Environmental influence on coastal phytoplankton and zooplankton diversity: a multivariate statistical model analysis

In a marine ecosystem, the diversity of phytoplankton can influence the diversity of zooplankton, or vice versa, and both can be affected by the environmental factors. In this study, we used principal component analysis (PCA) to identify the major sources of influence on the coastal water near an industrial park, following by construction of structural equation model (SEM) to determine the direct and indirect effect of the factors on phytoplankton and zooplankton diversity. PCA results indicated that the coastal area was mainly affected by riverine discharge (represented by high PC factor loadings of transparency and turbidity) and seasonal change (represented by temperature). SEM further suggested that both riverine discharge and seasonal influences can directly affect phytoplankton diversity, but indirectly affected zooplankton diversity via changes in phytoplankton. Using PCA to determine the sources of influence followed by construction of SEM allowed us to understand the relative importance of the environmental factors, direct or indirect, on phytoplankton and zooplankton diversity. When environmental changes occur, a new SEM could be constructed using the same category of physical and biological data and then compared to the current model to verify whether the environmental changes were the cause of alterations in planktonic communities in the area.     

Characterization of hydrocarbon contaminated areas by multivariate statistical analysis: Case studies

Analysis of soil gases is a relatively rapid and inexpensive method to delineate and measure hydrocarbon contamination in the subsurface caused by diesel or gasoline. Techniques originally developed for petroleum exploration have been adapted to tracking hydrocarbons which have leaked or spilled at or below the earth's surface.Discriminant analysis (a multivariate statistical technique) is used to classify soil gas samples of C₁ to C₇ hydrocarbons as biogenic (natural soil gases) or thermogenic (contaminant hydrocarbons). Map plots of C₁ to C₇ total interstitial hydrocarbons, C₂ to C₇ interstitial hydrocarbons, and C₁/C _n rations are used to further delineate and document the extent and migration of contamination.Three case studies of the technique are presented: each involves leakage of hydrocarbons from underground storage tanks. Soil gas analysis clearly defines the spread of contamination and can serve as the basis for the correct placement of monitoring wells. The method proved to be accurate, rapid, and cost-effective; it therefore has potential for widespread application to the identification of soil and groundwater contaminated by hydrocarbons.     

Analysis of mosses and topsoils for detecting sources of heavy metal pollution: multivariate and enrichment factor analysis

In order to assess the contribution of emission sources to the pollution of areas remote from industrial facilities, a combined approach of enrichment factor analysis and multivariate statistics was used for detecting the origin of heavy metal pollution in the Zlatibor ecosystem, in Serbia. Samples of moss (Pleurozium schreberi, Hylocomium splendens, Scleropodium purum, Hypnum cupressiforme and Thuidum delicatulum) and of topsoil (0-5 cm) were collected in 2005. The concentrations of seven heavy metals (Cd, Cr, Cu, Mn, Ni, Pb and Zn) were determined in moss and soil samples by atomic absorption spectrometry. The results obtained by enrichment factor analysis and two multivariate statistical methods, principal component analysis and cluster analysis, enabled discrimination of the lithologic and anthropogenic sources of heavy metals in the mosses. Enrichment factors, calculated to evaluate the contribution to the metal content in moss from anthropogenic sources, revealed pollution of the investigated area by Cd and Pb, originating from long-range transport and fossil fuel burning.     

Principal response curves technique for the analysis of multivariate biomonitoring time series

Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been used to analyse complex data sets. This paper discusses the methods currently in use and introduces the principal response curves method, which overcomes the problem of cluttered graphical results representation that is a great drawback of most conventional methods. To illustrate this, two example data sets are analysed using two ordination techniques, principal component analysis and principal response curves. Whereas PCA results in a difficult-to-interpret diagram, principal response curves related methods are able to show changes in community composition in a diagram that is easy to read. The principal response curves method is used to show trends over time with an internal reference (overall mean or reference year) or external reference (e.g. preferred water quality or reference site). Advantages and disadvantages of both methods are discussed and illustrated.     

Classification and management issues of Greek lakes under the European Water Framework Directive: a DPSIR approach

The European Water Framework Directive (WFD) establishes a framework for the protection of water bodies. The aim of the Water Framework Directive (WFD) is to ensure sustainable management of groundwater, freshwater and marine water in the European Union, such that a minimum "good ecological status" will be obtained by 2015. We used background morphometric, hydrologic, physico-chemical and biological data, in order to highlight the ecological status of the natural Greek lakes using also the Driving-Pressure-State-Impact-Response (DPSIR) approach in order to link the driving forces and further the pressures with the present state of the lakes' ecosystems. In addition detailed biodiversity and habitat typology data are provided. The assessment of the DPSIR approach under the view of WFD provided a quite clear identification of pressures and the subsequent impacts of the qualitative status of the Greek lakes.     

Application of a battery of biomarkers in mussel digestive gland to assess long-term effects of the Prestige oil spill in Galicia and Bay of Biscay: correlation and multivariate analysis

According to published reports, selected cell and tissue-level biomarkers revealed alterations in the health status of mussels collected after the Prestige oil spill (POS) in Galicia and the Bay of Biscay (2003-2006). Presently, univariate and multivariate analysis provides a comprehensive view of the whole data set. Correlation and principal component analysis (PCA) were performed using biomarkers corresponding to or derived from those data, say: induction, AOX(exp), and inhibition, AOX(eff), of Acyl-CoA oxidase enzyme activity; membrane labilisation period, LP, volume density, Vv(LYS), and surface-to-volume ratio, S/V(LYS), of digestive cell lysosomes; volume density of neutral lipids, Vv(NL); volume density of basophilic cells, Vv(BAS), mean luminal radius to mean epithelial thickness in digestive gland epithelium, MLR/MET; gonad index, GI; cumulative intensity of inflammatory responses, CI(IR) and parasitic infestation, CI(PI). The PCA results were used to classify sampling times and localities by means of hierarchical cluster analysis. Biomarkers were more frequently correlated with naphthalene than with total PAH concentration in mussel tissues. Many biological parameters were correlated with each other in agreement with their involvement in toxic processes or their role in the mussels' response against environmental insult. Naphthalene appears to be the most effective toxic herein, which supports that the reported effects may be mainly due to POS. According to the PCA, 4 groups of biomarkers explained 67.37% variability and the factors that most strongly contributed were S/V(LYS), CI(IR), Vv(BAS) and MLR/MET. These factors were used in the hierarchical cluster analyses carried out. The cluster analysis performed to classify sampling times discriminated 3 significantly different periods after POS: (a) a high affection period (until April-04); (b) the beginning of the recovery; and (c) an advanced recovery status in which geographical variability increased (from April-05 onwards). As regards the clustering of localities, major geographical differences were not detected but three outliers were identified: (a) early and highly impacted localities in Galicia and Bay of Biscay; (b) Agui?o that was characterised by extremely high Vv(BAS) during 2003; and (c) localities that were subjected to persistent chronic pollution.     

Assessment of trace element contamination in sediment cores from the Pearl River and estuary,South China: geochemical and multivariate analysis approaches

Twenty-four major and trace elements and the mineralogical composition of four sediment cores along the Pearl River and estuary were analyzed using ICP-AES, ICP-MS, and X-ray diffraction (XRD) to evaluate contamination levels. The dominant minerals were quartz, kaolinite, and illite, followed by montmorillonite and feldspars, while small amounts of halite and calcite were also observed in a few samples. Cluster analysis (CA) and principal component analysis (PCA) were performed to identify the element sources. The highest metal concentrations were found at Huangpu, primarily due to wastewater treatment plant discharge and/or the surreptitious dumping of sludge, and these data differed from those of other sources. Excluding the data from Huangpu, the PCA showed that most elements could be considered as lithogenic; few elements are the combination of lithogenic and anthropogenic sources. An antagonistic relationship between the anthropogenic source metals (K, Ba, Zn, Pb, Cd, Ag, Tl, and U) and marine source metals (Na, Mg, Ti, V, and Ca) was observed. The resulting normalized Al enrichment factor (EF) indicated very high or significant pollution of Cd, Ag, Cu, Zn, Mo, and Pb at Huangpu, which may cause serious environmental effects. Conflicting results between the PCA and EF can be attributed to the background values used, indicating that background values must be selected carefully.     

Determination of volatile organic compounds pollution sources in malaysian drinking water using multivariate analysis

A field investigation was conducted at all water treatment plants throughout 11 states and Federal Territory in Peninsular Malaysia. The sampling points in this study include treatment plant operation, service reservoir outlet and auxiliary outlet point at the water pipelines. Analysis was performed by solid phase micro-extraction technique with a 100 μm polydimethylsiloxane fibre using gas chromatography with mass spectrometry detection to analyse 54 volatile organic compounds (VOCs) of different chemical families in drinking water. The concentration of VOCs ranged from undetectable to 230.2 μg/l. Among all of the VOCs species, chloroform has the highest concentration and was detected in all drinking water samples. Average concentrations of total trihalomethanes (THMs) were almost similar among all states which were in the range of 28.4--33.0 μg/l. Apart from THMs, other abundant compounds detected were cis and trans-1,2-dichloroethylene, trichloroethylene, 1,2-dibromoethane, benzene, toluene, ethylbenzene, chlorobenzene, 1,4-dichlorobenzene and 1,2-dichloro - benzene. Principal component analysis (PCA) with the aid of varimax rotation, and parallel factor analysis (PARAFAC) method were used to statistically verify the correlation between VOCs and the source of pollution. The multivariate analysis pointed out that the maintenance of auxiliary pipelines in the distribution systems is vital as it can become significant point source pollution to Malaysian drinking water.     

Assessment of temporal and spatial water quality in international Gomishan Lagoon,Iran, using multivariate analysis

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO₂), nitrate (NO₃), biological oxygen demand (BOD), phosphate (PO₄), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R², RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.     

Strategies to optimize monitoring schemes of recreational waters from Salta,Argentina: a multivariate approach

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively, and cluster analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments, and Vaqueros and La Caldera rivers were the most similar. Canonical correlation analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and principal component analysis allowed finding relationships among the nine measured variables in all aquatic environments. Variable’s loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros rivers were influenced by recreational activities. Discriminant analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved.     

Monitoring particulate matter levels and climate conditions in a Greek sheep and goat livestock building

Atmospheric pollutants from livestock operations influence air quality inside livestock buildings and the air exhausted from them. The climate that prevails inside the building affects human and animal health and welfare, as well as productivity, while emissions from the building contribute to environmental pollution. The aim of this study was to examine the variation of two climatic parameters (namely temperature and relative humidity) and the levels of particulate matter of different sizes (PM10-PM2.5-PM1), as well as the relationships between them, inside a typical Greek naturally ventilated livestock building that hosts mainly sheep. The concentration of particles was recorded during a 45-day period (27/11-10/1), while temperature and relative humidity were observed during an almost 1-year period. The analysis revealed that the variation of outdoor weather conditions significantly influenced the indoor environment, as temperature and relative humidity inside the building varied in accordance to the outside climate conditions. Temperature remained higher indoors than outdoors during the winter and extremely low values were not recorded inside the building. However, the tolerable relative humidity levels recommended by the International Commission of Agricultural Engineering (CIGR) were fulfilled only in 47% of the hours during the almost 1-year period that was examined. This fact indicates that although temperature was satisfactorily controlled, the control of relative humidity was deficient. The concentration of particulate matter was increased during the cold winter days due to poor ventilation. The maximum daily average value of PM10, PM2.5 and PM1 concentration equaled to 363, 61 and 30?μg/m(3) respectively. The concentration of the coarse particles was strongly influenced by the farming activities that were daily taking place in the building, the dust resuspension being considered as the dominant source. A significant part of the fine particles were secondary, which the production of could be attributed to an increase in relative humidity levels. It is concluded that measures have to be adopted in order to achieve sufficient ventilation and to reduce particulate matter levels.     

Identification of mangrove water quality by multivariate statistical analysis methods in Pondicherry coast,India

Different multivariate statistical analysis such as, cluster analysis, principal component analysis, and multidimensional scale plot were employed to evaluate the trophic status of water quality for four monitoring stations. The present study was carried out to determine the physicochemical parameters of water and sediment characteristics of Pondicherry mangroves—southeast coast of India, during September 2008–December 2010. Seasonal variations of different parameters investigated were as follows: salinity (10.26–35.20 psu), dissolved oxygen (3.71–5.33 mg/L), pH (7.05–8.36), electrical conductivity (26.41–41.33 ms⁻¹), sulfide (1.98–40.43 mg/L), sediment texture sand (39.54–87.31%), silt (9.89–32.97%), clay (3.06–31.20%), and organic matter (0.94–4.64%). pH, temperature, salinity, sand, silt, clay, and organic matter indicated a correlation at P < 0.01. CA grouped the four seasons in to four groups (pre-monsoon, monsoon, post-monsoon, summer) and the sampling sites in to three groups. PCA identified the spatial and temporal characteristics of trophic stations and showed that the water quality was worse in stations 3 and 4 in the Pondicherry mangroves.     

Determination of pollution trends in an abandoned mining site by application of a multivariate statistical analysis to heavy metals fractionation using SM&T-SES

The mobility, availability and persistence of Heavy Metals (HMs), As, Cd, Cu, Ni, Pb and Zn, in contaminated soils of a former abandoned mining area were evaluated by means of a sequential extraction scheme (SES) and applying a multivariate statistical analysis to the obtained data. Chemical partitioning of HMs in each sample was determined in four fractions (acid-soluble, reducible, oxidable and residual) following the Standard Measurements and Testing (SM&T) SES, formerly BCR-SES. Statistical evaluation of results by pattern recognition techniques allowed identification of groups of samples with similar characteristics and observations of correlations between variables, determining the pollution trends and distribution of HMs within the studied area. Typical metal-fraction association and metal availability characteristics of heavy metals have been depicted. The obtained results indicate an urgent need to attenuate the hazard in that area posed by high concentrations of toxic metals, which exceed the limits specified by different European legislations on soil reclamation.     

An application of multivariate analysis to acid rain data in Northern Italy to discriminate natural and man-made compounds

This paper presents the preliminary results of a study, the aim of which was to analyse the pluviometric and chemical rain data, recorded by a wet only network located in Northern Italy.More in detail, precipitations were collected on a weekly basis and chemical analysis was performed on pH, electric conductivity and Ca, Mg, Na, K, NH₄, NO₃, SO₄, Cl concentrations.The Principal Components Analysis pointed out that the first three components are enough to explain more than 90% of the variability of the parameters. Moreover each component may have a different physical interpretation, that is the first one is mainly related to the precipitation amount, while the second to the man made and natural sources and the last one to the sea/soil contribution.