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Atmospheric Environment 40 (2006) 1016–1029

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Parameters for the assessment of odour impacts on communities

Paul Henshawa,�, James Nicellb, Anamika Sikdara

aDepartment of Civil and Environmental Engineering, University of Windsor, Windsor, Ont., Canada, N9B 3P4bDepartment of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke St. West, Montreal, Que., Canada, H3A 2K6

Received 26 June 2004; received in revised form 27 October 2005; accepted 8 November 2005

Abstract

Odorous emissions are the cause of most air pollution complaints that are made by the public to regulatory agencies.

Methods need to be developed to objectively access the impact of odours so that odour-producing facilities have a pro-

active means of reducing their impact on surrounding communities. These methods must consider the sub- and supra-

threshold effects of odour over the range of concentrations that can be experienced as the odour disperses over a

community. In addition, the influence of local meteorological and terrain conditions can be accommodated through

conventional dispersion models. However, metrics of odour impact need to be developed which take into account the

concentration and frequency of odours at receptors, as well as the population density at those receptors. This paper

proposes and demonstrates the use of six impact parameters that may be used as metrics of the impact of a stationary

odour source on a community. These odour impact parameters are maximum concentration, maximum probability of

response, footprint area, concentration-weighted footprint area, probability-weighted footprint area and population

impact. The utility of these parameters were demonstrated by evaluating them to quantify the impact of an industrial

facility that produces an odorous chemical.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: Odor; Impact; Dispersion; Population response

1. Introduction

Odorous airborne emissions, particularly thoseodours arising from such operations as sewagetreatment plants, paint facilities, petroleum refi-neries, rendering plants, pulp mills, plastic and resinmanufacturers and chemical industries, are thecause of many public complaints (Leonardos,1996). A survey of 66 jurisdictions in NorthAmerica revealed that in the 28 jurisdictions thatdid have specific odour regulations, the use of a

e front matter r 2005 Elsevier Ltd. All rights reserved

mosenv.2005.11.014

ing author. Fax: +1519 971 3686.

ess: henshaw@uwindsor.ca (P. Henshaw).

single receptor dilution-to-threshold criterion wascommon (Law et al., 2002). This technique involvescollecting a sample of the odour at the source,quantifying the dilution level at which 50% of agroup of panellists can detect the odour (i.e., thethreshold), and subsequently modelling the disper-sion of the odour to determine if the threshold (orsome fixed multiple of the threshold, eg. Sheridanet al., 2004) will be exceeded downwind of thesource. Although this approach is common in manyjurisdictions, compliance with this type of regula-tory standard does not necessarily absolve theodour-producing facility from complying with otherregulations that are designed to protect the public

.

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from nuisances (Girard and Geisberger, 2000).Further, the determination of the odour concentra-tion at a single point downwind of the source, evenif it is the worst-case in terms of the number ofdilutions, may not adequately reflect impact on thecommunity because, in reality, the odour is experi-enced spatially and temporally by the populationover a complete range of dilutions.

To-date, the owners and operators of industrieslack sufficiently objective strategies for assessing theimpacts of odorous emissions on surroundingpopulations under the wide range of conditionsover which the odours would tend to be experiencedthroughout the community. Without an objectiveand effective means to assess the community impactof odours, facilities have little basis for: (1) assessingtheir current performance in terms of communityimpact; (2) tackling an odour problem until afterthe problem has become evident to the public;(3) prioritizing their approach to dealing withmultiple sources of odours within a given facilityonce an odour problem has been identified; and(4) comparing the relative effectiveness of varioustechnologies or management strategies in reducingcommunity impact. Therefore, the development ofnew strategies for odour impact assessment is required.

An odour impact model (OIM) has been used inrecent years to establish the dose–response relation-ships for populations exposed to odours (Poostchi,1985; Nicell, 1994, 2003). The curves of the OIMprovide estimates of the probability of response,usually expressed as probability of detection, anddegree of annoyance of the population as a functionof odour concentration. These relationships allowthe variability of the olfactory sensitivities ofindividuals and the offensiveness of the odour tobe included in odour characterization and, ulti-mately, in odour impact assessments. By combiningthe dose–response relationships from the OIM withdispersion modelling results, variables such asodour concentration, exhaust gas velocity and localgeographic and meteorological conditions can alsobe considered in odour impact assessments. Inaddition, modern dispersion models can also beused to calculate the frequency at which certaincontaminants exceed a specified concentration,thereby allowing the temporal variation in odourconcentration to also be included in odour impactassessments.

Dispersion model estimates of odour concentra-tions at different downwind locations may betransformed using the non-linear dose–response

profiles of the OIM to predict the zone of impactand the probability of response, and the corre-sponding degree of annoyance at any location(Nicell and St. Pierre, 1996; Nicell and Tsakaloyan-nis, 1997; Nicell, 1999). Once spatial variations inpopulation response have been predicted using thistechnique, it becomes possible to propose a varietyof impact parameters that can be used to quantifythe impact of an odour on a receptor or on thecommunity as a whole (Nicell, 2003).

This paper describes the use of several proposedimpact parameters as applied to an odour-emittingindustrial facility and illustrates how these para-meters may be used to quantify odour impacts indifferent ways. Due to the absence of annoyancedata for the OIM for this particular study, theimpact parameters evaluated in this study werebased solely on predictions of spatial variations inodour concentration and probability of response.

2. Methodology

2.1. Description of the industrial facility

The industrial facility consists of 145 buildings. Inthe Northeast cluster of six buildings, alignedapproximately with true North, there is a11.2m� 16.4m building with a tiered roof thatvaries in height from 7.8 to 13.4m. It is neither thetallest nor the largest building in the cluster.However, in this building, an odourous compoundis produced and poured into drums for shipping.Process equipment exhaust and the local exhaust forthe drumming process are directed through anoxidizing scrubber prior to being vented outthrough a single stack on the roof of that building.In addition, open areas of the building result in thepotential for fugitive emissions from the drummingprocess. Although drumming is essentially a batchprocess, the frequency and duration of the drum-ming led to modelling the emissions as continuous.

2.2. OIM

An OIM consists of a set of dilution–responserelationships for a particular odour source (Nicell,1994, 2003). During the odour test, each panellistwas exposed to a series of gas streams, of which onecontained the odour diluted with odour-free air andthe others consisted of odour-free air only. Eachpanellist was required to choose which stream s/heperceived as containing the odour and was asked to

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indicate whether s/he was guessing or certain aboutthe choice. The test was repeated in an ascendingseries of odour concentrations that covered a rangefrom imperceptible odour (i.e., 0% response) for allpanellists to 100% response. The highest dilution atwhich each panellist began to consistently andcorrectly identify the odorous gas stream amongthe odour-free blanks (at this and all subsequentlower dilution levels) and also stated that s/he wasconfident in her/his identification of these streamswas the basis for estimating that panellist’s responsethreshold.

Two sets of odour evaluation data were used tocalculate OIM parameters. The first set consisted oftwo samples taken from the exhaust of the oxidizingscrubber during the process of drumming of thechemical. These samples were used to establish theemission rate of odour from the stack. The secondset of three samples was taken from the indoor airnear the drumming station during the process ofdrumming. This was multiplied by an estimate ofthe net airflow rate through the open doors of thebuilding to determine the fugitive emission rate.Two commercial laboratories performed the collec-tion of dose–response data. One laboratory used aseven-member panel and a non-forced choicemethod in which the panellists were asked, if theyfelt it was possible, to identify which of the two gasstreams (one odorous and one clean air) containedthe odour. The second laboratory used a panel ofeight members, and a ternary forced-choice olfacto-metry technique involving one odorous stream andtwo streams of clean air. Although panellists wereasked to guess which of the streams contained theodour, only correct identifications, where thepanellists had also confirmed with certainty thatthey had detected the odour, were included inevaluating the probability of response. One samplewas analyzed by both laboratories.

In calculating the odour threshold using the OIMapproach, the percentage of panellists responding tothe odour at each dilution level was plotted againstthe number of dilutions of the source odour. Anonlinear regression analysis was performed usingthe software Sigmaplot (SPSS, Chicago, IL) to fitthe reduced data to the following two-parameterlogistic equation (Nicell, 2003):

P ¼100

1þ ðD=D50Þ1�pð Þ=p

, (1)

where P is the percent of the panellists (population)responding to the odour (%), D is dilutions of the

source odour (i.e., the volume of odorous gas plusdilution air, divided by the volume of odorous gas),D50 is the response threshold (in dilutions) and p isthe persistence of the odour. Eq. (1) was fit to theolfactometry data for the industrial samples in orderto estimate the values of D50 for each sample. Theconcentration of an odour, Cou, is expressed by thedimensionless ratio of D50/D, which for conveni-ence is assigned concentration units of odour units(OU) (Nicell, 2003). At the source, the emittedodour is undiluted (i.e., D ¼ 1), and thus has aconcentration in OU that has the same value as D50,expressed in dilutions (i.e., C ¼ D50=1). The sourceconcentration values were then used to quantify theodour emission rate (in OUm3 s�1) from the facilityby multiplying the volumetric emission rate (inm3 s�1) by the odour concentration (in OU).

As described by Nicell (2003), since the ratio D50/

D is equivalent to odour concentration, Cou, Eq. (1)can be modified to produce:

P ¼100

1þ ðCouÞp�1ð Þ=p

. (2)

The value of Cou is also equivalent to the ratioC/C50; where C and C50 are the concentrations ofthe pure chemical in air and the threshold of thepure chemical, respectively, expressed in typicalconcentration units of mass per unit volume (e.g., inmgm�3). Using the method described by Nicell(2003), the response data from the industrial stackand indoor air sources were normalized to provideP versus Cou data. The combined data were fit toEq. (2) to provide the best estimate of the odourpersistence for the emissions from this facility.

Although the odour was characterized using arelatively small number of panellists, it is assumed indeveloping the OIM that the collective response ofthe panellists reflects the response of the communityaffected by the odour.

2.3. Dispersion modelling

The USEPA dispersion model ISC-PRIME(plume rise model enhancements) was used becauseit is well-suited to simulating building downwash.ISC-PRIME calculates the concentration of pollu-tants downwind of a source assuming that theconcentration values follow a normal (Gaussian)distribution about the centreline of the downwindplume in the vertical and horizontal directions.These models are suited for sources of constantoutput and are based on the assumption that the

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wind direction and speed are constant through-out the modelled area for each time periodmodelled. The model calculates the concentra-tion at a number of receptors for every hour withinthe simulation period. Input and output of datawere accomplished using ISC View version 3.5software interface (Lakes Environmental, Waterloo,Canada).

Hourly meteorological data for the 5 yr period of1986–1990 for a community within 20 km of thefacility were used as a basis for modelling the rangeof impacts experienced over an extended period oftime. Rural dispersion coefficients and a 1 hraveraging time (the minimum available) for receptorconcentrations were selected. The stack was mod-elled as a point source on the rooftop with a stackheight of 15.4m, a stack gas velocity of 12.7m s�1,an odour emission rate of 4196OUm3 s�1 and astack gas temperature of 302K. Fugitive emissionswere modelled as a volume source with an estimatedemission rate of 4251OUm3 s�1 based on indoor airmeasurements of the odorant concentration (asdescribed above) and the air outflow rate throughthe open areas. The air flowrate was estimatedby multiplying the open area (3.9m2) by thewind velocity on the day of measurement(1.0m s�1) and adding the flowrate due to theimbalance of the exhaust and make-up air(2.8m3 s�1). According to ISC, a volume sourcemust be specified as a square in the plan view.Therefore, the shortest side of the building (11.2m)was used as the length of side of the volume source,and the source was assumed to occupy the Northend of the building. The height of the volume sourcewas 7.8m. The building profiles and other char-acteristics for the 145 buildings on the industrial sitewere used in the Building Profile Input Program(BPIP) module of ISC in order to incorporatebuilding downwash effects into the model. As well,the property line of the plant was defined using asite plan.

A uniform Cartesian grid of 6400 receptors wasused, which is the maximum allowable number ofreceptors with ISC-PRIME. The distance betweenadjacent receptors (grid increment) was set at 50mand the number of receptors in each of the x- and y-directions was 80. Hence, the receptor grid covereda modelled region of 3950� 3950m. The elevationof the receptor grid was set at the source base heightand the flat terrain option was selected. Theprogram was set to evaluate the maximum hourlyand the 99th percentile values of odour concentra-

tion (in OU) at each receptor. Eq. (2) wassubsequently used as a basis for transforming thepredicted odour concentrations at each receptorinto a corresponding probability of response.

Five emission scenarios were simulated. The‘‘point and volume’’ simulation included both pointand volume sources at their existing emission rates.The second and third simulations modelled pointand volume sources separately to determine theirrelative impact. In the fourth scenario, ‘‘half pointand volume’’, both the point and volume sourceswere modelled, but the emission rate for each wasreduced to half that of the initial case in order todemonstrate the sensitivity of the impact parametersto source reduction. In the fifth scenario, analternative for treating the fugitive emissions wassimulated. In this scenario it was assumed that thebuilding was closed and under negative pressure,and that the building exhaust vented through astack with similar height, diameter and exit velocityas the existing point source described above. Thegas exit temperature for this simulated stack was298K to correspond to typical temperatures insidethe building.

2.4. Population density map

To make the population density map, the streetboundary file for the area was obtained fromnational census data. The original file in ArcInfoformat was converted to ArcView version 3.2(Environmental Systems Research Institute, Red-lands, CA) format. In order to create a filecontaining the population density at each receptorpoint, the ArcView ‘attribute table’ containing thenumeric data associated with the population densitymap was merged with an ISC-PRIME output filemodified to contain only the UTM coordinates ofthe receptor grid points.

2.5. Evaluation of odour impact parameters

A series of odour impact parameters, as describedbelow, have been evaluated in this study, based onspatial variations in odour concentration predictedby the dispersion model and transformed using thedose–response relationships of the OIM. Thereceptor grid and associated concentration valuesproduced by ISC-Prime were exported to a file thatwas compatible with the contouring software Surferversion 7.0 (Golden Software, Golden, CO). TheGrid|Math function of Surfer was used to convert

ARTICLE IN PRESSP. Henshaw et al. / Atmospheric Environment 40 (2006) 1016–10291020

the concentration at each point to a probability ofresponse value using Eq. (2). Refined grids andcontours of odour concentration (in OU) andprobability of response (in %) were generated usingthe kriging function of Surfer.

Before evaluating the proposed odour impactparameters described below, the points on thereceptor grid that fell inside the plant boundarywere removed, as is the common practice in impactassessments, and in determining compliance withenvironmental regulations. The GridjSpline Smoothfunction was used to interpolate between thereceptor points in the 80� 80 mesh that had a gridspacing of 50m to produce a refined grid resolutionof 2m. The GridjBlank function was then used tozero those points that fell within the plant propertyline. The interpolation by spline smoothing wasperformed to provide a greater resolution ofpoints, especially near the plant boundary. WhenSurfer blanks out an area, the space around theperimeter of the blanked area between theblanked grid points and the non-blanked points isinterpreted as a slope from zero at the blankedpoint to the non-blanked value. In this study,this would essentially create a 50m swath aroundthe plant property line that would not be consideredin contouring and determining maximum values.Since the point of maximum concentration (outsidethe plant boundary) usually occurs immediatelyoutside the property line, increasing the gridresolution to 2m reduced the swath width andminimized errors in the calculation of impactparameters.

The GridjVolume function of surfer was used toevaluate odour impact parameters related to areasinside contours and volumes under contours. Areasinside selected contours were calculated using theGridjVolume function by taking the positive planararea above the desired contour. Volumes werecalculated as the positive volume contained betweena selected lower limit (usually 0) and the uppersurface defined by the grid of receptor values (e.g.,P or Cou). In the case of the evaluation ofpopulation impact (PI), as described below, thegrid under which the volume was to be calcu-lated was created by multiplying the values of P (in%) at each grid receptor by the correspondingpopulation density (in persons/m2) and dividing thisvalue by 100%. In this case, blanking out the plantarea was not required because the populationdensity grid was set to zero within the plantboundary.

3. Development of impact parameters

A variety of impact parameters can be proposedthat will capture various dimensions of odourimpact including odour concentration, probabilityof response and population density. Three generaltypes of impact parameters are envisioned, viz.point, area and volume parameters. The nature ofthese types of parameters is discussed below. Notethat when conducting the analysis of odour impact,it is possible to exclude particular areas from theimpact regions, such as the area defined by theboundaries of the facility, unpopulated areas suchas those extending over a lake, or areas in whichodorous impacts are deemed not to be of significantconcern (e.g., regions zoned for industrial orcommercial use). By including or excluding specificareas from the analysis, it becomes possible toaccount for the various land-use characteristics ofthe impacted region in odour impact assessments.

3.1. Point parameters

Point parameters may be used as single-locationmeasures of impact evaluated at the worst-casereceptor in the community or at a receptor ofparticular interest. For example, it can be claimedthat the impact of an odour will be directly relatedto the concentration at which it is experienced in thecommunity, with a higher extreme odour concen-tration corresponding to a greater impact. The peakodour concentration at a receptor, usually expressedin OU, is frequently used in many jurisdictions as abasis for testing compliance with environmentalregulations (Law et al., 2002; Mahin, 2001), andthus serves as a point impact parameter.

It can be claimed that the odour concentration,by itself, does not fully capture the nature of theimpact, since the value of odour concentration doesnot reflect the magnitude of the response of thepopulation; i.e., the impact of an odour is also likelyto be a function of the fraction of people whoexperience the odour. Nicell (2003) demonstratedthat two different odours could have the sameodour threshold but result in different responses inthe community. The differing responses are directlyrelated to odour persistence, where for a givenodour concentration, an odour with a higherpersistence will tend to have a greater impact, sincethe population has a higher probability of respond-ing to this type of odour over the range ofconcentrations at which it may be experienced

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(Nicell, 2003). Thus, in this study, the following twopoint odour impact parameters were evaluated:(1) maximum concentration of odour at a receptor,Cmax, and (2) maximum probability of response at areceptor, Pmax. Values of Cmax were determined byinstructing the dispersion model to determine thepeak hourly values and 99th percentile values ofodour concentration experienced in the community(excluding the region inside the boundaries of theindustrial facility), and selecting the highest value inthe receptor field. The values of Pmax weredetermined by evaluating Eq. (2) with Cou ¼ Cmax

and a persistence, p, equal to the overall persistenceevaluated for the odorous emissions from thefacility under study.

3.2. Area parameters

The point parameters described above reflect theimpact that is felt at a particular receptor, but donot reflect the magnitude of the impact as it isexperienced across the community. For example, itis possible that in two different odour emissionsituations the magnitude of Cmax and/or Pmax maybe quite similar. However, in one situation, theodour may be experienced only in a highly localizedarea, perhaps immediately adjacent to the facility,and in another it may be experienced in a broadregion over the community. Clearly, the situation inwhich a broad area is impacted is the worst of thetwo. Thus, area parameters are proposed based onthe assumption that the geographical extent of theregion that is impacted is directly related to themagnitude of the impact. This type of parameter is aregional measure of impact, and is evaluated as anarea inside a selected contour of either concentra-tion or probability of response. These area impactparameters represent the ‘‘footprint’’ of the odourimpact on a region and are designated as F(Cou) andF(P) for footprint areas inside selected contours ofconcentration (Cou) and probability of response (P),respectively:

F ðCouÞ ¼

ZZ

Cou

dxdy, (3)

F ðPÞ ¼

ZZ

P

dxdy; (4)

where the double-integrals are bounded by theselected contours of Cou or P. These boundingcontours must be selected such that Cou40 and

P40, otherwise the footprint area will be infinite inextent.

In this study, footprint areas have been evaluatedthat correspond to regions in which a concentrationof 1OU has been exceeded (i.e., F(1OU)) and fromwhich the area defined by the boundaries of thefacility have been excluded. It is noted that thefootprint area inside the 50% contour is numericallyequivalent to the footprint area inside the 1OUcontour since, by definition, at 1OU the probabilityof response is 50%.

3.3. Volume parameters

Footprint areas provide an indication of the sizeof the region in which an odour impact isexperienced, but do not reflect the varying magni-tudes of impact that are felt throughout smallerregions within the footprint. For example, at onepoint inside the footprint region bounded by the50% contour, there may be a higher concentrationof the odour to which 80% of the people mightrespond, and at another point a lower concentrationto which only 50% would respond. Both thesepoints would be counted equally when evaluatingthe footprint areas when, in fact, the impact wouldbe different at these locations. Volume parametersare defined as a means for adding an extradimension to area parameters that account for theinfluence of the magnitude of the odour within thebounded area.

In order to account for the differing magnitudesof impact felt throughout an impacted area, thefootprints of the odour may be weighted accordingto other dimensions that reflect odour impact—suchas odour concentration and probability of response.This weighting of local areas within the modelledgrid and their subsequent summation to give anoverall impact parameter is equivalent to evalua-ting the integral under contours defined by theweighting parameter. For example, it is possible toweight impacts at various locations by the concen-tration at receptors, which produces the followingequation:

CWFA ¼

ZZCouðx; yÞdxdy, (5)

where CWFA is the concentration-weighted foot-print area (in OUm2) and Cou(x, y) is the odourconcentration (in OU) at a receptor located atposition (x, y) (in m). Similarly, the footprint couldbe weighted according to the probability of response

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Fig. 1. Combined odour impact models for building exhaust

stack emissions (ST1 and ST2) and indoor air concentrations

(IA1, IA2 and IA3).

P. Henshaw et al. / Atmospheric Environment 40 (2006) 1016–10291022

at receptors as follows:

PWFA ¼

ZZPðx; yÞ

100%dxdy, (6)

where PWFA is the probability-weighted footprintarea (in m2) and P(x, y) is the probability ofresponse (in %) at a receptor located at position (x,y) (in m). The denominator of 100% is included toconvert the probability in percent into a fraction.This leads to the PWFA being a volume under thecontour with units of area only.

Note that it is not necessary to bound the doubleintegrals in Eqs. (5) and (6) by selected contours, aswas necessary in Eqs. (3) and (4) with Cou40 andP40. No bounds are necessary because, as theodour disperses over a large region, the concentra-tion and probability of response will tend to zero.Therefore, these weighted footprint areas have finitevalues even though they may be modelled over aninfinite region into which the odour is dispersed.

This type of analysis can be extended further toaccount for differences in population distributionthroughout the community. For example, if thepopulation density in the impact region is known,by evaluating the integral under the probabilitycontours it becomes possible to estimate the numberof people who would be able to respond to aparticular odour in the study region (Nicell, 2003).Therefore, for regions in which there is sparsehuman habitation, the impact would be low and fordensely populated areas the impact would be muchlarger. This can be expressed as follows:

PI ¼

ZZPðx; yÞ

100%Nðx; yÞdxdy, (7)

where PI is the population impact (in persons) andN(x, y) is the population density (in persons/m2) atlocation (x, y) (in m). The multiplication of N(x,y)�P(x, y)/100% ensures that only those personswho can actually respond to the odour at eachlocation will be counted in the impact assessment.

4. Results

4.1. Odour characterization

The characteristics of the five industrial samplesare shown in Fig. 1. The persistence and thresholdvalues shown for all five samples were determinedby fitting Eq. (1) to the probability of responseversus dilutions data for each sample. The persis-tence values varied from 0.19 to 0.28, which are

similar to the values measured for other odours,such as those from a foundry (p ¼ 0:320:43) and ahog farm (p ¼ 0:28) (Sikdar, 2001), and for severalpure chemical odorants in air (p ¼ 0:2120:45)(Nicell, 2003). Samples of the pure odorant com-pound being handled in the industrial facilityproduced an odour threshold between 22 and56 ugm�3, with a persistence of 0.32.

In order to combine the data from individualodour analyses, it was assumed that these analyseswere all conducted on odour that was of similarcharacter; i.e., differing only in terms of sourceconcentration, but with the same persistence. Thiswas necessary since the combined impact ofodorous emissions can only be evaluated if theyall have a common persistence (Nicell, 2003). Oncethe data was combined, an overall persistence valueof 0.21 was determined by fitting all data containedin Fig. 1 to Eq. (2).

4.2. Dispersion modelling and contour development

For illustrative purposes, the dispersion model-ling software was also run with a meteorological filecontaining only the data corresponding to the hourthat gave the maximum concentration in thereceptor field. The plume for this hour is shown inFig. 2(a). The footprint enclosed by the 1OUcontour (F(1OU)) has an area of 0.5 km2 (roughly2.5� 0.2 km). The CWFA, which is the sum ofvalues of concentration at each grid point times thearea of influence of that grid point, is 0.94OUkm2.Considering the population density over the region,199 persons would respond to the odour under this

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Fig. 2. Contours of values for the single 1 hr condition that results in the highest off-plant odour concentration over the course of a 5 yr

period resulting from point and fugitive emissions from an industrial facility: (a) odour concentrations (OU) and (b) probability of

response values (%).

Fig. 3. Contours of peak 1 hr values at each receptor over the course of a 5 yr simulation period resulting from point and fugitive emissions

from an industrial facility: (a) odour concentrations (OU) and (b) probability of response values (%).

P. Henshaw et al. / Atmospheric Environment 40 (2006) 1016–1029 1023

peak impact condition. Receptor A is the locationwhere a number of odour complaints have beenreceived in the past. Fig. 2(a) shows that thisreceptor receives sub-threshold concentrations ofodours under the conditions when the maximumconcentration occurs.

In the analyses that follow, dispersion modellingwas used to predict the hour-by-hour odourconcentrations experienced at each receptor over a5 yr period. ISC-PRIME was instructed to summar-ize the peak-hourly and 99th percentile odourconcentrations at each receptor. The peak-hourlyconcentrations were to be used when evaluatingimpact parameters that describe the extremes of

impacts that can be experienced in the community.The 99th percentile values were used to evaluate thesame parameters, but the removal of the top 1% ofvalues placed less emphasis on extreme events andweather conditions that occur less than 1% of thetime.

Fig. 3(a) shows the peak hourly concentrations ofthe odour in the receptor grid relative to thelocation of the industrial facility, stack and anapproximation of the plant property line. Ingeneral, the concentration increases evenly as onegets further from the source, although there is a‘‘finger’’ of higher concentration that extends south-east from the source. The area within which there

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Fig. 4. Contours of 99th percentile 1 hr values at each receptor over the course of a 5 yr period resulting from point and fugitive emissions

from an industrial facility: (a) odour concentrations (OU) and (b) probability of response values (%).

P. Henshaw et al. / Atmospheric Environment 40 (2006) 1016–10291024

would be at least one instance of response of theodour by the average person (i.e., Cou41OU) overthe 5 yr simulation period is slightly larger than themodelled area (3.95� 3.95 km). Similarly, Fig. 3(b)shows the peak probability of response contours inthe community, which were calculated using Eq. (2)and the peak hourly odour concentrations ofFig. 3(a). Note that, as expected according toEq. (2), the 50% probability contour correspondsexactly to the 1OU contour. However, it can beseen that over the entire modelled area, persons atall receptors would have at least a 20% probabilityof responding to the odour.

Figs. 4(a) and (b) show the locations of the 99thpercentile contours of hourly odour concentrationsand probabilities of response, respectively. Bycomparing Figs. 3(a) and 4(a), it is evident thatthe size of the impacted regions defined by selectedcontours is significantly less when the top 1% ofodour concentrations at each receptor are dis-carded. It may be argued that this is a morereasonable way to quantify odour impacts, sinceextreme events reflected by peak hourly values onlyoccur once during the modelled period (in this case1 h out of 5 yr of data). In the case of the 99thpercentile values, the concentrations shown areequalled or exceeded 1% of the time, which placesmuch less emphasis on rare meteorological events.However, it should be noted that 1% of the timecorresponds to a total period of 88 h (approximately4 days) each year—which, to some homeownersexperiencing odour impact, may be excessive. Forexample, the Cmax value at Receptor A, shown inFig. 3(a), is 9.9OU for the peak hour, but is reduced

to 1.1OU if the top 1% of values are excluded. Thismeans that, at this receptor, the odour concentra-tion is exceeding detectable levels (1OU) for theaverage person for 4 days per year. It should also benoted that the 99th percentile contours exhibit moreirregular shapes, meaning that the prevailing con-ditions in site-specific meteorology are more pro-nounced.

A comparison of Figs. 3(b) and 4(b) also showsthe effect of excluding the top 1% of the values issignificant. However, in the most affected area—adjacent to the plant on the West side, the fractionof the population responding to the odour is stillwell above 90%. On the other hand, Receptor A hasa level of response of 61% for the 99th percentilecase, as opposed to over 99.9% when peak hourvalues were used for the calculation. It should bereiterated that the peak at any receptor point is thehighest 1 hr concentration value out of 5 yr, whereasthe 99th percentile value excludes 88 h (approxi-mately 4 days) out of every year with higherconcentrations. Therefore, at this receptor, less than1% of the time, one would expect the odour bestrong enough for more than 61% of the populationto respond to it. The histogram (Fig. 5) may be usedto visualize the odour–frequency relationship. Thisshows the cumulative number of hours in eachconcentration category experienced at Receptor A.For 98.98% of the time (43376 out of 43824 h) theodour is undetectable by half the persons atReceptor A, assuming that these individuals haveodour-sensing characteristics similar to the rest ofthe population. If the top 1% of values (top 438values) were eliminated, the odour histogram would

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220

96

36 3315

32

1

11

4

43376

1

10

100

1000

10000

100000

< 1

1 to

22

to 3

3 to

44

to 5

5 to

66

to 7

7 to

88

to 9 >

9

Concentration Categories (OU)

To

tal H

ou

rs

Fig. 5. Frequency of occurrence of calculated odour concentrations at Receptor A.

P. Henshaw et al. / Atmospheric Environment 40 (2006) 1016–1029 1025

change radically, because all bars in the histogramwould be eliminated except the leftmost two.

4.3. Impact assessment

Odour impact parameters were calculated initiallyfor three emissions scenarios. The first scenarioincluded both the stack (point source) and fugitive(volume source) emissions. The second and thirdscenarios modelled included only the emissionsfrom the point and volume sources, respectively.Table 1 summarizes the impact parameters for thesethree scenarios based on both peak and 99thpercentile values. In all impact parameter evalua-tions, impacts inside the boundary of the facilitywere excluded from the analysis.

The maximum off-property odour concentrationusing peak values is 32.7OU under the point andvolume scenario. The corresponding value whenonly the lower 99th percentile of values is consid-ered is 8.8OU, and the location is the same.According to this simulation, 8.8OU are exceededless than 1% of the time, anywhere in thecommunity.

The maximum probability of response for anyoff-plant receptor is predicted to be 499.9% usingpeak values. This value occurs at the location ofmaximum concentration, i.e. where the concentra-tion is 32.7OU. Of course, the peak probability ofresponse can be calculated from the peak odourconcentration using Eq. (2). As was the case for the

maximum odour concentration, the maximumprobability of response was reduced when the top1% of values was excluded. However, its value onlydecreased from 99.9998% to 99.97%. It is clear thatat high odour concentrations, the maximum prob-ability of response is not sensitive to odour concen-tration changes.

The area enclosed by the 1OU contour is12.5 km2 when one considers peak values. Theentire modelled area (3.95� 3.95 km) less theblanked-out area enclosed inside the plant bound-aries (0.37 km2) is 15.2 km2. Looking at the extent ofthe modelled area enclosed in the 1OU contour inFig. 3(a), it seems logical that the footprint value beso high. If one considers the 99th percentile valuesat each receptor, the area value drops to 0.28 km2.The footprint thereby is an easily interpretedparameter to gauge odour impact.

However, there are points within the 1OUcontour where essentially all of the population(499%) would respond to the odour and pointswhere only half the population would be expected torespond to the odour, yet these points are givenequal weight in calculating the footprint area. Theconcentration- and probability-weighted footprintareas provide more weighting to those areas where ahigher percentage of the people would respond tothe odour. When using peak values and both pointand volume sources, the concentration-weightedfootprint area is 35.0OUkm2, and the probability-weighted footprint area is 11.7 km2. Note that the

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Table 1

Impact parameters calculated for the first three scenarios wherein both stack (point sources) and fugitive emissions (volume sources) are

active, and where each source is modelled separately

Impact parameter Modelled scenario

Point and volume sources Point source only Volume source only

Peak 99th percentile Peak 99th percentile Peak 99th percentile

Maximum concentration, Cmax,

at a receptor (OU)

32.7 8.79 1.20 (0.04) 0.67 (0.08) 32.7 (1.00) 8.78 (1.00)

Maximum probability, Pmax, at

a receptor (%)

499.9 499.9 66.2 (0.66) 18.1 (0.18) 499.9 (1.00) 499.9 (1.00)

Footprint area inside 1OU

concentration contour, F(1OU)

(km2)

412.5a 0.28 0.013 (0.00) 0.00 (0.00) 48.61a (0.69) 0.14 (0.5)

Concentration-weighted

footprint area, CWFA

(OUkm2)

435.0a 2.75 15.2 (0.43) 1.04 (0.38) 428.2a (0.80) 1.70 (0.62)

Probability-weighted footprint

area, PWFA (km2)

411.7a 0.40 1.31 (0.11) 0.014 (0.04) 48.93a (0.76) 0.20 (0.49)

Population impact, PI (persons) 7675 509 1213 (0.16) 19 (0.04) 6534 (0.85) 257 (0.50)

Bracketed terms are the ratios of the parameter values resulting from only one emission type relative to those experienced when both point

and volume sources were modelled.aImpact parameter could not be evaluated accurately since the bounding contours extended beyond the modelled area.

P. Henshaw et al. / Atmospheric Environment 40 (2006) 1016–10291026

actual values for these parameters will be greaterthan the values shown in Table 1 because of thelimited size of the modelled area. At the edges of themodelled area, the values of concentration andprobability of response are significantly above zero,so the calculation of CWFA and PWFA haveeffectively been truncated. The value of the PWFAdivided by the footprint area gives the average valueof probability. As stated before, the entire modelledarea was 15.2 km2, thus the average probability inthis area is 77% (i.e., 11.7/15.2� 100%), which isreasonable considering Fig. 3(b). The values of theCWFA and PWFA are reduced to 2.75OUkm2 and0.40 km2 if the 99th percentile value is considered ateach receptor.

The population density can also be incorporatedinto impact estimates in order to estimate thenumber of people affected by the odour. The valueof PI using peak concentrations is 7675 persons.Therefore, at least 90% of the population of thiscommunity would be expected to respond to theodour at some hour throughout the 5 yr period ifthe worst concentration at each point is considered.Of course, not all of these people would be expectedto respond to the odour at the same time. Inaddition, this statistic ignores the fact that underone wind condition, an individual at a receptorpoint may have a probability of responding to the

odour of 10%, while in another wind condition his/her probability of response could be 40%. The 10%value is ignored in this analysis because only thehighest value at each receptor is counted. That isone of the reasons that the number of peoplecalculated in this way is considered a minimum. Inaddition to being easily interpreted, the populationimpact is a useful parameter in that it takes intoaccount the fact that some of the areas where odourexists are actually unpopulated and would yield noimpact. As with the other parameters, if the 99thpercentile value at each receptor is used, theparameter value is drastically reduced, in this caseto 509 persons. Therefore, one would expect morethan 509 people, or 6% of the population, torespond to the odour approximately 1% of the time.

A comparison of the columns of Table 1 revealsthat the point source contributes minimally to thetotal impact. This was somewhat surprising as thestack and fugitive sources have nearly equalemission rates: i.e., 4196 and 4251OUm3 s�1,respectively. However, with only the point source,many impact parameters (Cmax, F(1OU), PWFA,PI) are reduced to less than one sixth of their valuescalculated using both point and volume sources.The maximum probability of response is reducedfrom 99.9998% to 66.2% (considering peak values),which is a reduction of about a third. Similarly, the

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concentration-weighted footprint area inside the1OU contour is reduced by 57%.

The values for these impact parameters evaluatedwith only fugitive emissions (i.e., volume source)are very similar, and in some cases the same asthe scenario that includes both point and volumesources (Table 1). This confirms that the fugitiveemissions contribute the most to odour impact. Themaximum concentration and maximum probabilityof response at a receptor are not affected by the lossof the point source. This indicates that the stackdisperses the odour further from the source than theground-level volume source, and out of the regionof maximum concentration. The greatest drop invalue of a parameter is for the footprint area, whichdropped 31% from the point and volume scenario,considering only peak values. Note that for anyimpact parameter, the sum of the parameters of thetwo sources considered separately is not equal to theparameter based on modelling of the sourcescombined. This is because the dispersion modellingprogram retains only the peak (or 99th percentile)value of the parameter at each receptor, and lesservalues are ignored. Thus, when modelling multiplesources, superposition of effects does not occur.

4.4. Emission reduction scenarios

Two emission reduction scenarios were evaluatedin order to explore how impact parameters can beused to assess the effectiveness of methods for

Table 2

Impact parameters calculated for the fourth and fifth scenarios wherein

Impact parameter Modelled emissio

Point and volume

emissions reduced

Peak

Maximum concentration, Cmax, at a receptor (OU) 16.3 (0.50)

Maximum probability, Pmax, at a receptor (%) 499.9 (1.00)

Footprint area inside 1OU concentration contour,

F(1OU) (km2)

46.00a (0.48)

Concentration-weighted footprint area, CWFA

(OUkm2)

417.5a (0.50)

Probability-weighted footprint area, PWFA (km2) 46.34a (0.54)

Population impact, PI (persons) 5074 (0.66)

Numbers in brackets indicate the ratio of the new parameter values to th

scenario shown in Table 1. Bracketed terms are the ratios of the parame

to those experienced when the original point and volume sources wereaImpact parameter could not be evaluated accurately since the boun

reducing impact. Reducing the total emissions by50% reduces the maximum concentration in thereceptor field by half (Table 2). The maximumprobability of response is not significantly changedfrom the value calculated by simulating both pointand volume sources, because the concentrationvalues lie in the upper flat part of the OIM(Fig. 1). The changes in these parameters hold truewhether considering peak values or 99th percentilevalues. When considering peak values, three of theother parameters—F(1OU), CWFA and PWFA,changed approximately 50% in response to thehalving of emission rates. The PI was not assensitive, decreasing by only about a third. How-ever, when one considers the 99th percentile values,the F(1OU), PWFA and PI are relatively sensitive,decreasing by more than 70%. An impact parameterthat is neutrally sensitive (decreases by half whenthe emission rates decrease by half) may be arguedto provide a more accurate representation of thetrue community impact. Clearly though, the selec-tion of which impact parameters are neutrallysensitive is dependant upon the receptor percentilelevel used.

In the final scenario, the industrial building waskept under negative pressure and the building airexhausted through a stack atop the source building,resulting in a reduction of impact parameters ascompared to the initial point and volume sourcescenario (Table 2). The maximum concentrationsconsidering peak and 99th percentile values were

emission rates were reduced

ns reduction scenario

sources,

by 50%

Point source and volume source vented

through a stack

99th percentile Peak 99th percentile

4.40 (0.50) 1.54 (0.05) 0.81 (0.09)

99.6 (1.00) 83.4 (0.83) 31.1 (0.31)

0.063 (0.23) 42.74a (0.22) 0.00 (0.00)

41.38a (0.50) 412.3a (0.35) 41.88a (0.68)

40.107a (0.27) 44.83a (0.41) 0.072 (0.18)

147 (0.29) 3630 (0.47) 84 (0.17)

ose calculated for the original point and volume source emissions

ter values resulting from the respective emission scenarios relative

modelled (see Table 1).

ding contours extended beyond the modelled area.

ARTICLE IN PRESSP. Henshaw et al. / Atmospheric Environment 40 (2006) 1016–10291028

reduced almost to their values with a point sourcealone (Table 1). This resulted in a noticeablereduction in the usually insensitive Pmax. Thefootprint area decreased the most with the newstack, because ground level and stack sources havebeen replaced by two stack sources, which, asobserved before, will dissipate the odour effectively.As in the case with a point source only and 99thpercentile values, the maximum concentration wasless than 1, so the footprint area was zero. In termsof the other parameters—CWFA, PWFA and PI,this emission reduction scenario is more effective atreducing community impact than the halving ofemission rates when one considers peak values atreceptors. When one considers 99th percentilevalues, halving emission rates is more effective onlyfor reducing the CWFA. A full analysis of these twoalternatives would require comparing the cost oftheir implementation for the same reduction inimpact.

5. Discussion

Many variations on the odour impact parametersdescribed above can be proposed. In any givensituation the influence of one or more parametersmay dominate. Furthermore, it is unlikely that therewill be one impact parameter that may be used torepresent the range of different types of odourimpacts that are experienced in a community. Forexample, while the PI may indicate the number ofpersons affected by the odour, and any reduction inthis parameter clearly translates to a reduction incommunity impact, it alone does not characterizethe effect on particularly impacted individuals, theway that Cmax or Pmax might. Therefore, it is notpossible to choose a single parameter that will bestreflect odour impact for all situations. Pointparameters should be chosen to describe the impactat either the most impacted location or at aparticular receptor. This approach would be mostsuitable when evaluating the impact at particularlysensitive receptors. Area parameters are useful whengauging the spatial extent of the odour impact as theodour disperses over a region. And, finally, volumeparameters are useful when assessing the degree ofimpact in the impacted region.

Additional dimensions to odour impact that havenot been considered in this study include the effectof averaging time and odour duration. A 1 hraveraging time was used here because it is theshortest time capable of being simulated by the

dispersion model. Clearly, though, odours can haveimpacts at much shorter times, and factors exist toaccommodate the increased maximum concentra-tion anticipated at shorter averaging times. How-ever, these factors must be built into the dispersionmodel as they vary with the stability of theatmosphere and, thus, from hour to hour. Relatedto this, the duration of odour at a receptor may beas important a consideration as its concentration.An odour that reaches a receptor and endures forseveral consecutive hours or days, even at relativelylow concentrations, may be perceived to be anannoyance by virtue of its duration. It is recom-mended that the impact of averaging time andodour duration should be investigated in the futureto determine their relevance in evaluating odourimpact. It is also suggested that future studiesshould be conducted to correlate the magnitude ofodour impact parameters such as those describedabove with the occurrence of odour complaints in acommunity. This would help narrow the range ofimpact parameters that should be evaluated in anygiven investigation.

The proposed approach is of practical signifi-cance to the owners and operators of any facilitythat produces or has the potential to produceodours. For example, the method can be used byindustries that are considering the implementationof various process changes, feedstock changes, oremission control technologies to reduce odorousemissions. In many cases, the effectiveness of thesestrategies in reducing odour impact is not usuallyknown until after they have been implemented atthe full-scale. However, through use of the proposedprocedure, dose–response data can be collectedfrom pilot plant studies and, in combination withdispersion modelling, be used to predict the odourimpact on a surrounding community. The proce-dure will facilitate the choice of the most effectiveapproach to reducing odour impact before imple-mentation at the full scale. This can translate intomonetary savings and improved relations withsurrounding populations, by allowing industries toquickly resolve conflicts concerning odour impactand by avoiding the implementation of ineffectiveodour control technologies.

6. Conclusions

This study demonstrated the utility of calculatedimpact parameters in assessing the odour impactof an industrial facility. The parameters were:

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maximum concentration, maximum probability ofresponse, footprint area, concentration-weightedfootprint area, probability-weighted footprint areaand population impact. These odour impact para-meters can be used to evaluate which source, ofmultiple sources producing odours of the samecharacteristics, is contributing the most to commu-nity impact. They can also be used to predict theeffectiveness of odour reduction strategies prior toimplementation. No single one of these parameterscompletely characterizes the impact of an odoursource on a community but, collectively, theyprovide the means for quantifying the various waysin which an impact can be experienced in thecommunity.

Acknowledgements

This report was part of a research projectsponsored by Environmental Science and Technol-ogy Alliance, Canada (ESTAC). In addition, fund-ing from the Natural Sciences and EngineeringResearch Council of Canada is acknowledged.

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