Mkhonto P MSc 2014

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Atmospheric Dispersion Modelling Study of a
Township within a Declared National Priority Area
Prince D. M. Mkhonto
Student Number: 802002732
Department of Geography, Environmental Management
and Energy Studies
Supervisor: Prof H. J. Annegarn
A Minor Dissertation submitted to the Faculty of Science, University of
Johannesburg, in partial fulfilment of the requirements of the degree Master of
Science in Environmental Management
December 2013

i
Affidavit
TO WHOM IT MAY CONCERN
This serves to confirm that I, Prince D.M. Mkhonto, I.D. No. 8204175604081, Student Number
802002732, enrolled for the qualification MSc (Environmental Management) in the Faculty of
Science, herewith declare that my academic work is in line with the Plagiarism Policy of the
University of Johannesburg, with which I am familiar.
I further declare that the work presented in the thesis:
Atmospheric Dispersion Modelling Study of a Township within a National Declared Priority
Area
Is authentic and original unless clearly indicated otherwise and in such instances full reference to
the source is acknowledged and I do not pretend to receive any credit for such acknowledged
quotations, and that there is no copyright infringement in my work. I declare that no unethical
research practices were used or material gained through dishonesty. I understand that plagiarism is
a serious offence and that should I contravene the Plagiarism Policy notwithstanding signing this
affidavit, I may be found guilty of a serious criminal offence (perjury) that would amongst other
consequences compel the UJ to inform all other tertiary institutions of the offence and to issue a
corresponding certificate of reprehensible academic conduct to whomever request such a certificate
from the institution.
Signed at Johannesburg on this ___________________
Signature ____________________________________
Prince D. M. Mkhonto
STAMP COMMISSIONER OF OATHS
Affidavit certified by a Commissioner of Oaths
This affidavit conforms with the requirements of the JUSTICES OF THE PEASE AND
COMMISSIONERS OF OATHS ACT 16 OF 1963 and the applicable Regulations published in the
GG GNR 1258 of 21 July 1972; GN 903 of 10 July 1998; GN 109 of 2 February 2001 as amended.

ii
Abstract
The use of atmospheric dispersion models to predict ground level pollutants concentrations
has been on an increase in South Africa in the last decade. At this stage National
Department of Environmental Affairs has published a draft document to provide guidelines
on the type or use of models. Most Air Quality Specialists in the country make use of the
United States Environmental Protection Agency approved atmospheric dispersion models
to conduct air quality investigations. These models were developed in the United States of
America after having considered the environmental set up and monitoring capabilities. In
light of the above, much of the required input data are not readily available and
calculations have been conducted to make up for the shortfall. For domestic emissions,
quantifying the emissions factors is proving to be a challenge for modellers. They calculate
emissions factors using different data sets from variable sources – sometimes the data are
not up to date. This variability could potentially compromise the output of the model. This
study aim was to model domestic emissions from an isolated rural township, Leandra, in
the Mpumalanga Province – located within a nationally declared Highveld air quality
management priority area – for two one month periods – in both the winter – July 2008 –
and the summer – October 2008. This was achieved by using a United States
Environmental Protection Agency approved AERMOD atmospheric dispersion model.
Hourly surface measured meteorology data were obtained from the Langverwacht ambient
air quality monitoring station and upper air data from the Irene monitoring station. The
data were screened for any suspect values, formatted and then pre-processed by AERMET
to be used by AERMOD. The study also investigated and compared the modelled
time-series and monitored time-series data. This study calculated the effective emissions
rate of 0.3 g PM10 s-1
m-2
by using a combination of monitored hourly PM10 concentrations
and dispersion modelling time series data, for a typical Highveld township. Furthermore,
the study revealed that, during winter when air is stagnant, Leandra was demonstrably
isolated from other emissions sources of strength in the region – i.e. power station and
domestic emissions were the dominant emissions sources. Under these circumstances,
indoor and outdoor emissions were above the acceptable standards – i.e. they constituted
unhealthy ambient air conditions. During summer – with the higher average wind speeds –
Leandra was under the influence of industrial sources and the argument of isolation was
not valid.

iii
Dedication
This work is dedicated to three very special people who will
neither get to read it nor understand it:
my late grandmother, Wanqasi Sibuyi,
my mother, Jeita Mathebula and
my late sister, Patience Mkhonto.

iv
Acknowledgments
I would like to thank and express my sincere appreciation to the following people and
organisations for their assistance in making this project a success:
 To Professor Harold Annegarn, thank you so much for the guidance, advice and the
follow ups even during my darkest moments. He continuously and generously gave
me constructive comments and stretched my capacity; allocating time in his busy
schedule. Thank you for also accommodating me in your house.
 To my wife, Patience Clara Mkhonto, thank you for your love, support and
encouragement. To my children, Nyiko, Nseketelo and Ntshembho – thank you for
the encouragement and the inspiration – I was once just a rural boy who grew up
looking after people’s goats to survive.
 To Airshed Professionals, thank you for taking me through the first baby steps in the
atmospheric dispersion modelling field. Special thanks to Nicolette Krause for taking
my hand and walking with me when I was in the dark. Thank you to Hanlie
Liebenberg-Enslin for giving me the opportunity to use your resources.
 To Eskom and Kristy Langerman, thank you for allowing me to use an Eskom
ambient air quality monitoring dataset.
 To Sasol and Owen Pretorius, thank you for your assistance and for facilitating
access and permission to use Sasol’s ambient air quality monitoring data.
 To the South African Weather Services, Xolile Ncipha and Hendrik Swart, thank you
for your assistance and for allowing me to use your upper air monitoring data.
 To Richard Huchzermeyer, thank you for taking the time to proof read two of the
chapters to ensure that gremlins in the language are addressed.
 To Edward Molepo and Ike Bogale, thank you for believing in me. I will always
keep it rural. Also many thanks to Riaan Grobbelaar, Kennedy Owuor and Moses
Mashiane. And to Lisanne Frewin for the final proof reading of this work.

v
Table of Contents
Affidavit....................................................................................................................i
Abstract ................................................................................................................... ii
Dedication .............................................................................................................. iii
Acknowledgments...................................................................................................iv
Table of Contents.....................................................................................................v
List of Figures ....................................................................................................... vii
List of Tables...........................................................................................................ix
List of Abbreviations................................................................................................x
1. Introduction............................................................................................................1
1.1 Background.....................................................................................................1
1.2 Strategy and measures ....................................................................................3
1.3 Study area selection rationale.........................................................................4
1.4 Importance of the study ..................................................................................5
1.5 Aim and objectives .........................................................................................5
1.5.1 Aim...................................................................................................5
1.5.2 Hypothesis ........................................................................................5
1.5.3 Objectives.........................................................................................6
2. Literature Review...................................................................................................7
2.1 Legislative framework....................................................................................7
2.2 Declared Air-Quality National Priority Areas................................................8
2.3 Particulate matter..........................................................................................15
2.4 Basa njengo Magogo method .......................................................................17
2.5 Atmospheric dispersion modelling...............................................................20
2.5.1 AERMOD dispersion model ..........................................................23
2.6 Emissions factors..........................................................................................25
3. Study Methodology ..............................................................................................26
3.1 Study area .....................................................................................................26
3.2 Monitored data..............................................................................................27
3.2.1 Surface meteorology data...............................................................27

vi
3.2.2 Upper air data .................................................................................30
3.2.3 Ambient monitored PM10 ...............................................................30
3.3 Data requirements and dispersion simulation...............................................31
3.3.1 AERMET pre-processor.................................................................31
3.3.2 Source data requirements ...............................................................32
3.3.3 Modelling domain ..........................................................................35
3.3.4 Building downwash consideration .................................................35
3.3.5 AERMOD dispersion model ..........................................................35
3.4 Strengths and shortcoming of the data..........................................................35
4. Results and Discussions .......................................................................................37
4.1 Monitored meteorology ................................................................................37
4.1.1 Local wind fields ............................................................................37
4.1.2 Temperature....................................................................................40
4.2 Ambient monitored PM10 concentration.......................................................42
4.3 AERMOD dispersion model results.............................................................44
4.4 Comparison of monitored and modelled concentration................................49
5. Conclusion and Recommendations.....................................................................52
References.......................................................................................................................55

vii
List of Figures
Figure 1. Locations of hot spots in the South Africa and the associated pollutants 2
Figure 2. A map of VTAPA indicating surrounding provinces and municipalities 9
Figure 3. Sources contribution of inhalable particulate emissions in the VTAPA 10
Figure 4. Monthly variation of domestic fuel burning 11
Figure 5. Diurnal variation of PM10 from VTAPA monitoring network 12
Figure 6. Highveld priority map indicating surrounding provinces and
municipality 13
Figure 7. Granny Mashinini indicating BnM fire-lighting steps 18
Figure 8. Comparison of traditional bottom-lit and BnM top-down fire lighting
methodologies 18
Figure 9. Photograph of the emissions from BnM imbawula on the front right
and a classical bottom-lit imbawula on the back left 19
Figure 10. Overview of air pollution modelling procedure 21
Figure 11. Type of models typical applied depending on problem 22
Figure 12. Data flow into AERMOD modelling system 23
Figure 13. Location of Leandra in the HPA 27
Figure 14. Location of the Leandra air quality monitoring station (yellow pin) 28
Figure 15. Location of Langverwacht station in Secunda (red dot) 29
Figure 16. Continuous PM10 monitor TEOM 1400a 31
Figure 17. Leandra area sources specified in AERMOD 33
Figure 18. PM10 mean diurnal variation used to calculate emissions factors 33
Figure 19. July period-wind rose 37
Figure 20. July day-time wind rose 38
Figure 21. July night-time wind rose 38
Figure 22. October period-wind rose 39
Figure 23. October day-time wind rose 40
Figure 24. October night-time wind rose 40
Figure 25. July and October 2008 hourly average temperature 41
Figure 26. July (lower) and October (upper) 2008 daily average temperature 42
Figure 27. July monitored diurnal PM10 hourly averages 43
Figure 28. July monitored PM10 daily average concentration 43
Figure 29. October monitored diurnal PM10 hourly averages 44
Figure 30. October monitored PM10 daily average concentration 44

viii
Figure 31. July modelled PM10 hourly average 45
Figure 32. July predicted PM10 daily average 45
Figure 33. October predicted PM10 hourly average 46
Figure 34. October predicted PM10 daily average 46
Figure 35. July modelled hourly average PM10 47
Figure 36. July modelled daily average PM10 47
Figure 37. October modelled hourly average PM10 48
Figure 38. October modelled daily average PM10 48
Figure 39. July monitored and modelled PM10 hourly average 49
Figure 40. July monitored (red) and modelled (blue) PM10 daily average 51
Figure 41. October monitored (red) and modelled (blue) PM10 hourly average 51
Figure 42. October monitored (red) and modelled (blue) PM10 daily average 51

ix
List of Tables
Table 1. Number of PM10 Exceedance of the 24-hour average ambient air
quality standard 11
Table 2. Distribution of PM10 per sector in the HPA 14
Table 3. Ambient air quality standards of PM10 for South Africa 16
Table 4. Ambient air quality standards of PM2.5 for South Africa 17
Table 5. Emissions factors of coal, paraffin and wood burning in household 25
Table 6. Variable emissions factors by hour of day 34

x
List of Abbreviations
AQA Air Quality Management Act 39 of 2004
APPA Atmospheric Pollution Prevention Act 45 of 1965
AUSPLUME Australian Gaussian regulatory model
AERMOD USEPA approved steady-state Gaussian dispersion mode
AERMET Meteorological data pre-processor for AERMOD
AERMAP Terrain pre-processor for AERMOD
CTMPLUS Complex Terrain Dispersion model
CALPUFF Multi-layer, multi species non-steady-state puff dispersion model
BnM Basa njengo magogo fire-lighting method – literally translates as
‘make fire like the old woman’
DEA Department of Environmental Affairs – previously known as
DEAT
DEAT Department of Environmental Affairs and Tourism
DME Department of Minerals and Energy
GIS Geographic information system
GMLM Govan Mbeki Local Municipality
HPA Highveld Priority Area
ISO 9001 International Organization for Standardization: Quality
Management System
ISCST3 Industrial Source Complex Short Term dispersion model
MEC Member of Executive Council
Nm3
Normal cubic meters
NEMA National Environmental Management Act 107 of 1998
MHz Mega-Hertz
TSP Total suspended particulate matter
PM10 Particulate matter with a diameter ≤ 10 µm
PM2.5 Particulate matter with a diameter ≤ 2.5 µm
SANAS South African National Accreditation System
SAWS South African Weather Service
TAPM Prognostic meteorological and air pollution dispersion model
TEOM Tapered Element Oscillating Microbalance
USEPA United States of America Environmental Protection Agency
UTM Universal Transverse Mercator
VTAPA Vaal Triangle Airshed Priority Area
WNPA Waterberg National Priority Area
WHO World Health Organisation

1
1. Introduction
1.1 Background
Air quality management issues are receiving growing attention in South Africa,
particularly in urban areas This attention has been given impetus by the passage of the Air
Quality Management Act No. 39 of 2004 (AQA) (DEAT, 2009a). Of particular concern are
high ground-level concentrations of air pollution in coal-burning townships. In these areas
coal is an accessible and affordable source of fuel, and thus it is the fuel of choice for many
lower income households. It provides a twofold benefit—it warms the house and allows
cooking to take place on the same heat source.
In their study, Lim et al. (2012) found that approximately 2.8 billion people worldwide
rely on coal and biomass as an energy source for cooking and heating. Of those an
estimated 18 million people in South Africa are found living in informal settlements and
townships (Wentzel, 2006). The inherent and associated problem with burning of coal and
biomass, particularly in poorly ventilated structures, is exposure to unhealthy levels of
indoor air pollution (WHO, 2003).
In South Africa, industrial and power generation plants are generally perceived to be major
sources of pollution. This is largely because of a weakness of the Atmospheric Pollution
Prevention Act 45 of 1965 (APPA). In 1992 it was acknowledged that South Africa’s
approach to pollution and waste management governance was inadequate (DEAT, 2000).
This was because APPA employed an approach that focused on source-based emissions
controls. This approach proved ineffective and lead to the development of pollution hot
spots in the country (Held et al., 1996; Zunckel, 1999; Scorgie et al., 2004; DEAT, 2009a),
(Figure 1).
In Gauteng Province, a study by Scorgie et al. (2003) found that domestic coal burning
was the largest contributor to air pollution – electricity generation contributed 5%,
industries and commercial organisations contributed 30% and domestic coal burning
contributed 65%. In their study, Liebenberg-Enslin et al. (2007) and DEA (2012a), found
that 5.14% and 6% of particulate matter was apportioned to domestic coal burning in Vaal
Triangle Airshed Priority and Highveld Priority Areas respectively. The coal burning
percentage might seem low at face value; however, it contributes significantly to
atmospheric pollutants in both informal and formal township settlements in South Africa

2
(Zunckel et al., 2006). Under stable meteorological conditions, the emissions from coal
burning accumulate in the boundary layer and often exceed the guideline values of ambient
air quality set by the Department of Environment and Tourism (DEAT) (Zunckel et al.,
2006).
Figure 1. Locations of hot spots in the South Africa and the associated pollutants
(Source: Scorgie et al, 2005)
The continued use of coal and wood (by a large portion of the South African population)
presents a cause for concern with regard to health risk potentials. Lim et al. (2012) found
that household air pollution from cooking with solid fuels killed approximately 4 million
people worldwide from 1990 – 2000. Additional, the Lim et al. (2012) study revealed that
millions more become ill with lung cancer and other lung diseases, cardiovascular disease
and cataracts. In terms of ‘Lost Healthy Life Years’, the study found that, household air
pollution is the second most important risk factor – globally – for women and girls (among
those examined) and the fifth most important risk factor for men and boys. In sub-Saharan
Africa, the household air pollution is the first critical factor for women and girls. In their
study, Scorgie et al. (2004) found that illness related to air pollution costs the South
African government an estimated R1.2 billion per annum in health care. Studies in the Vaal
triangle area have also shown that children exposed to coal smoke have an incidence
approximately ten times higher for respiratory tract disease when compared with children
living in nearby areas who are not exposed to smoke from incomplete coal combustion
processes (Terblanche et al., 1994).

3
Domestic coal burning has been found to be a significant source of particulate matter in the
townships (Matte, 2004). A study, by DEAT (2007), found that particulate emissions are a
major cause of poor ambient air quality in urban areas and this poor air quality has an
adverse impact on human health. Particulate matter is defined a complex mixture of
extremely small particles and liquid droplets (Holgate et al., 1999). Particle pollution is
made up of a number of components including: acids (such as nitrates and sulphates);
organic chemicals; metals; and soil or dust particles (Peavy et al., 1985). Particulate matter
can be divided into three classes: total suspended particulates (TSP), PM10 (particles with a
diameter ≤ 10 µm), and PM2.5 (diameter ≤ 2.5 µm) (Nuwarinda, 2007). Generally, the
sources of particulate matter can vary from road dust, imported regional aerosol, refuse
burning and mine tailings – and these sources can vary by season and by particle size
(Annegarn & Sithole, 1999). A study, by Brunekreef and Holgate (2002), demonstrated
that exposure to particulate matter of different size fractions is associated with an increased
risk of cardiovascular disease
1.2 Strategy and measures
The AQA provides a number of air quality management measures to address the air
pollution problems in South Africa. One of the management measures is the declaration of
priority areas. The Act stipulates that the Minister or Member of Executive Council (MEC)
“…may, by notice in the gazette, declare an area as a priority area if he or she reasonably
believes that; ambient air quality standards are being or may be, exceeded in the area or
any other situation may exist which is causing or may cause, significant negative impact
on air quality; and the area require a specific air quality management action to rectify the
situation.” Once an area has been declared a priority area, air quality management plans to
reduce the emissions must be developed and implemented. The Minister or MEC may,
through the government gazette, withdraw the declaration once the priority area has been
found to be in compliance with the ambient air quality standards for at least two years
(AQA, 2004).
To date, three areas have been declared national priority areas by the minister: namely
Vaal Triangle Airshed (VTAPA), Highveld Priority area (HPA) and the Waterberg-
National Priority Area (WNPA) (DEA, 2012b). VTAPA was the first to be declared – in
April 2006; HPA in November 2007 and WNPA in 2012. These areas were declared as
national priority areas because their boundaries cross over (political boundaries) into more

4
than one province. The Minister declared VTAPA and HPA to be priority areas because of
concerns about the elevated pollutant concentrations within the areas, specifically
particulate matter (DEAT, 2006). On the other hand, the declaration of WNPA was a
proactive approach, because of the likelihood of an expected increase in air pollution in the
area resulting from a new planned industrial development (DEA, 2012b).
The national priority areas are typically defined by a mix of highly industrial areas and
various smaller commercial activities, and are associated with offensive and noxious
gasses. In addition, the areas are interspersed with several dense low income settlements.
In their study, Terblanche et al. (1992) found that the international ambient health
standards for particulate matter were exceeded two and half times in the VTAPA. The
HPA area was found to be associated with poor ambient air quality and elevated
concentrations of criteria pollutants from both industrial and non-industrial sources (Held
et al., 1996). Particulate matter is one of the criteria pollutants specified in the national
ambient air quality standards for South Africa (DEAT, 2009b).
The air quality management plans – of both the VTAPA and HPA – consider the use of
Atmospheric Emissions Licensing, as enshrined in the AQA, to be the ideal mechanisms to
address the industrial emissions (DEAT, 2007; DEA, 2012a). The plans promote the
implementation of the Basa njengo Magogo (BnM) method as a cost-effective measure
towards addressing domestic emissions. The BnM method is an ‘upside-down-method’ of
burning fire with a proven potential of a 40-50% reduction in emissions (Wentzel, 2006).
Although various studies have proved its effectiveness in townships (such as Zamdela and
eMbalenhle), different areas still experience high ground-level concentrations of priority
pollutants which continue to exceed the hourly and daily averages of the national ambient
air quality standards (DEA, 2012a). This is because of the limited funding available to
promote the method (DME, 2005).
1.3 Study area selection rationale
Atmospheric dispersion models are conducted to facilitate the identification of area within
the declared national priority areas or any other area where ground air pollution levels have
the most impact. During the VTAPA and HPA studies, the CALPUFF dispersion model
was used (DEAT, 2007; DEA, 2012a). The model was set up using the measured
meteorological data as inputs. In both studies, the availability and quality of the measured
meteorological data were identified as one of the limitations. Given the sizes of the HPA

5
and the VTAPA – 3 600 km2
and 31 106 km2
respectively – (DEAT, 2007; DEA, 2012b)
and the proven various weather patterns (particularly in the HPA), the dispersion potentials
in these areas vary considerably (Nuwarinda, 2007; DEAT, 2007; DEA, 2012a). Because
of these variations, the expected performance of the model and the validation of the output
of the CALPUFF were not considered optimal. This study aims to investigate how the
dispersion model would perform for Leandra Township, located on the south-west of the
HPA. Leandra represents a township relatively isolated from the urban conglomerate of the
Witwatersrand and is also not too close to industrial activities. The study will also compare
the modelled time-series concentration and measured time-series for Leandra Township.
This will be achieved through the use of an AERMOD atmospheric dispersion model.
1.4 Importance of the study
The study is important because it will provide validation of AERMOD’s performance in a
South African context. The validation will provide a sound basis for decision-makers for
air quality offset projects, targeted at coal-burning communities and promoting healthy
communities. In addition, the results could provide a sound basis for a quantitative
prediction of the rate of uptake of the Basa njengo Magogo fire-lighting method (BnM),
towards attaining compliance with ambient air quality standards.
1.5 Aim and objectives
1.5.1 Aim
The aim of the study is to model emissions from a Leandra Township within the Highveld
Priority Area, in both the winter and summer, and to investigate and compare the modelled
time-series concentrations and monitored time-series data. This will be achieved through
the use of an AERMOD atmospheric dispersion model. This study focuses on an
investigation of the spatial and seasonal patterns of domestic emissions, and will not deal
with evaluating concentrations in relation to national ambient air quality standards.
1.5.2 Hypothesis
It is postulated that, by using a combination of monitored hourly PM10 concentrations and
dispersion modelling time series data, it is possible to calculate the effective emission rate
(g PM10 s-1
m-2
) for a typical Highveld township.

6
1.5.3 Objectives
The aim will be met by achieving the following objectives:
 to source ambient air quality monitored data ,for one year, from a surface station on
the Highveld close to an isolated Highveld township;
 to develop a diurnal emission model for the township from the monitored data for a
winter period;
 to develop a diurnal emission model for the township from the monitored data for a
summer period;
 to run an atmospheric dispersion model, with output set up to generate an hourly
time-series at a receptor site, selected as the location of the identified monitoring
station;
 to compare the time-series modelling results with the ambient air quality monitored
data, and to calculate an effective emissions factor and rate for the township (for a
typical township) by adjusting the emission factor so that the modelled and
monitored data match.
Based on the above introduction and objectives, a literature review of legal framework,
declared air-quality management priority areas, particulate matter, Basa njengo Magogo
fire lighting method and dispersion models was conducted.

7
2. Literature Review
2.1 Legislative framework
The constitution of South Africa contains the Bill of Rights, which is considered a key
milestone of democracy (DEAT, 2007). The environmental right, defined under the Bill of
Rights, , states (among other rights) that everyone in the Republic of South Africa should
live in an environment that is not harmful to their health and well-being (Constitution,
1996). Against this background, the government promulgates and implements
environmental legislations to give effect to this right.
In 2008, the National Environmental Management Act Number 107 (NEMA) was
promulgated as the framework and principle legislation to guide the management of the
environment. The principles defined under NEMA guide the interpretation, administration
and implementation of the Act and all the other laws or legislation concerned with the
protection or management of the environment in South Africa. Within this context, AQA
was promulgated in 2004 and replaced the Atmospheric Pollution Prevention Act 45 of
1965 (APPA).
APPA was replaced because it failed to set targets or standards would permit the
achievement of an environment that would not be harmful to the health and well-being of
South Africans (Scott, 2010). In 2006, DEAT revealed the Air Quality Management Act 39
of 2004 (AQA) which presented a distinct shift from an exclusively source based air
pollution control to a holistic and integrated based air quality management.
The objectives of AQA are to protect the environment by providing reasonable measures
for the prevention of air pollution and ecological degradation and securing ecologically
sustainable development while also promoting justifiable economic and social
development (AQA, 2004). AQA provides a number of air quality management measures
to address air pollution problems in South Africa. One of the management measures (set
out in Chapter 4 of the Act) is the declaration of national and or provincial priority areas.
The Act empowers the Minister to declare an area a national priority area and also
empowers the relevant Member of the Executive Council (MEC) to declare an area a
provincial priority area. An area is declared as a priority area if: either the Minister or the
MEC reasonably believes that the ambient air quality standards are being, or may be,
exceeded in the area; if any other situation exists which is causing, or may cause, a

8
significant negative impact on air quality in the area; and if the area requires specific air
quality management action to rectify the situation (AQA, 2004).
Following the declaration of a national priority area, both the national and provincial air
quality officers must develop an air quality management plan (to be approved by the
Minister) for implementation. The provincial air quality officer must do the same for a
provincial priority area. Before the Minister or MEC approves the air quality management
plan, a consultative process (as set out in Section 56 and 57 of AQA) must be followed.
Once the plan is approved, the Minister or MEC must publish it in the government gazette
within 90 days. The plan must aim at coordinating and addressing air quality management
issues in the area. Furthermore, the plan must make provision for the implementation of the
plan by a committee representing relevant stakeholders. The Minister or MEC, by notice in
the government gazette, can withdraw the declaration if the ambient air quality is found to
be in compliance with the ambient standards for at least two years (AQA, 2004).
2.2 Declared Air-Quality National Priority Areas
In April 2006 the Minister of the National Department of Environmental Affairs and
Tourism, (DEAT) declared the Vaal Triangle Airshed Priority Area (VTAPA) as the first
national priority area in South Africa (Figure 2). The VTAPA covers approximately
3 600 km2
, extending across the Free State and Gauteng Provinces and is contained within
Fezile Dabi and Sedibeng district municipalities (DEAT, 2006). The political boundaries
of local municipalities were used as boundaries for the priority area (Liebenberg-Enslin et
al., 2007). Within the VTAPA, Soweto was found to contain the highest population
density, followed by the Emfuleni Local Municipality (Liebenberg-Enslin et al., 2007).
Most of the households within these areas rely on coal, wood and paraffin as a primary
sources of energy. A study by Liebenberg-Enslin et al. (2007), which has been supported
by previous studies (Terblanche et al., 1992; Annegarn et al., 1999), found that more than
60% of air pollution in the townships emanates from coal burning during the winter
months. A priority pollutant of health concern associated with domestic coal and wood
burning is the particulate matter (DEAT, 2007). Figure 3 shows the source distribution of
particulate matter in the VTAPA, of which 5% is attributable to domestic burning. The
quantity (percentage) might appear insignificant, however because the low level exposure
in the townships is further compounded by poor dispersion during winter months, health
impacts are high (Scorgie et al., 2004; Annegarn et al., 1999).

9
Figure 2. A map of the Vaal Triangle Airshed Priority Area indicating included
provincial and municipal areas
(Source: Liebenberg-Enslin et al., 2007)

10
Figure 3. Sources contribution of inhalable particulate emissions in the VTAPA
(Source: Liebenberg-Enslin et al., 2007).
Table 2Table 1 indicates the number of exceedances for PM10 in the VTAPA compared
with the ambient air quality objectives (Liebenberg-Enslin et al., 2007). Generally,
domestic fuel burning intensities and related emissions during the winter seasons are found
to be high in the VTAPA (Figure 4). The ratio on Figure 4 is calculated based on the
estimated quantities of “heating-degree-days”. The heating-degree-days is a function of the
demand for residential space heating, which is directly linked to the amount of fuel
burning, and is strongly dependent on the minimum daily temperature (Annegarn &
Sithole, 1999). A thick haze of smoke, which reduces visibility, normally covers the coal
burning townships in the morning and evening throughout the winter season. The study by
Liebenberg-Enslin et al. (2007) confirmed the expected PM10 diurnal trends during a
winter month in VTAPA (Figure 5).

11
Table 1. Number of PM10 Exceedance of the 24-hour average ambient air quality
standard
Figure 4. Monthly variation of domestic fuel burning intensities and related
emissions generated

12
Figure 5. Diurnal variation of PM10 from VTAPA monitoring network
During the development of the VTAPA air quality management plan, the study made use
of the USEPA based CALPUFF dispersion model to predict which areas were most
affected by emissions. CALPUFF was selected as a suitable model, based on the size of the
area and its known performance in such areas. CALPUFF, because of its puff-based
formulations, is able to account for various effects, including spatial variability in
meteorological conditions, dry deposition and dispersion over a variety of spatial land
surfaces (Thomas, 2010). The simulation of plume fumigations and low wind speed
dispersion was also facilitated. CALMET was used to pre-process the hourly meteorology
data that was used in CALPUFF (Liebenberg-Enslin et al., 2007). The model
under-predicted ground daily and annual concentration of PM10 in Soweto and
over-predicted in areas falling outside the study area. Generally the model predicted well
for PM10 highest hourly and daily averages and under-predicted on annual average
concentrations when compared with the monitored data from the stations located within
other townships.
The VTAPA study experienced limitations; therefore, an assumption had to be made to
facilitate the performance of CALPUFF. The limitations included both the lack of accurate
and comprehensive ambient monitored data and the lack of an accurate emissions
inventory (Liebenberg-Enslin et al., 2007) –this should be understood within the context of

13
atmospheric dispersion model configuration. The models normally contain their own
inherent degree of uncertainty, ranging from -50% to 200% (Krause at el., 2008). The
uncertainty might relate to either a single issue or to the total sum of model physics, data
errors; and stochastic or turbulence in the atmosphere (Krause at el., 2008). Given the
limitations experienced in the VTAPA study and the inherent model uncertainty, it can be
expected that the input data may have compromised the model results.
Two years after the declaration of VTAPA, in 2007, the Highveld area was declared the
second national priority area. Similar to VTAPA the Highveld priority area is also
associated with poor ambient air quality and elevated concentrations of criteria pollutants
because of both industrial and non-industrial sources (Held et al., 1996). The priority area
(Figure 6) covers 31 106 km², includes parts of the Gauteng and Mpumalanga provinces;
the Ekurhuleni Metropolitan Municipality, three district municipality and nine local
municipalities lie within the priority area (DEA, 2012a).
Figure 6. Highveld priority map indicating surrounding provinces and
municipality
(Source: DEA, 2012a)
Coal is an important source of energy in the Highveld townships, where it is extensively
used for domestic purposes (Balmer, 2007). Although the majority of coal is used on the
Highveld for electricity generation by the national utility, Eskom, a high number of

14
households (especially those close to coal mines) record high coal consumption (Balmer,
2007). Often the cheapest and lowest grade available, with higher levels of impurities, is
used (Scorgie et al., 2004). Household coal use is estimated to be 3% of the total coal
consumption in South Africa, and an estimated 950 000 households use coal as the main
household energy source (Balmer, 2007).
The Highveld region has been the subject of studies – for the last 30 years– focusing on the
state of the region ambient air quality. These studies were largely because of presence of
coal reserves and the related industrial development. The Tyson et al. (1988) study found
high concentrations of SO2 in the area. Another similar study, by Held et al. (1996), found
a link between the poor ambient air quality in the area and potentially negative health
impacts. The poor overall ambient air quality was detailed by Scorgie et al. (2004) in the
Fund for Research into Industrial Development Growth and Equity (FRIDGE) study. A
study by DEA (2012a) in the HPA estimated that the total annual emissions of PM10 is
279 630 tons, of which approximately half is attributed to dust entrainment from the open
cast mine haul roads (Table 2). Approximately 6% can be attributed to domestic fuel
burning. As applies to the VTAPA, most townships in the HPA are also regarded as
priority zones because of the prevalent exceedances of ambient air quality standards (DEA,
2012a).
Table 2. Distribution of PM10 per sector in the HPA
(Source: DEA, 2012a)
Unlike in the VTAPA study, the HPA had a better representation in terms of ambient air
quality monitored data. This was because of the availability of data from the monitoring
network operated and maintained by major industries (such as Eskom and Sasol) (DEA,

15
2012a). The emission inventory for emissions sources was relatively complete and
Geographic Information System (GIS) based emissions quantification was used to resolve
emissions data gaps for the dispersion modelling (DEA, 2012a).
The atmospheric dispersion simulations were conducted, also using the CALPUFF model.
Domestic emissions were modelled as an area source, with temporal profiles to account for
the daily and seasonal variations (DEA, 2012a). The Index of Agreement (IOA) was used
to measure how well the CALPUFF model performed when compared with monitored data
(DEA, 2012a). The IOA provided a more consistent measure of model performance than
the correlation coefficient (Hurley, 2000). The modelled PM10 did not compare well with
the monitored data, and was consistently under-predicted across the stations (DEA, 2012a).
In June 2012, the Minister of Environmental Affairs declared the Waterberg-Bojanala area
as the third national priority area, based on a proactive and preventative approach of the
AQA and NEMA. The area within the boundary of the priority area lies in both the
Limpopo and North-West province. Several parts of the priority area (such as the
Waterberg district municipality in Limpopo Province) are currently regarded as pristine
environment (C&M, 2013). However, based on the planned energy industrial
developments in the areas and the potential trans boundary air pollution impacts between
the neighbouring country Botswana and South Africa, the Minister believed the air quality
standards may be exceeded in the near future (DEA, 2012b). The air quality management
plan development is in process at the time of writing this report.
In both the studies of the VTAPA and HPA, CALPUFF did not predict the daily and
annual PM10 average concentration from the domestic sources in line with the monitored
concentrations. This could be because of a number of factors, which were also discussed in
page 11. However the question remains: How would a USEPA approved dispersion model
perform in an isolated township? This study aims to answer that question, focusing on
domestic emissions from Leandra Township, located in the HPA.
2.3 Particulate matter
Particulate matter is emitted into the atmosphere by a number of anthropogenic and natural
sources. Major sources of particulate matter in the townships include: domestic coal
burning for space heating and cooking; road dust; and imported regional aerosols. Other
sources include: refuse burning and mine tailings. Particulates emanating from these

16
sources vary by season and by particle size (Annegarn & Sithole, 1999). Particulate matter
can be divided into three classes: total suspended particulates (TSP), PM10 (particles with a
diameter ≤10 µm), and PM2.5 (diameter ≤ 2.5 µm).
A number of studies (Dockery & Pope, 1996; Scorgie et al. 2004) have demonstrated that
atmospheric particulate matter in urban areas is linked to the daily number of
hospitalisations and deaths due to pulmonary and cardiac diseases. These studies showed
that measurements of thoracic and alveolar particles (PM10 and PM2.5) correlated well with
morbidity and mortality. PM10 is not only dangerous because of its inorganic chemistry but
also because of the complex organic materials it contains. These materials include:
benzene; 1-3 butadiene; polychlorinated biphenyl and polynuclear aromatic hydrocarbons,
all of which are known carcinogens (Holgate, 1999). In addition to the health effects in
humans, particulate matter has also been found to have an impact on the environment.
PM10 is a significant source of haze and its deposition in buildings is known to be a public
nuisance (Annegarn & Sithole, 1999; Scorgie et al., 2004). Larger particles which have
settled on water bodies, also change the acidity and nutrient balance in these environments,
which in turn impacts on the ecosystem (Thomas, 2010).
In response to the obvious health risks particulate matter causes, ambient air quality
standards for PM10 and PM2.5 have been set by DEAT (Table 3 and Table 4). These
involve daily and annual average concentrations and compliance dates. In recognition of
the health risks and the need for stricter standards, the DEA included ‘reduced limit’
targets, to come into effect in stages, starting from 2015 and continuing until 2030. The
ambient air quality standards for PM2.5 were introduced in 2012 and no monitored data is
readily available yet, therefore this study will focus on only PM10.
Table 3. Ambient air quality standards of PM10 for South Africa
(Source: DEAT, 2009b)

17
Table 4. Ambient air quality standards of PM2.5 for South Africa
(Source: DEA, 2012c)
2.4 Basa njengo Magogo method
A number of unsuccessful attempts, since 1960s, have been made in South Africa to
address domestic emissions from townships (Van Niekerk, 2006). In this study Van
Niekerk (2006) further indicated that attempts were technology driven and focused on
particular aspects (e.g. low-smoke stoves and electrifications). A breakthrough was
achieved in 1999 after the NOVA Institute, supported by Sasol Synfuels, piloted and
successfully introduced the Basa njengo Magogo (BnM) fire-lighting method to the
eMbalenhle community near Secunda (Van Niekerk & Swanepoel, 1999). The primary aim
of the project was to reduce the exposure of women and children to indoor air pollution to
in settlements relying on combustion fuels for domestic cooking and heating. The method
was initially called Basa Magogo (a translation of the Zulu words for ‘making fire’ and
‘grandmother’); after being perfected by Granny Mashinini, the method was renamed BnM
in her honour (Wagner et al., 2005). Figure 7 shows Granny Mashinini indicating BnM
fire-lighting method. It is not entirely a new invention—the upside-down fire-lighting
method was promoted in Soweto during the mid-eighties, and was known at the time as the
Scotch fire-lighting method (Annegarn, 2009 personal communication).

18
Figure 7. Granny Mashinini indicating BnM fire-lighting steps
The BnM method replaced the “classical method of fire lighting”. In the classical fire
lighting method (or bottom up approach) semi-volatile emissions from the heated coal rise
through the colder coal above, condense into droplets and escape to the atmosphere. The
condensed droplets cause a dense white plume of smoke (Figure 8). The BnM method has
wide range of environmental and social benefits when compared with the “classical
method of fire lighting”. Figure 9 illustrates the visual difference and the associated
advantages between the BnM method and the “classical method of fire lighting”.
Figure 8. Comparison of traditional bottom-lit and BnM top-down fire lighting
methodologies
(Source: DME, 2003)

19
Figure 9. Photograph of the emissions from BnM imbawula on the front right
and a classical bottom-lit imbawula on the back left
(Photo: Prince Mkhonto)
To validate the findings of largely qualitative studies, the Council for Scientific and
Industrial Research (CSIR) conducted an experiment, under controlled laboratory
conditions, to gather quantitative data on the reduction in particulate emissions associated
with the BnM fire-lighting method. The study found that the particulate emissions from
BnM average 87% less than the emissions from conventional method (Le Roux et al.,
2005).
Various studies reported a wide range of benefits associated with BnM implementation.
Schoonraad and Swanepoel (2003), in their survey of BnM at the Harry Gwala informal
settlement, found that a coal saving was recorded at 70 kg per winter month. Findings from
similar studies (Van Niekerk & Swanepoel, 1999; Wentze, 2006; Balmer, 2007) identified
the following benefits as being directly associated with the BnM fire-lighting method:
 Environmental – the method reduced the ambient air pollution caused by the use of
household coal in a relatively short space of time, with between 80% to 87% less
particulate matter being emitted (when compared with the conventional method).
 Financial benefits – The household savings of coal consumption of between 20 and
50%.

20
 Health benefits – social benefit of reduced respiratory diseases and consequent
savings in the health care cost carried by the economy overall (associated with air
pollution).
The BnM method has been implemented since early 1999 in different areas. These areas
include: eMbalenhle in the Mpumalanga province; Zamdela in the Free State Province;
Orange Farm in Gauteng Province (DME, 2005). To date, annual campaigns in winter
season have been implemented in different informal settlements and townships. The
campaigns involve the use of scarce resources (in terms of time, finances and human input)
and hence, despite the benefits, the BnM is still not implemented continuously and
consistently.
2.5 Atmospheric dispersion modelling
Atmospheric dispersion models are mathematical simulations of the physics and chemistry
governing the transport, dispersion and transformation of pollutants from their source/s to
the receiving environment (Bluett et al., 2004). Atmospheric dispersion models can also be
defined as a means to estimate downwind impacts, given the pollutant sources physical
parameters, emissions rate, local topography and meteorology of the area (Peavy et al.,
1985). In South Africa, as in many developed countries, authorities are increasingly relying
on atmospheric dispersion models as a means to evaluate various emission control
strategies (DEA, 2012d).
Most modern atmospheric dispersion models are computer-based programs. Figure 10
shows the overview of the standard steps and the dataset required to successfully set up
and run the atmospheric dispersion model (Bluett et al., 2004). As indicated in Figure 10,
meteorology is fundamental for the dispersion of pollutants, because it is the primary factor
in determining the diluting effect of the atmosphere (Peavy et al., 1985). In addition,
meteorology is thought to be at the heart of the relationship between air pollution and
health in that any variation in the physical and dynamic properties of the atmosphere, on
time scales from hours to days, can play a major role in influencing air quality (Holgate et
al., 1999). The ground-level concentrations, resulting from a discharge of pollutants,
change according to the weather – particularly prevailing wind conditions. Meteorology
conditions, by controlling the reaction rates, also influences the chemical and physical
process involved in the formation of a variety of secondary pollutants (Bluett et al., 2004).
Any changes in weather could influence emissions whether it is at the onset of cold or

21
warm spells or resulting from increases or decreases in heating and cooling needs (Holgate
et al., 1999; Kastner & Rotach, 2004). It is, therefore, important for meteorology to be
carefully considered when modelling is performed.
Figure 10. Overview of air pollution modelling procedure
(Source: Bluett, 2004)
To date the most commonly used atmospheric dispersion models are steady-state Gaussian
plume models. They are based on a mathematical approximation of plume behaviour and
are the easiest models to use (Thomas, 2010). More recently, better ways of describing the
spatially varying turbulence and diffusion characteristics within the atmosphere have been
developed (Perry et al., 2004). The “new generation” dispersion models adopt a more
sophisticated approach to describing diffusion and dispersion, using the fundamental
properties of the atmosphere rather than relying on general mathematical approximations
(Bluett et al., 2004). This enables better treatment of difficult situation – such as complex
terrain and long-distance transportation of pollutants (Perry et al., 2004).
The atmospheric dispersion models have inherent performance limitations. Even the most
sophisticated models cannot predict the precise location, magnitude and timing of
ground-level concentrations with 100% accuracy (Bluett et al., 2004). However, most
models used today, especially the USEPA approved models, have been through a thorough

22
model evaluations process and the modelling results are reasonably accurate, provided the
appropriate model and input data are used (Krause et al., 2008).
One of the key elements of an effective dispersion modelling study is choosing an
appropriate model to match the scale of impact and complexity of a particular emissions
release (Hall et al., 2002). In their study, Bluett et al. (2004) indicated the two principal
issues to consider when choosing the most appropriate model are: terrain and meteorology
effects; and human health and amenity effects. Most developed countries use the following
models for regulatory purposes: Gaussian plume models (such as AUSPLUME, USEPA
ISCST3, USEPA approved AERMOD and CTMPLUS); and advanced models (such as
CALPUFF and TAPM) (Bluett et al., 2004).
Figure 11 illustrates the types of models typically applied to particular scenarios,
dependent on their scale and complexity (Bluett et al., 2004). The width of the band
associated with each model type is roughly proportional to the number of modellers
currently using that particular type. In medium complexity atmospheric and topographical
conditions, Gaussian plume models can produce reliable results. In highly complex
atmospheric and topographical conditions, advanced puff and particulate models and
meteorological modelling may be required to achieve a similar degree of accuracy (Hall et
al., 2002). In choosing the most appropriate model it is important to understand the model
limitations and apply it in scenarios that match its capabilities (Bluett et al., 2004). The
USEPA approved AERMOD model was selected as the appropriate model for this study.
Figure 11. Type of models typical applied depending on problem
(Source: Bluett, 2004)

23
2.5.1 AERMOD dispersion model
AERMOD was developed in 1995, by the American Meteorological Society and
Environmental Protection Agency, further reviewed in 1998 and formally replaced the
ISCST3 in 2000 as a preferred regulatory model (Venkatram, 2008). It is an advanced
dispersion model because it has a better capacity for dealing with a more complex
meteorological dataset (Cimorelli et al., 2003). One of the major improvements that
AERMOD has brought to the applied dispersion modelling is that it takes into accounts the
meander effects on coherent plume in stable condition with current state-of-the-art
planetary boundary layer parameterisation (Perry et al., 2004).
The AERMOD modelling system consists of the model itself (AERMOD) and two
stand-alone input data pre-processors: the meteorological pre-processor (AERMET) and
terrain pre-processor (AERMAP) (Venkatram, 2008). Figure 12 indicates the data flow and
processing stages in the AERMOD modelling system. The main purpose of AERMET is to
provide the meteorological pre-processor with available meteorological data for organising
into a format suitable for use by the AERMOD (USEPA, 2004a). In addition, the
AERMAP pre-processor characterises the terrain and general receptor grids for the
AERMOD dispersion model (Perry et al., 2004).
Figure 12. Data flow into AERMOD modelling system
(Source: USEPA, 2004a)
AERMOD is a “near field, steady-state guideline model” in that it assumes that
concentrations at all distances during a modelled hour are governed by a set of hourly
meteorological inputs, which are held constant (Cimorelli et al., 1994). Using available
meteorological data and similarity theory scaling relationships, AERMOD constructs

24
hourly gridded vertical profiles of required meteorological variables: including wind speed;
wind direction; potential temperature; and vertical and horizontal turbulences (which are
used by the model to calculate plume rise, as well as transport and dispersion of each
plume) (Perry et al., 2004). Furthermore, the AERMOD model uses hourly sequential
pre-processed meteorological data to estimate concentrations at receptor points for
averaging times ( i.e. ranging from one hour to many years) (Cimorelli et al., 2003).
In their study, Hanna et al. (1999) stated that AERMOD uses a relatively simple approach
that incorporates the current concepts about flow and dispersion in the complex terrain.
Where appropriate the plume is modelled as either impacting and or following the terrain.
This approach has been designed to be physically realistic and simple to implement, while
avoiding the need to distinguish between simple, intermediate and complex terrains (as
required by other regulatory models) (Perry et al., 2004). Based on an advanced
characterisation of both the atmospheric boundary layer turbulence structure and the
scaling concepts, the model is applicable to rural and urban areas, flat and complex terrain,
surface and elevated releases, and multiple sources (including point and area sources)
(Perry et al., 2004) – hence its suitability for this study.
The AERMOD model is capable of handling multiple sources, including point, area and
volume sources types (USEPA, 2004b). Several source groups may be specified in a single
run, with the source contribution combined for each group. The model contains algorithms
for modelling the effects of aerodynamic downwash from nearby buildings on point source
emissions (USEPA, 2004b). Source emissions rates can be treated as constant throughout
the modelling period, or may be varied by month, season, hour-of-day, or other optional
periods of variation. The variable emissions rates factors may be specified for a single
source or for a group of sources. The user may also specify a separate file of hourly
emissions rates for part or all of the sources included in a particular model run (USEPA,
2004b). The limitation of the AERMOD is that spatial varying wind fields, caused by
topography or other factors, cannot be included. The range of uncertainty of the model
predictions could be between -50% to 200% (Krause et al., 2008). In their study, Krause et
al. (2008) also stated that AERMOD prediction accuracy improved with strong winds and
during calm atmospheric conditions. Further, the study pointed out that the model was
designed for the US environment; various difficulties were experienced when compiling
the AERMET required dataset in South Africa. The main data shortfalls identified were:
lack of national meteorological dataset; limited upper air data; and surface meteorological

25
stations seldom monitor all the required parameters (such as solar radiation, cloud cover
and humidity).
2.6 Emissions factors
Emissions factors are regarded as one of the fundamental tools in air quality management;
in the sense that they are used to develop emissions control strategies, ascertaining the
effects of sources and the associated mitigation strategies (USEPA, 2009). In both the
VTAPA and HPA studies, emissions rates were calculated from the emissions factors
given (Table 5); the number of households was sourced from the Census 2001 and the
quantity of fuel consumed was calculated based on the existing literature
(Liebenberg-Enslin et al., 2007; DEA, 2012a). The data used to calculate the emissions
rates were obtained from different and varied sources. The number and types of households
and fuel-uses vary from community to community. This variability of data sources has a
potential to negatively influence (i.e. cause inaccurate) emissions analyses. This study used
the combination of monitored hourly PM10 concentrations and dispersion modelling time
series data to calculate site specific emissions rate to mitigate against the model’s
limitations.
Table 5. Emissions factors of coal, paraffin and wood burning in household
(Source: Liebenberg-Enslin et al., 2007)

26
3. Study Methodology
The study aimed to model emissions from an isolated township within a national declared
priority area for two months, one month in the winter and another one month period in the
summer. This would be achieved through the use of AERMOD atmospheric dispersion
model. A set of surface and upper meteorological measured data would be obtained and the
site-specific emissions factors calculated. The monitored meteorology data and emissions
factors were required to successfully setup and run the atmospheric dispersion model. The
study also investigated and compared the modelled time-series and monitored time-series
data. This chapter discusses the study area, all the dispersion model required input datasets
and monitored data.
3.1 Study area
The Highveld Priority Area (HPA) includes parts of Gauteng and Mpumalanga provinces,
with Ekurhuleni metropolitan municipality, and three District Municipalities: Gert Sibande
(including the local Municipalities of Govan Mbeki, Dipaliseng, Lekwa, Msukaligwa and
Pixley ka Seme); Sedibeng (includes the Lesedi local municipality) and Nkangala
(including the Delmas, Emalahleni and Steve Tshwete local municipalities). Leandra town
is located close to the centre of the HPA in the Govan Mbeki local municipality in
Mpumalanga Province (Figure 13). Leandra is representative of a township relatively
isolated from the urban conglomerate of the Witwatersrand and also not too close to the
industrial activities – hence the selection as an appropriate study area. The Sasol Synfuels
Complex, Secunda, is located ~4 km to the east. The only observed commercial activities
in the areas are informal car repairs and panel beaters. Leandra is approximately 8 km2
in
area, with 8 892 households including shacks based on Census 2011 (StatsSA 2011). The
majority of the residential units consist of single dwellings with an average size of the
350 m2
(GMLM, 2006). Census 2011 found that 61% of the houses and shacks are
electrified, 49% of the household still rely on coal, paraffin, animal dung and wood as the
main source of heat and cooking. This percentage is expected to be higher during winter
season.

27
Figure 13. Location of Leandra in the HPA
3.2 Monitored data
3.2.1 Surface meteorology data
The Leandra air quality monitoring station has been in operation since 1992 and is used
primarily to monitor compliance with the ambient air quality standards. The station is
operated and maintained by Eskom. It is located at latitude 28 55’ 58.9” E and longitude
26 22’ 01.1” S, ~800 m from the township (Figure 14). The station continuously monitors
and records meteorology parameters (wind speed, wind direction, ambient temperature,
rainfall and relative humidity) and the ambient concentration of PM10 and SO2. The data
sets are available in hourly values. The station has been receiving ad hoc maintenance
attention from Eskom and, as a result part of the data set is suspect. 80% of the
meteorology data set, which was made available, was suspect and it was decided that the
dataset should not be used for input into the model. Instead the study obtained a
meteorological dataset from the Kendal air quality monitoring station, located
approximately 45 km north of Leandra. This dataset was also incomplete and also deemed
unsuitable for use.

28
Figure 14. Location of the Leandra air quality monitoring station (yellow pin)
The study used the meteorology datasets from the Langverwacht station in Secunda. The
Langverwacht air quality station is situated ~9 km to the west of the Sasol Synfuels
Complex, approximately 45 km east of Leandra – the GPS location of the station is
26º33.5" S, 29º06.45" E (Figure 15). The station is operated and maintained by Sasol and
is SANAS accredited. The station is used primarily to monitor compliance with the
ambient air quality standards of pollutants associated with the Sasol Synfuels Complex
operations. The weather patterns between Secunda and Leandra were not expected to be
significantly different. The two areas fall within the Mpumalanga Highveld and share
typical atmospheric weather patterns. Because of its reliability and SANAS accreditation,
it was considered justified to use the data to set up the AERMOD and to simulate the
emissions for Leandra Township.

29
Figure 15. Location of Langverwacht station in Secunda (red dot)
At the Langverwacht ambient air quality monitoring station, the wind speed is measured
using a sensor which has a four-bladed helicoid propeller. When the propeller rotates it
produces an AC sine wave voltage signal, which the station computer converts into a
numerical number and this number is then recorded. The propeller has a threshold
sensitivity of 1 m s-1
or 3.6 km h-1
. Wind direction is also measured through a sensor, a
rugged yet lightweight vane. Vane angle is sensed by a precision potentiometer. The
potentiometer generates a voltage that the station computer program processes into a
reading of angular displacement. Relative humidity and temperature are measured by a
single probe sensor. The dry-bulb thermometer of the sensor indicates the temperature of
the air; the wet-bulb thermometer measures the cooling caused by the evaporation of the
moisture on the bulb (Sasol, 2009).
The meteorological dataset was obtained and then screened for quality. Any suspect data
were removed (e.g. out of range for angular data, or negative values of velocity). The
July 2008 and October 2008 datasets were found to have more than 80% available, which
were the best two months during the 2008 monitoring period. Data gaps were caused by
equipment failures and unplanned power outages. The July 2008 and October 2008 data
were then selected and considered adequate for the model and the study.

30
3.2.2 Upper air data
The upper air data were obtained from the closest monitoring station – located in Irene,
Pretoria sited at +28°12´37.2” E and -25°54´39.6” S. The station is operated and
maintained by the South African Weather Service (SAWS). The station monitors pressure,
air temperature, humidity and both wind speed and wind direction. The parameters are
measured every 10 s using a radiosonde, a data receiver/digitiser and an antenna. The data
measuring system is manually operated and the devices are synchronised using satellite
signals. The antenna uses the data transmitting frequency 403 MHz. The data receiver and
digitiser also act as a radio receiver for the signal from the antenna. The measuring system
also includes an analogue to digital converter.
For ascending, the radiosonde is attached to a balloon, filled with hydrogen. The rate of
ascent is set at 360 m min-1
. Radiosondes are powered by a small battery (6 V up to 18 V),
which is well insulated with polystyrene so it can operate at extremely low temperatures.
The data sequence is then synchronised with the data sequence of the radiosonde, by
recognising the START and STOP signals. The remainder of the sequences are then
allocated to the correct channels by the processor. The pressure, temperature and humidity
values are calculated from their respective signals. The wind speed and direction
information, are calculated from the GPS values. These values are stored in the memory, in
a specific format.
According to SAWS procedures, the operator follows a strict safe working practice for
filling the balloon with hydrogen (a hazardous gas). SAWS has developed and
implemented procedures that comply with the guidelines and standards issued by the
World Meteorology Organization. The SAWS website indicates that the data management
systems also comply with the requirements of ISO 9001:2008 quality standards.
3.2.3 Ambient monitored PM10
Ambient ground concentrations for PM10 were sourced from the Leandra ambient air
quality monitoring station for validation of the AERMOD simulated concentration. PM10 is
monitored using an ambient continuous monitor tapered element oscillating microbalance
(TEOM Model 1400a) in real-time. The TEOM monitor incorporates an inertial balance
that directly measures the mass collected on an exchangeable filter cartridge by monitoring
the corresponding frequency changes of a tapered element. The sample flows through the

31
filter, where PM10 is collected, and then continues through the hollow tapered element on
its way to an active volumetric flow control system and vacuum pump. TEOM contains a
module that monitors and records sampling flow rate, filter mass measurements, ambient
temperature and barometric pressure measurements. The TEOM mass transducer does not
require recalibration because it is constructed from non-fatiguing materials. Its mass
calibration may be verified, using Mass Calibration Verification Kit that contain filter of
known mass. Active volumetric flow is maintained by mass flow controllers whose set
points are constantly adjusted in accordance with the measured ambient temperature and
pressure.
Figure 16. Continuous PM10 monitor TEOM 1400a
3.3 Data requirements and dispersion simulation
3.3.1 AERMET pre-processor
The surface meteorology data (wind speed, wind direction, relative humidity and
temperature) and the upper air data (pressure, wind speed, wind direction and air
temperature) were available in a Microsoft Excel spreadsheet, in an hourly format, for the
two 2008 monitoring periods. Standard deviation, cloud cover and ceiling height were
calculated. Standard deviation was calculated based on measured wind direction and solar
radiation. Cloud cover measurements were based on the ratio of the measured solar
radiation and calculated solar radiation. For the days with cloud cover, the ceiling height

32
was assumed to be 2 000 ft above ground at the mid-level of the cumulus clouds associated
with South African thunderstorms. The data were screened and any suspect data replaced
with 9999, a model default value, then used for the AERMET pre-processor model. After
the hourly surface and upper data files were uploaded onto AERMET, the associated
Geographical Points System coordinates in Latitude and Longitude, and the base elevation
expressed in meters were specified. The coordinates were sourced from the Google Earth
system. The hourly surface data files were formatted using AERMET SCRAM option
(MET144) and the upper air data file using the NCDC TD-6201 fixed length. AERMET
was run and output files generated for AERMOD.
3.3.2 Source data requirements
The Google Earth map of Leandra was uploaded into AERMOD as a base map. Using the
model tools the area was divided into four polygons area sources (Figure 17). AERMOD
automatically specified the X and Y coordinates and calculated sizes for each polygon. The
use of four polygons area sources was preferred to using a single polygon for the entire
area to facilitate and better account for the emissions from domestic burning. Release
height was specified at 3 m. To calculate the emission factors and rate, the following steps
were followed:
 a month when the air quality monitoring station was predominantly receiving wind
from the township was selected – July 2008;
 diurnal average concentrations were generated (Figure 18) and the normalised mean
diurnal variation was used as a model for the hourly domestic emission factors;
 monitored and modelled concentrations were compared to the determine the
emissions factors and rate;
 the hourly variable emission factors were calculated (Table 6) (the emission factor
was a multiplier of the emission rate determined for Leandra).
 the emission rate was used as an adjustable parameter to modify modelling output
concentrations to match the monitored concentrations. (The determined effective
emission rate1
for Leandra is 0.3 g PM10 s-1
m -2
.)
1
This emission factor could vary, depending on the density of houses (which could be determined from
satellite images or airborne remote sensing images by counting the number of dwellings). This further
calculation was outside the scope of this dissertation.

33
Figure 17. Leandra area sources specified in AERMOD
Figure 18. PM10 mean diurnal variation used to calculate emissions factors
Actual emissions (AE) for evening fires (18:00 to 24:00) were calculated as:
(1)

34
Similarly for morning fires AEam
(2)
But as each fire was the same for each burn, it was therefore assumed that fewer fires had
been lit in the mornings.
(3)
(4)
Table 6 (next page) shows the Variable emissions factors by hours of the day.
Table 6. Variable emissions factors by hour of day

35
3.3.3 Modelling domain
Firstly, the PM10 was simulated with the location of the air quality monitoring station
indicated as a single discrete receptor. Building influences were ignored and flat terrain
specified. Secondly, the uniform Cartesian grid receptor network was selected over the
entire area to plot the contours of the simulated ground PM10 concentration. The uniform
Cartesian grid receptor network covered a length of 9 942 m on the X Axis and 5 213 m on
the Y Axis, with 441 receptors. AERMOD simulated the ground PM10 concentration for
each of the gridded points.
3.3.4 Building downwash consideration
Building heights were not taken into account in the dispersion setup because of the low
potential for building down-wash effects in the area. The height of the imbawula and stove
chimneys are low and the sizes of the chimneys and houses are relatively equal – for this
reason the houses will not interfere with the air flow characteristics and therefore do not
cause building down-wash effects.
3.3.5 AERMOD dispersion model
AERMOD was setup using the surface and upper air output files generated by AERMET.
The projection parameters were set up on Universal Transverse Mercator (UTM), zone 35
for South Africa, datum set on World Geodetic System 1984. The geophysical parameters
for the area were obtained from the Google Earth system and specified in the model. The
five default wind speed categories were used. The default categories were 1.54; 3.09; 5.14;
8.23 and 10.8 m s-1
. The model was set up for the hourly runs for July 2008, using variable
emissions rates to allow for the diurnal variations. For October 2008, the emissions
strength was reduced to 0.1 g PM10 s-1
m-2
to tallow for seasonal emission variations (lower
in summer because there is no heating demand). The model output file was generated for
analysis.
3.4 Strengths and shortcoming of the data
The meteorology data from the Leandra air quality monitoring station contained a high
proportion of missing data, and was considered inadequate for the dispersion model. The
study then used meteorology data obtained from the SANAS accredited Langverwacht air
quality monitoring station, situated in Secunda, approximately 45 km east of Leandra.
Although Leandra and Secunda experience typical Mpumalanga Highveld atmospheric

36
weather patterns, a possibility of marginal difference still exists. The upper air data used as
an input into the model was sourced from the nearest station, situated in Irene,
approximately 100 km away from Leandra. The station is SANAS accredited and well
maintained by SAWS and the data is regarded as credible. However, the longer distance
between two areas means variable atmospheric patterns between the two points – this can
be regarded as a shortcoming.
The Leandra air quality monitoring station has undergone ad hoc external calibration with
break-down challenges. However, the monitored ground concentration for PM10 data for
July and October 2008 is regarded as credible and what could be expected from a domestic
coal burning township. The emissions factors were calculated from the monitored PM10
concentration. This can be considered as strength, since the data used were site specific.

37
4. Results and Discussions
4.1 Monitored meteorology
4.1.1 Local wind fields
To characterise the dispersion potential of Leandra Township, reference was made to
hourly average meteorological data recorded at the Langverwacht station during the study
periods, July 2008 and October 2008. Parameters taken into account in the characterisation
of the dispersion potential include: wind speed; wind direction; and ambient air
temperature. Three wind roses for: (i) the overall July 2008 period; (ii) the day-time and
(iii) the night-time – are shown in Figure 19, Figure 20 and Figure 21 respectively. The
wind roses are comprised of 16 spokes, each representing the direction from which the
wind blew during the period recorded. The colours indicate the categories of wind speed.
The dotted circles provide information regarding the frequency of occurrence of wind
speed and direction categories. In the wind roses in Figure 19 and Figure 20, each dotted
circle represents a 3% frequency of occurrence and in Figure 21 the circles represent 4%
frequency. The figures indicated in the centre of the circle describe the frequency of calms
occurred – i.e. periods during which the wind speed was below 1 m s-1
.
Figure 19. July period-wind rose

38
Figure 20. July day-time wind rose
Figure 21. July night-time wind rose
In July 2008 the prevailing wind directions were westerly, south-westerly, north-westerly.
Wind speed at or higher than 8 m s-1
were mainly from the north-west and south-west.
Calm conditions occurred for 4% of the time. During July 2008 the diurnal air flow for the
area was characterised mainly by variations in north-westerly, westerly and south-western
winds. North-westerly, westerly and south-westerly dominated day times and
south-westerly night-times. The night-time domination meant the air quality monitoring

39
station was able to measure most of the emissions from the township during the evening
domestic burning peak hours. During the night-time, there was significant decrease in the
frequency of wind occurrence from the south-west and an increase in frequency of wind
occurrence from north-east – a variation of approximately 16%.
Wind roses for October 2008, during (i) the overall period, (ii) the day-time and (iii) the
night-time are shown in Figure 22, Figure 23 and Figure 24 respectively. In October 2008,
the prevailing wind directions were north-westerly and north-easterly. Winds speeds at or
higher than 8 m s-1
were mainly from the north-west and north-east. October 2008
experienced 0.7% calm conditions, with the average wind speed of 4.2 m s-1
– compared
with July 2008 when 5.9% calm conditions were experienced with an average wind speed
of 2.3 m s-1
. The diurnal air-flow variation was quite evident, mainly between
north-westerly and north-easterly. Fewer emissions were monitored at the air quality
monitoring station.
Figure 22. October period-wind rose

40
Figure 23. October day-time wind rose
Figure 24. October night-time wind rose
4.1.2 Temperature
Within the atmospheric science context, air temperature assists in both determining the
effects of plume buoyancy (the larger the temperature difference between the plume and
the ambient air, the higher the plume is able to rise), and in following the development of
the mixing and inversion layers (Krause et al., 2008). In addition, the temperature provides
a direct indication of a number of households likely to burn coal and wood for heating and

41
cooking. Figure 25 shows the contrasting ambient hourly average temperature, between
July 2008 and October 2008, measured at Langverwacht air quality monitoring station. The
lowest average hourly temperature of 4°C was measured at 08:00 on a morning in July.
This cold resulted in an increased in the amount of coal and wood burned for morning
domestic activities. The highest midday temperature in winter was measured at 21°C
during the day – compared with 27°C in October. Given the times, these temperatures did
not have a major influence on emissions levels because most people were at work or
school. As expected, July was a much colder month than October. July experienced daily
average temperatures of approximately 10°C; October experienced temperatures averaging
~20°C (Figure 26). With the lower winter temperatures many households can be expected
to burn more coal and wood for heating and cooking purposes than the quantities used in
summer. Households mainly use imbawula to burn coal. The imbawula tend to be poorly
designed and the emissions temperature is usually not high enough to encourage the plume
to disperse far from the source.
Figure 25. July and October 2008 hourly average temperature

42
Figure 26. July (lower) and October (upper) 2008 daily average temperature
4.2 Ambient monitored PM10 concentration
Coal is combusted using a home-made imbawula. The fires are initiated outside the houses
and, once the most of the coal and few pieces of wood have caught fire, the imbawula is
brought inside the houses for cooking and heating. This activity is the main source of air
pollution in Leandra. During cold weather, particularly in the evenings, the area
experiences low inversions and all habitants will be exposed to domestic emissions – even
if they are not burning imbawula within their own households. Furthermore unpaved, dusty
roads within the township contribute to poor air quality during windy seasons.
Elevated levels of pollution are known to occur in townships, particularly during winter
months and at lower levels in summer (Liebenberg-Enslin et al., 2007). A similar trend of
PM10 concentration was recorded by Leandra air quality station during the study period.
Figure 27 indicates the diurnal variations measured at the Leandra air quality monitoring
station. The highest average hourly concentration for PM10 was measured at 255 µg Nm-3
.
The ambient daily ambient air quality standard for PM10 was exceeded 19% of the time,
with highest measured at 242 µg Nm-3
on 18 July 2008 (Figure 28). The highest hourly
average concentration for PM10 in October 2008 was measured at 74 µg Nm-3
(Figure 28).
Figure 29 indicates the daily concentration of PM10 for October 2008 did not exceed the
daily ambient air quality standard. However, the concentrations measured in October are
still considered high for township during a summer month. This indicates emissions from
other contributing sources originated east of Leandra.

43
Figure 27. July monitored diurnal PM10 hourly averages
Figure 28. July monitored PM10 daily average concentration

44
Figure 29. October monitored diurnal PM10 hourly averages
Figure 30. October monitored PM10 daily average concentration
4.3 AERMOD dispersion model results
The AERMOD dispersion model was undertaken to predict the second highest hourly and
daily ground levels average concentration for PM10 during July and October 2008. (The
highest was considered to be further from reality.) Figure 31 shows the predicted PM10
diurnal concentrations for July 2008, using the Leandra air quality monitoring station as
the single discrete receptor. The model predicted the typical diurnal variations associated
with domestic emissions from a township during the winter months. During the period
between 09:00 and 16:00 in July, the model predicted zero. The second highest hourly
average ground level concentration for PM10 in July was 250 µg Nm-3
. This is considered
to be within the expected range of domestic emissions from the township. On the daily

45
average concentration, the model predicted the highest concentration at 210 µg Nm-3
(Figure 32). The model performance in July is considered to be within the expected range.
Figure 31. July modelled PM10 hourly average
Figure 32. July predicted PM10 daily average
For October 2008, the model predicted the highest hourly average concentration at 03:00 in
the morning of 100 µg Nm 3 for PM10 and, from 08:00 to 17:00, the prediction was
0 µg Nm-3
(Figure 33). The model predicted domestic emissions overestimated monitored
concentrations in October. However, in light of the prevailing wind, which was measured
as predominantly easterly in direction, the model was predicting emissions which included
outside sources. The model predicted the highest daily concentration of PM10 at
140 µg Nm-3
in October 2008 (Figure 34).

46
Figure 33. October predicted PM10 hourly average
Figure 34. October predicted PM10 daily average
To generate contour plots, a second simulation was conducted with a uniform Cartesian
grid receptor network specified across the study area. Figure 35 and Figure 36 indicate the
July 2008 predicted second highest hourly and daily average PM10 concentrations
respectively. The average concentrations at the centre of the township were 300 µg Nm-3
for hourly and daily and the concentrations decreased with distance. A similar trend was
predicted in October (Figure 37 and Figure 38) at much lower concentrations. The model
predicted that the township was the source of emissions and, at the same time, the area
where the emissions would impact the most. However it is possible that, even though the
high hourly and daily average concentrations were predicted to occur at certain locations,
this may have only been true for one day during the entire period of domestic coal and
wood burning during this study.

47
Figure 35. Contours of second highest 1-hour modelled PM10 concentrations for
July. Dotted red rectangles indicate residential zones entered into the
model as the PM10 source areas.
July. Source areas marked as dotted rectangles.

48
October. Dotted red rectangles indicate residential zones entered into the
model as the PM10 source areas.
October. Source areas marked as dotted rectangles.

49
4.4 Comparison of monitored and modelled concentration
Atmospheric dispersion models are a mathematical simulation of: how pollutant/s behaves
from the source/s; how the pollution is influenced by the atmospheric conditions; and
through to the receiving environment. Since atmospheric dispersion is a stochastic
phenomenon, it is important to validate the simulated output by comparing with the actual
measured concentrations (Rao, 2005). The literature review (Chapter 2) points out that,
even with a “perfect” model, it is likely that deviation from the measured concentration can
occur, either because of a single factor or a combination of model configuration,
atmospheric chemistry and unpredictable human behaviour. However, by comparing the
simulated and measured concentrations, the source of errors can be identified and
corrective actions implemented to improve model performance (Krause et al., 2008).
Figure 39. July monitored and modelled PM10 hourly average

50
Figure 39 shows the AERMOD predicted hourly PM10 average concentrations, compared
with the monitored data recorded during the July 2008 modelling period. The model
overestimated concentrations during the first eight hours of the morning, and
underestimated from 18:00 to 20:00. At three points – 08:00, 18:00 and 21:00 – the model
predicted the same PM10 concentrations as those measured. The domestic emissions
appeared to reach the Leandra ambient air quality monitoring station an hour later. This is
logical because the air quality monitoring station is located approximately 800 m
south-east of the township. In July 2008 the wind direction was measured blowing from
the western and south western direction for more than 60% of the time. The model
predicted the typical diurnal trends associated with the township emissions in winter, with
the highest hourly average concentration of PM10 of 240 µg Nm-3
predicted at 20:00 –
compared with that measured at PM10 270 µg Nm-3
at 03:00 in the morning.
The model generally predicted the up and down trend on daily average concentrations
similar to those measured (Figure 40). The highest daily average concentration of PM10
was predicted at 210 µg Nm-3
on the 18 July 2008 – compared with the measured at
250 µg Nm-3
on the 10th
July 2008. In July, the overall predicted concentrations fell within
the same concentration range as the measured. During October 2008, the model predicted
high concentrations during early hours of the morning and late at night. The station
monitored PM10 concentrations of a rolling hourly average of 60 µg Nm-3
– compared with
the predicted average of 28 µg Nm-3
(Figure 41). The predicted concentrations did not
represent reality. The model over predicted the daily average concentration in a trend
contrasting with the monitored (Figure 42). However, the model predicted a zero
concentration for 50% of the modelling period, with highest daily concentration at
140 µg Nm-3
on the 28 October 2008 – compared with the concentration monitored at
105 µg Nm-3
on the 14 October 2008. The measured concentration pointed to a constant
source of PM10 located in easterly direction. Given the above anomalies, it could be
expected that the model would not be able to accurately predict domestic emissions.

51
Figure 40. July monitored (red) and modelled (blue) PM10 daily average
Figure 41. October monitored (red) and modelled (blue) PM10 hourly average
Figure 42. October monitored (red) and modelled (blue) PM10 daily average

52
5. Conclusion and Recommendations
The aim of the study was to model domestic coal combustion emissions from an isolated
township within a declared national priority area, for two one-month periods, one each in
winter and summer, and to investigate and compare the modelled time-series and
monitored time-series data. To achieve this aim the following was done:
 Leandra, a rural township within the Highveld National Priority area, was selected as
a study area;
 July 2008 and October 2008 hourly surface measured meteorology data (wind speed,
wind direction, rainfall, relative humidity, ambient temperature) were obtained from
the Langverwacht air quality monitoring station;
 Upper air data (wind speed, wind direction, rainfall, relative humidity, ambient
temperature) was obtained from the SAWS Irene upper air monitoring station;
 Upper air and surface data were screened, merged and pre-processed by AERMET to
be suitable for input into the AERMOD dispersion model;
 Emissions factors were calculated using the monitored and modelled concentrations;
 The AERMOD dispersion model was then set up and run;
 Modelled PM10 concentrations were compared with the monitored concentrations.
In establishing the relationship between air pollution from the township and meteorological
parameters, it was observed that, during the coldest morning (4ºC, measured on 06th
and
10th
July 2008 at 08:00), domestic coal burning was relatively high; an hour later PM10 was
measured at 210 µg Nm-
³, the highest morning value observed during the study period. The
Leandra ambient air quality monitoring station is located approximately 800 m from the
township: therefore emissions reached the station with an approximate delay of one hour
under stagnant wind conditions.
When analysing wind direction, in relation to the location of the station and the township,
the results showed that during July 2008 the station measured PM10 originating from
domestic emissions for more than 60% of the time. The opposite was observed during
October 2008, with wind coming from the east. Notably, AERMOD predicted PM10
concentrations from the township better during July 2008 when compared with the October
2008 predictions.

53
For July, the model predicted the diurnal variations associated with typical winter
conditions in the township. For October 2008, the model over-predicted the PM10
concentrations for both the early hours of the morning and the late hours of the night. Wind
direction was mainly from the east. These predictions did not conclusively point to a
particular source of emissions.
In exploring the dispersion of PM10 from the area, the model produced dispersion contours
for second highest hourly and daily concentrations over the study area. The study
discovered that PM10 concentrations are highest at 300 µg Nm-3
in the centre of the study
area and reduced rapidly with increased distance from the edge of the township. It was
found, from the diurnal plots, cleaner air disperses the previous night’s emissions the
following morning during the winter. The results of this study confirm that ambient air
pollution is high over the township because of the emissions from the township itself.
Under these circumstances, indoor and outdoor emissions are above the accepted standards
– i.e. they constitute unhealthy ambient air conditions.
The study has demonstrated that it is possible to determine an effective emissions rate for a
Highveld coal-burning township (0.3 g PM10 s-1
m-2
) and the hourly variable emissions
factors reflecting the pattern of domestic energy use. During winter, when the air is
stagnant over the Highveld, results demonstrated that Leandra (as a typical Highveld
township) was atmospherically isolated from other strong emission sources in the region
(power stations, oil and metallurgical industries), i.e. local domestic emissions are the
dominant source generating the observed high ambient particulate matter concentrations.
During summer, with higher average wind speeds, the atmosphere over Leandra was under
the influence of regional industrial sources, so the argument for atmospheric isolation was
not valid for summer months. Furthermore, this result confirmed that the AERMOD
dispersion model can be used for simulating dispersion of township emissions in a South
African context with a satisfactory level of confidence, provided that input parameters are
correct. (This proviso applies specifically to the time of day activity factors reflecting local
domestic energy use patterns, and appropriate effective emissions factors). Assuming
uniform emission rates over the day, or ignoring seasonal variations, will not lead to
realistic dispersions results, and will produce erroneous human exposure factors.

54
This study recommends that air quality monitoring stations should be located in the centre
of the residential areas, primarily to eliminate directional limitations that may be
encountered in similar future studies.
Furthermore, domestic emissions from townships should be reduced by: promoting
improved stoves (designed to emit less particulate matter); promoting the use of the Basa
njengo Magogo method (to ignite coal for heating and cooking); and by requiring all new
houses to be constructed with passive energy efficiency features (such as insulated
ceilings), to reduce heat demand from coal combustion.

55
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