Rapid and Convenient Assessment of Trace Element Contamination in Agricultural Soils through Slurry-TXRF and Ecological Indices: The Ñuble Region, Chile as a Case Study

Abstract

The study aims to evaluate the applicability of the slurry-TXRF method for estimating background contents and ecological indices in a rapid and convenient way. For this reason, the agricultural soils of the Itata Valley were used as a case study, where 48 soil samples were collected and analyzed. This rapid, minimally sample-intensive, and simultaneous multi-element quantification technique presented high accuracy but lower precision (approx. 20% RSD) compared to the classic total reflection X-ray fluorescence and flame/graphite furnace atomic absorption spectrometry methods, which require sample digestion. Due to the analytical characteristics of Slurry-TXRF, it can be concluded that the lower precision is likely compensated for, and this method represents a valuable alternative for the rapid and efficient assessment of trace element contamination in agricultural soils. The regional median concentrations of Cr, Ni, Cu, Zn, and Cd in the Itata Valley surface soils were found to be 63.7, 9.57, 31.0, 41.1, and 0.56 mg kg⁻¹, respectively, with corresponding upper limits of 47.6, 6.82, 17.0, 30.7, and 0.284 mg kg⁻¹. The ecological indices, including the geoaccumulation index, contamination factor, enrichment factor, and degree of contamination, suggest moderate levels of contamination in the region.

1. Introduction

Soil contamination by heavy metals and trace elements is a long-standing issue affecting developed and developing countries alike, which is primarily due to human activities such as mineral extraction [1], energy production [2], agriculture and livestock management [3]. To address this issue, both governmental and scientific organizations have invested in studies to evaluate contamination levels and develop faster and more reliable assessment platforms [4].

In environmental geochemistry, the understanding of geochemical background and threshold levels is crucial for determining the distinction between naturally occurring levels of elements and human-caused pollution [5].

Quantitative studies in soils may utilize various analytical techniques, including classical methods such as inductively coupled plasma mass spectrometry (ICP-MS), flame atomic absorption spectroscopy (FAAS), inductively coupled plasma emission spectroscopy (ICP-ES), graphite furnace atomic absorption spectroscopy (GF-AAS), and cold vapor atomic absorption spectrometry (CV-AAS).

In environmental contamination studies, total X-ray fluorescence spectroscopy (TXRF) has been suggested as a reliable and convenient alternative for determining trace element concentrations. The advantage of this method is that it can be performed with or without sample digestion, making it a recommendable option [6,7,8,9]. Additionally, the versatility of TXRF has been demonstrated in both biological and environmental applications [10]. The slurry-TXRF method has been used for the analysis of biological samples [10,11,12], drugs [13,14], soils [15], rocks [16], and archeological samples [17]. According to Natalí et al. [18], among the various sample preparation techniques for TXRF, slurry-TXRF is ranked as the third most widely used method, following digestion and minimal treatment methods. The slurry preparation technique for TXRF has the advantage of reducing the risk of sample contamination and streamlining the analysis process. This is achieved by eliminating the need for the digestion step and minimizing washing requirements, thus saving time [18].

This study aims to evaluate the suitability of the slurry-TXRF analytical technique for the quantification of trace elements such as Cr, Ni, Cu, Zn, and Cd in agricultural soils and thus for use as an easy and fast way to estimate contamination levels by means of different ecological indices.

2. Materials and Methods

2.1. Study Area

This study focuses on a geographic area that is recognized as a wine subregion in southern Chile. The territory spans an approximate area of 311,418 hectares, which constitutes 29% of the area of the Ñuble Region. Of the total area, 13,030 hectares are used for agricultural purposes, mainly for wine production, and these soils will be the subject of investigation [19].

The Itata Valley is characterized by the presence of heritage grape varieties, including País, Muscat of Alexandria, and Cinsault strains, which are the most significant in terms of cultivated hectares. The region is considered an emerging player in the wine market and is distinguished for its production of organic wines.

As the name suggests, the Itata Valley is closely tied to the Itata River basin, which runs from east to west across the valley. It is situated in the Central Valley and Coastal Range, with elevations not exceeding 400 m above sea level. The presence of the Coastal Range results in climatic differentiation between the western slopes, which receive more rainfall than the more arid eastern slopes. However, in general, the study area is located in a Mediterranean climate area with clearly marked four seasons that include rainy and dry seasons [19]. Due to this, one year of study seems to be adequate because it allows us to evaluate the performance of the slurry-TXRF method under different climatic scenarios.

On the other hand, the soil found in this valley is primarily derived from the coastal mountain range and can be classified into three different parent materials:

• Eroded metamorphic rock: This soil type exhibits the presence of shale, sandstone, phyllite, and slates. It has a clayey soil texture and slow water infiltration. Typically, this type of soil is found in high positions within areas characterized by steep or complex variable slopes, resulting in the formation of Catena due to the topography and drainage characteristics.
• Granite origin: Derived from granite and diorite rocks, this soil type has a clayey texture and low water infiltration. It is found in a hilly topography with complex and variable slopes, making it susceptible to water erosion.
• Fine alluvial sediment: This soil type is derived from the deposition of large amounts of fine alluvial sediments, which resulted from the fluvio-glacial sediments of the Andes Mountain glaciation during the Quaternary period. The thickness of the deposit varies considerably due to the influence of rivers in the area, which transport significant amounts of sediment, particularly fine material. These sediments have formed soils with a loamy–clayey texture and poor drainage characteristics [20].

In summary, the soil in this valley is primarily derived from eroded metamorphic rock, granite and diorite, and fine alluvial sediments. Each of these parent materials exhibits distinct characteristics that influence soil texture, water infiltration, and susceptibility to water erosion [20].

2.2. Soil Sampling

The agricultural soil sampling was conducted in two phases: June and October of 2022. The samples were taken from 48 points located in 5 vineyards (A, B, C, D, E) within the Itata Valley (Figure 1) using the combined method in X, described by the Servicio Agricola y Ganadero de Chile [21]. For each sampling point, approximately two kilograms of soil were collected, consisting of five sub-samples taken from the corners and center of a 1 m × 1 m cross, to form a composite sample. Samples were taken from the topsoil layer (0–20 cm), with plant cover removed if present, and from deep soil (150 cm) using a Dutch Auger. The topsoil layer is the portion most influenced by human activities, whereas deep soil is relatively undisturbed and considered free of anthropogenic contamination.

The samples were packaged in polyethylene bags for transportation to the laboratory. Once there, the samples were dried in an oven at 40 °C for 48 h and sieved through a nylon sieve to remove coarse material and other residues, and only the fine material (<2 mm) was stored in hermetically sealed plastic bags for subsequent analysis.

2.3. Slurry Sample Preparation

The soil slurry samples were prepared in 2.5 mL sterile Eppendorf vials. To prepare the slurry, 30 mg of dry and grounded soil was added to the vial, which was followed by the addition of 1500 μL of Triton-X as a surfactant and 10 μL of Galio (Ga) standard solution at a concentration of 1000 mg L⁻¹ (TraceCERT, ICP standard, Fluka) as an internal standard. The slurries were homogenized using an electric shaker for 300 s. Subsequently, 10 μL of the homogenized slurry was dispensed onto the 30 mm diameter quartz sample carrier for TXRF analysis. To avoid potential sedimentation, the sample was deposited immediately after homogenization.

Before depositing the sample, a siliconized film was formed on the quartz glass disc reflector using a silicone solution in isopropanol (Serva GmbH & Co., Heidelberg, Germany). The sample was then dried using an infrared lamp.

2.4. Quantification of Element Concentrations and Method Validation

This study is focused on the analysis of chromium, copper, nickel, zinc, and cadmium, which are listed as priority trace elements by the US Environmental Protection Agency (US-EPA). The concentrations of these elements were determined using a S4 T-Star benchtop TXRF spectrometer manufactured by Bruker AXS Microanalysis GmbH. The spectrometer consists of an X-ray tube made of molybdenum and tungsten, which are both capable of producing X-rays with a maximum power of 50 W by accelerating electrons to a potential of 50 kV. The spectrometer also includes a 150 nm carbon-nickel multilayer monochromator and a high-resolution Peltier-cooled XFlash silicon drift detector with an energy resolution of less than 149 eV at 100 kcps, as referenced to the manganese Kα line of 135.9 keV.

The quantitative analysis was conducted by identifying the most intense fluorescent emission lines (Kα) of the elements at 5.41 keV, 7.48 keV, 8.02 keV, 8.63 keV, and 23.17 keV, respectively, which are free of spectral interference due to overlapping of fluorescent signals, as occurs frequently for other elements in environmental matrices such as arsenic and lead [22] or cadmium and potassium when a benchtop TXRF spectrometer with a low-power molybdenum X-ray tube is used [23]. The acquisition of the counts in each channel during the 2500 s measurement and the following deconvolution process was performed using TStar 1.0 software, which was designed by Bruker© for the analysis of X-ray spectral data.

To validate the slurry-TXRF method, the element concentrations in a certified reference soil material (Loamy Clay 2) were determined and compared with those obtained through sample digestion and quantification by TXRF and Flame Atomic Absorption Spectrometry (F-AAS), which is the method recommended by the SAG analysis protocol for soils and sludges [24]. The sample digestion was performed in Teflon tubes by weighing approximately 1 g (accuracy 0.01 g) of dry soil and adding 25 mL of 100% HNO₃, which was followed by homogenization and digestion at 60 °C overnight and at 120 °C for 1 h. The sample was then cooled and mixed, heated to 140 °C until a volume of about 5 mL remained, and cooled to less than 60 °C; 5 mL of 70% HClO₄ was added before the sample underwent a final digestion at 220 °C for 30 min. The sample was then filtered and diluted to 100 mL [24].

2.5. Exploratory Data Analysis

To summarize the data using a reduced set of new variables without losing much information, find data sets, and classify the observations into defined groups and relate them to each other, an exploratory data analysis was performed.

Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used in this study. The aim of PCA and HCA is to understand the relationship between variables and determine potential sources of heavy metals by evaluating the correlation between variables and grouping similar observations. This study’s approach aligns with prior research described by Facchinelli et al. [25]. Pirouette 3.11 software was used to perform a mean-centered transformation of the database and then conduct PCA and HCA. The Euclidean distance metric was used to calculate the distances between the observations in the cluster analysis, and the complete linkage method was used as the linkage criteria.

2.6. Background Values

The natural or background values were established by analyzing the deep soil samples (DS) [26]. The identification of soil samples with an unusually high element concentration is a challenge, as there is no universally accepted method for determining threshold values [27,28]. The method known as the median absolute deviation method (MAD; the median ± 2MAD) was used in this study to identify these values.

2.7. Ecological Indices

In order to assess and quantify the potential contamination in soils, a variety of environmental quality indicators are frequently employed [29,30]. The use of ecological indices is a common approach in evaluating the origin of trace element accumulation in soils, either from natural processes or from human activities [31,32]. The potential contamination of trace metals was assessed in this study using three ecological indicators: the geoaccumulation index (IGEO), the enrichment factor (EF), and the contamination factor (CF). These ecological indices are widely accepted and have been used for decades to evaluate the degree of contamination [32,33,34,35,36].

The geoaccumulation index (IGEO) (1) depends on:

$$IGEO=lo{g}_2\left(\frac{Ci}{1.5Cb}\right)$$

(1)

where the measured content of the element in the soil (Ci) and the estimated background value of that element in the soil (Cb) are used. In order to interpret the IGEO, Müller [35] classified it into seven contamination levels: uncontaminated (IGEO ≤ 0), slightly contaminated (0 < IGEO ≤ 1), moderately contaminated (1 < IGEO ≤ 2), moderately to heavily contaminated (2 < IGEO ≤ 3), heavily contaminated (3 < IGEO ≤ 4), heavily to extremely contaminated (4 < IGEO ≤ 5), and extremely contaminated (IGEO > 5). This classification scheme is based on the findings of Müller [35] and Sutherland et al. [36].

The enrichment factor (EF) (2) is an indicator that assesses the level of contamination in soil by standardizing the content of the target element against a reference element. The reference element is selected based on its low variability in soil samples and is used as a control for natural occurrences. The most frequently used reference elements are Sc, Mn, Ti, Al, and Fe [37]. In the present study, Fe was selected as the reference element due to its low variability in the agricultural soils of the Itata Valley. Fe is one of the main components of the Earth’s crust and is primarily influenced by the soil matrix [33]. The EF depends on:

$$EF=\left[\frac{Ci}{CFei}\right]/\left[\frac{Cb}{CFeb}\right]$$

(2)

where Cb is the reference or background content of the element obtained by the MAD method, and CFeb is the reference or background content of Fe. EF values can be categorized into five levels: from deficiency to minimal enrichment (EF < 2), moderate enrichment (2 < EF < 5), significant enrichment (5 < EF < 20), very high enrichment (20 < EF < 40), and extremely high enrichment (EF > 40).

The contamination factor (CF) (3) is used to determine the magnitude of potential contamination in soil. It is calculated as the quotient between the concentration of the examined element in the soil (Ci) and the background values obtained by the MAD method (Cb)

$$CF=\frac{Ci}{Cb}$$

(3)

CF categories were proposed by Hakanson [34], and they include low contamination (CF < 1), moderate contamination (1 < CF < 3), significant contamination (3 < CF < 6), and very high contamination (CF > 6) [34,38].

Subsequently, the degree of contamination (CDEG) (4) is defined as the sum of the individual contamination factors as expressed in the following equation:

$$CDEG=\sum CF$$

(4)

As with the CF, the CDEG is divided into four groups: low contamination (CDEG < 8), moderate contamination (8 < CDEG < 16), considerable contamination (16 < CDEG < 32) and very high contamination (CDEG > 32) [34].

3. Results

3.1. Method Comparison and Validation

In

Table 1. Figures of merit for slurry-TXRF, digestion-TXRF and classical spectrometry methods (FAAS/GF-AAS) applied to soil reference material (Loamy Clay 2) (n = 3).

Slurry-TXRF
	Certified (mg kg−1)	Results (mg kg−1)	RSD	t-Test p (0.05)	AnalyticalSensitivity ([mg kg−1]−1)	LOD (mg kg−1)
Cd	58.4 ± 1.99	61.7 ± 7.0	11.3	0.476	15.2	0.091
Cr	43.8 ± 2.42	48.9 ± 11.1	22.7	0.480	6.85	0.289
Cu	5.68 ± 0.467	6.92 ± 1.59	23.0	0.265	30.1	0.061
Fe	8320 ± 646	7801 ± 1700	21.8	0.647	0.89	0.153
Ni	6.63 ± 0.508	7.83 ± 1.44	18.4	0.245	29.6	0.079
Zn	74.8 ± 3.02	66.6 ± 10.9	16.4	0.249	12.5	0.051
Digestion-TXRF
Cd	58.4 ± 1.99	56.5 ± 5.2	9.20	0.586	108.3	0.013
Cr	43.8 ± 2.42	38.2 ± 5.9	15.4	0.203	162.5	0.029
Cu	5.68 ± 0.467	5.21 ± 0.89	17.1	0.463	443.0	0.008
Fe	8320 ± 646	7531 ± 1352	18.0	0.413	13.4	0.017
Ni	6.63 ± 0.508	6.21 ± 1.21	19.5	0.609	256.9	0.010
Zn	74.8 ± 3.02	67.5 ± 6.7	9.92	0.160	220.8	0.007
GF-AAS a/F-AAS b
Cd a	58.4 ± 1.99	59.6 ± 0.7	1.17	0.380	21.4	0.032
Cr a	43.8 ± 2.42	45.8 ± 2.5	5.49	0.378	8.43	0.933
Cu b	5.68 ± 0.467	6.8 ± 0.73	10.7	0.091	0.790	17.6
Fe b	8320 ± 646	8243 ± 745	9.04	0.899	0.152	3.40
Ni b	6.63 ± 0.508	6.15 ± 0.73	11.9	0.403	0.723	2.34
Zn b	74.8 ± 3.02	71.5 ± 4.5	6.29	0.351	0.625	1.30

^a GF-ASS: Graphite Furnace Atomic Absorption Spectrometry; ^b FAAS: Flame Atomic Absorption Spectrometry.

, element concentration values, measurement errors (standard deviation), relative standard deviation (RSD), analytical sensitivity, and limit of detection (LOD) are compared for slurry-TXRF, digestion-TXRF and F-AAS methods with certified reference material (Loamy Clay 2). The results were obtained by analyzing three soil sub-samples following the slurry procedure and three soil sub-samples digested in separate vessels.

It can be seen that when analyzing and quantifying the soil samples directly without a previous digestion process (slurry-TXRF method), there are no significant differences with the certified value, but the precision is lower compared to the quantifications made by means of TXRF and FAAS or GF-AAS with the samples previously digested in acid.

The analytical sensitivities observed for slurry-TXRF and digestion-TXRF are as expected for fluorescence-based techniques, which are characterized by high sensitivity.

The lowest LODs are obtained from fluorescence-based techniques, while slurry-TXRF in particular is a method that increases the LODs.

3.2. Trace Elements Concentration

In

Table 2. Statistical summary of trace metal concentrations (mg kg⁻¹) in soils of the Itata Valley.

	Median	Min	Percentile					Max	MAD a
			5	25	50	75	95
Cr	63.68	32.11	35.86	47.31	63.73	91.06	133.22	165.10	6.15
Ni	9.57	3.77	4.80	6.53	9.76	13.27	19.64	22.50	0.89
Cu	30.95	12.52	14.12	18.68	31.34	41.35	57.00	62.31	0.95
Zn	49.14	20.06	26.73	37.89	49.26	59.17	87.92	115.30	1.75
Cd	0.56	0.23	0.25	0.34	0.56	0.74	1.03	1.12	0.03

TXRF in particular is a method that increases the LODs. ^a Median absolute deviation.

, a statistical summary of the concentrations of Cr, Ni, Cu, Zn, and Cd in topsoil samples is presented. The overall concentration of trace elements in the soil samples collected from the Itata Valley (n = 48) decreases in the following order: Cr > Zn > Cu > Ni > Cd. Great differences between the minimum and maximum concentrations are observed for every element.

In Figure 2, the boxplots for Cr, Ni, Cu, Zn, and Cd concentrations results are presented. The distributions of Cr, Cu, and Zn are asymmetrical, with longer tails at higher concentrations due to the presence of a relatively small fraction of high values. As displayed in the graph, there is an outlier in the higher concentration tail of Zn.

3.3. Exploratory Analysis: Correlation and Possible Sources

The hierarchical clustering, which examines trace metal concentrations in topsoils of the Itata Valley (Figure 3), first splits into two main groups (with a similarity coefficient of 0.00) Cr and Ni, Cu, Zn, Cd. Two groups comprise the later cluster, Zn-Cu (0.56 similarity coefficient) and Cd-Ni (0.72 similarity coefficient).

The applied PCA explains 96.83% of the variance with the first two principal components (

Table 3. Variance explained for each principal component of the PCA.

	Variance	Percent (%)	Cumulative (%)
PC1	52,386.45	90.4016	90.4016
PC2	3729.35	6.4356	96.8372
PC3	1751.07	3.0218	99.8590
PC4	81.67	0.1409	99.9999
PC5	0.05	0.0001	100.0000

), and it corroborates the results already obtained by HCA. Analysis of the PCA loadings (Figure 4) shows that the first component explains the most variance (90.4%), with Cr being the most differentiating element of the samples. Meanwhile, Ni and Cd present a great correlation.

3.4. Background Values and Assessment of Potential Contamination

With the background upper limits established (

Table 4. Percentage of samples above the upper limits of the natural background value (median + 2MAD) for each metal studied.

	Cr	Ni	Cu	Zn	Cd
Background Upper Limit (mg kg−1)	47.6	6.82	17.0	30.7	0.284
Outliers	35	34	44	42	44
Percentage	72.92	70.83	91.67	87.50	91.67

), it is possible to determine how many samples have contents that exceed them (outliers). As described in Table 3, the vast majority of the samples (between 70% and 90% of them depending on element) have higher contents than these limits, suggesting external contributions of heavy metals.

The existence of external contributions of heavy metals is to be expected in an intensively used soil such as agricultural soil. Further information on the contamination level is obtained from the ecological indices (Figure 5) that can be calculated with background concentrations.

The IGEO results indicate that contamination levels for Cr, Ni, Cu, Zn, and Cd tend to be slight. Meanwhile, there is a pair of samples that indicate greater contamination but which can still be considered moderate.

The contamination factor (CF), which tends to classify soil as more contaminated than the IGEO because the former does not consider possible natural variations, indicates that most of the sampling points present moderate contamination for all the elements of interest, with specific points exhibiting considerable contamination that coincide with those classified as the most contaminated by the IGEO.

The enrichment factor corroborates the findings obtained by the IGEO and CF, since most of the sampling points are moderately enriched in Cr, Ni, Cu, Zn, and Cd compared to Fe concentrations, which present a minimum vertical variation in the soil layer.

The observed differences in the indices correspond to their considerations in the mathematical formulas. While the CF compares surface concentrations with background concentrations in a simple way, the IGEO introduces a correction factor in its mathematical formula that considers possible geogenic contributions to the surface. The EF goes even further and normalizes the contents with the concentrations of a low-variability element such as iron (Fe), correcting for any endogenous contribution. That explains the different severities of each index.

Finally, the degree of global contamination with respect to the elements studied (Figure 6) indicates that the agricultural soils of the Itata Valley present low to moderate contamination, with specific points where contamination is considerable.

4. Discussion

The lower precision obtained for the slurry-TXRF quantification method compared to the quantifications made by means of TXRF and FAAS or GF-AAS is an expected result, considering that in the TXRF technique, the conditions and complexity of the matrix are highly influential factors at the moment of signal quantification. Nonetheless, the relative standard deviations (RSD) are around 20% depending on the element, which is acceptable based on the concentration levels in this study and considering the acceptability criteria in similar studies [18,39].

The slightly lower analytical sensitivity in the case of slurry-TXRF is very well explained by the presence of the matrix and the technical impossibility of obtaining sufficiently small and homogeneous particle sizes, increasing background noise. Still, even for slurry-TXRF, the analytical sensitivity is higher for Cu, Fe, Ni, and Zn compared to FAAS, and it is very similar for Cd and Cr compared to GF-AAS.

LODs are explained in the same way. Fluorescence-based techniques tend to obtain the lowest LODs due to its great sensitivity [40,41,42], while slurry-TXRF increases the LODs due to the increase in background noise from the presence of matrix particles in the sample.

Since slurry-TXRF enables the simultaneous determination of multiple elements without the need for extensive sample preparation, resulting in savings in both time and reagents, it can be concluded that the lower precision is likely compensated for. Moreover, previous studies have shown that the accuracy of slurry-TXRF is comparable to other commonly used techniques for trace element analysis in soil samples [18,39]. Therefore, slurry-TXRF represents a valuable alternative for the rapid and efficient assessment of trace element contamination in agricultural soils.

Differences in the concentrations of elements indicate the presence of spatial heterogeneity, which can be attributed to variations in the soil composition and potential anthropogenic contributions. This idea gains strength when finding a sample that stands out from the others due to its higher concentrations of zinc, which indicates that natural concentrations are possibly affected by external sources.

The two main groups obtained from the hierarchical clustering can possibly be explaining the controlling anthropogenic (Cr) and geogenic (Ni, Cu, Zn and Cd) factors. Two groups comprise the geogenic cluster, Zn-Cu (0.56 similarity coefficient), and Cd-Ni (0.72 similarity coefficient). These two groups can be explained due to possible common sources. Even though the origin of most of these metals is reportedly geogenic, there are anthropogenic sources that could be influencing these concentrations and groupings.

For example, the main anthropogenic sources of Cu and Zn pollution in European soils are pesticides [43], which may have their origin in agricultural use itself or come from forestry, which is undoubtedly the main economic activity in the area and would expose these soils through atmospheric transportation and deposition and through irrigation water.

The main source of Cd in European agricultural soils is phosphate-based fertilizers, which are also the main source of Ni in these soils [43]. This would explain the correlation and grouping by source of these two metals.

The same information could explain the solitary grouping of Cr. As the contribution of phosphate fertilizers to chromium concentrations in agricultural soils is so evident [43], the algorithm is forced to classify it separately from other metals, because the vast majority of chromium present in topsoil is likely to come from this anthropogenic source. The interpretation of these correlations as common sources is strengthened since the results are similar for both HCA and PCA.

In general, the moderate levels of contamination founded in the soils, understood as the degree of impact relative to background contents, are expected in a high-use soil such as agricultural soil [44,45,46,47]. The potential for adverse biological effects on resident communities is not necessarily reflected in the results obtained for specific soil ecological indices using the slurry-TXRF method. To properly assess the possible risk to human health, it is necessary to consider factors such as the ability of an elemental species in its different forms to migrate from soil through plant parts and become available for consumption [2,30,45].

5. Conclusions

In conclusion, this study highlights several important points regarding the slurry-TXRF quantification method for assessing trace element contamination in agricultural soils. The lower precision and slightly lower analytical sensitivity of slurry-TXRF compared to other techniques such as TXRF, FAAS, and GF-AAS are expected due to the complexity of the matrix and the presence of background noise. However, the relative standard deviations (RSD) obtained for slurry-TXRF are acceptable based on the concentration levels and results of similar studies. Despite the lower precision, the simultaneous determination of multiple elements and the savings in time and reagents make slurry-TXRF a valuable alternative for the rapid and efficient assessment of trace element contamination in agricultural soils.

In the case of the agricultural soils of the Itata Valley, the study reveals spatial heterogeneity in the concentrations of elements, indicating variations in soil composition and potential anthropogenic contributions. The hierarchical clustering analysis suggests the presence of both geogenic and anthropogenic factors controlling the concentrations of certain elements. Common sources such as agricultural pesticide use, forestry activities, and phosphate-based fertilizers are likely influencing the concentrations and groupings of Cu, Zn, Cd, Ni, and Cr in the soils.

Regarding the contamination levels in the agricultural soils of the Itata Valley, the results indicates that moderate levels of contamination were found, but this does not necessarily imply adverse biological effects on resident communities.

Overall, this study demonstrates the usefulness of the slurry-TXRF method for assessing trace element contamination in agricultural soils, enabling the acquisition of valuable knowledge regarding ecological indices, sources, and spatial distribution of elements in a rapid and convenient manner.