Universidad Nacional de Chimborazo
NOVASINERGIA 2019, Vol. 2, No. 2, junio-noviembre (68-74)
ISSN: 2631-2654
https://doi.org/10.37135/unach.ns.001.04.07
Research Article
http://novasinergia.unach.edu.ec
Drinking water wastage through sanitary equipment
Desperdicio de agua a través del equipo sanitario
Alfonso Arellano
*
, Carlos Izurieta , Carlos Bravo , Alexis Merino , Danilo Yépez
Facultad de Ingeniería, Universidad Nacional de Chimborazo, Avda. Antonio José de Sucre, Km 1.5 Vía a Guano,
Riobamba, Ecuador, 060108; cizurieta@unach.edu.ec; cbravo.fic@unach.edu.ec; amerino.fic@unach.edu.ec;
dyepez@unach.edu.ec
* Correspondence: aarellano@unach.edu.ec
Recibido 16 mayo 2019; Aceptado 08 noviembre 2019; Publicado 10 diciembre 2019
Abstract:
Considering the climatic changes, the overall urbanization problems, and the
technological advances, drinking water demand should always be a matter to
research yet. In developing countries, many people still do not get enough
drinking water to satisfy their basic needs. There is insufficient technical
information to apply in water management to optimize drinking water
consumption (DWC) and distribute it better. This study identifies the
correlation between DWC and sanitary devices (SD). It provides some models
to calculate the DWC by knowing how many SD there are in a residential
building. The primary data obtained from 11 Ecuadorean cities contains
information about social, economic, climatic, demographic, anthropogenic
characteristics, and DWC for six months. Some SD outliers are handled with a
Box plot. We got some lineal models with a perfect or robust correlation
(R>0.75; p-value< 0.05) in big cities. In medium cities, the Sanitary equipment
(SE) is used to calculate the DWC by another lineal model (R=0.4315; p-
value=0.0097). These mathematical models are essential tools to define DWC
optimization policies. The DWC increases because of the number of SD
increases in medium and big cities. Water wastage occurs through excessive
SD in residential buildings.
Keywords:
Consumption, drinking water, sanitary devices, wastage, water
Resumen:
Considerando los cambios climáticos, los problemas urbanísticos y los avances
tecnológicos, la demanda de agua potable seguirá siendo un tema de
investigación. En países en desarrollo mucha gente todavía no tiene suficiente
agua potable para satisfacer sus necesidades básicas. No hay suficiente
información técnica aplicada en la gestión del agua para optimizar los
consumos de agua (DWC) y para mejorar su distribución. Este estudio
identifica la correlación entre DWC y los aparatos sanitarios (SD). Provee
modelos para calcular DWC a través del número de SD que hay en un edificio
residencial. La información primaria obtenida en 11 ciudades ecuatorianas
contiene las características sociales, económicas, climáticas, demográficas y
antropogénicas y acerca de los consumos de agua potable durante seis meses.
Algunos valores atípicos fueron procesados a través de los diagramas de cajas
y bigotes. Utilizando análisis estadístico descriptivo se encontraron modelos
lineales con una perfecta o muy fuerte correlación (R>0.75; p valor< 0.05) en
las ciudades grandes. En las ciudades medianas, el equipo sanitario (SE) sirve
para calcular el DWC a través de otro modelo lineal (R=0.4315; p-
valor=0.0097). Estos modelos matemáticos son herramientas importantes para
definir políticas para optimización del consumo de agua potable. En las
ciudades grandes y medianas, el consumo de agua potable aumenta cuando el
número de aparatos sanitarios (SD) aumenta. El desperdicio de agua potable
ocurre a través de un excesivo número de aparatos sanitarios en los edificios
residenciales.
Palabras clave:
Agua, agua potable, consumo, desperdicio, dispositivos sanitarios.
http://novasinergia.unach.edu.ec 69
1 Introduction
There is still a considerable population globally that
do not have drinking water while other people have it
too much. The water distribution systems (WDS) do
not have enough tools to balance the inequitable
distribution. Some authors direct their research to the
economic approach through tariff systems proposals
(Sahin, Bertone & Beal, 2017; Santopietro et al.,
2018). Others Scientists are especially concerned
about improving WDS (Tricarico, de Marinis,
Gargano & Leopardi, 2007) because the freshwater
availability (Rodell et al., 2018) is becoming scarce,
and the urbanization increases together with the
human comfort and the necessity to supply drinking
water as well (UNESCO, 2009).
The climatic change also impacts freshwater
availability (Rodell et al., 2018) and consequently
affects the drinking water consumption DWC. Since
these events seem to be unavoidable, some authors
have taken other directions to optimize the DWC.
Some reports demonstrate that water consumption
rises when the number of dishwashers and clothes
washing machines increases at home (Morote
Seguido, 2017).
Other Authors go further and quantify water use per
person in residential buildings (Matos, Teixeira,
Duarte & Bentes, 2013). Morote Seguido (2017) did a
comprehensive bibliographic review about DWC. He
identified the following factors: social, demographic,
economic, management, psychological, urban, and
climatological factors, which affect the DWC. The
demographical and climatological incidence was
recently quantified by Arellano, Bayas, Meneses &
Castillo (2018)
From the demographic approach, the DWC shows
different patterns in small, medium, and big cities
(Arellano et al., 2018). This report keeps the same
range of city sizes and quantifies the DWC per capita
upon the number of sanitary devices at home. It aims
to provide some linear models to quantify the DWC
by knowing how many sanitary devices there are in a
residential house. The SD is easy to identify when
designs and building construction need authorization
by WDS managers. The models allow managers to set
up tariffs or bonds to the users to change the water
consumption habits. This article contributes to water
conservation as well as water management literature.
2 Methodology
2.1 Cities characteristics
The cities are located in three geographical regions in
Ecuador, with different socio-economical and climatic
characteristics. Arellano et al. ( 2018) reported three
groups of cities (small, medium, and big) with DWC
similarities. This paper analyzes the variables in each
group of cities. Cities' population were withdrawn
from national census reports carried out in 2010 (Table
1).
2.2 Samples
Every month the researchers wrote down the water
consumption data from residential buildings
micrometers. The data had registered m
3
consumption
with a decimal fraction and converted to liters per
month. The fields’ work in 11 cities started in 2013
and finished in 2015 (Carrillo & Quintero, 2013;
Montenegro & Tapia, 2014; Morillo & Luna, 2013;
Barreno, 2015; Cáceres & Rubio, 2015; Noriega,
2015; Patiño & Pino, 2014; Sagñay & Carguachi,
2015; Samaniego & Muela, 2015).
The field's work on each city took six months. The
whole field information contained mainly: how many
people live in a residential building, how many and
which sanitary devices they have. Therefore, we could
know how much drinking water per person is
consumed and how many sanitary devices per person.
Table 1: Cities, population, size, and samples.
Population range
Size
City
Population
inhabitants 2010
500 – 8000
Small
Columbe
526
Cubijíes
588
Guamote
2648
Chambo
4459
Quimiag
5257
Guano
7758
8000 – 30000
medium
Joya Sachas
11480
Macas
18984
Guaranda
23874
30000 – 150000
Big
Ventanas
38168
Riobamba
146324
http://novasinergia.unach.edu.ec 70
2.3 Variables
The DWC is expressed as per capita consumption
(PCC) (l/person-day). The primary information
shows sanitary, social, and economic
characteristics from each house sampled. The
sanitary devices (SD) are the toilet, washbasin,
shower, kitchen sink, and clothes washing machine
(CWM). We call sanitary equipment (SE) to all
devices together. The SD and SE area expressed
in units per person. Since people's habits are
somewhat different, this study includes SD more
often used in South American countries. Therefore,
the bath, as well as dishwasher machines, were not.
2.4 Data process
The Statistical software R was applied to make a
descriptive statistical assessment due to a small
amount of data.
We applied the simple lineal regression technique
to infer data from others. We calculated the
Pearson correlation coefficient R to determine a
parameter variation related to another parameter
(SD related CPC or SE related CPC). We also
calculated the Spearman determination coefficient
R². The R² values are between 0 and 1. The closer
to 1, the better the model is. When R² values are
higher than 0.6, the relation is significant. When R
is between 0.50 and 0.75, the correlation is
considerable. When R is between 0.75 and 0.90,
the correlation is very strong. When the R is
between 0.90 and 1.00, the correlation is perfect
(Hernández, Fernández & Baptista, 2006).
A correlation will be statistically significant when
p < 0.05. It will be statistically significant when p
< 0.001 (less than one in a thousand chance of
being wrong). When the pattern showed a lineal
tendency, the model is:
𝑦 = 𝑚𝑥 + 𝑏 + 𝜀
where the constants m and b are determined by the
least square method.
3 Results and Discussion
3.1 Big cities (30000-150000
inhabitants)
When the analysis is done individually, between
each sanitary device and the DWC, there is a
perfect correlation between the toilet, washbasin,
shower, and CWM, respectively, with PCC values
(R² >0.8; p<0.05). There is a strong correlation
between the kitchen sink and PCC (R² >0.60;
p<0.05). The p-value so low means it is significant
from a statistical point of view.
The equations state:
y= monthly average drinking water per
capita consumption (l/ person-day).
x= number of each sanitary device
individually (toilet, washbasin, shower, kitchen
sink, and clothes washing machines) per person
(units/person).
These equations could be used to predict the DWC
from a residential building by counting the number
of sanitary devices.
When we consider SD as a set, we call it sanitary
equipment (SE). Figure 3 plots SE against drinking
water per capita consumption. The lineal model
obtained is considerable and highly significant
statistically (Pearson R = 0.6146 y p-valor = 0.001,
table 2). The DWC increases when SD and SE
increase. It seems that water consumption
increases because there is more sanitary device
rather than water necessities. Will water demand
dropdown if people would not have many SD at
home? It confirms that water consumption
increases and that sanitary water users increase at
home (Morote Seguido, 2017).
In both cases, either with sanitary devices
individually or as equipment, the models could
predict the DWC in big cities.
3.2. Medium cities (8000-30000
inhabitants)
In the medium-sized cities (Guaranda's data was
removed), the relations between al SD and PCC
are considerable (R > 0.5), but they do not have
statistical significance because their p-values are
much higher than 5% (Table 3). Therefore we did
not calculate linear equations.
3.3 Small cities (8000-30000
inhabitants)
In the cities' smaller than 8000 people, the results
are much different from the previous ones. There
is no statistical significance between sanitary
devices and PCC (p-value is much higher than 5%,
table 4), and there is no acceptable correlation (R
values are too low). The DWC in small cities does
not depend on the number of sanitary devices at
all. The correlation between DWC and SE in small
cities is harmful and is not valid from a statistical
point of view (Pearson R =-0.2166, table 4)
although it is highly significant (p-value of 0.0568,
figure 5). Perhaps other factors affect the DWC in
small cities because the graphic trends are
different from big and medium cities.
http://novasinergia.unach.edu.ec 71
Figure 1: Sanitary devices per capita from 11 cities.
Figure 2: Sanitary devices per capita from 9 cities.
Table 2: Sanitary devices and equipment, versus drinking water per capita consumption in big cities (30000-150000
inhabitants).
Sanitary device
R
2
p-value
Equation
Toilet
0.908
0.0003
y = 165.86x + 101.2
Wash basin
0.8902
0.0004
y = 160.23x + 107.65
Shower
0.8954
0.0004
y = 221.69x + 100.59
Kitchen sink
0.6046
0.0231
y = 621.7x + 53.306
Clothes Washing Machine (CWM)
0.9573
0.0001
y = 434.48x + 145.79
Sanitary equipment
0.3777
0.001
y = 103.5x + 163.94
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
Riobamba
Guaranda
Ventanas
Macas
Joya Sachas
Guano
Quimiag
Chambo
Guamote
Cubijíes
Columbe
sanitary device/person
City
toilet washbasin shower kitchen sink clothes washing machine
0.00
0.20
0.40
0.60
0.80
Riobamba
Ventanas
Macas
Joya Sachas
Guano
Quimiag
Guamote
Cubijíes
Columbe
Sanitary device / person
City
toilet washbasin shower kitchen sink clothes washing machine
http://novasinergia.unach.edu.ec 72
Figure 3: Drinking water consumption and sanitary equipment in big cities (30000-150000 inhabitants).
Table 3: Sanitary devices and equipment, versus drinking water per capita consumption in medium cities (8000-30000
inhabitants).
Sanitary devices
R
2
R
p-value
Equation
Toilet
0.3023
0.5498
0.2010
-
Washbasin
0.3093
0.5561
0.1948
-
Shower
0.3285
0.5731
0.1786
-
Kitchen sink
0.3457
0.5880
0.1650
-
Clothes Washing Machine
0.5439
0.7375
0.0585
-
Sanitary equipment
0.1862
0.4315
0.0097
y = 190.54x + 180.82
The research about water quality, water cost, total
inflation, and leaking will provide more
information. Figure 1 shows high peaks in two
cities (Guaranda and Chambo).
Applying the box plots to identify the outliers and
remove the data from those cities, we obtain figure
2, which diminishes the data. However, when the
sanitary equipment SE is drawn against the
drinking water per capita consumption (Figure 4),
it yields a medium correlation (R=0.4315; R
2
=
0.1862) with statistically highly significance (p-
value=0.0097).
The DWC could be predicted by counting all
sanitary devices and applying the following lineal
model. It is valid for residential buildings.
The R-value is relatively low, perhaps due to a lack
of data.
Where y is the label for monthly average drinking
water consumption (l/person-day); and X
represents the number of toilets, washbasins,
showers, kitchen sinks, and clothes washing
machines together (units/person).
Table 4: Sanitary devices and equipment, versus
drinking water per capita consumption in small cities
(less than 8000 inhabitants).
Sanitary
devices
R
2
R
p-value
Toilet
0.0138
-0.1175
0.6768
Wash basin
0.0043
-0.0656
0.8171
Shower
0.0439
-0.2095
0.4536
Kitchen sink
0.2130
-0.4615
0.0833
Clothes
Washing
Machine
0.1866
-0.4320
0.0735
Sanitary
equipment
0.0469
-0.2166
0.0568
130
150
170
190
210
230
250
270
290
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2
Drinking water per capita Consumption
l/person
-day)
Sanitary equipment per cápita (unit/person)
http://novasinergia.unach.edu.ec 73
Figure 4: Drinking water consumption and sanitary equipment in medium cities (8000 a 30000 inhabitants).
Figure 5: Sanitary equipment and drinking water consumption per capita (less than 8000 inhabitants).
4 Conclusion
The drinking water consumption (DWC) in
medium and big cities depends directly on the
number of sanitary devices (SD). The clothes
washing machines (CWM) lineal models seem to
give better results than the other devices'
equations. In big cities, the CWM and DWC got a
perfect correlation, with high statistical
significance. In medium cities, the same device
and DWC model got a considerable correlation
with statistical significance. In big cities, the
sanitary equipment and DWC model also got a
considerable correlation with high statistical
significance. In medium cities, an unacceptable
correlation was got. We can not calculate the DWC
in small cities because it does not correlate with
SD.
It seems people misuse water when they have too
many sanitary devices in medium and big cities.
The DWC rises when SD rises, too (Morote
Seguido, 2017). If we see the other way around,
the DWC will diminish if the number of SD
diminish. This correlation gives a significant
contribution to defining awareness' strategies to
optimize DWC in Ecuador.
The equations yield useful values to fix the water
tariff in the SD number function in a residential
building (Santopietro et al., 2018). The more SD,
the higher the water tariff. Santopietro et al.
mention the economic value of water recognized
by the International Conference on Water and
Sustainable Development (ICWE). If it is so, these
mathematical models help calculate the water
distribution cost based on the SD number.
Santopietro et al. calculated the WDS
rehabilitation cost, but he does not mention the
consumption cost. The WDS managers could
apply these models to set up differentiated rates for
water consumption. The more sanitary devices at
home, the higher the tariff to pay.
Conflict of Interest
The authors declare there is not conflict of interest
at all.
y = 190.54x + 180.82
R² = 0.1862
140
180
220
260
300
340
380
420
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80
Drinking water per capita
consumption (l/person
-day)
Sanitary equipment per cápita (units/person)
y = -169.4x + 239.35
R² = 0.0469
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
450.00
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80
Drinking water per capita
consumption (l/person
-day)
)
Sanitary equipment per capita (unit/person)
http://novasinergia.unach.edu.ec 74
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