Stormwater ponds are artificial structures that are critical components of stormwater management systems in many Canadian cities. They serve to prevent flooding of urban areas during excess rainfall. Stormwater ponds also contribute to environmental health by allowing the settlement of dirt and solids from stormwater to the bottom of the pond. As a result, the sediments of stormwater ponds can become enriched with potentially harmful contaminants. The health risks posed to anglers by contact with stormwater and sediments and consumption of fish from stormwater ponds are not well characterized. The City of Lacombe (Alberta) is a municipality with two stormwater ponds stocked with sterile fish for angling. Alberta Health collected water, sediment and fish from these two ponds over two seasons (fall 2010 and spring 2011) and analyzed the samples for a suite of contaminants. Water samples were collected from three sites at each pond and three depths for each site (n=40; nine samples plus one replicate sample per pond per season). Sediment samples were collected from the same three sites at each pond (n=12; three samples per pond per season). Fish samples (rainbow trout) were collected in fall 2010 (n=18; eight from East Pond and ten from Len Thompson Pond). For the contaminant analysis, all samples (water, sediment and fish) were tested for parent and alkylated polycyclic aromatic hydrocarbons (PAHs). Additionally, water samples were tested for routine chemicals, trace metals, pesticides and volatile organic compounds (VOCs), and fish muscle tissue was tested for total mercury.

The Waterbodies dataset is comprised of area features: lakes, intermittent waterbodies, islands, and rivers wide enough to be represented as an area feature (e.g. St. Lawrence River, Mackenzie River). In a few exceptional cases, islands had to be represented by "holes" in the polygons in the Waterbodies dataset. Some area features have been subdivided and several types of virtual linear features serve to separate them. Features in this dataset are linked (by an attribute) to their corresponding flow line in the Drainage Network Skeleton. Therefore the Waterbodies dataset may be used in conjunction with the Drainage Network Skeleton for analytical applications. The Islands dataset is comprised of area and linear features: islands within inland waters and the waterbodies and single line rivers within these islands. Oceanic islands are not included as they are part of the coastline component of the Drainage Network Skeleton dataset. The Islands dataset exists to complete the cartographic representation of Canadian hydrology. The Islands dataset is not logically connected with the Drainage Network Skeleton, and can not be used for analytical applications. It should be noted that flow lines of the Drainage Network Skeleton do not take into account of the existence of islands and therefore do not necessarily flow around them. In a few exceptional cases, islands had to be represented by "holes" in the polygons in the Waterbodies dataset. Some islands themselves contain waterbodies and rivers, not significant for network analysis. However, in order to support a complete cartographic representation such waterbodies and rivers have been added to the Islands dataset. The National Scale Frameworks Hydrology data consists of area, linear and point geospatial and attribute data for Canada's hydrology at a national scale. It provides a representation of Canada's surface water features, and data completeness reflects the content of the source, the original Vector Map level 0 (VMAP0) revision 4 hydrographic layers, except where revision editing has been performed. Key value-added characteristics include river flow direction, connectivity and the tagging of geographical name keys to selected rivers, lakes and islands included in the Concise Gazetteer of Canada.The Atlas Frameworks are a set of integrated base map layers which form part of a larger National Scale Frameworks data collection. These data have been compiled at a scale of 1:1 000 000 with the primary goal being to indicate correct relative positioning with other framework layers rather than absolute positional accuracy.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

This dataset uses RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) satellite images to identify open water regions within ice-covered rivers during winter, with the aim to assess hydrokinetic resources near remote communities reliant on diesel fuel for electricity generation. The data is processed with the HyRASS, a machine learning-based SAR image processing and classification algorithm.Disclaimer:This dataset was designed to identify open water regions within ice-covered rivers for assessing hydrokinetic resources near remote communities reliant on diesel fuel for electricity generation and is subject to the following limitations: • This dataset was derived from RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) satellite images. While these images are generally reliable, they are subject to inherent limitations, including resolution constraints, potential distortion, and occasional inaccuracies in real-time conditions capture. • The HyRASS algorithm is designed to pinpoint open water areas using satellite images, with a particular emphasis on RCM quad polarization (QP) imagery. This specialization means that its effectiveness depends on the accessibility of this specific type of imagery. Consequently, the data it produces might not cover a broad spectrum of time periods. For more reliable results, it's essential to classify areas more regularly, ensuring that detected open water regions are consistent over time.This dataset is intended for preliminary assessment and should not be the sole basis for making critical decisions or investments related to hydrokinetic energy projects. Further validation and in-depth analysis are strongly recommended, and users should conduct their own due diligence and additional research to verify the data accuracy and relevance for specific applications. By accessing and using this dataset, users acknowledge and accept these disclaimers. The providers of this dataset explicitly absolve themselves of any responsibility or liability for any consequences arising from the use, reliance upon, or interpretation of this dataset. Users are advised that their use of the dataset is at their own risk, and they assume full responsibility for any actions or decisions made based on the information contained therein. This disclaimer is in accordance with applicable laws and regulations, and by accessing or utilizing the dataset, users agree to release the providers of this dataset from any legal claims, damages, or liabilities that may arise from such use.

PURPOSE:To record hourly water temperatures throughout the Margaree watershed.DESCRIPTION:The Department of Fisheries and Oceans (DFO) has been deploying water temperature monitoring equipment since spring 1993 in the Margaree River watershed. Coverage has changed throughout the time series and there is little documentation regarding equipment used. In recent years data have been collected using VEMCOs. USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

PURPOSE:This study is part of a two-decade series of research aimed to provide a comprehensive synthesis of the effects of harvest and environmental change on fisheries in Great Bear Lake. The main objectives are to assess demographic traits and the current status of harvested species, with a focus on evaluating sustainable harvest levels of lake trout, a cold-adapted species with a relatively narrow thermal niche. As part of this research, trends in water quality and primary productivity are monitored to evaluate potential effects of change on fisheries. DESCRIPTION:Great Bear Lake, one of the largest lakes in North America, contains culturally and recreationally important fish species. Great Bear Lake is located in the sub-Arctic and Arctic Circle. As part of a two-decade series of research aimed to provide a comprehensive synthesis of the effects of harvest and environmental change on fisheries in Great Bear Lake, the main objectives of this study are to assess demographic traits and the current status of harvested species, with a focus on evaluating sustainable harvest levels of lake trout, a cold-adapted species with a relatively narrow thermal niche. As part of this research, trends in water quality and primary productivity are monitored to evaluate potential effects of change on fisheries. From 2021 to 2024, surface water temperature data was collected at depths of 0.1 to 1.0 meters using an RBR Maestro3 through partnered community-led and community/Fisheries and Oceans Canada/university partner collaborative sampling. The project has strong community involvement, including youth through the Guardian Program, to facilitate capacity building and community leadership in the long-term monitoring of Great Bear Lake fisheries and the aquatic ecosystem. This data is an extension of baseline data sets on water quality on the lake. These data will contribute to a better understanding cumulative impacts of climate change on the functioning of large northern lake ecosystems and provide a benchmark for monitoring further change. This data will be important for developing effective strategies for maintaining community-led aquatic monitoring and managing natural resources, particularly fish, which are expected to be increasingly important to communities with declines in other country foods such as caribou.We acknowledge the data were collected in the Sahtú Settlement Area and are made publicly available with the agreement of the Délı̨nę Renewable Resources Council (Délı̨nę Ɂehdzo Got’ı̨nę (Renewable Resources Council)). Collaborators include: the Community of Délı̨nę partners (data collection), Délı̨nę Renewable Resource Council, Sahtú Renewable Resource Board, and University of Manitoba. Community of Délı̨nę partners and field workers that participated in data collection include Chris Yukon, Archie Vital, Ted Mackienzo, Daniel Baton, Lloyd Baton, Simon Neyelle, and Stanley Ferdanan.Funding and logistical support was provided by: Northwest Territories Cumulative Impact Monitoring, Sahtú Renewable Resource Board, the Polar Continental Shelf Program and Fisheries and Oceans Canada.

Waterfowl and mammals harvested and trapped at various locations in the oil sands region and in reference locations are assessed for contaminant burdens and toxicology. Wildlife samples are obtained from local hunters and trappers. Tissue samples are analysed for concentrations of oil sands-related contaminants (heavy metals, polycyclic aromatic hydrocarbons, and naphthenic acids). Dead and moribund birds collected from tailing ponds are also evaluated for levels and effects of contaminants.

This greenhouse gas emissions dataset shows area fugitive emission from tailings ponds and mine face at the oil sands mining facilities in Alberta. The data were reported between 2011 and 2021 under the Technology Innovation and Emission Reduction Regulation and preceding regulations in Alberta. The produced bitumen and the diluent flared/wasted volumes reported in the Alberta Energy Regulator's statistical report ST-39 are included in the dataset for reference.

This spatial data represents the boundaries of Manitoba's forest sections. Forest sections are administrative areas comprised of Forest Management Units (FMU's). There are 14 uniquely named forest sections in Manitoba, 9 of which are capable of growing commercial forests.Manitoba's f orest sections are administrative areas comprised of Forest Management Units (FMU's). There are 14 uniquely named forest sections in Manitoba, 9 of which are capable of growing commercial forests. The Aspen Parkland forest section in the south along with the northern forest sections of Boreal Shield, Taiga Shield, Hudson Plains and Southern Arctic are incapable of growing commercial forests. The four northern forest sections were previously called the 'white zone' and all have retained the previous white zone forest section number of 10. The northern forest section boundaries are based on the following ecozones:Hudson Plains: A subarctic area encompassing the coastal areas of Hudson Bay. The area is formed into a wide, level plain, characterised by poor drainage that has resulted in large and numerous peatlands, lakes, coastal marshes, and tidal flats. Alder, willow, black spruce, and tamarack are the most common tree species.Taiga Shield: Terrain is typically flat or with rolling hills caused by glacial retreat; long eskers and uplands are common. Shallow soils remain damp year-round and regularly freeze and thaw; this leads to tilted growing trees, sometimes called ‘drunken forests’. The northern edge of the forest section is delineated by the tree line. Black spruce, jack pine, birch, tamarack, white spruce, balsam fir, trembling aspen, and balsam poplar are common tree species.Southern Arctic: The southern boundary designated the tree line. Moraines, eskers, kettle lakes, and ponds are common. Permafrost occurs in a continuous sheet throughout the section; polygonal hummocks often result from the freeze and thaw of the soils.Boreal Shield : This forest section represents the upper boundary of the boreal shield ecozone, characterised by long, cold winters and warm summers. Permafrost is widespread. Uplands and lowland tree species are common. Soil varies from poorly drained muskeg to glacially-deposited sand. Coniferous trees include white and black spruce, balsam fir, jack pine, and tamarack; hardwood tree species include birch, trembling aspen, and balsam poplar. Forest fires and insect outbreaks are the natural drivers of forest succession. The ten forest sections south of forest section 10 are sometimes referred to as the 'green zone' and include the following: Pineland, Aspen Parkland, Mountain, Interlake, Lake Winnipeg East, Churchill, Nelson River, Hayes River, Saskatchewan River and Highrock. Fields Included: S ECTION : Forest section number . SECTION_NAME : Forest section name .

Air emissions from oil sands development can come from a number of sources including industrial smokestacks, tailings ponds, transportation, and dust from mining operations. Air quality monitoring under the Joint Canada-Alberta Implementation Plan for the Oil Sands is designed to determine the contribution of emissions from oil sands activities to local and regional air quality and atmospheric deposition both now and in the future. Deposition data include: - Passive Sampling of PACs deployed for two month periods across a network of 17 sites - Active sampling of PACs at three sites to inform the amount of dry deposition - Particulate metals (24 hour integrated samples following the one in six day National Air Pollution Surveillance (NAPS) cycle)

All available bathymetry and related information for Swan Lake were collected and hard copy maps digitized where necessary. The data were validated against more recent data (Shuttle Radar Topography Mission 'SRTM' imagery and Indian Remote Sensing 'IRS' imagery) and corrected where necessary. The published data set contains the lake bathymetry formatted as an Arc ascii grid. Bathymetric contours and the boundary polygon are available as shapefiles.