Functional Data Analysis for Environmental Pollutants and Health

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

El Niño as a predictor of round sardinella distribution along the northwest African coast

The El Niño Southern Oscillation (ENSO) produces global marine environment conditions that can cause changes in abundance and distribution of distant fish populations worldwide. Understanding mechanisms acting locally on fish population dynamics is crucial to develop forecast skill useful for fisheries management. The present work addresses the role played by ENSO on the round sardinella population biomass and distribution in the central-southern portion of the Canary Current Upwelling System (CCUS). A combined physical-biogeochemical framework is used to understand the climate influence on the hydrodynamical conditions in the study area. Then, an evolutionary individual-based model is used to simulate the round sardinella spatio-temporal biomass variability. According to model experiments, anomalous oceanographic conditions forced by El Niño along the African coast cause anomalies in the latitudinal migration pattern of the species. A robust anomalous increase and decrease of the simulated round sardinella biomass is identified in winter off the Cape Blanc and the Saharan coast region, respectively, in response to El Niño variations. The resultant anomalous pattern is an alteration of the normal migration between the Saharan and the Mauritanian waters. It is primarily explained by the modulating role that El Niño exerts on the currents off Cape Blanc, modifying therefore the normal migration of round sardinella in the search of acceptable temperature conditions. This climate signature can be potentially predicted up to six months in advance based on El Niño conditions in the Pacific.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Cluster analysis of angiotensin biomarkers to identify antihypertensive drug treatment in population studies

Background: The recent progress in molecular biology generates an increasing interest in investigating molecular biomarkers as markers of response to treatments. The present work is motivated by a study, where the objective was to explore the potential of the molecular biomarkers of renin-angiotensin-aldosterone system (RAAS) to identify the undertaken antihypertensive treatments in the general population. Population-based studies offer an opportunity to assess the effectiveness of treatments in real-world scenarios. However, lack of quality documentation, especially when electronic health record linkage is unavailable, leads to inaccurate reporting and classification bias. Method: We present a machine learning clustering technique to determine the potential of measured RAAS biomarkers for the identification of undertaken treatments in the general population. The biomarkers were simultaneously determined through a novel mass-spectrometry analysis in 800 participants of the Cooperative Health Research In South Tyrol (CHRIS) study with documented antihypertensive treatments. We assessed the agreement, sensitivity and specificity of the resulting clusters against known treatment types. Through the lasso penalized regression, we identified clinical characteristics associated with the biomarkers, accounting for the effects of cluster and treatment classifications. Results: We identified three well-separated clusters: cluster 1 (n = 444) preferentially including individuals not receiving RAAS-targeting drugs; cluster 2 (n = 235) identifying angiotensin type 1 receptor blockers (ARB) users (weighted kappa κw = 74%; sensitivity = 73%; specificity = 83%); and cluster 3 (n = 121) well discriminating angiotensin-converting enzyme inhibitors (ACEi) users (κw = 81%; sensitivity = 55%; specificity = 90%). Individuals in clusters 2 and 3 had higher frequency of diabetes as well as higher fasting glucose and BMI levels. Age, sex and kidney function were strong predictors of the RAAS biomarkers independently of the cluster structure. Conclusions: Unsupervised clustering of angiotensin-based biomarkers is a viable technique to identify individuals on specific antihypertensive treatments, pointing to a potential application of the biomarkers as useful clinical diagnostic tools even outside of a controlled clinical setting

A Bayesian hierarchical approach for spatial analysis of climate model bias in multi-model ensembles

Coupled atmosphere–ocean general circulation models are key tools to investigate climate dynamics and the climatic response to external forcings, to predict climate evolution and to generate future climate projections. Current general circulation models are, however, undisputedly affected by substantial systematic errors in their outputs compared to observations. The assessment of these so-called biases, both individually and collectively, is crucial for the models’ evaluation prior to their predictive use. We present a Bayesian hierarchical model for a unified assessment of spatially referenced climate model biases in a multi-model framework. A key feature of our approach is that the model quantifies an overall common bias that is obtained by synthesizing bias across the different climate models in the ensemble, further determining the contribution of each model to the overall bias. Moreover, we determine model-specific individual bias components by characterizing them as non-stationary spatial fields. The approach is illustrated based on the case of near-surface air temperature bias in the tropical Atlantic and bordering regions from a multi-model ensemble of historical simulations from the fifth phase of the Coupled Model Intercomparison Project. The results demonstrate the improved quantification of the bias and interpretative advantages allowed by the posterior distributions derived from the proposed Bayesian hierarchical framework, whose generality favors its broader application within climate model assessment

Spatio-temporal quantification of climate model errors in a Bayesian framework

Numerical output from coupled atmosphere-ocean general circulation models is a key tool to investigate climate dynamics and the climatic response to external forcings, and to generate future climate projections. Coupled climate models are, however, affected by substantial systematic errors or biases compared to observations. Assessment of these systematic errors is vital for evaluating climate models and characterizing the uncertainties in projected future climates. In this paper, we develop a spatio-temporal model based on a Bayesian hierarchical framework that quantifies systematic climate model errors accounting for their underlying spatial coherence and temporal dynamics. The key feature of our approach is that, unlike previous studies that focused on empirical and purely spatial assessments, it simultaneously determines the spatial and temporal features of model errors and their associated uncertainties. This is achieved by representing the spatio-temporally referenced data using weighting kernels that capture the spatial variability efficiently while reducing the high dimensionality of the data, and allowing the coefficients linking the weighting kernels to temporally evolve according to a random walk. Further, the proposed method characterizes the bias in the mean state as the time-invariant average portion of the spatio-temporal climate model errors. To illustrate our method, we present an analysis based on the case of near-surface air temperature over the southeastern tropical Atlantic and bordering region from a multi-model ensemble mean of historical simulations from the fifth phase of the Coupled Model Intercomparison Project. The results demonstrate the improved characterization of climate model errors and identification of non-stationary temporal and spatial patterns