Chemometric modeling of main contamination sources in surface waters of Portugal

Rain water samples were collected to study the chemical composition of wet atmospheric precipitation (first event) over the Lucknow city in the northern Indo-gangetic alluvial plains. The samples were collected in the month of July, 2005 from different sites. The wet precipitation samples were analyzed for pH, EC, major ions (HCO(-3), Cl(-), SO4(2-), NO(-3), PO4(-3), F(-), Na(+), K(+), Ca(2+), Mg(2+), NH4+) and heavy metals (As, Cd, Al, Co, Cr, Cu, Fe, Mn, Mo, Ni, Hg, Pb, Se, Sn, Ti, V, Zn). The pH values of wet precipitation samples ranged between 6.5 and 8.7. The analysis of linear regression applied to the set of studied variables and computation of neutralization factors showed that neutralization occurred in precipitation samples and Ca(2+) had the maximum neutralization capacity. It was found that Cl(-), [Formula: see text], Ca(2+), Mg(2+), Na(+) and K(+) in the precipitation samples originated mainly from crustal/anthropogenic sources in the region. On an average Fe, and Al accounted for >72% of the total concentration of trace metals in the wet precipitation samples followed by Zn (>10%). Enrichment factors calculated for heavy metals over reference background level in seawater and Earth's crust showed relatively higher enrichment of Zn. The principal component analysis (PCA) identified the possible sources of ionic species and heavy metals in the wet precipitation samples.

Section: Principal Components Analysismentioning

confidence: 99%

Hydrochemistry of Wet Atmospheric Precipitation Over an Urban Area in Northern Indo-Gangetic Plains

Singh

Malik

et al. 2006

“…Domestic sewage of over one million inhabitants from Porto, the Portugal's second largest city, as well as industrial effluents from several industries (food processing, textile, tannery, ceramic, metal platting), are still discharged, mostly without treatment, directly into the estuary and its tributaries (Guerreiro and Pereira, 2002). In the Douro River basin, types of contaminants referred in the literature include high levels of heavy metals (Bettencourt and Ramos, 2003;Mucha et al 2003), and organic compounds (Azevedo et al, 2000;Lacorte et al, 2001;Tauler et al, 2001;Tauler et al, 2004). Station D (St D: 41 • 01 45 N, 8 • 38 44 W) was located in an intertidal zone affected by a small river that crosses several intensive agricultural areas, characterized by extensive pesticide use, receiving also untreated domestic effluents.…”

Section: Sampling Stationsmentioning

confidence: 99%

The Use of Mytilus Galloprovincialis Acetylcholinesterase and Glutathione S-Transferases Activities as Biomarkers of Environmental Contamination Along the Northwest Portuguese Coast

Moreira

Guilhermino

2005

With the aim of using Mytilus galloprovincialis acetylcholinesterase (AChE) and glutathione S-transferases (GST) activities as biomarkers of environmental contamination in risk assessment studies along the northwest Portuguese coast, the objective of this study was to provide background information related to: (1) baseline values of these enzymatic activities both in reference and contaminated areas and their responsiveness as indicators of exposure to environmental contaminants; (2) the importance of seasonal variations of such biomarkers in M. galloprovincialis along this area. For this, the activity of these enzymes was seasonally determined in mussels collected from four sites, including a reference and three stations with different contamination sources along the referred area. Statistically significant differences on both enzymatic activities were found among the four sampling stations, at the four sampling periods. In comparison to the reference station, lower AChE and higher GST activity values were found in mussels collected in stations potentially contaminated by pesticides and domestic/industrial effluents and in mussels collected in the vicinity of an oil refinery and an industrial/mercantile harbour, respectively. The results obtained in this work highlighted the potential suitability of these biomarkers to be used as components of environmental monitoring programs in risk assessment studies along the northwest Portuguese coast. Since a seasonal variation in both enzymatic activities was found, the possible implications of such variability in the use of these enzymes as environmental biomarkers are also discussed.

“…PCA assuming a linear model to explain the observed data variance using a reduced number of components decomposes the X matrix of n obj =rows (samples) and n var =columns (variables), according to (Tauler et al 2004):…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…Principal components analysis (PCA), an exploratory data analysis method allows for identification of major contamination sources (principal components) and estimation of their spatial and temporal distributions (Tauler et al 2004), whereas APCS method is used to quantify the contributions of all sources to each measured pollutant (Thurston and Spengler 1985). Subsequent multiple linear regression (MLR) of sample mass concentration on these APCS derives estimated mass concentration of each source.…”

Section: Introductionmentioning

confidence: 99%

Receptor modeling for source apportionment of polycyclic aromatic hydrocarbons in urban atmosphere

Singh

Malik

Kumar

et al. 2007

This study reports source apportionment of polycyclic aromatic hydrocarbons (PAHs) in particulate depositions on vegetation foliages near highway in the urban environment of Lucknow city (India) using the principal components analysis/absolute principal components scores (PCA/APCS) receptor modeling approach. The multivariate method enables identification of major PAHs sources along with their quantitative contributions with respect to individual PAH. The PCA identified three major sources of PAHs viz. combustion, vehicular emissions, and diesel based activities. The PCA/APCS receptor modeling approach revealed that the combustion sources (natural gas, wood, coal/coke, biomass) contributed 19-97% of various PAHs, vehicular emissions 0-70%, diesel based sources 0-81% and other miscellaneous sources 0-20% of different PAHs. The contributions of major pyrolytic and petrogenic sources to the total PAHs were 56 and 42%, respectively. Further, the combustion related sources contribute major fraction of the carcinogenic PAHs in the study area. High correlation coefficient (R2 > 0.75 for most PAHs) between the measured and predicted concentrations of PAHs suggests for the applicability of the PCA/APCS receptor modeling approach for estimation of source contribution to the PAHs in particulates.