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Amendment Basic Assessment: Air Quality Specialist Baseline Study and

Saldanha Air Quality Permit Amendment Basic Assessment: Air

Quality Specialist Baseline Study and Impact Assessment

Report Prepared for

Transnet Limited

Report No

Saldanha Air Quality Permit

Amendment Basic Assessment: Air Quality Specialist Baseline Study and

Impact Assessment

Report Prepared for

Transnet Limited

eport No 399449/42A

September 2009

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NADH/nadh 399449_Saldanha Air Quality BA_Air Quality Study_FINAL September 2009

Saldanha Air Quality Permit Basic

Assessment: Air Quality Specialist Baseline Study and Impact

Assessment

Transnet Limited

SRK Project Number 399449

SRK Consulting 2A IBM House

54 Norfolk Terrace Westville, 3630

South Africa

P O Box 1969 Westville

3630 South Africa

Tel: (031) 279-1200 Fax: (031) 279-1204

[email protected]

September 2009 Compiled by: Reviewed by: _________________________ _________________________ D Naidoo VS Reddy (Pr. Sci. Nat) VS Reddy

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Executive Summary The Transnet iron ore handling facility at the Port of Saldanha, some 120 km north of Cape Town,

receives and stockpiles iron ore mined in the Northern Cape. The ore is then loaded onto ships and

exported. The facility is currently authorised to export 45 million tonnes per annum (MTPA) of iron

ore and holds a temporary air quality permit in terms of the Atmospheric Pollution Prevention Act

(Act 45 of 1965) for throughput of 47 MTPA, of which 2 MTPA are routed directly to Saldanha

Steel.

Transnet has identified operational inefficiencies at the iron ore handling facility and estimates that

once the inefficiencies have been addressed, the facility can process up to 60 MTPA of iron ore.

Kumba’s new Sishen-South mine in the Northern Cape ensures that an additional supply of iron ore

is available to meet these throughput targets. To achieve this throughput, Transnet needs to apply for

an amendment to its air quality permit. In terms of the EIA Regulations under the National

Environmental Management Act, any changes to facilities requiring an amendment of a permit

related to the release of emissions require that a Basic Assessment (BA) be undertaken.

SRK Consulting (SRK) has been appointed by Transnet to conduct the BA to inform the proposed

amendment of the air quality permit. As part of the BA process, SRK appointed specialists to carry

out the necessary baseline studies and impact assessments (hereinafter referred to as the ‘specialist

studies’), which will inform the BA undertaken by SRK.

Impact Assessment

Although the results of the modelling for the 31 MTPA, 47 MTPA and 60 MTPA throughput levels

are presented in this report, the impact assessment focuses on 60 MTPA, with 47 MTPA being the

baseline condition. Where necessary, comment will be given on changes in impacts relative to

31 MTPA. The change in the concentration of PM10 and TSP dust emissions due to the increased

throughput at the IOHF is the only environmental aspect covered in this study. Other emission

sources, e.g. emissions from ships and the Salkor shunting yard, were reviewed but were not

considered to be significant when compared to dust emissions due to ore handling, and were

therefore not assessed in detail.

Residents in the towns surrounding the site are the potential sensitive receptors in this study.

Surrounding towns include Vredenburg to the north, Saldanha and Blue Water Bay to the west and

Langebaan to the south east of the IOHF. The environmental and nuisance impacts of change in

ambient PM10 and dust fallout levels in these areas were therefore assessed.

Assessment of Impacts for 47 MTPA (no-go alternativ e)

Impacts associated with changes in ambient PM 10 concentrations for 47 MTPA throughput

PM10 is of concern as it has a potential impact on health as respirable dust. As shown in the

47 MTPA isopleth maps (Appendix A, Figure 7 and 8), PM10 will affect the project area and

communities beyond the project boundary for unmitigated conditions. However, with mitigation the

PM10 footprint will decrease considerably to the project area.

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The impact is considered to be of high (negative) significance without mitigation, as shown in the

table below and determined as follows:

• Extent – The extent was rated regional because areas beyond the project area will be affected by

PM10 such as Vredenburg.

• Intensity – The intensity is medium because processes will be altered, but able to continue

around the project area.

• Duration – The duration of the impact will be long term, because it will coincide with the life of

the operations.

• Probability – The impact is probable and in this case provisions will have to be made.

• Confidence – The confidence level is high because on site visuals as well as dispersion

modelling give a good assessment of the impact.

The impact is considered to be of low (negative) significance with mitigation, as shown in the table

below and determined as follows:

• Extent – Local because under mitigated conditions the PM10 footprint will decrease around the

project area and will not affect towns or communities greater than 5 km away.

• Intensity – The intensity is low because the impact will alter the environment, but not change

any natural processes or functions in the project area.


the operations.


• Confidence – The confidence level is high because on-site visuals as well as dispersion


Extent Intensity Duration Consequence Probability Significance Status Confidence

Without mitigation

Regional Medium Long-term High Probable HIGH – ve High

2 2 3 7

Key mitigation measures: • Pave all unpaved roads; • Housekeeping should occur more frequently and should include the following:

o Sweep all paved surfaces (e.g. roads) within the terminal daily; and o Sweep or remove dust piles from unpaved surfaces that contain deposits of material that could lead to

fugitive dust emissions, e.g. dust falling from the conveyor belts. This should happen at least weekly or more often if necessary to prevent accumulation of material;

• Maintain an iron ore moisture content of 1.2%;

• Cover all transfer points and conveyor belts, where practical, to reduce wind speeds and re-entrainment of dust into the atmosphere during ore transfer;

• Ensure optimal performance of cartridge filters systems that are installed at the dust extraction system at the tipplers;

• Use a chemical suppressant (as a substitute to moisture control especially when it is difficult to maintain a stable moisture level over a long period of time).

With mitigation

Local Low Long-term Low Probable LOW – ve High

1 1 3 5

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Impacts associated with changes in dust fallout con centrations for 47 MTPA throughput (no-go alternative)

Dust fallout is primarily a measure of nuisance, which occurs when a fine, powdery substance settles

on the ground as an adverse secondary effect. Figures 9 and 10 in Appendix A show the unmitigated

modelled scenario for dust fallout as well as the mitigated scenario respectively and provide an

indication of the impacts that dust fallout will have in the project area and surrounding communities.

Dust fallout is measured in four areas around the project area, and these areas can be defined as

discrete receptors.



• Extent – Regional because areas beyond the project area will be affected by dust fallout such as

Vredenburg.




the operations.

• Probability – The impact is probable, and in this case provisions will have to be made.





• Extent – Local because under mitigated conditions the extent of the dust fallout will only be

significant around the project area and will not affect towns or communities greater than 2 km

away.




the operations.




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Without mitigation

Regional Medium Long-term High Probable High – ve High

2 2 3 7






• Ensure optimal performance of cartridge filter systems that are installed at the dust extraction system at the tipplers;


With mitigation


1 1 3 5

Assessment of Impacts for 60 MTPA The proposed 60 MTPA throughput will increase the footprint of both PM10 and dust fallout, under

unmitigated conditions, when compared to the actual throughput of approximately 31 MTPA, which

is being achieved at the IOHF currently. Mitigation measures have to be implemented to full effect if

the change from 31 MTPA to 60 MTPA is considered. The size of the area affected will increase,

and the impact will still occur on a regional scale under unmitigated conditions. Under mitigated

conditions both the footprints for PM10 and dust fallout will decrease and the area impacted will be

on a local scale.

Impacts associated with changes in ambient PM 10 concentrations for 60 MTPA throughput



• Extent – Regional because areas beyond the project area will be affected by PM10 such as

Vredenburg




the operations.






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• Extent – Local because under mitigated conditions the extent of the impact will be significant

around the project area and will not affect towns or communities greater than 5 km away.




the operations.

• Probability – The impact is probable and in this case provisions will have to be made in case of

significant influences.




Without mitigation


2 2 3 7








With mitigation


1 1 3 5

Impacts associated with changes in dust fallout (nu isance) concentrations for 60 MTPA throughput

The impact is considered to be of very high (negative) significance without mitigation, as shown in

the table below and determined as follows:


Vredenburg and Langebaan.

• Intensity – The intensity is high because of the high dust volume and its staining properties.


the operations.




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around the project area and will not affect towns or communities greater than 5km away.




the operations.

• Probability – The impact is possible and in this case provisions will have to be made in case of

significant influences.




Without mitigation

Regional High Long-term Very High Probable VERY HIGH – ve High

2 3 3 8








With mitigation


1 1 3 5

Conclusions and Recommendations

Conclusions

Based on the findings of this assessment, the following are concluded:

• The increase in throughput of iron ore at the IOHF from 47 MTPA to 60MPTA will result in an

increase in ambient PM10 and dust fallout levels in the surrounding environment for unmitigated

and mitigated conditions.

• The relative increase in ambient PM10 and dust fallout levels for the unmitigated scenario will be

greater from 31 MTPA to 60 MTPA than from 47 MTPA to 60 MTPA.

• The implementation of appropriate mitigation measures will result in ambient PM10 and dust

fallout levels that will be significantly below the proposed South African standards and

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guidelines for PM10 and dust fallout in Saldanha, Blue Water Bay and Vredenburg and levels

that are lower than unmitigated conditions for the current 31 MTPA.

• Of the various mitigation scenarios considered, the optimum mitigation scenario is maintenance

of a minimum 1.2% moisture content in the ore throughout the facility, at least 50% dust

suppression efficiency, paving of all roads (except those within the stockpiles), sweeping of

paved roads and good housing keeping (i.e. cleaning up of spills and any fugitive dust that

occurs within the facility).

• Dust mitigation is an imperative for the proposed increased throughput, and this must be

considered to be an operational activity and not an add-on to daily operations.

Recommendations

Mitigation measures that are usually recommended for an operation of this nature are included

below. Some if not all of these measures will be implemented as part of the Phase 1B (47 MTPA)

expansion. However these measures also need to be implemented and maintained for the 60 MTPA

scenario as a minimum:

• Maintain an iron ore moisture content of 1.2% (more detail provided later in this section),

• Continue using a chemical suppressant (as a substitute to moisture control especially when it is

difficult to maintain a stable moisture level over a long period of time);

• Pave all unpaved roads except for those between the stockpiles;

• Reduce vehicle speeds on roads;

• Improve housekeeping at all times to include the following:

o Sweep all paved surfaces (e.g. roads) within the terminal daily; and

o Sweep or remove dust piles from unpaved surfaces that contain deposits of material that

could lead to fugitive dust emissions, e.g. dust falling from the conveyor belts. This should

happen at least weekly or more often if necessary to prevent accumulation of material;

• Ensure optimal performance of cartridge filters systems that are installed at the dust extraction

system at the tipplers;

• Install wind shields/breakers along the conveyor belt system where this has not been

implemented;

• Reduce fugitive dust emissions from miscellaneous sources such as dust coming off the return

conveyors;

• Ensure that all transfer points and conveyor belts are covered, where practical, in order to reduce

wind speeds and re-entrainment of dust into the atmosphere during ore transfer, where this has

not been done. Maintain existing covers; and

• Cover any bare ground in the areas surrounding the main operational area with suitable

vegetation that will be able to grow in the area.

In addition, if possible, wind breakers such as trees in areas located upwind of the facility could be

installed / planted.

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Of the listed measures, moisture control, paving of unpaved road surfaces, sweeping of paved roads,

enclosure of transfers points with mist sprayers and housekeeping were the primary mitigation

measures that were considered during modelling.

The mitigation measures used during the modelling phase of this study were largely guided by

measures that the engineers from Transnet have recommended based on tests conducted by Kumba

Resources and engineering and designs that were considered for the expansion of the IOHF. In

addition to this, these measures are primarily what are practical and most effective for an operation

of this nature. Details on the possible dust mitigation measures are presented in the following

sections.

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Moisture Control

For the purposes of the modelling study, the mitigation measures considered included increasing and

maintaining the moisture content of iron ore at 1%, 1.2%, and 1.4% by use of sprayers at various

points in the operations, especially at materials handling points. The increased moisture content will

promote aggregation of small dust particles to larger particles, which will reduce the amount of

windblown dust from various sources. The modelling results (discussed in Section 8) showed that

increasing the residual moisture content of the ore to 1% resulted in a significant decrease in dust

emissions, and hence a decrease in ambient PM10 and dust fallout is predicted. The results of the

simulation for the three levels of moisture content indicated that maintenance of a moisture content of 1.2% in the ore yielded an optimum level dust emission reduction and hence the optimum

PM10 and dust fallout levels in the surrounding environment.

Paving of Roads

Transnet have paved all road surfaces outside the stockpiles, but within stockpiles roads are

unpaved. The modelling results as present in Section 8 indicate that this measure would contribute to

a significant reduction of dust emissions, thus reducing the footprint of the impacted area. It should

be noted that the model assumed that the road surfaces will be swept and dust on the road

surfaces kept to a minimum or negligible level.

Enclosure of Transfer Points

For the purposes of this study, the efficiency of dust reduction that would result from the enclosure

and installation of mist sprayers over all transfers points up to the sampling plant was not known.

Hence different modelling efficiencies at transfer points were modelled, i.e. 50% and 75%. (It should

be noted that these were found to be optimum efficiencies based on the findings of the Phase 2

study.) This study has found that for 60 MTPA iron ore throughput, a minimum dust suppression

efficiency at transfer points of 50% is required.

Housekeeping

Housekeeping will contribute to a significant reduction of dust emissions from fugitive sources at the

IOHF. Whilst this is not a primary source of dust, it is a secondary source that will significantly

reduce the levels of dust emissions especially given that such sources are difficult to manage, due to

the their variability and spatial extent during very windy periods. Activities or areas that need

particular attention include with respect to housekeeping:

• Sweeping of road surfaces and open surface water channels;

• Removal of dust accumulating under the conveyor belts and transfer points; and

• Vegetate bare or open ground within the IOHF that could be a source of windblown dust.

Tippler Dust Filter Plant

The tipplers need to operate at efficiencies that meet the design specifications at all times i.e. with

total dust emission levels below 10 mg/Nm3.

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Performance Monitoring Measures

The following performance monitoring measures are recommended:

• Install and maintain a dust fallout and ambient monitoring network (including the weather

stations) to measure and monitor PM10 and dust fallout levels emitted by the facility. Ensure that

the network remains in good working order. It is important to note that a well run monitoring

network with a >95% data availability will help monitor the impacts from the IOHF and any

mitigation measures that have been implemented ;

• Expand the monitoring network to include measurement of dust fallout and PM10 levels in the

Langebaan area (baseline and background monitoring) and a PM10 monitor closer to the IOHF in

order to more accurately monitor dust emissions that are largely due to the IOHF;

• Develop a system to monitor and rapidly respond to spills;

• Monitor the effectiveness of the proposed dust suppression system based on moisture content of

the ore and investigate, identify and implement opportunities for optimisation of the system if it

contributes to a further decrease in dust levels;

• Conduct periodic audits of water sprayers and systems to ensure good working condition,

resulting in efficient reduction in wind erosion;

• Conduct periodic and independent audits of the monitoring systems and implementation of

operational management plans to ensure that the system is being maintained properly and that

the outputs of the monitoring system are providing suitable data for support in decision making;

and

• Conduct a performance audit on staff that have been appointed to manage the various dust

control and mitigation systems recommended in this study.

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Table of Contents

1 Introduction and Background ....................... .............................................................. 1

1.1 Intoduction ......................................................................... Error! Bookmark not defined.

1.2 Project Location ................................................................................................................ 2

1.3 Project Description ............................................................................................................ 3

2 Objectives and Scope of Work ...................... .............................................................. 3

2.1 Objectives ......................................................................................................................... 3

2.2 Scope of Work .................................................................................................................. 4

3 Assumptions and Limitations ....................... .............................................................. 5

4 Air Quality Legislation ........................... ...................................................................... 5

4.1 Local and International Air Quality Guidelines and Standards ........................................... 5

4.2 Pollutants .......................................................................................................................... 6

4.2.1 Particulate Matter .................................................................................................................... 6

4.2.2 Dust Deposition or Dust Fallout .............................................................................................. 6

5 Meteorology and Pollution Dispersion in Saldanha Ba y, Western Cape ................. 7

5.1 Synoptic Climatology of Southern Africa ........................................................................... 7

5.2 Regional Climatology of the Western Cape ....................................................................... 9

5.3 Local Climate of Saldanha .............................................................................................. 10

5.3.1 Rainfall .................................................................................................................................. 10

5.3.2 Ambient Air Temperature ...................................................................................................... 11

5.3.3 Wind ...................................................................................................................................... 12

5.3.4 Atmospheric Stability and Mixing Depth................................................................................ 17

5.3.5 Summary of Description of the Weather in Saldanha ........................................................... 17

5.4 Ambient Dust Monitoring ................................................................................................. 18

5.4.1 PM10 Monitoring ..................................................................................................................... 18

5.4.2 Dust Fallout ........................................................................................................................... 26

6 Emissions Inventory ............................... ................................................................... 26

6.1 Emissions Calculations and Methodology ....................................................................... 26

6.2 Potential Sources of Air Emissions .................................................................................. 26

6.2.1 Transfer Points ...................................................................................................................... 29

6.2.2 Stacking and Reclaiming ....................................................................................................... 30

6.2.3 Dust Generation by Vehicles on Roads ................................................................................ 31

6.2.4 Wind Erosion ......................................................................................................................... 33

6.2.5 Wagon Tipplers ..................................................................................................................... 36

6.2.6 Ship Loading ......................................................................................................................... 36

6.3 Summary of Emissions ................................................................................................... 36

6.3.1 Unmitigated Emission Loads ................................................................................................. 36

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6.3.2 Mitigation Scenario 1 - 1.2% moisture content and 50% control efficiency at the transfer points ..................................................................................................................................... 37

6.3.3 Mitigation Scenario 2 - 1.2% moisture content and 75% control efficiency at the transfer points ..................................................................................................................................... 39

7 Dispersion Modelling and Model Inputs ............. ...................................................... 40

7.1 Description of the Air Dispersion Model .......................................................................... 40

7.2 Model Input Data ............................................................................................................. 41

7.2.1 Meteorological Data .............................................................................................................. 41

7.2.2 Receptor Grid ........................................................................................................................ 41

7.2.3 Particle Size and Density ...................................................................................................... 41

7.2.4 Source Data .......................................................................................................................... 41

8 Dispersion Modelling Results ...................... ............................................................. 45

8.1 Dust Dispersion Modelling Results .................................................................................. 45

8.2 Discussion of Dust Dispersion Modelling Results ............................................................ 45

8.2.1 PM10 Modelling Results for 31 MTPA.................................................................................... 46

8.2.2 Dust Fallout Modelling Results for 31 MTPA ........................................................................ 47





8.3 Model Calibration ............................................................................................................ 50

9 Impact Assessment ................................. ................................................................... 52

9.1 Assessment of Impacts for 47 MTPA .............................................................................. 52

9.1.1 Impacts associated with changes in ambient PM10 concentrations for 47 MTPA throughput52

9.1.2 Impacts associated with changes in dust fallout concentrations for 47 MTPA throughput ... 54

9.2 Assessment of Impacts for 60 MTPA .............................................................................. 55

9.2.1 Impacts associated with changes in ambient PM10 concentrations for 60 MTPA throughput55

9.2.2 Impacts associated with changes in dust fallout (nuisance) concentrations for 60 MTPA throughput ............................................................................................................................. 56

10 Conclusions and Recommendations.................... .................................................... 58

10.1 Conclusions .................................................................................................................... 58

10.2 Recommendations .......................................................................................................... 58

10.2.1 Moisture Control .................................................................................................................... 59

10.2.2 Paving of Roads .................................................................................................................... 59

10.2.3 Enclosure of Transfer Points ................................................................................................. 60

10.2.4 Housekeeping ....................................................................................................................... 60

10.2.5 Tippler Dust Filter Plant ......................................................................................................... 60

10.3 Performance Monitoring Measures ................................................................................. 60

11 References ........................................ .......................................................................... 62

Appendices ........................................ .............................................................................. 64

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Appendix A: Isopleth maps ...................................................................................................... 64

Appendices ........................................ .............................................................................. 65

Appendix B: Impact Assessment Methodology ........................................................................ 65

List of Tables Table 4.1: Air quality guidelines and standards for PM10 ................................................................. 6

Table 4.2: SANS 1929:2004. Target values given in mg/m2/day ..................................................... 6

Table 5.1: Rainfall data for Geelbek and Langebaanweg for 2008 ................................................ 10

Table 5.2: Maximum, minimum and average temperatures in oC at Geelbek for 2008 .................. 11

Table 5.3: Maximum, minimum and average temperatures in oC at Langebaanweg for 2008 ....... 11

Table 5.4: Average daily and monthly PM10 levels (µg/m3) for Vredenburg for the period January 2008 to May 2009 ........................................................................................................ 19

Table 5.5: Average daily and monthly PM10 levels (µg/m3) for Blue Water Bay for the period January 2008 to May 2009 .......................................................................................... 20

Table 5.6: Dust fallout results for the period March 2008 to April 2009 in mg/m2/day .................... 25

Table 6.1 Potential sources of air emissions and their importance as a source of air pollution related to the IOHF. ..................................................................................................... 27

Table 6.2 Derived moisture contents at transfer points for winter and summer for unmitigated conditions. ................................................................................................................... 30

Table 6.3: Ranking of unmitigated dust sources using calculated annual emission loads for PM10 and TSP for 31 MTPA, 47 MTPA and 60 MTPA .......................................................... 37

Table 6.4: Ranking of mitigated dust sources using calculated annual emission loads for PM10 and TSP for 31 MTPA, 47 MTPA and 60 MTPA (1.2% moisture content and paved roads - 50 % mitigation efficiency) ........................................................................................... 38

Table 6.5: Ranking of mitigated dust sources using calculated annual emission loads for PM10 and TSP for 31 MTPA and 60 MTPA (1.2% moisture content and paved roads- 75 % mitigation efficiency) .................................................................................................... 39

Table 7.1: Summary of particle diameter, corresponding measured mass fraction and particle density up to 1000 um ................................................................................................. 41

Table 7.2: 31 MTPA, 47 MTPA and 60 MTPA dust emission rates used for modelling (unmitigated)42

Table 7.3: 31 MTPA, 47 MTPA and 60 MTPA dust emission rates used for modelling 1.2% ore moisture content, mitigation at 50% efficiency ............................................................. 43

Table 7.4: 31 MTPA, 47 MTPA and 60MTPA dust emission rates used for modelling 1.2% ore moisture content, mitigation at 75% efficiency ............................................................. 44

Table 8.1 Monitored and predicted PM10 average maximum 24-hr concentrations for 31 MTPA ... 46

Table 8.2 Monitored and predicted maximum dust fallout concentrations for 31 MTPA ................. 47

Table 8.3: Predicted average maximum 24-hour ambient PM10 modelled concentrations for 47 MTPA .......................................................................................................................... 48

Table 8.4: Monitored and predicted maximum dust fallout concentrations for 47 MTPA ................ 49

Table 8.5: Predicted average maximum 24-hour ambient PM10 modelled concentrations for 60 MTPA ......................................................................................................................... 49

Table 8.6: Monitored and predicted maximum dust fallout concentrations for 60 MTPA ................ 50

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Table 8.7: Predominant wind direction and wind speed for the modelled periods ......................... 50

Table 8.8: Monitored and modelled dust concentrations for 31 MTPA .......................................... 51

List of Figures Figure 1.1: Location of Saldanha Bay ............................................................................................. 2

Figure 5.1: Annual variations in the positions of the South Atlantic and South Indian anticyclones . 8

Figure 5.2: Important features of the surface atmospheric circulation over southern Africa ............. 9

Figure 5.3: Rainfall at Geelbek and Langebaanweg for 2008 ........................................................ 10

Figure 5.4: Averaged monthly temperatures for Geelbek and Langebaanweg in 2008 .................. 11

Figure 5.5: Annual all hours daytime and night time all hours wind roses for Geelbek .................. 13

Figure 5.6: Seasonal wind roses for Geelbek ................................................................................ 14

Figure 5.7: Annual all hours daytime and night time all hours wind roses for Langebaanweg ....... 15

Figure 5.8: Seasonal wind roses for Langebaanweg ..................................................................... 16

Figure 5.9: Location of the PM10 and Dust fallout monitoring points .............................................. 24

Figure 5.10 Average dailyAveraged monthly concentrations by month for Vredenburg for the period January 2008 to May 2009 .......................................................................................... 25

Figure 5.11 Average dailyAveraged monthly concentrations by month for Blue Water Bay for the period January 2008 to May 2009 ............................................................................... 25

Figure 5.12: Dust fallout for the period March 2008 to April 2009 .................................................. 26

Figure 6.1: Relationship between particle sizes and threshold friction velocities ........................... 33

Figure 6.2: Contours of normalised surface wind speeds (surface wind speed (Us) / approach wind speed (Ur)) .................................................................................................................. 35

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Glossary

Adiabatic: Occurring without the gain or loss of heat. When a gas is compressed under adiabatic

conditions, its pressure increases and it temperature rises without the gain or loss of heat.

Deposition: The act of depositing, especially laying down of matter by a natural process.

Dust Fallout: The deposition of a fine, powdery substance that settles on the ground as an adverse

unwanted secondary effect.

Efficiency: The ratio of the effective or useful output to the total input in any system

Emission: A substance discharged into the air.

Fugitive Emission: An emission that tends to be inconstant, transient and uncontrolled.

Inversion: An atmospheric condition in which the air temperature rises with increasing altitude

holding surface down and preventing dispersion of pollutants.

Micron: A metric unit of length equal to one millionth of a meter

Mitigated : To moderate (a quality or condition) in force or intensity; in order to alleviate a problem.

Nanometer: (nm) is a unit of length, equal to one billionth of a meter. It can be written in scientific

notation as 1 x 10-9 m meaning 1/1,000,000,000 meters.

PM10: Inhalable particulates with an aerodynamic diameter of less than 10 µm.

PM2.5: Respirable particulates with an aerodynamic diameter of less than 2.5 µm.

Receptors: An exposed individual or group that respond to sensory stimuli.

Reclaiming: To retrieve material to be used for future functions.

Rehabilitation: To restore to good condition, operation, or capacity.

Stacking: Creating a large, usually conical pile of material arranged for outdoor storage.

Stockpiles: To accumulate and maintain a supply of material for future use.

Tippler : A place where loaded rail wagons are emptied by tipping.

Transfer Point: The point where the material is transferred from one piece of equipment to another

(e.g. conveyor).

Total Suspended Particles: (TSP) defined as all particulates with an aerodynamic diameter less

than 100 µm.

Wind rose: Computerized output that visually summarizes wind patterns in an area.

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List of Acronyms

APPA Atmospheric Pollution Prevention Act (No 45 of 1965)

BA Basic Assessment

oC Degrees Celcius

DEAT Department of Environmental Affairs and Tourism

EIA Environmental Impact Assessment

HP High Pressure

hPa HectoPascals

IFC International Finance Corporation

IOHF Iron Ore Handling Facility

ISCST Industrial Source Complex Short Term

mm millimeter

m/s meters per second

mg/Nm3 milligrams per natural cubic meter at standard temperature and pressure

MTPA Million Tonnes Per Annum

NEMAQA National Environmental Management: Air Quality Act (No 39 of 2004)

NOx Oxides of Nitrogen

PM Particulate Matter

PM10 Particulate Matter (less than 10 micrometers in diameter

PM2.5 Particulate Matter (less than 2.5 micrometers in diameter

RAMSAR An international treaty for the conservation and sustainable utilization of wetlands

SANS South African National Standards

SAWS South African Weather Services

sL Silt Loading

SO2 Sulphur Dioxide

SRK SRK Consulting

TLV Threshold Limit Value

TP Transfer Point

TPA Total Per Annum

TSP Total Suspended Particles

US-EPA United States Environmental Protection Agency

VOC Volatile Organic Compounds

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WB World Bank

WHO World Health Organization

µm micrometer

399449_Saldanha Air Quality BA_Air Quality Study_FINAL

Partners AN Birtles, JCJ Boshoff, MJ Braune, JM Brown, CD Dalgliesh, JR Dixon, DM Duthe, WC Joughin, PR Labrum, DJ Mahlangu, GP Nel, VS Reddy, PN Rosewarne, PE Schmidt, DJ Venter, HG Waldeck, ML Wertz,

Directors AJ Barrett, JR Dixon, DJ Mahlangu,

Associates AH Bracken, BM Engelsman, DJD Gibson,

Consultants AC Burger, BSc (Hons); IS CameronGA Jones, PrEng, PhD; TR StaceyDW Warwick, PrSci Nat, BSc (Hons)

SRK Consulting (South Africa) (Pty) Ltd

July 2009

399449\42A

Saldanha Air Quality Permit Baseline Study and Impact Assessment

1 Introduction and Background1.1 Introduction

The Transnet iron ore handling facility at the Port of Saldanha, some 120 km north of Cape Town,


exported. The facility is currently authorised to export 45

ore and holds a temporary air quality permit

(Act 45 of 1965) for throughput of 47 MTPA, of which

Transnet has identified operational inefficiencies at the iron ore handling facility and estimates that

the facility, once the inefficiencies have been addressed, can process up to 60

Kumba’s new Sishen-South mine in the Northern Cape

is available to meet these throughput targets.

an amendment to its air quality permit.

Environmental Management Act, any chang

related to the release of emissions require that a Basic Assessment (BA)

SRK Consulting (SRK) has been appointed by Transnet to conduct

amendment of the air quality permit. As part of the BA process, SRK appointed specialists to carry


studies’), which will inform the BA undertaken by SRK.

Air Quality Specialist Baseline Study and Impact Assessment.

JCJ Boshoff, MJ Braune, JM Brown, CD Dalgliesh, JR Dixon, DM Duthe, R Gardiner, T Hart, GC Howell. Labrum, DJ Mahlangu, RW McNeill, HAC Meintjes, BJ Middleton, MJ Morris, GP Murray, WA Naismith,

Rosewarne, PE Schmidt, PJ Shepherd, VM Simposya, AA Smithen, PJ Terbrugge, KM Uderstadt,ML Wertz, A Wood

JR Dixon, DJ Mahlangu, BJ Middleton, MJ Morris, PE Schmidt, PJ Terbrugge

DJD Gibson, SA McDonald, M Ristic, JJ Slabbert, CF Steyn, D Visser, MD Wanless

IS Cameron-Clarke, PrSci Nat, MSc; JAC Cowan, PrSci Nat, BSc (Hons), JH de Beer, PrSci Nat, MSc;Stacey, PrEng, DSc; OKH Steffen, PrEng, PhD; RJ Stuart, PrTech Eng, GDE;

Nat, BSc (Hons)

SRK Consulting (South Africa) (Pty) Ltd

Reg No 1995.012890.07

Saldanha Air Quality Permit Amendment Basic Assessment: Air Quality Specialist Baseline Study and Impact Assessment

and Background



exported. The facility is currently authorised to export 45 million tonnes per annum (MTPA) of iron

air quality permit in terms of the Atmospheric Pollution Prevention Act

for throughput of 47 MTPA, of which 2 MTPA are routed directly to

operational inefficiencies at the iron ore handling facility and estimates that

the facility, once the inefficiencies have been addressed, can process up to 60

South mine in the Northern Cape ensures that an additio

is available to meet these throughput targets. To achieve this throughput, Transnet needs to apply for

an amendment to its air quality permit. In terms of the EIA Regulations under the National

Environmental Management Act, any changes to facilities requiring an amendment of a permit

related to the release of emissions require that a Basic Assessment (BA) be undertaken.

SRK Consulting (SRK) has been appointed by Transnet to conduct the BA to inform the proposed

uality permit. As part of the BA process, SRK appointed specialists to carry


studies’), which will inform the BA undertaken by SRK. This document present

Air Quality Specialist Baseline Study and Impact Assessment.

Section A, 2nd Floor IBM House 54 Norfolk Terrace, off Blair Atholl Drive 3630 WESTVILLE PO Box 1969 3630, WESTVILLE e-Mail: [email protected] URL: http://www.srk.co.za Tel: +27 (0) 31 279 1200 Fax: +27 (0) 31 279 1204

WA Naismith, Uderstadt,

Cape Town +27 (0) 21 659 3060 Durban +27 (0) 31 279 1200 East London +27 (0) 43 748 6292 Johannesburg +27 (0) 11 441 1111 Kimberley +27 (0) 53 861 5798 Pietermaritzburg+27 (0) 33 345 6311 Port Elizabeth +27 (0) 41 509 4800 Pretoria +27 (0) 12 361 9821 Rustenburg +27 (0) 14 594 1280 Dar-es-Salaam +25 (5) 22 260 1881 Harare +263 (4) 49 6182

Nat, MSc;

1

Basic Assessment: Air Quality Specialist



million tonnes per annum (MTPA) of iron

in terms of the Atmospheric Pollution Prevention Act

MTPA are routed directly to Mittal Steel.

operational inefficiencies at the iron ore handling facility and estimates that

the facility, once the inefficiencies have been addressed, can process up to 60 MTPA of iron ore.

that an additional supply of iron ore

To achieve this throughput, Transnet needs to apply for

In terms of the EIA Regulations under the National

es to facilities requiring an amendment of a permit

undertaken.

the BA to inform the proposed

uality permit. As part of the BA process, SRK appointed specialists to carry


This document presents the findings of the

SRK Consulting

Saldanha Air Quality Permit BA: Air Study Page 2

Nadh/Nadh 399449_Saldanha Air Quality BA_Air Quality Study_FINAL September 2009

1.2 Project Location The Port of Saldanha lies approximately 120 km north of Cape Town in Saldanha Bay on the West

Coast of South Africa (see Figure 1.1). The main towns in the vicinity of the Transnet iron ore

handling facility are Saldanha, Langebaan and Vredenburg, with surrounding formal and informal

residential areas. They fall within the Saldanha Bay Local Municipality, which had an estimated

population of 81 121 in 2006 (Census 2004).

Figure 1.1: Location of Saldanha Bay

The Langebaan Lagoon, a wetland of international importance and a registered RAMSAR site, is

located to the south-east of the Port of Saldanha and connected to Saldanha Bay.

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1.3 Project Description Iron ore is currently delivered to the handling facility at the Port of Saldanha by train from the mines

in the Northern Cape. The iron ore is offloaded by wagon tipplers, from where it is transported to

stockpiles by conveyor1.

Iron ore is loaded onto and removed from stockpiles by four stacker/reclaimers that perform both

functions. Different ore grades are stockpiled separately and blended to the specific export

requirements after reclamation. The reclaimed iron ore is transported by conveyor via a sampling

building to two shiploaders, which load the ore onto bulk carriers that are moored at the iron ore

jetty at the Port.

Dust is emitted during the handling of the iron ore at the iron ore handling facility (IOHF), and

nuisance and health issues have been identified as primary concerns of the adjacent communities

which have established around the Port. Transnet has embarked on a dust mitigation programme that

includes, amongst others, the covering of most conveyors and installation of sprinklers at iron ore

transfer points, water cannons at the stockpiles and dust monitoring systems.

Transnet has identified operational inefficiencies at the iron ore handling facility. Addressing those

inefficiencies is estimated to allow the facility to achieve an iron ore throughput of up to 60 MTPA.

The facility currently has a temporary permit to handle up to 47 MTPA, an amendment to the permit

to handle 60MTPA must be applied for.

The higher volume of iron ore that the facility will be able to handle will result in the tipplers

handling more iron ore wagons than currently required, and more ships calling at the Port to receive

the iron ore. However, the infrastructure at the handling facility, such as the number of stockyards

and amount and type of handling equipment, will not change, as the increased throughput will be

achieved by greater operational efficiency, which is expected to result in reduced work stoppage,

equipment down time and iron ore spillage. Additional iron ore from the new Sishen-South mine

will provide the necessary ore supply.

2 Objectives and Scope of Work 2.1 Objectives

The following objectives were identified for the air quality specialist study:

• Describe current air quality conditions and determine current air quality impacts associated with

dust emissions emanating from the activities at the iron ore handling facility at the Port of

Saldanha, i.e. current conditions of approximately 31 MTPA throughput for the 12 month period

from April 2008 to March 2009;

• Assess and consider the potential impacts on the environment associated with the proposed

development (including the No-Go alternative) with respect to air quality impacts associated

with dust emissions emanating from the activities at the iron ore handling facility at the Port of

Saldanha for a throughput capacity of 60 MTPA;

1 A limited amount of iron ore is directly loaded onto ships without being stockpiled first.

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• Identify and recommend suitable mitigation measures and assess impacts with these mitigation

measures in place; and

• Provide supporting information for an air quality permit application for a 60 MTPA throughput.

2.2 Scope of Work The following scope of work was undertaken to meet the above objectives:

• Review of relevant information, including existing air quality monitoring data, previous air

quality impact assessment studies, site plans, available meteorological data and project

description, as well as suitable and available information from the draft Phase 2 (expansion to

93 MTPA) EIA.

• Review of relevant air quality legislation in South Africa and the associated air quality standards

to inform the assessment of impacts;

• Description and quantification of projected air emissions from the operations at the handling

facility and other sources in the areas surrounding the handling facility expected on completion

of the Phase 1B expansion (i.e. handling of 47 MTPA), which forms the baseline of the project2.

• Development of a representative emissions inventory for the handling facility operating at the

proposed increased throughput of 60 MTPA of iron ore.

• Determination, where possible, of significant seasonal differences in levels of air emissions from

the handling facility.

• Prediction of ambient PM10 (i.e. particulates with an aerodynamic diameter of less than 10 µm)

and dust fallout concentrations in the environment surrounding the handling facility following

the increase in iron ore throughput to 60 MTPA using an air dispersion model.

• Identification and verification of areas and receptor populations for air emissions generated by

the operations at the iron ore handling facility.

• Identification of impacts associated with the proposed increase of iron ore throughput at the

handling facility to 60 MTPA on the air quality of the surrounding area.

• Assessment of the significance of the identified air quality impacts based on the results of the

dispersion model against relevant national and international standards, using the SRK prescribed

impact assessment methodology.

• Comparative assessment of the air quality impacts associated with the ‘no-go’ alternative

(handling of 47 MTPA) and the proposed project (handling of 60 MTPA).

• Identify and recommend practical mitigation and management measures to reduce potential

negative air quality impacts on the surrounding environment beyond the boundaries of the

handling facility and enhance positive impacts of the project.

• Assessment of the effectiveness of proposed mitigation measures by re-rating the impact,

assuming the mitigation is employed, using the prescribed impact assessment methodology.

2 Environmental approval for export of up to 47 MTPA has been granted. The required changes to the iron ore handling facility, falling within the scope of the Phase 1B expansion, are currently being implemented.

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• Recommendation of methods for monitoring the ongoing effectiveness of proposed mitigation

measures related to air quality issues, and on-going air quality impacts if considered necessary.

• Liaison with the health specialist as required and provision of information regarding the levels

and distribution of PM10 and dust to inform the health specialist study.

3 Assumptions and Limitations The following assumptions and limitations apply:

• Dust (PM10 and dust fallout) is the main and primary pollutant of concern that has been

identified and assessed in this study.

• Other pollutants, such as gaseous and particulate emissions from the ships and motor vehicles,

were reviewed but were not considered to be significant when compared to dust emissions due to

ore handling and were therefore not assessed.

• The US EPA Industrial Source Complex Short Term dispersion model (ISCST3) is used for the

dispersion modelling. The model is most accurate when applied to flat or gently rolling terrain

with fairly strong wind speeds, during neutral atmospheric conditions. It is said to have an

uncertainty range within a factor of two under such circumstances, i.e. uncertainty could range

between -50% and 200% (US-EPA, 1995). Other model-related assumptions and limitations are

presented in Section 7.

• Only dust emissions (PM10 and TSP) from the ore handling facility were modelled.

• For modelling purposes, the worst case scenario has been used, i.e. that the entire ore handling

infrastructure is operational at the same time. The model is therefore taking a conservative

approach, thereby increasing the probability that the predicted results fall within or below the

predicted results.

4 Air Quality Legislation 4.1 Local and International Air Quality Guidelines and Standards

In South Africa, the main legislation with respect to air quality is the National Environment

Management: Air Quality Act, No. 39 of 2004 (NEM: AQA). Since the NEM: AQA is in the

process of being implemented, certain aspects of previous air quality legislations viz. the

Atmospheric Pollution Prevention Act No. 45 of 1965 (APPA) are still applicable to air quality

issues. An important factor in the approach to air quality management in NEM:AQA is the

introduction of national ambient air quality standards. These standards will provide the targets for air

quality management plans and a measure of the effectiveness of these management plans. The

NEM: AQA provides for the identification of priority pollutants and priority areas which are affected

by these pollutants, as well as determining ambient standards with respect to these pollutants and

areas. Reference to other guidelines and standards, such as World Bank (WB/IFC) and World Health

Organization (WHO), have been consulted and presented in this report for comparative purposes.

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4.2 Pollutants Pollutants such as Volatile Organic Compounds (VOCs), Nitrous Oxides (NOx) and Sulphur Dioxide

(SO2) will be emitted, on a smaller scale, by activities associated with the IOHF. However, for this

study, dust has been identified as the primary pollutant of concern. Dust as a pollutant can be split

into two categories, i.e. ambient particulate matter (PM) and dust fallout. The former is usually

associated with health and nuisance factors and latter primarily associated with nuisance. The

potential impacts and guidelines for these two parameters are presented in the following sections.

4.2.1 Particulate Matter

Air quality guidelines for particulates for various particle size fractions, including total suspended

particles (TSP) and respirable particles or PM10, are presented in Table 4.1. PM10 is of concern as it

has a potential impact on health.

Table 4.1: Air quality guidelines and standards for PM10

Standard or Guideline Maximum 24-Hour Concentration (µg/m 3)

Annual Average Concentration (µg/m 3)

South African Standard1 1802 60 Proposed South African Standards (based on the SANS 1929:2004) 753 403

SA Proposed _ March 2009 754 404 World Bank(WB)/IFC 1505 705

World Health Organization(WHO)

1506 706

1007 507

758 308 509 209

1. As listed in the NEM: Air Quality Act No. 39 of 2004

2. The 24 limit may not be exceeded more than 3 times a year

3. SANS 1929 – South African National Standard – Ambient Air Quality – limits for common pollutants. Also approved South African standards as published in the government gazette of 9th June 2006

4. As listed in the NEM: Air Quality Act No. 39 of 2004 ambient standards for comment March 2009.

5. World Bank/IFC, 2007.Environmental, Health and Safety guidelines. General EHS guidelines: Environmental. Air emissions and ambient air quality

6. WHO interim target-1 (IT-1) – World Health Organization air quality guidelines global update 2005



9. WHO guideline (AQG) – World Health Organization air quality guidelines global update 2005

4.2.2 Dust Deposition or Dust Fallout

South African National Standards (SANS) also published dust deposition standards that are based on

the cumulative South African dust fall level in SANS 1929:2004. The target, action and alert

thresholds are presented in Table 4.2. Dust deposition is addressed in this study since it is considered

to be a measure of nuisance dust.

Table 4.2: SANS 1929:2004. Target values given in m g/m 2/day

Level Dust fall rate (D)

(mg/m 2/day, 30-day average)

Permitted frequency of exceeding dust fallout rate

Target 300 -

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Action residential 600 Three within any year, no two sequential months

Action industrial 1 200 Three within any year, not two sequential months

Alert threshold 2 400 None. First incidence of dust fall rate being exceeded requires remediation and compulsory report to relevant authorities.

5 Meteorology and Pollution Dispersion in Saldanha Bay, Western Cape Meteorological conditions play a major role in the dispersion and accumulation of air pollutants.

Waste substances released into the atmosphere can be diluted by diffusion, removed by fallout,

washout and atmospheric reactions and created due to solar radiation through photochemical

reactions (Wanta, 1968). Horizontal dispersion is determined by wind speed and wind direction,

which have a powerful influence over the rate of pollutant dispersion and dictate the geographical

location and distance that a pollutant will be transported. Vertical dispersion depends on the stability

of the atmosphere, which generally depends on the high and low pressure systems, the thermal state

of the atmosphere and the height of the mixing depth. A stable atmosphere is associated with a low

degree of dilution and high air pollution potential. An unstable atmosphere is linked to intense

turbulent mixing of the air resulting in dispersion and low air pollution potential (Turco, 2002).

Overall, pollution levels are governed by wind characteristics and changes in the stability profile of

the atmosphere.

Pollution concentrations fluctuate in response to different wind systems therefore it is important to

account for these atmospheric processes in order to account for the atmosphere’s potential to

disperse pollutants (Preston-Whyte et al., 1980). Wind can be categorised as macro, meso and micro-

scale circulations. Macro-scale ventilation is dependent on synoptic weather systems that control the

circulation conditions of an area and the frequency of weather perturbations. Meso-scale ventilation

is the local wind that is induced due to thermal and topographical properties of the area. Such

circulations include land-sea breezes and valley or mountain winds. Micro-scale winds such as

mechanical turbulence develop as a result of surface roughness and friction from buildings and other

obstacles (Turco, 2002).

5.1 Synoptic Climatology of Southern Africa South Africa is located in the subtropical high pressure belt, where subsidence, high pressure and

atmospheric stability are predominant features. Three high pressure (HP) cells dominate over South

Africa. The South Atlantic HP cell is located off the west coast, the Continental HP cell reigns over

the interior of the country and the South Indian HP cell resides over the east coast. This results in the

annual mean circulation of the atmosphere over South Africa being anticyclonic (Steynor, 2006).

During winter, the anticyclonic circulation over South Africa is well established and is at its most

intense. The continental HP cell migrates north over the country and the South Indian HP cell shifts

westward towards the east coast from the summer position of 88°E to 65°E in winter. The South

Atlantic HP cell moves from its summer position at 4°W to its winter position at 16°W, migrating

away from the western coast (Preston-Whyte et al., 1976). Anticyclones result in subsidence in the

atmosphere, which increase the incidence and duration of calm winds, clear skies, lowered humidity

and little precipitation (Preston-Whyte et al., 1980).

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Figure 5.1: Annual variations in the positions of t he South Atlantic and South Indian anticyclones

Source: Preston-Whyte et al., 2000

Conversely, during summer, a low pressure system dominates over the interior of the country due to the slight southward shift of the continental HP cell. The South Atlantic HP cell moves towards the east, over the Western Cape, and the South Indian HP shifts eastwards, causing the high pressure conditions over the eastern coast to diminish. Extremely stable atmospheric conditions that can persist for long periods are the result of the semi-permanent and subtropical continental anticyclones. They are found to occur at a frequency of 70% and 20% in winter and summer respectively.

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Figure 5.2: Important features of the surface atmos pheric circulation over southern Africa

Source: Preston-Whyte et al., 2000

The south easterly trade winds generally affect South Africa throughout the year. However, during

winter, the high pressure cells shift northward, causing the circumpolar westerly waves to displace

the easterly trade winds and dominate over South Africa. The westerly belt is associated with the

migration of isolated low pressure cells and cyclones around the coast or across the country towards

the east. During summer the high pressure belt shifts southwards and the easterly trade winds

displace the westerly waves to resume its influence over the country.

During summer localised weather systems to the east of the south-easterly trade winds causes

turbulence and uplift and the potential for precipitation over the eastern part of the country, resulting

in summer rains. On the western side of the easterly waves, upper-level convergence and surface-

level divergence causes clear conditions with no precipitation over the western part of the country.

During winter, westerly waves significantly influence the weather of the country. Upper-level

divergence and surface-level convergence occurs to the rear of the trough, which causes uplift and

cloud formation resulting in precipitation and winter rains over the western coast. Rainfall will also

occur with the passing of cold fronts, which are associated with the westerly waves. Rainfall has a

positive effect on pollution control as the water droplets act as nuclei onto which dust and pollutants

will collect and deposit onto the ground. This is known as “scrubbing” of the atmosphere.

Along the coastline, sea and land breeze circulations influence the diurnal wind variation and

ultimately govern the transport of atmospheric pollutants. During the daytime, the land heats rapidly

while the sea retains its cool temperature. The warm air over the land rises causing a low pressure to

develop. The cool air over the sea subsides and flows down the pressure gradient, causing a sea-land

breeze to develop. The converse is true for night time conditions, where the air above the land cools

due to a lack of insulation, while the air above the sea remains warm. A land-sea breeze will

therefore prevail during the night (Diab, pers comm., 2007).

5.2 Regional Climatology of the Western Cape The Western Cape has a diverse climate, with many distinct micro- and macroclimates created by

the varied topography and the influence of both the Indian (warm water) and Atlantic (cold water)

oceans, thus climatic statistics can vary greatly over short distances. Most of the province is

considered to have a Mediterranean climate with cool, wet winters and warm, dry summers. The

interior Karoo has a semi-arid climate with cold, frosty winters and hot summers with occasional

thunderstorms. The south coast of the province has a maritime climate with cool, moist winters and

mild, moist summers. Thunderstorms are generally rare in the province, except in the Karoo interior,

with most precipitation being of a frontal nature. Extremes of heat and cold are common inland, but

rare near the coast. Snow is a common winter occurrence on the higher lying ground. However, frost

is relatively rare in coastal areas and many of the heavily cultivated valleys.

Summer temperatures in December to February range from around 15 to 27 ˚C, whilst in the winter

months of June to August average temperatures are between 7 to 20 ˚C. The average annual rainfall

is about 788 mm, most of which occurs during the winter season.

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5.3 Local Climate of Saldanha

5.3.1 Rainfall

Rainfall is an important parameter with respect to air quality, which deteriorates during the dry

season and improves during the wet season. During the wet season, air pollution, and more

specifically in this case, dust particles, are removed from the atmosphere. Dust emissions are

suppressed due to the damp soil conditions and increased vegetation cover. During the dry season,

dust emission levels increase as the soils dry up and the vegetation cover decreases.

Saldanha Bay experiences wet winters and dry summers. Precipitation is responsible for “scrubbing”

the atmosphere and increasing the deposition of pollutants, reducing atmospheric concentrations.

Table 5.1 presents the rainfall data from the South Africa Weather Service (SAWS) for Geelbek,

located near the Langebaan Lagoon, and Langebaanweg, located to the east of the Port of Saldanha.

Figure 5.3 presents the data graphically.

Table 5.1: Rainfall data for Geelbek and Langebaanw eg for 2008

Site Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Geelbek 3.7 13.5 9.1 32.3 50.9 52.5 68 43.8 64.6 3 28 34 437.4

Langebaanweg 1.8 3.8 0.4 12.2 41.1 39.8 66.6 40.6 38.2 4.6 20.4 5.8

278.4

Figure 5.3: Rainfall at Geelbek and Langebaanweg fo r 2008

Figure 5.3 compares the rainfall data for both the Geelbek and Langebaanweg. The data shows that

rainfall is seasonal with winters experiencing more rainfall than summers in both areas. Geelbek

does however experience more rainfall than Langebaanweg over the monitored period and during the

summer months. This is probably due to its closer proximity to the coast.

0

10

20

30

40

50

60

70

80

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ra

infa

ll (

mm

)

Month

Rainfall

Geelbek

Langebaanweg

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5.3.2 Ambient Air Temperature

Ambient air temperature is important, both for determining the effect of plume buoyancy (the larger

the temperature difference between the emissions plume and the ambient air, the higher the plume is

able to rise), and determining the development of the mixing depth and inversion layers. Daytime

temperatures peak during the summer months and are lower during the winter months. Temperatures

drop significantly at night during the winter months. Table 5.2 presents temperature data for

Geelbek and Table 5.3 presents data for Langebaanweg. Figure 5.4 presents the average monthly

temperatures for the period 1 January 2008 to 31 December 2008.

Table 5.2: Maximum, minimum and average temperature s in oC at Geelbek for 2008


Max (oC) 32.2 36.6 32.3 35.6 30.1 23.6 24.1 26.2 30.9 30.4 32.9 33.4

Min (0C) 5.5 3.4 3.4 0.3 6.7 -0.6 0.1 -1.8 -0.2 1.8 4.2 5.4

Average 18.9 18.2 17.5 15.7 15.7 12.6 11.5 11.7 11.7 14.8 16.2 17.9

Table 5.3: Maximum, minimum and average temperature s in oC at Langebaanweg for 2008


Max (oC) 38.3 38.0 35.8 36.7 30.0 24.6 23.7 26.7 31.3 31.9 37.5 39.8

Min (0C) 12.3 12.2 11.6 7.6 16.1 4.3 3.2 3.1 3.2 4.8 7.8 9.3

Average 22.0 21.7 21.1 18.2 8.6 13.3 12.0 12.6 12.5 16.2 18.3 20.6

Figure 5.4: Averaged monthly temperatures for Geelb ek and Langebaanweg in 2008

0

5

10

15

20

25


Te

mp

era

ture

(oC

)

Month

Average Monthly Temperatures for Geelbek and

Langebaanweg

Geelbek

Langebaanweg

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5.3.3 Wind

The wind field for an area is an important parameter with respect to air quality. Whilst rainfall and

temperature attenuate the behaviour and concentration of a pollution plume after the release of a

pollutant into the atmosphere, wind can generate dust emissions and thereafter control the dispersion

of an emissions plume. The degree to which winds influence dispersion depends on wind speed.

Higher wind speeds result in longer travel distance and dilution of the pollutants and lower, more

stable wind conditions result in shorter travel distance and build-up of pollutant levels (especially

gases) over a smaller area.

The wind data for annual, daytime, night time and seasonal wind roses for Geelbek are presented in

Figures 5.5 and 5.6. The annual, daytime night time and seasonal wind roses for Langebaanweg are

presented in Figure 5.7 and Figure 5.8. The wind data that were used to create the wind roses was

acquired from SAWS.

The Langebaanweg and Geelbek weather stations are the two closest SAWS stations to the Port of

Saldanha, which suggests they give the best indication of weather conditions affecting the port. The

Langebaanweg weather station is situated to the east of Saldanha and the Geelbek weather station is

situated to the south. Wind data recorded at the Geelbek weather station will give an indication of

how the activities at the IOHF will affect residential areas to the north of the facility such as Blue

Water Bay and Vredenburg, whereas wind data from Langebaanweg will indicate how areas to the

west are being affected by the IOHF and if areas such as Vredenburg are likely to be impacted by

activities at the IOHF and other sources.

The prevailing winds for Geelbek (Fig. 5.5) are relatively consistent throughout the year. The winds

predominantly range from south to south-south easterly, with a northerly wind prevailing late at

night (Fig. 5.5d). Similar patterns are observed during the day and at night. A look at the synoptic

weather patterns for the area suggests that the regional weather is dominated by the South Atlantic

and Continental High Pressure systems.

The average annual wind speed for Geelbek is 4.0 m/s, with calm winds occurring 11.98% of the

time. The average wind speed during the day is 4.9 m/s with wind speeds rarely exceeding 11.1 m/s.

The night time component was split into two periods, 18h00 – 23h00 and 00h00 – 06h00. The

average winds speed decreases to 3.6 m/s between 18h00 - 23h00 and 2.5 m/s between 00h00 –

06h00.

Wind roses were also created for the different seasons (Fig. 5.6) i.e. for summer, autumn, winter and

spring. Summer and spring, which are the warmer seasons, have higher wind speeds, 4.6 m/s and

4.5 m/s respectively, than the cooler winter (3.6 m/s) and autumn (3.6 m/s). This is representative of

the fact that warmer temperatures are associated with higher wind speeds. The south, south-south-

easterly are the dominant wind vectors during the summer, autumn and spring seasons, with a north

wind vector occurring during the winter months which is also present during autumn.

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Figure 5.5: Annual all hours daytime and night time all hours wind roses for Geelbek

Source: SAWS

WRPLOT View - Lakes Environmental Software

WIND ROSE PLOT:

Geelbek1 January 2008 to 20 June 2009 (All Hours)

COMMENTS: COMPANY NAME:

SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

4%

8%

12%

16%

20%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 11.98%

TOTAL COUNT:

12884 hrs.

CALM WINDS:

11.98%

DATA PERIOD:

2008-2009 Jan 1 - Dec 3100:00 - 23:00

AVG. WIND SPEED:

4.00 m/s

DISPLAY:

Wind SpeedDirection (blowing from)

Figure 5.5a: Annual wind rose – all hours


WIND ROSE PLOT:

Geelbek1 January 2008 to 20 June 2009 (Day Time 06:00-18:0 0)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

5%

10%

15%

20%

25%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 6.51%

TOTAL COUNT:

6978 hrs.

CALM WINDS:

6.51%

DATA PERIOD:

2008-2009 Jan 1 - Dec 3106:00 - 18:00

AVG. WIND SPEED:

4.93 m/s

DISPLAY:


Figure 5.5b: Annual wind rose – day time

06h00-18h00


WIND ROSE PLOT:

Geelbek1 January 2008 to 20 June 2009 (Night time 18:00-23 :00)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

6%

12%

18%

24%

30%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 11.92%

TOTAL COUNT:

3222 hrs.

CALM WINDS:

11.92%

DATA PERIOD:

2008-2009 Jan 1 - Dec 3118:00 - 23:00

AVG. WIND SPEED:

3.64 m/s

DISPLAY:


Figure 5.5c: Annual wind rose - night time

18h00 - 23h00


WIND ROSE PLOT:

Geelbek1 January 2008 to 20 June 2009 (Night Time 00:00-06 :00)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

3%

6%

9%

12%

15%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 23.07%

TOTAL COUNT:

3758 hrs.

CALM WINDS:

23.07%

DATA PERIOD:

2008-2009 Jan 1 - Dec 3100:00 - 06:00

AVG. WIND SPEED:

2.47 m/s

DISPLAY:


Figure 5.5d: Annual wind rose - night time

00h00 - 06h00

SRK Consulting



Figure 5.6: Seasonal wind roses for Geelbek

Source: SAWS


WIND ROSE PLOT:

GeelbekSummer (All Hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

6%

12%

18%

24%

30%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 12.54%

TOTAL COUNT:

3573 hrs.

CALM WINDS:

12.54%

DATA PERIOD:

2008-2009 Check Date Range Report00:00 - 23:00

AVG. WIND SPEED:

4.59 m/s

DISPLAY:


Figure 5.6a: December to February

(Summer) – all hours


WIND ROSE PLOT:

GeelbekAutumn (All Hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

4%

8%

12%

16%

20%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 12.50%

TOTAL COUNT:

4416 hrs.

CALM WINDS:

12.50%

DATA PERIOD:

2008-2009 Mar 1 - May 3100:00 - 23:00

AVG. WIND SPEED:

3.56 m/s

DISPLAY:


Figure 5.6b: March to May (Autumn) – all

hours


WIND ROSE PLOT:

GeelbekWinter (All Hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

7/2/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

4%

8%

12%

16%

20%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 11.24%

TOTAL COUNT:

2687 hrs.

CALM WINDS:

11.24%

DATA PERIOD:

2008-2009 Jun 1 - Aug 3100:00 - 23:00

AVG. WIND SPEED:

3.56 m/s

DISPLAY:


Figure 5.6c: June to August (Winter) all

hours


WIND ROSE PLOT:

GeelbekSpring (All Hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

5%

10%

15%

20%

25%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 10.94%

TOTAL COUNT:

2184 hrs.

CALM WINDS:

10.94%

DATA PERIOD:

2008-2009 Sep 1 - Nov 3000:00 - 23:00

AVG. WIND SPEED:

4.45 m/s

DISPLAY:


Figure 5.6d: September to November

(Spring) - all hours

SRK Consulting



Figure 5.7: Annual all hours daytime and night time all hours wind roses for Langebaanweg

Source: SAWS


WIND ROSE PLOT:

Langebaanweg1 January 2008 to 20 June 2009 (All Hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

5%

10%

15%

20%

25%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 6.18%

TOTAL COUNT:

12879 hrs.

CALM WINDS:

6.18%

DATA PERIOD:

2008-2009 Jan 1 - Dec 3100:00 - 23:00

AVG. WIND SPEED:

4.11 m/s

DISPLAY:


Figure 5.7a: Annual wind rose – all hours


WIND ROSE PLOT:

Langebaanweg1 January 2008 to 20 June 2009 (All Hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

4%

8%

12%

16%

20%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 4.42%

TOTAL COUNT:

6976 hrs.

CALM WINDS:

4.42%

DATA PERIOD:

2008-2009 Jan 1 - Dec 3106:00 - 18:00

AVG. WIND SPEED:

4.80 m/s

DISPLAY:


Figure 5.7b: Annual wind rose – day time

06h00-18h00


WIND ROSE PLOT:

Langebaanweg1 January 2008 to 20 June 2009 (Night Time 18:00-23 :00)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

6%

12%

18%

24%

30%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 4.32%

TOTAL COUNT:

3218 hrs.

CALM WINDS:

4.32%

DATA PERIOD:

2008-2009 Jan 1 - Dec 3118:00 - 23:00

AVG. WIND SPEED:

4.02 m/s

DISPLAY:


Figure 5.7c: Annual wind rose - night time

18h00 - 23h00


WIND ROSE PLOT:

Langebaanweg1 January 2008 to 20 June 2009 (Night Time 00:00-06 :00)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

5%

10%

15%

20%

25%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 11.26%

TOTAL COUNT:

3758 hrs.

CALM WINDS:

11.26%

DATA PERIOD:

2008-2009 Jan 1 - Dec 3100:00 - 06:00

AVG. WIND SPEED:

2.90 m/s

DISPLAY:


Figure 5.7d: Annual wind rose - night time

00h00 - 06h00

SRK Consulting



Figure 5.8: Seasonal wind roses for Langebaanweg

Source: SAWS


WIND ROSE PLOT:

LangebaanwegSummer (All hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

6%

12%

18%

24%

30%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 2.33%

TOTAL COUNT:

3568 hrs.

CALM WINDS:

2.33%

DATA PERIOD:

2008-2009 Check Date Range Report00:00 - 23:00

AVG. WIND SPEED:

5.02 m/s

DISPLAY:


Figure 5.8a: December to February

(Summer) – all hours


WIND ROSE PLOT:

LangebaanwegAutumn (All hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

5%

10%

15%

20%

25%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 5.57%

TOTAL COUNT:

4416 hrs.

CALM WINDS:

5.57%

DATA PERIOD:

2008-2009 Mar 1 - May 3100:00 - 23:00

AVG. WIND SPEED:

3.85 m/s

DISPLAY:


Figure 5.8b: March to May (Autumn) – all

hours


WIND ROSE PLOT:

LangebaanwegWinter (All hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

3%

6%

9%

12%

15%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 12.10%

TOTAL COUNT:

2687 hrs.

CALM WINDS:

12.10%

DATA PERIOD:

2008-2009 Jun 1 - Aug 3100:00 - 23:00

AVG. WIND SPEED:

3.31 m/s

DISPLAY:


Figure 5.8c: June to August (Winter) all

hours


WIND ROSE PLOT:

LangebaanwegSpring (All hours)


SRK Consulting

MODELER:

D Naidoo

DATE:

6/25/2009

PROJECT NO.:

399449-42A

NORTH

SOUTH

WEST EAST

6%

12%

18%

24%

30%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 6.50%

TOTAL COUNT:

2184 hrs.

CALM WINDS:

6.50%

DATA PERIOD:

2008-2009 Sep 1 - Nov 3000:00 - 23:00

AVG. WIND SPEED:

4.12 m/s

DISPLAY:


Figure 5.8d: September to November

(Spring) - all hours

SRK Consulting



The prevailing winds for Langebaanweg (Fig. 5.7) are south, south-south-westerly and south west,

and are relatively consistent throughout the year; there is also a presence of a north wind vector

during the year. Similar patterns are observed during the day and at night. The average wind speed

for all hours at Langebaanweg is 4.11m/s (Fig. 5.7a). Day time wind speeds average 4.80 m/s

(Fig. 5.7b). The night time component is split into two periods as seen with the Geelbek wind roses.

The period between 18h00 and 23h00 (Fig. 5.7c) has an average wind speed of 4.02 m/s and the

00h00 -06h00 period (Fig. 5.7d) has an average wind speed of 2.90 m/s.

Seasonal wind roses were also created for the different seasons at Langebaanweg (Fig. 5.8) i.e. for

summer, autumn, winter and spring. Summer and spring, which are the warmer seasons, have higher

wind speeds, 5.0 m/s and 4.1 m/s respectively, than the cooler winter (3.3 m/s) and autumn

(3.85 m/s) seasons.

5.3.4 Atmospheric Stability and Mixing Depth

Vertical transport of pollutants and aerosols is governed by the degree of thermal turbulence and the

depth of the mixing layer. The mixing layer will extend to the lowest elevated inversion and the

extent of mixing may be determined by the level of a local surface inversion layer. Surface inversion

layers have a strong influence over pollution accumulation at a local level.

Surface temperature inversions occur when warm air overlays a cold air mass, preventing air from

mixing and thereby enforcing atmospheric stability. Four inversion types exist, namely marine,

regional subsidence, high pressure and radiation inversions. Marine inversions develop during the

day as the sun heats the air over the land causing it to rise, which creates a pocket of low pressure

into which cool ocean air will flow. Regional inversions may also occur over the coastal areas as air

from the mountains move to lower altitudes, warming adiabatically. The warm air settles over the

cool coastal air, trapping the air, reinforcing atmospheric stability. High pressure temperature

inversions occur due to anti-cyclonic systems that promote the sinking of air masses, which warm

adiabatically, trapping cooler air and concentrating pollutants and aerosols as dilution is minimised.

The most common temperature inversion is the radiation inversion which develops at night time,

especially during winter. Under calm, cloudless conditions, thermal radiation is released from the

earth’s surface which warms the air layers at higher altitudes while the surface air cools. The warmer

air traps the cooler air and reduces the extent of mixing and dilution. The degree of atmospheric

stability and the depth of the mixing layer govern the extent to which pollutants will disperse and

dilute and ultimately have a strong control over pollution levels at a local level.

5.3.5 Summary of Description of the Weather in Sald anha

The rainfall in Saldanha is seasonal, with wet winters and dry summers. The average annual rainfall

is 278 mm, most of which occurs during the wet season. The temperatures at Saldanha peak during

the summer months and are lower during the winter months, dropping below zero during some

nights. The wind at Saldanha is predominantly south-south east during summer, spring and autumn,

with a dominant northerly and north-north westerly component during winter. Average wind speeds

are higher during the warmer months. During the cooler months, the winds are calmer with

exceptions of short periods when high speed winds are observed.

SRK Consulting



5.4 Ambient Dust Monitoring PM10 and dust fallout are currently being monitored at locations that are seen as sensitive receptors

to the IOHF at the Port of Saldanha. PM10 is monitored at Blue Water Bay and Vredenburg, and dust

fallout is monitored at Vredenburg, the National Ports Authority (NPA) offices and the Port. Figure

5.9 shows the location of the four monitoring stations.

5.4.1 PM10 Monitoring

A continuous PM10 monitoring network was set up in Vredenburg and Blue Water Bay. Tables 5.4

and 5.5 show the 24-hour PM10 concentrations for Vredenburg and Blue Water Bay respectively, for

the period between January 2008 and May 2009. PM10 data for the period February 2008 to June

2008 was not available for both monitoring stations at the time when this report was compiled.

There were no recorded exceedances of the 180 µg/m3 (South African Standard – as listed in

NEMAQA No. 39 of 2004) or 75 µg/m3 (SANS 1929:2004) guidelines for either monitoring station

during the sampling period. The highest recorded 24hr PM10 concentration for Vredenburg was

73.17 µg/m3 during February 2009, and the highest recorded 24hr PM10 concentration for Blue Water

Bay was 59.5 µg/m3, also during February 2009. The annual averages for Vredenburg and Blue

Water Bay are 16.5 µg/m3 and 19.28 µg/m3 respectively for 2008.

The data is presented graphically in Figures 5.10 and 5.11.

SRK Consulting



Table 5.4: Average daily and monthly PM 10 levels (µg/m 3) for Vredenburg for the period January 2008 to May 2009

Day of Month Jan-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 May-09

1 12.80 11.00 31.08 7.04 11.25 25.96 13.17 8.88 20.88 41.58 14.29

2 12.90 7.50 13.71 11.33 9.67 22.13 7.13 11.00 24.63 57.88 4.00

3 25.60 5.40 31.13 16.21 20.63 23.50 5.08 34.83 23.67 42.88 8.21

4 10.30 3.50 27.00 14.54 25.25 22.75 10.17 55.46 49.46 28.46 7.58

5 12.60 7.80 26.96 16.63 25.75 20.79 16.04 73.17 42.79 8.96 3.58

6 6.80 8.90 19.13 34.48 14.00 25.67 11.21 51.71 27.63 20.00 5.52

7 9.80 8.60 12.33 20.13 26.42 27.88 13.83 33.46 35.04 37.54 5.42

8 28.80 10.00 3.13 20.00 25.22 47.33 11.04 20.54 33.00 33.29 17.83

9 27.10 29.71 12.63 17.00 24.71 32.54 12.79 19.78 53.33 16.04 14.88

10 29.50 22.88 19.33 20.33 27.58 20.26 18.29 33.04 32.46 17.71 15.00

11 22.40 15.70 16.38 13.88 32.79 17.48 17.58 59.08 30.67 18.92 13.04

12 13.60 16.43 19.75 9.83 10.38 21.67 16.88 26.67 23.96 13.83 16.50

13 18.10 5.71 26.38 18.29 28.96 12.21 6.25 32.25 15.63 29.54 16.67

14 19.30 21.17 27.38 18.92 10.79 27.88 26.04 17.39 30.42 11.75

15 12.50 15.13 22.96 16.79 19.21 26.88 43.00 22.63 27.92 5.83

16 12.83 13.96 28.58 12.21 22.00 26.54 27.25 30.38 56.21 18.38

17 11.96 13.22 25.50 32.42 23.17 18.75 29.63 39.67 38.58 24.75

18 19.29 9.38 16.61 32.08 24.50 16.54 16.42 38.75 8.13 15.17

19 15.88 12.33 10.04 40.21 10.46 37.75 19.08 40.33 9.46 10.75

20 21.13 11.04 14.67 29.46 7.79 33.79 28.63 31.83 27.63 19.79

21 13.17 11.50 19.92 10.08 9.13 24.63 12.88 21.75 27.00 25.96

22 6.71 12.71 25.75 9.33 13.92 15.71 27.83 24.00 28.50 10.79

23 9.46 12.42 34.88 17.42 19.58 10.33 67.38 36.96 6.52 10.38

24 7.50 12.83 39.46 18.17 21.50 6.71 24.13 40.08 15.54 4.21

25 8.00 7.63 30.75 19.21 19.38 6.83 20.29 38.46 6.83 6.67

26 12.83 13.79 24.25 26.42 18.46 21.83 27.04 21.21 8.75 14.92

SRK Consulting



Day of Month Jan-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 May-09

27 10.79 14.92 28.75 14.50 21.21 27.75 26.00 17.04 15.17 21.88

28 10.48 12.04 27.83 11.35 14.79 42.83 26.50 14.50 9.29 8.54

29 9.76 7.96 13.50 10.77 4.96 35.54 23.29 4.13 11.00

30 13.83 11.25 14.13 14.04 7.29 39.50 30.67 16.50 10.08

31 20.75 17.46 13.13 9.79 34.38 7.25

Monthly average 17.47 12.33 15.74 20.71 20.53 19.40 19.00 31.50 30.21 23.44 12.28

Note:

Where months have been excluded this implies that no data is available.

Blank cells imply that no data is available.

Table 5.5: Average daily and monthly PM 10 levels (µg/m 3) for Blue Water Bay for the period January 2008 to May 2009

Day of Month Jan-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 May-09

1 19.9 12.90 13.92 28.50 11.61 11.67 34.25 16.48 13.13 37.67 33.54 17.65

2 17.7 10.70 21.08 15.61 18.75 11.75 28.79 14.81 14.25 25.68 40.13 13.52

3 22.8 8.20 20.50 29.38 17.83 16.46 30.67 14.55 27.00 15.90 37.88 13.92

4 20.1 9.80 20.92 39.79 17.75 25.71 24.17 18.13 33.38 33.08 27.00 13.91

5 14.9 11.80 15.75 33.33 28.88 22.04 25.25 19.09 49.71 58.50 9.14 10.33

6 9.7 13.80 18.25 24.61 30.08 13.00 31.63 22.30 37.33 44.96 15.33 13.82

7 19.6 11.00 24.46 12.04 16.38 18.33 40.42 16.39 40.42 21.55 32.00 12.67

8 27.7 8.50 29.46 6.75 19.17 21.50 38.00 14.86 20.43 23.58 25.46 21.92

9 27.5 12.40 57.67 12.67 10.67 29.42 37.92 17.70 17.20 51.38 13.83 25.79

10 24.8 10.22 40.17 16.38 19.52 19.17 25.58 24.96 18.08 31.52 19.88 25.75

11 5.7 10.54 30.39 21.63 17.71 19.58 19.04 25.26 33.08 22.05 16.17 17.00

12 12.3 8.25 24.17 23.71 13.79 15.38 21.92 22.54 29.35 24.26 13.38 14.33

13 27.3 12.88 18.92 27.58 12.79 27.71 17.92 17.78 21.91 12.05 28.29 15.29

SRK Consulting



Day of Month Jan-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 May-09

14 27.7 15.57 18.08 18.58 18.63 19.17 25.33 21.42 17.75 18.00 38.08 16.29

15 4.8 14.83 23.00 15.13 23.17 13.71 20.46 37.58 32.67 35.76 17.63 14.35

16 18.21 26.25 17.79 22.33 11.79 24.00 33.25 31.83 17.06 34.54 17.75

17 35.38 29.92 14.33 23.25 23.71 20.71 17.83 30.46 23.92 27.38 31.33

18 12.71 30.13 12.38 21.63 24.00 33.67 22.54 19.22 23.50 7.82 16.46

19 7.79 26.50 18.46 9.42 37.08 20.29 28.25 14.95 20.29 9.38 19.42

20 11.13 17.17 16.33 16.79 34.46 17.26 37.63 25.75 16.50 22.50 17.52

21 7.96 19.96 17.73 21.25 9.50 16.59 26.96 19.65 22.79 32.46 21.42

22 7.42 16.58 18.54 15.71 10.63 20.00 18.21 27.26 20.83 21.13 10.46

23 12.58 13.83 14.46 20.67 19.17 26.18 16.75 59.50 18.92 6.86 15.65

24 14.54 17.46 19.65 27.50 15.08 31.79 18.00 33.25 22.54 9.17 9.04

25 12.54 16.13 7.04 50.58 13.33 24.42 17.08 22.52 18.21 7.65 9.88

26 16.58 18.04 18.50 25.25 25.59 23.63 17.63 25.65 17.42 9.63 14.74

27 11.83 19.54 19.61 18.33 13.63 32.13 24.70 16.18 17.63 19.86 22.78

28 19.83 18.00 16.67 30.25 14.13 15.54 40.50 27.86 16.38 8.61 10.00

29 13.96 14.41 7.53 13.71 16.00 11.87 33.58 33.08 5.54 13.79

30 7.67 17.21 10.79 10.45 22.46 17.70 25.08 26.33 13.46 13.04

31 14.17 39.88 14.67 23.13 14.88 29.38 10.13

Monthly average 18.83 12.76 23.15 18.52 19.95 19.17 25.17 22.47 16.13 25.83 20.12 16.13

Note:

Where months have been excluded this implies that no data is available.

Blank cells imply that no data is available.

SRK Consulting



Figure 5.9: Location of the PM 10 and Dust fallout monitoring points

SRK Consulting



Figure 5.10 Average daily concentrations by month f or Vredenburg for the period January 2008 to May 2009

Figure 5.11 Average daily concentrations by month f or Blue Water Bay for the period January 2008 to May 2009

Figure 5.10 and Figure 5.11 represent average daily PM10 concentrations by month for Vredenburg

and Blue Water Bay respectively. During the months that the PM10 concentrations at Vredenburg are

higher than Blue Water Bay, it can be suggested that this is either the result of higher wind speeds

carrying dust from the IOHF north, or local dust generation at Vredenburg. Vredenburg is situated

some distance away from the iron ore terminal, whilst Blue Water Bay is much closer. It is a strong

possibility that the measurements reflect local dust generation since PM10 monitors usually measure

0.00

30.00

60.00

90.00

120.00

150.00

180.00

PM

10

(u

g/m

3)

Month

Average daily PM10 data for Vredenburg for

the period 1 January 2008 to 31 May 2009

PM10 SANS (1929:2004) South African Standard

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particles from sources that are very close to the monitor, as particles settle out over distance, with the

sensitivity of the monitoring point decreasing as the distance from the source increases beyond 1 km.

5.4.2 Dust Fallout

A continuous dust fallout monitoring network which was set up by independent consultants in three

locations, the Port Jetty, the NPA offices and at Vredenburg, measures dust fallout levels. Table 5.6

presents the dust fallout results for the period March 2008 to April 2009 and Figure 5.12 presents the

data graphically.

There were no exceedances of the action industrial limit of 1200 mg/m2/day or the action residential

limit of 600 mg/m2/day for the entire period. All results are in range of the target limit of

300 mg/m2/day. Monitoring data for Blue Water Bay were not available at the time of writing this

report.

Figure 5.12: Dust fallout for the period March 2008 to April 2009

.

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Table 5.6: Dust fallout results for the period Marc h 2008 to April 2009 in mg/m 2/day

Monitoring station

Mar- 08 Apr-08 May-08 June-08 July-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09

Port Jetty 265.07 200.59 419.84 173.39 268.36 422.88 345.43 177.50 61.00 33.00 131.89 247.93 278.91. 108.11

NPA Offices 65.20 45.91 184.13 198.39 101.90 385.40 374.43 87.52 28.00 251.00 373.64 178.91 88.0 233.75

Vredenburg 274.26 122.17 38.10 3.39 37.26 48.26 27.35 66.56 48.00 50.00 0.00 101.78 123.92 121.32

NB: Colouring of shaded values corresponds to colours in the legend

Legend

Level Dust Fallout rate

Target 300 mg/m2/day

Action Residential 600 mg/m2/day

Action Industrial 1200 mg/m2/day

Action Industrial 2400 mg/m2/day

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6 Emissions Inventory 6.1 Emissions Calculations and Methodology

Data used to establish an emissions inventory should be site specific or from continuous emission

monitors. However, this data was unavailable. Therefore, specifically developed emission factors

were used as a surrogate in order to quantify the emissions from each source. The majority of the

emission factors used to develop the emissions inventory are described in Airchief 12 (US EPA

2005). More specifically, the document that was used in this case was the US-EPA AP-42 document

entitled “Compilation of Air Pollution Emission Factors”. The document provides the most widely

used emission factors that are regularly revised and reviewed. Predictive emission factor equations

that are empirically derived are available to calculate the dust emission load for material handling,

industrial wind erosion and vehicle entrainment from paved and unpaved roads.

The predictive emission factor equations incorporate particle drift potential, which allows for the

estimation of emissions for specific particle size ranges (i.e., PM10 and TSP). This is an important

factor, as the impact of fugitive dust on air quality is dependent on the drift potential or the degree to

which the particles will diffuse. The drift potential in turn depends on the initial release height, the

terminal settling velocity (which depends on the particle diameter) and the atmospheric turbulence.

Due to their greater density, larger particles settle at ground level close to the source while finer

particles diffuse over a larger distance and come to rest further away from the source.

6.2 Potential Sources of Air Emissions This section lists the activities at the iron ore handling facility that are likely to be the primary

sources of air emissions and associated pollutants for current and proposed operations. A number of

sources have been identified as having the potential to have an impact on the baseline air quality as a

result of the increase in throughput to 60 MTPA. A summary of the various sources, the likely

pollutants to be emitted and their relative significance as a source of air pollution is presented in

Table 6.2. The individual sources are discussed in more detail in the sections below.

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Table 6.1 Potential sources of air emissions and t heir importance as a source of air pollution relate d to the IOHF.

Activity Timing Pollutant Type Significance as a source of air

pollution

Comment

Shunting at Salkor Yard Current, operational

Dust, SO2, NOx and VOCs

Low Dust emissions could occur from the loaded ore wagons. However the wagons are usually covered, and the increase in the number of wagons is unlikely to result in a significant increase in dust emissions. Furthermore, should the wagons not be covered, any erodible dust will have been blown off the surfaces of the ore during the journey from the Northern Cape or settled to the bottom of the wagon. Exhaust emissions from the diesel locomotives are expected to increase. However these are not continuous emissions as the locomotives are not continuously in operation and, given the low base of current emissions, the change in emission levels is not expected to be significant.

Tippling Current, operational

Dust Low to medium This activity has the potential to be a major source due to the high energy nature of tippling or transfer of ore from the rail wagon onto the conveyor system and the volume of ore that will be transferred. However, the tipplers are fitted with a dust extraction system and bag filter unit that collects dust emissions that are generated. When the dust collection and cleaning systems are operational as per the suppliers specifications, dust emissions are <50mg/Nm3 (Airshed, 2006), a level that is considerably lower than the DEAT emission limit of 50 mg/Nm3. It should be noted that the design specification for the dust cleaning system claim that a dust loading of 10mg/Nm3 in the stack emissions can be achieved. The IOHF has upgraded the dust collection system to meet the design specifications at the end of 2007 and this is expected to be continuously maintained for the duration of operation at the IOHF.

Transfer points including ship loading, stacking and reclaiming

Current, operational

Dust Medium to high All transfer points have the potential to be significant sources of dust because of the high energy and mechanical nature of the operation. Sprayers and covers have been introduced to reduce dust at the transfer points as recommended by previous air impact assessment studies. These measures are expected to remain in place for future operations.

Stockpiles Current, operational

Dust Medium to high This source is potentially a significant source of dust primarily due to the height of the stockpiles at the IOHF and their exposure to winds. However once a stockpile has been exposed to wind, the dust is blown off the surface layer and a capping protects the ore underneath, hence no further dust will be generated from that stockpile until it is disturbed. Hence dust emissions from this source will be variable in nature but when conditions are conducive to peak dust emissions then the impact from this source could be quite significant. Dust emissions from this source are usually controlled by use of water sprayers and chemical dust suppression techniques, which aim to maintain an ore moisture content of at least 1.2%. Infrastructure has been installed to keep the ore stockpiles moist for current and future operations.

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Activity Timing Pollutant Type Significance as a source of air

pollution

Comment

Roads, fugitive dust and unpaved areas

Current, operational

Dust, SO2, VOCs, Greenhouse gases

Medium to high The gaseous exhaust emissions from vehicles on the roads are not expected be a significant source of air emissions and will therefore have a low significance. Dust emissions resulting from the entrainment of dust as a result of vehicles travelling on paved and unpaved roads could be a major source of dust. Unpaved surfaces and roads are the primary source of dust from this source. The IOHF has paved all the unpaved road surfaces except for the roads in-between the stockpiles.

Ore ships in the harbour Current, operational

Exhaust dust, SO2, VOCs, Greenhouse gases

Low Contributions of air emissions from this source is also expected to increase, albeit off a relatively low base, due to the increase in the volume of shipping traffic that will occur in the harbour. Although it is a possibility, it is unlikely that ambient concentrations of the associated gas emissions will increase significantly, because only three ships are allowed to wait inside the harbour at any one time, while two are berthed. Emissions will only take place during berthing and de-berthing, however this will not increase the ambient concentration significantly with the increase in the number of trips into the harbour. Hence this source is not considered to be a major source of air emissions.

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6.2.1 Transfer Points

Transfer points are those points in the iron ore handling chain where iron ore is transferred from one

belt or piece of equipment to another, where dust emissions are likely to be produced. The facility

currently has ten transfer points, the first six of which are located to the north and south of the

stockpiles, where ore is transferred from one conveyor belt to the next. The seventh transfer point is

found at the sampling plant, where ore is transferred from the conveyor belt that leads from the

stockpiles to the conveyor belt that feeds the sampling plant. The eighth transfer point is situated

where ore is transferred from the sampling plant onto the conveyor that feeds the ship loaders. The

last two transfer points are located where the ore is transferred from the conveyors to the ship

loaders, which load it into the ship’s cargo hold. The transfer points described are not always used

simultaneously. Loading schedules determine which handling routes and hence transfer points are

being used (Transnet Projects, 2007). For the purposes of this study it was assumed that all of the

transfer points are operational at any given moment (which becomes more realistic with the

proposed increase in throughput, but also represents the worst case scenario that produces

conservative results).

Dust suppression at transfer points is achieved by atomising sprayers which seal the transfer point

with a mist spray that captures dust emissions. Fine particles are most vulnerable to wind erosion as

they are disaggregated easily. Moisture causes these fine dust particles to attach onto the larger

particles, thereby reducing windblown emissions of fine particles. It is evident that moisture content

is affected by the varying seasons. Winter is associated with a higher precipitation, and this will

increase the moisture content of the iron ore, thereby suppressing emissions. Non-climatic

parameters, such as the nature, volume and ore types also affect dust generation from transfer points

and were taken into account in Equation 6.1 used to predict emissions from transfer points.

4.1

3.1

2

2.20016.0

=M

U

kxE (kg/ton) Equation 6.1

Where,

E = Total PM10 Emissions (kg of dust / tonne transferred)

k = Particle size multiplier (0.35 for PM10 and 0.74 for TSP) (dimensionless)

U = mean wind speed (m/s)

M = moisture content of ore (%)

The value 0.0016 is a constant and 0.35 is the particle size multiplier for PM10. The calculation of

Total Suspended Particulates (TSP) dust emissions requires that the constant be multiplied by the

TSP multiplier of 0.74. The particle size multiplier accounts for the varying physical and chemical

characteristics associated with PM10 and TSP. It should be noted that, whilst generic emission factors

are available, Equation 6.1 was used to develop site specific emission factors to take account of site

specific wind speeds and moisture content.

The hourly data for the period 1 January 2008 to June 2009 for both weather stations were used. This

allowed daily and seasonal wind speed variation to be taken into account when determining

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emissions due to materials handling operations at the IOHF. Hence, an hourly emission rate was

calculated for every hour of every day for the period.

Moisture content of the ore varies with the various types of ore available. The moisture content

estimates used for the purposes of dispersion modelling were based on measurements taken at the

IOHF by Airshed (Airshed, 2006). It was assumed that the iron ore at the transfer points that handle

ore from the tippler to the stockpiles (TP 1-3) has the same moisture content as the received ore

(1.69% in winter and summer). Moisture contents of 1.33% in winter and 0.19% in summer were

used for the iron ore at the transfer points between the reclaimers and the sampling plant (TP 4-5).

The measured moisture content at the various transfer points are presented in Table 6.2. This

represents unmitigated conditions as the moisture contents were determined in 2006 before the

current mitigation measures were put in place.

Based on information provided by Transnet, three different levels of moisture content were assumed,

and the 1.2% moisture content was modelled for the mitigated scenarios. This was determined to be

the minimum moisture content to achieve optimum results with respect to dust suppression at this

facility. Any intrinsic moisture level above this level is maintained in the modelling scenarios. This

will have a positive effect by suppressing dust emissions at the transfer points. In addition to this,

Transnet has installed covers and mist sprayers over all fixed transfer points before the sampling

plant as a dust mitigation measure. The utilisation of a chemical wax suppressant was not considered

in the modelling because there was insufficient data on the efficiency of the various products that are

available on the market. However it should be noted that chemical dosing is currently being used at

the port at present. Chemical suppressants work in a similar manner as moisture content, i.e. they

reduce dust emissions by physically aggregating finer particles. However, the main difference is that

the chemical suppressants are not too sensitive to changes in ambient moisture content and hence

will last longer and therefore require less frequent application.

Table 6.2 Derived moisture contents at transfer po ints for winter and summer for unmitigated conditions.

Transfer Point

Winter (%)

Summer (%)

Location

1 1.69 1.69 North of stockpiles, receives ore from tipplers and loads it onto conveyors to be stockpiled 2 1.69 1.69

3 1.69 1.69

4 1.33 0.19 South of stockpiles, receives ore from stockpiles and transfers it onto a conveyor that leads towards the sampling plant 5 1.33 0.19

6 1.35 0.27 At the sampling plant, receives ore from stockpiles and transfers it onto a conveyor that feeds the sampling plant 7 1.35 0.27

8 1.84 0.96 North of sampling plant, ore is transferred from the sampling plant onto the conveyor that feeds the ship loaders

9 1.84 0.96 At the ship loading sites where the ore is transferred from the conveyors to the ship-loaders 10 1.84 0.96

1B no. 1 1.69

1.69 North of stockpiles, receives ore from tipplers and loads it on to conveyors to be stockpiled

1B no. 2 1.35 0.27 South of stockpiles, receives ore from stockpiles and transfers it onto a conveyor that leads towards the sampling plant

Source: Airshed (2006)

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6.2.2 Stacking and Reclaiming

Under unmitigated conditions, stacking and reclaiming of ore is responsible for emitting large

quantities of dust. At present (with a throughput of approximately 31 MTPA), Stacker/Reclaimers

exist, using a bucket reclaiming system. The Stacker/Reclaimers transfer ore from the conveyor belt

onto the stockpiles according to ore grade, customer requirements and predetermined configurations.

The Stacker/Reclaimers also reclaim ore using a bucket loading system that transfers ore from the

stockpiles onto conveyor belts that feed the sampling plant and ultimately deliver ore to the ship

loading system.

The predictive equation used to calculate the dust emission from the Stacker/Reclaimers is the same

as that applied to the transfer points (Equation 6.1). The moisture content was assumed to be 1.33%

during winter and 0.187% during summer. The summer moisture level represents the worst case

scenario. The total emission load measured in kilograms per ton was multiplied by the load per year

(32 million tons for current), which was multiplied by the PM10 fraction of the total silt content, i.e.

particulate matter < 1 mm. In order to calculate the PM10 fraction, the silt content of lumpy ore

(which is 0.95% according to US EPA, 2005, Table 13.2.4-1) was multiplied by the percentage of

PM10 that is found in silt (0.32%).

6.2.3 Dust Generation by Vehicles on Roads

Unpaved Roads

It is often found that dust generated by vehicles travelling on unpaved roads on industrial sites

accounts for the greatest dust emissions. Surface material on the road is pulverised due to the force

of the vehicle’s wheels travelling on the unpaved roadway. The resulting smaller particles are lifted

and dropped by the rotating movement of the wheels, which causes the loose particles to collect on

the roadway. Strong air currents in turbulent shear with the surface caused by the turbulent wake

preceding a vehicle act on the road surface after the vehicle has passed, which causes the erosion and

lifting of loose particles from the road surface. The quantity of dust emitted from the unpaved

roadway is directly proportional to the volume of traffic, in a linear fashion.

Roadway emissions depend on parameters that characterise the condition of a particular road and the

vehicle traffic. Parameters include average vehicle speed, vehicle weight, number of wheels per

vehicle and road surface texture and moisture (US EPA, 2005). The above mentioned parameters

have been given specific values based on reference values outlined in the US-EPA AP-42 document.

The equation used to quantify emissions from unpaved roads for the Saldanha iron ore export

terminal is shown in Equation 6.2.

9.28132

xWsL

kEba

= Equation 6.2

where,

E = emissions in g of particulates per vehicle kilometre travelled (g/VKT)

k = particle size multiplier (4.9 for TSP and 1.5 for PM10) (dimensionless)

a = empirical constant (0.7 for TSP and 0.9 for PM10)

b = empirical constant (0.45 for TSP and PM10)

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sL = silt content of road surface material (%)

W = mean vehicle weight (tonnes)

The silt content of the road surface material was taken to be 10% and the mean vehicle mass was

22 tons. A total of 1712 vehicle kilometres are travelled per day on site, of which 932 km were

travelled on unpaved roads (Airshed, 2006) prior to the commencement of the road paving program.

Paved Roads

Vehicle traffic, including maintenance vehicles, haul trucks, visitor vehicles and plant personnel, is

also responsible for particulate emissions that result from the entrainment of loose material from

paved roads. The amount of loose material on the paved road surface is the result of a balance that is

formed between the deposition and removal of silt. Deposition can occur due to material spillage

from trucks and wind erosion from adjacent sources and unpaved roads.

Emissions from paved roads are dependent on the extent of “silt loading” (sL) found on the roadway

surface and, to a lesser degree, the average weight of the vehicles that travel on the road. Silt loading

encompasses the mass of the silt-size material that is equal to or less than 75 micron in diameter, per

unit area of the road surface. It is the product of the silt fraction and the total loading. The seasonal

variation in the silt content of the surface material can be characterised by including the sL in the

predictive emission estimation equation.

There are various predictive emission equations for paved roads, namely general, urban, industrial

and heavy industrial paved road equations. In this case, the industrial paved road PM10 equation,

presented as Equation 6.3 (US-EPA, 2005) was used. It should be noted that emissions related to

vehicle fleet exhausts, brake wear and tyre wear were not subtracted from the total emission load

since this could not be calculated. However, these emissions are usually a negligible portion of

emissions associated with paved surfaces. No correction was applied for precipitation as well. This

was taken into account during the modelling phase by allowing for reduced emissions during the wet

periods.

Equation 6.3

where,

E = emissions in g of particulate matter per vehicle kilometre travelled (g/VKT)

K = particle size multiplier (24 for TSP and 4.6 for PM10) (g/VKT)

sL = silt content of road surface material (%)

W = average weight (tons) of the vehicles travelling the road

C = emission factor for 1980’s vehicle fleet exhaust, brake wear and tyre wear (0.1317 g/VKT

for PM10).

Site-specific data regarding the silt loading on paved roads was unavailable, therefore data from US-

EPA (2005) was referenced. Forty eight silt loading samples from nine different sites from iron and

steel plants were collected and analysed by the EPA. The mean silt loading content was found to be

9.7%. The average weight of the vehicles was assumed to be 10.76 tonnes (this is the average weight

CWsL

kE −= 5.165.0 )3

()2

(

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of cars and trucks that travel on the road) and the vehicle kilometres travelled on paved roads were

approximately 880 km for activities prior to the commencement of the road paving program during

Phase 1A.

6.2.4 Wind Erosion

Open storage piles (stockpiles) give rise to a significant amount of fugitive dust due to mechanical

disturbance of the material by the prevailing winds. Emission loads can be suppressed by wetting the

material, which promotes the aggregation of fine particles to the surfaces of larger particulates. The

stockpile surfaces are non-homogenous material impregnated with particles that are larger than 1 cm

in diameter (non-erodible particles). Dust generation levels also depend on the shape of the

stockpile, which influences the wind field. The size of the particulates and their distribution

throughout the stockpile affects the potential of wind erosion, the nature of the dust plume and the

deposition rate. Generally, larger dust particles settle closer to the source while smaller particles are

transferred larger distances, affecting a wider area. The wind speed, amount of precipitation and

ground cover are parameters that influence the dust generation levels.

Dust is emitted from open stockpiles when the threshold wind speed is exceeded (US EPA, 1992).

The threshold friction velocity is defined as the minimum wind velocity that is required to initiate

particle motion. It is influenced by the particle size and wind shear stress on the surface (Fig 6.1).

Small particles, with a diameter less than 60 microns (µm), form strong cohesion forces between

other particles; therefore a higher threshold friction velocity exists. Larger particles, with a diameter

greater than 60 microns, experience a trend where the threshold friction velocity decreases as the

diameter of the particle decreases.

Figure 6.1: Relationship between particle sizes and threshold friction velocities

Source: Airshed 2006

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Field testing of exposed materials using a wind tunnel by the EPA revealed that threshold wind

speeds exceed 5 m/s at 15 cm or 10 m/s at 7 m above the surface of the stockpile. Particulates have a

short half life, causing the emission rates to decay rapidly during an erosion event. This occurs as

natural crusting and removal of the erodible material results in a reduction in the erosion potential of

the stockpile. The erosion potential increases rapidly with increasing wind speed. Consequently,

high dust emissions are related to gusts with high magnitudes that have the ability to disturb the

erodible material. The wind tunnel tests found that when typical values for threshold wind speed at

15 cm are corrected to 7-10m (known as wind sensor heights), the mean hourly wind speeds are

insufficient to cause wind erosion from flat surfaces (US-EPA, 2000). Erosion of stockpiles is

anticipated to occur at wind speeds greater than 5.4 m/s.

The erosion potential function for an exposed dry surface is given in Equation 6.4.

(Equation 6.4)

where,

P = Erosion potential for a dry, exposed surface

u* = Friction velocity (m/s)

u*t = Threshold friction velocity (m/s)

The threshold friction velocity used in this case was 0.25 m/s, which is the value prescribed for the

particle fraction below 1mm in diameter (USEPA, 2005).

The friction velocity (u*) was calculated using Equation 6.5.

Equation 6.5

where,

u* = Friction velocity (m/s)

u+10 = Fastest mile (m/s) at 10 m for period between disturbances

To calculate the emission factor for wind entrained particulates, subject to the number of

disturbances, Equation 6.6 was used.

(Equation 6.6)

where,

E = emission factor (g/m2 per annum)

k = Particle size multiplier (0.5 for PM10 and 1 for TSP) (dimensionless)

N = Number of disturbances per year

P = Erosion potential for a dry, exposed surface

)(25)(58 **2**tt uuuuP −+−=

)(053.0 10* += uu

∑=

=N

iiPkE

1

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It is important to note that each area of the erodible surface that experiences a different number of

disturbances per year should be treated separately during the calculation. Wind speed varies across

the stockpile surface, which causes variation in dust emission rates. The windward side of the

stockpile is exposed to the same wind speeds as the top of the pile. The US EPA has developed a

ratio which involves the surface wind speed (denoted as us) to the approach wind speed (ur). This

ratio depends on the surface area of the stockpile that is exposed to wind.

Figure 6.2 shows a conical and an oval shaped pile with a flat top and a 37 degree side slope as used

in wind tunnel tests by the US EPA. The contours show the areas across the stockpiles that possess

the same

Ur

Us

ratio, which were grouped together in order to calculate the emission load. The red

and yellow areas with a

Ur

Us

ratio, of 1.1 and 0.9 respectively denote the areas of highest erosion

potential in stockpiles, while the green (0.2) and blue (0.6) areas have the lowest erosion potential.

Round stockpiles have a lower erosion potential than oval stockpiles as indicated in Figure 6.2

Figure 6.2: Contours of normalised surface wind spe eds (surface wind speed (Us) / approach wind speed (Ur))

Source: USEPA, 2005

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6.2.5 Wagon Tipplers

Ore is off-loaded from the rail trucks at one of the two wagon tipplers. The tippler offloads a pair of

rail wagons in 90 seconds at a rate of 65 tonnes per minute. The tipplers are enclosed in a building

designed to extract dust that is emitted during off-loading. The building has a dust extraction

cartridge filter plant that comprises a Donaldson Cartridge Filtration System, which extracts the

fugitive dust that is generated. The extraction system removes the dust-laden air through a 324-

ultraweb-filtration filter at a rate of 3600 cubic meters per minute. The resulting off-gas from the two

tipplers is released through a stack 22 meters above ground level.

The off-gas released from the stack should have a particulate load of approximately 10 mg/m3, at

0°C and at local atmospheric pressure. However, sampling conducted by Ecoserv in 2006 reveals

that the particulate load emitted from the stacks is 50 mg/m3. It is important to note that this detected

amount is below the DEAT emission level of 120 mg/m3. Transnet upgraded the bag filter units

during 2007 and undertook further isokinetic emission tests on the tippler stacks and recorded levels

of 4.9 and 2.9 mg/Nm3 for Tipplers 1 and 2 respectively.

6.2.6 Ship Loading

The two ship loaders will remain during the proposed expansion, with no additional ship loaders

being constructed. However, there will be an increase in emissions from the resultant increase in

throughput at the port, but it is not expected to be significant.

6.3 Summary of Emissions

6.3.1 Unmitigated Emission Loads

The calculated annual unmitigated emission loads for all phases are presented in Tables 6.3. It

should be noted that this scenario includes any natural mitigation such as moisture levels above 1.2%

that is encountered in some part of the facility during winter. The unmitigated source responsible for

the highest PM10 emissions for current (modelling was based on 31 MTPA) and proposed

(60 MTPA) iron ore throughput levels are the transfer points, followed by stacking and reclaiming,

roads, wind erosion from exposed sources such as stockpiles, tipplers and ship loading. The

emissions from roads are not expected to increase due to the paving of the roads that have taken

place.

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Table 6.3: Ranking of unmitigated dust sources usin g calculated annual emission loads for PM 10 and TSP for 31 MTPA, 47 MTPA and 60 MTPA

Iron ore throughput

level

Source PM10 (TPA) TSP (TPA)

31 MTPA

Transfer points 674.2 1385.9

Stacking & reclaiming 305.2 627.3

Roads- Paved & Unpaved surfaces 327.2 1159.9

Wind erosion 201.5 403.1

Tippler 94.63 94.6

Ship loading 18.0 37.3

47 MTPA





Tippler 94.6 94.6


60 MTPA





Tippler 94.6 94.6


6.3.2 Mitigation Scenario 1 - 1.2% moisture content and 50% control efficiency at the transfer points

According to the emissions inventory, the mitigated sources responsible for the highest PM10

emissions for current (31 MTPA), approved (47 MTPA) and proposed (60 MTPA) iron ore

throughput levels, the roads followed by transfer points. This scenario assumed that:

• A constant ore moisture content of 1.2% is maintained;

• Mitigation measures such as bag filter units at the tipplers are operating at optimal efficiency;

• Unpaved roads are paved and swept;

• Spillages are removed and returned to the main stockpile at least weekly or more often to

prevent spilled material from accumulating;

• Conveyor belts are cleaned with a scraper continuously and spillages under the conveyors are

removed before spilled material builds up, at least weekly; and

• Dust emissions from transfer points are reduced by 50% relative to unmitigated conditions (50%

dust control efficiency).

3 Only TSP are being monitored. To be conservative, it was assumed that all TSP are PM10.

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The ranking of significance of the mitigated sources for 31 MTPA and 47 MTPA are roads, followed

by the transfer points, wind erosion, stacking and reclaiming, tippler and ship loading. The

60 MPTA iron ore throughput levels are the transfer points followed by the roads, stacking and

reclaiming, wind erosion, ship loading and tippler.

Table 6.4: Ranking of mitigated dust sources using calculated annual emission loads for PM 10 and TSP for 31 MTPA, 47 MTPA and 60 MTPA (1.2% moi sture content and paved roads - 50 % mitigation efficienc y)

Iron ore throughput level


31 MTPA





Tippler 19.7 19.7

Ship loading 14.0 29

47 MTPA






Tippler 19.7 19.7

60 MTPA






Tippler 19.7 19.7

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6.3.3 Mitigation Scenario 2 - 1.2% moisture content and 75% control efficiency at the transfer points

According to Table 6.5, the mitigated source responsible for the highest PM10 emissions for current

(31 MTPA) iron ore throughput levels, when a constant ore moisture content is maintained at 1.2%

and mitigation measures are operating at 75% efficiency (dust control at transfer points), are the

roads, followed by transfer points, wind erosion, stacking and reclaiming, tippler and ship loading.

For 60 MTPA throughput levels the predicted ranking of sources is roads, transfer points, stacking

and reclaiming, wind erosion, ship loading and tippler. The increase in throughput would change the

order of ranking of the sources as some of these sources are more affected than others.

Table 6.5: Ranking of mitigated dust sources using calculated annual emission loads for PM 10 and TSP for 31 MTPA and 60 MTPA (1.2% moisture con tent and paved roads- 75 % mitigation efficiency)

Iron ore throughput level


31 MTPA





Tippler 19.7 19.7


60 MTPA






Tippler 19.7 19.7

For this scenario, only the current 31 MTPA and proposed 60 MTPA throughput scenarios are

compared, since a 50% operational efficiency control at transfer point was considered adequate for

47MTPA. These results can be compared with the results presented in Table 6.5.

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7 Dispersion Modelling and Model Inputs 7.1 Description of the Air Dispersion Model

Dispersion models act as powerful tools to ascertain the ambient concentrations and deposition

levels that are potentially emitted from various sources. Models determine the spatial and temporal

patterns in the ground level concentrations and deposition of particles using parameters including

source configuration, emission strengths and meteorological characteristics. This study used the US

EPA Industrial Source Complex Short-Term dispersion model (ISCST Version 3) and AERMOD

(Version 5.6) to model dust emissions arising from the proposed expansion of the IOHF to 60

MTPA. The AERMOD model was used only as a back-up to the ISCST3 model, which had been

used for previous modelling studies (simulating throughput levels of 38 MTPA, 45 MTPA,

67 MTPA and 90 MTPA) and was thus employed in this assessment again for the purposes of

consistency and comparability of the outputs.

The ISCST3 is based on the steady state Gaussian Plume model, which can be applied to multiple

point, area and volume sources. The model can predict the spatial distributions of concentrations for

1-hour, 24-hour, 1-month and annual averaging periods. As the length of the period that is being

modelled increases, in terms of years or months, the model becomes more accurate. Hence, for the

purposes of this study, a meteorological data set covering the period January 2008 to May 2009 was

used. This period was also selected because it coincided with the period for the ore throughput data

that was available and the availability of comparable ambient monitoring data.

Like most air dispersion models, the ISCST3 possesses certain limitations. The model cannot

include spatially varying wind fields due to factors such as topography, and it cannot simulate wind

calm conditions, i.e. wind speeds below 1 m/s. Under such wind calm conditions, the model

overestimates the concentration, therefore US EPA limits these conditions to 1 m/s.

The model is most accurate when applied to flat or gently rolling terrain with fairly strong wind

speeds, during neutral atmospheric conditions. It is said to have an uncertainty range within a factor

of two under such circumstances, i.e. uncertainty could range between -50% (over-prediction) and

200% (under-prediction) (US-EPA, 1995). The study area can be described as relatively flat,

therefore the flat terrain function was used for the model simulations, which is considered to be

suitable for the conditions.

Input data for the model include:

• Meteorological data;

• Information regarding the nature of the receptor grid; and

• Source emission data.

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7.2 Model Input Data

7.2.1 Meteorological Data

Hourly average meteorological data, including wind speed, wind direction, a measure of atmospheric

turbulence, ambient air temperature and mixing height are required. Limited suitable on-site data

was available from the SAWS Langebaanweg station (17 month data set from January 2008 to May

2009), which was used to model the emissions.

7.2.2 Receptor Grid

A Uniform Cartesian grid was established for the model. The grid covered an area of 23.2 km by

25.5 km with a grid spacing of 750 m by 750 m, comprising of 1120 receptor points.

7.2.3 Particle Size and Density

Airshed (2006) conducted a particle size analysis of an ore sample for the fraction less than 1 mm.

For the purposes of this study, the particle diameter and mass fraction presented in Table 7.1 were

used as input into the model. It was assumed that suspended particulate matter comprised all

particles that were <75 microns in diameter, which is similar to the silt size fraction used to calculate

the unpaved roads emission. This was the maximum particle size that was used for the dust fallout

modelling. The specific gravity for crushed iron ore was obtained from SI metric website

(www.simetric.co.uk/si_materials.htm). The specific gravity of crushed iron ore ranges from 2100 –

2900 kg/m3 and an average of 2500 kg/m3 (2.5 g/cm3) was used as input in the model to predict dust

fallout levels. It should be noted that for PM10 only the calculated PM10 emission rates were used,

and for dust fallout the calculated TSP emission rates were used. These calculations are explained in

Section 6 of this report.

Table 7.1: Summary of particle diameter, correspond ing measured mass fraction and particle density up to 1000 um

Particle Diameter (microns)

Mass Fraction (0 to 1) Particle Density (kg/m 3)

1 0.0095 2500

10 0.0063 2500

20 0.0046 2500

53 0.0039 2500

100 0.0066 2500

1000 0.9691 2500

7.2.4 Source Data

The emission rates were calculated using the methodologies described in Section 6. PM10 and total

dust fallout concentrations were modelled for 31 MTPA, 47 MPTA and 60MTPA. Emission rates for

the sources that were modelled for each iron ore throughput level at different levels of mitigation

efficiency are presented in the Tables 7.3 to 7.5.

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Table 7.2: 31 MTPA, 47 MTPA and 60 MTPA dust emissi on rates used for modelling (unmitigated)

Iron ore throughput

level

Source Type of source

No of sources

PM10 Emission Rate per source

(g/s)

TSP Emission Rate (g/s)

31 MTPA

Tippler Point 2 3.00 3.00

Small Stockpile Area 1 0.89 1.78

100 m wide stockpile Area 1 4.40 8.80

Large stockpile Area 3 2.20 4.40

Stacker-Reclaimer Volume 3 3.23 5.18

Transfer Points (1-3) Volume 3 0.33 0.67




Ship Loading Volume 2 0.29 0.59

Unpaved roads onsite Line 3 9.49 32.14

Paved Line 1 0.89 4.60

47 MTPA













60 MTPA



100m width stockpile Area 1 4.40 8.80


Stacker-Reclaimer Area 4 3.75 12.83

Transfer Points (1-3, 1B1) Volume 4 0.64 1.31







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Table 7.3: 31 MTPA, 47 MTPA and 60 MTPA dust emissi on rates used for modelling 1.2% ore moisture content, mitigation at 50% efficiency

Iron ore throughput

level


No of sources

PM10 Emission Rate per

source (g/s)


31 MTPA

Tippler 1 Point 1 0.297 0.297

Tippler 2 Area 1 0.327 0.324











Paved Line 1 0.886 4.63

47 MTPA


Tippler 2 Area 1 0.327 0.327











Paved Line 1 0.886 4.63

60 MTPA













Paved Line 1 0.886 4.63

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Table 7.4: 31 MTPA, 47 MTPA and 60MTPA dust emissio n rates used for modelling 1.2% ore moisture content, mitigation at 75% efficiency

Iron ore throughput

level


No of sources

PM10 Emission Rate per

source (g/s)


31 MTPA


Tippler 2 Area 1 0.327 0.324











Paved Line 1 0.886 4.63

47 MTPA


Tippler 2 Area 1 0.327 0.324











Paved Line 1 0.886 4.63

60 MTPA













Paved Line 1 0.886 4.63

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8 Dispersion Modelling Results The dust modelling results are presented as ambient PM10 and dust fallout results. PM10 refers to the

respirable fraction of dust that is present in the ambient air. The PM10 fraction looks at the fraction of

the dust that is most likely to have an impact on human health. Dust fallout represents the nuisance

fraction of the dust i.e. all dust that will fall out of the atmosphere, e.g. dust settling on surfaces,

including wind blown dust from other natural and man-made sources.

8.1 Dust Dispersion Modelling Results Predicted maximum average 24-hour (daily) concentrations for PM10 and TSP were modelled using

the US-EPA approved ISCST3 model. The output of the model is an isopleth map representing

ambient PM10 levels and dust fallout concentrations at ground level that were interpolated for each

receptor grid point. The maximum 24-hour average plots display the highest predicted ambient

concentration over a 24 hour period over the entire period that was modelled. Therefore the daily

maximum concentration that is predicted may represent the worst case scenario which is generally

associated with high pollution potential meteorological conditions, such as inversions and strong

winds.

8.2 Discussion of Dust Dispersion Modelling Results It is important to note that even though the model results are valuable in terms of trends and areas of

impact, there is a degree of uncertainty associated with the model outputs. Uncertainty is raised in

the emission inventory and air dispersion model in general due to the fact that the model relies on

various assumptions that are made regarding the nature, location and extent of certain activities

conducted at the site. The model outputs are presented as isopleth maps. Maps that are discussed in

the following sections are included in Appendix A.

On each of these maps, the following isopleths (lines connecting points with the same numerical

value, a type of ‘contour lines’) have been plotted:

1) PM10 maps:

• 10 µg/m3 - as an indication of below-guideline extent of footprint;

• 50 µg/m3 - World Health Organization (WHO);

• 75 µg/m3 - SANS (1929:2004) / S.A. Proposed;

• 150 µg/m3 - World Bank (WB/IFC); and

• 180 µg/m3 - South African Standards.

2) Dust fallout map:

• 300 mg/m2/day – Target limit (SANS 1929:2004);

• 600 mg/m2/day – Action residential (SANS 1929:2004);

• 1200 mg/m2/day – Action industrial (SANS 1929:2004); and

• 2400 mg/m2/day – Alert threshold (SANS 1929:2004).

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8.2.1 PM10 Modelling Results for 31 MTPA

Figures 1 to 3 in Appendix A display the predicted maximum 24-hour PM10 concentrations in the

area surrounding the IOHF for 31 MTPA iron ore throughput for unmitigated (Figure 1) and

mitigated conditions (Figure 2 shows 1.2% moisture content4 and 50% mitigation efficiency,

Figure 3 shows 1.2% moisture content and 75% mitigation efficiency).

A comparison of the predicted ambient PM10 concentrations against measured levels at the two

residential stations in Blue Water Bay and Vredenburg is presented in Table 8.1, which also

indicates the location of the monitoring points; actual monitored concentration ranges and modelled

concentration range for the area in the vicinity of the monitoring points.

Table 8.1 Monitored and predicted PM 10 average maximum 24-hr concentrations for 31 MTPA

Monitor Point Location

Monitored Concentration

(µg/m 3)1

Modelled Concentration unmitigated 2

(µg/m 3)

Modelled Concentration

mitigated 2 (1.2% moisture content-

50% efficiency) (µg/m 3)


mitigated 2 (1.2% moisture content-

75% efficiency) (µg/m 3)

Blue Water Bay Average: 20.82 Max: 59.90 Min: 4.80

109.43 7.77 5.18

Vredenburg Average:19.51 Max: 73.17 Min: 3.13

7.77 1.32 0.97

Notes:

(1) Monitored data represent present conditions for 2008/9 when the terminal throughput was 31 MTPA.

(2) Modelled data conditions where throughput at the terminal is at 31 MTPA.

The predicted maximum 24-hr PM10 concentration for the Blue Water Bay monitoring point is

109.43 µg/m3 should no mitigation measures be applied to control dust emissions from the terminal.

This predicted concentration is much higher than the maximum monitored PM10 24-hr concentration

of 59.90 µg/m3 and the modelled mitigated concentrations of 7.77 µg/m3 for 1.2% moisture content

and 50% efficiency and 5.18 µg/m3 1.2% moisture content and 75% efficiency. The data from the

model indicates that, should no mitigation measures be implemented at the IOHF, the maximum

PM10 concentrations at the Blue Water Bay PM10 monitoring point will exceed the proposed South

African PM10 24-hour standard level of 75 µg/m3.

However, the lower measured concentration suggests that for the period from January 2008 to May

2009, when the actual throughput was 31 MTPA, PM10 contributions from the IOHF to the Blue

Water Bay monitoring point were lower. This indicates that mitigation measures were in place at the

facility over this period, resulting in an average 24-hour PM10 concentration of 20.82 µg/m3 and a

maximum concentration of 59.90 µg/m3. However, the modelled concentrations for 50% and 75%

mitigation efficiency are lower, suggesting that mitigation efficiency below 50% was achieved at the

facility in the period under review.

4 Transnet have proposed that the ore moisture content be maintained at a level between 1% and 1.4% to achieve optimum dust emission control. For the purposes of this assessment, model simulations were run for ore moisture content of 1.2%, which was determined to be the optimum moisture level with respect to the control of dust emissions. In cases where the residual moisture content in the ore was already above 1.2%, this level was maintained during modelling.

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The simulated unmitigated maximum PM10 concentration for Vredenburg is 7.77 µg/m3, which is

lower than the monitored maximum concentration of 73.17µg/m3, suggesting that the Vredenburg

monitoring point could also be monitoring other local sources of dust, such as construction activities

and/or Saldanha Steel. The modelled concentration for the mitigated scenario for Vredenburg is

1.32 µg/m3 (1.2% moisture content and 50% mitigation efficiency) and 0.97 µg/m3 (1.2% moisture

content and 75% mitigation efficiency), which is significantly lower than the predicted unmitigated

concentration and the monitored concentration of 19.51 µg/m3.

The PM10 dispersion footprint observed in each mitigation scenario decreases considerably when

compared to the unmitigated scenario with the 75 µg/m3 and 50 µg/m3 footprints decreasing to

within the footprint of the IOHF (see Figures 1 to 3, Appendix A).

8.2.2 Dust Fallout Modelling Results for 31 MTPA

Dust monitors have been installed at strategic points in and around the IOHF. The monitoring points

were modelled as discrete receptors in the ISCST3 model, which allows for easy comparison

between the monitored and modelled concentrations at the monitoring points. Table 8.2 presents the

predicted maximum dust fallout concentrations for a throughput 31 MTPA of iron ore. It should be

noted that dust fallout is a measure of nuisance dust (from a range of man-made and natural sources)

and exceedances of the SANS guideline levels suggests an increase in nuisance levels for the various

defined categories (See Section 4.2).

Table 8.2 Monitored and predicted maximum dust fall out concentrations for 31 MTPA

Co-ordinates 3 Description of

monitoring point

Average Monthly

Monitored Concentration (mg/m 2/day)

Modelled Concentration (unmitigated) 2

(mg/m 2/day)


(1.2% moisture

content-50% efficiency) 2

(mg/m 2/day)


(1.2% moisture

content-75% efficiency) 2

(mg/m 2/day)

32˚ 59’ 43.85” S 17˚ 58’ 24.32” E

Blue Water

Bay1

Not monitored 518.19 116.59 96.93

33˚ 00’ 10” S 17˚ 59’ 34.57”E

Port Jetty 223.85

12076.07 2393.35 2215.59

32˚ 59’ 32.42” S 17˚ 59’ 25.06” E

NPA Buildings

185.44

2280.52 522.09 465.43

32˚ 54’ 53.20” S 17˚ 59’ 22.03” E

Vredenburg 75.88

123.03 23.09 21.95

Notes:

(1) No monitored concentration for Blue Water Bay (2) Modelled data representing 31 MTPA conditions. (3) The monitoring locations can be viewed in Figure 5.9 in Section 5 of this report.

These results suggest that, if no mitigation measures are implemented at the IOHF, contributions to

the dust fallout from the IOHF can be expected and therefore nuisance associated with dust will

increase. However, if mitigation measures are implemented, contributions to dust fallout are

expected to be very low at Vredenburg and below target levels at Blue Water Bay.

By introducing the mitigation measures in the model, the predicted dust deposition footprint

decreases significantly relative to the unmitigated scenario. A 1.2% moisture content, paving of

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unpaved roads and 50% efficiency of dust control at the transfer points result in the footprint of the

target 24-hr dust fallout guideline of 300 mg/m2/day being limited to a radius of no more that 2 km

around the IOHF. However, the alert threshold guideline of 2400 mg/m2/day (SANS 1929:2004 dust

deposition guidelines) will still have a footprint that would remain within the Port boundaries, which

will represent the activities still occurring in the port (see Appendix A, Figures 11, 12 and 13). It

should be noted that the predicted concentrations represent the maximum daily dust fallout

concentrations at any single receptor point, which means that the guideline value will not be

exceeded all the times but only under certain (worst scenario) conditions such as very high wind

speeds and/or failure of dust mitigation conditions during a specific 24-hour period.


The maximum 24-hour ambient PM10 concentrations modelled for Blue Water Bay and Vredenburg

are presented in Table 8.3. The model predicts an increase in ambient PM10 levels at Blue Water Bay

and Vredenburg relative to a throughput of 31 MTPA, confirming that an increase in ore throughput

will result in increased ambient PM10 concentrations.

However, the application of appropriate mitigation measures in the model yields predicted levels that

are considerably lower than the unmitigated scenario at a throughput of 31 MTPA, and a negligible

increase when compared to the mitigated scenario at a throughput of 31MTPA. Furthermore,

maintaining a 50% mitigation efficiency at the transfer points, 1.2% moisture content of the iron ore,

paving roads and improved housekeeping while handling 47 MTPA at the facility results in ambient

concentrations at Blue Water Bay and Vredenburg that are significantly below the proposed 24-hour

SA standard of 75 µg/m3. The footprint area of the 75 µg/m3 PM10 exposure is then expected to be

confined to a radius of 1 km around the IOHF, e.g. it does not reach surrounding residential

areas(see Figures 7 and 8, Appendix A).

Table 8.3: Predicted average maximum 24-hour ambien t PM10 modelled concentrations for 47 MTPA


Modelled Concentration range (unmitigated)

(µg/m 3)

Modelled Concentration mitigated (1.2% moisture content-50% efficiency)

(µg/m 3)

Blue Water Bay 170.77 12.96

Vredenburg 17.5 2.55


The predicted maximum daily dust fallout (nuisance dust) concentrations for Blue Water Bay and

Vredenburg are presented in Table 8.4. If no mitigation measures are implemented, impacts on the

surrounding areas could be significant, with the 600 mg/m2/day residential guideline value being

exceeded in Saldanha and the southern parts of Vredenburg (Figure 9, Appendix A). However,

implementing mitigation measures with 50 % efficiency at the transfer points, maintaining a 1.2%

moisture content, paving of roads and improved housekeeping will result in the footprint for the area

where dust fallout levels exceed the 600 mg/m2/day decreasing to within a radius of <1km from the

port. This is comparable to a similar scenario at 31MTPA iron ore throughput (see Figures 10 and

12, Appendix A).

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Table 8.4: Monitored and predicted maximum dust fal lout concentrations for 47 MTPA

Co-ordinates Description of

monitoring point

Average Monthly


Modelled Concentration (unmitigated)

(mg/m 2/day)

Modelled Concentration (1.2%

moisture content-50% efficiency)

(mg/m 2/day)

32˚ 59’ 43.85” S 17˚ 58’ 24.32” E

Blue Water Bay

Not monitored 1946.36 171.89

33˚ 00’ 10” S 17˚ 59’ 34.57”E

Port Jetty 223.85

19280.14 1712.15

32˚ 59’ 32.42” S 17˚ 59’ 25.06” E

NPA Buildings

185.44

5432.98 670.13

32˚ 54’ 53.20” S 17˚ 59’ 22.03” E

Vredenburg 75.88

299.17 28.03


The maximum 24-hour ambient PM10 concentrations modelled for Blue Water Bay and Vredenburg

for an iron ore throughput of 60 MTPA are presented in Table 8.5. For this scenario, ambient PM10

concentrations are predicted to further increase at the Blue Water Bay and Vredenburg PM10

monitoring stations if no mitigation measures are applied (see Figure 4, Appendix A).

However, the implementation of mitigation measures with 50% efficiency at the transfer points,

maintaining 1.2% moisture content and paving roads is expected to result in a significant reduction

in PM10 concentrations, with predicted levels being below the proposed SA guideline of 75 µg/m3 in

both Blue Water Bay and Vredenburg (see Figures 5 and 6, Appendix A). If a 75% control

efficiency is achieved at the transfer points, then maximum PM10 concentrations are expected to

decrease to levels comparable to or lower than those predicted for 50% control efficiency at the

transfer points for 31 and 47 MTPA throughput.

Table 8.5: Predicted average maximum 24-hour ambien t PM10 modelled concentrations for 60 MTPA


Modelled Concentration range (unmitigated)

(µg/m3)


(µg/m3)


(µg/m3)

Blue Water Bay 197.57 25.45 8.98

Vredenburg 12.36 2.19 1.29

NB: Associated health risks with these levels are discussed in the Health Specialist Study

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The predicted maximum daily dust fallout concentrations for Blue Water Bay and Vredenburg are

presented in Table 8.6. If no mitigated measures are implemented, impacts on the surrounding areas

could be significant, with the 600 mg/m2/day residential guideline value being exceeded in an area

encompassing greater Saldanha and the southern parts of Vredenburg (see Figure 14, Appendix A).

However, implementing mitigation measures with 50% efficiency at the transfer points, maintaining

a 1.2% moisture content of the ore and paving roads will result in the footprint for the area where

dust fallout exceeds 600 mg/m2/day decreasing to within a radius of <2km from the port (see Figure

15, Appendix A).

Table 8.6: Monitored and predicted maximum dust fal lout concentrations for 60 MTPA

Description of

monitoring point

Average Monthly


Modelled Concentration (unmitigated)

(mg/m 2/day)

Modelled Concentration (1.2% moisture

content-50% efficiency)

(mg/m 2/day)


(1.2% moisture

content-75% efficiency)

(mg/m 2/day)

Blue Water Bay

Not monitored 2685.18 312.94 136.80

Port Jetty 223.85

24808.26 3104.03 2665.55

NPA Buildings 185.44

5864.46 812.64 580.74

Vredenburg 75.88

176.29 34.38 23.79

8.3 Model Calibration The model was run for one specific day during different months, using the weather conditions that

were experienced during that day. These periods were chosen based on the predominant wind

directions. Table 8.7 shows the predominant wind direction and wind speed for the chosen periods.

Table 8.7: Predominant wind direction and wind spe ed for the modelled periods

Date Predominant wind direction

Predominant wind speed (m/s)

25 October 2008 South-south-east 5.7-8.8

28 January 2009 South- east 5.7-8.8

9 March 2009 South-east 3.6-5.7

Two different scenarios were run for PM10:

• 31 MTPA unmitigated; and

• 31 MTPA mitigated, with an ore moisture content of 1.2% and 50% mitigation efficiency.

The modelled PM10 concentrations were then compared to monitored concentrations for the same

days. Table 8.8 shows the monitored and modelled PM10 concentrations for both the unmitigated and

mitigated scenario.

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Table 8.8: Monitored and modelled dust concentratio ns for 31 MTPA

Description of monitoring point

Average Daily Monitored

Concentration (µg/m 3)

Modelled Concentration - Unmitigated

(µg/m 3)

Modelled Concentration – Mitigated (1.2 %

moisture and 50% efficiency

(µg/m 3)

25 Oct 2008

Blue Water Bay 50.58 5.51 1.01

Vredenburg 30.75 0.63 0.09

28 Jan 2009

Blue Water Bay 40.50 32.06 3.40

Vredenburg 42.83 0.64 0.07

9 March 2009

Blue Water Bay 51.38 15.42 2.01

Vredenburg 53.33 0.89 0.10

All of the monitoring points recorded measurements that were below the modelled concentrations

for PM10 during the selected days. The modelled results for Vredenburg were significantly lower

than the actual measurements. During these periods, wind was blowing from a south-south-easterly

and south-easterly direction and wind speeds were at a medium level, indicating that particles from

the Port were unlikely to have reached Vredenburg, or would have been blown in a north-westerly

direction past the town under the conditions prevailing on this particular day. It is thus likely that the

measured PM10 concentrations at Vredenburg were not contributed in their majority by the IOHF,

and the monitoring point at Vredenburg was instead recording local dust sources.

In Table 8.8, the actual recorded PM10 concentrations at Blue Water Bay and Vredenburg were

higher than the modelled concentrations. The modelled data for the 28 January 2009 at Blue Water

Bay shows concentrations fairly close to the actual recorded concentration for that day.

An increase of 50% efficiency in the 1.2% mitigation scenario decreases the modelled PM10

concentrations significantly compared to the monitored concentrations. This indicates that the IOHF

is not the only source contributing to PM10 concentrations at Blue Water Bay.

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9 Impact Assessment Although the results of the modelling for the 31 MTPA, 47 MTPA and 60 MTPA throughput levels

are presented in this report, the impact assessment focuses on 60 MTPA, with 47 MTPA being the

baseline condition. Where necessary, comment will be given on changes in impacts relative to

31 MTPA as discussed in Section 8. The change in the concentration of PM10 and TSP dust

emissions due to the increased throughput at the IOHF is the only environmental aspect covered in

this study. Other emission sources, e.g. emissions from ships and the Salkor shunting yard, have

been reviewed and were not considered to be significant contributors to the ambient air pollution

(see Table 6.1).

Residents in the towns surrounding the site are the potential sensitive receptors in this study.

Surrounding towns include Vredenburg to the north, Saldanha and Blue Water Bay to the west and

Langebaan to the south east of the IOHF. The environmental and nuisance impacts of change in

ambient PM10 and dust fallout levels in those areas were therefore assessed in this study.

The impact assessment methodology used was provided by SRK as part of the Terms of Reference

for this study. A detailed description of the methodology used is given in Appendix B. The

significance of the impacts was assessed according to the following criteria:

• Extent – the area in which the impact will be experienced;

• Intensity – the magnitude or size of the impact; and

• Duration – the timeframe for which the impact will be experienced.

Using these criteria a consequence rating will be determined.

The probability of the impact occurring will be determined according to the following definitions:

Probability– the likelihood of the impact occurring

Improbable < 40% chance of occurring

Possible 40% - 70% chance of occurring

Probable > 70% - 90% chance of occurring

Definite > 90% chance of occurring

Once that probability and consequence have been determined, the significance of the impacts are

assessed stating whether it will be a positive or negative impact and a level of confidence for both

the mitigated and unmitigated scenarios.

9.1 Assessment of Impacts for 47 MTPA

9.1.1 Impacts associated with changes in ambient PM 10 concentrations for 47 MTPA throughput

PM10 is of concern as it has a potential impact on health as respirable dust. As shown in the

47 MTPA isopleth maps (Appendix A, Figure 7 and 8), PM10 will affect the project area and

communities beyond the project boundary for unmitigated conditions. However, with mitigation the

PM10 footprint will decrease considerably to the project area.

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• Extent – The extent was rated regional because areas beyond the project area will be affected by

PM10 such as Vredenburg.




the operations.






• Extent – Local because under mitigated conditions the PM10 footprint will decrease around the

project area and will not affect towns or communities greater than 5 km away.




the operations.





Without mitigation


2 2 3 7








With mitigation


1 1 3 5

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9.1.2 Impacts associated with changes in dust fallo ut concentrations for 47 MTPA throughput

Dust fallout is primarily a measure of nuisance, which occurs when a fine, powdery substance settles

on the ground as an adverse secondary effect. Figures 9 and 10 in Appendix A show the unmitigated

modelled scenario for dust fallout as well as the mitigated scenario respectively and provide an

indication of the impacts that dust fallout will have in the project area and surrounding communities.

Dust fallout is measured in four areas around the project area, and these areas can be defined as

discrete receptors.




Vredenburg.




the operations.






• Extent – Local because under mitigated conditions the extent of the dust fallout will only be

significant around the project area and will not affect towns or communities greater than 2 km

away.




the operations.




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Without mitigation


2 2 3 7







With mitigation


1 1 3 5

9.2 Assessment of Impacts for 60 MTPA The proposed 60 MTPA throughput will increase the footprint of both PM10 and dust fallout, under

unmitigated conditions, when compared to the actual throughput of approximately 31 MTPA, which

is being achieved at the IOHF currently. Mitigation measures have to be implemented to full effect if

the change from 31 MTPA to 60 MTPA is considered. The size of the area affected will increase,

and the impact will still occur on a regional scale under unmitigated conditions. Under mitigated

conditions both the footprints for PM10 and dust fallout will decrease and the area impacted will be

on a local scale.

9.2.1 Impacts associated with changes in ambient PM 10 concentrations for 60 MTPA throughput



• Extent – Regional because areas beyond the project area will be affected by PM10 such as

Vredenburg




the operations.







around the project area and will not affect towns or communities greater than 5 km away.

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the operations.





Without mitigation


2 2 3 7








With mitigation


1 1 3 5

9.2.2 Impacts associated with changes in dust fallo ut (nuisance) concentrations for 60 MTPA throughput

The impact is considered to be of very high (negative) significance without mitigation, as shown

in the table below and determined as follows:


Vredenburg and Langebaan.

• Intensity – The intensity is high because of the high dust volume and its staining properties.


the operations.







around the project area and will not affect towns or communities greater than 5km away.

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the operations.

• Probability – The impact is probably and in this case provisions will have to be made.




Without mitigation

Regional High Long-term Very High Probable VERY HIGH – ve High

2 3 3 8








With mitigation


1 1 3 5

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10 Conclusions and Recommendations 10.1 Conclusions

Based on the findings of this assessment, the following are concluded:

• The increase in throughput of iron ore at the IOHF from 47 MTPA to 60MPTA will result in an

increase in ambient PM10 and dust fallout levels in the surrounding environment for unmitigated

and mitigated conditions.

• The relative increase in ambient PM10 and dust fallout levels for the unmitigated scenario will be

greater from 31 MTPA to 60 MTPA than from 47 MTPA to 60 MTPA.

• The implementation of appropriate mitigation measures will result in ambient PM10 and dust

fallout levels that will be significantly below the proposed South African standards and

guidelines for PM10 and dust fallout in Saldanha, Blue Water Bay and Vredenburg (Figs. 4 to 6

and 14 to 16, Appendix A) and levels that are lower than unmitigated conditions for the current

31 MTPA.

• Of the various mitigation scenarios considered, the optimum mitigation scenario is maintenance

of a minimum 1.2% moisture content in the ore throughout the facility, at least 50% dust

suppression efficiency, paving of all roads (except those within the stockpiles), sweeping of

paved roads and good housing keeping (i.e. cleaning up of spills and any fugitive dust that

occurs within the facility).

• Dust mitigation is an imperative for the proposed increased throughput, and this must be

considered to be an operational activity and not an add-on to daily operations.

10.2 Recommendations Mitigation measures that are usually recommended for an operation of this nature are included

below. Some if not all of these measures will be been implemented as part of the Phase 1B

(47 MTPA) expansion. However these measures also need to be implemented and maintained for the

60 MTPA scenario as a minimum:

• Maintain an iron ore moisture content of 1.2% (more detail provided later in this section),

• Continue using a chemical suppressant (as a substitute to moisture control especially when it is

difficult to maintain a stable moisture level over a long period of time);

• Pave all unpaved roads except for those between the stockpiles;

• Reduce vehicle speeds on roads;

• Improve housekeeping at all times to include the following:

o Sweep all paved surfaces (e.g. roads) within the terminal daily; and

o Sweep or remove dust piles from unpaved surfaces that contain deposits of material that

could lead to fugitive dust emissions, e.g. dust falling from the conveyor belts. This should

happen at least weekly or more often if necessary to prevent accumulation of material;

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• Ensure optimal performance of cartridge filters systems that are installed at the dust extraction

system at the tipplers;

• Install wind shields/breakers along the conveyor belt system where this has not been

implemented;

• Reduce fugitive dust emissions from miscellaneous sources such as dust coming off the return

conveyors;

• Ensure that all transfer points and conveyor belts are covered, where practical, in order to reduce

wind speeds and re-entrainment of dust into the atmosphere during ore transfer, where this has

not been done. Maintain existing covers; and

• Cover any bare ground in the areas surrounding the main operational area with suitable

vegetation that will be able to grow in the area.

In addition, if possible, wind breakers such as trees in areas located upwind of the facility could be

installed / planted.

Of the listed measures, moisture control, paving of unpaved road surfaces, sweeping of paved roads,

enclosure of transfers points with mist sprayers and housekeeping were the primary mitigation

measures that were considered during modelling.

The mitigation measures used during the modelling phase of this study were largely guided by

measures that the engineers from Transnet have recommended based on tests conducted by Kumba

Resources and engineering and designs that were considered for the expansion of the IOHF. In

addition to this, these measures are primarily what are practical and most effective for an operation

of this nature. Details on the possible dust mitigation measures are presented in the following

sections.

10.2.1 Moisture Control

For the purposes of the modelling study, the mitigation measures considered included increasing and

maintaining the moisture content of iron ore at 1%, 1.2%, and 1.4% by use of sprayers at various

points in the operations, especially at materials handling points. The increased moisture content will

promote aggregation of small dust particles to larger particles, which will reduce the amount of

windblown dust from various sources. The modelling results (discussed in Section 8) showed that

increasing the residual moisture content of the ore to 1% resulted in a significant decrease in dust

emissions, and hence a decrease in ambient PM10 and dust fallout is predicted. The results of the

simulation for the three levels of moisture content indicated that maintenance of a moisture content of 1.2% in the ore yielded an optimum level dust emission reduction and hence the optimum

PM10 and dust fallout levels in the surrounding environment.

10.2.2 Paving of Roads

Transnet have paved all road surfaces outside the stockpiles, but within stockpiles roads are

unpaved. The modelling results as present in Section 8 indicate that this measure has contributed to a

significant reduction of dust emissions, thus reducing the footprint of the impacted area. It should be

noted that the model assumed that the road surfaces will be swept and dust on the road surfaces

kept to a minimum or negligible level.

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10.2.3 Enclosure of Transfer Points

For the purposes of this study, the efficiency of dust reduction that would result from the enclosure

and installation of mist sprayers over all transfers points up to the sampling plant was not known.

Hence different mitigation efficiencies for the transfer points were modelled, i.e. 50% and 75%. (It

should be noted that these were found to be optimum based on the findings of the Phase 2 study.)

This study has found that for 60 MTPA iron ore throughput, a minimum dust suppression efficiency of 50% is required.

10.2.4 Housekeeping

Housekeeping will contribute to a significant reduction of dust emissions from fugitive sources at the

IOHF. Whilst this is not a primary source of dust, it is a secondary source that will significantly

reduce the levels of dust emissions especially given that such sources are difficult to manage, due to

the their variability and spatial extent during very windy periods. Activities or areas that need

particular attention include with respect to housekeeping:

• Sweeping of road surfaces and open surface water channels;

• Removal of dust accumulating under the conveyor belts and transfer points; and

• Vegetate bare or open ground within the IOHF that could be a source of windblown dust.

10.2.5 Tippler Dust Filter Plant

The tipplers need to operate at efficiencies that meet the design specifications at all times i.e. with

total dust emission levels below 10 mg/Nm3.

10.3 Performance Monitoring Measures The following performance monitoring measures are recommended:

• Maintain dust fallout and ambient monitoring network (including the weather stations) to

measure and monitor PM10 and dust fallout levels emitted by the facility. Ensure that the

network remains in good working order. It is important to note that a well run monitoring

network with a >95% data availability will help monitor the impacts from the IOHF and any

mitigation measures that have been implemented ;

• Expand the monitoring network to include measurement of dust fallout and PM10 levels in the

Langebaan area and a PM10 monitor closer to the IOHF in order to more accurately monitor dust

emissions that are largely due to the IOHF;

• Develop a system to monitor and rapidly respond to spills;

• Monitor the effectiveness of the proposed dust suppression system based on moisture content of

the ore and investigate, identify and implement opportunities for optimisation of the system if it

contributes to a further decrease in dust levels;

• Conduct periodic audits of water sprayers and systems to ensure good working condition,

resulting in efficient reduction in wind erosion;

• Conduct periodic and independent audits of the monitoring systems and implementation of

operational management plans to ensure that the system is being maintained properly and that

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the outputs of the monitoring system are providing suitable data for support in decision making;

and

• Conduct a performance audit on staff that have been appointed to manage the various dust

control and mitigation systems recommended in this study.

D Naidoo VS Reddy (Pr.Sci.Nat)

Scientist Partner

SRK Consulting

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11 References

Books and Reports:

Airshed Planning Professionals. 2006. Baseline Air Quality Assessment for the Bulk Ore Terminal –

Saldanha Bay (Western Cape), Report No.: APP/06/-02 Rev 0.0.

Cosijn C. and Tyson, P.D. (1996). Stable discontinuities in the atmosphere over South Africa. South

African Journal of Science, vol. 92.

DEAT. 2006. Government Gazette, National Environmental Management Air Quality Act, 2004,

No. 28899.

SANS 1929. 2005. South African National Standard, Ambient air Quality – Limits for common

pollutants.

Ecoserv, 2005. Ambient dust reporting SAPO Monitoring Network November 2002 to October

2003, Report No. AS0162.

Ecoserv, 2005. Ambient dust reporting SAPO Monitoring Network November 2003 to October

2004, Report No. AS0162.

Kornelius, G., Mudeme, L. and le Roux, N. 2006. Atmospheric Impact of the Holcim Roodeport

Extensions, Report APP/06/MW-01-Rev.04.

Preston-Whyte, R. A., Diab, R. D. and Tyson, P. D. 1976. Towards an Inversion Climatology of

Southern Africa: part 2, non-surface inversions in the lower atmosphere. South African Geographical

Journal, vol. 59, no. 1.

Preston-Whyte, R. A. and Diab, R. D. 1980. Local weather and Air Pollution Potential: the case of

Durban. Environmental Conservation, vol. 7, no. 3.

Transnet. 2007. Saldanha Iron Ore Terminal Water Supply and Demand Report.

US EPA. 2005. Compilation of Air Pollution Emission Factors (AP-42),Version 12, as contained in

the Air CHIEF 12 (AIR Clearinghouse for Inventories and Emission Factors) CD-ROM (compact

disk read only memory), EPA 454/C-05-001, US Environmental Protection Agency, Research

Triangle Park, North Carolina, June 2005.

US EPA. 1992. Fugitive Dust Background Document and Technical Information Document for Best

Available Control Measures, EPA-450/2-92-004, U.S. Department of Commerce, Springfield, VA,

September 1992

Wanta, R. C. 1968. Meteorology and Air Pollution and its effects, Ch 7 in: Stern, A. C. (ed.), Air

Pollution, Volume 1: Air Pollution and its effects, Academic Press, New York.

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Internet sources:

BAAQMD (Bay Area Air Quality Management District). undated. Emission Inventory,

http://www.baaqmd.gov/pln/emission_inventory.htm, accessed 9 May 2007.

DEAT. 2005. Northern Cape Atmosphere and Climate particulate matter concentrations.

http://www.environment.gov.za/, accessed 12/03/07.

Steynor, A. 2006. Introduction to South African Climate. http://www.csag.uct.ac.za/module_1,

accessed 21/02/07.

Williamson, S. J. 1973. Fundamentals of Air Pollution, Chapter 12, Control of Air Pollution.

USEPA. 2006. Particulate Matter. http://www.epa.gov/oar/particlepollution/, accessed 26.01.07

Personal communication:

Diab, R. D. 2007. Air Dispersion Modelling Workshop, personal communication, University of

Kwazulu-Natal, Durban.

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Appendices

Appendix A: Isopleth maps

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Appendices

Appendix B: Impact Assessment Methodology

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Impact Assessment Methodology

The significance of all potential impacts that would result from the proposed project is determined in

order to assist decision-makers. The significance rating of impacts is considered by decision-makers,

as shown below.

INSIGNIFICANT : the potential impact is negligible and will not have an influence on the

decision regarding the proposed activity.

VERY LOW : the potential impact is very small and should not have any meaningful influence

on the decision regarding the proposed activity.

LOW : the potential impact may not have any meaningful influence on the decision regarding

the proposed activity.

MEDIUM : the potential impact should influence the decision regarding the proposed activity.

HIGH : the potential impact will affect a decision regarding the proposed activity.

VERY HIGH : The proposed activity should only be approved under special circumstances.

The significance of an impact is defined as a combination of the consequence of the impact

occurring and the probability that the impact will occur. The significance of each identified impact5

must be rated according to the methodology set out below:

Step 1 – Determine the consequence rating for the impact by adding the score for each of the three criteria (A-C) listed below: Rating Definition of Rating Score

A. Extent– the area in which the impact will be experienced

None 0

Local Confined to project area – the site and surrounding areas/communities 1

Regional Exceeds project area but within the Western Cape region 2

National South Africa (or beyond) 3

B. Intensity– the magnitude or size of the impact

None 0

Low Natural and/or social functions and processes are negligibly altered 1

Medium Natural and/or social functions and processes continue albeit in a modified way 2

High Natural and/or social functions or processes are severely altered 3

C. Duration– the time frame for which the impact will be experienced

None 0

Short-term Up to 2 years 1

Medium-term 2 to 15years 2

Long-term More than 15 years 3

5 This does not apply to minor impacts which can be logically grouped into a single assessment.

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The combined score of these three criteria corresponds to a Consequence Rating, as follows: Combined Score (A+B+C) 0 – 2 3 – 4 5 6 7 8 – 9

Consequence Rating Not significant Very low Low Medium High Very high

Example 1:

Extent Intensity Duration Consequence

Regional Medium Long-term High

2 2 3 7

Step 2 – Assess the probability of the impact occurring according to the following definitions:

Probability– the likelihood of the impact occurring

Improbable < 40% chance of occurring

Possible 40% - 70% chance of occurring

Probable > 70% - 90% chance of occurring

Definite > 90% chance of occurring

Example 2:

Extent Intensity Duration Consequence Probability


2 2 3 7 Probable

Step 3 – Determine the overall significance of the impact as a combination of the consequence and probability ratings, as set out below:

Significance Rating Consequence Probability

Insignificant Very Low & Improbable

Very Low & Possible

Very Low Very Low & Probable

Very Low & Definite

Low & Improbable

Low & Possible

Low Low & Probable

Low & Definite

Medium & Improbable

Medium & Possible

Medium Medium & Probable

Medium & Definite

High & Improbable

High & Possible

High High & Probable

High & Definite

Very High & Improbable

Very High & Possible

Very High Very High & Probable

Very High & Definite

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Example 3:

Extent Intensity Duration Consequence Probability Significance


2 2 3 7 Probable HIGH

Step 4 – Note the status of the impact (i.e. will the effect of the impact be negative or positive?)

Example 4:

Extent Intensity Duration Consequence Probability Significance Status


2 2 3 7 Probable HIGH – ve

Step 5 – State your level of confidence in the assessment of the impact (high, medium or low).

Depending on the data available, you may feel more confident in the assessment of some impact than others. For example, if you are basing your assessment on extrapolated data, you may reduce the confidence level to low, noting that further groundtruthing is required to improve this.

Example 5:


Regional

Medium Long-term High

2 2 3 7 Probable HIGH – ve High

Step 6 – Identify and describe practical mitigation measures that can be implemented effectively to reduce the significance of the impact. The impact should be re-assessed following mitigation, by following Steps 1-5 again to demonstrate how the spatial extent, intensity, duration and/or probability change after implementation of the proposed mitigation measures.

Example 6: A completed impact assessment table


Without mitigation


2 2 3 7

With mitigation

Local Low Long-term Low Improbable VERY LOW – ve High

1 1 3 5

In the report, mitigation measures must be described as either:

Essential: must be implemented and are non negotiable; and

Optional: must be shown to have been considered and sound reasons provided by the proponent if

not implemented.

Step 7 – Summarise all impact significance ratings as follows in your executive summary:

Impact Consequence Probability Significance Status Confidence

Impact 1: XXXX Medium Improbable LOW –ve High

With Mitigation Low Improbable VERY LOW High

Impact 2: XXXX Very Low Definite VERY LOW –ve Medium

With Mitigation: Not applicable

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SRK Report Distribution Record

Complete this form and include it as the final page for each copy of the report produced.

Report No. 399449/42A

Copy No.

Name/Title Company Copy Date Authorised by

S Reuther SRK – Western Cape SBU 1 Sep 09 VS Reddy

Library Dur SRK Consulting 2 Sep 09 VS Reddy

Library Jnb SRK Consulting 3 Sep 09 VS Reddy

File SRK Consulting 4 Sep 09 VS Reddy

Approval Signature:

This report is protected by copyright vested in SRK Consulting. It may not be reproduced or

transmitted in any form or by any means whatsoever to any person without the written permission of

the copyright holder, SRK.

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