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     

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 assessment of polluted soils

This 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.     

Chemometrics in the assessment of the sustainable development rule implementation

The 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 %.     

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.     

Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties

The ability of multivariate analysis methods such as hierarchical cluster analysis, principal component analysis and partial least squares-discriminant analysis (PLS-DA) to achieve olive oil classification based on the olive fruit varieties from their triacylglycerols profile, have been investigated. The variations in the raw chromatographic data sets of 56 olive oil samples were studied by high-temperature gas chromatography with (ion trap) mass spectrometry detection. The olive oil samples were of four different categories (“extra-virgin olive oil”, “virgin olive oil”, “olive oil” and “olive-pomace” oil), and for the “extra-virgin” category, six different well-identified olive oil varieties (“hojiblanca”, “manzanilla”, “picual”, “cornicabra”, “arbequina” and “frantoio”) and some blends of unidentified varieties. Moreover, by pre-processing methods of chemometric (to linearise the response of the variables) such as peak-shifting, baseline (weighted least squares) and mean centering, it was possible to improve the model and grouping between different varieties of olive oils. By using the first three principal components, it was possible to account for 79.50% of the information on the original data. The fitted PLS-DA model succeeded in classifying the samples. Correct classification rates were assessed by cross-validation.     

An advanced multivariate statistical approach to study coastal sediment data

The present paper deals with the application of classical and fuzzy principal components analysis to a large data set from coastal sediment analysis. Altogether 126 sampling sites from the Atlantic Coast of the USA are considered and at each site 16 chemical parameters are measured. It is found that four latent factors are responsible for the data structure (“natural”, “anthropogenic”, “bioorganic”, and “organic anthropogenic”). Additionally, estimating the scatter plots for factor scores revealed the similarity between the sampling sites. Geographical and urban factors are found to contribute to the sediment chemical composition. It is shown that the use of fuzzy PCA helps for better data interpretation especially in case of outliers.     

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     

Contamination assessment of heavy metal in surface sediments of the Wuding River, northern China

The heavy metal contents and the contamination levels of the surface sediments of the Wuding River, northern China, were investigated. Heavy metal concentration ranged in μg g⁻¹: 50.15–71.91 for Cr, 408.1–442.9 for Mn, 20.11–43.59 for Ni, 17.51–20.1 for Cu, 68.32–89.57 for Zn, 0.2–0.38 for Cd and 15.08–16.14 for Pb in the Wuding River sediments. The enrichment factor (EF) and the geo-accumulation index (Igeo) demonstrated that the sediments of the Wuding River had been polluted by Cd, Cr and Ni, which mainly originated from anthropogenic sources, whereas the sediments had not been polluted by Zn, Pb, Cu and Mn, which were derived from the crust. In addition, the assessment results of EF and Igeo suggested that the sediments of the Wuding River was “moderately” polluted by Cd and “unpolluted to moderately” polluted by Cr and Ni. The elevated urban sewage discharges and agriculture fertilizers usage in river basin are the anthropogenic sources of these heavy metals in river.     

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.     

Distribution of selected trace elements in samples of sediment,suspension and water taken along the Isar River,Bavaria

The three phases “dissolved solids”, “suspended solids” and “sediment” of four sampling sites along the river Isar were analysed by INAA. In these as well as in the different grain-size fractions between<2 and 63 μm 17 trace elements were determined. Compared with the values of other rivers in Middle Europe the river Isar is still below the levels of significant pollution.     

Chemometric application in classification and assessment of monitoring locations of an urban river system

The study presents the application of selected chemometric techniques: cluster analysis, principal component analysis, factor analysis and discriminant analysis, to classify a river water quality and evaluation of the pollution data. Seventeen stations, monitored for 16 physical and chemical parameters in 4 seasons during the period 1999-2003, located at the Bagmati river basin in Kathmandu Valley, Nepal were selected for the purpose of this study. The results allowed, determining natural clusters of monitoring stations with similar pollution characteristics and identifying main discriminant variables that are important for regional water quality variation and possible pollution sources affecting the river water quality. The analysis enabled to group 17 monitoring sites into 3 regions with 5 major discriminating variables: EC, DO, CL, NO₂N and BOD. Results revealed that some locations were under the high influence of municipal contamination and some others under the influence of minerals. This study demonstrated that chemometric method is effective for river water classification, and for rapid assessment of water qualities, using the representative sites; it could serve to optimize cost and time without losing any significance of the outcome.     

Multivariat-statistische Untersuchungen zur Probenahmestrategie in der Flie?gew?sseranalytik

Summary A strategy for representative sampling in the analysis of river water is presented. Temporal and local variations of the concentration of 11 different water components in a selected river are recorded by means of suitable samples and the multivariate-statistical methods factor analysis and multidimensional variance and discriminant analysis in an optimal manner. By means of these results and the relation of the ascertained fluctuations of concentrations to the anthropogenic pollution in the river it is possible to draw practical conclusions with regard to sampling.     

Portable Fibre Optic Oxygen Sensor Based on Room-Temperature Phosphor escence Lifetime

 A portable fibre optic instrument for oxygen sensing based on luminescence lifetime is presented. The instrument is based on measurement of the quenching by oxygen of the room temperature phosphorescence (RTP) emitted by aluminium tris(8-hydroxy-7-iodo-5-quinolinesulfonic acid) chelate incorporated in an inorganic matrix by sol–gel technology. The comparatively long RTP lifetime of this sensing material (450 μs) and the large singlet–triplet splitting (λ_exc 390 nm, λ_em 590 nm) allow the use of simple opto-electronic circuits and low-cost processing electronics. Standard electronic components have been applied in development of the low-cost lifetime-measurement instrumentation presented here. Two optical sensor configurations, “flow-through cell” and “probe”, have been designed and evaluated for the determination of very low levels of oxygen, in gaseous argon streams and in waters. The basic technology, design parameters and performance characteristics of the optical sensors are discussed. Applications to determination of dissolved oxygen in river, tap and sewage waters are described. The advantages of luminescence lifetime measurements over conventional RTP intensity measurements for oxygen sensing are discussed. Received April 1, 1999. Revision November 4, 1999.     

Archaeometric Analysis of Ancient Copper Artefacts by Laser-Induced Breakdown Spectroscopy Technique

Twelve archaeological copper objects from the burial site of “Fontino” cave, near Grosseto, (around 2500–2000 B.C.) were analysed using laser-induced breakdown spectroscopy. Qualitative results and a preliminary study of the samples’ composition are reported and used to make a quantitative estimate; based on these results, the samples were classified using principal components statistical analysis. The perspectives of using laser-induced breakdown spectroscopy for archaeometric analysis are also discussed.     

Lab-on-a-chip systems for biomedical and environmental monitoring

During the last decade, pocket-sized analytical equipment based on the lab-on-a-chip approach has become available. These chips, in combination with portable electronic equipment, are applicable in, for example, point-of-care ion analysis of body fluids, forensics, identification of explosives, tracking of pollution in environmental or waste waters, monitoring nutrients in agricultural or horticultural water, controlling quality in food production, or process control in chemical industry. This paper discusses several demonstrator systems with applications in these fields.     

Simultaneous Determination of Seven Bioactive Compounds in Chinese Medicine “QI-SHEN-YI-QI” Dropping Pill by LC-UV and LC-ELSD

A high performance liquid chromatography coupled with ultraviolet detection and evaporative light scattering detection (HPLC-UV-ELSD) method was developed for simultaneously determining seven bioactive components, i.e. danshensu, protocatechuic aldehyde, salvianolic acid B, notoginsenoside R₁, ginsenoside Rg₁, ginsenoside Rb₁, and astragaloside IV in “QI-SHEN-YI-QI” dropping pill, a widely used traditional Chinese medicine (TCM) for treating cardiovascular disease. The chromatographic separation was performed on a Zorbax Stable Bond C₁₈ column using gradient elution with acetonitrile and water with acceptable validation results such as linearity and recovery. The recoveries of the seven investigated compounds ranged from 93.3 to 100.2% with RSD values less than 5%. More importantly, this proposed method was successfully used to determine the seven compounds in nine batches of “QI-SHEN-YI-QI” dropping pills, which indicated that the proposed method could be readily utilized as a quality control method for this TCM preparation.     

Chemometric investigations on the differentiated evaluation of element trace analysis in river waters

The combination of sequential leaching methods for a first assessment of the kind of species in river sediments with multivariate-statistical methods (like factor analysis) for identifying anthropogenic and/or geogenic loading is useful for the differentiated characterization of the pollution state of a river. Electrochemical investigations, planned on the basis of statistical design and following empirical modelling, enables quantitative conclusions on the binding forms of heavy metals in river waters. Deposition-remobilisation effects of heavy metals in the complex system river water-river sediment can be described by PLS modelling.