technical note: packington power plant: dispersion of emissions … · 2020. 9. 4. · packington...

1 © Amec Foster Wheeler Environment & Infrastructure UK Limited

June 2015 Doc Ref: S:\E&I\Projects\30356 REM Sita Packington\D - Design\Final_Tech_Note 15379i1.docx

Technical note: Packington Power Plant: Dispersion of Emissions to Air from Landfill Gas Engines

1. Introduction

Amec Foster Wheeler Environment and Infrastructure UK Ltd (‘Amec Foster Wheeler’) has been

commissioned by SITA UK Limited (SITA) to undertake an air quality assessment to determine the impact

from the operation of 7 established landfill gas engines, as well as an additional proposed engine, at

Packington Power Plant, West Midlands.

Following the recent installation of 7 Jenbacher engines from 2012 – 2014, a proposed eighth engine is due

to be commissioned. The necessity for Engine 8 has arisen due to the requirement to maintain generation

output at 7MW whilst another engine is operating at reduced output or is offline for routine maintenance –

Engine 8 will provide additional engine capacity for this to be possible. Initially, the location for the proposed

eighth engine was in the Engine House. However due to space constraints, Engine 8 will now be installed at

the south end of site. The proposed new engine will be a either a Jenbacher JCG320 or a CAT 3516.

In this assessment the model has been developed to identify the maximum likely short-term and long-term

impacts on local air quality during operation under a ‘worst-case’ emission scenario. That is to say that it has

been assumed that all 8 engines operate continuously throughout the year at maximum load.

1.1 Aims and Objectives

The overall aim of this technical note is to present the results of dispersion emissions to air from Packington

Power Plant. The impacts of 7 established on-site engines, as well as an eighth proposed engine, have been

assessed using detailed dispersion modelling and the H1 methodology.

1.2 Site and Operator

The site address is:

Packington Power Plant

SITA UK Ltd

Packington Lane

Little Packington

West Midlands

CV7 7HN

Grid Reference: SP213846.



The general site location is shown in Figure 1.1 below, the location of the Packington Power Plant (PPP)

compound installation boundary is shown in Figure 1.2, and the layout of the engines in Figure 1.3 (proposed

Engine 8 circled in red).

Figure 1.1 Packington Landfill Site Location

Figure 1.2 Packington Landfill Installation Boundary

Site Location



Figure 1.3 Packington Power Plant Layout

2. Methodology

2.1 The Dispersion Model

This study uses the ADMS 5.1 model, which was developed in the UK by Cambridge Environmental

Research Consultants (CERC) in collaboration with the Meteorological Office, National Power and the

University of Surrey. ADMS is termed a ‘new generation’ model, parameterising stability and turbulence in

the planetary boundary layer (PBL) by the Monin-Obukhov length and the boundary layer depth. This

approach allows the vertical structure of the PBL to be more accurately defined than by the stability

classification methods of earlier dispersion models such as R91 or ISC.

Like R91 and ISC, ADMS adopts a symmetrical Gaussian profile of the concentration distribution in the

vertical and crosswind directions in neutral and stable conditions. However, unlike R91 or ISC, the ADMS

vertical concentration profile in convective conditions adopts a skewed Gaussian distribution to take account

of the heterogeneous nature of the vertical velocity distribution in the Convective Boundary Layer (CBL).

Numerous model validation studies have demonstrated that ADMS is acknowledged to be fit for the purpose

of EIA and EP by the Environment Agency and planning authorities.

2.2 Process Emissions

This assessment has considered emissions to air from a total of 8 landfill gas engines. For the purposes of

this assessment, emissions of oxides of nitrogen (NOx), carbon monoxide (CO), volatile organic compounds

(VOCs) and non-methane VOCs (NMVOCs) have been assessed. Tables 2.1 and 2.2 detail the model input

parameters and emission rates.



Table 2.1 Model Input Parameters

Source Name Stack Height (m) Diameter (m) Velocity (m/s) Temperature (°C)

Engine 1 8.0A 0.35 32.98 450

Engine 2 8.0 A 0.35 32.98 450

Engine 3 8.0 A 0.35 32.98 450

Engine 4 3.4 A 0.35 32.98 450

Engine 5 3.4 A 0.35 32.98 450

Engine 6 3.4 A 0.35 32.98 450

Engine 7 3.4 A 0.35 32.98 450

Engine 8 3.8 A 0.35 38.34 450

A 4 m deducted off total stack height to account for stack location in rail cutting.

Table 2.2 Engine Emission Rates

Source Name Type NOx (g/s) CO (g/s) VOCs (g/s) NMVOCs (g/s)

Engines 1-7 Jenbacher J320 0.56 1.57 1.12 0.08

Engine 8 CAT 3516 0.65 1.83 1.31 0.10

2.3 Meteorology

For meteorological data to be suitable for dispersion modelling purposes, a number of meteorological

parameters need to be measured on an hourly basis. These parameters include wind speed, wind direction,

cloud cover and temperature. There are only a limited number of sites where the required meteorological

measurements are made. The year of meteorological data that is used for a modelling assessment can also

have a significant effect on ground level concentrations.

This assessment has used meteorological data recorded at Birmingham Airport during the 2010-2014 period.

The meteorological station is located approximately 3.4 km to the south-west of site and is the nearest

synoptic station to the site offering data in a suitable format for the model. Previous assessments have used

meteorological data from Coleshill, which is located approximately 2.5 km north of site, however it was

deemed prudent to use data from Birmingham Airport for this assessment as more recent data was

available. This means that results from this assessment are not directly comparable with previous

assessments that use different meteorological data.

The following Figures 2.1 to 2.5 show the wind roses from Birmingham Airport for each year modelled, which

illustrate the frequency and distribution of wind directions and wind speeds.



Figure 2.1 2010 Windrose Figure 2.2 2011 Windrose

Figure 2.3 2012 Windrose Figure 2.4 2013 Windrose

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Figure 2.5 2014 Windrose

2.4 Surface Characteristics

The predominant surface characteristics and land use in a model domain have an important influence in

determining turbulent fluxes and, hence, the stability of the boundary layer and atmospheric dispersion.

Factors pertinent to this determination are detailed below.

Surface Roughness

Roughness length, z0, represents the aerodynamic effects of surface friction and is physically defined as the

height at which the extrapolated surface layer wind profile tends to zero. This value is an important

parameter used by meteorological pre-processors to interpret the vertical profile of wind speed and estimate

friction velocities which are, in turn, used to define heat and momentum fluxes and, consequently, the degree

of turbulent mixing in the boundary layer.

The surface roughness length is related to the height of surface elements; typically, the surface roughness

length is approximately 10% of the height of the main surface features. Thus, it follows that surface

roughness is higher in urban and congested areas than in rural and open areas. Oke (1987) and CERC

(2003) suggest typical roughness lengths for various land use categories (Table 2.3).


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Table 2.3 Typical Surface Roughness Lengths for Various Land Use Categories

Type of Surface Z0 (m)

Ice 0.00001

Smooth Snow 0.00005

Smooth Sea 0.0002

Lawn Grass 0.01

Pasture 0.2

Isolated Settlement (farms, trees, hedges) 0.4

Parkland, woodlands, villages open suburbia 0.5-1.0

Forests/cities/industrialised areas 1.0-1.5

Heavily Industrialised Areas 1.5-2.0

Increasing surface roughness increases turbulent mixing in the lower boundary layer. With respect to

elevated sources under neutral and stable conditions, conflicting impacts in terms of ground level

concentrations often occur due to:

The increased mixing can bring portions of an elevated plume down towards ground level,

resulting in increased ground level concentrations closer to the emission source; however

The increased mixing increases entrainment of ambient air into the plume and dilutes plume

concentrations, resulting in reduced ground level concentrations further downwind from an

emission source.

The overall impact on ground level concentration is, therefore, strongly correlated to the distance of a

receptor from the emission source.

2.5 Buildings

Any large object has an impact on atmospheric flow and air turbulence within the locality of the object, which

affects the dispersion of material emitted from nearby stacks. This can result in maximum ground level

concentrations that are significantly different (generally higher) from those encountered in the absence of

buildings. The building ‘zone of influence’ is generally regarded as extending a distance of 5L (where L is the

lesser of the building height or width) from the foot of the building in the horizontal plane and three times the

height of the building in the vertical plane.

Using these criteria, Table 2.4 and Figure 2.6 identify those buildings included in the model set-up.

Table 2.4 Modelled Buildings

Building Name X Y Height (m) Length (m) Width (m) Angle (°)

Engine Building 421410 284673 5 25 15 20



Figure 2.6 Building and Emission Source Visualisation

2.6 Terrain

The concentrations of an emitted pollutant found in elevated, complex terrain differ from those found in

simple level terrain. There have been numerous studies on the effects of topography on atmospheric flows.

The UK ADMLC provides a summary of the main effects of terrain on atmospheric flow and dispersion of

pollutants (Hill et al., 2002):

“Plume interactions with windward facing terrain features:

Plume interactions with terrain features whereby receptors on hills at a similar elevation to

the plume experience elevated concentrations;

421390 421400 421410 421420 421430

284630

284640

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284670

284680

284690

Engine_1

Engine_2Engine_3

Engine_4

Engine_5

Engine_6

Engine_7

Engine_8

Engine_Building*

Building

Point or jet source



Direct impaction of the plume on hill slopes in stable conditions;

Flow over hills in neutral conditions can experience deceleration forces on the upwind slope,

reducing the rate of dispersion and increasing concentrations; and

Recirculation regions on the upwind side of a hill can cause partial or complete entrainment

of the plume, resulting in elevated ground level concentrations.

Plume interactions with lee sides of terrain features:

Regions of recirculation behind steep terrain features can rapidly advect pollutants towards

the ground culminating in elevated concentrations; and

As per the upwind case, releases into the lee of a hill in stable conditions can also be

recirculated, resulting in increased ground level concentrations.

Plume interactions within valleys:

Releases within steep valleys experience restricted lateral dispersion due to the valley

sidewalls. During stable overnight conditions, inversion layers develop within the valley

essentially trapping all emitted pollutants. Following sunrise and the erosion of the inversion,

elevated ground level concentrations can result during fumigation events; and

Convective circulations in complex terrain due to differential heating of the valley side walls

can lead to the impingement of plumes due to crossflow onto the valley sidewalls and the

subsidence of plume centrelines, both having the impact of increasing ground level

concentrations.”

These effects are most pronounced when the terrain gradients exceed 1 in 10 i.e., a 100 m change in

elevation per 1 km step in the horizontal plane. Figure 2.7 provides a visualisation of topography within the

model domain.

Figure 2.7 Visualisation of Topography within Model Domain

N



2.7 Model Domain and Receptors

Modelled Domain

A 2 km x 2 km Cartesian grid centred on the site, with a receptor resolution of 40 m, was modelled to assess

the impact of atmospheric emissions from the proposed development on local air quality. This resolution is

considered suitable for capturing the maximum process contribution from site emissions.

Human Receptors

The receptors considered were chosen based on places where people may be located, judged in terms of

the likely duration of their exposure to pollutants and proximity to the site. Details of the locations of human

receptors considered in the assessment are given in Table 2.5 and Figure 2.8 below.

Model predictions are made at a height of 1.5 m above ground level, representative of the typical breathing

zone height.

Table 2.5 Modelled Human Receptors

Receptor Name X Y Distance from Site (km)

Packington Hall 422180 283980 1.03

Woodbine Cottage 421411 284860 0.22

House to East 421990 284720 0.59

Common Farm 420025 284883 1.40

Church Farm 421175 284416 0.33

Rectory Cottage 421088 284495 0.36

Beam Ends 421462 284956 0.30

Brook Farm 421555 285058 0.43

Old School House 421842 285149 0.66

North Lodge 421940 284899 0.58

Rockwood Cottage 422013 284722 0.61



Figure 2.8 Location of Human Receptors

Note: Red symbol marks centre of site

Ecological Receptors

The Environment Agency’s Horizontal Guidance Note H1 requires detailed dispersion modelling to be carried

out based on local receptors. Using this guidance, internationally and nationally-designated ecological sites

(i.e. SPAs, SACs, SSSIs, Ramsar sites and NNRs) within 15 km of the development, and locally-designated

ecological sites within 2 km of the development (e.g. LNRs, ancient woodland etc.) need to be assessed.

The receptors included in the assessment are detailed in Table 2.6 and Figure 2.9.

Table 2.6 Modelled Ecological Receptors


Coleshill and Bannerly Pools SSSI 421019 285906 1.28

Bickenhill Meadows SSSI 416027 381723 6.14

Berkswell Marsh SSSI 422734 279921 4.93

Hoar Park Wood SSSI 426728 292901 9.80

Whitacre Heath SSSI 420952 292332 7.67

Tilehill Wood SSSI 427597 279214 8.25

River Blythe SSSI 1 421813 284925 0.48







Butlers Moors Wood (Potential LWS) 421509 284591 60 m

Figure 2.9 Location of Ecological Receptors



2.8 Conversion of NO to NO2

Emissions of NOx from combustion processes are predominantly in the form of nitrogen monoxide (NO).

Excess oxygen in the combustion gases and further atmospheric reactions cause the oxidation of NO to

nitrogen dioxide (NO2). NOx chemistry in the lower troposphere is strongly interlinked in a complex chain of

reactions involving Volatile Organic Compounds (VOCs) and Ozone (O3). Two of the key reactions

interlinking NO and NO2 are detailed below:

(R1)

(R2)

Where hv is used to represent a photon of light energy (i.e., sunlight).

Taken together, reactions R1 and R2 produce no net change in O3 concentrations, and NO and NO2 adjust

to establish a near steady state reaction (photo-equilibrium). However, the presence of VOCs and CO in the

atmosphere offer an alternative production route of NO2 for photolysis, allowing O3 concentrations to

increase during the day with a subsequent decrease in the NO2:NOx ratio.

However, at night, the photolysis of NO2 ceases, allowing reaction R2 to promote the production of NO2, at

the expense of O3, with a corresponding increase in the NO2:NOx ratio.

Near to an emission source of NO, the result is a net increase in the rate of reaction R2, suppressing O3

concentrations immediately downwind of the source, and increasing further downwind as the concentrations

of NO begin to stabilise to typical background levels (Gillani and Pliem 1996).

Given the complex nature of NOx chemistry, the Environment Agency’s Air Quality Modelling and

Assessment Unit (AQMAU) has adopted a pragmatic, risk based approach in determining the conversion

rate of NO to NO2 which dispersion model practitioners can use in detailed assessments. AQMAU guidance

advises that the source term should be modelled as NOx (as NO2) and then suggests a tiered approach

when considering ambient NO2:NOX ratios:

Screening Scenario: 50 % and 100 % of the modelled NOx process contributions should be

used for short-term and long-term average concentration, respectively. That is, 50 % of the

predicted NOx concentrations should be assumed to be NO2 for short-term assessments and

100 % of the predicted NOx concentrations should be assumed to be NO2 for long-term

assessments;

Worst Case Scenario: 35 % and 70 % of the modelled NOx process contributions should be

used for short-term and long-term average concentration, respectively. That is, 35 % of the

predicted NOx concentrations should be assumed to be NO2 for short-term assessments and 70

% of the predicted NOx concentrations should be assumed to be NO2 for long-term

assessments; and

Case Specific Scenario: Operators are asked to justify their use of percentages lower than 35 %

for short-term and 70 % for long-term assessments in their application reports.

In line with the AQMAU guidance, this assessment has used the ‘Worst Case Scenario’ approach in

determining the conversion rate of NO to NO2 as a robust assumption.

3. Assessment Criteria

3.1 Criteria Appropriate to the Assessment

Table 3.1 sets out those Air Quality Standards (AQSs) and Environmental Assessment Levels (EALs) that

are relevant to this assessment.



Table 3.1 Relevant AQSs and EALs

Pollutant AQS/EAL Averaging Period Value (µg m-3)

Nitrogen dioxide (NO2) AQS Annual mean 40

AQS 1-hour mean, not more than 18 exceedences a year (equivalent of 99.79 Percentile)

200

Oxides of nitrogen (NOx) - Ecological Receptors Only

AQS Annual mean 30

EAL Daily mean 75

CO AQS 8-hour mean 10,000

EAL 1-hour mean 30,000

Critical Loads Relevant to the Assessment

The Air Pollution Information System (APIS) provides specific information on the potential effects of nitrogen

deposition on various habitats and species. This information, relevant to habitats of some of the ecological

receptors considered in this assessment, is presented in Table 3.2

Table 3.2 Critical Loads Relevant to this Assessment

Habitat and Species Specific Information

Critical Load (kg N ha-1 yr-1)

Specific Information Concerning Nitrogen Deposition

Temperate and Boreal Forests

10-20 Increased nitrogen deposition in mixed forests increases susceptibility to secondary stresses such as drought and frost, can cause reduced crown growth. Also can reduce the diversity of species due to increased growth rates of more robust plants.

Hay Meadow 20-30 The key concerns are related to changes in species composition following enhanced nitrogen deposition. Indigenous species will have evolved under conditions of low nitrogen availability. Enhanced Nitrogen deposition will favour those species that can increase their growth rates and competitive status e.g. rough grasses such as false brome grass (Brachypodium pinnatum) at the expense of overall species diversity. The overall threat from competition will also depend on the availability of propagules.

Oak Woodland 10-15 Increased nitrogen deposition in Oak forests increases susceptibility to secondary stresses such as drought and frost, can cause reduced crown growth.

4. Background Air Quality

4.1 Continuous Monitoring Data

DEFRA sponsors a network of continuous air quality monitoring stations in the UK; the Automatic Urban and

Rural Network (AURN). However, no such monitoring stations are located within North Warwickshire.



4.2 Passive Monitoring Data

SITA have been monitoring the local NO2 levels since August 2011 using a local diffusion tube network.

Data is available for the twelve month period prior to the commissioning of the engines at PPP and shows an

average NO2 concentration of 22.8 µg/m3. Results from the 2014 diffusion tube monitoring can be found in

the 2014 Diffusion Tube Survey Results Report1. The location of the local diffusion tubes is shown in Figure

4.1.

Figure 4.1 SITA Operated Diffusion Tubes 2014

4.3 Background Concentrations used in the Assessment

The data for the background concentration used in this assessment are given in Table 4.1 below. Data from

DEFRA’s UK Air Information Resource (UKAIR) using the Pollution Climate Mapping (PCM) model

developed by AEARicardo has been used.

Background mapped concentrations of NOx for each 1km grid square closest to the ecological receptors are

detailed in Table 4.2.

The annual average process contribution is added to the annual average background concentration, to give

a total concentration at each receptor location. This total concentration can then be compared against the

relevant AQS and the likelihood of an exceedance determined.

It is not technically rigorous to add predicted short-term or percentile concentrations to ambient background

concentrations not measured over the same averaging period, since peak contributions from different

sources would not necessarily coincide in time or location. Without hourly ambient background monitoring

data available it is difficult to make an assessment against the achievement or otherwise of the short-term

1 Amec Foster Wheeler (Dec 2014) Packington Diffusion Tube Survey – 2014 Results.



AQS. For the current assessment, conservative short-term background levels have been derived by applying

a factor of two to the annual mean data as per the recommendation in H1 Annex (f).

Table 4.1 Maximum Short-term and Long-term Ambient Background Concentrations at any Human Receptor

Pollutant Short-term Mean Long-term Mean

NO2 (µg m-3) 45.0 22.5

CO (µg m-3) 764 382

VOCs (µg m-3) - -

NMVOCs1(µg m-3) 0.94 0.47

Note: Benzene data used as a proxy compound

Table 4.2 Long-term Ambient Background concentrations at Designated Ecological Receptors

Receptor NOX Long-Term (Annual) Mean (µg m-3)

River Blythe 14.80

Coleshill and Bannerly Pools, Coleshill Pool Wood 15.86

Whitacre Heath3 20.18

Hoar Park Wood3 13.76

Bickenhill Meadows3 19.72

Berkswell Marsh3 19.71

Tilehill Wood3 19.94

Notes: 1. Values based on UK-AIR estimates to the 2011 base year. 2. Long-term emissions only are assessed at ecological receptors. 3. At a distance of greater than 2km included for information only.

Comparing the nitrogen dioxide background concentrations from UKAIR and the most recent 12-month

diffusion tube data, the UKAIR values are reported as lower than the diffusion tube data. As the diffusion

tube data incorporates emissions from the on-site engines that are already operational, the background

concentration data from UKAIR (industrial component removed) has been used in this assessment. This has

been deemed a robust decision as it means that emissions to air from PPP are accounted for in the

modelled results only.



5. Results

The following tables details the maximum concentrations at human and ecological receptors for each

pollutant considered in this assessment for long-term and short-term impacts.

5.1 Impacts at Human Receptors

Predicted ground-level concentrations have been derived from atmospheric dispersion modelling of process

emissions from the Packington landfill site. Ground- level concentrations for the human receptors considered

are presented in Tables 5.1 to 5.4 for each of the pollutants modelled.

Table 5.1 NO2 Annual and 1-hour Mean Impacts at Human Receptors

Receptors Annual Mean 99.79%-ile 1-hour mean

AQS (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS

AQS (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS

Packington Hall 40 1.07 18.44 46.1% 200 13.73 48.47 24.2%

Woodbine Cottage 40 14.97 33.45 83.6% 200 58.85 95.80 47.9%

House to East 40 1.97 20.44 51.1% 200 22.86 59.81 29.9%

Common Farm 40 0.20 22.72 56.8% 200 5.85 50.89 25.4%

Church Farm 40 4.55 23.02 57.6% 200 31.21 68.16 34.1%

Rectory Cottage 40 3.26 21.73 54.3% 200 27.95 64.89 32.4%

Beam Ends 40 8.84 27.32 68.3% 200 37.39 74.33 37.2%

Brook Farm 40 5.53 24.97 62.4% 200 25.78 64.66 32.3%

Old School House 40 2.75 22.19 55.5% 200 20.06 58.94 29.5%

North Lodge 40 2.71 21.18 53.0% 200 21.68 58.63 29.3%

Rockwood Cottage 40 1.84 18.79 47.0% 200 22.62 56.52 28.3%

Note: AQS = Air Quality Standard; PC = Process Contribution; PEC = Predicted Environmental Concentration (PC + Background)

Long-term and short-term NO2 PCs and PECs at the nearest human receptors are below the AQS and, as

such, no adverse effects on the local human populations are expected.

Figures 5.1 and 5.2 shows the contour plot for annual NO2 and for 1-hour mean NO2 respectively, for the

worst-case year.



Figure 5.1 Annual Average Concentration of NO2, 2011 meteorological year.

5

100



Figure 5.2 99.79% percentile 1-hour mean NO2, 2012 meteorological year

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50



Table 5.2 CO 1-Hour Mean and 8-Hour Mean Impacts at Human Receptors

Receptors Maximum 1-hour mean Maximum rolling 8-hour mean

EAL (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS

AQS (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS

Packington Hall 30,000 722 1424 4.7% 10,000 139 841 8.4%

Woodbine Cottage 30,000 240 942 3.1% 10,000 534 1236 12.4%

House to East 30,000 104 868 2.9% 10,000 142 906 9.1%

Common Farm 30,000 329 1093 3.6% 10,000 40.6 805 8.0%

Church Farm 30,000 298 1062 3.5% 10,000 249 1013 10.1%

Rectory Cottage 30,000 351 1115 3.7% 10,000 215 979 9.8%

Beam Ends 30,000 262 1026 3.4% 10,000 351 1115 11.1%

Brook Farm 30,000 215 979 3.3% 10,000 259 1023 10.2%

Old School House 30,000 257 1021 3.4% 10,000 158 922 9.2%

North Lodge 30,000 235 999 3.3% 10,000 181 945 9.5%

Rockwood Cottage 30,000 125 833 2.8% 10,000 137 845 8.5%


Short-term CO PCs and PECs at the nearest human receptors are below the AQS/EAL and, as such, no

adverse effects on the local human receptors are expected.

Table 5.3 VOC Annual and 1-Hour Mean Impacts at Human Receptors

Receptors Maximum 1-Hour Mean Annual Mean

AQO (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS

AQS (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS

Packington Hall - 103 - - - 3.48 - -

Woodbine Cottage - 515 - - - 49.5 - -

House to East - 171 - - - 6.34 - -

Common Farm - 74.6 - - - 0.66 - -

Church Farm - 235 - - - 14.5 - -

Rectory Cottage - 212 - - - 10.5 - -

Beam Ends - 250 - - - 29.2 - -

Brook Farm - 187 - - - 18.2 - -

Old School House - 153 - - - 9.02 - -

North Lodge - 183 - - - 8.81 - -

Rockwood Cottage - 168 - - - 5.94 - -


There are no AQSs available for either long-term or short-term VOCs – results are presented for information

only.



Table 5.4 NMVOC Annual Mean Impacts at Human Receptors

Receptors Annual Mean

AQS (µg m-3) PC (µg m-3) PEC (µg m-3) %PEC of AQS

Packington Hall 5 0.25 1.11 22.3%

Woodbine Cottage 5 3.56 4.43 88.5%

House to East 5 0.46 1.39 27.8%

Common Farm 5 0.05 0.98 19.6%

Church Farm 5 1.05 1.98 39.6%

Rectory Cottage 5 0.76 1.69 33.8%

Beam Ends 5 2.11 3.04 60.8%

Brook Farm 5 1.31 2.25 45.0%

Old School House 5 0.65 1.58 31.7%

North Lodge 5 0.63 1.57 31.4%

Rockwood Cottage 5 0.43 1.28 25.7%


Long-term NMVOC PCs and PECs at local human receptors are below the AQS, even with the assumption

that all of the emitted NMVOC are a single species (benzene). Therefore no adverse effects on the local

human populations are expected.

5.2 Impacts at Ecological Receptors

Table 5.5 summarises the results of the dispersion modelling assessment for estimating atmospheric

concentrations of NOx at the ecological receptors considered in this assessment.

Table 5.5 NOx Impacts at Ecological Receptors

Receptors Annual Mean Maximum 24-Hour Mean

AQS (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS

EAL (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS

Coleshill and Bannerly Pools SSSI

30 1.40 17.26 57.5% 75 16.77 48.5 64.6%

Bickenhall Meadows SSSI

30 0.04 19.76 65.9% 75 0.93 40.4 53.8%

Berkswell Marsh SSSI 30 0.14 19.85 66.2% 75 1.79 41.2 54.9%

Hoar Park Wood SSSI 30 0.06 13.82 46.1% 75 0.89 28.4 37.9%

Whitacre Heath SSSI 30 0.15 20.33 67.8% 75 1.93 42.3 56.4%

Tilehill Wood SSSI 30 0.09 20.02 66.7% 75 3.08 43.0 57.3%

River Blythe SSSI 1 30 5.82 20.62 68.7% 75 33.92 63.5 84.7%




Receptors Annual Mean Maximum 24-Hour Mean

AQS (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS

EAL (µg m-3)

PC (µg m-3)

PEC (µg m-3)

%PEC of AQS



Butlers Moors Wood 30 32.76 47.57 158.6% 75 222.49 252.1 336.1%

Denbigh Spinney 30 0.76 16.74 55.8% 75 15.90 47.9 63.8%

Holywell Brook 30 2.56 16.98 56.6% 75 24.65 53.5 71.3%

Siding Wood 30 2.02 16.82 56.1% 75 23.69 53.3 71.1%

Packington Park 30 2.31 16.73 55.8% 75 22.99 51.8 69.1%

Mulliners Rough 30 1.10 16.04 53.5% 75 7.75 37.6 50.2%

Todds Rough 30 2.84 18.70 62.3% 75 21.03 52.7 70.3%

Potential LWS 1 30 2.66 18.52 61.7% 75 26.65 58.4 77.8%

Potential LWS 2 30 2.16 18.02 60.1% 75 21.71 53.4 71.2%


Long-term NOx PCs at most of the ecological receptors are below the relevant AQO. The one exception is

the predicted NOx concentration at Butlers Moors Wood. However, looking at the diffusion tube data, results

have consistently shown to be below the AQS of 30 µg m-3.

Furthermore, there is a general understanding that the annual average AQO for vegetation (30 µg m-3 NOx)

does not apply (for compliance purposes) at specified distances from agglomerations (20 km) or other built

up areas, industrial installations, or motorways (5 km). The areas falling within the specified distances have

been referred to as “exclusion zones”. This general understanding is not given explicitly within the Air Quality

Standards Regulations 2010, but can be inferred from the reference to the macro-scale siting of sampling

points (Schedule 1, Part 2 of the Regulations). The locations considered in this assessment lie within 20 km

of the Birmingham conurbation and within 5 km of the M6. Therefore, the AQS for the protection of

vegetation would not apply.

The 24 hour mean NOx PCs at the majority of ecological receptors are below the relevant level. For some

receptors however, there are some exceedences. However the 75 µg m-3 daily mean EAL is derived from the

2000 World Health Organisation (WHO) Air Quality Guidelines for Europe. The WHO state in this document

that, with reference to the daily mean NOx guideline level:

“There is insufficient data to provide these levels with confidence at present.”

Consequently, it is considered that greater emphasis should be placed on achievement with the more

established NOx annual mean AQS.

The River Blythe, as a water body, will not be sensitive to the concentrations of air pollutants, the overriding

issue will be the run-off of pollutants from the surrounding land and discharges into the river.

5.3 Nitrogen Deposition

Table 5.6 contains the results of the deposition analysis for nitrogen.



Table 5.6 Nitrogen Deposition rates at Ecologically-Designated Sites

Receptor CLmin (kg N ha-1 y-1)

Maximum Predicted PC (kg N ha-1 yr-1)

Background Deposition Rate (kg N ha-1 y-1)

PEDR (kg N ha-1 y-1)

% PC of CLmin

Coleshill and Bannerly Pools SSSI

10 0.14 38.22 38.36 1.4%

Bickenhall Meadows SSSI

20 0.00 20.58 20.58 0.0%

Berkswell Marsh SSSI 10 0.01 50.26 50.27 0.1%

Hoar Park Wood SSSI 10 0.01 45.78 45.79 0.1%

Whitacre Heath SSSI N/A 0.01 13.02 13.03 -

Tilehill Wood SSSI 15 0.01 43.68 43.69 0.1%

River Blythe SSSI 1 N/A 0.59 - - -




Note: CL = Critical Load – the CL selected for each designated site relates to its most N-sensitive habitat (or a similar surrogate) listed on the site citation for which data on Critical Loads are available. PC = process Contribution. PEDR = Predicted Environmental Deposition Rate (= PC + background).

The contribution to the deposition rates from the modelled engine emissions is very small when compared to

the background data, and is unlikely to result in degradation of the ecologically-designated sites; even if the

process contribution was completely eliminated, the ecologically-designated sites would experience

deposition levels significantly higher than the minimum critical load. The model has assumed that all 8

engines operate continuously at maximum output. In reality they will operate to a lesser extent, as the eighth

engine will only be used when another engine is operating at reduced output or is offline for maintenance.

This will mean that the annual average impact will be lower than the modelled predictions.

6. Conclusions

This assessment has used detailed dispersion modelling to undertake an assessment of potential air quality

impacts due to engine emissions to air from Packington Power Plant.

The assessment indicates that predicted environmental concentrations of key pollutants emitted by PPP are

below the appropriate standard/guideline value at all local human receptors. For ecological receptors, long-

term NOx remains below the relevant AQS for all receptors, except for Butlers moors Wood – however

monitoring by passive diffusion tube has shown concentrations to be consistently below the annual AQS. For

those ecological receptors where background deposition rates are already predicted to exceed the critical

load, the impact from the 8 landfill gas engines over the entire range of the designated area is not

considered significant.

The assessment is conservative in terms of the site operational scenario, hours of operation and the year of

meteorological data used to derive maximum concentrations, and therefore the assessment represents the

worst-case emissions scenario.

technical note: packington power plant: dispersion of emissions … · 2020. 9. 4. · packington...

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