handbook of atmospheric science || pollutant dispersion modeling

18.1 INTRODUCTION

It is often necessary for operators of industrialprocesses, developers, or regulatory authorities toquantify the air pollution impact of an existing orproposed scheme (e.g. a power station or a road) forthe purposes of licensing or planning. A predictivetool such as an atmospheric dispersion model canbe useful in this respect, as it provides a means of calculating air pollution concentrations nearground level in the vicinity of the emitting source,given information about the emissions and theprevailing meteorology. It is the concentration ofthe pollutant near ground level that is important indetermining whether there are any potential ill effects, e.g. on human health; air quality standardsand guidelines are set in terms of concentrationvalues, not source rates.

The aim of this chapter is to provide details onwhat types of model are available, how they shouldbe used, and their limitations. The information re-quired to run dispersion models, the type of resultsthat are produced, and how these can be inter-preted are described. The case studies illustratehow the results of various dispersion models canbe used in an air quality assessment and highlightsome of the problems that are encountered in themodeling approach. A detailed technical descrip-tion of the theory of atmospheric dispersion is provided in Chapter 10.

Dispersion models take a number of forms,from simple nomograms and spreadsheets to so-phisticated computer programs. It is important

that the model is appropriate for the complexity of the study (DoE 1995a). Factors such as model accuracy and validation need to be taken into ac-count (see Case study 5). It is also important to as-sess the availability of both meteorological andemissions data; some models do not require detailed meteorological data because they make broad assumptions about the prevailinglocal climate, while others may require quite detailed information on meteorology and emissions.

18.2 EMISSION SOURCESRECOGNIZED BY ATMOSPHERIC

DISPERSION MODELS

The three main sources of air pollution modeledare:1 Emissions from vehicle exhausts on roads,which generally make the greatest contribution toair pollution in urban areas.2 Controlled industrial, commercial, and domes-tic emissions from chimneys.3 Fugitive emissions (e.g. leakages from industrialplant, or particulate matter from mineral extrac-tion schemes; see Chapter 9).These sources may be divided into three main categories that are recognized by dispersion models:1 Point sources. These are individual chimneys.The simpler models can treat only one stack at a time (EA 1998), though more sophisticated

18 Pollutant Dispersion Modeling

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Handbook of Atmospheric Science: Principles and ApplicationsEdited by C.N. Hewitt, Andrea V. Jackson

Copyright © 2003 by Blackwell Publishing Ltd

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programs can include a very large number of stackssimultaneously.2 Line sources. Emissions from road traffic areusually treated as lines, where each straight linerepresents a segment of road. The simplest modelsmay treat only one straight section of road (Buckland & Middleton 1997), whereas the moreadvanced programs can handle a very large numberof roads, including bridges and street canyons(Benson 1992).3 Area sources. A cluster of point or line sources(e.g. a large number of vehicles in a parking lot)may be treated as an area source. Similarly, a largecity may be split into a number of grid squares(each with both traffic and industrial emissions),and the emissions from each square treated as anarray of area sources.Volume sources and puff releases can also be modeled using more specialized programs, butsuch applications are less common in the quantifi-cation of air pollution impacts, and are not dis-cussed further in this chapter. Roads and industrialchimneys are by far the most commonly modeledsources of air pollution.

The type of model that is used is dependentupon the pollutant released, and the appropriateperiod over which concentrations will be consid-ered. Most pollutants that are commonly investi-gated are released as buoyant gases (e.g. sulfurdioxide emissions from a power station chimney),and most dispersion models have been developedto treat the atmospheric dispersion of these hotplumes. Some of the more sophisticated modelsalso incorporate formulae for taking into accountdeposition processes that may occur in the atmos-phere (such as washout of pollutants due to rain-fall, or the gravitational settling of particles —seeCase study 4). Some models also attempt to ac-count for chemical reactions that may occur dur-ing the transport of pollutants in the atmosphere,although most models are suited for unreactive(inert) species only (see Case studies 2 and 6).Moreover, pollutants such as nitrogen dioxide andPM10 have both primary and secondary sources,and there are few dispersion models that can reliably calculate secondary pollutant concentra-

tions on a local scale for practical applications.However, a number of empirical relationships areavailable to describe the conversion of nitric oxideto nitrogen dioxide (Fisher et al. 1999), which maybe applied to the results of modeling the concen-tration of total nitrogen oxides.

18.3 ASSESSMENT CRITERIAFOR THE RESULTS OFDISPERSION MODELS

Air quality standards and guidelines for differentpollutants are expressed in various averaging periods and percentiles, depending on whether thepollutant has acute effects (which can arise aftershort periods of exposure) or chronic effects (re-sulting from exposure over a long period) onhuman health or vegetation. For example, the airquality objectives that have been set within theUK Air Quality Strategy (DETR 2000a) are basedon averaging periods ranging from 15 minutes toone year. Some of these UK air quality criteria areshown in Table 18.1; the results of dispersion mod-els need to be compared against standards of thistype. Therefore, it is important that dispersionmodels calculate air pollutant concentrations inthe appropriate averaging periods and statistics.Moreover, special applications of dispersion mod-els (e.g. for assessing odor impacts —see Case study1) may require estimates of concentrations oververy short time scales, i.e. a few seconds.

18.4 METEOROLOGICAL DATAREQUIREMENTS OF ATMOSPHERIC

DISPERSION MODELS

A detailed discussion of meteorology is presentedin Chapter 10 and a simplified treatment of the parameters that are required by many dispersionmodels is given below. At the simplest level, highly convective conditions and high windspeeds often give rise to highest ground-level con-centrations of pollutants emitted from chimneys.In contrast, calm, low wind speed conditions are

Pollutant Dispersion Modeling 505

Table 18.1 Examples of UK Air Quality Strategy objectives for the protection of human health.

Pollutant Concentration Measured as Date to be achieved by

Benzene 16.25mg m-3 (5p.p.b.) Running annual mean End of 2003

5mg m-3 (1.5p.p.b.) Running annual mean End of 2010

1,3-Butadiene 2.25mg m-3 (1p.p.b.) Running annual mean End of 2003

Carbon monoxide 10mgm-3 (8.6p.p.b.) Running annual mean End of 2003

Lead 0.5mg m-3 Annual mean End of 2004

0.25mg m-3 Annual mean End of 2008

Nitrogen dioxide 200mg m-3 (105p.p.b.) not to be exceeded One-hour mean End of 2005

more than 18 times a year

40mg m-3 (21p.p.b.) Annual mean End of 2005

Sulfur dioxide 350mg m-3 (132p.p.b.) not to be exceeded One-hour mean End of 2004


125mg m-3 (47p.p.b.) not to be exceeded 24-hour mean End of 2004

more than three times a year

266mg m-3 (100p.p.b.) not to be exceeded 15-minute mean End of 2005


more likely to give rise to the highest concentra-tions from road traffic emissions.

The main factors that affect the dilutionprocess are the wind speed, the height at which thesubstance is emitted, and atmospheric turbulence.Only if the wind direction is roughly aligned between the point of release and the point at whichan individual is standing will that individual besubject to any exposure. An individual standing inthe open air within an urban area will be subject tothe influence of many sources within the area, andhence will be exposed to some extent to trafficfumes, regardless of wind direction.

Many polluting sources are released from tallchimneys; this is to ensure that sufficient mixingand dilution occurs before the pollutants reach theground, in order to render concentrations harmless.In many cases the emissions are hotter than the sur-rounding air when released from the chimney,which results in the plume being buoyant. This, to-gether with the velocity of the plume, means thatextra dilution occurs before the substance reaches

the ground. The height to which the released mate-rial rises depends on atmospheric conditions anddispersion models are designed to take this “plumerise” into account (see Case study 3).

In recent years dispersion models have focusedon improving the description of dispersion and tur-bulence in the atmosphere at heights well abovethe ground (Weil 1992). This is important for calculating atmospheric concentrations of emis-sions from tall chimneys, such as those at powerstations (HMIP 1996). These new models rely on abetter understanding of the lower layers of the at-mosphere (Chapter 10). However, many modelssimplify the issue by categorizing the turbulenceof the atmosphere into six or seven stability class-es, the “Pasquill” categories A to G. Pasquill cate-gory A represents very convective conditionswhere surface heating generates additional turbu-lence over that caused by the wind, category D represents neutral conditions and categories F and G represent a very stable, low-turbulence situation.

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downwash effects, as several buildings greater than 6m in

height were proposed adjacent to the chimney. The emission

rate of hydrogen sulfide was taken to be the manufacturer’s guar-

anteed limit, to ensure a degree of conservatism in the

assessment.

Results and assessmentThe highest short-term concentration was predicted to occur at a

dwelling to the north of the site, with a value of 0.45 p.p.b. The

highest short-term concentration at the northern site boundary

was predicted to be 0.33 p.p.b. These concentrations were

derived by multiplying the model predicted one-hour mean

concentrations by 10. These short-term concentrations are well

below the environmental criterion of 1 p.p.b. for a “recognizable”

odor for H2S.

ConclusionsA chimney height of 15m would be adequate to ensure that

emissions of H2S from the sewage treatment plant stack would

not give rise to a “recognizable” odor beyond the site boundary.

The model results suggest that even a faint odor (i.e. at the

detection threshold for H2S) is unlikely.

This conclusion assumes that the meteorology during 1997,

1998, and 1999 would be representative of future conditions at

the site. It is noteworthy that fugitive emission of hydrogen

sulfide (e.g. uncontrolled leaks) were not included in the

assessment, as their emission characteristics could not be reliably

quantified. Moreover, it was assumed that H2S would behave as

an inert species during its dispersion and dilution, and there

would be no synergistic effects with other air pollutants that

would alter its odorous properties.

Table 18.2 Emission parameters for proposed sewagetreatment plant.

Stack height (m) 15

Stack diameter (m) 0.8

Volumetric flow rate at 20°C (m3 s-1) 7.643

Normalized flow rate (Nm3 s-1) 7.121

Exit temperature (°C) 20

H2S emission concentration — manufacturer’s 20

guaranteed limit at 0°C (p.p.b.)

Exit velocity (ms-1) 15

H2S emission rate (g s-1) 0.000216

Case study 1 An assessment ofthe odor impact of a proposed

sewage treatment plant

Key features of studyThe purpose of the study was to determine whether emissions of

hydrogen sulfide from the proposed 15m chimney at a new

sewage treatment plant were likely to give rise to any odor

beyond the site boundary, where complaints from local residents

could result. The plant was in a rural area, with no other

significant sources of hydrogen sulfide, either natural or

anthropogenic. Therefore, the background concentration of H2S

was taken to be zero. The assessment criterion was taken to be a

“recognizable” odor. The published odor detection threshold

for hydrogen sulfide is 0.5 p.p.b. (Woodfield & Hall 1994), which

would result in a very faint odor. A “recognizable” odor would be

2–5 times the detection threshold, i.e. 1.0–2.5 p.p.b.

An understanding of the relationship between the mean

and fluctuating plume concentrations is important for

assessing odors. Over a short time scale (i.e. a few seconds), the

unsteady character of the dispersion process (due to constantly

varying wind speed and direction) leads to large excursions of

peak concentrations from the hourly mean. Most atmospheric

dispersion models predict one-hour averages as the shortest

averaging period. However, the human nose can readily per-

ceives these peaks in fluctuating concentrations of odorous

substances.

Measurements of hourly meteorological parameters re-

presentative of the site (from a nearby airport) were available

for a three-year period (1997, 1998, and 1999). It is advisable

to consider more than a single year of meteorology as the

frequency distribution of wind speed, direction, and stability

can vary from year to year. A Gaussian dispersion model was used

to calculate one-hour mean hydrogen sulfide concentrations.

As the perception of an odor can occur over a matter of

seconds, the one-hour mean predictions were extrapolated to

short-term peaks, by multiplying by a factor of 10 (Hall &

Kukadia 1993).

MethodologyThe model input parameters are shown in Table 18.2. Receptors

for the modeling were located at the site boundary in eight

directions from the chimney round the compass at 45°

intervals, and at selected properties beyond the site boundary,

representative of residential and amenity areas in the neighbor-

ing community. The model took into account building


Case study 2 Impact of highnitrogen oxides emissions

during start-up conditions at apower station

Key features of studyStart-up of turbines at power stations can result in temporary

emissions of nitrogen oxides (NOx) far greater than the emission

rate during routine operation. Start-up conditions could prevail

several times a year, i.e. after maintenance or emergency

shut-downs. Start-up emissions need to be modeled to assess the

short-term impact of nitrogen dioxide (NO2) on the community.

The national air quality standard for one-hour mean NO2 con-

centrations was 287mg m-3.

MethodologyThe turbine manufacturer provided the emissions data shown

in Table 18.3. The NOx emission rate (expressed as an hourly

average) was calculated from an emission of 133kg h-1 over the

first hour (the start-up period).

The dispersion model was used to predict one-hour mean NOx

concentrations at a line of receptors (at intervals of 50m)

directly downwind of the stack. A matrix of 20 stability and wind

speed combinations (Table 18.4) were used as meteorological in-

puts to the model. These combinations collectively represent con-

ditions that prevail for 99% of the time at the site (as shown by

inspecting a long-term frequency analysis of stability and wind

speed recordings for the site).

The dispersion model could not take into account the chemi-

cal transformation of the primary-emitted NO to NO2 in the at-

mosphere. Therefore, the model is suitable only for predicting

Table 18.3 Emission parameters for power stationduring start-up.

Stack height (m) 60

Stack diameter (m) 7

Exit temperature (°C) 88

Exit velocity (ms-1) 14.2

NOx emission rate (g s-1) 36.9

Table 18.4 Predicted one-hour mean ground level NOx concentrations during start-up.

Pasquill Wind Maximum NOx Maximum NO2 Distance downwind stability speed concentration concentration at which maximum category (ms-1) (mgm-3) (mgm-3) concentration occurs (m)

A 1 94 47 <50

A 2.5 198 99 <50

B 1 4 2 3500

B 2.5 84 42 200

B 4.5 116 108 150

C 1 <1 <1 3500

C 2.5 12 6 1500

C 4.5 38 19 550

C 6.5 54 27 500

C 9.5 54 27 450

C 15 44 22 400

D 1 <1 <1 3200

D 2.5 2 1 1650

D 4.5 18 9 1150

D 6.5 34 17 850

D 9.5 46 23 750

E 2.5 4 2 4500

E 4.5 10 5 1400

F 1 <1 <1 150

F 2.5 <1 <1 850

(Continued on p. 508)

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0 200 400 600 800 1000 1200 1400 1600 18000

20

40

60

80

100

120B: 4.5 m s–1

C: 9.5 m s–1

A: 2.5 m s–1

D: 9.5 m s–1

E: 4.5 m s–1E: 1 m s–1

Distance downwind of stack (m)

One-

hour

mea

n N

O2 c

once

ntr

ati

on (

mg m

–3)

Fig. 18.1 Predicted one-hourmean contribution of stack to ground-level NO2concentration (mgm-3) duringstart-up of turbines. Worstcase wind speed in eachstability category.

concentrations of total NOx, and a subsequent allowance must

be made to account for how much of this NOx is in the form of

NO2 at ground level (Janssen et al. 1988; Bange et al. 1991).

Studies have shown that the principal NOx emitted by power

station stacks is nitric oxide (NO). NO is oxidized to NO2, primari-

ly by reaction with ozone (O3). The formation of NO2 proceeds

relatively slowly because the available O3 is rapidly depleted.

Additional O3 enters the plume as it mixes with the surrounding

air to continue the reaction, but this is generally a slow process

(Janssen et al. 1988). Within 5km of a source, less than 20%

of the NOx is in the form of NO2, under stable atmospheric

conditions. During unstable atmospheric conditions with more

mixing, up to 50% of the NO may be converted to NO2. Many

modeling studies assume that 50% of the NOx in a power station

plume is in the form of NO2 at ground level, and this factor was

applied to the modeled NOx concentrations in this study.

Results and assessmentThe maximum predicted NOx and NO2 concentrations for each

stability and wind speed combination are shown in Table 18.4,

together with the distance downwind of the stack where this

peak occurs. The NO2 results are displayed as concentration pro-

files in Fig. 18.1. The maximum predicted NO2 concentration was

108mg m-3, occurring 150m downwind of the stack during con-

vective atmospheric conditions (i.e. warm, sunny days) with a

wind speed of about 5m s-1. The annual mean NO2 concentra-

tion in the area was estimated at 25–30 mg m-3, based on

national mapping of NO2 concentrations (measured using net-

works of diffusion tubes). Even when this annual mean was

added to the maximum predicted short-term concentration of

108mg m-3, the air quality standard of 287mg m-3 for a one-

hour mean NO2 concentration is not approached.

ConclusionsStart-up emissions of NOx from the power station were unlikely to

cause breaches of the relevant air quality standard for NO2.

For the prediction of long-term pollutant con-centrations, such as the annual average, it is oftenfaster for a model to use a statistical summary ofmeteorological data, which could be in the form ofa “Pasquill stability frequency analysis” —thisdescribes the frequency distribution of wind speed,direction, and Pasquill stability category for anumber of years. Models in which short-termtime-averaged concentrations over periods ofabout one hour are calculated may not require me-

teorological data (as specific combinations of thekey parameters can be user-defined), although ref-erence to the frequency of specific combinations of wind speed, direction, and stability for the areacan be very useful for the interpretation of results.Models may also be run to predict a sequence ofhourly concentrations using a long sequence ofhourly meteorological data. Generally, dispersionmodels assume that meteorological conditions remain steady over one-hour periods. Statistical


and sequential hourly meteorological data setswill give different results when calculating peakone-hour mean concentrations, but should givesimilar results when used to calculate annualmeans. Sequential hourly data sets can be a verypowerful tool in investigating pollution episodesbut only if the emission characteristics correspon-ding to the period of the episode are known.

18.5 TYPES OF ATMOSPHERICDISPERSION MODEL

At the simplest level, predictions of air qualitymay be made using a nomogram, workbook, orspreadsheet, i.e. a “screening” model. Other mod-els are computer-based, using a PC program. Themost advanced dispersion models need very detailed meteorological inputs, and may require a workstation or very powerful PC on which to run.

There is a whole class of models based on theGaussian formulation, in which the vertical andhorizontal profiles of the plume concentration fol-low the shape of a Gaussian function (a symmetricbell-shaped distribution), which has convenientmathematical properties. A Gaussian model alsoassumes that one of the seven stability categories,together with wind speed, can be used to representany atmospheric condition when it comes to cal-culating dispersion. Within each stability categorythe plume spread has a different dependence ondownwind distance (Turner 1994).

Until recently, the Gaussian distribution wasconsidered to be the best mathematical approxi-mation of plume behavior for short averaging periods, and Gaussian models were generally usedin most applications. More recently it has been recognized that there are different turbulence anddiffusion characteristics within the atmosphere atdifferent heights (see Chapter 10). The so-called“new generation” dispersion models adopt a moresophisticated approach to define vertical profilesof the turbulence parameters, and modify theGaussian distribution under the more convective(unstable) conditions. These “new generation”models are not restricted to describing plume

shape according to a finite number of dispersioncategories. Few Gaussian models can interpolatebetween discrete dispersion categories.

18.5.1 Screening models

In order to simplify the running of a computermodel, some models already contain preset meteorological conditions and certain emis-sions data, and such models will usually cal-culate worst-case concentrations. The screening models consist of empirical results from field ob-servations or wind tunnel experiments, or calcula-tions of advanced models under clearly specifiedconditions. Screening models are a very useful way of quickly gaining an initial impression of the air pollution impact of the scheme under investigation.

18.5.2 Computerized dispersion models

Most dispersion models are computer-based programs suitable for a desktop PC. Their main differences to screening models are that:1 They require more information on the source,e.g. diurnal traffic flow data for a road rather thansimply a peak hour or annual average flow.2 They can take greater account of hour-by-hourvariations in meteorology, e.g. by use of sequen-tial, hourly meteorological data from a representa-tive meteorological observing site.3 The results may be more accurate than those ofscreening models, but only if detailed and accuratemeteorological and emissions data are available.4 More than one type of source can be treated, e.g.point, area, and volume sources could be modeledsimultaneously.5 They are usually multisource, i.e. a large number of emission sources can be modeled simultaneously.6 Their output choices can be more versatile, i.e. awide range of averaging periods may be modeled.7 Special effects such as atmospheric photochem-istry, complex terrain, and buildings effects can betaken into account by some models.8 Some of the more advanced models incorporatethe most up-to-date treatment of the meteorology

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of the atmosphere, if the necessary meteorologicalparameters are available.Brief descriptions of some widely used models aregiven in the Appendix to this chapter for the pur-pose of illustrating relative capabilities; the list isnot exhaustive, and does not indicate any type ofapproval or endorsement.

18.6 INPUT DATA REQUIREMENTS

All dispersion models need data on the pollutantemission rate. These need not necessarily be exact,as in many cases useful decisions can be madechoosing highest likely emissions to represent aworst case (e.g. by assuming that emissions from achimney are at the limits defined by the regulator,or that traffic on a road is at its peak-hour level —see Case studies 1 and 6 respectively). Most modelsalso require details on how the pollutant is beingreleased, and the environment into which the release occurs. It is also necessary to define the locations at which the impact of the emissions will be predicted (the “receptor” locations).

Where the model assumes that the emissionsare not chemically transformed in the atmosphere,then the predicted concentration is directly pro-portional to the emission rate, i.e. if the emissionrate is doubled, the predicted ground-level concen-tration also doubles. The collation of accurateemissions data is therefore extremely important(Vawda 1998).

18.6.1 Emissions data for chimneys

The emission rate is normally given in grams persecond (gs-1) or equivalent, convenient units, andmay be derived from measurements of pollutantconcentrations in the stack gases, plant manufac-turers’ specifications, or process characteristicssuch as fuel consumption, composition of fuel orfeedstock —emission factors enable one to calcu-late emissions from such surrogate data. When cal-culating the emission rate, consideration must alsobe given as to whether the operation is continuous or“batch” (i.e. the process runs for a specified period oftime), or whether there are specific scenarios that

might give rise to higher emissions, such as duringstart-up (see Case study 2). If a model is being used topredict annual average concentrations, an averageor typical emission rate will be adequate. However,if short-term impacts are of interest (e.g. hourly or15-minute average concentrations), a number of“worst-case” scenarios should also be included.

Some of the release conditions that need to bedefined for dispersion models are used to calculatethe plume rise, from the buoyancy and momen-tum of the exhaust gases. These parameters are:• the height of the stack;• the diameter of the stack at the exit point;• the temperature of the emissions at the exitpoint;• the velocity of the emissions at the exit point.

18.6.2 Emissions data for roads

The emission rate for a specific section of road is usually required by a dispersion model in grams permeter per second (gm-1 s-1). The user may have toprecalculate the line source emission rate based on aknowledge of individual vehicle type emission fac-tors (DETR 1998). Emissions from individual roadvehicles are dependent upon a number of factors, in-cluding the type of vehicle and the speed at whichthe vehicle is traveling. The emissions data requiredfor road traffic emissions modeling include:• the length of road section;• the volume of traffic, e.g. vehicles per hour, vehi-cles per day, or the maximum hourly traffic flow;• speed of traffic;• vehicle mix (fraction of gasoline and diesel auto-mobiles, light goods vehicles and heavy goods vehicles, buses, etc.);• whether the road is in a street canyon, elevated,or in a cutting.A notable feature of most road traffic models isthat they take some account of the extra atmos-pheric mixing and turbulence caused by the move-ment of vehicles.

18.6.3 Local environmental conditions

Both nearby buildings and complex topographycan have a significant effect upon the dispersion


Case study 3 Carbon monoxideemissions from two municipal

waste incinerators

Key features of studyA company operates two incinerators (Sites A and B) on opposite

sides of a small town in Neverland. Each site has a single

chimney. The pollutant of concern from the stacks is carbon

monoxide. The Industrial Source Complex (ISC) model was used

to predict the cumulative concentrations of carbon monoxide

from the two stacks. The predicted concentrations, after making

an allowance for background levels, were compared against the

relevant air quality criterion for carbon monoxide, to assess the

combined air quality impact of the processes.

MethodologyNeverland has no national air quality standard or guideline for

carbon monoxide. Therefore, the air quality assessment makes

use of guidelines set by the World Health Organization (1987).

These have been set for the protection of human health, and

represent levels below which there should be no risk of harm. The

one-hour mean guideline has been set at 30mgm-3 (25 p.p.m.),

and the eight-hour mean guideline has been set at 11.4mgm-3

(10 p.p.m.).

No ambient monitoring data for carbon monoxide were avail-

able specifically for the town in which the two factories were

sited. Therefore, use was made of national statistics, which

suggested that the annual mean background concentration (i.e.

away from the influence of busy roads) was about 0.6mgm-3.

Even close to busy roads, carbon monoxide concentrations were

unlikely to exceed 5mgm-3 as an eight-hour mean. The highest

one-hour mean carbon monoxide concentration near busy roads

during adverse weather conditions was unlikely to exceed

10mg m-3.

The Industrial Source Complex (ISC) model was run using one

year of sequential hourly meteorological data from the nearest

observing station. The model calculated the maximum one-hour

mean carbon monoxide concentration as well as the maximum

running eight-hour mean. The model allows the effects of aero-

dynamic downwash on the dispersion of the plume to be taken

into account; therefore, building heights, dimensions, and loca-

tions were defined in relation to the location of the stacks. The

emissions parameters required by the model are shown in Table

18.5 — these were provided by the operator.

The data in Table 18.5 suggest that dispersion and dilution of

the emissions from Site A will be less effective than from Site B.

This is because the emissions at Site A occur from a shorter stack,

and with a lower exit velocity, which would result in less plume

rise. Moreover, the mass emission rate from the Site A stack is

higher than at Site B, such that higher ground-level concentra-

tions of CO are expected close to Site A.

Results and assessmentThe model predictions for the combined carbon monoxide

contribution of the two stacks are shown in Table 18.6. The

maximum off-site one-hour mean concentration is 6.5mgm-3,

occurring at the eastern boundary of Site A. The maximum off-

site eight-hour mean carbon monoxide concentration was also

predicted to occur at this location, with a value of 1.7mgm-3.

Only the predictions of off-site concentration were reported, as

the air quality assessment was concerned with the impact on the

community, and not workplace exposure.

The total carbon monoxide concentrations (i.e. predicted con-

tribution from Sites A and B added to the worst-case estimates of

Table 18.5 Emission parameters for the dispersionmodeling.

Stack on Stack on Site A Site B

Carbon monoxide emission rate (g s-1) 185 115

Stack exit temperature of gases (K) 325 325

Stack exit velocity of gases (ms-1) 20 28

Stack height (m) 40 60

Stack diameter (m) 0.8 0.3

Table 18.6 Maximum predicted carbon monoxideconcentration contributed by stacks.

Running One-hour eight-hour

mean mean (mgm-3) (mgm-3)

Maximum concentration (occurring 6.5 1.7

at Site A boundary, 300m east

from stack at Site A)

Maximum concentration at boundary 1.5 0.5

of Site B (occurring 50m east of

stack at Site B)

Maximum concentration at a 2.3 1.0

residential property beyond the

site boundary


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characteristics of a plume. Many dispersion mod-els contain algorithms to take account of these ef-fects, although it must be accepted that the resultsshould be treated with a greater degree of caution.Buildings may cause a plume to come to groundmuch closer to the stack than otherwise expected,causing significantly higher pollutant concentra-tions. As a general guide, building downwash prob-lems may occur if the stack height is less than twoand a half times the height of nearby buildings (seeCase study 1). To take account of local building effects, models require information on the dimen-sions and location of the structures in relation tothe stack. A number of different building wake algorithms are available.

Plumes can impact directly on hillsides undercertain meteorological conditions (resulting inmuch higher ground-level concentrations thatwould occur over flat terrain), or valleys may trapemissions during low-level inversions. The moresophisticated models can take account of these terrain effects (though this is an area of active research), and require the input of contour heightsin the area surrounding the stack. Terrain effectsare unlikely to be significant where the hills have a slope of less than about 10%.

The model user is frequently required to definethe “surface roughness,” a term that describes the

degree of ground turbulence caused by the passageof winds across surface structures. Surface rough-ness is much greater in urban areas (due to the pres-ence of tall buildings) than in rural areas.Calculations of dispersion that take account of thegreater aerodynamic roughness of the surfacestructures in urban areas tend to predict higherconcentrations closer to the stack than calcula-tions under equivalent conditions that assumetypical rural roughness.

The model user needs to define the receptor locations. It is good practice to identify the nearby,sensitive locations (e.g. dwellings, amenity areas)to the source, though many models allow the userto specify a “grid” of receptor locations, such thatthe results can be presented as isopleths superim-posed on a base map of the area (see Case study 4).When setting up a receptor grid it is important toensure that there are sufficient receptor points to be able to reliably predict the magnitude and location of the maximum concentration; if the grid of receptor points is too widely spaced, themaximum concentration may be missed.

18.6.4 Availability of input data

The results of a dispersion model are only as good asthe data that are put in. For example, it would not be

the background levels) are compared in Table 18.7 against the

WHO guidelines. It is clear that even when the impact of the two

factories is superimposed on the highest concentrations likely

to arise near busy roads, the air quality guidelines are not

approached.

Table 18.7 Total carbon monoxide concentrations compared with air quality guidelines.

Maximum Worst-case predicted estimate of Air

contribution background Total qualityof the stacks level concentration guideline

One-hour mean (mgm-3) 6.5 10 16.5 30

Eight-hour mean (mgm-3) 1.7 5 6.7 11.4

ConclusionsThe impact of the two incinerators on carbon monoxide concen-

trations in the local community is not significant, in terms

of either incrementing background levels or approaching the

relevant health-based air quality criteria.


Case study 4 Nuisanceassessment for deposited dust

from a proposed opencast coal site

Key features of studyAn opencast coal site can be a significant source of dust, causing

soiling of surfaces at properties within 500m of the boundary.

The nuisance impact of a proposed open-cast coal mine on the

local community was carried out by modeling the dispersion and

deposition of particulates, using the Fugitive Dust Model (FDM).

This model can predict dust deposition rates as well as the more

common parameter of interest in air quality studies, the ambient

concentration of fine particles. Estimates of the existing, back-

ground dust deposition rate were in the range 10–50mgm-2

day-1 (averaged over a month), with higher peaks close to

agricultural tilling. Emission factors for particulates from various

dust-generating activities at the site (Table 18.8) were derived

from an authoritative database (US EPA 1991).

FDM makes the basic assumption that deposition from the

dust plume is proportional to the airborne dust concentra-

tion close to the ground. Deposition is treated through two

parameters: the gravitational settling velocity and the deposi-

tion velocity. The gravitational settling velocity accounts for the

removal of particulate matter from the atmosphere due to gravi-

ty. Since only the larger particles have sufficient mass to over-

come turbulent eddies, this mechanism is significant only for the

larger size ranges. The deposition velocity accounts for the

removal of particles by all other means, including impaction

and adsorption.

The FDM model can treat point, area, and line sources. For the

purpose of this study, aggregate dropping, loading onto dumper

trucks, and localized scraping of surface material were treated as

point sources. Emissions of dust from haul routes and perimeter

bunds were treated as line sources, and wind blow from

stockpiles was treated as area sources.

Table 18.8 Examples of US EPA emission factor equations for particulates <30mm.

Vehicle movements on E (kg vehicle-1 km-1) = k 1.7 (s/12) (S/48) k, particle size multiplier, e.g. 0.8 for <30mm; s,

unpaved haul routes (W/2.7)0.7 (w/4)0.5 (365 - p)/365 silt content; S, average vehicle speed; W, average

vehicle weight; w, number of tyres; p, number of

days in year with >0.25mm of rainfall

Bulldozing/scraping E (kg h-1) = (2.6s1.2)/M1.3 s, silt content; M, moisture content

Material handling/transfer E (kg t-1) = [k 0.0016 (U/2.2)1.3]/(M/2)1.4 k, particle size multiplier (e.g. 0.8 for <30mm); M,

moisture content; U, wind speed, read directly

from meteorological file

MethodologyThe village of Little Blakenham lies to the northeast of the pro-

posed site for the opencast coal mine, downwind of the prevail-

ing winds in the area (as shown by a 10-year wind-rose

representative of the locality: Fig. 18.2). These long-term meteo-

rological data were input to the FDM model, such that the results

of deposited dust concentrations represent a 10-year mean. The

emissions data were based on the year of most intense mining ac-

tivity at the site, to represent a worst case. Examples of the emis-

sion factor equations are shown in Table 18.8 (US EPA 1991).

Information on site activities (e.g. number of vehicle movements

on haul routes, quantities of material handled) necessary for cal-

culating particulate emission rates from the emission factor

equations was provided by the developer, and is summarized in

Table 18.9. It is acknowledged that the most significant sources

of dust at surface mineral sites are mechanical handling opera-

tions and haulage of material on unsurfaced site roads (DoE

1995b).

Dust deposition rates were modeled for a number of key

properties in the village close to the proposed opencast site, and

also over a uniform grid of receptors extending 3km in all direc-

tions from the site with spacings of 10m.

Results and assessmentA dust deposition rate of 200–350mgm-2 day-1 (averaged over

a month) is frequently cited in the UK as the level above which

nuisance complaints may arise (IIRS 1991). Washington state

has adopted a nuisance standard of 187mgm-2 day-1, that in

use in West Australia is 133mgm-2 day-1, and the standard in

Germany is 350–650mgm-2 day-1. However, it should be noted

that these various standards apply to sampling equipment with

different efficiencies.

The maximum predicted long-term dust deposition rate at a

sensitive location was predicted as 16mgm-2 day-1, occurring at

the closest property to the northeast of the opencast site, at a dis-

tance of 100m from the site boundary (Fig. 18.3). Even if this is


514 yasmin vawda

7 a.m. to 7 p.m., Monday to Friday 7 a.m.

Working hours to 1 p.m., Saturday

Moisture content of topsoil, subsoil, 8%

and overburden

Silt content of all materials handled 4%

Moisture content of coal stockpiles 6%

Height of coal stockpiles 4m

Average weight of vehicles on haul 30 t

routes

Number of wheels for site vehicles 6

Average speed of vehicles 20kmh-1

Area of coal stockpiles 1000m2

Number of haul routes 4

Total length of haul routes 500m

Table 18.9 Operational details and site characteristics for proposed opencast coal site in year of peak activity.

236,642 obs.1.5% calm

3.4% variable

20%

10%

5%

0% Knots

1–10

11–16

17–27

28–33

>33

Season: annual

Period of data: January 1986 to December 1995

Altitude: 10 m a.m.s.l.

Fig. 18.2 Ten-year wind rosefor meteorological observingstation representative ofLittle Blakenham.

7 a.m. to 7 p.m., Monday to Friday 7 a.m.

Working hours to 1 p.m., Saturday

Duration of topsoil, subsoil, and 4 weeks

overburden removal

Maximum height of overburden 4m

stockpile

Time to build overburden stockpile 20–25 weeks

to maximum height

Area of overburden stockpile 2500m2

Total area of perimeter bunds 2000m2

Time to construct perimeter bunds 4 weeks

Maximum depth for excavation 30m

Time to reach maximum depth 30 weeks

Vehicle movements on haul routes 10 vehicles h-1 as a

daily average on

each haul route

(Continued)


Park FmPark Fm

DerrickhillDerrickhill Rookery FmRookery Fm

Scale: 1 cm = 200cm = 200 mproposed opencastproposed opencastcoal site

WestleygreenWestleygreenFm InghamsInghams

CottageCottageFm

woodlandwoodlandGonGon

BlackacreBlackacreHillHillBroomvaleBroomvale

Fm

TheTheCommonCommon

LittleLittleBlakenhamBlakenham

RedRedHoHo

SycamoreSycamoreFm

13

WaterWaterParkPark

NettlesteadNettlestead

60

504545 30

The ElmsThe Elms

PHPH

N OfftonPtPt 2323

PHPH

SomershamSomershamP

ChurchChurchFm

6666TheTheClampsClamps

Leak HallLeak Hall

Little ParkLittle ParkFm GroveGrove

FmValleyValleyFm

4343ill Fmill Fm FlowtonFlowton

TyeHoHo

5454

WoodlandsWoodlandsFm

BullenhallBullenhall

Rost ComyComy

RuttersRuttersFm

4747

50

4040 3030

10

20

HallHall

4848 B1113

B1113

G

4949

88

12

16

Derrickhill Rookery Fm

Scale:1 cm = 200 m

Proposed opencastcoal site

WestleygreenFm Inghams

CottageFm

woodlandGon

BlackacreHillBroomvale

Fm

TheCommon

LittleBlakenham

RedHo

SycamoreFm

13

WaterPark

Nettlestead

60

5045 30

The Elms

PH

N OfftonPt 23

PH

SomershamP

ChurchFm

66TheClamps

Leak Hall

Little ParkFm Grove

FmValleyFm

43ill Fm Flowton

TyeHo

54

WoodlandsFm

Bullenhall

Rost Comy

RuttersFm

47

50

40 30

10

20

Hall

48 B1113

G

49

88

12

16

4

Park Fm

Fig. 18.3 Isopleths of predicted long-term dust deposition rates (mgm-2 day-1) contributed by proposedopencast coal mine. (With kind permission of the Ordnance Survey © Crown Copyright. NC/00/971.)

added to a background dust deposition rate of 50mgm-2

day-1, the various nuisance criteria are not exceeded.

ConclusionsThe dispersion modeling has shown that operations at the pro-

posed opencast coal site are unlikely to cause soiling nuisance at

nearby properties. However, the study did not model the contri-

bution of the opencast site to ambient fine particulate concen-

trations, which would be necessary for a comparison against

health-based air quality standards for airborne PM10 concentra-

tions. The study did not attempt to take into account the reduc-

tion in emission rates that could be achieved by means of

mitigation measures, e.g. good site practices such as watering of

haul routes and covering of stockpiles.

meaningful to attempt to predict the highest one-hour mean NO2concentration close to a road if onlydaily average traffic data were available; the peakhour vehicle flows and speeds (or at least some esti-mate of these quantities) are required. Similarly,the contribution of a power station to an observedSO2 episode is unlikely to be indicated by modelingbased on the annual average SO2 emission rate; in-

formation is required on the variation of SO2releaserates on an hourly basis, coincident with the periodof the pollution episode. Advanced models can takeaccount of diurnal, daily, and monthly variations inthe emission parameters, based on traffic flowchanges and/or industrial output cycles, fuel usescenarios, etc. Such a model may be used if the inputdata are available at this level of detail.

516 yasmin vawda

18.7 OUTPUT DATAAND INTERPRETATION

The output from dispersion models usually con-sists of concentration values, which can be pre-sented in many different ways. Concentrations areusually expressed as milligrams or micrograms percubic meter of air (mgm-3 and mgm-3 respectively).Concentrations of polluting gases may also be ex-pressed as ratio of the volume of the substance tothe volume of air —parts per million (p.p.m.) orparts per billion (p.p.b.).

The concentration of a pollutant will vary al-most instantaneously because of the turbulence inthe atmosphere. For practical purposes, concentra-tions calculated by dispersion models are ex-pressed as averages over specified time periods(Beychok 1994). Concentrations are usually calcu-lated over sequences of one-hour periods, eachwith an associated meteorological condition and,if known, different hourly emission rates. Se-quences of calculated hourly average concentra-tions are not always of direct use in air pollutionapplications and can be processed in various ways.The annual mean concentration is the simplestlong-term output result from dispersion models.

18.8 BACKGROUND AIR QUALITY

Dispersion models can only predict ground-levelconcentrations arising from the sources that havebeen input to the model. In all situations, therewill be an additional pollutant component arisingfrom those sources that have not been included.These may be local sources (such as nearby roads)or distant sources (in other parts of the country oreven neighboring countries). The backgroundcomponent can be significant in determiningwhether air quality standards are exceeded or not,and must be given careful consideration.

For situations where the annual average concen-tration is predicted, it is relatively straightforwardto include the background component. For exam-ple, modeling predictions for a road in a town may in-dicate a maximum annual average benzene contri-bution of 1 p.p.b. from the road at the nearest recep-

tor. Data from a nearby automatic monitoring sta-tion, but away from the influence of the road beingmodeled, may indicate that the annual average isabout 0.5 p.p.b. (i.e. the urban background concen-tration). The total concentration can be calculatedby simply adding 1 + 0.5 = 1.5 p.p.b. The annual av-erage concentration modeled from a chimney stackcould be treated in exactly the same way.

However, it is more difficult to assess the effects of the background component where peakone-hour concentrations are being considered. Inthis case, it is necessary to treat low-level (i.e.roads) and high-level (i.e. chimneys) sources in adifferent way.

For example, the maximum one-hour concen-tration of NOx predicted by a dispersion model toarise from the road may be 40 p.p.b. under worst-case atmospheric conditions. These conditions arelikely to be the same as those giving rise to an ele-vated background, e.g. calm, stable conditions dur-ing the wintertime. Data from a nearby automaticmonitoring station, away from the direct influenceof the road, may indicate that the maximum wintertime one-hour concentration is about 75p.p.b. The total concentration can be estimated byadding 75 + 40 = 115 p.p.b.

In a different example, the maximum one-hourmean NOxcontribution from a chimney stack maybe predicted by a dispersion model to be 50 p.p.b. Inthis case, the weather conditions that cause thehighest concentration from the stack (high windspeed, convective conditions) are unlikely to be the same as those causing an elevated back-ground —during calm, stable conditions the impact of the stack emissions is likely to be very low. The total concentration is therefore estimated by adding the peak one-hour stack con-tribution to the annual average background, wherethe latter is again estimated from a suitable urbanbackground monitoring station.

18.9 CHOICE OFDISPERSION MODEL

The Royal Meteorological Society has publishedguidelines that seek to promote best practice in the


Table 18.10 Available traffic data (vehicles h-1) for Straight Street.

Hour Cars Buses Light goods vehicles (LGV) Heavy goods vehicles (HGV) Motorcycles Total

08.00–09.00 1185 176 115 33 5 1514

09.00–10.00 906 186 146 42 3 1283

10.00–11.00 667 163 121 68 11 1030

11.00–12.00 670 165 129 61 8 1033

12.00–13.00 664 154 108 50 8 984

13.00–14.00 679 151 107 44 4 985

14.00–15.00 762 164 120 45 5 1096

15.00–16.00 740 166 126 60 6 1098

16.00–17.00 923 186 118 47 7 1281

17.00–18.00 854 179 73 29 4 1139

No traffic data available for period 18.00–08.00 hours.

Case study 5 Validation of tworoad traffic dispersion models

against ambient monitoring data

Key features of studyA city council needed to know how reliable the results of two road

traffic dispersion models (AEOLIUSF and CAR INTERNATIONAL)

would be for predicting carbon monoxide concentrations for the

city center. The local authority would then be in a position to

choose the better tool in fulfilling its urban planning and regula-

tory duties with respect to air pollution (DETR 1997). For this rea-

son, a validation study was undertaken of the two models, by

comparing model predictions against continuous monitoring

data for carbon monoxide, collected over a period of three

months at a kerbside site on Straight Street.

MethodologyThe available traffic data for Straight Street are shown in Table

18.10. In the absence of further details, it was assumed that

these flows were representative of every day in the week over the

three-month study period. A constant speed of 15kmh-1 was

defined for AEOLIUSF and a canyon height of 20m was assumed

for Straight Street.

It is noteworthy that both AEOLIUSF and CAR INTERNATION-

AL can treat only a single road, whereas the monitoring data will

reflect the influence of emissions from many roads in the city cen-

ter. Therefore, care must be taken to estimate a realistic urban

background concentration for the assessment. The three-month

mean background carbon monoxide concentration was

estimated at 0.4 p.p.m. from data collated from another con-

tinuous monitoring station in the city center, which was distant

from all major roads.

Traffic data collated by local authorities often do not include a

detailed breakdown of vehicle types, to correspond with the more

detailed information on vehicle emission factors that are avail-

able (DETR 1998). Therefore, assumptions need to be made on

the vehicle mix based on national statistics. The assumptions

made for this study were as follows:

• half the HGVs were medium goods vehicles (3.5–7.5 t) and

half were 7.5–17 t;

• of the LGVs, half had gasoline engines and half had diesel

engines;

• of the automobiles, 80% had gasoline engines with catalytic

converters, and 20% had diesel engines.

The carbon monoxide emission factors are shown in Table 18.11.

These were the most up-to-date figures for urban situa-

tions available from national government (DETR 1998).

The Dutch emission factors in CAR INTERNATIONAL were

overwritten.

The only meteorological observing station with hour-by-hour

readings of the necessary meteorological parameters (as re-

quired by AEOLIUSF) was 10km from the city center (at the local

airport), with very different physiography. However, no other

suitable meteorological data were available. The format of the

meteorological data file required by AEOLIUSF is shown in

Table 18.12. CAR INTERNATIONAL requires only an average

wind speed over the period of interest; the three-month mean

wind speed was found to be 4.3m s-1.

The monitoring station recorded carbon monoxide concentra-

tions as 15-minute averages. These data were processed to

derive the three-month mean concentration, the maximum one-

hour and eight-hour means, and the 98th percentile of 24-hour,

eight-hour, and one-hour means, for comparison against the

outputs of AEOLIUSF and CAR INTERNATIONAL.


518 yasmin vawda

Table 18.12 Example of meteorological data (one day) required by AEOLIUSF.

Year Month Day Hour Direction Temperature Pressure U10

1997 4 1 0 230 9.5 1013 8.7

1997 4 1 1 230 9.2 1013 9.3

1997 4 1 2 220 9.6 1013 7.7

1997 4 1 3 220 9.1 1013 7.2

1997 4 1 4 230 9.6 1013 7.2

1997 4 1 5 240 8.9 1013 6.2

1997 4 1 6 240 9.4 1013 8.2

1997 4 1 7 260 9.3 1013 7.7

1997 4 1 8 260 9.4 1013 6.7

1997 4 1 9 260 9.8 1013 6.7

1997 4 1 10 270 10.8 1013 8.2

1997 4 1 11 260 10.9 1013 8.2

1997 4 1 12 270 10.5 1013 7.7

1997 4 1 13 240 10.6 1013 7.7

1997 4 1 14 250 10.8 1013 8.2

1997 4 1 15 260 11.0 1013 8.2

1997 4 1 16 270 10.2 1013 7.2

1997 4 1 17 260 10.1 1013 7.2

1997 4 1 18 260 8.9 1013 6.7

1997 4 1 19 250 8.4 1013 5.1

1997 4 1 20 250 8.3 1013 5.1

1997 4 1 21 250 8.8 1013 5.7

1997 4 1 22 250 8.7 1013 7.2

1997 4 1 23 270 8.7 1013 6.2

Results and assessmentCAR INTERNATIONAL underpredicted the three-month mean

carbon monoxide concentration by 25% (Table 18.13), but gave

good agreement against monitoring data for the 98th percentile

of 24-hour means and the 98th percentile of eight-hour means.

However, the model underpredicted the 98th percentile of

one-hour means.

AEOLIUSF underpredicted the three-month mean con-

centration (Table 18.13), but this could be due to the

assumption of zero traffic over the nighttime period, as

traffic data were available only for the daytime. AEOLIUSF

also underpredicted the maximum running eight-hour

mean concentration and the maximum one-hour mean

concentration.

ConclusionsThe validation study showed that both models had a tendency to

underestimate carbon monoxide concentrations for the city

center. The factors that could be responsible for these under-

predictions are:

1 The carbon monoxide emission factors published by the

government could have been too low, and not suitable for con-

Table 18.11 Carbon monoxide emission factors(urban driving cycle) used in CARINTERNATIONAL and AEOLIUSF models.

Emission factor Vehicle type (gkm-1 vehicle-1)

Motorcycles 22

Diesel cars 0.08

Gasoline cars (catalysts) 4.13

Gasoline light goods vehicle (LGV) 13.57

Diesel light goods vehicle (LGV) 0.26

Bus/coach 17.65

Medium goods vehicles (3.5–7.5 t) 4.39

Heavy goods vehicle (HGV) (7.5–17 t) 5.25

(Continued)


monitoring) to be already close to the air qualitystandard, then the use of a multisource model,which can treat both chimney and road trafficemissions, would be advisable.

Screening models may be most appropriatewhen the meteorological data lack the reliabilityand accuracy that would warrant the use of moresophisticated dispersion models; screening models usually require little or no meteorologicalinput, and therefore cannot take account of localconditions.

When providing meteorological data to themore advanced computerized models, careful consideration needs to be given to the choice ofmeteorological observing station from which sequential or statistical data sets are purchased;

gested, city-center driving conditions. These published emission

factors were not speed related.

2 The estimate of the background carbon monoxide concentra-

tion may have been too low.

3 The assumptions made on the details of vehicle mix on

Straight Street could have been in error. Straight Street may have

had a vehicle type distribution very different from that of the

national trunk roads.

Table 18.13 Comparison of carbon monoxide modeling results against monitoring data.

Measured concentration

Modeling results*

Statistic (p.p.m.) (monitoring data) AEOLIUSF CAR INTERNATIONAL

Maximum running eight-hour 3.3 2.8 —

means

Maximum one-hour mean 5 4.2 —

Three-month means 1.1 0.7 0.8

98th percentile of 24-hour 1.8† — 2.0

means

98th percentile of eight-hour 2.4‡ — 2.3

means

98th percentile of one-hour 4.9 — 2.7

means

* Including an allowance for background concentrations.

†98th percentile of daily means.

‡98th percentile of running eight-hour means.

It would be advisable for the local authority to repeat the valida-

tion exercise after making suitable adjustments and refinements

to these factors in turn (i.e. a sensitivity analysis), before either

model could be used routinely for modeling air quality in the city

center for regulatory purposes.

use of mathematical models of atmospheric dis-persion (DoE 1995b). Should the results of a screen-ing model suggest that an air quality standardcould be approached, it would be prudent to use acomputerized Gaussian model. Some areas may beprone to unusual weather patterns (e.g. coastal locations) or there may be complex topographicalfeatures (e.g. deep valleys): in these cases, the impact of even small industrial plant may need to be investigated using models that can incorpo-rate these effects. If the polluting potential of a pro-posed development is high (e.g. NOx from a largepower station in a densely populated urban area), if there are other significant sources nearby (e.g.majors road and an incinerator), and if local background concentrations are known (from

520 yasmin vawda

similar meteorological conditions. There are realfluctuations in concentrations about the averagecaused by variations in meteorological and emis-sion conditions that are not treated within a model.This issue is not significant when one is using amodel to calculate long-term average concentra-tions over, say, one year. If the prediction of an annu-al mean value lies within ±50% of the measure-ment, the results of a dispersion model would usual-ly be deemed to be acceptable. However, dispersionmodels are less accurate in their predictions of maximum short-term averaged concentrations and high percentile values (see Case study 5). More-over, most dispersion models have been compiled tooverestimate concentrations, in order to ensure aconservative approach for regulatory purposes.

Different models designed to calculate thesame quantity will produce different answers. Thereason why there is not a single right answer is thatmodel developers have to make choices about theformulae they use and choose from the literatureone that is practical, has been tested against meas-urements (i.e. validated), and is consistent withthe other parts of the model; not all model develop-ers will make the same choice. Some will placegreater emphasis on one aspect rather than another and this is why models differ.

APPENDIX: LIST OF MODELS

This list of commonly used atmospheric disper-sion models is provided as a guide. The list is notexhaustive, and many other models with compara-ble features are reported in the scientific literature.

Traffic emissions models

The Design Manual for Roads and Bridges (DMRB)is a screening model developed by the UK Trans-port Research Laboratory for single roads, based ona series of graphs and tables. It predicts pollutantconcentrations in the averaging periods and statis-tics directly comparable against UK air qualitystandards and objectives. The model contains UKvehicle exhaust emission factors, which are speed-and year-related.

geographic proximity alone may not be the onlycriterion. Coastal influences, and the situationwith respect to hills and valleys, can have profoundeffects on the local wind flow and turbulence.

The choice of model depends on the relevant averaging time required of the results. If the objec-tive is to compare the modeling results againstsome available monitoring data (e.g. to validate themodel predictions for a particular situation, andtherefore lend it some credibility as a forecastingtool —see Case study 5), then the model resultsshould be for the same averaging periods as themeasurements. The receptors for the modelingstudy must be coincident with the monitoringequipment; accurate geographic referencing ofsources and receptors is required. If the purpose ofthe dispersion modeling is to assess compliancewith national air quality standards, the dispersionmodel should, ideally, generate results for thesame averaging periods as the standards. However,only a few of the advanced models can generatepercentile statistics. Empirical corrections to predictions for other sampling periods can be applied provided that there are sufficient data from monitoring sites in similar locations (DETR 2000b). A few models have a concentrationfluctuations module, which can be used to modelaveraging periods of less than an hour; for othermodels, appropriate empirical corrections may beapplied to the predicted one-hour mean results toestimate shorter-term concentrations (see Casestudy 1).

18.10 ACCURACY OF DISPERSIONMODELING PREDICTIONS

The accuracy of model predictions depends mostlyon the quality of the input data. It is not possible tocalculate accurately the concentrations for a specified one-hour time interval because one can-not know with any certainty (unless there are verydetailed measurements available) which meteoro-logical and emission conditions applied precisely tothat particular interval. Dispersion models havebeen designed to calculate the average concentra-tion over a large number of time intervals subject to


CAR INTERNATIONAL is a screening modeldeveloped by TNO Environmental Sciences andthe RIVM National Institute of Public Health andthe Environment for Dutch regulatory purposes.The model is widely used in the Netherlands. Themodel calculates a long-term average concentra-tion and the 98th percentile of one-hour, 24-hour,and eight-hour means.

AEOLIUSF is a screening model that has beendeveloped by the UK Meteorological Office for sin-

Case study 6 Prediction of short-term NO2 concentrations

close to a road

Key features of studyA residential property was situated 10m from the kerb of a busy,

straight road. The one-hour mean NO2 concentrations during the

most common weather conditions at the site, during rush hour

traffic conditions, were predicted using the CALINE4 dispersion

model. CALINE4 incorporates a photochemistry module, which

calculates downwind NO2 concentrations depending upon the

distance, wind speed, ambient temperature, and background

concentrations of NO, NO2, and O3.

MethodologyThe input data required by the model are summarized in

Table 18.14. The NOx emission rate is determined by a number of

factors, including the vehicle mix along the road during the peak

hour (categorized by means of the number of heavy duty vehicles

and light duty vehicles) and the vehicle speed during the rush hour.

The weighted mean NOxemission rate is derived from the LDV and

HDV emission rates at vehicle speeds of 40kmh-1. The most com-

monly occurring stability and wind speed combination at the site

(based on nearby synoptic observations) was Pasquill category

D, with a wind speed of 7m s-1. A worst-case wind direction

directly from the road to the receptor was assumed for the model-

ing. The background concentrations of NO, NO2, and O3 were

derived from empirical monitoring data to describe conditions

relevant to neutral stability and moderate or high wind speed

conditions.

Results and assessmentThe predicted NO2 concentration at the property was 110 p.p.b.

There is no need to take separate account of the background level

of NO2 as this was automatically considered by the model. The

Table 18.14 Input data for CALINE4 dispersionmodel.

Peak hour 2-way flow of light 2000 vehicles h-1

duty vehicles (LDV)

Peak hour 2-way flow of heavy 150 vehicles h-1

duty vehicles (HGV)

Total peak hour 2-way vehicle 2150 vehicles h-1

flow

Peak hour vehicle speed 40kmh-1

Weighted mean NOx emission 4.98g km-1 vehicle-1

rate

Atmospheric stability Neutral (Pasquill category D)

Wind speed 7m s-1

Ambient temperature 15°C

Background concentrations NO = 10p.p.b.

NO2 = 25p.p.b.

O3 = 35p.p.b.

Wind direction Perpendicular to road,

directly toward receptor

predicted NO2 concentration exceeds the WHO air quality guide-

line of 105 p.p.b.

ConclusionsThe NO2 concentration at the property arising from peak-hour

NOx emissions along the road would give cause for concern. If the

WHO guideline is exceeded during neutral, moderate wind

speed conditions, the margin of exceedance would be even

greater during stable, low wind speed weather conditions (e.g.

cold foggy mornings and evenings), which could coincide with

peak-hour traffic flows. A detailed assessment of the frequency of

the worst-case wind direction (i.e. directly from the road to the

receptor) would be helpful in assessing the likely frequency of

breaches of the air quality guideline.

gle, street canyon situations. It calculates hour-by-hour concentrations based on hourly meteorologyread from a preprocessed data file.

CAL3QHC is a US EPA model designed to handle near-saturated and/or overcapacity trafficconditions and complex intersections. It predictsone-hour mean concentrations.

The California Line Source Model (CALINE)was developed by the California Department ofTransportation and the US Federal Highways

522 yasmin vawda

model calculates one-hour, monthly, and annualaverages, and it uses statistical meteorologicaldata.

The Rough Terrain Diffusion Model (RTDM) isa US EPA Gaussian model capable of predictingshort-term concentrations arising from pointsources in complex terrain. It calculates one-houraverages only. RTDM requires on-site hourlymeasurements of turbulence intensity, verticaltemperature difference, horizontal wind shear,and wind profile exponents.

The Industrial Source Complex (ISC) is a USEPA multisource Gaussian model, capable of predicting both long-term and short-term concen-trations arising from point, area, and volumesources. Gravitational settling of particles can beaccounted for using a dry deposition algorithm;wet deposition and depletion due to rainfall canalso be treated. Effects of buildings can be consid-ered. The model has urban and rural dispersion coefficients.

The Atmospheric Dispersion Modeling System(ADMS) is a “new generation” multisource disper-sion model from the UK. Specific features includethe ability to treat both dry and wet deposition,building wake effects, complex terrain, and coastalinfluences. ADMS allows the use of point, area,volume, and line sources, and can predict bothlong-term and short-term (down to one-secondmean) concentrations. Urban and rural dispersioncoefficients are included, and percentile calcula-tions are possible.

AERMOD is a US EPA update of the ISC model,intended to be the “new generation” model for USregulatory purposes. It contains improved algo-rithms for convective and stable boundary layers,for computing vertical profiles of wind, turbulenceand temperature, and for the treatment of all typesof terrain.

The INDIC AirViro system differs from otherPC-based models in that it requires a UNIX work-station and needs complex physiographic and meteorological configuration by the software supplier. Unlike most Gaussian models which relyupon meteorological information collected from asingle site, the INDIC AirViro model describes a

Agency. It can model junctions, street canyons,parking lots, bridges, and underpasses; it includes aphotochemistry model to predict downwind con-centrations of NO2 from NO emitted by vehicleexhausts. CALINE predicts one-hour mean concentrations, and hourly meteorological condi-tions are user-defined.

The Point, Area and Line (PAL) Source Modeluses the same Gaussian line source algorithms asCALINE, but has special area source algorithmsfor treating edge effects more accurately. This facility is useful for modeling parking lots, small areas of cities, and airports.

Industrial source models

D1 Stack Height Calculations is a screening procedure based on nomograms, which estimates achimney height that would ensure that ground-level concentrations of a pollutant did not exceed aspecified standard or guideline for that pollutantfor more than about five minutes, under weatherconditions that are likely to occur 98% of the time.

Guidance for Estimating the Air Quality Impact of Stationary Sources is a screeningmethod published by the UK Environment Agencythat provides precalculated dispersion results forstack emissions expressed as nomograms. Theseare based on a large number of computations usingthe ADMS model. The predicted pollutant concen-trations are directly comparable against UK airquality objectives.

SCREEN is a US EPA model that uses worst-case meteorological data. It can model a singlepoint, area, or volume source, and can take accountof building wake effects. It also has a limited ability to treat terrain above stack height.

R91 is a Gaussian model developed by a Work-ing Group led by the UK National RadiologicalProtection Board. It has been used in the past by UKregulatory bodies as a reference model. R91 originally consisted of nomograms, but PC ver-sions are commercially available. DISTAR cantreat only flat terrain and cannot model buildingwake effects, although reports by NRPB show howthe basic R91 model can include these effects. The


pattern of small-scale winds based upon the sur-face characteristics. The INDIC AirViro model in-terfaces with a sophisticated emissions databasecapable of accepting point sources (i.e. stacks), areasources, and line sources (i.e. traffic), and detaileddiurnal, seasonal, and production variations ofemissions (both traffic and industrial). The INDIC system can be applied using a number ofGaussian model options, and a street canyonmodel option.

The Fugitive Dust Model (FDM) is a US EPAGaussian model for predicting the dispersion anddeposition of particulate matter, e.g. dust from sur-face mineral workings. Point, line, and areasources can be modeled, using long-term statisti-cal frequency analyses of meteorological con-ditions. The particle size distribution of the emissions must be user-defined. Emission factorsare usually derived from a US EPA database.

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