Geochemical Signature and Risk Assessment of Potential Toxic Elements in Intensively Cultivated Soils of South-West Punjab, India

Abstract

Soil contamination with potentially toxic elements (PTEs) in the Malwa region belt of Punjab, India, can be a serious concern as a result of intensive agricultural practices and overuse of agrochemicals. The main objectives of the present study were to evaluate the spatial distribution, geochemical signature, and contamination level/health risk of PTEs in 76 soil samples (0–10 cm) collected from the three districts viz. Muktar, Faridkot, and Moga of Punjab, India. The result shows that PTEs concentrations vary widely in the region, with Fe and Mn distribution patterns being mostly coherent with each other. When compared to the Indian natural soil background values, the average concentration of Pb and Zn were higher than the limit, only Pb exceeded the average values of the world background and upper continental crust (UCC). Spatial autocorrelation plotted with a local indicator of spatial association (LISA) in GeoDa software version 1.18 was used to identify hotspots. A positive spatial autocorrelation (>0.2) was indicated with Moran’s I values for Pb, V, Mn, Cu, and Cr, being highest for Pb. A principal component analysis (PCA) identified the major geo-chemical patterns of Fe-Al-V-Cr and TOC-Mn-Zn-HCO₃⁻, which were positively loaded on PC1. This indicates that Fe/Al-oxyhydroxides and organic matter play a dominant role in controlling metal mobility in soils. This can be further substantiated with the Spearman’s rank correlation values. The contamination factor (CF) indicates that only Pb and Zn (15.7% and 3.9% samples, respectively) were under high risk. This could be due to the excessive application of chemical fertilizers. The large range of degree of contamination (C_deg) values suggests that there are variations in the degree of soil pollution due to PTEs. A little over 3.9% of samples had significant contamination, compared to 72.3% of samples with low contamination and 23.6% of samples with moderate contamination. Human non-carcinogenic and carcinogenic risk levels were investigated. The hazard index (HI) values for adult ranged from 0.00 to 0.2, and values for children ranged from 0.009 to 1.2. These findings suggest that both children and adults are not at potential risk, except in a few locations. Overall, the results of this study provide the current baseline status of toxic elements in agricultural soil. This would be helpful for developing strategies for sustainable management of the soil resources in the region, as well as for future monitoring programs of the soil quality in the Malwa region as a whole, to track any changes in the contamination levels over time.

1. Introduction

Contamination of agricultural soil with potentially toxic elements (PTEs) has been reported from many parts of the world and poses a risk to both human and animal health [1,2,3,4,5]. Due to their ability to bind to a variety of substances, soil particles can be thought of as a significant sink for pollutants that may come from a variety of anthropogenic and natural sources [5,6,7,8]. In the last few decades, growing populations and our evolving way of life have led to increased food consumption, placing an immense strain on soil resources [9,10,11]. For instance, to ensure global food security by 2050, food production must rise by up to 70% [7,12]. To meet this demand, agricultural practices have become more intensive, leading to increased use of pesticides, fertilizers, and other chemicals [13,14,15,16]. Consequently, significant volumes of these fertilizers, including nitrogen (N), phosphorus (P), potassium (K), and compound/mixed variants, are regularly applied to agricultural soils to ensure adequate macronutrient supply [6,17]. It was estimated that in 2019, over 220 million tons (MT) of commercial fertilizers and liming materials were utilized worldwide, predominantly in agricultural landscapes [18,19]. In terms of overall fertilizer consumption, India is the leading country in the South Asian Association of Regional Cooperation (SAARC) and the world’s second-largest consumer of fertilizer [20]. In the fiscal year 2019–2020, the total consumption of nutrients (N + P₂O₅ + K₂O) amounted to 28.97 MT, with nitrogen (N), phosphorus pentoxide (P₂O₅), and potassium oxide (K₂O) individually consumed at 18.86 MT, 7.46 MT, and 2.64 MT, respectively [21]. A variety of fertilizer products are currently employed to cultivate crops and meet the growing demand for food. Urea, ammonium sulphate (AS)/ammonium chloride (ACl), diammonium phosphate (DAP), single superphosphate (SSP), and muriate of potash (MoP), as well as NP/NPK complex fertilizers, are predominant in the Indian market [21]. The prevalence of chemical fertilizers in India’s agricultural practices is indeed significant, aiding in meeting the nation’s food demands. However, this can also lead to soil contamination as a significant amount of PTEs has been reported in different fertilizers worldwide [21,22,23,24]. Concentrations of Cd, Pb, Cu, and Zn at 0.1 to 170 mg/kg, 1 to 300 mg/kg, 7 to 225 mg/kg, and 50 to 1450 mg/kg were reported in P fertilizers [22]. Whereas, Cd, Pb, Cu, and Zn ranged from 0.05 to 8.5 mg/kg, 1 to 15 mg/kg, 2 to 1450 mg/kg, and 1 to 42 mg/kg in N fertilizers, respectively [22]. However, this reliance results in the accumulation of PTEs such as arsenic (As), copper (Cu), lead (Pb), manganese (Mn), chromium (Cr), mercury (Hg), cobalt (Co), vanadium (V), nickel (Ni), fluoride (F), cadmium (Cd), selenium (Se), molybdenum (Mo), iron (Fe), and zinc (Zn) in the soil [24,25,26]. While these substances are necessary for many biological processes in moderation, high concentrations can be harmful to biological systems [27,28,29]. Their toxicity, persistence, and lack of natural degradation make them major threats to human health and the environment [30,31,32,33,34]. Furthermore, the accumulation of PTEs in soil is identified as detrimental to soil fertility, plant growth, and overall ecosystem functioning [29,35,36,37]. This accumulation poses a risk as these elements have the potential to accumulate in plants and subsequently move up the food chain [8,38,39,40].

The scale of soil degradation in India, estimated by the National Bureau of Soil Survey and Land Use Planning, is staggering, with approximately 30% of the country soil classified as degraded [41]. The majority of Indian states are severely impacted by soil degradation; the worst-affected states are Punjab, Haryana, Gujarat, Maharashtra, Andhra Pradesh, and Telangana [28,35,41,42,43]. It is believed that intensive farming methods, extensive use of chemicals and fertilizers, and inadequate irrigation management are some of the causes of soil degradation in these states. To maintain sustainable land use practices, preserve agricultural productivity, and protect the livelihoods of millions of people in India, this widespread soil degradation must be addressed [2,44].

Punjab is referred to as the “Granary of India” [45] because it supplies a majority of the food in the country [46]. Agriculture is the primary occupation for the state population. Approximately 35.45% of wheat and 25.53% of rice produced in 2018–2019 came from Punjab, India, according to the statistical abstract report for 2021 [46]. Even though Punjab makes up 1.53% total land area of India, the majority of the cropped area is made up of wheat and rice. The fertilizer consumption of Punjab state is significantly higher (247 kg/ha) as compared to the Indian (165.8 kg/ha) and world average (140.5 kg/ha) [47,48]. In Punjab soil, PTEs concentrations varied from 154 to 422.1 mg/kg, 0.4 to 118 mg/kg, 0.4 to 58.1 mg/kg, 0.1 to 19.2 mg/kg, 3.9 to 27.9 mg/kg, 0.1 to 30 mg/kg, BDL to 75.7 mg/kg, and 0.1 to 98.1 mg/kg for Mn, Pb, Cu, Co, Ni, Cd, Cr, and Zn, respectively [46]. The majority of the PTE concentrations were under the limit with the exception of Cd and Pb at some places in the agricultural soil of Punjab [45,46,49,50,51,52], particularly in districts like Faridkot, Moga, and Muktsar, where 92%, 89%, and 90% of the area is under agriculture. These districts, renowned for their agricultural productivity, face heightened risks due to intensive farming practices, industrial activities, and urbanization. In addition to promoting economic development and growth, the interaction of these variables has resulted in contamination of PTEs in the soil. Since the dawn of civilization, the study area is one of the areas that has seen extensive agriculture. There has not been much in-depth research conducted on the potentially toxic element contamination in the soil, and there is a lack of health risk evaluation in the area under study. However, other research emphasizes the negative impacts of PTEs on soil quality, agricultural productivity, and human health in soil samples taken from different parts of Punjab [45,50,51,52,53]. There may indeed be a lack of specific studies focusing on soil contamination with PTEs in the selected region of Punjab. In light of these challenges, understanding the distribution, concentration, and ecological effects of PTEs in the soil of the Faridkot, Moga, and Muktsar districts is of paramount importance. Thus, the current study was initially carried out to evaluate the elements (As, Cu, Pb, Cr, V, Ni, Fe, and Zn) that may be hazardous in agricultural soils of selected districts of Punjab. Afterwards, we determine the relationship between PTEs and other soil quality parameters by using principal component analysis (PCA) and correlation analysis. Finally, a variety of pollution indices are used to determine the level of pollution and assess health risk. Such understanding forms the foundation for devising effective mitigation strategies aimed at safeguarding environmental and human well-being. By unraveling the complexities of soil contamination and its ecological ramifications, stakeholders can work towards sustainable land management practices that ensure the continued prosperity of Punjab agricultural landscapes while preserving the health of its ecosystems and communities.

2. Materials and Methods

2.1. Study Area Description

The study area, located in southwestern Punjab, India, encompasses three districts, Muktsar, Faridkot, and Moga (Figure 1), situated on the Indo–Gangetic alluvial plain within the western part of the Sutlej basin; the region is primarily agricultural. The landuse/landcover (LULC) map depicts how most of the area was dominated with agriculture followed by built-up area and then bare land (Figure 1a). The climate of southwestern Punjab is hot, dry (average annual rainfall 420 mm), and arid with scorching summers and cold winters [54,55,56]. The elevation of the study area ranged between 191 and 204 msl. Geologically, the region’s lithology is primarily composed of grey micaceous sand, silt, clay, oxidized silt–clay with kankar, and micaceous sand, with patches of yellowish-brown loose sand with or without kankar (Figure 1b). Geological formations consist of thick sequences of Quaternary deposits ranging from the mid-Pleistocene to the Recent age (Figure 1c). Moga is notable for having the highest annual rainfall as compared to Faridkot and Muktsar. Alluvial plains and sand dunes make up its terrain, which is primarily made up of desert and sierogem soil in Muktsar and Moga, whereas Faridkot is distinguished by sandy loam soil and alluvium. The principal aquifer in the study area is the alluvium, with an older alluvium and newer alluvium serving as major aquifers (Figure 1d). Various minerals are found in the region, including feldspars, quartz, muscovite, amphibole, biotite, anhydrite (gypsum), fluorite, kaolinite, and chlorite, contributing to the geological diversity of the area [54,55,56].

2.2. Collection of Soil Samples

A total of 76 soil samples were collected from the districts of Muktsar, Faridkot, and Moga during the year 2022–2024 covering a total area of 6221 km². Dry fly ash and bottom fly samples were also collected from the coal-based thermal power plant situated in Punjab, India. The sampling sites were mapped using QGIS 3.28 software (Figure 1a) with one sample per grid (8 × 8 km²). Using a stainless-steel auger, soil samples were taken from agricultural fields at a depth of 0 to 10 cm. From each sampling location, about 1 kg of soil was gathered and brought to the lab in double-zip plastic bags that had been previously cleaned with ethanol and double-distilled water. Moreover, soil samples were sieved through a mesh with pore sizes of 2 mm after being air-dried at ambient temperature. After that samples were kept in fresh polythene bags for further analysis.

2.3. Analysis of Soil Physicochemical Properties

Soil pH and electrical conductivity (EC) were assessed in 1:5 soil: water suspension through the IS method [58,59]. The mixture was shaken for 2 h, and the supernatant was filtered and used for measurement. Total organic carbon (TOC) content was determined using the Walkley–Black rapid titration method [60]. Available phosphorus levels were examined utilizing the Olsen method and measured using a spectrophotometer [61].

2.4. Analysis of PTEs in Soil

A total of 10 elements, comprising PTEs (Zn, Cu, Pb, Ni, Cr, Al, As, Fe, Mn, and V), were determined using MP-AES (Agilent 4210, Agilent Technologies, Mulgrave, Victoria, Australia). Prior to analysis, soil and ash samples were acid-digested using reversed aqua regia (1:3 v/v; HCl: HNO₃ (USEPA 3052)) in a microwave digester (Milestone Ethos Easy) [50]. Following the process of digestion, samples were diluted and filtered using PTFE syringe filters with a pore size of 0.45 μm before being injected into the instrument. Quality control measures for MP-AES data included the analysis of duplicate samples and certified reference materials (CRMs: GBM303-4, MRGeo08). The obtained data showed a high degree of consistency with mentioned CRMs. The Relative Standard Deviation (% RSD) was used to ensure the precision, and it varied within ±10% for most of the elements.

2.5. Statistical and Geospatial Analysis of Data

Descriptive statistics such as minimum (min), maximum (max), average, and standard deviation (SD), and multivariate statistical analyses such as PCA and Spearman’s rank correlation, were performed using R software, version 4.2.3. Before performing the PCA, the raw data for each parameter were transformed using the centered log ratio (CLR) method in order to ensure data consistency and reliability and to minimize closure issues [4] using Equation (1) [62] provided below.

$$CLR\left(x\right)=(\log \left(x_1/g\left(x\right)\right),\log (x_1/g\left(x\right))), \log \left(x_N/g\left(x\right)\right)$$

(1)

Using CLR-PCA, the original variables are changed into principal components (PCs), which are new, uncorrelated variables. Using QGIS (version 3.28) software Kriging interpolation tools, the spatial distribution of several parameters was plotted. The geographical correlation between those parameters is shown in the geospatial plots. The spatial autocorrelation and (Moran’s Index) and Local Indicator of Spatial Association (LISA), along with their z-score and p-value, were calculated using GeoDa 1.18 software [63].

2.6. Soil Pollution Indices Calculation

2.6.1. Contamination Factor

The contamination factor (CF) was utilized as a method to evaluate the pollution potential of specific elements present in soils [64]. It was determined using Equation (2) [65].

$$CF=\frac{C_{metal}}{C_{control}}$$

(2)

Here, ‘C_metal’ represents the concentration of PTEs in the contaminated samples, while ‘C_control’ represents the concentration of PTEs in background samples. A regional control value derived from Indian natural background soils [66,67] was used in this study. For As, the world background soil value [68] was used, as this element was absent in the Indian reference values. The degree of contamination is then categorized based on the ‘CF’ values provided in Supplementary Table S1 [69].

2.6.2. Degree of Contamination

The contamination degree (C_deg) of a specific sampling location is determined by adding up the contamination factors for all elements present, as indicated in Equation (3) [69,70].

$$C_{deg}={{\displaystyle \sum }}_{i=1}^{n}CF$$

(3)

where ‘n’ represents the number of pollutants analyzed and ‘CF’ denotes the contamination factor. The categorization of C_deg [69] is outlined in Supplementary Table S2.

2.6.3. Geo-Accumulation Index (I_geo)

Muller introduced the geo-accumulation index (I_geo) as a method to assess the contamination of trace elements in sediments [70], and it has since become a widely adopted approach for evaluating soil contamination [71]. The calculation of I_geo has been realized as per Equation (4) [65]:

$$I_{geo}=lo{g}_2 \frac{C_{metal}}{I.5\times C_{metal \left(control\right)}}$$

(4)

where ‘C_metal’ represents the concentration of the metal in the sample under study, while ‘C_{metal (control)}’ denotes the geochemical background value [68] derived from Indian natural soil data. The factor 1.5 serves as a correction factor, aimed at mitigating the influence of potential variations in background or control values that could be attributed to local terrain effects. The classification of I_geo index values into six distinct classes [69] is detailed in Supplementary Table S3.

2.6.4. Health Risk Assessment Index

Human health risk assessment is the process of determining the kind and likelihood of harmful health effects in people who might be exposed to substances in polluted environmental media. Oral ingestion, inhalation, and dermal contact are the three main ways that people are exposed to hazardous substances. Nevertheless, oral consumption and dermal absorption are thought to be the primary exposure channels for heavy metals in soil [72]. The average daily intake (ADI) of chemicals in soils for these three pathways is determined with Equations (5)–(7) [72].

$$AD{I}_{oral}=\frac{C\times I{R}_{ing}\times EF\times ED}{BW\times AT}\times {10}^{-6}$$

(5)

$$AD{I}_{dermal}=\frac{C\times SA\times SAF\times ABS\times EF\times ED}{BW\times AT}\times {10}^{-6}$$

(6)

$$AD{I}_{inhalation}=\frac{C\times I{R}_{inh}\times EF\times ED}{BW\times AT}$$

(7)

where ‘C’ represents the concentration of heavy metals in the soil (mg kg⁻¹); ‘ADI_oral’, ‘ADI_dermal’, and ‘ADI_inh’ are average daily intake values from soil ingestion, dermal absorption, and inhalation, respectively (mg kg⁻¹ day⁻¹); ‘EF’ is the exposure frequency, 350 days per year⁻¹; ‘ED’ is the exposure duration, 30 years for adults and 6 years for children [3,73]; and ‘IR_ing’ is the ingestion rate (mg day⁻¹), 100 mg kg⁻¹ for adults and 200 mg kg⁻¹ for children. According to USEPA (2002), the exposed skin area (SA) is 5700 cm² for adults and 2800 cm² for children; the skin adherence factor (SAF) is 0.07 mg cm⁻² for adults and 0.2 mg cm⁻² for children; and ‘IR_inh’ represents inhalation rate in (m³/day) [73].

The USEPA (2002) defines ‘ABS’ as the dermal absorption factor, which is 0.001 for both adults and children. ‘PEF’ on the other hand, is the particle emission factor, which is 1.36 × 10⁹ m³ kg⁻¹ and relates the concentration of a pollutant in the soil to the concentration of a respirable particle in the air due to fugitive dust emissions from contaminated soils. ‘BW’ is the body weight, which is 20 kg for children and 70 kg for adults. Finally, ‘AT’ is the averaging time, which is 70 (lifetime) × 365 days for carcinogens (As, Cr, and Ni) and ‘ED’ × 365 days for non-carcinogens [73].

Non-Carcinogenic Risk Assessment

The hazard quotient (HQ), which is determined as the ratio of the average daily intake and a reference dose (RfD), can be expressed as Equation (8) [72]. It is commonly used to describe non-carcinogenic risks.

$$HQ=\frac{ADI}{RfD}$$

(8)

where ‘RfD’ is the reference dosage (mg kg⁻¹ day⁻¹) of PTEs. This is the highest concentration of a PTE that is safe for human health. The reference dosage for non-dairy ingestion of PTEs (RfD_ing; mg kg⁻¹ day⁻¹) for adults and children in soil was taken into account in this study. The USEPA (2002) [73] offers a technique to evaluate dermal absorption exposure to chemicals when reference doses are lacking. This method involves multiplying the soil oral reference dosage by a gastrointestinal absorption factor in order to determine dermal risk [73].

In order to evaluate the combined non-carcinogenic effects of a number of contaminants, the hazard index (HI) is calculated by adding together the HQ values of each element. The formula for the HI can be expressed as Equation (9) [72].

$$HI=\sum H{Q}_i=\sum \frac{AD{I}_i}{Rf{D}_i}$$

(9)

where $H{Q}_i$ represents the Hazard Quotient for the ith element, $AD{I}_i$ denotes average daily intake, and $Rf{D}_i$ represents the reference dose for the same. It is unlikely that the exposed person will suffer negative health impacts if the HI value is less than 1. On the other hand, in the event that the HI value surpasses 1, there exists a possibility of a non-carcinogenic health consequence, with a probability that tends to rise with the HI value [3,74].

Carcinogenic Risk Assessment

The process of estimating carcinogenic risks involves computing the cumulative likelihood that an individual would experience cancer during their lifetime due to exposure to a probable carcinogen. According to USEPA (2010), the slope factor (SF) directly translates the average lifetime intake of a toxin into the incremental chance of a person developing cancer [75,76]. Cancer risk can be calculated using Equation (10) [72].

$$CR=ADI\times SF$$

(10)

where ‘CR’ is cancer risk, ‘SF’, is the carcinogenicity slope factor (mg kg⁻¹ day⁻¹). The cumulative cancer risk from all chemicals and pathways is used when there are many carcinogenic pollutants present. Supplementary Table S9 also shows the RfD and SF from non-dietary consumption, skin contact, and inhalation of the eight heavy metals.

3. Results and Discussion

3.1. Physicochemical Properties of Soil

The results of the physicochemical analysis of the soil samples are summarized in Table 1. Soil pH ranged from 6.9 to 8.57, indicating that most of the samples were alkaline in nature. These results are in line with earlier studies on the agricultural soils of Bathinda, Talwandi Sabo, and Amritsar [77,78]. One essential measure of the health of the soil is electrical conductivity (EC). It offers insightful information on a number of functional and quality aspects of the soil including crop yields, plant nutrient accessibility, and soil microbial activity. It also contributes significantly to important soil processes, such as the release of methane, carbon dioxide, and nitrogen oxides which are greenhouse gases. As illustrated in Table 1, the EC of all soil samples ranged from 125 to 1703 μS cm⁻¹, with an average value of less than 4500 μS cm⁻¹, suggesting that the soils in the study area are not saline. In agricultural soil, EC may arise from the accumulation of soluble fertilizers, pesticides, and salts. Irrigation with elevated EC-containing groundwater also contributes the rise in soil EC in the area. Concentrations of dissolved HCO₃⁻ ranged between 150 and 1050 mg/kg with a mean value of 491.3 mg/kg, and TKN ranged between 0.06 and 0.2% with a mean value of 0.1% (Table 1), which represented the soil in the area was very low in nitrogen. The range of TOC in the soil was 0.07% to 2.2%, which signifies the variability and low content of organic matter content within the soil. TOC represents the accumulation of organic compounds derived from the decomposition of soil organisms, cells, and tissues of soil organisms and various forms of plant and animal remains. The sandy loam texture of the soils under study may be the cause of their low organic matter content due to their low retention capacity. Table 1 provides a statistical summary of PTE concentrations in soil samples. There is a wide variation of the metal concentrations in agricultural soil, with iron (Fe) and aluminum (Al) having the highest concentrations. The order of the average metal concentrations (mg/kg) in descending order is as follows: Al (11,541.8), Fe (10,382), V (1289.5), Mn (186.3), Zn (67.47), Pb (31.59), Cr (22.99), Cu (12.02), Ni (3.96), and As (1.7) (Table 1). To make comparisons easier, metal concentrations from the world background soil and the Indian natural background were used (Table 2). While the mean concentration of Pb (31.5 mg/kg) was found to be higher than the World background soil [68], the mean concentration of Zn (67.4 mg/kg) was found to be higher than the Indian natural background value [67].

3.2. Comparison of Soil PTE Concentrations with Other Studies of Punjab and Other Reference Values

The soil metal concentration data were compared with other studies conducted in Punjab and reference values from different parts of the world have been summarized in Table 2. The result showed that the soil in various parts of Punjab is contaminated with a wide range of PTEs. The following particular ranges were noted: Zn ranged from 2.9 to 120.1, Cu from 0.9 to 58.1, Pb from 3.8 to 90, Mn from 14.3 to 184.2, Ni from 0.5 to 56, Cr from 0.8 to 87.7, and As from 1.8 to 115.7 (mg/kg) [42,49,52,53,79,80,81,82,83,84,85]. When compared to the previous studies of Punjab (Table 2), the majority of the metals in the present study exhibit within the range, with elevated levels reported from Bathinda [9], Rupnagar and Ludhiana [85]. The average soil PTEs concentrations were compared with Indian natural soil [67], world background values [68], reference soil China [86], reference soil USA [87], reference soil Spain [88], Upper continental crust [89] as summarized in Table 2. For instance, the concentration of Zn in the study site soil is approximately three times higher than the Indian natural soil background value and almost the same as the world background soil value. Similarly, the soil at the study site has substantially lower Cu concentrations than both background values, indicating reduced potential for Cu contamination. The concentrations of Pb vary greatly among various locations of Punjab. For example, Rajewal reported the lowest average Pb concentration (3.83 mg/kg) in agricultural soil [80], which is significantly lower than the levels found in the current study. On the other hand, SAS Nagar in Punjab had the highest average Pb concentration (90.02 mg/kg) [81], which is roughly three times greater than what was found in the current study. Zn concentrations in the study samples were found to be higher than the Indian natural soil background values in various districts, including Harike, Barnala, Ludhiana, Rupnagar, SAS Nagar, Amritsar, and Batla [49,52,81,82,83,84,85]. The concentration of As exceeded the world background value in the Ludhiana and Bathinda districts of Punjab [9,52]. Chromium levels exceeded the concentration of the world background value in the Rajewal, Tibbi Taiba, and Bathinda districts of Punjab (Table 2) [9,80].

The Pb levels in the study site soil are notably elevated, surpassing the Indian natural soil background value [67] and approaching the world background soil value [68] (Table 2). All the PTEs were found under the levels of the reference soil China [86], except for Pb, while Pb and Zn levels were higher when compared with USA reference soil [87] and Upper continental crust [89]. While only Pb content was higher in the study soil than the Reference soil of Spain [88] (Table 2). Overall, the data suggest that the soil at the study site is contaminated with various PTEs, posing potential risks to the environment and human health. The disparities observed in the heavy metal concentrations between the study site soil and the background values can be attributed to various factors related to various anthropogenic activities.

Groundwater and canal water serve as primary irrigation sources in the study area, yet the specified PTEs are not significantly present except U and F^- in these groundwater and canal sources [90,91]. Research by Krishan et al. (2021) supports this observation, indicating that the concentrations of metals such as Zn, Cu, and Pb in groundwater and canals met Indian standards [92]. This suggests that these water sources are unlikely to be major contributors to heavy metal contamination in the agricultural soil of Punjab. The study site, being an agricultural center, shows exacerbated PTE contamination due to related activities. Conversely, studies already highlighted the substantial impact of fertilizer usage on soil contamination in the region [3,81,93]. High levels of PTEs, including Zn, Cu, and Pb, have been detected in soils treated with chemical fertilizers, indicating their role as significant contributors to soil contamination. Additionally, a positive correlation between fertilizer application rates and PTE concentrations in soil samples has been reported earlier, further underscoring the association between fertilizer usage and soil contamination in the region [94,95]. Therefore, while groundwater and canal water may not be major sources of heavy metal contamination, fertilizer application emerges as a prominent factor contributing to soil pollution in Punjab.

Table 1. Descriptive statistics of soil physicochemical properties and PTEs.

	Unit	Min	1st Quartile	Median	Mean	3rd Quartile	Max	SD
pH	-	6.9	7.5	7.7	7.7	8	8.5	0.3
EC	μS/cm	125	284.2	403.4	476.9	532.8	1703	312.2
Salinity	mg/kg	107	239.2	351.2	414.7	475.6	1544	280
HCO3−	mg/kg	150	277.5	500	491.3	650	1050	228.4
TKN	%	0.06	0.1	0.17	0.1	0.2	0.2	0.06
AP	mg/kg	40.3	92.4	103.5	100.5	113.7	132.4	17.7
TOC	%	0.07	0.7	1	1.1	1.5	2.2	0.4
Zn	mg/kg	1	17.5	48.2	67.4	74.3	971.4	120
Cu	mg/kg	2.8	6.5	10.5	12.0	15.8	41.9	7.4
Pb	mg/kg	0.2	6.4	11.5	31.5	47.4	179	40.4
Mn	mg/kg	0.1	107.2	164.2	186.3	223.3	672.1	117.7
V	mg/kg	41.8	672.4	1136.2	1289.5	1816.5	3142.1	765.9
Ni	mg/kg	0.8	1.7	3.85	3.9	5.8	17	2.7
Al	mg/kg	346.3	7002.1	9479	11,541.8	15,077.5	33,462.4	6420.6
Cr	mg/kg	6.3	15.4	22.5	22.9	29.4	49.2	10
As	mg/kg	0.7	1.3	1.7	1.7	1.9	2.6	0.42
Fe	mg/kg	422	6973	9964	10,382	12,743	33,437	5233.9

Min: Minimum, Max: Maximum, SD: Standard deviation, EC: Electrical Conductivity, TKN: Total Kjeldal Nitrogen, AP: Available Phosphorous, TOC: Total Organic Carbon.

Table 1. Descriptive statistics of soil physicochemical properties and PTEs.

	Unit	Min	1st Quartile	Median	Mean	3rd Quartile	Max	SD
pH	-	6.9	7.5	7.7	7.7	8	8.5	0.3
EC	μS/cm	125	284.2	403.4	476.9	532.8	1703	312.2
Salinity	mg/kg	107	239.2	351.2	414.7	475.6	1544	280
HCO3−	mg/kg	150	277.5	500	491.3	650	1050	228.4
TKN	%	0.06	0.1	0.17	0.1	0.2	0.2	0.06
AP	mg/kg	40.3	92.4	103.5	100.5	113.7	132.4	17.7
TOC	%	0.07	0.7	1	1.1	1.5	2.2	0.4
Zn	mg/kg	1	17.5	48.2	67.4	74.3	971.4	120
Cu	mg/kg	2.8	6.5	10.5	12.0	15.8	41.9	7.4
Pb	mg/kg	0.2	6.4	11.5	31.5	47.4	179	40.4
Mn	mg/kg	0.1	107.2	164.2	186.3	223.3	672.1	117.7
V	mg/kg	41.8	672.4	1136.2	1289.5	1816.5	3142.1	765.9
Ni	mg/kg	0.8	1.7	3.85	3.9	5.8	17	2.7
Al	mg/kg	346.3	7002.1	9479	11,541.8	15,077.5	33,462.4	6420.6
Cr	mg/kg	6.3	15.4	22.5	22.9	29.4	49.2	10
As	mg/kg	0.7	1.3	1.7	1.7	1.9	2.6	0.42
Fe	mg/kg	422	6973	9964	10,382	12,743	33,437	5233.9

Min: Minimum, Max: Maximum, SD: Standard deviation, EC: Electrical Conductivity, TKN: Total Kjeldal Nitrogen, AP: Available Phosphorous, TOC: Total Organic Carbon.

Table 2. Comparison of average value of PTEs (mg/kg) in this study with other studies from Punjab, India and worldwide reference limits.

Locations	Zn	Cu	Pb	Mn	V	Ni	Al	Cr	As	Fe	References
This study	67.4	12	31.5	186.3	1289.5	3.9	11,541.8	22.9	1.7	10,382
Jalandhar City, Punjab	2.9	0.9		14.3		0.56		0.81	1.8	13.8	[42]
SBS Nagar, Punjab	80.1	15.4		184.2				19		749.6	[79]
Jalalabaad, Punjab		9.0	6.6					29.5			[80]
Rajewal, Punjab		9.1	3.83					86.9			[80]
Yousufpur, Punjab			5.5								[80]
Tibbi Taiba, Punjab		3.0	7					87.7			[80]
Doomniwala, Punjab		13.3	5.8					67			[80]
Harike, Punjab	59	18.7	6.5	374				22.3		17,700	[83]
Barnala, Punjab	55.7	17.1	19.3	36	55.2	27.6		64.8	9.9	25,722	[84]
Mansa, Punjab			15.6			47.7	10,700	37.5	8.1	14,000	[53]
Bathinda, Punjab	114.7	28.8	6.6			56		94	15	28,803	[9]
Ludhiana, Punjab	71.5	40.3	31.5				21.9		115.7		[52]
Amritsar, Punjab	96.5	58.1	24.8			24.7					[49]
Rupnagar and Ludhiana, Punjab	70.1	30.5	47.5	342		33.3		36.2		1498	[85]
SAS Nagar, Punjab	32.6	5.1	90			6.5		5.2	2.7		[81]
Batla, Punjab	120.1	4.1	4.0	152.4		12.7				488.2	[82]
Indian natural soil background	22.1	56.5	13.1			27.7		114			[66]
World background soil	67.8	28.2	28.4	571		17.8		70.9	11.4		[68]
Reference soil China	74	23	27			27		61	11.2		[86]
Reference soil USA	55	21	17	380		15		41	5.5		[87]
Reference soil Spain	192		30			43		73			[88]
Upper continental crust	52	14	17	900		19		35	2		[89]
Reference soil Brazil	59.9	35.1	17			13.2		40.3			[4]

Table 2. Comparison of average value of PTEs (mg/kg) in this study with other studies from Punjab, India and worldwide reference limits.

Locations	Zn	Cu	Pb	Mn	V	Ni	Al	Cr	As	Fe	References
This study	67.4	12	31.5	186.3	1289.5	3.9	11,541.8	22.9	1.7	10,382
Jalandhar City, Punjab	2.9	0.9		14.3		0.56		0.81	1.8	13.8	[42]
SBS Nagar, Punjab	80.1	15.4		184.2				19		749.6	[79]
Jalalabaad, Punjab		9.0	6.6					29.5			[80]
Rajewal, Punjab		9.1	3.83					86.9			[80]
Yousufpur, Punjab			5.5								[80]
Tibbi Taiba, Punjab		3.0	7					87.7			[80]
Doomniwala, Punjab		13.3	5.8					67			[80]
Harike, Punjab	59	18.7	6.5	374				22.3		17,700	[83]
Barnala, Punjab	55.7	17.1	19.3	36	55.2	27.6		64.8	9.9	25,722	[84]
Mansa, Punjab			15.6			47.7	10,700	37.5	8.1	14,000	[53]
Bathinda, Punjab	114.7	28.8	6.6			56		94	15	28,803	[9]
Ludhiana, Punjab	71.5	40.3	31.5				21.9		115.7		[52]
Amritsar, Punjab	96.5	58.1	24.8			24.7					[49]
Rupnagar and Ludhiana, Punjab	70.1	30.5	47.5	342		33.3		36.2		1498	[85]
SAS Nagar, Punjab	32.6	5.1	90			6.5		5.2	2.7		[81]
Batla, Punjab	120.1	4.1	4.0	152.4		12.7				488.2	[82]
Indian natural soil background	22.1	56.5	13.1			27.7		114			[66]
World background soil	67.8	28.2	28.4	571		17.8		70.9	11.4		[68]
Reference soil China	74	23	27			27		61	11.2		[86]
Reference soil USA	55	21	17	380		15		41	5.5		[87]
Reference soil Spain	192		30			43		73			[88]
Upper continental crust	52	14	17	900		19		35	2		[89]
Reference soil Brazil	59.9	35.1	17			13.2		40.3			[4]

3.3. Geospatial Distribution of PTEs in Soil

The geographic distribution of PTEs aids in hotspot differentiation and the identification of the likely PTE source in the soil regime. Within the study area, there were significant fluctuations in the spatial variance of total PTEs (Cr, Zn, Pb, Mn, Al, and Fe) (Figure 2a–f). The Zn concentration increased from Muktsar to Moga, according to the geographical variation of PTEs; a similar pattern was also seen for Mn and Fe (Figure 2) due to the binding adsorption of these PTEs on the surface of Fe/Al-oxyhydroxides. The distribution of Pb and Zn was almost opposite in the area, it has been also confirmed in a Spearman’s rank correlation plot where a negative association was found in between Zn and Pb. The concentration of Pb was higher in the Muktsar than in Faridkot and Moga regions. It showed that the Pb in soil was due to anthropogenic activities. Compared to Faridkot and Moga, the majority of Muktsar had a lower concentration of aluminum, and an almost similar pattern was seen in Cr (Figure 2d,e). The north-eastern region of Moga and the southwest region of Faridkot had higher levels.

3.4. Weight Matrix and Spatial Autocorrelation

The 76 samples were assigned a neighborhood structure using spatial weight matrices, and 10 PTEs’ spatial autocorrelation was evaluated. Supplementary Table S4 displays the global Moran’s I values with their p-value and z-score (Supplementary Table S4). The test results for Moran’s I significance ranged from p < 0.001 to 0.5 (Supplementary Table S4). While As, Zn, Fe, and Ni showed low Moran’s I values near zero, Pb, Cr, Cu, Al, Mn, and V displayed considerable and positive spatial autocorrelation on the weight matrix as well as first-order queen contiguity (Supplementary Table S4). The strength of a spatial autocorrelation is generally correlated with the absolute value of Moran’s I, and the significance of a geographical structure is correlated with the size of the standardized metric [96]. The ability to compare the significant spatial patterns of many variables or of the same variable with various calculation settings is one of the advantages of the standardized Moran’s I. A significant positive Moran’s I value of Pb, V, Mn, Cr, Cu, and Al with a significance level (p-value ≤ 0.01), showed the positive spatial autocorrelation of PTEs in the area.

3.5. Spatial Autocorrelation of Selected Elements in Soil

The Local Indicator of Spatial Association (LISA) provides information on the details of spatial variability. The selected distance indicates the local spatial pattern (high-high, low-low, high-low, low-high, and no significance) where the spatial dependency of Al, Cr, Mn, Zn, Cu, Fe, Ni, Pb, and V was strongest [97]. Moreover, half of the samples of the 10 PTEs showed no meaningful geographical pattern, despite the fact that they all had a substantial global spatial positive correlation (Supplementary Table S4). The substantial spatial clusters accounted for 42.1% of Pb, 28.9% of Zn, 26.3% of Mn, and 26.3% of V, while around 20% of the other PTE samples were part of this greater spatial pattern (Figure 3a–j, Supplementary Figure S1), with the exception of V. The low-low pattern of the 10 PTEs dominated the overall geographic distribution as trace elements in soils (Figure 3). Certain noteworthy spatial patterns may point to robust, continuing enrichment processes of certain PTEs in the soils of southwest Punjab. The notable geographical patterns of these PTEs, in contrast to the spatial randomness, showed that the underlying enrichment processes were more stable, which would make cleanup more challenging. Furthermore, the outliers in the evaluation of soil PTEs, including Ni and As in this study, may reflect possible polluted sites. In the event that more spatial interpolation is generated, the outliers should use a more sophisticated geo-statistics approach rather than being randomly eliminated [96].

3.6. Multivariate Statistical Analysis

Spearman’s rank correlation matrix is frequently used to evaluate the degree of similarity and relationships between elements. Spearman’s rank correlation coefficients (ρ < 0.05) were calculated for all parameters using clr data (Figure 4). Correlation strengths were classified as follows: strong (ρ = ±0.7 to ±0.9), moderate (ρ = ±0.4 to ±0.69), weak (ρ = ±0.1 to ±0.39), and none or very weak (ρ = 0 to ±0.1) [87]. Zinc exhibited moderate to strong positive associations with Cu (ρ = 0.45), Mn (ρ = 0.83), V (ρ = 0.72), Al (ρ = 0.74), Cr (ρ = 0.46), and Fe (ρ = 0.67). Manganese showed strong positive associations with V (ρ = 0.76), Al (ρ = 0.83), Cr (ρ = 0.63), and Fe (ρ = 0.76). Aluminum also had strong positive associations with Cr (ρ = 0.73) and Fe (ρ = 0.96). Parameters such as AP and TOC played a significant role in the accumulation and release of PTEs in the soil [98,99,100]. In this study, TOC exhibited moderate-to-strong positive associations with PTEs such as Cu, V, Mn, Al, Cr, and Fe, while showing negative associations with Pb and Ni (Figure 4). Due to the various processes involved in the adsorption of metals and metalloids by soil TOC, TOC can influence the distribution of these elements in the soil [101]. The relationship between TOC and PTEs, including heavy metals and metalloids, is dependent on specific soil conditions, despite TOC’s crucial role in regulating these substances [102]. No significant correlation was observed between TKN and PTEs in the soil (Figure 5). Iron displayed moderate-to-strong positive associations with Zn, Mn, Ni, and Al, while it was weakly associated with Cu, V, and Cr. This indicates that PTEs are adsorbed onto the surface of Fe/Al-hydroxides which prevents their mobilization from the soil [100]. Understanding the origin of heavy metals requires more than a single correlation analysis. Consequently, PCA is widely used to determine the properties and sources of heavy metals in both anthropogenic and natural soils [6,97].

In clr-transformed PCA, five principal components (PCs) with eigenvalues > 1 were extracted, describing 75.1% of the total variance. The first two PCA axes together accounted for 47.6% (PC1 29.8% and PC2 17.8%) of the total variance (Figure 5). Parameters such as Zn, Al, Mn, Fe, and V were positively loaded in with PC1, and their positive association was also found in the Spearman’s correlation plot (Figure 4). All the elements loaded in the PC1 were found below the world background value and Indian natural soil after comparison except Zn. The significant association of Fe with PTEs represents that that Fe/Al-oxyhydroxides are the major scavengers of these metals in the soils. PC2 was positively loaded with Pb and Cu, and the same was observed in the correlation plot (Figure 4 and Figure 5), whereas Pb and Ni were negatively loaded in PC1 and positively loaded in PC2 (Figure 5). The concentrations of Zn and Pb exceeding the Indian natural background values could be attributed to several factors, including the atmospheric deposition of fly ash, extensive use of agrochemicals, wastewater discharge, and the presence of coal-based thermal power plants, which significantly contribute to PTE contamination in the soil in this region. Elevated concentrations of PTEs have been observed in dry fly ash and bottom ash collected from various nearby industrial sources. Specifically, dry fly ash contains Mn, Cu, Pb, Zn, As, Ni, and Cr at concentrations of 638 mg/kg, 116 mg/kg, 187.5 mg/kg, 38.3 mg/kg, 99 mg/kg, and 306.5 mg/kg, respectively. Similarly, bottom ash exhibits Mn, Zn, Pb, As, Ni, and Cr concentrations of 596 mg/kg, 98.4 mg/kg, 128.9 mg/kg, 96.6 mg/kg, 30.6 mg/kg, 76.6 mg/kg, and 281.9 mg/kg, respectively (

Table 3. Concentration (mg/kg) of PTEs in different soil additives (such as fly ash, bottom ash, and chemical and organic fertilizers).

Sources	As	Cd	Cr	Ni	Pb	Zn	Mn	References
Fly Ash	38.3	-	306.5	99.0	187.5	116	638	This study
Bottom Ash	30.6	-	281.9	76.6	128.9	96.6	596	This study
P fertilizers	-	0.1–170	-	-	1–300	50–1450	-	[22]
N fertilizers	-	0.05–8.5	-	-	1–15	1–42	-	[22]
Lime fertilizers	-	0.04–0.1	-	-	2–125	10–450	-	[22]
Manure fertilizers	-	0.3–0.8	-	-	2–60	15–250	-	[22]
Solid waste	-	0.8–224	-	154–1534	23–530	116–23,321	494–19,964	[103]
Organic fertilizer	-	5.87	-	-	49.62	664.27	-	[104]
Urea	-	0.8	-	-	-	-	-	[105]
DAP	-		236.00	-	108.14	174.54	-	[105]
SSP	-	12.35	83.00		128.47	216.14		[105]

DAP: Diammonium Phosphate, SSP: Single Super Phosphate.

). Solid-waste dumping can also contribute to PTE concentrations as it leaches down and is accumulated in the soil. In a study by Moturi et al. (2004), the Pb, Zn, and Cu concentrations ranged from 23 to 530 mg/kg, 116 to 23,321 mg/kg, and Cu 147 to 10,144 mg/kg, respectively in the solid waste [103]. Fertilizers are another critical source of Zn and Pb in agricultural soils. For instance, nitrogen (N) fertilizers, phosphorus (P) fertilizers, lime fertilizers, and manure contain substantial amounts of Pb, measured at 15 mg/kg, 300 mg/kg, 125 mg/kg, and 60 mg/kg, respectively [22,104,105]. Zn concentrations in these fertilizers are also considerable, with values of 42 mg/kg in N fertilizers, 1450 mg/kg in P fertilizers, 450 mg/kg in lime fertilizers, and 250 mg/kg in manure (Table 3) [22,104,105]. This extensive use of contaminated fertilizers substantially contributes to the elevated Zn and Pb levels observed in the agricultural soils of this region [106]. Weintraub and Schimel (2003) found a substantial correlation between nitrogen and carbon mineralization based on the considerable connection (ρ = 0.59) between TKN and TOC in PC4 [107]. Moderate-to-strong positive associations between TOC and the majority of inorganic elements suggests that TOC does significantly influence inorganic elements in the irrigated soil [108]. Arsenic was positioned differently in the soil PCA than phosphorus, which demonstrated their competing behaviour and may have had an impact on the ability to retain As [78,109].

3.7. Environmental Pollution Level Assessment

The contamination factor and C_deg values were computed using the background values (Indian natural soil [66,67] and world background value [68] for As) for a subset of PTEs (As, Cr, Cu, Fe, Mn, Ni, Pb, and Zn). The CF values between elements differed significantly. The maximum CF in surface soils fell in the following order: Zn > Pb > Mn > Fe > Cu > Ni > Cr > As (Supplementary Table S5). The contamination factor of Zn had a median of 2.18 and ranged from 0.04 to 43.95 (Figure 6a). Pb ranged from 0.01 to 13.66 with a median value of 0.88, Cu ranged from 0.05 to 0.07 with a median of 0.1 (Figure 6a). The median value of Mn was found to be 0.78 (Figure 6a). The contamination factor of Ni ranged from 0.02 to 0.61 with a median value of 0.13, and Cr varied from 0.05 to 0.43, with a median of 0.19 (Figure 6a). The contamination factor of Zn, Pb, and Mn were significant, only 15.7% and 3.9% samples of Pb and Zn were under the high-risk category (Supplementary Table S5). About 40.7% and 18.4% samples of Zn and Pb were moderately polluted, whereas 30% of Mn samples fall under this category. The highest CFs of Zn, Pb, and Mn were found in the Dharmkot, Dabra, and Mandwala villages of Moga, Muktsar, and Faridkot, respectively, whereas the lowest CFs of Zn, Pb, and Mn were found in Midda village of Muktsar. The average C_deg value was 7.23, with values ranging from 1.13 to 48.20. The Dargah Saidan village in Moga has the highest C_deg values. The Sangat Pura village in Moga recorded the lowest C_deg values. The large range of C_deg values suggests that there are variations in the degree of metal pollution in the study area soil. A little over 3.9% of samples had significant contamination, compared to 72.3% of samples with low contamination and 23.6% of samples with moderate contamination (Supplementary Figure S2).

3.8. Geo-Accumulation Index

To assess trace element contamination in sediments, the geo-accumulation index (I_geo) has become a popular tool for assessing soil contamination [71]. For PTEs, the majority of samples have an I_geo value falling into class 0 (I_geo < 0), uncontaminated (Figure 6b, Supplementary Table S6). For PTEs like Zn, Pb, and Mn, 34.21%, 72.37%, and 88.16% of the samples fall under class (0), while I_geo values for PTEs such as Cu, Ni, Cr, and Fe fall under the class (0) in approximately all the samples (Figure 6b). Class 1 had around 34.21% of Zn samples, 2.63% of Pb samples, and 10.53% of Mn samples (Figure 6b). Class 2 had approximately 10.53% of the Pb samples, 27.63% of the Zn samples, and 1.32% of the Mn samples (Figure 6b). Very few samples were found in the class representing significant contamination, while the majority of samples fell into the uncontaminated category. The highest I_geo value for Zn was observed in the village of Dharmkot in Moga and the lowest value was found in the Midda village of Muktsar. The highest I_geo value of Pb was found in the Dabra village in Muktsar and the lowest value was found in the village of Midda in Muktsar. The soil in the area was contaminated with Zn, Pb, and Mn which may be due to anthropogenic activities in the area.

3.9. Human Health Risk Assessment

In order to aid in the assessment of potential health concerns that toxic compounds may pose to people, the USEPA developed the health risk assessment (HRA) method. Children as well as adult were evaluated for the possible health risk posed by soil PTE contamination; HQ took into consideration the “soil-to-human” exposure pathway. A genuine number of HQ is the end result of a series of calculations. No negative consequences should be anticipated if HQ is less than unity [73,110]. This explains why comparable literature has utilized HRA so frequently. However, it has several drawbacks. Firstly, it cannot possibly be unique for every exposed human because it is based on rigid assumptions about things like exposure duration, body weight, air volume inhaled, etc. [111]. Secondly, it is unable to account for significant health-related issues like pre-existing health problems, genetic problems, etc. Thirdly, it does not indicate certainty but rather a possible risk. Despite the widespread knowledge of these drawbacks, the most crucial method for determining potential health hazards associated with PTE exposure is the health-related risk assessment (HRA). In summary, health risk assessment is a powerful strategy due to its simplicity, but the results should only be viewed as a preliminary risk assessment of the possible harm associated with chemical use. Due to reports that the majority of exposures that have an impact on human health occur through the mouth, this pathway was investigated in this case. The non-carcinogenic health risk has also been investigated since it considers all PTEs that have been evaluated and is regarded as an essential first step in evaluating health risks in the study areas.

3.9.1. Non-Carcinogenic Risks Assessment

The non-carcinogenic health risk indices (hazard quotient (HQ) and hazard index (HI)) were estimated for selected toxic elements through three potential exposure pathways (ingestion, inhalation, and dermal contact) for adults and children; results are listed in Supplementary Table S7. This indicates a similar pattern of non-carcinogenic risk for adults and children, with higher risk noticeable for Pb. The Pb (HQ) contribution to the total obtained value of HI was determined to be 90.76% for adults and 90.77% for children. On the other hand, for adults and children alike, the total HQ values for Zn, Cu, and Ni were less than 6.8% of the HI value. The latter is a tool used to assess the cumulative non-carcinogenic health risk over exposure to multiple contaminants [73]. If the hazard index (HI) is <1, it suggests that the exposed individual is unlikely to experience obvious adverse health effects. However, there is a possibility that non-carcinogenic consequences might appear if the HI > 1. According to Man et al. (2010), there is a tendency for the probability of these effects to rise with the hazards index [112]. In this instance, the percentage of soils with a hazard index between 0.2 and 1 was roughly 27.63%, and 71.05% of soils had a HI lower than 0.2 for children. A hazard index of less than 0.2 is present in all proportions of soils for adults. Thus, it can be said that because of their physiological differences, children are more susceptible to the non-carcinogenic effects of PTE exposure than adults. One common way that soil contaminants enter children is through their habit of sucking their fingers [113]. As a result, children can unintentionally ingest high amounts of soil.

3.9.2. Carcinogenic Risks Assessment

Carcinogenic risks were assessed for Cr, Ni, and As, due to the availability of carcinogenic slope factors (SF) for these PTEs. The carcinogenic risks from PTE exposure from soils were presented for non-dietary ingestion, dermal contact, and inhalation pathways [3]. The average carcinogenic risk values for As, Ni, and Cr were calculated for both children and adults. The findings demonstrated that these risks fell within acceptable levels Supplementary Table S8 [3], with values above 1.00 × 10⁻⁶, which is considered to have significant health effects. Risks surpassing 1.00 × 10⁻⁴ are viewed as unacceptable. Specifically, the average carcinogenic risk values for children were as follows: 2.47972 × 10⁻⁵ for arsenic, 8.72964 × 10⁻⁸ for nickel, and 7.0652 × 10⁻⁶ for chromium. For adults, the average carcinogenic risk values were 3.55578 × 10⁻⁶ for As, 5.25091 × 10⁻⁸ for Ni, and 1.52223 × 10⁻⁷ for Cr. Overall, the values indicate that while there is some risk associated with exposure to these PTEs, it is considered manageable and not likely to result in significant health effects. The average carcinogenic risk values varied between children and adults, with slightly higher values observed for children compared to adults for all three elements. This is because children are particularly vulnerable to heavy-metal exposure due to various factors such as additional routes of exposure (e.g., breastfeeding and placental exposure), high-risk behaviours (e.g., hand-to-mouth activities and higher risk-taking activity in adolescence), and physiological differences (e.g., higher comparative uptakes and lower toxin elimination rates) [112].

4. Conclusions

In the present study, contamination factor (CF), degree of contamination (C_deg), geo-accumulation index (I_geo), Moran’s index (MIs), spatial autocorrelation, principal component analysis (PCA), and health risk from different pathways were used to identify the geochemical signature, spatial distribution pattern, and environmental/human health risk of PTEs in the agricultural soils of the Malwa region. Among the analyzed PTEs, the average concentration of Pb exceeded the reference values of the world background, Indian natural soil, and upper continental crust. The average concentration of Zn exceeded the Indian natural soil, but was below the world background value. Elevated levels of Zn and Pb in the soil may be due to excessive use of chemical fertilizers and additives such as fly ash and manures. Arsenic, Zn, Fe, and Ni showed low Moran’s I values near zero while Pb, Cr, Cu, Al, Mn, and V displayed considerable and positive spatial autocorrelation on the weight matrix and first-order queen contiguity. The highest Moran’s I value was recorded for Pb with a p-value = 0.001, representing its positive spatial autocorrelation. More than half of the samples of the PTEs had no significant spatial pattern. The significant spatial clusters accounted for 42.1% of Pb, 28.9% of Zn, 26.3% of Mn, and 26.3% of V. Multivariate results show moderate-to-strong positive associations of Fe with PTEs such as Zn, Al, Mn, and Ni, indicating that Fe/Al-oxyhydroxides are the primary scavengers of these metals. Contamination factor results showed that, on the basis of degree of contamination, a little over 3.9% of samples had significant contamination, compared to 72.3% of samples with low contamination and 23.6% of samples with moderate contamination. Hazard quotient values for adults were much lower than 1 but children HQ values were close to 1. The value of the HI indicates that there is no potential risk for adults and children both, except in Dabra Village in Muktsar, where the HI value for children exceeded 1. The HI further supports our earlier conclusions that there is not a potential risk for adults from PTE concentrations in the soil. However, continuous soil quality monitoring is essential for sustainable management of soil resources and ecological restoration of the region.