anna savage report jaqu air quality methodology modelling

JAQU Air Quality Methodology Modelling Report

AQ2

October 2019


AECOM

Quality information

Prepared by Checked by Verified by Approved by

Alice Gurung

Graduate Air Quality Consultant

Alistair Thorpe

Principal Air Quality Consultant

Anna Savage

Associate Air Quality Director

Gareth Collins

Regional Director

Revision History

Revision Revision date Details Authorized Name Position

1 May 2019 Working Draft GC Gareth Collins Regional Director

2 September 2019 Updated Working Draft

GC Gareth Collins Regional Director

3 September 2019 Updated Working Draft – comments from SU

4 October 2019 Final for OBC GC Gareth Collins Regional Director

Distribution List

# Hard Copies PDF Required Association / Company Name


AECOM

Prepared for:

Portsmouth City Council and Joint Air Quality Unit (JAQU)

Prepared by:

Anna Savage

Principal Air Quality Consultant

E: [email protected]

AECOM Limited

Midpoint, Alencon Link

Basingstoke

Hampshire RG21 7PP

United Kingdom

T: +44(0)1256 310200

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© 2019 AECOM Limited. All Rights Reserved.

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“Client”) in accordance with generally accepted consultancy principles, the budget for fees and the

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and referred to herein has not been checked or verified by AECOM, unless otherwise expressly stated

in the document. No third party may rely upon this document without the prior and express written

agreement of AECOM.


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Table of Contents

1. Introduction ................................................................................................................................... 5

2. Monitoring Data ............................................................................................................................. 6

3. Emissions Model ........................................................................................................................... 8

4. Air Quality Dispersion Model ........................................................................................................ 9

4.1 Study Area .......................................................................................................................... 9

4.2 Choice of Model .................................................................................................................. 9

4.3 Street Canyons ................................................................................................................... 9

4.4 Tunnels / Flyovers ............................................................................................................ 10

4.5 Gradient Effects ................................................................................................................ 10

4.6 Receptors ......................................................................................................................... 11

4.7 Traffic Input Data .............................................................................................................. 11

4.8 Road Traffic NOx-NO2 ...................................................................................................... 12

4.9 Background Contributions ................................................................................................ 12

4.10 Model Verification ............................................................................................................. 12

5. Model Predictions ....................................................................................................................... 12

5.1 Baseline ............................................................................................................................ 12

5.2 Future With-Measures Projections ................................................................................... 13

Appendix A Supporting Information ....................................................................................................... 14

Appendix B Quality Assurance of Monitoring Data ............................................................................... 28

QA / QC of automatic monitoring ................................................................................................ 28

QA / QC of diffusion tube monitoring .......................................................................................... 30

Figures

Figure 1 PCM Links and 50m Buffer .................................................................................................... 15 Figure 2 Study Area ............................................................................... Error! Bookmark not defined. Figure 3 Location of Street Canyons ................................................................................................... 16 Figure 4 Location of Flyovers and Bridges .......................................................................................... 17 Figure 5 Exceedances of the Annual Mean NO2 Limit Value, 2017 ....... Error! Bookmark not defined. Figure 6 Exceedances of Annual Mean NO2 Limit Value, 2018 .......................................................... 18 Figure 7 Diffusion Tube and Automatic Monitoring Locations ............................................................... 19 Figure 8 Wind Rose, Thorney Island (2018 data) ................................................................................ 20 Figure 9 ANPR Camera Sites. ............................................................................................................. 21 Figure 10 Locations of New Diffusion Tubes for 2018 ............................ Error! Bookmark not defined. Figure 11: Euro Composition from ANPR compared to national fleet ................................................... 22

Tables

Table 1 Annual Mean NO2 Concentrations at PCC Monitoring Sites Exceeding the Limit Value .......... 6 Table 2 ADMS-Roads Model Setup Parameters .................................................................................... 9 Table 3 Details of automatic monitoring locations ................................................................................ 23 Table 4 Details of diffusion tube locations ............................................................................................ 24


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1. Introduction

This report constitutes the ‘Local Plan Air Quality Modelling Methodology Report (AQ2)’ and

supplements the information provided in the Local Plan Air Quality Modelling Tracking Table (AQ1).

AQ2 (and AQ1) will remain ‘live’ documents and will be updated as required during the course of the

various stages of the study.

On 26 July 2017, the government published the UK plan for tackling roadside nitrogen dioxide (NO2)

concentrations (‘the UK Plan’) to bring NO2 concentrations within the European Union (EU)’s statutory

annual limit value of 40 micrograms per cubic metre (µg/m3) in the shortest possible time. The

Department for Environment, Food and Rural Affairs and the Department for Transport’s Joint Air

Quality Unit (JAQU) is responsible for overseeing the delivery of the UK Plan, which includes

supporting local authorities and other organisations on the delivery of local measures in their area.

On 23 March 2018 the government directed 33 English local authorities with shorter-term NO2

problems to undertake studies to establish whether there are measures they can take to reduce NO2

air pollution in their areas in the shortest possible time.

On 5 October 2017 the government published a supplement to the UK Plan that identified 8 of the

initial 33 local authorities to have identified more persistent long term exceedances. Under the terms

of the Environment Act 1995, the government has issued a Ministerial Direction to this group of local

authorities to develop a Local Plan to identify measures that could bring forward compliance dates

within the shortest possible time. Portsmouth City Council (PCC) is one of these local authorities.

PCC is required to submit a Full Business Case (FBC) to JAQU by 31 October 2019 at the latest,

unless statutory consultation is required. If so, an Outline Business Case (OBC) is due by this

deadline.

PCC has previously worked with AECOM to undertake an initial targeted feasibility study, submitted to

JAQU in September 2018. This study included local modelling for the two non-compliant links and for

AQMA No. 6 to identify the main causes and extent of exceedances, as well as to determine the level

of emissions reductions required to achieve compliance, and potential measures that could bring this

forward. The study concluded that PCM link 48196 may achieve compliance in 2020 (one year earlier

than predicted by the PCM model) and PCM link 18113 in 2023. Introducing a combination of

measures such as bus retrofitting, reducing private car use and promoting uptake of cleaner vehicles

may bring forward compliance to 2019 and 2022, respectively. Local modelling suggested that road

links within AQMA No. 6 are predicted to achieve compliance in 2024, with a combination of measures

potentially bringing forward compliance to 2022.

AECOM has now been appointed by PCC to provide further air quality modelling support for this

study. This work will input into target determination to understand the extent of exceedances across

the wider study area, as illustrated in Figure 2 in Appendix A. Further modelling of measures against

a Clean Air Zone (CAZ) baseline will be conducted to identify a preferred option or package of

measures that will bring forward compliance in the shortest time possible, for incorporation into the

Full Business Case.


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2. Monitoring Data

PCC operate three automatic monitoring stations: one kerbside site and two roadside sites. These

are equipped with NETCEN approved analysers and run by a NETCEN trained local site operator

(LSO), who follows the same procedures as those of the Defra’s automatic and rural urban network

(AURN) sites. Routine calibrations are conducted every two weeks, and six-monthly services are

conducted through a service contract.

PCC also run a large NO2 diffusion tube monitoring network. The tubes are supplied and analysed by

Gradko International Ltd using the 50% TEA in acetone preparation method. Gradko achieved 100%

satisfactory scores in all recent rounds of the AIR-PT scheme – an independent scheme set up by

Defra to provide quality assurance/quality control services. The diffusion tubes are bias adjusted

using local bias factors from tubes co-located with PCC’s automatic analysers.

Details of all automatic and diffusion tube monitoring sites are given in Tables 5 and 6 in Appendix A.

Full details of the Quality Assurance procedure for PCC’s air quality monitoring activities is provided in

Appendix B.

A summary of the measured NO2 concentrations at monitoring sites that have recorded exceedances

of the Limit Value within the last 5 years is given in Table 1. Error! Reference source not found.

and Figure 6 in Appendix A presents the locations of key monitoring sites, indicating whether they

exceeded the Limit Value in 2017 and 2018.

Table 1 Annual Mean NO2 Concentrations at PCC Monitoring Sites Exceeding the Limit Value

Site ID Site Type Monitoring Type NO2 Annual Mean Concentration (μg/m3)

2014 2015 2016 2017 2018

1 Roadside Diffusion Tube 42.57 44.33 43.52 38.8 42.92






26 Kerbside Diffusion Tube 40.81 43.12 49.16 43.09 46.02



44 Roadside Diffusion Tube Monitoring started in 2018 40.41







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Site ID Site Type Monitoring Type NO2 Annual Mean Concentration (μg/m3)

116 Roadside Diffusion Tube Monitoring started in 2017 42.56 N/A

117 N/A Diffusion Tube Monitoring started in 2018 50.42




C2 Kerbside Automatic 45.68 38.4 41.21 44.6 40.57


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3. Emissions Model

The latest version of Emissions Factor Toolkit (EFT) will be used as the emissions model for this study

to calculate road traffic oxides of nitrogen (NOx) emissions1. Version 9 of the EFT has been

specifically released for local authorities to undertake their Local Plan Development studies for JAQU.

The tool uses the latest COPERT 5 NOx emission factors to allow users to define their vehicle fleet

composition by vehicle type, fuel type and Euro standard as well as a fleet projection tool and

petrol/diesel tool for adjusting Euro standard compositions using local data.

Link-specific pollutant emission rates and annual emissions totals will be calculated for each road in

the study domain. The link-specific emission rate outputs from the EFT are linked to the respective air

quality model scenario to enable concentration predictions to be made at identified discrete and

gridded receptors. The link-specific annual emissions outputs from the EFT will enable appropriate

source-apportionment analyses to be performed, particularly with respect to links identified by the

PCM model as being non-compliant and other roads identified by local modelling and monitoring as

being non-compliant.

Further details of the setup of the EFT are given in the AQ3 report.

1 v9.1b is the latest version issue in September 2019 and has been used for the most recent model runs.


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4. Air Quality Dispersion Model

4.1 Study Area

The study area for the Local Plan encompasses most of Portsea Island. The northern end of the

study area incorporates the M27 / A27 route corridor and a portion of Cosham. The southern extent

of the study area is approximately defined by the route of the A288.

The study area includes the main city centre area, the non-compliant links identified in Defra’s PCM

model, and other local roads of concern such as those with the designated AQMAs. It is also wide

enough to ensure the impacts of any traffic displacement effects due to the implementation of

measures aimed at bringing forward compliance can be appropriately evaluated. Figure 2 in

Appendix A outlines the study area in relation to the AQMAs.

4.2 Choice of Model

Air quality modelling will use Cambridge Environmental Research Consultant’s (CERC) Atmospheric

Dispersion Modelling System for Roads (ADMS-Roads) v4.1, which is recognised by Defra and JAQU

as a suitable air quality modelling software package for predicting pollutant concentrations from road

emissions sources and in assessing the impacts of low emission measures such as low emission

zones or clean air zones. ADMS-Roads uses advanced algorithms for the height-dependence of wind

speed, turbulence and stability to produce concentration predictions of specified pollutants at

specified discrete and/or gridded receptor locations.

This model software is commonly used by local authorities in undertaking their local air quality

management studies and has been applied previously for local plan modelling within Portsmouth and

neighbouring authorities.

Details of the proposed ADMS model setup are given in Table 2.

Table 2 ADMS-Roads Model Setup Parameters

Model Parameter / Variable ADMS Roads Model Input

Surface roughness at source (and met site)

0.5m to represent open suburbia (0.2m at met site to represent less built up land use)

Minimum Monin-Obukhov length for stable conditions

30m to represent cities and towns

Terrain types Flat, except where gradients identified

Receptor locations x, y coordinates determined by GIS, z=various

Pollutants NOx from road traffic. This is converted to NO2 as part of the model post processing

Emission factors EFT Version 9.1b

Meteorological data 1 year (2018) hourly sequential data from Thorney Island Met Office meteorological station

Model output Long-term annual mean NOx concentrations

4.3 Street Canyons

With respect to street canyon effects, the road network and detailed OS mapping with address base

and building layer data will be used to facilitate use of the ‘Advanced Street Canyon’ module within


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ADMS-Roads. Figure 2 in Appendix A shows an indication of streets initially identified as being street

canyons, based on TG16 (paragraph 7.408), which states that:

“..Although street canyons can generally be defined as narrow streets where the height of buildings

on both sides of the road is greater than the road width, there are numerous example whereby

broader streets may also be considered as street canyons where buildings result in reduced

dispersion and elevated concentrations (which may be demonstrated by monitoring data).”

Street canyons were identified by measuring the road width (building façade to building façade) and

the heights of the building; narrow streets where the height of buildings on both sides of the road was

greater than the road width were classed as a street canyon. Google Streetview and Google Earth

were used to measure the road widths and building heights.

The Advanced Street Canyon module will be applied to the relevant links included in the air quality

model domain, thereby modifying the dispersion of vehicle emissions from a road source according to

the presence and properties of the canyon walls on one or both sides of the carriageway. The

predicted pollutant concentrations are affected both inside and outside of the respective canyon when

using this model option.

The model performance will be reviewed to identify where modelling roads identified as street

canyons results in improvement in model predictions.

4.4 Tunnels / Flyovers

The locations of bridges and flyovers have been reviewed to identify the relevance of these with

respect to air quality modelling for the PCM links being investigated. Flyovers will be represented

within ADMS-Roads by assigning road elevations to the respective links using of elevations from OS

digital terrain model (DTM) data. The road elevations in metres will be extracted using GIS and will

be cross-checked against Google Earth elevation.

To establish the relative source (elevated road) height, this is determined in relation to the receptor

height. The elevation of source is calculated using the following formula:

Elevation of source= Measured source height - Measured receptor height

For example, if the source height was 100m, but the receptor height was 90m, this is represented in

the dispersion model by the height difference (10m) rather than absolute values.

In cases where the roads have different source/receptor height relationships, the source roads will be

grouped by height, a maximum of 3 height groups; significantly elevated, moderately elevated and

non-elevated. More details on the modelling of the flyovers can be found on the guidance ‘Dispersion

modelling of elevated roads and tunnels’ by JAQU.

The locations of flyovers and bridges (over roads and railway) identified within the study area are

shown in Figure 3 in Appendix A.

Based on observations from Google maps, and local knowledge, no tunnels have been identified in

the study area.

4.5 Gradient Effects

The effects of road gradients on vehicle emissions, particularly heavy duty vehicles (HDVs), should

ideally be represented in the air quality modelling appropriately. OS DTM (land heights) data was

used to calculate road gradients for all modelled road links within the study area. Gradient effects will

be calculated and applied to all road links where gradient exceeds 2.5%, in accordance with Defra’s

LAQM.TG16 methodology and associated information provided by JAQU.

Road gradients were calculated using GIS and the following procedure. The ITN network was used to

determine road segment start and end points (using the tool 'Feature Vertices to Points', selecting

option 'BOTH_ENDS'). These point features were subsequently attributed with height information

derived from the 2m Environment Agency LiDAR DTM surface (using the tool 'Add Surface

Information'). The slope for each road segment was then calculated using the formula: slope =

(change in the start of the road link -y/change in end of road link- x)*100. The resulting data was


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joined back to the original road data using osm_id to present the gradients of each road spatially in

GIS. PCC were consulted on the locations of the identified gradients, and it was concluded that

gradient effects only needed to be considered for Portsdown Hill Road, north of the A27/M27. This is

outside of the model domain.

4.6 Receptors

The ADMS-Roads model will be used to predict concentrations at individual receptor locations. A

number of receptor points will be defined at a height of 2m and 4m distance from the kerb along each

of the modelled road links – both PCM model links and other road links (see Figure 2).

All modelled receptors will be chosen to meet the siting requirements set out in the Air Quality

Directive (AQD), i.e.

At least 25m from major junctions, such as junctions between PCM model links and signalised

intersections;

Be representative of at least 100m road length;

Be in locations where there is access to the general public (e.g. not in the carriageway, or within

the central reservation, unless there is pedestrian access).

Not be in locations of no fixed habitation, or locations such as offices or industrial installations

where health and safety at work legislation applies.

A sub-set of receptors will be chosen along the road links in order to assess compliance with the NO2

limit value. These receptors will be located to have a pair of receptors on each road link as a

minimum (i.e. on either side of the road). Each receptor will be assigned to the nearest modelled road

link, and the receptor with the highest predicted concentration for each link will be recorded. This

process, known as target determination, allows a quality assurance check to be conducted by JAQU,

and enables local modelling results to be compared to the PCM model outputs.

4.7 Traffic Input Data

It is proposed that the Solent Regional Traffic Model (SRTM) will be used in this study. The SRTM

has a base year of 2015 and future projections for 2018 and 2026 so the outputs will need to be

factored to the air quality model years. The traffic model will provide flows for AM, PM and interpeak

periods for cars, light goods vehicles (LGVs), and HDVs. Buses can be split out using the public

transport module. Vehicle speed data will be provided from the model for these time periods. Some

assumptions will need to be made on the overnight period for the air quality modelling and time-

varying emission factors will be applied to the data across the day.

The local vehicle fleet proportions will be based on those provided in the EFT version 9.1b for the

model assessment year, for urban roads (outside London). Vehicle Euro compositions within the EFT

will be modified using data from the Automatic Number Plate Recognition (ANPR) camera survey that

will be undertaken for the study. A map of the ANPR survey locations can be found in Figure 7 in

Appendix A. For roads where no ANPR survey data are available, the fleet data will be based on an

average fleet for the city centre, as this will be more representative of the local vehicle fleet than the

default data in the EFT.

The data from the ANPR camera survey is provided in AQ3. Initial evidence from a 24-hour ANPR

survey conducted for PCC in AQMA No. 6 in October 2018 indicates that the vehicle Euro fleet is

likely to be different to the national average values in the EFT. Indicative Euro compositions for petrol

and diesel cars, diesel LGVs and buses, based on the October 2018 survey, are shown in Table 1 in

in Appendix A. Heavy Goods Vehicle (HGV) data are not shown as insufficient data were gathered

from the survey for these vehicles.

To enable a realistic representation of road locations, each road link included in the model will be

digitised based on the OS Mastermap ITN Centreline using GIS software to accurately follow actual

road alignments and represent road junctions. Road widths and canyon widths (where applicable) will

be measured based on the OS Mastermap topographical layer, verified by Google Streetview.


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4.8 Road Traffic NOx-NO2

The EFT v 9 (currently v9.1b) will be used to determine the primary NO2 fraction (f-NO2) for each road

link in the model. Where the f-NO2 values vary, then a different fraction may be applied in the model

on an area or link-specific basis, following the guidance provided by JAQU. The ADMS-Roads model

will be run to calculate road NOx concentrations from the road sources only, and these will be

converted to NO2 during post-processing of results using the Defra’s NOx-NO2 calculator by

specifying the year, local authority and All UK traffic mix.

4.9 Background Contributions

This study will model NOx emissions from road transport sources. Therefore, the contribution from all

other sources will be taken from Defra’s LAQM background maps based on 2017 for the relevant 1km

grid square. Any road transport sources that have been modelled will be subtracted from the

background map using Defra’s NOx sector removal tool. The year of the background mapping will

correspond to the model year. If activity data are available for ships and on-side port operations, then

the study will consider explicitly modelling these too.

The NO2 concentrations from the background maps will be compared to concentrations at PCC’s

background monitoring site (C4) to determine whether they are appropriate for use. For example, in

2017, the annual mean NO2 concentration at C4 was 19 µg/m3, which is lower than those in the

background maps which range from 22-24 µg/m3.

4.10 Model Verification

Local model verification will be conducted against PCC’s monitoring data to ensure the model outputs

are in good agreement with measured concentrations. These sites are provided in Table 3 and 4 in

the appendix.

All monitoring sites been reviewed to identify those which are suitable for model verification. Sites

with inadequate data capture, kerbside sites and urban background locations will be considered as to

whether they should be excluded from the verification process.

It is noted that the PCM centreline is not highly accurate, so once the detailed road network has been

correctly aligned to the road centre line, further scrutiny of the monitoring sites and data will be

undertaken. However, it is considered that these monitoring sites are likely to be most appropriate for

the purposes of model verification for target determination.

The locations of candidate monitoring locations, diffusion tube stations and automatic monitoring

stations, are shown in Appendix A.

Details on model performance and uncertainty (root mean squared error - RMSE) will be provided as

part of the model verification process, as per section 7.536 onwards of LAQM.TG16. Detailed

information on the model verification will be given in AQ3.

5. Model Predictions

5.1 Baseline

For this study, it is proposed that the base model year will be 2018; modelling will be undertaken in

early 2019, ratified monitoring data and meteorological data for 2018 are available. This purpose of

the base model is for model verification against local monitoring data and to establish the current

situation with respect to annual mean NO2 concentrations.

Hourly average meteorological data (wind speed, wind direction, cloud cover, temperature, pressure,

rainfall and relatively humidity) for the dispersion modelling will be taken from the Thorney Island met

office meteorological station (Lat. 50.817; Lon. -0.917; elevation: 3m), which is approximately 15km

from the study area. This was considered to be the closest and most representative site and has

been used in previous modelling studies for the city. A wind rose based on 2018 is shown in Figure 6

in Appendix A. The dominant wind direction in this year was from the southwest (270 degrees).


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Detailed dispersion modelling of a future baseline year will be conducted using the ADMS-Roads

model, in the same manner as the 2018 baseline year (i.e. with the same meteorological file and

model verification factor(s)). Based on some initial local modelling undertaken for the Targeted

Feasibility Study, the earliest data where compliance potentially may be achieved with measures

would be 2019 for compliance link 48196, 2022 for compliance link 18114 and 2024 for AQMA6

(London Road).

Based on an assessment that the earliest time a Clean Air Zone (CAZ) could be implemented is 2022,

it is proposed that future modelling is undertaken for this year. Any measures put in place between

2018 and 2022 not explicitly modelled as part of this study would need to be included in the future

baseline as part of the SRTM outputs.

If the results of the modelling show that compliance is achieved by the future model year for any of

the road links, then JAQU’s future-year projection factors would be used to estimate the intermediate

year between the modelled 2018 base year and future base year when compliance may be achieved.

If compliance is not achieved on all road links in the future base year, then additional detailed

modelling would need to be conducted for additional future years.

The fleet composition for the modelled future years will be projected from the baseline fleet

information obtained from the ANPR camera survey. The fleet projection tool within the EFT v 9.1b

will be used to project future fleets. There are two options to do this within the EFT, the first assumes

that the fleet composition in the future year has the same differences to the base year and the second

assumes the fleet converges towards the default national predictions for that year. This projection tool

also takes in account of penetration of more stringent vehicles in the fleet, including Euro 6d. For this

study, option 1 is the most reasonable assumption, as the ANPR study showed that the fleet is

generally older than national assumptions (see Figure 8 in Appendix A). The separate petrol/diesel

tool provided by JAQU will also be used to update the split of cars by fuel type with the latest

information from DfT.

The EFT will be run to calculate NOx emissions (g/km/s) for the proposed future year for the baseline

situation. In addition, annual future baseline emissions will be calculated in the future (using 2026

traffic data) for NOx, PM10 and carbon dioxide (CO2). The purpose of these calculations is to compare

long term costs and benefits of the modelled options.

5.2 Future With-Measures Projections

Future modelling for “with-measures” scenarios will consider the same year as for the future base

year in order to identify the impacts due to the measures only. For CAZ modelling, compliant and non-

compliant vehicles will be separated out in the EFT to understand their emissions contributions. As

with the future baseline, NO2 concentrations in intermediate years will be projected based on JAQU’s

projection factors. The measures to be modelled will be confirmed for the Strategic Outline Case

(SOC) and will be compared against a CAZ benchmark.

As for the baseline year, the EFT will be run to calculate future fleet emissions of NOx, PM10 and CO2

for a future year using 2026 traffic outputs to inform options appraisal.


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Appendix A Supporting Information


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Figure 1 PCM Links and 50m Buffer


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Figure 2 Location of Street Canyons


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Figure 3 Location of Flyovers and Bridges


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Figure 4 Measured Exceedances of Annual Mean NO2 Limit Value, 2018


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Figure 5 Monitoring Locations


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Figure 6 Wind Rose, Thorney Island (2018 data)

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6

3.1

10

5.1

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(knots)

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Wind speed

0°

30°

60°

90°

120°

150°

180°

210°

240°

270°

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1200


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Figure 7 ANPR Camera Sites.


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Figure 8: Euro Composition from ANPR compared to national fleet


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Table 3 Details of automatic monitoring locations

Site ID Site Name

Site Type X OS Grid Ref

Y OS Grid Ref

Pollutants Monitored

In AQMA?

Monitoring Technique

Distance to Relevant Exposure (m) (1)

Distance to kerb of nearest major road (m) (2)

Inlet Height (m)

C2 London Road

Kerbside 464925 102129 NO2 PM2.5 PM10

Y Chemiluminescent, HORIBA's APDA- 372

1.8m of the kerbside further to the south of the station

1m 1.8m

C4 Gatcombe Park Primary School

Urban Background

465403 103952 NO2 PM10 PM2.5 O3

N Chemiluminescent, FDMS

0m 119 m 2.5m

C6 Burrfields Road

Roadside 466004 102348 NO2 PM10

N Chemiluminescent, Eberline

0.5m 4.5m of Burrfields Road & 5.5m of Copnor Road

1.8m

C7 Mile End Road

Roadside 464397 101270 NO2 PM2.5 PM10

Y Chemiluminescent HORIBA's APDA- 372

2m 6.5m 1.8m

DEFRA Anglesea Road

Roadside 463835 100259 NO2 PM10

Y Chemiluminescent; FDMS

2m 6.5m 1.8m


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Table 4 Details of diffusion tube locations

Site ID

Site Name Site Type X OS Grid Ref

Y OS Grid Ref

Pollutants Monitored

In AQMA?

Distance to Relevant Exposure (m) (1)

Distance to kerb of nearest road (m) (2)

Tube collocated with a Continuous Analyser?

Height (m)

1 Lord Montgomery Way (FST) Roadside 463872 99874 NO2 Y 0 3.7m N 2m

2 12 Chadderton Gardens (CG-12) Urban background 463705 99371 NO2 N 0 N/A N 2m

3 High Street (HS-121A) Roadside 463408 99460 NO2 Y 0 3.1m N 2m

4 Queen Street (QS-Col 30) Roadside 463190 100390 NO2 Y N/A 3m N 2m

5 119 Whale Island Way (WIW-119) Roadside 464230 102194 NO2 N 0 16.23m N 2m

6 88 Stanley Road (SR-88) Roadside 464331 102197 NO2 N 0 9.88m N 2m

7 138 Lower Derby Road (LDR-138) Urban background 464291 102279 NO2 N 0 37.57m N 2m

8 492 Hawthorn Crescent (HC-492) Urban background 466690 104355 NO2 N 0 34m N 2m

9 6 Northern Road (NR-6) Roadside 465621 105528 NO2 N 0 5.43m N 2m

10 20 Stroudley Avenue (SA-20) Urban background 467107 104850 NO2 N 0 N/A N 2m

11 Anchorage Road (AR-Col6) Roadside 466869 103457 NO2 N 11.76M 6.56m N 2m

14 4 Merlyn Drive (MD-4) Roadside 466109 103736 NO2 N 0 11.26m N 2m

15 29 Milton Road (MR-29) Roadside 466120 101324 NO2 N 0 7.04m N 2m

16 Parade Court, London Road (LR-PC) Roadside 465474 104205 NO2 N 5.32m 5.15m N 2m

18 4 Milton Road (MR-4) Roadside 466097 101332 NO2 N 0 6.13m N 2m

19 7 Velder Avenue (VA-7) Roadside 466392 100226 NO2 Y 0 4.44m N 2m

20 136 Eastney Rd (ER-136) Roadside 466712 99415 NO2 N 0 6.23m N 2m

21 118 Albert Road (AR-116) Roadside 465209 98964 NO2 N 0 2.36m N 2m

22 2 Victoria Road North (VRN-2) Roadside 464778 99306 NO2 N 0 5.53m N 2m

23 106 Victoria Road North (VRN-106) Roadside 464974 99766 NO2 N 2.37m 2.42m N 2m

24 221 Fratton Road (FR-221) Roadside 465111 100737 NO2 Y 0 4.21m N 2m

25 117 Kingston Rd (KR-117) Roadside 465036 101547 NO2 Y 0 2.46m N 2m

26 The Tap London Road (Tap) Kerbside 464900 101976 NO2 Y 0 1.91m N 2m

30 Market Tavern (Mile End Rd) (MT) Roadside 464478 101457 NO2 Y 0 12.73m N 2m

32 Larch Court, Church Rd (CR-Corner) Roadside 464559 100980 NO2 N 0 5.84m N 2m

34 Sovereign Gate, Commercial Rd (UF) Roadside 464425 100893 NO2 Y 0 4.40m N 2m

35 Hampshire Terrace (AM) Roadside 463837 99759 NO2 N 0 4.9m to 10.74m

N 2m

36 Elm Grove (EG-103) Roadside 464501 99329 NO2 N 0 2.26m N 2m

54 Anglesea Road, Victoria Park, Column 234 (AR-VP-Col)

Roadside 463835 100257 NO2 N 0 1.5 N 2 m

55 Gunwharf Road, Column 12 (GWR-Col12) Roadside 463224 99590 NO2 n 0 1.5 m N 2m

56 Gunwharf Road, Column 4 (GWR-Col4) Roadside 463261 99782 NO2 N 0 1.5 m N 2m

58 St Georges Street-9 (St GS-9) Roadside 463487 99659 NO2 N N/A 6 N 2m

65 Mooring Way-12 (MW-12) Roadside 466681 100373 NO2 N 11.76M 1.5 m N 2m

70 Milton Primary School (ER-DS) Roadside 466667 99546 NO2 N 0 5 m N 2m


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92 Locksway Road-13 (LR-13) Roadside 466525 99736 NO2 N 0 2.5 m, N 2m

104 219 Jervis Road Urban background 464120 102717 NO2 N 0 4 m N 2m

105 Column 8 Tipner Urban background 464097 102773 NO2 N N 2m

106 24 Tipner Urban background 464046 102932 NO2 N 0 N 2m

107 Column 3 Tipner Urban background 464058 103007 NO2 N 0 N 2m

112 Medina School Fratton Road (MS1) Urban background 465116 101029 NO2 N 0 30 m N 2m

113 Medina School Fratton Road (MS2) Roadside 465119 101015 NO2 N 5.32m 30 m N 2m

114 233 Southampton Road Roadside 462331 105651 NO2 N 0 6 m N 2m

115 Catholic Church St Agatha's Church Market Way

Roadside 464953 100705 NO2 Y 0 4 m N 2m

116 Catholic Cathedral Alfred Road Roadside 463891 100479 NO2 N 0 5m N 2m

42 Kingston Crescent-Admiral Drake PH- (KC-ADPH)

Roadside 464552 101940 NO2 Y 0 N 2m

43 Kingston Crescent-Vanguard House (KC-VH) Urban background 464774 101922 NO2 N 0 N 2m

44 Market Way-24 (MW-24) Roadside 464336 100833 NO2 Y 0 N 2m

45 Market Way-79 (MW-79) Roadside 464344 100808 NO2 Y N/A N 2m

46 Market Way-Column 5 (MW-Col5) Roadside 464339 101273 NO2 N 0 N 2m

47 Stamshaw Road West (1) Roadside 464586 102125 NO2 N 0 N 2m

48 Stamshaw Road East (28) Urban background 464597 102119 NO2 N 0 N 2m

49 Half Moon Street-The Ship and Castle(PH) (HMS-S&CPH)

Urban background 463042 100315 NO2 N 0 N 2m

50 Queen Street-47 (QS-47) Roadside 463388 100398 NO2 N 0 N 2m

51 Queen Street-57 (QS-57) Urban background 463333 100395 NO2 N 0 N 2m

52 Queen Street-Column 29 Roadside 463235 100412 NO2 N 11.76M N 2m

53 Anglesea Road Station-DEFRA (AR-Station) Roadside 463835 100258 NO2 N 0 Y 2m

57 Saint Jude School-Column 7 (StJSc-Col7) Urban background 463503 99362 NO2 N 5 0.5 m N 2m

59 Milton Road- Across the road from Column 42 on the fence (MR-Opposite Col42)

Roadside 466263 100334 NO2 N 1.5 m N 2m

60 Milton Road- Column 42 (MR-Col42) Roadside 466201 100478 NO2 N 5.32m N 2m

61 Milton Road-1 to 10 Southwick House (MR- SH(Fence))

Roadside 466136 100610 NO2 N 0 N 2m

62 Milton Road-12 Hambrook House (MR-HH) Roadside 466165 100573 NO2 Y 0 N 2m

63 Milton Road-209 (SR-209) Roadside 466354 100172 NO2 N 0 N 2m

64 Milton Road-Summerson Lodge (MR-SL) Roadside 466326 100165 NO2 N 0 N 2m

66 Velder Avenue-1 (VA-1) Roadside 466267 100216 NO2 N 0 N 2m

67 Velder Avenue-23 (VA-23) Roadside 466457 100253 NO2 N 2.37m N 2m

68 Velder Avenue-36 (VA-36) Roadside 466501 100277 NO2 Y 0 N 2m

69 Velder Avenue-Column 4 (VA-Col4) Roadside 466396 100248 NO2 Y 0 N 2m

71 Havant Road-19 (HR-19) Kerbside 465711 105624 NO2 Y 0 N 2m

72 Northern Road-60 (NR-60) Roadside 465657 105577 NO2 N 0 N 2m

73 Northern Road-52 Roadside 465653 105544 NO2 Y 0 N 2m

74 Northern Road-Column 38 (NR-Col38) Roadside 465610 105383 NO2 Y 0 N 2m

75 Southampton Road-1-6 Chipstead House (SR-CH)

Roadside 465618 105619 NO2 N 0 N 2m

76 Copnor Road-142 (CR-142) Roadside 466002 102053 NO2 Y 0 N 2m

77 Copnor Road-Copnor School Playground (CR-School)

Roadside 466008 102097 NO2 N 0 N 2m


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78 Goldsmith Avenue-3 (GA-3) Roadside 466523 99599 NO2 N 0 N 2m

79 Goldsmith Avenue-Column 1 (GA-Col1) Kerbside 466555 99598 NO2 Y 1.8 m N 2m

80 Albert Road -147 (AR-147) Urban background 465204 98978 NO2 N 0 N 2m

81 Albert Road Column 22 (AR-Col22) Roadside 465278 98968 NO2 N 0.5 M N 2m

82 Albert Road-106 t0 108 On Waverley Road (AR-WR)

Roadside 465178 98945 NO2 Y 2m N 2m

83 Albert Road-141 (AR-141) Roadside 465166 98982 NO2 n N 2m

84 Albert Road-145 on Lawrence Road (AR-145) Roadside 465198 98996 NO2 N N 2m

85 Albert Road-96 (AR-96) Urban background 465150 98968 NO2 N 5 N 2m

86 Fawcett Road-91 (FR-91) Roadside 465201 99734 NO2 N N/A N 2m

87 Fawcett Road- Priory School (FR-PSc) Roadside 465183 99904 NO2 N N 2m

88 Lawrence Road -1 to 8 Brandon House (LR-BH)

Urban background 465186 98996 NO2 N 0 N 2m

89 Waverley Road-114 (WR-114) Urban background 465190 98946 NO2 N N 2m

90 Baffins Road-18 (BR-18) Urban background 466095 100813 NO2 N 0 N 2m

91 Baffins Road-3 (BR-3) Urban background 466070 100819 NO2 N 0 N 2m

93 Victoria Road North-40 (Nursery) (VRN-40 Nursery)

Roadside 464826 99500 NO2 N 0 N 2m

94 2&3 Selbourne Terrace Roadside 465162 100077 NO2 N 11.76M N 2m

95 189 Collins Place Fratton Roadside 465109 100005 NO2 N 0 N 2m

96 Mary Rose Centre, Albert Road Urban background 465465 98937 NO2 N 0 N 2m

97 29 Rowan Court, Goldsmith Avenue Roadside 465896 99852 NO2 N 5.32m N 2m

98 13-29 Eastern Road Roadside 466700 100591 NO2 N 0 N 2m

99 64-80 Eastern Road Roadside 466727 100572 NO2 Y 0 N 2m

100 340 Havant Road Roadside 467783 105677 NO2 N 0 N 2m

101 Havant Road Column 52 Roadside 467693 105687 NO2 N 0 N 2m

102 Hillside & Wymering Centre Roadside 464585 105714 NO2 N 0 N 2m

103 UTC Portsmouth Roadside 465556 103968 NO2 N 2.37m N 2m

108 137 London Road Roadside 464951 102418 NO2 Y 0 N 2m

109 122/124 London Road Roadside 464961 102383 NO2 Y 0 N 2m

110 2a/2b Gladys Avenue Roadside 464913 102419 NO2 Y N 2m

111 Column 3 Gladys Avenue Roadside 464898 102414 NO2 Y N 2m

117 Alfred Road Column 9 (AR-Col 9) Kerbside 463901 100508 NO2 N N 2.5m

118 Alfred Road Column 12 (AR-Col 12) Roadside 463951 100531 NO2 N N 2.5m

119 Alfred Road -left of St Agatha's bus shelter (MW-StABS)

Kerbside 464098 100748 NO2 N N 2.5m

120 Alfred Road Opposite MW-StABS (MW-OppStABS)

Kerbside 464086 100765 NO2 N N 2.5m

121 46 London Road (LR-46) Roadside 464930 102071 NO2 Y N 2.5m

122 47 London Road (LR-47) Roadside 464918 102090 NO2 Y N 2.5m

124 Hillsley Road Column 23 (HR-Col23) Urban Background 462491 106553 NO2 N N 2.5m

125 7 Tudor Crescent (TC-7) Urban Background 465624 104626 NO2 N N 2.5m

126 Column 32 Port Way (PW-Col32) Roadside 463756 105253 NO2 N N 2.5m

127 133 Southampton Road (SR-133) Roadside 463536 105652 NO2 N N 2.5m

131 16 London Road on Chichester Road (CR-PP) Roadside 464925 101969 NO2 Y N 2.5m


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132 Column 50 Milton Road (MR-Col50) Roadside 466344 100139 NO2 Y N 2.5m

133 Labour Party Club Holbrook Road (HR-LPC) Roadside 464882 100475 NO2 N N 2.5m

134 Labour Party Club Coburg Street (CS-LPC) Roadside 464919 100464 NO2 N N 2.5m


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Appendix B Quality Assurance of Monitoring Data

QA / QC of automatic monitoring

Continuous Air Quality Monitoring, Quality Assurance and Quality Control PCC manages four air quality-monitoring stations. These are all fully equipped with PCC DEFRA / NETCEN approved real-time automatic continuous monitoring analysers. These are sophisticated automatic monitoring systems housed in purpose built air-conditioned enclosures. These analysers measure and record in real-time a combination of NO2, PM10 and PM2.5. PCC compiled continuous air quality monitoring data for the Further Assessment using Horiba’s APNA-370, NO2 based on the chemiluminescent analysis method. Routine site operations PCC employs a dedicated staff member to operate the network of continuous air quality monitoring stations. He is trained in all aspects of the monitoring processes including routine site operations, field calibrations and data ratification. He is also the NETCEN trained Local Site Operator (LSO) for the local affiliated AURN station. This is to ensure that both a high-level of accurate data and an acceptable percentage of data capture are obtained. All automatic monitoring equipment has both routine remote calibration check and routine (fortnightly) on-site checks. They also have maintenance visits, which follow documented procedures that stem from equipment manuals, manufacturer instructions and the UK Automatic Network Site Operators Manual. Routine visits include:

visual inspection of the station

regular inlet-filter changes

regular sampling head-cleaning and airflow

a two-point calibration of the NO2 analyser using a zero-air scrubber and a Nitric Oxide (NO) gas

on-site

AIR LIQUIDE supplies the NOx span gas with the concentration certificate. This gas is traceable

to national standards

All equipment fitted within each station’s enclosure (e.g. sample meteorological sensors, pumps, air conditioning units, modem etc.) is subject to independent routine maintenance and support via a service contract with Horiba. This includes:

six-monthly minor service and equipment check visits by the manufacturer for Horiba’s analysers

and approved engineers covering all non-Horiba equipment following national protocols and

traceable QA/QC procedures. Horiba is ISO 9001 accredited and carries out similar or identical

support work for a number of AURN network stations across the UK

six-monthly major service where a full multi-point calibration is carried out on the NO2 analyser,

using zero-air, NO and NO2 span gas (again traceable to national standards) meaning the

analyser data slope and offset factors are reset. In addition to multi-point calibration the following

checks are carried out:

linearity

noise

response time, leaks and flow

converter efficiency

stability of the on-site gas calibration cylinder.

The local AURN station is also subject to external audit. Site Inter-calibration checks carried out by

National Environmental Technology Centre Network engineers prior to each Horiba’s major service.


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Horiba also carries out non-routine site visits in response to equipment failure to the same standards.

Contract arrangements ensure that visits are carried out within two to three days of the notification of

call-out in order to minimise data loss.

All routine and non-routine site visits are fully documented and detail all works carried out, including

any adjustments, modifications and repairs completed.

Calibration check methods

The calibration procedure for NOx for sites C2, C4, C6 and C7 is based on a two point zero / span

calibration check being performed at intervals of two weeks. The calibration procedure for the NOx

analyser of the C4 AURN network was based on three points, the third being span NO2 to check the

NO2 Converter. However this was changed to two point calibration check. The methodology for the

calibration procedure is followed according to the manufacturers’ instruction handbooks:

pre-calibration check - the site condition and status of the analyser is recorded prior to the zero /

span check being conducted

zero check – the response of the analyser to the absence of the gas being monitored. The

stations were fitted with an integrated scrubber system incorporating a set of scrubbers,

Hopcalite, activated charcoal, Purafil and Drierite, to generate a dried gas with none of the

monitored pollutants. All were changed at least every six months but Hopcalite is changed more

frequently due to the high levels of humidity in Portsmouth. These were changed with to be fitted

with synthetic air cylinders supplied by Air Liquide UK Ltd

span check – the response of the analyser to the presence of the gas of a known concentration.

Traceable gases are used for calibration checks supplied as part of the maintenance contract

post calibration check - the site condition and status of the analyser upon completion of all

checks

all Horiba’s APNA-370 analysers have their own built in data storage facility. They are built in a

multi-drop set up. The calibration checks are done directly through the front panel. Each analyser

zero / span check is fully documented with records being kept centrally

Automatic data handling

All the stations are remotely accessible from a desktop computer at the civic offices via a telemetry

linkage by either landline or GSM system. The telemetry linkage software used is ‘Data

Communication Server’. It is set on a daily auto-dial collection mode for data retrieval. It is also set to

run calibration checks every three days.

Once the connection is established, the ‘Data Communication Server’ software retrieves the overnight

auto-calibration first and stores it in a temporary database and a calibration factor is generated

according to the following steps:

instrument span, F = C/(Vs-Vz) and

pollutant concentration (ppb) = Fx(Va-Vz) where:

C is the set gas value on the gas certificate

Vs span value

Vz zero span value

Va is the sample value as recorded by the analyser.

Raw measured data retrieved from the station data logger(s) is then subject to the calculated

correction factors and stored in the final database as corrected. The latter is then made readily

available to be queried via the ‘IDAZRW Central Station’, database access software.

Instrument status and internal auto-calibration data can be viewed in addition to the corrected

collected measured monitoring data.


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The air quality data ratification is carried out manually from this station.

Manual data handling

All collected data is screened or validated by visual examination to see if there are any unusual

measurements. The affected data is then flagged in the database. Any further remaining suspicious

data, such as large spikes, ‘flat-lines’ and excessive negative data is flagged for more detailed

investigation. ‘IDAZRW Central Station’ is capable to trace back any change made at all times with

the administrator’s name. An original raw dataset is always kept in the data processing software.

When data ratification has been completed the data is then made available for further statistical and

critical examination for reporting purposes.

Air quality monitoring data can be imported manually into a Microsoft Excel spreadsheet. This scaled

data (where values are above the lower detectable limit is considered to be valuable data) is then

further converted to generate data in the National Air Quality Objective format to enable direct

comparison to the standards. A file of raw data is always kept for reference in the database.

QA / QC of diffusion tube monitoring

Monitoring technique The continuous NO2 monitoring network is complemented by a secondary network of passive NO2 tubes that are located in suspected air quality hot spots. In addition, tubes are located at the relevant continuous monitoring sites to enable data adjustment. At a selection of sites three tubes are exposed simultaneously and the data compared. Where the data is consistent, the results are averaged. Where the tubes results show significant differences the data is discounted. This method provides a cost-effective means of monitoring a wide range of monitoring locations. The accuracy of tubes however is variable depending on the tube handling procedures, the specific tube preparation, adsorbent mixture and the analysing laboratory. These tubes are supplied and analysed by Gradko International Ltd.

PCC’s NO2 diffusion tubes are prepared by the supplier using 50% Triethanolamine (TEA) in acetone.

These tubes were exposed for one-month periods in accordance with LAQM.TG (16) guidance.

Tube Handling Procedures Once received by post, NO2 tubes are stored in cool location within the supplied packaging until use. The tube end caps are not removed until the tube has been placed at the monitoring location at the start of the monitoring period. The exposed tubes are recapped at the end of the monitoring period and returned as quickly as possible to a clean cool storage environment then sent to GIL for analysis. Laboratory QA / QC GIL is a UKAS accredited company for the analysis of NO2. GIL take part in the WASP scheme on a quarterly basis. An inter-comparison of results from other laboratories demonstrates that GIL’s performance is good in terms of accuracy and precision. Data Ratification Once analysed, the NO2 diffusion tubes results which, were significantly within the documented limit of detection, were laboratory blank corrected. The returned results are closely examined on a monthly basis to identify any spurious data (e.g. very high or very low data). The data is subjected to a further series of corrections for the monitored period under consideration:

Firstly, PCC use the data from the local collocation study of NO2 diffusion tubes to calculate the

bias following the approach prescribed in Box 6.4 of LAQM TG (16) using the appropriate

continuous monitoring data from the local air quality monitoring network for individual NO2

monitored site according to the site criteria

Secondly, the estimation of the NO2 annual mean is deduced for individual NO2 diffusion tube

monitored locations following the approach prescribed in Box 6.5 of LAQM TG (16) using data

from both Portsmouth and Southampton AURN stations


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The corrected results are then reported and used for comparison only, i.e. not for verification

processes in the Further Assessment (Review and Assessment process).


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aecom.com


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anna savage report jaqu air quality methodology modelling

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