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The release of greenhouse gases from the large organic carbon stock in permafrost deposits in the circumarctic regions may accelerate global warming upon thaw. The extent of this positive climate feedback is thought to be largely controlled by the microbial degradability of the organic matter preserved in these sediments. In addition, weathering and oxidation processes may release inorganic carbon preserved in permafrost sediments as CO2, which is generally not accounted for. We used C-13 and C-14 analysis and isotopic mass balances to differentiate and quantify organic and inorganic carbon released as CO2 in the field from an active retrogressive thaw slump of Pleistocene-age Yedoma and during a 1.5-years incubation experiment. The results reveal that the dominant source of the CO2 released from freshly thawed Yedoma exposed as thaw mound is Pleistocene-age organic matter (48-80%) and to a lesser extent modern organic substrate (3-34%). A significant portion of the CO2 originated from inorganic carbon in the Yedoma (17-26%). The mixing of young, active layer material with Yedoma at a site on the slump floor led to the preferential mineralization of this young organic carbon source. Admixtures of younger organic substrates in the Yedoma thaw mound were small and thus rapidly consumed as shown by lower contributions to the CO2 produced during few weeks of aerobic incubation at 4 degrees C corresponding to approximately one thaw season. Future CO2 fluxes from the freshly thawed Yedoma will contain higher proportions of ancient inorganic (22%) and organic carbon (61-78%) as suggested by the results at the end, after 1.5 years of incubation. The increasing contribution of inorganic carbon during the incubation is favored by the accumulation of organic acids from microbial organic matter degradation resulting in lower pH values and, in consequence, in inorganic carbon dissolution. Because part of the inorganic carbon pool is assumed to be of pedogenic origin, these emissions would ultimately not alter carbon budgets. The results of this study highlight the preferential degradation of younger organic substrates in freshly thawed Yedoma, if available, and a substantial release of CO2 from inorganic sources.
Supervised machine learning to assess methane emissions of a dairy building with natural ventilation
(2020)
A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach.