Temporal and Spatial Distributions of Waste Facilities and Solid Waste Management Strategies in Rural and Urban Saskatchewan, Canada

Abstract

Saskatchewan has the highest number of landfills per capita in Canada. Given the lower population density and the skewed spatial population distribution, comprehensive analysis of municipal solid waste management systems in Saskatchewan is inherently difficult. Most of the published waste studies however focus on city-level waste management, and there is a lack of literature with respect to the rural areas. In this study, landfills and transfer stations are examined temporally and spatially using Geographic Information System. Landfills and transfer stations from 2017 and 2020 were plotted against census division land area, annual budget, and population density to study temporal changes. Saskatchewan witnessed a 54% reduction in the number of landfills and a 55% increase in number of transfer stations between 2017 and 2020. The replacement of landfills with transfer stations are more noticeable in divisions 8, 9, and 16. Regression analysis is conducted, and landfill closure operation show no obvious correlation to division land area, annual budget, or population density. Rural division 18, representing Northern Saskatchewan, has approximately 45% of the land area in the province and has the lowest population density. The findings suggest different waste management strategies are required for urban and rural areas. The results of this study will help policy makers to better implement solid waste management strategies in urban and rural areas.

Perspective of solid waste generation in Albania based on a combination of correlation and interpolation

“…The results presented, similar to those of Mushtaq et al (2020), show that SW management varies from region to region, due to factors, such as demographic, socio-economic and accepted policy. Several studies (Kukula 2016;Ghosh and Ng 2021) indicate that waste management strategies depend on the approach of each administrative unit to the most decisive factors. We found a significant correlation in most of the administrative units, which can be used to implement decision-making strategies in each of them.…”

Integrating Geographic Information System network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level

“…The absence or inaccessibility of municipal landfills in the vicinity of population centers is causing serious health and environmental issues in the Canadian rural areas (Patrick 2018 ; Keske et al 2018 ). This is especially important when some jurisdictions have started to close landfills and move toward landfill regionalization (Ghosh and Ng 2021 ; Richter et al 2021a ), making strategical landfill siting an urgent necessity. For example, Saskatchewan started to reduce the number of rural landfills (SARM 2017 ), without a comprehensive landfill selection and reduction plan.…”

“…Accessibility of waste facilities appears to be an important issue in regionalization of Canadian waste management system (Ghosh and Ng 2021 ; Karimi et al 2022b ). A larger area coverage of a landfill helps reduce illegal disposal sites by providing accessibility to neighboring populations (Patrick 2018 ; Keske et al 2018 ; Quesada-Ruiz et al 2019 ; Karimi et al 2022a ).…”

“…Most published studies focus on waste management system optimization within an urban area or a metropolis, where waste generation sites are well defined and geographically precise. However, the design of a full-scale waste management system containing both urban and rural areas is more challenging and is much less addressed in the literature (Ghosh and Ng 2021 ), probably due to the difficulties associated with identification of population centers and waste generation sites outside of the city boundary. In this study, identification of population centers is done by RS nighttime light satellite (NTL) data.…”

Hospital waste generation during the first wave of COVID-19 pandemic: a case study in Delhi

“…Data accuracy and reliability is a key challenge in the development of an evidence-based waste management system (Richter et al 2019;Ghosh and Ng, 2021), especially during the pandemic. For example, different modeling approaches such as the uses of lagged inputs and distinct time series (Vu et al 2021a) and separated waste fractions (Vu et al 2021b) were attempted to minimize uncertainties in data and variations in waste recycling behaviors during the COVID pandemic.…”