Uptake of the neurotoxicant monomethylmercury (MeHg) from fish and marine mammals continues to present a public health concern in Canada and elsewhere. However, fish and marine mammals are key diet items contributing to food security for some Indigenous populations in Canada. Mercury (Hg) exposure is estimated assuming that 100% of Hg is methylated, that 100% will be absorbed by the consumer and that cooking does not affect MeHg concentrations. Some of these assumptions do not correspond to our current state of knowledge. The aim of this study was to assess the impact of additional variables on Hg exposure equation using probabilistic risk analysis. New variables tested were (1) the proportion of methylated Hg compared to total Hg (pMeHg, %), (2) the relative absorption factor (RAF, %) expressed as bioaccessibility and (3) the mass loss factor (MLF, unitless) that represents the loss of moisture during cooking, known to increase MeHg concentration in fish and mammals. For the new variables, data from literature were used in order to set point estimate values that were further used in the probabilistic risk analysis. Modelling results for both fish and marine mammals indicate that adding these new variables significantly influenced estimates of MeHg exposure (Mood’s median test, p < 0.05). This study highlights that the evaluation of exposure to MeHg is sensitive to pMeHg, RAF and MLF, and the inclusion of these variables in risk assessment should be considered with care. Further research is needed to provide better food-dependent, population-specific estimates of RAF and MLF before formal inclusion in exposure estimates.

The decreasing cost of implementation and increasing regulatory incentive to lower energy use have led to an increased adoption of distributed energy resources in recent years. This increased adoption has been further fueled by a surge in energy consciousness and the expansion of energy-saving products and technologies. To lower reliance on the electrical grid and fully realize the benefits of distributed energy resources, many consumers have also elected to use battery systems to store generated energy. For owners of multiple buildings, or multiple owners willing to share the operational cost, building clusters may be formed to more effectively take advantage of these distributed resources and storage systems. The implementation of these systems in existing buildings introduces the question of what makes a “good” building cluster. Furthermore, the scalable nature of distributed energy sources and storage systems create countless possibilities for system configuration. Through comparison of unique two-building clusters from a stock of five buildings with a given distributed energy resource (in this case, a solar photovoltaic panel array) and energy storage system, we develop a fundamental understanding of the underlying factors that allow building clusters to be less reliant on the utility grid and make better use of energy generation and storage systems.

The energy consumption of buildings has traditionally been driven by the consumption habits of building occupants. However, with the proliferation of smart building technologies and appliances, automated machine decisions are beginning to impart their influence on building energy behavior as well. This is giving rise to a disconnect between occupant energy behavior and the overall energy consumption of buildings. Consequently, researchers can no longer leverage building energy consumption as a proxy for understanding human energy behavior. This paper addresses this problem by exploiting the habitual and sequential nature of human energy consumption. By studying the chronology of human energy actions, the results of this work present a promising new approach for non-intrusively learning about human energy behavior directly from building energy demand data.