Some guidelines are necessary to help environmental managers of industrial plants and environmental technicians of Environmental Regulatory offices in order to avoid mistakes when choosing dispersion modelling software.

Diaz C.¹; Cartelle D.² and Barclay J.³

⁽¹⁾ SVPA, Gerena, Spain ⁽²⁾ Troposfera, Ferrol, Spain ⁽³⁾ independent consultant, Auckland, New Zealand

   Corresponding author: carlosdiaz@olores.org

   Competing interests: The author has declared that no competing interests exist.

   Academic editor: Carlos N Díaz.

   Content quality: This paper has been peer reviewed by at least two reviewers. See scientific committee here

   Citation:Diaz C., Cartelle D., Barclay J., 2014, Revision of Regulatory Dispersion Models, an Important Key in Environmental Odour Management, Ist International Seminar of Odours in the Environment, Santiago, Chile, www.olores.org

   Copyright: 2014 olores.org. Open Content Creative Commons license., It is allowed to download, reuse, reprint, modify, distribute, and/or copy articles in olores.org website, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

   Keywords: subhourly data, topography, average time, peak to mean relation, calm and light winds

   Acronyms: APGNO: Activity that may Potentially Generate Nuisance by Odours, IEA: Integrated Environmental Authorisation, AAE: Authorisation for Atmospheric Emissions, EIA: Environmental Impact Assessment, FB: Fractional Bias; NMSE Normalised Mean Square Error; GM Geometric Mean Bias, WWTP: Waste water Treatment Plant.

Abstract

Using dispersion modelling to evaluate the odour impact of an activity is quite common in the case of environmental odour emissions. However, some guidelines are necessary to help environmental managers of industrial plants and environmental technicians of Environmental Regulatory offices in order to avoid mistakes when choosing dispersion modelling software. In this article, some key aspects related with the election of a dispersion model for odours are detailed. Also, some important issues are commented where some work is necessary to adapt the dispersion models used nowadays to the reality of the odour emissions.

1. Introduction

Modelling of the Dispersion of atmospheric pollutants is today a routine method in environmental air quality management. In the particular case of environmental odour emissions, dispersion models have become indispensable given the difficulty of obtaining a reliable value of odour concentration in immision.

The use of dispersion models helps in the prediction of the impacts on air quality from industrial emission at their sources and it is a valuable argument to propose effective control strategies. Therefore, dispersion models are routinely used for creating the reports needed to apply for Integrated Environmental Authorisations (IEAs) [15] [16] and other environmental authorisation procedures, such as Authorisation for Atmospheric Emissions (AAE) [17].

The results of the dispersion models are currently used to establish limit values for emissions into the atmosphere by an Activity that can Potentially Generate Nuisance Odours (APGNOs), or to remove the guilt of industries in incidents where the impact of odour affects air quality.

There is no public body in Spain regulating the type of dispersion model that should be used in each case, although there are reference guidelines for good practice [5] [11]. This is not surprising, as there are currently few countries which regulate dispersion models for IEAs.

An important exception is Germany, where there is a regulated dispersion model called AUSTAL2000[19], whose use is almost exclusively for the processing of IEAs and AAEs for certain activities subject to German law on air quality. Legislation [7] does not directly refer to the Lagrangian AUSTAL2000 model as the official regulatory model, but the specifications included therein are so strict that they exclude de facto many other dispersion models.

Spain is gradually standardising criteria in line with the other countries belonging to the European Union. In Europe there is currently a forum for air quality modelling (FAIRMODE), which aims to harmonise criteria and standardise methods and procedures for the use of dispersion models in EU member countries, under Directive 2008/50/CE on atmospheric air quality and Cleaner Air For Europe (CAFE). However, the dispersion modelling of odours is not specifically addressed in this initiative.

Therefore, regarding odour modelling, in Europe is quite common to look to the other side of the ocean for references. [4]

The aim of this paper is to review the various commonly used regulatory dispersion models in order to assess the odour impact of an activity that is subject to an environmental authorisation procedure. In addition, key issues that should be considered when choosing a particular dispersion model will be discussed. Finally, these models' limitations will be described as well as areas that need further research.

2. Regulatory models

One of the European Union FAIRMODE initiative objectives is to harmonise the use of dispersion models; they give advice on the most appropriate type of model for each specific case, however there is no guide for modelling odours in an Environmental Impact Assessment (EIA).

The U.S. Environmental Protection Office (US EPA) has a list of "alternative models" that constitute an open list where any dispersion model can be listed, similar to what occurs in Europe. Any technician can use any model as long as they justify the choice of a particular model in each case.

The US EPA also defines "recommended dispersion models" for EIAs. The two main EPA models recommended for regulatory purposes are the gaussian AERMOD plume model and the CALPUFF lagrangian puff model.

US EPA recommends the AERMOD model to estimate air quality at a local level (up to 50 km), that is, this model is recommended for most studies related with odour emitting industries.

The CALPUFF model is used to assess air quality in the case of transportation over large distances and in those cases where the AERMOD model cannot be used because of a complex land topography, where uses are not standard and where wind circulation can render steady state estimations redundant. That is, if there are sea or lake breezes, wind flows near coastlines, prevailing calm conditions, thermal inversions, recirculation and spraying conditions.

Therefore, according to the US EPA criteria, the dispersion model of use when trying to establish the environmental odour impact of an industrial activity is the gaussian AERMOD model, while the CALPUFF lagrangian model is used only for complex cases.

In Germany it is not possible to use a gaussian model for an EIA of an activity that is subject to an environmental authorisation procedure [7]. The model AUSTAL2000 is a lagrangian particle model developed for the German Environment Agency and used as reference in this country. The "g" version of the model (Gerüch meaning odour in German) popularly known as AUSTAL2000g is specifically tailored for modelling odours.

Besides the three dispersion models cited in this article, there are a significant number of other dispersion models. For example, the European Topic Centre on Air and Climate Change has listed 142 dispersion models [18] in its database.

With such a high amount of dispersion models, the industry that seeks advice or the technician who is responsible for conducting an EIA is faced with the dilemma of choosing the most appropriate model. Neither the industry nor the environmental technician is an "expert" in modelling. So, which model select? Gaussian, Lagrangian or Eulerian?

It is also important to consider that the cost of a model usually increases with its complexity and necessary computational resources, as follows:

Eulerian model >> Lagrangian model >> Gaussian model

There is a tendency to label the quality of the models according to their complexity. This sometimes causes errors in the choice of dispersion model, since such a choice should be based on the adequacy of the model to the case study. From this point of view, a model based on the gaussian solution could be sufficient to solve a complex problem and vice versa, an eulerian model may not be adequate for a simple study. The key is to align the selection criteria and validate methods and results.

In this sense, the Environment Agency of England and Wales has published an interesting guide on the choice of dispersion models for odour predictions, which generally only contemplates gaussian models such as ADMS or AERMOD and does not mention lagrangian models.

The following exposes aspects to consider when choosing a dispersion model to estimate odour impact from industrial activity that seeks an authorisation from the competent environmental administration.

3. Topography, atmospheric conditions and emission source type.

The dispersion of odours is mainly influenced by the topography surrounding the source emitting the odours and atmospheric conditions [9].

The main parameters used to describe the atmospheric conditions are environmental temperature, the mixed layer, the type of atmospheric stability, wind speed and direction, relative humidity and solar radiation [8].

In this regard, the type of atmospheric stability has a substantial impact on the dispersion of odours [10]. Atmospheric stability is calculated in a simplified manner, with Pasquill stability classes (1961), which classifies different weather situations into 6 categories (A - F) under unstable, neutral and stable conditions. Under unstable atmospheric conditions (Pasquill A and B) odours travel a short-distance, whereas under stable conditions (E, F) they can travel further. Similarly, an increase in the ambient temperature results in an increase in the distance over which odorous compounds are transported.

The type of emission source is also crucial when it comes to properly conducting a study on dispersion. The height of odorous substances emitted is a key factor in their dispersion. In the specific case of the impact of odours by sources emitted at ground level, dispersal usually occurs a few metres above the ground and slowly. Consequently, the mixing layer height is not usually a very influential factor in dispersion for these cases, while it is when emissions occur at a high altitude.

Likewise, at specific sources the emission parameters such as flow rate or temperature are generally well controlled, while in diffused or surface sources we must assume certain estimated emission conditions and parameters such as lateral and vertical dispersion coefficients. In these cases, an experienced modeller is essential to obtain reasonable values.

4. Time average.

Selecting a suitable time average is one of the inconveniences that must be addressed when calculating the dispersion emission of odours at a source. Let's take a look at an example: suppose we take a snapshot with a camera of a plume of gas coming out of a stack.

The result would be similar to this.

Fig 1. Instant snapshot of a plume

A snapshot of the plume of smoke shows the ripple of the plume influenced by atmospheric turbulence.

If we adjusted the exposure time of the camera so that instead of taking a snapshot we took the photo with a time frame of 10 minutes the result would look similar to this:

Fig 2. Snapshot of a plume with 10 min exposure time

If the exposure time of the camera is increased, the photograph captures both ripples as well as the internal spread of the plume and the details of the ripples begin to fade so that the edge of the plume will not be as accurate.

The use of average times eliminates very high or very low fluctuations in the smoke plume concentration. This is key when trying to model odours.

An hour is a "de facto" average time taken for modelling chemical compounds emitted in a stack.

The most common chemical compounds studied in dispersion models are SO₂, NO₂ o PM₁₀, PM_2,5, O₃, Pb, benzene, CO, As, Ni and Cd. All these compounds have their hourly and/or daily and/or annual exposure values regulated in European legislation. It has sense here to get hourly averaged times.

However, the perception of odours occurs in a range from a few seconds to a few minutes, that is, during short exposure times. Therefore, in the case of odours it has more sense to take a photo within the shortest possible average time.

When the average time in which we will take the "photo" is less than one hour, it is called subhourly, as opposed to the hourlyaverage time.

For over 5 years, the Spanish State Meteorological Agency (AEMET for its acroynm in Spanish) has provided data from surface meteorological observation stations recorded every ten minutes [1]. However, no study on the dispersion of odours using subhourly data has been published yet.

This may be because most dispersion models today are struggling to deal with this type of data. For example, version 5 of the lagrangian puff model CALPUFF was incapable of processing subhourly data and it has not been until version 6 that this possibility has been included [13].

Unfortunately, the version approved by the US EPA is still 5.

The perception of odours occurs in a range from a few seconds to a few minutes, that is, in short exposure times. Therefore in the case of odours it is more accurate to take the photo with the shortest possible average time and is therefore advisable to work with subhourly or, and why not, subminute meteorological data.

However, this requires rethinking the established limit values of odours based on hourly percentiles, which will be discussed below.

5. Peak to mean ratio.

The results of a modelling are often expressed in the form of odour contour lines (or isopleths) connecting points with the same frequency of occurrence for mean hourly concentrations per cubic metre (in ou_E/m³) at a given percentile. It is often expressed as P98 (98th percentile) of measured concentrations or C_{98, 1 hour.}

These contours encompass the area where in 98% of the hours in the year the maximum hourly mean concentration does not exceed a certain value. For example, the contour line of 5 ou_E/m³ in a map of odours implies that the hourly mean concentration does not exceed 5 odour units in 365 days a year (8760 hours). If the contour line is 5 ou_E/m³ P98, the hourly mean concentration does not exceed 5 odour units per cubic metre in 175 hours (2% of annual hours).

However, it must be remembered that the above information refers to mean hourly concentrations. In the previous example, the precise concentration in the area within the isoline of 5 ou_E/m³ P98 can reach maximum values of, say, 30 ou_E/m³, in any case far from the average hourly concentration.

Fig 3. Peak to mean

An example of this phenomenon is shown in figure 3. The isoline affecting the receptor at approximately 4.7 ou_E/m³. If the limit established by legislation for the affected receptors is 5 ou_E/m³, the immission level, let's not consider uncertainties, is below the regulatory limit. However, as can be seen in the graph, both minute averaged values and the real values exceed the limit of 5 ou_E/m³ in many cases. The consequence of this is that although the hourly limit may be below 5 ou_E/m³, the receptors will be exposed to a higher concentration in terms of minutes or seconds (response time the nose takes to perceive a change in smell), exceeding 5 ou_E/m³.

In 1973, Smith (1973) postulated that the average and maximum concentration in the near field of the source could be described using the following formula.

C_p = C_m (t_p/t_m)^-p

Where C_p is maximum concentration in a time interval t_p, C_m is the mean concentration over time t _m, and p is an exponent.

The following map shows the odour impact of a Wastewater Treatment Plant (WWTP) using a peak to mean ratio.

Fig 4. 5-min averaged odour isopleths contour map.

In practical terms, in this particular case the result of each hourly value was multiplied by 1.64. In this way, for example, the dark green line corresponding to a C_{98, 1 hour} = 61 ou_E/m³ is transformed into a value greater than C_{98, 5 min} = 100 ou_E/m³.

Similarly an hourly limit of C_{98, 1 hour} = 5 ou_E/m³ will be transformed in this case to a larger value of C_{98, 5 min} = 8.2 5 ou_E/m³, which marks the limit value from which, in this particular case, adopting corrective measures to prevent an impact of odours should be considered.

6. Treatment in calm conditions

Most WWTPs tend to be located at low points in the community, and are often located close to water bodies and in moderate terrain. Worst case dispersive conditions for these facilities tend to occur at night due to increased stability associated with very light winds, low turbulence and persistent radiation inversions which act to restrict the vertical dispersion of odours released near the ground. Stagnation conditions are usually associated with rapid nighttime cooling of air near the valley surface. Increased temperatures above the nocturnally cooled surface create an inversion that prohibits mixing. Odours emitted into this stable nocturnal environment will either accumulate if calm conditions prevail or else will flow downwind with the drainage flow. [3].

This phenomenon will be named here as the "disconnection button" phenomenon. The disconnection button effect appears when residents of towns mearby to an industrial plant perceive the odour impact at dusk, attributing the incident to a "disconnection of some filter" to "save costs". That is, residents think that someone has switched off the button of the filter of an industrial activity, which is why they believe that it smells worse, when in fact the odour emission is due to a simple atmospheric phenomenon called thermal inversion.

It is in these moments also when industrial activities should pay special attention to their emissions, for example by regulating processes if weather conditions are unfavourable.

Build-up or retention of odours over several hours is common at this stage and they will only disperse with the onset of increased turbulence usually at sunrise.

Steady state plume models such as the gaussian models AERMOD or ADMS are not able to simulate the stagnation of odours. For one, the model has no prior knowledge of the previous hours’ meteorological or emission conditions as each hour is treated independent of the next. Also, the model has no causality effects so material is carried instantaneously and without changing to the receptors which may be many miles away.

Furthermore, the inverse wind speed dependence of the steady-state plume equation causes plume models to break down during low wind speed or calm conditions. Steady state models either set the concentration to zero for the hour of calm conditions, or, it forces the wind to a minimum speed, usually 1 m/s or greater, which means that the plume will travel to infinity even within the first hour.

Odours are in most of the cases perceived during calm conditions, therefore the use of gaussian models such as AERMOD or ADMS might be right for other pollutants, but not for odours.

7. Future studies

Generally, the emissions of nuisance odours are formed by a complex mixture of many odorous substances from mercaptans, hydrogen sulphide, terpenes, hydrocarbons, ammoniated compounds etc. Many of these compounds are highly reactive in the atmosphere, a few are polar and miscible indifferent solutions and others are clearly apolar, and they all have different decaying rates in the atmosphere. Thus, methyl mercaptan has a decay in the atmosphere rate of 1.2 to 8.4 hours [14]. Methyl sulphide lasts approximately 1 day (Kelly and Smith, 1990), dimethyl disulphide disintegrates in the atmosphere by photochemical reactions with hydroxyl radicals and its mean lingering in the air time is estimated at 4 hours and hydrogen sulphide has a decay in the atmosphere time of up to 3 days.

These are therefore non-conservative contaminants, that is, their chemical structure changes over time.

From this perspective, it seems important to know the chemical properties of odours in order to model first-order chemical conversions as well as their deposition via dry and wet, and thus be able to apply them in dispersion models. In reverse order, using models without chemical mechanisms can treat odours conservatively, but the model will have a worse approximation.

A tool used to simplify studying the influence of the chemical composition of an odour is based on the use of odour indices[6] [12]. However, a series of techniques is usually necessary for making a large sample and chemical analysis equipment with very low detection levels to achieve an adequate characterisation without leaving behind relevant chemicals in very small concentration.

More research and development into scientific channels is therefore required to try to find an answer to this question and in order to further knowledge regarding the dispersion of odours into the environment.

Adequate data sets are also needed to validate the odour dispersion models of different activities in addition those ones that already exist [2] [9] [8].

8. Conclusions

The US EPA instructs that the dispersion model used to establish the environmental odour impact of an industrial activity is the gaussian AERMOD model, while the CALPUFF lagrangian model is used only for complex cases.

However, the case of odour in the air is most often a complex case, for example in regards to low sources (WWTPs, farms, slaughterhouses, waste management plants, composting plants, slurry evaporation ponds, etc.) using a gaussian model is not recommended.

Most odour incidents are generated during calm or very low wind speeds which do not facilitate the dispersion of an odour, producing the "disconnected button" phenomenon. In these cases using the gaussian model is not recommended.

It is also not advisable to use the gussian models for sources near bodies of water (sea, estuaries, wide rivers, etc.) or on steep elevations in terrain (mountains, valleys, etc.)

The perception of odours occurs within a range of a few seconds to a few minutes, that is, in short exposure times, so it is important to work with subhourly data when modelling odours, provided that such data is available.

This decision is frequently a dilemma for the engineer who does not know what model to use or the cost difference between models in the achievement of their end goal: obtaining an Environmental Authorisation. It is also a dilemma for environmental technicians who evaluate EIA projects in their achievement of their ultimate goal: A better environment for their community.

There are still elements that need to be further investigated such as the chemical mechanisms of odours to avoid conservative treatment of odours, perhaps grouping "odour types" by their chemical nature, so that it will be possible in the future to treat in a different way odours emitted by a farm or a rendering, and those produced by a paper factory or a refinery.

Bibliography

1: AEMET, AEMET amplía y detalla observaciones y predicciones en su web, 2009, http://www.aemet.es/es/zona_portada_destacada/politicadatos

2: Bächlin, W.; Rühling, A.; Lohmeyer, A., 2002. Bereitstellung von validierungs-daten für geruchsausbreitungs-modelle-naturmessungen., Ministerio de Medio Ambiente de Baden-Württemberg

3: Barclay J. et al., Potential Problems Using Aermod To Implement Current Odor Regulations For WWTPs, 2013

4: Capelli L., Dentoni L., Sironi S., Guillot JM, Experimental Approach for the Validation of Odour Dispersion Modelling , 2012

5: Departamento de Medio Ambiente, Planificación Territorial, Agricultura y Pesca. Dirección de Planificación Ambiental., Guía de Buenas Prácticas para la elaboración de modelos de dispersión. Gobierno Vasco., 2012

6: Diaz C., 2010. Método para determinar y caracterizar los compuestos más relevantes en las emisiones de olor en procesos industriales., olores.org

7: Díaz Jiménez, C. , 2009. El Apasionante Estudio de la Relación de Máximos al Promedio., Olores.org

8: German Ministry of Environment, Technische Anleitung zur Reinhaltung der Luft, 2002

9: Guo, H., L. D. Jacobson, D. R. Schmidt, and R. E. Nicolai, 2001. Calibrating INPUFF-2 model by resident-panelists for long-distance odor dispersion from animal production sites, Applied Eng. in Agric

10: Jacobson, L. D., H. Guo, D. R. Schmidt, R. E. Nicolai, J. Zhu, and K. A. Janni, 2005. Development of the OFFSET model for determination of odor-annoyance-free setback distances from animal production sites: Part I. Review and experiment. Trans., ASAE

11: Jacobson, L. D., H. Guo, D. Schmidt, and R. E. Nicolai, 2000. Development of an odor rating system to estimate setback distances from animal feedlots: Odor from feedlots - setback estimation tool (OFFSET),

12: RETEMCA, Web Ibérica sobre modelización de la contaminación atmosférica http://www.ciemat.es/MCAportal/portal.do?IDM=62&NM=2,

13: Schlegelmilch M., 2009. Methoden zur Bewertung und Verminderung von Geruchsemissionen. GERUCHSMANAGEMENT,

14: SRC, CALPUFF Version 6 Announcement, 2006, http://www.src.com/calpuff/download/Mod6_Files/Version6Announcement.pdf

15: Varios, 1992. Agency for Toxic Substances and Disease Registry, U.S. Department of Health and Human Services,

16: , Directiva 96/61/CE, del Consejo, de 24 de septiembre, relativa a la prevención y al control integrado de la contaminación., 1996

17: , Ley 16/2002 de 1 de julio de Prevención y Control integrados de la Contaminación (IPPC)., 2002

18: , Real Decreto 100/2011, de 28 de enero, por el que se actualiza el catálogo de actividades potencialmente contaminadoras de la atmósfera y se establecen las disposiciones básicas para su aplicación., 2011

19: , , 2014, http://pandora.meng.auth.gr/mds/strquery.php?wholedb

20: , . AUSTAL2000. Manual. ,