On the validity of grid and trajectory models of urban air pollution

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

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.

Mitigation of severe urban haze pollution by a precision air pollution control approach

Severe and persistent haze pollution involving fine particulate matter (PM_2.5) concentrations reaching unprecedentedly high levels across many cities in China poses a serious threat to human health. Although mandatory temporary cessation of most urban and surrounding emission sources is an effective, but costly, short-term measure to abate air pollution, development of long-term crisis response measures remains a challenge, especially for curbing severe urban haze events on a regular basis. Here we introduce and evaluate a novel precision air pollution control approach (PAPCA) to mitigate severe urban haze events. The approach involves combining predictions of high PM_2.5 concentrations, with a hybrid trajectory-receptor model and a comprehensive 3-D atmospheric model, to pinpoint the origins of emissions leading to such events and to optimize emission controls. Results of the PAPCA application to five severe haze episodes in major urban areas in China suggest that this strategy has the potential to significantly mitigate severe urban haze by decreasing PM_2.5 peak concentrations by more than 60% from above 300 μg m⁻³ to below 100 μg m⁻³, while requiring ~30% to 70% less emission controls as compared to complete emission reductions. The PAPCA strategy has the potential to tackle effectively severe urban haze pollution events with economic efficiency.

REGIONAL PHOTOCHEMICAL AIR QUALITY MODELING:Model Formulations, History, and State of the Science

▪ Abstract  Regional air quality models have been used for scientific investigation of trace species dynamics for over two decades and are now beginning to take a central position in air quality management. In particular, they have been used for studying the transport and fate of atmospheric acids, photochemical oxidants (e.g. ozone), and more recently, aerosols. Such models are based on numerically solving the mass conservation equations for a chemically interacting system of species and are applied to horizontal domains of 1000s of kms. Primary applications include assessing the response of pollutant concentrations to emissions controls, quantifying the flux of pollutants across and out of a region, and understanding the impact of specific processes on pollutant concentrations.