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1
Center for Advancing Research
in Transportation Emissions,
Energy and Health
U.S. Department of Transportation Universit...
2
“The Impact of
Different
Validation
Datasets on Air
Quality Modeling
Performance”
3
•Not possible to make sufficient air pollution exposure
measurements for epidemiological and health impact
assessments ...
4
LUR modeling
• Repeated measurements
(passive samplers) at N
sites
• Average measurements
over longer term and
adjust fo...
5
LUR modeling
• BUILDINGS  Local land use, Area/number of buildings, m2/N(umber)
• TRAFLOAD  Local road network, Total ...
6
AD modeling
7
•Models are only validated using one validation dataset
•Their estimates at select receptor points are
sometimes general...
8
•Objective 1  explore the effect of different
validation datasets on the validation results of two
commonly used air qu...
9
Study area
10
Annual (2009) NO2 and NOx
LUR model
Validation against 4
different datasets
Spatial resolution
analysis
Estimate at exa...
11
Measurement
campaign and
dataset (n = 126)
Pollutants
measured Measurement device
Year and time
interval for final
data...
12
Annual (2009) NO2 and NOx
LUR model
Validation against 4
different datasets
Spatial resolution
analysis
Estimate at exa...
13
Results: validation against different datasets
Models combination Validation dataset
ESCAPE
NOx
diffusion
tubes
(n=41)
...
14
AD vs. LUR annual average
NO2/NOx Estimates (µg/m3) at
 46,452 specified output
points centering each 100m x
100m grid...
15
Results: spatial resolution of estimates
Validation dataset
ESCAPE NOx
diffusion tubes
(n=41)
ESCAPE NO2
diffusion tube...
16
Results: spatial resolution of estimates
Validation dataset
ESCAPE NOx
diffusion tubes
(n=41)
ESCAPE NO2
diffusion tube...
17
• LUR and AD models validated against four different datasets
Summary and discussion
18
• LUR and AD models validated against four different datasets
• LUR and AD model estimates made at different spatial re...
19
• LUR and AD models validated against four different datasets
• LUR and AD model estimates made at different spatial re...
20
• LUR and AD models validated against four different datasets
• LUR and AD model estimates made at different spatial re...
21
• There is value of validating modeled air quality data against various
datasets
Conclusions
22
• There is value of validating modeled air quality data against various
datasets
• The spatial resolution of the models...
23
• There is value of validating modeled air quality data against various
datasets
• The spatial resolution of the models...
24
• There is value of validating modeled air quality data against various
datasets
• The spatial resolution of the models...
25
• There is value of validating modeled air quality data against various
datasets
• The spatial resolution of the models...
26
27
Thank you!
Haneen Khreis
H-khreis@tti.tamu.edu
@HaneenKhreis
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How do air quality models perform with different validation datasets and different spatial resolution setups?

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This paper explores the performance of two commonly used air quality models: dispersion models and land-use regression models. Both models are widely used in air pollution epidemiological studies and in health impact assessment studies. In this work, we looked at how the choice of the validation dataset impacts the performance of air quality models and the insights gleaned from their validation. We also looked at whether the spatial resolution for the models' setup impacts the performance of air quality models and the insights gleaned from their validation. We saw that R-squared almost halved when the air quality models' estimates were made at the centroid of the 100x100m grid in which the validation point fell, instead of at the exact location of the validation point. We also saw that the different validation datasets give very different insights.

Dispersion models and land-use regression models are widely used in air pollution epidemiological studies and in health impact assessment studies. As such the performance of these air quality models have implications on the ability of epidemiological studies to pick up associations between the exposures and the health outcomes of interest, and the ability of health impact assessment studies to quantify the impacts accurately. This work demonstrated the value of validating modeled air quality data against various datasets to obtain a better understanding of the performance of models and the value of reporting these validation results. Also, the work suggested that the spatial resolution of the models’ estimates has a significant influence on the validity at the application point. These results should be considered when air quality models are used to assign human exposures and study the health effects/impacts of these exposures. Significant work is still needed to improve the performance of air quality models and their ability to pick up the variations of air pollution levels and especially the higher and more variable levels that are related to traffic. Significant work is also still needed to account for the factors that underlie this variation in epidemiological and health impact assessment studies, especially time activity patterns of the exposed populations

Publié dans : Environnement
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How do air quality models perform with different validation datasets and different spatial resolution setups?

  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”
  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) • Atmospheric dispersion (AD) Background
  4. 4. 4 LUR modeling • Repeated measurements (passive samplers) at N sites • Average measurements over longer term and adjust for temporal variations • Regression model to combine measurements with GIS-based predictors • 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 •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 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
  12. 12. 12 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
  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 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%
  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% -15% -29%
  17. 17. 17 • LUR and AD models validated against four different datasets Summary and discussion
  18. 18. 18 • LUR and AD models validated against four different datasets • LUR and AD model estimates made at different spatial resolution and validated against four different datasets Summary and discussion
  19. 19. 19 • LUR and AD models validated against four different datasets • LUR and AD model estimates made at different spatial resolution and validated against four different 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
  20. 20. 20 • LUR and AD models validated against four different datasets • LUR and AD model estimates made at different spatial resolution and validated against four different 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 • Validation results based on actual points’ locations were generally much better than at a grid level Summary and discussion
  21. 21. 21 • There is value of validating modeled air quality data against various datasets Conclusions
  22. 22. 22 • There is 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
  23. 23. 23 • There is 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
  24. 24. 24 • There is 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 assessments where estimates of air quality models at select points are extrapolated to larger areas/populations Conclusions
  25. 25. 25 • There is 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 assessments where estimates of air quality models at select points are extrapolated to larger areas/populations • Can improve understanding of the most influential uncertainties/errors across full-chain health impact assessment Conclusions
  26. 26. 26
  27. 27. 27 Thank you! Haneen Khreis H-khreis@tti.tamu.edu @HaneenKhreis

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