Volcanic air pollution, known as vog (volcanic smog) has recently become a major issue in the Hawaiian
islands. Vog is caused when volcanic gases react with oxygen and water vapor. It consists of a mixture of
gases and aerosols, which include sulfur dioxide and other sulfates. The source of the volcanic gases is
the continuing eruption of Mount Kilauea. This paper studies predicting vog using statistical methods.
The data sets include time series for SO2 and SO4, over locations spanning the west, south and southeast
coasts of Hawaii, and the city of Hilo. The forecasting models include regressions and neural networks,
and a frequency domain algorithm. The most typical pattern for the SO2 data is for the frequency domain
method to yield the most accurate forecasts over the first few hours, and at the 24 h horizon. The neural
net places second. For the SO4 data, the results are less consistent. At two sites, the neural net generally
yields the most accurate forecasts, except at the 1 and 24 h horizons, where the frequency domain
technique wins narrowly. At one site, the neural net and the frequency domain algorithm yield
comparable errors over the first 5 h, after which the neural net dominates. At the remaining site, the
frequency domain method is more accurate over the first 4 h, after which the neural net achieves smaller
errors. For all the series, the average errors are well within one standard deviation of the actual data at all
the horizons. However, the errors also show irregular outliers. In essence, the models capture the central
tendency of the data, but are less effective in predicting the extreme events.