AIR DISPERSION MODELLING

APRIL 2016
Prepared by
Rajat Nag (15202684)
Under the guidance of Dr. Tom Curran
School of Biosystems & Food Engineering
Agriculture and Food Science Department
University College Dublin, Belfield, Dublin 4, Ireland
Module: Advanced Air Pollution
Module Code: BSEN40110
AIR DISPERSION MODELLING
FOR SO2 EMISSION FROM
A STEEL PLANT IN ZENICA VALLEY,
BOSNIA AND HERZEGOVINA

EXECUTIVE SUMMARY
The list of key statistics and summary will guide to the reader having a quick review on the
steel plant at Zenica Valley and the scope of this Air Dispersion Modelling.
Importance of the project
Due to SO2 pollution the number of cancer patient
was increased by 20% during 2002 to 2011 in
Zenica valley
Scope
Investigate the influence of a steel plant in terms
of SO2 emission with the help of Air Dispersion
Modelling
Owner of the steel plant ArcelorMittal
Major limitation of operation
Data source: Only literature
Simple model with Screen View; not access to
complex software like AERMOD
Intended audience The people (90,000) of Zenica Valley
Permissible concentration of SO2
Short-term exposures: 500 μg/m3
Long-term exposures: 20 μg/m3
Wind data analyzer Windographer
Location: Coordinates 44°13'28" N, 17°54'11" E
Type of production
Hot rolled steel products (rebars, wire rod, mesh,
lattice girders, and classic construction armature)
Steel production capacity
1,000,000 tonnes (Produced 700,000 tonnes in
2012)
Employees 3000
Terrain profile calculator Google earth
Source type Point source
Dispersion coefficient Urban
Receptor height 1.65 m
Emission rate 582.75 g/s
Stack height 120 m
Stack inside diameter 5 m
Stack gas exit velocity 11.32 m/s
Stack gas exit temperature 373.15 K
Ambient air temperature 293 K
Terrain type Complex
Meteorology type EPA’s Screen 3 model used
Governing Stability Class E: slightly stable
Wind velocity for worst combination 2.5 m/s
Method of superimposition of
concentration contour on the map
Auto cad and Google earth
Abatement technologies: Filter used Beta Attenuation Monitor BAM ($ 8,500,000)
Measured concentration of SO2 from
the dispersion modelling
1354 µg/m3
within 1.5 km from the source
Recorded concentration of SO2 on
17th of December 2013
1392 µg/m3

TABLE OF CONTENT
SL NO DESCRIPTION PAGE NO
1 Acknowledgements 1
2 List of abbreviations 1
3 Abstract 2
4 Introduction & Objectives 2
5 Literature review 3
6 Methodology and Assumptions 15
7 Results and Discussions 25
8 Limitations and future work 28
9 Conclusion 28
10 References 29
11 Appendix I
LIST OF FIGURES
SL.
NO.
REFERENCE
NO
DESCRIPTION PAGE
NO
1 Figure 4.1
Location of Zenica Valley
2
2 Figure 5.1 Photograph of the steel plant and two chimneys 4
3 Figure 5.2 Surface Ironworks and orographic 3D model of Zenica basin 4
4 Figure 5.3 Schematic diagram of UV. Fluorescence Method 5
5 Figure 5.4 Schematic diagram of Conductimetric Method 6
6 Figure 5.5 The basic illustration of a Gaussian plume model of smoke 7
7 Figure 5.6
Wind tunnel experiment to establish the building downwash
effect 8
8 Figure 5.7 Eddy formed on the plan in a wind tunnel experiment 8
9 Figure 5.8
Pollutant concentrations as velocity vectors in the canyon for
the (a) reference and (b) parked cars models at a wind speed
of 2.5 m/s in perpendicular wind conditions
9
10 Figure 5.9 Drop of SO2 from 2004 to 2013 9
11 Figure 5.10 Trend of land based emissions 10
12 Figure 5.11 Concentration of SO2 in Europe 12

SL.
NO.
REFERENCE
NO
DESCRIPTION PAGE
NO
13 Figure 5.12 Commercially available FGD technologies 13
14 Figure 5.13
Pie chart of percentage shares (capacity) of the three FGD
technologies installed
13
15 Figure 5.14 Schematic diagram of Wet FGD Technologies 14
16 Figure 5.15 Schematic diagram of Dry FGD Technologies 14
17 Figure 6.1 Pollutant source from the factory 16
18 Figure 6.2 Input window of Screen View 16
19 Figure 6.3 Input window for terrain profile in Screen View 17
20 Figure 6.4 a - Flat terrain, b - point of measurement, c – complex terrain 17
21 Figure 6.5 The data extraction from the NASA server (open resource) 18
22 Figure 6.6
Wind speed variation over time (from January 2015 to
February 2016)
19
23 Figure 6.7 The maximum wind speed noted for recent time 19
24 Figure 6.8 Wind rose diagram: Wind speed 20
25 Figure 6.9 Rose diagram: Surface temperature 20
26 Figure 6.10
Superimposed rose diagram on the valley showing direction
of prevailing wind
21
27 Figure 6.11
Diurnal profile based on the topographical conditions:
analysed in windograph. Speed marked in blue, Temperature
in red
21
28 Figure 6.12 Terrain profile in the direction of Wind-15 degree 22
29 Figure 6.13 Terrain profile in the direction of Wind-30 degree 22
30 Figure 6.14: Terrain profile in the direction of Wind-45 degree 23
31 Figure 7.1
Automated distance vs. concentration - Terrain height = 0.00
m 25
32 Figure 7.2
Discrete distance vs. concentration - Terrain height = 0.00
m. Distances considered 200m, 500m, 750m, 1000m,
1250m, 1500m
25
33 Figure 7.3 The result representing the final model of the study 26
34 Figure 7.4
Concentration of SO2 recorded in the winter time in Zenica
valley. 26
35 Figure 7.5 The concentration contours for our model 27
36 Figure 7.6 The concentration distribution of SO2 in Zenica valley 27

SL.
NO.
REFERENCE
NO
DESCRIPTION PAGE
NO
37 Figure 8.1
Comparison of the output from a. screen view and b.
AERMOD
28
LIST OF TABLES
SL.
NO.
REFERENCE
NO
DESCRIPTION PAGE
NO
1 Table 5.1 Input data from literature 5
2 Table 5.2
Air quality standards for SO2 as given in the EU Ambient
Air Quality Directive and WHO AQG 10
3 Table 5.3 Permissible exposure set by WHO 11
4 Table 6.1
Input data of the steel factory owned by Arcelor Mittal in
Zenica for screen view 15
5 Table 6.2 Data Set summary from Windographer 22
6 Table 6.3: Summary of the relative elevation 23
7 Table 6.4
Input data of the steel factory owned by Arcelor Mittal in
Zenica for screen view 24

1
1. ACKNOWLEDGEMENTS
The author of the report would like to thank Dr. Tom Curran, Gerry Murphy and David
Kelleghan for their helpful suggestions and guidance.
2. LIST OF ABBREVIATIONS
AQG Air Quality Guideline
BAM Beta Attenuation Monitor
BAT Best Available Technique
BREFs Best Available Techniques reference documents
EPA Environmental Protection Agency
EU European Union
FGD Flue Gas Desulfurization
NASA National Aeronautics and Space Administration
PAH Polycyclic Aromatic Hydrocarbons
PM Particulate Matter
SO2 Sulphur dioxide
tpy ton per year
ULSD Ultra-low Sulphur Diesel Fuel
USEPA United States Environmental Protection Agency
UV Ultra Violet
VOC Volatile Organic Compound
WHO World Health Organization

2
3. ABSTRACT
Fossil fuel burning due to industrialization has been the major source of sulphur dioxide (SO2)
emission other than the active volcanoes. In order to determine the emissions from an
individual point source, the steel plant owned by Arcelor Mittal in Zenica valley, Bosnia and
Herzegovina has been monitored. Standard Gaussian model has been used in the form of Screen
View software. The wind analysis was carried out with a software called Windographer which
accessed the GIS data from the website of NASA. The valley profile and superimposition of
the final pollution measurement in the form of stress contours have been created by Google
earth. The modelling was a very important study to estimate the possible concentration of SO2
after air dispersion. According to the report published by the Cantonal Institute for Public
Health there was a significant increase of cancer patient by 20% during 2002 to 2011 in Zenica
valley. A series of graphs are presented to lead us to the final model and finally the result was
compared with the actual measurements done at site.
4. INTRODUCTION AND OBJECTIVE
The steel Factory chosen for this report is owned by the world largest steel producer – the
ArcelorMittal Corporation. The factory, originally state-owned was privatized and Arcelor-
Mittal is now the main shareholder, with the government owning a symbolic share. In July
2008, the factory restarted integrated steel production after the facilities were damaged and
closed down during the Yugoslavian civil war in the 1990s. The factory in Zenica produces hot
rolled products (rebars, wire rod, mesh, lattice girders, and classic construction armature)
mainly for the Balkan, EU and North African markets with a capacity of 1,000,000 tonnes
(Environmental Justice Atlas, 2016)) per year. It Produced 700,000 tonnes steel product in
2012.
The town is located in a small, narrow valley (Figure 4.1) – 14 kilometres from Janić to
Vranduk, and is in between two mountains that are less than 2 kilometres apart. Because of this
from November to February, a toxic cloud forms over the city which traps all the substances
rising from the chimneys of steel plants and the other factories. Since 2008, the analyses of air
quality showed that pollution in Zenica was exceeding EU and Bosnia and Herzegovina
standards, often reaching alarming levels.
Figure 4.1: Location of Zenica Valley

3
The number of cancer patients has increased by the hundreds each year, and especially from
2007 onwards, i.e. since the full production of steel was resumed (Caucaso, O., 2012) however
there are diverging opinions about how much of the epidemic can be blamed on the steel plant.
In December 2012, after a several years of sending demand letters for installation of filters in
the plants, the citizens of Zenica organised a large protest attended by several thousand people.
Citizens demand filters for the smoke stacks to reduce toxic gases and heavy metals; as well as
an independent, publicly- controlled center for monitoring emissions led by the local university
and the local government.
On the 15th of November 2013, ArcelorMittal, has finally installed a filter in blast furnace and
proudly announced this would lower their emissions below the acceptable levels. In this way
the company admitted they have been breaking the law over the years. They did this at the
official ceremony attended by representatives of cantonal and local authorities in charge of
making and enforcing those laws. Citizens considered ArcelorMittal should not be
acknowledged for installing the filter, as the damage has already been done. However, the
company failed to give an apology to the citizens for doing so. The citizens wonder why this
was not done before, as the investment of 12 million BAM ($ 8,500,000)
(Usa.arcelormittal.com, 2016) for the filters represent a low level investment to ArcelorMittal,
having in mind it is a third most profitable company in Bosnia.
In this report the emission of SO2 is modelled and compared with the actual measurement done
at site. This study is very important for future prediction of emissions from a similar plant
irrespective of geographical aspects. The permissible concentration of SO2 in different
countries are also presented in this report. Climate data from 2015 onwards is taken into
account for this study. There are certain limitations of the model which are also illustrated in
methodology and assumption section.
The objective of the study is to investigate the influence of a steel plant in terms of SO2
emission with the help of Air Dispersion Modelling.
5. LITERATURE REVIEW
SO2 sources and effects
Sulphur dioxide (SO2) is one of the most common air pollutants in the world. It comes to the
environment from volcanoes and industrial processes, particularly combustion of fossil fuels
loaded with sulphur compounds. It is a colourless gas, the characteristic sharp odour, is heavier
than air, which at elevated concentrations in the air is detrimental to the human organism,
especially in the respiratory tract. It causes cough, bronchitis and fatigue, and higher
concentrations have toxic effects, Goletic and Imamovic (2013). Also, it causes acid rain in the
form of H2SO4 resulting harmful effects on wildlife, vegetation. In winter, the heating facilities,
SO2 exists in the air of towns and settlements in higher concentrations.
Background of the site
Every winter, as a rule, Zenica valley experiences episodes of high air pollution (Figure 5.1)
due to increased emissions of SO2 and adverse weather conditions which are characterized by
a stable atmosphere. High air pollution produced by the formation of inversion layer due to the
descent of cold air in the valley so that the layer of colder (denser) air found under a layer of
warm air (Goletic and Imamovic, 2013). Below the inversion layer accumulate pollutants and

4
worsening air quality. For these reasons it is necessary to ensure the control and evaluation of
the level of air pollution with SO2 or other pollutants that significantly pollute the air.
Figure 5.1: Photograph of the steel plant and two chimneys
Modelling of air pollution is an important tool for assessing air pollution in industrial and urban
areas located in the deep basins such as the Zenica basin. In the deep valley of Zenica, limited
by high mountains, lies a steel mill production capacity of 1 million t/y of steel. More than
90,000 people are exposed to emissions of various pollutants. The area of iron works and the
terrain profile is shown in Figure 5.2.
Figure 5.2. Surface Ironworks and orographic 3D model of Zenica basin, Goletic and
Imamovic (2013)

5
Measurements
The input data (Table 5.1) for the modelling are taken from the peer reviewed journal.
However, in practical cases where the data is not available from the journals field
measurements are required
Table 5.1: Input data from literature, Goletic and Imamovic (2013)
Parameters Description /
Value
Unit where
applicable
Remarks
Source type Point source Chimney
Emission rate 582.75 g/s SO2
Stack height 120 m
There measurement techniques are reported below;
1. Ultraviolet fluorescence Method
a. This method is based on the principle that SO2 molecules absorb ultraviolet
(UV) light and become excited at one wavelength,
b. SO2 + hrt → SO2*
c. then decay to a lower energy state emitting UV light at a different wavelength.
d. SO2* → SO2 + h nu 2
e. The intensity of fluorescence is proportional to the SO2 concentration.
f. Fluorescence SO2 Analyzer consists of a hydrocarbon scrubber, fluorescence
chamber, W light source, photoelectric detector, electronics, etc.
(as shown Figure 5.3).
Figure 5.3: Schematic diagram of UV. Fluorescence Method (Gec.jp, 2016)
g. Hydrocarbon Scrubber: The hydrocarbon scrubber shall remove hydrocarbons
contained in ambient air, which are excited with W light and consequently emit
fluorescence. The SO2 molecules pass through the hydrocarbon scrubber
unaffected.
Ambient
sample air
Hydrocarbon
Scrubber
Flourescence
Chamber
Photoelectric
Detector
Electronics Outputs
Pumping
out
UTV Light
Source

6
h. Fluorescence Chamber The fluorescence chamber shall have a structure to
efficiently emit the fluorescence of SO2.
i. UV Light Source: The exciting light source shall generate W light energy by an
electric discharge and so on.
j. Photoelectric Detector: The photoelectric detector shall be located adjacent to
the fluorescence chamber via an optical filter which selectively passes the
fluorescence to an electrical signal of required level.
2. Conductimetric Method
a. This is the method to measure continuously the concentration of SO2 in ambient
sample air from change in the conductivity of absorbent (hydrogen peroxide
solution acidified by sulfuric acid) which appears when the ambient sample air
passes through the absorbent by air bubbling. This method shall include the
following two types of measurements.
b. Accumulative Measurement: The measurement to indicate and record the
concentration of sulphur dioxide in ambient sample air corresponding to the
increment of the conductivity of absorbent with making absorption of sulphur
dioxide in a fixed amount of absorbent by bubbling a fixed amount of ambient
sample air for a fixed period of time.
c. Instantaneous Measurement: The measurement to measure and record
continuously the concentration of sulphur dioxide in ambient sample air by
measuring change in the conductivity of absorbent developed by sulphur
dioxide absorption through the contact of ambient sample air with the absorbent
at a fixed ratio of flow rate.
d. Remark: This method is applicable when the measurement is negligibly affected
by those gases which are dissolved in the absorbent causing the conductivity
change, e.g., chlorine, ammonia and carbon dioxide, or when the affection to
the measurement by these gases can be removed.
e. A composition example of conductimetric SO2 analyser is shown as Figure 5.4.
Figure 5.4: Schematic diagram of Conductimetric Method (Gec.jp, 2016)
Ambient
sample air
Electronics
Pumping out Outputs
Absorbent
pump
Absorbent
tank
Drain
tank
Gas absorption part

7
3. Coulometry
This is the method to measure continuously the concentration of sulphur dioxide in
ambient sample air detecting with the electrodes the change of bromine concentration
decreased by the reaction of SO2 in ambient sample air and bromine which is
dissociated in the electrolyte of potassium bromide by electrolysis.
4. Flame Photometry
This is the method to continuously measure sulphur compounds in ambient sample air
as the concentration of SO2 by measuring the light intensity with a photomultiplier tube
utilizing the light emission phenomenon which appears in the near ultraviolet region
when the sulphur compounds are thermally decomposed in a hydrogen flame.
Methodology
The formation of plumes of smoke (pick-up, transport, diffusion and deposition) under the
direct influence of the defined hour of meteorological data that may result of in the situ
measurements or in the alternative estimated. The origin of the coordinate system for each
source and for each hour of the calculation, is located on the surface as the sources of pollution,
and the absolute position of the receptor network nodes are translated into the local coordinate
system of pollution sources. For a given source, the concentration of SO2 at a distance on the
downstream (Figure 5.5) is represented by the following equation (1) (Goletic and Imamovic,
2013).
Equation (5.1)
Where, D - member of the dissolution, K - constant conversion (m), Q - emission of pollutants
SO2 (g/m3), Us - wind speed at the exit of the chimney (m/s), V – Vertical member, y -
Horizontal distance downstream from the headquarters of smoke (m), z – horizontal lateral
distance from the headquarters of smoke (m), λ - the concentration of pollutants SO2 (μg/m3),
σy - standard deviation of the horizontal dispersion, σz - standard deviation of the vertical
dispersion.
Figure 5.5. The basic illustration of a Gaussian plume model of smoke

8
Meteorological data
Meteorological conditions such as solar irradiation, ambient temperature, humidity, wind speed
and direction have great influence on air dispersion modelling. Once released to the
atmosphere, particulate matters are subjected to several atmospheric processes governing their
sources and sinks in the air (cited in Amodio et al. 2009). Heterogeneous reactions (photo-
oxidations) and gas-particle partitioning are the main transformation processes of PAHs; these
processes are dependent on the different meteorological conditions. Amodio (2009) presented
the influence of wind speed and direction on air dispersion modelling. The concentration was
found to be decreased in the cold season as a function of wind speed. Hence stable or slightly
stable situation may rise the probability of worst effect of air pollution.
Building Downwash
With the help of wind tunnel effect, Pournazeri et al, (2012) investigated the effect of building
downwash on air dispersion modelling. Due to the formation of eddy (Figure 5.6) the pollutants
are stuck into the blue zone and cannot mix well with the atmosphere.
Figure 5.6: Wind tunnel experiment to establish the building downwash effect
When the stack height is relatively small compared to the total height of a building there is an
effect of the same eddy formation but this time it formed on the plan (Figure 5.7) of the
building, Gupta et al. (2012).
Figure 5.7: Eddy formed on the plan in a wind tunnel experiment, Gupta et al. (2012)

9
Even the building geometry, parking pattern of cars in a street has significant impact on the
presence of pollutants in air. Gallagher et al. (2013) investigate the influence of parking pattern
of cars on Pearse street (Figure 5.8).
Figure 5.8: Pollutant concentrations as velocity vectors in the canyon for the (a) reference
and (b) parked cars models at a wind speed of 2.5 m/s in perpendicular wind conditions
Air quality standards
The EU-28 urban population was exposed to only a few exceedances of the Sulphur dioxide
(SO2) EU daily limit value in 2013. However, 37% of the EU‑28 urban population was exposed
to SO2 levels exceeding the WHO AQG in 2012.
Figure 5.9: Drop of SO2 from 2004 to 2013 (European Environment Agency, 2015)

10
The hourly and the daily limit values for the protection of human health were exceeded at only
two urban stations in Bulgaria in 2013, out of some 1390 stations measuring SO2 in 35
European countries.
Achieving the 6th EAP objectives of “levels of air quality that do not give rise to significant
negative impacts on, and risks to human health and the environment” means, for the natural
environment, no exceedance of critical loads and levels. For human health, the situation is more
complex as there is no known safe level of exposure for some pollutants such as particulate
matter and ground level ozone. There is strong health evidence, however, that measures taken
to reduce (Figure 5.10) these pollutants will have beneficial effects for the EU population.
Figure 5.10: Trend of land based emissions (EU Directive 2005/1132 & 1133/EC)
To achieve these objectives, SO2 emissions will need to decrease by 82%, NOx emissions by
60%, VOCs by 51%, ammonia by 27% and primary PM2.5 by 59% relative to emissions in
2000. The permissible limits are listed in Table 5.2.
Table 5.2: Air quality standards for SO2 as given in the EU Ambient Air Quality Directive
and WHO AQG

11
There has been a trend for decreasing exposure to SO2 over the past few decades, and, since
2007, the exposure of the urban population to concentrations above the daily limit value has
been under 0.5%. The EU‑28 urban population exposed to SO2 levels exceeding the WHO
AQG (20 μg/m3 as daily mean) in 2011–2012 amounted to about 36–37% of the total urban
population. Proportions have been constantly decreasing since 2004, when 64% of the EU‑28
urban population was exposed to SO2 levels exceeding the WHO AQG. In 2013, the highest
concentrations and exceedances of the annual critical level for the protection of vegetation from
SO2 occurred in Romania, Poland and Serbia, with six exceedances recorded in total. As in
previous years, none of these exceedances occurred at rural locations, where the critical loads
are supposed to apply.
WHO sets the exposure types for SO2 as per following;
 Short-term exposures
Controlled studies involving exercising asthmatics indicate that a proportion experience
changes in pulmonary function and respiratory symptoms after periods of exposure to
SO2 as short as 10 minutes.
 Long-term exposures (over 24-hours)
Early estimates of day-to-day changes in mortality, morbidity or lung function in
relation to 24-hour average concentrations of SO2 were necessarily based on
epidemiological studies in which people are typically exposed to a mixture of
pollutants. The limits are noted as in Table 5.3.
Table 5.3: Permissible exposure set by WHO, WHO (2005)
Pollutant Exposure types Permissible concentration
SO2
Short-term exposures 500 μg/m3
Long-term exposures 20 μg/m3
Best Available Techniques (BAT) from US EPA
The reduction of SO2 is primarily focused on fossil-fuel combustion sources. Reductions can
be achieved through the use of lower sulfur–containing fuel and/or installation of wet or dry
scrubbers. The economic impact analysis for an option such as dry scrubbing can show an
economic gain, as the waste may be saleable for the manufacture of wallboard. The following
provides information about each possible SO2 emission reduction option, based on past
experience and research of similar applications.
 Ultra-low Sulfur Diesel Fuel (ULSD): Because of its reduced sulfur content, ULSD is
capable of achieving significant reductions in SO2 emission rates. ULSD, while
marginally more expensive than No. 1 diesel, is an easy, environmentally practical
means of achieving emissions reductions without the need to install or maintain any
new equipment or after-treatment device. The use of this fuel in place of the standard
diesel is a strong candidate for the BAT for SO2 reduction.
 Environmental Impacts: In addition to the positive reduction in SO2 emissions (directly
proportional to the difference in sulfur content), the ULSD has a co-benefit of resulting
in slightly lower NOx emissions. Through the refining process to remove sulfur, there
is likely to be a slight reduction in elemental nitrogen, which translates to potentially
lower NOx emissions.

12
 Energy Impacts: The combustion of ULSD does not require any additional energy
consumption. The refinery producing the ULSD, however, will require more energy.
 Economic Impacts: The additional cost of ULSD is approximately $0.05 per gallon.
Assuming maximum firing rate of 147.6 gal/hr and 500 hr per year of operation, the
economic impact would be $2,300/tpy of SO2 reduced.
 SO2 emissions will be directly proportional to the sulfur content of the oil being burned.
Residual oil typically is refined into three sulfur content categories: 1) 2.2%, 2) 1.0%,
and 3) 0.5%. This case study has assumed a project specification of 1.0% residual oil.
The following are SO2 control alternatives.
o Flue Gas Desulfurization: A post-combustion flue gas desulfurization (FGD)
system uses an alkaline reagent to absorb SO2 in the flue gas and produce a
sodium and calcium sulfate compound. These solid sulfate compounds are then
removed in downstream equipment. FGD technologies are categorized as wet,
semi-dry, or dry, depending on the state of the reagent as it leaves the absorber
vessel. These processes are either re-generable (such that the reagent material
can be treated and reused) or non-re-generable (in which case all waste streams
are de-watered and discarded). Wet re-generable FGD systems are attractive
because they have the potential for better than 95% SO2 control, have minimal
wastewater discharges, and produce a saleable sulfur product. The economic
impact was determined to be $570/tpy
o Cleaner Fuel Substitution: SO2 reductions can be realized simply by using
distillate oil rather than residual oil. Based on published emission factors, SO2
emissions would be 73% less if distillate oil were burned. Although residual oil
and distillate oil prices fluctuate day to day, the current price differential is $0.62
per gallon. Assuming an annual fuel use based on full operation for 8,760 hours
per year.
o According to EU Best Available Techniques reference documents (BREFs),
Annual Mean SO2 for entire Europe is limited to 0 – 10 µg/m3 (Figure 5.11)
however there are are some exceptional cases have been identified.
Legends Annual Mean
SO2 [µg/m3]
≤ 5
5 - 10
10 - 20
20 - 25
> 25
Figure 5.11: Concentration of SO2 in Europe, Source: Eea.europa.eu. (2016)

13
Abatement technologies
Commercially available FGD technologies can “conventionally” be classified as oncethrough
and regenerable (Figure 5.12), depending on how sorbent is treated after it has sorbed SO2. 4
In once-through technologies, the SO2 is permanently bound by the sorbent, which must be
disposed of as a waste or utilized as a by-product (e.g., gypsum). In regenerable technologies,
the SO2 is released from the sorbent during the regeneration step and may be further processed
to yield sulfuric acid, elemental sulfur, or liquid SO2. The regenerated sorbent is recycled in
the SO2 scrubbing step. Both once-through and regenerable technologies can be further
classified as wet or dry. In wet processes, wet slurry waste or by-product is produced, and flue
gas leaving the absorber is saturated with moisture. In dry processes, dry waste material is
produced and flue gas leaving the absorber is not saturated with moisture.
Figure 5.12: Commercially available FGD technologies (United States Environmental
Protection Agency [USEPA], 2000a)
It has been observed that the use of this technology is distributed as the Figure 5.13 over the
world. Wet technology has the highest share of 86.8% over the world.
Figure 5.13: Pie chart of percentage shares (capacity) of the three FGD technologies installed
(USEPA, 2000a)

14
 Wet FGD Technologies: The overall reactions (raw material and end products are
highlighted in equation 5.2 and 5.3) in the absorber and in the reaction tank can be
summarized by:
SO2 + CaCO3 + 1/2H2O → CaSO3.1/2H2O +CO2 Equation (5.2)
SO2 + 1/2O2 + CaCO3 + 2H2O → CaSO4. 2H2O +CO2 Equation (5.3)
The schematic diagram for Wet FGD technology is presented in Figure 5.14.
Figure 5.14: Schematic diagram of Wet FGD Technologies (USEPA, 2000a)
 Dry FGD Technologies: Primary reactions (raw material and end products are
highlighted in equation 5.4, 5.5 and 5.6) in the spray dryer are as follows:
Ca(OH)2 + SO2 → CaSO3.1/2H2O + 1/2H2O Equation (5.4)
Ca(OH)2 + SO3 + H2O → CaSO4.2H2O Equation (5.5)
CaSO3 + 1/2O2 → CaSO4 Equation (5.6)
The schematic diagram for Dry FGD technology is presented in Figure 5.15.
Figure 5.15: Schematic diagram of Dry FGD Technologies (USEPA, 2000a)

15
6. METHODOLOGY AND ASSUMPTIONS
Model 1: Basic model by Screen View
In our study is based on Gaussian plume model and we can perform our analysis in Screen
View software developed by Lekes environmental corporation. To start with the model a
simple scenario is used to generate initial simulation results. The data collected from literature
are presented in the Table 6.1 below.
Table 6.1: Input data of the steel factory owned by Arcelor Mittal in Zenica for screen view
Value
Unit where
applicable
Remarks
Coordinates
44°13'28" N Hypothetical location @
centre of two stacks17°54'11" E
Steel production capacity 1,000,000 tonnes Produced 700,000 tonnes in
2012
Employees 3000
Source type Point source Chimney
Receptor height 1.65 m Average height of human 1.6
Stack height* 120 m
Stack gas exit velocity* 11.32 m/s
Stack gas exit temperature* 373.15 K
Ambient air temperature* 293 K Default
Terrain type** Simple
Nature of terrain** Flat
Distance considered Automated
and discrete
Shortest distance to property
line is 200 m
Fumigation Not
considered
Not applicable for urban
Building downwash* Not
considered
Meteorology type** Full All stability classes and wind
speed considered
Minimum distance
considered
100 m
Maximum distance
considered
50000 m
*Parameter to be changed in model 1a,1b,1c,1d,1e
** Parameter to be changed in model 2,3,4
The coordinates are taken as the mid point of two stacks (figure 6.1) of the plant. The data
taken from the journal is the summation of the emission from the plant.

16
Figure 6.1: Pollutant source from the factory
The simple model requires emission rate, stack height, stack inside diameter, stack gas exit
velocity, stack gas exit temperature, ambient air temperature (Figure 6.2).
Figure 6.2: Input window of Screen View
Here the height of receptor above ground is set to 1.65 m according to the average height of
man (1.7 m) and woman (1.6 m). However, people residing on multi-stories may face worse
effect than people walking on the ground. Now we need to give the input for the terrain profile
accordingly (Figure 6.3).

17
Figure 6.3: Input window for terrain profile in Screen View
We need to select the range of automated distance plot and we can also give the input for the
discrete distance at which we want to get the result for the concentration of the emission. The
graphical explanation of different terrain profile is mentioned in Figure 6.4.
a
b
c
Figure 6.4: a - Flat terrain, b - point of measurement, c – complex terrain
After successful operation of first model the parameters are changed (only one at a time) to
have an overview on the impact associated with the results. Different scenarios are mentioned
below.
 Model 1: Simple model based on Table 1.
 Model 1a: Change of single parameter (Height of stack from 120 to 200m) in model 1
 Model 1b: Change of single parameter (exit velocity from 11.32m/s to 22.64m/s) in
model 1
 Model 1c: Change of single parameter (Stack gas exit temperature from 373.15K to
323K) in model 1
 Model 1d: Change of single parameter (Ambient air temperature from 293K to 273K)
in model 1

18
 Model 1e: Change of single parameter (Building downwash from ‘not considered’ to
‘considered’) in model 1
 Model 2a to 2g: Advanced model considering wind data generated by Windographer
o Model 2a to 2f: Change of stability class
 a) Very Unstable
 b) Unstable
 c) Slightly Unstable
 d) Neutral
 e) Slightly Stable
 f) Stable
o Model 2g: Change of wind speed from 1m/s to 4m/s
 Model 3a and 3b: Advanced model considering wind data generated by Windographer
and terrain profile
o Model 3a: Change of terrain from Flat terrain to elevated terrain (valid only up
to 1km from the source)
o Model 3b: Change of terrain from simple terrain to complex terrain
 Model 4: Final model considering worst case scenario
Model with Windographer
Windographer is a software to model the wind data. It can download the satellite data from the
server of NASA may be for past 100 years. But here wind data from January 2015 till March
2016 is analysed. Here we need to mention the global coordinate of the desired location. Next
the nearest station (Station D in Figure 6.5) is selected to extract the wind data. It takes a lot of
time to download a set of data for decades. In my case the software was able to perform the
extraction within 15 minutes. After the analysis the software prepares a wind rose diagram
showing the direction vector and the magnitude of the wind.
Figure 6.5: The data extraction from the NASA server (open resource)

19
Windographer summarizes the wind speed variation over a period of time (Figure 6.6). With
this analysis we can note easily the minimum (zero) and maximum (11.4 m/s) wind speed for
that region. However, it does not give us the duration and the direction of wind. Here we
checked the wind speed at 120m height because it meets the criteria of our stack height.
Figure 6.6: Wind speed variation over time (from January 2015 to February 2016)
We can zoom in to any data in the Figure 6.7 at any instant of time and present the fragments
easy to read. It has been found that in recent time (February 2016) the speed reached its
maximum limit however it is always preferable to work with a long period say, 100 years.
Because a higher wind speed might be discovered with a greater return period for
meteorological data.
Figure 6.7: The maximum wind speed noted for recent time
In the following wind rose diagram (Figure 6.8) presents the wind speed, direction vector
(towards centre) and the duration of the wind. The length of each section presents the
magnitude of the wind, the area presents the occurrence period of the wind. Direction vectors
are presented in the form of 360-degree rotation. The most prevailing wind can be traced from
the highest area under the segments.

20
Figure 6.8: Wind rose diagram: Wind speed
Prevailing wind speed is recorded as 2m/s to 6m/s (average of 4m/s) blowing from NNE and
SSW directions. Next we need to superimpose the diagram in google earth so that we can
predict the direction of terrain profile needs to be considered. Figure 6.9 also note the range of
surface temperature in Zenica valley.
Figure 6.9: Rose diagram: Surface temperature
In the Figure 6.10 the direction of wind is marked and it is understood that the cold air coming
from the NNE direction is the governing wind during winter time. The arrow follows the valley
line between two consecutive mountains.

21
Figure 6.10: Superimposed rose diagram on the valley showing direction of prevailing wind
The diurnal profile is also presented in the Figure 6.11. It shows the temperature variation
during a day and the corresponding wind speed. The lowest temperature and wind speed both
recorded as lowest during 3am and 4 am in the early morning.
Figure 6.11: Diurnal profile based on the topographical conditions: analysed in windograph.
Speed marked in blue, Temperature in red
In summary the windographer lists the following meteorological data mentioned in the Table
6.2. Now we need to consider all of the stability classes of atmosphere in the screen view model
and observe the changes in results.

22
Table 6.2: Data Set summary from Windographer
Start date 01/01/2015 00:00
End date 01/03/2016 00:00
Duration 14 months
Length of time step 60 minutes
Calm threshold 0 m/s
Mean temperature 7.48 °C
Mean pressure 91.52 kPa
Mean air density 1.135 kg/m3
Model 3a and 3b: Advanced model considering wind data generated by Windographer and
terrain profile
After marking the location of the plant the direction vectors of the prevailing winds are
marked on the map (Figure 6.12). Then the cross sections are computed changing the cursor
on the valley profile on google earth.
Figure 6.12: Terrain profile in the direction of Wind-15 degree
Figure 6.13 and 6.14 also presents the terrain profile of the region for 30-degrees and 45-
degree wind direction respectively.

23
After listing (table 6.3) all the values from zero to five kilometre from the centre of the
emission the maximum value is taken for the worst combination. As the screen view can plot
circular contours at a time we should look for the maximum possible concentration of SO2 in
the air.
Table 6.3: Summary of the relative elevation
Distance
(m)
Elevation in meter (worst
combination profile)
Relative Elevation (m)
Datum
Wind
15
Wind
30
Wind
45
Wind
15
Wind
30
Wind
45
Maximum of
all scenarios
0 310 310 310 310 0 0 0 0
500 310 380 310 310 70 0 0 70
1000 310 450 320 375 119 10 65 119
1500 310 480 350 450 170 40 140 170
2000 310 530 420 525 220 110 215 220
2500 310 530 475 580 220 165 270 270
3000 310 700 550 700 390 240 390 390
3500 310 800 615 805 490 305 495 495
4000 310 900 540 775 590 230 465 590
4500 310 947 440 750 637 130 440 637
5000 310 800 420 720 490 110 410 490
With the above stated data, the complex model is performed in screen view and the worst
scenario is detected after generation of a set of graphs. After performing the final model, the
concentration of pollutant is categorised with a colour code from lowest value (green) to
highest one (red). Finally, the circular concentration contours are drawn in Auto cad and
superimposed on the Google earth with proper scale. The maximum concentration is compared
with some actual scenario as a part of the result validation.

24
The data selected for worst case scenario are presented in Table 6.4.
Table 6.4: Input data of the steel factory owned by Arcelor Mittal in Zenica for screen view
Value
Unit where
applicable
Remarks
Source type Point source chimney
Receptor height 1.65 m average height of human 1.6
Stack height 120 m
Ambient air temperature 293 K default
Terrain type Complex
Nature of terrain For complex terrain, EPA’s Screen 3 model used
Distance considered Automated
and discrete
Fumigation Not
considered
Not applicable for urban
Building downwash Not
considered
No such influence
Meteorology type For complex terrain, EPA’s Screen 3 model used.
Stability class: e, slightly stable
Wind velocity 2.5m/s (Appendix I)
Minimum distance
considered
100 m
Maximum distance
considered
50000 m

25
7. RESULTS AND DISCUSSIONS
Model 1: Simple model based on Table 1
The graph (Figure 7.1 and 7.2) presents the relation between the concentration of SO2 and the
distance travelled by the pollutant. It resembles the fact that theoritically there is zero
concentration at the bottom of the stack. It rockets up the highest peak with a concentration of
670 µg/m3
within 2 km and then it starts to slump to 480 µg/m3
when it reaches 3.5 km. Again
it starts to increase and gains 550 µg/m3
concentration while crossing 5 km landmark. Then it
starts to follow the path of parabolla and touches 140 µg/m3
at the distance of 50 km.
Figure 7.1: Automated distance vs. concentration - Terrain height = 0.00 m
Due to sharp increase of the initial concentration, the x- axis is further extended in second graph
(Figure 7.2).
Figure 7.2: Discrete distance vs. concentration - Terrain height = 0.00 m. Distances
considered 200m, 500m, 750m, 1000m, 1250m, 1500m
To avoid repetition of the graphs a series of graphs and the observations are described in
Appendix I. Based on the observation the worst case scenario has been chosen for the final
model.
100
200
300
400
500
600
700
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
Automated Distance Vs. Concentration
Terrain Height = 0.00 m.
(ug/m**3)
Distance (m)
100
200
300
400
500
600
700
200 400 600 800 1000 1200 1400 1600
Discrete Distance Vs. Concentration
(ug/m**3)
Distance (m)

26
Model 4: Final model considering worst case scenario
After performing the final model, the graph (Figure 7.3) is generated. The maximum
concentration lies between 1 km and 2 km from the source. It records maximum of 1346 µg/m3
which is roughly thrice the maximum allowable limit of SO2 in the air.
Figure 7.3: The result representing the final model of the study
Due to high concentration of the SO2 the government was forced to install a concentration
measurement detector on a roof top of one of the multi storied building (Figure 7.4). The
concentration showing on the LCD board is one of the highest concentration of SO2 measured
in Zenica valley which is very close to our calculated concentration of 1346 µg/m3
.
Figure 7.4: Concentration of SO2 recorded in the winter time in Zenica valley.
(Source: Environmental Justice Atlas, 2016)
Now the concentration of the pollutant is plotted in the form of stress contours (Figure 7.5) in
Auto cad. The green colour represents the lowest concentration while red denotes the highest.
1000.000, 1346.000
1500.000, 1354.000
2000.000, 1212.000
2500.000, 482.900
3000.000, 365.300
3500.000, 288.600
4000.000, 235.300
4500.000, 196.600
5000.000, 167.400
200
400
600
800
1000
1200
1400
1000 1500 2000 2500 3000 3500 4000 4500 5000
Complex Terrain Distance Vs. Concentration
(ug/m**3)
Distance (m)

27
Figure 7.5: The concentration contours for our model
Now the contour block is superimposed on the map with the help of Google earth creating a
circle of radius 5 km. Finally, we can get a quick overview from the Figure 7.6 about how far
the polluted air is spread with how much concentration.
Figure 7.6: The concentration distribution of SO2 in Zenica valley
Distance (m) Concentration µg/m
3
0 0
1000 1346
1500 1354
2000 1212
2500 482
3000 365
3500 288
4000 235
4500 196
5000 167

28
8. LIMITATIONS AND FUTURE WORK
The concentration distribution of the model outcome is circular which is only valid for the
simple and flat terrain without any obstacles and the area must have an equal distribution of
wind. In reality the concentration pattern is different. The comparison of the typical images
generated by Screen View and AERMOD are mentioned in Figure 8.1. So being an advanced
model AERMOD gives us the actual scenario (Gibson et al. 2009) of the concentration
distribution over an effected area. The vertical wind effect is to be considered also in advanced
model.
a. Circular contours of concentration b. Realistic pattern of concentration
Figure 8.1: Comparison of the output from a. screen view and b. AERMOD
Secondly the model only calculates the ground level concentration of the pollutant. A statistical
analysis is required to categorize the number of people effected in the region computing high
rise buildings, distance from the source, and the number of occupants. After the computation,
a series of model to be run to find out the number of people who are in different state of
vulnerable situation in terms of exposure to different concentration of SO2. It is observed that
beyond 2.5 kilometre from the source the concentration is under allowable limit. The stress
contours help us to have a quick overview on the pollution scenario of the valley. The study
was not limited to any geographical or meteorological aspects. The methodology of the studied
model is applicable for feasibility analysis of any steel plant in terms of SO2 dispersion located
any part of the world.
9. CONCLUSION
The model successfully computes the level of concentration of SO2 in terms of variation of
distance from the source of the pollution. The maximum concentration recorded in Zenica
valley matches the computed result with a narrow margin. It is also observed that the maximum
concentration of SO2 exceeds its allowable limit proposed by WHO and European standards
with an excess of third multiple order. As a result, the number of cancer patients increased by
20% during 2002 to 2011 in Zenica valley (Cantonal Institute for Public Health). Even though
the installation of the filter (12 million worth BAM filter) on 17th of December 2013, the
measuring station in Zenica have recorded 1,392 µg/m3
of SO2 in the air.

29
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Appendix I
Model 1a: Change of single parameter (Height of stack from 120 to 200m) in model 1
Observation: As stack height increases the pollutant gets the opportunity to mix well with
the atmosphere, hence the concentration drops down (Figure A1).
Model 1 Model 1a
Figure A1: Comparison of the results (Automated distance vs. concentration) of model 1 and
model 1a
Model 1b: Change of single parameter (exit velocity from 11.32m/s to 22.64m/s) in model 1
Observation: As exit velocity increases the pollutant gets the opportunity to mix well with
the atmosphere, hence the concentration drops down (Figure A2).
Model 1 Model 1b
Figure A2: Comparison of the results of model 1 and model 1b
Model 1c: Change of ‘Stack gas exit temperature’ from 373.15K to 323K) in model 1
Observation: As stack gas exit temperature decreases the brawnier motion of the gas
molecules slow down causing poor mix with the atmosphere, hence the concentration
increases (Figure A3).
Model 1 Model 1c
Figure A3: Comparison of the results of model 1 and model 1c
100
200
300
400
500
600
700
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
600
700
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
50
100
150
200
250
300
350
400
450
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
600
700
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
200
400
600
800
1000
1200
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)

Model 1d: Change of single parameter (Ambient air temperature from 293K to 273K) in
model 1
Observation: As the ambient temperature drops the difference between the exit temperature
and ambient temperature increases which leads better condition for equilibrium causing better
mixing, hence the concentration drops down (Figure A4) but in small quantity. However,
there is a problem of inversion.
Model 1 Model 1d
Figure A4: Comparison of the results of model 1 and model 1d
Model 1e: Change of single parameter (Building downwash from ‘not considered’ to
‘considered’) in model 1
Observation: There is negligible impact (Figure A5) of the change in modelling because the
height of stack (120 m) is quite higher than the building height (15m).
Model 1 Model 1e
Figure A5: Comparison of the results of model 1 and model 1d
Model 2a to 2f: Change of stability class
Observation: As the stability class changes from ‘very unstable’ to ‘stable’ the concentration
of pollutant observed in the result increases. The area under the curve is also highest or the
scenario f (Figure A6).
a: very unstable b: Unstable
100
200
300
400
500
600
700
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
600
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
600
700
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
600
700
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
600
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
600
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)

c: Slightly unstable d: Neutral
e: Slightly stable f: Stable
Figure A6: Comparison of the results (automated distance vs. concentration) of the model
with 1m/s wind velocity for different stability cases
Model 2g: Change of wind speed from 1m/s to 4m/s
Observation: Concentration drops (Figure A7) when the velocity of wind increases causing
better dispersion into the atmosphere.
Model 2f: Stable wind with 1m/s wind
velocity
Model 2g: Stable wind with 4m/s wind
velocity
Figure A7: Comparison of the results (automated distance vs. concentration) of 2f and 2g
Model 3a: Change of terrain from Flat terrain to elevated terrain (valid only up to 1km from
the source)
Observation: The concentration increases (Figure A8) for elevated terrain because the
elevation gives rise to the higher exposure of the polluted air. However in our case up to 2 km
from the source the terrain is flat, beyond this there is a mix of elevated and flat terrain. So it
is better to have a complex terrain in our next scenario.
100
200
300
400
500
600
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
50
100
150
200
250
300
350
400
450
500
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
50
100
150
200
250
300
350
400
450
500
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
600
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
100
200
300
400
500
600
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)
50
100
150
200
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
(ug/m**3)
Distance (m)

Model 2f: Stable wind with 1m/s wind
velocity for flat terrain
Model 3a: Stable wind with 1m/s wind
velocity for elevated terrain
Figure A8: Comparison of the results (automated distance vs. concentration) of 2g and 3a
Model 3b: Change of terrain from simple terrain to complex terrain
Observation: There is a drastically change of concentration (Figure A9) when we change the
terrain type from simple to complex. Complex terrain is more vulnerable to the air pollution
as the air gets less chance to mix with surroundings and the elevation effect causes increase in
the concentration of SO2 exposure.
Model: Slightly stable wind with 2.5m/s
wind velocity for simple terrain
Model 3b: Slightly stable wind with 2.5m/s
wind velocity for complex terrain
Figure A9: Comparison of the results (automated distance vs. concentration) of simple
terrain and complex terrain
10
20
30
40
50
60
70
80
90
200 400 600 800 1000 1200 1400 1600
(ug/m**3)
Distance (m)
500
1000
1500
2000
2500
3000
3500
0 100 200 300 400 500 600 700 800 900 1000 1100
(ug/m**3)
Distance (m)
50
100
150
200
250
300
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
(ug/m**3)
Distance (m)
200
400
600
800
1000
1200
1400
1000 1500 2000 2500 3000 3500 4000 4500 5000
Complex Terrain Distance Vs. Concentration
(ug/m**3)
Distance (m)

AIR DISPERSION MODELLING

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