Seasonal influences on surface ozone variability in continental South Africa and implications for air quality

Although elevated surface ozone (O₃) concentrations are observed in many areas within southern Africa, few studies have investigated the regional atmospheric chemistry and dominant atmospheric processes driving surface O₃ formation in this region. Therefore, an assessment of comprehensive continuous surface O₃ measurements performed at four sites in continental South Africa was conducted. The regional O₃ problem was evident, with O₃ concentrations regularly exceeding the South African air quality standard limit, while O₃ levels were higher compared to other background sites in the Southern Hemisphere. The temporal O₃ patterns observed at the four sites resembled typical trends for O₃ in continental South Africa, with O₃ concentrations peaking in late winter and early spring. Increased O₃ concentrations in winter were indicative of increased emissions of O₃ precursors from household combustion and other low-level sources, while a spring maximum observed at all the sites was attributed to increased regional biomass burning. Source area maps of O₃ and CO indicated significantly higher O₃ and CO concentrations associated with air masses passing over a region with increased seasonal open biomass burning, which indicated CO associated with open biomass burning as a major source of O₃ in continental South Africa. A strong correlation between O₃ on CO was observed, while O₃ levels remained relatively constant or decreased with increasing NO_x, which supports a VOC-limited regime. The instantaneous production rate of O₃ calculated at Welgegund indicated that ∼40 % of O₃ production occurred in the VOC-limited regime. The relationship between O₃ and precursor species suggests that continental South Africa can be considered VOC limited, which can be attributed to high anthropogenic emissions of NO_x in the interior of South Africa. The study indicated that the most effective emission control strategy to reduce O₃ levels in continental South Africa should be CO and VOC reduction, mainly associated with household combustion and regional open biomass burning.

1 Introduction

High surface O₃ concentrations are a serious environmental concern due to their detrimental impacts on human health, crops and vegetation (NRC, 1991). Photochemical smog, comprising O₃ as a constituent together with other atmospheric oxidants, is a major air quality concern on urban and regional scales. Tropospheric O₃ is also a greenhouse gas that directly contributes to global warming (IPCC, 2013).

Tropospheric O₃ concentrations are regulated by three processes, i.e. chemical production–destruction, atmospheric transport, and losses to the surface through dry deposition (Monks et al., 2015). The photolysis of nitrogen dioxide (NO₂) in the presence of sunlight is the only known way of producing O₃ in the troposphere (Logan, 1985). O₃ can recombine with nitric oxide (NO) to regenerate NO₂, which will again undergo photolysis to regenerate O₃ and NO. This continuous process is known as the NO_x-dependent photostationary state (PSS) and results in no net production or consumption of ozone (null cycle). However, net production of O₃ in the troposphere occurs outside the PSS when peroxy radicals (HO₂ and RO₂) alter the PSS by oxidizing NO to produce “new” NO₂ (Cazorla and Brune, 2010), resulting in net O₃ production. The main source of these peroxy radicals in the atmosphere is the reaction of the hydroxyl radical (OH^•) with volatile organic compounds (VOCs) or carbon monoxide (CO) (Cazorla and Brune, 2010).

O₃ precursor species can be emitted from natural and anthropogenic sources. Fossil fuel combustion is considered to be the main source of NO_x in South Africa, which includes coal-fired power generation, petrochemical operations, transportation, and residential burning (Wells et al., 1996; Held et al., 1996). Satellite observations indicate a well-known NO₂ hotspot over the South African Highveld (Lourens et al., 2012) attributed to industrial activity in the region. CO is produced from three major sources, i.e. fossil fuel combustion, biomass burning, and the oxidation of methane (CH₄) and VOCs (Novelli et al., 1992). Anthropogenic sources of VOCs are largely due to industrial and vehicular emissions (Jaars et al., 2014), while biogenic VOCs are also naturally emitted (Jaars et al., 2016). Regional biomass burning, which includes household combustion for space heating and cooking, agricultural waste burning, and open biomass burning (wild fires), is a significant source of CO, NO_x, and VOCs (Macdonald et al., 2011; Crutzen and Andreae, 1990; Galanter et al., 2000; Simpson et al., 2011) in southern Africa. In addition, stratospheric intrusions of O₃-rich air to the free troposphere can also lead to elevated tropospheric O₃ concentrations (Diab et al., 1996, 2004). O₃ production from natural precursor sources, the long-range transport of O₃, and the injections from stratospheric O₃ contribute to background O₃ levels, which is beyond the control of regulators (Lin et al., 2012).

Since O₃ concentrations are regulated in South Africa, O₃ monitoring is carried out across South Africa through a network of air quality monitoring stations established mainly by provincial governments, local municipalities, and industries (http://www.saaqis.org.za, last access: 30 November 2017). High O₃ concentrations are observed in many areas within the interior of South Africa, which exceed the South African standard O₃ limit, i.e. an 8 h moving average of 61 ppb (e.g. Laakso et al., 2013). These exceedances can be attributed to high anthropogenic emissions of NO_x and VOCs in dense urban and industrial areas (Jaars et al., 2014), regional biomass burning (Lourens et al., 2011), and O₃-conducive meteorological conditions (e.g. sunlight). Since O₃ is a secondary pollutant, high levels of O₃ can also be found in rural areas downwind of city centres and industrial areas. In order for South Africa to develop an effective management plan to reduce O₃ concentrations by controlling NO_x and VOC emissions, it is important to determine whether a region is NO_x or VOC limited. However, O₃ production has a complex and non-linear dependence on precursor emissions (e.g. NRC, 1991), which makes its atmospheric levels difficult to control (Holloway and Wayne, 2010). Under VOC-limited conditions, O₃ concentrations increase with increasing VOCs, while a region is considered NO_x limited when O₃ production increases with increasing NO_x concentrations. Results from a photochemical box model study in South Africa, for instance, revealed that the Johannesburg–Pretoria megacity is within a VOC-limited regime (Lourens et al., 2016). VOC reductions would, therefore, be most effective in reducing O₃, while NO_x controls without VOC controls may lead to O₃ increases. In general, it is considered that O₃ formations in regions close to anthropogenic sources are VOC limited, while rural areas distant from source regions are NO_x limited (Sillman, 1999).

Previous assessments of tropospheric O₃ over continental South Africa have focused on surface O₃ (Venter et al., 2012; Laakso et al., 2012; Lourens et al., 2011; Josipovic et al., 2010; Zunckel et al., 2004), as well as free tropospheric O₃ based on soundings and aircraft observations (Diab et al., 1996, 2004; Thompson, 1996; Swap et al., 2003). Two major field campaigns (SAFARI-92 and SAFARI 2000) were conducted to improve the understanding of the effects of regional biomass burning emissions on O₃ over southern Africa. These studies indicated a late winter–early spring (August and September) maximum over the region that was mainly attributed to increased regional open biomass burning during this period, while Lourens et al. (2011) also attributed higher O₃ concentrations in spring in the Mpumalanga Highveld to increased regional open biomass burning. A more recent study demonstrated that NO_x strongly affects O₃ levels in the Highveld, especially in winter and spring (Balashov et al., 2014). A regional photochemical modelling study (Zunckel et al., 2006) has attempted to explain surface O₃ variability, which found no dominant source(s) of elevated O₃ levels.

Figure 1 Location of the four measurement sites in South Africa.

The aim of the current study is to provide an up-to-date assessment of the seasonal and diurnal variations in surface O₃ concentrations over continental South Africa, as well as to identify local and regional sources of precursors contributing to surface O₃. Another objective is to use available ambient data to qualitatively assess whether O₃ formation is NO_x or VOC limited in different environments. An understanding of the key precursors that control surface O₃ production is critical for the development of an effective O₃ control strategy.

2 Methodology

2.1 Study area and measurement stations

Continuous in situ O₃ measurements obtained from four research stations in the north-eastern interior of South Africa, indicated in Fig. 1, which include Botsalano (25.54^∘ S, 25.75^∘ E, 1420 m a.s.l.), Marikana (25.70^∘ S, 27.48^∘ E, 1170 m a.s.l.), Welgegund (26.57^∘ S, 26.94^∘ E, 1480 m a.s.l.), and Elandsfontein (26.25^∘ S, 29.42^∘ E, 1750 m a.s.l.), were analysed. This region is the largest industrial (indicated by major point sources in Fig. 1) area in South Africa, with substantial gaseous and particulate emissions from numerous industries, domestic fuel burning, and vehicles (Lourens et al., 2012, 2011), while the Johannesburg–Pretoria megacity is also located in this area (Fig. 1). A combination of meteorology and anthropogenic activities has amplified the pollution levels within the region. The seasons in South Africa correspond to typical austral seasons, i.e. winter from June to August, spring from September to November, summer from December to February and autumn from March to May. The climate is semi-arid with an annual average precipitation of approximately 400 to 500 mm (Klopper et al., 2006; Dyson et al., 2015), although there is considerable inter-annual variability associated with the El Niño–Southern Oscillation (ENSO) phenomenon. Precipitation in the north-eastern interior occurs mostly during the austral summer, from October to March, whereas the region is characterized by a distinct cold and dry season from May to September, i.e. late autumn to mid-spring, during which almost no precipitation occurs. During this period, the formation of several inversion layers is present in the region, which limits the vertical dilution of air pollution, while more pronounced anticyclonic recirculation of air masses also occurs. This synoptic-scale meteorological environment leads to an accumulation of pollutants in the lower troposphere in this region, which can be transported for several days (Tyson and Preston-Whyte, 2000; Garstang et al., 1996). The SAFARI-92 and SAFARI 2000 campaigns indicated that locations in southern Africa, thousands of kilometres apart, are linked through regional anticyclonic circulation (Swap et al., 2003).

2.1.1 Botsalano

The Botsalano measurement site is situated in a game reserve in the North West Province of South Africa, which is considered to be representative of regional background air. The surrounding vegetation is typical of a savannah biome, consisting of grasslands with scattered shrubs and trees (Laakso et al., 2008). The area is quite sparsely populated and has no local anthropogenic pollution sources (Laakso et al., 2008; Vakkari et al., 2013). The western Bushveld Igneous Complex, where numerous platinum, base metal, vanadium, and chromium mining–smelting industries are situated, is the largest regional anthropogenic pollution source, with the Rustenburg area located approximately 150 km to the east. Botsalano is also occasionally impacted by plumes passing over the industrialized Mpumalanga Highveld and the Johannesburg–Pretoria megacity (Laakso et al., 2008; Vakkari et al., 2011). In addition, the site is influenced by seasonal regional savannah wildfires during the dry period (Laakso et al., 2008; Vakkari et al., 2011; Mafusire et al., 2016). Measurements were conducted from 20 July 2006 until 5 February 2008 (Laakso et al., 2008; Vakkari et al., 2011, 2013).

2.1.2 Marikana

The Marikana measurement site is located within the western Bushveld Igneous Complex, which is a densely populated and highly industrialized region, where mining and smelting are the predominant industrial activities. Marikana is a small mining town located approximately 30 km east of Rustenburg and approximately 100 km north-west of Johannesburg. The measurement site is located in the midst of a residential area, comprising low-cost housing settlements and municipal buildings (Hirsikko et al., 2012; Venter et al., 2012). Anthropogenic emissions from household combustion, traffic, and industry in the wider region have a strong influence on the measurement site (Venter et al., 2012). Data were collected from 8 February 2008 to 16 May 2010 and have been previously used in other studies (Venter et al., 2012; Vakkari et al., 2013; Petäjä et al., 2013; Hirsikko et al., 2012, 2013).

2.1.3 Welgegund

This measurement site is approximately 100 km west of Johannesburg and is located on a commercial arable and pastoral farm. The station is surrounded by grassland savannah (Jaars et al., 2016). The station can be considered a regionally representative background site with few local anthropogenic sources. Air masses arriving at Welgegund from the west reflect a relatively clean regional background. However, the site is, similar to the Botsalano station, at times impacted by polluted air masses that are advected over major anthropogenic source regions in the interior of South Africa, which include the western Bushveld Igneous Complex, the Johannesburg–Pretoria megacity, the Mpumalanga Highveld, and the Vaal Triangle (Tiitta et al., 2014; Jaars et al., 2016; Venter et al., 2017). In addition, Welgegund is also affected by regional savannah and grassland fires that are common in the dry season (Vakkari et al., 2014). The atmospheric measurement station has been operating at Welgegund since 20 May 2010, with data measured up until 31 December 2015 utilized in this study.

2.1.4 Elandsfontein

Elandsfontein is an ambient air quality monitoring station operated by Eskom, the national electricity supply company, primarily for legislative compliance purposes. This station was upgraded and co-managed by researchers during the EUCAARI project (Laakso et al., 2012). The Elandsfontein station is located within the industrialized Mpumalanga Highveld at the top of a hill approximately 200 km east of Johannesburg and 45 km south-south-east of eMalahleni (previously known as Witbank), which is a coal mining area (Laakso et al., 2012). The site is influenced by several emission sources, such as coal mines, coal-fired power-generating stations, a large petrochemical plant, and traffic emissions. Metallurgical smelters to the north also frequently impact the site (Laakso et al., 2012). The Elandsfontein dataset covers the period 11 February 2009 until 31 December 2010 during the EUCAARI campaign (Laakso et al., 2012).

2.2 Measurements

A comprehensive dataset of continuous measurements of surface aerosols, trace gases, and meteorological parameters has been acquired through these four measurement sites (Laakso et al., 2008, 2012; Vakkari et al., 2011, 2013; Venter et al., 2012; Petäjä et al., 2013). In particular, measurements of ozone (O₃), nitric oxide (NO), nitrogen dioxide (NO₂), and carbon monoxide (CO), as well as meteorological parameters, such as temperature (^∘C) and relative humidity (RH, %), were used in this study. Note that Botsalano, Marikana, and Welgegund measurements were obtained with the same mobile station (first located at Botsalano, then relocated to Marikana and thereafter permanently positioned at Welgegund), while Elandsfontein measurements were conducted with a routine monitoring station. O₃ concentrations at Welgegund, Botsalano, and Marikana research stations were measured using the Environment SA 41M O₃ analyser, while a Monitor Europe ML9810B O₃ analyser was utilized at Elandsfontein. CO concentrations were determined at Welgegund, Botsalano, and Marikana with a Horiba APMA-360 analyser, while CO was not measured at Elandsfontein. NO_x (NO+NO₂) concentrations were determined with a Teledyne 200AU NO∕NO_x analyser at Welgegund, Botsalano and Marikana, whereas a Thermo Electron 42i NO-NO2-NOx analyser was used at Elandsfontein. Temperature and RH were measured with a Rotronic MP 101A instrument at all the sites.

Data quality at these four measurement sites was ensured through regular visits to the sites, during which instrument maintenance and calibrations were performed. The data collected from these four stations were subjected to detailed cleaning (e.g. excluding measurements recorded during power interruptions, electronic malfunctions, calibrations, and maintenance) and the verification of data quality procedures (e.g. corrections were made to data according to in situ calibrations and flow checks). Therefore, the datasets collected at all four measurement sites are considered to represent high-quality, high-resolution measurements as indicated by other papers (Laakso et al., 2008, 2012; Petäjä et al., 2013; Venter et al., 2012; 2011; Vakkari et al., 2013). Detailed descriptions of the data post-processing procedures were presented by Laakso et al. (2008) and Venter et al. (2012). The data were available as 15 min averages and all plots using local time (LT) refer to local South African time, which is UTC + 2.

In order to obtain a representative spatial coverage of continental South Africa, O₃ data from an additional 54 ambient monitoring sites were selected. These included O₃ measurements from 18 routine monitoring station measurements (SAAQIS) for the period from January 2012 to December 2014 (downloaded from the JOIN web interface https://join.fz-juelich.de, last access: 15 July 2017; Schultz et al., 2017) and 36 passive sampling sites located in the north-eastern interior of South Africa where monthly O₃ concentrations were determined for 2 years from 2006 to 2007 (Josipovic, 2009). Spatial analyses were conducted with a geographic information system mapping tool (ArcGIS software), which used ordinary kriging to interpolate the O₃ concentrations measured at the 58 sites in order to build the spatial distribution. The interpolation method involved making an 80/20 % split of the data (80 % for model development, 20 % for evaluation), in which 20 % was used to calculate the root-mean-square error (RMSE =0.2804331). Optimal model parameters were selected using an iterative process and evaluated on the basis of the best performance statistics obtained (reported in the ArcGIS kriging output), with particular emphasis on minimizing the RMSE. The extent of area was 23.00154974 (top), −29.03070026 (bottom), 25.74238974 (left), and 32.85246366 (right).

2.3 Air mass history

Individual hourly 4-day back trajectories for air masses arriving at an arrival height of 100 m above ground level were calculated for the entire measurement period at each monitoring site, using HYSPLIT 4.8 (Hybrid Single-Particle Lagrangian Integrated Trajectory model) (Stein et al., 2015; Draxler and Hess, 1998). The model was run with the GDAS meteorological archive produced by the US National Weather Service's National Centre for Environmental Prediction (NCEP) and archived by ARL (Air Resources Laboratory, 2017). Overlay back trajectory maps were generated by superimposing individual back trajectories onto a southern African map divided into $\mathrm{0.5}{}^{\circ }\times \mathrm{0.5}{}^{\circ }$ grid cells. In addition, source maps were compiled by assigning each grid cell with a mean measured O₃ and CO concentration associated with trajectories passing over that cell, similar to previous methods (Vakkari et al., 2011, 2013; Tiitta et al., 2014). A minimum of 10 trajectories per cell were required for the statistical reliability.

2.4 Modelling instantaneous production rate of O₃

The only speciated VOC dataset available and published in South Africa exists for Welgegund (Jaars et al., 2016, 2014), which could be used to model instantaneous O₃ production at this site. The concentration of these biogenic and anthropogenic VOCs was obtained from grab samples taken between 11:00 and 13:00 LT over the course of two extensive field campaigns conducted from February 2011 to February 2012 and from December 2013 to February 2015. During this time, six trace gases, 19 biogenic VOCs, and 20 anthropogenic VOCs, including 13 aromatic and seven aliphatic compounds were measured. The VOC reactivity was calculated from the respective rate coefficients of each VOC with ^•OH radicals obtained from chemical kinetic databases such as JPL, NIST, and the MCM (e.g. Jaars et al., 2014) to estimate ozone production at 11:00 LT at Welgegund. Specifically, each VOC reactivity was then summed to obtain the total VOC reactivity for each measurement, i.e. VOC reactivity $=\sum k_{\mathrm{1}i}[\mathrm{VOC}{]}_I$. The major contributors to VOC reactivity are depicted in Fig. A1 and include, in approximate order of contribution, o-xylene, CO, styrene, p,m-xylene, toluene, ethylbenzene limonene, isoprene, α-pinene, β-pinene, and hexane. Of note, key compounds such as methane are not included, which could contribute to VOC reactivity, and therefore this VOC reactivity can only be a lower estimate. However, if a global ambient concentration of 1.85 ppm and a rate of oxidation by ^•OH radicals of $\mathrm{6.68}\times {\mathrm{10}}^{-\mathrm{15}}$ cm³ molec⁻¹ s⁻¹ are assumed (Srinivasan et al., 2005), a VOC reactivity of 0.3 s⁻¹ would be obtained and would therefore account for a small increase in the VOC reactivity calculated in Figs. A1 and 10.

A mathematical box model was applied to model O₃ production as a function of VOC reactivity and NO₂ concentrations. This model involves three steps, i.e. (1) the estimation of HO_x (sum of ^•OH and ${\mathrm{HO}}_{\mathrm{2}}^{•}$ radicals) production, (2) the estimation of the ^•OH radical concentration, and (3) the calculation for O₃ production (Murphy et al., 2006; Geddes et al., 2009). The VOC concentrations are the limiting factor in the ability to model O₃ production at Welgegund since only data for the 11:00 to 13:00 LT grab samples were available (Fig. A1). Therefore, the model approach does not coincide with peak O₃ typically observed around 14:00 to 15:00 LT and therefore likely represents a lower estimate.

Figure 2 Seasonal and diurnal variation in median O₃ concentrations at Welgegund, Botsalano, Marikana, and Elandsfontein. The O₃ measurement periods varied among sites, which combined spanned a period from July 2006 to December 2015.

The production rate of HO_x (P(HO_x)) depends on the photolysis rate of O₃ ($J_{{\mathrm{O}}_{\mathrm{3}}}$), concentration of O₃, and vapour pressure of water (Jaeglé et al., 2001). The photolysis rate proposed for the Southern Hemisphere, i.e. $J_{{\mathrm{O}}_{\mathrm{3}}}=\mathrm{3}\times {\mathrm{10}}^{-\mathrm{5}}$ s⁻¹ (Wilson, 2015), was used, from which P(HO_x) was calculated as follows:

$$P\left({\mathrm{HO}}_x\right)=\mathrm{2}{J}_{{\mathrm{O}}_{\mathrm{3}}}{k}_{{\mathrm{O}}_{\mathrm{3}}}\left[{\mathrm{O}}_{\mathrm{3}}\right]\left[{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\right],$$

and estimated to be 6.09×10⁶ molec cm⁻³ s⁻¹ or 0.89 ppbv h⁻¹ (calculated for a campaign O₃ average of 41 ppbv and a campaign RH average of 42 % at 11:00 LT each day) at STP. The P(HO_x) at Welgegund is approximately a factor of 2 lower compared to other reported urban P(HO_x) values (Geddes et al., 2009). The factors and reactions that affect [^•OH] include

• linear dependency between ^•OH and NO_x due to the reaction $\mathrm{NO}+{\mathrm{HO}}_{\mathrm{2}}\to {}^{•}\mathrm{OH}+{\mathrm{NO}}_{\mathrm{2}}$, until ^•OH begins to react with elevated NO₂ concentrations to form HNO₃ ($\mathrm{OH}+{\mathrm{NO}}_{\mathrm{2}}+\mathrm{M}\to {\mathrm{HNO}}_{\mathrm{3}}+\mathrm{M}$);

• P(HO_x) affected by solar irradiance, temperature, O₃ concentrations, and humidity; and

• partitioning of HO_x among RO₂, HO₂, and OH.

[^•OH] was calculated at 11:00 LT each day as follows:

$$\begin{array}{ll}{\displaystyle }& {\displaystyle }A=k_{\mathrm{5}\mathrm{eff}}{\left({\displaystyle \frac{\mathrm{VOC}\mathrm{reactivity}}{k_{\mathrm{2}\mathrm{eff}}\left[\mathrm{NO}\right]}}\right)}^{\mathrm{2}}\\ {\displaystyle }& {\displaystyle }B=k_{\mathrm{4}}\left[{\mathrm{NO}}_{\mathrm{2}}\right]+\mathit{\alpha }\times \mathrm{VOC}\mathrm{reactivity}\\ {\displaystyle }& {\displaystyle }C=P\left({\mathrm{HO}}_x\right)\\ {\displaystyle }& {\displaystyle }\left[\mathrm{OH}\right]={\displaystyle \frac{-B+\sqrt{B^{\mathrm{2}}+\mathrm{24}C\times A}}{\mathrm{12}\times A}}.\end{array}$$

The instantaneous production rate of O₃, P(O₃), could then be calculated as a function of NO₂ levels and VOC reactivity. A set of reactions used to derive the equations that describe the dependence of the ^•OH, peroxy radicals (${\mathrm{HO}}_{\mathrm{2}}^{•}+{\mathrm{RO}}_{\mathrm{2}}^{•}$), and P(O₃) on NO_x is given by Murphy et al. (2006), which presents the following equation to calculate P(O₃):

$$\begin{array}{ll}{\displaystyle }P\left({\mathrm{O}}_{\mathrm{3}}\right)& {\displaystyle }=k_{\mathrm{2}\mathrm{eff}}\left[{\mathrm{HO}}_{\mathrm{2}}+{\mathrm{RO}}_{\mathrm{2}}\right]\left[\mathrm{NO}\right]\\ {\displaystyle }& {\displaystyle }=\mathrm{2}\times \mathrm{VOC}\mathrm{Reactivity}\times \left[\mathrm{OH}\right],\end{array}$$

where k_2eff is the effective rate constant of NO oxidation by peroxy radicals (chain propagation and termination reactions in the production of O₃). The values of the rate constants and other parameters used as input parameters to solve the equation above can be found in Murphy et al. (2006) and Geddes et al. (2009).

Figure 3 The main (central) map indicating spatial distribution of mean surface O₃ levels during springtime over the north-eastern interior of southern Africa ranging between 23.00 and 29.03^∘ S and between 25.74 and 32.85^∘ E. The data for all sites were averaged for years when the ENSO cycle was not present (by examining sea surface temperature anomalies in the Niño 3.4 region). Black dots indicate the sampling sites. The smaller maps (top and bottom) indicate 96 h overlay back trajectory maps for the four main study sites, over the corresponding springtime periods.

3 Results and discussion

3.1 Temporal variation in O₃

In Fig. 2, the monthly and diurnal variations for O₃ concentrations measured at the four sites in this study are presented (time series plotted in Fig. A2). Although there is some variability among the sites, monthly O₃ concentrations show a well-defined seasonal variation at all four sites, with maximum concentrations occurring in late winter and spring (August to November), which is expected for the South African interior as indicated above and previously reported (Zunckel et al., 2004; Diab et al., 2004). In Fig. A3 monthly averages of meteorological parameters and total monthly rainfall for Welgegund are presented to indicate typical seasonal meteorological patterns for continental South Africa. These O₃ peaks in continental South Africa generally point to two major contributors of O₃ precursors, i.e. open biomass burning (wild fires) (Vakkari et al., 2014) and increased low-level anthropogenic emissions, e.g. increased household combustion for space heating and cooking (Oltmans et al., 2013; Lourens et al., 2011). In addition to the seasonal patterns of O₃ precursor species, during the dry winter months, synoptic-scale recirculation is more predominant and inversion layers are more pronounced, while precipitation is minimal (e.g. Tyson and Preston-Whyte, 2000). These changes in meteorology result in the build-up of precursor species that reach a maximum in August–September when photochemical activity starts to increase. The diurnal concentration profiles of O₃ at the four locations follow the typical photochemical cycle, i.e. increasing during daytime in response to maximum photochemical production and decreasing during the night-time due to titration with NO. O₃ levels peaked from midday to afternoon, with a maximum at approximately 15:00 (LT, UTC + 2). From Fig. 2, it is also evident that night-time titration of O₃ at Marikana is more pronounced, as indicated by the largest difference between daytime and night-time O₃ concentrations in comparison to the other sites, especially compared to Elandsfontein where night-time concentrations of O₃ remain relatively high in winter.

Figure 4 Seasonal cycle of O₃ at rural sites in other parts of the world. The dots indicate monthly median (50th percentile) and the upper and lower limits the 25th and 75th percentiles, respectively, for monthly O₃ concentrations. The data are averaged from May 2010 to December 2014, except in a few instances in which 2014 data were not available.

3.2 Spatial distribution of O₃ in continental South Africa

Figure 3 depicts the spatial pattern of mean surface O₃ concentrations over continental South Africa during springtime (September–October–November), when O₃ is usually at a maximum, as indicated above. Also presented in Fig. 3, are 96 h overlay back trajectory maps for the four main study sites over the corresponding springtime periods. The mean O₃ concentration over continental South Africa ranged from 20 to 60 ppb during spring. From Fig. 3, it can be seen that O₃ concentrations at the industrial sites Marikana and Elandsfontein were higher than O₃ levels at Botsalano and Welgegund. As mentioned previously, Elandsfontein is located within the industrialized Mpumalanga Highveld with numerous large point sources of O₃ precursor species. It is also evident from Fig. 3 that rural measurement sites downwind from Elandsfontein, such as Amersfoort, Harrismith, and Glückstadt had significantly higher O₃ concentrations, which can be attributed to the formation of O₃ during the transport of precursor species from source regions. Lourens et al. (2011) indicated that higher O₃ concentrations were associated with sites positioned in more rural areas in the Mpumalanga Highveld. Venter et al. (2012) attributed high O₃ concentrations at Marikana, which exceeded South African standard limits on a number of occasions, to the influence of local household combustion for cooking and space heating, as well as to regional air masses with high O₃ precursor concentrations. Higher O₃ concentrations were also measured in the north-western parts of Gauteng, at sites situated within close proximity to the Johannesburg–Pretoria megacity, while the rural Vaalwater site in the north also has significantly higher O₃ levels. From Fig. 3, it is evident that O₃ can be considered a regional problem, with O₃ concentrations being relatively high across continental South Africa during spring. Figure 3 also clearly indicates that the four research sites where surface O₃ was assessed in this study are representative of continental South Africa.

3.3 Comparison with international sites

In an effort to contextualize the O₃ levels measured in this study, the monthly O₃ concentrations measured at Welgegund were compared to monthly O₃ levels measured at monitoring sites in other parts of the world (downloaded from the JOIN web interface https://join.fz-juelich.de; Schultz et al., 2017) as indicated in Fig. 4. Welgegund was used in the comparison since it had the most extensive data record, while the measurement time period considered was from May 2010 to December 2014. The seasonal O₃ cycles observed at other sites in the Southern Hemisphere are comparable to the seasonal cycle at Welgegund, with slight variations in the time of year when O₃ peaks, as indicated in Fig. 4. Cape Grim, Australia; GoAmazon T3 Manancapuru, Brazil; Ushuaia, Argentina; and Cerro Tololo, Chile, are regional background GAW (Global Atmosphere Watch) stations with O₃ levels lower than the South African sites. However, the O₃ concentrations at Cerro Tololo, Chile, are comparable to Welgegund. Oakdale, Australia, and Mutdapilly, Australia, are semi-rural and rural locations, which are influenced by urban and industrial pollution sources and also had lower O₃ concentrations compared to Welgegund.

The northern hemispheric O₃ peak over mid-latitude regions is similar to seasonal patterns in the Southern Hemisphere where a springtime O₃ maximum is observed (i.e. Whiteface Mountain Summit, Beltsville, Ispra, Ryori, and Seokmo-Ri Ga). However, there are other sites in the Northern Hemisphere where a summer maximum is more evident (Vingarzan, 2004), i.e. Joshua Tree and Hohenpeissenberg. The discernible difference between the hemispheres is that the spring maximum in the Southern Hemisphere refers to maximum O₃ concentrations in late winter and early spring, while in the Northern Hemisphere, it refers to a late spring and early summer O₃ maximum (Cooper et al., 2014). The spring maximum in the Northern Hemisphere is associated with stratospheric intrusions (Zhang et al., 2014; Parrish et al., 2013), while the summer maximum is associated with photochemical O₃ production from anthropogenic emissions of O₃ precursors being at its highest (Logan, 1985; Chevalier et al., 2007). Maximum O₃ concentrations at background sites in the United States and Europe are similar to values at Welgegund in spring with the exception of Joshua Tree National Park in the United States, which had significantly higher O₃ levels. This is most likely due its high elevation and deep boundary layer (∼4 km a.s.l.) during spring and summer allowing free-tropospheric O₃ to be more effectively mixed down to the surface (Cooper et al., 2014). Maximum O₃ levels at the two sites in East Asia (Ryori and Seokmo-Ri Ga) were also generally higher than at Welgegund, especially at Seokmo-Ri Ga.

3.4 Sources contributing to surface O₃ in continental South Africa

As indicated above (Sect. 3.1), the O₃ peaks in continental South Africa usually reflect increased concentrations of precursor species from anthropogenic sources during winter, as well as the occurrence of regional open biomass burning in late winter and early spring. In addition, stratospheric O₃ intrusions during spring (Lefohn et al., 2014) could also partially contribute to increased surface O₃ levels.

3.4.1 Anthropogenic and open biomass burning emissions

A comparison of the O₃ seasonal cycles at background and polluted locations is useful for source attribution. From Fig. 2, it is evident that daytime O₃ levels peaked at Elandsfontein, Marikana and Welgegund during late winter and spring (August to October), while O₃ levels at Botsalano peaked later in the year during spring (September to November). This suggests that Elandsfontein, Marikana and Welgegund were influenced by increased levels of O₃ precursors from anthropogenic and open biomass burning emissions (i.e. NO_x and CO indicated in Figs. A4 and A5, respectively – time series plotted in Figs. A7 and A8), while O₃ levels at Botsalano were predominantly influenced by regional open biomass burning (Fig. A5). Although Welgegund and Botsalano are both background sites, Botsalano is more removed from anthropogenic source regions than Welgegund is (Sect. 2.1.3), which is therefore not directly influenced by the increased concentrations of O₃ precursor species associated with anthropogenic emissions during winter. Daytime O₃ concentrations were the highest at Marikana throughout most of the year, which indicates the influence of local and regional sources of O₃ precursors at this site (Venter et al., 2012). In addition, a larger difference between O₃ concentrations in summer and winter–spring is observed at Marikana compared to Welgegund and Botsalano, which can be attributed to local anthropogenic emissions (mainly household combustion) of O₃ precursors at Marikana.

Figure 5 Source area maps of (a) O₃ concentrations and (b) CO concentrations for the background sites Welgegund and Botsalano. The black star represents the measurement site and the colour of each pixel represents the mean concentration of the respective gas species. At least 10 observations per pixel are required.

O₃ concentrations at Elandsfontein were lower compared to the other three sites throughout the year, with the exception of the winter months (June to August). The major point sources at Elandsfontein include NO_x emissions from coal-fired power stations and are characterized by high-stack emissions, which are emitted above the low-level night-time inversion layers. During daytime, downwards mixing of these emitted species occurs, which results in daytime peaks of NO_x (as indicated in Fig. A4 and by Collett et al., 2010) and subsequent O₃ titration. In contrast, Venter et al. (2012) indicated that, at Marikana, low-level emissions associated with household combustion for space heating and cooking were a significant source of O₃ precursor species, i.e. NO_x and CO. The diurnal pattern of NO_x and CO (Figs. A4 and A5, respectively) at Marikana was characterized by bimodal peaks during the morning and evening, which resulted in increased O₃ concentrations during daytime and night-time titration of O₃, especially during winter. Therefore, the observed differences in night-time titration at Marikana and Elandsfontein can be attributed to different sources of O₃ precursors, i.e. mainly low-level emissions (household combustion) at Marikana (Venter et al., 2012) compared to predominantly high-stack emissions at Elandsfontein (Collett et al., 2010). The higher O₃ concentrations at Elandsfontein during winter are most likely attributed to the regional increase in O₃ precursors.

Figure 6 Spatial distribution of fires in 2007, 2010, and 2015 from the MODIS burnt area product. Blue stars indicate (from left to right) Botsalano, Welgegund, Marikana, and Elandsfontein.

The spring maximum O₃ concentrations can be attributed to increases in widespread regional biomass burning in this region during this period (Vakkari et al., 2014; Lourens et al., 2011). Biomass burning has strong seasonality in southern Africa, extending from June to September (Galanter et al., 2000), and is an important source of O₃ and its precursors during the dry season. In an effort to elucidate the influence of regional biomass burning on O₃ concentrations in continental South Africa, source area maps of O₃ were compiled by relating O₃ concentrations measured with air mass history, which are presented in Fig. 5a. Source area maps were only generated for the background sites Welgegund and Botsalano since local sources at the industrial sites Elandsfontein and Marikana would obscure the influence of regional biomass burning. In addition, maps of spatial distribution of fires during 2007, 2010, and 2015 were compiled with the MODIS Collection 5 burnt area product (Roy et al., 2008, 2005, 2002) and are presented in Fig. 6.

The highest O₃ concentrations measured at Welgegund and Marikana were associated with air masses passing over a sector north to north-east of these sites, i.e. southern and central Mozambique, southern Zimbabwe, and south-eastern Botswana. O₃ concentrations associated with air masses passing over central and southern Mozambique were particularly high. In addition to O₃ source maps, CO source maps were also compiled for Welgegund and Botsalano, as indicated in Fig. 5b. It is evident that the CO source maps indicated a similar pattern to that observed for O₃, with the highest CO concentrations corresponding with the same regions where O₃ levels are the highest. From the fire maps in Fig. 6, it can be observed that a large number of fires occur in the sector, associated with higher O₃ and CO concentrations, with the fire map indicating, in particular, a high fire frequency occurring in central Mozambique. During 2007, more fires occurred in Botswana compared to the other two years, which is also reflected in the higher O₃ levels measured at Botsalano during that year for air masses passing over this region. Open biomass burning is known to emit more CO than NO_x, while CO also has a relatively long atmospheric lifetime (1 to 2 months; Kanakidou and Crutzen, 1999) compared to NO_x (6 to 24 h, Beirle et al., 2003) and VOCs (a few hours to a few weeks; Kanakidou and Crutzen, 1999) emitted from open biomass burning. Enhanced CO concentrations have been used previously to characterize the dispersion of biomass burning emissions over southern Africa (Mafusire et al., 2016). Therefore, the regional transport of CO and VOCs (and NO_x to a lesser extent) associated with biomass burning occurring from June to September in southern Africa can be considered an important source of surface O₃ in continental South Africa (Fig. A5).

Figure 7 Simultaneous measurements of O₃ (daily 95th percentile), CO (daily average ppb), and RH (daily average) from 07:00 to 18:00 LT at Welgegund, Botsalano, and Marikana.

3.4.2 Stratospheric O₃

Elevated levels of tropospheric O₃ may also be caused by stratospheric intrusion of O₃-rich air (Zhang et al., 2014; Parrish et al., 2013; Lin et al., 2012), especially on certain days during late winter and spring when O₃ is the highest on the South African Highveld (Thompson et al., 2014). However, the importance of the stratospheric source over continental South Africa has not yet been specifically addressed. The assessment of meteorological fields and air quality data at high-elevation sites is required to determine the downward transport of stratospheric O₃. Alternatively, stratospheric O₃ intrusions can be estimated through concurrent in situ measurements of ground-level O₃, CO, and humidity since stratospheric intrusions of O₃ into the troposphere are characterized by elevated levels of O₃, high potential vorticity, low levels of CO, and low water vapour (Stauffer et al., 2017; Thompson et al., 2015, 2014). Thompson et al. (2015) defined low CO as 80 to 110 ppbv, while low RH is considered <15 %. In Fig. 7, the 95th percentile O₃ levels (indicative of “high O₃”) corresponding to low daily average CO concentrations (<100 ppb) are presented together with the daily average RH. Only daytime data from 07:00 to 18:00 LT were considered in order to exclude the influence of night-time titration. From Fig. 7, it is evident that very few days complied with the criteria indicative of stratospheric O₃ intrusion, i.e. high O₃, low CO, and low RH, which indicates a very small influence of stratospheric intrusion on surface O₃ levels. However, it must be noted that the attempt in this study to relate surface O₃ to stratospheric intrusions is a simplified qualitative assessment and more quantitative detection methods should be applied to understand the influence of stratospheric intrusions on surface O₃ for this region.

3.5 Insights into the O₃ production regime

The relationship among O₃, NO_x, and CO was used as an indicator to infer the O₃ production regime at Welgegund, Botsalano, and Marikana (no CO measurements were conducted at Elandsfontein as indicated above) since no continuous VOC measurements were conducted at each of these sites. However, as indicated in Sect. 2.4, a 2-year VOC dataset was available for Welgegund (Jaars et al., 2016, 2014), which was used to calculate the instantaneous production rate of O₃ as a function of NO₂ levels and VOC reactivity (Geddes et al., 2009; Murphy et al., 2006).

3.5.1 The relationship among NO_x, CO, and O₃

In Fig. 8, the correlations among O₃, NO_x, and CO concentrations at Welgegund, Botsalano, and Marikana are presented, which clearly indicate higher O₃ concentrations associated with increased CO levels, while O₃ levels remain relatively constant (or decrease) with increasing NO_x. The highest O₃ concentrations occur for NO_x levels below 10 ppb since the equilibrium between photochemical production of O₃ and chemical removal of O₃ shifts towards the former, i.e. greater O₃ formation. In general, there seems to exist a marginal negative correlation between O₃ and NO_x (Fig. A6) at all four sites, which is a reflection of the photochemical production of O₃ from NO₂ and the destruction of O₃ through NO_x titration. These correlations among NO_x, CO, and O₃ indicate that O₃ production in continental South Africa is limited by CO (and VOCs) concentrations, i.e. VOC limited.

Figure 8 Mean O₃ concentration averaged for NO_x and CO bins. Measurements were only taken from 11:00 to 17:00 LT when photochemical production of O₃ was at a maximum.

This finding shows a strong correlation between O₃ and CO and suggests that high O₃ can be attributed to the oxidation of CO in the air masses; i.e. as long as there is a sufficient amount of NO_x present in a region, CO serves to produce O₃. Although NO_x and VOCs are usually considered the main precursors in ground-level O₃ formation, CO acts together with NO_x and VOCs in the presence of sunlight to drive photochemical O₃ formation. According to Fig. 8, reducing CO emissions should result in a reduction in surface O₃ and it is assumed that this response is analogous to that of VOCs. It is, however, not that simple since the ambient NO_x and VOC concentrations are directly related to the instantaneous rate of production of O₃ and not necessarily to the ambient O₃ concentration at a location, which is the result of chemistry, deposition, and transport that have occurred over several hours or a few days (Sillman, 1999). Notwithstanding the various factors contributing to increased surface O₃ levels, the correlation between ambient CO and O₃ is especially relevant given the low reactivity of CO with respect to ^•OH radicals compared to most VOCs, which implies that the oxidation of CO probably takes place over a timescale of several days. It seems that the role of CO is of major importance in tropospheric chemistry in this region, where sufficient NO_x is present across continental South Africa and biogenic VOCs are relatively less abundant (Jaars et al., 2016), to fuel the O₃ formation process.

3.5.2 Seasonal change in O₃–precursors relationship

Seasonal changes in the relationship between O₃ and precursor species can be indicative of different sources of precursor species during different times of the year. In Fig. 9, the correlations between O₃ levels and NO_x and CO are presented for the different seasons, which indicate seasonal changes in the dependence of elevated O₃ concentrations on these precursors. The very high CO concentrations relative to NO_x, i.e. high CO-to-NO_x ratios, are associated with the highest O₃ concentrations, which are most pronounced (highest CO∕NO_x ratios) during winter and spring. This indicates that the winter and spring O₃ maximum is primarily driven by increased peroxy radical production from CO and VOCs. The seasonal maximum in O₃ concentration coincides with the maximum CO concentration at the background sites, while the O₃ peak occurs just after June–July when CO peaked at the polluted site Marikana (Fig. A5). This observed seasonality in O₃ production signifies the importance of precursor species emissions from open biomass burning during winter and spring in this region, while household combustion for space heating and cooking is also an important source of O₃ precursors, as previously discussed. The strong diurnal CO concentration patterns observed during winter at Marikana (Fig. A5) substantiate the influence of household combustion on CO levels, as indicated by Venter et al. (2012).

Figure 9 Seasonal plots of the relationship among O₃, NO_x, and CO at Welgegund, Botsalano, and Marikana.

3.5.3 O₃ production rate

In Fig. 10, P(O₃) as a function of VOC reactivity calculated from the available VOC dataset for Welgegund (Sect. 2.4) and NO₂ concentrations is presented. O₃ production at Welgegund during two field campaigns, specifically at 11:00 LT, was found to range between 0 and 10 ppbv h⁻¹. The average P(O₃) values over the 2011 to 2012 and the 2014 to 2015 campaigns combined were 3.0±1.9 and 3.2±3.0 ppbv h⁻¹, respectively. The dashed black line in Fig. 10, called the ridge line, separates the NO_x- and VOC-limited regimes. To the left of the ridge line is the NO_x-limited regime, when O₃ production increases with increasing NO_x concentrations. The VOC-limited regime is to the right of the ridge line, when O₃ production decreases with increasing NO_x. According to the O₃ production plot presented, approximately 40 % of the data are found in the VOC-limited regime area, which would support the regional O₃ analysis conducted for continental South Africa in this study. However, the O₃ production plot for Welgegund transitions between NO_x- and VOC-limited regimes, with Welgegund being in a NO_x-limited production regime the majority of the time, especially when NO_x concentrations are very low (<1 ppb). As indicated in Sect. 2.4, limitations to this analysis include limited VOC speciation data, as well as a single time-of-day grab sample. The O₃ production rates can therefore only be inferred at 11:00 LT despite O₃ concentrations peaking during the afternoon at Welgegund. Therefore, clean background air O₃ production is most likely NO_x limited (Tiitta et al., 2014), while large parts of the regional background of continental South Africa can be considered VOC limited.

Figure 10 Contour plot of instantaneous O₃ production (P(O₃)) at Welgegund using daytime (11:00 LT) grab sample measurements of VOCs and NO₂. The blue dots represent the first campaign (2011–2012), and the red dots indicate the second campaign (2014–2015).

3.6 Implications for air quality management

3.6.1 Ozone exceedances

The South African National Ambient Air Quality Standard (NAAQS) for O₃ is an 8 h moving-average limit of 61 ppbv with 11 exceedances allowed annually (Government Gazette, 2009). Figure 11 shows the average number of days per month when this O₃ standard limit was exceeded at the four measurement sites. It is evident that the daily 8 h O₃ maximum concentrations regularly exceeded the NAAQS threshold for O₃ and the number of exceedances annually allowed at all the sites, including the most remote of the four sites, Botsalano. At the polluted locations of Marikana and Elandsfontein, the O₃ exceedances peak early on in the dry season (June onwards), while at the background locations of Welgegund and Botsalano, the highest numbers of exceedances occur later in the dry season (August to November). These relatively high numbers of O₃ exceedances at all the sites (background and industrial) highlight the regional O₃ problem in South Africa, with background sites being impacted by the regional transport of O₃ precursors from anthropogenic and biomass burning source regions.

Figure 11 Monthly number of exceedances of the daily 8 h O₃ maximum (i.e. highest value of all available 8 h moving averages in that day) above 61 ppbv at Welgegund, Botsalano, Marikana, and Elandsfontein.

3.6.2 O₃ control strategies

As indicated above (Sect. 3.4 and 3.5), O₃ formation in the regions where Welgegund, Botsalano, and Marikana are located can be considered VOC limited, while the highly industrialized region with high NO_x emissions where Elandsfontein is located could also be considered VOC limited. Rural remote regions are generally considered to be NO_x limited due to the availability of NO_x and the impact of biogenic VOCs (BVOCs) (Sillman, 1999). However, Jaars et al. (2016) indicated that BVOC concentrations at a savannah grassland were at least an order of magnitude lower compared to other regions in the world. Therefore, very low BVOC concentrations, together with high anthropogenic emissions of NO_x in the interior of South Africa, result in VOC-limited conditions at background sites in continental South Africa.

It is evident that reducing CO and VOC concentrations associated with anthropogenic emissions, e.g. household combustion, vehicular emissions, and industries, would be the most efficient control strategy to reduce peak O₃ concentrations in the interior of South Africa. It is also imperative to consider the seasonal variation in the CO and VOC source strength in managing O₃ pollution in continental southern Africa. This study also revealed the significant contribution of biomass burning to O₃ precursors in this region, which should also be considered when implementing O₃ control strategies. However, since open biomass burning in southern Africa is of anthropogenic and natural origin, while O₃ concentrations in continental South Africa are also influenced by trans-boundary transport of O₃ precursors from open biomass burning occurring in other countries in southern Africa (as indicated above), it is more difficult to control. Nevertheless, open biomass burning caused by anthropogenic practices (e.g. crop residue, pasture maintenance fires, opening burning of garbage) can be addressed.

4 Conclusions

A spatial distribution map of O₃ levels in the interior of South Africa indicated the regional O₃ problem in continental South Africa, which was signified by the regular exceedance of the South African air quality standard limit. The seasonal and diurnal O₃ patterns observed at the four sites in this study resembled typical trends for O₃ in continental South Africa, with O₃ concentrations peaking in late winter and early spring (see Zunckel et al., 2004), while daytime O₃ corresponded to increased photochemical production. The seasonal O₃ trends observed in continental southern Africa could mainly be attributed to the seasonal changes in emissions of O₃ precursor species and local meteorological conditions. Increased O₃ concentrations in winter at Welgegund, Marikana, and Elandsfontein reflected increased household combustion for space heating and the trapping of low-level pollutants near the surface. A spring maximum observed at all the sites was attributed to increased regional open biomass burning. Significantly higher O₃ concentrations, which corresponded with increased CO concentrations, were associated with air masses passing over a region in southern Africa, where a large number of open biomass burning occurred from June to September. Therefore, the regional transport of CO associated with open biomass burning in southern Africa was considered a significant source of surface O₃ in continental South Africa. A very small contribution from the stratospheric intrusion of O₃-rich air to surface O₃ levels at the four sites was indicated.

The relationship among O₃, NO_x, and CO at Welgegund, Botsalano, and Marikana indicated a strong correlation between O₃ and CO, while O₃ levels remained relatively constant (or decreased) with increasing NO_x. Although NO_x and VOCs are usually considered to be the main precursors in ground-level O₃ formation, CO can also drive photochemical O₃ formation. The seasonal changes in the relationship between O₃ and precursors species also reflected the higher CO emissions associated with increased household combustion in winter and open biomass burning in late winter and spring. The calculation of the P(O₃) from a 2-year VOC dataset at Welgegund indicated that at least 40 % of O₃ production occurred in the VOC-limited regime. These results indicated that large parts in continental South Africa can be considered VOC limited, which can be attributed to high anthropogenic emissions of NO_x in this region. It is, however, recommended that future studies should investigate more detailed relationships among NO_x, CO, VOCs, and O₃ through photochemical modelling analysis, while concurrent measurement of atmospheric VOCs and ^•OH would also contribute to the better understanding of surface O₃ in this region.

In this paper, some new aspects of O₃ for continental South Africa have been indicated, which must be taken into consideration when O₃ mitigation strategies are deployed. Emissions of O₃ precursor species associated with the concentrated location of industries in this area could be regulated, while CO and VOC emissions associated with household combustion and regional open biomass burning should also be targeted. However, emissions of O₃ precursor species related to factors such as household combustion associated with poor socio-economic circumstances and long-range transport provide a bigger challenge for regulators.

Appendix A

Figure A1 Individual VOC reactivity time series. In the calculation of instantaneous O₃ production (P(O₃)), CO was treated as a VOC.

Figure A2 Time series of monthly median O₃ concentrations for each hour of the day at the four sites.

Figure A3 Monthly averages of meteorological parameters at Welgegund to show typical seasonal patterns in continental South Africa. In the case of rainfall, the total monthly rainfall values are shown.

Figure A4 Seasonal and diurnal variation in NO_x at Welgegund, Botsalano, Marikana, and Elandsfontein (median values of NO_x concentration were used).

Figure A5 Seasonal and diurnal variation in CO at Welgegund, Botsalano, and Marikana (median values of CO concentration were used). Note that CO was not measured at Elandsfontein.

Figure A6 Scatter plots of O₃ vs. NO_x for daytime (09:00 to 16:52 LT), and night-time (17:00 to 08:52 LT) at Welgegund, Botsalano, Marikana, and Elandsfontein. The correlation coefficient (r) has a significance level of $p<{\mathrm{10}}^{-\mathrm{10}}$, which means that r is statistically significant (p<0.01).

Figure A7 Time series of monthly median NO_x concentrations for each hour of the day at the four sites.

Figure A8 Time series of monthly median CO concentrations for each hour of the day at the four sites.