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    The Regional Air Quality Deterministic Prediction System FireWork (RAQDPS-FW) carries out physics and chemistry calculations, including emissions from active wildfires, to arrive at deterministic predictions of chemical species concentration of interest to air quality, such as fine particulate matter PM2.5 (2.5 micrometers in diameter or less). Geographical coverage is Canada and the United States. Data is available at a horizontal resolution of 10 km. While the system encompasses more than 80 vertical levels, data is available only for the surface level. The products are presented as historical, annual or monthly, averages which highlight long-term trends in cumulative effects on the environment.

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    With the changing climate conditions, marine traffic along Canada’s coastal regions has increased over the past couple of decades and the need to improve our state of preparedness for oil-spill-related emergencies is critical. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Between 2010 and 2017, georeferenced high-definition videography and photos were collected for various study sites across coastal Canada. The study areas include Beaufort Sea, Mackenzie Delta channels and Banks Island in the western Canadian Arctic; James Bay, Hudson Bay, Nunavik, Resolute Bay, Victoria Strait, Baffin Island and Coronation Gulf in the eastern Canadian Arctic; Labrador, Bay of Fundy and Chedabucto Bay in Atlantic Canada and Kitimat, Haida Gwaii and Burrard Inlet in the northern Pacific. Data was collected during ice-free and low tide conditions (where applicable) between July and September. Low-altitude helicopter surveys were conducted at each study site to capture video of the shoreline characteristics. In addition to acquiring videography, ground-based observations were recorded in several locations for validation. Shoreline segmentation was then carried out by manual interpretation of the oblique videography and the photos aided by ancillary data. This involved splitting and classifying the shoreline vectors based on homogeneity of the upper intertidal zone. Detailed geomorphological information (i.e. shoreline type, substrate, slope, height, accessibility etc.) describing the upper intertidal, lower intertidal, supratidal and backshore zones was extracted from the video and entered into a geospatial database using a customized data collection form. In addition, biological characteristics like biobands, water features, fauna, human use etc. observed along the coast were recorded. The data was also validated through ground samples (when available) and a second interpreter QA (quality analysis) was performed on each dataset (excluding Nunavik) to ensure high quality and consistency. The final dataset contains segments ranging in length from 150 metres to 2500 metres. In total, from 2010 to 2017, within the 14 study sites, about 26,150 km of shoreline were mapped.

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    This dataset includes historical observations of the daily depth of snow on the ground (daily climate element 013) made at Environment and Climate Change Canada (ECCC) sites by manual ruler or by a sonic sensor equipped autostations. The history of the ECCC daily snow depth observing program is provided in Brown et al. (2021) along with a detailed intercomparison of ruler and sonic sensor observations that showed manual observations typically reporting more snow than a nearby sonic sensor. The database includes a measurement method flag to differentiate between the two methods. This update extends the Canadian historical daily snow depth database (originally provided on the Canadian Snow CD in 2000) up to the end of the 2016/17 snow season. The same procedures were applied to fill missing values and QC data - see MSC (2000) and Brown and Braaten (1998). This latest update was carried out for snow seasons 2000/01 to 2016/17, where a snow season is defined as starting August 01 and ending July 31, and the data merged with the previous update made in 2004 covering the period up to snow season 2003/04. The 2000-2003 period was included in this update to take account of additional data in the ECCC climate archive that were unavailable in 2004. Daily snow depth data for the period after July 31, 2017 can be downloaded using the daily climate data extraction tool at the Canadian Centre for Climate Services (https://climate-change.canada.ca/climate-data/#/daily-climate-data). Warning: The method used to reconstruct snow cover is likely to considerably underestimate snow depth from underestimation of solid precipitation, and the assumption that fallen snow immediately attains a density of 300 kg/m3 upon reaching the ground. Reconstructed snow depths are not considered reliable for data gaps larger than ~14 days.

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    As indicated in the Notice with respect to certain quaternary ammonium compounds in Canadian commerce — Phase 1 (the Notice), and in accordance with section 49 of the Canadian Environmental Protection Act, 1999 (CEPA), the Government of Canada is providing a summary of the information received in response to the Notice, which was published in the Canada Gazette, Part I, on November 17, 2018 under section 46 of CEPA. The Notice collected information from manufacturers and importers (e.g., quantities and uses) on approximately 800 quaternary ammonium compounds (QACs) for the 2017 calendar year. The information will be used to support priority setting, and inform future risk assessments. The files in the resources section below offer an overview of the information gathered under this initiative including: type of submission; reported substances; substances that are manufactured or imported; industrial sectors involved; substance functions and commercial applications; and intended use (in commercial activity, in consumer activity, and by children). It should be noted that these documents do not include an assessment of the potential risks these substances may pose to the environment or to the health of Canadians. To complement the information summary document, a compilation of the non-confidential information received is available for download in Excel and CSV formats and includes a list of the substances for which no information was submitted. Important information about this summary: To protect confidential business information, in some cases quantitative data is presented in ranges and qualitative data is excluded. Qualitative data includes information that characterizes and categorizes information [e.g., company names, North American Industry Classification System (NAICS) codes, specified uses, Substance Function Codes, and Application Codes]. Since the dataset does not include confidential business information, the figures presented may be an underestimate. Code 999 "Other" was provided for a Substance Function Code or an Application Code, it indicates that the codes listed in the Notice did not apply and a description of the function or use was required. Please refer to the Notice or guidance document for further details on data collected. Supplemental Information Useful links: http://gazette.gc.ca/rp-pr/p1/2018/2018-11-17/html/notice-avis-eng.html https://www.canada.ca/en/health-canada/services/chemical-substances/chemicals-management-plan/initiatives/transparency-risk-assessment-activities.html https://www.canada.ca/en/health-canada/services/chemical-substances/inventory-updates.html https://www.canada.ca/content/dam/eccc/documents/pdf/pded/quats/Guidance-manual-QAC.pdf https://www.canada.ca/content/dam/eccc/documents/pdf/pded/quats/20190312-QAC-s-46-substances-en-fr.xlsx

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    Hotspots are locations where wildfires are actively burning, identified from infrared satellite imagery. Hotspot locations are provided by NASA and NOAA, and further processed by Natural Resources Canada's Canadian Wildland Fire Information System. This layer contains the hotspots that are selected to be used as input for the Regional Air Quality Deterministic Prediction System FireWork (RAQDPS-FW) to enable forecasting air quality while taking into account wildfire emissions. Geographical coverage is Canada and the United States. The products are presented as historical annual compilations which highlight long-term trends in cumulative effects on the environment.

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    As part of a water quality survey, stream samples were collected throughout the Big Creek, Lynn River, and Nanticoke Creek Watershed in Southern Ontario in 2008. The project was undertaken to examine stream water quality under base flow conditions and was done in support of Environment and Climate Change Canada’s ongoing work to assess the status of the waters of the Great Lakes Basin. Sample collection was done at roadside stream crossings using a stainless steel bucket. Field parameters (temperature, specific conductivity, dissolved oxygen, pH) were determined on site using a YSI 600QS Sonde. Water quality parameters (major anions, major cations, select dissolved metals, ammonium) were analyzed at the groundwater labs at the Canada Centre for Inland Waters, Burlington, Ontario. Dissolved organic carbon concentrations were determined at the Environmental Geochemistry Lab, University of Waterloo. Stable isotope ratios of water (δ2H, δ18O) were analyzed at the Environmental Isotope Lab, University of Waterloo.

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    This project examined the use of parasites of slimy sculpin (Cottus cognatus) as indicators of water quality in the Athabasca River. Specimens of C. cognatus were sampled from four tributaries to assess patterns of helminth parasite community structure in this fish and to study the composition and diversity of its parasite communities in relation to water quality. All data are a part subject of a publication containing method details, full QA/QC, interpretation and conclusions: Braicovich, P. E., McMaster, M., Glozier, N. E., & Marcogliese, D. J. (2020). Distribution of parasites of slimy sculpin Cottus cognatus Richardson, 1836 (Scorpaeniformes: Cottidae) in the Athabasca drainage, Alberta, Canada, and their relation to water quality. Parasitology research, 119(10), 3243–3254. doi.org/10.1007/s00436-020-06819-9

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    This database contains model output files that were used in the 2021 Arctic Monitoring and Assessment Programme (AMAP) Assessment Report on Short-lived Climate Forcers (SLCFs) and subsequent journal publications. The datasets are stored here: http://crd-data-donnees-rdc.ec.gc.ca/CCCMA/products/AMAP/ Type0 model files are basic historical simulations. Types 1 and 2 are model sensitivity simulations with regionally perturbed emissions and type 3 are future simulations under varying emission scenarios. Please see the file "AMAPdatabaseREADME.xlxs" at the data location for further explanation.

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    With the changing climate conditions, marine traffic along Canada’s coastal regions has increased over the past couple of decades and the need to improve our state of preparedness for oil-spill-related emergencies is critical. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Between 2013 and 2019, georeferenced high-definition videography and photos were collected for various study sites along the west coast. The study areas include the mainland, inlets, channels and islands along the BC coast starting from Kitimat in the north to Quadra Island in the south, including Haida Gwaii and North Vancouver Island in the west and Burrard Inlet in the extreme south. Data was collected during low tide conditions (where applicable) between July and September. Low-altitude helicopter surveys were conducted at each of the study site to capture video of the shoreline characteristics. In addition to acquiring videography, ground-based observations were recorded in several locations for validation. Shoreline segmentation was then carried out by manual interpretation of the oblique videography and the photos aided by ancillary data. This involved splitting and classifying the shoreline vectors based on homogeneity of the upper intertidal zone. Detailed geomorphological information (i.e. shoreline type, substrate, slope, height, accessibility etc.) describing the upper intertidal, lower intertidal, supratidal and backshore zones was extracted from the video and entered into a geospatial database using a customized data collection form. In addition, biological characteristics like biobands, water features, fauna, human use etc. observed along the coast were recorded. The data was also validated through ground samples (when available) and a second interpreter QA (quality analysis) was performed on the dataset to ensure high quality and consistency. The final dataset contains segments ranging in length from 150 metres (45 metres for study areas surveyed in 2018-19) to 2500 metres. In total, from 2013 to 2019, about 15,000 km of shoreline were segmented.

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    With the changing climate conditions, marine traffic along Canada’s coastal regions has increased over the past couple of decades and the need to improve our state of preparedness for oil-spill-related emergencies is critical. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Between 2010 and 2016, georeferenced high-definition videography and photos were collected for various study sites along the north coast of Canada. The study areas include Beaufort Sea, Mackenzie Delta channels and Banks Island in the western Canadian Arctic and James Bay, Hudson Bay, Nunavik, Resolute Bay, Victoria Strait, Baffin Island and Coronation Gulf in the eastern Canadian Arctic. Data was collected during ice-free and low tide conditions (where applicable) between July and September. Low-altitude helicopter surveys were conducted at each study site to capture video of the shoreline characteristics. In addition to acquiring videography, ground-based observations were recorded in several locations for validation. Shoreline segmentation was then carried out by manual interpretation of the oblique videography and the photos aided by ancillary data. This involved splitting and classifying the shoreline vectors based on homogeneity of the upper intertidal zone. Detailed geomorphological information (i.e. shoreline type, substrate, slope, height, accessibility etc.) describing the upper intertidal, lower intertidal, supratidal and backshore zones was extracted from the video and entered into a geospatial database using a customized data collection form. In addition, biological characteristics like biobands, water features, fauna, human use etc. observed along the coast were recorded. The data was also validated through ground observations (when available) and a second interpreter QA (quality analysis) was performed on each dataset (excluding Nunavik) to ensure high quality and consistency. The final dataset contains segments ranging in length from 150 metres to 2500 metres. In total, from 2010 to 2016, within the 8 study sites, about 16,800 km of shoreline were segmented.