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.