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Although road surface friction is considered the most effective performance measure for maintenance operations, it is not commonly used due to the high cost of collection. As a result, most jurisdictions use subjective visual indicators that qualitatively describe the state of the road surface, even though they create measurement inconsistencies and offer less detailed maintenance tracking. For maintenance personnel to transition into using friction, the collection cost must be reduced. This paper attempts to do so by proposing a low-cost, machine learning-based method for predicting road surface friction using dash camera imagery and demonstrates its feasibility through a case study. The dataset used for this project was collected in the City of Edmonton, AB, Canada during its 2021/2022 winter season. Three models were developed using tree-based algorithms, where all three displayed high performance with an average root mean squared error of 0.0796 or 79.3% accuracy based on RMSPE. [ABSTRACT FROM AUTHOR]

The driving forces controlling the long-term total ozone change are investigated using the total ozone data from Edmonton (53.55°N, 114.0°W), Canada, over 1958–2018. By using the slow feature analysis method, the driving force signal for monthly mean total ozone time series is extracted. It is found that the driving force has fluctuated at higher values since the mid-1980s, and the corresponding total ozone has varied at lower values. These variation patterns indicate that the ozone loss since the mid-1980s is correlated with the higher values of the driving force. It is also found that the driving force exhibits sharp values following some explosive volcanic events, indicating that explosive volcanic eruptions act as a driving factor for ozone depletion. In order to analyze the scale structure of the driving force, the wavelet analysis method is applied. The results show that the spectral scales of the driving force peak at 2.4, 7.0, 21.4, 28.5, and 42.8 years. Among them, the 21.4 year periodicity, which corresponds to the 22-year solar magnetic cycle, is the basic one, and it modulates other mode of oscillations. More importantly, the 21.4 year cycle is modulated in amplitude by a signal with a much longer time scale. This long-scale modulating signal represents the impacts of the ozone depleting substances (ODSs) in the stratosphere, and it controls the long-term evolution of total ozone. Since the turn of the century, as the ODSs decline, a slow recovery of total ozone is expected. [ABSTRACT FROM AUTHOR]

Waste collection management plays a crucial role in controlling pandemic outbreaks. Electric waste collection systems and vehicles can improve the efficiency and effectiveness of sanitary processes in municipalities worldwide. The waste collection routing optimization involves designing routes to serve all customers with the least number of vehicles, total traveling distance, and time considering the vehicle capacity. This paper proposes a dynamic location-arc routing optimization model for electric waste collection vehicles. The proposed model suggests an optimal routing plan for the waste collection vehicles and determines the optimal locations of the charging stations, dynamic charging arcs, and waste collection centers. A genetic algorithm and grey wolf optimizer are used to solve the large-sized random generated NP-hard location-arc routing problems. We present a case study for the city of Edmonton in Canada and show the grey wolf optimizer outperforms the genetic algorithm. We further demonstrate the total number of waste collection centers, charging stations, and arcs for dynamic charging needed to ensure a minimum required service for electric vehicles throughout Edmonton's entire waste collection system. [Display omitted] • The waste collection routing optimization optimizes routes serving customers. • Optimized routes use minimum vehicles, distance, and time with capacitated vehicles. • A dynamic location-arc routing optimization is proposed for electric waste collection vehicles. • Genetic algorithms and grey wolf algorithms solve the large NP-hard problem. • A case study in Canada shows grey wolf algorithm outperforms the genetic algorithm. [ABSTRACT FROM AUTHOR]

Changes in temperature and precipitation are expected to alter the seasonal distribution of surface water supplies in snowmelt-dominated watersheds. A realistic assessment of future climate change and inter-annual variability is required to meet a growing demand for water supplies in all major use sectors. This study focuses on changes in climate and runoff in the North Saskatchewan River Basin (NSRB) above Edmonton, AB, Canada, using the MESH (Modélisation Environnementale communautaire—Surface Hydrology) model. The bias-corrected ensemble of Canadian Regional Climate Model (CanRCM4) data is used to drive MESH for two 60-year time periods, a historical baseline (1951–2010) and future projection (2041–2100), under Representative Concentration Pathway (RCP) 8.5. The precipitation is projected to increase in every season, there is significant trend in spring (0.62) and fall (0.41) and insignificant in summer (0.008). Winter extreme minimum temperature and summer extreme maximum temperature are increasing by 2–3 °C in the near future and 5–6 °C in the far future. Annual runoff increases by 19% compared to base period. The results reveal long-term hydrological variability enabling water resource managers to better prepare for climate change and extreme events to build more resilient systems for future water demand in the NSRB. [ABSTRACT FROM AUTHOR]