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

doi:10.3390/atmos12080983

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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Abstract

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 language	English
Article number	983
Number of pages	21
Journal	Atmosphere
Volume	12
Issue number	8
DOIs	https://doi.org/10.3390/atmos12080983
Publication status	Published - 30 Jul 2021

Bibliographical note

Funding Information:
This research was funded by the UK Natural Environment Research Council (NERC) project WM-Air, grant number NE/S003487/1. The authors appreciate the University of Birmingham?s BlueBEAR HPC service (http://www.bear.bham.ac.uk, accessed on 23 April 2021) for providing the computational resource. The authors would like to acknowledge local authorities within WM for provision of local air quality measurement and modelling data and for their review of local emissions data, with special thanks to John Grant and Curtis Dean in Walsall council. The authors also thank Transport for West Midlands (TfWM) and Birmingham City Council for provision of traffic data, previous modelling and reports.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

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

ASJC Scopus subject areas

Environmental Science (miscellaneous)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3390/atmos12080983Licence: Creative Commons: Attribution (CC BY)

ZhongJ2021UsingFinal published version, 20.8 MBLicence: Creative Commons: Attribution (CC BY)

West Midlands Air Quality Improvement Programme
Sevinc, D., Pope, F., Bloss, W., Chapman, L., Thomas, N., Shi, Z., Bartington, S., Bryson, J., Jowett, S., Muller, C., MacKenzie, R., Harrison, R., Ferranti, E. & Cai, X.
Natural Environment Research Council
1/01/19 → 31/12/24
Project: Research Councils

Cite this

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

abstract = "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.",

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note = "Funding Information: This research was funded by the UK Natural Environment Research Council (NERC) project WM-Air, grant number NE/S003487/1. The authors appreciate the University of Birmingham?s BlueBEAR HPC service (http://www.bear.bham.ac.uk, accessed on 23 April 2021) for providing the computational resource. The authors would like to acknowledge local authorities within WM for provision of local air quality measurement and modelling data and for their review of local emissions data, with special thanks to John Grant and Curtis Dean in Walsall council. The authors also thank Transport for West Midlands (TfWM) and Birmingham City Council for provision of traffic data, previous modelling and reports. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

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AU - Cai, Xiaoming

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Projects

West Midlands Air Quality Improvement Programme

Cite this