AIR QUALITY AND GREENHOUSE GAS ASSESSMENT HAIL CREEK TRANSITION PROJECT

Transcription

1 AIR QUALITY AND GREENHOUSE GAS ASSESSMENT HAIL CREEK TRANSITION PROJECT Rio Tinto Coal Australia May 2015 Job Number Prepared by Todoroski Air Sciences Pty Ltd Suite 2B, 14 Glen Street Eastwood, NSW 2122 Phone: (02) Fax: (02)

2 Air Quality and Greenhouse Gas Assessment Hail Creek Transition Project Author(s): Aleks Todoroski Philip Henschke Position: Director Atmospheric Physicist Signature: Date: 15/05/ /05/2015 DOCUMENT CONTROL Report Version Date Prepared by Reviewed by DRAFT /03/2015 P Henschke A Todoroski DRAFT /04/2015 P Henschke A Todoroski FINAL /04/2015 P Henschke A Todoroski FINAL /05/2015 P Henschke A Todoroski This report has been prepared in accordance with the scope of works between Todoroski Air Sciences Pty Ltd (TAS) and the client. TAS relies on and presumes accurate the information (or lack thereof) made available to it to conduct the work. If this is not the case, the findings of the report may change. TAS has applied the usual care and diligence of the profession prevailing at the time of preparing this report and commensurate with the information available. No other warranty or guarantee is implied in regard to the content and findings of the report. The report has been prepared exclusively for the use of the client, for the stated purpose and must be read in full. No responsibility is accepted for the use of the report or part thereof in any other context or by any third party.

3 TABLE OF CONTENTS 1 INTRODUCTION PROJECT BACKGROUND Existing Hail Creek Mine Project Description LOCAL SETTING AIR QUALITY OBJECTIVES Preamble Particulate matter Legislative framework Environmental Authority EPML National Environment Protection Measure Queensland Other air pollutants EXISTING ENVIRONMENT Local climate Local meteorological conditions Ambient air quality Particulate matter monitoring NO 2 and CO Monitoring MODELLING SCENARIO Emission estimation Particulate matter Power station and flaring operations DISPERSION MODELLING APPROACH Introduction Modelling methodology Meteorological modelling Dispersion modelling DISPERSION MODELLING RESULTS Particulate matter Predicted maximum 24-hour average PM 2.5 concentrations Predicted annual average PM 2.5 concentrations Predicted maximum and 6 th highest 24-hour average PM 10 concentrations Predicted annual average PM 10 concentrations Predicted annual average TSP concentrations Predicted annual average dust deposition levels Dust mitigation and management Power station and flaring GREENHOUSE GAS ASSESSMENT Introduction Greenhouse Gas Inventory Emission sources... 35

4 9.2.2 Emission factors Summary of greenhouse gas emissions Contribution of greenhouse gas emissions Greenhouse gas management CONCLUSIONS REFERENCES LIST OF APPENDICES Appendix A Emission Calculation Appendix B Isopleth Diagrams

5 LIST OF TABLES Table 4-1: EPP (Air) ambient air quality objectives for particulate matter... 8 Table 4-2: EPP (Air) ambient air quality objectives for other pollutants... 9 Table 5-1: Monthly climate statistics summary Moranbah Water Treatment Plant Table 5-2: Monthly climate statistics summary - Mackay Aero Table 5-3: Available 24-hour average PM 10 monitoring data collected at North Toowoomba and The Gap (Mt Isa) (µg/m³) Table 5-4: Estimated background level for particulate matter Table 5-5: 1-hour annual average NO 2 monitoring data collected at North Toowoomba, South Gladstone and Pimlico (µg/m³) Table 5-6: 8-hour average CO monitoring data collected at Woolloongabba and North Toowoomba (mg/m³) Table 5-7: Estimated background level for CO and NO Table 6-1: Estimated emissions for the Project (kg of TSP) Table 6-2: Stack parameters for power station and flare units Table 7-1: Surface observation stations Table 7-2: Distribution of particles Table 8-1: Particulate matter modelling predictions of the Project Table 8-2: CO and NO 2 modelling predictions of the Project Table 9-1: Summary of quantities of materials estimated for the Project Table 9-2: Summary of emission factors Table 9-3: Summary of CO 2-e emissions for the project (t CO 2-e) Table 9-4: Summary of CO 2-e emissions per scope (t CO 2-e) LIST OF FIGURES Figure 3-1: Privately-owned sensitive receptors... 5 Figure 3-2: Topography surrounding the Project location... 6 Figure 5-1: Monthly climate statistics summary Moranbah Water Treatment Plant Figure 5-2: Monthly climate statistics summary - Mackay Aero Figure 5-3: Hail Creek AWS location Figure 5-4: Annual and seasonal windroses for Hail Creek AWS (4 October September 2014) Figure 6-1: Indicative mine plan for modelling scenario Figure 6-2: Indicative flare and power station layout Figure 7-1: Representative snapshot of wind field for the Project Figure 7-2: Windroses from CALMET extract (cell ref 2858) Figure 7-3: Meteorological analysis of CALMET extract (cell ref 2858)... 28

6 1 1 INTRODUCTION Todoroski Air Sciences has prepared this report for Rio Tinto Coal Australia (hereafter referred to as the Proponent). It provides an assessment of the potential air quality impacts and greenhouse gas emissions associated with the proposed extension of open cut operations and development of an underground mining operation at the Hail Creek Mine (hereafter referred to as the Hail Creek Transition Project [the Project]). This report has been prepared in consideration of the Department of Environment and Heritage Protection s (DEHP) Information request for an amendment application for an Environmental Authority and the DEHP s Guideline Application requirements for activities with impacts to air and incorporates the following aspects: A background and description of the Project and proposed operations; A review of the existing meteorological and air quality data to characterise the environment surrounding the Project site; A description of the dispersion modelling approach used to assess potential air quality impacts; Presentation of the predicted results; Discussion of the potential air quality impacts as a result of the Project; and An assessment of the potential greenhouse gas emissions associated with the Project.

7 2 2 PROJECT BACKGROUND 2.1 Existing Hail Creek Mine The Hail Creek Mine is an open cut mining operation managed by Rio Tinto Coal Australia (RTCA) for the Hail Creek Joint Venture. The Hail Creek Mine is located wholly within the Surface Rights Area of Mining Lease (ML) 4738 on land tenure held by the Hail Creek Joint Venture and operations at the Hail Creek Mine are undertaken in accordance with Environmental Authority (EA) EPML Construction of the Hail Creek Mine commenced in 2001 with first coal was produced in Coal is mined by conventional open cut strip mining methods. Mining generally progresses from west to east with mining of the Elphinstone Seam preceding mining of the underlying Hynds Seam. The deeper Fort Cooper Seam is also periodically mined in areas where it is close to the Hynds Seam, subject to production schedule constraints. Overburden is removed using truck and shovel/excavator fleets and emplaced in both out-of-pit and in-pit waste emplacements, interburden between the two coal seams is removed using draglines and directly placed within previously mined areas. ROM coal is excavated then transported by truck for stockpiling or direct loading to the crusher before being conveyed to the coal handling and preparation plant (CHPP) for processing. Product coal is stockpiled and reclaimed into a train loading bin for rail transport to the Dalrymple Bay Coal Terminal or Hay Point Coal Terminal for export. Dust mitigation measures for the Hail Creek Mine are outlined in the Plan of Operations in accordance with Condition A3 of EA EPML Project Description The Project would involve the extension of open cut operations and enable the development of an underground mining operation, entirely contained within the existing Surface Rights Area of ML Relevant to this assessment, the Project includes the following activities: Continuation of mining operations at the Hail Creek Mine, producing up to 20 million tonnes per annum (Mtpa) of run-of-mine (ROM) coal, consistent with the currently approved ROM coal extraction rate. Continuation of open cut mining west of Hail Creek. Open cut mining operations with associated in-pit and out-of-pit waste rock emplacements to the east of Hail Creek. Extension of open cut mining areas to the north and south of the existing open cut. Underground mining activities using a combination of both development and conventional longwall mining methodologies for coal seam extraction of both the Elphinstone and Hynds coal seams. Construction and operation of infrastructure to remove gas from the coal seam to enable a safe underground operating environment and for beneficial use within the Project Area for mining purposes or flaring.

8 3 Construction of a gas fired power plant (supplied with gas from the coal seam) to supply electricity to the Hail Creek Mine. Continued backfilling of open pit voids and waste rock emplacements with waste rock and CHPP coarse rejects. A detailed description of the Project is provided in the main text of the Environmental Assessment Report.

9 4 3 LOCAL SETTING The Project is located within the Bowen Basin coalfield in central Queensland, approximately 120 kilometres (km) southwest of Mackay and 35km northwest of Nebo. The immediate land use surrounding the Project is predominately rural land used for low intensity cattle grazing on native pasture adjacent to the Homevale National Park. There are few receptors in the area, with the three closest dwellings located approximately 5.2km, 9.1km and 13.3km respectively from the Project. Figure 3-1 presents the location of the Project in relation to privately-owned sensitive receptors of relevance to this assessment. Figure 3-2 presents a three dimensional visualisation of the topography within which the Project is located. The surrounding topography is characterised by generally flatter terrain associated with the valley floor of the upper catchment of Hail Creek. Elevated terrain is located to the immediate west of the Project in the Mt Gotthardt Range and to the northeast in the Clarke Range. These topographical features would play a significant role in defining the local wind distribution patterns of the Project area.

10 5 Figure 3-1: Privately-owned sensitive receptors

11 6 Figure 3-2: Topography surrounding the Project location

12 7 4 AIR QUALITY OBJECTIVES 4.1 Preamble Air quality objectives are benchmarks set to protect the general health and amenity of the community in relation to air quality (particularly in metropolitan areas). The sections below identify the potential air emissions generated by the Project and the relevant air quality objectives. 4.2 Particulate matter Particulate matter consists of dust particles of varying size and composition. Air quality goals refer to measures of the total mass of all particles suspended in air defined as the Total Suspended Particulate matter (TSP). The upper size range for TSP is nominally taken to be 30 micrometres (µm) as in practice particles larger than 30 to 50µm will settle out of the atmosphere too quickly to be regarded as air pollutants. Two sub-classes of TSP are also included in the air quality goals, namely PM 10, particulate matter with aerodynamic diameters of 10µm or less, and PM 2.5, particulate matter with aerodynamic diameters of 2.5µm or less. Mining activities generate particles in all the above size categories. The great majority of the particles generated are due to the abrasion or crushing of rock and coal and general disturbance of dusty material. These particulate emissions will generally be larger than 2.5µm as sub-2.5µm particles are usually generated through combustion processes or as secondary particles formed from chemical reactions rather than through mechanical processes that dominate emissions on mine sites. Combustion particulate matter can be more harmful to human health as the particles have the ability to penetrate deep into the human respiratory system, due to their size and can be comprised of acidic and carcinogenic substances. A study of the particle size distribution from mine dust sources in 1986 conducted by the State Pollution Control Commission (SPCC) of 120 samples found that PM 2.5 comprised approximately 4.7 percent (%) of the TSP, and PM 10 comprised approximately 39.1% of the TSP in the samples (SPCC, 1986). The emissions of PM 2.5 occurring from mining activities are small in comparison to the total dust emissions and in practice, the concentrations of PM 2.5 in the vicinity of mining dust sources are likely to be low. 4.3 Legislative framework Environmental Authority EPML The HCM currently operates in accordance with EA EPML EA EPML conditions are reproduced below. The air quality-related A1 A2 Dust monitoring must be undertaken on a monthly basis for the first twelve (12) month period after the commencement date of this environmental authority and thereafter at intervals not exceeding five (5) years at the monitoring locations detailed in the Plan of Operations. The environmental authority holder will respond within twenty-eight (28) days to complaints with a dust monitoring campaign, that demonstrate that concentrations do not exceed one hundred

13 8 and fifty (150) micrograms per cubic metre (1µg/m 3 ) (PM 10, twenty-four (24) hour average) at the lease boundary monitoring points. A3 Dust mitigation measures that detail how the risk of dust exceedances will be reduced must be detailed in the Plan of Operations National Environment Protection Measure The National Environment Protection Council Act 1994 and subsequent amendments define the National Environment Protection Measures (NEPM) as instruments for setting environmental objectives in Australia. The NEPM Ambient Air Quality specifies national ambient air quality standards and goals for air pollutants including; carbon monoxide (CO), nitrogen dioxide (NO 2), sulfur dioxide (SO 2), lead (Pb) and particulate matter (PM 10 and PM 2.5). The NEPM Air Toxics includes monitoring investigation guidelines for five compounds classified as air toxics: benzene, formaldehyde, benzo(a)pyrene, toluene and xylenes. These standards and goals were developed for metropolitan areas to represent the level of exposure of the general population, and are not necessarily applicable to major sources (such as coal mining operations) in rural environments Queensland In Queensland, air quality is managed under the Environmental Protection Act 1994 (the EP Act), the Environmental Protection Regulation 2008 (the EP Regulation) and the Environmental Protection (Air) Policy 2008 (EPP (Air)). The purpose of the EPP (Air) is to achieve the objectives of the Act in relation to the air environment. Schedule 1 of the EPP (Air) specifies the air quality objectives for enhancing or protecting environmental values applicable to: Health and well-being; Aesthetic environment; Health and biodiversity of ecosystems; and Agriculture. Table 4-1 summarises the air quality goals that are included in the EPP (Air) and are relevant to this study. Table 4-1: EPP (Air) ambient air quality objectives for particulate matter Pollutant Averaging period Impact Objectives Particulate matter (PM 10)* 24 hours Total 50µg/m³ Particulate matter (PM 2.5) 24 hours Total 25µg/m³ Annual Total 8µg/m³ Total suspended particles (TSP) Annual Total 90µg/m³ *Allows for exceedance of up to five days each year. The DEHP suggests a deposited dust objective, relevant to coal dust, of 133mg/m 2 /day (or 3.7 to 4.1g/m²/month) as recommended by the New Zealand Ministry for the Environment (DEHP, 2013).

14 9 The DEHP objective for deposited dust is specified as a daily value, monthly average. This value can only be measured on a monthly basis in actual practice. 4.4 Other air pollutants Emissions of carbon monoxide (CO) and nitrogen dioxide (NO 2) can be expected from the combustion of coal seam methane gas used in power generation and flaring. CO is a colourless, odourless and tasteless gas generated from the incomplete combustion of fuels when carbon molecules are only partially oxidised. It can reduce the capacity of blood to transport oxygen in humans resulting in symptoms including headache, nausea and fatigue. atmosphere is relatively unstable and readily reacts with oxygen. CO in the NO 2 gas is reddish-brown in colour (at high concentrations) with a characteristic odour and can irritate the lungs and lower resistance to respiratory infections such as influenza. NO2 belongs to a family of reactive gases called nitrogen oxides (NO X). These gases form when fuel is burned at high temperatures, mainly from motor vehicles, power generators and industrial boilers (USEPA 2011). It is important to note that when formed, NO2 is generally a small fraction of the total NO X generated. It is noted that in addition to CO and NO 2 formation, a small amount of particulate matter (PM 10) may be generated from the power station and flare operation. Considering the potential emission in the context of the surrounding mining activities, it is unlikely that the proposed power station and flare operation would make any significant contribution to an increase in the particulate matter level in the surrounding areas and therefore has not been investigated in this assessment. Table 4-2summarises the air quality goals that are included in the EPP (Air) and are relevant to this study. Table 4-2: EPP (Air) ambient air quality objectives for other pollutants Pollutant Averaging period Impact Objectives Carbon Monoxide (CO) 8 hours* Total 11mg/m³ Nitrogen Dioxide (NO 2) 1 hour* Total 250µg/m³ Annual Total 62µg/m³ *Allows for exceedance of up to one day each year.

15 10 5 EXISTING ENVIRONMENT This section describes the existing environment including the climate and ambient air quality in the area surrounding the Project. 5.1 Local climate Long-term climatic data from the Bureau of Meteorology (BoM) weather stations at Moranbah Water Treatment Plant (Site No ) and Mackay Aero (Site No ) were used to characterise the local climate in the proximity of the Project. The Moranbah Water treatment Plant station (Moranbah) is located approximately 65km southwest of the Project and has since been decommissioned in The Mackay Aero station (Mackay) is located approximately 90km northeast of the Project and is the nearest available operating weather station. Table 5-1 and Figure 5-1 present a summary of data from the Moranbah station collected over a 23 to 39-year period for the various meteorological parameters. Table 5-2 and Figure 5-2 present a summary of data from the Mackay station collected over a 12 to 29-year period for the various meteorological parameters. The data indicate that December is the hottest month with a mean maximum temperature of 34.0ºC and 30.7ºC respectively at the Moranbah and Mackay stations. July as the coldest with a mean minimum temperature of 9.9ºC and 11.2ºC. Rainfall is characterised by a wet season and a dry season with rainfall peaking during the summer months and declining in winter and spring. The annual average rainfall at Moranbah is noticeably lower compared to Mackay with levels of 613 millimetres (mm) and mm respectively. The data show December is the wettest month at Moranbah with an average rainfall of 103.9mm over 5.9 days and February is the wettest month at Mackay with an average rainfall of mm over 14.5 days. September is the driest month at both stations with an average rainfall of 9.1mm over 1.4 days and 25.6mm over 2.6 days at Moranbah and Mackay respectively. Relative humidity levels exhibit greater variability over the day at the Moranbah station when compared to the Mackay site. Mean 9am relative humidity levels range from 58% in October to 74% in February at Moranbah and 63% in October and November to 79% in April at Mackay. Mean 3pm relative humidity levels vary from 30% in September to 48% in February at Moranbah and 60% in October to 72% in February at Mackay. Mean wind speeds show some variability over the day and season. As expected mean wind speeds at Mackay are higher compared to Moranbah due to it coastal location. The mean 9am wind speeds range from 5.3km/h in July to 8.4km/h in October to December at Moranbah and 12.2km/h in July to 17.8km/h in March at Mackay. The mean 3pm wind speeds vary from 6.6km/h in June to 9.6km/h in February at Moranbah and 20.5km/h in May and July to 23.8km/h in March at Mackay.

16 11 Table 5-1: Monthly climate statistics summary Moranbah Water Treatment Plant Parameter Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Temperature Mean max. temperature ( o C) Mean min. temperature ( o C) Rainfall Rainfall (mm) Mean No. of rain days ( 1mm) am conditions Mean temperature ( o C) Mean relative humidity (%) Mean wind speed (km/h) pm conditions Mean temperature ( o C) Mean relative humidity (%) Mean wind speed (km/h) Source: Bureau of Meteorology, 2015 Figure 5-1: Monthly climate statistics summary Moranbah Water Treatment Plant

17 12 Table 5-2: Monthly climate statistics summary - Mackay Aero Parameter Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Temperature Mean max. temperature ( o C) Mean min. temperature ( o C) Rainfall Rainfall (mm) Mean No. of rain days ( 1mm) am conditions Mean temperature ( o C) Mean relative humidity (%) Mean wind speed (km/h) pm conditions Mean temperature ( o C) Mean relative humidity (%) Mean wind speed (km/h) Source: Bureau of Meteorology, 2015 Figure 5-2: Monthly climate statistics summary - Mackay Aero

18 Local meteorological conditions The Proponent operates a weather station to assist with the environmental management of site operations. The position of this station is shown in Figure 5-3. Annual and seasonal windroses prepared from data collected from October 2013 to September 2014 are presented in Figure 5-4. Analysis of the windroses shows that on an annual basis, winds from the east dominate the distribution with a lesser portion of winds originating from the southeast quadrant. The summer, autumn and winter wind distributions indicate a similar pattern to the annual distribution with winds from the east dominating followed by winds from the southeast quadrant. During spring, the wind distribution is more varied compared to the other seasons with dominate winds from the northnorthwest and ranging clockwise to the east-southeast. The windroses show a wind distribution pattern that is typical of the expected patterns for this area considering the location of the station in relation to local terrain features.

19 14 Figure 5-3: Hail Creek AWS location

20 15 Figure 5-4: Annual and seasonal windroses for Hail Creek AWS (4 October September 2014)

21 Ambient air quality The main sources of particulate matter in the wider area of the Project would include active mining, agricultural activities and emissions from anthropogenic activities such as motor vehicle exhaust and other commercial or industrial activity. Ambient particulate matter monitoring for the Project site and the wider area is not readily available, therefore, it is not possible to accurately quantify the existing background level for each of the assessed pollutants at the Project site, and thus an estimate needs to be made. To do this, a desktop review of NEPM air quality monitoring data for all sites in Queensland was conducted. The review found that most of the monitoring sites are located in coastal areas either in built up urban areas or near ports Particulate matter monitoring Of the available data, the North Toowoomba and The Gap (Mt Isa) are the only sites that are inland locations and would be more likely to record similar trends in dust levels in the vicinity of the Project site compared to the other available data from the monitors located either in built up coastal areas urban areas or near ports. We note that there would be differences in soil types, geography and emission sources at these locations compared with the Project site, and for example data from the Gap at Mt Isa may potentially show higher dust levels than may occur in this area. However, in the absence of sufficient site specific data to use in the assessment, these data have been used and are believed likely to provide a conservative estimate of the actual background level for the Project site. Monitoring data collected at the North Toowoomba site for the period and The Gap site for the period are presented in Table 5-3 and were used in estimating the background level for this assessment. Table 5-3: Available 24-hour average PM 10 monitoring data collected at North Toowoomba and The Gap (Mt Isa) (µg/m³) Average Percentile North Toowoomba 75 th percentile th percentile The Gap (Mt Isa) 75 th percentile th percentile It was assumed that of the available data, the measured 75th percentile of the 24-hour average PM 10 data is a representative background level for other non-mining sources in the wider area (e.g. agriculture and anthropogenic activities) and the measured 50th percentile of the 24-hour average PM 10 data is a representative level for annual average PM 10 concentration. It is noted that use of these data as the background level is likely to lead to an overestimate of the total cumulative impact which is the increment from mining activities from the Project plus the background level.

22 17 The above approach is necessary as only the 75th percentile and 50th percentile value of the Queensland NEPM monitoring data are available to approximate the 70th percentile and annual average results. This is considered to be conservative as, for example, the Victorian EPA 1 allow the use of a (lower) 70th percentile level as an appropriate estimate of background 24-hour values in modelling, and experience with monitoring data near coal mines shows that the annual average results are less than the 50th percentile levels (as the majority of the data are in the lowest part of the measured range). No ambient monitoring data for PM 2.5, TSP and dust deposition are available for the North Toowoomba and The Gap (Mt Isa) sites. Therefore, conservative background PM 2.5, TSP and dust deposition levels were estimated based on the following assumptions below and the results are presented in Table hour average PM 2.5 concentration of 25µg/m³ is equivalent to a 24-hour average PM 10 concentration of 50µg/m³; Annual average PM 2.5 concentration of 8µg/m³ is equivalent to an annual average PM 10 concentration of 30µg/m³; and Annual average dust deposition of 4g/m²/month is equivalent to an annual average PM 10 concentration of 30µg/m³. These assumptions are considered to be conservative based on the actual ratio of PM 2.5 to PM 10 and deposited dust to PM 10 measured in the air near other coal mines. The annual average TSP concentration was estimated by assuming that the annual average PM 10 concentration is 39.1% of the annual average TSP concentration based on a study of the distribution of particle sizes near mining dust sources in 1986 (SPCC, 1986). Table 5-4: Estimated background level for particulate matter Pollutant Averaging period Units Background level 24 hour µg/m³ 20.1 PM 10 Annual µg/m³ hour µg/m³ 10.0 PM 2.5 Annual µg/m³ 3.6 TSP Annual µg/m³ 35.0 Dust deposition Annual g/m²/month The Victorian Government s State Annual Environment Protection Policy (Air Quality Management), SEPP (2001) states at Part B, 3(b) Proponents required to include background data where no appropriate hourly background data exists must add the 70th percentile of one year s observed hourly concentrations as a constant value to the predicted maximum concentration from the model simulation. In cases where a 24-hour averaging time is used in the model, the background data must be based on 24-hour averages.

23 NO 2 and CO Monitoring There are numerous sites in Queensland that monitor ambient NO 2 levels, however the majority of these are located either in built up coastal areas urban areas or near ports and are not likely to record similar trends as in the vicinity of the Project site. This is due the greater number of NO 2 emissions sources associated with increased anthropogenic activity such as vehicles and combustion that occurs in these locations. However, in the absence of sufficient site specific data to use in the assessment, data from the North Toowoomba, South Gladstone and Pimlico sites have been used and are likely to provide a conservative estimate of the actual background level for the Project site. Monitoring data collected at the North Toowoomba site for the period, the South Gladstone site for the and Pimlico site for the period are presented in Table 5-5 and were used in estimating the background level for this assessment. Table 5-5: 1-hour annual average NO 2 monitoring data collected at North Toowoomba, South Gladstone and Pimlico (µg/m³) Average Percentile North Toowoomba 90 th percentile Annual South Gladstone 90 th percentile Annual Pimlico 90 th percentile Annual The measured 90th percentile data of 1-hour average NO 2 concentrations were applied to the background estimates as a conservative measure and are likely to lead to an overestimate of the total cumulative impact. Woolloongabba and North Toowoomba are the only sites in Queensland that measure ambient CO levels. The monitoring data collected at the Woolloongabba for the period and the North Toowoomba site for the period are presented in Table 5-3 and were used in estimating the background level for CO in this assessment. Table 5-6: 8-hour average CO monitoring data collected at Woolloongabba and North Toowoomba (mg/m³) Average Percentile Woolloongabba 90 th percentile North Toowoomba 90 th percentile Table 5-7 summarises the estimated background levels of CO and NO 2 applied in the assessment.

24 19 Table 5-7: Estimated background level for CO and NO 2 Pollutant Averaging period Units Background level CO 8 hour mg/m³ hour µg/m³ 52.1 NO 2 Annual µg/m³ MODELLING SCENARIO The modelling conducted has generally followed a maximum-case scenario which corresponds with the use of maximum mining fleet and disturbance footprint (nominal Year 2022). The mine plan year assessed represents the maximum ROM coal extraction rate and maximum product material movement within the Project area for the indicative mine schedule. This case would represent the maximum levels of dust generation from mining activities and the situation with the maximum potential for air quality impacts. Open cut mining in the assessed scenario occurs concurrently to the east and west of Hail Creek, with overburden emplacement occurring in areas behind the progression of mining. ROM coal extracted is from the active pit areas and transported by haul truck to the existing CHPP for processing before being stockpiled and dispatched off-site via rail transport. Reject material generated at the CHPP is emplaced in the allocated co-disposal areas located to the east and the south of the infrastructure area. A small quarry operation is also proposed to occur to the east of the Hail Creek and would generate material required for the construction of haul roads and other related infrastructure at the site. An indicative mine plan for the modelling scenario is presented in Figure 6-1. The plan shows that large parts of the site undergo active mining in the scenario assessed. Other activities assessed for the Project include the proposed operation of a small power generation facility and local flares to manage the coal seam methane gas generated at the site from the underground mining operations. The quantities of coal seam methane gas likely to be generated by the Project have been estimated by Palgas (Palgas, 2014). The potential air quality impacts associated with the proposed operations have been based on the predicted maximum annual amount coal seam methane gas produced for the mine life. It is estimated that approximately 70 per cent of coal seam methane gas produced from the pre and post drainage streams would be utilised to produce electricity, 28 per cent would be flared and two per cent would be lost to the atmosphere during the collection process. The proposed power generation facility would consist of 12 power generation units able to produce approximately 3MW. The flaring operation would be distributed across the gas drainage network at various node points in the pipeline network and would consist of approximately 60 small flares. Figure 6-2 presents an indicative layout of the power station and flares modelling in the assessment.

25 20 Figure 6-1: Indicative mine plan for modelling scenario

26 21 Figure 6-2: Indicative flare and power station layout

27 Emission estimation Particulate matter For the chosen modelling scenario selected to represent the maximum-case operation over the life of the Project, the rate of dust emission has been calculated by analysing the various types of dust generating activities taking place and utilising suitable emission factors. The emission factors applied are considered the most representative for determining dust generation rates for the proposed activities. The emission factors were sourced from both Australian studies and United States EPA (US EPA) developed documentation. Total dust emissions from all significant dust generating activities for the Project are presented in Table 6-1. Detailed emission inventories and emission estimation calculations are presented in Appendix A. The estimated dust emissions presented in Table 6-1 reflect the use of applied dust mitigation currently being implemented at the Hail Creek Mine which includes the use of dust suppression for drilling operations and use of watering as dust suppression for trafficked areas. Due to the seasonal extremes in rainfall, only an average level of dust control by watering has been applied, as outlined in the right hand sections of the detailed emissions inventory table in Appendix A. The assumed control levels due to watering result in more dust emissions than might occur in practice, making the results conservative and likely to overestimate actual emissions. Table 6-1: Estimated emissions for the Project (kg of TSP) Activity TSP emission (kg/y) OB - Stripping Topsoil with scraper 5,800 OB - Scraper unloading Topsoil 4,000 OB - Excavator loading Topsoil to haul truck 350 OB - Hauling to Topsoil dump 3,226 OB - Emplacing at Topsoil dump 350 OB - Dragline 841,317 OB - Drilling 8,850 OB - Blasting 124,451 OB - Excavator loading OB to haul truck 523,215 OB - Hauling to dump 5,026,050 OB - Emplacing at dump 523,215 OB - Dozers on OB (pit/dump/rehab) 4,486,006 CL - Dozers ripping/pushing/clean-up 1,350,775 CL - Loading ROM coal to haul truck 1,705,525 CL - Hauling ROM to hopper 1,081,398 CHPP - Unloading ROM to hopper 1,705,525 CHPP - Rehandle ROM at hopper 170,552 CHPP - Dozer pushing ROM coal 991,654 CHPP - Conveying ROM 263 CHPP - Crushing ROM 54,772 CHPP - Conveying Product 578 CHPP - Loading Product coal to stockpile 2,057 CHPP - Dozer pushing Product coal 229,127 CHPP - Conveying Product to train 252 CHPP - Loading Product coal to train 2,057 CHPP - Loading rejects 1,495

28 23 Activity TSP emission (kg/y) CHPP - Hauling rejects 171,441 CHPP - Unloading rejects 1,495 WE - Overburden emplacement areas 2,148,269 WE - Open pit 915,840 WE - ROM stockpiles 7,809 WE - Product stockpiles 17,896 QUA - Loading material to crusher 437 QUA - Crushing material 675 QUA - Screening material 3,125 QUA - Unloading material to stockpile 437 QUA - Loading material to truck 437 QUA - Hauling to emplacement area 16,130 QUA - Unloading material 437 Grading roads 549,931 Total TSP emissions (kg/yr) 22,677,219 OB overburden, CL coal, CHPP coal handling and preparation plant, BYP bypass, WE - wind erosion, QUA - quarry Note: Totals may vary slightly due to rounding. A small amount of particulate matter (PM 10) may be generated from the power station and flare operation. Considering this potential emissions in the context of the surrounding mining activities, it is unlikely that the proposed power station and flare operation would make any significant contribution to an increase in the particulate matter level in the surrounding areas and therefore has not been investigated in this assessment Power station and flaring operations Air emissions associated with the power station and flaring operations were calculated based on parameters provided by the Proponent and estimated stack concentrations (see Table 6-2). Typically, NO X emissions emitted from stack sources consist for the larger part (~95 per cent) of nitrogen monoxide (NO) and for a small part (~5 per cent) of NO 2. After emission from the stack, NO is converted to NO 2 through oxidation with atmospheric ozone (O 3) (Janssen et al., 1988). The rate of this reaction is governed by the level of ozone in the air, air dispersion and other meteorological factors such as temperature. The reactions are complicated and most pronounced in urban areas with high ozone and other levels of pollution which do not generally arise at the Project. This assessment has conservatively assumed that all NO X emissions from the proposed operations would be emitted as NO 2. The predicted results are therefore likely to overestimate the actual impacts significantly (potentially up to 20 times higher than may actually occur) and provide an indication of a worst case impact. Table 6-2: Stack parameters for power station and flare units Parameter Unit Power station Flare Number of stacks / sources Stack height m Stack diameter m Exit velocity m/s Temperature o C 400 1,200 NO X emission rate g/s CO emission rate g/s

29 24 7 DISPERSION MODELLING APPROACH 7.1 Introduction The following sections are included to provide the reader with an understanding of the model and modelling approach. The DEHP EIS information guideline for air specifies generic TOR requirements that outline situations where various air dispersion models should be used and require that modelling be undertaken in accordance with the NSW EPA "Approved Methods for the Modelling and Assessment of Air Pollutants in NSW" (NSW DEC, 2005). For this assessment the CALPUFF modelling suite is applied to dispersion modelling. The CALPUFF model is an advanced "puff" model that can deal with the effects of complex local terrain on the dispersion meteorology over the entire modelling domain in a three dimensional, hourly varying time step. CALPUFF is an air dispersion model approved by NSW EPA for use in air quality impact assessments. As recommended by the DEHP EIS information guideline for air, the CALPUFF and CALMET model is setup in general accordance with methods provided in the NSW EPA document "Generic Guidance and Optimum Model Setting for the CALPUFF Modeling System for Inclusion into the 'Approved Methods for the Modeling and Assessments of Air Pollutants in NSW, Australia" (TRC, 2011). 7.2 Modelling methodology Modelling was undertaken using a combination of TAPM and the CALPUFF Modelling System. The CALPUFF Modelling System includes three main components: CALMET, CALPUFF and CALPOST and a large set of pre-processing programs designed to interface the model to standard, routinely available meteorological and geophysical datasets. TAPM is a prognostic air model used to simulate the upper air data for CALMET input. The meteorological component of TAPM is an incompressible, non-hydrostatic, primitive equation model with a terrain-following vertical coordinate for three dimensional simulations. The model predicts the flows important to local scale air pollution, such as sea breezes and terrain induced flows, against a background of larger scale meteorology provided by synoptic analysis. CALMET is a meteorological model that uses the geophysical information and observed/simulated surface and upper air data as inputs and develops wind and temperature fields on a three dimensional gridded modelling domain. CALPUFF is a transport and dispersion model that advects "puffs of material emitted from modelled sources, simulating dispersion processes along the way. meteorological field generated by CALMET. It typically uses the three dimensional CALPOST is a post processor used to process the output of the CALPUFF model and produce tabulations that summarise the results of the simulation.

30 Meteorological modelling The TAPM model was applied to the available data to generate a three dimensional upper air data file for use in CALMET. The centre of analysis for the TAPM modelling used is 21deg29min south and 148deg35.5min east (665300mE, mN). The simulation involved four nesting grids of 30km, 10km, 3km and 1km with 35 vertical grid levels. CALMET modelling used a nested approach where the three dimensional wind field from the coarser grid outer domain is used as the initial guess (or starting) field for the finer grid inner domains. This approach has several advantages over modelling a single domain. Observed surface wind field data from the near field as well as from far field monitoring sites can be included in the model to generate a more representative three dimensional wind field for the modelled area. Off domain terrain features for the finer grid domain can be allowed to take effect within the finer domain, as would occur in reality, also the coarse scale wind flow fields give a better set of starting conditions with which to operate the finer grid run. The CALMET initial domain was run on a 100 x 100km area with a 2km grid resolution and refined for a second domain on a 50 x 50km grid with a 1km grid resolution and further refined for a final domain on a 30 x 30km grid with a 0.3km grid resolution. The available meteorological data for September August 2014 from four surrounding meteorological monitoring sites were included in this run. Table 7-1 outlines the parameters used from each station. Three dimensional upper air data were sourced from TAPM output. Table 7-1: Surface observation stations Weather Stations Parameters WS WD CH CC T RH SLP Hail Creek AWS Mackay Aerodrome (BoM) (Station No ) Proserpine Airport (BoM) (Station No ) Hay Point (BoM) (Station No ) WS = wind speed, WD= wind direction, CH = cloud height, CC = cloud cover, T = temperature, RH = relative humidity, SLP = sea level pressure Local land use and detailed topographical information was included in the simulation to produce realistic fine scale flow fields (such as terrain forced flows) in surrounding areas, as shown in Figure 7-1. The figure illustrates the degree to which the model is able to consider the effects of local terrain on the wind field, as shown by the channelling of winds along valley s, around terrain and the increased wind speed over ridges and decreased winds in sheltered valleys. Closer examination of the figure also shows that the modelled wind field responds to the terrain features of mine pit and dump area that skew the winds very near to the Project. The use of advanced meteorological modelling such as this, for each hour of the full year allows more accurate predictions of any potential impacts to be made.

31 26 CALMET generated meteorological data were extracted from a point within the CALMET domain and are graphically represented in Figure 7-2 and Figure 7-3. Figure 7-2 presents the annual and seasonal windroses from the CALMET data. The CALMET modelling results reflect the expected wind distribution patterns of the area based on consideration of the measured data and the expected terrain effects on the prevailing winds. As can be seen in Figure 7-2, the wind distribution pattern from that point in the CALMET domain is generally similar to the wind distribution recorded by the Hail Creek AWS (see Figure 5-4). Figure 7-3 includes graphs of the temperature, wind speed, mixing height and stability classification over the modelling period and shows statistical properties consistent with what is expected to occur in the area. Figure 7-1: Representative snapshot of wind field for the Project

32 27 Figure 7-2: Windroses from CALMET extract (cell ref 2858)

33 28 Figure 7-3: Meteorological analysis of CALMET extract (cell ref 2858)

34 Dispersion modelling The CALPUFF modelling is based on the application of three particle size categories; fine particulates, coarse matter and rest. The distribution of particles for each particle size category was derived from measurements in the SPCC (1986) study and is presented in Table 7-2. Table 7-2: Distribution of particles Particle category Size range Distribution (1) Fine particulates 0 to 2.5µm 4.68% of TSP Coarse matter 2.5 to 10µm 34.4% of TSP Rest 10 to 30µm 60.92% of TSP (1) Particle distribution sources from SPCC (1986) Each particle-size category is modelled separately and later combined to predict short-term and longterm average concentrations for PM2.5, PM10, and TSP. Dust deposition was predicted using the proven dry deposition algorithm within the CALPUFF model. Particle deposition is expressed in terms of atmospheric resistance through the surface layer, deposition layer resistance and gravitational settling (Slinn and Slinn, 1980 and Pleim et al., 1984). Gravitational settling is a function of the particle size and density, simulated for spheres by the Stokes equation (Gregory, 1973). CALPUFF is capable of tracking the mass balance of particles emitted into the modelling domain. For each hour CALPUFF tracks the mass emitted, the amount deposited, the amounts remaining in the surface mixed layer or the air above the mixed layer and the amount advected out of the modelling domain. The versatility to address both dispersion and deposition algorithms in CALPUFF, combined with the three dimensional meteorological and land use field generally result in a more accurate model prediction compared to other Gaussian plume models (Pfender et al., 2006). Emissions from each activity occurring at the Project were represented by a series of volume sources and were included in the CALPUFF model via an hourly varying emission file. Meteorological conditions associated with dust generation (such as wind speed) and levels of dust generating activity were considered in calculating the hourly varying emission rate for each source. It should be noted that as a conservative measure, the effect of the precipitation rate (rainfall) in reducing dust emissions has not been considered in this assessment.

35 30 8 DISPERSION MODELLING RESULTS The dispersion model predictions for the assessed scenario are presented in this section. The Projectonly and total (cumulative) levels were predicted for: maximum 24-hour average PM 2.5 concentrations; annual average PM 2.5 concentrations; maximum and 6 th highest 24-hour average PM 10 concentrations; annual average PM 10 concentrations; annual average TSP concentrations; and annual average dust (insoluble solids) deposition rates. It is important to note that when assessing impacts per maximum and 6 th highest 24-hour average PM 2.5 and PM 10 concentrations; the predictions show the highest predicted 24-hour average concentrations that occur at each point within the modelling domain for the worst day (a 24-hour period) in the one year modelling period. The figures (Appendix B) showing 24-hour average levels represent every worst case day, at every point over the whole year combined into one plot, and do not represent a single worst case day snapshot. When trying to assess the total (cumulative) 24-hour average impacts based on model predictions, challenges arise as the predicted impacts are often overestimated due to several factors. The first is the use of a regulatory model which is specifically designed to not under-predict any result when operated correctly. All regulatory models, therefore over-predict results to some degree. Other factors include the model's inherent limitations in realistically considering spatial and temporal variability, especially over short timeframes. Further issues are associated with identification and quantification of emissions from other non-modelled (background) sources over relatively short, 24- hour average periods. These factors should be taken into consideration whenever assessing impacts based on 24-hour average predictions. Each of the sensitive receptors shown in Figure 3-1 were assessed individually as discrete receptors with the predicted results presented in tabular form for the assessed year. Associated isopleth diagrams of the dispersion modelling results are presented in Appendix B. 8.1 Particulate matter Table 8-1 presents the model predictions at each of the privately-owned sensitive receptors. Figure B-1 to Figure B-11 in Appendix B present isopleth diagrams of the predicted modelling results for each of the assessed pollutants.

36 31 Receptor ID Table 8-1: Particulate matter modelling predictions of the Project PM 2.5 (µg/m³) 24-hour average Annual average Maximum 24- hour average PM 10 (µg/m³) 6 th highest 24-hour average Annual average Potential incremental impact due to the Project TSP (µg/m³) Annual average DD (g/m²/month) Annual average Air quality goals Fort Cooper Carrinyah Homestead <0.1 Kemmis Creek <0.1 Total potential impact due to the Project and other sources Air quality goals * Fort Cooper Carrinyah Homestead Kemmis Creek *Levels equivalent to 133mg/m 2 /day per month Predicted maximum 24-hour average PM 2.5 concentrations Figure B-1 shows the predicted maximum 24-hour average PM 2.5 concentrations due to emissions from the Project in isolation. The results in Table 8-1 indicate that all privately-owned receptors are predicted to experience maximum 24-hour average concentrations below the air quality goal of 25µg/m³ due emissions from the Project and other sources Predicted annual average PM 2.5 concentrations Figure B-2 shows the predicted annual average PM 2.5 concentrations due to emissions from the Project in isolation and Figure B-8 shows the predicted total impact from the Project and other sources. The results in Table 8-1 indicate that all privately-owned receptors are predicted to experience levels, due to emissions from the Project and other sources, below the air quality goal of 8µg/m³ Predicted maximum and 6 th highest 24-hour average PM 10 concentrations Figure B-3 and Figure B-4 show the predicted maximum and 6 th highest 24-hour average PM 10 concentrations, respectively, due to emissions from the Project in isolation. The results in Table 8-1 indicate that predicted levels are below the relevant air quality objectives, with the exception of predictions at the Fort Cooper receptor where predicted levels of PM 10, 24 hour exceed the air quality objective for the maximum case scenario. Air dispersion models have a tendency to overestimate predicted levels when averaging over short time periods. When considering this in conjunction with the conservative dust emission estimates, it is likely that actual levels would be lower.

37 32 An analysis was conducted to investigate how often dust levels due to the Project may be elevated at Fort Cooper and some further investigations into potential effects of applying operational reactive and proactive dust mitigation strategies to minimise the potential for these impacts occurring. In the event of a complaint, air quality monitoring would be conducted to validate the model predictions and inform the implementation of air quality mitigation measures, if required. In this instance, air quality mitigation measures would be investigated in consultation with the sensitive receptor, and could include modification of Project operations or at-receiver mitigation measures Predicted annual average PM 10 concentrations Figure B-5 shows the predicted annual average PM 10 concentrations due to emissions from the Project in isolation and Figure B-9 shows the predicted total impact from the Project and other sources. The results in Table 8-1 indicate that all privately-owned receptors are predicted to experience levels, due to emissions from the Project and other sources, below the air quality goal of 30µg/m³ Predicted annual average TSP concentrations Figure B-6 shows the predicted annual average TSP concentrations due to emissions from the Project in isolation and Figure B-10 shows the predicted total impact from the Project and other sources. The results in Table 8-1 indicate that all privately-owned receptors are predicted to experience levels, due to emissions from the Project and other sources, below the air quality goal of 90µg/m³ Predicted annual average dust deposition levels Figure B-7 shows the predicted annual average dust deposition levels due to emissions from the Project in isolation and Figure B-11 shows the predicted total impact from the Project and other sources. The results in Table 8-1 indicate that all privately-owned receptors are predicted to experience total dust deposition levels below the relevant air quality goal of 133mg/m²/day (or g/m²/month) from the Project and other sources. 8.2 Dust mitigation and management Operational dust mitigation strategies and management measures would be implemented to minimise potential for dust impacts during mining operations in the surrounding environment including: continuing application of wet suppression of dust on unsealed haul roads; and rehabilitation of areas as they become available to reduce the overall area of exposed soil and the revegetation of areas locks up soil thus making it difficult for dust to escape the surface reducing the potential for wind erodible dust. In the event of a complaint, air quality monitoring would be conducted to validate the model predictions and inform the implementation of air quality mitigation measures, if required, where quality objectives are exceeded. In this instance, air quality mitigation measures would be

38 33 investigated in consultation with sensitive receptor, and could include modification of Project operations or at-receiver mitigation measures 8.3 Power station and flaring Table 8-2 presents the model predictions at each of the privately-owned sensitive receptors. The results indicate that all of the predicted values would be below the relevant criteria. Figure B-12 to Figure B-17 in Appendix B present isopleth diagrams of the predicted modelling results for each of the assessed pollutants. Analysis of the predicted CO and NO 2 impacts in Table 8-2 show minimal incremental impact at the sensitive receptor locations. The total (cumulative) impact with the addition of estimated background levels (see Section 5.3.2) indicates that predicted CO and NO 2 concentrations at the sensitive receptor locations would also be well below the relevant criteria. Receptor ID Table 8-2: CO and NO 2 modelling predictions of the Project CO (mg/m³) NO 2 (µg/m³) 8-hour average 1-hour average Annual average Potential incremental impact due to the Project Air quality goals Fort Cooper Carrinyah Homestead <0.1 Kemmis Creek Total potential impact due to the Project and other sources Air quality goals Fort Cooper Carrinyah Homestead Kemmis Creek

39 34 9 GREENHOUSE GAS ASSESSMENT 9.1 Introduction Dynamic interactions between the atmosphere and surface of the earth create the unique climate that enables life on earth. Solar radiation from the sun provides the heat energy necessary for this interaction to take place, with the atmosphere acting to regulate the complex equilibrium. A large part of this regulation occurs from the "greenhouse effect" with the absorption and reflection of the solar radiation dependent on the composition of specific greenhouse gases in the atmosphere. Over the last century, the composition and concentration of greenhouse gases in the atmosphere has increased due to increased anthropogenic activity. Climatic observations indicate that the average pattern of global weather is changing as a result. The measured increase in global average surface temperatures indicate an unfavourable and unknown outcome if the rate of release of greenhouse gas emissions remain at the current rate. This assessment aims to estimate the predicted emissions of greenhouse gases (GHG) to the atmosphere due to the Project and to provide a comparison of the direct emissions from the Project at the state and national level. 9.2 Greenhouse Gas Inventory The National Greenhouse Accounts (NGA) Factors document published by the Department of the Environment defines three scopes (Scope 1, 2 and 3) for different emission categories based on whether the emissions generated are from "direct" or "indirect" sources. Scope 1 emissions encompass the direct sources from the project defined as: "...from sources within the boundary of an organisation as a result of that organisation's activities" (Department of the Environment, 2014c). Scope 2 and 3 emissions occur due to the indirect sources from the project as: "...emissions generated in the wider economy as a consequence of an organisation's activities (particularly from its demand for goods and services), but which are physically produced by the activities of another organisation" (Department of the Environment, 2014c). For the purpose of this assessment, emissions generated in all three scopes defined above provide a suitable approximation of the total GHG emissions generated from the Project. Scope 3 emissions can often result in a significant component of the total emissions inventory; however, these emissions are often not directly controlled by the Project. These emissions are understood to be considered in the Scope 1 emissions from other various organisations related to the Project. The primary contribution of the Scope 3 emissions from the Project occurs from the transportation of the product coal and from the end use of the product coal. Scope 3 emissions also have the potential to arise from a greater number of sources associated with the operation of the Project. As these are often difficult to quantify due to the diversity of sources and relatively minor individual contributions, they have not been considered in this assessment.

40 Emission sources Scope 1 and 2 GHG emission sources identified from the operation of the Project are the fugitive release of methane gas from mining operations, the onsite combustion of diesel fuel, the onsite consumption of electricity, the onsite use of explosives and the conversion of coal seam gas for power generation and flaring. Scope 3 emissions have been identified as resulting from the purchase of diesel, electricity for use onsite, transport of product to its final destination and the final use of the product. Estimated quantities of materials that have the potential to emit GHG emissions associated the Project have been summarised in Table 9-1 below. These estimates are based on a conservative upper limit of the assumed maximum production throughout the life of the Project. The assessment provides a reasonable worst case approximation of the potential GHG emissions for the purpose of this assessment. Year ROM coal Open Cut (tonnes) Table 9-1: Summary of quantities of materials estimated for the Project ROM coal - Underground Product coal Diesel (tonnes) (tonnes) (kl) Electricity (MWh) Explosives (tonnes) 1 18,000,000-12,732, , ,594 90, ,000,000-14,484, , , , ,000,000-13,921, , , , ,000,000-12,743, , , , ,000,000-13,067, , , , ,000,000-11,869, , , , ,000,000-12,062, , , , ,000,000-14,134, , ,567 93, ,913,689-13,485, , , , ,594, ,992 11,214, , ,460 62, ,121,107 5,221,033 7,130 57, ,071,177 8,255,697 3,155 81, ,777,443 8,992,773 3,396 87, ,317,327 8,562,400 3,233 81, ,904,483 7,066,226 2,748 70, ,289,292 8,487,873 3,222 82, ,660,797 7,807,881 3,006 76, ,487,800 8,194,851 3,289 85, ,734,360 7,681,474 3,374 88, ,059,438 7,966,811 3,485 91, ,932,025 8,754,434 3,784 97, ,796,312 8,645,746 3,737 95, ,295,970 9,109,107 3,907 99, ,470,256 6,577,213 2,933 75, ,195,016 7,218,847 3,182 81, ,157,661 7,180,235 3,169 81, ,533,265 8,389,873 3,640 93, ,281,870 7,281,925 3,211 82, ,254,486 6,369,550 2,860 73, ,009,521 7,055,861 3,118 77, ,680,842 5,889,112 2,663 65, ,282,354 2,012,528 1,156 26,989 -

41 36 Year ROM coal Open Cut (tonnes) ROM coal - Underground (tonnes) Product coal (tonnes) Diesel (kl) Electricity (MWh) Explosives (tonnes) Total 225,508, ,881, ,157,813 1,996,017 4,275,898 1,197,073 Scope 3 emissions for the transport and final use of the coal may have the potential to vary in the future depending on the market situation at the time. These assumptions include emission factors for the transport modes of rail and shipping and the associated average weighted distance travelled for the export coal Emission factors To quantify the amount of carbon dioxide equivalent (CO 2-e) material generated from the Project, emission factors have been obtained from the NGA Factors (Department of the Environment, 2014c) and other sources as required and are summarised in Table 9-2. Table 9-2: Summary of emission factors Type Energy content factor Emission factor CO 2 CH 4 N 2O Units Scope Fugitive emissions Open cut tonnes CO 2-e/t ROM 1 Diesel kg CO 2-e/GJ Electricity kg CO 2-e/kWh Explosives (ANFO/Emulsion) tonnes CO 2-e/t product 1 Power generation kg CO 2-e/GJ 1 Flaring tonnes CO 2-e/t flared 1 Rail (1) t CO 2-e/Mt-km 3 Ship (1) t CO 2-e/Mt-km 3 Thermal kg CO 2-e/GJ 3 Coking coal kg CO 2-e/GJ 3 (1) Todoroski Air Sciences (2014) Estimates of the amount of fugitive emissions released from the underground operation for the Project were provided by Palgas (2014). The method used to estimate the fugitive emissions was obtained from the National Greenhouse and Energy Report Technical Guidelines (Department of Climate Change and Energy Efficiency, 2012). Product coal from the site is transported by rail to the Hay Point Coal Terminal and then transferred to coal loaders before being shipped to its final destination. The approximate rail distance is taken to be 380km (return distance). The product coal is assumed to be generally distributed to locations in Asia, East Asia and Europe based on the current distributions. The approximate shipping distance of 13,000km, 18,000km and 34,000km (return distances) is based on destinations in the Asian, East Asian and European markets, respectively, and have been applied in the estimates. The emissions generated from the end use of coal produced by the Project have assumed that the product coal is consumed as either coking coal or thermal coal. As it is difficult to estimate emissions from its use in other countries, this assessment has assumed the emissions generated would be equivalent to those generated in Australia.

42 Summary of greenhouse gas emissions Table 9-3 summarises the estimated annual CO 2-e emissions due to the operation of the Project. Table 9-3: Summary of CO 2-e emissions for the project (t CO 2-e) Fugitive - Fugitive - Transport Transport Final use Final use Diesel Electricity Explosives Power generation Flaring Year Open Cut Underground (RAIL) (SHIP) (Thermal) (Coke) Scope 1 Scope 1 Scope 3 Scope 2 Scope 3 Scope 1 Scope 1 Scope 1 Scope 1 Scope 3 Scope 3 Scope 3 Scope , ,727 25, ,631 28,027 15, , ,113 12,295 80, ,491 12,064,260 21,728, , ,805 31, ,101 30,029 17,328 1,110, ,472 22,003 91, ,814 12,717,982 25,781, , ,150 34, ,962 29,204 18,450 1,234, ,383 16,190 87, ,582 10,990,827 26,080, , ,379 32, ,274 28,291 17,205 1,241, ,320 9,704 80, ,200 11,793,116 22,043, , ,360 37, ,740 28,045 19,522 1,250, ,908 10,147 82, ,307 11,283,061 23,458, , ,664 38, ,516 30,416 19,715 1,268, ,216 13,079 74, ,504 10,742,175 20,787, , ,433 34, ,469 26,075 18,673 1,304, ,834 24,069 76, ,481 10,086,771 22,003, , ,252 33, ,909 31,924 15,812 1,320, ,846 24,457 89, ,955 35,152,484 1,156, , ,032 43, ,797 32,227 20,129 1,323, ,469 22,099 85, ,579 32,065,508 2,656, , ,137 26, ,903 27,750 10,609 1,427, ,518 19,299 70, ,609 25,121,907 3,837, ,238 1,459 46,370 7,442-1,609, ,093 23,997 32, ,708 2,590,014 11,397, , ,257 10,634-1,686, ,422 23,932 52, ,354 4,095,430 18,022, , ,697 11,346-1,567, ,336 24,504 56, ,830 4,461,074 19,631, , ,100 10,609-1,594, ,177 30,099 54, ,197 4,245,834 18,693, , ,413 9,214-1,821, ,216 37,067 44, ,040 3,494,882 15,436, , ,066 10,764-1,900, ,031 37,049 53, ,110 4,198,579 18,542, , ,234 9,988-1,918, ,405 38,343 49, ,774 3,849,471 17,069, , ,859 11,051-1,934, ,441 36,025 51, ,101 3,878,262 18,086, , ,313 11,445-1,948, ,187 33,099 48, ,036 3,355,062 17,249, , ,986 11,874-1,976, ,082 33,959 50, ,713 3,479,690 17,890, , ,592 12,614-1,986, ,360 31,761 55, ,986 3,823,702 19,659, , ,071 12,369-2,007, ,163 32,129 54, ,015 3,776,230 19,415, , ,303 12,888-2,031, ,362 33,503 57, ,473 3,978,614 20,455, , ,133 9,811-2,024, ,504 18,427 41, ,365 2,872,750 14,769, , ,143 10,615-2,018, ,278 16,954 45, ,618 3,152,999 16,210, , ,046 10,600-1,748, ,008 13,639 45, ,497 3,136,134 16,124, , ,597 12,133-1,768, ,385 13,386 52, ,957 3,664,472 18,840, , ,789 10,719-1,119,279 84,188 8,143 45, ,084 3,180,550 16,352, , ,562 9, , ,956 2,782,049 14,303, , ,161 10, , ,663 3,081,811 15,844, , ,779 8, , ,560 2,572,206 13,224, , ,861 3, , , ,019 4,519,373 TOTAL 3,323,639 4,681, ,000 3,238, , ,844 44,272,477 6,816, ,359 1,844,690 16,011, ,566, ,276,443

43 Contribution of greenhouse gas emissions Table 9-4 summarises the emissions associated with the project based on Scopes 1, 2 and 3. Table 9-4: Summary of CO 2-e emissions per scope (t CO 2-e) Year Scope 1 Scope 2 Scope 3 Scope , ,631 34,623,913 1,105, ,134, ,101 39,445,373 2,321, ,230, ,962 37,985,661 2,412, ,141, ,274 34,675,555 2,317, ,224, ,740 35,605,630 2,399, ,290, ,516 32,323,633 2,479, ,394, ,469 32,887,246 2,556, ,401, ,909 37,226,930 2,600, ,500, ,797 35,610,457 2,701, ,306, ,903 29,691,532 2,479, ,900,618 46,370 14,316,245 1,946, ,966,839 66,257 22,634,599 2,033, ,854,175 70,697 24,655,193 1,924, ,944,801 66,100 23,475,154 2,010, ,248,893 57,413 19,374,143 2,306, ,329,387 67,066 23,271,654 2,396, ,361,833 62,234 21,408,090 2,424, ,351,724 68,859 22,478,760 2,420, ,332,850 71,313 21,087,358 2,404, ,370,486 73,986 21,870,673 2,444, ,356,874 78,592 24,032,427 2,435, ,382,199 77,071 23,733,972 2,459, ,421,712 80,303 25,005,825 2,502, ,241,111 61,133 18,055,934 2,302, ,219,624 66,143 19,817,205 2,285, ,911,506 66,046 19,711,248 1,977, ,929,822 75,597 23,031,694 2,005, ,220,275 66,789 19,990,376 1,287, ,715 59,562 17,485,913 67, ,413 63,161 19,369,538 71, ,185 52,779 16,166,635 59, ,120 21,861 5,525,404 24,981 Total 59,927,014 3,238, ,573,972 63,165,652 Annual average 1,872, ,207 24,892,937 1,973,927 The estimated annual greenhouse emissions for Australia for the 2013 to 2014 period was Mt CO2-e (Department of the Environment, 2014b). In comparison, the conservative estimated annual average greenhouse emission over the 32-year life of the Project is 1.97Mt CO2-e (Scope 1 and 2). Therefore, the annual contribution of greenhouse emissions from the Project in comparison to the Australian greenhouse emissions for the 2013 to 2014 period is conservatively estimated to be approximately 0.36 per cent. At a state level, the estimated greenhouse emissions for Queensland in the period was Mt CO 2-e (Department of the Environment, 2014a). The annual contribution of greenhouse

44 39 emissions from the Project in comparison to the Queensland greenhouse emissions for the period is conservatively estimated to be approximately 1.30 per cent. The estimated greenhouse gas emissions generated in all three scopes are based on approximated maximum quantities of materials. Therefore the estimated emissions for the Project are considered conservative. 9.5 Greenhouse gas management The Project would utilise various mitigation measures to minimise the overall generation of greenhouse gas emissions. These measures would include developing a basis for identifying and implementing energy efficiency opportunities and mitigation measures for various activities. Examples of various mitigation and energy management measures to reduce GHG emissions are as follows: Monitor the consumption of fuel and regularly maintain diesel powered equipment to ensure operational efficiency; Monitor the total site electricity consumption and investigate avenues to minimise the requirement; Utilisation of coal seam methane gas to generate electricity for use at the Project; Flare coal seam methane gas; Provide energy awareness programs for staff and contractors; and Minimise the production of waste generated onsite.

45 40 10 CONCLUSIONS The study has described the potential air quality impacts that may arise from the Project. The assessment utilises advanced air dispersion modelling methods and focuses on potential air quality impacts from the Project in isolation (incrementally) and cumulatively with the addition of existing ambient background levels. The dispersion modelling predictions show that that predicted levels are below the relevant air quality objectives, with the exception of predictions at the Fort Cooper receptor where predicted levels of PM 10, 24 hour exceed the air quality objective for the maximum case scenario. It is noted that the air dispersion model predictions include a high level of conservatism in both the emission estimation and the applied background air quality levels which would tend to result in an overestimation of the actual impacts. In the event of a complaint, air quality monitoring would be conducted to validate the model predictions and inform the implementation of air quality mitigation measures, if required. In this instance, air quality mitigation measures would be investigated in consultation with the sensitive receptor, and could include modification of Project operations or at-receiver mitigation measures. The study predicts that the incremental air quality impact arising from the power station and flare operations would be minor at the surrounding sensitive receptors. Cumulative air quality impacts are also expected to be below the relevant air quality criteria at these locations. The greenhouse gas assessment conservatively calculates the annual Scope 1 and Scope 2 emission generated from the Project to be 1.97Mt CO2-e. Relative to the annual greenhouse gas emissions from Australia and Queensland, it is estimated the proposal would contribute approximately 0.36 and 1.30 per cent respectively.

46 41 11 REFERENCES Bureau of Meteorology (2015) Climate statistics for Australian locations, Bureau of Meteorology website. Department of Climate Change and Energy Efficiency (2012) National Greenhouse and Energy Reporting System Measurement Technical Guidelines for the estimation of greenhouse gas emissions by facilities in Australia, Department of Climate Change and Energy Efficiency, July DEHP (2013) Coal Dust Management, Department of Environment and Heritage Protection website. (last updated: 20 August 2013) Department of the Environment (2014a) State and Territory Greenhouse Gas Inventories , Department of the Environment, April Department of the Environment (2014b) Quarterly Update of Australia s National Greenhouse Gas Inventory: June 2014, Department of the Environment, December Department of the Environment (2014c) National Greenhouse Accounts Factors Australian National Greenhouse Accounts, Department of the Environment, December 2014 Update. Gregory P. H. (1973) "The microbiology of the atmosphere", Halstead Press, New York. Janssen, L. H. J. M., van Wakeren, J. H. A., van Duuren, H. and Elshout, A. J. (1988) "A Classification of NO oxidation rates in power plant plumes based on atmospheric conditions". Atmospheric Environment, Volume 22, Number 1, Katestone Environmental Pty Ltd (2010) "NSW Coal Mining Benchmarking Study: International Best Practice Measures to Prevent and/or Minimise Emissions of Particulate Matter from Coal Mining", Katestone Environmental Pty Ltd prepared for DECCW, NPI (2012) "Emission Estimation Technique Manual for Mining Version 3.1", National Pollutant Inventory, January ISBN NSW DEC (2005) Approved Methods for the Modelling and Assessment of Air Pollutants in NSW, August 2005.

47 42 Palgas (2014) Hail Ck_EA Application Gas & Vent Inputs, memo prepared by Palgas for RTCA, September Pfender W., Graw R., Bradley W., Carney M. And Maxwell L. (2006) "Use of a complex air pollution model to estimate dispersal and deposition of grass stem rust urendiniospores at landscape scale", Agriculture and Forest Meteorology, Vol 139. Pleim J., Venkatram A. and Yamartino R. J. (1984) "ADOM/TADAP model development program, Vol 4, The Dry Deposition Model", Ministry of the Environment, Rexdale, Ontario, Canada. SEPP (2001) State Environment Protection Policy (Air Quality Management), Victoria Government Gazette S December Slinn S. A. and Slinn W. G. N. (1980) "Predictions for particle deposition on natural waters", Atmospheric Environment, Vol 14. SPCC (1986) "Particle size distributions in dust from open cut mines in the Hunter Valley", Report Number Prepared for the State Pollution Control Commission of NSW by Dames & Moore, 41 McLaren Street, North Sydney, NSW, Todoroski Air Sciences (2014) Air Quality and Greenhouse Gas Assessment Warkworth Continuation 2014, prepared by Todoroski Air Sciences on behalf of EMGA Mitchell McLennan, June TRC (2011) "Generic Guidance and Optimum Model Settings for the CALPUFF Modeling System for Inclusion into the Approved Methods for the Modeling and Assessments of Air Pollutants in NSW, Australia", Prepared for the NSW Office of Environment and Heritage by TRC Environmental Corporation. US EPA (1985 and updates) Compilation of Air Pollutant Emission Factors, AP-42, Fourth Edition United States Environmental Protection Agency, Office of Air and Radiation Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina Note this reference is now a web-based document. US EPA (2011) "Health Effects of Pollution", United States Environmental Protection Agency website

48 Appendix A Emission Calculation

49 A-1 Hail Creek Mine Dust Emission Calculation The mining schedule and mine plan design provided by the Proponent have been combined with emissions factor equations that relate to the quantity of dust emitted from particular activities based on intensity, the prevailing meteorological conditions, and composition of the material being handled. Emission factors and associated controls have been sourced from the US EPA AP42 Emission Factors (US EPA, 1985 and Updates), the State Pollution Control Commission document "Air Pollution from Coal Mining and Related Developments" (SPCC, 1983), the National Pollutant Inventory document Emission Estimation Technique Manual for Mining, Version 3.1 (NPI, 2012) and the NSW EPA document, NSW Coal Mining Benchmarking Study: International Best Practise Measures to Prevent and/or Minimise Emissions of Particulate Matter from Coal Mining, prepared by Katestone Environmental (Katestone, 2010). The emission factor equations used for each dust generating activity are outlined in Table A-1 below. Detailed emission inventories for the modelled year is presented in Table A-2.

50 A-2 Table A-1: Emission factor equations Activity Emission factor equation Variables Control Source Topsoil removal by scraper EF = kg/mg - - US EPA, 1985 Scraper unloading EF = 0.02 kg/mg - - US EPA, 1985 Drilling (overburden) EF = 0.59 kg/hole - 70% - dust suppression US EPA, 1985 NPI, 2012 Blasting (overburden) EF = A 1.5 kg/blast A = area to be blasted (m²) - US EPA, 1985 Loading / emplacing material (overburden/topsoil) Hauling on unsealed surfaces Dozers on overburden Dozers on coal EF = k ( U M ) kg/tonne EF = ( ) k (s )0.7 ( M 3) 0.45 kg/vkt Ktsp = 0.74 U = wind speed (m/s) M = moisture content (%) S = silt content (%) M = average vehicle gross mass (tonnes) EF = 2.6 s1.2 M 1.3 kg/hour S = silt content (%) M = moisture content (%) EF = 35.6 s1.2 M 1.4 kg/hour S = silt content (%) M = moisture content (%) - NPI, % - watering of trafficked areas US EPA, US EPA, US EPA, 1985 Loading / emplacing coal EF = 0.58 kg/tonne M = moisture content (%) - US EPA, 1985 M1.2 Loading product coal to stockpile EF = k ( U M ) kg/tonne Ktsp = 0.74 U = wind speed (m/s) M = moisture content (%) Grading roads EF = s 2.5 kg/vkt S = speed of grader (km/hr) - - US EPA, 1985 US EPA, 1985 Conveying material Transfers Crushing Screening Wind erosion (coal stockpiles) Wind erosion EF = 0.4 kg ha /hour - - SPCC, 1983 EF = k ( U1.3 M 1.4 Ktsp = 0.74 ) kg tonne U = wind speed (m/s) - NPI, M = moisture content (%) EF = kg Mg - - US EPA, 1985 EF = kg Mg - - US EPA, 1985 EF = 1.9 ( s p ) 365 ( ) ( f S = silt content (%) ) kg/ha yr 15 p = No. days / year when rainfall is >0.25mm f = % time wind speed is greater than 5.4m/s - NPI, 2012 EF = 0.4 kg ha /hour - - SPCC, 1983

51 A-3 Table A-2: Emission inventory ACTIVITY TSP emission (kg/y) Intensity Units Emission Factor Units Variable 1 Units Variable 2 Units Variable 3 Units Variable 4 Units Variable 5 Units Variable 6 Units OB - Stripping Topsoil with scraper 5, ,000 tonnes/year kg/t OB - Scraper unloading Topsoil 4, ,000 tonnes/year kg/t OB - Excavator loading Topsoil to haul truck ,000 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Hauling to Topsoil dump 3, ,000 tonnes/year kg/t 240 tonnes/load 5.0 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control OB - Emplacing at Topsoil dump ,000 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Dragline 841,317 26,479,086 bcm/year kg/m 3 7 drop height in m 2 moisture content in % OB - Drilling 8,850 50,000 holes/year 0.59 kg/hole 70 % Control OB - Blasting 124, blasts/year 622 kg/blast 20,000 Area of blast in square metres OB - Excavator loading OB to haul truck 523, ,988,021 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Hauling to dump - 1 2, ,164 tonnes/year kg/t 300 tonnes/load 4.1 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,818 6,515,400 tonnes/year kg/t 300 tonnes/load 5.6 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,597 48,406,653 tonnes/year kg/t 300 tonnes/load 3.9 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,391 12,927,229 tonnes/year kg/t 300 tonnes/load 1.7 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,812 8,920,885 tonnes/year kg/t 300 tonnes/load 4.5 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,729 35,164,078 tonnes/year kg/t 300 tonnes/load 2.3 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,861 16,816,714 tonnes/year kg/t 300 tonnes/load 3.5 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,469 1,481,916 tonnes/year kg/t 300 tonnes/load 3.1 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,757 49,219,169 tonnes/year kg/t 300 tonnes/load 2.9 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,674 21,859,831 tonnes/year kg/t 300 tonnes/load 9.4 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,343 1,152,959 tonnes/year kg/t 300 tonnes/load 11.2 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,944,148 64,914,233 tonnes/year kg/t 300 tonnes/load 10.3 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Hauling to dump ,238 31,420,788 tonnes/year kg/t 300 tonnes/load 8.4 km/return trip 3.5 kg/vkt 2.0 % silt content 345 Ave GMV (tonnes) 75 % Control OB - Emplacing at dump ,164 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,402 6,515,400 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,709 48,406,653 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,622 12,927,229 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,611 8,920,885 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,535 35,164,078 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,428 16,816,714 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump - 8 2,593 1,481,916 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,131 49,219,169 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,254 21,859,831 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,018 1,152,959 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,597 64,914,233 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Emplacing at dump ,985 31,420,788 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % OB - Dozers on OB (pit/dump/rehab) 4,486, ,056 hours/year 16.7 kg/h 10 silt content in % 2 moisture content in % CL - Dozers ripping/pushing/clean-up 1,350,775 29,784 hours/year 45.4 kg/h 8 silt content in % 5 moisture content in % CL - Loading ROM coal to haul truck - 1 2,606 31,001 tonnes/year kg/t 5 moisture content in % CL - Loading ROM coal to haul truck ,910 3,020,062 tonnes/year kg/t 5 moisture content in % CL - Loading ROM coal to haul truck ,947 1,165,001 tonnes/year kg/t 5 moisture content in % CL - Loading ROM coal to haul truck ,272 7,698,797 tonnes/year kg/t 5 moisture content in % CL - Loading ROM coal to haul truck ,233 1,739,325 tonnes/year kg/t 5 moisture content in % CL - Loading ROM coal to haul truck , ,917 tonnes/year kg/t 5 moisture content in % CL - Loading ROM coal to haul truck ,804 3,910,868 tonnes/year kg/t 5 moisture content in % CL - Loading ROM coal to haul truck ,038 2,533,917 tonnes/year kg/t 5 moisture content in % CL - Hauling ROM to hopper - 1 2,631 31,001 tonnes/year kg/t 240 tonnes/load 26.3 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CL - Hauling ROM to hopper ,447 3,020,062 tonnes/year kg/t 240 tonnes/load 17.1 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CL - Hauling ROM to hopper ,083 1,165,001 tonnes/year kg/t 240 tonnes/load 14.1 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CL - Hauling ROM to hopper ,298 7,698,797 tonnes/year kg/t 240 tonnes/load 13.2 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CL - Hauling ROM to hopper ,709 1,739,325 tonnes/year kg/t 240 tonnes/load 29.4 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CL - Hauling ROM to hopper , ,917 tonnes/year kg/t 240 tonnes/load 20.6 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CL - Hauling ROM to hopper ,313 3,910,868 tonnes/year kg/t 240 tonnes/load 20.5 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CL - Hauling ROM to hopper ,508 2,533,917 tonnes/year kg/t 240 tonnes/load 11.8 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CHPP - Unloading ROM to hopper 1,705,525 20,285,888 tonnes/year kg/t 5 moisture content in % CHPP - Rehandle ROM at hopper 170,552 2,028,589 tonnes/year kg/t 5 moisture content in % CHPP - Dozer pushing ROM coal 991,654 18,615 hours/year 53.3 kg/h 8 silt content in % 5 moisture content in % CHPP - Conveying ROM ha 3,504 kg/ha/year CHPP - Crushing ROM 54,772 20,285,888 tonnes/year kg/t CHPP - Conveying Product ha 3,504 kg/ha/year CHPP - Loading Product coal to stockpile 2,057 11,187,386 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 10 moisture content in % CHPP - Dozer pushing Product coal 229,127 18,615 hours/year 12.3 kg/h 5 silt content in % 10 moisture content in % CHPP - Conveying Product to train ha 3,504 kg/ha/year CHPP - Loading Product coal to train 2,057 11,187,386 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 10 moisture content in % CHPP - Loading rejects 1,495 8,130,771 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 10 moisture content in % CHPP - Hauling rejects -1 24,683 4,065,385 tonnes/year kg/t 240 tonnes/load 1.9 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CHPP - Hauling rejects ,758 4,065,385 tonnes/year kg/t 240 tonnes/load 11.2 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control CHPP - Unloading rejects 1,495 8,130,771 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 10 moisture content in % WE - Overburden emplacement areas 2,148, ha 3,504 kg/ha/year WE - Open pit 915, ha 3,504 kg/ha/year WE - ROM stockpiles 7, ha 2,603 kg/ha/year 8 silt content in % 45 No. days rainfall>0.25mm 7.8 % WS>5.4m/s WE - Product stockpiles 17, ha 1,627 kg/ha/year 5 silt content in % 45 No. days rainfall>0.25mm 7.8 % WS>5.4m/s QUA - Loading material to crusher ,000 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % QUA - Crushing material ,000 tonnes/year kg/t QUA - Screening material 3, ,000 tonnes/year kg/t QUA - Unloading material to stockpile ,000 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % QUA - Loading material to truck ,000 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % QUA - Hauling to emplacement area 16, ,000 tonnes/year kg/t 240 tonnes/load 20.0 km/return trip 3.1 kg/vkt 2.0 % silt content 266 Ave GMV (tonnes) 75 % Control QUA - Unloading material ,000 tonnes/year kg/t average of (WS/2.2)^1.3 in m/s 2 moisture content in % Grading roads 549, ,520 km 0.62 kg/vkt 8 speed of graders in km/h Total TSP emissions (kg/yr) 22,677,219

52 Appendix B Isopleth Diagrams

53 B-1 Figure B-1: Predicted maximum 24-hour average PM 2.5 concentrations due to emissions from the Project (µg/m³)

54 B-2 Figure B-2: Predicted annual average PM 2.5 concentrations due to emissions from the Project (µg/m³)

55 B-3 Figure B-3: Predicted maximum 24-hour average PM 10 concentrations due to emissions from the Project (µg/m³)