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Odour impact assessment by means of dynamic olfactometry, dispersionmodelling and social participation

Selena Sironi, Laura Capelli*, Paolo C�entola, Renato Del Rosso, Sauro PierucciPolitecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, P.za Leonardo Da Vinci, 32, 20133 Milano, Italy

a r t i c l e i n f o

Article history:Received 29 May 2009Received in revised form15 October 2009Accepted 21 October 2009

Keywords:Odour episodesSource identificationOdour samplingOdour measurementRenderingCalpuff

a b s t r a c t

This work discusses how it is possible to assess odour impact in presence of multiple similar sources byillustrating a case study. The study was conducted on an area of northern Italy comprising three smallmunicipalities where four rendering plants are located near to each other. Based on the emission dataresulting from olfactometric surveys conducted in different periods of the year the overall odouremission rate emitted by each plant were evaluated, showing that the major contributor to the odourimpact on the territory was plant 2. These data were linked with meteorological and orographical data inorder to evaluate odour dispersion with a model (Calpuff). The results of the odour dispersion modellingconfirmed the outcomes of the olfactometric survey and they were further validated through a “ques-tioning” survey, conducted with the aim of involving the population by means of questionnaires forreporting the perceived odour episodes, which showed a good correspondence (86.5%) between odourperceptions and simulated odour immissions.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

When more odour emitting industrial activities, such as sewagetreatment plants, waste treatment or disposal facilities, paintfacilities, petroleum refineries, rendering plants, pulp mills, plasticand resin manufacturers and chemical industries, are present ona restricted area, an odour nuisance problem could be generated(Leonardos, 1996; Henshaw et al., 2006). Even though it is univer-sally recognized that the exposure to odours generally representa nuisance more than a risk for human health (Fransses et al., 2002;Luginaah et al., 2000), odour exposure may nonetheless causeeffects on human activities (Gostelow et al., 2001; Shusterman,1992). Effects of this kind of pollution may be: i) impairment of thequality of the environment; ii) damages to properties, plants oranimal; iii) harm or discomfort to any person; iv) impairment of thesafety of any person; v) rendering any property, plant or animalunsuitable for human use; vi) loss of enjoyment of normal use ofproperty; vii) interference with business activities (Nicell, 2009).Prolonged exposure to odours can cause undesired reactionsranging from emotional stresses such as states of anxiety, unease,

headache or depression to physical symptoms such as eye irrita-tion, respiratory problems, nausea or vomiting (National ResearchCouncil Committee on Odours, 1979).

Several experiences tend to include odours among the pollut-ants that have to be controlled and subject to specific regulations.As a matter of fact, odours are considered to be one of the majorcauses of public complaints to the competent authorities (Blum-berg and Sasson, 2001). Population living near odour emittingactivities rely on local authorities (e.g., municipal by-law officers,police, and fire or health units), on regional agencies or directly onthe personnel involved in the odour emitting operation for theproblem elimination or reduction. Following to internal or externalsolicitations for odour impact reduction by the competent author-ities, often there is a bouncing of responsibility of the odournuisance between the involved subjects, i.e. between the differentodour emitting activities on the territory. The question is evenmorecomplicated if more similar plants are present. In this case it is notuseful to search for chemical tracers referable to a specific sourcebecause of the homogeneity of the different gaseous emissions(Sohn et al., 2009).

The present work discusses how it is possible to assess odourimpact in presence of multiple similar sources by illustrating a casestudy. Based on the emission data resulting from olfactometricsurveys conducted in different periods of the year the overall odouremission rate emitted by each plant can be calculated as the sum ofthe odour emission rate values relevant to all odour sources. These

* Corresponding author. Tel.: þ39 02 23993206; fax: þ39 02 23993291.E-mail addresses: selena.sironi@polimi.it (S. Sironi), laura.capelli@polimi.it

(L. Capelli), paolo.centola@polimi.it (P. C�entola), renato.delrosso@polimi.it (R. DelRosso), sauro.pierucci@polimi.it (S. Pierucci).

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data can be linked with meteorological and orographical data inorder to evaluate odour dispersion with a suitable mathematicalmodel.

Furthermore, it is possible to conduct a “questioning” surveywith the aim of involving the population and making them activelytake part to the odour impact assessment study by means ofquestionnaires for reporting the odour episodes on the territory(Gallego et al., 2008). This information can be used as a tool for theevaluation of the model applied for the odour emission dispersionsimulation.

2. Materials and methods

2.1. Site description

The study was conducted on an area of northern Italycomprising three small municipalities (each having less than10 000 inhabitants), where four different rendering plants arelocated near to each other (Fig. 1).

The rendering plants at issue treat animal by-productsbelonging to category 2 (plant 2) and category 3 (plants 1, 3 and 4)according to the European reference regulation (EC 1774, 2002) andthey are equipped with different systems for the treatment of theirgaseous effluents. These abatement systems are based on theprinciples of wet scrubbing or combustion.

Table 1 reports the data relevant to the capacity of thefour plants and their functioning periods, in h d�1 and in d y�1,respectively.

2.2. Emission sources

Plants 2 and 4 are characterized by the presence of point sourcesonly, whereas plants 1 and 3 have both point and area sources(Bockreis and Steinberg, 2005; Hudson and Ayoko, 2008). Tables 2and 3 report the physical characteristics relevant to all the odoursources of the four plants, which were determined experimentally.

In the four monitored plants there are also fugitive sources,which are difficult to determine and may be significantly differentamong the plants. These emissions weren't considered in this studybecause, based on past experience on this kind of plants, there is anevidence that the contribution of the fugitive emissions to the

overall odour impact is small with respect to the other emissionsources (e.g., conveyed emissions through stacks and wastewatertreatment tanks).

2.3. Sample collection

Two odour sampling andmeasurement trials were conducted inorder to characterize the emissions from all the odour sources lis-ted in Tables 2 and 3. The trials took place in two different periodsof the year: a “hot” one (June 2008) and a cold one (October 2008),respectively, in order to improve the evaluation of the odour impactof the four rendering plants by taking account of different meteo-rological conditions.

Sampling on point sources (i.e. conveyed emissions, e.g.,through a stack) is carried out by sucking part of the odorousairflow into an 8-L sampling bag in Nalophan� equipped witha Teflon� inlet tube by means of a depression pump (Capelli et al.,2008).

Sampling on passive area sources (i.e. liquid surfaces without anoutward flow, e.g., wastewater treatment tanks) entails moredifficulties. It is performed using a wind tunnel system, whichconsists of a hood that simulates the wind action on the liquidsurface to bemonitored (Jiang and Kaye, 2001; Frechen et al., 2004).In this case, we used a specific wind tunnel made in poly-ethyleneterephtalate (PET), whichwas positioned over the emittingsurface. A neutral air stream is introduced at known airflow ratefrom an air bottle into the hood. Air samples are then collected inthe outlet duct using the same methodology as for point sources.The wind tunnel used during the experimentation has a circularsection inlet and outlet duct, of 0.08 m diameter. The central bodyof the hood used has a rectangular section chamber of 0.25 mwidth, 0.08 m height and 0.5 m depth. Inside the inlet duct there isa perforated stainless steel grid and inside the divergent thatconnects this duct to the central body of the hood there are threeflow deflection vanes (Capelli et al., 2009).

2.4. Olfactometric analyses

Dynamic olfactometry is a sensorial technique that allows todetermine the odour concentration (cod) of an odorous air samplerelating to the sensation caused by the sample directly on a panel ofopportunely selected people. cod is expressed in European odourunits per cubic metre (ouE m�3), and it represents the number ofdilutions with neutral air that are necessary to bring the odoroussample to its odour detection threshold concentration. The analysisis carried out by presenting the sample to the panel at increasingconcentrations by means of a particular dilution device called anolfactometer, until the panel members start perceiving an odourthat is different from the neutral reference air. The cod is thencalculated as the geometric mean of the odour threshold values ofeach panellist. As defined by the EN 13725 (2003), the individualthreshold estimate is defined by the two presentations in onedilution series, sorted on growing odour concentration, wherea certain change in response from “false” to a consistently “true”response occurs. The individual threshold estimate is calculated asFig. 1. Studied area and localization of the four rendering plants.

Table 1Plants capacity and functioning times.

Plant Capacity (t y�1) Functioning (h d�1) Functioning (d y�1)

1 30 000 24 3002 21 000 12 2603 30 000 24 3004 31 000 14 260

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the geometric mean of the dilution factors of the two definedpresentations.

An olfactometer model TO8 produced by ECOMA GmbH, basedon the “yes/no” method, was used as a dilution device. Thisinstrument with aluminium casing has 4 panellist places in sepa-rate open boxes. Each box is equipped with a sniffing port instainless steel and glass, and a push-button for “yes” (odourthreshold). The measuring range of the TO8 olfactometer startsfrom a maximum dilution factor of 1:65 536, with a dilution stepfactor 2. All the measurements were conducted within 30 h aftersampling, relying on a panel composed of 4 panellists.

2.5. Dispersion model

The model used for the simulation of the emission dispersion isthe CALPUFF model (Wang et al., 2006). This model is realized byEarth Tech Inc. for the California Air Resources Board (CARB) andthe U.S. Environmental Protection Agency (US EPA).

CALPUFF is a non-stationary puff atmospheric dispersion model.It is suitable for the estimation of emission from single or multipleindustrial sources. It allows to calculate dry and wet deposition,building downwash, dispersion from point, area and volumesources, the gradual plume rising as a function of the distance fromthe source, the influence of the orography on dispersion, and thedispersion in case of weak or absent wind. The dispersion coeffi-cients are obtained from the turbulence parameters (u*, w*, LMO),instead of being calculated from the PasquilleGiffordeTurnerstability classes. This means that the turbulence is described bycontinuous functions, not by discrete ones. During the periods inwhich the boundary layer has a convective structure, the concen-tration distribution inside each puff is Gaussian on the horizontalplanes, but asymmetric on the vertical planes, i.e. it takes account ofthe probability distribution function of the vertical speeds. In otherwords, the model simulates the effects on dispersion due toascending and descending air movements that are typical of theday's hottest hours and due to big scale vortex (Progress, 2006).

The model needs three different kinds of input data:orographical, meteorological and emission data.

As far as orography is concerned: the dimensions of the spatialgrid on the simulation domain are 4000 m � 4000 m, witha receptor every 100 m. The domain was chosen in order to includethe four monitored plants and the three municipalities where theodour is a nuisance.

Table 4 reports the meteorological parameters used for thedispersion modelling. The output parameters of the pre-processorused for the calculation of the micrometeorological variables arelisted in Table 5.

As emission data, the results of the olfactometric analysesconducted on the four rendering plants can be used. Furthermore,the data needed as input for the model are not the odour concen-tration values, but the Odour Emission Rate (OER) values, expressedin ouE s�1, associated with each considered odour source.

In the case of point sources (Table 2), the OER can be calculatedsimply by multiplying the odour concentration value (in ouE m�3)by the normalized airflow (in m3 s�1).

The evaluation of the OER relevant to area sources, e.g., waste-water treatment tanks (Table 3), requires the calculation of theSpecific Odour Emission Rate (SOER), which is expressed inouE s�1 m�2. Once the odour concentration value of a samplecollected at the outlet of the wind tunnel is determined, it ispossible to obtain the SOER bymultiplying the odour concentration(ouEm�3) with the flow rate of the inlet air (m3 s�1) and dividing bythe base area of the central body of the hood (m2). The OER is finallyobtained as the product of the SOER value and the emitting surfaceof the considered source (m2) (Sironi et al., 2006). The OER isa function of the air velocity, i.e. the wind speed, on the liquidsurface. Once the OER relevant to the sampling conditions is eval-uated, the OER for any other air velocity (i.e. wind speed) can becalculated as follows (Sohn et al., 2003):

OERv2 ¼ OERv1

�v2v1

�12

According to this equation, for the odour dispersion modellingstudy, the odour emissions from the area sources (OERs) were

Table 2Characteristics of the point sources.

Plant Emission source Measured airflow (m3 h�1) Temperature (K) Stack height (m) Section (m2) Diameter (m) Air speed (m s�1)

1 Scrubber E1 outlet 28 000 298 25 0.11 0.95 11.0Steam boiler E2 outlet 3000 483 5 0.63 0.35 8.5

2 Steam boiler E2 outlet 7000 613 7 0.2 0.45 12.0Scrubber E3 outlet 5500 300 10 0.16 0.49 8.2Scrubber E4 outlet 16 000 298 10 0.16 0.65 13.5

3 Steam boiler E1 outlet 3000 476 6 0.14 0.48 4.6Steam boiler E2 outlet 3000 476 6 0.14 0.48 4.6Scrubber E3 outlet 5000 296 10 0.196 0.50 7.0

4 Therm. comb. E1 outlet 35 000 463 7 0.94 1.10 10.2Steam boiler E2 outlet 3500 448 7 0.4 0.40 7.7Steam boiler E3 outlet 3500 448 7 0.4 0.40 7.7

Table 3Characteristics of the area sources.

Plant Source Area (m2) Equivalent diameter (m)

1 Oxidation tank 200 16.0Sedimentator 85 10.4

3 Accumulation/denitrification tank 14.1 4.2Oxidation tank 133 13.0Sedimentator 18.5 4.9

Table 4Meteorological parameters used for the dispersion modelling.

Meteorologicalparameter

Type of data Unit of measurement Period

Temperature Hourly average �C From 01/01/2007to 31/12/2007Wind speed Hourly average m s�1

Wind direction Predominant (1 h) sexagesimal degreeNet solar radiation Hourly average W m�2

Relative humidity Hourly average %Rainfall Total (1 h) mm

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calculated for each hour of the simulation domain based on thecurrent wind speed.

Two odour impact studies by dispersion modelling were con-ducted twice, based on the data of the first (June 2008) and of thesecond (October 2008) odour sampling and measurement trial,respectively.

2.6. Odour episodes reporting and model validation

Between June and October 2008, a “questioning” survey wasconducted, with the aim of collecting reports of perceived odourepisodes. Several public assemblies were organized in order toinform and sensitize the population.

The periods (date and time) during which odours from themonitored plants were perceived were reported by the inhabitantsof the three municipalities involved in the study on simple ques-tionnaire forms distributed during 5 months of observation period(from June to October 2008). The questionnaires were non-anon-ymous, i.e. name and full address of the citizen registering theodour episodes had to be indicated. Fig. 2 indicates the position ofthe citizens who took part to this survey.

The odour episodes reports were compared with the results ofthe odour dispersion simulation by application of the mathematicaldispersion model (Calpuff). In correspondence of the periodsduring which the presence of odours was reported by the citizens,

the dispersion model was applied using the emission data and themeteorological data relevant to the period in order to simulateodour dispersion on the territory at the moment of the perception.This way it was possible to verify if the odour plume simulated bythemodel effectively reached the receptor in correspondence of theodour episode report.

3. Results and discussion

3.1. Olfactometric survey

Table 6 shows the results of the olfactometric survey. Columns 3and 4 report the odour concentration values measured in the I andII survey, conducted in June 2008 and October 2008, respectively.Columns 6 and 7 report the OER values relevant to each odour

Table 5Parameters calculated by the micrometeorological model.

Micrometeorologicalparameter

Symbol Type of data Calculation method

Surface heat flux Qh Hourly average Thomson, 2000Friction velocity u* Hourly averageMonin-Obukhov length LMO Hourly averageConvective velocity scale w* Hourly average

Mixing height MH Hourly average Scire et al., 2000

Fig. 2. Localization of the citizens who reported the odour episodes.

Table 6Results of the olfactometric surveys.

Plant Sampling point cod (ouE m�3) Q (m3 h�1) OER (ouE s�1) Abatem. eff. (%)

I surv. II surv. I surv. II surv. I surv. II surv.

1 Steam boiler E1 inlet 390 000 420 000 3000 _ _ _ _Steam boiler E1 outlet 1800 2400 3000 1500 2000 99 99Scrubber E2 inlet 16 000 10 000 28 000 _ _ _ _Scrubber E2 outlet 6900 3100 28 000 53 667 24 111 57 69Oxidation tank 190 100 _ 211 111 _ _Sedimentator 270 96 _ 128 45 _ _

2 Steam boiler E2 inlet 390 000 440 000 7000 _ _ _ _Steam boiler E2 outlet 1300 2300 7000 2528 4472 99 99Scrubber E3 inlet 55 000 52 000 5500 _ _ _ _Scrubber E3 outlet 39 000 52 000 5500 59 583 79 444 30 0Scrubber E4 inlet 18 000 33 000 16 000 _ _ _ _Scrubber E4 outlet 13 000 28 000 16 000 57 778 124 444 30 15

3 Steam boilers inlet 40 000 49 000 3000 _ _ _ _Steam boiler E1 outlet 300 570 3000 250 475 99 99Steam boiler E2 outlet 360 200 3000 300 167 99 99Scrubber E3 inlet 28 000 15 000 5000 _ _ _ _Scrubber E3 outlet 4900 2000 5000 6806 2778 83 59Accum./denitr. tank 130 1100 _ 10 86 _ _Oxidation tank 130 170 _ 96 126 _ _Sedimentator 100 140 _ 10 14 _ _

4 Therm. combustor inlet 16 000 62 000 35 000 _ _ _ _Therm. combustor outlet 1200 170 35 000 11 667 1653 93 99Steam boilers inlet 770 1300 3500 _ _ _ _Steam boiler E2 outlet 540 64 3500 525 62 30 91Steam boiler E3 outlet 340 110 3500 331 107 56 95

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source. As explained above (Section 2.5), for point sources the OERis calculated as the product of the odour concentration and theairflow (column 5), whereas in the case of area sources the OER isobtained by multiplying the SOER by the emitting area (i.e. thesurface of the wastewater treatment tank). The last two columnsof Table 6 report the odour abatement efficiency values relevant tothe odour reducing facilities present on the monitored plants(scrubbers, steam boilers and thermal combustors). The odourabatement efficiency is evaluated as follows (Sironi et al., 2007):

Effð%Þ ¼ cod;IN � cod;OUTcod;IN

$100

Based on these results it is possible to make some consider-ations about the plants odour emissions.

Fig. 3. Map of the 98th percentile of the hourly peak odour concentration valuesobtained using the emission data derived from the I olfactometric survey (June 2008).

Fig. 4. Mapof the98thpercentileof thehourlypeakodourconcentrationvaluesobtainedusing the emission data derived from the II olfactometric survey (October 2008). Ta

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First, the overall OER relevant to each of the four plants can beevaluated by adding the OER values of all respective (point or area)sources. This way of calculating the overall OER representsa simplification, as it doesn't account for possible interactionsbetween different odours which may make different emissions notadditive. The overall OER was evaluated to be comprised between2.6 � 104 and 5.5 � 104 ouE s�1 for plant 1, 1.2 � 105 and2.1�105 ouE s�1 for plant 2, 3.6�103 and 7.5�103 ouE s�1 for plant 3and 1.8 � 103 and 1.3 � 104 ouE s�1 for plant 4. It can be highlightedthat the overall OER of plant 2 is at least one order of magnitudeabove the overall OER values relevant to the other plants.

The reasons for this may be on one hand the low abatementefficiency of the odour control systems and on the other hand thetypology of the treated material, as plant 2 is the only one treatinganimal by-products belonging to category 2.

This kind of material is delivered to the rendering plant ina more advanced state of decomposition (compared to category 3by-products which arrive refrigerated and relatively fresh), thusgiving rise to higher odour concentrations. The odour controlsystems installed at plant 2 seem to be inadequate for the abate-ment of such odour concentrations, even if their efficiency wasoptimized. Typical abatement efficiencies of wet scrubbers inrendering plants are between 50% and 60%, and even particular careand optimization of a scrubber doesn't make its efficiency higherthan 80% (Sironi et al., 2007). Considering that inlet odourconcentrations in a plant treating category 2 material arecomprised between 50 000 ouE m�3 and 60 000 ouE m�3, with anoptimized efficiency of 80%, outlet concentrations would be10 000e12 000 ouE m�3, thus remaining unacceptably high and notcomparable with the outlet concentrations of the other plants.

The OER values associated with the wastewater treatment tanksare rather low, i.e. some orders of magnitude below the OER levelsof the point sources, even if the tank size is significant (e.g., theoxidation tanks of plants 1 and 3).

3.2. Simulation of odour dispersion

For each receptor of the simulation grid and for each hour of thesimulation period, the model calculates the hourly mean odour

concentration. These values must be multiplied by a peak-to-meanratio, in order to obtain the peak odour concentration for eachreceptor and for each hour of the time domain. In general, the peak-to-mean ratio can be evaluated as a function of wind velocity,stability and distance from the source (Schauberger et al., 2000). Inthis case, we decided to use a peak-to-mean ratio of 2.3, accordingto the technical document about the modelling and assessment ofair pollutants published by the Department of Environment andConservation of New SouthWales (DEC 361, 2005). From thematrixof the ground peak odour concentration values the 98th percentileswere extracted. The results of the odour dispersion simulation cantherefore be represented in maps reporting the isopleths relevantto the 98th percentile of the hourly peak concentrations (Figs. 3and 4, relevant to the I and II survey, respectively).

In agreement with the outcomes of the olfactometric survey, themodel results show clearly how the odour impact relevant to plant2 largely prevails with respect to the odour impact relevant to theother three rendering plants.

It is important to highlight that there exist other, well estab-lished alternatives to the chosen 98th percentile methodology forodour impact evaluation by dispersion modelling. One noteworthymethod is for example the concept of odour hour as defined by theGerman guideline about odour immissions (GIRL, 2008). The choiceof the 98th percentile method is due to the fact that this is the

Table 8Odour episodes reported by the citizens living in the second municipality.

Receptor 15 16 17 18 19

Episode Date Time

12 06/08 23.00 23.1013 07/08 18.30 19.1014 15/08 15.20 11.00e15.15 7.0015 18/08 14.55 14.15e15.30e16.05e

18.30e20.3016 19/08 17.20 18.0017 20/08 21.45 9.2518 21/08 16.15 7.10e18.2019 23/08 12.50 23.10 22.0020 08/09 15.30e18.15e20.45 21.00

Table 9Odour episodes reported by the citizens living in the third municipality.

Receptor 20 21 22 23 24

Episode Date Time

21 25/09 All day long From 8.00all day long

All day long 18.30

22 28/09 All day long 1.00 8.3023 29/09 All day long All day long 19.3024 02/10 All day long 8.00

Fig. 5. Map of the ground peak odour concentration values on the 27th August 2008,between 7am and 8am.

Table 10Correspondences between odour perceptions and simulated immissions.

Municipality no. 1 Total no. of odour episodes 49No. of correspondences perceptions-model 41% of correspondences perceptions-model 83.7%

Municipality no. 2 Total no. of odour episodes 20No. of correspondences perceptions-model 18% of correspondences perceptions-model 90.0%

Municipality no. 3 Total no. of odour episodes 20No. of correspondences perceptions-model 18% of correspondences perceptions-model 90.0%

Total Total no. of odour episodes 89No. of correspondences perceptions-model 77% of correspondences perceptions-model 86.5%

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approach actually considered for regulation purposes in LombardyRegion (Italy) (Sironi and Capelli, 2009).

3.3. Questioning and model comparison

The results of the questioning survey are represented by tablesreporting the odour episodes registered by the citizens living inthe three municipalities near the monitored rendering plants(Tables 7e9).

These odour episodes were compared with the results of theodour dispersion simulation by dispersion modelling. The odourepisodes were compared with the hourly peak concentrationvalues calculated by the model in correspondence of the hour ofeach odour episode.

As an example, Fig. 5 illustrates the correspondence betweenthe map resulting from the application of the dispersion model inthe period between 7am and 8am on the 27th August 2008 and theodour episode no. 11 reported by receptors no. 5, 6, 7 and 10 at thesame time.

The agreement between odour reports by the citizens anddispersion model was evaluated by verifying if, in correspondenceof each episode, the spot of the reported odour perception fellinside the isoline relevant to an odour concentration of 1 ouE m�3.This value was chosen based on the fact that this is defined as theodour threshold concentration of 50% of a population, i.e. the odourconcentration at which 50% of a population would start perceivingan odour. In general, a good correspondence between odour reportsby the citizens and dispersion model results was observed. Thepercentages of correspondence are reported in Table 10.

The comparison between odour perceptions and simulatedodour immissions showed an accuracy of 86.5% in terms of corre-spondence. This percentage is rather high and therefore adds to thevalidation of the applied simulation procedure.

4. Conclusions

This work shows how it is possible to assess odour impact inpresence of multiple similar sources by illustrating a case study.

The olfactometric survey allowed to identify the major contrib-utor to the odour impact on themonitored area, which turned out tobe plant 2 (1.2e2.1 �105 ouE s�1). The reason therefore may be thetypology of the treated material (belonging to category 2 instead ofcategory 3 treated by the other plants) and the low abatementefficiency of the adopted odour control systems (scrubbers).Furthermore, the model application allowed to quantify the odourimpact relevant to the four monitored plants on the surroundings.

In agreement with the outcomes of the olfactometric survey, themodel results confirm that the odour impact relevant to plant 2largely prevails with respect to the odour impact relevant to theother three rendering plants.

The questioning survey conducted with the aim of involving thecitizens and collecting their reports of perceived odour episodes fitswith the results of the odour immissions (86.5% of correspondencebetween odour perceptions and simulated odour immissions)simulation by dispersion modelling and therefore adds to theevaluation of the applied simulation procedure.

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