Environment International (Nov 2023)
Modelling the dispersion of particle number concentrations in the West Midlands, UK using the ADMS-Urban model
Abstract
Ultrafine particles (UFPs) are respirable particles with a diameter less than 100 nm, which some studies have associated with adverse effects upon health. UFPs are currently not regulated as the health evidence is insufficient and very few observational data are available in most cities. The 2021 WHO Global Air Quality Guidelines highlighted the pressing issue of UFPs and provided a good practice statement for UFPs, which recommends that more measurement and modelling studies are implemented in future. Particle number concentrations (PNC) are the most common metric for UFPs as this fraction normally dominates the total ambient PNC in urban environments. This study simulates the dispersion of particle number concentrations in the West Midlands (a metropolitan area), UK using the local scale ADMS-Urban model, which is an advanced quasi-Gaussian plume dispersion modelling system. ADMS-Urban implements a physics-based approach to represent the characteristics of the atmospheric boundary layer and has been widely used in the dispersion modelling of air pollutants. It can represent a variety of source types (such as road and grid emissions) occurring in urban environments and requires a range of input data. Particle number was used as a passive scalar, with no inclusion of aerosol microphysics within the model, as a first implementation in the ADMS-Urban model for the West Midlands, UK. Evaluation was conducted by comparing the modelled (from a receptor run) and measured data at the Birmingham Air Quality Supersite. Overall, the model performed well although there was a slight underestimation for PNC. Based on the modelling output from a contour run, PNC maps at a variety of spatial scales (i.e. street scale, ward level and local authority level) and temporal resolutions (i.e. annual, 24-hour, and 1-hour) were generated. PNC mapping could be linked to local population and health data for potential epidemiological studies.
