Download - The EMEP/EEA Emissions Inventory Guidebook
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The EMEP/EEA Emissions Inventory Guidebook
Dr Chris Dore Chair of the TFEIP
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Contents
1. Accuracy In Emission Inventories2. Principles of Uncertainty3. Uncertainty Tools4. Conclusions5. Discussion Points
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Does it matter?!• Actually, it is not very important for demonstrating
compliance with targets• But key for trying to reflect the real world.
1. Accuracy
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Some starting considerations…• Point sources vs area sources• Source/fuel mix• Activity data – trends with time vs absolute• EFs – variations across time series, applicability• Completeness vs guidebook• Completeness vs real world• Mapping emissions• Projections & scenarios
1. Accuracy
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1. Accuracy
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NOx NMVOC SO2 NH3 PM2.5
Waste & OtherAgricultureProcesses & Product UseOther MobileRoad TransportDomestic & Inst. Comb.Industrial CombustionPower Stations
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2. Principles of Uncertainty Uncertainty analysis is generally used to represent
“accuracy”
Point sources- combination of random independent errors Area sources- one EF, prone to bias.
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Propagation of Errors• Assign uncertainty to AD and EF
– from measurement, default ranges, expert judgement• “,… root the sum of the squares…”
• Simple mathematical combination of elements to give an uncertainty for the total emission.
3. Uncertainty Tools (1)
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Monte-Carlo Analysis
3. Uncertainty Tools (2)
• Uncertainty profiles, accounts for inter-dependencies...• Much better tool, but more challenging to use.
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Trend Uncertainties• Standard tool used for assessing the uncertainty
in the trend included in the Guidance.
3. Uncertainty Tools (3)
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Strengths• Methodologies common with GHGs (UNFCCC)• Standard mathematical approaches for assessing
uncertainty• Simple methods available.
3. Uncertainty Tools
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Weaknesses• Low uncertainty does not necessarily mean good
accuracy!– incomplete inventory, use of inappropriate EFs etc.
• Uncertainty ranges applied to EFs are usually no better than a guess!– Not usually enough data points for a statistical analysis
• Error propagation analysis is too simple– Does not account for interdependencies/biases etc.
• Modellers want uncertainty on mapped emissions.
3. Uncertainty Tools
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Development of New Tools• Moran’s co-efficient
– A mathematical metric of spatial autocorrelation (chess board = -1, random = 0, uniform = +1).
– Indicates adjacent grid cell dependencies
• Uncertainty of mapped emissions
3. Uncertainty Tools
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Development of New Tools• Uncertainty of mapped
emissions• Combination of emissions
uncertainty with mapping uncertainty
3. Uncertainty Tools
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Learning from the Past
3. Uncertainty Tools
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NOx SNAPSHOT
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SO2(1970-2011)SO2 SNAPSHOT
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Important Considerations• EF uncertainties are not robust enough• Error propagation analysis - too simple?• Uncertainty analysis does not indicate the ability to
represent the real world• Modellers want uncertainty on mapped emissions.
• … we need to improve what we are delivering!• … and in particular better explain what it
represents.
4. Conclusions
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Some Questions1. Can we improve current EF uncertainties?
2. Should we all be using Monte-Carlo analysis?
3. Can we add to/adjust uncertainty results to give an indication of real-world representation?
4. Can tools be developed that better provide the information that users need?
5. What resources do we have to support this?
5. Discussion Points
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THANK-YOU FORYOUR ATTENTION