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Hk icth2016 14th_june2016_htw_website version

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Assessing the contribution of traffic to outdoor air pollution and health effects

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Hk icth2016 14th_june2016_htw_website version

  1. 1. AIR POLLUTION AND HEALTH EFFECTS: THE CONTRIBUTION OF TRAFFIC? The uncertainties in the full chain of traffic activity-fleet composition-vehicle emissions-air pollution dispersion- individual exposure- health effects Session: Jenga - The suspense builds as the stakes get higher Haneen Khreis, 2nd International Conference on Transport and Health, San Jose, 13-15 June, 2016
  2. 2. Estimating the Human Exposure to Traffic-Related Air Pollution ■ Monitoring stations and personal measurements (relatively reliable) ■ Monitors: locations based on regulatory, not scientific, purposes and are limited ■ Personal measurements: small timeframes/populations ■ Logistic and costs concerns
  3. 3. TRAP is highly heterogeneous ■ Experiment: walk across the designated route to the left (12 trips: 6 AM peak, 6 PM peaks) - stop at each circle (microenvironment) for 20 seconds, ultrafine particles measurement, device: handheld Condensation Particle Counter 3007
  4. 4. 0 50000 100000 150000 200000 250000 300000 16:38… 16:39… 16:40… 16:41… 16:41… 16:42… 16:43… 16:44… 16:45… 16:46… 16:47… 16:47… 16:48… 16:49… 16:50… 16:51… 16:52… 16:52… 16:53… 16:54… 16:55… 16:56… 16:57… 16:58… 16:58… 16:59… 17:00… 17:01… 17:02… 17:03… 17:04… 17:04… 17:05… 17:06… 17:07… 17:08… 17:09… 17:09… 17:10… 17:11… 17:12… 17:13… 17:14… 17:15… 17:15… 17:16… 17:17… 17:18… 17:19… 17:20… 17:21… BackgroundSite BackgroundSite IntersectionCorner BusStop RoadCrossing BusStop IntersectionCorner RoadCrossing IntersectionCorner IntersectionCorner RoadCrossing
  5. 5. Estimating the Human Exposure to Traffic-Related Air Pollution ■ Surrogates for Human Exposure – Proximity to ‘major roads’. – Vehicles counts (& composition). – Land Use Regression models. – Geostatistical interpolation (GIS). – Hybrid models (mixture, activity diaries, home monitors, indices) (2). – And dispersion models…….
  6. 6. Dispersion modelling ■ Estimate levels of TRAP exposures used in health effects analysis most commonly come from routine air quality monitoring stations and land-use regression models ■ These are assumed to be traffic-related pollutants ■ There are only a few studies using air pollution dispersion modelling in the literature, perhaps due to relatively severe data demands and the laborious process associated with the modelling ■ When using dispersion modelling, evaluating the health effects of TRAP exposures becomes a part of a broad effort which starts with the assessment of source (vehicle) activity and associated emissions
  7. 7. Air pollution: the contribution of traffic? 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 Transport policy Emissions Ambient air quality Exposure/ dose Human healthHEI, 2003
  8. 8. Step 1 ■ Step 1: Determine traffic activity using a traffic assignment model to provide, for each road in the network, data on traffic flow and speed and vehicle kilometres driven (source activity rate) Transport policy Emissions Ambient air quality Exposure/ dose Human healthHEI, 2003 Step 1
  9. 9. Step 1 1. A road network (supply); The road network specifies the physical structure of the roads upon which trips occur 2. A trip matrix (demand); The trip matrix specifies the number of trips from zone i to zone j for all zones modelled in the network ■  Trips are then allocated to routes based on the total flows along links in the network and corresponding network costs (e.g. times or average generalized cost) calculated ■ The simulation model then takes the route allocated traffic and creates another more accurate cost-flow relationships by working out the delays that will occur at the junctions due to these assigned flows ■ The assignment and simulation loops run iteratively, until an equilibrium point is reached at which the costs (e.g., times) are optimised
  10. 10. Step 1
  11. 11. Step 1 ■ Outputs – Traffic average speed (AM peak hour, Inter-peak hour, PM peak hour) – Traffic flow (passenger car units/hour) ■ Acceptance criteria (in comparison to real life): ■ Traffic flow: G𝐸𝐻 = 2(𝑀−𝐶)2 𝑀+𝐶 – GEH of less than ‘5.0’ is considered a good match between the modelled and observed hourly volumes, and 85% of the volumes in a traffic model should achieve this criteria (240 links) ■ Traffic speed (52 journeys) – Average difference between modelled and observed journey times within 15% for all time periods  Modelled journey times are consistently faster than observed, in all time periods, suggesting that congestion is under-represented in the models Time period Test 1 2 3 4 5 AM 40% 77% 40% 88% 73% IP 46% 89% 46% 95% 78% PM 42% 73% 42% 82% 63%
  12. 12. Conclusions ■ SATURN is a static equilibrium model, intended to provide long-run estimates of vehicle flow, rather than a snapshot of what happened on a particular day ■ Does not consider the effect of acceleration and deceleration at junctions, and has a uniform emission through a junction based on average speed ■ Validation is difficult because the cost of obtaining network wide, long-run observed counts is prohibitive ■ And because there is usually significant uncertainty in the observed count data ■ Validation is conducted at the project scale ■ Sensitivity testing on the model show that the model is remarkably stable at the aggregate level, but less so at the link level
  13. 13. Step 2 ■ Step 2: Determine vehicle proportions on the road in preparation for emission factoring Transport policy Emissions Ambient air quality Exposure/ dose Human healthHEI, 2003 Step 2
  14. 14. Step 2 ■ Typical sources of this information – Automatic number plate recognition (limited in place and time, date of registration usually used to determine emission standards) – Local data collected by the council (counters, limited in place and time, no emission standard available) – National data collected at counter points and complied with local data (same limitations as local data) – Solicited projects (limited in space and time, can be biased due to measurement technique) – National atmospheric emission inventories national average (National data and projections)
  15. 15. Step 3 ■ Step 3: Using/developing an emission model that applies emission factors for the vehicle activity rate, to calculate pollutant loads emitted by vehicles Transport policy Emissions Ambient air quality Exposure/ dose Human healthHEI, 2003 Step 3
  16. 16. Step 3 Current/standard approach  Average-speed emission functions sourced from the “Computer Programme to Calculate Emissions from Road Transport”, referred to as COPERT 4  COPERT 4 (v10.0) is a model developed and coordinated by the European Commission's Joint Research Centre and the European Environment Agency  COPERT used by most European Countries in the compilation of national emission inventories
  17. 17.  Passenger cars (diesel and petrol, 5 emission standards)  Light good vehicles (diesel and petrol, 5 emission standards, 3 weight classes)  Heavy good vehicles (diesel, 5 emission standards, 13 weight classes)  Buses (diesel, 6 emission standards/ technology, 3 weight classes)  Coaches (diesel, 6 emission standards/ technology, 2weight classes)
  18. 18. Step 3 Driving cycle NEDC (worked) WLTP class 3a (proposed) Bradford real-world driving cycle Duration (s) 1,220 (20.3 minutes) 1,800 (30 minutes) 117,471 (1958 minutes ≈33 hours) Distance (km) 11 23 657 % idling b time 26 13 31 % acceleration time 20 44 27 % deceleration time 15 42 25 % cruising c time 39 1 17 Average speeds with stops (km/h) 32.5 46.3 20.1 Average speeds without stops (km/h) 44.7 53.2 29.9 Maximum speed (km/h) 120 131 100 Maximum acceleration (m/s) 1.06 1.88 4.72 Maximum deceleration (m/s) -1.39 -1.52 -6.67 % time spent at 0-40 km/h 65 49 81 % time spent at >40-60 km/h 16 19 18 % time spent at > 60 km/h 19 32 1
  19. 19. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 10 20 30 40 50 60 70 80 COPERT Diesel Passenger Car EURO 4 New Diesel Passenger Car EURO 4
  20. 20. Emission standard Diesel passenger cars PHEM0 PHEMG Average (g/km) Range (g/km) Average (% change from PHEM0) Range (% change from PHEM0) Euro 0 488.5 165.4 - 2131.5 507.3 (3.84) 81.02 (-51.02) - 2159.4 (1.31) Euro 1 448.4 153.0 - 1932.7 464.6 (3.61) 74.89 (-51.05) - 1952.1 (1.00) Euro 2 417.8 138.0 - 1803.9 430.1 (2.94) 70.74 (-48.74) - 1825.5 (1.20) Euro 3 488.3 157.3 - 2087.5 501.5 (2.70) 92.9 (-40.94) - 2110.9 (1.12) Euro 4 499.5 155.3 - 2134.6 510.9 (2.28) 108.5 (-30.14) - 2132.2 (-0.11) Euro 5 381.2 145.9 - 1244.5 390.2 (2.35) 88.95 (-39.03) - 1258.9 (1.15) Euro 6 362.6 142.4 - 1062.5 371.8 (2.53) 90.31 (-36.58) - 1064.0 (0.14) Emission standard Petrol passenger cars PHEM0 PHEMG Average (g/km) Range (g/km) Average (% change from PHEM0) Range (% change from PHEM0) Euro 0 827.1 202.0 - 3470.4 842.2 (1.83) 176.4 (-12.67) - 3513.6 (1.24) Euro 1 613.8 168.3 - 2852.2 624.6 (1.76) 112.4 (-33.21) - 2885.7 (1.17) Euro 2 583.7 161.0 - 2725.4 592.3 (1.47) 110.3 (-31.49) - 2760.6 (1.29) Euro 3 579.3 161.8 - 2693.8 589.0 (1.67) 107.6 (-33.50) - 2726.5 (1.21) Euro 4 632.1 157.4 - 3032.0 640.8 (1.38) 122.4 (-22.24) - 3058.0 (0.86) Euro 5 381.6 141.1 - 1286.8 390.2 (2.25) 104.6 (-25.87) - 1285.4 (-0.11) Euro 6 330.7 129.5 - 959.9 339.5 (2.66) 95.5 (-26.25) - 979.6 (2.05) STEP 3
  21. 21. Conclusions ■ Current emission factors unreliable, especially at lower speeds ■ There is a lack of clarity about the underlying data? ■ Emission factors are based on limited observations, which differ from the activities to which they are applied ■ Using a small vehicle sample to represent a vehicle fleet of millions ■ Driving cycle not representative ■ Not considering all actual environmental conditions in emission estimation ■ Validation of urban emission models through comparison of observed–predicted values has never been done, as it is not feasible to monitor emissions from all vehicles travelling on a road ■ Neither is it possible to accurately quantify uncertainties in emissions due to the complex and varied nature of the data and calculations associated with emission estimation
  22. 22. Step 4 ■ Step 4: Using a dispersion model to simulate emissions dispersion across study area Transport policy Emissions Ambient air quality Exposure/ dose Human healthHEI, 2003 Step 4
  23. 23. STEP 4Combine mobile source emissions with stationary source emissions (from industries, etc.)
  24. 24. Step 4 Modelled and measured hourly concentrations of NOx between 12th and 16th November 1996 at Birmingham West
  25. 25. Step 4 Comparison of ADMS-Urban and Airviro predictions with wind speed
  26. 26. Conclusions ■ Inaccuracies in results of dispersion modelling arise due to: – Inherent variability caused by random turbulence – Accuracy and coverage of the meteorological and emission input data – The effect of complex surface features such as buildings – Not accounting for street canyon effects – Accuracy of background emission inventories – Unrealistic diurnal profiles – Spatial misalignment – and inherent model uncertainty (i.e., is the model formulation correct and appropriate to the required output?) ■ The way in which model errors are dealt with depends on the error type ■ Sometimes not dealt with/ validated ■ Systematic errors (bias), where the same error trend is apparent at all times, can be addressed by calibrating the model using a fudge factor ■ Random error (uncertainty) may still remain after model calibration and comparison of model  add an error band around the modelled mean in which the true value is thought to lie
  27. 27. Step 5 ■ Step 5: Assign/temporally resolve modelling outputs to addressed of interest Transport policy Emissions Ambient air quality Exposure/ dose Human healthHEI, 2003 Step 5
  28. 28. Step 5 – Home location Standard – Work/study location Sometimes – Commute Rarely – Elsewhere Rarely ■ E.g. (systematic review of 38 studies )  TRAP exposures almost exclusively assigned based on the participants’ residential addresses (35 studies – 3 looked at school and daycare centres) ■ Only 8 recent studies, 5 of which published after 2014, considered children’s mobility at ages when exposure at the residential address is less relevant and assigned time-weighted TRAP exposures at daycare-centres and schools alongside residence
  29. 29. Conclusions ■ Exposures at the home address not necessarily true exposure ■ Exposure variation may be masked ■ Limited knowledge on effects of TRAP peaks/short term exposures ■ Very difficult to disentangle effects of different exposure windows  well correlated
  30. 30. Step 6 ■ Step 6: Assess associations between exposures and health outcomes Transport policy Emissions Ambient air quality Exposure/ dose Human healthHEI, 2003 Step 6
  31. 31. Final conclusions ■ Sources of error – Traffic activity – Vehicle proportions – Vehicle emission standards – Emission factors (non-linear functions of traffic speed) – Spatial alignment of sources and addresses – Space-time resolution – Application and compatibility of models – Dispersion modelling configuration – Modelled vs true exposures – Reliability of health outcomes diagnosis
  32. 32. Recommendations ■ More effort into exhaustive exercises of the chain’s validation – at each step and overall ■ Communicating sources of error and implications to people using modelling outputs – epidemiologists and policy makers ■ There is a need of improving the accuracy of input at each step of the chain – explore data sets available and other options ■ Are these results good enough to be used in policy?

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