Modeling Emissions Sources and Characterization in a Dense Low-Income Township Near Industrial Zones and Mine Dumps (Case of Tsakane, South Africa).

Abstract

The sources of pollution in Tsakane township, which is situated within the City of Ekurhuleni in the province of Gauteng, South Africa, are investigated in this paper. The City of Ekurhuleni has the most industrial activities reported on South Africa's National Atmospheric Emission Inventory System (NAEIS), accounting for 40% of all listed activities in the country. The problem of suburban air pollution in South Africa is mainly associated with dense low-income areas like townships. The aim of this paper was to investigate atmospheric concentration correlation parameters, emissions roses, and probability modelling functions in order to analyse and classify significant emission sources affecting the township. Sulfur dioxide, nitrogen dioxide, ozone, and PM10 were the focus of the investigation. The probability functions for identifying and characterizing unknown or hidden sources of pollution were developed using hourly data. Furthermore, K-clustering algorithm analysis technique was used to provide graphical context for sources. PM10, ozone, sulfur dioxide, and nitrogen dioxide have all been identified as having directional pollution sources that are problematic and the results provide baseline data for a detailed understanding of current emission levels and possible sources.