A research report submitted to the Faculty of Geosciences, University of the Witwatersrand for the degree of Master of Science Johannesburg, 2015 / Quantifying or estimating emission uncertainty for particulate matter from stationary sources in South Africa. The estimation of measurement uncertainty with regards to hazardous air pollution emissions from stationary sources is currently the most uncertain element associated with obtaining relevant, valid stack emission data in South Africa. This project is aimed at developing an appropriate method to evaluate the uncertainty associated with particulate matter measurements conducted for stationary source emissions in the South African context. A series of In-Stack measurements were taken in accordance with recognized international methodology (ISO 9096:1992, and 2003) on two different industrial processes, representing a best and worst case scenario. A comparison between the two scenarios was made in an attempt to establish what components of the sampling technique have the greatest error. The effect of cumulative errors in the sampling train as well as external factors that may influence the results were evaluated and included in the final estimate of uncertainty. Some of the factors used included the sampling location, industrial process and external environmental factors. The overarching goal of this project was to establish an estimate of the cumulative uncertainty on the final emission values obtained, inclusive of both analytical, field sampling and process related variables that may result in a cumulative error associated with quantifying stationary source particulate matter emission values. The results of the study found that the estimated combined expanded uncertainty for both sets of data was calculated to be between 62 – 72%. Upon closer analysis of the data it was ascertained that the data obtained were inadequate and the calculation of the uncertainty of the results both with the compliant and non-compliant sampling campaigns revealed that the variability of the results was too great for both scenarios to make any statistically valid observations or conclusions about the data. In lieu of this, and considering the significant costs, time and labour involved in order to obtain enough data to enable adequate quantification of an uncertainty budget for the results obtained, the author has developed an alternative tool for assessing the quality and reliability of reported emission figures. The author has developed what he has named a sampling suitability matrix, this tool although subjective in nature will add significant value (in the authors opinion) to the interpretation of the quality and reliability of the final emission results reported. The intention of this tool is to be incorporated as supplementary information into all emission reports in future. This will enable the plant operator and regulator to assess the quality of reported data and final emission results, thus assisting in establishing whether the plant is in compliance with their Air Emission License (AEL) requirements or not.

A thesis submitted to the Faculty of Science, University of the Witwatersrand, in fulfillment of the requirements for the degree of Master of Science. Johannesburg, 2017. / The Highveld is identified as an air pollution ‘hotspot’ area where pollutant concentrations are elevated due to the high density of industrial and non-industrial air pollution sources. To enhance air quality across the Highveld, it was declared a priority area to manage and monitor pollutants to reduce their negative impact on the environment and society. Hence, the aim of this study was to investigate ambient air pollution across the Highveld Priority Area (HPA), using ground-level SO2, NOx and surface O3 concentrations, meteorological parameters and Moderate resolution imaging spectroradiometer (MODIS) atmosphere products, for January to December 2011, to develop new modelling techniques to aid in the management of air pollution. Results show the annual mean trace gas concentrations of SO2, NOx and surface O3 were 12.14, 14.75 and 28.77 ppb, respectively. SO2 and NOx concentrations were highest during winter at an average of 17.56 and 20.96 ppb, where surface O3 concentrations were highest during spring at an average of 32.82 ppb. Diurnal patterns of SO2 and surface O3 were similar, where a midday peak occurred. NOx concentrations instead showed peaks during traffic hours. Ambient air temperature, solar radiation, relative humidity, wind speed and rainfall levels peaked during summer. Atmospheric pressure was relatively stable throughout the year. Winds typically ranged from N to E up to April and from S to NW from May. Very little variation in SO2 and NOx concentrations was explainable by meteorology, 4 to 29 % and 5 to 23 %, while the influence of meteorology on surface O3 concentrations was more significant, 23 to 53 %. Spatial multiple regression statistical models using a cross validation approach for model validation were made over a number of temporal scales. The model fitting and validation processes indicated that the models were not a good fit as only up to 69, 74 and 58 % of SO2, NOx and surface O3 concentrations with high root means square error (RMSE) values of up to 22.10, 15.56 and 18.59 ppb, respectively, could be explained by the models. This process revealed the potential to model pollutants across the HPA, and as a pilot study future work can be based on this study. It is clear that spatial modelling for pollution estimation and management is necessary as seen by the frequent exceedances of the national and international ambient air quality standards. / XL2017

No description available.

Thesis (MTech (Environmental Health))--Cape Peninsula University of Technology, 2010 / Air pollution is a global problem and it can also have a larger impact in developing countries like South-Africa. Mossel Bay was one of the regions in the Western-Cape that was rated to have potentially poor air quality. With above mentioned in mind, the research was done in Bayview. Bayview is a upper income suburb of Mossel-Bay. The suburb is surrounded by industrial activities that increased the possibility of a bad status of the air. This research measured the concentrations of the primary pollutants, SO2, NO2, PM10, O3 en Benzine, as well as the meteorological data for a period of one year as from the 1st October 2008 to the 30th of September 2009. The monitoring was done by using the mobile air quality monitoring station of the Western Cape Department of Environmental Affairs and Development Planning's that was situated in Mossel Bay. The research also correlated with the human aspects of air quality control and the monitoring results. The quality of life of the Bayview residents was measured by using a structured questionnaire. The questionnaire had amongst others, obtained the symptoms of certain air quality related diseases that the 114 respondents have recorded for the responding period of air quality monitoring. Other aspects that were researched were obtaining the social status, exposure, and work exposure and health consciousness of the respondents. Air quality surveys can fail if not put in the context of the perceptions of the affected communities. Therefore the perceptions of the respondents were also tested by a structured questionnaire. Aspects of perceptions that were tested were amongst others, the visual influence of perceptions, exposure, social status and the perception of the hazard. The results of the monitoring station for the period from 1st October 2008 to 30 September 2009, were compared with the proposed standards of the National Environmental Management: Air Quality Act (Act 39 of 2004) South-African National Ambient Air Quality Standards, as well as the SANS 1929 target values for PM10. There were no exceedences of the measured pollutants against the National Air Quality Standards. The results found that the SANS 1929 standards were also not exceeded for SO2, NO2, O' and C6H6. The concentrations of PM10 equaled the SANS standards of 75pg/m3 on a few occasions. However, the SANS 1929 daily target values of 50pg/m3 were exceeded on a few occasions. Overall though, the air quality status of the research area was within the legislative conditions. Twenty six point three percent (26.3% n = 30) of the respondents did not indicate any symptoms of any air quality related illnesses during the study period.

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. Johannesburg, August 2015. / Petrol and diesel fumes are known to be anthropogenic sources of air pollutants that have a negative impact on both environmental and human health. In developing countries, attendants are still employed to pump fuel for customers. In South Africa gas pump attendants refuel vehicles with various octane unleaded petrol, lead replacement petrol (LRP) and diesel on a daily basis. Attendants are particularly at risk to adverse health effects associated with inhalation of hazardous air pollutants (HAPs). Of increasing concern in recent years are the volatile organic compounds (VOCs), with particular reference to the six aromatic hydrocarbons (benzene, toluene, ethyl benzene and three isomeric xylenes), namely the BTEX.

M.Sc. (Geography) / Severe Aeolian deposition of windblown dust from mine tailings storage facilities (TSFs) is a common phenomenon on the Witwatersrand, especially during the spring windy season. For communities around tailings storage facilities, this poses health and environmental challenges. This dissertation estimates the risk of adverse health effects resulting from human exposure to hazardous elements in particulate matter (sub 20 μm diameter) for selected tailings storage facilities: East Rand Gold and Uranium Company (ERGO); East Rand Proprietary Mine (ERPM); Crown Gold Recoveries (CGR) and Durban Roodepoort Deep (DRD). Samples of surface material from these TSFs were analysed for heavy metal content using the ICP-MS method. Other than the expected gold, five heavy metals (arsenic, cadmium, chromium, lead and uranium) exhibited enrichments in the tailings material significantly above average (greater than a factor of 2) crustal composition. These elements were selected for comprehensive risk assessment through airborne exposure routes. The mean ambient particulate concentration in air of 540 μg m-3 (used in the risk calculations) was based on a conservative worst-case exposure scenario. U.S. Environmental Protection Agency (US EPA) risk assessment methods were used to determine the inhalation and ingestion hazard quotient and hazard indices for adults and children. The sum of the hazard indices was below the non-cancer benchmark (hazard indices 1.0) considered to be acceptable for a lifetime exposure. The risk cancer included the excess life cancer risk for the inhalation and the ingestion risk. The total risk for both exposures was within the range of 1 in 1 000 000 to 100 in 1 000 000 - taken as “acceptable risk” by the U.S. Environmental Protection Agency for adults and children.

Lephalale Municipality is a predominantly rural Municipality with 38 villages, two townships (Marapong and Onverwacht) and one town, Lephalale. Lephalale, formerly known as Ellisras, is a town situated in the “heart of the Bushveld” in Limpopo province. The town is growing rapidly and more industries are becoming concentrated within this small town. The construction of Medupi power station which is underway and other projects such as the expansion of Grootegeluk mine (coal 3 and 4 projects), and road developments in the area; have led to concern about the ambient air quality of the area. Other possible future projects are the Coal to Liquid project by Sasol and the Coal Bed Methane project by Anglo American Thermal Coal. The purpose of this study is to determine the ambient air quality impact of the Matimba power station in the Lephalale area. The AERMOD model and ambient air quality data obtained from Eskom’s Grootstryd and Marapong monitoring stations were used to assess the ambient air quality of Lephalale. Sulphur dioxide and Nitrogen oxides were investigated. Both the model’s results and the ambient air quality monitoring data indicated that the power station contributes to high -ground level concentrations of Sulphur dioxide. AERMOD simulated the nitrogen oxides results as nitrogen dioxide. From the study it is concluded that the power station is not the only source of nitrogen oxides. Nitrogen oxides concentrations were associated with low-level sources. The relationship between the criteria pollutants in this study was assessed. The study found that there is no relationship between sulphur dioxide and nitrogen oxides. This finding was used to support the idea that sulphur dioxide and nitrogen oxides are from different sources. It was also established that seasonality has an influence on the ground level concentrations of pollutants in the area.

M. Tech. Environmental Health / Particulate Matter (PM) is a complex, heterogeneous mixture of smoke, soot, dust, salt, acids, and metals. Particulate Matter varies in concentration, size, chemical composition, surface area and sources of origin. Given the known ambient particulate pollution problem, the potential health risks posed by PM to the population of eMbalenhle are unknown. eMbalenhle (the study area) is a township located in Mpumalanga Province, about 12 km from Secunda. The area is surrounded by industries, power stations and mines, all of which are recognised emitters of PM. The main aim of this study was to assess the health risks posed by ambient PM10 exposure to the population of eMbalenhle.

M.Sc. (Environmental Management) / The use of atmospheric dispersion models to predict ground level pollutants concentrations has been on an increase in South Africa in the last decade. At this stage National Department of Environmental Affairs has published a draft document to provide guidelines on the type or use of models. Most Air Quality Specialists in the country make use of the United States Environmental Protection Agency approved atmospheric dispersion models to conduct air quality investigations. These models were developed in the United States of America after having considered the environmental set up and monitoring capabilities. In light of the above, much of the required input data are not readily available and calculations have been conducted to make up for the shortfall. For domestic emissions, quantifying the emissions factors is proving to be a challenge for modellers. They calculate emissions factors using different data sets from variable sources – sometimes the data are not up to date. This variability could potentially compromise the output of the model. This study aim was to model domestic emissions from an isolated rural township, Leandra, in the Mpumalanga Province – located within a nationally declared Highveld air quality management priority area – for two one month periods – in both the winter – July 2008 – and the summer – October 2008. This was achieved by using a United States Environmental Protection Agency approved AERMOD atmospheric dispersion model. Hourly surface measured meteorology data were obtained from the Langverwacht ambient air quality monitoring station and upper air data from the Irene monitoring station. The data were screened for any suspect values, formatted and then pre-processed by AERMET to be used by AERMOD. The study also investigated and compared the modelled time-series and monitored time-series data. This study calculated the effective emissions rate of 0.3 g PM10 s-1 m-2 by using a combination of monitored hourly PM10 concentrations and dispersion modelling time series data, for a typical Highveld township. Furthermore, the study revealed that, during winter when air is stagnant, Leandra was demonstrably isolated from other emissions sources of strength in the region – i.e. power station and domestic emissions were the dominant emissions sources. Under these circumstances, indoor and outdoor emissions were above the acceptable standards – i.e. they constituted unhealthy ambient air conditions. During summer – with the higher average wind speeds – Leandra was under the influence of industrial sources and the argument of isolation was not valid.

Thesis (MScEng)--University of Stellenbosch, 2000. / ENGLISH ABSTRACT: Air pollution is a major concern III the Cape Metropole. A major contributor to the air pollution problem is road transport. For this reason, a national vehicle emissions study is in progress with the aim of developing a national policy regarding motor vehicle emissions and control. Such a policy could bring about vehicle emission control and regulatory measures, which may have far-reaching social and economic effects. Air pollution models are important tools 10 predicting the effectiveness and the possible secondary effects of such policies. It is therefore essential that these models are fundamentally sound to maintain a high level of prediction accuracy. Complex air pollution models are available, but they require spatial, time-resolved information of emission sources and a vast amount of processing power. It is unlikely that South African cities will have the necessary spatial, time-resolved emission information in the near future. An alternative air pollution model is one that is based on the Gaussian Plume Model. This model, however, relies on gross simplifying assumptions that affect model accuracy. It is proposed that statistical and mathematical analysis techniques will be the most viable approach to modelling air pollution in the Cape Metropole. These techniques make it possible to establish statistical relationships between pollutant emissions, meteorological conditions and pollutant concentrations without gross simplifying assumptions or excessive information requirements. This study investigates two analysis techniques that fall into the aforementioned category, namely, Non-linear Time Series Analysis (specifically, the method of delay co-ordinates) and Singular Spectrum Analysis (SSA). During the past two decades, important progress has been made in the field of Non-linear Time Series Analysis. An entire "toolbox" of methods is available to assist in identifying non-linear determinism and to enable the construction of predictive models. It is argued that the dynamics that govern a pollution system are inherently non-linear due to the strong correlation with weather patterns and the complexity of the chemical reactions and physical transport of the pollutants. In addition to this, a statistical technique (the method of surrogate data) showed that a pollution data set, the oxides of Nitrogen (NOx), displayed a degree of non-linearity, albeit that there was a high degree of noise contamination. This suggested that a pollution data set will be amenable to non-linear analysis and, hence, Non-linear Time Series Analysis was applied to the data set. SSA, on the other hand, is a linear data analysis technique that decomposes the time series into statistically independent components. The basis functions, in terms of which the data is decomposed, are data-adaptive which makes it well suited to the analysis of non-linear systems exhibiting anharmonic oscillations. The statistically independent components, into which the data has been decomposed, have limited harmonic content. Consequently, these components are more amenable to prediction than the time series itself. The fact that SSA's ability has been proven in the analysis of short, noisy non-linear signals prompted the use of this technique. The aim of the study was to establish which of these two techniques is best suited to the modelling of air pollution data. To this end, a univariate model to predict NOx concentrations was constructed using each of the techniques. The prediction ability of the respective model was assumed indicative of the accuracy of the model. It was therefore used as the basis against which the two techniques were evaluated. The procedure used to construct the model and to quantify the model accuracy, for both the Non-linear Time Series Analysis model and the SSA model, was consistent so as to allow for unbiased comparison. In both cases, no noise reduction schemes were applied to the data prior to the construction of the model. The accuracy of a 48-hour step-ahead prediction scheme and a lOO-hour step-ahead prediction scheme was used to compare the two techniques. The accuracy of the SSA model was markedly superior to the Non-linear Time Series model. The paramount reason for the superior accuracy of the SSA model is its adept ability to analyse and cope with noisy data sets such as the NOx data set. This observation provides evidence to suggest that Singular Spectrum Analysis is better suited to the modelling of air pollution data. It should therefore be the analysis technique of choice when more advanced, multivariate modelling of air pollution data is carried out. It is recommended that noise reduction schemes, which decontaminate the data without destroying important higher order dynamics, should be researched. The application of an effective noise reduction scheme could lead to an improvement in model accuracy. In addition to this, the univariate SSA model should be extended to a more complex multivariate model that explicitly encompasses variables such as traffic flow and weather patterns. This will explicitly expose the inter-relationships between the variables and will enable sensitivity studies and the evaluation of a multitude of scenarios. / AFRIKAANSE OPSOMMING: Die hoë vlak van lugbesoedeling in die Kaapse Metropool is kommerwekkend. Voertuie is een van die hoofoorsake, en as gevolg hiervan word 'n landswye ondersoek na voertuigemissie tans onderneem sodat 'n nasionale beleid opgestel kan word ten opsigte van voertuigemissie beheer. Beheermaatreëls van so 'n aard kan verreikende sosiale en ekonomiese uitwerkings tot gevolg hê. Lugbesoedelingsmodelle is van uiterste belang in die voorspelling van die effektiwiteit van moontlike wetgewing. Daarom is dit noodsaaklik dat hierdie modelle akkuraat is om 'n hoë vlak van voorspellingsakkuraatheid te handhaaf. Komplekse modelle is beskikbaar, maar hulle verg tyd-ruimtelike opgeloste inligting van emmissiebronne en baie berekeningsvermoë. Dit is onwaarskynlik dat Suid-Afrika in die nabye toekoms hierdie tydruimtelike inligting van emissiebronne gaan hê. 'n Alternatiewe lugbesoedelingsmodel is dié wat gebaseer is op die "Guassian Plume". Hierdie model berus egter op oorvereenvoudigde veronderstellings wat die akkuraatheid van die model beïnvloed. Daar word voorgestel dat statistiese en wiskundige analises die mees lewensvatbare benadering tot die modellering van lugbesoedeling in die Kaapse Metropool sal wees. Hierdie tegnieke maak dit moontlik om 'n statistiese verwantskap tussen besoedelingsbronne, meteorologiese toestande en besoedeling konsentrasies te bepaal sonder oorvereenvoudigde veronderstellings of oormatige informasie vereistes. Hierdie studie ondersoek twee analise tegnieke wat in die bogenoemde kategorie val, naamlik, Nie-lineêre Tydreeks Analise en Enkelvoudige Spektrale Analise (ESA). Daar is in die afgelope twee dekades belangrike vooruitgang gemaak in die studieveld van Nie-lineêre Tydreeks Analise. 'n Volledige stel metodes is beskikbaar om nie-lineêriteit te identifiseer en voorspellingsmodelle op te stel. Dit word geredeneer dat die dinamika wat 'n besoedelingsisteem beheer nie-lineêr is as gevolg van die sterk verwantskap wat dit toon met weerpatrone asook die kompleksiteit van die chemiese reaksies en die fisiese verplasing van die besoedelingstowwe. Bykomend verskaf 'n statistiese tegniek (die metode van surrogaatdata) bewyse dat 'n lugbesoedelingsdatastel, die okside van Stikstof (NOx), melineêre gedrag toon, alhoewel daar 'n hoë geraasvlak is. Om hierdie rede is die besluit geneem om Nie-lineêre Tydreeks Analise aan te wend tot die datastel. ESA daarenteen, is 'n lineêre data analise tegniek. Dit vereenvoudig die tydreeks tot statistiese onafhanklike komponente. Die basisfunksies, in terme waarvan die data vereenvoudig is, is data-aanpasbaar en dit maak hierdie tegniek gepas vir die analise van nielineêre sisteme. Die statisties onafhanklike komponente het beperkte harmoniese inhoud, met die gevolg dat die komponente aansienlik makliker is om te voorspel as die tydreeks self. ESA se effektiwitiet is ook al bewys in die analise van kort, hoë-graas nie-lineêre seine. Om hierdie redes, is ESA toegepas op die lugbesoedelings data. Die doel van die ondersoek was om vas te stel watter een van die twee tegnieke meer gepas is om lugbesoedelings data te analiseer. Met hierdie doelwit in sig, is 'n enkelvariaat model opgestel om NOx konsentrasies te voorspel met die gebruik van elk van die tegnieke. Die voorspellingsvermoë van die betreklike model is veronderstelom as 'n maatstaf van die model se akkuraatheid te kan dien en dus is dit gebruik om die twee modelle te vergelyk. 'n Konsekwente prosedure is gevolg om beide die modelle te skep om sodoende invloedlose vergelyking te verseker. In albei gevalle was daar geen geraasverminderings-tegnieke toegepas op die data nie. Die akuraatheid van 'n 48-uur voorspellingsmodel en 'n 100-uur voorspellingsmodel was gebruik vir die vergelyking van die twee tegnieke. Daar is bepaal dat die akkuraatheid van die ESA model veel beter as die Nie-lineêre Tydsreeks Analise is. Die hoofrede vir die ESA se hoër akkuraatheid is die model se vermoë om data met hoë geraasvlakke te analiseer. Hierdie ondersoek verskaf oortuigende bewyse dat Enkelvoudige Spektrale Analiese beter gepas is om lugbesoedelingsdata te analiseer en gevolglik moet hierdie tegniek gebruik word as meer gevorderde, multivariaat analises uitgevoer word. Daar word aanbeveel dat geraasverminderings-tegnieke, wat die data kan suiwer sonder om belangrike hoë-orde dinamika uit te wis, ondersoek moet word. Hierdie toepassing van effektiewe geraasverminderings-tegniek sal tot 'n verbetering in model-akkuraatheid lei. Aanvullend hiertoe, moet die enkele ESA model uitgebrei word tot 'n meer komplekse multivariaat model wat veranderlikes soos verkeersvloei en weerpatrone insluit. Dit sal die verhoudings tussen veranderlikes ten toon stel en sal sensitiwiteit-analises en die evaluering van menigte scenarios moontlik maak.