Prediction of soil pyrogenic carbon contents from Rock-Eval&#174; thermal analysis: a machine-learning based model
Authors: Lebrun Thauront Johanne , Luca Bellè Severin , Schiedung Marcus , Delahaie Amicie , Stojanova Marija , Baudin François , Barré Pierre , Abiven Samuel 
Published: March 2024

Pyrogenic carbon (PyC) is a continuum of aromatic and condensed organic molecules. It represents about 15&#160;% of organic carbon in soils and sediments1. However, there is a discrepancy in the literature regarding quantification of PyC: different methods that are currently considered as reference differ largely in their results1,2. Indeed, most methods used to quantify PyC are based on different operational principles (e.g. chemical, thermal or physical stability of PyC, molecular markers) and consequently, they do not cover the same range of the PyC continuum2. In addition, most of them are expensive and/or time consuming. Here, we propose a new PyC quantification method based on Rock-Eval&#174; thermal analysis, thought to be rapid, inexpensive and comparable to the previous methods toolbox. Rock-Eval&#174; thermal analysis has been successfully introduced to the field of soil carbon analysis in the last two decades and allowed to distinguish between various pools of soil carbon (inorganic carbon, stable and active organic carbon) using a single analysis of combined pyrolysis and thermal oxidation3,4. In this study, we formulate the hypothesis that Rock-Eval&#174; thermal analysis in combination with predictive modelling is suitable to quantify PyC in soil matrices.To build and validate such a model, we chose soil samples originating from contrasting climate zones and parent material and with varying properties including clay content and mineralogy, iron oxide speciation and content, pH, cation-exchange capacity and organic carbon content. We measured PyC using a set of established methods (i.e. CTO-375, BPCA and HyPy) and acquired Rock-Eval&#174; thermograms. Then, we identified the relevant features for PyC quantification in the thermograms by applying several machine-learning approaches. This work adds a new soil carbon pool to the ones already accessible from Rock-Eval&#174; thermal analysis and allows an efficient and rapid quantification of PyC in soils, which is needed for large-scale studies of soil carbon pools.(1) Reisser, M.; Purves, R. S.; Schmidt, M. W. I.; Abiven, S. Pyrogenic Carbon in Soils: A Literature-Based Inventory and a Global Estimation of Its Content in Soil Organic Carbon and Stocks. Front. Earth Sci. 2016, 4 (August), 1&#8211;14. https://doi.org/10.3389/feart.2016.00080.(2) Hammes, K.; Smernik, R. J.; Skjemstad, J. O.; Schmidt, M. W. I. Characterisation and Evaluation of Reference Materials for Black Carbon Analysis Using Elemental Composition, Colour, BET Surface Area and 13C NMR Spectroscopy. Appl. Geochemistry 2008, 23 (8), 2113&#8211;2122. https://doi.org/10.1016/j.apgeochem.2008.04.023.(3) Disnar, J. R.; Guillet, B.; Keravis, D.; Di-Giovanni, C.; Sebag, D. Soil Organic Matter (SOM) Characterization by Rock-Eval Pyrolysis: Scope and Limitations. Org. Geochem. 2003, 34 (3), 327&#8211;343. https://doi.org/10.1016/S0146-6380(02)00239-5.(4) C&#233;cillon, L.; Baudin, F.; Chenu, C.; Houot, S.; Jolivet, R.; K&#228;tterer, T.; Lutfalla, S.; Macdonald, A.; Van Oort, F.; Plante, A. F.; Savignac, F.; Souc&#233;marianadin, L. N.; Barr&#233;, P. A Model Based on Rock-Eval Thermal Analysis to Quantify the Size of the Centennially Persistent Organic Carbon Pool in Temperate Soils. Biogeosciences 2018, 15 (9), 2835&#8211;2849. https://doi.org/10.5194/bg-15-2835-2018.