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The Waste Water as a Source of Information for the
Urban Agglomerations Management
Martina Drahošová
e-Europe Research &
Development Center
Faculty of Management,
Comenius University in
Bratislava
Bratislava, Slovakia
martina.drahosova@fm.uniba.sk
Peter Balco
Department of Information
Systems
Faculty of Management,
Comenius University in
Bratislava
Bratislava, Slovakia
pater.balco@fm.uniba.sk
Lukáš Pokorný
BPUG™ SLOVENSKO
Bratislava, Slovakia
bpug@bpug.sk
Ryan Razani
BPUG™ SLOVENSKO
Bratislava, Slovakia
bpug@bpug.sk
Abstract— This paper presents a clear relationship between
agglomeration, smart cities and waste management in Slovak
republic. The main contribution of this work is to investigate the
relationship between smart cities management and the
wastewater coming from the households to waste water
treatment plant. It has been shown that the gathered information
from the waste water is very useful for identifying the locations
of pollution caused by medicaments and drugs in a city. We
provide a brief overview of smart cities, followed by statistical
information about world’s population and the population of cities
within Slovak republic. Moreover, the key information about the
locations and the times of pollution is briefly discussed.
Exploiting the network theory and the tree data structure used in
this work, enables one to localize the collection points and to
create the infrastructure for automatized collection of data.
Compared to previous work, we identify the pollution points
more effectively and more economically. Therefore, this makes
the solution attractive for criminology of drug distribution points
in the city. Subsequently, it leads to cleaner, safer and more
environmentally friendly cities.
Keywords— agglomeration, smart cities, waste management,
waste water, pollution, efficiency and conservation
I. INTRODUCTION
In this turbulent time full of pollution and criminality, it is
important to identify the pollution points in the agglomerations.
However, it is difficult to localize these points of pollution. The
purpose of this paper is to clarify the relationship between
agglomeration, smart cities and waste management in Slovak
republic. We demonstrate how the waste plays an important
key for gathering information about the citizens and their
habits. This information can help to manage urban
agglomerations more effectively. In this context, using this
data more effectively offers valuable services to citizens. In
addition, the information about the pollution points can help to
eliminate pollution which ensures greener environment for
inhabitants.
We begin by distinguishing the three types of cities and
agglomeration, namely: city proper, urban agglomeration, and
metropolitan area. Then we outline the principles and
the characteristics of smart cities and waste management.
Next, we
present the statistical information about growing tendency of
world’s population and the population of Slovak republic.
Finally, we discuss about the case study on using the gathered
data from waste water of inhabitants of Bratislava, capital of
Slovak Republic. This is followed by a discussion about
pollution from the medicaments and drugs, especially because
these substances are not totally removed from water by using
the waste water treatment plant.
II. LITERATURE REVIEW
A. Cities and Agglomerations
We can agree that cities are hubs of government, commerce
and transportation that make them places where large numbers
of people are living and working. However, it is not easy to
define the geographical limits of a city. Because there are not
standardized international criteria which are determining the
boundaries of a city. Usually there are many different
definitions of a city boundary, which vary from city to city.
The definition “city proper”, describes a city according to
an administrative boundary. A second definition, named the
“urban agglomeration”, considers the extent of the contiguous
urban area, or built-up area, to delineate the city’s boundaries.
A third approach of the city, known as the “metropolitan area”,
defines its boundaries according to the degree of economic and
social interconnectedness of nearby areas. It is identified by
interlinked commerce or commuting patterns, etc. [1].
The agglomeration of capital of Slovak republic Bratislava
is illustrated in Fig. 1. The territory and settlement structure of
the Bratislava region consists of 73 municipalities, one of
which has the status of the capital of the Slovak Republic
(Bratislava) and 6 have the status of the city (Malacky,
Stupava, Svätý Jur, Pezinok, Modra, Senec) [2].
In Fig. 1, the city proper, highlighted in blue color, is
shown as heart of the city – city center (down town). The grey
areas are urban agglomeration of the city – city districts.
Metropolitan area is demonstrated by beige color, i.e.,
Malacky, Pezinok and Senec.
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Fig. 1. Bratislava Region - city proper, urban agglomeration,
metropolitan area [3]
B. Smart Cities
In nowadays rapid increase of urban worldwide population,
it is important to think about and understand the so-called
smart city concept. The importance to handle these challenges
activates many cities around the world to discover more
effective and smarter ways to manage them. These kinds of
cities are more likely to be described with the tag of smart city.
One of the possibilities to conceptualize a smart city is as a
symbol of sustainability and livability of the city [4].
Smart city is an urban environment, where is utilizing ICT
and other important technologies to boost efficiency of
constant city operations and quality of services (QoS) offered
to inhabitants. Formally, smart city is defined by experts
considering various aspects and perspectives as we define here.
A well-known definition expresses that the smart city connects
physical, social, business, and ICT infrastructure to improve
the intelligence of the city [5]. The other definition says that
smart city is an advanced modern city which utilizes ICT and
other technologies to improve quality of life (QoL),
competitiveness, operational efficacy of urban services, while
guarantee the resource availability for present and future
generations in terms of social, economic, and environmental
aspects [6]. The highest goal of inceptive smart cities was to
enhance the QoL of urban citizens by decreasing the
contradiction between demand and supply in various
functionalities [7]. Accommodating QoL demands and in
particular modern smart cities focus on sustainable and
efficient solutions for energy management, transportation,
health care, governance, and many more, in order to meet the
extreme necessities of urbanization [8].
Smart city consists of attributes known as characteristics,
infrastructure, and themes. As introduced in [10], the smart city
concept, shown in Fig. 2, comprises of four major attributes
namely: sustainability, quality of life (QoL), urbanization, and
smartness. Attributes can further split into sub-attributes. For
instance, sub-attributes of infrastructure and governance,
pollution and waste, energy and climate change, social issues,
economics, and health fall under sustainability attribute.
Sustainability is defined as the ability of a city to maintain an
ecological balance, while performing city operation. The
general well-being (i.e. emotional and financial) of individuals
and societies manifests the QoL enhancement. Urbanization
attribute concerns the technological, economical,
infrastructural, and governing aspects of the transformation
from rural to urban areas. The smartness attribute is based upon
the social, environmental, and economic standards of the city
and its inhabitants [9].
Fig. 2. Characteristics of a Smart City [9]
C. Waste Management
Waste is mainly a by-product of consumer-based lifestyles
that drive much of the world’s economies. Solid waste
managers need to value the global context of solid waste and
its interconnections to economies and local and global
pollution. Solid waste is the most visible and adverse by-
product of a resource-intensive, consumer-based economic
lifestyle. Moreover, greenhouse gas emissions, water pollution
and endocrine disruptors are similar by-products to our urban
lifestyles. However, the long-term sustainability of today’s
global economic structure is beyond the scope of this paper
[11].
Composition of waste shows us cultural and technological
trends and varies greatly between different continents and
regions over time. There are many technical aspects involved
in creating more sustainable and fair waste management
services. While ashes from heating and cooking, e.g., were
reported as large components of household waste in North
America until the middle of the last century, plastic appears
only since the 1970s as a separate recorded substance [12].
Most municipal solid waste generated worldwide what
represents 70 per cent, is still deposited at landfills and waste
dumps. While 19 per cent is officially recycled or treated by
mechanical or biological treatments and a small proportion is
incinerated (11 per cent) [13].
1) Purification of waste water in Agglomerations
As we already mentioned for our project is important water
purification process in the sewage treatment plant, we are
demonstration here this process:
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• Mechanical - cleaning involves the removal of waste water
from both coarse and fine impurities by cremation and
sedimentation.
• Biological - cleaning is carried out by microorganisms that
degrade sewage for oxygen or no oxygen.
• Chemically - some systems also work on the principle of
chemical cleansing, when they are precipitated into flakes
with the help of precipitating impurities.
waste water treatment plants are most commonly used with
the activation of the aerobic segregation of sludge. The
microorganisms swirl in the activation tank with the aeration
device and, together with other technical processes, can purify
wastewater with an efficiency of 90-95 per cent. Waste water
purifiers are suitable for objects with a constant supply of
contaminated water, e. g. permanently inhabited [14].
III. STATISTICAL INFORMATION ON POPULATION
A. World
In United Nations: The World’s Cities in 2016 – Data
Booklet [15], is stated that approximately 54.5 per cent of the
world’s population lived in urban settlements. By 2030, urban
areas are estimated to house 60 per cent of people globally and
one in every three people will live in cities with at least half a
million inhabitants. 1.7 billion people were living in a city with
at least 1 million inhabitants in 2016. These numbers represent
23 per cent of the world’s population. This is expected to
increase by 27 per cent of people worldwide by 2030.
While the population in all city size classes is estimated to
increase, from 2016 to 2030, the rural population is estimated
to decline slightly. Rural areas were home to more than 45 per
cent of the world’s population in 201. However, this proportion
is expected to fall to 40 per cent by 2030.
A minority of people who reside in megacities (with
population of 500 million people), represent 6.8 per cent of the
global population in 2016. However, as these cities increase in
both size and number, they will become home to a growing
share of the population. It is estimated that 730 million people
will live in cities with at least 10 million inhabitants by 2030.
This population growth estimate represents 8.7 per cent of
people globally.
Fig. 3. World’s population by size class of settlement, 1990-2030 [15]
B. Slovakia
There are currently 138 cities and 2883 municipalities in
Slovakia, which are part of the 79 districts and 8 self-governing
regions.
As shown in Table I, Slovak republic has area of
49034203728.00 square meters and the population density is
110.93.
TABLE I. P
OPULATION
D
ENSITY
–
SR
[16]
Indicato
r
2017
Area (Square meters) 49034203728.00
Population density (Person per square
kilometer)
110.93
Permanently living mid-year population
(Person)
5439231.50
a.
Statistical Office of the SR
In Table II, we demonstrate the size groups of
municipalities in Slovak republic. The results presented here
show that the biggest cities are in the group size from 100 000
and more residents. This group consists of two cities, the
capital of Slovak republic Bratislava and the second biggest
city is Košice. Together in both cities, there are 668 659 of
inhabitants living permanently.
TABLE II. S
IZE
G
ROUPS OF
M
UNICIPALITIES
–
SR
[17]
Indicators
(Groups)
2017
N
umber of
municipalities
(Number in
units)
Permanently
living population
on 31 December
(Person)
99 residents or less 404 50100
100 to 199 residents 267 41213
200 to 499 residents 724 251006
500 to 999 residents 762 542588
1K to 1 999 residents 569 800286
2K to 4 999 residents 296 860373
5K to 9 999 residents 63 423599
10K to 19 999 residents 34 484090
20K to 49 999 residents 28 809388
50K to 99 999 residents 8 553031
100K and more residents 2 668659
b.
Statistical Office of the SR
As illustrated above, there are ten cities in Slovak republic
with at least 50000 of inhabitants, which are considered as
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smart cities. These ten biggest cities are: Bratislava, Košice,
Prešov, Žilina, Bánska Bystrica, Nitra, Trnava, Trenčín,
Martin, Poprad, Prievidza [18]. In our case study, we discuss
about the biggest city in Slovak republic that is the capital
Bratislava.
IV. METHODOLOGY AND ANALYSIS
Waste becomes informative, as it is possible to find out
what substances the citizens of agglomerations are discharging
into the sewer system. Sewage treatment does not involve
cleaning substances such as drugs and pharmaceuticals. These
substances pollute the underground water, which can then
reach to the households. As these substances get back into the
rivers where the fish live, and it gets into the food we consume.
This is becoming dangerous for the population.
A. Network Theory
In order to realize a more efficient data collection, data
analysis and subsequently interpretation of the data, we needed
to create a model infrastructure based on network theory.
In general, network theory is associated with the study of
graphs and is represented either in symmetric relation or in
asymmetric relation among discrete objects. Network theory
finds various application such as logistic networks, gene
regularity networks, metabolic networks, the World Wide
Web, ecological networks, epistemological networks and social
networks. It is applied in multiple disciplines, including
biology, computer science, business, economics, particle
physics, operations research, and most commonly, in sociology
[19].
Fig. 4. Tree Data Structure [20]
In our case, we used the tree data structure principle. Fig. 4
provides the structure of the city of Bratislava, with respect to
the structure of sewage collection points. It can be observed
that the city has five city zones and seventeen city districts.
In addition to the type of waste (information) that comes to
the root, we are also interested in the distance between the root
and the child node in our model. This model of network
infrastructure, which uses network theory, is extrapolated to
urban agglomeration. This extrapolation was conducted by
identifying locations that were key to our collection of waste
water samples, which were then subjected to the required
analysis.
Let us consider this model from outlet, which represents the
wastewater treatment plant. By analysing this waste sample,
we identify medicaments and drugs in the waste water.
Subsequently, in the second step, we naturally look at which
path from the child node to the root distributes waste that
contains increased medicine or drug content.
Applying this iterative process enables us to get closer to
the source that pollutes wastewater by medicaments and drugs.
The prerequisite is to ensure a good sampling and subsequent
analysis for each collection place (root connection - child
node).
Fig. 5. Tree Data Structure of Waste Water Treatment Plant
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B. Business Intelligence
Business Intelligence is defined as "knowledge gained
about a business through the use of various hardware/software
technologies which enable organizations to turn data into
information” [21].
“The processes, technologies and tools needed to turn data
into information and information into knowledge and
knowledge into plans that drive profitable business action. BI
encompasses data warehousing, business analytics and
knowledge management” [22].
Data collection is not automated as we are at the beginning
of our project. This means that all data is collected manually,
then analyzed and evaluated.
V. PRACTICAL PART
In view of the experimental data, we present a few outlets,
because our goal is to automate data collection to be able to
most effectively obtain desired outcomes. We can classify the
data output into several types:
A. Localization Graph
Graph 1. Localization graph of collection of waste water
Graph 1. illustrates collection points of waste water in one
of the city districts. It shows the path of the waste water from
the child nodes to the root. The root represents the waste water
treatment plant where the waste water is purified. As we
mentioned above, the waste water can be purified with an
efficiency of 90-95 per cent. That being said, the undesired
substances can still exist in this purified water as fragments of
medicaments and drugs.
B. Time Graph (weekly)
Graph 2. demonstrates the days of the week distribution
and contamination of waste water by medicaments and drugs.
This graph provides information about the waste water which
is constantly contaminated by medicaments. Hence, there are
not big variations in their amount. However, in case of
contamination by drugs, there are substantial differences in
amounts according to week days. The largest amount of
contamination by drugs are during the weekends.
Graph 2. Weekly graph of medicaments and drugs in waste water
By automating the process of collection of these data, we
can daily collect and more properly analyse waste water from
different districts of the city. This will lead to better and more
effective identification of distribution of drugs in a city. In the
next part of the paper, we discuss approaches for using the
acquired data.
VI. RESULTS AND IMPACTS
There are two approaches to using the acquired data:
1. We can implement technologies that are able to remove
these undesirable substances from wastewater. Such a solution
is a major financial investment that may not be efficient and
effective. The ineffectiveness and possible inefficiency of this
solution lies in the fact that the list of medicines and drugs is
constantly changing, and it is therefore necessary to update the
infrastructure of these undesirable substances for their removal
from wastewater. This solution can provide water purification
but does not remove socially undesirable effects.
2. We can identify the main sources of these undesirable
waste products and try to ensure the disposal of these products
through the use of public power. Although, this approach may
seem undemocratic, but it can help to reduce crime, improve
the health of the population and eliminate unwanted social
incidents.
VII. CONCLUSION
The motivation behind our work is to automatically collect
the desired data from waste water treatment plant and to
analyse, report and interpret the data about the pollution. The
main contribution of this work is to investigate the relationship
between smart cities management and the waste water coming
from the households to waste water treatment plant. It has been
shown that the gathered information from the waste water is
very useful for identifying the locations of pollution caused by
medicaments and drugs in a city. Therefore, this paper aims to
deliver two possible solutions: 1. To implement the
1234567
Amout of medicaments/drugs
Week Days
Weekly graph of medicaments
and drugs in water waste
Medicaments Drugs
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technologies that are capable of removing these undesirable
substances from wastewater, 2. To identify the main sources of
these undesirable waste products and try to ensure the disposal
of these products through the use of public power. In this
paper, we have highlighted the danger of this pollution, which
can affect the population health of the city. Compared to
previous work, we identify the pollution points more
effectively and more economically. Therefore, this makes the
solution attractive for criminology of drug distribution points
in the city. Subsequently, it leads to cleaner, safer and more
environmentally friendly cities. It is important to point out that
when these problems are not addressed, the undesirable
substances infiltrate the underground water. As a result, these
undesirable substances are fed to the households. For this
reason, they endanger the health of both children and the adult
population. As citizens receive the drug and drug residues in
their bodies, they become resistant to certain types of drugs.
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