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Air Quality Modelling
This document provides an overview of air quality modelling. It discusses key guidance documents, defining the modelling domain by identifying affected roads and receptors, model setup including input parameters like emissions factors and traffic data, and model verification through comparing predictions to monitoring data to improve the model. The goal is to use better inputs to produce better outputs from the dispersion model and accurately assess air quality impacts.
Air Quality Modelling
- 1. Air Quality Modelling MarkChapman, Air Quality and Odour Discipline Manager mark.chapman@mouchel.com 01483 731378 07976 344 311
- 2. Air Quality Modelling MarkChapman, Air Quality and Odour Discipline Manager mark.chapman@mouchel.com 01483 731378 07976 344 311
- 3. 33 Air Quality ModellingOverview • Key Guidance Documents • Defining the AQ Modelling Domain • Model Setup – Input Parameters – Road Traffic Emissions; • Model Verification and Adjustment
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- 5. 55 Defining The AQModelling Domain Brief Reminder: DMRB Screening Criteria (Guidance) For Local Air Quality ‘Affected roads’ are those that meet any of the following criteria: • road alignment will change by 5 m or more; or • daily traffic flows will change by 1,000 AADT or more; or • Heavy Duty Vehicle (HDV) flows will change by 200 AADT or more; or • daily average speed will change by 10 km/hr or more; or • peak hour speed will change by 20 km/hr or more.
- 6. 66 Defining The AQModelling Domain For Regional Emissions ‘Affected roads’ are those that are expected to have : • a change of more than 10% in AADT; or • a change of more than 10% to the number of HDV; or • a change in daily average speed of more than 20 km/hr. However… Air Quality Assessments are Receptor Based not Source Based
- 7. 77 Defining The AQModelling Domain In addition to identifying ‘Affected roads’: • Receptors within 200m of Affected roads; – Including future receptors • Roads (including non-affected) within 200m of these receptors; • Locations where it is anticipated that EU Limit Values are likely to be exceeded in the Opening Year; – Air Quality Management Areas (AQMA) – Monitoring Locations
- 8. 88 Air Quality Modelling– Affected Roads
- 9. 99 Air Quality Modelling- Final Study Area
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- 11. 11111111 Model Setup –The ‘basics’ Air Quality Concentration = … Emission • Factors derived from speeds, fleet composition (LDV/HDV), fleet age (EURO Standard) X Activity • Magnitude of each factor for local network X Dispersion • Chemistry - Oxidation reactions, etc. • Physics – Turbulence, Meteorology, etc. Better inputs = Better outputs
- 12. 12121212 Model Setup –Input Parameters
- 13. 13131313 Model Setup –Input Parameters Internal to Model: • Terrain Data • Meteorological Data • Surface Roughness (Horizontal Dispersion) • Monin-Obukhov Length (Vertical Dispersion) • Road Traffic Emissions Data
- 14. 14141414 Model Setup –Input Parameters External Processing: • Local Monitoring Data – Model Verification and Adjustment • Background Concentrations – National Mapping – Local Monitoring Data • NOx to NO2 Empirical Formulae (Basic Chemistry)
- 15. 15151515 Model Setup –Road Traffic Emissions 2010 LDV vs HDV Nox Emissions 0 2 4 6 8 10 12 14 16 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 Speed (kph) Emissions LDVs HDVs Optimum speed for HDVs is 60-70 kph Optimum speed for LDVs is 45-50 kph x16 x20 x14 x10
- 16. 16161616 Model Setup –Road Traffic Emissions 2010 Motorway NOx Emissions 0 500 1000 1500 2000 2500 3000 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 Speed (kph) EmissionRate LDVs HDVs Cumulative Optimum speed for LDVs is 45-50 kph Optimum speed for HDVs is 60-70 kph Optimum speed for entire motorway fleet is 55-65 kph Best Resolution Fleet Breakdown
- 17. 17171717 Model Setup –Road Traffic Emissions NOx Emissions 2010-2025 0 500 1000 1500 2000 2500 3000 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 LDVs HDVs Cumulative 45% Reduction in Nox Emissions from 2011 to 2016 72% Reduction in Nox Emissions from 2011 to 2021
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- 19. 19191919 Model Verification andAdjustment The predicted results from a dispersion model may differ from measured concentrations for a large number of reasons: • estimates of background concentrations; • meteorological data uncertainties; • uncertainties in source activity data such as traffic flows and emissions factors; • model input parameters such as roughness length, minimum Monin-Obukhov; and • overall model limitations; and • uncertainties associated with monitoring data, including locations.
- 20. 20202020 Model Verification andAdjustment • checks on: – traffic data; – road widths; – distance between sources and monitoring; – monitoring data; – estimates of background concentrations; • consideration of speed estimates on roads in particular at junctions where speed limits are unlikely to be appropriate; • consideration of source type, such as roads and street canyons;
- 21. 21212121 Model Verification andAdjustment
- 22. 22222222 Model Verification andAdjustment
- 23. 23232323 Model Verification andAdjustment
- 24. 24242424 Model Verification andAdjustment
- 25. 25252525 Model Verification andAdjustment
- 26. 26262626 Model Verification andAdjustment
- 27. Mark Chapman Air Qualityand Odour Discipline Manager mark.chapman@mouchel.com 01483 731 378 07976 344 311