Up-to-date information on present and near-future air quality help people avoid exposure to high levels of air pollution. We apply different machine learning models to significantly improve traditional forecasts of PM₁₀, NO_x, and O₃ in Stockholm, Sweden. It is shown that forecasts of all air pollutants are improved by the input of lagged measurements and taking calendar information into account. The final modelled errors are substantially smaller than uncertainties in the measurements.