Transfer of heavy metals in topsoils, based on sequential extraction and statistical tools in Northeast Brazil

doi:10.21203/rs.3.rs-3328679/v1

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Transfer of heavy metals in topsoils, based on sequential extraction and statistical tools in Northeast Brazil

https://doi.org/10.21203/rs.3.rs-3328679/v1

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Trace metals are associated with various organic and inorganic components, appearing in different chemical forms, which regulate their solubility, mobility, and availability to biological systems. Given this, it is necessary to know the chemical form in which these elements are found since this defines their potential for remobilization. Thus, this work aimed to evaluate the distribution and mobility of Pb, Cu, Cd, Cr, Ni, and Zn in eight types of soils from Northeast Brazil. The concentration in the fractions was determined through sequential extraction using the procedure outlined by the Community Bureau of Reference (BCR). Cu was extracted using oxidizing conditions and in the residual fraction. The Principal Component Analysis (PCA) and Pearson correlation matrix were used in fifty topsoil samples to investigate the relationship between metals and physical-chemical parameters. The results revealed that metals were prevalent in the residual fraction. Cd, Ni, and Zn were primarily detected in the mobile fraction. Cd and Pb were associated with Fe and Mn oxides, suggesting the mobility of these metals within the environment. The organic matter and texture influenced the mobility of metals in the soil. The PCA analysis shows that the pH correlates positively with Ni, Cu, and Cr and negatively with Zn, Cd, and Pb. The parameters analyzed showed significant variations across different soil classes, highlighting their diverse nature. The findings highlighted the significance of sequential metal extraction in soil and the utilization of PCA to predict changes in solubility and determine its availability to plants.

Trace elements

soil

environmental geology

geologic maps

chemical analysis

availability

Soil is a receiving medium for many potentially polluting and toxic substances. Its condition as an interface between the biosphere (land biomass, marine biomass, and man), the lithosphere (crust, soils, and sediments), the hydrosphere (freshwater and seawater), and the atmosphere makes it a transit station for contaminants (Coringa et al., 2016).

Evaluating and characterizing soils, especially those influenced by contaminants, is one of the most significant environmental challenges. Considering this, several types of research have been devised to know the dynamics of pollutants within the soil system. Among these, heavy metals occupy a prominent place and are being intensively studied (Alovisi et al., 2020; Fernández et al., 2022; Khadhar et al., 2020).

The potential danger of contaminants in soils arises not just from their overall concentration but primarily from their bioavailability. Heavy metals can exhibit toxicity to organisms and must be chemically accessible. Consequently, more than relying on the total content alone is required to gauge the risk involved accurately (Siahcheshm et al., 2022). Metals bound loosely to chemical species exhibit high availability, as they can easily dissolve with a slight change in soil conditions like pH or redox potential. In contrast, metals tightly bound to chemical species demonstrate strong stability.

Significant changes in environmental conditions, which generally do not occur in nature without human influence, are required to become soluble. If the higher concentrations of metals are in this condition, their bioavailability is low, and consequently, the risk of toxicity is negligible. Thus, chemical speciation studies allow for obtaining bioavailability and risks associated with trace metals (Hernandez and Jimenez, 2012; Kennou et al., 2015).

Operational speciation involves identifying and quantifying different chemical forms of an element within a sample. This is typically done using a simple or sequential extraction process, where various chemical extractors are applied in succession to the same model. Each extraction phase has distinct conditions compared to the previous one. This method is commonly employed in soil analysis due to its analytical simplicity, utilizing instrumental procedures accessible in most laboratories (Emurotu, 2021; Siahcheshm et al., 2022).

Sequential extractions do not lead to complete chemical speciation but may have a realistic character of great interest in terms of the behavior of an element as a contaminant. The metal contents (which may not always represent the total) can be associated with their mobility and ability to be transferred to living organisms. In summary, a sequential extraction can measure approximately the distribution of trace elements in the various stages of soil and their relative mobility (Coringa et al., 2016).

There are different sequential extraction schemes, all practices with the same procedure. To harmonize the various techniques used, the European Community BCR 1992 conducted a study involving multiple extractors and their application (Fernández et al., 2004; Ure et al., 1993). The BCR method established three steps for extracting metals associated with acid-soluble, reducible, and oxidizable phases. However, due to specific challenges encountered with the BCR scheme, the original protocol was reviewed and optimized, resulting in a revised three-step procedure.

This work employed a method encompassing four extraction fractions: metal in exchangeable ions and carbonates, a metal associated with Fe and Mn oxides, metal bound to organic matter, and residual or lithogenic phase. It is essential to employ excellent speciation methods to determine the total concentration and different chemical species accurately.

The primary objective of this research is to study the availability of metals to plants or humans by analyzing the presence of Cd, Cr, Cu, Ni, Pb, and Zn extracted using the BCR sequential extraction method. The methodology was verified using the Certified Reference Material (CRM) BCR-701. Furthermore, statistical analysis was used to identify the relationship between soil physicochemical characteristics and fractions of heavy metals.

2.1 Study Area

The study area was the State of Pernambuco, Northeast region of Brazil. It occupies a territorial area of 98,311.6 km² and has a population of 9,674,73 (IBGE, 2021). The samples were selected in eight different soil types, which were distributed as follows: alfisols (18%), aridisols (8%), entisols (Lithic Orthents, Psamments, and Quartzipsamments) (30%), gelisols (12%), oxisols (10%) and ultisols (20%) (USDA, 2014). The collected soil for this study had a caatinga cover, a unique Brazilian biome characterized by drought-resistant and withered scrub trees throughout most of the year (Fernández et al., 2022; MMA, 2018). Figure 1 details the region's soil type in the 50 topsoil samples collected.

Pernambuco experiences a tropical climate characterized by distinct local variations influenced by its geographical location, limited rainfall, and the interplay of meteorological systems in the area. Pernambuco has two seasons: a rainy season spanning from March to June and a dry season covering the remaining months.

The climate is milder in the state's countryside because of the Borborema Plateau. During winter, temperatures in some cities can drop to 10°C or even 5°C, while on the Pernambuco coast, the average temperature ranges from 25°C to 30°C.

2.2 Sample Collection and Preparation

Trenches of 0.7 x 0.7 m were opened according to the procedures established by EMBRAPA (EMBRAPA, 2009). The collected samples were conditioned in plastic bags and labeled. They were air-dried and then sifted through a 2 mm sieve, disregarding stones or other debris. Afterward, they were macerated in agate mortar with a reduction of granulometry to pass through a sieve of 75 µm (ABNT/ASTM 200). The humidity was determined by weighing 0.1 g of each sample, using an oven at 105°C until constant weight.

The mobility of heavy metals is limited, resulting in their accumulation within the uppermost layers of soil and only leaching in minimal quantities to lower horizons (Zewge et al., 2022). Consequently, when these metals are introduced onto soil surfaces due to human activities, their concentrations diminish with increasing depth, and their availability depends on each soil's specific characteristics. Considering this characteristic, soil samples were collected at a maximum depth of 10 cm to 20 cm.

Soil pH was measured with a combined electrode submerged in a soil water suspension in a 1:2.5 ratio. Determining soil density involves obtaining the sample mass by weighing it and determining its volume. The volume was obtained by collecting a soil sample with an undisturbed structure through a cylinder of known internal volume.

A dry combustion method with CHN elemental analyzer (TruSpec CHN LECO® 2006, St. Joseph, USA) was used to determine the carbon concentration. The soil's organic matter was estimated from its organic carbon content, considering a carbon content representing 58% of soil organic matter. Granulometric analysis (sand, clay, and silt) was conducted using the pipette method, following the EMBRAPA (2017) guidelines.

The sequential extraction method was used to determine the concentrations of Cd, Cr, Cu, Ni, Pb, and Zn in the available fractions. This method was based on the protocol outlined in the BCR Program, which involved a series of steps designed to isolate and quantify these metals in various forms (Ure et al., 1993). The methods used for processing the samples were divided into four stages.

In the initial (first) stage, was extracted the exchangeable ions and those bound to carbonates. It was combined 1.0 g of each soil sample with 40 mL of a 0.11 mol.L^-1 acetic acid solution (HOAc) within polyethylene containers to achieve this. The mixture was agitated for 16 hours at ambient temperature. Subsequently, the contents of the polyethylene containers were moved into tubes and subjected to centrifugation. The resulting supernatant was collected and preserved in the refrigerator at 4°C until analysis. The remaining residues were rinsed with 20 mL of ultrapure water from a Milli-Q system (with a resistivity of 18.0 µS.cm^-1) and then centrifuged for 10 minutes. The washing solution was discarded, ensuring no loss of solid residues from the containers.

In the second step, elements bound to Fe-Mn oxides were extracted. After the first step, each sample's residue was mixed with a 40 mL hydroxylamine hydrochloride solution with a 0.5 mol.L^-1 concentration. The solution was acidified with HNO₃ to pH 1.5 before being added to the residue. The mixture was agitated for 16 hours at room temperature, and the resulting extract was separated from the residue through centrifugation. The supernatant was preserved at 4°C for later analysis, while the residue underwent the washing procedure outlined in the first step.

In the third step, elements bound to organic matter were extracted. Considering the sample treatment, 10 mL of H₂O₂ 8.8 mol.L^-1 (30 v.v^-1) was added to the second stage residue, digested for one hour at room temperature, and manually shaken to facilitate the mixture of the reaction. Digestion was continued for one hour in a thermostated bath at 85°C, the bath temperature being increased to about 99°C − 99.8°C to reduce the volume to a few milliliters. Then, 10 mL of H₂O₂ was added with the beaker covered, heating in a thermostated bath at 85°C for 1 hour.

Afterward, the vessel was opened, and the bath temperature was increased to evaporate the sample to dryness. Then, 50 mL of 1 mol.L^-1 ammonium acetate solution (C₂H₇NO₂₎ was added to the residue, and the mixture was shaken for 16 hours at room temperature. After completing this step, the precipitate was centrifuged as described earlier, and the resulting extract was stored at 4°C until analysis.

In the fourth step, the residual fraction was extracted. At this stage, the residue prepared in the third step was transferred to a Pyrex vessel to avoid loss and 7 mL of 12 mol.L^-1 HCl and 2.3 mL of 15.8 mol.L^-1 HNO₃, waiting for digestion at room temperature for 16 hours. The mixture's temperature was raised gradually with a heating plate for two hours until it boiled. The residues were filtered and transferred into 25 mL volumetric flasks after the vessel was cooled to room temperature, and the volume was completed with HNO₃ (2 v.v^-1).

2.3 Measurement System

The metals were determined using the Flame Atomic Absorption Spectrometry technique, using a VARIAN® equipment, model Spectr AA-220FS. The background correction was performed using a hollow cathode lamp deuterium. Acetylene was used as the fuel gas, and air as the oxidant. For the quantification of the metals, calibration curves were prepared from the dilution of stock solutions of 1000 µg.mL^-1 of each metal (Spectrosol from Merck, Darmstadt, Germany). The concentration ranges of the curves were 0.53-2.0 mg.L^-1 for Cd and Zn and 0.11-2.0 mg.L^-1 for Cr, Cu, Ni, and Pb.

The efficiency of the sequential extraction method was evaluated using the CRM BCR- 701 (lake sediment), which has certified values for the available fractions and informative values for direct material analysis.

2.4 Statistical analysis

This study employed Principal Component Analysis (PCA), Varimax rotation, and Kaiser-Meyer-Olkin normalization. PCA is a mathematical technique that converts a collection of potentially interrelated variables into a more concise set of uncorrelated variables known as principal components. The initial principal component effectively captures the most significant variability inherent in the data. Subsequent components sequentially account for the residual variability beyond what the preceding components have already justified. This approach facilitates the examination of interconnections among the observed variables. This method analyzes relationships among the observed variables (Fernández et al., 2017; 2022).

As shown in Table 1, the content of Cd, Cr, Cu, Ni, Pb, and Zn in the sample CRM BCR-701, associated with the four fractions: metal in the form of exchangeable ions, and carbonates (Step 1), a metal associated with oxides of Fe and Mn (Step 2), metal bound to organic matter (Step 3) and residual or lithogenic phase (Step 4), analyzed by the FAAS technique.

Samples that could not determine the chemical element's content were indicated as less than the detection limit (< DL). In each extraction step, the number En was calculated. The results obtained are close to the certificates and reflect the method's efficiency; the number En demonstrates this with values between − 1 and 1.

Table 1

Values of certified and obtained concentrations (mg.kg^− 1) of the chemical elements in the BCR-701, analytical uncertainties at the 95% confidence level, and the average number of En
Metal		Step 1			Step 2			Step 3			Step 4
Cd	Certificate value	7.34	±	0.35	3.77	±	0.28	0.27	±	0.06	0.13*	±	0.08
	Obtained value	7.69	±	0.82	4.24	±	0.65	< DL			< DL
	En	0.39			0.66
Cr	Certificate value	2.26	±	0.16	45.7	±	2.0	143	±	7	62.5*	±	7.4
	Obtained value	2.50	±	0.80	47.70	±	0.64	141	±	5	54.7	±	3.6
	En	0.30			0.95			-0.15			-0.95
Cu	Certificate value	49.3	±	1.7	124	±	3	55.2	±	4.0	38.5*	±	11.2
	Obtained value	50.2	±	2.6	129	±	21	54.8	±	0.73	29.7	±	2.9
	En	0.30			0.23			-0.11			-0.76
Ni	Certificate value	15.4	±	0.9	26.6	±	1.3	15.3	±	0.9	41.4*	±	4.0
	Obtained value	17.3	±	2.7	31.8	±	6.0	12.9	±	2.2	42.2	±	1.5
	En	0.69			0.84			-0.98			0.18
Pb	Certificate value	3.18	±	0.21	126	±	3	9.3	±	2.0	11.0*	±	2.0
	Obtained value	3.00	±	0.05	128	±	0.77	10.6	±	1.3	7.6	±	2.5
	En	-0.82			0.52			0.53			-0.59
Zn	Certificate value	205	±	6	114	±	5	45.7	±	4.0	95*	±	13
	Obtained value	218	±	14	116	±	7	42.82	±	0.78	99.3	±	1.8
	En	0.84			0.20			-0.71			0.32
* informative value

The percentages of Cd, Cr, Cu, Ni, Pb, and Zn in the acid-soluble (Step 1), reducible (Step 2), oxidizable (Step 3), and residual (Step 4) fractions are presented in Fig. 2. The figure shows the distribution of the 50 soil samples in each fraction.

The acid-soluble fraction reveals the presence of heavy metals that could be released into the environment in the event of increased acidity. This fraction poses the highest environmental risk due to its increased mobility.

The acid-soluble fraction levels of Cd, Cr, Cu, Ni, Pb, and Zn ranged from 0.12 to 1.24 mg.kg^-1; 0.11 to 2.53 mg.kg^-1; 0.12 to 0.68 mg.kg^-1; 0.16 to 10 mg.kg^-1; 0.75 to 15 mg.kg^-1 and 0.34 to 4.6 mg.kg^-1, respectively. In acid-soluble fractions, Cd, Ni, and Zn are in high proportion, with 28%, 17%, and 8.9%, respectively. The same behavior was observed by Khadhar (2020).

The reducible fraction of heavy metal is the metal content bound to iron and manganese oxides that would be released if the substrate was subjected to more reducing conditions. The levels of Cd, Cr, Cu, Ni, Pb, and Zn in this fraction ranged from 0.20 to 1.6 mg.kg^-1; 0.39 to 37 mg.kg^-1; 0.17 to 13 mg.kg^-1; 0.11 to 26 mg.kg^-1; 2.2 to 44 mg.kg^{- 1} and 0.41 to 9.9 mg.kg^-1, respectively. In step 2, the median percentage values were 40%, 11%, 12%, 18%, 22%, and 11% for Cd, Cr, Cu, Ni, Pb, and Zn, respectively. According to Huang (2017), adding Cd to Mn oxide lowered the adsorption of this metal. Cd and Pb are consistently higher in step 2, indicating that both are mobile in the environment. Studies in the Vale do Ribeira region indicate that Pb transport is mainly associated with Fe and Mn oxides and hydroxides (Tramonte, 2014). Pb, having a large ionic radius, can occupy adsorption spaces with low binding energies. Thus, the adsorption of Pb by the oxides of Fe and Mn is considered the main retention process of this metal in the soil.

The oxidizable fraction shows the amount of metal bound to the organic matter and the sulfides, which would be released into the environment if the conditions became oxidative. The levels of Cd, Cr, Cu, Ni, Pb, and Zn for this fraction ranged from 0.66 to 1.35 mg.kg^-1; 0.77 to 22 mg.kg^-1; 0.10 to 17 mg.kg^-1; 0.10 to 12 mg.kg^-1; 11 to 26 mg.kg^{- 1} and 0.27 to 15 mg.kg^-1, respectively. The abundance of copper (Cu) observed in step 3 can be attributed to its strong attraction to soluble organic ligands. The creation of these complexes has the potential to enhance the mobility of copper in soils notably (Ghayoraneh and Qishlaqi, 2017; Khadhar et al., 2020). According to EPA (1992), the predominant proportion of the total zinc (Zn) in contaminated soils was linked with iron and manganese oxides. In our study, more Zn content was found in the residual fraction, indicating the influence of the natural substrate of the soil. The Cd fraction was the highest obtained in this step; similar results were found in Chavez (2016).

The residual metal fraction is bound by the strongest association with the crystalline structures of the minerals, and it is not always easy to separate them from the extracted material. In the modified BCR protocol, residual fractions underwent digestion using aqua regia. The concentration ranges of Cd, Cr, Cu, Ni, Pb, and Zn within this fraction were as follows: 0.16 to 29 mg.kg^-1, 0.87 to 82 mg.kg^-1, 0.93 to 54 mg.kg^-1, 0.77 to 154 mg.kg^-1, 8 to 39 mg.kg^-1, and 0.63 to 57 mg.kg^-1, respectively. Generally, this step revealed the most substantial metal fraction. This finding implies a reduced pollution risk associated with these elements, as the residual fraction contains metals unlikely to be released under typical environmental conditions.

The geo-edaphic parameters become essential to evaluate the sensitivity of the soil in terms of the presence of pollutants. When metals are introduced to the soil surface, their downward transport is limited unless the soil's metal retention capacity is exceeded or the metal's interaction with the waste matrix increases mobility. Factors like the breakdown of the organic waste matrix shifts in pH, redox potential, or variations in soil solution composition due to different remediation methods or natural weathering processes can contribute to this enhanced metal mobility. For this reason, the relationship between these, in particular pH, soil density, granulometry, carbon, and organic matter concentration, with the contents of metals found, was studied (Table 2).

Table 2

Pearson's correlation matrix for carbon (C), organic matter (OM), sand, clay, silt, pH, density (Dens.), Cd, Cr, Cu, Ni, Pb, and Zn
	C	OM	Sand	Clay	Silt	pH	Dens.	Cd	Cr	Cu	Ni	Pb	Zn
C	1	1
OM	1	1
Sand	-0.33	-0.33	1
Clay	0.66	0.66	-0.66	1
Silt	-0.47	-0.47	0.04	-0.45	1
pH	-0.36	-0.36	0.17	-0.36	0.76	1
Dens.	-0.16	-0.16	0.66	-0.62	0.26	0.09	1
Cd	0.29	0.29	-0.27	0.41	-0.32	-0.37	-0.24	1
Cr	0.43	0.43	-0.40	0.61	-0.10	0.10	-0.40	-0.01	1
Cu	0.05	0.05	0.08	0.18	0.06	0.34	-0.24	0.14	0.51	1
Ni	0.15	0.15	-0.28	0.40	0.20	0.39	-0.48	-0.14	0.78	0.71	1
Pb	0.32	0.32	-0.11	0.23	-0.35	-0.58	0.01	0.63	-0.15	-0.25	-0.44	1
Zn	0.19	0.19	-0.01	0.14	0.07	-0.02	0.04	0.44	-0.01	0.20	0.05	0.31	1

Lead is positively correlated with cadmium and zinc; the latter two, in turn, also establish a direct relationship with each other so that the samples that present the highest contents of one of them also have the highest ranges of the others. The elements Cr, Ni, and Cu also show a good correlation.

The relationship between metals and other parameters was studied through PCA analysis. Three components were selected through the factor analysis with the PCA method, as seen in Fig. 3. The primary principal component indicates that 70% of the variability is attributed to substantial influences from clay, C, OM, sand, density, silt, and pH. This component possesses the largest count of elements with loadings exceeding 0.5, highlighting a predominant correlation with geo-edaphic parameters. The secondary component explains 58% of the variance and encompasses Cr, Ni, Cu, Pb, and pH. In this case, the Pb was also partially represented in Factor 3, with a more significant factor load, suggesting a quasi-independent behavior in this group. Factor 3 is dominated by Cd, Pb, and Zn, accounting for 34% of the total variance.

The PCA analysis shows that, in most fractions, the metals Zn, Cd, and Pb negatively correlated with the pH; the opposite happened with Ni, Cu, and Cr. The pH level plays a crucial role in the extraction of metals from soil samples. As the pH decreases, the availability of negatively charged sites for cation adsorption decreases while the number of sites for anion adsorption increases. Research by Sungur (2014) demonstrated that the adsorption of cationic metals increases with higher pH values. Notably, a significant increase in metal retention occurs when the pH exceeds 7.0. Moreover, when the soil pH surpasses 7.0, the concentrations of all six heavy metals notably decrease as soil pH values rise.

This pH-dependent behavior in the adsorption reactions of cationic metals can be attributed, at least in part, to the preferential adsorption of hydrolyzed metal species compared to free metal ions. The proportion of hydrolyzed metal species tends to rise with increasing pH levels, as outlined by the EPA (1992). Most metals are more available at acidic pH because they are less strongly adsorbed, except As, Mo, Se, and Cr, which are more mobile at alkaline pH (Coringa et al., 2016).

Ni and Pb contents in the F1 fraction exhibited positive correlations, aligning with the findings of Sungur (2014) in the context of agricultural soils. Conversely, a negative correlation was observed between soil pH and Cd in the F2 and F3 fractions, while a positive correlation was evident in the F4 fraction. pH displayed positive correlations, notably with the less mobile F3 and immobile F4 fractions, showing connections with Zn and Cu. Zinc hydrolyzes when pH exceeds 7.7, leading to strong adsorption onto soil surfaces. Furthermore, zinc forms complexes with inorganic and organic ligands, influencing its interaction with the soil surface's adsorption sites, as the EPA (1992) noted. Ni exhibited a positive correlation with pH in both mobile and immobile phases. This implies that, beyond just soil pH and other parameters, considering organic matter content is essential for comprehending the impact of soil properties on Ni availability within the soil.

In soil, chromium has two potential oxidation states: trivalent chromium, Cr³⁺, and hexavalent chromium, Cr⁶⁺. Among these, Cr⁶⁺ ions are more harmful than Cr³⁺ ions and can be transformed into Cr³⁺ through reduction reactions. This reduction process becomes more rapid as the soil pH decreases. In natural environments, the movement and accessibility of chromium generally increase as soil pH rises due to the conversion of soluble Cr⁶⁺ into soluble Cr³⁺ under acidic conditions. This research has established a positive connection between soil chromium levels and pH values across all fractions. This behavior is similar to that found by other researchers (Zeng et al., 2011).

Beyond soil pH, the presence of organic matter (OM) in soil is another highly significant factor influencing the availability of heavy metals. Organic matter interacts with metals, leading to the formation of exchange complexes or chelates. The adsorption can be so strong that they are stabilized, as in the case of Cu, or they form very stable chelates, as can happen with Pb and Zn. Organometallic complexes are often included, facilitating metal solubility, availability, and dispersion because soil organisms can degrade them. This leads to the persistence of toxicity.

The influence of organic matter on metals' availability has been extensively investigated. Research has indicated that as the organic matter content in soils decreases, the adsorption of heavy metals onto soil components also decreases. Additionally, dissolved organic matter in soils can enhance the movement and absorption of heavy metals by plant roots, as reported by Zeng et al. in 2011. Altering soil pH to a higher level could mobilize metals because of the intricate interactions in soils abundant with dissolved organic matter, as noted by the EPA (1992).

In general, the relationship between metals presents in the soil and organic matter content showed limited significance. Among them, Cr exhibited the most notable positive correlation. Cr³⁺ can create soluble organic complexes when interacting with natural organic matter in the surrounding environment, subsequently becoming accessible for biological processes (Zeng et al., 2011). This finding explains this study's positive correlation between extractable Cr contents and organic matter.

A positive relationship exists between organic matter and carbon content with Zn in the F2 fraction and Cr, Ni, and Pb in the F3 and F4 fractions. A negative relationship was found with Cu in the F2 fraction. The similarities and differences were due to metal affinities against organic carbon and organic matter.

Organic matter is strongly related to soil texture; sandy soils with low clay content present low OM concentration. Fine textured soils are likely to originate from secondary minerals, which are easily altered and generally represent the primary source of heavy metals. Coarse-textured soils comprise primary minerals, such as quartz, with low levels of heavy metals. Clay soils retain more metals by adsorption, while sandy soils lack fixation capacity; therefore, contamination in deeper layers is more likely (Gharaibeh et al., 2019; Reyes et al., 2021).

Cationic metals like Cd and Pb solubility decreases in calcareous soil with increasing pH (EPA, 1992). This is consistent with the results obtained in the current study for cadmium in fractions 1, 2, and 3. Our research showed an excellent correlation between clay and Cd, although this relationship was insignificant for Pb.

Clay minerals, carbonates, hydrous iron, and manganese oxides can adsorb cadmium, or cadmium may undergo precipitation as cadmium carbonate, hydroxide, or phosphate. The behavior of cadmium in the soil is primarily influenced by pH, similar to other cationic metals. When the soil is acidic, cadmium solubility rises, resulting in minimal adsorption by soil colloids, hydrous oxides, and organic matter. In contrast, at pH levels above 6, cadmium binds to the solid soil phase or precipitates, leading to a significant reduction in cadmium solution concentrations. Lead exhibits a strong attraction to organic ligands, and forming such complexes can notably enhance lead mobility in the soil (EPA, 1992).

The soil texture also influenced nickel, which correlates well with clay in the studied fractions. Ni tends to attach itself to clays, as well as to iron and manganese oxides and organic matter. As a result, it becomes separated from the soil solution (Dai et al., 2018; Rasheed et al., 2019). Surface soils can be chelated, forming chelates of considerable solubility, and clay can form stable complexes that can even express their distribution in the soil profile. Both inorganic and organic ligands can form complexes with nickel, enhancing its mobility within soils (EPA, 1992).

Research indicates that Cr and Ni frequently interact with clay minerals throughout pedogenic processes. As a result, they exist as structural components of clay minerals rather than as exchangeable ions on the surface of clays (Ghayoraneh and Qishlaqi, 2017). Cr in soils has little mobility and can form complexes with Fe and Mn oxides, as well as with organic matter and the clay mineral fraction. This element was the one that most correlated with clay in this research.

In this study, heavy textured soils (sand and silt) correlate negatively with most metals (Fig. 3), and a good correlation between them. There was a positive correlation between organic matter and clay content with the metals analyzed but not with pH for most elements. Silt content only correlated positively with Cu in F2 and F4 fractions and poorly with other metals.

Soils with greater texture and elevated pH levels attenuated effectively, whereas sandy and low-pH soils exhibited less efficient metal retention. Clay soils containing low-pH oxides displayed relatively good anion preservation regarding anionic metals. Similar to cationic metals, lighter-textured soils showed lower effectiveness in retaining anions.

Regarding the different types of soil found in the region: alfisols, aridisols, entisols (Lithic Orthents, Psamments, and Quartzipsamments), gelisols, oxisols, and ultisols, in Fig. 4, we can see how their behavior was related to the physical-chemical parameters studied. In the case of gelisols, determining the content of sand, silt, and clay was impossible.

As seen in Fig. 4, entisol has a higher content of sand and a lower content of silt and clay when compared to other soils. This fact is explained because these soils do not have well-defined horizons, so there needed to be more time to accumulate fine materials (EMBRAPA, 2018). It is also worth noting that aridisols and oxisol have high clay content and low sand content.

The pH value was lower in soils with higher OM and C content (gelisols, oxisols, and ultisols). According to the literature, entisol and aridisol soils have a higher pH when compared to alfisol, gelisols, oxisols, and ultisols (EMBRAPA, 2018). In our study, the first ones had a higher pH, with an average value of 6.73 and 6.21 for aridisol and entisol, respectively.

Figure 4B shows that aridisols have high Ni, Cu, and Cr contents, with mean values of 103 mg.kg^-1, 46 mg.kg^-1, and 65 mg.kg^-1, respectively. A similar result was found by Perez (2019) in the province of San Juan, Argentina. On the other hand, in the study by AL-Garni (2017) in Saudi Arabia, relatively low concentrations of Ni, Cr, and Cu were found.

Studies indicate that the concentration of Cd, Cr, Cu, Ni, Pb, and Zn in different types of soils can vary depending on several factors, including the origin and history of the soil and human activity in the area. In areas without significant influence of human activities, these metals' concentration seems relatively low compared to other types of soils. However, the concentration of these metals can be more significant in mining areas or other activities that can release heavy metals into the soil.

BCR sequential extraction showed that the most extensive metal content was found in the residual fraction; however, it should be noted that the presence of metals was evident in the other fractions. The PCA analysis determined that pH, organic matter (OM), and texture are the primary factors influencing the mobility of heavy metals in the soil. A considerable variation in the results was observed when comparing the studied parameters across different soil types, suggesting that the region's geology and climate significantly influence these variables. The application of multivariate statistics is a helpful tool to assess the impact of different pollution sources.

The data presented in this study will significantly contribute to other environmental research, providing valuable quantitative evidence highlighting the necessity for regulating agricultural and industrial activities. Moreover, statistical tools can provide a foundation for successful conservation. Utilizing these tools can drive the development of precise policy measures and legislation to safeguard soils from long-term environmental heavy metal accumulation.

Acknowledgments

The authors thank the Radioecology and Environmental Control Laboratory of the UFPE Department of Nuclear Energy and the Northeastern Regional Center for Nuclear Sciences (CRCN-NE) for their chemical analyses.

Funding

This work was supported by the Foundation for Science and Technology of Pernambuco (FACEPE) [Grant #BFP-0009-3.09/17, APQ-0245-3.01/21, DCR-0039-3.01/21, BFP-0101-3.01/22 and PIBIC-0095-4.06/22], the National Council for Scientific and Technological Development (CNPq) [Grant #380789/2023-0], the Coordination for the Improvement of Higher Education Personnel (CAPES) [Grant #88882.379364/2019-01], and Notice PROPG #06/2022 of the Dean of Graduate Studies at the Federal University of Pernambuco [Grant number 23076.052525/2022-26].

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

Zahily Herrero Fernández and José Araújo dos Santos Júnior: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft. Zahily Herrero Fernández, José Araújo dos Santos Júnior, Romilton dos Santos Amaral, Artur Paiva Coutinho and Mariana Brayner Cavalcanti Freire Bezerra.: Resources, Visualization, Supervision, Project administration. Josineide Marques do Nascimento Santosa, Marvic Ortueta Milan, Lino Angel Valcarcel Rojas, and Taiwo Saheed Yinusa: Investigation, Writing - Review & Editing, Visualization. Robert Fernandes De Melo, Marcela Ferreira Marques de Oliveira and Yasmin Marques dos Santos: Writing - Review & Editing, Visualization. All authors reviewed the manuscript.

AL-Garni, S. M., Murtaza, G., Basahi, J. M., & Maghbooli, M., 2017. Assessment of heavy metals in soils of Al-Madinah Al-Munawwarah, Saudi Arabia. Int. J. Environ. Res. Public Health 14, 1045.
Alovisi, A.M.T., Cassol, C.J., Nascimento, J.S., Soares, N.B., da Silva Junior, I.R., da Silva, R.S., da Silva, J.A.M., 2020. Soil factors affecting phosphorus adsorption in soils of the Cerrado, Brazil. Geoderma Reg. 22, e00298. https://doi.org/10.1016/j.geodrs.2020.e00298
Chabukdhara, M., Nema, A.K., 2013. Heavy metals assessment in urban soil around industrial clusters in Ghaziabad, India: Probabilistic health risk approach. Ecotoxicol. Environ. Saf. 87, 57–64. https://doi.org/10.1016/j.ecoenv.2012.08.032
Chavez, E., He, Z.L., Stoffella, P.J., Mylavarapu, R.S., Li, Y.C., Baligar, V.C., 2016. Chemical speciation of cadmium: An approach to evaluate plant-available cadmium in Ecuadorian soils under cacao production. Chemosphere 150, 57–62. https://doi.org/10.1016/j.chemosphere.2016.02.013
Coringa, J. do E.S., Pezza, L., Coringa, E. de A.O., Weber, O.L. dos S., 2016. Distribuição geoquímica e biodisponibilidade de metais traço em sedimentos no Rio Bento Gomes, Poconé - MT, Brasil. Acta Amaz. 46, 161–174. https://doi.org/10.1590/1809-4392201502215
Dai, Z., Wang, L., Tang, H., Sun, Z., Liu, W., Sun, Y., Su, S., 2018. Chemosphere Speciation analysis and leaching behaviors of selected trace elements in spent SCR catalyst. Chemosphere 207, 440–448. https://doi.org/10.1016/j.chemosphere.2018.05.119
EMBRAPA, 2018. Sistema brasileiro de classificação de solos. Embrapa Solos.
EMBRAPA, 2017. Manual de métodos de análise de solo, 3a ed. Brasília, DF.
EMBRAPA, 2009. Manual de análises químicas de solos, plantas e fertilizantes, 2nd ed. Embrapa Informação Tecnológica, Brasília, DF.
Emurotu, J.E., 2021. Chemical speciation and potential mobility of some metals of selected farmland in Kogi State, North Central Nigeria. Niger. J. Basic Appl. Sci. 28, 48–55. https://doi.org/10.4314/njbas.v28i1.7
EPA, 1992. Ground Water Issue. Behavior of metals in soils. https://doi.org/10.1056/NEJMoa030660
Fernández, E., Jiménez, R., Lallena, A.M., Aguilar, J., 2004. Evaluation of the BCR sequential extraction procedure applied for two unpolluted Spanish soils. Environ. Pollut. 131, 355–364. https://doi.org/10.1016/j.envpol.2004.03.013
Fernández, Z., dos Santos Júnior, J.A., dos Santos Amaral, R., De França, E.J., Menezes, R.S.C., Brayner Cavalcanti Freire Bezerra, M., Paiva Coutinho, A., Valcarcel Rojas, L.A., Santos, J.M. do N., Dias Bezerra, J., Nóbrega de Araújo, E.E., 2022. Pernambuco Caatinga: relevance of soil chemical composition for biodiversity conservation. Chem. Ecol. 38, 108–121. https://doi.org/10.1080/02757540.2021.2023134
Fernández, Z.H., dos Santos Júnior, J.A., dos Santos Amaral, R., Alvarez, J.R.E., da Silva, E.B., De França, E.J., Menezes, R.S.C., de Farias, E.E.G., do Nascimento Santos, J.M., 2017. EDXRF as an alternative method for multielement analysis of tropical soils and sediments. Environ. Monit. Assess. 189, 447–456. https://doi.org/10.1007/s10661-017-6162-5
Gharaibeh, M.A., Marschner, B., Heinze, S., Moos, N., 2019. Spatial distribution of metals in soils under agriculture in the Jordan Valley. Geoderma Reg. 20, e00245. https://doi.org/10.1016/j.geodrs.2019.e00245
Ghayoraneh, M., Qishlaqi, A., 2017. Concentration, distribution and speciation of toxic metals in soils along a transect around a Zn/Pb smelter in the northwest of Iran. J. Geochemical Explor. 180, 1–14. https://doi.org/10.1016/j.gexplo.2017.05.007
Hernandez, M.C., Jimenez, J.C., 2012. Effects of soil water content and organic matter addition on the speciation and bioavailability of heavy metals. Sci. Total Environ. 423, 55–61. https://doi.org/10.1016/j.scitotenv.2012.02.033
Huang, X., Chen, T., Zou, X., Zhu, M., Chen, D., Pan, M., 2017. The adsorption of Cd(II) on manganese oxide investigated by batch and modeling techniques. Int. J. Environ. Res. Public Health 14. https://doi.org/10.3390/ijerph14101145
IBGE-Instituto Brasileiro de Geografia e Estatística, 2021. URL http://www.ibge.gov.br/estadosat/perfil.php?lang=_EN&sigla=pe
Kennou, B., El Meray, M., Romane, A., Arjouni, Y., 2015. Assessment of heavy metal availability (Pb, Cu, Cr, Cd, Zn) and speciation in contaminated soils and sediment of discharge by sequential extraction. Environ. Earth Sci. 74, 5849–5858. https://doi.org/10.1007/s12665-015-4609-y
Khadhar, S., Sdiri, A., Chekirben, A., Azouzi, R., Charef, A., 2020. Integration of sequential extraction, chemical analysis and statistical tools for the availability risk assessment of heavy metals in sludge amended soils. Environ. Pollut. 263, 114543. https://doi.org/10.1016/j.envpol.2020.114543
MMA, 2018. Caatinga [WWW Document]. URL http://www.mma.gov.br
Perez, A., Silva, H., Nolta, N., & Maturano, G., 2019. Assessment of copper, lead, nickel, and zinc contamination in soils of a mining region in San Juan Province, Argentina. Environ. Sci. Pollut. Res. 26, 9815–9826.
Rasheed, Kolawole; Solomon, Akinnawo; Aiyesanmi, A., 2019. Chemical speciation and fractionation study of heavy metals in top sediment deposit of Owena river, Nigeria. Phys. Sci. Int. J. 21, 1–13.
Reyes, A., Cuevas, J., Fuentes, B., Fernández, E., Arce, W., Guerrero, M., Letelier, M.V., 2021. Distribution of potentially toxic elements in soils surrounding abandoned mining waste located in Taltal, Northern Chile. J. Geochemical Explor. 220, 106653. https://doi.org/10.1016/j.gexplo.2020.106653
Siahcheshm, K., Orberger, B., Wagner, C., 2022. Bioavailability and heavy metals speciation assessment in the contaminated soils of Doustbaglu mineralized area, NW Iran. Environ. Earth Sci. 81. https://doi.org/10.1007/s12665-021-10162-2
Sungur, A., Soylak, M., Yilmaz, E., Yilmaz, S., Ozcan, H., 2014. Characterization of Heavy Metal Fractions in Agricultural Soils by Sequential Extraction Procedure: The Relationship Between Soil Properties and Heavy Metal Fractions. Soil Sediment Contam. 24, 1–15. https://doi.org/10.1080/15320383.2014.907238
Tramonte, K.M., 2014. Estudo da disponibilidade de metais em sedimentos do sistema estuarino-lagunar de Cananéia-Iguape (Doutorado). Universidade de São Paulo, Brasil.
Ure, A., Quevauvillert, P., Muntaus, H., Griepinkt, B., 1993. Speciation of heavy metals in soils and sediments. An account of the improvement and harmonization of extraction techniques undertaken under the auspices of the BCR of the Commission of the European Communities. Int. J. Environ. Anal. Chem. 51, 135–151.
USDA, 2014. Keys to soil taxonomy, Soil Conservation Service. https://doi.org/10.1109/TIP.2005.854494
Zewge, F., Chandravanshi, B. S., Dagnaw, D. C, 2022. Spatial distribution and potential sources of heavy metals in urban soils of Addis Ababa, Ethiopia. Bull. Environ. Contam. Toxicol. https://doi.org/10.1007/s00128-022-03545-5
Zeng, F., Ali, S., Zhang, H., Ouyang, Y., Qiu, B., Wu, F., Zhang, G., 2011. The influence of pH and organic matter content in paddy soil on heavy metal availability and their uptake by rice plants. Environ. Pollut. 159, 84–91. https://doi.org/10.1016/j.envpol.2010.09.019

No competing interests reported.

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Transfer of heavy metals in topsoils, based on sequential extraction and statistical tools in Northeast Brazil

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Abstract

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1. Introduction

2. Material and methods

2.1 Study Area

2.2 Sample Collection and Preparation

2.3 Measurement System

2.4 Statistical analysis

3. Results and Discussion

Conclusions

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