Odour Dispersion Modelling Explained: The Science Behind Predicting Odour Impact
Odour dispersion modelling provides a scientific, data-driven way to predict how odours travel and disperse in the atmosphere. It helps to predict how emissions move from a source and how they may impact different locations.
Unlike observational or qualitative assessments, dispersion modelling uses mathematical calculations combined with meteorological data to simulate real-world atmospheric behaviour.
This provides a detailed understanding of odour emissions under a wide range of environmental conditions.
What is Odour Dispersion Modelling?
Odour dispersion modelling is a mathematical tool. It is a computer-based simulation technique used to predict how odour emissions spread through the air.
Modelling uses established atmospheric dispersion algorithms to estimate:
- How far odours travel.
- How they dilute over distance.
- How often they may be detectable at specific locations.
Typically, we show the outputs as contour maps. They represent predicted odour concentrations across a geographic area. These predictions are based on defined emission sources and long-term weather data.
How Odour Dispersion Models Work
Dispersion models simulate how odours move through the atmosphere by combining emission data with environmental conditions. They integrate the following elements:
| Model Component | What It Includes | Why It Matters in Dispersion Modelling |
|---|---|---|
| Emission Sources | • Point sources (e.g. stacks, vents) • Area sources (e.g. lagoons, storage areas) • Volume sources (e.g. building emissions). Each source is assigned an emission rate to represent its contribution to overall odour output. | Defines where odours originate and how strongly they are released into the atmosphere. This is the starting point for all modelling calculations. |
| Meteorological Data | Key inputs include: • Wind speed • Wind direction • Atmospheric stability • Mixing height • Temperature profiles | Determines how odours travel and disperse over time under different atmospheric conditions. Typically, several years of data are used to ensure robust long-term predictions. |
| Terrain & Surface Characteristics | Models account for: • Building effects (wake and downwash) • Surface roughness • Elevation and topography. | Local geography influences airflow patterns and dispersion behaviour near receptors. |
Common Odour Modelling Software
At Silsoe Odours, our odour consultants use specialist atmospheric dispersion software to provide accurate, site-specific predictions.
The most commonly used systems include:
- ADMS (Atmospheric Dispersion Modelling System)
- AERMOD
These tools apply validated dispersion algorithms to predict how emissions behave under real atmospheric conditions.
Each system can model complex scenarios and varying emission conditions over time.
What Dispersion Modelling Outputs Show
We typically present the results of odour dispersion modelling in visual and numerical formats.
These may include:
- Odour concentration contour maps.
- Likely frequency of exceedance at receptors.
- Predicted peak and average concentrations.
- Spatial distribution of odour impact.
These outputs help visualise how odours may behave across a site and surrounding area under different conditions.
Iterative Modelling: Testing Scenarios Before Implementation
One of the most powerful aspects of odour modelling is iterative scenario testing. We can test various operational scenarios before you put them into practice, by tweaking and comparing specific variables. This allows comparison of how changes in design or operation affect predicted dispersion patterns.
Common scenario variations include:
- Stack height changes.
- Emission rate adjustments.
- Different operational conditions.
- Mitigation system performance.
For example, a site may test multiple stack heights or emission-control options to identify the most effective solution.
A piggery might model stack heights of 20, 30, and 40 metres. They can then select the most effective stack height based on data provided by odour dispersion modelling.
Limitations of Odour Dispersion Modelling
While odour dispersion modelling is a powerful predictive tool, it is based on assumptions and input data quality.
Key limitations include:
- Variability in real-world emissions.
- Uncertainty in meteorological conditions.
- Simplification of complex atmospheric behaviour.
- Sensitivity to input accuracy.
As a result, models should always be interpreted as a predictive tool rather than a direct measurement of real-world conditions.
How Dispersion Modelling Fits Within Odour Assessment Work
Odour dispersion models are rarely used in isolation. They form part of a wider toolkit of odour assessment methods. These approaches vary from field-based observation to laboratory analysis and predictive simulation. Each method provides different types of data, depending on the stage and complexity of a project.
The table below explains how dispersion modelling fits alongside other key odour assessment techniques.
For a wider overview of how dispersion modelling is used within planning and development decision-making, see our guide to Odour Impact in Planning & Development.
| Odour Assessment Method | Purpose | How It Supports Dispersion Modelling |
|---|---|---|
| Odour Measurement (Overview) | Provides an overview of methods for measuring odour, including a range of techniques. | Establishes the broader measurement framework that underpins all odour assessment work. |
| Odour Sampling Surveys & Testing | Collects odour samples directly from emission sources for laboratory analysis. | Provides quantitative emission data that can be used as direct inputs for dispersion models. |
| Sniff Surveys | Field-based assessment carried out by trained assessors walking defined routes and recording odour observations. | Helps identify the presence of real-world odour and its patterns, which can be used to contextualise or validate model outputs. |
| Stack Sampling | Collects samples directly from stacks or ducted sources under controlled conditions. | Produces accurate source emission data that can be used to define model inputs for point sources. |
| Biofilter Sampling | Measurement of odour emissions from treatment systems such as biofilters. | Assesses mitigation performance and provides post-mitigation emission data for modelling scenarios. |
| Odour Modelling in Planning | Explains how odour modelling is used within environmental and planning contexts. | Provides context on where dispersion modelling sits within wider assessment strategies and how it links to decision-making frameworks. |
Speak to our Odour Specialists
Odour dispersion modelling provides a scientific method for understanding how odours behave.
If you need support to understand odour emissions, we are ready to help. We can provide detailed modelling, iterative scenario analysis and expert interpretation tailored to your project.
Call: 01525 860222
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References: Environment Agency Review of Dispersion Modelling for Odour Predictions
Article originally published 15th March 2022. Updated 15th April 2026.
