Grant: Discovery Grants Program - Individual

Solid waste management is vital to public health and safety, and it is directly or indirectly related to more than half of the 17 United Nation's Sustainable Development Goals. Canadians generate more solid waste than most industrialized nations and we recycle less. Canadians also export tonnes of waste to other developing countries, triggering many ethical and equality issues. Waste management in Canada is economically significant, as the industry represents a $3.5billion+ sector. This proposal focuses on the development of advanced analytical techniques to support the design and optimization of a sustainable waste management system (WMS). A sustainable WMS will minimize our environmental footprint, contribute to a circular economy, and improve access to waste management service in sparsely populated regions. There is a worldwide trend on the use of data-driven techniques in waste studies and evidence-based waste policy development. It is however difficult to effectively design WMS and implement waste regulations if the attributes of a sustainable WMS are not well understood and the key analytical tools are missing. The 5-year objectives are to [1a] develop an original set of jurisdictionally comparable metrics for WMS evaluation, [1b] advance time series waste modeling techniques to improve WMS modeling, [2a] develop waste management regions using weighted Thiessen polygons, and [2b] optimize WMS in remote and northern regions using selected spatial statistics. The new WMS metrics developed in 1a will help us to benchmark Canadian WMS with other countries, identifying key WMS attributes. The advances in time series modeling techniques derived from 1b will be used to examine the WMS attributes temporally, helping us to design a viable WMS. Geospatially, WMS regionalization minimizes environmental risks and reduces operation cost. However, the sciences behind the topological tessellation tools on WMS regionalization are lagging. Weighted Thiessen polygons are proposed in 2a in an effort to avoid political biases and to establish a true data-driven WMS. The pseudo-3D WMS tessellation built in 2a will then be optimized in 2b. The optimized tessellation will challenge the status quo and improve environmental sustainability and social equality in remote regions, such as northern Canada. The realizations of the study objectives will fundamentally change how we implement WMS in Canada and beyond, and will ultimately bring us closer to the answer of this long-standing question whether an upper limit on waste diversion rate exists. The training plan is carefully crafted to provide diverse opportunities to the 11 HQP on data driven techniques, machine learning tools, time series modeling, and tessellation optimization. The new analytical tools generated will create a novel line of research and versatile, job-ready skill set for HQP, contributing to an advanced knowledge based economy.