Air pollutants and mental health of children in a rural region using compositional spatio-temporal models

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This doctoral thesis addresses the relationship between the air pollutants particulate matter (PM10), nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), carbon monoxide (CO), and mental disorders in children and adolescents in a rural area with limited pollution monitoring stations. Its innovative approach lies in the fusion of multiple analytical approaches to thoroughly examine the impact of atmospheric pollutants on the following mental disorders: attention deficit hyperactivity disorder (ADHD), anxiety, mental health issues, and eating disorders. The methodological contribution is combining spatio-temporal models, Bayesian inference with the Integrated Nested Laplace Approximation (INLA), and Compositional Data Analysis (CoDa) with a total. This combination allows for a detailed analysis of atmospheric pollutant concentrations in spatial and temporal terms, considering both overall pollution and trade-offs between pollutants, including covariates and providing a more precise predictive framework for rural areas with limited monitoring infrastructure. In a first step, pollutant concentrations are estimated in census tracts with no monitoring stations. In a second step, estimated concentrations are used as predictors of mental health. The most relevant results in the first step indicate that the trade-off between NO2 and O3 exhibits the highest variability and the best predictive accuracy in both time and space. Total pollution levels rank second in variability but have low spatial predictive accuracy. The most relevant results in the second step are that higher exposure to NO2, O3, and SO2 affects behavioural and developmental disorders, while anxiety is linked to the concentration of PM10, O3, and SO2. Furthermore, overall pollution increases the risk of ADHD and eating disorders. In summary, children living in rural areas are not exempt from health risks related to air pollution, and the combination of spatio-temporal models, Bayesian inference, and compositional data analysis with a total, makes it possible to estimate the relationship between mental health problems and pollutant concentrations ​
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