Modelling source contributions to ambient particulate matter in Kwadela, Mpumalanga
The rapid growth of urban areas all over the world has deteriorated the quality of the atmosphere and ambient environment. PM with a diameter of 10μm and less contributes greatly to critical health impacts. Developing countries such as SA face a great health and environmental problem concerning this matter, as ambient PM levels are particularly high in low-income settlements. At least 64% of SA's population reside in urban environments being affected by these harmful conditions on a daily basis. This evidently requires proper regulations and air quality control in urban environments, especially in low-income settlements, being something most developing countries, including SA, do not have in place. Air dispersion models are important for regulatory purposes and optimising appropriate site-specific abatement strategies that support local environmental policymaking. However, using Gaussian dispersion models as a tool to govern urban air quality has some challenges: variation of ambient air pollution levels in low-income urban areas; uncertainties pertaining to model inputs when simulating intra-urban air quality using a Gaussian dispersion model; the sensitivity of simulated ambient PM to model inputs when modelling intra-urban air pollutants; and uncertainty about the relative contribution of different pollution sources. The overall aim of this study is to assess the performance of a steady-state Gaussian dispersion model to simulate ambient air quality inside a low-income urban area on the South African Highveld. Air quality regulations largely focus on industrial and commercial emitters. In low-income urban areas, a variety of other sources also contribute to air pollution and need to be assessed in order to inform regulatory efforts. This setting is significantly different than the ones where these models were originally developed and with inputs that are typically used. In order to determine this, the challenges previously mentioned will be addressed as follows: firstly, the ambient PM loading and variability of five low-income settlements are characterised; secondly, a summary of the variability of model inputs when simulating intra-urban air quality using a Gaussian dispersion model, AERMOD, is given in the literature review; then, the sensitivity of simulated ambient PM to model inputs in Kwadela, Mpumalanga, is evaluated by comparing different dispersion modelling scenarios; and lastly, the relative contribution of domestic fuel burning, windblown dust, waste burning and surrounding coal-fired industrial sources to PM10 in Kwadela is modelled and assessed. The results confirm that local sources do contribute greatly and dominantly to the PM levels of Kwadela, especially windblown dust and domestic fuel burning as opposed to other surrounding pollution sources such as nearby industries. Considering some limitations of the model, the study confirms that a Gaussian dispersion model is an effective tool to use when simulating intra-urban ambient air quality.