Simulation of outdoor air pollution in Southampton
Keywords:Air pollution, road traffic
The city of Southampton is committed to monitoring and reducing outdoor air pollution, in particular, Nitrogen Dioxide (NO2) and Particulate Matters (PM2.5/PM10), which have been linked with adverse health effects under short- or long-term exposure. This project investigates the air pollution contributed mainly by road sources, which consist of major and minor roads in Southampton, by using Atmospheric Dispersion Modelling Software (ADMSRoads), and provides further understanding on the air pollution contributions based on qualitative and quantitative analysis. The model was validated by comparing the modelled concentration with the observed data from the Southampton AURN pollution monitoring station. This study has found out that although the simulation exhibits a tendency to underpredict pollution concentrations, the dynamics of the model were relatively promising as it managed to capture the trends over time of the concentration of air pollutants relatively consistently. Therefore, future improvements to the model may be made by applying correction factors to overcome the bias offset to obtain more realistic predictions of air quality. The simulation has also correctly predicted poorer air quality within the Air Quality Management Areas (AQMA) declared by Southampton City Council (SCC), which implies that road sources have a notable contribution towards the air pollution. The advantages of this model are that it can be quickly altered to predict response to future policy actions and that it has sufficient resolution to be used for epidemiological studies linking air pollution with the prevalence of health conditions in the city. The findings so far indicate that further pollution control measures are still warranted as most of the pollutant concentrations from road sources exceed the latest (2021) WHO air quality guidelines developed to protect public health from effects of exposure to air pollutants.
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Copyright (c) 2022 Bryan Ming De Gan, Matthew Loxham, Christina Vanderwel
This work is licensed under a Creative Commons Attribution 4.0 International License.