Projects: Projects for Investigator |
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Reference Number | NE/F003919/1 | |
Title | Probabilistic estimation of changes in BVOC emissions due to projected land-use change: atmospheric and policy implications | |
Status | Completed | |
Energy Categories | Renewable Energy Sources(Bio-Energy, Other bio-energy) 25%; Renewable Energy Sources(Bio-Energy, Production of transport biofuels (incl. Production from wastes)) 75%; |
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Research Types | Basic and strategic applied research 100% | |
Science and Technology Fields | ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences) 100% | |
UKERC Cross Cutting Characterisation | Sociological economical and environmental impact of energy (Environmental dimensions) 80%; Sociological economical and environmental impact of energy (Policy and regulation) 20%; |
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Principal Investigator |
Dr P (Paul ) Palmer No email address given School of Geosciences University of Edinburgh |
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Award Type | R&D | |
Funding Source | NERC | |
Start Date | 01 July 2008 | |
End Date | 30 June 2011 | |
Duration | 36 months | |
Total Grant Value | £338,315 | |
Industrial Sectors | Transport Systems and Vehicles | |
Region | Scotland | |
Programme | ||
Investigators | Principal Investigator | Dr P (Paul ) Palmer , School of Geosciences, University of Edinburgh (99.999%) |
Other Investigator | Prof M (Mark ) Rounsevell , School of Geosciences, University of Edinburgh (0.001%) |
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Web Site | ||
Objectives | The broad science objective of our proposed work is to quantify the magnitude and uncertainty of the impact of projected land-use change on BVOC emissions and tropospheric O3. We have five specific science objectives: 1) Develop a low-dimension model of global land use change suitable for probabilistic studies. 2) Generate an ensemble of probabilistic land use projections for 2010, 2030 and 2050 conditional on the IPCC-SRES scenarios and considering a range of socio-economic development pathways. 3) Derive spatial distributions of plant functional types from the ensemble of land use projections 4) Quantify the range and likelihood of BVOC emissions from the ensemble of projected PFT distributions. 5) Quantify the magnitude and distribution of tropospheric O3 and its photochemical pr ecursors using the ensemble of BVOC emission scenarios for 2010, 2030 and 2050. | |
Abstract | Humans have altered Earth's natural land surface for thousands of years, e.g., agriculture, deforestation, biomass burning. Scientists have studied extensively how these activities alter the biogeochemical cycles (e.g., water and carbon), and how they affect surface albedo and consequently the radiative balance of the atmosphere. Vegetation emit a number of biogenic hydrocarbons (isoprene, C5H8,represents the largest emission) with chemical lifetime of days or less that can lead to the production of ozone, a surface air pollutant and a greenhouse gas in the upper troposphere. We know from in situ measurements that different vegetation emit these reactive hydrocarbons at different rates so that altering the spatial patterns of vegetation will alter budgets of biogenic hydrocarbons with serious implications for surface ozone. We know that tropical ecosystems emit ~70% of the global isoprene budget. A good understanding of how human activity will alter Earth's natural land cover underpins climate projections of the next few decades. The main scientific objective of this proposal is to reduce uncertainty in land-use change projections related to human activity and subsequent changesin the magnitude and distribution of biogenic hydrocarbon emissions and surface ozone. Projecting human activity and its role in land-use change is difficult, involving many different socio-economic issues. However, it is an integral part of the Earth system and if we are to improve climate projections we must consider this human element. Up until now, the human contribution to land-use changehas been considered independent of the rest of the climate system. Past work has focused primarily on developing complex models that consider a whole range of socio-economic phenomena but thiscomplexity effectively limits the exploration of alternative socio-economic futures because of computational constraints. What we propose is to develop a simpler model, with only a few describing parameters,that captures many of the broad scale features of the more complex models. Such a model enables a more comprehensive exploration (and uncertainty analysis) of alternative futures. This probabilistic approach allows us to determine the moststatistically likely future. In our work we will use the developed model to explore the most likely human-induced land-use change over the next 40years. An excellent example of a current socio-economic driver of land-use change is biofuel production. Biofuels represent an arguably cleaner alternative to burning traditional fossil fuels and is increasingly being used as an ingredient in car diesel fuel and in power stations. Palm oil is themost prominent biofuel, which is grown largely in Southeast Asia. Production of palm oil, and associated rates oftropical deforestation, is increasing rapidly in response to demand, representing significant changes in tropical land-cover. In many cases hydrocarbon emissions from palm oil are larger than theindigenous cropsso that increased biofuel production effectively increases the global biogenic hydrocarbon budget with (yet unquantified) implications for surface air pollution. Future biofuel production will be considered in the land-use model. We will estimate emissions of biogenic hydrocarbons using inventories based in situ measurements so that for projection of land-use we will have an associated distribution of biogenic hydrocarbon emissions. To quantify the impact of these emissions on surface air pollution we will use a start-of-the art computer model of atmospheric chemistry and transport. Running such a model is computationally intensive for a large ensemble of biogenic hydrocarbon emission inventories so we will limit our calculation to the minimum, maximum and most likely global biogenic hydrocarbon emissions. Our calculations will significantly reduce uncertainty on the human element on land-use change and implications for surface air pollution. | |
Data | No related datasets |
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Projects | No related projects |
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Publications | No related publications |
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Added to Database | 08/09/08 |