The worldwide increase in fire events has attracted global attention, as potentially toxic elements (PTEs) have been widely recognised within the produced ash. Ash is transported, dispersed by wind, and deposited into the soil and surficial waters even far from fires. Considering that their composition can be enriched in PTEs, they represent a potential hazard for humans and other animals exposed to airborne particles and, afterwards, to resuspended matter, even at a considerable distance from the source. This study aimed to assess the environmental impact of fire events that occurred during the 2017 summer season at two different sites in the Campania region (Southern Italy). One of the fires affected a waste disposal site west of Caserta, and one involved a forest on the slopes of Mt. Somma-Vesuvius, a few kilometres southeast of Naples, the regional capital. Changes to the PTE concentration in the topsoil in the surroundings of both sites, after the fire events, were investigated. Enrichment factors (EFs) of a selection of PTEs were determined by comparing geochemical data from two sampling campaigns, one completed before and one after the fire events. A combined application of multivariate statistics (based on robust principal component analysis; RPCA) and geospatial analysis was used to determine the materials affected by the fire on the slopes of Mt. Somma-Vesuvius, and roughly locate their place. Specifically, a statistically significant enrichment of Hg was identified in the topsoil of both study areas. In addition, in soil samples collected at Mt. Somma-Vesuvius, more PTEs showed significant changes in their concentration. For both areas, Hg enrichments were related to the deposition of ash proceeding from waste burning; furthermore, as regards the soil of the Vesuvian area, Cr and Cd enrichments were associated with the fallout of ash generated during biomass combustion, and the increase in Cu and Zn concentrations was linked to the burning of crops on cultivated lands. Apart from the specific results obtained, concerning the examined case studies, the methods applied can be seen as a reliable option to determine the compositional characteristics of materials burned during a fire event, even with the prospect of improving the eventual assessment process of the related environmental hazards.

Environmental risks often stem from contamination driven by chemical stressors introduced from multiple sources, either geogenic or anthopogenic. Differentiating between anthropogenic chemical anomalies and those inherent to the environment is crucial. This distinction is essential for defining feasible remediation objectives. This study applied univariate and multivariate statistical techniques to analyse geochemical data from over 7000 topsoil samples in Campania (Southern Italy), over an area of approximately 13,600 km². A key step in the methodology was applying Normal Score Transformation (NST), which stabilized the variance of the dataset, pulling the extreme outliers back to normal ranges, making it more suitable for multivariate analysis. Principal Component Analysis (PCA) was performed, and four components were selected; the spatialization of their scores revealed four primary independent sources controlling geochemical variability across the region. Specifically, two distinct volcanic districts were identified, plus a siliciclastic and an anthropogenic component. The integration of RGB composite maps further refined this differentiation, emphasising the coexistence or the predominance of one component over the other. The methodological approach demonstrated here provides valuable insights for environmental risk assessment and remediation planning in geochemically complex and anthropized regions.

This study investigates the application of Compositional Data Analysis (CoDA) and multivariate statistical techniques to geochemical data from the soils of the Campania region. The dataset examined includes 3571 soil samples analyzed for 37 chemical elements. Principal Component Analysis (PCA) was employed to reduce the dataset’s dimensionality and identify key relationships between elements. The first PCA identified groups of highly correlated variables, which were then reduced to 20 representative elements for a second PCA. The three most significant principal components (PC1, PC2, and PC3) explained approximately 65% of the total variability. PC1 (accounting for 29.97% of variability) revealed an anticorrelation between Ti, La, and Sc with Au, Hg, and Ag, with positive scores primarily located in the inland Apennine areas. PC2 (21.8%) was dominated by Na, K, and Cu, with positive scores corresponding to volcanic deposits, aligning with the dispersion patterns of historical Vesuvian eruption products. PC3 (11%) was associated with Ca and S, with higher scores found in the alluvial plains and inland areas. These results demonstrate the efficacy of CoDA in minimizing spurious correlations and uncovering latent relationships between elements, thereby enhancing the interpretation of natural and anthropogenic processes influencing soil variability in the region.

This study applies a hotspot-based spatial statistical approach to investigate the spatial distribution of chemical elements and to improve regional geochemical baseline estimation in topsoils affected by widespread anthropogenic influence. Specifically, this study applied the Getis–Ord Gi* Hotspot analysis on over 7000 topsoil samples from the Campania region (southern Italy), focusing on 21 variables. The analysis revealed statistically significant clusters of high and low concentrations, closely aligned with regional geological features. Elevated levels of As, Ba, Be, Bi, Cu, Sr, Th, Tl, U, and V were mainly observed in soils developed on volcanoclastic deposits, whereas Co, Cr, Ni, and Mn were more common in soils on siliciclastic units. Cd, Hg, Pb, Sb, Sn, and Zn exhibited clustered anomalies in major urban and industrial areas, indicating anthropogenic sources. For these elements, baseline values were estimated. Traditional statistical methods, which primarily rely on data distribution, often overlook spatial autocorrelation, leading to biased thresholds, particularly in areas with widespread contamination. The hotspot-based approach addresses this limitation by excluding hotspot clusters from the calculation of the 95% Upper Tolerance Limit (UTL95-95), thereby providing baseline thresholds uninfluenced by human activity. Comparison with other data-driven methods showed consistent trends across lithologies, although the hotspot-based approach tended to yield slightly lower thresholds, reflecting its responsiveness to spatial patterns.

Geochemical prospecting is a valuable tool for investigating the influence of the geological context on the composition of stream sediment and determining the existence of any natural or anthropogenic geochemical anomaly within a river catchment basin. Several indices have been proposed in scientific literature to assess sediment's environmental quality and the ecological condition of catchment basins. In environmental studies, these indices are usually based on applying ratios among the raw geochemical composition of the sediment at the sampling location and a value assumed as a reference for the undisturbed conditions (Background/baseline). However, the reference values are often determined while overlooking the potential influence of river dynamics on the variability of sediment composition, and this can compromise the robustness of the contamination assessment.As a matter of fact, the chemical composition of a stream sediment sample is representative of the relative upstream catchment basin. The Sample Catchment Basin (SCB) method, which accounts for the dynamic nature of rivers, has been largely used in literature to correct the dilution effect impacting the composition of stream sediment, aiming at determining reliable geochemical background values to be used for mineral prospecting.The main purpose of the study was to check if the use of the correction of the dilution effect to determine background values could also improve the performance of some contamination indices, favouring a more effective and accurate assessment of the environmental degradation affecting a river basin.The Sarno River, known for its susceptibility to contamination from urban and industrial sources, served as a pertinent case study. A total of 96 samples were used to define the zone of influence of each sample through the elaboration of geomorphological and hydrological features; then, the background concentrations of each element were estimated by calculating the weighted average element content based on the areal proportions of lithologic units in each sample catchment basin. The values deriving from this step were then used as a reference to calculate the degree of contamination for each SCB.The contamination indices were calculated and mapped by using both non-diluted and dilution-corrected background data.A comparative analysis was performed among the results obtained to assess if sensitive changes occurred to the spatial and statistical distribution of used indices and to determine if an improvement in performance could be obtained. This study represents a methodological benchmark for future research focusing on environmental risk assessment of stream sediment.