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How to cite this thesis

Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/ M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved from: https://ujdigispace.uj.ac.za (Accessed: Date).

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

(Source: Liebenberg-Enslin et al., 2007).

Figure 4. Monthly variation of domestic fuel burning intensities and related

emissions generated

(Source: Liebenberg-Enslin et al., 2007).

12

Figure 5. Diurnal variation of PM10 from VTAPA monitoring network

(Source: Liebenberg-Enslin et al., 2007).

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.

Figure 36. Contours of second highest 24-hour modelled PM10 concentrations for

July. Source areas marked as dotted rectangles.

48

Figure 37. Contours of second highest 1-hour modelled PM10 concentrations for

October. Dotted red rectangles indicate residential zones entered into the

model as the PM10 source areas.

Figure 38. Contours of second highest 24-hour modelled PM10 concentrations for

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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