1. Uncertainty management
Tinus Pulles
4th JOINT UNECE Task Force & EIONET Workshop on
Emission Inventories and Projections
WARSAW, 22 – 24 SEPT 2003
TRAINING ON THE ASSESSMENT OF AIR QUALITY AND EMISSION MEASUREMENTS UNDER THE EUROPEAN COMMISSION’S DIRECTIVES
Wednesday, 25th September– Friday, 27th September 2002
Joint Research Centre, Ispra, Italy
Parallel session 2– Emission Inventories
Uncertainty management
Tinus Pulles
TNO-MEP
PO Box 342
7300 AH Apeldoorn
The Netherlands

2. Issues in this presentation
The concept of “data quality”
Why manage uncertainties?
How to manage uncertainties
How to report uncertainties
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Uncertainties in Emission Inventories

3. Perspectives on data Quality
Quality high if …
Scientist
Scientific debate:
search for weaknesses and errors; falsification
… it produces predictions that are confirmed
Policy maker
Political debate:
search for consensus and agreement; compromise
… everybody involved agrees
Lawyer
Judicial debate:
search for proof or doubt; persuasion
… it convinces a judge or jury
True ?
Accepted ?
Convinced ?
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Uncertainties in Emission Inventories

4. Verification Validation
Guidelines applied ?
validation
Real world
emissions
Emission
inventory
True ?
verification
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Uncertainties in Emission Inventories

5. Why manage uncertainties ?
“Good practice” in scientific work
It imply needs to be done!
To identify weak spots in the inventory
Improvement of inventory
Requested by conventions and protocols
Report uncertainties
Convince expert review teams
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Uncertainties in Emission Inventories

6. How to manage uncertainties
Good practice
Follow guidlines
Key source analysis
Selection of methods: decision trees
Documentation
QA/QC
Estimate uncertainties
Data need:
Input uncertainties (EFs, ARs )
Probability distributions
Combining input uncertainties
Tier 1: simple spreadsheet calculation
Tier 2: Monte Carlo simulation
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Uncertainties in Emission Inventories

7. Uncertainties in Emission Inventories
Uncertainty analysis
Quality Control
Quality Assessment
Uncertainty
Analysis
Ground Thruth
Verification
validation
accepted
verification
true
Qualitative
Quantitative
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Uncertainties in Emission Inventories

8. Qualitative Uncertainty Analysis
UNFCCC (TCCCA)
“Synthesis and Assessment Report”
Compare national submissions:
Earlier submissions of the country
Implied emission factors between countries
Activity rates with international statistics
Expert review teams
Ask questions to national experts
UNECE / CLRTAP
That’s why we’re here
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Uncertainties in Emission Inventories

9. Quantitative Uncertainty Analysis
Uncertainty estimates for input data
Activity data
Statistics office?
Emission factors
Literature
Databases
Expert judgement
Combine uncertainties into one over all uncertainty estimate:
Tier 1: relatively simple spreadsheet method
Tier 2: Monte Carlo simulation
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Uncertainties in Emission Inventories

10. Tier 1 Uncertainty Analysis
Simple approach using a “square root of sum of squares” algorithm
Is statistically correct if
Standard deviations are small compared to point estimate
All PDFs are normal distributions
This is not the case
Therefore:
Tier 1 produces a proxy or indicator of uncertainty, not a statistical value
How does it look like?
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Uncertainties in Emission Inventories

11. Tier 2 Uncertainty Analysis
Monte Carlo simulation
(Re)build inventory model in a spreadsheet
Choose probability density functions (PDF) for all ARs and EFs
Run a Monte Carlo add in
@Risk
Cristal Ball
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Uncertainties in Emission Inventories

12. How to report uncertainties
95 percent confidence intervals
IPCC Good Practice and Uncertainty Management report proposes a table. It might be similar for other conventions!
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Uncertainties in Emission Inventories

13. Conclusions: Issues in this presentation
The concept of “data quality”
Complex issue, depends on (perspective of) user
We are interested in the policy perspective
Qualitative and quantitative aspects
Good practice
Quality criteria: reliability & accuracy
Why manage uncertainties?
How to manage uncertainties
How to report uncertainties
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Uncertainties in Emission Inventories

14. Conclusions: Issues in this presentation
The concept of “data quality”
Quality criteria: reliability & accuracy
Accuracy: mainly scientific understanding
Are data good?
Reliability: mainly application oriented understanding:
Are data good enough?
Why manage uncertainties?
How to manage uncertainties
How to report uncertainties
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Uncertainties in Emission Inventories

15. Conclusions: Issues in this presentation
The concept of “data quality”
Quality criteria: reliability & accuracy
Why manage uncertainties?
Because it is good practice
Because the client requires it
To find weak spots in the inventory
To improve the quality of the inentory
How to manage uncertainties
How to report uncertainties
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Uncertainties in Emission Inventories

16. Conclusions: Issues in this presentation
The concept of “data quality”
Quality criteria: reliability & accuracy
Why manage uncertainties?
How to manage uncertainties
Apply the IPCC Guidelines and Good Practice Guidance
Key source analysis & Decision trees
Estimate uncertainties
QA/QC: adequate documentation
How to report uncertainties
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Uncertainties in Emission Inventories

17. Conclusions: Issues in this presentation
The concept of “data quality”
Quality criteria: reliability & accuracy
Why manage uncertainties?
How to manage uncertainties
How to report uncertainties
Estimate using Tier 1 or Tier 2
In a predefined table as defined in CRF
Additional information in National Inventory Report
Get it accepted by the expert review team
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Uncertainties in Emission Inventories

18. Uncertainties in Emission Inventories
Thank you
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Uncertainties in Emission Inventories