Chemometric analysis of the water purification process data

Available online at www.sciencedirect.com

Talanta 74 (2007) 153–162

Chemometric analysis of the water purification process data I. Stanimirova a , M. Połowniak b , R. Skorek b , A. Kita b , E. John b , F. Buhl b , B. Walczak a,∗ b

a Department of Chemometrics, Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland Department of Analytical Chemistry, Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland

Received 2 February 2007; received in revised form 20 May 2007; accepted 24 May 2007 Available online 2 June 2007

Abstract The aim of this work was to show usefulness of chemometric analysis in processing of the data describing production of drinking water in the Silesian region of Poland. Water samples have been collected within the period of 1 year and the quality of water was characterized by a number of physical, chemical and microbiological parameters. Principal component analysis (PCA) and STATIS (Structuration des Tableaux A Trois Indices de la Statistique) were employed to obtain the knowledge about the complete water treatment process. PCA makes it possible to uncover seasonal changes influencing the water treatment process. In particular, it was found out that the salt content, hardness and conductivity of water tend to obtain higher levels in winter rather than in summer, and the relatively lower acidity is also to be expected in winter. The sensory quality of water is considerably improved over the consecutive purification steps. Complementary information about the individual technological units of the process is gained with the STATIS approach. The obtained results show that the water produced by the two independent filtering branches of the water plant is of similar quality and the prescribed quality characteristics of drinking water are fulfilled. © 2007 Elsevier B.V. All rights reserved. Keywords: STATIS; PCA; Drinking water; Quality control

1. Introduction Nowadays, the quality control of drinking water is of major concern. The Silesian region situated in the urban area of South Poland is supplied with drinking water by the water treatment plants of the Upper Silesian Waterworks. This company is one of the biggest in Europe and embraces several surface and deep water treatment plants. It has a capacity of an average combined process flow of 1.5 million m3 day−1 . Up to date, over 0.5 million m3 day−1 of drinking water are redistributed among the three and a half million inhabitants of the Upper Silesian agglomeration. In order to meet the required quality specifications, raw water passes through several filtering systems before reaching the municipal pipeline system of 1030 km length. The natural water sources for the Goczalkowice surface water plant are the Vistula and the Sola rivers collected by the Goczalkowice and Czaniec reservoirs, respectively. Water from each

∗

Corresponding author. Tel.: +48 32 359 2115; fax: +48 32 259 9978. E-mail address: [email protected] (B. Walczak).

0039-9140/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.talanta.2007.05.044

source flows through an individual technological system of general treatment. Then the parallel purification systems merge into one that utilizes secondary ozonation, granular activated carbon filtration and chlorination as a disinfection process. The two parallel systems of water filtering allow periodical conservation and backflushing of the filters of one of the two systems, if necessary, without breaking the purification process and water supply [1]. The aim of this work is to obtain more detailed information about the global changes of the monitored physico-chemical parameters and the levels of inorganic elements at different water treatment units. Moreover, it is important to trace a possible seasonal impact upon the filtering process and consequently, upon the quality of the final product. It is also essential to confirm that the water passing through the two independent filtering branches is of comparable quality. In order to meet the aims of this study, water samples were collected at different steps of the technological process twice per month within the 1-year period (24.10.2005–11.09.2006) and a comprehensive analysis of the various different water

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quality parameters was performed, like assessing the levels of the main inorganic components (F− , Cl− , NO3 − , SO4 2− , Na+ , K+ ), trace elements (Al, Cu, Ba, Fe, Mn, Zn, Ca, Mg, Li), turbidity, color, hardness, biochemical oxygen demand, conductivity, pH, quantity of dissolved oxygen, alkalinity, acidity and the quantity of total solids. Although each step of the process is controlled at the water plant separately, this study aims to produce a global picture of the process and the factors that influence it. Two chemometric techniques are going to be employed to analyze the collected analytical data. These are the classical principal component analysis, PCA [2], and a less popular technique known as STATIS (the acronym holds for Structuration des Tableaux A Trois Indices de la Statistique) [3,4]. PCA is one of the most often applied dimensionality reduction techniques used for data compression and visualization. It can reveal similarity among the water samples collected at each filtering step and the relationship among the measured parameters. However, classical PCA can be applied to the two-way data tables only, for instance to the data organized as (time of sampling × filtering steps) × parameters. Complementary information can be obtained exploring the three-way data structure with the STATIS approach, i.e., the data that are arranged as, e.g., filtering steps × time of sampling × parameters. Application of STATIS to such type of the data is interesting, because the method allows uncovering and relatively easy interpretation of complex relationships among the parameters measured at the consecutive filtering steps, what often cannot be achieved with the conventional two-way methods.

2. Experimental 2.1. Stages in water purification The first technological system of the Goczalkowice water plant, called Go-cza I, processes water of the Goczalkowice reservoir located on the Vistula river. It utilizes pre-ozonation (Fig. 1, step 1), coagulation with alum (Al2 (SO4 )3 ) (step 3), and the flocculation and sedimentation units. After a rapid sand filtration (step 4), water flows trough the pumps (step 7) and is mixed with the water passing through the second filtering system. The second filtering system, Go-cza II, uses raw water from both the Goczalkowice and the Czaniec reservoir. Technologically, it consists of the analogous water treatment units, i.e., of preozonation (step 2), coagulation with Al2 (SO4 )3 at step 5 and the rapid sand filtration of water (step 6). However, a more effective, advanced and compact clarifier/flocculator unit (Degremont) is employed for the coagulation process. The consecutive step is a common secondary ozonation (step 8). Then the water passes through the granular activated carbon filtration unit (step 9) and finally, the process of chlorination (step 10) takes place. Eventually, the water enters the municipal pipeline system [5]. The consecutive steps of water filtering are schematically given in Fig. 1. 2.2. Sampling and analysis All water samples were collected directly at each of the 10 filtering units schematically presented in Fig. 1. They were stored

Fig. 1. Flowchart of the stages in water treatment at the Goczalkowice water plant.

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in plastic bottles for an easy and secure transportation to the laboratory [6]. The sampling campaign took place twice per month within the 1-year period (24.10.2005, 7.11.2005, 21.11.2005, 5.12.2005, 19.12.2005, 4.01.2006, 16.01.2006, 30.01.2006, 13.02.2006, 27.02.2006, 13.03.2006, 27.03.2006, 10.04.2006, 24.04.2006, 8.05.2006, 22.05.2006, 5.05.2006, 19.06.2006, 3.07.2006, 17.07.2006, 7.08.2006, 28.08.2006, 11.09.2006). Due to the renovation process, the coagulation and rapid sand filtration units at Go-cza I (see Fig. 1) were closed within the period from 27.03.2006 till 5.06.2006 and for this reason the respective measurements were not performed. The quality of drinking water was characterized by 29 chemical, physical and microbiological parameters such as dissolved oxygen (DOXY), biochemical oxygen demand (BOD5 ), the percentage of saturated oxygen (SOXY), total hardness (HARD), chemical oxygen demand (COD), alkalinity (ALK), acidity (AC), the content of total solids (TS), the content of non-volatile (NVS) and volatile (VS) substances, temperature (TEMP), pH, conductivity (COND), turbidity (TUR), color (COL), anions (F− , Cl− , NO3 − , SO4 2− ), cations (Na+ , K+ ) and metals (Al, Cu, Ba, Fe, Mn, Zn, Ca, Mg, Li). The employed analytical procedures were performed according to the current Polish standards issued by the Polish Committee for Standardization [7–21]. In general, the ion chromatographic method (IC) was used for determination of the cations and anions. The relative standard deviation of the IC method is less than 5% for all the ions. All metals were analysed by the inductively coupled plasma optical emission spectrometry (ICP-OES). The standard calibration procedure using five standards was adopted and all the measurements were done in triplicate. The relative standard deviation of the technique was 3–5% for all the elements. Several parameters like saturated oxygen, pH, turbidity, color, conductivity and temperature were measured directly at the sampling place. The uncertainty of the measurements is reported in Refs. [6,14,15,17,20]. Titration methods were adopted for determination of the dissolved oxygen, hardness, biochemical oxygen demand, chemical oxygen demand, acidity and alkalinity with the accuracy ±0.05 ml determined in replicates. The contents of the total solids and of the non-volatile and volatile substances were determined by use of the routine gravimetric analytical method. The measurements were performed in replicates with the accuracy ±1.10−5 g. 3. Theory 3.1. Principal component analysis Principal component analysis (PCA) is usually chosen to uncover information hidden in the complex data sets [2]. For the studied data set, it allows to reveal the relationship between the water samples collected over the investigated time span and the parameters measured for the consecutive filtering units, and also it can elucidate the relationships among these parameters. PCA allows projection of the data from a high dimensional space onto a space spanned by a few uncorrelated factors, called principal components (PCs). These principal components are obtained as a weighted sum of the original variables. The weights are

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called loadings and show the importance of the parameters in construction of the PCs. Several algorithms can be used to perform PCA like, e.g., the singular value decomposition (SVD) approach [2] and the non-linear iterative partial least squares (NIPALS) algorithm [22]. In recent years, the so-called kernel PCA approach has gained a lot of attention in chemometrics, due to its computational efficiency [23,24]. PCA is a very widely applied method and therefore it will not be further explained in this place. 3.2. STATIS The acronym STATIS stands for Structuration des Tableaux A Trois Indices de la Statistique [3,4,25], which can be translated as “structuring the three-way data sets in statistics”. As the name suggests, STATIS explores the three-way structure of the data. It highlights similarities among the individual data tables constituting the three-way array. This is done with use of a special weighting scheme. STATIS can be viewed as PCA of the unfolded data, where the variance of each data table constituting the three-way array is weighted. In fact, the weighting scheme is responsible for the three-way character of the method. The weights are used to define the so-called STATIS compromise, as a weighted sum of the individual cross-product matrices [3]. They reflect an “agreement” between each individual data table and the compromise. Including the weights that reflect an agreement between the individual tables and the compromise makes the STATIS compromise robust. The table that agrees with the compromise the least has the weight close to zero. In that way, the influence of atypical observations is diminished. The weights are determined as the normalized elements (their sum is equal to one) of the first eigenvector of the so-called RV matrix, which holds the RV coefficients. The RV coefficient [26] is a measure of closeness between any two cross-product matrices. It is positive and its value varies in the range of (0, 1). The two cross-product matrices are more similar, if their respective RV coefficient is closer to one. STATIS has very good visualization properties. Distribution of objects can be visualized on the plane spanned by the principal components selected after PCA of the compromise matrix and the representation is called the compromise plot. Because of the weighting scheme, another way of data representation can additionally be used. A plot displaying each individual crossproduct matrix projected on the compromise plot can be drawn, i.e., the plot that shows the position of each point on the compromise plot as a weighted center of the individual positions of this point. Such plot enables, e.g., an easy interpretation of the possible changes in chemical composition at each step of purification in the water treatment plant. Several STATIS compromises can be obtained by reorganizing the three-way array in various different ways. If the data have the same dimension for the columns or rows, three different arrangements of the data array are possible, resulting in the three different ways of data analysis with STATIS. Since the STATIS method uses the cross-product matrices, it also allows working with the data that do not have the same dimensions for the columns or rows. In this latter case, the compromise is

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obtained for the objects, for which all the data tables have the same dimensions. 4. Results and discussion For a preliminary inspection of the data structure, principal component analysis (PCA) was applied. For this purpose, the analytical data were organized in a two-way table (sampling time × filtering steps) × parameters of the dimension 218 × 29. Each object in this table corresponds with the sample collected at a given technological unit at a definite sampling time and is characterized by 29 measured parameters. As it has been mentioned before, the measurements of the coagulation and the rapid sand filtration units were not recorded at Go-cza I within the period from 27.03.2006 till 5.06.2006, due to the ongoing renovation activities. Then the data were preprocessed, using the so-called autoscaling procedure [2]. This data transformation was required, since the variables have been measured in different units and appeared at the various different ranges. Autoscaling is performed by subtracting the corresponding column mean from each data element and dividing it by the corresponding column standard deviation. As a result, the variables have the unitary variances (scale) and consequently, they have the same importance in the PCA analysis. The results obtained from PCA on the autoscaled data are shown in Fig. 2. The first three principal components explain about 60% of the total data variance (see Fig. 2a). The total data variance is distributed over all PCs, which indicates that the data compression is not very efficient, i.e., there is no high correlation structure in the data. However, the PCA analysis allows drawing some general conclusions about the studied water purification process. The first principal component, PC 1, accounts for 30.48% of the total data variance. It is associated mainly with conductivity (COND), the quantity of the total solids (TS), Ca, Cl− , hardness (HARD), the quantity of the non-volatile solids (NVS) and to a lesser degree with Na+ , F− and Ba (see Fig. 2b). Therefore, this factor reflects the salt content in the water samples and can be conditionally called the ‘salt’ factor. The second principal component, PC 2 (explaining 20.18% of the total data variance), contrasts the two groups of the parameters: color (COL), turbidity (TUR), Fe, chemical oxygen demand (COD), pH and BOD5 with the negative loading values on the one hand, and the saturated (SOXY), and dissolved oxygen (DOXY) with the positive loading values on the other (see Fig. 2b). These parameters are associated with the smell, taste and appearance of drinking water. The third principal component, PC 3 (see Fig. 2c), is associated with the Mg content (the negative loading value) and the water acidity, AC (the positive loading value). A diffuse distribution of samples is observed in the PC 1–PC 2 score plot (see Fig. 2d and e). To enable better visualization and easier interpretation of the general effects, the objects are colored (in gray scale). The intensity of the color corresponds with the sampling time (Fig. 2d) and the filtering units (Fig. 2e). Fig. 2d shows a clear seasonal tendency along PC 1. The samples found on the left-hand side of the PC 1–PC 2 plot (light gray) were collected in the winter period (21.11.2005–10.04.2006), whereas those placed on the right-hand side of the plot were col-

lected in summer. Combining information from the PCA score and the loading plots, it comes out that the salt content, hardness and conductivity of the water samples appear higher in winter than in the summer at all the technological units. This is not a striking observation though, since coagulation, flocculation, sedimentation, and the activated carbon and sand filtration are the strongly temperature-dependent processes [24]. As with the temperature decrease the viscosity of water increases, the rate of sedimentation decreases. Therefore, the coagulation and sedimentation processes may be less efficient in winter than in summer. Filtration of water is also slowed down at lower temperatures. Adsorptivity of the activated carbon increases as the temperature decreases. It has been reported that in general, hardness and conductivity of water decrease with an increase of temperature at all water purification steps. The same tendency was observed within the framework of this study. The change of parameters that reflect the sensory quality of water at the consecutive technological units is the main effect observed along PC 2 (see Fig. 2e). The levels of Fe, COD, pH and BOD5 decrease, and the color and turbidity diminish in the consecutive filtering steps, while the quantities of both the saturated and dissolved oxygen increase. The quantities of the saturated and dissolved oxygen are essential to interpreting biological and chemical processes going on in water. The higher levels thereof in water are indicative of lower contamination. From Fig. 2e it comes out that the quality of water is considerably better in the checkpoints located after the parallel pre-ozonation units 1 and 2 at Go-cza I and Go-cza II, respectively, than before these units. An additional seasonal effect related to the acidity of water takes place along PC 3. The samples situated in the lower part of the PC 1–PC 3 plot (see Fig. 2f) have been collected mainly in the autumn and winter (24.10.2005–16.01.2006). They are characterized by a low acidity and high levels of Mg. In general, pH decreases with an increase of the temperature and consequently, the lower acidity of water is expected in winter rather than in summer. However, the presence of carbonates and of the other acidic species in water can modify this effect. In an agreement with this observation, the highest acidity was found for the samples collected in the summer (24.04.06–11.09.06), for which the conductivity, the quantity of the total solids, Ca, Cl− , hardness, and the quantity of the non-volatile solids are at the low levels (see Fig. 2f). The effect of an increased temperature contributes to the increased concentrations of the organic acids and CO2 that ultimately might result in an increase of microbiological action, consequently leading to a higher acidity. The results of PCA demonstrate the main effects influencing production of drinking water. However, PCA does not allow for a detailed description of the processes at the individual technological steps. Such possibility arises, when exploring the three-way structure of the collected analytical data with aid of the STATIS approach. For this purpose, the autoscaled data were organized in a three-way array as parameters × sampling time × filtering steps. As it has already been mentioned before, certain data for the coagulation and rapid sand filtration units at Go-cza I are missing, which results in smaller dimensions of the columns in the third and

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Fig. 2. Results of PCA for the autoscaled data: (a) cumulative percentage of the explained data variance as a function of the principal components number. (b) Projection of parameters on the plane spanned by PC 1 and PC 2. (c) Projection of parameters on the plane spanned by PC 1 and PC 3. (d) Projection of samples on the plane spanned by PC 1 and PC 2. The samples are presented in gray scale according to the sampling time. (e) Projection of samples on the plane spanned by PC 1 and PC 2. The samples are presented in gray scale according to the purification steps. (f) Projection of samples on the plane spanned by PC 1 and PC 3. The samples are presented in gray scale according to the sampling time.

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Fig. 3. Results of STATIS for the autoscaled data: (a) the PC 1–PC 2 compromise plot for the parameters, (b) the PC 1–PC 3 compromise plot for the parameters, (c) the PC 1–PC 2 compromise plot for the water purification units and (d) convex hulls of parameters in the PC 1–PC 2 compromise plot for the water purification units.

the fourth table. The PC 1–PC 2 and PC 1–PC 3 compromise plots for the parameters are presented in Fig. 3a and b. Distributions of the parameters in the plots resemble distributions of the parameters observed on the loading plots of PCA, except for the fact that now the PC 2 axis has a reversed sign. However, the sign change does not influence an overall interpretation. The principal components explain the same relationships between the explanatory parameters. The compromise plot presented in Fig. 3c shows the changes in the quality of the water passing through the various filtering steps. It is obtained from STATIS using the data array, each table of which is organized as filtering steps × parameters. Three principal components explain about 87% of the data variance. Assessing the two figures (Fig. 3a and c), it comes out that after the pre-ozonation steps at Go-cza I (step 1) and Go-cza II (step 2), the water still has the high levels of Fe, COD, pH, BOD5 and turbidity, as well as an intensive color, whereas the quantities of the saturated and dissolved oxygen are at the low

levels. At these two steps, the water is additionally characterized by the high conductivity (COND), the quantity of total solids (TS), Ca, Cl− , hardness (HARD) and the quantity of the non-volatile solids (NVS). However, the quality of water considerably improves with the successive filtering units. A decrease of these parameters combined with a quantitative increase of the saturated and dissolved oxygen is observed. This conclusion has already been drawn, when describing the results of PCA, but it is even more apparent, when applying the STATIS approach. As mentioned in the theory part, each point on the compromise plot is a weighted center of the individual locations of this point. Therefore each filtering step in Fig. 3c can be presented as a weighted center of all the parameters at this step. Thus a convex hull passing through individual parameters can be drawn to better visualize the relationship between the parameters. Fig. 3d shows that the convex hulls for step 1 (solid line), step 2 (solid line), step 8 (dashed line) and step 9 (doted line) do not considerably overlap, which points out to certain global changes of

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Fig. 4. A comparison of pre-ozonation steps at Go-cza I and Go-cza II: (a) a convex hull of parameters on the PC 1–PC 2 compromise plot for the pre-ozonation step at Go-cza I, (b) the PC 1–PC 2 plot of sampling times for the pre-ozonation step at Go-cza I, (c) a convex hull of parameters in the PC 1–PC 2 compromise plot for pre-ozonation step at Go-cza II and (d) the PC 1–PC 2 plot of sampling times for the pre-ozonation step at Go-cza II.

the parameters at these purification steps. However, such a complex plot is not easily legible and therefore interpretation will be continued, using separate plots. Because of the difference in sampling times, the plot describing the time trends at each filtering unit can also be constructed. Let us consider filtering steps 1 and 2. Fig. 4 shows distribution of the parameters (Fig. 4a and c) and time (Fig. 4b and d). In Fig. 4a, projections of the parameters Fe, color intensity, turbidity, COD and pH are placed far away from the compromise point along PC 1 and PC 2. They have the high absolute score values. Combining information about the distribution of the parameters and the time in Fig. 4a and b, it comes out that Fe, color intensity, turbidity, COD and pH are at the high levels within the whole sampling period. However, the highest levels of these parameters were found on 3.07.06, 5.06.06, 19.06.06 and 10.04.06. A similar conclusion can also be drawn from Fig. 4c and d.

A comparison of water quality after the coagulation process at Go-cza I (step 3) and Go-cza II (step 5) is made, based on Fig. 5a and c. Fig. 5a shows that conductivity (COND), Cl− , Ca, hardness and NVS have the high absolute score values on PC 1 and PC 2, while Mn has the high absolute score value on PC 1. The high absolute score value on PC 2 was also found for Al. Fig. 5c describes similar tendency for these parameters, except for Mn, which has a low absolute score value. Taking into the account the distribution of time (see Fig. 5b and d), it can be pointed out that these parameters are at high levels within the whole sampling period. However, the higher levels are characteristic of the winter rather than the summer time. As it was already mentioned before, this is so due to the strong temperature effect on coagulation. The presence of Al in the water is in a sense not surprising, since alum is used as a coagulant. At the same time, such parameters as Fe, color intensity, turbidity, COD and pH are found at the low levels indicating a considerable improvement of the water quality. The

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Fig. 5. (a) A convex hull of parameters in the PC 1–PC 2 compromise plot for the coagulation at Go-cza I, (b) the PC 1–PC 2 plot of sampling times for the coagulation at Go-cza I, (c) a convex hull of parameters in the PC 1–PC 2 compromise plot for the coagulation at Go-cza II, (d) the PC 1–PC 2 plot of sampling times for the coagulation at Go-cza II, (e) a convex hull of parameters in the PC 1–PC 2 compromise plot for the rapid sand filtration at Go-cza I and (f) a convex hull of parameters in the PC 1–PC 2 compromise plot for the rapid sand filtration at Go-cza II.

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Fig. 6. (a) A convex hull of parameters in the PC 1–PC 2 compromise plot for the secondary ozonation, (b) the PC 1–PC 2 plot of sampling times for the secondary ozonation and (c) a convex hull of parameters in the PC 1–PC 2 compromise plot for the granular activated carbon filtration.

consecutive rapid sand filtration allows a decrease of the Al and Mn concentrations, which is revealed from the projections in Fig. 5e and f. Both figures show the low absolute score values of Al and Mn. For the sake of brevity, figures that describe the tendency that involves the sampling time were omitted. It is important to emphasize that the water from the parallel purifying units is of a similar quality. The quality of the water does not change after the pumping unit (step 7). Surprisingly enough, after the secondary ozonation there appear the increased levels of Cu and Zn. An increase of these concentrations is indicated by their higher score values on PC 1 and PC 2 (see Fig. 6a), when compared with the score values at the preceding water filtering steps (see Fig. 5e and f). This tendency proves true for the entire sampling period. The higher levels are characteristic of the winter rather than the summer period (see Fig. 6b). After the granular activated carbon filtration, the even higher levels of Zn are observed (see Fig. 6c). The reason might be related to the strong desorption of zinc from the carbon filter surface. A substantial decrease of the Zn concentration is noticed after chlorination of the water. Presently, neither the

national nor the European regulations require the control of the Zn concentration in drinking water. However, the presence of Zn lends the water an undesired taste and appearance. It should be emphasized that the drinking water quality in the Upper Silesian region is fulfilled according to the national and European standards. This means that Cu, Mn, Na, Mg, Fe, Cl− , chemical oxygen demand and color are found in the aliquots 30 times lower than those indicated by the norms, whereas the levels of Al, F− , NO3 − , SO4 2− , hardness, turbidity and conductivity are 10 times lower than those required by the national regulations. 5. Conclusions The analysis of the collected data describing production of drinking water shows that the major improvement of the water quality is achieved after the coagulation steps 3 and 5 at Go-cza I and Go-cza II, respectively. The two parallel technological units produce water of a comparable quality. However, the Go-cza II unit is more efficient, compared with Go-cza I, due to the faster coagulation process.

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The PCA analysis helps to get a general insight in the studied water purification treatment. The STATIS method provides complementary and detailed information by considering the three-way data structure. The effect of the temperature changes, influencing the water treatment process within the 1-year period is also revealed with the PCA and the STATIS approach. The STATIS approach can further be employed as a tool for the water quality control. However, such a control requires regular and frequent water sampling at a given purification unit. Similar application of STATIS to the process control data was described in Ref. [27]. Acknowledgments The Authors would like to thank the Upper Silesian Waterworks in Katowice and the Colleagues of the Goczalkowice water treatment plant for the opportunity to collect water samples and for the fruitful discussions. Dr. R. Michalski from the Institute of Environmental Engineering, the Polish Academy of Science in Zabrze, Poland, is kindly acknowledged for providing the ion chromatographic data. References [1] I. Zimoch, B. Koba, K. Trybulec, in: M. Elektorowicz, M.M. Soza´nski (Eds.), Proceedings of the XVIIIth National, the VIth International Scientific and Technical Conference on Water Supply and Water Quality, PZiTS, Pozna´n, Poland, 2004, pp. 743–749. [2] B.M.G. Vandeginste, D.L. Massart, L.M.C. Buydens, S. de Jong, P.J. Lewi, J. Smeyers-Verbeke, Handbook of Chemometrics and Qualimetrics, Part B, Elsevier, Amsterdam, The Netherlands, 1998. [3] Ch. Lavit, Y. Escoufier, R. Sabatier, P. Traissac, Comput. Stat. Data Anal. 18 (1994) 97. [4] I. Stanimirova, B. Walczak, D.L. Massart, V. Simeonov, C.A. Saby, E. Di Crescenzo, Chemomet. Intell. Lab. Syst. 73 (2004) 219. [5] I. Zimoch, A. Szostak, A. Gawlik, M. Czechowski, in: M. Elektorowicz, M.M. Soza´nski (Eds.), Water Supply and Water Qusality, Proceedings of the XVIIIth National, the VIth International Scientific and Technical Conference, PZiTS, Pozna´n, Poland, 2004, pp. 751–762. [6] Water Quality, Sampling, PN-EN 25667-1, 2003.; Water Quality, Sampling, PN-EN 25667-2, 1999.; Water Quality, Sampling, PN-EN ISO 5667-3, 2002.

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