Water Resour Manage (2007) 21:1797–1812
DOI 10.1007/s11269-006-9128-x
Water Quality in the Río Lerma, Mexico: An Overview
of the Last Quarter of the Twentieth Century
Jacinto Elías Sedeño-Díaz & Eugenia López-López
Received: 31 October 2005 / Accepted: 30 November 2006 / Published online: 10 January 2007
# Springer Science + Business Media B.V. 2007
Abstract The Río Lerma basin is the most important watershed in the Central Plateau of
Mexico. Major urban, industrial, agricultural and livestock regions are located in its
catchment area. Regarded as a center of endemism for its fish fauna diversity, it is also the
most polluted watercourse in Mexico. This study assesses spatial and long temporal
variations in water quality over the last 25 years with two approaches: the use of a water
quality index multiplicative and weighted (WQI) and a principal component analysis
(PCA). The general rating scale for WQI range on a 0–100 with 100 indicating highest
water quality. WQI scores ranging from 26.53 to 67.44 denote Rio Lerma water is not fit
for drinking, requires treatment for most industrial and crop uses, and is suitable for coarse
fish only. Navigation is impracticable and inexistent. PCA shows the monitoring stations
arrayed along a set of environmental parameter gradients. Several endemic fish species
have been lost: two silver side food fishes are extinct, two more species (one of them a food
fish) are endangered, and another three are threatened.
Key words water quality index . multivariate analysis . Río Lerma . Mexico
1 Introduction
The Río Lerma basin, in the Central Plateau, is among the most important water systems in
Mexico. It has benefited large towns and cities (Toluca, Querétaro, Salamanca, Guanajuato,
Morelia and others, representing 11% of the total population of Mexico, approximately 11
million people, in less than 3% of Mexican territory), making this region one of the most
densely populated in the country and contributing to the growth of agriculture and livestock
areas like El Bajío and La Piedad. Major industrial parks have been developed, such as
Salamanca, city where is located one of the most productive oil refineries in Mexico, and
also food industry facilities, chemical and pharmaceutical plants, pulp and paper mills,
J. E. Sedeño-Díaz (*) : E. López-López
Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico, Mexico
e-mail: [email protected]
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Water Resour Manage (2007) 21:1797–1812
distilleries and beverage factories are located in the municipalities of Lerma, Toluca,
Atlacomulco, Celaya, Irapuato, and Morelia.
Development in the Lerma-Chapala basin is largely sustained by intense water use.
Industries in the basin generate around 130,500 t year−1 biochemical oxygen demand
(BOD) coming from urban waste and 424,260 t year−1 chemical oxygen demand (COD)
coming from industrial discharges. These organic and inorganic pollutant loads and a
scarcity of wastewater treatment capacity have intensified water quality problems and
severely reduced water availability. Diffuse pollution caused by drainage containing
fertilizer and pesticide residues from irrigated agricultural areas, together with solid wastes
washed away by rainfall from rural households lacking domestic waste disposal systems for
excreta and rubbish, have also contributed to the water quality problems (Mestre-Rodríguez
1997). In addition, the Río Lerma flows into Lake Chapala, which is the most important
water distribution center in the region, furthermore their water is the source of drinking
water for the Guadalajara City, the second larger city of Mexico, and its metropolitan area
with a current population of 3.34 millions of inhabitants.
The hydraulic system that is handled in the Río Lerma basin is based on numerous
reservoirs since the headstream of the river. The topography of this region does not permit
to build large storages for the water; however, the region includes 16 large reservoir of a
total of 552 reservoirs (Cottler and Gutiérrez 2005) which help to regulate erratic run-off
from year to year. They have also helped considerably to reduce flooding risks, however, as
a result of an excess of nutrients derived from untreated effluents, the reservoirs have been
seriously affected by eutrophication process (López-López and Sedeño-Díaz 2006).
Due to the high endemism of its fish fauna, the Lerma-Chapala system has been used to
characterize the Lerma Province, one of three in the Nearctic Region (López-López and
Díaz-Pardo 1991). Natural resources in this area have sustained severe exploitation, as in
the case of the white fish Chirostoma spp. and several goodeid fishes. The Río Lerma is
now one of the most polluted rivers in Mexico. Its major sources of contaminants are
industrial and municipal wastewaters discharged along the main river channel. High
pollution has seriously affected its water quality, causing the extinction of several native
species of the fauna and flora (Athié 1987; López-López and Díaz-Pardo 1991; Soto-Galera
et al. 1998 and Soto-Galera et al. 1999).
Physical and chemical monitoring of the water quality of the Río Lerma has long been
practiced. The available data bank allowed the application of indices to detect water quality
trends. Although many countries maintain water quality records, little has been published
on the long time water quality evolution. Some papers deal with the study of sediment cores
(Gousset et al. 1999), which reflect the history of industrial activity in a basin and reveal the
presence of recalcitrant pollutants, but not display the limitations of the resource for specific
uses.
The aquatic environment is subject to temporal and spatial variations in quality due to
internal and external factors. Meybeck and Helmer (1992) suggest the quality of the aquatic
environment can be defined as a set of concentrations, speciations and physical partitions of
inorganic or organic substances. Thus, water quality management had until recently relied
on monitoring of chemical factors (Logan 2001). Ideally, however, water quality should be
assessed using physical, chemical and biological parameters in order to provide a full
spectrum of information for adequate water management (Iliopoulou-Georgudaki et al.
2003).
Factors that can complicate water quality assessment include the multidimensional
nature of the water-quality concept (Shultz 2001). Analysis and interpretation of large sets
Water Resour Manage (2007) 21:1797–1812
1799
of unprocessed data is often difficult (Štambuk-Giljanović 2003a) and evaluating overall
water quality is no easy task, particularly when different criteria are applied to different
water uses (Bordalo et al. 2001). The use of indices is proposed as a means of aggregating
dimensions of the water-quality concept in order to infer trends in watershed environmental
quality (Shultz 2001).
Water quality indices are intended as a simple, readily understandable tool for managers
and decision makers to transmit information on the quality and potential uses of a given water
body, based on various criteria (Štambuk-Giljanović 2003a). A water quality index (WQI) is
an arithmetical device used to translate large data sets on water quality into a single
cumulatively derived number representing a certain level of water quality (Miller et al.
1986; Štambuk-Giljanović, 2003a).
WQIs can also be used to aggregate data on water quality parameters at different times
and in different places and to translate this information into a single value defining the
period of time and spatial unit involved (Shultz 2001). Numerous studies on water quality
assessment have made use of WQIs (Miller et al. 1986; Al-Ami et al. 1987; Sahu et al.
1991; Štambuk-Giljanović 1999; Bordalo et al. 2001; Štambuk-Giljanović 2003a, b).
Chemometrics can be defined as the application of mathematical, statistical, graphical or
symbolic methods to maximize the chemical information. One of the primary goals of
Chemometrics is to reduce the number of dimensions needed to accurately portray the
characteristics of the data set. The most frequently used projection technique is PCA
(Massart and Vander 2004; Møller et al. 2005).
Principal Component Analysis (PCA) was developed to summarize and make easier the
interpretation of multivariate data sets (Gauch 1989). PCA reduces a multidimensional
complex data set into two or three dimensions by computing principal components or
factors (Demayo and Steel 1992; Kowalkowski et al. 2006). In our study, PCA was used to
produce an ordination of independent descriptors based on water quality parameters and
display spatial and temporal trends in water quality in the Río Lerma.
The purpose of this paper is to assess the evolution of water quality in the Río Lerma
from 1975 to 1999 and its implications for major land use, based on data from the Water
Quality Monitoring Network of the National Water Commission, the regulatory agency for
water resources in Mexico.
2 Materials and Methods
2.1 Study Area
The Rio Lerma with a length of 750 km originates in the Central Plateau of Mexico at an
altitude above 3,000 m above sea level (masl). The river empties in Lake Chapala
(1,510 masl) which is the largest tropical lake in Mexico (Fig. 1). The Lerma River basin, is
a tropical region with an average temperature of 21°C, an area of 54,400 km2 (less than 3%
of the Mexican territory) and an average rainfall of 735 mm year−1, mainly concentrated in
the summer, from which a mean run-off of 5.19 km3 is derived. The River Santiago arises
from Lake Chapala and flows westwards finally reaching the Pacific Ocean (MestreRodríguez 1997). Based on topographical features, the basin is divided into three
subprovinces: Upper, Middle and Lower Río Lerma. Wastewater discharges in the Upper
Río Lerma have their origin in chemical, metal-mechanic, textile and other industries. The
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Water Resour Manage (2007) 21:1797–1812
Middle Río Lerma receives wastewater contributions from an oil refinery, fertilizer and
power plants, fruit-packing houses, poultry farms and the food industry, as well as pig
farms and tanneries. Also significant is diffuse pollution from the major agricultural region
of El Bajío. Lastly, in the Lower Río Lerma, discharges originate in livestock, particularly
pig farms although there are also some food industry contributions.
Navigation in the Río Lerma is virtually impossible due to its morphology. It is less than
25 m wide at its widest sections and less than 3 m deep in its deepest zones, the usual depth
being 1.5 m. Furthermore, the slope of the watercourse makes navigation impracticable, and
the river is not currently used for transportation purposes. The main use of the Río Lerma is
to supply water for industrial use and crop irrigation, and given its importance as a center of
fish endemism, the protection of its aquatic life should be a priority.
2.2 Water Quality Data
The water quality data were retrieved from the National Water Commission data bank via the
files of the Water Quality Monitoring Network. A total of 17 monitoring stations were
considered for assessment (Fig. 1), based on main river channel location and distribution in all
three subprovinces, and were selected on the basis of their persistence in the monitoring data
set through this period. Monitoring design includes field sampling each 3 months and
laboratory analyses were conducted as per Mexican regulations, which are based in turn on
APHA (1971 and 1976).
Fig. 1 Main land use and location of monitoring stations in the Río Lerma basin
Water Resour Manage (2007) 21:1797–1812
1801
2.3 Water Quality Index
Water quality was assessed using the multiplicative weighted index proposed by Dinius
(1987), which is of the form:
WQI ¼
n
Y
IiWi
i¼1
Where:
WQI
Ii
wi
n
Water Quality Index, a number from 0 to 100
Subindex of parameter, a number from 0 to 100
n
P
Unit weight of parameter, a number from 0 to 1; and
wi ¼ 1
i¼1
Number of parameters
Ott (1978), Landwehr and Deininger (1976), Walski and Parker (1974) and Gupta et al.
(2003) showed multiplicative indices are superior because a geometric mean is less affected
by extreme values than an arithmetic mean. In addition, recently several authors have
applied this index (Shiow-Me et al. 2004; Gupta et al. 2003; Gergel et al. 2002, and Zoppou
1999).
Twelve parameters enter into the formulation of this WQI: dissolved oxygen (DO),
biochemical oxygen demand (BOD), total and fecal coliforms, alkalinity, hardness,
chloride, conductivity, pH, nitrate, color and water temperatures. The mean annual values
of each parameter were used to calculate subindex functions from which the WQI was then
derived. All calculations were performed with Microsoft Office Excel 2003. Table 1 show
the subindex functions and weighted values used for each parameter, which subsequently
were incorporated to the WQI algorithm to obtain the WQI scores. Thus, WQI scores in this
paper are based on annual averages. The water quality map was plotted with Surfer 6.01
software using ordinary kriging without drift interpolation (Keckler 1996), which allows to
get a 3-D plot.
2.4 Principal Component Analysis (PCA)
This analysis was applied to assess the significance of parameters that explain the patterns
of the monitoring stations. The PCA was applied on the basis of the dataset of the mean
annual values of the 12 water quality parameters. Classical PCA is based on the
decomposition of a covariance/correlation matrix by eigenvalue (spectral) decomposition
or by the decomposition of real data matrixes. The correlation matrix consisting of the 12
water quality parameters for the WQI was used for PCA; all the assessments were carried
out with XLSTAT Pro 7.1 software and varimax rotation. A t-Student test was carried out to
compare mean WQI scores among study periods.
3 Results
3.1 Water Quality Index
The mean and range (minimum and maximum) values of WQI score, for each monitoring
station in the whole period studied are provided in Table 2. Even when seasonal variations
in each annual cycle in tropical systems are well documented, in this case study we focus
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Water Resour Manage (2007) 21:1797–1812
Table 1 Sub indices and weighted values of variables in the WQI
Parameter
Subindex Ii
Weighted
value (wi)
Dissolved oxygen (percent saturation)
Biochemical oxygen demand 5-day (mg/l)
Nitrate (mg/l)
Total coliforms (MPN coliforms/ml)
Fecal coliforms (MPN coliforms/ml)
Alkalinity (mg/l)
Hardness (mg/l)
Chloride (mg/l)
Temperature (°C) where Ta = air temperature Ts =
water temperature
Conductivity (μmhos/cm)
pH (units)
IOD ¼ 0:82ðDOÞ þ 10:56
IDBO ¼ 108ðBODÞ0:3494
INO3 ¼ 125ð N Þ0:2718
IColTot ¼ 136ðTotColÞ0:1311
ICol Fec ¼ 106ðE: coliÞ0:1286
IALC ¼ 110ðALK Þ0:1342
IDUR ¼ 552ðHaÞ0:4488
ICloruros ¼ 391ðCl Þ0:4488
IT C ¼ 102:004 0:0382jTa Ts j
0.109
0.097
0.090
0.090
0.116
0.063
0.065
0.074
0.077
Color (Pt-Co units)
ICOND ¼ 506ðSPC Þ3315
IpH ¼ 100:6803þ0:1856ðpH Þ pH < 6:9
IpH ¼ 100 6:9 pH 7:1
IpH ¼ 103:650:2216ðpH Þ pH > 7:1
ICOLOR ¼ 127ðC Þ0:2394
0.079
0.077
0.063
on long-term variations (25 years). WQI scores indicate water quality throughout the
period of study and along the entire river course was less than 70 on a scale of 0–100
having 100 as highest water quality (Table 2). Mean WQI along the river ranged from
32.22 to 43.6 (monitoring stations 4 and 8, respectively), with a global average of 38.10±
6.02 during the study period. Station 4 in 1982 had the lowest WQI score and station 3 in
1999 the highest. The spatial and temporal mean variation of WQI scores are shown in
Figs. 2 and 3, respectively. From 1975 to 1994, mean WQI scores were lower than to 41,
while from 1995 to 1999, the WQI scores ranged from 44.7 to 45.5 (Fig. 3). A minimal but
significant (α=0.01) improvement in water quality over time is evident when comparing
these periods.
Figure 4 shows the water quality map based on WQI scores in a 3D Surfer plot. During
the whole study period, the five sites (1, 2, 4, 5, and 6 in the upper portion of the river),
associated with industrial parks, had the lowest water quality scores (except monitoring
station 3). WQI values were higher in the monitoring stations furthest downstream
(monitoring stations 7 to 17) due to dilution by tributary inflow and moderate contributions
of industrial wastewaters. The spatial pattern apparent from the onset of the study period
remains unchanged and it is only in the last 5 years (1995–1999) that water quality
improves, reaching the higher WQI means scores from 44.75 to 45.54.
3.2 PCA
Correlation matrix of the PCA (Table 3), shows in bolds significant values (α=0.05) of
correlation between particular parameters: dissolved oxygen is negatively related with
BOD, chloride, alkalinity, specific conductance, hardness and total coliforms. Parameters
that are related with mineralization have high interdependence among them and with
organic pollution (BOD) and fecal contamination.
According to the eigenvalue-one criterion (Kowalkowski et al. 2006), only the three first
eigenvalues was taken into account (eigenvalues >1); the remainder principal components
(PC) were eliminated (Table 4). The cumulative variance for the first three principal
components is 79.94% of the total variance of the original dataset (Table 4 and Fig. 5).
1975
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Mean
SD
Min
Max
Year
38.95
39.35
39.75
40.16
40.56
40.79
40.56
43.90
41.02
44.04
38.14
32.62
39.85
36.91
33.72
30.69
34.82
38.56
36.49
47.71
49.83
48.72
50.13
47.06
40.60
5.30
30.69
50.13
1
38.63
39.59
42.49
37.09
35.89
34.96
34.82
33.62
33.97
32.97
34.55
31.23
33.19
34.47
35.31
29.58
30.70
32.36
39.51
42.81
40.28
41.05
40.35
31.09
35.85
3.89
29.58
42.81
2
39.05
39.57
39.78
37.09
34.03
31.84
30.62
30.48
32.05
30.86
32.71
31.88
40.54
39.20
37.85
36.51
36.22
41.76
39.26
48.82
49.01
48.14
46.58
67.44
39.22
8.34
30.48
67.44
3
Monitoring station
37.62
37.44
36.95
34.37
32.16
28.72
26.43
27.34
30.14
28.74
30.87
32.54
26.69
31.72
29.27
26.77
26.85
27.31
37.33
39.03
36.12
36.03
36.12
36.62
32.22
4.33
26.43
39.03
4
36.37
35.81
36.69
33.85
32.15
32.48
32.52
30.59
29.32
29.96
28.55
33.97
28.26
34.84
28.91
29.74
28.89
29.50
35.62
40.01
38.05
39.72
37.85
35.68
33.31
3.74
28.26
40.01
5
39.71
36.33
39.94
31.22
34.73
32.80
36.93
33.03
36.09
32.64
39.93
36.02
32.92
34.14
27.12
29.76
32.29
33.69
37.51
42.91
42.49
45.53
41.54
36.28
36.06
4.49
27.12
45.53
6
43.03
42.25
48.93
45.75
43.77
42.19
41.91
44.20
49.74
39.57
40.94
40.54
38.33
39.39
39.09
42.67
36.75
36.99
46.38
48.15
45.90
49.25
46.45
34.34
42.77
4.23
34.34
49.74
7
39.31
40.65
47.80
41.38
41.37
37.71
39.84
39.23
43.70
43.07
43.16
40.25
40.07
41.19
44.77
41.23
38.96
33.91
50.34
50.60
53.54
48.95
51.95
53.38
43.60
5.36
33.91
53.54
8
41.00
45.99
41.09
47.91
44.81
43.39
42.80
44.02
46.33
40.43
43.04
40.66
38.84
40.70
34.11
37.78
39.41
42.38
37.44
47.92
47.57
48.43
46.67
45.91
42.86
3.83
34.11
48.43
9
39.52
42.64
43.16
36.48
39.88
37.50
37.70
36.19
34.95
42.13
36.68
37.70
36.79
37.30
35.55
36.37
36.25
34.88
33.79
46.47
47.60
47.80
46.67
48.68
39.70
4.71
33.79
48.68
10
38.22
40.35
40.35
36.53
43.15
36.95
36.95
34.19
39.60
40.31
39.08
35.66
36.41
35.65
33.19
35.37
35.61
35.45
33.15
44.27
48.35
49.63
46.47
47.00
39.25
4.86
33.15
49.63
11
34.92
38.09
36.23
32.43
33.20
30.23
27.82
26.79
31.11
31.43
34.16
30.48
26.53
34.80
30.16
32.71
31.24
31.13
31.09
43.87
39.09
34.57
39.67
41.10
33.45
4.44
26.53
43.87
12
Table 2 WQI scores of the monitoring stations of the Río Lerma during the study period
35.56
31.44
37.70
32.63
33.77
32.86
32.90
30.37
32.15
30.73
33.75
33.76
33.67
34.13
29.35
30.90
33.17
30.07
32.17
47.42
42.85
33.95
43.81
42.41
34.65
4.76
29.35
47.42
13
38.05
38.81
36.55
36.13
32.50
34.28
32.65
32.00
31.37
31.82
33.86
32.58
32.27
36.99
36.62
33.17
34.46
30.10
31.89
39.75
43.77
42.82
43.20
43.62
35.80
4.26
30.10
43.77
14
33.52
40.87
37.68
35.66
36.12
35.15
36.24
31.89
37.87
35.91
32.77
37.64
39.10
37.89
33.44
34.88
34.93
36.43
34.10
46.96
49.09
48.69
43.54
44.95
38.14
4.99
31.89
49.09
15
40.12
41.86
44.27
43.83
37.15
34.14
35.16
34.15
33.86
35.37
34.40
40.49
42.99
38.39
37.01
37.45
34.50
35.17
42.01
50.62
49.26
50.85
54.88
55.19
40.96
6.78
33.86
55.19
16
37.19
37.12
42.20
39.11
33.60
33.02
35.10
36.71
39.76
33.67
37.83
35.97
37.54
36.63
37.44
34.90
36.76
33.49
46.48
46.79
46.64
46.69
47.82
50.26
39.28
5.28
33.02
50.26
17
SD
38.28
2.32
39.30
3.25
40.68
3.80
37.74
4.74
36.99
4.36
35.24
4.07
35.35
4.59
34.63
5.47
36.65
5.96
35.51
5.05
36.14
4.17
35.53
3.51
35.53
4.98
36.73
2.57
34.29
4.47
34.15
4.33
34.22
3.37
34.30
4.15
37.92
5.58
45.54
3.65
45.26
4.81
44.75
5.63
44.92
4.94
44.77
8.98
Global mean
38.10
Global SD
6.02
Absolute minimum 26.43
Absolute maximum 67.44
Mean
33.52
31.44
36.23
31.22
32.15
28.72
26.43
26.79
29.32
28.74
28.55
30.48
26.53
31.72
27.12
26.77
26.85
27.31
31.09
39.03
36.12
33.95
36.12
31.09
Min
43.03
45.99
48.93
47.91
44.81
43.39
42.80
44.20
49.74
44.04
43.16
40.66
42.99
41.19
44.77
42.67
39.41
42.38
50.34
50.62
53.54
50.85
54.88
67.44
Max
Water Resour Manage (2007) 21:1797–1812
1803
1804
Water Resour Manage (2007) 21:1797–1812
55
8
50
16
3
7
9
10 11
1
17
45
15
6
2
14
13
40
WQI
12
4 5
35
30
25
Number = monitoring station
20
0
100
200
300
400
500
Km from headstream to river mouth in Lake Chapala
Fig. 2 Spatial variation of mean WQI scores for monitoring station in the Río Lerma from 1975 to 1999.
The bar denotes ± SD
These first three PC were later rotated according to varimax rotation in order to make
interpretation easier (Table 4). In this table, communalities show that all parameters have
been described to an acceptable level (above 0.60). Consequently, the first three principal
components can be considered significant in the analysis.
Following the criteria of Kowalkowski et al. (2006), those components loadings higher
than 0.6 may be taken into consideration for the interpretation of the PC analysis (Table 4).
In the first PC the significant parameters were: alkalinity, chloride, hardness, BOD, and
specific conductance with positive relation, while the DO indicate a negative relation. This
signifies that high values of BOD are inversely related with the DO concentrations, which
in turn are related to mineralization (high specific conductance, chloride, alkalinity and
hardness).
In the second PC, total and fecal coliforms as well as color, are significant parameters
with a negative relation; while pH shows a positive correlation coefficient. The last PC
shows information prominent upon nitrates and water temperature with a positive relation.
The PCA biplot of the annual mean environmental parameters and the 17 monitoring
stations is shown in Fig. 6, each point is representing a monitoring station and also the
respective mean WQI score were placed in parenthesis. The first two PC (with eigenvalues
of 5.31 and 2.37, respectively) account for 63.91% of the total variance. A close look at
Fig. 6 shows that monitoring stations with WQI scores higher than 40 are on the right side,
while WQI scores lower than 40 are on the left side. In the same way, inversely correlated
parameters (DO – BOD) are distributed in opposite sides on PC 1. Sites associated with the
highest dissolved oxygen and lowest BOD concentrations are on the left side of the PC1axis (7, 8, 9, 10, 11, 15, 16), while those monitoring stations with highest BOD,
conductivity and total coliforms are on the right side (1, 2, 4, 5, 12, 13, 14), these
monitoring stations are the more polluted, which also are associated with industrial parks in
Water Resour Manage (2007) 21:1797–1812
1805
60
55
50
WQI
45
40
35
30
25
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
20
Time
Fig. 3 Temporal variation of the mean WQI scores for the 17 monitoring stations in the Río Lerma. The bar
denotes ± SD
the Upper Río Lerma. On the other hand monitoring stations 7, 8, 9, 10, 11, 15 and 16,
associated with agricultural areas and far away of large population centers, have lower
inputs of industrial wastewaters and display an improvement in water quality. Thus, a water
quality gradient is displayed along the PC1-axis extending from monitoring stations with
Fig. 4 3D plot of annual WQI scores for each monitoring station of Río Lerma from 1975 to 1999
1
0.457
0.735
0.519
0.769
0.842
0.580
0.030
−0.749
0.047
−0.033
1
0.813
0.164
0.451
0.201
0.818
0.688
0.781
−0.224
−0.745
0.346
0.114
Chloride
1
0.546
0.297
0.731
0.400
−0.073
−0.509
−0.441
−0.203
−0.537
0.169
Total
coliforms
1
0.658
0.587
0.319
0.376
0.103
0.388
−0.246
Fecal
coliforms
Italicized significant values α=0.050 (two tail test).
Alkalinity
Chloride
Fecal Coliforms
Total Coliforms
Color
Conductivity
BOD
Hardness
Nitrates
Dissolved
Oxygen
pH
Water
Temperature
Alkalinity
Table 3 Correlation matrix of water quality variables of PCA
−0.221
−0.003
1
0.410
0.469
0.133
0.461
−0.194
Color
0.358
0.480
1
0.563
0.529
0.219
−0.631
Conductivity
−0.128
−0.167
1
0.367
−0.065
−0.819
BOD
0.113
0.030
1
−0.455
−0.539
Hardness
−0.007
0.329
1
0.143
Nitrates
−0.023
0.065
1
Dissolved
oxygen
1
0.195
pH
1
Water
temperature
1806
Water Resour Manage (2007) 21:1797–1812
Water Resour Manage (2007) 21:1797–1812
1807
Table 4 Varimax rotated components
Variances explained by rotated components
Percent variance
Cummulative variance
Alkalinity
Chloride
Fecal coliforms
Total coliforms
Color
Specific conductance
BOD
Hardness
Nitrates
Dissolved oxygen
PH
Water temperature
1
39.475
39.475
Rotated loadings
2
24.439
63.914
1
2
0.966
0.863
0.156
0.536
0.228
0.775
0.767
0.799
−0.306
−0.840
0.262
0.014
3
16.027
79.941
Communalities
3
0.049
−0.404
−0.862
−0.704
−0.750
−0.119
−0.449
0.111
−0.479
0.170
0.689
0.071
0.132
0.082
0.155
−0.295
0.248
0.596
−0.132
−0.122
0.703
0.071
0.458
0.793
0.953
0.914
0.791
0.870
0.677
0.969
0.808
0.666
0.817
0.740
0.754
0.635
elevated BOD and fecal coliforms to stations with high levels of dissolved oxygen,
corresponding with WQI scores ranged from 32.22 to 43.60. Then, the first PC
characterizes the allochthonous inputs of organic matter, mainly through municipal and
industrial wastewaters into the Río Lerma, segregating those monitoring stations with
lowest organic pollution inputs and highest DO concentrations.
Similarly, the Fig. 6 shows that second PC separates in the upper portion the monitoring
stations with alkaline pH values and highest nitrates concentrations (1, 3, 12, 14), while in
the lower portion, are monitoring stations 2, 4, 5, 6, 13, 15, 16, and 17, with highest fecal
coliforms counts. The positive values of this component rather characterize an alkaline
medium. The negative values of this PC will characterize the microbiologic pollution
6
Fig. 5 Scree-plot for the PCA of
the water quality parameters from
Río Lerma monitoring stations
44.29%
5
Eigenvalue
4
3
19.79%
15.85%
2
7.41%
1
4.95%
2.39% 2.33%
1.14% 0.92% 0.61%
0.22% 0.04%
0
0
1
2
3
4
5
6
7
Component
8
9
10
11
12
1808
Water Resour Manage (2007) 21:1797–1812
(40.60)
1
2
1.5
(35.80)
1
14
pH
(39.22)
3
PC2 axis
(33.45)
0.5
(42.77)
(43.60)
7
Conductivity
Alkalinity
Water Temperature
8
Hardness
Nitrates
DO
0
(40.96)
(40.60)
10
-0.5
(40.60)
9
Chloride
Color
BOD
16
(36.06) 6
(34.65) 13
(39.28) 17
Total Coliforms
(38.14) 15
Fecal Coliforms
(35.85)
(42.86)
12
(32.22)
4
2
11
-1
(33.31)
5
-1.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
PC1 axis
Fig. 6 PCA-derived biplot of the water quality parameters and monitoring stations, based on the mean
annual values of the 12 environmental variables and 17 monitoring stations. Number in parenthesis = WQI
scores
associated to reducing and acid medium, resulted from microbial decomposition activity.
Taking as a set, the parameters analyzed with PCA display the water quality patterns of
the monitoring stations, identifying individual stations correlated with each environmental
factor.
4 Discussion
The WQI aggregates diverse parameters and their dimensions into a single score, displaying
a picture of water quality in the Río Lerma, besides linking water use to absolute values
connoting fit or unfit, polluted or unpolluted. The mean WQI score, checked against the
general rating scale for water quality (Fig. 7), indicates that Río Lerma water is not fit for
use in the public water supply system, requires extensive treatment for most industry and
crop use, and is suitable for coarse fish only. Johnson et al. (1997) found that major
differences in stream chemistry among sub-basins of the Saginaw River were closely
related to land use. Water quality in the Saginaw River was affected by prevalent land use
more than by point discharges. In this study, the Upper Río Lerma is characterized by
industrial and municipal wastewater discharges, the Middle Río Lerma by diffuse pollution
and industrial wastewater, and the Lower Río Lerma by discharges of livestock origin
mainly. Certain river stretches showed minimal recovery (7, 8, 9, 16, 17), possibly due to
their distance from major wastewater discharge areas as well as the transport and
breakdown of organic matter brought about by this dynamic water system.
Water Resour Manage (2007) 21:1797–1812
Level of
Pollution
(100 = Best)
1809
Water Uses
Public Water
Recreation
Supply
Fish
Shellfish
Agricultural
Industrial
Purification Not
Necessary
Purification Not
Necessary
100
Purification Not
Necessary
90
Minor
Purification
Required
Acceptable for Acceptable for
All Fish
All Water Sports
Acceptable for
All Shellfish
80
70
Necesary
Treatment
Becoming more
Extensive
60
Becoming
Polluted Still
Acceptable
Bacteria Count
Marginal for
Sensitive Fish
Marginal for
Sensitive
Shellfish
Doubtful for
Sensitive Fish
Doubtful for
Sensitive
Shellfish
Hardy Fish Only
Hardy Shellfish
Only
Minor
Minor
Purification for Purification for
Industries
Crops Requiring
requiring High
High Quality
Quality Water
Water
No treatment
Necessary for
Most Crops
No treatment
Necessary for
Normal Industry
Extensive
treatment for
Most Crops
Extensive
treatment for
Most Industry
Use Only for
Very Hardy
Crops
Rough Industrial
Use Only
Not Acceptable
Not Acceptable
50
Doubtful
40
Doubtful for
Water Contact
Coarse Fish
Only
30
Coarse Shellfish
Only
Obvious
Pollution
Appearing
20 Not Acceptable
Not Acceptable
10
Not Acceptable
Obvious
Pollution - Not
Acceptable
0
Fig. 7 General rating scale for water quality (taken from Dinius 1987)
The mean spatial variation of WQI scores along the Río Lerma showed that water
quality is strongly polluted in the upper portion, later improved in the Middle portion and
again gets worse in the Lower portion (Fig. 2). This pattern could be associated to major
land uses along the Río Lerma.
On the other hand, temporal variations of WQI scores showed that a minimal but
significant (α=0.01) improvement of water quality occurred after 1994. From 1975 to
1994, mean WQI scores were lower than 41, while 1995–1999, the WQI ranged from 44.7
to 45.5. Modification to the Mexican environmental legislation in December 1992 affected
all hydraulic resources (enactment of National Water Law). It included new procedures for
water pollution control, wastewater treatment, formation of Basin Councils for more
efficient water use and actions seeking the improvements of water bodies. These measures
were particularly applied in the Lerma basin and their effects are possibly apparent in the
last 5 years of our study.
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Water Resour Manage (2007) 21:1797–1812
From the standpoint of methodology, none of the physicochemical or microbiological
parameters by itself (used as analytical values) are sufficient to give a full picture of the
water quality of a river (Battegazzore and Renoldi 1995). Hence there is necessary data
analysis in order to determine general patterns that account for the set of parameters being
evaluated. PCA showed monitoring stations 7, 8 and 9, in the last portion of the Upper Río
Lerma, have the highest levels of dissolved oxygen and are plotted opposite to
environmental parameters indicative of pollution, such as BOD and total coliforms. On
the opposite side of these monitoring stations are those with high BOD, and total coliforms.
Consequently, PCA allowed us to segregate the monitoring stations along an environmental
gradient and link them to major land uses, e.g. parameters associated with municipal and
industrial discharges, such as BOD, or nutrient loads such as nitrogen (nitrate) associated
with agricultural areas. PCA also highlighted the parameters directly affecting water quality,
which are not apparent in the final WQI score such is the case of inverse relation BOD and
DO, that contribute to characterization of monitoring stations more polluted as well as those
less polluted.
WQI scores indicate water quality in the Río Lerma is not adequate for protection and
conservation of aquatic life. López-López and Díaz-Pardo (1991) pointed out several Río
Lerma species have been lost and others displaced. Similarly, reduced water quality has
brought to the lost of fish habitat as well as changes in the distribution and abundance
patterns of the native fish species, particularly in the Upper Río Lerma where the main
environmental impact is felt, although all subprovinces have been damaged in its fish fauna
(Soto-Galera et al. 1998). In particular and in accordance with the standard regulation on
the protection of species in Mexico, NOM-059-ECOL-2001 (Diario Oficial de la
Federación 2002), two native Río Lerma fish species are now extinct (Chirostoma charari
and C. compressum). Two more (Algansea barbata and Hubbsina turneri), are listed as
endangered and another three (Skiffia lermae, S. bilineata and Allotoca dugesi) are in threat
of extinction. All aquatic ecosystems are to some extent capable of assimilating
environmental stresses such as effluent discharge or excessive nutrients, but when stress
exceeds the capacity of the ecosystem to absorb it, the system develops symptoms of
environmental degradation and becomes unhealthy, with an expected decline in biodiversity
(Loeb 1994), such is the case of the Río Lerma.
The WQI integrates the results of the environmental parameters into a single score in
time and space, which allows water quality to be viewed in terms of a numerical value that
qualifies possible water uses. PCA, on the other hand, points out the importance of certain
environmental parameters for water quality trends. Both methods summarize the
multidimensional nature of the water quality concept in order to analyze its trends in time
and space from different perspectives.
5 Conclusions
WQI in Río Lerma make evident the cumulative impacts from point and non-point
pollution sources of a variety of land uses in the basin. Also let us detect the incipient
progress in water quality resulting from water quality management practices after 1992;
although overall water quality remains very low.
This study confirms what is stated in the literature regarding Río Lerma pollution levels.
Río Lerma is among the most degraded watercourses in Mexico, with WQI scores from
26.53 (not acceptable for most uses) to a high of 67.44 (treatment required for use in the
Water Resour Manage (2007) 21:1797–1812
1811
public water supply system and not recommended for sensitive fish). Prior to 1995, Río
Lerma WQI scores stayed below 40, i.e., not acceptable for most uses or requiring
treatment before use. PCA permitted the identification of indicator parameters affecting
water quality in the different monitoring stations. Both WQI and PCA indicate Lerma
waters are exposed to organic and microbiologic pollution (BOD, total and fecal
coliforms). The water quality map provides a picture of the evolution of water quality in
time and space. Adjacent land use was a better indicator of water quality, the Upper Río
Lerma with predominant industrial land uses having the lowest WQI scores.
Ideally, water quality should be assessed with properly analyzed physical, chemical and
biological parameters so as to arrive at a full spectrum of information for adequate water
management. However, such a study involves more time and expense than do biological
parameters, like bioindicators. It is widely agreed that the advantage of monitoring water
quality through bioindicators lies in the fact that biological communities reflect the overall
environmental quality as well as the assimilated effects of different stress agents, thus
providing a rough measure of the ecological impact of the latter and of fluctuating
environmental conditions.
This study reviews the water quality of the Río Lerma in the last quarter of the twentieth
century. The Río Lerma is one of the major watersheds in central Mexico, where demand
for water in terms of both quality and quantity is much greater than the natural supply of
this resource. Thus, recovery of this watercourse should be a priority for the country. Water
quality improvement in the Lerma system must take into consideration the human demand
for this resource, its uses, and its significance for fish fauna diversity in a center of
endemism in Mexico.
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