Although traditional field-based methods for evaluating soil degradation and contamination, and assessing restoration outcomes are essential, they are labour-intensive, time-consuming, and spatially constrained. Integrating remote sensing with in-situ observations and modelling enhances the ability to map degradation, identify hotspots and drivers, and quantify recovery trajectories. This session combines the in-situ evaluation of the relationship between soil quality and environmental and human health, via exposure to contaminants, with the use of remote and proximal sensing to monitor and assess soil degradation and its impacts on environmental and human health risk. We invite colleagues to present their research and to establish new cross-cutting, multidisciplinary collaborations aimed at proposing solutions and identifying soil health–related risks, as well as risks to environmental and human health. We welcome contributions using UAV, airborne, and satellite data together with in-situ and proximal sensors (DRS, XRF, EM, GPR), including: (i) indicator retrieval (e.g., SOC, texture, moisture, vegetation stress, contaminant proxies); (ii) bare-soil compositing and time-series workflows; (iii) physics- (or process-) based versus machine-learning approaches; (iv) sensor and data fusion; (v) spectral libraries and transferability; and (vi) case studies tracking degradation and recovery trajectories under diverse management actions, amendments, mitigation, or remediation strategies. The aim is to develop scalable, reproducible workflows that produce decision-ready outputs for assessing land degradation, planning restoration, and reducing environmental and human health risks.
Recent literature shows a strong increase in publications using UAV technology [1], and more specifically for soil-related applications such as the prediction of SOC on agricultural land. This technology holds great promises for SOC mapping, particularly in the context of carbon farming, for which temporal monitoring at field-plot scale is required. However, before this technology can be widely used, key questions remain regarding accuracy, temporal sensitivity, and cost-effectiveness compared to satellite remote sensing or geostatistical approaches.
Using a two-season field monitoring over an 11.25 ha-plot, with three replicates of five different tillage practices and a total of 75 sampling points, we aim to address these questions. During the first campaign (October 2024), a multispectral UAV (10 bands from 444 to 842 nm, VNIR) was used, while during the second campaign (May 2025), a hyperspectral UAV (~500 bands from 400 to 2500 nm, VNIR-SWIR) was deployed. To our knowledge, this is the first attempt to map SOC using VNIR-SWIR hyperspectral UAV. In parallel, for both seasons, soil samples were collected for laboratory SOC analysis and spectral measurements under controlled conditions (dried and sieved samples).
We used machine learning models (PLSR, RF, SVM) to predict SOC, comparing spectra derived from UAV imagery, Sentinel-2 (S2) data, and laboratory spectra. For this purpose, the dataset was split into 2/3 for calibration and 1/3 for validation, and this procedure was repeated randomly 100 times. The same data partitioning was used to evaluate a kriging approach.
The first result shows that surface SOC concentration is strongly dependant on tillage practices, with a mean seasonal change of 1.2 g.kg⁻¹ (±1.3 g.kg⁻¹) over a 10-20 g.kg⁻¹ range. This also raises questions about the importance of acquiring spectral data close in time to soil sampling. For UAV, S2, and kriging approaches, model performance was lower in May than in October, with decreases of 1.1, 0.8, and 0.5 in RPIQ, respectively. This suggests more favorable surface and/or sky conditions in October, despite wetter soils and sparse vegetation.
In October, multispectral UAV achieved high prediction accuracy comparable to laboratory spectroscopy (RPIQ ≈ 3.1), followed by S2 and kriging (RPIQ = 2.7 and 2.3, respectively). The same ranking was observed in May, however, the performance of the hyperspectral UAV decreased substantially and became similar to S2 (RPIQ = 2.1 vs. 1.9). Adverse weather conditions may partly explain this decline. Reducing calibration sampling density did not significantly degrade UAV accuracy, indicating potential for cost reduction. Our results suggest that future UAV-based studies should systematically compare their results with alternative methods and not only report prediction performance, but also explicitly address cost-effectiveness and temporal monitoring constraints.
[1] S. A. H. Mohsan, N. Q. H. Othman, Y. Li, M. H. Alsharif, and M. A. Khan, “Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends,” Intell. Serv. Robot., vol. 16, no. 1, pp. 109–137, Mar. 2023, doi: 10.1007/s11370-022-00452-4.
How to cite: Merlet, H., Fouad, Y., Michot, D., Pichelin, P., Bertin, P., Savoie, A., Martin, L., Zayani, H., Beaucher, E., Rouault, F., Fabre, C., and Vaudour, E.: Evaluating UAV spectroscopy for monitoring soil organic carbon in agricultural fields, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10375, https://doi.org/10.5194/egusphere-egu26-10375, 2026.
Mountainous landscapes face numerous threats stemming from population growth, economic development, and climate change. The unique and fragile climatic conditions in mountain areas are susceptible to all changes that may occur due to anthropogenic activities (i.e., overgrazing, agricultural intensification, deforestation, land abandonment, infrastructure and construction activities, and unplanned settlement), exacerbated by extreme weather events like intensified rainfall, causing soil erosion, and prolonged drought. Land degradation occurs through the interaction of multiple processes in different regions and areas, and these processes are particularly complex and susceptible to accelerating factors in mountainous regions. Given Türkiye's topography, 58% of the land cover consists of mountainous areas; the land degradation processes in these areas, which constitute an essential segment of the Alpine–Himalayan orogenic system, remain poorly understood.
Therefore, this study aims to develop a land degradation map in mountainous areas in Türkiye. In this regard, we compiled primary land degradation indicators for mountainous landscapes, including the normalized difference vegetation index, bare soil fraction, soil moisture, grassland, human footprint, and aridity data, at a 1km resolution for the year 2000. Here, we combine Mann-Kendall trend analyses and overlay analyses in selected mountainous areas, which comprise 5% of the total study area, to evaluate our approach for testing mapping capabilities.
Our initial results indicated that NDVI was a decreasing indicator for land degradation. Localized degradation hotspots were identified specifically; 21% of the total area showed degradation over the last 23 years. Another important aspect we observe is a statistically significant negative trend in the grassland across 3.18% of the total study area. Whereas the human footprint exhibits varying trends that are not readily associated with land degradation. Taking all these preliminary findings into account, this study will provide the first "Land Degradation Map of Mountainous Landscapes in Türkiye" upon the completion of the land degradation assessment approach.
How to cite: Cuhadar, E., Görüm, T., Avcıoğlu, A., Akbaş, A., and Fidan, S.: Land Degradation Processes in Mountainous Landscapes in Türkiye: Preliminary Results, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-411, https://doi.org/10.5194/egusphere-egu26-411, 2026.
Understanding soil–crop interactions in salinization-prone agroecosystems is critical for sustainable management, yet these interactions are highly dynamic and influenced by multiple drivers. Soil apparent electrical conductivity (ECa) serves as an integrative indicator of soil properties affecting crop performance, while vegetation indices such as NDVI provide high-resolution information on crop development. Linking these datasets across space and time can reveal how soil constraints emerge and evolve during the growing season, informing adaptive management strategies. We propose a spatiotemporal correlation framework combining proximal soil sensing (ECa) with remote sensing imagery (NDVI) to identify periods and zones where soil conditions either promote or limit crop growth. Positive correlations indicate favorable conditions, whereas negative correlations signal stress factors such as water scarcity or salinity. The approach was tested in irrigated systems in southern Spain—including maize, cotton, tomato, and sugar beet—under both non-saline and salinization-prone scenarios. Results show that correlation patterns shift throughout the season, reflecting changes in soil water and salinity dynamics and their impact on crop development. This integrative workflow demonstrates the potential of combining proximal and remote sensing for diagnosing soil-driven variability and guiding precision agriculture. Integrating proximal and remote sensing technologies enables more effective monitoring and management of soil–crop interactions across fields.
Acknowledgements
This work was supported by grant PID2023-149609OR-I00, funded by MICIU/AEI/10.13039/501100011033 and by FEDER, EU. JLGF and MRR acknowledge their PhD grants PRE2020-095133 and PREP2023-001774 funded by MICIU/AEI/10.13039/501100011033 and by “ESF Investing in your future”.
How to cite: Vanderlinden, K., Ramos Rodríguez, M., Gómez Flores, J. L., Farzamian, M., and Martínez García, G.: Integrating Proximal Soil Sensing and Remote Sensing to Track Soil Constraints and Crop Responses in Salinization-Prone Environments., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10797, https://doi.org/10.5194/egusphere-egu26-10797, 2026.
Open-pit mining generally produces severe land degradation that constrains vegetation establishment and limits carbon uptake. Within a CO2-capture-oriented restoration project, we monitored vegetation development in five experimental plots affected by mining disturbance. One plot was fertilized with a commercial liquid product and four were amended with four artificial soil formulations that were placed on the degraded land. All plots were vegetated by planting seeds. The aim was to quantify and compare temporal trajectories of vegetation spectral response, as a proxy for canopy development, plant functioning and restoration performance under different artificial soils.
We conducted a two-year monthly remote sensing analysis from January 2024 to December 2025 using Sentinel-2 surface reflectance imagery. For each plot, cloud- and shadow-masked imaginery were generated and summarized over plot-scale regions of interest. We analysed multi-spectral responses combining near-infrared and short-wave infrared information and a set of vegetation and moisture indices (e.g., NDVI, EVI, SAVI, red-edge based indices, and SWIR-derived moisture metrics) to capture changes in greenness, structure and water status. Temporal patterns were evaluated through trend and seasonal descriptors, inter-annual anomalies, and between-treatment contrasts to assess the consistency of artificial soils effects across years and climatic phases. The remote sensing time series were interpreted in the context of restoration objectives, emphasizing indicators relevant to biomass accumulation and potential CO2 sequestration.
We observed different soil-dependent spectral trajectories across the two-year period, as well as different persistence of vegetation signals during summer stress, and recovery after disturbance periods. Plots amended with artificial soils designed to improve water retention and nutrient availability exhibited earlier and more stable increases in NIR reflectance and vegetation indices, alongside lower SWIR-based stress signatures, compared with less ameliorative formulations. These differences suggest that amendments composition can modulate vegetation establishment and function at reclaimed mine sites, with direct implications for carbon capture potential. This demonstrates the utility of high-frequency Sentinel-2 monitoring to deliver reproducible, plot-scale indicators of restoration performance to support artificial soil selection, and adaptive management in CO2-oriented mine land reclamation.
This work was funded by the European Union under the Horizon Europe programme through the C-SINK project (Grant Agreement No. 101080377; GAP-101080377). VA thanks for her postdoctoral Ramón y Cajal contract RYC2024-048710-I funded by MICIU/AEI/10.13039/501100011033 and the FSE+.
How to cite: Asensio, V., Sánchez-Poyal, A., Estrada, A., Leboreiro, R., López Cortijo, A., Gómez, D., Díaz, P., R. Gallego, J. L., and Salgado, L.: Sentinel-2 monitoring of vegetation spectral trajectories in open-cast mine plots treated with artificial soils: implications for CO2 capture , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10320, https://doi.org/10.5194/egusphere-egu26-10320, 2026.
The transition to a sustainable bio-economy faces a crucial challenge: obtaining raw materials without competing for arable land needed for food production. This problem is particularly urgent in Europe, where around 80% of the land is used for settlements, agricultural and forestry production, and infrastructure. Land scarcity requires innovative approaches to raw material cultivation. Contaminated soils, unsuitable for food crops, offer a promising alternative for industrial cultivation. However, the widespread adoption of industrial crops in these soils is limited by key challenges such as determining how contaminants affect plant growth and yield under stress conditions, as well as the dynamics of those contaminants.
This research addresses these challenges by monitoring, testing and optimizing phytoremediation strategies to improve soil health while producing bio-based products for the textile industry. The proposed approach is demonstrated in an experimental field trial at the former Nitrastur fertilizer plant (Asturias, Spain). For this study, we present an integrated monitoring framework that combines proximal and remote detection techniques with chemical analysis to assess soil contamination through vegetation response, using biomass production as a functional indicator of soil recovery.
Terrestrial Laser Scanning (TLS) was used to acquire high-density 3D point clouds, from which volumes were calculated to subsequently estimate the biomass of birch trees (Betula celtiberica) that were planted on the polluted soils twelve years ago. Structural parameters such as tree height, trunk diameter (using multiple geometric estimation methods) were extracted and indirect biomass measurements to characterize vegetation growth with different levels of soil contamination. Unmanned aerial vehicle (UAV) images also complemented TLS data by supporting plot delineation, site-scale visualization and spatial contextualization.
These structural observations were integrated with soil chemical analyses to quantify contamination levels, as well as spectral indices of vegetation and soil to assess soil health. This novel and integrated monitoring approach made it possible to assess the relationships between contamination levels and biomass production by providing key information on soil health status and its recovery process.
This work was funded by the European Union under the Horizon Europe program through the pHYBi project (Grant Agreement No. 101156439; CBE JU) and the INTERSOIL project (PID2023-147718NB-I00, AEI/Spain, FEDER/EU).
How to cite: Moriano, L., Cabo, C., Peña-Álvarez, V., Salgado, L., Cuesta, C., and R. Gallego, J. L.: Integrated monitoring of phytoremediation through analysis of biomass production and soil recovery on a contaminated site, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17677, https://doi.org/10.5194/egusphere-egu26-17677, 2026.
Energy dispersive X-ray fluorescence (EDXRF) is an efficient, nondestructive, and cost-effective analytical method for the detection of multiple heavy metal elements in soil via X-rays. This method has been extensively applied in rapid on-site screening of soil, indicating a promising market outlook. The analysis accuracy of energy dispersion X-ray fluorescence spectrometry (XRF) for detecting heavy metal in agricultural soils is severely depending on complex matrix effect（such as soil organic matter, soil type, parent material, and texture），thereby posing a challenge in fast and precise monitoring soil contamination.
To calibrate the XRF detection, this study proposes a Gaussian mixture clustering-multilevel model (GMC-MLM) to enhance XRF accuracy for Cd in agricultural soils，which considers the interactive effects of organic matter, soil type, parent material, and texture during XRF detection. Using a Gaussian Mixture Clustering Model (GMC) to pre-classify the sample detection results, we adopt laboratory measurements as the true values and employ a multi-level modeling (MLM) approach for integrated modeling. This approach aims to further eliminate residual errors in the quantitative analysis of instrumental detection. Additionally, to address the complexity of soil matrices in different regions, we propose a technical method for constructing XRF detection corrections localized knowledge bases to address the inconsistency of modeling parameters across regions.
Hilly cultivated land was designated as the experimental area in Hubei Province, China. A total of 350 soil samples were collected to ensure uniform spatial distribution across the region, which covers approximately 3,855 km². These samples encompass 4 categories of parent materials, 7 soil types, and 3 texture types. To verify the effectiveness, a comparative analysis was conducted with conventional calibration methods, including linear regression, random forest and support vector machine. The results demonstrate that the Gaussian Mixture Clustering-Multilevel Model (GMC-MLM) can effectively disentangle the nested distribution characteristics of XRF detection errors. The correlation coefficient between the XRF detection results and ICP-MS test results for the corrected samples can reach 0.9085, with 74% of the corrected samples having a relative error of less than 30%. When the number of knowledge base sample points is 50, the RMSE (Root Mean Squared Error), and REM (Relative Error of Mean) are 0.7347, 3.7014%, respectively. It can be observed that the model has good extrapolation capability, and with the increase in the number of knowledge base sample points, the correction effect based on the knowledge base gradually stabilizes. This knowledge base-based GMC-MLM calibration method not only can be embedded into XRF detection instruments to correct XRF detection results in different regions of China but also provides theoretical support for the establishment of a nationwide soil sample knowledge base. This study also provides technical references for the popularization and application of EDXRF in on-site detection of farmland soils.
How to cite: Gao, Y., Zhang, Z., Zhou, Y., Dong, S., and zhao, Y.: Quantitative Determination of Cd Using Energy Dispersion XRF Based on Gaussian Mixture Clustering-Multilevel Model Recalibration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10611, https://doi.org/10.5194/egusphere-egu26-10611, 2026.
Armed conflicts generate long-lasting environmental disturbances, with soils acting as critical sinks for toxic residues such as heavy metals and phosphorus. Soil contamination directly affects environmental and human health through exposure pathways linked to food production, soil fertility, and downstream water systems. While military training ranges have been extensively studied, contamination dynamics in recently bombarded civilian landscapes remain poorly documented. This study addresses this gap by documenting soil contamination in South Lebanon following the 2023–present conflict. A community-driven sampling strategy was implemented across bombed villages, engaging 85 local participants to ensure safe and representative coverage under restricted access conditions. A total of 200 soil samples were collected and analyzed for total and reactive phosphorus, as well as cadmium (Cd), lead (Pb), copper (Cu), zinc (Zn), and nickel (Ni). Laboratory analyses were conducted with dual-lab verification at the American University of Beirut and the Lebanese University to ensure data reliability under constrained analytical conditions. Results revealed elevated concentrations of Ni (up to 228 mg/kg) and Cd (up to 9 mg/kg), exceeding WHO soil guideline values, while Pb, Cu, and Zn were closer to background levels but exhibited localized hotspots. Reactive phosphorus concentrations indicated inputs consistent with excessive fertilization, reflecting residues associated with white phosphorus munitions. These patterns indicate heterogeneous, hotspot-dominated contamination rather than uniform spatial gradients. The enrichment of Ni and Cd above health thresholds points to acute ecological and human health risks through soil–food–water exposure pathways, while phosphorus residues highlight additional stress on soil quality and downstream aquatic systems. Overall, this study demonstrates how adapted sampling methodologies, community participation, and dual-laboratory verification can generate robust soil quality indicators in conflict-affected environments, supporting soil health risk assessment, monitoring, and remediation strategies relevant to sustainable soil management and human health protection.
How to cite: Dirani, L., Zurayk, R., Faaour, N., Kanaan, T., and Hamade, E.: Environmental Legacies of Armed Conflict: Soil Contamination and Citizen Participation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19170, https://doi.org/10.5194/egusphere-egu26-19170, 2026.
Soils in industrial cities act as long-term sinks for anthropogenic contaminants and can provide valuable information on emission sources. This study identifies and compares the dominant pollution sources in soils of two industrial cities in Kazakhstan, Pavlodar and Ust-Kamenogorsk, using source apportionment of polycyclic aromatic hydrocarbons (PAHs) and potentially toxic elements (PTEs). Ust-Kamenogorsk exhibits a robust technogenic footprint. Surface soils are highly enriched in Zn (up to ~600 mg kg-1), Pb (~208 mg kg-1), Cu (~74 mg kg-1), Cd (up to 6.4 mg kg-1), and As (~20 mg kg-1), forming a persistent geochemical anomaly. Source apportionment revealed four major contributors. The dominant factor is non-ferrous metallurgy, clearly associated with elevated Cd-Pb-Zn-Cu loadings in topsoil. A second factor is linked to coal combustion and fly ash deposition, characterized by Fe-Al-Cr-V associations. The natural mineralogical background was distinguished as a separate source dominated by carbonate and oxide-related elements (Ca, Mn, Ba). A fourth source, marked by strong As-Se coupling, is attributed to emissions from pyrometallurgical processing.
In contrast, Pavlodar soils demonstrated lower contamination levels and a more diffuse anthropogenic signal. Three integrated factors were identified: 1) a mixed lithogenic-industrial source combining Al, Fe, Cr, Ni, and V, reflecting both parent material and refining-related activities; 2) a traffic- and construction-related source defined by Cu, Zn, Pb, Ca, and Na, indicative of abrasion products, cement dust and historical lead residues; and 3) a minor geochemical factor dominated by Co-Se, likely attributable to local lithological heterogeneity. Total PAH concentrations range from 0.82 to 3.53 µg g-1 in Pavlodar and 0.18 to 11.31 µg g-1 in Ust-Kamenogorsk. In Pavlodar, the prevalence of indeno[1,2,3-cd]pyrene and naphthalene indicates road materials and traffic emissions, while medium-molecular-weight PAHs, contributing up to one-third of the PAH burden, are consistent with coal combustion. Ust-Kamenogorsk shows elevated PAHs levels with distinct spatial hotspots, confirming intense industrial influence. These results demonstrate that industrial specialization controls soil pollution regimes, with Ust-Kamenogorsk representing an extreme case of metallurgy-driven contamination and Pavlodar reflecting mixed urban–industrial loading. These findings highlight the need for targeted remediation and prioritized urban environmental management.
How to cite: Radelyuk, I., Bektassov, M., Zhumadildinova, A., and Baimatova, N.: Source apportionment of soil pollution in industrial cities of Kazakhstan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1348, https://doi.org/10.5194/egusphere-egu26-1348, 2026.
Glyphosate and its degradation product AMPA, are ecotoxic, recurrent and persistent in agricultural soils, susceptible to overland transport by runoff, and sediment erosion due to their strong sorption affinities. We hypothesize that eroded sediments of different sizes have differing sorbed concentrations and relative contributions to glyphosate and AMPA transport, due to different specific surface areas and adsorption site abundances. Hence, we conducted a flume experiment of glyphosate-polluted sediment erosion in a rainfall simulator. After 1 hour of rainfall (90 mm water), 10% of applied glyphosate degraded to AMPA. The top 2 cm of soil retained 68% of the total glyphosate-equivalent (including AMPA) mass, while runoff and eroded sediment accounted for 8% and 10% respectively. Amongst pesticides transported overland, runoff (61%) and eroded sediment (39%) were similarly important for glyphosate, but eroded sediment (95%) transported remarkably more AMPA than runoff (5%), although glyphosate sorption affinities are typically larger. Small sediments (<0.25mm) constituted 75% of eroded sediment counts, but carried 60% of sediment-phase glyphosate and 85% of sediment-phase AMPA mass. In <0.25mm sediments, unlike glyphosate, AMPA breakthrough concentrations were substantially greater than in larger sediments. As water and pollutant mass exchanges between various environmental compartments (soil moisture, soil grains, runoff, eroded sediment, biodegradation) are highly dynamic, equilibrium sorption affinities alone may not fully characterize the predominant modes of pollutant transport, which may vary across spatio-temporal scales. Therefore, pollutants that are preferentially transported by eroded sediments, particularly small sediments, should be identified and prioritized in future research, due to potentially amplified environmental impacts.
How to cite: Yang, X., Silva, V., and Tang, D.: Sorptive diffuse pollutant transport under runoff-erosion potentially dominated by small sediments , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9307, https://doi.org/10.5194/egusphere-egu26-9307, 2026.
Soil degradation and contamination pose significant risks to environmental quality, food security, and human health, particularly in intensively managed agricultural systems. Agricultural soils act both as sinks and secondary sources of chemical contaminants, regulating their mobility, persistence, and transfer to other environmental compartments such as groundwater and surface water. Among these contaminants, pesticides are of particular concern due to their widespread use, frequent co-occurrence as complex mixtures, and the formation of transformation products with uncertain environmental and toxicological profiles. Yet pesticide contamination is still predominantly assessed in surface soils, implicitly assuming rapid dissipation and limited relevance of subsurface layers.
In reality, soils are vertically structured and dynamic systems in which hydrological processes, soil properties, and climatic drivers, such as monsoons or heavy rainfall, can facilitate the downward transport, accumulation, and long-term persistence of pesticide residues beyond the surface layer. Empirical evidence on how pesticide mixtures redistribute across soil depths and seasons under field conditions remains scarce, especially in subtropical agroecosystems subject to intense rainfall pulses. This knowledge gap limits accurate exposure assessment and weakens soil quality indicators used to protect environmental and human health.
Here, we investigated depth- and season-resolved pesticide contamination in a subtropical agricultural landscape by analyzing 181 pesticides and metabolites in 246 soil samples collected from 41 agricultural sites. Samples were obtained from three depths: 0–5 cm (surface soil), 15–20 cm (plough layer), and 55–60 cm (deep soil) during pre-monsoon (June 2024) and post-monsoon (February 2025) periods. Sixty-one compounds were detected above quantification limits, with pesticide mixtures present in nearly all surface and plough-layer soils and in 71% of deep soils. Metabolites were particularly prominent at depth and frequently exceeded parent compounds in detection frequency, indicating transformation-driven persistence in subsurface horizons.
Comparisons with commonly used property-based mobility and persistence indicators (Koc, DT50, and GUS) revealed systematic mismatches between predicted and observed field behavior, including the deep occurrence of compounds classified as non-leaching or low-risk. These discrepancies highlight the limitations of equilibrium-based screening approaches under hydrologically dynamic conditions. To address this, we developed field-derived indices integrating seasonal occurrence, vertical mobility, and inter-seasonal carry-over with hazard classification. This approach identified several current-use pesticides, including clothianidin, carbendazim, bifenthrin, and difenoconazole, as high-priority compounds for routine monitoring due to their persistence and high toxicity to non-target organisms, including humans.
Our findings demonstrate that subsurface pesticide mixtures represent an overlooked exposure compartment in agricultural soils. Incorporating depth-resolved, metabolite-inclusive monitoring and field-based behavioral indicators into soil quality assessment and regulatory frameworks is essential for improving environmental risk evaluation, protecting groundwater resources, and safeguarding environmental and human health.
How to cite: Shah, S. S., van Dam, J., Silva, V., Singh, A., Bundela, D. S., Osman, R., Mohrmann, J. B., Yadav, R. K., Geissen, V., and Ritsema, C.: Hidden below the surface: Depth- and season-resolved pesticide mixtures in agricultural soils, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11092, https://doi.org/10.5194/egusphere-egu26-11092, 2026.
Pyrolysis-derived products are increasingly being researched to improve soil and manage organic by-products from agroforestry and livestock operations. However, their chemical behavior and agronomic safety vary with feedstock and pyrolysis conditions. Eucalyptus bark contains phenolic and tannin compounds with allelopathic effects, while sheep wool contains nitrogen and sulfur compounds, which, at low pyrolysis temperatures, may produce ecotoxic substances. In soil solution, these compounds may reduce seed germination and plant growth. Therefore, assessing the ecotoxicology of leachates from pyrolysis-derived products is important to ensure the safe agricultural use of products from a circular economy.
This study assessed the ecotoxicology of leachates from pyrolysis-derived products using bioassays with two sensitive plant species. Pyrolysis-derived products included Eucalyptus bark (EB), sheep wool residues (SWR), and mixed EB/SWR at various ratios (100% EB, 100% SWR, 30/70, 40/60, 60/40, and 70/30). Feedstocks were pyrolyzed at various temperatures (150 to 500 °C) and residence times (10, 20, and 30 minutes). Simulated leachates (DIN extraction) were obtained from the raw eucalyptus biomass (EM) and pyrolysis-derived products and used in a bioassay (filter paper, n=5 replicates/treatment, n=25 seeds/replicate) with Lactuca sativa and Allium cepa..
The bioassays were carried out in a growth chamber maintained at a temperature of 25 ± 1 °C and a photoperiod of 16 h light and 8 h dark. The leachates were characterized by the elemental analyses of the macronutrients (K, Ca, Mg, P, and Na) and the ecotoxicity was determined by the measuring the percentage of germination, the time to 50% germination (T₅₀), the total dry biomass, as well as the total dry plant biomass and roots and shoots elongations. The statistics were conducted using ANOVA and the means were separated using Dunan’s multiple range test for the means which were significantly different at a 5% level (p ≤ 0.05).
The leachates presented differences in their chemical characteristics. Higher pyrolysis temperatures result in leachates with a higher pH and greater concentrations of Ca, Mg, and K. Conversely, leachates from unprocessed eucalyptus biomass and low-temperature pyrolysis products exhibited higher electrical conductivities and concentrations of elements. The final germination percentages were relatively similar across all treatments for both species, indicating a relatively robust germination response to a broad range of chemical characteristics of the leachates The remaining plant parameters were more sensitive to the treatment effects. Low-temperature pyrolysis treatments contributed to the reduction of root elongation and total dry biomass. On the other hand, leachates from pyrolysis-derived products obtained at temperatures greater than or equal to 400 °C positively influenced overall seedling performance, particularly the mixtures of eucalyptus bark and sheep wool. Pyrolysis temperature played a vital role in determining leachate quality and potential ecotoxicity. The products of high-temperature pyrolysis (≥400 °C) produced leachates with lower ecotoxicity and, in some instances, positive effects on early seedling development. Conversely, unprocessed biomass and low-temperature pyrolysis products contained leachate constituents capable of inducing unfavorable physiological responses in sensitive plants.
Keywords: Simulated Leachates, Bioassay; Pyrolysis; Lettuce; Onion; sheep wool wastes
How to cite: da Paixão Oliveira, L., Esposito, E., Santos, E., and Arán, D.: Feedstock and Pyrolysis Effects on the Ecotoxicological Quality of Leachates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14337, https://doi.org/10.5194/egusphere-egu26-14337, 2026.
This study proposes a method for estimating the risk associated with the ingestion of soil contaminated with lead (Pb), considering the nature of the source and the parameters that influence its bioavailability. Statistical variables related to the solubility and bioavailability of Pb are used, including pH, electrical conductivity (EC), particle size distribution, mineralogical composition, and bioaccessibility/bioassimilability. These parameters allow indicators to be generated and an algorithm based on probability distributions to be constructed, which requires in-depth knowledge of the source materials in order to assess the health risk derived from the concentration of the metal.
To this end, 186 samples from areas affected by mining activities in the Region of Murcia (southeast Spain) and nearby soils were analysed. The samples were screened, homogenised and characterised by determining pH, EC, particle size, total and extractable Pb content, oral bioaccessibility tests and mineralogical analysis by X-ray diffraction.
The main objective was to develop a flexible and applicable procedure to estimate whether a soil poses a risk to human health in residential settings. The methodology is based on variables with a significant correlation with total Pb content and on parameters related to its solubility and bioavailability. The expected result is a global risk indicator, useful for taking corrective measures at contaminated sites that may be used for residential purposes. This approach allows for the incorporation of new indicators or the modification of existing ones, provided they are based on knowledge of the source of contamination.
Statistical analysis makes it possible to explain the uncertainty associated with parameters such as mineralogy, bioaccessibility and granulometry, offering valid results. Mineralogy is confirmed as a key factor in estimating risk, although it requires prior classification of the components present. The results show a significant correlation between the non-carcinogenic risk calculated using accessible Pb content and the risk estimated by the proposed indicator, with no false negatives. In some cases, risk overestimation (false positives) was detected, attributable to particular characteristics of the site, with values above the permissible limit but with a hazard index (HI) below unity.
This procedure is an effective tool for assessing risks in critical mining areas and planning preventive measures, contributing to the safe management of lead-contaminated soils.
How to cite: Hernandez, M., Martínez Sanchez, M. J., Martinez Martinez, L. B., Perez Sirvent, C., Hernandez Perez, C., and Bech, J.: A Probabilistic Approach for Estimating Human Health Risk from Lead-Contaminated Soils , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15096, https://doi.org/10.5194/egusphere-egu26-15096, 2026.
The increasing reliance on chemically intensive agriculture has led to serious environmental issues, such as the problem of agricultural waste, soil degradation, and water contamination. Also, the widespread use of tetracycline and silver-based products has caused persistent contamination that threatens the environment and human health. Additionally, large amounts of agricultural waste, such as corncobs, are generated annually, i.e., in 2023, over 1.17 billion metric tons of waste were generated, and they are often underutilized. Converting waste into biochar (BC), a material with potential uses in soil regeneration, pollutant removal, and carbon sequestration, offers a sustainable solution to all these issues (Zheng et al., 2023). However, sometimes the physicochemical properties of this material may be unsuitable for its intended use, and modification may be necessary (An et al., 2023). This study explores NH4OH-modified corncob biochar as an eco-friendly, low-cost adsorbent for the removal of silver ions (Ag+), silver nanoparticles (AgNPs), and tetracycline (TC), aiming to improve agricultural waste management and protect the environment.
The biochar (BC) was produced from corncob (C) at 700 °C for 1 h, with a heating rate of 12 °C/min. The 25% NH4OH solution was used to chemically alter the biochar surfaces. 20 grams of biochar were introduced in a 1:5 (s/v) ratio to 100 ml of NH4OH in a magnetic stirrer and then, the mixture was stirred at room temperature for 2 h. The excess ammonium hydroxide was then eliminated using a paper filter. The resulting biochar (BCM) mixture was heated to 300°C for around 2 h in a furnace after being allowed to air dry overnight. Adsorption of the pollutants was conducted in batch experiment, with an initial concentration of 100 mg/L for Ag+, 100 mg/L for AgNPs, and 10 mg/L for tetracycline at a pH of 6. The concentration of Ag+ was measured using a silver-ion-selective electrode, while that of AgNPs, using a UV-Vis spectrophotometer at 437 nm. The concentration of tetracycline was determined using HPLC.
The performed modification changed the surface functional groups of BC. In the FTIR spectra, the introduction of amine groups was observed. The basic functional group increased by 31.8%. In addition, the hydrophobicity of the solid was reduced by 23.68%. BCM had point of zero charge (pHPZC) of 9.98, specific surface area (SBET) of 144 m2/g, whilst the content of basic and acidic functional groups equalled 8.13 mmol/g and 4.75 mmol/g, respectively. Adsorption of all adsorbates reduces in mg/g as the dose of the adsorbent increases from 0.01 to 0.1 g. There was an insignificant difference in the absorbed amounts of Ag+ and AgNPs at pH 3, 6, and 9, whilst TC demonstrated a significant difference, with the highest adsorption recorded at pH 9. TC and AgNPs demonstrated competitive interaction, while TC and Ag+ showed synergistic interaction in the bi-sorbate system. Modified biochar offers an excellent efficiency in the removal of most pollutants from the environment. Its highest observed adsorption capacity was towards AgNPs and equalled 26.73 mg/g.
Keywords: Antibiotics, contaminant adsorption, crop residue, agrochemicals, modified biochar
How to cite: Sama, D. K., Siryk, O., Grygorczuk-Płaneta, K., and Szewczuk-Karpisz, K.: NH4OH-modified corncob biochar as a potential agent for improving soil properties, immobilizing organic and inorganic pollutants, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19680, https://doi.org/10.5194/egusphere-egu26-19680, 2026.
In situ stabilization methods using chemical amendments are widely applied to reduce the mobility of toxic metallic elements in abandoned mine soils. However, the long-term stability of amended soils under climate change–induced thermal stress remains uncertain. This study evaluated the effects of elevated temperature on the leaching behavior and stabilization efficiency of metallic elements in amendment-treated mine soils. Two mine soils were amended with limestone (LS) and steel slag (SS) and subjected to thermal aging at 90 °C for eight weeks, simulating accelerated weathering under the SSP5–8.5 climate scenario. Sequential extraction and batch leaching experiments were conducted to assess physicochemical transformations and the mobility of metallic elements. Thermal aging significantly increased the initial and cumulative leaching of Cu, Zn, As, and Cd compared with ambient conditions. Regression analysis identified Al and Fe as key geochemical regulators of metal mobilization under high-temperature stress, with Al hydrolysis–induced acidification enhancing the solubility of cationic elements and reductive dissolution of Fe oxides promoting As release. Although LS and SS treatments consistently reduced the leachability relative to unamended controls, elevated temperature partially weakened their stabilization efficiency. These findings demonstrate that climate-driven thermal stress can induce secondary mobilization of metallic elements through Al- and Fe-mediated processes, highlighting the need to assess the long-term durability of soil amendment–based remediation strategies under future extreme climate conditions.
How to cite: Hyun, S., Seo, Y., and Park, M.: Effect of thermal treatment based on SSP5-8.5 climate scenario on the leachability of toxic metallic elements from amended mine soils, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9579, https://doi.org/10.5194/egusphere-egu26-9579, 2026.
Tuzköy village (Nevşehir, Cappadocia, Türkiye) is known as an epidemic location for mesothelioma cases due to exposure to erionite fibers. In this study, health risk assessment of potentially toxic elements (PTEs) in soil and bedrocks of the village has been interpreted, as well. 87 topsoil and 10 rock samples were geochemically analysed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Hazard Index (HI) was calculated through As, Ba, Be, Cd, Co, Mo, Ni, Pb, Sb, Se, Sn, and Zn results, while As, Be, Cd, Ni, Pb results were used for determination of Incremental Lifetime Cancer Risk (ILCR) values. In addition, contamination factor (CF), geo-accumulation index (Igeo) and pollution load index (PLI) were calculated.
Risk assessment results demonstrate that median and mean values of HI for adults are 0.280 and 0.376 respectively, and these values of ILCR for adults are equal to 9.93E-05, 1.34E-04. Moreover, median and mean values of HI for children are determined as 1.774 and 2.371 respectively, and these values of ILCR are equal to 5.97E-04, 8.4E-04. For children, both HI and ILCR values are above acceptable levels. Based on HI values, adults are not susceptible to risk as both the median and mean are below 1, but the mean ILCR value is above the acceptable limit of 1E-04.
Spatial risk maps indicate higher levels of risk in the northwest and eastern parts of the village for adults while the risk for children is widespread across the entire area, and is also considerably high in the same regions. Arsenic enrichment is considered as the main contributor to both HI and ILCR values. To conclude, in addition to erionite-related fiber exposure, arsenic-related soil contamination poses a significant health concern in Tuzköy, especially for children.
Keywords: Soil contamination, geochemical risk assessment, arsenic, incremental lifetime cancer risk, hazard index
How to cite: Kılıç, A., Toksoy Köksal, F., Çubukçu, H. E., Koca Akçay, G., Demir, A., İlgen, H. G., Demir, S., and Aykasım, D.: Geochemical Risk Assessment Of Elements in Tuzköy, Türkiye, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1178, https://doi.org/10.5194/egusphere-egu26-1178, 2026.
Soil salinity is a major form of land degradation in irrigated agroecosystems, directly affecting crop productivity, soil health, and long-term sustainability. While proximal sensing techniques such as electromagnetic induction (EMI) are widely used for field-scale salinity mapping, accurately predicting low to moderate salinity levels remains challenging due to strong spatial heterogeneity, depth-dependent processes and limited transferability across fields. This study proposes a data fusion framework integrating proximal sensing (EMI and gamma-ray spectrometry), soil profile-based salinity metrics, and machine learning models to improve soil salinity estimations across four irrigated regions in Arizona and California, United States: Imperial Valley, Yuma, Salinas Valley and Colorado River Indian Tribes (CRIT). A total of 1,015 samples were collected. Electrical conductivity of saturated paste extract (ECe) from samples at 10, 40 and 120 cm, was aggregated into a profile weighted indicator ECew (0-120 cm) to better match the effective sensing depth of EMI measurements. Multiple modelling scenarios were evaluated using apparent electrical conductivity obtained from EMI at nominal depth ranges of 1.6 m and 2.3 m, gamma-ray data, soil taxonomy, and depth information. Six regression algorithms (Ridge, Random Forest, Gradient Boosting, Support Vector Regression, XBoost and CatBoost) were tested using random K-Fold and Leave-One-Field-Out (LOFO) cross-validation to assess spatial transferability.
Results show that profile integration and multi-sensor fusion improve predictive robustness. Across regions, jack-knifed mean square prediction error (MSPE) values ranged from 0.10 to 2.16 relative error, with the highest accuracy in Yuma (MSPE=0.10-0.13) and Imperial Valley (MSPE=0.12-0.27) (good to excellent accuracy), intermediate performance in CRIT (MSPE=0.15-0.36) and clear limitations in Salinas (MSPE=0.16-2.16). The use of ECew consistently reduced prediction error and improved spatial transferability under LOFO validation. Tree-based and boosting models outperformed linear and kernel-based approaches for depth-specific ECe, while Ridge regression proved most robust for ECew. Spiking analysis further demonstrated that incorporating small fraction of local ground-truth samples markedly improved LOFO performance, highlighting a practical balance between sampling effort and predicting accuracy. Overall, the fusion of indirect sensor observations and soil profile-based salinity metrics, enable scalable and transferable mapping of low to moderate soil salinity for operational applications.
How to cite: Matendo, S., G. Anderson, R., H. Skaggs, T., and Scudiero, E.: Data fusion of proximal sensing and machine learning for soil salinity mapping in irrigated systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22415, https://doi.org/10.5194/egusphere-egu26-22415, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 2
EGU26-19058 | ECS | Posters virtual | VPS17
Integrated environmental Assessment of multielement Contamination in Mining-Impacted Soils and Leachates: A Case Study from Northeastern AlgeriaWed, 06 May, 14:42–14:45 (CEST) vPoster spot 2
Abandoned mining sites are a major source of long-term soil contamination by potentially toxic elements. This study assessed the environmental risk of metal-contaminated soils from the Sidi Kamber mine (northeastern Algeria). Mining residues are spread into the surrounding areas and the Oued Es-Souk, a river that supplies the Guenitra dam.This dam is the main drinking water reserve in the Skikda region..
This study is based on the geochemical and ecotoxicological analysis of 16 soil samples, from four stations distributed along the Oued Es-Souk until the dam. Samples were taken at two depths (0–25 cm and 25–50 cm) during both the dry and wet seasons. in Metal availability was evaluated through simulated leachate analyses, while soil properties (pH, fertility and pseudo-total elemental concentrations) were determined using conventional methods. Ecotoxicological bioassays were conducted to assess the biological effects of both soils and leachates in two plant species (Allium cepa and Lactuca sativa), focusing on seed germination, root elongation, and total biomass production as sensitive indicators of phytotoxicity. Soil pollution indices, including the Igeo-Geoaccumulation Index and the CF-Contamination Factor were calculated to quantify contamination levels and identify the most critical elements.
The soils showed a very variable conductivity (510–3460 μS/cm) and a pH ranging from neutral to slight acid (5.15–7.54), with a tendency towards acidification during dry season. The leachates, less saline, were systematically acid (pH ~5). The organic carbon and some available nutrients contents were relatively low confirming low soil fertility.
The upstream location had the lowest Zn, Mn, Cu, and Pb concentrations in pseudo-total fraction recorded in wet season (194, 480, 16.5, and 88 mg/kg, respectively). Despite being the lowest on the site, these levels exceeded benchmarks reported by Dutch Target Values or AFNOR standards. The highest concentrations are located at the surface of the soils and at specific points, reflecting a localized accumulation. Mobility Index (MI = Av/PT) ranked metals in descending order of mobility: Cd>Zn>Ni>Cu>Pb>Cr>Fe. Contamination Factors confirm a significant polluting heritage: the pseudo-total contents of Zn, Pb and Cd are considerably enriched (CF>10 in many cases) compared to local natural levels. The geoaccumulation index classifies metals into three categories: strong to extreme contamination for Cd and Pb (Igeo>3); moderate accumulation for Zn and S (1<Igeo<2); and low to natural levels for Fe, Mn, Cr and Ni (Igeo=0).
Inhibition index specially on Lactuca, shows that root growth is much more sensitive than germination. If it is very little affected (indices from -0.06 to +0.01), the length of the roots varies greatly, from a marked inhibition (-33.5% for the most toxic sample) to a significant stimulation (+46.5%). Among all the relationships studied, it is between pH and germination that the negative correlation is the most marked..
Overall, this integrated approach provides a comprehensive framework for assessing the environmental risks associated with abandoned mining.
This work was funded by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the projects UIDB/04129/2020 and UID/04129/2025 (LEAF) and LA/P/0092/2020 (TERRA).
How to cite: Cedah, S., Fekrache, F., Aran, D., and Santos, E.: Integrated environmental Assessment of multielement Contamination in Mining-Impacted Soils and Leachates: A Case Study from Northeastern Algeria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19058, https://doi.org/10.5194/egusphere-egu26-19058, 2026.
EGU26-19259 | ECS | Posters virtual | VPS17
Evaluation of soil contamination surrounding an abandoned ore processing plant in Northeastern Algeria: spatial variability and seasonality effectWed, 06 May, 14:45–14:48 (CEST) vPoster spot 2
Abandoned ore processing plants represent critical, long-term sources of environmental contamination and therefore constitute an important field of research for the subsequent rehabilitation of the area.
The objective of this study was the evaluation of the level and spreading of soil contamination by trace metal elements in the vicinity of an abandoned ore processing plant in northeastern Algeria. Superficial soil samples were collected from 8 sampling stations located upstream, downstream, and directly at the abandoned ore processing plant during the wet and dry seasons. An environmental assessment of soil samples was conducted through the analysis of physicochemical characteristics: pH, electrical conductivity, and concentration of nutrients and potentially toxic elements in the available and total fractions.
Soil samples showed marked spatial variability in pH values and electrical conductivities although, in general, soils collected in the both seasons showed an acid pH (3.66-4.19) and low-moderate EC (250-446 µS/cm)The total concentrations of S, Fe, Cr, As, Cu, Pb and Zn were elevated in all soil samples, exceeding the maximum values permitted for industrial land use according to soil legislation in several countries (e.g. Canada). For Ni and Cd, only some soil samples exceeded the maximum allowed values. The variation in the elements' availability revealed clear spatial heterogeneity between locations upstream and downstream of the abandoned ore processing plant. However, values remained consistently high near the plant regardless of position, which confirmed its role as the primary contamination source through multidirectional dispersion via runoff and wind.
This work was funded by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the projects UIDB/04129/2020 and UID/04129/2025 of LEAF-Linking Landscape, Environment, Agriculture and Food, Research Unit and LA/P/0092/2020 of Associate Laboratory TERRA
How to cite: Djemli, M., Boudeffa, K., Fekrache, F., Arán, D., and Santos, E.: Evaluation of soil contamination surrounding an abandoned ore processing plant in Northeastern Algeria: spatial variability and seasonality effect, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19259, https://doi.org/10.5194/egusphere-egu26-19259, 2026.
