Physico-chemical properties of soil
Within sampling sites, the arithmetic means for a few physicochemical parameters (pH, electrical conductivity (EC), and soil organic carbon (SOC) as well as the available nutrients (nitrogen (N), phosphorus (P), and potassium (K) differed significantly (F (10, 610) = 1039.82, p < 0.000). Table 1 shows the descriptive statistics of the selected soil physicochemical parameters (minimum, maximum, arithmetic mean, and standard deviation (Mean ± SD)). The mean ± SD values from both sampling sites (control and along roadsides) in Bhomoraguri RF were: 6.140.16 and 7.310.14, 0.260.11 and 0.520.21, 0.670.35 and 1.300.27 for SOC (%), and 0.260.11 and 0.520.21 for EC (μS/cm). Similarly, the available soil nutrients (mg kg-1) N, P, and K reported from the two samples had mean ± SDs of 32.642.15 and 19.491.28, 2.940.98 3.651.22, and 27.4419.08 and 12.908.97 for N, P, and K, respectively. Balipara RF, on the other hand, observed a similar tendency of fluctuation in values between the control site and the roadside.
For Bhomoraguri and Balipara RF, the independent t-test revealed that the two sampling sites from each forest were statistically significant at t = -0.57, df = 85.29, p < 0.000; and t = 40.39, df = 94.68, p < 0.000, respectively. In samples taken from control sites, the pH of the soil was determined to be strong to moderately acidic (5.27 and 6.14), while it was neutral to basic (6.27 and 7.31) in samples collected along the roadsides. The substantial leaching of exchangeable bases in natural forests due to heavy rainfall conditions may have influenced the acidic character of soil examined in the control area. According to Dutta et al. (2021), high leaching and rainy circumstances improve the exchange of bases and may promote the release of protons due to biochemical weathering. The relatively high pH (6.27 and 7.31) in the samples collected along roads, on the other hand, could be due to the fact that discharged metals from automobile lubricants, vehicular emissions, frequent road repair, and road constructions in highly dense and fast-moving trafficked roads, traversing through these RFs, can modify the physicochemical properties of surface soils by de-acidifying and oxidizing them. Acidic soils enhance heavy metal mobility and adsorption in soil particles, while alkaline soils reduce mobility throughout the soil matrix, according to Adhikari & Bhattacharyya (2015).
Soil organic carbon (SOC) in soil samples collected from the control site ranged from 1.3 to 3.85 %, indicating a high amount of organic carbon in soils, whereas those collected along roadsides ranged from 1.01 to 3.01 %, indicating a slight decrease in organic carbon in soil samples compared to those collected from the control site. Although the amount of SOC depends on a variety of parameters (texture, climate, vegetation, and land use pattern), metal contaminants discharged along roadsides may have influenced the minor variance seen in this study. These metals may have an impact on litter decomposition, nitrogen exchange, and trace metal concentrations, as well as general soil health. The presence of stable litterfall and decomposition, which were then released from the vegetation in the forest ecosystem, could explain the relatively high level of SOC reported in the control sites. As a result, it plays an important role in improving soil fertility and plant productivity.
Soil electrical conductivity (EC) measured along the roadside was 0.52 and 0.13 μS/cm, respectively, compared to 0.26 and 0.07 μS/cm at the control site for Bhomoraguri and Balipara RF. The observed differences between the two sampling sites in the investigated forests could be owing to the discharge and leaching of several lime-related metals containing a significant amount of soluble salts. The salt-related slugs, which result from the chemical used in the shattered stone, blended tar to produce tarmac, play a big role when it comes to road repairs and construction. The available N, P, and K concentrations vary significantly between the two sampling sites. The levels of N and K were found to be higher in samples collected from the control (32.64 and 45.11 mg kg-1 for N; 27.44 and 14.07 mg kg-1 for K, respectively) than in samples collected from roadsides (19.49 and 26.93 mg kg-1 for N; 12.90 and 6.61 mg kg-1 for K, respectively) in Bhomoraguri and Balipara RF.
Contrary to popular belief, available P levels were higher in soil samples taken from roadsides (12.90 and 18.19 mg kg-1, respectively) than in the control sites (2.94 and 14.68 mg kg-1). The main source of worry about the decreased available P in the control site might be attributed to the high uptake and fixation of P by the dominant forest tree species (Tectona grandis), which used and stored a large amount of P. Not only that, but the quick utilisation of iron and aluminium by huge trees and regenerating plants, as documented by (Shrestha and Kafle 2020), could have had a part in the reduction of available P in the samples from the control sites. Furthermore, high P availability in soil samples collected near roadsides could be attributed to vehicular discharge of P-related compounds.
The concentration of metal pollutants in surface soils
The present investigation found a significant difference in metal pollutant concentrations between the two sampling sites (control and along roadsides) at the 95 % level (t = -33.65, df = 58.37, p < 0.000). The metal pollutant evaluated in soil samples collected from two places within the study forests was positive (Table 2). In Bhomoragiri RF, metal concentrations varied from 0.13 to 0.46 and 1.91 to 6.90 mg kg-1 for Cd; 0.29 to 0.59 and 4.87 to 9.53 mg kg-1 for Cr; 2.83 to 5.67 and 50.39 to 100.78 mg kg-1 for Ni; and 8.43 to 15.08 and 113.05 to 202.32 mg kg-1 for Pb in the control site and along roadsides, respectively. In Balipara RF, metal concentrations varied from 0.004 to 0.14 and 0.06 to 2.19 mg kg-1 for Cd; 0.24 to 7.41 and 3.98 to 122.69 mg kg-1 for Cr; 0.36 to 2.95 and 5.66 to 46.36 mg kg-1 for Ni; and 3.17 to 13.56 and 31.85 to 136.12 mg kg-1 for Pb. Metal concentrations were greater in soil samples taken along roadsides (for example, Ni had a mean of 4.37 mg kg-1) than in control locations (Ni = 78.01 mg kg-1). Pb had the highest content, with 137.83 and 60.67 mg kg-1 for Bhomoraguri and Balipara RF, respectively, followed by Ni (78.01 and 18.86 mg kg-1), and Cd had the lowest concentration (2.97 and 0.53 mg kg-1).
The mean concentrations of metal contaminants in surface soil were in the sequence Pb>Ni>Cr>Cd in all of the forests tested. However, for all metals studied, the mean concentration was higher in Bhomoraguri RF than in Balipara RF. This could be owing to the ongoing large-scale bridge construction project along the Brahmaputra River, which runs through the forests. As a result, there are a variety of road diversions in the area, all of which have a serious concern for tree removal to maximize the number of vehicles passing the RF. The creation of these temporary road diversions results in a significant shift in road geometry and vehicle speed. Devi et al. (2019) said that road geometry has a direct impact on vehicle deceleration and acceleration, hence controlling metal pollutant load discharge and emissions from vehicular engines, as well as wear and tear. Despite considerable differences in concentration levels among the examined metal pollutants (Cd, Cr, Ni, and Pb), the mean concentrations were determined to be below-allowed limits when compared to the Indian standards guideline for natural and roadside soils (Bhatia et al. 2015; Kaur et al. 2020). According to Adimalla (2020) and Kaur et al. (2020), the majority of the examined metals were even lower than the safe level of other international guidelines. For example, the mean Cd concentration along the roadside in Bhomoraguri and Balipara RF was 2.97 and 0.53 mg kg-1, respectively, although the permitted Cd limits in soil for natural and roadside soils vary from 3 to 6 mg kg-1 (Bhatia et al. 2015; Kaur et al. 2020). Pb had the widest concentration range (113.05-202.32 mg kg-1); however, the mean values were lower than the Indian soils' value limitations. The higher concentrations of metal pollutants in soil samples collected along roadsides compared to control sites suggest that the source of these metals could be automobile lubricants discharged due to road repair and construction, break tears, and vehicular emissions caused by road repair and construction fast-moving traffic. Metal contaminants and their compounds could have been released and deposited faster due to these anthropogenic activities. The adsorption and mobility of these meta pollutants in the soil may increase the toxicity of biological systems, lowering plant species production. Mobility of metals such as Cr is a function of soil pH, oxidizing, and reducing conditions, according to Gowd et al. (2010). Still, Pb varies significantly with soil type and may be released by motor vehicle exhaust fumes. Although the concentration of Cd was lower than that of other metal pollutants, it has a high toxicity profile (Shi et al. 2019); therefore, it should not be overlooked.
The concentrations of metal contaminants found in this study agree with roadside soil and road dust urban parks in Delhi, India, by Siddiqui et al. (2020). Similarly, except for Pb, the concentrations of Cd and Cr in the current investigation followed the same trend as the findings reported by Singh et al. (2015) in roadside soils of Varanasi, India. The concentrations of metal contaminants in the present study, on the other hand, were significantly lower than those reported by Devi et al. (2019) in highway road dust traversing through Assam's Kaziranga National Park. Furthermore, it was difficult to establish accurate comparisons from the present study's findings due to a lack of similar studies from the region. However, some metal pollution concentration studies from various land uses in the region were compared for justification (Table 3).
Furthermore, the results of this study were compared to published metal pollution concentrations in other Indian states. The results of this investigation matched those published by Adhikari and Bhattacharyya (2015), Bhatia et al. (2015), Kumar et al. (2018), and Kaushik et al. (2021). However, it was discovered in contrast to the findings of Kaur et al. (2020) and Sharma et al. (2018), whose results had a lower concentration than the findings of the present study. The amounts of metal contaminants in the current study, on the other hand, were significantly lower than those reported by Adimalla (2020) in the urban soils of Indian cities. In addition, the results of this study were compared to those of other road-related studies conducted in China, Japan, Greece, and Spain (cited in Devi et al. 2019, p. 1394). There were considerable differences in the concentration of metal contaminants discovered.
The disparities in metal pollutant concentrations observed across all comparisons could be attributable to the different sources of metal pollutants. Some of these investigations were conducted in places that were impacted by industrial pollution, urbanization, or substantial agriculture. Thus, the elevated concentration could be attributable to the outflow of industrial wastewater, home sewage, and fertilizer applications. However, as previously stated, the empirical reason for substantial augmentation from the present investigation is difficult to identify because the current study dealt with soil samples polluted mainly by a single source of anthropogenic activity (i.e., vehicular emissions and automobile discharge). According to Shi et al. (2019), the concentration of metal pollutants is influenced by the dominant anthropogenic activity in a given location. For example, in agricultural areas, the use of Cd-containing phosphate fertilizer could result in high levels of Cd in the soil. The high concentration of Pb-containing chemicals in a specific ecosystem results from industrialization and motor vehicle emissions into the atmosphere. In majority of the literatures, we looked at, we found considerable concentrations of metals along the roadside. For example, Pb concentrations in China ranged from 77.3 to 408 mg/kg; Greece had a mean of 301 mg/kg; and Spain had a mean of 514 mg kg-1 (Table 3). As a result, the greater concentrations of metals like Pb (ranging from 31.85 to 202.32 mg kg-1) and Ni (5.66 to 100.78 mg kg-1) in our study, particularly on roadways, support the hypothesis that they are the result of automotive discharge and motor vehicle emissions. This concentration could reach uncontrollable levels, causing environmental damage to nearby species. Excess Nickel (Ni) in soils, for example, causes a variety of physiological and macroscopic responses, including chlorosis and necrosis, which impede plant development and root growth and inhibit the growth of primary roots (Adimalla 2020). Furthermore, according to the US Department of Health and Human Services' Priority List of Hazardous Substances, metals such as lead (Pb) rank second, cadmium (Cd) rank seventh, and hexavalent chromium (Cr (vi)) rank seventeen based on prevalence, toxicity, and possible harm (Jha et al. 2016). These metals are among the top twenty on the list, and their persistence in the environment makes them potentially dangerous. As a result, their accumulation in surface soils and subsequent transmission from soil to plants is a major worry (Malik et al. 2010).
Evaluation of metals pollution and Ecological risk assessments
Table 4 shows the results of the geo-accumulation Index (Igeo) and pollution index (PI) values for the metal contaminants studied. In Bhomoraguri RF, the Igeo values for Cd, Cr, Ni, and Pb, Ni were 0.86, -4.44, 0.87, and 2.79, respectively. While at Balipara RF, Cd, Cr, Ni, and Pb values were -2.09, -3.82, -1.40, and 1.46, respectively. In all of the forests tested, the Igeo values of the assessed metal pollutant along highways varied significantly, ranging from uncontaminated to moderately contaminated, and moderately contaminated to heavily contaminated levels. However, all soil samples collected from the control site (away from roads) exhibited uncontaminated levels (i.e., Igeo ≤ 0). Pb (2 < Igeo < 3) was found to have the highest environmental contamination in the surface soils by metal contaminants within sites, whereas Cr and Cd had the lowest levels. The mean values for Cd, Cr, Ni, and Pb along roadsides were 2.97, 0.07, 2.82, and 10.52 in Bhomoraguri RF, and 0.53, 0.18, 0.68, and 4.63 in Balipara RF, respectively, based on an overall assessment of soil samples with respect to the pollution Index (PI). The studied soils revealed three graded levels of pollution according to the PI criteria (i.e, low, medium, and high levels). Pb (10.52±2.07) had the highest amount of pollution, followed by Cd and Ni, and the lowest level was observed by Cr (0.07±0.02) in Bhomoraguri RF, according to the findings. All metals (Cd, Cr, and Ni) were classed as low levels of pollution in Balipara RF, with mean values of 0.53, 0.18, and 0.68, respectively, with the exception of Pb, which was categorized as a high level of pollution with a mean value of 4.63. Overall contamination was in the following order: Pb> Ni> Cd> Cr. This indicated that the surface soils along roads traveling through these Reserved Forests were moderately to heavily contaminated, with Pb and Ni being the primary contaminants. The high concentrations and abundance of Pb and Ni in surface soils around roads could be accredited to automotive emissions and automobile discharges due to periodic road repairs and construction.
Table 5 summarizes the nemerov index (PIN) results and the ecological risk index (Ei). The analyzed metal pollutants in the surface soils, according to the categories defined by Hu et al. (2013) for the Nemerov Index (PIN), showed that Cd, Ni, and Pb were seriously polluting, while Cr was in the safe category in Bhomoraguri RF, with mean values of 5.31, 3.25, 13.21, and 0.09, respectively. In Balipara RF, however, there was a slight pollution for Cd (1.59) and Ni (1.28) as well as a substantial pollution for Pb (8.04). These findings imply that Pb, Cd, and Ni contribute significantly to environmental pollution. In Bhomoraguri RF, the results of the ecological risk index revealed that Cd was the most significant contributor to ecological risk, with a mean concentration of 88.97, followed by Pb (Ei = 52.61), Ni (Ei = 14.08), and finally Cr (Ei = 0.14). Meanwhile, for Cd, Cr, Ni, and Pb, the ecological risk mean values in Balipara RF were 1.02, 0.02, 0.22, and 2.31, respectively. According to Hakanson's criteria of 1980 for ecological risk assessment (if 80 ≤ Ei < 160) and if 40 < Ei ≤ 80), Cd (88.97) and Pb (52.61) were found in the categories of considerable potential ecological risk and moderately potential ecological risk, respectively, in the analyzed soil samples in Bhomoraguri RF. Cr and Ni, on the other hand, were classified as having a low ecological risk. The aggregate mean value of the examined soils in Balipara RF, on the other hand, falls within the category of low ecological risk. Although the overall amounts of metals in Balipara RF were classified as low ecological risk, this result shows that Cd and Pb were possible polluting and ecological risk factors in the examined forests. The independent t-test found a statistically significant difference between the two forests analyzed. Apart from road-related discharge and emission, the large variations in potential ecological risks reported in Bhomoraguri RF versus Balipara RF could be attributable to the ongoing bridge construction project and the regular outflow of the Brahmaputra around the Forest Reserve.
According to Tomlinson et al. (1980), the overall site characterization using the pollution load index (PLI) and the integrated pollution index (IPI) revealed that the examined sites (roadsides) in Bhomoraguri and Balipara RF were severely contaminated and highly polluted, respectively. With mean values of 1.43 and 0.69 for Bhomoraguri and Balipara RF, respectively (Table 6). The results on PLI were classed as severe contaminated (if 0.5 ≤ PLI < 1), while the IPI criteria, classified as high polluted (if IPI > 2.0), (Singh et al. 2015), with mean values of 16.37 and 6.02 for Bhomoraguri and Balipara RF, respectively. The findings show that national highways passing through RF provide a possible ecological risk to forest surface soils. This suggests that huge amounts of metal contaminants are released into forest soils due to traffic emissions, which could have a considerable influence on species' lifespan. Furthermore, because of their long-term buildup, high concentrations might cause pollution. The end outcome of all of this could pose a major environmental threat. As a result, as Dutta et al. (2021) indicated, monitoring metal concentrations in soils is critical for preventing any anomalous buildup.
The current study's findings are consistent with prior research (Kumar et al. 2018; Devi et al. 2019) that found trace metal accumulation along India's roadsides, as well as pollution and ecological hazards. For example, according to Sarma et al. (2017), roadsides are regularly exposed to metals such as Pb, Cd, and Ni due to vehicle emissions on the National Highway. Similarly, Rai et al. (2014) pointed out that vehicle traffic is one of the most significant sources of pollution in the environment. Gowd et al. (2010), metal pollutants such as Cd, Ni, and Pb are mostly discharged into the environment through motor vehicle exhaust fumes, automotive discharge, and lead pipe corrosion. Furthermore, Alloway (2012) found that cities provide the greatest levels of Pb metals in urban soils in the United States. As a result, the deposition of heavy metals along road surface soils may spread to other parts of the environment, with a variety of repercussions, including reduced plant productivity. This is because the uptake of these metals by plants can produce metabolic stress by preventing photosynthesis, respiration, and transpiration (Trombulak and Frissell 2000; Adhikari and Bhattacharyya 2015).
Tree biomass stocking potential
A total of 24 dominant tree species were quantified for biomass (i.e., 12 trees from each forest) to assess the impacts of the examined metal pollutants (Cd, Cr, Ni, and Pb) on tree biomass stocking and production potential in the forests. From the control site and along roadsides of Bhomoraguri RF, totals of 111.05 Mg ha-1 with an average of 9.25 ± 1.09 stocking per tree (Mg tree-1) and 97.41 Mg ha-1 with an average of 8.12± 1.12 Mg tree-1 were reported, respectively (Table 7). These results equated to 203.77 Mg ha-1 and 168.02 Mg ha-1 CO2 sequestration by tree species. In Balipara RF, 98.76 Mg ha-1 was recorded, with an average of 8.23 ± 1.90 stockings per tree (Mg tree-1), and 89.82 Mg ha-1 with an average of 7.49± 1.85 Mg tree-1. The CO2 equivalent value was estimated to be 181.23 and 154.93 Mg ha-1. Tectona grandis L.f. (19.18 Mg ha-1), Artocarpus integer (Thunb.) Merr (12.18 Mg ha-1), Dalbergia sissoo Roxb (11.87 Mg ha-1), and Ficus carica L (9.40 Mg ha-1) were the tree species that produced the most biomass and carbon stocks in Bhomoraguri RF. The dominant biomass stocks in Balipara RF, on the other hand, were Ficus carica L (25.20 Mg ha-1), Mimusops elengi L (17.09 Mg ha-1), and Stereospermum chelonoides DC (9.99 Mg ha-1). For Bhomoraguri and Balipara RF, the topmost biomass and carbon accumulator were found in Tectona grandis L.f. and Ficus carica L., respectively. Despite the slight difference in biomass stocking between the control site and the site along roads, an independent sample t-test revealed that the difference was not statistically significant; t = 0.476, df = 60, p = 0.636; and t = 0.298, df = 82, p = 0.766, for Bhomoraguri and Balipara RF, respectively. According to the study, metal pollutants may negatively affect tree biomass stocks and productive potential.
Correlation between physicochemical properties, metal pollutants, and tree biomass stocks
A Pearson's correlation coefficient (r) and hierarchical cluster analysis (CA) were used to determine the degree of a relationship and similarity between metal pollutants, soil physicochemical parameters, and tree biomass stocks. Table 8 and Fig. 2 illustrate the results of the matrix for these variables. The soil metals were shown to be correlated with physicochemical parameters (pH, EC, and SOC), as well as available soil nutrients (N, P, and K) at p < 0.000. A strong positive association was discovered between pH and metals (Cd (95%), Cr (93%), Ni (80%), and Pb (98%)). In contrast, a weak positive correlation was observed between EC and metals (Cd (18%), Cr (12%), Ni (5%), and Pb (20%)). According to the findings, soil pH has a significant impact on metal pollutant concentrations. On the other hand, SOC had a negative relationship with metal pollutants, with -63 %, -81 %, -86 %, and -67 %, respectively, for Cd, Cr, Ni, and Pb. Similar to available soil nutrients, metal pollutants were strongly negatively correlated, with coefficient (r) values of -73 %, -83 %, -84 %, and -74 % for N; -98 %, -85 %, -97 % and -81 % for P; and -73 %, -91 %, -75 %, and -79 % for K. SOC and soil nutrients such as N, P, and K concentrations were shown to be adversely linked with soil metal pollutant concentrations in the present study. This shows that SOC may have a significant impact on the distribution patterns of their enrichments. According to Shi et al. (2019), soil organic matter can operate as a carrier and binding agent for heavy metals or play an important role in their distribution patterns.
The four metals studied (Cd, Cr, Ni, and Pb) were significantly associated when correlation analysis was performed. Cd and Cr (94%), Cd and Ni (74%), Cd and Pb (97%), Cr and Ni (88%), Cr and Pb (90%), and Ni and Pb (76%) were among the metal pollutants that exhibited a very high positive connection in the correlation matrix. The significant positive association found among the metals in this study suggests that these metals have a similar origin. As stated by Siddiqui et al. (2020), this common source could be linked to vehicular emissions, as well as wear and strain on automotive parts. The four metal contaminants shown to be strongly negatively correlated with tree biomass were found to be. TB-Pb (-80%), TB-Ni (-79%), TB-Cr (-76%), and TB-Cd (-71%) were the correlation coefficient matrix in order. Metal pollutants are the most important elements impacting plant physiological activity and productivity, which may have an influence on tree biomass stocks, according to a significant negative association discovered between tree biomass stock and metal pollutants. This is backed up by the findings of (Gonçalves et al. 2020), who discovered the impacts of Cd and Pb accumulation in various plant tissues, as well as the resulting restrictions on material translocation.
Cluster analysis (CA) was used to find interrelationships and distinguish between clusters and sub-clusters based on their commonalities. As a measure of similarity, the Ward's technique and the Euclidean distance were utilized (Kumar et al. 2018; Shi et al. 2019). It was also utilized to back up the results of the correlations observation using the Pearson correlation coefficient (r). Based on Fig. 2, three main clusters (groups) were identified, each with its own set of features. Cr, Pb, Ni, Cd, and EC belong to the first cluster. The pH, available N, SOC, and available P are found in the second cluster, while available K and tree biomass stock values are found in the third. Cluster one was divided into two sub-groups: Cr, Pb, and Ni in one, and EC and Cd in the other. Cluster two was likewise broken into two sub-groups: pH and available N in one, and SOC and available P in the other. The correlation coefficient was found to correspond with the groupings of soil parameters, metal pollutants, and values for tree biomass stocks. This connection grouping supports the idea that the metal contaminants under investigation have common sources of enrichment in surface soils, which could be substantially linked to automotive emissions in this situation.
Predicting the effects of metal pollutant concentrations on tree biomass stocking potential
A stepwise multiple regression analysis was done to estimate the impacts of metal pollutants (Cd, Cr, Ni, and Pb) on tree stocking potential (Table 9). The findings revealed that these metals accounted for 63.4 % of the variations in tree biomass (F (2, 28) = 27.49, P < 0.000). Metal concentrations in surface soils have a variable percentage of effects on tree biomass stocks. The uniqueness contributions of the metals (predictors) were significant for Cd (β = 1.94, t = 2.56, P = 0.015), and Cr (β = 8.23, t = -2.69, P = 0.001), but not for Ni (β = -0.34, t = -1.32, P = 0.198, and Pb (β = -0.49, t = -1.88, P = 0.07). The overall regression models revealed a strong negative relationship between metals (Cd, Cr, Ni, and Pb) and tree biomass stocks, with linear models Ŷ = 36. 478 + 61.488Cd, R2= 0.62, p < 0.001; Ŷ = 34.488-29.413Cr, R2= 0.64, p < 0.001; Ŷ = 43.690-0.112Ni, R2 = 0.63, p < 0.001; Ŷ = 42.590-0.249Pb, R2 = 0.66, p < 0.001; Ŷ = 36.478+61.488 Cd + (-29.413 Cr) + (Cd*Cr), R2 = 0.63, p < 0.001; and Ŷ = 43.690+ (-0.112Ni) + (-0.249Pb) + (Ni*Pb), R2 = 0.66, p < 0.00, for Cd, Cr, Ni, Pb, (Cd*Cr) and (Ni*Pb), respectively.