Suchergebnisse

Air quality assessment regarding metals and metalloids using experimental measurements is expensive and time consuming due to the cost and time required for the analytical determination of the levels of these pollutants. According to the European Union (EU) Air Quality Framework Directive (Directive 2008/50/EC), other alternatives, such as objective estimation techniques, can be considered for ambient air quality assessment in zones and agglomerations where the level of pollutants is below a certain concentration value known as the lower assessment threshold. These conditions occur in urban areas in Cantabria (northern Spain). This work aims to estimate the levels of As, Cd, Ni and Pb in airborne PM10 at two urban sites in the Cantabria region (Castro Urdiales and Reinosa) using statistical models as objective estimation techniques. These models were developed based on three different approaches: partial least squares regression (PLSR), artificial neural networks (ANNs) and an alternative approach consisting of principal component analysis (PCA) coupled with ANNs (PCA-ANN). Additionally, these models were externally validated using previously unseen data. The results show that the models developed in this work based on PLSR and ANNs fulfil the EU uncertainty requirements for objective estimation techniques and provide an acceptable estimation of the mean values. As a consequence, they could be considered as an alternative to experimental measurements for air quality assessment regarding the aforementioned pollutants in the study areas while saving time and resources. ; The authors gratefully acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through the Project CMT2010-16068. The authors also thank the Regional Environment Ministry of the Cantabria Government for providing the PM10 samples at the Castro Urdiales and Reinosa sites.

Soil organic matter is seriously affected by fires and suffers changes in stock, composition, and distribution. In the North-West side of the Cantabrian Range (northern Spain) fires are very common. In order to develop a cartographic technique to map areas with high carbon stocks caused by fire, we test a technique based on calibrated VIS-NIR soil organic carbon models and hyperspectral images. Total (TOC) and oxidizable carbon (OC) were measured in 89 soil samples. The samples were scanned with VIS-NIR spectrometer (400–2500 nm), and the spectra were resampled to the hyperspectral image channels. Spectroscopic models for TOC and OC were fitted (R2>0.81) using partial least squares regression (PLSR). The predictions were regionalized to the hyperspectral image and the results validated with a new soil population consisting of 12 Valeri plots collected in burned slopes of the study area under heather vegetation. In soil samples, TOC and OC values are highly correlated (R = 0.92), and the coefficients of the PLSR models have a similar pattern, which suggests similar organic components. Nevertheless, there are significant differences in the values of the regression coefficients, much higher in the TOC model except at 560 and 2054 nm that might be interpreted as labile carbon components, and at 1590 nm. At this wavelength the coefficient of TOC is positive and OC is negative, and it could be interpreted as hydrocarbons components present in the TOC model. ; This work was funded by the Government of Asturias through the project SV-PA-13-ECOEMP-40. J. Peón acknowledges a PhD Grant from the University of Oviedo and a PhD Grant "Severo Ochoa" from the Government of Asturias (BP14-104). We also acknowledge INTA for providing the images and for the geometric and atmospheric correction.

RESUMEN: La legislación europea permite que la evaluación de la calidad del aire se pueda efectuar únicamente mediante técnicas de modelización y/o de estimación objetiva en aquellas zonas en las que los niveles de contaminantes se encuentren por debajo de un cierto umbral inferior de evaluación. Esta Tesis Doctoral tiene como objetivo el desarrollo de una metodología para la estimación de los niveles en aire ambiente de metales y semimetales, y la evaluación de la adecuación de dicha metodología como herramienta de evaluación de la calidad del aire de acuerdo con los criterios de calidad establecidos por la legislación europea para las técnicas de modelización y de estimación objetiva. La metodología propuesta se fundamenta en el empleo de modelos estadísticos desarrollados en base a técnicas de regresión por mínimos cuadrados parciales (PLSR) y de redes neuronales artificiales (ANNs), que permiten determinar la concentración en inmisión de los contaminantes objeto de estudio en zonas urbanas a escala local en función de los niveles de otros contaminantes atmosféricos y de las condiciones meteorológicas, lo que reduciría las necesidades de medidas experimentales para su posterior análisis en laboratorio. ; ABSTRACT: The European legislation permits that air quality assessment may be conducted solely by modelling and/or objective estimation techniques in those zones and agglomerations where the levels of pollutants are below a certain lower assessment threshold. This Thesis aims to develop a methodology for the estimation of the levels of metals and metalloids in ambient air, and the evaluation of the adequacy of this methodology as an air quality assessment tool according to the quality criteria established by the European legislation for modelling and objective estimation techniques. The proposed methodology involves the development of statistical models based on partial least squares regression (PLSR) and artificial neural networks (ANNs), which allows the determination of the immission concentration of the studied pollutants in urban areas at a local scale as a function of the levels of other air pollutants and the meteorological conditions, reducing the number of experimental measurements for their subsequent analysis in the laboratory. ; Este trabajo ha sido realizado en el grupo de investigación Desarrollo de Procesos Químicos y Control de Contaminantes (DePRO) perteneciente al Departamento de Ingenierías Química y Biomolecular de la Universidad de Cantabria. La investigación ha sido financiada por el Ministerio de Economía y Competitividad de España (MINECO) a través del proyecto Herramientas para la evaluación de los metales pesados y compuestos orgánicos persistentes (COPs) en aire ambiente a escala local/regional con referencia CTM2010-16068. Durante la ejecución del presente trabajo, su autor, Germán Santos Bregel, ha disfrutado de una ayuda predoctoral de Formación de Personal Investigador (FPI) del Ministerio de Economía y Competitividad de España concedida por resolución de 21 de julio de 2011, con referencia BES-2011- 047908. Asimismo, Germán Santos Bregel ha disfrutado también de una ayuda económica a la movilidad predoctoral del Ministerio de Economía y Competitividad de España concedida por resolución de 23 de abril de 2013, con referencia EEBB-I-13-07691, para la realización de una estancia breve de investigación de tres meses de duración en el centro de investigación Maison de la Recherche en Environnement Industriel (MREI) adscrito a la Université du Littoral Côte d'Opale en la ciudad de Dunkerque (Francia). Mi más sincero agradecimiento a dichas instituciones.

This work was partially supported by University of Granada (Spain) within the framemork of the funding corresponding to program 'precompetitive research projects for young researchers'. Funding for open access charge: University of Granada/CBUA. AMJC wish to acknowledge the Department of Economic Transformation, Industry, Knowledge and Universities belong to Regional Andalusia Government (Spain) for the Postdoctoral fellowship (DOC_00121). In addition, AAC wants to express their sincere gratitude to the Spanish Ministry of Universities for a pre-doctoral fellowship FPU (FPU20/04711, Formaci ' on del Profesorado Universitario). ; Spatially offset Raman spectroscopy (SORS) is a novel technique capable of measuring samples through the original packaging and recovering the spectra without the contribution of surface layers. Here, a portable SORS equipment was used to measure 62 samples of margarines and fat spreads through the original plastic container. Chemometric tools were used to analyse the data obtained. A total of 25 classification models were developed based on: (i) geographical origin, (ii) vegetable oils and (iii) some significant minor constituents present in the samples. Partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and soft independent modelling of class analogy (SIMCA) were used for model classification. Quantitative analysis using the partial least squares regression (PLSR) method was also performed to determine the total fat content. In parallel, a benchtop conventional Raman spectrometer was used to analyse the same samples, develop the models with the same training and validation sets in order to compare the results. The calculated classification performance metrics showed better classification models from SORS data than conventional Raman spectroscopy (CRS), highlighting the one-class SIMCA models for margarines containing phytosterols, olive oil or linseed oil. These models exhibited very high predictability (performance parameters with values equal to or ...

Air quality assessment, required by the European Union (EU) Air Quality Directive, Directive 2008/50/EC, is part of the functions attributed to Environmental Management authorities. Based on the cost and time consumption associated with the experimental works required for the air quality assessment in relation to the EU-regulated metal and metalloids, other methods such as modelling or objective estimation arise as competitive alternatives when, in accordance with the Air Quality Directive, the levels of pollutants permit their use at a specific location. This work investigates the possibility of using statistical models based on Partial Least Squares Regression (PLSR) and Artificial Neural Networks (ANNs) to estimate the levels of arsenic (As), cadmium (Cd), nickel (Ni) and lead (Pb) in ambient air and their application for policy purposes. A methodology comprising the main steps that should be taken into consideration to prepare the input database, develop the model and evaluate their performance is proposed and applied to a case of study in Santander (Spain). It was observed that even though these approaches present some difficulties in estimating the individual sample concentrations, having an equivalent performance they can be considered valid for the estimation of the mean values - those to be compared with the limit/target values - fulfilling the uncertainty requirements in the context of the Air Quality Directive. Additionally, the influence of the consideration of input variables related to atmospheric stability on the performance of the studied statistical models has been determined. Although the consideration of these variables as additional inputs had no effect on As and Cd models, they did yield an improvement for Pb and Ni, especially with regard to ANN models. ; This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the Projects CTM2010-16068 and CTM2013-43904R. Germán Santos thanks MINECO for his FPI research fellowship (BES-2011-047908).