Detailed Dispersion Modelling - Defra, UK · PDF fileTHE FIRST PHASE REVIEW: A SUMMARY DETAILED DISPERSION MODELLING 45 while 1% were planning to change. The remaining ... ICI Katalco

THE FIRST PHASE REVIEW: A SUMMARY � D E T A I L E D D I S P E R S I O N M O D E L L I N G

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while 1% were planning to change. The remainingfew percent did not know either way.5.1 INTRODUCTION

The atmospheric dispersion of pollution is theprocess by which pollutants are mixed or dilutedand transported in the atmosphere. Thepollutant concentration varies in time and spacedepending on the distribution of pollutionsources, meteorology and topography. The actualconcentrations which are observed are the resultof a balance between a number of competingprocesses, principally the emissions ofcompounds into the atmosphere and theirsubsequent dispersion and removal from theatmosphere. Numerical models can be used tosimulate the dispersion process and are veryuseful tools for predicting the ambient airquality effects of a single pollution emissionsource or a group of sources.

Three first phase authority groups were specificallytasked with modelling dispersion around industrialsources. These were Ribble Valley where modellingwas focused on emissions from sources in Clitheroe,Merseyside where modelling of sources around theMersey Estuary was carried out and Avon wheremodelling was undertaken on sources aroundAvonmouth. Other authorities undertook the largertask of urban area modelling, incorporating allsources in an overall air quality management tool.

Both the Merseyside and Ribble Valley groupscarried out dispersion modelling studies usingthe following widely used models R91, ISC andADMS. The R91 model is a Gaussian plumemodel developed by the Working Group onAtmospheric Dispersion. A Gaussian model is onein which the variation in pollutant concentrationacross the plume profile is a Gaussian (orNormal) curve centred on the plume centre line.The vertical and horizontal standard deviations,σz and σy increase with distance downwind asturbulent mixing spreads the pollutant throughthe depth of the boundary layer. Themethodology was published by the NationalRadiological Protection Board (NRPB) as report

R91 (Clarke, 1979). The model is presented as aseries of nomograms.The Industrial Source Complex (ISC) version 2 isthe US equivalent to the R91 model and wasdeveloped by the US Environmental ProtectionAgency during the 1970s. Like R91, ISC uses aGaussian plume concentration profile. ISC isused with various modifications to modelmultiple point source emissions from stacks,stacks that have buildings nearby, area sourcesand volume sources. The model has 2 versions,ISCLT (long term) and ISCST (short term). TheISCST model includes a short term facility tocalculate 1-hour to 24-hour averages, thusenabling an assessment of episodic peakconcentrations. The ISCLT model include a long-term average facility to calculate seasonal andannual averages by incorporating long termhistorical meteorological information. Animportant difference from R91 is the use of onlytwo surface roughness lengths: urban and rural.

ADMS (version 2) is a dispersion model developedin the UK by CERC (Cambridge EnvironmentalResearch Consultants) with support from anumber of Government bodies and privatecompanies. It includes a more rigorous treatmentof the boundary layer and vertical dispersion,particularly under “unstable” conditions when airat the surface is driven to rise by the solarheating of the ground. In neutral and stableconditions, ADMS treats dispersion as Gaussian.The ADMS treatment of unstable conditionsmeans that the maximum concentrations due tostack emissions are larger and closer to the sourcethan for simple Gaussian models. It is generallyaccepted that the mathematical model in ADMS isan improvement over the simple Gaussian modelsfor the assessment of stack plume dispersion.

5.2 MODELLING INDUSTRIALSOURCES IN MERSEYSIDE

This group was asked to undertake a comparison ofcalculated concentrations of pollutants fromindustrial sources in the area using three models:

Detailed DispersionModelling 5

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R91, ISC and ADMS (version 2).

5.2.1 Input data requirements

Source data input were very similar for all threemodels (Emissions data were discussed in Chapter4). In addition, the three models were similar in theirtreatment of topographical features in that only ageneral description of the surrounding landscape isrequired. The ADMS module for treatment ofcomplex terrain was not used. The MeteorologicalOffice can supply input meteorological data for alarge number of locations around the UK and, ingeneral, data from one of these sites will be suitablyrepresentative of the modelling site. Longer termmeteorological summary data are available in filesfor direct input to ADMS and to ISCLT. Hourlysequential data can also be obtained from theMeteorological Office for input to the ADMS modelfor direct estimation of individual hourly averagesfrom which estimates of maximum hourly averagesand running averages can be derived.

5.2.2 Model outputs and flexibility

R91 and ISC are simple models to use. ISC wasdeveloped by the US EPA to be used by both industryand regulators. It is widely used by industry andenvironmental consultants in Europe and the UK topredict concentrations from industrial sources. Themodel processes long term meteorological data at avery fast rate and calculation of annual averages canbe very efficient. However, the model cannot predictrunning averages or percentile values. Although ISCcan be downloaded free of charge from the USEPAinternet site, this version does not have a userfriendly front end so this basic form is unsuitable foruse by inexperienced modellers. More user-friendlyversions of the model are readily available frommodel providers listed in the Departments’ Guidancedocument Dispersion Modelling for Air QualityReview and Assessment (DETR et al., 1998a).

The number and type of outputs from the ADMSmodel are clearly greater than with the ISC andR91 models. The ability of the ADMS model tocalculate running averages and percentile values isof benefit to end-users whose task it is to comparemodelled results with air quality objectives. TheADMS models can also be used in a GIS formatwhich is an advantage if a local authority or theend-user has access to this software. A majordrawback of ADMS is the long processing timerequired, particularly when hourly sequential data

are used together with emissions estimates fromseveral sources and a grid of receptor points.5.2.3 Model Results

ADMS can provide hour-by-hour estimates ofpollutant concentrations enabling frequencydistributions of concentrations to be derived.Statistical summaries of ADMS-modelled andmeasured concentrations of SO2, PM10 and VOCare given in Tables 5.1, 5.2 and 5.3. The hour-by-hour predicted and measured hourly averageconcentrations of sulphur dioxide are shown inFigure 5.1. Industrial emissions of sulphurdioxide are relatively well quantified and are thelargest contributor to ambient concentrations. Theagreement between ADMS modelled andmeasured concentrations over the longer term(months) is relatively good, but over the shorterterm (hours) the agreement can be poor(correlation co-efficient for hourly data was 0.34).For example, the average ADMS modelled andmeasured SO2 concentration over a three monthperiod was very similar (Table 5.1) but, while themaximum hourly modelled and measuredconcentration was very similar, they did not occuron the same day. The maximum concentrations forR91 and ISC did not compare as well with themaximum measured concentrations.

TABLE 5.1. STATISTICAL SUMMARY OF

HOURLY AVERAGE ADMS2-PREDICTED AND

MEASURED SO2 CONCENTRATIONS AT THE

AIR QUALITY MONITORING STATION AT

SPEKE (JANUARY TO MARCH 1997).

Modelled Industrial Measured

Emissions SO2

SO2 (µg m–3)* (µg m–3)

Maximum 422 427

Mean 23 21

50th percentile 8 12




* Model data filtered to remove zero values

ADMS-modelled concentrations of PM10 and VOCand measurements agreed less well. For PM10only primary emissions from industry weremodelled and, the modelled average andmaximum concentrations were much smallerthan measured values. There are many sources



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contributing to ground-level concentrations inaddition to industrial sources, including roadtransport, domestic combustion, mining,quarrying and construction, so this is perhapsnot surprising. This illustrated the need to takeaccount of other components of particles to giveestimates of ground level concentrations. ForVOCs, the modelled concentrations were morethan an order of magnitude larger thanmeasured values. This is due to the difficulty ofmodelling the ambient concentrations due to theemissions from Stanlow refinery. Theseemissions were available as total VOCs ratherthan specific compounds and the compoundsemitted may have been different from the groupof hydrocarbons measured. In addition, VOCs areemitted from multiple sources with the refinerycomplex and from a range of heights. The detailsof these release points were not known and therefinery was modelled as an area source withemissions at ground level, very much a worse

case. It will clearly be difficult to model theimpact of major VOC sources such as oilrefineries for review and assessment purposesand direct measurements may be necessarywhere emissions are a cause of concern.



MEASURED PM10 CONCENTRATIONS AT THE



Modelled Industrial Measured

Emissions PM10

PM10 (µg m–3) (µg m–3)

Maximum 12 225

Mean 1 21

50th percentile 0.004 17



Measured data

Sulp

hu

r d

ioxi

de

(µg

m-3

)

Modelled value

22-Jan

450

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3-Feb7-Feb

11-Feb15-Feb

19-Feb23-Feb

27Feb3-Mar

7-Mar11-Mar

15-Mar19-Mar

23-Mar27-Mar

31-Mar

Figure 5.1. Comparison of measured hourly average sulphur dioxide concentrations at Liverpool Speke during January to March, 1997and the predictions of ADMS-2 using sequential meteorological data. Only contributions from the identified point sources wereincluded in the model calculations.

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MEASURED VOC CONCENTRATIONS AT THE



Modelled Measured*

VOC (ppb) VOC (ppb)

Maximum 16783 188

Mean 245 11

50th percentile 0.5 1.25




* Total hydrocarbons (25 hydrocarbon compounds as

defined in national automatic monitoring network.

5.3 MODELLING INDUSTRIALSOURCES IN RIBBLE VALLEY

The Ribble Valley authority modelled thedispersion of pollutants emitted by the mainindustrial sources around Clitheroe using ADMS,ISCLT3 and R91. They contracted consultants,CERC, to undertake this task. All three modelswere used to predict the long term averageconcentrations of SO2, particles and NO2 in a 12km x 12 km area around Clitheroe for both thecurrent and 2005 cases. Emissions from the majorindustrial sources are given in Table 5.4.

TABLE 5.4. EMISSIONS OF SO2 AND NOX (AS

NO2)(g/s) FOR THE CURRENT AND 2005 CASES

FROM THE MAJOR SOURCES IN THE RIBBLE

VALLEY AREA.

Source Emission Emission Emission Emission

rate of SO2 rate of NO2 rate of SO2 rate of NO2

- current - current - 2005 - 2005

Castle Cement 221 89.9 22.1 89.9

Tarmac 9.87 1.1 9.91 1.1

ICI Katalco 3.33 5.55 3.33 2.77

All calculations were carried out in flat terrain inorder to compare the results between the threemodels. The effect of complex terrain on groundlevel concentrations was not large, except in verystable conditions, and in particular the effect ofthe quarry was limited (Carruthers et al., 1997).

Only the results from ADMS were compared to thenational air quality objectives and the air qualitymonitoring data from the established station atChatburn. The other models were not used in thisexercise due to practical difficulties in setting upthe calculations and a lack of certain modellingfeatures within the available version of each model.

The modelling results show that the national airquality objective (100 ppb [or 267 µg m–3] 99.9thpercentile of 15 minute mean concentrations) forSO2 is exceeded very near to the industrial sourcesfor the current emissions case and may still be veryclose to the limit for the proposed emissions case,although still in a very localised area (Figure 5.2and 5.3) Further from the sources theconcentration of SO2 is predicted to be significantlylower in 2005 based on the proposed changes toemissions from the three sources.

Concentrations of NOx resulting from industrialemissions were small for the current case and withthe proposed future emissions case, the annualaverage concentration of NOx is reduced by about50% (Figures 5.4 and 5.5). This has been achievedmainly by the abatement equipment beinginstalled at ICI Katalco. Some reduction can alsobe seen in the contribution from Tarmac due to theincreased stack heights of three sources.

5.4 URBAN AREA MODELLINGIN THE WEST MIDLANDS

The West Midlands Group undertook the task ofcomparing the performance of two air qualitymanagement tools, Indic Airviro and ADMS-Urbanfor modelling the dispersion of three primarypollutants: carbon monoxide, total oxides ofnitrogen and PM10.

Both models require a database of emissions (point,line and area sources) and appropriatemeteorological data. Emissions data had beenpreviously collected by the London Research Centreon behalf of DETR (LRC, 1996). The WestMidlands is a complex urban area with airpollutant emissions from many industrial pointsources as well as the road transport, domestic andother dispersed sources typical of any urban area.The emissions inventory included 3,468 linesources, 1020 point sources and 1,277 area sources.For modelling, additional information was needed.This included stack data (height, heat content and



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0 1 2

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100

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100

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150

150

150200

200

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3 4 5 6 7 8 9 10 Kilometres

N

Figure 5.2 Current 99.9th percentile of 15 minute average concentration of sulphur dioxide from all sources (µg m–3)as predicted using ADMS.

0 1 2 3 4 5 6 7 8 9 10 Kilometres

50

50

50

50

100

N

Figure 5.3 2005 99.9th percentile of 15 minute average concentration of sulphur dioxide from all sources (µg m–3)as predicted using ADMS.

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N

0 1 2 3 4 5 6 7 8 9 10 Km

223

3

2

11

11

1

Figure 5.4 Current annual mean concentration of NOx from all sources (µg m–3) as predicted using ADMS.

0 1 2 3 4 5 6 7 8 9 10 Km

N

2

1

111

1

1

2 23

3

Figure 5.5 2005 annual mean concentration of NOx from all sources (µg m–3) as predicted using ADMS.


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so on), process operating times and diurnalvariation in traffic flow.Five years of synoptic meteorological data wereobtained from the Meteorological Office and wereused to compile summer and winter scenarios forseasonal dispersion modelling.

5.4.1 Emissions data for Airviro

Airviro (Swedish Hydrometeorological Institute),can be used to predict short term air quality (i.e.predictions of ambient concentrations hour byhour) or long term air quality (mean or percentile).It requires a database of emissions (point, line andarea) and appropriate meteorology. Airviro workson a UNIX workstation rather than a PC and someaspects of setting it up are therefore relativelycomplex. The emissions data requirements are alsorelatively demanding. Once set up, however, thesystem is potentially a powerful tool for air qualityassessment. Like any model. however, thedispersion model contained within the Airvirosoftware suite is subject to error and uncertainty.

Transfer of the urban emissions inventory databaseinto the model was found to be relativelystraightforward. Airviro has no fixed limit on thenumber of sources and a database of the 5699sources in the West Midlands was created in themodelling software. Information on the releaseconditions from point sources required formodelling was not available. Thus parameters suchas height and temperature of the discharge, effluxvelocity and dimensions of neighbouring buildingswere estimated. It was noted that these variablesare unlikely to change from year to year andcollation of these data would improve the accuracyof the modelling results.

Most information on emissions from roads requiredby the model was available from the DETR WestMidlands emissions inventory. The number of laneswas required and was not available in theinventory but a default of 2 was used for mostroads, 4 for dual carriageways and 6 for motorwayswithin the model. Eight road types are availablewhich is a more detailed classification system thanthat used in the emissions inventory.

5.4.2 Emissions data for ADMS-Urban

The West Midlands emissions inventory was inputinto the model, which uses a Microsoft Accessdatabase, by copying and pasting data for groups of

sources from the spreadsheet package used byLRC. This model has a limit of 1500 sources whichwas too restrictive for a large conurbation of over5000 sources.

As with Airviro, a number of discharge dimensionvariables are required in the dispersioncalculations which were not supplied in theinventory. Most road traffic information requiredby the model was available in the inventory.However, the following parameters were notavailable and default settings were used:

• number of lanes• elevation of road• canyon height• road width

5.4.3 Comparison with measurements

Two examples of the comparison of hour-by-hourmeasurements and modelled concentrations areshown in Figure 5.6. Figure 5.6(a) shows hourlymean CO concentrations between November 12and November 16 1996 at Wolverhampton Centre.The highest concentrations were observed duringthe morning and evening of November 13 and thiswas predicted reasonably well by Airviro. ADMS-Urban under-predicted concentrations on thisoccasion. Figure 5.6(b) shows modelled andmeasured concentrations of nitrogen oxides atBirmingham West during the same episode.Airviro overpredicts concentrations on the 13th byabout a factor of 2. The concentrations atBirmingham West peaked later in the evening onthe 13th than at Wolverhampton. This is usuallydue to very poor dispersion overnight such thatany emissions from night-time traffic or othersources have a magnified effect on concentrations.This effect was not reproduced by the models inthis case. On the 14th and 15th, measuredconcentrations were not particularly high butAirviro predicted very high concentrations, up to afactor of 5 too large. ADMS-Urban predicted themorning peak concentrations to within 20% of themeasured values on these days.

These examples are typical of the behaviour foundat other times and sites. Minor fluctuations in winddirection induced the models to “see” or “miss” aneffect from a source at a particular location. Also, itshould be noted that the calm conditions that leadto pollution episodes are outside the range ofmeaningful dispersion calculations and therefore

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12-Nov 960:00

12-Nov 9612:00

13-Nov 960:00

13-Nov 9612:00

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600

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Pollu

tan

t C

on

cen

trat

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(µg

m-3

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Measured dataIndic AirviroADMS Urban v.1.42

Figure 5.6a Modelled and measured hourly mean concentrations of CO between 12th and 16th November, 1996 at Wolverhampton Centre

12-Nov 960:00

12-Nov 9612:00

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Measured dataIndic AirviroADMS Urban v.1.42

Figure 5.6b Modelled and measured hourly concentrations of NOx between 12th and 16th November 1996 at Birmingham West.


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test the models to the limit. Both models generallyfollowed the patterns of measured values but oftennot too closely. Sometimes Airviro performed betterthan ADMS but at other times the performanceswere reversed. At other times, both modelspredicted high concentrations which were notrecorded by monitoring instruments.Airviro is capable of providing maps of relevantstatistics of the frequency distribution of theconcentrations of the pollutants. In the WestMidlands study calculations were carried out foroxides of nitrogen, carbon monoxide and PM10particles. For each pollutant, mean, 95th, 96th,97th, 98th and 99th percentiles were calculated.Figure 5.7 shows the map of winter meanconcentration of nitrogen oxides. Point by pointcomparison with monitoring site results indicatesthat the mapped winter mean NOx concentrationsare about a factor of two too high.

The output from both ADMS-Urban and Airviro inthis case highlights the need for model validationwith monitoring results in such large conurbations.A key element of the review and assessment of air

quality is the use of “screening” tools to assess therisk of specific air quality objectives being exceededat the end of 2005. Several of the First PhaseAuthorities tested screening tools including:

• The Design Manual for Roads and Bridges, amethodology for estimating the impact of major roadson air quality (The Highways Agency, 1994).

• AEOLIUS, a model of the effect of street canyons (i.e.where the height of the buildings are significantcompared to the width of the road) which wasdeveloped by the Meteorological Office.

• The preliminary draft review and assessment guidancecirculated by the then Department of the Environmentprior to the commencement of the First Phase studies.

5.5 DISPERSION MODELLING IN AVONMOUTH

The Group in Avonmouth also undertook dispersionmodelling using Indic Airviro and ADMS-Urbanand compared the performance of both modelsagainst monitoring data for total oxides of nitrogen.

0 2 4 6 8 10 12 14 16 181 km

Airviro results

Gaussian model

Winter scenario

West Midlandswithout Coventry

Total oxides of nitrogen

Mean values

4080120160200240280

µg m-3

Figure 5.7 Map of West Midlands showing winter mean concentrations of NOx as determined by Airviro.

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Like the West Midlands Group, emissions datawere input to both models using those datacompiled by the London Research Centre (1997) onbehalf of the Governments. This urban inventorycovered the city of Bristol and surrounding areasincluding the coastal area of Avonmouth totallingan area of 166 km2. Industrial and road traffic

emissions were calculated using local data anddomestic and other low-level dispersed emissionswere added from the National AtmosphericEmissions Inventory.

Results from dispersion modelling were comparedwith monitoring data from the national automatic


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NO

x co

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ntr

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pp

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AirviroMonitoringADMS-Urban

Figure 5.8 Comparison of ADMS-Urban and Airviro predictions with monitoring data (16-21st January 1995).

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THE FIRST PHASE REVIEW: A SUMMARY � E S T A B L I S H M E N T O F M O N I T O R I N G S I T E S

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network site at Bristol Centre. This site isrepresentative of the urban centre and is located ina pedestrianised walkway 43 m from a major road.Although both dispersion models used includestreet canyon options these were not used in thisstudy. Differences in the models resulted in terraindata being input into Indic Airviro but not toADMS-Urban. Both models are able to processsequential hourly meteorological data.

A scenario was run for a typical week (16th-23rdJanuary, 1995) where predictions were made overthe short-term (hours). A diurnal traffic profilewas included into the input data which assumed atraffic split of 93% LGV and 7% HGV. Comparisonwith monitoring data showed that predictedconcentrations from both models were of the sameorder and were of a similar magnitude to themonitored NOx concentrations. Like in the WestMidlands study the highest concentrations were, ingeneral, during the morning and evening rushhours but where peaks in the monitoringconcentration were seen the predictedconcentrations from both models were often muchhigher, for example, early on 17th, late on 18th,early on 19th (Figure 5.8). On the whole, though,Indic Airviro over-predicted and ADMS-Urbanunder-predicted the monitored concentrations.Comparisons between predicted concentrations andwind speeds showed that the greatest over-estimates were during periods of relative calmwhen dispersion models are particularly tested(Figure 5.9). Predicted concentrations are notablyhigh when wind speeds are less than 2 m s-1 and itis recommended that wind speeds less than 1 m s-1

are not used in ADMS-Urban (CERC, 1997).

5.6 URBAN AREA MODELLINGIN LONDON

The London First Phase Group used two models toinvestigate air quality in the London wide area,Indic Airviro and ADMS-Urban for both NO2 andSO2. Comparison with monitoring data from anumber of sites was carried out. The modellingwas undertaken for two areas of London, Centraland the East Thames corridor.

5.6.1 Emissions data

The modelling for this study was undertakenbefore the publication of the urban inventory forLondon (LRC, 1998). However, emissions data

were supplied by the South East Institute of PublicHealth which required some manipulation prior toinput into the models. Part A point sourceemission data along with the other requiredparameters for dispersion modelling, such as stackheight, discharge temperature, were readilyavailable but similar information for Part Bprocesses was very limited. Point sources outsidethe study areas had a significant influence on theregion’s SO2 emissions and so were included in theemissions inventory for modelling purposes.Emissions data from roads were calculated usingfactors from the Design Manual for Roads andBridges (DMRB). Average traffic flows and speedsfor roads with a flow greater than 25,000 vehiclesper day were used in the modelling study. A diurnalvariation in traffic flow was used for CentralLondon. Background emissions were also inputinto both models on a 1 km grid square basis.

5.6.2 Predicted results from ADMS-Urban

ADMS was used to calculate hourly averagedconcentrations of SO2 and NO2 at 5 automaticmonitoring sites for every hour in two three monthstudy periods (May-July and October-December,1995). This allowed the modelled data at each siteto be compared with the monitored concentrationsover a variety of meteorological conditions.Percentiles of concentration were calculated andcompared for each site (Tables 5.7 and 5.8).Typical run times for each of the three monthperiods were 2-3 hours.

Comparison of the mean and the 95th percentilehourly average concentrations of SO2 calculated byADMS with that observed at each of themonitoring sites for both the summer and winterperiods generally showed good model performance(Table 5.7). Agreement with the higher percentilesshows more variability and the model often over-predicted up to about a factor of 2. Analysis of themeteorological data suggested that ADMSpredicted highest concentrations when the windwas from the east (during the winter) which werecaused by emissions from the large power stationsalong the East Thames corridor which highlightedthe need to obtain time-dependent emission datafrom large point sources.

Nitrogen dioxide concentrations calculated byADMS generally showed reasonable agreementwith measured data, though the model does tend to

56

THE FIRST PHASE REVIEW: A SUMMARY � I N T R O D U C T I O N

TABLE 5.7 SUMMARY MEASURED AND ADMS MODELLED SO2 DATA (ppb) DURING THE

SUMMER AND WINTER PERIODS AT DIFFERENT SITES.

Summer WinterSite Percentile Measured Modelled Measured Modelled

concentration concentration concentration concentration

Greenwich* mean 4 5 6 795th 17 22 20 2898th 33 33 31 5799.9th 106 222 64 227100th 178 250 96 230

Bexley mean 10 7 - -95th 36 29 - -98th 77 49 - -99.9th 254 277 - -100th 304 299 - -

City* mean 8 7 9 895th 28 23 24 2598th 42 32 31 4899.9th 131 456 71 177100th 178 429 121 187

Bloomsbury* mean 8 7 10 895th 26 19 27 2398th 38 29 39 3999.9th 119 149 83 169100th 149 413 104 178

Westminister* mean 10 6 - -95th 27 15 - -98th 44 28 - -99.9th 96 139 - -100th 141 152 - -

* Monitored data supplied by South East Institute of Public Health

TABLE 5.8 SUMMARY MEASURED AND ADMS MODELLED NO2 DATA (ppb) DURING THE

SUMMER AND WINTER PERIODS AT DIFFERENT SITES.



Greenwich* mean 23 14 22 895th 48 39 41 2298th 64 71 48 4199.9th 87 157 83 178100th 92 208 89 264

Bexley* mean 19 11 22 895th 43 47 37 2598th 56 93 43 4099.9th 86 214 72 142100th 128 256 91 306

City* mean 38 29 64 1595th 85 80 100 3498th 106 117 121 7199.9th 149 293 174 220100th 164 341 192 280

Bloomsbury* mean 34 27 37 1595th 66 76 56 3898th 88 106 64 6599.9th 132 234 85 189100th 176 292 112 220

Westminister* mean 42 25 - -95th 82 68 - -98th 105 96 - -99.9th 182 205 - -100th 246 222 - -

* Monitored data supplied by South East Institute of Public Health


57

under-estimate during the winter (Table 5.8). Thiscould be because the NO2 component of total NOxemissions which was assumed to be 5% for allsources in the modelling was under-estimatedwhen the percentage NOx contribution of non-roadsources is likely to be higher. The modelledmaximum concentrations are generally over-predicted by about a factor of 2.

5.6.3 Predicted results from IndicAirviro

Indic Airviro was used to model NO2 and SO2 overthe same three month periods (May - July andOctober - December 1995). Comparisons betweenmodelled and monitored data were then made. ForNO2 the results showed that for the CentralLondon monitoring stations, the model predictionswere up to 34% of monitored concentrations, except

at Westminster in summer and City of London inwinter where the calculated seasonal mean is lessthan the measured value (Table 5.9). For the EastThames corridor stations, the model predictions ofseasonal means agree very well with themonitoring data. For the Central London stations,the model generally over-predicted the 98thpercentile, by factors of 2 - 4. For the East Thamescorridor stations, the model is accurate to within40% for the 98th percentile.

Comparisons between modelled and measured datafor SO2 are shown in Table 5.10 which shows goodagreement between the modelled and measuredseasonal means for the East Thames corridorstations. It was thought that this may be a resultof the SO2 emissions data for this region beingmore accurate than for Central London. AtBloomsbury and the City of London sites, the

TABLE 5.9 SUMMARY MEASURED AND INDIC AIRVIRO MODELLED NO2 DATA (µg m–3) DURING

THE SUMMER AND WINTER PERIODS AT DIFFERENT SITES.



Bloomsbury mean 65 91 70 10698th 147 400 124 464

Westminister mean 82 74 69 8298th 207 118 374 304

Bridge Place mean 64 75 65 8098th 195 132 120 337

City of London mean 74 84 123 8798th 203 356 232 301

Bexley* mean 36 41 43 4598th 83 115 83 89

Greenwich* mean 42 45 43 5498th 126 76 93 86

* East Thames Corridor monitoring stations

TABLE 5.10 SUMMARY MEASURED AND INDIC AIRVIRO MODELLED SO2 DATA (µg m–3) DURING

THE SUMMER AND WINTER PERIODS AT DIFFERENT SITES.



Bloomsbury mean 22 66 26 8699th 132 221 122 262

Bridge Place mean 58 29 40 3499th 144 163 72 149

City of London mean 22 56 25 6099th 137 274 109 224

Bexley* mean 28 23 19 1899th 307 236 127 167

Greenwich* mean 17 16 18 2199th 165 166 104 328

* East Thames Corridor monitoring stations

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model over-predicted the seasonal means (by up toa factor of 3 ) whereas at Bridge Place, the modelunder-predicted (within a factor of 2). During thewinter months, the model over-predicted the 99thpercentile concentration at all sites, by factorsranging from 1.3 to 3.2. During the summermonths, the model over-predicted the 99thpercentile at the Central London stations (factorsof 1.1 to 2), under-predicted by 23% at Bexley, butgave good agreement at Greenwich.

5.7 DISPERSION MODELLINGIN BELFAST

Belfast City Council were asked to predictconcentrations of sulphur dioxide and PM10 in 2005under a variety of scenarios and determine if it islikely that the National Air Quality Strategyobjectives for these pollutants would be achieved ornot. Belfast is unlike many of other cities in the UKin that it experiences high ambient concentrations ofSO2 and PM10. This is principally due to high use ofcoal, solid fuels and oil within domestic properties.The scenarios modelled include:

A. High and low uptake of natural gas upon itsintroduction to Belfast;

B. A 15% improvement in the efficiency of Belfast’sbuildings;

C. Zero coal consumption;D. Reduced sulphur emissions, due to a ban on the sale

of high-sulphur petroleum cokes and legislation onthe sulphur content of oil and diesel;

E. The closure of Belfast West power station.

These scenarios were run for a base period whichwas winter 1995/96.

An emissions database was compiled by CERC whoalso undertook the dispersion modelling. Focuswas given to domestic emissions but point sourcesand transport were also included. The informationgained during a market survey on the use of solidfuel in Belfast (see Section 4.8) proved useful indetermining emissions. Total emissions estimatedare shown in Table 5.11 where the majority of theSO2 and PM10 emissions are emitted from the twopower stations NIE West and Kilroot.

The dispersion model ADMS-Urban was used inthis study together with meteorological data fromBelfast International Airport. The prevailing winddirection is generally south to south westerly butthe dominant wind direction during this study

period was found to be easterly and therefore maynot be representative of typical winter conditions. TABLE 5.11 TOTAL EMISSIONS FROM

DIFFERENT SOURCES TYPES (g s-1) IN

BELFAST ESTIMATED FOR WINTER 1995/96

Source SO2 PM10

Road Network 3.63 4.84

Stacks < 30 m high 59.3 3.56

Stacks > 30 m high 55.1 3.31

NIE West and Kilroot 927 95

All Point Sources 1040 102

Oil Burning 46.7 3.4

Solid Fuel 94.6 10.2

All Household Fuel Use 141.3 13.5

Belfast is located in between the Antrim hills to thewest and the coast to the east. ADMS-Urban cantake such complex terrain into account duringdispersion calculations using the complex flowmodel FLOWSTAR. However, this was not used inthis study as the run times would have beensignificantly increased. Alternatively the modelwas run with a terrain file using a 64 x 64 grid,covering a 39 km x 35 km area, to take intoaccount as much of the surrounding hilly terrain aspossible. Model outputs were validated with themonitoring data from the two national monitoringstations, Belfast Centre and Belfast East. For SO2the mean modelled concentration at Belfast Centrewas within 2 ppb of that measured while the99.9th percentile of 15 minute means was over-predicted by the model by over 100 ppb, or 42%.Conversely, at Belfast East monitoring site thepredicted mean concentration was around 50% ofthe measurements, while the 99.9th percentile of15 minute means was under-predicted by almost 80ppb, or 20%.

Generally, it was found that domestic emissions ofSO2 were by far the greatest contributor to bothmean and 99.9th percentile concentrations duringthe winter study period. Point sources accountedfor less than 16% of the mean concentrations whileroad emissions contributed to about 20%. Thepower stations, despite having high emissions, didnot contribute greatly to the predictedconcentrations during the winter as highestconcentrations resulting from such large pointsources tend to occur in summer when convectiveatmospheric conditions bring the plume to groundclose to source. During the typical stableatmospheric winter conditions such plumes tend to



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travel kilometres and significantly disperse beforereaching ground level.Concentrations of PM10 were consistently under-predicted in the model output when compared tomeasured data. However, the model only predictedprimary particles and the addition of secondaryand coarse particles was not made beforecomparison of the predictions and measurementswere made.

Various scenarios were modelled during this studyone of which was the increased use of natural gas(Scenario A). It was thought that the vastmajority of conversions to natural gas for domesticheating purposes will be from solid fuel users,especially in the public sector. For commerce, itwas expected that there will be 20% conversionfrom oil to gas amongst the smaller users (lessthan 75,000 therms per year) and 50% for largerusers. This uptake of gas was expected to deliver a15% reduction in the 99.9th percentile of 15 minuteSO2 concentrations at Belfast Centre. A 30%reduction in energy use in the Belfast housingstock within 10 years, of which 15% will come fromreduced fuel use (Scenario B), was predicted toreduce the 99.9th percentile of 15 minute means by13%. Scenario C was based on zero coalconsumption where it was assumed that alldomestic coal users would have switched to a non-polluting form of heating, such as gas. Onlyemissions from oil burning were modelled whichresulted in a reduction of the 99.9th percentile of15 minute averages of 59%. Scenario D was basedon reduced sulphur emissions where domesticemissions were recalculated on the basis that nopetroleum coke was burnt and that the limitsulphur content in diesel reduces from 0.2% to0.05% and that the maximum sulphur content ingas oil decreases, also to 0.05%. This resulted in areduction in the 99.9th percentile of 15 minuteaverages of 40%. The closure of Belfast Westpower station (Scenario E) was predicted have noeffect on the 99.9th percentile of 15 minute means.Under typical stable winter atmospheric conditions

the plume will not impinge on the city.

The assumptions in the scenarios outlined abovewere combined into a scenario which, it isintended, will be representative of Belfast by 2005,although for domestic emissions it was assumedthat there would be a % uptake of natural gasrather than zero coal consumption. It waspredicted that the 99.9th percentile of 15 minuteaverages would reduce by between 49 and 68%depending on the level of uptake of natural gas.Under this basis the National Air Quality Strategyobjective is unlikely to be met at either BelfastCentre (111-116 ppb) or Belfast East (190-215 ppb)national monitoring stations.

The predicted concentration of PM10 providedsimilar trends in reductions as seen in the SO2predictions, except that the reductions in percentilestatistics are generally lower. It was predicted thatthe National Air Quality Strategy objective for PM10would not be met in 2005. Predictions were 102-109µg m–3 at Belfast Centre and 66-69 µg m–3 at BelfastEast depending on the uptake of natural gas.

The influence of point sources on pollutantconcentrations in Belfast is thought to be smallcompared to the influence from domestic andvehicular emissions. For SO2, domestic emissionsare the dominant contributor.

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