Use of Unmanned Aerial Vehicles for Monitoring of Air Pollution
Generated by Stationary Sources
Pavel Jirava and Michal Kuřík
Institute of System Engineering and Informatics, Fac Econ & Adm, University of Pardubice,
Studentská 95, Czech Republic
Keywords: UAV, Stationary Sources of Air Pollution, Ringelmann Scale, Data, Smoke Plume.
Abstract: This paper is focused on the use of unmanned aerial vehicles for the monitoring of air pollution by stationary
sources. A quadrocope with digital image capture was used for experiments. The data thus obtained was
processed and the Ringelmann scale method for determining the darkness of the smoke plume was applied..
The values obtained are relevant and can be used to determine the rate of air pollution by a stationary source.
The advantage of this proposed procedure are the low costs of realization and reduction of the influence of the
human factor on determining the darkness of the smoke and finally no need for direct access to the stationary
source of air pollution.
1 INTRODUCTION
The aim of the paper is to explore selected
possibilities offered by unmanned aerial vehicle in
the field of monitoring of small stationary sources of
air pollution. It is a combination of two areas,
namely the protection of the environment and
unmanned aerial vehicles (UAV). Both areas evolve
over time and it is only a matter of a relatively short
period of time before they begin to mutually
interfere and support each other. UAVs are
multifunctional devices offering a wide range of
uses, including in this area. In the experimental part,
the possibility of using the UAV for data collection
has been verified. For this purpose, several test
flights were carried out, during which videos for
subsequent analysis were made.
Based on legislation in the Czech Republic, the
method of determining the darkness of the smoke is
described. Ringelmann scale is used to evaluate data
(Uekoetter, 2005). This is applied to recorded data
using computer software. This is essentially a digital
determination of the darkness of the smoke plume of
a stationary source of air pollution.
Already today, using the UAV in the field of
emissions monitoring is often considered. This
specialized field is developing very fast. For
example, the European Maritime Safety Agency
deals with "Remote Pilot Aircraft System (RPAS)
services in the maritime environment. RPAS has an
on-board gas analyzer draws samples of air and
monitors of SOx, NOx and CO
2
levels. However,
this is a relatively expensive technology (EMSA,
2017). Another example is UAV based smoke plume
detection system controlled via the short message
service through the GSM network, proposed system
consist of two parts: flying hardware and embedded
kit for finding smoke plume. This system is
dependent on the operation of GPS networks
(Ramanatha, 2016). The aim of this paper is, among
other things mentioned above, to show a very simple
and inexpensive way to use the UAV for the
detection and evaluation of air pollution. A micro
UAV with standard camera and optics was used. We
used the free software to process the data. The total
cost of experiments (including UAV, insurance,
pilot registration fees, flight fee, free SW) did not
exceed EUR 1000. The economic aspect and the cost
of UAV use is a very important factor in its wider
expansion.
2 UNMANNED AERIAL
VEHICLES
An UAV, sometimes referred to as a drone, is
generally perceived as an unmanned aircraft. It can
be controlled remotely at the pilot's eye or outside
Jirava P. and KuÅ
´
k M.
Use of Unmanned Aerial Vehicles for Monitoring of Air Pollution Generated by Stationary Sources.
DOI: 10.5220/0006510801500155
In Proceedings of the International Conference on Computer-Human Interaction Research and Applications (CHIRA 2017), pages 150-155
ISBN: 978-989-758-267-7
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
the pilot's reach. In some cases, the distance between
the pilot and the drones may be several thousand
kilometers, as is the case for military UAV. Another
option is the autonomous movement on a previously
planned route. Nowadays computer technology is at
such a level that it allows a completely independent
movement, which is, however, limited by the
legislative conditions of operation. (Karas, 2016)
One of the biggest advantages over piloted
machines is the lower cost of operation and
acquisition of the machine. It cannot be said that this
applies to all UAVs. Some are more expensive than
ordinary planes. But with commercially available
drones, it's a fact. If you need to scan a small area,
deploying these resources is much more economical
than conventional methods (Saadatseresht, 2015).
They are also a suitable solution if you need to
scan or explore only a small area or area where the
classic procedure cannot be used. Their further
advantage is therefore the reach and permeability of
the unfavorable terrain. If allowed constructively,
they can start or land perpendicular, which can be
used in places where such operations are not
necessary for ordinary aircraft. (Rehak, 2012)
Another great advantage is maneuverability and
safety. They can be used in natural disasters and
other situations where rescuers, firefighters, etc.
would otherwise endanger life. (Karas, 2016)
This contribution is also based on some articles
on air quality monitoring. This is mainly research on
the use of quadcopter for monitoring air pollution
(Zhaoyan et al., 2017), and methodology for smoke
detection and monitoring (Gómez-Rodríguez, 2002;
Gómez-Rodríguez, 2003)
The disadvantage of drones can be flight time,
low load capacity. This does not have to be the case
for special army UAVs, which are able to use the
combustion engines to travel to one tank for
thousands of kilometers and can last for several tens
of hours in the air. Commercially available drones
are not capable of so long flights. There are dozens
or hundreds of possibilities and ways to use the
drones. Each field or profession brings another
(d'Oleire-Oltmanns et al., 2012).
The UAV can be seen as a dynamic system that
can be modeled and analyzed using various tools
(Ibl, 2016). No unified international legislation
exists for the operation of UAV. Therefore, it is
necessary to read the laws for the airspace in which
the flight is to be conducted before flight (Cracknell,
2017).
From the historical point of view, there has been
and still remains the greatest benefit of an UAV for
military and military purposes, whether used for
protection, search and rescue of missing persons,
monitoring, communications, exploration or as a
weapon.
In recent years, UAV have become available to
civilians as well. Due to their variability and
features, the most common purpose of the job is to
simplify work and cost effectiveness. They also
provide the ability to represent a person in
potentially dangerous situations, thereby
contributing to increased safety. A wide range of
applications also stems from the use of various
special sensors.
All drones usage can be broken down according
to the following division:
Aerial photography
Aerial video
Aerial monitoring
Space and terrain mapping
Special applications in conjunction with
special sensors
Transport and logistics
Entertainment (Rodríguez-Canosa et al,
2012.; Towler et al., 2012; Karas, 2016)
3 AIR POLLUTION
In each developed country, air pollution is regulated
by the applicable law. This paper is based on the
Czech Air Protection Act 201/2012 Sb.
The sources of air pollution by origin are divided
into natural and anthropogenic. We mean natural
sources that are not of human origin. This may be,
for example, volcanic activity or dust storms. The
other group is anthropogenic sources related to
human activity, namely industrial and agricultural
production, electricity and heat generation, transport
and waste disposal. We have focused here on
anthropogenic stationary sources.
Depending on location, we can divide our
sources into ground, elevated and elevated. We also
divide the sources of pollution according to their
layout, point, line and bulk order. The point source
can be, for example, a chimney.
A stationary source is, according to the Act on
Air Protection, a stationary technical unit which is
further indivisible and in which the fuels are
oxidised in order to use the released heat.
The list of polluting sources and their
categorization together with the requirements for
individual categories can be found in Appendix 2 to
the Act on Air Protection (The Act No. 201/2012,
2012). We place stationary sources in the following
categories:
Energetics - Combustion of fuels
Heat treatment of waste, waste
management and sewage
Energy - others
Production and processing of metals and
plastics
Processing of minerals
Chemical industry
Food, woodworking and other industries
Livestock farming
Use of organic solvents
Petrol handling
In order to protect human health and the
environment, limitations on the quantity of airborne
substances discharged by stationary sources are
established. These are emission limits, immission
limits and emission ceilings (Jirava et al , 2010) .
The degree of air pollution by smoke from a
stationary source can be evaluated by means of a
Ringelmann scale. This is a visual method where the
observer visually assesses the darkness of the smoke
plume at the outlet of the chimney. Ringelmann
scale, which consists of six rectangular arrays (see
Figure 1), each representing a given degree of
darkness of smoke.
Figure 1: Ringelmann's scale with RGB values.
Grade 0 corresponds to 0% of black on a white
background with definition. Reflectivity 80%.
Grade 1 corresponds to 20% of black on a white
background.
Grade 2 corresponds to 40% of black on a white
background.
Grade 3 corresponds to 60% of black on a white
background.
Grade 4 corresponds to 80% of the black on a white
background.
Grade 5 corresponds to 100% black on a white
background.
4 EXPERIMENTS
The experiments and calculations performed were as
follows. Firstly, UAV flights were conducted and
data capture was performed using a digital camera.
With the UAV, one stationary source of pollution
was repeatedly scanned under different
meteorological conditions. The obtained data was
preprocessed and digitally processed (Panus and
Simonova, 2005). The outcome was the
determination of smoke darkness according to the
Ringelmann scale.
4.1 Flights and Data Collection
For the experiments, conventional commercially
available UAVs were used (Parrot AR Drone, 2017).
This is a quadrocope controlled by smartphone or
tablet (Benson, 2017). Communication of the control
device (mobile phone) with the machine takes place
via Wi-Fi, which limits the operating range to 50
meters. The UAV has the following features:
720p 30fps HD camera (4)
Photo format: JPEG
Connection: Wi-Fi
With internal frame: 380 g
With external frame: 420 g
Processor: ARM Cortex A8 1 GHz
RAM: DDR2 1 GB at 200 MHz
USB: High-speed USB 2.0 for extensions
Gyroscope: 3 axles, accuracy of 2,000°/second
Accelerometer: 3 axles, accuracy of +/- 50 mg
Magnetometer: 3 axles, accuracy of 6°
The take-off of an UAV was carried out on a
private property with the permission of the owner
and did not leave it for the duration of the flight.
Also, safe horizontal distance from surrounding
buildings and persons not directly involved in the
flight was maintained. As a stationary source of air
pollution a combustion boiler with a manual solid
fuel supply was used, where the chimney mouth was
on the same land from which the UAV was started.
The Figure 2 below shows a graph with the
important flight data. The y axis on the left refers to
the altitude of the flight. The second axis on the left
shows the values related to the flight velocity in
meters per second. The top speed that the UAV
reached was 3.7 m / s, 13.3 km / h. The vertical axis
on the right shows the percentage of battery charge.
At the beginning of the flight, the battery was
charged almost 80%. After five minutes, this value
dropped by half. The time is captured on the x axis.
According to the chart, the flight lasted for just over
five minutes
Figure 2: Flight data.
4.2 Data Processing
During the flight, a digital record of smoke plume
was taken (Aber et al., 2010). Appropriate images
were separated from it, as shown in Figure 3 (with
free SW Avidemux 2.6). Subsequently, only the
smoke plumes were separated (using free graphical
SW GIMP v. 2.8.22). In order to determine the
average values of the RGB components of smoke
plume, it is necessary to place the smoke plume on a
transparent background that does not affect the mean
values of the individual component (realized with
free graphical SW GIMP v. 2.8.22).
Figure 3: Digital image of smoke plume.
In the following figure 4, the smoke plume is
excluded from five different pictures taken from one
video. The interval between the slides is five
seconds. For each of the smoke plume, the RGB
values are determined by the procedure described
above in this paper. In the table 1 are summarized
outputs from one flight. In the first column is
computed RGB value. In the second column is the
closest grayscale from left (based on the Ringelmann
scale). In the third column is the closest grayscale
from right (based on the Ringelmann scale). In the
last column is computed value for pictures a,b,c,d,e.
The last row of the table is the total result.
a
b
c
d
e
Figure 4: Preprocessed smoke plumes.
Table 1: Computed values.
RGB
values
Ringel. L
Ringel.
R
Final
value
a
0 1
210,8
202,2
205,4
255
255
255
209
210
212
1
b
0 1
229,0
221,2
212,9
255
255
255
209
210
212
1
c
1 2
193,2
187,3
197
,
6
209
210
212
167
169
172
1
d
0 1
239,8
232,6
228,8
255
255
255
209
210
212
0
e
1 2
201,3
193,1
192,9
209
210
212
167
169
172
1
Final
Ringelmann
value
1
4.3 Discussion
The proposed procedure (see Fig. 5) appears to be
applicable according to the obtained results.
Digitization and computer processing minimize the
impact of human factor on final results. These could,
of course, be supported by other methods such as
Gas chromatography, Dynamic Optochemistry,
Gravimetric analysis, or Spectroscopy. These
methods, however, require more complex and
expensive devices (Wolf and Witt, 2000; SenseFly,
2011). Meteorological conditions also affect
measurement. For example, strong wind gusts can
not only affect the smoke plume but also endanger
the UAV.
We believe that the measurements must be
carried out by an authorized person. The question
remains whether it is necessary for this person to be
the pilot of an UAV or only to be present when
carrying out the measurements. An operator could
be another person who has all the permits needed to
make a flight (licence). The officers of the
competent authorities would not have to have the
necessary pilot licenses at all. They would only
perform their own measurements.
Figure 5: Experiments - block diagram.
5 CONCLUSIONS
In this work we dealt with the problem of
determining the darkness of smoke plume from a
stationary source of air pollution using the UAV.
The smoke darkness can be determined using the
Ringelmann scale. This process is usually realized
manually and the measurement is thus largely
affected by human error. The proposed procedure
should eliminate this human error. The experiment
was as follows (figure 5 - left side block diagram).
First we collected data using UAV. The obtained
digital records were pre-processed and modified for
the RGB component decomposition. These were
then compared with the Ringelmann scale using the
software and finally the resulting value was
assigned. In addition, this procedure does not require
direct access to a source of pollution because it uses
the UAV.
The whole process can be done by an embedded
system on board of the UAV (see figure 5 – right
side diagram). The results may be sent via GPS to
the ground stations. But that is only the aim of future
research.
A disadvantage may be the varying legal
regulation of UAV issues in individual countries.
The application of the method would thus have to be
considered from the point of view of the country's
legislation.
ACKNOWLEDGEMENTS
This work was supported by the projects No. SGS
2017_SG19 of the Ministry of Education, Youth and
Sports of CR with title “Models Synthesis and
Analysis for Implementation Support of Smart Cities
and Regions Concept” at the Faculty of Economics
and Administration, University of Pardubice.
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