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.
Highlights
Air pollution has become the biggest environmental risk for public health both globally and locally [1,2,3,4]
This paper presents the results of a novel approach to running ADMS-Urban, where task farming has been used to spatially parallelise the model configuration, and each run component has been executed on an HPC—Bluebear at the University of Birmingham
For the purpose of model evaluation, the model was first run in a “Receptor” Mode for 32 air quality measurement sites within the West Midlands (WM) over the whole year of 2016, with measured concentration data obtained from local authorities and Defra’s AURN [12]
Summary
Air pollution has become the biggest environmental risk for public health both globally and locally [1,2,3,4]. Owing to the advanced development of Internet of Things, low-cost sensors [13] are increasingly used for air quality measurements, as indicative measures These techniques can enable the dense network of air quality monitoring required for building smart cities. Other monitoring approaches, such as mobile measurements using bicycles [14,15] and vehicles [16], generate air quality information at both high temporal and spatial resolutions within relatively small domains, while satellite measurements can provide a globally consistent air quality monitoring service at a coarse spatial resolution [17]. The monitored Chilbolton concentration was multiplied by the ratio of the annual average concentration at a rural area bordering the West Midlands to that at Chilbolton based on Defra’s background concentration maps [69]
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