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104 Projects

  • Canada
  • 2010

10
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  • Funder: NIH Project Code: 5U62PS024510-03
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  • Funder: SNSF Project Code: 128212
    Funder Contribution: 61,700
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  • Funder: SNSF Project Code: 123461
    Funder Contribution: 73,250
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  • Funder: UKRI Project Code: NE/E00511X/1
    Funder Contribution: 324,555 GBP

    On June 15 2006, the World Wildlife Federation (WWF) released a report called 'Killing them Softly', which highlighted concern over the accumulation and toxic effects of persistent organic pollutants present in Arctic wildlife, particularly marine mammals such as the Polar Bear. The Times newspaper ran a full-page article summarising this report and detailed 'legacy' chemicals such as DDT and polychlorinated biphenyls (PCBs), as well as the rise in 'new' chemical contaminants such as brominated flame retardents and perfluorinated surfactants, which are also accumulating in arctic fauna and adding an additional toxic risk. The high levels of these contaminants are making animals like the Polar Bear less capable of surviving the harsh Arctic conditions and dealing with the impacts of climate change. The work in this proposal intends to examine how these chemicals are delivered to surface waters of the Arctic Ocean, and hence the base of the marine foodweb. Persistent organic pollutants reach the Arctic via long-range transport, primarily through the air from source regions in Europe, North America and Asia, but also with surface ocean currents. The cold conditions of the Arctic help to promote the accumulation of these chemicals in snow and surface waters and slows any breakdown and evaporative loss. However, the processes that remove these pollutants from the atmosphere, store them in snow and ice and then transfer them to the Arctic Ocean are poorly understood, and yet these processes may differ depending on the chemcial in question. For example, some chemicals are rather volatile (i.e. they have a tendency to evaporate), so while they can reach the Arctic and be deposited with snowfall they are unlikely to reach the ocean due to ltheir oss back to the atmosphere during the arctic summer. On the other hand, heavier, less volatile chemicals, become strongly bound to snow and particles and can be delivered to seawater during summer melt. Climate change and a warmer world are altering the Arctic and affecting pollutant pathways. For example, the number of ice-leads (large cracks in the sea-ice that give rise to 'lakes' of seawater) are increasing. As a result, the pathways that chemical pollutants take to reach ocean waters are changing and may actually be made shorter, posing an even greater threat to marine wildlife. During ice-free periods, the ocean surface water is in contact with the atmosphere (rather than capped with sea-ice) and airborne pollutants can dissolve directly into cold surface waters. Encouragingly, there is evidence that some of the 'legacy' pollutants are declining in the arctic atmosphere, but many 'modern' chemicals are actually increasing in arctic biota and work is required to measure their input and understand their behaviour in this unusual environment. For example, in sunlit surface snow following polar sunrise (24 h daylight), some of these compounds can degrade by absorbing the sunlight, and in some cases, this can give rise to more stable compounds that subsequently enter the foodchain. Therefore, the quantity of chemical pollutant that is deposited with snowfall and the chemical's fate during snowmelt are important processes to address, especially to understand the loading and impact of these pollutants on the marine ecosystem. This project aims to understand these processes, and to understand which type of pollutants and their quantities pose the greatest threat to wildlife.

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  • Funder: NIH Project Code: 5R01GM076990-03
    Funder Contribution: 209,736 USD
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  • Funder: SNSF Project Code: 121419
    Funder Contribution: 118,520
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  • Funder: UKRI Project Code: NE/F001673/1
    Funder Contribution: 15,818 GBP

    Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

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  • Funder: NIH Project Code: 3R01DA021525-03S2
    Funder Contribution: 99,524 USD
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  • Funder: NIH Project Code: 5R01HL073975-02
    Funder Contribution: 263,655 USD
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  • Funder: NSF Project Code: 0914115
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Advanced search in
Projects
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
104 Projects
  • Funder: NIH Project Code: 5U62PS024510-03
    more_vert
  • Funder: SNSF Project Code: 128212
    Funder Contribution: 61,700
    more_vert
  • Funder: SNSF Project Code: 123461
    Funder Contribution: 73,250
    more_vert
  • Funder: UKRI Project Code: NE/E00511X/1
    Funder Contribution: 324,555 GBP

    On June 15 2006, the World Wildlife Federation (WWF) released a report called 'Killing them Softly', which highlighted concern over the accumulation and toxic effects of persistent organic pollutants present in Arctic wildlife, particularly marine mammals such as the Polar Bear. The Times newspaper ran a full-page article summarising this report and detailed 'legacy' chemicals such as DDT and polychlorinated biphenyls (PCBs), as well as the rise in 'new' chemical contaminants such as brominated flame retardents and perfluorinated surfactants, which are also accumulating in arctic fauna and adding an additional toxic risk. The high levels of these contaminants are making animals like the Polar Bear less capable of surviving the harsh Arctic conditions and dealing with the impacts of climate change. The work in this proposal intends to examine how these chemicals are delivered to surface waters of the Arctic Ocean, and hence the base of the marine foodweb. Persistent organic pollutants reach the Arctic via long-range transport, primarily through the air from source regions in Europe, North America and Asia, but also with surface ocean currents. The cold conditions of the Arctic help to promote the accumulation of these chemicals in snow and surface waters and slows any breakdown and evaporative loss. However, the processes that remove these pollutants from the atmosphere, store them in snow and ice and then transfer them to the Arctic Ocean are poorly understood, and yet these processes may differ depending on the chemcial in question. For example, some chemicals are rather volatile (i.e. they have a tendency to evaporate), so while they can reach the Arctic and be deposited with snowfall they are unlikely to reach the ocean due to ltheir oss back to the atmosphere during the arctic summer. On the other hand, heavier, less volatile chemicals, become strongly bound to snow and particles and can be delivered to seawater during summer melt. Climate change and a warmer world are altering the Arctic and affecting pollutant pathways. For example, the number of ice-leads (large cracks in the sea-ice that give rise to 'lakes' of seawater) are increasing. As a result, the pathways that chemical pollutants take to reach ocean waters are changing and may actually be made shorter, posing an even greater threat to marine wildlife. During ice-free periods, the ocean surface water is in contact with the atmosphere (rather than capped with sea-ice) and airborne pollutants can dissolve directly into cold surface waters. Encouragingly, there is evidence that some of the 'legacy' pollutants are declining in the arctic atmosphere, but many 'modern' chemicals are actually increasing in arctic biota and work is required to measure their input and understand their behaviour in this unusual environment. For example, in sunlit surface snow following polar sunrise (24 h daylight), some of these compounds can degrade by absorbing the sunlight, and in some cases, this can give rise to more stable compounds that subsequently enter the foodchain. Therefore, the quantity of chemical pollutant that is deposited with snowfall and the chemical's fate during snowmelt are important processes to address, especially to understand the loading and impact of these pollutants on the marine ecosystem. This project aims to understand these processes, and to understand which type of pollutants and their quantities pose the greatest threat to wildlife.

    visibility9
    visibilityviews9
    downloaddownloads15
    Powered by Usage counts
    more_vert
  • Funder: NIH Project Code: 5R01GM076990-03
    Funder Contribution: 209,736 USD
    more_vert
  • Funder: SNSF Project Code: 121419
    Funder Contribution: 118,520
    more_vert
  • Funder: UKRI Project Code: NE/F001673/1
    Funder Contribution: 15,818 GBP

    Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

    more_vert
  • Funder: NIH Project Code: 3R01DA021525-03S2
    Funder Contribution: 99,524 USD
    more_vert
  • Funder: NIH Project Code: 5R01HL073975-02
    Funder Contribution: 263,655 USD
    more_vert
  • Funder: NSF Project Code: 0914115
    more_vert