Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line
Geoinformation technologies and methods of spatial analysis of emissions in the border regions have been developed and GIS based software has been created for estimating mass of carbon dioxide (CO2) emissions that goes through border line. Described mathematical models of processes of CO2 emissions...
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irk-123456789-584242014-03-24T03:01:43Z Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line Lesiv, M. Bun, R. Интеллектуальные системы планирования, управления, моделирования и принятия решений Geoinformation technologies and methods of spatial analysis of emissions in the border regions have been developed and GIS based software has been created for estimating mass of carbon dioxide (CO2) emissions that goes through border line. Described mathematical models of processes of CO2 emissions in the energy sector in the border regions take into account the meteorological data. Spatial analysis of carbon dioxide transport processes has been done for Ukrainian – Polish border zone in consideration with wind rose. Описаны геоинформационные технологии и методы пространственного анализа эмиссий парниковых газов в приграничных регионах и создано программное обеспечение для численного моделирования процессов переноса диоксида углерода через границу. Предложенные математические модели процессов эмиссии углекислого газа в энергетическом секторе западных регионов Украины для вычисления перемещения атмосферных масс учитывают метеорологические условия, а именно – розу ветров. Пространственный анализ эмиссий углекислого газа был сделан для украинско-польской пограничной полосы. Описано геоінформаційні технології та методи просторового аналізу емісій парникових газів в прикордонних регіонах та створено програмний засіб для числового моделювання процесів переносу діоксиду вуглецю через лінію кордону. Запропоновані математичні моделі процесів емісії вуглекислого газу в енергетичному секторі західних регіонів України для обчислення переміщення атмосферних мас враховують метеорологічні умови, а саме – розу вітрів. Просторовий аналіз емісій вуглекислого газу зроблено для українсько-польської прикордонної смуги. 2010 Article Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line / M. Lesiv, R. Bun // Штучний інтелект. — 2010. — № 4. — С. 322-329. — Бібліогр.: 5 назв. — англ. 1561-5359 http://dspace.nbuv.gov.ua/handle/123456789/58424 004.942:519.876.5 en Штучний інтелект Інститут проблем штучного інтелекту МОН України та НАН України |
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Интеллектуальные системы планирования, управления, моделирования и принятия решений Интеллектуальные системы планирования, управления, моделирования и принятия решений |
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Интеллектуальные системы планирования, управления, моделирования и принятия решений Интеллектуальные системы планирования, управления, моделирования и принятия решений Lesiv, M. Bun, R. Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line Штучний інтелект |
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Geoinformation technologies and methods of spatial analysis of emissions in the border regions have been developed and GIS based software has been created for estimating mass of carbon dioxide (CO2) emissions that goes through border line. Described mathematical models of processes of CO2 emissions in the energy sector in the border regions take into account the meteorological data. Spatial analysis of carbon dioxide transport processes has been done for Ukrainian – Polish border zone in consideration with wind rose. |
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Lesiv, M. Bun, R. |
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Lesiv, M. Bun, R. |
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Lesiv, M. |
| title |
Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line |
| title_short |
Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line |
| title_full |
Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line |
| title_fullStr |
Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line |
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Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line |
| title_sort |
geoinformation technologies and spatial analysis of carbon dioxide transport through border line |
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Інститут проблем штучного інтелекту МОН України та НАН України |
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2010 |
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Интеллектуальные системы планирования, управления, моделирования и принятия решений |
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http://dspace.nbuv.gov.ua/handle/123456789/58424 |
| citation_txt |
Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line / M. Lesiv, R. Bun // Штучний інтелект. — 2010. — № 4. — С. 322-329. — Бібліогр.: 5 назв. — англ. |
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Штучний інтелект |
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2025-07-05T09:39:36Z |
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2025-07-05T09:39:36Z |
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1836799372640124928 |
| fulltext |
«Искусственный интеллект» 4’2010 322
5L
UDC 004.942:519.876.5
M. Lesiv, R. Bun
Lviv Polytechnic National University, Lviv, Ukraine
myroslava.lesiv@gmail.com
Geoinformation Technologies and Spatial
Analysis of Carbon Dioxide Transport
Through Border Line
Geoinformation technologies and methods of spatial analysis of emissions in the border regions have been
developed and GIS based software has been created for estimating mass of carbon dioxide (CO2) emissions
that goes through border line. Described mathematical models of processes of CO2 emissions in the energy sector
in the border regions take into account the meteorological data. Spatial analysis of carbon dioxide transport
processes has been done for Ukrainian – Polish border zone in consideration with wind rose.
Introduction
It is very important to have full information about country’s greenhouse gases (GHG)
emissions for participating in new international obligations concerning reducing and monito-
ring emissions, verification and fulfillment of economical and ecological commitments. Main
sources of emissions/absorptions processes are located very irregularly on country territory.
Analysis of spatial distributions of GHG emissions from different sectors of human activi-
ties on regional level is useful and valuable for projecting effective nature protecting tools,
study the ways of reducing emissions and uncertainties of GHG inventories.
Already existed mathematical models of processes of GHG emissions in the energy
sector do not consider atmospheric diffusion and influence of wind profile. So development
of mathematical models that will take into account these factors is relevant task. The aim
of research is to develop mathematical models of processes of GHG emissions in Energy
sector in the western regions bordering Ukraine, taking into account gas transportation, to
be more detailed – models, that will give an opportunity to calculate the amount of carbon
dioxide that goes through fixed border line.
Carbon dioxide (CO2) properties
CO2 is a naturally occurring gas in the atmosphere and one of the main greenhouse
gases as it transmits visible light but absorbs strongly in the infrared and near-infrared. CO2
is also a pollutant, as it is toxic in higher concentrations: 1 % (10,000 ppm) will make some
people feel drowsy. Concentrations of 7 % to 10 % cause dizziness, headache, visual and
hearing dysfunction, and unconsciousness within a few minutes to an hour.
The behavior of dense gases such as CO2 can be quite different than for passive tra-
cers (with neutral buoyancy) due to density effects. For example, a plume of CO2 will spread
laterally over flat terrain even in the absence of wind because it is denser than the surroun-
ding air [1]. A concentrated CO2 plume will generally hug the ground and cross terrain con-
tours to reach the lowest elevation. Driven by density effects, a CO2 plume will spread more
quickly than a neutrally-buoyant gas which spreads only through diffusion, giving the coun-
Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport...
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ter-intuitive result that ground level concentrations can drop more quickly for a dense gas than
for a passive gas tracer under calm conditions [2]. The dispersion of CO2 is highly depen-
dent on ambient winds. In weak winds, density (buoyancy) effects dominate the dispersion
process; in high winds, the gas begins to act more as a passive pollutant, with the ambient
wind overwhelming density effects. Topographic depressions inhibit the movement of the
CO2 plume under weak winds. At wind speeds of greater than 2 m/s, the plume is quickly
swept out of the domain and diluted, even for rolling hills of amplitude 50 m [1].
Mathematical background
Mathematical model that consider physical properties of carbon dioxide has been
built. It involves two cases: windy and calm conditions.
Data required:
− meteorological data including annual average wind rose;
− emission data (mass of carbon dioxide in Gg, source coordinates);
− border line divided into elementary line type objects and their coordinates.
At any point and any time moment wind can be represented as a vector having a direc-
tion and value (speed). Although the wind has three dimensions, it is common only to con-
sider the horizontal components of the wind. The wind direction is the direction from which
the wind comes. The wind is considered in meters per second, m/s, and at the vertical height
of 10 meters about ground.
The mass of carbon dioxide that goes through border line is directly proportional to
the emission rate. It is therefore important to use emission rates that are as accurate as pos-
sible. Calculated emissions in Energy sector from every elementary area object of investi-
gated regions are used as sources of emission [3].
Using digital map the Ukrainian – Polish border line was divided into elementary line
objects. In order to estimate mass of carbon dioxide that transports through border line there
were made assumptions [2], [4]:
− Mass that is emitted from the source is assumed to remain in the atmosphere. None
of the gas is removed through chemical reaction nor is lost at the ground surface through re-
action, gravitational settlings, or turbulent impaction.
− Steady – state conditions: meteorological conditions are assumed to persist unchan-
ged with time, over the time period of transport from the source to elementary line object
of border line.
− The average mass profiles at any distance in the crosswind direction, horizontal (per-
pendicular to the pass of transport) are well represented by a Gaussian, or normal distribution.
− Mass of gas that goes through border line is assumed to be direct proportional to con-
centration on border line.
− Surface roughness is not considered.
The Gaussian distribution is used to describe the crosswind and vertical distributions
that result from turbulent mixing that causes dispersion [4], [5]. The height of this curve is
described by following function:
( ) ],
2
exp[
2
1)( 2
2
σ
µ
πσ
−
−=
xxf (1)
where µ is the x position where the center of the distribution occurs. The magnitude of the
peak of the distribution is
π2
1 . The shape of the distribution in the horizontal, whether nar-
Lesiv M., Bun R.
«Искусственный интеллект» 4’2010 324
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row or broad, is determined by the magnitude of standard deviation, σ . The principle disad-
vantage of the Gaussian distribution is that it extends from –∞ one side to +∞ on the other.
Real plume spreading will be finite. However, from a practical standpoint, the height of the
Gaussian distribution is very small beyond the limits of σ4± [4].
It is assumed that x axis is oriented in the wind direction The y axis is oriented in
crosswind direction.
As mass rate of CO2 that passes through border line is directly proportional to gas
concentration, the next formula takes place:
( ) ),,(, yxcKyxM v ∗= (2)
where M (x, y) is the distribution of mass in crosswind direction, g/m; vK is the coefficient
which value depends on effective volume of gas plume, m2; c(x,y) is the gas concentration
in crosswind direction, g/m3.
Instantaneous gas concentrations in crosswind direction can be described by formula:
( )
,
2
2),(
2
2
2
2/3
y
y
zyx
E eMyxc σ
σσσπ
−
= (3)
where EM is the total mass of emissions in g; xσ is the standard deviation of concentration
distribution in the puff in the upwind-downwind direction; yσ is the standard deviation of
concentration distribution in the crosswind direction, m, at the downwind distance x; zσ is
the standard deviation of concentration distribution in the vertical direction, m, at the down-
wind distance x [4].
The speed of the wind mainly serves to give the download position of the center of
the puff. Wind speed may influence the dispersion indirectly because the dispersion para-
meters zyx σσσ ,, may be functions of wind speed. Much less is known of dispersion in the
upwind-downwind direction than is known of lateral and vertical dispersion. In general, one
should expect the xσ to be the same as yσ .
For calculation of yσ and zσ parameters based on measurements of atmospheric tur-
bulence over flat plains are used. As it has been observed when air is stable, vertical mixing
is inhibited, and when there is strong solar heating of the surface, there may be strong con-
vective activity with large vertical motions. There have been produced empirical results for
the variation of yσ and zσ for six stability classes: A – extremely unstable, B – moderately
unstable, C– slightly unstable, D – neutral, E – slightly stable, F – moderately stable.
Guidelines are given for estimating the stability class from wind speed, cloud cover and
time of day in Table 1.
Table 1 – Guidelines for determining Pasquill – Gifford stability classes [4]
Day with insolation Night Surface wind
speed, m/s Strong Moderate Slight Overcast 8/4≥
low cloud
8/3≤
cloud
2 A A – B B – –
2 – 3 A-B B C E F
3 – 5 B B – C C D E
5 – 6 C C – D D D D
6 C D D D D
Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport...
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Approximate power-law functions (4) can be specified for quasi-instantaneous sour-
ces corresponding to the Pasquill stability classes [4] (Table 2).
., d
z
b
y cxax == σσ (4)
Table 2 – Quasi-instantaneous Power Functions [4]
Pasquill
stability a b σy
100 m
σy
4 km c d σz
100 m
σz
4 km
A 0,18 0,92 12,45 371 0,72 0,76 23,8 393
B 0,14 0,92 9,69 288 0,53 0,73 15,3 226
C 0,1 0,92 6,92 206 0,34 0,72 9,4 133
D 0,06 0,92 4,15 124 0,15 0,70 3,8 50
E 0,045 0,91 2,97 85 0,12 0,67 2,6 31
F 0,03 0,90 1,89 52 0,08 0,64 1,5 16
“G” 0,02 0,89 1,21 32 0,05 0,61 0,8 8
If to substitute the valuable c (x, y) (formula (3)) into formula (2):
( )
.
2
2),(
2
2
2
2/3
y
y
zyx
E
v eMKyxM σ
σσσπ
−
∗= (5)
The area under curve ),( yxM is assumed to be equal to EM , total mass of emissions (see
Figure 1):
∫
∞
∞−
= dyyxMM E ),(
or
( )
.
2
2
2/3∫
∞
∞−
∗=
zyx
E
VE
M
KM
σσσπ
(6)
From (6):
zxVK σπσ= .
Final formula for mass distribution in crosswind direction is:
.
2
1),(
2
2
2 y
y
E eMyxM σ
π
−
∗=
Figure 1 – Mass distribution in crosswind direction
Lesiv M., Bun R.
«Искусственный интеллект» 4’2010 326
5L
Mass that goes through elementary line object ),,( 21 yyxM gas (see Figure 1) can be
estimated by following formula:
.
2
1),,(
2
2
2
1
2
21
y
yy
y y
Egas eMyyxM σ
σπ
−
∫ ∗= (7)
Figure 2 – Mass distribution in case of calm conditions
In case of calm conditions the average mass profiles at any distance in any direction
cannot be represented by a Gaussian curve. So it is assumed that mass of gas has even distri-
bution in all directions.
Mass of carbon dioxide that passes elementary line object is directly proportional to
the value of angle α (in radians) that is created by two radii drawn from the ends of line ob-
ject to source as it is shown in Figure 2. Using cosine rule, here the formula (8) for estima-
ting rate of mass:
π2
2
arccos
),,(
222
−+
=
ac
bca
McbaM E
calm
gas (8)
where calm
gasM is the mass of gas in calm conditions; a,b are the radii connected source cent-
re and ends of elementary line object, m; c is the length of elementary line object, m.
Figure 3 – Moving from coordinate system of digital map to another one with centre
in the source of emissions and x axis oriented in the wind direction
y
source x
a
b
c
α
(x1;y1)
(x2;y2)
Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport...
«Штучний інтелект» 4’2010 327
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As in practice there is a need to work with big number of sources of carbon dioxide
emissions and different wind directions, so there were developed formulas below (9) – (10)
for moving from one coordinate system of digital map to another with center in source of emis-
sions and x axis oriented in the wind direction.
Variables in Figure 3: x,y are the coordinates of emission source in “old” coordinate
system; x', y' are the new coordinates with centre in source of emissions and axis x oriented
in the wind direction; (x1,y1) are the coordinates of ends of line type object in map coordinate
system; α is the angle between wind direction and x axis; β is the angle between vector, con-
nected source and receptor, with x axis.
),sin(),cos( '
1
'
1 αβαβ −=−= ayax (9)
where ),( '
1
'
1 yx are the coordinates of ends of line type object in new coordinate system with
centre in source point and axis x oriented in wind direction.
The value of angle β can be calculated using formulas (10):
=>
=>
<=
>=
><
−
−
−
<>
−
−
−
<<
−
−
−
>>
−
−
=
.,,2
;,,0
;,,
2
3
;,,
2
;,,
;,,2
;,,
;,,
11
11
11
11
11
1
1
11
1
1
11
1
1
11
1
1
yyxx
yyxx
yyxx
yyxx
yyxx
xx
yyarctg
yyxx
xx
yyarctg
yyxx
xx
yyarctg
yyxx
xx
yyarctg
π
π
π
π
π
π
β (10)
Numerical modeling
For estimating mass that goes through border line as input data there were used avera-
ge annual wind rose for western Ukrainian regions (wind speed, and direction with frequen-
cy), and digital maps with information about carbon dioxide emissions in Energy sector of
western regions in Ukraine on elementary objects level [1].
The tools of geoinformation system (GIS) MapInfo was used. Additional menu was crea-
ted on the panel, based on developed mathematical model, and programmed on MapBasic.
It gives opportunities to build border line, select border zone of fixed width with emissions
sources, and calculate mass that passes through selected border line (see Figure 4).
For calculations the Ukraine – Polish border line was chose and border zone with 100 km
width was selected. As a result the digital map with information about mass of carbon dioxi-
de that goes through border line on elementary object level was created. Figure 5 shows the
Lesiv M., Bun R.
«Искусственный интеллект» 4’2010 328
5L
distribution of mass of carbon dioxide that goes through Ukraine – Polish border line on ele-
mentary line object level from south to north.
Figure 4 – MapInfo menu
Figure 5 – Mass distribution of CO2 across border line from south to north
Conclusions
Created mathematical model that takes into account meteorological data and is based
on geoinformational technologies gives the opportunity to estimate rate of mass of carbon
dioxide that goes through border line. Made estimations for Ukrainian – Polish border line
and border zone with 100 km width show that only 15 % of CO2 emitted in this zone are tran-
sported through border line from Ukraine to Poland. Creation of common geoinformation
system for GHG spatial inventory, and formation of georeferenced databases with input da-
ta and inventory results for Ukrainian – Polish border regions is in perspective. Developed
methods and geoinformation technologies can be used for estimating mass that transports
through border line as well.
References
1. Chowa F.K. Modeling the effects of topography and wind on atmospheric dispersion of CO2 surface lea-
kage at geologic carbon sequestration / Fotini K. Chowa, Patrick W. Granvolda, Curtis M. Oldenburgb //
Greenhouse Gas Control Technologies 9 (GHGT-9), Energy Procedia. – University of California: Berke-
ley Published by Elsevier Ltd., 2009. – Vol. 1, Issue 1. – P. 1925-1932.
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2. Good practice guide for atmospheric dispersion modelling / Prepared by the National Institute of Water
and Atmospheric Research, Aurora Pacific Limited and Earth Tech Incorporated for the Ministry for the En-
vironment. – Ministry for the Environment, 2004. – 142 p.
3. Hamal Kh. Geoinformation technology for spatial analysis of greenhouse gas emissions in Energy sector :
thesis for a candidate’s degree : 05.13.06 / Khrystyna Hamal. – Lviv : Lviv Polytechnic National Univer-
sity, 2009. – 246 p.
4. Turner D.B. Workbook of atmospheric dispersion estimates: an introduction to dispersion modelling /
D. Bruce Turner. – Boca Raton, Florida, USA: CRC Press, 1994. – 194 p.
5. Markiewicz M. Mathematical modelling of heavy gas dispersion / M. Markiewicz / Models and techniqu-
es for health and environmental hazards assessment and management. Part 2. – Air Quality Modelling. –
Institute of Atomic Energy, Otwock-Swierk, Poland, 2006. – P. 281-299.
М.Ю. Лесив, Р.А. Бунь
Геоинформационные технологии и пространственный анализ процессов
переноса диоксида углерода через границу
Описаны геоинформационные технологии и методы пространственного анализа эмиссий парниковых
газов в приграничных регионах и создано программное обеспечение для численного моделирования процессов
переноса диоксида углерода через границу. Предложенные математические модели процессов эмиссии
углекислого газа в энергетическом секторе западных регионов Украины для вычисления перемещения
атмосферных масс учитывают метеорологические условия, а именно – розу ветров. Пространственный
анализ эмиссий углекислого газа был сделан для украинско-польской пограничной полосы.
М.Ю. Лесів, Р.А. Бунь
Геоінформаційні технології та просторовий аналіз процесів
переносу діоксиду вуглецю через кордон
Описано геоінформаційні технології та методи просторового аналізу емісій парникових газів в прикордонних
регіонах та створено програмний засіб для числового моделювання процесів переносу діоксиду вуглецю
через лінію кордону. Запропоновані математичні моделі процесів емісії вуглекислого газу в енергетичному
секторі західних регіонів України для обчислення переміщення атмосферних мас враховують метеорологічні
умови, а саме – розу вітрів. Просторовий аналіз емісій вуглекислого газу зроблено для українсько-польської
прикордонної смуги.
Статья поступила в редакцию 21.06.2010.
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