Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Wctr2016 innovation grantpaper_its_webpage

42 views

Published on

Critical Issues in Estimating Human Exposure to Traffic-related Air Pollution: Advancing the Assessment of Road Vehicle Emissions Estimates

Published in: Environment
  • Be the first to comment

  • Be the first to like this

Wctr2016 innovation grantpaper_its_webpage

  1. 1. Institute for Transport Studies FACULTY OF ENVIRONMENT Critical Issues in Estimating Human Exposure to Traffic-related Air Pollution: Advancing the Assessment of Road Vehicle Emissions Estimates Haneen Khreis @WCTR 2016 Shanghai, 10-15 July 2016
  2. 2. Acknowledgements James Tate Karen Lucas Tony May Mark Nieuwenhuijsen WCTRS Ph.D. Innovation Grant
  3. 3. o Road traffic is a dominant source of urban air pollution o Historic & current trends suggest ongoing increases in vehicle fleet and number of people exposed to traffic- related air pollution (TRAP) o Traffic-related emissions and air pollution difficult to quantify and control Background
  4. 4. Increased awareness of the adverse health effects associated with TRAP o All-cause and cardiovascular mortality, cardiovascular morbidity, respiratory morbidity, lung function, COPD, birth outcomes, cancer, cognitive and psychomotor development, congenital anomalies, fertility rates, obesity, diabetes Background
  5. 5. Some open questions o Which (multi-)pollutants? o Which vehicle fleet (fuels, emission standards, vehicle classes)? o Where to intervene? o What is the accuracy and precision of the health effect estimates? o Are vehicle emission standards adequate? o Are air quality guidelines adequate?
  6. 6. Open questions Compliance, effectiveness Atmospheric transport, chemical transformation, and deposition Human time-activity in relation to indoor and outdoor air quality; Uptake, deposition, clearance, retention Susceptibility factors; mechanisms of damage and repair, health outcomes Regulatory action Traffic emissions Traffic air pollution Exposure/ dose Human healthHEI, 2003 At the mercy of the emission inputs/ factors
  7. 7. Current emission estimation methodology o Vehicle emissions (g/km), for a certain pollutant and a vehicle type, are functions of average speed over a trip E= f ( ҧ𝑣) o In Europe, functions are sourced from COPERT  functions unreliable, especially at lower average speeds A typical range of the variability of individual measurements for emission factors for gasoline passenger cars of Euro 3 technology
  8. 8. New emission estimation methodology o Develop a new set of average-speed emission functions  transparent and replicable o Better account for real-world driving conditions e.g. urban stop-start driving/ congestions (where emissions are underestimated) o Explore real-world driving conditions effects on emissions  road gradient o Compare the new functions with the current/ standard emission estimation methodology and highlight differences o Apply both methodologies to the traffic air pollution  exposure/dose  human health effects (childhood asthma) chain Regulatory action Traffic emissions Exposure/ dose Human health Traffic air pollution
  9. 9. Study area
  10. 10. Test routes
  11. 11. Logging real-world driving +
  12. 12. Emission estimation Emissionstandard(Pre-EurotoEuro5) Roadgradient–separateruns Second-by-second emission estimates for each simulated vehicle Gradient = 0 Gradient = X
  13. 13. Average-speed emission functions development Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km Average emissions g/km
  14. 14. Results
  15. 15. Results Statistic Micro-trip distance (km) Micro-trip time (s) Micro-trip average speed (km/h) Minimum 0.001389 2.00 0.2857 1st quartile 0.034583 29.00 4.3604 Median 0.157222 59.00 11.7600 Mean 0.467699 77.95 15.0534 3rd quartile 0.590556 98.00 24.6097 Maximum 12.694220 919.00 78.9278 33 hours of driving  1406 micro-trips
  16. 16. Results o For each vehicle class, fuel type and Euro emission standard, (micro-trip) average-speed emission function was developed o R2 ranging from 0.54 to 0.98 o Low relationships are a product of the inability of the statistical models to address large scatter o Including road grade in model estimates highly increases the scatter and decreases R2
  17. 17. Gradient vs non-gradient scenarios
  18. 18. Comparison with COPERT Passenger car – diesel pre-Euro Passenger car – diesel Euro 1 Passenger car – diesel Euro 2 Passenger car – diesel Euro 3 Passenger car – diesel Euro 4 Passenger car – diesel Euro 5 Diesel passenger cars (-1325%, 33%; DE5)
  19. 19. Comparison with COPERT Passenger car – petrol pre-Euro Passenger car – petrol Euro 1 Passenger car – petrol Euro 2 Passenger car – petrol Euro 3 Passenger car – petrol Euro 4 Passenger car – petrol Euro 5 Petrol passenger cars (-338%, 32%; DE5)
  20. 20. Comparison with COPERT SD Buses diesel pre-Euro SD Buses diesel Euro 1 SD Buses diesel Euro 2 SD Buses diesel Euro 3 SD Buses diesel Euro 4 SD Buses diesel Euro 5 Single Decker buses (-4287%, 39%; DE5 - SCR) 0 20 40 60 80 100 0 50 100 COPERT SD Buses EURO 0 New SD Buses EURO 0 0 20 40 60 80 100 0 50 100 COPERT SD Buses EURO 1 New SD Buses EURO 1 0 20 40 60 80 100 0 50 100 COPERT SD Buses EURO 2 New SD Buses EURO 2 0 20 40 60 80 100 0 50 100 COPERT SD Buses EURO 3 New SD Buses EURO 3 0 20 40 60 80 100 0 50 100 COPERT SD Buses EURO 4 New SD Buses EURO 4 0 20 40 60 80 100 0 50 100 COPERT Euro 5 SCR New Euro 5 SCR
  21. 21. Gradient vs non-gradient scenario
  22. 22. Summary • New average-speed emission functions developed tailored for Bradford driving and underpinned by: • High resolution real-world driving cycles ++ • Modelled emissions (PHEM; verified but a model) - • And a micro-trip averaging approach +? • Results confirm different emission estimates at lower average speeds • Implications to practice unclear  modelled journey times are consistently faster than observed, suggesting that congestion is under- represented in traffic models • To minimize errors in the road traffic emission inventory, a series of improvements regarding activity data should be implemented and vehicle class specific driving cycles should be obtained • Inaccurate emissions and TRAP estimates can bias exposure-response functions  downward bias?
  23. 23. Questions?

×