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Using Task Farming to Optimise a Street-Scale Resolution Air Quality Model of the West Midlands (UK)
2021
Atmosphere
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
doi:10.3390/atmos12080983
fatcat:y2gd7z47rjej5kkjxu5qhv5jhi