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The Impact of Different Validation Datasets on Air Quality Modelling Performance

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The Impact of Different Validation Datasets on Air Quality Modelling Performance, Transportation Research Board, Washington DC, 7–11 January 2018.

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The Impact of Different Validation Datasets on Air Quality Modelling Performance

  1. 1. 1 Center for Advancing Research in Transportation Emissions, Energy and Health U.S. Department of Transportation University Transportation Centers Program HaneenKhreis, PhD www.carteeh.org
  2. 2. 2 “The Impact of Different Validation Datasets on Air Quality Modeling Performance” – paper # 18- 01950
  3. 3. 3 •Not possible to make sufficient air pollution exposure measurements for epidemiological and health impact assessments  143,472 children •Many studies rely on air pollution modeling •Commonly used models include: • Land use regression (LUR) modeling • Atmospheric dispersion (AD) modeling Background
  4. 4. 4 LUR modeling • Repeated measurements (passive samplers) at N sites • Average measurements over longer term (usually a year) and adjust for temporal variations • Regression model to combine measurements with GIS-based predictor data within certain buffers • Apply model to non- measured locations
  5. 5. 5 LUR modeling • BUILDINGS  Local land use, Area/number of buildings, m2/N(umber) • TRAFLOAD  Local road network, Total traffic load of all roads in a buffer (sum of (traffic intensity*length of all segments)), Veh. Day-1 m • NATURAL  Semi-natural and forested areas, m2 • HEAVYTRAFMAJOR  Heavy-duty traffic intensity on nearest major road, Veh. Day-1
  6. 6. 6 AD modeling
  7. 7. 7 •These models are only validated using one validation dataset • Their estimates at select receptor points are sometimes generalized to larger areas •This may lead to unsatisfactory validation and/or inaccurate insights about the models’ performance and suitability for large-scale application Background
  8. 8. 8 •Objective 1  explore the effect of different validation datasets on the validation results of two commonly used air quality models •Objective 2  explore the effect of the model estimates’ spatial resolution on the models’ validity at different locations Objectives
  9. 9. 9 Study area
  10. 10. 10 Annual (2009) NO2 and NOx LUR model Validation against 4 different datasets Spatial resolution analysis Estimate at exact location of validation point Estimates at centroid of 100x100m grid in which validation point fell AD model Validation against 4 different datasets Spatial resolution analysis Estimates at exact locations of validation point Estimates at centroid of 100x100m grid in which validation point fell Methods
  11. 11. 11 Annual (2009) NO2 and NOx LUR model Validation against 4 different datasets Spatial resolution analysis Estimate at exact location of validation point Estimates at centroid of 100x100m grid in which validation point fell AD model Validation against 4 different datasets Spatial resolution analysis Estimates at exact locations of validation point Estimates at centroid of 100x100m grid in which validation point fell Methods
  12. 12. 12 Measurement campaign and dataset (n = 126) Pollutants measured Measurement device Year and time interval for final dataset Locations and purpose of measurements ESCAPE diffusion tubes (n=41) NO2 and NOx Ogawa badges 2009 (annualized) At the façade of homes of study subjects as the primary objective of the ESCAPE project was to characterize residential exposures and associated health CBMDC diffusion tubes (n=29) NO2 “Diffusion tubes” 2009 (annualized) Three sites were not close to main road whilst the rest were kerbside sites at 0.5-5m from the nearest road, monitoring undertaken to review and assess air quality progress de Hoogh diffusion tubes (n=48) NO2 Palmes tubes Four 2-week periods during 2007-2008 Close to the front door of 48 homes of study subjects from the Born in Bradford cohort to characterize their residential exposures and compare with future ESCAPE work CBMDC fixed-site monitoring (n=8) NO2 Automatic urban network chemiluminescence 2009 (annualized) Two sites were classified as urban background whilst the rest were kerbside sites at 1.5-2 m from the nearest road, monitoring undertaken to review and assess air quality progress Methods
  13. 13. 13 Results: validation against different datasets Models combination Validation dataset ESCAPE NOx diffusion tubes (n=41) ESCAPE NO2 diffusion tubes (n=41) CBMDC NO2 diffusion tubes (n=29) De Hoogh NO2 diffusion tubes (n=48) CBMDC NO2 fixed-site monitoring (n=8) ADmodel COPERT dispersion model NOx at points (varying background) R2 = 0.30 COPERT dispersion model NO2 at points (varying background) R2 = 0.33 R2 = 0.20 R2 = 0.59 R2 = 0.24 LURmodel NOx LUR estimates at points R2 = 0.58 NO2 LUR estimates at points R2 = 0.54 R2 = 0.21 R2 = 0.61 R2 = 0.38 (r= 0.62)
  14. 14. 14 AD vs. LUR annual average NO2/NOx Estimates (µg/m3) at  46,452 specified output points centering each 100m x 100m grid across 40 * 33 km Results: spatial resolution of estimates
  15. 15. 15 AD vs. LUR Modeling Model and statistic COPERT-based dispersion model (traffic) LUR model Minimum 10.03 (0.40) 0.00064 1st quartile 15.93 22.00 Median 17.80 (0.78) 24.93 Mean 19.47 (2.45) 24.91 3rd quartile 20.34 32.10 Maximum 110.15 (93.14) 95.18
  16. 16. 16 Results: spatial resolution of estimates Validation dataset ESCAPE NOx diffusion tubes (n=41) ESCAPE NO2 diffusion tubes (n=41) CBMDC NO2 diffusion tubes (n=29) de Hoogh NO2 diffusion tubes (n=48) CBMDC NO2 fixed-site monitorin g (n=8) LURmodels NOx LUR estimates at points R2= 0.58 NOx LUR estimates at raster R2= 0.35 NO2 LUR estimates at points R2= 0.54 R2= 0.21 R2= 0.61 R2= 0.38 (r= 0.62) NO2 LUR estimates at raster R2= 0.31 R2= 0.06 R2= 0.32 R2= 0.38 (r=- 0.61) -23%
  17. 17. 17 Results: spatial resolution of estimates Validation dataset ESCAPE NOx diffusion tubes (n=41) ESCAPE NO2 diffusion tubes (n=41) CBMDC NO2 diffusion tubes (n=29) de Hoogh NO2 diffusion tubes (n=48) CBMDC NO2 fixed-site monitorin g (n=8) LURmodels NOx LUR estimates at points R2= 0.58 NOx LUR estimates at raster R2= 0.35 NO2 LUR estimates at points R2= 0.54 R2= 0.21 R2= 0.61 R2= 0.38 (r= 0.62) NO2 LUR estimates at raster R2= 0.31 R2= 0.06 R2= 0.32 R2= 0.38 (r=- 0.61) -23% -15% -29%
  18. 18. 18 • LUR and AD model estimates were validated against four different validation datasets Summary and discussion
  19. 19. 19 • LUR and AD model estimates were validated against four different validation datasets • LUR and AD model estimates were made at different spatial resolution and validated against four different validation datasets Summary and discussion
  20. 20. 20 • LUR and AD model estimates were validated against four different validation datasets • LUR and AD model estimates were made at different spatial resolution and validated against four different validation datasets • The validation metrics varied substantially (R2 0.20 – 0.61) based on • which model was used • which validation dataset was used • whether exposure estimates were made at exact validation point or at centroid of containing grid Summary and discussion
  21. 21. 21 • LUR and AD model estimates were validated against four different validation datasets • LUR and AD model estimates were made at different spatial resolution and validated against four different validation datasets • The validation metrics varied substantially (R2 0.20 – 0.61) based on • which model was used • which validation dataset was used • whether exposure estimates were made at exact validation point or at centroid of containing grid • The validation results based on the actual points’ locations were generally much better than at a grid level Summary and discussion
  22. 22. 22 • There is a value of validating modeled air quality data against various datasets Conclusions
  23. 23. 23 • There is a value of validating modeled air quality data against various datasets • The spatial resolution of the models’ estimates has a significant influence on the validity at the application point (even at 100m level) Conclusions
  24. 24. 24 • There is a value of validating modeled air quality data against various datasets • The spatial resolution of the models’ estimates has a significant influence on the validity at the application point (even at 100m level) • Have implications for epidemiological studies disregarding time-activity patterns or using location proxies Conclusions
  25. 25. 25 • There is a value of validating modeled air quality data against various datasets • The spatial resolution of the models’ estimates has a significant influence on the validity at the application point (even at 100m level) • Have implications for epidemiological studies disregarding time-activity patterns or using location proxies • Have implications for health impact assessment studies where estimates of air quality models at select receptor points are extrapolated and assumed to apply to larger areas and populations Conclusions
  26. 26. 26 • There is a value of validating modeled air quality data against various datasets • The spatial resolution of the models’ estimates has a significant influence on the validity at the application point (even at 100m level) • Have implications for epidemiological studies disregarding time-activity patterns or using location proxies • Have implications for health impact assessment studies where estimates of air quality models at select receptor points are extrapolated and assumed to apply to larger areas and populations • Can improve understanding of the most influential uncertainties/errors across full-chain health impact assessment Conclusions
  27. 27. 27
  28. 28. 28 Thank you! Haneen Khreis H-khreis@tti.tamu.edu Haneen.khreis@isglobal.org

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