Absolute Principal Component Scores Research Articles

Chemometrics in the assessment of the sustainable development rule implementation

AbstractThe sustainable development rule implementation is tested by the application of chemometrics in the field of environmental pollution. A data set consisting of Cd, Pb, Cr, Zn, Cu, Mn, Ni, and Fe content in bottom sediment samples collected in the Odra River (Germany/Poland) is treated using cluster analysis (CA), principal component analysis (PCA), and source apportionment techniques. Cluster analysis clearly shows that pollution on the German bank is higher than on the Polish bank. Two latent factors extracted by PCA explain over 88 % of the total variance of the system, allowing identification of the dominant “semi-natural” and “anthropogenic” pollution sources in the river ecosystem. The complexity of the system is proved by MLR analysis of the absolute principal component scores (APCS). The apportioning clearly shows that Cd, Pb, Cr, Zn and Cu participate in an “anthropogenic” source profile, whereas Fe and Mn are “semi-natural”. Multiple regression analysis indicates that for particular elements not described by the model, the amounts vary from 4.2 % (Mn) to 13.1 % (Cr). The element Ni participates to some extent to each source and, in this way, is neither pure “semi-natural” nor pure “anthropogenic”. Apportioning indicates that the whole heavy metal pollution in the investigated river reach is 12510.45 mg·kg−1. The contribution of pollutants originating from “anthropogenic sources” is 9.04 % and from “semi-natural” sources is 86.53 %.

Determination of PM 2.5 sources using time-resolved integrated source and receptor models

Multivariate statistical techniques are applied to particulate matter (PM) and meteorological data to identify the sources responsible for evening PM spikes at Sunland Park, NM (USA). The statistical techniques applied are principal components analysis (PCA), redundancy analysis (RDA), and absolute principal components scores analysis (APCSA), and the data evaluated are 3-h average (6–9 p.m.) PM 2.5 mass and chemical composition and 1-h average PM 2.5 and PM 10 mass and environmental data collected in the winter of 2002. Although the interpretation of the data was complicated by the presence of sources which are likely changing in time (e.g. brick kilns), the multivariate analyses indicate that the evening high PM 2.5 is associated with burning-activities occurring to the south of Sunland Park, and these emissions are characterized by elevated Sb, Cl −, and elemental carbon; ∼68% of the PM 2.5 mass can be attributed to this source. The PM 10 evening peaks, on the other hand, are mainly caused by resuspended dust generated by vehicular movements south of the site and transported by the local terrain-induced drainage flow.

A comparison between two receptor models to determine the source apportionment of atmospheric pollutants

AbstractTwo frequently used factor analysis (FA) methods, Absolute Principal Components Scores (APCS) and Target Transformation Factor Analysis (TTFA), are compared by examining two cases with different complexity on sources and parameters. The effect of the noise contribution on the data is evaluated.By applying the methods to simulated data matrices, it has been found that the APCS method characterizes the source matrix better than TTFA method. APCS method also reconstructs the sample better than TTFA. In addition both methods are applied to real data collected during a monitoring campaign in Taranto city (South Italy). Copyright © 2006 John Wiley & Sons, Ltd.

Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques—a case study

Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the data set on water quality of the Gomti river (India), generated during three years (1999–2001) monitoring at eight different sites for 34 parameters (9792 observations). This study presents usefulness of multivariate statistical techniques for evaluation and interpretation of large complex water quality data sets and apportionment of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Three significant groups, upper catchments (UC), middle catchments (MC) and lower catchments (LC) of sampling sites were obtained through CA on the basis of similarity between them. FA/PCA applied to the data sets pertaining to three catchments regions of the river resulted in seven, seven and six latent factors, respectively responsible for the data structure, explaining 74.3, 73.6 and 81.4% of the total variance of the respective data sets. These included the trace metals group (leaching from soil and industrial waste disposal sites), organic pollution group (municipal and industrial effluents), nutrients group (agricultural runoff), alkalinity, hardness, EC and solids (soil leaching and runoff process). DA showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. It rendered five parameters (temperature, total alkalinity, Cl, Na and K) affording more than 94% right assignations in temporal analysis, while 10 parameters (river discharge, pH, BOD, Cl, F, PO 4, NH 4–N, NO 3–N, TKN and Zn) to afford 97% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Further, receptor modeling through multi-linear regression of the absolute principal component scores (APCS-MLR) provided apportionment of various sources/factors in respective regions contributing to the river pollution. It revealed that soil weathering, leaching and runoff; municipal and industrial wastewater; waste disposal sites leaching were among the major sources/factors responsible for river quality deterioration.

Multivariate statistical assessment of polluted soils

AbstractThis study deals with the application of several multivariate statistical methods (cluster analysis, principal components analysis, multiple regression on absolute principal components scores) for assessment of soil pollution by heavy metals. The sampling was performed in a heavily polluted region and the chemometric analysis revealed four latent factors, which describe 84.5 % of the total variance of the system, responsible for the data structure. These factors, whose identity was proved also by cluster analysis, were conditionally named “ore specific”, “metal industrial”, “cement industrial”, and “steel production” factors. Further, the contribution of each identified factor to the total pollution of the soil by each metal pollutant in consideration was determined.

Source contributions to fine particulate matter in an urban atmosphere

This paper proposes a practical method for estimating source attribution by using a three-step methodology. The main objective of this study is to explore the use of the three-step methodology for quantifying the source impacts of 24-h PM 2.5 particles at an urban site in Seoul, Korea. 12-h PM 2.5 samples were collected and analyzed for their elemental composition by ICP-AES/ICP-MS/AAS to generate the source composition profiles. In order to assess the daily average PM 2.5 source impacts, 24-h PM 2.5 and polycyclic aromatic hydrocarbons (PAH) ambient samples were simultaneously collected at the same site. The PM 2.5 particle samples were then analyzed for trace elements. Ionic and carbonaceous species concentrations were measured by ICP-AES/ICP-MS/AAS, IC, and a selective thermal MnO 2 oxidation method. The 12-h PM 2.5 chemical data was used to estimate possible source signatures using the principal component analysis (PCA) and the absolute principal component scores method followed by the multiple linear regression analysis. The 24-h PM 2.5 source categories were extracted with a combination of PM 2.5 and some PAH chemical data using the PCA, and their quantitative source contributions were estimated by chemical mass balance (CMB) receptor model using the estimated source profiles and those in the literature. The results of PM 2.5 source apportionment using the 12-h derived source composition profiles show that the CMB performance indices; χ 2, R 2, and percent of mass accounted for are 2.3%, 0.97%, and 100.7%, which are within the target range specified. According to the average PM 2.5 source contribution estimate results, motor vehicle exhaust was the major contributor at the sampling site, contributing 26% on average of measured PM 2.5 mass (41.8 μg m −3), followed by secondary sulfate (23%) and nitrate (16%), refuse incineration (15%), soil dust (13%), field burning (4%), oil combustion (2.7%), and marine aerosol (1.3%). It can be concluded that quantitative source attribution to PM 2.5 in an urban area where source profiles have not been developed can be estimated using the proposed three-step methodology approach.

Multivariate Statistical Assessment of Air Quality: A Case Study

The present paper deals with the application of several chemometrical methods (cluster and principal components analysis, source apportioning on absolute principal components scores) to an aerosol data collection from Unterloibach, Austria. It is shown that seven latent factors explaining almost 80% of the total variance are responsible for the data structure and are conditionally identified as “secondary aerosol”, “mineral dust”, “oil burning”, “lead smelter”, “coal burning”, “salt” and “fertilizer” emission sources. Furthermore, the contribution of each identified source to the formation of the particle total mass and chemical compounds total concentration is calculated. Thus, a reliable assessment of the air quality in the region is performed. The requirements of the sustainability concept for ecological indicators in this case is easily transformed into a multivariate statistical problem taking into account not separate indicators but the specific multivariate nature of aerosol pollution.

Source contributions to ambient VOCs and CO at a rural site in eastern China

Ambient data on volatile organic compounds (VOCs) and carbon monoxide (CO) obtained at a rural site in eastern China are analyzed to investigate the nature of emission sources and their relative contributions to ambient concentrations. A principal component analysis (PCA) showed that vehicle emissions and biofuel burning, biomass burning and industrial emissions were the major sources of VOCs and CO at the rural site. The source apportionments were then evaluated using an absolute principal component scores (APCS) technique combined with multiple linear regressions. The results indicated that 71%75% (average7standard error) of the total VOCemissions were attributed to a combination of vehicle emissions and biofuel burning, and 7%73% to gasoline evaporation and solvent emissions. Both biomass burning and industrial emissions contributed to 11%71% and 11%70.03% of the total VOCemissions, respectively. In addition, vehicle emissions and biomass and biofuel burning accounted for 96%76% of the total CO emissions at the rural site, of which the biomass burning was responsible for 18%73%. The results based on PCA/ APCS are generally consistent with those from the emission inventory, although a larger relative contribution to CO from biomass burning is indicated from our analysis. r 2004 Elsevier Ltd. All rights reserved.

Application of multivariate statistical analysis to superficial soils around a coal burning power plant

The Thermoelectric Complex Jorge Lacerda (TCJL), located in the Santa Catarina State, Brazil, is the largest coal burning thermoelectric complex of Latin America and consists of seven power plants with a total capacity of 832 MWe. In order to estimate the contribution of the atmospheric releases from the TCJL to the elemental composition of surface soils around it, forty-five samples were collected at up to a distance of 8 km. Forty-two elements were determined by ICP-MS and ICP-AES after total acid dissolution. The technique of principal component analysis was employed to identify the major sources that contribute to surface soil composition. Additionally, a source apportioning using multiple regression on absolute principal component scores was performed in order to obtain quantitative information about the contribution of the different identified sources on the soil composition. Based on the results obtained, four sources were identified as the main contributors to the surface soil elemental composition. One of them was related to TCJL because it retains volatile elements enriched on fly ash and released from powerhouse stacks.

SOURCE APPORTIONMENT OF FINE AIRBORNE PARTICULATE MATTER COLLECTED IN THE MEXICO CITY METROPOLITAN AREA

Samples of fine airborne particulate matter ( PM 2.0) have been collected at three sites located in the Mexico City Metropolitan Area (MCMA) during July, August, and September 2000. The elemental concentrations of these samples were determined by PIXE so that twelve elements ( S , Cl , K , Ca , Ti , Mn , Fe , Ni , Cu , Zn , Br and Pb ) were consistently detected. These results produced a database, which was statistically analysed; first by Principal Components so to identify the pollution sources, and, subsequently, by Absolute Principal Component Scores analysis in order to estimate the corresponding source apportionment contributions. Other elements such as V and Cr were also detected in around 20% of the samples and, therefore, they were not considered statistically significant. Thus, these elements, as well as some others found only occasionally and in the trace range, were left out of the discussion. All calculations were performed using a standard statistical package following the method developed by Thurston and Spengler. Four main pollution sources were identified and the source apportionments were found in good agreement with other estimations found in the literature. The results were obtained individually for the three sampling sites, but the complete results are presented for just one of the sites in order to simplify this presentation.

Environmetric modeling and interpretation of river water monitoring data.

This environmetric study deals with modeling and interpretation of river water monitoring data from the basin of the Saale river and its tributaries the Ilm and the Unstrut. For a period of one year of observation between September 1993 and August 1994 a data set from twelve campaigns at twenty-nine sampling sites from the Saale river and six campaigns from the river Ilm at seven sampling sites and from river Unstrut at ten sampling sites was collected. Twenty-seven chemical and physicochemical properties were measured to estimate the water quality. The application of cluster analysis, principal components analysis, and apportioning modeling on absolute principal components scores revealed important information about the ecological status of the region of interest:identification of two separate patterns of pollution (upper and lower stream of the rivers);identification of six latent factors responsible for the data structure with different content for the two identified pollution patterns; anddetermination of the contribution of each latent factor (source of emission) to the formation of the total concentration of the chemical burden of the river water. As a result more objective ecological policy and decision making is possible.

Environmetric modeling of emission sources for dry and wet precipitation from an urban area

Monitoring data from chemical analysis of rainwater and aerosol samples collected in an urban area have been interpreted by the use of environmetric approaches. An attempt was done to compare the data set structures of both type of precipitation and to estimate the contribution of different anthropogenic and naturally occurring emission sources to the total mass of the wet and dry precipitation. It was found that three latent factors explaining over 80% of the total variance of the set are responsible for the rainwater set structure—‘sea spray’, ‘soil dust’, and ‘anthropogenic’. Only two were the latent factors explaining the dominant part of the variance in the case of aerosol samples—‘anthropogenic’ and ‘natural’. It is shown that the anthropogenic influence for aerosol samples is more complex that that of rainwater samples and represents interaction between typical anthropogenic sources and natural emitters. Additionally, a source apportioning using multiple regression on absolute principal component scores is performed in order to obtain qualitative information about the impact of the different identified emission sources on the urban environment.

Source apportionment of gaseous atmospheric pollutants by means of an absolute principal component scores (APCS) receptor model

A multivariate statistical method has been applied to apportion the atmospheric pollutant concentrations measured by automatic gas analyzers placed on a mobile laboratory for air quality monitoring in Taranto (Italy). In particular, Principal Component Analysis (PCA) followed by Absolute Principal Component Scores (APCS) technique was performed to identify the number of emission sources and their contribution to measured concentrations of CO, NOx, benzene toluene m+p-Xylene (BTX). This procedure singled out two different sources that explain about 85% of collected data variance.

Danube River Water Data Modelling by Multivariate Data Analysis

A data set (48×19) consisting of Danube river water analytical data collected at Galati site, Romania, during a four-year period has been treated by principal components analysis (PCA). The PCA indicated that seven latent factors (“hardness”, “biochemical”, “waste inlets”, “turbidity”, “acidity”, “soil extracts” and “organic wastes”) are responsible for the data structure and explain over 80 % of the total variance of the system. Its complexity is further proved by the application of multiple linear regression analysis on the absolute principal components scores (APCS) where the contribution of each natural or anthropogenic sources in the factor formation is shown. The apportioning makes clear that each variable participates to a different extent to each source and, in this way, no pure natural or pure anthropogenic influence could be determined. No specific seasonality for the variables in consideration is found.

Chemical characterization and source identification/apportionment of fine and coarse air particles in Thessaloniki, Greece

The distribution of air particulate mass and selected particle components (trace elements and polycyclic aromatic hydrocarbons (PAHs)) in the fine and the coarse size fractions was investigated at a traffic-impacted urban site in Thessaloniki, Greece. 76±6% on average of the total ambient aerosol mass was distributed in the fine size fraction. Fine-sized trace elemental fractions ranged between 51% for Fe and 95% for Zn, while those of PAHs were between 95% and 99%. A significant seasonal effect was observed for the size distribution of aerosol mass, with a shift to larger fine fractions in winter. Similar seasonal trend was exhibited by PAHs, whereas larger fine fractions in summer were shown by trace elements. The compositional signatures of fine and coarse particle fractions were compared to that of local paved-road dust. A strong correlation was found between coarse particles and road dust suggesting strong contribution of resuspended road dust to the coarse particles. A multivariate receptor model (multiple regression on absolute principal component scores) was applied on separate fine and coarse aerosol data for source identification and apportionment. Results demonstrated that the largest contribution to fine-sized aerosol is traffic (38%) followed by road dust (28%), while road dust clearly dominated the coarse size fraction (57%).

Composition and sources of aerosol in a central African rain forest during the dry season

During the Experiment for Regional Sources and Sinks of Oxidants (EXPRESSO‐96), size‐fractionated aerosol samples were collected in November and December 1996 at a ground site in the tropical rain forest at the N'doki National Park (NNP) in the Republic of Congo. The samples were analyzed for up to 26 elements using particle‐induced X‐ray emission. Elements related to mineral dust and pyrogenic aerosol exhibited greater concentrations during the daytime, while aerosol produced by the rain forest exhibited higher concentrations at night. Samples were also collected at two levels on the tower, above and below the canopy, to characterize vegetation sources. Absolute principal component analysis (APCA) identified three major aerosol source types in each size fraction, which explained more than 90% of the data variance. The fine‐size fraction contained mineral dust (Al, Si, Ca, Ti, and Fe), pyrogenic (black carbon, K, and Zn), and marine/anthropogenic sulfur components. The coarse‐size fraction included a mineral dust (Al, Si, Ca, Ti, Mn, and Fe) and two primary biogenic components consisting of K, P, Zn, and S. Absolute principal component scores were calculated for the components of APCA, and temporal trends were compared to 7 day isopycnic backward trajectories. Consistent relationships between the temporal trends of the fine fraction aerosol components and meteorological patterns were observed. Trade wind air masses transported biomass burning and mineral dust aerosol to NNP during the first half of the experiment. The fine fraction sulfur component correlated well with the pyrogenic activity before the change in meteorological patterns halfway through the experiment. The fine and coarse sulfur concentrations nearly doubled in the latter part of the experiment as a monsoon circulation brought sulfur‐enriched aerosol from the Atlantic Ocean. Various industrial activities on the coast of Cameroon and Gabon probably contributed to the high sulfur concentrations as well.

Multivariate statistical interpretation of coastal sediment monitoring data

Multivariate statistical analysis of sediment data (input matrix 122 x 15) collected from 122 sampling sites from the western coastline of the USA and analyzed for 15 analytes indicates that the data structure could be explained by four latent factors. These factors are conditionally named "anthropogenic", "organic", "natural", and "hot spots". They explain over 85% of the total variance of the data system, which is an acceptable value for the PCA model. The receptor models obtained after regression of the mass on the absolute principal components scores ensures reliable estimation of the contribution of each possible natural or anthropogenic source to the mass of each chemical component. It can be concluded that the region of interest reveals a different pattern of pollution compared with the eastern coastline treated statistically in a previous study.

Environmetrical interpretation of analytical data of marine organisms from the black sea

The environmetrical analysis of the benthic organisms data set from a Black sea region has revealed new information concerning the chemical content and the bioindicating abilities of polychaeta (Melina palmata), Crustacea (Aspendopsis ostroumovi) and molluscs (Mytilus gallo‐provincialis). The application of various multivariate statistical approaches like cluster and principal component analysis, linear regression and partial least square modeling, source apportioning makes it possible to understand in a better way the properties of the benthic organism as collectors of pollutants in a total and a more specific mode. It is shown that heavily polluted coastal zones are indicated in the same way by all benthic species but some specificity could be detected when moderately polluted zones are considered. In this case polychaeta accumulated preferably Co, Cr, Cu and Pb Crustacea ‐ As, Cd and Ni and molluscs ‐ Zn to a limited extent. PCA identified three latent factors (“anthropogenic flotation”;, “anthropogenic galvanic”; and “naturally occurring") which explain about 65% of the total variance of the system and determine the data set structure. The source apportioning on the absolute principal component scores proved that none of the metals is quantitatively linked with only one anthropogenic or natural source. The linear regression and PLS models have indicated that a reliable prognosis of the pollution on some naturally occurring chemical components (e.g. linear regression on Zn for Cr and Ni) or combination of them (PLS modeling on Mn/Zn or on Mn/Zn/Fe for the rest of the pollutants) could be achieved.

CLUSTER AND PRINCIPAL COMPONENT ANALYSIS OF THE SOLVENT EFFECTS ON THE C˭O STRETCHING FREQUENCY OF ACETOPHENONE

The solvatochromic shift of the C˭O stretching frequency of acetophenone is studied by cluster (CA) and principal component analysis (PCA) confirming our previous results about the determining contributions to the polarity, polarizability, and electrophilicity of the medium on this effect. It is shown that the used combination of Varimax mode of PCA and multiple regression (apportioning modeling) makes it possible to obtain absolute principal component scores (APCS), which include the solvent parameters describing definite solute–solvent interactions. The adequateness of the suggested chemometric model is checked by “reverse” CA of 25 solvents as objects with respect to the APCS calculated.

Effects of Outdoor Pyrotechnic Displays on the Regional Air Quality of Western Washington State

ABSTRACT Data from a PM25 (i.e., Dp < 2.5 mm) particulate matter monitoring network was used to quantify the effects of outdoor pyrotechnic displays on the regional air quality of western Washington State. Linear regression and principal component analysis demonstrated that the fine par-ticulate matter generated by these displays was primarily composed of Sr, K, V, Ti, Ba, Cu, Pb, Mg, Al, S, Mn, Zn,and soot. The maximum 24-hour averaged PM2.5 mass concentration apportioned to the pyrotechnic displays by absolute principal component scores regression analysis was 18.5 mg/m3. The majority of this mass (54%) was composed of K and S, which originated from the combustion of black powder. The distribution of smoke emissions from the displays closely resembled the population distribution of western Washington. The PM2.5 aerosol monitoring network tracked the pyrotechnic smoke plume for a period of two days as it was advected by low-level winds. The geometric mass mean diameter of the K particles was ~0.7 mm after transport of ~100 km. In the absence of rain, which is the primary sink for particles of this size, the particulate matter generated by the pyrotechnic displays could have an atmospheric residence time of more than one week.