Resumen

BELELIE, Monray D; BURGER, Roelof Ρ; MKHATSHWA, Gabi  y  PIKETH, Stuart J. Assessing the impact of Eskom power plant emissions on ambient air quality over KwaZamokuhle. Clean Air J. [online]. 2019, vol.29, n.1, pp.29-37. ISSN 2410-972X.  http://dx.doi.org/10.17159/2410-972x/2019/v29n1a2.

Coal-fired power plants are considered a major source of criteria air pollutants. The existence of such activities close to densely populated areas has an impact on human health and more generally on the environment. The impact of a pollutant typically depends on its residence time and the existence of background concentration levels. This study evaluates the dispersion of PM₂₅, SO₂ and NO_X emissions from Eskom power plants (Arnot, Hendrina, and Komati) located close to KwaZamokuhle Township. AERMOD was used to assess the contribution of each plant to the air quality of the township. This steady-state dispersion model was used to simulate surface concentrations (1-hour, 24-hour and annual average concentrations) on a 50km domain for 2015-2017. The modelled results together with data obtained from Eskom's KwaZamokuhle monitoring site were used to estimate the extent to which these power plants contribute to the ambient air quality of KwaZamokuhle Township. The results confirm that the power plants do contribute to concentrations of PM₂₅, SO₂, and NO_x in the ambient air of the township. However, based on a comparison between the modelled and monitored data, it was inferred that power plants are not the only significant source of these criteria pollutants. Evidence from temporal variations in the monitored data shows that domestic burning is likely the major contributor since the variability is more closely associated with burning habits. It is therefore likely that existing regulatory strategies that focus mostly on the industrial sector may not be successful in improving ambient air quality in low-income settlements like KwaZamokuhle.

Palabras clave : Coal-fired power plants; emissions; AERMOD; atmospheric dispersion modelling; PM₂₅; SO₂; NO_X.

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