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Meteorological marvel: unlocking weather data to protect people from pollution

Air Quality and Odour By Curtis Oliver-Smith, Graduate Consultant, Air Quality – 23 February 2024

a mountain covered in fog with trees in the foreground


Curtis Oliver Smith in a blue shirt against a marble wall

Curtis Oliver-Smith

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You might have wondered, why are some days smoggier than others? The answer is weather, because it plays a vital role in the dispersion of air pollution. On a wet and windy day, conditions will suppress emissions of dust and particulates such as those from vehicle exhaust, while a hot, dry and humid day may lead to elevated pollution levels due to limited wind dispersion.

When Cundall’s Air Quality team looks at projects to ascertain the air quality, we also need to understand local weather at the site. Atmospheric dispersion modelling is used to produce air quality assessments to inform planning, and for developing an indoor air quality plan. The goal is to evaluate the effect on occupants of the building.

To carry out the atmospheric dispersion modelling, the software we use (such as ADMS) requires the input of local meteorological variables. These need to be in a specific format with the file extension. met, similar to how a Word file has the extension .docx, or an Excel file .xlx. This .met file type can then be understood by the software so it can unlock the important information held within, including the chosen weather station, temperature, humidity, wind speed, wind direction, cloud cover and precipitation.

We need these met data files in an hourly sequential data format and had previously been purchasing them from external providers. However, we have found an innovative alternative to purchasing the data! Using R, a free programming software for statistical computing and graphics, Cundall’s Air Quality team has extracted meteorological variables from the National Oceanic and Atmospheric Administration (NOAA) Integrated Surface Database (ISD) with a bespoke script and adapted functions inside R studio.

Now, using coordinates, meteorological station codes (the numeric codes to identify a land weather station) and a data viewer, we can select multiple years and run analyses on yearly averages and data capture percentages. This enables the most suitable meteorological stations to be selected for the modelling software. This substantially reduces lead times and means the data can be interrogated in-house, providing greater confidence with our dispersion modelling, as the meteorological data input is delivered in a more technical and robust manner.

In most cases, processing involves data stitching from other stations for data infilling, in a bid to improve the level of data capture to 100%. This consists of various techniques, including interpolation, cross-referencing, and trend analysis. This information will then be provided to the internal team or to the client as a metafile, as a transparent way of communicating the process.

Windrose figures and annual statistics can also be produced alongside file generation, which feeds into our air quality assessments. A section of the air quality assessment is focused on local meteorology, for which a Cundall-trained meteorologist carries out an analysis that justifies multiple aspects of our evaluation.

The Air Quality team can utilise this process across all projects requiring meteorological data, both within the UK and internationally. We can also provide this service to other technical disciplines globally, facilitated by discussions focusing on understanding their meteorological data, enabling us to adapt our service for their local clients’ needs. Furthermore, we can provide this as a service to external customers in the hope that by providing meteorological data with suitable interpretation, we can help facilitate top-class modelling capabilities across the air quality modelling sector.

For the people our clients’ projects serve there is a general wellbeing benefit from all of this, as having improved modelling helps ensure air quality mitigations can be fully tailored to local conditions.