The primary objective of this session is to facilitate a comparative analysis of various methods, encompassing both cutting-edge monitoring and data processing techniques, to offer a real-time assessment of the advantages and disadvantages associated with the diverse approaches presented. Researchers utilizing geochemical data for the assessment of the impact of human activities on the environment or for exploration purposes are encouraged to participate in this session.
While studies focusing on individual matrices are welcomed, research that derives insights from integrated plans involving multiple matrices, including biological ones, is particularly sought after.
Contributions that emphasize data processing techniques utilizing multivariate analysis, machine learning, geostatistics, and other spatial or non-spatial analytical methods are especially encouraged, particularly when they address the compositional nature of geochemical data.
Orals: Thu, 1 May | Room -2.15
It has been revealed that significant disparities in both the organic matter source and hydrocarbon generation characteristics of lacustrine sedimentary environments, causing challenges in the assessment of continental shale oil prospects . Lacustrine-sourced shale oil resources in China exhibits notable longitudinal and vertical heterogeneity, which poses a substantial challenge in objectively assessing geological resources and shale oil prospects, especially in a region characterized by overall low thermal evolution . Advanced pyrolysis or bulk kinetic experiments are invaluable tools to refine the understanding of petroleum generation timing . Nevertheless, such experiments are expensive and time-consuming and hence cannot be executed on extensive sets of samples to capture the overall lateral and vertical variability that a source formation may inherit .In this study, we proposed a new method to rapidly evaluate the hydrocarbon generation characteristics of lacustrine source rocks utilizing anomalies in the Rock-Eval pyrolysis parameter Tmax across various lacustrine shales.
The workflow is depicted as flows: The analysis workflow starts with the selection of samples with TOC exceeding 1 wt.%, given the economic exploration potential of these shales. Subsequently, these samples are categorized into low and high maturity profiles based on the measured vitrinite reflectance (Ro). The two maturity profiles are further classified into low and high Tmax classes using machine learning data analysis. The kmeans clustering method in the Python library scikit-learn was utilized to classify different Tmax values to specific classes . In certain instances, a third cluster or class may be necessary, depending on the data structure. Samples in the “low Tmax” class typically exhibit high Production Index (PI = S1/(S1+S2)) while the Hydrogen Index (HI: = S2/TOC*100) values decrease with increasing maturity. In contrast, the “high Tmax” class maintains consistently high HI and low PI at different maturity levels. This analysis workflow facilitates the identification of distinct hydrocarbon generation characteristics for source rocks at different maturity levels based on the Tmax values.
Overall, the “low Tmax” class shows characteristics of early hydrocarbon generation, low activation energy, and wide hydrocarbon generation windows, while the “high Tmax” class shows characteristics of late hydrocarbon generation, high activation energy, and narrow hydrocarbon generation windows. Notably, these diverse hydrocarbon generation characteristics are mainly related to the composition of the primary organic matter, a correlation that can be confirmed through organic petrographical observations.
This analysis workflow is validated with three examples. There are a great data pool of Tmax，and it is recommended to shift the focus towards source rocks that host organic matter favorable for early oil generation. This involves identifying rocks with low Tmax values and hence low activation energy, as they are indicative of conditions conducive to the initiation of oil generation. When it comes to in-situ heating, The exact prediction of hydrocarbon generation processes enables a more precise calculation of current geological recoverable resources. This study has important guiding significance for oil and gas exploration.
Fig. 1. Workflow for determining hydrocarbon generation characteristics of source rocks by a classification according to Tmax variability.
How to cite: Liu, S., Liu, S., Misch, D., Shi, X., Bian, C., Zhao, W., and Zhu, R.: A new approach to evaluate hydrocarbon generation characteristics by pyrolysis Tmax in lacustrine shale oil plays, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7643, https://doi.org/10.5194/egusphere-egu25-7643, 2025.
The traditional kinetic model reflects the impact of fixed pressure on hydrocarbon generation in thermal simulation experiments, but the pressure in experiments differs from the overpressure in formation, affecting the evaluation of hydrocarbon generation. In this study, a new parallel first-order reaction kinetic model for hydrocarbon generation in relation to formation overpressure retardation is proposed and its application is illustrated.
According to the impact of pressure on activation energy (E) and pre-exponential factor (A), the pressure factor is introduced into the Arrhenius formula:
k=A·exp(p/a)·exp(-(n⋅p+E)/RT)
Where k is the reaction rate at 1 bar, R is the universal gas constant, T is the absolute temperature (K), p is pressure (MPa), a and n are the impact factors of pressure on A and E respectively.
By calculating n and a, the new model can simulate hydrocarbon generation under any temperature and pressure and is no longer limited by experimental conditions.
Sample from Baiyun Depression in the Pearl River Mouth Basin were selected to carry out a gold-tube thermal simulation, and the kinetic parameters of natural gas generation were calculated by using the new model. Since the traditional model reflects the impact of experimental pressure, the new model calculates the kinetic parameters without the impact of pressure, therefore, the average activation energy (Ea) calculated by the traditional model is greater than that of the new model, and the impact of pressure is reflected by impact factors (n and a)(Fig. 1).
Figure 1 Comparison of kinetic parameters of natural gas generation calculated by traditional model and new model
The new model reflects the inhibition effect of overpressure on natural gas generation (Fig. 2). According to the information on fluid inclusions, The kerogen began to generate mass gas at 23 Ma. The calculation results of the traditional model show that kerogen starts to enter the large-scale gas generation stage at 32 Ma, which is inconsistent with the time of overpressure formation. When the formation pressure coefficient is 1.8, the mass hydrocarbon generation time calculated by the new model is about 24 Ma, which is more consistent with the geological reality. The new model proves that natural gas generation is retarded under the impact of overpressure.
Figure 2 Comparison of the conversion rate of natural gas of the traditional model and the model in relation to overpressure under geological conditions.The black line reflecting the influence of pressure under experimental conditions; the blue line is the history of natural gas generation without the impact of overpressure; the orange line is the history of hydrocarbon generation under formation overpressure.
How to cite: Jiang, J. and Li, J.: Overpressure retardation of hydrocarbon generation: a new kinetic model considering the effects of pressure and its application, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8179, https://doi.org/10.5194/egusphere-egu25-8179, 2025.
Polycyclic aromatic hydrocarbons (PAHs) are a group of toxic organic pollutants that originate from the incomplete combustion of organic matter. Due to lipophilic and hydrophobic properties, PAHs tend to be adsorbed by soil particles. Source apportionment of soil-bound PAHs contributes to controlling emissions and protecting the ecological environment. This research investigated source and distribution characteristics of PAHs in soils from Campania region of Italy, especially the natural sources. The total PAH concentrations ranged from N.D. to 4191 ng/g soil (dry weight). The data do not follow either a normal or lognormal distribution, but rather absolutely the multifractal distribution. Spatially distributed PAHs have experienced different degrees of superposition on the basis of multifractal spectrums curves with asymmetric upper convex. In addition, multifractal spectrum curves are all in a right hook shape, representing that low-values are dominant in the Campania area. The local singularity analysis shows an enrichment phenomenon that is not identified by the spatial interpolation method. The singularity values of PAHs were significantly correlated with TOC, but not significant with pH and population density. As opposed to concentrations, singularity indexes mainly reflecting the influence of soil properties. The fractal method was successfully used to separate the natural sources from the anthropogenic contributions of PAHs. Our results indicate that PAHs mixing distributions may be decomposed into natural background, anthropogenic background, and point source pollution. The background field was attributed to the thermal effects of geological processes, whereas anthropogenic anomaly was associated with anthropogenic activities. In sum, our study provides evidence of natural sources evidence that volcanic events have key effects on the distribution of PAHs, and shows that anthropogenic sources of PAHs are related to regional industrialization and urbanization status.
How to cite: Shi, C. and Qu, C.: Decoupling natural and anthropogenic polycyclic aromatic hydrocarbons in the soil environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2421, https://doi.org/10.5194/egusphere-egu25-2421, 2025.
We investigate the intricate patterns associated with space-time dynamics displayed by (i) surface topography (Z) and (ii) reaction rates (R) resulting from direct nano-scale imaging of calcite-water interfaces subject to dissolution. The analysis rests on a space-time variogram modeling approach. The latter has been suggested as promising in unveiling major patterns exhibited by hydrogeological quantities across large scale aquifer systems. Transferability of the associated theoretical and operational framework to interpret nano-scale geochemical scenarios is here assessed for the first time. We do so upon taking advantage of recent high-resolution experiments attained through Atomic Force Microscopy (AFM) and targeting the evolution of the interface between a calcite crystal and water as driven by mineral dissolution processes. Upon relying on the ensuing large data-set, key variability patterns are identified through (i) efficient sampling of the spatial domain via quasi-random Sobol sequences and (ii) the use of a Harmonic Covariance Estimator (HCE) to model the space-time variogram of Z. The resulting (sample) space-time variogram exhibits visibly periodic oscillations at specific spatial and temporal lags. These patterns highlight the interaction taking place between the spatial structure and temporal dynamics in hydrogeological processes. We also explore the theoretical bases of the relationship between the variograms of Z and R. Corresponding results offer valuable insights into the spatial and temporal correlation of calcite dissolution dynamics. Our findings enable one to link space-time dynamics of crystal topography and the ensuing dissolution rates to corresponding traits of space-time variograms. Hence, they constitute the basis for potential applications associated with the possibility of providing estimates of the way these complex processes evolve at nano-scale resolutions, thus driving chemical weathering of minerals constituting the Earth’s interior.
How to cite: Varouchakis, E., Recalcati, C., Ceresa, L., Riva, M., and Guadagnini, A.: Geostastical modeling of space-time dynamics calcite dissolution at the nanoscale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12283, https://doi.org/10.5194/egusphere-egu25-12283, 2025.
Many environmental and geological phenomena are inherently complex, shaped by the interplay of physical, chemical, biological, and anthropogenic factors. These interactions often result in spatial asymmetries, where high and low values exhibit distinct statistical behaviors. For instance, a contaminant field may simultaneously reflect natural and anthropogenic sources, producing unique spatial patterns that challenge conventional analysis.
Despite significant advancements in geostatistics, multivariate spatial models remain limited. The linear model of coregionalization (LCM) dominates the field but assumes symmetrical dependencies and Gaussian behavior. These assumptions restrict its ability to capture the structural complexity of multivariate spatial data, potentially obscuring meaningful relationships or introducing misleading correlations.
This presentation introduces a stochastic methodology for simulating non-Gaussian multivariate random fields using a non-linear model of coregionalization (N-LCM). The proposed approach accounts for rank asymmetry (differing spatial dependencies for low and high values) and directional asymmetry (variations in spatial dependence across directions). It supports multiple dependencies between variables, allowing some to exhibit Gaussian behavior while others display non-Gaussian characteristics. Pseudo-admissible N-LCMs are approximated through spectral decomposition, with negative eigenvalues replaced by zero.
The methodology leverages an adapted Generalized Fast Fourier Transform Moving Average (G-FFTMA) algorithm for multivariate non-Gaussian geostatistical simulations, offering a flexible and efficient framework for analyzing and simulating complex datasets. Synthetic examples demonstrate the method’s ability to uncover meaningful spatial patterns. Additionally, a real-world case study highlights the duality between natural contamination and anthropogenic emissions from a smelter in Quebec, Canada. This case study emphasizes the methodology’s capability to analyze geochemical processes influenced by human activities and environmental interactions.
This research advances geostatistics and multivariate analysis, providing new insights into geological and environmental processes at the Earth’s surface.
How to cite: Lauzon, D., Hörning, S., and Bárdossy, A.: A Novel Framework for Stochastic Simulation of Multivariate Non-Gaussian Random Fields in Environmental and Geological Studies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7198, https://doi.org/10.5194/egusphere-egu25-7198, 2025.
The geochemical background/baseline is a key parameter for assessing environmental contamination and identifying potential risks to ecosystems and human health. However, the determination of these values requires careful handling of geochemical data. In practice, the geochemical background of chemical elements can be determined by both empirical and geostatistical methods. In addition, depending on the sampling scale and the area's natural landscape-geochemical characteristics, especially in biogeochemical provinces, the use of a single approach can lead to bias. Therefore, combining both approaches and incorporating several methods of geochemical data processing and spatial clustering is needed to unveil the hidden patterns and delineate representative areas, where separate processes and factors (natural and anthropogenic) condition the contents and distribution of chemical elements. In addition, adding auxiliary information related to the geological setting, soil types and potential sources of contamination can ensure the refinement of the data processing and increase the reliability of the estimated background/baseline values. This study aims to determine the geochemical background/baseline of arsenic (As) in the Lori region (Armenia) by dividing the soil data set into homogeneous subsamples using an algorithm combining data transformation, hot spot analysis (Local Moran I), univariate outlier detection and concept of normal distribution. The results of the study showed that the application of the Local Moran I index allows to identify clusters of samples (of high-high (HH) values, low-low (LL) values and not-significant (NS) values) that have a clear spatial separation. The boxplots of the As contents in the identified subsamples showed that outliers and extreme values are presented. After the elimination of these values, normal distribution was confirmed (Shapiro-Wilk test). The median value of As was 13 mg/kg, 16 mg/kg and 24 mg/kg for LL, NS and HH values, respectively. Meanwhile, the 95 percentile of the LL and NS values were 16 mg/kg and 21.4 mg/kg, respectively. The cluster of HH values spatially covers an area known for its natural mineralization, mining sites and Cu smelter, implying some level of anthropogenic quantities of As which is superimposed on the natural contents. Therefore, the estimated value for this area can be considered as a geochemical baseline rather than a geochemical background. The results of this study showed that in the case of biogeochemical provinces where natural mineralization and anthropogenic activities are presented, several background/baseline values can be determined. The algorithm proposed in this study can be used for other elements and serve as a justified approach to separate homogeneous subsamples and delineate areas for the application of these reference values.
How to cite: Tepanosyan, G., Poghosyan, Z., Gevorgyan, A., Davtyan, K., and Sahakyan, L.: Determination of geochemical background/baseline values in a biogeochemical province (a case study of the Lori region, Armenia), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6105, https://doi.org/10.5194/egusphere-egu25-6105, 2025.
To investigate particulate material dynamics, 48 soil, river bottom sediment, and river suspended particulate matter (SPM) samples were collected using a stratified sampling method in the Koppány River Basin, Hungary. Samples were analyzed via inductively coupled plasma mass spectrometry for the concentration of 44 elements, encompassing heavy metals, "light" metals, and rare earth elements. Multivariate statistical methods, particularly hierarchical cluster analysis and principal component analysis (PCA) were applied to identify patterns and drivers of material distribution across the catchment.
The PCA results revealed distinct partitioning of particulate material sources and transport behaviors. The first principal component (PC1) distinguished SPM samples from soil and sediment samples, underscoring the contrasting geochemical signatures of material mobilized during different flow conditions. The second principal component (PC2) separated SPM samples collected during low flow conditions from those collected during high flow conditions, reflecting hydrological influences on particulate transport and source contributions. Notably, spatial differences between the upper and lower parts of the catchment were found to be less significant than the temporal dynamics driven by flow conditions.
These preliminary findings highlight the pivotal role of hydrology in governing the geochemical composition of suspended materials and provide insights into sediment dynamics in hilly river basins. The study demonstrates the utility of multivariate approaches in disentangling complex interactions between geological and hydrological processes in catchment systems.
How to cite: Kardos, M. K., Jolánkai, Z., and Clement, A.: Applying fingerprinting methods on multielement measurements to track sediment transport in a small erosion-prone hilly catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12894, https://doi.org/10.5194/egusphere-egu25-12894, 2025.
The Muyira sector is one of ten sectors of the Nyanza district in the Amayaga region of Rwanda. Amayaga region is part of the country's drought-prone areas, with high groundwater dependence. Most inhabitants in the area depend on groundwater for drinking and domestic purposes. Therefore, a hydrogeochemical characterization and assessment of groundwater quality in the study area was carried out using a combined application of hydrochemical models, multivariate statistical techniques, and GIS-based ordinary kriging interpolation on seven (7) borehole water samples. This study aimed to determine the concentrations and spatial distribution of various ions, groundwater quality issues, and the geochemical processes contributing to groundwater chemistry. The abundance of major cations in the groundwater is in the order Na+ > Ca2+ > K+ > Mg2+, whereas that of the major anions varies in the order HCO3− > SO42− > Cl−. Ca-Mg-Na-HCO3 water type is common in the area, possibly due to the dissolution of magnesite and silicate minerals in the basement rocks. Also, results indicate weak acids (i.e., HCO3−) dominance over strong acids (i.e., SO42− and Cl−). Ion exchange reactions and magnesite and silicate minerals weathering primarily control the area's groundwater chemistry. The results of the Pollution Index for Groundwater (0.29-0.55) and Groundwater Quality Index (29.14-53.68) indicate groundwater in the area is suitable for drinking. The sodium percentage (36.88–78.20%, mean of 57.83%), magnesium ratios (13.90–94.66, mean of 35.70), and sodium adsorption ratio (4.63–16.92, mean of 11.78) suggests that groundwater in the study area is suitable for irrigation purposes.
How to cite: Ngendahayo, E., Impuhwezayo, M., Nkurunziza, E., and Nsengiyumva, J. N.: Hydrogeochemical characterization and assessment of groundwater quality in Muyira Sector, Rwanda., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12634, https://doi.org/10.5194/egusphere-egu25-12634, 2025.
The hyperaccumulation behaviour of PTEs is observed in many edible plants. However, the role of soil geochemistry and the human health risks associated with the uptake of PTEs by hyperaccumulator plants remain poorly understood. This study analyses 10 topsoil and Brassica rapa samples collected from volcanic and clay soils, comparing the contents of As, Cd, Hg and Pb and assessing their relative health risks. To account for geochemical variations in volcanic and clay soils, samples were collected from two Italian regions (Sicily and Campania) characterized by different geological settings. The results indicate that volcanic soils exhibit higher concentrations of PTEs than clay soils, with Hg levels exceeding precautionary limits established by EU soil quality standards. Notably, Campania shows the highest concentrations of PTEs in soils, attributable to evolved magmatic products with tephritic-phonolitic composition. In clay soils, Sicilian samples reveal significant enrichment in Cd, while As, Hg and Pb are more concentrated in Campanian clays. Soil quality standards are not exceeded in clay soils. Regarding plant tissues, concentrations of Cd, Hg and Pb in edible organs (stems and leaves) exceed FAO-WHO standards in most samples from volcanic soils, with values up to 3, 8, and 14 times higher than the standards. Plants grown in clay soils show lower concentrations of PTEs than those grown in volcanic soils; only one Sicilian sample exhibits concentrations of Cd, Hg, and Pb 4, 1.5, and 6 times above FAO-WHO standards, respectively. The bioconcentration factor (BCF) and translocation factor (TF) confirm the hyperaccumulating behaviour of Brassica rapa, with concentrations of PTEs in roots and stems sometimes exceeding those present in the soil. Risk analysis revealed that total cancer risk and target hazard quotient are unacceptable for adults and children who consume Brassica rapa from volcanic soils. Both parameters generally show acceptable values in clay soils, with alarming levels only for high consumption rates. Finally, although Sicilian soils are generally impoverished in PTEs, Brassica rapa samples from this region exhibit higher levels than those from Campania. Therefore, while soil geochemistry is a crucial factor in metal absorption by Brassica rapa, other parameters (e.g., climatic, environmental, and biological) also play a significant role.
How to cite: Ambrosino, M., Puglia, G. D., Di Salvo, E. M., Saini, S., Cicero, N., and Cicchella, D.: The role of soil geochemistry in the absorption of potentially toxic elements (PTEs) by edible hyperaccumulator plants: the case of Brassica rapa in volcanic and clay soils., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18176, https://doi.org/10.5194/egusphere-egu25-18176, 2025.
Soils result from physical, chemical, and biological processes that affect rocks and their weathered products. In historical times, natural processes have also been widely influenced by human activity (such as industrial production, motor vehicle mobility, waste disposal, and agricultural practices). Consequently, soils represent a reservoir of chemical elements and compounds with extreme spatial variability across Earth's surface.
Defining the distribution of chemical elements and their anomalies and understanding the nature of factors controlling their spatial variability is essential for those committed to environmental issues management, especially when effects on ecosystems and living beings must be addressed, targeting the development of remedial actions.
In recent years, with the rapid data volume growth, effective methods are required for data analytics for large geochemical datasets. Spatial machine learning technologies have been proven to have the potential to reveal hidden patterns based on geochemical information. In this study, a spatial clustering technique of Getis-Ord Gi* statistic was performed on 21 characterizing elements using more than 7000 topsoil samples (~ 7300) proceeding from the Campania region territory in southern Italy.
The analysis found spatial clusters of significantly high (hot spots) and low values (cold spots) for the selected elements, showing a strong correlation with the geological features of the study area, particularly volcanic and siliciclastic units.
Volcanic units were associated with high concentrations of elements such as As, Ba, Be, Bi, Cu, Sr, Th, Tl, U, and V, while siliciclastic units were associated with high values of Co, Cr, Ni, and Mn. Additionally, the high concentration of Cd, Hg, Pb, Sb, Sn and Zn showed a clear association with the region's main urban and industrial centres.
The results highlight the power of spatial clustering techniques in discriminating geogenic from anthropogenic processes and identifying hidden spatial patterns, thus offering valuable insights for environmental studies and management.
How to cite: Albanese, S., Iannone, A., Zhang, C., Guarino, A., De Falco, A., and Pacifico, L. R.: Hotspot analysis for discriminating geochemical anomalies in the soil of an intensely anthropized volcanic region in Italy., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17328, https://doi.org/10.5194/egusphere-egu25-17328, 2025.
Posters on site: Thu, 1 May, 16:15–18:00 | Hall X4
The interaction between the unique geochemical characteristics of the soils and the social, environmental, climatic and biodiversity factors give distinctive properties to the wines of a specific area, defining the terroir of a wine. While climatic, ecological, and social aspects remain relatively stable within the limited extension of terroirs, soil geochemistry can change notably. Variations in soil geochemistry play a significant role as they influence vine health, grape quality and, ultimately, the flavour profile of the wine. This study aims to highlight the chemical and granulometric differences occurring in the Taurasi terroir (Southern Italy) to improve its management and enhance the diverse flavours and aromas of the wines. The Taurasi terroir encompasses an area of 245 km², within which soils belong to three different geochemical domains (clay, volcanic, and carbonate soils). Chemical elements affecting colour and aroma (Cu, Fe), taste (Na, K), metabolism and photosynthesis (B, Mn, Zn), or potentially toxic elements for vines (Al, As) were analysed in 100 topsoil samples distributed across the three geochemical domains. Additionally, granulometry and organic carbon content were also analysed to assess soil's ability to retain water and microbial populations. Results revealed significant compositional differences among the three geochemical domains that inevitably reflect in wine characteristics. Except for Mn, volcanic soils were enriched in all analysed elements, while carbonate soils were depleted. Following the order of volcanic soils – clay soils – carbonate soils, the average concentrations of analysed elements were as follows: As (16 - 7 - 3 mg/kg); Al (5.5 - 3.2 - 1.8 %); B (24 - 10 - 6 mg/kg); Cu (78 - 52 - 28 mg/kg); Fe (3.2 - 2.7 - 1.8 %); K (1.6 - 0.7 - 0.4 %); Mn (950 - 1470 - 820); Na (0.6 - 0.05 - 0.05 %); Zn (105 - 62 – 58 mg/kg). From a granulometric perspective, volcanic soils were coarse-grained, followed by carbonate and clay soils. The average granulometry for the three geochemical domains is as follows: volcanic soils (72% sand, 13% silt, 5% clay); carbonate soils (26% sand, 58% silt, 16% clay); clay soils (5% sand, 13% silt, 72% clay). Average organic carbon values were also favourable for volcanic soils (4.5%), followed by carbonate soils (3.2%) and clay soils (1.4%). These results show that significant compositional and granulometric differences within the Taurasi terroir are reflected in the expression of grapes and wines produced. Therefore, this study provides key tools for micro-zoning terroirs to enhance diverse flavour, colour, and aroma expressions within the same terroir.
How to cite: Cicchella, D., Ambrosino, M., Guagliardi, I., and Albanese, S.: Harnessing soil geochemistry and granulometry for optimal terroir management and wine profiling: insights from the Taurasi terroir of southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17982, https://doi.org/10.5194/egusphere-egu25-17982, 2025.
Qinghai Province, in northwest China's low-density population areas, is known for its high-altitude landscape and abundant surface water. This study investigates 48 sediment samples from 19 quintessential plateau lakes distributed in the Qilian Mountains, Qaidam Basin, Yellow River source regions, and Yangtze River source regions of Qinghai Province, focusing on 7 potentially toxic elements (PTEs ) and 16 priority polycyclic aromatic hydrocarbons (PAHs). Compared to other plateau lake sediments domestically and internationally, concentrations of PTEs ( Ni, Zn, Pb, Cu, Cr, Co, and As) and PAHs (Nap, Acy, Ace, Flu, Phe, Ant, Fla, Pyr, BaA, Chr, BbF, BkF, BaP, IcdP, DahA, and BghiP) in the studied sediment samples were relatively low. Spatial distribution characteristics of PTEs and PAHs contents show that the similar trend between the four regions was like that: Qaidam Basin> Yangtze River source>Yellow River source> Qilian Mountains. Additionally, positive matrix factorization (PMF) and species sensitivity distributions (SSD) are employed to assess pollution source levels and ecological risks in the study lake regions,respectively.The study reveals that the total average concentrations of PTEs and PAHs in the sediments of the 19 Qinghai lakes were individually 132.93 mg/g and 27.64 ng/g, which were quite low compared to lake sediments in other plateau regions both domestically and internationally. PMF analysis identified fisheries, animal husbandry, mining, rock weathering, and agriculture as PTE sources.While oil leakage, combustion, and auto emissions were identified as PAH sources. It is found that SSD-based health risk assessment shows the risks was far below the acceptable threshold (0.1). However, the highest risks were concentrated mainly in the downstream areas of the estuaries and near tourist and agricultural sites, such as the studied sites in Keluke Lake (Qaidam Basin), Eling Lake (Yellow River source region), and Tuosu Lake (Qaidam Basin). Notably, Cu, As, and Phe exhibited higher ecological risk indices. In general, despite the current research indicating low and negligible health risks posed by PTEs and PAHs in these plateau lakes, it is necessary to keep monitoring and controlling to prevent any escalation of ecological risks in the fragile lake environment.
How to cite: Sun, W., Zhang, J., Wang, W., and Qu, C.: Sources apportionment and ecological risk assessment of potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) in surface sediments of plateau Lakes of Qinghai Province, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10689, https://doi.org/10.5194/egusphere-egu25-10689, 2025.
The 21st century is the era of big data, where scientific research under the new paradigm of data and model-driven knowledge discovery has become the new trend in the scientific field. Under the framework of the international science project "Deep-time Digital Earth" (DDE), the research team led by Dr. Wang Tao has constructed the OnePetrology database for magmatic rocks. This database adopts a three-in-one approach of "data + mapping + research," based on the magmatic rock knowledge system, with samples as the core, with self-developed tools to build a magmatic rock database system that integrates big data aggregation and mapping analysis functions.
The OnePetrology database system includes backend services (cloud), a website (Web), and a scientific research work platform (desktop). The data mainly comes from publicly published literature, tests conducted by the research team, laboratory test data, etc. The data types include basic information on magmatic rock rock types, occurrences, spatial locations, as well as geochronology, geochemistry, (Sr-Nd-Hf-Pb-O) isotopes, and non-traditional (or emerging) isotope data, involving global important orogenic belts, cratons, and some oceans (ocean drilling data).
The database has preliminarily completed the framework construction and has incorporated a portion of the data, initially forming methods and processes for data aggregation. Currently, there are two ways to contribute data: first, volunteers enter data in the data "data submission portal" set up for this purpose. The database system has currently built 22 thematic databases and welcomes more disciplinary experts to come to the magmatic rock database to build their own thematic databases.
The core idea of the database's functionality is to combine big data and software tools for scientific research and exploration. Taking the website as an example, it provides data filtering tools and mapping analysis tools. Data filtering tools include spatial filtering and attribute filtering: spatial filtering can pull cross-sections (set radius), rectangles, custom polygons, global tectonic units, global cratons, China and neighboring areas' main tectonic units, and other search methods, while attribute filtering can set search conditions, value ranges, and fuzzy queries for all fields. Spatial filtering and attribute queries can be used separately or in combination, with query results displayed in table form, spatial distribution maps, and mapping functions. The mapping functions currently built in the database include TAS, Pearce, SiO2-K2O, ACNK-CNK, 2D Density, Heatmap, Profile, etc., each supporting secondary filtering and grouping, facilitating users to quickly discover data patterns. Currently, online analysis can provide processing capabilities for about 20,000 samples (assuming average desktop computing capabilities). If you need to process larger data volumes, please download the desktop software from the homepage of the website (https://dde.igeodata.org).
In summary, the DDE OnePetrology magmatic rock database has preliminarily constructed the capabilities for magmatic rock data aggregation and mapping analysis, with other planned functions being built step by step. We warmly welcome more interested experts to participate in the construction and use of the magmatic rock database and to offer your valuable suggestions.
How to cite: Ding, Y., Wang, T., Tong, Y., and wang, C.: An easy way to build database and analyze data using OnePeterology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20237, https://doi.org/10.5194/egusphere-egu25-20237, 2025.
Environmental risks are often linked to contamination processes driven by various chemical stressors introduced into the environment from specific sources. These sources may be anthropogenic, stemming from human activities, or natural, associated with the geolithological context and geological weathering processes. It is crucial to distinguish between chemical anomalies resulting from anthropogenic inputs and those arising from the inherent compositional characteristics of the natural environment, which may not be remediable. This differentiation is essential for establishing reliable and practical remediation objectives.
When anthropogenic activities release waste products into the environment in airborne, liquid, or solid forms, these materials typically possess distinct chemical compositions. Such compositions frequently involve associations of substances that can disrupt environmental equilibria. This study employed both univariate and multivariate statistical techniques to analyze geochemical data from over 7,000 topsoil samples collected in the Campania region of Southern Italy. The objective was to develop an operational model for assessing environmental risks by identifying their sources. The database encompasses the concentrations of 52 chemical elements for each sample, with data georeferenced to facilitate spatial analysis, delineate geochemical patterns, and correlate anomalies with known human or natural sources.
The complex geological setting of the Campania region, combined with the diverse sources of anthropogenic contamination, rendered Principal Component Analysis (PCA) an especially effective method for identifying element associations that predominantly influence the variability of the geochemical data. PCA was conducted using a selection of 21 variables, resulting in the identification of four significant principal components (PCs) that account for the majority of the observed data variability:
- PC1 (42% of total variance), characterized by Th, Be, As, U, V, and Bi.
- PC2 (16% of total variance), characterized by Sb, Zn, Hg, Pb, Sn, and Cd.
- PC3 (10% of total variance), characterized by Mn, Ni, Cr, and Co.
- PC4 (9% of total variance), characterized by Ba, Cu, and Sr.
The scores of the components for each sample were spatially plotted and classified to enhance their interpretability. The legend for the component scores was centered at zero, indicating the minimal contribution of the covered areas to overall component variability; higher absolute values identified areas where the featured elemental association was more significant.
The analysis effectively differentiated soils predominantly influenced by natural contributions, such as loose materials from the volcanic centers of Campania (e.g., Mt. Roccamonfina, Campi Flegrei, and Mt. Somma-Vesuvius) (PC1 and PC4) and weathering products from the region's siliciclastic deposits (PC3). Furthermore, the PCA found areas subjected to considerable anthropogenic pressure concerning the association of Sb, Zn, Hg, Pb, Sn, and Cd (PC2). These findings underscore the effectiveness of multivariate analysis, particularly PCA, in discriminating between geogenic and anthropogenic processes and, further, in distinguishing among various anthropogenic sources. This methodological approach offers valuable insights for managing environmental risks and prioritizing remediation efforts.
How to cite: Iannone, A., Zhang, C., Guarino, A., De Falco, A., Pacifico, L. R., and Albanese, S.: The application of Principal Component Analysis to reveal the contributions of various natural and anthropogenic sources to the chemical composition of soil., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16437, https://doi.org/10.5194/egusphere-egu25-16437, 2025.
The Sarno River Basin (SRB) is among the most polluted in Europe, with contamination deriving from industrial activities, including tanneries and canneries, as well as intensive agriculture and dense urbanization. It is located in the Campania region in Southern Italy, in the southwestern portion of the Campania Plain, between the Somma–Vesuvius volcanic complex to the west and the Lattari Mountains carbonate reliefs to the south.
The SRB is characterized by the presence of high concentrations of some priority organic pollutants, deriving from their use in various processes linked to human activities (e.g., agriculture, industry, mining, vehicular traffic). The soils of the basin, predominantly fine-grained alluvial and volcanic deposits, provide an ideal matrix for the retention of these pollutants, further enhanced by the high organic matter content.
Organochlorine pesticides (OCPs) are synthetic organic compounds extensively utilized in agriculture as insecticides and fungicides during the mid-20th century and, subordinately, in the medical field. These chemicals are among the most common soil contaminants, especially in highly industrialized and anthropized areas; they show a high environmental persistence and are generally characterized by a marked tendency towards bioaccumulation and biomagnification along trophic chains due to their lipophilic character.
The study aims to assess the geochemical-environmental conditions of the SRB, through GIS-based maps and univariate statistical analysis, also establishing the nature of their potential emission sources. For the purpose, over an area of about 500 km2 a total of 42 topsoil samples were collected to be analyzed to determine the concentration levels of 24 OCPs.
To investigate the distribution pattern of concentrations, the compounds were grouped into six classes:
- I) dichloro-diphenyl-trichloroethane (DDT) and its isomers and metabolites (DDE, DDD), whose Ʃ6DDTs concentrations represent on average 68.7% of the total OCPs and ranges from a minimum of 0.021 µg/kg to a maximum of 339 µg/kg;
- II) hexachlorocyclohexane (HCH) isomers (α, β, γ, δ), with Ʃ4HCHs representing 2.64% and ranging from 0.013 µg/kg to 7.84 µg/kg;
- III) aldrin, dieldrin, and endrin, whose Ʃ3Drins varies from 0.010 µg/kg to 71.7 µg/kg and constitutes the 6.61%;
- IV) heptachlor, chlordane (α, γ), and nonachlor (cis, trans), constituting on average 1.39% with Ʃ5Chlors from 0.016 µg/kg to 0.94 µg/kg;
- V) Endosulfan (α, β), and Endosulfan sulfate, whose Ʃ3Endos varies from 0.010 µg/kg to 19.3 µg/kg and represents 14.5%;
- VI) mirex, methoxychlor and hexachlorobenzene, constituting 6.18%, with total values from 0.010 µg/kg to 7.01 µg/kg.
Because some OCPs tend to degrade over time and the technical pesticides (i.e., DDT, HCH, chlordane, endosulfan) consist of precise percentage of the different molecules, the ratio between the parent compound and its metabolites can be used as pollution sources indicators. This helps identify whether the concentrations are attributable to fresh or historical use of these substances. The analyzed isomeric ratios showed that, although most OCPs are banned, recent applications of pesticide mixtures still contribute to high soil concentrations in some parts of the study area.
How to cite: Guarino, A., Iannone, A., De Falco, A., and Albanese, S.: Environmental Distribution and Source Analysis of Organochlorine Pesticides in the Soils of the Sarno River Basin, Southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18593, https://doi.org/10.5194/egusphere-egu25-18593, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 4
EGU25-10723 | Posters virtual | VPS19
Investigation of polycyclic aromatic hydrocarbons in the soils of Benevento Province, ItalyTue, 29 Apr, 14:00–15:45 (CEST) | vP4.7
Benevento Province, located in the Campania region of Italy, may experience environmental quality impacts from neighboring developed areas such as Naples and Caserta. Previous studies have suggested that some agricultural chemicals from Naples, such as hexachlorobenzene, may be transported through the air to rural areas of Benevento. Additionally, high concentrations of polycyclic aromatic hydrocarbons (PAHs) have been detected in Naples and Caserta, making Benevento Province a potential PAH "sink." This study systematically investigated the occurrence of PAHs in soil from Benevento Province, southern Italy, and their correlations with environmental factors, soil-air exchange processes, and health risks. Over 95% of sampling sites exhibited ∑16PAHs concentrations at non-polluted levels (9.50-1188 ng/g, mean = 55.0 ± 152 ng/g), and four-ring PAHs were the dominant pollutants contributing to 28.3% of ∑16PAHs. The spatial distribution of PAHs presented significant heterogeneity, with hotspots concentrated near landfills. The results of Positive Matrix Factorization (PMF) model showed that the main sources of PAHs were vehicle emissions, coal/biomass combustion, and petroleum products volatilization/leakage, contributing 42.2%, 40.2%, and 17.6%, respectively. Most of PAHs correlated significantly with total organic carbon in the soil and population density, while only Benzo(b)fluoranthene (BbF) showed a significantly negative correlation with pH. The mass inventory of ∑16PAHs ranged from 0.94 to 29.4 tons, averaging 2.45 tons. The synergistic effects of pollution hotspots and the persistent accumulation of PAHs in the soil suggested that the soil might act as a secondary source of PAHs. Toxicity equivalent and probabilistic risk assessments indicated that health risks from PAHs remained within acceptable limits.
How to cite: Qu, C. and Pu, C.: Investigation of polycyclic aromatic hydrocarbons in the soils of Benevento Province, Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10723, https://doi.org/10.5194/egusphere-egu25-10723, 2025.
EGU25-4641 | Posters virtual | VPS19 | Highlight
Size distribution of elemental components in atmospheric particulates from a typical industrial and mining city of Central ChinaTue, 29 Apr, 14:00–15:45 (CEST) | vP4.8
As one of crucial factor in atmospheric particulate matter, elemental components exhibit distinct distribution features within different particle size ranges. Crustal elements (such as Al, Si, Fe, Ca, Mg) are primarily concentrated in coarse particulate matter, whereas elements originating from anthropogenic pollution sources (such as heavy metal elements including Pb, Zn, Cd, As, Cr) are more frequently distributed in fine particulate matter. Furthermore, some specific elements may also exhibit peak concentrations in particular particle size, which is closely related to their sources and formation processes. In recent years, there are still some challenges and deficiencies. Further research is needed on the particle size distribution characteristics of complex pollution sources (such as industrial emissions and traffic emissions). Additionally, there is a need to enhance the understanding of the transformation mechanisms and health effects of elemental components within particulate matter. This study selected a typical industrial and mining city to investigate particle size distribution characteristics of elemental components in atmospheric particulate matter. Anderson Eight-Stage Particulate Impactor Sampler was used to collect atmospheric particulate matter in the urban area of Huangshi during winter and summer. Nine particle size range samples were obtained spanning from 0 to 0.4 µm, 0.4 to 0.7 µm, 0.7 to 1.1 µm, 1.1 to 2.1 µm, 2.1 to 3.3 µm, 3.3 to 4.7 µm, 4.7 to 5.8 µm, 5.8 to 9.0 µm, and 9.0 to 10 µm. Energy Dispersive X-Ray Fluorescence Spectrometry (ED-XRF) was employed to determine the concentrations of 17 elemental components, including S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sb, Ba, and Pb. Elements Ca, S, Fe, K, Zn, Ba, and Pb were identified as the primary pollutants during the sampling period. All the elemental concentrations exhibited distinct seasonal variations, demonstrating higher levels in winter compared to summer. Each element demonstrated distinct particle size distribution characteristics with peak concentrations for most elements occurring in the 5.8 to 9.0 µm range and peaks for the remaining elements in the 0.4 to 1.1 µm range. The highest elemental concentrations in both summer and winter were mainly distributed in the 5.8 to 9.0 µm and 0.7 to 1.1 µm size ranges. In summer, most elemental concentrations were negatively correlated with relative humidity. However, in winter, there was no significant correlation with relative humidity. Rainfall had a certain scavenging effect on elements but was also influenced by other meteorological factors. Element S had the highest enrichment factor values in both summer and winter. Element Cl was highly enriched in finer particle size fractions in both seasons. Most elements were slightly enriched across all particle size fractions. Principal component analysis further identified the main sources as soil dust and wind-blown sand, coal combustion, vehicle exhaust emissions, biomass burning, mining and construction activities, and other pollution sources. These findings contribute to the formulation of effective pollution control measures and the protection of public health.
How to cite: Liu, H., Zhang, J., Zhan, C., Liu, S., Liu, T., and Xiao, W.: Size distribution of elemental components in atmospheric particulates from a typical industrial and mining city of Central China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4641, https://doi.org/10.5194/egusphere-egu25-4641, 2025.
