scorgie and kornelius naca 2009rev01

8/8/2019 Scorgie and Kornelius NACA 2009Rev01

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MODELLING OF ACID DEPOSITION OVERTHE SOUTH AFRICAN HIGHVELD

Yvonne Scorgie1

and Gerrit Kornelius2

1ENVIRON Australia Pty Ltd, 100 Pacific Highway, Sydney, NSW 2060, Australia, [email protected]

2

Airshed Planning Professionals Pty Ltd, P O Box 5260, Halfway House 1685, South Africa, [email protected]

Abstract

Trends in Nitrogen and Sulphur deposition were modelled over the South African Highveld using theintegrated Gaussian puff modeling system CALPUFF. Modelled S deposition correlated with previousmeasurements for the central Highveld. Annual total S deposition rates were predicted to exceed 3 kg/ha-year over much of the Highveld, with a peak of over 35 kg/ha-year over the central Highveld and a lower peak(20 kg/ha-year) over the southern Vaal Triangle. Dry and wet deposition rates were found to varyindependently resulting in significant spatial and temporal variations in ratios of dry to wet deposition. Thecontribution of dry S deposition was predicted to decrease from over 40% in the northwest parts of theHighveld to less than 10% in south eastern parts. Peak dry deposition contributions were simulated to occur

near low level SOx sources (>60%). Wet S deposition was projected to dominate in the vicinity of highstacks. Predicted N deposition rates were lower than measured rates due in part to gaseous ammoniadeposition not having been accounted for in the modelling. Improved N deposition estimates could beachieved through the inventory and modelling of ammonia releases.

Keywords: Acid deposition, sulphur, nitrogen, Highveld, CALPUFF.

1. Introduction

Acid deposition over the South African Highveld hasbeen investigated for over two decades, promptedby a growth in anthropogenic emissions and relatedconcerns about potential degradation of soil andwater resources. Long-term trends in acid

deposition, and the likelihood and timing of criticalload exceedances have been speculated about.The contribution of combustion processes to aciddeposition, and the relative impact of suchdeposition on water quality compared to otherpollutant sources are also of interest.

Regional acid deposition modelling wasundertaken for the Highveld to inform a broaderstudy into the effects of air pollutants on soils, watercatchments and ecosystems. The modelling aimedto address the following information gaps identifiedfrom the review of past studies:

Spatial and interannual trends in total sulphate

(S) and nitrogen (N) deposition and ratios ofwet to dry deposition.

Trends in the contribution of sulphur dioxide(SO2) and sulphate to S deposition

Contributions of individual gaseous and particleconstituents to total N deposition.

Relative source contributions to S and Ndeposition.

Variations in S and N deposition during high,low and average rainfall years.

Historical and future trends in acid deposition.Aspects of this regional study addressed in thispaper include the modelling approach adopted, data

inputs, and select study findings. Spatial and intra-

annual variations in S and N deposition arepresented for a recent base case period (2000/1),and projected longer-term (1920 to 2020) trends inS and N deposition presented and discussed.

1.2. Previous Acid Deposition Studies

Wet deposition has been monitored on the Highveldsince 1985, providing the foundation for areasonable understanding of wet deposition rates(Wells et al., 1987; Wells, 1989; Botha et al., 1990;Turner, 1990; Bluff et al. 1991; Snyman et al., 1991;Wells, 1993; Turner, 1993; Piketh and Annegarn,1994; Turner et al. 1996; Zunckel et al., 1994, 1996,1999; Galpin and Turner, 1999a,b; Galpin and Held,2002). Wet deposition measurement and analysistechniques are primarily based on methods used bythe US-EPA and by the Warren Springs Laboratoryin the United Kingdom. Although the process of wetdeposition monitoring requires meticulous care, the

procedure is relatively inexpensive andstraightforward.The possible significance of dry deposition, given

the relative aridity of the Highveld climate, wasraised in the 1980s and early 1990s (Wells, 1989;Turner, 1993; Wells, 1993). Dry deposition may beeither measured directly or indirectly computed byinferential methods from the measurement ofambient air pollutant concentrations. Wells (1993)recommended the application of the inferentialmethod for the estimation of dry deposition rates onthe Highveld. Turner et al. (1995) demonstrated theeffectiveness of the inferential technique for use onthe Highveld, with estimated S deposition ratescomparable with published data. Inferential


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methods have remained a preferred approach forestimating dry deposition for the Highveld (Zunckelet al., 1996, 1999; Mphepya and Held, 1999).

Atmospheric modelling of deposition has beenundertaken for the Highveld since the late 1990s.Models used have included the US-EPA Lagrangianpuff CALPUFF model, the MATCH Eulerian multi-layered 3D model developed by the SwedishMeteorological and Hydrological Institute, theLagrangian-Eulerian Diffusion (LED) model, and theCAMx Eulerian photochemical dispersion modeldeveloped by ENVIRON (Zunckel et al. 2000a,2000b; Scorgie et al., 2002; Fourie et al., 2005).

Total annual S deposition rates are documentedin the above literatures as peaking in the range of50 to 80 kg/ha-year over the main source area.Annual S deposition rates over the greater Highveldhave been generally estimated to be over 8 kg/ha-year, with deposition rates of greater than 1 kg/ha-year predicted to occur over the entire north-easternparts of the country.

The literature is more divergent with regard to thecontribution of dry S deposition to total S depositionrates on the Highveld. Estimations range from dryS deposition constituting 30% to 80% of totaldeposition, with temporal and spatial variations insuch ratios being poorly addressed by monitoringstudies. Inaccuracies may arise due to ratios beingcalculated based on wet and dry deposition ratesmeasured at different locations.

Nitrogen deposition rates for the Highveld areprimarily published by Turner (1993), Galy-Lacauxet al. (2003, 2008) and Mphepya et al. (2004, 2005).Dry deposition rates at Amersfoort (situated on theHighveld to the southeast of the main source areas)and Louis Trichardt (remote site) have beencalculated based on inferential modelling, using asinput ambient NO2 and NH3 concentrations. Wetdeposition was calculated from the measured NH4

+

and NO3-

concentrations in precipitation and meanannual rainfall

(1).

Total N deposition at Amersfoort has beenquantified as 15 kg N/ha-year, comprising 63% wetdeposition (9.5 kg N/ha-year) and 37% drydeposition (5.6 kg N/ha-year). The large differencein total N deposition between Amersfoort (15 kgN/ha-year) and Louis Trichardt (9 kg N/ha-year)illustrates the influence of industrial activities on thecentral Mpumalanga Highveld.

Based on measurements at Amersfoort, Ndeposition has been found to comprise: 42% drydeposition of gases (NO, NO2, HNO3, NH3), 33%wet deposition of ammonium nitrate, 24% wetdeposition of ammonium sulphate and nitric acidand


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3.2. Source and Emissions Data

3.2.1. Emission Scenarios

Hydrological year, 1 October 2000 to 30 September2001, was selected for model verification and basecase acid deposition characterisation. This yearwas selected due to it being an average rainfall

year. Source and emissions data were also morereadily available for this period.Several break point years, selected on the basis

of projected historical and future emissions, weresimulated using base case (2000/1) meteorology topredict long-term (~1920 to 2020) trends inatmospheric deposition.

3.2.2. Sources Inventoried

Sources inventoried are as follows:

Power generation (primarily coal-fired powergeneration for the national grid);

Industrial sources, including combustion and

process emissions; Household fuel burning, including coal, wood,

LPG and paraffin burning;

Vehicle tailpipe emissions, including petrol- anddiesel-driven vehicles;

Biomass burning (agricultural and wild fires);and

Institutional and commercial fuel burning (whereavailable).

Anthropogenic fuel burning activities and biomassburning emissions account for the bulk of ambientSO2 and NO2 concentrations and associatedatmospheric S and N deposition rates on the

Highveld (Annegarn et al., 2007). Sources of NOxand/or SOx emissions which were not quantifiedand included in the modelling include: spontaneouscombustion within coal storage piles and coaldiscard dumps and natural sources such asbiogenic emissions.

3.2.3. Emission Projections

Preliminary base case modelling indicated thatlarge industrial and power generation sourcesdominate regional acid deposition predictions.More emphasis was therefore placed on theaccurate projection of emissions for such sources.

Historical source and emissions data for Eskompower stations were made available for the 1927 to2007 period. Future power station emissions weretaken to comprise existing and recommissionedpower stations operating at capacity, in addition totwo new 4800 MW power stations (Kusile PS nearexisting Kendal PS; and Golf PS south ofSasolburg). Kusile PS, is under construction andexpected to come on line between 2012 and 2014.Golf PS is proposed for construction and, pendingits approval, will come on line prior to 2019. Bothnew power stations will implement wet fluidized gasdesulphurisation (FGD) with a SO2 control efficiencyof 90-95%. Power generation forecasts for post2025 are uncertain. It is conjectured that if South

Africa is still reliant on coal, new power stations arelikely to be built in the Waterberg (situated to thenorth of the study domain).

The quantification of current and historicalemissions from major industrial sources werebased on: data supplied by companies, theemissions inventory compiled during the VaalTriangle Air Quality Management Plan (AQMP)development project (Liebenberg-Enslin et al.,2008), and the Department of Environmental Affairsand Tourism (DEAT) 1994 Emissions Inventory forScheduled Processes.

Emission projections were not available for largeindustries. Initially emissions were projected basedon economic growth predictions for petrochemical,metallurgical and pulp and paper sectors.Emissions are however unlikely to increase in linewith industrial growth due to tightening regulationsunder the 2004 National Air Quality Act. Given thelocation of major industries within polluted airshedswhich are being subject to air quality management,it is feasible that industrial emissions in 2020 will beequivalent to or lower than current emissions.Emission data for 2007 were therefore taken to beindicative of future emissions for existing majorindustries.

Data from previous studies (Scorgie et al., 2004;Scorgie and Thomas, 2006) were used to quantifybase case emissions for smaller industries,household fuel burning, vehicle tailpipe releasesand vegetation burning. Past and future emissionprojections were based on published trends inmanufacturing volumes, GDP, total household andelectrified household numbers, and fuel sales data.

3.2.4. Base Case Emission Estimates

Major sources were defined as any facility emittingover 10 ktpa of either SOx or NOx, and includedlarge operational coal-fired power stations andmajor industrial complexes.

Total annual SO2 emissions were estimated to be1,463 ktpa, comprising primarily major sourceemissions (96.9%), with minor contributions byvehicles (1.6%), other industry (0.8%), householdfuel burning (0.3%) and biomass burning (0.04%).

Total annual NOx emissions were estimated to be685 ktpa, with significant contributions by major

source emissions (76.9%) and vehicles (21.6%),and minor contributions by other industries (0.6%),residential fuel burning (0.5%) and biomass burning(0.5%).

Areas of maximum emissions are centralisedover the eastern Mpumalanga Highveld and theVaal Triangle. Elevated stack emissions (>100 m)account for 93% of the total SO2 emissionsestimated from all sources (with stacks >250 maccounting for 62% of total SO2 emissions). Interms of NOx emissions, elevated stack emissions(>100 m) account for 70% of the total emissionsestimated from all sources (with stacks >250 m

accounting for 50% of total NOx emissions).


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3.2.5. Breakpoint Year Emissions

Over the 1950 to 2007 period, quantifiable SO2emissions increased significantly from less than 30ktpa to almost 1.9 Mtpa. Quantifiable NOx (as NO)emissions are predicted to have increased over thesame period from about 60 ktpa to over 900 ktpa.By 2020, SO2 and NOx emissions are estimated to

be 2.26 Mtpa and 1.23 Mtpa respectively.Suitable historical and future break point yearswere selected to assess the significance of changesin the magnitude and location of emissions in termsof spatial and interannual variations in aciddeposition rates (Figure 1). Predicted SO2 and NOxemissions for breakpoint years selected areillustrated in Figure 2 and Figure 3 respectively.

3.3. Modelling

3.3.1. Meteorological Modelling

CALMET was used to simulate the meteorological

field based on representative land use,topographical, upper air and surface meteorologicaldata. Meteorological data was input for 19 surfacestations, including the South African WeatherServices (SAWS) Johannesburg, Irene,Vereeniging, Witbank, Leandra, Ermelo,Standerton, Newcastle, Verkykkop and Bethalstations and Eskoms Elandsfontein, Majuba 1,Majuba 3, Kendal 2, Leandra, Makalu, Palmer,Verkykkop and Camden stations. Upper air datafrom SAW S ETA-model stations and tworadiosonde stations, Irene and Bethlehem, wereused. The three dimensional meteorological

dataset generated included model projections atground level, 20m, 200m, 500m, 1500m, and3000m above ground thus parameterising theatmosphere within valley layers, transitional layersand atmospheric layers located above the terrain.

3.3.2. Dispersion Modelling

Gas phase reactions for SOx and NOx werecomputed internally by the CALPUFF model usingthe RIVAD/ARM3 Scheme. This scheme treats theNO and NO2 conversion process in addition to theNO2 and total NO3 and SO2 to SO4 conversions,with equilibrium between gaseous HNO3 andammonium nitrate aerosol. Use was made of site-specific ozone measurement data (together withmodelled radiation intensity) as surrogates for theOH concentration during the daytime when gasphase free radical chemistry is active. Hourlyvarying ozone data were input from EskomsVerkykkop, Palmer, Elandsfontein and Kendal 2stations.

Site-specific deposition velocities were input inthe CALPUFF to facilitate dry deposition modelling.Such velocities were specified as seasonal-average24-hour cycles of deposition velocities for SO2, NO,NO2 and HNO3.

Wet deposition is determined by the scavengingcoefficient which in turn is a function of the

characteristics of the pollutant (solubility, reactivity)as well as the nature of the precipitation. Defaultvalues of the scavenging coefficient for SO2,sulphate, NOx, HNO3 and nitrate included in theCALPUFF model for liquid precipitation were used.Hourly precipitation data for 92 stations were inputto improve the accuracy of wet depositionpredictions.

Total S deposition modelled included wet and drydeposition of gaseous SO2 and particulate SO4.Total N deposition comprises dry deposition of NO,NO2, HNO3, NO3 and ammonium sulphate inaddition to wet deposition of HNO3, NO3 andammonium sulphate.

To account for the contribution of ammoniumsulphate and ammonium nitrate it was assumedthat the predicted SO4 and NO3 are completelyneutralized by NH4 with the following factors beingapplied: 0.292 x SO4 and 0.226 x NO3. Thismethod is frequently applied in the US to accountfor the ammonium associated with SO4 and NO3.

3.3.2. Study Limitations

SO2 can be formed in the atmosphere through thereaction of hydrogen sulphide and ozone (thermalgas-phase photo-oxidation). Substantial H2Semissions occur on the Highveld, with largeindustrial sources on the Mpumalanga Highveldalone accounting for over 70 ktpa. The modellingapproach adopted did not allow for atmosphericSO2 formation due to H2S emissions.

Ammonia emissions were not quantified norsimulated during the study. Given that drydeposition of gaseous ammonia has been

estimated to account for ~30% of total N deposition(Galy-Lacaux et al., 2008), the omission ofammonia from the study is expected to affect theaccuracy of total N deposition rates (including themagnitude and spatial patterns of such ratespredicted) and the accuracy of projected wet to dryN deposition ratios.

3.4. Results

3.4.1. Model Verification

Predicted ambient SO2, SO4, NO, NO2 and NO3concentrations correlated well with measured

concentrations for the central Highveld. AtElandsfontein ratios of modelled to measured levelswere within the range of 0.6 to 1.0 for annualaverages and 0.7 to 1.1 for peak concentrations.

Predicted wet S deposition is of a similar order ofmagnitude as that measured by the acid rainmonitoring network, with ratios of predicted tomeasured deposition rates being in the range of 0.6to 1.6. Although predictions are higher than thosepublished for Amersfoort (ratio of 1.4 to 1.6) it isnotable that higher rainfall occurred in 2000/1 (720mm) compared to the averaged 1996-8 (510 mm)and 1985-1992 (614 mm) periods for which

measurements are published.


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The magnitude and spatial variation in simulatedwet S deposition were also found to be comparableto the rates modelled by Zunckel et al. (2000a), withwet S deposition rates of >3 kg/ha/annum occurringover the entire Highveld and rates of >10kg/ha/annum over the main source areas.

The predicted dry S deposition compares closelywith the deposition modelled using the inferentialmethod published by Mphepya and Held (1999) andZunckel 1999 for both Elandsfontein and Palmer.There is also a good correlation at the downwindstations of Elandsfontein and Amersfoort with theLagrangian-Eulerian Diffusion (LED) dispersionmodelling undertaken by Fourie et al. (2005).

Concurrent dry and wet S deposition could onlybe found in the literature for two sites, Amersfoort(1996-8) and Suikerbosrand (November 1992 March 1993). At Amersfoort, dry S deposition wasmeasured to be 33% of total deposition. Thepercentage of dry S deposition predicted atAmersfoort for 2000/1 by this study is comparable(29%). At Suikerbosrand, dry S deposition wasmeasured to be 44% of total deposition. Thepercentage dry S deposition predicted for 2000/1 atthat site by this study is comparable (40%).

Deposition of SO2 was predicted to account for92% of the total S deposition at Elandsfontein, withthe remainder (8%) due to SO4 particle deposition.This is within the range of values published in theliterature, with Turner et al. (1995) estimating thatSO4 contributed 5% of total S deposition, andZunckel (1999) documenting that SO4 accounts for20% of S deposition at this station.

Predicted total wet N deposition is comparable tomeasured values published by Turner (1993) closerto the central Highveld area, with modelled valuescomprising 50% to 90% of measured values atAmersfoort and Ermelo respectively. Predicted wetN deposition rates however only represent 20% to30% of the measured wet deposition rates at themore peripheral stations (Ladysmith, Vryheid, PietRetief).

Predicted total N deposition at Amersfoort waslower than the total measured N deposition ratespublished for Amersfoort by Galy-Lacaux et al.(2003), with the ratio of predicted to measureddeposition being 0.3. The underprediction is due inpart to dry deposition of ammonia gas not havingbeen accounted for in the predictions.

Predicted and measured N depositionconstituents (excluding ammonia gas drydeposition) were comparable. Total N depositionwas predicted and measured to be dominated bywet deposition of nitrate and ammonium nitrate(~50%), with total wet deposition accounting forover 80% of the total deposition.

It is notable that if gaseous NH3 deposition wasaccounted for in the modelling, the contribution ofnitrate and ammonium nitrate would be reducedfrom ~50% to ~30% with dry gaseous NO, NO2,HNO3 deposition being more significant (~40%).

Accounting for dry NH3 deposition, total wetdeposition would account for less than 60% of thetotal N deposition at Amersfoort.

3.4.2. Predicted Base Case Sulphur Deposition

The total annual S deposition maximum occurs overthe central Highveld (between Witbank, Secundaand Bethal). Deposition rate peaks of >35

kg/ha/year are predicted to occur in the vicinity oflarge point sources including coal-fired powerstations, petrochemical plants and large steelworks.A second maximum, smaller in magnitude andspatial extent, occurs over the southern VaalTriangle, with peak deposition rates of >20kg/ha/year predicted to occur to the east ofSasolburg (Figure 4).

Wet and dry removal of gaseous SO2 contributessignificantly to predicted total annual S depositionmaximums. Dry removal of SO2 was predicted tocontribute most significantly to peak depositionrates in the vicinity of low level major industrial

sources. Wet deposition of gaseous SO2 washowever projected to be a more effective removalmechanism for sulphur emitted from high stackssuch as those of the large-scale power stations.

Significant spatial and temporal variations in theratio of wet to dry S deposition were predicted tooccur, and partially explain the range of ratiospresented in the literature. The spatial variationsare due to a range of factors including: spatialvariations in rainfall (magnitude, frequency), and thelocation and height of significant SOx sources.

The contribution of dry S deposition to total Sdeposition was predicted generally to decrease

from over 40% in the northwest parts of the studyarea to less than 10% in the south-east. Thehighest contribution of dry deposition occurred inthe vicinity of significant low level SOx sources, suchas to the west of Witbank (>60% dry) and atVanderbijlpark (>50% dry). In the vicinity of large-scale power stations and petrochemical plants withhigh stacks, wet S deposition was predicted toaccount for over 70% of total S deposition.

Higher dry S deposition rates occur in thesummer and winter over the central Highveld.Averaged inferred deposition velocities for SO2 areestimated to be larger during summer and smallest

during winter. This is attributable to increased solarradiation, leaf area index (LAI), the photosyntheticactivity of vegetation and variations inmeteorological variables such as temperature andsurface wetness which occurs during summer. Thehigher deposition velocities during summer arehowever offset by the lower ambient SO2concentrations which occur. In contrast, the effectof the lower deposition velocities during winter isoffset by increased ambient SO2 concentrations.

No distinct diurnal pattern is evident in wet Sdeposition, as is to be expected. Neither are diurnaltrends in total S deposition apparent due to the

contribution of wet S deposition to total S deposition


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rates. A diurnal trend is however evident in dry Sdeposition, with the morning peak coinciding withconvective mixing of elevated plumes to ground.

The bulk of the dry deposition is due to daytimedeposition of SO2 over the Highveld associated withthe mixing down of plumes emitted from tall powerplant stacks within the daytime convective boundarylayer. At night vertical down mixing of plumes isinhibited due to stable boundary layer conditions.

3.4.3. Predicted Base Case Nitrogen Deposition

The omission of ammonia emissions from themodelling is expected to result in an underpredictionof dry N deposition (and consequently anunderprediction of total N deposition andexaggeration of wet/dry deposition ratios). Studyfindings presented should therefore be taken asindicative of NOx emission related deposition only.

The total N deposition maximum was predictedto occur at a more easterly location compared tothe S deposition maximum with the peak centred

between Bethal and Ermelo, reflecting the projectedwet N deposition pattern (Figure 5). Wet Ndeposition was estimated to account for over 80%of total deposition over the entire modelling domain.

Total N deposition was predicted to bedominated by wet deposition of nitrate andammonium nitrate, with time taken for formation ofthese products and spatial variations in rainfallaccounting for the deposition maximum beinglocated more remotely from significant NOx sourceareas. Higher rainfalls generally occurred over theeastern parts of the study domain.

Dry N deposition, which is primarily due to dry

deposition of gaseous NO, NO2 and HNO3, waspredicted to peak over the central Highveld,coincident with the widespread elevated NO2concentrations projected. Over the entire modellingdomain contributions of specific species to dry Ndeposition were predicted as follows: NO (~20%)NO2 (~40%) and HNO3 (~40%).

As indicated above, model results are expectedto under predict total N deposition due to gaseousammonia deposition not having been accounted for.The percentage contribution of wet N deposition tototal N deposition is likely to be significantlyoverstated due to the omission of dry deposition of

gaseous ammonia (which has been estimated tocontribute over 30% of total N deposition).

3.4.4. Long-term Trends in Acid Deposition

Predictions for breakpoint years were based on theuse of meteorology for 2000/1 (average rainfallyear). Trends in Highveld SO2 and NOx emissionsover breakpoint years and resultant total S and Ndeposition at specific discrete receptor points aredepicted in Figure 6 and Figure 7. Predicted spatialvariations in total S deposition for breakpoint yearsare illustrated in Figure 8.

The relationship between emissions of acid

deposition precursors and resultant deposition

fluxes is complex and, in many cases, non linear.This non-linearity is expressed as differences in therate of change of emissions and total deposition,differences in the response of wet and drydeposition rates to emission trends, and spatialvariations in deposition trends.

In the USA, strong near-linear correlations werefound between large scale SO

2emission reductions

and large reductions in sulphate concentrations inprecipitation in the Northeast, one of the areas mostaffected by acid deposition (NADP, 2007). In theUK however, significant non-linearities have beennoted in the relationship between sulphur emissionsand deposition over the past two decades (Fowleret al., 2007). In the case of the US, the locationsand emission heights of sources remained relativelyunchanged despite reductions in emissions fromsuch sources. Whereas in the UK, there have beensignificant changes in the location and configurationof sources with urban emissions becoming moreprominent as industrial emissions have increasinglybeen reduced.

No clear linear relationship is projected betweenSO2 emissions and catchment receptor Sdeposition during the 1948 to 1979 period (Figure6). This is to be expected since the location ofmajor SO2 sources varied significantly over thisperiod, with peak source areas switching fromWitbank (pre 1950) to Witbank, Vereeniging andGermiston (1950-5), and subsequently includingSasolburg (1956-1962). Thereafter, Komati PSsituated near Bethal became the dominant powergeneration source (1963-1967).

Increasingly power generation shifted to theMpumalanga Highveld and large industrial sourceswere commissioned in the region. By the late1970s / early 1980s, power generation emissionsprimarily occurred over the Mpumalanga Highveldand other major sources (e.g. Highveld Steel andVanadium) were commissioned. Throughout the1980s emissions intensified over this region withDuvha and Matla power stations beingcommissioned and their operating capacity steppedup, and Sasol 2 and 3 becoming operational atSecunda.

The significance of the Mpumalanga Highveldregion as the dominant source of SO2 emissionspersisted throughout the 1990s and 2000s. Thisregion is projected to become even more significantby 2020 as existing power stations increase theiroutput and the planned Kusile PS near Witbank iscommissioned.

Given continued SO2 emissions from elevatedstacks situated on the Mpumalanga Highveld, theprojected linear relationship between SO2emissions and predicted S deposition during the1980 to 2020 period is conceivable (Figure 6). Theclosest relationship is found with deposition trendsprojected for the Olifants and Komati catchmentswhich are situated, along with the Elandsfonteinreceptor, within the major source region.


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The increasing intensity of emissions on theMpumalanga Highveld over the past three decadesis also apparent in projected spatial N depositionpatterns and deposition rates predicted forcatchment receptors (Figure 7).

4. Conclusions

Regional acid deposition modelling was undertakenfor the Highveld to address information gapsregarding intra-annual trends and spatial variationsin S and N deposition, and to project longer-termtrends in deposition rates.

Dispersion model results correlated reasonablywell with measured SO2, SO4, NO, NO2 and NO3concentrations and S deposition rates for thecentral Highveld. This correlation reinforced theearlier finding by Zunckel et al. (2000a) thatHighveld emissions account for well over 80% ofthe total S deposition occurring in the region. Themodelling domain and methodological approach

was therefore considered suitable for simulating Sdeposition trends within the hydrologicalcatchments on the central Highveld. Model resultsgenerally underpredicted ambient concentrationsand deposition rates at peripheral sites situated atthe outer extents of the modelling domain.

Annual total S deposition rates exceed 3 kg/ha-year over much of the Highveld, with a peak of over35 kg/ha/year over the central Highveld (betweenWitbank, Secunda and Bethal) and a lower peak(20 kg/ha-year) over the southern Vaal Triangle.

Spatial and temporal trends in dry and wetdeposition vary independently, thus accounting for

significant spatial and temporal trends in ratios ofdry to wet deposition. The contribution of dry Sdeposition was predicted to decrease from over40% in the northwest parts of the study domain toless than 10% in the south-eastern corner. Thehighest contribution of dry deposition occurred nearlow level SOx sources (>60%), whereas wet Sdeposition was predicted to account for over 70% oftotal S deposition in the vicinity of high stacks.According to global modelling by Dentener et al.(2006), wet deposition was found to contributebetween 50% and 70% of the total global SOxdeposition.

Predicted dry and total N deposition rates werelower than measured rates due in part to gaseousammonia deposition not having been accounted forin the modelling. The percentage contribution ofwet N deposition to total N deposition is likely to besignificantly overstated due to the omission of drydeposition of gaseous ammonia, which haspreviously been estimated to contribute over 30% oftotal N deposition. Study findings presented aretherefore indicative of NOx emission related Ndeposition only. Improved N deposition estimatescould be achieved through the inventory andmodelling of ammonia releases and biogenic NOx

sources.

Highveld SO2 and NOx emissions have grownsignificantly over the past eighty years from


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Scorgie Y., Burger L.W. and Annegarn H.J., 2004,Socio-Economic Impact of Air Pollution ReductionMeasures - Task 2: Establishment of SourceInventories, and Task 3: Identification andPrioritisation of Technology Options, Reportcompiled on behalf of National EconomicDevelopment and Labour Council (NEDLAC)under the Fund for Research into IndustrialGrowth and Equity (FRIDGE), 2004.

Scorgie Y. and Thomas R., 2006, EskomMpumalanga Highveld Cumulative ScenarioPlanning Study, Air Pollution ComplianceAssessment and Health Risk Analysis ofCumulative Operations of Current, Return toService and Proposed Eskom Power StationsLocated within the Mpumalanga and GautengProvinces, Project completed by Airshed PlanningProfessionals Pty Ltd on behalf of EskomHoldings Ltd, Report No. APP/06/ESKOM-05 Rev1.0, October 2006.

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sulphur deposition over Southern Africa resultingfrom scheduled Highveld emissions, Paperpresented at the National Association for CleanAir Conference "Lessons from the Past - Solutionfor the Future", held at Mount Amanzi,Hartebeespoort, 14 & 15 September 2000.


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Figures

Historical Trend in Annual Sulphur Dioxide Emissions from Major Industrial Sources

(with selected Break Point Years indicated)

-

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

1927

1929

1931

1933

1935

1937

1939

1941

1943

1945

1947

1949

1951

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

SO2Emissions(ktpa)

Other Major Sources

Eskom Power Stations

Figure 1. Historical trends in annual SO2 emissions from major sources with selected break pointyears indicated

Highveld SO2 Emissions for Breakpoint Years

-

500

1,000

1,500

2,000

2,500

1948 1951 1961 1965 1974 1979 1984 2000 2006 2020

Emissions(ktpa)

Other Sources

Major Industry

Power Generation

Figure 2. Annual SO2 emissions for breakpoint years selected


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Highveld NOx (as NO) Emissions for Breakpoint Years

-

200

400

600

800

1,000

1,200

1948 1951 1961 1965 1974 1979 1984 2000 2006 2020

Emissions(ktpa)

Other Sources

Major Industry

Power Generation

Figure 3. Annual NOx emissions for breakpoint years selected

Figure 4. Predicted annual S deposition over the Highveld for the base case year (2000/1) and dry Sdeposition as a percentage of total S deposition


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Figure 5. Predicted annual wet and total N deposition over the Highveld for the base case year(2000/1)

Total SO2 Emissions and Total Sulphur Deposition for Emission Break Years

(modelling using average rainfall meteorological year, 2000-1)

0

5

10

15

20

25

30

35

40

45

1940 1950 1960 1970 1980 1990 2000 2010 2020 2030

Break Years

AnnualSDeposition(kgS/ha/annum

-

500

1,000

1,500

2,000

2,500

SO2Emissions(ktpa)

Sabie catchment deposition (kg/ha/year)

Komati catchment deposition (kg/ha/year)

Olifants catchment deposition (kg/ha/year)

Sandspruit catchment deposition (kg/ha/year)

Elandsfontein deposition (kg/ha/year)

SO2 Emissions (ktpa)

Figure 6. Trends in Highveld SO2 emissions and predicted total sulphur deposition rates at selecteddiscrete receptors during breakpoint years


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Total Nox (as NO) Emissions and Total Nitrogen Deposition for Emission Break Years

(modelling using average rainfall meteorological year, 2000-1)

0

2

4

6

8

10

12

14

1940 1950 1960 1970 1980 1990 2000 2010 2020 2030

Break Years

AnnualNDeposition(kgN/ha/annum)

-

200

400

600

800

1,000

1,200

NOxEmissions(ktpa)

Sabie catchment deposition (kg/ha/year)

Komati catchment deposition (kg/ha/year)

Olifants catchment deposition (kg/ha/year)

Sandspruit catchment deposition (kg/ha/year)

Elandsfontein deposition (kg/ha/year)

NOx Emissions (as NO, ktpa)

Figure 7. Trends in Highveld NOx (as NO) emissions and predicted total nitrogen deposition rates atselected discrete receptors during breakpoint years

Figure 8. Predicted spatial variations in total S deposition for breakpoint years (kg/ha/year).Predictions for breakpoint years were based on the use of meteorology for 2000/1 (average rainfallyear).

scorgie and kornelius naca 2009rev01

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