1. Dr Diane Keogh Independent Air Quality Scientist Sept 2017
West Gate Tunnel Project
Air Quality Impact Assessment
Dr Diane Keogh Independent Air Quality Scientist Sept 2017

2. Presentation Overview
Health effects
Buffer distances
Scientifically-based particle emission factors
Air Quality Impact Assessment
Particle emission factors used
Ultrafine particles
Omissions in AQIA
Recommendations

3. Principles underpinning presentation
People & diesel don’t mix
Emission factors should be scientifically-based
Technologies for mitigation & filtration systems available to provide clean air in 2017
Projects and/or components of projects contributing to breaching of air quality standards should not proceed

4. Human Respiratory System
World Health Organisation declared diesel exhaust a cancer-causing agent associated with an increased risk of lung cancer
(IARC, 2012).

5. Particle size determines where in the respiratory system particles they lodge
PM10
PM2.5
Ultrafine particles

6. High Polluting Australian Truck Fleet
By Age by Engine Emission Standard (above 4.5t GVM)
In % not Euro V compliant
30% manufactured when there were no emissions standards
30%
(ABS, 2014, as cited in Truck Industry Council, 2015)

7. High polluting truck fleet 2016
In 2016 for trucks registered in Victoria in
terms of vehicle kilometres travelled only:-
29% of rigid trucks (> 3.5t GVM) and
47% of articulated trucks
were manufactured in 2011 or later
(Euro V or higher compliant) (ABS, 2016).

8. Buffer/Separation Distances

9. Buffer Distances Background levels generally 300-500m from roadway
California planning legislation requires School Boards work with local agencies to identify any incompatible land uses within 400m of a proposed new school site
Incompatible land uses incl. freeways & busy traffic corridors, >50,000 vehicles per day in rural areas & >100,000 in urban areas; health assessments and other actions are necessary (Wickham, 2012)
Emma McLean Kindergarten is within 200m of West Gate Freeway which > 200,000 vehicles p.d. pre-Project

10. Buffer Distances Dangerous Goods Vehicles
California Dept of Education recommend distances for schools of at least 1,500 feet (approx. 457 metres) from roads transporting:
diesel, propane, gasoline, oxygen, chlorine, pesticides, or other poisonous gases or combustible gases are transported (USEPA, 2015).
The Hyde Street ramp will carry dangerous goods and will be next to Donald McLean & within 200m of the Emma McLean Kindergarten.

11. Scientifically-based particle emission factors

12. What is an emission factor?
Motor vehicles produce exhaust & non-exhaust
(brake and tyre wear) emissions
An emission factor is a value representing the amount of a pollutant emitted when an activity is performed:-
eg., how many grams of PM2.5 or PM10 different vehicle types emit for each kilometre driven
the number of ultrafine particles different vehicle types emit for each kilometre driven

13. Many methods used to derive emission factors
Measurements on/near
roads or in tunnels
Testing vehicles in labs
Basing on fuel consumption
Remote sensing methods

14. Quotes from the Air Quality Conclave West Gate Tunnel Project
“13. The representativeness of emission factors used in modelling determines the quality of modelling.
Agreed.”
“19. There are numerous, relevant, real world emission factors for PM2.5 and PM10 available in the international published literature which could have been used in the modelling.
This item was not agreed by Frank Fleer.”

15. Extensive international literature review
> 900 published particle emission factors
Examined 667 particle emission factors
One emission factor can represent a single vehicle, or the average for a group of vehicles or a vehicle fleet
Hence, these 667 represent a relatively very large sample of motor vehicles

16. Number of emission factors
Statistical models were used to derive average particle emission factors
Particle Metric
Number of emission factors
Explanatory variables,
at a 95% confidence level
Co-efficient of Determination
(R2 model value)
Particle Number (ultrafine particles)
156
Vehicle Type
Instrumentation
0.86
Total Particle Mass
199
No explanatory variables identified
Null model
Particle Volume 57
Vehicle Type, Size range measured, Speed Limit on the Road (<=60 and >60 km/hr)
0.93
PM1 44
Fuel Type
0.87
PM2.5 85
0.65
PM10
126
Road Type
0.47
Total Sample Size
667
(Keogh et al., 2010)

17. 16 Model variables identified
Particle Metric
Size Range Measured
Instrumentation and Method
Vehicle type - LDV, HDV, Bus, Fleet
Fuel Types
Drive Cycles
Average Vehicle Speed
Engine Power

18. 16 Model variables cont’d
Country of Study
Study Location (tunnel, road, dyno)
Road Type
Speed Limit on the Road
Road Classes < 80 km/hr, ≥ 80 km/hr;
≤ 60 km/hr, > 60 km/hr
Average No Vehicles in Fleet per day
Heavy Duty Vehicle Share (%)
Number of HDVs in fleet

19. Average particle emission factors derived from the statistical models
Particle metric
Fleet
HDV
LDV
Bus
Particle number, 1014 particles per km
7.26 65
3.63
3.08
Particle volume, cubic cm per km
≤ 60 km/hr
0.07
0.93
0.03 --
> 60 km/hr
0.04
0.41
0.05
PM1 mg per km 36
287 16
PM2.5 mg per km 60
302 33
299
PM10 mg per km, for different Road Types:-
Boulevard
4815
454
4130
Urban
688
538
156
1089
Freeway
200
2500
285
Highway 66
840
141
Motorway 77
213 63
Rural Area 67
394 46
Tunnel
Dynamometer
306
1019 14
313
(Keogh et al., 2010)

20. An inventory of motor vehicle particle emissions in South-East Qld covering from ultrafine to PM10 found HDVs although only travelling 6% of total vehicle kilometres travelled in the region, contributed more than half the particle number (ultrafine particles) and PM1 emissions (Keogh et al., 2009)

21. Air Quality Impact Assessment (AQIA)
The AQIA modelling used the SAME PIARC-derived emission factors to model particle emissions:-
a) at ventilation stacks and
b) on surface roads
These are different environments that generate different quantities of particle emissions.
“The in-tunnel fleet mix differs substantially from the average
on-road fleet, leading to lower emissions by a factor of about 2” (Smit et al., 2017).
PIARC emission factors are not suitable for modelling surface road emissions.

22. PIARC Particle Emission Factors
Rafael López Guarga, Chairman, The Spanish National Committee, established in 1934 & one of the oldest PIARC National Committees, whose members were involved in the working group that prepared the PIARC (2012) report used by the AQIA to source emission factors provided the following advice by
Rafael López Guarga, Chief Engineer of the Demarcation of State Highways in Aragon Ministry of Development, Zaragoza , Spain, Telf.: / Fax:

23. PIARC Particle Emission Factors
 Rafael López Guarga, Chairman, The Spanish National Committee, advised in correspondence:-
“Please note that the PIARC reports are usually prepared by specialists from many countries which join in working groups, discuss the shared information and propose the best practice criteria or guidance. However, the information used is not shared and, in many cases, is not publicly available . “
“… please note that the emission factors provided in these reports do not fit to the standard emission factors obtained for the typical motor vehicles tests, as the driving cycles have been specifically developed to represent in-tunnel driving behaviour.”

24. Methods employed to ‘estimate’ PM2
Methods employed to ‘estimate’ PM2.5 and PM10 emission factors used in the AQIA modelling

25. PM2.5 emission factors PCP = petrol passenger cars PCD = passenger diesel cars
“PCP PM2.5 emission factors were derived from the PIARC PCD PM2.5 emission factors, scaled by the ratio of PCP to PCD PM2.5 emission factors contained in the National Pollutant Inventory (NPI) Emission Estimation Technique Manual for Combustion Engines Version 3.0, June 2008 (NPI 2008)” (Golders, 2017).
PIARC do not publish emission factors for PM2.5 exhaust emissions
The AQIA ‘derived’ these by converting Opacity factors (visibility percentages) in PIARC (2012) to PM2.5, using a conversion value, scaled by a ratio in NPI (2008). The conversion & ratio values used were not disclosed in AQIA report.
PM2.5 non-exhaust emission factors were sourced from PIARC (2012)
These surrogate values for PM2.5 exhaust emissions have not been derived from rigorous scientific measurement studies of real world motor vehicle emissions.

26. Table 1 Comparison of PM2.5 emission factors in the West Gate Tunnel AQIA & those derived from a statistical analysis of published emission factors (n=85)
PM2.5 : mg/vkt 
Vehicle class
AQIA
Exhaust only
0% grade
80 kph
Exhaust & Non-Exhaust
0% grade, 80 kph
Statistical analysis
Keogh et al., 2010
Sample size = 85
Model fit R2 = 0.65
Petrol passenger car
0.78
28.8
LDV <= 5 t 33
LCV petrol/diesel
23.3
51.3
HCV
105
209
HDV >= 3.5 t
302

27. PM10 emission factors in AQIA
“PM10 emission factors were derived from the sum of PIARC PM2.5 exhaust
and non-exhaust emission factors, multiplied by a factor of approximately
1.8. This factor was based on in-stack emission data for an existing
Australian road tunnel” (Golders, 2017).
Derivation of Surrogate PM10 emission factors
= Surrogate PM2.5 exhaust + PIARC non-exhaust PM2.5 emission
x 1.8
This is not a valid method to derive PM10 emission factors. These surrogate values for PM10 have not been derived from rigorous scientific measurement studies of real world motor vehicle emissions and substantially underestimate PM10 emissions

28. Table 2 AQIA LDV & HDV PM10 emission factors are SUBSTANTIALLY
LOWER than those derived from statistical analysis (n-126)
PM10 : mg/vkt
Vehicle class
AQIA
Exhaust only
0% grade,
80 kph
Exhaust & Non-Exhaust
0% grade, 80 kph
Statistical analysis Keogh et al., 2010
Sample size = 126
Model fit R2 = 0.47
Road Type
Petrol passenger car
0.84
50.4
LDV <= 5 t
156
Urban < 80 kph
LCV petrol/diesel
24.5
89.8
141
Urban Major
>= 80 kph
HCV
112
366
HDV >= 3.5 t
538
840

29. Comparison of particle emission factors
IMPORTANT NOTE:
These surrogate PM10 emission factors are not scientifically robust & do not compare well with those derived from statistical analysis of 126 published emissions factors
Their use in the modelling of the AQIA, in my opinion, will have very substantially underestimated PM10 emission levels

30. Omissions in the AQIA modelling

31. Surface roads (exhaust & non-exhaust) Where is the remodelling that includes non-exhaust emissions for the other 10 roads, ramps, 2022 & scenarios & also Annual Averages?
(Fleer, Golder Associates, 2017, Presentation, p. 22)

32. Surface roads – exhaust only Where is the remodelling that includes non-exhaust emissions for these roads and the new Annual Averages?
(Fleer, Golder Associates, 2017, p. 21)

33. Missing from the AQIA
Very little detail on method used to ‘derive’ surrogate PM2.5 & PM10 emission factors (2 paras), does not clearly state equations, visibility conversion factor & NPI ratio values
Do not report which specific values of ‘derived’ emission factors, in terms of road grade, used for each road & ventilation stacks
Where brake & tyre wear emissions in modelling have been included, their values & source are not reported
No reason why 2010 & not 2016 Victoria fleet used
No comparisons with historical measurement data

34. Important Modelling NOT presented in the AQIA
Brake & tyre wear not modelled for 10 roads, ramps, 2022, scenarios & Annual Averages, could increase emissions by 26%
PM10 emission factors severely underestimated , require investigation & assessment using a literature review, derivation method not scientifically robust
Real world emission factors, not PIARC-’derived’ tunnel emission factors, should be used to remodel surface roads and ramps
Ramp emissions appear not to have been counted in assessing Project impact, which was ventilation + background emissions
An inventory of ultrafine particles pre-Project would be useful

35. Health risks at Spotswood
Due to the combined effect of:-
200,000 + vehicles using the West Gate Freeway
+ another 37,000 vehicles the Project will add
+ more PM emissions from diesel truck fleet using
the Hyde Street ramp and
+ the already current exceedances of PM hour
average measured at the Donald McLean Reserve
These pose a SIGNIFICANT HEALTH RISK to residents, Reserve users & children 3-5 years at Emma McLean Kindergarten, who spend up to 10 hours per day outside

36. Project Health Risks
The Hyde Street truck ramp is TOO close to sensitive populations & Donald McLean is already polluted.
People exercising at Donald McLean will be subject to greater health risks due to their higher respiration rates
Millers Road will have additional 7000 trucks passing by residential houses with no buffer distance

37. Ultrafine particles
Most vehicle particles ultrafine size, with known health effects
At least an inventory should be done pre-Project
Standards not currently available, but efforts are underway in Europe to introduce ambient ultrafine particle standards
Euro VI/6 vehicle standards prescribe particle number (ultrafine particle) limits:-
6 x particle number/km for LDVs (diesel & petrol)
6 or 8 x 1011 particle number/kilowatt hour (dep. drive cycle)
for HDVs (diesel) (ICCT, 2016)
Corporate social responsibility & precautionary principle to monitor ultrafines, consider the time it took for the
‘leaded petrol’ case

38. Ultrafine particle inventory
Simple approach can be taken for an inventory
Emission factor for that class of vehicle x number of those vehicles travelling on the road x length of road in kilometres, sum all calculations.
Road tunnels maximise exposure to dangerous ultrafine particles and other pollutants necessitating removal of particles.
Measurements in the M5 East West Tunnel found ultrafine particle concentrations can reach 1000 times higher than those found in urban areas (QUT, 2009).

39. Question on Project Impact

40. Golder Associates - Technical Memorandum – Response to West Gate Tunnel Project Inquiry and Advisory Committee Interim Advice - 31/8/2017, p. 5
“19. Section 5.2 “….Hyde St off ramps….There is no assessment of the impact of emissions from the off ramps in the EES or Technical Report G”. “Section 5.2(i) “There is no data presented in the AQIA on the impact of increased traffic on local streets being used as feeder roads to the freeway or for the impact of off-ramps on sensitive receptors”.
Response: “As shown in Table 55 of Technical Report G a number of on and off ramps were included in the surface roads modelling assessment, including the Hyde Street on and off ramps, with the impact on adjacent residential properties assessed. “

42. Project Impact = Ventilation stack emissions
+ Background Emissions
If the Hyde Street and other ramps emissions modelling was included in the surface road modelling, were the Hyde Street and other ramp emissions excluded from the Project Impact total above?

43. Modelling Recommendations
An extensive literature review and evaluation of the PM2.5 and PM10 PIARC-derived emission factors used in the modelling is needed to assess their validity
If found unsuitable, ALL relevant scenarios be remodelled with suitable emission factors
Exhaust and non-exhaust modelling should be done for ALL roads and ALL ramps
An ultrafine inventory be prepared

44. AQIA Report Recommendations
State which specific PIARC-derived emission factor values, according to road grade, were used for ventilation stacks and each individual road modelled
List brake & tyre wear emission factors & source
Explain reasons why eg., background data was used eg., as later periods had missing values; why vehicle fleet not 2016 was used, etc.
Should report remodelled scenarios that include brake and tyre wear emissions for all scenarios, including Annual Averages

45. Recommendations for existing EPRs
LPP1 Minimise design footprint
Don’t build this Hyde St ramp at Spotswood unless it is a fully enclosed structure with a filtration and full deluge system for spills and accidents, find another solution, build elsewhere, use another form eg., tubes, underground, undersea, new design etc

46. Recommendations for existing EPRs
AQP4 Ambient Air Quality
AQP6 Air Quality during construction
BP4 Impacts on operation of Council facilities
LVP4 Vegetation screening
If Hyde Street ramp goes ahead, plant suitable vegetation and relevant fixed barriers at Donald McLean Reserve and Emma McLean Kindergarten
Conduct a full air quality assessment at Emma McLean and a health and air quality study with respiratory physicians to monitor children’s health
Establish anti-idling policy for trucks

47. Recommendations for existing EPRs
AQP4 Ambient Air Quality
TP2 Traffic monitoring
Permanent particulate matter monitoring on busy roads, near sensitive sites eg., Donald McLean Reserve, Emma McLean Kindergarten, and truck routes, eg Millers Rd
Measurement campaigns for ultrafine particles and remote sensing to identify high polluters

48. Recommendations for existing EPRs
AQP2 Zero portal emissions
Install filtration systems in tunnels & bikeways, winding up windows insufficient mitigation
LPP2 Recreation Facilities
LPP1 Minimise design footprint
Review separation distances for sensitive populations as per USA guidelines

49. Recommendations for existing EPRs
AQP4 Ambient Air Quality
BP5 Business Involvement Plan
Clearly communicate to the public the ACTUAL mitigation activities planned, make AQ monitoring data publicly available in a timely manner

50. Recommendations IAC and to Minister
Financial penalties for high polluters
Adoption of Euro VI/6 Standards

51. References
Australian Bureau of Statistics (ABS). (2016) Survey of Motor Vehicle Use, Australia, 12 months ended 30 June Data cubes. Retrieved from
Fleer, F. (2017). West Gate Tunnel Project IAC, Air Quality Impact Assessment, Golders Associates [Presentation], Retrieved from
Golder Associates Pty. Ltd. (2017). West Gate Tunnel Project IAC Air Quality Expert Conclave Statement. Report Number R- Rev0, August. Retrieved from _Air_Quality_dated_August_2017.pdf
Golder Associates Pty. Ltd. (Golders). (2017). West Gate Tunnel Project. Technical Report G- Air Quality Impact Assessment Report. Report No R-Rev0. Retrieved from
International Agency for Research on Cancer (IARC) WHO. (2012). IARC: Diesel engine exhaust carcinogenic (Press release No. 213 ed.).
International Council on Clean Transportation (ICCT). (2016). Briefing: A technical summary of Euro 6/VI vehicle emission standards. Retrieved from
Keogh, D.U., Ferreira, L.  & Morawska, L. (2009) . Development of a particle number and particle mass vehicle emissions inventory for an urban fleet. Environmental Modelling & Software, 24(11), Retrieved from
Keogh, D.U., Kelly, J., Mengersen, K.L., Jayaratne, R., Ferreira, L. & Morawska, L. (2010). Derivation of motor vehicle tailpipe particle emission factors  suitable for modelling urban fleet emissions and air quality assessments. Environmental Science and Pollution Research, 17(3),  Retrieved from
QUT. (2009). Tunnels concentrate air pollution by up to 1000 times. Retrieved from
Smit, R., Kingston, P., Wainwright, D. H. & Tooker, R. (2017). A tunnel study to validate motor vehicle emission prediction software in Australia. Atmospheric Environment 151,
Truck Industry Council. (2015). Fleet report Retrieved from industrycouncil.org/res/file/TIC%20Fleet%20report%202015%20screen%20view%20-%20A3%20format.pdf 
United States Environmental Protection Agency (USEPA). (2015). Best practices for reducing near road pollution exposure at schools. Retrieved from
Wickham, L. (2012). Separation distances for roads, a discussion document prepared for Auckland Council. Retrieved from

52. Thank you.