, in T2, the significant variation of organic carbon is only observed forlandscape position, with an F-value of 5.25 and a p-value of 0.02. These findings suggest that the distribution of organic carbon varies significantly across different landscape positions and horizons, and this variation is more pronounced in T3 than in T2.

The average transect-1 value for CaCO₃was 6.69 ± 1.06%, showing a coefficient of variation (CV) of 15.82%. When considering the weighted mean for CaCO₃, the soils in the transect can be arranged in ascending order as follows: P2 with 7.29%, P4 with 6.90%, P5 with 6.85%, P3 with 6.39%, 6.17 (P1), and P6 with 5.91%. The CEC of soils on upper slopes exhibits a gradual increase in CEC from the Ap horizon to the B horizons in all soils, regardless of slope position. The A horizons have a medium CEC with a mean value of 12.45 ± 4.24 cmol (p+) kg^-1. On the other hand, the B horizons have a mean CEC of 24.21 ± 8.46 cmol (p+) kg^-1, which is nearly double the CEC of the Ap horizons. The variability in the CEC of the B horizons is moderate, with a coefficient of variation (CV) of 34.94%. The overall transect mean CEC is 19.87 ± 8.93cmol (p+) kg^-1, indicating a high variability with a CV of 44.94% (rated as high). The CEC is generally rated as high, except in P3 where it is rated as medium as reported by Moore in 2001. The statistical analysis using two-way ANOVA indicates that there is a noteworthy difference in CEC for T2 across different landscape positions (F = 4.34, ƿ=0.04) and horizons (F = 4.65, ƿ=0.02). On the other hand, the analysis also revealed a significant variation in CEC between horizons for T3 (F = 5.58, ƿ=0.01). These findings suggest that the CEC values are not uniform across different landscape positions and horizons, and the differences are statistically significant.

Furthermore, the CEC of the soils was positively correlated with the clay content, with an R² value of 0.76** and an F value of 79.86 at 2, 31 degrees of freedom. The regression equation representing this relationship is provided below.

CEC (cmol (p+) kg^-1) = 1.04 (clay %) -2.23 with t statistic of 10.3 and confidence interval (95% level) of β (0.83 to 1.24).

4.5 Elemental composition

The high SiO₂ content in granites potentially contributes to the limited dissolution of quartz. In contrast to Si, Al, and Na, Fe is remobilized from source rocks to the weathered materials. The negative correlation between SiO₂ and Fe₂O₃ suggested that this remobilization could be attributed to the precipitation of Fe-oxides and the dissolution of quartz (Ndjigui et al., 2013). The presence of calcareous nodules and dolomite leads to elevated CaO contents in granites, which in turn increases the MgO content. The similar elemental composition and distribution patterns indicate that the argillic B horizons have higher Fe₂O₃ concentrations compared to the A horizons, suggesting the comigration of Fe with clay (Bhaskar et al., 2004). The varying concentrations of Fe₂O₃ and Al₂O₃ in different horizons indicate that these elements are mobilized within the soil pedon due to active soil formation processes, as supported by the findings of Bera et al. (2015). These elemental concentrations are greater in the B horizons compared to the A horizons but are lower in lower slopes. These observations align with the conclusions drawn by Nandy et al. (2013) and Babechuk et al. (2014).

The upper slopes of the soil exhibit a gradual decrease in the total P₂O₅ content as one delves deeper into its layers. This decline is further emphasized by the weighted mean values of 488 mg kg^-1 for P1 and 351 mg kg^-1 for P2. Moving on to the middle slopes, a similar decreasing trend is observed, with a weighted mean of 498 mg kg^-1 for P3 and 407 mg kg^-1 for P4. These findings align with the conceptual model of phosphorus dynamics during long-term ecosystem development proposed by Walker and Syers(1976). Moreover, previous studies conducted by Cross and Schlesinger (1995), as well as Yang et al. (2013), have reported consistent patterns.

The statistical analysis of the total P content, as determined by two-way ANOVA, revealed a significant difference between the horizons. This is evidenced by an F value of 5.38 and a p value of 0.01 at 3 and 23 degrees of freedom. However, no significant difference was observed between the various landscape positions, as indicated by an F value of 0.65 and a p value of 0.54. These findings are in line with the research conducted by Day et al. 1987, which suggests a correlation between total P and clay. In fact, a regression equation has been derived to express the direct relationship between total P and clay and organic carbon. This equation, Total P = 288.32 + 1.82 (clay %) + 567.28 (Organic carbon %), demonstrates an R² value of 0.78 and an F value of 56.77 at 2 and 30 degrees of freedom. This relationship between total P and SOC is further supported by the studies conducted by Hou et al. (2018)and Boke et al. (2015), highlighting the coupling between phosphorus and carbon. The analysis conducted using the two-way ANOVA method revealed a marked difference in the overall potassium content among different horizons and landscape positions. The statistical values obtained, with an F-value of 13.24 and a p-value of less than 0.001, demonstrated the significance of these differences. These findings align perfectly with the research conducted by Osodeke et al. (2014) and Tijjani and David (2017), further validating the accuracy of the results. To establish a comprehensive understanding of the relationship between total potassium and clay, a third-order polynomial equation was derived. This equation, which yielded an R-squared value of 0.28 at 30 degrees of freedom, proved to be statistically significant at a 5% level. Additionally, the correlation between total potassium and organic carbon (OC) was found to be positive and exponential, with an R-squared value of 0.31. This correlation was statistically significant at a 1% level, indicating that even a slight increase in OC led to a small increment in total potassium. The equation provided, Total K (mg kg^-1) = 3669 − 0.56 (OC %), can be used to approximate the variability of total potassium in these soils, explaining 31.3% of the observed variability. These results are consistent with the findings of previous studies conducted by Kabala et al. (2009); Singh and Mishra (2012), Athokpam et al. (2013), and Patel et al. (2014). The soil profiles display a range of correlation coefficients with ƿ values less than 0.01 and 0.05 at 75 degrees of freedom, demonstrating the interdependence of elemental oxides. These correlations were interpreted in accordance with Mukaka (2012). The correlation analysis reveals a highly negative correlation between SiO₂ and Al₂O₃ (r = -0.93**), a significant low positive correlation with K₂O (r = 0.29*), and a low negative correlation with P2O5 (r = -0.23*). Fe₂O₃ and Mn exhibit a low negative correlation (r = -0.24*). P₂O₅ displays a significant but weak negative correlation with MgO (r = -0.42**) and K₂O (r = -0.24*), but a moderately positive correlation with Mn (r = 0.68**). Additionally, a low positive and significant correlation is observed between Zn and Cu (r = 0.41**, Table 12). These findings are consistent with similar relationships observed in weathered granitic soil profiles at high altitudes in the NE Lesser Himalaya (Ghasera and Rashid 2022).

Table 12

Correlation matrix of elemental oxides in agricultural soils of Palamaner
Variable	SiO₂	Al₂O₃	Fe₂O₃	P₂O₅	K₂O	Na₂O	CaO	MgO	Cu	Mn	Zn
SiO₂	1
Al₂O₃	-0.93**	1
Fe₂O₃	0.11	-0.32**	1
P₂O₅	-0.23*	0.25*	-0.22	1
K₂O	0.29*	-0.19	-0.12	-0.24*	1
Na₂O	-0.04	-0.04	0.06	0.08	0.01	1
CaO	0.01	-0.04	0.04	-0.21	0.12	0.09	1
MgO	-0.11	0.11	-0.03	-0.42**	0.22	0.18	0.60**	1
Cu	-0.06	0.00	0.10	-0.01	0.02	-0.07	-0.02	-0.20	1
Mn	-0.04	0.09	-0.24*	0.68**	-0.04	0.12	-0.26*	-0.34**	-0.25*	1
Zn	-0.13	0.02	0.20	0.04	-0.29*	-0.16	-0.37**	-0.29*	0.41**	-0.19	1

The concentration of copper in the Ap horizon was greater than in the B horizon, with a mean of 36.02 ± 8.79 mg kg^-1 and a coefficient of variation (CV) of 24.41%. The enrichment of copper in the surface horizons can be partially attributed to natural bioaccumulation, as supported by Kabata (2011), Alloway (2013), and Weber et al. (2018). Across the entire transect, the mean concentration of copper was 29.40 ± 8.37 mg kg^-1, with a CV of 28.48%. The distribution of copper varied with depth, with P1, P2, P4, and P6 showing an erratic tendency, while P3 and P5 exhibited a downward trend. The multiple regression analysis revealed a significant relationship between total copper and several factors, including cation exchange capacity (CEC), pH, and organic carbon (OC). The equation representing this relationship is as follows: Total Cu (mg kg-1) = 80.8 + 21.0 OC (%) − 0.30 CEC (cmol (p+) kg^-1) − 7.6 pH. The regression model had a strong correlation with an R² value of 0.8. These findings are consistent with the studies conducted by Abbas et al. (2003), Singh and Dhaliwal (2012), and Sharma et al. (2014), who also reported a relationships between total copper, CEC, and pH. The statistical analysis confirmed the significance of the regression model (F at 3, 23) = 12.5, p < 0.001, R² = 0.62, R²adj = 0.57). The concentration of total iron in the soils ranged from 0.87 to 6.47%. In the top slopes, the iron content varied from 3.29 to 2.52% (P1 and P2), 4.05 to 2.94% (P3 and P4) in the intermediate slopes, and 3.12 to 3.87% (P5 and P6) in the lower slopes. The values of total free iron oxide concentrations were significantly higher in the subsoil compared to the surface horizons. This difference was attributed to the co-translocation of iron with clay through eluviation - illuviation processes, as supported by previous studies (Agbenin 2003 and Jelic et al. 2011). The strong correlation between iron and clay (r = 0.617***) further supports the co-migration of these elements. The equation for approximating the total iron content (%) is as follows: Total Fe (%) = 4.5 + 0.2 Clay (%) − 0.1 CEC (cmol (p+) kg^-1) + 3.7 OC (%) − 0.5 CaCO₃ (%). The R square (R²) value of 0.6 indicates that the predictors (Xi) explain 60% of the variance in Y. The adjusted R square value of 0.5 suggests that the model accounts for 50% of the variance. The coefficient of multiple correlation (R) is 0.8, indicating a strong correlation(Mukka, 2012). In a study conducted in MajuliriverIsland, Assam, India, revealed that total Fe is influenced by OC, clay, CEC, and sand, with an R² value of 85% (Bhaskar et al. 2017).

The soils of T2 had varying levels of manganese, with the upper slopes having the highest content and the lower slopes having the lowest. The trend of manganese content with depth was irregular in most pedons, except for P7 and P11, which showed an increasing and decreasing trends, respectively. The two-way ANOVA showed that there was no significant variation in total manganese content between horizons and landscape positions. However, the total manganese content significantly varied with the CaCO₃ of soils, which was represented by a regression equation. Some studies suggest that organic matter and calcium carbonate play crucial roles in retaining manganese in calcareous soils. The Ap horizon in transect-3 had an average total Zn of 41.35 ± 6.57 mg kg^-1, with a variability of 15.89%. Meanwhile, the B horizon had an average of 34.41 ± 10.12 mg kg^-1, with a CV of 29.40%. The overall transect had an average of 36.63 ± 9.58 mg kg^-1, with a CV of 26.15%. Pedon 13, 15, and 17 exhibited a decreasing trend with depth, while the remaining pedons exhibited an irregular trend. The two-way ANOVA revealed a significant difference in total zinc content between horizons (F = 4.77; p = 0.02 level at 3 and 23 degrees of freedom) and landscape positions (F value of 4.61 and p value of 0.03). The zinc content also significantly varied with sand and organic carbon contents of the soils, as expressed by the regression equation with an R² of 0.51 and F value of 11.39 on 2 and 22 degrees of freedom: Total zinc = 39.73–0.21 (sand %) + 39.34 (organic carbon %).

The results of the one-way analysis indicate a significant variation in total Cu between the slopes, with an F value of 4.6 and a ƿ value of 0.01 level (Table 13). However, the slope did not have a significant effect on the other elements studied. Further analysis using the Turkey-HSD test revealed a significant difference of 6.9 between the upper and lower levels, with a standard error of 1.6 and a critical mean of 5.5 (Table 14). However, the impact of the landscape on copper levels in soils has not been determined. In the Galicia (Spain) river valley, sediments were found to have higher copper levels than nearby vineyard soils (Fernandez et al., 2008). Additionally,Rusjan et al. (2007) reported that copper levels were highest on terraces among plains, plateaus, and terraces.

Table 13

One way ANOVA of the elemental composition of the soils
Elements	MSS		F statistics	ƿ value	Df
Elements	Between the slopes	Within slopes			2,75
SiO₂(%)	32.3	52.7	0.6	0.5
R₂O₃(%)	24.5	36.8	0.7	0.5
Al₂O₃(%)	37.1	40.7	0.9	0.5
Fe₂O₃(%)	1.6	3.0	0.5	0.6
P₂O₅(%)	0.0002	0.001	0.1	0.99
K₂O(%)	0.008	0.007	1.0	0.4
Na₂O(%)	0.003	0.001	1.9	0.2
CaO(%)	0.2	0.2	1.0	0.4
MgO(%)	0.02	0.06	0.3	0.8
Cu(mg/kg)	323.4	70.4	4.6	0.01
Mn(mg/kg)	4190	3227.2	1.3	0.3
Zn (mg/kg)	47.2	76.7	0.6	0.5

Table 14

Turkey HSD results for Cu
Pairof slopes	Difference	SE	Q	Lower CI	Upper CI	Critical Mean	p-value
Upper to middle	2.2	1.6	1.3	-3.4	7.8	5.6	0.6
Upper to lower	6.9	1.6	4.2	1.3	12.4	5.5	0.01
Middle to lower	4.7	1.7	2.8	-0.9	10.3	5.6	0.1

4.6 Evaluation of metal pollution

In semi-aridenvironments, the order of major elemental oxides in the soil profiles increases as follows: <Cu < Zn < Mn < P < Na < K < Mg < Ca < K < Fe < Al < Si, indicating lower levels of chemical weathering. Soils with high CEC values and clay proportions exhibit the greatest enrichment in SiO₂, Al₂O₃ and Cu. The EF valuesare mostly lower than 2, reveal typical major element concentrations of granitic rocks and Climo sequences. According to Hernandez et al. (2003), EF values ranging from 0.5 to 2 are associated with natural variability from granitic rocks in tropical environments. The I_geo index results for major and trace elements in the soil profiles indicate that both A and B horizons are unpolluted, as evidenced by negative Igeo values. Additionally, the contamination factor (CF) results show low to moderate contamination of Cu in the subsoil, while the pollution load index (PLI) values equal to or greater than 1 may be attributed to mineralization rather than pollution. These findings suggest that agricultural activities are safe to carry out on granitic landscapes. Similar results were reported in the granitic soils of Labunwa, Near Idanre, Southwestern Nigeria (Asowata and Akinwumiju, 2020). The Nemerov comprehensive index values for the elements indicate slight contamination, with values ranging from 1 to 2. Therefore, the study area is considered none to slightly polluted with Cu. Similar findings were reported in agricultural soils of Singhbhum shear zone, India (Giri et al. 2017). Among these indices, a moderate positive correlation was observed between the PLI and mCd with r value of 0.60**(ƿ = 0.01; Table 15).

Table 15

Correlation matrix of pollution indices in the soils
Variable	SiO₂	Al₂O₃	Fe₂O₃	P₂O₅	K₂O	Na₂O	CaO	MgO	Cu	Mn	Zn	PLI	mCd
SiO₂	1.00
Al₂O₃	-0.93**	1.00
Fe₂O₃	0.11	-0.32**	1.00
P₂O₅	-0.23*	0.25*	-0.22	1.00
K₂O	0.29*	-0.19	-0.12	-0.24*	1.00
Na₂O	-0.04	-0.04	0.06	0.08	0.01	1.00
CaO	0.01	-0.04	0.04	-0.21	0.12	0.09	1.00
MgO	-0.11	0.11	-0.03	-0.42**	0.22	0.18	0.60**	1.00
Cu	-0.06	0.00	0.10	-0.01	0.02	-0.07	-0.02	-0.20	1.00
Mn	-0.04	0.09	-0.24*	0.68**	-0.04	0.12	-0.26*	-0.34**	-0.25*	1.00
Zn	-0.13	0.02	0.20	0.04	-0.29*	-0.16	-0.37**	-0.29*	0.41**	-0.19	1.00
PLI	-0.41**	0.30*	0.10	0.55**	-0.03	0.35**	0.15	-0.02	0.23*	0.36**	0.11	1.00
mcd	-0.63**	0.58**	0.08	0.42**	-0.14	0.03	0.10	-0.01	0.65**	0.11	0.35**	0.63**	1.00
PS	0.07	-0.04	-0.19	0.28*	-0.25*	0.05	-0.28*	-0.28*	-0.11	0.37**	0.00	-0.03	-0.10
*significant at 1% level, significant at 5% level

4.7 Cluster analysis

A total of 78 instances of contamination indices from three sets of soil transect data were utilized to form three clusters using a proximity matrix with the Euclidean distance method. Cluster-1 comprises 12 instances, consisting of 7 horizons from T3 and 5 horizons from T1 soils. Interestingly, out of the 5 horizons in T3, all belong to Bt, while the remaining 2 horizons are Ap. On the other hand, the T1 horizons consist of 3 Ap horizons and 2 B horizons. Cluster-2 encompasses 24 instances, with 13 horizons from upper slopes, 7 from middle slopes, and 4 from lower slopes. In the third cluster, there are 42 instances containing various horizons from the three transects. Out of these 42 instances, 17 consisted of Bt horizons, 8 consisted of Ap horizons, and the remaining instances were associated with other horizons. The mean values for each element under investigation, along with pollution indices, are presented in Table 16 according to the clusters. The mean Cf factor for SiO₂ is greater than 1 in cluster-2 and 3, while for Al₂O₃, it is greater than 1 in cluster-1 and 3. As for Cu, the mean Cf is greater than 1 in all clusters. Among the other pollution indices, the PLI and Ps are both greater than 1, indicating slight contamination. However, the degree of contamination is higher than 6 in cluster-1 and 3. The results of the one-way ANOVA reveal significant variations between the clusters, yielding an F value of CF for SiO₂, Al₂O₃, P₂O₅, and Cu at a significance level of ƿ=0.001, while for Na₂O, it is at a significance level of ƿ=0.05. Among the pollution indices, PLI, mCd, and Cd concentration significantly differed among the clusters under investigation at a significance level of ƿ=0.001 (Table 16).

Table 16

One way ANOVA data for pollution indices
Variable	MSS	F value	Significance
SiO₂	0.234	38.461	0.000**
Al₂O₃	3.618	42.701	0.000**
Fe₂O₃	0.023	0.753	0.475
P₂O₅	0.201	7.767	0.001**
K₂O	0.001	1.600	0.209
Na₂O	0.000	3.496	0.035*
CaO	0.014	1.135	0.327
MgO	0.005	0.393	0.677
Cu	1.561	18.395	0.000**
Mn	0.023	1.604	0.206
Zn	0.041	2.849	0.064
PLI	0.000	19.001	0.000**
mCd	0.072	109.819	0.000**
PS	0.015	0.643	0.529
Cd	8.710	109.819	0.000**

Table 17

PCA analysis
Variance	PC1	PC2	PC3	PC4	PC5
Eigenvalues	3.41	2.45	2.07	1.52	1.15
Proportion	0.24	0.18	0.15	0.11	0.08
Cumulative Proportion	0.24	0.42	0.57	0.68	0.76

4.8 PCA analysis

The results obtained from the analysis underwent principal component (PC) analysis, utilizing varimax rotation with Kaiser normalization. This procedure was used to identify the origins of the heavy metals and the potential factors contributing to their concentrations in the soil samples under study (Table. 17). The findings revealed the presence of 5 factors with eigenvalues exceeding 1.0, collectively accounting for 76% of the total variance in the analysis. Notably, the factor loading of metals exceeding 0.5 (highlighted in italics in Table.17) is considered significant for data interpretation. Component 1, responsible for 24% of the total variance, exhibited initial eigenvalues of 3.41 displaying higher positive loadings for SiO2 (natural) but negative loadings for Al₂O₃. Furthermore, correlation analysis demonstrated significant associations between these metals at a significance level of 0.01 (Table 18), suggesting a geogenic source for the metals. Component 2 displays initial eigenvalues of 2.45 and exhibits higher loadings for CaO, MgO, Mn metals, which account for 18% of the total variance in the region characterized by granitic structures and agricultural development. Component 3, with initial eigenvalues of 2.07 and explaining 15% of the variance, is dominated by Cu and Zn with high factor loadings with intense mango and vine production systems. Factor 4 explains 11% of the total variance with negative loading of Na₂O contributing with high factor loadings. The metal associations can be ascribed to geogenic (embedded in the mineralogy of the soil). None of the trace element concentrations exceeded the permissible limits, in the hilly terrains of Palamaner in semiarid environments where geochemical process strongly associated with the physicochemicalproperties of soils to protect soil quality in the region.

Table 18

Correlations of Principal Components with Original Variables
Soil variable	PC1	PC2	PC3	PC4	PC5
SiO₂	0.74	-0.40	-0.15	-0.41	0.17
Al₂O₃	-0.70	0.33	0.26	0.54	0.01
Fe₂O₃	0.13	0.19	-0.46	-0.42	-0.49
P₂O₅	-0.71	-0.43	0.23	-0.22	0.09
K₂O	0.36	0.18	0.21	-0.25	0.69
Na₂O	-0.09	0.09	0.35	-0.53	-0.39
CaO	0.21	0.64	0.36	-0.23	-0.03
MgO	0.29	0.69	0.43	0.04	-0.13
Cu	-0.32	0.29	-0.62	-0.25	0.38
Mn	-0.44	-0.60	0.44	-0.23	0.12
Zn	-0.32	-0.02	-0.76	0.06	-0.10

Eighteen soil profiles in three soil transects on granitic landscapes of the Palamaner division of Chittoor district were studied to determine the variability of soil properties inrelation to slope position and utilizing elemental composition of soils for assessing contamination in agricultural lands.More than fifty percent of soils have red subsoils enriched with clayey argillic horizons with high cation exchange capacity and base saturation more than 60%.The CEC is strongly related with clay(r = 0.76**, ƿ=0.001) while bulk density has weakly correlated with organic carbon (r = 0.34*, ƿ=0.05). Two-way ANOVA revealed significant variation of organic carbon and CEC with respect to landscape position and horizons but not in pH with respect to landscape position only. These soils displayed variations in elemental composition in relation to geographical position with less influence of parent rock leading to uniqueness of individual profiles.The enrichment ratio of more than 1 for SiO₂ and Cu indicates mild enrichment while negative values of I_geoindex for all elements indicate no pollution.A degree of contamination (Cd) > 6 and Nemerov index < 2 indicate that these soils are slightly polluted. Three clusters were derived using pollution indices revealing significant variations between the clusters for SiO₂, Al₂O₃ and Cu. Five components were derived from the PCA analysis with eigen values > 1and high loading for SiO2 indicating a natural origin. In PC3, Cu and Zn accountedfor 15% of variance in intensively cultivated agricultural lands.

Author Contributions

Ms. B. Jayasree was responsible for selecting transects and collecting soil samples from various horizons as part of her PhD program at the Department of Soil Science and Agricultural Chemistry, S.V. Agricultural College, Tirupati. She then processed and analyzed these samples in the laboratory. Dr. M.V. S. Naidu, the Chairperson of the advisory committee, provided guidance in study design, preparation of the manuscript, writing, reviewing, and editing. Dr. B.P. Bhaskar, the Co-chairman, assisted Jayasree in interpretation of the results, writing and editing her manuscript. The final manuscript was thoroughly reviewed and approved by all the authors prior to submission.

Funding

This research received financial support from Andhra Pradesh Agricultural University, India, with Acharya N.G Ranga serving as the funding authority.

Notes

The authors declare that there are no competing financial interests. Additionally, the datasets employed in this study can be made available for sharing upon request to the corresponding author.

Acknowledgement

The authors would like to extend their gratitude to the office staff and technical officer for their invaluable technical assistance in completing this project. Additionally, we would like to express our sincere appreciation to the laboratory of the Department of Soil Science and Agricultural Chemistry for providing us with the necessary laboratory facilities. We would also like to thank Dr.S.C. Ramesh Kumar for his help in cluster analysis and PCA analysis.

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Transect Approach for Assessing Major and Trace Elemental Contamination in Agricultural Soils of the Palamaner Division, Chittoor District, Andhra Pradesh, India

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Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Description of the study sites

2.2 Soil transect technique

2.3 Sample collection and processing

2.4 Laboratory analysis

2.4.1 Particle size and chemical analysis

2.4.2 Elemental analysis

2.5 Environmental indicesfor assessing anthropogenic influence

2.5.1 Enrichment factor (EF)

2.5.2 Igeo accumulation index

2.5.3 Contamination factor(Cf), degree of contamination (Cd) and modified degree of contamination(mCd)

2.5.4 Pollution load index

2.5.5 Nemerow comprehensive index

2.6 Statistical analysis

3. Results

3.1 Particle size distribution,bulkdensity,pHand EC of the soil transects

3.2 Chemical characteristics of the soil transects

3.3 Horizon wisevariations in particle size and chemical properties

3.4 Elemental composition

3.5 Enrichment ratio

3.6 Igeo index

3.7 Pollution assessment

4. Discussion

4.1 Soil transects

4.2 Soil morphology

4.3 Particle size distribution and bulkdensity

4.4 Chemical characteristics

4.5 Elemental composition

4.6 Evaluation of metal pollution

4.7 Cluster analysis

4.8 PCA analysis

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1

2.5.2 I_geo accumulation index