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We have found 37 datasets for the keyword " pyrolysis". You can continue exploring the search results in the list below.
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Geothermal Radiogenic Heat Production
Background:More than 80% of the heat produced in the Earth's crust comes from granitoid rocks. When granitoid rocks form they naturally concentrate radioactive elements such as U, Th, and K, and the radiogenic decay of these elements is an exothermic reaction. The radioactive decay of these elements within a granitoid body may generate local heat anomalies and elevated geothermal gradient at relatively shallow crustal levels. In combination with other local rock properties (e.g, porosity, permeability, thermal conductivity), radiogenic heat has the potential to generate a geothermal resource. The decay of radioactive elements converts mass into radiation energy, which in turn gets converted to heat. While all naturally radioactive isotopes generate some heat, significant heat generation only occurs from the decay of 238 U ,235 U ,232 Th and 40 K. Therefore, potential heat production is governed by the concentrations of U ,Th and K in the rock. In igneous rocks, radiogenic heat production is dependent on the bulk chemistry of the rock and decreases from acidic (e.g. granite) through basic to ultra basic rock types. Therefore, granites with anomalously high concentrations of U ,Th and K are targets for calculating potential radiogenic heat production. Potential radiogenic heat production (A)from plutonic rocks can be calculated using this equation:A (\\u03BCW/m 3 )=10 -5 \\u1D29 (9.52c u +2.56c K +3.48c Th )where "c" is the concentration of radioactive elements "U" and "Th" in ppm, and "K" in %; and "\\u1D29" is the rock density. Heat production constants of the natural radio-elements U, Th, K are 9.525x10 -5 , 2.561x10 -5 and 3.477x10 -9 W/kg, respectively.Data and Methods:Geochemical data from \~1760 samples of plutonic rocks from Yukon are used to calculate potential heat production. The calculated values for radiogenic heat production (A) are plotted over the mapped distribution of Paleozoic and younger plutonic rocks and major crustal faults are also shown for reference.
Tracing carbon flow and trophic structure of a coastal Arctic marine food web using highly branched isoprenoids and carbon, nitrogen and sulfur stable isotopes
PURPOSE:In this study, we examined the structure and function of the Southampton Island marine food web across 149 species of benthic and pelagic invertebrates, fishes, marine mammals and seabirds collected from 2016 to 2019, to provide a baseline for future studies that aim to quantify temporal changes in food web structuring. More specifically,we used a multi-biomarker approach combining stable isotopes and HBIs to: (i) determine the vertical trophic structure of the marine food web, (ii) investigate the contribution of benthic and pelagic-derived prey to the higher trophic level species of the Arctic food web, and (iii) determine the role of ice algae and phytoplankton carbon source use across different trophic levels and compartments (pelagic and benthic). By shedding new light on the functioning of the Southampton Island food web and specifically how the contribution of ice algae and benthic habitat shapes its structure, these results will be relevant to adaptive management and conservation initiatives implemented in response to anthropogenic stressors and climate change. DESCRIPTION:Climate-driven alterations of the marine environment are most rapid in Arctic and subarctic regions, including Hudson Bay in northern Canada, where declining sea ice, warming surface waters and ocean acidification are occurring at alarming rates. These changes are altering primary production patterns that will ultimately cascade up through the food web. Here, we investigated (i) the vertical trophic structure of the Southampton Island marine ecosystem in northern Hudson Bay, (ii) the contribution of benthic and pelagic-derived prey to the higher trophic level species, and (iii) the relative contribution of ice algae and phytoplankton derived carbon in sustaining this ecosystem. For this purpose, we measured bulk stable carbon, nitrogen and sulfur isotope ratios as well as highly branched isoprenoids in samples belonging to 149 taxa, including invertebrates, fishes, seabirds and marine mammals. We found that the benthic invertebrates occupied 4 trophic levels and that the overall trophic system went up to an average trophic position of 4.8. The average δ34S signature of pelagic organisms indicated that they exploit both benthic and pelagic food sources, suggesting there are many interconnections between these compartments in this coastal area. The relatively high sympagic carbon dependence of Arctic marine mammals (53.3 ± 22.2 %) through their consumption of benthic invertebrate prey, confirms the important role of the benthic subweb for sustaining higher trophic level consumers in the coastal pelagic environment. Therefore, a potential decrease in the productivity of ice algae could lead to a profound alteration of the benthic food web and a cascading effect on this Arctic ecosystem.Collaborators:Centre for Earth Observation Science, University of Manitoba, Winnipeg, Manitoba, Canada - R´emi Amiraux, C.J. Mundy, Jens K. Ehn, Z.A. Kuzyk.Quebec-Ocean, Sentinel North and Takuvik, Biology Department, Laval University, Quebec, Quebec, Canada - Marie Pierrejean.Scottish Association for Marine Science, Oban, UK - Thomas A. Brown.Department of Natural Resource Sciences, McGill University, Ste. Anne de Bellevue, Quebec, Canada - Kyle H. Elliott.Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada - Steven H. Ferguson, Cory J.D. Matthews, Cortney A. Watt, David J. Yurkowski.School of the Environment, University of Windsor, Windsor, Ontario, Canada - Aaron T. Fisk.Science and Technology Branch, Environment and Climate Change Canada, Ottawa, Ontario, Canada - Grant Gilchrist.College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, AK, USA - Katrin Iken.Department of Earth Sciences, University of New Brunswick, Fredericton, NB, Canada - Audrey Limoges.Department of Integrative Biology, University of Windsor, Windsor, Ontario, Canada - Oliver P. Love, Wesley R. Ogloff.Department of Arctic Biology, The University Centre in Svalbard, Longyearbyen, Norway - Janne E. Søreide.
pH in CaCl2 - Soil Landscape Grids of Canada, 100m
Predicted soil pH in CaCl2 at a defined depth range (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm). The degree of acidity or alkalinity of a soil expressed in terms of the pH scale.
Coal Fields of BC with Coal Bed Methane Potential
A polygon coverage showing the location of coalfields with coalbed methane (cbm) potential in BC
Concentrations of suspended particulate matter (SPM) in the St. Lawrence
The layer provides information on suspended particulate matter (SPM) concentrations by area. There is a natural interaction phenomenon between hydrocarbons and SPM, that creates hydrocarbon-SPM aggregates. The SPM in the water column, hence has an effect on hydrocarbon capacity to sink to the bottom in aggregate form (Gong et collab., 2014 ; Fitzpatrick et collab., 2015, cited in Centre d'expertise en analyse environnementale du Québec, 2015). Additional InformationThe suspended particulate matter data for this layer are derived from multiple sources given the need to cover the St. Lawrence portion from Montreal to Anticosti. The layer has been cut into 6 different zones. Denis Lefaivre, a researcher at Maurice-Lamontagne Institute, has provided the coordinates of the points allowing the delimitation of areas. The values in each zone are derived from different studies carried out at different times. The references are cited below for each of the polygons from West to East, as well as for the summary:1- Department of Sustainable Development, Environment and Climate Change and Environment and Climate Change Canada, 2016. Recommendations for Suspended Matter Management (ESM) during dredging activities. Quebec. 64 pages and appendices. http://planstlaurent.qc.ca/fileadmin/publications/diverses/Registre_de_dragage/Recommandations_dragage.pdf2- D'Anglejan, B. 1990. Recent Sediments and Sediment Transport Process in the St. Lawrence Estuary. In Oceanography of a Large-Scale Estuarine System: The St. Lawrence, edited by M. I. El-Sabh and N. Silverberg. New York: Springer-Verlag, 109-153.3- Silverberg, N., and B. Sundby. 1979. Observations in the maximum turbidity of the St. Lawrence estuary. Can. J. Earth Sci. 16: 939-950.4- Michel Lebeuf, 2016.Unpublished personal data.Collected between 2015-2016 for research purposes.5- Sundby, B. 1974. Distribution and Transport of Suspended Particulate Matter in the Gulf of St. Lawrence. Canadian Journal of Earth Sciences11 (11): 1517-1533.6- Gong, Y., X. Zhao, Z. Cai, S. E. O'Reilly, X. Hao and D. Zhao. 2014. A review of oil, dispersedoil and sediment interactions in the aquatic environment: Influence on the fate, transportand remediation of oil spills. Marine Pollution Bulletin, vol. 79: 1-2, p.16-33. 7- Fitzpatrick, F.A., M.C., Boufadel, R., Johnson, K., Lee, T.P., Graan, A.C., Bejarano, Z.,Zhu, D., Waterman, D.M., Capone, E., Hayter, S.K., Hamilton, T., Deffer, M.H.,Garcia, et J.S., Hassan. 2015. Oil-particle interactions and submergence from crudeoil spills in marine and freshwater environments – Review of the science and futurescience needs. U.S. Geological Survey Open-file report 2015-2016, 33 p.8- Centre d'expertise en analyse environnementale du Québec,2015.Hydrocarbures pétroliers : caractéristiques, devenir et criminalistique environnementale –Études GENV222 et GENV23, Évaluation environnementale stratégique globale sur leshydrocarbures. Ministère du Développement durable, de l’Environnement et de la Lutte contreles changements climatiques, 41 p. et annexes.9- CSL – Centre Saint-Laurent, 1997. Le Saint-Laurent : dynamique et contamination des sédiments, Montréal, Environnement Canada – Région du Québec, Conservation de l’environnement, 127 p. (coll. BILAN Saint-Laurent). [Rapport thématique sur l’état du Saint-Laurent].
Wet Deposition Maps
Patterns of wet deposition of the nitrate (NO3), non-sea-salt sulfate (xSO4) and ammonium (NH4) ions across areas of Canada and the United States are based on measurements of precipitation depth and ion concentrations in precipitation samples. xSO4 refers to the wet deposition of sulfate with the sea-salt sulfate contribution removed at coastal sites. These measurements were collected and quality controlled by their respective networks: in Canada, the federal Canadian Air and Precipitation Monitoring Network (CAPMoN) and provincial or territorial networks in Alberta, New Brunswick, the Northwest Territories, Nova Scotia, Ontario and Quebec. In the United States, wet deposition measurements were made by two coordinated networks: the National Atmospheric Deposition Program (NADP) / National Trends Network (NTN) and the NADP/Atmospheric Integrated Research Monitoring Network (AIRMoN). Only data from sites that were designated as regionally representative were used in the mapping. Wet deposition amounts were interpolated by ordinary kriging using ArcMap Geostatistical Analyst. The map is limited to the contiguous U.S. and southeastern or southern Canada because outside that region, the interpolation error exceeds 30% due to the larger distances between stations. Links to annual and five-year average maps are available in the associated resources.
Organic Matter Content of Cultivated Soils
This map displays the percentage of organic matter in the surface layer of cultivated soils in the agricultural region of Alberta. Soil organic matter (SOM) is derived primarily from the decomposition of plant biomass. SOM improves both the physical and chemical properties of soil and has beneficial effects on agricultural soil quality. SOM is reported on the map as a percentage using the following classes: less than 2 (very low), 2 to 4 (low), 4 to 6 (medium), 6 to 8 (high) and greater than 8 (very high).This resource was created in 2002 using ArcGIS.
Contaminants in fish database
The contaminants in fish database is a compilation of contaminant data analysed from fish tissue at the Fresh Water Institute from 1970 to 2005. Data include lab number, region, analysis, organs, species, lake, form (whole fish, headon dressed, headless dressed), weight, and length and contaminant concentrations. Total mercury was the predominant contaminant measured. Results were expressed as ppm or ppb based on the parameter analyzed. Concentrations are expressed based on wet weight.
Releases of harmful substances to water - Releases of arsenic to water by facility
The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. These indicators track facility-based releases to water of 4 substances that are defined as toxic under the Canadian Environmental Protection Act, 1999: mercury, lead, cadmium and arsenic and their compounds. For each substance, data are provided at the national, regional (provincial and territorial) and facility level, as well as by source. The indicators inform Canadians about releases to water of these 4 substances from facilities in Canada. The Releases of harmful substances to water indicators also help the government to identify priorities and develop or revise strategies to inform further risk management and to track progress on policies put in place to reduce or control these 4 substances and water pollution in general.Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated.Canadian Environmental Sustainability Indicators: https://www.canada.ca/environmental-indicators
Pan-Canadian predictive model of Carbonatite-hosted REE and Nb deposits
A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7
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