Effect of atmospheric pollution on the health of soccer players using generalized additive models

Due to the rapid development of the economy, increasing level of urbanization, and industrialization, air pollution has transformed from the traditional coal-fired type to the composite pollution of coal-fired type and motor vehicle exhaust. With this transformation, the excessive release of many gases results in drastic climatic changes, global warming, ozone layer depletion, and more, which dramatically affect human lives in many sectors including sports. Based on the above, this research employs the Generalized Additive Model (GAM) to study and predict the effect of atmospheric pollution on the health of soccer players. First, the paper evaluates the impact of air pollution on the physical health of soccer players. It starts with a rigorous evaluation of the influence of various atmospheric pollutants on the health of athletes. Second, it investigates data analysis using numerous tests, including the Augmented Dickey–Fuller test, to evaluate the stationarity of the data and choose the most suitable models for further examination. Third, the research explores the complex correlations between meteorological factors and atmospheric pollutants by considering the dynamic interplay of environmental variables on health results. This analysis offers a nuanced understanding of how meteorological conditions affect pollutant concentrations and their potential health implications. Finally, the research constructs a prediction model that incorporates these complicated associations by employing GAMs with cubic spline functions to capture the non-linear interactions among multiple factors influencing soccer players' health. This advanced modeling technique offers a full examination of air pollution's complex effects, opening the way for useful findings in the disciplines of public health, environmental management, and sports medicine. The experimental results show that the proposed GAM outperforms the other models in terms of accuracy (85%), precision (88%), F1-Score (87%), consistency (90%), recall (86%), specificity (91%), and sensitivity (82%). These remarkable achievements highlight the importance of the proposed GAM in predicting the effect of air pollution on soccer players' health.