The increasing production of wastewater and sewage sludge (SS) is a major environmental challenge of the 21^st century, while the need for sustainable waste management and resource recovery drives the development of innovative technologies for sludge utilization. The thermochemical conversion of SS through pyrolysis to biochar (BC) is a promising “waste-to-resource” strategy, as it allows both the reduction of sludge volume and the production of a functional, value-added material.

This study aims to examine sewage sludge-derived biochar (BCxSS), focusing on its characterization methods, the effect of pyrolysis conditions on its physicochemical properties, and its practical applications in water and wastewater treatment. By applying a structured PRISMA-based methodology, 170 studies concerning the production, modification, and environmental utilization of BCxSS were studied. The results showed that pyrolysis conditions, and particularly pyrolysis temperature, have a major influence on the properties of the BC, such as yield, ash content, pH, specific surface area, porous structure, and surface functional groups. Furthermore, BCxSS effectively removes heavy metals, dyes, phenolic compounds, and emerging organic micropollutants, such as pharmaceuticals and antibiotics. These removals occur through mechanisms such as physical adsorption, ion exchange, surface complexation, and π-π interactions. BCxSS is also attracting attention as a precursor for catalysts capable of degrading persistent pollutants. Despite these advances, the application of BCxSS for the adsorption and inactivation of pathogenic microorganisms and antibiotic resistance genes remains limited, revealing a critical research gap. Understanding BC-microorganism interactions is vital, given the significant public and environmental health risks posed by waterborne pathogens.

Overall, BCxSS provides a tangible example of circular economy in practice, transforming wastewater treatment byproducts into valuable resources that reduce waste and mitigate pollution.

How to cite: Gkogkou, E. V., Isari, E. A., Grilla, E., Manariotis, I. D., Kalavrouziotis, I. K., and Kokkinos, P.: Sewage sludge-derived biochar as a circular “waste-to-resource” strategy for wastewater treatment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10788, https://doi.org/10.5194/egusphere-egu26-10788, 2026.

Numerous recent publications have demonstrated the relationship between random reflectance and the proportion of the fully carbonized fraction (equivalent to the fusinite maceral) contained in a biochar sample. These findings have motivated international frameworks and independent carbon registries to consider random reflectance among the core analytical proxies required to assess biochar permanence in soil. However, skepticism for the application of the proxy still persists, with main challenges revolving around the aspects of data acquisition and data interpretation. This is mostly attributed to the fact that the methods applied in the microscopic study of biochar were originally developed and standardized for the study of coal, where the calculation of the average and standard deviation of a 100 measurements on collotelinite, accompanied by a histogram showing the frequency distribution of the measured values, is often enough to describe and report this optical property.

Even though biochar samples are petrographically much simpler than coal and other sedimentary rocks, they have peculiarities that require a more careful consideration when applying standard petrographic techniques for their study. Biochar manufacturing conditions play a major role in the extent of the carbonization degree of the feedstock and this in turn has an impact on the heterogeneity of the formed biochar grains often resulting in complex distributions of the reflectance values, not always accurately captured in the basic descriptive statistics (mean and standard deviation, etc.), particularly in the case of polymodal distributions. For this reason a higher number of measurements, ranging between 300-500, on fields of view selected along a regular grid, is required to acquire meaningful average and standard deviation values, as opposed to coal samples, where a “run of the sample” on parallel traverses where collotelinite occurs is a common practice.  

Advanced descriptive statistics have long been successfully used for the evaluation of grain size analysis of clastic sedimentary rocks for the assessment of reservoir properties and depositional environment. This study attempts to investigate the frequency and probability distributions and derived advanced descriptive statistics of random reflectance measurements acquired from 50 plant-based biochar samples, in order to characterize their heterogeneity with regards to the proportion of the fully carbonized fraction. The calculated advanced descriptive statistics include the coefficient of variation and confidence intervals, measures of central tendency (median and mode), measures of dispersion (variance and interquartile range), shape parameters (skewness and kurtosis), and probability-related measures (probability density function, cumulative distribution function, and percentiles, particularly those associated with the established “inertinite benchmark”-IBR_o2). In addition to those, the study attempts a comparison between the IBR_o fractions determined by the measurement of 3-4 points per field of view, against those determined by just measuring the point located at the crosshair of each field of view, together with the convergence of the acquired set of measurements to the mean and median of each sample. Findings are expected to contribute to a mathematically more robust characterization of the acquired datasets, providing greater rigor in how this data can be utilized with regards to the assessment of biochar carbon permanence.

How to cite: Siavalas, G., Alami Sounni, K., and Camps Arbestain, M.: Advanced descriptive statistics of random reflectance measurements on plant-based biochars-do they even matter?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19240, https://doi.org/10.5194/egusphere-egu26-19240, 2026.

Abstract: Understanding how vegetation patterns control gravity erosion, such as avalanches, landslides, and mudflows, in slope–gully systems under heavy rainfall, remains a key challenge on the Chinese Loess Plateau. To address this, five 1-h simulated rainfalls were conducted, at an intensity of 1.4 mm/min, on experimental plots. These plots featured a gentle slope of 3° and a gully sidewall of 70°, and were covered with different vegetation patterns. Our results show that high-coverage grass on the gentle slope effectively reduced avalanche magnitude. The plot with 85% grass coverage had the lowest average avalanche volume, at 109.6 cm³, across the five rainfall experiments. Conversely, the excessive restoration of woody vegetation, or planting woody vegetation near the gully shoulder line, markedly increased landslide scale. Across the five rainfalls, the average landslide volume was 1,202.7 cm³ in the plot with 85% tree coverage and 983.3 cm³ in the plot with 60% shrub coverage along the gully shoulder line––both nearly triple that of bare land. Mudflow volumes in most of the plots accounted for less than 10% of the total gravity erosion. Avalanche and landslide volumes were significantly correlated with root mass density, silt content, bulk density, and organic matter, with all correlation coefficients exceeding 0.45. Consequently, implementing high-coverage grass on gentle slopes is one of the most effective strategies for mitigating gravity erosion on gully sidewalls.

Keywords: Gravity erosion; Vegetation pattern; Slope–gully systems; Grass; Loess Plateau

How to cite: Ran, G. and Xu, X.: High-coverage grass on slope–gully systems effectively mitigates gravity erosion in the Loess Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6345, https://doi.org/10.5194/egusphere-egu26-6345, 2026.

Plant roots actively modify the physical properties of the soil in their area by secreting mucilage. Chia seed mucilage (CSM) is used as a model for plant root exudates primarily because of its similarities in physicochemical properties to natural root mucilage and its easy extraction in substantial, consistent quantities for laboratory experiments to study plant-soil-water relations. CSM can form highly viscous solutions at low concentrations and exhibits excellent properties, including water-holding capacity, surface tension, and emulsion stabilization. Most previous studies focused on chia seed mucilage as a conceptual model to describe the effect of mucilage on soil hydraulic properties, solute movement and gas diffusion in soil. However, the interactions between CSM and heavy metals have not been studied yet. Here, we showed the role of CSM as a bio-adsorbent for the removal of heavy metals and contaminants. Due to its sensitivity, non-destructivity, and simplicity, molecular fluorescence spectroscopy has been used to provide qualitative and quantitative information on the interaction between natural dissolved organic matter and metal ions. CSM was extracted from hydrated chia seeds and characterized using fluorescence Excitation-Emission Matrices combined with the Parallel Factor Analysis (EEM-PARAFAC) method. The binding interactions of CSM fluorescent components with heavy metals were quantified using fluorescence quenching titration and the Stern-Volmer model. Competitive binding studies were also conducted using one heavy metal as the quenching agent in the presence of competing heavy metal ions. Unconstrained PARAFAC modelling with two to four components was performed separately on 61 EEMs obtained from different concentrations of CSM samples, and the final component scores were determined through core consistency analysis, split-half analysis, and examination of the explained variance percentages. Protein-like (tryptophan & tyrosine) substances were the main fluorescent components identified by EEM-PARAFAC. The quenching titration results showed that the fluorescence intensity of CSM fluorescent components decreased with increasing heavy metal concentration under various environmental conditions. This strong quenching effect implies the binding ability of CSM to heavy metals and its significance in understanding metal toxicity, bioavailability, and transport in soil and natural waters.

How to cite: abrha, K. A. and Arye, G.: Adsorption of heavy metals to chia seeds' mucilage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7583, https://doi.org/10.5194/egusphere-egu26-7583, 2026.

Multiple cropping systems are widely adopted as a key climate adaptation strategy to ensure food security in China, however, they also impose significant pressure on freshwater ecosystems. While trade-offs between yield and water use are well-documented, the spatiotemporal impacts of specific rotation systems on aquatic biodiversity remain poorly quantified at fine spatial scales. To address this, we present a high-resolution framework integrating 30m-resolution crop rotation maps with monthly gridded characterization factors to quantify the potential fraction of species loss (PDF) at the grid level. This approach specifically distinguishes the impacts of seasonal rotation patterns from annual aggregates. We anticipate three key findings: (1) the identification of seasonal biodiversity hotspots driven by groundwater reliance in winter; (2) a quantification of the biodiversity leakage or savings resulting from China’s Crop Rotation and Fallow Policy; and (3) the revelation of spatial mismatches between agricultural intensification and ecosystem vulnerability. By shifting from a static, national-level perspective to a dynamic, spatially explicit one, this study underscores the urgency of incorporating seasonal biodiversity footprints into climate-smart agricultural policymaking to achieve food targets with ecosystem integrity.

How to cite: Niu, Q. and Zhao, D.: Unveiling the Invisible Ecological Cost: Seasonal Biodiversity Footprint of Crop Rotation in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7358, https://doi.org/10.5194/egusphere-egu26-7358, 2026.

Process-based crop models such as AquaCrop are widely used to assess crop responses to irrigation management and climate variability. However, large-scale scenario exploration and optimization are often constrained by the high computational cost of repeated model simulations. In this study, we develop a machine-learning surrogate model to emulate AquaCrop-OSPy (ACOSP) simulated wheat yields under a wide range of soil-moisture-triggered irrigation (SMT) thresholds and daily maximum irrigation limits (MaxIrr) across 26 growing seasons in Northwest India. A simulation ensemble of 21,840 ACOSP runs was generated by systematically varying SMT (0–100%) and MaxIrr (0–40 mm day⁻¹) for each season. The surrogate model was trained using season-wise climate variability, seasonal precipitation and irrigation strategy parameters (Season, SMT, MaxIrr) as predictors, with wheat yield (t ha⁻¹) as the target variable. We implemented and compared Random Forest and XGBoost regression models using a time-based train–test split to avoid information leakage across seasons. The best-performing XGBoost model explained ~87–90% of the inter-season and management-driven yield variability in the independent test period, while maintaining computational runtimes several orders of magnitude lower than ACOSP. Feature-importance analysis showed that SMT was the dominant explanatory factor, followed by climate-driven seasonal variability, whereas MaxIrr primarily influenced high-stress scenarios. The surrogate model successfully reproduced non-linear yield responses and threshold behaviour, suggesting strong potential for near-real-time decision support and large-scale scenario exploration. This work demonstrates that machine-learning surrogates can complement process-based crop models by enabling rapid evaluation of irrigation strategies, uncertainty assessment, and future climate scenario testing at regional scales. The developed framework is transferable to other regions, crops, and water-limited environments, offering a scalable pathway toward computationally efficient agricultural water-management assessment.

How to cite: Garg, D. and Kumar, H.: Developing a machine-learning Surrogate Model to rapidly predict wheat yields under Soil-Moisture-Triggered irrigation strategies in Northwest India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10703, https://doi.org/10.5194/egusphere-egu26-10703, 2026.