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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]

Background: Ongoing legal and social discrimination, and stigmatization of people with lived experience of drug use (PWLE) continues to contribute to overdose-related deaths in Canada. The involvement of PWLE working in harm reduction services has proven effective in decreasing drug-related harms among PWLE; however, there exist unintended negative impacts. PWLE working in harm reduction services risk overextending themselves beyond employment parameters (e.g., emotional labor) with few systems in place (e.g., employment advocacy) for support. While meaningful participation of PWLE in harm reduction programs is critical to addressing the overdose crisis, their labor in Canada's overdose response commands further investigation and recognition. This paper examines some of the benefits and negative aspects of working in harm reduction among PWLE. Methods: Fifty qualitative surveys were completed by PWLE working in harm reduction services from across Canada at the National 2018 Stimulus conference held in Edmonton, Alberta. The surveys focused on the benefits and negatives of 'peer' employment and recommendations for organizational transformation through short answer written sections. Surveys were analyzed thematically using NVivo, informed by critical perspectives on substance use, with attention to key re-occurring themes on employment equity. Results: While participants described multiple benefits of working in harm reduction services, such as the valuing of their expertise by fellow 'peers,' growing skill sets, countering stigma, and preventing overdose deaths, issues of workplace equity were significantly identified. Stigma, tokenism, workplace discrimination, including power and pay inequities, as well as lack of worker compensation and benefits were identified as key factors persisting in the everyday experiences of participants. Conclusion: Continued exposure to stigma, workplace discrimination, and/or power imbalances, combined with the impact of high stakes employment (e.g., dealing with overdose deaths), can have significant consequences for PWLE working in harm reduction, including burn out. Policy recommendations include large-scale structural changes that address inequities of hierarchical 'peer' employment for PWLE, including increased leadership roles for diverse PWLE, pay equity and benefits, unionization, as well as more supportive working environments attentive to the intersecting social-structural factors (poverty, criminalization, racism, gendered violence) impacting the everyday lives of PWLE working in harm reduction. [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]

This paper applies urban and environmental sociological theory to survey data on self-reported sustainable consumption practices, using a matched sample of central city and suburban residents in Edmonton, Alberta. We use cluster analysis to create an ordinal typology of four types of consumers, conduct an analysis of variance to characterize the resultant clusters, and perform logistic regression to predict the net effect of urban and neighbourhood context on sustainable consumption practices. We find that neighbourhood and environmental attitude are the strongest predictors of sustainable consumption practices. We conclude by arguing many sustainable activities are more difficult to incorporate into daily routine when residing in the suburban neighbourhood. While suburban residents may feel strongly that they should consume less, their geographic location appears to significantly constrain their ability to meaningfully reduce their own consumption. This urban Canadian case study has implications for middle class environmental practices in other North American urban and suburban settings. [ABSTRACT FROM AUTHOR]

This paper examines the demand for hockey game trips among metropolitan and nonmetropolitan residents of Alberta, Canada. Using data on both revealed and stated preference game-trip behavior from a telephone survey conducted throughout Alberta, we estimate the effect of ticket prices, team quality, arena amenities, and capacity on the latent demand for National Hockey League hockey games. We find that lower ticket prices, higher team quality, and additional capacity encourage attendance. In the status quo scenario, consumer surplus per game is $50 for those who had attended hockey games and about 50% less for those who had not attended games. Exploiting the stated preference data, we develop a number of other consumer surplus estimates. We also include travel costs in the estimation of the demand function and estimate the full value of the game trip considering both ticket prices and travel costs. Sold-out arenas in Calgary and Edmonton generate annual consumption benefits of $40 and $35 million when only ticket prices are used to calculate consumer surplus (i.e., excluding travel costs). Considering the full-price consumer surplus for the Calgary Flames of $103 per game trip, the annual consumption benefits may be as high as $82 million. [ABSTRACT FROM AUTHOR]

This paper reviews the factors and criteria for the design of new and the retrofitting of existing dry detention ponds to enhance removal of total suspended solids (TSS) from stormwater. Detention time is discussed as the most important factor affecting TSS removal. Two-stage facilities and multi-level outlet design are important means of enhancing TSS removal in dry ponds. Two dry ponds within the city of Edmonton were selected to evaluate their TSS removal. The level of expected TSS removal is low owing to the relatively short detention times for both ponds. Methods for retrofitting the dry ponds to enhance TSS removal are discussed.Key words: dry pond, stormwater, TSS removal, detention time, retrofitting.Cet article passe en revue les facteurs et critères de conception de nouveaux étangs de rétention, et de modification de ceux déjà existants, avec pour but d'améliorer la capacité d'enlèvement des substances solides totales en suspension (SST) contenues dans les eaux de ruissellement. Le temps de rétention est examiné en tant que facteur principal affectant l'enlèvement des SST. Les installations à deux étages et la conception de sorties multi-niveaux sont des procédés importants qui améliorent l'enlèvement des SST dans les étangs. Deux étangs de la Ville d'Edmonton ont été sélectionnés et leur capacité de rétention a été évaluée. Le niveau d'enlèvement des SST escompté est bas compte-tenu des temps de rétention relativement courts de ces deux étangs. Les méthodes de modification des étangs visant à améliorer l'enlèvement des SST sont examinées.Mots clés : étang, eaux de ruissellement, enlèvement des SST, temps de rétention, réajustement.[Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

Announces the 95th annual meeting of the Poultry Science Association, Inc. slated in July 16 to 19, 2006 at Alberta, Edmonton, Canada. Program chairmen; Papers and Presentation; Participants.

In the past few years, artificial neural networks (ANNs) have been used in describing and modelling wastewater treatment processes. Artificial neural network models can be identified without a detailed knowledge of the kinetics of the system to be modelled. Also, ANN models can potentially contain a great deal of information about the system itself, including the same type of information contained in conventional deterministic models. The fact that these models can be continuously updated with minimal resource requirements makes them very attractive for application in a real-time control scenario. In the current paper, applications of ANNs in the field of wastewater treatment performance prediction are reviewed. In addition, this paper presents a case study that reports some comprehensive modelling work to develop nonlinear neural network prediction models for the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest sewage treatment facility in Edmonton, Alberta. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) technology has evolved from the experimental stage into actual industrial applications. To achieve this significant transition, careful planning and adjustment are required. This paper illustrates such an example in the water treatment industry. The project objective is to upgrade the ANN models from a previous research project and install the system on-line in the Rossdale Water Treatment Plant in Edmonton, Alberta, Canada, to forecast raw water colour one day ahead. The article discusses the important issues and techniques to upgrade the neural network model to the actual application. Furthermore, sufficient communication is also required between the designers and the users to address the applicability and user friendly issues in model implementation. Failure in communication can render the whole process ineffective. Possible improvements are also recommended for the future on-line applications. [ABSTRACT FROM AUTHOR]

This paper examines the application of the artificial neural network (ANN) modeling technique to model a lime softening process at a full-scale drinking water treatment facility. The modeling was done for the Rossdale Water Treatment Plant (WTP) operated by EPCOR Water Services Inc. in Edmonton, Alberta. It was determined that ANN can model a lime clarifier accurately and with superior performance to other modeling methods. During the development stage, a prediction of alum clarifier pH also becomes necessary, and a very accurate inferential (virtual) sensor for pH was developed using ANN. The ANN models were also integrated with the Supervisory Control and Data Acquisition (SCADA) system of the plant so that real-time predictions of lime doses and effluent total hardness could be monitored. It was shown that the performance of the ANN models that were developed using average daily values for the parameters also work well when they are executed in real time. [ABSTRACT FROM AUTHOR]

This paper reports on the application of artificial neural network (ANN) techniques for predicting the concentration of trihalomethanes (THMs) in finished water at the E.L. Smith Water Treatment Plant (WTP) in Edmonton, Alberta, Canada. The formation of THMs in finished water involves many complex chemical reactions and interactions that are difficult to model using conventional methods. The formation of THMs has been found to be correlated to raw and treated water quality characteristics such as colour, pH, and temperature and chemical addition such as chlorine, alum, and powder activated carbon (PAC). Three models were derived using raw water, post clarification water, and a combination of raw and post clarification water parameter inputs. The model that most successfully predicted the concentration of THMs in finished water is the model that uses clarifier effluent parameter inputs. This model can be used at the E.L. Smith WTP for early detection of potentially high THM concentrations in finished water and gives plant operators enough advanced warning to reduce THM precursors. With an adequate understanding of water treatment plant processes and THM formation potential it will be fairly easy for any water treatment facility, which has a few years of historical plant data, to develop its own ANN model for predicting the formation of THM in finished water. [ABSTRACT FROM AUTHOR]