Janssens Iris, Servotte Thomas, Calogiuri Tullia, Mortier Steven, Niron Harun, Corbett Thomas, Poetra Reinaldy P, Rieder Lukas, Van Tendeloo Michiel, Singh Abhijeet, Latré Steven, Vlaminck Siegfried E, Hartmann Jens, van Groenigen Jan Willem, Neubeck Anna, Vidal Alix, Janssens Ivan A, Hagens Mathilde, Vicca Sara, Verdonck Tim
Department of Computer Science, University of Antwerp - imec - IDLab, Antwerp, Belgium.
Department of Mathematics, University of Antwerp - imec - IDLab, Antwerp, Belgium.
Open Res Eur. 2025 Jul 3;5:71. doi: 10.12688/openreseurope.19252.2. eCollection 2025.
The optimization of enhanced mineral weathering as a carbon dioxide removal technology requires a comprehensive understanding of what drives mineral weathering. These drivers can be abiotic and biotic and can interact with each other. Therefore, in this study, an extensive 8-week column experiment was set up to investigate 30 potential drivers of mineral weathering simultaneously.
The setup included various combinations of rock types and surface areas, irrigation settings, biochar and organic amendments, along with various biota and biotic products such as earthworms, fungi, bacteria and enzymes; each varying in type or species and quantity. The resulting changes in dissolved, solid, and total inorganic carbon (∆TIC), and total alkalinity were calculated as indicators of carbon dioxide removal through mineral weathering. Three machine learning models, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest and eXtreme Gradient Boosting (XGB) regression, were used to predict these indicators. Dominant drivers of the best performing model were investigated using SHapley Additive exPlanations (SHAP).
SHAP analysis revealed that each CDR indicator was influenced by different factors. However, key drivers were consistently abiotic, though biota also made a significant contribution to the predictions. The most representative CDR indicator, ∆TIC, was predominantly driven by steel slag addition and mixed rock grain sizes but was also substantially impacted by earthworms and microbes.
These findings provide valuable insights into the complex interplay of numerous abiotic and biotic factors that affect mineral weathering, highlighting the potential of machine learning to unravel complex relationships in biogeochemical systems.
作为一种二氧化碳去除技术，强化矿物风化的优化需要全面了解驱动矿物风化的因素。这些驱动因素可以是生物的和非生物的，并且它们之间可以相互作用。因此，在本研究中，我们开展了一项为期8周的广泛柱实验，以同时研究30种潜在的矿物风化驱动因素。
实验设置包括岩石类型和表面积、灌溉设置、生物炭和有机改良剂的各种组合，以及各种生物群和生物产物，如蚯蚓、真菌、细菌和酶；每种在类型、物种和数量上均有所不同。计算溶解无机碳、固体无机碳和总无机碳（∆TIC）以及总碱度的变化，作为通过矿物风化去除二氧化碳的指标。使用三种机器学习模型，即最小绝对收缩和选择算子（LASSO）、随机森林和极端梯度提升（XGB）回归，来预测这些指标。使用SHapley加性解释（SHAP）研究表现最佳模型的主要驱动因素。
SHAP分析表明，每个二氧化碳去除指标受不同因素影响。然而，关键驱动因素始终是非生物因素，尽管生物群对预测也有显著贡献。最具代表性的二氧化碳去除指标∆TIC主要受钢渣添加和混合岩石粒度的驱动，但也受到蚯蚓和微生物的显著影响。
这些发现为影响矿物风化的众多非生物和生物因素之间的复杂相互作用提供了有价值的见解，突出了机器学习在揭示生物地球化学系统复杂关系方面的潜力。
