Biochar (BC) is often used as an additive in anaerobic digestion (AD) to increase yield and/or to stabilise the process when the manure content is increased. Unfortunately, BC is rarely described in detail in terms of its raw material sources, production processes, and structural, physical and chemical properties to allow correlation with its effects on AD. It is an open question whether microorganisms from AD can penetrate into different biochar pore types, depending on their wood origin. In this paper, we describe the preparation (temperatures, treatment times, yields) and characterisation shrinkage, density, pore sizes, pore size distribution, specific surface area, ash, volatiles, fixed carbon, elemental composition, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), benzene, toluene, ethyl-toluene, xylene (BTEX) and volatile halogenated hydrocarbons (VHH) of BC cubes of Scots pine (Pinus sylvestris) and common beech (Fagus sylvatica) powder made from this BC in addition to commercial charcoal powder. The pore size distribution determined by mercury porosimetry differs from that determined by 3D-reflected light microscopy. After incubating BC cubes in AD, the cubes were mechanically cleaned and cut into two pieces. Microorganisms were detected inside the cubes by fluorescence microscopy. Particle size and wood source determine the influence of BC on AD.

Reliable estimation of biochar’s calorific value is essential for optimizing its use as a renewable energy source. Traditional bomb calorimetry provides accurate measurements but is hindered by its destructive, time-consuming nature, limiting the high-throughput screening capabilities needed for large-scale deployment. In this study, an innovative, non-destructive approach utilizing laser-induced breakdown spectroscopy (LIBS) combined with advanced multivariate analysis is presented for predicting the Higher Heating Value (HHV) of biochar derived from pine and beech biomass. The developed empirical model incorporates spectral signatures of key elements: carbon, hydrogen, nitrogen, sulfur, and oxygen. Model validation using 36 independent biochar samples revealed a statistically significant correlation between experimentally measured and LIBS-predicted HHVs (p-value = 0.045, t-statistic = 2.08). The developed model yielded a mean absolute error (MAE) of 1.33 MJ kg⁻¹ and a root mean square error (RMSE) of 1.72 MJ kg⁻¹. The findings demonstrate the feasibility of using LIBS-derived elemental data for rapid HHV estimation and provide a basis for further model refinement through the inclusion of additional biochar types and calibration datasets. The model effectively captures the complex nonlinear relationships between spectral features and energy content, addressing the heterogeneity inherent in biochar matrices. These findings highlight LIBS’s potential as a rapid, scalable, and environmentally sustainable tool for real-time biochar evaluation. Implementing this approach could significantly accelerate biomass resource assessment, optimize bioenergy production, and advance sustainable energy management strategies aligned with global environmental goals.