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A skeleton of a new ceratopsid dinosaur, Eotriceratops xerinsularis gen. et sp. nov., is described in this paper. It is the first associated vertebrate skeleton found within the upper 20 m of the Horseshoe Canyon Formation. Eotriceratops xerinsularis is a large chasmosaurine that differs from other chasmosaurines in a unique set of features in the premaxilla, nasal horn core, squamosal frill, and epijugal. The most striking of those features includes an extremely tall, non-recessed narial process of the premaxilla; the presence of greatly elongate, spindle-shaped epoccipitals on the squamosal frill; a deep, well-demarcated fossa on the anteroventral surface of the squamosal frill; a sharply conical epijugal with a pronounced proximoposterior process and separate fossa-like facets for the jugal and quadratojugal; and the presence of an obliquely extending vascular trace meeting a transverse vascular trace ventrally on the anterior surface of the nasal horn core. Our phylogenetic analysis suggests that E. xerinsularis is nested within a clade including Triceratops, Diceratops, and Torosaurus, which are all from late Maastrichtian deposits. The upper 20 m of the Horseshoe Canyon Formation comprises a coal-rich interval (Carbon–Thompson coal zone, unit 5), which previously has been assigned to upper Maastrichtian magnetochrons 31n and 30r, and the Mancicorpus gibbus miospore subzone. The ceratopsid specimen was collected from between the Carbon and Thompson coal seams, and thus, is inferred to (1) occur near the top of magnetochron 31n and (2) have an age of 67.6–68.0 Ma. Large chasmosaurine ceratopsids, such as Triceratops and Torosaurus, have not previously been described from the Horseshoe Canyon Formation or from magnetochron 31n or the M. gibbus miospore subzone. Thus, Eotriceratops is distinctly older than any other ceratopsid in the Triceratops group, and the discovery of E. xerinsularis helps fill a biostratigraphic gap between early and late Maastrichtian chasmosaurines. [ABSTRACT FROM AUTHOR]

Recent archaeological excavations at a deeply stratified site in northwest Calgary, Alberta have uncovered four occupation layers spanning the interval from 8000 to 2000 BP. Depending on the dating one accepts for the Holocene thermal maximum, two or more of these occupations occurred during this interval of aridity. This site thus provides a record of human occupation during the Archaic (Middle Prehistoric I) period in the Calgary area. More importantly, the site occurs in an upland setting, apparently far removed from reliable sources of water and normal environments of deposition. Yet, the cultural deposits occur in sediments which extend 2 m below surface and include five sedimentary units as well as a minimum of three pedogenic units. This paper presents the results of geoarchaeological research on the changing landscape for the site and notes the implications of these changes for the study of Archaic manifestations on the northern Plains. [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]

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]

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]

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 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]