Principal component analysis an appropriate tool for water quality.pdf

7/26/2019 Principal component analysis an appropriate tool for water quality.pdf

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Ecological Modelling 178 (2004) 295311

Principal component analysis: an appropriate tool for water qualityevaluation and managementapplication to a tropical lake system

Bernard Parinet a,, Antoine Lhote a,b, Bernard Legube a

a Laboratoire de Chimie de lEau et de lEnvironnement, UMR CNRS 6008, ESIP; 40 Avenue du Recteur Pineau, 86022 Poitiers, Franceb Laboratoire de Chimie de lEau INP-HB, BP 1093 Yamoussoukro, Cote dIvoire

Received 10 April 2003; received in revised form 3 February 2004; accepted 12 March 2004

Abstract

An eutrophic lake system characteristic of IvoryCoast provided us with the opportunity to check that the values of all analytical

variables are linked to both causes and effects of eutrophication (feedback effect). Therefore, none of these values can accurately

describe a trophic state alone. To solve this difficulty we suggest here, that relationships between analytical variables are able to

generate better descriptors than variables themselves. We show that principal component analysis (PCA) using coefficients of

linear regression is, by construction, an appropriate tool for this purpose.

The graphic representations obtained underline that: (i) the first principal component is linked to the trophic potential and

the second one to the trophic level; (ii) the graphical locations of the different lakes studied are consistent with their apparent

features; (iii) allochthonous inputs have a spreading effect on the graphic representation. Extension of this model to other lakes,

located in the same geographical area, was successfully carried out. Furthermore, it has been shown that it is possible to reducethe number of analytical parameters to four (pH, conductivity, UV absorbance at 254 nm and permanganate index for raw water)

without notably impairing the quality of the PCA representation. Moreover, these very simple parameters are easier to quantify

than classical one (nutrients, chlorophyll-a, etc.) and make their use easier for the water resources management.

2004 Elsevier B.V. All rights reserved.

Keywords: Tropical water quality; Lake eutrophication; Macrophyte; Algae; Principal component analysis (PCA)

Abbreviations: T, water temperature; cond, electrical conduc-tivity; EH , redox potential (with standard hydrogen electrode as

reference); DO, dissolved oxygen; SS, suspended solids; PO4-P,

orthophosphate ions; Ptot, total phosphorus; PIRW, permanganate

index in acidic medium on raw water; PIFW, permanganate index

in acidic medium on filtered water; Chl-a, chlorophyll a; UV abs,

UV absorbance at 254 nm; Na, sodium ions; K, potassium ions;

NH4, ammonium ions; NO3-N, nitrate ions; Ca, calcium ions; Mg,

magnesium ions Corresponding author. Tel.: +33-5-49453918;

fax: +33-5-49453768.

E-mail address: [email protected]

(B. Parinet).

1. Introduction

In order to identify and classify the different trophic

states of waters (lakes or rivers), two main types oftrophic indicators have been and are still being used

(Pesson, 1980), belonging to biocenosys (biologi-

cal factors) or biotope (physico-chemical factors).

The aim of the biological approach of eutrophica-

tion is to measure its impact on the environments

biodiversity. Thus, several classification indexes have

been drawn up (Woodiviss, 1964; Vernaux, 1982;

Kelly, 1998; Seele et al., 2000). Working with such

indexes requires quite complex analysis since it is

necessary to identify the local fauna and flora (Dodds

0304-3800/$ see front matter 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.ecolmodel.2004.03.007


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296 B. Parinet et al. / Ecological Modelling 178 (2004) 295311

et al., 1998; Stambuck-Giljanovic, 1999). For the

physico-chemical approach, the aim is to quantify

the trophic state of an aquatic environment by mea-

suring a number of physico-chemical parameters(Carlson, 1977; Ryding and Rast, 1994). It is obvious

that the two approaches are similar since the biodi-

versity of an aquatic environment is conditioned by

the physico-chemical quality of its water (Gara and

Coimbra, 1998;Thornton, 1987).

As for the physico-chemical approach, the study of

the eutrophication process of superficial waters faces

an important difficulty: the choice of analytical pa-

rameters that are the most appropriate to describe the

phenomenon (Moss, 1998).

Although it is currently admitted that nitrogen,

phosphorus and chlorophyll parameters cannot beignored (OCDE, 1982; Salas and Martino, 1990),

the values of all analytical variables are more or

less linked to both causes and effects of eutrophi-

cation (feedback effect). Therefore, neither their

intrinsic values nor derived index, can satisfactorily

describe the trophic state of the aquatic system by

itself (Hakanson, 2000).

In fact, it seems obvious that eutrophication pro-

cesses modify chemical equilibriums, and act on the

relationships that link each variable to the others

(Strain and Yeats, 1999).The aim of this study is to verify that these relation-

ships make up a set of informations that could pro-

vide a good way of characterising the state of the sys-

tem. Although the relationships linking all variables by

pairs are not always linear, the whole set of coefficients

obtained from linear regression is probably a better

criterion of waters trophic state than the variables

themselves. Moreover, proceeding this way indirectly

takes into account all the physico-chemical, biological,

morphological and hydrological parameters of lakes.

However, it is generally not easy to find a suitableaquatic system that is able to illustrate this. The lakes

studied here present the rare advantage of being sup-

plied by the same streams running across a restricted

geographical zone of geological and climatic simi-

larity. Moreover, the trophic characteristics of these

waters are altered by their passage through different

agricultural and urban zones. So, it become easy to

compare their different behaviours.

Such a system provides us with the opportunity

to verify the precedent assertion. In previous studies

(Lhote, 2000; Parinet et al., 2001) we showed that the

feedback effect was an important feature of the be-

haviour of these lakes. We concluded that the intrin-

sic values of analytical parameters are not sufficient tomake a correct assessment of their nature and trophic

status.

Given the complexity of the process, a multidi-

mensional statistical treatment of collected variables

should be looked for. The well-known method of prin-

cipal components analysis (PCA), using coefficients

of linear correlation offers this possibility (Wenning

and Erickson, 1994; Aruga et al., 1993).Over the last

20 years, this method has been widely used in many

fields dealing with the study of the natural environ-

ment, (Tomassone et al., 1993)including eutrophica-

tion of water (Reisenhofer et al., 1995; Vega et al.,1998; De Ceballos et al., 1998; Perona et al., 1999).

However, as far as we know, and given the way it has

been used, it has not yet provided answers to the ques-

tions this kind of study generally poses. Nevertheless,

the originality of the lake system under study provided

us with the opportunity to test the relevance of this

tool.

2. Materials and methods

2.1. Location of the site under study

The town of Yamoussoukro is located in the centre

of Ivory Coast, 250 km to the north-west of Abidjan,

at about 65 North latitude. A set of lakes was built

there on two connected rivers (Fig. 1).We studied ten

of them, numbered from 1 to 10. The surfaces of the

lakes and of their drainage basins are given inTable 1.

They are usually less than 3 m deep.

2.2. General description of the lakes

The trophic status of the lakes are direct conse-

quence of their own local situation. Thus, lakes 14

located in an area of low urban density are colonised

by phytoplankton.

Lake 5, located in the centre of the town, receives

domestic wastewaters. This lake had been, over a long

period of time, entirely covered with water hyacinths

(Eichhornia crassipes), a very invasive floating macro-

phytes (Bard et al., 1991) and rooted macrophytes like


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B. Parinet et al. / Ecological Modelling 178 (2004) 295311 297

Fig. 1. Yamoussoukros lake system.


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Table 1

Drainage basins and lake areas

Lake number

1 2 3 4 5 6 7 8 9 10

Lake area (km2) 0.15 0.14 0.08 0.09 0.45 0.10 0.08 0.10 0.10 0.11

Drainage basin area (km2) 7.5 1.25 1.00 1.10 3.75 2.05 1.45 1.10 1.00 3.80

lotuses (Nelumbo nucifera). During the study period,

following the manual removal of the macrophytes in

July 1995, the water was strongly colonised by al-

gae, which can be noted from high concentrations of

chlorophyll-a, close to 200g/l.

Lake 6 presents a similar situation; it was almost en-

tirely covered byE. crassipesin the first period of our

study (before July 97) and was then clear by manualremoval. As the elimination of water hyacinths highly

modified the characteristics of this lake, we will anal-

yse the two periods separately. Therefore, numbers 6a

and 6b refer to lake 6 for the first and second period,

respectively.

Lake 7 receives wastewater from a densely popu-

lated area. Although this lake was also part of our

work, the results from this lake will not be shown here

because the water is closer to a wastewater pool rather

than of lake.

Finally, lakes 9 and 10 are almost completelycovered with lotuses (N. nucifera) which are rooted

macrophytes, with few water lettuces (Pistia stra-

tiotes), while lake 8 is periodically colonised by water

lilies (Nymphea lotus) and algae.

Table 2 sums up the state of colonisation of the lakes

by aquatic plants.

Table 2

Colonisation of the studied lakes by aquatic plants

Lake Macrophytes Estimation of the algae density from the Chl-a concentration

1 A few Lotuses upstream +

2 ++

3 +++

4 ++++

5 Lotuses, Pistia, Hyacinths (1020% covered) ++++

6 Hyacinths before July 97 ++++(after July 97)

7 Pistia and others (usually 100% covered) +++++(if no macrophytes)

8 Water lilies, Lotuses, Hyacinths (10% covered) +++

9 Lotuses and Hyacinths (up to 95% covered in June 1995) +

10 Lotuses, Pistia and others (100% covered until July 1998)

2.3. Physico-chemical analyses

To follow up the water quality of the 10 stud-

ied lakes, 21 sampling stations were chosen, usually

at the entrance and exit of each lake. The constant

sampling period for all the lakes was a 2 h one

(from 8 to 10 a.m.). Sampling and analysis of the

18 physico-chemical parameters taken into accountwas carried out between April 1996 and April 1998

(twice a month during the rainy season and once a

month otherwise). At each location, 1 l of water was

sampled, 50 cm below the surface; 250 ml were then

transferred into a brown glass bottle, for later analy-

sis of chlorophyll. After in situ analysis, bottles were

kept in the dark in a cooler.

Analytical methods followed normalised French

standard methods (AFNOR, 1994). The following

parametersT, pH, cond, EH, DO were determined in

situ, and the others (SS, PO4-P, Ptot, NO3-N, NH4,PIRW, PIFW, Chl-a, UV abs, Ca, Mg, Na and K) in

the laboratory within a 3 h delay. The floating macro-

phytes could not be quantified, because no satisfac-

tory method is available. Their surface density on the

lake depending on the orientation and strength of the

wind.


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3. Results and discussion

3.1. Data treatment

As previously mentioned, the measurement of 18

chemical and physical variables were carried out

twice a month during the rainy season and once a

month during otherwise on 21 sampling sites and

on 9 lakes. Eleven thousand analysis was carried

out during 23 months. A detailed statistical study

(ANOVA, Box plots, etc.), tests and more comments

on this large data base can be found through pre-

vious published works (Lhote, 2000; Parinet et al.,

2001).

As PCA is a non parametric method of classifi-

cation, it makes no assumptions about the under-lying statistical distribution of the data (Vega et

al., 1998; Helena et al., 2000; Kalin et al., 2000;

Wunderlin et al., 2001). Nevertheless, in conjunc-

tion with the KolmogorovSmirnov test, it could be

found (Table 3) that most variables were normally

distributed particularly when applied to individual

lake. When applied to the nine lakes, some vari-

ables could differ from normality, especially in the

case of nutrients (P-PO4, Ptot, N-NH4 and N-NO3)

that were the less normally distributed variables.

For these parameters, we obtained approximatelynormal distribution with a Ln (x + a) transfor-

mation.

To examine the suitability of these data for factor

analysis, KaiserMeyerOlkin (KMO) and Bartletts

tests were performed. KMO is a measure of sam-

pling adequacy that indicates the proportion of vari-

ance which is common variance, i.e. which might

be caused by underlying factors. High value (close

to 1) generally indicates that factor analysis may

be useful, which is the case in this study: KMO

= 0.85 (Table 4). If KMO test value is less than

0.5, factor analysis will not be useful. Bartletts test

of sphericity indicates whether correlation matrix is

an identity matrix, which would indicate that vari-

ables are unrelated. The significance level which is

0 (Table 4) in this study (less than 0.05) indicate

that there are significance relationships among vari-

ables.

Finally, PCA was applied to normalized data, and

so the covariance matrix coincides with the correlation

matrix.

3.2. Comments on physico-chemical parameters

evolution

This part presents a synthesis of the measurements.Figs. 2 and 3show the mean values of some studied

parameters for each lake, over the 2 years of the study.

A simplified comment is given here for pH, conduc-

tivity and alkalines (Na) ions, PO4-P, Ptot, and Chl-a.

3.2.1. pH

Significant spatial variation of pH was noted

(Fig. 2a). This could be essentially explained here

by the physico-chemical and biological reactions due

to the presence of aquatic vegetation. Comparison

of lakes 6a and 6b demonstrates that the low pH of

water is a consequence of the macrophytes growth.

On the other hand the relatively higher pH in lakes

2, 3, 4, 5, 6b and 8, compared to lake 1 are probably

due to the presence of phytoplankton.

Such observations underline the fact that the envi-

ronment has a strong feedback effect on the pH.

3.2.2. Conductivity and alkaline ions

Fig. 2c and 2d relative to conductivity and con-

centrations of sodium ions are logically quite similar,

since conductivity depends particularly on alkaline

ions in the studied waters. The important increase ofthese parameters from lake 4 to lake 5 is probably due

to the discharge of domestic wastewater, as demon-

strated by the high value of conductivity of lake 7

(500S/cm) which can be considered as the first col-

lector of Yamoussoukros wastewaters. Therefore, in

this lake system, conductivity will be dependent of

the degree of pollution from urban inputs.

On an other hand, value of conductivity in lake

6a (covered with hyacinths) is lower than its value

in lake 6b (after hyacinths removal). The meaning of

this observation is that conductivity is also dependingon the nature eutrophication processes. Such obser-

vation highlights again the feedback effect on con-

ductivity.

3.2.3. Phosphate and total phosphorus

It is generally admitted that phosphorus (Martin,

1987) plays an important role in the development of

aquatic plants, and is, in most cases, considered as

the limiting factor of eutrophication in temperate lakes

(Vollenweider et al., 1980).


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Table 4

Correlation matrix (a) and level of significance (b)

T pH Cond Ptot SS O2 PO4 EH PIRW PIFW NH4 NO3 Chl-a Ca

(a)a

T 1.000

pH 0.540 1.000

Cond 0.089 0.096 1.000

Ptot 0.071 0.244 0.529 1.000SS 0.394 0.724 0.183 0.652 1.000

O2 0.460 0.813 0.193 0.031 0.489 1.000

PO4 0.290 0.378 0.120 0.235 0.154 0.523 1.000

EH 0.421 0.560 0.280 0.203 0.258 0.722 0.681 1.000 .

PIRW 0.328 0.490 0.507 0.671 0.745 0.264 0.082 0.030 1.000

PIFW 0.293 0.221 0.556 0.560 0.463 0.057 0.269 0.154 0.811 1.000

NH4 0.181 0.246 0.512 0.266 0.084 0.332 0.140 0.346 0.327 0.395 1.000

NO3 0.082 0.115 0.186 0.329 0.082 0.210 0.580 0.370 0.324 0.397 0.229 1.000

Chl-a 0.486 0.701 0.434 0.609 0.836 0.520 0.157 0.302 0.780 0.529 0.024 0.077 1.000

Ca 0.218 0.451 0.601 0.122 0.274 0.485 0.141 0.424 0.121 0.002 0.270 0.018 0.125 1.0

K 0.091 0.066 0.917 0.481 0.188 0.178 0.120 0.262 0.498 0.542 0.514 0.164 0.430 0.4

Na 0.225 0.103 0.902 0.634 0.379 0.063 0.170 0.209 0.672 0.707 0.466 0.277 0.589 0.3

Mg 0.223 0.049 0.606 0.160 0.072 0.090 0.178 0.023 0.316 0.398 0.305 0.056 0.245 0.4

Abs 0.186 0.306 0.521 0.478 0.054 0.462 0.794 0.0657 0.417 0.582 0.445 0.664 0.121 0.2

(b)b

T

pH .000

Cond 0.124 0.107

Ptot 0.179 0.001 0.000

SS 0.000 0.000 0.008 0.000

O2 0.000 0.000 0.006 0.345 0.000

PO4 0.000 0.000 0.058 0.001 0.022 0.000

EH 0.000 0.000 0.000 0.004 0.000 0.000 0.000

PIRW 0.000 0.000 0.000 0.000 0.000 0.000 0.142 0.350

PIFW 0.000 0.002 0.000 0.000 0.000 0.228 0.000 0.022 0.000

NH4 0.009 0.001 0.000 0.000 0.138 0.000 0.034 0.000 0.000 0.000

NO3 0.144 0.067 0.008 0.000 0.142 0.003 0.000 0.000 0.000 0.000 0.001

Chl-a 0.000 0.000 0.000 0.000 0.000 0.000 0.020 0.000 0.000 0.000 0.380 0.158

Ca 0.002 0.000 0.000 0.056 0.000 0.000 0.033 0.000 0.057 0.492 0.000 0.406 0.052

K 0.119 0.194 0.000 0.000 0.007 0.010 0.059 0.000 0.000 0.000 0.000 0.016 0.000 0.0

Na 0.002 0.089 0.000 0.000 0.000 0.207 0.013 0.003 0.000 0.000 0.000 0.000 0.000 0.0

Mg 0.002 0.261 0.000 0.018 0.176 0.120 0.010 0.385 0.000 0.000 0.000 0.235 0.001 0.0

Abs 0.008 0.000 0.000 0.000 0.241 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.057 0.0

KMO test: measure of sampling adequacy: if close to 1, PCA may be useful (KMO test of sampling adequacy: 0.850). Significance level of Barletts te

significance relationship among variables (Bartletts test of sphericity: significance level: 000).a Grey boxes: value of pearson correlation >0.6.b Significance values: in the greyed boxes indicate less significance (only 10 values >0.2).


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Fig. 2. Average value and standard deviations for each lake: (a) pH; (b) DO; (c) cond; (d) Na; (e) SS; (f) Chl-a.

The quite low values of PO4-P (Fig. 3e) as well asits

important variability with the allochthonous input led

to high standard deviations. The analysis of this figure

shows that phosphate concentration is not linked to

chlorophyll-a (compareFigs. 2f and 3efor lakes 3 and5). Lake 5, with chlorophyll-a concentration twice that

of lake 3 has the same phosphate concentration. A low

value of phosphate concentration can be measured in

a mesotrophic lake (little phosphorus inputs) as well

as in an hypereutrophic one (available phosphate is

consumed by biomass).

Nonetheless, PO4-P concentration seems to depend

on the nature of the biomass. Indeed, the highest

PO4-P concentrations were those of lakes colonised

by macrophytes (lakes 6a and 10): lake 6a was cov-

ered with water hyacinths and lake 10 was partly

covered by P. stratiotes associated to lotuses rooted

in the sediments.

This result has to be linked to EH value in these

lakes, which was about 150 mV compared to 350 mVin other lakes (Table 3). Indeed, a reducing environ-

ment (EH < 200 mV) leads to a release of mineral

phosphate accumulated in sediments (Ryding and

Rast, 1994).

As for lakes with a high phytoplanktonic biomass,

they are characterised by a generally lower level of

phosphate.

Measurements of total phosphorus (Fig. 3f), car-

ried out on the raw water after mineralization, include

the quantity of phosphorus contained in phytoplank-


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Fig. 3. Average value and standard deviation for each lake: (a) PIRW; (b) UV abs; (c) NO3-N; (d) NH4; (e) PO4-P; (f) Ptot.

ton and other aquatic organisms. For that reason, they

give an apparently better representation of the trophic

state of the environment in the case of colonisation by

phytoplankton.

Fig. 3fis quite similar to that showing the evolution

of chlorophyll (Fig. 2f)for the first five lakes, whichconfirms the link between total phosphorus and phy-

toplankton (both linked to external load).

However, in the case of colonisation by macro-

phytes, another interpretation of total phosphorus

should be made. This parameter is one of those

used in the trophic classification (OCDE, 1982).

Applied to our system, the values of total phospho-

rus and chlorophyll-a mainly correspond to hyper-

eutrophic lakes. However, this classification does

not take into account the great differences exist-

ing among the states of the lakes, as seen pre-

viously.

3.2.4. Chlorophyll-a

Chlorophyll-a concentration is considered as a good

indicator of the phytoplanktonic biomass (Forsgergand Ryding, 1980; Cloot and Ros, 1996). The high

increase of this parameter between lake 1 (located in

a rural area) and lake 5 (urbanised area) can be ex-

plained by urban wastewaters. It must be reminded

that the first four lakes are fed by the same stream,

then their differences in composition is mainly due to

the nature of their inputs. For the first four lakes, we

can note an opposite evolution of DO concentration

(Fig. 2b) and Chlorophyll-a (Fig. 2f). We could con-

sider this, as surprising result, but it has to be noticed


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that measurements were carried out in the morning

when oxygen production by phytoplankton has not yet

compensated its nocturnal consumption.

Lakes 1 and 6a cannot be labelled using the sametrophic state, even if they are both very poor in

chlorophyll-a lake 6a is colonised by macrophytes

that prevent light from penetrating into the water.

Photosynthesis is thus blocked and phytoplankton

cannot develop. Furthermore, as Nakai et al. (1996)

mention, macrophytes may release algaecide con-

stituents. On the other hand, lake 6b results, show that

once hyacinths have been removed, the concentration

in chlorophyll-a rapidly increases, until it reaches that

of lake 5.

These results clearly show that trophic states are

multiform and that those with macrophytes growthmust be separated from those with algae growth.

3.3. Interpretation with the use of principal

component analysis (PCA)

Because of the feedback effect, which depends on

the particular characteristics of each lake, the above

comments pointed out that the intrinsic values of ana-

lytical data are not sufficient to make a correct assess-

ment of the trophic status of these lakes.

The trophic levels should therefore be evaluatedfrom other criteria, which will indirectly take into ac-

count relations between analytical parameters.

In this section, we examine how application of

principal component analysis, using correlation coef-

ficients, can describe the various trophic states of this

aquatic system.

3.3.1. Analysis of the 18 variables from the 10 lakes

The study of the main variables presented above

with the examination of the correlation matrix

(Table 4) shows, for all lakes, a good consistencebetween the results. For instance we can observe a

good correlation between some couple of variables

Chl-a and PIRW, SS and pH, cond and Na or K,

DO and pH. The correlation between SS and pH

which could, at first, appear surprising, is easily ex-

plained by the fact that, in most lakes, SS are made

up of algae biomass which affects the pH (photo-

synthesis). Concerning the correlation between pH

and Chl-a (Table 5), it can be noted that the value

of this correlation coefficient depends strictly on the

Table 5

R-value of pH/Chl-a correlation

Lake R value of pH/Chl-a correlation

1 0.1452 0.354

3 0.171

4 0.625

5 0.624

6a 0.254

6b 0.821

8 0.748

9 0.07

10 0.77

considered lake. In fact, its the same for all others

couples.

The principal component analysis showed that the

eigenvalues of the two first principal components rep-

resent up to 62% of the total variance (PC135.3%; PC227.2%) of the observations. This percentage rises up

to 75.5% when taking into account three components.

However, considering the large number of variables

studied (18), we decided for greater clarity, to plot

factor loadings on a PC1PC2axes plane (Fig. 4a). To

correctly interpret this graph, the factor loadings for

each variable on the unrotated components must be

taken into account, as shown in Table 6. The twelveparameters shown in the greyed boxes of this table

are well represented on the plane under consideration,

either by the first component (cond, Na, K, PIFW,

PIRW, Ptot, UV abs) or by the second (pH, DO, EH,

SS, Chl-a).

A close look at Fig. 4ashows that well correlated

variables with mineral character (Na, K and cond),

contribute to the construction of component 1, as well

as PIFW which is rather characteristic of organic mat-

ter. The observation of data shows that these variables

are linked to allochthonous inputs due to urban pol-lution in lakes 5, 6 and 8. Therefore, the first compo-

nent favours the characterisation of allochthonous in-

puts. The positive values on component 1 correspond

to important inputs, and the negative values to low

inputs.

DO, pH, EH and, to a lesser degree, T, contribute

to the construction of component 2. The positive val-

ues of this component will characterise a colonisation

of phytoplanktonic type. Indeed, in an aquatic envi-

ronment, photosynthesis brings a simultaneous rise in


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Fig. 4. Loadings of the 18 experimental variables (a) and scores of the lakes on the plane defined by principal components 1 and 2

obtained by the 18 experimental variables (b).

pH, DO and EH in the epilimnion, in the conditions

of this study (Fig. 2a and 2b). The negative values

of this component rather characterise a reducing and

acid medium, resulting from macrophytes colonisa-

tion (Fig. 2a), the vegetal cover lowering the water

temperature. Thus, this component characterises thenature of the plants, which colonise the water, and the

intensity of their development.

SS and Chl-a, located next to diagonal XX sepa-

rating the positive values of components 1 and 2, are

characteristic of external inputs with phytoplanktonic

development (simultaneous influence of components

1 and 2 in their positive values). UV abs, PO4-P and

NH4, next to diagonal YY, are characteristic of al-

Table 6

Loadings of the principal components 1 and 2

Variable Component 1 Component 2 Variable Component 1 Component 2

Na 0.918 0.114 NO3-N 0.429 0.289

Cond 0.856 0.165 pH 0.135 0.901

K 0.836 0.145 O2 0.077 0.886

PIFW 0.825 0.132 EH 0.300 0.786

PIRW 0.812 0.407 SS 0.492 0.696

Ptot 0.759 0.139 Chl-a 0.635 0.678

UW abs 0.695 0.518 T 0.189 0.638

NH4 0.522 0.371 PO4-P 0.296 0.611

Mg 0.519 0.495 Ca 0.323 0.530

lochthonous inputs with growth of macrophytes (re-

ducing environment leading to a release of phosphates

and a reduction of nitrate into ammonia). The pres-

ence of macrophytes means a rise in UV abs (Fig. 3b)

and a low temperature value.

3.3.2. Analysis of the lakes with 18 variables

With the same approach as onFig. 4a (build with

18 variables), Fig. 4bshows the scores of each lake

during the period of the study.

In relation to component 1 (characteristic of al-

lochthonous inputs), the position of all lakes is com-

pletely in agreement with observations drawn in the

commented results: low allochthonous inputs for lakes


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1, 2, 3 and 9 and important for lakes 8, 5 and 6 (lakes

4 and 10 being intermediate).

In relation to component 2 (characteristic of nature

and development of biomass), lakes 6a, 9 and 10, cov-ered with macrophytes, were to be found in the neg-

ative part of this component while lakes 2, 3, 4, 8, 5

and 6b are in the positive part, subject to greater phy-

toplanktonic growth.

The positions of lakes 6a and 6b in relation to com-

ponent 2 confirm the choice of this component for a

characterisation of the nature of the biomass present

in the water. In fact, lake 6a was covered by hyacinths

while lake 6b had been undergoing phytoplanktonic

development after they were removed. The similar po-

sition of lake 6 in relation to component 1 in its two

configurations (6a and 6b) logically justifies that al-lochthonous inputs have changed little between the

periods of study. The type of biomass seems then to

be independent of allochthonous inputs.

Lake 9 is partially covered with lotuses. Its position

on this graph is effectively that of a lake with a low

colonisation by macrophytes.

As for lake 1 with little allochthonous inputs and

little aquatic plant colonisation, it is found at an ex-

pected position on the graph.

To sum up, the lakes that evolve from area 1 to area

2 (arrow 1 onFig. 4b) will be increasingly colonised

Fig. 5. Month by month scores of the lakes 1, 5 and 10.

by phytoplankton as long as allochthonous inputs in-

crease. The lakes evolving from area 3 to area 4 (ar-

row 2 on Fig. 4b) will be increasingly colonised by

floating macrophytes. Rooted macrophytes are foundmainly in the shallow lakes of area 3 for which al-

lochthonous inputs are low, the nutrients being in the

sediments. As for an evolution from area 4 to area 2

(observed for lake 6) and from area 3 to area 1 (not

observed), it depends on the outcome of the competi-

tion between the plants.

For this kind of water, it seems acceptable to say

that the trophic potential increases along component

1. However, it is necessary to make a distinction based

on the kind and the quantity of biomass produced.

Component 2 seems to be a good representation of the

trophic level.

3.3.3. Time patterns analysis

Fig. 5shows the scores of lakes 1, 5 and 10 (month

by month) between October 1996 and April 1998 on

the plane defined by the components 1 and 2.

It is interesting to note that scores for each month

are distributed in particular zones of the plane,

which depend both on the water quality of the lake

and its seasonal evolution. This remark could be

taken into account for good management of water

bodies.


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For example, the points corresponding to July 1997

and April 1998 appear quite characteristic for each

lake.

Lake 1 for example, is represented by points whichare located in a small area, which means that the qual-

ity of its water depends little on the season, whereas

the points representing lake 5 (which is in an urban

zone), cover a larger area. This indicates that its wa-

ter quality depends on the season and consequently on

the allochthonous inputs.

The points representing lake 10 (covered with lo-

tuses and P. stratiotes) also spread into a larger area

in the zone corresponding to macrophytes.

The months of July 1997 and April 1998 are shown

on the outer extremities of component 1, characteristic

of allochthonous inputs. These results can be easilyinterpreted by taking into account the rainfall shown

onFig. 6.

The month of July 1997 had a low rainfall (29 mm).

It comes at the end of the rainy season, and followed

June, which had a particularly high rainfall (263 mm).

Lake water was diluted by the rainfall of the previ-

ous months, and the soil was too washed for the al-

lochthonous input to be high.

Fig. 6. Rainfall.

The points representing July 1997 for the three lakes

under consideration are located on the lower values

of component 1 (allochthonous inputs component),

which further confirms the preceding hypotheses.On the other hand, for the month of April 1998,

the situation is reversed. This month is at the begin-

ning of the rainy season, and the rainfall brings to the

lakes the organic and mineral matter accumulated dur-

ing the three previous months. In that case, the points

representing the three lakes are on the side of the high

values of component 1.

In fact, this observation applies to all the lakes of

this system, which shows that the allochthonous inputs

essentially linked to rainfall runoff plays a major role

on the lakes behaviour.

To sum up, the allochthonous inputs have a spread-ing effect on the graphic representation while waters

that receive few of these inputs have a condensed rep-

resentation.

This observation could be used to establish a crite-

rion in order to make a seasonal follow up of the qual-

ity of waters. Moreover, the evolution of the shapes of

these graphical surfaces could provide information on

the kind of problems the water under study encoun-


14/17


Fig. 7. Variance of factor scores for PC1 and PC2 components for the 10 studied lakes.

ters. For example, plotting of the variance of com-

ponents 1 and 2 scores versus lake number (Fig. 7)

could provide a representation of water quality evolu-

tion for each lake during the studied period. Variance

of factor score 1 gives information about the seasonalvariation of allochthonous inputs, while variance of

factor score 2 gives information about biological or

physico-chemical evolution of the lakes. The annual

evolution of the sum of these two values can be use

as a water quality index.

3.3.4. Interpretation through the PCA using a

reduced number of variables

We may observe that some variables are well corre-

lated. Consequently, it seems possible to simplify this

model. Therefore, we propose here to study how therepresentations of variables and lakes evolve when a

more restricted number of variables are taken into ac-

count.

Among the set of variables that strongly con-

tribute to the construction of the two first compo-

nents, we chose to consider the global ones, because

they are more representatives of the whole system.

Their two-by-two correlation include necessarily

the correlation of other variables, which depend on

them.

Four parameters easy to measure were selected: pH

(as an indicator of nature of biomass), conductivity

(as an indicator of external inputs), UV abs and PIRW

(as indicators of dissolved and particular organic mat-

ter). We can notice that total organic carbon (TOC)could be probably used instead of PIRW. Conductiv-

ity and pH contribute respectively to the construction

of component 1 and component 2, UV abs and PIRW

contribute to the construction of both components and

are located in two different half planes of the graph

(Fig. 8a).Although phosphorus and nitrogen are usu-

ally considered as important parameters for this kind

of study, we did not consider them in this small-scale

model of variables. We will explain this choice latter.

Moreover, it has to be noted that the chosen variables

(except to some extend for conductivity) are more de-

pendent on the effects of eutrophication than on the

causes.Fig. 8ashows the position of the four selected

variables loadings on the principal components 1 and

2. It can be noted that the four variables loadings re-

main in unchanged positions compared to the eighteen

initial ones. Therefore, the signification of the compo-

nents remains the same than in the previous case. The

results obtained with four variables (Fig. 8b) show that

the absence of the other variables does not alter the

model (for the studied case).


15/17


Fig. 8. Loadings of the four selected experimental variables (a) and scores of the 10 lakes on the plane PC1PC2 obtained by the four

selected variables (b).

3.3.5. Extension of the model to other lakes in the

Yamoussoukro area

Five other lakes were studied in the Yamous-

soukro area in order to test the upgradeability of the

model. For lakes in the whole, one (and two for some

lakes) analytical campaigns were conducted (Lhote,

2000).

The Kossou lake (40 km from Yamoussoukro)is a reservoir of very pure water, with no macro-

phytes and very few chlorophyll-a. The Yabra lake

(20 km from Yamoussoukro) is entirely covered withPistia. The Basilique lake (in Yamoussoukro City)

is entirely covered with Echornia crassipes. The

C.F.P. lake (in Yamoussoukro City) is characterised

by an intermediate urban environment with low

chlorophyll-a concentrations (inferior to lakes 3 and

4), but high conductivity and no macrophytes. The

I.N.S.E.T. lake (7 km from Yamoussoukro) was built

on springs and receives wastewater. It contains many

algae.

For this model, the estimated scores (Fig. 9)of the

five additional lakes were computed by multiplying

the mean values of their five normalised variables by

factor score coefficients. As it could be shown, the

five lakes are totally in accordance with the findings

and the summary description made. It can be noted

that Yabra lake and Basilique lake are indeed in a

macrophyte zone and that the I.N.S.E.T. lake, contain-

ing many algae is in an expected position. Although

these 5 lakes are not supplied by the same waters as

the 10 reference lakes, their PCA representation is in

good agreement with their physico-chemical and bi-

ological features. Consequently, the extension of this

methodology to other tropical water seems possible. In

the same way, it is now possible to envisage building

larger PCA models taking into account a great number

of different tropical lakes.

Fig. 9. Extension of the PCA model (with four variables) to other

lakes in the Yamoussoukro area.


16/17


4. Conclusion

From the study of the behaviour of these lakes,

it is obvious that the feedback effect can be ap-plied to eutrophication processes, but also to other

physico-chemical and biological ones. This feed-

back effect could be extended to every lake in

tropical but also in temperate climates whatever the

kind of biomass that colonises them. When such a

phenomenon appears, the state of equilibrium of the

aquatic medium is modified. Therefore, we observe a

change in every relation linking analytical variables.

By construction, PCA made with correlation coeffi-

cients, takes into account these changes, and become

an easy and appropriate tool for such a description.

Based on an ideal lacustrian tropical system, this

study tried to show that a precise description could be

made. It also showed that it was possible to simplify

the description (without impairing its quality) by the

use of only four simple parameters: conductivity,

pH, permanganate index (in acidic medium) and UV

absorbance (at 254 nm).

It seemed a priori iconoclastic to describe such a

lake system without considering nutrients (nitrogen

and phosphorus) or morphology contributions. Al-

though, values of analytical variables are linked to

both causes and effects of eutrophication, nutrientsare mostly linked to causes and become unpredictable

variables (because of their allochthonous character).

Consequently, it is better to consider only variables

that are mostly linked to effects.

Acknowledgements

The authors thank the UNDP/GEF project IWC/94/

G31 Aquatic weed control in water bodies for im-

proving/restoring biodiversity, for financial support.

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