Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to AQ modelling with real world traffic emissions
Similar to AQ modelling with real world traffic emissions (20)
Recently uploaded
Recently uploaded (20)
AQ modelling with real world traffic emissions
- 1. 1© Ricardo-AEA LtdRicardo Energy & Environment in Confidence AQ modelling with real world traffic emissions Scott Hamilton, PhD Tech Lead- Air Quality Modelling scott.hamilton@ricardo.com Nicola Masey, PhD Senior Consultant- Air Quality Modelling CAZANZ19 16-18th September, 2019 Queenstown, New Zealand
- 2. 2© Ricardo-AEA LtdRicardo Energy & Environment in Confidence This talk… The project Emissions Dispersion models Python workflow controllers Results
- 3. 3© Ricardo-AEA LtdRicardo Energy & Environment in Confidence The project
- 4. 4© Ricardo-AEA LtdRicardo Energy & Environment in Confidence The project UK street with a road traffic problem- traffic microsimulation model (4 periods) Exceeds national NO2 standards Annual mean ~70 µgm3 (standard is 40 µgm3) Unusually high emissions combined with topography = compliance problem NO2 is the main pollutant of concern in UK urban areas
- 5. 5© Ricardo-AEA LtdRicardo Energy & Environment in Confidence The project- technical objective Harness traffic microsimulation outputs without losing any information and create air quality models that can support local compliance assessment and regional implications of local traffic interventions.
- 6. 6© Ricardo-AEA LtdRicardo Energy & Environment in Confidence The project
- 7. 7© Ricardo-AEA LtdRicardo Energy & Environment in Confidence GRAL domain (1m) Local RapidAir domain (1m) Regional RapidAir domain (3m) Nested model approach 3m regional (RapidAIR) 1m sub-regional (RapidAIR) 1m local (GRAL)
- 8. 8© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Modelling technology stack Emissions OPUS remote sensing / RapidEMS Local dispersion GRAL Regional dispersion RapidAIR Python workflow managers
- 9. 9© Ricardo-AEA LtdRicardo Energy & Environment in Confidence OPUS / COPERT emission model
- 10. 10© Ricardo-AEA LtdRicardo Energy & Environment in Confidence https://ee.ricardo.com/transport/vehicle-emissions-monitoring OPUS remote sensing
- 11. 11© Ricardo-AEA LtdRicardo Energy & Environment in Confidence RapidEMS comparison with OPUS measurements- fleet Comparison for generalised vehicle categories
- 12. 12© Ricardo-AEA LtdRicardo Energy & Environment in Confidence COPERT 5 calibrated with OPUS measurements- technology Emissions from remote sensing measurements (pink uphill, blue non-uphill). The grey dots denote corresponding COPERT factors.
- 13. 13© Ricardo-AEA LtdRicardo Energy & Environment in Confidence RapidAIR
- 14. 14© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Dispersion modelling program for (mainly) road traffic emission sources. Written in python 3. automates much of the workflow for dispersion modelling for road t • Traffic emissions model- RapidEMS (COPERT5) • Road dispersion model (based on USEPA AERMOD) • Street canyon model (based on AEOLIUS/OSPM) • Practically unlimited domain size / resolution • Decouples computation from no. of sources / receptors / met hours • Meteorology- sources data, processes, runs AERMET • Lots of utilities (data viewers, simple GIS tools etc) • Complete reproducibility and auditability Masey, N., Hamilton, S. and Beverland, I. (2018). Development and evaluation of the RapidAir dispersion model, including the use of geospatial surrogates to represent street canyon effects. Accepted: Environmental Modeling and Software RapidAIR
- 15. 15© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Road NO2 example, 3 x 3 m resolution for London Clock time for the road dispersion model is about 200 sec. Scenarios are very quick to iterate through When the model has run we can sample any of the many hundreds of millions of receptor locations
- 16. 16© Ricardo-AEA LtdRicardo Energy & Environment in Confidence GRAL
- 17. 17© Ricardo-AEA LtdRicardo Energy & Environment in Confidence GRAL- Graz Lagrangian Model • Developed by TU Graz • Lagrangian air quality model • Particle tracking within a 3D wind field • Mesoscale GRAMM model provides initial and boundary conditions meteorology • Effect of buildings / terrain on flow included • Combination of capabilities and the domain made it an attractive candidate air quality model
- 18. 18© Ricardo-AEA LtdRicardo Energy & Environment in Confidence GRAL- Graz Lagrangian Model GRAL simulations are quite computationally costly We needed hundreds of GRAL runs for this domain Used the grouping feature to model each road link separately Created dispersion database Each link ‘looked up’ and scaled according to real emissions Greatly decreased the computation, though it was complex to set up
- 19. 19© Ricardo-AEA LtdRicardo Energy & Environment in Confidence GRAL- Graz Lagrangian Model Each dispersion grid for each link matched to the scenario emission rate. Its then scaled and combined to make the final map. Each link has a unique ID • Uphill? • In scenario? Output grid scaled by ratio of unit emission to actual
- 20. 20© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Methodological challenge- big workflow control issues Many road links Time variation in emissions- some interventions were in specific hours Presenting the appropriate emissions for each link to dispersion models Compute dispersion in GRAL, match hourly ‘field’ to the right emissions Recombine the dispersion fields to make one ‘map’ per scenario Extract results at exposure points Do all of this reproducibly
- 21. 21© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Python coding of custom model controllers
- 22. 22© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Whilst we’ve got great tools, methods and are somewhat capable humans- this study was way too complex for a person to manage reproducibly
- 23. 23© Ricardo-AEA LtdRicardo Energy & Environment in Confidence …..QA
- 24. 24© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Example python business logic for a single link Match emission rate to the road by the link ID code Inside Clean Air Zone? What hour is it? Calculate emissions and compile emission factor library Uphill or not? Match to GRAL dispersion grid for that link/hour/scenario Weight GRAL grid by ratio of actual Vs idealised emissions Compile > 300 GRAL results to one ‘map’ Repeat for all scenarios/years Automate extraction of receptor results Lookup correct emission rate from library for specifics of the link activity, time and setting Validate model and scale results
- 25. 25© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Results
- 26. 26© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Baseline local scale model
- 27. 27© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Demolition scheme- local model
- 28. 28© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Red areas = a reduction in NO2 concentrations with demolition. Blue = an increase in NO2 concentrations with demolition The canyon concentrations reduce by ~5-8 micrograms but still exceed the NO2 standard by several micrograms at roadside footpaths (though not residential receptors. Demolition scheme difference plot
- 29. 29© Ricardo-AEA LtdRicardo Energy & Environment in Confidence RapidAIR baseline result- regional analysis ~5000 km2 Motorways Minor roads
- 30. 30© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Highlights RapidAIR and GRAL worked well together The traffic microsimulation model was faithfully ‘translated’ into AQ Use of python workflow managers saved a huge amount of work- guaranteed reproducibility across hundreds of thousands of calculations QA was very simple despite there being hundreds of models/runs Scenarios were simple to run – ‘drop in’ a new traffic model No spreadsheet calculations at all First use of GRAL in the UK (to our knowledge, we’d do it again)
- 31. 31© Ricardo-AEA LtdRicardo Energy & Environment in Confidence T.Hanks! scott.hamilton@ricardo.com https://ee.ricardo.com/air-quality https://ee.ricardo.com/air-quality/city-scale-air-quality
Editor's Notes
- Steep uphill gradient not well represented in emission models Street canyon inside a steep valley Receptors at various heights and VERY close to sources Previous modelling studies using Gaussian models didn’t capture these issues very well 70 µg/m3 annual mean NO2
- Remote real-world emissions factors were measured at two locations within the study area. One of these locations captured emissions of vehicles travelling uphill, while the other recorded non-uphill emissions. The OPUS Inspection AccuScan RSD-5000 instrument was used to make the measurements (Figure 1). Emissions measurements were made over a 4-week period at varying times of day to capture both peak and off-peak traffic. Data collected by Automatic Number Plate Recognition (ANPR) camera was referenced with a national vehicle database to extract vehicle type and euro standard. This allowed average emission rates to be calculated from the OPUS data.
- Homing in on some individual vehicle types we can see that in general, the emission model compares better with measurements for the ‘not uphill’ cases- but uphill emissions are not well characterised in the emission model. Hence, for a domain like this with a clear issue with climbing traffic- remote sensing helps nudge the emissions towards a more realistic representation.
- Some neat things it does: Traffic emissions model built in (1 million links in 1 minute, covers NOx, fNO2, NH3, CO2, PM10, PM2.5, gradients, builds an inventory, source apportionment…) Road dispersion model (1m resolution possible) Street canyon model Area source model e.g. for large dispersed sources (e.g. domestic, shipping) Unlimited domain size and resolution (testing with 3 billion locations) Domain splitting unlimited domain size Met data handling- met data gathering, filling, substitution, running AERMET Automatic handling of background values (in the UK) Model scaling can be done automatically Lots of utilities (data viewers, simple GIS tools etc) Various empirical NOx NO2 chemistry options (with road NOx, fNO2 effects) Interactive plotting (in a customisable GUI) GUI driven option (in a customisable GUI)