Characterizing lightning NOx production over South Africa

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Characterizing lightning NO

x

production

over South Africa

BI Maseko

orcid.org 0000-0002-8089-3966

Dissertation submitted in fulfilment of the requirements for the

degree

Master of Science in Geography and Environmental

Management

at the North-West University

Supervisor:

Dr RP Burger

Co-supervisor:

Dr GT Feig

Graduation May 2019

28474783

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ABSTRACT

Nitrogen oxides (NOx= nitric oxide (NO) + Nitrogen dioxide (NO2)) are toxic air pollutants and play

a significant role in tropospheric chemistry. Global NOx hotspots are the industrialized regions of

the United States, Europe, the Middle East, East Asia and the eastern parts of South Africa. Lightning is one of the tropospheric NOx sources and the only natural source far from the Earth’s

surface. NOx poses a threat to air quality and the health of humans; as a result, NO2 is regulated

by the National Ambient Air Quality Standard (NAAQIS). It plays a role in the formation of particulate matter (PM) and tropospheric ozone (O3), which are both linked to adverse health and

climate effects. The NOx budget mainly comprises of anthropogenic rather-than natural sources.

However, lightning is known to be the main source of tropospheric NOx globally, it is, therefore

important to understand its contribution to the national and global NOx budget. This study

characterizes the spatiotemporal variability of lightning and the associated NOx production over

South Africa. The South African Weather Service operates a network of lightning detectors (LDN) over South Africa, which monitors cloud-to-ground lightning strikes. The system theoretically has a detection efficiency of 90% and a location accuracy of 0.5 km. The lightning events recorded by the LDN are used to approximate the influence of lightning on the NOx load over South Africa,

and to develop a gridded dataset of lightning-produced NOx (LNOx) emissions over the country

for the period 2008-2015. An emission factor of 11.5 kg NO2/flash was employed to calculate the

LNOx budget of 270 kt NO2/year. The calculated LNOx was 14% of the total NOx emission

estimates published in the EGDARv4.2 dataset for the year 2008. The results indicate that the NOx production is not distributed uniformly, with elevated areas having high LNOx production and

the western parts of the country depicting lower emissions. LNOx production peaks in the late

afternoon when thunderstorm activity starts to occur. Seasonally, the LNOx production was higher

during summer, due to meteorological conditions being favourable for thunderstorm occurrence. In winter, the production was low due to most rainfall occurring because of frontal systems passing along the coastline of the country. Overall, the results indicate that both lightning and industrial NOx sources are essential in evaluating NOx and tropospheric O3 chemistry over South Africa.

LNOx emissions are projected to increase with climate change, which can lead to a rise in

tropospheric O3. Having LNOx emission inventory as input into air quality modelling will improve

model performance and forecasting, and the understanding of the sensitivity of ambient pollution to changes in lightning emission. This will improve the regional emission inventories to inform chemical transport modelling so that the contribution of natural and anthropogenic sources can be better understood.

Keywords: Lightning flash density, Nitrogen oxides, Lightning Detection Network, South Africa,

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ACKNOWLEDGEMENTS

I would firstly like to thank Dr Gregor Feig and Dr Roelof Burger as supervisors of this dissertation for their guidance and support. Within your busy schedules, you created time for me and believed in the value of this work.

My gratitude also goes to the Water Research Commission (WRC) for funding my studies. Thank you to the South African Weather Service (SAWS) for providing me with the

lightning data used as part of the study and time to undertake this research.

To my colleagues (Morne Gijben and Bheki Sibiya) who assisted or provided advice where needed with regards to this study.

Thank you to Musa Mkhwanazi for providing me with the Lightning Detection Network maps.

Many thanks also go to Dr Estelle de Coning for the advice and support provided during the early phases of the study.

I would also like to acknowledge the European Commission Joint Research Centre for distribution of the global inventories data used as part of the study.

To my family (Patrick and Buhlebendalo Lukhele) thank you so much for your support and motivation throughout this study.

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LIST OF TABLES

Table 1 Major Industrial Operations by Province (Department of Environmental Affairs,

2012)... 17

Table 2 Intra-cloud to Cloud-to-Ground lightning flash ratio from different literature ... 47

Table 3 Estimates of LNOx production per flash from different studies ... 48

Table 4: The Intergovernmental Panel on Climate Change (IPCC) 1996 code, with the names and description of sectors ... 49

Table 5: Average lightning flash counts per season for eight years (2008-2015). ... 58

Table 6: Comparison of seasonal total lightning flash percentages ... 60

Table 7: Annual provincial flash density (flashes/km2_{) ... 61}

Table 8: Seasonal and annual LNOx production over South Africa excluding Lesotho and Swaziland ... 64

Table 9: NO2 emission from EDGARv4.2, Eskom, Sasol and LNOx for the year 2008 over South Africa in kt NO2/year ... 71

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LIST OF FIGURES

Figure 1: Tropospheric NO2 column in 1015 molecules/cm2for March 2018 (Tropospheric

Emission Monitoring Internet Service) ... 6 Figure 2: Tropospheric vertical NO2 column over the Highveld of South Africa (Lourens et

al., 2012) ... 7 Figure 3: Monthly variation of mean values of O3 for Lephalale (red), Mokopane (Green),

and Thabazimbi (Blue) in the Waterberg area (Feig et al., 2016) ... 8 Figure 4: Daily variation of mean values of O3 for Lephalale (red), Mokopane (Green), and

Thabazimbi (Blue) in the Waterberg area (Feig et al., 2016) ... 9 Figure 5: Reactions involving the NOx family in CO oxidation (Seinfeld and Pandis, 2006) ... 14

Figure 6: Primary energy consumption in South Africa (Fisher and Downes, 2015) ... 17 Figure 7: Number of cars in South Africa for the years 1990 to 2009 (Department of

Environmental Affairs, 2012) ... 20 Figure 8: Consumption of petrol and diesel in South Africa from 1988 to 2009 (Department

of Environmental Affairs, 2012) ... 20 Figure 9: Main source of heating for households in 2010 (Department of Environmental

Affairs, 2012) ... 22 Figure 10: A photograph showing Central Ameican biomass burning (National Aeronautics

and Space Agency, 2007) ... 24 Figure 11: Global annual lightning flash density (flashes/km2_{) based on a 0.25º grid from}

the Lightning Imaging Sensor (LIS) (Gill, 2008) ... 25 Figure 12: Annual LPATS lightning strikes density for the year 2002 (Bhavika, 2007) ... 27 Figure 13: Annual distribution of lightning measured using LIS data from January 1999 to

December 2004 (Collier et al., 2006) ... 28 Figure 14: Seasonal distribution of lightning measured using LIS data from January 1999

to December 2004 (Collier et al., 2006) ... 28 Figure 15: The life cycle of a thunderstorm cell (Mountain Wave Weather, 2017) ... 30

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Figure 16: Average vertical distribution of the percentage of LNOx mass (computed as

mass of N) for each of three regimes (Pickering et al., 1998) ... 33 Figure 17: Vertical distribution functions for a) CG and b) IC flashes used in the LNOx

algorithm. Note that the ground surface is at about 1.5 km MSL in the

region of interest (DeCaria et al., 2005) ... 34 Figure 18: Topographical map of South Africa, indicating altitude above Mean Sea Level

(AMSL) in meters (m). Lesotho and the nine provinces of South Africa

are indicated (Simpson, 2013) ... 38 Figure 19: Mean annual precipitation in millimetres (mm), over South Africa (Schulze,

2012)... 39 Figure 20: Seasonal distribution of rainfall over 30 year period: Winter (top left), spring

(top right), summer (bottom left), autumn (bottom right) (de Coning,

2013)... 40 Figure 21: Position of the 19 Vaisala lightning detection sensors over South Africa (South

African Weather Service) ... 41 Figure 22: Position of the 25 Vaisala lightning detection sensors over South Africa (South

African Weather Service) ... 42 Figure 23: (a) Detection efficiencies and location accuracies of the LDN before the

upgrades and (b) currently (South African Weather Service) ... 43 Figure 24: Average number of lightning ground flashesin (flashes/km2_{/year) over South}

Africa for the eight year period (2008-2015) ... 52 Figure 25: Left: Average annual lightning ground flash densities per square km for all

lightning flashes over South Africa for the five year period from 2006 to 2010 (Gijben, 2012); and Right: lightning ground flash density map for

2006 as derived from LDN data (Gill, 2008) ... 52 Figure 26: Lightning ground flash density map of South Africa: after CSIR (1994) (Gill,

2008)... 53 Figure 27: Relationship between the topography of South Africa (m AMSL) and lightning

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Figure 28: Monthly distribution of the average number of lightning flash for eight years

(2008-2015) over South Africa ... 57 Figure 29: Seasonal distribution of lightning ground flashes per km2_{over South Africa for}

the eight year period (2008-2015): summer (top left), autumn (top right), winter (bottom left) and spring (bottom right) ... 59 Figure 30: Total annual number of flashes per province for 2008 through to 2015 over

South Africa ... 61 Figure 31: Average number of total LNOx in (kg (NO2)/km²/year) over South Africa ... 63

Figure 32: Seasonal total LNOx (kg (NO2)/km²) available in a 1km gridded format: summer

(top left), autumn (top right), winter (bottom left), spring (bottom right) ... 65 Figure 33: Monthly estimates of LNOx over South Africa ... 66

Figure 34: Annual diurnal variation of LNOx production over South Africa for the year 2015 .... 67

Figure 35: Seasonal diurnal variation of LNOx production over South Africa for the year

2015 ... 68 Figure 36: Annual LNOx per province over South Africa (solid bar represents the median,

the red is the 25 and 75 percentiles and the dashed line represents the

10 and 90 percentiles of interannual variability ... 69 Figure 37: NO2 emission from EDGARv4.2, Eskom, Sasol and LNOx for the year 2008

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CHAPTER 1 INTRODUCTION

This Section gives a background of South Africa’s air quality and the contribution of lightning-produced nitrogen oxides (LNOx) thereof. It also points out some of the studies done on lightning

in South Africa and the gap that exists in LNOx research. The importance of quantification of LNOx

is discussed, also the study aims and objectives. 1.1. Rationale

Poor air quality is one of the key environmental concerns in South Africa, as it poses a serious threat to the well-being of the people of South Africa. One of the key pollutants with adverse health and environmental impacts are nitrogen oxides (NOx) and particulate matter (PM). NOx have

effects that are felt on humans and the environment, but are also important reagents in atmospheric chemical processes that result in the creation of secondary atmospheric pollutant, like ozone (O3). PM have more critical health and environmental impacts in their own right.

To alleviate the effects of air pollution, it is essential to have a comprehensive understanding of the sources of the atmospheric pollutants to manage emissions. Management of air quality requires that the quantity of pollutants released into the atmosphere from sources be known to determine how much of the emissions need to be reduced to achieve acceptable levels. Furthermore, understanding of pollutant sources is essential for atmospheric chemical transport modelling, which is a valuable tool in comprehending the distribution of the pollutants and their potential impacts (Monks et al., 2015). Areas that are strongly affected by poor air quality in South Africa include the Mpumalanga Highveld, Vaal Triangle area, and the Waterberg Bojanala area. These areas have been affirmed as air quality priority areas in terms of section 19 of the Air Quality Act (Department of Environmental Affairs and Tourism, 2005). The primary sources of pollutant in these areas are coal-fired power plants, vehicles, domestic fuel burning, metallurgical and petrochemical industries (Department of Environmental Affairs, 2011; Venter et al., 2012). Carbon monoxide (CO), Ozone (O3), particulate matter (PM10 and PM2.5), nitrogen oxides (NOx)

and sulphur dioxide (SO2) are pollutants that are listed as being of particular concern in South

Africa (Department of Environmental Affairs, 2013). These criteria pollutants are regulated by the National Ambient Air Quality Standard (NAAQS), as they have adverse health and environmental impacts (Department of Environmental Affairs, 2012). Nitrogen oxides are important in many ways:

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 They pose a threat to human life and animals, long-term exposure to NO2 may reduce the

functionality of the lungs and increase the risk for children and the elderly in getting diseases such as bronchitis (World Health Organization, 2013);

 NOx is vital in climate and atmospheric chemistry, it reacts with volatile organic compounds

(VOCs) in the existence of sunlight to form O3 (Chameides et al., 1992), which in the

troposphere has adverse health impacts and is a greenhouse gas (GHG) (World Health Organization, 2013).

 Additionally NOx is a precursor for the formation of secondary atmospheric aerosols, and

it is involved in acid deposition (Josipovic, 2009).

Nitrogen oxides are discharged into the air from both anthropogenic and natural sources. The total global NOx budget is dominated by the anthropogenic sources rather-than natural sources

(Pickering et al., 2014). Therefore, research on natural sources of NOx in general and lightning in

particular, have received less attention. This is mainly due to the high spatiotemporal variability associated with detection of lightning (Beirle et al., 2005). While this remains the case, lightning-produced NOx (LNOx) makes about 10 to 15% of the global NOx budget and is a leading source

in the upper troposphere. Consideration of the dynamics of LNOx production is essential in

understanding atmospheric chemical processes and the ambient concentrations of NOx and O3.

Other sources of NOx in the upper troposphere maybe the emissions from aircraft or NOx

transported from the stratosphere (Bond et al., 2002).

Fossil fuel combustion is the most important source of NOx, mainly through traffic and large power

plants (Hudman et al., 2007). Other contributors are the burning of biomass, soil and lightning emissions (van der A. et al., 2008). In the troposphere, lightning is the main source of nitrogen oxides (Chameides et al., 1977; Tie et al., 2002). Most global estimates of LNOx are around 5

[2-8] Tg N/year (Nitrogen mass units per year) (Schumann and Huntrieser, 2007). In a study done over Highveld region by Ojelede et al., (2008), the annual LNOx production was estimated to be

65 kt NO2/year, which was 9% of the NOx emitted from coal-powered plants for the same year.

The lightning discharge consists of the Intra-cloud (IC), and cloud-to-ground (CG) discharges. Both types of lightning discharges are capable of producing NOx (Gallardo and Cooray, 1996).

Lightning is mainly associated with convective thunderstorms in the atmosphere. When lightning is produced during active thunderstorms, it heats the atmosphere to approximately 30 000 K, separating the oxygen (O2) and nitrogen (N2) molecules in the atmosphere (Ardaseva et al.,

2017). When the temperature decreases to about 2000 K, it is through the Zeldovich mechanism that nitrogen oxides are formed (Ardaseva et al., 2017). The Zeldovich mechanism is a chemical mechanism for producing nitric oxide (NO) and nitrogen dioxide (NO2) from molecular nitrogen in

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Nitrogen oxides in the mid-upper troposphere participate in the production of O3 (Price et al.,

1997). In the presence of sunlight, NO2 is broken down photochemically to form nitric oxide (NO)

and a single oxygen radical; the oxygen atom then combines with molecular oxygen atom to form O3 (Seinfeld and Pandis, 2006). The health effects of tropospheric O3 are one of the compelling

reasons to fully understand NOx concentration in the troposphere (Ojelede et al., 2008).

There is limited research regarding the role of production of NOx by lightning in regional

atmospheric chemistry, of the studies that exist most concentrate on the global LNOx production

(Biazar and McNider, 1995; Beirle et al., 2010; Finney et al., 2016). The global studies of LNOx

might not be applicable at the regional scale, because they may not capture the number of lightning strikes occurring within a region, the variability involving the production of NOx per flash,

and the varying ratio of IC and CG flashes (Bond et al., 2002; Schumann and Huntrieser, 2007; Ott et al., 2010; Banerjee et al., 2014).

Even though it has been recognized that lightning has an impact on total NOx production, national

and regional air pollution emission inventories typically focus on anthropogenic sources (Allen et

al., 2012). Accurate knowledge of NOx source distribution is needed especially for atmospheric

chemistry modelling (Delmas et al., 1997; Finney et al., 2016; Gressent et al., 2016). 1.2. Problem statement

Previous studies done on lightning in South Africa have concentrated on 1) atmospheric electricity and lightning processes (Ojelede et al., 2008), 2) spatial frequency, and distribution for the preservation of tangible and electrical infrastructure (Ojelede et al., 2008), 3) the danger it poses to humans and animals (Gijben, 2012) and 4) forecasting of convective storms for up to 12 hours (Papadopoulos et al., 2005).

Several studies have been conducted to improve the estimation of the global budget of NOx

(DeCaria et al., 2005; Schumann and Huntrieser, 2007; Beirle et al., 2010; Ott et al., 2010). However, Ojelede et al. (2008) conducted a study to estimate LNOx production over the Highveld

of South Africa for the year 2002, utilizing lightning data obtained from the Lightning Position and Tracking System (LPATS) network. The purpose of the system was to give lightning warnings and analyze fault for the national electrical power supply system The LPATS only records CG strokes, and the lightning strikes were not automatically grouped into a lightning flash. The LPATS has detection efficiency (DE) of 80% and comprises of six sensors over the eastern half of the country. While this previous study provides some information on the dynamics of LNOx

production, it has some short-comings including the limited spatial extent, the short study period, a lower DE and uncertainty related to the grouping of strokes into flashes.

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As there have been relatively insufficient studies done on lightning NOx emissions, it leads to a

gap in South Africa and adequate LNOx emissions information is not available for air quality

modelling or planning. Gridded emission records are crucial inputs for Chemical Transport Models (CTMs) and climate models (Zheng et al., 2017). This study will provide LNOx emissions over the

South African region, which can be used as input data into atmospheric chemical transport modelling over South Africa. Gijben (2012) published an updated lightning climatology using lightning data from Lightning Detection Network (LDN) that is managed by the South African Weather Service (SAWS). The LDN was installed in 2005 and has a 90% DE and location accuracy of 0.5 km.

Information from the improved lightning detection system allows for the high-resolution identification of areas of intense lightning occurrence. It also provides an opportunity to investigate the spatiotemporal characteristics of LNOx production, which can then be used as input into

atmospheric chemical transport modelling and to improve air quality modelling. A time series of LNOx can be created looking at daily, weekly and monthly emissions based on the data. The

study by Ojelede et al. (2008) used an emission factor from (Price et al., 1997). In this study an emission factor from Schumann and Huntrieser (2007) is used, which is regarded to be a good estimate of LNOx production (Beirle et al., 2010).

1.3. Aims and objectives

The study aim is to determine the contribution of lightning to total atmospheric NOx emissions

over South Africa to improve regional emission inventories to be able to inform chemical transport modelling so that the contribution of natural and anthropogenic sources can be better understood. The study has the following objectives:

1. To characterize spatial and temporal lightning distribution over South Africa.

2. To characterize the spatial and temporal dynamics of LNOx production over South Africa.

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CHAPTER 2 LITERATURE REVIEW

The availability of reactive forms of nitrogen is an essential element in the earth system, since it is a building block of many biological components such as proteins and deoxyribonucleic acid (DNA), however there is a limited amount of naturally available reactive nitrogen. When available in the biosphere nitrogen can be involved in a chain of biological and biogeochemical processes known as the nitrogen cascade (Galloway et al., 2004; Gruber and Galloway, 2008; Fowler et al., 2013). A single atom of reactive nitrogen can sequentially be involved in processes such as tropospheric O3 formation with impacts on the health of humans and the ecosystem productivity,

acid deposition to soil and water bodies resulting in acidification and eutrophication; and alterations in plants (Thompson et al., 2014; Erisman et al., 2013). While most nitrogen fixation that is occurring is the result of anthropogenic activities, lightning is one of the most important naturally occurring sources of NOx in the atmosphere.

The chapter briefly gives an overview of the importance of lightning on the generation of NOx and

the impact it has on health, O3, PM, and acid deposition. It also details how NOx is formed through

atmospheric chemical processes and the importance of different sources of NOx. This study

focuses on lightning-produced NOx (LNOx), thus understanding the variability of lightning (and

hence LNOx) in space and time is important.

2.1. Introduction

Lightning can influence atmospheric chemistry through its production of reactive nitrogen species (Labrador et al., 2005; Schumann and Huntrieser, 2007). Furthermore it is considered to be a major source of NOx in the troposphere (Schumann and Huntrieser, 2007; Ju et al., 2014), where

NOx is an essential precursor for tropospheric O3 (Seinfeld and Pandis, 2006). NOx emissions into

the atmosphere have a significant impact on global GHGs (Houghton et al., 2001). NOx directly

affects human and ecosystem health but also does so indirectly through the formation of O3, PM

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Satellite analysis of total NO2 vertical column density has identified some NOx hot spots over the

industrialized areas of the United States, Europe, Middle East, East Asia and South Africa (Figure 1). The hotspot over South Africa is in the north eastern parts of the country (Highveld). This area contributes 90% of the industrial NOx emissions (Lourens et al., 2012). The Mpumalanga Province

is one of the world’s largest NO2 hotspot, with 12 coal fired power plants situated in the area

(Health Effects Institute, 2018). Other sources of NOx in the Highveld region include; metallurgical

smelters, road transport, biomass burning, and human settlements (Lourens et al., 2012).

Figure 1: Tropospheric NO2 column in 1015 molecules/cm2for March 2018 (Tropospheric

Emission Monitoring Internet Service)

Another area depicting high NO2 concentration is the Johannesburg-Pretoria (Jhb-Pta) megacity

(Figure 2). The leading sources of pollution in these areas are emissions from traffic, biomass burning (veld fires), domestic combustion, and industrial activities (Lourens et al., 2012). In the morning and evening, the concentration of NO2 in these cities exceeds that of the Highveld due

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The NO2 emitted by the coal-fired power stations is transported across Mpumalanga to the

Jhb-Pta mega-cities, exposing 8 million people in the area to poor air (Health Effects Institute, 2018). The Greenpeace proposes that all coal-powered power plants that do not comply with the minimum emission standard (MES) be demolished, as these emissions can be deadly to humans (Health Effects Institute, 2018). Moreover, South Africa emits large amounts of NO2 than allowed

in China and Japan (Health Effects Institute, 2018). Knowledge of where these NOx hotspots is

vital as NO2 is a precursor for tropospheric O3, aerosol nitrate (NO3-) and hydroxyl radicals (OH).

Knowledge of NOx hotspots helps policymakers to put measures in place for controlling

emissions.

Figure 2: Tropospheric vertical NO2 column over the Highveld of South Africa (Lourens et

al., 2012)

Seasonally, NO2 and SO2 peak in winter whereas O3 peak in the springtime (Kgabi and Sehloho,

2012) The seasonal variation of NO2 concentration is due to elevated incidences of combustion

for domestic heating during winter and the difference in meteorological conditions during winter and summer. In winter, the conditions are more stable, and the surface inversion layer formation prevents vertical atmospheric mixing, keeping the pollutants produced at ground level and preventing the mixing of high-level stack emissions to the ground (Lourens et al., 2011). In summer the conditions are unstable; this increases the vertical motion and dispersion in the atmosphere, but also allows the down mixing of high-level emission sources (Lourens et al., 2011).

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The peak of O3 concentration during spring (Figure 3) is owed to various contributing factors,

including the intensifying solar radiation which promotes photochemical reactions of O3

precursors that were accrued during winter (Zunckel et al., 2004), while the stable conditions still prevail in spring. Another contributing factor is the anthropogenically formed O3 that accumulates

in the invasion layer owing to the long lifetime of O3 in winter (~ 200 days) (Kgabi and Sehloho,

2012). A general representation of the variation of O3 can be specified according to declining

concentration from spring, through summer, autumn, and winter.

Observations have indicated that the concentration of O3 rises on clear days, with intensifying

solar radiation and temperature (Han et al., 2011). South Africa is generally associated with clear, sunny skies with gentle winds. Thus, the pollutants in the atmosphere are not readily distributed, but accumulate in the stable boundary layer (van Tienhoven, 1999). In winter the accumulation of pollutants is significant due to the atmosphere being dry and highly stable (Tyson and Preston-Whyte, 2000).

Figure 3: Monthly variation of mean values of O3 for Lephalale (red), Mokopane (Green),

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The diurnal variation of O3 (Figure 4) indicates a daytime peak in concentration and a lower

concentration at night time, as the O3 formation is dependent on radiant energy emitted by the

sun (Feig et al., 2016). The O3 cycle is affected by weather conditions and the levels of precursors

(NOx) (Han et al., 2011).

Figure 4: Daily variation of mean values of O3 for Lephalale (red), Mokopane (Green), and

Thabazimbi (Blue) in the Waterberg area (Feig et al., 2016)

Future tropospheric O3 concentration depends on the imminent precursor emissions, together

with the changes in meteorological variables, including temperature and atmospheric moisture (Steiner et al., 2006). Various studies have suggested that future lightning activity is projected to intensify due to climate change. At the upper troposphere LNOx is more efficient in producing

tropospheric O3, thus developing a regional LNOx emission inventory will assist in understanding

the sensitivity of the increase in lightning activity with climate change to O3 in the troposphere

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2.1.1 Health impacts of ambient NOx

Nitrogen oxides have an effect on human’s health; short-term exposure can worsen asthma, cause coughing or breathing problems (The United States Environmental Protection Agency, 2016). Long-term exposure to NO2 may cause asthma development and possibly increase

vulnerability to infections in the respiratory system. Individuals with asthma, children and older people are commonly at higher risk of being affected by NO2. The health impacts include a decline

in lung function and increased risks of respiratory infections (Environmental Protection Agency, 1999).

Indoor air pollution contains pollutants such as PM, CO, CO2, NO2, SO2, and VOCs, which have

negative health impacts. Individuals get exposed to NOx indoors, through the use of stoves for

cooking and space heating (Bernard et al., 2001; Gul et al., 2011). These pollutants infiltrate far down into the lungs and are linked to some respiratory diseases, as well as low birth weight, cataracts, and blindness (Smith et al., 2011). The acute respiratory infections are the most common reason for loss of life among children who are younger than five years of age in developing countries. In the year 2000, about 1.5 million deaths resulted due to indoor air pollution (Khalequzzaman et al., 2010).

Outdoor NO2 from natural and anthropogenic sources may also affect levels of indoor pollutants,

through open windows or ventilation systems of a building (Gul et al., 2011). The indoor concentrations of NO2 are also subject to geographical, seasonal and diurnal variation (Garrett et

al., 1999). The indoor pollutants levels are usually higher in winter months, as opposed to summer months, due to increased space heating, poorer ventilation and higher outdoor concentrations (Zota et al., 2005).

The indoor concentration varies in various countries, owing to the different types of fuel used and the rate at which it is consumed. The indoor NO2 levels can also vary significantly within homes

due to the contributing factors stated above (Topp et al., 2004). Areas that these health consequences are intensified are the developing countries (van den Berg, 2015).

2.1.2 The role of NOx in tropospheric ozone formation

Tropospheric O3 is not naturally formed. It is created by chemical reactions between NOx and

VOCs in the presence of sunlight. Large quantities of O3 can warm the troposphere, and induce

climatic changes over a long period (Nishanth et al., 2012). O3 has harmful impacts on human

health, crops and forest ecosystem, which brings about the need to advance understanding of the association between O3 and NOx (Hassan et al., 2013). The impact of ozone on agriculture is

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People who are more susceptible to air containing O3 include those with chronic medical

conditions such as asthma, children and the elderly and outdoor workers (The United States Environmental Protection Agency, 2017). The health problems associated with O3 are chest pain,

throat irritation, and airway inflammation. Ozone can exacerbate bronchitis, emphysema, and asthma, and can also affect sensitive vegetation and ecosystems (Vet et al., 2014).

2.1.3 The role of NOx in secondary aerosol formation

Particulate matter (PM) is formed when NOx reacts with ammonia, water, and other compounds

(World Health Organization, 2013). The small particle aerosols can enter deep into the lungs, and some may even get into the blood-stream. Short-term and long-term exposure health impacts include worsening of asthma and respiratory symptoms; death from lung cancer, cardiovascular and respiratory diseases (Pope III et al., 2004). Furthermore, a smaller percentage of cardiopulmonary (3%) and lung cancer deaths (5%) are thought to be caused by PM on a global scale (World Health Organization, 2013).

The correlation between high concentrations of PM and SO2 and the rise in mortality was

established by the 1970s (Pope III et al., 2002). Even though there is a link between PM and mortality rate, understanding of the underlying biological mechanisms remains limited (Pope III et al., 2004).

2.1.4 The role of NOx in acid deposition

NOx emission does not only have negative impacts on human health but has environmental

effects, such as eutrophication and acidification (Scholes et al., 2007). Acidification is still a potential environmental problem for developing countries in the context of increasing emissions (Josipovic, 2009). Acidification effects are primarily associated with the atmospheric deposition of nitrogen and sulphur compounds. Atmospheric species are removed from the atmosphere in three ways: 1) chemical transformation, 2) wet deposition (acidic rain) and 3) dry deposition. Acid rain is formed when NO2 interacts with water in the form of rain, and it is damaging to plants,

trees, and human-made structures (Baukal, 2005; Josipovic, 2009).

The major sources of SO2 and NOx that causes acid rain include burning of fossil fuels to produce

electricity, manufacturing, vehicles and heavy equipment, oil refineries and other industries. Acid rain is not only a problem for those living close to the source but also those who live far, because SO2 and NOx can be blown by the wind over long distances and across borders (United States

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Acid rain causes the release of aluminum into the soil, which is harmful to the trees and can flow into lakes and streams, endangering the aquatic life. Trees in areas that are affected by acid rain are usually seen to be dead or in their dying stage. Additionally, acidic rain washes away the nutrients and minerals from the soil that the trees need to grow (The United States Environmental Protection Agency, 2017).

2.2. Chemistry of the atmosphere

While O3 is an important atmospheric pollutant, many natural or industrial processes do not

directly emit it; instead it is formed through atmospheric chemical processes involving the occurrence of nitrogen oxides, sunlight and VOCs. The following section outlines the basic photochemical cycle of NO, NO2, and O3 and highlights how perturbations in the concentrations

of the precursors can drive the production of ozone in the troposphere.

2.2.1 Photochemistry of NOx

Tropospheric ozone is formed when NOx and VOCs reacts in the presence of heat and sunlight

(Seinfeld and Pandis, 2006). A simplified version of the reaction is: 𝑁𝑂2+ 𝑉𝑂𝐶 + ℎ𝑣 → 𝑂3 (Equation 2.1)

Ozone formation process in the lower troposphere starts with the photolysis of NO2, after which

NO rapidly reacts with O3 to re-generate NO2 (Reaction 2.2 to 2.4). Therefore, ozone remains

static depending on the speed of NO2 photolysis and the NO2/NO ratio (Hassan et al., 2013). This

process is catalyzed by hydrogen oxide radicals (HOx= hydroxyl radical (OH) and hydroperoxyl

radical (HO2)) and NOx (Naja and Lal, 2002).

𝑁𝑂₂+ ℎ𝑣 → 𝑁𝑂 + 𝑂 (Equation 2.2) 𝑂 + 𝑂₂+ 𝑀 → 𝑂₃+ 𝑀 (Equation 2.3)

Where M is any inert molecule, which is needed to stabilize the reaction by removing excess energy.

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If reaction 2.2 to 2.4 were the only processes that convert NO into NO2, the concentration of O3

would remain constant; this is termed the null cycle. However, in the presence of VOCs, this equilibrium is shifted, and the concentration of O3 increases after NO is transformed to NO2 due

to the formation of radicals (Seinfeld and Pandis, 2006). The formation of NO2 in equation 2.4

depends on the amount of O3, therefore actions to minimize NOx can lead to a reduction in NO2,

which would increase O3 since there will be less NO to react with and removing it from the

atmosphere.

The lifetime of NOx is short (1 to 2 days) at the surface and is about two weeks in the upper

troposphere. The short lifetime of NOx at the surface is due to reaction 2.5.

𝑁𝑂₂+ 𝑂𝐻 + 𝑀 → 𝐻𝑁𝑂₃ (Equation 2.5)

In the upper troposphere lifetime of NOx is longer, with most of NOx in the form of NO, the net

removal of NOx by reaction 2.5 is slowed down considerably (Seinfeld and Pandis, 2006). At night

nitrate (NO3) is formed by reaction of NO2 and O3, which can the form peroxyacetyl nitrate (PAN)

N2O5 on reaction with another NO2 molecule. The occurrence of PAN is essential in that it acts as

a reservoir molecule, which can result in the long distance transportation of NO2.

𝑁𝑂₂+ 𝑂₃ → 𝑁𝑂₃+ 𝑂₂ (Equation 2.6)

𝑁𝑂₃+ 𝑁𝑂₂+ 𝑀 ↔ 𝑁₂𝑂₅+ 𝑀 (Equation 2.7)

Overall, it can be said that this reaction does not contribute much to the increase of O3 in the

upper troposphere.

2.2.2 Carbon monoxide (CO) oxidation

In the troposphere carbon monoxide (CO) reacts with the hydroxyl radical (OH), the hydrogen atom (H) formed (Equation 2.8) combines with O2 to form hydroperoxyl radical (HO2).

𝐶𝑂 + 𝑂𝐻 → 𝐶𝑂₂+ 𝐻 (Equation 2.8) 𝐻 + 𝑂₂ → 𝐻𝑂₂+ 𝑀 (Equation 2.9)

Reaction 2.8 and 2.9 can be simplified as: 𝐶𝑂 + 𝑂𝐻 → 𝐶𝑂₂+ 𝐻𝑂₂ (Equation 2.10)

The addition of H to O2 weakens the O-O bond in O2. Thus the HO2 reacts more freely than O2.

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The NO2 formed participates in the photochemical NOx cycle in reaction 2.2 and 2.4 to form O3.

When there is very low NOx, ozone is destroyed.

𝐻𝑂₂+ 𝑂₃ → 𝐶𝑂₂+ 𝑂₂ (Equation 2.12)

The net reaction is given in equation 2.13: 𝐶𝑂 + 2𝑂₂ → 𝐶𝑂₂+ 𝑂₃ (Equation 2.13)

Otherwise, HO2 reacts with itself to produce hydrogen peroxide (H2O2)

𝑂𝐻₂+ 𝑂𝐻₂ → 𝐻₂𝑂₂+ 𝑂₂ (Equation 2.14)

The hydrogen peroxide is a temporary reservoir for HOx; it is water-soluble and is removed from

the atmosphere within a week by deposition. It can also undergo the process of photolysis or react with OH:

𝐻₂𝑂₂+ ℎ𝑣 → 𝑂𝐻 + 𝑂𝐻 (Equation 2.15) 𝐻₂𝑂₂+ 𝑂𝐻 → 𝐻𝑂₂+ 𝐻₂𝑂 (Equation 2.16)

HOx and NOx are removed from the system by reaction 2.17:

𝑂𝐻 + 𝑁𝑂2→ 𝐻𝑁𝑂3 (Equation 2.17)

While the photolysis of NO2 results in a steady-state O3 cycle, CO oxidation leads to an increases

in O3 due to the reaction that the HO2 radical undergoes with NO in reaction 2.11, producing an

additional O3 molecule because the NO2 molecule formed from reaction 2.11 did not require an

O3 molecule in its formation (Figure 5).

Figure 5: Reactions involving the NOx family in CO oxidation (Seinfeld and Pandis, 2006)

HO2 hv NO NO2 O3 OH O3 HNO3

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2.3. Sources of atmospheric NOx

Nitrogen oxides are released into the atmosphere from anthropogenic and natural sources. Anthropogenic sources comprise of industrial activities, fossil fuel combustion, transportation and power plants. Natural sources include lightning and soil (Lin, 2012). Emissions from industries and vehicles contribute extensively to the problems of air pollution (Hassan et al., 2013). In the Jhb-Pta megacity, the primary source of NOx is vehicles. The NOx emissions peak at hours when

there is a lot of traffic, in the morning and late afternoon when people are commuting to and from work at 06:00-09:00 Local Time (LT) and 16:00-18:30 LT, respectively (Lourens et al., 2016). NOx

concentration is not only highest around areas of heavy traffic in the city of Johannesburg, but also around Kempton Park. The peak in NOx around Kempton Park is owed to the emissions from

OR Tambo Airport and Kelvin power station in the vicinity (Naidoo et al., 2017). Other sources are biomass burning and domestic combustion, while on the Mpumalanga Highveld industrial activities are the main emission sources of NOx. Since they operate 24 hours there is no diurnal

variation of the emissions (Lourens et al., 2016).

Air pollutants concentration is assumed to be lower in rural areas than in the cities due to higher industrial activities and increased traffic in the latter (Hassan and Basahi, 2013). However, high O3 concentrations have been detected over rural areas within urbanized countries, due to

anthropogenic actions and biogenic emissions where there is limited industrial activity (Nishanth

et al., 2012). Transportation of O3 and its precursors from urban areas, and photochemical O3

also contribute to high concentrations of O3 in rural areas (Nishanth et al., 2012).

Of the different NOx sources, the majority is caused by human activity rather than the natural

activities. In the United States of America (USA), the highest contributor to NOx concentration is

mobile sources (road vehicles, boats, aeroplanes etc.). However, in South Africa, the emissions profile differs from other countries, the industrial sources are the most significant source (Department of Environmental Affairs, 2012). The industries over the Highveld of Mpumalanga emit large amounts of SO2, NOx, and other gas species from power generation, petro-chemical

and metallurgical processes (Josipovic, 2009). About 75% of the industries located at the Highveld region contributing approximately 90% of emitted NOx in South Africa (Collett et al.,

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2.3.1 Power stations

A significant source of NOx is the production of electricity through coal-fired power stations. South

Africa is part of the major coal producers and consumers in the world (Pretorius et al., 2014). The electricity generation in South Africa is mainly reliant on coal since it is affordable and largely available. The South African energy sector is one of the primary emitters of criteria pollutants in the country, and fired power stations constitute 90% of the sector. A large number of coal-fired power plants are found in the Highveld region of Mpumalanga, placed close to one another producing very high emission concentrations in the region (Collett et al., 2010). Eighty-one percent of the national coal is used for the production of electricity in the country.

The Electricity Supply Commission (Eskom), which is owned by the state is one of the largest energy utilities in the world and produces about 95% of South Africa’s electricity and 45% of Africa’s electricity (Eskom, 2011). Privately owned coal-fired power plants, municipalities and Sasol generates the remaining 5% of the electricity in South Africa. The main contributor to SO2

and NOx emissions in South Africa is the energy sector, followed by the industrial, commercial

and institutional fuel burning, vehicle emissions, and biomass and domestic burning (Pretorius et al., 2015).

Although power station emissions are the largest contributor to SO2 and NOx they are not the

chief cause of health effects from air quality in South Africa (Pretorius et al., 2015). The emissions from power station are usually diluted in the atmosphere before reaching human lungs as the pollutants are emitted through tall stacks. Due to high electricity cost and unreliable supply, poor South Africans are prevented from switching to electricity (Pretorius et al., 2015), thus being the most affected by NOx emission from domestic burning.

Pretorius et al. (2015) did a study on future projections for worst case and best-case scenarios of criteria pollutants (PM, SO2 and NO2) in South African coal-fired power stations. The results

indicated that NOx would increase by 40% (1559 kt NO2/year) in 2030 from the 2015 baseline

value (1160 kt NO2/year), whereas in the best case scenario it will decrease by 10% (1005 kt

NO2/year) from 2015 baseline value (1094 kt NO2/year).

In the year 2014, the primary energy consumption in South Africa was reported to be 70% coal, with energy consumption from oil being 23%, natural gas (3%), nuclear energy (3%) and renewable energy (23%) (Figure 6). The majority of the country’s power generation depends on coal production (Fisher and Downes, 2015), which is the highest emitter of NOx. This process of

producing NOx is referred to as thermal NOx; it is highly reliant on temperature and is the most

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Figure 6: Primary energy consumption in South Africa (Fisher and Downes, 2015)

2.3.2 Industrial emissions

Air pollution effects are the primary concern in highly industrial areas, as industries are the leading energy consumer and rely mostly on fossil fuels, particularly coal (Leaner et al., 2009). Most of the South African industries are in the metallurgical sector, with other industrial sectors being less dominant (Department of Environmental Affairs, 2012). Table 1 shows major industrial processes by province.

Table 1 Major Industrial Operations by Province (Department of Environmental Affairs, 2012)

Province Major Industrial operations Associated Main Pollutants Eastern Cape  Brickworks

 Animal reduction (tanneries and Rendering Plants)  Waste incineration  PM, SO2, CO2, CO  CO2, PM, CH4, NH3, VOCs  PM, SO2, VOCs, CO2, CO

Free State  Waste incineration

 Organic/inorganic industries  Asphalt plants  PM, CO, CO2, VOCs  PM, VOCs, SO2, NH3, CO2, NOx  PM, SO2, NOx, CO, Hg, Pb, VOCs

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Province Major Industrial operations Associated Main Pollutants Gauteng  Metallurgical  Ceramic/Brickworks  Organic/inorganic industries  HF, PM, CO, SO2, NO2  PM, SO2, CO2, CO, VOCs  PM, VOCs, SO2, NH3, CO2, NOx

KwaZulu Natal  Pulp and Paper/Wood Products  Ceramic/Brickworks  Organic/inorganic industries  Asphalt plants  PM, CO2, SO2, H2S, VOC, Cl2  PM, SO2, CO2, CO  PM, VOCs, SO2, NH3, CO2, NOx  PM, SO2, NOx, CO, Hg, Pb, VOCs Limpopo  Incineration  Ceramic/Brickworks  Wood products

(Sawmills and Charcoal)

 PM, SO2, VOCs, CO2,

CO

 PM, SO2, CO2, CO

 NO2, VOCs and PM

Mpumalanga  Power Generation

 Ceramic/Brickworks  Wood Products  Metallurgical Industries  PM, NO2, SO2, CO and Mercury (Hg)  PM, NO2, VOCs  PM, NO2, SO2, CO and Mercury (Hg)  PM, CO2, SO2, H2S, VOC, CI2

North West  Mining Operations

 Ceramic/Brickworks  Incineration (Medical waste)  PM and vehicle emissions  PM, SO2, CO2, CO, VOCs  PM, SO2, CO2, CO, VOCs

Northern Cape  Mining Operations

 Ceramic/Brickworks

 PM and vehicle

emissions

 PM, SO2, CO2, CO,

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Province Major Industrial operations Associated Main Pollutants Western Cape  Ceramic/Brickworks

 Metallurgical

 Animal Reduction matter

 PM, SO2, CO2, CO, VOCs  PM, SO2, CO2, CO, VOCs, HF, NOx  CO2, PM, CH4, NH3, VOCs

Poor land use planning in South Africa, led for industrial developments to be placed near areas where people live (Leaner et al., 2009). The adverse impact of polluted air on the environment are mostly experienced during industrial operations, However, they can still be experienced long after industrial operations have ended, in some cases (Gauteng Province Department of Agriculture and Rural Development, 2012).

2.3.3 Vehicle emissions

The transport sector contributes significantly to negative environmental impacts. The increase in contribution is mainly due to traffic growth (Nicolay, 2000). The Environmental Protection Department of Hong Kong (HKEPD) deduced that CO, NOx, and particulates originate mostly

from vehicular emissions. Emissions of particulates and NOx were shown to be quite high in cities

and always above the Hong Kong Air Quality Objective (HKAQO) (Tong et al., 2000). These pollutants contribute to the drop in air quality, particularly in the cities.

Vehicle emissions in South African urban areas may be responsible for 60-70% of NOx. The

number of vehicles on the road increase due to lack of efficient public transportation (Figure 7), as a result people opt to use private vehicles. The increase number of vehicles on the road leads to more fuel consumption and emission of pollutants (Figure 8) (Department of Environmental Affairs, 2012). According to the National Traffic Information System (2018), the number of private vehicles has been recorded to be 11 238 480 as of the 31st_{of October 2018, which is an increase}

of ± 2 700 000 since 2009. The Gauteng province is on the lead with 39% followed by the Western Cape and KwaZulu Natal provinces with 16% and 14% respectively (National Traffic Information System, 2018). Steg (2003) suggested that other reasons for traffic growth could be that private vehicles are more comfortable, quicker, private, convenient and flexible for running errands on one trip as opposed to public transport, hence the growth in private vehicles on the road.

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Figure 7: Number of cars in South Africa for the years 1990 to 2009 (Department of

Environmental Affairs, 2012)

Figure 8: Consumption of petrol and diesel in South Africa from 1988 to 2009 (Department

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The two diurnal peaks of NO2 in cities are dominated by traffic emissions, with a high

concentration in the early morning and late afternoon during rush-hour (Han et al., 2011). Traffic levels are reduced over weekends (Saturday and Sunday) when there is less traffic on the roads (Venter et al., 2012).

2.3.4 Domestic fuel burning

Domestic fuel burning releases copious amounts of different trace gases into the atmosphere such as SO2, NO2, NOx, CO, O3, VOCs, hydrocarbons and particulates which have environmental,

climatic and health effects (Naidoo, 2014). Wood is used as a dominant fuel type in most African countries, primarily for cooking and heating purpose (Brocard et al., 1998; Uhunamure et al., 2017). All these sources add to the total burden of air pollutants in the region (Josipovic, 2009). The use of coal, oil, and wood for heating of space and cooking is more common in low income households and informal settlements. Most homes use coal and wood because they are multipurpose fuels (Naidoo, 2014). These fuels can be utilized for food preparation and lighting purposes, as well as warming purposes (Nkosi et al., 2018). Utilization of wood and coal is a cause for both environmental and health concerns for communities residing in the low-income settlements.

The primary source of energy for heating and lighting in 2010 was electricity, at 56.8% for the country (Figure 9), however, due to electricity being so expensive most poor households do not use the energy carrier for heating, and opt to use it for lighting as this is affordable (Department of Environmental Affairs, 2012). People living in informal settlements continue to use coal for cooking and space warming due to the availability and affordability of the fuel. Households with higher incomes used coal less frequently and preferred to use electricity instead (Naidoo, 2014).

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Figure 9: Main source of heating for households in 2010 (Department of Environmental

Affairs, 2012)

Household combustion plays a role in the diurnal NO2 peaks in non-urban areas, which peaks

early in the morning and in the late afternoon into the evening, similar to traffic emissions (Venter

et al., 2012). In winter, the diurnal peak of NO2 shows that domestic burning is most likely the

most significant source of NO2. Space heating increases in winter, more so during the night-time.

Seasonal variation shows an increase in NOx from summer to winter, which could be the result of

space heating in colder days, as well as the development of low-level inversion layer in winter, which traps low-level emissions. On Sundays the diurnal variation of NO2 observed slightly differs,

this could be due to that many people resume their day late, leading to reduced/delayed early morning cooking and space heating (Venter et al., 2012). It has been reported that a large number of households living in informal settlements (57%) had no access to electricity. The homes with electricity still preferred domestic burning for cooking and heating needs (The Housing Development Agency, 2013).

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Domestic burning emits lower SO2 and NOx emissions, however, it has the highest impact on

health. This is because domestic burning emissions are near humans (at ground level) as opposed to emissions from tall stack power plants. Another contributing factor is the peak of emissions when there is poor atmospheric dispersion, the discharge of pollutants in highly populated regions influences both the outdoor and indoor pollution concentrations (Scorgie et al., 2003).

2.3.5 Biomass burning

Biomass burning (Figure 10) is a significant source of trace gases and PM emissions to the troposphere. Emissions from biomass burning are carbon monoxide (CO), methane, carbon dioxide (CO2), VOC’s and NOx (Tsutsumi et al., 1999). The large amounts of trace gases emitted

from biomass burning play a vital part in the chemistry of the atmosphere (Koppmann et al., 2005). According to the EDGARv3.2FT2000 global emissions inventory (Olivier et al., 2005), open biomass burning produced 20% of NOx and 51% of the global CO emissions in 2000, it was also

estimated to generate 26-73% of primary fine organic PM and 33-41% global fine black carbon (BC) PM emissions globally (Wiedinmyer et al., 2010). The PM and trace gases estimates are essential in characterizing air quality problems (Wiedinmyer et al., 2010).

Excessive biomass burning occurs on the African savannas and is accountable for 1/3 of the global biomass burning emission of NOx (van der A. et al., 2008). The fires generally occur

throughout the dry season, with few fires (<5%) occurring during the wet season. The fires that occur during the wet season are associated with lightning events (Archibald et al., 2012). It is crucial for total global emissions and has both local and global climatic effects (Koppmann et al., 2005).

South Africa is susceptible to large-scale veld fires and bush-fires throughout the dry season producing high levels of O3 precursors (Venter et al., 2012), which cause danger to life,

destruction to property and the environment. Almost all biomass burning emissions are believed to result from fires that were set by human activities (Koppmann et al., 2005). Humans set fires for different purposes, such as agricultural expansion, weed and residue burning, bush control and harvesting practices. One must also bear in mind that although biomass burning is high during the dry season, it also depends on site, land use and vegetation type (Delmas et al., 1997).

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Figure 10: A photograph showing Central Ameican biomass burning (National Aeronautics

and Space Agency, 2007)

2.4. Variability of lightning in space and time

Lightning activity is highly variable on both temporal and spatial scale; this makes it challenging to estimate global lightning frequency. It also varies from year to year and between individual thunderstorm events (Bhavika, 2007). In-order It is recommended to use data for at least 11 years to get a stable long term average values for lightning climatology (Rakov and Uman, 2003). The study by Bhavika (2007) explains in detail the developments around lightning research from the first global study of lightning activity that was conducted by Brooks in 1925.

The National Aeronautics and Space Agency’s (NASA’s) Optical Transient Detector (OTD) was designed to detect lightning by looking for small transient changes in light intensity during daytime and night time. Using lightning measurements from OTD, the first comprehensive results including global annual averages and seasonal distribution of lightning were obtained for a five-year period. However, the OTD instrument was only operational from 1995 to 2000 (Christian et al., 2003). It was established that lightning occurred mostly over landmasses with an average land: ocean ratio of 10:1 thus validating the results obtained by (Orville and Henderson, 1986). Refer to Christian et al., (2003) for comprehensive insight on the study. The global flash rate was found to be 45 flashes s-1_{with an estimated uncertainty of ±5 flashes s}-1_.

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The Lightning Imaging Sensor (LIS) is the follow-up version of the OTD, on-board the Tropical Rainfall Measuring Mission (TRMM) platform (Beirle, 2004). This sensor can detect lightning during the day and can observe “total lightning” over tropical regions of the globe. Similar results were obtained with LIS measurements as with the OTD instrument (Figure 12).

Figure 11: Global annual lightning flash density (flashes/km2_{) based on a 0.25º grid from}

the Lightning Imaging Sensor (LIS) (Gill, 2008)

In various regional studies of lightning activity, it shows that lightning is more frequent during summer months, and peaks during afternoon and evenings. However, the factors leading to enhanced convective activity may differ (Bhavika, 2007).

Research in Africa is limited, due to limited lightning data. Global lightning detection satellites provide limited spatial and temporal coverage. In Christian et al. (2003) study the Congo Basin was identified as the lightning ‘hotspot’ of the world. The Congo Basin is situated in the equatorial belt region. LIS results confirmed that lightning activity in the Congo Basin occurs throughout the year, with flash densities exceeding 50 km-2_a-1_{. Lightning flash densities decrease north and}

south of the equatorial belt with secondary peaks occurring over the eastern Highveld of South Africa. Lightning activity over Gaborone, Botswana was reported by Jayaratne and Ramachandran (1998) using a CGR3 flash counter. It was found that temperature and humidity play a vital role in increasing lightning activity during summer months (Jayaratne, 1993). Anderson, (1971) used flash counters to report lightning flash densities in Zimbabwe.

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South Africa has had more progress in lightning research than any other African country. During the 1970’s and 1980’s, South Africa participated in comprehensive flash counter studies producing ground flash density maps based on lightning flash counter observation (Rakov and Uman, 2003). A network of four lightning recording stations was established in Gauteng (Silverton, Bapsfontein, Diepsloot and Westfort) to provide a testing ground for calibration and development of lightning flash counters (Anderson et al., 1978).Calibration testing was undertaken using sky cameras capable of photographing 360º azimuth simultaneously.

Results showed a broad spread of errors in locational accuracy in the lightning network data, whereas the sky cameras located flashes within 60 km of occurrence. However, where the sky cameras lacked in the number of records due to being confined to observation during the night, lightning flash counters provided broader and more complete background data (Kidder and Van Niekerk, 1971).

In 1993, Eskom commissioned the first electronic Lightning Position and Tracking System (LPATS) ground-based lightning location network in South Africa, centered on the eastern Highveld region. The main purpose for installing the system was to investigate the effects of lightning on transmission lines and to establish optimal line networks. The research was not focused on lightning distribution and its characteristics and was used primarily for Eskom to evaluate their transmission line network (Bhavika, 2007).

Ojelede et al., (2008) conducted research, which produced results on lightning characteristics in South Africa using the LPATS data. Lightning strike density maps were produced for January to December 2002. Figure 13 shows annual LPATS lightning strikes density for 2002. High concentrations of lightning activity occur in the Highveld region and over the escarpment with densities decreasing away from this peak (Ojelede et al., 2008). The location of sensors influences this high concentration over the Highveld region, since they are concentrated over the Highveld region and the detection efficiency decreased with distance from the core region of coverage. Comprehensive lightning patterns for the entire country could not be determined accurately using LPATS data (Ojelede et al., 2008).

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Figure 12: Annual LPATS lightning strikes density for the year 2002 (Bhavika, 2007)

Collier et al. (2006) showed the annual and seasonal distribution of lightning occurrence over Southern Africa using LIS data from January 1999 to December 2004. The annual distribution in Figure 14 shows high activity over the Highveld and escarpment, and considerable activity over the eastern coastline. The seasonal variation (Figure 15) shows a peak in summer (DJF) and minimum activity throughout winter (JJA). The eastern coastline shows maximum activity during autumn (FMA) and minimum in spring (ASO).

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Figure 13: Annual distribution of lightning measured using LIS data from January 1999 to December 2004 (Collier et al., 2006)

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2.5. Lightning formation

Lightning is an atmospheric discharge of energy that occurs during a thunderstorm. It can arise from CG, between two thunderclouds (cloud-to-cloud) or within the same thundercloud (IC) (Simpson, 2013). The convective cloud, which is associated with lightning, is the cumulonimbus cloud (CB). However, not all CB clouds are associated with lightning (Rakov and Uman, 2003). Thunderstorms may last for 30 minutes to over few hours depending on the number of convective cells that form. Therefore, these cells can be characterized as single, multi or super. Multi-cell and super-cell storms are known for their vigorous nature, increased precipitation and lightning activities, and usually last for several hours (Rakov and Uman, 2003).

Thunderstorm life cycle occurs in three stages (Figure 11); 1) the developing/cumulus stage, 2) mature stage and 3) dissipating stage (Simpson, 2013). The cumulus stage is dominated by updrafts, and there is no precipitation during this phase. During the cumulus stage thunderclouds form, as warm, moist air rises and cools at the dry adiabatic lapse rate. When the relative humidity in a rising and cooling parcel exceeds saturation, water vapor condenses on airborne particulate matter to form cumulus cloud (Rakov and Uman, 2003).

The release of the latent heat during condensation allows this moisture and surrounding air to remain buoyant, and the atmosphere remains unstable. As the rising air parcel passes the freezing level, some water begins to freeze. Particles that remain liquid at temperatures below 0ºC are known as super cooled water particles. Liquid water particles and ice particles, also know at graupel, exist at temperatures between 0ºC and -40ºC in the mixed phase region where most electrification is thought to occur (Rakov and Uman, 2003). As the vertical growth of cumulus clouds continues, the thunderstorm clouds known as CB begin to form.

The mature stage is reached when precipitation that reaches the ground occurs. At this stage, rain, and sometimes hail and snow, falls downwards through the cloud and pulls the adjacent air downward, generating strong downdrafts alongside the updrafts (Moran and Morgan, 1994). A thunderstorm cell reaches its maximum intensity toward the end of this stage. More lightning that is frequent, heavy rainfall, hail, strong surface winds and tornadoes may develop at this phase. The characteristic anvil cloud top forms towards the end of the storm, as the strong updrafts reach stable layers in the tropopause, and the moist air dissipates horizontally.

Once the downdraft has spread throughout the thundercloud, the dissipating stage has been reached. The dissipation stage is characterized by subsiding air. The subsiding air is warmed by the adiabatic compression, relative humidity drops, precipitation is gradually reduced and ends, and convective clouds slowly vaporize (Moran and Morgan, 1994).

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Figure 15: The life cycle of a thunderstorm cell (Mountain Wave Weather, 2017)

Lightning formation starts with thunderstorm electrification. Various theories have been investigated concerning thunderstorm electrification process; however, the widely accepted theory is the non-inductive theory also known as the graupel-ice mechanism. This process generally occurs when ice crystals and graupel particles collide within the cloud, allowing the separation of electric charge (Saunders, 1992).

Reynolds et al. (1957) infer that the downward moving graupel become negatively charged after colliding with the upward moving ice crystals, where the positive charge is being removed. The positively charged ice crystal is transported to the upper part of the cloud by the updraft, whereas the negatively charged particle is positioned at the lower part of the cloud. Once the charge separation has occurred in the cloud, the build-up of positive and negative charges in the upper regions and the lower regions of the cloud will continue until the electric stresses are such that a lightning discharge will occur (Krehbiel, 1986). Numerous studies explain the propagation of lightning from a thundercloud to the ground using a stepped leader originating in the cloud and extending to the ground (Rakov and Uman, 2003).

Characterizing lightning NOx production over South Africa