Environ Monit AssessDOI 10.1007/s10661-009-1270-5

Concentrations, distributions and critical level exceedanceassessment of SO2, NO2 and O3 in South Africa

Miroslav Josipovic · Harold J. Annegarn ·Melanie A. Kneen · Jacobus J. Pienaar ·Stuart J. Piketh ·

Received: 10 June 2009 / Accepted: 3 December 2009© Springer Science+Business Media B.V. 2009

Abstract South Africa has been identified as asource of industrial pollution that is significant ata global scale. This study was designed to providequantitative information, by direct measurement,across northeastern South Africa, which includesthe highly industrialised Mpumalanga Highveld.The specific aim of the study was to evaluatewhether or not acidic atmospheric pollution posesa threat to soils, plants and water bodies of SouthAfrica. To address this aim, a network of 37passive sampling sites was established to mea-sure monthly mean concentrations of near-surfaceSO2, NO2 and ozone. The area covered extendedover the northern and eastern interior of SouthAfrica while avoiding sources of local emissionssuch as towns, mines and highways. The field cam-paign was conducted between August 2005 andSeptember 2007. Spatial distributions and tempo-ral trends for these pollutant gases were assessed.

M. Josipovic (B) · H. J. Annegarn · M. A. KneenUniversity of Johannesburg, Johannesburg,South Africae-mail: mickyj@uj.ac.za

J. J. PienaarNorth-West University, Potchefstroom, South Africa

S. J. PikethUniversity of The Witwatersrand, Johannesburg,South Africa

Critical levels analysis comparisons were madeagainst applicable air quality standards, guidelinesand limits to evaluate the potential for adverseatmospheric pollution impacts on regional envi-ronments. The assessment indicates that only inthe central source area of the South African in-dustrial Highveld are some levels exceeded. Inremote areas, including the sensitive forested re-gions of the Drakensberg escarpment, pollutantconcentrations are below the critical thresholdsfor environmental damage.

Keywords South Africa · Exceedance ·Industrial pollution · Critical levels

Introduction

Forest dieback and irreversible acidification oflakes linked to serious loss of fish stocks and othersensitive aquatic species in Scandinavia in themiddle of the twentieth century were attributedto deposition of acidic species (acid rain) orig-inating from the UK (Oden 1968). Ever sincethese experiences became known, there have beenglobal and local concerns that such ecologicaldamage due to acid rain in other regions down-wind of large coal-fired power plants and indus-tries are and could be repeated (Kuylenstiernaand Hicks 2002; Kuylenstierna et al. 1995, 2001;Kuylenstierna and Chadwick 1989; Whelpdaleand Kaiser 1996; Wesely and Hicks 1977).

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Countries in Western Europe and NorthAmerica responded to air pollution problemsand through policies and coordinated measuresmanaged to reduce substantially the emissions ofSO2, the primary acidic precursor, although NOx

emissions still remain a problem. In the meantime,later research has shown that pollutants such asvolatile organic compounds and ammonia are alsoinvolved in processes that lead to acidification(McCormick 1997).

The science and knowledge on acid pollutionhas made rapid progress, especially in countriesof Western Europe and North America. Mostof the industrialised countries operate their ownlocal and countrywide atmospheric monitoringprogrammes. At the global level, the WorldMeteorological Organisation (Miller 1992; Georgii1982) has been operating a Background Monito-ring Network, and the UN EnvironmentProgramme (UNEP 2008) coordinates global andregional monitoring through its Global Environ-ment Monitoring System. In Europe, monitoringis done under the Cooperative Programme forMonitoring and Evaluation of the Long-RangeTransmission of Air Pollutants in Europe underthe UNECE (UNECE 2008). Research onemissions has been also performed by the Inter-national Institute for Applied Systems Analysis,which has developed an interactive computersimulation called the Regional Air PollutionInformation and Simulation. There are severalmonitoring networks in the USA, such as theNational Atmospheric Deposition Program,which collects data from nearly 200 sites acrossthe USA. A major project, called the NationalAcid Precipitation Program (NAPAP 2008), waslaunched in 1980 to study the causes and potentialeffects of acid deposition in and around the USA.The final report was issued in 1990, raising morequestion than it answered (McCormick 1997).

In contrast to declining SO2 emissions inEurope and North America in response to strin-gent government regulation, emissions are ris-ing in many developing nations across the world(McCormick 1997; Held et al. 1996; Galloway1995). There is an opportunity for policy makersin developing countries to take into account thepotential acidification impacts associated with in-creasing emissions so that they can avoid some of

the costly mistakes that have been made during in-dustrial development in Europe and North Amer-ica. The knowledge obtained in these regions withregard to acidic deposition and the effects of theacidification can be used and adopted to localconditions for this purpose.

Previous experience shows that, in order tomanage acid deposition, emissions of the acidify-ing precursor species need to be controlled. Theacidification of soil and surface water has indirecteffects, including the cumulative impacts of bothwet and dry sulphur (S) and nitrogen (N) deposi-tion. Reducing emissions cannot result in the im-mediate recovery of affected ecosystems (Bishopand Hultberg 1995), and the major risks of thecumulative effects of acidification in developingcountries lie in the future as emissions increase(Kuylenstierna et al. 2001). Therefore, the estab-lishment of quantifiable estimates of acidificationof the environment in developing regions still re-quires attention.

The primary research question for this study isto what extent are industrial emissions from thecentral power generating region (MpumalangaHighveld) influencing the concentrations awayfrom the source region, taking into account pre-vailing circulation of the air masses over the south-ern African subcontinent?

Very few studies have been undertaken inSouth Africa on the impacts of gaseous pollu-tant species on non-commercial vegetation species(Emberson et al. 2001, 2003; Van Tienhoven andScholes 2003). Past research explored the effectsof air pollution on natural vegetation (grassland,Fynbos and Acacia), commercial forests (Euca-lyptus and Pinus species) and agricultural crops(maize, sunflowers, dry beans, potatoes, tomatoand tobacco). The results from past studies wereinconclusive (Van Tienhoven and Scholes 2003;Emberson et al. 2003). More studies need to beundertaken in order to determine critical levelsfor local vegetation with confidence. Thus, as thesecond objective, possible exceedances of damagethresholds have been assessed.

Critical levels for vegetation are described indifferent ways for different pollutants. These arecurrently expressed as mean concentrations, cu-mulative exposures or fluxes through plant stom-ata. Critical levels for vegetation are defined as the

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“concentration, cumulative exposure or cumula-tive stomatal flux of atmospheric pollutants abovewhich direct adverse effects on sensitive vegeta-tion may occur according to present knowledge”(Manual on Methodologies and Criteria for Mod-elling and Mapping Critical Loads 2004). Criticallevel exceedance can thus show the differencebetween the critical level and the monitored ormodelled air pollutant concentration, cumulativeexposure or cumulative flux.

The scope of this study was relatively sim-ple in its design, although extensive in its spa-tial and temporal extent. It involved establishinga network of passive monitoring sites over thenortheastern sector of South Africa and collat-ing monthly concentration samplers over two an-nual cycles. This was necessary in order to obtainmedium term averages, in addition to annual andseasonal variations.

The strategy for selecting sampling locationswas designed to capture the regional influences ofthe industrial pollution, avoiding local influences(towns, industries and highways). The spatial dis-tribution of the concentrations were analysed inorder to determine direction and magnitude of theinfluence of industrial emissions within the studydomain. The final step was to assess exceedanceof international and local air pollution standards,guidelines and limits, including health-based andvegetation indicators. Literature states that thecurrent pollutant concentrations experienced inmany developing countries can result in severedamage to vegetation and that, without appropri-ate control measures, such damage is likely toworsen if the pollution emissions increase in thefuture (Emberson et al. 2001). Vegetation (naturaland crops) is regarded as the most sensitive com-ponent of ecosystems, and thus, the vegetationcritical level exceedances were assessed againstthe concentrations from the monitoring program.

Method

Monthly passive samplers have been exposed at37 sites for a period of 2 years in the northeast-ern portion of South Africa. This allowed theconcentration distribution of SO2, NOx and O3

to be evaluated from the Mpumalanga Highveldindustrial source region. Critical levels were as-sessed based on measured (mean monthly) pol-lutant concentrations only. The disadvantage ofa relatively long averaging period of 1 month isoffset against the advantage of a dense grid ofremote measuring locations. In addition, a criticallevels survey of the long-term potential damageto the environment from industrial emissions ofthese gas species has been enabled.

Study design

The air pollutant concentration network for thisresearch was designed for the measurement ofSO2 and NO2, originating from coal fired powerplants on the industrial Highveld, and constrainedto lie within the political boundary of SouthAfrica. Although prevailing winds are from thenorthwest, seasonal stagnation and recirculationnecessitated a network design covering the en-tire inland area of South Africa (Freiman andPiketh 2003). Extension beyond the boundariesof South Africa would have added value butwas constrained by practical considerations. Thearea of concern can be represented geometricallyas a 600 × 600-km array rectangle, centred onWitbank, Mpumalanga Province of South Africa.

A reference site, intentionally in the zone ofhighest pollution, was chosen at the approxi-mate centre of the industrial Highveld. This site,Elandsfontein (site 17), is within visual sight ofseveral of the Mpumalanga coal-fired power sta-tions. Other sources of air pollution in this re-gion include major ore smelters, multiple surfacecoal mines and a major coal liquefaction andpetrochemical complex. Elandsfontein served asan indicator of maximum ground level concentra-tions of the pollution levels in the industrialisedHighveld.

Passive sampling of trace gases

Trace gases were monitored using the pas-sive sampling technology as applied in the In-ternational Geosphere Atmosphere Chemistry/Deposition of Biogeochemically Important Trace

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Species [IGAC-DEBITS-Africa (IDAF)] net-work (Pienaar et al. 2003). Such measurementsare based on the molecular diffusion of gasesand species-specific collection on impregnated fil-ters specific to each pollutant measured. Thesesamplers have been developed according to theDEBITS procedures based on the work of Ferm(1991) and Ferm et al. (1994). While passive mon-itors have previously been demonstrated in SouthAfrica, these have either been used in remoteareas with only a small number of samplers (typ-ically at four to six sites) or in intense urbansurveys (Zunckel et al. 2004; Kuylenstierna andHicks 2002). Passive samplers have been testedand validated since the beginning of 1998 in theIDAF network at six African stations (Al Ourabiand Lacaux 1999; Lacaux 1999). This techniquehas also been tested in different tropical and sub-tropical regions (Ferm and Rodhe 1997).

In Ayers et al. (1998), precision of the passivesamplers, expressed as mean percentage differ-ence between duplicates, was in the range 5–10%for NO2 and 10–25% for SO2. In addition, in Cruzet al. (2004), it is stated that methods precisionas a relative standard deviation for three simulta-neously applied SO2 passive samplers was within10% and, when compared to active monitoringmethods under real conditions, used in urban andindustrial areas, showed an overall accuracy of

15%. For O3, error margins of 15% are statedbased on comparisons with continuous ozone an-alyzers (Tang and Lau 2000).

The passive sampling monitoring network

Monitoring sites were selected at integral one-degree grid intersections, with some additionalsites positioned at half-degree grid intervals(Fig. 1). To avoid the impacts from local air pol-lution sources, care was taken to position the sitesaway from urban centres, industrial point sourcesor other local disturbances. Samplers were alsolocated away from major roads. Sites were mostlyin rural farmland, forestry and natural reserves,sparsely inhabited and often non-electrified loca-tions. In instances were the grid point was close topotential interferences, sites were shifted from theoriginal fixed grid point by distances of not morethan 15 km.

Passive samplers where exposed in thefield for 30-day intervals, corresponding tocalendar months for 2 years (two annual cycles)(September 2005 to August 2007). The monitor-ing sites were jointly managed with fieldvolunteers who serviced them on a monthlybasis. Samples were prepared in the laboratoryand couriered or mailed to the site operators.

Fig. 1 Extent of the studyarea within South (largermap) and sub-SaharanAfrica (a map inset). Thesites included in threedifferent sites categoriesare indicated

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Ambient air quality standards and guidelinesapplied as critical levels

Measured ambient pollutant concentrations havebeen compared to several sets of appropriate stan-dards. The monitoring interval for all sites was30 days, and this has been compared with criticallevels for averaging periods of 1 month or longer.A range of South African and international airquality standards have been utilised (Table 1).Exceedance of a critical level occurs when themeasured mean concentration is greater than thecritical level concentration.

The first two sets of standards (NEMA andSANS) are concerned with human (and animal)health protection, while the third (UNECE: CLR-TAP 2005) and fourth (UN WHO AOT) areconcerned with limits for vegetation. Previous re-search on the effects of air pollution on vegetationin South Africa was assessed using trace limits spe-cific to South Africa—the reviewed literature didnot contain a conclusive value for use as a criticallevel. Although there is ongoing research on theimpact of ozone on vegetation in South Africa, noresults had been published by the time this study

concluded. No vegetation-specific guidelines orstandards for South African vegetation have beenlegislated. Thus, for the purpose of this assess-ment, the widely used Convention on Long-RangeTransboundary Air Pollution concentration limitswere used. Important to emphasise here is thatthe monthly means do not allow for the calcula-tion of the accumulated hours above the 40 ppbthreshold—which is the basis for O3 AOT40 ap-proach. Thus, for this study, a simplified approachwas adopted of using values, 30 and 40 ppb, asthreshold limit values with which to compare themonthly mean O3 values.

Quality assurance and quality control

The use of an accredited atmospheric chemistrylaboratory with its own quality assurance and con-trol measures was considered to be of paramountimportance. The atmospheric chemistry labora-tory of the Atmospheric Chemistry ResearchGroup, of Northwest University (PotchefstroomCampus) as part of the IGBP/IGAC/DEBITSprogramme of remote deposition monitoring

Table 1 Atmospheric ambient standards and European critical levels used as critical levels for trace gas species SO2, NO2and O3 in this study

Sources Averaging period Applicability Units SO2 NO2 O3

SA NEMA: Annual mean Human ppm 0.019 0.05 n/aAQA 39 (2004) and (and animal) μg m−3 50 (50)b 95.5a (40)b n/aSANS: 1929 (2004) health

UN - WHO Annual mean Human ppb n/a n/a n/a2000/2005 AQ (and animal μg m−3 50c (10–30)d 40 (40)b n/aGuidelines health)

UNECE - CLRTAP Annual mean and Agricultural crops μg m−3 30 30 n/a(for SO2 and NO2) half-year (winter) mean (Semi-) natural vegetation μg m−3 20 n/a

for (semi)-natural and Forest ecosystems μg m−3 20 n/aagricultural vegetation Lichen μg m−3 10 n/a

UN WHO AOT: Annual and Vegetation Ppb n/a n/a 30 andOzone monthly growing season 40e

thresholds (cut-offconcentration forthe AOT 30 and40 critical levels)

aRe-calculated from NEMA standard = 0.05 ppmbRe-calculated from SANS 1929 (2004) standardcWHO Guidelines for the protection of human health (UN WHO 2000)dThe critical level of eco-toxic effects (issued by WHO for Europe); a range is given to account for different sensitivities ofvegetation types (UN WHO 2000)eCut-off concentration for the AOT 30 and 40 critical levels, UN WHO Europe (2000)

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programme undertook all the laboratory andanalysis work.

Each sample was marked with an identificationnumber, enclosed in separate plastic vials (dis-tinct colour for each gaseous species), sealed, andstored in dry, cool pre-packaging for transporta-tion. Samplers were sent to each monitoring siteby either registered mail or courier services. Afterexposures, the samples were retrieved and storedfor batch transport to the analytical laboratory.Protocols were communicated and explained tothe site operators and volunteers in the field as aset of simple steps.

Throughout the sampling campaign, measureswere undertaken for field quality assurance. Theseranged from duplicate sampling, seasonal site vis-its to monitors and procedure inspection of themonitoring campaign. Suspect sampler containers(such as those that were open and those arriv-ing late) were marked and handed to the labfor discarding. As a result, the laboratory estab-lished a relatively small number of not exposed,overexposed and beyond detection limit samplers,and those were dealt as per standard labora-tory procedures. Furthermore, a number of sam-plers went missing during transport—these werereplaced if notified in time or treated as non-exposures.

The results where divided into two annual cy-cles, September to August. Rate of valid samplereturn for the first annual cycle was below 60%and insufficient to meet quality control criteria.However, for the second annual cycle, the datareturn was above 80% and for the 2-year periodwas over 70%, which is an adequate return rateon which to derive valid statistical results.

For internal quality control purposes, compar-isons were made between the two annual cyclesof the monthly, seasonal and annual means. Inaddition, data analysis included checking for ex-treme concentrations that prompted the scientificflagging of outliers (greater than two standarddeviations). Any samples with concentration val-ues beyond three standard deviations above themean were excluded from further data analyses.In addition, for readings that exceeded two stan-dard deviations above the mean, the entire chainof sampling, chemical analysis reports and dataprocessing was re-inspected.

Results

Criteria pollution concentrationsover the South African Highveld

SO2 concentrations and regional distribution

The mean SO2 concentrations for the annual cycleSeptember 2006 to August 2007 are presented inFig. 2. Six locations recorded average concentra-tion above 6 μg m−3. These included Standerton(site 1), Amersfoort (site 2), Piet Retief (site 3),Elandsfontein (site 17), Heilbron (site 18), andPotgietersrus (site 21). These sites are all directlywithin the main source region, or lie downwindfrom, the industrial Highveld.

The annual mean concentrations at the remain-ing sites were all below 6 μg m−3, with the plusone standard deviation lying below 10.5 μg m−3.Those sites with higher variations are downwindof the source areas. These are Standerton (site 1),Piet Retief (site 3), Gluckstad (site 8), Utrecht(site 27), Amsterdam (site 29), and Malelane(site 30). The site at Thabazimbi (site 34) indi-cated occasional high concentrations of SO2 de-spite being to the north of the main sources onthe Highveld. The likely sources that contributedthe ambient SO2 are the Thabazimbi iron oremine and smelter, which were both situated up-wind from the site. This site is also impacted bytwo other possible sources of SO2, namely theMatimba Power station and the Grootegluk open-cast mine, both in Limpopo Province.

The spatial distribution of SO2 over theHighveld during 2005 and 2006 shows that thehighest concentrations are centred directly overthe Mpumalanga industrial Highveld. This re-gion is dominated by the influence of high con-centrations at Elandsfontein elevated concentra-tions of SO2 were also found at Standerton andAmersfoort that are directly downwind of thedominant sources. With the exception of a loca-tion NW of Potgietersrus/Mokopane (site 21), themore distant sites have much lower concentrationsin both years, while the furthest sites have the low-est concentrations. Potgietersrus (site 21) has twopossible influences for slightly higher mean andmaximum concentrations. Matimba power stationlies ∼150 km upwind, while 8 km to the west is

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Fig. 2 Two-year mean SO2 concentrations, standard deviation, maximum and minimum (September 2005 to August 2007)

Mokopane Platinum mine, although it does notappear that this mine has a smelter on site.

Overall, the observation of SO2 concentrationsand the spatial distributions behave as expectedand confirm the conclusions from prior studiesbased on dispersion modelling (Scorgie et al.2004a, b).

NO2 concentrations and regional distribution

An evaluation of the inter-annual differences forNO2 showed that concentrations did not divergesubstantially from one year to the next, withdifferences ranging from −0.6 to +0.8 μg m−3

(−10% to +52% from 2-year means; Fig. 3).Elandsfontein (site 17) has mean and maximumconcentrations much greater than any of theother sites, similar to the spatial distribution ofSO2.

The sites with higher concentrations were posi-tioned either within the industrial Highveld sourcearea, Standerton (sites 1), Amersfoort (site 2) andElandsfontein (site 17), or were downwind of thesource area, Piet Retief (site 3), Harrismith (site 6),

Newcastle (site 7) and Gluckstad (site 8). Themaximum NO2 concentration at Elandsfontein(site 17) was only 2.5 times greater than the meanNO2 at all other sites, whereas for SO2 concentra-tions, Elandsfontein (site 17) was 6.5 times greaterthan the background mean. For the majority ofthe remaining sites, mean concentrations werebetween 2 and 3 μg m−3, exceptat Steelpoort (site23) and Thohoyandou (site 37), which recordedconcentrations ∼4 μg m−3. There are chrome–manganese smelters within the Steelpoort valley,which may account for the high NOx at Steelpoort(site 37). There are no known large industries nearThohoyandou (site 37) and the traffic emissionsfrom a small town such as this should not generateelevated NOx at a site ∼10 km distant.

This observation confirms that the sites chosenwere appropriately remote from direct influencesfrom the main emission points on the industrialHighveld or from other local strong emissionsources of NOx, with the two exceptions noted.The lowest mean NO2 (1.3 μg m−3) was recordedat Skukuza (site 12) on the Mpumalanga Lowveld.Other low mean NO2 values, below 2 μg m−3,

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Fig. 3 NO2 mean, standard deviation, maximum and minimum concentrations over 2 years (September 2005 to August2007)

occurred at sites Hluhluwe (site 9), Kosi Bay(site 16) and Pongola (site 28), all on the northernKwaZulu–Natal coastal plain, presumably well-ventilated by clean marine air.

For the annual mean NO2 concentrations, spa-tial concentrations contours for both annual cyclesare compared. The highest NO2 concentrationsat sites on the industrial Highveld close to coal-fired power plants leads to the contours plots withthe central red/yellow isopleths almost having acircular distribution, surrounded by otherwise rel-atively uniformly low background field. Contoursfor the 2 years of sampling are similar.

The industrial Highveld has been clearly delin-eated as a zone of elevated NO2 concentrations.The areas surrounding it show the rapidly de-creasing concentrations. For the sites positionedsouthwest and northwest and to a lesser extent,southeast slightly elevated concentrations aredetectable. The overall observation of NO2

over the study area again compares well to theknown transport and dispersion patterns and re-gional scale dispersion models (Scorgie et al.2004a, b).

O3 concentrations and regional distribution

An evaluation of the inter-annual differences forO3 show that concentrations diverged more from1 year to the next than for the other gassesanalysed. Changes ranged between −6.7 and+2.4 ppb in absolute terms and between −30%and +14% changes from the bi-annual mean inrelative terms.

In contrast to wide variations in the mean con-centrations at different sites for SO2 and NO2,O3 mean concentrations lie in a narrow band (be-tween 14 and 23 ppb; Fig. 4). For the majority ofthe sites, the mean measured concentrations werearound 20 ppb. The maximum monthly mean con-centration of 43 ppb, recorded near Thabazimbi(site 37). Lowest O3 values were <5 ppb but stillwell above the detection limit of 0.1 ppb.

Ozone concentrations were not enhanced in thecentral industrial region, as was the case for SO2

and NO2. This is expected, as ozone is a secondarypollutant, and the conversion of ozone precursorshappens during air transport from source regions.There is no evidence in the medium term averages

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Fig. 4 Mean, standard deviation, maximum and minimum O3 monthly concentrations for the period September 2005 toAugust 2007

of enhanced ozone downwind from the Gautengconurbation. A marginally enhanced concentra-tion of 26 ppb at Standerton (site 1) in September2005 to August 2006 annual cycle did not recur thefollowing annual cycle September 2006 to August2007.

The slightly higher concentrations in the north-ernmost sites Tolwe (site 35), Louis Trichardt(site 36) and Thohoyandou (site 37) may be a realeffect. The averages are consistently higher andthe standard deviations lower.

Critical (concentration) levelexceedance assessment

The exceedance assessments of mean concentra-tions of SO2, NO2, and O3 against specific meanlevels (defined as critical levels) for different peri-ods of measurements are presented in the follow-ing section.

Critical level exceedance assessment for SO2

Comparisons between SO2 annual mean valuesfor each site and critical levels are shown in

Fig. 5. Overall, three sites exceeded one or an-other critical level. The most stringent criticallevel is for lichen, exceeded at Elandsfontein (site17), Standerton (site 1) and Amersfoort (site 2).At only one site, Elandsfontein, in the heart ofthe industrial Highveld, was the CLRTAP criti-cal level for forest and (semi)-natural vegetationexceeded. In addition, SO2 concentration at thissite approached the CLRTAP critical level foragricultural vegetation.

The prevailing winter meteorology is dictatedby a lower dispersion potential, so winter monthsshould have higher trace gas concentrations thanin summer, for constant emission rates. This gen-eral observation is supported by the seasonal con-centrations from five representative sites, chosenin the industrial Highveld, downwind from theindustrial Highveld and at remote sites. Graphsof seasonal trends for these three categories areshown in Fig. 6. The selected categories of differ-ent sites are:

• Remote sites [one far north near LouisTrichardt (site 36), and a site southeast of theindustrial Highveld near Gluckstad (site 8)].

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AveSep05-Aug06AveSep06-Aug07SA NEMA: AQA 39, 2004 and SANS: 1929, 2004UNECE CLRTAP, 2004(WHO adopted) - Agricultural VegetationUNECE CLRTAP, 2004 (WHO adopted) - Semi-natural and Forest VegetationUNECE CLRTAP, 2004 (WHO adopted) - Sensitive Lichen

Fig. 5 Annual mean SO2 concentrations for two annual cycles (September 2005 to August 2006 and September 2006 toAugust 2007) compared to various annual SO2 critical levels

• One site in the centre of the industrialHighveld [Elandsfontein, near Kriel (site 17)].

• Two sites downwind from the industrialHighveld (near Standerton (site 1) and nearAmersfoort (site 2)].

To highlight the seasonal trends of SO2, monthlyconcentrations have been averaged by zone andby 3-month moving averages (Fig. 6). The summermeans are lowest and mid-winter means high-est. The factor from lowest to highest seasonal

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Fig. 6 Moving three-monthly SO2 means over two annual cycles (September 2005 to August 2006 and September 2006 toAugust 2007) for three different categories of sites. Different critical levels are the same as in Fig. 5

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differences are from ∼2 to ∼3.5 times greater forthe industrial Highveld zone (category 1) whilemuch less marked close to the source areas (cat-egory 2) and at remote zones (category 3).

Critical level exceedance assessment for NO2

There was no exceedance for NO2 mean annualresults for any of the critical levels. The meanannual values of NO2 for each site were muchlower than all the specified critical levels (Fig. 7).

This general observation is supported by theseasonal (3-month moving mean) concentrationsfrom five representative sites, chosen in the in-dustrial Highveld, downwind from the industrialHighveld and at remote sites (Fig. 8). There islittle seasonal variation of NO2 at any of the sites.Once again, the gradient with spatial reductionfrom the highest in the Highveld centre to theremote rural background is evident.

For the industrial Highveld, seasonal NO2 con-centrations peak during spring and are ∼25%higher than the lowest values that occur duringsummer. For the downwind sites, NO2 shows nodistinct seasonal variation. For remote sites, thereis a variation from minimum to maximum of

∼80%, with a slight enhancement during summermonths.

These relatively constant mean NO2 concentra-tions show no trends that would indicate stronginfluences from lightning produced NOx (LNOx).LNOx peaks strongly in summer over SouthAfrican Highveld, while lighting frequencies dur-ing three winter months comprise <1% (Ojeledeet al. 2008). Total LNOx production in the tro-posphere over the South African Highveld regionwas estimated to be ∼10% of power plant produc-tion (Ojelede et al. 2008).

Critical level exceedance assessment for O3

Annual mean O3 values are assessed for ex-ceedance of the annual critical levels for all thesites in Fig. 9. In the case of ozone, no exceedanceof the measured annual mean was found for either40 or 30 ppb concentration thresholds in either ofthe two annual cycles, although concentrations atseveral sites were relatively close to the 30 ppbthreshold level. If error margins are taken into ac-count, then a few sites only come closer the lower30 ppb threshold—Standerton (site 1), Gluckstad

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Fig. 7 Mean annual NO2 concentrations for two annual cycles (September 2005 to August 2006 and September 2006 toAugust 2007) compared to annual critical levels

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Oct/Nov

/Dec

Nov/D

ec/Ja

n

Dec/Ja

n/Feb

Jan/

Feb/M

ar

Feb/M

ar/A

pr

Mar

/Apr

/May

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ay/Ju

n

May

/Jun/

Jul

Jun/

Jul/A

ug

Jul/A

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ep

Time

NO

2 C

on

c. (

ug

m-3

)

Means by category 1 site (Industrial Highveld)Means by category 2 sites (Downwind from Highveld)Means by category 3 sites (Remote rural)

Fig. 8 Thee-month moving average NO2 concentrations over two annual cycles (September 2005 to August 2006, andSeptember 2006 to August 2007) for three different site categories

(site 8), Memel (site 26), Tolwe (site 35) and LouisTrichardt (site 36)—and not exceeded it.

Seasonal trends at the industrial Highveld,downwind and remote sites are shown in Fig. 10.

All three sites show lowest concentrations duringwinter, as expected.

The remote sites show the strongest summerenhancement of ozone concentration. In contrast,

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Sites

O3

Co

nc.

(p

pb

)

Sep05-Aug06 Mean Sep06-Aug07 Mean

Lower ozone threshold (30 ppb) Higher ozone threshold (40 ppb)

Fig. 9 Mean annual O3 concentrations for two annual cycles (September 2005 to August 2006 and September 2006 toAugust 2007) compared to O3 annual average critical levels. The error bars are set at 15% (Tang and Lau 2000)

Environ Monit Assess

15

20

25

30

35

40

Aug/S

ep/O

ct

Sep/O

ct/Nov

Oct/Nov

/Dec

Nov/D

ec/Ja

n

Dec/Ja

n/Feb

Jan/

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ar

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ar/A

pr

Mar

/Apr

/May

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n

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/Jun/

Jul

Jun/

Jul/A

ug

Jul/A

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ep

Time

O3

Co

nc.

(p

pb

)Means by category 1 site (Industrial Highveld)Means by category 2 sites (Downwind from Highveld)Means by category 3 sites (Remote rural)Critical Level 30 ppb Critical Level 40 ppb

Fig. 10 Three-month moving average O3 over two annual cycles (September 2005 to August 2006 and September 2006 toAugust 2007) for three different site categories

the pattern for the industrial Highveld appearserratic and does not follow the common under-standing that ozone production in the troposphereis highest during summer due to greater solarradiation. The mean for remote sites and slightlyless for the sites downwind from industrial High-veld show the highest records for spring monthsand secondarily for late winter. However, the in-dustrial Highveld category site shows the highestrecords for mid-summer months. Increasing ozoneconcentrations are observed in late winter andearly spring. This pattern is logical, as this seasonhas increased pyrogenic ozone precursors fromwild fires to the north and within South Africa(Swap et al. 2003) and, together with increasedsunlight and daytime, results in higher readings forremote sites. Overall, Fig. 10 shows highly vari-able regional ozone generation and distribution.Certain temporal and spatial regional trends canbe recognised. However, both monthly and long-term averages are below the critical levels.

When the exceedance was assessed through thedifferent sites categories, site category 1 (centralindustrial Highveld) reached the lower thresholdlevel once in late summer, while sites of category2 (downwind industrial Highveld) reached it oncein late spring only. In category 3 (remote rural),ozone slightly exceeded the lower threshold once

in late spring. This temporal trend and spatialpattern confirm ozone assessment findings basedon the annual averages.

Discussion and conclusions

The concentrations and spatial distributions forSO2, NO2 and O3 were measured monthly overtwo annual cycles at 37 sites across the northernand eastern areas of South Africa. Low concen-trations were recorded upwind and in areas re-mote from the Mpumalanga industrial Highveld.Increased concentrations were recorded in thecentre and immediately downwind from High-veld source region. Inter-annual differences forSO2 and NO2 were not substantial, althoughSO2 showed larger inter-annual variations thanNO2. Seasonal trends for SO2 were apparent,winter having the highest concentrations. Secondhighest concentrations occurred in spring or au-tumn, depending on site. For NO2, the seasonaltrends were less obvious. However, summer withlate spring and early autumn had slightly ele-vated levels compared to the remainder of theyear.

Ozone concentrations had distinctly differentspace and time distribution patterns. Increased

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concentrations were recorded outside of the in-dustrial pollution area towards the edges of thestudy area, both south and north, compared tothe central area over the Mpumalanga Highveld.In terms of the inter-annual ozone comparison,concentrations for the majority of sites did notfluctuate widely from year to year. In terms of sea-sonal comparisons, late winter and most of springhad elevated ozone concentrations, with summerlower and autumn the lowest concentrations.

There were multiple exceedances of criticallevels for SO2 in the Highveld industrial sourcearea. Only the lowest critical level was exceededfor the sites located downwind. SO2 critical levelswere not exceeded at the remote locations stationseither downwind or upwind from the source. Thesites downwind and closer to the industrial High-veld are influenced by SO2 emissions from knownpower plant and industrial sources, while remotesites do not show elevated SO2 distribution.

NO2 did not exceed any critical level at anysite. However, NO2 concentrations were higherat sites within and downwind of the industrialHighveld source area, compared to remote sites.Although far below the critical levels, a temporaltrend is recognizable, indicating relative increasesduring the summer. Observed seasonal trends ofNO2 do not reflect the known pattern of light-ning produced NOx (strong summer production,close to zero winter production); thus. LNOx isnot expected to be the significant contributor tothe observed concentrations. Nevertheless, fur-ther investigations of the contribution of LNOx

to the budget of NO2 in the boundary layer arerecommended.

Ozone exceedance of the lowest concentra-tion critical level occurred at several sites. Ex-ceedances occurred for remote sites located bothnorth and southeast off the industrial Highveld.No exceedance of higher critical level was mea-sured. Ozone exceedance for the growing seasonshows a similar pattern. The representative sitesand their means showed that a slight maximumduring late winter and early spring. The distrib-ution of ozone is close to uniform on a regionalbasis, without a localised maximum on the in-dustrial Highveld that was characteristic of SO2

and NO2, confirming the mixed origin of ozoneprecursors and the regional nature of ozone. The

most sensitive plant species like lichens will benegatively affected in the central study region.

Acknowledgements The study was a joint project ofDepartment of Geography, Environmental Managementand Energy Studies, University of Johannesburg; Clima-tology Research Group, University of the Witwatersrand,Johannesburg, and Atmospheric Chemistry ResearchGroup, North-West University, Potchefstroom. Thanks areextended to all those involved, with special thanks to Dr K.Martins for the laboratory work conducted at the NorthWest University. The study was funded jointly by AngloAmerican Corp, Eskom and Sasol, with matching contribu-tions from the Department of Trade and Industry’s THRIPprogramme. M. Josipovic acknowledges the support of aNRF Grant Holder’s Bursary, an Oppenheimer MemorialTrust Fellowship (16068) and a Sasol Fund supplementarybursary through the Faculty of Science, UJ. The work wassupported in part by an NRF Focus Area Project grant toHJA—Sustainability Studies using GIS & Remote SensingFA2005040600018.

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