Research on Water Quality for Evaluation Using the Water Quality Index and Multivariate Statistical Approach of Evrenye Stream (Kastamonu, Türkiye)

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Pol. J. Environ. Stud. Vol. XX, No. X (XXXX), X-X
Original Research
Research on Water Quality for Evaluation
Using the Water Quality Index and
Multivariate Statistical Approach of
Evrenye Stream (Kastamonu, Türkiye)
Ekrem Mutlu1, Arzu Aydin Uncumusaoglu2*
1Kastamonu University, Faculty of Humanities and Social Sciences, Department of Geography, Kastamonu, Türkiye
2Giresun University, Environmental Engineering Department, Giresun, Türkiye
Received: 4 June 2024
Accepted: 15 August 2024
Abstract
This study investigates and evaluates the spatial and temporal variations in the water quality of the
Evrenye Stream by identifying the main pollutant sources using a water quality index and a multivariate
statistical approach. Water quality data were obtained monthly in 2021 by considering 28 parameters
from 10 stations. The parameters (PCA) constituting the main components of the water body are also
used in calculating the water quality index. PCA was aected by four main factors explaining 83.69%
of the total variance and pollutants from heavy metals are the pollutant source of this stream. It is
thought that mining enterprises located in the river basin may be responsible for this pollution. The
result of the Water Quality Index (WQI), which is applied according to the annual average data values,
is generally determined as quality water. In the future, freshwater management of the basin, monitoring
is recommended.
Keywords: irrigation quality, temporal-spatial variations, water quality index, principal components
analysis, hierarchical cluster analysis
DOI: 10.15244/pjoes/192355 ONLINE PUBLICATION DATE:
*e-mail: arzu.a.uncumusaoglu@gmail.com
Tel.: +90 454 310 17 40
Fax: +90 454 310 17 49
Introduction
Aquatic life is very important for all biological life
and human activities, from the smallest organism to
the largest one. Although ¾ of the world is covered
with water, only 2.5% of it is fresh water. Of this
freshwater, 70% is in glaciers, soil, atmosphere, and
underground waters and is not usable. The rapid growth
of the world population with a constant amount of
water available increases the need for water on a daily
basis and it creates water stress. As stated in a report
by the World Health Organization and United Nations
Children's Fund, 3 out of 10 individuals on earth have
no reliable tap water in their houses, whereas 6 out of 10
individuals have no sucient sanitation [1]. Given this
joint monitoring report, it was determined that many
individuals, especially those living in rural areas, had no
access to reliably managed water and sanitation services.

Ekrem M., Arzu A.U.
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It is expected that, as of the year 2050, the number of
countries having water problems and the number of
people living under those conditions will signicantly
increase, and almost half of the global population, which
the United Nations projects to be 10 billion as of the
year 2050, will have water insuciency problem.
The availability of water in sucient amounts
and at sucient quality is very important for aquatic
ecosystems and organisms and it is among the main
natural sources that will inuence food safety,
sustainable development, and human future. The release
of domestic and industrial wastes into rivers, barrages,
lakes, and seas without sucient treatment poses an
important risk for these ecologic systems [2]. The
recipient of radioactivity originating from point and
non-point sources, and organic and inorganic matters,
wastes, detergents, heavy metals, pesticides, and oil
and its derivatives is the water [3]. These pollutants
have toxic eects on the organisms in the aquatic
ecosystem and they also threaten human health via the
food chain [4]. For a healthy human life, food chain, and
hydrological cycle, it is very important to protect current
water sources and regularly monitor the quality. Since
water is a vital component of all ecosystems and human
life, there are ongoing studies on the characteristics,
availability, and quality of water aquatic ecosystems.
For eective water management, it is very important
to collect reliable data on water quality, determine
the spatial and temporal changes in water quality on a
timely basis, detect pollutant sources, reveal the water
quality in its simplest form, check pollution in natural
waters, and take measures.
In recent years, the water quality index (WQI)
has become an important and popular instrument in
assessing especially freshwater quality by presenting
water quality data incorporating dierent units and
parameters in a more understandable form and allowing
water quality experts and non-expert individuals to gain
information about water quality and use this information
easily, rapidly, and understandably [5, 6]. Principal
component analysis (PCA), cluster analysis (CA),
principal component analysis (PCA), factor analysis
(FA), and irrigation water quality parameters are
extensively employed in the assessment of water quality,
enabling the interpretation of intricate datasets [7-10].
These methods also make it easier to determine water
pollution sources, determine and interpret the natural
and anthropogenic factors inuencing the temporal
and spatial changes in water quality, and suggest fast
solutions for pollution problems [11-13]. Moreover, they
also oer saving from time and money thanks to the
information they provide to rapidly interpret the data
and monitor the study area. The present study aims
to determine monthly, seasonal, and spatial changes
in water samples taken from stations representing the
Evrenye Stream, reveal water quality characteristics,
and determine the pollution level and pollutants by
using both WQI and statistical methods in interpreting
the data more simplistically and understandably, as well
as having pollutants monitored in future studies in order
to take measures.
Methods and Materials
Study Area
The Evrenye Stream, which is situated in the
Western Black Sea region of Anatolia, reaches the sea
in a village where the sun rises and sets over the sea.
The distance to the Kastamonu city center is 102 km,
and it is 12 km to the İnebolu district center (Fig. 1).
The stream, which is in the district land that is high and
has a rough structure, has deep beds to send its water
to the sea. In general, the typical Black Sea climate is
seen in the region. The level of precipitation is much
higher in winter months in comparison to summer. The
annual mean temperature in İnebolu is 10.9 °C, while
the annual mean precipitation is 1060 mm. The annual
mean temperature is approx. 18.1 °C.
Sampling and Analysis Methodology
Water samples were taken from 10 stations on the
Evrenye Stream between January and December 2021
(Fig. 1). The sampling has been conducted on a monthly
monitoring basis. Water samples for heavy metal
analysis were collected using 2.5 L sample containers
pre-washed with acid and then the samples were
transported to the laboratory in a large thermos and
stored in a refrigerator at 4°C until analysis [11].
In-situ measurements including dissolved oxygen
(DO), water temperature (WT), electrical conductivity
(EC), and salinity were made using a YSI 556 MPS
multi-parameter instrument. Phosphate phosphorus
(PO4
-3-P), chemical oxygen demand (COD), ammonium
nitrogen (NH4
+-N), nitrite-nitrogen (NO2
- N), nitrate-
nitrogen (NO3
- N), biological oxygen demand (BOD5),
total hardness (TH), total alkalinity (TA), sodium
(Na+1), sulte (SO3
-2), sulfate (SO4
-2), magnesium (Mg+2),
calcium (Ca+2), chlorine (Cl-), and potassium (K+)
parameters were analyzed using standard laboratory
methods [6, 14, 15]. The analyses of metals including
zinc (Zn+2), nickel (Ni+2), cadmium (Cd+2), copper (Cu+2)
fer rous (Fe+2), and lead (Pb+2) were carried out by Perkin
Elmer Optima 2000 DV ICP-OES [6, 14, 15].
Irrigation Quality Parameters
Agricultural products of high quality can be obtained
through the use of quality soil, proper irrigation water,
and accurate agricultural practices. The chemical
properties of irrigation water can directly impact the
presence of deciencies or toxicity in herbal products,
and may also have an indirect eect on nutrient
availability. To assess the irrigation water quality of the
Evrenye Stream, various parameters including sodium

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absorption rate (SAR), residual sodium carbonate (RSC),
sodium percentage (% Na), magnesium hazard (MH),
and Kelley Ratio (KR) values were calculated using
Formulas (1-5) respectively (Table 1) [6, 16]. Following
the computation of these values, the concentrations of
elements were transformed from mg L-1 to meq L-1 [6,
17, 18].
(1)
(2)
(3)
(4)
(5)
Water Quality Index (WQI)
In assessing the water quality of the Evrenye
Stream, the Water Quality Index (WQI), a simple yet
comprehensive indicator, was calculated as described by
Wang et al. [19](6).
Fig. 1. Study area with the sampling points.

Ekrem M., Arzu A.U.
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(6)
The term “wi” represents the eigenvalue for each
principal component from the PCA results performed
with 28 parameters representing the relative importance
of each water quality parameter and the weight attributed
to each parameter based on the factor loading [6, 20, 21]
(7); ci” is the detected concentration of the parameters
in the water samples; si is the guideline value for each
parameter [14, 19, 22]. Wi is the relative weight and is
calculated by the equation [20, 21] (7):
(7)
Five classications are made for the interpretation
of the calculated WQI values: 0 ≤ WQI < 50 indicates
excellent water quality, 50 ≤ WQI < 100 indicates good
water quality, 100 ≤ WQI < 200 indicates poor water
quality, 200 ≤ WQI < 300 indicates very poor water
quality and WQI>300 indicates water not suitable for
drinking [20, 21].
Statistical Analysis Methodology
The statistical analysis of the data obtained was
carried out using the IBM SPSS 25 statistical package
program. Descriptive statistical analysis and signicance
tests (0.01 and 0.05) were conducted through One-Way
ANOVA analysis of parameters to identify dierences
between stations and seasons. Tukey's multiple tests
were employed to determine signicant dierences
between mean values and Pearson's correlation index
(PCI) was utilized to analyze the correlations among the
parameters [23].
For the spatial and temporal elucidation of the
Evrenye Stream's surface water quality, large datasets
underwent multivariate Hierarchical Cluster Analysis
(HCA), a technique aimed at classifying datasets into
clusters based on similarities or dierences [19]. Ward’s
method served as the similarity criterion for HCA [24].
Principal Component Analysis (PCA) was employed to
assess spatial and temporal variations in water quality,
preceded by Kaiser–Meyer–Olkin (KMO) and Bartlett's
tests. With a KMO value of 0.886 and Bartlett's test
yielding a signicant result (P = 0), the PCA test was
deemed suitable for the purpose. Data were standardized
using Z-scale transformation to avoid misclassication
due to signicant dierences in the datasets [13].
Results and Discussion
Water Quality Analysis
Twenty-eight physicochemical parameters were
examined using the water samples collected monthly
from 10 stations on the Evrenye Stream for a year
(January-December 2021). During the period of the
study, the dierences between the mean values of the
stations were found to be statistically signicant (P
< 0.05). Nevertheless, no signicant dierences were
observed between seasons in terms of annual mean
values (P > 0.05). Upon examination of the analysis
results, the average and standard deviation values
of the parameters are as follows: DO = 11.21±1.46
mg L-1, salinity =0.42±0.17‰, pH=8.68±0.20, WT
=11.10±6.72°C, EC = 320.22±28.17 μS cm
-1, COD
=0.94±0.67 mg L-1, SS matter =2.35±1.30 mg L-1, BOD5
= 0.40±0.39 mg L-1, [Cl-] = 7.61±1.33 mg L-1, [PO4
3-] =
0.02±0.02 mg L-1, [SO4
2]-= 32.14±25.74 mg L
-1, [SO3
2-
] = 1.68±1.10 mg L
-1, [Na+] = 42.41±10.89 mg L
-1, [K+]
= 7.08±1.58 mg L
-1, TH = 266.11±15.40 mg L
-1, TA
=276.55±15.52 mg L-1, [Mg2+] =19.52±0.65 mg L-1, [Ca+2]
=18.99±1.08 mg L-1, [NO2
-] = 0.0004±0.0005 mg L-1,
[NO3
-] =1.06±0.59 mg L-1, [NH4
+] = 0.0006±0.0002 mg
L-1, [Fe2+] = 0.008±0.004 mg L-1, [Pb2+] =1.15±0.56 µg
L-1, [Cu2+] =13.63±7.45 µg L-1, [Cd 2+] =0.62±0.40 µg L-1,
[Hg2+] = 0.0061±0.004 µg L-1, [Ni2+] = 5.78±2.90 µg L-1,
and [Zn2+] = 7.26±5.05 µg L-1.
Temporal Changes in Water Quality
In aquatic ecosystems, crucial correlations such as
bacterial activity, photosynthesis, nutrient availability,
and stratication provide valuable insights into temporal
changes in water quality. In aquatic ecosystems,
Dissolved Oxygen (DO), essential for organism
Na (%) SAR RSC Mg hazard KR
Water class Water class Water class Water class Water class
<20 Excellent 0–10 Excellent <1.25 Safe/good <50 Suitable <1 Suitable
20–40 Good 10–18 Good 1.25–2.50 doubtful >50 Unsuitable >1 Unsuitable
40–60 Permissible 18–26 Fair >2.50 Unsuitable
60–80 Doubtful >26 Poor
>80 Unsuitable
Table 1. Standard and calculated irrigation water quality indices for sodium percentage (Na (%)), sodium absorption rate (SAR), residual
sodium carbonate (RSC), Mg hazard, and Kelley Ratio (KR) in waters (meq L-1) (Ravikumar et al., 2013; Jehan et al., 2020; Mutlu and
Aydın Uncumusaoğlu, 2022).

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survival, reached its peak in July at 12.77 mg L-1, while
its lowest recorded level was observed in September
at 7.27 mg L-1. DO exhibited a statistically signicant
negative correlation (P < 0.05) with salinity, pH, WT,
COD, BOD5, [NO2-], and [Cd2+] (r > 0.500) (Table 2).
For aquatic life, the DO level in water should exceed
5 mg L-1. The stream poses no danger in terms of DO
levels. Regarding DO, the stream was classied as
Class I, signifying high-quality water, according to
the standards outlined in the Surface Water Quality
Regulation of Turkey (SWQR) and the World Health
Organization (WHO) guidelines [14, 22].
The salinity of water for agriculture is important in
terms of plant health, soil quality, irrigation systems,
and environmental sustainability. The use of saline and
drainage waters in irrigation due to insucient water
quantity and quality can lead to agricultural challenges
[25].
Corresponding to the regional climate, the lowest
salinity level was recorded as 0.21 psu in December,
a winter month, while the highest level was observed
in September (0.81 psu). Salinity demonstrates a
statistically signicant (P < 0.05) and positive correlation
with the concentrations of WT, EC, SS, COD, BOD5,
[SO4
2-], [SO3
2-], and [NO3
-] (r > 0.750) as shown in Table
2. The Evrenye Stream poses no threat to agriculture or
aquatic life in terms of salinity. The pH value shows a
rising trend peaking in December (9.10) before declining
to its lowest point in March (8.28). This uctuation is
attributed to environmental photosynthetic activity. Low
pH values enhance the toxicity of heavy metals and
pose a risk to the aquatic ecosystem. This parameter
exhibits a statistically signicant (P < 0.05) and positive
correlation with salinity, WT, EC, SS, COD, BOD5, as
well as the concentrations of, [PO4
3-], [SO3
2-], [NO3
-],
and [Cd2+] (r > 0.50) as indicated in Table 2. Considering
the general chemical and physicochemical parameters
in standards set by the WHO and SWQR, this stream's
pH level falls into Class I (6-9), indicating 'high-quality
water' [14, 22].
The water temperature reached its lowest level
(4.1°C) in February and its highest level in September
(26.10°C). WT exhibits a statistically signicant (P <
0.05) and positive correlation with EC, SS, COD, BOD5,
as well as the concentrations of [SO4
2-] and [SO3
2-] (r
> 0.750), while it demonstrates a signicant negative
correlation with chloride concentration [Cl-] (Table 2).
WT poses no threat to organisms inhabiting the water.
The electrical conductivity of water, inuenced by
the solubility of rocks in contact with the water, peaked
in September (380.62 μS cm-1) and reached its lowest
level in December (272.26 μS cm-1) in this stream. EC
level exhibits a statistically signicant (P < 0.05) and
positive correlation with salinity, EC, SS, COD, BOD5,
[SO4
2-], [SO3
2-], and [NO3
-] (r > 0.750) (Table 2). The
mean EC value in the Evrenye Stream falls into Class
II (less polluted water) according to SWQR (<1000 μS
cm-1) standards [14, 22]. The EC value obtained in this
study is considerably higher than the results reported in
a previous study on the Karaboğaz Stream [10].
The Suspended Solids (SS) level is inuenced
by the volume of water brought in by oods and the
phytoplankton carried by precipitation water into the
stream. In the Evrenye Stream, the lowest SS levels were
recorded in March, September, October, November, and
December (0.48 mg L-1), but they increased steadily
until September, reaching the highest level (5.40 mg L-1).
The level of SS demonstrates a statistically signicant
(P < 0.05) and positive correlation with salinity, WT,
EC, COD, BOD5, as well as the concentrations of [SO4
2-
], [SO3
2-], and [NO3
-] (r > 0.750) as presented in Table
2. The total SS in the Amba River, the main source of
wastewater and sewage discharge into rivers, was found
to be signicantly higher than observed in this study
[26].
COD, one of the parameters determining if the
water quality originates from the organic matter, ranged
between 0.22 (all months) and 2.46 mg L-1 (September)
in the present study. This parameter has a statistically
signicant (P<0.05) and positive relationship with
salinity, WT, EC, SS, BOD5, [SO4
2-], [SO3
2-], and [NO3
-]
(r>0.750) (Table 2). This parameter remained below the
dened threshold level, thus classied as Class I "high-
quality water" (˂ 25 mg L-1) according to standards
outlined by WHO and SWQR [14, 22]. In the Evrenye
Stream, BOD5, which is an indicator of water pollution
level, reached its lowest levels in November and
December, and the highest level in July (1.98 mg L-1).
BOD5 level was found to have a statistically signicant
(P<0.05) and positive relationship with salinity, WT,
EC, SS, COD, [SO4
2-], and [SO3
2-] (r>0.750) (Table 2). In
this current investigation, BOD5 levels remained below
the threshold established by SWQR, thus qualifying
as Class I "high-quality water" (˂ 4 mg L-1) according
to guidelines outlined by standards [14, 22]. The
concentration of chloride ions in the stream exhibited
its lowest measurement in August (4.10 mg L-1) and its
highest measurement in June (10.50 mg L-1). Chloride
level was found to have a statistically signicant
(P<0.05) and negative relationship with salinity, pH,
COD, EC, SS, BOD5, WT, [SO4
2-], and [SO3
2-] and a
negative relationship with DO and [PO4] (Table 2).
COD, a parameter indicating the presence of organic
matter in water, varied between 0.22 (all months) and
2.46 mg L-1 (September) in the current study. This
parameter demonstrates a statistically signicant (P
< 0.05) and positive correlation with salinity, WT,
EC, SS, BOD5, as well as the concentrations of [SO4
2-
], [SO3
2-], and [NO3
-] (r > 0.750) as illustrated in Table
2. The COD levels remained below the threshold and
were classied as Class I 'high-quality water' (˂ 25 mg
L-1) according to the standards [14, 22]. In the Evrenye
Stream, the Biological Oxygen Demand, an indicator of
water pollution, reached its lowest levels in November
and December and its highest level in July (1.98 mg
L-1). BOD5 levels showed a statistically signicant (P <
0.05) and positive correlation with salinity, WT, EC, SS,

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COD, (r > 0.750) (Table 2). The levels of BOD5 exhibited
a statistically signicant (P < 0.05) and positive
correlation with salinity, WT, EC, SS, COD as well as
the concentrations of [SO4
2-], and [SO3
2-] (r > 0.750) as
delineated in Table 2.
In the scope of this investigation, BOD5 levels
remained below the threshold stipulated by SWQR
guidelines, thus qualifying the water as Class I 'high-
quality' (˂ 4 mg L-1) according to standards outlined by
standards [14, 22]. Furthermore, the concentration of
chloride ions in the stream reached its nadir in August
(4.10 mg L-1) and its zenith in June (10.50 mg L-1).
Chloride levels demonstrated a statistically signicant
(P < 0.05) and negative correlation with salinity, pH,
COD, EC, SS, BOD5, WT, [SO4
2-], and [SO3
2-], and a
negative correlation with DO and [PO4
3-] (Table 2).
Throughout the year, the phosphate ion concentration
in the Evrenye Stream remained consistently low,
with a peak observed in July at 0.078 mg L-1. It was
undetectable in January and displayed a gradual increase
thereafter. The phosphate ion levels in this stream
fall within Class I (<0.16 mg L-1), complying with the
threshold limit established by SWQR guidelines, thus
earning classication as Class I 'high-quality water' as
per standards [14, 22]. The sulfate ion concentration in
the Evrenye Stream ranged from 0.22 mg L-1 (February)
to 77.53 mg L-1 (September), within the acceptable limit
of 90 mg L-1 for sulfate in natural waters. Statistically
signicant (P < 0.05) and positive correlations were
observed between sulfate ion levels and salinity, WT,
EC, SS, COD, BOD5, [SO3
2-], and [NO3
-] concentration (r
> 0.750) as depicted in Table 2.
The sulde ion concentration in this stream ranged
from 0.03 mg L-1 (February) to 4.22 mg L-1 (September).
It exhibited a statistically signicant (P < 0.05) and
positive correlation with salinity, pH, WT, EC, SS, COD,
BOD5, [SO4
2-], and [NO3
-] (r > 0.750) as illustrated in
Table 2. Based on sulde ion concentration, the stream
was categorized as Class II (≤2 mg L-1) 'Less polluted
water' according to standards [14, 22]. It is noteworthy
that the sulde concentration in this stream surpassed
that of Bektaş Pond, indicating a variation in pollution
levels [27].
The sodium ion concentration in the Evrenye Stream
reached its minimum level in April at 26.00 mg L-1 and
peaked in June at 75.52 mg L-1, maintaining consistency
with the original statement's meaning. Sodium levels
exhibited a signicant (P < 0.05) and positive correlation
with Total Alkalinity (TA), copper ion concentration,
and zinc ion concentration (r > 0.750) (Table 2). The
sodium value range in the Tigris River (Iraq) was found
to be considerably higher than observed in this study
[28]. Potassium ions are a contributing factor to water
taste, with their concentration in the Stream observed
at its nadir in January (3.18 mg L-1) and zenith in June
(10.08 mg L-1).
In natural waters, Total Hardness (TH), calcium,
and magnesium chloride vary based on concentrations
of nitrate bicarbonate compounds, as well as slightly
depending on the concentrations of strontium, ferrous,
and aluminum ions. In the current investigation, the
Total Hardness (TH) exhibited its minimum level in
February (244.02 CaCO3 mg L-1) and reached its peak
in June (307.06 CaCO3 mg L-1), while demonstrating a
statistically signicant (P < 0.05) and positive correlation
with [NO3
-], [Na+], and TA with a correlation coecient
exceeding 0.750, as presented in Table 2. In this study,
TH was lowest in February (244.02 CaCO3 mg L-1) and
highest in June (307.06 CaCO3 mg L-1). This parameter
showed a statistically signicant (P < 0.05) and positive
correlation with [NO3
-], [Na+], and TA (r > 0.750) (Table
2). Total alkalinity ranged between 255.19 and 320.04
CaCO3 mg L-1, similar to TH. The parameter displayed
a statistically signicant (P < 0.05) and positive
correlation with magnesium concentration (r = 0.729)
as documented in Table 2. Calcium and magnesium
cations present in water contribute to enhancing soil
permeability, thus underscoring their signicance in
determining the suitability of stream water for irrigation
purposes. The magnesium concentration within the
Evrenye Stream ranged from 18.76 mg L-1 in February
to 22.58 mg L-1 in July. Magnesium levels exhibited a
statistically signicant (P < 0.05) and positive correlation
with calcium concentration (r > 0.750) as indicated in
Table 2. Moreover, calcium levels were observed to be
at their lowest in October (17.62 mg L-1) and peaked in
April (22.02 mg L-1) within the scope of this study.
Within the context of this research, the nitrite
concentration in the Evrenye Stream varied between
0 and 0.0038 mg L-1, reaching its peak in April. The
concentration of nitrate nitrogen ranged between 0.28
and 2.42 mg L-1. The stream falls into Class I for nitrate
nitrogen (≤ 0.01 mg L-1) and Class II 'less polluted
water' for nitrate nitrogen (<10 mg L-1) [14, 22]. In a
previous study on the Seydisuyu River, the nitrate value
was found to be higher than in the present study [29].
The ammonium concentration in the Evrenye Stream
ranged between 0.0004 (January, February) and 0.0017
(December) mg L-1, indicating Class I (< 0.2 mg L-1)
ammonium nitrogen, which corresponds to 'high-quality
water' [14, 22].
In an aquatic environment, autotrophic bacteria
require ferrous ions to secrete many enzymes. The
ferrous concentration within the Evrenye Stream
uctuated between 0.0010 and 0.0230 mg L-1. This
parameter exhibited a statistically signicant (P < 0.05)
and positive correlation with [Na2+], [Cu2+], TH, TA,
[Mg2+], [Ca2+], [Pb2+], [Cu2+], [Zn2+], and [Hg2+] with
correlation coecients exceeding 0.700, as outlined in
Table 2. In this study, the ferrous concentration was
found not to reach a hazardous level (36-101 mg L-1) [14,
22].
Heavy metals with anthropogenic origins may
accumulate in the livers, kidneys, and muscles of
aquatic organisms [6]. In this research, the lead
concentration exhibited a range of 0.20 to 2.60 µg L-1,
reaching its peak in July. Moreover, lead demonstrated
a positive relationship with [Cu2+] and [Zn2+] ions, with

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Table 2. Pearson correlation matrix between variables.
DO Salinity pH WT EC SS COD BOD5 Cl-PO4
3- SO4
2- SO3
2- Na+K+TH TA Mg2+ Ca2+ NO2
-NO3
-NH4
+Fe+2 Pb+2 Cu+2 Cd+2 Hg+2 Ni+2 Zn+2
DO 1
S-.431** 1
pH -.572** .721** 1
WT -.506** .865** .574** 1
EC -.465** .91** .605** .869** 1
SS -.454** .869** .683** .758** .916** 1
COD -.682** .849** .689** 0.84** .866** .874** 1
BOD5 -.502** .838** .65** .834** .884** .916** .888** 1
Cl-.5** -.555** -.354** -.749** -.575** -.436** -.687** -.606** 1
PO4
3- -.243** .563** .6** .265** .524** .549** .418** .532** -.126 1
SO4
2- -.288** .873** .593** .813** .897** .912** .836** .87** -.444** .434** 1
SO3
2- -.484** .917** .737** .82** .934** .957** .906** .89** -.525** .54** .92** 1
Na+.308** .257** .218* -.063 .217* .38** .135 .106 .365 .228** .403** .366** 1
K+-.110 .466** .481** .193* .438** .672** .405** .481** .056** .515** .506** .59** .658 1
TH .013 .588** .486** .343** .536** .6** .449** .425** .099** .431** .719** .618** .782** .592** 1
TA .085 .569** .473** .307** .499** .576 .403 .402 .116** .454** .687** .588** .809** .633** .982** 1
Mg2+ .139 .504** .426 .186* .454** .582 .274 .447 .135** .672** .514** .543** .685** .763** .687** .729** 1
Ca2+ .099 .393** .376** .124 .362** .587** .28** .385** .195* .411** .514** .5** .738** .852** .689** .729** .792 1
NO2
--.545** .285** .361** .226* .288** .38** .466** .375** -.203* .271** .3** .343** -.059 .219* .145 .077 .012 .204* 1
NO3
--.321** .84** .675** .658** .829** .843** .713** .716** -.249** .535** .841** .869** .506** .566** .732** .711** .618** .542** .201* 1
NH4
+-.004 .047 .438** -.072 -.085 .129 .063** .084** .111 .097 .044 .119* .311** .291** .102 .136 .191* .239** -.020 .147 1
Fe+2 .127 .163 .28** -.098 .205* .365** .202** .193** .144 .357** .353** .353** .733** .596** .634** .64** .616** .664** .140 .366** .206* 1
Pb+2 .109 .308** .176 .118 .445** .587** .311** .389** .112 .266** .568** .518** .691** .636** .627** .599** .623** .753** .192* .52** .024 .791** 1
Cu+2 .147 .145 .194* -.176 .218* .392** .166** .171** .249** .402** .334** .335** .759** .66** .6** .611** .619** .724** .203* .37** .119 .894** .849 1
Cd2+ -.744** .538** .618** .389** .628** .732** .737 .632** -.289** .529** .518** .688** .191** .528** .335** .276** .309** .37** .602** .577** .094 .346** .429** .448** 1
Hg2+ -.267** .424** .467** .155 .467** .605** .473** .447** .060 .588** .531** .513** .544 .665 .668** .677** .537** .643** .437** .566** .041 .613** .577** .725** .691** 1
Ni+2 -.087 .311** .274** .059 .418** .598** .328** .359** .202* .358** .487** .498** .672** .714** .61** .584** .616** .772** .359** .538** .035 .721** .874** .825 .615** .755** 1
Zn+2 .097 .327** .232* .132 .431** .58** .356** .404** .066 .323** .592** .522** .757** .704** .735** .743 .651** .777** .182* .538** .062 .842** .895** .861** .422** .718** .843** 1
Note: * Correlation is signicant at the 0.05 level (2-tailed). **Correlation is signicant at the 0.01 level (2-tailed).

Ekrem M., Arzu A.U.
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a correlation coecient exceeding 0.750 as outlined in
Table 2. It was determined that the lead concentration
posed no threat within the range of 1.2-14 µg L-1[14].
The copper concentration in the Evrenye Stream
ranged between 0 and 32 µg L-1, with the highest level
recorded in May. It exhibited a signicant positive
relationship with [Na2+], [Pb2+], and [Ni2+] parameters
(r > 0.750) (Table 2). According to SWQR, this stream
was considered very hazardous in terms of copper (1.3-
5.7 µg L-1) [14, 22]. Copper values found in Swat and
Huaihe streams were higher than those observed in the
present study [19, 30]. The cadmium level in this stream
ranged between 0.1 µg L-1 (February and August) and
1.8 µg L-1, with the highest value recorded in October.
Pearson’s correlation analysis indicated that [Cd2+]
had a signicant and strong relationship with SS, pH,
COD, BOD5, and [Mg2+], and a negative relationship
with DO (Table 2). According to SWQR, the cadmium
concentration in this stream was deemed dangerous
and classied as Class IV (<1.5 µg L-1), indicating 'very
polluted water. The cadmium concentration in the
Yağlıdere stream was found to be higher, similar to the
present study, whereas the concentration in the Chenab
River was observed to be low [31, 32].
In the present study, mercury concentration ranged
between 0.001 and 0.0210 µg L-1 and the highest
level was observed in October. This parameter was
signicantly (P < 0.05) and highly positively correlated
with SS, K, TH, TA, [Ca2+], [Pb2+], [Ni2+], and [Zn2+]
(r>0.60) (Table 2). It was determined that, according to
the inland water quality criteria, Evrenye Stream was
found to not be dangerous (<0.07 µg L-1) [14, 22].
The nickel concentration observed in this study
varied between 2.0 and 13.0 µg L-1, with the highest
recorded value occurring in April. In the present study,
nickel doesn’t pose a danger. Zinc concentration in
this stream ranged between 0.0 and 23.00 µg L-1. The
highest concentration was found in May. Given the
results obtained from PCA, zinc was found to have a
statistically signicant (P < 0.05) and highly positive
relationship with [Hg2+], [Ni2+], [Cu2+], [Pb2+] [Fe2+],
[Ca2+], TA, and TH (r>0.70). According to guidelines.,
zinc concentration doesn’t pose a threat to this aquatic
environment (5.33-76 µg L-1) [14, 22].
Irrigation Water Quality Analyses
As the Evrenye Stream is used for agricultural
activities, irrigation water quality parameters such as
% Na, SAR, RSC, MH, and Kelley ratio (KR) were
calculated and the results are given in Table 1 [6, 17, 30].
% Na is one of the parameters used to determine sodium
damage from irrigation waters. The mean % Na value
in this stream was found to be 30.37; the lowest value
was observed in Station 1 in April (30.16% Na), whereas
the highest value was found in Station 7 in June (51.89%
Na). This stream was deemed "permissible" in terms of
% Na (Table 1) [17, 30].
In this research, the average Sodium Adsorption
Ratio (SAR) values, utilized for identifying sodium-
related damage, were determined to be 1.63 meq L-1.
The minimum SAR value was recorded at Station 1
during April (1.01 meq L-1), while the maximum SAR
value was observed at Station 10 in June (2.76 meq L-1)
[17, 30].
Considering the SAR value, an irrigation water
quality parameter, the Evrenye Stream was considered
"perfect" (0-6 meq L-1) (Table 1) [17, 30]. Since the
RSC value of this stream was found to be negative,
there is no risk of sodium damage in irrigation.
Elevated magnesium concentration in water causes
soil salinization and has a detrimental impact on plant
growth and yield [18]. The Evrenye Stream's MH value
was determined to be between 60.43 and 64.40, which is
suitable for irrigation (MH > 50) (Table 1). Kelley ratio
is calculated as the ratio of [Na+] to [Ca2+] and [Mg2+]. A
Kelley ratio higher than 1 indicates excessive sodium in
water. This study's Kelley ratio was found to be higher
than 1 in May and June in Stations 7, 8, 9, and 10;
suggesting that the stream can be considered suitable for
irrigation [16].
Water Quality Index Analysis
Water quality parameters of Evrenye Stream were
analyzed using WQI. To calculate the indices, weight
loads were determined considering the distribution of
PCA to loads of its main components (Table 3), and
standard thresholds were used in calculations [13, 14, 22,
33]. To calculate the index by the PCA analysis result,
[Cu2+], [Zn2+], [Pb2+], [Na+], [Ni2+], [Fe2+], [Ca2+], TA,
TH, WT, Salinity, EC, [SO3
2-], [SO4
2-], BOD5, COD, SS,
[Cd2+], [NO2
-], DO, and [NH4
+] parameters were used
[33]. The WQI value of Evrenye Stream calculated using
the annual mean values (98.65) was found to be “good
water quality”. In the present study, the lowest WQI
value was found in December (81.13) and the highest
one in May (126.36) (Fig. 2).
The water quality parameters of the Evrenye
Stream were analyzed using the WQI. To calculate the
indices, weight loads were determined, considering the
distribution of Principal Component Analysis (PCA) to
loads of its main components (Table 3), and standards
thresholds were used in calculations [14, 22, 33]. In
the calculation of the index based on the PCA analysis
result, the parameters [Cu2+], [Zn2+], [Pb2+], [Na+], [Ni2+],
[Fe2+], [Ca2+], TA, TH, WT, Salinity, EC, [SO3
2-], [SO4
2-
], BOD5, COD, SS, [Cd2+], [NO2
-], DO, [NH4
+] were
utilized [6, 33].
The WQI value of the Evrenye Stream, calculated
using the annual mean values (98.65), was found to be
categorized as "good water quality" (50 ≤ WQI <100).
In the present study, the lowest WQI value was observed
in December (81.13), and the highest one was in May
(126.36) (Fig. 2). Given these values, it can be stated that
the stream is almost in the "good water quality" class in
terms of drinking water. WQI of the Evrenye Stream,

Research on Water Quality for Evaluation Using... 9
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computed based on the annual mean values (98.65), was
determined to fall within the category of "good water
quality" (50 ≤ WQI <100). Within the scope of this
study, the lowest WQI value was recorded in December
(81.13), while the highest was observed in May (126.36)
(Fig. 2). These ndings suggest that the stream largely
Eigenvalue Relative
eigenvalue Variable Loading
value
Relative loading value
on the same PC
Weight (relative eigenvalue ×
relative loading value)
19.536 0.407 [Cu2+]0.925 0.120 0.049
[Zn2+]0.919 0.120 0.049
[Pb2+]0.885 0.115 0.047
[Na+]0.869 0.113 0.046
[Ni2+]0.869 0.113 0.046
[Fe2+]0.86 0.112 0.046
[Ca2+]0.841 0.109 0.045
TA 0.767 0.100 0.041
TH 0.752 0.098 0.040
28.992 0.384 WT 0.976 0.138 0.053
Salinity 0.920 0.130 0.050
EC 0.918 0.129 0.050
[SO3
2-]0.873 0.123 0.047
[SO4
2-]0.868 0.122 0.047
BOD5 0.867 0.122 0.047
COD 0.862 0.121 0.047
SS 0.811 0.114 0.044
32.98 0.127 [Cd2+]0.782 0.501 0.043
[NO2
-]0.778 0.499 0.043
DO 0.751 0.324 0.041
41.925 0.0821 [NH4
+] 0.81 1.00 0.082
23.432
Table 3. The weights assigned to the 21 variables in the water samples from the Evrenye Stream.
Fig. 2. Monthly Change of Evrenye Stream’s Water Quality Index (WQI).

Ekrem M., Arzu A.U.
10
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meets the criteria for "good water quality," particularly
with regards to its suitability for drinking purposes.
Both spatial and temporal analyses were conducted
using complex and multivariate statistical methods,
employing 28 parameters obtained from water samples
collected monthly from 10 stations over a year.
Hierarchical Clustering Analysis was utilized to unveil
temporal and spatial disparities/similarities and to
delineate distinct groups. HCA utilized mean values of
the parameters grouped by seasons and stations. The
results for the Evrenye Stream illustrate the clustering
of stations based on dierences/similarities, as seen in
Fig. 3. Cluster B demonstrates a higher similarity ratio
in contrast to Cluster A. The study area is spatially
segmented into an upper basin (St 1-6) and a lower basin
(St 7-10). Cluster B (Upper Basin) denotes the initial
origin stations of the river, while Cluster B (Lower
Basin) characterizes the area where the river meets the
waters of the Black Sea (Fig. 3).
The temporal dierences in variations were
examined through HCA employing seasonal mean
values (Fig. 4). The similarity ratio among the members
of Cluster A surpasses that of Cluster B within the two
clusters. Cluster A encompasses the winter and spring
seasons, there is Cluster B encompasses the summer and
fall seasons. Consequently, two seasonal clusters were
identied, albeit without a distinct demarcation between
wet and dry seasons. This outcome was consistent with
the ndings of the ANOVA test.
Prior to conducting principal component analysis
(PCA), the Kaiser-Meyer-Olkin (KMO) and Bartlett
tests were administered on the datasets to assess their
suitability for PCA. To ascertain the appropriate number
of principal components, conrmation was sought by
observing the point at which the number of principal
components exceeded "1" before a discernible break in
the scree plot [27].
As a result of the PCA, it was determined that four
principal components adequately represented the data
from the Evrenye Stream (Table 4). These principal
components accounted for 83.69% of the total variance
in the dataset and exhibited eigenvalues >1 (Table 4, Fig.
5). Consistent with the approach employed by Liu et
al., the PCA factor loadings were classied as "strong"
(greater than 0.75), "moderate" (0.75-0.50), and "weak"
(0.50-0.30) [34].
The rst principal component, explaining 34.06% of
the total variance, exhibits a strong positive loading on
parameters such as [Cu2+], [Zn2+], [Pb2+], [Na+], [Ni2+],
[Fe2+], [Ca2+], TA, and TH (r>0.750). This component
reects the inuence of heavy metal content, originating
from soil or rock, on the water. The impact of mining
activities in the study area is evident through this
rst factor. Conversely, it was observed that the total
Fig. 3. The dendrogram illustrates clusters of variables. Fig. 4. Clusters of variables (A: Autumn, Sm: Summer, Sp:
Spring and W: Winter).
Fig. 5. Component plot in the rotated space.

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hardness and total alkalinity in water originate from
basin geology, soluble salts, and atmospheric deposition,
all stemming from the soil or rock structure [35, 36].
The second principal component, explaining 32.11%
of the total variance, also exhibits a high loading similar
to the rst component. This component shows a strong
positive loading with WT, salinity, EC, BOD5, COD,
SS, [SO3
2-], and [SO4
2-]. It can be asserted that this
component represents a non-point pollutant source,
resulting from the interaction of soil and rocks along
with surface ow inuenced by atmospheric deposition
and temperature [7, 30, 37, 38].
The third component, explaining 10.64% of the
total variance, demonstrates a strong positive loading
on [Cd2+] and [NO2
-], and a strong negative loading on
DO. Cadmium exhibits high solubility in water, and this
solubility increases with rising temperature. The mining
activity in the basin serves as a source of cadmium. Since
the stream water is utilized in agricultural activities, the
presence of cadmium in the food chain poses a risk to
Variable PC 1 PC 2 PC 3 PC 4
Eigenvalues 9.54 8.99 2.98 1.92
Variance (%) 34.06 32.11 10.64 6.875
Cumulative (%) 34.06 66.17 76.81 83.69
Cu2+0.925 -0.073 0.212 0.009
Zn2+ 0.919 0.213 0.082 -0.112
Pb2+ 0.885 0.195 0.103 -0.193
Na+0.869 0.048 -0.228 0.228
Ni2+ 0.869 0.121 0.345 -0.068
Fe2+ 0.860 -0.011 0.114 0.078
Ca2+ 0.841 0.185 0.036 0.230
TA 0.767 0.407 -0.200 0.223
TH 0.752 0.437 -0.139 0.171
Mg2+ 0.736 0.303 -0.131 0.352
K+0.725 0.259 0.188 0.344
Hg2+ 0.711 0.225 0.449 0.123
WT -0.102 0.976 0.047 -0.032
Salinity 0.204 0.920 0.083 0.188
EC 0.258 0.918 0.177 -0.036
SO3
20.363 0.873 0.219 0.142
SO4
2- 0.427 0.868 0.047 -0.003
BOD50.192 0.867 0.277 0.088
COD 0.135 0.862 0.404 0.056
SS 0.436 0.811 0.281 0.123
NO30.466 0.748 0.050 0.229
Cl−0.361 -0.715 -0.209 0.161
Cd2+ 0.326 0.419 0.782 0.133
NO2
-0.113 0.175 0.778 -0.027
DO 0.262 -0.450 -0.751 -0.125
NH4
+0.083 -0.067 -0.010 0.810
pH 0.156 0.594 0.329 0.622
PO4
3- 0.349 0.372 0.274 0.429
Table 4. Varimax rotated the factor matrix for the data set.

Ekrem M., Arzu A.U.
12
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organisms in the basin, as well as to human health. The
negative loading of dissolved oxygen in this principal
component is expected due to the inverse relationship
between these parameters.
The nal component explains 10.64% of the total
variance and is characterized by a high positive loading
on [NH4
+]. Rainwater, being one of the most eective
solvents globally, partially dissolves nitrogenous matter
in the soil, resulting in the transfer of nitrogenous
compounds to the water. Elevated levels of [NH4
+] ions
may be found in wells in agricultural areas, indicating
that nitrogenous articial fertilizers are inltrating
underground through rainwaters, posing a risk to well
waters.
Conclusions
In the world, 2 million individuals, mainly children,
die annually because of intestinal infections due to
inappropriate water usage and poor hygienic conditions.
A quality life can be achieved only with quality
freshwater, as well as improving the available water and
sustainable usage.
In this study, by making use of water quality data
obtained monthly for a year from 10 stations in Evrenye
Stream located in a basin with excellent natural beauty,
water quality was determined. Besides that, in order to
determine the suitability of this stream for aquatic life
and irrigation purposes, WQI was calculated using the
principal components of PCA analysis. Furthermore,
temporal and spatial analyses were performed using
multivariate statistical methods. Because of the mining
company in the basin, this stream was found to be very
risky to both aquatic organisms and agricultural use,
since the heavy metal content reaches humans. All the
organisms drinking the underground and surface waters
are included in the foot chain by being aected by these
chemicals. Moreover, the agricultural products are also
polluted. In conclusion, a severe health risk arises for
all organisms. It is recommended to take necessary
precautions, continue sampling as monitoring, and carry
out the controls.
In the present study, according to WHO, SWQR, and
regulations and considering the general chemical and
physical parameters set inland surface water sources,
the Evrenye Stream was found to be “less polluted
water” in terms of electrical conductivity and nitrate
concentration. However, since the copper and cadmium
ions were found to be much higher than the desired
limits for irrigation and drinking waters, stream water
is classied as “very polluted water” (Class I-V). The
stream water was determined to be suitable for irrigation
purposes based on the % Na, SAR, RSC, MH, and KR
parameters.
The results obtained from the statistical analyses
applied to the achieved data clusters (ANOVA, Pearson’s
correlation, HCA, and PCA) corroborated each other.
Naming the basin as the upper and lower basin was a
result of spatial distinction based on the HCA. Winter
and spring seasons were observed to exhibit greater
similarity compared to summer and fall seasons. As a
result of PCA analysis conducted on the water quality
data of this stream, it was determined that four principal
components represented the water mass. The main
pollution sources of the Stream were mining companies
in the basin, temperature, anions reaching the water as
a result of pesticides and fertilizers used in agricultural
activities, and point and non-point pollutants.
WQI calculated using the annual mean values of
parameters yielded “quality water” character in general.
However, since it was observed that the quality tended
to decline in the course of time during the freshwater
management of the basin, it was determined that the
stream should be monitored. The continuity of life
depends on the water. Mining activities consume water
and also pollute it. Moreover, considering the pollution
arising from agricultural activities, widely used
chemical fertilizers and pesticides should be controlled
and animal wastes should be prevented from reaching
the water. While developing the action plans for water
pollution in stream basins, the use of statistical modeling
allows for interpreting the raw data and understanding
them more clearly. Continuous monitoring is one of the
suggestions for sustainable water.
Acknowledgments
The authors declare that no funds, grants, or other
support were received during the preparation of this
manuscript.
Conict of Interest
The authors declare no conict of interest.
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