Utilizing innovative input data and ANN modeling to predict atmospheric gross beta radioactivity in Spain

Abstract

This study introduces a new methodology aimed at predicting gross $\beta$ levels in the atmosphere. The methodology incorporates input data consisting of local meteorological and synoptic variables, alongside temporal lags and residence time of air masses, to predict gross $\beta$ activity concentration in the atmosphere. Weekly measurements conducted between January 2006 and December 2017 at various sampling sites across diverse locations with different climatic and geographical conditions in Spain were utilized. A high-performance Artificial Neural Network (ANN) model was constructed for this purpose. Across all locations, strong linear relationships are evident between predicted and actual values, with correlation coefficients (R) ranging from 0.86 to 0.92. Higher R values indicate a more robust correlation. Additionally, R-squared values, ranging from 0.7320 to 0.8502, further affirm the model’s ability to explain a significant proportion of the variance in gross $\beta$ activity. Moreover, the relatively low Mean Squared Error (MSE) values, spanning from 0.00966 to 0.11115, and Mean Absolute Error (MAE) values, ranging from 0.08176 to 0.11747, underscore the model’s accuracy in gross $\beta$ activity prediction. The predictive capabilities of the models are robustly demonstrated, showcasing promising results. According to the results of the sensitivity analysis, the category of synoptic parameters has the most important influence on the prediction of atmospheric $\beta$ radioactivity levels, namely air temperature, potential temperature and relative humidity. Regarding the residence time of the air masses, the periods spent over land or water have the most effect on gross $\beta$ levels.