Desalination 246 (2009) 496–505

Descriptive and multivariable analysis of the physico-chemical

and biological parameters of Sfax wastewater treatment plant

Amira Oualia,*, Chafai Azrib, Khaled Medhioubb, Ahmed Ghrabia

aCERTE, Laboratoire Traitement et Recyclage des Eaux Usées, BP 273, 8020 Soliman, TunisiaEmail: w_princesse@yahoo.fr

bISEPIS, Unité de Recherche Etude et Gestion des environnements Côtiers et Urbains, 3018 Sfax, Tunisia

Received 10 September 2007; revised 19 April 2008; accepted 26 April 2008

Abstract

A set of quantitative descriptive and analytical data from the wastewater treatment plant (WWTP) of Sfax,

located on the south-east of Tunisia, has been processed by multivariate statistical techniques in order to investigate

the evolution of the wastewater quality over a period of 12 years (1984–1996) for six physico-chemical and bio-

logical parameters. The experimental 6×12 matrix was analysed by principal component analysis (PCA). The

exploration of the correlation matrix allowed to uncover strong associations between some variables (BODi COD

i,

SSiBDO

o, COD

oSS

o) as well as a lack of association between the others (T

waterand SS

o). PCA showed the existence

of up to three significant PCs which account for 74% of the variance. The first one assigned to water with relatively

low organic load, whereas the second and the third assigned to water with an average and high organic and mineral

loads. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and inter-

pretation of large complex data sets with view to get better information about the water quality and design of mon-

itoring network for effective management of water resources.

Keywords: Aerated lagoon; Multivariable analysis; Organic load; PCA; Wastewater treatment plant

1. Introduction

Wastewater is becoming increasingly impor-

tant in integrated water resources management

because of the scarcity of water resources and the

need of environmental protection [1]. Waste-

water use in agriculture is widespread throughout

the world. In arid and semi-arid areas, there is a

growing need to reuse wastewater not only to

reduce waste discharges to surface waters but

also to improve potential water resource [2]. In

many zones, as a result of the anthropogenic

influences urban, industrial and agricultural

activities wastewater are discharged in natural

areas [3,4] or reused in agriculture without any

treatment and cause many human and animal*Corresponding author.

Presented at the: MEDA WATER International Conference on Sustainable Water Management, Tunis, March 21–24 2007.

0011-9164/09/$– See front matter © 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.desal. 00 .0 .02 8 4 58

sanitary risks [5,6]. Treatment of this potential

resource becomes imperative before use.

Treatment of wastewater in ponds is probably

the most ancient means of waste treatment known

to humans. Oxidation ponds are also called stabi-

lization ponds or lagoons and they serve mostly

small rural areas where land is available at low

cost. They are used also for secondary treatment

of wastewater or as polishing ponds.

The present paper examines the case of an

aerated wastewater lagoon. An aerated waste-

water lagoon, or any subsection of the overall

lagoon, should function as (a) an oxidation reac-

tor for soluble BOD, (b) a clarifier for volatile

solids, and (c) a benthal aerobic/anaerobic

digester for settled particulates. The distribution

of these functions can vary within a lagoon due

to seasonal temperature variations, aeration, and

light intensity [7].

In view of the temporal variations of physico-

chemical and biological parameters regular mon-

itoring programs are required for reliable

estimates of the water quality. In wastewater

treatment process performance and operation are

measured continuously. This results in a complex

data matrix comprised of a large number of

physico-chemical parameters [7], which are

often difficult to interpret drawing meaningful

conclusions [8] and demanding a structured

approach to monitoring and analysis of the

process. Multivariate analysis provides a

methodology to extract and structure information

from large amounts of data. The multivariate

treatment of environmental data is useful for evi-

dencing temporal and spatial variations caused

by natural and anthropogenic factors linked to

seasonality [8–17]. It has been used to monitor

industrial processes for several decades [18]. It

has also been applied to wastewater treatment

operation [19–21]. These statistical techniques

and exploratory data analysis are the appropriate

tools for a meaningful data reduction and inter-

pretation of multi-consistent physical and chem-

ical measurements [22].

The multivariate statistical techniques such as

principal component analysis (PCA) have widely

been used as unbiased methods in analysis of

water-quality data for drawing meaningful infor-

mation [16,17,23–30]. PCA is a fundamental and

one of the most popular multivariate statistics

based on monitoring methods [31].

2. Materials and methods

2.1. Description of Sfax wastewater treatmentplant facilities

The WWTP of Sfax is an aerated lagoon

(Fig. 1). It has been functional since 18 January

1983. It is conceived to purify the urban waste-

water with a daily average flow rate of 24,000

m3/d and a maximum daily flow rate of 32,000

m3/d with a hydraulic retention time of 5.2 days.

The wastewater treatment process is made up of

a primary treatment and a secondary or biological

treatment.

In primary treatment, wastewater enters the

plant and is screened for large objects. Next grit,

such as sand and gravel, is removed in a grit

removal system.

In secondary treatment, biodegradable organics

and nutrients are removed from the sewage by bio-

logical treatment. Sewage from primary treatment

is passed to the aerated lagoon for biological treat-

ment. Air is supplied continuously into the lagoon

equipped with 14 aerators for the growth and

reproduction of micro-organisms which assimilate

and decompose the organic matter in the sewage.

Finally, the effluent from lagoon is settled in two

clarifier tanks. The sludge is collected and treated

by thickening and drying beds. The layout of Sfax

WWTP is represented by Fig. 1.

2.2. Monitored parameters and analyticalmethods

Under the water-quality monitoring program

of the WWTP of Sfax, samples were collected

A. Ouali et al. / Desalination 246 (2009) 496–505 497

each month during the 12 years period (1984–

1996). COD and SS sampling were restricted to

the period from 1991 to 1996.

Water temperature was measured on the site

using mercury thermometer. The biochemical

oxygen demand (BOD) was measured following

the respirometric method with OxiTop-nanome-

ters (AFNOR No. T90103). The chemical oxy-

gen demand (COD) was measured by chemical

oxidization method (AFNOR No. T90101) and

the suspended solids (SS) were determined by

filtration and drying at 110°C (AFNOR No.

T90105).

2.3. Data treatment and multivariate statisticalmethods

Multivariate analysis of the WWTP of Sfax

was performed through PCA [30,31]. PCA can be

described as a method to project high dimen-

sional measurement space with significantly

fewer dimensions. A set of quantitative analytical

data from the WWTP, has been processed by

multivariate statistical techniques in order to

investigate the evolution of the water quality dur-

ing 6 years. The original matrix consisted of six

physico-chemical variables determined monthly

over a period of 6 years. The experimental matrix

(6 × 12) was analysed by PCA.

3. Results and discussion

3.1. Descriptive analysis

3.1.1. The flow rate of water

The average flow rate of wastewater was

increasing during the period from 1984 to 1996

(Table 1). It fluctuates between 4.60 × 105 and

7.57 × 105 m3. During the selected period, the

flow rate variation appears very important and

depends on the water consumption by the sfaxian

A. Ouali et al. / Desalination 246 (2009) 496–505

Fig. 1. Schematic representation of Sfax wastewater treatment.

498

agglomeration. However, we cannot omit the

effect of rainfall (fall and winter seasons) on the

flow rate of waters drained toward the WWTP

(the sewerage system gets both domestic water

and run-off) (Fig. 2).

3.1.2. Debris captured by screening

The capture of debris by the screening is cres-

cent during the selected period (1984–1996).

Their mean evolution is manifested by three

plateaus (Table 1):

• From 1984 to 1987: a monthly average value

oscillates between 5 and 10 m3. The fall and

winter values were very raised in comparison

with those of spring and summer. This possibly

displayed the seasonal contribution of the rainy

time.

• The period from 1988 to 1991 was character-

ized by a relatively reduced contribution on

debris. An exception made for December 1988

and October 1989. It translated the influence of

rainfall on the contributions on debris. The

heavy rainfall increased notably flow rate of

waters and their constituents (in particular

debris) in the network.

• From 1992 to 1996: the monthly average

value increased to reach values between 6 and

35 m3. The marked variation of monthly val-

ues translated the non-negligible run-off con-

tributions.

3.1.3. Sands evacuated by decantation

Table 1 shows that average evolution of sands

evacuated by decantation did not coincide neither

with the flow rate of water nor with debris kept

by screening. Studies done at the level of the grit

removal system confirmed its bad working

[34,35]. They proved a resuspension of sands by

A. Ouali et al. / Desalination 246 (2009) 496–505

Table 1

Average of the quality of raw and treated wastewater

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

Monthly flow

(× 105 m3) 5.03 4.60 5.29 4.60 4.66 5.48 5.88 6.07 6.72 6.35 6.64 7.57 7.13

Monthly debris kept

by screening (m3) 5.80 6.03 8.45 1.93 3.36 5.52 3.90 4.42 8.16 10.88 12.66 17.35 15.50

Monthly sands

evacuated in the 38.22 44.38 56.92 42.38 51.54 62.13 61.71 62.83 60.25 52.21 50.25 69.75 65.50

settling tanks (m3)

BOD input

(mg O2/L) 237 211 278 240 242 264 287 390 422 459 668 729 651

BOD output

(mg O2/L) 28,7 31,1 29,8 31,4 29,1 29,4 31,5 50,7 55,8 54,0 101,2 110,9 114,5

BOD removal

efficiency (%) 88 86 89 87 88 89 89 87 87 88 85 85 82

COD input

(mg O2/L) 944 771 819 1019 1033 926

COD output

(mg O2/L) 201 264 195 310 253 278

COD removal

efficiency (%) 78.5 65.2 75.6 69,9 74,0 69,6

SS input (mg/L) 353 304 421 446 467

SS output (mg/L) 43 44 39 60 45

SS removal

efficiency (%) 88 87 91 87 91

499

pumping that limits the decantation process.

Sands escaped from the grit removal system and

discerning in the oxidation basins increase the

water turbidity and decantation process. These

phenomena tend to disturb the water ecology

(disturbance of the biomass, reduction in the

hydraulic retention time and increase of bottom

sludge).

3.1.4. Biochemical oxygen demand (BOD)

The average of BOD at the input of the station

shows the increasing values during the period

from 1984 to 1996. It presents a succession of

three plateaus with different evolution (Table 1,

Fig. 3):

• from 1984 to 1989: the monthly average with a

weak growth appears characterized by values

oscillating between 200 and 300 mg O2/L;

• from 1990 to 1993: the monthly average is

more increasing and reach values near of 400 g

O2/L;

• from 1994 to 1996: the monthly average with

strong growth was distinguished by values

raised of 700 g O2/L;

The variability in the BOD values, at the input

of the WWTP, translated the irregular organic

A. Ouali et al. / Desalination 246 (2009) 496–505

0

200000

400000

600000

800000

1000000

1200000

1984 1986 1988 1990 1992 1994 1996

Year

Flow (m3)

minimal

maximal

average

Fig. 2. Minimal, maximal and average of flow rate treated by wastewater treatment of Sfax.

0

4000

8000

12000

16000

20000

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

Nominal load Evolution of load

Lo

ad

(B

OD

Kg

/d)

Year

Fig. 3. Evolution of organic load (BOD), compared to nominal load of Sfax wastewater treatment.

500

matter enrichment of water, which is possibly

dependent on citizen life conditions.

The monthly output average values of the

BOD draw practically the same evolution as at

the input of the WWTP. The organic matters

enrichment of outputs waters is more accentuated

in the second and the third plateau. It possibly

results in the biologic aspect of the WWTP that

could appear in the following manner:

• From 1984 to1990: a relatively steady biologic

activity, explained by a monthly mean BOD

removal efficiency about 90% and values at the

output complied with the discharge norm. This

period is characterized by a good mineraliza-

tion of organic biodegradable compound in the

oxidation basin. This period is characterized by

a good biologic activity: equilibrium between

the contribution in organic matters, the pro-

duced biomass and the biodegradation.

• From 1991 to 1996: a relatively perturbed bio-

logic activity was explained by the important

organic load at the upstream and downstream

of the station. To the downstream the load that

passes the norm displayed of an unbalanced

biologic environmental aspect. Otherwise, the

input organic load increase displayed of an over

exploitation of the station essentially during

years 1994, 1995 and 1996. It has as effect a

massif biomass production, decrease of the

oxygen reserves and evolution of the state to

anoxia. However, the elevated BOD removal

efficiency (>80%) could be explained by the

tampon effect of an aerated lagoon, in case of a

high organic load.

3.1.5. Chemical oxygen demand (COD) andsuspended solid (SS)

During the period from 1991 to 1996, The

COD average at the input was characterised by

two antagonistic evolutions: the first was decreas-

ing. It was characterized by values about 800 g

O2/L, whereas the second was increasing with

values relatively important about 1000 g O2/L.

The input and output COD average did not show

a clear similarity. At the downstream, the load

above norm shows an unbalanced chemical

aspect with a limitation of oxygen reserves in oxi-

dation basins and sway of the COD removal effi-

ciency 65–75%.

During the period from 1991 to 1996, the

input SS mean evolution fluctuates between 300

and 500 g/L. It was similar with the one of COD,

with a minimum during the period from 1991 to

1993 and a maximum during the period from

1994 to 1996.

A. Ouali et al. / Desalination 246 (2009) 496–505

Table 2

Correlation matrix (r = 0,2º3 pour n = 72 and α = 0.05)

Input load Output load Removal efficiency

Twater

BODi

CODi

SSi

BODo

CODo

SSo

ABOD ACOD ASS

Twat

1

Input load BODi

0.214 1

CODi

0.19 0.639 1

SSi

−0.163 0.354 0.42 1

Output load BODo

0.022 0.806 0.43 0.27 1

CODo

−0.016 0.439 0.44 0.26 0.521 1

SSo

−0.58 −0.002 0.07 0.24 0.175 0.341 1

Removal ABOD 0.255 −0.22 −0.02 −0.05 −0.738 −0.46 −0.34 1

efficiency ACOD 0.111 0.044 0.27 0.05 −0,194 −0.72 −0.27 0.45 1

ASS 0.413 0.275 0.25 0.49 0.06 −0.138 −0.7 0.234 0.301 1

NB. i: input of WWTP, o: output of WWTP, A: removal efficiency.

501

A. Ouali et al. / Desalination 246 (2009) 496–505

Table 3

Eigenvalue and their percentages on the principal compo-

nents

Principal Variance Cumulative

components Eigenvalue explained (%) variance (%)

PC1 3,327 33,3 33,3

PC2 2,691 26,9 59,9

PC3 1,395 13,9 73,8

The SS oscillates between 200 and 800 g/L. It

translated the irregular wastewater enrichment in

particular matters depends probably on the urban

activity (domestic and industrial).

The monthly output SS average is similar to

COD (Table 1). It testifies to the SS enrichment in

oxidizable matters and their participation in the

chemical processes. These matters accentuate the

oxygen demand and limit reserves for the oxida-

tion of dissolved and colloidal matter.

The SS retention (97–91%) and their weak

contents at the output of the oxidation basins pos-

sibly translated both decantation and dissolution

processes.

3.2. Principal component analysis (PCA)

Water quality of the WWTP of Sfax was reg-

ularly conducted over a period of 6 years (1991–

1996) at the inlet and outlet of the station. All the

samples were analysed for various parameters

(BOD, COD, SS).

The PCA was carried out by diagonalization

of the correlation matrix, so the problems arising

from different measurement scales and numerical

ranges of the original variables are avoided, since

all variables are automatically auto scaled to

mean zero and variance unit.

PCA identified three factors, which are respon-

sible for the data structure explaining 74% of the

total variance of the data set and allowed to group

the selected parameters according to common fea-

tures as well as to evaluate the incidence of each

group on the overall variation in water quality dur-

ing the period from 1991 to 1996 (Table 3).

Table 4 summarises the PCA results including

the loadings. The amount of variance spanned by

each principal component (PC) is also shown in

Table 2. Following the criteria of Cattel and

Jaspers [36] we have retained three PCs and

explaining 74% of the variance or information

contained in the original data set. The absolute

value of the loadings is an indicator of the partic-

ipation of variables in the PCs, and in Table 1 the

maximum contribution reached by each original

variable is highlighted.

PC1 explained 33.3 of the variance and was

contributed by BODinput

, CODoutput

, SSinput

,

BODouput

, SSoutput

, ACOD, ABOD, ASS; which

were negatively correlated with the water temper-

ature. All input and output parameters were found

significantly correlated and uncorrelated with the

removal efficiency. These correlations can be

explained on the basis of a normal operating con-

dition of the WWTP. PC1 was found uncorrelated

with the SS removal efficiency. This correlation

can be attributed to a particular behaviour of the

particular matters and therefore disturbed water

ecology.

PC2 explains 26.9% of the variance and was

mainly participated by water temperature, output

Table 4

Loadings of physico-chemical variables on three signif-

icant principal components

Variables PC1 PC2 PC3

(Axis 1) (Axis 2) (Axis 3)

Twater

0.150 −0.609 −0.588

BOD (input) −0.699 −0.547 −0.015

Inlet load COD (input) −0.529 −0.570 0.283

SS (input) −0.437 −0.378 0.569

BOD (output) −0.864 −0.197 −0.146

Outlet load COD (output) −0.825 0.107 −0.246

SS (output) −0.465 0.633 0.502

ACOD 0.450 −0.519 0.511

Removal ABOD 0.699 −0.333 0.220

efficiency ASS 0.092 −0.849 −0.034

Variance % 33.3 26.9 13.9

502

A. Ouali et al. / Desalination 246 (2009) 496–505

Fig. 4. Factorial distribution of variable space on the plan

1 × 2.

parameters and their removal efficiency. This

component was correlated negatively with the

input parameters already correlated with the

water temperature. These correlations can be

explained on the basis of seasonal features in the

monitoring region.

PC3 explains 17% of the variance and was

contributed by COD, SS and the water tempera-

ture. The input parameters (COD and SS) were

correlated positively, whereas those of the output

were correlated negatively. This component was

correlated with the output parameters (COD, SS).

This correlation can be attributed to the seasonal

contribution in oxidizable particulate (industrial

wastewater)

For better refining the above-mentioned

groupings, the recourse to plane projections is of

a great interest. The following paragraphs include

the factorial distribution of the variables in the

1 × 2 and 1 × 3 plans.

3.2.1. Projection on the plan 1×2

According to this factorial representation we

notice a distribution according to four groups

illustrated in Fig 4.

– a first group was representative of the input

and the output water quality. It was con-

tributed by the input and output parameters

BOD (input), BOD (output), COD (input), COD

(output), and SS (input) which were correlated

negatively to axis 1. The BOD (input), COD

(input), and SS (input) were characterized by a

significant positive correlation. These correla-

tions can be explained by a common transport

of an organic and mineral polluting load along

the WWTP. BOD (output) and COD (output)

were correlated positively appeared also corre-

lated with those at the input. They possibly

translated purification dependent on the input

load.

– a second group was related positively to axis 1.

It was formed by the BOD, COD removal effi-

ciency. This group confirms the dependence of

the residual output oxidizable substances on

their input load.

– a third group was contributed by SS which

were uncorrelated with the water temperature

and the SS removal efficiency. The deviation

of the SS removal efficiency from the position

occupied by the BOD and the COD removal

efficiency of the second group could inform us

of particular particulate load behaviour.

– a fourth group located in the negative part of

axis 2; it was contributed by the water temper-

ature (Twater

) and the SS removal efficiency. Its

antagonistic position with the SS (outlet) can

be translated possibly by the effect of the sea-

sonal particulate contributions on the effi-

ciency of the WWTP.

3.2.2. Projection on the plan 1 × 3

It comes out from this projection (Fig. 5) a

new group contributed by the following parame-

ters: SS (input), SS (output), COD (output) which

were correlated positively. This group was

opposed to the water temperature. It could prob-

ably show the effect of seasonal polluting matter

on the ecology of the station. The positive corre-

lation between the SS and COD showed the

enrichment the SS (inlet) on oxidizable soluble

(chemical oxidation).

503

A. Ouali et al. / Desalination 246 (2009) 496–505

Fig. 5. Factorial distribution of the variable space on the

plan 1 × 3.

4. Conclusion

Water-quality monitoring programs generate

complex multidimensional data that need multi-

variate statistical treatment for their analysis and

interpretation of the underlying information. An

experimental 6 × 12 matrix was found and

analysed by multivariate statistical procedures.

The inspection of the correlation matrix of six

variables showed the existence of strong correla-

tions between variables.

PCA allowed the reduction of the six vari-

ables to three PCs to explain 74% of the vari-

ance of the original data set. PC1 (33.3 of

variance) was found uncorrelated with the SS

removal efficiency. This correlation can be

attributed to a particular behaviour of the partic-

ulate matters consequently a disturbed water

ecology. The PC2 (26.9 of variance) can be

assigned to seasonal contribution in organic and

mineral matters according to the citizen life con-

ditions. PC3 (17% of variance) can be assigned

to chemical and biochemical contributions in

utilizable particulates.

Thus, the multivariable statistical analysis

served as an excellent exploratory tool in analysis

and interpretation of complex data set on water

quality and understanding their temporal and spa-

tial variations.

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