Arisido, Maeregu Woldeyes (2015) Functional Data Analysis for Environmental Pollutants and Health. [Tesi di dottorato]
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The adverse health effect of exposure to high pollutant concentration has been the focus of many recent studies. This is particularly true for ground level ozone which is considered in the present thesis. The effect has been estimated at different geographic locations, and it has been shown that it may be spatially heterogeneous. Within such widely accepted studies, two major issues arise which are the focus of this thesis: how to best measure daily individual exposure to a pollutant and how the health effect of the exposure is affected by geographic location both in strength and shape. The first issue is related to the fact that the concentration of ozone varies widely during the day, producing a distinctive daily pattern. Traditionally, the daily pattern of the pollutant is collapsed to a single summary figure which is then taken to represent daily individual exposure. In this thesis, we propose a more accurate approaches to measure pollutant exposure which address the limitations in the use of the standard exposure measure. The methods are based on principle of functional data analysis, which treats the daily pattern of concentration as a function to account for temporal variation of the pollutant. The predictive efficiency of our approach is superior to that of models based on the standard exposure measures. We propose a functional hierarchical approach to model data which are coming from multiple geographic locations, and estimate pollutant exposure effect allowing daily variation and spatial heterogeneity of the effect at once. The approach is general and can also be considered as the analogue of the multilevel models to the case in which the predictor is functional and the response is scalar.
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Numerosi studi recenti hanno mostrato l'effetto dannoso che l'esposizione a elevate concentrazioni di inquinanti ha sulla salute umana.
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