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RapidAIR- a new urban dispersion modelling platform for air quality analysis in cities

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Presented at the 2018 Joint Conference on ABaCAS and CMAS-Asia-Pacific in Beijing, China, May 22nd

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RapidAIR- a new urban dispersion modelling platform for air quality analysis in cities

  1. 1. 1© Ricardo-AEA LtdRicardo Energy & Environment in Confidence RapidAIR- a new urban dispersion modelling platform for air quality analysis in cities Scott Hamilton1, Nicola Masey1, Tianlin Niu2, David Carslaw1 1. Ricardo, UK 2. Ricardo, China Presented at the 2018 Joint Conference on ABaCAS and CMAS-Asia-Pacific in Beijing, China, May 22nd
  2. 2. 2© Ricardo-AEA LtdRicardo Energy & Environment in Confidence RapidAir dispersion model What it is and how it works Why we made it How it compares with other models Examples of application ‘Time to interpretation’ Our remote sensing work UK insights Linking remote sensing to modelling Very important for both disciplines Questions Answers (maybe…) 800 0 Time (seconds) …for an 800 second talk Random catastrophic events Histogram of presentation topics
  3. 3. 3© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Ricardo- engineers working in AQ since the 1950s 140 air quality experts in measurements, inventories, dispersion modelling and policy support
  4. 4. 4© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Recent achievement- UN TFEIP award https://ricardo.com/news-and-media/press-releases/ricardo-awarded-%E2%80%98most-complete%E2%80%99-inventory-for-uk-e
  5. 5. 5© Ricardo-AEA LtdRicardo Energy & Environment in Confidence RapidAIR™
  6. 6. 6© Ricardo-AEA LtdRicardo Energy & Environment in Confidence
  7. 7. 7© Ricardo-AEA LtdRicardo Energy & Environment in Confidence What is RapidAir®? Dispersion modelling suite for (mainly) road traffic sources. We wrote it in python 2.7, using an open source stack including numpy, gdal and scipy. which automates much of the workflow for dispersion modelling for road t • Traffic emissions model- COPERT 5 written in pandas • Road dispersion model (based on AERMOD) • Street canyon model (based on AEOLIUS/OSPM) • Area source model (based on AERMOD) • Practically unlimited domain size and resolution • Met data- sourcing, processing and AERMET modelling • Lots of utilities (data viewers, simple GIS tools etc) • Complete reproducibility and auditability GUI) 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
  8. 8. 8© 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
  9. 9. 9© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Data RapidAir NO2 model RapidAir NO2 in a GIS RapidAir NO2 in a GIS RapidAir NO2 in Google Earth RapidAir NO2 in Google Earth We use London a lot as a test case The city has open access road emissions mapped to shapefiles, AQ measurements, buildings data Unrestricted access to input datasets is crucial to run this model
  10. 10. 10© Ricardo-AEA LtdRicardo Energy & Environment in Confidence How is it different to other road source models? The central model in RapidAIR is AERMOD which is a preferred model of the USEPA for road traffic sources. RapidAIR uses a convolution modelling approach similar to those used in computer vision to greatly reduce computational overhead (several orders of magnitude). That said, the model produces almost identical results to AERMOD for the same inputs. Convolution modelling allows us to decouple run time from the number of sources and receptor locations- both are essentially unlimited in RapidAIR. In other road source dispersion models run times can be measured in days, RapidAIR run times are measured in seconds- how do the results differ from other models?
  11. 11. 11© Ricardo-AEA LtdRicardo Energy & Environment in Confidence y = 1.0234x R² = 0.9902 0 20 40 60 80 100 120 0 20 40 60 80 100 120 RAPIDAIR(ANNUALMEANNOX) AERMOD (ANNUAL MEAN NOX) RapidAIR and AERMOD (annual mean, NOx, ugm3) Run time 5 hrs Vs 0.5 seconds Run time 5 hrs Vs 0.5 seconds The model produces very similar concentration distributions to other models across large receptor networks for the same emissions and meteorological inputs. How does it compare? y = 0.9446x R² = 0.9535 0 20 40 60 80 100 0 10 20 30 40 50 60 70 80 90 RAPIDAIR(ANNUALMEANNOX) ADMS URBAN (ANNUAL MEAN NOX) RapidAIR and ADMS Urban (annual mean, NOx µg/m3) Run time 5 hrs Vs 0.5 seconds
  12. 12. 12© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Representative workflow- processing emissions Emission polylines (.shp) Road points (.shp) Road emission grid (.tif) 1 2 3 1. Spatially allocate emissions to line sources 2. Convert the lines to ‘point’ sources- this helps with geometry issues 3. Convert point sources to area sources at the desired model resolution This is saved as a .tif file, and processed by RapidAIR as a numpy array
  13. 13. 13© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Representative workflow- dispersion Road emission grid (.tif) Concentration surface Not to scale AERMOD kernel Convolution 1. Convert emissions to array 2. Run AERMET/AERMOD 3. ‘Convolve’ emission grid with AERMOD output grid 4. Produce concentration surface 5. ‘Time to interpretation’ is dramatically reduced 1 2 3 4
  14. 14. 14© Ricardo-AEA LtdRicardo Energy & Environment in Confidence ‘What if’ analysis is very efficient- lets test a Clean Air Zone Model domain • 400 million discrete points • 10m resolution • 150km x 200km area (30,000km2) • Run time ~150 sec • Emissions modelled in our RapidEMS module (140,000 links in the UK wide model, run time a few seconds)
  15. 15. 15© Ricardo-AEA LtdRicardo Energy & Environment in Confidence
  16. 16. 16© Ricardo-AEA LtdRicardo Energy & Environment in Confidence NO2 in 2016 – the baseline
  17. 17. 17© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Beijing Case Study / Model Test Background info:  We modelled NOx and NO2 from road traffic for the whole city-  16410 km2  21.71 million people  265477 road links  Inside the 5th Ring road-  667 km2  About 10 million people  About 100000 road links Emission scenarios :  Business as usual-  Year 2013  Ordinary Traffic Control  Weekday Average Flows  PC1-  Year 2014  Odd-Even  APEC traffic controls Thankyou to the team in the School of Environment at Tsinghua University for allowing us to run the test case with their excellent traffic emissions inventory data.
  18. 18. 18© Ricardo-AEA LtdRicardo Energy & Environment in Confidence RapidAIR Test Case- Beijing Annual mean NOx µg/m3 10 x 10m resolution Run time: ~200 seconds
  19. 19. 19© Ricardo-AEA LtdRicardo Energy & Environment in Confidence City Area Comparison  In 5th Ring Road NOX Concentration (on-road vehicles source):  2013 Weekday average:32.2μg/m³  2014 APEC average:30.8μg/m³  We didn’t model building effects, though RapidAIR can do that 2014 APEC NOx (10m resolution; 5th Ring Road) Base Case NOx (10m resolution; 5th Ring Road)
  20. 20. 20© Ricardo-AEA LtdRicardo Energy & Environment in Confidence 2013 Weekday, 东长安街, 47.1 μg/m³ 2014 APEC, 东长安街, 43.3 μg/m³ 2013 Weekday, 西直门, 47.9 μg/m³ 2014 APEC, 西直门, 42.4 μg/m³
  21. 21. 21© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Cross Road Concentration Profiles 东三环 W → E N → S NW → SE 长安街 机场高速
  22. 22. 22© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Conversion of long term average NOx to NO2  Without measurement data of NOX, NO2 and O3, the NO2/NOX ratio is calculated using the simple scheme below (USEPA & Ricardo’s case studies).  The ratio is applied to the NOx grid in RapidAIR with an array function in GDAL  NO2 Concentration is estimated as: C(NO2) = (NO2 / NOX)%*Cmodelled(NOX) + Cbackground(NO2) µg.m-3
  23. 23. 23© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Remote sensing Dr David Carslaw and team
  24. 24. 24© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Things have moved on since our first vehicle emissions testing over 50 years ago
  25. 25. 25© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Our remote sensing equipment https://ee.ricardo.com/transport/vehicle-emissions-monitoring
  26. 26. 26© Ricardo-AEA LtdRicardo Energy & Environment in Confidence What are the data telling us?
  27. 27. 27© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Emission deterioration (NOx) with increased mileage This phenomena is reflected in UK emission factors or inventories
  28. 28. 28© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Temperature sensitivity of NOx emissions This phenomena is not reflected at all in UK emission factors or inventories
  29. 29. 29© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Emissions of NOx from diesel Euro 5/V and Euro 6/VI vehicles
  30. 30. 30© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Linking remote sensing to emissions and dispersion modelling Conceptual framework
  31. 31. 31© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Remote Sensing Interpret Compare Localise Diagnose Statistical analysis of data from the measurement campaign Placing the measurements in the context of current local evidence- compare with local emissions models Tune local scale emission models to reflect the new evidence. We can change coefficients in for example COPERT Create/update air quality models with the localised emissions. RapidAIR is a good candidate model to make use of RS data given its speed and efficiency ‘Time to interpretation’ and indeed action is reduced 1 2 3 4 Consolidating remote sensing data with air quality models
  32. 32. 32© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Thanks to the organisers and to you for your kind attention scott.hamilton@ricardo.com
  33. 33. 33© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Scott Hamilton, PhD Knowledge Leader, Air Quality Modelling Environmental Evidence and Data Practice Ricardo Energy and Environment scott.hamilton@ricardo.com
  34. 34. 34© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Supplementary material
  35. 35. 35© Ricardo-AEA LtdRicardo Energy & Environment in Confidence NO2 annual mean concentrations in building footprints, 2008 UK example, concentrations in building footprints
  36. 36. 36© Ricardo-AEA LtdRicardo Energy & Environment in Confidence NO2 cross road profile of concentrations- M74 in Glasgow baseline best available tech BAT plus no diesel LDV Exposure zone ~150m 0 80
  37. 37. 37© Ricardo-AEA LtdRicardo Energy & Environment in Confidence High resolution run: from 10m down to 3m 2014 APEC 3m resolution2014 APEC 10m resolution ~200 seconds to compute
  38. 38. 38© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Model Validation — Background Point Name Detail Measured NO2 Modelled NOX Define As 1001 A 定陵 城市清洁对照点 15.39 0.48 City_BG MYSC 密云 水库 京东北区域背景 传输点 9.08 0.01 NE_BG YL 永乐店 京东南区域背景 传输点 60.87 3.09 SE_BG DGC 东高村 京东区域背景传 输点 33.84 3.37 E_BG YF 榆垡 京南区域背景传 输点 27.21 4.60 S_BG LLH 琉璃河 京西南区域背景 传输点 33.85 1.08 SW_BG BDL 八达岭 京西区域背景传 输点 24.89 0.14 W_BG 1002A as background (0-traffic source)
  39. 39. 39© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Beijing NO2 model validation In 5th Ring Road Note: uncertain coordinate of traffic control points Some are placed right on the road surface Some on the roadside as expected Surban Points 8 City National Control Points The NO2 concentration at suburban points are affected little from road emission dispersion: e.g. 15% contribution from traffic except MY
  40. 40. 40© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Ricardo has world- class expertise in vehicle emissions measurement
  41. 41. 41© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Emissions of NOx from Euro 5/V and Euro 6/VI buses split by location
  42. 42. 42© Ricardo-AEA LtdRicardo Energy & Environment in Confidence Relevant references for RapidAIR and its development The development group for RapidAIR has published and presented the model for peer review 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 for publication: Environmental Modeling and Software Hamilton, S., Masey, N. and Beverland, I. (2017). Development and validation of a rapid urban scale dispersion modelling platform. In: 17th Annual CMAS Conference. [online] Chapel Hill: CMAS, University of North Carolina. Available at: https://www.cmascenter.org/conference//2017/abstracts/hamilton_development_validation_2017.pdf Hamilton, S., Masey, N., Niu, T. and Carslaw, D. (2018). RapidAIR- a new urban dispersion modelling platform for air quality analysis in cities. In: 2018 Joint International Conference on ABaCAS and CMAS-Asia-Pacific. [online] Beijing: CMAS-Asia-Pacific. Available at http://www.abacas-dss.com/Conference2018/ConferenceAgenda.aspx Hamilton, S. (2018). Air Quality Modelling (in RapidAIR) of New and Emerging Vehicle Technologies – What Can They Deliver in Scotland?. In: Dispersion Model User Group Conference. London: Institution of Environmental Sciences Hamilton, S. (2018). Clean Air Zones- big models for big questions (RapidAIR). In: Scottish Air Quality Database and Website Annual Seminar. [online] Glasgow: Scottish Government. Available at: http://www.scottishairquality.co.uk/news/reports?view=seminars&id=565 Gillespie J, Masey N, Heal M R, Hamilton S, Beverland I J (2017) Estimation of spatial patterns of urban air pollution over a 4-week period from repeated 5-min measurements. Atmospheric Environment. 150, 295-302. Masey N, Gillespie J, Heal M R, Hamilton S, Beverland I J (2017) Influence of wind-speed on short duration NO2 measurements using Palmes and Ogawa passive diffusion samplers. Atmospheric Environment, 160, 70-76.

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