air quality impact assessment for the swaziland rail link rail link/davel to... · 2014. 6. 25. ·...

Air Quality Impact Assessment for theSwaziland Rail Link - 3: Upgrade andconstruction of the Mpumalanga sec-tion, Davel to Nerston

Report to the

Department of Environmental Affairson behalf of

Aurecon

Final Report2013AUR-0339— AQIA RN 130477 AUR - ver 1

November 2013

Report Title Air Quality Impact Assessment for the Swaziland Rail Link - 3: Up-grade and construction of the Mpumalanga section, Davel to Nerston

Project Number 2013AUR-0339

Report Number AQIA RN 130477 AUR - ver 1

Client Aurecon

Authors

Roelof BurgerMartin van NieropAnja van BastenGondwana Environmental SolutionsTel: 011472 3112Email: [email protected]

Modeller Roelof Burger

Date Submitted November 2013

Copyright This report contains GES intellectual property, and may not be usedby any other party for sourcing of competitive proposals, as a basisfor performing the work described therein, or for any other com-mercial purpose, without the prior written permission of GES andAurecon.

How to cite GESZA, 2013: Air Quality Impact Assessment for the SwazilandRail Link - 3: Upgrade and construction of the Mpumalanga section,Davel to Nerston., Gondwana Environmental Solutions, PO Box158 Florida Hills, Johannesburg, 2013AUR-0339-AQIA RN 130477AUR - ver 1.

Swaziland Rail Link Air Quality Final ReportApplication 3

EXECUTIVE SUMMARY

Gondwana Environmental Solutions is contracted to do an air quality impact assessment of theproposed Swazi Rail Link between Davel in Mpumalanga and Nsezi near Richardsbay in KwazuluNatal. The project has the potential for significant social gain, as well as relieving pressure from theexisting Richards Bay Coal Line. It includes an intergovernmental memorandum of understandingbetween the South African and Swaziland governments. This report assess the potential air qualityimpacts from the construction and operation of the Swazi Rail Link.

The project has been separated into three different environment impact assessment applica-tions with the Department of Environmental Affairs. This report deals with the third applica-tion, amely, upgrade and construction of the Mpumalanga section, Davel to Nerston (DEA Ref14/12/16/3/3/2/553).

Emissions were estimated using a detailed assessment of the proposed operations. During peakoperations, a total of 150 Ml of diesel is estimated to be used annually. This would be the result of16 trains per direction per day, the maximum practical capacity. Considerable reductions in emis-sions is possible by upgrading the control technologies of the diesel locomotive engines. Furtherreductions can also be obtained by limiting idle times. A conservative estimate of annual emissionsfor the peak operations, as well as a comprehensive mitigated scenario is presented below:

Estimated emissions for the operational phaseScenario 𝑁𝑂𝑥 (T/annum) 𝑃𝑀10 (T/annum) 𝐻𝐶 (T/annum) 𝐶𝑂 (T/annum)

Worst-case 13 307 336 673 4394Mitigated 1 070 24 116 1318

The AERMOD dispersion model was used to assess the impact of operations on the ambientconcentrations of particulate matter, nitrogen dioxide, carbon oxide and hydrocarbons. Receptorswas placed at 50m, 100m, 150m, 300m, 500m, 1000m, 1500, 2000m, 3000m, 5000m, 7000m and10000m. Additional receptors was placed at all schools in the vicinity of the railway line, as well asall suburbs in the area. Meteorological data from South African Weather Service stations as well asnumerical weather model analysis of upper-air conditions was used as inputs to AERMOD.

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Maximum contributions to ambient concentrations for the operational phasePollutant Period Worst-case Mitigated

𝑃𝑀10 24hr 0.789 0.110𝑃𝑀10 annual 0.486 0.035𝑁𝑂2 1hr 190.437 15.317𝑁𝑂2 annual 22.165 1.783𝐶𝑂 1hr 62.900 18.870Benzene annual 1.121 0.193

This assessment therefore finds that this project has a category B according to the equator principles.The potential impact of the operations on human health and the environment are generally site-specific and readily addressed through mitigation measures. The contribution of rail transport tothe national budget of agents of climate change are significant, but falls outside the scope of thisassessment.

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CONTENTS

1 General information 11.1 Project identification information . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Applicant details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.2 Facility identification and physical address of facility . . . . . . . . . . . . 21.1.3 Environmental authorization reference number (EIA ref nr) . . . . . . . . . 21.1.4 Modelling contractor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Project background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.1 Objectives of air quality assessment . . . . . . . . . . . . . . . . . . . . . 31.2.2 Proposed activity under consideration . . . . . . . . . . . . . . . . . . . . 3

1.3 Project location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3.1 Area maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.4 Land use determination in modelling domain . . . . . . . . . . . . . . . . . . . . . 71.5 Elevation data and resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Emissions characterisation 82.1 Construction phase of the project . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2 Operational phase of the project . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3 Proposed emissions included in the assessment . . . . . . . . . . . . . . . . . . . 10

3 Meteorological overview 123.1 Surface data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2 Upper-air data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4 Baseline air quality assessment 174.1 Air quality legislation and standards . . . . . . . . . . . . . . . . . . . . . . . . . 174.2 Background concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5 Modelling the ambient air quality 215.1 Screening analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215.2 Aermod modelling assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.2.1 Proposed modelling setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 225.2.2 Modelling results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

6 Recommended mitigation measures 256.1 Construction phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256.2 Operational phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

7 Conclusions 27

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FIGURES

1.1 The proposed project aims to upgrade and construct a railway line between Davel inMphumalanga through Swaziland to Richardsbay. . . . . . . . . . . . . . . . . . . 4

1.2 Detailed layout of the proposed project. . . . . . . . . . . . . . . . . . . . . . . . 51.3 A regional perspective of the proposed activities. Included is topography, major land

use clases, weather stations and previous quality monitoring sites. . . . . . . . . . 6

3.1 Wind rose of hourly averaged winds as used in the modelling study. The data arefrom the SAWS weather office at Ermelo. . . . . . . . . . . . . . . . . . . . . . . 13

3.2 Wind rose of hourly averaged winds as used in the modelling study for each hour ofthe day. The data are from the SAWS weather office at Ermelo. . . . . . . . . . . . 14

3.3 Wind rose of hourly averaged winds as used in the modelling study for each monthof the year. The data are from the SAWS weather office at Ermelo. . . . . . . . . . 15

3.4 The modelled boundary layer height. . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.1 Ambient measurements of pollutants in Kwadela, Mpumalanga (?). . . . . . . . . . 194.2 Ambient measurements of the diurnal distribution of pollutants in Kwadela, Mpumalanga

(?). The box-and-whiskers shows the minimum, 25%, median, 75% and maximumvalues. The red lines shows the average for that hour. . . . . . . . . . . . . . . . . 20

5.1 Screening analysis of two emission scenarios using the SCREEN3 model. . . . . . 22

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1. GENERAL INFORMATION

Contents1.1 Project identification information . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Applicant details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.2 Facility identification and physical address of facility . . . . . . . . . . . 2

1.1.3 Environmental authorization reference number (EIA ref nr) . . . . . . . . 2

1.1.4 Modelling contractor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Project background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Objectives of air quality assessment . . . . . . . . . . . . . . . . . . . . 3

1.2.2 Proposed activity under consideration . . . . . . . . . . . . . . . . . . . 3

1.3 Project location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.1 Area maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.4 Land use determination in modelling domain . . . . . . . . . . . . . . . . . . . . 7

1.5 Elevation data and resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Gondwana Environmental Solutions (Pty) Ltd (GES) was appointed by Aurecon Ltd to un-dertake a specialist air quality investigation of the proposed Swazi Rail Link between Davel andRichardsbay as part of the process of preparing an Environmental Impact Assessment (EIA). Themain objective of the project is to assess the impact of the proposed activities on the ambient airquality of the surrounding areas. This section provides an overview of the proposed project.

The project has been separated into three different applications with DEA:

1. Upgrade of the Davel Yard and associated infrastructure (DEA Ref 14/12/16/3/3/2/551)

2. Upgrade of the KZN section, Golela to Nsezi (DEA Ref 14/12/16/3/3/2/552)

3. Upgrade and construction of the Mpumalanga section, Davel to Nerston (DEA Ref 14/12/16/3/3/2/553)

The Nerston to Golela section will be submitted to the Swaziland authorities.

1.1 Project identification information

1.1.1 Applicant detailsTransnet Group Planning

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1.1.2 Facility identification and physical address of facilityThe project will upgrade the Davel to Nerston rail link to accomodate the envisaged 200 wagontrains. Permission was obtained to proceed with the impact assessment while the land acquisitionprocess is underway. An overview of the proposed railway line is presented in section ??.

1.1.3 Environmental authorization reference number (EIA ref nr)DEA Ref 14/12/16/3/3/2/553

1.1.4 Modelling contractorGES provides government, industry and private agencies with sophisticated and appropriate envi-ronmental solutions across a wide range of disciplines including air quality monitoring and man-agement, data management services, water quality monitoring and a broad range of environmentalservices to assist clients with compliance to environmental legislation. GES has performed numer-ous specialist air quality assessments. The following specialists form part of the GES team.

Roelof Burger

Roelof Burger completed his BSc (hons) degree at the University of Pretoria in 1998. He has morethan 10 years of experience in Atmospheric Science and computer programming. In 2002 he didan introductory the United States Environmental Protection Agency (US-EPA) course in dispersionmodelling. Mr Burger worked at the South African Weather Service until 2004 and after that wasan associate researcher at the University of the Witwatersrand until early 2012. He is currentlyworking as an atmospheric scientist at North-West University (NWU) in Potchefstroom. Workingas an atmospheric consultant since 2005, he has co-authored many dispersion modelling impactassessments. Mr Burger has taught introductory and intermediate courses in atmospheric dispersionmodelling at university and industry level.

Dr Martin van Nierop

Dr Martin van Nierop has a doctorate in Chemical Engineering from the University of the Witwa-tersrand, Johannesburg (Wits). While studying for his doctorate, Dr van Nierop was employed atthe University as a research officer. Dr van Nierop became interested in Research management, andmanaged a number of large contract research projects at the University. One of these was a study ofthe Brown Haze air pollution problem over Cape Town. Dr van Nierop has been involved in severalatmospheric impact assessment studies since then.

Anja van Basten

Anja van Basten completed her BA (Hons) degree in Physical Geography cum laude at Wits in1988. She taught high school Geography between 1989 and 1994. She has worked for GES sinceJune 2009. She completed an the AMS/EPA Regulatory Model (AERMOD) course in March 2010.

1.2 Project backgroundA multinational strategic rail corridor is planned by Transnet. The rail link will connect Lothair inSouth Africa to the heavy haul Richards Bay Coal Line through the existing Swaziland rail link. The



proposed project will relieve pressure from the existing Richards Bay Coal Line. Regional supportfor the project was confirmed and an Inter Governmental Memorandum of Understanding betweenthe South African and Swaziland governments has been signed.

1.2.1 Objectives of air quality assessmentThe air quality assessment report on the possible impact of the Swazi Rail Link development onambient air quality. This part of the study focus on the third phase of the project, namely theupgrade and construction of the Mpumalanga railway line, Davel to Nerston.

The objectives of this air quality impact assessment (AIA) are to:

∙ Assess qualitatively the potential air quality impacts of emissions during the constructionphase of the Mpumalanga railway line upgrade.

∙ Assess quantitatively the potential air quality impacts of pollutants from the combustion ofdiesel fuel from locomotives during the operation phase of the Project.

∙ Propose mitigation measures for each phase to prevent or reduce any adverse air quality im-pacts on the receiving environment.

∙ Assess qualitatively the potential air quality impacts of emissions during the constructionphase of the Swaziland railway line upgrade.

∙ Assess quantitatively the potential air quality impacts of pollutants from the combustion ofdiesel fuel from locomotives during the operation phase of the Project.

∙ Propose mitigation measures for each phase to prevent or reduce any adverse air quality im-pacts on the receiving environment.

1.2.2 Proposed activity under considerationThe operation of trains is planned around a single railway line from Davel to Nsezi with crossingloops originally at 40 kilometres apart (figure ??). This spacing is to be changed to 20km apart whentraffic increases warrant it. This arrangement will provide a theoretical capacity of 12 and 24 trainsper day per direction for Phase 1 Phase 2 respectively. This equates to a practical capacity, at 65%operating efficiency, of 8 and 16 trains per day per direction. It is assumed that 336 operational daysper year will be achievable.

The rail link will be used for coal and general freight. Upgrades to the rail link is needed toallow for a train length of 2562m, i.e. 200 CCL or 160 GFB wagons. Traction is provided by up to6 Class 43 equivalent diesel locomotives positioned at the front, the centre and the rear of the trainin Distributed Power (DP) mode. The train length makes allowance for longitudinal stretch in theconsist. Trains consisting of up to 200 CCL/CR wagons loaded to 26 tons/axle can be hauled to therespective Ports (Richards Bay and Maputo) provided that they are fitted with air-brakes and F-typecouplers, and distributed power technology is employed.



Figure 1.1: The proposed project aims to upgrade and construct a railway line between Davel inMphumalanga through Swaziland to Richardsbay.

1.3 Project location

1.3.1 Area mapsThis section of the rail link will upgrade an existing line and construct a new section between Davelan Nerston (figure ??).



Figure 1.2: Detailed layout of the proposed project.



Figure 1.3: A regional perspective of the proposed activities. Included is topography, major landuse clases, weather stations and previous quality monitoring sites.



1.4 Land use determination in modelling domainThe national landcover dataset preduced by the CSIR was used. ??) shows the main landcoverclasses used for classifying the nature of the modelling domain.

1.5 Elevation data and resolutionThe Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) was used in thisstudy (figure ??). This dataset has a resolution of around 90 meters.

It can be seen that the area around Davel has relatively simple terrain, featuring gently rollinghills. In a 50km radius, the topography varies by less than 500 meters. Davel and the proposed railline is on the highest ridge in the area.



2. EMISSIONS CHARACTERISATION

Contents2.1 Construction phase of the project . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Operational phase of the project . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Proposed emissions included in the assessment . . . . . . . . . . . . . . . . . . . 10

Emission estimates are typically the biggest uncertainty in an AIA. Since the overall objectiveof any AIA is to protect human and environmental health, it is important to understand that conser-vative estimates will be made at every step of emissions estimation. Two operational scenarios areconcidered. First the construction phase of the proposed project, thereafter the operational phase.

2.1 Construction phase of the projectThe construction phase will be relatively short. It will also result in mainly nuisance impacts in theform of dust. Large uncertainties are associated with emission estimates for these type of activities,resulting mostly in fugitive emissions. These factors therefore do not justify a full modelling assess-ment for the construction phase of this project. However, the nuisance and other possible impactsshould still be managed. Best practise and possible mitigation strategies are therefore recommendedfor the construction activities.

It will include emissions from on-site heavy-duty off-road vehicles, other light-duty vehicles anddust emissions as a result of the construction activities. The most important emissions will be 𝑁𝑂𝑥

from the vehicles and dust from the earthworks. It will also result in mainly nuisance impacts in theform of dust.

2.2 Operational phase of the projectCombustion of diesel results in the following emissions:

∙ volatile organic compounds (𝑉 𝑂𝐶𝑠) and other hydrocarbons (𝐻𝐶)

∙ carbon monoxide (𝐶𝑂)

∙ nitrogen oxides (𝑁𝑂𝑥)

∙ particulate matter with an aerodynamic diameter smaller than 10 𝜇𝑔 (𝑃𝑀10)

∙ particulate matter with an aerodynamic diameter smaller than 2.5 𝜇𝑔 (𝑃𝑀2.5)

∙ sulphur dioxide (𝑆𝑂2)



The pollutants of most concern and for which there exist ambient standards in South Africainclude 𝐶𝑂, nitrogen dioxide (𝑁𝑂2), 𝑃𝑀10 and benzene (𝐶6𝐻6) (one of the 𝐻𝐶s). Although 𝑆𝑂2

is a pollutant of concern, the emission factors strongly depend of the fuel characteristics, which isnot known, and furthermore, the contribution is likely not significant.

Emissions for diesel locomotives have been quantified by the US-EPA ?. Emissions factors fordifferent duty cycle diesel locomotives are shown in table ??. The different duty cycles correspondto the age of the technology (as shown in brackets). Seperate emissions standards are given forline-haul and switch mode of operation.

Table 2.1: US-EPA Locomotive emission standards (g/bhp.hr)

Duty Cycle 𝐻𝐶 𝐶𝑂 𝑁𝑂𝑥 𝑃𝑀10

Tier 0 (1973-1992)Line-haul 1.00 5.0 8.0 0.22Switch 2.10 8.0 11.8 0.26Tier 1 (1993-2004)Line-haul 0.55 2.2 7.4 0.22Switch 1.20 2.5 11.0 0.26Tier 2 (2005-2011)Line-haul 0.30 1.5 5.5 0.10Switch 0.60 2.4 8.1 0.13Tier 3 (2012-2014)Line-haul 0.30 1.5 5.5 0.10Switch 0.60 2.4 5.0 0.10Tier 4 (2015 or later)Line-haul 0.14 1.5 1.3 0.03Switch 0.14 2.4 1.3 0.03

To calculate the total annual emissions from the diesel locomotives, the information on the num-ber of locomotives, the annual fuel consumption rates of diesel for each train types and line types,and the mode of the locomotives operation (line-haul or switch modes) is required. The speed ofoperation (throttle notch), as well as the idle characteristics also have an impact on emissions. TheUS-EPA uses average characteristics as presented in table ??.

A detailed analysis of the proposed design capacity were done in the pre-feasibility stage (?).The track design allow for a train length of 2562m, or 200 wagons for coal and 160 wagons forgeneral freight. Class 43 (or equivalent) type diesel electric locomotives have been assumed for theentire train service. Traction is provided by up to 6 Class 43 equivalent diesel locomotives positionedat the front, the centre and the rear of the train in Distributed Power (DP) mode.

Three terminals will be used for operations. These include Davel, Nsese and Phuzumoya. Nsesein Richardsbay will be the base of operations. Activites at Nsese will include traction changes,load consolidation/distributions and fuelling. Davel will be the secondary terminal. Activities atDavel include traction changes, load consolidation/distributions and secondary fuelling. A junctionterminal will be located in Phuzumoya in Swaziland. Activities at Phuzumoya will include junctionand secondary fuelling.



Table 2.2: Throttle notch weighting factors for diesel locomotives (?)

Throttle notch Line-haul Switch

Idle 38.0 59.8Dynamic brake 12.5 0.0Notch 1 6.5 12.4Notch 2 6.5 12.3Notch 3 5.2 5.8Notch 4 4.4 3.6Notch 5 3.8 3.6Notch 6 3.9 1.5Notch 7 3.0 0.2Notch 8 16.2 0.8

Trains start off in 50 wagon lengths at 20 ton axle loads during 2017. By 2020 almost allpossible combinations with current wagon types are performed and 25% of coal trains run at 26 tonaxle loads. By 2030 approximately 50% of all wagons are high capacity wagons running at 20 tonaxle loads whilst some coal runs at 26 tons. At 2040 the majority of bulk trains run in 200 wagonlengths at 26 ton axle loads. All general freight trains then run at maximum lengths.

Coal from other areas will probably be transported in light-loaded jumbo wagons and 100 wagonblocks initially. The train axle loading and length will evolve to the maximum permissible axleloading and length over time.

A worst case scenario at full capacity is assumed for the purpose of an AIA. The maximumpracticle design capacity estimated scenario are shown in table ??. Slight differences in train fre-quencies exist between the Davel to Phuzumoya and the Phuzumoya to Nsese sections. For thisAIA, the maximum of the two are used. A conservative estimate of 150 Ml of diesel per annum areassumed. This include haul-line and switch mode operations.

Table 2.3: Maximum annual estimated fuel consumption during the operational phase.

Trains / Litre Litre MegaTrain direction diesel diesel litre dieselcomposition / day / train / day / annum

100w/20tal 1 15 010 15 010150160w/20tal 7 24 141 144 846

200w/26tal 8 35 089 280 712

2.3 Proposed emissions included in the assessmentConditions in South Africa will be slightly different that those assumed by the . The locomotive fleetis likely older and the maintenance might not be to the same standard as those used in for testing bythe . It is also not certain what the exact specification of the locomotives used on this rail line will



be. For the purpose of the AIA, concervative estimates of operations, as well as emission factorsshould account for these uncertainties.

The AIA therefore models two scenarios: scenario A assumes old, badly maintained tier 0 lo-comotives and scenario B assumes new locomotives with the best available emission control tech-nology, or tier 4. This approach provides the absolute worst possible impact as well as the benefitsobtainable from using best available technology. The final set of emission factors used for modellingthe two scenarios are shown in figure ??.

It is further assumed that 15% of fuel is spent on swtich mode operations an 85% for line-haul.Combining the emission factors in table ?? with the fuel estimates in table ?? leads to total estimatedannual emissions for the peak of operations ??.

Annual contributions of 13307 T/annum 𝑁𝑂𝑥, 336 T/annum 𝑃𝑀10, 673 T/annum hydro carbonsand 4394 T/annum 𝐶𝑂 makes up a significant portion of the national budget for the worst casescenario.

Table 2.4: Locomotive emission estimates used in this analysis (g/l)

Duty Cycle 𝑁𝑂𝑥 𝑃𝑀10 𝐻𝐶 𝐶𝑂

Scenario A (worst case)Line-haul 83.5 2.1 4.1 27.5Switch 95.6 2.4 5.5 32.1Scenario B (mitigated)Line-haul 0.14 1.5 1.3 0.03Switch 0.14 2.4 1.3 0.03

Table 2.5: Total estimated annual emissions for the peak operations of the Swazi Rail Link(T/annum).

Scenario 𝑁𝑂𝑥 𝑃𝑀10 𝐻𝐶 𝐶𝑂

A: Worst-case 13 307 336 673 4394B: Mitigated 1 070 24 116 1318



3. METEOROLOGICAL OVERVIEW

Contents3.1 Surface data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 Upper-air data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Meteorological data are central to modelling dispersion of pollutants. The available monitoringdata are shown in figure ??. Data from Bethal, Carolina and Ermelo were evaluated for this assess-ment. The Ermelo weather office were judged to be the most complete and reliable and forms thecore of the meteorological data.

3.1 Surface dataDispersion of atmospheric pollutants is a function of the prevailing wind characteristics at any site.The vertical dispersion of pollution is largely a function of the wind field. The wind speed deter-mines both the distance of downwind transport and the rate of dilution of pollutants. The generationof mechanical turbulence is similarly a function of the wind speed, in combination with the surfaceroughness.

Meteorological data from the the South African Weather Service (SAWS) weather office in Er-melo was used (figure ??). Data for 2012 was used to construct a complete year of data. Numericalweather analysis for the area was used to suppliment the monitoring data. Some variables neededfor the dispersion model, for example cloudiness or incoming solar radiation, are not routinely mea-sured in South Africa. The numerical weather model data was used for these purposes.

Hourly averaged winds for Ermelo (figure ??) show a bimodel distribution of wind speeds.One maxima between 3 and 4 m/s and one between 5 and 8 m/s. The average wind speed is 4.12m/s. Wind directions variations seem fairly uniform from the north. Only Southern winds areuncommmon.

The diurnal distribution of winds (figure ??) shows the typical backing from an Easterly com-ponent at midnight to a Westerly component by midday and then veering back to an Easterly. Windspeeds are higher during the day.

The most dominant summer patter shows relatively strong South Easterly winds in January (fig-ure ??). Wind speeds reach a minimum in Autumn. They then back towards the West, increasingslightly until another maximum is reached during peak Spring in October. Spring winds have astrong Northerly component.



Figure 3.1: Wind rose of hourly averaged winds as used in the modelling study. The data are fromthe SAWS weather office at Ermelo.



Figure 3.2: Wind rose of hourly averaged winds as used in the modelling study for each hour of theday. The data are from the SAWS weather office at Ermelo.



Figure 3.3: Wind rose of hourly averaged winds as used in the modelling study for each month ofthe year. The data are from the SAWS weather office at Ermelo.



3.2 Upper-air dataThe closest upper-air station is the SAWS stations at Irene in Pretoria and Durban. For this reason,numerical weather analysis data was used to estimate the vertical stratification of the atmosphere.

The AERMET model uses the hourly surface observations along with the vertical thermody-namic profile closest to sunrise to estimate the boundary layer height. Figure ?? shows the resultantatmospheric boundary layer (ABL) heights. The mechanical ABL is formed by turbulence asso-ciated with winds, whereas the convective ABL is driven by solar heating. The typical diurnalpattern captured by AERMET in figure ?? boosts confidence that the meteorological pre-processoris correctly modelling the atmosphere.

Figure 3.4: The modelled boundary layer height.



4. BASELINE AIR QUALITY ASSESSMENT

Contents4.1 Air quality legislation and standards . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.2 Background concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.1 Air quality legislation and standardsThe National Environmental Management: Air Quality Act 39 of 2004 (NEM:AQA) has shiftedthe approach of air quality management from source-based control to receptor-based control. Thebasis of this approach is the control of all major sources, including mining, industrial, vehiclesand domestic sources in terms of ambient air concentrations, and is the responsibility of LocalGovernment.

The Act makes provision for the minister or MEC to prescribe measures for the control of dust inspecified places or areas, either in general or by specified machinery or in specified instances. Thiscan take the form of guidelines or standards. Guidelines provide a basis for protecting public healthfrom adverse effects of air pollution and for eliminating, or reducing to a minimum, those contam-inants of air that are known or likely to be hazardous to human health and well-being. Once theguidelines are adopted as standards, they become legally enforceable. These guidelines/standardsprescribe the allowable ambient concentrations of pollutants which are not to be exceeded duringa specified time period in a defined area. If the air quality guidelines/standards are exceeded, theambient air quality is poor and the potential for health effects is greatest.

The National Ambient Air Quality Standards for the criteria pollutants were published in De-cember 2009 (?). The values of the National Ambient Air Quality Standards, as well as referencemethods and compliance dates for PM 10 (particulate matter with an aerodynamic diameter of lessthan 10 m) are presented in table ??.



Table 4.1: National ambient air quality standards (?)

Averaging Frequncy of CompliancePeriod Concentration Exceedance Date

Nitrogen Dioxide (𝑁𝑂2)1 hour 200 𝜇g/m3 (106ppb) 88 Immediate1 year 40 𝜇g/m3 (21ppb) 0 ImmediateParticulate Matter (𝑃𝑀10)24 hours 120 𝜇g/m3 4 Immediate24 hours 75 𝜇g/m3 4 1 January 20151 year 50 𝜇g/m3 0 Immediate1 year 40 𝜇g/m3 0 1 January 2015Carbon Monoxide (𝐶𝑂)1 hour 30 mg/m3 (26 ppm) 88 Immediate8 hour 10 mg/m3 (8.7 ppm) 11 ImmediateBenzene (𝐶6𝐻6)1 year 10 𝜇g/m3 (3.2 ppb) 0 Immediate1 year 5 𝜇g/m3 (1.6 ppb) 0 1 January 2015

4.2 Background concentrationsDavel is situated in the Highveld Priority Area. As such, although not in the immediate vicinity, it issurrounded by large industrial sources. Several monitoring studies have been conducted in the area(see figure ??). The most recent of these were conducted in Davel/Kwadela during the 2013 monthswith the worst dispersion potential (figure ?? and figure ?? as presented by ?). These measurementsare representative of the air quality around low income households. It therefore provides a perfectbaseline for the current AIA.

Typical diurnal patterns driven by domestic cooking and heating are visible in the diurnal distri-bution (figure ??). Isolated high peaks in 𝐶𝑂 and 𝑆𝑂2 suggest an impact for industry and biomassburning in the area. 𝐶𝑂 values are relatively low and below the national guidelines. particulate mat-ter (𝑃𝑀 ) (𝑃𝑀10 and 𝑃𝑀2.5), 𝑁𝑂𝑥 and 𝑆𝑂2 are especially high during times of domestic burning.

It is evident that the ambient concentrations of pollutants in low income areas are poor. Evenisolated villages are expected to have high 𝑃𝑀 values. Any contribution to ambient 𝑃𝑀 and 𝑁𝑂𝑥

would therefore be significant.



Figure 4.1: Ambient measurements of pollutants in Kwadela, Mpumalanga (?).



Figure 4.2: Ambient measurements of the diurnal distribution of pollutants in Kwadela,Mpumalanga (?). The box-and-whiskers shows the minimum, 25%, median, 75% and maximumvalues. The red lines shows the average for that hour.



5. MODELLING THE AMBIENT AIR QUALITY

Contents5.1 Screening analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5.2 Aermod modelling assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.2.1 Proposed modelling setup . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.2.2 Modelling results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Dispersion modelling is the most cost effective way to evaluate the impact of a proposed activityon ambient air quality. A conservative approach to estimating the emission factors insure that theimpact of the proposed activity is overestimated and that potential adverse impacts therefore beidentified.

For this assessment, a screening model (SCREEN3) was run to show the worst-case scenario asa function of distance from the railway line. This will be discussed in section ??. Aermod modellingruns were then performed for each place where the railway line crossed close to settlements (section??).

5.1 Screening analysisA screening analysis provides a benchmark to which more refined modelling can be compared.The SCREEN3 model uses simple Gaussian dispersion assumptions, combined with cavity andplume-rise algorithms, to calculate the maximum hourly ambient concentration of a pollutant as afunction of distance from the source. The input requirements for a screening model is restricted tothe emission characteristics and some basic assumptions about the topography and land cover in thearea.

The emission factors in table ?? was used to model the worst-case scenario (red line) and mit-igated scenario for each of the four pollutants (figure ??). Emissions were assumed to be from anarea source at 10 meter height.

The screening model results show that relatively high 𝑁𝑂2 values are expected close to therailway line. The smaller contributions of 𝑃𝑀10, 𝐶𝑂 and 𝐻𝐶 are also limited to with a few hundredmeters of the railway line. The contribution of 𝑁𝑂2 is more than half that of the national guideline.



Figure 5.1: Screening analysis of two emission scenarios using the SCREEN3 model.

5.2 Aermod modelling assessment

5.2.1 Proposed modelling setupModelling the impact of a railway line pose a unique challenge. The impact of a railway line onambient air quality will only be within a couple of kilometers from the railway line. The railwayline itself, however, can span over hundreds of kilometers. Regulatory models that could model oversuch large domains, like Californian puff model (CALPUFF), does not do well in modelling impactsclose to the source. On the other hand, regulatory models that are well tuned for near field impactscannot be used to model domains larger than a few tens of kilometers. Furthermore, modelling linesources like a railways is computationally expensive.

The solution to the above challenges is to run the model for multiple domains where an impactis suspected. Judging from the screening model results, this would only be when settlements arewithin a couple of kilometer from the railway line.

The emission factors in table ?? was used to model the two scenarios for the four pollutants foreach of the modelling domains in table ??.

The default regulatory options for AERMOD was used. Receptors was placed at 50m, 75, 100m,150m, 300m, 500m, 1000m, 1500, 2000m, 3000m, 5000m, 7000m and 10000m from the railway



Table 5.1: Modelling domains used for this assessment.

Modelling Center CenterTown/Settlement domain Latitude Longitude

Davel 40x40km -26.4556 29.6651Breyten 40x40km -26.3217 29.974Ermelo 40x40km -26.5001 30.014Lothair 40x40km -26.3908 30.4379

line. One special receptors were also placed in each suburb, as well as other receptors to representschools.

Examples of the setup files for the model runs are shown in Appendix A.

5.2.2 Modelling resultsThe results of all the model runs are presented in table ??. Each value in the table represents themaximum modelled contribution from the proposed project’s operational activities to the ambientconcentration for each of the 32 model runs. The averaging periods corresponding to the nationalambient air quality standards for each pollutant are shown. (table ??).

The maximum modelled contributions are similar to the screening analysis. The maximumunmitigated contribution to 24 hourly averaged 𝑃𝑀 concentration is 0.789 𝜇𝑔/𝑚3. Mitigated 𝑃𝑀falls to almost zero (below 0.11 in table ??).

𝑁𝑂𝑥 levels are high, but do not exceed the ambient standard. The maximum contribution tohourly averaged 𝑁𝑂2 is 190 𝜇𝑔/𝑚3 (table ??). It is likely however background during some timesand in some areas might result in 𝑁𝑂2 exceedences.

Maximum contribution to hourly averaged 𝐶𝑂 is 62.9 𝜇𝑔/𝑚3 (table ??). This is far belowambient standards.

The only hydrocarbon for which a ambient standard exist is benzene. For this AIA it was as-sumed that all 𝐻𝐶 are benzene. Even with this very conservative assumption, the contribution toannual averaged ambient concentrations are small.

The mitigated scenario show dramatic decreases in contribution to ambient levels of all pollu-tants and all areas.

Plots showing the spatial impacts of all pollutants and all modelling scenarios are shown inAppendix B.



Table 5.2: Aermod modelling of the proposed operation contribution to background ambient con-centrations for the different modelling runs (𝜇g/m3).

Pollutant Period Model run Worst-case Mitigated

𝑃𝑀 24hr M1 0.789 0.110𝑃𝑀 annual M1 0.170 0.024𝑃𝑀 24hr M2 0.592 0.045𝑃𝑀 annual M2 0.381 0.029𝑃𝑀 24hr M3 0.537 0.039𝑃𝑀 annual M3 0.486 0.035𝑃𝑀 24hr M4 0.682 0.052𝑃𝑀 annual M4 0.424 0.032𝑁𝑂2 1hr M1 190.437 15.317𝑁𝑂2 annual M1 22.165 1.783𝑁𝑂2 1hr M2 94.058 7.563𝑁𝑂2 annual M2 16.448 1.323𝑁𝑂2 1hr M3 76.728 6.168𝑁𝑂2 annual M3 19.496 1.567𝑁𝑂2 1hr M4 121.081 9.740𝑁𝑂2 annual M4 17.203 1.384𝐶𝑂 1hr M1 62.900 18.870𝐶𝑂 1hr M2 31.068 9.321𝐶𝑂 1hr M3 25.342 7.606𝐶𝑂 1hr M4 39.988 12.006benzene annual M1 1.121 0.193benzene annual M2 0.832 0.144benzene annual M3 0.986 0.169benzene annual M4 0.870 0.150



6. RECOMMENDED MITIGATION MEASURES

6.1 Construction phaseStandard mitigation measures are recommended for the construction phase. These include:

∙ Use of enclosures, screens and sheeting to contain dust

∙ Use of paved / surfaced and cleaned haul routes

∙ Use of water suppression and wheel washing

∙ Choice of location and facilities for site storage where required

∙ Location of dust generating activities

∙ Transport route selection and location

∙ No burning on site and close to settlements

∙ Conduct any slash burning (glossary term) in compliance with open burning permit require-ments

∙ Minimize the amount of disturbance and areas cleared of vegetation

∙ Revegetate disturbed areas as soon as possible after disturbance

∙ Use dust abatement techniques on unpaved, unvegetated surfaces

∙ Enact fugitive dust and vehicle emission controls

∙ Establish and enforce peed limits to reduce airborne fugitive dust

∙ When feasible, shut down idling construction equipment

∙ Keep soil moist while loading into dump trucks to minimize fugitive dust

∙ Keep soil loads below the freeboard of the truck to minimize fugitive dust

∙ Minimize drop heights when loaders dump soil into trucks

∙ Tighten gate seals on dump trucks

∙ Cover dump trucks before traveling on public roads

∙ When possible, schedule construction activities during periods of low winds to reduce fugitivedust



6.2 Operational phaseHuge reduction in emissions from diesel locomotives can be obtained by upgrading the engines.The US-EPA measured improvements in different duty cycle tiers (table ??). The modeled improve-ments in this assessment is even greater since the worst case scenario assumed badly maintainedlocomotives. These mitigation measures are however very expensive and the costs have to outweighthe benefits.

Table 6.1: Reductions from levels of existing standards (?)

Standards tier 𝑃𝑀10 𝑁𝑂𝑥

Remanufactured Tier 0 60 15-20Remanufactured Tier 1 50Remanufactured Tier 2 50Tier 3 50Tier 4 90 80All tiers - idle emissions 50 50



7. CONCLUSIONS

Locomotive diesel engine emissions comprises a substantial protion of the national 𝑃𝑀 and 𝑁𝑂𝑥

inventory. This relative contribution is likely to increase over time as emissions from other sourcesdecrease.

On a local scale, unmitigated contributions to daily and annual averaged ambient concentrationsof 𝑃𝑀10 are not really significant. Hourly and annual contributions to 𝑁𝑂𝑥 are significant. Unmit-igated 𝑁𝑂𝑥 emissions will also have a regional significance by contribution to secondary pollutantslike ozone (𝑂3). High background 𝑂3 levels due to industrial sources and regional biomass burningmakes this a potential area of concern. This should however be part of a larger national strategy toreduce emissions from diesel locomotives.

It is possible to almost eliminate the impact of the operational phase of the proposed projectthrough mitigation measures. Re-manufacturing old engines and replacing old locomotives withnewer cleaner models will be costly, but drastically reduce all emissions. The impact matrix for theproposed activity is shown in table ??.

Table 7.1: Air quality impact assessment matrix using the prime resources impact rating methodol-ogy

Potential air quality impactEnvironmental significance

Before mitigation After mitigationM D S P Total SP M D S P Total SP

Construction phaseDust releases from earthworks 8 2 2 3 36 M 4 2 2 2 16 LConstruction vehicle exhaust 4 2 2 3 24 L 4 2 2 3 24 LOperational phaseContribution to ambient 𝐶𝑂 6 3 2 4 44 L 2 2 2 2 12 LContribution to ambient 𝑃𝑀10 6 3 2 4 44 L 2 2 2 2 12 LContribution to ambient 𝑁𝑂2 8 4 3 4 60 M 2 2 2 2 12 LContribution to ambient 𝐶6𝐻6 6 3 2 4 44 L 2 2 2 2 12 L

This assessment therefore rate this project as a category B impact according to the equator prin-ciples. The potential impact of the operations on human health and the environment are generallysite-specific and readily addressed through mitigation measures.



BIBLIOGRAPHY

Piketh, S., Burger, R. and Pauw, C., 2013: Indoor household air pollution measurements fromsolid fuel (coal) combusion in Mpumalanga, South Africa: in ‘16th IUAPPA World Clean AirCongress’. iii, 18, 19, 20

RSA, 2009: National Air Quality Standards, Government Gazette 534(32816 on 24 December2009), 6 – 9. 17, 18

Transnet, 2013: Transnet Group Planning Swaziland Rail Link: FEL-2 Pre-Feasibility Report, Tech-nical report: 303044. 9

USEPA, 1989: Emission Standards for Locomotives and Locomotive Engines, US Federal Register63(73), 18978–19084.URL: http://www.epa.state.oh.us/portals/32/pdf/FRwebpage forCorrectionsClarificationrule3-2010.inter2.pdf 9

USEPA, 2008: Control of Emissions of Air Pollution From Locomotive Engines and Ma-rine Compression-Ignition Engines Less Than 30 Liters per Cylinder, US Federal Register73(126), 37096–37350. 10, 26


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