- Email: [email protected]
Odour impact assessment by means of dynamic olfactometry, dispersion modelling and social participation
Atmospheric Environment 44 (2010) 354e360
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Odour impact assessment by means of dynamic olfactometry, dispersion modelling and social participation ntola, Renato Del Rosso, Sauro Pierucci Selena Sironi, Laura Capelli*, Paolo Ce Politecnico 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
a b s t r a c t
Article history: Received 29 May 2009 Received in revised form 15 October 2009 Accepted 21 October 2009
This work discusses how it is possible to assess odour impact in presence of multiple similar sources by illustrating a case study. The study was conducted on an area of northern Italy comprising three small municipalities where four rendering plants are located near to each other. Based on the emission data resulting from olfactometric surveys conducted in different periods of the year the overall odour emission rate emitted by each plant were evaluated, showing that the major contributor to the odour impact on the territory was plant 2. These data were linked with meteorological and orographical data in order to evaluate odour dispersion with a model (Calpuff). The results of the odour dispersion modelling confirmed the outcomes of the olfactometric survey and they were further validated through a “questioning” survey, conducted with the aim of involving the population by means of questionnaires for reporting the perceived odour episodes, which showed a good correspondence (86.5%) between odour perceptions and simulated odour immissions. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Odour episodes Source identification Odour sampling Odour measurement Rendering Calpuff
1. Introduction When more odour emitting industrial activities, such as sewage treatment plants, waste treatment or disposal facilities, paint facilities, petroleum refineries, rendering plants, pulp mills, plastic and resin manufacturers and chemical industries, are present on a restricted area, an odour nuisance problem could be generated (Leonardos, 1996; Henshaw et al., 2006). Even though it is universally recognized that the exposure to odours generally represent a nuisance more than a risk for human health (Fransses et al., 2002; Luginaah et al., 2000), odour exposure may nonetheless cause effects on human activities (Gostelow et al., 2001; Shusterman, 1992). Effects of this kind of pollution may be: i) impairment of the quality of the environment; ii) damages to properties, plants or animal; iii) harm or discomfort to any person; iv) impairment of the safety of any person; v) rendering any property, plant or animal unsuitable for human use; vi) loss of enjoyment of normal use of property; vii) interference with business activities (Nicell, 2009). Prolonged exposure to odours can cause undesired reactions ranging from emotional stresses such as states of anxiety, unease,
* Corresponding author. Tel.: þ39 02 23993206; fax: þ39 02 23993291. E-mail addresses: [email protected] (S. Sironi), [email protected] ntola), [email protected] (R. Del (L. Capelli), [email protected] (P. Ce Rosso), [email protected] (S. Pierucci). 1352-2310/$ e see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.10.029
headache or depression to physical symptoms such as eye irritation, respiratory problems, nausea or vomiting (National Research Council Committee on Odours, 1979). Several experiences tend to include odours among the pollutants that have to be controlled and subject to specific regulations. As a matter of fact, odours are considered to be one of the major causes of public complaints to the competent authorities (Blumberg and Sasson, 2001). Population living near odour emitting activities rely on local authorities (e.g., municipal by-law officers, police, and fire or health units), on regional agencies or directly on the personnel involved in the odour emitting operation for the problem elimination or reduction. Following to internal or external solicitations for odour impact reduction by the competent authorities, often there is a bouncing of responsibility of the odour nuisance between the involved subjects, i.e. between the different odour emitting activities on the territory. The question is even more complicated if more similar plants are present. In this case it is not useful to search for chemical tracers referable to a specific source because of the homogeneity of the different gaseous emissions (Sohn et al., 2009). The present work discusses how it is possible to assess odour impact in presence of multiple similar sources by illustrating a case study. Based on the emission data resulting from olfactometric surveys conducted in different periods of the year the overall odour emission rate emitted by each plant can be calculated as the sum of the odour emission rate values relevant to all odour sources. These
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
data can be linked with meteorological and orographical data in order to evaluate odour dispersion with a suitable mathematical model. Furthermore, it is possible to conduct a “questioning” survey with the aim of involving the population and making them actively take part to the odour impact assessment study by means of questionnaires for reporting the odour episodes on the territory (Gallego et al., 2008). This information can be used as a tool for the evaluation of the model applied for the odour emission dispersion simulation. 2. Materials and methods
355
Table 1 Plants capacity and functioning times. Plant
Capacity (t y1)
Functioning (h d1)
Functioning (d y1)
1 2 3 4
30 21 30 31
24 12 24 14
300 260 300 260
000 000 000 000
overall odour impact is small with respect to the other emission sources (e.g., conveyed emissions through stacks and wastewater treatment tanks).
2.1. Site description
2.3. Sample collection
The study was conducted on an area of northern Italy comprising three small municipalities (each having less than 10 000 inhabitants), where four different rendering plants are located near to each other (Fig. 1). The rendering plants at issue treat animal by-products belonging to category 2 (plant 2) and category 3 (plants 1, 3 and 4) according to the European reference regulation (EC 1774, 2002) and they are equipped with different systems for the treatment of their gaseous effluents. These abatement systems are based on the principles of wet scrubbing or combustion. Table 1 reports the data relevant to the capacity of the four plants and their functioning periods, in h d1 and in d y1, respectively.
Two odour sampling and measurement trials were conducted in order to characterize the emissions from all the odour sources listed in Tables 2 and 3. The trials took place in two different periods of the year: a “hot” one (June 2008) and a cold one (October 2008), respectively, in order to improve the evaluation of the odour impact of the four rendering plants by taking account of different meteorological conditions. Sampling on point sources (i.e. conveyed emissions, e.g., through a stack) is carried out by sucking part of the odorous airflow into an 8-L sampling bag in NalophanÔ equipped with a TeflonÔ inlet tube by means of a depression pump (Capelli et al., 2008). Sampling on passive area sources (i.e. liquid surfaces without an outward flow, e.g., wastewater treatment tanks) entails more difficulties. It is performed using a wind tunnel system, which consists of a hood that simulates the wind action on the liquid surface to be monitored (Jiang and Kaye, 2001; Frechen et al., 2004). In this case, we used a specific wind tunnel made in polyethyleneterephtalate (PET), which was positioned over the emitting surface. A neutral air stream is introduced at known airflow rate from an air bottle into the hood. Air samples are then collected in the outlet duct using the same methodology as for point sources. The wind tunnel used during the experimentation has a circular section inlet and outlet duct, of 0.08 m diameter. The central body of the hood used has a rectangular section chamber of 0.25 m width, 0.08 m height and 0.5 m depth. Inside the inlet duct there is a perforated stainless steel grid and inside the divergent that connects this duct to the central body of the hood there are three flow deflection vanes (Capelli et al., 2009).
2.2. Emission sources Plants 2 and 4 are characterized by the presence of point sources only, whereas plants 1 and 3 have both point and area sources (Bockreis and Steinberg, 2005; Hudson and Ayoko, 2008). Tables 2 and 3 report the physical characteristics relevant to all the odour sources 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 different among the plants. These emissions weren't considered in this study because, based on past experience on this kind of plants, there is an evidence that the contribution of the fugitive emissions to the
2.4. Olfactometric analyses
Fig. 1. Studied area and localization of the four rendering plants.
Dynamic olfactometry is a sensorial technique that allows to determine the odour concentration (cod) of an odorous air sample relating to the sensation caused by the sample directly on a panel of opportunely selected people. cod is expressed in European odour units per cubic metre (ouE m3), and it represents the number of dilutions with neutral air that are necessary to bring the odorous sample to its odour detection threshold concentration. The analysis is carried out by presenting the sample to the panel at increasing concentrations by means of a particular dilution device called an olfactometer, until the panel members start perceiving an odour that is different from the neutral reference air. The cod is then calculated as the geometric mean of the odour threshold values of each panellist. As defined by the EN 13725 (2003), the individual threshold estimate is defined by the two presentations in one dilution series, sorted on growing odour concentration, where a certain change in response from “false” to a consistently “true” response occurs. The individual threshold estimate is calculated as
356
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
Table 2 Characteristics of the point sources. Plant
Emission source
Measured airflow (m3 h1)
Temperature (K)
Stack height (m)
Section (m2)
Diameter (m)
Air speed (m s1)
1
Scrubber E1 outlet Steam boiler E2 outlet
28 000 3000
298 483
25 5
0.11 0.63
0.95 0.35
11.0 8.5
2
Steam boiler E2 outlet Scrubber E3 outlet Scrubber E4 outlet
7000 5500 16 000
613 300 298
7 10 10
0.2 0.16 0.16
0.45 0.49 0.65
12.0 8.2 13.5
3
Steam boiler E1 outlet Steam boiler E2 outlet Scrubber E3 outlet
3000 3000 5000
476 476 296
6 6 10
0.14 0.14 0.196
0.48 0.48 0.50
4.6 4.6 7.0
4
Therm. comb. E1 outlet Steam boiler E2 outlet Steam boiler E3 outlet
35 000 3500 3500
463 448 448
7 7 7
0.94 0.4 0.4
1.10 0.40 0.40
10.2 7.7 7.7
the geometric mean of the dilution factors of the two defined presentations. An olfactometer model TO8 produced by ECOMA GmbH, based on the “yes/no” method, was used as a dilution device. This instrument with aluminium casing has 4 panellist places in separate open boxes. Each box is equipped with a sniffing port in stainless steel and glass, and a push-button for “yes” (odour threshold). The measuring range of the TO8 olfactometer starts from a maximum dilution factor of 1:65 536, with a dilution step factor 2. All the measurements were conducted within 30 h after sampling, relying on a panel composed of 4 panellists.
2.5. Dispersion model The model used for the simulation of the emission dispersion is the CALPUFF model (Wang et al., 2006). This model is realized by Earth Tech Inc. for the California Air Resources Board (CARB) and the 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 multiple industrial sources. It allows to calculate dry and wet deposition, building downwash, dispersion from point, area and volume sources, the gradual plume rising as a function of the distance from the source, the influence of the orography on dispersion, and the dispersion in case of weak or absent wind. The dispersion coefficients are obtained from the turbulence parameters (u*, w*, LMO), instead of being calculated from the PasquilleGiffordeTurner stability classes. This means that the turbulence is described by continuous functions, not by discrete ones. During the periods in which the boundary layer has a convective structure, the concentration distribution inside each puff is Gaussian on the horizontal planes, but asymmetric on the vertical planes, i.e. it takes account of the probability distribution function of the vertical speeds. In other words, the model simulates the effects on dispersion due to ascending and descending air movements that are typical of the day'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 spatial grid on the simulation domain are 4000 m 4000 m, with a receptor every 100 m. The domain was chosen in order to include the four monitored plants and the three municipalities where the odour is a nuisance. Table 4 reports the meteorological parameters used for the dispersion modelling. The output parameters of the pre-processor used for the calculation of the micrometeorological variables are listed in Table 5. As emission data, the results of the olfactometric analyses conducted on the four rendering plants can be used. Furthermore, the data needed as input for the model are not the odour concentration values, but the Odour Emission Rate (OER) values, expressed in ouE s1, associated with each considered odour source. In the case of point sources (Table 2), the OER can be calculated simply by multiplying the odour concentration value (in ouE m3) by the normalized airflow (in m3 s1). The evaluation of the OER relevant to area sources, e.g., wastewater treatment tanks (Table 3), requires the calculation of the Specific Odour Emission Rate (SOER), which is expressed in ouE s1 m2. Once the odour concentration value of a sample collected at the outlet of the wind tunnel is determined, it is possible to obtain the SOER by multiplying the odour concentration (ouE m3) with the flow rate of the inlet air (m3 s1) and dividing by the base area of the central body of the hood (m2). The OER is finally obtained as the product of the SOER value and the emitting surface of the considered source (m2) (Sironi et al., 2006). The OER is a function of the air velocity, i.e. the wind speed, on the liquid surface. Once the OER relevant to the sampling conditions is evaluated, the OER for any other air velocity (i.e. wind speed) can be calculated as follows (Sohn et al., 2003):
OERv2 ¼ OERv1
1 v2 2 v1
According to this equation, for the odour dispersion modelling study, the odour emissions from the area sources (OERs) were
Table 4 Meteorological parameters used for the dispersion modelling.
Table 3 Characteristics of the area sources. Plant Source
Area (m2) Equivalent diameter (m)
1
Oxidation tank Sedimentator
200 85
3
Accumulation/denitrification tank 14.1 Oxidation tank 133 Sedimentator 18.5
16.0 10.4 4.2 13.0 4.9
Meteorological parameter
Type of data
Unit of measurement Period
Temperature Wind speed Wind direction Net solar radiation Relative humidity Rainfall
C Hourly average Hourly average m s1 Predominant (1 h) sexagesimal degree Hourly average W m2 Hourly average % Total (1 h) mm
From 01/01/2007 to 31/12/2007
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
357
Table 5 Parameters calculated by the micrometeorological model. Micrometeorological parameter
Symbol
Type of data
Calculation method
Surface heat flux Friction velocity Monin-Obukhov length Convective velocity scale
Qh u* LMO w*
Hourly Hourly Hourly Hourly
Thomson, 2000
Mixing height
MH
Hourly average
average average average average
Scire et al., 2000
calculated for each hour of the simulation domain based on the current wind speed. Two odour impact studies by dispersion modelling were conducted twice, based on the data of the first (June 2008) and of the second (October 2008) odour sampling and measurement trial, respectively. 2.6. Odour episodes reporting and model validation
Fig. 2. Localization of the citizens who reported the odour episodes.
Between June and October 2008, a “questioning” survey was conducted, with the aim of collecting reports of perceived odour episodes. Several public assemblies were organized in order to inform and sensitize the population. The periods (date and time) during which odours from the monitored plants were perceived were reported by the inhabitants of the three municipalities involved in the study on simple questionnaire forms distributed during 5 months of observation period (from June to October 2008). The questionnaires were non-anonymous, i.e. name and full address of the citizen registering the odour episodes had to be indicated. Fig. 2 indicates the position of the citizens who took part to this survey. The odour episodes reports were compared with the results of the odour dispersion simulation by application of the mathematical dispersion model (Calpuff). In correspondence of the periods during which the presence of odours was reported by the citizens,
the dispersion model was applied using the emission data and the meteorological data relevant to the period in order to simulate odour dispersion on the territory at the moment of the perception. This way it was possible to verify if the odour plume simulated by the model effectively reached the receptor in correspondence of the odour episode report. 3. Results and discussion 3.1. Olfactometric survey Table 6 shows the results of the olfactometric survey. Columns 3 and 4 report the odour concentration values measured in the I and II survey, conducted in June 2008 and October 2008, respectively. Columns 6 and 7 report the OER values relevant to each odour
Table 6 Results of the olfactometric surveys. Plant
1
2
3
4
Sampling point
cod (ouE m3)
Q (m3 h1)
I surv.
II surv.
Steam boiler E1 inlet Steam boiler E1 outlet Scrubber E2 inlet Scrubber E2 outlet Oxidation tank Sedimentator
390 000 1800 16 000 6900 190 270
420 000 2400 10 000 3100 100 96
Steam boiler E2 inlet Steam boiler E2 outlet Scrubber E3 inlet Scrubber E3 outlet Scrubber E4 inlet Scrubber E4 outlet
390 000 1300 55 000 39 000 18 000 13 000
Steam boilers inlet Steam boiler E1 outlet Steam boiler E2 outlet Scrubber E3 inlet Scrubber E3 outlet Accum./denitr. tank Oxidation tank Sedimentator Therm. combustor inlet Therm. combustor outlet Steam boilers inlet Steam boiler E2 outlet Steam boiler E3 outlet
OER (ouE s1)
Abatem. eff. (%)
I surv.
II surv.
I surv.
II surv.
3000 3000 28 000 28 000 _ _
_
_
_ 53 667 211 128
_
_ 99 _ 57 _ _
_ 99 _ 69 _ _
440 000 2300 52 000 52 000 33 000 28 000
7000 7000 5500 5500 16 000 16 000
_
_
_ 59 583 _ 57 778
_
_ 99 _ 30 _ 30
_ 99 _ 0 _ 15
40 000 300 360 28 000 4900 130 130 100
49 000 570 200 15 000 2000 1100 170 140
3000 3000 3000 5000 5000
_
_
_ _ _
_ 99 99 _ 83 _ _ _
_ 99 99 _ 59 _ _ _
16 000 1200 770 540 340
62 000 170 1300 64 110
35 000 35 000 3500 3500 3500
_ 93 _ 30 56
_ 99 _ 91 95
1500
2000 24 111 111 45
2528
4472 79 444 _ 124 444
250 300 _
475 167 _
6806 10 96 10 _ 11 667 _ 525 331
2778 86 126 14 _ 1653 _ 62 107
21.30
8.30
8.10
7.15 8.10 13.45e16.15 18.00 8.30 8.00e18.00 7.00e13.00
7.30 7.30
9.00 7.00e19.00
11.03e12.45e14.45e18.35e19.55 19.10e19.35e20.00 8.55 5.55e21.45 7.35e20.00e20.45e22.25 11.20 7.20e11.30e11.50e13.45e 15.45e18.15 14.00e16.00 18.00e19.00 9.00 8.30e17.30 7.00e13.30 15.30
15.30e20.30
10.00e22.30 10.00e22.30
14.00e16.00 18.00e19.00 9.00 8.30e17.30 7.00e13.30
9.00 9.00 9.30e23.00 9.30
24/07 25/07 28/07 30/07 31/07 01/08 05/08 26/08 27/08 3 4 5 6 7 8 9 10 11
8.00e22.45 9.15
9.00 9.00 17.35e18.30 9.40e10.30 21/07 22/07 1 2
17.00 9.00
7.00 17.00 19.30
17.35 7.40e8.03e8.55e 9.30e9.45e10.08e10.45 11.10e11.25e11.55 17.30 7.30e9.00
6 4 3 Time Episode
2 1
Date Receptor
Table 7 Odour episodes reported by the citizens living in the first municipality.
Fig. 4. Map of the 98th percentile of the hourly peak odour concentration values obtained using the emission data derived from the II olfactometric survey (October 2008).
5
Based on these results it is possible to make some considerations about the plants odour emissions.
9.30
9.30 7.00
7
source. As explained above (Section 2.5), for point sources the OER is calculated as the product of the odour concentration and the airflow (column 5), whereas in the case of area sources the OER is obtained by multiplying the SOER by the emitting area (i.e. the surface of the wastewater treatment tank). The last two columns of Table 6 report the odour abatement efficiency values relevant to the odour reducing facilities present on the monitored plants (scrubbers, steam boilers and thermal combustors). The odour abatement efficiency is evaluated as follows (Sironi et al., 2007):
cod;IN cod;OUT $100 cod;IN
8.00
11.15
10 9 8 Fig. 3. Map of the 98th percentile of the hourly peak odour concentration values obtained using the emission data derived from the I olfactometric survey (June 2008).
Effð%Þ ¼
7.00
11
12
13
20.50 21.45
18.00 17.15e20.30e23.50 7.00e17.40 8.00e18.30 6.30e17.45e21.00 7.00e18.00e23.30 18.30 8.30e19.00 17.15e20.40
17.30 7.30e17.20
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
14
358
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
359
Table 8 Odour episodes reported by the citizens living in the second municipality. Receptor
15
16
Episode
Date
Time
12 13 14 15
06/08 07/08 15/08 18/08
23.00 18.30 15.20 14.55
16 17 18 19 20
19/08 20/08 21/08 23/08 08/09
17.20 21.45 16.15 12.50 15.30e18.15e20.45
17
18
19
23.10 19.10 11.00e15.15 14.15e15.30e16.05e 18.30e20.30 18.00 9.25 7.10e18.20 23.10 21.00
7.00
22.00
First, the overall OER relevant to each of the four plants can be evaluated by adding the OER values of all respective (point or area) sources. This way of calculating the overall OER represents a simplification, as it doesn't account for possible interactions between different odours which may make different emissions not additive. The overall OER was evaluated to be comprised between 2.6 104 and 5.5 104 ouE s1 for plant 1, 1.2 105 and 2.1 105 ouE s1 for plant 2, 3.6 103 and 7.5 103 ouE s1 for plant 3 and 1.8 103 and 1.3 104 ouE s1 for plant 4. It can be highlighted that the overall OER of plant 2 is at least one order of magnitude above the overall OER values relevant to the other plants. The reasons for this may be on one hand the low abatement efficiency of the odour control systems and on the other hand the typology of the treated material, as plant 2 is the only one treating animal by-products belonging to category 2. This kind of material is delivered to the rendering plant in a more advanced state of decomposition (compared to category 3 by-products which arrive refrigerated and relatively fresh), thus giving rise to higher odour concentrations. The odour control systems installed at plant 2 seem to be inadequate for the abatement of such odour concentrations, even if their efficiency was optimized. Typical abatement efficiencies of wet scrubbers in rendering plants are between 50% and 60%, and even particular care and optimization of a scrubber doesn't make its efficiency higher than 80% (Sironi et al., 2007). Considering that inlet odour concentrations in a plant treating category 2 material are comprised between 50 000 ouE m3 and 60 000 ouE m3, with an optimized efficiency of 80%, outlet concentrations would be 10 000e12 000 ouE m3, thus remaining unacceptably high and not comparable with the outlet concentrations of the other plants. The OER values associated with the wastewater treatment tanks are rather low, i.e. some orders of magnitude below the OER levels of the point sources, even if the tank size is significant (e.g., the oxidation tanks of plants 1 and 3).
Fig. 5. Map of the ground peak odour concentration values on the 27th August 2008, between 7am and 8am.
concentration. These values must be multiplied by a peak-to-mean ratio, in order to obtain the peak odour concentration for each receptor and for each hour of the time domain. In general, the peakto-mean ratio can be evaluated as a function of wind velocity, stability and distance from the source (Schauberger et al., 2000). In this case, we decided to use a peak-to-mean ratio of 2.3, according to the technical document about the modelling and assessment of air pollutants published by the Department of Environment and Conservation of New South Wales (DEC 361, 2005). From the matrix of the ground peak odour concentration values the 98th percentiles were extracted. The results of the odour dispersion simulation can therefore be represented in maps reporting the isopleths relevant to the 98th percentile of the hourly peak concentrations (Figs. 3 and 4, relevant to the I and II survey, respectively). In agreement with the outcomes of the olfactometric survey, the model results show clearly how the odour impact relevant to plant 2 largely prevails with respect to the odour impact relevant to the other three rendering plants. It is important to highlight that there exist other, well established alternatives to the chosen 98th percentile methodology for odour impact evaluation by dispersion modelling. One noteworthy method is for example the concept of odour hour as defined by the German guideline about odour immissions (GIRL, 2008). The choice of the 98th percentile method is due to the fact that this is the
3.2. Simulation of odour dispersion For each receptor of the simulation grid and for each hour of the simulation period, the model calculates the hourly mean odour
Table 9 Odour episodes reported by the citizens living in the third municipality. Receptor
20
Episode
Date
Time
21
25/09
All day long
22 23 24
28/09 29/09 02/10
All day long All day long All day long
21
From 8.00 all day long
22
23
24
All day long
18.30
1.00 All day long
8.30 19.30 8.00
Table 10 Correspondences between odour perceptions and simulated immissions. Municipality no. 1
Total no. of odour episodes No. of correspondences perceptions-model % of correspondences perceptions-model
49 41 83.7%
Municipality no. 2
Total no. of odour episodes No. of correspondences perceptions-model % of correspondences perceptions-model
20 18 90.0%
Municipality no. 3
Total no. of odour episodes No. of correspondences perceptions-model % of correspondences perceptions-model
20 18 90.0%
Total
Total no. of odour episodes No. of correspondences perceptions-model % of correspondences perceptions-model
89 77 86.5%
360
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
approach actually considered for regulation purposes in Lombardy Region (Italy) (Sironi and Capelli, 2009). 3.3. Questioning and model comparison The results of the questioning survey are represented by tables reporting the odour episodes registered by the citizens living in the three municipalities near the monitored rendering plants (Tables 7e9). These odour episodes were compared with the results of the odour dispersion simulation by dispersion modelling. The odour episodes were compared with the hourly peak concentration values calculated by the model in correspondence of the hour of each odour episode. As an example, Fig. 5 illustrates the correspondence between the map resulting from the application of the dispersion model in the period between 7am and 8am on the 27th August 2008 and the odour episode no. 11 reported by receptors no. 5, 6, 7 and 10 at the same time. The agreement between odour reports by the citizens and dispersion model was evaluated by verifying if, in correspondence of each episode, the spot of the reported odour perception fell inside the isoline relevant to an odour concentration of 1 ouE m3. This value was chosen based on the fact that this is defined as the odour threshold concentration of 50% of a population, i.e. the odour concentration at which 50% of a population would start perceiving an odour. In general, a good correspondence between odour reports by the citizens and dispersion model results was observed. The percentages of correspondence are reported in Table 10. The comparison between odour perceptions and simulated odour immissions showed an accuracy of 86.5% in terms of correspondence. This percentage is rather high and therefore adds to the validation of the applied simulation procedure. 4. Conclusions This work shows how it is possible to assess odour impact in presence of multiple similar sources by illustrating a case study. The olfactometric survey allowed to identify the major contributor to the odour impact on the monitored area, which turned out to be plant 2 (1.2e2.1 105 ouE s1). The reason therefore may be the typology of the treated material (belonging to category 2 instead of category 3 treated by the other plants) and the low abatement efficiency of the adopted odour control systems (scrubbers). Furthermore, the model application allowed to quantify the odour impact relevant to the four monitored plants on the surroundings. In agreement with the outcomes of the olfactometric survey, the model results confirm that the odour impact relevant to plant 2 largely prevails with respect to the odour impact relevant to the other three rendering plants. The questioning survey conducted with the aim of involving the citizens and collecting their reports of perceived odour episodes fits with the results of the odour immissions (86.5% of correspondence between odour perceptions and simulated odour immissions) simulation by dispersion modelling and therefore adds to the evaluation of the applied simulation procedure. References Blumberg, D.G., Sasson, A., 2001. Municipal hotlines and automated weather stations as a tool for monitoring bad odour dispersion: the northern Negev case. Journal of Environmental Management 63, 103e111.
Bockreis, A., Steinberg, I., 2005. Measurement of odour with focus on sampling techniques. Waste Management 25, 859e863. ntola, P., Il Grande, M., 2008. A comparative and Capelli, L., Sironi, S., Del Rosso, R., Ce critical evaluation of odour assessment methods on a landfill site. Atmospheric Environment 42, 7050e7058. ntola, P., 2009. Design and validation of a wind Capelli, L., Sironi, S., Del Rosso, R., Ce tunnel system for odour sampling on liquid area sources. Water Science and Technology 59, 1611e1620. DEC 361, 2005. Approved Methods for the Modelling and Assessment of Air Pollutants in New South Wales. Department of Environment and Conservation, Sydney, New South Wales. EC European Parliament and Council Regulation No. 1774, 2002. Laying down health rules concerning animal by-products not intended for human consumption. OJ L 273, 1e155. EN 13725, 2003. Air Quality e Determination of Odour Concentration by Dynamic Europe en de Normalisation, Brussels. 1e70. Olfactometry. Comite Fransses, E.A.M., Staatsen, B.A.M., Lebret, E., 2002. Assessing health consequences in an environmental impact assessment. The case of Amsterdam Airport Schiphol. Environmental Impact Assessment Review 22, 633e653. €ser, C., 2004. Aerodynamic performance of a lowFrechen, F.B., Frey, M., Wett, M., Lo speed wind tunnel. Water Science and Technology 50, 57e64. n, M., Guardino, X., 2008. Gallego, E., Soriano, C., Roca, F.X., Perales, J.F., Alarco Identification of the origin of odour episodes through social participation, chemical control and numerical modelling. Atmospheric Environment 42, 8150e8160. GIRL, 2008. Directive on Odor in Ambient Air. (Geruchsimmissions-Richtlinie, GIRL). Ministry of the Environment and Conservation, Agriculture and Consumer € sseldorf. Protection of the German State of North Rhine-Westphalia, Du Gostelow, P., Parsons, S.A., Stuetz, R.M., 2001. Odour measurements for sewage treatment works. Water Research 35, 579e597. Henshaw, P., Nicell, J., Sikdar, A., 2006. Parameters for the assessment of odour impacts on communities. Atmospheric Environment 40, 1016e1029. Hudson, N., Ayoko, G.A., 2008. Odour sampling 1: physical chemistry considerations. Bioresource Technology 99, 3982e3992. Jiang, J.K., Kaye, R., 2001. Sampling techniques for odour measurement. In: Stuetz, Richard, Frechen, Franz-Bernd (Eds.), Odours in Wastewater Treatment: Measurement, Modelling and Control. IWA Publishing, London, UK, pp. 98e117. Leonardos, G., 1996. Review of odour control regulations in the USA. In: Odors, Indoor and Environmental Air. Proceedings of a Specialty Conference. Air & Waste Management Association, Bloomington, MN, pp. 73e84. Luginaah, I.N., Taylor, S.M., Elliott, S.J., Eyles, J.D., 2000. A longitudinal study of the health impacts of a petroleum refinery. Social Science & Medicine 50, 1155e1166. National Research Council Committee on Odours, 1979. Odors from Stationary and Mobile Sources. Board on Toxicology and Environmental Hazards, Assembly of Life Sciences. National Research Council, National Academy of Sciences, Washington, DC. Nicell, J.A., 2009. Assessment and regulation of odour impacts. Atmospheric Environment 43, 196e206. Progress, 2006. Environmental Impact Studies by Atmospheric Dispersion Simulation Technical document A0025r000, url: http://www.olfattometria.com/ download/A0025_Studi-impatto-mediante-simulazione_ENG.pdf. Schauberger, G., Piringer, M., Petz, E., 2000. Diurnal and annual variation of the sensation distance of odour emitted by livestock buildings calculated by the Austrian odour dispersion model (AODM). Atmospheric Environment 34, 4839e4851. Scire, J.S., Robe, F.R., Fernau, M.E., Yamartino, R.J., 2000. A User's Guide for the CALMET Meteorological Model, Version 5. Earth Tech Inc., Concord, MA. Shusterman, 1992. Critical review: the health significance of environmental odor pollution. Archives of Environmental Health 47, 76e87. ntola, P., Del Rosso, R., Il Grande, M., 2006. Odour emission Sironi, S., Capelli, L., Ce factors for the prediction of odour emissions from plants for the mechanical and biological treatment of MSW. Atmospheric Environment 40, 7632e7643. ntola, P., Del Rosso, R., Il Grande, M., 2007. Odour emission Sironi, S., Capelli, L., Ce factors for assessment and prediction of Italian rendering plants odour impact. Chemical Engineering Journal 131, 225e231. Sironi, S., Capelli, L., 2009. Personal communication. Regione Lombardia: technical working group on odour emissions from stationary sources. Sohn, J.H., Smith, R., Yoong, E., Leis, J., Galvin, G., 2003. Quantification of odours from piggery effluent ponds using an electronic nose and an artificial neural network. Biosystems Engineering 86, 399e410. Sohn, J.H., Pioggia, G., Craig, I.P., Stuetz, R.M., Atzeni, M.G., 2009. Identifying major contributing sources to odour annoyance using a non-specific gas sensor array. Biosystems Engineering 102, 305e312. Thomson, D.J., 2000. The Met Input Module ADMS 3 Documentation Paper Number P/05/01/00. The Met Office, Exeter, Devon, UK. Wang, L., Parker, D.B., Parnell, C.B., Lacey, R.E., Shaw, B.W., 2006. Comparison of CALPUFF and ISCST3 models for predicting downwind odor and source emission rates. Atmospheric Environment 40, 4663e4669.
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Odour impact assessment by means of dynamic olfactometry, dispersion modelling and social participation ntola, Renato Del Rosso, Sauro Pierucci Selena Sironi, Laura Capelli*, Paolo Ce Politecnico 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
a b s t r a c t
Article history: Received 29 May 2009 Received in revised form 15 October 2009 Accepted 21 October 2009
This work discusses how it is possible to assess odour impact in presence of multiple similar sources by illustrating a case study. The study was conducted on an area of northern Italy comprising three small municipalities where four rendering plants are located near to each other. Based on the emission data resulting from olfactometric surveys conducted in different periods of the year the overall odour emission rate emitted by each plant were evaluated, showing that the major contributor to the odour impact on the territory was plant 2. These data were linked with meteorological and orographical data in order to evaluate odour dispersion with a model (Calpuff). The results of the odour dispersion modelling confirmed the outcomes of the olfactometric survey and they were further validated through a “questioning” survey, conducted with the aim of involving the population by means of questionnaires for reporting the perceived odour episodes, which showed a good correspondence (86.5%) between odour perceptions and simulated odour immissions. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Odour episodes Source identification Odour sampling Odour measurement Rendering Calpuff
1. Introduction When more odour emitting industrial activities, such as sewage treatment plants, waste treatment or disposal facilities, paint facilities, petroleum refineries, rendering plants, pulp mills, plastic and resin manufacturers and chemical industries, are present on a restricted area, an odour nuisance problem could be generated (Leonardos, 1996; Henshaw et al., 2006). Even though it is universally recognized that the exposure to odours generally represent a nuisance more than a risk for human health (Fransses et al., 2002; Luginaah et al., 2000), odour exposure may nonetheless cause effects on human activities (Gostelow et al., 2001; Shusterman, 1992). Effects of this kind of pollution may be: i) impairment of the quality of the environment; ii) damages to properties, plants or animal; iii) harm or discomfort to any person; iv) impairment of the safety of any person; v) rendering any property, plant or animal unsuitable for human use; vi) loss of enjoyment of normal use of property; vii) interference with business activities (Nicell, 2009). Prolonged exposure to odours can cause undesired reactions ranging from emotional stresses such as states of anxiety, unease,
* Corresponding author. Tel.: þ39 02 23993206; fax: þ39 02 23993291. E-mail addresses: [email protected] (S. Sironi), [email protected] ntola), [email protected] (R. Del (L. Capelli), [email protected] (P. Ce Rosso), [email protected] (S. Pierucci). 1352-2310/$ e see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.10.029
headache or depression to physical symptoms such as eye irritation, respiratory problems, nausea or vomiting (National Research Council Committee on Odours, 1979). Several experiences tend to include odours among the pollutants that have to be controlled and subject to specific regulations. As a matter of fact, odours are considered to be one of the major causes of public complaints to the competent authorities (Blumberg and Sasson, 2001). Population living near odour emitting activities rely on local authorities (e.g., municipal by-law officers, police, and fire or health units), on regional agencies or directly on the personnel involved in the odour emitting operation for the problem elimination or reduction. Following to internal or external solicitations for odour impact reduction by the competent authorities, often there is a bouncing of responsibility of the odour nuisance between the involved subjects, i.e. between the different odour emitting activities on the territory. The question is even more complicated if more similar plants are present. In this case it is not useful to search for chemical tracers referable to a specific source because of the homogeneity of the different gaseous emissions (Sohn et al., 2009). The present work discusses how it is possible to assess odour impact in presence of multiple similar sources by illustrating a case study. Based on the emission data resulting from olfactometric surveys conducted in different periods of the year the overall odour emission rate emitted by each plant can be calculated as the sum of the odour emission rate values relevant to all odour sources. These
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
data can be linked with meteorological and orographical data in order to evaluate odour dispersion with a suitable mathematical model. Furthermore, it is possible to conduct a “questioning” survey with the aim of involving the population and making them actively take part to the odour impact assessment study by means of questionnaires for reporting the odour episodes on the territory (Gallego et al., 2008). This information can be used as a tool for the evaluation of the model applied for the odour emission dispersion simulation. 2. Materials and methods
355
Table 1 Plants capacity and functioning times. Plant
Capacity (t y1)
Functioning (h d1)
Functioning (d y1)
1 2 3 4
30 21 30 31
24 12 24 14
300 260 300 260
000 000 000 000
overall odour impact is small with respect to the other emission sources (e.g., conveyed emissions through stacks and wastewater treatment tanks).
2.1. Site description
2.3. Sample collection
The study was conducted on an area of northern Italy comprising three small municipalities (each having less than 10 000 inhabitants), where four different rendering plants are located near to each other (Fig. 1). The rendering plants at issue treat animal by-products belonging to category 2 (plant 2) and category 3 (plants 1, 3 and 4) according to the European reference regulation (EC 1774, 2002) and they are equipped with different systems for the treatment of their gaseous effluents. These abatement systems are based on the principles of wet scrubbing or combustion. Table 1 reports the data relevant to the capacity of the four plants and their functioning periods, in h d1 and in d y1, respectively.
Two odour sampling and measurement trials were conducted in order to characterize the emissions from all the odour sources listed in Tables 2 and 3. The trials took place in two different periods of the year: a “hot” one (June 2008) and a cold one (October 2008), respectively, in order to improve the evaluation of the odour impact of the four rendering plants by taking account of different meteorological conditions. Sampling on point sources (i.e. conveyed emissions, e.g., through a stack) is carried out by sucking part of the odorous airflow into an 8-L sampling bag in NalophanÔ equipped with a TeflonÔ inlet tube by means of a depression pump (Capelli et al., 2008). Sampling on passive area sources (i.e. liquid surfaces without an outward flow, e.g., wastewater treatment tanks) entails more difficulties. It is performed using a wind tunnel system, which consists of a hood that simulates the wind action on the liquid surface to be monitored (Jiang and Kaye, 2001; Frechen et al., 2004). In this case, we used a specific wind tunnel made in polyethyleneterephtalate (PET), which was positioned over the emitting surface. A neutral air stream is introduced at known airflow rate from an air bottle into the hood. Air samples are then collected in the outlet duct using the same methodology as for point sources. The wind tunnel used during the experimentation has a circular section inlet and outlet duct, of 0.08 m diameter. The central body of the hood used has a rectangular section chamber of 0.25 m width, 0.08 m height and 0.5 m depth. Inside the inlet duct there is a perforated stainless steel grid and inside the divergent that connects this duct to the central body of the hood there are three flow deflection vanes (Capelli et al., 2009).
2.2. Emission sources Plants 2 and 4 are characterized by the presence of point sources only, whereas plants 1 and 3 have both point and area sources (Bockreis and Steinberg, 2005; Hudson and Ayoko, 2008). Tables 2 and 3 report the physical characteristics relevant to all the odour sources 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 different among the plants. These emissions weren't considered in this study because, based on past experience on this kind of plants, there is an evidence that the contribution of the fugitive emissions to the
2.4. Olfactometric analyses
Fig. 1. Studied area and localization of the four rendering plants.
Dynamic olfactometry is a sensorial technique that allows to determine the odour concentration (cod) of an odorous air sample relating to the sensation caused by the sample directly on a panel of opportunely selected people. cod is expressed in European odour units per cubic metre (ouE m3), and it represents the number of dilutions with neutral air that are necessary to bring the odorous sample to its odour detection threshold concentration. The analysis is carried out by presenting the sample to the panel at increasing concentrations by means of a particular dilution device called an olfactometer, until the panel members start perceiving an odour that is different from the neutral reference air. The cod is then calculated as the geometric mean of the odour threshold values of each panellist. As defined by the EN 13725 (2003), the individual threshold estimate is defined by the two presentations in one dilution series, sorted on growing odour concentration, where a certain change in response from “false” to a consistently “true” response occurs. The individual threshold estimate is calculated as
356
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
Table 2 Characteristics of the point sources. Plant
Emission source
Measured airflow (m3 h1)
Temperature (K)
Stack height (m)
Section (m2)
Diameter (m)
Air speed (m s1)
1
Scrubber E1 outlet Steam boiler E2 outlet
28 000 3000
298 483
25 5
0.11 0.63
0.95 0.35
11.0 8.5
2
Steam boiler E2 outlet Scrubber E3 outlet Scrubber E4 outlet
7000 5500 16 000
613 300 298
7 10 10
0.2 0.16 0.16
0.45 0.49 0.65
12.0 8.2 13.5
3
Steam boiler E1 outlet Steam boiler E2 outlet Scrubber E3 outlet
3000 3000 5000
476 476 296
6 6 10
0.14 0.14 0.196
0.48 0.48 0.50
4.6 4.6 7.0
4
Therm. comb. E1 outlet Steam boiler E2 outlet Steam boiler E3 outlet
35 000 3500 3500
463 448 448
7 7 7
0.94 0.4 0.4
1.10 0.40 0.40
10.2 7.7 7.7
the geometric mean of the dilution factors of the two defined presentations. An olfactometer model TO8 produced by ECOMA GmbH, based on the “yes/no” method, was used as a dilution device. This instrument with aluminium casing has 4 panellist places in separate open boxes. Each box is equipped with a sniffing port in stainless steel and glass, and a push-button for “yes” (odour threshold). The measuring range of the TO8 olfactometer starts from a maximum dilution factor of 1:65 536, with a dilution step factor 2. All the measurements were conducted within 30 h after sampling, relying on a panel composed of 4 panellists.
2.5. Dispersion model The model used for the simulation of the emission dispersion is the CALPUFF model (Wang et al., 2006). This model is realized by Earth Tech Inc. for the California Air Resources Board (CARB) and the 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 multiple industrial sources. It allows to calculate dry and wet deposition, building downwash, dispersion from point, area and volume sources, the gradual plume rising as a function of the distance from the source, the influence of the orography on dispersion, and the dispersion in case of weak or absent wind. The dispersion coefficients are obtained from the turbulence parameters (u*, w*, LMO), instead of being calculated from the PasquilleGiffordeTurner stability classes. This means that the turbulence is described by continuous functions, not by discrete ones. During the periods in which the boundary layer has a convective structure, the concentration distribution inside each puff is Gaussian on the horizontal planes, but asymmetric on the vertical planes, i.e. it takes account of the probability distribution function of the vertical speeds. In other words, the model simulates the effects on dispersion due to ascending and descending air movements that are typical of the day'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 spatial grid on the simulation domain are 4000 m 4000 m, with a receptor every 100 m. The domain was chosen in order to include the four monitored plants and the three municipalities where the odour is a nuisance. Table 4 reports the meteorological parameters used for the dispersion modelling. The output parameters of the pre-processor used for the calculation of the micrometeorological variables are listed in Table 5. As emission data, the results of the olfactometric analyses conducted on the four rendering plants can be used. Furthermore, the data needed as input for the model are not the odour concentration values, but the Odour Emission Rate (OER) values, expressed in ouE s1, associated with each considered odour source. In the case of point sources (Table 2), the OER can be calculated simply by multiplying the odour concentration value (in ouE m3) by the normalized airflow (in m3 s1). The evaluation of the OER relevant to area sources, e.g., wastewater treatment tanks (Table 3), requires the calculation of the Specific Odour Emission Rate (SOER), which is expressed in ouE s1 m2. Once the odour concentration value of a sample collected at the outlet of the wind tunnel is determined, it is possible to obtain the SOER by multiplying the odour concentration (ouE m3) with the flow rate of the inlet air (m3 s1) and dividing by the base area of the central body of the hood (m2). The OER is finally obtained as the product of the SOER value and the emitting surface of the considered source (m2) (Sironi et al., 2006). The OER is a function of the air velocity, i.e. the wind speed, on the liquid surface. Once the OER relevant to the sampling conditions is evaluated, the OER for any other air velocity (i.e. wind speed) can be calculated as follows (Sohn et al., 2003):
OERv2 ¼ OERv1
1 v2 2 v1
According to this equation, for the odour dispersion modelling study, the odour emissions from the area sources (OERs) were
Table 4 Meteorological parameters used for the dispersion modelling.
Table 3 Characteristics of the area sources. Plant Source
Area (m2) Equivalent diameter (m)
1
Oxidation tank Sedimentator
200 85
3
Accumulation/denitrification tank 14.1 Oxidation tank 133 Sedimentator 18.5
16.0 10.4 4.2 13.0 4.9
Meteorological parameter
Type of data
Unit of measurement Period
Temperature Wind speed Wind direction Net solar radiation Relative humidity Rainfall
C Hourly average Hourly average m s1 Predominant (1 h) sexagesimal degree Hourly average W m2 Hourly average % Total (1 h) mm
From 01/01/2007 to 31/12/2007
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
357
Table 5 Parameters calculated by the micrometeorological model. Micrometeorological parameter
Symbol
Type of data
Calculation method
Surface heat flux Friction velocity Monin-Obukhov length Convective velocity scale
Qh u* LMO w*
Hourly Hourly Hourly Hourly
Thomson, 2000
Mixing height
MH
Hourly average
average average average average
Scire et al., 2000
calculated for each hour of the simulation domain based on the current wind speed. Two odour impact studies by dispersion modelling were conducted twice, based on the data of the first (June 2008) and of the second (October 2008) odour sampling and measurement trial, respectively. 2.6. Odour episodes reporting and model validation
Fig. 2. Localization of the citizens who reported the odour episodes.
Between June and October 2008, a “questioning” survey was conducted, with the aim of collecting reports of perceived odour episodes. Several public assemblies were organized in order to inform and sensitize the population. The periods (date and time) during which odours from the monitored plants were perceived were reported by the inhabitants of the three municipalities involved in the study on simple questionnaire forms distributed during 5 months of observation period (from June to October 2008). The questionnaires were non-anonymous, i.e. name and full address of the citizen registering the odour episodes had to be indicated. Fig. 2 indicates the position of the citizens who took part to this survey. The odour episodes reports were compared with the results of the odour dispersion simulation by application of the mathematical dispersion model (Calpuff). In correspondence of the periods during which the presence of odours was reported by the citizens,
the dispersion model was applied using the emission data and the meteorological data relevant to the period in order to simulate odour dispersion on the territory at the moment of the perception. This way it was possible to verify if the odour plume simulated by the model effectively reached the receptor in correspondence of the odour episode report. 3. Results and discussion 3.1. Olfactometric survey Table 6 shows the results of the olfactometric survey. Columns 3 and 4 report the odour concentration values measured in the I and II survey, conducted in June 2008 and October 2008, respectively. Columns 6 and 7 report the OER values relevant to each odour
Table 6 Results of the olfactometric surveys. Plant
1
2
3
4
Sampling point
cod (ouE m3)
Q (m3 h1)
I surv.
II surv.
Steam boiler E1 inlet Steam boiler E1 outlet Scrubber E2 inlet Scrubber E2 outlet Oxidation tank Sedimentator
390 000 1800 16 000 6900 190 270
420 000 2400 10 000 3100 100 96
Steam boiler E2 inlet Steam boiler E2 outlet Scrubber E3 inlet Scrubber E3 outlet Scrubber E4 inlet Scrubber E4 outlet
390 000 1300 55 000 39 000 18 000 13 000
Steam boilers inlet Steam boiler E1 outlet Steam boiler E2 outlet Scrubber E3 inlet Scrubber E3 outlet Accum./denitr. tank Oxidation tank Sedimentator Therm. combustor inlet Therm. combustor outlet Steam boilers inlet Steam boiler E2 outlet Steam boiler E3 outlet
OER (ouE s1)
Abatem. eff. (%)
I surv.
II surv.
I surv.
II surv.
3000 3000 28 000 28 000 _ _
_
_
_ 53 667 211 128
_
_ 99 _ 57 _ _
_ 99 _ 69 _ _
440 000 2300 52 000 52 000 33 000 28 000
7000 7000 5500 5500 16 000 16 000
_
_
_ 59 583 _ 57 778
_
_ 99 _ 30 _ 30
_ 99 _ 0 _ 15
40 000 300 360 28 000 4900 130 130 100
49 000 570 200 15 000 2000 1100 170 140
3000 3000 3000 5000 5000
_
_
_ _ _
_ 99 99 _ 83 _ _ _
_ 99 99 _ 59 _ _ _
16 000 1200 770 540 340
62 000 170 1300 64 110
35 000 35 000 3500 3500 3500
_ 93 _ 30 56
_ 99 _ 91 95
1500
2000 24 111 111 45
2528
4472 79 444 _ 124 444
250 300 _
475 167 _
6806 10 96 10 _ 11 667 _ 525 331
2778 86 126 14 _ 1653 _ 62 107
21.30
8.30
8.10
7.15 8.10 13.45e16.15 18.00 8.30 8.00e18.00 7.00e13.00
7.30 7.30
9.00 7.00e19.00
11.03e12.45e14.45e18.35e19.55 19.10e19.35e20.00 8.55 5.55e21.45 7.35e20.00e20.45e22.25 11.20 7.20e11.30e11.50e13.45e 15.45e18.15 14.00e16.00 18.00e19.00 9.00 8.30e17.30 7.00e13.30 15.30
15.30e20.30
10.00e22.30 10.00e22.30
14.00e16.00 18.00e19.00 9.00 8.30e17.30 7.00e13.30
9.00 9.00 9.30e23.00 9.30
24/07 25/07 28/07 30/07 31/07 01/08 05/08 26/08 27/08 3 4 5 6 7 8 9 10 11
8.00e22.45 9.15
9.00 9.00 17.35e18.30 9.40e10.30 21/07 22/07 1 2
17.00 9.00
7.00 17.00 19.30
17.35 7.40e8.03e8.55e 9.30e9.45e10.08e10.45 11.10e11.25e11.55 17.30 7.30e9.00
6 4 3 Time Episode
2 1
Date Receptor
Table 7 Odour episodes reported by the citizens living in the first municipality.
Fig. 4. Map of the 98th percentile of the hourly peak odour concentration values obtained using the emission data derived from the II olfactometric survey (October 2008).
5
Based on these results it is possible to make some considerations about the plants odour emissions.
9.30
9.30 7.00
7
source. As explained above (Section 2.5), for point sources the OER is calculated as the product of the odour concentration and the airflow (column 5), whereas in the case of area sources the OER is obtained by multiplying the SOER by the emitting area (i.e. the surface of the wastewater treatment tank). The last two columns of Table 6 report the odour abatement efficiency values relevant to the odour reducing facilities present on the monitored plants (scrubbers, steam boilers and thermal combustors). The odour abatement efficiency is evaluated as follows (Sironi et al., 2007):
cod;IN cod;OUT $100 cod;IN
8.00
11.15
10 9 8 Fig. 3. Map of the 98th percentile of the hourly peak odour concentration values obtained using the emission data derived from the I olfactometric survey (June 2008).
Effð%Þ ¼
7.00
11
12
13
20.50 21.45
18.00 17.15e20.30e23.50 7.00e17.40 8.00e18.30 6.30e17.45e21.00 7.00e18.00e23.30 18.30 8.30e19.00 17.15e20.40
17.30 7.30e17.20
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
14
358
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
359
Table 8 Odour episodes reported by the citizens living in the second municipality. Receptor
15
16
Episode
Date
Time
12 13 14 15
06/08 07/08 15/08 18/08
23.00 18.30 15.20 14.55
16 17 18 19 20
19/08 20/08 21/08 23/08 08/09
17.20 21.45 16.15 12.50 15.30e18.15e20.45
17
18
19
23.10 19.10 11.00e15.15 14.15e15.30e16.05e 18.30e20.30 18.00 9.25 7.10e18.20 23.10 21.00
7.00
22.00
First, the overall OER relevant to each of the four plants can be evaluated by adding the OER values of all respective (point or area) sources. This way of calculating the overall OER represents a simplification, as it doesn't account for possible interactions between different odours which may make different emissions not additive. The overall OER was evaluated to be comprised between 2.6 104 and 5.5 104 ouE s1 for plant 1, 1.2 105 and 2.1 105 ouE s1 for plant 2, 3.6 103 and 7.5 103 ouE s1 for plant 3 and 1.8 103 and 1.3 104 ouE s1 for plant 4. It can be highlighted that the overall OER of plant 2 is at least one order of magnitude above the overall OER values relevant to the other plants. The reasons for this may be on one hand the low abatement efficiency of the odour control systems and on the other hand the typology of the treated material, as plant 2 is the only one treating animal by-products belonging to category 2. This kind of material is delivered to the rendering plant in a more advanced state of decomposition (compared to category 3 by-products which arrive refrigerated and relatively fresh), thus giving rise to higher odour concentrations. The odour control systems installed at plant 2 seem to be inadequate for the abatement of such odour concentrations, even if their efficiency was optimized. Typical abatement efficiencies of wet scrubbers in rendering plants are between 50% and 60%, and even particular care and optimization of a scrubber doesn't make its efficiency higher than 80% (Sironi et al., 2007). Considering that inlet odour concentrations in a plant treating category 2 material are comprised between 50 000 ouE m3 and 60 000 ouE m3, with an optimized efficiency of 80%, outlet concentrations would be 10 000e12 000 ouE m3, thus remaining unacceptably high and not comparable with the outlet concentrations of the other plants. The OER values associated with the wastewater treatment tanks are rather low, i.e. some orders of magnitude below the OER levels of the point sources, even if the tank size is significant (e.g., the oxidation tanks of plants 1 and 3).
Fig. 5. Map of the ground peak odour concentration values on the 27th August 2008, between 7am and 8am.
concentration. These values must be multiplied by a peak-to-mean ratio, in order to obtain the peak odour concentration for each receptor and for each hour of the time domain. In general, the peakto-mean ratio can be evaluated as a function of wind velocity, stability and distance from the source (Schauberger et al., 2000). In this case, we decided to use a peak-to-mean ratio of 2.3, according to the technical document about the modelling and assessment of air pollutants published by the Department of Environment and Conservation of New South Wales (DEC 361, 2005). From the matrix of the ground peak odour concentration values the 98th percentiles were extracted. The results of the odour dispersion simulation can therefore be represented in maps reporting the isopleths relevant to the 98th percentile of the hourly peak concentrations (Figs. 3 and 4, relevant to the I and II survey, respectively). In agreement with the outcomes of the olfactometric survey, the model results show clearly how the odour impact relevant to plant 2 largely prevails with respect to the odour impact relevant to the other three rendering plants. It is important to highlight that there exist other, well established alternatives to the chosen 98th percentile methodology for odour impact evaluation by dispersion modelling. One noteworthy method is for example the concept of odour hour as defined by the German guideline about odour immissions (GIRL, 2008). The choice of the 98th percentile method is due to the fact that this is the
3.2. Simulation of odour dispersion For each receptor of the simulation grid and for each hour of the simulation period, the model calculates the hourly mean odour
Table 9 Odour episodes reported by the citizens living in the third municipality. Receptor
20
Episode
Date
Time
21
25/09
All day long
22 23 24
28/09 29/09 02/10
All day long All day long All day long
21
From 8.00 all day long
22
23
24
All day long
18.30
1.00 All day long
8.30 19.30 8.00
Table 10 Correspondences between odour perceptions and simulated immissions. Municipality no. 1
Total no. of odour episodes No. of correspondences perceptions-model % of correspondences perceptions-model
49 41 83.7%
Municipality no. 2
Total no. of odour episodes No. of correspondences perceptions-model % of correspondences perceptions-model
20 18 90.0%
Municipality no. 3
Total no. of odour episodes No. of correspondences perceptions-model % of correspondences perceptions-model
20 18 90.0%
Total
Total no. of odour episodes No. of correspondences perceptions-model % of correspondences perceptions-model
89 77 86.5%
360
S. Sironi et al. / Atmospheric Environment 44 (2010) 354e360
approach actually considered for regulation purposes in Lombardy Region (Italy) (Sironi and Capelli, 2009). 3.3. Questioning and model comparison The results of the questioning survey are represented by tables reporting the odour episodes registered by the citizens living in the three municipalities near the monitored rendering plants (Tables 7e9). These odour episodes were compared with the results of the odour dispersion simulation by dispersion modelling. The odour episodes were compared with the hourly peak concentration values calculated by the model in correspondence of the hour of each odour episode. As an example, Fig. 5 illustrates the correspondence between the map resulting from the application of the dispersion model in the period between 7am and 8am on the 27th August 2008 and the odour episode no. 11 reported by receptors no. 5, 6, 7 and 10 at the same time. The agreement between odour reports by the citizens and dispersion model was evaluated by verifying if, in correspondence of each episode, the spot of the reported odour perception fell inside the isoline relevant to an odour concentration of 1 ouE m3. This value was chosen based on the fact that this is defined as the odour threshold concentration of 50% of a population, i.e. the odour concentration at which 50% of a population would start perceiving an odour. In general, a good correspondence between odour reports by the citizens and dispersion model results was observed. The percentages of correspondence are reported in Table 10. The comparison between odour perceptions and simulated odour immissions showed an accuracy of 86.5% in terms of correspondence. This percentage is rather high and therefore adds to the validation of the applied simulation procedure. 4. Conclusions This work shows how it is possible to assess odour impact in presence of multiple similar sources by illustrating a case study. The olfactometric survey allowed to identify the major contributor to the odour impact on the monitored area, which turned out to be plant 2 (1.2e2.1 105 ouE s1). The reason therefore may be the typology of the treated material (belonging to category 2 instead of category 3 treated by the other plants) and the low abatement efficiency of the adopted odour control systems (scrubbers). Furthermore, the model application allowed to quantify the odour impact relevant to the four monitored plants on the surroundings. In agreement with the outcomes of the olfactometric survey, the model results confirm that the odour impact relevant to plant 2 largely prevails with respect to the odour impact relevant to the other three rendering plants. The questioning survey conducted with the aim of involving the citizens and collecting their reports of perceived odour episodes fits with the results of the odour immissions (86.5% of correspondence between odour perceptions and simulated odour immissions) simulation by dispersion modelling and therefore adds to the evaluation of the applied simulation procedure. References Blumberg, D.G., Sasson, A., 2001. Municipal hotlines and automated weather stations as a tool for monitoring bad odour dispersion: the northern Negev case. Journal of Environmental Management 63, 103e111.
Bockreis, A., Steinberg, I., 2005. Measurement of odour with focus on sampling techniques. Waste Management 25, 859e863. ntola, P., Il Grande, M., 2008. A comparative and Capelli, L., Sironi, S., Del Rosso, R., Ce critical evaluation of odour assessment methods on a landfill site. Atmospheric Environment 42, 7050e7058. ntola, P., 2009. Design and validation of a wind Capelli, L., Sironi, S., Del Rosso, R., Ce tunnel system for odour sampling on liquid area sources. Water Science and Technology 59, 1611e1620. DEC 361, 2005. Approved Methods for the Modelling and Assessment of Air Pollutants in New South Wales. Department of Environment and Conservation, Sydney, New South Wales. EC European Parliament and Council Regulation No. 1774, 2002. Laying down health rules concerning animal by-products not intended for human consumption. OJ L 273, 1e155. EN 13725, 2003. Air Quality e Determination of Odour Concentration by Dynamic Europe en de Normalisation, Brussels. 1e70. Olfactometry. Comite Fransses, E.A.M., Staatsen, B.A.M., Lebret, E., 2002. Assessing health consequences in an environmental impact assessment. The case of Amsterdam Airport Schiphol. Environmental Impact Assessment Review 22, 633e653. €ser, C., 2004. Aerodynamic performance of a lowFrechen, F.B., Frey, M., Wett, M., Lo speed wind tunnel. Water Science and Technology 50, 57e64. n, M., Guardino, X., 2008. Gallego, E., Soriano, C., Roca, F.X., Perales, J.F., Alarco Identification of the origin of odour episodes through social participation, chemical control and numerical modelling. Atmospheric Environment 42, 8150e8160. GIRL, 2008. Directive on Odor in Ambient Air. (Geruchsimmissions-Richtlinie, GIRL). Ministry of the Environment and Conservation, Agriculture and Consumer € sseldorf. Protection of the German State of North Rhine-Westphalia, Du Gostelow, P., Parsons, S.A., Stuetz, R.M., 2001. Odour measurements for sewage treatment works. Water Research 35, 579e597. Henshaw, P., Nicell, J., Sikdar, A., 2006. Parameters for the assessment of odour impacts on communities. Atmospheric Environment 40, 1016e1029. Hudson, N., Ayoko, G.A., 2008. Odour sampling 1: physical chemistry considerations. Bioresource Technology 99, 3982e3992. Jiang, J.K., Kaye, R., 2001. Sampling techniques for odour measurement. In: Stuetz, Richard, Frechen, Franz-Bernd (Eds.), Odours in Wastewater Treatment: Measurement, Modelling and Control. IWA Publishing, London, UK, pp. 98e117. Leonardos, G., 1996. Review of odour control regulations in the USA. In: Odors, Indoor and Environmental Air. Proceedings of a Specialty Conference. Air & Waste Management Association, Bloomington, MN, pp. 73e84. Luginaah, I.N., Taylor, S.M., Elliott, S.J., Eyles, J.D., 2000. A longitudinal study of the health impacts of a petroleum refinery. Social Science & Medicine 50, 1155e1166. National Research Council Committee on Odours, 1979. Odors from Stationary and Mobile Sources. Board on Toxicology and Environmental Hazards, Assembly of Life Sciences. National Research Council, National Academy of Sciences, Washington, DC. Nicell, J.A., 2009. Assessment and regulation of odour impacts. Atmospheric Environment 43, 196e206. Progress, 2006. Environmental Impact Studies by Atmospheric Dispersion Simulation Technical document A0025r000, url: http://www.olfattometria.com/ download/A0025_Studi-impatto-mediante-simulazione_ENG.pdf. Schauberger, G., Piringer, M., Petz, E., 2000. Diurnal and annual variation of the sensation distance of odour emitted by livestock buildings calculated by the Austrian odour dispersion model (AODM). Atmospheric Environment 34, 4839e4851. Scire, J.S., Robe, F.R., Fernau, M.E., Yamartino, R.J., 2000. A User's Guide for the CALMET Meteorological Model, Version 5. Earth Tech Inc., Concord, MA. Shusterman, 1992. Critical review: the health significance of environmental odor pollution. Archives of Environmental Health 47, 76e87. ntola, P., Del Rosso, R., Il Grande, M., 2006. Odour emission Sironi, S., Capelli, L., Ce factors for the prediction of odour emissions from plants for the mechanical and biological treatment of MSW. Atmospheric Environment 40, 7632e7643. ntola, P., Del Rosso, R., Il Grande, M., 2007. Odour emission Sironi, S., Capelli, L., Ce factors for assessment and prediction of Italian rendering plants odour impact. Chemical Engineering Journal 131, 225e231. Sironi, S., Capelli, L., 2009. Personal communication. Regione Lombardia: technical working group on odour emissions from stationary sources. Sohn, J.H., Smith, R., Yoong, E., Leis, J., Galvin, G., 2003. Quantification of odours from piggery effluent ponds using an electronic nose and an artificial neural network. Biosystems Engineering 86, 399e410. Sohn, J.H., Pioggia, G., Craig, I.P., Stuetz, R.M., Atzeni, M.G., 2009. Identifying major contributing sources to odour annoyance using a non-specific gas sensor array. Biosystems Engineering 102, 305e312. Thomson, D.J., 2000. The Met Input Module ADMS 3 Documentation Paper Number P/05/01/00. The Met Office, Exeter, Devon, UK. Wang, L., Parker, D.B., Parnell, C.B., Lacey, R.E., Shaw, B.W., 2006. Comparison of CALPUFF and ISCST3 models for predicting downwind odor and source emission rates. Atmospheric Environment 40, 4663e4669.