Characterization of Polycyclic Aromatic Hydrocarbon Profiles by Multivariate Statistical Analysis

2011 ◽  
Vol 1 (2) ◽  
pp. 1-14
Author(s):  
D. J. Marino ◽  
E. A. Castro ◽  
L. Massolo ◽  
A. Mueller ◽  
O. Herbarth ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Principal Component ◽  
Emission Sources ◽  
Multivariate Statistical ◽  
Data Set ◽  
Polycyclic Aromatic ◽  
The City ◽  
Sampling Times ◽  
Sample Identity

In the present study, statistical methods based on multivariate analyses such as the Descriptive Discriminant Analysis (DDA) and Principal Component Analysis (PCA) were applied to determine relationships between particle sizes and the composition of the associated semi-volatile compounds, in addition to evaluating these observations in relation to the emission sources, study areas, sampling campaigns and season. Results from the DDA showed that the PAHs distributions give the best discrimination capacity within the data set, whereas the PAH distribution in intermediate particle fractions incorporates noise in the statistical analysis. The PCA was useful in identifying the main emission sources in each study area. It showed that in the city of La Plata the most important pollution sources are traffic emissions and the industrial activity associated with oil and petrochemical plants. In Leipzig, the main sources are those associated with traffic and also a power plant. The combined PCA and DDA methods applied to PAH distributions is a valuable tool in characterizing types of emissions burdens and also in obtaining a differentiation of sample identity according to study areas and sampling times.

Characterization of Polycyclic Aromatic Hydrocarbon Profiles by Multivariate Statistical Analysis

2013 ◽  
pp. 102-116
Author(s):  
D. J. Marino ◽  
E. A. Castro ◽  
L. Massolo ◽  
A. Mueller ◽  
O. Herbarth ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Principal Component ◽  
Emission Sources ◽  
Multivariate Statistical ◽  
Data Set ◽  
Polycyclic Aromatic ◽  
The City ◽  
Sampling Times ◽  
Sample Identity

In the present study, statistical methods based on multivariate analyses such as the Descriptive Discriminant Analysis (DDA) and Principal Component Analysis (PCA) were applied to determine relationships between particle sizes and the composition of the associated semi-volatile compounds, in addition to evaluating these observations in relation to the emission sources, study areas, sampling campaigns and season. Results from the DDA showed that the PAHs distributions give the best discrimination capacity within the data set, whereas the PAH distribution in intermediate particle fractions incorporates noise in the statistical analysis. The PCA was useful in identifying the main emission sources in each study area. It showed that in the city of La Plata the most important pollution sources are traffic emissions and the industrial activity associated with oil and petrochemical plants. In Leipzig, the main sources are those associated with traffic and also a power plant. The combined PCA and DDA methods applied to PAH distributions is a valuable tool in characterizing types of emissions burdens and also in obtaining a differentiation of sample identity according to study areas and sampling times.

scholarly journals Identification of emission sources of particle-bound polycyclic aromatic hydrocarbons in the vicinity of the industrial zone of the city of Novi Sad

2013 ◽  
Vol 67 (2) ◽  
pp. 337-348 ◽  
Author(s):  
Natasa Jovcic ◽  
Jelena Radonic ◽  
Maja Turk-Sekulic ◽  
Mirjana Vojinovic-Miloradov ◽  
Srdjan Popov
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Heavy Traffic ◽  
Principal Component ◽  
Ambient Air ◽  
Emission Sources ◽  
Industrial Zone ◽  
Polycyclic Aromatic ◽  
Novi Sad ◽  
The City

Data on polycyclic aromatic hydrocarbons (PAHs) in ambient air accessed at selected locations in the vicinity of the industrial zone of the city of Novi Sad, Serbia, have been presented and analyzed in order to determine seasonal and spatial variations and to identify emission sources of particle-bound PAHs. Previous studies have demonstrated that the major contributors of PAHs in urban areas are the emissions from vehicle exhaust, and emissions releases from industrial processes like aluminium production, creosote and wood preservation, waste incineration, cement manufacture, petrochemical and related industries, commercial heat/power production etc. The sampling campaigns have been conducted at three sampling sites, during the two 14-day periods. The first site was situated near industrial area, with a refinery, power plant and heavy-traffic road in the vicinity. The second site was located nearby the heavy traffic area, especially busy during the rush hour. The third site was residential district. Summer sampling period lasted from June 26th to July 10th 2008, while sampling of ambient air during the winter was undertaken from January 22nd to February 5th 2009. Eighty-four (84) air samples were collected using a high volume air sampler TCR Tecora H0649010/ECHO. 16 US EPA polycyclic aromatic hydrocarbons were determined in all samples using a gas chromatographer with a mass spectrometer as a detector (Shimatzu MDGC/GCMS-2010). The total average concentrations of PAHs ranged from 1.21 to 1.77 ng/m3 during the summer period and from 6.31 to 7.25 ng/m3 in the winter. Various techniques, including diagnostic ratio (DR) and principal component analysis (PCA), have been used to define and evaluate potential emission sources of PAHs. Diagnostic ratio analysis indicated that vehicles, diesel or/and gasoline, industrial and combustion emissions were sources of PAHs in the vicinity of the industrial zone. Additionally, principal component analysis was used to constrain the potential sources. The results showed that vehicles are the predominant source of particle-bound PAHs during the whole year, and stationary sources (thermal power and heating plant, oil refinery, individual furnaces) during the winter period.

scholarly journals An Exploratory Multivariate Statistical Analysis to Assess Urban Diversity

2019 ◽  
Vol 11 (14) ◽  
pp. 3812 ◽  
Author(s):  
Lorena Salazar-Llano ◽  
Marti Rosas-Casals ◽  
Maria Isabel Ortego
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Urban Systems ◽  
Multivariate System ◽  
Multiple Factor Analysis ◽  
Multivariate Statistical ◽  
Sustainability Challenges ◽  
The City

Understanding diversity in complex urban systems is fundamental in facing current and future sustainability challenges. In this article, we apply an exploratory multivariate statistical analysis (i.e., Principal Component Analysis (PCA) and Multiple Factor Analysis (MFA)) to an urban system’s abstraction of the city’s functioning. Specifically, we relate the environmental, economical, and social characters of the city in a multivariate system of indicators by collecting measurements of those variables at the district scale. Statistical methods are applied to reduce the dimensionality of the multivariate dataset, such that, hidden relationships between the districts of the city are exposed. The methodology has been mainly designed to display diversity, being understood as differentiated attributes of the districts in their dimensionally-reduced description, and to measure it with Euclidean distances. Differentiated characters and distinctive functions of districts are identifiable in the exploratory analysis of a case study of Barcelona (Spain). The distances allow for the identification of clustered districts, as well as those that are separated, exemplifying dissimilarity. Moreover, the temporal dependency of the dataset reveals information about the district’s differentiation or homogenization trends between 2003 and 2015.

Relationship Between Major And Trace Elements In Ulaanbaatar Soils: A Study Based On Multivariate Statistical Analysis

2019 ◽  
Vol 12 (3) ◽  
pp. 199-212 ◽  
Author(s):  
Elena V. Shabanova ◽  
Ts. Byambasuren ◽  
G. Ochirbat ◽  
Irina E. Vasil'eva ◽  
B. Khuukhenkhuu ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Analysis ◽  
Principal Components ◽  
Multivariate Statistical Analysis ◽  
Urban Soils ◽  
Thermal Power ◽  
Principal Component ◽  
Major Elements ◽  
Multivariate Statistical ◽  
Data Set

This article focuses on the relationships between major (Si, Al, Mg, Fe, Ca, Na, K, S, P and Ti) and potentially toxic trace (Ag, As, B, Ba, Bi, Co, Cd, Cr, Cu, F, Ge, Mo, Mn, Li, Ni, Pb, Sb, Sn, Sr, Tl, V and Zn) elements in Ulaanbaatar surface soils and also sources of the trace elements in the soils distinguished by the methods of multivariate statistical analysis. Results of exploratory data analysis of 325 Ulaanbaatar soil samples show the accumulation of Ca, S, B, Bi, Cu, Mo, Pb, Sb, Sn, Sr and Zn in urban soils. The major elements were grouped by cluster analysis in tree associations characterizing main soil fractions: sandy P-(K-Na-Si), clayey (Mg-Ti-Fe-Al) and silty (S-Ca). The factor analysis shows that silty fraction is enriched in major elements of both natural and anthropogenic origin. The principal component analysis from 32 variables extracted nine principal components with 82.49% of the cumulative explained variance. The results of cluster and factor analyses well agree and reaffirm the enrichment causes of potentially toxic elements are a coal combustion at thermal power stations (B, Bi, Ca, Mo, S and Sr) and traffic emissions (Cu, Pb, Sn and Zn). Spatial distributions of trace elements in the districts of Ulaanbaatar city were obtained by ordinary kriging. It is illustrated that the different principal components define the various origins and patterns of accumulation of trace elements in soils. The supplementation of data set by the concentration of organic carbon and the species of elements could help to identify the sources of such elements as P, Ni, Al, Fe, Ca, Ba, Bi, Cr, Zn, Sr and Sb in urban soils more completely.

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

scholarly journals STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1

2019 ◽  
Vol 32 (1) ◽  
pp. 200-210
Author(s):  
Antônio Italcy de Oliveira Júnior ◽  
Luiz Alberto Ribeiro Mendonça ◽  
Sávio de Brito Fontenele ◽  
Adriana Oliveira Araújo ◽  
Maria Gorethe de Sousa Lima Brito
Keyword(s):  
Organic Matter Content ◽  
Principal Component ◽  
Matter Content ◽  
Multivariate Statistical ◽  
Statistical Tool ◽  
Environmental Characterization ◽  
Hierarchical Grouping ◽  
Two Factors ◽  
Total Data

ABSTRACT Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

scholarly journals Identification of the dominant hydrogeochemical processes and characterization of potential contaminants in groundwater in Qingyuan, China, by multivariate statistical analysis

RSC Advances ◽  
2018 ◽  
Vol 8 (58) ◽  
pp. 33243-33255 ◽  
Author(s):  
Zuobing Liang ◽  
Jianyao Chen ◽  
Tao Jiang ◽  
Kun Li ◽  
Lei Gao ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Water Source ◽  
Hydrogeochemical Processes ◽  
Karst Aquifers ◽  
Multivariate Statistical ◽  
Drinking And Irrigation ◽  
Karst Areas

In karst areas, groundwater is an important water source for drinking and irrigation purposes; however, karst aquifers are vulnerable and recovery from damage is difficult.

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