Pedo-chemical characterization
Soil textural distributions
Textural analysis of soil is an important factor for studying the PC or HM pollution in soil because the concentration of PC elements, essential nutrients, and other living organisms depends on soil texture. The textural analysis exhibits that the majority of soils are mostly sandy and classified as fluvisols, a type of entisols developed by the fluvial deposits following annual flood events. In general, the sizes of soil particles affect the concentration of HM. The sizes of soil particles decrease with an increase in the concentration of HM in soil64. The surface soils depicted that 39.78–79.46% with a mean of 66.72% is sand, 6–40% with a mean of 15.48% is silt, and 13.38–22.56% with a mean of 17.94% is clay. For the sub-surface soils, 35.60–81.36% with a mean of 69.36%, 5–48% with a mean of 13.75% and 11.43–20.50% with a mean of 15.11% is sand, silt, and clay respectively (Table 2). Thus sub-surface soils are sandier than the surface. Furthermore, Shepard Triangle Diagram65 is used to classify the soils (Fig. 4a,b).
Classification of soil samples on Shepard triangle diagram. (a) surface soil, (b) sub-surface soil.
This diagram showed that 83.34, 13.33 and 3.33% of soils were sandy loam, sandy clay loam and loam of surface level. Besides, 90, 3.33 and 6.67% of soils were sandy loam, sandy clay loam and loam of sub-surface level respectively.
Soil pH and EC
The pH is an important chemical property of soil as it controls precipitation and adsorption which are the principal mechanisms of metal retention and affect metal dynamics in soil64. It plays a vital role in soil fertility as it controls the movement of HM and activates the macronutrients and trace elements in soil26,66. The present study found that surface soils were moderately acidic while the sub-surface was slightly acidic (surface pH = 5.49 and sub-surface pH = 6.11). The mean pH values of surface and sub-surface soils indicate that surface soil has nutrient deficiencies and toxicities (iron, manganese, and aluminum) and other elements (calcium, magnesium, nitrogen, phosphorous, and potassium) become less available for absorption by plants whereas sub-surface soil is an ideal soil condition for most plants. The pH of surface soils ranges from 4.48 to 6.43 while in the sub-surface soils, it ranges from 4.54 to 7.01 (Table 2). Based Ficklin–Caboi diagram, 76.67% of surface soil and 16.67% of sub-surface soils lie under acid high metal while 23.33% and 83.33% lie under near natural high metal respectively (Fig. 5a, b). Electrical conductivity (EC) is used to indicate the salinity of soil and water67,68. The present study found a low concentration of EC in the soils (0.05–0.69 dsm−1 with a mean of 0.20 dsm−1 for the surface soils and 0.03–0.37 dsm−1 with a mean value of 0.11 dsm−1 for the sub-surface soils (Table 2 and Fig. 6a,b). This result reveals that the surface soils are more saline than the sub-surface soils.
Classification of soil samples on Ficklin–Caboi diagram displaying HM load vs pH. (a) surface soils, (b) sub-surface soils.
Spatial distribution of pedo-chemical (PC) parameters of the studied soils. (a) surface soil condition, (b) sub-surface soil condition (Note: units of the PC parameters are to be referred to Table 2).
Organic carbon
Organic carbon (OC) is also a significant parameter of agricultural soil because it maintains the bio-availability of HM in soil29. A low concentration of OC in soil reduces the soil microbial diversity and biomass through decreasing mineralization. The concentration of OC (%) in the surface soils ranged from 0.15 to 0.53 while it varied from 0.19 to 0.66 for the sub-surface soils (Table 2 and Fig. 6a,b). These results indicate that the microbial diversity and biomass of sub-surface soils are lower than those of surface-level.
Soil moisture
Soil moisture also impacts the soil organisms and interacts with contaminants of soil. Hence, it affects the solubility of HM and the bioavailability of soil. It was found that the availability of soil moisture in surface soils ranged from 0.80 to 11.40% with a mean of 2.88% whereas it varied from 0.60 to 12% with a mean of 2.31% in the sub-surface soils (Table 2). Thus, the percentage of soil moisture concentration in the surface soils is more than the sub-surface level (Fig. 6a,b).
Cation exchange capacity
Soil CEC is a significant chemical property that reflects soil functions like structural stability, nutrient accessibility, pH, and reaction to fertilizers64. It is a measure of the soil’s capability to bind interchangeable cations64,66. Our study findings indicate that the CEC (meq/100gm) of surface soils was found to be 2–5.44 with a mean of 3.99 while it was found to be 2.52–11.48 with a mean of 6.77 in the sub-surface soils (Table 2 and Fig. 6a,b). These results also indicate that the CEC of sub-surface soils has more ability to bind or hold the exchangeable cations than surface soils.
Heavy metals (Cu, Zn, Fe, Mn, B)
The concentration of HM is in the order of Fe > Mn > Cu > Zn > B based on the mean value of HM in both levels of soils (Fig. 6a,b). The concentration of Cu, Zn, Fe, Mn, and B in surface soils ranged from 0.57 to 1.94 mg/kg, 0.30 to 1.25 mg/kg, 16.95 to 52.53 mg/kg, 10.64 to 26 mg/kg and 0.12 to 0.45 mg/kg respectively whereas they ranged from 0.69 to 1.82 mg/kg, 0.35 to 1.78 mg/kg, 13.85 to 34.69 mg/kg, 7.50 to 20.42 mg/kg and 0.15 to 0.41 mg/kg in the sub-surface soils (Table 2). According to the mean values of the studied HM, the concentration of Zn, Fe, and Mn in surface soils is higher than in the sub-surface level. However, the concentration of Cu is lower than the sub-surface and interestingly the mean value of B is the same in both levels of samples. This study also found that the concentration of HM in the soils lies below the permissible limits (Cu = 100 mg/kg, Zn = 300 mg/kg, Fe = 2000 mg/kg, Mn = 50,000 mg/kg, and B = 30 mg/kg) based on Salem et al.66.
Heavy metals contamination
Contamination factor
It was found that the CF of Cu, Zn, Fe, and Mn in surface soils ranged from 0.010 to 0.034, 0.014 to 0.057, 0.00053 to 0.00164 and 0.051 to 0.124 respectively while it ranged from 0.012 to 0.032, 0.016 to 0.081, 0.00043 to 0.00108 and 0.036 to 0.098 in the sub-surface soils. Thus, these results show that all CF values lie below 1 (< 1), indicating that soils have a low level of contamination. From the obtained mean CF values of HM, it is observed that the CF values of Zn, Fe, and Mn of surface soils are higher than the sub-surface soils, however, the CF value of Cu is lower than the sub-surface. According to the average CF values of HM, they were ranked in the order of Mn > Zn > Cu > Fe in both levels of soils (Fig. 7a,b).
Spatial distribution of indices values of studied soils. (a) surface soil condition, (b) sub-surface soil condition.
Contamination degree (Cd), and potential contamination index (Cp)
The spatial distribution of Cd of surface soils was found to be 0.095–0.188 with a mean value of 0.134 whereas it was found to be 0.081–0.174 with a mean value of 0.114 in the sub-surface soils (Fig. 7a,b). The Cd of the studied HM is very low in all the soils. The study exhibits that all the Cd values from both level soils lie below 8C degrees which indicates that the metals have a low degree of contamination. The Cd values of surface soils are higher than sub-surface soils as compared to the range and mean of Cd values. The Cp values ranged from 0.00053 to 0.00164 in the surface soils whereas, in the sub-surface soils, they ranged from 0.00046 to 0.092 (Fig. 8a,b). The Cp values of the sub-surface soils are higher than the surface. All the calculated Cp values from both levels of soils lie below 1 which indicates that all the soils have low potential contamination of studied chars. All the Cp values are calculated from Fe as the highest concentration metal in soils but one Cp value is calculated from Mn (location ID. 9 of sub-surface soil) as it is the highest metal concentration. The Cp value of Zn is the highest among the other parameters.
Spatial distribution of indices values of studied soils. (a) surface soil condition, (b) sub-surface soil condition.
Enrichment factor
The EF values of Cu, Zn, and Mn of surface soils were found to be 6.97–47.69, 8.48–67.09, and 41.39–142.48 respectively whereas they were found to be 11.89–51.03, 16.38–101.84 and 44.56–211.69 in the sub-surface soils (Fig. 7a,b). Based on the calculated EF mean values, it is observed that the EF values of sub-surface soils’ HM are higher than the surface soils. It is also observed that the EF value of Mn (in both levels) is high among the other investigated metals. According to their EF mean values, the investigated Cu, Zn, and Mn HM of surface soils are classified as severe, very severe, and extremely severe respectively. However, at the sub-surface level, they are classified as very severe, very severe, and extremely severe (Table 2). Moreover, they are ranked in the order of Mn > Zn > Cu for both levels of soil.
Geoaccumulation index (Igeo)
The study found that the Igeo value of surface soils Cu, Zn, Fe, and Mn was 0.0020–0.0069, 0.0027–0.00114, 0.00011–0.00033 and 0.010–0.025 respectively whereas they were 0.0025–0.0065, 0.0032–0.00162, 0.00009–0.00022 and 0.007–0.020 in the sub-surface soils in the study. Therefore, these results show that all the calculated Igeo values lie below 1 (< 1) and Class 1 which indicates that soils are uncontaminated to moderately contaminated. The calculated Igeo mean values of HM reveal that the Igeo values of Zn, Fe, and Mn of sub-surface soils are lower than the sub-surface level, although the Igeo value of Cu is higher than the surface. Moreover, based on the mean Igeo values of HM, they were ranked in the order of Mn > Zn > Cu > Fe in both levels of soils (Fig. 8a,b).
Pollution index and pollution load index
In this study, the calculated pollution index values of surface soils ranged from 0. 040 to 0.093 with a mean value of 0.062 while it ranged from 0.031 to 0.073 in the sub-surface soils (Table S2 and Fig. 8a,b). Thus, based on these results, the pollution index values of surface soils are higher than the sub-surface. It indicates that the pollution level is also high in surface soils. Moreover, according to the classification of pollution index values, all the soils’ pollution index indices lie below 0.70 indicating the unpolluted nature of all the samples (from both levels). The PLI was found to be 0.11–0.020 in surface soils while 0.009–0.17 in the sub-surface soils (Fig. 8a,b). Hence, these results indicate that the pollution load of HM in surface soils is more than in the sub-surface.
Ecological risk assessment
The ecological risk index (RI) shows that the RI values of surface soils ranged from 0.15 to 0.26 while it ranged from 0.14 to 0.25 in the sub-surface soils (Table S3). The RI values of surface soils are higher than the sub-surface. Hence, the ecological risk from the surface soil is higher than the sub-surface. Moreover, all the soils’ RI values lie under 150 indicating that all the samples from both levels have low ecological risk (Fig. 8a,b).
The Eir values of Cu, Zn, and Mn in the surface soils were 0.050–0.172, 0.014–0.057, and 0.051–0.124 respectively. However, they were found to be 0.061–0.161, 0.016–0.081 and 0.036–0.098 in the sub-surface soils (Table S3). The mean values of Eir show that the Eir values of Zn and Mn of surface soils are higher than the sub-surface but Cu is lower than the sub-surface soils. It is also found that Eir value of Cu (in both levels) is high among the other investigated metals. The mean Eir values of Cu, Zn, and Mn, Eir values of each HM in all the soils from both levels were less than 40 and classified as low potential ecological risk. Therefore, the studied HM in soils has a low potentiality for ecological risk. Moreover, they are ranked as Cu > Zn > Mn for both levels of soil (Fig. 9).
Box plot showing the variations in the heavy metal pollution indices.
Prioritization of surface and sub-surface soil using TOPSIS
Prioritization of surface and sub-surface soil is vital for the management and mitigation of soil pollution and its ecological risk using TOPSIS14. Pollution and ecological risk assessing indices such as Cd, Cp, EF, Igeo, MPI, PLI, Er, RI and MRI have been analyzed using the TOPSIS MCDM technique for evaluating the priority of surface and sub-surface soil pollution. The performance rank (Pi) portrays that a higher value of Pi indicates lower soil pollution and vice versa. Hence, rank 1 is the best location while rank 30 is the worst location in terms of soil pollution. The calculated Pi values of surface and sub-surface soils are mentioned in supplementary (Table S4). The final relative closeness score of the ideal solution was found to be 0.29–0.81 with a 0.65 mean value in the surface soils while it was found to be 0.04–0.99 with a 0.94 mean value in the sub-surface soils. These results indicate that the sub-surface soils have lower soil pollution than the surface soils. Furthermore, the final score or Pi values are classified into five groups such as very low (< 0.4), low (0.4–0.5), medium (0.5–0.6), high (0.6–0.7), and very high (> 0.7) (Fig. 10a,b).
(Source: prepared by the authors using ArcGIS software-version 10.2).
Prioritization of char soils based on the concentration of HM (Pi). (a) surface soils, (b) sub-surface soils
Geostatistical modeling
This study found that all the semi-variogram models (spherical, exponential, Gaussian, and circular) have arisen as best fit models for OK and SK interpolation methods for some elements as there was no difference in the ME, MSE, RMSE, ASR, and RMSSE values (Table S5). It was observed that the circular semi-variogram model of the OK technique is the best-fit model compared to the other two models (SK and IDW) for pH of surface soils while all the studied semi-variogram model of the SK technique is the best-fit model compared to other two models (OK and IDW) for pH of sub-surface soils (Figs. S1 and S2). Similarly, for Cu, the Gaussian semi-variogram model of the OK technique is the best-fit model for surface soils while all the studied semi-variogram models of the SK technique are the best-fit model for sub-surface soils. Inversely, in case of OC, CEC, and Zn, all studied the semi-variogram models of SK, SK and OK interpolation techniques appeared the best-fit model for surface soils while Gaussian, exponential and circular semi-variogram models of SK interpolation technique are the best-fit models for sub-surface soils. Regarding Mn and B, all the studied semi-variogram models of the SK interpolation technique are the best fit for both levels of soils. Moreover, in case of sand, clay, and Fe, the Gaussian semi-variogram model of SK and OK interpolation technique was observed as the best-fit model for both levels of soils but for sand and clay, SK was observed for surface soils. And OK for sub-surface soils. However, in case of Fe, inverse results were found. For moisture, the spherical semi-variogram model of the SK interpolation technique is the best fit for both levels of soil. Further, for EC and Silt, the circular and exponential/Gaussian semi-variogram models of the OK interpolation technique are the best-fit model for surface soils while Gaussian of SK and OK is the best-fit model for surface soils respectively. Hence, based on these results, the kriging interpolation technique (OK and SK) with the studied semi-variogram model provides better performance for each variable (Table 3). Furthermore, 61.54% of surface soil sample variables and 76.92% of sub-surface soil sample variables fit with the SK interpolation technique and the rest with the OK interpolation technique. So, the SK technique is expected as the most accurate interpolation model for OC, CEC, sand, clay, moisture, Cu, Mn, and B in the surface soils while it is for pH, EC OC, CEC, moisture, Cu, Zn, Fe, Mn and B for sub-surface soils as compared to the other studied techniques (OK and IDW). The elements like a nugget, range, partial sill, and lag size value of best-fit semivariogram models are extracted by using ArcGIS (version 10.2). Later, sill, nugget/sill, and the effect of nugget/sill are also calculated from obtained values for this study (Table 3).
