Module manager: Steve Arnold
Email: Steve Arnold s.arnold@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
Principles of Air Quality Modelling: Local Dispersion
This module is not approved as an Elective
Millions of people die prematurely every year due to exposure to harmful air pollution. In this module, students will learn how computer models are used to understand and predict large-scale distributions of key air pollutants harmful to health. The module covers essential aspects of model design and theory, application of air pollution models to key global regions, and the use of models in estimating population health risks from air pollution exposure. Students will produce their own analysis of model data in a series of hands-on data science sessions.
Overall aim:
To produce learners who have an appreciation of the key components of regional air pollution models and how application of models is used to investigate regional pollutant distributions and their impacts.
Objectives
The module will provide students with:
- Knowledge of key components of regional air pollution models, and considerations in model design.
- Understanding of emissions inventories and their incorporation into models.
- An appreciation of the use of models in separating and quantifying different influences (meteorology, emissions, chemistry) on regional air pollutant distributions for primary and secondary pollutants.
- Understanding of the role of models, alongside measurements, in advancing understanding.
- Understanding of the application of models to quantify air pollution impacts (e.g. health) in key global regions, using case study examples.
- An appreciation of the use of modelling in determining key sectoral sources (anthropogenic and natural) in controlling air pollutant distributions in different global regions.
- Opportunity to analyse regional air pollution model output, linking to fundamental processes, and evaluating with observations.
- Practical assessment of a specific policy intervention based on model-predicted changes in air pollution distributions.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Articulate key aspects of model design in relation to capturing key process drivers for different air pollutants (PM2.5, ozone) on regional scales;
2. Critically assess strengths and limitations of key aspects of different model designs in simulating key air pollution processes;
3. Use appropriate experimental design and a simple model to evaluate impacts of changes in atmospheric process understanding on air pollutants;
4. Evaluate the response of air pollution distributions to policy interventions or changes in specific processes;
5. Develop practical knowledge of the application of modelling to key air pollution problems in different world regions;
6. Describe the methodology and understand the assumptions behind health impact assessment modelling for PM2.5 and ozone pollution.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Critically assess scientific evidence to draw conclusions regarding the effectiveness of interventions or policy changes;
2. Analyse and present complex geophysical data using computational data science tools;
3. Design model experiments to test hypotheses or theory;
4. Present scientific information using effective visualisation, and interpretation for non-expert audiences;
5. Apply knowledge to novel situations to derive conclusions and predict system behaviour;
6. Demonstrate an appreciation of uncertainty in modelling approaches, and key limitations of modelling methodologies.
Competence Standards
On successful completion of the module students will have demonstrated the following competence standards:
1. Collate information from diverse sources, and analyse, and interpret data to reinforce understanding and develop solutions to problems.
2. Synthesise information and present it in an effective way to different audiences.
3. Apply knowledge to novel situations and tasks to demonstrate understanding of complex problems.
4. Apply data science and computational tools to processing and analysing complex datasets.
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 18 | 1 | 18 |
| Practical | 6 | 2 | 12 |
| Seminar | 2 | 2 | 4 |
| Private study hours | 116 | ||
| Total Contact hours | 34 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Students will have the opportunity to receive formative feedback on practical work via discussion and feedback from staff and demonstrators in computer sessions throughout the semester, and on their understanding of core material via in-class quizzes.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assignment | OTLA | 60 |
| Assignment | Coursework | 40 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 29/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team