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Modelling Source Contributions to Ambient Particulate Matter in Kwadela, Mpumalanga K Bosman

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Modelling Source Contributions to Ambient Particulate Matter in Kwadela, Mpumalanga K Bosman Modelling source contributions to ambient particulate matter in Kwadela, Mpumalanga K Bosman orcid.org 0000-0001-5714-7060 Dissertation submitted in fulfilment of the requirements for the degree Masters of Science in Geography and Environmental Management at the North-West University Supervisor: Prof SJ Piketh Co-supervisor: Dr RP Burger Graduation July 2019 22258108 I DEDICATION This thesis is dedicated to my LORD Jesus Christ, my dear mother Chantal Burnett and my grandfather Willem Renier Bosman II “And there are also many other things which Jesus did, the which, if they should be written every one, I suppose that even the world itself could not contain the books that should be written. Amen.” John 21:25 (KJV) III PREFACE This study forms part of an intensive air quality sampling campaign conducted in Kwadela, a small low-income settlement between Bethal and Ermelo, Mpumalanga. The aim of this campaign is to quantify the baseline air quality during summer and winter in a typical low-income, domestic fuel burning community. Ambient air quality standards (AAQS) for particulate matter (PM) are exceeded, confirming that Kwadela has poor air quality. The 99th percentile of the daily averaged 3 3 PM10 is 96µg/m , and for PM2.5 it is 60µg/m . Judging from the diurnal profile of PM, with peaks in the mornings and evenings, domestic fuel burning is mainly responsible for these high ambient PM values. This is noteworthy, since Kwadela has fewer than 1 000 households and is located +/- 45km from the nearest industrial source. This also implies that fuel for cooking purposes is enough to raise ambient PM levels to above air quality standards. This campaign is funded by South African Synthetic Oil Liquid (SASOL). This study was presented in a full paper and an oral presentation at the National Association of Clean Air (NACA) Conference in 2014. I had the privilege of attending two air dispersion modelling courses: American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and California Puff Model (CALPUFF), one in April 2014 presented by Prof Piketh and Dr Burger, and in August 2014, presented by Lakes Environmental, Prof and Mrs Thé. 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. These areas are home to a significant portion of the South African population. 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. 1. Characterise the ambient PM loading and variability of five low-income urban areas on the South African Highveld. 2. Evaluate the sensitivity of simulated ambient PM to model inputs in Kwadela, Mpumalanga, by comparing different dispersion modelling scenarios. 3. Model and assess the relative contribution of domestic fuel burning, windblown dust, waste burning and surrounding coal-fired industrial sources to ambient PM concentrations in the small low-income settlement of Kwadela, Mpumalanga IV The data and methodologies used to accomplish these objectives are discussed in Chapter 3. Objectives 1 and 2 are addressed in Chapter 4, and objective 3 is addressed in Chapter 5. V ACKNOWLEDGEMENTS I would like to extend my gratitude to the following individuals and organisations for their support during the course of my research: Prof Stuart Piketh for the financial support in everything I needed and more, his input in my research, as well as all the opportunities being able to attend relevant courses and conferences. I feel honoured to have worked under such an esteemed scientist. I am grateful for Dr Roelof Burger for his guidance, encouragement and unending willingness to help in all facets of my research; I am privileged to have benefitted from the expertise of such a respected scientist. Thank you to the North-West University for giving me the opportunity to do a master’s degree and receiving an education of such excellent quality and for all the research support and the facilities made available to use for my research. I hereby acknowledge and thank SASOL and Electricity Supply Commission (ESKOM) for the financial support during my research. Thank you South African Weather Services (SAWS), for the provision of surface and upper air data, and South African Air Quality Information System (SAAQIS) for providing ambient air quality data of the low- income settlements in Gauteng and the Free State and air quality monitoring site locations in South Africa (SA). Thanks to Mr Jasper Dreyer (NWU) for assisting me with the soil classification of the study area and all the detail thereof. Beanca van den Berg, Brigitte Language, and Corne Grové are thanked for helping to collect data during the Kwadela campaign. Thank you to Jaun van Loggerenberg, for assisting me with some of the maps, having excellent skills in GIS, for his encouragement and companionship, for always being prepared to help during my research and being a very good friend. Thank you to Reinhardt Hauptfleisch for his support and companionship, and being a good friend. Nopasika Mabadi is thanked for her friendship, love, guidance and encouragement. Thank you to my dearest mother for her unending support, inspiration, and love during my research. VI ABSTRACT 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. Keywords: AERMOD, dispersion modelling, domestic fuel burning, low-income settlements, particulate matter VII TABLE OF CONTENTS Dedication .............................................................................................................. I Preface.................................................................................................................. IV Acknowledgements ............................................................................................. VI Abstract ............................................................................................................... VII List of tables ....................................................................................................... XII List of figures ..................................................................................................... XIII List of pollutants and elements ....................................................................... XVII Abbreviations
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