Factor analytical study on water quality in Lake Saimaa, Finland

Multivariate data analysis methods (4-way Candecomp-PARAFAC model solved with Multilinear Engine (ME-1)) were used to interpret the data of over two decades to study the changes in the water of Lake Saimaa in Finland. Earlier studies have shown that it is difficult to extract the natural background from the other sources of variation. By using the multilinear model three interpretable factors representing natural and anthropogenic processes could be extracted. The natural long-term variation, seasonal fluctuation and dilution of discharges in the recipient area could be extracted into their own factors, which could be easily visualized. The variation could be also presented with estimated variation in the water quality parameters caused by each of these natural or anthropogenic processes.     

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. Received January 24, 2001. Revision July 6, 2001.     

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.     

Mass spectrometric investigations of water extracts of the river Elbe for the determination of potential inputs of pollutants into the North Sea

A non-target screening by gas chromatography-mass spectrometry has been carried out on water extracts of the river Elbe in order to obtain an overview of organic compounds being dissolved or bound to suspended matter in the Elbe. Samples of 1001 volume were taken at the freshwater border at Stade and extracted by liquid-liquid extraction with pentane. Before GC-MS analysis, the water extracts were fractionated into 15 subsamples by HPLC on an SiO₂ column. A sensitivity of ca. 50 to 250 pg/l was reached for the GC-MC analysis. Several hundreds of natural and anthropogenic compounds could be identified or at least grouped into a compound class. The presence of a number of compounds could be verified from earlier investigations, in addition a great number of anthropogenic compounds were described which have previously not been reported for the river Elbe.     

Multivariate statistical interpretation of coastal sediment monitoring data

Multivariate statistical analysis of sediment data (input matrix 122 × 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.     

Harmonized protocol and certified reference material for the determination of extractable contents of phosphorus in freshwater sediments – A synthesis of recent works

An analytical protocol for the determination of the extractable phosphorus contents in freshwater sediments has been harmonized through interlaboratory studies in the frame of the Standards Measurements and Testing Program of the European Commission. A homogeneous and stable sediment reference material has been prepared and certified on the basis of this protocol named SMT protocol, and will be available in spring of 2001. The SMT protocol, together with the reference material, are useful tools in the field of water management, especially at a time when quality assurance and data comparability are of paramount importance in laboratory analysis. The knowledge of the bioavailable forms of phosphorus is important not only for sediments but also for sludge and soils. Therefore, the SMT protocol could be extended to these materials and new CRMs could be prepared. The SMT protocol was used in a study of a reservoir, which allowed to calculate the P stock, therefore helping to predict the restoration delay of the lake. The paper describes the protocol and the CRM, and gives a brief outline of the case study. Received: 14 November 2000 / Revised: 25 January 2001 / Accepted: 31 January 2001     

Assessment of Water Quality for Human Consumption

 This study deals with the application of chemometric approaches (cluster analysis and principal components analysis) to a potable water monitoring demonstrated on a data set from the region of Kavala, Greece, being analysed according to the standard instructions and directives of the European Union. It is shown that the data classification by cluster analysis and data structure modeling by principal components analysis reveals similar results, namely four different patterns of water source sites are identified depending on the geographical site location (near to Nestos river, near to Strimon river, elevated sites and near-to-coast sites). Three latent factors, explaining over 85% of the total variance, are responsible for the data structure as follows: “water acidity (anthropogenic)”, “water hardness (natural)” and the “marine factor”. Their importance for the different sites is related to the site location. Finally, it is recommended to involve the environmetric data treatment as a substantial standard procedure in assessment of the quality of water intended for human consumption. Received October 18, 2001; accepted June 24, 2002     

Chemometric analysis of groundwater quality data of alluvial aquifer of Gangetic plain, North India

Water quality data set from the alluvial region in the Gangetic plain in northern India, which is known for high fluoride levels in soil and groundwater, has been analysed by chemometric techniques, such as principal component analysis (PCA), discriminant analysis (DA) and partial least squares (PLS) in order to investigate the compositional differences between surface and groundwater samples, spatial variations in groundwater composition and influence of natural and anthropogenic factors. Trilinear plots of major ions showed that the groundwater in this region is mainly of Na/K-bicarbonate type. PCA performed on complete data matrix yielded six significant PCs explaining 65% of the data variance. Although, PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types (dug well, hand-pump and surface water). However, a visible differentiation between the water samples pertaining to two watersheds (Khar and Loni) was obtained. DA identified six discriminating variables between surface and groundwater and also between different types of samples (dug well, hand pump and surface water). Distinct grouping of the surface and groundwater samples was achieved using the PLS technique. It further showed that the groundwater samples are dominated by variables having origin both in natural and anthropogenic sources in the region, whereas, variables of industrial origin dominate the surface water samples. It also suggested that the groundwater sources are contaminated with various industrial contaminants in the region.     

Multi-way modeling of hydro-chemical data of an alluvial river system--a case study

A large data set pertaining to water quality of an alluvial river was analyzed using multi-way data analysis methods with a view to extract the hidden information, spatial and temporal variation trends in the river water quality. Four-way data (8 monitoring sites × 22 water quality variables × 10 monitoring years × 12 sampling months) analysis was performed using PARAFAC and Tucker3 models. A two component PARAFAC model, although explained 35.1% of the data variance, could not fit to the data set. Tucker3 model of optimum complexity (2,3,1,3) explaining 39.7% of the data variance, allowed interpretation of the data information in four modes. The model explained spatial and temporal variation trends in terms of water quality variables during the study period and revealed that sampling sites in mid-stretch of the river were dominated mainly by the variables of anthropogenic origin. The results delineated the mid stretch of the river as critical from pollution point of view and also identified summer months as having high influence on river water quality in this stretch. The information regarding spatial and temporal variations in water quality generated by the four-way modeling of data would be useful in developing long-term water resources management strategies in the river basin.     

Study of seasonal variation and source characteristic of PM10 of Shanghai urban atmosphere using PIXE

This study is a one-year monitoring of the inhalable particulate matter (PM10) of Shanghai (from January 2006 to December 2006) to study PM10 pollution. Proton-induced X-ray emission (PIXE) was used to investigate the chemical elements in Shanghai PM10. The study finds seasonal variation in both mass concentration and of chemical elements in PM10. The results of the enrichment factor show that the chemical elements in the inhalable particles could be divided into two categories, soil elements from earth crust and anthropogenic pollution elements. The high enrichment factors suggest that anthropogenic activities were the dominant source for elements such as S, Cu, Cl, Zn, Pb and Br. Strong correlation of K, Ca, Fe and Ti, from factor analysis, indicates these elements coming from earth crust or soil, S, Zn and Pb from industrial pollution and/or traffic and Cl from coal combustion.     

Multivariate statistical analysis of coastal sediment data

Multivariate statistical analysis of sediment data (information matrix 123 × 16) from the Gulf of Mexico, USA shows that the data structure is defined by four latent factors conditionally called “inorganic natural”, “inorganic anthropogenic”, “bioorganic” and “organic anthropogenic” explaining 39.24%, 23.17%, 10.77% and 10.67% of the total variance of the data system, respectively. The receptor model obtained by the application of the PCR approach makes it possible to apportion the contribution of each chemical component for the latent factor formation. A separation of the contribution of each chemical parameter is achieved within the frames of “natural” and “anthropogenic” origin of the respective heavy metal or organic matter to the sediment formation process. This is a new approach as compared to the traditional “one dimensional” search with a limited number of preliminary selected tracer components. The model suggested divides natural from anthropogenic influences and allows in this way each participant in the sediment formation process to be used as marker of either natural or anthropogenic effects. Received: 20 March 1999 / Revised: 1 June 1999 / Accepted: 3 June 1999     

Multivariate statistical analysis of coastal sediment data

Multivariate statistical analysis of sediment data (information matrix 123 × 16) from the Gulf of Mexico, USA shows that the data structure is defined by four latent factors conditionally called “inorganic natural”, “inorganic anthropogenic”, “bioorganic” and “organic anthropogenic” explaining 39.24%, 23.17%, 10.77% and 10.67% of the total variance of the data system, respectively. The receptor model obtained by the application of the PCR approach makes it possible to apportion the contribution of each chemical component for the latent factor formation. A separation of the contribution of each chemical parameter is achieved within the frames of “natural” and “anthropogenic” origin of the respective heavy metal or organic matter to the sediment formation process. This is a new approach as compared to the traditional “one dimensional” search with a limited number of preliminary selected tracer components. The model suggested divides natural from anthropogenic influences and allows in this way each participant in the sediment formation process to be used as marker of either natural or anthropogenic effects. Received: 20 March 1999 / Revised: 1 June 1999 / Accepted: 3 June 1999     

Arsenic speciation and other parameters of surface and ground water samples of Jamshoro,Pakistan

This study evaluated and interpreted complex data sets of water samples collected from different sampling origins of ground water (hand pump and tube well) and surface water (municipal, river and canal). The aim was to provide information concerning the apportionment of pollution sources to obtain better information about water quality and possible distribution of As with respect to its speciation. The As (III) formed complex with ammonium pyrrolidinedithiocarbamate (APDC) and extracted by surfactant-rich phases in the non-ionic surfactant Triton X-114, while total iAs in water samples was adsorbed on titanium dioxide (TiO₂) and determined by electrothermal atomic absorption spectrometry. The accuracy of the proposed methodologies was confirmed by standard addition method. The recoveries of As (III) and total inorganic arsenic (iAs) were found to be >98%. The results revealed that the ground water of the area under study was more contaminated as compared to surface water samples. The mean concentration of As (III) and As (V) in the surface water samples was found to be 15.8 and 6.00?µg?L^?1, respectively, whereas, in the case of ground water samples, the contents of As (III) and As (V) ranged from 6.20 to 51.0 and 6.40 to 53.0?µg?L^?1, respectively. Principal component analysis performed on a combined (tube well and hand pump) samples data set extracted two significant factors explaining more than 60% of total variance, which suggested that the contamination sources might be natural or anthropogenic.     

Preconcentration and determination of trace amounts of lead (II) as thenoyltrifluoroacetone complex with dibenzo-18-crown-6 by synergistic extraction and atomic absorption spectrometry

A new method for the preconcentration and determination of trace amounts of lead at the μg/L level in natural waters has been established based on the formation of the thenoyltrifluoroacetone (TTA) complex with dibenzo-18-crown-6 (DB18C6) by means of synergistic extraction and back-extraction combined with atomic absorption spectrometry (AAS). The effect of various factors (synergism with TTA and DB18C6, shaking time, preconcentration factor, composition of the extracted species, and foreign ions etc.) on the extraction and back-extraction of lead has been investigated in detail. The lead-TTA chelate in o-dichlorobenzene forms a stable adduct with DB18C6 as Pb(TTA)₂ DB18C6. The stability constant (β) of the adduct determined by curve fitting method was log β = 4.2. The amount of lead in natural waters such as tap water (Kanazawa University) and Kakehashi river (Komatsu City) determined by the present method was found to be 0.64 ± 0.02 μg/L and 5.10 ± 0.03 μg/L, respectively. Received: 14 July 1997 / Revised: 3 November 1997 / Accepted: 20 January 1998     

Heavy metal determination in atmospheric particulate matter by Instrumental Neutron Activation Analysis

Instrumental Neutron Activation Analysis (INAA) is employed for its important analytical properties. Fundamentally, INAA is a multi-elemental technique allowing the determination of about 40 elements with a good Limit of Detection. In this paper we applied this nuclear technique to study the element composition in PM10 determining about 30 elements.25 filters were collected in downtown Rome from October 1999 to April 2000 and irradiated at the nuclear reactor Triga Mark II (ENEA-Casaccia Laboratories). The γ-ray measurements have allowed the quali- and quantitative analysis. The element levels in PM10 with the relative correlations have been determined: basically, the concentrations are very low.Furthermore, the enrichment factors of all elements will be reported in order to understand the natural or anthropogenic origins of the particulate matter: some elements may be attributed to long-range transport phenomena from other natural and/or anthropogenic sources.     

N-way modelling of sediment monitoring data from Mar Menor lagoon, Spain

The present paper deals with the application of Tucker3 modelling to a sediment monitoring data set from the area of Mar Menor coastal lagoon (Spain). The aim of the study is to model and interpret the fractionation of heavy metals in the suspended particulate matter and sediment fractions resulting by sedimentation processes. Since the lagoon is seriously influenced by anthropogenic activities the modelling aims an assessment of the environmental hazard, too. After application of various scaling and centering procedures and estimation of the model dimensionality, an optimal (3, 3, 3) Tucker3 model was chosen for data interpretation. Using the model output (factor loadings connected to the four main core elements) it could be concluded that the heavy metal concentrations in the suspended particulate matter and sediment fractions increase in order Cu > Mn > Zn ≈ Pb > Cd and could be examined as estimation of basic levels for all heavy metals caused by different sedimentation processes. The second important core element summarizes the anthropogenic influence of the mining activity in the region. The third important core element shows the different mobility of the heavy metals. The fourth important core element should be related to the specific sediment formation at one of the sampling location.     

Qualitative interpretation of physico-chemical and isotopic parameters in the Krka River (Croatia) assessed by multivariate statistical analysis

Multivariate statistical analyses were applied on the measured physico-chemical (Cd, Pb, Cu, Zn, Mg, Ca, O₂, alkalinity, temperature, pH, SAS, DOC and DIC) and isotopic parameters (δ¹³C and δ¹⁸O) to estimate and distinguish anthropogenic from natural influences to the water system of the Krka River. Analyses were conducted on the data collected during six years from twelve sampling sites. On the basis of orientation, positioning and grouping of parameters arranged by biplots, four main hypotheses were defined and finally statistically confirmed. Thereof, two main and distinct processes occurring in the Krka River could be highlighted: (i) upstream pollution, caused by the inflow of untreated waste-waters of city of Knin and (ii) downstream self-purification, caused by the sedimentation and/or co-precipitation of pollutants coupled by the inflow of clean subterranean water (groundwater recharge). Grouping of (i) hydrological and carbon cycle connected parameters, and (ii) anthropogenically influenced correlated parameters were proposed as a result of statistical analysis. Regarding the pH, it is shown that a stream section influenced by the subterranean inflow of Zrmanja River is statistically significantly different for all sampling campaigns during six years, being lower for about 0.5 pH unit.     

Microstuctural analysis and determination of PM10 emission sources in an industrial Mediterranean city

Scientists are interested in knowing more about the control of sources which contribute to environmental pollution. Air pollution has two main sources: anthropogenic and natural sources. The natural contributions to environmental pollution can be assessed, but cannot be totally controlled. while the emissions from the anthropogenic sources can be controlled. These air pollutants can be dispersed and transferred by winds in the atmosphere. The focus area of this study is the Mediterranean basin. The most important winds in this area are the land and sea breezes. Scanning Electron Microscopy (SEM) was applied to characterize the morphology of the PM10 samples in order to identify possible emission sources for the occuring pollution. Energy Dispersive X-ray Spectroscopy (EDS) was performed for the elemental analysis and chemical characterization of the PM10 samples. The analysis showed that the PM10 samples can be divided into three different groups: the samples containing mineral phases, the compounds from combustion processes and the particles emitted from high-temperature processes.      

A model to set measurement quality objectives and to establish measurement uncertainty expectations in analytical chemistry laboratories using ASTM proficiency test data

A model is presented that correlates historical proficiency test data as the log of interlaboratory standard deviations versus the log of analyte concentrations, independent of analyte (measurand) or matrix. Analytical chemistry laboratories can use this model to set their internal measurement quality objectives and to apply the uncertainty budget process to assign the maximum allowable variation in each major step in their bias-free measurement systems. Laboratories that are compliant with this model are able to pass future proficiency tests and demonstrate competence to laboratory clients and ISO 17025 accreditation bodies. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/ 10.1007/s007690100398-y. Received: 31 March 2001 Accepted: 11 September 2001     

Optimization and evaluation of mixed-bed chemisorbents for extracting fission and activation products from marine and fresh waters

Chemically selective chemisorbents are needed to monitor natural and engineered waters for anthropogenic releases of stable and radioactive contaminants. Here, a number of individual and mixtures of chemisorbents were investigated for their ability to extract select fission and activation product elements from marine and coastal waters, including Co, Zr, Ru, Ag, Te, Sb, Ba, Cs, Ce, Eu, Pa, Np, and Th. Conventional manganese oxide and cyanoferrate sorbents, including commercially available Anfezh and potassium hexacyanocobalt(II) ferrate(II) (KCFC), were tested along with novel nano-structured surfaces (known as Self Assembled Monolayers on Mesoporous Supports or SAMMS) functionalized with a variety of moieties including thiol, diphosphonic acid (DiPhos-), methyl-3,4 hydroxypyridinone (HOPO-), and cyanoferrate. Extraction efficiencies were measured as a function of salinity, organic content, temperature, flow rate and sample size for both synthetic and natural fresh and saline waters under a range of environmentally relevant conditions. The effect of flow rate on extraction efficiency, from 1 to 70 mL min(-1), provided some insight on rate limitations of mechanisms affecting sorption processes. Optimized mixtures of sorbent-ligand chemistries afforded excellent retention of all target elements, except, Ba and Sb. Mixtures of tested chemisorbents, including MnO(2)/Anfezh and MnO(2)/KCFC/Thiol (1-3 mm)-SAMMS, extracted 8 of the 11 target elements studied to better than 80% efficiency, while a mixture of MnO(2)/Anfezh/Thiol (75-150 μm)-SAMMS mixture was able to extract 7 of the 11 target elements to better than 90%. Results generated here indicate that flow rate should be less of a consideration for experimental design if sampling from fresh water containing variable amounts of DOM, rather than collecting samples from salt water environments. Relative to the capability of any single type of chemisorbent tested, optimized mixtures of several sorbents are able to increase the number of elements that can be efficiently and simultaneously extracted from natural waters.