Novasinergia 2022, 5(2), 174-192. https://doi.org/10.37135/ns.01.10.10 http://novasinergia.unach.edu.ec
Research article
Influence of geomorphology and flow on the water quality of Guano River,
Ecuador
Influencia de la geomorfología y el caudal en la calidad del agua del río Guano, Ecuador
Nelly Guananga1, Benito Mendoza2* , Freddy Guananga1, Jaime Bejar1, Carlos Carbonel3, Sandra
Noemí Escobar Arrieta , Absalón Wilberto Guerrero Rivera4
1Escuela Superior Politécnica de Chimborazo, Riobamba, Ecuador, 060106; nguananga@espoch.edu.ec;
freddy.guananga@espoch.edu.ec; bejarjaime@gmail.com
2Universidad Nacional de Chimborazo, Riobamba, Ecuador, 060150
3Universidad Nacional Mayor de San Marcos, Ciudad Universitaria, Lima, Perú, 15081, ccarbonelh@unmsm.edu.pe
4Universidad Estatal de Milagro, Milagro, Ecuador, 091701; aguerreror@unemi.edu.ec
*Correspondence: benitomendoza@unach.edu.ec
Citación: Guananga, N., Mendoza,
B., Guananga, F., Bejar, J., Carbonel,
C., Escobar, N., & Guerrero, A.,
(2022). Influence of geomorphology
and flow on the water quality of
Guano River, Ecuador.
Novasinergia. 5(2). 174-192.
https://doi.org/10.37135/ns.01.10.10
Recibido: 03 diciembre 2021
Aceptado: 27 junio 2022
Publicación: 05 julio 2022
Novasinergia
ISSN: 2631-2654
Abstract: The present study determines the water quality of
the Guano River in Ecuador through the water quality indices
WQI-NSF, WQI-Dinius, and a variant of the WQI-Dinius
index that includes the average slope of the riverbed and the
flow. To obtain qualitative values of water quality that allow
better use of the river water. The results obtained with the
three indices show that there is slight contamination in river
sections caused by human activities, decreased flow, and
wastewater discharge. Furthermore, the work shows that
when applying the WQI-Dinius modified, the values of the
weights of the water quality are lower concerning the other
indices. But, even when the WQI-Dinius modified values are
common, the valuation range for agricultural use is similar
among the three indices, maintaining the criterion that the
Guano River is slightly contaminated. Therefore, treating the
water before using it in agricultural activities is necessary.
Keywords: Flow, geomorfología, solpe, water quality, WQI.
Copyright: 2022 derechos
otorgados por los autores a
Novasinergia.
Este es un artículo de acceso
abierto distribuido bajo los
términos y condiciones de una
licencia de Creative Commons
Attribution (CC BY NC).
(http://creativecommons.org/licens
es/by/4.0/).
Resumen: El presente estudio determina la calidad del agua del río
Guano en Ecuador mediante los índices de calidad del agua ICA-
NSF, ICA-Dinius y una variante al índice ICA-Dinius que incluye
la pendiente media del cauce y el caudal. Para de esta manera obtener
valores cualitativos de calidad de agua que permitan un mejor
aprovechamiento del agua del río. Los resultados obtenidos con los
tres ínidces muestran que existe contaminación leve en ciertos
tramos del río, provocada por las actividades humanas, la
disminución del caudal en el río y por la descarga de aguas
residuales. Por otra parte, muestra que al aplicar el ICA-Dinius
modificado los valores de las ponderaciones de la calidad del agua son
más bajos respecto a los otros índices. Aún cuando los valores
presentados por ICA- Dinius modificado son bajos, el rango de
valoración para uso agropecuario es similar entre los tres índices,
manteniendo el criterio que el río Guano es levemente
contaminado. Por lo tanto, es necesario dar tratamiento al agua del
río antes de usarla en actividades agropecuarias.
Palabras clave: Calidad del agua, caudal, geomorfología, ICA,
pendiente.
Novasinergia 2022, 5(2), 174-192 175
1. Introduction
Water is the most abundant natural resource on the planet; its quantity is
approximately 1.385 billion km3. Out of this overall volume, as little as 1% is usable
freshwater; 81% of it is present in glaciers and polar zones, while the remaining 18% is
distributed among soil moisture, lakes, atmospheric vapor, rivers, and living organisms
(Bitsch et al., 2021). All lifeforms on our planet, including flora, fauna, and human beings,
have developed due to water availability (Singh, Yadav, Pal, & Mishra, 2020). Ecuador
possesses a significant quantity of water resources; its average total runoff is 43,500 m3 per
inhabitant a year, four times greater than the world average of 10800 m3 per inhabitant
(Machado, dos Santos, Alves, & Quindeler, 2019). Water quality is at risk due to human
activities near water sources. These activities are usually related to urban areas, mining
areas, oil exploitation, and agriculture. These activities generate pollutant discharges with
high concentrations of organic matter, nitrogen, phosphorus, heavy metals, and
hydrocarbons (Ustaoğlu, Tepe, & Taş, 2020). Agricultural activity is extensive in the
province of Chimborazo due to favorable climatic and geographical conditions (Moreano-
Logroño & Mancheno-Herrera, 2020). In the last ten years, the Guano River has been used
mainly in agricultural activities; in its course, it receives sanitary, agricultural, and industrial
discharges. In addition, its flow has been reduced by 50%, changing the water quality and
affecting the balance of the aquatic ecosystem, the soil, and peoples health (Shakir,
Chaudhry, & Qazi, 2012).
To study the characteristics of water resources, quality indexes are used to verify whether
the water complies with the specifications for its intended use; in addition, the effects of
pollutants need to be assessed (Akhtar et al., 2021; Gupta & Gupta, 2021; Nong, Shao, Zhong,
& Liang, 2020; Uddin, Nash, & Olbert, 2021; Villa-Achupallas, Rosado, Aguilar, & Galindo-
Riaño, 2018). These indexes allow researchers to gather information on trends and identify
river disturbances sources. Additionally, these indexes are necessary to study the
characteristics of water resources and their quality to ensure the balance between human
activities and the water ecosystem (Rivera, Encina, Muñoz-Pedreros, & Mejias, 2004). The
US National Sanitation Foundation - water quality index (WQI-NSF) is used worldwide for
this type of study. This method referred to here as Ramirezs approach, is based on
characteristics of North American rivers, which relate physical-chemical variables to
average weights assigned to each for evaluating the specific pollution type (Gradilla-
Hernández et al., 2020; Nugraha, Cahyo, & Hardyanti, 2020).
Works carried out in the area it is shown that the Guano River is affected by human
activities, such as the excessive use of water for irrigation and the reception of wastewater
(Castillejos & Arévalo, 2018; Castillo-López, Salas-Cisneros, Logroño-Veloz, & Vinueza-
Veloz, 2021; Cevallos, 2015; Quevedo, 2020). But in these works, contamination is not related
to other characteristics present in the area, such as geomorphology and flow.
Therefore, this work proposes a novel study to determine the water quality of the Guano
River, using a variation of the WQI-Dinius that includes variables such as the average slope
and the rivers flow. In addition, this work compares the results obtained with this variant
in the index with WQI-NSF and WQI-Dinius.
Novasinergia 2022, 5(2), 174-192 176
2. Methodology
2.1. Sampling sites
The Guano River basin (Figure 1) is located in the Ecuadorian highland between
Tungurahua and Chimborazo; the river is the product of the thaws from Chimborazos
volcano and the runoffs generated in the Igualata moorland. The rivers source is
downstream of Andaluza, in the area of Llío, where the Agags and Puluchaca streams merge
at 3090 masl. The Guano River flows from northwest to southeast and runs into the Chambo
River after traveling 21 km. Runoff from slopes in the area feeds the Guano River on its
course (Chidichimo et al., 2018).
Figure 1: Location of the Guano River basin.
According to the vegetation cover and the use of the soil (Table 1), the main anthropic
activity is agriculture since it has a higher percentage of the area than the rest of the micro
basin areas. In addition, the rate of population area corresponds mainly to the town of
Guano, which sits on the banks of the river and is the one that shows the most significant
interference in its quality. To determine the sampling points, vegetation and land use
information (Mendoza et al., 2021) were analyzed (Table 1). The river was traversed from
the upper part to the river mouth, corroborating what was identified in the characteristics
of land use and vegetation data.
Table 1: Area of the vegetal coverage and use of soil.
Vegetal coverage and use of soil
Area (km2)
Natural forest
3.60
Crops
277.58
Grass
28.01
Paramo
69.44
Cities
5.65
In addition, the anthropic activities that affect the environmental conditions of the same are
identified from the preliminary information on the cover and soil use. The river was
explored from the mouth, identifying the characteristics of vegetation cover and soil use
Novasinergia 2022, 5(2), 174-192 177
and the human activities that affect the rivers environmental conditions; thus, 29
observation points of anthropic activities were found (Table 2).
Table 2: Observation points of anthropic activities with their UTM coordinates.
Code
Description
Y
Masl.
P_RIVER 1
Guano river, Llío
9826549
3120
P_CHANNEL 1
Irrigation Channel 1
9822858
2800
P_DISCHARGE 1
Wastewater discharge Colegio Pérez Guerrero
9822398
2760
P_DISCHARGE 2
Wastewater discharge 80 m before the town of Guano1
9822235
2725
P_DISCHARGE 3
Waste water discharge beginning of the town of Guano
9822241
2720
P_DISCHARGE 4
Waste water discharge 70 m downstream
9822178
2720
P_DISCHARGE 5
Waste water discharge 25 m downstream
9822169
2720
P_DISCHARGE 6
Waste water discharge 27 m downstream
9822159
2720
P_DISCHARGE 7
Waste water discharge 25 m downstream
9822135
2720
P_DISCHARGE 8
Discharge of wastewater before the town of Guano
9822121
2720
P_SLOPE 1
Spring Park of the slopes
9822074
2720
P_SLOPE 2
Spring Park of the slopes
9822046
2720
P_DISCHARGE 6
Discharge of residual water 40 m after the park of the
slopes
9822014
2720
P_SLOPE 3
Spring Park of the slopes
9822004
2720
P_DISCHARGE 7
Discharge of residual water 40 m after the park of the
slopes
9821994
2720
P_SLOPE 4
Spring Park of the slopes
9821980
2720
P_DISCHARGE 8
Discharge of residual water 40 m after the park of the
slopes
9821969
2720
P_SLOPE 5
Spring Park of the slopes
9821922
2720
P_RIVER 2
Guano River, before Pebble Spinning Mill
9821879
2680
P_DISCHARGE 9
Wastewater discharge Pebble Spinning Mill
9821858
2680
P_RIVER 3
Guano River, before unloading Santa Teresita sector
9821894
2673
P_DISCHARGE 10
Waste water discharge Santa Teresita
9821860
2640
P_RIVER 4
Guano River, before the Chingazo Canal - Pungal
9821806
2640
P_CHANNEL 3
Canal Chingazo - Pungal
9821776
2607
P_SLOPE 6
Spring of the Elenes
9821465
2600
P_SLOPE 7
Spring of the Elenes
9821436
2600
P_DISCHARGE 13
Wastewater discharge San José Alto
9819720
2560
P_DISCHARGE 14
Waste water discharge Quimiac sector
9819519
2556
P_RIVER 5
Guano River, before the mouth of the Chambo River
9817696
2480
From these sampling points: P_RIVER1, P_RIVER 2, P_RIVER 3, P_RIVER 4, and P_RIVER
5 were selected to determine the water quality of the Guano River concerning the
interference of human activities. In addition to these sampling points, by areas, from the
upper part to the mouth of the river. Once these sampling points were chosen, over the time
frame encompassing July to November 2018 (dry season), the water samples were taken in
triplicate for 18 days at each monitoring point, giving 240 pieces for water quality analysis.
All models were collected by the authors manually in plastic containers. For the physical-
chemical parameters, the bottle (1000 mL) was submerged 20 cm below the water surface
Novasinergia 2022, 5(2), 174-192 178
with the peak of the bottle in the direction of the current until filled the bottle free of bubbles
that may form at the mouth of the bottle. For the microbiological analysis, 100 mL of sample
was taken in a sterile plastic container (Rice, Baird, & Eaton, 2017).
2.2. Parameters used in the indexes
Laboratory analysis for water was performed according to the Standard Methods for the
Examination of Water and Wastewater (23rd ed.) (Rice et al., 2017) as described below:
The Electrometric Method 4550-H+ B for pH was carried out with a Model HI99121 pH
meter, using a model HI1230B electrode by HANNA INSTRUMENTS of Woonsocket,
Rhode Island, United States, which enables a measuring range from pH 2.00 to 16.00. The
method consists of shaking a 100 mL aliquot of water to ensure homogeneity. Then the
electrode is immersed in the sample for 1 min and the pH value is read when the equipment
stabilizes.
The Electrical Conductivity Method 2510 B was performed using a Model SEVEN
COMPACT CONDUCTIVITY S230 conductivity meter, with an electrode Cond probe InLab
710 by METTLER TOLEDO of Greifensee, Switzerland. The range of measurement was from
0.001 to 1000 mS/cm. The method involves shaking an aliquot of 100 mL of water to ensure
homogeneity. Then, the electrode was immersed in the sample for 1 min, and the
conductivity value was read when the equipment stabilized.
The Total Dissolved Solids Method 2540 C was conducted with the previous equipment,
with a measuring range from 0.00 mg/L...1000 g/L. The method involves shaking an aliquot
of 100 mL of water to ensure homogeneity. Then the electrode is immersed in the sample
for 1 min, and the total dissolved solids value is read when the equipment stabilizes.
The Membrane Electrode Method 4500-O G for determining Dissolved Oxygen was carried
out with the Model HI98198 OD and a model HI764113 electrode by HANNA
INSTRUMENTS of Woonsocket, Rhode Island, United States; the measuring range was
from 0.00 to 50 mg/L. This method consists of introducing the electrode into the river bed
so that the water covers the electrode membrane completely. The equipment is allowed to
stabilize for 1 min, and the optical density (OD) value is reported as a concentration in mg/L.
The Nephelometric Method 2130 B for determining Turbidity is carried out with a Model
HI93703 (HANNA INSTRUMENTS of Woonsocket, Rhode Island, United States) with a
measuring range from 0.00 a 1000 FTU. This method involves gently agitating the sample
for 1 min; then the sample is poured into the cell of HANNA INSTRUMENTS HI93700d tr
of Woonsocket, Rhode Island, United States. The turbidity value is read when the
equipment stabilizes and all air bubbles disappear.
A modified phosphates Method 4500-P-E was applied with a range of 0.02 to 2.50 mg/L PO4-
3. This method is carried out with the spectrophotometer HACH DR 5000 of Loveland,
Colorado, United States, using sample cells of 10 mL (HACH 2495402 5000 of Loveland,
Colorado, United States). This method involves gently agitating the sample for 1 min before
placing it into the cell. Then, the contents of one PhosVer3 Reagent Powder Pillow (HACH,
catalog number: 2106069 5000 of Loveland, Colorado, United States) is added to the cell; a
blue color develops if phosphorus is present in the sample. If so, the sample cell should be
Novasinergia 2022, 5(2), 174-192 179
closed immediately and shaken vigorously for 20-30 s. After this, the sample should be
allowed to stand still for 2 min. Next, start program 490 P with the spectrophotometer set to
a wavelength of 880 nm. Insert the blank into the cell holder, push zero, and the display
shows 0.00 mg/L PO4-3. Then, the prepared sample cell is cleaned with reagent, and the
prepared sample is inserted into the cell holder; results are displayed in mg/L PO4-3.
The Nitrogen Method (Nitrate) 4500 NO3- E modified to HACH method 8039 had a
measuring range from 0.3 to 30.0 mg/L NO3-. This method is carried out with a HACH DR
5000 spectrophotometer and Model HACH 2495402 sample cells of 10 mL 5000 of Loveland,
Colorado, United States. This method involves gently agitating the sample for 1 min. Then,
the sample is placed in the cell, and the contents of one NitraVer 5 Reagent Powder Pillow
are added. The sample cell was closed immediately, shaken vigorously for 60 s, and let to
still stand for 5 min. Next, prepare the blank and fill it in a second sample cell. Start program
355 N with wavelength set to 500 nm. Zero the instrument for the blank, clean the sample
cell with reagent, and introduce the sample into the cell holder; results will be shown in
mg/L NO3-.
EDTA Titrimetric Method 2340 C for Total hardness (mgCaCO3/L). This method needs 25
mL of sample. First, one adds to the sample 1 to 2 mL buffer solution (ammonium chloride
and ammonium hydroxide) to give a pH of 10.0 to 10.1. Next, 1 to 2 drops of indicator
solution or an appropriate amount of dry-powder indicator formulation (Eriochrome Black
T -NET) are added. Add standard EDTA (0.01M) titrant slowly, under continuous magnetic
stirring, until the last reddish tinge disappears. The last few drops should be added at 3 to
5 s intervals. At the endpoint, the solution typically turns blue.
The Titration Method 2320 CB for Alkalinity (mgCaCO3/L). In this method, 25 mL of sample
1 to 2 drops indicator solution (Methyl Orange) are added and titrated with a standard 0.1
N sulfuric acid solution. The reagent should be added slowly, under continuous stirring,
until the sample color changes to purple.
Biochemical Oxygen Demand (BOD) 5210 B. 5-Day BOD modified to VELP BOD EVO
Sensor of Usmate, Italy; for this measurement, one used a BOD sensor set consisting of a
BOD Sensor, a dark glass bottle, an alkali holder to absorb the carbon dioxide, and a stirring
bar. BOD (mg/L) value will be obtained directly from the display at any time, even after five
days. For 5 days, the set is kept in an incubator VELP SCIENTIFICA FOC 120I at 20 °C in
Usmate, Italy. Then, a magnetic stirrer is inserted in an amber glass bottle (500 mL), and the
BOD sensor is installed. The sensor reports that the value after 5 days is the one that
determines the BOD5.
Chemical Oxygen Demand (COD) 5220 D, Closed Reflux, Colorimetric Method. One used
HACH DR 5000 spectrophotometer 5000 of Loveland, Colorado, United States. Samples
were gently agitated for 1 min and held in a vial reagent HACH LR (range 3-150 mg COD/L)
at an angle of 45º. A clean pipet was utilized for dispensing 2.00 mL of sample to the vial.
The same procedure was used for another vial filled with deionized water and utilized as
the blank. After closing the vial it should be held by the cap, over a sink. The content of the
vial can be mixed by inverting the vial gently several times. Next, vials are placed in A
preheated DRB200 reactor for 2 h at 120 ºC. After turning off the heat, vials should cool in
Novasinergia 2022, 5(2), 174-192 180
the reactor for 20 min to 120 ºC or less and then cool to room temperature in a tube rack.
The spectrophotometer should be set at 420 nm, and 430 COD LR program should be
started. Samples are recorded relative to the blank, and results are displayed in mg/L COD.
Membrane filter technique for members of the coliform group 9222 modified to Petrifilm
Coliform Count Plate of Northern Minnesota, United States. This method consists of gently
agitating samples for 1 min and placing Petrifilm Coliform Count Plates on the surface. Lift
the top film and, with Pipettor or equivalent held perpendicularly to the plate, place 1 mL
of sample or diluted sample onto the center of the bottom film. Prevent pushing sample off
film to avoid entrapping air bubbles. Do not let top film drop. With the flat side down, place
3MTM PetrifilmTM Spreader on top film over inoculum. Gently apply pressure on the 3M
Petrifilm Spreader to distribute inoculum over a circular area before the gel is formed. Do
not twist or slide the spreader. Lift 3M Petrifilm Spreader. Wait a minimum of 1 min for the
gel to solidify. Incubate plates clear side up in stacks of up to 20. It may be necessary to
humidify the incubator MEMMERT model BE500. 3M Petrifilm Coliform Count Plates can
be counted using the 3MTM Petrifilm Plate Reader on a standard colony counter or other
illuminated magnifiers. Colonies may be isolated for further identification. Lift the top film
and pick the colony from the gel.
The flow was obtained with the float method, this is based on the speed-area principle,
where (n) cross-sectional areas (depth and width) were measured, and the velocity was
obtained from the time the float takes to travel a distance (5m). The Manning coefficient
corrects the flow (Davids et al., 2019). Flow information of the different sampling points was
obtained according to equation (1).
󰇛󰇜 (1)
Where: Q is the flow rate (m3/s), V is the velocity (m/s), AT is the transverse area (m2), K is a
correction factor (Manning coefficient) for rivers with a depth greater than 15 cm.
From the heights of the contour lines, the tool-created TIN is used to develop the digital
terrain elevation model (DEM). Owing to the transform tool of TIN, the elevation raster of
the study area is generated. From this raster, the Spatial Analysis slope tool is used to
spatially determine the Slope (Sc) in the basin. This is done in the ArcGis 10.1 software
(Mendoza et al., 2021).
2.2. Water Quality Indexes
In this study, the river course was divided into transects according to the natural
conditions and the human activities present, leaving five monitoring points for studying the
water quality; these points were chosen based on the pressure on different transects, as
shown in table 2. The methodology of the water quality indices applied at the sampling
points is described below.
The first methodology is the WQI-NSF, proposed by the National Sanitation Foundation;
this is used to assess changes in water quality in specific sections of rivers at different times.
The calculations of this method were carried out by weighting according to the parameter
type (Table 3); that is, a percentage value was assigned to each parameter analyzed, their
total sum being 1. This value was then transformed into a percentage value, with a range
Novasinergia 2022, 5(2), 174-192 181
from 0 to 100 (Akhtar et al., 2021; Gupta & Gupta, 2021; Mukate, Wagh, Panaskar, Jacobs, &
Sawant, 2019; Uddin et al., 2021; Ustaoğlu et al., 2020). Finally, equation (2) was used to
calculate the WQI.
 

 (2)
where Wi is the weighting coefficient for parameter i, I is the index for each parameter, and
n is the total number of parameters.
Table 3. Parameter of quality index WQI-NSF (Akhtar et al., 2021; Gupta & Gupta, 2021; Mukate et al., 2019; Uddin et al.,
2021; Ustaoğlu et al., 2020)
Parameter
Weigth
DO
0.17
Faecal Coliforms
0.16
pH
0.11
BOD
0.11
Nitrates
0.10
Phosphates
0.10
Temperature
0.10
Turbitity
0.08
Dissolved solids
0.07
The second methodology described by Dinius determines the water quality of the sample
according to the degree of water pollution. Thus, it will have a quality index close to 0 for
utterly contaminated water. The index will be 100 for water with excellent conditions
(Hoseinzadeh, Khorsandi, Wei, & Alipour, 2015; Mukate et al., 2019; Zotou, Tsihrintzis, &
Gikas, 2019, 2020). Subsequently, this index indicates that a correction should be made to
the results (Table 4). Each parameter has a weighting value of W that allows obtaining the
corresponding WQI; the weight for each parameter was given in table 4.
Table 4. Parameter of WQI-Dinius (Hoseinzadeh et al., 2015; Mukate et al., 2019; Zotou et al., 2019, 2020)
Parameter
I for WQI calculation
W for WQI calculation
Dissolved Oxygen - OD
0.82*OD + 10.56
0.109
Chemical Oxygen Demand - COD
108 (COD)-0.3494
0.097
Total Coliforms - CT
136 (ColiTotal)-0.1311
0.090
Fecal Coliforms -CF
106 (EColi)-0.1286
0.116
Conductivity
506 (SPC)-0.3315
0.079
Chloride
391 (CL)-0.3480
0.074
Total Hardness
552(Hardness)-0.4488
0.065
Alkalinity
110(Alc)-0.1342
0.063
pH < 6.9
100.6803+0.1856(pH)
0.077
pH = 6.9 7.1
1
pH > 7.1
103.65+0.2216(pH)
Nitrates
125(N)-0.2718
0.09
Color Pt-Co
127(Color)-0.2394
0.063
Turbidity
102.004-0.382|Ta-Ts|
0.077
Novasinergia 2022, 5(2), 174-192 182
The numerical evaluation of the WQI-Dinius is obtained from the geometric mean (Equation
(3)):
  
 (3)
where Wi are the specific weights assigned to each parameter (i), and weighed between 0
and 1, so the sum is equal to 1. Qi is the quality of the parameter (i), which depends on its
concentration, and is rated from 0 to 100. PI represents the multiplication of the variables Q
elevated to power W.
To contemplate the geomorphology and flow of the Guano River in the calculation of water
quality, the WQI-Dinius was modified; therefore three steps were considered: (i) selecting
the parameters, (ii) determining the sub-indexes, and (iii) determining the index by
aggregation (Mukate et al., 2019; Samboni et al., 2007). For this purpose, the selection of
parameters was separated into groups as follows: (a) organic matter: dissolved oxygen in %
saturation and mg/L, biochemical oxygen demand and chemical oxygen demand, (b)
bacteriological matter: total coliforms and fecal coliforms, (c) physical characteristics of
water: color, Turbidity and electrical conductivity, (d) inorganic matter: alkalinity, hardness,
chlorides, hydrogen ion concentration (pH), suspended solids, and total dissolved solids,
(e) nutrients: nitrates, nitrites, phosphates, total phosphorus, and sulfates, (f)
geomorphology characteristics: mean average of the Slope, Slope of the river course in the
area under study, and flow. To apply the WQI-Dinius Modified, equation (4) was used. The
I values were obtained from table 4, and the geomorphological characteristics (I) are equal
to 1. The importance of W (WQI- Dinius Modified) for this method are described in table 5.
 

 (4)
where Wi is the weighting coefficient for parameter i, I is the index for each parameter, and
n is the total number of parameters.
The importance of parameter groups is identified for this case. Then the importance of the
parameters within the parameter group is identified and the weight value is given at the
end
Table 5: Weighing (W) for WQI- Dinius Modified.
Importance
between
groups
Parameter
Weighing (W)
Dinius
Importance
between
parameters
Weighing (W)
WQI- Dinius
Modified
1
Dissolved Oxygen
0.109
1
10.9
COD
0.097
2
9.7
2
Fecal Coliforms
0.116
1
11.6
Total Coliforms
0.09
2
9
3
Flow
-
1
7.25
Average slope of the main cause
-
2
6.45
4
Nitrates
0.09
1
9
5
Conductivity
0.079
1
7.9
Turbidity
0.077
2
7.7
pH
0.077
1
7.7
Total Hardness
0.065
2
6.5
Alkalinity
0.063
3
6.3
Novasinergia 2022, 5(2), 174-192 183
The criteria that were used to determine the quality of the water once calculated with the
WQI-NSF, Dinius-WQI, and modified WQI are shown in table 6 (Akhtar et al., 2021;
Gradilla-Hernández et al., 2020; Gupta & Gupta, 2021; Hoseinzadeh et al., 2015; Mukate et
al., 2019; Nong et al., 2020; Nugraha et al., 2020; Uddin et al., 2021; Ustaoğlu et al., 2020;
Zotou et al., 2020, 2019). The results obtained by this methodology are analyzed according
to the information in table 5 to identify whether the quality is excellent or bad as endpoints
of the valuation.
Table 6: General criteria of WQI (Akhtar et al., 2021).
Type of use
Color
Evaluation range
Quality description
Treatment
USE IN
AGRICULTURE
E
90-100
EXCELLENT
It does not require purification to
be consumed
A
79-90
ACCEPTABLE
Minor purification is needed for
crops that require high water
quality
LC
50-79
SLIGHTLY
CONTAMINATED
Treatment required for most crops
C
30-50
CONTAMINATED
Treatment required for most crops
FC
20-30
STRONGLY
CONTAMINATED
Use only in very resistant crops
EC
0-20
EXCESSIVE
Inacceptable for irrigation
3. Results
The geomorphology of the Guano River (Table 7) shows that the micro-basin is small
according to the area. The average slope of the micro-basin is medium-rough, the sections
of the leading cause have medium-rough slopes in the upper part, and the lower part has
gentle slopes.
Table 7: Geomophology of the Guano river.
Parameter
Initials
Unit
Value
Area
A
km2
384.28
Perimeter
P
km
94.26
Length of the main channel
Lc
km
39.15
The average slope of the basin
Sm
%
13.74
The average slope of the main channel
Sc
%
16.35
Slope first section
Sc1
%
11.2
Slope the second section
Sc2
%
3.93
Slope third section
Sc3
%
0.07
Slope fourth section
Sc4
%
2.59
Slope the fifth section
Sc5
%
2.39
In the high areas, the Guano River has an average flow of 0.68 m3/s, reaching the mouth with
a flow of 1.83 m3/s. The flow decreases in the central region because there are irrigation
Novasinergia 2022, 5(2), 174-192 184
channels along the river that redirect water from the river’s natural course (Table 8). Still,
the flow recovers because springs provide additional fresh water to the river. Moreover, the
physical-chemical and microbiological parameters that constitute the WQI were analyzed
and performed for the five sampling points, as shown in Tables 9, 10, 11, 12, and 13.
Table 8: Average flow of the Guano river at the sampling points (m3/s).
Sampling
P_RIVER 1
P_RIVER 2
P_RIVER 3
P_RIVER 4
P_RIVER 5
Sampling 1
0.79
0.97
0.94
1.83
2.14
Sampling 2
0.81
0.99
0.96
1.87
2.19
Sampling 3
0.60
0.74
0.71
1.39
1.63
Sampling 4
0.73
0.90
0.87
1.70
1.99
Sampling 5
0.75
0.92
0.90
1.74
2.04
Sampling 6
0.56
0.69
0.66
1.29
1.51
Sampling 7
0.68
0.84
0.81
1.58
1.85
Sampling 8
0.70
0.86
0.83
1.62
1.90
Sampling 9
0.52
0.64
0.62
1.20
1.41
Sampling 10
0.64
0.78
0.76
1.47
1.72
Sampling 11
0.65
0.80
0.77
1.51
1.76
Sampling 12
0.48
0.59
0.57
1.12
1.31
Sampling 13
0.59
0.73
0.70
1.37
1.60
Sampling 14
0.61
0.74
0.72
1.40
1.64
Sampling 15
0.73
0.90
0.87
1.70
1.99
Sampling 16
0.83
1.02
0.99
1.93
2.26
Sampling 17
0.85
1.05
1.02
1.98
2.31
Sampling 18
0.63
0.78
0.75
1.47
1.72
Table 9: Results of the physical-chemical and microbiological analysis in P_RIVER 1.
Sampli
ng
pH
Conduc
tivity
Temperat
ure
Dissolved
Oxygen
Turbid
ity
ST
D
Phosph
ate
Nitra
te
Total
hardness
Alkalinit
y
BOD
COD
Total
Colifor
ms
Fecal
Coliforms
-
µS/cm
ºC
mg/L
NTU
mg/
L
mg/L
mg/
L
mg
CaCO3 /L
mg
CaCO3/L
mg
O2/L
mg/L
ufc/100
mL
ufc/100 mL
1
7.75
617
17.15
6.39
5.80
273
0.99
15.47
292
54.40
1.89
16.80
321
130
2
7.80
599
17.56
5.96
6.30
279
1.80
10.14
311
48.93
2.15
27.39
239
59
3
7.60
616
16.82
5.03
9.11
272
0.98
10.60
291
48.93
1.65
16.43
333
83
4
7.67
651
17.20
4.77
10.42
275
0.89
10.27
270
50.02
1.55
12.05
662
141
5
6.89
657
18.05
5.72
13.63
302
1.11
24.18
304
56.23
3.66
21.18
399
141
6
7.62
933
17.98
5.72
8.98
366
1.96
20.77
393
53.93
1.01
20.81
587
147
7
7.54
795
15.98
4.20
8.08
435
0.81
7.64
304
46.81
3.42
21.18
408
136
8
7.00
966
15.94
4.94
10.18
673
1.02
11.12
397
48.02
3.64
20.08
500
154
9
7.61
644
16.08
7.35
7.79
310
1.11
20.42
320
59.74
2.04
18.56
424
172
10
7.66
626
16.48
6.91
8.29
315
2.02
13.38
342
53.72
2.32
30.26
315
78
11
7.46
643
15.75
5.99
11.10
310
1.10
13.99
319
53.72
1.78
18.16
440
110
12
7.52
678
16.13
5.73
12.41
311
1.00
13.56
296
54.93
1.67
13.31
874
186
13
6.75
684
16.97
6.68
15.62
338
1.24
31.92
334
61.74
3.94
23.40
527
186
14
7.48
960
16.90
6.68
10.97
402
2.20
21.81
431
59.21
1.59
23.00
775
194
15
7.40
822
14.91
5.15
10.06
471
0.91
10.08
334
51.40
3.69
23.40
539
179
16
6.86
993
14.87
5.90
12.17
709
1.15
14.67
436
52.72
3.92
22.19
660
203
17
7.47
671
15.01
8.31
9.78
346
1.24
26.95
352
65.59
2.20
20.51
559
227
18
7.52
653
17.56
7.87
10.28
352
2.26
17.67
376
58.99
2.50
33.44
416
103
Novasinergia 2022, 5(2), 174-192 185
Table 10: Results of the physical-chemical and microbiological analysis in P_RIVER 2.
Sampl
ing
pH
Conducti
vity
Tempera
ture
Dissolved
Oxygen
Turbid
ity
ST
D
Phosph
ate
Nitr
ate
Total
hardness
Alkalinit
y
BOD
COD
Total
Coliform
s
Fecal
Coliforms
-
µS/cm
ºC
mg/L
NTU
mg
/L
mg/L
mg/
L
mg
CaCO3 /L
mg
CaCO3/L
mg
O2/L
mg/L
ufc/100
mL
ufc/100 mL
1
7.90
721
18.95
7.39
4.19
197
0.94
14.71
350
65.28
2.27
20.15
988
314
2
7.94
700
19.40
6.89
4.55
201
1.71
9.64
374
58.71
2.58
32.86
734
143
3
7.74
720
18.59
5.82
6.58
197
0.93
10.07
349
58.71
1.98
19.72
1025
201
4
7.81
761
19.01
5.52
7.53
198
0.85
9.76
324
60.02
1.86
14.46
2038
340
5
7.02
768
19.94
6.62
9.85
218
1.06
22.99
365
67.47
4.39
25.41
1229
340
6
7.76
1091
19.86
6.62
6.49
264
1.87
19.74
471
64.71
1.71
24.97
1806
355
7
7.68
929
17.66
4.85
5.83
314
0.77
7.26
365
56.17
4.11
25.41
1256
328
8
7.13
1129
17.62
5.71
7.36
486
0.97
10.57
476
57.61
4.36
24.10
1538
372
9
7.75
752
17.77
8.50
5.63
224
1.05
19.41
384
71.68
2.45
22.27
1304
414
10
7.80
732
18.21
8.00
5.99
228
1.92
12.72
410
64.46
2.78
36.31
969
188
11
7.60
752
17.40
6.92
8.02
224
1.04
13.29
383
64.46
2.14
21.79
1353
266
12
7.66
792
17.83
6.63
8.96
225
0.95
12.89
356
65.91
2.01
15.98
2690
449
13
6.87
799
18.76
7.72
11.29
244
1.18
30.34
400
74.08
4.73
28.08
1622
449
14
7.61
1123
18.68
7.72
7.93
291
2.09
20.73
517
71.05
1.91
27.60
2384
469
15
7.54
961
16.47
5.96
7.27
340
0.86
9.58
400
61.67
4.43
28.08
1657
433
16
6.99
1160
16.43
6.82
8.79
512
1.09
13.95
523
63.26
4.70
26.63
2030
490
17
7.61
784
16.58
9.61
7.07
250
1.18
25.62
422
78.70
2.64
24.61
1721
547
18
7.66
763
19.40
9.10
7.42
254
2.15
16.80
450.56
70.78
3.00
40.12
1279
248
Table 11: Results of the physical-chemical and microbiological analysis in P_RIVER 3.
Sampli
ng
pH
Conducti
vity
Tempera
ture
Dissolved
Oxygen
Turbid
ity
ST
D
Phosph
ate
Nitr
ate
Total
hardness
Alkalinit
y
BOD
COD
Total
Coliform
s
Fecal
Coliforms
-
µS/cm
ºC
mg/L
NTU
mg
/L
mg/L
mg/
L
mg
CaCO3 /L
mg
CaCO3/L
mg
O2/L
mg/L
ufc/100
mL
ufc/100 mL
1
6.71
613
16.11
6.28
3.56
168
0.80
12.50
298
55.49
1.93
17.13
839
267
2
6.75
595
16.49
5.86
3.87
171
1.46
8.19
318
49.90
2.19
27.93
624
121
3
6.58
612
15.80
4.94
5.59
168
0.79
8.56
296
49.90
1.69
16.76
871
171
4
6.64
647
16.16
4.69
6.4
169
0.72
8.30
275
51.02
1.58
12.29
1732
289
5
6.70
652
16.95
5.62
8.37
185
0.90
19.54
310
57.35
3.73
21.60
1044
289
6
6.59
928
16.88
5.62
5.52
225
1.59
16.78
400
55.00
1.41
21.23
1535
302
7
6.53
790
15.01
4.13
4.96
267
0.65
6.17
310
47.74
3.49
21.60
1067
279
8
6.66
959
14.97
4.86
6.25
413
0.83
8.98
405
48.97
3.71
20.48
1307
316
9
6.59
640
15.10
7.22
4.79
190
0.89
16.50
327
60.93
2.08
18.93
1108
352
10
6.63
622
15.48
6.80
5.09
194
1.63
10.82
349
54.79
2.36
30.86
823
160
11
6.46
639
14.79
5.88
6.82
190
0.89
11.30
325
54.79
1.82
18.52
1150
226
12
6.51
674
15.15
5.63
7.62
191
0.81
10.95
302
56.02
1.71
13.58
2287
382
13
6.57
679
15.94
6.56
9.59
208
1.01
25.79
340
62.97
4.02
23.87
1378
382
14
6.47
954
15.88
6.56
6.74
247
1.78
17.62
440
60.39
1.62
23.46
2026
399
15
6.41
817
14.00
5.07
6.18
289
0.73
8.15
340
52.42
3.77
23.87
1409
368
16
6.41
986
13.97
5.80
7.48
436
0.93
11.86
444
53.77
4.00
22.63
1726
417
17
6.47
666
14.10
8.16
6.01
213
1.00
21.78
359
66.90
2.24
20.92
1463
465
18
6.51
649
16.49
7.74
6.31
216
1.83
14.28
383
60.16
2.55
34.10
1087
211
Novasinergia 2022, 5(2), 174-192 186
Table 12: Results of the physical-chemical and microbiological analysis in P_RIVER 4.
Sampli
ng
pH
Conducti
vity
Tempera
ture
Dissolved
Oxygen
Turbid
ity
ST
D
Phosph
ate
Nitr
ate
Total
hardness
Alkalinit
y
BOD
COD
Total
Coliform
s
Fecal
Coliforms
-
µS/cm
ºC
mg/L
NTU
mg
/L
mg/L
mg/
L
mg
CaCO3 /L
mg
CaCO3/L
mg
O2/L
mg/L
ufc/100
mL
ufc/100 mL
1
7.68
570
16.32
5.94
6.83
321
1.01
15.87
266
49.67
1.73
15.33
333
84
2
7.73
554
16.70
5.54
7.41
328
1.85
10.40
284
44.67
1.96
25.00
286
38
3
7.53
570
16.00
4.68
10.72
321
1.00
10.87
265
44.67
1.51
15.00
340
54
4
7.60
602
16.37
4.44
12.26
323
0.92
10.53
246
45.67
1.42
11.00
528
91
5
6.83
607
17.17
5.32
16.04
355
1.14
24.80
277
51.33
3.34
19.33
378
91
6
7.55
863
17.10
5.32
10.57
430
2.01
21.30
358
49.23
1.51
19.00
485
95
7
7.47
735
15.20
3.90
9.50
511
0.83
7.83
277
42.73
3.14
19.33
383
87
8
6.94
893
15.17
4.59
11.98
791
1.05
11.40
362
43.83
3.22
18.33
435
99
9
7.54
595
15.30
6.83
9.17
364
1.13
20.94
292
54.53
1.86
16.94
392
110
10
7.59
579
15.68
6.43
9.75
371
2.07
13.73
312
49.04
2.12
27.63
330
50
11
7.39
595
14.98
5.57
13.06
364
1.12
14.34
291
49.04
1.63
16.58
401
71
12
7.46
627
15.35
5.33
14.60
366
1.03
13.90
270
50.14
1.53
12.16
649
120
13
6.69
632
16.15
6.21
18.38
398
1.28
32.74
305
56.36
3.60
21.36
451
120
14
7.41
888
16.08
6.21
12.91
473
2.25
22.37
393
54.05
1.45
21.00
592
125
15
7.33
760
14.18
4.79
11.84
554
0.93
10.34
305
46.92
3.38
21.36
457
115
16
6.80
918
14.15
5.48
14.32
834
1.18
15.05
398
48.13
3.47
20.26
526
131
17
7.40
620
14.28
7.72
11.51
408
1.27
27.65
321
59.88
2.01
18.72
469
146
18
7.45
604
16.70
7.32
12.09
414
2.32
18.12
343
53.85
2.28
30.53
387
66
Table 13. Results of the physical-chemical and microbiological analysis in P_RIVER 5.
Sampli
ng
pH
Conducti
vity
Tempera
ture
Dissolved
Oxygen
Turbid
ity
ST
D
Phosph
ate
Nitr
ate
Total
hardness
Alkalinit
y
BOD
COD
Total
Coliform
s
Fecal
Coliforms
-
µS/cm
ºC
mg/L
NTU
mg
/L
mg/L
mg/
L
mg
CaCO3 /L
mg
CaCO3/L
mg
O2/L
mg/L
ufc/100
mL
ufc/100 mL
1
7.16
590
18.96
6.15
7.07
333
1.05
16.42
276
51.41
1.79
15.87
189
52.00
2
7.20
573
17.28
5.73
7.67
339
1.91
10.76
294
46.23
2.03
25.88
141
24.00
3
7.02
590
18.63
4.84
11.09
333
1.04
11.25
275
46.23
1.56
15.53
197
33.00
4
7.08
623
21.08
4.60
12.69
334
0.95
10.90
255
47.27
1.47
11.39
391
56.00
5
6.36
629
19.84
5.51
16.60
367
1.18
25.67
287
53.13
3.46
20.01
236
56.00
6
7.03
894
20.80
5.51
10.94
445
2.08
22.05
371
50.95
1.76
19.67
346
59.00
7
6.96
761
19.87
4.04
9.83
529
0.86
8.11
287
44.23
2.67
20.01
241
54.00
8
6.46
924
20.87
4.75
12.40
819
1.09
11.80
375
45.37
2.89
18.98
295
61.00
9
7.03
616
17.90
7.07
9.49
377
1.17
21.68
303
56.44
1.93
17.54
250
69.00
10
7.07
599
16.23
6.66
10.09
384
2.14
14.21
323
50.76
2.19
28.59
186
31.00
11
6.89
616
17.57
5.76
13.51
377
1.16
14.85
302
50.76
1.68
17.16
260
44.00
12
6.95
649
20.02
5.52
15.11
379
1.06
14.39
280
51.90
1.58
12.58
516
74.00
13
6.23
654
18.78
6.43
19.02
412
1.32
33.88
315
58.34
3.73
22.11
311
74.00
14
6.90
919
19.75
6.43
13.36
490
2.33
23.15
407
55.95
1.50
21.73
457
78.00
15
6.83
787
18.82
4.96
12.25
574
0.96
10.70
315
48.56
2.89
22.11
318
72.00
16
6.33
950
19.82
5.68
14.82
864
1.22
15.57
412
49.81
2.56
20.97
389
81.00
17
6.90
642
16.85
7.99
11.91
422
1.32
28.61
332
61.97
2.11
19.38
330
91.00
18
6.94
625
17.28
7.58
12.51
429
2.40
18.76
355
55.74
2.05
31.59
245
41.00
Figure 2 shows the mean values obtained through the three indices for July to November
2018. The values are between 59 and 73. It is observed that July presents high values and
October low values. Once the samples were analyzed, the quality index was determined via
Novasinergia 2022, 5(2), 174-192 187
three methods, as described, for each sampling point (Table 14). The results show two types
of quality and water, acceptable (A) and slightly contaminated (LC), predominating the LC
classification in the three indices for type A.
Figure 2: Values for WQI-NSF, WQI-Dinius and WQI-Dinius Modified.
Table 14: Results of WQI index for the Guano River.
SAMPLE
WQI-
NSF
WQI-
Dinius
WQI-Dinius
Modified
Sampling 1
A
A
LC
Sampling 2
LC
A
LC
Sampling 3
LC
A
LC
Sampling 4
LC
A
LC
Sampling 5
LC
LC
LC
Sampling 6
LC
A
LC
Sampling 7
LC
LC
LC
Sampling 8
LC
LC
LC
Sampling 9
A
LC
LC
Sampling 10
LC
A
LC
Sampling 11
LC
A
LC
Sampling 12
LC
LC
LC
Sampling 13
LC
LC
LC
Sampling 14
LC
LC
LC
Sampling 15
LC
LC
LC
Sampling 16
LC
LC
LC
Sampling 17
LC
LC
LC
Sampling 18
LC
LC
LC
4. Discussion
According to the results, the Guano River is a small micro-basin, with slopes ranging
from medium-rough to gentle. It also shows the results of the slopes in the sections studied
since the river slopes range from medium-rough to soft. In addition, it is observed that the
flow at the sampling points varies depending on human activities and natural conditions.
In the upper part of the river (P_RIVER 1), the flow is small; at point P_RIVER 2, it increases
55
57
59
61
63
65
67
69
71
73
75
JULY AUGUST SEPTEMBER OCTOBER NOVEMBER
WQI
WQI-NSF WQI-Dinius WQI-Dinius Modified
Novasinergia 2022, 5(2), 174-192 188
a little due to the effect of the runoff of the sector. From point P_RIVER 3 the flow decreases,
because there are irrigation canals (Castillo-López et al., 2021; Mendoza et al., 2021;
Quevedo, 2020). P_RIVER 4 and P_RIVER 5 show an increase in the flow due to the presence
of a spring that again provides water to the river (Chidichimo et al., 2018). Moreover, the
water quality results also depend on anthropic and natural conditions. In other matters, the
water quality results also depend on the anthropic and natural conditions.
From a geomorphological point of view, it is evident that the slope influences the water
quality because the effect of the slope on the rivers is essential; it allows the self-purification
of the water with high slopes (Marimón-Bolívar, Jiménez, Toussaint-Jiménez, &
Domínguez, 2021; Šaulys, Survile, & Stankevičiene, 2019; Toussaint-Jimenez, Marimon-
Bolivar, & Dominguez, 2020).
This is perceptible in the water quality in the upper part of the river, where there are
medium-rough slopes, allowing the presence of surface runoff, oxygenation of the water,
and the dissolution of pollutants. The slope is gentle in the middle and lower part of the
river, minimizing self-purification conditions. In addition, 88% of the river area is affected
by agricultural activity, extending from 3000 to 2480 masl. The actions of towns (San Andres
and Guano) are notoriously detrimental to the water quality by wastewater discharges
directly into the riverbed.
Furthermore, Guano's artisan activities, such as leather and textile garment making,
produce organic contaminants, including detergents, dyes, and heavy metals. Furthermore,
non-technical agriculture has deteriorated the water quality indicators. This includes the
riparian forests, which have disappeared almost entirely from the river banks, causing
erosion and drag of the materials (Quevedo, 2020). In this context, the water quality
assessment was carried out at the five sampling points of the fundamental cause; the values
shown are the average of the 5 points. The qualitative evaluation of the water quality of the
Guano River is: WQI-NSF values acceptable (A) in 2 samples and slightly contaminated (LC)
in the rest of the samples. WQI-Dinius values seven samples as good (A) and 11 as slightly
soiled (LC). In the case of the modified WQI, the evaluation is somewhat contaminated (LC)
in all the samples. That is to say; water treatment is necessary to improve its condition so
that it should not affect the quality of the crops.
5. Conclusions
The three indices reveal that the water is slightly contaminated and must be treated
before use. The WQI-Dinius Modified gives lower values concerning the other two indices,
as it shows the effects of flow and slope in determining water quality. When there is less
flow and the water is contaminated in areas of human activity, such as areas with
wastewater discharge. In this context, the water Quality with WQI-NSF and WQI-Dinius
has been used and validated in several rivers worldwide. Therefore, the results obtained
with these indices for the Guano River are considered valid, showing that the river is slightly
contaminated.
The study of the water quality of the Guano River allowed us to see the approximation to
reality of the WQI-Dinius modified since when comparing them with WQI-NSF and WQI-
Novasinergia 2022, 5(2), 174-192 189
Dinius, the results are lower. Still, the qualitative assessment is similar regarding the water
quality along the river. In the same way, it was possible to assess how the slope and flow
parameters affect the value of the WQI-Dinius Modified since it was noticed that there is a
more significant contamination in the areas with slope and low flow, other areas with higher
flow and greater slope. In the lower part of the river, the water quality improves due to the
greater volume of water and the presence of springs, which allows the dilution of pollutants
and oxygenation of the water.
Although the index shows somewhat different values, it should be studied in greater detail,
with a more significant number of physical-chemical data, for several years and in other
rivers with similar characteristics. In the same way, the sampling of the parameters should
be carried out in the dry season, where there is less flow. The effect of the flow and the slope
on the self-purification of the river water would probably be observed better: This is because
the slope and the flow are new parameters in the WQI that need further study for the
method to be reliable. In addition, the results in rivers already studied must be validated to
verify if this can contribute to improving this type of water quality study.
Interest conflict
The funders had no role in the study design; in the collection, analysis or
interpretation of data; in the writing of the manuscript or in the decision to publish the
results.
Authors’ contributions
Following the internationally established taxonomy for assigning credits to authors
of scientific articles (https://casrai.org/credit/). The authors declare their contributions in the
following matrix:
Guananga, N.
Mendoza, B.
Guananga, F.
Bejar, J.
Carbonel, C.
Escobar, S.
Guerrero, A.
Conceptualization
Formal Analysis
Investigation
Methodology
Resources
Validation
Writing-review & editing
References
Akhtar, N., Ishak, M. I. S., Ahmad, M. I., Umar, K., Md Yusuff, M. S., Anees, M. T.,
Almanasir, Y. K. A. (2021). Modification of the Water Quality Index (WQI) Process
for Simple Calculation Using the Multi-Criteria Decision-Making (MCDM) Method:
Novasinergia 2022, 5(2), 174-192 190
A Review. Water, 13(7), 905. https://doi.org/10.3390/W13070905
Bitsch, B., Raymond, S. N., Buchhave, L. A., Bello-Arufe, A., Rathcke, A. D., & Schneider, A.
D. (2021). Dry or water world? How the water contents of inner sub-Neptunes
constrain giant planet formation and the location of the water ice line. Astronomy &
Astrophysics, 649, L5. https://doi.org/10.1051/0004-6361/202140793
Castillejos, P., & Arévalo, P. (2018). Diatomeas epilticas como bioindicadoras de eutrofizacin en
la microcuenca del ro “Guano”, provincia de Chimborazo (Trabajo para obtener el título
de Máster en Gestión Ambiental). Quito:Ecuador. Universidad Internacional SEK.
Retrieved from http://link.springer.com/10.1007/978-3-319-59379-
1%0Ahttp://dx.doi.org/10.1016/B978-0-12-420070-8.00002-
7%0Ahttp://dx.doi.org/10.1016/j.ab.2015.03.024%0Ahttps://doi.org/10.1080/07352689.
2018.1441103%0Ahttp://www.chile.bmw-motorrad.cl/sync/showroom/lam/es/
Castillo-López, G., Salas-Cisneros, P., Logroño-Veloz, M. A., & Vinueza-Veloz, M. F. (2021).
Hexavalent Chromium in Waters for Human Consumption and Irrigation in the
Guano Canton. ESPOCH Congresses: The Ecuadorian Journal of S.T.E.A.M., 524532.
https://doi.org/10.18502/ESPOCH.V1I1.9592
Cevallos, C. (2015). Caracterización de la calidad hídrica de la Microcuenca del río Guano (Trabajo
final de titulación), Riobamba:Ecuador, Escuela Superior Politécnica de Chimborazo.
Retrieved from http://dspace.espoch.edu.ec/handle/123456789/4061
Chidichimo, F., Mendoza, B. T., De Biase, M., Catelan, P., Straface, S., & Di Gregorio, S.
(2018). Hydrogeological modeling of the groundwater recharge feeding the Chambo
aquifer, Ecuador. AIP Conference Proceedings, 2022(1), 020003.
https://doi.org/10.1063/1.5060683
Davids, J. C., Rutten, M. M., Pandey, A., Devkota, N., David Van Oyen, W., Prajapati, R., &
Van De Giesen, N. (2019). Citizen science flow-an assessment of simple streamflow
measurement methods. Hydrology and Earth System Sciences, 23(2), 10451065.
https://doi.org/10.5194/HESS-23-1045-2019
Gradilla-Hernández, M. S., de Anda, J., Garcia-Gonzalez, A., Montes, C. Y., Barrios-Piña, H.,
Ruiz-Palomino, P., & Díaz-Vázquez, D. (2020). Assessment of the water quality of a
subtropical lake using the NSF-WQI and a newly proposed ecosystem specific water
quality index. Environmental Monitoring and Assessment, 192(5), 119.
https://doi.org/10.1007/S10661-020-08265-7
Gupta, S., & Gupta, S. K. (2021). A critical review on water quality index tool: Genesis,
evolution and future directions. Ecological Informatics, 63, 101299.
https://doi.org/10.1016/J.ECOINF.2021.101299
Hoseinzadeh, E., Khorsandi, H., Wei, C., & Alipour, M. (2015). Evaluation of Aydughmush
River water quality using the National Sanitation Foundation Water Quality Index
(NSFWQI), River Pollution Index (RPI), and Forestry Water Quality Index (FWQI).
Desalination and Water Treatment, 54(11), 29943002.
https://doi.org/10.1080/19443994.2014.913206
Machado, A. V. M., dos Santos, J. A. N., Alves, L. M. C., & Quindeler, N. da S. (2019).
Novasinergia 2022, 5(2), 174-192 191
Contributions of Organizational Levels in Community Management Models of Water
Supply in Rural Communities: Cases from Brazil and Ecuador. Water, 11(3), 537.
https://doi.org/10.3390/W11030537
Marimón-Bolívar, W., Jiménez, C., Toussaint-Jiménez, N., & Domínguez, E. (2021). Use of
Neural Networks to Estimate a Global Self-Purification Capacity Index for Mountain
Rivers: A Case Study in Bogota River Basin. Earth Systems and Environment 2021, 1
13. https://doi.org/10.1007/S41748-021-00248-Z
Mendoza, B., Fiallos, M., Iturralde, S., Santillán, P., Guananga, N., Bejar, J., Sándor, Z.
(2021). Determination of field capacity in the Chibunga and Guano rivers micro-
basins. F1000Research, 10. https://doi.org/10.12688/F1000RESEARCH.28143.1
Moreano-Logroño, J. A., & Mancheno-Herrera, C. A. (2020). Analysis of the productivity
and competitiveness of the agricultural sector in Ecuador. Dominio de las Ciencias, 6(5),
412428.
https://www.dominiodelasciencias.com/ojs/index.php/es/article/download/1610/307
3
Mukate, S., Wagh, V., Panaskar, D., Jacobs, J. A., & Sawant, A. (2019). Development of new
integrated water quality index (IWQI) model to evaluate the drinking suitability of
water. Ecological Indicators, 101, 348354.
https://doi.org/10.1016/J.ECOLIND.2019.01.034
Nong, X., Shao, D., Zhong, H., & Liang, J. (2020). Evaluation of water quality in the South-
to-North Water Diversion Project of China using the water quality index (WQI)
method. Water Research, 178, 115781. https://doi.org/10.1016/J.WATRES.2020.115781
Nugraha, W. D., Cahyo, M. R. D., & Hardyanti, N. (2020). The Influence of Land Use To River
Water Quality Level by Using The Water Quality Index Of National Sanitation Foundation
(WQI-NSF) Method (Case Study: Klampok River, Semarang District). In The 5th
International Conference on Energy, Environmental and Information System
(ICENIS). E3S Web of Conferences, 202, 04006.
https://doi.org/10.1051/E3SCONF/202020204006
Quevedo, D. J. T. (2020). Determinacin de la vulnerabilidad hdrica del ro Guano de la provincia
de Chimborazo, en cantidad y calidad y su disponibilidad frente al cambio climtico
(Monografía previo a la obtención del título de Licenciada en Ciencias de la
Educación, mención en Educaión especial y Preescolar). Cuenca;Ecuador.
Universidad el Azuay. Retrieved from
http://dspace.uazuay.edu.ec/bitstream/datos/7646/1/06678.pdf
Rice, E., Baird, R., & Eaton, A. (2017). Standard Methods for the Examination of Water and
Wastewater ed-23rd. Washington DC: American Public Health Association (APHA),
American Water Works Association (AWWA) and Water Environment Federation
(WEF). Retrieved from https://www.standardmethods.org/
Rivera, N. R., Encina, F., Muñoz-Pedreros, A., & Mejias, P. (2004). La Calidad de las Aguas
en los Ríos Cautín e Imperial, IX Región-Chile. Información Tecnológica, 15(5), 89101.
https://doi.org/10.4067/S0718-07642004000500013
Novasinergia 2022, 5(2), 174-192 192
Samboni, E., Carvajal, Y., & Escobar, J. C. (2007). A review of physical-chemical parameters
as water quality and contamination indicators. Ingeniería e Investigación, 27(3), 172
181. http://www.scielo.org.co/pdf/iei/v27n3/v27n3a19.pdf
Šaulys, V., Survile, O., & Stankevičiene, R. (2019). An Assessment of Self-Purification in
Streams. Water, 12(1), 87. https://doi.org/10.3390/W12010087
Shakir, A., Chaudhry, A. S., & Qazi, J. I. (2012). Impact of anthropogenic activities on
physico-chemical parameters of water and mineral uptake in Catla catla from river
Ravi, Pakistan. Environmental Monitoring and Assessment,185, 28332842.
https://doi.org/10.1007/S10661-012-2753-3
Singh, J., Yadav, P., Pal, A. K., & Mishra, V. (2020). Water Pollutants: Origin and Status. In
D. Pooja, P. Kumar, P. singh, & S. Patil (Eds.), Sensors in Water Pollutants Monitoring:
Role of Material (pp.520). https://doi.org/10.1007/978-981-15-0671-0_2
Toussaint-Jimenez, N., Marimon-Bolivar, W., & Dominguez, E. (2020). Estimation of a
global self-purification capacity index for Mountain Rivers from water quality data
and hydrotopographic characteristics. 2020 Congreso Internacional de Innovacion y
Tendencias en Ingenieria, CONIITI.
https://doi.org/10.1109/CONIITI51147.2020.9240307
Uddin, M. G., Nash, S., & Olbert, A. I. (2021). A review of water quality index models and
their use for assessing surface water quality. Ecological Indicators, 122, 107218.
https://doi.org/10.1016/j.ecolind.2020.107218
Ustaoğlu, F., Tepe, Y., & Taş, B. (2020). Assessment of stream quality and health risk in a
subtropical Turkey river system: A combined approach using statistical analysis and
water quality index. Ecological Indicators, 113, 105815.
https://doi.org/10.1016/J.ECOLIND.2019.105815
Villa-Achupallas, M., Rosado, D., Aguilar, S., & Galindo-Riaño, M. D. (2018). Water quality
in the tropical Andes hotspot: The Yacuambi river (southeastern Ecuador). Science of
The Total Environment, 633, 5058. https://doi.org/10.1016/J.SCITOTENV.2018.03.165
Zotou, I., Tsihrintzis, V. A., & Gikas, G. D. (2019). Performance of Seven Water Quality
Indices (WQIs) in a Mediterranean River. Environmental Monitoring and Assessmen,
191. https://doi.org/10.1007/S10661-019-7652-4
Zotou, I., Tsihrintzis, V. A., & Gikas, G. D. (2020). Water quality evaluation of a lacustrine
water body in the Mediterranean based on different water quality index (WQI)
methodologies. Journal of Environmental Science and Health, Part A, 55(5).
https://doi.org/10.1080/10934529.2019.1710956