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Article

Using Task Farming to Optimise a Street-Scale Resolution Air Quality Model of the West Midlands (UK)

1
School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
2
Cambridge Environmental Research Consultants, Cambridge CB2 1SJ, UK
3
Birmingham City Council, Birmingham B5 5BD, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Patrick Armand
Atmosphere 2021, 12(8), 983; https://doi.org/10.3390/atmos12080983
Received: 28 May 2021 / Revised: 20 July 2021 / Accepted: 26 July 2021 / Published: 30 July 2021
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. View Full-Text
Keywords: air pollution; air quality modelling; ADMS-Urban; high performance computing; HPC; West Midlands air pollution; air quality modelling; ADMS-Urban; high performance computing; HPC; West Midlands
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MDPI and ACS Style

Zhong, J.; Hood, C.; Johnson, K.; Stocker, J.; Handley, J.; Wolstencroft, M.; Mazzeo, A.; Cai, X.; Bloss, W.J. Using Task Farming to Optimise a Street-Scale Resolution Air Quality Model of the West Midlands (UK). Atmosphere 2021, 12, 983. https://doi.org/10.3390/atmos12080983

AMA Style

Zhong J, Hood C, Johnson K, Stocker J, Handley J, Wolstencroft M, Mazzeo A, Cai X, Bloss WJ. Using Task Farming to Optimise a Street-Scale Resolution Air Quality Model of the West Midlands (UK). Atmosphere. 2021; 12(8):983. https://doi.org/10.3390/atmos12080983

Chicago/Turabian Style

Zhong, Jian, Christina Hood, Kate Johnson, Jenny Stocker, Jonathan Handley, Mark Wolstencroft, Andrea Mazzeo, Xiaoming Cai, and William J. Bloss 2021. "Using Task Farming to Optimise a Street-Scale Resolution Air Quality Model of the West Midlands (UK)" Atmosphere 12, no. 8: 983. https://doi.org/10.3390/atmos12080983

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