Statistical Analysis of NO2 Emissions from Eskom’s Majuba Coal-Fired Power Station in Mpumalanga, South Africa

Abstract

Gaseous emissions from coal combustion during electricity generation continue to be a challenge in South Africa. To meet the regulatory limits, it is crucial to understand the statistical distribution of such emissions from the power generating plants. The current paper characterises the nitrogen dioxide (NO2) emissions from Eskom’s Majuba coal-fired power station by making use of the quantile–quantile (QQ) plots and derivative plots of three statistical parent distributions, namely, the Weibull, Lognormal, and Pareto distributions. These distributions are fitted and compared according to their tail heaviness as they cater for data that may have tails lighter or heavier than that of the Exponential distribution. Of the three distributions evaluated here, the Lognormal gave the best fit for the full body of the data according to the QQ and derivative plots, and the goodness-of-fit tools (bootstrap Kolmogorov–Smirnov (KS), Anderson–Darling (AD), Akaike Information Criterion (AIC), Schwarz’s Bayesian Information Criterion (BIC), and the BIC-corrected Vuong test for non-nested distributions). The Lognormal distribution also gave the best fit for the overall upper tail, while at the very top six largest NO2 emission observations in the upper tail, a Pareto-type tail was observed. The practical implication of a heavy tail like the Pareto is that it models more frequent larger sized NO2 emissions compared to lighter tails like the Weibull and Lognormal tails. The methods used in this study give a framework on how emissions of NO2 from a coal-fired power station can be modelled using statistical parent distributions whilst also taking into account the distribution of the data in the tails which is mostly ignored when fitting statistical parent distributions. Understanding the distribution of the upper tail is very important since higher and rare emissions are of the most concern and are dangerous to human health and the environment.