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

DetectorElectronics 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 stacks 17°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. ConcentrationTerrain 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. ConcentrationTerrain Height = 0.00 m.

(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/m3

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)

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AIR DISPERSION MODELLING