Water Quality Assessment and Apportionment of Pollution Sources
-
Upload
ovane-tiana-ywa-alam -
Category
Documents
-
view
221 -
download
0
Transcript of Water Quality Assessment and Apportionment of Pollution Sources
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
1/20
Analytica Chimica Acta 538 (2005) 355374
Water quality assessment and apportionment of pollution sources ofGomti river (India) using multivariate statistical techniques
a case study
Kunwar P. Singh a,, Amrita Malika, Sarita Sinha b
a Environmental Chemistry Section, Industrial Toxicology Research Centre, Post Box 80, MG Marg, Lucknow 226 001, Indiab National Botanical Research Institute, Rana Pratap Marg, Lucknow 226 001, India
Received 31 October 2004; received in revised form 30 January 2005; accepted 1 February 2005
Available online 9 March 2005
Abstract
Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA) and discriminant
analysis (DA) were applied to the data set on water quality of the Gomti river (India), generated during three years (19992001) monitoring at
eight different sites for 34 parameters (9792 observations). This study presents usefulness of multivariate statistical techniques for evaluation
and interpretation of large complex water quality data sets and apportionment of pollution sources/factors with a view to get better information
about the water quality and design of monitoring network for effective management of water resources. Three significant groups, upper
catchments (UC), middle catchments (MC) and lower catchments (LC) of sampling sites were obtained through CA on the basis of similarity
between them. FA/PCA applied to the data sets pertaining to three catchments regions of the river resulted in seven, seven and six latent
factors, respectively responsible for the data structure, explaining 74.3, 73.6 and 81.4% of the total variance of the respective data sets. These
included the trace metals group (leaching from soil and industrial waste disposal sites), organic pollution group (municipal and industrial
effluents), nutrients group (agricultural runoff), alkalinity, hardness, EC and solids (soil leaching and runoff process). DA showed the best
results for data reduction and pattern recognition during both temporal and spatial analysis. It rendered five parameters (temperature, totalalkalinity, Cl, Na and K) affording more than 94% right assignations in temporal analysis, while 10 parameters (river discharge, pH, BOD,
Cl, F, PO4, NH4N, NO3N, TKN and Zn) to afford 97% right assignations in spatial analysis of three different regions in the basin. Thus,
DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in
water quality. Further, receptor modeling through multi-linear regression of the absolute principal component scores (APCS-MLR) provided
apportionment of various sources/factors in respective regions contributing to the river pollution. It revealed that soil weathering, leaching and
runoff; municipal and industrial wastewater; waste disposal sites leaching were among the major sources/factors responsible for river quality
deterioration.
2005 Elsevier B.V. All rights reserved.
Keywords: Gomti river; Water quality management; Cluster analysis; Factor analysis; Principal component analysis; Discriminant analysis; Source apportion-
ment
1. Introduction
The surface water quality is a matter of serious concern
today. Rivers due to their role in carrying off the munici-
pal and industrial wastewater and run-off from agricultural
Corresponding author. Tel.: +91 522 2508916; fax: +91 522 2628227.
E-mail address:kpsingh [email protected] (K.P. Singh).
land in their vast drainage basins are among the most vul-
nerable water bodies to pollution. The surface water quality
in a region is largely determined both by the natural pro-
cesses (precipitation rate, weathering processes, soil erosion)
and the anthropogenic influences viz. urban, industrial and
agricultural activities and increasing exploitation of water
resources [1,2]. The municipal and industrial wastewater dis-
charge constitutes the constant polluting source, whereas, the
0003-2670/$ see front matter 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.aca.2005.02.006
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
2/20
356 K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374
surface run-off is a seasonal phenomenon, largely affected by
climate in the basin. Seasonal variations in precipitation, sur-
face run-off, ground water flow and water interception and
abstraction have a strong effect on river discharge and subse-
quently on the concentration of pollutants in river water [3].
Since, rivers constitute the main inland water resources for
domestic, industrial and irrigation purposes, it is imperativeto prevent and control the rivers pollution and to have reliable
information on thequality of water for effective management.
In view of the spatial and temporal variations in the hydro-
chemistry of rivers, regular monitoring programs are required
for reliable estimates of the water quality. This results in a
huge and complex data matrix comprised of a large number
of physico-chemical parameters, which are often difficult to
interpret and draw meaningful conclusions[4]. Further, for
effective pollution control and water resource management,
it is required to identify the pollution sources and their quan-
titative contributions.
The Gomti river, a major tributary of the Ganga river sys-
temin India,originates from a natural reservoir in theforested
area (elevation of about 200 m; North latitude 28
34
andEast longitude 8007) in Uttar Pradesh. The river traverses
a total distance of about 730 km before finally merging with
the Ganga river near Varanasi. It drains a catchments area
of about 25,800 km2. Kathna, Sarayan, Reth, Luni, Kalyani
and Sai rivers are the tributaries of the Gomti river. Lucknow
(population about 3.5 million), Sultanpur (population about
0.2 million) and Jaunpur (population about 0.2 million) are
Fig. 1. Map showing the water quality monitoring sites on the Gomti river.
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
3/20
K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374 357
the three major urban settlements on the banks of the river
(Fig. 1).The river serves as a major source of domestic water
supply of the Lucknow city, the State capital of Uttar Pradesh.
Subsequently, the river receives back the untreated domestic
wastewater from Lucknow city (about 450 mld), Jagdishpur,
Sultanpur and Jaunpur towns and effluents from a few indus-
tries (distilleries, sugar mills, chemical and others) directlyduring its course. The river during its course of about 730 km
receives pollution load both from the point and non-point
sources. It receives agricultural run-off from its vast catch-
ments area directly or through its tributaries and wastewater
drains (45 numbers). The Gomti river has been identified as
one of the most polluted rivers in India. Asa part of the Ganga
Action Plan, a vast database on its quality has been gener-
ated through regular monitoring of the river during the last
about a decade for its regeneration and management under
the National Rivers Conservation Program (NRCP).
The application of different multivariate statistical tech-
niques viz., cluster analysis (CA), discriminant analysis
(DA), principal component analysis (PCA)/factor analysis(FA), source apportionment by multiple linear regression on
absolute principal component scores (APCS-MLR) for inter-
pretation of the complex databasesoffers a better understand-
ing of water quality in the study region. These techniques also
permit identification of the possible factors/sources that are
responsible for the variations in water quality and influence
the water system and in apportionment of the sources, which,
thus offers valuable tool for developing appropriate strategies
for effective management of the water resources[510].
In the present paper, the large data base obtained during
the three years monitoring program (9792 observations) was
subjected to different multivariate statistical techniques witha view to extract information about the similarities or dissim-
ilarities between the sampling sites, identification of water
quality variables responsible for spatial and temporal varia-
tions in river water quality, the hidden factors explaining the
structure of the database, the influence of the possible sources
(natural and anthropogenic) on the water quality parameters;
and the source apportioning for estimation of the contribution
of possible sources on the concentration of determined water
quality parameters of the Gomti river.
2. Methods
2.1. Monitoring sites
In the present study, total eight sites, namely Neemsar
(site 1), Bhatpur (site 2), Gaughat (site 3), Mid-Lucknow
(site 4), Pipraghat (site 5), Gangaganj (site 6), downstream
of Sultanpur (site 7) and Jaunpur (site 8) were selected on the
Gomti river under the river quality-monitoring network. The
sampling network was designed to cover a wide range of de-
terminants at key sites, which reasonably represent the water
quality of the river system accounting for tributary and inputs
from wastewater drains that have impact on downstream wa-
ter quality. The first three sites (13) are located in the area
of relatively low river pollution and are upstream of the Luc-
know city. Other three sites (46) are located in the region
of high river pollution as there are a number of wastewater
drains (27 numbers) and two highly polluted tributaries emp-
tying in to the river in this stretch. The last two sites (7 and
8) are in the downstream region of moderate river pollutionas the river considerably recovers in the course (Fig. 1).
2.2. Sampling and chemical analysis
Water samples were collected each month at three points
(1/4, 1/2 and 3/4) across the river width at all the eight
sites with a view to monitor changes caused by the sea-
sonal hydrological cycle during the study period (January
1999December 2001). Sampling, preservation and trans-
portation of the water samples to the Laboratory were as per
standard methods[11]. The Gomti river discharge was mea-
sured at each of the eight sites along with sampling followingthe areavelocity method using the calibrated water current
meter. Water temperature was measured on thesite using mer-
cury thermometer. All other parameters were determined in
laboratory following the standard protocols[11]. The sam-
ples were analysed for 33 parameters, namely temperature,
pH, electrical conductivity, total alkalinity, total hardness,
calcium hardness, total solids, total dissolved solids, total
suspended solids, dissolvedoxygen, 5-days biochemical oxy-
gen demand, chemical oxygen demand, ammonical nitrogen,
nitrate nitrogen, total kjeldahl nitrogen, chloride, fluoride,
sulfate, phosphate, sodium, potassium, calcium, magnesium,
total coliform, faecal coliform, cadmium, chromium, iron,
manganese, copper, lead, zinc and nickel. Different water
quality parameters, their units and methods of analysis are
summarized inTable 1.The analytical data quality was en-
sured through careful standardization, procedural blank mea-
surements, spiked and duplicate samples. The ionic charge
balance of each sample was within5%. The laboratory also
participated in regular national program on analytical quality
control (AQC). Basic statistics of the 3-years data set on river
water quality is summarized inTable 2.
2.3. Data treatment and multivariate statistical methods
Correlation structure between the variables was stud-
ied using the Spearman R coefficient as a non-parametric
measure of the correlation between the variables [12]. In
the present study, the temporal variations of the river wa-
ter quality parameters (Table 1) were evaluated through
season-parameter correlation matrix using the Spearman
non-parametric correlation coefficient (Spearman R). The
water quality parameters were grouped in three different sea-
sons (winter, summer and monsoon) and each assigned a
numerical value in the data file, which as a variable corre-
sponding to the season was correlated (pair by pair) with all
the measured parameters.
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
4/20
358 K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374
Table 1
Water quality parameters, abbreviations, units and analytical methods as measured during 19992001 for the Gomti river water
Variables Abbreviations Units Analytical methods
Discharge Dis m3 s1 Current meter
Temperature Temp C Mercury thermometer
Electrical conductivity EC S cm1 Electrometric
PH pH pH unit pH-meter
Total solids TS mg l1 GravimetricTotal dissolved solids TDS mg l1 Gravimetric
Total suspended solids TSS mg l1 Gravimetric
Total alkalinity T-Alk CaCO3mg l1 Titrimetric
Total hardness T-Hard CaCO3mg l1 Titrimetric
Calcium hardness Ca-Hard CaCO3mg l1 Titrimetric
Dissolved oxygen DO mg l1 Winkler azide method
Biochemical oxygen demand BOD mg l1 Winkler azide method
Chemical oxygen demand COD mg l1 Dichromate reflex method
Chloride Cl mg l1 Spectrophotometric
Fluoride F mg l1 Spectrophotometric
Phosphate PO4 mg l1 Spectrophotometric
Sulphate SO4 mg l1 Spectrophotometric
Potassium K mg l1 Flame photometer
Sodium Na mg l1 Flame photometer
Calcium Ca mg l1 Flame AASMagnesium Mg mg l1 Flame AAS
Ammonical nitrogen NH4N mg l1 Spectrophotometric
Nitrate nitrogen NO3N mg l1 Spectrophotometric
Total kjeldahl nitrogen TKN mg l1 Spectrophotometric
Total coliform T. Coli MPN/100 ml Multiple tube method
Feacal coliform F. Coli MPN/100 ml Multiple tube method
Cadmium Cd mg l1 ICP-OES
Chromium Cr mg l1 ICP-OES
Iron Fe mg l1 ICP-OES
Manganese Mn mg l1 ICP-OES
Lead Pb mg l1 ICP-OES
Copper Cu mg l1 ICP-OES
Zinc Zn mg l1 ICP-OES
Nickel Ni mg l1 ICP-OES
Multivariate analysis of the river water quality data setwas
performed through CA, DA, PCA, FA and APCS-MLR tech-
niques. DA was applied on raw data, whereas, CA, PCA and
FA were applied on experimental data standardized through
z-scale transformation in order to avoid misclassification due
to wide differences in data dimensionality [8,9]. Standardiza-
tion tends to minimize the influence of difference of variance
of variables and eliminates the influence of different units of
measurement and renders the data dimensionless.
2.3.1. Cluster analysis
Cluster analysis groups the objects (cases) into classes
(clusters) on the basis of similarities within a class and dis-
similarities between different classes. The results of CA help
in interpreting the data and indicate patterns [3,10].In hier-
archical clustering, clusters are formed sequentially by start-
ing with the most similar pair of objects and forming higher
clusters step by step. Hierarchical agglomerative CA was per-
formed on the normalized data set (mean of observations
over the whole period) by means of the Wards method us-
ing squared Euclidean distances as a measure of similarity
[13].Cluster significance was determined using the criterion
of 0.66Dmax[5].
Cluster analysis was applied to the river water quality data
set with a view to group the similar sampling sites (spatial
variability) spread over the river stretch and in the resulted
dendrogram, the linkage distance is reported as Dlink/Dmax,
which represent the quotient between the linkage distance
for a particular case divided by the maximal distance, mul-
tiplied by 100 as a way to standardize the linkage distance
represented ony-axis[9,10,12].
2.3.2. Discriminant analysis
Discriminant analysis determines the variables that dis-criminate between two or more naturally occurring groups.
It constructs a discriminant function (DF) for each group [14]
as in Eq.(1):
f(Gi) = ki +
n
j=1
wijpij (1)
whereiis the number of groups (G),kithe constant inherent
to each group, n the number of parameters used to classify
a set of data into a given group, wj the weight coefficient,
assigned by DA to a given selected parameter (pj).
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
5/20
Table 2
Range, mean and S.D. of different water-quality parameters at different locations of the Gomti river during 19992001
Parameters Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
Discharge (m3 s1) Range 6.137.3 6.336.5 5.645.2 6.952.6 8.853.8 14.358.5
Mean 15.70 16.84 16.85 22.49 22.08 28.95
S.D. 8.93 8.48 11.36 14.48 13.80 14.39
Temperature (C) Range 18.035.0 15.033.0 15.033.0 16.035.0 17.034.0 16.033.0
Mean 26.71 26.21 26.44 26.71 26.44 26.44
S.D. 5.26 5.36 5.29 5.66 5.33 5.25 EC (S cm1) Range 208.3443.3 210.0462.0 150.0460.3 216.7511.7 230.0588.3 256.7606.7
Mean 356.80 379.30 369.38 411.43 452.49 458.18
S.D. 60.32 69.38 78.64 81.82 93.52 103.20
PH Range 7.98.7 7.98.7 8.08.8 6.08.7 7.48.7 7.48.7
Mean 8.34 8.33 8.36 8.05 7.92 7.98
S.D. 0.17 0.18 0.18 0.40 0.27 0.28
TS (mg l1) Range 182.5372.0 200.0359.3 226.7414.9 205.3432.9 186.0440.0 268.0470.1
Mean 282.5 285.97 287.65 311.73 323.83 330.12
S.D. 44.57 39.21 40.40 53.13 56.58 48.56
TDS (mg l1) Range 142.3300.0 173.3302.7 173.3300.0 165.9366.7 148.7386.7 185.7380.0
Mean 236.60 246.22 247.84 265.98 276.87 283.08
S.D. 35.57 37.56 27.82 44.99 48.90 45.03
TSS (mg l1) Range 5.686.0 16.086.7 16.1108.9 8.7138.0 13.9124.4 11.3167.2
Mean 44.95 39.58 38.39 45.30 47.23 48.00
S.D. 22.16 18.37 21.10 29.94 26.15 33.97
T-Alk (mg l1) Range 99.3238.0 85.3258.0 110.7261.3 112.0274.7 113.3292.0 120.0289.3
Mean 189.13 196.48 197.70 206.68 213.98 216.88
S.D. 40.12 47.21 47.69 47.58 48.83 50.30
T-Hard (mg l1) Range 52.0230.7 45.3236.0 49.3244.0 78.7254.7 60.0262.7 46.7280.0
Mean 157.67 168.58 170.56 184.04 193.92 193.28
S.D. 49.53 51.96 56.48 50.61 55.95 58.89
Ca-Hard (mg l1) Range 44.0149.3 40.0156.0 37.3156.7 12.7169.3 53.3184.0 40.0200.0
Mean 89.80 89.05 90.31 105.24 112.65 112.50
S.D. 28.18 26.63 31.3 34.00 29.42 32.09
DO (mg l1) Range 3.610.6 3.810.5 4.110.4 0.07.8 0.05.4 1.96.3
Mean 7.41 7.19 7.17 4.05 0.95 3.68
S.D. 1.77 1.78 1.81 2.02 1.52 1.00
BOD (mg l1) Range 0.88.9 1.19.5 1.110.7 5.431.5 6.335.8 4.724.7
Mean 3.35 3.56 3.55 14.09 18.93 12.95
S.D. 1.74 1.86 1.83 6.17 7.70 4.33
COD (mg l1) Range 2.619.3 6.420.0 6.221.5 11.858.3 8.176.3 8.239.4
Mean 10.76 11.89 11.88 29.33 38.68 27.52
S.D. 4.10 3.36 3.17 10.93 14.23 7.50
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
6/20
Table 2 (Continued)
Parameters Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
Cl (mg l1) Range 0.75.8 1.06.3 0.210.3 2.017.3 3.323.7 3.321.3
Mean 2.99 3.86 4.45 7.35 11.26 11.74
S.D. 1.21 1.47 2.05 3.51 5.04 5.41
F (mg l1) Range 0.10.9 0.090.91 0.170.97 0.170.91 0.191.05 0.161.01
Mean 0.39 0.41 0.42 0.47 0.53 0.52
S.D. 0.16 0.18 0.17 0.17 0.20 0.20
PO4(mgl1) Range 0.020.15 0.020.34 0.0310.0 0.150.55 0.091.19 0.12.07
Mean 0.06 0.09 0.37 0.23 0.44 0.49
S.D. 0.04 0.06 1.65 0.09 0.30 0.43
SO4(mgl1) Range 3.511.8 2.912.5 3.813.8 4.153.6 4.218.8 6.522.5
Mean 7.65 8.53 9.10 12.94 13.48 15.13
S.D. 2.29 2.42 2.58 7.43 3.57 4.01
K (mg l1) Range 2.210.2 3.09.2 3.39.1 3.222.2 3.626.7 3.911.8
Mean 4.18 4.89 5.08 7.01 7.45 6.59
S.D. 1.42 1.62 1.45 3.89 3.88 2.05
Na (mg l1) Range 11.439.3 7.943.7 15.446.8 15.471.1 17.385.5 19.078.7
Mean 27.72 29.80 31.92 37.70 43.42 43.37
S.D. 6.56 8.01 7.55 10.39 12.61 12.90
Ca (mg l1) Range 17.659.7 16.062.4 14.962.7 5.167.7 21.373.6 16.080.0
Mean 35.92 35.62 36.12 42.10 45.06 45.00
S.D. 11.3 10.65 12.52 13.60 11.77 12.83
Mg (mg l1) Range 1.535.5 1.333.6 0.1641.6 1.441.3 1.636.2 1.640.6
Mean 16.29 19.09 19.26 18.91 19.51 19.39
S.D. 9.92 9.47 12.15 10.31 9.24 10.84
NH4N (mg l1) Range 0.00.26 0.00.42 0.040.51 0.124.57 0.091.66 0.051.37
Mean 0.05 0.14 0.19 0.62 0.51 0.34
S.D. 0.08 0.10 0.11 0.77 0.40 0.29
NO3N (mg l1) Range 0.020.72 0.030.84 0.00.85 0.041.50 0.072.23 0.112.14
Mean 0.16 0.16 0.17 0.50 0.83 0.85
S.D. 0.17 0.14 0.16 0.38 0.60 0.58
TKN (mg l1
) Range 0.044.35 1.554.25 0.163.84 2.544.85 2.947.95 2.457.25 Mean 2.84 2.86 2.59 3.72 4.15 3.95
S.D. 0.89 0.68 0.85 0.53 1.18 0.99
T. Coli (MPN/100ml) Range 201.42E06 277.0E + 04 331.7E + 05 409.7E + 09 5.4E+ 058.9E+ 10 1.6E+ 038.4E+ 0
Mean 4.7E + 04 1.1E + 04 8.2E + 03 9.7E + 08 2.8E + 09 5.1E + 05
S.D. 2.4E + 05 1.9E + 04 2.8E + 04 2.6E + 09 1.5E + 10 1.4E + 06
F. Coli (MPN/100ml) Range 201.4E + 06 277.0E + 04 331.7E + 05 409.7E + 09 5.4E+ 058.9E+ 10 1.6E+ 031.7E+ 0
Mean 4.7E + 07 1.1E + 04 8.1E + 03 9.7E + 08 2.7E + 09 9.4E + 05
S.D. 2.4E + 05 1.9E + 04 2.8E + 04 2.6E + 09 1.5E + 10 3.0E + 06
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
7/20
Cd (mg l1) Range 0.00.003 0.00.007 0.00.002 0.00.004 0.00.006 0.00.002
Mean 0.0003 0.001 0.0004 0.001 0.001 0.0003
S.D. 0.0006 0.001 0.0005 0.001 0.001 0.0005
Cr (mg l1) Range 0.00.048 0.00.054 0.00.019 0.00.014 0.00.048 0.00.049
Mean 0.006 0.004 0.003 0.003 0.006 0.007
S.D. 0.01 0.009 0.004 0.004 0.009 0.010
Fe (mg l1) Range 0.07517.064 0.05920.397 0.03214.32 0.079.736 0.1813.338 0.010.494
Mean 3.211 2.847 2.234 1.982 2.446 2.472
S.D. 3.346 3.754 3.120 2.299 2.714 2.917
Pb (mg l1) Range 0.00.107 0.00.114 0.00.077 0.00.097 0.00.124 0.00.142
Mean 0.024 0.021 0.017 0.019 0.021 0.022
S.D. 0.023 0.022 0.017 0.019 0.022 0.025
Cu (mg l1) Range 0.00.102 0.00.139 0.00.179 0.00.108 0.00.369 0.00.136
Mean 0.018 0.015 0.014 0.016 0.034 0.016
S.D. 0.027 0.027 0.035 0.023 0.071 0.031
Mn (mg l1) Range 0.0030.601 0.0041.50 0.0030.432 0.0140.507 0.0020.426 0.0030.318
Mean 0.120 0.136 0.075 0.082 0.097 0.111
S.D. 0.111 0.247 0.085 0.098 0.080 0.076
Zn (mg l1) Range 0.0020.325 0.0040.430 0.00.166 0.00.086 0.0180.289 0.0010.455
Mean 0.093 0.075 0.042 0.030 0.086 0.073
S.D. 0.088 0.100 0.039 0.021 0.056 0.086
Ni (mg l1) Range 0.0040.057 0.0040.082 0.0020.038 0.0040.033 0.0070.058 0.0060.035
Mean 0.016 0.016 0.014 0.014 0.016 0.016
S.D. 0.009 0.012 0.008 0.006 0.008 0.008
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
8/20
362 K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374
In this case study, three groups both for temporal (three
seasons) and spatial (three sampling regions) evaluations
have been selected. DA was applied to raw data by using the
standard, forward stepwise and backward stepwise modes
to construct DFs to evaluate both the spatial and temporal
variations in river water quality. The sites (spatial) and the
seasons (temporal) were the grouping (dependent) variables,while all the measured parameters constituted the indepen-
dent variables.
2.3.3. Principal component analysis/factor analysis
Principal component analysis provides information on the
most meaningful parameters, which describe whole data set
rendering data reduction with minimum loss of original in-
formation[3,12,15]. It is a powerful technique for pattern
recognition that attempts to explain the variance of a large set
of inter-correlated variables and transforming into a smaller
set of independent (uncorrelated) variables (principal com-
ponents). The principal component (PC) is expressed as:
zij= ai1x1j+ ai2x2j+ ai3x3j+ + aimxmj (2)
wherea is the component loading, z the component score,x
the measured value of a variable, i the component number,j
the sample number, andmthe total number of variables.
Factor analysis attempts to extract a lower dimensional
linear structure from the data set. It further reduces the con-
tribution of less significant variables obtained from PCA and
the new group of variables known as varifactors (VFs) is ex-
tracted through rotating the axis defined by PCA. In FA, the
basic concept is expressed in Eq.(3),
zji = af1f1i + af2f2i + af3f3i + + afmfmi + efi (3)
wherezis the measured value of a variable,athe factor load-
ing, f the factor score, e the residual term accounting for
errors or other sources of variation, i the sample number, j
the variable number, andmthe total number of factors.
The two methods, PCAand FA, in principle, are expressed
as similar equations, however, the difference lies in the fact
that in earlier one, the PC is expressed as a linear combination
of measured variables, while, in case of FA, measured vari-
able is expressed as a combination of factors and the equation
contains the residual term and thus, a VF can include unob-servable, hypothetical, latent variables[3,10,12,15].
Principal component analysis/factor analysis was per-
formed on correlation matrix of rearranged data (all observa-
tions for each group of sites), so that it explains the structure
of the underlying data set. The correlation coefficient matrix
measures how well the variance of each constituent can be
explained by relationship with each of the others[8].PCA
of the normalized variables (water quality data set) was per-
formed to extract significant PCs and to further reduce the
contribution of variables with minor significance; these PCs
were subjected to varimax rotation (raw) generating VFs.
2.3.4. Receptor modeling (APCS-MLR)
Receptor modeling approach based on multi-linear regres-
sion of the absolute principalcomponent score (APCS-MLR)
is a widely employed statistical technique for source appor-
tionment of environmental contaminants in air pollution stud-
ies [5,6,16,17]. It has recently been applied to water pollution
source apportionment also[9].It is based on the assumptionthat the total concentration of each contaminant is made up
of the linear sum of elemental contributions from each of the
jpollution source components collected at the receptor site,
Zjk =
p
j=1
wijpjk (4)
where zjk is the normalized concentration of contaminant
(variable),j the number of pollution sources, wij the factor
loadings, the coefficient matrix of the components relating
the pollution sources to their elemental concentrations; and
pjkthe factor scores, the value of thejth sources components
on observationkin Eq.(4).Bothwijandpjkare dimension-less.
Since,zjkin Eq. (4) is normalized valueof variables, it can-
not be used directly for computation of quantitative source
contributions, the normalized factor scores determined in
Eq. (4) were converted to un-normalized APCS following
the method reported elsewhere[18].The contribution from
each factor was then estimated by multiple linear regression
(MLR), using the APCS values as the independent variables
and the measured concentration of the particular contaminant
as the dependent variable, as
Mjk = ai0 +
p
j=1
Aij(APCS)jk (5)
whereMjkis the contaminants concentration; ai0the average
contribution of the jth contaminant from sources not deter-
mined by PCA/FA,Aijthe linear regression coefficient for the
ith contaminant and thejth factor, and (APCS)jkthe absolute
factor score for thejth factor with thekth measurement. The
values forMjk, ai0andAijhave the dimensions of the original
concentration measurements.
After determination of the number and identity of pos-
sible sources influencing the river water quality in three
different catchments regions (UC, MC and LC) by using
PCA/FA (Table 7), source contributions were computed
through APCS-MLR technique. Quantitative contributions
from each source for individual parameter/contaminant were
compared with their measured values.
3. Results and discussion
3.1. Spatial similarity and site grouping
Cluster analysis was applied to detect spatial similarity
for grouping of sites under the monitoring network. It ren-
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
9/20
K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374 363
Fig. 2. Dendogram showing sampling site clusters on the Gomti river.
dered a dendrogram (Fig. 2),grouping all the eight sampling
sites on the river into three statistically significant clusters
at (Dlink/Dmax) 100 < 70. The clustering procedure gener-ated three groups of sites in a very convincing way, as the
sites in these groups have similar characteristic features and
natural background source types. Cluster 1 (sites 13), clus-
ter 2 (sites 46) and cluster 3 (sites 7 and 8) correspond to
a relatively low pollution, very high pollution and moderate
pollution regions, respectively. It implies that for rapid as-
sessment of water quality, only one site in each cluster may
serve as good in spatial assessment of the water quality as
the whole network. It is evident that the CA technique is
useful in offering reliable classification of surface waters in
the whole region and will make possible to design a future
spatial sampling strategy in an optimal manner. Thus, thenumber of sampling sites in the monitoring network will be
reduced, hence cost without loosing any significance of the
outcome. There are other reports[5,6,9,10,12], where simi-
lar approach has successfully been applied in water quality
programs.
3.2. Spatial and temporal variations in river water
quality
The temporal variations of the river water quality parame-
ters (Table 2) were evaluated through season-parameter cor-
relation matrix, which showed that all the measured param-
eters were found significantly (p < 0.05) correlated with the
season, except TSS, BOD, COD, F, SO4,KandTKN.Among
these, temperature exhibited highest correlation (Spearman)
coefficient (R = 0.69). Other parameters exhibiting high cor-
relation with season were total alkalinity (R =0.55), total
hardness (R =0.52), Na (R =0.52) and EC (R =0.51).
The season-correlated parameters can be taken as represent-
ing the major source of temporal variations in water qual-
ity. In view of the source types in the river catchments,
these correlations can be explained on the basis of seasonal
features in the monitoring region. Wide seasonal variations
in temperature and river discharge round the year can be
attributed to the high seasonality in various water quality
parameters.
Temporal variations in water quality were further eval-
uated through DA. Temporal DA was performed on raw
data after dividing the whole data set into three seasonal
groups (winter, summer and monsoon). Discriminant func-
tions (DFs) and classification matrices (CMs) obtained fromthe standard, forward stepwise and backward stepwise modes
of DA are shown in Tables 3 and 4. In forward stepwise
mode, variables are included step-by-step beginning with the
more significant until no significant changes are obtained,
whereas, in backward stepwise mode, variables are removed
step-by-step beginning with the less significant until no sig-
nificant changes are obtained. The standard DA mode, con-
structed DFs including 31 parameters are shown in Table 3.
The coefficients for the total coliform bacteria group were
zero. Both the standard and forward stepwise mode DFs
using 31 and 21 discriminant variables, respectively, ren-
dered the corresponding CMs assigning 97% cases correctly
(Tables 3 and 4). However, in backward stepwise mode DAgave CMs with 94% correct assignations using only five dis-
criminant parameters (Tables 3 and 4) with a little different
match for each season compared with the forward stepwise
mode. Forward stepwise DA showed that temperature, alka-
linity, Cl, Na and K are followed by a second group of param-
eter formed by discharge, pH, TDS, T-Hard, Ca-Hard, DO,
BOD, F, SO4, PO4, NO3N, TKN, Cr, Pb, Cu and Ni but less
significant as could be seen from the difference in percentage
of correct assignations between the backward and forward
DA modes (Tables 3 and 4).Further, a much less significant
third group of remaining 10 parameters is evident from the
standard mode DA assignations. Thus, the temporal DA re-sults suggest that temperature, alkalinity, Cl, Na and K are
the most significant parameters to discriminate between the
three different seasons, which means that these five param-
eters account for most of the expected temporal variations
in the river water quality (Table 3). This also suggests that
the anthropogenic pollution, which is the major river pol-
lution problem, mainly due to discharge of wastewater into
the river does not discriminate between the seasons and is a
regular source throughout the year. The trend obtained was
also supported by the analysis of the results on the raw data
set.
As identified by DA, box and whisker plots of the selected
parameters showing seasonal trends are given in Fig. 3. The
variation of temperatureshows a clear-cutseasonal effect. To-
tal alkalinity during three seasons showed increasing trend
in summers over winters and decline during monsoon and
this may be attributed to enhanced weathering process dur-
ing the summers in the catchments, which during monsoon
months declines due to excessive dilution. Chloride, sodium
andpotassium ions in the river water showedsimilar trends of
increase during summers over winters and declining during
the monsoon.
Spatial DA was performed with the same raw data set
comprised of 31 parameters after grouping into three major
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
10/20
364 K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374
Table 3
Classification functions (Eq.(1)) for discriminant analysis of temporal variation in Gomti river water
Parameters Standard mode Forward stepwise mode Backward stepwise mode
Winter
coefficientaSummer
coefficientaMonsoon
coefficientaWinter
coefficientaSummer
coefficientaMonsoon
coefficientaWinter
coefficientaSummer
coefficientaMonsoon
coefficienta
Discharge 0.236 0.194 0.347 0.243 0.196 0.352
Temperature 0.018 1.510 1.319 0.351 1.141 0.980 3.349 4.645 4.444PH 174.33 172.77 168.36 166.47 164.70 160.69
EC 0.140 0.159 0.150
TS 0.268 0.272 0.231
TDS 0.532 0.502 0.457 0.276 0.247 0.239
TSS 0.616 0.636 0.583
T-Alk 0.947 1.091 0.883 0.958 1.113 0.901 0.549 0.664 0.480
T-Hard 0.175 0.195 0.188 0.122 0.137 0.131
Ca-Hard 0.262 0.468 0.139 0.394 0.253 0.541
DO 0.880 0.782 0.796 0.620 0.495 0.525
BOD 3.539 3.718 3.446 3.857 3.889 3.684
COD 0.054 0.175 0.103
Cl 4.312 3.964 3.953 3.823 3.443 3.463 0.721 0.392 0.336
F 12.748 17.463 14.319 7.539 11.758 9.208
SO4 0.580 0.329 0.476 0.611 0.308 0.450
PO4 1.560 1.621 1.341 1.438 1.520 1.230K 1.515 0.849 0.918 1.841 1.273 1.283 0.045 0.751 0.446
Na 2.009 2.541 2.275 1.408 1.898 1.664 0.536 1.002 0.808
NH4N 14.100 13.524 12.996
NO3N 0.744 0.424 1.282 2.838 3.944 4.958
TKN 10.395 9.409 10.695 10.047 8.970 10.301
T. Coli 0.000 0.000 0.000
Cd 2724.57 3147.42 3049.37
Cr 251.54 260.71 127.294 408.08 422.14 269.94
Fe 2.293 2.372 2.063
Pb 225.93 217.66 169.348 266.83 259.30 220.02
Cu 142.91 118.11 118.847 230.23 210.45 204.71
Mn 3.657 1.062 5.869
Zn 26.81 30.240 26.118
Ni 1207.24 1210.55 1003.92 1288.99 1313.35 1167.95
Constant 877.165 914.368 838.95 825.301 858.663 788.357 82.364 129.214 92.562a Discriminant function coefficient for winter, summer and monsoon seasons correspond towijas defined in Eq.(1).
Table 4
Classificationmatrixfor discriminantanalysisof temporalvariationin Gomti
river water
Monitoring seasons % Correct Season assigned by DA
Winter Summer Monsoon
Standard DA mode
Winter 96.9 93 2 1
Summer 96.9 3 93 0
Monsoon 96.9 0 3 93
Total 96.9 96 98 94
Forward stepwise DA mode
Winter 95.8 92 3 1
Summer 96.9 3 93 0
Monsoon 96.9 0 3 93
Total 96.5 95 99 94
Backward stepwise DA mode
Winter 93.8 90 5 1
Summer 94.8 5 91 0
Monsoon 93.8 0 6 90
Total 94.1 95 102 91
classes of UC, MC and LC as obtained through CA. The site
(clustered) was the grouping (dependent) variable, while all
the measured parameters constituted the independent vari-
ables. Discriminant functions and classification matrices ob-
tained from the standard, forward stepwise and backward
stepwise modes of DA are shown in Tables 5 and 6. Sim-
ilar to the temporal DA, the standard DA mode constructs
DFs including 31 parameters (Table 5),the coliform bacte-
ria group coefficients are zero again. Both the standard and
forward stepwise mode DFs using 31 and 22 discriminant
parameters, respectively, rendered the corresponding CMs
assigning more than 97% cases correctly (Tables 5 and 6).
The backward stepwise mode DA gave CMs with slightly
less than 97% correct assignations using only 10 discriminant
parameters (Tables 5 and 6).Backward stepwise DA shows
that discharge, pH, BOD, Cl, F, SO4, NH4N, NO3N, TKN
and Zn are the discriminating parameters in space. The cor-
rect assignations (97%) by DA for three different site clusters
(UC, MC and LC) further confirmed the adequacy of DA and
the grouping pattern coincides with our previous spatial CA.
Both CA and DA predict important differences in water qual-
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
11/20
K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374 365
ity due to impact from Lucknow city. DA shows that there
are significant differences between these three regions (UC,
MC and LC), which are expressed in terms of 10 discrimi-
nating parameters. Hence, DA rendered a considerable data
reduction.
Box and whisker plots of some selected discriminating pa-
rameters identified by spatial DA (backward step mode) were
constructed to evaluate different patterns associated with spa-
tial variations in river water quality (Fig. 4).Mean discharge
of the river shows a steady increase with the river course. It
Fig. 3. Temporal variations: (a) temperature; (b) total alkalinity; (c) chloride; (d) sodium; (e) potassium in Gomti river water.
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
12/20
366 K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374
Table 5
Classification functions (Eq.(1)) for discriminant analysis of spatial variations in Gomti river water
Parameters Standard DA mode Forward stepwise DA mode Backward stepwise DA mode
UCa
coefficientbMCc
coefficient
LCd
coefficient
UC
coefficient
MC
coefficient
LC
coefficient
UC
coefficient
MC
coefficient
LC
coefficient
Discharge 0.680 0.971 1.245 0.630 0.911 1.188 0.294 0.519 0.764
Temperature 0.232 0.032 0.116 0.296 0.101 0.198EC 0.107 0.112 0.102
PH 173.59 167.799 173.171 169.112 163.578 168.991 158.038 152.04 158.002
TS 0.124 0.146 0.117 0.243 0.224 0.237
TDS 0.403 0.407 0.392
TSS 0.438 0.444 0.393 0.045 0.045 0.012
T-Alk 0.453 0.438 0.488 0.409 0.402 0.440
T-Hard 0.152 0.150 0.151
Ca-Hard 0.112 0.152 0.112 0.080 0.120 0.079
DO 0.430 0.639 0.453 0.246 0.452 0.277
BOD 1.237 1.988 1.414 1.703 2.413 2.011 1.781 2.367 2.022
COD 0.314 0.293 0.395
Cl 3.540 3.125 2.458 2.847 2.413 1.805 1.408 0.912 0.278
F 5.880 4.311 12.407 19.104 17.433 24.854 15.044 11.116 19.842
SO4 1.733 1.278 0.964
PO4 1.353 1.500 1.988 1.484 1.633 2.103 1.202 1.338 1.792K 1.463 1.355 01.297
Na 1.565 1.458 1.505 1.325 1.209 1.244
NH4N 5.033 8.741 6.304 5.488 9.058 7.027 2.951 7.299 4.725
NO3N 8.698 5.159 9.651 8.847 4.935 9.753 14.644 12.619 17.054
TKN 8.032 9.097 6.321 8.040 9.066 6.288 6.091 6.530 4.120
T. Coli 0.000 0.000 0.000
Cd 2261.17 2564.128 3045.26 953.68 1279.52 1751.65
Cr 97.198 26.087 108.666
Fe 0.698 0.857 1.730 0.297 0.067 0.770
Pb 167.217 154.37 156.351
Cu 119.202 155.499 167.689 82.956 122.64 132.41
Mn 7.490 10.66 21.85 16.704 13.678 1.372
Zn 41.558 33.678 46.851 42.200 34.681 47.987 43.615 39.167 54.37
Ni 260.491 316.299 321.337 458.318 525.86 495.55
Constant 819.742 792.008 849.818 789.624 763.29 821.70 674.153 645.499 699.582a Upper catchments includes sites 13.b Coefficients for different catchments correspond to wijas defined in Eq.(1).c Middle catchments includes sites 46.d Lower catchments includes sites 7 and 8.
is due to the inputs from its tributaries and 45 wastewater
drains emptying into the river directly. Trends for pH, BOD,
NH4N, NO3N and TKN suggest for high load of dissolved
organic matter in the MC region added by 28 wastewater
drains from Lucknow town pouring about 450 mld of raw
wastewater in to the river directly leading to anaerobic con-
ditions which results in formation of ammonia and organic
acids. Hydrolysis of these acidic materials causes a decrease
of water pH in this region. Similar trends of spatial variations
observed for BOD, NH4N and TKN suggest vast difference
in pollution load and sources in three catchments regions of
the river. Chloride and zinc show increasing trends from UC
to LC and this reflects additive nature of the two relatively
conservative constituents. Similar trends of spatial variations
observed for BOD, NH4N and TKN suggest vast difference
in pollution load and sources in three catchments regions of
the river. An increase in F level in river water at MC sug-
gests its origin in municipal and industrial wastewater in this
zone.
3.3. Data structure determination and source
identification
Principal component analysis/factor analysis was applied
to the normalized data sets (26 variables) separately for
the three different spatial regions viz. UC, MC and LC,
as delineated by CA technique, to compare the composi-
tional patterns between the analyzed water samples and to
identify the factors that influence each one. The input data
matrices (variables cases) for PCA/FA were [26 108]
for UC and MC, and [26 72] for LC regions. PCA of
the three data sets evolved seven PCs each for first two
regions (UC and MC) and six PCs for last region (LC)
with eigenvalue >1, explaining 74.4, 73.6 and 81.4% of
the total variance in respective water quality data sets.
Equal numbers of VFs were obtained for three regions
through FA performed on the PCs. Corresponding VFs,
variables loadings and variance explained are presented in
Table 7.
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
13/20
K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374 367
For the dataset pertaining to UC, among the seven VFs,
the VF1 explaining 31.6% of total variance has strong neg-
ative loadings (>0.70) on Cd, Cr, Fe, Mn and Ni and mod-
erate negative loading on Pb. Thus, it represents the metal
group. VF2 explaining 11.4% of the total variance has strong
positive loadings on organic pollution parameters, BOD and
COD, and moderate loading on TKN, thus, basically repre-
sents the organic pollution group. VF3 has strong positive
loadings on TS and TDS and moderate positive loadings on
Na and SO4, which can be interpreted as a mineral compo-
nent of the river water. This clustering of variables points to
a common origin for these minerals, likely from dissolution
of limestone and gypsum soils in the river catchments [3].
It may be noted that gypsum is widely used as soil modi-
Fig. 4. Spatial variations: (a) discharge; (b) pH; (c) BOD; (d) chloride; (e) fluoride; (f) phosphate; (g) ammonical nitrogen; (h) total kjeldahl nitrogen; (i) zinc;
(j) nitrate-nitrogen in Gomti river water.
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
14/20
368 K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374
Fig. 4. (Continued).
Table 6
Classification matrix for discriminant analysis of spatial variations in Gomti
river water
Monitoring regions % Correct Regions assigned by DA
UCa MCb LCc
Standard DA mode
UCa 99.1 107 1 0
MCb 96.3 2 104 2
LCc 95.8 2 1 69
Total 97.2 111 106 71
Forward stepwise DA mode
UCa 99.1 107 1 0
MCb 97.2 1 105 2
LCc 95.8 2 1 69
Total 97.6 110 107 71
Backward stepwise DA mode
UCa 99.1 107 1 0
MCb 95.4 3 103 2
LCc 95.8 2 1 69
Total 96.9 112 105 71
a Upper catchments includes sites (13).b Middle catchments includes sites (36).c Lower catchments includes sites (7 and 8).
fier in the river catchments. VF4 explaining relatively lower
variance (6.45%) has strong negative loadings on alkalin-
ity, hardness and DO and moderate negative loadings on Na
and Cl ions. This VF represents natural sources of these pa-
rameters in catchments from soil weathering and subsequent
run-off. VF5 has strong loading on Cu and moderate loading
on NO3N. This indicates influence of industrial activities
VF6 has strong negative loading on K and moderate nega-
tive loading on Cl. This VF also represents natural source
as these ions have origin in the catchments soils. VF7 has
strong positive loading on NH4N. This represents influenceof agricultural runoff from the soil as nitrogenous fertilisers
are extensively used in this region.
For the data set pertaining to water quality in MC region,
among the seven VFs, VF1 explaining 27% of total variance
has strong positive loadings on TS and TDS and moderate
loadings on Na and K. This basically represents the solids
group. This clustering point to common sources of natural
processes of dissolution of soil constituents mainly carbon-
ates. VF2, explaining about 18% of the variance, has strong
positive loadings on metals (Cr, Fe, Mn and Ni), whereas, a
moderate loading on Pb, Cu and Zn. Thus, it basically repre-
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
15/20
K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374 369
Table 7
Loadings of experimental variables (26) on significant principal components for (a) UC dataset, (b) MC dataset, (c) LC dataset
Variables VF1 VF2 VF3 VF4 VF5 VF6 VF7
UC dataset (seven significant principal components)
EC 0.123 0.026 0.257 0.834 0.098 0.073 0.047
TS 0.028 0.017 0.876 0.184 0.089 0.004 0.055
TDS 0.157 0.008 0.870 0.223 0.044 0.128 0.073
T-Alk 0.374 0.053 0.257 0.777 0.160 0.244 0.013
T-Hard 0.182 0.093 0.092 0.774 0.163 0.010 0.047
DO 0.246 0.344 0.004 0.812 0.048 0.120 0.032
BOD 0.028 0.891 0.097 0.103 0.058 0.001 0.091
COD 0.076 0.868 0.010 0.106 0.097 0.120 0.182
Cl 0.235 0.033 0.140 0.599 0.010 0.593 0.092
F 0.079 0.314 0.154 0.017 0.492 0.398 0.282
PO4 0.031 0.037 0.263 0.222 0.180 0.349 0.018
SO4 0.096 0.177 0.617 0.091 0.399 0.143 0.037
K 0.204 0.004 0.148 0.022 0.127 0.844 0.028
Na 0.336 0.008 0.515 0.593 0.075 0.297 0.060
NH4N 0.039 0.103 0.031 0.045 0.022 0.041 0.868
NO3N 0.267 0.189 0.051 0.420 0.657 0.152 0.061
TKN 0.144 0.544 0.120 0.006 0.286 0.326 0.198
T. Coli 0.009
0.177
0.316 0.211 0.134 0.155
0.481Cd 0.721 0.160 0.024 0.084 0.064 0.010 0.181
Cr 0.832 0.028 0.020 0.198 0.128 0.029 0.139
Fe 0.825 0.028 0.119 0.378 0.248 0.149 0.001
Pb 0.674 0.193 0.152 0.010 0.494 0.212 0.124
Cu 0.305 0.072 0.060 0.135 0.824 0.042 0.016
Mn 0.895 0.016 0.181 0.181 0.145 0.032 0.056
Zn 0.414 0.419 0.280 0.218 0.320 0.020 0.301
Ni 0.908 0.025 0.002 0.247 0.002 0.105 0.014
Eigenvalue 8.21 2.97 2.40 1.68 1.62 1.31 1.15
% Total variance 31.58 11.40 9.23 6.45 6.25 5.04 4.42
Cumulative % variance 31.58 42.98 52.21 58.66 64.91 69.95 74.37
MC dataset (seven significant principal components)
EC 0.357 0.187 0.080 0.329 0.041 0.802 0.081
TS 0.876 0.084 0.084 0.028 0.024 0.191 0.050
TDS 0.787 0.092 0.150 0.211 0.002 0.409 0.067T-Alk 0.425 0.209 0.034 0.200 0.033 0.806 0.079
T-Hard 0.070 0.058 0.015 0.027 0.108 0.900 0.076
DO 0.100 0.031 0.725 0.257 0.176 0.032 0.236
BOD 0.265 0.068 0.893 0.005 0.095 0.027 0.113
COD 0.146 0.108 0.892 0.158 0.145 0.082 0.023
Cl 0.409 0.046 0.091 0.609 0.163 0.523 0.108
F 0.229 0.072 0.123 0.265 0.689 0.145 0.129
PO4 0.268 0.050 0.162 0.607 0.090 0.186 0.084
SO4 0.095 0.137 0.003 0.470 0.142 0.272 0.271
K 0.512 0.195 0.407 0.329 0.306 0.008 0.164
Na 0.630 0.216 0.099 0.347 0.0001 0.478 0.125
NH4N 0.020 0.160 0.011 0.012 0.066 0.003 0.859
NO3N 0.063 0.092 0.159 0.821 0.101 0.041 0.040
TKN 0.036 0.093 0.168 0.395 0.620 0.037 0.109
T. Coli 0.090 0.016 0.297 0.241 0.154 0.145 0.358
Cd 0.123 0.055 0.324 0.291 0.258 0.236 0.234
Cr 0.161 0.754 0.043 0.179 0.010 0.120 0.021
Fe 0.173 0.820 0.065 0.125 0.002 0.425 0.004
Pb 0.142 0.614 0.004 0.022 0.546 0.051 0.072
Cu 0.036 0.685 0.174 0.117 0.441 0.039 0.008
Mn 0.011 0.849 0.186 0.021 0.021 0.249 0.045
Zn 0.153 0.624 0.323 0.229 0.007 0.062 0.135
Ni 0.091 0.850 0.055 0.295 0.015 0.146 0.020
Eigenvalue 6.99 4.72 2.19 1.68 1.40 1.08 1.06
% Total variance 26.89 18.17 8.41 6.47 5.40 4.14 4.08
Cumulative % variance 26.89 45.06 53.47 59.94 65.34 69.48 73.56
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
16/20
370 K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374
Table 7 (Continued)
Variables VF1 VF2 VF3 VF4 VF5 VF6 VF7
LC dataset (six significant principal components)
EC 0.636 0.424 0.019 0.376 0.446 0.101
TS 0.161 0.184 0.010 0.894 0.043 0.012
TDS 0.408 0.236 0.023 0.823 0.048 0.023
T-Alk 0.600 0.458 0.050 0.434 0.435 0.075
T-Hard 0.324 0.366 0.064 0.364 0.571 0.035DO 0.120 0.481 0.314 0.194 0.631 0.006
BOD 0.076 0.040 0.885 0.074 0.039 0.140
COD 0.122 0.071 0.863 0.177 0.175 0.007
Cl 0.821 0.217 0.037 0.276 0.283 0.110
F 0.646 0.183 0.427 0.131 0.042 0.235
PO4 0.857 0.038 0.014 0.195 0.194 0.108
SO4 0.452 0.490 0.262 0.074 0.327 0.262
K 0.833 .098 0.175 0.045 0.055 0.178
Na 0.649 0.359 0.019 0.417 0.310 0.080
NH4N 0.090 0.054 0.148 0.217 0.743 0.244
NO3N 0.160 0.182 0.091 0.020 0.200 0.724
TKN 0.182 0.300 0.758 0.168 0.137 0.175
T. Coli 0.111 0.432 0.473 0.329 0.075 0.312
Cd 0.008 0.710 0.105 0.209 0.027 0.457
Cr 0.127 0.887 0.152 0.193 0.057 0.274Fe 0.309 0.821 0.108 0.169 0.189 0.330
Pb 0.155 0.630 0.398 0.003 0.026 0.558
Cu 0.053 0.414 0.247 0.048 0.003 0.784
Mn 0.249 0.844 0.098 0.142 0.152 0.261
Zn 0.124 0.802 0.042 0.132 0.143 0.122
Ni 0.242 0.898 0.058 0.086 0.055 0.203
Eigenvalue 11.03 3.97 2.01 1.71 1.43 1.01
% Total variance 42.44 15.28 7.74 6.57 5.50 3.87
Cumulative % variance 42.44 57.73 65.47 72.04 77.54 81.41
Bold-faced values represent strong loadings.
sents a toxic metals group. VF3 has strong positive loadingboth on BOD and COD, whereas, a negative strong loading
on DO. It is, thus, a group of purely organic pollution indica-
tor parameters. This VF represents anthropogenic pollution
sources and can be explained that high levels of dissolved or-
ganic matterconsume large amounts of oxygen, which under-
goes anaerobic fermentation processes leading to formation
of ammonia and organic acids. Hydrolysis of these acidic
materials causes a decrease of water pH values [3]. VF4
has strong positive loading on NO3N and moderate positive
loading on Cl and PO4. Thus, it represents the nutrients group
of pollutants which points to some source of wastewater and
runoff. VF5 has moderate positive loading on F, while mod-
erate negative loadings on TKN and Pb. This region (MC) isknown for high fluoride in soils and groundwater. VF6 has
strong positive loadings on EC, alkalinity and hardness and
a moderate positive loading on Cl. This basically represents
the salts group. VF7 has strong negative loading on NH4N
alone, and thus indicating the influence of domestic waste
and agricultural runoff.
Lastly, for the data set representing the LC region, among
total six significant VFs, the first one (VF1) explaining about
42.5% of the total variance has strong positive loadings on
K, Cl, and PO4 and moderate positive loadings on EC, al-
kalinity, F and Na. It basically represents the ionic group
of salts. The phosphate has its origin in soils due to use ofphosphatic fertilisers in this region. VF2 explaining more
than 15% of total variance has strong negative loadings on
Cd, Cr, Fe, Mn, Zn and Ni. It also has moderate negative
loading on Pb. Thus, it basically represents the toxic met-
als group. VF3, a group of organic pollution indicators, has
strong positive loadings on BOD, COD and TKN. VF4 has
strong positive loadings on TS and TDS. This factor loaded
with solids indicates towards their origin in run-off from the
fields with high load of solids and waste disposal activi-
ties. VF5 has strong negative loading on NH4N, whereas,
a moderate negative loadings on hardness and DO. VF6
has strong positive loadings on NO3N and Cu. It also has
moderate positive loading on Pb and represents industrialwaste.
From the PCA/FA loadings, it is evident that for all the
three regions, major groups of parameters (strong loadings)
emerged are the trace metals group (leaching from soil, and
industrial waste disposal sites), organic pollution group (rep-
resenting influencesfrom pointsources suchas municipaland
industrial effluents), nutrient parameters group (represent-
ing influences from non-point sources such as agricultural
runoff and atmospheric deposition), alkalinity, hardness, EC
and solids group (soil leaching/erosion followed by runoff
process).
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
17/20
K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374 371
Table 8
Source contribution to Gomti river water in the (a) UC region, (b) MC region, (c) LC region
Parameters Source type Observed
mean (O)
Measured
mean (M)
Ratio
(O/M)
R2
SM S1 S2 S3 S4 S5 S6 S7
UC regiona
EC (S cm1) 30.26 5.88 105.37 251.23 392.74 368.49 1.07 0.80
TS (mg l1
) 44.22 2.32 211.35 32.64 290.54 285.4 1.02 0.81TDS (mg l1) 52.22 173.42 32.67 258.31 243.6 1.06 0.86
T-Alk (mg l1) 23.68 67.62 150.47 241.77 194.44 1.24 0.90
T-Hard (mg l1) 5.37 28.37 175.54 209.24 165.6 1.26 0.68
DO (mg l1) 4.28 6.22 10.50 7.26 1.45 0.86
BOD (mg l1) 4.23 0.09 4.32 3.49 1.24 0.83
COD (mg l1) 3.08 0.74 10.08 13.90 11.51 1.21 0.83
Cl (mg l1) 0.82 3.46 4.28 3.77 1.14 0.80
F (mg l1) 0.38 0.02 0.06 0.46 0.41 1.14 0.61
PO4(mg l1) 0.08 0.07 0.03 0.17 0.17 1.00 0.28
SO4(mg l1) 0.78 8.42 0.54 0.09 9.80 8.43 1.17 0.61
K (mg l1) 1.90 1.33 0.48 3.71 3.50 1.06 0.79
Na (mg l1) 18.64 15.90 0.82 35.36 29.81 1.19 0.83
NH4N (mg l1) 0.15 0.15 0.13 1.15 0.77
NO3N (mg l1) 0.12 0.05 0.01 0.18 0.17 1.08 0.75
TKN (mg l1
) 1.00 1.43 0.57 0.02 0.19 3.21 2.76 1.16 0.56T. Coli (MPN/100 ml) 3129 78206 81335 22365 3.64 0.45
Fe (mg l1) 1.07 2.21 3.27 2.76 1.18 0.92
Pb (mg l1) 0.033 0.013 0.046 0.02 2.21 0.82
Cu (mg l1) 0.016 0.024 0.041 0.016 2.54 0.80
Mn (mg l1) 0.19 0.191 0.11 1.73 0.89
Zn (mg l1) 0.011 0.038 0.047 0.017 0.114 0.07 1.63 0.67
Ni (mg l1) 0.025 0.025 0.015 1.62 0.90
Cr (mg l1) 0.006 0.001 0.007 0.005 1.60 0.77
MC regionb
EC (S cm1) 80.25 165.97 5.83 18.34 208.33 478.71 440.70 1.09 0.93
TS (mg l1) 55.25 227.45 7.00 3.43 0.88 27.71 1.76 323.48 321.89 1.00 0.82
TDS (mg l1) 37.36 179.52 5.34 5.78 52.07 280.06 275.31 1.02 0.87
T-Alk (mg l1) 17.12 101.57 5.74 107.45 231.88 190.41 1.22 0.92
T-Hard (mg l1) 47.43 18.76 135.40 2.81 204.4 190.41 1.07 0.84
DO (mg l1) 2.19 0.53 0.26 0.19 0.26 3.42 2.90 1.18 0.69
BOD (mg l1) 8.71 0.72 4.60 0.02 0.79 0.49 0.50 15.82 15.3 1.03 0.90
COD (mg l1) 6.47 8.71 2.08 8.33 1.13 2.20 2.73 0.19 31.84 31.84 1.00 0.88
Cl (mg l1) 9.42 2.91 12.32 10.12 1.22 0.86
F (mg l1) 0.3 0.22 0.08 0.59 0.51 1.16 0.66
PO4(mg l1) 0.33 0.07 0.11 0.51 0.39 1.32 0.52
SO4(mg l1) 5.17 2.49 1.47 0.94 3.97 0.97 15.01 13.85 1.08 0.42
K (mg l1) 6.32 1.06 0.65 8.03 7.02 1.14 0.69
Na (mg l1) 34.80 2.49 13.28 50.57 41.50 1.22 0.82
NH4N (mg l1) 0.48 0.04 0.52 0.49 1.07 0.77
NO3N (mg l1) 0.16 0.17 0.08 0.07 0.27 0.06 0.81 0.73 1.12 0.73
TKN (mg l1) 2.59 0.17 0.14 0.12 0.22 0.73 3.97 3.94 1.01 0.59
T. Coli (MPN/100 ml) 3.8E+09 2.3E+08 2.0E+09 3.5E+09 9.5E+09 1.5E+10 0.63 0.33
Cd (mg l1) 0.0005 0.0001 0.0001 0.0001 0.0007 0.0003 2.49 0.69
Fe (mg l1) 2.40 1.71 4.12 2.27 1.81 0.90
Pb (mg l1) 0.005 0.02 0.014 0.001 0.039 0.021 1.87 0.71
Cu (mg l1) 0.035 0.002 0.001 0.005 0.043 0.022 1.96 0.71
Mn (mg l1) 0.052 0.114 0.166 0.097 1.71 0.82
Zn (mg l1) 0.029 0.064 0.016 0.009 0.118 0.063 1.85 0.59
Ni (mg l1) 0.006 0.004 0.011 0.02 0.016 1.29 0.84
Cr (mg l1) 0.008 0.001 0.001 0.009 0.005 1.72 0.64
LC regionc
EC (S cm1) 36.18 89.58 234.04 152.86 512.65 425.83 1.20 0.94
TS (mg l1) 57.56 11.01 1.85 271.28 341.69 318.44 1.07 0.86
TDS (mg l1) 9.05 27.11 4.12 241.61 7.80 0.54 290.23 270.68 1.07 0.90
T-Alk (mg l1) 46.15 147.68 81.55 275.38 206.01 1.34 0.96
T-Hard (mg l1) 27.24 135.64 117.12 280.00 183.76 1.52 0.70
DO (mg l1) 2.97 1.89 3.77 8.62 7.02 1.23 0.78
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
18/20
372 K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374
Table 8 (Continued)
Parameters Source type Observed
mean (O)
Measured
mean (M)
Ratio
(O/M)
R2
SM S1 S2 S3 S4 S5 S6 S7
BOD (mg l1) 5.31 1.02 6.33 5.31 1.19 0.82
COD (mg l1) 1.52 0.82 0.59 13.92 2.24 19.09 16.18 1.18 0.83
Cl (mg l1) 5.21 5.73 3.76 14.70 11.63 1.26 0.89
F (mgl1
) 0.39 0.15 0.54 0.43 1.25 0.71PO4 (mg l1) 0.01 0.13 0.10 0.25 0.23 1.06 0.82
SO4 (mg l1) 6.79 2.90 4.53 2.11 5.11 21.44 18.11 1.18 0.69
K (mgl1) 2.72 1.92 1.09 0.46 6.19 5.45 1.18 0.69
Na (mg l1) 11.58 24.92 13.45 49.94 40.74 1.23 0.83
NH4N (mg l1) 0.01 0.07 0.15 0.23 0.18 1.26 0.69
NO3N (mg l1) 0.18 0.07 0.08 0.09 0.41 0.38 1.10 0.63
TKN (mg l1) 0.62 0.31 1.70 0.62 0.05 3.30 2.87 1.15 0.78
T. Coli. (MPN/100 ml) 6937 18722 28576 22647 86880 72160 1.20 0.63
Cd (mg l1) 0.002 0.0003 0.0018 0.0008 2.38 0.77
Fe (mg l1) 2.18 5.17 0.39 7.73 4.62 1.67 0.96
Pb (mg l1) 0.027 0.01 0.006 0.043 0.027 1.57 0.89
Cu (mg l1) 0.011 0.011 0.027 0.049 0.033 1.49 0.85
Mn (mg l1) 0.051 0.19 0.014 0.005 0.261 0.156 1.67 0.90
Zn (mg l1) 0.041 0.194 0.008 0.244 0.138 1.76 0.71
Ni (mg l1
) 0.006 0.019 0.003 0.001 0.03 0.022 1.33 0.92Cr (mg l1) 0.001 0.012 0.003 0.001 0.017 0.008 2.2 0.94
a SM: miscellaneous sources; S1: industrial waste; S2: municipal and industrial waste; S3: soil runoff 1; S4: weathering and runoff; S5: agricultural runoff;
S6: soil runoff 2; S7: waste site runoff.b SM: miscellaneous sources; S1: municipal and industrial waste 1; S2: industrial waste; S3: municipal and industrial waste 2; S4: agricultural runoff; S5:
soil runoff 1; S6: soil runoff 2; S7: waste site runoff.c SM: miscellaneous sources; S1: agricultural runoff; S2: industrial waste 1; S3: municipal and industrial waste; S4: soil runoff; S5: waste site runoff; S6:
industrial waste 2.
The results from temporal PCA/FA suggested that most
of the variations in water quality are explained by the
soluble salts (natural), toxic metals (industrial), nutrients
(non-point) and organic pollutants (anthropogenic). Inthis study, FA did not result in much data reduction, as
we still need 1417 parameters (about 5565% of the
26 parameters) to explain 7482% of the data variance
in three catchments regions (Table 7ac). However, FA
served as a means to identify those parameters, which have
greatest contribution to temporal variation in the river water
quality and suggested possible sets of pollution sources
in each of the catchments regions of the river. Similar
approach based on PCA/FA for evaluation of temporal and
spatial variations in water quality has earlier been used
[3,9,10,12,15].
However, from the PCA/FA results, it may convincingly
be presumed that in all the three catchments regions under
study, the river pollution is mainly from soil weathering and
agricultural run-off, leaching from solid waste disposal sites
and domestic and industrial wastewater disposal. These find-
ings are also supported by the catchments source/activity in-
ventory.
3.4. Source apportionment
Results of receptor modeling through APCS-MLR
for source apportionment in three different catchments
regions of the Gomti river as the contributions of the possible
sources (identified through PCA/FA) in various water quality
parameters are presented inTable 8ac. As evident from the
correlation coefficients, the multiple regression exhibited
good adequacy between the measured and predicted values.Further, the ratio of mean observed and measured values of
almost all the water quality variables suggest goodness of
the receptor modeling approach to the source apportionment
of river water. MLR on APCS showed that except for a few,
most of the parameters are influenced by sources (PCs) iden-
tified as industrial waste disposal site leaching, domestic and
industrial waste disposal, agricultural runoff, weathering and
runoff from catchments. However, the miscellaneous uniden-
tified sources in all the three regions also contribute to the
river water pollution for most of the water quality variables.
In all the three regions (UC, MC and LC), major sources
influencing the river water quality are industrial waste
disposal sites leaching, domestic and industrial waste, soil
weathering and agricultural land runoff. Further, the uniden-
tified miscellaneous sources also contribute significantly
to the river water quality variations (Table 8ac). Relative
contributions of different sources for major water quality
variables identified for the region through DA (Fig. 5ac)
suggest that the miscellaneous unidentified sources along
with the natural weathering, industrial waste sites leaching,
domestic and industrial wastewater, agricultural soil leaching
and runoff from catchments are the major sources/factors
contributing most to the river water quality in all the three
regions.
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
19/20
K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374 373
Fig. 5. (a) Source contributions for selected water quality parameters of Gomti river in the UC region: SM = miscellaneous sources; S1 = industrial waste;
S2 = municipal and industrial waste; S3 = soil runoff 1; S4 = weathering and runoff; S6 = soil runoff 2; S7 = waste site runoff. (b) Source contributions for
selected water quality parameters of Gomti river in the MC region: SM = miscellaneous sources; S1 = municipal and industrial waste 1; S2 = industrial waste;
S3 = municipal and industrial waste 2; S4 = agricultural runoff; S5 = soil runoff 1; S6 = soil runoff 2; S7 = waste site runoff. (c) Source contributions for selected
water quality parameters of Gomti river in the LC region: SM = miscellaneous sources; S1 = agricultural runoff; S2 = industrial waste 1; S3 = municipal and
industrial waste; S4 = soil runoff; S5 = waste site runoff; S6 = industrial waste 2.
4. Conclusions
In this study, hierarchical CA grouped the sampling sites
into three clusters of similar characteristics reflecting the wa-
ter quality characteristics. The extracted grouping informa-
tion can be used in reducing the number of sampling sites
without missing much information. FA/PCA helped in iden-
tifying the factors/sources responsible for river water quality
variations in three differentregions.VFs obtained from FA in-
dicate that the parameters responsible for water quality varia-
tions aremainly related to trace metals(leaching from soil and
industrial waste sites), soluble salts (natural), organic pollu-
tion and nutrients (anthropogenic). DArendered an important
data reduction as it uses only five parameters (temperature,
total alkalinity, Cl, Na and K) affording more than 94% right
assignations in temporal analysis, while 10 parameters (river
discharge, pH, BOD, Cl, F, PO4, NH4N, NO3N, TKN and
Zn) to afford 97% right assignations in spatial analysis of
three different regions in the basin. Thus, DA allowed reduc-
tion in dimensionality of the large data set, delineating a few
indicator parameters responsible for large variations in water
quality. Further, receptor modeling through multi-linear re-
gression of the absolute principal component scores (APCS-
MLR) provided apportionment of various sources/factors in
respective regions contributing to the river pollution. It re-
vealed that agricultural soil weathering, leaching and runoff;
municipal and industrial wastewater; waste disposal sites
leaching were among the major sources/factors responsible
for river quality deterioration. Thus, this study presents use-
fulness of multivariate statistical techniques in water qual-
ity assessment, identification and apportionment of pollution
sources/factors with a view to get better information about
-
7/25/2019 Water Quality Assessment and Apportionment of Pollution Sources
20/20
374 K.P. Singh et al. / Analytica Chimica Acta 538 (2005) 355374
the water quality and design of monitoring network/strategy
for effective management of water resources.
Acknowledgements
The authors thank the National River Conservation Direc-
torate (NRCD), Ministry of Environment and Forests, Gov-
ernment of India for financial support and Director, ITRC,
Lucknow for encouragement. Suggestions and help provided
by Prof. V. Simeonov (Faculty of Chemistry, University of
Sofia, Bulgaria) and Prof. D.A. Wunderlin (Facultad de Cien-
cias Quimicas, Universidad National de Cordoba, Argentina)
in multivariate analysis of data are thankfully acknowledged.
References
[1] S.R. Carpenter, N.F. Caraco, D.L. Correll, R.W. Howarth, A.N.
Sharpley, V.H. Smith, Ecol. Appl. 8 (1998) 559.[2] H.P. Jarvie, B.A. Whitton, C. Neal, Sci. Total Environ. 210/211
(1998) 79.
[3] M. Vega, R. Pardo, E. Barrado, L. Deban, Water Res. 32 (1998)
3581.
[4] W. Dixon, B. Chiswell, Water Res. 30 (1996) 1935.
[5] P. Simeonova, V. Simeonov, G. Andreev, Cen. Eur. J. Chem. 2 (2003)
121.
[6] V. Simeonov, P. Simeonova, R. Tsitouridou, Chem. Eng. Ecol. 11
(2004) 450.
[7] K. Bengraine, T.F. Marhaba, J. Hazard. Mater. B 100 (2003) 179.
[8] C.W. Liu, K.H. Lin, Y.M. Kuo, Sci. Total Environ. 313 (2003) 77.
[9] V. Simeonov, J.A. Stratis, C. Samara, G. Zachariadis, D. Voutsa,
A. Anthemidis, M. Sofoniou, Th. Kouimtzis, Water Res. 37 (2003)
4119.
[10] K.P. Singh, A. Malik, D. Mohan, S. Sinha, Water Res. 38 (2004)
3980.
[11] APHA, Standard Methods for the Examination of Water and Wastew-
ater, 20th ed., American Public Health Association, Washington, DC,
1998.
[12] D.A. Wunderlin, D.M.D. Pilar, A.M. Valeria, P.S. Fabiana, H.A.
Cecilia, B.M. De Los Angeles, Water Res. 35 (2001) 2881.
[13] M. Otto, Multivariate methods, in: R. Kellner, J.M. Mermet, M. Otto,
H.M. Widmer (Eds.), Analytical Chemistry, Wiley/VCH, Weinheim,
Germany, 1998, p. 916.
[14] R.A. Johnson, D.W. Wichern, Applied Multivariate Statistical Anal-
ysis, 3rd ed., Prentice-Hall International, Englewood Cliffs, New Jer-
sey, USA, 1992, p. 642.
[15] B. Helena, R. Pardo, M. Vega, E. Barrado, J.M. Fernandez, L. Fer-nandez, Water Res. 34 (2000) 807.
[16] Y.S. Fung, L.W.Y. Wong, Atmos. Environ. 29 (1995) 2041.
[17] E. Swietlicki, R. Krejei, Nucl. Instr. Meth. Phys. B 109/110 (1996)
519.
[18] G.D. Thurston, J.D. Spengler, Atmos. Environ. 19 (1985) 9.