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SH20 Manukau Harbour Crossing Project – Assessment of Environmental Effects
Appendix 7 – Air Quality Assessment
SH20 Manukau Harbour Crossing Project
Appendix 7: Air Quality Assessment
Page i
Status: Final 19/05/2006
SH20 Manukau Harbour Crossing Project – Assessment of Environmental Effects
Appendix 7 – Air Quality Assessment
Assessment of the Effects on Air Quality for the SH20 Manukau Harbour
Crossing Project
A Report for Transit New Zealand.
By
The National Institute of Water and Atmospheric Research Ltd.
Authors:
Guy Coulson and Shanju Xie
Reviewed by:
Jeff Bluett and Neil Gimson
NIWA Report Number AKL-2006-021
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Status: Final 19/05/2006
SH20 Manukau Harbour Crossing Project – Assessment of Environmental Effects
Appendix 7 – Air Quality Assessment
Table of Contents
1 Introduction ................................................................................................................................ 1
1.1 Scope of this report ........................................................................................................... 1
1.2 National Environmental Standards.................................................................................... 2
2 Description of existing environmental conditions....................................................................... 4
2.1 Local Receiving Environment............................................................................................ 4
2.2 Current traffic numbers and predictions for 2021 .............................................................. 4
2.3 Air quality in Auckland ....................................................................................................... 7
2.4 Background concentrations............................................................................................. 10
2.5 Long term trends ............................................................................................................. 12
2.6 Air quality effects ............................................................................................................. 14
2.6.1 Local and community scale effects ............................................................................. 14
2.6.2 Regional and national scale effects............................................................................. 15
2.7 Pollution compounds due to traffic .................................................................................. 15
3 Assessment criteria: Choosing sections of roadway to model................................................. 17
4 Inputs for the model ................................................................................................................. 19
4.1 Meteorological data ......................................................................................................... 19
4.2 Predicted traffic flows ...................................................................................................... 19
4.3 Emission factors modelling.............................................................................................. 19
4.3.1 Fleet composition ........................................................................................................ 19
4.3.2 State of tuning ............................................................................................................. 20
4.3.3 Cold start ..................................................................................................................... 20
4.3.4 Congestion .................................................................................................................. 20
4.3.5 Gross emitters ............................................................................................................. 20
4.4 Emissions Model ............................................................................................................. 20
5 Modelling ................................................................................................................................. 22
5.1 Choice of model .............................................................................................................. 22
5.1.1 Modelling locations...................................................................................................... 22
5.2 Modelling using measured traffic data............................................................................. 23
5.2.1 Errors and uncertainty ................................................................................................. 23
5.2.2 Measured traffic flows ................................................................................................. 24
5.2.3 Emission factors .......................................................................................................... 24
5.3 2003 Modelling Results ................................................................................................... 24
5.3.1 Predicted maximum CO concentrations...................................................................... 25
5.3.2 Predicted maximum 1-hour average NO2 concentrations ........................................... 25
5.3.3 Predicted maximum PM10 concentrations ................................................................... 26
5.3.4 Comparison with measurements................................................................................. 26
6 Comparing the Effects of the Project and Do-Minimum Scenarios in 2021. ............................ 28
6.1 Predicted maximum CO concentrations.......................................................................... 28
6.1.1 For the Project in 2021 ................................................................................................ 28
6.1.2 For the Do-minimum scenario in 2021 ........................................................................ 28
6.1.3 Comparison between the Project and the Do-minimum scenario in 2021 .................. 29
6.2 Predicted NO2 concentrations ......................................................................................... 29
6.2.1 For the Project in 2021 ................................................................................................ 29
6.2.2 For the Do-minimum scenario in 2021 ........................................................................ 30
6.2.3 Comparison between the Project and the Do-minimum scenario in 2021 .................. 30
6.3 Predicted maximum PM10 concentrations ....................................................................... 31
6.3.1 For the Project in 2021 ................................................................................................ 31
6.3.2 For the Do-minimum scenario in 2021 ........................................................................ 32
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Appendix 7 – Air Quality Assessment
6.3.3 Comparison between the Project and the Do-minimum scenario in 2021 .................. 33
6.4 Summary of modelling results ......................................................................................... 33
7 Assessment of modelling results ............................................................................................. 34
7.1 Addition of Background values........................................................................................ 34
7.2 Initial Screening............................................................................................................... 34
7.2.1 CO ............................................................................................................................... 34
7.2.2 NO2 .............................................................................................................................. 35
7.2.3 PM10 ............................................................................................................................ 36
8 Conclusion ............................................................................................................................... 38
8.1 Errors and uncertainty ..................................................................................................... 38
9 References .............................................................................................................................. 39
Appendix 1: NO2 - NOx relationships .............................................................................................. 41
Appendix 2: Receptor Points .......................................................................................................... 44
Appendix 3: Model input files .......................................................................................................... 49
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SH20 Manukau Harbour Crossing Project – Assessment of Environmental Effects
Appendix 7 – Air Quality Assessment
Assessment of the Effects on Air Quality for the SH20 Manukau Harbour Crossing
Project: Executive summary
This report is prepared for Transit New Zealand. NIWA have been engaged to carry out modelling
studies of a proposed new bridge across the Manukau Harbour at Mangere and an associated
road widening scheme (The Project).
Key points
• The Air Quality Assessment analysed two scenarios being a “do minimum” and the Project.
• Emissions and roadside concentrations of three key vehicle pollutants: Carbon Monoxide
(CO), Nitrogen Dioxide (NO2) and Particulate Matter less than ten micrometres in diameter
(PM10) were modelled for two road scheme scenarios. The results were analysed in terms
of applicable environmental standards and guidelines;
• Roadside CO concentrations for both scenarios are predicted not to exceed the National
Environmental Standard (NES) and do not present a significant environmental hazard;
• NO2 concentrations for both scenarios are not predicted to cause a breach of the NES;
• NO2 concentrations due to emissions are relatively high in both cases, with roadside
+concentrations for the Project being generally higher than those for the do-minimum
scenario; and
• PM10 concentrations are similar for both scenarios, and are not predicted to exceed the
NES and do not present a significant environmental hazard.
Initial screening assessments used traffic flow predictions provided by Opus. The flow predictions
were based upon two scenarios;
1) The “do minimum” scenario, which assumes that any existing road schemes in the
vicinity with resource consent are completed but no further schemes are carried out;
and
2) The current proposal, which assumes that the Project is carried out.
This initial screening identified eight sections or links where the most significant deleterious effects
on air quality would be expected. The emissions from the traffic using these links were modelled
for both the above scenarios using the Ausroads Model. The model was used to calculate ground
level concentrations of the three major vehicle exhaust pollutants (CO, NO2 and PM10). The results
are reported at the roadside and at Receptor points (predominantly places where people live and
work, including schools and hospitals but also other sensitive areas such as parks and amenity
space) up to 200m from the roadside.
A simple comparison of the model results for the two scenarios shows that concentrations from CO
emissions are similar for both scenarios and in all cases the concentrations are low. On the
proposed new motorway sections, NO2 concentrations at the roadside due to traffic emissions are
potentially higher than those from the do-minimum scenario. PM10 emissions are slightly, but not
significantly, higher from the Project than from the do-minimum scenario.
The emissions calculated above are purely the effects of the tailpipe emissions of traffic. To assess
the effect the road scheme will have on total ambient concentrations of the species modelled they
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Appendix 7 – Air Quality Assessment
have to be considered in the context of background (existing) concentrations. This has implications
on meeting the NES.
Since the concentrations of CO and PM10 are similar in both the Do-minimum and the Project
scenarios, they will have similar effects on compliance with the NES. In both cases ambient
concentrations are expected to be below NES limits.
There is a difference between the two scenarios for NO2 with the emissions from the Project being
generally higher than the Do-minimum scenario. In both cases however, the expected values are
below the NES.
The increased NO2 concentrations from the Project compared to the Do-minimum scenario are a
result of the fact that NO2 concentrations tend to increase more rapidly with vehicle speed than
other pollutants. The improvement per vehicle in CO and PM10 emissions due to improved traffic
flow is offset by the increase in vehicle numbers so that the two scenarios yield similar values. The
more free flowing conditions of the Project lead to higher speeds and hence higher NO2
concentrations.
The sections modelled were the ones where the most significant deleterious differences between
the two scearios were expected. There are some sections of road in the area surrounding the
scheme; particularly SH1 to the East where traffic volumes are expected to fall if the Project goes
ahead compared to if it does not. This would be expected to lead to an improvement in air quality
in the immediate vicinity of those sections, which may offset any degraded air quality in the
immediate vicinity of the SH20.
Since there is not expected to be any breach of the NES, mitigation measures are not considered
necessary.
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Appendix 7 – Air Quality Assessment
1 Introduction
This report is prepared for Transit New Zealand. NIWA have been engaged to carry out modelling
studies for the Project. The proposed duplicate bridge is to be built alongside the existing bridge at
Mangere and adjoining sections of SH20 upgraded to motorway as part of a wider scheme to link
motorways around Auckland.
1.1 Scope of this report
NIWA was asked by Opus to conduct an assessment of the effects on air quality from the Project.
This assessment considered two scenarios;
• The “Do minimum” option, which assumes that any existing road schemes in the vicinity
with resource consent are completed but no further schemes are carried out; and
• The current proposal, which assumes that the Project is carried out.
The aim of the assessment is to compare the effects on air quality between the two scenarios.
To make this assessment this report considers the following;
1) the current state of the local environment using measurement data from local
monitoring stations. (Section 2)
2) methods - assessment criteria, choice of model, vehicle emissions factors and
meteorological data (Section 3)
3) results of model runs for this proposed road scheme and the “do nothing” alternative.
(Section 4)
4) analysis of results, interpretation, effects on air quality and compliance with the NES
(Section 5)
It is a complex task to specifically link air pollution emissions from traffic operations to ambient air
quality, as the emissions are highly variable, and eventual ambient concentrations are strongly
affected by the weather and local features. There is no simple concept of “air pollution” –
degraded air quality comprises a number of contaminants, which can behave differently from each
other, and can be emitted in different amounts from vehicles depending on a number of complex
factors.
On balance, there are likely to be five principle drivers that control local air quality for any given
traffic area: -
Principle 1. Vehicle Numbers: Lower numbers of vehicles in the traffic flow, for any type of
fossil fuel powered vehicle, leads to better air quality; conversely higher numbers lead to poorer air
quality.
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Principle 2. Traffic Flow: For a given volume of traffic, free flow (and avoidance of congestion),
results in lower emissions, and hence better air quality.
Principle 3. State of Tuning: Vehicles in the fleet that are better maintained, and regularly
tuned, have lower emissions. (This not necessarily related to the vehicle age).
Principle 4. Exposure and Corridors: Built areas, road-side structures, and local terrain that
are more open around the road environment result in better ventilation, better dispersion of
pollutants, and better local air quality.
Principle 5. Technology: Pollution emissions are a direct consequence of burning fuel. Any
move to improve efficiency, use emissions control technology, or adopt newer hybrid or electric
vehicles will improve local air quality.
Changes in any one of these factors can have a change in the resultant air quality. Although
transport activities have the potential to discharge a large number of contaminants, the major
contributors to degraded air quality are Carbon Monoxide (CO); Nitrogen Dioxide (NO2) and
Particulate Matter of less than ten micrometers in diameter (PM10). Therefore to assess the effects
on air quality we have chosen to model the concentration of these three pollutant species the
maximum concentrations of which are prescribed in law by the NES, as indicators of air quality.
The predicted concentrations of these species emitted by traffic in each of the two scenarios are
then compared with each other and with the NES
1.2 National Environmental Standards
Discussion in this report will be based upon the effects of the proposed road scheme on air quality
in relation to the NES.
The NES came into effect on 1st September 2005. They place a legal requirement on local and
regional councils that concentrations of certain pollutants must be kept below given thresholds.
The pollutants covered by the standards and the concentration threshold values are shown in
Table 1-1 below.
Allowable
Contaminant Standard Time Average exceedences per
year
Carbon monoxide (CO) 10 mg/m3 8 hours 1
Nitrogen dioxide (NO2) 200 µg/m3 1 hour 9
Ozone (O3) 150 µg/m3 1 hour 0
Particles (PM10) 50 µg/m3 24 hours 1
350 µg/m3 1 hour 9
Sulphur dioxide (SO2)
3
570 µg/m 1 hour 0
Table 1-1. The National Environmental Standards
The regulations accompanying the NES state that all areas of the country must comply with these
standards as of 1st September 2005. The exception is PM10 which must be complied with by 31st
August 2013. After these dates councils will not be allowed to issue resource consents for any
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Appendix 7 – Air Quality Assessment
activity that will cause the ambient concentrations of the relevant pollutants in the surrounding area
to exceed the value permitted in the standards (MfE 2005).
Model outputs will be given in units and averaging times appropriate for comparison with the NES.
In addition ARC has adopted its own guideline values for other averaging times or differing
locations (see Proposed Auckland Regional Plan: Air, Land & Water
http://www.arc.govt.nz/arc/environment/air/air-publications.cfm) and these will also be given for
comparison where appropriate. The relevant ARC target values are shown in Table 1-2 below
Contaminant Target Averaging Time
Particles (PM10) 33µg/m3 24 hour
3
Particles (PM10) 13µg/m Annual
Nitrogen dioxide (NO2) 132µg/m3 1 hour
3
Nitrogen dioxide (NO2 ) 66µg/m 24 hour
3
Carbon monoxide (CO) 20mg/m 1 hour
3
Carbon monoxide (CO) 6mg/m 8 hour
Table 1-2. The ARC target values for compounds of interest
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2 Description of existing environmental conditions
2.1 Local Receiving Environment
The area immediately surrounding the roads in question is known as the Local Receiving
Environment. It is generally accepted that concentrations of pollution due to traffic decreases with
distance from the roadside until, by 200m away it is indistinguishable from the background
(Highways Agency 2003). It is therefore common practice to model the concentrations of such
pollutants at a range of distances from the roadside to 200m away or at places within this area
where people might reasonably be expected to be exposed to traffic pollution for significant
periods. These places are known as Receptor Points. They are predominantly places where
people live and work, including schools and hospitals but also other sensitive areas such as parks
and amenity space.
The area of interest stretches from Hillsborough Road to the West to SH1 to the East and from the
airport to the South to Mt Albert Road to the North. It is shown in Figure 2-1
Road
Figure 2-1. The area around the Project
2.2 Current traffic numbers and predictions for 2021
The Project will increase the capacity of the corridor. Additional capacity will reduce traffic
congestion and improve traffic flow, and attract additional traffic into the corridor. Decongestion of
traffic will reduce vehicle emissions. On the other hand, additional traffic volume will increase
vehicle emissions. Compared to the without-crossing scenario, it is likely that the overall vehicle
emissions will rise, potentially increasing the adverse effects on local air quality.
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The Auckland Regional Transport Model used for traffic predictions provides estimates of traffic
over ten year intervals. Results are available for 2001, 2011 and 2021. The new bridge is intended
to be in place by 2013, therefore model numbers for the next available year (i.e. 2021) are used in
this study. Current traffic numbers (2004 Auckland City Council, Transit) and predicted numbers for
the crossing and surrounding roads are shown in Table 2-1. The table shows that traffic numbers
on the roads (Annual Average Daily Traffic –AADT) in the vicinity of the crossing are predicted to
approximately double between 2004 and 2021 even under the do-minimum scenario. Unless there
are significant changes in vehicle technology or emissions regulations in the intervening time this
will certainly lead to degradation of air quality regardless of traffic flow conditions.
The overall difference between the Do-minimum and the Project is that total traffic numbers on the
roads listed in Table 2-1 are predicted to be about 10% higher under the Project scenario than the
Do-minimum. This difference is not spread evenly across the network: Numbers are predicted to
be about 20% to 30% higher on the motorway sections of SH20 in the Project scenario but
numbers on SH1, the Great South Road and roads connecting to them are predicted to be up to
10% lower. Numbers in some of the roads connecting to SH20 will be higher under the Project
scenario. In parts of Neilson Street, numbers could be 70% higher. Overall, an increase of about
10% in traffic numbers across the local network is probably not significant in terms of air quality so
traffic flow conditions will have a more significant impact.
Link Road Location 2004 2021 Do- 2021 The
number minimum Project
AADT
AADT AADT
1 SH20 Hillsborough Road to Queenstown Road 47540 91868 119230
2 SH20 Queenstown Road to Neilson Street 73050 128279 155102
3 SH20 Mangere Bridge 90230 140847 175846
4 SH20 Rimu Road to Walmsley Road 68860 115864 146313
5 SH20 Walmsley Road to SH20A 67610 121252 146269
6 SH20 SH20 to Massey Road 35160 65875 81935
7 SH20A SH20A to Kirkbride Road 36640 76918 81321
8 Mahunga Drive Past Marae - 18423 18869
9 Coronation Road South of Rimu Road - 13140 13339
10 McKenzie Road South of Walmsley Road - 18487 19029
11 Favona Road East of Robertson Road - 29696 29609
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Appendix 7 – Air Quality Assessment
12 Neilson Street West of Onehunga Mall 225551 29355 49881
2
13 Neilson Street East of Onehunga Mall 18872 45474 49798
3
14 Selwyn Street North of Church Street 5270 18698 24847
4
15 Church Street East of Onehunga Mall 12032 10335 11209
16 Church Street West of Neilson Street 57015 30061 28597
17 Queenstown Road North of Beachcroft Avenue - 37811 35743
18 Trafalgar Street East of Pah Road 6570 13201 11856
6
19 Beachcroft Avenue East of Pah Road 6168 21025 17343
20 SH1 North of SEART 139640 185836 182358
21 SH1 South of Mt Wellington Highway 111800 151672 143786
7
22 Great South Road South of Sylvia Park Road 23615 40277 35649
23 Great South Road South of Mangere Road - 48199 47956
8
24 Great South Road North of Ellerslie-Panmure Highway 14483 46785 44129
9
25 Mays Road East of Mt Smart Road 7375 9740 8567
10
26 Mt Smart Road East of Mays Road 16341 26396 26984
27 Hillsborough Road North of SH20 1870411 29287 30282
1 2 3 4 5 6
West of Victoria St 2005; West of Edinburgh St 2005; North of Grey St 2004; 2005; West of Selwyn St 2004;
7 8 9 10
East of Pleasant St 2004 ; North of Portage Rd 2005; South of Campbell Rd 2005; South of Felix St 2005; West
11
of Waitangi Rd 2004; South of Herd Rd 2005
Table 2-1 Traffic numbers in 2004 compared to predicted numbers in 2021
The links chosen to model in this study are of two distinct types:
1. Sections one to six of the main arterial SH20; and
2. Sections twelve and fourteen, which are local connecting roads.
The areas bordering these roads are a mix of residential and industrial in the case of the main road
and a mix of residential, industrial and commercial - the shopping area around Onehunga Mall – in
the case of the connecting roads. Within 200 m distance from the roadside of the Project, there are
some sensitive air quality receptors. They include the Marae, Hillsborough Hospital, and Onehunga
High School.
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Appendix 7 – Air Quality Assessment
There are no air quality monitoring data available for the Scheme area. The nearest monitoring site
is at Penrose, which has a similar mix of activities, including the proximity of major arterial roads
and therefore may be representative of the local environment around the Manukau Bridge. There
are no CO measurements from Penrose so measurements from Takapuna have been used as
representative of the area
2.3 Air quality in Auckland
There is an air quality monitoring network in the Auckland region, which has been run by the ARC
and the Ministry for the Environment (MfE) for many years. The network provides a good indication
of the regional air quality. The location of the sites is shown in Figure 2-2, although not all of these
sites are necessarily in operation or able to provide the particular data needed for this report. The
latest available data is for 2004. All air quality monitoring data used in this section are provided by
the ARC and MfE and used with permission. Tables 2-2 to 2-4 below show the measured levels of
CO, NO2 and PM10 at several sites in Auckland in 2004.
Location Site type Units Maximum Annual Median 95th
Average Percentile
Queen St Traffic mg/m3 10.1 1.5 1.3 3.7
(Car Park)
Khyber Traffic mg/m3 8.6 2.2 1.9 5.1
Pass
Takapuna Residential/ mg/m3 5.9 0.5 0.3 2.0
Traffic
Henderson Residential mg/m3 4.0 0.6 0.4 1.7
Pakuranga Residential mg/m3 6.0 0.8 0.6 2.5
Table 2-2. 8 hour average CO measurements at Auckland monitoring sites during 2004 (NES value 10mg/m3)
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Appendix 7 – Air Quality Assessment
Location Site type Units Maximum Annual Median 95th
Average Percentile
Queen Traffic µg/m3 226.8 56.1 52.4 99.1
Street
Khyber Traffic µg/m3 270.6 57.5 49.4 134.7
Pass
Penrose Traffic µg/m3 98.8 23.6 22.1 50.1
/Industrial
Takapuna Residential/ µg/m3 124.1 25.6 24.1 52.3
Traffic
Henderson Residential µg/m3 82.7 16.6 11.6 48.3
Mt Eden Residential µg/m3 66.2 17.3 13.8 43.6
Musick Rural µg/m3 69.5 8.5 4.5 32.3
Point
Table 2-3. 1 hour average NO2 measurements at Auckland monitoring sites during 2004 (NES value 200 µg/m3)
Location Site type Units Maximum Annual Median 95th
Average Percentile
Queen St Traffic µg/m³ 52.6 22.9 21.5 37.9
Khyber Traffic µg/m³ 47.4 21.7 21.6 32.3
Pass
Penrose Traffic µg/m³ 45.2 19.2 18.4 32.3
/Industrial
Takapuna Residential µg/m³ 59.5 20.4 19.2 34.6
/Traffic
Henderson Residential µg/m³ 45.3 17.5 16.4 28.6
Mt Eden Residential µg/m³ 37.3 15.3 14.7 25.2
Table 2-4. 24 hour average PM10 measurements at Auckland monitoring sites during 2004 (NES value 50 µg/m3)
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In 2004, at the monitoring sites from which data were available there was one measurement that
was above that allowed under the NES for CO (10.1 mg/m3 at Queen St). The NES did not come
into force until 2005, however, had they been, this would have constituted an exceedence of the
NES. The standard allows one exceedence per year so overall, the standard would not have been
breached.
The NO2 standard value was exceeded 15 times at Khyber Pass Road and twice at Queen St;
none of these exceedences were coincident between the two locations. The standard allows nine
exceedences per year so had the NES been applicable, it would have been breached in Auckland
in 2004
The PM10 standard value was exceeded once each at the Queen St and Takapuna sites. The
standard allows one exceedence per year so overall, the standard would have been breached in
Auckland in 2004.
Whangaparaoa
N
Northcote
Takapuna
Queen St
Sky Tower
Hobson St Khyber Pass
Mt Albert Musick Point
Pakuranga
Henderson
Dominion Rd Penrose
Mt Eden East Tamaki
Mangere
Manurewa
10 km
Pukekohe
Figure 2-2 Auckland air quality monitoring sites.
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Appendix 7 – Air Quality Assessment
2.4 Background concentrations
The modelling study carried out for this report estimates emissions of CO, NO2 and PM10 from the
traffic using the road. These modelled results need to be superimposed upon the omnipresent
background concentrations of these species in order to estimate their effects on the local air
quality. Therefore, it is necessary to choose a representative value for the background
concentrations.
In order to estimate background values with which to assess the effects of the proposed road
scheme, measurements from residential areas can be used, i.e. an urban area not exposed to
heavy traffic. There are no truly background urban monitoring sites in Auckland. All sites, even
those in residential areas tend to be roadside sites with a major influence from nearby traffic. This
makes it difficult to establish a reasonable value for the underlying background concentrations of
pollutants.
To remove the effects of the traffic, measurements made between the hours of 2.00am and
4.00am, when traffic numbers would be expected to be at their lowest were examined. Available
measurements for the past five years from three residential locations; Takapuna (in the grounds of
a school in North Auckland), Mt Eden (in Auckland City) and Henderson (in West Auckland) were
used. Other sources of background pollutants would be domestic heating and cooking (CO and
PM10 from solid fuels and NO2 from natural gas) and industrial sources. These activities would also
be expected to be at a minimum during these hours so concentrations of species of interest would
also be expected to be a minimum. Therefore, values from these times and locations would be
expected to represent a lower limit for background concentrations.
The process is straightforward for NO2 concentrations. Data are available in one hour averages
and the NES is expressed in the same units. Therefore it is possible to extract night-time values
that are directly comparable to the whole day value and to the NES.
For PM10, one hour average data are available but the NES is expressed in twenty four hour
average units: it is not possible to extract night-time values from the 24hour dataset or to
reconstruct 24hour averages from night-time values extracted from the one hour dataset. It is
however, possible to extract an annual average and maximum value for the night-time and whole
day which can then be compared to give an indication of the difference between the two situations.
The situation is similar to PM10 for CO: data are available in one hour averages but the NES is
expressed in 8 hour averaging times.
Table 2-5, below shows the five year annual average and maximum values for concentrations of
the species of interest at residential monitoring sites. Measurements made between the hours of
2.00 am and 4.00 am are compared to the values of all hourly measurements.
Averaging Night-time measurement All measurements
time
Annual Annual
Species Units Average Maximum Average Maximum
CO 1 hour mg/m³ 0.4 3.2 0.6 9.8
NO2 1 hour µg/m³ 13.2 45.4 21.7 90.1
PM10 1 hour µg/m³ 15.3 97.0 16.6 261.4
Table 2-5 Night time air quality measurements at Auckland residential monitoring sites
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The ratios of the night-time measurements and the whole day measurements are shown in Table
2-6. It shows that the annual average whole day values are about 1.6 times the night-time values
for CO and NO2 but about the same for PM10. The maximum values are in the range two to three
times higher than the night-time values.
CO NO2 PM10
Annual
1.6 1.6 1.1
Average
Maximum 3.1 1.9 2.7
Table 2-6. ratios of night-time measurements and whole-day measurements
For NO2, the comparison to the NES is straightforward because it is averaged over the same time
as specified in the standard. The values for CO and PM10 need to be converted into appropriate
averaging times for comparison with the NES. To do this the five year annual average and
maximum values from the 8 hour CO and 24 hour PM10 datasets were scaled using the ratios in
the table above. For example, the average annual maximum 24 hour PM10 value at residential sites
over the past five years is 42.4 µg/m³ (from Table 2-7), if this is divided by the appropriate ratio of
night-time to whole-day measurements given in Table 2-6 (2.7) it yields the value 15.7 µg/m³. This
is the equivalent of an annual maximum “night-time 24 hour average” PM10 concentration. The
calculated values for PM10 and CO are shown in Table 2-7 below along with the 1 hour values for
NO2.
Averaging Night-time measurement All measurements
Species time Units
Annual Annual
Average Maximum Average Maximum
CO 8 hours mg/m³ 0.4 1.4 0.6 4.4
NO2 1 hour µg/m³ 13.2 45.4 21.7 90.1
PM10 24 hours µg/m³ 15.1 15.7 16.6 42.4
Table 2-7. Night-time and annual concentrations of pollutants in units appropriate to the NES
That the night-time annual average, the night-time maximum and whole-day annual average for
PM10 all have a similar value indicates that this may be an appropriate value to choose. This is
further corroborated by correlation with CO concentrations. CO and PM10 from combustion tend to
be correlated in the atmosphere. CO can only come from combustion but PM10 can come from non
anthropogenic sources such as sea salt, wind-blown dust and biomass burning. Therefore a
regression plot of CO against PM10 should have an intercept that is the PM10 from non combustion
sources i.e. the “natural” background. A plot of CO against PM10 for Takapuna in 2004 is shown in
Figure 2-3. The intercept and hence the residual PM10 concentration is 15.5 µg/m³. That this is
similar to the other values suggests that this is a suitable value for the background value. By
inference the background value for CO and NO2 may also be a combination of night-time annual
average, the night-time maximum and whole-day annual average. It is most convenient to
represent this combination as the whole-day annual average for all species.
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y = 0.009x + 15.497
160.0
R2 = 0.4127
140.0
120.0
PM10 concentration (ug/m3)
100.0
80.0
60.0
40.0
20.0
0.0
0 1000 2000 3000 4000 5000 6000 7000 8000
CO concentration (ug/m3)
Figure 2-3. A plot of CO against PM10 at Takapuna in 2004
2.5 Long term trends
The measurement values used for the background calculation are from the five years up to 2004.
Previously unpublished work at NIWA (Xie 2006) shown in figures 2-4 to 2-6 concluded the
following
• It is possible that the concentrations of CO are decreasing over time at Khyber Pass,
• It is possible that the concentrations of NO2 are increasing over time at Penrose, but no
clear trend at Mt Eden,
• It is possible that the concentrations of PM10 are decreasing over time at Penrose and Mt
Eden.
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Khyber Pass Rd site maximum
25
95th Percentile
average
CO (mg m -3) 20
15
10
5
0
1996 1997 1998 1999 2000 2001 2002
Year
Figure 2-4. 1-hour CO concentrations recorded at Khyber Pass Rd.
30
25
NO2 (µ g m )
20
-3
15
10
Penrose
5
Mt Eden
0
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Year
Figure 2-5. Annual average NO2 concentrations recorded at Penrose and Mt Eden sites.
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40
PM10 (µ g m-3) 30
20
10
Penrose
Mt Eden
0
1995
1996
1997
1998
1999
2000
2001
2002
Year
Figure 2-6. Annual average PM10 concentrations recorded at Penrose and Mt Eden sites
However the data upon which this indicative analysis was based were limited in both temporal and
spatial senses. To allow more robust conclusions to be drawn about the longer term trends in
contaminant concentrations, would require a data set that is longer in duration and covers a
greater number of monitoring sites.
2.6 Air quality effects
The potential adverse effects of pollutants discharged from vehicles that are predicted to use the
proposed road scheme are considered from the point of local and community effects, and regional
and national effects.
2.6.1 Local and community scale effects
Local and community scale effects of potential concern are the adverse effects on human health.
These adverse effects are principally caused by the primary pollutants carbon monoxide (CO),
nitrogen dioxide (NO2) and inhalable particulates (PM10). The potential adverse effects on human
health are established by estimating the concentrations of pollutants discharged from the traffic
using the proposed road scheme using a dispersion model. The estimated concentrations are then
added to background levels and finally compared to the relevant ambient air quality guideline (MfE,
2002), targets (ARC, 2001), and the NES (MfE, 2003).
Note that the effects can be very localised and will vary from section to section. A modelled section
can be of the order of about 50m long. For some sections, the sensitive receptors may be very
close to the alignment, while for some sections, the sensitive receptors may locate outside the
highly affected areas. Therefore, the effects of some sections may be high, while the effects of
sections with comparable traffic flow/emissions may be insignificant.
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2.6.2 Regional and national scale effects
Regional air quality is strongly affected by emissions from fossil fuelled transport. These emissions
are the primary cause of degraded air quality in Auckland, for almost all the contaminants listed in
the MfE guidelines. They are also the primary cause of visibility degradation (‘the brown cloud’) in
Auckland, especially on calm winter mornings. Wider scale effects of traffic pollution also include
the greenhouse effect due to the release of CO2 and photochemical smog. However, these effects
are beyond the scope of this report
2.7 Pollution compounds due to traffic
Carbon monoxide (CO). Road transport is responsible for a significant proportion of total
emissions of carbon monoxide. It is rapidly absorbed by the blood, reducing its oxygen
carrying capacity. It is a relatively stable compound that takes part only slowly in
atmospheric chemical reactions. It contributes indirectly to the greenhouse effect by
depleting atmospheric levels of hydroxyl radicals and thus slowing the destruction of
methane, which is a powerful greenhouse gas.
Oxides of nitrogen (NOX). Most of the NOX produced by road vehicles is emitted as NO.
In the air it is oxidised to NO2, which is more toxic, affecting the respiratory system. Oxides
of nitrogen are important in atmospheric chemistry, contributing to photochemical smog
formation and acid deposition. Some of the products of reactions involving NOX are
powerful greenhouse gases.
Particulate Matter (PM). Particles may be emitted from the exhaust, through the
resuspension of road surface dust, and are generated by abrasion from tyre, brake and
road surface wear. Diesel exhaust contains much higher particle concentrations (in terms
of mass) than petrol exhaust. These emissions comprise carbonaceous material onto
which a wide range of organic and inorganic compounds may be adsorbed. Exhaust
particle emissions are generally fine, with an aerodynamic diameter of less than 1.0
micron. Particles are also formed through a range of atmospheric chemical processes
which result in the formation of secondary particles such as, nitrates and sulphates, which
are associated with the acidification of water courses.
The size and composition of aerosols have been demonstrated to have an impact on their
health effects, Neuberger et al., [2004] report a significant impact of the carbonaceous
fraction of PM2.5 on breathing patterns of healthy subjects, and Ghio and Devlin, [2001]
show an influence of chemical composition of particulate on observed respiratory effects.
Wyzga, [2002] indicates that PM10 is associated with respiratory diseases and PM2.5 with
cardiovascular diseases. Location is also of significance; Hoek et al. [2002] report an
increase of cardiopulmonary mortality associated with distance from main roads.
Hydrocarbons (HC).
The term is used generally to include all organic compounds emitted (both in the exhaust
and by evaporation from the fuel system) and includes many hundreds of different species.
Some hydrocarbon compounds, such as benzene and 1,3-butadiene, are toxic or
carcinogenic. The reactivity of hydrocarbon species varies widely but they are important
precursors of photochemical smog, acidic and oxidising compounds. They contribute
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directly and indirectly to the greenhouse effect. The composition of HC emissions is
strongly influenced by the composition of the fuel, so changes in fuel specifications can
modify their impacts. There are insufficient data available in New Zealand to be able to
model hydrocarbons with any confidence. Hydrocarbon production from vehicles closely
follows CO production so CO may be used as an indicator for hydrocarbons.
Sulphur dioxide (SO2). SO2 concentrations in Auckland are low. Road transport
represents only a very minor source of sulphur emissions. Concentrations may have been
slightly elevated at heavily trafficked roadside locations in the past but because the
maximum permitted sulphur content of road fuels has recently been reduced, the
contribution is now much lower. Road transport is not a significant source of sulphur
dioxide.
Lead (Pb). Lead is a recognised neurotoxin. Formerly, lead compounds, mainly in the form
of fine particles, were widely emitted by petrol vehicles using leaded petrol, but the
phasing out of leaded petrol has reduced concentrations to levels well below those
considered harmful except in a very few locations where there remain industrial or other
non-traffic sources of lead pollution. Road transport is no longer considered a significant
source of airborne lead pollution.
Carbon dioxide (CO2). CO2 is a major product of the combustion of all carbon containing
materials. It is the most abundant man-made greenhouse gas in the atmosphere. Carbon
dioxide is not considered in local air quality assessment since it is not toxic and causes no
adverse environmental effects on a local scale but is included in regional assessments as
it is a greenhouse gas.
Ozone (O3). Ozone differs from the other pollutants in that it is not produced directly from
emission sources, but is created by photochemical reactions in the atmosphere involving
oxides of nitrogen, hydrocarbons and other compounds. Because road transport is a major
source of the compounds involved in the reactions, it is an important contributor to ground
level O3 concentrations. However, for several reasons, it is not included in the initial
assessment. Near to roads, the amount of O3 in the air is governed mainly by the reaction
between NO and O3, to produce NO2. Because roads provide an excess of NO from the
traffic emissions, the reaction proceeds until most of the O3 is depleted, and consequently,
O3 levels near to roads tend to be low.
Polycyclic aromatic hydrocarbons (PAH). Polycyclic aromatic hydrocarbons are
produced by all types of combustion. By far the most significant sources are specific
industrial processes such as aluminium production, coke ovens and anode baking. Given
the relatively low contribution to total PAH from road transport, and the absence of
appropriate transport emission data, it is not considered in road assessments (Highways
Agency 2003).
Trace metals. Little is known about road traffic emissions, but they are likely to be less
important than those from industrial processes. Road traffic is likely to be a relatively minor
source so they are not considered in road assessments.
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3 Assessment criteria: Choosing sections of roadway to model
Initial screening assessments were based upon traffic flow predictions provided by Opus. The flow
predictions were based upon two scenarios;
1. The “Do minimum” option, which assumes that any existing road schemes in the vicinity
with resource consent are completed but no further schemes are carried out
2. The current proposal, which assumes that the Project is carried out.
Predicted traffic flows for both these scenarios for 2021 were provided by Opus, broken down into
some thirty nine sections, including slip roads for proposed new motorway junctions. Flows were
provided as AADT, Morning and Evening peak hourly flows and hourly flows at other times.
A comparison of the two scenarios showed that at nine of the sections the predicted flows went
down between scenario 1 and scenario 2 (i.e. building the proposed scheme would cause the
traffic numbers to decrease in that section compared to not building it). Of the remainder, a further
15 sections would experience small but not significant increases in traffic numbers. The average
increase over all the sections would be 23% with a standard deviation of 36%, so at this stage any
increase of 20% or less was considered to be “in the noise”. Also at this stage, slip roads were
discounted as the relative flows are reasonably small.
Because a high percentage change in a small traffic volume may be less significant than a small
change in a high volume, a combination of percentage increase in traffic flows and absolute
numbers was used in order to estimate where the most significant effects would come from
amongst the remaining sections. The two numbers were multiplied together and the result used to
rank the changes in individual sections in order of significance. When this was done, eight sections
stood out from the rest as having a higher risk of degraded air quality than other locations.
These are shown in the Table 3-1 and depicted in Figure 3-1 below;
Section number Road Location
1 SH20 Hillsborough Road to Queenstown Road
2 SH20 Queenstown Road to Neilson Street
3 SH20 Mangere Bridge
4 SH20 Rimu Road to Walmsley Road
5 SH20 Walmsley Road to SH20A
6 SH20 SH20A to Massey Road
12 Neilson Street West of Onehunga Mall
14 Selwyn Street North of Church Street
Table 3-1. Sections of the road scheme modelled in this study
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Figure 3-1. Sections of the road scheme modelled in this study
These sections of the proposed road scheme were then subjected to a detailed modelling analysis.
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4 Inputs for the model
Note: Copies of the input files for the model are available upon request. Details of where they can
be obtained are given in Appendix 3.
4.1 Meteorological data
The 1996 Auckland Airport meteorological dataset produced by NIWA was used as the model
input. The dataset is classified into the Classification A category in the Good Practice Guide for
Dispersion Modelling (Mfe, 2004). Such datasets have been:
“• produced using recommended methods
• subjected to peer review
• employed in a relatively large number of studies and resource consent applications.”
Therefore, the 1996 Auckland Airport meteorological dataset is considered appropriate and robust
for this project.
4.2 Predicted traffic flows
Predicted traffic flows have been provided by Opus. The diurnal pattern of daily traffic volume is
allocated according to the measured pattern on the Mangere Bridge during March 2003 (Figure 4-
1).
10
Proportion of daily traffic (%)
8
6
4
2
0
1 3 5 7 9 11 13 15 17 19 21 23
Hour
Figure 4-1. Proportion of daily traffic volume measured on the Manukau Harbour Bridge during March 2003 (weekdays
only) (data source: Transit).
4.3 Emission factors modelling
There are several variables to take into account when modelling the emissions from traffic.
4.3.1 Fleet composition
The fraction of various types of vehicles in the fleet driving along the roads of interest has a huge
influence on the emissions, as different types and sizes of vehicles emit very different amounts of
pollution. Assessments and modelling assume an average fleet profile, but the local variations can
be substantial. In a similar manner, the way the fleet composition profile changes with time has a
large effect, especially when considering options more than a few years into the future.
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4.3.2 State of tuning
Similarly, the state of tuning of the vehicles determines their emissions. At any given time, many
vehicles will be out of tune to various degrees. Again an average profile is used, but this can be
different locally, and can change substantially over time.
4.3.3 Cold start
Before the engine warms up, vehicles can emit larger amounts than when they achieve their
normal operating temperature. This time varies for different vehicles, but is generally within the
first 3-5 km of the trip. This factor needs to be accounted for, and can be significant, as many
surveys show that a high proportion of trips in NZ are less than 5 km.
4.3.4 Congestion
Vehicles generally emit least amounts when they are travelling at their optimal design speed.
Emission rates increase for both higher and lower speeds, and during acceleration. In congested
traffic conditions, the emissions can be substantially higher (in some cases twice as much) than in
free flow conditions for CO and volatile organic compounds (VOC), but not necessarily for NOx and
PM10.
4.3.5 Gross emitters
Vehicle emissions testing shows that in many cases the total air pollution emissions on a road can
be dominated by a small percentage of vehicles - the 'gross emitters'. This factor may be one
cause of the high variability in monitoring results - even when total vehicle counts are consistent.
One poorly tuned large truck or bus can emit as much air pollution as 100 well tuned private cars.
4.4 Emissions Model
Emissions factors used in this study have been extracted from NZTER. It should be noted that
whilst NZTER is generally considered to be the official source of traffic emission factors for New
Zealand, it now some seven or eight years old. Work is being carried out to update it but it has not
been published yet. It is, however, accepted as best available data.
The traffic flows supplied by Opus use a split of 95% Light Duty Vehicles (LDV i.e. cars and vans)
and 5% Heavy Duty vehicles (HDV i.e. lorries, trucks and busses) for all modelled sections except
Neilson Street (section number 12) which has a split of 88%LDV and 12%HDV
The same splits were used to extract emissions factors from NZTER with the LDV category further
broken down into 84% petrol and 16% diesel. All HDV are assumed to be diesel. These numbers
are compiled into a single value for a single “composite vehicle” as shown below;
For the Project -
Link number 1to 6: free flow plus 10% cold start for peak and non-peak hours;
Link number 12 & 14: congested plus 20% cold start for peak hours, interrupted plus 20%
cold start for non-peak hours.
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For the do-minimum -
Link number 1 to 5: congested plus 10% cold start for peak hours, free flow plus 10% cold
start for non-peak hours;
Link number 6: free flow plus 10% cold start for peak and non-peak hours;
Link number 12 & 14: congested plus 20% cold start for peak hours, interrupted plus 20%
cold start for non-peak hours.
Link CO NOx PM10
number Peak Non-peak Peak Non-peak Peak Non-peak
1 to 6 1.69 1.69 1.47 1.47 0.04 0.04
12 5.27 4.19 1.83 1.84 0.07 0.06
14 5.65 4.48 1.15 1.13 0.05 0.04
Table 4-1. Emission factors for the Project in 2021 (g/km per vehicle)
Link CO NOx PM10
number Peak Non-peak Peak Non-peak Peak Non-peak
1 to 5 2.53 1.69 0.78 1.47 0.03 0.04
6 1.70 1.70 1.45 1.45 0.03 0.03
12 5.27 4.19 1.83 1.84 0.07 0.06
14 5.65 4.48 1.15 1.13 0.05 0.04
Table 4-2. Emission factors for do-minimum in 2021 (g/km per vehicle)
These PM10 results are for exhaust pipe emissions only and take no account of other sources such
as tyre and brake wear and resuspended road dust, which are not well understood and hence
difficult to quantify. Various studies suggest that the contribution to total particulate matter could be
equivalent to between 10% and 100% of the contribution from vehicle exhausts (Highways Agency
2003, Kuschel and Bluett 2002 and references therein)
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5 Modelling
The near road dispersion model, CALINE4 (the California Line Source Model), is commonly used
in New Zealand to predict the traffic derived ground level concentrations of air pollutants. Based on
the methodology of CALINE4, EPA Victoria (Australia) has released the dispersion model
AusRoads. Improvements include removing some artificial limitations and easier data entry. Details
of both CALINE4 and AusRoads have been discussed in the Good Practice Guide for Dispersion
Modelling (MfE, 2004). For this project, AusRoads is used to predict the ground level
concentrations of the various contaminants discharged from the vehicles.
There are a large number of variables to consider in this assessment, and in order to make the
question and analysis manageable, it is necessary to make some assumptions. The following
assumptions were made:
• The emission rate used is for a composite vehicle;
• A realistic and conservative traffic flow scenario is used;
• The modelled traffic volumes for the year 2021 are representative of the actual flows likely
to occur;
• Non-tailpipe emissions of PM10 are omitted from the model.
Any number of other assumptions could have been made, but the matrix of results would become
large and confusing. These assumptions are considered appropriate for the scenarios being
examined and will produce a conservative (high) estimate of the concentrations of pollutants.
5.1 Choice of model
CALINE4 is an updated and expanded version of CALINE3 which is the United States
Environmental Protection Agency’s (USEPA) preferred roadway model (US EPA, 2005). CALINE4
has been verified using data from five independent field studies (Benson, 1989). In New Zealand,
an Auckland study showed that the prediction of CO concentrations using NZTER emissions data
and CALINE4 were within the accepted limits of accuracy for dispersion models (Kuschel and
Bluett, 2002).
CALINE4 is commonly used in New Zealand to predict the traffic derived ground level
concentrations of air pollutants (MfE, 2004). For this study, AusRoads, the equivalent of CALINE4,
is considered suitable for predicting the near-road impact of vehicle emissions for the project.
5.1.1 Modelling locations
Links and modelling locations are shown in Figure 5-1. Concentrations are calculated at two kinds
of locations: Receptor points are described in section 2.2 above and since concentrations
decrease with increasing distances from roadsides, we also calculate concentrations at different
distances from the roadsides (roadside locations). We report concentrations at 10 m from the
roadsides of motorways (links #1-#5 after widening, link #6), 3 m for suburban roads (links #12 and
#14). A list and maps of the locations of receptor points used in the model are given in Appendix
2.
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