Scottish Urban Air Quality Steering Group Modeling and Monitoring Workshop - Alan Hills Presentation - Aberdeen Air Modelling Pilot Study Update

1. Aberdeen Air Modelling Pilot Study Update
Dr Alan Hills - Unit Manager – OceanMet

2. •
Pilot Study Objectives: (Key Partners: Aberdeen City Council, Transport Scotland and Glasgow City Council).
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Approaches to Problem Solving.
•SEPA Environmental and Spatial Informatics.
•What have we learned so far?
•Traffic and Emissions Data.
•Building Air Quality Models.
•Data Visualisation and Analysis.
•Complex Modelling Approaches.
•Pilot Study Key Messages.
•Future Work.
•Acknowledgements.
Presentation Structure

3. Need: To help SEPA to contribute to a Modelling and Monitoring Framework to support the Scottish Government Low Emission Strategy. Sponsored by the SUAQSG.
Objectives
•Contribute to a problem solving approach to urban air quality modelling using techniques from other disciplines and fields.
•Develop innovative ways of making Air Quality modelling information more accessible and interactive.
•Evaluate different approaches to air quality modelling and contribute to the development of best practice guidance.
•Develop an understanding of current air modelling approaches and how they vary.
•Contribute to the development of guidance for estimating emissions in Scotland.
•Develop techniques for mitigating model uncertainty.
Pilot Study Need and Objectives

4. •
Key Points (Malcolm Sparrow Training)
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Define the problem to be solved.
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Gather all available data/information and analyse/visualise.
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Identify critical data and address uncertainty.
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Turn data into information.
•Key Points OceanMet Implementation
•Data and modelling offer complimentary perspectives on the problem.
•Data and modelling can feed each other, further refining problem analysis.
•Data and Modelling are uncertain and imperfect and this must be managed.
•Modelling is good for looking at system behaviour.
•Important features in data not displayed in modelling.
Problem Solving
Data Analysis/Visualisation
Modelling

5. SEPA ESIU Informatics Hub (ESIU – Environmental & Spatial Informatics Unit) TIBCO – Spotfire, S+ Also R

6. SEPA ESIU Informatics Hub (Water Framework Directive Dashboard) Web Based Interactive Data Analysis/Visualisation

7. Traffic Count Points: Car
Traffic Count Time Series: Car
Traffic Count Data - Interactive Analysis

8. Traffic Flow Data - Interactive Analysis

9. Traffic Data – Key Lessons So Far - 1
•Detailed Traffic Data Are Essential For:
•Understanding the Problem.
•Identifying Potential Improvement Measures.
•Setting up Traffic Models.
•Setting up Good Air Quality Models.
•Detailed, Extensive And High Quality Data in Aberdeen, BUT:
•Traffic Data Most often collected for Road Network Management.
•May not always be suitable for Air Quality Modelling = Uncertainty.
•Small detailed counts more common.
•Traffic Flow Estimation From Count Data = Potential Uncertainty.
•Flow Data May Not Be Routinely Archived.
•Standardised Traffic Data In Consistent Format Are Essential
•Must Be Designed with Air Quality Modelling in Mind.

10. Traffic Data – Key Lessons So Far - 2
•Important to gain an understanding of areas of Congestion.
•Congestion or Stagnation Factors have been applied in Various Modelling Studies.
•Urban Traffic Management & Control And SCOOT Data are likely to be useful. Prof. Margaret Bell (Newcastle University).
•ANPR can provide more detailed insights into the “Residence Time” of certain types of vehicles, (e.g., Taxis) – Sheffield LEZ Study.
•Do we have a national stock of available Traffic Data, where Required?
•Opportunities For Better User of Traffic Modelling and Sat. Nav. Data.

11. Estimating Emissions – Key Lessons So Far - 1
•Estimating Emissions can be a “Black Art”!
•Some Evidence that Published Emission Factors do not always represent the local Fleet (Sheffield Low Emission Work).
•Automatic Number Plat Recognition (ANPR) and Vehicle Emission Measurement System (VEMS) are powerful tools for reducing uncertainty in Emissions.
•Future Emission Estimates Heavily Reliant On “Expected Performance” of New Euro VI Standard – Uncertainty!
•Should we deal with Future Emissions on a more Probabilistic Basis?
•Likely to Require Agreed Approach And Guidance.

12. Building Air Quality Models – Key Lessons So Far
•Relatively Straightforward for Standard Models!
•ArcGIS Tools have been developed to Speed Up Model Build.
•Need to better understand the limitations of Gaussian Modelling.
•Constant Desire for Model Inter-comparison. Is this useful?

13. Aberdeen Pilot Project – Air Quality Data (Interactive Analysis)

14. Aberdeen Pilot Project – Air Quality & Met. Data (Interactive Analysis)

15. Aberdeen Pilot Project – Modelling Output (Interactive Analysis)

16. Aberdeen Pilot Project – Modelling Input/Output (Interactive Analysis)

17. Aberdeen Pilot Project – Modelling Input/Output
(Interactive Analysis)

18. Data Analysis/Visualisation – Key Lessons So Far
•Interactive Data Analysis and Visualisation is now more efficient and output is easier to share new Software Packages.
•Data/Modelling may be more easily turned into Information and shared with a large audience over the Web.
•Interactive Tools have the potential to yield new insights into data or modelling output, promoting better understanding.
•Efficient comparison of modelling and data may allow better management of uncertainty.
•Potential for interactive tools to allow Scenario Testing, prior to detailed studies.
•Could this approach compliment or replace “static” reporting?
•Great Potential for Communicating Information.

19. Aberdeen Pilot Project – Complex Modelling (Computational Fluid Dynamics - CFD)

20. Aberdeen Pilot Project – Complex Modelling (MISKAM)

21. Complex Modelling – Key Lessons So Far
•Localised Air Quality Issues?, Complex Models Appropriate?
•Gaussian Models Not Always Suitable.
•CFD Modelling May Require Vehicle Induced Turbulence.
•Output May be Helpful in Communicating Current Impacts.
•MISKAM is a useful Intermediate Approach to CFD.
•May Help to Manage Uncertainty.
•May Help to Design Monitoring Networks and Exposure Assessment Studies.

22. •
Strategic Plan for Traffic Data Collection is Essential
•
Traffic Data Should Serve Many Needs:
•
Air Quality
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Road Network Management
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Traffic Modelling
•Modelling and Data Analysis are equally important
•Measures to Improve Air Quality Require Careful Thought and Input From Many Stakeholders.
•Must Mitigate Uncertainty.
•Tools For Communication Are Essential.
•Monitoring and Modelling Framework Required.
•Partnership Working Essential to Success.
Pilot Study Key Messages…So Far

23. •
Contribute to the LES Modelling and Monitoring Working Group
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Refine and Further Develop Data Vis./Analysis Products
•R&D on the statistical analysis of modelling output – Prof. Marian Scott – Glasgow University, Lauren Sim, Dr Francesco Finazzi – University of Bergamo.
•Examine Feasibility of “Unit Release” Scenario Testing Approach.
•Report on Pilot Aberdeen Pilot study and contribute to a Data/Modelling Framework.
•Further Develop Complex Air Modelling Tools – CFD and MISKAM.
•Continue to work with Key Partners and Develop new Contacts within the Community. Particularly on Emission Estimates.
Future Work

24. •
Aberdeen City Council (Aileen Brodie), Transport Scotland (Drew Hill, Stephen Thomson), Glasgow City Council (Dominic Callaghan, Vincent McInally).
•
Scottish Government (Andrew Taylor).
•SEPA Airmod Group (Alan McDonald, Andrew Malby, Eddy Barratt, Fraser Gemmell).
•SEPA Colleagues (Colin Gillespie, Colin Gray, Mark Hallard, ESIU, Martin Marsden).
•Michael Glotz-Richter - Senior project manager 'sustainable mobility‘ – Bremen City
•Scottish Urban Air Quality Steering Group
•AECOM, SiAS, IBI Group
•CEH (Stefan Reis, Massimo Vieno)
•THANK YOU
Acknowledgements

25. •
Decision Support – Understand/Protect/Improve Environment
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Estimate Risks and Uncertainties – Explain to Non-Experts
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Modelling/Data Advice Services – Guidance Online
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Key Role in Assessing Third Party Modelling – Methods/Data
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Historical Emphasis on Point Sources and Emergency Planning In Air Quality
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Aberdeen Pilot Project Undertaken to:
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Develop Urban Air Quality Modelling Capability
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Translate experience in other topics to Urban Air Quality
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Particularly Modelling and Data Analysis/Visualisation
Modelling & Data Vis./Analysis - SEPA
£ vs. Risk

26. Modelling & Data Vis./Analysis – Example Bremen

27. Modelling & Data Vis./Analysis – Example Bremen

28. Modelling & Data Vis./Analysis – Example Bremen

29. Modelling & Data Vis./Analysis – Example Netherlands