Emission Inventory Report

WEST RAND DISTRICT MUNICIPALITY: EMISSION INVENTORY FOR 2011

Report No. uMN014-12 i

EMISSION INVENTORY FOR

THE WEST RAND DISTRICT

MUNICIPALITY

Issued by: Issued to:

uMoya-NILU Consulting (Pty) Ltd

P O Box 20622

Durban North, 4016

South Africa

MD Mokoena

Air Quality Officer

West Rand District Municipality

Corner Park and 6th Streets

Randfontein

2000

May 2012


Report No. uMN014-12 ii

This report has been produced for the West Rand District Municipality by uMoya-NILU Consulting (Pty) Ltd. The intellectual property contained in this report

remains vested in uMoya-NILU Consulting (Pty) Ltd. No part of the report may be reproduced in any manner without written permission from the West Rand District Municipality and uMoya-NILU Consulting (Pty) Ltd.

When used as a reference this report should be cited as follows:

uMoya-NILU (2012): Emission Inventory for the West Rand District Municipality, report

for the West Rand District Municipality, uMoya-NILU Consulting (Pty) Ltd, Report No.

uMN014-12.


Report No. uMN014-12 iii

EXECUTIVE SUMMARY

Introduction:

Substances released into the air can affect the health of the environment, residents,

animals and plants. Air emissions are the quantity of a substance, measured as mass of

substance per time unit, released into the atmosphere from a source. Sources of air

emissions include industrial facilities, transportation, home heating, agriculture, forest

fires and many others.

The West Rand District Municipality (WRDM) comprises four local municipalities, namely

Mogale City, Randfontein, Merafong and Westonaria. The WRDM is undertaking its first

air emissions inventory, which will then have to be updated on a regular basis to account

for emission changes. The results of the emission inventory will be used to shape the

way air quality is improved in the WRDM. The emission inventory is fundamental to the

development, implementation, monitoring and evaluation of the WRDM’s air quality

strategy. The emission inventory is also used as a major input to atmospheric dispersion

models.

The base year relevant to the emission inventory is 2011, which means that all emission

estimates will be based on 2011 activity data. The primary focus of the emission

inventory will be on the following criteria pollutants:

Sulphur dioxide (SO2)

Nitrogen oxides (NOx)

Carbon monoxide (CO)

Particulate matter (PM, PM10)

Lead (Pb)

The United States Environmental Protection Agency (USEPA) regulates these pollutants

by developing health-based air quality standards. In addition to these pollutants,

emissions will also be estimated for VOC and benzene. Emissions will be reported as

emission rates, of which the most common units are ton/day or kg/year. Pollutant

emission rates will be estimated by a combination of approaches, including the emission

factor approach and emissions monitoring.

Categorisation of Source in WRDM:

The emission sources in the WRDM are grouped into three main emission types (Figure

A) based on their characteristics, namely, point, mobile and area sources.

Point sources are sub-divided into the two categories of listed activities, i.e., large

industries regulated by Section of the National Environmental Management: Air Quality

Act (Act 39 of 2004) (AQA), and smaller industrial processes with boilers. Mobile


Report No. uMN014-12 iv

sources include on-road motor vehicles, ships, aircraft and locomotives. Only on-road

motor vehicles will be considered in this study as there are no harbours or airports in the

WRDM.

Figure A: Categorisation of emission sources in the WRDM

Due to the absence of credible information on agricultural activities in the WRDM, this

source is excluded from the study. However, the prescribed burning of crops is covered

as part of biomass burning.

Listed activities and small industrial processes:

Combustion devices found in industries are key emitters of criteria pollutants (SO2, NOx,

CO and PM10) and toxic air pollutants such as benzene, toluene and xylene. Data to

estimate emissions from industries was gathered with the aid of questionnaires. This

was supplemented by personal interviews with industry representatives and site visits.

The methods used to estimate industrial emissions were the emission factor approach

and calculations based on emission testing undertaken by the industries. The key types

of data required for the emission factor approach include the types of fuel (coal, fuel-oil,

diesel and gas) used and the consumption rates of the fuels. Questionnaires were issued

to 66 industries in the WRDM. It was discovered that several industries were not

sources of atmospheric emissions. The general response from industries was mixed,

with some being very cooperative and others not at all. A breakdown of emissions from

WRDM Atmospheric Emission Inventory

Point Mobile Area

Listed Activities Motor Vehicles

Small Industrial Processes

Domestic Burning

Agricultural

Biomass Burning

Tailings Dams


Report No. uMN014-12 v

the individual industries is contained in Section 4 of this report. The following table

provides a summary per local municipality:

Table A: Breakdown of industrial emission rates per local municipality in

WRDM, ton/day

Local

Municipality

Emission Rate (ton/day)

SO2 NOx CO PM10 VOC Benzene Lead

Mogale City 3.111 2.449 383.680 2.190 0.013 0.001 0.030

Randfontein 1.184 0.297 0.194 0.530 1.492 0.017 0

Westonaria 0.626 0.119 0.078 11.577 0.001 0 0

Merafong 0 0 0 0.004 0 0 0

Total 4.921 2.864 383.952 14.297 1.506 0.017 0.030

The largest industrial source of SO2 emissions is Mogale Alloys at 2.27 ton/day. Its

contribution to total SO2 emissions exceeds 46%, implying that significant reductions in

industrial SO2 emissions in the WRDM could be achieved by focussing reduction efforts

solely on Mogale Alloys. The other industries that have recorded notable SO2 emissions

are the Foodcorp Grocery Division and West End Clay Brick. Emissions of NOx from

industries in the WRDM are also low at 2.86 ton/day.

The local municipality that produces the largest quantity of industrial emissions is Mogale

City. However, it is interesting to note that the highest emissions of PM10 are from

Westonaria. This is primarily due to the many mining operations taking place there.

Motor vehicles:

A motor vehicle is defined as an on-road vehicle that derives its power for propulsion

from the combustion of fossil fuel. The most common types of motor vehicles that

operate in the WRDM are cars, vans (light-duty vehicles), buses and trucks (heavy-duty

vehicles). Cars are fuelled by both diesel and petrol (gasoline), whereas trucks are only

fuelled by diesel. Pollution from vehicles arises from the by-products of the combustion

process (emitted via the exhaust system), from evaporation of the fuel itself from the

fuel tank and from brakes and tyre wear. The pollutants produced include SO2, NOx, CO,

PM10, VOC and lead.

Motor vehicle emissions were estimated by using the Tier 1 approach proposed by the

European Environmental Agency (EEA). The key types of data required for this approach

are fuel sales data and emission factors. Fuel sales data was sourced from the

Department of Energy, which collates fuel sales data for the oil companies. Data was

available for all the local municipalities with the exception of Merafong. Emission factors

were sourced from the EEA.

The total emissions estimated from motor vehicles are presented below in Table B.


Report No. uMN014-12 vi

Table B: Breakdown of motor vehicle emission rates per local municipality in

WRDM, ton/day

Local

Municipality

Emission Rates (ton/day)

SO2 NOx CO PM10 VOC Lead

Mogale City 0.143 5.329 27.278 0.341 2.977 0.000

Randfontein 0.046 1.911 11.228 0.099 1.215 0.000

Westonaria 0.042 1.742 10.243 0.090 1.108 0.000

Total 0.231 8.982 48.749 0.530 5.300 0.000

The largest quantity of motor vehicle emissions are from Mogale City, followed by

Randfontein and Westonaria. Motor vehicle emissions in Mogale City, on average, make

up approximately 60% of total motor vehicle emissions in the WRDM. With respect to

individual pollutants, the pollutant emitted in the greatest quantity from motor vehicles

in the WRDM is CO at 48.749 ton/day. This is followed by NOx at 8.982 ton/day and

VOC at 5.3 ton/day. The largest source of VOC is gasoline-fuelled passenger cars. PM10

from diesel engines is considered to be one of the most dangerous pollutants from motor

vehicles with regard to human health. PM10 emissions from motor vehicles in the WRDM

are estimated to be 0.53 ton/day or 193 558 kg/year. The largest source of PM10

emissions is high-sulphur diesel and consequently light-duty trucks and heavy-duty

vehicles (trucks and buses). Due to the phase-out of lead from fuels, total lead

emissions from motor vehicles in the WRDM are low at 5 kg/year.

Tailings dams:

Tailings are the residue of the milling process used to extract valuable metals from

mined ores. There are currently approximately 52 active and inactive tailings dams in

the West Rand District Municipalities owned by the various gold mines located in the

areas. A total of 14 tailings dams were identified in Mogale City, 2 in Randfontein, 11 in

Westonaria and 23 in Merafong.

Tailings dams are examples of open areas that provide substantially large un-vegetated

areas that are exposed to wind erosion. They are a major source of dust and particulate

emissions. The estimation of particulate emissions is based on the USEPA methodology

for wind erosion of open aggregate storage piles and exposed areas in industrial facilities

provided in Chapter 13 of the USEPA 42 (USEPA, 2006). The total estimated emissions

from tailings dams are presented in Table C.

Table C: Breakdown of tailings dams emission rates per local municipality

Local Municipality PM Emission Rate

(kg/year)

PM Emission Rate

(ton/day)

% of

Total

Mogale City 1 797 629 4.92 10.95

Randfontein 1 925 291 5.28 11.75

Westonaria 3 956 869 10.83 24.10


Report No. uMN014-12 vii

Merafong 7 734 055 21.21 47.21

Total 16 467 289 42.24

The local municipality that emits the largest quantity of PM emissions from tailings dams

is Merafong at 21.21 ton/day, which is also the municipality with the greatest number of

tailings dams. More than 47% of all PM emissions from tailings dams are emitted from

Merafong. Significant quantities of emissions also originate from Westonaria, where

many tailings dams are also located. Since there is no information on what proportion of

the PM consists of PM10, it is assumed that all PM is PM10 which is representative of a

worst-case scenario.

Domestic burning:

The three primary application categories relating to domestic fuel burning are cooking,

lighting and space heating. The primary fuels used in South Africa for domestic

purposes are coal, paraffin, liquefied petroleum gas (LPG) and wood. Domestic use of

fuels is restricted largely to informal, low-income and densely populated settlements.

The combustion of these fuels is a significant source of air pollution, especially during

winter. The impact on air quality from residential fire emissions is fairly significant,

considering that the release of pollutants occurs close to ground level at relatively low

temperatures.

Domestic coal burning contributes to the emission of PM10, SO2, NOx, CO and benzene.

The emission factor approach was used to estimate emissions from domestic burning.

Data was sourced on the number of households in the WRDM, the consumption of fuels

by these households and emission factors for the various fuels burned in households.

The estimated emissions from domestic burning are presented in the table below.

Table D: Breakdown of domestic burning emission rates per local municipality

in WRDM, ton/day

Local

Municipalities


SO2 NOx CO PM10 VOC Benzene

Mogale City 0.087 0.011 0.781 0.019 0.023 0.000

Randfontein 0.040 0.005 0.339 0.007 0.008 0.000

Westonaria 0.068 0.009 0.536 0.010 0.012 0.000

Merafong 0.001 0.000 0.009 0.000 0.000 0.000

Total 0.196 0.025 1.665 0.036 0.044 0.000

Emissions of all pollutants, with the exception of benzene, can be described as

significant. The combustion of coal and paraffin results in high emissions of SO2 due to

the high sulphur content in these fuels. The local municipality that produces the largest

quantity of emissions from domestic burning is Mogale City. This is directly attributable

to the high number of households in Mogale City that use coal for cooking and space

heating. Westonaria is the municipality that produces the second highest quantity of


Report No. uMN014-12 viii

emissions. Westonaria is the local municipality where the consumption of paraffin is the

greatest. A high number of households use paraffin in Mogale City for cooking, lighting

and space heating.

Biomass burning:

Biomass burning is generally categorised into wildfires and prescribed (controlled)

burning. A wildfire is a large-scale natural combustion process that consumes various

ages, sizes, and types of flora growing outdoors in a geographical area. Consequently,

wildfires are potential sources of large amounts of air pollutants. Prescribed burning

activities include fires that are intentionally started for a variety of reasons such as fuel

reduction for wildfire prevention, regeneration after logging operations, ecosystem

maintenance, land clearing, and agricultural land management. Emissions of PM, CO,

NOx and VOC from wildfires are estimated by using the emission factor approach.

Emissions of SO2 from biomass burning are considered to be negligible.

The emission factor method requires data on the area burned (in hectares) by a fire and

fuel loading (mass of forest fuel/unit land area burned). Data on area burned was

sourced from the Meraka Institute that uses satellite remote sensing techniques to

identify burned areas. For this study, their analyses of burned area consisted of a spatial

overlay with aggregation, performed in a spatial relational database.

The total burned area for 2011 was 782.6 km2, compared to a total estimated area of

the WRDM of 4 087 km2. The burned area represents 20% of the total area of the

WRDM. This does not however imply that 20% of the total surface area of the

municipality was burned as a single location could be burned several times in a year.

A total of 3 651 fires occurred in 2011, meaning that there were approximately 10 fires

in a day in the WRDM. The highest number of fires for both 2010 and 2011 (average of

4.4 a day) occurred in Merafong, by a significant margin. The lowest number of fires

occurred in Randfontein. The results of the estimation of emissions from biomass

burning are presented below in Tables E.

Table E: Breakdown of biomass burning emission rates per local municipality in

WRDM, ton/day

Local

Municipality


NOx CO PM10 VOC

Mogale City 3.638 127.802 10.856 21.923

Merafong City 7.740 271.913 23.097 46.643

Randfontein 1.763 61.941 5.261 10.625

Westonaria 3.156 110.866 9.417 19.018

Total 16.297 572.522 48.632 98.208

The total emissions from biomass burning can be described as significant. This is

primarily due to the high number of fires that occur in the district municipality.


Report No. uMN014-12 ix

Significant quantities of all pollutants are emitted into the atmosphere from biomass

burning. The pollutant emitted in the highest quantity if CO at 572.522 ton/day. There

are also significant quantities of VOC and PM emitted at 98.208 ton/day and 48.632

ton/day, respectively. In line with the highest number of fires there, the highest

quantity of emissions as a result of biomass burning are from Merafong.

Total emissions:

Total emissions of all pollutants from all sources in the WRDM are presented below in

Table F.

Table F: Total emissions from all sources in the WRDM, ton/day

Source Emission Rate (ton/day)


Industries 4.921 2.864 383.952 14.297 1.506 0.017 0.030

Motor vehicles 0.231 8.982 48.749 0.530 5.300

0.000

Domestic burning 0.196 0.025 1.665 0.036 0.044

Tailings dams

42.24

Biomass burning

16.297 572.522 48.632 98.208

Total 5.348 28.167 1 006.889 105.736 105.058 0.017 0.030

The emission rates contained in the above tables provide useful information on which

sources to focus when developing emission reduction initiatives. A total of 5.348

ton/day of SO2 are emitted in the WRDM. Industries are the most significant contributor

to this total (>92.0%), due mainly to the combustion of coal.

A total of 28.167 ton/day of NOx emissions are produced in the WRDM, approximately 5

times more than SO2. The largest producer of NOx emissions is from biomass burning.

Wildfires and prescribed burning activities cause nitrogen to be oxidised to NOx. It is

estimated that a total of 16.297 ton/day of NOx emissions are produced in this way. The

other notable sources of NOx emissions are motor vehicles at 8.982 ton/day and

industries at 2.864 ton/day.

A total of 1 006.889 ton/day of CO emissions are produced, greater than both SO2 and

NOx. However, this does not necessarily mean that CO will pose a greater danger to the

health and well-being of residents in the WDM. CO normally causes negative health

impacts at high concentrations, whereas SO2 and NOx cause negative health impacts at

much lower concentrations. The two most significant sources of CO emissions are

biomass burning and industries at 572.522 ton/day and 383.952 ton/day, respectively.

As expected, the tailings dams produce no CO emissions, while motor vehicles produce

48.749 ton/day.

PM10 emissions are produced by all sources identified in this study. The quantity of PM10

emissions produced in the WRDM are greater than both SO2 and NOx. PM10 is recognised


Report No. uMN014-12 x

as a pollutant of great concern across the world due to its high prevalence and negative

health impacts. The total quantity of PM10 emitted in the WRDM was estimated at

105.736 ton/day. Biomass burning (48.632 ton/day) and the tailings dams (42.24

ton/day) have been identified as the major sources of PM10 emissions in the WRDM.

Industries are also responsible for a significant PM10 emissions rate of 14.297 ton/day.

VOCs consist of a range of organic pollutants that react photo-chemically with NOx in the

presence of sunlight to form ozone (O3), one of the 6 criteria pollutants and known to

have negative health impacts. The most notable source of VOCs is biomass burning at

98.208 ton/day. Emissions of one of the compounds classified as a VOC, namely,

benzene, was estimated separately in the study. Benzene emissions from the

petrochemical storage depot in Tarlton have been estimated at 6 306 kg/year.

Lead emissions originate from Castle Lead Works (11 031 kg/year) and motor vehicles

(5 kg/year). The low quantity of lead emissions from motor vehicles is primarily due to

the phase-out of lead in fuels.


Report No. uMN014-12 xi

TABLE OF CONTENTS

1. INTRODUCTION ............................................................................................... 1 1.1 Background ........................................................................................................................... 1 1.2 Base Year ............................................................................................................................. 2 1.3 Pollutants .............................................................................................................................. 2 1.4 Source Types ........................................................................................................................ 3 1.5 Time Interval ......................................................................................................................... 4 1.6 General Emission Estimation Methodology .......................................................................... 4

2. TERMS OF REFERENCE ...................................................................................... 5

3. EMISSIONS INVENTORIES................................................................................. 6 3.1 Background ........................................................................................................................... 6 3.2 Emission Estimation Methodologies ..................................................................................... 8

3.2.1 Continuous Emissions Monitoring (CEM) ............................................................... 8 3.2.2 Source Testing ........................................................................................................ 9 3.2.3 Mass Balance .......................................................................................................... 9 3.2.4 Emission Models and Factors ................................................................................. 9

4. CATEGORISATION OF SOURCES IN THE WRDM .................................................. 11

5. LISTED ACTIVITIES AND SMALL INDUSTRIAL PROCESSES .................................. 14 5.1 Description of Emissions .................................................................................................... 14 5.2 Methodology ....................................................................................................................... 15

5.2.1 Data Gathering ...................................................................................................... 15 5.2.1 Estimation of Emissions ........................................................................................ 17

5.3 Results of Emissions Estimations ....................................................................................... 19 6. MOTOR VEHICLES .......................................................................................... 31

6.1 Description of Emissions .................................................................................................... 31 6.2 Methodology ....................................................................................................................... 32 6.3 Results of Emission Estimations ........................................................................................ 35

7. TAILINGS DAMS ............................................................................................. 40 7.1 Description of Emissions .................................................................................................... 40 7.2 Methodology ....................................................................................................................... 41 7.2.1 Data Gathering ................................................................................................................... 41 7.2.2 Estimation of Emissions ..................................................................................................... 41 7.3 Results of Emission Estimates ........................................................................................... 43

8. DOMESTIC BURNING ...................................................................................... 46 8.1 Description of Emissions .................................................................................................... 46 8.2 Methodology ....................................................................................................................... 46

8.2.1 Data Gathering ...................................................................................................... 46 8.2.2 Estimation of Emissions ........................................................................................ 48

8.3 Results of Emission Estimates ........................................................................................... 48 9. BIOMASS BURNING ........................................................................................ 50

9.1 Description of Emissions .................................................................................................... 50 9.2 Methodology for Estimating Emissions............................................................................... 51 9.3 Results of Emissions Estimation ........................................................................................ 53

10. EMISSIONS SUMMARY .................................................................................... 55

11. CONCLUSIONS AND RECOMMENDATIONS ......................................................... 60

12. REFERENCES ................................................................................................. 63

APPENDIX A – STAKEHOLDER WORKSHOP ............................................................. 64

APPENDIX B – DESCRIPTIONS OF POLLUTANTS AND THEIR HEALTH EFFECTS ............ 68

APPENDIX C – EMISSION INVENTORY QUESTIONNAIRE ........................................... 70

APPENDIX D - GUIDELINE DOCUMENT ................................................................... 77

APPENDIX E – STORAGE TANK EMISSIONS ............................................................ 92

APPENDIX F – LOADING GANTRY EMISSIONS ......................................................... 94

APPENDIX G – FUGITIVE EMISSIONS..................................................................... 96


Report No. uMN014-12 xii

GLOSSARY OF ACRONYMS, TERMS AND UNITS

AEL Atmospheric emission license

API American Petroleum Industry

AQA The National Environmental Management: Air Quality Act (No. 39 of

2005)

AQMP Air quality management plan

BAT Best Available Technology

BTEX Benzene, toluene, ethyl benzene and xylene

CALPUFF The Californian Puff Model, a USEPA approved Gaussian-Lagrangian

air dispersion model

C Degrees Celsius

CEM Continuous emissions monitoring

CO Carbon monoxide

CONCAWE Oil Companies European Association for Environment, Health and

Safety in Refining

DEA The Department of Environmental Affairs

EF Emission factor

EFRT External floating roof tank

EIA Environmental Impact Assessment

Emission

The direct or indirect release of substances, vibrations, heat or noise

from individual or diffuse sources in an installation into the air, water

or land.

FRT Fixed roof tank

g/s Grams per second

HFO Heavy fuel oil

LDAR Leak detection and repair

LPG Liquefied petroleum gas

m/s Meters per second

mg/m3 Milligrams per cubic meter

NEMA National Environmental Management Act (Act No. 107 of 1998)

Nm3/h Normal cubic meters per hour

NOx Oxides of nitrogen, collectively groups nitrogen oxide and nitrous

oxide

NPI National Pollutant Inventory of Australia

O3 Ozone

Pb Lead

PM10 Particulate matter with aerodynamic diameter < 10 microns

PM Particulate matter

ppb Parts per billion

SAWS South African Weather Service

SO2 Sulphur dioxide

TANKS US EPA model to calculate emissions from fuel storage tanks


Report No. uMN014-12 xiii

TOC Total organic compounds

USEPA The United States Environmental Protection Agency

µg/m3 Micrograms of gaseous substance in one cubic metre of total gas

VKT Vehicle kilometres travelled

VOC Volatile organic compounds

WHO The World Health Organisation

WRDM West Rand District Municipality


Report No. uMN014-12 1

1. INTRODUCTION

1.1 Background

Substances released into the air can affect the health of the environment, residents,

animals and plants. Air emissions are the quantity of a substance released to the

atmosphere from a source. There are a variety of sources that are responsible for air

emissions, these include both naturally occurring and man-made. Sources of air

emissions include industrial facilities, transportation, home heating, agriculture, forest

fires and many others.

Air pollution comes from many sources, so it is important to know the contribution each

one makes in order to develop the best approaches for improving air quality. The West

Rand District Municipality (WRDM) is undertaking its first air emissions inventory, which

will then have to be updated on a regular basis to account for emission changes. The

results of the emission inventory will be used to shape the way air quality is improved in

the WRDM. The emission inventory is fundamental to the development, implementation,

monitoring and evaluation of the WRDM’s air quality strategy. The emission inventory is

also used as the major input to atmospheric dispersion models.

The WRDM is located in the province of Gauteng and comprises four local municipalities,

namely Mogale City, Randfontein, Merafong and Westonaria (see Figures 1.1 and 1.2).

The North West province is located to the north, west and south of the municipality.

Figure 1.1: Map showing location of WRDM in Gauteng Province



Figure 1.2: Map showing local municipalities in WRDM

The economic profile of the West Rand is characterised by agriculture, industrial and

mining activities, of which the latter two contribute largely to pollution and air quality

related problems in the region. In order to address and manage air pollution challenges,

a comprehensive air emission inventory is required for each local municipality.

The WRDM developed an Air Quality Management Plan (AQMP) in 2010 and one of the

gaps identified was the lack of a comprehensive emission inventory. The emission

inventory is intended to provide WRDM with essential information required to combat air

pollution and improve the quality of air within the region. This study was commissioned

in response to this response to the gap identified in the AQMP.

1.2 Base Year

The base year relevant to the emission inventory is 2011, which means that all emission

estimates will be based on 2011 activity data (fuel consumption rates, cleaning devices

efficiencies, traffic counts, etc.). No consideration will be given to proposed changes

intended to improve future air quality performance that have not been implemented by

2011. However, there will be instances where 2011 data will not be available. In such

cases, the year relating to the data will be explicitly stated.

1.3 Pollutants

The primary focus of the emission inventory will be on criteria pollutants, although

hazardous air pollutants (HAPs) will be estimated where permitted by the availability of

the necessary emission factors. The USEPA makes a clear distinction between criteria



pollutants and HAPs. Criteria pollutants are the six most commonly found air pollutants

that can harm human health or the environment. They include:




Particulate matter (PM, PM10)

Ozone (O3)

Lead (Pb)

The USEPA regulates these pollutants by developing health-based air quality standards.

Emissions for five of the six criteria pollutants will be estimated as part of this project.

Ozone will be excluded as it is, strictly speaking, not an emission but is formed by the

photochemical reaction of nitrogen oxides with non-methane volatile organic compounds

(NMVOC).

HAPs, on the other hand, are pollutants that cause or may cause cancer or other serious

health effects. However, these effects normally occur at high concentrations not

commonly found in the ambient environment, but in occupational environments such as

in the vicinity of chemical plants or chemical storage facilities. The standards developed

for HAPs are therefore occupational standards and are applied to workers working in

facilities that produce or store these pollutants. Examples are toluene (found in

gasoline), methylene chloride (found in paint stripper) and perchlorethylene (emitted

from dry cleaners). The USEPA has compiled a list of 187 HAPs, the majority of which

are organic in nature. However, certain non-organic HAPs such as hydrogen sulphide

(H2S), hydrogen fluoride (HF) and mercury also do exist. The full list of HAPs can be

accessed on the following link:

http://www.epa.gov/ttn/atw/188polls.html

1.4 Source Types

The project scope will include the following three primary source categories:

Point sources

Non-point sources

Mobile sources

Point sources are generally large industries with high stacks and high emission loads.

The USEPA defines a point source as any industry emitting >10 ton/annum of a criteria

pollutant or a combination of criteria pollutants. South Africa has not yet adopted a

formal definition for point sources. The following are typical examples of industries

classified as point sources:

Oil refineries

Power plants




Pulp and paper mills

Metallurgical industries

Non-point or area sources are smaller sources of similar activity that are grouped

together, which when taken collectively, produce a significant amount of air pollution.

There are several categories of area sources including biogenic sources, small industrial

sources (tank farms, landfills, waste water treatment plants, etc.), agricultural sources,

domestic burning sources and minor road sources.

Mobile sources are classified as on-road or non-road sources. As the name suggests,

on-road sources include all motor vehicles that travel on road such as cars, vans, trucks

and buses. Non-road sources refer to vehicles that are not confined to a road such as

ships, boats, aeroplanes, construction equipment and farming equipment.

1.5 Time Interval

Emissions will be reported as emission rates (versus emission concentrations), that is,

the mass of pollutants emitted in a time unit interval. The most common emission rate

units are ton/day or kg/annum. Reporting in daily averaging units such as ton/day is

important as it allows seasonal variations during the course of the year to be accounted

for. Seasonal variations occur when industries operate for certain months of the year

and not for others. This results in the uneven distribution of emissions during the year.

Variations in emissions also occur for motor vehicle emissions such that emissions rates

are higher during weekdays and lower during weekends. This variation can only be

demonstrated if emission rates are reported on a daily basis.

The unit of ton/day will be reported to two decimal places, whereas the unit of kg/year

will be reported to zero decimal places.

1.6 General Emission Estimation Methodology

Activity data will be obtained from industry groups, government departments and other

service providers. Pollutants emission rates will then be estimated by combining activity

data with emission factors. Where available, source emission test data will be used in

preference to emission factors for industrial and commercial sources. The emissions will

be assigned to the four local municipalities for all sources. Emissions will then be

calculated for days and years using emissions factors derived from various

internationally recognised sources. Emission estimation techniques for all source types

have been based on either published United States Environmental Protection Agency

(USEPA) or Australian (i.e. NPI) methodologies.



2. TERMS OF REFERENCE

The following items broadly define the scope of work for the project:

A. Estimation of emissions from the following biogenic and anthropogenic sources:

Veld fires, windblown dust, etc.

Area sources (human settlements)

Point sources:

Industrial (including mining)

Commercial

Agricultural activities:

Crop related

Animal Breading

Mobile sources:

On- road mobile

Off- road mobile

B. Inclusion of the following pollutants in the inventory:

Criteria pollutants

Organic air toxins

C. Inclusion of the Mogale, Randfontein, Merafong and Westonaria local municipal areas

in the inventory.



3. EMISSIONS INVENTORIES

3.1 Background

An air emissions inventory is an accounting of air pollutant emissions released over a

given time for a given political or geographic area. It can include point (e.g. industrial

stacks), area (e.g. domestic burning) and mobile (e.g. cars, trucks, and rail) sources. Air

emissions inventories can include emissions from both anthropogenic (man-made) and

natural (e.g. biomass burning, vegetation, soil, etc.) sources.

Air emission inventories are fundamental components of air quality management

systems. Air emissions must be measured before they can be managed and reduced.

Other components of an air quality management system include goals, policies, ambient

objectives, source emission standards, dispersion modelling, ambient air and source

emission monitoring, environmental reporting, approvals, inspections, enforcement and

research. Air emissions inventories are needed to provide regulators, industry and the

public with easy access to the best possible data to make informed decisions. They are

also needed in order to develop and evaluate emission reduction scenarios. Examples of

emission sources are shown in Figure 3.1.

Figure 3.1: Illustration showing typical emission sources

In an emission inventory, all sources of pollution within an area are listed, and details

are provided of the locations and masses of pollutants emitted. Sources of pollution are

divided into the following categories:

Point sources - Emissions from single activities of considerable size, like industrial

plants, power plants and incinerators are characterised by emissions from

individual stacks. It is important to identify the stacks, and to collect information

about their height, physical parameters and the exact location so that it is

spatially resolved.



Mobile sources - Emissions from road traffic, streets and highways, and railways.

Emissions are usually estimated based on transportation data (e.g. traffic

counts).

Area sources - Area sources are smaller or more diffuse sources of pollution (e.g.

home heating, public services, veld fires, agricultural activities). Input data for

estimating emissions from these sources are provided on an area basis either for

administrative areas, such as counties, municipalities or for regular grids.

There are several widely used categories to characterise pollutants. These categories

depend on the level of prevalence of the pollutants and the severity of their impacts on

human beings. The three categories that relate particularly to the petroleum industry

are criteria pollutants, toxic (hazardous) air pollutants and nuisance pollutants.

Criteria pollutants:

The USEPA lists criteria pollutants as the six most commonly found air pollutants that

can harm human health or the environment. They include sulphur dioxide (SO2),

nitrogen oxides (NOx), carbon monoxide (CO), particulate matter (PM), ozone (O3), and

lead (Pb). The USEPA regulates these pollutants by developing health-based air quality

standards.

Toxic air pollutants:

Toxic air pollutants are pollutants that cause or may cause cancer or other serious health

effects. However, their effects normally occur at high concentrations or concentrations

not commonly found in the ambient environment. The impacts of toxic air pollutants are

generally occupational in nature and occur at the point of production. The standards

developed for toxic air pollutants are therefore primarily occupational in nature and

applied to the work place. There are various types of toxic air pollutants that originate

from petrochemical refineries, most of which are organic in nature (collectively referred

to as total organic compounds or TOCs) and formed by the volatilization of organic

compounds. For petrochemical facilities, the following have been identified as the key

toxic air pollutants:

Benzene

Toluene

Ethyl benzene

Xylene

They are collectively referred to as the BTEX group of compounds.

Nuisance pollutants:

In addition to criteria and toxic air pollutants, which are associated with negative health

impacts, there are also pollutants that do not affect one’s health but rather quality of

life. These are the so-called nuisance pollutants and included in this category are



pollutants such as hydrogen sulphide (H2S) (odour impact), mercaptans (odour impact)

and dust fallout (nuisance impact).

The emissions inventory starts with identifying all relevant sources in the industry. For

each source that may emit air pollution and contribute to exposure, data must be

collected on the:

Type of source (e.g. point, line, and area source)

Location of source

Amount of emission

Variation of the emissions with time (hour of the day, day of the week and year).

When developing an emission inventory, it is imperative that a base year is selected.

This will serve as a reference for future emissions inventories. Emissions inventories are

typically updated every three to five years.

Emissions data serves as the primary input for air dispersion modelling in providing

spatially referenced emission rates from sources such as industries. Emission

inventories are the starting point in the development of air quality management systems

and provide data for:

Establishing a baseline for future planning.

Setting emission limits and reduction targets for industries through permitting.

Tracking environmental performance of industries (and regulators).

Identifying sources and problem areas.

Generating public interest in air quality.

3.2 Emission Estimation Methodologies

The methodologies most commonly used by petroleum refineries to estimate emissions

to the atmosphere are direct measurement methods: continuous emissions monitoring

(CEM), source testing; and indirect methods: mass balance calculations, emission

models and factors, and engineering estimates.

3.2.1 Continuous Emissions Monitoring (CEM)

CEMs are proven technologies for monitoring emissions directly and continuously. CEMs

are used to determine flue gas flow rates, analyse the gas, measure the contaminant

concentrations, and log the data. They can be the most accurate method of quantifying

emissions but are the most costly option. In several countries, regulations require CEMs

to monitor SO2 and NOx emissions from sulphur recovery unit (SRU) incinerators,

fluidised catalytic crackers (FCCs) and boilers. SO2 and NOx emissions are perceived to

be the most significant pollutants, and these three sources are generally the largest at

refineries under normal operating conditions. Most CEMs require certification and

verification of quality assurance/control (QA/QC) activities, as well as routine

maintenance. Continuous monitoring can be done using either an ‘extractive CEM,’ in

which case the sample gas is extracted from the emission stream and transported to a



gas analyser for the measurement and recording of the contaminant concentration, or an

‘in-situ CEM,’ which measures and analyses the emissions directly in a stack. The main

benefits of an extractive CEM is that the instruments are not subject to heat, vibration,

and corrosive conditions. Maintenance is easier at ground level and analysers for

extractive CEMs are generally less expensive than those for in-situ systems. The

disadvantages are that sample lines can leak, freeze, or clog, and pollutants can be lost

to adsorption, scrubbing effects, or condensation. The main benefits of an in-situ CEMs

are minimisation of sample loss and elimination of the costly sampling and conditioning

system. However, maintenance and replacement inside the stack are more difficult and

calibration gas must be taken to the analyser location.

3.2.2 Source Testing

Source testing, also known as stack testing or stack sampling, is the regulatory standard

in South Africa. Undertaken by trained and experienced staff during normal operating

conditions, using accredited methods, and at appropriate intervals, source testing can

provide accurate annual emission estimates. It is required to determine compliance with

a country’s emission standards or permit discharge limits. They are also required for

certification of CEMs, and emission factors are often a collection of source tests at

various operating rates.

3.2.3 Mass Balance

A mass balance calculation applies the law of conservation that the mass of material

entering and leaving a process unit remains unchanged provided there is no

accumulation in the unit. The cost depends on the availability of accurate data and staff

time. The general equation for the mass (M) balance calculation is:

Min=Mout + Maccumulated/depleted

A typical refinery example is the combustion of fuel oil containing sulphur. If it is

assumed that all the sulphur in oxidised to SO2, then the following equation could be

used to estimate SO2 emissions:

SO2 Emissions (kg) = Consumption Rate of fuel (m3/day) x S Content (mg/m3) x (No. of

Days Units Operates) x (MW of SO2/S) x 1(kg)/106 (mg)

3.2.4 Emission Models and Factors

Emission models and factors are widely used to measure air emissions from refineries.

If default data are not applicable to local conditions or type of facility, emission models

require detailed input of data, such as meteorological data or equipment specifications.



Examples of emission models used in the petroleum refining sector include the USEPA’s

TANKS and WATER9 models, both of which can be used to calculate VOCs and other air

contaminants. An emission factor is a simplified emission model that relates emissions

from a source to some activity associated with the source. A large number of published

emission factors are available for many processes, and they are generally the least

costly method and the easiest to apply. The USEPA provides ratings of reliability with its

AP-42 emission factors.



4. CATEGORISATION OF SOURCES IN THE WRDM

The emission sources in the WRDM are grouped into three main emission types (Figure

4.1), based on their characteristics, namely, point, mobile and area sources. Each of the

main emission sources are further categorised into various key emission source

categories, based on the nature of the emission sources.

The point emission source category includes stationary emission sources identified

individually due to the quantity or nature of their atmospheric emissions. This category

is sub-divided into two key emission source categories:

Listed activities, i.e., large industries regulated by Section of the National

Environmental Management: Air Quality Act (Act 39 of 2004) (AQA)

Smaller industrial processes with boilers.

Figure 4.1: Categorisation of emission sources in the WRDM

The mobile emission source category includes emission sources along a defined line. It

includes all on-road mobile sources (these are vehicles operated on the streets and

highways, such as motorcycles and cars) and non-road mobile sources (consisting of all

vehicles and equipment not routinely operated on streets and highways, such as trains,

ships and aircrafts). Since there are no ports or harbours in the WRDM, the only mobile

source in the district municipality is road traffic or on-road motor vehicles.

WRDM Atmospheric Emission Inventory

Point Mobile Area

Listed Activities Motor Vehicles

Small Industrial Processes

Domestic Burning

Agricultural

Biomass Burning

Tailings Dams



The area emission source category encompasses a large number of diverse emission

sources and it includes facilities whose individual emissions do not qualify them as point

sources (individually they emit smaller quantities of pollutants but, collectively, they can

release significant quantities of pollutants) and those emissions sources for which

datasets do not exist to locate the emissions any more specifically. This category is sub-

divided into six key emission source categories:

Domestic burning (for cooking, lighting and space heating)

Agricultural activities

Biomass burning

Tailings dams

Due to the absence of credible information on agricultural activities in the WRDM, this

source is excluded from the study. However, the prescribed burning of crops will be

covered as part of biomass burning.

A brief summary of the main pollutants emitted in the WRDM and their primary sources

are contained in Table 4.2.

Table 4.1: Pollutants and sources at petroleum refineries

Air Pollutant Main Sources

Sulphur dioxide Listed activities

Small industrial processes

Motor vehicles

Domestic burning

Biomass burning

Nitrogen oxides Listed activities


Motor vehicles

Domestic burning

Biomass burning

Particulate matter Listed activities


Motor vehicles

Domestic burning

Biomass burning

Tailings dams

Carbon monoxide Listed activities


Motor vehicles

Domestic burning

Biomass burning

Volatile organic

compounds

Listed activities




Motor vehicles

Domestic burning

Biomass burning

There are many small industrial processes in the WRDM that are sources of criteria

pollutants and HAPs due to the use of boilers in those facilities. There are several listed

activities, but none of these could be classified as major industries in the league of

power stations, crude oil refineries and pulp and paper mills. These listed activities are

regulated and operate in accordance with conditions specified in their atmospheric

emission licenses (AELs).

Many listed activities and small industrial processes in the WRDM have boilers for the

production of steam, which is used for the purpose of heating. The boilers are primarily

small to medium in size with a heat input rating of less than 50 MW, the threshold for

classification as a listed activity. The combustion of fossil fuels such as coal, diesel, gas

and heavy fuel oil (HFO) in these boilers results in emissions of SO2, NOx, CO, PM10 and

VOC.

There are also several gold mines located across the WRDM, owned and operated by the

major gold mining conglomerates in South Africa. Some of these mines are classified as

listed activities while others are not. Those that are not are exclusively involved in the

extraction of precious metals, and not the refining of the metals. Refining entails

combustion, which is a key source of air pollutants, whereas extraction is not. Extraction

type mining processes are primarily a source of dust and PM10.

Interspersed across the WRDM are also numerous tailings dams, which are major

sources of dust and PM10. The burning of biomass such as agricultural crops and bushes

is a source of PM10 and VOCs. Motor vehicles burn petrol and diesel, which are classified

as fossil fuels due to their origin from crude oil. The burning of petrol and diesel in

motor vehicles produces emissions such as SO2, NOx, CO, PM10 and VOC.



5. LISTED ACTIVITIES AND SMALL INDUSTRIAL PROCESSES

5.1 Description of Emissions

There are several listed activities and small industrial processes in the WRDM that

operate boilers, the most common type of combustion device in the municipality. The

primary purpose of these boilers is to produce steam for heating. S.21 of the National

Environmental Management: Air Quality Act, 2004 (AQA) defines listed activities and

legislates the need for these activities to operate in compliance with an AEL. None of the

boilers in the WRDM is classified as listed activities as their heat input ratings do not

exceed 50 MW (threshold for being classified as a listed activity). Many of these boilers

will however be classified as controlled emitters in the proposed regulation for boilers

with heat input ratings exceeding 10 MW but less than 50 MW.

Combustion devices are key emitters of criteria pollutants (SO2, NOx, CO and PM10) and

toxic air pollutants such as benzene, toluene and xylene. The greenhouse gases, CO2,

CH4, and nitrous oxide (N2O), are also produced during gas combustion.

Figure 5.1: Picture of an industrial boiler process

According to CONCAWE, combustion processes comprise boilers, furnaces, gas turbines,

gas engines, diesel engines, incinerators and flares. A large number of industrial

processes and facilities make use of industrial boilers for steam generation. Industrial

boilers use a range of fuels depending on boiler size and design characteristics, and on

the availability/proximity of fuel. In many cases, the fuel is a by-product or waste

product from other processes. The volume and nature of the emissions from combustion



in boilers differs depending on the fuel composition, fuel consumption, boiler design and

operation, and the emission and pollution control devices in use.

When fuels burn, they produce various pollutants. The non-combustible portion of the

fuel remains as solid waste. The coarser, heavier waste is called “bottom ash” and is

extracted from the burner, and the lighter, finer portion is “fly ash” and is usually

emitted as particulates through the stack. Products of incomplete combustion include

CO, SOx, NOx, acid gases and VOCs. Metals and their compounds may also be entrained

(i.e. carried forward by a stream of gas or vapour of fine liquid droplets).

Process heaters such as furnaces are used extensively in refineries to supply the heat

necessary to increase the temperature of feed materials to reaction or distillation level.

The fuel burned may be refinery fuel gas, natural gas, residual fuel oils, or combinations,

depending on economics, operating conditions, and emission requirements. Process

heaters may also use CO-rich regenerator flue gas as fuel.

In the WRDM, the most commonly used fuels are coal, followed by gas, diesel and heavy

fuel oil (HFO). A total of 25 industries have been identified with either one or multiple

combustion units.

5.2 Methodology

5.2.1 Data Gathering

The steps in gathering information from industries are illustrated with the aid of the flow

charts below in Figures 4.1. The following points regarding the information gathering

process are highlighted:

Emissions inventory questionnaires serves as the principle information gathering

documents for industries.

Guideline documents were developed to assist industries in completing the

questionnaires.

The questionnaires were accompanied by the official letter from the WRDM.

The questionnaires, once completed by the industry, were signed off by the

authorised company representative to certify the correctness of the information

submitted.

Industries were allowed one month for completing the questionnaires.

Industries could use consultants to assist if in-house capabilities do not exist.

Questionnaires with missing data were referred back to the industries. Once the

questionnaires were adequately completed, the previous incomplete versions

were disposed of.

Data was subjected to quality control before final input into the database. This

involved tasks such as reality checks, sample calculations, etc.



Figure 5.1: Flowchart showing steps in gathering information

The emissions inventory questionnaires were developed in Microsoft Word format to

allow for ease of completion by the industries. The questionnaires were received from

industries in hard copy or electronic formats. The hard copies were signed by the senior

company official. The hard copies were stored in a Master File and stores in a safe

location until hand-over to the WRDM. Only the most updated questionnaires or latest

revisions were kept on file. Revisions that were found to be incompletely filled were

disposed of once the corrected versions were received.

The electronic copies were saved in an emissions inventory folder called ‘WRDM

Emissions Inventory_2010’. Write-access to the folder was only assigned to emission

inventory team members.

Industries were identified by initially reviewing existing databases such as those

compiled by the team that developed the WRDM’s air quality management plan. This

was complemented by a drive-around with Musa Zwane of the WRDM to identify

industries that did not appear on the databases. The drive around was based on the

Determine list of industries to be

inventoried

Issue notification to industries on legal

requirement to complete

questionnaire

Follow - up with non - respondents

Contact facility to resolve outstanding

issues

Review and code facility

data

Problems?

Inventory database

uMoya-NILU

Yes

No

To Facility

Distribute questionnaires

To facility

From facility

Calculate emission rates using

emission factors

Receive completed questionnaires

Compare emission rates with

questionnaire

Problems?

Yes

No



identification of industries with stacks, which pointed to the existence of combustion

devices such as boilers and furnaces on those premises.

Industries that experienced difficulties in completing the questionnaires were assisted in

two ways. Firstly, industries were invited to visit the offices of the WRDM, where

consultants from the emission inventory development team provided basic training in

completing the questionnaires. Secondly, personal visits were made by Benton Pillay

and Musa Zwane to companies to assist them in gathering the required data and

completing the questionnaires. These two measures yielded very positive results.

5.2.1 Estimation of Emissions

The methods used to estimate emissions from listed activities and small industrial

processes using emission factors are briefly described in this section. These methods

apply mostly to boilers. However, in some instances, emission rates were provided by

the industries. Several industries had also undertaken stack sampling, albeit with

service providers not competent to provide this service. The results of these stack

sampling campaigns have also been used to estimate emissions from the industries

concerned.

Sulphur dioxide:

The quantity of SO2 emitted from combustion processes depends on the mass fraction of

sulphur in the fuel burnt. According to CONCAWE, the following equation can be used to

estimate SO2 emissions from combustion processes (Concawe, 2009):

Emission rate (kg/year) = 2000 × A × MFS (1)

Where,

A = mass of fuel consumed (ton/year)

MFS = mass fraction of sulphur in fuel

This equation assumes complete combustion of sulphur to SO2. The composition of

sulphur in coal generally varies between 0.5 and 1.3%. A conservative estimate of 1%

(or mass fraction of 0.01) was however used in cases where the sulphur content was not

known.

The composition of sulphur in HFO is generally high at 3.5% or 0.035 (m/m). HFO is

therefore the fuel that produces the highest emissions of sulphur per unit mass of fuel

burnt when compared with coal, diesel and gas. Sasol gas has the lowest sulphur

content of 0.001875%, thus making it the cleanest burning fuel. This value was

estimated from information provided by Sasol that the sulphur content of its gas is < 15

mg/m3. Using the density of Sasol gas of 0.80 kg/m3, it was possible to estimate the

maximum sulphur content as a mass percentage of 0.001875%, which represents a



conservative estimate. The composition of sulphur in diesel is 500 ppm (m/m), which is

commercially available from most suppliers, including the oil companies. In terms of

mass fraction, this equates to a value of 0.0005.

Nitrogen oxides:

Environment Australia has developed a set of emission estimation guidelines entitled

“Emission Estimation Technique Manual” for a range of industrial sector sources

(Environment Australia, 2008). The contents of these manuals are based on

internationally recognised sources such as the USEPA’s AP-42 and the European Union’s

CORINAIR. The major advantage of using the Environment Australia guidelines is the

reporting of numbers in metric units. For instance, NOx emission factors are reported in

units of kg/m3. The other important advantage is the availability of emission factors for

refinery fuel gas. Most other sources only provide emission factors for natural gas and

suggest that this be used as a surrogate for fuel gas.

According to Environment Australia, the following equation can be used to estimate

emissions from combustion processes using emission factors:

Emission rate (kg/year) = A × EF × CE (2)

Where,

A = mass of fuel consumed (ton/year)

EF = uncontrolled emission factor (kg pollutant/ton fuel burnt)

CE = control efficiency of the emission from the use of a control device

The NOx emission factor for the uncontrolled combustion of coal is 3.8 kg/ton. This

implies that for every ton of coal combusted, an average of 3.8 kg of NOx is emitted.

The use of an emission control device will result in a reduction of the emission factor,

based on the efficiency of the control device. For instance, a reduction efficiency of 50%

will result in the emission facto being reduced by half to 1.9 kg/ton. The boilers in use in

the WRDM are generally not equipped with emission control devices such as low NOx

burners for reduction of NOx emissions. The emission factor of 3.8 kg/ton will therefore

stay unchanged for this study.

The NOx emission factor for the uncontrolled combustion of natural gas (similar to Sasol

gas) from boilers of <30 MW is 2.16 kg/ton. The relevant emission factor for the

uncontrolled combustion of residual oil (similar to HFO) is 7.32 kg/ton and for diesel it is

2.72 kg/ton. As with SO2, emissions of NOx are the highest when residual oil is burnt.

Carbon monoxide:

Equation (2) above is also used for the estimation of CO emissions from combustion

processes. The CO emission factor for uncontrolled combustion of coal in boilers is 2.5

kg/ton. There are currently no emission control devices available for small boilers to



reduce CO emissions. The emission factor of 2.5 kg/ton is therefore considered

acceptable.

For residual oil combustion, a CO emission factor 0.67 kg/ton is specified by

Environment Australia. This value is almost three times higher for gas combustion at

1.82 kg/ton. The CO emission factor drops to 0.68 kg/ton for diesel, a value similar to

that for residual oil.

Particulate matter:

As with all other pollutants, PM10 emissions vary with the type of fuel combusted and the

duty of the combustion device. An additional factor that has an influence on PM10

emissions is the ash content of the coal when combustion takes place in wall-fired,

tangentially-fired or wet bottom boilers or in cyclone furnaces. The greater the ash

content, the greater the emissions of PM10. For the conventional spreader stoker type

boiler, which is the type primarily used in the WRDM, ash content does not have a

significant influence on PM10 emissions.

The USEPA (2005) provides emission factors of 33 kg/ton for PM and 6.6 kg/ton for PM10

for coal combustion from boilers with a spreader stoker feed configuration. For natural

gas, Environment Australia provides an emission factor of 0.16 kg/ton for boilers rated <

30 MW. For residual oil, the emission factor for PM10 is even less at 0.0542 kg/ton, while

it is 0.14 kg/ton for diesel.

Volatile organic compounds (VOCs):

VOC emissions by definition include all emissions of volatile organics with the exception

of methane. These are the compounds that participate in the photochemical reactions

that lead to the generation of ground-level ozone. Methane is a greenhouse gas (GHG)

and is not involved in photochemical reactions. Emissions of VOCs from combustion

processes are estimated by using the emission factor method and equation (2), as

presented by Environment Australia. In line with the USEPA, Environment Australia

provides an emission factor of 0.03 kg/ton of VOC emissions from the uncontrolled

combustion of coal.

With respect to natural gas, the VOC emission factor increases to 0.119 kg/ton. For

residual oil and boilers rated < 30 MW, the VOC emission factor is low at 0.04 kg/ton,

and decreases further to 0.0272 kg/ton for diesel. VOC emission factors are generally

higher for fuels that are more volatile, such as natural gas.

5.3 Results of Emissions Estimations

The industry databases referenced in this study were found not to be comprehensive.

Several of the industries contained in the databases no longer existed and several others



were not sources of atmospheric emissions. Many industries were also very small and

could not be considered as significant sources of air pollutants. During the drive-around

referred to in the methodology section, several industries were identified that were

inexplicably absent from the industry databases. The following is the final list of

industries included in the emission inventory:

Table 5.1: Industries in the WRDM

Local

Municipality Industry

Source of

Emissions

Listed

Activity

Mogale City

Goud Saad/Krugersdorp Mill Unconfirmed No

Krugersdorp Crematorium Yes Yes

Majesty oil Mills Yes No

Boltonia Meats Yes No

Pace Oils (The Old Oil Man) No No

Rely Metpro Yes No

Foodcorp Piemans Pantry Yes No

Mogale Alloys Yes Yes

African Brick Yes Yes

Castle Lead Works Yes Yes

Chemiphos SA Yes Yes

Exol Oil Refinery Yes No

Advance Seed No No

Auto Commodities Unconfirmed

Avima No No

Blancom International Products No No

William Tell Yes Yes

Clariant Southern Africa No No

Sima Yes No

Fima Films SA No No

The Energy Company No No

Duys Roto Moulders No No

Ceramic Industries Limited Yes Yes

AARD Mining No No

Perlite Mining Unconfirmed

SAB Yes No

Lafarge Ready Mix Yes Yes

Yusuf Dadoo Hospital Yes No

Plascon Yes Yes

Cam Chem Unconfirmed

Pace Oils Yes No

Drift Supersand No No

Janho Quarries and Crushing Yes No

Executive Bricks and Paving Yes No

Krugersdorp Abattoir Yes No

Sasko Mills Yes No

Chemico SA Unconfirmed

Leratong Hospital Yes No

Geratech Zirconium Beneficiation Unconfirmed

Nimag Yes No

Transnet Pipelines Yes No

Sachi Chemicals No No



Transvaal Rubber Yes No

Isover No No

Galvaglo Yes No

Cobra Watertech Yes No

Bull Brand Foods Yes No

Randfontein

Aranda Textile Mills Yes No

B&S Steel Fabrication Non-existent

Blitz Engineering Non-existent

Wilma Continental Oil Yes No

Cosmos Dairy Yes No

Gemtex Textile Mill Yes No

Meadow (Astral Foods) Yes No

Foodcorp Grocery Division Yes No

Randfontein Hospital No No

Supreme Petfood/ V-Oils Yes No

Tiger Brands Yes No

Ultimate Feeds Unconfirmed No

Vesuvius Rand Steel Non-existent No

Gold One No No

Armco Superlite Yes Yes

SA Oil Yes No

Transnet Pipelines Yes Yes

Cremos Crematorium Yes

Westonaria

BASF Construction Chemicals Yes No

Goldfields South Deep Gold Mine Yes Yes

Goldfields Kloof Gold Mine Yes Yes

Goldfields Driefontein Mine Yes Yes

West End Clay Brick Yes Yes

Merafong

Corobrick Driefontein Yes Yes

Carletonville Transport and Plant Hire No No

Fochville Hospital No No

Western Deep Levels Hospital No No

Leslie Williams Private Hospital (on

property of Goldfields)

No No

Khutsong Medical Centre No No

Fochville Abattoir Non-existent

Durban Roodepoort Deep Gold Mine

(Blyvoor)

Unconfirmed Yes

Harmony Elandsrand Gold Mine Unconfirmed Yes

AngloGold Ashanti Mponeng Mine Unconfirmed Yes

AngloGold Ashanti Tau Tona Mine Cannot locate Yes

AngoGold Ashanti Savuka Mine Unconfirmed Yes

DRD Gold (Blyvooruitchzit Mine) Cannot locate Yes

A total of 37 emission inventory questionnaires were submitted to industries in Mogale

City. Of these, 15 industries returned their questionnaires either completely or partially

completed. A further 12 were determined not to be a source of emissions during the



course of telephonic discussions with their representatives (see Table 5.1). The

following 4 industries in Mogale City that did not submit questionnaires were contacted

telephonically to source fuel data (type, consumption rate, sulphur content, etc.) needed

to estimate their pollutant emission rates:

Foodcorp Piemans Pantry

SIMA

Yusuf Dadoo Hospital

Leratong Hospital

The following industries expressed their desire not to participate in the study with

responses that were generally rude and uncooperative:

Goud Saad/Krugersdorp Mill

Cam Chem

The other industries in Mogale City such as Boltonia Meats, Rely Metpro, Perlite Mining,

Lafarge Cement, Krugersdorp Abbatoir and John Turner and Sons have generally been

evasive and uncooperative.

Exol Oil Refinery would like to be included in the study, but the company was in the

process of conducting an air quality study and wished to submit information at a later

stage.

Of the 19 industries listed in Randfontein, a total of 9 returned their questionnaires.

Personal visits had to be paid to several of these industries after they initially failed to

submit their questionnaires on time. During these visits, the emission inventory

questionnaires were completed. A total of 2 industries were confirmed as not being

sources of emissions, a further 2 remain unconfirmed (due to a lack of response) and a

further 3 were found to no longer exist in the Randfontein Municipality. Gemtex Textile

Mill did not submit their questionnaire, but the company was contacted telephonically to

obtain their boiler fuel data. Aranda Textile Mill remains evasive and difficult to source

information from.

A total of 5 industries were contacted in Westonaria. BASF Construction Chemicals and

West End Bricks submitted their emission inventory questionnaires, whereas the 3

Goldfields Mines provided copies of their draft AEL application forms. Most of the

information required to estimate their emission was contained in the AEL application

forms.

Industries in the Merafong Municipality, of which a total of 13 were identified, generally

proved to be the most uncooperative. Emission inventory questionnaires were issued to

the following industries:

Corobrik

Leslie Williams Private Hospital

Harmony Elandsrand Gold Mine



AngloGold Ashanti Mponeng Mine

AngoGold Ashanti Savuka Mine

A total of 4 industries were found not to be a source of emissions through telephonic

discussions and 2 could not be located. From Table 5.1, it is clear that the majority of

industries are located in Mogale City, followed by Randfontein, Merafong and Westonaria.

The largest type of industries are the precious metal mines, but smelting operations are

absent from the majority of mines in the WRDM. These mines primarily extract ore,

which is transported to other areas where ore beneficiation in the form of smelting takes

place. Smelting operations result in emissions of combustion pollutants such as SO2,

NOx, CO and PM10. Since smelting operations are absent, the primary pollutant produced

is dust from ore extraction. The estimation of these dust emissions, primarily from the

handling and storage of ore, is a complex task that mines should estimate with the aid of

air quality consultants. However, since the extraction takes place underground, dust

emissions are not expected to be significant. Dust is classified as a nuisance pollutant

that has no health impacts but affect one’s quality of life. Dust is the cause of many

complaints and results in the soiling, contamination, structural corrosion and damage to

precision equipment, machinery and computers. Although highly dependent upon local

sources, dust typically comprises of windblown dust, fine sand, mist, fly ash, pulverised

coal and ore. Transport distances range typically from <1 m to <2 km. South Africa

currently has dust fallout standards in place. The Department of Environmental Affairs

(DEA) has published limit values (SANS 1929) for dust deposition and these are used for

assessment purposes. The 1 200 mg/m2/day threshold is taken as an action level for

remedial action.

For the reasons described here, there was not a huge focus on dust emissions from

mining operations. The only mining group that disclosed the existence of smelting

operations was Goldfields at its South Deep, Kloof and Driefontein gold mines. However,

the emissions data provided by Goldfields is not complete and lacks the detail required

for the compilation of a comprehensive emission inventory. The data provided by

Goldfields was nevertheless used in this study, but all the mines should be encouraged

to undertake more detailed emission inventories of their processes when the WRDM

emission inventory is updated in the future.



Figure 5.1: Gold Fields South Deep twin shaft vent shaft

An important aspect in the reduction of atmospheric emissions is the phasing out of the

so-called “dirty fuels”. These are generally the fuels with high sulphur contents that

when combusted result in high emissions of SO2 and other pollutants. Coal and HFO are

generally classified as dirty fuels and should be discouraged from use by industries.

These are unfortunately also the most abundant and cheapest fuels. The use of clean

fuels such as gas and LPG should be promoted. The following table provides the

consumption of fuels by those industries that did submit the data.

Table 5.2: Fuels and quantities consumed by industries in the WRDM

Local


Fuel Consumption Rate

Coal

(ton/year)

Fuel Oil

(ton/year)

Gas

(m3/year)

Diesel

(ton/year)

Mogale City

Majesty Oil Mills 4 800

Transvaal Rubber 16

South African Breweries 9 855

Sphinx Acrylic Bathroom

Ware

2 839

Mogale Alloys 88 413

Plascon Luipaardsvlei 128 228

Independent

Crematoriums of SA

13 941

Chemiphos 115

Castle Lead Works 320 033

Cobra Watertech 360 000

William Tell Industries 562

Galvaglo Not reported

BullBrand Foods 4 800

Foodcorp Piemans Pantry 739

SIMA 2 124 521

Yusuf Dadoo Hospital 2 520



Leratong Hospital 1 800

Randfontein

Meadow Feeds 1 932 70

Wilma Continental Oils 12 000

Cosmos Dairy 18

Foodcorp Grocery Division 14 400

Westonaria

BASF Construction

Chemicals

Goldfields South Deep

Gold Mine

Goldfields Kloof Gold Mine

Goldfields Driefontein Mine

West End Clay Brick 11 424

Total 141 944 1 486 2 949 562 34

From the above table, coal and gas appear to be the most widely used fuels. A total of

141 944 tons of coal a year are burnt by industries in the WRDM. Of this total, 62% is

consumed by Mogale Alloys, by far the single largest consumer of coal in the district

municipality. Other large coal consumers include the Foodcorp Grocery Division, West

End Clay Brick, Wilma Continental Oils and South African Breweries (SAB).

There are essentially two types of oils, namely, HFO and LFO (light fuel oil). HFO is

generally associated with high levels of sulphur and is consequently a dirty fuel. A few

industries use LFO and HFO, either as their primary fuel or as a backup to coal. The

latter scenario exists in case coal-fired boilers fail or are decommissioned for statutory

maintenance reasons. The largest consumer of oil is the Foodcorp Piemans Pantry,

followed by William Tell Industries. As indicated previously, HFO is a dirty fuel and its

use should therefore be discouraged in the WRDM.

The use of gas, notably Sasol gas, is relatively prevalent in the WRDM. SIMA, a

company that manufacturers steam for commercial purposes, is the largest consumer of

gas at 2 124 521 Nm3/year. Other large users of gas include Cobra Watertech and

Castle Lead Works. In contrast to HFO, gas is recognised as the cleanest fuel.

The following table presents the emission rates estimated from the various industries

that submitted information in the form of questionnaires, AEL application forms, results

of stack sampling or via telephonic interviews.

The total SO2 emissions from all industries in the WRDM are estimated to be 1 796 219

kg/year or 4.92 ton/day. This figure is relatively small if compared to the SO2 emissions

from a standard crude oil refinery in South Africa, which can vary from 6 to 18 ton/day.

SO2 emission rates from local power stations are even greater at between 200 and 750

ton/day of SO2.



Table 4.2: Emission rates from industries in WRDM, kg/year

Local


Emission Rate (kg/year)


Mogale City

Majesty Oil Mills 48 000 18 240 12 000 31 680 144

Transvaal Rubber 16 43 11 2

South African Breweries 70 956 37 449 24 638 65 043 296

Sphinx Acrylic Bathroom Ware 13 4 229

Mogale Alloys 828 560 777 270 139 979 420 622 570 2 652 2 140

Plascon Luipaardsvlei 3 051 937 172 11 855

Independent Crematoriums of SA 1 291 733 335 35 2 44

The Old Oil Man

Automotive Gasoil Limited

Chemiphos 289 34 21 231 5

Geratech

Castle Lead Works 9 313 5 298 37 4 38 8 847

Cobra Watertech 1

William Tell Industries 39 312 4 111 376 30 22

Nimag 15 100

Galvaglo 4 012

BullBrand Foods 48 000 18 240 12 000 31 680 144

Foodcorp Piemans Pantry 259 5 411 495 40 30

SIMA 25 9 518 2 855 258 253

Yusuf Dadoo Hospital 50 400 9 576 6 300 16 632 76

Leratong Hospital 36 000 6 840 4 500 11 880 54

Randfontein

Meadow Feeds 24 224 7 854 4 877 12 755 61

Wilma Continental Oils 120 000 45 600 30 000 79 200 360

Cosmos Dairy 18 49 12 3

Foodcorp Grocery Division 288 000 54 720 36 000 95 040 432



Tiger Consumer Brands 6 469

Transnet Pipelines - Tarlton

Refractionator

261 112 4 564

Transnet Pipelines - Tarlton Tank Farm 282 624 1 513

Westonaria

BASF Construction Chemicals 500

Goldfields South Deep Gold Mine 26 224 000

Goldfields Kloof Gold Mine 5 200

Goldfields Driefontein Mine 3 920 550

West End Clay Brick 228 480 43 411 28 560 75 398 343

Merafong Corobrik 1 292

Total 1 796 219 1 045 349 140 142 614 5 218 323 549 503 6 306 11 031

Table 4.3: Emission rates from industries in WRDM, ton/day

Local




Mogale City

Majesty Oil Mills 0.132 0.050 0.033 0.087 0.0004

Transvaal Rubber 0.0001

South African Breweries 0.194 0.103 0.068 0.178 0.0008

Sphinx Acrylic Bathroom Ware 0.0006

Mogale Alloys 2.270 2.130 383.505 1.706 0.007 0.0059

Plascon Luipaardsvlei 0.0084 0.0026 0.0005 0.0023

Independent Crematoriums of SA 0.0035 0.002 0.0009 0.0001 0.0001

The Old Oil Man

Automotive Gasoil Limited

Chemiphos 0.0008 0.0001 0.0001 0.0006

Geratech

Castle Lead Works 0.026 0.015 0.0001 0.0001 0.024

Cobra Watertech



William Tell Industries 0.108 0.0113 0.001 0.0001

Nimag 0.041

Galvaglo 0.011

BullBrand Foods 0.132 0.050 0.033 0.087 0.0004

Foodcorp Piemans Pantry 0.0007 0.0148 0.0014 0.0001 0.0001

SIMA 0.0001 0.0261 0.0078 0.0007 0.0007

Yusuf Dadoo Hospital 0.138 0.026 0.017 0.046 0.0002

Leratong Hospital 0.099 0.019 0.012 0.033 0.0001

Randfontein

Meadow Feeds 0.066 0.022 0.013 0.035 0.0002

Wilma Continental Oils 0.329 0.125 0.082 0.217 0.001

Cosmos Dairy 0.0001

Foodcorp Grocery Division 0.789 0.150 0.099 0.2604 0.0012

Tiger Consumer Brands 0.018

Transnet Pipelines - Tarlton

Refractionator

0.715 0.013

Transnet Pipelines - Tarlton Tank

Farm

0.774 0.004

Westonaria

BASF Construction Chemicals 0.0014

Goldfields South Deep Gold Mine 0.0001 0.614

Goldfields Kloof Gold Mine 0.014

Goldfields Driefontein Mine 10.741

West End Clay Brick 0.626 0.119 0.078 0.207 0.0009

Merafong Corobrik 0.004

Total 4.9211 2.8640 383.9524 14.2968 1.5055 0.0173 0.0302



Table 4.5: Breakdown of emission rates per local municipality in WRDM, kg/year

Local

Municipality



Mogale City 1 135 471 893 714 140 043 164 799 208 4 571 229 11 031

Randfontein 432 242 108 223 70 889 193 467 544 589 6 077 0

Westonaria 228 506 43 411 28 560 4 225 648 343 0 0

Merafong 0 0 0 1 292 0 0 0

Total 1 796 219 1 045 349 140 142 614 5 218 323 549 503 6 306 11 031

Table 4.6: Breakdown of emission rates per local municipality in WRDM, ton/day

Local

Municipality



Mogale City 3.111 2.449 383.680 2.190 0.013 0.001 0.030

Randfontein 1.184 0.297 0.194 0.530 1.492 0.017 0

Westonaria 0.626 0.119 0.078 11.577 0.001 0 0

Merafong 0 0 0 0.004 0 0 0

Total 4.921 2.864 383.952 14.297 1.506 0.017 0.030



The impact of these SO2 emissions on ambient air quality should be determined through

air quality monitoring or atmospheric dispersion modelling. However, based on emission

rates, the initial expectation is that SO2 ambient air quality levels should be low and

below health-based air quality guidelines and standards. The largest source of SO2

emissions is Mogale Alloys at 2.27 ton/day. Its contribution to total SO2 emissions

exceeds 46%, implying that significant reductions in industrial SO2 emissions in the

WRDM could be achieved by focussing reduction efforts on Mogale Alloys. The reason for

Mogale Alloys’ high SO2 emissions is directly related to its high consumption of coal. The

other industries that have recorded notable SO2 emissions are the Foodcorp Grocery

Division and West End Clay Brick. Although the Foodcorp Pieman’s Pantry is a large

consumer of oil, it is a clean oil called low burning fuel (LBF) with a sulphur content of

0.005%, compared to the typical 3% of HFO.

Emissions of NOx from industries in the WRDM are also low at 2.86 ton/day. This is in

line with international trends which suggest that other sources such as motor vehicles

are more prominent sources of NOx emissions than industries.

The breakdowns of emission rates for each local municipality reveal that the largest

quantity of atmospheric emissions from industries is produced in Mogale City. This is

followed by Randfontein, Westonaria and Merafong. Of the total of 4.921 ton/day of SO2

emitted from industries in the WRDM, 3.111 ton/day are emitted from industries in

Mogale City and 1.184 ton/day are emitted from industries in Randfontein. The total SO2

emissions from industries in these two local municipalities is 4.295 ton/day. Emissions

from the other two local municipalities, namely, Westonaria and Merafong are there

insignificant.

However, it is interesting to note that the highest emissions of PM10 are from

Westonaria. This is primarily due to the numerous mining operations taking place there.

There are also many mines located in Merafong, but as mentioned earlier, the industries

in Merafong were not very forthcoming in providing emissions information, so it was not

possible to estimate SO2 emissions from mines in Merafong.



6. MOTOR VEHICLES


A motor vehicle is defined as an on-road vehicle that derives its power for propulsion

from the combustion of fossil fuel (NPI, 2000). The most common types of motor

vehicles that operate in the WRDM are passenger cars, vans (light-duty vehicles), buses

and trucks (heavy-duty vehicles). The energy to propel vehicles comes from burning

fuel in an engine. Cars are fuelled by both diesel and petrol, whereas trucks are only

fuelled by diesel. Pollution from vehicles arises from the by-products of the combustion

process (emitted via the exhaust system) and from evaporation of the fuel itself from

the fuel tank. Particulate matter is also emitted from brakes and tyre wear.

Various types of pollutants are produced in the combustion process. A range of VOCs

are produced because the fuel is not completely burnt (oxidised) during combustion.

NOx results from the oxidation of nitrogen at high temperature and pressure in the

combustion chamber. CO is generated when carbon in the fuel is partially oxidised

rather than fully oxidised to CO2. SO2 and lead are derived from the sulphur and lead in

fuels. Particulate matter is produced from the incomplete combustion of fuels, additives

in fuels and lubricants, and worn material that accumulates in the engine lubricant.

These additives and worn materials also contain trace amounts of various metals and

their compounds which may be released as exhaust emissions.

Evaporative emissions come mainly from petrol (diesel fuel has a much lower vapour

pressure) and consist of VOCs and small amounts of lead. These emissions may occur in

several ways:

Diurnal Losses: As the ambient air temperature rises during the day, the

temperature of fuel in the vehicle’s fuel system increases and increased vapour is

produced.

Running Losses: Heat from the engine and exhaust system can vaporise gasoline

when the car is running.

Hot Soak Losses: Because the engine and exhaust system remain hot for a period

of time after the engine is turned off, gasoline evaporation continues when a car

is parked.

Resting Losses: Vapour may be lost from the fuel system or the evaporative

emission control system as a result of permeation through rubber components

and other leaks.

Another type of emission that arises from the use of motor vehicles is dust emissions

from roads. As the vehicle’s tyres turn, particles on the road are crushed and re-

suspended into the atmosphere.

Motor vehicles continue to play a dominant role in causing air pollution. In the European

Union as a whole, on and off road vehicles are the largest sources of CO, NOx and



NMVOC emissions (Andrias, et al, 1994). Forecasts indicated that vehicles would remain

a major emissions source there until the various Euro standards have been

implemented. In densely populated urban areas, vehicles can be a major source of

exposure to PM as well. Road vehicles account for a majority of NOx and black smoke

emissions in many major cities in the world.

Motor vehicles are also major emissions sources in the United States (US) and Japan. In

the densely populated cities of the US, where the air pollution problem is especially

severe, the USEAP has projected that highway vehicles will account for approximately

38% of the total NOx inventory and 22% of the total VOC inventory in 2005, in spite of

the introduction of tighter motor vehicle standards in the 1990 Clean Air Act. It is

increasingly clear that motor vehicles are also the major source of pollution problems in

the developing world.

6.2 Methodology

Motor vehicle emissions are generally estimated by using a combination of the top-down

(high-level) and bottom-up (detailed) approaches. The top-down approach essentially

entails the use of emission factors with fuels sales data on a broad spatial basis, say, a

municipality. Data requirements and resources to estimate emissions based on the top-

down approach are not significant. The bottom-up approach, on the other hand, entails

the use of emission factors with detailed activity data such vehicle counts, road data,

vehicle parc data, and fuel consumption data. The bottom-up approach provides results

that are more accurate than the top-down approach, but requires substantially greater

resources. The data requirements for the bottom-down approach are substantial and

therefore more onerous. The preferred approach in estimating motor vehicle emissions

is therefore a combination of both the top-down and bottom-up approaches.

Top-down approach: The key types of data required for this approach are fuel sales data

and emission factors. The European Environmental Agency (EEA) publishes a guideline

book with a comprehensive list of methodologies for estimating atmospheric emissions

from a variety of sources (EEA, 2010). One of the chapters deals specifically with

exhaust emissions from road transport. The EEA identifies the following key categories

of motor vehicles:

Passenger cars

Light-duty trucks (<3.5 ton)

Heavy-duty vehicles (>3.5 ton) including buses

Motorcycles

The EEA also identifies a range of pollutants emitted from motor vehicles including the

following:

Ozone precursors (CO, NOx, VOCs)

Greenhouse gases (CO2, CH4, N2O)

Acidifying substances (NH3, NOx, SO2)



Particulate matter mass (PM)

Carcinogenic species (PAHs and POPs)

Toxic substances (dioxins and furans)

Heavy metals (e.g. lead)

However, due to the scope of this study, the only pollutants that will be considered are

SO2, NOx, CO, PM10, NMVOC, benzene and lead. The EEA proposes the use of the

following equation to estimate these emissions (with the exception of SO2) based on a

top-down or Tier 1 approach:

Ei = Σj (Σm (FCj,m × EFi,j,m)) (1)

Where,

Ei = emission of pollutant i (g),

FCj,m = fuel consumption of vehicle category j using fuel m (kg),

EFi,j,m = fuel consumption-specific emission factor of pollutant i for vehicle

category j and fuel m (g/kg).

Since emissions of SO2 are dependent on the sulphur content of the fuel burnt, the EEA

proposes the following equation to estimate SO2 emissions:

ESO2,m = 2 x kS,m x FCm (2)

Where,

ESO2,m = emissions of SO2 per fuel m (g),

kS,m = weight related sulphur content in fuel of type m (g/g fuel),

FCm = fuel consumption of fuel m (g).

The emission factors, presented in the table below, that are to be used in equation (1),

have been developed by the EEA. The emission factors apply to the two primary fuels

used in motor vehicles, namely, gasoline and diesel.

Table 6.1: Emission factors to estimate motor vehicle emissions

Category Fuel Emission Factor (g/kg Fuel)

NOx CO PM10 VOC Lead

Passenger cars Gasoline 14.5 132 0.037 14 0.000017

Diesel 11 4.7 1.7 1.1 0.0000325

Light-duty trucks Gasoline 24 155 0.03 14 0.000017

Diesel 15 11 2.8 1.75 0.0000325

Heavy-duty vehicles Diesel 37 8 1.2 1.6 0.0000325

The emission factors suggest that passenger car engines are more efficient at burning

fuel with respect to both gasoline and diesel when compared to light-duty trucks and

heavy-duty vehicles. For instance, burning a kg of gasoline in a passenger car will only



result in 14.5 g of NOx emissions, whereas burning a similar quantity of gasoline in a

light-duty truck will emit a larger 24 g of NOx.

The use of these emission factors requires data on quantity of fuel consumed. The

organisation that historically collated fuel sales data for South Africa was the South

African Petroleum Industries Association (SAPIA). However, this role was recently

transferred to the Department of Energy (DoE), which was approached to provide fuel

sales data for the WRDM. Data is reported by the DoE for the major motor vehicle fuels

used in South Africa, namely:

Unleaded petrol of 95 octane, ULP95,

Unleaded petrol of 93 octane ULP93,

Lead replacement petrol of 95 octane (LRP95),

Lead replacement petrol of 93 octane (LRP93),

Diesel of 50 ppm sulphur (diesel50) and

Diesel of 500 ppm (diesel500).

The most recent year of data that the DoE has available for release into the public

domain is 2009. However, data is not available for Merafong.

Fuels sales data, as provided by the DoE, are presented below in Table 6.2. It is

important to note that fuels sales data does not necessarily translate into fuel

consumption data, the data actually required to estimate atmospheric emissions by

using equations (1) and (2) above. As a point in illustration, motor vehicle owners may

fill up their tanks in Randfontein, but drive directly to the City of Joburg, where the

majority of the fuel may be burnt and emission produced. The reverse may also apply,

where fuel is purchased in the City of Joburg, but burnt in Ranfontein. This creates an

off-setting effect. With this as the backdrop, the assumption therefore made in this

study, as with other similar studies, is that fuel sold in an area is roughly consumed in

that area. This then implies that the fuel sales data of Table 6.2 is assumed to be fuel

consumed in the WRDM.

Table 6.2: Fuel sales data for 2009

Fuel Name Local Municipality Consumption

(Litres/Year)

Diesel50 Mogale City 7 591 333

Diesel50 Randfontein 643 513

Diesel50 Westonaria 143 524

Diesel500 Mogale City 59 429 601

Diesel500 Randfontein 18 178 579

Diesel500 Westonaria 16 802 458

LRP93 Mogale City 29 965 859

LRP93 Randfontein 15 095 843

LRP93 Westonaria 15 429 549



ULP93 Mogale City 54 102 061

ULP93 Randfontein 23 325 459

ULP93 Westonaria 20 034 878

LRP95 Mogale City 522 136

ULP95 Mogale City 10 997 783

ULP95 Randfontein 1 510 309

ULP95 Westonaria 963 334

The data in Table 6.2 suggests that Mogale City is, by a significant margin, the largest

consumer of all the various grades and types of motor vehicle fuels. However, the data

provides no indication of how much fuel is consumed by each category of motor vehicle.

This was addressed by making the following assumptions:

All gasoline is consumed by passenger cars,

All low-sulphur (50 ppm) diesel is consumed by passenger cars,

High-sulphur diesel is consumed by both light-duty trucks and heavy-duty

vehicles.

The third bullet point above requires data on the split in rations between light-duty

trucks and heavy-duty vehicles in the WRDM. SAPIA (2008) provides a useful source of

data for the South African vehicle population. Although the data is applicable to 2007, it

could be confidently used for 2011 as a ratio is required and not actual numbers. It is

expected that although the actual numbers will differ, the ratio between light-duty trucks

and heavy-duty vehicles will be similar from year to year. Since the SAPIA data is

reported according to provinces, the data for Gauteng will be used for the WRDM. From

the data, the ratio between the numbers of light-duty trucks and heavy-duty vehicles is

4.62 to 1. This implies that for every heavy-duty vehicle on the road, there are 4.62

light-duty trucks. This ratio was used to apportion the consumption of diesel500.

Bottom-up approach: In order to estimate total transport emissions for the WRDM,

various types of data was collected such as:

Vehicle distribution (to describe the vehicle fleet by defining the fraction of each

vehicle class);

The average mileage travelled for each vehicle class (where “class” refers to

vehicle size and fuel type);

Emission factors (for CO, HC, NOx, PM10 and SO2) for each vehicle class (including

technology levels such as EURO 1, EURO 2, EURO 3, EURO 4 or pre-EURO);

Fuel types (only diesel and petrol)

6.3 Results of Emission Estimations

The following tables present the results of emission estimations from motor vehicles

separately per municipality and as a total for the WRDM.



Table 6.3: Breakdown of motor vehicle emission rates from Mogale City

Category Fuel Consumption

(kg/year)

Emission Rates (kg/year) Emission Rates (ton/day)

SO2 NOx CO PM10 VOC Lead SO2 NOx CO PM10 VOC Lead

Passenger cars

Gasoline 71 690 879 7169 1 039 518 9 463 196 2 653 1 003 672 1 0.020 2.848 25.927 0.007 2.750 0.000

Diesel50 5 693 500 569 62 628 26 759 9 679 6 263 0 0.002 0.172 0.073 0.027 0.017 0.000

Diesel500 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Light-duty trucks

Gasoline 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Diesel50 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Diesel500 36 641 466 36641 549 622 403 056 102 596 64 123 1 0.100 1.506 1.104 0.281 0.176 0.000

Heavy-duty vehicles

Diesel50 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Diesel500 7 930 735 7931 293 437 63 446 9 517 12 689 0 0.022 0.804 0.174 0.026 0.035 0.000

Total 52 311 1 945 205 9 956 458 124 444 1 086 747 3 0.143 5.329 27.278 0.341 2.977 0.000

Table 6.4: Breakdown of motor vehicle emission rates from Randfontein


(kg/year)



Passenger cars

Gasoline 29 948 708 2995 434 256 3 953 229 1 108 419 282 1 0.008 1.190 10.831 0.003 1.149 0.000

Diesel50 482 635 48 5 309 2 268 820 531 0 0.000 0.015 0.006 0.002 0.001 0.000

Diesel500 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Light-duty trucks

Gasoline 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Diesel50 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Diesel500 11 208 047 11208 168 121 123 289 31 383 19 614 0 0.031 0.461 0.338 0.086 0.054 0.000

Heavy-duty vehicles

Diesel50 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Diesel500 2 425 887 2426 89 758 19 407 2 911 3 881 0 0.007 0.246 0.053 0.008 0.011 0.000

Total 16 677 697 444 4 098 193 36 222 443 308 1 0.046 1.911 11.228 0.099 1.215 0.000



Table 6.5: Breakdown of motor vehicle emission rates from Westonaria


(kg/year)



Passenger cars

Gasoline 27 320 821 2 732 396 152 3 606 348 1 011 382 491 0 0.007 1.085 9.880 0.003 1.048 0.000

Diesel50 107 643 11 1 184 506 183 118 0 0.000 0.003 0.001 0.001 0.000 0.000

Diesel500

0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Light-duty

trucks

Gasoline

0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Diesel50

0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Diesel500 10 359 597 10 360 155 394 113 956 29 007 18 129 0 0.028 0.426 0.312 0.079 0.050 0.000

Heavy-duty

vehicles

Diesel50

0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Diesel500 2 242 247 2 242 82 963 17 938 2 691 3 588 0 0.006 0.227 0.049 0.007 0.010 0.000

Total

15 345 635 693 3 738 748 32 891 404 327 1 0.042 1.742 10.243 0.090 1.108 0.000

Table 6.6: Breakdown of total motor vehicle emission rates per local municipality, kg/year

Local

Municipality

Emission Rates (kg/year)


Mogale City 52 311 1 945 205 9 956 458 124 444 1 086 747 3

Randfontein 16 677 697 444 4 098 193 36 222 443 308 1

Westonaria 15 345 635 693 3 738 748 32 891 404 327 1

Total 84 332 3 278 342 17 793 399 193 558 1 934 382 5



Table 6.7: Breakdown of total motor vehicle emission rates per local municipality, ton/day

Local

Municipality

Emission Rates (ton/day)


Mogale City 0.143 5.329 27.278 0.341 2.977 0.000

Randfontein 0.046 1.911 11.228 0.099 1.215 0.000

Westonaria 0.042 1.742 10.243 0.090 1.108 0.000

Total 0.231 8.982 48.749 0.530 5.300 0.000


39

From the table above, it is clear that the highest quantity of motor vehicle emissions is from

Mogale City, followed by Randfontein and Westonaria. Motor vehicle emissions in Mogale City, on

average, make up approximately 60% of total motor vehicle emissions in the WRDM.

With respect to individual pollutants, the pollutant emitted in the greatest quantity from motor

vehicles in the WRDM is CO at 48.749 ton/day. This is followed by NOx at 8.982 ton/day and

NMVOC at 5.3 ton/day. The largest source of VOC is gasoline-fuelled passenger cars. Diesel PM

is considered to be one of the most dangerous pollutants from motor vehicles with regard to

human health. PM10 is the key indicator of diesel PM. PM10 emissions from motor vehicles in the

WRDM are estimated to be 0.53 ton/day or 193 558 kg/year. The largest source of PM10

emissions is high sulphur diesel and consequently light-duty trucks and heavy-duty vehicles

(trucks and buses). Due to the phase-out of lead from fuels, total lead emissions from motor

vehicles in the WRDM is low at 5 kg/year.


40

7. TAILINGS DAMS


Tailings are the residue of the milling process used to extract valuable metals from mined ores.

During this process, ores are milled and finely ground, and then treated in a flotation and/or

hydrometallurgical plant. The extracted metal represents a small percentage of the whole ore

mass and so, the vast majority of the mined material ends up as finely-ground slurry. Tailings

contain all other constituents of the ore except for the majority of the extracted metal. These

consist of heavy metals and other substances at concentration levels that can be toxic to biota in

the environment. Moreover, tailings contain the chemicals added during the milling process,

although these levels and types are generally not of major concern. After milling, these

contaminants are better available for dispersion into the environment than in the original ore

because of their finer particle size and higher surface area. Furthermore, the mechanical stability

of the tailings mass is poor because its small grain size and high water content.

Figure 7.1: Picture of a tailings dam

Most mill tailings produced worldwide are dumped in large surface impoundments ("tailings

dams"). In other cases, tailings are processed for use as backfill in underground mine workings.

The embankments of these large impoundments are typically constructed as earth-fill dams.

Although water-retention dams are suitable for use, their cost is too high. Tailings storage

facilities (or dams) are designed and engineered to accept an on-going supply of leach residue

and tailings from the flotation circuits. They are therefore in a continual state of growth phase

until reaching the limits of their design, both laterally and vertically. The slurry is pumped from

the tailings thickeners and sprayed on the tailings dam. Here, the solids settle out and the water

is returned to the process. Tailings dams resemble large flat-topped stockpiles with tapered sides.

The active part of the tailings dam consists of moist slurry. Inactive portions consist of dry silt.

The dry un-vegetated portions of tailings dams are sources of wind entrained dust.


41

There are currently approximately 52 active and inactive tailings dams in the West Rand District

Municipalities owned by the various gold mines located in the areas. The material on the beaches

is very fine with a high silt content material which dries to form a weak crust which is easily

broken to expose the fine powder-like silt beneath. When exposed this material is entrained into

the atmosphere by wind. The sides of the tailings dam are relatively well vegetated which is an

effective way to reduce the dust entrainment by wind. Due to the significance of its size and their

vertical dimensions, the tailings dams provide a considerable area of dry material that is exposed

to wind. While active and predominantly moist, the potential for dust entrainment by wind is

dampened, but it may become a significant source of particulate matter if it is not vegetated and

is allowed to dry out, particularly during windy conditions.

7.2 Methodology


Tailings dams are examples of open areas that provide substantially large un-vegetated areas that

are exposed to wind erosion. The task of data gathering consisted essentially of scanning the

WRDM using Google Earth to identify tailings/slimes dams. Each of the tailings dams identified in

this manner was then provided with an identity number ranging from 1 to 54 (TD1 to TD54). A

marked-up Google Earth map showing all the tailings dams was then sent to the mines in the

WRDM by Musa Zwane so that the mines could identify which of the 54 tailings dams belonged to

them.


This section describes the methodology used to estimate emission rates of PM10 and total

suspended particulates (TSP) from tailings dams.

The estimation of particulate emissions is based on the USEPA methodology for wind erosion of

open aggregate storage piles and exposed areas in industrial facilities provided in Chapter 13 of

the USEPA 42 (USEPA, 2006). The following methodology was followed:

Using Google Earth, the WRDM was scanned for tailings dams. A total of 54 were

identified.

The lateral dimensions of each tailings dam was estimated by demarcating these using

Google Earth (see examples in Figure 4.1). The average height of each source was

obtained from Google Earth;

Each area source was divided into three areas: a windward area, the top of the area and a

leeward area;

The area of potential wind erosion of the tailings dam was calculated based on the variable

vegetation cover and on the surface water area;


42

A function was developed to determine the relationship between wind speed and the

emission rate, or entrainment, based on the approach recommended by the USEPA (2006);

Average daily wind speed and direction data for the WRDM was obtained from the South

African Weather Service (SAWS);

The wind dependant emission rates of particulate matter in g/m2/s are presented in

Section 7.3.

Figure 7.2: Tailings dams as identified in Google Earth with red boundaries for

estimating approximate areas


43

7.3 Results of Emission Estimates

The results of PM emission estimations from tailings dams in the WRDM are presented in Tables

7.3 and 7.4.

The 3 largest dams in terms of PM emissions in the WRDM are TD18 which belongs to Gold 1 and

emits 4.35 ton/day, followed by TD30 which belongs to Goldfields Driefontein and emits 2.3

ton/day, and TD22 which also belongs to Gold 1 and emits 2.03 ton/day.

Table 7.3: PM emission rates from tailings dams in WRDM

Tailings

Dam

Local

Municipality Mine

PM Emission

Rate

(kg/year)

PM

Emission

Rate

(ton/day)

TD1 Mogale City 143 525 0.39














TD18 Randfontein Gold 1 – Mill Site 1 587 153 4.35

TD19 Randfontein 338 138 0.93

TD20 Westonaria 507 048 1.39


TD22 Westonaria Gold 1 – Old No. 4 741 547 2.03

TD23 Westonaria Goldfields – Kloof 1 426 515 1.17


TD25 Westonaria Goldfields – South Deep TS 404 990 1.11

TD26 Westonaria Goldfields – South Deep SS 283 222 0.78


TD28 Westonaria Goldfields – Kloof 2 571 019 1.56

TD29 Merafong Goldfields – Kloof 3 639 158 1.75

TD30 Merafong Goldfields – Driefontein 4 839 258 2.30



44

TD32 Merafong 50 540 0.14




TD36 Merafong 238 302 0.65


TD38 Merafong 431 126 1.18


TD40 Merafong 509 238 1.40

TD41 Merafong 661 428 1.81

TD42 Merafong 319 508 0.88

TD43 Merafong 132 037 0.36

TD44 Merafong 193 277 0.53

TD45 Merafong 261 332 0.72

TD46 Merafong 133 961 0.37


TD48 Merafong 615 928 1.69

TD49 Merafong 440 915 1.21

TD50 Merafong 442 300 1.21


TD52 Westonaria Goldfields – Kloof Venterspost 2 147 065 0.40

TD53 Westonaria Goldfields – Kloof Venterspost 1 99 950 0.27

TD55 Randfontein Gold 1 - Lindium Not estimated

TD56 Randfontein Gold 1 – Dump 20 Not estimated

Total 15 413 844 42.24

A total of 14 tailings dams were identified in Mogale City, 4 in Randfontein, 11 in Westonaria and

23 in Merafong. An additional 2 tailings dams (TD9 and TD54) that were initially identified as

being located in Mogale City, were later found to be located in the City of Joburg.

The total PM emissions from tailings dams was estimated at 42.24 ton/day. This implies that an

average 42 tons of PM enter the ambient environment of the WRDM on a daily basis from tailings

dams. The estimation of PM10 emissions, a subset of PM emissions, is not possible as the

emission estimation methodology used in this study only applies to PM.


45

Table 7.4: Breakdown of PM emission rates per local municipality

Local Municipality PM Emission Rate

(kg/year)

PM Emission Rate

(ton/day)

% of

Total

Mogale City 1 797 629 4.92 11.64

Randfontein 1 925 291 5.28 12.5

Westonaria 3 956 869 10.83 25.63

Merafong 7 734 055 21.21 50.21

Total 15 413 844 42.24

The local municipality that emits the largest quantity of PM emissions from tailings dams is

Merafong at 21.21 ton/day, which is also the municipality with the greatest number of tailings

dams. More than 50% of all PM emissions from tailings dams are emitted from Merafong. A

significant quantity of emissions also originate from Westonaria, where many tailings dams are

also located.


46

8. DOMESTIC BURNING


The three primary application categories relating to domestic fuel burning are:

Cooking

Lighting

Space heating

These are also the categories under which StatsSA reports the number of households utilising

fuel. The primary fuels used in South Africa for domestic purposes are coal, paraffin, liquefied

petroleum gas (LPG) and wood. Domestic use of fuels is restricted largely to informal, low-income

and densely populated settlements. The combustion of these fuels is a significant source of air

pollution, especially during winter. The impact on air quality from residential fire emissions is

fairly significant, considering that the release of pollutants occurs close to ground level at

relatively low temperatures (causing a lack of buoyancy of the plume). The low-level release

implies that the pollutants are released into the stable surface inversion layer, where dispersion is

inhibited and pollutants tend to accumulate close to the source. High ambient concentrations may

result near the source under these conditions. The relatively low fire temperature implies that the

combustion process is often incomplete.

Domestic coal burning contributes to the emission of particulate matter, notable PM2.5 and PM10.

Other criteria pollutants such as SO2 and CO are also emitted in substantial quantities as a result

of coal burning, particularly when low-grade, high sulphur coal is burned. Domestic burning of

wood (in addition to veld fire burning) results in the release of fine particulate emissions (PM2.5) as

well as NO2, CO and benzene. Domestic coal and wood combustion in informal settlements and

rural areas have been identified through various studies to be, potentially, one of the greatest

sources of airborne particulates and gaseous emissions to be inhaled in high concentration (i.e.

before dispersion and fallout processes can ameliorate impact).

Although many households are electrified, informal households predominantly use a contribution

of this fuel mix (coal, paraffin, LPG, wood, animal dung and other waste materials are used to a

smaller extent) primarily due their availability and affordability, although factors such as cultural

traditions also play a role in the continuing use of other fuels. Population density and growth also

play a significant part, amongst a variety of additional factors.

8.2 Methodology


The estimation of emissions from domestic burning commenced with the sourcing of data on the

number of households in the WRDM utilising fuels for domestic purposes (cooking, lighting, space


47

heating). The most appropriate source for this information was the census data from StatsSA’s

Household Services Community Survey 2007. The census data reports on the number of

households burning fuel in an area, which for our purposes are the four local municipalities of the

WRDM. The limitation of the information is recognised to be the fact that the information is not

current. The study should ideally utilise data for 2011, however, the most recent data available

was for 2007. The number of households consuming fuels are presented in Tables 5.1 to 5.3 for

the four local municipalities.

Table 8.1: Number of households using fuels for cooking

Fuel Mogale City Randfontein Westonaria Merafong

Coal 235 131 71 16

Paraffin 14 248 8 578 16 729 349

LPG 1 184 406 512 15

Wood 1 122 376 417 79

Table 8.2: Number of households using fuels for lighting


Paraffin 3 035 1 900 7 102 17

LPG 189 0 216 0

Table 8.3: Number of households using fuels for space heating


Coal 4 108 1 631 2 431 16

Paraffin 10 374 7 123 13 689 237

LPG 1 303 262 286 58

Wood 4 763 1 024 1 327 222

Having obtained data on the number of households consuming fuels, the next logical step was to

determine the quantity of fuels consumed per household. One of the outputs of the extensive

research undertaken in developing the FRIDGE (Fund for Research into Industrial Development,

Growth and Equity) report was data on the quantities of fuels consumed in specific geographical

areas of South Africa. The quantity of fuels consumed varies with geographical areas due to

several reasons such as climate (more fuels consumed in colder areas) and extent of development

(more fuels consumed in rural areas). For instance, the quantity of coal consumed in the

eThekwini Municipality (warmer and more developed) is 0.042 ton/year/household, which is

significantly less than the 0.615 ton/year/household consumed by households in the Vaal Triangle

(colder and less developed).

The FRIDGE report does not specifically present fuel consumption data for the WRDM. It was

therefore decided to select an area that most closely reflects the situation in the WRDM, in this

case, Johannesburg. The total fuel consumption figures contained in the table below for the local


48

municipalities of the WRDM were based on household level fuel consumption figures for

Johannesburg.

Table 8.4: Total fuel consumption for households in the WRDM

Fuel Total Consumption (ton/year)

Mogale City Randfontein Westonaria Merafong

Coal 65.85 36.71 19.89 4.48

Paraffin 446.53 268.83 524.29 10.94

LPG 5.63 1.93 2.44 0.07

Wood 20.07 6.73 7.46 1.41

From the table above, it is clear that the greatest quantity of coal, paraffin and wood are

consumed in Mogale City, while the greatest quantity of paraffin is consumed in Westonaria. In

contrast, the lowest quantities of all fuels are consumed in Merafong.


Having determined fuel consumption associated with domestic burning in the WRDM, the next

important step was to source appropriate emission factors for the criteria pollutants. The FRIDGE

report also served as a useful reference source for emission factors of criteria pollutants from

domestic burning. The following table presents these emission factors.

Table 5.5: Emission factors identified for the estimation of household fuel combustion

emissions

Fuel Units Emission Factors

SO2 NOx VOCs PM10 CO Benzene

Coal g/kg 19 1.5 5 4.1 187.4 0.0134

Paraffin g/l 8.5 1.5 0.09 0.2 44.9 0

LPG g/kg 0.01 1.4 0.5 0.07 13.6 0

Wood g/kg 0.18 5 22 15.7 114.6 0.9

8.3 Results of Emission Estimates

By applying the emission factors of Table 5.5 to the fuel consumption rates of Table 5.4, it was

possible to estimate pollutant emission rates from domestic burning. These are presented below

in Table 8.6.


49

Table 8.6: Breakdown of emission rates from domestic burning per local municipality,

kg/year

Local

Municipalities



Mogale City 31 609 3 864 285 016 6 842 8 496 96

Randfontein 14 774 1 821 123 902 2 544 3 078 23

Westonaria 24 814 3 242 195 704 3 635 4 316 28

Merafong 356 73 3 277 126 165 5

Total 71 553 9 000 607 899 13 147 16 056 152

Table 8.6: Breakdown of emission rates from domestic burning per local municipality,

ton/day

Local

Municipalities



Mogale City 0.087 0.011 0.781 0.019 0.023 0.000

Randfontein 0.040 0.005 0.339 0.007 0.008 0.000

Westonaria 0.068 0.009 0.536 0.010 0.012 0.000

Merafong 0.001 0.000 0.009 0.000 0.000 0.000

Total 0.196 0.025 1.665 0.036 0.044 0.000

Emissions of all pollutants, with the exception of benzene, can be described as significant. The

combustion of coal and paraffin result in high emissions of SO2 due to the high sulphur content in

these fuels. The local municipality that produces the largest quantity of emissions from domestic

burning is Mogale City. This is directly attributable to the high number of households in Mogale

City that use coal for cooking and space heating. Westonaria is the municipality that produces the

second highest quantity of emissions. Westonaria is the local municipality where the consumption

of paraffin is the greatest. A high number of households use paraffin in Mogale City for cooking,

lighting and space heating.


50

9. BIOMASS BURNING


Biomass burning is generally categorised into wildfires and prescribed (controlled) burning.

Wildfires:

A wildfire is a large-scale natural combustion process that consumes various ages, sizes, and

types of flora growing outdoors in a geographical area. Consequently, wildfires are potential

sources of large amounts of air pollutants that must be considered when trying to relate emissions

to air quality. Wildfires occur both naturally (e.g. through lightning strikes) and through arson. It

is often difficult to determine whether a wildfire has been deliberately lit or has occurred naturally.

The size and intensity, even the occurrence, of a wildfire depend directly on such variables as

meteorological conditions, the species of vegetation involved and their moisture content, and the

weight of consumable fuel per acre (available fuel loading). Once a fire begins, the dry

combustible material is consumed first. If the energy release is large and of sufficient duration,

the drying of green, live material occurs, with subsequent burning of this material as well. Under

proper environmental and fuel conditions, this process may initiate a chain reaction that results in

a widespread conflagration.

The factors that affect the rate of spread of a wildfire are:

Weather (wind velocity, ambient temperature, relative humidity);

Fuels (fuel type, fuel bed array, moisture content, fuel size); and

Topography (slope and profile).

Figure 9.1: Photograph of a wildfire – a significant source of emissions


51

Prescribed burning:

The most effective method of controlling wildfire emissions is to prevent the occurrence of

wildfires by various means. A frequently used technique for reducing wildfire occurrence is

"prescribed" or "hazard reduction" burning. This type of managed burn involves combustion of

litter and underbrush to prevent fuel build-up under controlled conditions, thus reducing the

danger of a wildfire. Although some air pollution is generated by this preventive burning, the net

amount is believed to be a relatively smaller quantity than that produced by wildfires.

Prescribed burning activities include fires that are intentionally started for a variety of reasons

such as fuel reduction for wildfire prevention, regeneration after logging operations, ecosystem

maintenance, land clearing, and agricultural land management (NPI, 1999). The amount and type

of prescribed burning and wildfires will vary significantly between different geographical areas and

airsheds. The quantity and composition of emissions from different types of burning are also

highly variable. For forest and grassland fires (both wild and prescribed), the area of land burned

will vary greatly depending on climatic conditions. Fuel loadings may also vary from year to year,

and with the time of year that the burn occurs. These two factors combined lead to large

variations in the amount of material consumed in fires from one year to the next.

For agricultural burning, the amount of fuel burned will depend on the annual crop harvest and

farming practices in the airshed. The amount of consumable fuel in a particular area has a major

impact on emissions from burning. Different species composition and hence different plant

material types have different burning qualities. Thus, forests, grasses, and crops such as wheat

and barley have different emission-generating characteristics.

The major air pollutant of concern is the smoke produced. Smoke from prescribed fires is a

complex mixture of carbon, tars, liquids, and different gases. This open combustion source

produces particles of widely ranging size, depending to some extent on the rate of energy release

of the fire. The major pollutants from wildland burning are PM, CO, and VOCs. NOx are emitted at

rates of from 1 to 4 g/kg burned, depending on combustion temperatures. Emissions of SOx are

negligible. PM emissions depend on the mix of combustion phase, the rate of energy release, and

the type of fuel consumed. All of these elements must be considered in selecting the appropriate

emission factor for a given fire and fuel situation.

9.2 Methodology for Estimating Emissions

Emissions of PM, CO, NOx and VOC from wildfires are estimated by using the following equations:

Fi = Pi L (1)

Ei = Fi A = Pi L A (2)

Where:


52

Fi = Emission factor (mass of pollutant/unit area of forest consumed)

Pi = Yield for pollutant "i" (mass of pollutant/unit mass of forest fuel consumed)

= 8.5 kilograms per ton (kg/ton) for PM

= 70 kg/ton for CO

= 2 kg/ton for NOx

= negligible for SOx

L = Fuel loading consumed (mass of forest fuel/unit land area burned)

A = Land area burned

Ei = Total emissions of pollutant "i" (mass pollutant)

The USEPA has developed fuel loading values for most types of vegetation found in the United

States of America. Other countries such as Australia have also followed suit and developed their

own fuel loading values. However, the same cannot be said about South Africa. In the absence

of this data locally, it was decided to use the average fuel loading values, as estimated by the

USEPA, for a wide range of vegetation types in the US. The fuel loading values adopted for this

study are presented below in Table 9.1:

Region Fuel Loading

(ton/hectare)

Emission Factor (kg/hectare)

NOx CO PM10 VOC

WRDM 38 76 2 670 227 458

The emission factors for prescribed burning are different to those from wildfires and appear to be

lower in magnitude. These emission factors are dependent on information relating to the burn

event such as the fuel used and the phase (flaming, smouldering, etc.) of burning. It was

therefore decided to apply the wildfire methodology to all the fires in the WRDM, whether they are

wildfires or prescribed burning. This is a conservative approach and will yield the highest

emissions possible.

To apply the emission factor method in estimating emissions from wildfires requires information

on the area burned. The organisation in South African that is the primary source of such

information is the Meraka Institute, which is an operating unit of the CSIR. One of the business

and research areas that the Meraka Institute is involved in is remote sensing. The Remote

Sensing Research Unit (RSRU) conducts activities related to remote sensing and earth observation

application development.

The group focuses on computationally-intensive remote sensing problems by applying their

advanced remote sensing, geomatics, image processing, machine learning and time-series

analysis skills. The group comprises electronic engineers, computer scientists and environmental

remote sensing specialists. Earth surface properties (such as fires) are observed from satellites.

One of their main areas of focus is the tracking of fires, namely, active fires, burnt area mapping

and fire danger modelling.


53

For this study, their analyses of burned area consisted of a spatial overlay with aggregation,

performed in a spatial relational database. The data used for burned areas was derived from a

merge of the so-called Giglio and Roy algorithms for MODIS burned area detection. Boundary

data was from the 2011 version of the Municipal Demarcation Board datasets. Burned areas were

collected into each area for each year by a spatial join/overlay and a temporal boundary query

before being aggregated to form counts and area calculations. The count may include the same

area more than once, as a fire can partially burn the area of a pixel observation more than once in

a year. Also, each burned area is not necessarily 463 m x 463 m (the minimum area size

identified by the satellite as burned); rather, what the algorithm says is that enough of a pixel has

burned to flag the whole pixel as “burned”. The implication is that the area estimate has an

inherent commission error. This is generally more than offset by the fact that many fires are not

detected, particularly those that are small. Overall, the estimate is likely to be conservative, with

more omission than commission errors.

9.3 Results of Emissions Estimation

Burned area for the local municipalities of the WRDM, as determined by the Meraka Institute, are

presented below in Table 9.2.

Table 9.2: Yearly burned area data for the WRDM

Local Municipality No. of

Fires (1)

Yearly Burned Area

Accumulation (m2) (2)

2010:

Mogale City 926 198 505 694

Randfontein 401 85 961 969

Merafong 1 450 310 835 050

Westonaria 1 041 223 158 129

Total 3 818 818 460 842

2011:

Mogale City 815 174 710 735

Merafong 1 734 371 715 846

Randfontein 395 84 675 755

Westonaria 707 151 558 883

Total 3 651 782 661 219

Notes:

1) A count of 463m x 463m pixels in a given year, for a given LM, that are flagged as “Burned” by one or other of

two algorithms for detecting burned areas from MODIS data.

2) An aggregation of the area of all the “Burned” pixels that are counted in a given year, for a given LM. Unit of

measure is m2. To get to hectares, divide by 10 000.

The discrepancy in burned areas between 2010 are 2011 is a mere 4%, thus relatively

insignificant. As this study is for 2011, emission estimations were based on 2011 data.


54

The total burned area for 2011 was 782.6 km2, compared to a total estimated area of the WRDM

of 4 087 km2. The burned area represents 20% of the total area of the WRDM. This does not

however imply that 20% of the total surface area of the municipality was burned as a single

location could be burned several times in a year.

The total of 3 651 fires in 2011 implies that there were approximately 10 fires in a day in the

WRDM. The highest number of fires for both 2010 and 2011 (average of 4.4 a day) occurred in

Merafong, by a significant margin. The lowest number of fires occurred in Randfontein.

The results of the estimation of emissions from biomass burning in the WRDM using the

methodologies described above are presented below in Tables 9.3 and 9.4.

Table 9.3: Breakdown of emission rates from biomass burning per local municipality,

kg/year

Local

Municipality


NOx CO PM10 VOC

Mogale City 1 327 802 46 647 766 3 962 439 8 001 752

Merafong City 2 825 040 99 248 131 8 430 515 17 024 586

Randfontein 643 536 22 608 427 1 920 446 3 878 150

Westonaria 1 151 848 40 466 222 3 437 355 6 941 397

Total 5 948 225 208 970 545 17 750 756 35 845 884

Table 9.3: Breakdown of emission rates from biomass burning per local municipality,

ton/day

Local

Municipality


NOx CO PM10 VOC

Mogale City 3.638 127.802 10.856 21.923

Merafong City 7.740 271.913 23.097 46.643

Randfontein 1.763 61.941 5.261 10.625

Westonaria 3.156 110.866 9.417 19.018

Total 16.297 572.522 48.632 98.208

The total emissions from biomass burning can be described as significant. This is primarily due to

the high number of fires that occur in the district municipality.

Significant quantities of all pollutants are emitted into the atmosphere from biomass burning. The

pollutant emitted in the highest quantity if CO at 572.522 ton/day. There are also significant

quantities of VOC and PM emitted at 98.208 ton/day and 48.632 ton/day, respectively. In line

with the highest number of fires there, the highest quantity of emissions as a result of biomass

burning is emitted from Merafong.


55

10. EMISSIONS SUMMARY

Total emissions of all pollutants from all sources in the WRDM are presented below in Table 10.1

and 10.2.

Table 10.1: Total emissions from all sources in the WRDM, kg/year

Source Emission Rate (kg/year)


Industries 1 796 219 1 045 349 140 142 614 5 218 323 549 503 6 306 11 031

Motor vehicles 84 332 3 278 342 17 793 399 193 558 1 934 382

5

Domestic burning 71 553 9 000 607 899 13 147 16 056 152

Tailings dams

15 413 844

Biomass burning

5 948 225 208 970 545 17 750 756 35 845 884

Total 1 952 104 10 280 917 367 514 457 38 589 628 38 345 825 6 458 11 036

Table 10.2: Total emissions from all sources in the WRDM, ton/day

Source Emission Rate (ton/day)


Industries 4.921 2.864 383.952 14.297 1.506 0.017 0.030

Motor vehicles 0.231 8.982 48.749 0.530 5.300

0.000

Domestic burning 0.196 0.025 1.665 0.036 0.044

Tailings dams

42.24

Biomass burning

16.297 572.522 48.632 98.208

Total 5.348 28.167 1 006.889 105.736 105.058 0.017 0.030

The emission rates contained in the above tables provide useful information on which sources to

focus when developing emission reduction initiatives.

SO2 emissions originate from the combustion of fossil fuels. A total of 5.348 ton/day of SO2

emissions are produced in the WRDM. Industries are the most significant contributor to this total

(>92.0%), due mainly to the combustion of coal. SO2 emissions also emanate from motor

vehicles, due to the combustion of diesel, and domestic burning, due to the combustion of coal.

No SO2 emissions are produced by tailings dams and biomass burning.

A total of 28.167 ton/day of NOx emissions are produced in the WRDM, approximately 5 times

more than SO2. NOx emissions are produced from the oxidation of naturally occurring nitrogen

species in the ambient environment during combustion processes. The atmosphere is composed

of approximately 80% nitrogen. As such, the largest producer of NOx emissions is biomass

burning. Wildfires and prescribed burning activities cause nitrogen to be oxidised to NOx. It is

estimated that a total of 16.297 ton/day of NOx emissions are produced in this way. The other

notable sources of NOx emissions are motor vehicles at 8.982 ton/day and industries at 2.864

ton/day. Some NOx emissions originate from domestic burning, but the quantity is insignificant

when compared to the other major sources described above .


56

A total of 1 006.889 ton/day of CO emissions are produced in the WRDM, greater than both SO2

and NOx. However, this does not necessarily mean that CO will pose a greater danger to the

health and well-being of residents in the WDM. CO normally causes negative health impacts at

high concentrations, whereas SO2 and NOx cause negative health impacts at much lower

concentrations. The two most significant sources of CO emissions are biomass burning and

industries at 572.522 ton/day and 383.952 ton/day, respectively. As expected, the tailings dams

produce no CO emissions, while motor vehicles produce 48.749 ton/day.

PM10 emissions are produced by all sources identified in this study. The quantity of PM10

emissions produced in the WRDM are greater than both SO2 and NOx. PM10 is recognised as a

pollutant of great concern across the world due to its high prevalence and negative health

impacts. The total quantity of PM10 emitted in the WRDM was estimated at 105.736 ton/day.

Biomass burning (48.632 ton/day) and the tailings dams (42.24 ton/day) have been identified as

the major sources of PM10 emissions in the WRDM. Industries are also responsible for a significant

PM10 emissions rate of 14.297 ton/day.

VOCs consist of a range of organic pollutants that react photo-chemically with NOx in the presence

of sunlight to form ozone (O3), one of the 6 criteria pollutants and known to have negative health

impacts. The most notable source of VOCs is biomass burning at 98.208 ton/day. Emissions of

one of the compounds classified as a VOC, namely, benzene, was estimated separately in the

study. Benzene emissions from the petrochemical storage depot in Tarlton have been estimated

at 6 306 kg/year.

Lead emissions originate from Castle Lead Works (11 031 kg/year) and motor vehicles (5

kg/year). The low quantity of lead emissions from motor vehicles is primarily due to the phase-

out of lead in fuels.

Emission contributions of individual sources are presented graphically with the aid of pie graphs in

Figures 10.1 to 10.5 below.


57

Figure 10.1: Graphical presentation of SO2 emissions from the WRDM

The graph above clearly shows that industries are the dominant source with regard to SO2

emissions in the WRDM. Industries account for 92.0% of all SO2 emissions, followed by motor

vehicles at 4.3% and domestic burning at 3.7%. There are no SO2 emissions from tailings dams,

while SO2 emissions from biomass burning could be described as negligible. The primary fossil

fuel which results in the production of SO2 emissions is coal, used by both industries and

households.

Figure 10.2: Graphical presentation of NOx emissions from the WRDM

With regard to NOx emissions, the two primary sources in the WRDM are biomass burning and

motor vehicles, with overall contributions of 57.9% and 31.9%, respectively. The total

92

.0%

4.3

%

3.7

%

0.0

%

0.0

%

SO2 EMISSIONS FROM THE WRDM

Industries

Motor vehicles

Domestic burning

Tailings dams

Biomass burning

10.2%

31.9%

0.1%

0.0%

57.9%

NOX EMISSIONS FROM THE WRDM

Industries

Motor vehicles

Domestic burning

Tailings dams

Biomass burning


58

contribution of NOx emissions from industries are lower at 10.2%, whereas NOx emissions from

domestic burning are insignificant at 0.1%.

Figure 10.3: Graphical presentation of CO emissions from the WRDM

The greatest source of CO emissions is biomass burning with an overall contribution of 56.9%.

This is followed by another significant contributor, industries, at 38.1%. CO emissions from motor

vehicles make up 4.8% of total CO emissions in the municipality.

Figure 10.4: Graphical presentation of PM10 emissions from the WRDM

The two most significant sources of PM10 emissions in the WRDM are biomass burning at a 46.0%

contribution and tailings dams at a 39.9% contribution. As with most other pollutants, industries

make a notable contribution to total PM10 emissions of 13.5%.

38.1%

4.8%

0.2% 0.0%

56.9%

CO EMISSIONS FROM THE WRDM

Industries

Motor vehicles

Domestic burning

Tailings dams

Biomass burning

13.5%

0.5%

0.0%

39.9%

46.0%

PM10 EMISSIONS FROM THE WRDM

Industries

Motor vehicles

Domestic burning

Tailings dams

Biomass burning


59

Figure 10.5: Graphical presentation of VOC emissions from the WRDM

The key source of VOC emissions in the WRDM is also biomass burning at an overall contribution

of 93.5%. Motor vehicles also contribute at a much lower 5.0%, whereas VOC emissions from

industries are low at 1.4%. VOC emissions from other sources are negligible.

1.4

%

5.0

% 0

.0%

0

.0%

93

.5%

VOC EMISSIONS FROM THE WRDM

Industries

Motor vehicles

Domestic burning

Tailings dams

Biomass burning


60

11. CONCLUSIONS AND RECOMMENDATIONS

The following are the key conclusions in relation to this study:

1. The 7 major sources of atmospheric emissions identified in the WRDM under their relevant

categories are:

Point – Listed activities and small industrial processes

Mobile – Motor vehicles

Area – Domestic burning, agricultural, biomass burning and tailings dams

2. Due to the absence of credible information on agricultural activities in the WRDM, this

source is excluded from the present study. However, the prescribed burning portion of

agricultural activities is accounted for under biomass burning.

3. Combustion devices (boilers, furnaces, heaters, etc.) in the WRDM are key emitters of

criteria pollutants (SO2, NOx, CO and PM10) and toxic air pollutants such as benzene,

toluene and xylene.

4. A total of 66 industries were identified and contacted to in the information gathering phase

of the project.

5. The primary fuels used by industries in the WRDM are coal (different grades), fuel oil

(heavy and light), diesel and gas.

6. Coal is the most widely used fuel with Mogale Alloys consuming 62 % of the total.

7. The following fuel consumption rates were determined from industries in the WRDM:

Coal – 141 944 ton/year

Fuel oil – 1 486 ton/year

Gas – 2 949 562 m3/year

Diesel – 34 ton/year

8. The total emission rates of pollutants from industries in the WRDM are:

SO2 – 4.921 ton/day

NOx – 2.864 ton/day

CO – 383.680 ton/day

PM10 – 14.297 ton/day

VOC – 1.506 ton/day

Benzene – 0.017 ton/day

Lead – 0.03 ton/day

9. The largest source of industrial SO2 emissions is Mogale Alloys, which emits an average of

2.27 ton/day or 46% of the total industrial SO2 emissions.

10. There are numerous mines located in the WRDM, but refining/smelting do not take place at

the majority these mines, with the result that the primary emissions from mines are PM,

notably dust, from ore extraction.

11. The only mining group that disclosed the existence of smelting/refining operations was

Goldfields at its South Deep, Kloof and Driefontein gold mines.

12. The estimation of emissions from motor vehicles was based on fuel sales data, sourced

from the Department of Energy. The following fuel sales figures apply to the WRDM:

Gasoline - 95 587 839 litres/year


61

Diesel50 - 7 591 333 litres/year

Diesel500 - 59 429 601 litres/year

13. The total emission rates of pollutants from motor vehicles in the WRDM are:




PM10 – 0.530 ton/day


14. The highest emission rates from motor vehicles originate from Mogale City.

15. The key pollutant emitted from tailings dams is PM. The tailings dams that produce the

largest quantity of PM emissions belong to Gold 1 (4.35 ton/day), Goldfields Driefontein

(2.3 ton/day), and another to Gold 1 (2.03 ton/day).

16. PM emission rates from tailings dams for the local municipalities are as follows:

Mogale City - 4.92 ton/day

Randfontein - 5.28 ton/day

Westonaria - 10.83 ton/day

Merafong - 21.21 ton/day

17. The local municipality from where the highest PM emissions from tailings dams occurs is

Merafong (21.21 ton/day or >50 % of total),

18. The primary applications in domestic fuel burning are cooking, lighting and space heating

using coal, paraffin, LPG and wood as the primary fuels.

19. The total estimated emissions from domestic burning are:




PM10 – 0.036 ton/day


20. The local municipality that produces the largest quantity of emissions from domestic

burning is Mogale City due to the large number of homes using fuels there.

21. Wildfires and controlled burning (biomass burning) are a significant source of NOx, CO,

PM10 and VOC.

22. There were a total of 3 818 and 3 651 significant fires in the WRDM in 2010 and 2011,

respectively. The highest number of fires at approximately 1 600 per year were identified

in Merafong.

23. The emission rates from biomass burning have been estimated as follows:



PM10 – 48.632 ton/day


24. Total emissions of all sources in the WRDM are as follows:

SO2 - 5.348 ton/day



62

CO – 1 006.889 ton/day

PM10 – 105.736 ton/day


Benzene – 0.017 ton/day

Lead - 0.030 ton/day

25. Industry is the largest source of SO2 emissions, while biomass burning is the largest source

of NOx, CO, PM10 and VOC emissions.

The following key recommendations are proposed to be undertaken after completion of this study:

1. Emissions from agricultural activities were not estimated in this study as a result of the

difficulty in sourcing credible data. A more detailed investigation is required to gather data

on the types of agricultural activities and the size of these activities.

2. SO2 reduction efforts from industries should focus on Mogale Alloys, which contributes to

more than 46% of total SO2 emissions from industries.

3. Exol Oil Refinery was excluded from the study as the company was in the process of

conducting an air quality study and was not ready to submit its emission inventory

questionnaire on time. It is therefore important to contact Exol Oil Refinery to acquire the

necessary information to determine their emissions.

4. Merafong was excluded from the estimation of emissions from motor vehicles as fuel sales

data was not available for Merafong. Once data from Merafong is available, the motor

vehicle emission estimation should be updated.

5. Emissions from 2 tailings dams, namely, Gold 1 – Dump 20 and Gold 1 – Lindium, were

not estimated as the emission inventory team were only recognised their existence once all

the emission estimations had been completed. It therefore recommended that emissions

from these 2 tailings dams be estimated.

6. An air quality management system consists of 3 primary components, namely, monitoring,

emission inventories and dispersion modelling. The WRDM currently has an air quality

monitoring network in place and with the completion of this study, will have an emission

inventory. It is therefore recommended that a dispersion model be developed to predict

air quality in the WRDM. With the development of a dispersion model, it is vitally

important that dispersion modelling capabilities be developed amongst officials in the

WRDM.


63

12. REFERENCES

1. Andrias, Samaras and Zierock. The Estimation of the Emissions of Other Mobile Sources

and Machinery Subparts Off-Road Road Vehicles and Machines, Railways and Inland

Waterways in The European Union, September 1994.

2. Environment Australia. Emissions Estimation Technique Manual, Aggregate Emissions from

Motor Vehicles. Version 1.0. 22 November 2000.

3. European Environmental Agency. EMEP/EEA Air Pollutant Emission Inventory Guidebook.

Exhaust Emission from Road Transport. 2010.

4. South African Petroleum Industries Association. Petrol and Diesel in South Africa and the

Impact on Air Quality. November 2008.

5. USEPA, Compilation of Air Pollutant Emission Factor, AP-42, Fifth Edition, Volume I:

Stationery Point and Area Sources, 2005.


64

APPENDIX A – STAKEHOLDER WORKSHOP


65

A stakeholder workshop was held 30 March 2012 to workshop the emission inventory to industrial

representatives. The aim of the workshop was to communicate the objectives of the emission

inventory study, provide some theoretical background in emission inventories to delegates,

provide an update of the project and source information from delegates to improve the accuracy

and completeness of the emission inventory. The meeting invitation is presented below.

Figure A.1: Invitation to the WRDM stakeholder workshop

2 March 2012

Dear Stakeholder

INVITATION TO WRDM EMISSION INVENTORY WORKSHOP

The West Rand District Municipality (WRDM) is in the process of compiling an atmospheric

emission inventory. The project has commenced with the identification of emission sources

and the gathering of data to estimate emissions. As a stakeholder in the WRDM, you are

invited to attend a workshop to discuss the various emission sources and the approach to

compiling the emission inventory. Your contribution to enhance the efficacy of the emission

inventory will be greatly appreciated.

The details of the workshop are as follows:

Date: 27 March 2012

Time: Registration for this workshop will be from 08:30 – 09:00.

The workshop is from 09:00 – 12:00

Venue:Imbizo Chamber, WRDM Offices, Corner Park and Sixth Streets

Please confirm your acceptance of this invitation by emailing Noma Mkhize on

[email protected]. If you require further information, please contact Noma or

Benton Pillay on 031 266 7357.

Yours sincerely

Benton Pillay

mailto:[email protected]


66

The table below lists the delegates that attended the workshop:

Table A.1: Attendance list from stakeholder workshop

Name Organisation

Robert Gilmore Harmony

Norman Davies Harmony

Thihanedzwi Ratshibvumo Harmony

Geoff Gilfillan Krugersdorp Crematorium

D Coetzee Krugersdorp Crematorium

Danny Ramsuchit Gold Fields

Tanith Stuart Chemiphos

Llewelyn Stuart Chemiphos

Petrus Leach Sasko Mills

Kenneth Mpadisang Leratong Hospital

Werner Slabbert Cobra Watertech

Michael Jacobs BASF Westonaria

Margot Saner Margot Saner Associates

Sane Simindla Ceramic Industries Limited

Lydia Mudilambi GDARD

Marins Grobler Mogale City Municipality

Grany Dlamini WRDM

Tshishma Galobedeher GDAR

Musa Zwane WRDM

Zakhele Dlamini WRDM

Yegeshni Naiker uMoya-NILU

Bheki Shongwe uMoya-NILU

Benton Pillay uMoya-NILU

A total of 16 respondents to the invitation that indicated they would attending the workshop, did

not turn up. The following agenda was adopted for the workshop.

Table A.2: Emission inventory workshop agenda

TIME ITEM PRESENTER

08:30 – 09:00 Registration

09:00 – 09:05 Welcome WRDM

09:05 – 09:15 Introduction of delegates All

09:15 – 09:45 Basic concepts in emission inventories uMoya-NILU

09:45 – 10:15 Identification of emission sources in WRDM uMoya-NILU

10:15 – 10:30 Tea

10:30 – 10:45 Project terms of reference uMoya-NILU

10:45 – 11:15 Update on project uMoya-NILU


67

11:15 – 11:45 General discussion All

11:45 – 12:00 Way forward, thanks and closure

The following sources of emissions, as defined by the workshop delegates, were identified:

1. Tailings dams:

a. Over-burden dust

b. Mine dust

c. Waste rock dumping dust

d. Slimes dams dust

e. Mining/gold plants

f. Mine dumps

g. Mine heaps

2. Motor vehicles:

a. Trucks, cars, etc.

b. CO2 and CO from vehicles

c. Vehicle emissions

d. Locomotive emissions

3. Domestic burning:

a. Household fuel/coal burning

b. Household low grade fuel/paraffin

c. Outside fires for cooking

4. Industrial emissions

a. Emissions from boilers

b. Boilers, storage facilities

c. Chemical industry stacks

d. Cremator plumes

e. Dust from stockpiles

f. Landfills

g. Waste disposals

h. Incinerators

5. Biomass burning

a. Annually during winter

The workshop was effective in confirming the sources of emissions identified as part of this study.

The one source identified during the workshop that was not initially identified was locomotives.

The source will need to be explored further to determine if it is a significant source in the WRDM.

The one source that the workshop failed to identify was agricultural activities.

The workshop delegates from industries displayed good understanding of what encompasses an

emission inventory. An observation of the delegates attending the workshop was that they were

the ones that submitted their emission inventory questionnaires and were generally cooperative.


68

APPENDIX B – DESCRIPTIONS OF POLLUTANTS AND THEIR HEALTH EFFECTS

The following descriptions of the pollutants and their effects are all directly cited from or based on

the descriptions found in the “Handbook for Criteria Pollutant Inventory Development” (EPA-

OAQPS, 1999).

Carbon monoxide (CO):

Carbon monoxide is a colourless, odourless, and poisonous gas produced by incomplete burning of

carbon in fuels. The biggest part of the CO emissions comes from transportation sources. Other

major CO sources are wood-burning stoves, incinerators, and fuel combustion at industrial

sources. When CO is inhaled, it enters the bloodstream, and reduces the delivery of oxygen to

organs and tissues.

Nitrogen oxides (NOx):

Nitrogen oxides are important precursors to both ozone and acid rain, and as a result may affect

not only human health, but also both terrestrial and aquatic ecosystems. Nitrogen oxides can

interact with other compounds in the air to form PM. Nitrogen oxides form when fuel is burned at

high temperatures. The two major emissions sources are motor vehicles and stationary fuel

combustion sources such as electric utility and industrial boilers. The major mechanism for the

formation of nitrogen dioxide (NO2) in the atmosphere is the oxidation of the primary air pollutant

nitric oxide (NO). When inhaled, nitrogen dioxide can irritate the lungs, cause bronchitis and

pneumonia, and lower resistance to respiratory infections.

Sulphur dioxide (SO2):

Sulphur dioxide is a colourless, pungent gas that is a respiratory irritant and like NOx, is a

precursor to acid rain. SO2 can also interact with other compounds in the air to form PM. Thus,

sulphur compounds in the air contribute to visibility impairment. Ambient SO2 results largely from

stationary sources such as coal and oil combustion, steel mills, refineries, pulp and paper mills,

and nonferrous smelters.

Particulate matter (PM):

Air pollutants called particulate matter include dust, dirt, soot, smoke, and liquid droplets. PM

originates from a variety of sources, such as:

Natural sources such as windblown dust and fires;

Combustion sources such as motor vehicles, power generation, fuel combustion at

industrial facilities, residential fireplaces, and wood stoves. Combustion sources emit

particles of ash or incompletely burned materials;


69

Activities such as materials handling, crushing and grinding operations, and travel on

unpaved roads; and

Interaction of gases (such as NH3, SO2, NOx, and VOC) with other compounds in the air to

form PM.

The chemical and physical composition of PM may vary depending on the location, time of year,

and meteorology. “Fine” particles (PM2.5) are generally emitted from combustion sources.

Sulphate and nitrate secondary particles represent significant components of PM2.5. “Coarse”

particles (PM10) can be emitted from sources including windblown dust, travel on unpaved roads,

and materials handling.


70

APPENDIX C – EMISSION INVENTORY QUESTIONNAIRE


71

1. GENERAL COMPANY INFORMATION

1.1 Name of company:

1.2 Physical address:

1.3 Postal address:

1.4 Name of contact person:

1.5 Title of contact person:

1.6 Telephone number of contact person:

1.7 Fax number of contact person:

1.8 E-mail address of contact person:

1.9 Number of Employees:

1.10 Approximate site coordinates - X: Y:

1.11 Nature of business:

1.12 S.21 Category and sub-category:

1.13 Total Plant emissions (tons/year):

SO2

NOX

CO

PM10

PM2.5 Lead VOC

1.14 Normal Operating

Schedule

1.15 Monthly Throughput (% of total for year – must add up to

100 %)

Hrs/Day Days/

Wk Wk/Yr Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Questionnaires to be submitted within 1 month from date of receipt to Noma Mkhize on fax

number on email [email protected]. In case of queries, Noma can be reached on 031 266

7357.

For official use only

Facility I.D.: Revision: Date Received:


72

2. POINT SOURCE DATA

2.1 Stack Data

Stack

Name/No.

X

Coordinate

Y

Coordinate

Stack

Height

Stack

Diameter

Exit Gas

Temp.

(oC)

Exit Gas

Velocity

(m/s)

Exit Gas

Flow Rate

(m3/s)

e.g. S111

E

N 57 m 2.5 m 156 15 74

2.2. Cleaning Device Data

Stack

Name/No. Cleaning Device Type

Component

Removed

Cleaning

Device

Availability

(%)

Cleaning

Efficiency

e.g. S111 Electrostatic Precipitator PM10 80% 85%


73

2.3. Fuel Data

Combustion Device

Name/No. Fuel Name

Sulphur

Content

(%)

Fuel Ash

Content

(%)

Heating

Value if Gas

(kJ/kg)

Fuel

Consumption

Rate

(ton/month)

e.g. Coal Boiler B11 Coal 1.2 12 90 000

2.4 Combustion Device Data

Combustion Device

Name/No.

(as above)

Heat Input

Rating

(MW)

Fuel Name

Firing Method

(wall, cell-burner

or tangential)

Stoker Type

(spreader,

underfeed,

overfeed)

e.g. Coal Boiler B11 30 coal


74

2.5. Process Production Data

Process

Name/No.

Product

Produced

Production

Rate

(ton/month)

Listed Activity

Category – S.21

Year of

Validity

2.6. Process Emission Data

Please add additional rows if required.

Process

Name/No.

Stack

Name/No.

(leave

blank if no

stack)

Pollutant

Emitted

Emission

Rate

(kg/year)

Basis of Calculation

(Measurement,

emission factor,

mass balance)

Year of

Validity

e.g. Coal Boiler B11 S111

SO2 2840 CEM

2010

NOx 607 Emission factor - USEPA

CO 485 Emission factor – USEPA

PM10 19 Mass balance

NMVOC 12 Emission factor – USEPA

Pb 0,56 Emission factor – Corinair


75

3. AREA SOURCE DATA

Process

Name/No.

Listed

Activity

Category –

S.21

Component

Emitted

Emission

Rate

(kg/year)

Year of

Validity

Co-ordinates of Boundary of Process Area

1 2 3 4

X Y X Y X Y X Y

E.g.

Final Product Tank

Storage

2.2

Benzene 123

2009

58.92" N

42.39" E

2

58.88" N

51.37" E

47.66" N

51.19" E

48.87" N

42.32" E

Toluene 710

Ethybenzene 34

Xylene 344


76

4. DECLARATION

I, the undersigned, hereby declare that I have personally examined and I am familiar

with the information and statements herein and further certify this information and

statements are true, accurate and complete.

Name of person completing form:

Designation:

Signature:

Date:

Name of authorised company representative:

Designation:

Signature:

Date:


77

APPENDIX D - GUIDELINE DOCUMENT


78

CONFIDENTIALITY

The WRDM and uMoya-NILU respect the confidentiality of data submitted by industries

and will treat data marked accordingly as confidential. If you have any special concerns

about confidentiality, please contact uMoya-NILU.

The following information will be kept confidential if clearly marked:

Production rates

Trade secrets (information that reveals secret processes or methods of

manufacture or production)

Information not considered confidential includes:

Emission rates

Emission point data

Type of emissions control equipment

Type of emitting equipment

Information that you consider confidential should be clearly marked CONFIDENTIAL.


79

NOMENCLATURE AND ABBREVIATIONS

Symbol Definition

AP-42 USEPA Compilation of Air Pollutant Emission Factors document

Area source Area sources are smaller sources of similar activity that are

grouped together, which when taken collectively, produce a

significant amount of air pollution.

BTEX Benzene, toluene, ethylbenzene and xylene

CO Carbon monoxide

CEM Continuous emissions monitor

Diffuse emissions Refers to pollution entering the atmosphere from a large non-point

(area) source and not confined to a stack, duct or vent. These

emissions generally include equipment leaks (fugitive), storage

tanks, wastewater treatment, maintenance operations, emissions

from bulk handling or processing of raw materials, windblown dust

and other specific industrial operations.

HAP Hazardous air pollutant

Hg Mercury

NO2 Nitrogen dioxide

NOx Oxides of nitrogen

O3 Ozone

ppb parts per billion

QC Quality control

Pb Lead

PM Particulate matter

PM2.5 Particulate matter with an aerodynamic diameter of less than 2.5

microns

PM10 Particulate matter with an aerodynamic diameter of less than 10

microns

Point source A source that emits more than 75 tons per year of any one or a

combination of criteria pollutants.

SO2 Sulphur dioxide

µg/m3 Microgram per cubic meter

USEPA United States Environmental Protection Agency

NMVOC Non-methane volatile organic compounds

WRDM West Rand District Municipality


80

TABLE OF CONTENTS

FOREWORD 3

1. GENERAL COMPANY INFORMATION 4

2. STACK DATA 4

3. CLEANING DEVICE DATA 4

4. FUEL DATA 5

5. COMBUSTION DEVICE DATA 5

6. PRODUCTION DATA 5

7. PROCESS EMISSION DATA 6


81

FOREWORD

The West Rand District Municipality (WRDM) is compiling a comprehensive emission

inventory of emissions in the district municipality. The emissions inventory will contain

data on point and non-point (area) sources. The WRDM has enlisted the services of

uMoya-NILU to complete this project.

An emissions inventory details the amounts and types of air pollutants released into the

air. It is a comprehensive listing by source of air pollution emissions. The emissions

inventory questionnaire serves as the principle means of gathering data from industries.

In summary, the questionnaire includes general information on the industry, stack data,

cleaning device data, process fuel consumption data, fuel burning process data and

process emission data. This guideline is intended to assist industries in completing the

various sections of the questionnaire.

The pollutants that fall within the scope of this project are the so-called criteria and

hazardous air pollutants (HAPs). Criteria pollutants are the six most commonly found air

pollutants that can harm human health or the environment. They include:



Particulate matter (PM)


Lead (Pb)

The USEPA has listed 187 HAPs which cause or may cause cancer or other serious health

effects. However, their effects normally occur at high concentrations or concentrations

not commonly found in the ambient environment. Most HAPs are organic in nature,

although non-organic toxics also exist e.g. mercury, hydrogen sulphide and hydrogen

fluoride. The complete list of toxic air pollutants can be accessed on the USEPA website

on the following link:


The project focuses on two types of industry sources, namely, point sources and area

sources. Point source emissions are released from stacks and are generally of a high

emission rate. Area source emissions, on the other hand, originate from a large area

and are smaller than point source emissions. Typical examples of area sources are

storage tanks or landfills.

Please note that the inventory is for the year 2010. All information provided must

therefore be relevant to 2010. Do not provide information based on future plans for

reduction.



82

Questionnaires are to be submitted within 1 month from date of receipt to Noma Mkhize

on email [email protected]. In case of queries, Noma can be contacted on 031 266

7357.


83

1. GENERAL COMPANY INFORMATION

1.1 to 1.9

Fill in the business details of your company in the spaces provided on the form.

1.10 Fill in the latitude and longitude (Y and X coordinates) that represents the central

point of your company’s premises. This information can be obtained from Google Earth.

1.11 Provide a concise description of the business activity of your company.

1.12 Select the category and sub-category from the table below for point and area

sources that best describes your company:

Table 1.1: Section 21 Listed Activities

CATEGORY SUB-CATEGORY

1. Combustion Installations 1.1 Solid fuel combustion installations

1.2 Liquid fuel combustion installations

1.3 Solid biomass combustion installations

1.4 Gas combustion installations

2. Petroleum industry, the

production of gaseous and

liquid fuels as well as

petrochemicals from crude oil,

gas and biomass

2.1 Combustion installations

2.2 Storage and handling of petroleum

products

2.3 Industry fuel oil recyclers

3. Carbonisation and coal

gasification


3.2 Coke production and coal gasification

3.3 Tar production

3.4 Char, charcoal and carbon black

production

3.5 Electrode paste production

4. Metallurgical industry

4.1 Drying


4.3 Primary aluminium production

4.4 secondary aluminium production

4.5 Sinter plants

4.6 Basic oxygen furnace steel making

4.7 Electric arc furnace and steel making

(primary and secondary)

4.8 Blast furnace operations

4.9 Ferro-alloy production

4.10 Foundries

4.11 Agglomeration operations

4.12 Pre-reduction and direct reduction

4.13 Lead smelting

4.14 Production and processing of zinc, nickel

and cadmium

4.15 Processing of arsenic, antimony, beryllium,

chromium and silicon

4.16 Smelting and converting of sulphide ores

4.17 Precious and base metal production and

refining


84

4.18 Vanadium ore processing

4.19 Production and casting of bronze and

brass, ands casting copper

4.20 Slag processes

4.21 Metal recovery

4.22 Hot dip galvanising

4.23 Metal spray

5. Mineral Processing, Storage

and Handling

5.1 Storage and handling of ore and coal

5.2 Clamp kilns for brick production

5.3 Cement production (using conventional

fuels and raw materials)

5.4 Cement production (using alternate fuels

and/or resources)

5.5 Lime production

5.6 Glass and mineral wool production

5.7 Ceramic production

5.8 Macadam preparation

5.9 Alkali processes

6. Organic chemicals industry 6.1 Organic chemicals manufacturing

7. Inorganic chemicals industry 7.1 Primary production and use in

manufacturing of ammonia, fluorine,

chlorine, and hydrogen cyanide

7.2 Primary production of acids

7.3 Primary production of chemical fertiliser

7.4 manufacturing activity involving the

production, use in manufacturing or

recovery of antimony, arsenic, beryllium,

cadmium, chromium, cobalt, lead,

mercury, selenium, by the application of

heat

7.5 Production of calcium carbide

7.6 Production of phosphorus and phosphorus

salts not mentioned elsewhere

8. Disposal of hazardous and

general waste

9. Pulp and paper manufacturing

activities, including by-products

recovery

9.1 Lime recovery kilns

9.2 Alkali waste chemical recovery furnaces

9.3 Copeland alkali waste chemical recovery

process

9.4 Chlorine dioxide plant

9.5 Wood drying and the production of

manufactured wood products

10. Animal matter processing

1.13 Fill in the total pollutant or bubble emissions from all the processes in your

company. Use the information from Section 7 by adding the totals for each pollutant.

1.14 & 1.15

The objective of operating schedule data is to ensure that calculations for emission rates

are more accurate. Emission rate equations using the emission factor method take into

account the number of operational hours per day, the number of operational days per

week and the number of operational weeks per year.


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Monthly throughput data is necessary to obtain a finer resolution for emission rates by

month versus annual emission rates that are normally reported. This information is

important since not all processes are operational continuously throughout the year.

Monthly emission rate data is especially useful for dispersion modelling purposes. The

results of modelling could be used to highlight varying trends in monthly air quality

levels.

2. POINT SOURCE DATA

The information required in this section relates to emissions from stacks, which are

generally regarded as point sources.

2.1 Stack Data

An important input into air dispersion models is stack data. The coordinates (latitudes

and longitudes) of the stack are required to plot them on GIS maps. Furthermore, stack

data is an important input into air dispersion models.

Fill in the data as detailed in Section 2 of the questionnaire for every stack on your

premises. The stack name or number is a company specific name or number that you

use to identify the stack. If your stack is a square stack, provide the dimensions at the

stack exit point.

2.2 Cleaning Device Data

Identify the pollution control equipment or devices used to control emissions in your

processes. Provide details including the percent cleaning efficiency, associated stack

number, etc. of the cleaning devices that your company is currently using. Review

operational records to determine the historical availability of your cleaning devices.

Cleaning devices inevitably need to be decommissioned for maintenance related

activities. This compromises the availability of the cleaning device. Cleaning efficiency

does not always meet design specification. In this case, report cleaning efficiency based

on trial run data and actual performance measurements.

The following are examples of cleaning device technologies:

Electrostatic precipitator

Water/steam injection (gas turbines)

Fabric filter

Lean/staged combustion

Scrubber

Flue gas recirculation

Cyclone

Biofilter


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Low NOx burner

Activated carbon filter

Selective catalytic reduction

Selective non-catalytic reduction

Incinerator

2.3 Fuel Data

The combustion of fuels results in the production of gaseous and particulate air

pollutants. It is necessary to identify the various types of fuels used in your processes.

For every process, list the type of fuel used and the consumption rate of the fuel. Some

of the common fuels include:

Bituminous coal

Heavy fuel oil

Light fuel oil

Marine fuel oil

Diesel

Jet fuel/kerosene

Paraffin

Fuel gas

Methane-rich gas

LPG

Wood

Also provide an estimation of the sulphur content, heating value and ash content of the

various fuel types used.

2.4 Combustion Device Data

Information on the firing method, heating value of fuel used, and stoker type of the

combustion devices will assist in selecting the most appropriate emission factor for the

emission rate calculation. The USEPA reports emission factors as a function of firing

method and type of burners used. Please provide information relating to the maximum

operating period for the various sources. Be sure to include the maximum number of

hours per day and days per week the fuel burning appliance will operate for.

2.5 Process Production Data

Air emissions do not only originate from the combustion of fuels. There are several non-

combustion type processes that give rise to air pollution. For example, the production of

sulphuric acid using elemental sulphur as a raw material results in the formation of

sulphur dioxide as a process emission. The oil refining industry produces sulphur dioxide

as a process emission from catalytic crackers.


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The primary objective of this section is to identify those processes in your company that

cause air pollution, whether or not they are combustion type processes. The production

rates of these processes are required to assist in selecting the correct emission factors.

2.6 Process Emission Data

Emission Estimation Techniques:

This section requires a listing of all the processes in your company together with the

emissions resulting from those processes and their emission rates. Please report on the

emissions of criteria and toxic air pollutants:

The following is a list of criteria pollutants:

SO2

NOx

CO

PM

Lead

If possible, speciate the PM into PM10 and PM2.5 if the particle size distribution of your

particulate emissions are known. These particles have aerodynamic diameters of less

than 10 and 2.5 microns, which make them small enough to enter the lungs and cause

negative health effects.

The USEPA has listed 187 HAPs (see Foreword for website link). Please also report the

emissions of these pollutants if your facility produces any of them. Several of these

toxic air pollutants are volatile organic compounds (VOCs). Please report on the

emissions of volatile organic compounds from the various processes in your facility.

Please mention the basis for the estimation. In general, there are four types of emission

estimates techniques (EET) that may be used to estimate emissions from your facility.

The four types are:

Mass balance

Engineering calculations

Direct measurement or

Emission factors

Select the EET that is most appropriate for your facility. For example, the mass balance

option may be chosen to best estimate the fugitive particulate emissions from stockpiles,

direct measurement option for losses from pumps and flanges and the emission factors

option when estimating losses from storage tanks.

It is important to note that EETs relate principally to average process emissions.

Emissions resulting from non-routine events are rarely routinely included in EETs.


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However, it is important to recognise that emissions resulting from significant operating

excursions and/or accidental situations e.g. flaring events, spills etc. will also need to be

estimated and added to process emissions when calculating total emissions for reporting

purposes.

All the sources of releases into the air must be specified. Describe the equipment or

type of operation that will be generating the emission, e.g. sandblasting operations,

flaring, tank storage, chemical processing, incineration, etc. The estimated annual

emissions in tons per year for each component must also be calculated and submitted.

Examples of procedures used to calculate emission estimates are outlined below:

a) Emissions from combustion sources:

Multiply the maximum input firing rate of the combustion equipment in Giga Joules per

hour, or the maximum rate of discharge by the emission factors for the appropriate fuel

used, multiplied by the maximum duration of operation. Express the contaminant

estimate in tons per year, e.g. sulphur dioxide (ton/year):

Natural gas fired industrial boiler, 155.5 GJ/h, low NOx burner controlled:

15.5 GJ/h x (2.57 g/GJ suspended + 3.11 g/GJ condensable) x 16 hours/day x 6

days/week x 52 weeks/year x 16-6 tons/g = 0.44 ton/year.

b) Emissions from solvent emitting sources:

Multiply the projected or last year’s annual solvent use by the product density and

percent volatility as provided on the Material Safety Data Sheets from the supplier, e.g.

total organic compounds, TOC (ton/year):

5000 litres/year x 0.88 kg/L density x 40% volatile x 10-3 tons/kg = 1.76 tons/year

The amount (kg per ton) and concentration (mg/per m³) of each relevant component of

the emissions shall be specified. (Other units may be used if this is more appropriate).

If the operation concerns facilities that are already operative, both the current amounts

released and the amounts expected in the future shall be stated.

If the amounts released vary with time, both the maximum and average emission rates

must be stated.

Specify the averaging period for mean amounts per time unit and concentration. This

may for example be one hour, one shift (8 hours), 24 hours, one week or one year,

depending on the pattern of operations and the parameters in question.

The averaging period for the maximum emission rate must also be specified.

Instantaneous measurements or rates per 15 minutes, per hour or per 24-hour period

may be used depending on both the pattern of operations and the method of

measurement.


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c) An example of a calculation using the mass balance technique:

A ferromanganese smelter is in the initial stages of its EIA prior to commissioning. An

air quality assessment is required for the plant’s Air Emissions Licence (AEL)

application, which needs to be submitted with the EIA. This application must include

dispersion modelling. The plant is not yet in operation, so no emission data is

available. A mass balance has been drawn up by the Process Engineer of a similar plant

with an identical process. The plant will process 30 tons of ore per day, 7 days per

week. According to the mass balance, 11.5 kg of particulates (as Mn) is given off for

every 1496.6 kg of material input. This is considered to be the only particulate

emission from this process. The plant will be fitted with a bag-house that typically

operates at a 98% efficiency.

E = A X EF X {1- (ER/100)}

Where, E - Emission rate (g/s)

A - Activity rate (tons per second)

EF - Emission factor (g/ton)

ER - Emission reduction efficiency (%)

A = 30 tons per day / 86400 (tons per second)

EF = 11 500 (g/ton)

ER = 98 (%)

Therefore:

E = 0.000347 x 11 500 x {1- (98/100)}

= 0.07981 g/s


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3. AREA SOURCE DATA

Companies that do not have stacks as a result of not having any combustion devices

(boilers, heaters, furnaces, etc.), should report their emissions as area sources. These

are smaller sources of similar activity that when grouped together produce a significant

amount of air pollution. A common example is the emission of dust from a cement

manufacturing facility. The nature of this operation results in the production of dust

emissions from several small sources. Together, these sources combine to produce

significant dust levels. Another example is the emission of volatile organic compounds

(VOCs) from storage tanks. Depending on the nature of the product being stored, the

VOCs emissions could include several of the toxic air pollutants. For the storage of

gasoline, the toxic air pollutants emitted include benzene, toluene and xylene. This type

of area source is illustrated in Figure 3.1.

Figure 3.1: A tank farm for final product storage

In completing Section 3 of the questionnaire, please provide a concise description of the

processes. For example, the area source represented in Figure 3.1 could be described as


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‘Final Product Tank Storage’. Then enter the S.21 category and sub-category relating to

the process.

Then list the pollutants emitted and their emission rates in columns 3 and 4.

The co-ordinates that define the area must then be entered. Please remember to enter

latitude and longitudes. The example of the tank farm in Figure 3.1 can be used to

illustrate this. The red rectangle defines the boundaries of the area of the tank farm.

The co-ordinates are the latitudes and longitudes of the four corners of the rectangle.

By using a similar approach, plot the approximate area of your area sources. Then

determine the co-ordinates of the 4 points of the area. If the area you have defined has

more than 4 points, supply the co-ordinates on a blank page, which must be attached to

the questionnaire. The co-ordinates for your area sources can be identified by using

Google Earth. Identify your facility in Google Earth. Then zoom in and locate the co-

ordinates of the 4 points of your area sources.


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APPENDIX E – STORAGE TANK EMISSIONS

Description of Emissions:

Emission losses from storage tanks containing organic liquids, especially highly volatile

liquids, occur because of evaporative losses of the liquid during storage and as a result

of changes in the liquid level. The emission rates are dependent on whether the tank is

of fixed roof or floating roof configuration. The two significant types of emissions from

fixed roof tanks are standing storage losses and working losses. Standing storage loss is

the expulsion of vapour from tanks through vapour expansion and contraction, which is

the result of changes in temperature and barometric pressure. This loss occurs without

any change in liquid level in the tank. The loss from filling and emptying the tank is

called working loss. Evaporation during filling operations is a result of an increase in the

liquid level in the tank. As the liquid level increases, the pressure inside the tank

exceeds the relief pressure and vapours are expelled from the tank. Evaporative loss

during emptying occurs when air drawn into the tank during liquid removal becomes

saturated with organic vapour and expands, thus exceeding the capacity of the vapour

space.

Figure E.1: External floating roof storage tanks

The total emissions from floating roof tanks are the sum of standing storage losses and

withdrawal losses. Withdrawal losses occur as the liquid level, and thus the floating roof,

is lowered. Some liquid remains on the inner tank wall surface and evaporates.

Standing storage loss from floating roof tanks include rim seal and deck fitting losses.

Other potential standing storage loss mechanisms include breathing losses as a result of

temperature and pressure changes.


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Emissions are naturally greater for more volatile products such as gasoline and smaller

for less volatile products such as diesel. The key emissions from refinery storage tanks

are a range of organic compounds that include hexane, benzene, toluene, ethyl benzene,

xylene, and 1,2,4 trimethyl benzene.

Methodology for Estimating Emissions:

To estimate emissions from storage tanks, the USEPA’s TANKS model was used. The

equations used in the model were developed by the American Petroleum Institute (API).

These are well-documented in Chapter 7 of the USEPA’s AP-42 2. TANKS allows the

input of specific information on storage tanks (e.g. tank type, dimensions, construction

and paint condition), liquid fuel contents, handling protocols (e.g. type of fuel, annual

product throughput and number of turnovers per year) and site-specific ambient

meteorological information. Speciation of the emission into its resultant components

was based on the composition of the components in their liquid phase. The compounds

selected for speciation were benzene, toluene, ethyl benzene and xylene.

The model also requires the input of representative meteorological data. Climatologically

representative wind, temperature, pressure and solar radiation data for South Durban

was obtained from the South African Weather Service (SAWS) and the eThekwini

Municipality.

The gathering of information for input into the TANKS model was achieved with the use

of a questionnaire that was issued to Engen. The questionnaire (see Appendix A) was

developed with the intention of sourcing information required to estimate emissions by

using the TANKS model.

The following assumptions were made in the estimation of storage tank emissions:

1. Where maximum liquid levels were not provided, a value equivalent to 95% of

tank/shell height was used.

2. Where average liquid levels were not provided, a value equivalent to 50% of

tank/shell height was used.

3. Slops were assumed to be gasoline in the TANKS model.

Results of Emissions Estimation:

The results of storage tank emission estimations are presented below for the Transnet

Pipelines Tarlton Tank Farm and Refractionator.


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APPENDIX F – LOADING GANTRY EMISSIONS


Emissions from empty cargo tanks (road tankers, rail tankers and marine vessels) occur

when organic vapours are displaced to the atmosphere by the liquid being loaded into

the tanks. The principal methods of loading are splash loading, submerged fill pipe and

bottom loading. High levels of vapour generation and losses occur in the splash loading

method. These organic vapours originate from various sources. These include:

Vapours formed due to evaporation of residual product from the previous load in

the empty tank.

Vapours transferred to the tanks from the vapour balance system as product is

being unloaded.

Vapours formed in the tanks as a result of the product being loaded.

Figure F.1: Typical road loading gantry - a source of VOC emissions

The products loaded at a refinery range in volatility from the highly volatile such as

gasoline to the low volatility products such as fuel oil. Emissions are naturally

proportional to the volatility of the product. The types of emissions are organic in

nature.


The loss of vapours from loading and offloading activities of refined petroleum products

involving rail tankers, road tankers and marine vessels is detailed in the USEPA’s AP-42,

Section 5.2, entitled “Transportation and Marketing of Petroleum Liquids”.

The USEPA has developed expressions for the estimation of petroleum emissions from

loading operations with a probable error of ± 30%. Inputs to the expressions include


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the quantities of products loaded, their vapour pressures and their molecular weights.

The expressions also require saturation factors which were determined by the USEPA

through empirical tests. Saturation factors are dependent on the type of loading. The

highest saturation factors occur for splash loading, whereas the lowest occur for

submerged and bottom loading.

The products considered in the emissions inventory are TOCs, benzene, toluene, ethyl

benzene and xylene.

The information required to estimate emissions from loading gantries was sourced from

Transnet Pipelines by using an emission inventory questionnaire.


Data on quantities loaded at the Wentworth Depot could not be acquired by Engen

Refinery as the depot operates independently of the refinery. The Wentworth Depot was

therefore excluded from this estimation. Loading gantries at the Tara Road Depot are

not equipped with vapour capture devices. Therefore, 100% of vapour emissions

generated through loading and offloading processes enter the atmosphere. The table

below contains data used in the estimation of emissions from loading gantries.


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APPENDIX G – FUGITIVE EMISSIONS


Fugitive emission leaks from process equipment constitute a considerable portion of VOC

emissions from facilities that process or store organic chemicals. These emissions cause

safety problems due to their common characteristics such as flammability. The types of

equipment from which leaks typically occur are block valves, control valves, pump seals,

compressor seals, pressure relief valves, flanges and connectors, open-ended lines and

sampling connections.

Figure G.1: Process equipment which are prone to leakages

Leaks from equipment occur due to several reasons. These include sub-standard

installation of equipment (e.g. flanges not adequately tightened), general wear and tear

of equipment components (gaskets wearing out after several years in use), incorrect

selection of packing materials (incorrect type of seals used in pumps) and corrosion

(which results in reduced wall thickness). The quantity of emissions from leaking

equipment is dependent on the contents of the pipeline. The more volatile the product,

the higher the rate of product loss from the leak. Leaks are typically higher when the

products involved are light hydrocarbons. In similar vein, leaks from heavy

hydrocarbons such as fuel oil and bitumen are very low.


The USEPA’s Protocol for Equipment Leak Emission Estimates presents four methods for

estimating emissions from leaking equipment, commonly referred to as fugitive

emissions. Three of the methods are dependent on the use of screening (measurement)

data. The method commonly employed to conduct measurements of fugitive emission

leak concentrations is the leak detection and repair (LDAR) programme. The

implementation of an LDAR programme is costly, but the most accurate method. Engen

contracted SNC Lavalin of Canada to undertake an LDAR programme at the refinery.


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The programme involves the measurement of leaks on 120,000 estimated points in the

refinery.

The following components that are considered to be the major sources of fugitive

emissions, were considered for the study:

Manual valves

Control valves

Compressor seals

Agitator seals

Pressure relief valves

Flanges and connectors

Open-ended lines

Sampling points

All process lines containing less than 10% (m/m) VOCs were excluded from the study.

Fugitive emissions of VOCs are measured in accordance with USEPA Method 21 entitled

‘Determination of Volatile Organic Compound Leaks’. The measurement of VOC

concentration (in ppm) at each component was undertaken with the aid of portable

flame ionisation detector (FID).

A correlation equation was used to calculate the emission rate (mass/time) of VOCs from

a component with measured VOC concentration data serving as the primary input. The

source of the correlation equation was the ‘Protocol for Equipment Leak Emission

Estimates’. Speciation to BTEX was based on the composition of BTEX in crude oil and

was applied to the total VOC emissions, not unit-specific VOC emissions. The following

composition of the BTEX components in crude oil was used in the speciation:

Benzene – 0.6%

Toluene – 1.00%

Ethyl benzene – 0.40%

Xylene – 1.40%


The results of the fugitive emission estimates were sourced from Engen’s Leak Detection

and Repair Program Report 5. Emissions were estimated for Units 2, 22, 41, 42, 43, 44,

45, 68 and 71, comprising a total of 60 000 points. It is estimated that there are

120,000 points in the refinery. To adjust for this shortcoming, the total emission rate

was extrapolated for 120 000 points by multiplying the emission rate estimated for

60,000 points by two.

Emissions of VOC and BEX are presented in the table below:


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Table 13.1: Total emissions from fugitive sources

POLLUTANT

EMISSION

RATE (kg/year)

EMISSION

RATE (ton/day)

TOC 375,815.60 1.030

Benzene 2,254.89 0.006

Toluene 3,758.16 0.010

Ethyl benzene 1,503.26 0.004

Xylene 5,261.42 0.014

Total organic emissions from fugitive sources were estimated at 375.82 ton/year or 1.87

ton/day. Benzene’s emission losses contributed approximately 0.5 % or 2.25 ton/day to

this total.

Emission Inventory Report

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