An environmental study by factor analysis of surface seawaters in the Gulf of Valencia (Western Mediterranean)

Analytica Chimica Acta 394 (1999) 109±117

An environmental study by factor analysis of surface seawaters in the Gulf of Valencia (Western Mediterranean) M.M. Moralesa,b, P. MartõÂb,c, A. Llopisb, L. Camposb, S. Sagradod,* a

Unidad de EpidemiologõÂa ClõÂnica, Hospital Universitario Dr. Peset, Valencia, Spain Unidad de Salud PuÂblica, Higiene y Sanidad Ambiental, Facultad de Farmacia, Universidad de Valencia, C/Vivente AndreÂs EstelleÂs s/n 46100 Burjassot, Valencia, Spain c Laboratorio de Salud PuÂblica, Centro de Salud PuÂblica de Valencia, ConsellerõÂa de Sanitat i Consum, Valencia, Spain d Departamento de QuõÂmica AnalõÂtica, Facultad de Farmacia, Universidad de Valencia, C/Vivente AndreÂs EstelleÂs s/n, 46100 Burjassot, Valencia, Spain b

Received 17 August 1998; received in revised form 28 January 1999; accepted 17 February 1999

Abstract A study is made on the quality of coastal waters in the Gulf of Valencia (Spain) in terms of contamination markers including microbiological agents, toxic heavy metals and nutrients that adversely affect the environment. Relationships are also established between these factors and other physical and chemical parameters. A multivariate analysis is conducted where a total of 14 parameters are established for 919 water samples corresponding to 52 sampling points along the coast of the province of Valencia ± speci®cally, total and fecal coliforms, fecal streptococci, Ni(II), Zn(II), Pb(II), Cd(II), Cu(II) and Cr(VI) concentrations, nitrates, phosphates, dissolved oxygen and ®nally pH and conductivity. Principal components analysis allows the characterization of the coastal water quality of the study zone, establishing the sources and types of contamination, and identifying the littoral areas associated to the different types of contamination. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Environmental; Contaminants; Factor analysis; Seawater

1. Introduction The quality of bathing waters is habitually established in terms of the classical fecal contamination markers or indicators, i.e., total coliforms, fecal coliforms and fecal streptococci [1]. These indicators are correlated to the risk of developing infectious gastrointestinal disorders [2]. However, a number of epidemiological studies have been unable to establish a clear *Corresponding author. Tel.: +34-96-38-64-878; fax: +34-96-3864-953; e-mail: [email protected]

dose-response relation between the degree of fecal contamination of the waters and the incidence of slight disorders affecting the eyes, nose, throat and ears [3]. In view of their toxicity and/or bioaccumulation capacity, heavy metals are considered as important contaminants that must be closely monitored [4]. Absorption of metals in humans such as zinc or copper can be regulated by the body [5,6], and is low in the case of nickel [7], i.e. these metals are not regarded as particularly dangerous. In contrast, however, lead and cadmium accumulate in seafood and can cause chronic diseases in humans [8,9]. In turn, hexavelant

0003-2670/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved. PII: S 0 0 0 3 - 2 6 7 0 ( 9 9 ) 0 0 1 9 8 - 1

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chromium can be able to penetrate the organism both orally and percutaneously, causing irritation of the skin and mucosas [10]. On the other hand, nitrogen and phosphate (nutrients) can cause excess phytoplankton proliferation and the establishment of eutrophism, particularly in closed systems, with adverse consequences for coastal ¯ora and fauna [11]. Microbiological contaminants are usually determined by standard methods [12]. Trace metals in seawater are habitually assayed by spectroscopic techniques, such as AAS [13], ICP-AES [14] or XRF [15]. In general, these methods involve prior extraction/ preconcentration steps. In this sense, anodic-stripping voltametry (ASV) [16] or adsorptive cathodic-stripping voltametry (ACSV) [17] have been found to afford improved results [18,19]. The use of ultraviolet spectrophotometry for the determination of nitrates in marine environments has been described [12,20]. On the other hand, phosphates can be assayed colorimetrically [12,21], and dissolved oxygen levels can be established by electrochemical methods [12]. Principal components analysis (PCA) is an important chemometric tool [22,23] that seeks to establish combinations of variables capable of describing the principal data tendencies observed. In mathematical terms, PCA relies upon an eigenvector decomposition of the covariance or correlation matrix. The technique has received numerous applications in the life sciences [24], and has been successfully used to study coastal water contamination [21]. The purpose of the present study is to identify the sources and the types of contamination in the Gulf of Valencia (Spain) by means of PCA, and making use of VARIMAX [25] rotation of the principal components. Likewise, the relations between the study variables are investigated, and associations are established among contaminated littoral areas, the observed contamination patterns and geographical zones. 2. Experimental 2.1. Samples The study covered a one-year period between March 1992 and March 1993; an average of 15 determinations was made per sampling station (range

8±42), totaling 52 sampling zones and 919 samples. The samples were collected in polyethylene ¯asks previously cleaned with nitric acid 2 M, distilled water and subsequently with the seawater sample itself. The ¯asks were transported to the laboratory at 48C, and the samples were analyzed within 3 h after sampling. The 14 parameters determined for each sample were: zinc (Zn), cadmium (Cd), Lead (Pb), copper (Cu), nickel (Ni), hexavelant chromium (Cr), conductivity (CON), pH (pH), nitrates (NO3) and phosphates concentrations, dissolved oxygen O2 and three microbiological contaminants, total coliforms (TC), fecal coliforms (FC) and fecal streptococci (FS). Between bracket appear the labels that for the variables are employed in the text and subsequent tables. 2.2. Analytical methods (reagents and apparatus) The divalent metal ions (Zn(II), Cd(II), Pb(II) and Cu(II)) were determined by ASV using an automatic polarograph VA 646 (Metrohm), according to the procedure described elsewhere [26]. Divalent nickel and hexavelant chromium (Ni(II) and Cr(VI)) were in turn determined by ACSV using the same equipment and following the method described in [26]. The rest of the study parameters were determined by the techniques indicated in [12]; potentiometric pH measurements were made with a Hanna Instruments pHmeter, and conductivity measurements were made at 258C using a Crison CM 2002 conductimeter. Dissolved oxygen was assayed by the membrane electrode method using a WTW model Oxi-91 oxymeter equipped with a WTW model EO90 oxygen electrode. Phosphates were determined by the vanadomolybdophosphoric acid colorimetric method, while nitrates were assayed according to the ultraviolet spectrophotometric screening method using a Shimadzu 160 UV spectrophotometer for both parameters. Finally, bacteriological testing was conducted by the membrane ®lter procedure, employing 0.45 mm HA-type membranes. All chemicals used were of analytical reagent grade. 3. Results and discussion Table 1 shows some of the statistics derived from the univariate analysis. The table does not re¯ect the

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Table 1 Univariate statistics corresponding to the total samples analyzed Variable

Units

Maximum

Mean

Median

Percentile 95%

Zn Cd Pb Cu Ni Cr CON pH NO3 O2 TC FC FS

mg/l mg/l mg/l mg/l mg/l mg/l mS/cm

166 2 52 13 11.990 8.319 64 500 8.380 28 111 420 000 70 000 24 000

2.202 0.245 0.899 1.2 0.769 0.165 49 400.80 8.166 3.091 84.8 2146.67 789.57 233.89

1 0.18 0.52 0.74 0.536 0.111 49 700 8.180 3 87 30 11 11

6.3 0.72 2.8 3.9 1.848 0.472 57 900 8.3 7 100 6600 2860 780

mg/l % sat. Cfu/100 ml Cfu/100 ml Cfu/100 ml

data corresponding to phosphate concentration, since these compounds were only detected in 17 of the 919 samples analyzed, and always at low concentrations (0.1±1 mg P/l). As a result, this parameter was excluded from the posterior multivariate statistical analysis. A 91913 data matrix was used for the multivariate analysis (having eliminated the variable phosphate concentration). The data were autoscaled prior to analysis. Table 2 re¯ects the eigenvalues and the variance percentages (accounted for and accumulative) corresponding to the principal components (PCs). This relatively spread-out distribution among components is quite common when working with environmental data, particularly with problems of scant structure, as in our case. In the present study, most of the samples exhibit very low contamination levels, and thus do not contribute to the formation of the PCs (exhibiting a relatively ``spherical'' structure of the data matrix). However, this is intrinsic to the nature of the problem under study. The dif®culty thus centers on the de®nition of the criterion best suited to decide how many components should be retained to perform rotation of the PCs. At this point, we wish to stress that our main interest centers on detecting the main data variation sources ± which are obviously concentrated in the ®rst PCs. In order to decide upon the number of components, it seems reasonable to focus on the chemical aspect (i.e., search for those components of chemical/environmental signi®cance). This is what we have done, considering the groups of objects responsible for formation of the different PCs,

and the groups of variables related to them. Thus, we discarded those components formed arti®cially by very few objects, and which moreover already stood out in one or more of the ®rst components (e.g., exhibiting markedly higher scores than the rest of objects, which were grouped without any de®ned structure ± a ``spherical structure'' ± around the coordinates origin). Such components contributed nothing new in terms of seawater contamination. This measure reduced the number of PCs to six, which exhibiting an eigenvalue of over 1 and accounted for 67% of the total data variance.

Table 2 Eigenvalues and variance percentages (accounted for and accumulative) corresponding to the principal components Principal component

Eigenvalue

Accounted for variance percentage

1 2 3 4 5 6 7 8 9 10 11 12 13

2.62168 1.64875 1.34623 1.11416 1.03392 1.00486 0.94567 0.90464 0.77317 0.58841 0.52111 0.32087 0.17653

20.2 12.7 10.4 8.6 8.0 7.7 7.3 7.0 5.9 4.5 4.0 2.5 1.4

Accumulative variance percentage 20.2 32.8 43.2 51.8 59.7 67.5 74.7 81.7 87.6 92.2 96.2 98.6 100.0

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Fig. 1. Loading plots (at top) and score plots (at bottom), corresponding to the first four factors following VARIMAX rotation of the principal components.

Additionally, we have performed a cross-validation, obtaining the ®rst local minimum of the residual variance for six PCs. The choice of six PCs minimizes the risk of modeling the noise associated with the rest of the components. A VARIMAX rotation was performed to secure increased PCs chemical/environmental signi®cance [25]. Fig. 1 (at top) re¯ects the loading plots corresponding to the ®rst two factors (at right) and factors 3 and 4 (at left). From these plots it can be deduced that the ®rst factor (F1) is related to the three bacteriological contamination parameters (loadings 0.93, 0.88 and 0.89 for TC, FC and FS, respectively). The second factor (F2) is in turn associated to the three heavy metals Ni, Zn and Cu (loadings 0.78, 0.71 and 0.53, respectively). The third factor in order of importance (F3) is related to the variables NO3 and CON, though with loadings of opposite sign (loadings 0.81 and ÿ0.81, respectively), re¯ecting a negative correlation between the two parameters. On the other hand, the next factor (F4) is related to the metals not associated to F2, i.e., Pb, Cd and Cr. In the case of factor 5 (F5), the positive loadings for Cu and pH (0.57 and 0.59,

respectively) and the negative loading for Cr (0.61) stand out. In this sense, F5 is dif®cult to interpret. Lastly, factor 6 (F6) is exclusively related to the parameter O2 (loading 0.89). Based on the above, factor F1 (i.e., the ``microbiological factor'') may involve an urban origin, where waste disposal from populated areas increases fecal contents in the affected coastal waters [2]. For this factor, conductivity exhibits a low negative loading (ÿ0.06), in agreement with a slight decrease in the salinity of these waters contaminated by urban waste. On the other hand, factor F2 (i.e., the ``metal factor'') is associated to the three less toxic metal ions. This contamination may be accounted for by the industrial waste dumping into rivers, drainage systems, gullies and ravines, and agricultural irrigation systems (which abound in the province of Valencia). This agrees with the important presence in the province of galvanizing and metal working industries in the form of small family enterprises and workshops. An observation that stands out in relation to this ``metal factor'' is the negative loading value of the variable pH (ÿ0.23). This could be related to the

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regulation mechanism of metals in seawater. In effect, particles in suspension act as heavy metal scavengers in the course of sedimentation, involving a series of chemical and physical processes including adsorption, absorption, the formation of complexes, ion exchange and inclusion within the particles [27]. The basic nucleus of these particles consists of ferric hydroxide [28], the formation of which is favored by increased pH values. This in turn agrees with the negative correlation observed between heavy metal concentration and pH. The third factor (F3), where increased nitrate concentrations are found to be associated to diminished conductivity values, points to the contamination of agricultural origin. In effect, irrigation waters from cultured land dissolve the nitrates and nitrogenous compounds present in fertilizers, and ultimately drain into the sea. This ``earthen'' factor is related to the super®cial waters that traverse the numerous irrigation zones in the province of Valencia and drain into rivers, ravines and ditches, etc., on their way to the sea. Alternatively, these nitrate-contaminated waters may ®rst drain into certain coastal lagoons found along the coast, followed by release into the coastal bathing waters. This contamination in turn causes a marked decrease in salinity, with a corresponding drop in conductivity. Mention should be made of factor 4 which, unlike factor 2, appears exclusively linked to the three most toxic metals (particularly Pb and Cd). This observation could be explained by the activities of the important ceramics industry present in the province of Valencia. In effect, the use of pigments containing Pb and Cd is widespread in this sector [29,30]. Lastly, attention is drawn to the absence of any relation between the dissolved oxygen levels and the rest of the variables investigated. Fig. 1 (at bottom) shows the scores of the different samples with respect to the ®rst four PCs. The ®gures do not include 773 samples with scores of fewer than two for all four factors simultaneously. This represents 84.1% of the total and implies that only 15.9% of the samples exhibit some form of signi®cant contamination. These are the minority samples that establish the observed pattern of environmental contamination, for they determine the main orientations in data variance (despite the fact that most samples re¯ect low contamination).

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On the other hand, Fig. 1 shows that a number of sampling areas adjust very well to some of the axes; this indicates that they fundamentally exhibit one of the speci®c forms of contamination identi®ed, while other samples simultaneously present more than one form of contamination. The open character of the Gulf of Valencia, where winds and currents easily displace the super®cial waters, can explain this. With the aim of measuring the degree of in¯uence of the sampling zone upon the latent structure of the data matrix, we have employed to strategy known as Discriminant Partial Least Squares, (Discriminant PLS) where use is made of a dummy or indicator variable (in our case the sampling zone). In our setting, this response y-variable is chosen as a diagnostic variable [31], assigning each object a value of between 1 (zone more to the North) and 52 (zone more to the South) according to the sampling point to which the sample belongs. The results obtained suggest a relatively scant in¯uence with respect to the PCA ®ndings (at least as regards the most important factors). As an example, the y-variable variance accounted for was less than 6%, regardless of the number of PLS components (latent variables) used. The percentages of variance explained for matrix X by the latent variables are similar (albeit a little lower) than those afforded by the PCs of the PCA model; however, four are the latent variables that give rise to the ®rst local minimum in the residual variance according to the cross-validation results. This suggests that the relevant information in terms of contamination centers on the ®rst four PCs. The relations between variables and PCs (PCA model) or latent variables (PLS model) with respect to the ®rst four PCs are not substantially modi®ed, though the second latent variable is more related to variables NO3 and COND, while the ``metallic'' variables are more related to latent variable 1 and especially 3 and 4. Some samples exhibit considerably higher scores than the rest; in certain cases, such types of data may alter the latent structure of the principal components. This points to the need for study of the anomalous point in order to determine their potential in¯uence upon the PCA. Since the score tracings shown only afford a qualitative impression of the possible anomalous points, more quantitative criteria should be adopted to characterize the latter. In this sense, the use of control charts based on the residual Q and Hotelling T2

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Fig. 2. Residual Q versus Hotelling T2 plot showing the most contaminated samples.

statistics and their respective con®dence limits has been suggested to detect samples beyond control with respect to the PCA [32]. Alternatively, a single tracing (Q versus T2) may be employed to unify this information [33]. Here, the T2 statistic has been used along with the Q residual, not in the form of control graphs (for no variable such as time exists), but in the way of the so-called ``in¯uence plots'' (residual versus leverage). Fig. 2 shows this plot corresponding to the PCA model using six PCs. The Q and T2 values are represented for each sample, together with the corresponding con®dence limits (95% probability) for both statistics. Those samples exhibiting high T2 values (i.e., far removed from the multivariate mean) re¯ect unusual variation of these points within the PCA model, while those samples with high residual Q values (i.e., far removed from the hyperplane of the ®rst six PCs) represent unusual variation of the points beyond or outside of the model [32]. In con®rmation of the above results, Fig. 2 reveals a large number of samples located within the statistics con®dence limits in comparison to those located above one or both limits. Some of the latter have been

labeled (using the point number in the original matrix) on the tracing. The position in the plot allows classi®cation of the anomalous samples. Thus, a sample such as number 847 (with high microbiological, Zn, Cd and nitrate scores) appears with high T2 values in representation of important contamination within the general contamination tendency observed. However, those samples that preferentially exhibit high Q values represent contamination partially associated to some concrete variable. For example, sample number 538 exhibits a high Zn concentration, though the values of the rest of parameters are normal. An additional utility of these graphical representations is their use as control plots of the analytical methods employed (detection of errors, instrument drift or calibration errors, etc.). As an example, sample number 763 shows normal values for all parameters except pH (6.3), while the rest of samples score between 7.26 and 8.38 for this same parameter. In our case this pH value was con®rmed experimentally, though in general the position of sample number 763 in the Q versus T2 plot would have alerted to a possible error linked in this case to the pH-meter used.

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In order to evaluate the in¯uence of these samples, the PCA was repeated, eliminating the points labeled in Fig. 2. In general, few changes were noted in the new model established. The variance distribution among the PCs did not change appreciably, though the order of importance differed as regards the previous sequence of factors F4, F5 and F6, and the relations between PCs and variables changed. In the new PCA model, the relative importance of dissolved oxygen and Cr levels increased. The latter parameter was found to be dissociated from Pb and Cd. In any case, the signi®cance of the ®rst three contamination sources remained unchanged. The fact that some samples are anomalous in the ``statistical'' sense does not imply that such points should be eliminated if experimentation shows them to be ``correct'' and thus part of the chemical/environmental problem. The values of the variables corresponding to the points marked in Fig. 2 were experimentally con®rmed (repeating the analysis),

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i.e., they effectively re¯ect the contamination ``peaks'' in the coastal waters of the Gulf of Valencia. Fig. 3 shows a map of the sampling zones related to a concrete contamination factor (F1, F2, F3 or F4); these sampling zones show several samples with high score values on only one of the loading plot axes (Fig. 1, at top). In addition, the map shows those samples exhibiting contamination peaks in accordance with Fig. 2. A number of other elements are also shown related to the routes by which contaminating wastes drain into the coastal waters, i.e., most populated cities with their corresponding sewage networks, the rivers and gullies or ravines that drain industrial, agricultural and urban waste, irrigation systems, and the coastal lagoon (i.e., the Albufera) located immediately to the South of the city of Valencia. The contaminated stretches of rivers have been marked, in accordance to data provided by the regional environmental authorities (the ConsellerõÂa de Medio Ambiente de la Comunidad Valenciana).

Fig. 3. Map comprising the coastal region where the contaminated samples were obtained (see Fig. 2), related to the main types of contamination (F1, microbiological; F2, metal; F3, ``earthen''; F4, ceramics industry). Terrestrial elements that influence coastal waters contamination include major urban nuclei (>50 000 inhabitants): the Port of Sagunto to the North, Valencia and Torrente at center, GandõÂa to the South, and JaÂtiva inland, principal rivers and gullies (from North to South: the Palancia river, the Barranco de Carraixet, the Turia river at the city of Valencia, the JuÂcar river, and the Serpis river), and marshlands (the Marjal del Moro to the North, the Albufera at center, and the Marjal de Xeresa i GandõÂa to the South).

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The above allows us to draw a number of conclusions regarding terrestrial contribution and contamination. However, in view of the marked diversi®cation and dispersion of both the contaminant conduction routes to the sea and the populated, industrial and agricultural zones in the present study, it is dif®cult to establish extra geographical variables for joint analysis with the environmental data. To this dif®culty we must also add the possible seasonal nature of waste disposal, processing plant activity/ standby, and variations in population and industrial and agricultural activities. It is thus very complicated to establish potential quantitative (or even signi®cant) relations between geographical variables and environmental data. We therefore consider it opportune to limit the conclusions to qualitative aspects. As an example, the zone of greatest microbiological contamination (F1) is located to the North of the city of Valencia, in agreement with the fact that this entire stretch of coast (reaching to the North of the province, at the Port of Sagunto) presents a higher population density (over 400 inhabitants/km2 versus less than 200 inhabitants/km2 to the South of the city). The observation of high microbiological contamination levels in samples 847, 333 and 781 con®rms this. This same zone also stands out as regards type F2 contamination. In effect, immediately to the North of Valencia capital, the zone of Alboraia, close to the mouth of a major gully (the Barranco de Carraixet), yields sample 475, re¯ecting Cu and F1 type contamination. F4 contamination areas are also located to the North (e.g., sample 727 exhibits high Pb concentrations), particularly in the port of Sagunto, at the mouth of the Palancia river. To the South of the city of Valencia, different forms of contamination are observed coinciding with the areas under the in¯uence of the coastal lagoon (or Albufera). The latter receives drainage from an extensive system of agricultural irrigation canals and sewage networks. Here microbiological contamination is also found, though to a lesser degree than in the North, along with industrial waste products (illustrated by type F2 sample number 568, containing high Pb levels). This region registers type F3 ``terrigenous'' contamination, which increases towards the southern limits of the province (at the town of GandõÂa). Such contamination may be explained by the in¯uence of the JuÂcar river (with the largest ¯ow of the entire study

region), and by the greater average rainfall ®gures recorded to the South of the province (600±900 mm versus 300±600 mm in the North). Coinciding with the mouth of the Serpis river, many samples were found to show heavy metal contamination: number 771 (especially Cr but also Zn, Pb and Ni), 279 (Zn and Ni), 583 (Zn), 360 (Ni) and 119 (Cr) ± in addition to F2 type contamination. These results agree with the fact that this river drain an industrial zone specialized in metal plating activities. 4. Conclusions The results of the present study point to the existence of three main independent types of contamination in the coastal waters of the Gulf of Valencia: microbiological, metal, and ``earthen'', each respectively of urban, industrial and agricultural origin. These forms of contamination are in turn related to different terrestrial features including rivers and urban nuclei, etc. The analysis of coastal water contamination thus contributes to characterize the terrestrial contamination foci. On the other hand, these contamination patterns in the Gulf of Valencia are supported by a relatively limited number of samples, since low contamination levels predominate in most of the seawater samples. Those zones with samples associated to one or more types of contamination moreover exhibit a number of point peaks of high contamination, particularly of heavy metal origin. This suggests the existence of local major waste disposal over short periods of time. This type of study may also provide a reference for similar present and future research for appraising the effects of waste disposal vigilance and control programs, or the implementation of new puri®cation and processing systems. Finally, deteriorated coastal water quality may have repercussions on human health due to direct exposure (skin contact and/or ingestion) while bathing, and to the consumption of contaminated seafood. It is thus advisable to monitor those areas exhibiting type F2, F4 in addition to F1 contamination (the latter being habitually used to establish bathing area aptitude in accordance with European Community regulations).

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