Forecasting 7BE concentrations in surface air using time series analysis

7Be is a cosmogenic radionuclide widely used as an atmospheric tracer, whose evaluation and forecasting can provide valuable information on changes in the atmospheric behavior. In this study, measurements of 7Be concentrations were made each month during the period 2007-2015 from samples of atmospheric aerosols filtered from the air. The aim was to propose a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to develop an explanatory and predictive model of 7Be air concentrations. The Root Mean Square Error (RMSE) and the Adapted Mean Absolute Percentage Error (AMAPE) were selected to measure forecasting accuracy in identifying the best historical data time window to explain 7Be concentrations. A measure based on the variance of forecast errors was calculated to determine the impact of the model uncertainty on forecasts. We concluded that the SARIMA method is a powerful explanatory and predictive technique for explaining 7Be air concentrations in a longterm series of at least eight years of historical data to forecast 7Be concentration trends up to one year in advance.