We need to continually improve our air quality modelling ability to better understand and address the impacts of air pollution. One way to achieve this is to run our air pollution simulations at a higher spatial resolution, which gives a more detailed result. The problem with this approach, though, is that running models at a higher resolution is more time-consuming, even using a supercomputer. Two projects, led by Dr. Ben Drummond of the Met Office under the SPF Clean Air programme, have been looking to address the problem, producing ever-higher resolution air quality models and forecasts that we can use to help people to reduce their exposure to air pollution.

A new NAME for air quality

One of these projects has involved developing a new high-resolution version of the Met Office’s Numerical Atmospheric-dispersion Modelling Environment (NAME), a computer model of how chemicals disperse through the atmosphere. Originally developed to track radioactive material following nuclear accidents, the more recent versions of the model are used for many more purposes.

The team developed and tested an air quality version of NAME at a horizontal resolution of only 2.2 km, 30 times finer than the current operational air quality model. The results from this new model configuration compared favourably with existing models and with measured values of air pollution. It was also used to investigate the impacts of wildfires on UK air quality, a problem that is growing in importance as the UK starts to see increasing numbers of wildfires. The team plan further work on this model, which will form the basis of the next generation of operational air quality forecast systems.

More than the sum of its parts: A Multi-Model Air Quality System for Health Research

Another high-resolution modelling project the team have been involved with is running the MAQS-Health modelling system on the Met Office’s supercomputing system. The MAQS-Health system models many air pollutants at a range of spatial scales and time periods, accounting for physical and chemical processes in the atmosphere. This project developed new code to convert Met Office model data into a format that MAQS-Health can use, before assessing its performance at simulating roadside air pollution. This is an area that MAQS-Health is expected to perform well, as it can simulate traffic emissions from the road network in much greater detail than other modelling approaches.

Led by Dr. David Carruthers at Cambridge Environmental Research Consultants under the SPF Clean Air programme, MAQS-Health combines multiple models into a world-leading modelling system. This means it can work seamlessly between different scales of model output, from regional models that cover entire countries, down to local models that can look at individual city streets. The system can also work at a variety of timescales according to what is needed, from hourly all the way up to annual data. A major advantage of this approach is that the model accounts for the physical and chemical processes going on in the atmosphere at all relevant scales of space and time, thanks to the multiple different models it draws on.

The system was developed in collaboration with user groups who use air pollution data, such as health researchers, to ensure it meets their needs. The team developed a new verification system allowing users to evaluate the system’s predictions and explore how well the models perform. The data can then be used to create maps of air pollution severity, allowing ready use in a range of applications.

These applications benefit from the integrated nature of MAQS-Health and the many spatial scales it can work at. The system can be used to create larger-scale data across a city to evaluate the impact of low-traffic neighbourhoods on residents’ health, or finer-scale data at street level to investigate how people’s personal exposure to air pollution varies according to where they live. The system can also be used at a national scale for air quality forecasting, or to evaluate the effect of a change in government policy on air pollution.

The MAQS-Health system forms the air quality modelling component of a major modelling framework, bringing together air pollutant data and modelling into a single UK programme for air quality analysis. This gives insights across the issue of air quality, from emissions to atmospheric processes to the conditions people live with. Ultimately, MAQS-Health helps us investigate the impact air quality has on health and wellbeing, and the effectiveness of policies designed to tackle it. This, and other projects under the SPF Clean Air programme, are providing world-class scientific advances to help improve air quality.

Back to all