Using task farming to optimise a street-scale resolution air quality model of the West Midlands (UK)

Jian Zhong, Christina Hood, Kate Johnson, Jenny Stocker, Jonathan Handley, Mark Wolstencroft, Andrea Mazzeo, Xiaoming Cai, William James Bloss

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High resolution air quality models combining emissions, chemical processes, dispersion and dynamical treatments are necessary to develop effective policies for clean air in urban environments, but can have high computational demand. We demonstrate the application of task farming to reduce runtime for ADMS-Urban, a quasi-Gaussian plume air dispersion model. The model represents the full range of source types (point, road and grid sources) occurring in an urban area at high resolution. Here, we implement and evaluate the option to automatically split up a large model domain into smaller sub-regions, each of which can then be executed concurrently on multiple cores of a HPC or across a PC network, a technique known as task farming. The approach has been tested for a large model domain covering the West Midlands, UK (902 km2), as part of modelling work in the WM-Air (West Midlands Air Quality Improvement Programme) project. Compared to the measurement data, overall, the model performs well. Air quality maps for annual/subset averages and percentiles are generated. For this air quality modelling application of task farming, the optimisation process has reduced weeks of model execution time to approximately 35 h for a single model configuration of annual calculations.
Original languageEnglish
Article number983
Number of pages21
Issue number8
Publication statusPublished - 30 Jul 2021


  • air pollution
  • air quality modelling
  • ADMS-Urban
  • high performance computing
  • HPC
  • West Midlands


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