Redistribution of Soil Organic Matter by Permafrost Disturbance in the Canadian High Arctic

With increased warming in the Arctic, permafrost thaw may induce localized physical disturbance of slopes. These disturbances, referred to as active layer detachments (ALDs), redistribute soil across the landscape, potentially releasing previously unavailable carbon (C). In 2007–2008, widespread ALD activity was reported at the Cape Bounty Arctic Watershed Observatory in Nunavut, Canada. Our study investigated organic matter (OM) composition in soil profiles from ALD-impacted and undisturbed areas. Solid-state 13C nuclear magnetic resonance (NMR) and solvent-extractable biomarkers were used to characterize soil OM. Throughout the disturbed upslope profile, where surface soils and vegetation had been removed, NMR revealed low O-alkyl C content and biomarker analysis revealed low concentrations of solvent-extractable compounds suggesting enhanced erosion of labile-rich OM by the ALD. In the disturbed downslope region, vegetation remained intact but displaced material from upslope produced lateral compression ridges at the surface. High O-alkyl content in the surface horizon was consistent with enrichment of carbohydrates and peptides, but low concentrations of labile biomarkers (i.e., sugars) suggested the presence of relatively unaltered labile-rich OM. Decreased O-alkyl content and biomarker concentrations below the surface contrasted with the undisturbed profile and may indicate the loss of well-established pre-ALD surface drainage with compression ridge formation. However, pre-ALD profile composition remains unknown and the observed decreases may result from nominal pre-ALD OM inputs. These results are the first to establish OM composition in ALD-impacted soil profiles, suggesting reallocation of permafrost-derived soil C to areas where degradation or erosion may contribute to increased C losses from disturbed Arctic soils.

Numerical Modelling of Eddy Current Probes for CANDU® Fuel Channel Inspection

Multi-frequency Eddy Current (EC) inspection with a transmit-receive probe (two horizontally offset coils) is used to monitor the Pressure Tube (PT) to Calandria Tube (CT) gap of CANDU® fuel channels. Accurate gap measurements are crucial to ensure fitness of service; however, variations in probe liftoff, PT electrical resistivity, and PT wall thickness can generate systematic measurement errors. Validated mathematical models of the EC probe are very useful for data interpretation, and may improve the gap measurement under inspection conditions where these parameters vary. As a first step, exact solutions for the electromagnetic response of a transmit-receive coil pair situated above two parallel plates separated by an air gap were developed. This model was validated against experimental data with flat-plate samples. Finite element method models revealed that this geometrical approximation could not accurately match experimental data with real tubes, so analytical solutions for the probe in a double-walled pipe (the CANDU® fuel channel geometry) were generated using the Second-Order Vector Potential (SOVP) formalism. All electromagnetic coupling coefficients arising from the probe, and the layered conductors were determined and substituted into Kirchhoff’s circuit equations for the calculation of the pickup coil signal. The flat-plate model was used as a basis for an Inverse Algorithm (IA) to simultaneously extract the relevant experimental parameters from EC data. The IA was validated over a large range of second layer plate resistivities (1.7 to 174 µΩ∙cm), plate wall thickness (~1 to 4.9 mm), probe liftoff (~2 mm to 8 mm), and plate-to plate gap (~0 mm to 13 mm). The IA achieved a relative error of less than 6% for the extracted FP resistivity and an accuracy of ±0.1 mm for the LO measurement. The IA was able to achieve a plate gap measurement with an accuracy of less than ±0.7 mm error over a ~2.4 mm to 7.5 mm probe liftoff and ±0.3 mm at nominal liftoff (2.42±0.05 mm), providing confidence in the general validity of the algorithm. This demonstrates the potential of using an analytical model to extract variable parameters that may affect the gap measurement accuracy.