DUGiDocsAir 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
L'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc-sa/4.0/