QUALITY WATER INDICES ESTIMATION
IN RIVERS AFECTED BY INDUSTRIAL DISCHARGES
Ribeiro, J.; Sousa, A.J.; Ribeiro, L.; Pereira, H.G.
CVRM – Centro de Geo-Sistemas / IST – Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Fax: 351 – 21 – 841 74 42 e-mail: jribeiro@alfa.ist.utl.pt
ABSTRACT
For dealing with the problem of estimation river water quality downstream of a
industrial effluent discharge point, a global methodology approach was developed in the
scope of a BRITE project (RIVERMOD). This methodology, based on Discriminant and
Correspondence Analysis, aims at the estimation of river water quality affected by an
industrial discharge.
The main point underlying the proposed methodology regards a new procedure to
combine in a single quantitative index the biological and physical-chemical parameters
that contribute to water quality in rivers. Based on monthly monitoring of variables
dissolved oxygen, pH, conductivity, temperature, chemical oxygen deficiency,
sulphates, nitrates, phosphates, Cu and Fe concentrations, and semesterly sampling of
macro-invertebrate taxa (this last variable, denoted biotic diversity, categorises the
pollution present in the river based on the presence/absence of certain macroinvertebrate
species related to the specific environmental conditions), a synthetic index was
developed to account for water quality in a river located in the South of Portugal.
In order to get an homogeneous data set, the biological-physical-chemical parameters
were categorised in classes for the same time periods. The definition of these classes is
based on the experts experience and international recommendations of how the
measured variables affect water quality.
The quantitative index construction procedure relies on the establishment of two
extreme ideal poles for water quality – denoted ‘GOOD’ and ‘BAD’ – by selecting the
classes of the measured variables where experts encapsulate the ‘best’ and ‘worse’
theoretical characteristics of each variable. Expert guesses providing the weights
assigned to the classes of the available biological-physical-chemical parameters
measured at 14 monitoring stations for a total of 139 months are given in Table 1.
Submitting the matrix of the two above defined poles to the Correspondence Analysis
algorithm in accordance with the work of Pereira (1988), following Benzécri (1980), a
single factorial axis is obtained, which constitutes a quantitative scale opposing the two
extreme configurations of the selected attribute classes (vd. Fig 1). Projecting, as
supplementary lines coded in complete disjunctive form, the experimental set of stationmonths onto the factorial axis, these points are sorted by their co-ordinates in the axis,
which constitute the values of the quantitative index (ranging from –1 to +1). The
histogram of the index values obtained by this procedure for the experimental stationmonths is given in Fig. 2, where it can be noticed that ‘good’ points display a negative
index, reflecting the low value of contaminant attributes.
Table 1 – Weights defining the ‘GOOD’ and ‘BAD’ poles for river water quality.
Variables
Biotic diversity based on
macro-invertebrate taxa
Dissolved Oxigen
(%)
Temperature
(ºC)
PH
Conductivity
(S/cm)
Chemical Oxygen
Deficiency
(mg/l)
Sulphates
(mg/l)
Nitrates
(mg/l)
Phosphates
(mg/l)
Cu
(mg/l)
Fe
(mg/l)
Codes
Classes
Good Pole
Bad Pole
BD 1
BD 2
BD 3
BD 4
BD 5
dissolv. Oxygen 1
dissolv. Oxygen 2
dissolv. Oxygen 3
Temperature 1
Temperature 2
pH 1
pH 2
pH 3
Conductivity 1
Conductivity 2
Conductivity 3
COD 1
COD 2
COD 3
Sulphates 1
Sulphates 2
Nitrates 1
Nitrates 2
Nitrates 3
Phosphates 1
Phosphates 2
Phosphates 3
Copper 1
Copper 2
iron 1
iron 2
[0;1]
]1;2]
]2;3]
]3;4]
]4;5]
[ 0 ; 50 ]
] 50 ; 90 ]
> 90
[ 0 ; 20 ]
] 20 ; 30 ]
[0;6]
]6;9]
] 9 ; 14 ]
[ 0 ; 400 ]
] 400 ; 1500 ]
> 1500
[ 0 ; 10 ]
] 10 ; 40 ]
> 40
[ 0 ; 400 ]
] 400 ; 3200 ]
[ 0 ; 25 ]
] 25 ; 50 ]
> 50
[ 0 ; 0.54 ]
] 0.54 ; 0.94 ]
> 0.94
[ 0 ; 0.005 ]
> 0.005
[ 0 ; 0.3 ]
> 0.3
0.00
0.01
0.05
0.14
0.80
0.00
0.09
0.91
0.50
0.50
0.00
1.00
0.00
0.95
0.05
0.00
0.90
0.10
0.00
0.99
0.01
0.97
0.02
0.01
0.96
0.03
0.01
0.98
0.02
0.96
0.04
0.80
0.14
0.05
0.01
0.00
0.91
0.09
0.00
0.02
0.98
0.50
0.00
0.50
0.00
0.05
0.95
0.00
0.10
0.90
0.01
0.99
0.01
0.02
0.97
0.01
0.03
0.96
0.03
0.97
0.03
0.97
Fig. 1 – Histogram of modalities.
Fig. 2 – Experimental histogram of station-months.
Based on the river geometry sketched in Fig. 3, the average values of the index per
station is shown in Fig. 4, for dry (May or August or September) and wet (November or
February) months. As expected a serious decrease on water quality is noticed after the
effluent discharge.
N
Effluent
er
Riv
a
Gu
ras
Oei
N
di
J
iv e
sR
iro
rr e
r
r
ve
E
I
M
K
Ri
F
L
H
a
an
G
Ca
D
C
River
Vascão
B
A
Sampling stations
Scale:
0
5
10 km
Fig. 3 – Location of the sampling stations.
Fig. 4 – Average values of the index per station.
On establishement of this index, the estimation of this new quantitative variable is
performed by adjusting space-time geostatistics to the specific features of the problem
(Pereira et al., 2000).
REFERENCES
Benzécri, J.-P., 1980 – Pratique de l’analyse des données. Dunod, 442 p.
Pereira, H.G., 1988 – Case study on application of qualitative data analysis to an
uranium mineralization. C. F. Chung et al. (eds.), Quantitative Analysis of Mineral and
Energy Resources, Reidel, 1988, p. 617-624.
Pereira, H.G.; Ribeiro, J.; Sousa, A.J.; Ribeiro, L.; Lopes, A.; Serôdio, J. – Forecasting
river water quality indices. Accepted to 6th International Geostatistics Congress, April
2000.