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SOURCE IDENTIFICATION OF ENVIRONMENTAL POLLUTANTS USING CHEMICAL ANALYSIS AND POSITIVE MATRIX FACTORIZATION
Get PDFMultivariate modeling techniques are successfully used in different areas of environmental research because of their ability to process large data sets. The main objective of their application lies in the determination of data structures and hidden information which account for the data set variability.
This thesis work seeks to explore the application of the positive matrix factorization (PMF) technique to different geochemical data sets on three spatial scales: local, pan-regional and pan-European. In particular, we focus on PMF identification of pollutants/contamination sources (e.g., anthropogenic and natural pollution) and chemical/physical processes (e.g., mineralization, weathering and corrosion) characterizing the data sets under examination.
PMF analysis was carried out on four data sets with different spatial scale:
at local scale, geochemical characteristics of soil samples at the abandoned Coren del Cucì mine dump were examined. A GIS-based approach was also combined with PMF results for a better source resolution. Five factors were determined: (i) two geo-morphological backgrounds characteristic of the area outside the dump; (ii) a source of mineralization situated inside the waste disposal area; and (iii) two different geochemical anomaly zones;
at a national level, eleven alpine lakes site in the Northern Italy were considered. X-ray fluorescence analyses on lake sediments were evaluated by PMF. Four interpretable mineralogical/chemical features were identified: (i) phosphate and sulphur source; (ii) carbonates; (iii) silicates; and (iv) heavy metal-bearing minerals. Also, to properly modify input information, a new PMF factor was determined, explaining a possible Pb contamination source;
in the pan-regional context, sediments of the Danube River basin, which cover an area of 817.000 km2, flowing through nine European countries, were analysed. The objective was to draw out information about the natural vs. anthropogenic origin of heavy metals and to determine the role of tributaries. Three factors were identified: (i) a carbonate component characterized by Ca and Mg; (ii) an alumino-silicate component dominated by Si and Al content and the presence of some metals attributed to natural processes; (iii) an anthropogenic source identified by Hg, S, P and some heavy metals load. Considering only the tributaries input, an additional source probably attributed to the use of fertilizers in agriculture was determined;
finally, a pan-European data set comprising sewage sludge from European waste water treatment plants was obtained. The final objective was to link the silver content to the increasingly use of silver nanoparticles in a variety of house-hold and personal care products. Here, method validation procedure was applied to the measured elements in order to compute correct uncertainties to be used in PMF application. The four resulting factors could be described by: (i) copper dissolution from water pipe lines; (ii) engineered silver nanoparticles load; (iii) anthropogenic influence suggested by the presence of different metals; and (iv) iron variation due to the use of this element for phosphorus removal in sewage sludge.
These studies provide first evidence that PMF could be successfully applied to geochemical data sets at different spatial scale
Geochemical characterization of an abandoned mine site: a combined positive matrix factorization and GIS approach compared with principal component analysis
No full textStatisticalmethods are increasingly used for geochemical characterization of contaminated sites. The geochemical
characteristics of the abandoned Coren del Cucì mine dump (Upper Val Seriana, Italy) were modelled by principal
component analysis (PCA) and positivematrix factorization (PMF) of 56 soil samples analyzed for 11 elements and
pH. PCA and PMF were used to investigate how different approaches deal with the preset type of data. PCA was
performed on two data subsets—samples inside and outside the dump—recognized by cluster analysis. PMF was
performed on the whole data set. However, a GIS-based approach was combined with PMF for better factor resolution.
Three main principal components (PCs) were identified inside the dump: (i) the local ore mineralization;
(ii) the background/regional metal content of rocks; and (iii) the variability of Cd. Two main PCs were obtained
outside the dump: (i) the background/regionalmetal content of rocks; and (ii) the local ore elements. Five factors
were determined by PMF: (i) two background geo-morphological characteristics of the area outside the dump;
(ii) a source ofmineralization situated inside the waste disposal area; and (iii) two different geochemical anomaly
zones. PMFwas found to be useful for estimating the number and composition of sources or processes that govern
data characterized by heterogeneous behavior. In contrast to the application of PCA, no data pre-treatments procedures
are needed to apply PMF
Waste rock characterisation supporting a better exploitation and remediation decision-making
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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