Canadian Nuclear Safety Commission

Early detection of damage in reinforced concrete structures is critical for ensuring structural safety and reducing maintenance costs. Acoustic emission monitoring offers a passive means of capturing stress-induced damage activity; however, conventional analysis approaches rely heavily on manual interpretation of signal features. This paper proposes an artificial intelligence framework that integrates acoustic emission data with machine learning for automated early failure detection. A one-class support vector machine is employed to learn baseline undamaged behavior and identify deviations associated with damage initiation. The approach is experimentally validated using pull-out tests on reinforced concrete specimens with varying configurations. Results demonstrate that the proposed framework detects damage onset in real time and provides reliable early warning prior to visible failure. Feature-level analysis further highlights the relative effectiveness of different acoustic emission parameters for damage characterization.

International designs for safe disposal of spent nuclear fuel in deep geological repositories (DGRs) typically include placing spent fuel in metallic used fuel containers (UFCs), surrounded by low permeability swelling clay buffers, in stable host rock formations, generally below the water table. Understanding the impact of groundwater chemistry on repository performance is an important aspect of DGR safety assessments, including the geochemical interaction of copper-coated UFCs with that groundwater. Research into copper corrosion in DGR relevant environments often uses sodium chloride solutions as analogues for DGR groundwater or as supporting electrolytes in electrochemical studies. It is, however, unclear whether solutions that mimic the expected ion composition of the groundwater will alter copper corrosion behaviour. Here, electrochemical experiments were used, in combination with surface analyses, to determine the corrosion behaviour of Cu in different solution compositions. Copper was exposed to 0.5 M NaCl, 0.25 M NaCl, and an analogue of groundwater found in a Canadian DGR hosted in crystalline rocks of the Precambrian Canadian Shield (based on the previously developed CR-10 model water). It was determined that the presence of oxygen (i.e., oxic vs. anoxic), the ionic strength, and the solution’s composition all impact the corrosion behaviour. A direct relationship between ionic strength and corrosion rate was determined with corrosion rates in solutions with a 0.5 mol/L ionic strength being twice that of solutions with a 0.25 mol/L ionic strength, particularly in oxic environments. The ion composition had an impact on copper corrosion in all instances with the groundwater analogue resulting in lower corrosion rates than the NaCl solutions.

Radiation-based medical techniques and devices provide significant benefits to patients through the diagnosis, treatment, and management of illness and disease. Documenting trends and frequency of use offer important insights into radiation protection and help address gaps in the documentation of medical exposures. Here, we present the retrospective Canadian data collected for the recent United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) global survey on medical exposure. The global survey included three modality categories: diagnostic and interventional radiology, nuclear medicine, and radiotherapy and reports the total number of devices, physicians, examinations, and procedures. Due to the inability to collect high-quality dose data from Canadian sources, the average doses for specific examinations and treatments were estimated using internationally pooled data. The total annual per capita dose from medical exposures was determined to be 1.56 mSv, excluding radiotherapy, resulting in approximately 47% of all radiation doses received by Canadians, compared to natural, industrial, and consumer product sources. This assessment of Canadian medical radiation exposures contributes to global improvement of patient protection, helps establish trends, and identifies where Canadian data collection is lacking, particularly dose data.

The Canadian Nuclear Safety Commission (CNSC) is Canada's nuclear regulatory body responsible for regulating the development, production, and use of nuclear energy and the production, possession, and use of nuclear substances. The CNSC achieves this through a highly regimented licensing process for activities under its mandate that includes engagement with the public and Indigenous Nations and communities who may be potentially affected by the activity to be licenced. This engagement is needed to disseminate objective scientific and technical information and ensure confidence in the regulatory competence of the CNSC. The risk of receiving unwanted exposures from licenced activities and how that exposure would impact a population's health, quality of life, and way of life are among the concerns routinely communicated to the CNSC during engagement. The CNSC typically addresses such concerns through the conveyance of anticipated doses, how those anticipated doses adhere to regulatory criteria, and how those anticipated doses compare to other more commonplace exposures such as from medical procedures. This article explores the disability adjusted life year (DALY) as a potential metric for improving existing radiological risk communication. The DALY is used in public health to quantify a population's burden of disease in terms of years of healthy life lost. Radiation exposure can be potentially included as one of many risk factors that already impact a population's health. In doing so, a more intuitive understanding of radiation risk and how it will impact a population's quality of life may be achieved.

The City of Toronto (Ontario) is Canada's largest urban area in population and areal extent. As a result, elevated activity from urban, industrial, and transportation sectors has increased atmospheric carbon-12 (12C) and carbon-13 (13C) from fossil fuel combustion. Excess 12C and 13C mask natural levels of atmospheric carbon-14 (14C), a phenomenon known as the Suess Effect. Consequently, measurements of atmospheric 14C can be used to quantify anthropogenic fossil fuel contributions. With southern Ontario having an air sampler run by Environment and Climate Change Canada (ECCC) in northern Toronto, there is opportunity to expand our understanding of the atmospheric carbon inventory. Using dendrochronological methods, tree-ring 14C measurements were obtained and compared with background 14C levels to assess urban 14C-depletion across Toronto via six trees representing different urban microclimates. One tree located near the ECCC air sampler closely tracked measured atmospheric 14C levels (Spearman's rho=0.93). While most samples analyzed were 14C-depleted, some years also experienced 14C-enrichment due to elevated 14C emissions from the Pickering Nuclear Generating Station. Trees located in urban parks recorded 14C levels closer to background levels. In general, tree rings effectively record 14C variations, reflecting both fossil fuel and nuclear contributions. Meteorological analysis indicates that proximity to the Pickering Nuclear Generating Station and lake-influenced air masses affects 14C uptake, suggesting that Toronto's atmospheric carbon inventory is spatially complex and that urban sampling sites must be evaluated individually.