5. 55
Defining The AQ Modelling 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 AQ Modelling 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 AQ Modelling 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
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
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)
19. 19191919
Model Verification and Adjustment
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 and Adjustment
• 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;
